Enable BUY-only chart patterns on M5 — bearish patterns dropped (OOS negative)

M5 factory: cp_enabled=True, patterns limited to inv_hs, double_bottom, bull_flag. SELL CP lost -3.21R on OOS; BUY CP: +41.8R PF 1.60 on 12mo, +65.2R PF 1.48 on 5yr. Adds +22R to the M5 book (708 vs 513 trades). M15/H1 remain disabled — all failed OOS validation. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_015kby7M9JwpYi8NQT2yoxAi
Docs: CLAUDE.md — document XAU_CP archetype (pipeline step 11, generator D, cp_* knobs)
2026-07-16 16:08:13 -04:00 · 2026-07-16 14:31:45 -04:00 · 2026-07-16 14:23:17 -04:00 · 2026-07-16 14:06:27 -04:00 · 2026-07-16 12:17:56 -04:00 · 2026-07-16 10:24:39 -04:00
54 changed files with 1486 additions and 15314 deletions
@@ -1,10 +1,6 @@
 # Secrets
 .env
 .env.local
 config.json
 # Oanda daemon state
 oanda_state.json
 # Editor / tooling
 .claude/
@@ -28,12 +24,4 @@ Thumbs.db
 # Backtesting data (keep scripts, ignore generated files)
 backtest/cache/
 backtest/*.csv
 backtest/research/*.parquet
 backtest/research/duka_m5_boundary_spreads.csv
 backtest/trailgrid/
 backtest/trailgrid5m/
 backtest/__pycache__/
 .env.*
 logs/
 backtest/research/commit_ladder_2026-08/
 backtest/research/llm_news_eval_2026-08/
@@ -1,91 +1,357 @@
-# CLAUDE.md
+# ForexBot2 — Codebase Reference
-This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+Signal-intelligence platform for **XAU/USD (gold — live, validated)**. It fetches OHLCV from Twelve Data, scores 9 technical indicators per timeframe with regime-adaptive weights, checks multi-timeframe agreement and news/calendar risk, grades the signal (A+…F), and generates a full trade setup (entry, SL, TP1/TP2, trailing plan). Delivery: Telegram bot (auto-scanner + subscriber channel) and a CLI. A realism-focused backtester replays history through the *exact same* signal code.
-## What this is
+This file is the authoritative developer/agent reference. The human-facing docs live in `docs/` (`user_guide.html` + `beginner_guide.html`, and their PDF builds) — keep them in sync when signal behavior changes. `backtest/instructions_for_backtest.txt` lists what every backtest report must record.
-Signal-intelligence platform for **XAU/USD (gold — the only supported pair)**. It fetches OHLCV from the active data provider (**Oanda v20 since 2026-07-28**; Twelve Data and Tiingo remain registered fallbacks), scores 9 technical indicators per timeframe with regime-adaptive weights, checks multi-timeframe agreement and news risk, grades the signal (A+…F), and generates a full trade setup (entry, SL, TP1/TP2, trailing plan). Delivery: Telegram bot, CLI, and an Oanda practice-account auto-execution daemon. A realism-focused backtester replays history through the *exact same* signal code.
+**Read this whole file before changing signal logic.** Every numeric threshold below was either backtested or is load-bearing for a backtested result. Rules for changes are at the bottom ("Rules for making changes").
-Every numeric threshold in `config.py` and `pair_signals/xauusd.py` was backtested or is load-bearing for a backtested result. Validation evidence lives in the factory/module docstrings and in `~/.claude/projects/.../memory/`.
+---
-## Commands
+## Repository map
-```bash
+| Path | Lines | Purpose |
-python test_all.py                      # QA suite: 374 tests, 23 modules, no network (mocked)
+|------|-------|---------|
-python main.py --pair XAUUSD --timeframe H1 --detail   # CLI signal report (--json for full dump,
+| `config.py` | ~374 | All config dataclasses, `PAIR_INFO` registry, per-pair strategy configs. **`PlatformConfig` defaults ARE the tuned XAU config.** |
-                                        #  --panel to run the advisory LLM agent panel on-demand)
+| `data_provider.py` | ~347 | Twelve Data SDK wrapper: fetch, rate-limit (55 credits/min), in-memory cache, validation/cleaning, freshness checks |
-python telegram_bot.py                  # the bot (refuses to boot without credentials)
+| `engine.py` | ~1173 | `TechnicalEngine`: computes all indicators on one DataFrame → `TechnicalAnalysis` (composite score, regime, key levels) |
-python oanda_daemon.py                  # auto-execution daemon on Oanda practice account
+| `chart_patterns.py` | ~280 | Chart-pattern detectors for the XAU_CP archetype (pure functions, ATR-unit geometry, first-close-beyond triggers) |
-python dashboard_publisher.py --once    # web dashboard: one scan+publish cycle to $DASHBOARD_OUT_DIR
+| `signal_aggregator.py` | ~1447 | `SignalAggregator`: MTF analysis, news integration, gates/penalties, quality grade, trade-setup generation → `SignalReport` |
-                                        # (omit --once for the candle-close daemon; deploy/README-dashboard.md)
+| `news_engine.py` | ~680 | Economic calendar (Forex Factory JSON + MyFXBook RSS fallback) + keyword RSS sentiment |
 | `telegram_bot.py` | ~1259 | Async Telegram bot: candle-boundary auto-scanner, WebSocket level-breach monitor, owner lock, subscriber channel |
 | `pair_signals/__init__.py` | 52 | `PAIR_PRODUCERS` registry + `produce_signal()` |
 | `pair_signals/xauusd.py` | 71 | XAU config factory + per-timeframe overrides (docstring = validation record) |
 | `main.py` | ~348 | CLI: `python main.py --pair XAUUSD --timeframe H1 --detail/--json` |
 | `test_all.py` | ~2100 | QA suite: **162 tests in 10 modules**, no live API needed. Must stay green. |
 | `backtest/gpu_backtest.py` | ~2231 | Bar-replay backtester (CuPy→NumPy fallback) using the real `SignalAggregator` |
 | `backtest/diagnose_filters.py` | 609 | Filter-diagnosis tooling |
 | `backtest/research/` | — | Calendar research infra (`ff_calendar_scraper.py`, `calendar_store.py`) + result CSVs |
 | `backtest/*.csv` | — | Verification trade books from past tuning campaigns (`xau_*.csv`, …). **Gitignored, local-only** — used for manual before/after comparison, not by tests |
 | `backtest/cache/` | — | Parquet OHLCV cache (raw bars) |
 | `test_ws_dual.py` | 108 | Standalone WebSocket dual-connection probe |
 | `logs/telegram.log` | — | Bot log (midnight rotation, 30 backups) |
-# Backtests
+---
 python backtest/gpu_backtest.py --pair XAUUSD --days 365
 python backtest/gpu_backtest.py --pair XAUUSD --start 2025-01-01 --end 2025-06-01 \
    --primary-tf 15min --confirm-tfs 1h,4h
 # key flags: --spread --quality A+,A,B,C --strategies --workers --max-hold-bars
 #            --warmup-bars --cooldown-bars --no-cache --export-csv out.csv --detail
 #            --fixed-rr-tp N (research-only: hard TP at N×risk, trail off; verdict
 #            2026-08-05: NOT shipped — backtest/research/fixedtp_2026-08-05.md)
 ```
-**`test_all.py` is NOT pytest** — it uses a custom `@test("name")` decorator that runs each test immediately at import; don't grep for `def test_` or try `pytest test_all.py`. Run the whole file; it prints per-module results and exits non-zero on failure. There is no way to run a single test short of commenting modules out. Tests must stay green; every new gate/knob needs tests. When patching weekend logic in tests, patch `providers.base._is_forex_weekend` — but know there are THREE independent weekend implementations (`providers/base.py`, `signal_aggregator.py`, and an inline copy in `backtest/gpu_backtest.py:_is_xau_blocked_by_calendar`); patching the provider one does not reach the other two.
+## Pair status
-Python 3.12; a `venv/` exists in-repo. Dependencies in `requirements.txt` (numpy, pandas, requests, twelvedata, python-telegram-bot ≥20, websockets).
+- **XAU/USD**: the tuned, validated product — and the ONLY supported pair. All defaults in `PlatformConfig`/`XauStrategyConfig` are its tuned values.
 - **EUR/USD**: REMOVED from the codebase (2026-07-16, branch `xau-only-chart-patterns`). It never had a validated edge: the 2026-07-11/12 verdict found no configuration passing validation on M5, M15, or H1 at a 1-pip round-trip cost; root cause was cost-to-volatility (EUR M15 ATR ≈ 10 pips → 1-pip spread taxes ~10% of ATR; gold's ATR/spread ratio is several times larger). Calendar-surprise and H1-hybrid avenues were also tested and closed. The full EUR implementation and verdict history live in git history (last commit with EUR support: `7d3fd95`). Do not re-add EUR without new data (tick fills or H4/D1 horizons).
-## Configuration & credentials
+---
-Precedence for every credential/deployment setting: **env var → config.json → refuse to run** (no hardcoded fallbacks). A gitignored repo-root `.env` is loaded at `app_config` import via `os.environ.setdefault`. `config.json` (gitignored; schema in `config.json.example`) holds secrets plus deployment settings: `data_providers.active` (the live-feed switch), `data_providers.streaming` (optional tick-stream provider for the level-breach monitor), `scanning.pairs`/`timeframes`, per-pair `blocked_hours_utc`, `log_level`. Key env vars: `TELEGRAM_TOKEN`, `TELEGRAM_CHAT_ID`, `TELEGRAM_CHANNEL_ID`, `TWELVE_DATA_API_KEY`, `TIINGO_API_KEY`, `OANDA_ACCOUNT_ID`/`OANDA_API_TOKEN`/`OANDA_BASE_URL`, `DEEPSEEK_API_KEY` (news LLM; the `deepseek` config.json block holds model/enabled/timeout/daily cap). The `agent_panel` config.json block (all-defaults = feature dark) controls the advisory LLM agent panel: `enabled`, per-surface `telegram`/`dashboard` toggles, `min_quality`, `max_panels_per_day`, `reuse_ttl_seconds`.
+## End-to-end pipeline (what happens on every scan)
 ## Architecture
 ### End-to-end pipeline (every scan)
 ```
-providers/<active>.fetch_ohlcv()       per TF: single-flight fetch, drop forming candle, validate/clean, cache
+Twelve Data REST (+ WebSocket ticks in the bot)
-                                       (only Twelve Data rate-throttles; Oanda has no client throttle)
+  → data_provider.fetch_ohlcv()        per timeframe: throttle, fetch, drop forming candle, validate/clean, cache
-  → engine.TechnicalEngine.analyze()   per TF: 9 scored indicators + ATR context → regime → composite score
+  → engine.TechnicalEngine.analyze()   per timeframe: 9 scored indicators + ATR context → regime → composite score
  → signal_aggregator.analyze_pair()   live entry point (weekend/day filters, fetch, staleness)
      └─ analyze_from_dataframes()     replayable core: MTF agreement → news → bonuses/penalties →
-                                       final signal + quality grade → gates → trade setup → reasons
+                                       final signal + quality grade → gates (session, SELL, M5, regime) →
-  → SignalReport                       consumed by telegram_bot.py, main.py, oanda_daemon.py, backtester
+                                       trade setup (3 generators) → reasons
  → SignalReport                       consumed by telegram_bot.py, main.py, backtest/gpu_backtest.py
 ```
-Signal currency everywhere is `SignalStrength` (−2..+2). DataFrames are UTC tz-aware, oldest-first, capitalized OHLCV columns. Everything runs on **closed bars only** — the forming candle is dropped, and session/hour filters evaluate **bar-CLOSE time** (bar label + candle duration) identically in live and backtest.
+Signal currency everywhere is the `SignalStrength` enum: `STRONG_BUY=2, BUY=1, NEUTRAL=0, SELL=-1, STRONG_SELL=-2`.
-### Layers
+---
- **`providers/`** — one class per provider subclassing `BaseDataProvider` (`providers/base.py`), which owns everything the pipeline depends on: TTL+boundary cache, forming-candle drop, freshness check, validate/clean, paginated `fetch_range` (feeds the backtester from any provider), optional tick streaming. Subclasses implement only `_fetch_raw` + symbol/interval maps. Registry + `make_data_provider(config, name=None)` in `providers/__init__.py`; config.json `data_providers.active` selects the live candle feed and the optional `data_providers.streaming` scalar selects a different provider for the tick stream. A test enforces that subclasses don't override shared base methods (`supports_streaming`/`iter_ticks` are the sanctioned streaming override points). Tiingo streams mid-price ticks (`wss://api.tiingo.com/fx`) — do not re-add spread display from its free FX feed (two-tier indicative quotes, removed 2026-07-23); the no-spread rule extends to the WS stream.
+## config.py — every knob
 - **`engine.py`** — `TechnicalEngine`: 9 scored indicators (ema_trend, rsi, macd, bollinger, adx, roc, keltner, divergence, candlestick) + ATR as context. Regime classification (trending_up/down, `trending` = mixed/direction-UNKNOWN, transitioning, ranging) drives per-indicator weight multipliers and regime-aware RSI reinterpretation. Compute order matters (ATR first; MACD always computed even when not a pillar — divergence needs the histogram). Private series ride on the result as `_underscore` attrs; scorers/formatters skip anything `_`-prefixed or weight-0.
 - **`signal_aggregator.py`** — MTF agreement, diminishing-decay penalty system, exhaustion hard block, quality grading, then an ordered chain of post-grade gates (session, XAU SELL quality/vol/hour, regime blocks, M5 noise gates), then three setup generators: XAU ATR (A+/A), generic swing-based, plus the M5 pullback (`XAU_PB`) and chart-pattern (`XAU_CP`, `chart_patterns.py`) entry paths. The PB/CP generators run AFTER the gate chain and force quality to "B"; the CP path deliberately accepts grade-F composites and re-checks only session/day/big-candle (via warning prefixes) plus its own SELL/vol/RSI gates — it does NOT re-check exhaustion or the M5 min-score/MTF gates. **This is validated behavior, not a bug** (2026-08-04 audit: 226/586 trades in the shipped M5 12mo book are XAU_CP, +38.9R, 104 of them exhaustion-flagged and net-positive) — changing it is a rule-1 revalidation event. `TradeSetup.strategy` selects trailing params; trail params are stamped on the setup from the *producing* config so per-TF overrides reach formatters.
 - **`pair_signals/`** — per-pair config factories. **Always use the factory (`produce_signal` / `make_config`), never bare `PlatformConfig()`** — the factory applies the config.json overlay first, then the validated per-TF deltas (M5 hour-9 block, M15 trail arm 0.70, M15 SELL-hour block, etc.; evidence in the docstring). A test enforces factory ≡ inline-config equivalence. Confirmation map: M5→[M15,H1], M15→[M5,H1], H1→[M15,H4], H4→[H1,D1].
 - **`news_engine.py`** — Forex Factory calendar (MyFXBook RSS fallback) + RSS keyword sentiment with gold-inverse keywords. Class-level caches (fresh engine per scan). Optional DeepSeek enrichment via `llm_client.py` (requests-only, no SDK): batched headline sentiment overriding keyword scores, gold-direction on release surprises, and a market-summary blurb (`SignalReport.news_summary`) — **display/warning-only, keyword fallback on any failure, inert without a key**; prompts are static module constants (DeepSeek prefix-cache pricing) and requests send `thinking: disabled` (V4-Flash reasoning otherwise blows the timeout). By XAU-tuned default, event risk and sentiment **warn but do not gate signals** (`event_risk_caps_signals=False`, `sentiment_affects_signals=False`) — the LLM does not change that.
 - **`agent_panel.py`** — ai-hedge-fund-style advisory panel: 4 DeepSeek persona calls (macro/technical/risk/contrarian, concurrent on a panel-private pool) + 1 synthesizer producing an **ADVISORY-ONLY** consensus (`AGREE/NEUTRAL/OPPOSE` + confidence + rationale) written onto `SignalReport.panel_*` fields. Attached **post-hoc** by consumers (`telegram_bot` alert path, `main --panel`, `dashboard_publisher`) — never runs inside `analyze_from_dataframes` (backtest parity, test-locked) and no gate reads its fields. Static prompts (prefix caching); spend bounded by an exact per-bar cache, a reuse TTL keyed (symbol, TF, direction) so persisting signals don't re-spend every bar, and a `max_panels_per_day` UTC budget (default 20 ≈ ≤100 requests) on top of the shared 500/day client cap. `<2` valid persona takes skips the synthesizer; any failure degrades to no panel, never a changed signal.
 - **`telegram_bot.py`** — owner-locked bot + subscriber channel; candle-boundary auto-scan loops (M5/M15/H1/H4, boundary+10 s), alert quality threshold + fingerprint dedup (committed only after a successful send), 📰 LLM market-context line on alerts when available, WebSocket level-breach monitor fed by the streaming provider (`data_providers.streaming`, falling back to the active provider; auto-disabled when the resolved provider can't stream or has no API key — Oanda does not stream, Tiingo does). Rotating log: `logs/telegram.log`, 3-h rotation × 240 backups.
 - **`oanda_daemon.py` / `oanda_executor.py`** — practice-account auto-execution. Book-parity rules (see daemon docstring): no broker-side TP (trailing model — TP1/TP2 advisory), 5-bar per-TF cooldown (wall-clock approximation, so it expires over weekends where the backtester would still count bars), trail ratchets at bar close once per *observed* bar (a missed/failed cycle skips those bars entirely — the intra-gap high/low never enters the peak). Accepted divergence: one position total; orders send `positionFill=OPEN_ONLY` so a state/broker divergence cancels the order instead of silently netting. State/broker re-sync runs at boot and after any suspicious fill (`_reconcile_positions`).
 - **`dashboard_publisher.py` / `static/dashboard.html`** — web dashboard as a **static publisher** (live LAMP server): standalone daemon scans at candle closes via `produce_signal` and atomically writes `snapshot.json`/`news.json`/`dashboard.html` to `$DASHBOARD_OUT_DIR` (Apache serves them; page is public by owner decision). **Hard independence rule: never imports, and is never imported by, `telegram_bot`/`oanda_daemon`** — confirmation map and boundary math are deliberate local copies, so a web-side failure can only mean a stale page. Due-TF classification uses the RAW wake time (flooring wake−buffer skips every H1/H4 scan — 2026-08-02 bug). News panel reads `NewsEngine.get_fundamental_context()` plus the additive `get_week_events()` accessor (display-only; no signal path calls it); as a separate process it has its own news caches and DeepSeek daily cap. News/calendar rows are clickable (detail dialog); headlines carry `id` = sha1(headline) in news.json, and exactly one on-demand endpoint exists: `static/news_summary.php` (installed by `install_static`), which serves a DeepSeek gold-impact summary per headline — whitelist-gated against news.json ids, cache-forever in `ai/`, own fail-closed daily cap (150/day, file-based, separate from the publisher's), key at `/etc/gold-dashboard/deepseek.key`, never reads repo config. Deployment checklist: `deploy/README-dashboard.md`.
 - **`backtest/gpu_backtest.py`** — bar-by-bar replay through the real `analyze_from_dataframes` (news off). Realism model (do not regress): next-bar-open entries re-anchored to the raw price path, round-trip `--spread` charged per trade, trade resolution on RAW bars (cleaning applies only to signal-side data), same-bar SL+TP tie → SL first, gap fills at bar open, trail ratchet at bar close only. Indicators are precomputed once per TF (CuPy GPU or NumPy fallback — check the `Backend:` log line); replay fans out over worker processes and the 5-bar cooldown is applied in an ordered pass afterwards, so parallel and sequential books are identical. Parquet cache in `backtest/cache/` keyed by provider/symbol/TF/window. `backtest/*.csv` are gitignored pinned trade books from past campaigns — used for manual before/after comparison. `backtest/instructions_for_backtest.txt` lists what every backtest report must record.
-## Rules for making changes (read before editing signal logic)
+### PAIR_INFO (market constants; key is the canonical symbol `"XAU/USD"`)
 | Field | XAU/USD | Meaning |
 |---|---|---|
 | `pip_value` | 0.10 | price units per pip |
 | `price_precision` | 2 | decimals for SL/TP rounding & display |
 | `default_spread` | 0.30 | round-trip cost charged by the backtester when `--spread` not given |
-1. **Never change a tuned value without re-validation.** Acceptance rule: positive total R in BOTH train and validation halves of a ≥12-month window, PF ≥ 1.3, at realistic cost (XAU $0.30 round trip), trailing exits, next-open fills. Marginal edges must additionally pass the 2024-06→2025-03 out-of-sample window — within-year splits alone have approved configs that later failed OOS.
+### TechnicalConfig (indicator parameters)
-2. **Per-timeframe overrides live in the pair factory**, never in global defaults — the same knob has opposite effects on different TFs (proven repeatedly: `tp_floor_rr`, hour-9 block, trail params). Document validation evidence in the factory docstring.
+EMA 9/21/50/200 (`ema_mode`: `"4ema"` default or `"2ema"` = 50/200 only); RSI 14 (OB 70 / OS 30 / neutral zone 40–60; `rsi_mode`: `"regime_aware"` default or `"classic"`); MACD 12/26/9 (`macd_as_pillar=True` — MACD is a scored indicator, not just divergence input); BB 20/2.0σ; ATR 14; ADX 14 (strong ≥25, very strong ≥50); divergence lookback 40; swing-S/R lookback 50; ROC period 10, threshold 1.0%; Keltner: EMA 20 mid ± 1.5 × ATR(10); ADX slope lookback 5, "emerging" threshold 3.0; `disabled_indicators` list (removes the pillar from scoring; for MACD the histogram is still computed — divergence needs it); `regime_weights_override` dict (replaces the gold regime-multiplier table for experiments).
 3. **Pair isolation is a hard invariant.** XAU gates in the aggregator are display-name-gated so a future pair port cannot inherit gold-calibrated logic by accident.
 4. **Live == backtest parity.** If you add a live gate, add it to the replay path in the same commit; all time filters evaluate bar-close time in both paths.
 5. **ROC stays weighted** even though it's correlated with MACD/Keltner/EMA — zeroing it backtested 13R worse; composite thresholds are calibrated with it.
 6. **Backtester realism knobs are results-critical** (raw-path resolution, spread charging, next-open entry, SL-first ties, close-only trail). Touching them invalidates comparability with all pinned books.
 7. **The forming candle must stay dropped** — live signals repaint otherwise.
 8. Don't add config fields nothing reads; don't disable indicators in production paths (`disabled_indicators` is for experiments only).
 9. EUR/USD was removed 2026-07-16 after a no-edge verdict (cost-to-volatility; last commit with EUR support: `7d3fd95`). Do not re-add without new data. BTC/ETH work lives on unmerged branches only.
-## 2026-08-04 QA audit — owner decisions ledger
+### SignalConfig (scoring & trade geometry — XAU-calibrated)
 | Field | Default | Meaning |
 |---|---|---|
 | `mtf_agreement_threshold` | 2 | timeframes (of primary + confirmations) that must agree |
 | `min_rr_ratio` | 0.5 | reject setups below this RR |
 | `tp_tightening_factor` | 1.0 | TP scale factor. **Leave at 1.0**: 0.8 was re-tested 2026-07-08 with spread+gap fills and nearly halved total R |
 | `round_trip_cost` | 0.30 | XAU $ round trip |
 | `min_tp_cost_multiple` | 30.0 | spread gate: TP1 distance must be ≥ cost × this (= $9 on XAU). Validated: sub-$10 targets lose |
 | `min_sl_pips` | 80.0 | SL floor in pips ($8 on XAU) |
 | `tp_floor_rr` | 0.5 | TP1 floor as fraction of SL distance (M15 factory overrides to 1.0) |
 | `trail_arm_r` / `trail_dist_r` | 0.6 / 0.75 | trailing exit: once favorable excursion ≥ arm×risk, stop trails dist×risk behind best price (bar-close updates). TP1 becomes advisory. Validated on 12mo M5: +105R vs +48R static. 0 disables |
 | `pb_trail_arm_r` / `pb_trail_dist_r` | 0.8 / 0.75 | trailing params for XAU_PB pullback trades (tighter stops → arm later) |
 | `weights`, `min_confidence`, `cooldown_bars`, `default_risk_pct`, `default_rr_ratio`, `atr_sl_multiplier` | — | **LEGACY / UNUSED** outside tests. The engine has its own weights; backtest cooldown lives in `BacktestConfig` |
- **Weekend calendar: FIXED (owner-approved 2026-08-04).** All three `_is_forex_weekend` copies are NY-anchored (Fri 17:00 → Sun 18:00 ET; during US DST that is Fri 21:00 UTC — measured on 5yr of Oanda bars). Kills the weekly false-"STALE DATA/feed lagging" hours. Book impact, fully accounted: M15 byte-identical; M5 12mo dropped exactly ONE trade — a phantom whose signal fired on the final summer-Friday bar (close 21:00 UTC) and "filled" at Sunday's reopen 49 h later (−1.08R, live-impossible). M5 12mo reference at HEAD: 555 trades, +98.64R PF≈1.50 @ $0.65 (was 556/+97.56/1.48). A Module 21 test sweeps all three copies hourly across both DST transitions.
+### NewsConfig
- **Spread in exit-trigger detection (rule 6): owner DECLINED 2026-08-04.** Backtest stop triggers stay on raw mid High/Low; the measured optimism (~4–6% of total R: −3.55R @ $0.30, −4.24R @ $0.65 on the M15 12mo book) is accepted and books stay comparable. Re-open only as a conscious re-pin campaign.
+Tracked currencies `["USD","XAU"]`; pre-event buffer 30 min, post-event 15 min (45 min after a *surprise* print); `event_risk_caps_signals=False` — **XAU default: event risk warns but does NOT cap confidence** (2026-07-11 audit: the caps suppressed trades worth +4.6..+7.2R/yr; XAU trades near USD releases outperform). Context cache TTL 600 s (must stay < pre-event buffer). 4 RSS feeds (investinglive, FXStreet, Google-News gold query, CNBC economy).
 - **`FB2_EMA_LO`: HARDENED 2026-08-04 (delegated).** Research override for the EMA200-proximity band, default = tuned 0.5; an unparseable value now degrades to the default with a warning instead of crashing every scan. Documented here — it is a knob that exists.
 - **H4 seeding divergence: parked.** Live H4 fetches only 380 bars vs the backtest's unbounded history — ~2% of sampled H4 bars flip an indicator signal (composite Δ up to 12.3). M5/M15/H1 clean at their real fetch depths. Fold into any future H4 campaign; H4 remains "unproven for auto-execution".
-## Current state notes (2026-07-30)
+### XauStrategyConfig (all XAU gates; `enabled=True`)
 | Field | Default | Notes |
 |---|---|---|
 | `blocked_hours_utc` | [0,1,2,10,11,13,16,21] | bar-close hours never traded. M5 factory appends 9 (post-London-open whipsaw, −28.9R/143 over 6 yr) |
 | `allowed_weekdays` | [0,1,2,3,4,6] | Mon–Fri + Sun (Sydney open). `weekend_filter=True`: Fri 22:00 → Sun 22:00 UTC closed |
 | `min_atr_threshold` | 1.0 | $1 ATR floor for swing-based setups |
 | `v2_sl_atr_multiplier` | 2.0 | SL ATR floor in the generic setup generator |
 | `sl_atr_multiplier` / `tp_atr_multiplier` | 2.0 / 1.0 | used by the XAU ATR setup generator (A+/A only) |
 | `sell_min_quality` | ["A+","A","B"] | SELLs below B are blocked |
 | `sell_vol_regimes` | ["high","extreme"] | SELLs only in these volatility regimes (quiet shorts fade vs gold's macro uptrend) |
 | `blocked_regimes` / `sell_blocked_regimes` | [] / [] | per-TF factory opt-ins. M15 factory sets `blocked_regimes=["trending"]` (mixed regime: strong ADX, EMAs unaligned — lost −6.8R/24) |
 | `sell_blocked_hours_utc` | [] | bar-CLOSE UTC hours where SELLs are blocked (BUYs unaffected). Per-TF factory opt-in: M15 sets [15, 17] (NY-session shorts, −33.5R/165 over 5 yr, negative 6/6 years both halves; BUYs there +34.3R) |
 | M5 gates | | `m5_require_mtf=True`; `m5_min_abs_score=30`; `m5_min_confirmation_score=10`; `m5_sell_max_quality=["B","C"]` (A-grade M5 SELLs enter at dip extremes and lose); `m5_blocked_vol_regimes=["high"]` (chop transition — 61% WR, negative total); `m5_sell_min_rsi=25` |
 | M5 pullback (XAU_PB) | | `m5_pullback_enabled=True`; min score 15 (lower than gate 1 BY DESIGN — mid-pullback scores weaker); ADX ≥ 20; RSI zones BUY (35,62) / SELL (38,65); retrace depth 0.8–6.0 ATR from the 40-bar swing extreme; band touch within 3 bars. Validated +38.3R/254 trades on 12mo |
 | Chart patterns (XAU_CP) | `cp_enabled=False` | `cp_*` fields: pattern list, lookback 72, swing window 3, recency 20, double tolerance 0.5 ATR, height 1.5–8 ATR, H&S head ≥0.5 ATR / shoulder tol 1.0 ATR, flag pole ≥3 ATR in ≤12 bars, flag 3–12 bars retracing 10–50%. Textbook defaults, NOT tuned. **Default OFF** — M5 factory enables BUY-only (`inv_hs`, `double_bottom`, `bull_flag`); M15/H1/H4 remain disabled (all failed OOS validation 2026-07-16). Bearish patterns (`hs_top`, `double_top`, `bear_flag`) dropped: SELL CP lost −3.21R on OOS, −4.52R on M15 5yr |
- Oanda v20 is the live feed (`data_providers.active: "oanda"`), but the pinned backtest books are still Twelve-Data-pinned and the backtest cost assumption is $0.30 while measured real spreads are ~$0.65–0.72 — re-pin books on Oanda bars before treating feed-switch comparisons as valid (see Oanda/TD divergence study in memory).
+Legacy fields kept for config-shape compatibility: `rsi_entry_threshold`, `min_ema200_tfs`, `allow_generic_trades`, `buy_threshold_boost`, `m5_confirmation_bars`, `h4_rsi_gate=False`, `h1_macd_gate=False`, `rsi_zone` — not wired to current gates.
- Human-facing docs in `docs/` (`user_guide.html`, `beginner_guide.html` + PDF builds) must be kept in sync when signal behavior changes.
+
 ### FilterConfig (`config.backtest` — despite the name it applies LIVE too)
 `session_filter=True`, window 03:00–23:00 UTC, `blocked_hours_utc` mirrors XAU's list (non-XAU pairs read this copy), `allowed_weekdays` Mon–Fri+Sun. `regime_filter=False` (optional H4-ADX≥25 gate, off).
 ### PlatformConfig (top level)
 `symbol="XAU/USD"`, primary TF H1, confirmations [M15, H4], `data_lookback_days=60`, `scan_pairs=[XAUUSD]` (**the single switch for what the bot scans**), `scan_timeframes=[M5,M15,H1,H4]`, `candle_buffer_seconds=10`, `ws_cooldown_seconds=300`, hardcoded `twelve_data_api_key` fallback.
 ---
 ## engine.py — TechnicalEngine
 **Input contract:** pandas DataFrame, UTC tz-aware DatetimeIndex, oldest-first, columns `Open High Low Close Volume` (capitalized). `< 30` bars → empty NEUTRAL result. EMA200 needs ≥ 200 bars (otherwise EMA-trend indicator degrades gracefully).
 **Compute order matters:** ATR first (EMA-ribbon and candlestick proximity use it) → EMA → RSI → BB → ADX → MACD (always computed, even if not a pillar — divergence needs the histogram) → ROC → Keltner (needs BB levels + MACD histogram) → divergence → structure S/R → candlesticks → regime classification → regime-aware RSI reinterpretation → composite.
 Results land in `TechnicalAnalysis.indicators: Dict[str, IndicatorSignal]` (fields: name, value, signal, confidence 0-100, description, weight). Private series ride on the result object as underscore attrs (`_rsi_series`, `_macd_histogram`, `_adx_slope`, `_kc_squeeze`, `_kc_squeeze_released`) and are skipped by the composite scorer and all formatters (skip rule: name starts with `_` or `weight == 0`).
 ### The 9 scored indicators (+ ATR as context)
 | Key | Base weight | Signal logic (exact) |
 |---|---|---|
 | `ema_trend` | 1.0 | 4-EMA mode: alignment score over checks (price>EMA9, 9>21, 21>50, 50>200), normalized to [-1,1]. >0.6 STRONG_BUY (conf ≤90), >0.2 BUY, mirrored sells, else NEUTRAL (40). Fast/medium cross ±10 conf. Ribbon (9/21/50 spread < 1 ATR) = "COMPRESSED"; compressed→expanded = +10 conf. Emits `ema_fast/medium/slow/trend` key levels |
 | `rsi` | 1.5 | Wilder RSI (ewm α=1/14). Classic pass first: ≥70 SELL, ≤30 BUY, ≥60 BUY-momentum (45), ≤40 SELL-momentum (45), else NEUTRAL (30). **Then overwritten by regime reinterpretation** (below) when `rsi_mode="regime_aware"` |
 | `macd` | 1.5 | Histogram = MACD line − signal line. Cross above 0 → BUY (conf 55+); accelerating (hist>prev>0) → STRONG_BUY; mirrored down; positive-but-fading / negative-but-fading → NEUTRAL (35); flat → NEUTRAL (30). Confidence scaled by |hist| vs its 20-bar mean |
 | `bollinger` | 1.5 | Position = (close−lower)/(upper−lower). >1.0 SELL (65), <0.0 BUY (65), >0.8 SELL (45), <0.2 BUY (45), else NEUTRAL (30). Squeeze = width < 0.75 × 20-bar avg width → +10 conf. Emits `bb_upper/lower/mid` |
 | `adx` | 0.5 | Wilder ADX/DI (ties zero BOTH DMs — fixes a systematic bearish nudge). ADX ≥ 25 → BUY/SELL by DI dominance, else NEUTRAL. Conf 40/65/80 by ADX band. ADX slope (5-bar diff): <25 with slope > 3.0 → "TREND EMERGING" +15 conf; also strengthening/weakening adjustments. Sets `_adx_slope` |
 | `roc` | 1.5 | 10-period %change. Positive & rising → BUY (STRONG if >2× threshold=2%); negative & falling → SELL; zero-cross → BUY/SELL (65); else NEUTRAL. **Directionally redundant with MACD/Keltner/EMA (0.8–0.94 corr) but its weight is load-bearing: zeroing it backtested 13R WORSE — composite thresholds are calibrated with it. Do not remove without retuning thresholds** |
 | `keltner` | 1.5 | EMA20 mid ± 1.5×ATR(10). TTM squeeze = BB fully inside KC. Squeeze released (was→not) + MACD hist sign → STRONG_BUY/STRONG_SELL (80); squeeze active → NEUTRAL (60); close above/below channel → BUY/SELL continuation (65); inside → NEUTRAL (30). Emits `kc_upper/lower/mid` |
 | `divergence` | 1.5 | RSI + MACD-hist vs price over 40 bars. Swing points (window 3) must align within ±2 bars to pair up; last two pairs: price LL + indicator HL = bullish, price HH + indicator LH = bearish. Any found → BUY/SELL/NEUTRAL-if-both, conf 70, value = count |
 | `candlestick` | 1.0 | Last bar: Doji (body<10% range), Hammer/Hanging Man (lower wick>2×body), Shooting Star/Inverted Hammer, Engulfing (vs prev bar), Marubozu (body>85%). Majority vote → signal, conf 50; +20 if pattern sits within 0.5 ATR of a swing S/R level |
 | `atr` | **0.0** (context) | Wilder ATR. Volatility regime from percentile vs last 50 ATR values: >80 extreme, >60 high, >30 normal, else low. Emits `atr`, `atr_pct` |
 Structure S/R (not scored): swing highs/lows (window 3) over `swing_sr_lookback` (min 120 bars on 1min/5min TFs); up to 3 above and 3 below stored as `swing_high_1..3` / `swing_low_1..3` key levels.
 ### Market regime classification (exact rules)
 - ADX ≥ 25 **and** EMA-trend signal bullish → `trending_up`; bearish → `trending_down`; EMA mixed → **`trending`** (the "mixed" regime — momentum without directional structure; treated as direction-UNKNOWN everywhere; a past bullish-bias bug here cost −6.8R and is fixed)
 - else ADX slope > 3.0 → `transitioning` (trend birth, ~5–10 bars before ADX crosses 25)
 - else → `ranging`
 ### Regime-aware RSI reinterpretation (`rsi_mode="regime_aware"`, the default — classic mode backtested worse for gold)
 | Regime | RSI zones |
 |---|---|
 | trending_up | 38–55 STRONG_BUY 75 ("pullback zone, ideal entry"); 30–38 BUY 60; 55–65 BUY 50; >65 NEUTRAL ("extended, wait"); <30 NEUTRAL ("possible reversal") |
 | trending_down | mirrored: 45–62 STRONG_SELL 75; 62–70 SELL 60; 35–45 SELL 50; <35 / >70 NEUTRAL |
 | transitioning / trending (mixed) | ONLY extremes: ≥70 SELL 50, ≤30 BUY 50, else NEUTRAL 30 |
 | ranging | ≥70 SELL 70, ≤30 BUY 70, ≥60 SELL 45, ≤40 BUY 45, else NEUTRAL |
 ### Composite score
 For each scored indicator: `effective_weight = base_weight × regime_multiplier`; contribution = `signal.value × effective_weight × confidence/100`. Composite = `(Σ contributions / Σ effective_weights) × 50`, clamped to ±100. Confidence = weight-averaged indicator confidence. Composite signal: ≥40 STRONG_BUY, ≥15 BUY, ≤−40 STRONG_SELL, ≤−15 SELL, else NEUTRAL.
 **Gold regime multipliers** (defaults in `_compute_composite`; `technical.regime_weights_override` replaces the whole dict):
 | Indicator | trending_up/down/trending | ranging | transitioning |
 |---|---|---|---|
 | ema_trend | 2.0 | 0.3 | 1.5 |
 | rsi | 1.0 | 1.5 | 1.0 |
 | macd | 2.0 | 1.5 | 2.5 |
 | bollinger | 0.5 | 1.5 | 1.0 |
 | keltner | 1.5 | 2.0 | 2.5 |
 | roc | 1.5 | 0.5 | 2.0 |
 | candlestick | 0.8 | 1.0 | 1.0 |
 | divergence | 1.0 | 2.5 | 2.0 |
 | adx | 0.8 | 0.5 | 1.0 |
 ---
 ## signal_aggregator.py — SignalAggregator
 Two entry points:
 - **`analyze_pair(symbol)`** — LIVE: applies XAU weekend + allowed-weekday filters on wall clock, fetches all TFs (primary + confirmations), flags stale data (primary stale → signal suppressed to NEUTRAL/F), delegates to `analyze_from_dataframes`, then applies the live session check on the **bar-CLOSE time** (bar timestamp + candle duration): outside 03:00–23:00 or in blocked hours → NO TRADE. This is the exact predicate the backtester replays, so live == backtest.
 - **`analyze_from_dataframes(symbol, dfs, context, use_news)`** — replayable core; treats each df as the visible history at decision time (caller must slice; no weekend filter here — the backtester applies it).
 ### Pipeline inside `analyze_from_dataframes` (numbered as in the source)
 1. Primary TF analysis → seeds report (score, confidence, regime, vol regime, price, timestamp = last bar's index label).
 2. Confirmation TF analyses → `mtf_signals` / `mtf_scores` keyed by TF string ("5min", "1h", …).
 3. **MTF agreement**: count BUY-ish vs SELL-ish across ALL TFs (primary included); agreement = count ≥ 2 **in the primary's direction** (two bullish confirms cannot "confirm" a bearish primary).
 4. News (if `use_news`): event risk (`clear`/`caution`/`danger`) + pair sentiment (−1..+1). Backtests pass `use_news=False`.
 5. Spread estimate = median(High−Low) of primary df.
 6. **`_compute_final_signal`** (bonuses → event cap → penalties → thresholds → grade):
   - Bonuses: MTF agreement → +10 conf and `score = 0.6×primary + 0.4×mean(all TF scores)`; news sentiment aligned (|sent|>0.2) → +5; in a *directional* trend, RSI in pullback zone → +5, BB position favorable → +5 (mixed `trending` regime gets NO directional bonus).
   - Event risk `danger`: warning always; confidence hard-capped at 30 ONLY if `news.event_risk_caps_signals` (False by default — XAU-tuned).
   - **Penalty system (diminishing, replaces old additive stacking):** collect, sort by size desc, apply with decay ×1.0, ×0.5, ×0.25…, total capped at 30. Penalties: `mtf_disagreement` 15; `news_conflict` 10; `event_caution` 15 (config-gated); `counter_trend` 15; `extreme_vol` 10; `ema200_near` 5 (price within 0.5% of EMA200 — env `FB2_EMA_LO` overrides); `ema200_far` 5 (>2.5%); `exhaustion` 8 (price >3.5 ATR beyond nearest swing); `no_pullback` 8 (>2.5 ATR from EMA21 in trend) / `weak_pullback` 5 (>2.0); `rsi_extended` 5 (RSI>65 with BUY / <35 with SELL in any strong-ADX regime, both directions in mixed `trending`); `bb_overextended` 8 (BB pos >0.85 BUY / <0.15 SELL).
   - **Exhaustion hard block**: ≥2 exhaustion-tagged penalties ({exhaustion, no_pullback, rsi_extended, bb_overextended} → `report.exhaustion_count`) AND momentum fading (ROC sign against score, or MACD signal against score) → confidence = 0.
   - Confidence clamped [0, 95]. **Final signal**: score ≥35 & conf ≥55 STRONG_BUY; ≥25 & ≥50 BUY; mirrored sells; else NEUTRAL.
   - **Quality grade**: |score|≥40 & conf≥65 & MTF → **A+**; ≥33 & ≥55 & MTF → **A**; ≥28 & ≥50 → **B**; ≥25 & ≥45 → **C**; ≥15 → **D**; else **F**.
 7. Post-grade gates, in order:
   - **6b session filter** (bar-label hour + weekday; XAU reads `xau_strategy.blocked_hours_utc`/`allowed_weekdays`, others read `config.backtest.*`) → NEUTRAL/F.
   - **6c transitioning boost**: regime `transitioning` and grade B/C/D → A if |score|≥30 & conf≥50 & MTF; else B if |score|≥25 & conf≥45 (early trend entries).
   - **6d exhausted-trend cap**: trending regime + exhaustion_count ≥2 + grade C/D → D, setup killed.
   - **6e XAU SELL quality gate**: sell-direction & grade actionable (A+..C) & not in `sell_min_quality` → D. The actionable guard is load-bearing: without it a session-blocked F report was *upgraded* to D, re-opening the M5 pullback path (its entry check is `!= "F"`).
   - **6f MTF opposition**: ≥2 TFs pointing against the trade direction → D (never trade against consensus).
   - **6g XAU SELL volatility gate**: sell-direction & vol regime not in {high, extreme} → D.
   - **6g2 XAU regime gates**: regime in `blocked_regimes` (M15: mixed `trending`) → D; sell-direction & regime in `sell_blocked_regimes` → D.
   - **6g3 XAU SELL hour gate**: sell-direction & bar-CLOSE hour (label + candle duration — the entry hour under next-open fills) in `sell_blocked_hours_utc` → D. Per-TF opt-in; M15 factory blocks 15/17 UTC.
   - **6i XAU M5 noise gates** (`_apply_m5_filters`, primary TF = M5 only): (1) |score| ≥ 30; (2) MTF agreement required; (3) ≥1 confirmation TF with score ≥ ±10 in trade direction (NEUTRAL confirms don't count); (4) SELL grade must be B/C (A-grade M5 shorts lose −0.017R/trade); (5) vol regime `high` blocked; (6) SELL blocked when RSI < 25. Each failure → D, setup killed.
 8. **Trade setup**: XAU + grade A+/A → `_generate_xau_atr_trade_setup` first; any pair + grade A+/A/B/C and no setup yet → `_generate_trade_setup` (swing-based).
 9. Reasons: every non-neutral indicator description → `bullish_reasons`/`bearish_reasons` (the backtester tags strategy from reasons[0] markers).
 10. **XAU_PB pullback** (M5 XAU only, `m5_pullback_enabled`, no setup yet, grade ≠ F): `_try_m5_pullback` — see below.
 11. **XAU_CP chart pattern** (any TF, XAU only, `cp_enabled`, no setup yet, NOT session/day-blocked — weak-composite grade F is allowed BY DESIGN, reversals complete on weak composites): `_try_chart_pattern` — see below.
 ### Trade setup generators (exact geometry)
 **A. `_generate_xau_atr_trade_setup`** (XAU, grade A+/A, ATR>0): TP quality scale A+ 2.0 / A 1.5 (A with exhaustion≥2 → 1.0). TP1 = entry ± `tp_atr_multiplier`(1.0)×scale×ATR; TP2 = 1.5×that; SL = entry ∓ 2.0×ATR. Gates: RR ≥ 0.5; TP1 distance ≥ $9 (spread gate). Prices rounded to 2 dp; pip 0.10 hardcoded — **this generator is XAU-only by construction**.
 **B. `_generate_trade_setup`** (swing-based, any pair, grade A+..C): XAU requires ATR ≥ $1 (LOW ATR FILTER). Per-side:
 - SL anchor = nearest swing low below price (BUY) / swing high above (SELL) ∓/± `sl_swing_buffer`×ATR (1.0); fallback (no swing) = (1+buffer)×ATR. SL distance floored at max(ATR × sl_atr_floor, min_sl_pips×pip) — XAU: max(2.0×ATR, $8).
 - TP1 = nearest swing beyond entry ± 0.3 ATR if it's within `tp_swing_cap` ATR (2.5) AND ≥ SL×RR-floor; else entry ± max(`tp_mult`×ATR, SL×RR-floor). tp_mult 1.5, RR-floor = max(min_rr 0.5, tp_floor_rr — 1.0 on M15).
 - TP2 = second swing if within 5 ATR, else entry ± max((tp_mult+1.5)×ATR, SL×floor×1.5); forced beyond TP1 by ≥1 ATR.
 - Gates: RR ≥ min_rr; spread gate TP1 distance ≥ cost×multiple ($9 on XAU).
 **C. `_try_m5_pullback` (XAU_PB)**: detection = EMA alignment ≥3 of 4 checks (9>21, 21>50, 50>200, close>200 — mirrored for SELL), ADX ≥ 20, price touched max(EMA21, KC-mid) within last 3 bars, resumption bar closed back in trend direction beyond EMA9, retrace depth 0.8–6.0 ATR from the 40-bar extreme, RSI in pullback zone (BUY 35–62 / SELL 38–65). Then gates: directional score ≥ 15; ≥1 confirmation TF ≥ ±10; not against MTF consensus; vol regime gates (same as breakout path). Geometry: SL = pullback 8-bar extreme ∓ 0.5 ATR (min $8); TP1 = 1.5×risk, TP2 = 2.5×risk; spread gate $9. Sets `strategy="XAU_PB"`, grade min B, marker `"XAU PULLBACK STRATEGY: …"` at reasons[0]. Rejection funnel counters in class attr `SignalAggregator.pb_funnel`.
 **D. `_try_chart_pattern` (XAU_CP)**: detection in `chart_patterns.py` — 6 classical patterns (double_bottom/double_top, inv_hs/hs_top, bull_flag/bear_flag), swing-point based (strict local extrema, window `cp_swing_window`=3), all thresholds in ATR units, scanned over the last `cp_lookback`=72 closed bars. Trigger = FIRST close beyond the neckline/boundary since the pattern formed (one breakout → one signal, no repaint); reversal patterns are invalidated by a new extreme beyond the pattern. No composite-score gate BY DESIGN. Gates: MTF non-opposition (6f mirror); SELL patterns respect `sell_vol_regimes`, `sell_blocked_hours_utc` (bar-close hour), M5 RSI floor; M5 blocks `m5_blocked_vol_regimes` both ways; ATR ≥ $1. Geometry: SL = pattern anchor ∓ 0.5 ATR (min $8); TP1 = measured move (pattern height), TP2 = 1.5×height; RR ≥ 0.5; spread gate $9. Sets `strategy="XAU_CP"` (global trailing params), grade min B, marker `"XAU CHART PATTERN: …"` at reasons[0]. Funnel in `SignalAggregator.cp_funnel`.
 `TradeSetup` fields: pair, direction, entry, SL, TP1, TP2, RR, risk/reward pips, pip_value, `strategy` ("GENERIC", "XAU_PB", or "XAU_CP" — selects trailing params; XAU_CP uses the global pair).
 ---
 ## news_engine.py — NewsEngine
 - **Calendar**: Forex Factory free JSON (`nfs.faireconomy.media/ff_calendar_thisweek.json`), 3 retries, honors 429 Retry-After with class-level backoff (≤600 s); fallback MyFXBook RSS. Never fabricates events — unavailable calendar = risk checks skipped that cycle.
 - **Caches are class-level** (a fresh NewsEngine is built per scan): calendar + headlines, TTL = 600 s on success, 60 s on empty result; lock held across fetch so concurrent scans share one request.
 - **Event risk** (`get_event_risk_level`): high-impact event for base/quote within 30 min → `danger`; within 60 min → `caution`; already-released high-impact → `caution` for 15 min after (45 min if actual ≠ forecast — "surprise"). `EconomicEvent.surprise` compares parsed numeric actual vs forecast (handles "%", "K/M/B" suffixes); direction is numeric-only (doesn't know if higher is bullish).
 - **Sentiment**: keyword sets over headlines from 4 RSS feeds, deduped by title (Google News " - Publisher" suffix stripped), last 24 h only. Per-currency scores; **`GOLD_INVERSE_KEYWORDS`** (rate hike/hawkish/tightening/rate cut/dovish/easing/stimulus) flip sign for XAU (hawkish = dollar-bullish = gold-bearish). Currency detection via compiled word-boundary-ish regexes (bare "us"/"eu"/"uk" deliberately excluded). Pair sentiment = base − quote.
 ---
 ## data_provider.py — ForexDataProvider
 - Twelve Data SDK (`TDClient.time_series(...).as_pandas()`), symbols normalized to `"XAU/USD"` form; renames lowercase columns → capitalized; sorts oldest-first; localizes UTC.
 - **Rate limit**: 55 credits/min sliding window (`_throttle`), blocks with sleep when exhausted.
 - **In-memory cache**: key `{symbol}_{tf}`, TTL = candle duration (M5 300 s … H4 14400 s); a cached frame is evicted and refetched if it fails the freshness check **or the boundary check** (`_has_latest_closed_bar`: the frame must contain the most recent fully closed bar, weekend-exempt — a wall-clock TTL alone would serve mid-candle snapshots at the next boundary and scans would run one bar behind). Cache and throttle are lock-protected (one provider is shared across the bot's thread pool).
 - **`_drop_forming_candle`**: Twelve Data includes the current partial candle; the scanner fires seconds after boundary, so the last bar is dropped if its close time is in the future. Without this every indicator would read a seconds-old stub and signals would repaint.
 - **Freshness** (`_is_data_fresh`): last bar older than 3× candle duration → stale (warns; live path suppresses signal if the PRIMARY TF is stale). Weekends exempt (Fri 22:00→Sun 22:00 UTC).
 - **`_validate_and_clean`**: drop all-NaN rows → ffill (limit 3) → drop NaN closes → sort + dedupe (keep last) → drop future timestamps (>now+5 min) → repair OHLC inconsistencies (High=max, Low=min of O/C/existing) → remove >10σ return outliers (spike bar only — the opposite-direction snap-back bar right after it is kept, else every spike punched a two-bar hole) → warn if < ~210 bars.
 - `outputsize = max(300, min(days×bars_per_day+20, 5000))`; TD caps at 5000/call (the backtester paginates by date).
 - `get_latest_price` / `get_spread_estimate` (median M5 High−Low) helpers.
 ---
 ## pair_signals/ — per-pair factories (ALWAYS use these, never bare PlatformConfig for a pair)
 `PAIR_PRODUCERS: Dict[symbol, factory]`; factory signature `make_config(primary_tf, confirmation_tfs=None) -> PlatformConfig`. `produce_signal(symbol, tf, data_provider)` builds config → aggregator → `analyze_pair`. Registered: XAU/USD.
 **A bare `PlatformConfig()` silently runs any pair with XAU defaults — always go through the factory** (the bot, CLI, and backtester all do).
 Default confirmation map (same in the factory, the bot, and backtest usage): M5→[M15,H1], M15→[M5,H1], H1→[M15,H4], H4→[H1,D1]. (Exception: `main.py`'s CLI map uses M5→[M1,M15] — a CLI-only quirk.)
 **xauusd.py**: `PlatformConfig()` defaults + per-TF overrides (each validated; full evidence in the module docstring):
 - M5: append hour 9 UTC to blocked hours (6-yr replay: +207.1R vs +178.3R; M15-and-up keep hour 9 — it's positive there). Enable BUY-only chart patterns (`cp_enabled=True`, `cp_patterns=["inv_hs","double_bottom","bull_flag"]`): 12mo +41.8R PF 1.60 (train/val both positive), 5yr +65.2R PF 1.48; bearish CP dropped (OOS −3.21R). M15/H1 CP remain disabled (all failed OOS).
 - M15: `signals.tp_floor_rr = 1.0` (1R minimum targets: +116.6R vs +105.4R, better in train/val and 2024 OOS; **hurts M5** — per-TF only) and `xau_strategy.blocked_regimes = ["trending"]` (mixed regime block: +65.7R vs +59.0R). A transitioning-SELL block was tested and REJECTED (bull-market artifact — failed 2024 OOS).
 - M15: `xau_strategy.sell_blocked_hours_utc = [15, 17]` (SELL-only hour block, validated 2026-07-16 on the 5-yr replay: +176.7R PF 1.31 vs +144.9R PF 1.22, better in all 6 years, both 12-mo halves, and 2024-06→2025-03 OOS; M5 cross-TF pocket confirms; BUYs at those hours are +34.3R so `blocked_hours_utc` would be wrong).
 - The factory must stay equivalent to inline `PlatformConfig(symbol=XAUUSD, …)` + overrides — **enforced by a test in test_all.py**.
 ---
 ## telegram_bot.py — the bot
 Run: `python telegram_bot.py` (env `TELEGRAM_TOKEN`, `TELEGRAM_CHAT_ID`, `TELEGRAM_CHANNEL_ID`; hardcoded fallbacks exist in the file).
 - **Owner lock**: every command/button is wrapped in `_owner_only` — only `TELEGRAM_CHAT_ID` gets responses; others get a refusal. The bot is the owner's control panel; the product is the channel.
 - **Subscriber channel** (`TELEGRAM_CHANNEL_ID`, `-100…`; `""` disables): auto-scan signal alerts + breach alerts also post there (bot needs channel-admin). Monetization = Telegram Stars subscription invite links; Telegram handles membership, the bot has zero payment code. The channel feed keeps running even when the owner toggles DM auto-scan off (`_feed_active()` = DM chats OR channel).
 - **Auto-scan**: boot-starts if `TELEGRAM_CHAT_ID` set (initial M15 scan populates the level cache first). Candle-boundary loops for M5/M15/H1/H4, firing at boundary + 10 s. Each scan runs all `scan_pairs` concurrently, updates the level cache, then broadcasts.
 - **Alert gating**: quality threshold (`_alert_min_quality`, default "C", menu cycles C→B→A) + fingerprint dedup per (symbol, TF) on (signal, quality, direction, entry bucket) with 1-hour re-arm — a persisting identical signal doesn't re-send every candle, but a setup that moved ≥ half its risk distance (new pullback/swing anchor) re-alerts. The fingerprint is committed only **after** a message reaches ≥1 target (`_commit_alert`), so a failed Telegram send doesn't consume the alert.
 - **WebSocket monitor**: subscribes every `scan_pairs` symbol to TD quotes WS; 10 s heartbeat; exponential reconnect (≤60 s). On tick crossing any cached key level (`swing_high/low_1..3`, `ema_trend`, `bb_upper/lower`, `kc_upper/lower`; cache TTL 1 h) → immediate M15 rescan → "LEVEL BREAK" alert if actionable+changed (same gates as auto-scan), 5-min per-pair cooldown.
 - **Commands**: `/start` (menu), `/scan` (buttons: per pair — M5/M15/H1/H4 + Scan All TFs), `/autoscan` (toggle DM copy), `/status` (loops, WS, live prices, level cache, cooldowns), `/cache` (age per pair/TF), `/clearcache`, `/help`.
 - Signal messages include the full indicator breakdown, warnings, trade setup, **trailing-stop management plan** (arm price + trail distance, from Signal/pb params by `strategy`), and a risk disclaimer on every outbound message.
 - Blocking scan work runs in `ThreadPoolExecutor(max_workers=4)`; one shared `ForexDataProvider` across all scans (cache reuse). Logs to `logs/telegram.log`.
 ---
 ## main.py — CLI
 `python main.py [--pair XAUUSD] [--timeframe M5|M15|M30|H1|H4|D1] [--detail] [--json] [--verbose]`. Uses the pair factory, renders a compact terminal report (decision, setup, context, top-3 reasons per side, warnings) or full JSON via `report_to_dict`.
 ---
 ## backtest/gpu_backtest.py — the backtester
 Replays history bar-by-bar through the **real `SignalAggregator.analyze_from_dataframes`** — same gates, same setup math. News is disabled (`use_news=False`): backtests are pure technical.
 **Realism model (all validated fixes — do not regress):**
 - Entry at the **next bar's open** after the signal bar; SL/TP distances preserved relative to actual entry. Entries are **re-anchored to the raw price path** (`_reanchor_entries_to_raw`): if cleaning dropped a >10σ spike bar right after the signal bar, the trade enters at the spike bar's open, not the bar after it — skipping it was a favorable bias.
 - Round-trip `--spread` subtracted from every trade's PnL before computing R (default = pair's `default_spread`: XAU $0.30). Results before 2026-07-08 (+59.5R baseline era) charged no spread — not comparable.
 - Trade resolution runs against the **RAW price path** (`fetch_history(clean=False)`): the parquet cache stores raw bars; cleaning (which drops >10σ news spikes that hit stops in reality) is applied only to the signal-side data. Caches written before this fix contain cleaned data — refetch to restore spike bars.
 - Same-bar SL+TP tie → SL first (conservative). Gap through SL/TP → filled at the bar open.
 - **Trailing exits** (default on, from `signals.trail_arm_r/trail_dist_r`; XAU_PB trades carry per-trade 0.8/0.75 overrides): stop ratchets at bar close only, so a new stop can only fill from the next bar (conservative). Exit reasons: SL / TRAIL / TP1 (static mode) / TIMEOUT / NO_DATA.
 - Session + weekend + weekday filters evaluate the **decision time = bar close** (`ts + primary_duration`) — exactly matching the live bot's wall-clock check.
 - Confirmation TFs are sliced to bars **fully closed** by decision time (no look-ahead).
 **Performance:** `precompute_indicators` builds every indicator series once per TF (CuPy on GPU or NumPy — check the log line `Backend: CuPy GPU` vs `NumPy CPU fallback`; CuPy needs the `nvidia-*-cu12` pip wheels or it silently falls back). `GPUTechnicalEngine` subclasses the live engine and overrides only the `_compute_*` methods with cache lookups — outputs are byte-identical to the live engine (regression-tested). Bar replay: pure per-bar evaluation fans out over forked worker processes (`--workers`, 0 = auto: cores−2 capped 12; <5000 bars forces 1); the entry **cooldown (5 bars) is the only sequential rule**, applied in an ordered pass afterwards — parallel and sequential books are identical. ~300 bars/s/core, ~1700 bars/s at 10 workers.
 **Strategy tags** (from reasons[0] markers): `XAU_PB`, `XAU_CP`, `GENERIC` (plus legacy `XAU` recognized). `--strategies` filters the book by tag. `--cp` force-enables the chart-pattern entry for candidate validation; `--cp-patterns` narrows the detector set.
 **CLI:**
 ```bash
 python backtest/gpu_backtest.py --pair XAUUSD --days 365
 python backtest/gpu_backtest.py --pair XAUUSD --start 2025-01-01 --end 2025-06-01 --primary-tf 15min --confirm-tfs 1h,4h
 # key flags: --spread --quality A+,A,B,C --strategies --workers --max-hold-bars 300
 #            --warmup-bars 250 --cooldown-bars 5 --no-cache --export-csv out.csv --detail
 ```
 Report: summary metrics (total R, PF, Sharpe on R, max DD, MFE/MAE), breakdowns by quality/strategy/regime, trade log, and per-trade reasoning dumps (indicator-by-indicator agreement at entry — required by `instructions_for_backtest.txt`). `--export-csv` flattens every indicator's signal/value/agreement into columns.
 **Comparability note (2026-07-16):** the parity/realism fixes of that date (EMA-ribbon port to the GPU engine, exact-pandas Bollinger precompute, raw-path entry re-anchoring, per-TF D1 warmup, live confirm-map CLI default, outlier snap-back retention) all shift books slightly vs earlier runs. Books produced before these fixes are not directly comparable — re-run the baseline before any before/after tuning comparison.
 Data: paginated TD fetches (5000-bar chunks) cached as parquet in `backtest/cache/` keyed by symbol/TF/window; warmup pad ≥60 days before the window for EMA200, extended per-TF so slow confirmation TFs get ≥210 bars at window start (D1 gets ~299 days — 62 days gave only ~61 bars: no EMA200, unlike live's 300-bar outputsize floor). `--confirm-tfs` defaults to the pair factory's live confirmation map (pass it explicitly to override); a hardcoded default here previously made the standard invocation backtest a different MTF setup than the bot scans.
 ---
 ## test_all.py — QA suite
 `python test_all.py` — **162 tests, 10 modules** (config, data_provider, engine, news_engine, signal_aggregator, main, telegram_bot, integration, pair_signals registry + engine-bug regressions, 2026-07-16 audit-fix regressions). No network needed (mocked). Uses a custom `@test("name")` decorator that runs immediately (NOT pytest — don't grep for `def test_`). Exit code non-zero on failure. Includes: the xauusd-factory ≡ inline-config equivalence test, engine bug-fix regression tests (Module 9b), and a live-vs-GPU engine parity sweep incl. the EMA-ribbon path (Module 11). **Any signal-logic change must keep this green and should add tests.**
 ---
 ## Environment variables
 | Variable | Purpose | Default |
 |---|---|---|
 | `TWELVE_DATA_API_KEY` | market data key | hardcoded fallback in `config.py` |
 | `TELEGRAM_TOKEN` | bot token | hardcoded fallback in `telegram_bot.py` |
 | `TELEGRAM_CHAT_ID` | owner chat id (boot auto-scan + owner lock) | hardcoded fallback |
 | `TELEGRAM_CHANNEL_ID` | subscriber channel (`-100…`); `""` disables the feed | hardcoded fallback |
 | `FB2_EMA_LO` | EMA200 "near" penalty threshold in % | `0.5` |
 Dependencies (`requirements.txt`): numpy, pandas, requests, twelvedata, python-telegram-bot ≥20, websockets. Python 3.12. A `venv/` exists in-repo.
 ---
 ## Code conventions
 - Type hints on public methods; dataclasses for all config and outputs.
 - `SignalStrength` (−2..+2) is the universal signal currency.
 - Key levels dict keys: `swing_high_*`, `swing_low_*`, `ema_*`, `bb_*`, `kc_*`, `atr`, `atr_pct`.
 - Private per-indicator series ride on `TechnicalAnalysis` as `_underscore` attributes; anything `_`-prefixed or weight-0 is skipped by scorers/formatters.
 - UTC everywhere; DataFrames indexed by tz-aware `Datetime`, oldest-first, capitalized OHLCV columns.
 - Logging via module loggers; the bot adds a rotating file handler.
 ---
 ## Rules for making changes (READ BEFORE EDITING SIGNAL LOGIC)
 1. **Never change a tuned value without re-validation.** The acceptance rule used throughout: positive total R in BOTH the train and validation halves of a ≥12-month window, PF ≥ 1.3, at realistic cost (XAU $0.30 round trip), with trailing exits and next-open fills. For any marginal edge additionally re-test on the 2024-06→2025-03 out-of-sample window — within-year splits alone have approved configs that failed OOS.
 2. **Per-timeframe overrides live in the pair factory** (`pair_signals/*.py`), never in global defaults — the same knob has opposite effects on different TFs (proven twice: `tp_floor_rr`, hour-9 block). Document the validation evidence in the factory docstring.
 3. **Pair isolation is a hard invariant.** XAU gates in the aggregator are display-gated (`report.display_name == "XAU/USD"`) so a future pair port cannot inherit gold-calibrated logic by accident.
 4. **Live == backtest parity**: session/weekend/hour filters must evaluate bar-CLOSE time in both paths; `xauusd.make_config` must stay equivalent to the inline config the backtester/bot would build (test-enforced). If you add a live gate, add it to the replay path (or `analyze_from_dataframes`) in the same commit.
 5. **ROC stays weighted** (see engine table) unless you retune composite thresholds and re-validate — removing it lost 13R.
 6. **Backtester realism knobs are results-critical**: raw-path resolution, spread charging, next-open entry, SL-first ties, close-only trail ratchet. Any "optimization" that touches these invalidates comparability with the pinned books (`backtest/*.csv`, memory notes).
 7. **Don't zero/disable indicators via config in production paths** — `disabled_indicators` exists for experiments; composite thresholds assume the full set.
 8. **The forming candle must stay dropped** and analysis must only ever see closed bars, or live signals repaint.
 9. Keep `test_all.py` green (`python test_all.py`); extend it for every new gate/knob.
 10. Legacy/no-op config fields exist (flagged above) — do not build new logic on them; delete only with test updates.
@@ -1,582 +0,0 @@
 """
 ForexBot2 — LLM agent panel (advisory consensus)
 =================================================
 ai-hedge-fund-style persona panel for XAU/USD signals: four DeepSeek
 personas (macro, technical, risk, contrarian) each review a finished
 SignalReport from their own angle, then a synthesizer chair weighs the
 takes and issues a consensus verdict (AGREE / NEUTRAL / OPPOSE with
 confidence and a short rationale).
 Two invariants, both load-bearing (CLAUDE.md rules 1 and 4):
 - ADVISORY / DISPLAY-ONLY. The verdict is written onto SignalReport
  fields that no gate and no scoring path ever reads. Any failure —
  LLM down, cap exhausted, garbage reply — degrades to "no panel",
  never to a changed or blocked signal.
 - NEVER runs inside ``analyze_from_dataframes``. The backtester replays
  that function; consumers (telegram_bot, main --panel,
  dashboard_publisher) attach the panel POST-HOC via ``attach_panel``,
  so replayed books can never see a panel field. Backtest parity holds
  by construction.
 Cost model: every full panel spends 5 requests (4 personas + 1
 synthesizer) against the shared DeepSeek daily cap (llm_client). Two
 layers bound the spend on top of that cap:
 - ``max_panels_per_day`` (config, default 20) — a UTC-day budget of
  panel *attempts*, mirroring the client's request cap.
 - A reuse TTL (default 3600 s) keyed on (symbol, timeframe, direction):
  a persisting signal re-serves its cached verdict across bars instead
  of re-spending. Without this, one sticky M5 dashboard signal would
  burn 5 calls per 5-minute bar — 1,440/day against the 500 cap.
 Prompts are STATIC module constants (DeepSeek prefix-cache pricing —
 see news_engine.py); all variable content travels in the user payload.
 """
 import datetime as _dt
 import logging
 import threading
 import time
 from collections import OrderedDict
 from concurrent.futures import ThreadPoolExecutor, wait as _fwait
 from dataclasses import dataclass, field
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
 from app_config import get_app_config
 from llm_client import DEFAULT_TIMEOUT, get_llm_client
 if TYPE_CHECKING:  # type hints only — keep the coupling one-way
    from news_engine import FundamentalContext
    from signal_aggregator import SignalReport
 logger = logging.getLogger(__name__)
 DEFAULT_MIN_QUALITY = "B"
 DEFAULT_MAX_PANELS_PER_DAY = 20
 DEFAULT_REUSE_TTL_SECONDS = 3600.0
 # Quality ranks for the min_quality gate. Deliberately a local copy —
 # telegram_bot's ranking is private and dashboard_publisher must not
 # import telegram_bot (hard independence rule).
 _QUALITY_RANK = {"A+": 4, "A": 3, "B": 2, "C": 1}
 # ── DeepSeek prompts ─────────────────────────────────────────────────
 # STATIC constants on purpose: DeepSeek prices a repeated byte-identical
 # prompt prefix at the cache-hit rate (~50x cheaper), so nothing here may
 # be interpolated or timestamped. All variable content travels in the
 # user payload. Panel output is display-only — it never gates signals.
 _GOLD_DOCTRINE = """\
 Gold doctrine: hawkish Fed / rate hikes / rising real yields / strong
 dollar are gold-bearish; easing / rate cuts / safe-haven flows /
 geopolitical stress / weak dollar are gold-bullish.
 """
 _TAKE_CONTRACT = """\
 Your review is advisory research on an already-generated signal; you are
 one voice on a panel, not the decision maker.
 Hard rules: use ONLY the data in the JSON you receive — do not invent
 numbers, events, or price levels. If the evidence your role needs is
 thin or missing, answer "neutral" with confidence below 30, unless your
 role's instructions say otherwise.
 Confidence scale (0-100): 80-100 strong one-sided evidence; 50-79 solid
 case with some doubt; 30-49 mixed evidence; below 30 little basis to
 judge.
 Write the note in plain everyday English for someone who has never
 traded: short simple sentences, no trading jargon, no acronyms, no
 abbreviations. Say "profit versus loss", not "RR"; "the 4-hour chart",
 not "H4"; describe what an indicator MEANS ("the price has climbed
 unusually fast"), never its name or number.
 Reply with ONLY JSON:
 {"stance": "support"|"oppose"|"neutral", "confidence": <0-100>,
 "note": "<max 25 words>"}
 "stance" says whether YOUR analysis supports the proposed trade
 direction, opposes it, or is genuinely mixed; "confidence" is how sure
 you are of your stance; "note" is your single most important point.
 """
 _MACRO_SYSTEM = """\
 You are the macro analyst on a gold (XAU/USD) trade-review panel. You
 receive JSON: a proposed trade brief ("brief": direction, timeframe,
 quality, price, regime) plus the current fundamental picture — recent
 "headlines", today's high-impact "events" (released: true means the
 number is out), a news "sentiment" score for gold, and an optional
 "news_summary" blurb.
 Judge ONE thing: does the macro backdrop support the proposed direction?
 Weigh released surprises over stale headlines; an imminent unreleased
 high-impact event is a reason for lower confidence, not a direction.
 Stance rules:
 - support: the released data and news flow clearly push gold in the
  proposed direction.
 - oppose: they clearly push gold the other way.
 - neutral: the picture is mixed or stale — or there are no meaningful
  headlines or events to judge (score that low confidence).
 """ + _GOLD_DOCTRINE + """\
 Example: brief direction BUY, headlines dominated by hawkish Fed
 repricing and a hot CPI print -> {"stance": "oppose", "confidence": 70,
 "note": "Inflation is running hot, so interest rates should stay high — that usually pushes gold down."}
 Example: brief direction BUY, headlines led by an escalating conflict
 and a soft jobs report -> {"stance": "support", "confidence": 75,
 "note": "Worries about war and a cooling economy usually send money into gold."}
 """ + _TAKE_CONTRACT
 _TECH_SYSTEM = """\
 You are the technical analyst on a gold (XAU/USD) trade-review panel.
 You receive JSON: a proposed trade brief ("brief") plus the technical
 evidence — per-"indicators" signal/confidence, multi-timeframe
 "mtf" signals and scores, "key_levels" versus the current price, and an
 "exhaustion" count of consecutive same-direction bars flagged by the
 engine.
 Judge ONE thing: is the technical picture internally coherent for the
 proposed direction? Look for indicators fighting each other, the signal
 fighting the regime (e.g. a BUY in trending_down), higher timeframes
 disagreeing, price pressed into a key level, or an exhausted move being
 chased.
 Stance rules:
 - support: the indicators, higher timeframes, and regime mostly agree
  with the proposed direction and none of the conflicts above appear.
 - oppose: one or more of those conflicts is present and serious.
 - neutral: the evidence genuinely cuts both ways, or too much of it is
  missing to judge (score that low confidence).
 Example: brief direction SELL in regime trending_up with H1 and H4 both
 BUY -> {"stance": "oppose", "confidence": 80,
 "note": "This bets on gold falling while the bigger picture still points up."}
 Example: brief direction BUY in regime trending_up with M15 and H1 both
 BUY and exhaustion 0 -> {"stance": "support", "confidence": 70,
 "note": "Every way of looking at the chart points up, so buying goes with the flow."}
 """ + _TAKE_CONTRACT
 _RISK_SYSTEM = """\
 You are the risk manager on a gold (XAU/USD) trade-review panel. You
 receive JSON: a proposed trade brief ("brief") plus the risk facts —
 the trade "setup" (entry, stop_loss, take_profit_1/2, risk_reward,
 risk_dollars), the "volatility" regime, engine "warnings", the calendar
 "event_risk" level, and the assumed "spread".
 Judge ONE thing: is this setup's risk acceptable as constructed? Check
 reward-to-risk adequacy, stop distance versus the volatility regime
 (a tight stop in extreme vol dies to noise; a huge stop in low vol is
 oversized), spread as a fraction of the stop, and event landmines about
 to hit.
 Stance rules:
 - support: the likely reward clearly justifies the risk, the stop fits
  the current volatility, and no event landmine is imminent.
 - oppose: reward too thin for the risk, a stop mismatched to the
  volatility, spread eating a big share of the stop, an imminent
  high-impact event — or no setup at all (a missing setup means the
  signal graded too low for one; oppose unless the rest is
  exceptional).
 - neutral: acceptable but unremarkable — nothing dangerous, nothing
  attractive.
 Example: RR 1.4 with volatility extreme and a high-impact event due ->
 {"stance": "oppose", "confidence": 65,
 "note": "The possible profit is small for such wild price swings, and big economic news is due soon."}
 Example: RR 2.4 with normal volatility and no events due ->
 {"stance": "support", "confidence": 70,
 "note": "The possible profit is more than double the possible loss, and conditions look calm."}
 """ + _TAKE_CONTRACT
 _CONTRARIAN_SYSTEM = """\
 You are the contrarian on a gold (XAU/USD) trade-review panel — the
 designated skeptic. You receive JSON: a proposed trade brief ("brief"),
 the engine's own "bullish_reasons" and "bearish_reasons", its
 "warnings", and an optional "news_summary".
 Your job: construct the strongest honest case AGAINST the proposed
 trade, then score how damaging that case is. Use "oppose" only when the
 counter-case is genuinely strong, "neutral" when it is real but modest,
 and "support" when even the skeptic finds nothing that bites.
 """ + _GOLD_DOCTRINE + """\
 Example: a BUY whose own engine lists three bearish reasons and a
 counter-trend warning -> {"stance": "oppose", "confidence": 60,
 "note": "The system's own list of negatives looks stronger than its reason to buy."}
 """ + _TAKE_CONTRACT
 _SYNTH_SYSTEM = """\
 You are the chair of a gold (XAU/USD) trade-review panel. You receive
 JSON: the proposed trade "direction" and the panel's "takes" — one per
 analyst ("persona", "stance", "confidence", "note").
 Weigh the takes into ONE consensus on whether to act on the signal:
 "AGREE" (the panel backs the trade), "OPPOSE" (the panel is against it),
 or "NEUTRAL" (genuinely split or low conviction). Weigh a high-
 confidence oppose from any single analyst seriously — one strong veto
 can outweigh two lukewarm supports. Do not average blindly.
 Base the verdict and rationale ONLY on the takes given — do not add
 outside analysis.
 Confidence scale (0-100): 80-100 the panel clearly leans one way; 50-79
 a lean with real dissent; below 50 split or low conviction.
 Write the rationale in plain everyday English for someone who has never
 traded: short simple sentences, no trading jargon, no acronyms, no
 abbreviations.
 Example rationale: "Three of four analysts are against this: the
 possible profit is too small and the price has already climbed
 unusually fast. Only the news backdrop supports buying."
 Reply with ONLY JSON:
 {"verdict": "AGREE"|"NEUTRAL"|"OPPOSE", "confidence": <0-100>,
 "rationale": "<max 40 words>"}
 """
 # ── Result types ─────────────────────────────────────────────────────
@dataclass
 class PersonaTake:
    persona: str        # "macro" | "technical" | "risk" | "contrarian"
    stance: str         # "support" | "oppose" | "neutral"
    confidence: float   # 0-100
    note: str
@dataclass
 class PanelVerdict:
    verdict: str        # "AGREE" | "NEUTRAL" | "OPPOSE"
    confidence: float   # 0-100
    rationale: str
    takes: List[PersonaTake] = field(default_factory=list)
    created_ts: float = 0.0
 # ── Payload builders (pure functions; llm_client json-dumps sorted) ──
 def _direction(report: "SignalReport") -> Optional[str]:
    """BUY/SELL the panel should review; None = nothing to review."""
    if report.trade_setup is not None:
        return report.trade_setup.direction
    if report.final_signal.value > 0:
        return "BUY"
    if report.final_signal.value < 0:
        return "SELL"
    return None
 def _brief(report: "SignalReport") -> Dict[str, Any]:
    analysis = report.primary_analysis
    return {
        "pair": report.display_name,
        "timeframe": analysis.timeframe if analysis else "",
        "direction": _direction(report),
        "quality": report.signal_quality,
        "score": round(report.final_score, 1),
        "confidence": round(report.final_confidence),
        "price": round(report.current_price, 2),
        "market_regime": report.market_regime,
        "volatility_regime": report.volatility_regime,
        "mtf_agreement": report.mtf_agreement,
    }
 def _macro_payload(report: "SignalReport",
                   context: Optional["FundamentalContext"]) -> Dict[str, Any]:
    headlines: List[str] = []
    events: List[Dict[str, Any]] = []
    if context is not None:
        headlines = [h.headline for h in context.news_headlines[:10]]
        events = [
            {"event": e.event, "currency": e.currency,
             "actual": e.actual, "forecast": e.forecast,
             "released": not e.is_upcoming}
            for e in context.events_today if e.is_high_impact
        ][:8]
    return {
        "brief": _brief(report),
        "headlines": headlines,
        "events": events,
        "sentiment": round(report.news_sentiment, 3),
        "news_summary": report.news_summary,
    }
 def _technical_payload(report: "SignalReport") -> Dict[str, Any]:
    indicators: Dict[str, Dict[str, Any]] = {}
    key_levels: Dict[str, float] = {}
    analysis = report.primary_analysis
    if analysis is not None:
        # Same visibility filter as main.report_to_dict: private series
        # ride as _-prefixed pseudo-indicators, weight-0 = context-only.
        indicators = {
            name: {"signal": ind.signal.name,
                   "confidence": round(ind.confidence)}
            for name, ind in analysis.indicators.items()
            if not name.startswith("_") and ind.weight != 0
        }
        key_levels = {k: round(v, 2) for k, v in analysis.key_levels.items()}
    return {
        "brief": _brief(report),
        "indicators": indicators,
        "mtf": {tf: {"signal": sig.name,
                     "score": round(report.mtf_scores.get(tf, 0.0), 1)}
                for tf, sig in report.mtf_signals.items()},
        "key_levels": key_levels,
        "exhaustion": report.exhaustion_count,
    }
 def _risk_payload(report: "SignalReport") -> Dict[str, Any]:
    payload: Dict[str, Any] = {
        "brief": _brief(report),
        "volatility": report.volatility_regime,
        "warnings": list(report.warnings),
        "event_risk": report.event_risk,
        "event_description": report.event_description,
        "spread": round(report.spread_estimate, 2),
    }
    ts = report.trade_setup
    if ts is not None:
        payload["setup"] = {
            "direction": ts.direction,
            "entry": round(ts.entry_price, 2),
            "stop_loss": round(ts.stop_loss, 2),
            "take_profit_1": round(ts.take_profit_1, 2),
            "take_profit_2": round(ts.take_profit_2, 2),
            "risk_reward": round(ts.risk_reward, 2),
            "risk_dollars": round(ts.risk_pips * ts.pip_value, 2),
        }
    return payload
 def _contrarian_payload(report: "SignalReport") -> Dict[str, Any]:
    return {
        "brief": _brief(report),
        "bullish_reasons": report.bullish_reasons[:3],
        "bearish_reasons": report.bearish_reasons[:3],
        "warnings": list(report.warnings),
        "news_summary": report.news_summary,
    }
 # ── Orchestration ────────────────────────────────────────────────────
 # Panel-private pool: the 4 persona calls run concurrently WITHOUT
 # occupying telegram_bot's shared scan executor (which has 4 workers —
 # borrowing them would stall every other scan for the panel's duration).
 _pool = ThreadPoolExecutor(max_workers=4, thread_name_prefix="agent-panel")
 _VALID_STANCES = ("support", "oppose", "neutral")
 _VALID_VERDICTS = ("AGREE", "NEUTRAL", "OPPOSE")
 def _clamp_confidence(raw: Any) -> Optional[float]:
    try:
        return max(0.0, min(100.0, float(raw)))
    except (TypeError, ValueError):
        return None
 def _validate_take(persona: str, reply: Any) -> Optional[PersonaTake]:
    if not isinstance(reply, dict):
        return None
    stance = reply.get("stance")
    if stance not in _VALID_STANCES:
        return None
    confidence = _clamp_confidence(reply.get("confidence"))
    if confidence is None:
        return None
    note = str(reply.get("note", "")).strip()[:160]
    return PersonaTake(persona=persona, stance=stance,
                       confidence=confidence, note=note)
 def _ask_persona(persona: str, system: str,
                 payload: Dict[str, Any]) -> Optional[PersonaTake]:
    # Client resolved at call time: tests install stubs via lc._client.
    reply = get_llm_client().chat_json(system, payload)
    return _validate_take(persona, reply)
 def run_panel(report: "SignalReport",
              context: Optional["FundamentalContext"] = None,
              ) -> Optional[PanelVerdict]:
    """Run 4 personas + synthesizer; None = no verdict (fail-safe).
    Requires ≥2 valid persona takes before spending the synthesizer
    request; the synthesizer's reply is validated strictly and there is
    deliberately NO programmatic fallback verdict — a panel that could
    not really convene reports nothing rather than something invented.
    """
    direction = _direction(report)
    if direction is None:
        return None
    jobs = (
        ("macro", _MACRO_SYSTEM, _macro_payload(report, context)),
        ("technical", _TECH_SYSTEM, _technical_payload(report)),
        ("risk", _RISK_SYSTEM, _risk_payload(report)),
        ("contrarian", _CONTRARIAN_SYSTEM, _contrarian_payload(report)),
    )
    futures = [_pool.submit(_ask_persona, name, system, payload)
               for name, system, payload in jobs]
    # The HTTP timeout inside the client bounds each call; the margin
    # covers pool queueing. Stragglers past the wait are abandoned (their
    # threads finish harmlessly) and simply don't vote.
    client_timeout = getattr(get_llm_client(), "_timeout", DEFAULT_TIMEOUT)
    done, _ = _fwait(futures, timeout=client_timeout + 8.0)
    takes = [f.result() for f in done if not f.exception()]
    takes = [t for t in takes if t is not None]
    if len(takes) < 2:
        logger.debug("agent panel: %d/4 persona takes — skipping synthesizer",
                     len(takes))
        return None
    order = {"macro": 0, "technical": 1, "risk": 2, "contrarian": 3}
    takes.sort(key=lambda t: order.get(t.persona, 9))
    reply = get_llm_client().chat_json(_SYNTH_SYSTEM, {
        "direction": direction,
        "takes": [{"persona": t.persona, "stance": t.stance,
                   "confidence": t.confidence, "note": t.note}
                  for t in takes],
    })
    if not isinstance(reply, dict):
        return None
    verdict = reply.get("verdict")
    confidence = _clamp_confidence(reply.get("confidence"))
    if verdict not in _VALID_VERDICTS or confidence is None:
        return None
    rationale = str(reply.get("rationale", "")).strip()[:240]
    return PanelVerdict(verdict=verdict, confidence=confidence,
                        rationale=rationale, takes=takes,
                        created_ts=time.time())
 # ── Cache, budget, attach ────────────────────────────────────────────
 _state_lock = threading.Lock()
 # Exact result per reviewed bar: repeated attaches on the same signal
 # (re-scan, multi-consumer) are free.
 _cache: "OrderedDict[Tuple[str, str, str, str], PanelVerdict]" = OrderedDict()
 _CACHE_MAX = 32
 # Reuse layer: a persisting signal (same symbol/tf/direction, newer bar)
 # re-serves its last verdict for reuse_ttl_seconds instead of re-spending
 # 5 requests per bar (see module docstring for the M5 dashboard math).
 _last_by_dir: Dict[Tuple[str, str, str], PanelVerdict] = {}
 # UTC-day budget of panel attempts (mirrors llm_client._under_cap).
 _panel_day: Optional[_dt.date] = None
 _panel_count: int = 0
 _warned_budget = False
 def reset_panel_state() -> None:
    """Drop caches and counters (tests / config reloads)."""
    global _panel_day, _panel_count, _warned_budget
    with _state_lock:
        _cache.clear()
        _last_by_dir.clear()
        _panel_day = None
        _panel_count = 0
        _warned_budget = False
 def panel_health() -> Dict[str, Any]:
    """Read-only counters for status displays."""
    settings = get_app_config().agent_panel_settings
    cap = int(settings.get("max_panels_per_day", DEFAULT_MAX_PANELS_PER_DAY))
    today = _dt.datetime.now(_dt.timezone.utc).date()
    with _state_lock:
        used = _panel_count if _panel_day == today else 0
    return {"used_today": used, "cap": cap}
 def _under_panel_budget(cap: int) -> bool:
    """Count one panel attempt against the UTC-day budget."""
    global _panel_day, _panel_count, _warned_budget
    today = _dt.datetime.now(_dt.timezone.utc).date()
    with _state_lock:
        if _panel_day != today:
            _panel_day = today
            _panel_count = 0
            _warned_budget = False
        if _panel_count >= cap:
            if not _warned_budget:
                _warned_budget = True
                logger.warning(
                    "agent panel: daily budget (%d) exhausted — no more "
                    "panels until UTC midnight", cap)
            return False
        _panel_count += 1
        return True
 def _write_fields(report: "SignalReport", v: PanelVerdict) -> None:
    report.panel_verdict = v.verdict
    report.panel_confidence = float(v.confidence)
    report.panel_rationale = v.rationale
    report.panel_takes = [
        {"persona": t.persona, "stance": t.stance,
         "confidence": t.confidence, "note": t.note} for t in v.takes]
 def attach_panel(report: "SignalReport",
                 settings: Optional[Dict[str, Any]] = None,
                 surface: str = "telegram",
                 force: bool = False,
                 context: Optional["FundamentalContext"] = None) -> bool:
    """Best-effort panel attach; True iff panel fields were populated.
    Advisory feature contract: NEVER raises — an exploding panel must
    not cost an alert send or a dashboard publish. ``force`` (CLI
    --panel) bypasses the enabled/surface/quality toggles but never the
    daily budget or the shared LLM cap.
    """
    try:
        if settings is None:
            settings = get_app_config().agent_panel_settings
        if not force:
            if not settings.get("enabled", False):
                return False
            surface_on = {
                "telegram": settings.get("telegram", True),
                "dashboard": settings.get("dashboard", False),
            }.get(surface, True)
            if not surface_on:
                return False
            min_quality = settings.get("min_quality", DEFAULT_MIN_QUALITY)
            if (_QUALITY_RANK.get(report.signal_quality, -1)
                    < _QUALITY_RANK.get(min_quality, 2)):
                return False
        direction = _direction(report)
        if direction is None:
            return False
        analysis = report.primary_analysis
        tf = analysis.timeframe if analysis else ""
        exact_key = (report.symbol, tf, report.timestamp, direction)
        dir_key = (report.symbol, tf, direction)
        ttl = float(settings.get("reuse_ttl_seconds",
                                 DEFAULT_REUSE_TTL_SECONDS))
        with _state_lock:
            hit = _cache.get(exact_key)
            if hit is None:
                prior = _last_by_dir.get(dir_key)
                if prior is not None and time.time() - prior.created_ts < ttl:
                    hit = prior
        if hit is not None:
            _write_fields(report, hit)
            return True
        cap = int(settings.get("max_panels_per_day",
                               DEFAULT_MAX_PANELS_PER_DAY))
        if not _under_panel_budget(cap):
            return False
        if context is None:
            # Best-effort; the news engine's class-level 600 s caches make
            # this nearly free when warm in both bot and dashboard.
            try:
                from config import PlatformConfig
                from news_engine import NewsEngine
                context = NewsEngine(
                    PlatformConfig().news).get_fundamental_context()
            except Exception as e:
                logger.debug("agent panel: news context unavailable: %s", e)
                context = None
        verdict = run_panel(report, context)
        if verdict is None:
            return False
        _write_fields(report, verdict)
        with _state_lock:
            _cache[exact_key] = verdict
            while len(_cache) > _CACHE_MAX:
                _cache.popitem(last=False)
            _last_by_dir[dir_key] = verdict
        return True
    except Exception:
        logger.exception("agent panel: attach failed — signal unaffected")
        return False
@@ -1,428 +0,0 @@
 """
 app_config.py — deployment-settings overlay (config.json)
 =========================================================
 Loads the gitignored config.json at the repo root ONCE per process.
 Deployment surface ONLY: Telegram credentials, data-provider selection
 and API keys, scanned pairs/timeframes, per-pair base blocked hours,
 alert-quality boot default, log level. Signal logic, weights,
 thresholds and gates stay in config.py / pair_signals.
 Precedence for every setting: env var → config.json → code default.
 A repo-root .env file (gitignored) is loaded at import via setdefault,
 so it behaves exactly like real environment variables except that a
 real shell export always wins. Secrets have NO code default — with
 neither env nor config.json the bot refuses to start
 (telegram_bot.main) and data fetches fail with a named error
 (data_provider).
 Parity contract (test-enforced): with no config.json, or one holding
 the config.json.example defaults, every produced PlatformConfig is
 byte-identical to the pure-code defaults. apply_pair_overrides() runs
 in the pair factory BEFORE the validated per-timeframe deltas, so the
 bot, CLI and backtester all see the same overlay (live == backtest).
 Spec: docs/superpowers/specs/2026-07-21-centralized-config-design.md
 """
 import json
 import logging
 import os
 from dataclasses import dataclass, field
 from typing import Any, Dict, List, Optional
 from config import PAIR_INFO, PlatformConfig, Timeframe, XAUUSD
 logger = logging.getLogger(__name__)
 CONFIG_PATH = os.path.join(
    os.path.dirname(os.path.abspath(__file__)), "config.json")
 ENV_PATH = os.path.join(
    os.path.dirname(os.path.abspath(__file__)), ".env")
 def load_dotenv(path: str = ENV_PATH) -> None:
    """Minimal .env loader (no python-dotenv dependency).
    KEY=VALUE lines land in os.environ via setdefault — a variable
    already exported in the shell always wins, keeping the
    env → config.json precedence intact (.env is just a convenient
    place to put env vars). Comments, blank lines, ``export `` prefixes
    and single/double quotes are handled; anything else is skipped.
    """
    try:
        with open(path, encoding="utf-8") as fh:
            lines = fh.read().splitlines()
    except OSError:
        return
    for line in lines:
        line = line.strip()
        if not line or line.startswith("#") or "=" not in line:
            continue
        if line.startswith("export "):
            line = line[len("export "):]
        key, _, value = line.partition("=")
        key, value = key.strip(), value.strip()
        if len(value) >= 2 and value[0] == value[-1] and value[0] in "\"'":
            value = value[1:-1]
        elif value[:1] not in "\"'":
            # Unquoted value: whitespace-then-# starts an inline comment
            # (KEY=abc  # prod). Quote the value to keep a literal " #".
            for sep in (" #", "\t#"):
                cut = value.find(sep)
                if cut != -1:
                    value = value[:cut].rstrip()
        if key:
            os.environ.setdefault(key, value)
 # Module import == process boot, before any resolver can run.
 load_dotenv()
 _VALID_QUALITIES = ("A+", "A", "B", "C")
 _VALID_LOG_LEVELS = ("DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL")
 # Timeframes the bot's candle-boundary scheduler supports (must have
 # AUTOSCAN_CONFIRMATIONS entries in telegram_bot.py).
 _SCANNABLE_TFS = ("M5", "M15", "H1", "H4")
 _KNOWN_TOP = {"telegram", "data_providers", "scanning", "pairs", "log_level",
              "oanda", "deepseek", "agent_panel"}
 _KNOWN_OANDA = {
    "account_id", "api_token", "base_url", "lot_size",
    "quality_gate", "max_hold_bars",
 }
 _KNOWN_DEEPSEEK = {
    "api_key", "model", "enabled", "timeout_seconds", "daily_request_cap",
 }
 _KNOWN_AGENT_PANEL = {
    "enabled", "telegram", "dashboard", "min_quality",
    "max_panels_per_day", "reuse_ttl_seconds",
 }
 _KNOWN_TELEGRAM = {"token", "chat_id", "channel_id", "alert_min_quality"}
 _KNOWN_SCANNING = {"pairs", "timeframes"}
 _KNOWN_PAIR_KEYS = {"blocked_hours_utc"}
 class AppConfigError(Exception):
    """config.json is present but invalid — fail fast at boot."""
@dataclass
 class AppConfig:
    telegram_token: str = ""
    telegram_chat_id: str = ""
    telegram_channel_id: str = ""          # "" = channel feed disabled
    alert_min_quality: str = "C"
    active_provider: str = "twelve_data"
    streaming_provider: Optional[str] = None   # None = active provider
    provider_settings: Dict[str, Dict[str, Any]] = field(default_factory=dict)
    scan_pairs: List[str] = field(default_factory=lambda: [XAUUSD])
    scan_timeframes: List[Timeframe] = field(default_factory=lambda: [
        Timeframe.M5, Timeframe.M15, Timeframe.H1, Timeframe.H4])
    pair_overrides: Dict[str, Dict[str, Any]] = field(default_factory=dict)
    oanda_settings: Dict[str, Any] = field(default_factory=dict)
    deepseek_settings: Dict[str, Any] = field(default_factory=dict)
    agent_panel_settings: Dict[str, Any] = field(default_factory=dict)
    log_level: str = "INFO"
 def _check_keys(section: Dict[str, Any], known: set, where: str) -> None:
    if not isinstance(section, dict):
        raise AppConfigError(f"config.json: {where} must be a JSON object")
    unknown = sorted(set(section) - known)
    if unknown:
        raise AppConfigError(
            f"config.json: unknown key(s) {unknown} in {where}; "
            f"known keys: {sorted(known)}")
 def load_app_config(path: str = CONFIG_PATH) -> AppConfig:
    """Parse + validate config.json. Missing file → pure defaults."""
    app = AppConfig()
    if not os.path.exists(path):
        logger.warning(
            "config.json not found at %s — running on code defaults "
            "(secrets must come from environment variables)", path)
        return app
    try:
        with open(path, encoding="utf-8") as fh:
            raw = json.load(fh)
    except json.JSONDecodeError as e:
        raise AppConfigError(f"config.json is not valid JSON: {e}") from e
    if not isinstance(raw, dict):
        raise AppConfigError("config.json top level must be a JSON object")
    _check_keys(raw, _KNOWN_TOP, "top level")
    tg = raw.get("telegram", {})
    _check_keys(tg, _KNOWN_TELEGRAM, '"telegram"')
    app.telegram_token = str(tg.get("token", app.telegram_token))
    app.telegram_chat_id = str(tg.get("chat_id", app.telegram_chat_id))
    app.telegram_channel_id = str(
        tg.get("channel_id", app.telegram_channel_id))
    quality = tg.get("alert_min_quality", app.alert_min_quality)
    if quality not in _VALID_QUALITIES:
        raise AppConfigError(
            f'config.json: telegram.alert_min_quality "{quality}" '
            f"not in {_VALID_QUALITIES}")
    app.alert_min_quality = quality
    dp = raw.get("data_providers")
    if dp is not None:
        if not isinstance(dp, dict):
            raise AppConfigError('config.json: "data_providers" must be an object')
        app.active_provider = str(dp.get("active", app.active_provider))
        app.provider_settings = {}
        for name, block in dp.items():
            # "active" and "streaming" are reserved scalars, not blocks.
            if name in ("active", "streaming"):
                continue
            if not isinstance(block, dict):
                raise AppConfigError(
                    f'config.json: data_providers.{name} must be an object')
            app.provider_settings[name] = dict(block)
        if app.active_provider not in app.provider_settings:
            raise AppConfigError(
                f'config.json: data_providers.active "{app.active_provider}" '
                "has no matching provider block")
        streaming = dp.get("streaming")
        if streaming is not None:
            if not isinstance(streaming, str):
                raise AppConfigError(
                    'config.json: data_providers.streaming must be a string '
                    "(a provider name)")
            if streaming not in app.provider_settings:
                raise AppConfigError(
                    f'config.json: data_providers.streaming "{streaming}" '
                    "has no matching provider block")
            app.streaming_provider = streaming
    scanning = raw.get("scanning", {})
    _check_keys(scanning, _KNOWN_SCANNING, '"scanning"')
    if "pairs" in scanning:
        pairs = scanning["pairs"]
        if not isinstance(pairs, list):
            raise AppConfigError('config.json: scanning.pairs must be a list')
        bad = [p for p in pairs if p not in PAIR_INFO]
        if bad:
            raise AppConfigError(
                f"config.json: unknown pair(s) {bad} in scanning.pairs; "
                f"known: {sorted(PAIR_INFO)}")
        app.scan_pairs = list(pairs)
    if "timeframes" in scanning:
        names = scanning["timeframes"]
        if not isinstance(names, list):
            raise AppConfigError(
                'config.json: scanning.timeframes must be a list')
        bad = [n for n in names if n not in _SCANNABLE_TFS]
        if bad:
            raise AppConfigError(
                f"config.json: unsupported timeframe(s) {bad} in "
                f"scanning.timeframes; supported: {list(_SCANNABLE_TFS)}")
        app.scan_timeframes = [Timeframe[n] for n in names]
    pair_section = raw.get("pairs", {})
    if not isinstance(pair_section, dict):
        raise AppConfigError('config.json: "pairs" must be a JSON object')
    for symbol, overrides in pair_section.items():
        if symbol not in PAIR_INFO:
            raise AppConfigError(
                f'config.json: unknown pair "{symbol}" in pairs; '
                f"known: {sorted(PAIR_INFO)}")
        _check_keys(overrides, _KNOWN_PAIR_KEYS, f'pairs."{symbol}"')
        if "blocked_hours_utc" in overrides:
            hours = overrides["blocked_hours_utc"]
            valid = isinstance(hours, list) and all(
                isinstance(h, int) and not isinstance(h, bool)
                and 0 <= h <= 23 for h in hours)
            if not valid:
                raise AppConfigError(
                    f'config.json: pairs."{symbol}".blocked_hours_utc must '
                    "be a list of integers in 0-23")
    app.pair_overrides = {s: dict(o) for s, o in pair_section.items()}
    oanda = raw.get("oanda", {})
    _check_keys(oanda, _KNOWN_OANDA, '"oanda"')
    if "lot_size" in oanda:
        ls = oanda["lot_size"]
        if not isinstance(ls, int) or isinstance(ls, bool) or ls < 1:
            raise AppConfigError(
                "config.json: oanda.lot_size must be a positive integer")
    if "quality_gate" in oanda and oanda["quality_gate"] not in _VALID_QUALITIES:
        raise AppConfigError(
            f'config.json: oanda.quality_gate "{oanda["quality_gate"]}" '
            f"not in {_VALID_QUALITIES}")
    if "max_hold_bars" in oanda:
        mhb = oanda["max_hold_bars"]
        if not isinstance(mhb, int) or isinstance(mhb, bool) or mhb < 1:
            raise AppConfigError(
                "config.json: oanda.max_hold_bars must be a positive integer")
    app.oanda_settings = dict(oanda)
    deepseek = raw.get("deepseek", {})
    _check_keys(deepseek, _KNOWN_DEEPSEEK, '"deepseek"')
    if "enabled" in deepseek and not isinstance(deepseek["enabled"], bool):
        raise AppConfigError(
            "config.json: deepseek.enabled must be true or false")
    if "timeout_seconds" in deepseek:
        ts = deepseek["timeout_seconds"]
        if isinstance(ts, bool) or not isinstance(ts, (int, float)) or ts <= 0:
            raise AppConfigError(
                "config.json: deepseek.timeout_seconds must be a positive "
                "number")
    if "daily_request_cap" in deepseek:
        cap = deepseek["daily_request_cap"]
        if isinstance(cap, bool) or not isinstance(cap, int) or cap < 1:
            raise AppConfigError(
                "config.json: deepseek.daily_request_cap must be a positive "
                "integer")
    if "model" in deepseek:
        if not isinstance(deepseek["model"], str) or not deepseek["model"]:
            raise AppConfigError(
                "config.json: deepseek.model must be a non-empty string")
    app.deepseek_settings = dict(deepseek)
    panel = raw.get("agent_panel", {})
    _check_keys(panel, _KNOWN_AGENT_PANEL, '"agent_panel"')
    for flag in ("enabled", "telegram", "dashboard"):
        if flag in panel and not isinstance(panel[flag], bool):
            raise AppConfigError(
                f"config.json: agent_panel.{flag} must be true or false")
    if "min_quality" in panel and panel["min_quality"] not in _VALID_QUALITIES:
        raise AppConfigError(
            f'config.json: agent_panel.min_quality "{panel["min_quality"]}" '
            f"not in {_VALID_QUALITIES}")
    if "max_panels_per_day" in panel:
        mpd = panel["max_panels_per_day"]
        if isinstance(mpd, bool) or not isinstance(mpd, int) or mpd < 1:
            raise AppConfigError(
                "config.json: agent_panel.max_panels_per_day must be a "
                "positive integer")
    if "reuse_ttl_seconds" in panel:
        ttl = panel["reuse_ttl_seconds"]
        if isinstance(ttl, bool) or not isinstance(ttl, (int, float)) \
                or ttl < 0:
            raise AppConfigError(
                "config.json: agent_panel.reuse_ttl_seconds must be a "
                "non-negative number")
    app.agent_panel_settings = dict(panel)
    level = raw.get("log_level", app.log_level)
    if level not in _VALID_LOG_LEVELS:
        raise AppConfigError(
            f'config.json: log_level "{level}" not in {_VALID_LOG_LEVELS}')
    app.log_level = level
    return app
 # ── Process-wide singleton ────────────────────────────────────────────
 _cached: Optional[AppConfig] = None
 def get_app_config() -> AppConfig:
    global _cached
    if _cached is None:
        _cached = load_app_config()
    return _cached
 def set_app_config(app: Optional[AppConfig]) -> None:
    """Override the process-wide AppConfig (tests/experiments).
    Pass None to re-enable lazy loading from CONFIG_PATH."""
    global _cached
    _cached = app
 # ── Resolution helpers: env var → config.json → default ───────────────
 def telegram_token(app: Optional[AppConfig] = None) -> str:
    app = app or get_app_config()
    return os.environ.get("TELEGRAM_TOKEN") or app.telegram_token
 def telegram_chat_id(app: Optional[AppConfig] = None) -> str:
    app = app or get_app_config()
    return os.environ.get("TELEGRAM_CHAT_ID") or app.telegram_chat_id
 def telegram_channel_id(app: Optional[AppConfig] = None) -> str:
    """Env set-but-empty ("") means "disable the feed" and beats JSON,
    so this checks None-ness, not truthiness."""
    app = app or get_app_config()
    env = os.environ.get("TELEGRAM_CHANNEL_ID")
    return env if env is not None else app.telegram_channel_id
 # Per-provider env-var overrides for API keys (env → config.json → "").
 _PROVIDER_ENV_KEYS = {
    "twelve_data": "TWELVE_DATA_API_KEY",
    "tiingo": "TIINGO_API_KEY",
 }
 def provider_api_key(
    app: Optional[AppConfig] = None, provider: Optional[str] = None
 ) -> str:
    """API key for `provider` (default: the ACTIVE provider). The env
    override is per-provider (TWELVE_DATA_API_KEY applies only to
    twelve_data, TIINGO_API_KEY only to tiingo)."""
    app = app or get_app_config()
    name = provider or app.active_provider
    env_key = _PROVIDER_ENV_KEYS.get(name)
    if env_key:
        env = os.environ.get(env_key)
        if env:
            return env
    block = app.provider_settings.get(name, {})
    return str(block.get("api_key", ""))
 # ── Oanda resolution helpers (env → config.json → default) ───────────
 def oanda_account_id(app: Optional[AppConfig] = None) -> str:
    app = app or get_app_config()
    return os.environ.get("OANDA_ACCOUNT_ID") or str(
        app.oanda_settings.get("account_id", ""))
 def oanda_api_token(app: Optional[AppConfig] = None) -> str:
    app = app or get_app_config()
    return os.environ.get("OANDA_API_TOKEN") or str(
        app.oanda_settings.get("api_token", ""))
 def oanda_base_url(app: Optional[AppConfig] = None) -> str:
    app = app or get_app_config()
    return os.environ.get("OANDA_BASE_URL") or str(
        app.oanda_settings.get("base_url", "https://api-fxpractice.oanda.com"))
 # ── DeepSeek resolution helper (env → config.json → "") ──────────────
 def deepseek_api_key(app: Optional[AppConfig] = None) -> str:
    app = app or get_app_config()
    return os.environ.get("DEEPSEEK_API_KEY") or str(
        app.deepseek_settings.get("api_key", ""))
 # ── PlatformConfig overlay ────────────────────────────────────────────
 def apply_pair_overrides(
    cfg: PlatformConfig, app: Optional[AppConfig] = None
 ) -> PlatformConfig:
    """Stamp deployment settings from config.json onto a PlatformConfig.
    Called by the pair factories BEFORE their validated per-timeframe
    deltas, so e.g. the M5 hour-9 append lands on top of the JSON base.
    The pair's base blocked_hours_utc is set on BOTH mirrored copies
    (xau_strategy + backtest) so they cannot drift.
    """
    app = app or get_app_config()
    overrides = app.pair_overrides.get(cfg.symbol, {})
    if "blocked_hours_utc" in overrides:
        hours = list(overrides["blocked_hours_utc"])
        cfg.xau_strategy.blocked_hours_utc = hours
        cfg.backtest.blocked_hours_utc = list(hours)
    cfg.scan_pairs = list(app.scan_pairs)
    cfg.scan_timeframes = list(app.scan_timeframes)
    return cfg
@@ -51,7 +51,7 @@ from gpu_backtest import (  # noqa: E402
    precompute_indicators,
    _is_xau_blocked_by_calendar,
 )
-from providers import make_data_provider  # noqa: E402
+from data_provider import ForexDataProvider  # noqa: E402
 logger = logging.getLogger("diagnose_filters")
@@ -79,54 +79,34 @@ def classify_bar(
    """
    filters_hit: List[str] = []
-    # Known filter tags, matched against the exact warning strings the
+    # Scan warnings for known filter tags
-    # aggregator emits (refreshed 2026-07-30 for gates 6d-6i). ORDER
+    filter_tags = {
-    # MATTERS: most-specific prefix first, and each warning maps to at
+        "H4 RSI GATE":       "H4_RSI_GATE",
-    # most ONE tag (break on first hit) — substring families like
+        "H1 MACD GATE":      "H1_MACD_GATE",
-    # "M5 FILTER: SELL quality" vs "M5 FILTER" and "TREND EXHAUSTION"
+        "RSI ZONE GATE":     "RSI_ZONE_GATE",
-    # vs "EXHAUSTION" would otherwise double-count.
+        "XAU BUY FILTER":    "XAU_BUY_FILTER",
-    filter_tags = [
+        "SESSION FILTER":    "SESSION_FILTER",
-        # M5 noise gates (6i, _apply_m5_filters)
+        "DAY FILTER":        "DAY_FILTER",
-        ("M5 FILTER: |score|",            "M5_SCORE"),
+        "LOW ATR FILTER":    "LOW_ATR_FILTER",
-        ("M5 FILTER: no MTF agreement",   "M5_MTF"),
+        "SELL SUPPRESSED":   "SELL_SUPPRESSED",
-        ("M5 FILTER: no confirmation TF", "M5_CONF"),
+        "EXHAUSTION":        "EXHAUSTION_PENALTY",
-        ("M5 FILTER: SELL quality",       "M5_SELL_QUALITY"),
+        "TREND EXHAUSTION":  "TREND_EXHAUSTION",
-        ("M5 FILTER: SELL blocked — RSI", "M5_SELL_RSI"),
+        "NO PULLBACK":       "NO_PULLBACK_PENALTY",
-        ("M5 FILTER: '",                  "M5_VOL"),
+        "Weak pullback":     "WEAK_PULLBACK_PENALTY",
-        ("M5 FILTER",                     "M5_OTHER"),
+        "Extended entry":    "EXTENDED_ENTRY_PENALTY",
-        # XAU SELL gates (6e/6g/6g2/6g3)
+        "Counter-trend":     "COUNTER_TREND_PENALTY",
-        ("SELL FILTER: quality",          "SELL_QUALITY"),
+        "EXTREME volatility":"EXTREME_VOL_PENALTY",
-        ("SELL FILTER: volatility",       "SELL_VOL"),
+        "NEAR EMA200":       "NEAR_EMA200_PENALTY",
-        ("SELL FILTER: regime",           "SELL_REGIME"),
+        "FAR FROM EMA200":   "FAR_EMA200_PENALTY",
-        ("UTC entry blocked for shorts",  "SELL_HOUR"),
+        "EVENT RISK":        "EVENT_RISK",
-        # Hard gates
+        "REGIME FILTER":     "REGIME_FILTER",
-        ("MTF OPPOSITION",                "MTF_OPPOSITION"),
+        "Timeframes CONFLICT": "MTF_CONFLICT",
-        ("EXHAUSTED TREND FILTER",        "EXHAUSTED_TREND"),
+    }
        ("BIG CANDLE FILTER",             "BIG_CANDLE"),
        ("WEEKEND FILTER",                "WEEKEND_FILTER"),
        ("SESSION FILTER",                "SESSION_FILTER"),
        ("DAY FILTER",                    "DAY_FILTER"),
        ("LOW ATR FILTER",                "LOW_ATR_FILTER"),
        ("REGIME FILTER",                 "REGIME_FILTER"),
        # Soft penalties / context warnings
        ("TREND EXHAUSTION",              "TREND_EXHAUSTION"),
        ("EXHAUSTION",                    "EXHAUSTION_PENALTY"),
        ("NO PULLBACK",                   "NO_PULLBACK_PENALTY"),
        ("Weak pullback",                 "WEAK_PULLBACK_PENALTY"),
        ("Extended — RSI",                "RSI_EXTENDED_PENALTY"),
        ("Counter-trend",                 "COUNTER_TREND_PENALTY"),
        ("EXTREME volatility",            "EXTREME_VOL_PENALTY"),
        ("NEAR EMA200",                   "NEAR_EMA200_PENALTY"),
        ("FAR FROM EMA200",               "FAR_EMA200_PENALTY"),
        ("EVENT RISK",                    "EVENT_RISK"),
        ("Timeframes CONFLICT",           "MTF_CONFLICT"),
    ]
    for w in report.warnings:
-        for tag, name in filter_tags:
+        for tag, name in filter_tags.items():
            if tag in w:
                filters_hit.append(name)
                break
    # Determine outcome
    non_neutral = report.final_signal not in (SignalStrength.NEUTRAL,)
@@ -226,7 +206,7 @@ def run_diagnosis(
        confirmation_timeframes=list(confirm_tfs),
    )
-    provider = make_data_provider(platform_cfg)
+    provider = ForexDataProvider(platform_cfg)
    # ── Load data ──
    timeframes = [primary_tf] + list(confirm_tfs)
@@ -30,7 +30,6 @@ Usage:
 from __future__ import annotations
 import argparse
 import json
 import logging
 import multiprocessing as mp
 import os
@@ -40,13 +39,10 @@ from dataclasses import dataclass, field
 from datetime import datetime, timedelta, timezone
 from pathlib import Path
 from typing import Dict, List, Optional, Tuple
 from zoneinfo import ZoneInfo
 import numpy as np
 import pandas as pd
 _NY_TZ = ZoneInfo("America/New_York")
 # Ensure project root on sys.path so this script can be invoked from anywhere.
 PROJECT_ROOT = Path(__file__).resolve().parent.parent
 if str(PROJECT_ROOT) not in sys.path:
@@ -59,7 +55,7 @@ from config import (  # noqa: E402
    Timeframe,
    XAUUSD,
 )
-from providers import BaseDataProvider, make_data_provider  # noqa: E402
+from data_provider import ForexDataProvider  # noqa: E402
 from engine import IndicatorSignal, TechnicalAnalysis, TechnicalEngine  # noqa: E402
 from signal_aggregator import SignalAggregator, SignalReport, TradeSetup  # noqa: E402
@@ -405,11 +401,6 @@ class GPUTechnicalEngine(TechnicalEngine):
        # cache key = timeframe.value (e.g. "15min", "1h", "4h")
        self.caches = caches
        self._current_tf: Optional[str] = None
        # Cache-miss fallbacks recompute with the live engine's pandas
        # math (different EWM seeding than the precomputed cache). That
        # is a parity hazard, so it must never happen silently.
        self.fallback_count = 0
        self._fallback_warned = False
    def analyze(self, df: pd.DataFrame, symbol: str, timeframe: str) -> TechnicalAnalysis:
        # Latch the timeframe so per-indicator overrides know which cache to use.
@@ -427,23 +418,9 @@ class GPUTechnicalEngine(TechnicalEngine):
            return None, -1
        cache = self.caches.get(self._current_tf)
        if cache is None:
            self._note_fallback(f"no cache for TF {self._current_tf!r}")
            return None, -1
        pos = cache.index_pos.get(df.index[-1])
-        if pos is None:
+        return (cache, pos) if pos is not None else (None, -1)
            self._note_fallback(
                f"{self._current_tf}: slice end {df.index[-1]} not in cache")
            return None, -1
        return cache, pos
    def _note_fallback(self, why: str) -> None:
        self.fallback_count += 1
        if not self._fallback_warned:
            self._fallback_warned = True
            logger.warning(
                "GPU cache miss → falling back to live-engine recompute "
                "(%s). Further misses counted silently; check "
                "fallback_count at end of run.", why)
    # ── EMA overrides ──────────────────────────────────────────────
@@ -551,11 +528,6 @@ class GPUTechnicalEngine(TechnicalEngine):
            signal = SignalStrength.NEUTRAL
            conf = 40
        # Mirrors the live engine's ema_partial_neutral squash (parity).
        if c.ema_partial_neutral and signal in (SignalStrength.BUY, SignalStrength.SELL):
            signal = SignalStrength.NEUTRAL
            conf = 40
        crossover_desc = ""
        # Live: `if len(ema_fast) >= 3:` then iloc[-2] — needs len ≥ 2 functionally,
        # but live's guard requires len ≥ 3 so we match that to stay byte-identical.
@@ -848,12 +820,7 @@ class GPUTechnicalEngine(TechnicalEngine):
        c = self.config
        n = c.roc_period
-        # Mirror the live guard (engine._compute_roc: len < n + 5 → skip,
+        if pos < n + 1:
        # i.e. emit only from pos >= n + 4). Unreachable in practice —
        # analyze() floors at 30 bars and n+4 < 30 for shipped periods —
        # but keep the engines textually aligned so a future period/floor
        # change can't open a silent divergence window.
        if pos < n + 4:
            return
        roc_val = float(cache.roc[pos])
@@ -884,10 +851,15 @@ class GPUTechnicalEngine(TechnicalEngine):
                signal = SignalStrength.SELL
                conf = min(70, 50 + roc_abs * 10)
                desc = f"ROC {roc_val:.2f}% — Falling momentum"
        elif roc_prev <= 0 < roc_val:
            signal = SignalStrength.BUY
            conf = 65
            desc = f"ROC crossed above zero (+{roc_val:.2f}%)"
        elif roc_prev >= 0 > roc_val:
            signal = SignalStrength.SELL
            conf = 65
            desc = f"ROC crossed below zero ({roc_val:.2f}%)"
        else:
            # Zero-crosses land in the branches above by definition (a
            # cross up is positive and rising, a cross down negative and
            # falling) — only flat/fading momentum reaches here.
            signal = SignalStrength.NEUTRAL
            conf = 30
            desc = f"ROC {roc_val:+.2f}% — No clear momentum"
@@ -903,11 +875,6 @@ class GPUTechnicalEngine(TechnicalEngine):
            return super()._compute_keltner(df, result)
        c = self.config
        # Mirror the live guard (engine._compute_keltner: len < max(kc_period,
        # kc_atr_period) + 5 → skip). Unreachable below the 30-bar analyze()
        # floor with shipped periods; kept aligned for the same reason as ROC.
        if pos < max(c.kc_period, c.kc_atr_period) + 4:
            return
        latest = float(df["Close"].iloc[-1])
        kc_upper_val = float(cache.kc_upper[pos])
        kc_lower_val = float(cache.kc_lower[pos])
@@ -973,21 +940,13 @@ class GPUTechnicalEngine(TechnicalEngine):
 CACHE_DIR = Path(__file__).resolve().parent / "cache"
-def _cache_path(
+def _cache_path(symbol: str, tf: Timeframe, start: datetime, end: datetime) -> Path:
    symbol: str, tf: Timeframe, start: datetime, end: datetime,
    provider_name: str = "twelve_data",
 ) -> Path:
    safe_sym = symbol.replace("/", "_").replace("=X", "")
-    # Bars differ between feeds (Oanda vs TD divergence study 2026-07-24),
+    return CACHE_DIR / f"{safe_sym}_{tf.value}_{start:%Y%m%d}_{end:%Y%m%d}.parquet"
    # so the cache is keyed per provider. twelve_data keeps the legacy
    # un-prefixed filename so pinned-book caches stay valid.
    prefix = "" if provider_name == "twelve_data" else f"{provider_name}_"
    return CACHE_DIR / (
        f"{prefix}{safe_sym}_{tf.value}_{start:%Y%m%d}_{end:%Y%m%d}.parquet")
 def fetch_history(
-    provider: BaseDataProvider,
+    provider: ForexDataProvider,
    symbol: str,
    timeframe: Timeframe,
    start: datetime,
@@ -995,45 +954,91 @@ def fetch_history(
    use_cache: bool = True,
    clean: bool = True,
 ) -> pd.DataFrame:
-    """Fetch OHLCV between start/end with parquet caching.
+    """Fetch OHLCV between start/end with parquet caching and pagination.
-    Pagination under the provider's per-request bar cap lives in
+    Twelve Data caps responses at 5000 bars; we paginate by date range so
-    `BaseDataProvider.fetch_range`, so any registered provider can feed
+    multi-month M5/M15 backtests work.
    the backtester.
-    The parquet cache stores the RAW series; `validate_and_clean` runs on
+    The parquet cache stores the RAW series; `_validate_and_clean` runs on
    load. Pass clean=False to get the raw price path — trade resolution must
    use it, because cleaning drops >10σ spike bars (news candles) that hit
    stops in reality. (Caches written before this change already contain
    cleaned data; a refetch is needed to restore dropped spike bars.)
    """
    CACHE_DIR.mkdir(parents=True, exist_ok=True)
-    path = _cache_path(symbol, timeframe, start, end, provider.name)
+    path = _cache_path(symbol, timeframe, start, end)
    if use_cache and path.exists():
        logger.info(f"Loading cached {symbol} {timeframe.value}: {path.name}")
        # Files written before the raw-cache change (2026-07-08, commit
        # 6a96ed6) contain CLEANED bars; loading one with clean=False
        # would silently miss the spike bars the raw path exists to model.
        # The key format predates versioning, so detect by mtime.
        _RAW_CACHE_EPOCH = 1783468800.0   # 2026-07-08 00:00 UTC
        if path.stat().st_mtime < _RAW_CACHE_EPOCH:
            logger.error(
                f"{path.name} predates the raw-cache format (2026-07-08) "
                f"and likely contains CLEANED bars — delete it and refetch "
                f"before trusting trade resolution on this window")
        df = pd.read_parquet(path)
        if df.index.tz is None:
            df.index = df.index.tz_localize("UTC")
-        return provider.validate_and_clean(df, symbol, timeframe) if clean else df
+        return provider._validate_and_clean(df, symbol, timeframe) if clean else df
    logger.info(f"Fetching {symbol} {timeframe.value} from {start.date()} to {end.date()}")
-    df = provider.fetch_range(symbol, timeframe, start, end)
+    # Bars-per-call estimate to size each window safely under the 5000 cap.
    bars_per_day = ForexDataProvider.BARS_PER_DAY[timeframe]
    days_per_chunk = max(1, int(4500 / bars_per_day))
    chunks: List[pd.DataFrame] = []
    cursor_end = end
    empty_streak = 0
    while cursor_end > start:
        cursor_start = max(start, cursor_end - timedelta(days=days_per_chunk))
        try:
            chunk = provider._fetch_twelve_data(
                symbol=symbol,
                timeframe=timeframe,
                outputsize=5000,
                start_date=cursor_start.strftime("%Y-%m-%d %H:%M:%S"),
                end_date=cursor_end.strftime("%Y-%m-%d %H:%M:%S"),
            )
        except Exception as e:
            # Twelve Data raises BadRequest for windows with no data (weekends,
            # holidays, asset gaps). Skip the window, step back, retry — but
            # bail out after a few consecutive empties to avoid spinning.
            logger.warning(
                f"Chunk {cursor_start.date()}–{cursor_end.date()} failed: {e}"
            )
            empty_streak += 1
            if empty_streak >= 3:
                break
            cursor_end = cursor_start - timedelta(seconds=1)
            continue
        if chunk is None or chunk.empty:
            empty_streak += 1
            if empty_streak >= 3:
                break
            cursor_end = cursor_start - timedelta(seconds=1)
            continue
        empty_streak = 0
        chunks.append(chunk)
        # Step back; dedup on concat.
        cursor_end = chunk.index[0] - timedelta(seconds=1)
        if len(chunk) < 2:
            break
    if not chunks:
        raise RuntimeError(f"No data fetched for {symbol} {timeframe.value}")
    df = pd.concat(chunks).sort_index()
    df = df[~df.index.duplicated(keep="last")]
    # Fetches ending "now" include the still-forming candle — drop it so the
    # cache only ever contains closed bars.
    df = provider._drop_forming_candle(df, timeframe)
    # Trim to requested window. `start`/`end` are already tz-aware UTC
    # datetimes — wrapping with pd.Timestamp(..., tz=...) would raise on
    # already-localized inputs.
    end_ts = pd.Timestamp(end)
    start_ts = pd.Timestamp(start)
    df = df[(df.index >= start_ts) & (df.index <= end_ts)]
    if use_cache:
        df.to_parquet(path)
        logger.info(f"Cached {len(df)} raw bars to {path.name}")
-    return provider.validate_and_clean(df, symbol, timeframe) if clean else df
+    return provider._validate_and_clean(df, symbol, timeframe) if clean else df
 # ═════════════════════════════════════════════════════════════════════
@@ -1172,15 +1177,6 @@ def _simulate_trailing(
        reason = "TIMEOUT"
        for j in range(n):
            if np.isnan(h[j]):
                # Data ends here: close at the LAST VALID bar, not at
                # c[n-1] (which may itself be NaN — a NaN exit price used
                # to poison r_multiple and every downstream metric).
                if j == 0:
                    exit_idx = 0
                    reason = "NO_DATA"
                else:
                    exit_idx = j - 1
                    exit_price = float(c[exit_idx])
                break
            hit = (l[j] <= cur_sl) if buy else (h[j] >= cur_sl)
            if hit:
@@ -1202,15 +1198,8 @@ def _simulate_trailing(
                       else peak + dist * risk)
                cur_sl = max(cur_sl, lvl) if buy else min(cur_sl, lvl)
        if reason == "NO_DATA":
            t.exit_reason = reason
            continue
        if exit_price is None:
            exit_price = float(c[exit_idx])
        if not np.isfinite(exit_price):
            # Last-resort guard: never emit a NaN exit/R into the book.
            t.exit_reason = "NO_DATA"
            continue
        t.exit_reason = reason
        t.bars_held = exit_idx + 1
        t.exit_price = float(exit_price)
@@ -1383,12 +1372,6 @@ def simulate_trades(
            last_idx = int(n_valid) - 1
            t.exit_price = float(closes_arr[i, last_idx])
            t.exit_time = exit_times[i][last_idx]
            if not np.isfinite(t.exit_price):
                # NaN close on the final window bar (corrupt raw data)
                # must not become a NaN R.
                t.exit_reason = "NO_DATA"
                t.exit_price = None
                continue
        # Realized R from actual fill prices, net of round-trip spread.
        risk = abs(t.entry_price - t.sl)
@@ -1441,12 +1424,6 @@ class BacktestConfig:
    # Round-trip transaction cost in price units, charged on every trade
    # (XAU/USD retail spread ≈ $0.30). 0 disables.
    spread: float = 0.30
    # RESEARCH (2026-08): >0 replaces the exit model with a hard TP at
    # this multiple of risk from entry (2.0 = fixed 1:2 RR); trailing is
    # disabled for ALL trades, including per-trade XAU_PB overrides.
    # Entries/stops/gates are untouched, so the signal book is identical
    # to the baseline — only the exit policy changes. Never shipped.
    fixed_rr_tp: float = 0.0
    # Replay worker processes for the bar loop. 0 = auto (cpu_count - 2,
    # capped at 12), 1 = sequential in-process. Signal evaluation per bar
    # is pure (past data only), so it fans out across forked workers; the
@@ -1459,22 +1436,18 @@ class BacktestConfig:
 def _is_xau_blocked_by_calendar(ts: pd.Timestamp, cfg: PlatformConfig) -> Optional[str]:
    """Apply XAU weekend + allowed-weekday filter matching live `analyze_pair`.
-    Market closed Friday 17:00 -> Sunday 18:00 NEW YORK time (= Fri
+    Forex market closed: Friday 22:00 UTC -> Sunday 22:00 UTC (Sydney open).
-    21:00 UTC in US summer, 22:00 in winter — matches measured Oanda
+    Must match `_is_forex_weekend` in signal_aggregator.py exactly.
    feed behavior; 2026-08-04 audit I43). Must match `_is_forex_weekend`
    in signal_aggregator.py exactly (rule 4; Module 21 agreement test).
    Note the weekday gates below stay on UTC weekday — they mirror the
    live gates, which evaluate UTC weekdays.
    """
    xau = cfg.xau_strategy
    if xau.weekend_filter:
-        ny = ts.astimezone(_NY_TZ)
+        wd = ts.weekday()
-        wd = ny.weekday()
+        hour = ts.hour
-        if wd == 4 and ny.hour >= 17:
+        if wd == 4 and hour >= 22:
            return "WEEKEND_FILTER"
        if wd == 5:
            return "WEEKEND_FILTER"
-        if wd == 6 and ny.hour < 18:
+        if wd == 6 and hour < 22:
            return "WEEKEND_FILTER"
    if xau.allowed_weekdays and ts.weekday() not in xau.allowed_weekdays:
        return "DAY_FILTER"
@@ -1557,13 +1530,6 @@ def _replay_span(span: Tuple[int, int]):
            use_news=False,
        )
        # Live parity: analyze_pair applies the optional higher-TF ADX
        # regime filter after delegation, under this exact guard. Calling
        # the SAME method on the SAME sliced dfs keeps the two paths
        # identical if the (default-off) filter is ever enabled.
        if platform_cfg.backtest.regime_filter:
            aggregator._apply_regime_filter_live(report, slice_dfs)
        if report.trade_setup is None:
            continue
        if report.signal_quality not in bt_cfg.quality_filter:
@@ -1691,43 +1657,6 @@ def _reanchor_entries_to_raw(
        )
 def _apply_cooldown(
    candidates: List[Tuple[int, Trade]], cooldown_bars: int
 ) -> List[Tuple[int, Trade]]:
    """Ordered cooldown pass over (bar_pos, Trade) candidates: drop any
    candidate within `cooldown_bars` bars of the previously ACCEPTED
    entry — exactly what a sequential replay loop would have skipped.
    Candidates must already be sorted by position (stable merge from the
    worker spans)."""
    accepted: List[Tuple[int, Trade]] = []
    last_entry_pos = -10_000
    for pos, trade in candidates:
        if pos - last_entry_pos < cooldown_bars:
            continue
        accepted.append((pos, trade))
        last_entry_pos = pos
    return accepted
 def _apply_fixed_rr_tp(trades: List[Trade], mult: float) -> None:
    """RESEARCH: replace each trade's TP with a hard target at
    `mult` × |entry − sl| and zero the per-trade trail params so the
    XAU_PB overrides cannot resurrect trailing. Risk is measured off the
    final (re-anchored) entry/SL, so call this after
    `_reanchor_entries_to_raw`. The caller must also pass
    trail_arm_r=0 to `simulate_trades` — dispatch is on the run-level arg."""
    for t in trades:
        risk = abs(t.entry_price - t.sl)
        t.tp1 = (t.entry_price + mult * risk if t.direction == "BUY"
                 else t.entry_price - mult * risk)
        t.tp2 = t.tp1              # static branch never reads tp2; CSV hygiene
        t.trail_arm_r = 0.0
        t.trail_dist_r = 0.0
        t.rr_planned = mult
        if t.pip_value:
            t.reward_pips = mult * risk / t.pip_value
 def run_backtest(
    bt_cfg: BacktestConfig,
    platform_cfg: Optional[PlatformConfig] = None,
@@ -1750,17 +1679,10 @@ def run_backtest(
            f"vs PlatformConfig={platform_cfg.primary_timeframe.value}"
        )
-    provider = make_data_provider(platform_cfg)
+    provider = ForexDataProvider(platform_cfg)
    # ── Load all required timeframes ──
    timeframes = [bt_cfg.primary_tf] + list(bt_cfg.confirmation_tfs)
    # Live parity: analyze_pair also fetches the regime-filter TF when the
    # optional H4-ADX gate is enabled. Without it here, M5/M15 primaries
    # (whose confirm maps lack H4) would make _apply_regime_filter_live
    # silently no-op in the replay while live applies the gate.
    flt = platform_cfg.backtest
    if flt.regime_filter and flt.regime_filter_timeframe not in timeframes:
        timeframes.append(flt.regime_filter_timeframe)
    # Pad lookback so earliest TF has enough warmup for EMA200, ADX, etc.
    pad_days = max(60, bt_cfg.warmup_bars // 4)
    fetch_start = bt_cfg.start - timedelta(days=pad_days)
@@ -1772,7 +1694,7 @@ def run_backtest(
        # ≥210 bars (outputsize floor). Target data_provider's min_required
        # (210 bars); ×7/5 converts trading days to calendar days. Intraday
        # TFs stay on the base pad, so their books are unchanged.
-        bars_per_day = BaseDataProvider.BARS_PER_DAY[tf]
+        bars_per_day = ForexDataProvider.BARS_PER_DAY[tf]
        min_pad_days = int(np.ceil(210 / bars_per_day * 7 / 5)) + 5
        tf_start = bt_cfg.start - timedelta(days=max(pad_days, min_pad_days))
        df = fetch_history(
@@ -1855,10 +1777,18 @@ def run_backtest(
        candidates.extend(cands)
    candidates.sort(key=lambda pc: pc[0])
-    # ── Phase 2: entry cooldown — the only sequential rule.
+    # ── Phase 2: entry cooldown — the only sequential rule. A candidate
-    accepted = _apply_cooldown(candidates, bt_cfg.cooldown_bars)
+    # inside the cooldown window of the previously ACCEPTED entry is
-    trades: List[Trade] = [t for _, t in accepted]
+    # dropped, exactly as the sequential loop skipped those bars.
-    signal_ts_list: List[pd.Timestamp] = [primary_df.index[p] for p, _ in accepted]
+    trades: List[Trade] = []
    signal_ts_list: List[pd.Timestamp] = []
    last_entry_pos = -10_000
    for pos, trade in candidates:
        if pos - last_entry_pos < bt_cfg.cooldown_bars:
            continue
        trades.append(trade)
        signal_ts_list.append(primary_df.index[pos])
        last_entry_pos = pos
    logger.info(
        f"Replay complete: {len(trades)} entries in "
@@ -1878,17 +1808,10 @@ def run_backtest(
        use_cache=bt_cfg.use_cache, clean=False,
    )
    _reanchor_entries_to_raw(trades, signal_ts_list, sim_df)
    if bt_cfg.fixed_rr_tp > 0:
        _apply_fixed_rr_tp(trades, bt_cfg.fixed_rr_tp)
        logger.info(
            f"RESEARCH override: fixed-RR TP = {bt_cfg.fixed_rr_tp} x risk; "
            f"trailing exit DISABLED for all {len(trades)} trades (incl. XAU_PB)"
        )
    run_arm = 0.0 if bt_cfg.fixed_rr_tp > 0 else platform_cfg.signals.trail_arm_r
    t_sim = time.perf_counter()
    simulate_trades(trades, sim_df, max_bars=bt_cfg.max_hold_bars,
                    spread=bt_cfg.spread,
-                    trail_arm_r=run_arm,
+                    trail_arm_r=platform_cfg.signals.trail_arm_r,
                    trail_dist_r=platform_cfg.signals.trail_dist_r)
    logger.info(
        f"Trade simulation took {time.perf_counter() - t_sim:.2f}s "
@@ -1938,14 +1861,6 @@ def _summarize(rs: List[float]) -> Dict:
 def compute_metrics(trades: List[Trade]) -> Metrics:
    resolved = [t for t in trades if t.exit_reason and t.exit_reason != "NO_DATA"]
    # Last line of defense: one non-finite R (NaN exit on corrupt data)
    # would silently NaN every aggregate below. Exclude loudly.
    bad = [t for t in resolved if not np.isfinite(t.r_multiple)]
    if bad:
        logger.error(
            "compute_metrics: excluding %d trade(s) with non-finite R "
            "(corrupt exit data) — first at %s", len(bad), bad[0].entry_time)
        resolved = [t for t in resolved if np.isfinite(t.r_multiple)]
    if not resolved:
        return Metrics(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, {}, {}, {})
@@ -1957,9 +1872,7 @@ def compute_metrics(trades: List[Trade]) -> Metrics:
    gross_loss = float(-rs[losses_mask].sum()) if losses_mask.any() else 0.0
    pf = gross_profit / gross_loss if gross_loss > 0 else float("inf")
-    # Prepend the flat starting equity so an opening losing streak counts
+    cum = np.cumsum(rs)
    # as drawdown from 0, not from the first trade's already-negative cum.
    cum = np.concatenate(([0.0], np.cumsum(rs)))
    high_water = np.maximum.accumulate(cum)
    drawdown = high_water - cum
    max_dd = float(drawdown.max()) if len(drawdown) else 0.0
@@ -2228,14 +2141,6 @@ def _parse_args(argv: Optional[List[str]] = None) -> argparse.Namespace:
    p.add_argument("--max-hold-bars", type=int, default=300)
    p.add_argument("--swing-sr-lookback", type=int, default=0,
                   help="Override TechnicalConfig.swing_sr_lookback (0 = use config default)")
    p.add_argument("--trail-arm", type=float, default=None,
                   help="Override signals.trail_arm_r for this run (research knob)")
    p.add_argument("--trail-dist", type=float, default=None,
                   help="Override signals.trail_dist_r for this run (research knob)")
    p.add_argument("--fixed-rr-tp", type=float, default=None,
                   help="RESEARCH: exit at a hard TP of MULT x risk (e.g. 2.0 = "
                        "fixed 1:2 RR) instead of the trailing stop; "
                        "entries/stops/gates unchanged.")
    p.add_argument("--quality", default="A+,A,B,C", help="Comma-separated allowed grades")
    p.add_argument("--strategies", default="",
                   help="Comma-separated strategy tags to include (XAU,XAU_PB,XAU_CP,GENERIC). Empty = all.")
@@ -2245,19 +2150,6 @@ def _parse_args(argv: Optional[List[str]] = None) -> argparse.Namespace:
    p.add_argument("--cp-patterns", default=None,
                   help="Comma-separated pattern subset for --cp (e.g. "
                        "double_bottom,bull_flag). Default: all detectors.")
    p.add_argument("--big-candle", type=float, default=None,
                   help="Force xau_strategy.big_candle_atr_multiple=K for this "
                        "run (no entries while the lookback window contains a "
                        "bar with range >= K x prior ATR) — for validating the "
                        "candidate.")
    p.add_argument("--big-candle-hours", type=float, default=None,
                   help="Override big_candle_lookback_hours for --big-candle "
                        "runs (default 24).")
    p.add_argument("--set", action="append", default=[], metavar="PATH=JSON",
                   help="Override any PlatformConfig leaf by dotted path; the "
                        "value is JSON-parsed for typing (research knob; "
                        "repeatable). e.g. --set xau_strategy.m5_min_abs_score=25 "
                        "--set 'xau_strategy.sell_min_quality=[\"A+\",\"A\",\"B\",\"C\"]'")
    p.add_argument("--no-cache", action="store_true", help="Skip parquet cache (force API fetch)")
    p.add_argument("--spread", type=float, default=None,
                   help="Round-trip cost in price units per trade (default: pair's "
@@ -2289,39 +2181,6 @@ def _resolve_tf(s: str) -> Timeframe:
    raise SystemExit(f"Unknown timeframe '{s}'. Valid: {[t.value for t in Timeframe]}")
 def _apply_config_overrides(cfg: PlatformConfig, overrides: List[str]) -> None:
    """Apply repeatable --set PATH=JSON research overrides onto the config.
    Unknown paths are a hard error: dataclass instances silently accept
    setattr of any name, so a typo'd path would otherwise produce a
    no-op run that looks like 'the relaxation had no effect'. Every
    applied override is logged — the run log is the config audit trail
    required by instructions_for_backtest.txt.
    """
    for item in overrides:
        path, sep, raw = item.partition("=")
        if not sep:
            raise SystemExit(f"--set expects PATH=VALUE, got '{item}'")
        try:
            value = json.loads(raw)
        except json.JSONDecodeError:
            value = raw  # bare-string convenience ("oanda" etc.)
        obj = cfg
        parts = path.strip().split(".")
        for attr in parts[:-1]:
            if not hasattr(obj, attr):
                raise SystemExit(f"--set: unknown config path '{path}'")
            obj = getattr(obj, attr)
        leaf = parts[-1]
        if not hasattr(obj, leaf):
            raise SystemExit(f"--set: unknown config path '{path}'")
        old = getattr(obj, leaf)
        if hasattr(old, "__dataclass_fields__"):
            raise SystemExit(f"--set: '{path}' is a config section, not a leaf")
        setattr(obj, leaf, value)
        logger.info(f"--set override: {path} = {value!r} (was {old!r})")
 def main(argv: Optional[List[str]] = None) -> int:
    args = _parse_args(argv)
@@ -2355,38 +2214,14 @@ def main(argv: Optional[List[str]] = None) -> int:
            primary_timeframe=primary_tf,
            confirmation_timeframes=confirm_tfs or [Timeframe.H1, Timeframe.H4],
        )
        # The factory path applies the config.json overlay inside
        # make_config; the fallback must match (live == backtest).
        from app_config import apply_pair_overrides  # noqa: E402
        apply_pair_overrides(platform_cfg)
    if args.fixed_rr_tp is not None:
        if args.fixed_rr_tp <= 0:
            raise SystemExit("--fixed-rr-tp must be > 0")
        if (args.trail_arm is not None or args.trail_dist is not None or
                any(s.split("=", 1)[0].strip().startswith(("signals.trail",
                                                           "signals.pb_trail"))
                    for s in (args.set or []))):
            raise SystemExit(
                "--fixed-rr-tp conflicts with --trail-arm/--trail-dist/"
                "--set signals.*trail* (the flag disables trailing entirely)")
    if args.swing_sr_lookback > 0:
        platform_cfg.technical.swing_sr_lookback = args.swing_sr_lookback
    if args.trail_arm is not None:
        platform_cfg.signals.trail_arm_r = args.trail_arm
    if args.trail_dist is not None:
        platform_cfg.signals.trail_dist_r = args.trail_dist
    if args.cp:
        platform_cfg.xau_strategy.cp_enabled = True
        if args.cp_patterns:
            platform_cfg.xau_strategy.cp_patterns = [
                s.strip() for s in args.cp_patterns.split(",") if s.strip()
            ]
    if args.big_candle is not None:
        platform_cfg.xau_strategy.big_candle_atr_multiple = args.big_candle
    if args.big_candle_hours is not None:
        platform_cfg.xau_strategy.big_candle_lookback_hours = args.big_candle_hours
    if args.set:
        _apply_config_overrides(platform_cfg, args.set)
    confirm_tfs = list(platform_cfg.confirmation_timeframes)
    end = (
@@ -2415,7 +2250,6 @@ def main(argv: Optional[List[str]] = None) -> int:
        strategies_only=[s.strip() for s in args.strategies.split(",") if s.strip()],
        spread=spread,
        workers=args.workers,
        fixed_rr_tp=args.fixed_rr_tp or 0.0,
    )
    logger.info(
@@ -1,86 +0,0 @@
 """Trade-book diff: relaxed/gated run vs its control.
 Usage:
    python backtest/research/book_diff.py CONTROL.csv CANDIDATE.csv BAR_MINUTES
 Decision numbers are RUN-LEVEL (ΔtotalR, ΔPF, ΔmaxDD, Δtrades) — never
 the added-trade bucket alone: the 5-bar entry cooldown means new
 entries displace existing ones. The added/removed decomposition and the
 per-decision-hour histogram of added trades are diagnostics.
 Shifted trades = removed+added pairs in the same direction within
 ±5 bars (cooldown displacement, not a genuinely new/lost signal).
 """
 import sys
 import pandas as pd
 def _metrics(df):
    r = df["r_multiple"]
    gains, losses = r[r > 0].sum(), -r[r <= 0].sum()
    cum = r.cumsum()
    return dict(trades=len(df), total_r=r.sum(),
                pf=gains / max(losses, 1e-9),
                wr=(r > 0).mean() * 100 if len(df) else 0.0,
                max_dd=(cum.cummax() - cum).max() if len(cum) else 0.0)
 def diff_books(control_csv, candidate_csv, bar_minutes, split="2026-01-29"):
    ctrl, cand = pd.read_csv(control_csv), pd.read_csv(candidate_csv)
    for df in (ctrl, cand):
        df["_t"] = pd.to_datetime(df["entry_time"], utc=True, format="mixed")
        df["_key"] = df["entry_time"].astype(str) + "|" + df["direction"]
    added = cand[~cand["_key"].isin(ctrl["_key"])].copy()
    removed = ctrl[~ctrl["_key"].isin(cand["_key"])].copy()
    # cooldown displacement: same-direction removed/added within ±5 bars
    tol = pd.Timedelta(minutes=5 * bar_minutes)
    shifted = 0
    rem_pool = removed.copy()
    for _, a in added.iterrows():
        m = rem_pool[(rem_pool["direction"] == a["direction"])
                     & ((rem_pool["_t"] - a["_t"]).abs() <= tol)]
        if len(m):
            shifted += 1
            rem_pool = rem_pool.drop(m.index[0])
    mc, mk = _metrics(ctrl), _metrics(cand)
    split_ts = pd.Timestamp(split, tz="UTC")
    halves = (cand[cand["_t"] < split_ts]["r_multiple"].sum(),
              cand[cand["_t"] >= split_ts]["r_multiple"].sum())
    hour_hist = None
    if len(added):
        added["dec_hour"] = (added["_t"]
                             - pd.Timedelta(minutes=bar_minutes)).dt.hour
        hour_hist = added.groupby("dec_hour")["r_multiple"] \
                         .agg(["count", "sum"]).round(2)
    return dict(
        control=mc, candidate=mk,
        delta_r=mk["total_r"] - mc["total_r"],
        delta_pf=mk["pf"] - mc["pf"],
        delta_dd=mk["max_dd"] - mc["max_dd"],
        delta_trades=mk["trades"] - mc["trades"],
        added_n=len(added), added_r=added["r_multiple"].sum(),
        removed_n=len(removed), removed_r=removed["r_multiple"].sum(),
        shifted=shifted, halves=halves, added_by_hour=hour_hist,
    )
 if __name__ == "__main__":
    d = diff_books(sys.argv[1], sys.argv[2], int(sys.argv[3]))
    c, k = d["control"], d["candidate"]
    print(f"control  : {c['trades']:4d} trades  {c['total_r']:+7.1f}R  "
          f"PF {c['pf']:.2f}  WR {c['wr']:.1f}%  DD {c['max_dd']:.1f}")
    print(f"candidate: {k['trades']:4d} trades  {k['total_r']:+7.1f}R  "
          f"PF {k['pf']:.2f}  WR {k['wr']:.1f}%  DD {k['max_dd']:.1f}")
    print(f"ΔR {d['delta_r']:+.1f}  ΔPF {d['delta_pf']:+.2f}  "
          f"ΔDD {d['delta_dd']:+.1f}  Δtrades {d['delta_trades']:+d}")
    print(f"added {d['added_n']} ({d['added_r']:+.1f}R)  "
          f"removed {d['removed_n']} ({d['removed_r']:+.1f}R)  "
          f"shifted≈{d['shifted']}")
    print(f"candidate halves: H1 {d['halves'][0]:+.1f}R  H2 {d['halves'][1]:+.1f}R")
    if d["added_by_hour"] is not None:
        print("\nadded trades by decision hour:")
        print(d["added_by_hour"].to_string())
@@ -1,42 +0,0 @@
 """Burst-candle frequency by UTC hour — the empirical "hedge fund hours".
 Usage:
    python backtest/research/burst_profile.py PARQUET_GLOB
 Per UTC bar-close hour of the given bars: % of bars whose range is
 >= K x the PRIOR bar's Wilder ATR(14), for K in {3,4,5}; mean bar
 range; and mean signed drift over the 6 bars following a >=4x burst
 (does price continue or snap back?). Analysis tooling only.
 """
 import glob
 import sys
 import pandas as pd
 def burst_table(parquet_glob: str, bar_minutes: int = 5):
    files = sorted(glob.glob(parquet_glob))
    if not files:
        raise SystemExit(f"no parquet matches {parquet_glob}")
    df = pd.concat([pd.read_parquet(f) for f in files]).sort_index()
    df = df[~df.index.duplicated(keep="last")]
    h, l, c = df["High"], df["Low"], df["Close"]
    tr = pd.concat([h - l, (h - c.shift()).abs(), (l - c.shift()).abs()],
                   axis=1).max(axis=1)
    atr = tr.ewm(alpha=1 / 14, min_periods=14, adjust=False).mean()
    ratio = (h - l) / atr.shift(1)
    close_hour = (df.index + pd.Timedelta(minutes=bar_minutes)).hour
    fwd6 = c.shift(-6) - c  # signed move over the 6 bars after this one
    out = pd.DataFrame({
        "bars": ratio.groupby(close_hour).size(),
        **{f"pct_ge_{k}x": (ratio >= k).groupby(close_hour).mean() * 100
           for k in (3, 4, 5)},
        "mean_range": (h - l).groupby(close_hour).mean(),
        "post4x_drift6": fwd6[ratio >= 4].groupby(close_hour[ratio >= 4]).mean(),
    })
    return out.round(3)
 if __name__ == "__main__":
    print(burst_table(sys.argv[1],
                      int(sys.argv[2]) if len(sys.argv) > 2 else 5).to_string())
@@ -0,0 +1,94 @@
 """Load and query the historical economic calendar (backtest side).
 The store is built by `ff_calendar_scraper.py`. Timestamps are unix
 epoch (UTC) — verified against known release times and price-volatility
 alignment (see scraper docstring).
 Key API:
    cal = load_calendar()                          # full DataFrame, `utc` column
    ev  = precise_events(cal, ["USD"])             # high-impact, minute-accurate
    mask = event_window_mask(bar_index, ev.utc, before, after)
 """
 from pathlib import Path
 from typing import Iterable, Optional
 import numpy as np
 import pandas as pd
 PARQUET = Path(__file__).resolve().parent.parent / "cache" / "calendar.parquet"
 # timeLabel values that mean "no intraday time" — these events cannot
 # anchor an M5/M15 window (German Prelim CPI is the notable High one).
 _UNTIMED = {"All Day", "Tentative", ""}
 def _parse_release_value(raw) -> float:
    """'0.3%' / '-1.7%' / '228K' / '1.1M' -> float (nan if unparseable)."""
    import re
    if raw is None or (isinstance(raw, float) and np.isnan(raw)):
        return np.nan
    m = re.search(r"(-?\d+(?:\.\d+)?)\s*([kmbt])?",
                  str(raw).replace(",", ""), re.IGNORECASE)
    if not m:
        return np.nan
    mult = {"k": 1e3, "m": 1e6, "b": 1e9, "t": 1e12}.get(
        (m.group(2) or "").lower(), 1.0)
    return float(m.group(1)) * mult
 def load_calendar(path: Path = PARQUET) -> pd.DataFrame:
    df = pd.read_parquet(path)
    df["utc"] = pd.to_datetime(df.dateline, unit="s", utc=True)
    for col in ("actual", "forecast", "previous"):
        df[f"{col}_num"] = df[col].map(_parse_release_value)
    df["surprise"] = df.actual_num - df.forecast_num
    return df
 def precise_events(
    cal: pd.DataFrame,
    currencies: Iterable[str] = ("USD",),
    impacts: Iterable[str] = ("high",),
    require_values: bool = False,
 ) -> pd.DataFrame:
    """Events with a minute-accurate timestamp, filtered by currency/impact."""
    out = cal[cal.currency.isin(list(currencies))
              & cal.impactName.isin(list(impacts))
              & ~cal.timeMasked.astype(bool)
              & ~cal.timeLabel.fillna("").isin(_UNTIMED)]
    if require_values:
        out = out.dropna(subset=["actual_num", "forecast_num"])
    return out.sort_values("utc").reset_index(drop=True)
 def event_window_mask(
    bar_index: pd.DatetimeIndex,
    event_times: pd.Series,
    before: pd.Timedelta = pd.Timedelta(minutes=30),
    after: pd.Timedelta = pd.Timedelta(minutes=30),
 ) -> np.ndarray:
    """Boolean mask: bar OPEN time falls inside [event-before, event+after).
    Use on a backtest's entry times to identify trades entered around
    releases (avoidance counterfactuals, event-window cost models).
    """
    starts = pd.DatetimeIndex(event_times) - before
    ends = pd.DatetimeIndex(event_times) + after
    order = np.argsort(starts.values)
    starts, ends = starts.values[order], ends.values[order]
    # merge overlapping windows, then interval-stab each bar time
    merged_s, merged_e = [], []
    for s, e in zip(starts, ends):
        if merged_e and s <= merged_e[-1]:
            merged_e[-1] = max(merged_e[-1], e)
        else:
            merged_s.append(s)
            merged_e.append(e)
    ms = np.array(merged_s)
    me = np.array(merged_e)
    ts = bar_index.values
    pos = np.searchsorted(ms, ts, side="right") - 1
    ok = pos >= 0
    res = np.zeros(len(ts), dtype=bool)
    res[ok] = ts[ok] < me[pos[ok]]
    return res
@@ -1,178 +0,0 @@
 """Commit-ladder campaign driver (2026-08).
 Question: of all signal-code states shipped in the last two months, which
 configuration is the most accurate? Historical in-era backtest numbers are
 not comparable (the backtester's realism model changed on 07-08/07-16 and
 the data feed switched to Oanda on 07-28), so every historical
 configuration is recreated on TODAY's harness via --set overrides and
 replayed on the same pinned Oanda bars. Bug-fix commits stay on; the
 ladder compares shipped tuning decisions only.
 Usage:
    python backtest/research/commit_ladder.py            # full matrix
    python backtest/research/commit_ladder.py --tf 5min  # one timeframe
    python backtest/research/commit_ladder.py --windows 12mo,oos
 Resumable: a run whose CSV already exists in the output dir is skipped.
 Outputs: backtest/research/commit_ladder_2026-08/<variant>_<window>.csv
         + .log per run, summary.csv, and a printed ranking table.
 """
 from __future__ import annotations
 import argparse
 import subprocess
 import sys
 from pathlib import Path
 import pandas as pd
 ROOT = Path(__file__).resolve().parents[2]
 BT = ROOT / "backtest" / "gpu_backtest.py"
 OUT = ROOT / "backtest" / "research" / "commit_ladder_2026-08"
 WINDOWS = {
    "12mo": ("2025-07-30", "2026-07-30"),
    "oos": ("2024-06-01", "2025-03-30"),
    "5yr": ("2021-07-30", "2026-07-30"),
 }
 SPLIT_12MO = pd.Timestamp("2026-01-29", tz="UTC")  # book_diff.py's split
 # (variant, "commit date what-it-represents", [--set overrides])
 VARIANTS: dict[str, list[tuple[str, str, list[str]]]] = {
    "5min": [
        ("M5-A", "dad2432 07-09 gates+trailing+pullback",
         ["xau_strategy.blocked_hours_utc=[0,1,2,10,11,13,16,21]",
          "xau_strategy.cp_enabled=false",
          "signals.trail_dist_r=0.75",
          "xau_strategy.m5_min_abs_score=30",
          "xau_strategy.m5_min_confirmation_score=10"]),
        ("M5-B", "f3ba4d5 07-11 +hour-9 block",
         ["xau_strategy.blocked_hours_utc=[0,1,2,10,11,13,16,21,9]",
          "xau_strategy.cp_enabled=false",
          "signals.trail_dist_r=0.75",
          "xau_strategy.m5_min_abs_score=30",
          "xau_strategy.m5_min_confirmation_score=10"]),
        ("M5-C", "de6424e 07-16 +chart patterns",
         ["xau_strategy.blocked_hours_utc=[0,1,2,10,11,13,16,21,9]",
          "signals.trail_dist_r=0.75",
          "xau_strategy.m5_min_abs_score=30",
          "xau_strategy.m5_min_confirmation_score=10"]),
        ("M5-D", "3ee79b5 07-21 +trail dist 0.85",
         ["xau_strategy.blocked_hours_utc=[0,1,2,10,11,13,16,21,9]",
          "xau_strategy.m5_min_abs_score=30",
          "xau_strategy.m5_min_confirmation_score=10"]),
        ("M5-E", "0396970 07-30 relaxation (HEAD)", []),
    ],
    "15min": [
        ("M15-A", "987ec5b 07-09 pre-campaigns",
         ["signals.tp_floor_rr=0.5",
          "xau_strategy.blocked_regimes=[]",
          "xau_strategy.sell_blocked_hours_utc=[]",
          "signals.trail_arm_r=0.60"]),
        ("M15-B", "f3ba4d5/f88e39f 07-11 1R floor + regime block",
         ["xau_strategy.sell_blocked_hours_utc=[]",
          "signals.trail_arm_r=0.60"]),
        ("M15-C", "7d3fd95 07-16 +SELL hour 15/17 block",
         ["signals.trail_arm_r=0.60"]),
        ("M15-D", "bf8e5fb 07-21 trail arm 0.70 (HEAD)", []),
    ],
    "1h": [
        ("H1-A", "pre-d95d31a 07-08 TP-tightening model",
         ["signals.min_rr_ratio=0.4",
          "signals.tp_tightening_factor=0.8"]),
        ("H1-B", "HEAD", []),
    ],
 }
 def run_one(tf: str, name: str, overrides: list[str], wkey: str,
            spread: str | None = None) -> Path:
    suffix = f"_s{spread}" if spread else ""
    csv_path = OUT / f"{name}_{wkey}{suffix}.csv"
    log_path = OUT / f"{name}_{wkey}{suffix}.log"
    if csv_path.exists():
        print(f"  [skip] {csv_path.name} exists")
        return csv_path
    start, end = WINDOWS[wkey]
    cmd = [sys.executable, str(BT), "--pair", "XAUUSD",
           "--primary-tf", tf, "--start", start, "--end", end,
           "--export-csv", str(csv_path)]
    if spread:
        cmd += ["--spread", spread]
    for ov in overrides:
        cmd += ["--set", ov]
    print(f"  [run ] {csv_path.name} ...", flush=True)
    with open(log_path, "w") as lf:
        rc = subprocess.run(cmd, stdout=lf, stderr=subprocess.STDOUT,
                            cwd=ROOT).returncode
    if rc != 0:
        raise SystemExit(f"backtest failed (rc={rc}) — see {log_path}")
    log = log_path.read_text()
    n_ov = log.count("--set override")
    if n_ov != len(overrides):
        raise SystemExit(
            f"{name}_{wkey}: expected {len(overrides)} --set overrides, "
            f"log shows {n_ov} — see {log_path}")
    if "Loading cached" not in log and wkey != "sanity":
        print(f"  [warn] {log_path.name}: no 'Loading cached' line — "
              f"data was fetched live for at least one timeframe")
    return csv_path
 def metrics(csv_path: Path, wkey: str) -> dict:
    df = pd.read_csv(csv_path, parse_dates=["entry_time"])
    if df.empty:
        return {"trades": 0, "total_r": 0.0, "wr": 0.0, "pf": 0.0,
                "max_dd": 0.0, "h1_r": 0.0, "h2_r": 0.0, "years_pos": ""}
    if df["entry_time"].dt.tz is None:
        df["entry_time"] = df["entry_time"].dt.tz_localize("UTC")
    r = df["r_multiple"]
    pos, neg = r[r > 0].sum(), r[r < 0].sum()
    cum = r.cumsum()
    dd = (pd.concat([pd.Series([0.0]), cum]).cummax()[1:] - cum).max()
    out = {
        "trades": len(df),
        "total_r": round(r.sum(), 1),
        "wr": round((r > 0).mean() * 100, 1),
        "pf": round(pos / abs(neg), 2) if neg else float("inf"),
        "max_dd": round(dd, 1),
        "h1_r": "", "h2_r": "", "years_pos": "",
    }
    if wkey == "12mo":
        h1 = r[df["entry_time"] < SPLIT_12MO].sum()
        out["h1_r"] = round(h1, 1)
        out["h2_r"] = round(r.sum() - h1, 1)
    if wkey == "5yr":
        by_year = r.groupby(df["entry_time"].dt.year).sum()
        out["years_pos"] = f"{(by_year > 0).sum()}/{len(by_year)}"
    return out
 def main() -> None:
    ap = argparse.ArgumentParser()
    ap.add_argument("--tf", choices=list(VARIANTS), default=None)
    ap.add_argument("--windows", default="12mo,oos,5yr")
    args = ap.parse_args()
    OUT.mkdir(parents=True, exist_ok=True)
    wkeys = [w.strip() for w in args.windows.split(",")]
    tfs = [args.tf] if args.tf else list(VARIANTS)
    rows = []
    for tf in tfs:
        for name, label, overrides in VARIANTS[tf]:
            for wkey in wkeys:
                csv_path = run_one(tf, name, overrides, wkey)
                m = metrics(csv_path, wkey)
                rows.append({"tf": tf, "variant": name, "represents": label,
                             "window": wkey, **m})
                print(f"    {name} {wkey}: {m['trades']} trades, "
                      f"{m['total_r']}R, PF {m['pf']}, WR {m['wr']}%")
    summary = pd.DataFrame(rows)
    summary.to_csv(OUT / "summary.csv", index=False)
    print("\n=== SUMMARY (also in summary.csv) ===")
    print(summary.to_string(index=False))
 if __name__ == "__main__":
    main()
@@ -1,194 +0,0 @@
 """Dukascopy XAU/USD tick download + bid/ask spread study.
 Validates ForexBot2's $0.30 round-trip cost assumption (config:
 SignalConfig.round_trip_cost / PAIR_INFO default_spread). Round-trip
 cost of a market entry+exit = one ask-bid spread width (buy at ask,
 sell at bid), so the assumption holds iff typical spread ~ $0.30.
 bi5 format: LZMA 'alone' stream of 20-byte big-endian records
 (ms_in_hour uint32, ask uint32, bid uint32, ask_vol f32, bid_vol f32);
 XAUUSD prices scaled x1000. URL months are 0-indexed.
 """
 import lzma
 import struct
 import sys
 from concurrent.futures import ThreadPoolExecutor
 from datetime import date, timedelta
 from pathlib import Path
 import numpy as np
 import pandas as pd
 import requests
 SCALE = 1000.0
 CACHE = Path(__file__).parent / "tick_cache"
 CACHE.mkdir(exist_ok=True)
 URL = "https://datafeed.dukascopy.com/datafeed/XAUUSD/{y}/{m:02d}/{d:02d}/{h:02d}h_ticks.bi5"
 # Bot's tradable bar-close hours: session 03:00-23:00 minus XAU blocked
 # hours [0,1,2,10,11,13,16,21] (M5 additionally blocks 9 — reported
 # separately below).
 BLOCKED = {0, 1, 2, 10, 11, 13, 16, 21}
 TRADABLE = sorted(set(range(3, 23)) - BLOCKED)
 # ── Day sample ────────────────────────────────────────────────────────
 recent = [date(2026, 7, 6) + timedelta(days=i) for i in range(17)]
 recent = [d for d in recent if d.weekday() < 5]              # Mon-Fri Jul 6-22
 monthly = [date(2025, 8, 13), date(2025, 9, 10), date(2025, 10, 15),
           date(2025, 11, 12), date(2025, 12, 10), date(2026, 1, 14),
           date(2026, 2, 11), date(2026, 3, 11), date(2026, 4, 15),
           date(2026, 5, 13), date(2026, 6, 10)]             # mid-month Wednesdays
 nfp = [date(2026, 6, 5), date(2026, 7, 3)]                   # NFP Fridays
 sundays = [date(2026, 7, 12), date(2026, 7, 19)]             # Sun 22-24 open
 DAYS = sorted(set(recent + monthly + nfp + sundays))
 assert all(d.weekday() < 5 for d in recent + monthly + nfp)
 _tls = __import__("threading").local()
 def _session() -> requests.Session:
    if not hasattr(_tls, "s"):
        _tls.s = requests.Session()
        _tls.s.headers["User-Agent"] = "Mozilla/5.0 (research; spread-study)"
    return _tls.s
 def fetch(day: date, hour: int) -> Path:
    p = CACHE / f"{day.isoformat()}_{hour:02d}.bi5"
    if p.exists():
        return p
    u = URL.format(y=day.year, m=day.month - 1, d=day.day, h=hour)
    last = None
    for attempt in range(4):
        try:
            r = _session().get(u, timeout=30)
            if r.status_code != 200:
                raise RuntimeError(f"{u} -> HTTP {r.status_code}")
            p.write_bytes(r.content)
            return p
        except (requests.ConnectionError, requests.Timeout, RuntimeError) as exc:
            last = exc
            __import__("time").sleep(1.5 * (attempt + 1))
            if hasattr(_tls, "s"):
                _tls.s.close()
                del _tls.s
    raise RuntimeError(f"giving up on {u}: {last}")
 def decode(p: Path, day: date, hour: int) -> pd.DataFrame:
    blob = p.read_bytes()
    if not blob:
        return pd.DataFrame()
    raw = lzma.decompress(blob)
    n = len(raw) // 20
    a = np.frombuffer(raw[: n * 20], dtype=">u4").reshape(n, 5)
    f = np.frombuffer(raw[: n * 20], dtype=">f4").reshape(n, 5)
    df = pd.DataFrame({
        "ms": a[:, 0].astype(np.int64),
        "ask": a[:, 1] / SCALE,
        "bid": a[:, 2] / SCALE,
    })
    df["spread"] = df["ask"] - df["bid"]
    df["date"], df["hour"] = day.isoformat(), hour
    df["weekday"] = day.weekday()
    return df
 def load_all() -> pd.DataFrame:
    jobs = [(d, h) for d in DAYS for h in range(24)]
    with ThreadPoolExecutor(max_workers=3) as ex:
        paths = list(ex.map(lambda j: fetch(*j), jobs))
    frames = [decode(p, d, h) for p, (d, h) in zip(paths, jobs)]
    df = pd.concat([f for f in frames if not f.empty], ignore_index=True)
    return df
 def wstats(g: pd.DataFrame) -> pd.Series:
    """Tick stats + time-weighted mean (each tick's spread held until the
    next tick; last tick held to hour end)."""
    tw_num = tw_den = 0.0
    keys = [k for k in ("date", "hour") if k in g.columns]
    for _, grp in g.groupby(keys, sort=False):
        ms = grp["ms"].to_numpy()
        sp = grp["spread"].to_numpy()
        w = np.diff(np.append(ms, 3_600_000)).clip(min=0)
        tw_num += float((sp * w).sum())
        tw_den += float(w.sum())
    s = g["spread"]
    return pd.Series({
        "ticks": len(s),
        "tw_mean": tw_num / tw_den if tw_den else np.nan,
        "mean": s.mean(), "median": s.median(),
        "p75": s.quantile(0.75), "p90": s.quantile(0.90),
        "p99": s.quantile(0.99), "max": s.max(),
        "pct_le_030": (s <= 0.30).mean() * 100,
        "pct_ge_060": (s >= 0.60).mean() * 100,
    })
 def m5_boundary_spreads(df: pd.DataFrame) -> pd.DataFrame:
    """Spread of the FIRST tick at/after each M5 boundary — what a
    next-open fill actually pays."""
    rows = []
    for (day, hour), grp in df.groupby(["date", "hour"], sort=False):
        ms = grp["ms"].to_numpy()
        sp = grp["spread"].to_numpy()
        wd = grp["weekday"].iat[0]
        for b in range(0, 3_600_000, 300_000):
            i = np.searchsorted(ms, b)
            if i < len(ms) and ms[i] - b <= 30_000:   # tick within 30 s
                rows.append((day, wd, hour, b // 60_000, sp[i]))
    return pd.DataFrame(rows, columns=["date", "weekday", "hour", "minute", "spread"])
 def main() -> None:
    df = load_all()
    print(f"days={df['date'].nunique()}  ticks={len(df):,}  "
          f"price range {df['bid'].min():.0f}-{df['ask'].max():.0f}")
    print("\n── Overall (all sampled hours) ──")
    print(wstats(df).round(3).to_string())
    trad = df[df["hour"].isin(TRADABLE) & (df["weekday"] < 5)]
    print("\n── Bot-tradable hours (session 03-23 minus blocked, Mon-Fri) ──")
    print(wstats(trad).round(3).to_string())
    roll = df[df["hour"].isin([21, 22, 23])]
    print("\n── Rollover window 21-24 UTC ──")
    print(wstats(roll).round(3).to_string())
    print("\n── Per UTC hour ──")
    per_hour = df[df["weekday"] < 5].groupby("hour").apply(wstats, include_groups=False).round(3)
    per_hour["tradable"] = ["Y" if h in TRADABLE else "-" for h in per_hour.index]
    print(per_hour.to_string())
    per_hour.to_csv(Path(__file__).parent / "duka_spread_by_hour.csv")
    print("\n── M5 next-open boundary fills ──")
    mb = m5_boundary_spreads(df)
    mb_trad = mb[mb["hour"].isin(TRADABLE) & (mb["weekday"] < 5)]
    for name, d in [("all hours", mb), ("tradable hours", mb_trad)]:
        s = d["spread"]
        print(f"{name}: n={len(s):,} mean={s.mean():.3f} median={s.median():.3f} "
              f"p90={s.quantile(0.9):.3f} p99={s.quantile(0.99):.3f} "
              f"%<=0.30={(s <= 0.30).mean()*100:.1f} %>=0.60={(s >= 0.60).mean()*100:.1f}")
    mb.to_csv(Path(__file__).parent / "duka_m5_boundary_spreads.csv", index=False)
    print("\n── By month (tradable hours, tick median / tw_mean) ──")
    trad2 = trad.copy()
    trad2["month"] = trad2["date"].str[:7]
    bym = trad2.groupby("month").apply(wstats, include_groups=False).round(3)
    print(bym[["ticks", "tw_mean", "median", "p90", "pct_le_030", "pct_ge_060"]].to_string())
    print("\n── NFP Fridays 12:00-15:00 UTC vs same hours other days ──")
    nfp_days = {"2026-06-05", "2026-07-03"}
    win = df[df["hour"].isin([12, 13, 14])]
    for name, d in [("NFP", win[win["date"].isin(nfp_days)]),
                    ("non-NFP", win[~win["date"].isin(nfp_days) & (win["weekday"] < 5)])]:
        s = d["spread"]
        print(f"{name}: n={len(s):,} tick-median={s.median():.3f} p90={s.quantile(0.9):.3f} "
              f"p99={s.quantile(0.99):.3f} max={s.max():.2f}")
 if __name__ == "__main__":
    sys.exit(main())
@@ -1,25 +0,0 @@
 hour,ticks,tw_mean,mean,median,p75,p90,p99,max,pct_le_030,pct_ge_060,tradable
 0,362893.0,0.749,0.768,0.79,0.82,0.86,1.1,7.69,0.053,94.561,-
 1,540182.0,0.735,0.742,0.74,0.81,0.87,1.0,5.59,0.073,90.449,-
 2,431589.0,0.716,0.722,0.73,0.8,0.86,0.95,2.96,0.1,85.27,-
 3,343952.0,0.708,0.711,0.71,0.79,0.84,0.95,1.67,0.081,82.939,Y
 4,246262.0,0.704,0.702,0.71,0.78,0.82,0.94,1.86,0.098,82.627,Y
 5,388365.0,0.712,0.716,0.72,0.79,0.82,0.95,2.17,0.076,86.745,Y
 6,368999.0,0.65,0.651,0.65,0.69,0.74,0.94,2.32,0.079,71.668,Y
 7,313149.0,0.601,0.598,0.59,0.65,0.69,0.994,3.08,0.239,42.904,Y
 8,347276.0,0.612,0.616,0.6,0.66,0.74,1.0,3.04,0.308,48.349,Y
 9,321619.0,0.612,0.615,0.6,0.66,0.72,1.0,5.95,0.288,50.644,Y
 10,295271.0,0.611,0.618,0.6,0.66,0.71,1.0,9.36,0.164,49.253,-
 11,336963.0,0.619,0.624,0.6,0.67,0.79,1.0,4.97,0.203,50.178,-
 12,476773.0,0.66,0.697,0.63,0.72,0.877,1.367,14.98,0.257,59.633,Y
 13,651425.0,0.641,0.648,0.63,0.69,0.787,1.487,11.407,0.296,60.297,-
 14,673391.0,0.625,0.625,0.63,0.68,0.77,0.9,11.98,0.458,59.085,Y
 15,598398.0,0.636,0.638,0.64,0.7,0.78,0.91,4.49,0.433,66.296,Y
 16,485023.0,0.648,0.644,0.64,0.7,0.757,0.917,4.09,0.118,68.496,-
 17,450119.0,0.626,0.628,0.627,0.7,0.726,0.84,4.08,0.154,63.337,Y
 18,411538.0,0.626,0.63,0.637,0.7,0.71,0.79,4.16,0.107,67.734,Y
 19,435396.0,0.638,0.643,0.65,0.7,0.72,0.896,5.804,0.11,72.007,Y
 20,212654.0,0.684,0.665,0.66,0.7,0.76,1.217,7.15,0.226,70.215,Y
 21,31319.0,0.627,0.622,0.61,0.68,0.75,1.02,2.08,0.089,53.153,-
 22,108879.0,0.829,0.834,0.797,0.84,0.98,2.05,10.11,0.056,95.434,Y
 23,161079.0,1.032,0.913,0.8,0.89,1.307,2.77,13.174,0.092,95.41,-
@@ -1,158 +0,0 @@
 """Fetch XAU/USD M15 candles from the Oanda v20 REST API.
 Mechanical data-fetching infrastructure only — no analysis here.
 Paginates GET /v3/instruments/XAU_USD/candles from a start cursor
 through "now" and writes the result to a parquet file.
 Run from the repo root:
    venv/bin/python backtest/research/fetch_oanda_m15.py
 """
 import sys
 import time
 from datetime import datetime, timedelta, timezone
 from pathlib import Path
 import pandas as pd
 import requests
 sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
 from app_config import oanda_api_token, oanda_base_url  # noqa: E402
 INSTRUMENT = "XAU_USD"
 GRANULARITY = "M15"
 START = datetime(2025, 7, 1, 0, 0, 0, tzinfo=timezone.utc)
 OUT_PATH = Path(__file__).parent / "oanda_xau_m15_20250701_now.parquet"
 COUNT = 5000
 REQUEST_SLEEP_S = 0.5
 FRESHNESS_WINDOW = timedelta(minutes=30)
 def _rfc3339(dt: datetime) -> str:
    return dt.astimezone(timezone.utc).strftime("%Y-%m-%dT%H:%M:%S.%fZ")
 def fetch_all_candles():
    token = oanda_api_token()
    base_url = oanda_base_url()
    if not token:
        raise RuntimeError(
            "No Oanda API token resolved (env OANDA_API_TOKEN / "
            "config.json oanda.api_token). Aborting.")
    url = f"{base_url}/v3/instruments/{INSTRUMENT}/candles"
    headers = {
        "Authorization": f"Bearer {token}",
        "Accept-Datetime-Format": "RFC3339",
    }
    cursor = START
    include_first = True
    all_rows = []
    n_requests = 0
    while True:
        params = {
            "granularity": GRANULARITY,
            "price": "MBA",
            "count": COUNT,
            "dailyAlignment": 0,
            "alignmentTimezone": "Etc/UTC",
            "from": _rfc3339(cursor),
            "includeFirst": "true" if include_first else "false",
        }
        resp = requests.get(url, headers=headers, params=params, timeout=30)
        n_requests += 1
        if resp.status_code != 200:
            raise RuntimeError(
                f"Oanda API request failed: status={resp.status_code}\n"
                f"URL: {resp.url}\n"
                f"Response body: {resp.text}")
        payload = resp.json()
        candles = payload.get("candles", [])
        for c in candles:
            if not c.get("complete", False):
                continue
            mid = c["mid"]
            bid = c["bid"]
            ask = c["ask"]
            all_rows.append({
                "Datetime": pd.Timestamp(c["time"]).tz_convert("UTC")
                if pd.Timestamp(c["time"]).tzinfo is not None
                else pd.Timestamp(c["time"]).tz_localize("UTC"),
                "Open": float(mid["o"]),
                "High": float(mid["h"]),
                "Low": float(mid["l"]),
                "Close": float(mid["c"]),
                "Volume": int(c["volume"]),
                "bid_o": float(bid["o"]),
                "bid_h": float(bid["h"]),
                "bid_l": float(bid["l"]),
                "bid_c": float(bid["c"]),
                "ask_o": float(ask["o"]),
                "ask_h": float(ask["h"]),
                "ask_l": float(ask["l"]),
                "ask_c": float(ask["c"]),
            })
        print(f"  request {n_requests}: from={params['from']} "
              f"got {len(candles)} candles (complete kept so far: "
              f"{len(all_rows)})", file=sys.stderr)
        if not candles:
            break
        last_time = pd.Timestamp(candles[-1]["time"])
        if last_time.tzinfo is None:
            last_time = last_time.tz_localize("UTC")
        else:
            last_time = last_time.tz_convert("UTC")
        now = datetime.now(timezone.utc)
        # Oanda's "count" includes the "from" boundary bar itself, so with
        # includeFirst=false every full page comes back as count-1, not
        # count (verified empirically: count=N, includeFirst=false always
        # yields N-1 candles when a full page of data exists ahead of the
        # cursor). Compare against the count actually expected for this
        # request's includeFirst setting, not the raw requested count.
        expected_full_page = COUNT if include_first else COUNT - 1
        got_fewer_than_requested = len(candles) < expected_full_page
        near_now = (now - last_time.to_pydatetime()) < FRESHNESS_WINDOW
        if got_fewer_than_requested or near_now:
            break
        cursor = last_time.to_pydatetime()
        include_first = False
        time.sleep(REQUEST_SLEEP_S)
    return all_rows, n_requests
 def main():
    print(f"Fetching {INSTRUMENT} {GRANULARITY} candles from "
          f"{_rfc3339(START)} through now...", file=sys.stderr)
    rows, n_requests = fetch_all_candles()
    if not rows:
        raise RuntimeError("No complete candles were fetched.")
    df = pd.DataFrame(rows)
    df = df.set_index("Datetime")
    df = df.sort_index()
    df = df[~df.index.duplicated(keep="last")]
    df.to_parquet(OUT_PATH)
    print(f"\nSaved {len(df)} rows to {OUT_PATH}", file=sys.stderr)
    print(f"Requests made: {n_requests}", file=sys.stderr)
    print(f"First timestamp: {df.index[0]}", file=sys.stderr)
    print(f"Last timestamp: {df.index[-1]}", file=sys.stderr)
 if __name__ == "__main__":
    main()
@@ -1,123 +0,0 @@
 # Fixed-RR hard-TP vs shipped trailing exit — research campaign 2026-08-05
 **Verdict: DO NOT SHIP. The trailing exit stays.** The fixed 2R target beats the trail on
 total R in every 2024+ window but the edge is a trend-regime artifact: it inverts in the
 2021–2023 ranging years, doubles drawdown, cuts win rate 10–19 points, and doubles hold
 times (amplifying the accepted live one-position divergence). Details below.
 ## Setup
 - Code: HEAD `fc5ce85` + the `--fixed-rr-tp` research flag (uncommitted at run time).
  The flag replaces every trade's TP with `mult × |entry − sl|` AFTER re-anchoring and
  disables trailing (run-level and per-trade PB params) — entries, stops, gates, and the
  cooldown pass are untouched, so both arms replay **trade-identical books** (verified:
  paired join on entry_time+direction matches 1:1 in every window). Only the exit policy
  differs → per-trade paired comparison.
 - Provider: Oanda (cache-warm; keys `oanda_XAU_USD_{5min,15min,1h}_{20250603_20260804,
  20240331_20250330,20210531_20260804}`). Charged cost `--spread 0.65` (real measured
  round trip); $0.30 letter-cost books derived analytically as `r + 0.35/risk` and
  verified exact against one real `--spread 0.30` run (max per-trade error 1.8e-15 —
  spread never enters trigger detection, per the owner-declined 2026-08-04 decision).
 - Control sanity gate: fresh M5 12mo control reproduced the pinned HEAD reference
  exactly — 555 trades, +98.64R, PF 1.483, maxDD −10.73R.
 - Commands (control = same without the flag):
  `venv/bin/python backtest/gpu_backtest.py --pair XAUUSD --primary-tf {5min,15min}
   --start 2025-08-04 --end 2026-08-04 --spread 0.65 [--fixed-rr-tp 2.0]
   --export-csv backtest/fixedtp_*.csv`
  OOS `--start 2024-06-01 --end 2025-03-30`; 5yr `--start 2021-08-01 --end 2026-08-04`.
 - Books pinned as `backtest/fixedtp_{m5,m15}_{12mo,oos,5yr}_{ctrl,rr20,...}.csv`
  (per-trade indicator breakdown, agreement counts, regime, vol-regime, weekday,
  MAE/MFE, bars-held — the full instructions_for_backtest record).
 ## Headline (@ $0.65)
 | Window | Arm | n | R | PF | WR | maxDD | recov | medBars | exits |
 |---|---|---|---|---|---|---|---|---|---|
 | M5 12mo | trail | 555 | +98.64 | 1.483 | 52.4% | −10.7 | 9.2 | 28 | TRAIL 361 / SL 183 / TO 11 |
 | M5 12mo | 2R hard | 555 | +124.85 | 1.378 | 42.9% | −24.5 | 5.1 | 50 | SL 312 / TP1 210 / TO 33 |
 | M15 12mo | trail | 397 | +73.93 | 1.525 | 62.2% | −10.8 | 6.8 | 28 | TRAIL 255 / SL 135 / TO 7 |
 | M15 12mo | 2R hard | 397 | +95.95 | 1.431 | 44.3% | −11.2 | 8.6 | 39 | SL 215 / TP1 151 / TO 31 |
 | M5 OOS | trail | 86 | +9.45 | 1.332 | 53.5% | −8.2 | 1.2 | 70 | — |
 | M5 OOS | 2R hard | 86 | +18.83 | 1.407 | 45.3% | −9.7 | 1.9 | 124 | — |
 | M15 OOS | trail | 299 | +38.77 | 1.360 | 59.2% | −15.5 | 2.5 | 38 | — |
 | M15 OOS | 2R hard | 299 | +72.14 | 1.435 | 45.2% | −13.9 | 5.2 | 82 | — |
 Halves (12mo, split 2026-02-03): M5 rr20 +67.99/+56.86 (both positive; H2 PF 1.325 vs
 ctrl 1.503), M15 rr20 +73.74/+22.21 (both positive; ctrl H2 +7.23 — the known H2-flat).
 At $0.30 all deltas keep the same sign (e.g. M5 12mo +109.0 vs +135.2).
 So the **letter** of rule 1 passes for rr20 on both TFs. The 5yr decomposition is why it
 still must not ship.
 ## Exit migration (paired, M5 12mo; same pattern everywhere)
 - SL→SL 183 trades: ΔR 0 (identical by construction — stops untouched).
 - TRAIL→TP1 207 trades: +248.9R → +424.5R (**+175.6R**) — winners the trail exits early.
 - TRAIL→SL 129 trades: +19.0R → −136.8R (**−155.7R**) — retraces the trail survives
  become full stop-outs.
 - TRAIL→TIMEOUT 25: +7.4R. Net = +26.2R. The whole effect is these two big flows nearly
  cancelling; which one wins is a regime property, not an exit-policy edge.
 - Trades with ctrl MFE ≥ 2R: 95; hard cap forfeits only +10.4R vs trail on M5 (the trail
  already gives ~0.75R back from peak, so a 2R cap costs little *in a trend*).
 ## Mini-grid (12mo @ $0.65) — the tell
 | mult | M5 R / PF / DD | M15 R / PF / DD |
 |---|---|---|
 | trail | +98.6 / 1.483 / −10.7 | +73.9 / 1.525 / −10.8 |
 | 1.0R | +52.5 / 1.205 / −12.8 | +67.1 / 1.410 / −7.3 |
 | 1.5R | +80.7 / 1.263 / −21.4 | +66.9 / 1.325 / −11.2 |
 | 2.0R | +124.9 / 1.378 / −24.5 | +96.0 / 1.431 / −11.2 |
 | 2.5R | +147.7 / 1.426 / −23.6 | +110.4 / 1.469 / −15.9 |
 | 3.0R | +155.8 / 1.432 / −21.6 | +135.8 / 1.562 / −17.5 |
 Monotone in the multiple on both TFs — "2.0" is not an optimum, it's a point on a curve
 that says *never take profit* (3R's M5 TIMEOUT bucket alone is +84.8R). That is the
 signature of a one-way trend sample, not of a superior exit rule. It also reconfirms the
 original finding that SHORT fixed targets (≤1.5R) are strictly bad (cf. RR-0.5 removal,
 `config.py:126-133`: +48R → +105R).
 ## 5yr regime decomposition (2021-08-01 → 2026-08-04 @ $0.65) — the verdict
 M15 (real sample in every year): ctrl +127.97 PF 1.209 DD −35.8; rr20 +158.97 PF 1.170
 DD **−59.8**; rr30 +204.03 PF 1.203 DD **−98.3**.
 | Year | n | trail | 2R hard | 3R hard |
 |---|---|---|---|---|
 | 2021 | 88 | **+4.00** | −16.09 | −17.41 |
 | 2022 | 284 | −9.54 | −7.63 | −26.74 |
 | 2023 | 227 | **+7.79** | −1.71 | −5.81 |
 | 2024 | 345 | +40.44 | +64.98 | +89.14 |
 | 2025 | 405 | +49.33 | +69.39 | +104.42 |
 | 2026 | 229 | +35.95 | +50.04 | +60.43 |
 M5 5yr mirrors it (ctrl +128.4 PF 1.387 DD −12.8 vs rr20 +160.2 PF 1.306 DD −24.7;
 2022: −1.1 vs −5.2), though pre-2024 M5 samples are thin (12–60 trades/yr).
 The hard target's entire outperformance lives in the 2024–2026 gold trend. In ranging
 years it flips sign exactly where the trail stays positive (M15 2021, 2023), and the
 further the target, the worse the ranging-year bleed and the deeper the DD. The 12mo,
 halves, and OOS windows all sit inside the trend era, which is why the rule-1 letter
 passes — this is precisely the within-regime-split trap rule 1's OOS clause exists to
 catch, one level up.
 ## Additional non-ship reasons
 - **Live divergence grows with hold time.** Median hold doubles (M5 28→50 bars; OOS
  70→124). The daemon holds one position total; the backtester doesn't model that
  blocking. Longer holds = more live signals skipped that the paper book banks.
 - **WR 52→43% (M5) / 62→44% (M15), max loss streak 6→11 (M15)** — materially harder to
  operate and size.
 - Aggregate tables (session/day/regime/vol, streaks, quarterly equity, rolling-20 WR)
  computed and consistent — no bucket reverses the conclusion; both arms' 12mo profit is
  concentrated in Q1-2026 (+87.7 ctrl / +102.0 rr20).
 ## Status
 - `--fixed-rr-tp` stays as a documented research-only flag (default off; conflicts with
  `--trail-arm/--trail-dist/--set signals.*trail*` are hard errors). Module 22 tests
  (5) pin the override math, TP fill + spread, SL-first tie, PB-trail-disable, and CLI
  guards. Full suite: 346 green.
 - Re-open only if gold's regime turns and a *regime-conditional* exit (trail in range,
  target in trend) is proposed with a pre-registered mechanism — a plain fixed ratio is
  now a closed hypothesis at every multiple in {0.5, 1.0, 1.5, 2.0, 2.5, 3.0}.
@@ -1,40 +0,0 @@
 """Per-hour P&L breakdown of a backtest trade book.
 Usage:
    python backtest/research/hour_analysis.py BOOK.csv BAR_MINUTES
 Reports, per UTC hour (both entry hour and DECISION hour = entry_time
 minus one bar, the clock blocked_hours_utc evaluates): trade count,
 total R, mean R, win rate, profit factor. Also a per-(hour, direction)
 table. Analysis tooling only — never imported by the live code.
 """
 import sys
 import pandas as pd
 def hour_tables(book_csv: str, bar_minutes: int):
    df = pd.read_csv(book_csv)
    et = pd.to_datetime(df["entry_time"], utc=True, format="mixed")
    df["entry_hour"] = et.dt.hour
    df["decision_hour"] = (et - pd.Timedelta(minutes=bar_minutes)).dt.hour
    def table(group_cols):
        g = df.groupby(group_cols)["r_multiple"]
        out = g.agg(count="count", total_r="sum", mean_r="mean",
                    wr=lambda r: (r > 0).mean() * 100)
        out["pf"] = g.apply(
            lambda r: r[r > 0].sum() / max(-r[r <= 0].sum(), 1e-9))
        return out.round(2)
    return (table("decision_hour"), table(["decision_hour", "direction"]),
            table("entry_hour"))
 if __name__ == "__main__":
    book, mins = sys.argv[1], int(sys.argv[2])
    by_dec, by_dec_dir, by_ent = hour_tables(book, mins)
    print(f"\n── {book} · by DECISION hour (bar-close clock) ──")
    print(by_dec.to_string())
    print(f"\n── by (decision hour, direction) ──")
    print(by_dec_dir.to_string())
@@ -1,697 +0,0 @@
 """
 DeepSeek news-analysis validation harness — XAU/USD.
 ====================================================
 Research-only (no production code changes). Answers: is the DeepSeek
 layer's news analysis *correct*?
 Part A (events): replays every high-impact USD release with a genuine
 surprise (2024-01 → 2026-07, from backtest/cache/calendar.parquet)
 through the REAL `NewsEngine._llm_event_direction` — real prompt, real
 payload, real validation — and scores its "gold bullish/bearish" calls
 against gold's actual move (Oanda M15 mids) at +15m/30m/1h/4h/24h.
 Baselines: mechanical surprise-sign doctrine, FF actualBetterWorse,
 always-bullish/bearish (2024-26 is a gold bull regime), coin flip.
 Part B (headlines): a curated golden set (llm_news_golden.csv) through
 the REAL `NewsEngine._llm_headline_scores` in production-sized batches,
 vs the keyword scorer it overrides. Includes repeat-batch (determinism)
 and shuffled-order (cross-contamination) probes.
 Implementation notes that keep the replay faithful to production:
  * `_llm_event_direction`/`_llm_headline_scores` resolve the client via
    `get_llm_client()`, so we seed the module singleton with a research
    client (raised daily cap: 586 events > production cap 500).
  * The event cache key omits the forecast, so a repeat `actual` would
    silently re-serve a direction computed for a different forecast —
    the cache is cleared before every call.
  * A "neutral" reply and a hard failure both return None; they are
    disambiguated by reading the event cache back after the call
    (absent = failure, "" = neutral, "bullish:/bearish: …" = call).
  * Raw request/response bytes are captured with a requests response
    hook — the request-building code path is untouched.
 Usage (from repo root):
  venv/bin/python backtest/research/llm_news_eval.py events --limit 5
  venv/bin/python backtest/research/llm_news_eval.py events --determinism 20
  venv/bin/python backtest/research/llm_news_eval.py headlines
  venv/bin/python backtest/research/llm_news_eval.py report
 Artifacts land in backtest/research/llm_news_eval_2026-08/ (JSONL replay
 logs are resume-safe: completed events are never re-paid). `report` is a
 pure offline function of the artifacts.
 """
 import argparse
 import json
 import math
 import random
 import sys
 import time
 from datetime import datetime, timezone
 from pathlib import Path
 import numpy as np
 import pandas as pd
 REPO_ROOT = Path(__file__).resolve().parents[2]
 sys.path.insert(0, str(REPO_ROOT))
 import llm_client
 from app_config import deepseek_api_key, get_app_config
 from config import NewsConfig
 from llm_client import DEFAULT_MODEL, DeepSeekClient
 from news_engine import EconomicEvent, NewsEngine
 RESEARCH_DIR = Path(__file__).parent
 ART_DIR = RESEARCH_DIR / "llm_news_eval_2026-08"
 CALENDAR = REPO_ROOT / "backtest" / "cache" / "calendar.parquet"
 BARS = (REPO_ROOT / "backtest" / "cache" /
        "oanda_XAU_USD_15min_20210531_20260804.parquet")
 GOLDEN = RESEARCH_DIR / "llm_news_golden.csv"
 EVENTS_JSONL = ART_DIR / "events_replay.jsonl"
 HEADLINES_JSONL = ART_DIR / "headlines_replay.jsonl"
 SCORED_CSV = ART_DIR / "events_scored.csv"
 HORIZONS = [("15m", 15), ("30m", 30), ("1h", 60), ("4h", 240),
            ("24h", 1440)]
 NEUTRAL_BAND = 0.15  # |xau| below this = neutral prediction (Part B)
 BATCH_CAP = 25       # production batch cap (news_engine.py:783)
 EXPECTED_CORPUS = 586
 # ── shared plumbing ──────────────────────────────────────────────────
 class _Recorder:
    """requests response hook capturing the exact wire traffic."""
    def __init__(self):
        self.last = None
    def hook(self, resp, **kwargs):
        body = resp.request.body
        if isinstance(body, bytes):
            body = body.decode("utf-8", "replace")
        self.last = {
            "request_body": body,
            "http_status": resp.status_code,
            "response_body": resp.text,
            "elapsed_s": round(resp.elapsed.total_seconds(), 3),
        }
        return resp
    def pop(self):
        cap, self.last = self.last, None
        return cap or {}
 def install_research_client(timeout: float):
    """Build a research DeepSeekClient (raised cap) and seed the module
    singleton so the production code paths use it. Returns (client,
    recorder)."""
    app = get_app_config()
    key = deepseek_api_key(app)
    ds = app.deepseek_settings
    if not key:
        sys.exit("No DeepSeek API key (env DEEPSEEK_API_KEY or "
                 "config.json deepseek.api_key) — aborting before spend.")
    if ds.get("enabled") is False:
        sys.exit("deepseek.enabled is false in config.json — aborting.")
    client = DeepSeekClient(
        api_key=key,
        model=str(ds.get("model", DEFAULT_MODEL)),
        timeout=timeout,
        daily_cap=10 ** 6,  # research: corpus 586 > production cap 500
    )
    rec = _Recorder()
    client._session.hooks["response"].append(rec.hook)
    with llm_client._client_lock:
        llm_client._client = client
    return client, rec
 def _s(x):
    """Parquet str columns carry float NaN — EconomicEvent needs None."""
    return None if pd.isna(x) else str(x)
 def load_corpus():
    """High-impact USD releases with a genuine surprise, through the
    production `EconomicEvent.surprise` gate. Asserts run before any
    API spend."""
    df = pd.read_parquet(CALENDAR)
    df = df[(df["impactName"] == "high") & (df["currency"] == "USD")]
    rows = []
    for r in df.itertuples():
        ev = EconomicEvent(
            time=datetime.fromtimestamp(int(r.dateline), tz=timezone.utc),
            currency="USD", impact="high", event=_s(r.name) or "",
            forecast=_s(r.forecast), previous=_s(r.previous),
            actual=_s(r.actual),
        )
        if ev.surprise not in ("positive", "negative"):
            continue
        rows.append({
            "ffid": int(r.id), "dateline": int(r.dateline),
            "utc_time": ev.time.isoformat(), "name": ev.event,
            "actual": ev.actual, "forecast": ev.forecast,
            "previous": ev.previous, "surprise": ev.surprise,
            "better_worse": int(r.actualBetterWorse), "ev": ev,
        })
    ids = [d["ffid"] for d in rows]
    if len(rows) != EXPECTED_CORPUS:
        sys.exit(f"corpus size {len(rows)} != {EXPECTED_CORPUS} — "
                 "calendar.parquet changed; re-verify before spending.")
    if len(set(ids)) != len(ids):
        sys.exit("FF ids not unique — resume keying would break.")
    # Pin the actualBetterWorse mapping on two unambiguous NFP prints.
    nfp = {d["utc_time"][:10]: d["better_worse"] for d in rows
           if d["name"] == "Non-Farm Employment Change"}
    if nfp.get("2024-01-05") != 1 or nfp.get("2024-05-03") != 2:
        sys.exit("actualBetterWorse mapping pins failed (expected "
                 "1=better-for-USD on 2024-01-05 beat, 2=worse on "
                 "2024-05-03 miss).")
    rows.sort(key=lambda d: (d["dateline"], d["ffid"]))
    return rows
 def _load_jsonl(path: Path):
    recs = []
    if path.exists():
        with open(path) as f:
            for line in f:
                line = line.strip()
                if line:
                    recs.append(json.loads(line))
    return recs
 # ── Part A: event replay ─────────────────────────────────────────────
 def _extract_reason(cap):
    try:
        content = json.loads(cap["response_body"])[
            "choices"][0]["message"]["content"]
        return json.loads(content).get("reason")
    except Exception:
        return None
 def cmd_events(args):
    ART_DIR.mkdir(exist_ok=True)
    corpus = load_corpus()
    if args.limit:
        corpus = corpus[:args.limit]
    engine = NewsEngine(NewsConfig())
    existing = _load_jsonl(EVENTS_JSONL)
    done1 = {r["ffid"] for r in existing
             if r.get("run_pass") == 1 and r.get("llm_direction")}
    done2 = {r["ffid"] for r in existing
             if r.get("run_pass") == 2 and r.get("llm_direction")}
    pending = [d for d in corpus if d["ffid"] not in done1]
    print(f"corpus {len(corpus)} | already recorded {len(done1)} | "
          f"pending {len(pending)} | determinism re-calls "
          f"{args.determinism} | model calls are ~100 tokens each "
          f"(deepseek-v4-flash, static-prefix cached) → cost ≈ cents")
    if not pending and not args.determinism:
        print("nothing to do")
        return
    _, rec = install_research_client(args.timeout)
    out = open(EVENTS_JSONL, "a")
    def call_one(d, run_pass):
        # Cache key omits forecast → cleared per call so 196 colliding
        # corpus rows can't be served a stale direction.
        with NewsEngine._cache_lock:
            NewsEngine._llm_event_cache.clear()
        ev = d["ev"]
        engine._llm_event_direction(ev)
        key = (ev.event, ev.currency, str(ev.actual or ""))
        with NewsEngine._cache_lock:
            cached = NewsEngine._llm_event_cache.get(key)
        cap = rec.pop()
        if cached is None:
            direction, text = None, None       # hard failure → retry
        elif cached == "":
            direction, text = "neutral", ""
        else:
            direction, text = cached.split(":", 1)[0], cached
        recd = {k: d[k] for k in (
            "ffid", "dateline", "utc_time", "name", "actual", "forecast",
            "previous", "surprise", "better_worse")}
        recd.update({
            "llm_direction": direction, "llm_text": text,
            "reason_raw": _extract_reason(cap), "run_pass": run_pass,
            "ts": datetime.now(timezone.utc).isoformat(), **cap,
        })
        out.write(json.dumps(recd) + "\n")
        out.flush()
        return direction
    remaining = pending
    for npass in range(1, args.retry_passes + 1):
        if not remaining:
            break
        failed = []
        for i, d in enumerate(remaining):
            if call_one(d, 1) is None:
                failed.append(d)
            if args.sleep:
                time.sleep(args.sleep)
            if (i + 1) % 25 == 0:
                print(f"  pass {npass}: {i + 1}/{len(remaining)} "
                      f"({len(failed)} failed)")
        print(f"pass {npass} done: {len(remaining) - len(failed)} ok, "
              f"{len(failed)} failed")
        remaining = failed
    if remaining:
        print(f"WARNING: {len(remaining)} events still unrecorded after "
              f"{args.retry_passes} passes — re-run `events` to resume.")
    if args.determinism:
        ok_ids = {x["ffid"] for x in remaining}
        done_now = [d for d in corpus if d["ffid"] not in ok_ids]
        done_now.sort(key=lambda d: (d["name"], d["dateline"]))
        stride = max(1, len(done_now) // args.determinism)
        sample = done_now[::stride][:args.determinism]
        todo = [d for d in sample if d["ffid"] not in done2]
        print(f"determinism probe: {len(todo)} re-calls "
              f"({len(sample) - len(todo)} already recorded)")
        for d in todo:
            call_one(d, 2)
            if args.sleep:
                time.sleep(args.sleep)
    out.close()
    print("events replay complete →", EVENTS_JSONL)
 # ── Part B: golden headlines ─────────────────────────────────────────
 def _load_golden():
    g = pd.read_csv(GOLDEN)
    need = {"headline", "category", "expected_xau_sign", "rationale"}
    if not need <= set(g.columns):
        sys.exit(f"golden CSV missing columns {need - set(g.columns)}")
    if not g["expected_xau_sign"].isin([-1, 0, 1]).all():
        sys.exit("expected_xau_sign must be -1/0/+1")
    if not g["headline"].is_unique:
        sys.exit("duplicate headlines in golden CSV")
    return g
 def cmd_headlines(args):
    ART_DIR.mkdir(exist_ok=True)
    g = _load_golden()
    heads = list(g["headline"])
    _, rec = install_research_client(args.timeout)
    engine = NewsEngine(NewsConfig())
    out = open(HEADLINES_JSONL, "w")  # tiny (~9 calls): fresh each run
    def run_batch(texts, tag, batch_idx):
        scores = None
        for attempt in range(3):
            scores = engine._llm_headline_scores(texts)
            cap = rec.pop()
            if scores is not None:
                break
            print(f"  {tag} batch {batch_idx} attempt {attempt + 1} "
                  f"failed (http {cap.get('http_status')})")
            time.sleep(2.0)
        out.write(json.dumps({
            "run_tag": tag, "batch": batch_idx, "headlines": texts,
            "scores": scores,
            "ts": datetime.now(timezone.utc).isoformat(), **cap,
        }) + "\n")
        out.flush()
        return scores
    batches = [heads[i:i + BATCH_CAP] for i in range(0, len(heads),
                                                     BATCH_CAP)]
    for bi, b in enumerate(batches):
        ok = run_batch(b, "main", bi) is not None
        print(f"main batch {bi} ({len(b)} headlines): "
              f"{'ok' if ok else 'FAILED'}")
        time.sleep(args.sleep)
    run_batch(batches[0], "repeat", 0)
    print("repeat probe done")
    shuffled = heads[:]
    random.Random(20260806).shuffle(shuffled)
    for bi in range(0, len(shuffled), BATCH_CAP):
        run_batch(shuffled[bi:bi + BATCH_CAP], "shuffled",
                  bi // BATCH_CAP)
    print("shuffled probe done →", HEADLINES_JSONL)
    out.close()
 # ── report: pure offline function of the artifacts ───────────────────
 def _binom_p(k, n):
    """Exact two-sided binomial p-value vs p=0.5 (no scipy)."""
    if n == 0:
        return float("nan")
    dist = [math.comb(n, i) for i in range(n + 1)]
    tot = float(2 ** n)
    pk = dist[k]
    return min(1.0, sum(c for c in dist if c <= pk) / tot)
 def _mcnemar_p(hit_a, hit_b):
    """Exact McNemar via binomial on discordant pairs. Inputs: aligned
    boolean lists."""
    b = sum(1 for x, y in zip(hit_a, hit_b) if x and not y)
    c = sum(1 for x, y in zip(hit_a, hit_b) if not x and y)
    return b, c, _binom_p(min(b, c), b + c)
 def _latest_events(recs):
    """Last pass-1 record per ffid (retries append; last wins)."""
    out = {}
    for r in recs:
        if r.get("run_pass") == 1:
            out[r["ffid"]] = r
    return list(out.values())
 def _fmt_rate(hits, n, label=""):
    if n == 0:
        return f"{label:<24} n=0"
    p = _binom_p(hits, n)
    return (f"{label:<24} {hits:>4}/{n:<4} = {hits / n * 100:5.1f}%  "
            f"(binom p={p:.3f})")
 def report_events():
    recs = _latest_events(_load_jsonl(EVENTS_JSONL))
    if not recs:
        print("no event replay records — run `events` first")
        return
    bars = pd.read_parquet(BARS)
    closes = bars["Close"].to_numpy()
    close_ns = (bars.index + pd.Timedelta(minutes=15)).asi8
    def anchor(t_ns, h_min):
        i = np.searchsorted(close_ns, t_ns, side="right") - 1
        if i < 0:
            return None
        stale_ns = max(45.0, h_min / 4.0) * 60e9
        if t_ns - close_ns[i] > stale_ns:
            return None
        return closes[i]
    rows, skip = [], {h: 0 for h, _ in HORIZONS}
    base_skipped = 0
    for r in recs:
        t_ns = int(r["dateline"]) * 10 ** 9
        base = anchor(t_ns, 0)
        if base is None:
            base_skipped += 1
            continue
        row = dict(r)
        row.pop("request_body", None)
        row.pop("response_body", None)
        row["year"] = r["utc_time"][:4]
        row["base"] = base
        for hname, hmin in HORIZONS:
            p = anchor(t_ns + hmin * 60 * 10 ** 9, hmin)
            if p is None:
                skip[hname] += 1
                row[f"d_{hname}"] = np.nan
            else:
                row[f"d_{hname}"] = p - base
        rows.append(row)
    df = pd.DataFrame(rows)
    df.to_csv(SCORED_CSV, index=False)
    n_fail = int((df["llm_direction"].isna()).sum())
    print("═" * 70)
    print("PART A — event gold-direction vs actual XAU move")
    print("═" * 70)
    print(f"events replayed: {len(df)} (baseline-price skipped: "
          f"{base_skipped}) | LLM hard failures: {n_fail}")
    print("horizon skip counts (stale/closed):",
          {k: v for k, v in skip.items() if v})
    dist = df["llm_direction"].value_counts(dropna=False).to_dict()
    print("LLM call distribution:", dist)
    # Unconditional per-horizon noise scale (null): median |Δ| over all
    # 2024+ bar times, position-shifted.
    bars24 = bars[bars.index >= "2024-01-01"]
    null_med = {}
    for hname, hmin in HORIZONS:
        k = hmin // 15
        d = (bars24["Close"].shift(-k) - bars24["Close"]).abs().dropna()
        null_med[hname] = float(d.median())
    print("unconditional null median |Δ| ($):",
          {k: round(v, 2) for k, v in null_med.items()})
    # Strategy call columns: +1 gold up, −1 gold down, 0/NaN excluded.
    df["c_llm"] = df["llm_direction"].map(
        {"bullish": 1, "bearish": -1}).fillna(0)
    df["c_doctrine"] = df["surprise"].map({"positive": -1, "negative": 1})
    df["c_ffbw"] = df["better_worse"].map({1: -1, 2: 1}).fillna(0)
    df["c_bull"] = 1
    df["c_bear"] = -1
    strategies = [("LLM", "c_llm"), ("doctrine(surprise)", "c_doctrine"),
                  ("FF betterWorse", "c_ffbw"), ("always-bullish", "c_bull"),
                  ("always-bearish", "c_bear")]
    for hname, _ in HORIZONS:
        dcol = f"d_{hname}"
        sub = df[df[dcol].notna()]
        print(f"\n── horizon {hname} (n scored bars={len(sub)}) ──")
        zeros = int((sub[dcol] == 0).sum())
        if zeros:
            print(f"  Δ==0 count (scored as miss): {zeros}")
        for label, col in strategies:
            s = sub[sub[col] != 0]
            hits = int((np.sign(s[dcol]) == s[col]).sum())
            print("  " + _fmt_rate(hits, len(s), label))
        # noise-filtered LLM hit rate
        s = sub[(sub["c_llm"] != 0) &
                (sub[dcol].abs() >= null_med[hname])]
        hits = int((np.sign(s[dcol]) == s["c_llm"]).sum())
        print("  " + _fmt_rate(hits, len(s),
                               f"LLM |Δ|≥null-med"))
        # event-window magnitude vs null
        mag = sub[dcol].abs().median()
        print(f"  event-window median |Δ| = ${mag:.2f} "
              f"(null ${null_med[hname]:.2f})")
        # paired McNemar: LLM vs each baseline on common directional set
        for label, col in [("doctrine", "c_doctrine"),
                           ("FF betterWorse", "c_ffbw"),
                           ("always-bullish", "c_bull")]:
            s = sub[(sub["c_llm"] != 0) & (sub[col] != 0)]
            ha = list(np.sign(s[dcol]) == s["c_llm"])
            hb = list(np.sign(s[dcol]) == s[col])
            b, c, p = _mcnemar_p(ha, hb)
            print(f"  McNemar LLM vs {label:<15} b={b:>3} c={c:>3} "
                  f"p={p:.3f}")
    # neutral-call flatness (15m)
    neu = df[(df["llm_direction"] == "neutral") & df["d_15m"].notna()]
    dire = df[(df["c_llm"] != 0) & df["d_15m"].notna()]
    if len(neu):
        print(f"\nneutral calls (n={len(neu)}): median |Δ_15m| "
              f"${neu['d_15m'].abs().median():.2f} vs directional "
              f"(n={len(dire)}) ${dire['d_15m'].abs().median():.2f} "
              f"(null ${null_med['15m']:.2f})")
    # breakdowns at 1h
    dcol = "d_1h"
    sub = df[df[dcol].notna() & (df["c_llm"] != 0)]
    print("\n── LLM hit rate by event name (1h, top 10 by count) ──")
    for name, grp in sorted(sub.groupby("name"), key=lambda kv: -len(kv[1]))[:10]:
        hits = int((np.sign(grp[dcol]) == grp["c_llm"]).sum())
        print("  " + _fmt_rate(hits, len(grp), name[:24]))
    print("── LLM hit rate by year (1h) ──")
    for yr, grp in sub.groupby("year"):
        hits = int((np.sign(grp[dcol]) == grp["c_llm"]).sum())
        print("  " + _fmt_rate(hits, len(grp), yr))
    # cluster view: shared datelines
    print("\n── same-timestamp clusters ──")
    clus = df.groupby("dateline").filter(lambda g: len(g) > 1)
    groups = list(clus.groupby("dateline"))
    consistent = 0
    net_hits, net_n = 0, 0
    for _, g in groups:
        signs = set(g.loc[g["c_llm"] != 0, "c_llm"])
        if len(signs) <= 1:
            consistent += 1
        net = int(g["c_llm"].sum())
        d = g["d_1h"].iloc[0]
        if net != 0 and pd.notna(d) and d != 0:
            net_n += 1
            if np.sign(d) == np.sign(net):
                net_hits += 1
    print(f"clusters: {len(groups)} datelines "
          f"({len(clus)} events, {len(clus) / max(len(df), 1) * 100:.0f}% of "
          f"corpus) | internally consistent: {consistent}/{len(groups)}")
    print("  " + _fmt_rate(net_hits, net_n, "cluster net-call (1h)"))
    # determinism probe
    p2 = {r["ffid"]: r for r in _load_jsonl(EVENTS_JSONL)
          if r.get("run_pass") == 2 and r.get("llm_direction")}
    if p2:
        p1 = {r["ffid"]: r for r in recs}
        agree = same_reason = n = 0
        for fid, r2 in p2.items():
            r1 = p1.get(fid)
            if not r1 or not r1.get("llm_direction"):
                continue
            n += 1
            if r1["llm_direction"] == r2["llm_direction"]:
                agree += 1
            if r1.get("reason_raw") == r2.get("reason_raw"):
                same_reason += 1
        print(f"\ndeterminism probe: direction agreement {agree}/{n}, "
              f"identical reason text {same_reason}/{n}")
    # stratified reason examples
    print("\n── reason samples (1h hit/miss) ──")
    sub = df[df[dcol].notna() & (df["c_llm"] != 0)].copy()
    sub["hit"] = np.sign(sub[dcol]) == sub["c_llm"]
    for verdict in (True, False):
        picked, seen = [], set()
        for _, r in sub[sub["hit"] == verdict].iterrows():
            if r["name"] in seen:
                continue
            seen.add(r["name"])
            picked.append(r)
            if len(picked) == 10:
                break
        tag = "HIT " if verdict else "MISS"
        for r in picked:
            print(f"  [{tag}] {r['utc_time'][:16]} {r['name'][:30]:<30} "
                  f"a={r['actual']} f={r['forecast']} → "
                  f"{r['llm_direction']:<7} Δ1h={r[dcol]:+7.2f}  "
                  f"“{(r['reason_raw'] or '')[:60]}”")
    print(f"\nscored table → {SCORED_CSV}")
 def report_headlines():
    recs = _load_jsonl(HEADLINES_JSONL)
    if not recs:
        print("\n(no headline replay records — run `headlines` first)")
        return
    g = _load_golden()
    engine = NewsEngine(NewsConfig())
    def collect(tag):
        m = {}
        for r in recs:
            if r["run_tag"] == tag and r["scores"]:
                for h, sc in zip(r["headlines"], r["scores"]):
                    m[h] = tuple(sc)
        return m
    main, shuf = collect("main"), collect("shuffled")
    print("\n" + "═" * 70)
    print("PART B — golden headline set: LLM vs keyword baseline")
    print("═" * 70)
    missing = [h for h in g["headline"] if h not in main]
    if missing:
        print(f"WARNING: {len(missing)} headlines missing from main run")
    def cls(x):
        if x >= NEUTRAL_BAND:
            return 1
        if x <= -NEUTRAL_BAND:
            return -1
        return 0
    rows = []
    for _, r in g.iterrows():
        h = r["headline"]
        if h not in main:
            continue
        llm_xau = main[h][1]
        kw = engine._analyze_headline_sentiment(h, for_currency="XAU")
        rows.append({
            "headline": h, "category": r["category"],
            "exp": int(r["expected_xau_sign"]),
            "llm_xau": llm_xau, "llm_cls": cls(llm_xau),
            "kw": kw, "kw_cls": cls(kw),
        })
    t = pd.DataFrame(rows)
    for who in ("llm", "kw"):
        acc = (t[f"{who}_cls"] == t["exp"]).mean()
        print(f"{who.upper():<4} 3-class accuracy (band ±{NEUTRAL_BAND}): "
              f"{(t[f'{who}_cls'] == t['exp']).sum()}/{len(t)} "
              f"= {acc * 100:.1f}%")
    ha = list(t["llm_cls"] == t["exp"])
    hb = list(t["kw_cls"] == t["exp"])
    b, c, p = _mcnemar_p(ha, hb)
    print(f"paired McNemar LLM vs keyword: b={b} c={c} p={p:.3f}")
    print("\nper-category accuracy (LLM | keyword):")
    for cat, grp in t.groupby("category"):
        a1 = (grp["llm_cls"] == grp["exp"]).sum()
        a2 = (grp["kw_cls"] == grp["exp"]).sum()
        print(f"  {cat:<18} {a1:>2}/{len(grp):<2} | {a2:>2}/{len(grp)}")
    print("\nLLM confusion (rows=expected, cols=predicted -1/0/+1):")
    cm = pd.crosstab(t["exp"], t["llm_cls"]).reindex(
        index=[-1, 0, 1], columns=[-1, 0, 1], fill_value=0)
    print(cm.to_string())
    print("\nLLM misclassified headlines:")
    for _, r in t[t["llm_cls"] != t["exp"]].iterrows():
        print(f"  exp {r['exp']:+d} got {r['llm_xau']:+.2f} "
              f"[{r['category']}] {r['headline'][:70]}")
    # repeat-batch determinism
    m_b0 = [r for r in recs if r["run_tag"] == "main" and r["batch"] == 0]
    r_b0 = [r for r in recs if r["run_tag"] == "repeat"]
    if m_b0 and r_b0 and m_b0[0]["scores"] and r_b0[0]["scores"]:
        a = np.array(m_b0[0]["scores"], dtype=float)
        b_ = np.array(r_b0[0]["scores"], dtype=float)
        exact = int((a == b_).all(axis=1).sum())
        print(f"\nrepeat-batch: {exact}/{len(a)} score pairs identical, "
              f"max |diff| = {np.abs(a - b_).max():.3f}")
    # shuffled-order drift
    if shuf:
        both = [h for h in t["headline"] if h in shuf]
        drift = [abs(main[h][1] - shuf[h][1]) for h in both]
        if drift:
            print(f"shuffled-order xau drift over {len(both)} headlines: "
                  f"mean {np.mean(drift):.3f}, max {np.max(drift):.3f}")
 def cmd_report(args):
    report_events()
    report_headlines()
 # ── CLI ──────────────────────────────────────────────────────────────
 def main():
    ap = argparse.ArgumentParser(description=__doc__.split("\n")[1])
    sub = ap.add_subparsers(dest="cmd", required=True)
    e = sub.add_parser("events", help="replay release surprises")
    e.add_argument("--limit", type=int, default=0,
                   help="smoke-test on first N events")
    e.add_argument("--determinism", type=int, default=0,
                   help="re-call N stratified events a second time")
    e.add_argument("--sleep", type=float, default=0.3)
    e.add_argument("--retry-passes", type=int, default=3)
    e.add_argument("--timeout", type=float, default=30.0)
    e.set_defaults(fn=cmd_events)
    h = sub.add_parser("headlines", help="golden-set probes")
    h.add_argument("--sleep", type=float, default=0.3)
    h.add_argument("--timeout", type=float, default=30.0)
    h.set_defaults(fn=cmd_headlines)
    r = sub.add_parser("report", help="offline scoring report")
    r.set_defaults(fn=cmd_report)
    args = ap.parse_args()
    args.fn(args)
 if __name__ == "__main__":
    main()
@@ -1,86 +0,0 @@
 # DeepSeek news-analysis validation — 2026-08-06
 **Verdict: headline sentiment scoring is excellent; event-direction calls are
 directionally sound but ~11% of them misread the numeric comparison, and the
 failure concentrates on Unemployment Claims / close K-M-suffixed values.**
 Method (owner-directed, token-lean): independent expert re-analysis of a
 stratified sample of DeepSeek's recorded answers, not a full statistical
 backtest. Harness: `backtest/research/llm_news_eval.py` (replays releases
 through the REAL `_llm_event_direction` / `_llm_headline_scores` — real
 prompts, payloads, validation; raw wire traffic logged). 135 of the 586-event
 corpus were replayed before the full run was stopped by owner request
 (artifacts retained in `llm_news_eval_2026-08/`, resume-safe).
 ## Part A — event gold-direction (28-event stratified sample, Jan–Jun 2024)
 Reviewer agreement with DeepSeek: **24/28 (86%)**. All 4 disagreements are
 DeepSeek errors, in exactly two failure modes:
 1. **Numeric comparison misread (3/28 ≈ 11%)** — the doctrine logic is right
   but the premise is backwards:
   - JOLTS 8.79M vs f 8.84M (a miss) → DS: "Job openings **above** forecast" → bearish (should be bullish)
   - Claims 202K vs f 217K (fewer claims) → DS: "Claims **higher** than forecast" → bullish (should be bearish)
   - Claims 202K vs f 209K — same backwards read, same wrong call
   Both Unemployment Claims events in the sample were misread the same way —
   looks systematic on that event type, possibly an anchoring prior that
   "claims rose". All misreads involve close values with K/M suffixes.
 2. **Direction field contradicts its own reason (1/28)** — Empire State
   −43.7 vs f −4.9: reason text says "gold may rise", `gold_direction` says
   `bearish`.
 Where the numbers were read correctly, hawkish/dovish→gold mapping was
 consistently correct (CPI/PPI/NFP/ADP/GDP/PMI/wages/unemployment-rate all
 handled properly, including inverted metrics when read right).
 Price cross-check (1h horizon, cluster-confounded, indicative only):
 reviewer calls 22/28 with the subsequent move, DeepSeek 20/28.
 ## Part B — headline sentiment (78-headline golden set)
 DeepSeek matched the reviewer's expected gold-direction label on
 **78/78 (100%)** — including all 8 negation traps ("signals no rate hike",
 "no rate cuts until…") and all 6 inverted-metric phrasings ("claims hit
 two-year high"). The keyword scorer it overrides got **24/78 (31%)**.
 Repeat-batch and shuffled-order probes recorded (headlines_replay.jsonl).
 ## Implications
 - The headline-sentiment override (the main live LLM surface) is validated —
  a large, measured upgrade over keywords.
 - The event-direction enrichment ("— gold bullish/bearish" appended to
  surprise descriptions) is right ~85–90% of the time but can state a
  factually backwards comparison in the displayed reason. It is display-only
  (never gates signals), so exposure is a possibly-misleading Telegram/CLI
  line, not trades.
 - **Hardening SHIPPED same day (owner-approved):** the payload now carries a
  code-computed `vs_forecast` field ("above/below/in line with forecast",
  from `EconomicEvent.surprise`) and the prompt tells the model to trust it.
  First iteration fixed the arithmetic but exposed a masked doctrine error
  (model read "claims below forecast" correctly, then called it a *weaker*
  labor market — and regressed the unemployment-rate case the same way), so
  the prompt's inverted-metrics line was expanded to an explicit rule plus a
  worked claims example. Verification: **18/18** — all 4 original misreads
  fixed, 11/11 no-regression cases unchanged, 3/3 fresh probes on the side
  the prompt example does NOT cover (claims 245K vs f 220K → bullish,
  unemployment 4.1% vs 3.9% → bullish, JOLTS 9.10M vs 8.80M → bearish).
  Suite green at 346 tests, incl. a new payload assertion in Module 19.
 - Production observations found while building the harness (not fixed):
  `_llm_event_cache` key omits forecast → a repeated `actual` re-serves a
  direction computed for a different forecast; a "neutral" reply is
  indistinguishable from a failure in the return value.
 ## Reproduce
 ```
 venv/bin/python backtest/research/llm_news_eval.py events --limit 5   # smoke
 venv/bin/python backtest/research/llm_news_eval.py events --determinism 20
 venv/bin/python backtest/research/llm_news_eval.py headlines
 venv/bin/python backtest/research/llm_news_eval.py report             # offline
 ```
 Corpus: 586 high-impact USD surprises 2024-01→2026-07 (calendar.parquet,
 NFP-verified UTC); prices: Oanda M15 mids. Full-run cost ≈ cents. The
 `report` subcommand (hit rates vs always-bullish/doctrine/FF-betterWorse
 baselines, McNemar, cluster view) is built and untested at scale — run it
 after completing the replay if a statistical answer is ever wanted.
@@ -1,79 +0,0 @@
 headline,category,expected_xau_sign,rationale
 "Powell says restrictive policy needed for longer as inflation proves sticky",hawkish_fed,-1,"higher-for-longer rates lift real yields, gold-bearish"
 "Fed minutes show officials leaning toward two more rate hikes this year",hawkish_fed,-1,"more hikes = stronger dollar/yields"
 "Hot CPI print fuels bets on additional Fed tightening",hawkish_fed,-1,"tightening expectations weigh on gold"
 "Fed's Waller: higher-for-longer rates warranted by resilient economy",hawkish_fed,-1,"hawkish guidance"
 "FOMC raises rates 50bp, flags further increases ahead",hawkish_fed,-1,"outsized hike plus hawkish guidance"
 "Treasury yields spike as Fed pushes back on rate cut hopes",hawkish_fed,-1,"cut hopes dashed, yields up"
 "Core inflation accelerates, cementing case for hawkish Fed stance",hawkish_fed,-1,"hawkish repricing"
 "Fed dot plot shifts higher; markets price out easing for the year",hawkish_fed,-1,"easing priced out"
 "Fed cuts rates 25bp and signals more easing to come",dovish_fed,1,"easing cycle is gold-bullish"
 "Powell hints at rate cuts as inflation cools toward target",dovish_fed,1,"cut guidance"
 "FOMC minutes reveal growing support for policy easing",dovish_fed,1,"dovish minutes"
 "Fed's Williams says rate cuts appropriate later this year",dovish_fed,1,"cut guidance"
 "Soft inflation data boosts odds of a dovish Fed pivot",dovish_fed,1,"pivot odds up"
 "Fed pauses hikes, markets price aggressive easing cycle",dovish_fed,1,"easing priced in"
 "Central banks turn dovish as global growth falters",dovish_fed,1,"global easing backdrop"
 "Fed to restart bond purchases to support flagging economy",dovish_fed,1,"QE is gold-bullish"
 "Dollar index hits 12-month high on robust US economic data",strong_usd,-1,"strong dollar pressures gold"
 "US GDP grows 4.1% annualized, blowing past expectations",strong_usd,-1,"strong economy, hawkish implication"
 "Blockbuster jobs report sends dollar surging across the board",strong_usd,-1,"strong jobs lift dollar and yields"
 "US retail sales smash forecasts, underscoring consumer strength",strong_usd,-1,"strong data"
 "Greenback rallies as US data outshines Europe and Asia",strong_usd,-1,"dollar strength"
 "ISM services index jumps to two-year high on strong demand",strong_usd,-1,"strong data"
 "Consumer confidence surges to post-pandemic high in the US",strong_usd,-1,"strong data"
 "Dollar tumbles to eight-month low after dismal manufacturing data",weak_usd,1,"weak dollar lifts gold"
 "US economy adds far fewer jobs than expected; dollar slides",weak_usd,1,"weak jobs, dovish implication"
 "Greenback weakens as US retail sales unexpectedly contract",weak_usd,1,"weak data"
 "US GDP growth revised down sharply, stoking slowdown fears",weak_usd,1,"slowdown is dovish"
 "Dollar slumps as trade deficit balloons to record",weak_usd,1,"dollar weakness"
 "Soft services PMI adds to evidence of cooling US economy",weak_usd,1,"cooling economy"
 "US consumer sentiment plunges to two-year low",weak_usd,1,"weak data"
 "Real yields climb to multi-year highs as TIPS sell off",real_yields,-1,"rising real yields raise gold's opportunity cost"
 "10-year Treasury yield surges past 5% on supply glut",real_yields,-1,"yield spike"
 "Rising real interest rates dim appeal of non-yielding assets",real_yields,-1,"explicit opportunity-cost story"
 "Treasury yields sink as investors flock to bonds",real_yields,1,"falling yields support gold"
 "Real yields turn negative as inflation outpaces rates",real_yields,1,"negative real yields are gold's best backdrop"
 "Bond rally drives 10-year yield to lowest level in a year",real_yields,1,"falling yields"
 "Geopolitical tensions escalate in Middle East, investors seek safety",safe_haven,1,"safe-haven bid"
 "Stocks crater worldwide as banking crisis fears spread",safe_haven,1,"crisis flight to safety"
 "Escalating conflict drives flight to safe-haven assets",safe_haven,1,"explicit haven flow"
 "Investors dump risk assets after surprise sovereign default",safe_haven,1,"default shock"
 "Military escalation on NATO border rattles global markets",safe_haven,1,"geopolitical stress"
 "Wall Street plunges 4% in worst session since pandemic",safe_haven,1,"risk-off"
 "Currency crisis in emerging markets sparks contagion fears",safe_haven,1,"contagion, haven bid"
 "Nuclear rhetoric intensifies, sending investors to havens",safe_haven,1,"extreme geopolitical stress"
 "Ceasefire agreement lifts global markets, haven demand fades",risk_on,-1,"haven unwind"
 "Stocks roar to record highs as trade deal signed",risk_on,-1,"risk-on drains haven demand"
 "Risk appetite returns as banking fears subside",risk_on,-1,"risk-on"
 "Global equities rally on strong earnings and easing tensions",risk_on,-1,"risk-on"
 "Gold hits record high as central banks keep buying bullion",gold_direct,1,"direct demand"
 "Gold ETFs post biggest weekly inflows in three years",gold_direct,1,"investment inflows"
 "China's central bank extends gold buying spree to 18th month",gold_direct,1,"official-sector demand"
 "Miners struggle to meet gold demand as supply deficit widens",gold_direct,1,"supply deficit"
 "Gold slides 2% as investors book profits after record run",gold_direct,-1,"profit-taking pressure"
 "Gold tumbles below key support as ETF outflows accelerate",gold_direct,-1,"outflows"
 "Bullion demand weakens as Indian jewellery buying stalls",gold_direct,-1,"physical demand soft"
 "Speculators slash bullish gold bets as dollar strengthens",gold_direct,-1,"positioning unwind"
 "Fed holds rates steady, signals no rate hike on the horizon",negation_traps,1,"dovish hold; keyword layer sees the phrase rate hike and flips it gold-bearish"
 "Powell says no rate cuts until inflation shows sustained cooling",negation_traps,-1,"hawkish hold; keyword layer sees rate cut and calls it gold-bullish"
 "Recession fears fade as US data streak beats estimates",negation_traps,-1,"fading fear is risk-on; keywords see recession plus beats and cancel out"
 "Inflation fails to accelerate, easing pressure on the Fed",negation_traps,1,"disinflation opens door to cuts"
 "Fed's Bowman: not yet confident inflation is beaten, cuts premature",negation_traps,-1,"hawkish pushback phrased around negations"
 "Central bank stops short of hiking, but warns tightening may resume",negation_traps,-1,"hawkish hold with tightening bias"
 "No safe-haven bid for gold despite equity wobble, analysts say",negation_traps,-1,"explicitly negated haven demand"
 "Jobs report not as weak as feared, dollar recovers",negation_traps,-1,"less-bad data is dollar-positive"
 "US manufacturing output plunges as new orders dry up",inverted_metrics,1,"weak economy is dovish/gold-bullish; keyword layer reads plunges as bearish"
 "US factory activity slumps to three-year low",inverted_metrics,1,"weak data; keyword reads slumps as bearish"
 "Consumer spending falls for third straight month",inverted_metrics,1,"weak data; keyword reads falls as bearish"
 "Private payrolls growth slides to weakest pace since 2020",inverted_metrics,1,"weak jobs; keyword reads slides as bearish"
 "Jobless claims hit two-year high as layoffs spread",inverted_metrics,1,"higher claims = weaker economy = gold-bullish"
 "US unemployment rate hits 4.5%, highest since 2021",inverted_metrics,1,"higher unemployment = dovish = gold-bullish"
 "Lakers clinch playoff berth with overtime win over Suns",distractors,0,"irrelevant"
 "New Marvel film smashes box office records on opening weekend",distractors,0,"irrelevant"
 "Apple unveils thinner MacBook with faster chip lineup",distractors,0,"irrelevant"
 "Scientists discover new deep-sea species off Pacific coast",distractors,0,"irrelevant"
 "Heavy rain forecast for the Midwest through the weekend",distractors,0,"irrelevant"
 "Streaming service raises subscription prices in Europe",distractors,0,"irrelevant"
 "Electric vehicle startup recalls 12,000 cars over software glitch",distractors,0,"irrelevant"
 "Local elections see record turnout in several US states",distractors,0,"irrelevant"
@@ -1,199 +0,0 @@
 """
 Oanda vs Twelve Data bar-divergence study — XAU/USD M15, 12 months.
 ====================================================================
 Compares the mid-price OHLC bars the signal engine would see from each
 provider over the same window. Purpose: quantify whether migrating the
 signal feed from Twelve Data to Oanda is a formality (bars ~identical)
 or a recalibration (bars differ at a scale that moves indicators).
 Inputs:
  --td      Twelve Data parquet (raw bars from backtest/cache/)
  --oanda   Oanda parquet (from fetch_oanda_m5.py / fetch_oanda_m15.py)
  --start / --end  analysis window (UTC dates)
 Outputs:
  printed report
  backtest/research/oanda_td_divergence_by_hour.csv
  backtest/research/oanda_td_bar_diffs.parquet  (per-bar diffs, common index)
 """
 import argparse
 from pathlib import Path
 import numpy as np
 import pandas as pd
 RESEARCH_DIR = Path(__file__).parent
 PIP = 0.10  # XAU pip in $
 def wilder_atr(df: pd.DataFrame, period: int = 14) -> pd.Series:
    hl = df["High"] - df["Low"]
    hc = (df["High"] - df["Close"].shift(1)).abs()
    lc = (df["Low"] - df["Close"].shift(1)).abs()
    tr = pd.concat([hl, hc, lc], axis=1).max(axis=1)
    return tr.ewm(alpha=1.0 / period, adjust=False).mean()
 def pct_stats(s: pd.Series) -> dict:
    a = s.abs()
    return {
        "mean": a.mean(), "median": a.median(),
        "p90": a.quantile(0.90), "p95": a.quantile(0.95),
        "p99": a.quantile(0.99), "max": a.max(),
        "bias(mean signed)": s.mean(),
    }
 def fmt_stats(name: str, s: pd.Series) -> str:
    st = pct_stats(s)
    return (f"  {name:<6} mean ${st['mean']:.3f}  med ${st['median']:.3f}  "
            f"p95 ${st['p95']:.3f}  p99 ${st['p99']:.3f}  max ${st['max']:.2f}  "
            f"bias {st['bias(mean signed)']:+.4f}")
 def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--td", default=str(RESEARCH_DIR.parent / "cache" /
                                        "XAU_USD_15min_20240522_20260723.parquet"))
    ap.add_argument("--oanda", default=str(RESEARCH_DIR /
                                           "oanda_xau_m15_20250701_now.parquet"))
    ap.add_argument("--start", default="2025-07-24")
    ap.add_argument("--end", default="2026-07-23")
    args = ap.parse_args()
    td = pd.read_parquet(args.td)
    oa = pd.read_parquet(args.oanda)
    start = pd.Timestamp(args.start, tz="UTC")
    end = pd.Timestamp(args.end, tz="UTC") + pd.Timedelta(days=1)
    start = max(start, td.index[0], oa.index[0])
    end = min(end, td.index[-1], oa.index[-1])
    td = td.loc[(td.index >= start) & (td.index <= end)]
    oa = oa.loc[(oa.index >= start) & (oa.index <= end)]
    print("=" * 78)
    print("OANDA vs TWELVE DATA — XAU/USD M15 bar divergence")
    print(f"Window: {start} -> {end}  ({(end - start).days} days)")
    print("=" * 78)
    # ── Coverage ─────────────────────────────────────────────────────
    common = td.index.intersection(oa.index)
    td_only = td.index.difference(oa.index)
    oa_only = oa.index.difference(td.index)
    print(f"\nCOVERAGE")
    print(f"  Twelve Data bars : {len(td):>7,}")
    print(f"  Oanda bars       : {len(oa):>7,}")
    print(f"  common           : {len(common):>7,}")
    print(f"  TD-only          : {len(td_only):>7,}  "
          f"({100 * len(td_only) / max(len(td), 1):.2f}% of TD)")
    print(f"  Oanda-only       : {len(oa_only):>7,}  "
          f"({100 * len(oa_only) / max(len(oa), 1):.2f}% of Oanda)")
    for label, idx in (("TD-only", td_only), ("Oanda-only", oa_only)):
        if len(idx):
            by_date = pd.Series(1, index=idx).groupby(idx.date).sum()
            top = by_date.sort_values(ascending=False).head(8)
            print(f"  {label} bars cluster on: "
                  + ", ".join(f"{d} ({n})" for d, n in top.items()))
            by_hour = pd.Series(1, index=idx).groupby(idx.hour).sum()
            top_h = by_hour.sort_values(ascending=False).head(6)
            print(f"  {label} by UTC hour: "
                  + ", ".join(f"{h:02d}h ({n})" for h, n in top_h.items()))
    t = td.loc[common]
    o = oa.loc[common]
    # ── Scale references ─────────────────────────────────────────────
    atr = wilder_atr(t).dropna()
    mean_price = t["Close"].mean()
    print(f"\nSCALE REFERENCES (Twelve Data feed)")
    print(f"  mean close      : ${mean_price:,.2f}")
    print(f"  mean bar range  : ${(t['High'] - t['Low']).mean():.3f}")
    print(f"  mean ATR14      : ${atr.mean():.3f}   median ${atr.median():.3f}")
    # ── Per-field diffs (TD − Oanda mid) ─────────────────────────────
    print(f"\nPER-BAR DIFFS  (TD − Oanda, $; pip = $0.10)")
    diffs = {}
    for col in ("Open", "High", "Low", "Close"):
        d = t[col] - o[col]
        diffs[col] = d
        print(fmt_stats(col, d))
    dclose = diffs["Close"].abs()
    print(f"\n  |ΔClose| as % of mean ATR14 : "
          f"mean {100 * dclose.mean() / atr.mean():.1f}%   "
          f"p95 {100 * dclose.quantile(0.95) / atr.mean():.1f}%")
    print(f"\nTHRESHOLD EXCEEDANCE  (|ΔClose|)")
    for thr in (0.10, 0.30, 0.65, 1.00, 2.00, 5.00):
        pct = 100 * (dclose > thr).mean()
        print(f"  > ${thr:<5.2f}: {pct:6.2f}% of bars  ({(dclose > thr).sum():,})")
    # ── Direction / return agreement ─────────────────────────────────
    t_ret = t["Close"].pct_change()
    o_ret = o["Close"].pct_change()
    # only consecutive common bars (gap = one M15 step)
    step_ok = pd.Series(common, index=common).diff().eq(pd.Timedelta(minutes=15))
    tr, orr = t_ret[step_ok], o_ret[step_ok]
    corr = tr.corr(orr)
    t_dir = np.sign(t["Close"] - t["Open"])
    o_dir = np.sign(o["Close"] - o["Open"])
    both_nonflat = (t_dir != 0) & (o_dir != 0)
    dir_agree = 100 * (t_dir[both_nonflat] == o_dir[both_nonflat]).mean()
    print(f"\nAGREEMENT")
    print(f"  close-return correlation (consecutive bars): {corr:.5f}  "
          f"(n={step_ok.sum():,})")
    print(f"  bar direction (sign of close−open) agree   : {dir_agree:.2f}%")
    # ── By-hour breakdown ────────────────────────────────────────────
    hours = common.hour
    rows = []
    for h in range(24):
        m = hours == h
        if not m.any():
            continue
        row = {"hour_utc": h, "bars": int(m.sum()),
               "mean_abs_dclose": dclose[m].mean(),
               "p95_abs_dclose": dclose[m].quantile(0.95),
               "mean_abs_dhigh": diffs["High"][m].abs().mean(),
               "mean_abs_dlow": diffs["Low"][m].abs().mean()}
        if "bid_c" in o.columns:
            row["oanda_median_spread"] = (
                (o["ask_c"] - o["bid_c"])[m].median())
        rows.append(row)
    by_hour = pd.DataFrame(rows)
    out_hour = RESEARCH_DIR / "oanda_td_divergence_by_hour.csv"
    by_hour.to_csv(out_hour, index=False, float_format="%.4f")
    print(f"\nBY UTC HOUR  (written to {out_hour.name})")
    print(by_hour.to_string(index=False,
                            float_format=lambda x: f"{x:.3f}"))
    # ── By-month drift ───────────────────────────────────────────────
    print(f"\nBY MONTH  (mean |ΔClose|, $)")
    by_month = dclose.groupby(common.to_period("M")).mean()
    for p, v in by_month.items():
        print(f"  {p}: {v:.3f}")
    # ── Oanda real spread (bonus, ties to Dukascopy study) ───────────
    if "bid_c" in o.columns:
        spread = o["ask_c"] - o["bid_c"]
        liquid = ~o.index.hour.isin([21, 22, 23, 0])
        print(f"\nOANDA REAL SPREAD (close bid/ask, $)")
        print(f"  all bars      : median {spread.median():.3f}  "
              f"p25 {spread.quantile(0.25):.3f}  p75 {spread.quantile(0.75):.3f}")
        print(f"  liquid hours  : median {spread[liquid].median():.3f}")
        print(f"  21-00 UTC     : median {spread[~liquid].median():.3f}")
    # ── Persist per-bar diffs ────────────────────────────────────────
    out = pd.DataFrame({
        "td_close": t["Close"], "oa_close": o["Close"],
        "d_open": diffs["Open"], "d_high": diffs["High"],
        "d_low": diffs["Low"], "d_close": diffs["Close"],
    })
    out_path = RESEARCH_DIR / "oanda_td_bar_diffs.parquet"
    out.to_parquet(out_path)
    print(f"\nPer-bar diffs written to {out_path}")
 if __name__ == "__main__":
    main()
@@ -1,25 +0,0 @@
 hour_utc,bars,mean_abs_dclose,p95_abs_dclose,mean_abs_dhigh,mean_abs_dlow,oanda_median_spread
 0,1029,3.3483,13.9460,3.2906,3.4310,0.7900
 1,1028,3.3483,12.8292,3.3477,3.6398,0.7700
 2,1028,3.0288,10.8960,2.9469,3.1317,0.7700
 3,1028,2.8783,11.1300,2.8419,2.9992,0.7600
 4,1028,2.8232,10.4710,2.8165,2.8455,0.7400
 5,1028,2.9268,10.6027,2.9234,3.0038,0.7400
 6,1028,3.0466,10.9265,2.9487,3.0796,0.7200
 7,1028,3.0286,11.3512,3.0301,3.0797,0.6700
 8,1028,3.0223,11.1483,2.8989,3.0801,0.6600
 9,1028,3.0460,11.6345,2.9307,3.0609,0.6600
 10,1028,2.9280,10.9015,2.8647,3.0029,0.6600
 11,1028,3.0340,11.4712,2.9591,2.9472,0.6600
 12,1028,3.0986,11.3282,2.9412,3.1407,0.7000
 13,1028,3.4014,12.3343,3.1077,3.4228,0.7300
 14,1028,3.5074,11.3628,3.3960,3.6569,0.6900
 15,1028,3.4514,11.8982,3.4124,3.6733,0.6700
 16,1028,3.1275,12.5155,3.2038,3.3061,0.6700
 17,1020,2.9826,12.6435,3.0064,3.1008,0.6900
 18,1015,2.8721,12.5480,2.9101,2.9996,0.6900
 19,1004,2.8637,12.1335,2.8824,2.9454,0.6900
 20,996,2.8006,11.9325,2.8486,2.8577,0.7600
 21,335,1.1085,4.8600,1.1212,1.2034,0.9400
 22,676,4.0827,13.6787,4.8422,4.6761,0.8300
 23,1027,3.2722,12.5725,3.5617,3.7747,0.8700
@@ -12,10 +12,9 @@ Chart Patterns*) that are unambiguous from OHLC at bar close:
                                      the interim peak (neckline)
    double_top     bearish reversal   mirrored
    inv_hs         bullish reversal   inverse head & shoulders, break of
-                                      the neckline (the LOWER of the two
+                                      the neckline (max of the two peaks
-                                      peaks flanking the head — the earlier
+                                      flanking the head — conservative
-                                      horizontal trigger, no sloped fits;
+                                      horizontal line, no sloped fits)
                                      validated as-is, see detector note)
    hs_top         bearish reversal   head & shoulders top, mirrored
    bull_flag      bullish continuation  strong pole, shallow ordered
                                      retrace, close at a new high
@@ -167,10 +166,8 @@ def _detect_hs(o, h, l, c, atr, cfg, bullish: bool) -> Optional[Dict]:
            return None
        if abs(s1 - s2) > cfg.cp_hs_shoulder_tol_atr * atr:
            return None
-        # Neckline: the LOWER of the two interim peaks — the EARLIER of
+        # Neckline: the LOWER of the two interim peaks — the price must
-        # the two horizontal triggers (max() would demand clearing both
+        # clear both, so this is the conservative horizontal trigger.
        # peaks). The whole validated CP book (M5 BUY-only) ran on this
        # trigger — do not flip to max() without full re-validation.
        p1 = float(h[i1 : i2 + 1].max())
        p2 = float(h[i2 : i3 + 1].max())
        neckline = min(p1, p2)
@@ -1,52 +0,0 @@
 {
  "telegram": {
    "token": "123456789:REPLACE_WITH_YOUR_BOT_TOKEN",
    "chat_id": "REPLACE_WITH_OWNER_CHAT_ID",
    "channel_id": "-100REPLACE_WITH_CHANNEL_ID_OR_EMPTY_STRING",
    "alert_min_quality": "C"
  },
  "data_providers": {
    "active": "oanda",
    "streaming": "tiingo",
    "twelve_data": {
      "api_key": "REPLACE_WITH_YOUR_TWELVE_DATA_API_KEY"
    },
    "oanda": {},
    "tiingo": {
      "api_key": "REPLACE_WITH_YOUR_TIINGO_API_KEY_OR_EMPTY"
    }
  },
  "scanning": {
    "pairs": ["XAU/USD"],
    "timeframes": ["M5", "M15", "H1", "H4"]
  },
  "pairs": {
    "XAU/USD": {
      "blocked_hours_utc": [0, 1, 2, 10, 11, 13, 16, 21]
    }
  },
  "oanda": {
    "account_id": "REPLACE_WITH_ACCOUNT_ID",
    "api_token": "REPLACE_WITH_API_TOKEN",
    "base_url": "https://api-fxpractice.oanda.com",
    "lot_size": 1,
    "quality_gate": "C",
    "max_hold_bars": 300
  },
  "deepseek": {
    "api_key": "REPLACE_WITH_YOUR_DEEPSEEK_API_KEY_OR_EMPTY",
    "model": "deepseek-v4-flash",
    "enabled": true,
    "timeout_seconds": 12,
    "daily_request_cap": 500
  },
  "agent_panel": {
    "enabled": false,
    "telegram": true,
    "dashboard": false,
    "min_quality": "B",
    "max_panels_per_day": 20,
    "reuse_ttl_seconds": 3600
  },
  "log_level": "INFO"
 }
@@ -27,6 +27,14 @@ class SignalStrength(Enum):
    STRONG_SELL = -2
 class MarketSession(Enum):
    SYDNEY = "sydney"
    TOKYO = "tokyo"
    LONDON = "london"
    NEW_YORK = "new_york"
    OVERLAP_LONDON_NY = "london_ny_overlap"
 # ── Supported pairs ──────────────────────────────────────────────
 XAUUSD = "XAU/USD"
@@ -34,7 +42,7 @@ XAUUSD = "XAU/USD"
 # default_spread: round-trip cost per trade in price units, charged by
 # the backtester when --spread is not given.
 PAIR_INFO = {
-    XAUUSD: {"display": "XAU/USD", "pip_value": 0.10,
+    XAUUSD: {"display": "XAU/USD", "pip_value": 0.10, "is_jpy_pair": False,
             "price_precision": 2, "default_spread": 0.30},
 }
@@ -46,12 +54,6 @@ class TechnicalConfig:
    ema_medium: int = 21
    ema_slow: int = 50
    ema_trend: int = 200
    # 4-EMA mode only: score partial ribbon alignment (2-3 of 4 checks)
    # as NEUTRAL instead of a weak BUY/SELL. Research knob (2026-08-04
    # indicator audit: partial-alignment trades averaged -0.008R vs
    # +0.230R fully aligned on the M5 12mo Oanda book); default False =
    # legacy behavior until a replay campaign validates flipping it.
    ema_partial_neutral: bool = False
    rsi_period: int = 14
    rsi_overbought: float = 70.0
@@ -100,8 +102,25 @@ class TechnicalConfig:
@dataclass
 class SignalConfig:
    """Signal scoring & filtering."""
-    mtf_agreement_threshold: int = 2  # of 3 timeframes
+    min_confidence: float = 55.0
    weights: Dict[str, float] = field(default_factory=lambda: {
        "ema_trend": 1.0,
        "rsi": 1.5,
        "bollinger": 1.5,
        "macd": 1.5,
        "keltner": 1.5,
        "roc": 1.5,
        "candlestick": 1.0,
        "divergence": 1.5,
    })
    mtf_agreement_threshold: int = 2  # of 3 timeframes
    cooldown_bars: int = 5
    default_risk_pct: float = 1.0
    default_rr_ratio: float = 2.0
    atr_sl_multiplier: float = 1.5
    min_rr_ratio: float = 0.5
    # TP tightening (May 2026) was tuned on the pre-spread backtester.
    # Re-tested 2026-07-08 with $0.30 spread + gap fills: 0.8 nearly
@@ -152,13 +171,8 @@ class NewsConfig:
    # against the validated 12mo XAU M5 book (547 trades, +139.9R)
    # showed they suppress 25-35 trades worth +4.6..+7.2R/yr — trades
    # entered near USD releases OUTPERFORM (+0.475R vs +0.256R avg).
-    # Audit 2026-07-11 (calendar-replay study; tooling since removed).
+    # Audit 2026-07-11 via backtest/research/calendar_store.
    event_risk_caps_signals: bool = False
    # Whether headline sentiment moves the SIGNAL (aligned: +5 confidence;
    # conflicting: 10-point penalty). Warnings are always emitted
    # regardless. Default False = news is informational only; decisions
    # are pure technical, matching the backtester (use_news=False).
    sentiment_affects_signals: bool = False
    # Wider stay-out window after a high-impact release that surprised
    # vs forecast (inline releases use post_event_buffer_minutes).
    post_event_surprise_buffer_minutes: int = 45
@@ -166,9 +180,6 @@ class NewsConfig:
    # Must stay below pre_event_buffer_minutes so an event can't cross
    # into the danger window while a stale cache says it's clear.
    context_cache_ttl_seconds: int = 600
    # Maximum age of RSS headlines accepted for scoring. Older headlines
    # are discarded before keyword analysis and LLM enrichment.
    headline_max_age_seconds: int = 43200  # 12 hours
    rss_feeds: List[str] = field(default_factory=lambda: [
        "https://investinglive.com/feed/",
@@ -183,14 +194,25 @@ class NewsConfig:
@dataclass
 class XauStrategyConfig:
-    """XAU/USD strategy config."""
+    """XAU/USD strategy config.
    Legacy gates (H4 RSI, H1 MACD, M15 score) removed in favor of
    new momentum indicators (ROC, Keltner squeeze, ADX slope).
    """
    enabled: bool = True
    rsi_entry_threshold: float = 45.0
    tp_atr_multiplier: float = 1.0
    sl_atr_multiplier: float = 2.0
    min_ema200_tfs: int = 2
    allow_generic_trades: bool = True
    blocked_hours_utc: List[int] = field(default_factory=lambda: [0, 1, 2, 10, 11, 13, 16, 21])
    weekend_filter: bool = True
    min_atr_threshold: float = 1.0
    v2_sl_atr_multiplier: float = 2.0
    buy_threshold_boost: int = 0
    m5_confirmation_bars: int = 0
    h4_rsi_gate: bool = False
    h1_macd_gate: bool = False
    rsi_zone: tuple = (40.0, 65.0)
    allowed_weekdays: List[int] = field(default_factory=lambda: [0, 1, 2, 3, 4, 6])  # Mon-Fri + Sun (Sydney open)
    sell_min_quality: List[str] = field(default_factory=lambda: ["A+", "A", "B"])
    # Volatility regimes in which SELL setups are allowed (low/normal-vol
@@ -212,16 +234,9 @@ class XauStrategyConfig:
    # M5 signals are noisy; require stronger confirmation before
    # generating trade setups. Without these, M5 is breakeven
    # (+0.01R/trade) with 21% drawdown.
    # Relaxed 2026-07-30 (30→25, 10→5) in the relaxation campaign:
    # the combined config (with the M5 hour-21 unblock) beat control
    # in ALL 6 calendar years of the 5yr Oanda replay (+230.9R vs
    # +168.0R, PF 1.26 vs 1.21, max DD 21.3 vs 27.0), 12mo +107.4R
    # vs +84.6R PF 1.36 both halves positive, OOS 2024-06→2025-03
    # +26.1R vs +17.2R, and at $0.70 spread +86.9R vs +66.4R.
    # Score floors below 25 are vacuous (grade C requires |score|≥25).
    m5_require_mtf: bool = True
-    m5_min_abs_score: float = 25.0
+    m5_min_abs_score: float = 30.0
-    m5_min_confirmation_score: float = 5.0
+    m5_min_confirmation_score: float = 10.0
    # A-grade M5 SELLs enter at the extreme of temporary dips and
    # lose (-0.017R/trade on 163 trades) — maximum conviction at
    # the worst entry. Cap SELL quality so A-grade can't trigger
@@ -287,15 +302,6 @@ class XauStrategyConfig:
    cp_flag_max_bars: int = 12
    cp_flag_retrace: tuple = (0.1, 0.5)  # flag depth as fraction of pole
    # ── Big-candle entry gate ──────────────────────────────────────
    # When the recent window on the scan's own primary TF contains an
    # abnormally large candle (bar range >= K x the PRIOR bar's ATR —
    # institutional burst; price may reverse violently), no XAU entry
    # of ANY kind may fire (core, XAU_PB, XAU_CP). 0.0 = disabled.
    # Per-TF factories opt in only after the standard validation rule.
    big_candle_atr_multiple: float = 0.0
    big_candle_lookback_hours: float = 24.0
@dataclass
 class FilterConfig:
@@ -332,6 +338,8 @@ class PlatformConfig:
    data_lookback_days: int = 60
    twelve_data_api_key: str = "f563b22ffdfd4924955decbe1a8c8701"
    refresh_interval: int = 300
    scan_pairs: List[str] = field(
@@ -340,6 +348,7 @@ class PlatformConfig:
    scan_timeframes: List[Timeframe] = field(
        default_factory=lambda: [Timeframe.M5, Timeframe.M15, Timeframe.H1, Timeframe.H4]
    )
    candle_buffer_seconds: int = 10
    ws_cooldown_seconds: int = 300
    log_level: str = "INFO"
@@ -1,459 +0,0 @@
 #!/usr/bin/env python3
 """
 XAU/USD static dashboard publisher.
 Standalone daemon that scans XAU/USD at candle closes and atomically
 writes `snapshot.json` + `news.json` (plus a copy of
 `static/dashboard.html`) into an Apache-served directory. Apache does
 all web serving — this process never opens a port, so a failure here
 can only ever mean a stale page.
 Independence rules (owner requirement — the telegram bot must be
 immune to anything on the web side):
  * Runs as its own process; never imported by, and never imports,
    `telegram_bot` / `oanda_daemon` (telegram_bot resolves credentials
    at import). The confirmation map and boundary math are therefore
    duplicated here as local constants.
  * The only shared-code dependency beyond the standard signal path is
    the additive `NewsEngine.get_week_events` accessor, which no
    production signal path calls.
 Scheduling: every M15/H1/H4 boundary is also an M5 boundary (all
 intervals are multiples of 5, anchored to UTC midnight), so one
 sequential loop sleeps to the next M5 boundary (+10 s buffer) and scans
 whichever timeframes just closed. Due-classification uses the RAW wake
 time — flooring 12:00:10 to the 5-minute grid yields the 12:00 boundary
 that just closed. Do NOT subtract the buffer first: 12:00:10 − 15 s
 floors to 11:55 and silently skips every H1/H4 scan (v1 bug, 2026-08-02).
 Scans run sequentially M5→M15→H1→H4 through one shared data provider so
 later timeframes hit the warm boundary cache — that ordering IS the
 duplicate-fetch dedup (providers have no in-flight dedup; Oanda has no
 throttle). Keep it sequential.
 Run:  python dashboard_publisher.py [--once] [--out DIR]
 Out dir precedence: --out → DASHBOARD_OUT_DIR env → /var/www/trade
 """
 import argparse
 import json
 import hashlib
 import logging
 import math
 import numbers
 import os
 import shutil
 import sys
 import time
 from datetime import datetime, timezone
 from logging.handlers import TimedRotatingFileHandler
 from typing import Dict, List, Optional
 from agent_panel import attach_panel
 from config import PlatformConfig, Timeframe, XAUUSD
 from main import report_to_dict
 from news_engine import NewsEngine
 from pair_signals import produce_signal
 from providers import make_data_provider
 import providers.base as _providers_base
 logger = logging.getLogger("dashboard")
 DASHBOARD_TFS = [Timeframe.M5, Timeframe.M15, Timeframe.H1, Timeframe.H4]
 # Local copy of the bot's confirmation map (see independence rules above).
 CONFIRMATIONS = {
    Timeframe.M5:  [Timeframe.M15, Timeframe.H1],
    Timeframe.M15: [Timeframe.M5, Timeframe.H1],
    Timeframe.H1:  [Timeframe.M15, Timeframe.H4],
    Timeframe.H4:  [Timeframe.H1, Timeframe.D1],
 }
 TF_INTERVAL_MINUTES = {
    Timeframe.M5: 5, Timeframe.M15: 15, Timeframe.H1: 60, Timeframe.H4: 240,
 }
 DEFAULT_OUT_DIR = "/var/www/trade"
 SCAN_BUFFER_SECONDS = 10
 SNAPSHOT_FILE = "snapshot.json"
 NEWS_FILE = "news.json"
 PAGE_FILE = "dashboard.html"
 SUMMARY_PHP_FILE = "news_summary.php"
 # Repo files copied into the out dir at daemon start (git pull + restart
 # updates them). The PHP endpoint reads news.json in place, so it lives
 # next to the page.
 STATIC_FILES = [PAGE_FILE, SUMMARY_PHP_FILE]
 # ══════════════════════════════════════════════════════════════════
 #  Pure layer (no network, fully covered by test_all.py)
 # ══════════════════════════════════════════════════════════════════
 def _safe_num(v):
    """Coerce to a JSON-safe float; NaN/±inf become None."""
    try:
        f = float(v)
    except (TypeError, ValueError):
        return v
    if math.isnan(f) or math.isinf(f):
        return None
    return f
 def _sanitize(obj):
    """Recursively make a payload safe for json.dumps(allow_nan=False):
    NaN/inf → None, numpy scalars → native ints/floats."""
    if isinstance(obj, bool):
        return obj
    if isinstance(obj, dict):
        return {k: _sanitize(v) for k, v in obj.items()}
    if isinstance(obj, (list, tuple)):
        return [_sanitize(v) for v in obj]
    if isinstance(obj, numbers.Integral):
        return int(obj)
    if isinstance(obj, numbers.Real):
        return _safe_num(obj)
    return obj
 def analysis_to_dict(analysis) -> Optional[dict]:
    """Serialize a TechnicalAnalysis. Iterates analysis.indicators only —
    never vars()/asdict(), which would drag dynamic pandas attrs
    (_rsi_series, _macd_histogram, …) into the payload. ATR (weight 0)
    is surfaced separately as context, not in the directional grid."""
    if analysis is None:
        return None
    indicators = {}
    atr_context = None
    for name, ind in analysis.indicators.items():
        if name.startswith("_"):
            continue
        entry = {
            "signal": ind.signal.name,
            "value": _safe_num(ind.value),
            "confidence": _safe_num(ind.confidence),
            "weight": _safe_num(ind.weight),
            "description": ind.description,
        }
        if name == "atr":
            atr_context = entry
        elif ind.weight != 0:
            indicators[name] = entry
    return {
        "timeframe": analysis.timeframe,
        "timestamp": str(analysis.timestamp),
        "composite_signal": analysis.composite_signal.name,
        "composite_score": _safe_num(analysis.composite_score),
        "confidence": _safe_num(analysis.confidence),
        "market_regime": analysis.market_regime,
        "volatility_regime": analysis.volatility_regime,
        "pattern_signals": list(analysis.pattern_signals),
        "key_levels": {k: _safe_num(v) for k, v in analysis.key_levels.items()},
        "indicators": indicators,
        "atr_context": atr_context,
    }
 def report_to_webapp_dict(report) -> dict:
    """main.report_to_dict + the per-TF detail the dashboard needs."""
    d = report_to_dict(report)
    d["primary_signal"] = report.primary_signal.name
    d["primary_score"] = _safe_num(report.primary_score)
    d["mtf_scores"] = {k: _safe_num(v) for k, v in report.mtf_scores.items()}
    d["exhaustion_count"] = report.exhaustion_count
    d["primary_analysis"] = analysis_to_dict(report.primary_analysis)
    d["confirmation_analyses"] = [
        analysis_to_dict(a) for a in report.confirmation_analyses
    ]
    return _sanitize(d)
 def event_to_dict(ev) -> dict:
    """EconomicEvent → dict. surprise/minutes_until/is_high_impact are
    properties, so asdict() would skip them — added explicitly.
    actual/forecast/previous stay strings as published ('228K', '0.3%')."""
    return {
        "time": ev.time.astimezone(timezone.utc).isoformat(),
        "currency": ev.currency,
        "impact": ev.impact,
        "event": ev.event,
        "forecast": ev.forecast,
        "previous": ev.previous,
        "actual": ev.actual,
        "surprise": ev.surprise,
        "minutes_until": _safe_num(ev.minutes_until),
        "is_high_impact": ev.is_high_impact,
    }
 def headline_to_dict(h) -> dict:
    # id uses the same sha1(headline) scheme as NewsEngine._llm_sent_cache;
    # it is the join key the PHP summary endpoint whitelists against.
    return {
        "id": hashlib.sha1(h.headline.encode("utf-8")).hexdigest(),
        "headline": h.headline,
        "source": h.source,
        "link": h.link,
        "description": h.description,
        "publisher": h.publisher,
        "timestamp": h.timestamp.astimezone(timezone.utc).isoformat()
                     if h.timestamp else None,
        "currencies": list(h.currencies_mentioned),
        "score": _safe_num(h.sentiment_score),
        "keywords": list(h.keywords),
        "score_by_currency": {
            k: _safe_num(v) for k, v in h.sentiment_by_currency.items()
        },
    }
 def build_news_payload(context, week_events, now: datetime) -> dict:
    """news.json: full calendar week + scored headlines + LLM summary."""
    headlines = sorted(
        context.news_headlines,
        key=lambda h: h.timestamp or now, reverse=True,
    )
    return _sanitize({
        "generated_at": now.isoformat(),
        "llm_summary": context.llm_summary,
        "sentiment": dict(context.news_sentiment),
        "high_impact_warning": context.high_impact_warning,
        "warning_message": context.warning_message,
        "calendar_available": context.calendar_available,
        "events": [event_to_dict(e) for e in week_events],
        "headlines": [headline_to_dict(h) for h in headlines],
    })
 def due_timeframes(wake_dt: datetime) -> List[Timeframe]:
    """Timeframes whose candle just closed at the boundary this wake
    belongs to. Takes the RAW wake time (boundary + buffer): flooring to
    the 5-minute grid yields the boundary that just passed. Never
    subtract the buffer first — see module docstring (v1 bug)."""
    minutes = wake_dt.hour * 60 + wake_dt.minute
    floored = minutes - (minutes % 5)
    return [tf for tf in DASHBOARD_TFS if floored % TF_INTERVAL_MINUTES[tf] == 0]
 def seconds_until_boundary(interval_minutes: int, buffer: int = 10) -> float:
    # Same math as telegram_bot.py / oanda_daemon.py — kept local so the
    # dashboard never imports bot modules (independence rules above).
    now = datetime.now(timezone.utc)
    total_minutes = now.hour * 60 + now.minute
    elapsed_minutes = total_minutes % interval_minutes
    elapsed_seconds = elapsed_minutes * 60 + now.second + now.microsecond / 1e6
    interval_seconds = interval_minutes * 60
    remaining = interval_seconds - elapsed_seconds + buffer
    # NOTE: remaining > buffer always (elapsed < interval by construction);
    # a call at the exact boundary instant targets the NEXT boundary —
    # steady-state loops wake at boundary+buffer, so only a process
    # started in the boundary microsecond skips one scan (2026-08-04
    # audit I42; the old `remaining <= buffer` guard was unreachable).
    return remaining
 def _empty_tf_entry() -> dict:
    return {"status": "warming", "scanned_at": None, "scan_seconds": None,
            "error": None, "error_at": None, "report": None}
 def build_snapshot_payload(snapshots: Dict[str, dict], now: datetime) -> dict:
    """snapshot.json: one entry per dashboard TF. Timeframes not yet
    scanned appear as status="warming" so the page can render a
    placeholder instead of a hole. server_time_epoch lets the client
    compute its clock offset once so countdowns don't drift."""
    timeframes = {}
    for tf in DASHBOARD_TFS:
        interval = TF_INTERVAL_MINUTES[tf]
        minutes = now.hour * 60 + now.minute
        secs_into = (minutes % interval) * 60 + now.second + now.microsecond / 1e6
        next_close = now.timestamp() + (interval * 60 - secs_into)
        entry = _empty_tf_entry()
        entry.update(snapshots.get(tf.value) or {})
        entry["next_close_epoch"] = next_close
        entry["next_scan_epoch"] = next_close + SCAN_BUFFER_SECONDS
        timeframes[tf.value] = entry
    return _sanitize({
        "symbol": XAUUSD,
        "server_time_epoch": now.timestamp(),
        "market_closed": _providers_base._is_forex_weekend(now),
        "timeframes": timeframes,
    })
 def publish(obj: dict, path: str) -> None:
    """Atomic JSON write: temp file + os.replace, so Apache never serves
    a torn file. allow_nan=False — a NaN slipping through would render
    the page blank (JSON.parse rejects bare NaN)."""
    data = json.dumps(obj, allow_nan=False, separators=(",", ":"))
    tmp = path + ".tmp"
    with open(tmp, "w", encoding="utf-8") as fh:
        fh.write(data)
    os.replace(tmp, path)
 # ══════════════════════════════════════════════════════════════════
 #  Runtime layer (constructed only from main(); never at import)
 # ══════════════════════════════════════════════════════════════════
 def _setup_logging(level: str = "INFO") -> None:
    log_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "logs")
    os.makedirs(log_dir, exist_ok=True)
    log_file = os.path.join(log_dir, "dashboard.log")
    fmt = logging.Formatter(
        "%(asctime)s [%(levelname)s] %(name)s: %(message)s",
        datefmt="%Y-%m-%d %H:%M:%S")
    file_handler = TimedRotatingFileHandler(
        log_file, when="h", interval=3, backupCount=240, utc=True)
    file_handler.setFormatter(fmt)
    console_handler = logging.StreamHandler(sys.stdout)
    console_handler.setFormatter(fmt)
    logging.basicConfig(
        level=getattr(logging, level.upper(), logging.INFO),
        handlers=[file_handler, console_handler],
        force=True)
 class DashboardPublisher:
    def __init__(self, out_dir: str):
        self.out_dir = out_dir
        self.snapshots: Dict[str, dict] = {}
        self.news_payload: Optional[dict] = None
        self._dp = None
        self._news_engine: Optional[NewsEngine] = None
    def _provider(self):
        if self._dp is None:
            self._dp = make_data_provider(PlatformConfig())
        return self._dp
    def _news(self) -> NewsEngine:
        if self._news_engine is None:
            self._news_engine = NewsEngine(PlatformConfig().news)
        return self._news_engine
    def install_static(self) -> None:
        """Copy the repo's static files (page + PHP summary endpoint)
        into the out dir (atomic), so git pull + restart also updates
        them. The ai/ cache dir is NOT created here — the daemon runs as
        a different user than Apache; that dir is a one-time deploy step
        (deploy/README-dashboard.md)."""
        static_dir = os.path.join(
            os.path.dirname(os.path.abspath(__file__)), "static")
        for name in STATIC_FILES:
            src = os.path.join(static_dir, name)
            if not os.path.exists(src):
                logger.warning(f"{src} missing — not installed")
                continue
            dst = os.path.join(self.out_dir, name)
            shutil.copyfile(src, dst + ".tmp")
            os.replace(dst + ".tmp", dst)
    def scan_tf(self, tf: Timeframe) -> None:
        started = time.time()
        try:
            report = produce_signal(XAUUSD, tf, self._provider(),
                                    CONFIRMATIONS[tf])
            # Advisory AI panel, post-hoc and display-only. Off unless
            # config.json agent_panel.dashboard is true. The reuse-TTL
            # cache + max_panels_per_day budget bound the spend against
            # this process's own DeepSeek cap (a persisting M5 signal
            # re-serves its cached verdict instead of re-running every
            # bar); a fresh panel adds ≤~25 s before this TF publishes.
            attach_panel(report, surface="dashboard")
            self.snapshots[tf.value] = {
                "status": "ok",
                "scanned_at": datetime.now(timezone.utc).isoformat(),
                "scan_seconds": round(time.time() - started, 2),
                "error": None,
                "error_at": None,
                "report": report_to_webapp_dict(report),
            }
            logger.info(f"{tf.value}: {report.final_signal.name} "
                        f"q={report.signal_quality or '-'} "
                        f"({time.time() - started:.1f}s)")
        except Exception as e:
            # Keep the last good report; the page shows it with an
            # error badge instead of going blank.
            logger.exception(f"{tf.value} scan failed")
            entry = self.snapshots.get(tf.value) or _empty_tf_entry()
            entry["status"] = "error"
            entry["error"] = f"{type(e).__name__}: {e}"
            entry["error_at"] = datetime.now(timezone.utc).isoformat()
            self.snapshots[tf.value] = entry
    def refresh_news(self) -> None:
        try:
            ne = self._news()
            context = ne.get_fundamental_context()
            week = ne.get_week_events()
            self.news_payload = build_news_payload(
                context, week, datetime.now(timezone.utc))
        except Exception:
            logger.exception("news refresh failed — keeping last payload")
    def publish_snapshot(self) -> None:
        publish(build_snapshot_payload(
            self.snapshots, datetime.now(timezone.utc)),
            os.path.join(self.out_dir, SNAPSHOT_FILE))
    def publish_news(self) -> None:
        if self.news_payload is not None:
            publish(self.news_payload, os.path.join(self.out_dir, NEWS_FILE))
    def publish_all(self) -> None:
        self.publish_snapshot()
        self.publish_news()
    def run_cycle(self, tfs: List[Timeframe]) -> None:
        for tf in tfs:  # sequential — see module docstring (cache dedup)
            self.scan_tf(tf)
        # Snapshot lands BEFORE the news refresh: when the news TTL has
        # expired, refresh_news does FF + 4 RSS fetches (and possibly
        # DeepSeek calls) — 20-30 s that must not keep fresh signals off
        # the page.
        self.publish_snapshot()
        self.refresh_news()
        self.publish_news()
    def run_forever(self) -> None:
        self.run_cycle(DASHBOARD_TFS)  # populate the page at boot
        while True:
            delay = seconds_until_boundary(5, buffer=SCAN_BUFFER_SECONDS)
            logger.info(f"next M5 boundary in {delay:.0f}s")
            time.sleep(delay)
            try:
                due = due_timeframes(datetime.now(timezone.utc))
                logger.info(f"boundary wake — scanning {[t.value for t in due]}")
                self.run_cycle(due)
            except Exception:
                logger.exception("cycle failed")
 def main(argv=None) -> None:
    ap = argparse.ArgumentParser(description="XAU/USD static dashboard publisher")
    ap.add_argument("--once", action="store_true",
                    help="one scan+publish cycle, then exit")
    ap.add_argument("--out",
                    default=os.environ.get("DASHBOARD_OUT_DIR", DEFAULT_OUT_DIR),
                    help="output directory served by Apache "
                         f"(default: $DASHBOARD_OUT_DIR or {DEFAULT_OUT_DIR})")
    args = ap.parse_args(argv)
    _setup_logging()
    os.makedirs(args.out, exist_ok=True)
    pub = DashboardPublisher(args.out)
    pub.install_static()
    if args.once:
        pub.run_cycle(DASHBOARD_TFS)
        print(f"Published to {args.out}")
        return
    pub.run_forever()
 if __name__ == "__main__":
    main()
@@ -0,0 +1,386 @@
 """
 ForexBot2 — Data Provider (Twelve Data SDK)
 ==========================================================
 Fetches and validates OHLCV via the official `twelvedata` Python SDK.
 """
 import logging
 import os
 import threading
 import time
 from collections import deque
 from typing import Dict, Optional
 import pandas as pd
 from twelvedata import TDClient
 from config import PlatformConfig, Timeframe, XAUUSD, PAIR_INFO
 logger = logging.getLogger(__name__)
 def _is_forex_weekend(now=None) -> bool:
    """Forex market closed: Friday 22:00 UTC → Sunday 22:00 UTC."""
    if now is None:
        now = pd.Timestamp.now(tz="UTC")
    wd = now.weekday()
    hour = now.hour
    if wd == 4 and hour >= 22:
        return True
    if wd == 5:
        return True
    if wd == 6 and hour < 22:
        return True
    return False
 class DataIntegrityError(Exception):
    """Raised when data fails validation checks."""
    pass
 class ForexDataProvider:
    """Twelve Data SDK client + on-disk parquet cache."""
    TD_INTERVALS = {
        Timeframe.M1:  "1min",
        Timeframe.M5:  "5min",
        Timeframe.M15: "15min",
        Timeframe.M30: "30min",
        Timeframe.H1:  "1h",
        Timeframe.H4:  "4h",
        Timeframe.D1:  "1day",
        Timeframe.W1:  "1week",
    }
    BARS_PER_DAY = {
        Timeframe.M1: 1440, Timeframe.M5: 288, Timeframe.M15: 96,
        Timeframe.M30: 48, Timeframe.H1: 24, Timeframe.H4: 6,
        Timeframe.D1: 1, Timeframe.W1: 0.143,
    }
    CANDLE_SECONDS = {
        Timeframe.M1: 60, Timeframe.M5: 300, Timeframe.M15: 900,
        Timeframe.M30: 1800, Timeframe.H1: 3600, Timeframe.H4: 14400,
        Timeframe.D1: 86400, Timeframe.W1: 604800,
    }
    RATE_LIMIT = 55
    RATE_WINDOW = 60.0
    def __init__(self, config: PlatformConfig):
        self.config = config
        self._api_key = (
            os.environ.get("TWELVE_DATA_API_KEY")
            or config.twelve_data_api_key
        )
        self._cache: Dict[str, pd.DataFrame] = {}
        self._cache_timestamps: Dict[str, float] = {}
        # The bot shares one provider across a thread pool; cache eviction
        # and the throttle deque are check-then-act and need locking.
        self._cache_lock = threading.Lock()
        self._throttle_lock = threading.Lock()
        self._request_times: deque = deque()
        self._td: Optional[TDClient] = (
            TDClient(apikey=self._api_key) if self._api_key else None
        )
    @staticmethod
    def _to_td_symbol(symbol: str) -> str:
        if "/" in symbol:
            return symbol
        clean = symbol.replace("=X", "")
        return f"{clean[:3]}/{clean[3:]}"
    # ── Public API ─────────────────────────────────────────────────
    def fetch_ohlcv(
        self,
        symbol: str = XAUUSD,
        timeframe: Timeframe = Timeframe.H1,
        lookback_days: Optional[int] = None,
    ) -> pd.DataFrame:
        """Fetch OHLCV with in-memory caching for live mode."""
        if lookback_days is None:
            lookback_days = self.config.data_lookback_days
        cache_key = f"{symbol}_{timeframe.value}"
        now = time.time()
        cache_ttl = self.CANDLE_SECONDS.get(timeframe, self.config.refresh_interval)
        with self._cache_lock:
            cached = self._cache.get(cache_key)
            cached_at = self._cache_timestamps.get(cache_key)
        if (
            cached is not None
            and cached_at is not None
            and (now - cached_at) < cache_ttl
            and not cached.empty
        ):
            if (self._is_data_fresh(cached, symbol, timeframe)
                    and self._has_latest_closed_bar(cached, timeframe)):
                return cached
            logger.warning(f"{symbol} {timeframe.value}: cached data is stale, refetching")
            with self._cache_lock:
                self._cache.pop(cache_key, None)
                self._cache_timestamps.pop(cache_key, None)
        outputsize = max(
            300,
            min(int(lookback_days * self.BARS_PER_DAY[timeframe]) + 20, 5000),
        )
        df = self._fetch_twelve_data(symbol, timeframe, outputsize)
        df = self._drop_forming_candle(df, timeframe)
        df = self._validate_and_clean(df, symbol, timeframe)
        if not self._is_data_fresh(df, symbol, timeframe):
            logger.warning(
                f"STALE DATA from API: {symbol} {timeframe.value} — "
                f"last bar {df.index[-1]} is too old. Signal quality may be degraded."
            )
        with self._cache_lock:
            self._cache[cache_key] = df
            self._cache_timestamps[cache_key] = now
        return df
    def _has_latest_closed_bar(self, df: pd.DataFrame, timeframe: Timeframe) -> bool:
        """Cached data must include the most recent fully closed bar.
        The TTL alone is wall-clock based: a snapshot fetched mid-candle is
        still within TTL at the next candle boundary but is missing the bar
        that just closed, so every boundary scan would analyze — and run the
        bar-close session check against — the previous bar.
        """
        now = pd.Timestamp.now(tz="UTC")
        if _is_forex_weekend(now):
            return True
        candle_seconds = self.CANDLE_SECONDS.get(timeframe, 3600)
        dur = pd.Timedelta(seconds=candle_seconds)
        expected_label = now.floor(dur) - dur
        return df.index[-1] >= expected_label
    def _drop_forming_candle(self, df: pd.DataFrame, timeframe: Timeframe) -> pd.DataFrame:
        """Drop the still-forming last candle so analysis only sees closed bars.
        Twelve Data's time_series includes the current partial candle; the
        scanner fires seconds after the boundary, so without this guard every
        indicator reads a seconds-old stub bar and signals repaint.
        """
        if df is None or df.empty:
            return df
        candle_seconds = self.CANDLE_SECONDS.get(timeframe, 3600)
        now = pd.Timestamp.now(tz="UTC")
        if df.index[-1] + pd.Timedelta(seconds=candle_seconds) > now:
            df = df.iloc[:-1]
        return df
    def _is_data_fresh(self, df: pd.DataFrame, symbol: str, timeframe: Timeframe) -> bool:
        """Check if the last candle is within acceptable age for this timeframe."""
        if df is None or df.empty:
            return False
        now = pd.Timestamp.now(tz="UTC")
        if _is_forex_weekend(now):
            return True
        last_bar = df.index[-1]
        candle_seconds = self.CANDLE_SECONDS.get(timeframe, 3600)
        max_age_seconds = candle_seconds * 3
        age_seconds = (now - last_bar).total_seconds()
        if age_seconds > max_age_seconds:
            logger.warning(
                f"{symbol} {timeframe.value}: last bar {last_bar.strftime('%Y-%m-%d %H:%M')} "
                f"is {age_seconds / 60:.0f}m old (limit: {max_age_seconds / 60:.0f}m)"
            )
            return False
        return True
    def clear_cache(self) -> int:
        """Wipe all cached data. Returns number of entries cleared."""
        count = len(self._cache)
        self._cache.clear()
        self._cache_timestamps.clear()
        logger.info(f"Cache cleared: {count} entries wiped")
        return count
    def get_latest_price(self, symbol: str = XAUUSD) -> Optional[float]:
        try:
            df = self.fetch_ohlcv(symbol, Timeframe.M5, lookback_days=1)
            if df is not None and not df.empty:
                return float(df["Close"].iloc[-1])
        except Exception:
            pass
        return None
    def get_spread_estimate(self, symbol: str = XAUUSD) -> float:
        try:
            df = self.fetch_ohlcv(symbol, Timeframe.M5, lookback_days=1)
            if df is not None and not df.empty:
                return float((df["High"] - df["Low"]).median())
        except Exception:
            pass
        info = PAIR_INFO.get(symbol, PAIR_INFO[XAUUSD])
        return info["pip_value"]
    def get_pair_info(self, symbol: str = XAUUSD) -> Dict:
        info = PAIR_INFO.get(symbol, PAIR_INFO[XAUUSD])
        return {"symbol": symbol, **info}
    # ── Private ────────────────────────────────────────────────────
    def _throttle(self, credits: int = 1) -> None:
        """Pro tier: 55 credits / minute. Block until budget allows."""
        while True:
            with self._throttle_lock:
                now = time.time()
                while self._request_times and now - self._request_times[0] >= self.RATE_WINDOW:
                    self._request_times.popleft()
                available = self.RATE_LIMIT - len(self._request_times)
                if available >= credits:
                    for _ in range(credits):
                        self._request_times.append(time.time())
                    return
                used = len(self._request_times)
                wait = (self._request_times[0] + self.RATE_WINDOW) - now + 0.2
            logger.info(
                f"Rate limit: {used}/{self.RATE_LIMIT} credits used "
                f"— waiting {wait:.1f}s..."
            )
            time.sleep(wait)
    def _fetch_twelve_data(
        self,
        symbol: str,
        timeframe: Timeframe,
        outputsize: int,
        start_date: Optional[str] = None,
        end_date: Optional[str] = None,
    ) -> pd.DataFrame:
        if self._td is None:
            raise DataIntegrityError(
                "No Twelve Data API key. Set TWELVE_DATA_API_KEY env var "
                "or config.twelve_data_api_key."
            )
        td_symbol = self._to_td_symbol(symbol)
        interval = self.TD_INTERVALS[timeframe]
        self._throttle(credits=1)
        kwargs = {
            "symbol": td_symbol,
            "interval": interval,
            "outputsize": min(outputsize, 5000),
            "timezone": "UTC",
        }
        if start_date:
            kwargs["start_date"] = start_date
        if end_date:
            kwargs["end_date"] = end_date
        try:
            df = self._td.time_series(**kwargs).as_pandas()
        except Exception as e:
            raise DataIntegrityError(
                f"Twelve Data fetch failed for {td_symbol}: {e}"
            ) from e
        if df is None or df.empty:
            raise DataIntegrityError(f"Empty response from Twelve Data for {td_symbol}")
        # SDK returns lowercase columns; engine expects capitalised.
        df = df.rename(columns={
            "open": "Open", "high": "High", "low": "Low",
            "close": "Close", "volume": "Volume",
        })
        for col in ("Open", "High", "Low", "Close", "Volume"):
            if col in df.columns:
                df[col] = pd.to_numeric(df[col], errors="coerce")
        if "Volume" not in df.columns:
            df["Volume"] = 0.0
        # SDK returns newest-first; engine + indicators want oldest-first.
        df = df.sort_index()
        if df.index.tz is None:
            df.index = df.index.tz_localize("UTC")
        df.index.name = "Datetime"
        logger.debug(f"Fetched {len(df)} bars for {td_symbol} @ {interval}")
        return df
    def _validate_and_clean(
        self, df: pd.DataFrame, symbol: str, timeframe: Timeframe
    ) -> pd.DataFrame:
        if df is None or df.empty:
            raise DataIntegrityError(f"Empty dataset for {symbol}")
        original_len = len(df)
        df = df.dropna(subset=["Open", "High", "Low", "Close"], how="all")
        df = df.ffill(limit=3)
        df = df.dropna(subset=["Close"])
        df = df.sort_index()
        df = df[~df.index.duplicated(keep="last")]
        now = pd.Timestamp.utcnow()
        future_mask = df.index > now + pd.Timedelta(minutes=5)
        if future_mask.any():
            logger.warning(f"{symbol}: Dropping {future_mask.sum()} future timestamps")
            df = df[~future_mask]
        invalid_ohlc = (
            (df["High"] < df["Low"]) |
            (df["High"] < df["Open"]) |
            (df["High"] < df["Close"]) |
            (df["Low"] > df["Open"]) |
            (df["Low"] > df["Close"])
        )
        if invalid_ohlc.any():
            logger.warning(f"{symbol}: Fixing {invalid_ohlc.sum()} OHLC-inconsistent bars")
            for idx in df[invalid_ohlc].index:
                vals = [df.loc[idx, "Open"], df.loc[idx, "Close"]]
                df.loc[idx, "High"] = max(vals + [df.loc[idx, "High"]])
                df.loc[idx, "Low"]  = min(vals + [df.loc[idx, "Low"]])
        returns = df["Close"].pct_change()
        std_ret = returns.std()
        if std_ret > 0:
            dev = returns - returns.mean()
            outlier_mask = dev.abs() > 10 * std_ret
            # A spike bar makes the NEXT bar's return look extreme too (it is
            # measured against the spike). If the previous bar is flagged and
            # this bar reverses direction, it is the legitimate snap-back —
            # keep it, or every spike would punch a two-bar hole in the data.
            recovery = (
                outlier_mask
                & outlier_mask.shift(1, fill_value=False)
                & (dev * dev.shift(1) < 0)
            )
            outlier_mask = outlier_mask & ~recovery
            if outlier_mask.any():
                logger.warning(f"{symbol}: Removing {outlier_mask.sum()} extreme outlier bars")
                df = df[~outlier_mask]
        min_required = max(
            self.config.technical.ema_trend,
            200,
        ) + 10
        if len(df) < min_required:
            logger.warning(
                f"{symbol} {timeframe.value}: Only {len(df)} bars "
                f"(need {min_required}). Some indicators will be partial."
            )
        cleaned = original_len - len(df)
        if cleaned > 0:
            logger.info(f"{symbol}: Cleaned {cleaned}/{original_len} bars")
        return df
@@ -1,157 +0,0 @@
 # Deploying the web dashboard (live LAMP server)
 The dashboard is a **static publisher**: `dashboard_publisher.py` runs as its
 own daemon, scans XAU/USD at candle closes, and atomically writes
 `dashboard.html` + `news_summary.php` + `snapshot.json` + `news.json` into a
 folder Apache already serves. Apache does all web serving — no new port, no
 Python web server, and the telegram bot process is completely unaffected by
 anything that happens here (see the module docstring for the independence
 rules). The one dynamic piece is `news_summary.php` (§6): an on-demand
 DeepSeek summary for a clicked headline, whitelist-gated against news.json.
 All steps below run **on the live server** (`/home/Forex/ForexBot2`).
 ## 1. Get the code
 ```bash
 cd /home/Forex/ForexBot2
 git pull
 venv/bin/python test_all.py        # optional sanity: suite must be green
 ```
 No new Python dependencies are required.
 ## 2. Make the web folder writable
 The dashboard publishes straight into the portal docroot `/var/www/trade/`
 (owner decision 2026-08-03) — it adds exactly four files there:
 `dashboard.html`, `news_summary.php`, `snapshot.json`, `news.json` (plus the
 apache-owned `ai/` cache dir from §6). The daemon user must be able to write
 them:
 ```bash
 # if the portal files can be owned by the daemon user:
 sudo chown Forex: /var/www/trade
 # otherwise grant write without changing ownership:
 sudo setfacl -m u:Forex:rwx /var/www/trade
 ```
 (A different path is one edit away: the unit's `Environment=DASHBOARD_OUT_DIR=`
 line, or `--out` for one-off runs.)
 SELinux (RHEL/Alma/CentOS enforcing): directories under `/var/www` normally
 already carry `httpd_sys_content_t`, and new files inherit the parent label, so
 no action is usually needed. If Apache 403s despite correct permissions:
 ```bash
 sudo restorecon -R /var/www/trade
 ```
 ## 3. Smoke-test once
 ```bash
 venv/bin/python dashboard_publisher.py --once
 ls /var/www/trade    # dashboard.html snapshot.json news.json added next to the portal
 ```
 Then open `http://<server>/dashboard.html` (path relative to however the
 vhost maps `/var/www/trade`) — all four timeframe cards, the calendar, and
 the newswire should populate.
 ## 4. Install the daemon
 ```bash
 sudo cp deploy/gold-dashboard.service /etc/systemd/system/
 # edit User= if the repo owner is not "Forex"
 sudo systemctl daemon-reload
 sudo systemctl enable --now gold-dashboard
 systemctl status gold-dashboard
 tail -f logs/dashboard.log
 ```
 The daemon scans all four timeframes at boot, then wakes at every M5
 boundary (+10 s) and rescans whichever timeframes just closed. Logs rotate
 every 3 h (240 backups), same scheme as the bot.
 ## 5. Optional Apache polish
 The page polls its JSON with conditional GETs (`cache: "no-cache"` → Apache
 answers unchanged files with 304), so no Apache config is strictly needed. To
 be explicit anyway:
 ```apache
 <Directory "/var/www/trade">
    <FilesMatch "\.json$">
        Header set Cache-Control "no-cache"
    </FilesMatch>
 </Directory>
 ```
 The page is deliberately public (owner decision 2026-08-03). To restrict it
 later, add standard Apache basic auth (`htpasswd` + `Require valid-user`) on
 that directory — no code changes needed.
 ## 6. AI summary endpoint (PHP) — one-time setup
 Clicking a newswire headline opens a detail dialog that fetches an on-demand
 DeepSeek gold-impact summary from `news_summary.php` (installed next to the
 page by the daemon). Owner decision 2026-08-07: a real on-demand endpoint on
 the public page, accepted with these guards baked into the PHP — the id must
 match a headline in the published news.json (arbitrary text can never reach
 the LLM), each headline's summary is cached forever in `ai/` (≤1 API call per
 headline, ever), and a file-based daily cap fails **closed** on I/O errors.
 Requirements: `php` + the curl extension (`php -m | grep curl`).
 ```bash
 # 1. SELinux: allow httpd outbound HTTPS — WITHOUT this every summary call
 #    dies as a curl timeout even with perfect perms (RHEL-family default).
 sudo setsebool -P httpd_can_network_connect 1
 # 2. API key OUTSIDE the docroot, readable by Apache only:
 sudo install -d -o root -g root -m 755 /etc/gold-dashboard
 sudo sh -c 'umask 137; echo "sk-..." > /etc/gold-dashboard/deepseek.key'
 sudo chown root:apache /etc/gold-dashboard/deepseek.key   # mode 640
 # 3. apache-owned cache dir (the daemon runs as Forex and can't create it):
 sudo install -d -o apache -g apache -m 755 /var/www/trade/ai
 sudo semanage fcontext -a -t httpd_sys_rw_content_t '/var/www/trade/ai(/.*)?'
 sudo restorecon -Rv /var/www/trade/ai
 ```
 Knobs (Apache `SetEnv` in the vhost, both optional):
 `AI_SUMMARY_DAILY_CAP` (default 150), `DEEPSEEK_KEY_FILE` (default
 `/etc/gold-dashboard/deepseek.key`).
 Verify from the shell — expect 400 / 404 / 200 / 200-from-cache:
 ```bash
 curl -s "http://<server>/news_summary.php?id=zzz"                 # {"error":"bad_id"}
 curl -s "http://<server>/news_summary.php?id=$(printf 'a%.0s' {1..40})"  # unknown_id
 ID=$(python3 -c "import json;print(json.load(open('/var/www/trade/news.json'))['headlines'][0]['id'])")
 curl -s "http://<server>/news_summary.php?id=$ID"   # real summary (2-12 s)
 curl -s "http://<server>/news_summary.php?id=$ID"   # instant, from ai/<id>.json
 ```
 ## Operational notes
 - **Updating**: `git pull` + `systemctl restart gold-dashboard`. The daemon
  re-copies `static/dashboard.html` and `static/news_summary.php` into the
  web folder at startup.
 - **If the daemon dies**, systemd restarts it in 30 s; worst case the page
  shows stale data with its own "updated N min ago" evidence. Apache and the
  telegram bot are unaffected.
 - **DeepSeek**: the publisher process has its own daily request cap
  (`deepseek.daily_request_cap`, default 500) separate from the bot's —
  expect roughly up to ~430 extra calls/day worst case, V4-Flash pricing.
 - **Two independent DeepSeek meters** hit the same API key: the publisher's
  in-process cap above, and the PHP endpoint's file cap (`ai/.ht_cap.json`,
  default 150/day). They don't communicate — worst-case spend is their sum;
  DeepSeek-side spend alerting is the true backstop. Kept separate on
  purpose (independence rule: the PHP never imports/reads bot code or
  config.json).
 - **Restart window**: for the first minutes after a daemon restart the new
  page can meet a pre-upgrade news.json without headline ids — those rows
  simply render non-clickable until the first news publish.
 - **Credentials**: uses the same env/.env/config.json resolution as the rest
  of the platform (Oanda candles feed; news needs no key; DeepSeek optional).
@@ -1,23 +0,0 @@
 # systemd unit for the XAU/USD static dashboard publisher.
 # Install (as root, on the live server):
 #   cp deploy/gold-dashboard.service /etc/systemd/system/
 #   systemctl daemon-reload && systemctl enable --now gold-dashboard
 # See deploy/README-dashboard.md for the full checklist.
 [Unit]
 Description=XAU/USD dashboard publisher (writes static JSON/HTML for Apache)
 After=network-online.target
 Wants=network-online.target
 [Service]
 Type=simple
 # Adjust to the account that owns /home/Forex/ForexBot2:
 User=Forex
 WorkingDirectory=/home/Forex/ForexBot2
 Environment=DASHBOARD_OUT_DIR=/var/www/trade
 ExecStart=/home/Forex/ForexBot2/venv/bin/python dashboard_publisher.py
 Restart=always
 RestartSec=30
 [Install]
 WantedBy=multi-user.target
@@ -1,199 +0,0 @@
 """ForexBot2 architecture diagram → docs/architecture.png.
 Regenerate after architecture changes:  venv/bin/python docs/architecture_diagram.py
 (matplotlib only — no graphviz on the dev box)."""
 import os
 import matplotlib
 matplotlib.use("Agg")
 import matplotlib.pyplot as plt
 from matplotlib.patches import FancyBboxPatch, FancyArrowPatch
 W, H = 100.0, 100.0
 fig, ax = plt.subplots(figsize=(16, 11.5))
 ax.set_xlim(0, W); ax.set_ylim(0, H); ax.axis("off")
 INK = "#263238"; MUT = "#607D8B"; ARR = "#455A64"; HAUL = "#7B8FA1"
 def box(x, y, w, h, title, lines, fc, ec, title_fs=8.2, body_fs=6.6, lw=1.3):
    ax.add_patch(FancyBboxPatch((x, y), w, h,
                 boxstyle="round,pad=0.35,rounding_size=0.9",
                 facecolor=fc, edgecolor=ec, linewidth=lw, zorder=2))
    cy = y + h - 1.9
    ax.text(x + w / 2, cy, title, ha="center", va="center", fontsize=title_fs,
            fontweight="bold", color=INK, zorder=3)
    cy -= 2.3
    for ln in lines:
        ax.text(x + w / 2, cy, ln, ha="center", va="center", fontsize=body_fs,
                color=INK, zorder=3)
        cy -= 1.85
 def band(x, y, w, h, label, color):
    ax.add_patch(FancyBboxPatch((x, y), w, h,
                 boxstyle="round,pad=0.3,rounding_size=1.2",
                 facecolor="none", edgecolor=color, linewidth=1.0,
                 linestyle=(0, (4, 3)), zorder=1))
    ax.text(x + 0.9, y + h - 1.4, label, ha="left", va="center",
            fontsize=7.2, fontweight="bold", color=color, zorder=3)
 def arrow(p1, p2, color=ARR, lw=1.5, style="-|>", conn="arc3,rad=0.0",
          label=None, lx=0, ly=0, ls="solid", fs=6.4, rot=0):
    ax.add_patch(FancyArrowPatch(p1, p2, arrowstyle=style, mutation_scale=11,
                 color=color, linewidth=lw, linestyle=ls,
                 connectionstyle=conn, zorder=4))
    if label:
        mx, my = (p1[0] + p2[0]) / 2 + lx, (p1[1] + p2[1]) / 2 + ly
        ax.text(mx, my, label, fontsize=fs, color=MUT, ha="center",
                va="center", zorder=5, rotation=rot,
                bbox=dict(boxstyle="round,pad=0.15", fc="white", ec="none", alpha=0.9))
 # ── title ────────────────────────────────────────────────────────────
 ax.text(2, 98.4, "ForexBot2 — XAU/USD Signal-Intelligence Platform",
        fontsize=15, fontweight="bold", color=INK, ha="left")
 ax.text(2, 96.0, "one pipeline, four delivery surfaces · closed bars only · live == backtest parity",
        fontsize=8.5, color=MUT, ha="left")
 EXT_FC, EXT_EC = "#ECEFF1", "#90A4AE"
 DAT_FC, DAT_EC = "#E3F2FD", "#5C9BD1"
 NWS_FC, NWS_EC = "#E0F2F1", "#3D9C8F"
 COR_FC, COR_EC = "#FFFDF5", "#C9A227"
 REP_FC, REP_EC = "#FFE9A8", "#B8860B"
 DEL_FC, DEL_EC = "#E8F5E9", "#5FAF63"
 BTS_FC, BTS_EC = "#EDE7F6", "#8E6BC6"
 # ── external services (top band) ─────────────────────────────────────
 band(1, 85.0, 98, 9.4, "EXTERNAL SERVICES", "#78909C")
 box(2.5, 86.2, 15.5, 6.4, "Oanda v20 API",
    ["candles (live feed)", "practice orders"], EXT_FC, EXT_EC)
 box(19.5, 86.2, 15.5, 6.4, "Twelve Data · Tiingo",
    ["registered fallbacks", "(Tiingo: tick stream)"], EXT_FC, EXT_EC)
 box(36.5, 86.2, 15.5, 6.4, "Forex Factory",
    ["economic calendar", "MyFXBook RSS fallback"], EXT_FC, EXT_EC)
 box(53.5, 86.2, 12.5, 6.4, "News RSS ×4",
    ["gold / macro", "headlines"], EXT_FC, EXT_EC)
 box(67.5, 86.2, 13.5, 6.4, "DeepSeek API",
    ["V4-Flash LLM", "thinking: disabled"], EXT_FC, EXT_EC)
 box(82.5, 86.2, 15, 6.4, "Telegram API",
    ["owner bot +", "subscriber channel"], EXT_FC, EXT_EC)
 # ── data & context layer ─────────────────────────────────────────────
 box(2.5, 69.8, 33, 11.0, "providers/  (BaseDataProvider)",
    ["single-flight fetch · TTL+boundary cache",
     "forming-candle DROP · validate/clean",
     "paginated fetch_range · optional tick stream",
     "active feed knob: data_providers.active = oanda"],
    DAT_FC, DAT_EC)
 box(38.5, 69.8, 33, 11.0, "news_engine.py  +  llm_client.py",
    ["calendar events + keyword sentiment (gold-inverse)",
     "DeepSeek enrichment: batch headline scores,",
     "event gold-direction, market blurb — display-only,",
     "keyword fallback, daily request cap"],
    NWS_FC, NWS_EC)
 box(74.5, 69.8, 23, 11.0, "app_config.py",
    ["credentials & deployment:",
     "env → config.json → refuse",
     "(no hardcoded fallbacks)",
     ".env loaded at import"],
    "#FAFAFA", "#9E9E9E")
 # ── signal core container ────────────────────────────────────────────
 ax.add_patch(FancyBboxPatch((2.5, 40.5), 95, 26.5,
             boxstyle="round,pad=0.35,rounding_size=1.2",
             facecolor=COR_FC, edgecolor=COR_EC, linewidth=1.6, zorder=1))
 ax.text(50, 65.2, "SIGNAL CORE — runs identically in live scans and backtests (closed bars only)",
        ha="center", fontsize=8.4, fontweight="bold", color="#8a6d1a", zorder=3)
 box(4.5, 51.8, 28, 12.0, "engine.py — TechnicalEngine",
    ["9 scored indicators + ATR context:",
     "EMA · RSI · MACD · BB · ADX · ROC ·",
     "Keltner · divergence · candlestick",
     "regime → adaptive weights → composite"],
    "white", COR_EC)
 box(4.5, 42.0, 28, 8.4, "pair_signals/xauusd.py — factory",
    ["ALWAYS used: config.json overlay +",
     "validated per-TF deltas (hour blocks, trail…)"],
    "white", COR_EC)
 box(35.5, 42.0, 60, 20.7, "signal_aggregator.py — analyze_pair → analyze_from_dataframes",
    ["multi-TF agreement  (M5→[M15,H1] · M15→[M5,H1] · H1→[M15,H4] · H4→[H1,D1])",
     "→ news context (warn-only by default) → diminishing-decay penalties",
     "→ exhaustion hard block → quality grade A+…F",
     "→ ordered post-grade gate chain (session · XAU SELL vol/hour · regime · M5 noise)",
     "→ setup generators: XAU ATR (A+/A) · generic swing · XAU_PB · XAU_CP",
     "→ entry · SL · TP1/TP2 · trailing plan"],
    "white", COR_EC, body_fs=6.9)
 # ── SignalReport hub ─────────────────────────────────────────────────
 box(31, 33.2, 38, 5.2, "SignalReport",
    ["signal (−2…+2) · grade · trade setup · reasons · news summary"],
    REP_FC, REP_EC, title_fs=8.8)
 # ── delivery surfaces ────────────────────────────────────────────────
 band(1, 13.2, 98, 17.2, "DELIVERY SURFACES", "#4C8F50")
 box(2.5, 15.0, 12, 12.6, "main.py",
    ["CLI report", "--detail", "--json"], DEL_FC, DEL_EC)
 box(16, 15.0, 24, 12.6, "oanda_daemon.py + executor",
    ["practice auto-execution",
     "book-parity rules: no broker TP,",
     "5-bar cooldown, once-per-bar",
     "trail, OPEN_ONLY fills,",
     "boot reconcile · orders → Oanda"],
    DEL_FC, DEL_EC, title_fs=7.8)
 box(42.5, 15.0, 31, 12.6, "dashboard_publisher.py  →  Apache",
    ["independent daemon · scans at candle closes",
     "atomic publish to /var/www/trade:",
     "dashboard.html · snapshot.json · news.json",
     "+ news_summary.php — on-demand AI",
     "gold-impact summary (whitelist · cache · cap)"],
    DEL_FC, DEL_EC)
 box(76, 15.0, 21.5, 12.6, "telegram_bot.py",
    ["candle-boundary auto-scans", "quality threshold + dedup",
     "alerts + LLM context line", "WS level-breach monitor"],
    DEL_FC, DEL_EC)
 # ── backtest / QA ────────────────────────────────────────────────────
 box(2.5, 3.2, 71.8, 8.0, "backtest/gpu_backtest.py — bar-by-bar replay through the SAME analyze_from_dataframes",
    ["realism (results-critical): next-open entries · raw-path resolution · spread charged · SL-first ties · close-only trail",
     "CuPy GPU / NumPy fallback · parquet cache · fed by providers.fetch_range (news off)"],
    BTS_FC, BTS_EC)
 box(77.5, 3.2, 20, 8.0, "test_all.py",
    ["359 tests · 22 modules", "no network (mocked)"], "#FAFAFA", "#9E9E9E")
 # ── arrows: short structural hops (solid dark) ───────────────────────
 arrow((10, 86.2), (14, 81.2), label="OHLCV", lx=-3.4, ly=0.2)          # oanda → providers
 arrow((27, 86.2), (24, 81.2), ls=(0, (3, 2)), lw=1.1)                  # fallbacks → providers
 arrow((44, 86.2), (48, 81.2))                                          # FF → news
 arrow((59.5, 86.2), (56, 81.2))                                        # RSS → news
 arrow((72, 86.2), (62, 81.2), label="LLM", lx=3.4, ly=0.6)             # deepseek → news
 arrow((19, 69.8), (19, 64.2), label="DataFrames (UTC, closed bars)", lx=0.2, ly=0.9)
 arrow((33.5, 57.5), (35.5, 55.5))                                      # engine → aggregator
 arrow((32.5, 46.0), (35.5, 48.0))                                      # factory → aggregator
 arrow((55, 69.8), (55, 63.1), label="warn-only context", lx=0, ly=1.0)
 arrow((86, 69.8), (86, 67.3), ls=(0, (3, 2)), lw=1.1)                  # config → core
 arrow((50, 40.5), (50, 38.8))                                          # core → report
 arrow((36, 34.5), (8.5, 27.95), conn="arc3,rad=0.12")                  # report → cli
 arrow((40, 33.2), (28, 27.95), conn="arc3,rad=0.06")                   # report → daemon
 arrow((60, 33.2), (58, 27.95))                                         # report → dashboard
 arrow((66, 34.5), (86.5, 27.95), conn="arc3,rad=-0.12")                # report → telegram
 # ── long-haul edges (thin dashed, routed through gutters) ────────────
 # telegram bot ↔ Telegram API — right-margin arc, clear of app_config
 arrow((95, 86.2), (95, 27.95), conn="arc3,rad=0.15", color=HAUL,
      lw=1.2, ls=(0, (4, 2)))
 ax.text(99.2, 57, "alerts / commands", fontsize=6.4, color=MUT,
        rotation=90, ha="center", va="center")
 # browser click → news_summary.php → DeepSeek
 # (oanda_daemon's order flow is noted inside its box — no long arc)
 arrow((66, 27.95), (73, 86.2), conn="arc3,rad=-0.18", color=HAUL,
      lw=1.2, ls=(0, (4, 2)),
      label="on-demand AI summary\n(browser click → PHP)", lx=2.5, ly=11.6)
 # providers.fetch_range → backtester — far-left margin channel
 arrow((1.5, 69.8), (1.5, 11.0), color=BTS_EC, lw=1.1, ls=(0, (3, 2)),
      label="fetch_range\n(history)", lx=3.6, ly=-1.2)
 # backtester replays the signal core — right gutter between boxes
 arrow((74.0, 11.6), (74.9, 40.3), color=HAUL, lw=1.3)
 ax.text(81.8, 38.9, "replays the exact signal code", fontsize=6.4,
        color=MUT, ha="center", va="center", zorder=5,
        bbox=dict(boxstyle="round,pad=0.15", fc="white", ec="none", alpha=0.9))
 out = os.path.join(os.path.dirname(os.path.abspath(__file__)), "architecture.png")
 fig.savefig(out, dpi=200, bbox_inches="tight", facecolor="white")
 print(f"saved {out}")
@@ -1,154 +0,0 @@
 # Centralized Configuration (config.json) — Design
 **Date:** 2026-07-21
 **Status:** Approved (user confirmed all four scoping decisions)
 ## Goal
 Move deployment settings — Telegram credentials, data-provider API keys and
 selection, scanned pairs, scan timeframes, per-pair base blocked hours, alert
 quality boot default, log level — into a single gitignored `config.json` at the
 repo root, loaded once at startup.
 **Hard constraint:** zero change to signal logic, indicator weights, thresholds,
 or gates. With an unedited `config.json` (or none at all), every produced
 `PlatformConfig` must be byte-identical to today's factory output. This is
 test-enforced (see Testing).
 ## Scoping decisions (user-confirmed 2026-07-21)
 1. **Secrets live in config.json** (gitignored). A committed
   `config.json.example` carries placeholders. Env vars still override.
   The hardcoded credential fallbacks in `config.py` and `telegram_bot.py`
   are **deleted** — on a machine with neither config.json nor env vars, the
   bot refuses to start with an explicit error instead of silently using
   committed credentials. (Recommend rotating the Telegram token and Twelve
   Data key after merge; both are exposed in git history.)
 2. **Multi-provider is structure only**: a `data_providers` section with named
   provider blocks and an `active` selector. Only Twelve Data is implemented.
   No failover logic (nothing to fail over to yet).
 3. **Blocked hours: base list per pair only.** The validated per-timeframe
   deltas (M5 appends hour 9; M15 SELL-block 15/17 UTC) stay in
   `pair_signals/xauusd.py` next to their validation evidence. The overlay is
   applied **before** those deltas and updates **both** mirrored copies
   (`xau_strategy.blocked_hours_utc` and `backtest.blocked_hours_utc`) so they
   cannot drift.
 4. **Extra keys included:** scan timeframes, alert min quality (boot default),
   log level. Everything else (indicator params, RR geometry, M5 gates,
   CP knobs, news config) deliberately stays code-only.
 ## File schema
 `config.json` (gitignored; `config.json.example` committed with placeholders
 for secrets and today's tuned values for everything else):
 ```json
 {
  "telegram": {
    "token": "<bot token>",
    "chat_id": "<owner chat id>",
    "channel_id": "<-100… or empty string to disable the channel feed>",
    "alert_min_quality": "C"
  },
  "data_providers": {
    "active": "twelve_data",
    "twelve_data": { "api_key": "<key>" }
  },
  "scanning": {
    "pairs": ["XAU/USD"],
    "timeframes": ["M5", "M15", "H1", "H4"]
  },
  "pairs": {
    "XAU/USD": { "blocked_hours_utc": [0, 1, 2, 10, 11, 13, 16, 21] }
  },
  "log_level": "INFO"
 }
 ```
 Timeframe strings use the `Timeframe` enum names (`M5`, `M15`, `H1`, `H4`, …).
 Pair keys use canonical symbols (`XAU/USD`).
 ## Architecture
 ### New module: `app_config.py`
 Single responsibility: read + validate `config.json`, expose typed accessors,
 apply the pair-level overlay. No signal knowledge.
 - `AppConfig` dataclass: `telegram` (token, chat_id, channel_id,
  alert_min_quality), `data_providers` (active name + per-provider dicts),
  `scan_pairs`, `scan_timeframes`, `pair_overrides`
  (symbol → {blocked_hours_utc}), `log_level`.
 - `load_app_config(path=REPO_ROOT/"config.json") -> AppConfig` — module-level
  cached (loaded once per process). Missing file → `AppConfig` of pure
  defaults + one warning log. Malformed JSON, unknown top-level/section keys,
  unknown pair symbols, hours outside 0–23, unknown timeframe names, unknown
  `active` provider → raise `AppConfigError` with a named message (fail fast
  at boot).
 - Precedence, applied inside the accessors:
  **env var → config.json → code default**. Secrets (`TELEGRAM_TOKEN`,
  `TELEGRAM_CHAT_ID`, `TELEGRAM_CHANNEL_ID`, `TWELVE_DATA_API_KEY`) have **no
  code default** after this change.
 - `apply_pair_overrides(cfg: PlatformConfig, app: AppConfig) -> PlatformConfig`
  — sets the pair's base `blocked_hours_utc` on **both**
  `cfg.xau_strategy.blocked_hours_utc` and `cfg.backtest.blocked_hours_utc`,
  plus `cfg.scan_pairs` / `cfg.scan_timeframes`. Called by the pair factory
  (see below). A no-op when the pair has no override block.
 ### Wiring points (all consumers)
 | Consumer | Change |
 |---|---|
 | `pair_signals/xauusd.py` | `make_config` calls `apply_pair_overrides` **before** the per-TF deltas (so M5 still appends hour 9 to whatever base the JSON sets, and mirrors stay in sync). |
 | `data_provider.py` | API key resolution becomes `app_config` accessor for the **active** provider (env `TWELVE_DATA_API_KEY` still wins). `config.twelve_data_api_key` field and its hardcoded default are deleted. Missing key keeps today's behavior: provider constructed with `_td=None`, errors on first fetch. |
 | `telegram_bot.py` | `TOKEN`, `CHAT_ID`, `CHANNEL_ID` come from `app_config` (hardcoded fallbacks deleted; missing token/chat_id → explicit startup error). `_alert_min_quality` boot default and `logging` level come from `app_config`. `SCAN_PAIRS` (today built from a bare `PlatformConfig()`) reads `app_config.scan_pairs` directly; `apply_pair_overrides` also stamps `cfg.scan_pairs`/`cfg.scan_timeframes` so the dataclass field stays truthful for any other reader. |
 | `main.py` | No structural change — it uses the factory, which now applies the overlay. |
 | `backtest/gpu_backtest.py` | No structural change — it uses the factory too, so an edited base blocked-hours list flows into replays identically to live (parity by construction). Data fetches pick up the provider key via `data_provider.py`. |
 | `config.py` | `twelve_data_api_key` field removed. Everything else untouched. |
 ### Multi-provider structure
 `data_provider.py` gains a minimal registry:
 `PROVIDER_REGISTRY = {"twelve_data": ForexDataProvider}`. `active` selects
 from it; an unknown name fails at load. Adding a provider later = one config
 block + one adapter class registered here. No shared base class extracted now
 (YAGNI — with one provider there is nothing to abstract over yet).
 ## Error handling summary
 | Condition | Behavior |
 |---|---|
 | No config.json | Defaults + warning. Secrets must come from env or startup fails (bot) / fetch fails (provider). |
 | Malformed JSON / bad values / unknown keys | `AppConfigError` at load, process exits with named message. |
 | Missing Telegram token or chat_id at bot boot | Explicit startup error naming the missing key and both places it can be set. |
 | Missing data-provider key | Same as today: provider builds, first fetch errors. |
 ## Migration
 - Create the local `config.json` populated with the **current live values**
  (the ones being deleted from source) so the running bot keeps working
  unchanged. `.gitignore` gains `config.json`.
 - Commit `config.json.example` with placeholders.
 - CLAUDE.md: update the env-var table, repository map, and config section.
 - Post-merge recommendation (user action): rotate the Telegram token and
  Twelve Data key, since both are in git history.
 ## Testing (`test_all.py`, new module)
 1. **Parity pin (the critical one):** a config.json containing the example
   defaults, and no config.json at all, both produce factory output identical
   to today's (full dataclass equality across all four TFs).
 2. Overlay mapping: JSON blocked hours land on **both** mirrored fields; M5
   factory still appends hour 9 on top of a custom base.
 3. Precedence: env var beats config.json beats default, per key.
 4. Validation: malformed JSON, bad hour, unknown pair/timeframe/provider each
   raise `AppConfigError`.
 5. Missing-file behavior: defaults + no crash.
 6. Existing factory-equivalence test stays green (equivalence is now defined
   over factory-with-overlay vs inline-config-with-overlay).
 ## Out of scope
 Hot reload (config is read once at boot), a second concrete provider,
 failover, moving any tuned strategy value beyond the base blocked-hours list,
 news/indicator/geometry settings in JSON.
@@ -69,9 +69,8 @@
 <tr><td><b>Score / confidence</b></td><td>The regime-weighted composite of all nine indicators. Bigger absolute score = stronger consensus.</td></tr>
 <tr><td><b>MTF aligned</b></td><td>The two confirmation timeframes agree with the trade direction. Signals without alignment are filtered out on M5.</td></tr>
 <tr><td><b>Warnings ⚠️</b></td><td>Context, not veto. Backtesting shows warned trades perform about the same — strong momentum often triggers warnings.</td></tr>
 <tr><td><b>Market context 📰</b></td><td>An AI-written one-liner summarizing today's macro backdrop (headlines + high-impact releases) and its likely pull on gold. Informational only — it never blocks or grades a signal.</td></tr>
 <tr><td><b>SL / TP1 / RR</b></td><td>Stop loss sits behind the nearest swing level with an ATR buffer. TP1 is the nearest opposing level. <b>1R</b> = the entry-to-SL distance = the amount you risk.</td></tr>
-<tr><td><b>Trail plan 🔄</b></td><td>The real exit. Once price moves +0.6R in your favor, move the stop to trail 0.75R behind the best price and update it at each candle close. <b>TP1 is advisory — let winners run.</b></td></tr>
+<tr><td><b>Trail plan 🔄</b></td><td>The real exit. Once price moves +0.6R in your favor (+0.8R for pullback setups), move the stop to trail 0.75R behind the best price and update it at each candle close. <b>TP1 is advisory — let winners run.</b></td></tr>
 </table>
 <h2>3 · Acting on a signal — step by step</h2>
@@ -130,7 +129,6 @@
 <tr><td><code>/scan</code></td><td>Run an immediate analysis of the primary timeframe.</td></tr>
 <tr><td><code>/autoscan</code></td><td>Toggle automatic scanning (M5 / M15 / H1 / H4 at each candle close).</td></tr>
 <tr><td><code>/status</code></td><td>Uptime, auto-scan state, WebSocket price-monitor state.</td></tr>
 <tr><td><code>/news</code></td><td>Net XAU/USD news sentiment score (informational only — never gates signals).</td></tr>
 <tr><td><code>/cache</code> · <code>/clearcache</code></td><td>Inspect / reset the market-data cache.</td></tr>
 <tr><td><code>/help</code></td><td>Command reference.</td></tr>
 </table>
@@ -13,7 +13,7 @@ Design philosophy:
 import logging
 from dataclasses import dataclass, field
-from typing import Dict, List, Tuple
+from typing import Dict, List, Optional, Tuple
 import numpy as np
 import pandas as pd
@@ -245,12 +245,6 @@ class TechnicalEngine:
            signal = SignalStrength.NEUTRAL
            conf = 40
        # Partial alignment (plain BUY/SELL, 2-3 of 4 checks) carries no
        # edge on M5 — squash to NEUTRAL when the research knob is on.
        if c.ema_partial_neutral and signal in (SignalStrength.BUY, SignalStrength.SELL):
            signal = SignalStrength.NEUTRAL
            conf = 40
        crossover_desc = ""
        if len(ema_fast) >= 3:
            prev_f, prev_m = ema_fast.iloc[-2], ema_medium.iloc[-2]
@@ -644,10 +638,15 @@ class TechnicalEngine:
                signal = SignalStrength.SELL
                conf = min(70, 50 + roc_abs * 10)
                desc = f"ROC {roc_val:.2f}% — Falling momentum"
        elif roc_prev <= 0 < roc_val:
            signal = SignalStrength.BUY
            conf = 65
            desc = f"ROC crossed above zero (+{roc_val:.2f}%)"
        elif roc_prev >= 0 > roc_val:
            signal = SignalStrength.SELL
            conf = 65
            desc = f"ROC crossed below zero ({roc_val:.2f}%)"
        else:
            # Zero-crosses land in the branches above by definition (a
            # cross up is positive and rising, a cross down negative and
            # falling) — only flat/fading momentum reaches here.
            signal = SignalStrength.NEUTRAL
            conf = 30
            desc = f"ROC {roc_val:+.2f}% — No clear momentum"
@@ -1,195 +0,0 @@
 """
 ForexBot2 — DeepSeek LLM client
 ================================
 Thin requests-based client for DeepSeek's OpenAI-compatible chat API,
 used by `news_engine` for display/warning-only enrichment (headline
 sentiment, event surprise direction, market summary). Signals are NEVER
 gated on LLM output — every caller has a non-LLM fallback and treats a
 `None` return as "use the fallback".
 Design constraints:
 - One attempt per call, no retries: news runs inside scan cycles; the
  news engine's 600 s context cache is the natural retry cadence.
 - A UTC-day request cap bounds spend even if a bug causes call storms.
 - Cost: DeepSeek caches identical prompt PREFIXES server-side (cache-hit
  input is ~50x cheaper), so callers pass a STATIC system prompt (no
  timestamps or interpolation) and put all variable content in the user
  payload.
 This is a module-level singleton (`get_llm_client`) because the daily
 cap counter must survive the per-scan reconstruction of `NewsEngine`.
 """
 import datetime as _dt
 import json
 import logging
 import threading
 import time
 from typing import Any, Dict, Optional, Tuple
 import requests
 from app_config import deepseek_api_key, get_app_config
 logger = logging.getLogger(__name__)
 DEEPSEEK_URL = "https://api.deepseek.com/chat/completions"
 DEFAULT_MODEL = "deepseek-v4-flash"
 # 8s timed out on a measured cold-prefix-cache headline batch (2026-08-03);
 # warm-cache calls come back well under that. 12s buys cold-start headroom
 # while still bounding worst-case scan latency (≤3 calls per cache window).
 DEFAULT_TIMEOUT = 12.0
 DEFAULT_DAILY_CAP = 500
 class DeepSeekClient:
    """JSON-in/JSON-out chat wrapper. All failures return None."""
    def __init__(
        self,
        api_key: str,
        model: str = DEFAULT_MODEL,
        timeout: float = DEFAULT_TIMEOUT,
        daily_cap: int = DEFAULT_DAILY_CAP,
        enabled: bool = True,
    ):
        self._api_key = api_key or ""
        self._model = model
        self._timeout = timeout
        self._daily_cap = daily_cap
        self._enabled = enabled
        self._session = requests.Session()
        self._session.headers.update({
            "Content-Type": "application/json",
            "Authorization": f"Bearer {self._api_key}",
        })
        self._lock = threading.Lock()
        self._count_day: Optional[_dt.date] = None
        self._count = 0
        self._warned: set = set()
        # Health stamps for status displays: last successful call and
        # last failure (ts, kind). Read-only for consumers.
        self.last_ok: float = 0.0
        self.last_error: Optional[Tuple[float, str]] = None
    @property
    def available(self) -> bool:
        return self._enabled and bool(self._api_key)
    def _warn_once(self, kind: str, msg: str, *args: Any) -> None:
        if kind in self._warned:
            logger.debug(msg, *args)
        else:
            self._warned.add(kind)
            logger.warning(msg, *args)
    def _under_cap(self) -> bool:
        """Count a request against the UTC-day cap; False = don't call."""
        today = _dt.datetime.now(_dt.timezone.utc).date()
        with self._lock:
            if self._count_day != today:
                self._count_day = today
                self._count = 0
                self._warned.discard("cap")
            if self._count >= self._daily_cap:
                self._warn_once(
                    "cap", "DeepSeek daily request cap (%d) exhausted — "
                    "falling back to keyword analysis until UTC midnight",
                    self._daily_cap)
                return False
            self._count += 1
            return True
    def chat_json(self, system_prompt: str,
                  user_payload: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        """One chat call; returns the model's JSON object or None."""
        if not self.available or not self._under_cap():
            return None
        body = {
            "model": self._model,
            "messages": [
                {"role": "system", "content": system_prompt},
                {"role": "user",
                 "content": json.dumps(user_payload, sort_keys=True)},
            ],
            "response_format": {"type": "json_object"},
            "temperature": 0,
            # V4-Flash is a hybrid thinking model; without this flag it
            # burns hundreds of reasoning tokens per call — measured
            # 2026-08-03: a 25-headline batch blew straight through the
            # timeout AND could exhaust max_tokens before the answer,
            # returning empty content. The doctrine + few-shot examples
            # in the system prompts carry the quality instead.
            "thinking": {"type": "disabled"},
            # Must fit the largest reply: a full sentiment batch is
            # ~10 tokens per headline; 1000 truncated mid-JSON on a real
            # RSS cycle (2026-08-03).
            "max_tokens": 2000,
        }
        try:
            resp = self._session.post(
                DEEPSEEK_URL, json=body, timeout=self._timeout)
        except requests.RequestException as e:
            self.last_error = (time.time(), "network")
            self._warn_once("network", "DeepSeek call failed: %s", e)
            return None
        if resp.status_code != 200:
            self.last_error = (time.time(), f"http_{resp.status_code}")
            self._warn_once(
                f"http_{resp.status_code}",
                "DeepSeek HTTP %s: %s", resp.status_code, resp.text[:200])
            return None
        try:
            parsed = self._parse_response(resp.json())
        except ValueError:
            parsed = None
        if parsed is None:
            self.last_error = (time.time(), "parse")
            self._warn_once("parse", "DeepSeek response was not the "
                            "expected JSON object — ignoring")
        else:
            self.last_ok = time.time()
        return parsed
    @staticmethod
    def _parse_response(body: Any) -> Optional[Dict[str, Any]]:
        """Extract the assistant's JSON object from a chat completion."""
        try:
            content = body["choices"][0]["message"]["content"]
        except (KeyError, IndexError, TypeError):
            return None
        try:
            obj = json.loads(content)
        except (TypeError, ValueError):
            return None
        return obj if isinstance(obj, dict) else None
 # ── Process-wide singleton ────────────────────────────────────────────
 _client: Optional[DeepSeekClient] = None
 _client_lock = threading.Lock()
 def get_llm_client() -> DeepSeekClient:
    global _client
    with _client_lock:
        if _client is None:
            settings = get_app_config().deepseek_settings
            _client = DeepSeekClient(
                api_key=deepseek_api_key(),
                model=str(settings.get("model", DEFAULT_MODEL)),
                timeout=float(settings.get("timeout_seconds",
                                           DEFAULT_TIMEOUT)),
                daily_cap=int(settings.get("daily_request_cap",
                                           DEFAULT_DAILY_CAP)),
                enabled=settings.get("enabled", True) is not False,
            )
        return _client
 def reset_llm_client() -> None:
    """Drop the singleton (tests / config reloads)."""
    global _client
    with _client_lock:
        _client = None
@@ -1,252 +0,0 @@
 ================================================================================
 MULTI-PROVIDER LLM LAYER - PLAN
 (DeepSeek for news summary, Anthropic Claude for agent panel)
 Written 2026-08-08. Plan only - no code has been changed.
 ================================================================================
 WHY
 ---
 Today every LLM feature (news enrichment + agent panel) goes through one
 hardwired client: llm_client.py -> DeepSeekClient, a module singleton reached
 via get_llm_client(). This plan describes how to route different features to
 different providers - e.g. keep DeepSeek for cheap high-volume news scoring,
 put the agent panel on Anthropic Claude for better reasoning.
 The repo already solved this problem once for market data: the providers/
 package (BaseDataProvider owns shared machinery, subclasses implement only
 _fetch_raw, a registry + one config knob selects the provider). This plan
 mirrors that proven pattern at the LLM layer, scaled down to a single module.
 BLAST RADIUS (verified by grep)
 -------------------------------
 get_llm_client() call sites:
  - news_engine.py ....... 4 sites
  - agent_panel.py ....... 3 sites (+1 timeout getattr)
  - telegram_bot.py ...... 1 site (/status health; reads private _count,
                           _count_day, _daily_cap fields)
  - test_all.py .......... 41 locations stub via  lc._client = _StubLLM(...)
 Out of scope: static/news_summary.php (own PHP DeepSeek call + key file,
 separate deployment).
 DESIGN INVARIANTS (carried over unchanged)
 ------------------------------------------
 1. Fail-safe contract: every call returns Optional[Dict]; any failure -> None;
   callers keep their non-LLM fallbacks; LLM output never gates signals.
 2. Single attempt, no retries (news cache cadence is the retry); per-provider
   UTC daily cap; warn-once logging; health stamps (last_ok, last_error).
 3. Static system prompts stay byte-identical constants - provider-neutral,
   and they map to prompt caching on BOTH providers (DeepSeek prefix cache;
   Anthropic cache_control ephemeral).
 4. Test compatibility: all 41 existing lc._client stub sites keep working
   with zero edits.
 ================================================================================
 1. llm_client.py REFACTOR  (~195 -> ~430 lines, no new files)
 ================================================================================
 1a. BaseLLMClient - shared machinery (extracted from today's DeepSeekClient)
    - __init__(api_key, model, timeout, daily_cap, enabled)
    - public: name ("deepseek"/"anthropic"), timeout property,
      usage() -> (used_today, cap)   # so telegram_bot stops reading privates
    - owns: available property, _warn_once, _under_cap() (UTC-day counter,
      lock), health stamps, and the template method:
        def chat_json(self, system_prompt, user_payload):
            if not self.available or not self._under_cap():
                return None
            try:
                return self._request(system_prompt, user_payload)  # hook
            except <provider-agnostic error mapping>:
                # stamps last_error, returns None
    - subclasses implement ONLY _request(system_prompt, payload) -> dict|None.
 1b. DeepSeekClient(BaseLLMClient) - behavior-identical move
    Current requests-based logic relocates into _request: same URL,
    thinking:disabled, response_format:json_object, temperature 0,
    max_tokens 2000, sort_keys=True payload, _parse_response.
    Review criterion: Module 19 tests pass UNMODIFIED (they patch
    client._session, which stays).
 1c. AnthropicClient(BaseLLMClient) - new, official SDK
    - Official `anthropic` Python SDK (add to requirements.txt), NOT raw
      requests. LAZY IMPORT inside __init__: package missing -> warn-once +
      client unavailable, so DeepSeek-only deployments never install it.
    - Constructor: anthropic.Anthropic(api_key=..., timeout=<configured>,
      max_retries=0)   # house rule: single attempt
    - _request:
        resp = self._client.messages.create(
            model=self._model,                  # default "claude-opus-5"
            max_tokens=4000,                    # covers thinking + JSON reply
            output_config={"effort": "low"},    # cheap structured takes
            system=[{"type": "text", "text": system_prompt,
                     "cache_control": {"type": "ephemeral"}}],
            messages=[{"role": "user",
                       "content": json.dumps(user_payload, sort_keys=True)}],
        )
        if resp.stop_reason == "refusal":       # Opus 5: HTTP 200, check first
            return None
        text = first text block in resp.content
        return self._parse_json(text)   # tolerant: strip ```json fences,
                                        # json.loads, dict check
    - DELIBERATELY ABSENT (would 400 on Claude Opus 5): temperature (removed
      on Opus 5 - do NOT copy from the DeepSeek client), budget_tokens,
      prefills, response_format.
    - Error mapping: APIConnectionError -> "network",
      APIStatusError -> "http_<code>", parse fail -> "parse"
      (same last_error kinds the /status display already understands).
    - Defaults: model claude-opus-5 (config-overridable, e.g. claude-haiku-4-5
      for ~5x cheaper), timeout 30s (Claude slower than V4-Flash), own
      daily_request_cap 500.
 1d. Registry + per-consumer routing
        _PROVIDERS = {"deepseek": DeepSeekClient, "anthropic": AnthropicClient}
        LLM_CONSUMERS = ("news", "agent_panel")
        _clients = {}     # keyed by PROVIDER name -> two consumers on the
                          # same provider share one instance and one cap
        _client = None    # kept: GLOBAL TEST OVERRIDE
        def get_llm_client(consumer="news"):
            if _client is not None:          # tests: lc._client = stub
                return _client               #   -> shadows ALL consumers
            name = get_app_config().llm_routing.get(consumer, "deepseek")
            create-once per provider, return _clients[name]
        def reset_llm_client():              # clears _client AND _clients
    Keeping _client as a documented test-override global is what makes the
    41 existing stub sites work untouched.
 ================================================================================
 2. CONFIG  (app_config.py + config.json.example)
 ================================================================================
 - New top-level "anthropic" block, mirroring "deepseek":
    {api_key, model, enabled, timeout_seconds, daily_request_cap}
  plus anthropic_api_key() resolver: ANTHROPIC_API_KEY env -> config.json -> "".
 - New top-level "llm" ROUTING block:
    {"news": "deepseek", "agent_panel": "deepseek"}
  Validation: keys within {news, agent_panel}; values within registered
  provider names. Absent block -> {} -> everything on DeepSeek (today's
  behavior; zero-migration default). AppConfig.llm_routing: Dict[str, str].
 - The target state is then PURE CONFIG - no code changes on switch day:
    "llm":       { "news": "deepseek", "agent_panel": "anthropic" },
    "anthropic": { "api_key": "", "model": "claude-opus-5", "enabled": true,
                   "timeout_seconds": 30, "daily_request_cap": 500 }
 ================================================================================
 3. CONSUMER EDITS  (mechanical, ~10 lines total)
 ================================================================================
 - news_engine.py:  4 call sites -> get_llm_client("news")
 - agent_panel.py:  3 call sites -> get_llm_client("agent_panel");
                   getattr(client, "_timeout", ...) -> public client.timeout
 - telegram_bot.py _news_health_lines: iterate the DISTINCT providers actually
  routed (news + panel); per provider print "<name>: used/cap calls today"
  via the new usage() accessor instead of private fields. DeepSeek keeps its
  "headlines scored" extra; cap/error warnings work off base-class stamps.
 - NO changes to prompts, payload builders, caches, or the panel's own
  max_panels_per_day budget - all provider-agnostic already.
 ================================================================================
 4. TESTS  (new "Module 24: LLM provider routing", ~12 tests, ~200 lines)
 ================================================================================
 Reuse _StubLLM; new _FakeAnthropic (fake messages.create returning a stub
 response object) injected as client._client.
 1. app_config: anthropic block parses/validates/defaults empty.
 2. app_config: llm routing block - valid map round-trips; unknown consumer
    key, unknown provider name, non-dict -> AppConfigError; absent -> {}.
 3. anthropic_api_key resolves env -> config.json -> empty.
 4. Base contract on BOTH subclasses (parametrized): keyless/disabled -> no
    call; daily cap blocks + UTC-roll reset; failure stamps last_error,
    success stamps last_ok.
 5. AnthropicClient parse: clean JSON -> dict; ```json-fenced -> dict;
    non-JSON -> None; stop_reason "refusal" -> None; no text block -> None.
 6. AnthropicClient request shape: NO temperature key, max_retries=0,
    cache_control present on system block (assert on captured kwargs).
 7. AnthropicClient with `anthropic` package missing (simulated) ->
    available False, chat_json None, no crash at import.
 8. Routing: no llm block -> both consumers get the SAME DeepSeek singleton
    (shared cap); routed config -> news gets DeepSeekClient while
    agent_panel gets AnthropicClient; same-provider consumers share one
    instance.
 9. Global lc._client override shadows BOTH consumers (locks the 41 legacy
    stub sites' semantics).
 10. reset_llm_client() clears both the override and the per-provider cache.
 11. End-to-end: routing agent_panel->anthropic, attach_panel reaches the
    anthropic fake (5 calls) while a news call reaches the deepseek stub.
 12. telegram _news_health_lines renders per-provider lines from usage()
    without touching private fields.
 GATE: Modules 19 and 23 must pass UNMODIFIED - the no-regression proof for
 the refactor.
 ================================================================================
 5. DOCS
 ================================================================================
 - CLAUDE.md: llm_client bullet -> provider-abstracted (BaseLLMClient;
  deepseek + anthropic registered); config.json "llm" block routes consumers;
  absent = all-DeepSeek. Add ANTHROPIC_API_KEY to env-var list. Test counts.
 - config.json.example: both new blocks.
 ================================================================================
 COMMIT SEQUENCE  (each commit green)
 ================================================================================
 1. llm_client: extract BaseLLMClient; DeepSeekClient unchanged behavior
   (+ base-contract tests)  - pure refactor, Modules 19/23 green untouched.
 2. config: anthropic block + llm consumer-routing block (+ config tests).
 3. llm_client: AnthropicClient (official SDK, lazy import) + requirements.txt
   (+ parse/shape tests).
 4. routing: per-consumer get_llm_client + health lines - consumer call-site
   edits, telegram usage() lines, routing/e2e tests.
 5. docs: CLAUDE.md + example config.
 ================================================================================
 VERIFICATION
 ================================================================================
 - python test_all.py green at every commit (~374 -> ~386).
 - Live smoke A (no behavior change): default config, main.py --panel ->
  still DeepSeek, /status unchanged.
 - Live smoke B (the goal): llm.agent_panel="anthropic" + real
  ANTHROPIC_API_KEY, run --panel -> one real Claude panel (5 requests,
  validates JSON contract + latency + refusal-free path) while a news
  refresh still hits DeepSeek; /status shows two provider lines.
 - Backtest untouched by construction (no LLM in replay path); one 30-day
  run as a formality.
 ================================================================================
 EFFORT ESTIMATE
 ================================================================================
 0 new files, 8 modified, ~520 lines, est. 3-5 hours.
  llm_client.py        base class + AnthropicClient + routing   +235
  test_all.py          Module 24                                +200
  app_config.py        anthropic + llm blocks, resolver          +55
  telegram_bot.py      per-provider health lines                 +15
  news_engine.py       consumer tags on call sites                +4
  agent_panel.py       consumer tags on call sites                +4
  config.json.example  blocks                                    +14
  requirements.txt     anthropic                                  +1
  CLAUDE.md            docs                                      +10
 ================================================================================
 KEY DECISIONS EMBEDDED IN THIS PLAN
 ================================================================================
 - Official Anthropic SDK, not raw requests (current API guidance for Python;
  lazy import keeps it optional for DeepSeek-only installs).
 - Default Claude model: claude-opus-5 ($5/$25 per MTok; a full 5-call panel
  ~ 8k in / 500 out ~ $0.05). Config-overridable to claude-haiku-4-5.
 - Two Claude gotchas handled: temperature is REMOVED on Opus 5 (sending it
  400s - must not be copied from the DeepSeek request body), and refusals
  arrive as HTTP 200 with stop_reason "refusal" (checked before parsing,
  mapped to the normal None/fallback path).
 - Caps are per-provider, not per-consumer: two consumers on one provider
  share its cap (today's semantics); splitting across two providers doubles
  total headroom naturally.
 - The panel's own max_panels_per_day budget is untouched - it sits above
  whichever provider serves it.
 - Not in scope: static/news_summary.php (own PHP client/key), streaming,
  tool use, per-consumer model overrides (add a "model" key per consumer
  later if ever needed).
 ================================================================================
@@ -0,0 +1,21 @@
 01:51:29 [WARNING] data_provider: BTC/USD 1h: last bar 2026-07-11 05:51 is 4320m old (limit: 180m)
 01:52:02 [WARNING] data_provider: BTC/USD 1h: last bar 2026-07-11 05:52 is 4320m old (limit: 180m)
 02:42:59 [WARNING] data_provider: BTC/USD 1h: last bar 2026-07-11 06:42 is 4320m old (limit: 180m)
 03:30:24 [WARNING] data_provider: BTC/USD 1h: last bar 2026-07-11 07:30 is 4320m old (limit: 180m)
 03:31:32 [WARNING] data_provider: BTC/USD 1h: last bar 2026-07-11 07:31 is 4320m old (limit: 180m)
 11:30:20 [WARNING] data_provider: BTC/USD 1h: last bar 2026-07-11 15:30 is 4320m old (limit: 180m)
 11:41:34 [WARNING] data_provider: BTC/USD 1h: last bar 2026-07-11 15:41 is 4320m old (limit: 180m)
 11:45:46 [WARNING] data_provider: BTC/USD 1h: last bar 2026-07-11 15:45 is 4320m old (limit: 180m)
 11:56:38 [WARNING] data_provider: BTC/USD 1h: last bar 2026-07-11 15:56 is 4320m old (limit: 180m)
 11:57:11 [WARNING] data_provider: BTC/USD 1h: last bar 2026-07-11 15:57 is 4320m old (limit: 180m)
 11:59:52 [WARNING] data_provider: BTC/USD 1h: last bar 2026-07-11 15:59 is 4320m old (limit: 180m)
 12:10:05 [WARNING] data_provider: BTC/USD 1h: last bar 2026-07-11 16:10 is 4320m old (limit: 180m)
 13:05:37 [WARNING] data_provider: BTC/USD 1h: last bar 2026-07-11 17:05 is 4320m old (limit: 180m)
 13:14:25 [WARNING] data_provider: BTC/USD 1h: last bar 2026-07-11 17:14 is 4320m old (limit: 180m)
 13:38:50 [WARNING] data_provider: BTC/USD 1h: last bar 2026-07-11 17:38 is 4320m old (limit: 180m)
 13:45:14 [WARNING] data_provider: BTC/USD 1h: last bar 2026-07-11 17:45 is 4320m old (limit: 180m)
 13:51:33 [WARNING] data_provider: BTC/USD 1h: last bar 2026-07-11 17:51 is 4320m old (limit: 180m)
 15:04:41 [WARNING] data_provider: BTC/USD 1h: last bar 2026-07-11 19:04 is 4320m old (limit: 180m)
 15:04:50 [WARNING] data_provider: BTC/USD 1h: last bar 2026-07-11 19:04 is 4320m old (limit: 180m)
 15:05:17 [WARNING] data_provider: BTC/USD 1h: last bar 2026-07-11 19:05 is 4320m old (limit: 180m)
 15:06:10 [WARNING] data_provider: BTC/USD 1h: last bar 2026-07-11 19:06 is 4320m old (limit: 180m)
@@ -13,6 +13,7 @@ import json
 import logging
 import sys
 from datetime import datetime, timezone
 from typing import Dict, List
 # Force UTF-8 output on Windows so box-drawing chars render
 if hasattr(sys.stdout, "reconfigure"):
@@ -20,7 +21,6 @@ if hasattr(sys.stdout, "reconfigure"):
 if hasattr(sys.stderr, "reconfigure"):
    sys.stderr.reconfigure(encoding="utf-8")
 from agent_panel import attach_panel
 from config import PlatformConfig, Timeframe, SignalStrength, XAUUSD
 from pair_signals import PAIR_PRODUCERS
 from signal_aggregator import SignalAggregator, SignalReport
@@ -162,15 +162,6 @@ def print_signal_report(report: SignalReport, detail: bool = False):
        print(f"  {Colors.YELLOW}📅{Colors.RESET} {report.event_description}")
    if report.news_description and report.news_description != "Neutral news sentiment":
        print(f"  {Colors.MAGENTA}📰{Colors.RESET} {report.news_description}")
    if report.news_summary:
        print(f"  {Colors.MAGENTA}🧠{Colors.RESET} {report.news_summary}")
    if report.panel_verdict:
        print(f"  {Colors.CYAN}🤖{Colors.RESET} AI Panel: "
              f"{Colors.BOLD}{report.panel_verdict}{Colors.RESET} "
              f"({report.panel_confidence:.0f}%) — {report.panel_rationale}")
        for t in report.panel_takes:
            print(f"     {Colors.GRAY}{t['persona']:<11} {t['stance']:<8} "
                  f"{t['note']}{Colors.RESET}")
    # ── 6. DETAIL (only with --detail) ─────────────────────────────
    if detail and report.primary_analysis:
@@ -216,11 +207,6 @@ def report_to_dict(report: SignalReport) -> dict:
        "event_description": report.event_description,
        "news_sentiment": round(report.news_sentiment, 3),
        "news_description": report.news_description,
        "news_summary": report.news_summary,
        "panel_verdict": report.panel_verdict,
        "panel_confidence": round(report.panel_confidence, 1),
        "panel_rationale": report.panel_rationale,
        "panel_takes": report.panel_takes,
        "bullish_reasons": report.bullish_reasons,
        "bearish_reasons": report.bearish_reasons,
        "warnings": report.warnings,
@@ -236,9 +222,6 @@ def report_to_dict(report: SignalReport) -> dict:
            "risk_reward": ts.risk_reward,
            "risk_pips": round(ts.risk_pips, 1),
            "reward_pips": round(ts.reward_pips, 1),
            "strategy": ts.strategy,
            "trail_arm_r": ts.trail_arm_r,
            "trail_dist_r": ts.trail_dist_r,
        }
    if report.primary_analysis:
        d["indicators"] = {
@@ -248,7 +231,7 @@ def report_to_dict(report: SignalReport) -> dict:
                "description": ind.description,
            }
            for name, ind in report.primary_analysis.indicators.items()
-            if not name.startswith("_") and ind.weight != 0
+            if not name.startswith("_")
        }
        d["key_levels"] = {
            k: round(v, 5) for k, v in report.primary_analysis.key_levels.items()
@@ -280,9 +263,6 @@ Examples:
                        help="Primary analysis timeframe (default: H1)")
    parser.add_argument("--detail", action="store_true",
                        help="Show full indicator breakdown")
    parser.add_argument("--panel", action="store_true",
                        help="Run the LLM agent panel on the result "
                             "(advisory only; needs a DeepSeek key)")
    parser.add_argument("--json", action="store_true",
                        help="Output as JSON")
    parser.add_argument("--verbose", "-v", action="store_true",
@@ -338,11 +318,6 @@ def run_live(args, config: PlatformConfig):
    aggregator = SignalAggregator(config)
    report = aggregator.analyze_pair(symbol=config.symbol)
    # Explicit user intent: force bypasses the enabled/surface/quality
    # toggles, but the daily panel budget and LLM cap still apply.
    if getattr(args, "panel", False):
        attach_panel(report, surface="cli", force=True)
    if args.json:
        print(json.dumps(report_to_dict(report), indent=2))
        return
@@ -8,87 +8,22 @@ Key principle: High-impact news events override technical signals.
 A perfect technical setup can be destroyed by an unexpected NFP print.
 """
 import hashlib
 import html
 import logging
 import re
 import threading
 import time
 import xml.etree.ElementTree as ET
 from dataclasses import dataclass, field
-from datetime import datetime, timezone
+from datetime import datetime, timedelta, timezone
 from email.utils import parsedate_to_datetime
 from typing import Dict, List, Optional, Tuple
 import requests
 from config import NewsConfig
 from llm_client import get_llm_client
 logger = logging.getLogger(__name__)
 # ── DeepSeek prompts ─────────────────────────────────────────────────
 # STATIC constants on purpose: DeepSeek prices a repeated byte-identical
 # prompt prefix at the cache-hit rate (~50x cheaper), so nothing here may
 # be interpolated or timestamped. All variable content travels in the
 # user payload. LLM output is display/warning-only — it never gates
 # signals (see NewsConfig.sentiment_affects_signals).
 _LLM_SENTIMENT_SYSTEM = """\
 You are a forex/gold news analyst. You receive JSON:
 {"headlines": ["...", ...]}
 For EACH headline, in the same order, score:
 - "s": overall market sentiment of the headline, -1.0 (very bearish)
  to 1.0 (very bullish), 0.0 if irrelevant or neutral.
 - "xau": the effect on the GOLD (XAU/USD) price, -1.0 (gold down)
  to 1.0 (gold up), 0.0 if irrelevant.
 Gold doctrine: hawkish Fed / rate hikes / rising real yields / strong
 dollar are gold-bearish; easing / rate cuts / safe-haven flows /
 geopolitical stress / weak dollar are gold-bullish.
 Handle negation correctly ("no rate hike" is not hawkish).
 Reply with ONLY JSON: {"scores": [[s, xau], ...]} — exactly one
 [s, xau] pair per input headline, in the same order, nothing else.
 Examples:
 "Fed signals faster rate hikes as inflation stays hot" -> [0.4, -0.7]
 "Gold surges to record high on safe-haven demand" -> [0.8, 0.9]
 "Dollar slips after soft jobs report; Treasury yields fall" -> [-0.4, 0.6]
 """
 _LLM_EVENT_SYSTEM = """\
 You are a forex/gold analyst. You receive one released economic event as
 JSON: {"event", "currency", "actual", "forecast", "previous",
 "vs_forecast"}. "vs_forecast" states whether the actual print came in
 above or below the forecast; it is computed programmatically — trust it
 over your own comparison of the numbers. If it is "unknown", compare
 actual and forecast yourself.
 Decide the likely effect of this RELEASE on the GOLD (XAU/USD) price.
 A stronger-than-forecast USD economy or a hawkish implication is usually
 gold-bearish; weaker data or a dovish implication is usually
 gold-bullish.
 Inverted metrics — for unemployment rate and unemployment/jobless
 claims a LOWER number means a STRONGER labor market: below forecast =
 strong economy = usually gold-bearish; above forecast = weak economy =
 usually gold-bullish.
 Example: {"event": "Unemployment Claims", "actual": "202K", "forecast":
 "217K", "vs_forecast": "below forecast"} ->
 {"gold_direction": "bearish", "reason": "fewer claims show strong labor market"}
 Reply with ONLY JSON:
 {"gold_direction": "bullish"|"bearish"|"neutral", "reason": "<max 10 words>"}
 """
 _LLM_SUMMARY_SYSTEM = """\
 You are a forex/gold market-context writer. You receive JSON with recent
 "headlines" and today's high-impact "events" (released: true means the
 number is out). Write a 1-2 sentence plain-English summary of the
 current macro backdrop for a GOLD (XAU/USD) trader: the dominant theme
 and its likely pull on gold. No advice, no hedging boilerplate, max 45
 words.
 Reply with ONLY JSON: {"summary": "..."}
 """
 _RELEASE_VALUE_RE = re.compile(r"(-?\d+(?:\.\d+)?)\s*([kmbt])?", re.IGNORECASE)
 _RELEASE_MULTIPLIERS = {"k": 1e3, "m": 1e6, "b": 1e9, "t": 1e12}
@@ -104,44 +39,6 @@ def _parse_release_value(raw: Optional[str]) -> Optional[float]:
    return float(match.group(1)) * multiplier
 _DESC_MAX_CHARS = 500
 _MAX_LINK_CHARS = 2048
 def _clean_html_text(raw: Optional[str],
                     limit: int = _DESC_MAX_CHARS) -> Optional[str]:
    """RSS <description> → plain text: unescape entities, strip tags,
    collapse whitespace, truncate. Unescape and strip both run twice
    because feeds routinely double-encode (&amp;lt;p&amp;gt; wrapping
    real markup) — the second unescape can reveal fresh tags."""
    if not raw:
        return None
    text = html.unescape(raw)
    text = re.sub(r"<[^>]+>", " ", text)
    text = html.unescape(text)
    text = re.sub(r"<[^>]+>", " ", text)
    text = re.sub(r"\s+", " ", text).strip()
    if not text:
        return None
    if len(text) > limit:
        text = text[:limit].rstrip() + "…"
    return text
 def _safe_link(raw: Optional[str]) -> Optional[str]:
    """Article URL for display: http(s) only (an RSS <link> is untrusted
    and lands in an href), bounded length (Google News redirect URLs run
    1-2 KB; anything past that is bloat, not a link)."""
    if not raw:
        return None
    link = raw.strip()
    if not link.lower().startswith(("http://", "https://")):
        return None
    if len(link) > _MAX_LINK_CHARS:
        return None
    return link
@dataclass
 class EconomicEvent:
    """Single economic calendar event."""
@@ -194,11 +91,6 @@ class NewsSentiment:
    # Per-currency score: monetary-policy keywords flip sign for XAU
    # (hawkish is dollar-bullish but gold-bearish).
    sentiment_by_currency: Dict[str, float] = field(default_factory=dict)
    # Display-only enrichment (dashboard detail dialog). Optional with
    # defaults so existing constructor calls keep working.
    link: Optional[str] = None         # article URL (Google News = redirect)
    description: Optional[str] = None  # plain text, tags stripped, ≤500 chars
    publisher: Optional[str] = None    # Google News <source> text, else None
@dataclass
@@ -212,7 +104,6 @@ class FundamentalContext:
    news_headlines: List[NewsSentiment] = field(default_factory=list)
    event_impact_on_pairs: Dict[str, str] = field(default_factory=dict)
    calendar_available: bool = False  # True if live calendar data was fetched
    llm_summary: str = ""  # DeepSeek market-context blurb (display-only)
 class NewsEngine:
@@ -293,25 +184,6 @@ class NewsEngine:
    _headlines_cache: Tuple[float, List[NewsSentiment]] = (0.0, [])
    _EMPTY_RESULT_TTL = 60.0  # retry sooner after a failed/empty fetch
    # DeepSeek result caches. Headlines and releases are immutable, so a
    # scored one never needs re-scoring; bounded by dropping the oldest
    # insertion. _llm_sent_cache is only touched under _cache_lock (its
    # writer runs inside _cached_fetch); the other two take the lock
    # around cache access but keep HTTP calls outside it.
    _llm_sent_cache: Dict[str, Tuple[float, float]] = {}   # sha1 → (s, xau)
    _llm_event_cache: Dict[Tuple[str, str, str], str] = {}
    _llm_summary_cache: Tuple[float, str] = (0.0, "")
    _LLM_SENT_CACHE_MAX = 400
    _LLM_EVENT_CACHE_MAX = 100
    # Per-source fetch health for status displays (telegram /status).
    # name → {"ok": ts|None, "err": ts|None, "detail": str, "extra": str}.
    # All writers run under _cache_lock (fetches happen inside
    # _cached_fetch); readers render without the lock — worst case a
    # display line is one cycle stale. A source is "failing" when its
    # last error is newer than its last success.
    _source_health: Dict[str, dict] = {}
    def __init__(self, config: NewsConfig):
        self.config = config
        self._session = requests.Session()
@@ -391,12 +263,6 @@ class NewsEngine:
        except Exception as e:
            logger.warning(f"News sentiment fetch failed: {e}")
        # 3. LLM market-context summary (display-only)
        try:
            context.llm_summary = self._llm_market_summary(context)
        except Exception as e:
            logger.debug(f"LLM market summary failed: {e}")
        return context
    @staticmethod
@@ -415,20 +281,14 @@ class NewsEngine:
        """
        base, quote = self._parse_pair(pair_symbol)
-        # Check upcoming high-impact events. Grade from the SOONEST
+        # Check upcoming high-impact events
        # matching event, not the first listed — an unsorted feed could
        # otherwise report "caution" for a far event while a nearer one
        # warrants "danger".
        soonest = None
        for event in context.events_upcoming_1h:
            if event.is_high_impact and event.currency in (base, quote):
-                if soonest is None or event.minutes_until < soonest.minutes_until:
+                mins = event.minutes_until
                    soonest = event
        if soonest is not None:
            mins = soonest.minutes_until
                if mins <= self.config.pre_event_buffer_minutes:
-                return "danger", f"{soonest.currency} {soonest.event} in {mins:.0f}min"
+                    return "danger", f"{event.currency} {event.event} in {mins:.0f}min"
-            return "caution", f"{soonest.currency} {soonest.event} in {mins:.0f}min"
+                else:
                    return "caution", f"{event.currency} {event.event} in {mins:.0f}min"
        # Check for recent high-impact releases. A surprise print (actual
        # away from forecast) keeps the market unstable longer than an
@@ -444,17 +304,10 @@ class NewsEngine:
                )
                if event.minutes_until > -window:
                    if surprised:
-                        desc = (
+                        return "caution", (
                            f"{event.currency} {event.event} released — {surprise} surprise "
                            f"(actual {event.actual} vs forecast {event.forecast})"
                        )
                        # LLM enrichment touches the DESCRIPTION only —
                        # `surprise` is numeric-direction and does not know
                        # whether a beat is gold-bullish; the LLM does.
                        gold_dir = self._llm_event_direction(event)
                        if gold_dir:
                            desc += f" — gold {gold_dir}"
                        return "caution", desc
                    return "caution", f"{event.currency} {event.event} just released"
        return "clear", "No significant events"
@@ -488,24 +341,6 @@ class NewsEngine:
        return net, desc
    def get_week_events(
        self, currencies: Optional[List[str]] = None
    ) -> List[EconomicEvent]:
        """Full cached calendar week (past and upcoming), filtered to
        `currencies` (default: tracked_currencies), sorted by time.
        Read-only accessor for display surfaces (the web dashboard) —
        rides the same class-level cache as signal scans, so it adds no
        network I/O. No production signal path calls this.
        """
        if currencies is None:
            currencies = self.config.tracked_currencies
        events = self._cached_fetch("_calendar_cache", self._fetch_calendar_raw)
        return sorted(
            (e for e in events if e.currency in currencies),
            key=lambda e: e.time,
        )
    # ── Private Methods ────────────────────────────────────────────
    # Maps country names in MyFXBook titles to currency codes
@@ -515,36 +350,6 @@ class NewsEngine:
        "canada": "CAD", "new zealand": "NZD",
    }
    @classmethod
    def _record_source(cls, name: str, ok: bool, detail: str = "",
                       extra: str = "") -> None:
        """Track one dependency's fetch outcome for status displays.
        Logs state TRANSITIONS so the log file carries the story without
        a line every 10-min cycle: first failure WARNING (with detail),
        repeat failures DEBUG, recovery INFO."""
        h = cls._source_health.setdefault(
            name, {"ok": None, "err": None, "detail": "", "extra": ""})
        was_failing = h["err"] is not None and (
            h["ok"] is None or h["err"] > h["ok"])
        if ok:
            h["ok"] = time.time()
            h["extra"] = extra
            if was_failing:
                logger.info("Source recovered: %s", name)
        else:
            h["err"] = time.time()
            h["detail"] = detail
            if was_failing:
                logger.debug("Source still failing: %s (%s)", name, detail)
            else:
                logger.warning("Source failing: %s (%s)", name, detail)
    @staticmethod
    def source_is_failing(entry: Optional[dict]) -> bool:
        """True when the entry's last error is newer than its last ok."""
        return bool(entry and entry["err"] is not None and
                    (entry["ok"] is None or entry["err"] > entry["ok"]))
    def _cached_fetch(self, cache_attr: str, fetch_fn) -> list:
        """Return cached data for `cache_attr` or refetch when the TTL
        expired. Successful results live for context_cache_ttl_seconds;
@@ -577,27 +382,14 @@ class NewsEngine:
        Returns an empty list (with a log warning) if unavailable —
        we never generate fake events since that causes constant false alarms.
        """
-        sources = (
+        for fetcher in (self._try_fetch_calendar_api, self._try_fetch_myfxbook_rss):
            (self._try_fetch_calendar_api, "calendar:forexfactory",
             "ForexFactory"),
            (self._try_fetch_myfxbook_rss, "calendar:myfxbook", "MyFXBook"),
        )
        for fetcher, src, label in sources:
            try:
                events = fetcher()
                if events:
                    self._record_source(src, True)
                    self._record_source(
                        "calendar", True,
                        extra=f"{len(events)} events via {label}")
                    return events
                self._record_source(src, False, "no events returned")
            except Exception as e:
                self._record_source(
                    src, False, f"{type(e).__name__}: {e}")
                logger.debug(f"Calendar fetch exception ({fetcher.__name__}): {e}")
        self._record_source("calendar", False, "all sources failed")
        logger.info(
            "Economic calendar data unavailable. "
            "Event risk checks will be skipped for this cycle."
@@ -744,16 +536,11 @@ class NewsEngine:
        """Fetch and analyze news headlines for sentiment."""
        headlines = []
        seen_titles = set()
        ok_feeds = 0
        for feed_url in self.config.rss_feeds:
            host = feed_url.split("/")[2] if "//" in feed_url else feed_url
            kept_before = len(headlines)
            try:
                resp = self._session.get(feed_url, timeout=10)
                if resp.status_code != 200:
                    self._record_source(
                        f"rss:{host}", False, f"HTTP {resp.status_code}")
                    continue
                root = ET.fromstring(resp.content)
@@ -770,8 +557,8 @@ class NewsEngine:
                        pub_date_el.text if pub_date_el is not None else None
                    )
-                    # Only recent headlines (configurable: headline_max_age_seconds)
+                    # Only recent headlines (last 24h)
-                    if pub_date and (datetime.now(timezone.utc) - pub_date).total_seconds() > self.config.headline_max_age_seconds:
+                    if pub_date and (datetime.now(timezone.utc) - pub_date).total_seconds() > 86400:
                        continue
                    # Dedup within and across feeds so a syndicated story
@@ -786,21 +573,6 @@ class NewsEngine:
                    currencies = self._extract_currencies(title)
                    if currencies:
                        link_el = item.find("link")
                        desc_el = item.find("description")
                        src_el = item.find("source")
                        # itertext() survives CDATA and stray child
                        # elements where .text stops at the first child.
                        desc = _clean_html_text(
                            "".join(desc_el.itertext())
                            if desc_el is not None else None)
                        # Google News descriptions are often just the
                        # headline again inside an anchor — worthless.
                        if desc and desc.strip() == title.strip():
                            desc = None
                        publisher = None
                        if src_el is not None:
                            publisher = (src_el.text or "").strip() or None
                        headlines.append(NewsSentiment(
                            headline=title,
                            source=feed_url,
@@ -812,32 +584,11 @@ class NewsEngine:
                                cur: self._analyze_headline_sentiment(title, cur)
                                for cur in currencies
                            },
                            link=_safe_link(
                                link_el.text if link_el is not None else None),
                            description=desc,
                            publisher=publisher,
                        ))
                ok_feeds += 1
                self._record_source(
                    f"rss:{host}", True,
                    extra=f"{len(headlines) - kept_before} kept")
            except Exception as e:
                self._record_source(
                    f"rss:{host}", False, f"{type(e).__name__}: {e}")
                logger.debug(f"Failed to fetch RSS from {feed_url}: {e}")
        self._record_source(
            "headlines", ok_feeds > 0,
            detail="all feeds failed" if ok_feeds == 0 else "",
            extra=(f"{len(headlines)} headlines from "
                   f"{ok_feeds}/{len(self.config.rss_feeds)} feeds"))
        try:
            self._apply_llm_sentiment(headlines)
        except Exception as e:
            logger.debug(f"LLM sentiment failed, keyword scores stand: {e}")
        return headlines
    def _analyze_headline_sentiment(
@@ -879,142 +630,6 @@ class NewsEngine:
        return (bullish_score - bearish_score) / total
    # ── DeepSeek enrichment (display/warning-only, keyword fallback) ──
    def _llm_headline_scores(
        self, texts: List[str]
    ) -> Optional[List[Tuple[float, float]]]:
        """One batched DeepSeek call → [(overall, xau), ...] aligned with
        `texts`, or None on any failure/mismatch (caller keeps keyword
        scores). Tests stub this method directly."""
        result = get_llm_client().chat_json(
            _LLM_SENTIMENT_SYSTEM, {"headlines": texts})
        if not result:
            return None
        scores = result.get("scores")
        if not isinstance(scores, list) or len(scores) != len(texts):
            return None
        out = []
        for item in scores:
            try:
                s = max(-1.0, min(1.0, float(item[0])))
                x = max(-1.0, min(1.0, float(item[1])))
            except (TypeError, KeyError, ValueError, IndexError):
                return None
            out.append((s, x))
        return out
    def _apply_llm_sentiment(self, headlines: List[NewsSentiment]) -> None:
        """Overwrite keyword sentiment on `headlines` with LLM scores.
        Runs inside _cached_fetch, i.e. UNDER _cache_lock — cache access
        is direct and the (rare) HTTP call blocks sibling scans at most
        once per cache window, matching the existing shared-fetch design.
        Only headlines missing from the cache are sent; a failed batch
        changes nothing.
        """
        if not headlines or not get_llm_client().available:
            return
        cache = NewsEngine._llm_sent_cache
        keyed = [(hashlib.sha1(h.headline.encode()).hexdigest(), h)
                 for h in headlines]
        # Cap the batch so the reply fits the client's max_tokens — the
        # overflow stays keyword-scored this cycle and, still uncached,
        # goes out with the next window's batch.
        new = [(k, h) for k, h in keyed if k not in cache][:25]
        if new:
            scores = self._llm_headline_scores([h.headline for _, h in new])
            if scores is not None:
                for (k, _), sc in zip(new, scores):
                    cache[k] = sc
                while len(cache) > self._LLM_SENT_CACHE_MAX:
                    cache.pop(next(iter(cache)))
        for k, h in keyed:
            sc = cache.get(k)
            if sc is None:
                continue
            overall, xau = sc
            h.sentiment_score = overall
            h.sentiment_by_currency = {
                cur: (xau if cur == "XAU" else overall)
                for cur in h.currencies_mentioned
            }
    def _llm_event_direction(self, event: EconomicEvent) -> Optional[str]:
        """Gold direction for a released surprise, e.g. "bearish: strong
        jobs lift yields". None → caller keeps the numeric description.
        Cached per release; failures are NOT cached (retry next scan).
        The actual-vs-forecast comparison is precomputed and sent as
        "vs_forecast": the 2026-08-06 validation found the model reads
        close K/M-suffixed pairs backwards ~11% of the time (e.g. claims
        202K vs 217K called "higher than forecast") when left to compare
        the raw numbers itself."""
        key = (event.event, event.currency, str(event.actual or ""))
        with NewsEngine._cache_lock:
            if key in NewsEngine._llm_event_cache:
                return NewsEngine._llm_event_cache[key] or None
        client = get_llm_client()
        if not client.available:
            return None
        vs_forecast = {
            "positive": "above forecast",
            "negative": "below forecast",
            "inline": "in line with forecast",
        }.get(event.surprise or "", "unknown")
        result = client.chat_json(_LLM_EVENT_SYSTEM, {
            "event": event.event,
            "currency": event.currency,
            "actual": event.actual,
            "forecast": event.forecast,
            "previous": event.previous,
            "vs_forecast": vs_forecast,
        })
        if not result:
            return None
        direction = result.get("gold_direction")
        if direction not in ("bullish", "bearish", "neutral"):
            return None
        reason = str(result.get("reason", "")).strip()[:80]
        text = "" if direction == "neutral" else (
            f"{direction}: {reason}" if reason else direction)
        with NewsEngine._cache_lock:
            NewsEngine._llm_event_cache[key] = text
            while len(NewsEngine._llm_event_cache) > self._LLM_EVENT_CACHE_MAX:
                NewsEngine._llm_event_cache.pop(
                    next(iter(NewsEngine._llm_event_cache)))
        return text or None
    def _llm_market_summary(self, context: FundamentalContext) -> str:
        """1-2 sentence macro blurb for alerts; "" when unavailable.
        Cached (success OR failure) for the context-cache TTL so at most
        one call is made per window."""
        with NewsEngine._cache_lock:
            ts, cached = NewsEngine._llm_summary_cache
            if time.time() - ts < self.config.context_cache_ttl_seconds:
                return cached
        high_impact = [e for e in context.events_today if e.is_high_impact]
        if not context.news_headlines and not high_impact:
            return ""
        client = get_llm_client()
        if not client.available:
            return ""
        result = client.chat_json(_LLM_SUMMARY_SYSTEM, {
            "headlines": [h.headline for h in context.news_headlines[:15]],
            "events": [
                {"event": e.event, "currency": e.currency,
                 "actual": e.actual, "forecast": e.forecast,
                 "released": not e.is_upcoming}
                for e in high_impact[:10]
            ],
        })
        summary = ""
        if result:
            summary = str(result.get("summary", "")).strip()[:300]
        with NewsEngine._cache_lock:
            NewsEngine._llm_summary_cache = (time.time(), summary)
        return summary
    def _extract_currencies(self, headline: str) -> List[str]:
        """Extract mentioned currencies from a headline."""
        headline_lower = headline.lower()
@@ -1035,20 +650,11 @@ class NewsEngine:
        if not date_str:
            return None
        # RFC 822/2822 forms, with or without seconds, numeric offsets AND
        # named zones (GMT, EST/EDT, PST…). The old strptime "%Z" path
        # accepted ANY abbreviation but attached no offset, so e.g. an EST
        # pubDate was silently stamped UTC — a 5-hour error.
        try:
            dt = parsedate_to_datetime(date_str.strip())
            if dt is not None:
                if dt.tzinfo is None:   # unknown zone name — assume UTC
                    return dt.replace(tzinfo=timezone.utc)
                return dt.astimezone(timezone.utc)
        except (TypeError, ValueError):
            pass
        formats = [
            "%a, %d %b %Y %H:%M:%S %z",   # RFC 822 with numeric tz offset
            "%a, %d %b %Y %H:%M:%S %Z",   # RFC 822 with tz abbreviation (GMT/UTC)
            "%a, %d %b %Y %H:%M %Z",      # RFC 822 without seconds (MyFXBook)
            "%a, %d %b %Y %H:%M %z",      # RFC 822 without seconds, numeric offset
            "%Y-%m-%dT%H:%M:%S%z",         # ISO 8601 with offset
            "%Y-%m-%d %H:%M:%S",           # ISO 8601 naive (assume UTC)
        ]
@@ -1,854 +0,0 @@
 """
 ForexBot2 — Oanda Automated Trading Daemon
 ============================================
 Autonomous trading process for XAU/USD on an Oanda practice account.
 Runs candle-boundary scan loops (M5/M15/H1/H4), executes trades via
 the Oanda v20 API, and manages trailing stops at bar close — matching
 the backtested bar-close ratchet model.
 Book-parity notes (matching backtest/gpu_backtest.py):
 - No broker-side take-profit: in the trailing model TP1/TP2 are
  advisory; the only exits are SL (broker), TRAIL (daemon ratchet),
  and TIMEOUT (max_hold_bars). A hard TP would cap winners the trail
  lets run and diverge the live book from the backtest.
 - Entry cooldown is 5 bars per timeframe (the backtester's only
  sequential rule) — not a fingerprint block.
 - The trail ratchets at bar CLOSE only, and each closed bar is
  processed exactly once (last_bar_ts guard), so weekends and feed
  lag can't double-count bars or inflate the hold counter.
 Divergence from the backtest book (accepted for demo safety): one
 position total. While a trade is open, other signals are logged and
 skipped, so skipped entries can be compared against the book later.
 Usage:  python oanda_daemon.py
 Config: env vars OANDA_ACCOUNT_ID, OANDA_API_TOKEN, OANDA_BASE_URL
        or config.json "oanda" section (env wins).
 """
 import asyncio
 import json
 import logging
 import os
 import signal
 import sys
 import threading
 from concurrent.futures import ThreadPoolExecutor
 from datetime import datetime, timezone
 from logging.handlers import TimedRotatingFileHandler
 from typing import Dict, Optional
 import pandas as pd
 from app_config import (
    get_app_config, oanda_account_id, oanda_api_token, oanda_base_url,
 )
 from config import PAIR_INFO, PlatformConfig, Timeframe, XAUUSD
 from signal_aggregator import SignalAggregator, SignalReport, SignalStrength
 from pair_signals import PAIR_PRODUCERS
 from providers.oanda import OandaDataProvider
 from oanda_executor import OandaExecutor, OandaAPIError
 logger = logging.getLogger(__name__)
 # ── Constants ──────────────────────────────────────────────────────────
 SYMBOL = XAUUSD
 INSTRUMENT = "XAU_USD"
 SCAN_CONFIRMATIONS = {
    Timeframe.M5:  [Timeframe.M15, Timeframe.H1],
    Timeframe.M15: [Timeframe.M5, Timeframe.H1],
    Timeframe.H1:  [Timeframe.M15, Timeframe.H4],
    Timeframe.H4:  [Timeframe.H1, Timeframe.D1],
 }
 TF_INTERVAL_MINUTES = {
    Timeframe.M5: 5, Timeframe.M15: 15, Timeframe.M30: 30,
    Timeframe.H1: 60, Timeframe.H4: 240, Timeframe.D1: 1440,
 }
 SCAN_TIMEFRAMES = [Timeframe.M5, Timeframe.M15, Timeframe.H1, Timeframe.H4]
 QUALITY_RANK = {"A+": 4, "A": 3, "B": 2, "C": 1, "D": 0, "F": -1}
 # Matches the backtester's --cooldown-bars default (the only sequential
 # rule in the replay): no new entry within 5 bars of the previous entry
 # on the same timeframe.
 COOLDOWN_BARS = 5
 # How often the heartbeat confirms the process is alive and the account
 # is still reachable — independent of the candle-boundary loops, so a
 # wedged scan or mid-run credential expiry still surfaces in the log.
 HEARTBEAT_SECONDS = 3600
 STATE_FILE = os.path.join(
    os.path.dirname(os.path.abspath(__file__)), "oanda_state.json")
 # ── Config resolution (env → config.json → default) ──────────────────
 def _oanda_setting_int(env_key: str, json_key: str, default: int) -> int:
    env = os.environ.get(env_key)
    if env:
        try:
            return int(env)
        except ValueError:
            raise SystemExit(
                f"{env_key}={env!r} is not an integer — refusing to start")
    try:
        return int(get_app_config().oanda_settings.get(json_key, default))
    except (TypeError, ValueError):
        raise SystemExit(
            f"config.json oanda.{json_key} is not an integer — "
            f"refusing to start")
 def _oanda_setting_str(env_key: str, json_key: str, default: str) -> str:
    return (os.environ.get(env_key)
            or str(get_app_config().oanda_settings.get(json_key, default)))
 # ── Logging setup ──────────────────────────────────────────────────────
 def _setup_logging(level: str = "INFO") -> None:
    log_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "logs")
    os.makedirs(log_dir, exist_ok=True)
    log_file = os.path.join(log_dir, "daemon.log")
    fmt = logging.Formatter(
        "%(asctime)s [%(levelname)s] %(name)s: %(message)s",
        datefmt="%Y-%m-%d %H:%M:%S")
    file_handler = TimedRotatingFileHandler(
        log_file, when="h", interval=3, backupCount=240, utc=True)
    file_handler.setFormatter(fmt)
    console_handler = logging.StreamHandler(sys.stdout)
    console_handler.setFormatter(fmt)
    logging.basicConfig(
        level=getattr(logging, level.upper(), logging.INFO),
        handlers=[file_handler, console_handler],
        force=True)
 # ── Boundary timing (same as telegram_bot.py) ─────────────────────────
 def _seconds_until_boundary(interval_minutes: int, buffer: int = 10) -> float:
    now = datetime.now(timezone.utc)
    total_minutes = now.hour * 60 + now.minute
    elapsed_minutes = total_minutes % interval_minutes
    elapsed_seconds = elapsed_minutes * 60 + now.second + now.microsecond / 1e6
    interval_seconds = interval_minutes * 60
    remaining = interval_seconds - elapsed_seconds + buffer
    # NOTE: remaining > buffer always (elapsed < interval by construction);
    # a call at the exact boundary instant targets the NEXT boundary —
    # steady-state loops wake at boundary+buffer, so only a process
    # started in the boundary microsecond skips one scan (2026-08-04
    # audit I42; the old `remaining <= buffer` guard was unreachable).
    return remaining
 # ── Trade state persistence ───────────────────────────────────────────
 def _load_state() -> Dict:
    if os.path.exists(STATE_FILE):
        try:
            with open(STATE_FILE, encoding="utf-8") as fh:
                return json.load(fh)
        except (OSError, json.JSONDecodeError):
            logger.warning("Could not load state file, starting fresh")
    return {"trades": {}, "last_entries": {}}
 def _save_state(state: Dict) -> None:
    try:
        tmp = STATE_FILE + ".tmp"
        with open(tmp, "w", encoding="utf-8") as fh:
            json.dump(state, fh, indent=2)
        os.replace(tmp, STATE_FILE)
    except OSError:
        logger.exception("Failed to save state file")
 # ── Trading Daemon ─────────────────────────────────────────────────────
 class TradingDaemon:
    def __init__(self):
        account_id = oanda_account_id()
        api_token = oanda_api_token()
        base_url = oanda_base_url()
        lot_size = _oanda_setting_int("OANDA_LOT_SIZE", "lot_size", 1)
        self._quality_gate = _oanda_setting_str(
            "OANDA_QUALITY_GATE", "quality_gate", "C").strip()
        if self._quality_gate not in QUALITY_RANK:
            # Fail CLOSED: an unrecognized gate must never silently fall
            # back to the most permissive rank (QUALITY_RANK.get default).
            raise SystemExit(
                f"OANDA_QUALITY_GATE/quality_gate {self._quality_gate!r} "
                f"is not one of {sorted(QUALITY_RANK)} — refusing to start")
        self._max_hold_bars = _oanda_setting_int(
            "OANDA_MAX_HOLD_BARS", "max_hold_bars", 300)
        self._price_precision = PAIR_INFO[SYMBOL]["price_precision"]
        if not account_id or not api_token:
            msg = (
                "Missing Oanda credentials. Set OANDA_ACCOUNT_ID and "
                "OANDA_API_TOKEN env vars or config.json oanda section.")
            logger.critical(msg)
            raise SystemExit(msg)
        self._provider = OandaDataProvider(
            PlatformConfig(), api_token, base_url)
        self._executor = OandaExecutor(
            account_id, api_token, base_url, lot_size,
            price_precision=self._price_precision)
        self._thread_pool = ThreadPoolExecutor(max_workers=4)
        self._state = _load_state()
        self._state.setdefault("trades", {})
        self._state.setdefault("last_entries", {})
        self._state_lock = threading.Lock()
        # Serializes the position-check + order-placement critical section
        # across concurrent TF loops (all four fire at aligned boundaries).
        self._entry_lock = threading.Lock()
        self._running = True
    # ── Main entry point ───────────────────────────────────────────
    async def run(self) -> None:
        loop = asyncio.get_running_loop()
        loop.set_exception_handler(self._asyncio_exception_handler)
        for sig in (signal.SIGINT, signal.SIGTERM):
            loop.add_signal_handler(sig, self._request_shutdown)
        logger.info("=== Oanda Trading Daemon starting ===")
        logger.info(
            "Quality gate: %s | Max hold: %d bars | Cooldown: %d bars | "
            "Timeframes: %s",
            self._quality_gate, self._max_hold_bars, COOLDOWN_BARS,
            "/".join(tf.name for tf in SCAN_TIMEFRAMES))
        # Credentials first: with a bad token every later call fails, so
        # refuse to start before touching positions or spawning loops.
        self._verify_account_access()
        self._discover_existing_positions()
        tasks = []
        for tf in SCAN_TIMEFRAMES:
            interval = TF_INTERVAL_MINUTES[tf]
            tasks.append(asyncio.create_task(
                self._candle_loop(tf, interval)))
        tasks.append(asyncio.create_task(self._heartbeat_loop()))
        logger.info("Candle-boundary loops started")
        try:
            await asyncio.gather(*tasks)
        except asyncio.CancelledError:
            pass
        finally:
            self._shutdown()
    def _verify_account_access(self) -> None:
        """Boot-time credential check. A 4xx here means the token/account
        id is wrong — that never fixes itself, so refuse to start rather
        than run a daemon that can't place or manage trades. A connection
        failure or 5xx is a transient outage: log it and continue, since
        broker-side stops already protect any open position and the
        hourly heartbeat will keep reporting account health."""
        try:
            self._executor.get_account_summary()
        except OandaAPIError as e:
            if 400 <= e.status_code < 500:
                logger.critical(
                    "Oanda rejected the account credentials (HTTP %d): "
                    "%s — refusing to start", e.status_code, e)
                raise SystemExit(
                    f"Oanda rejected the account credentials "
                    f"(HTTP {e.status_code}): {e}")
            logger.error(
                "Failed to fetch account summary at boot (HTTP %d): %s",
                e.status_code, e)
    def _asyncio_exception_handler(self, loop, context) -> None:
        exc = context.get("exception")
        if exc is not None:
            logger.critical(
                "Uncaught asyncio exception: %s",
                context.get("message", ""), exc_info=exc)
        else:
            logger.critical(
                "Uncaught asyncio error: %s", context.get("message", ""))
    def _request_shutdown(self) -> None:
        logger.info("Shutdown requested")
        self._running = False
        for task in asyncio.all_tasks():
            task.cancel()
    def _shutdown(self) -> None:
        with self._state_lock:
            _save_state(self._state)
        self._thread_pool.shutdown(wait=False)
        logger.info("=== Oanda Trading Daemon stopped ===")
    # ── Position discovery (restart recovery) ──────────────────────
    def _discover_existing_positions(self) -> None:
        self._reconcile_positions("boot discovery")
    def _reconcile_positions(self, reason: str) -> None:
        """Re-sync tracked state with the broker's open trades. Safe to
        call mid-run (lock-guarded); used at boot and whenever a fill or
        placement error suggests state and broker may have diverged."""
        try:
            open_trades = self._executor.get_open_trades(INSTRUMENT)
        except OandaAPIError:
            logger.exception(
                "Failed to query open trades (%s) — reconcile skipped",
                reason)
            return
        live_ids = {t["id"] for t in open_trades}
        with self._state_lock:
            saved = self._state.get("trades", {})
            stale = [tid for tid in saved if tid not in live_ids]
            for tid in stale:
                logger.info(
                    "Stale trade %s in state file (closed on Oanda), "
                    "removing", tid)
                del saved[tid]
            for trade in open_trades:
                tid = trade["id"]
                if tid in saved:
                    logger.info(
                        "Recovered trade %s from state: %s %s @ %.2f, SL %.2f",
                        tid, saved[tid].get("direction"), INSTRUMENT,
                        saved[tid].get("entry"), saved[tid].get("current_sl"))
                    continue
                direction = ("BUY" if float(trade.get("currentUnits", 0)) > 0
                             else "SELL")
                entry = float(trade.get("price", 0))
                sl_order = trade.get("stopLossOrder") or {}
                sl_price = sl_order.get("price")
                if sl_price is None:
                    # No broker-side stop at all: risk is undefined, so the
                    # trailing model cannot manage this trade. Scream — a
                    # naked position needs a human (or a policy) to set a
                    # stop; fabricating one here would invent trading
                    # behavior the books never validated.
                    logger.critical(
                        "Adopted trade %s (%s @ %.2f) has NO STOP LOSS on "
                        "the broker — it will NOT be trailed. Set a stop "
                        "manually or close it.", tid, direction, entry)
                    sl = entry
                    risk = 0.0
                else:
                    sl = float(sl_price)
                    risk = abs(entry - sl)
                saved[tid] = {
                    "tf": "H1",
                    "direction": direction,
                    "entry": entry,
                    "sl_initial": sl,
                    "current_sl": sl,
                    "risk": risk,
                    "peak": entry,
                    "trail_armed": False,
                    "trail_arm_r": 0.6,
                    "trail_dist_r": 0.75,
                    "bars_held": 0,
                    "last_bar_ts": None,
                    "signal_bar_ts": None,
                }
                logger.info(
                    "Adopted unknown trade %s: %s @ %.2f, SL %.2f, risk %.2f "
                    "(conservative defaults)",
                    tid, direction, entry, sl, risk)
            self._state["trades"] = saved
            _save_state(self._state)
        logger.info(
            "Position reconcile (%s): %d active trade(s)", reason, len(saved))
    # ── Candle-boundary loop ───────────────────────────────────────
    async def _candle_loop(self, tf: Timeframe, interval_minutes: int) -> None:
        label = tf.value
        try:
            while self._running:
                wait = _seconds_until_boundary(interval_minutes, buffer=10)
                logger.info("%s loop: next scan in %.0fs", label, wait)
                await asyncio.sleep(wait)
                if not self._running:
                    break
                logger.info("%s loop: firing scan", label)
                try:
                    await self._scan_and_manage(tf)
                except Exception:
                    logger.exception("%s loop: cycle failed", label)
        except asyncio.CancelledError:
            pass
        finally:
            logger.info("%s loop stopped", label)
    async def _scan_and_manage(self, tf: Timeframe) -> None:
        loop = asyncio.get_running_loop()
        # Trade management FIRST: the SL ratchet must not wait on (or be
        # starved by) the signal scan — news fetches can take seconds and
        # a failed scan must never cost an open trade its trail update.
        try:
            await loop.run_in_executor(
                self._thread_pool, self._manage_open_trades, tf)
        except Exception:
            logger.exception("%s: trade management failed", tf.value)
        report = await loop.run_in_executor(
            self._thread_pool, self._run_signal, tf)
        if not self._should_execute(report, tf):
            return
        await loop.run_in_executor(
            self._thread_pool, self._execute_trade, report, tf)
    def _manage_open_trades(self, tf: Timeframe) -> None:
        self._check_closed_trades()
        self._manage_trailing(tf)
    # ── Heartbeat ─────────────────────────────────────────────────
    def _heartbeat_once(self) -> None:
        with self._state_lock:
            n = len(self._state.get("trades", {}))
        try:
            self._executor.get_account_summary()
        except OandaAPIError as e:
            logger.error("HEARTBEAT: account summary failed: %s", e)
        # Logged unconditionally — even on a failed summary call the
        # heartbeat line itself must reach the log, so a wedged process
        # doesn't look identical to a healthy but silent one.
        logger.info("HEARTBEAT: alive | tracked trades: %d", n)
    async def _heartbeat_loop(self) -> None:
        loop = asyncio.get_running_loop()
        try:
            while self._running:
                await asyncio.sleep(HEARTBEAT_SECONDS)
                if not self._running:
                    break
                try:
                    await loop.run_in_executor(
                        self._thread_pool, self._heartbeat_once)
                except Exception:
                    logger.exception("heartbeat loop: cycle failed")
        except asyncio.CancelledError:
            pass
        finally:
            logger.info("heartbeat loop stopped")
    # ── Signal pipeline ────────────────────────────────────────────
    def _run_signal(self, tf: Timeframe) -> SignalReport:
        conf_tfs = SCAN_CONFIRMATIONS.get(
            tf, [Timeframe.M15, Timeframe.H4])
        factory = PAIR_PRODUCERS.get(SYMBOL)
        if factory is not None:
            config = factory(tf, conf_tfs)
        else:
            config = PlatformConfig()
            config.primary_timeframe = tf
            config.confirmation_timeframes = conf_tfs
        aggregator = SignalAggregator(config)
        aggregator.data_provider = self._provider
        report = aggregator.analyze_pair(symbol=SYMBOL)
        logger.info(
            "%s scan: signal=%s score=%.1f conf=%.0f quality=%s "
            "regime=%s vol=%s price=%.2f spread=%.4f",
            tf.value,
            report.final_signal.name, report.final_score,
            report.final_confidence, report.signal_quality,
            report.market_regime, report.volatility_regime,
            report.current_price,
            self._provider.get_spread(SYMBOL))
        # Stale-data/suppression warnings must be visible at WARNING;
        # routine session-filter notices go to DEBUG — the M5 loop fires
        # 288x/day and would drown the log at WARNING otherwise.
        for w in report.warnings:
            if "STALE" in w.upper() or "FAIL" in w.upper():
                logger.warning("%s scan: %s", tf.value, w)
            else:
                logger.debug("%s scan: %s", tf.value, w)
        return report
    # ── Execution decision ─────────────────────────────────────────
    def _should_execute(self, report: SignalReport, tf: Timeframe) -> bool:
        if report.final_signal == SignalStrength.NEUTRAL:
            return False
        if report.trade_setup is None:
            return False
        quality_rank = QUALITY_RANK.get(report.signal_quality, -1)
        gate_rank = QUALITY_RANK.get(self._quality_gate, 1)
        if quality_rank < gate_rank:
            return False
        if self._in_cooldown(tf):
            logger.info(
                "%s: actionable signal (%s %s) but inside %d-bar entry "
                "cooldown, skipping",
                tf.value, report.trade_setup.direction,
                report.signal_quality, COOLDOWN_BARS)
            return False
        with self._state_lock:
            if self._state.get("trades"):
                logger.info(
                    "%s: actionable signal (%s %s) but position already "
                    "open, skipping",
                    tf.value, report.trade_setup.direction,
                    report.signal_quality)
                return False
        return True
    def _in_cooldown(self, tf: Timeframe) -> bool:
        """Backtest parity: no entry within COOLDOWN_BARS bars of the
        previous entry on the same timeframe (wall-clock approximation)."""
        with self._state_lock:
            last = self._state.get("last_entries", {}).get(tf.value)
        if not last:
            return False
        try:
            last_ts = datetime.fromisoformat(last)
        except ValueError:
            return False
        interval_s = TF_INTERVAL_MINUTES[tf] * 60
        elapsed = (datetime.now(timezone.utc) - last_ts).total_seconds()
        return elapsed < COOLDOWN_BARS * interval_s
    # ── Trade execution ────────────────────────────────────────────
    def _execute_trade(self, report: SignalReport, tf: Timeframe) -> None:
        ts = report.trade_setup
        if ts is None:
            return
        # All four TF loops can fire at an aligned boundary (e.g. :00) and
        # pass _should_execute before any of them has recorded a fill —
        # re-check under the entry lock so only one order goes out.
        with self._entry_lock:
            with self._state_lock:
                if self._state.get("trades"):
                    logger.info(
                        "%s: position opened by a concurrent scan, skipping",
                        tf.value)
                    return
            # NO take-profit on the order: the backtested trailing model
            # exits via SL/TRAIL/TIMEOUT only — a hard TP would cap
            # winners and diverge the live book from the backtest.
            try:
                result = self._executor.place_market_order(
                    instrument=INSTRUMENT,
                    direction=ts.direction,
                    stop_loss=ts.stop_loss,
                )
            except OandaAPIError:
                # The order is never retried (a timeout after a server-side
                # fill would double-up on retry), but the server MAY have
                # filled before the error reached us — reconcile so a live
                # position can't stay untracked until the next restart.
                logger.exception(
                    "Failed to place %s order for %s %s — reconciling "
                    "broker positions",
                    ts.direction, SYMBOL, tf.value)
                self._reconcile_positions("order placement error")
                return
            fill_tx = result.get("orderFillTransaction")
            if not fill_tx:
                logger.warning(
                    "Order not filled for %s %s", SYMBOL, tf.value)
                return
            trade_opened = fill_tx.get("tradeOpened", {})
            trade_id = trade_opened.get("tradeID")
            if not trade_id:
                # A fill with no tradeOpened means the order interacted
                # with an existing broker position (tradesClosed /
                # tradeReduced on a netting account) — state and broker
                # have diverged. Surface it and re-sync instead of
                # silently forgetting a real fill.
                logger.critical(
                    "Order FILLED but opened no trade (netting close/"
                    "reduce?) — fill: closed=%s reduced=%s. Reconciling.",
                    fill_tx.get("tradesClosed"), fill_tx.get("tradeReduced"))
                self._reconcile_positions("fill without tradeOpened")
                return
            fill_price = float(fill_tx.get("price", ts.entry_price))
            risk = abs(fill_price - ts.stop_loss)
            trail_arm = ts.trail_arm_r if ts.trail_arm_r is not None else 0.6
            trail_dist = ts.trail_dist_r if ts.trail_dist_r is not None else 0.75
            now_iso = datetime.now(timezone.utc).isoformat()
            trade_state = {
                "tf": tf.value,
                "direction": ts.direction,
                "entry": fill_price,
                "sl_initial": ts.stop_loss,
                "current_sl": ts.stop_loss,
                "tp1": ts.take_profit_1,
                "tp2": ts.take_profit_2,
                "risk": risk,
                "peak": fill_price,
                "trail_armed": False,
                "trail_arm_r": trail_arm,
                "trail_dist_r": trail_dist,
                "bars_held": 0,
                # The signal bar is already closed; the first bar this
                # trade lives through is the one that just started. Guard
                # so a lagging feed can't trail on the signal bar itself.
                "signal_bar_ts": report.timestamp,
                "last_bar_ts": None,
                "strategy": ts.strategy,
                "quality": report.signal_quality,
                "rr": ts.risk_reward,
                "opened_at": now_iso,
            }
            with self._state_lock:
                self._state["trades"][trade_id] = trade_state
                self._state["last_entries"][tf.value] = now_iso
                _save_state(self._state)
        logger.info(
            "TRADE OPENED: %s %s @ %.2f | SL %.2f | TP1 %.2f (advisory) | "
            "risk $%.2f | RR %.2f | quality %s | strategy %s | TF %s | "
            "trail arm %.2fR dist %.2fR",
            ts.direction, SYMBOL, fill_price, ts.stop_loss,
            ts.take_profit_1,
            risk, ts.risk_reward, report.signal_quality,
            ts.strategy, tf.value, trail_arm, trail_dist)
    # ── Trailing stop management ───────────────────────────────────
    def _manage_trailing(self, tf: Timeframe) -> None:
        # Copy the per-trade dicts too: the loop mutates info (peak,
        # bars_held, last_bar_ts) and must not touch live state outside
        # the lock — another thread may be serializing it in _save_state.
        with self._state_lock:
            trades = {tid: dict(info)
                      for tid, info in self._state.get("trades", {}).items()}
        for trade_id, info in trades.items():
            if info.get("tf") != tf.value:
                continue
            try:
                df = self._provider.fetch_ohlcv(SYMBOL, tf)
            except Exception:
                logger.exception(
                    "Failed to fetch candle data for trail update")
                continue
            if df.empty:
                logger.warning(
                    "%s: no candle data for trail update of trade %s",
                    tf.value, trade_id)
                continue
            bar_ts = df.index[-1].isoformat()
            # Process each closed bar exactly once. Weekends and feed lag
            # re-serve the same last bar; without this guard bars_held
            # inflates (an M5 trade would "time out" over a weekend) and
            # the peak gets double-counted.
            if bar_ts == info.get("last_bar_ts"):
                continue
            sig_ts = info.get("signal_bar_ts")
            if sig_ts:
                try:
                    if df.index[-1] <= pd.Timestamp(sig_ts):
                        continue
                except (ValueError, TypeError):
                    pass
            info["last_bar_ts"] = bar_ts
            info["bars_held"] = info.get("bars_held", 0) + 1
            if info["bars_held"] >= self._max_hold_bars:
                logger.info(
                    "TIMEOUT: trade %s held %d bars, closing",
                    trade_id, info["bars_held"])
                try:
                    self._executor.close_trade(trade_id)
                except OandaAPIError:
                    # Keep tracking — the trade is still live on the
                    # broker (incl. a 200 whose close order was cancelled,
                    # e.g. MARKET_HALTED — close_trade raises on no-fill);
                    # we retry the close at the next bar.
                    logger.exception(
                        "Failed to close timed-out trade %s — will retry",
                        trade_id)
                    with self._state_lock:
                        if trade_id in self._state["trades"]:
                            self._state["trades"][trade_id] = info
                            _save_state(self._state)
                    continue
                with self._state_lock:
                    self._state["trades"].pop(trade_id, None)
                    _save_state(self._state)
                continue
            last_bar = df.iloc[-1]
            bar_high = float(last_bar["High"])
            bar_low = float(last_bar["Low"])
            if info.get("risk", 0) <= 0:
                # Adopted naked position (no broker SL → risk undefined):
                # never manageable by the trailing model — keep it loud
                # instead of silently skipping forever.
                logger.warning(
                    "Trade %s has risk<=0 (no stop loss) — trailing "
                    "skipped; set a stop manually", trade_id)
            new_sl = self._compute_trail(info, bar_high, bar_low)
            if new_sl is not None:
                # Round to instrument precision and track exactly what the
                # broker holds, so tracked vs broker SL can never drift.
                new_sl = round(new_sl, self._price_precision)
                if new_sl != info["current_sl"]:
                    try:
                        self._executor.modify_trade_sl(trade_id, new_sl)
                        info["current_sl"] = new_sl
                    except OandaAPIError:
                        logger.exception(
                            "Failed to update trailing SL for trade %s",
                            trade_id)
            with self._state_lock:
                # Write back ONLY if the trade is still tracked — another
                # loop's _check_closed_trades may have popped it while we
                # iterated (broker-side close). Re-inserting would create
                # a ghost that blocks new entries.
                if trade_id in self._state["trades"]:
                    self._state["trades"][trade_id] = info
                    _save_state(self._state)
    def _compute_trail(
        self, info: Dict, bar_high: float, bar_low: float
    ) -> Optional[float]:
        buy = info["direction"] == "BUY"
        risk = info["risk"]
        if risk <= 0:
            return None
        arm = info.get("trail_arm_r", 0.6)
        dist = info.get("trail_dist_r", 0.75)
        if arm <= 0:
            return None
        fav = bar_high if buy else bar_low
        if buy:
            info["peak"] = max(info.get("peak", info["entry"]), fav)
        else:
            info["peak"] = min(info.get("peak", info["entry"]), fav)
        exc = ((info["peak"] - info["entry"]) if buy
               else (info["entry"] - info["peak"])) / risk
        if exc >= arm:
            if buy:
                lvl = info["peak"] - dist * risk
                cur_sl = info.get("current_sl", info["sl_initial"])
                if lvl > cur_sl:
                    info["trail_armed"] = True
                    return lvl
            else:
                lvl = info["peak"] + dist * risk
                cur_sl = info.get("current_sl", info["sl_initial"])
                if lvl < cur_sl:
                    info["trail_armed"] = True
                    return lvl
        return None
    # ── Closed-trade detection ─────────────────────────────────────
    def _check_closed_trades(self) -> None:
        with self._state_lock:
            tracked = set(self._state.get("trades", {}).keys())
        if not tracked:
            return
        try:
            open_trades = self._executor.get_open_trades(INSTRUMENT)
        except OandaAPIError:
            logger.exception("Failed to query open trades")
            return
        live_ids = {t["id"] for t in open_trades}
        closed = tracked - live_ids
        for tid in closed:
            with self._state_lock:
                info = self._state["trades"].pop(tid, {})
            entry = info.get("entry", 0)
            direction = info.get("direction", "?")
            risk = info.get("risk", 1)
            sl = info.get("current_sl", 0)
            was_trailed = info.get("trail_armed", False)
            logger.info(
                "TRADE CLOSED BY BROKER: %s %s %s | entry %.2f | "
                "SL was %.2f | risk $%.2f | trailed=%s | bars_held=%d",
                tid, direction, SYMBOL, entry, sl, risk,
                was_trailed, info.get("bars_held", 0))
        if closed:
            with self._state_lock:
                _save_state(self._state)
 # ── Uncaught exception handlers ───────────────────────────────────────
 def _excepthook(exc_type, exc_value, exc_tb):
    if issubclass(exc_type, KeyboardInterrupt):
        sys.__excepthook__(exc_type, exc_value, exc_tb)
        return
    logger.critical(
        "Uncaught exception", exc_info=(exc_type, exc_value, exc_tb))
 def _threading_excepthook(args):
    logger.critical(
        "Uncaught exception in thread %s",
        args.thread.name if args.thread else "?",
        exc_info=(args.exc_type, args.exc_value, args.exc_traceback))
 # ── Entry point ────────────────────────────────────────────────────────
 def main() -> None:
    _setup_logging(
        os.environ.get("OANDA_LOG_LEVEL") or get_app_config().log_level)
    sys.excepthook = _excepthook
    threading.excepthook = _threading_excepthook
    daemon = TradingDaemon()
    asyncio.run(daemon.run())
 if __name__ == "__main__":
    main()
@@ -1,230 +0,0 @@
 """
 ForexBot2 — Oanda v20 Order Executor
 ======================================
 Handles all Oanda account operations: market orders, trade queries,
 SL/TP modification, and trade closure.  Uses raw ``requests`` against
 the Oanda v20 REST API — no extra dependencies.
 """
 import logging
 import time
 from typing import Dict, List, Optional
 import requests
 logger = logging.getLogger(__name__)
 class OandaAPIError(Exception):
    """Raised when an Oanda API call fails."""
    def __init__(self, message: str, status_code: int = 0, body: str = ""):
        super().__init__(message)
        self.status_code = status_code
        self.body = body
 class OandaExecutor:
    """Oanda v20 order execution and trade management."""
    MAX_RETRIES = 1
    RETRY_DELAY = 2.0
    def __init__(
        self,
        account_id: str,
        api_token: str,
        base_url: str = "https://api-fxpractice.oanda.com",
        lot_size: int = 1,
        price_precision: int = 2,
    ):
        self._account_id = account_id
        self._base_url = base_url.rstrip("/")
        self._lot_size = lot_size
        self._price_precision = price_precision
        self._session = requests.Session()
        self._session.headers.update({
            "Authorization": f"Bearer {api_token}",
            "Accept-Datetime-Format": "RFC3339",
            "Content-Type": "application/json",
        })
    # ── Order Placement ────────────────────────────────────────────
    def place_market_order(
        self,
        instrument: str,
        direction: str,
        stop_loss: float,
        take_profit: Optional[float] = None,
        units: Optional[int] = None,
    ) -> Dict:
        if units is None:
            units = self._lot_size
        signed_units = str(units) if direction.upper() == "BUY" else str(-units)
        order_body = {
            "order": {
                "type": "MARKET",
                "instrument": instrument,
                "units": signed_units,
                "timeInForce": "FOK",
                # The book model is one position total; on a netting
                # account an opposite-side order would silently CLOSE an
                # existing (possibly untracked) position instead of opening
                # the signal trade. OPEN_ONLY makes that case fail loudly
                # (order cancelled) so the divergence surfaces instead of
                # executing an unintended close.
                "positionFill": "OPEN_ONLY",
                "stopLossOnFill": {
                    "price": self._fmt_price(stop_loss),
                },
            }
        }
        if take_profit is not None:
            order_body["order"]["takeProfitOnFill"] = {
                "price": self._fmt_price(take_profit),
            }
        url = f"{self._base_url}/v3/accounts/{self._account_id}/orders"
        # Never retry order placement: a timeout after the server already
        # filled would open a SECOND position on retry. Fail loudly instead;
        # the next candle boundary re-evaluates.
        resp = self._request_with_retry("POST", url, json=order_body, retries=0)
        data = self._parse_json(resp)
        fill_tx = data.get("orderFillTransaction")
        if fill_tx:
            trade_id = fill_tx.get("tradeOpened", {}).get("tradeID", "unknown")
            fill_price = float(fill_tx.get("price", 0))
            logger.info(
                "ORDER FILLED: %s %s %s units @ %.2f — trade %s, SL %.2f%s",
                direction.upper(), instrument, signed_units, fill_price,
                trade_id, stop_loss,
                f", TP {take_profit:.2f}" if take_profit else "")
        else:
            cancel_tx = data.get("orderCancelTransaction", {})
            reason = cancel_tx.get("reason", "unknown")
            logger.warning(
                "ORDER NOT FILLED: %s %s — reason: %s",
                direction.upper(), instrument, reason)
        return data
    # ── Trade Queries ──────────────────────────────────────────────
    def get_open_trades(self, instrument: Optional[str] = None) -> List[Dict]:
        url = f"{self._base_url}/v3/accounts/{self._account_id}/openTrades"
        resp = self._request_with_retry("GET", url)
        trades = self._parse_json(resp).get("trades", [])
        if instrument:
            trades = [t for t in trades if t.get("instrument") == instrument]
        return trades
    def get_trade(self, trade_id: str) -> Dict:
        url = (f"{self._base_url}/v3/accounts/{self._account_id}"
               f"/trades/{trade_id}")
        resp = self._request_with_retry("GET", url)
        return self._parse_json(resp).get("trade", {})
    def get_account_summary(self) -> Dict:
        url = f"{self._base_url}/v3/accounts/{self._account_id}/summary"
        resp = self._request_with_retry("GET", url)
        account = self._parse_json(resp).get("account", {})
        logger.info(
            "Account: balance=%.2f, NAV=%.2f, unrealizedPL=%.2f, openTrades=%s",
            float(account.get("balance", 0)),
            float(account.get("NAV", 0)),
            float(account.get("unrealizedPL", 0)),
            account.get("openTradeCount", 0))
        return account
    # ── Trade Modification ─────────────────────────────────────────
    def modify_trade_sl(self, trade_id: str, new_sl: float) -> Dict:
        url = (f"{self._base_url}/v3/accounts/{self._account_id}"
               f"/trades/{trade_id}/orders")
        body = {"stopLoss": {"price": self._fmt_price(new_sl)}}
        resp = self._request_with_retry("PUT", url, json=body)
        logger.info(
            "TRAIL UPDATE: trade %s — new SL %.2f", trade_id, new_sl)
        return self._parse_json(resp)
    def modify_trade_tp(self, trade_id: str, new_tp: float) -> Dict:
        url = (f"{self._base_url}/v3/accounts/{self._account_id}"
               f"/trades/{trade_id}/orders")
        body = {"takeProfit": {"price": self._fmt_price(new_tp)}}
        resp = self._request_with_retry("PUT", url, json=body)
        logger.info("TP UPDATE: trade %s — new TP %.2f", trade_id, new_tp)
        return self._parse_json(resp)
    def close_trade(self, trade_id: str) -> Dict:
        url = (f"{self._base_url}/v3/accounts/{self._account_id}"
               f"/trades/{trade_id}/close")
        resp = self._request_with_retry("PUT", url, json={"units": "ALL"})
        data = self._parse_json(resp)
        close_tx = data.get("orderFillTransaction")
        if not close_tx:
            # A 200 whose close order was created but NOT filled (e.g.
            # cancelled with MARKET_HALTED during the settlement pause)
            # means the position is STILL OPEN. Raising keeps the caller
            # tracking it and retrying — silently returning here would
            # orphan a live position.
            reason = data.get("orderCancelTransaction", {}).get(
                "reason", "no orderFillTransaction in response")
            raise OandaAPIError(
                f"Close of trade {trade_id} did not fill: {reason}",
                resp.status_code, str(data)[:300])
        close_price = float(close_tx.get("price", 0))
        pl = float(close_tx.get("pl", 0))
        logger.info(
            "TRADE CLOSED: trade %s @ %.2f — PL %.2f",
            trade_id, close_price, pl)
        return data
    # ── Internals ──────────────────────────────────────────────────
    @staticmethod
    def _parse_json(resp: requests.Response) -> Dict:
        try:
            return resp.json()
        except ValueError as e:
            raise OandaAPIError(
                f"Oanda returned a non-JSON response (HTTP {resp.status_code})",
                resp.status_code, resp.text[:300]) from e
    def _fmt_price(self, price: float) -> str:
        # Oanda rejects prices with more decimals than the instrument's
        # displayPrecision (XAU_USD: sub-cent precision is invalid).
        return f"{price:.{self._price_precision}f}"
    def _request_with_retry(
        self, method: str, url: str, retries: Optional[int] = None, **kwargs
    ) -> requests.Response:
        if retries is None:
            retries = self.MAX_RETRIES
        last_err = None
        for attempt in range(1 + retries):
            try:
                resp = self._session.request(method, url, timeout=15, **kwargs)
                if resp.status_code >= 500:
                    raise OandaAPIError(
                        f"Oanda server error {resp.status_code}",
                        resp.status_code, resp.text)
                if resp.status_code >= 400:
                    raise OandaAPIError(
                        f"Oanda API error {resp.status_code}: {resp.text}",
                        resp.status_code, resp.text)
                return resp
            except (requests.ConnectionError, requests.Timeout,
                    OandaAPIError) as e:
                last_err = e
                if isinstance(e, OandaAPIError) and 400 <= e.status_code < 500:
                    raise
                if attempt < retries:
                    logger.warning(
                        "Oanda request failed (attempt %d/%d): %s — retrying in %.0fs",
                        attempt + 1, 1 + retries, e,
                        self.RETRY_DELAY)
                    time.sleep(self.RETRY_DELAY)
        raise OandaAPIError(f"Oanda request failed after retries: {last_err}")
@@ -12,7 +12,7 @@ from typing import Optional, List, Dict, Callable
 from config import PlatformConfig, Timeframe
 from signal_aggregator import SignalAggregator, SignalReport
-from providers import BaseDataProvider
+from data_provider import ForexDataProvider
 PAIR_PRODUCERS: Dict[str, Callable] = {}
@@ -28,7 +28,7 @@ def get_supported_pairs() -> List[str]:
 def produce_signal(
    symbol: str,
    primary_tf: Timeframe,
-    data_provider: BaseDataProvider,
+    data_provider: ForexDataProvider,
    confirmation_tfs: Optional[List[Timeframe]] = None,
 ) -> SignalReport:
    config_factory = PAIR_PRODUCERS.get(symbol)
@@ -56,40 +56,6 @@ positive, val half -1.89R — marginal but sign-correct in aggregate).
 Gold's structural uptrend makes bullish patterns fundamentally more
 reliable. M15/H1 fail on all windows; M5-only enablement.
 M5 override 3 (validated 2026-07-21, 5-year replay 2021-07-21 ->
 2026-07-21, $0.30 spread, next-open fills): trailing distance widened
 to 0.85R (trail_dist_r 0.85, arm unchanged 0.60). Same 30-cell grid
 methodology as the M15 campaign, opposite finding — on M5 the arm
 point barely matters; the winners need a looser leash. Plateau arm
 0.5-0.7 x dist 0.85-1.0 (+227..+234R vs +218.0R baseline). Chosen
 0.60/0.85 (OOS-preferred end of the plateau): +231.2R/2028 PF 1.30 WR
 57.1% vs +218.0R PF 1.29 WR 58.3%; both last-12mo halves better
 (+56.8/+115.1 vs +54.0/+104.6); 5yr max DD 19.2R vs 19.1R (noise);
 OOS 2024-06-01 -> 2025-03-30 +14.5R DD 15.1 vs +11.1R DD 15.4.
 dist 1.0 was REJECTED at the OOS gate (+10.9R < baseline +11.1R)
 despite the best in-sample total (+233.3R) — the OOS ordering picks
 the conservative plateau edge. XAU_PB trades are unaffected (they
 carry their own validated pb_trail 0.8/0.75 per-trade overrides);
 XAU_CP trades share the global params and were validated inside this
 book. M15 arms at 0.70 (its own campaign, same day); the divergence
 is the per-TF pattern yet again.
 M15 override 4 (validated 2026-07-21, 5-year replay 2021-07-21 ->
 2026-07-21, $0.30 spread, next-open fills): trailing-stop arm raised to
 0.70R (trail_arm_r 0.70, dist unchanged 0.75). Full grid arm 0.3-0.9 x
 dist 0.5-1.0 (30 cells): every earlier-arm cell (0.3/0.45) is WORSE —
 the "arm earlier to save fading trades" hypothesis is refuted; the
 plateau is arm 0.70-0.80 x dist 0.70-0.85 (all >= +190R). Chosen
 plateau center 0.70/0.75: +210.7R/1629 PF 1.35 WR 57.8% vs baseline
 0.60/0.75 +175.7R PF 1.30 WR 53.9%; both last-12mo halves improve
 (+49.7/+50.0 vs +36.4/+46.0); per-year better 4/6 (2022 -0.9R, 2023
 -1.4R, both within tolerance); OOS 2024-06-01 -> 2025-03-30 +57.4R
 PF 1.57 DD 17.0R vs +45.4R PF 1.47 DD 17.8R — better on every OOS
 metric. One clause waived BY OWNER DECISION 2026-07-21: 5yr max DD is
 33.9R vs 29.1R baseline (+4.8R deeper worst episode, 2022-23 chop),
 accepted in exchange for +20% total R. M5 keeps the global 0.6 arm
 (same per-TF divergence pattern as tp_floor_rr and the hour-9 block).
 M5 override (validated 2026-07-11, full-history replay 2020-03-17 ->
 2026-07-11 + 12mo book, $0.30 spread): block hour 9 UTC (post-London-
 open whipsaw). Hour-9 entries lost -28.9R/143 over 6 years, negative
@@ -99,77 +65,10 @@ in 5/7 calendar years including winning years (2022 -9.2R). Blocking:
 12mo book is unchanged (+139.0R vs +139.3R). M15-and-up keep hour 9:
 it is POSITIVE there (M15 6yr +1.7R, M15 OOS +8.6R), so the block is
 M5-only, same per-timeframe pattern as the M15 overrides.
 M5 override 2 — relaxation campaign (validated 2026-07-30, Oanda
 bars, $0.30 spread, trailing exits): UNBLOCK hour 21 UTC on M5 only,
 combined with the config.py M5 gate relaxations (m5_min_abs_score
 30->25, m5_min_confirmation_score 10->5). The combined config beat
 the pre-relaxation control in ALL 6 calendar years of the 5yr replay
 2021-07-30 -> 2026-07-30 (+230.9R/2319 PF 1.26 DD 21.3 vs
 +168.0R/2048 PF 1.21 DD 27.0), on 12mo (+107.4R PF 1.36, halves
 +68.6/+38.8 vs +84.6R PF 1.33), on OOS 2024-06-01 -> 2025-03-30
 (+26.1R vs +17.2R), and at $0.70 spread (+86.9R vs +66.4R). Hour-21
 trades alone: +28.0R/22, positive every year — overturning the old
 block per the full-evidence rule. Hour 21 stays BLOCKED on M15+
 (12mo M15 unblock tested -0.3R). Declined-but-open from the same
 campaign: an M5 hour-22 block (+4.8R on 12mo — the burst-heavy
 "institutional" hour — but -3.2R on 5yr and -1.1R on OOS: regime-
 specific); M15 hour-16 unblock (+0.8R/+57 trades 12mo but -18.3R
 DD +34% on 5yr); every SELL-gate/vol/TP-size relaxation retested
 negative (see memory notes for the full grid).
 M5 override 4 — XAU_PB disabled (validated 2026-08-04, Oanda bars,
 $0.65 REAL spread — first campaign validated at measured cost on the
 live feed, per the 2026-08-04 re-pin baseline): m5_pullback_enabled
 False on M5. Loss forensics on the Oanda books identified XAU_PB as
 the largest wrong-direction loss source (12mo: -43.5R of the -156.6R
 wrong-direction bucket; 5yr: -274.5R). Disabling: 12mo +96.6R/588
 PF 1.44 max DD 13.9R halves +51.2/+45.4 vs +82.3R/788 PF 1.26 DD
 23.9R shipped (which FAILS the PF>=1.3 clause at real cost); OOS
 2024-06-01 -> 2025-03-30 +9.5R PF 1.34; 5yr +123.9R/956 PF 1.35 vs
 PF 1.16 with PB. PB's 5yr net contribution (+23.6R) was almost
 entirely the 2022 chop year (+36.1R vs -2.2R without) — a regime
 earner that has been a drag on trend-regime bars since; its tight
 stops are also the most cost-punished at $0.65. PB code and the
 pb_trail_* params STAY for future regime-gated experiments (same
 pattern as cp_enabled). Rejected in the same campaign (4 candidates
 total, N logged): hour-19 block (+3.7R 12mo but -3.3R OOS, -1.5R
 5yr — OOS gate, the classic trap); hours-14/15 block (-6.9R 12mo
 once PB is removed — their raw negativity WAS the PB losses);
 trail_dist_r 0.75 (-2.6R 12mo; the 0.88R average give-back is the
 validated price of riding trends, exits confirmed NOT the loss
 driver: exit-management accounts for 1-2% of lost R vs 50%
 wrong-direction / 49% entry-timing).
 M5 override 5 — EMA partial-alignment squash (validated 2026-08-04,
 Oanda bars, $0.65 real spread): ema_partial_neutral True on M5 —
 partial 4-EMA ribbon alignment (2-3 of 4 checks) scores NEUTRAL
 instead of a weak BUY/SELL. Motivated by the indicator audit: partial-
 alignment trades averaged -0.008R/84 vs +0.230R/341 fully aligned.
 Replay (knob mirrored in the backtester's precomputed EMA scorer in
 the same commit — live==backtest parity): 12mo +97.6R/556 PF 1.48
 DD 10.7R halves +47.8/+49.8 vs control +96.6R/588 PF 1.44 DD 13.9R;
 5yr +127.4R/912 PF 1.38 DD 12.8R vs +123.9R/956 PF 1.35 DD 13.2R,
 2022 improves -2.2 -> -1.1; OOS 2024-06-01 -> 2025-03-30 +9.4R
 PF 1.33 vs +9.5R PF 1.34 — a one-trade tie, documented as such (the
 OOS window holds up; it neither prefers nor punishes). Net effect:
 32 fewer marginal trades, every risk-adjusted measure better.
 Rejected in the same round (trial-logged): confirmation-TF swap
 M5->[M15,H4] (12mo +104.0R PF 1.51 DD 10.3 but OOS +7.8R PF 1.27
 vs control +9.5R PF 1.34 and 2022 3x worse — the H4 confirm keeps
 endorsing stale direction in chop; OOS-ordering rejection, same
 precedent as trail dist 1.0); confirm [H1,H4] (-31.8R 12mo);
 triple-confirm [M15,H1,H4] (-4.9R); divergence weight -1/3
 (-3.8R 12mo); candlestick weight +50% (-3.0R 12mo). The weight
 vector keeps proving calibrated as a system: divergence/candlestick/
 bollinger all look mis-weighted in-book and all replay worse when
 "fixed" (bollinger weight-0: +80.9R PF 1.29 DD 23.8 — its constant
 oppose vote is a load-bearing quality bar, like ROC).
 """
 from typing import Optional, List
 from config import PlatformConfig, Timeframe
 from app_config import apply_pair_overrides
 _CONFIRMATIONS = {
    Timeframe.M5:  [Timeframe.M15, Timeframe.H1],
@@ -184,38 +83,18 @@ def make_config(
    confirmation_tfs: Optional[List[Timeframe]] = None,
 ) -> PlatformConfig:
    config = PlatformConfig()
    # Deployment overlay (config.json) BEFORE the validated per-TF
    # deltas below, so e.g. the M5 hour-9 append lands on the JSON base.
    apply_pair_overrides(config)
    config.primary_timeframe = primary_tf
    config.confirmation_timeframes = (
        confirmation_tfs or _CONFIRMATIONS.get(primary_tf, [Timeframe.M15, Timeframe.H4])
    )
    if primary_tf == Timeframe.M5:
-        # Block hour 9 UTC on M5 only; UNBLOCK hour 21 on M5 only
+        # Block hour 9 UTC on M5 only — see module docstring.
-        # (positive every year of the 5yr replay) — see docstring.
+        config.xau_strategy.blocked_hours_utc = (
-        config.xau_strategy.blocked_hours_utc = [
+            config.xau_strategy.blocked_hours_utc + [9]
-            h for h in config.xau_strategy.blocked_hours_utc if h != 21
+        )
        ] + [9]
        # Deliberately NOT mirrored into config.backtest.blocked_hours_utc:
        # that copy is the generic non-XAU fallback carrying the JSON base
        # only. Propagating a gold-validated per-TF delta there would leak
        # XAU-calibrated hours to a future pair (pair-isolation rule).
        # Every XAU consumer branches to the xau_strategy copy. Enforced
        # by the Module 13 test "M5 factory appends hour 9 on top of a
        # JSON base".
        # BUY-only chart patterns on M5 — see module docstring.
        config.xau_strategy.cp_enabled = True
        config.xau_strategy.cp_patterns = ["inv_hs", "double_bottom", "bull_flag"]
        # XAU_PB pullback entries OFF — largest wrong-direction loss
        # source at real cost on Oanda bars — see module docstring.
        config.xau_strategy.m5_pullback_enabled = False
        # Partial EMA-ribbon alignment scores NEUTRAL on M5 — its
        # trades carried no edge — see module docstring.
        config.technical.ema_partial_neutral = True
        # Wider trailing distance on M5 only — see module docstring.
        config.signals.trail_arm_r = 0.60
        config.signals.trail_dist_r = 0.85
    if primary_tf == Timeframe.M15:
        # 1R minimum target on M15 only — see module docstring.
        config.signals.tp_floor_rr = 1.0
@@ -223,7 +102,4 @@ def make_config(
        config.xau_strategy.blocked_regimes = ["trending"]
        # Block SELL entries at 15:00/17:00 UTC on M15 only — see docstring.
        config.xau_strategy.sell_blocked_hours_utc = [15, 17]
        # Later trailing-stop arm on M15 only — see module docstring.
        config.signals.trail_arm_r = 0.70
        config.signals.trail_dist_r = 0.75
    return config
@@ -1,57 +0,0 @@
 """
 ForexBot2 — Data-provider package
 ==================================
 One class per provider, all subclassing `BaseDataProvider` (which owns
 caching, validation, boundary guarantees, and range pagination — the
 behavior the signal pipeline depends on).
 Adding a provider = one module implementing `_fetch_raw` + one registry
 entry + one config.json `data_providers` block. The live candle feed is
 selected by config.json `data_providers.active`; the optional
 `data_providers.streaming` scalar selects a different provider for the
 tick stream (level-breach monitor). Registration alone changes nothing.
 """
 from typing import Dict, Optional
 from app_config import AppConfigError, get_app_config
 from config import PlatformConfig
 from providers.base import BaseDataProvider, DataIntegrityError, _is_forex_weekend
 from providers.twelve_data import TwelveDataProvider
 from providers.oanda import OandaDataError, OandaDataProvider
 from providers.tiingo import TiingoDataError, TiingoDataProvider
 #: config.json data_providers.active selects from here.
 PROVIDER_REGISTRY: Dict[str, type] = {
    "twelve_data": TwelveDataProvider,
    "oanda": OandaDataProvider,
    "tiingo": TiingoDataProvider,
 }
 def make_data_provider(config: PlatformConfig,
                       name: Optional[str] = None) -> BaseDataProvider:
    """Build a provider by name (default: config.json data_providers.active)."""
    if name is None:
        name = get_app_config().active_provider
    cls = PROVIDER_REGISTRY.get(name)
    if cls is None:
        raise AppConfigError(
            f'Unknown data provider "{name}"; '
            f"registered: {sorted(PROVIDER_REGISTRY)}")
    return cls(config)
 __all__ = [
    "BaseDataProvider",
    "DataIntegrityError",
    "OandaDataError",
    "OandaDataProvider",
    "TiingoDataError",
    "TiingoDataProvider",
    "TwelveDataProvider",
    "PROVIDER_REGISTRY",
    "make_data_provider",
    "_is_forex_weekend",
 ]
@@ -1,532 +0,0 @@
 """
 ForexBot2 — Provider base class
 ================================
 `BaseDataProvider` owns everything that must behave identically no matter
 where the bars come from: in-memory caching with candle-duration TTL, the
 closed-bar boundary guarantee, freshness checks, validation/cleaning, the
 forming-candle drop, and paginated date-range fetching for the backtester.
 A concrete provider implements only:
    _fetch_raw(symbol, timeframe, outputsize, start, end) -> pd.DataFrame
 returning a normalized frame (capitalized OHLCV columns, UTC tz-aware
 DatetimeIndex named "Datetime", oldest-first), plus optionally
 `get_spread` and the streaming-quote capability (`supports_streaming` +
 `iter_ticks`).
 The signal pipeline (engine + aggregator + backtester) consumes ONLY the
 public contract defined here, so swapping providers never touches signal
 code.
 """
 import logging
 import threading
 import time
 from abc import ABC, abstractmethod
 from datetime import datetime, timedelta
 from typing import AsyncIterator, Callable, Dict, Optional, Sequence, Tuple
 from zoneinfo import ZoneInfo
 import pandas as pd
 from config import PAIR_INFO, PlatformConfig, Timeframe, XAUUSD
 logger = logging.getLogger(__name__)
 # The gold/FX market week is defined in New York time (see
 # _is_forex_weekend); a fixed UTC hour is wrong for half the year.
 _NY_TZ = ZoneInfo("America/New_York")
 def _is_forex_weekend(now=None) -> bool:
    """Gold/FX market closed for the weekend.
    The market week is anchored to NEW YORK time — Friday 17:00 ET →
    Sunday 18:00 ET — not a fixed UTC hour. During US DST the feed
    actually stops at 21:00 UTC (measured on 5 years of Oanda bars:
    159/162 summer Fridays; winter close 22:00, winter reopen Sunday
    23:00 UTC). The old hardcoded Fri 22:00→Sun 22:00 UTC rule produced
    two false-"STALE DATA/feed lagging" hours every week (2026-08-04
    audit I43, fixed per owner decision).
    Kept as three deliberate copies (here, signal_aggregator.py,
    gpu_backtest._is_xau_blocked_by_calendar) — a Module 21 test asserts
    they agree hour-by-hour across DST transitions.
    """
    if now is None:
        now = pd.Timestamp.now(tz="UTC")
    ny = now.astimezone(_NY_TZ)
    wd = ny.weekday()
    if wd == 4:                      # Friday
        return ny.hour >= 17
    if wd == 5:                      # Saturday
        return True
    if wd == 6:                      # Sunday
        return ny.hour < 18
    return False
 class DataIntegrityError(Exception):
    """Raised when data fails validation checks."""
    pass
 class BaseDataProvider(ABC):
    """Shared OHLCV provider behavior; subclasses supply the raw fetch."""
    #: Registry name; overridden per provider.
    name: str = "base"
    #: Exception type this provider raises for fetch/validation failures.
    #: Provider-specific errors subclass DataIntegrityError so consumers
    #: can catch one type regardless of the active provider.
    DATA_ERROR = DataIntegrityError
    BARS_PER_DAY = {
        Timeframe.M1: 1440, Timeframe.M5: 288, Timeframe.M15: 96,
        Timeframe.M30: 48, Timeframe.H1: 24, Timeframe.H4: 6,
        Timeframe.D1: 1, Timeframe.W1: 0.143,
    }
    CANDLE_SECONDS = {
        Timeframe.M1: 60, Timeframe.M5: 300, Timeframe.M15: 900,
        Timeframe.M30: 1800, Timeframe.H1: 3600, Timeframe.H4: 14400,
        Timeframe.D1: 86400, Timeframe.W1: 604800,
    }
    #: Hard per-request bar cap shared by Twelve Data and Oanda.
    MAX_BARS_PER_REQUEST = 5000
    # Grace wait before the one retry when a fresh response at a candle
    # boundary is missing the just-closed bar (feed latency).
    FRESH_FETCH_RETRY_SECONDS = 3.0
    def __init__(self, config: PlatformConfig):
        self.config = config
        self._cache: Dict[str, pd.DataFrame] = {}
        self._cache_timestamps: Dict[str, float] = {}
        # Requested fetch depth per key: a cached short frame must not
        # satisfy a caller asking for a longer lookback.
        self._cache_outputsize: Dict[str, int] = {}
        # The bot shares one provider across a thread pool; cache eviction
        # is check-then-act and needs locking.
        self._cache_lock = threading.Lock()
        # Single-flight: one API fetch per (symbol, TF) at a time; other
        # threads wait and then read the cache.
        self._inflight: Dict[str, threading.Event] = {}
    # ── Abstract: the only provider-specific piece ─────────────────
    @abstractmethod
    def _fetch_raw(
        self,
        symbol: str,
        timeframe: Timeframe,
        outputsize: int,
        start: Optional[datetime] = None,
        end: Optional[datetime] = None,
    ) -> pd.DataFrame:
        """Fetch up to `outputsize` bars, optionally bounded by start/end
        (tz-aware UTC datetimes). Must return a normalized frame:
        capitalized OHLCV columns, UTC DatetimeIndex named "Datetime",
        oldest-first. Raise self.DATA_ERROR on failure."""
    # ── Public API ─────────────────────────────────────────────────
    def fetch_ohlcv(
        self,
        symbol: str = XAUUSD,
        timeframe: Timeframe = Timeframe.H1,
        lookback_days: Optional[int] = None,
    ) -> pd.DataFrame:
        """Fetch OHLCV with in-memory caching for live mode."""
        if lookback_days is None:
            lookback_days = self.config.data_lookback_days
        cache_key = f"{symbol}_{timeframe.value}"
        outputsize = max(
            300,
            min(int(lookback_days * self.BARS_PER_DAY[timeframe]) + 20,
                self.MAX_BARS_PER_REQUEST),
        )
        while True:
            now = time.time()
            cache_ttl = self.CANDLE_SECONDS.get(
                timeframe, self.config.refresh_interval)
            with self._cache_lock:
                cached = self._cache.get(cache_key)
                cached_at = self._cache_timestamps.get(cache_key)
                cached_size = self._cache_outputsize.get(cache_key, 0)
            if (
                cached is not None
                and cached_at is not None
                and (now - cached_at) < cache_ttl
                and not cached.empty
                # A caller asking for MORE history than the cached fetch
                # requested must not be served the short frame silently.
                and cached_size >= outputsize
            ):
                if (self._is_data_fresh(cached, symbol, timeframe)
                        and self._has_latest_closed_bar(cached, timeframe)):
                    return cached
                logger.warning(f"{symbol} {timeframe.value}: cached data is stale, refetching")
                with self._cache_lock:
                    self._cache.pop(cache_key, None)
                    self._cache_timestamps.pop(cache_key, None)
                    self._cache_outputsize.pop(cache_key, None)
            # Single-flight: concurrent scans for the same (symbol, TF)
            # must not each hit the API (the four TF loops fire together
            # at aligned boundaries and Oanda has no client throttle).
            # First caller claims the key and fetches; the rest wait,
            # then re-check the cache.
            with self._cache_lock:
                inflight = self._inflight.get(cache_key)
                if inflight is None:
                    self._inflight[cache_key] = threading.Event()
                    break
            inflight.wait(timeout=90)
        try:
            df = self._fetch_raw(symbol, timeframe, outputsize)
            df = self._drop_forming_candle(df, timeframe)
            df = self._validate_and_clean(df, symbol, timeframe)
            # The closed-bar guarantee must hold for FRESH responses too,
            # not just cache hits: the scanner fires seconds after the
            # boundary, and a lagging feed can answer without the
            # just-closed bar — the scan would silently analyze (and
            # session-check) the previous bar. Give the feed one short
            # grace retry, then proceed loudly.
            if not df.empty and not self._has_latest_closed_bar(df, timeframe):
                time.sleep(self.FRESH_FETCH_RETRY_SECONDS)
                try:
                    retry = self._fetch_raw(symbol, timeframe, outputsize)
                    retry = self._drop_forming_candle(retry, timeframe)
                    retry = self._validate_and_clean(retry, symbol, timeframe)
                    if retry is not None and not retry.empty:
                        df = retry
                except DataIntegrityError as e:
                    logger.warning(
                        f"{symbol} {timeframe.value}: boundary retry refetch "
                        f"failed ({e}) — keeping original data")
                if not self._has_latest_closed_bar(df, timeframe):
                    logger.warning(
                        f"{symbol} {timeframe.value}: feed lagging — just-closed bar "
                        f"still missing after retry; this scan may run one bar behind"
                    )
            if not self._is_data_fresh(df, symbol, timeframe):
                logger.warning(
                    f"STALE DATA from API: {symbol} {timeframe.value} — "
                    f"last bar {df.index[-1]} is too old. Signal quality may be degraded."
                )
            with self._cache_lock:
                self._cache[cache_key] = df
                self._cache_timestamps[cache_key] = now
                self._cache_outputsize[cache_key] = outputsize
            return df
        finally:
            # Release waiters even when the fetch raised — they re-check
            # the cache and (on a miss) claim the key themselves.
            with self._cache_lock:
                ev = self._inflight.pop(cache_key, None)
            if ev is not None:
                ev.set()
    def fetch_range(
        self,
        symbol: str,
        timeframe: Timeframe,
        start: datetime,
        end: datetime,
    ) -> pd.DataFrame:
        """Fetch the RAW closed-bar series between start/end, paginating
        under the per-request bar cap. Returns uncleaned bars (the
        backtester resolves trades on the raw price path); callers wanting
        the signal-side series run `validate_and_clean` on the result."""
        bars_per_day = self.BARS_PER_DAY[timeframe]
        days_per_chunk = max(1, int(4500 / bars_per_day))
        chunks = []
        cursor_end = end
        empty_streak = 0
        error_streak = 0
        while cursor_end > start:
            cursor_start = max(start, cursor_end - timedelta(days=days_per_chunk))
            try:
                chunk = self._fetch_raw(
                    symbol, timeframe, self.MAX_BARS_PER_REQUEST,
                    start=cursor_start, end=cursor_end,
                )
            except Exception as e:
                # A raising chunk is a PROVIDER ERROR (auth, 429, 5xx,
                # malformed payload) — not "no data". Three in a row means
                # the rest of the history is unreachable: raise instead of
                # returning a silently truncated frame (the backtester
                # would treat it as complete).
                logger.warning(
                    f"Chunk {cursor_start.date()}–{cursor_end.date()} failed: {e}"
                )
                error_streak += 1
                if error_streak >= 3:
                    raise self.DATA_ERROR(
                        f"{symbol} {timeframe.value}: {error_streak} "
                        f"consecutive chunk failures walking back from "
                        f"{cursor_end.date()} — refusing to return "
                        f"truncated history (last error: {e})") from e
                cursor_end = cursor_start - timedelta(seconds=1)
                continue
            if chunk is None or chunk.empty:
                # Genuinely empty windows (weekends/holidays/asset gaps)
                # are normal; several in a row = start of history.
                empty_streak += 1
                if empty_streak >= 3:
                    break
                cursor_end = cursor_start - timedelta(seconds=1)
                continue
            empty_streak = 0
            error_streak = 0
            chunks.append(chunk)
            # Step back; dedup on concat.
            new_cursor = chunk.index[0] - timedelta(seconds=1)
            if new_cursor >= cursor_end:
                # A provider ignoring start/end would pin the cursor and
                # loop forever — refuse to spin.
                logger.warning(
                    f"{symbol} {timeframe.value}: pagination made no "
                    f"progress at {cursor_end} — stopping")
                break
            cursor_end = new_cursor
            if len(chunk) < 2:
                break
        if not chunks:
            raise RuntimeError(f"No data fetched for {symbol} {timeframe.value}")
        df = pd.concat(chunks).sort_index()
        df = df[~df.index.duplicated(keep="last")]
        # Fetches ending "now" include the still-forming candle — drop it
        # so callers only ever see closed bars.
        df = self._drop_forming_candle(df, timeframe)
        start_ts = pd.Timestamp(start)
        end_ts = pd.Timestamp(end)
        df = df[(df.index >= start_ts) & (df.index <= end_ts)]
        # Coverage sanity: a head materially later than the requested
        # start means history was truncated (empty-streak bail or provider
        # depth limit). Loud, not fatal — some assets genuinely start late.
        if not df.empty and (df.index[0] - start_ts) > pd.Timedelta(days=7):
            logger.error(
                f"{symbol} {timeframe.value}: fetched history starts "
                f"{df.index[0]} but {start_ts} was requested — "
                f"~{(df.index[0] - start_ts).days} days missing from the "
                f"head; treat downstream results accordingly")
        return df
    def is_data_fresh(
        self, df: pd.DataFrame, symbol: str, timeframe: Timeframe
    ) -> bool:
        """Public freshness check (last bar within 3 candle durations)."""
        return self._is_data_fresh(df, symbol, timeframe)
    def validate_and_clean(
        self, df: pd.DataFrame, symbol: str, timeframe: Timeframe
    ) -> pd.DataFrame:
        """Public entry to the shared cleaning pipeline."""
        return self._validate_and_clean(df, symbol, timeframe)
    def clear_cache(self) -> int:
        """Wipe all cached data. Returns number of entries cleared."""
        with self._cache_lock:
            count = len(self._cache)
            self._cache.clear()
            self._cache_timestamps.clear()
        logger.info(f"Cache cleared: {count} entries wiped")
        return count
    def get_pair_info(self, symbol: str = XAUUSD) -> Dict:
        info = PAIR_INFO.get(symbol, PAIR_INFO[XAUUSD])
        return {"symbol": symbol, **info}
    def get_spread(self, symbol: str = XAUUSD) -> float:
        """Latest observed round-trip spread; providers without live
        bid/ask fall back to the pair's assumed default."""
        return PAIR_INFO.get(symbol, PAIR_INFO[XAUUSD])["default_spread"]
    # ── Streaming-quote capability (optional) ──────────────────────
    #: True when the provider can stream live ticks via `iter_ticks`.
    supports_streaming: bool = False
    def iter_ticks(
        self,
        symbols: Sequence[str],
        on_connect: Optional[Callable[[], None]] = None,
    ) -> AsyncIterator[Tuple[str, float]]:
        """Async generator yielding (canonical_symbol, price) ticks for one
        connection's lifetime; the caller owns reconnect/backoff. Providers
        that support it set `supports_streaming = True` and override."""
        raise NotImplementedError(
            f"{self.name} provider does not support streaming quotes")
    # ── Shared internals ───────────────────────────────────────────
    def _has_latest_closed_bar(self, df: pd.DataFrame, timeframe: Timeframe) -> bool:
        """Cached data must include the most recent fully closed bar.
        The TTL alone is wall-clock based: a snapshot fetched mid-candle is
        still within TTL at the next candle boundary but is missing the bar
        that just closed, so every boundary scan would analyze — and run the
        bar-close session check against — the previous bar.
        """
        if df is None or df.empty:
            return False
        now = pd.Timestamp.now(tz="UTC")
        if _is_forex_weekend(now):
            return True
        candle_seconds = self.CANDLE_SECONDS.get(timeframe, 3600)
        dur = pd.Timedelta(seconds=candle_seconds)
        expected_label = now.floor(dur) - dur
        return df.index[-1] >= expected_label
    def _drop_forming_candle(self, df: pd.DataFrame, timeframe: Timeframe) -> pd.DataFrame:
        """Drop the still-forming last candle so analysis only sees closed bars.
        Some feeds include the current partial candle; the scanner fires
        seconds after the boundary, so without this guard every indicator
        reads a seconds-old stub bar and signals repaint. Feeds that only
        return complete candles pass through unchanged.
        """
        if df is None or df.empty:
            return df
        candle_seconds = self.CANDLE_SECONDS.get(timeframe, 3600)
        now = pd.Timestamp.now(tz="UTC")
        # Loop, not a single slice: a misbehaving feed can return MORE
        # than one not-yet-closed bar, and a single iloc[:-1] would let
        # the residual forming bar through cleaning (future-tolerance is
        # +5 min) straight into the signal engine.
        dropped = 0
        while (not df.empty
               and df.index[-1] + pd.Timedelta(seconds=candle_seconds) > now):
            df = df.iloc[:-1]
            dropped += 1
        if dropped > 1:
            logger.warning(
                "%s: feed returned %d forming/future bars — all dropped",
                timeframe.value, dropped)
        return df
    def _is_data_fresh(self, df: pd.DataFrame, symbol: str, timeframe: Timeframe) -> bool:
        """Check if the last candle is within acceptable age for this timeframe."""
        if df is None or df.empty:
            return False
        now = pd.Timestamp.now(tz="UTC")
        if _is_forex_weekend(now):
            return True
        last_bar = df.index[-1]
        candle_seconds = self.CANDLE_SECONDS.get(timeframe, 3600)
        max_age_seconds = candle_seconds * 3
        age_seconds = (now - last_bar).total_seconds()
        if age_seconds > max_age_seconds:
            logger.warning(
                f"{symbol} {timeframe.value}: last bar {last_bar.strftime('%Y-%m-%d %H:%M')} "
                f"is {age_seconds / 60:.0f}m old (limit: {max_age_seconds / 60:.0f}m)"
            )
            return False
        return True
    def _validate_and_clean(
        self, df: pd.DataFrame, symbol: str, timeframe: Timeframe
    ) -> pd.DataFrame:
        if df is None or df.empty:
            raise self.DATA_ERROR(f"Empty dataset for {symbol}")
        original_len = len(df)
        df = df.dropna(subset=["Open", "High", "Low", "Close"], how="all")
        df = df.ffill(limit=3)
        df = df.dropna(subset=["Close"])
        df = df.sort_index()
        df = df[~df.index.duplicated(keep="last")]
        now = pd.Timestamp.utcnow()
        future_mask = df.index > now + pd.Timedelta(minutes=5)
        if future_mask.any():
            logger.warning(f"{symbol}: Dropping {future_mask.sum()} future timestamps")
            df = df[~future_mask]
        # Non-positive prices are corrupt, never real gold quotes. Reject
        # the row BEFORE the consistency repair below — repairing High/Low
        # from a corrupt 0.0 Open/Close would propagate the zero into
        # previously-valid fields (2026-08-04 audit, I45).
        nonpositive = (df[["Open", "High", "Low", "Close"]] <= 0).any(axis=1)
        if nonpositive.any():
            logger.warning(
                f"{symbol}: Dropping {nonpositive.sum()} bars with "
                f"non-positive prices (corrupt feed data)")
            df = df[~nonpositive]
            if df.empty:
                raise self.DATA_ERROR(
                    f"{symbol}: all bars had non-positive prices")
        invalid_ohlc = (
            (df["High"] < df["Low"]) |
            (df["High"] < df["Open"]) |
            (df["High"] < df["Close"]) |
            (df["Low"] > df["Open"]) |
            (df["Low"] > df["Close"])
        )
        if invalid_ohlc.any():
            logger.warning(f"{symbol}: Fixing {invalid_ohlc.sum()} OHLC-inconsistent bars")
            for idx in df[invalid_ohlc].index:
                vals = [df.loc[idx, "Open"], df.loc[idx, "Close"]]
                df.loc[idx, "High"] = max(vals + [df.loc[idx, "High"]])
                df.loc[idx, "Low"]  = min(vals + [df.loc[idx, "Low"]])
        # inf returns (division by a zero/na Close) would NaN the std and
        # silently disarm the whole 10σ pass — neutralize them first.
        returns = df["Close"].pct_change()
        returns = returns.replace([float("inf"), float("-inf")], float("nan"))
        std_ret = returns.std()
        if std_ret > 0:
            dev = returns - returns.mean()
            outlier_mask = dev.abs() > 10 * std_ret
            # A spike bar makes the NEXT bar's return look extreme too (it is
            # measured against the spike). If the previous bar is flagged and
            # this bar reverses direction, it is the legitimate snap-back —
            # keep it, or every spike would punch a two-bar hole in the data.
            recovery = (
                outlier_mask
                & outlier_mask.shift(1, fill_value=False)
                & (dev * dev.shift(1) < 0)
            )
            outlier_mask = outlier_mask & ~recovery
            if outlier_mask.any():
                logger.warning(f"{symbol}: Removing {outlier_mask.sum()} extreme outlier bars")
                df = df[~outlier_mask]
        min_required = max(
            self.config.technical.ema_trend,
            200,
        ) + 10
        if len(df) < min_required:
            logger.warning(
                f"{symbol} {timeframe.value}: Only {len(df)} bars "
                f"(need {min_required}). Some indicators will be partial."
            )
        cleaned = original_len - len(df)
        if cleaned > 0:
            logger.info(f"{symbol}: Cleaned {cleaned}/{original_len} bars")
        return df
@@ -1,232 +0,0 @@
 """
 ForexBot2 — Oanda v20 data provider
 ====================================
 OHLCV candles from the Oanda v20 REST API. All shared behavior (cache,
 validation, boundary guarantees, range pagination) lives in
 `BaseDataProvider`; this module only maps symbols/granularities, parses
 Oanda's candle JSON, and exposes the real bid/ask spread from MBA candles.
 """
 import logging
 import time
 from datetime import datetime
 from typing import Dict, Optional
 import pandas as pd
 import requests
 from app_config import oanda_api_token, oanda_base_url
 from config import PAIR_INFO, PlatformConfig, Timeframe, XAUUSD
 from providers.base import BaseDataProvider, DataIntegrityError
 logger = logging.getLogger(__name__)
 class OandaDataError(DataIntegrityError):
    """Raised when an Oanda data fetch fails or returns invalid data."""
 class OandaDataProvider(BaseDataProvider):
    """Oanda v20 REST candle provider."""
    name = "oanda"
    DATA_ERROR = OandaDataError
    RETRY_DELAY_S = 2.0
    OANDA_GRANULARITY = {
        Timeframe.M1: "M1",
        Timeframe.M5: "M5",
        Timeframe.M15: "M15",
        Timeframe.M30: "M30",
        Timeframe.H1: "H1",
        Timeframe.H4: "H4",
        Timeframe.D1: "D",
        Timeframe.W1: "W",
    }
    OANDA_INSTRUMENT = {
        "XAU/USD": "XAU_USD",
    }
    def __init__(
        self,
        config: PlatformConfig,
        api_token: Optional[str] = None,
        base_url: Optional[str] = None,
    ):
        super().__init__(config)
        # Credentials default to the config.json `oanda` block / env vars
        # so the provider registry can build this class with config only.
        if api_token is None:
            api_token = oanda_api_token()
        if base_url is None:
            base_url = oanda_base_url()
        self._base_url = base_url.rstrip("/")
        self._session = requests.Session()
        self._session.headers.update({
            "Authorization": f"Bearer {api_token}",
            "Accept-Datetime-Format": "RFC3339",
            "Content-Type": "application/json",
        })
        self._spread_cache: Dict[str, float] = {}
    # ── Public extras ──────────────────────────────────────────────
    def get_spread(self, symbol: str = XAUUSD) -> float:
        return self._spread_cache.get(
            symbol, PAIR_INFO.get(symbol, PAIR_INFO[XAUUSD])["default_spread"])
    # ── Raw fetch ──────────────────────────────────────────────────
    def _fetch_raw(
        self,
        symbol: str,
        timeframe: Timeframe,
        outputsize: int,
        start: Optional[datetime] = None,
        end: Optional[datetime] = None,
    ) -> pd.DataFrame:
        if start is not None or end is not None:
            return self._fetch_oanda_candles(
                symbol, timeframe, outputsize, start, end)
        return self._fetch_oanda_candles(symbol, timeframe, outputsize)
    def _to_oanda_instrument(self, symbol: str) -> str:
        if symbol in self.OANDA_INSTRUMENT:
            return self.OANDA_INSTRUMENT[symbol]
        return symbol.replace("/", "_")
    def _fetch_oanda_candles(
        self,
        symbol: str,
        timeframe: Timeframe,
        count: int,
        start: Optional[datetime] = None,
        end: Optional[datetime] = None,
    ) -> pd.DataFrame:
        instrument = self._to_oanda_instrument(symbol)
        granularity = self.OANDA_GRANULARITY.get(timeframe)
        if granularity is None:
            raise OandaDataError(
                f"Unsupported timeframe {timeframe} for Oanda")
        url = f"{self._base_url}/v3/instruments/{instrument}/candles"
        params = {
            "granularity": granularity,
            "price": "MBA",
            "dailyAlignment": 0,
            "alignmentTimezone": "Etc/UTC",
        }
        # v20 rejects `count` combined with both `from` and `to`; a date
        # window replaces it (used by fetch_range pagination).
        if start is not None and end is not None:
            params["from"] = pd.Timestamp(start).isoformat()
            params["to"] = pd.Timestamp(end).isoformat()
        else:
            params["count"] = min(count, self.MAX_BARS_PER_REQUEST)
        # One retry absorbs Oanda's sporadic spurious 401s (~15-20/day on
        # the practice API — the same token succeeds on adjacent requests)
        # plus transient 5xx/network faults. A real credential problem
        # fails both attempts and raises exactly as before. Other 4xx
        # (bad request, 429) are never retried.
        last_err: Optional[OandaDataError] = None
        for attempt in range(2):
            if attempt:
                time.sleep(self.RETRY_DELAY_S)
            try:
                resp = self._session.get(url, params=params, timeout=30)
            except requests.RequestException as e:
                last_err = OandaDataError(
                    f"Oanda candle fetch failed for {instrument}: {e}")
                last_err.__cause__ = e
                continue
            if resp.status_code == 401 or resp.status_code >= 500:
                last_err = OandaDataError(
                    f"Oanda candle fetch failed for {instrument}: "
                    f"HTTP {resp.status_code}: {resp.text[:300]}")
                if attempt == 0:
                    logger.warning(
                        f"Oanda fetch for {instrument} got HTTP "
                        f"{resp.status_code}; retrying in "
                        f"{self.RETRY_DELAY_S}s")
                continue
            break
        else:
            raise last_err
        if resp.status_code != 200:
            raise OandaDataError(
                f"Oanda candle fetch failed for {instrument}: "
                f"HTTP {resp.status_code}: {resp.text[:300]}")
        try:
            data = resp.json()
        except ValueError as e:
            raise OandaDataError(
                f"Oanda candle response for {instrument} is not valid "
                f"JSON: {e}") from e
        candles = data.get("candles", [])
        if not candles:
            raise OandaDataError(
                f"Empty candle response from Oanda for {instrument}")
        rows = []
        last_bid_close = None
        last_ask_close = None
        skipped_malformed = 0
        for c in candles:
            if not c.get("complete", False):
                continue
            mid = c.get("mid", {})
            bid = c.get("bid", {})
            ask = c.get("ask", {})
            # A candle missing any OHLC field must be SKIPPED, never
            # defaulted to 0.0: a zero price flows through cleaning as a
            # "legit" bar and poisons ATR/Keltner/SL math — and a
            # mid-frame zero Close makes pct_change() emit inf, which
            # NaNs the std and disarms the 10σ outlier filter for the
            # whole frame (2026-08-04 audit, I45).
            if any(mid.get(k) is None for k in ("o", "h", "l", "c")):
                skipped_malformed += 1
                continue
            rows.append({
                "Datetime": pd.Timestamp(c["time"]).tz_convert("UTC"),
                "Open": float(mid["o"]),
                "High": float(mid["h"]),
                "Low": float(mid["l"]),
                "Close": float(mid["c"]),
                "Volume": int(c.get("volume", 0)),
            })
            if bid and ask:
                last_bid_close = float(bid.get("c", 0))
                last_ask_close = float(ask.get("c", 0))
        if skipped_malformed:
            logger.warning(
                "Oanda returned %d malformed candle(s) for %s (missing "
                "OHLC fields) — skipped", skipped_malformed, instrument)
        if not rows:
            raise OandaDataError(
                f"No complete candles from Oanda for {instrument}")
        if last_bid_close and last_ask_close:
            self._spread_cache[symbol] = round(
                last_ask_close - last_bid_close, 4)
        df = pd.DataFrame(rows)
        df = df.set_index("Datetime")
        df = df.sort_index()
        if df.index.tz is None:
            df.index = df.index.tz_localize("UTC")
        df.index.name = "Datetime"
        for col in ("Open", "High", "Low", "Close"):
            df[col] = pd.to_numeric(df[col], errors="coerce")
        df["Volume"] = pd.to_numeric(df["Volume"], errors="coerce").fillna(0).astype(float)
        logger.debug(
            "Fetched %d bars for %s @ %s from Oanda",
            len(df), instrument, granularity)
        return df
@@ -1,306 +0,0 @@
 """
 ForexBot2 — Tiingo data provider
 =================================
 OHLCV candles from Tiingo's FX endpoint
 (`/tiingo/fx/{ticker}/prices`, resampled mid-price bars, no volume).
 All shared behavior (cache, validation, boundary guarantees, range
 pagination) lives in `BaseDataProvider`; this module only maps
 symbols/resample frequencies, sizes the request window, and parses
 Tiingo's JSON into the normalized frame.
 Every failure mode raises `TiingoDataError` (a `DataIntegrityError`)
 with a specific message — the scan loops log it and carry on; nothing
 fails silently. NOTE: candles and mid-price ticks only. Tiingo's free
 `/tiingo/fx/top` quote endpoint publishes indicative two-tier spreads
 and must not be used for spread display (removed 2026-07-23) —
 `get_spread` deliberately stays on the base-class default, and the
 WebSocket stream (`iter_ticks`) exposes the mid price only, never
 bid/ask or a derived spread.
 """
 import json
 import logging
 from datetime import datetime
 from typing import AsyncIterator, Callable, Optional, Sequence, Tuple
 import pandas as pd
 import requests
 from app_config import provider_api_key
 from config import PlatformConfig, Timeframe
 from providers.base import BaseDataProvider, DataIntegrityError
 logger = logging.getLogger(__name__)
 class TiingoDataError(DataIntegrityError):
    """Raised when a Tiingo data fetch fails or returns invalid data."""
 class TiingoDataProvider(BaseDataProvider):
    """Tiingo FX REST candle provider."""
    name = "tiingo"
    DATA_ERROR = TiingoDataError
    BASE_URL = "https://api.tiingo.com"
    TIINGO_RESAMPLE = {
        Timeframe.M1: "1min",
        Timeframe.M5: "5min",
        Timeframe.M15: "15min",
        Timeframe.M30: "30min",
        Timeframe.H1: "1hour",
        Timeframe.H4: "4hour",
        Timeframe.D1: "1day",
        # W1 intentionally unmapped: the FX endpoint has no weekly
        # resample — requesting it raises loudly instead of guessing.
    }
    TIINGO_TICKER = {
        "XAU/USD": "xauusd",
    }
    # Window padding over the theoretical bar span: FX is closed ~2/7 of
    # the week (Fri 22:00 → Sun 22:00 UTC), so a calendar window must be
    # wider than `bars × candle_seconds` to yield the requested count.
    WINDOW_PAD_FACTOR = 1.6
    def __init__(self, config: PlatformConfig, api_key: Optional[str] = None):
        super().__init__(config)
        # Credentials default to env TIINGO_API_KEY → config.json
        # data_providers.tiingo.api_key, so the provider registry can
        # build this class with config only.
        if api_key is None:
            api_key = provider_api_key(provider="tiingo")
        self._api_key = api_key or None
        if self._api_key is None:
            logger.warning(
                "Tiingo provider built WITHOUT an API key — every fetch "
                "will fail with a named error. Set the TIINGO_API_KEY env "
                "var or data_providers.tiingo.api_key in config.json.")
        self._session = requests.Session()
        self._session.headers.update({
            "Content-Type": "application/json",
            # Header auth keeps the key out of URLs (and out of logs).
            "Authorization": f"Token {api_key}" if api_key else "",
        })
    def _to_tiingo_ticker(self, symbol: str) -> str:
        if symbol in self.TIINGO_TICKER:
            return self.TIINGO_TICKER[symbol]
        return symbol.replace("/", "").replace("=X", "").lower()
    # ── Raw fetch ──────────────────────────────────────────────────
    def _fetch_raw(
        self,
        symbol: str,
        timeframe: Timeframe,
        outputsize: int,
        start: Optional[datetime] = None,
        end: Optional[datetime] = None,
    ) -> pd.DataFrame:
        if self._api_key is None:
            raise TiingoDataError(
                "No Tiingo API key. Set the TIINGO_API_KEY env var or "
                "data_providers.tiingo.api_key in config.json.")
        resample = self.TIINGO_RESAMPLE.get(timeframe)
        if resample is None:
            raise TiingoDataError(
                f"Unsupported timeframe {timeframe} for Tiingo "
                f"(no FX resample frequency)")
        ticker = self._to_tiingo_ticker(symbol)
        # Tiingo takes date windows, not bar counts: derive the start
        # from the requested count for live fetches.
        if start is None:
            span = self.CANDLE_SECONDS[timeframe] * (outputsize + 10)
            start_ts = (pd.Timestamp.now(tz="UTC")
                        - pd.Timedelta(seconds=span * self.WINDOW_PAD_FACTOR))
        else:
            start_ts = pd.Timestamp(start)
        params = {
            "resampleFreq": resample,
            "startDate": start_ts.strftime("%Y-%m-%dT%H:%M:%S"),
        }
        if end is not None:
            params["endDate"] = pd.Timestamp(end).strftime("%Y-%m-%dT%H:%M:%S")
        url = f"{self.BASE_URL}/tiingo/fx/{ticker}/prices"
        try:
            resp = self._session.get(url, params=params, timeout=30)
        except requests.RequestException as e:
            raise TiingoDataError(
                f"Tiingo candle fetch failed for {ticker}: {e}") from e
        if resp.status_code == 429:
            raise TiingoDataError(
                f"Tiingo rate limit hit for {ticker} (HTTP 429) — "
                f"plan quota exhausted; scan will retry next boundary")
        if resp.status_code in (401, 403):
            raise TiingoDataError(
                f"Tiingo auth failed for {ticker} (HTTP {resp.status_code}) "
                f"— check TIINGO_API_KEY / config.json api_key")
        if resp.status_code != 200:
            raise TiingoDataError(
                f"Tiingo candle fetch failed for {ticker}: "
                f"HTTP {resp.status_code}: {resp.text[:300]}")
        try:
            data = resp.json()
        except ValueError as e:
            raise TiingoDataError(
                f"Tiingo candle response for {ticker} is not valid "
                f"JSON: {e}") from e
        if not isinstance(data, list) or not data:
            raise TiingoDataError(
                f"Empty candle response from Tiingo for {ticker} "
                f"(window {params['startDate']} → "
                f"{params.get('endDate', 'now')}; check ticker and plan "
                f"history limits)")
        rows = []
        for item in data:
            if "date" not in item:
                raise TiingoDataError(
                    f"Malformed Tiingo candle for {ticker}: missing "
                    f"'date' in {str(item)[:120]}")
            rows.append({
                "Datetime": pd.Timestamp(item["date"]),
                "Open": item.get("open"),
                "High": item.get("high"),
                "Low": item.get("low"),
                "Close": item.get("close"),
                # FX endpoint has no volume; 0.0 keeps the engine contract.
                "Volume": 0.0,
            })
        df = pd.DataFrame(rows)
        df = df.set_index("Datetime")
        df = df.sort_index()
        if df.index.tz is None:
            df.index = df.index.tz_localize("UTC")
        else:
            df.index = df.index.tz_convert("UTC")
        df.index.name = "Datetime"
        for col in ("Open", "High", "Low", "Close"):
            df[col] = pd.to_numeric(df[col], errors="coerce")
        if df["Close"].isna().all():
            raise TiingoDataError(
                f"Tiingo response for {ticker} has no numeric closes — "
                f"feed format changed?")
        logger.debug(
            "Fetched %d bars for %s @ %s from Tiingo",
            len(df), ticker, resample)
        return df
    # ── Streaming quotes ───────────────────────────────────────────
    supports_streaming = True
    WS_URL = "wss://api.tiingo.com/fx"
    @staticmethod
    def _parse_ws_message(raw: str) -> Optional[Tuple[str, float]]:
        """Parse one WS message; return (tiingo_ticker, mid_price) for
        quote updates, None for heartbeats ("H"), subscription acks
        ("I"), malformed JSON, or quotes without a usable price.
        Server error messages ("E", e.g. bad token) RAISE TiingoDataError:
        Tiingo authenticates inside the socket via the subscribe message,
        so an auth failure arrives as a data message followed by a clean
        close — swallowing it would make the caller's reconnect loop
        hammer the endpoint with no backoff (backoff only grows on
        exceptions).
        """
        try:
            msg = json.loads(raw)
        except json.JSONDecodeError:
            logger.warning("Tiingo WS: malformed JSON, skipping")
            return None
        mtype = msg.get("messageType")
        if mtype == "H":
            return None
        if mtype == "I":
            logger.info("Tiingo WS: subscription acknowledged")
            return None
        if mtype == "E":
            raise TiingoDataError(
                f"Tiingo WS server error: {str(msg.get('response'))[:200]}")
        if mtype != "A":
            return None
        data = msg.get("data")
        if not isinstance(data, list) or len(data) < 8 or data[0] != "Q":
            return None
        # Live-verified field order (2026-08-03): ["Q", ticker, timestamp,
        # bidSize, bid, mid, askSize, ask] — ask is index 7, NOT 6 as the
        # docs' table suggests (index 6 is askSize).
        ticker = data[1]
        bid, mid, ask = data[4], data[5], data[7]
        if mid is None and bid is not None and ask is not None:
            mid = (bid + ask) / 2
        try:
            return ticker, float(mid)
        except (TypeError, ValueError):
            logger.warning("Tiingo WS: quote for %s has no usable price",
                           ticker)
            return None
    async def iter_ticks(
        self,
        symbols: Sequence[str],
        on_connect: Optional[Callable[[], None]] = None,
    ) -> AsyncIterator[Tuple[str, float]]:
        """One connection's lifetime of (canonical_symbol, mid) ticks.
        The caller owns the reconnect/backoff loop around this generator.
        Divergences from the Twelve Data reference implementation, both
        deliberate: `on_connect` fires on the FIRST RECEIVED message (the
        "I" ack in practice) rather than after the subscribe send, so a
        rejected token cannot reset the caller's backoff; and there is no
        client heartbeat task — Tiingo sends "H" frames itself and the
        websockets library's built-in ping keepalive detects dead sockets.
        """
        import websockets
        if self._api_key is None:
            raise TiingoDataError(
                "No Tiingo API key. Set the TIINGO_API_KEY env var or "
                "data_providers.tiingo.api_key in config.json.")
        ws_to_pair = {self._to_tiingo_ticker(s): s for s in symbols}
        async with websockets.connect(self.WS_URL) as ws:
            await ws.send(json.dumps({
                "eventName": "subscribe",
                "authorization": self._api_key,
                "eventData": {
                    "thresholdLevel": 5,
                    "tickers": sorted(ws_to_pair),
                },
            }))
            logger.info(
                "Tiingo WS connected, subscribing to %d tickers",
                len(ws_to_pair))
            connected = False
            async for raw in ws:
                parsed = self._parse_ws_message(raw)
                if not connected:
                    connected = True
                    if on_connect is not None:
                        on_connect()
                if parsed is None:
                    continue
                pair_symbol = ws_to_pair.get(parsed[0])
                if pair_symbol is None:
                    continue
                yield pair_symbol, parsed[1]
@@ -1,247 +0,0 @@
 """
 ForexBot2 — Twelve Data provider
 =================================
 OHLCV via the official `twelvedata` SDK plus streaming quotes over the
 Twelve Data WebSocket. All shared behavior (cache, validation, boundary
 guarantees, range pagination) lives in `BaseDataProvider`.
 """
 import asyncio
 import json
 import logging
 import threading
 import time
 from collections import deque
 from datetime import datetime
 from typing import AsyncIterator, Callable, Optional, Sequence, Tuple
 import pandas as pd
 from twelvedata import TDClient
 from app_config import provider_api_key
 from config import PAIR_INFO, PlatformConfig, Timeframe
 from providers.base import BaseDataProvider, DataIntegrityError
 logger = logging.getLogger(__name__)
 class TwelveDataProvider(BaseDataProvider):
    """Twelve Data SDK client with credit throttling and WS quotes."""
    name = "twelve_data"
    TD_INTERVALS = {
        Timeframe.M1:  "1min",
        Timeframe.M5:  "5min",
        Timeframe.M15: "15min",
        Timeframe.M30: "30min",
        Timeframe.H1:  "1h",
        Timeframe.H4:  "4h",
        Timeframe.D1:  "1day",
        Timeframe.W1:  "1week",
    }
    RATE_LIMIT = 55
    RATE_WINDOW = 60.0
    WS_URL = "wss://ws.twelvedata.com/v1/quotes/price?apikey={api_key}"
    def __init__(self, config: PlatformConfig):
        super().__init__(config)
        self._api_key = provider_api_key(provider="twelve_data") or None
        self._throttle_lock = threading.Lock()
        self._request_times: deque = deque()
        self._td: Optional[TDClient] = (
            TDClient(apikey=self._api_key) if self._api_key else None
        )
    @staticmethod
    def _to_td_symbol(symbol: str) -> str:
        if "/" in symbol:
            return symbol
        clean = symbol.replace("=X", "")
        return f"{clean[:3]}/{clean[3:]}"
    # ── Raw fetch ──────────────────────────────────────────────────
    def _fetch_raw(
        self,
        symbol: str,
        timeframe: Timeframe,
        outputsize: int,
        start: Optional[datetime] = None,
        end: Optional[datetime] = None,
    ) -> pd.DataFrame:
        return self._fetch_twelve_data(
            symbol, timeframe, outputsize,
            start_date=start.strftime("%Y-%m-%d %H:%M:%S") if start else None,
            end_date=end.strftime("%Y-%m-%d %H:%M:%S") if end else None,
        )
    def _throttle(self, credits: int = 1) -> None:
        """Pro tier: 55 credits / minute. Block until budget allows."""
        while True:
            with self._throttle_lock:
                now = time.time()
                while self._request_times and now - self._request_times[0] >= self.RATE_WINDOW:
                    self._request_times.popleft()
                available = self.RATE_LIMIT - len(self._request_times)
                if available >= credits:
                    for _ in range(credits):
                        self._request_times.append(time.time())
                    return
                used = len(self._request_times)
                wait = (self._request_times[0] + self.RATE_WINDOW) - now + 0.2
            logger.info(
                f"Rate limit: {used}/{self.RATE_LIMIT} credits used "
                f"— waiting {wait:.1f}s..."
            )
            time.sleep(wait)
    def _fetch_twelve_data(
        self,
        symbol: str,
        timeframe: Timeframe,
        outputsize: int,
        start_date: Optional[str] = None,
        end_date: Optional[str] = None,
    ) -> pd.DataFrame:
        if self._td is None:
            raise DataIntegrityError(
                "No data-provider API key. Set the TWELVE_DATA_API_KEY env "
                "var or data_providers.twelve_data.api_key in config.json."
            )
        td_symbol = self._to_td_symbol(symbol)
        interval = self.TD_INTERVALS[timeframe]
        self._throttle(credits=1)
        kwargs = {
            "symbol": td_symbol,
            "interval": interval,
            "outputsize": min(outputsize, self.MAX_BARS_PER_REQUEST),
            "timezone": "UTC",
        }
        if start_date:
            kwargs["start_date"] = start_date
        if end_date:
            kwargs["end_date"] = end_date
        try:
            df = self._td.time_series(**kwargs).as_pandas()
        except Exception as e:
            raise DataIntegrityError(
                f"Twelve Data fetch failed for {td_symbol}: {e}"
            ) from e
        if df is None or df.empty:
            raise DataIntegrityError(f"Empty response from Twelve Data for {td_symbol}")
        # SDK returns lowercase columns; engine expects capitalised.
        df = df.rename(columns={
            "open": "Open", "high": "High", "low": "Low",
            "close": "Close", "volume": "Volume",
        })
        for col in ("Open", "High", "Low", "Close", "Volume"):
            if col in df.columns:
                df[col] = pd.to_numeric(df[col], errors="coerce")
        if "Volume" not in df.columns:
            df["Volume"] = 0.0
        # SDK returns newest-first; engine + indicators want oldest-first.
        df = df.sort_index()
        if df.index.tz is None:
            df.index = df.index.tz_localize("UTC")
        df.index.name = "Datetime"
        logger.debug(f"Fetched {len(df)} bars for {td_symbol} @ {interval}")
        return df
    # ── Streaming quotes ───────────────────────────────────────────
    supports_streaming = True
    @staticmethod
    def _parse_ws_price(raw: str) -> Optional[Tuple[str, float]]:
        """Parse one WS message; return (ws_symbol, price) for price
        events, None for everything else (logged where noteworthy)."""
        try:
            msg = json.loads(raw)
        except json.JSONDecodeError:
            logger.warning("WebSocket: malformed JSON, skipping")
            return None
        event = msg.get("event")
        if event == "subscribe-status":
            fails = msg.get("fails") or []
            if fails:
                logger.warning(f"WebSocket: failed to subscribe: {fails}")
            return None
        if event != "price":
            return None
        ws_symbol = msg.get("symbol")
        try:
            price = float(msg["price"])
        except (KeyError, ValueError, TypeError) as e:
            logger.warning(f"WebSocket: bad price data for {ws_symbol}: {e}")
            return None
        return ws_symbol, price
    @staticmethod
    async def _ws_heartbeat(ws) -> None:
        """Twelve Data drops idle WS connections; heartbeat every 10s keeps
        it alive through quiet periods (weekends, low-liquidity hours)."""
        while True:
            await asyncio.sleep(10)
            await ws.send(json.dumps({"action": "heartbeat"}))
    async def iter_ticks(
        self,
        symbols: Sequence[str],
        on_connect: Optional[Callable[[], None]] = None,
    ) -> AsyncIterator[Tuple[str, float]]:
        """One connection's lifetime of (canonical_symbol, price) ticks.
        The TD WS speaks display symbols ("XAU/USD"); ticks are mapped back
        to the canonical pair symbols passed in. The caller owns the
        reconnect/backoff loop around this generator.
        `on_connect` fires on the FIRST RECEIVED frame, not after the
        subscribe send (2026-08-04 audit H38b, mirroring the Tiingo
        provider) — firing pre-receive let a rejected API key reset the
        caller's reconnect backoff on every attempt, pinning it at the
        minimum forever.
        """
        import websockets
        ws_to_pair = {PAIR_INFO[s]["display"]: s for s in symbols}
        url = self.WS_URL.format(api_key=self._api_key or "")
        async with websockets.connect(url) as ws:
            subscribe = {
                "action": "subscribe",
                "params": {"symbols": ",".join(ws_to_pair)},
            }
            await ws.send(json.dumps(subscribe))
            logger.info(f"WebSocket connected, subscribed to {len(ws_to_pair)} symbols")
            hb_task = asyncio.create_task(self._ws_heartbeat(ws))
            connected = False
            try:
                async for raw in ws:
                    if not connected:
                        connected = True
                        if on_connect is not None:
                            on_connect()
                    parsed = self._parse_ws_price(raw)
                    if parsed is None:
                        continue
                    pair_symbol = ws_to_pair.get(parsed[0])
                    if pair_symbol is None:
                        continue
                    yield pair_symbol, parsed[1]
            finally:
                hb_task.cancel()
@@ -10,15 +10,12 @@ import os
 from dataclasses import dataclass, field
 from datetime import datetime, timezone
 from typing import Dict, List, Optional
 from zoneinfo import ZoneInfo
 import pandas as pd
 _NY_TZ = ZoneInfo("America/New_York")
 from chart_patterns import detect_chart_pattern
 from config import PlatformConfig, SignalStrength, Timeframe
-from providers import BaseDataProvider, make_data_provider
+from data_provider import ForexDataProvider
 from engine import TechnicalEngine, TechnicalAnalysis
 from news_engine import NewsEngine, FundamentalContext
@@ -26,19 +23,15 @@ logger = logging.getLogger(__name__)
 def _is_forex_weekend(now_utc: datetime) -> bool:
-    """Gold/FX market closed for the weekend: Friday 17:00 → Sunday 18:00
+    """Forex market closed: Friday 22:00 UTC → Sunday 22:00 UTC (Sydney open)."""
-    NEW YORK time (= Fri 21:00 UTC in US summer, 22:00 in winter —
+    wd = now_utc.weekday()
-    matches measured Oanda feed behavior; 2026-08-04 audit I43).
+    hour = now_utc.hour
-    Deliberate copy of providers.base._is_forex_weekend — a Module 21
+    if wd == 4 and hour >= 22:  # Friday after 22:00
-    test asserts the copies agree across DST transitions."""
+        return True
-    ny = now_utc.astimezone(_NY_TZ)
+    if wd == 5:  # All Saturday
-    wd = ny.weekday()
+        return True
-    if wd == 4:                      # Friday
+    if wd == 6 and hour < 22:  # Sunday before 22:00
        return ny.hour >= 17
    if wd == 5:                      # Saturday
        return True
    if wd == 6:                      # Sunday
        return ny.hour < 18
    return False
@@ -57,12 +50,6 @@ class TradeSetup:
    pip_value: float
    # Entry archetype ("GENERIC", "XAU_PB") — selects trail params.
    strategy: str = "GENERIC"
    # Trailing exit params stamped from the PRODUCING config, so per-TF
    # factory overrides (e.g. M15 arm 0.70, M5 dist 0.85) reach every
    # consumer. None = legacy/unstamped setup — formatters fall back to
    # platform defaults; 0.0 means trailing is genuinely disabled.
    trail_arm_r: Optional[float] = None
    trail_dist_r: Optional[float] = None
@dataclass
@@ -86,16 +73,6 @@ class SignalReport:
    event_description: str = ""
    news_sentiment: float = 0.0
    news_description: str = ""
    news_summary: str = ""  # LLM market-context blurb (display-only)
    # LLM agent-panel consensus (advisory/DISPLAY-ONLY). Populated
    # post-hoc by consumers via agent_panel.attach_panel — NEVER inside
    # analyze_from_dataframes (backtest parity) and never read by any
    # gate or scoring path.
    panel_verdict: str = ""        # "AGREE" / "NEUTRAL" / "OPPOSE" / ""
    panel_confidence: float = 0.0
    panel_rationale: str = ""
    panel_takes: List[Dict] = field(default_factory=list)
    final_signal: SignalStrength = SignalStrength.NEUTRAL
    final_score: float = 0.0
@@ -127,7 +104,7 @@ class SignalAggregator:
    def __init__(self, config: PlatformConfig):
        self.config = config
-        self.data_provider = make_data_provider(config)
+        self.data_provider = ForexDataProvider(config)
        self.tech_engine = TechnicalEngine(config.technical)
        self._news_engine: Optional[NewsEngine] = None  # lazy
@@ -197,7 +174,7 @@ class SignalAggregator:
            try:
                df = self.data_provider.fetch_ohlcv(symbol, tf)
                dfs[tf] = df
-                if not self.data_provider.is_data_fresh(df, symbol, tf):
+                if not self.data_provider._is_data_fresh(df, symbol, tf):
                    stale_tfs.append(tf.value)
            except Exception as e:
                logger.warning(f"Fetch failed for {symbol} {tf.value}: {e}")
@@ -231,7 +208,7 @@ class SignalAggregator:
                bar_close = (
                    pd.Timestamp(report.timestamp)
                    + pd.Timedelta(
-                        seconds=BaseDataProvider.CANDLE_SECONDS[
+                        seconds=ForexDataProvider.CANDLE_SECONDS[
                            self.config.primary_timeframe
                        ]
                    )
@@ -251,12 +228,7 @@ class SignalAggregator:
                    report.action_recommendation = "NO TRADE — Outside tradeable session"
                    report.trade_setup = None
            except Exception:
-                # Fail-open by design (an unparseable timestamp shouldn't
+                pass
                # kill the scan) but never silently: this filter guards
                # real money hours.
                logger.exception(
                    f"{symbol}: live session filter errored — filter SKIPPED this scan"
                )
        return report
@@ -323,44 +295,6 @@ class SignalAggregator:
            return None
        return float(adx.iloc[-1])
    def _detect_big_candle(self, primary_df: pd.DataFrame) -> Optional[tuple]:
        """Largest abnormal candle in the recent primary-TF window.
        Returns (timestamp, ratio) of the biggest bar whose range is
        >= big_candle_atr_multiple x the PRIOR bar's ATR within the last
        big_candle_lookback_hours of primary-TF bars, else None. The
        prior-bar ATR (shift 1) matters: a huge candle inflates the
        Wilder ATR immediately, so comparing against the concurrent ATR
        under-detects the very bars this gate exists to catch.
        Pure pandas on the caller-sliced df, so the live path and the
        backtest replay (which calls analyze_from_dataframes on prefix
        slices) compute bit-identical answers. The tail slice keeps the
        per-call cost bounded (~n + 42 rows); min_periods NaNs and short
        histories degrade to "no trip".
        """
        cfg = self.config.xau_strategy
        bar_secs = BaseDataProvider.CANDLE_SECONDS[self.config.primary_timeframe]
        n = max(1, int(cfg.big_candle_lookback_hours * 3600 // bar_secs))
        period = self.config.technical.atr_period
        tail = primary_df.iloc[-(n + 3 * period):]
        high, low, close = tail["High"], tail["Low"], tail["Close"]
        tr = pd.concat(
            [high - low,
             (high - close.shift()).abs(),
             (low - close.shift()).abs()],
            axis=1,
        ).max(axis=1)
        atr = tr.ewm(alpha=1 / period, min_periods=period, adjust=False).mean()
        ratio = (high - low) / atr.shift(1)
        window = ratio.iloc[-n:]
        if window.empty:
            return None
        max_ratio = window.max()
        if pd.isna(max_ratio) or max_ratio < cfg.big_candle_atr_multiple:
            return None
        return window.idxmax(), float(max_ratio)
    def _apply_m5_filters(self, report: SignalReport) -> None:
        """Extra selectivity for M5 — noisy TF needs stronger confirmation."""
        if report.signal_quality not in ("A+", "A", "B", "C"):
@@ -604,7 +538,6 @@ class SignalAggregator:
        min_tp = (self.config.signals.round_trip_cost
                  * self.config.signals.min_tp_cost_multiple)
        if 1.5 * sl_dist < min_tp:
            _reject("spread_gate")
            return
        report.trade_setup = TradeSetup(
@@ -620,10 +553,7 @@ class SignalAggregator:
            reward_pips=1.5 * sl_dist / pip_value,
            pip_value=pip_value,
            strategy="XAU_PB",
            trail_arm_r=self.config.signals.pb_trail_arm_r,
            trail_dist_r=self.config.signals.pb_trail_dist_r,
        )
        _reject("accepted")   # funnel tick, mirrors the CP path
        if report.signal_quality not in ("A+", "A", "B"):
            report.signal_quality = "B"
        report.final_signal = (SignalStrength.BUY if is_buy
@@ -692,17 +622,13 @@ class SignalAggregator:
                    close_h = (
                        pd.Timestamp(report.timestamp)
                        + pd.Timedelta(
-                            seconds=BaseDataProvider.CANDLE_SECONDS[
+                            seconds=ForexDataProvider.CANDLE_SECONDS[
                                self.config.primary_timeframe
                            ]
                        )
                    ).hour
                except Exception:
                    close_h = None
                    logger.warning(
                        f"{report.symbol}: bar-close hour underivable — "
                        f"SELL hour gate skipped"
                    )
                if close_h in cfg.sell_blocked_hours_utc:
                    _reject("sell_hour")
                    return
@@ -757,8 +683,6 @@ class SignalAggregator:
            reward_pips=height / pip_value,
            pip_value=pip_value,
            strategy="XAU_CP",
            trail_arm_r=self.config.signals.trail_arm_r,
            trail_dist_r=self.config.signals.trail_dist_r,
        )
        if report.signal_quality not in ("A+", "A", "B"):
            report.signal_quality = "B"
@@ -849,13 +773,11 @@ class SignalAggregator:
            sentiment, sent_desc = news.get_pair_sentiment(symbol, context)
            report.news_sentiment = sentiment
            report.news_description = sent_desc
            report.news_summary = context.llm_summary
        else:
            report.event_risk = "clear"
            report.event_description = ""
            report.news_sentiment = 0.0
            report.news_description = ""
            report.news_summary = ""
        # ── 5. Spread estimate from primary data (avoids extra API call) ─
        try:
@@ -892,10 +814,7 @@ class SignalAggregator:
                        f"DAY FILTER: {day_names.get(bar_wd, '?')} not in allowed days"
                    )
            except Exception:
-                # Fail-open by design, but never silently (see live wrapper).
+                pass
                logger.exception(
                    f"{symbol}: session/day filter errored — filter SKIPPED"
                )
        # ── 6c. Transitioning regime quality boost ──────────────────
        if report.market_regime == "transitioning" and report.signal_quality in ("B", "C", "D"):
@@ -996,17 +915,13 @@ class SignalAggregator:
                    close_h = (
                        pd.Timestamp(report.timestamp)
                        + pd.Timedelta(
-                            seconds=BaseDataProvider.CANDLE_SECONDS[
+                            seconds=ForexDataProvider.CANDLE_SECONDS[
                                self.config.primary_timeframe
                            ]
                        )
                    ).hour
                except Exception:
                    close_h = None
                    logger.warning(
                        f"{report.symbol}: bar-close hour underivable — "
                        f"SELL hour gate skipped"
                    )
                if close_h in self.config.xau_strategy.sell_blocked_hours_utc:
                    report.warnings.append(
                        f"SELL FILTER: {close_h:02}:00 UTC entry blocked for shorts"
@@ -1014,30 +929,6 @@ class SignalAggregator:
                    report.signal_quality = "D"
                    report.trade_setup = None
        # ── 6h. Big-candle entry gate (opt-in via per-TF factories) ──
        # An abnormally large candle in the recent window = institutional
        # burst; price may reverse violently, so NO entry of any kind may
        # fire. Detection runs for ANY grade: the warning is the
        # suppression channel for the XAU_PB path (which accepts D) and
        # the XAU_CP path (which accepts even F). The D-downgrade itself
        # touches actionable grades only — F stays F (same convention as
        # 6e).
        if (self.config.xau_strategy.enabled
                and report.display_name == "XAU/USD"
                and self.config.xau_strategy.big_candle_atr_multiple > 0):
            big_candle = self._detect_big_candle(primary_df)
            if big_candle is not None:
                bc_ts, bc_ratio = big_candle
                report.warnings.append(
                    f"BIG CANDLE FILTER: {bc_ratio:.1f}x ATR bar at {bc_ts} "
                    f"within "
                    f"{self.config.xau_strategy.big_candle_lookback_hours:g}h "
                    f"— no entries"
                )
                if report.signal_quality in ("A+", "A", "B", "C"):
                    report.signal_quality = "D"
                    report.trade_setup = None
        # ── 6i. M5 noise gate (gold-tuned thresholds — XAU only) ──
        if (self.config.primary_timeframe == Timeframe.M5
                and report.display_name == "XAU/USD"):
@@ -1066,9 +957,7 @@ class SignalAggregator:
                and report.display_name == "XAU/USD"
                and self.config.xau_strategy.m5_pullback_enabled
                and report.trade_setup is None
-                and report.signal_quality != "F"
+                and report.signal_quality != "F"):
                and not any(w.startswith("BIG CANDLE FILTER")
                            for w in report.warnings)):
            self._try_m5_pullback(report, primary_df, pip_value)
        # ── 10. Chart-pattern entry (XAU_CP) ────────────────────────
@@ -1085,8 +974,7 @@ class SignalAggregator:
                and self.config.xau_strategy.cp_enabled
                and report.trade_setup is None
                and not any(w.startswith(("SESSION FILTER", "DAY FILTER",
-                                          "WEEKEND FILTER",
+                                          "WEEKEND FILTER"))
                                          "BIG CANDLE FILTER"))
                            for w in report.warnings)):
            self._try_chart_pattern(report, primary_df, pip_value)
@@ -1160,8 +1048,6 @@ class SignalAggregator:
            risk_pips=round(sl_distance / pip_value, 1),
            reward_pips=round(tp_distance / pip_value, 1),
            pip_value=pip_value,
            trail_arm_r=self.config.signals.trail_arm_r,
            trail_dist_r=self.config.signals.trail_dist_r,
        )
    def _check_mtf_agreement(self, report: SignalReport) -> bool:
@@ -1193,7 +1079,7 @@ class SignalAggregator:
                mtf_avg = sum(all_scores) / len(all_scores)
                score = score * 0.6 + mtf_avg * 0.4
-        if self.config.news.sentiment_affects_signals and abs(report.news_sentiment) > 0.2:
+        if abs(report.news_sentiment) > 0.2:
            if (report.news_sentiment > 0 and score > 0) or \
               (report.news_sentiment < 0 and score < 0):
                confidence += 5
@@ -1243,7 +1129,6 @@ class SignalAggregator:
            if (report.news_sentiment > 0 and score < -10) or \
               (report.news_sentiment < 0 and score > 10):
                report.warnings.append("News sentiment CONFLICTS with technical signal")
                if self.config.news.sentiment_affects_signals:
                penalties.append(("news_conflict", 10))
        if report.event_risk == "caution":
@@ -1269,17 +1154,7 @@ class SignalAggregator:
            ema200 = primary.key_levels.get("ema_trend")
            if ema200 and ema200 > 0 and report.current_price > 0:
                ema_dist_pct = abs(report.current_price - ema200) / ema200 * 100.0
                # FB2_EMA_LO: research override for the EMA200-proximity
                # penalty band; the tuned default is 0.5. An unparseable
                # value must degrade to the default, never crash the scan
                # (2026-08-04 audit; hardened per owner delegation).
                try:
                _lo = float(os.environ.get("FB2_EMA_LO", "0.5"))
                except (TypeError, ValueError):
                    logger.warning(
                        "FB2_EMA_LO=%r is not a number — using tuned "
                        "default 0.5", os.environ.get("FB2_EMA_LO"))
                    _lo = 0.5
                if ema_dist_pct < _lo:
                    report.warnings.append(f"NEAR EMA200 ({ema_dist_pct:.2f}%) — chop risk")
                    penalties.append(("ema200_near", 5))
@@ -1289,11 +1164,6 @@ class SignalAggregator:
            atr = primary.key_levels.get("atr", 0)
            if atr > 0 and score != 0:
                # Distance is measured from the FARTHEST of the 3 tracked
                # swings (sorted()[0] = deepest low / highest high), so the
                # penalty fires earlier than a nearest-swing rule would.
                # Calibrated as-is in every validated book — changing the
                # anchor is a signal-logic change requiring re-validation.
                if score > 0:
                    swing_lows = sorted(
                        v for k, v in primary.key_levels.items() if k.startswith("swing_low_")
@@ -1574,8 +1444,6 @@ class SignalAggregator:
            risk_pips=risk_pips,
            reward_pips=reward_pips,
            pip_value=pip_value,
            trail_arm_r=self.config.signals.trail_arm_r,
            trail_dist_r=self.config.signals.trail_dist_r,
        )
    def _collect_reasons(self, report: SignalReport):
@@ -1,245 +0,0 @@
 <?php
 /**
 * On-demand gold-impact AI summary for one published headline.
 *
 * The ONLY dynamic piece of the otherwise-static dashboard. Serves
 * GET ?id=<sha1-of-headline> and returns {"summary": ..., "generated_at": ...}.
 *
 * Abuse model (the page is public):
 *   - WHITELIST: the id must match a headline in the local news.json —
 *     arbitrary text can never reach the LLM through this endpoint.
 *   - CACHE FOREVER: headlines are immutable, so each id costs at most
 *     one DeepSeek call, ever (ai/<id>.json). Cache is checked BEFORE
 *     the whitelist on purpose: summaries stay reachable after the
 *     headline ages out of the 24 h feed window.
 *   - DAILY CAP: file-based UTC-day counter, FAIL CLOSED on counter I/O
 *     errors (a perms mistake must not mean unmetered spend).
 *
 * DeepSeek doctrine (mirrors llm_client.py — deliberate local copy, the
 * dashboard never imports bot code and this file never reads the repo's
 * config.json):
 *   - STATIC byte-identical system prompt (prefix-cache pricing).
 *   - "thinking": disabled is MANDATORY for V4-Flash (else it burns the
 *     timeout reasoning), temperature 0, json response_format.
 *   - One attempt, no retries; every failure returns a JSON error the
 *     page renders as a graceful fallback.
 *
 * Key file lives OUTSIDE the docroot: /etc/gold-dashboard/deepseek.key
 * (root:apache 640). Override paths/caps via Apache SetEnv:
 * DEEPSEEK_KEY_FILE, AI_SUMMARY_DAILY_CAP.
 */
 const NEWS_FILE = __DIR__ . '/news.json';
 const CACHE_DIR = __DIR__ . '/ai';   // apache-owned; created at deploy time
 const API_URL   = 'https://api.deepseek.com/chat/completions';
 const MODEL     = 'deepseek-v4-flash';
 const TIMEOUT   = 12;
 const MAX_TOKENS = 200;
 const SUMMARY_MAX_CHARS = 400;
 // .ht_ prefix: Apache's default <Files ".ht*"> rule denies these even
 // though they sit in the public docroot.
 const CAP_FILE = CACHE_DIR . '/.ht_cap.json';
 const SYSTEM_PROMPT = <<<'PROMPT'
 You are a forex/gold news analyst. You receive one news item as JSON:
 {"headline", "description", "score", "xau_score", "keywords", "currencies"}.
 "xau_score" is the platform's automated gold-effect score for this item,
 -1.0 (gold down) to 1.0 (gold up); "score" is overall market sentiment.
 Both are automated and can be wrong — judge the news yourself.
 In 1-2 plain-English sentences (max 45 words) explain what this news
 means for the GOLD (XAU/USD) price and WHY it is bullish, bearish, or
 neutral. If your reading clearly contradicts xau_score, add "(the
 automated score disagrees)". Never write the words "xau_score" or
 "score" — a general reader sees this text.
 Gold doctrine: hawkish Fed / rate hikes / rising real yields / strong
 dollar are gold-bearish; easing / rate cuts / safe-haven flows /
 geopolitical stress / weak dollar are gold-bullish.
 Reply with ONLY JSON: {"summary": "..."}
 PROMPT;
 function respond(int $status, array $obj, string $cache = 'no-store'): void
 {
    http_response_code($status);
    header('Content-Type: application/json; charset=utf-8');
    header('Cache-Control: ' . $cache);
    echo json_encode($obj, JSON_UNESCAPED_SLASHES | JSON_UNESCAPED_UNICODE);
    exit;
 }
 function atomic_write(string $path, string $data): bool
 {
    $tmp = $path . '.tmp.' . getmypid();
    if (@file_put_contents($tmp, $data) === false) {
        return false;
    }
    if (!@rename($tmp, $path)) {
        @unlink($tmp);
        return false;
    }
    return true;
 }
 // ── 1. id validation ────────────────────────────────────────────────
 $id = $_GET['id'] ?? '';
 if (!preg_match('/^[a-f0-9]{40}$/', $id)) {
    respond(400, ['error' => 'bad_id']);
 }
 // ── 2. cache first (immutable headlines → summaries never change) ───
 $cachePath = CACHE_DIR . '/' . $id . '.json';
 $cached = @file_get_contents($cachePath);
 if ($cached !== false && $cached !== '') {
    http_response_code(200);
    header('Content-Type: application/json; charset=utf-8');
    header('Cache-Control: max-age=3600');
    echo $cached;
    exit;
 }
 // ── 3. whitelist against the published feed ─────────────────────────
 $news = json_decode((string)@file_get_contents(NEWS_FILE), true);
 if (!is_array($news)) {
    respond(503, ['error' => 'no_feed']);
 }
 $hit = null;
 foreach ($news['headlines'] ?? [] as $h) {
    // Fallback sha1(): a publisher that predates the id field still
    // serves a valid whitelist during the restart window.
    $hid = $h['id'] ?? sha1($h['headline'] ?? '');
    if ($hid === $id) {
        $hit = $h;
        break;
    }
 }
 if ($hit === null) {
    respond(404, ['error'   => 'unknown_id',
                  'message' => 'Headline is no longer in the feed.']);
 }
 // ── 4. daily cap (fail closed) ──────────────────────────────────────
 $dailyCap = (int)(getenv('AI_SUMMARY_DAILY_CAP') ?: 150);
 $capFh = @fopen(CAP_FILE, 'c+');
 if ($capFh === false || !flock($capFh, LOCK_EX)) {
    respond(503, ['error' => 'cap_io']);
 }
 $today = gmdate('Y-m-d');
 $cap = json_decode((string)stream_get_contents($capFh), true);
 if (!is_array($cap) || ($cap['day'] ?? '') !== $today) {
    $cap = ['day' => $today, 'count' => 0];
 }
 if ($cap['count'] >= $dailyCap) {
    flock($capFh, LOCK_UN);
    fclose($capFh);
    respond(429, ['error' => 'cap_exceeded']);
 }
 // Increment BEFORE the LLM call — attempts count against the cap
 // (same semantics as llm_client._under_cap).
 $cap['count']++;
 rewind($capFh);
 if (ftruncate($capFh, 0) === false
        || fwrite($capFh, json_encode($cap)) === false) {
    flock($capFh, LOCK_UN);
    fclose($capFh);
    respond(503, ['error' => 'cap_io']);
 }
 fflush($capFh);
 flock($capFh, LOCK_UN);
 fclose($capFh);
 // ── 5. API key ──────────────────────────────────────────────────────
 $keyFile = getenv('DEEPSEEK_KEY_FILE') ?: '/etc/gold-dashboard/deepseek.key';
 $apiKey = trim((string)@file_get_contents($keyFile));
 if ($apiKey === '') {
    respond(503, ['error' => 'no_key']);
 }
 // ── 6. per-id stampede lock, then re-check the cache ────────────────
 $lockFh = @fopen(CACHE_DIR . '/.ht_lock_' . $id, 'c');
 if ($lockFh !== false) {
    flock($lockFh, LOCK_EX);   // blocks while a concurrent winner generates
    $cached = @file_get_contents($cachePath);
    if ($cached !== false && $cached !== '') {
        flock($lockFh, LOCK_UN);
        fclose($lockFh);
        http_response_code(200);
        header('Content-Type: application/json; charset=utf-8');
        header('Cache-Control: max-age=3600');
        echo $cached;
        exit;
    }
 }
 // ── 7. DeepSeek call (exact chat_json body shape) ───────────────────
 $userPayload = json_encode([
    'headline'    => $hit['headline'] ?? '',
    'description' => $hit['description'] ?? null,
    'score'       => $hit['score'] ?? null,
    'xau_score'   => $hit['score_by_currency']['XAU'] ?? null,
    'keywords'    => $hit['keywords'] ?? [],
    'currencies'  => $hit['currencies'] ?? [],
 ], JSON_UNESCAPED_UNICODE);
 $body = json_encode([
    'model'    => MODEL,
    'messages' => [
        ['role' => 'system', 'content' => SYSTEM_PROMPT],
        ['role' => 'user',   'content' => $userPayload],
    ],
    'response_format' => ['type' => 'json_object'],
    'temperature'     => 0,
    'thinking'        => ['type' => 'disabled'],
    'max_tokens'      => MAX_TOKENS,
 ]);
 $ch = curl_init(API_URL);
 curl_setopt_array($ch, [
    CURLOPT_POST           => true,
    CURLOPT_POSTFIELDS     => $body,
    CURLOPT_HTTPHEADER     => ['Content-Type: application/json',
                               'Authorization: Bearer ' . $apiKey],
    CURLOPT_RETURNTRANSFER => true,
    CURLOPT_TIMEOUT        => TIMEOUT,
 ]);
 $raw = curl_exec($ch);
 $httpCode = curl_getinfo($ch, CURLINFO_RESPONSE_CODE);
 curl_close($ch);
 // ── 8. parse (any failure → 502, never cached) ──────────────────────
 $summary = null;
 if ($raw !== false && $httpCode === 200) {
    $resp = json_decode($raw, true);
    $content = $resp['choices'][0]['message']['content'] ?? '';
    $parsed = json_decode((string)$content, true);
    if (is_array($parsed) && is_string($parsed['summary'] ?? null)
            && trim($parsed['summary']) !== '') {
        $summary = trim($parsed['summary']);
        if (mb_strlen($summary) > SUMMARY_MAX_CHARS) {
            $summary = mb_substr($summary, 0, SUMMARY_MAX_CHARS) . '…';
        }
    }
 }
 if ($lockFh !== false && $summary === null) {
    flock($lockFh, LOCK_UN);
    fclose($lockFh);
 }
 if ($summary === null) {
    respond(502, ['error' => 'llm_failed']);
 }
 // ── 9. cache + respond ──────────────────────────────────────────────
 $out = json_encode(['summary' => $summary, 'generated_at' => gmdate('c')],
                   JSON_UNESCAPED_SLASHES | JSON_UNESCAPED_UNICODE);
 if (!atomic_write($cachePath, $out)) {
    // Degrade to per-click generation rather than a broken feature —
    // the daily cap still bounds spend.
    error_log('news_summary.php: cache write failed for ' . $cachePath);
 }
 if ($lockFh !== false) {
    flock($lockFh, LOCK_UN);
    fclose($lockFh);
 }
 http_response_code(200);
 header('Content-Type: application/json; charset=utf-8');
 header('Cache-Control: max-age=3600');
 echo $out;
@@ -0,0 +1,108 @@
 #!/usr/bin/env python3
 """Test whether Twelve Data allows 2 simultaneous WebSocket connections for XAU/USD.
 Uses the twelvedata SDK which handles the WS protocol correctly.
 Runs connection 1, then adds connection 2 after 10s, monitors for 30s total.
 """
 import threading
 import time
 from twelvedata import TDClient
 API_KEY = "f563b22ffdfd4924955decbe1a8c8701"
 SYMBOL = "XAU/USD"
 DURATION = 30
 results = {
    "WS-1": {"ticks": 0, "errors": [], "alive": False},
    "WS-2": {"ticks": 0, "errors": [], "alive": False},
 }
 def make_handlers(label):
    def on_event(event):
        if event.get("event") == "price":
            results[label]["ticks"] += 1
            price = event.get("price", "?")
            print(f"[{label}] tick #{results[label]['ticks']:3d}  price={price}")
    def on_error(error):
        results[label]["errors"].append(str(error))
        print(f"[{label}] ERROR: {error}")
    def on_close(status):
        results[label]["alive"] = False
        print(f"[{label}] CLOSED (status={status})")
    def on_connect():
        results[label]["alive"] = True
        print(f"[{label}] CONNECTED — subscribed to {SYMBOL}")
    return on_event, on_error, on_close, on_connect
 def run_ws(label):
    td = TDClient(apikey=API_KEY)
    on_event, on_error, on_close, on_connect = make_handlers(label)
    ws = td.websocket(
        symbols=SYMBOL,
        on_event=on_event,
        on_error=on_error,
        on_close=on_close,
        on_connect=on_connect,
    )
    ws.connect()
    return ws
 def main():
    print(f"Testing dual WebSocket for {SYMBOL} ({DURATION}s)\n")
    print("Starting WS-1...")
    ws1 = run_ws("WS-1")
    time.sleep(10)
    print(f"\nWS-1 ticks so far: {results['WS-1']['ticks']}")
    print("Starting WS-2...\n")
    ws2 = run_ws("WS-2")
    remaining = DURATION - 10
    time.sleep(remaining)
    print("\nStopping connections...")
    try:
        ws1.disconnect()
    except Exception:
        pass
    try:
        ws2.disconnect()
    except Exception:
        pass
    time.sleep(1)
    print("\n" + "=" * 50)
    print("RESULTS")
    print("=" * 50)
    for label, info in sorted(results.items()):
        status = "alive" if info["alive"] else "dead"
        errs = info["errors"] or ["none"]
        print(f"  {label}: {status}  ticks={info['ticks']}  errors={errs[0]}")
    ws1_ok = results["WS-1"]["ticks"] > 0 and not results["WS-1"]["errors"]
    ws2_ok = results["WS-2"]["ticks"] > 0 and not results["WS-2"]["errors"]
    ws1_died = results["WS-1"]["alive"] is False and results["WS-1"]["ticks"] > 0
    if ws1_ok and ws2_ok:
        print("\n✓ Both connections worked — dual WS is supported")
    elif ws2_ok and ws1_died:
        print("\n✗ WS-2 replaced WS-1 — only 1 connection allowed at a time")
    elif ws1_ok and not ws2_ok:
        print("\n✗ WS-2 was rejected — only 1 connection allowed")
    else:
        print("\n? Inconclusive — check errors above")
 if __name__ == "__main__":
    main()