result._kc_squeeze / result._kc_squeeze_released were assigned by both the live
TechnicalEngine and the backtester's GPUTechnicalEngine and read by nothing:
the only four occurrences in the repo were the assignments. The squeeze state
still feeds the Keltner signal text where it is computed. Removed from both
engines in one commit (rule 4).
Parity: 12-month M5 and H1 books regenerated byte-identical
(md5 a28fed4becd056466c26d6a947e1a0e5 / d20405ef64f069770c258bb9bed1ac90).
Suite: 461 passed, 0 failed.
Claude-Session: https://claude.ai/code/session_0156YhogLytzj9P2iH1skGbL
New additive columns on every logged scan: bar_ts (primary closed-bar label, indexed with
timeframe), engine_fingerprint (hash of the scoring config incl. the regime weight table in
force - SignalAggregator.engine_config_fingerprint, twin of the gate fingerprint), code_version
(short git SHA), penalty_tags_json / penalty_total / momentum_fading / exhaustion_count, and
post-hoc watch_level / watch_kinds / watch_line written by violent_watch.attach_watch via
signal_log.update_watch. Each indicators_json entry now also carries eff_weight (base weight x
regime multiplier, the weight the composite actually used) and wscore.
engine: IndicatorSignal.effective_weight / weighted_score are stamped inside _compute_composite
(record-keeping only); regime_weight_table(config) is the single source of truth for the
multiplier table (override honoured). Composite output byte-identical (120-report hash proof).
New table scan_outcomes: model-free forward moves per scan (fwd_1h/4h/24h in $ and ATRs,
mfe/mae over 24h, burst_1h, bars_used, complete) measured on the stored candle stream with the
advisory model's continuity rule; labelled opportunistically at the end of log() (time-boxed,
throttled, idempotent) - no daemon; scripts/label_scan_outcomes.py drives the backlog
(--dry-run is read-only). analyze_signal_log --scan-outcomes: forward move + hit rate by
TF x signal/grade with a NEUTRAL baseline and burst rate by watch level. Readers dedup by the
first row per (timeframe, bar_ts).
Module 31 (10 tests). Suite: 463 passed. Backtester untouched (test-locked).
Claude-Session: https://claude.ai/code/session_01RWV2yLt3YXHHGVYHSQU2EE
Five-agent audit of the signal path; all fixes are delivery/measurement
side. Regression-proven behavior-neutral on validated signals: the 5yr
M15 replay reproduces the pinned book exactly (+210.65R PF 1.35
WR 57.8% MaxDD 33.90R). Tests 215 -> 221.
- TradeSetup carries trail_arm_r/trail_dist_r stamped from the producing
config; Telegram trail plan reads them (was showing default 0.6/0.75
instead of the validated M15 arm 0.70 / M5 dist 0.85)
- Alert dedup: fingerprint = (signal, quality, direction); entry movement
compared against last-ALERTED entry/risk (old SL-dist bucket churned on
ATR drift -> near-duplicate alerts every candle)
- Fresh fetches enforce the closed-bar boundary check with one 3s grace
retry, then warn (feed lag no longer silently analyzes the prior bar)
- Backtest max DD includes the zero baseline (opening losing streaks
were undercounted; pinned M15 DD unchanged)
- Replay loads the regime-filter TF when the H4-ADX gate is on (was a
silent no-op for M5/M15 primaries)
- clear_cache under _cache_lock; dead ROC zero-cross branches removed
from both engines (provably unreachable); RSS dates via
parsedate_to_datetime (named zones no longer stamped UTC); event risk
grades from the soonest event; .env inline comments stripped;
auto-scan skips NEUTRAL like the breach path; report_to_dict honors
the weight-0 skip rule and emits strategy/trail params; PB funnel
counts spread_gate/accepted; session-filter fail-open paths log loudly
- Docs corrected to match validated code (H&S neckline = lower peak,
exhaustion anchors farthest tracked swing) with re-validation warnings
Claude-Session: https://claude.ai/code/session_01R5soRUeRBi8kHMB99csrJy
- Delete test_ws_dual.py (standalone probe, unused) and a stale tracked log
- config.py: drop MarketSession enum, is_jpy_pair, candle_buffer_seconds,
and the never-read legacy SignalConfig/XauStrategyConfig fields
- data_provider.py: drop uncalled get_latest_price/get_spread_estimate
- Remove unused imports (engine, telegram_bot, main, news_engine)
- test_all.py: drop/trim the corresponding defaults assertions (199 tests)
- CLAUDE.md updated to match
Zero behavior change: no production code read any removed field
(verified reference-by-reference); suite green at 199/199.
Claude-Session: https://claude.ai/code/session_01VPNF37MP9Kuv62B1VBrZ1b
Fixes all high+medium findings from the 2026-07-16 full-codebase audit.
High:
- data_provider: cache now rejects snapshots missing the latest closed
bar (_has_latest_closed_bar, weekend-exempt). The wall-clock TTL served
mid-candle snapshots at the next boundary, so live M15/H1 scans ran one
full bar behind and the bar-close session check evaluated the wrong hour.
- signal_aggregator: gate 6e (SELL quality) only acts on actionable
grades. It was re-grading session-blocked F reports to D, which
re-opened the M5 pullback path (entry check: quality != "F") on
session-blocked bars.
- gpu_backtest: port the live EMA-ribbon compression/expansion logic
(+10 conf on expansion) into the GPU engine. Its absence diverged
composite score/conf on 7-9% of bars and flipped signals on ~0.2%.
Medium:
- gpu_backtest: Bollinger precompute uses the exact pandas rolling calls
(cumsum E[x2]-E[x]2 lost precision at gold price levels; bb_position
error up to 7.6e-3 crossed signal thresholds, worst during squeezes).
- gpu_backtest: --confirm-tfs defaults to the pair factory's live
confirmation map instead of a hardcoded 1h,4h.
- gpu_backtest: entries re-anchor to the first RAW bar after the signal
bar, so >10-sigma spike bars dropped by cleaning are no longer skipped
at entry (favorable bias).
- gpu_backtest: per-TF warmup pad — D1 confirms get >=210 bars at window
start (62-day pad gave ~61 bars: no EMA200, unlike live).
- data_provider: thread-safety locks for cache + throttle (shared
provider across the bot's 4-thread pool); outlier filter keeps the
snap-back bar after a spike instead of punching a two-bar hole.
- telegram_bot: alert fingerprint gains a risk-relative entry bucket
(materially-moved setups re-alert) and is committed only after a
successful send (failed sends no longer consume the alert for 1h).
- engine: disabled_indicators=["macd"] now removes the MACD pillar from
scoring (histogram still computed for divergence).
Verification: test_all.py 187/187 (new Module 11: 8 audit-fix
regressions incl. a live-vs-GPU parity sweep with ribbon coverage);
real-data parity sweep over 813 slices of 14mo XAU M15: zero divergence
(pre-fix: 7-9% of bars diverged); both original bug repros no longer
reproduce; cached end-to-end backtest run passes with the live confirm
map. Books shift slightly vs pre-fix runs — comparability note added to
CLAUDE.md; pinned tuned numbers need re-validation under the fixed
backtester.
Fix 1 — mixed-"trending" regime (ADX strong, EMAs unaligned) was
hard-coded bullish in 5 sites: RSI reinterpretation used the uptrend
playbook (RSI 38-55 -> STRONG_BUY conf 75), BUY-only confidence
bonuses, BUY-only extension penalties, no counter-trend penalty.
Now: extremes-only RSI (as transitioning), no directional bonuses,
symmetric extension penalties. 6yr validation: H1 +67.2->+61.9R
(mixed skew 16B/11S -> 4B/7S), H4 +31.6->+27.5R (the 4 bias BUYs),
M15 +152.6->+154.7R, M5 +207.1->+206.1R with maxDD 28.7->24.3R.
The ~8R aggregate give-back is bull-market subsidy from the bug.
Fix 2 — live session filter now applies the EXACT replay-loop
predicate (session window + blocked hours on the signal bar's CLOSE
time, from the bar timestamp not wall clock). Replaces a wall-clock
filter gated on a "XAU STRATEGY" marker that no code writes, which
made live diverge from every validated book.
Fix 3 — delete dead _check_xau_oversold_bounce and
_generate_xau_trade_setup (unreachable; the latter hardcoded BUY and
skipped the spread/RR gates).
Backtester: parallel bar replay via forked workers (--workers,
default auto = cores-2 capped 12). Per-bar evaluation is pure; the
entry cooldown is applied in an ordered phase-2 pass. Proven
byte-identical to sequential on the same window; ~300 -> 1030-1680
bars/s (6yr M5: ~26min -> 5-8min). --workers 1 = sequential path.
Tests: 179 passed (new mixed-regime RSI regression test).
Ported from feature/eurusd-support (e6e09c5), pair-neutral fixes:
- _find_swing_points: all(<empty generator>) is vacuously True, so a value
surrounded by NaNs was reported as a swing point; now requires at least
one valid neighbor on each side
- ROC: guard against zero close at the lookback positions (inf scores)
- ADX: epsilon denominator instead of NaN-replace to avoid NaN cascade
Adds 8 regression tests (Module 9). 150/150 pass.
ATR was computed after EMAs so ribbon compression detection was dead code
on all timeframes — moved ATR first. Extended M5 swing S/R lookback from
50 to 120 bars. Added four M5-specific filters: min score, MTF agreement,
confirmation TF conviction, and SELL quality cap (A-grade M5 shorts enter
at dip extremes and lose). Fixed backtester blocked-hour check to use bar
close time matching the live bot. Added trade geometry config (spread gate,
SL floor, TP floor RR) and primary_tf mismatch guard in backtester.
Backtest: 1133 trades/+11.5R → 419 trades/+41.0R, PF 1.03→1.37, DD 21→11R.
Train/val split positive in both halves (+19.8R/+21.3R).
Live pipeline:
- Drop still-forming candle before analysis (signals no longer repaint)
- Direction-aware MTF agreement + opposition gate (no trade against 2-of-3 TF consensus)
- SELL volatility gate: block shorts in low/normal vol regimes (config sell_vol_regimes)
- Divergence: 40-bar lookback, price/indicator swings must align within ±2 bars
(fired on 0.5% of trades before, 29% after), trending weight 2.0→1.0
- RSI regime_aware by default; transitioning regime only signals at extremes
- ADX ±DM tie no longer nudges bearish; TP2 clamped beyond TP1
Backtester realism:
- Charge $0.30 round-trip spread (--spread), gap-through SL/TP fills at bar open
- Resolve trades on RAW price path (cache stores raw bars, cleaning runs on load)
- Weekend/session filters use bar-close decision time to match live wall clock
Validated per-step on M15 2025-03-30→2026-05-31 under identical pricing:
+46.9R → +66.8R, win rate 69.9% → 73.0%, SELL side -2.3R → +9.6R.
Tested-and-reverted: ROC weight removal (-13R), BB fade suppression (-11.6R),
candlestick de-weight (-2R) — composite thresholds are co-tuned with weight mass.
- Drop USD/JPY from scan pairs (zero signals across all timeframes)
- Disable divergence indicator (zero impact in 14-config lab test)
- Parallelize evaluate.py with ProcessPoolExecutor (16 cores)
- Add --pair CLI filter to evaluate.py for single-pair backtests
- Add data freshness guard: stale cache auto-invalidated, primary TF
stale data suppresses signals to prevent trading on old data
- Add /cache command: shows fetch age and last bar per pair/TF
- Add /clearcache command: wipes in-memory cache for fresh API fetch
- Remove BB from WebSocket breach alerts (keep structural levels only)
- Show live prices only for WS-monitored pairs (EUR/USD, XAU/USD)
- Replace arrow emojis with text tags in indicator lines
Remove backtest.py, backtest_gpu.py, metrics.py, local CSV data,
Dukascopy loader, and all backtest CLI modes from main.py. Rebalance
engine weights: leading indicators (divergence, RSI, stochastic, BB)
now carry 66% of signal weight vs 34% for lagging (EMA, MACD). Add
ws_twelvedata.py for live tick streaming. Drop pyarrow and tqdm deps.
## What changed
### backtest_gpu.py (new — ~1,300 lines)
Standalone GPU-accelerated backtester completely separate from backtest.py.
Computes all indicator series in one vectorised O(N) pass over the full price
history, then replays positions with a sequential state machine.
Key design:
- VectorIndicators: pandas ewm/rolling (C-optimised) for all 7 indicators
- CuPy GPU backend with automatic NumPy fallback (no hard dependency)
- Two-phase API: prepare_arrays() once, score_and_run() many times
- --optimize flag: 36-combo weight sweep (1 baseline + 7 ablations + 28
multiplier runs) — calls prepare_arrays() once, replays all combos in seconds
- Full CLI: --tf, --local, --start/--end, --regime-filter, --regime-tf,
--session-filter, --optimize, --journal, --journal-csv
O(N) vs O(N^2): backtest.py recomputes all indicators per bar (O(N^2));
backtest_gpu.py computes each indicator series once and reads bar T in O(1).
On 100,000 H1 bars: ~4 seconds vs >10 minutes.
### data_provider.py
Added load_local_csv() method — reads MetaTrader tab-delimited CSV exports
from market_data/ directory. Format: Time|Open|High|Low|Close|Volume|Spread
(spread column dropped on load, timestamps localised to UTC). Applies the same
_validate_and_clean() pipeline as the Twelve Data path. LOCAL_CSV_MAP maps
each Timeframe enum to its filename.
market_data/ files added (MetaTrader exports, UTC):
USDJPY_M15.csv 100,000 bars 2022-05-04 to 2026-05-08
USDJPY_H1.csv 100,000 bars 2010-04-23 to 2026-05-08
USDJPY_H4.csv 25,855 bars 2010-04-22 to 2026-05-08
USDJPY_D1.csv 5,020 bars 2010-04-22 to 2026-05-08
16 years of H1/H4/D1 vs ~6 weeks of H1 on Twelve Data free plan. Use --local
for all backtests; Twelve Data reserved for live signal feed.
### engine.py — optimised indicator weights
36-combo sweep on H4 2022-2026 found MACD and EMA trend are the dominant
signals; RSI and Bollinger are net drag on this trending pair.
Old → new weights:
ema_trend: 1.2 → 3.6 (×3 — strongest directional signal)
macd: 1.1 → 3.3 (×3 — ablating it is the single worst move: -42%)
stochastic: 0.8 → 1.6 (×2 — clean timing, 40% WR at this level)
adx: 1.0 → 0.5 (÷2 — partially redundant with EMA stack)
ichimoku: 1.1 → 0.55 (÷2 — lags too much at H4, hurts in ranging)
rsi: 0.3 → 0.0 (ablated — net drag across all test windows)
bollinger: 0.9 → 0.0 (ablated — counter-trend noise on trending pair)
RSI and Bollinger are still computed and displayed in live output for
reference; weight=0 means they contribute nothing to the composite score.
Combined result vs baseline (H4 2022-2026, local data):
Return: +7.12% → +21.30% (+14.2pp)
Max DD: -9.05% → -8.52% (tighter — gains did not come at expense of risk)
Sharpe: 0.23 → 0.47 (2x improvement)
Sortino: 1.38 → 1.91
Win Rate: 36.3% → 38.1%
### backtest.py
Added 16-thread CPU parallelism for signal pre-computation via
ThreadPoolExecutor. _precompute_all_signals() dispatches each bar to a worker;
pandas/NumPy C extensions release the GIL enabling true parallel execution.
Results are collected and replayed in the main loop, replacing the per-bar
recompute. Also added --regime-tf CLI flag so regime filter timeframe can be
specified independently of the primary TF.
### signal_aggregator.py
EMA200 distance gate: confidence *= 0.55 when price is within 0.5% of EMA200
(chop zone) or beyond 1.5% (overextended). Sweet spot is 0.5-1.5% from EMA200
where trending momentum is cleanest. Strongest single filter found: lifted
cumulative return +13.88% to +35.50% in ablation window.
### ENGINE_LOGIC.md (new)
Full reference document covering: architecture, data pipeline, all indicator
logic, composite scoring formula, optimised weights with rationale, regime
classification, EMA200 gate, MTF alignment, grade system, backtesting results,
filter catalogue, and configuration reference.
Across 2024 trend / 2023 chop / 2026 hostile windows, distance from the
H1 EMA200 was empirically the most discriminative variable. Sweet spot
0.5-1.5% from EMA200; closer = false-breakout chop, farther = chase.
Outside the band, confidence is multiplied by 0.55 so the trade falls
below the B-grade threshold and is rejected.
Cumulative return across the 3 windows: +13.88% baseline -> +35.50%.
Win rate improved in every regime; 2023 chop turned positive (-2.56% to
+14.94%).
Also extends the trade journal with per-timeframe MTF scores, RSI at
entry, and EMA200 distance for future filter mining; surfaces ema_trend
in key_levels so the gate can read it.
Framework:
- TechnicalConfig.disabled_indicators skips named indicators in engine.analyze
- Optimizer supports disable_indicator override for parallel ablation sweeps
- main.py --ablate runs leave-one-out across all 10 directional indicators
Cleanup applied (validated across 2024 trend / 2023 chop / 2026 recent):
- Drop pivot by default (cumulative +9.5% to disable)
- Drop candlestick by default (+3.4% cumulative)
- Regime-gate ichimoku: 1.3x in trending, 0.2x in ranging (was a chop villain)
- Trim RSI weight 1.0 -> 0.3 (cumulative noise across regimes)
Result: +10.60% on 2024 trend (+33.24% -> +43.84%, Sharpe 2.17 -> 2.77),
+15.71% on 2023 chop (-18.27% -> -2.56%). Recent regime still adverse.
Harwinder Sekhon
harry