audit 2026-09-13: eleven delivery/measurement fixes; books byte-identical
Signal path
- A shipped setup always names a direction: grade C needs confidence
>= 45 (gate 6c grants B at >= 45) while BUY/SELL needs >= 50, so C/B
reports reached the alert gate as NEUTRAL with a full setup. The
backtester traded them (the replay never reads final_signal) while
Telegram rejected NEUTRAL first — 5/91 trades, +3.7R of +22.2R, in
the H1 12-month book. _attach_core_setup stamps the setup's direction
and repairs the pre-gate action text; no-setup reports stay NEUTRAL.
- analyze_pair fails the scan when ANY required frame is missing
("DATA UNAVAILABLE:", scan_kind='failed'); a lost confirmation frame
used to run with an unearned -15 MTF penalty.
Outcomes and labels
- advisory_outcome.resolve_locked_floor: a gap the database cannot fill
resolves at the locked trailing stop (GAP_LOCKED, result.floor) when
the stop had crossed the entry; the rebuild fires on both gap reasons
(rows 915/920: +1.14R / +0.84R floors; resolved sum -2.46R -> -0.48R).
- label_scan_outcomes: SCAN_LABEL_MAX_AGE_H = 72 h, stale rows never
anchor the shared bar stream, labeled_at bumped on re-examination,
_stream_since warns at its row cap (the Labor-Day pile would have
blinded scan_outcomes around 2026-09-14).
Providers and scheduling
- The daily settlement pause (Mon-Thu 17:00 New York) is a scheduled
closure for _has_latest_closed_bar (whole-span label walk-back) and
_is_data_fresh (closed seconds are not age); bot and dashboard skip
scans whose just-closed label sits in the pause (61 false stale rows
in three nights).
- fetch_range retries a failed page in place (three failures on one
window raise) and logs an ERROR for interior gaps of >3 unscheduled
bars.
- pd.Timestamp.utcnow() replaced in providers/base.py and engine.py;
requirements pin pandas<4, numpy<3.
- telegram_bot: boundary loops share one scan per bar with the breach
handler (_remember_scan keys on the analysed bar; breach defers 20 s
after an M15 boundary); the rotating file handler attaches in main()
only (the test suite used to write into logs/telegram.log).
- dashboard_publisher: catch_up_timeframes re-scans boundaries missed
while a cycle overran or the process was down.
Backtester and engine guards
- _replay_span returns the per-span GPU cache-miss count; run_backtest
sums, logs it (must be 0) and writes cache_fallbacks to the manifest.
- regime_weight_table raises on a partial regime_weights_override.
Verification: 519 tests; M5/M15 2-yr, M15 OOS, H1/H4 3-yr books
byte-identical before and after; live signals.db untouched (checked on
a copy). CLAUDE.md carries the dated entry and the blocked-hours union
semantics that every book was validated under.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0139xz1J6YccndA2be6yFnEo
This commit is contained in:
Harwinder Sekhon
@@ -348,6 +348,39 @@ def advance_outcome(
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return state
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def resolve_locked_floor(state: Dict[str, Any]) -> Dict[str, Any]:
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"""If an UNRESOLVABLE gap outcome had already trailed its stop past the
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entry, finish it at that stop as a FLOOR (the true outcome is at least
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this good) instead of discarding it. Returns the state (mutated)."""
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if state.get("status") != "UNRESOLVABLE":
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return state
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reason = str(state.get("reason", ""))
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# Both gap reasons ("lost continuity" / "contain an internal gap")
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# share this prefix; an invalid seed or evaluator error never floors.
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if not reason.startswith("stored candle snapshots"):
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return state
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progress = state.get("progress")
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setup = state.get("setup")
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if not isinstance(progress, dict) or not isinstance(setup, dict):
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return state
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entry = _finite(progress.get("entry_price"))
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stop = _finite(progress.get("current_stop"))
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risk = _finite(setup.get("stop_distance"))
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exit_at = progress.get("last_processed_bar")
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if (entry is None or stop is None or risk is None or risk <= 0
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or not exit_at):
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return state
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buy = setup.get("direction") == "BUY"
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locked = stop >= entry if buy else stop <= entry
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if not locked:
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return state
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_finish(state, "GAP_LOCKED", str(exit_at), stop)
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state["result"]["floor"] = True
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state["reason"] = (
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f"{reason} — resolved at the locked trailing stop (floor)")
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return state
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def canonical_r(state: Dict[str, Any]) -> Optional[float]:
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result = state.get("result")
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value = result.get("net_r_065") if isinstance(result, dict) else None
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@@ -1540,6 +1540,10 @@ def _replay_span(span: Tuple[int, int]):
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is applied by the caller in a sequential pass over the returned
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candidates, so parallel and sequential runs produce identical books.
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Must stay behaviorally identical to the original sequential loop.
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Returns (skipped, candidates, bar_rows, cache_fallbacks) — the last
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element is this span's GPU-cache-miss count, which the caller sums so
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a parity hazard inside a worker process cannot stay invisible.
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"""
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ctx = _REPLAY_CTX
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primary_df = ctx["primary_df"]
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@@ -1550,6 +1554,12 @@ def _replay_span(span: Tuple[int, int]):
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bt_cfg = ctx["bt_cfg"]
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primary_dur = ctx["primary_dur"]
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# One worker process handles several spans, so the engine's cache-miss
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# counter must be per span, not cumulative across them.
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engine = aggregator.tech_engine
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if hasattr(engine, "fallback_count"):
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engine.fallback_count = 0
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skipped: Dict[str, int] = {}
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candidates: List[Tuple[int, Trade]] = []
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is_xau = PAIR_INFO[bt_cfg.pair]["display"] == "XAU/USD"
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@@ -1719,8 +1729,9 @@ def _replay_span(span: Tuple[int, int]):
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if tag == "XAU_PB" else 0.0),
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)))
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return skipped, candidates, (replay_record.finish_bar_rows(bar_buf)
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if record_bars else None)
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return (skipped, candidates,
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(replay_record.finish_bar_rows(bar_buf) if record_bars else None),
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int(getattr(engine, "fallback_count", 0)))
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def _reanchor_entries_to_raw(
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@@ -1927,14 +1938,28 @@ def run_backtest(
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skipped_reasons: Dict[str, int] = {}
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candidates: List[Tuple[int, Trade]] = []
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bar_chunks: List = []
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for skips, cands, bars in results:
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cache_fallbacks = 0
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for skips, cands, bars, fallbacks in results:
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for reason, count in skips.items():
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skipped_reasons[reason] = skipped_reasons.get(reason, 0) + count
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candidates.extend(cands)
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if bars is not None:
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bar_chunks.append(bars)
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cache_fallbacks += fallbacks
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candidates.sort(key=lambda pc: pc[0])
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# The workers' counters die with their processes, so surface the run
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# total here and leave it on the parent engine for callers to read.
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aggregator.tech_engine.fallback_count = cache_fallbacks
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if cache_fallbacks > 0:
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logger.warning(
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f"GPU cache fallbacks: {cache_fallbacks} indicator recomputes "
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f"(one per _compute_* override per missed bar) fell back to the "
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f"live engine's pandas math (different EWM seeding) — parity "
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f"hazard; see GPUTechnicalEngine._note_fallback")
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else:
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logger.info("GPU cache fallbacks: 0")
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# ── Phase 2: entry cooldown — the only sequential rule.
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accepted = _apply_cooldown(candidates, bt_cfg.cooldown_bars)
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trades: List[Trade] = [t for _, t in accepted]
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@@ -1985,7 +2010,8 @@ def run_backtest(
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skipped=skipped_reasons, n_candidates=len(candidates),
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workers=workers,
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backend="CuPy GPU" if _GPU_AVAILABLE else "NumPy CPU fallback",
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cache_files=cache_names, replay_seconds=replay_seconds),
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cache_files=cache_names, replay_seconds=replay_seconds,
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cache_fallbacks=cache_fallbacks),
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sim_df=sim_df, bar_chunks=bar_chunks or None)
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logger.info(f"--record: wrote run to {bt_cfg.record_dir}")
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@@ -1055,6 +1055,9 @@ def write_run(record_dir, *, trades, book_df, bt_cfg, platform_cfg, aggregator,
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"max_hold_bars": bt_cfg.max_hold_bars,
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"fixed_rr_tp": bt_cfg.fixed_rr_tp,
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"workers": run_info.get("workers"),
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# >0 means some bar-analyses fell back to the live engine's pandas
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# math instead of the pre-computed cache (a parity hazard).
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"cache_fallbacks": run_info.get("cache_fallbacks"),
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"engine_fingerprint": engine_fp,
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"gate_fingerprint": gate_fp,
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"technical_config": dataclasses.asdict(platform_cfg.technical),
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+92
-6
@@ -26,6 +26,11 @@ whichever timeframes just closed. Due-classification uses the RAW wake
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time — flooring 12:00:10 to the 5-minute grid yields the 12:00 boundary
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that just closed. Do NOT subtract the buffer first: 12:00:10 − 15 s
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floors to 11:55 and silently skips every H1/H4 scan (v1 bug, 2026-08-02).
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A cycle can overrun its 5-minute slot (or the process can be down), so
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each wake also catches up the timeframes whose latest closed boundary is
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newer than their last scanned one (`catch_up_timeframes`) — without it the
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page served an hours-old H1/H4 report behind a fresh countdown
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(2026-09-13 audit).
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Scans run sequentially M5→M15→H1→H4 through one shared data provider so
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later timeframes hit the warm boundary cache — that ordering IS the
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@@ -46,10 +51,12 @@ import os
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import shutil
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import sys
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import time
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from datetime import datetime, timezone
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from datetime import datetime, timedelta, timezone
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from logging.handlers import TimedRotatingFileHandler
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from typing import Dict, List, Optional
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import pandas as pd
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from agent_panel import attach_panel
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from app_config import get_app_config, violent_watch_enabled
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from config import PlatformConfig, Timeframe, XAUUSD
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@@ -230,10 +237,60 @@ def due_timeframes(wake_dt: datetime) -> List[Timeframe]:
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"""Timeframes whose candle just closed at the boundary this wake
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belongs to. Takes the RAW wake time (boundary + buffer): flooring to
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the 5-minute grid yields the boundary that just passed. Never
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subtract the buffer first — see module docstring (v1 bug)."""
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subtract the buffer first — see module docstring (v1 bug).
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A timeframe whose just-closed bar would carry a label inside gold's
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daily settlement pause is skipped: no such bar exists, so the scan
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would only re-read the previous one (2026-09-13 audit)."""
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minutes = wake_dt.hour * 60 + wake_dt.minute
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floored = minutes - (minutes % 5)
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return [tf for tf in DASHBOARD_TFS if floored % TF_INTERVAL_MINUTES[tf] == 0]
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boundary = wake_dt.replace(
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hour=floored // 60, minute=floored % 60, second=0, microsecond=0)
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return [
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tf for tf in DASHBOARD_TFS
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if floored % TF_INTERVAL_MINUTES[tf] == 0
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and not _providers_base._is_daily_maintenance(
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boundary - timedelta(minutes=TF_INTERVAL_MINUTES[tf]))
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]
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def catch_up_timeframes(wake_dt: datetime,
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last_boundary: Dict[str, float]) -> List[Timeframe]:
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"""Timeframes whose most recent closed boundary (<= wake) is newer than
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the boundary of their last successful scan — i.e. boundaries that were
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missed while a cycle overran or the process was down. A TF never scanned
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is due. Excludes bars whose label sits in the daily settlement pause
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(same rule as due_timeframes).
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`due_timeframes` only ever names the timeframes whose boundary coincides
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with THIS wake, so an overrunning cycle (scan + advisory panel + news can
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take minutes) or a restart left the page serving the previous H1/H4
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report for up to 1 h / 4 h while advertising a fresh next_close_epoch
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(2026-09-13 audit)."""
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minutes = wake_dt.hour * 60 + wake_dt.minute
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out = []
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for tf in DASHBOARD_TFS:
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interval = TF_INTERVAL_MINUTES[tf]
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floored = minutes - (minutes % interval)
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last_closed = wake_dt.replace(
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hour=floored // 60, minute=floored % 60, second=0, microsecond=0)
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# No bar carries a label inside a scheduled closure (settlement
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# pause or weekend), so nothing can have been "missed" there — and a
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# weekend-blocked scan never advances the ledger, so without the
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# weekend half of this test every weekend wake would re-scan
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# M15/H1/H4 for nothing (~1,800 blocked rows per weekend).
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# (Whole-span rule, as in providers.base._has_latest_closed_bar: an
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# H4 bar labelled Sunday 20:00 UTC starts in the weekend but runs
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# through the reopen, so it exists.)
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label = last_closed - timedelta(minutes=interval)
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if (_providers_base._is_scheduled_closure(label)
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and _providers_base._is_scheduled_closure(
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last_closed - timedelta(seconds=1))):
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continue
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scanned = last_boundary.get(tf.value)
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if scanned is None or scanned < last_closed.timestamp():
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out.append(tf)
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return out
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def seconds_until_boundary(interval_minutes: int, buffer: int = 10) -> float:
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@@ -324,6 +381,9 @@ class DashboardPublisher:
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self.out_dir = out_dir
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self.snapshots: Dict[str, dict] = {}
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self._reports = {}
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# tf.value → epoch seconds of the boundary the last successful scan
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# belonged to; feeds catch_up_timeframes (2026-09-13 audit).
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self._last_boundary: Dict[str, float] = {}
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self.news_payload: Optional[dict] = None
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self._dp = None
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self._news_engine: Optional[NewsEngine] = None
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@@ -355,7 +415,14 @@ class DashboardPublisher:
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shutil.copyfile(src, dst + ".tmp")
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os.replace(dst + ".tmp", dst)
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def scan_tf(self, tf: Timeframe) -> None:
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def scan_tf(self, tf: Timeframe,
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boundary_epoch: Optional[float] = None) -> None:
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"""Scan one timeframe and store its snapshot entry.
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`boundary_epoch` is the fallback boundary recorded when the report
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carries no analysis (weekend/blocked or failed scan) or its bar
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label will not parse; normally the boundary comes from the report
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itself (bar label + interval)."""
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started = time.time()
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try:
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report = produce_signal(XAUUSD, tf, self._provider(),
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@@ -365,6 +432,19 @@ class DashboardPublisher:
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if violent_watch_enabled():
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attach_watch(report, tf,
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get_app_config().violent_watch_settings)
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# Which boundary this report belongs to: the bar LABEL it closed
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# on plus one interval. Recorded so a boundary missed by an
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# overrunning cycle or a restart is caught up on the next wake
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# instead of sitting stale (2026-09-13 audit).
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boundary = boundary_epoch
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if report.primary_analysis is not None:
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try:
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boundary = (pd.Timestamp(report.timestamp).timestamp()
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+ TF_INTERVAL_MINUTES[tf] * 60)
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except Exception:
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boundary = boundary_epoch
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if boundary is not None:
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self._last_boundary[tf.value] = boundary
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self._reports[tf.value] = report
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self.snapshots[tf.value] = {
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"status": "ok",
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@@ -449,9 +529,15 @@ class DashboardPublisher:
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logger.info(f"next M5 boundary in {delay:.0f}s")
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time.sleep(delay)
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try:
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due = due_timeframes(datetime.now(timezone.utc))
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now = datetime.now(timezone.utc)
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due = due_timeframes(now)
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logger.info(f"boundary wake — scanning {[t.value for t in due]}")
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self.run_cycle(due)
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missed = catch_up_timeframes(now, self._last_boundary)
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extra = [tf for tf in missed if tf not in due]
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if extra:
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logger.info(f"catch-up — scanning "
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f"{[t.value for t in extra]} (missed boundary)")
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self.run_cycle(due + extra)
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except Exception:
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logger.exception("cycle failed")
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@@ -12,6 +12,7 @@ Design philosophy:
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"""
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import logging
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import math
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from dataclasses import dataclass, field
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from typing import Dict, List, Optional, Tuple
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@@ -84,15 +85,65 @@ _DEFAULT_REGIME_WEIGHTS = {
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}
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def _validate_regime_override(override: Dict[str, Dict[str, float]]) -> None:
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"""Reject a PARTIAL ``regime_weights_override`` (raises ValueError).
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An override REPLACES the whole table, it does not patch it: whatever
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it omits is multiplied by 1.0 in _compute_composite, not by the
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shipped default. A half-filled research table (``--set
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technical.regime_weights_override=<JSON>``) would therefore silently
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run 1.0 where the default said 0.3 or 2.5, with no warning — so every
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regime and all nine scored indicators must be present, with a finite
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multiplier >= 0. The shipped M15 override is derived from the default
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table (pair_signals/xauusd._m15_regime_weights) and passes."""
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problems: List[str] = []
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want_regimes = set(_DEFAULT_REGIME_WEIGHTS)
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want_indicators = set(_TRENDING_WEIGHTS)
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got_regimes = set(override)
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if got_regimes != want_regimes:
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problems.append(
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f"missing regimes {sorted(want_regimes - got_regimes)}, "
|
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f"unexpected {sorted(got_regimes - want_regimes)}")
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for regime in sorted(got_regimes & want_regimes):
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weights = override[regime]
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if not isinstance(weights, dict):
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problems.append(f"regime '{regime}' is not an indicator -> "
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f"multiplier mapping: {weights!r}")
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continue
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got = set(weights)
|
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if got != want_indicators:
|
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problems.append(
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f"regime '{regime}' missing {sorted(want_indicators - got)}, "
|
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f"unexpected {sorted(got - want_indicators)}")
|
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for name in sorted(got & want_indicators):
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mult = weights[name]
|
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if (isinstance(mult, bool) or not isinstance(mult, (int, float))
|
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or not math.isfinite(mult) or mult < 0):
|
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problems.append(
|
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f"regime '{regime}' indicator '{name}' multiplier "
|
||||
f"{mult!r} is not a finite number >= 0")
|
||||
if problems:
|
||||
raise ValueError(
|
||||
"regime_weights_override is partial: " + "; ".join(problems)
|
||||
+ "; an override replaces the WHOLE table "
|
||||
"(see engine._DEFAULT_REGIME_WEIGHTS)")
|
||||
|
||||
|
||||
def regime_weight_table(config: TechnicalConfig) -> Dict[str, Dict[str, float]]:
|
||||
"""The regime -> indicator weight multipliers _compute_composite runs
|
||||
with (``regime_weights_override`` wins, else the gold-optimized
|
||||
defaults above).
|
||||
|
||||
A non-empty override must cover the WHOLE table (every regime, all
|
||||
nine scored indicators) — a partial one raises ValueError instead of
|
||||
silently multiplying the cells it omits by 1.0.
|
||||
|
||||
Pure and side-effect free: ONE source of truth shared by the composite
|
||||
and by signal_aggregator.engine_config_fingerprint. Returns a copy, so
|
||||
a caller can never mutate the shipped table."""
|
||||
override = getattr(config, "regime_weights_override", None)
|
||||
if override:
|
||||
_validate_regime_override(override)
|
||||
table = override if override else _DEFAULT_REGIME_WEIGHTS
|
||||
return {regime: dict(weights) for regime, weights in table.items()}
|
||||
|
||||
@@ -119,7 +170,7 @@ class TechnicalEngine:
|
||||
return TechnicalAnalysis(
|
||||
symbol=symbol,
|
||||
timeframe=timeframe,
|
||||
timestamp=str(pd.Timestamp.utcnow()),
|
||||
timestamp=str(pd.Timestamp.now(tz="UTC")),
|
||||
confidence=0.0,
|
||||
)
|
||||
|
||||
|
||||
+120
-18
@@ -65,6 +65,46 @@ def _is_forex_weekend(now=None) -> bool:
|
||||
return False
|
||||
|
||||
|
||||
def _is_daily_maintenance(now=None) -> bool:
|
||||
"""Gold's daily settlement pause: Monday-Thursday 17:00-17:59 New York
|
||||
(21:00 UTC in US summer, 22:00 in winter). No bar carries a label
|
||||
inside this hour. Friday 17:00 is the weekend (see _is_forex_weekend).
|
||||
Same rule as advisory_outcome._has_continuity's daily_maintenance."""
|
||||
if now is None:
|
||||
now = pd.Timestamp.now(tz="UTC")
|
||||
ny = now.astimezone(_NY_TZ)
|
||||
return ny.weekday() < 4 and ny.hour == 17
|
||||
|
||||
|
||||
def _is_scheduled_closure(now=None) -> bool:
|
||||
"""The feed is legitimately silent: weekend or daily settlement pause."""
|
||||
return _is_forex_weekend(now) or _is_daily_maintenance(now)
|
||||
|
||||
|
||||
def _scheduled_closed_seconds(start, end) -> float:
|
||||
"""Seconds of the [start, end) span that fall inside a scheduled
|
||||
closure (weekend or the daily settlement pause).
|
||||
|
||||
Bar age is measured from a bar LABEL, so at 21:30 UTC in summer the
|
||||
20:55 M5 bar looks 35 minutes old even though the feed could not have
|
||||
printed anything since 21:00. Subtracting the closed span keeps the
|
||||
3-candle freshness limit meaningful instead of failing it — and
|
||||
logging a false "feed lagging"/"STALE DATA" — every settlement hour
|
||||
(2026-09-13 audit: 61 suppressed scans on three nights).
|
||||
"""
|
||||
hour = pd.Timedelta(hours=1)
|
||||
closed = 0.0
|
||||
t = start.floor("h")
|
||||
steps = 0
|
||||
while t < end and steps < 24 * 10:
|
||||
if _is_scheduled_closure(t):
|
||||
closed += max(
|
||||
0.0, (min(t + hour, end) - max(t, start)).total_seconds())
|
||||
t += hour
|
||||
steps += 1
|
||||
return closed
|
||||
|
||||
|
||||
class DataIntegrityError(Exception):
|
||||
"""Raised when data fails validation checks."""
|
||||
pass
|
||||
@@ -276,7 +316,7 @@ class BaseDataProvider(ABC):
|
||||
chunks = []
|
||||
cursor_end = end
|
||||
empty_streak = 0
|
||||
error_streak = 0
|
||||
window_errors = 0
|
||||
while cursor_end > start:
|
||||
cursor_start = max(start, cursor_end - timedelta(days=days_per_chunk))
|
||||
try:
|
||||
@@ -286,23 +326,31 @@ class BaseDataProvider(ABC):
|
||||
)
|
||||
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).
|
||||
# malformed payload) — not "no data". RETRY THE SAME
|
||||
# WINDOW: until 2026-09-13 a failure advanced the cursor
|
||||
# past the window, so one or two NON-consecutive page
|
||||
# failures (Oanda's sporadic 401/5xx, even after the
|
||||
# provider's own retry ladder) silently dropped up to
|
||||
# days_per_chunk of bars — 15 days of M5 — out of the
|
||||
# middle of the frame, and only the HEAD was
|
||||
# coverage-checked below, so the backtester cached the hole
|
||||
# as complete history forever. Three failures on the SAME
|
||||
# window means it is unreachable: raise instead of
|
||||
# returning a frame with a hole in it.
|
||||
logger.warning(
|
||||
f"Chunk {cursor_start.date()}–{cursor_end.date()} failed: {e}"
|
||||
)
|
||||
error_streak += 1
|
||||
if error_streak >= 3:
|
||||
window_errors += 1
|
||||
if window_errors >= 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
|
||||
f"{symbol} {timeframe.value}: {window_errors} "
|
||||
f"consecutive failures fetching window "
|
||||
f"{cursor_start.date()}–{cursor_end.date()} — "
|
||||
f"refusing to return truncated history "
|
||||
f"(last error: {e})") from e
|
||||
continue # re-request the SAME window
|
||||
|
||||
window_errors = 0
|
||||
if chunk is None or chunk.empty:
|
||||
# Genuinely empty windows (weekends/holidays/asset gaps)
|
||||
# are normal; several in a row = start of history.
|
||||
@@ -313,7 +361,6 @@ class BaseDataProvider(ABC):
|
||||
continue
|
||||
|
||||
empty_streak = 0
|
||||
error_streak = 0
|
||||
chunks.append(chunk)
|
||||
# Step back; dedup on concat.
|
||||
new_cursor = chunk.index[0] - timedelta(seconds=1)
|
||||
@@ -348,6 +395,38 @@ class BaseDataProvider(ABC):
|
||||
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")
|
||||
# INTERIOR-gap audit (2026-09-13): the head check above is blind to
|
||||
# a hole in the MIDDLE of the frame, which is exactly what a
|
||||
# dropped page used to leave behind. Missing labels inside a
|
||||
# scheduled closure (weekend, daily settlement pause) are normal,
|
||||
# so only unscheduled runs are reported — and a real market holiday
|
||||
# (Christmas, Good Friday, July 4) looks identical to a lost page,
|
||||
# so this is LOUD BUT NOT FATAL and the frame is returned unchanged.
|
||||
if len(df) > 400_000:
|
||||
logger.info(
|
||||
f"{symbol} {timeframe.value}: interior-gap audit skipped "
|
||||
f"({len(df)} rows)")
|
||||
elif len(df) > 1:
|
||||
step = pd.Timedelta(seconds=self.CANDLE_SECONDS[timeframe])
|
||||
labels = df.index
|
||||
# Missing labels between each consecutive pair; >3 is the floor
|
||||
# worth a look (weekends/settlement hours leave 1-2 labels that
|
||||
# the NY-anchored closure rule does not cover).
|
||||
spans = ((labels[1:] - labels[:-1]) / step).astype("int64") - 1
|
||||
gaps = []
|
||||
for i in (spans > 3).nonzero()[0]:
|
||||
missing = sum(
|
||||
1 for k in range(1, int(spans[i]) + 1)
|
||||
if not _is_scheduled_closure(labels[i] + k * step))
|
||||
if missing > 3:
|
||||
gaps.append((labels[i], labels[i + 1], missing))
|
||||
if gaps:
|
||||
logger.error(
|
||||
f"{symbol} {timeframe.value}: {len(gaps)} interior "
|
||||
f"gap(s) of >3 unscheduled bars in fetched history — "
|
||||
f"first: {gaps[0]}; a page fetch may have been dropped "
|
||||
f"or the market was closed (holiday); verify before "
|
||||
f"trusting this window")
|
||||
return df
|
||||
|
||||
def is_data_fresh(
|
||||
@@ -398,7 +477,8 @@ class BaseDataProvider(ABC):
|
||||
|
||||
# ── Shared internals ───────────────────────────────────────────
|
||||
|
||||
def _has_latest_closed_bar(self, df: pd.DataFrame, timeframe: Timeframe) -> bool:
|
||||
def _has_latest_closed_bar(self, df: pd.DataFrame, timeframe: Timeframe,
|
||||
now=None) -> bool:
|
||||
"""Cached data must include the most recent fully closed bar.
|
||||
|
||||
The TTL alone is wall-clock based: a snapshot fetched mid-candle is
|
||||
@@ -408,12 +488,29 @@ class BaseDataProvider(ABC):
|
||||
"""
|
||||
if df is None or df.empty:
|
||||
return False
|
||||
if now is None:
|
||||
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
|
||||
# No bar ever carries a label inside a scheduled closure, so the
|
||||
# newest bar that MUST be present is the last label before it.
|
||||
# Without this walk-back every scan from 21:05 to 22:00 UTC in
|
||||
# summer evicted the cache, refetched twice and logged a false
|
||||
# "feed lagging" (2026-09-13 audit).
|
||||
# A label counts as "inside" only when the closure covers the bar's
|
||||
# whole span: a D1 bar labelled Sunday 00:00 UTC starts in the
|
||||
# weekend but runs through the Sunday reopen, so it exists and
|
||||
# must be present (68 such bars in the 3-year Oanda D1 cache).
|
||||
steps = 0
|
||||
last_second = dur - pd.Timedelta(seconds=1)
|
||||
while (_is_scheduled_closure(expected_label)
|
||||
and _is_scheduled_closure(expected_label + last_second)
|
||||
and steps < 2000):
|
||||
expected_label -= dur
|
||||
steps += 1
|
||||
return df.index[-1] >= expected_label
|
||||
|
||||
def _drop_forming_candle(self, df: pd.DataFrame, timeframe: Timeframe) -> pd.DataFrame:
|
||||
@@ -443,11 +540,13 @@ class BaseDataProvider(ABC):
|
||||
timeframe.value, dropped)
|
||||
return df
|
||||
|
||||
def _is_data_fresh(self, df: pd.DataFrame, symbol: str, timeframe: Timeframe) -> bool:
|
||||
def _is_data_fresh(self, df: pd.DataFrame, symbol: str, timeframe: Timeframe,
|
||||
now=None) -> bool:
|
||||
"""Check if the last candle is within acceptable age for this timeframe."""
|
||||
if df is None or df.empty:
|
||||
return False
|
||||
|
||||
if now is None:
|
||||
now = pd.Timestamp.now(tz="UTC")
|
||||
|
||||
if _is_forex_weekend(now):
|
||||
@@ -456,7 +555,10 @@ class BaseDataProvider(ABC):
|
||||
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()
|
||||
# Scheduled closures (weekend, daily settlement pause) are dead
|
||||
# time in which no bar can print — they must not count as age.
|
||||
age_seconds = ((now - last_bar).total_seconds()
|
||||
- _scheduled_closed_seconds(last_bar, now))
|
||||
|
||||
if age_seconds > max_age_seconds:
|
||||
logger.warning(
|
||||
@@ -482,7 +584,7 @@ class BaseDataProvider(ABC):
|
||||
df = df.sort_index()
|
||||
df = df[~df.index.duplicated(keep="last")]
|
||||
|
||||
now = pd.Timestamp.utcnow()
|
||||
now = pd.Timestamp.now(tz="UTC")
|
||||
future_mask = df.index > now + pd.Timedelta(minutes=5)
|
||||
if future_mask.any():
|
||||
logger.warning(f"{symbol}: Dropping {future_mask.sum()} future timestamps")
|
||||
|
||||
+2
-2
@@ -1,5 +1,5 @@
|
||||
numpy>=1.24
|
||||
pandas>=2.0
|
||||
numpy>=1.24,<3
|
||||
pandas>=2.0,<4
|
||||
requests>=2.28
|
||||
twelvedata>=1.2.0
|
||||
python-telegram-bot>=20.0
|
||||
|
||||
+61
-7
@@ -242,6 +242,36 @@ class SignalAggregator:
|
||||
except Exception as e:
|
||||
logger.warning(f"Fetch failed for {symbol} {tf.value}: {e}")
|
||||
|
||||
# A missing frame is a FAILED scan, never a degraded one. Losing a
|
||||
# CONFIRMATION frame used to let the scan continue with fewer
|
||||
# frames: _check_mtf_agreement needs >= 2 signals, so it returned
|
||||
# False and the composite took a 15-point mtf_disagreement penalty
|
||||
# the market never earned (A/A+ became impossible), while on
|
||||
# M15/H1/H4 a B/C setup could still ship on one confirmation with
|
||||
# no warning at all. The backtester never sees that state
|
||||
# (run_backtest raises on an empty frame), so it was a live-only
|
||||
# divergence. 2026-09-13 audit.
|
||||
missing = [tf for tf in timeframes if tf not in dfs]
|
||||
if missing:
|
||||
report = SignalReport(
|
||||
symbol=symbol,
|
||||
display_name=pair_info["display"],
|
||||
timestamp=str(now_utc),
|
||||
)
|
||||
report.final_signal = SignalStrength.NEUTRAL
|
||||
report.signal_quality = "F"
|
||||
report.trade_setup = None
|
||||
if self.config.primary_timeframe in missing:
|
||||
# Legacy text signal_log.gate_reason already recognises.
|
||||
report.warnings.append("Primary timeframe data unavailable")
|
||||
report.warnings.append(
|
||||
f"DATA UNAVAILABLE: {', '.join(tf.value for tf in missing)}"
|
||||
" — scan failed"
|
||||
)
|
||||
report.action_recommendation = "NO TRADE — market data unavailable"
|
||||
self._log_signal(report, log_source, dfs, failed=True)
|
||||
return report
|
||||
|
||||
report = self.analyze_from_dataframes(
|
||||
symbol=symbol,
|
||||
dfs=dfs,
|
||||
@@ -303,7 +333,7 @@ class SignalAggregator:
|
||||
def _log_signal(
|
||||
self, report: SignalReport, source: Optional[str],
|
||||
dataframes: Optional[Dict[Timeframe, pd.DataFrame]] = None,
|
||||
blocked: bool = False,
|
||||
blocked: bool = False, failed: bool = False,
|
||||
) -> None:
|
||||
"""Persist the scan to the signal-history DB (signal_log.py).
|
||||
|
||||
@@ -314,9 +344,11 @@ class SignalAggregator:
|
||||
is still written, just without the news JSON blobs. ``blocked``
|
||||
marks weekend/day early returns: no news fetch, no snapshots —
|
||||
a ~1 KB liveness row (or nothing, per signal_log.log_blocked_scans).
|
||||
A scan whose PRIMARY fetch failed is logged as ``scan_kind``
|
||||
``failed`` (price 0, no signal): liveness evidence that readers
|
||||
skip like ``blocked``, so it never pollutes NEUTRAL statistics."""
|
||||
A scan missing ANY required frame (primary or confirmation, or
|
||||
the regime-filter frame when that filter is on) is logged as
|
||||
``scan_kind`` ``failed`` (price 0, no signal): liveness evidence
|
||||
that readers skip like ``blocked``, so it never pollutes NEUTRAL
|
||||
statistics."""
|
||||
context = None
|
||||
if not blocked:
|
||||
try:
|
||||
@@ -325,9 +357,9 @@ class SignalAggregator:
|
||||
logger.debug("signal log: news context unavailable",
|
||||
exc_info=True)
|
||||
kind = "blocked" if blocked else "scan"
|
||||
if not blocked and dataframes is not None:
|
||||
primary = dataframes.get(self.config.primary_timeframe)
|
||||
if primary is None or primary.empty:
|
||||
primary = (dataframes or {}).get(self.config.primary_timeframe)
|
||||
if failed or (not blocked and dataframes is not None
|
||||
and (primary is None or primary.empty)):
|
||||
kind = "failed"
|
||||
extra = {"scan_kind": kind}
|
||||
for key, fingerprint in (
|
||||
@@ -1297,6 +1329,28 @@ class SignalAggregator:
|
||||
report.trade_setup = self._generate_xau_atr_trade_setup(report, atr)
|
||||
if report.trade_setup is None and report.signal_quality in ("A+", "A", "B", "C"):
|
||||
report.trade_setup = self._generate_trade_setup(report, pip_value)
|
||||
# A shipped setup always names a direction. The signal thresholds
|
||||
# (BUY/SELL need confidence >= 50) sit above the C grade floor
|
||||
# (>= 45), and gate 6c grants B at >= 45 in "transitioning", so a
|
||||
# C/B report could leave here as NEUTRAL with a full setup: the
|
||||
# backtester traded it (the replay never reads final_signal) while
|
||||
# the Telegram alert gate rejects NEUTRAL first (2026-09-13 audit:
|
||||
# 5/91 H1 12-mo book trades, +3.7R of +22.2R, were never alerted).
|
||||
# The generators derived the direction from the score sign — the
|
||||
# same sign every direction gate above used — so this changes
|
||||
# display and alert eligibility only; the trade books are unchanged.
|
||||
# A report no generator served stays NEUTRAL, as before.
|
||||
if (report.trade_setup is not None
|
||||
and report.final_signal == SignalStrength.NEUTRAL):
|
||||
report.final_signal = (SignalStrength.BUY
|
||||
if report.trade_setup.direction == "BUY"
|
||||
else SignalStrength.SELL)
|
||||
# The action text was written before the gate chain with the
|
||||
# old signal name; keep it consistent with the header.
|
||||
if report.action_recommendation.startswith("NEUTRAL"):
|
||||
report.action_recommendation = (
|
||||
report.final_signal.name.replace("_", " ")
|
||||
+ report.action_recommendation[len("NEUTRAL"):])
|
||||
|
||||
@staticmethod
|
||||
def _drop_stale_spread_gate_vetoes(report: SignalReport) -> None:
|
||||
|
||||
+127
-16
@@ -104,6 +104,7 @@ from advisory_outcome import (
|
||||
canonical_r,
|
||||
initial_outcome,
|
||||
normalize_timeframe,
|
||||
resolve_locked_floor,
|
||||
timeframe_value,
|
||||
)
|
||||
# Private on purpose — the scan-outcome labeller must apply EXACTLY the
|
||||
@@ -258,6 +259,7 @@ _GATE_PREFIXES = (
|
||||
"SESSION FILTER",
|
||||
"Primary timeframe stale",
|
||||
"Primary timeframe data unavailable",
|
||||
"DATA UNAVAILABLE:",
|
||||
"REGIME FILTER",
|
||||
"EXHAUSTION BLOCK",
|
||||
"EXHAUSTED TREND FILTER",
|
||||
@@ -278,6 +280,10 @@ SCAN_OUTCOME_WINDOW_H = 24
|
||||
# A 24 h window can only be judged once it has elapsed; the extra hour
|
||||
# leaves room for the last bar of the window to be stored.
|
||||
SCAN_LABEL_MIN_AGE_H = 25
|
||||
# An incomplete row older than this is FINAL: its window had a real hole
|
||||
# (a feed outage), which no later bar can fill. It is neither re-examined
|
||||
# nor allowed to anchor the shared bar stream for the rest of the batch.
|
||||
SCAN_LABEL_MAX_AGE_H = 72
|
||||
# Missing bars tolerated inside a window ON TOP of the scheduled closures
|
||||
# advisory_outcome._has_continuity already allows.
|
||||
SCAN_LABEL_MAX_GAP_BARS = 3
|
||||
@@ -399,6 +405,20 @@ def setup_key(setup: Optional[Dict[str, Any]], timeframe: Any) -> Optional[str]:
|
||||
))
|
||||
|
||||
|
||||
def _is_gap_reason(reason: Any) -> bool:
|
||||
"""True when an outcome was left UNRESOLVABLE by a candle gap.
|
||||
|
||||
advance_outcome writes TWO such reasons — "stored candle snapshots
|
||||
lost continuity" (no overlap with the last processed bar) and "stored
|
||||
candle snapshots contain an internal gap" — and each call site used to
|
||||
match only one of them, so the stream rebuild and the locked-floor
|
||||
rescue skipped half the gap rows. The substring clauses keep states
|
||||
written by older code inside the same net."""
|
||||
text = str(reason or "")
|
||||
return (text.startswith("stored candle snapshots")
|
||||
or "continuity" in text or "gap" in text)
|
||||
|
||||
|
||||
def gate_reason(
|
||||
warnings: Iterable[str],
|
||||
cleaned_warnings: Iterable[str],
|
||||
@@ -1150,12 +1170,19 @@ class SignalLogger:
|
||||
before = _dumps(json.loads(hit[0]))
|
||||
advanced = advance_outcome(json.loads(before), bars)
|
||||
if (advanced.get("status") == "UNRESOLVABLE"
|
||||
and "continuity" in str(advanced.get("reason", ""))):
|
||||
and _is_gap_reason(advanced.get("reason"))):
|
||||
union = self._stream_since(
|
||||
symbol, timeframe,
|
||||
json.loads(before)["progress"]["last_processed_bar"])
|
||||
if union:
|
||||
advanced = advance_outcome(json.loads(before), union)
|
||||
# The database could not fill the hole. A trade
|
||||
# whose trail had already locked profit still has a
|
||||
# real answer — its floor — so record that instead
|
||||
# of dropping the row out of the scorecard.
|
||||
if (advanced.get("status") == "UNRESOLVABLE"
|
||||
and _is_gap_reason(advanced.get("reason"))):
|
||||
advanced = resolve_locked_floor(advanced)
|
||||
after = _dumps(advanced)
|
||||
if after == before:
|
||||
self._conn.rollback()
|
||||
@@ -1194,6 +1221,11 @@ class SignalLogger:
|
||||
" WHERE symbol=? AND candles_json IS NOT NULL"
|
||||
" AND logged_at >= ? ORDER BY id LIMIT ?",
|
||||
(f'$."{key}"', symbol, since, int(max_rows))).fetchall()
|
||||
if len(rows) == int(max_rows):
|
||||
logger.warning(
|
||||
"signal_log: bar stream truncated for %s %s (key %s)"
|
||||
" since %s — hit the %s-row cap, later bars are missing",
|
||||
symbol, tf, key, since, int(max_rows))
|
||||
for (blob,) in rows:
|
||||
if not blob:
|
||||
continue
|
||||
@@ -1212,42 +1244,82 @@ class SignalLogger:
|
||||
|
||||
Candidates are rows that carry indicator snapshots (a real scan)
|
||||
and a price, whose signal bar closed more than 25 h ago and which
|
||||
have no COMPLETE label yet; the least recently labelled go first,
|
||||
so a permanently short stream can never starve the rest. Returns
|
||||
the number of rows actually WRITTEN — a row whose numbers did not
|
||||
change is skipped, which makes repeated passes idempotent and lets
|
||||
a batch driver stop at 0.
|
||||
have no COMPLETE label yet. An incomplete row is re-examined only
|
||||
while it is younger than SCAN_LABEL_MAX_AGE_H (counted from the
|
||||
newest logged row when the database is behind the clock) — beyond
|
||||
that its window had a real hole that no later bar can fill, so the
|
||||
label it already carries is final — and a stale row never anchors
|
||||
the shared bar stream either (a key that has nothing else due
|
||||
starts at the age bound plus one window). Both bounds are
|
||||
load-bearing: a handful of permanently incomplete rows (a feed
|
||||
outage) sort to the front of every pass, and anchoring the stream
|
||||
at their bar pushes its row cap past today, which silently labels
|
||||
every genuinely due row NULL and incomplete — the scan_outcomes
|
||||
table then goes blind. Returns the number of rows actually WRITTEN
|
||||
— a row whose numbers did not change is not rewritten (only its
|
||||
labeled_at is bumped, so it rotates to the back of the queue),
|
||||
which makes repeated passes idempotent and lets a batch driver
|
||||
stop at 0.
|
||||
|
||||
Bounded on purpose: at most ``budget`` candidates are examined and
|
||||
the pass stops after ``time_budget`` seconds (default 2 s) so the
|
||||
opportunistic call inside log() stays cheap. Per-row failures are
|
||||
logged and skipped."""
|
||||
moment = now or datetime.now(timezone.utc)
|
||||
cutoff = _utc(moment) - timedelta(hours=SCAN_LABEL_MIN_AGE_H)
|
||||
moment_utc = _utc(moment)
|
||||
cutoff = moment_utc - timedelta(hours=SCAN_LABEL_MIN_AGE_H)
|
||||
deadline = time.monotonic() + float(
|
||||
SCAN_LABEL_TIME_BUDGET if time_budget is None else time_budget)
|
||||
written = 0
|
||||
with self._lock:
|
||||
# Age is measured from the newest row when the database itself
|
||||
# is behind the clock (an imported snapshot on the dev box),
|
||||
# otherwise every row of a 3-day-old import would be declared
|
||||
# final on the first pass. On a live logger the two coincide.
|
||||
horizon = moment_utc
|
||||
newest = self._conn.execute(
|
||||
"SELECT logged_at FROM signals ORDER BY id DESC"
|
||||
" LIMIT 1").fetchone()
|
||||
try:
|
||||
horizon = min(horizon, _utc(datetime.fromisoformat(
|
||||
str(newest[0]).replace(" ", "T"))))
|
||||
except (TypeError, ValueError, IndexError):
|
||||
pass
|
||||
stale_before = horizon - timedelta(hours=SCAN_LABEL_MAX_AGE_H)
|
||||
# Deepest a stream may start when every row due for that key is
|
||||
# stale: one window before the age bound, so such a row still
|
||||
# gets its single final pass without dragging the stream back.
|
||||
anchor_floor = stale_before - timedelta(hours=SCAN_LABEL_MIN_AGE_H)
|
||||
# Row ids are monotonic in logged_at (a uniform ISO string), so
|
||||
# an id bound is the cheap, index-friendly way to leave stale
|
||||
# incomplete rows out of the re-examination set. bar_ts mixes
|
||||
# separators and must never be compared as a string.
|
||||
min_id = self._conn.execute(
|
||||
"SELECT MIN(id) FROM signals WHERE logged_at >= ?",
|
||||
(stale_before.isoformat(),)).fetchone()[0]
|
||||
if min_id is None:
|
||||
min_id = 2 ** 62 # nothing recent: re-examine none
|
||||
rows = self._conn.execute(
|
||||
"SELECT s.id, s.symbol, s.timeframe, s.price,"
|
||||
" COALESCE(s.bar_ts, json_extract(s.indicators_json,"
|
||||
" '$.\"' || s.timeframe || '\".timestamp')) AS bar,"
|
||||
" json_extract(s.indicators_json,"
|
||||
" '$.\"' || s.timeframe || '\".key_levels.atr') AS atr"
|
||||
" '$.\"' || s.timeframe || '\".key_levels.atr') AS atr,"
|
||||
" (o.signal_id IS NULL) AS unlabelled"
|
||||
" FROM signals s"
|
||||
" LEFT JOIN scan_outcomes o ON o.signal_id = s.id"
|
||||
" WHERE s.indicators_json IS NOT NULL AND s.price > 0"
|
||||
" AND (o.signal_id IS NULL OR o.complete = 0)"
|
||||
" AND (o.signal_id IS NULL OR (o.complete = 0 AND s.id >= ?))"
|
||||
" AND bar IS NOT NULL"
|
||||
" ORDER BY COALESCE(o.labeled_at, '') ASC, s.id ASC"
|
||||
" LIMIT ?", (int(budget),)).fetchall()
|
||||
" LIMIT ?", (int(min_id), int(budget))).fetchall()
|
||||
# One bar stream per (symbol, timeframe) for the whole batch,
|
||||
# built from the OLDEST bar in it — _stream_since is a scan
|
||||
# over every row logged since that bar, far too expensive to
|
||||
# repeat per candidate.
|
||||
due: List[Tuple[Any, ...]] = []
|
||||
oldest: Dict[Tuple[str, str], Tuple[datetime, str]] = {}
|
||||
for rowid, symbol, tf, price, bar, atr in rows:
|
||||
for rowid, symbol, tf, price, bar, atr, unlabelled in rows:
|
||||
try:
|
||||
stamp = _utc(datetime.fromisoformat(
|
||||
str(bar).replace(" ", "T")))
|
||||
@@ -1255,8 +1327,16 @@ class SignalLogger:
|
||||
continue
|
||||
if stamp > cutoff:
|
||||
continue # window has not elapsed yet
|
||||
# Stale = old by the bar it closed on AND by when it was
|
||||
# written (on a live database the two agree within minutes;
|
||||
# requiring both keeps a freshly imported or backfilled DB,
|
||||
# whose rows all carry today's logged_at, out of it).
|
||||
stale = stamp < stale_before and rowid < min_id
|
||||
if stale and not unlabelled:
|
||||
continue # final: a hole no later bar can ever fill
|
||||
key = (str(symbol), str(tf))
|
||||
if key not in oldest or stamp < oldest[key][0]:
|
||||
if not stale and (key not in oldest
|
||||
or stamp < oldest[key][0]):
|
||||
oldest[key] = (stamp, str(bar))
|
||||
due.append((rowid, symbol, tf, price, bar, atr, key))
|
||||
streams: Dict[Tuple[str, str], List[Dict[str, Any]]] = {}
|
||||
@@ -1265,8 +1345,13 @@ class SignalLogger:
|
||||
break
|
||||
try:
|
||||
if key not in streams:
|
||||
# 3 days of anchor is ~2,100 rows on the live DB;
|
||||
# the cap is a safety net, not the working limit.
|
||||
anchor = oldest.get(key)
|
||||
streams[key] = self._stream_since(
|
||||
symbol, tf, oldest[key][1])
|
||||
symbol, tf,
|
||||
anchor[1] if anchor else anchor_floor.isoformat(),
|
||||
max_rows=10000)
|
||||
label = scan_outcome(bar, tf, streams[key],
|
||||
_finite(atr), price)
|
||||
if label is None:
|
||||
@@ -1277,6 +1362,14 @@ class SignalLogger:
|
||||
+ " FROM scan_outcomes WHERE signal_id=?",
|
||||
(rowid,)).fetchone()
|
||||
if prior is not None and tuple(prior) == values:
|
||||
# Unchanged, so NOT counted as written — but its
|
||||
# queue position moves, or the same short-stream
|
||||
# rows would head every pass for ever.
|
||||
now_iso = datetime.now(timezone.utc).isoformat()
|
||||
self._conn.execute(
|
||||
"UPDATE scan_outcomes SET labeled_at=?"
|
||||
" WHERE signal_id=?", (now_iso, rowid))
|
||||
self._conn.commit()
|
||||
continue
|
||||
self._conn.execute(
|
||||
_SCAN_OUTCOME_INSERT,
|
||||
@@ -1376,6 +1469,9 @@ class SignalLogger:
|
||||
continue
|
||||
before = _dumps(state)
|
||||
advanced = advance_outcome(json.loads(before), union)
|
||||
if (advanced.get("status") == "UNRESOLVABLE"
|
||||
and _is_gap_reason(advanced.get("reason"))):
|
||||
advanced = resolve_locked_floor(advanced)
|
||||
after = _dumps(advanced)
|
||||
if after == before:
|
||||
continue
|
||||
@@ -1393,7 +1489,9 @@ class SignalLogger:
|
||||
"""Bounded retry for continuity-broken outcomes: re-freeze the
|
||||
seed from the row's own setup/indicators (at most ``max_reseeds``
|
||||
times per row) so the next resolver pass can walk the now-filled
|
||||
stream. Invalid-seed rows stay terminal."""
|
||||
stream. Invalid-seed rows stay terminal. A gap row that had already
|
||||
trailed its stop past the entry is finished at that locked floor
|
||||
instead of re-seeded; the returned list holds re-seeded ids only."""
|
||||
reseeded: List[int] = []
|
||||
with self._lock:
|
||||
rows = self._conn.execute(
|
||||
@@ -1403,8 +1501,21 @@ class SignalLogger:
|
||||
" AND advisory_event_id = id").fetchall()
|
||||
for rowid, tf, setup_json, ind_json, state_json in rows:
|
||||
old = json.loads(state_json) if state_json else {}
|
||||
if "continuity" not in str(old.get("reason", "")) and \
|
||||
"gap" not in str(old.get("reason", "")):
|
||||
if not _is_gap_reason(old.get("reason")):
|
||||
continue
|
||||
# The locked trailing stop lives ONLY in this progress
|
||||
# block, and a re-seed rebuilds the walk from scratch over
|
||||
# a stream whose hole is still there (the continuity check
|
||||
# is all-or-nothing), so the floor would be lost for good.
|
||||
# Finish such a row here; re-seed only what has nothing.
|
||||
floored = resolve_locked_floor(json.loads(_dumps(old)))
|
||||
if floored.get("status") == "RESOLVED":
|
||||
self._conn.execute(
|
||||
"UPDATE signals SET advisory_outcome_status=?,"
|
||||
" advisory_r_multiple=?, advisory_outcome_json=?"
|
||||
" WHERE id=?",
|
||||
("RESOLVED", canonical_r(floored),
|
||||
_dumps(floored), rowid))
|
||||
continue
|
||||
count = int((old.get("reseed") or {}).get("count", 0))
|
||||
if count >= max_reseeds or not setup_json:
|
||||
|
||||
+60
-4
@@ -49,9 +49,10 @@ import sys
|
||||
import threading
|
||||
import time
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
from datetime import datetime, timezone
|
||||
from datetime import datetime, timedelta, timezone
|
||||
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Set
|
||||
|
||||
import pandas as pd
|
||||
from telegram import InlineKeyboardButton, InlineKeyboardMarkup, Update
|
||||
from telegram.constants import ParseMode
|
||||
from telegram.error import BadRequest
|
||||
@@ -71,6 +72,7 @@ from app_config import (
|
||||
telegram_channel_id, violent_watch_enabled,
|
||||
)
|
||||
from providers import BaseDataProvider, make_data_provider
|
||||
from providers.base import _is_daily_maintenance
|
||||
from agent_panel import attach_panel, panel_health
|
||||
from llm_client import get_llm_client
|
||||
from market_outlook import maybe_run_outlook
|
||||
@@ -110,8 +112,12 @@ _file_handler = logging.handlers.TimedRotatingFileHandler(
|
||||
when="midnight", backupCount=30, encoding="utf-8",
|
||||
)
|
||||
_file_handler.setFormatter(_log_formatter)
|
||||
# Console only at IMPORT: `python test_all.py` imports this module, and an
|
||||
# import-time file handler wrote fixture alerts (2027 dates, 3300.00 prices)
|
||||
# into logs/telegram.log on whatever box ran the suite. main() attaches
|
||||
# _file_handler for real bot runs.
|
||||
logging.basicConfig(level=getattr(logging, _APP.log_level, logging.INFO),
|
||||
handlers=[_console_handler, _file_handler],
|
||||
handlers=[_console_handler],
|
||||
force=True)
|
||||
logging.getLogger("httpx").setLevel(logging.WARNING)
|
||||
logging.getLogger("httpcore").setLevel(logging.WARNING)
|
||||
@@ -545,7 +551,21 @@ def _bar_epoch(tf: Timeframe, now: Optional[float] = None) -> int:
|
||||
|
||||
|
||||
def _remember_scan(symbol: str, tf: Timeframe, report: SignalReport) -> None:
|
||||
_last_scan[f"{symbol}_{tf.value}"] = (_bar_epoch(tf), report)
|
||||
# File the report under the bar it actually ANALYSED, not the wall
|
||||
# clock at completion: a scan that straddles a boundary would land
|
||||
# under the new bar, so the boundary loop's own scan of that bar
|
||||
# found no cache entry and re-fetched/re-scored/re-logged the same
|
||||
# closed bar (15 duplicate M15 rows in five days — 2026-09-13 audit).
|
||||
epoch = _bar_epoch(tf)
|
||||
if report.primary_analysis is not None:
|
||||
try:
|
||||
# report.timestamp is the primary bar's OPEN label; the bar
|
||||
# closes (= the boundary this scan belongs to) one TF later.
|
||||
epoch = (int(pd.Timestamp(report.timestamp).timestamp())
|
||||
+ _TF_SECONDS_CACHE.get(tf, 900))
|
||||
except (ValueError, TypeError):
|
||||
pass # unparseable label → wall-clock boundary, as before
|
||||
_last_scan[f"{symbol}_{tf.value}"] = (epoch, report)
|
||||
|
||||
|
||||
async def _cached_or_scan(
|
||||
@@ -767,6 +787,16 @@ def _seconds_until_boundary(interval_minutes: int, buffer: int = 10) -> float:
|
||||
return remaining
|
||||
|
||||
|
||||
def _closed_bar_in_settlement(interval_minutes: int,
|
||||
now: Optional[datetime] = None) -> bool:
|
||||
"""True when the bar that just closed (label = now - interval) falls in
|
||||
gold's daily settlement pause — no such bar exists, so a scan would only
|
||||
re-read the previous bar (61 stale rows in the 2026-09 audit DB)."""
|
||||
label_time = ((now or datetime.now(timezone.utc))
|
||||
- timedelta(minutes=interval_minutes))
|
||||
return _is_daily_maintenance(label_time)
|
||||
|
||||
|
||||
# ── Violent-move watch (display-only) ────────────────────────────────
|
||||
# Per-process heads-up budget: {"day": date, "count": int, "sent": {...}}.
|
||||
_watch_state: Dict[str, Any] = {}
|
||||
@@ -800,7 +830,11 @@ async def _scan_timeframe(
|
||||
confirmation_tfs: List[Timeframe],
|
||||
) -> None:
|
||||
"""Scan all pairs for one timeframe and broadcast results."""
|
||||
tasks = [run_signal(symbol, tf, confirmation_tfs) for symbol in SCAN_PAIRS]
|
||||
# _cached_or_scan, not run_signal: the WS breach handler may already
|
||||
# have scanned this closed bar (and vice versa) — one fetch/score/log
|
||||
# per bar per TF (2026-09-13 audit).
|
||||
tasks = [_cached_or_scan(symbol, tf, confirmation_tfs)
|
||||
for symbol in SCAN_PAIRS]
|
||||
results = await asyncio.gather(*tasks, return_exceptions=True)
|
||||
reports: List[SignalReport] = []
|
||||
for i, result in enumerate(results):
|
||||
@@ -930,6 +964,9 @@ _pending_breach: Dict[str, tuple] = {}
|
||||
# A failed breach rescan shrinks the just-set cooldown to this retry
|
||||
# window instead of silencing the symbol for the full cooldown (H33a).
|
||||
_BREACH_RETRY_SECONDS = 30
|
||||
# A breach landing inside this many seconds after an M15 boundary waits for
|
||||
# the candle loop's scan (boundary+10s) and reuses it — 2026-09-13 audit.
|
||||
_BREACH_DEFER_SECONDS = 20.0
|
||||
|
||||
_cfg = PlatformConfig()
|
||||
_WS_COOLDOWN = _cfg.ws_cooldown_seconds
|
||||
@@ -1003,6 +1040,13 @@ async def _handle_breach(bot, pair_symbol: str, price: float, level_name: str) -
|
||||
logger.info(f"Level breach: {display} crossed {label} at {price} — rescanning M15")
|
||||
|
||||
try:
|
||||
# A tick a second after an M15 boundary would scan a bar the feed
|
||||
# may still be lagging on, and the candle loop (boundary+10s) would
|
||||
# then scan the same bar again. Wait for the loop and reuse it.
|
||||
elapsed = time.time() - _bar_epoch(Timeframe.M15)
|
||||
if elapsed < _BREACH_DEFER_SECONDS:
|
||||
logger.debug("deferring breach rescan to the boundary scan")
|
||||
await asyncio.sleep(_BREACH_DEFER_SECONDS - elapsed)
|
||||
report = await _cached_or_scan(pair_symbol, Timeframe.M15)
|
||||
_update_level_cache([report])
|
||||
except Exception:
|
||||
@@ -1152,6 +1196,11 @@ async def _candle_loop(bot, tf: Timeframe, interval_minutes: int) -> None:
|
||||
await asyncio.sleep(wait)
|
||||
if not _feed_active():
|
||||
break
|
||||
if _closed_bar_in_settlement(interval_minutes):
|
||||
logger.info(
|
||||
f"{label} auto-scan: skipped — bar label inside the "
|
||||
f"daily settlement pause")
|
||||
continue
|
||||
logger.info(f"{label} auto-scan: firing")
|
||||
try:
|
||||
await _scan_timeframe(
|
||||
@@ -1824,6 +1873,13 @@ def main() -> None:
|
||||
"and telegram.chat_id in config.json, or the TELEGRAM_TOKEN and "
|
||||
"TELEGRAM_CHAT_ID environment variables.")
|
||||
|
||||
# Only a real bot run writes the rotating log file: tests import this
|
||||
# module (an import-time handler logged their fixtures into
|
||||
# logs/telegram.log) and one of them calls main() to assert the refusal
|
||||
# above, so the attach sits after that guard — 2026-09-13 audit.
|
||||
if _file_handler not in logging.getLogger().handlers:
|
||||
logging.getLogger().addHandler(_file_handler)
|
||||
|
||||
_install_excepthooks()
|
||||
logger.info("Starting ForexBot2 Telegram bot…")
|
||||
|
||||
|
||||
+1015
-6
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user