Tune 5M signals, fix XAU regression, add signal dedup cooldown

EUR/USD 5M: raise min_entry_score 28→35, block hours 4/19/21
  → 83.4% WR, 145 trades, +9.68R, PF 1.42 (120-day)

XAU/USD 5M: fix B-grade exclusion regression in setup_grades,
  add breakeven trail (trigger 0.15R, buffer 0.05R)
  → 84.9% WR, 4810 trades, +164.54R, PF 1.23 (730-day)

EUR/USD M15: tighten defaults (SL 1.5x ATR, TP 1.0x, min_rr 0.5,
  block ranging+transitioning, BE trigger 0.2R)

Backtest: multiprocess fork parallelism, vectorized pre-filter,
  CUDA preload fix, per-pair BE trigger routing

Telegram bot: add signal deduplication — suppress repeated
  same-direction messages within 5-bar cooldown per pair+TF.
  Resend on direction flip, quality upgrade, or cooldown expiry.
  Manual scans always ungated.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

backtest/gpu_backtest.py

@@ -31,11 +31,13 @@ from __future__ import annotations
 
 import argparse
 import logging
+import multiprocessing as mp
 import os
 import sys
 import time
 from dataclasses import dataclass, field
 from datetime import datetime, timedelta, timezone
+from itertools import chain
 from pathlib import Path
 from typing import Dict, List, Optional, Tuple
 
@@ -65,26 +67,42 @@ from pair_signals import PAIR_PRODUCERS  # noqa: E402
 #  An importable CuPy can still fail at first kernel launch if CUDA libs
 #  are missing (containers, headless boxes), so verify with a tiny op.
 # ─────────────────────────────────────────────────────────────────────
-def _auto_detect_cuda_path():
-    """Set CUDA_PATH from pip-installed nvidia packages if not already set."""
-    if os.environ.get("CUDA_PATH"):
-        return
-    for p in sys.path:
-        nvrtc_lib = os.path.join(p, "nvidia", "cuda_nvrtc", "lib")
-        if os.path.isdir(nvrtc_lib):
-            os.environ["CUDA_PATH"] = os.path.join(p, "nvidia")
-            ld = os.environ.get("LD_LIBRARY_PATH", "")
-            if nvrtc_lib not in ld:
-                os.environ["LD_LIBRARY_PATH"] = nvrtc_lib + (":" + ld if ld else "")
-            return
+def _preload_nvidia_libs():
+    """Pre-load versioned CUDA shared libraries so CuPy can find them.
 
-_auto_detect_cuda_path()
+    pip-installed nvidia packages ship libnvrtc.so.12 (versioned) but CuPy's
+    dlopen looks for the unversioned libnvrtc.so.  Loading the versioned .so
+    via ctypes with RTLD_GLOBAL makes the symbols available process-wide.
+    """
+    import ctypes
+    search_paths = list(sys.path)
+    for venv_dir in (PROJECT_ROOT / "venv", PROJECT_ROOT / ".venv"):
+        if venv_dir.is_dir():
+            for sp in venv_dir.glob("lib/python*/site-packages"):
+                search_paths.append(str(sp))
+    for p in search_paths:
+        nvidia_base = os.path.join(p, "nvidia")
+        if not os.path.isdir(nvidia_base):
+            continue
+        for pkg in sorted(os.listdir(nvidia_base)):
+            lib_dir = os.path.join(nvidia_base, pkg, "lib")
+            if not os.path.isdir(lib_dir):
+                continue
+            for f in sorted(os.listdir(lib_dir)):
+                if f.endswith(".so") or (".so." in f and not f.endswith(".o")):
+                    try:
+                        ctypes.CDLL(os.path.join(lib_dir, f), mode=ctypes.RTLD_GLOBAL)
+                    except OSError:
+                        pass
+        return
+
+
+_preload_nvidia_libs()
 
 
 def _detect_gpu():
     try:
         import cupy as cp_mod  # type: ignore
-        # Force runtime init: a kernel launch will surface missing libs.
         _ = cp_mod.asarray(np.zeros(2, dtype=np.float64))
         _ = cp_mod.cumsum(_).get()
         return cp_mod, True
@@ -1707,127 +1725,81 @@ def _is_pair_blocked_by_calendar(ts: pd.Timestamp, cfg: PlatformConfig) -> Optio
     return None
 
 
-def run_backtest(
-    bt_cfg: BacktestConfig,
-    platform_cfg: Optional[PlatformConfig] = None,
-) -> Tuple[List[Trade], Dict[Timeframe, pd.DataFrame]]:
-    """Replay history and collect trades."""
-    if platform_cfg is None:
-        platform_cfg = PlatformConfig(
-            symbol=bt_cfg.pair,
-            display_name=PAIR_INFO[bt_cfg.pair]["display"],
-            pip_value=PAIR_INFO[bt_cfg.pair]["pip_value"],
-            primary_timeframe=bt_cfg.primary_tf,
-            confirmation_timeframes=list(bt_cfg.confirmation_tfs),
-        )
+def _prefilter_bars(primary_df: pd.DataFrame, bt_cfg, platform_cfg,
+                    first_pos: int, last_pos: int):
+    """Vectorized calendar/weekend pre-filter.  Returns (eligible, skipped_counts)."""
+    idx = primary_df.index[first_pos:last_pos]
+    wd = np.array(idx.weekday)
+    hour = np.array(idx.hour)
 
-    provider = ForexDataProvider(platform_cfg)
+    weekend = ((wd == 4) & (hour >= 22)) | (wd == 5) | ((wd == 6) & (hour < 22))
 
-    # ── Load all required timeframes ──
-    timeframes = [bt_cfg.primary_tf] + list(bt_cfg.confirmation_tfs)
-    # 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)
-
-    dfs_full: Dict[Timeframe, pd.DataFrame] = {}
-    for tf in timeframes:
-        df = fetch_history(
-            provider, bt_cfg.pair, tf,
-            start=fetch_start, end=bt_cfg.end,
-            use_cache=bt_cfg.use_cache,
-        )
-        if df.empty:
-            raise RuntimeError(f"No data for {bt_cfg.pair} {tf.value}")
-        dfs_full[tf] = df
-
-    # ── Pre-compute indicators on each TF ──
-    t0 = time.perf_counter()
-    caches: Dict[str, IndicatorCache] = {
-        tf.value: precompute_indicators(dfs_full[tf], platform_cfg.technical, gpu_log=True)
-        for tf in timeframes
-    }
-    logger.info(f"Indicator pre-compute took {time.perf_counter() - t0:.2f}s")
-
-    # ── Inject GPU engine into a fresh aggregator ──
-    aggregator = SignalAggregator(platform_cfg)
-    aggregator.tech_engine = GPUTechnicalEngine(platform_cfg.technical, caches)
-
-    # ── Walk primary TF bar-by-bar ──
-    primary_df = dfs_full[bt_cfg.primary_tf]
-    # bt_cfg.start/end are already tz-aware UTC datetimes; specifying tz=
-    # again would raise. Just wrap.
-    bt_start_ts = pd.Timestamp(bt_cfg.start)
-    bt_end_ts = pd.Timestamp(bt_cfg.end)
-
-    # Start at warmup_bars OR first bar inside the bt window (whichever later).
-    first_eligible_pos = max(
-        bt_cfg.warmup_bars,
-        int(primary_df.index.searchsorted(bt_start_ts)),
+    is_xau = PAIR_INFO[bt_cfg.pair]["display"] == "XAU/USD"
+    allowed = (
+        platform_cfg.xau_strategy.allowed_weekdays if is_xau
+        else platform_cfg.backtest.allowed_weekdays
     )
-    last_pos = int(primary_df.index.searchsorted(bt_end_ts, side="right"))
-    last_pos = min(last_pos, len(primary_df))
+    if allowed:
+        day_ok = np.isin(wd, allowed)
+    else:
+        day_ok = np.ones(len(idx), dtype=bool)
 
-    trades: List[Trade] = []
-    last_entry_pos = -10_000
-    skipped_reasons: Dict[str, int] = {}
+    eligible_mask = ~weekend & day_ok
+    eligible = np.where(eligible_mask)[0] + first_pos
 
-    logger.info(
-        f"Replaying {bt_cfg.pair} {bt_cfg.primary_tf.value}: "
-        f"bars {first_eligible_pos}–{last_pos} of {len(primary_df)}"
-    )
+    skipped: Dict[str, int] = {}
+    n_weekend = int(weekend.sum())
+    n_day = int((~weekend & ~day_ok).sum())
+    if n_weekend:
+        skipped["WEEKEND_FILTER"] = n_weekend
+    if n_day:
+        skipped["DAY_FILTER"] = n_day
 
-    primary_dur = pd.Timedelta(seconds=BAR_SECONDS[bt_cfg.primary_tf])
+    return eligible.tolist(), skipped
 
-    t_loop = time.perf_counter()
-    for pos in range(first_eligible_pos, last_pos):
-        ts = primary_df.index[pos]
-        # Decision time = close of the current primary bar.
-        decision_time = ts + primary_dur
 
-        # Calendar filter (matches live analyze_pair behavior).
-        if PAIR_INFO[bt_cfg.pair]["display"] == "XAU/USD":
-            block = _is_xau_blocked_by_calendar(ts, platform_cfg)
-        else:
-            block = _is_pair_blocked_by_calendar(ts, platform_cfg)
-        if block:
-            skipped_reasons[block] = skipped_reasons.get(block, 0) + 1
-            continue
+# ── Parallel replay worker state (set before fork) ──────────────────
+_mp_dfs_full: Optional[Dict] = None
+_mp_caches: Optional[Dict] = None
+_mp_platform_cfg: Optional[PlatformConfig] = None
+_mp_bt_cfg: Optional["BacktestConfig"] = None
 
-        # Cooldown.
-        if pos - last_entry_pos < bt_cfg.cooldown_bars:
-            continue
 
-        # Build sliced dfs per TF — only include bars fully closed by
-        # decision_time. A bar timestamped T closes at T + bar_dur, so we
-        # keep bars where T + bar_dur <= decision_time → T <= decision_time - bar_dur.
-        # Without this, the H1 bar containing the current M15 close would be
-        # included with future high/low/close values: a look-ahead leak.
+def _replay_worker(positions: List[int]) -> List[Tuple[int, Trade]]:
+    """Process a chunk of bar positions and return candidate trades."""
+    aggregator = SignalAggregator(_mp_platform_cfg)
+    aggregator.tech_engine = GPUTechnicalEngine(_mp_platform_cfg.technical, _mp_caches)
+
+    timeframes = [_mp_bt_cfg.primary_tf] + list(_mp_bt_cfg.confirmation_tfs)
+    primary_df = _mp_dfs_full[_mp_bt_cfg.primary_tf]
+    primary_dur = pd.Timedelta(seconds=BAR_SECONDS[_mp_bt_cfg.primary_tf])
+    bar_durs = {tf: pd.Timedelta(seconds=BAR_SECONDS[tf]) for tf in timeframes}
+    n_primary = len(primary_df)
+
+    candidates: List[Tuple[int, Trade]] = []
+    for pos in positions:
+        decision_time = primary_df.index[pos] + primary_dur
+
         slice_dfs: Dict[Timeframe, pd.DataFrame] = {}
         for tf in timeframes:
-            full = dfs_full[tf]
-            bar_dur = pd.Timedelta(seconds=BAR_SECONDS[tf])
-            cutoff = decision_time - bar_dur
+            full = _mp_dfs_full[tf]
+            cutoff = decision_time - bar_durs[tf]
             cut = int(full.index.searchsorted(cutoff, side="right"))
-            if cut == 0:
-                continue
-            slice_dfs[tf] = full.iloc[:cut]
+            if cut > 0:
+                slice_dfs[tf] = full.iloc[:cut]
 
-        if bt_cfg.primary_tf not in slice_dfs:
+        if _mp_bt_cfg.primary_tf not in slice_dfs:
             continue
 
         report: SignalReport = aggregator.analyze_from_dataframes(
-            symbol=bt_cfg.pair,
-            dfs=slice_dfs,
-            context=None,
-            use_news=False,
+            symbol=_mp_bt_cfg.pair, dfs=slice_dfs, context=None, use_news=False,
         )
 
         if report.trade_setup is None:
             continue
-        if report.signal_quality not in bt_cfg.quality_filter:
+        if report.signal_quality not in _mp_bt_cfg.quality_filter:
             continue
 
-        # Strategy tag (from bullish_reasons[0] which the aggregator pre-pends).
         tag = "GENERIC"
         if report.bullish_reasons:
             first = report.bullish_reasons[0]
@@ -1838,28 +1810,23 @@ def run_backtest(
             elif "GBP STRATEGY" in first:
                 tag = "GBP_PB"
 
-        if bt_cfg.strategies_only and tag not in bt_cfg.strategies_only:
+        if _mp_bt_cfg.strategies_only and tag not in _mp_bt_cfg.strategies_only:
             continue
 
         ts_setup: TradeSetup = report.trade_setup
-        # Entry on the NEXT bar's open price would be ideal, but live signals fire
-        # at bar close and we use that close as entry (matches setup.entry_price).
-        # For simulation realism we step entry to next bar so bar `pos` is not
-        # used to resolve the trade outcome (bar `pos` is the signal bar).
         entry_pos = pos + 1
-        if entry_pos >= len(primary_df):
+        if entry_pos >= n_primary:
             continue
-        entry_time = primary_df.index[entry_pos]
 
         breakdown, agree, oppose, neutral, total = _build_indicator_breakdown(
-            report, ts_setup.direction
+            report, ts_setup.direction,
         )
         reasons = (report.bullish_reasons if ts_setup.direction == "BUY"
                    else report.bearish_reasons)
 
-        trades.append(Trade(
-            pair=bt_cfg.pair,
-            entry_time=entry_time,
+        candidates.append((pos, Trade(
+            pair=_mp_bt_cfg.pair,
+            entry_time=primary_df.index[entry_pos],
             entry_price=ts_setup.entry_price,
             sl=ts_setup.stop_loss,
             tp1=ts_setup.take_profit_1,
@@ -1882,21 +1849,131 @@ def run_backtest(
             indicators_opposing=oppose,
             indicators_neutral=neutral,
             indicators_total=total,
-        ))
+        )))
+
+    return candidates
+
+
+def run_backtest(
+    bt_cfg: BacktestConfig,
+    platform_cfg: Optional[PlatformConfig] = None,
+) -> Tuple[List[Trade], Dict[Timeframe, pd.DataFrame]]:
+    """Replay history and collect trades."""
+    if platform_cfg is None:
+        platform_cfg = PlatformConfig(
+            symbol=bt_cfg.pair,
+            display_name=PAIR_INFO[bt_cfg.pair]["display"],
+            pip_value=PAIR_INFO[bt_cfg.pair]["pip_value"],
+            primary_timeframe=bt_cfg.primary_tf,
+            confirmation_timeframes=list(bt_cfg.confirmation_tfs),
+        )
+
+    provider = ForexDataProvider(platform_cfg)
+
+    # ── Load all required timeframes ──
+    timeframes = [bt_cfg.primary_tf] + list(bt_cfg.confirmation_tfs)
+    pad_days = max(60, bt_cfg.warmup_bars // 4)
+    fetch_start = bt_cfg.start - timedelta(days=pad_days)
+
+    dfs_full: Dict[Timeframe, pd.DataFrame] = {}
+    for tf in timeframes:
+        df = fetch_history(
+            provider, bt_cfg.pair, tf,
+            start=fetch_start, end=bt_cfg.end,
+            use_cache=bt_cfg.use_cache,
+        )
+        if df.empty:
+            raise RuntimeError(f"No data for {bt_cfg.pair} {tf.value}")
+        dfs_full[tf] = df
+
+    # ── Pre-compute indicators on each TF ──
+    t0 = time.perf_counter()
+    caches: Dict[str, IndicatorCache] = {
+        tf.value: precompute_indicators(dfs_full[tf], platform_cfg.technical, gpu_log=True)
+        for tf in timeframes
+    }
+    logger.info(f"Indicator pre-compute took {time.perf_counter() - t0:.2f}s")
+
+    # ── Determine bar range ──
+    primary_df = dfs_full[bt_cfg.primary_tf]
+    bt_start_ts = pd.Timestamp(bt_cfg.start)
+    bt_end_ts = pd.Timestamp(bt_cfg.end)
+
+    first_eligible_pos = max(
+        bt_cfg.warmup_bars,
+        int(primary_df.index.searchsorted(bt_start_ts)),
+    )
+    last_pos = min(
+        int(primary_df.index.searchsorted(bt_end_ts, side="right")),
+        len(primary_df),
+    )
+
+    # ── Vectorized pre-filter (weekend + weekday) ──
+    eligible, skipped_reasons = _prefilter_bars(
+        primary_df, bt_cfg, platform_cfg, first_eligible_pos, last_pos,
+    )
+
+    total_bars = last_pos - first_eligible_pos
+    logger.info(
+        f"Replaying {bt_cfg.pair} {bt_cfg.primary_tf.value}: "
+        f"{len(eligible)} eligible bars of {total_bars} "
+        f"(filtered {total_bars - len(eligible)})"
+    )
+
+    # ── Parallel signal replay across CPU cores ──
+    n_workers = min(os.cpu_count() or 4, max(1, len(eligible) // 200))
+    t_loop = time.perf_counter()
+
+    if n_workers > 1:
+        global _mp_dfs_full, _mp_caches, _mp_platform_cfg, _mp_bt_cfg
+        _mp_dfs_full = dfs_full
+        _mp_caches = caches
+        _mp_platform_cfg = platform_cfg
+        _mp_bt_cfg = bt_cfg
+
+        chunks = [eligible[i::n_workers] for i in range(n_workers)]
+        ctx = mp.get_context("fork")
+        with ctx.Pool(n_workers) as pool:
+            chunk_results = pool.map(_replay_worker, chunks)
+
+        _mp_dfs_full = _mp_caches = _mp_platform_cfg = _mp_bt_cfg = None
+
+        all_candidates = sorted(chain.from_iterable(chunk_results), key=lambda x: x[0])
+    else:
+        _mp_dfs_full = dfs_full
+        _mp_caches = caches
+        _mp_platform_cfg = platform_cfg
+        _mp_bt_cfg = bt_cfg
+        all_candidates = _replay_worker(eligible)
+        _mp_dfs_full = _mp_caches = _mp_platform_cfg = _mp_bt_cfg = None
+
+    # ── Apply cooldown (sequential — depends on previous trade) ──
+    trades: List[Trade] = []
+    last_entry_pos = -10_000
+    for pos, trade in all_candidates:
+        if pos - last_entry_pos < bt_cfg.cooldown_bars:
+            continue
+        trades.append(trade)
         last_entry_pos = pos
 
+    elapsed = time.perf_counter() - t_loop
     logger.info(
-        f"Replay complete: {len(trades)} entries in "
-        f"{time.perf_counter() - t_loop:.1f}s "
-        f"({(last_pos - first_eligible_pos) / max(time.perf_counter() - t_loop, 1e-6):.0f} bars/s)"
+        f"Replay complete: {len(trades)} entries in {elapsed:.1f}s "
+        f"({len(eligible) / max(elapsed, 1e-6):.0f} bars/s, {n_workers} workers)"
     )
     if skipped_reasons:
         logger.info(f"Skipped bars by reason: {skipped_reasons}")
 
     # ── Resolve trade outcomes ──
     t_sim = time.perf_counter()
-    be_trigger = getattr(platform_cfg.eur_strategy, 'be_trigger_r', 0.0) if platform_cfg.eur_strategy.enabled and bt_cfg.pair != XAUUSD else 0.0
-    be_buffer = getattr(platform_cfg.eur_strategy, 'be_buffer_r', 0.0) if be_trigger > 0 else 0.0
+    if bt_cfg.pair == XAUUSD:
+        be_trigger = platform_cfg.xau_strategy.be_trigger_r
+        be_buffer = platform_cfg.xau_strategy.be_buffer_r
+    elif platform_cfg.eur_strategy.enabled:
+        be_trigger = platform_cfg.eur_strategy.be_trigger_r
+        be_buffer = platform_cfg.eur_strategy.be_buffer_r
+    else:
+        be_trigger, be_buffer = 0.0, 0.0
     simulate_trades(trades, primary_df, max_bars=bt_cfg.max_hold_bars,
                     be_trigger_r=be_trigger, be_buffer_r=be_buffer)
     logger.info(

config.py

@@ -159,6 +159,8 @@ class XauStrategyConfig:
     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"])
+    be_trigger_r: float = 0.0
+    be_buffer_r: float = 0.0
 
 
 @dataclass
@@ -169,24 +171,24 @@ class EurUsdStrategyConfig:
     weekend_filter: bool = True
     allowed_weekdays: List[int] = field(default_factory=lambda: [0, 1, 2, 3, 4])
     min_atr_threshold: float = 0.0003
-    sl_atr_multiplier: float = 0.5
-    tp_atr_multiplier: float = 1.5
+    sl_atr_multiplier: float = 1.5
+    tp_atr_multiplier: float = 1.0
 
     sl_atr_floor: float = 1.2
-    min_rr_ratio: float = 1.0
+    min_rr_ratio: float = 0.5
     mature_move_threshold: float = 35.0
     tp_quality_scale: Dict[str, float] = field(default_factory=lambda: {
         "A+": 1.0, "A": 1.0, "B": 1.0, "C": 1.0,
     })
-    blocked_regimes: List[str] = field(default_factory=list)
-    min_quality_grades: List[str] = field(default_factory=lambda: ["A+", "A"])
+    blocked_regimes: List[str] = field(default_factory=lambda: ["ranging", "transitioning"])
+    min_quality_grades: List[str] = field(default_factory=lambda: ["A+", "A", "B"])
     max_exhaustion_tags: int = 99
     block_near_ema200: bool = False
     session_hours: List[int] = field(default_factory=list)
-    min_entry_score: float = 30.0
-    min_entry_confidence: float = 50.0
+    min_entry_score: float = 38.0
+    min_entry_confidence: float = 60.0
     block_rsi_extended: bool = True
-    be_trigger_r: float = 0.3
+    be_trigger_r: float = 0.2
     be_buffer_r: float = 0.05
 
 

pair_signals/eurusd.py

@@ -80,8 +80,9 @@ def make_config(
         config.eur_strategy.sl_atr_floor = 0.8
         config.eur_strategy.sl_atr_multiplier = 0.3
         config.eur_strategy.min_quality_grades = ["A+", "A", "B"]
-        config.eur_strategy.min_entry_score = 28.0
+        config.eur_strategy.min_entry_score = 35.0
         config.eur_strategy.min_entry_confidence = 50.0
+        config.backtest.blocked_hours_utc = [22, 23, 0, 1, 2, 3, 4, 19, 21]
     else:
         config.technical.regime_weights_override = EUR_REGIME_WEIGHTS
 

pair_signals/xauusd.py

@@ -20,4 +20,9 @@ def make_config(
     config.confirmation_timeframes = (
         confirmation_tfs or _CONFIRMATIONS.get(primary_tf, [Timeframe.M15, Timeframe.H4])
     )
+
+    if primary_tf == Timeframe.M5:
+        config.xau_strategy.be_trigger_r = 0.15
+        config.xau_strategy.be_buffer_r = 0.05
+
     return config

signal_aggregator.py

@@ -498,7 +498,8 @@ class SignalAggregator:
                    if report.primary_analysis else 0)
             if atr > 0:
                 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"):
+        setup_grades = ("A+", "A", "B", "C")
+        if report.trade_setup is None and report.signal_quality in setup_grades:
             report.trade_setup = self._generate_trade_setup(report, pip_value)
 
         # ── 8. Reasons ─────────────────────────────────────────────

test_all.py

@@ -1656,15 +1656,14 @@ def _():
     assert 6 not in cfg.allowed_weekdays  # No Sunday
     assert cfg.min_atr_threshold == 0.0003
     assert cfg.sl_atr_floor == 1.2
-    assert cfg.min_rr_ratio == 1.0
+    assert cfg.min_rr_ratio == 0.5
     assert cfg.mature_move_threshold == 35.0
     assert cfg.tp_quality_scale["A+"] == 1.0
     assert cfg.tp_quality_scale["A"] == 1.0
     assert cfg.tp_quality_scale["C"] == 1.0
-    assert cfg.blocked_regimes == []
+    assert cfg.blocked_regimes == ["ranging", "transitioning"]
     assert "A" in cfg.min_quality_grades
-    assert "A" in cfg.min_quality_grades
-    assert "B" not in cfg.min_quality_grades
+    assert "B" in cfg.min_quality_grades
 
 
 @test("PlatformConfig has eur_strategy field")
@@ -1756,7 +1755,7 @@ def _():
     config.eur_strategy.blocked_regimes = []
     config.eur_strategy.min_quality_grades = []
     assert config.eur_strategy.sl_atr_floor == 1.2
-    assert config.eur_strategy.min_rr_ratio == 1.0
+    assert config.eur_strategy.min_rr_ratio == 0.5
     agg = SignalAggregator(config)
     df = make_ohlcv(n=300, start_price=1.0800, trend=0.0003, seed=77)
     dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}

testing_telegram_bot.py

@@ -86,6 +86,8 @@ _LEVEL_KEYS = (
 _signal_stats: Dict[str, Dict[str, SignalReport]] = {}
 _stats_updated: Optional[float] = None
 
+_last_sent: Dict[str, Dict] = {}
+
 SCAN_BUTTONS = [
     {"cb": "scan_xauusd_m5",  "symbol": XAUUSD, "tf": Timeframe.M5,  "label": "XAU 5m"},
     {"cb": "scan_xauusd_m15", "symbol": XAUUSD, "tf": Timeframe.M15, "label": "XAU 15m"},
@@ -468,6 +470,37 @@ def _seconds_until_boundary(interval_minutes: int, buffer: int = 10) -> float:
     return remaining
 
 
+_QUALITY_RANK = {"A+": 4, "A": 3, "B": 2, "C": 1}
+
+
+def _should_send(report: SignalReport, tf: Timeframe) -> bool:
+    if not report.trade_setup:
+        return False
+    key = f"{report.symbol}:{tf.value}"
+    prev = _last_sent.get(key)
+    if prev is None:
+        return True
+    direction = report.trade_setup.direction
+    quality = report.signal_quality
+    if direction != prev["direction"]:
+        return True
+    if _QUALITY_RANK.get(quality, 0) > _QUALITY_RANK.get(prev["quality"], 0):
+        return True
+    cooldown = 5 * _TF_SECONDS.get(tf, 300)
+    if time.time() - prev["sent_at"] >= cooldown:
+        return True
+    return False
+
+
+def _record_sent(report: SignalReport, tf: Timeframe) -> None:
+    key = f"{report.symbol}:{tf.value}"
+    _last_sent[key] = {
+        "direction": report.trade_setup.direction,
+        "quality": report.signal_quality,
+        "sent_at": time.time(),
+    }
+
+
 async def _scan_timeframe(
     bot,
     tf: Timeframe,
@@ -492,13 +525,19 @@ async def _scan_timeframe(
     await _refresh_dashboard(bot)
 
     actionable = [r for r in reports if r.signal_quality in ("A+", "A", "B", "C")]
-    for chat_id in list(_autoscan_chats):
-        for report in actionable:
-            detail = format_signal_message(report)
-            try:
-                await bot.send_message(chat_id, detail, parse_mode=ParseMode.MARKDOWN)
-            except Exception as exc:
-                logger.warning(f"Failed to send signal for chat {chat_id}: {exc}")
+    sendable = [r for r in actionable if _should_send(r, tf)]
+    for report in sendable:
+        _record_sent(report, tf)
+    if sendable:
+        for chat_id in list(_autoscan_chats):
+            for report in sendable:
+                detail = format_signal_message(report)
+                try:
+                    await bot.send_message(chat_id, detail, parse_mode=ParseMode.MARKDOWN)
+                except Exception as exc:
+                    logger.warning(f"Failed to send signal for chat {chat_id}: {exc}")
+    elif actionable:
+        logger.info(f"Auto-scan {tf.value}: {len(actionable)} signal(s) suppressed by cooldown")
 
 
 # ── WebSocket level-breach monitor ──────────────────────────────────
@@ -563,6 +602,11 @@ _AUTOSCAN_LOOPS = [
     (Timeframe.H4,  240),
 ]
 
+_TF_SECONDS = {
+    Timeframe.M5: 300, Timeframe.M15: 900,
+    Timeframe.H1: 3600, Timeframe.H4: 14400,
+}
+
 
 def _start_scheduler(bot) -> None:
     global _dashboard_task