# source: https://raw.githubusercontent.com/ccaadaro/RL_Trading/5d0d1958a3766ee8c0990d3b232a131069d1422d/InstitutionalDollarStrategy.py """ Github_ccaadaro_RL_Trading__InstitutionalDollarStrategy__20260510_090356.py (Phase 8 — ZMQ Subscriber Shell) ******************************************************************************* * [RESEARCH CLOSED] — NOT DEPLOYABLE * * Status: ARCHIVED (2026-05-08) * * BTC P&L project reached a terminal kill criterion after Phase 12B final audit.* * No stable directional alpha or risk-adjusted BTC exposure strategy found. * ******************************************************************************* Architecture: market_data_daemon.py ──ZMQ PUB──► Github_ccaadaro_RL_Trading__InstitutionalDollarStrategy__20260510_090356 └── _ZmqListener thread │ ▼ (on each Dollar Bar) Features → LightGBM → HMM → Kelly │ ▼ _latest_signal (thread-safe dict) │ populate_indicators() │ populate_entry/exit_trend() │ Freqtrade order routing custom_stake_amount / custom_entry_price This strategy is a THIN SHELL. All data ingestion and intelligence computation happen in the daemon + the background listener thread, NOT in the Freqtrade polling loop. Freqtrade only manages the lifecycle of individual orders. WARNING: Not designed for native Freqtrade backtesting. Run exclusively with --dry-run or --live. """ import queue import sys import json import logging import threading import time import warnings from pathlib import Path from typing import Optional from concurrent.futures import ThreadPoolExecutor # ── Path bootstrap: must run BEFORE any third-party imports ────────────────── # Freqtrade uses importlib.util which does NOT inherit sys.path from the caller. # We resolve RL_Trading/ relative to THIS file's real location so that copies # placed in strategy subfolders (e.g. strategies/institutional/) still work. _THIS_FILE = Path(__file__).resolve() _STRAT_DIR = _THIS_FILE.parent # Support both: direct in strategies/ OR in strategies/institutional/ _RL_DIR = _STRAT_DIR / "RL_Trading" if not _RL_DIR.exists(): _RL_DIR = _STRAT_DIR.parent / "RL_Trading" for _p in [str(_RL_DIR), str(_RL_DIR.parent)]: if _p not in sys.path: sys.path.insert(0, _p) import lightgbm as lgb import numpy as np import pandas as pd import pandas_ta as ta try: import zmq _ZMQ_AVAILABLE = True except ImportError: _ZMQ_AVAILABLE = False from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import DecimalParameter from utils.signal_features import compute_ohlcv_features from utils.risk_directors import MahalanobisTurbulence, HMMRegimeModel from utils.position_sizer import FractionalKellySizer from utils.filters import SymmetricCUSUMFilter from utils.data_providers import MarketDataProvider, ZmqDollarBarProvider, FreqtradeCandleProvider logger = logging.getLogger(__name__) # ── ZMQ daemon topics (must match market_data_daemon.py) ───────────────────── TOPIC_DOLLAR_BAR = b"DOLLAR_BAR" TOPIC_BOOK_TICKER = b"BOOK_TICKER" ZMQ_DAEMON_ADDR = "tcp://127.0.0.1:5555" ZMQ_DIAG_ADDR = "tcp://127.0.0.1:5556" # Dashboard diagnostics PUB # Minimum Dollar Bars in the rolling buffer before running inference # Raised to 50 to satisfy min_periods in volatility calculations. MIN_BARS_FOR_INFERENCE = 50 class InstitutionalSignalEngine(threading.Thread): """ Background daemon thread that drives the full intelligence pipeline (features → LightGBM → HMM → sizing) using a MarketDataProvider. Results are written atomically into a shared signal dict. """ def __init__(self, alpha_model, meta_model, alpha_slow_model, turbulence_engine, hmm_model, hmm_model_htf, sizer, signal_store: dict, lock: threading.Lock, provider: MarketDataProvider): super().__init__(daemon=True, name="InstitutionalSignalEngine") self._alpha = alpha_model self._meta = meta_model self._alpha_slow = alpha_slow_model self._turb = turbulence_engine self._WINDOW_DAILY = 1000 # Default daily window for $2M Dollar Bars self._hmm = hmm_model self._hmm_htf = hmm_model_htf # 1-hour timeframe HMM self._sizer = sizer self._signal = signal_store self._lock = lock self._provider = provider self._zmq_addr = ZMQ_DAEMON_ADDR self._topic = TOPIC_DOLLAR_BAR self._running = True # Clock Decoupling State self._cusum = SymmetricCUSUMFilter() self._current_target_pos = 0.0 # HMM Robust Inference State (LTF — dollar bar resolution) self._hmm_fitted = False self._bars_since_fit = 0 # HTF HMM state (1-hour aggregated bars) self._htf_fitted = False self._htf_bars_since_fit = 0 # D-02 FIX: Temporal hysteresis for regime label. # Asymmetric: faster into bull (miss fewer rallies), slower into bear (avoid whipsaw). # Long-only: false negatives (blocked bull) cost 100% of the rally; # false positives (wrong bull) only cost fees if it reverses quickly. self._HYSTERESIS_TO_BULL = 3 # bear→bull: 3 bars confirmation (restored; 2 was over-firing) self._HYSTERESIS_TO_BEAR = 6 # bull→bear: require more confirmation self._pending_regime: Optional[str] = None self._pending_regime_count: int = 0 self._committed_regime: str = "unknown" self._last_hmm_diag: float = 0.0 self._last_alpha_diag: float = 0.0 # BYPASS HOLD: After a bypass entry, sustain the 10% floor for up to # _HYSTERESIS_TO_BULL bars so the position isn't killed before the HMM # commits. Cleared early if pending regime pivots away from bull or # alpha falls below 0.45 (model reversed). self._bypass_hold_bars: int = 0 # BUG #23 FIX: Coalescing state self._pipeline_inflight = False self._inflight_since = 0.0 self._last_t_processed = 0.0 # Sync with provider buffer if possible self._bar_buffer = getattr(provider, "_bar_buffer", []) self._executor = ThreadPoolExecutor(max_workers=1) # Dashboard diagnostics: fire-and-forget queue (thread-safe) self._diag_queue: queue.SimpleQueue = queue.SimpleQueue() self._last_hb = 0.0 # Institutional Stats Monitoring (Shadow-Live) self._stats = { "n_inferences": 0, "alpha_probs": [], "meta_probs": [], "spreads": [], "costs": [], "veto_count": 0, "entry_count": 0, "reasons": {}, "shadow_stats": [] # For detailed telemetry } self.shadow_selector_enabled = False self._alpha_v1 = None self._alpha_v2 = None def set_virtual_position(self, pos: float): """BUG #19 FIX: Thread-safe update of current position.""" with self._lock: self._current_target_pos = float(pos) def stop(self): self._running = False if self._executor: # BUG-Shutdown FIX: non-waiting shutdown self._executor.shutdown(wait=False) def _log_summary_stats(self): """Institutional Summary Log (Shadow-Live Audit)""" s = self._stats if s["n_inferences"] == 0: return alpha_arr = np.array(s["alpha_probs"]) meta_arr = np.array(s["meta_probs"]) logger.info( "\n" + "="*60 + "\n" f"[SHADOW-LIVE SUMMARY] Bars: {s['n_inferences']}\n" f"Alpha Prob: Mean={np.mean(alpha_arr):.3f} | P10={np.percentile(alpha_arr, 10):.3f} | P50={np.median(alpha_arr):.3f} | P90={np.percentile(alpha_arr, 90):.3f}\n" f"Meta Prob: Mean={np.mean(meta_arr):.3f} | P10={np.percentile(meta_arr, 10):.3f} | P50={np.median(meta_arr):.3f} | P90={np.percentile(meta_arr, 90):.3f}\n" f"Veto Rate: {(s['veto_count']/s['n_inferences'])*100:.1f}% | Entry Rate: {(s['entry_count']/s['n_inferences'])*100:.1f}%\n" f"Spreads: Mean={np.mean(s['spreads']):.1f} bps | Costs: Mean={np.mean(s['costs']):.1f} bps\n" f"Veto Reasons: {s['reasons']}\n" + "="*60 ) # Clear rolling window to avoid memory leak over days, but keep counts s["alpha_probs"] = [] s["meta_probs"] = [] s["spreads"] = [] s["costs"] = [] def _compute_v2_features(self, df: pd.DataFrame) -> pd.DataFrame: """Isolated computation of the 10 features used by v1/v2 specialists.""" close = df['close'].astype(float) log_close = np.log(close.clip(lower=1e-9)) df_v2 = pd.DataFrame(index=df.index) # Base 7 df_v2['return_3_bars_feature'] = log_close.diff(3).fillna(0) df_v2['return_5_bars_feature'] = log_close.diff(5).fillna(0) df_v2['vol_10_feature'] = log_close.diff().rolling(10).std().fillna(0) buy_vol = df.get('buy_volume', df.get('buy_vol', 0)).astype(float) sell_vol = df.get('sell_volume', df.get('sell_vol', 0)).astype(float) cvd = (buy_vol - sell_vol).cumsum() df_v2['cvd_slope_feature'] = cvd.diff(10).fillna(0) / 10 df_v2['aggressor_imbalance_feature'] = ((buy_vol - sell_vol) / (buy_vol + sell_vol).clip(lower=1e-9)).fillna(0) hma20 = ta.hma(close, 20) df_v2['hma_dist_feature'] = (close / hma20 - 1).fillna(0) # WVF Z-Score length, sma_len = 22, 20 highest_close = close.rolling(length).max() wvf = (highest_close - df['low']) / highest_close * 100 wvf_sma = wvf.rolling(sma_len).mean() wvf_std = wvf.rolling(sma_len).std() df_v2['wvf_zscore_feature'] = ((wvf - wvf_sma) / wvf_std.clip(lower=1e-9)).fillna(0) # Second-Order price_slope = close.diff(10).fillna(0) / 10 df_v2['cvd_divergence_feature'] = (np.sign(price_slope) != np.sign(df_v2['cvd_slope_feature'])).astype(float) df_v2['hma_slope_feature'] = hma20.diff(5).fillna(0) / 5 df_v2['vol_accel_feature'] = df_v2['vol_10_feature'].diff(5).fillna(0) return df_v2 def select_shadow_model(self, prob_v1, prob_v2, features_row, threshold=0.55): """Regime-aware selector for Shadow-Live auditing.""" wvf_z = features_row.get("wvf_zscore_feature", 0.0) cvd_div = features_row.get("cvd_divergence_feature", 0) # Priority 1: Panic Rebound (v2 is better at catching crashes in 2025) if wvf_z > 1.5: return prob_v2, "v2", "panic_rebound_wvf" # Priority 2: CVD Absorption (v2 detects order flow traps) if cvd_div == 1: return prob_v2, "v2", "cvd_absorption" # Priority 3: Dual Confirmation (Consensus) if prob_v1 > threshold and prob_v2 > threshold: return min(prob_v1, prob_v2), "consensus", "dual_confirmation" # Default: No edge regime return 0.5, "flat", "no_regime_edge" def _run_pipeline(self): """Converts bar buffer → features → alpha → regime → sizing.""" try: if not self._provider.is_ready(): return df = self._provider.get_latest_data() # 2. Buffer Enforcement (Institutional Safety) if len(df) < MIN_BARS_FOR_INFERENCE: # HEARTBEAT during buffering self._diag_queue.put({ "type": "shadow_selector", "data": { "model": "buffering", "reason": f"Accumulating bars ({len(df)}/{MIN_BARS_FOR_INFERENCE})", "final_alpha": 0.5, "prob_v1": 0.5, "prob_v2": 0.5, "wvf_z": 0.0, "cvd_div": 0, "hma_slope": 0.0, "ts": time.time() } }) return # Feature engineering from utils.signal_features import build_feature_matrix, SIGNAL_FEAT_COLS_V2 try: X = build_feature_matrix(df, eth_df=None, funding_series=None) except Exception as e: logger.warning("Feature engineering failed: %s", e, exc_info=True) return if len(X) < 20: return # Alpha Orcale inference (Fast + Slow Stacking) try: # 1. Fast Alpha (Dollar Bars) model_features = self._alpha.feature_name() X_fast = X[model_features].fillna(0.0) fast_preds = self._alpha.predict(X_fast) df["alpha_prob_fast"] = pd.Series(fast_preds).fillna(0.5).clip(0.0, 1.0) # 2. Slow Alpha (1h Ensemble Logic) alpha_slow = 0.5 alpha_slow_source = "not_loaded" alpha_slow_models = 0 if self._alpha_slow and isinstance(self._alpha_slow, dict): slow_models = self._alpha_slow.get("models", {}) weights = self._alpha_slow.get("weights", {}) alpha_slow_source = "loaded" alpha_slow_models = len(slow_models) logits = 0.0 total_w = 0.0 last_idx = len(X) - 1 for h, model in slow_models.items(): slow_feats = getattr(model, "feature_name", lambda: [])() # Align features X_s = X[slow_feats].fillna(0.0) if slow_feats else X # LGBMClassifier needs predict_proba p = model.predict_proba(X_s)[last_idx, 1] p = np.clip(p, 1e-6, 1-1e-6) w = weights.get(h, 1.0) logits += w * np.log(p / (1 - p)) total_w += w if total_w > 0: alpha_slow = 1.0 / (1.0 + np.exp(-logits / total_w)) else: alpha_slow_source = "loaded_no_weight" df["alpha_prob_slow"] = alpha_slow # 3. Stacked Prediction: Requirement of cross-timeframe agreement last_fast = float(df["alpha_prob_fast"].iloc[-1]) if abs(alpha_slow - 0.5) < 0.02: # If slow signal is neutral, we dampen the fast signal significantly df["alpha_prob"] = 0.5 + (last_fast - 0.5) * 0.3 else: is_bull = (alpha_slow > 0.51) and (last_fast > 0.51) is_bear = (alpha_slow < 0.49) and (last_fast < 0.49) if not (is_bull or is_bear): df["alpha_prob"] = 0.5 # kill signal else: df["alpha_prob"] = last_fast now = time.time() if now - self._last_alpha_diag >= 60.0: logger.info( "[ALPHA] fast=%.4f slow=%.4f final=%.4f slow_source=%s slow_models=%d", last_fast, float(alpha_slow), float(df["alpha_prob"].iloc[-1]), alpha_slow_source, alpha_slow_models, ) self._last_alpha_diag = now except Exception as e: logger.warning("Combined Alpha inference failed: %s", e, exc_info=True) return # --- PRE-SELECTOR SAFETY & COSTS --- with self._lock: l_sig = dict(self._signal) spread_bps = 0.0 mid = l_sig.get("mid", 0.0) if l_sig.get("best_ask") and l_sig.get("best_bid") and mid > 0: spread_bps = (l_sig["best_ask"] - l_sig["best_bid"]) / mid * 10000 spread_bps = spread_bps if (spread_bps > 0 and mid > 0) else 100.0 expected_cost_bps = 5 + spread_bps + 2 # 5 fee + spread + 2 slippage # Latency Watchdog data_age = time.time() - float(df.iloc[-1].get("ts", 0)) is_stale = data_age > 300 # 5 minutes # --- SHADOW SELECTOR PATH --- if self.shadow_selector_enabled and self._alpha_v1 and self._alpha_v2: try: df_v2 = self._compute_v2_features(df) latest_v2 = df_v2.iloc[-1] prob_v1 = 0.5 prob_v2 = 0.5 if is_stale or spread_bps > 15: alpha_prob_final = 0.5 selected_model = "flat" selector_reason = "stale_data" if is_stale else "toxic_spread" else: f_v1_names = ["return_3_bars_feature", "return_5_bars_feature", "vol_10_feature", "cvd_slope_feature", "aggressor_imbalance_feature", "hma_dist_feature", "wvf_zscore_feature"] f_v2_names = f_v1_names + ["cvd_divergence_feature", "hma_slope_feature", "vol_accel_feature"] X_v1 = df_v2[f_v1_names].tail(1).fillna(0) X_v2 = df_v2[f_v2_names].tail(1).fillna(0) prob_v1 = float(self._alpha_v1.predict(X_v1)[0]) prob_v2 = float(self._alpha_v2.predict(X_v2)[0]) # Entry threshold from signal engine or strategy params thr = 0.55 alpha_prob_final, selected_model, selector_reason = self.select_shadow_model( prob_v1, prob_v2, latest_v2, threshold=thr ) # TELEMETRY LOGGING telemetry = { "ts": time.time(), "prob_v1": prob_v1 if not (is_stale or spread_bps > 15) else 0.5, "prob_v2": prob_v2 if not (is_stale or spread_bps > 15) else 0.5, "final_alpha": alpha_prob_final, "model": selected_model, "reason": selector_reason, "wvf_z": float(latest_v2["wvf_zscore_feature"]), "cvd_div": int(latest_v2["cvd_divergence_feature"]), "hma_slope": float(latest_v2["hma_slope_feature"]), "spread_bps": spread_bps, "data_age": data_age } self._stats["shadow_stats"].append(telemetry) if len(self._stats["shadow_stats"]) > 1000: self._stats["shadow_stats"].pop(0) # BROADCAST TO DIAGNOSTICS (For monitor_shadow_selector.py) self._diag_queue.put({ "type": "shadow_selector", "data": telemetry }) logger.info("[SELECTOR] %s | Reason: %s | Alpha: %.3f (v1: %.3f, v2: %.3f)", selected_model, selector_reason, alpha_prob_final, prob_v1, prob_v2) # Update df for downstream Kelly/Sizer df["alpha_prob"] = alpha_prob_final except Exception as e: logger.warning("Shadow selector failed: %s", e, exc_info=True) # Meta-model "Gatekeeper" Features (Context-Aware) try: # 1. Feature Prep alpha_probs = df["alpha_prob"] df["alpha_prob_smooth"] = alpha_probs.ewm(span=10).mean() df["alpha_prob_zscore"] = (alpha_probs - alpha_probs.rolling(200).mean()) / alpha_probs.rolling(200).std() df["alpha_prob_percentile"] = alpha_probs.rolling(1000).rank(pct=True) df["alpha_signal_persistence"] = (alpha_probs > 0.55).astype(int).rolling(20).sum() # We'll compute turbulence percentile later after full turb computation except Exception as e: logger.warning("Meta-feature prep failed: %s", e) # Turbulence (rolling adaptive threshold) risk_vec = ["log_return_feature", "volatility_24_feature", "intraday_range_feature"] available_risk = [c for c in risk_vec if c in df.columns] if len(available_risk) < 2: logger.warning("Insufficient risk features (%s) — skipping pipeline", available_risk) return turb_series = self._turb.compute(df, available_risk) # D-05 FIX: NaN turbulence (first 1000-bar warmup) should NOT be filled with 0. # 0 turbulence = no penalty = full Kelly exposure during the riskiest cold-start period. # Fill with the 95th percentile of observed values (conservative) or a fixed high value. if turb_series is not None: turb_p95 = turb_series.quantile(0.95) if turb_series.notna().sum() > 10 else 5.0 turb_p95 = float(turb_p95) if not np.isnan(turb_p95) else 5.0 df["turbulence_score"] = turb_series.fillna(turb_p95) else: df["turbulence_score"] = 5.0 # conservative default adaptive_thr = self._turb.rolling_threshold(df["turbulence_score"]) # HMM Regime (Priority 3: Robust Online Inference) hmm_feats = ["log_return_feature", "volatility_24_feature"] # D-09 FIX: Expand HMM to use same 4D space as Mahalanobis turbulence. # aggressor_ratio and intraday_range_feature add microstructure signal # that distinguishes clean bull_calm from bull_calm with selling pressure. extended_feats = [ "log_return_feature", "volatility_24_feature", "aggressor_ratio", "intraday_range_feature", ] hmm_feats = [f for f in extended_feats if f in df.columns] or hmm_feats available_hmm = [c for c in hmm_feats if c in df.columns] try: if not self._hmm_fitted or self._bars_since_fit >= 500: logger.info("[HMM] Re-fitting model (interval=500 bars, window=%d)...", len(df)) self._hmm.fit(df, available_hmm) self._hmm_fitted = True self._bars_since_fit = 0 # Predict current state using the last 50 observations # Semantic labeling (Log Return is features[0]) assert self._hmm.features[0] == "log_return_feature", "HMM first feature must be log_return_feature for semantic labeling" current_regime_raw = self._hmm.predict_current(df.tail(50)) if current_regime_raw == "unknown": now = time.time() if now - self._last_hmm_diag >= 60.0: logger.warning( "[HMM] raw regime is unknown | features=%s fitted=%s state_map=%s tail_rows=%d", self._hmm.features, self._hmm_fitted, self._hmm.state_map, len(df.tail(50)), ) self._last_hmm_diag = now # D-02 FIX: Temporal hysteresis — only commit regime change after # N consecutive bars with the same new label (asymmetric: faster into bull). _bull_regimes = {"bull_calm", "bull_neutral", "high_vol_rebound"} _hysteresis_needed = ( self._HYSTERESIS_TO_BULL if current_regime_raw in _bull_regimes else self._HYSTERESIS_TO_BEAR ) if current_regime_raw == self._committed_regime: self._pending_regime = None self._pending_regime_count = 0 elif current_regime_raw == self._pending_regime: self._pending_regime_count += 1 if self._pending_regime_count >= _hysteresis_needed: logger.info( "[REGIME] Committed %s → %s after %d bars", self._committed_regime, current_regime_raw, self._pending_regime_count ) self._committed_regime = current_regime_raw self._pending_regime = None self._pending_regime_count = 0 else: self._pending_regime = current_regime_raw self._pending_regime_count = 1 current_regime = self._committed_regime if current_regime == "unknown" and current_regime_raw != "unknown": now = time.time() if now - self._last_hmm_diag >= 60.0: logger.info( "[HMM] raw=%s pending=%s pending_count=%d needed=%d committed=%s", current_regime_raw, self._pending_regime, self._pending_regime_count, _hysteresis_needed, self._committed_regime, ) self._last_hmm_diag = now df["hmm_semantic_regime"] = current_regime self._bars_since_fit += 1 except Exception as e: # BUG #22 FIX: exc_info=True logger.warning("HMM online inference failed: %s", e, exc_info=True) df["hmm_semantic_regime"] = "unknown" last = df.iloc[-1] # 5. Kelly Portfolio Sizing (The "Raw" Signal) # T2.7 Daily Volatility for Barrier Scaling # Labels are pt=1.5 * daily_vol; Kelly must match this scale to overcome costs. log_ret = df["log_return_feature"] min_periods = min(50, self._WINDOW_DAILY) daily_vol = log_ret.rolling(self._WINDOW_DAILY, min_periods=min_periods).std() * np.sqrt(self._WINDOW_DAILY) last_daily_vol = float(daily_vol.iloc[-1]) if not np.isnan(daily_vol.iloc[-1]) else 0.02 barrier_height = max(0.003, 1.5 * last_daily_vol) raw_target_pos = self._sizer.size_portfolio( probabilities=df["alpha_prob"], regimes=df["hmm_semantic_regime"], turbulence=df["turbulence_score"], adaptive_threshold=adaptive_thr, risk_scales=pd.Series(barrier_height, index=df.index), dof=len(available_risk), ).iloc[-1] # T2.4: ATR14 — trailing stop anchor (EWM for responsiveness) atr14 = None try: h_arr = df["high"].values.astype(float) l_arr = df["low"].values.astype(float) c_arr = df["close"].values.astype(float) pc_arr = np.concatenate([[c_arr[0]], c_arr[:-1]]) tr_arr = np.maximum(h_arr - l_arr, np.maximum(np.abs(h_arr - pc_arr), np.abs(l_arr - pc_arr))) atr14 = float(pd.Series(tr_arr).ewm(span=14, adjust=False).mean().iloc[-1]) if not np.isfinite(atr14): atr14 = None except Exception: pass # T2.5-Phase 5: Stacking and Regime Hardening last_oof = float(last["alpha_prob"]) # Kill-Switch 1: Regime Based # Total blackout in high-risk regimes for Longs hard_blackout = {"unknown", "panic_selloff", "bear_neutral"} if str(last["hmm_semantic_regime"]) in hard_blackout: raw_target_pos = 0.0 logger.info("[KILL-SWITCH] Regime %s -> Position zeroed", last["hmm_semantic_regime"]) # Kill-Switch 2: Volume Cluster Confirmation # Z-score of 24 bars. 1.0 means volume is 1 std above mean. vol_z = float(X["volume_zscore_24_feature"].iloc[-1]) if "volume_zscore_24_feature" in X.columns else 0.0 if last_oof > 0.52 and vol_z < 0.5: # signal but no volume pop raw_target_pos *= 0.5 # reduce half logger.info("[KILL-SWITCH] Weak Volume (z=%.2f) -> Position halved", vol_z) # Kill-Switch 3: Long-Only Filter if last_oof < 0.50: raw_target_pos = 0.0 logger.info("[KILL-SWITCH] Long-Only Filter active (alpha_prob < 0.50) -> Position zeroed") confidence_scale = min(1.0, (2.0 * abs(last_oof - 0.5)) / 0.10) raw_target_pos = float(raw_target_pos) * confidence_scale # Kill-Switch 4: Meta-Model Gatekeeper (Tradeability) meta_prob = 0.0 veto_reason = "none" allow_trade = True if self._meta: try: # Capture latest book data for spread calculation # (already calculated above for selector safety) # Calculate final meta-features df["turbulence_percentile"] = df["turbulence_score"].rolling(2000).rank(pct=True) # Costs # (already calculated above for selector safety) # Get canonical HMM state ID (0, 1, 2) hmm_state_id = self._hmm.predict_current_state(df.tail(50)) # Map names to match meta-model training script X_meta = pd.DataFrame([{ "alpha_prob": float(last_oof), # Raw probability "alpha_prob_smooth": float(df["alpha_prob_smooth"].iloc[-1]), "alpha_prob_zscore": float(df["alpha_prob_zscore"].iloc[-1]), "alpha_prob_percentile": float(df["alpha_prob_percentile"].iloc[-1]), "alpha_signal_persistence": float(df["alpha_signal_persistence"].iloc[-1]), "turbulence_score": float(df["turbulence_score"].iloc[-1]), "turbulence_percentile": float(df["turbulence_percentile"].iloc[-1]), "hmm_state": int(hmm_state_id), "volatility_24_feature": float(df["volatility_24_feature"].iloc[-1]), "aggressor_ratio": float(df["aggressor_ratio"].iloc[-1]) if "aggressor_ratio" in df.columns else 0.0, "l2_imbalance_feature": float(X["l2_imbalance_feature"].iloc[-1]) if "l2_imbalance_feature" in X.columns else 0.0, "spread_bps": float(spread_bps), "expected_cost_bps": float(expected_cost_bps) }]) meta_prob = self._meta.predict(X_meta)[0] # LOGGING EXHAUSTIVO (Per Inerence) if meta_prob < 0.60: raw_target_pos = 0.0 allow_trade = False veto_reason = "low_meta_conviction" if expected_cost_bps > 25: veto_reason = "prohibitive_costs" elif spread_bps > 15: veto_reason = "toxic_spread" elif float(last_oof) < 0.55: veto_reason = "alpha_weakness" # Update Stats self._stats["n_inferences"] += 1 self._stats["alpha_probs"].append(float(last_oof)) self._stats["meta_probs"].append(float(meta_prob)) self._stats["spreads"].append(float(spread_bps)) self._stats["costs"].append(float(expected_cost_bps)) if not allow_trade: self._stats["veto_count"] += 1 self._stats["reasons"][veto_reason] = self._stats["reasons"].get(veto_reason, 0) + 1 # Log Decisivo logger.info( "[SHADOW-LIVE] Inf #%d | Alpha=%.3f (Thr=0.55) | Meta=%.3f (Thr=0.60) | " "Allow=%s | VetoReason=%s | Regime=%s | Turb=%.2f | " "Spread=%.1f bps | Cost=%.1f bps | Pos: %.2f -> %.2f", self._stats["n_inferences"], float(last_oof), float(meta_prob), allow_trade, veto_reason, current_regime, float(last["turbulence_score"]), spread_bps, expected_cost_bps, self._current_target_pos, raw_target_pos ) # Summary Every 100 bars if self._stats["n_inferences"] % 100 == 0: self._log_summary_stats() except Exception as e: logger.warning("Meta-model inference failed: %s", e) # T2.5: Multi-TF regime filter — 1h aggregated HMM provides market context. # Acts as a soft multiplier on the LTF Kelly, not a hard gate. htf_regime = "unknown" htf_mult = 0.5 # conservative default when HTF not yet fitted try: if "t_close" in df.columns: df_htf = ( df.assign( _hour=pd.to_datetime(df["t_close"], unit="ms", utc=True).dt.floor("1h") ) .groupby("_hour", as_index=False) .agg( open =("open", "first"), high =("high", "max"), low =("low", "min"), close =("close", "last"), volume =("volume", "sum"), aggressor_ratio =("aggressor_ratio", "mean"), log_return_feature=("log_return_feature", "sum"), # sum = total log return volatility_24_feature=("volatility_24_feature","mean"), intraday_range_feature=("intraday_range_feature","mean"), ) .reset_index(drop=True) ) if len(df_htf) >= 20: htf_feats = [c for c in [ "log_return_feature", "volatility_24_feature", "aggressor_ratio", "intraday_range_feature", ] if c in df_htf.columns] if not self._htf_fitted or self._htf_bars_since_fit >= 50: logger.info("[HTF-HMM] Fitting on %d 1h bars...", len(df_htf)) self._hmm_htf.fit(df_htf, htf_feats) self._htf_fitted = True self._htf_bars_since_fit = 0 assert self._hmm_htf.features[0] == "log_return_feature" htf_regime = self._hmm_htf.predict_current(df_htf.tail(6)) self._htf_bars_since_fit += 1 except Exception as e: logger.warning("HTF regime failed: %s", e) # V-BOTTOM FIX: HTF as mult+floor instead of pure multiplication. # Floor allows minimum passthrough when LTF shows recovery signal, # preventing the multiplicative cascade from killing V-bottom entries. _HTF_CONFIG = { # (mult, floor) floor = min passthrough if LTF is recovering "bull_calm": (1.00, 0.00), "bull_neutral": (0.90, 0.00), # increased from 0.80 "high_vol_rebound": (0.75, 0.05), # increased from 0.60 "bear_neutral": (0.50, 0.00), # increased from 0.30 "bear_calm": (0.30, 0.05), # increased from 0.20 "panic_selloff": (0.00, 0.00), "unknown": (0.50, 0.00), } htf_mult, htf_floor = _HTF_CONFIG.get(htf_regime, (0.50, 0.00)) _ltf_recovery = current_regime in ("high_vol_rebound", "bull_calm", "bull_neutral") if htf_mult < 1.0: old_pos = raw_target_pos multiplicative = raw_target_pos * htf_mult if _ltf_recovery and htf_floor > 0: raw_target_pos = max(multiplicative, min(raw_target_pos, htf_floor)) else: raw_target_pos = multiplicative logger.debug( "[HTF] regime=%s mult=%.2f floor=%.2f ltf_recovery=%s → pos %.4f→%.4f", htf_regime, htf_mult, htf_floor, _ltf_recovery, old_pos, raw_target_pos, ) # 6. Event-Driven Hysteresis & Clock Decoupling # BUG CUSUM Freeze FIX: Use timestamps instead of local indices to avoid # freezing when the buffer saturates and shifts. last_ts = float(df.iloc[-1].get("t_close", 0)) if self._last_t_processed == 0: # Cold start: assume the previous bar was processed self._last_t_processed = float(df.iloc[-2].get("t_close", 0)) if len(df) >= 2 else 0 is_event = False for i in range(1, len(df)): t_i = float(df.iloc[i].get("t_close", 0)) if t_i <= self._last_t_processed: continue prev_p = float(df.iloc[i-1]["close"]) curr_p = float(df.iloc[i]["close"]) log_ret = np.log(curr_p / prev_p) # Priority 4: Dynamic threshold h_t = 3.5 * sigma_recent # V-BOTTOM FIX: Use MAD-based robust sigma to resist post-crash # outlier contamination (crash spike inflates std, blocking rebound detection). sigma_series = df["log_return_feature"].iloc[:i+1].tail(100) sigma = sigma_series.std() sigma_robust = sigma_series.abs().median() * 1.4826 # MAD→σ equivalent if not np.isnan(sigma_robust) and sigma_robust > 0: sigma_eff = min(sigma, sigma_robust * 2.0) else: sigma_eff = sigma cusum_h = 3.5 * sigma_eff if (not np.isnan(sigma_eff) and sigma_eff > 0) else 0.005 if self._cusum.check(log_ret, cusum_h): is_event = True self._last_t_processed = last_ts # Pre-compute threshold value (reused in bypass + diagnostics) _thr_val = None if adaptive_thr is not None and adaptive_thr.notna().any(): try: _v = float(adaptive_thr.iloc[-1]) if np.isfinite(_v): _thr_val = _v except Exception: pass # BYPASS HOLD: Sustain the 10% floor on bars following a bypass entry, # bridging the HMM lag until the regime commits or the thesis fails. # Cancelled early if pending regime pivots away from bullish targets or # alpha reverses below 0.45 (model conviction reversed). _bypass_applied = False _turb_last = float(last["turbulence_score"]) _turb_ok = (_thr_val is None) or (_turb_last < _thr_val) _bar_imbalance = float(last.get("book_imbalance", 0.0)) _imbalance_ok = _bar_imbalance >= -0.20 if self._bypass_hold_bars > 0: _bull_targets = {"bull_calm", "bull_neutral", "high_vol_rebound"} _hold_regime_ok = ( self._pending_regime in _bull_targets or self._committed_regime in _bull_targets ) _hold_alpha_ok = float(last["alpha_prob"]) >= 0.45 if _hold_regime_ok and _hold_alpha_ok: if raw_target_pos < 0.10: raw_target_pos = 0.10 self._bypass_hold_bars -= 1 logger.debug( "[BYPASS HOLD] Flooring at 10%% — %d bars remaining (regime=%s alpha=%.3f)", self._bypass_hold_bars, self._committed_regime, float(last["alpha_prob"]), ) else: logger.info( "[BYPASS HOLD] Cancelled — pending=%s committed=%s alpha=%.3f", self._pending_regime, self._committed_regime, float(last["alpha_prob"]), ) self._bypass_hold_bars = 0 # ALPHA BYPASS: When HMM is pending a bullish regime (not yet committed), # allow a 10% floor position if four independent signals agree: # 1. CUSUM fired (structural break) # 2. Alpha >= 0.65 (model conviction) # 3. Turbulence < adaptive threshold (regime is calm enough) # 4. L1 book imbalance >= -0.20 (not heavily sell-side dominated) # This bridges the HMM lag on V-bottom entries in long-only mode. if (is_event and float(last["alpha_prob"]) >= 0.65 and _turb_ok and _imbalance_ok and self._pending_regime in ("bull_calm", "bull_neutral", "high_vol_rebound") and raw_target_pos < 0.10): raw_target_pos = 0.10 _bypass_applied = True self._bypass_hold_bars = self._HYSTERESIS_TO_BULL # sustain for N bars logger.info( "[ALPHA BYPASS] Pending %s + alpha=%.3f + turb=%.3f < thr=%.3f + imb=%+.3f → floor 10%% (hold=%d bars)", self._pending_regime, float(last["alpha_prob"]), _turb_last, _thr_val if _thr_val is not None else float("inf"), _bar_imbalance, self._bypass_hold_bars, ) # Decide whether to commit the new target position # Rules: # 1. Must be a CUSUM event (Significant information arrival) # 2. Change must be > 1% absolute (Hysteresis gate to avoid jitter) # Priority 4: Asymmetric Hysteresis & Execution Discipline # Rules (User Spec): # 1. Entry: ONLY if target >= 10% # 2. Rebalance: ONLY if change >= 10% # 3. Exit: Permitted if reduction >= 5% OR target is zero # 4. Mandatory: Must be a CUSUM event pos_diff = abs(raw_target_pos - self._current_target_pos) should_update = False if is_event: # Case A: Entry (currently flat) if self._current_target_pos == 0: if raw_target_pos >= 0.10: # raised from 0.05 should_update = True # Case B: Exit / Reduction (reducing exposure) elif raw_target_pos < self._current_target_pos: if (pos_diff >= 0.05) or (raw_target_pos == 0): should_update = True # Case C: Increase / Rebalance elif raw_target_pos > self._current_target_pos: if pos_diff >= 0.10: should_update = True if should_update: logger.info( "[EXECUTION EVENT] Pos Update: %.4f -> %.4f (diff=%.4f)", self._current_target_pos, raw_target_pos, pos_diff ) self.set_virtual_position(raw_target_pos) # 7. Update Shared Signal Store (Atomic) with self._lock: self._signal.update({ "target_pos": float(self._current_target_pos), "regime": str(last["hmm_semantic_regime"]), "turbulence": float(last["turbulence_score"]), "alpha_prob": float(last["alpha_prob"]), "meta_prob": float(meta_prob), "veto_reason": str(veto_reason), "close": float(last["close"]), "ts": time.time(), "n_bars": len(df), "is_event": is_event, "atr14": atr14, "htf_regime": htf_regime, }) # 8. Dashboard Diagnostics (fire-and-forget, never blocks pipeline) try: def _sf(v, default=None): """Safe float: return None instead of NaN/Inf so JSON stays valid.""" try: f = float(v) return f if np.isfinite(f) else default except Exception: return default _thr = _sf(_thr_val) # already computed above self._diag_queue.put_nowait({ "target_pos": _sf(self._current_target_pos, 0.0), "raw_kelly": _sf(raw_target_pos, 0.0), "regime": str(last["hmm_semantic_regime"]), "pending_regime": self._pending_regime, "pending_regime_count": self._pending_regime_count, "pending_regime_threshold": _hysteresis_needed if self._pending_regime else None, "turbulence": _sf(last["turbulence_score"], 0.0), "turbulence_thr": _thr, "alpha_prob": _sf(last["alpha_prob"], 0.5), "close": _sf(last["close"]), "ts": time.time(), "n_bars": len(df), "is_event": is_event, "cusum_pos_sum": _sf(self._cusum._pos_sum, 0.0), "cusum_neg_sum": _sf(self._cusum._neg_sum, 0.0), "cusum_threshold": _sf(cusum_h, 0.0), "hmm_fitted": self._hmm_fitted, "bars_since_fit": self._bars_since_fit, "pipeline_inflight": False, # published at end of successful run "alpha_bypass": _bypass_applied, "bypass_hold_bars": self._bypass_hold_bars, "atr14": _sf(atr14), "confidence_scale": _sf(confidence_scale, 0.0), "htf_regime": htf_regime, "htf_multiplier": _sf(htf_mult, 0.5), "htf_floor": _sf(htf_floor, 0.0), "book_imbalance": _sf(_bar_imbalance, 0.0), "meta_prob": _sf(meta_prob, 0.0), "veto_reason": str(veto_reason), "expected_cost_bps": _sf(expected_cost_bps, 0.0), }) except Exception: pass # Never let diagnostics crash the pipeline logger.info( "Pipeline OK | bar=%d close=%.2f alpha=%.3f regime=%s target_pos=%.4f (event=%s)", len(df), float(last["close"]), float(last["alpha_prob"]), str(last["hmm_semantic_regime"]), float(self._current_target_pos), "YES" if is_event else "no", ) except Exception as e: logger.error("Unexpected error in pipeline worker: %s", e, exc_info=True) finally: self._pipeline_inflight = False def run(self): if not _ZMQ_AVAILABLE: logger.error("pyzmq not installed. ZMQ listener inactive.") return ctx = zmq.Context() sock = ctx.socket(zmq.SUB) sock.connect(self._zmq_addr) sock.setsockopt(zmq.SUBSCRIBE, TOPIC_DOLLAR_BAR) sock.setsockopt(zmq.SUBSCRIBE, TOPIC_BOOK_TICKER) # Non-blocking poll so we can check _running sock.setsockopt(zmq.RCVTIMEO, 1000) diag_ctx = zmq.Context() diag_sock = diag_ctx.socket(zmq.PUB) diag_sock.setsockopt(zmq.LINGER, 0) # don't block on close diag_sock.bind(ZMQ_DIAG_ADDR) # self._executor is now initialized in __init__ logger.info("ZMQ listener connected to %s | DIAG PUB at %s", self._zmq_addr, ZMQ_DIAG_ADDR) while self._running: try: # 1. Pipeline Stuck Safety if self._pipeline_inflight and (time.time() - self._inflight_since > 60): logger.warning("[SAFETY] Resetting stuck pipeline_inflight flag.") self._pipeline_inflight = False # 2. Non-blocking Recv try: parts = sock.recv_multipart(flags=zmq.NOBLOCK) if len(parts) >= 2: topic = parts[0] payload = json.loads(parts[1].decode()) if topic == TOPIC_DOLLAR_BAR: self._bar_buffer.append(payload) if len(self._bar_buffer) > 4000: self._bar_buffer = self._bar_buffer[-2000:] with self._lock: self._signal["n_bars"] = len(self._bar_buffer) self._signal["ts"] = time.time() # Keep Freqtrade Watchdog happy if not self._pipeline_inflight: self._pipeline_inflight = True self._inflight_since = time.time() try: self._executor.submit(self._run_pipeline) except Exception as e: self._pipeline_inflight = False logger.warning("Failed to submit pipeline: %s", e) elif topic == TOPIC_BOOK_TICKER: with self._lock: self._signal["best_bid"] = payload.get("bid") self._signal["best_ask"] = payload.get("ask") self._signal["mid"] = (payload.get("bid", 0) + payload.get("ask", 0)) / 2 if payload.get("bid") else 0 self._signal["book_imbalance"] = payload.get("l1_imb", 0.0) self._signal["ts"] = time.time() except zmq.Again: pass # 3. Periodic Heartbeat (Always runs even if no ZMQ messages) if time.time() - self._last_hb > 2.0: n_bars = len(self._bar_buffer) # Update ts in shared state to satisfy Freqtrade Watchdog with self._lock: self._signal["ts"] = time.time() self._signal["n_bars"] = n_bars if n_bars < 50: # Send as shadow_selector so monitor updates UI self._diag_queue.put({ "type": "shadow_selector", "data": { "model": "buffering", "reason": f"Accumulating bars ({n_bars}/50)", "final_alpha": 0.5, "prob_v1": 0.5, "prob_v2": 0.5, "wvf_z": 0.0, "cvd_div": 0, "hma_slope": 0.0, "ts": time.time() } }) else: self._diag_queue.put({ "type": "heartbeat", "ts": time.time(), "data": {"n_bars": n_bars, "status": "active"} }) self._last_hb = time.time() # 4. Drain Diagnostics Queue while not self._diag_queue.empty(): try: diag = self._diag_queue.get_nowait() diag_sock.send_multipart( [b"DIAGNOSTICS", json.dumps(diag, default=str).encode()], flags=zmq.NOBLOCK, ) except Exception: pass time.sleep(0.1) # Prevent CPU spiking in no-message loop except Exception as e: logger.error("ZMQ loop error: %s", e) time.sleep(1) sock.close() ctx.term() diag_sock.close() diag_ctx.term() logger.info("ZMQ listener stopped.") class Github_ccaadaro_RL_Trading__InstitutionalDollarStrategy__20260510_090356(IStrategy): """ Freqtrade execution shell backed by the institutional 7-layer pipeline. Receives completed Dollar Bars + book state from market_data_daemon.py via ZeroMQ PUB/SUB. All intelligence runs in a background thread. Run with: nohup python RL_Trading/services/market_data_daemon.py & freqtrade trade -s Github_ccaadaro_RL_Trading__InstitutionalDollarStrategy__20260510_090356 --dry-run """ INTERFACE_VERSION = 3 timeframe = "1m" startup_candle_count = 1 # Reduced from 100 for faster boot (strategy uses ZMQ) process_only_new_candles = True # Mute all native Freqtrade risk controls — the pipeline governs these minimal_roi = {"0": 100} stoploss = -0.99 # hard floor; custom_stoploss handles normal exits trailing_stop = False use_exit_signal = True use_custom_stoploss = True # BUG #21 FIX: Moved mutable state to instance level in bot_start def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self._alpha_model = None self._turbulence = None self._hmm = None self._sizer = None self._zmq_listener = None self._signal_engine = None self._signal_lock = threading.Lock() self._latest_signal = { "target_pos": 0.0, "regime": "unknown", "turbulence": 0.0, "alpha_prob": 0.5, "close": None, "ts": 0.0, "best_bid": None, "best_ask": None, "mid": None, "n_bars": 0, } self._boot_time = time.time() self._last_watchdog_log_time = 0.0 V_BAR_TARGET = 2_000_000.0 def _seed_buffer_from_cache(self) -> None: """ Pre-populate the bar buffer from the cached feather file so the pipeline fires immediately after restart instead of waiting for 60+ new bars. """ cache_path = _RL_DIR / "cache" / "dollar_bars_btc_2000000_regimes.feather" if not cache_path.exists(): logger.info("Cache not found at %s — buffer will fill from live stream.", cache_path) return try: import pyarrow.feather as feather df = feather.read_feather(str(cache_path)) # BUG #18 FIX: Recency validation using internal bar timestamp (t_close) if "t_close" in df.columns: last_ts = df["t_close"].iloc[-1] if last_ts > 1e11: last_ts /= 1000.0 age = time.time() - last_ts # Historical bars are valid for turbulence baseline even if old. # Only skip if data is absurdly stale (>365 days). if age > 86400 * 365: logger.warning("Cache data is too stale (%.0fd old) — skipping seeding.", age / 86400) return logger.info("Cache age: %.1fh", age / 3600) # Seed 2000 bars so MahalanobisTurbulence (window=1000) has enough # history to compute real distances on the first pipeline run. df = df.tail(2000).copy() # Derive t_open/t_close (unix milliseconds) from the feather's # datetime columns so the HTF aggregation can build 1h buckets # across the full seeded history. if "end_time" in df.columns and "t_close" not in df.columns: df["t_close"] = (pd.to_datetime(df["end_time"]).astype("int64") // 1_000_000) if "start_time" in df.columns and "t_open" not in df.columns: df["t_open"] = (pd.to_datetime(df["start_time"]).astype("int64") // 1_000_000) # D-10 FIX: Include microstructure columns when present in the feather. priority_cols = [ "t_open", "t_close", "open", "high", "low", "close", "volume", "buy_volume", "sell_volume", "aggressor_ratio", "notional", "trade_count", "cvd", "intraday_range_feature", "log_return_feature", "volatility_24_feature", ] existing = [c for c in priority_cols if c in df.columns] records = df[existing].to_dict(orient="records") # Seed 2000 bars into the provider if it's a ZMQ provider if isinstance(self._provider, ZmqDollarBarProvider): self._provider._bar_buffer = records logger.info("Buffer seeded with %d bars from cache (%s)", len(records), cache_path.name) except Exception as e: logger.warning("Cache seeding failed: %s", e) def bot_start(self, **kwargs) -> None: logger.info("[BASELINE-V1] Booting Institutional Hardened Candidate (Shadow-Live mode)...") # 1. Load Frozen Assets from Deployment Path deploy_path = _RL_DIR / "deployments" / "baseline_hardened_v1" model_path = deploy_path / "alpha_model.txt" meta_path = deploy_path / "gatekeeper.txt" self._alpha_model = None if model_path.exists(): self._alpha_model = lgb.Booster(model_file=str(model_path)) logger.info("[BASELINE-V1] Alpha Model loaded from deployment: %s", model_path) else: logger.error("[BASELINE-V1] CRITICAL: Alpha model missing in %s", model_path) self._meta_model = None if meta_path.exists(): self._meta_model = lgb.Booster(model_file=str(meta_path)) logger.info("[BASELINE-V1] Meta-Gatekeeper loaded from deployment: %s", meta_path) else: logger.error("[BASELINE-V1] CRITICAL: Meta-model missing in %s", meta_path) # 1.5 Shadow-Live Specialist Loading shadow_path = _RL_DIR / "deployments" / "candidate_v2" v1_path = shadow_path / "alpha_v1.txt" v2_path = shadow_path / "alpha_v2.txt" alpha_v1, alpha_v2 = None, None if v1_path.exists() and v2_path.exists(): alpha_v1 = lgb.Booster(model_file=str(v1_path)) alpha_v2 = lgb.Booster(model_file=str(v2_path)) logger.info("[SHADOW-LIVE] Specialist models v1/v2 loaded from %s", shadow_path) # 2. Instantiate Risk Engines self._turbulence = MahalanobisTurbulence(window=1000, step=250) self._hmm = HMMRegimeModel(n_components=3, n_init=3) self._hmm_htf = HMMRegimeModel(n_components=3, n_init=3) self._sizer = FractionalKellySizer(kelly_fraction=0.5, max_drawdown=0.10) # 3. Choose Data Provider if self.timeframe == "1h": logger.info("[PHASE 9] Using FreqtradeCandleProvider (1h candles)") self._provider = FreqtradeCandleProvider() else: logger.info("[PHASE 8] Using ZmqDollarBarProvider (%s)", ZMQ_DAEMON_ADDR) self._provider = ZmqDollarBarProvider(zmq_addr=ZMQ_DAEMON_ADDR, topic=TOPIC_DOLLAR_BAR) # Only start the ZMQ thread if using ZMQ self._provider.start() # 4. Start background signal engine self._signal_engine = InstitutionalSignalEngine( alpha_model=self._alpha_model, meta_model=self._meta_model, alpha_slow_model=None, turbulence_engine=self._turbulence, hmm_model=self._hmm, hmm_model_htf=self._hmm_htf, sizer=self._sizer, signal_store=self._latest_signal, lock=self._signal_lock, provider=self._provider ) # Activation of Shadow-Live features self._signal_engine.shadow_selector_enabled = True self._signal_engine._alpha_v1 = alpha_v1 self._signal_engine._alpha_v2 = alpha_v2 # 5. Pre-populate buffer SO seeding happens AFTER engine exists self._seed_buffer_from_cache() # 6. Kick off the engine thread self._signal_engine.start() logger.info("Institutional Signal Engine started") def bot_stopped(self) -> None: if self._signal_engine: self._signal_engine.stop() self._signal_engine.join(timeout=3) if hasattr(self, "_provider") and isinstance(self._provider, ZmqDollarBarProvider): self._provider.stop() def populate_indicators(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame: """ Thin shell: reads latest signal from the engine thread and stamps it onto every row of the Freqtrade candle frame. """ # Phase 9: If using 1h candles, update the provider with the latest data if isinstance(self._provider, FreqtradeCandleProvider): self._provider.update(dataframe) # Trigger immediate inference if possible if not self._signal_engine._pipeline_inflight: self._signal_engine._executor.submit(self._signal_engine._run_pipeline) with self._signal_lock: sig = dict(self._latest_signal) # snapshot # D-06 FIX: Reconcile virtual position with Freqtrade's actual fill state. # The listener tracks _current_target_pos independently from real fills. # Partial fills, rejections, or slippage create divergence over time. # Here we read the real exposure and sync it back to the listener. try: self._reconcile_actual_position() except Exception as e: logger.debug("[D-06] Position reconciliation skipped: %s", e) # DESIGN-04 FIX: Watchdog — alert if signal is stale (ZMQ listener may be dead) now = time.time() sig_ts = sig.get("ts", 0) sig_age = now - sig_ts if sig_ts > 0 else 0 # 1. Warmup / Boot timeout if sig.get("n_bars", 0) == 0 and (now - self._boot_time > 600): if now - self._last_watchdog_log_time > 300: logger.error("[WATCHDOG] No bars received after 10m. Daemon/ZMQ issue?") self._last_watchdog_log_time = now # BUG #20 FIX: Ensure columns exist before early return for col, default in [("target_pos", 0.0), ("regime", "unknown"), ("turbulence", 0.0), ("alpha_prob", 0.5), ("n_bars", 0)]: dataframe[col] = default return dataframe # 2. Stale signal timeout if sig_age > 1800 and sig.get("n_bars", 0) > 0: if now - self._last_watchdog_log_time > 300: logger.error( "[WATCHDOG] Signal is %.0fs old. ZMQ listener thread may be dead. " "FORCING target_pos=0 for safety.", sig_age ) self._last_watchdog_log_time = now # BUG #20 FIX: Emergency flat in BOTH locations (local copy and shared state) sig["target_pos"] = 0.0 dataframe["target_pos"] = 0.0 with self._signal_lock: self._latest_signal["target_pos"] = 0.0 # BUG-Watchdog FIX: Do NOT refresh ts here to allow sig_age to persist dataframe["target_pos"] = sig.get("target_pos", 0.0) dataframe["regime"] = sig.get("regime", "unknown") dataframe["turbulence"] = sig.get("turbulence", 0.0) dataframe["alpha_prob"] = sig.get("alpha_prob", 0.5) dataframe["n_bars"] = sig.get("n_bars", 0) return dataframe def _reconcile_actual_position(self) -> None: """ D-06: Sync _current_target_pos in the ZMQ listener with the real fill state tracked by Freqtrade's portfolio manager. Logic: - Read open trades for the current pair. - Compute actual exposure = open_trade_value / max_stake. - If actual exposure differs from the listener's virtual position by more than 3%, update the listener so hysteresis operates correctly. This runs every ~1m (each populate_indicators cycle) — lightweight. """ if not self._signal_engine: return try: from freqtrade.persistence import Trade open_trades = Trade.get_open_trades() except Exception: return # Not available in dry-run initial boot or test mode if not open_trades: actual_exposure = 0.0 else: # Mark-to-market reconciliation (current price) total_value = sum( t.amount * t.current_rate for t in open_trades ) try: max_stake = self.wallets.get_available_capital() + total_value actual_exposure = total_value / max_stake if max_stake > 0 else 0.0 except Exception: return # wallets not yet initialised virtual_pos = self._signal_engine._current_target_pos drift = abs(actual_exposure - virtual_pos) if drift > 0.03: # 3% reconciliation threshold logger.info( "[D-06] Position reconciliation: virtual=%.3f actual=%.3f drift=%.3f → syncing", virtual_pos, actual_exposure, drift ) self._signal_engine.set_virtual_position(actual_exposure) def populate_entry_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame: # Harmonized Entry Gate: Only enter if pipeline has committed target >= 10% dataframe["enter_long"] = 0 dataframe.loc[ (dataframe["target_pos"] >= 0.10) & (dataframe["regime"] != "unknown"), "enter_long" ] = 1 return dataframe def populate_exit_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame: dataframe["exit_long"] = 0 dataframe.loc[dataframe["target_pos"] <= 0.0, "exit_long"] = 1 return dataframe def custom_exit(self, pair: str, trade, current_time, current_rate: float, current_profit: float, **kwargs) -> Optional[str]: """ Institutional Exit Audit: distinguishes between Alpha reversal and Meta-veto. """ with self._signal_lock: targ = self._latest_signal.get("target_pos", 0.0) veto_reason = self._latest_signal.get("veto_reason", "none") alpha_prob = self._latest_signal.get("alpha_prob", 0.5) meta_prob = self._latest_signal.get("meta_prob", 0.0) if targ <= 0: if veto_reason != "none": logger.info("[EXIT-AUDIT] Meta-Veto exit: %s (meta_prob=%.3f)", veto_reason, meta_prob) return f"meta_veto_{veto_reason}" if alpha_prob < 0.50: logger.info("[EXIT-AUDIT] Alpha reversal exit (prob=%.3f)", alpha_prob) return "alpha_reversal" # Fallback return "pipeline_exit_zero" return None def custom_stake_amount(self, pair: str, current_time, current_rate: float, proposed_stake: float, min_stake: Optional[float], max_stake: float, leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: """Sets stake as a fraction of max_stake based on the current Kelly target.""" with self._signal_lock: targ = self._latest_signal.get("target_pos", 0.0) if targ <= 0: return 0.0 # Hard floor: do not open ultra-small positions stake = max_stake * targ if min_stake and stake < min_stake: return 0.0 return stake def custom_entry_price(self, pair: str, current_time, proposed_rate: float, entry_tag: Optional[str], side: str, **kwargs) -> float: """ Passive execution: anchor to best_bid for buys, best_ask for sells. Falls back to proposed_rate if no live book data is available. """ with self._signal_lock: best_bid = self._latest_signal.get("best_bid") best_ask = self._latest_signal.get("best_ask") if side == "buy" and best_bid and best_bid > 0: return float(best_bid) if side == "sell" and best_ask and best_ask > 0: return float(best_ask) return proposed_rate def custom_stoploss(self, pair: str, trade, current_time, current_rate: float, current_profit: float, **kwargs) -> float: """ T2.4: ATR-based trailing stop. Trails 2.5× ATR14 below the current price on every tick. Freqtrade keeps the highest stop seen, so this naturally trails upward. Falls back to the hard stoploss (-0.99) when ATR is unavailable. """ with self._signal_lock: atr14 = self._latest_signal.get("atr14", None) if not atr14 or atr14 <= 0 or current_rate <= 0: return self.stoploss stop_pct = (2.5 * atr14) / current_rate # Floor at 0.5% to avoid premature stop on ultra-low-vol bars; # cap at 10% to prevent excessively wide stops in panic conditions. return -float(np.clip(stop_pct, 0.005, 0.10))