feat: implement Professor AI recommendations v0.2.2 (5 critical fixes)
Exit Strategy v6.6 "Professor AI Validated" - All recommendations implemented FIX #1: Remove Misleading Debug Code - Removed manual trajectory calculation (line 1262-1269) - Trajectory predictor was CORRECT, debug comparison was WRONG - Cleaned up false "bug found" warnings FIX #2: Peak Detection Logic (CHECK 0A.4) - Detects approaching peak (vel > 0, accel < 0) - Holds position if peak within 30s and 15%+ profit ahead - Suppresses fuzzy exits during peak approach - Target: Peak capture 38% -> 70%+ - Added peak_hold_active field to PositionGuard FIX #3: London False Breakout Filter - London session + ATR ratio < 1.2 = whipsaw risk - Requires ML confidence 70% (instead of 60%) - Prevents false breakouts during low volatility - Implemented in main_live.py before signal logic FIX #4: Enhanced Kelly Partial Exit Strategy - Active for all profits >= tp_min * 0.5 (not just >$8) - Recommends partial exits for better peak capture - Full exit when Kelly suggests >70% close - Note: Actual partial close needs MT5 volume parameter (TODO) FIX #5: Unicode Encoding Fixes - Added UTF-8 encoding to file logger - Replaced all emoji (⚠️ -> [WARNING]) and arrows (-> -> ->) - No more UnicodeEncodeError on Windows console - Fixed in 11 src/*.py files Expected Performance: - Peak Capture: 38% -> 70%+ (+84%) - Avg Profit: $2.00 -> $4.50 (+125%) - Risk/Reward: 0.49 -> 1.2+ (+145%) - Win Rate: Maintain 76% Files Modified: - src/smart_risk_manager.py (peak detection, Kelly, unicode) - src/trajectory_predictor.py (unicode arrows) - main_live.py (London filter, UTF-8 encoding) - src/*.py (unicode cleanup: 11 files) - VERSION (0.2.1 -> 0.2.2) - CHANGELOG.md (comprehensive v0.2.2 docs) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
GifariKemal
co-authored by
Claude Sonnet 4.5
Claude Sonnet 4.5
parent
f36123ccaf
commit
0f9548e5fb
@@ -0,0 +1,349 @@
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"""
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Advanced Target Labeling for ML Model V3 - BINARY CLASSIFICATION
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=================================================================
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Implements Triple Barrier Method for high-quality BUY vs SELL signals.
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Key improvements over V2:
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1. Triple barrier: profit target, stop loss, time limit
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2. Binary classification: BUY (1) vs SELL (0) only - no HOLD class
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3. ATR-adaptive thresholds for balanced labeling
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4. Class balancing to 50/50 distribution
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5. Time barrier labels by final direction (always directional)
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Reference: "Advances in Financial Machine Learning" by Marcos Lopez de Prado
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"""
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import polars as pl
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import numpy as np
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from typing import Tuple, Dict
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from pathlib import Path
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import sys
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# Add src to path
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sys.path.insert(0, str(Path(__file__).parent.parent.parent))
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class TripleBarrierLabeling:
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"""
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Binary classification using triple barrier method.
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For each bar, we define:
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- Upper barrier (profit target): +profit_atr_mult * ATR
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- Lower barrier (stop loss): -stoploss_atr_mult * ATR
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- Vertical barrier (time limit): max_holding_bars
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Label = BUY (1) if upper barrier hit first or time barrier with positive return
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SELL (0) if lower barrier hit first or time barrier with negative return
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"""
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def __init__(
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self,
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profit_atr_mult: float = 0.5, # 50% of ATR for TP (balanced)
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stoploss_atr_mult: float = 0.5, # 50% of ATR for SL (symmetric RR 1.0)
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max_holding_bars: int = 20, # 5 hours on M15 (allow time to develop)
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):
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self.profit_atr_mult = profit_atr_mult
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self.stoploss_atr_mult = stoploss_atr_mult
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self.max_holding_bars = max_holding_bars
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def label_data(self, df: pl.DataFrame) -> pl.DataFrame:
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"""
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Apply triple barrier labeling to DataFrame (BINARY classification).
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Args:
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df: DataFrame with columns ['close', 'high', 'low', 'atr']
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Returns:
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DataFrame with additional columns:
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- target: 1 (BUY), 0 (SELL) - BINARY only, no HOLD
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- target_label: "BUY" or "SELL"
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- barrier_hit: which barrier was hit first
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- bars_to_barrier: how many bars until barrier hit
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- return_pct: actual return achieved (ATR-normalized)
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"""
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print(f" Starting Triple Barrier Labeling (BINARY: BUY vs SELL)...")
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print(f" Profit target: {self.profit_atr_mult} ATR")
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print(f" Stop loss: {self.stoploss_atr_mult} ATR")
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print(f" Max holding: {self.max_holding_bars} bars")
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# Convert to numpy for speed
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closes = df["close"].to_numpy()
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highs = df["high"].to_numpy()
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lows = df["low"].to_numpy()
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atrs = df["atr"].to_numpy()
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n = len(df)
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targets = np.zeros(n, dtype=np.int8)
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barriers_hit = np.zeros(n, dtype='U10') # 'profit', 'stoploss', 'time', 'none'
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bars_to_barrier = np.zeros(n, dtype=np.int32)
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returns_pct = np.zeros(n, dtype=np.float32)
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# For each bar, scan forward to find first barrier hit
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for i in range(n - self.max_holding_bars):
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entry_price = closes[i]
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entry_atr = atrs[i]
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if entry_atr == 0 or np.isnan(entry_atr):
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barriers_hit[i] = 'none'
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continue
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# Define barriers
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upper_barrier = entry_price + (self.profit_atr_mult * entry_atr)
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lower_barrier = entry_price - (self.stoploss_atr_mult * entry_atr)
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# Scan forward
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barrier_found = False
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for j in range(1, self.max_holding_bars + 1):
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if i + j >= n:
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break
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future_high = highs[i + j]
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future_low = lows[i + j]
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future_close = closes[i + j]
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# Check upper barrier (BUY signal if hit first)
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if future_high >= upper_barrier:
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targets[i] = 1 # BUY
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barriers_hit[i] = 'profit_long'
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bars_to_barrier[i] = j
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returns_pct[i] = (upper_barrier - entry_price) / entry_atr
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barrier_found = True
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break
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# Check lower barrier (SELL signal if hit first)
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if future_low <= lower_barrier:
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targets[i] = 0 # SELL (binary: 0)
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barriers_hit[i] = 'profit_short'
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bars_to_barrier[i] = j
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returns_pct[i] = (entry_price - lower_barrier) / entry_atr
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barrier_found = True
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break
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# If no barrier hit within time limit - use time barrier
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if not barrier_found:
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final_price = closes[min(i + self.max_holding_bars, n - 1)]
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return_atr = (final_price - entry_price) / entry_atr
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# Time barrier: ALWAYS label by final direction (no HOLD for binary)
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targets[i] = 1 if return_atr >= 0 else 0 # BUY if positive, SELL if negative
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barriers_hit[i] = 'time_up' if return_atr >= 0 else 'time_down'
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bars_to_barrier[i] = self.max_holding_bars
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returns_pct[i] = return_atr
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# Last few bars cannot be labeled (no forward data) - mark as unlabeled (-1)
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targets[-self.max_holding_bars:] = -1
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barriers_hit[-self.max_holding_bars:] = 'no_data'
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# Add to DataFrame
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df = df.with_columns([
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pl.Series("target", targets),
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pl.Series("barrier_hit", barriers_hit),
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pl.Series("bars_to_barrier", bars_to_barrier),
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pl.Series("return_pct", returns_pct),
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])
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# Add text labels (binary: BUY=1, SELL=0, unlabeled=-1)
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df = df.with_columns([
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pl.when(pl.col("target") == 1).then(pl.lit("BUY"))
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.when(pl.col("target") == 0).then(pl.lit("SELL"))
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.otherwise(pl.lit("UNLABELED"))
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.alias("target_label")
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])
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# Stats (exclude unlabeled from distribution)
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labeled_mask = targets >= 0
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n_buy = (targets[labeled_mask] == 1).sum()
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n_sell = (targets[labeled_mask] == 0).sum()
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n_unlabeled = (targets == -1).sum()
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n_total = n_buy + n_sell
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print(f"\n Target Distribution (BINARY):")
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print(f" BUY: {n_buy:6d} ({n_buy/n_total*100:5.2f}%)")
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print(f" SELL: {n_sell:6d} ({n_sell/n_total*100:5.2f}%)")
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print(f" Unlabeled: {n_unlabeled:6d} (last {self.max_holding_bars} bars)")
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# Quality metrics
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profit_barriers = (barriers_hit == 'profit_long') | (barriers_hit == 'profit_short')
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avg_bars_profit = bars_to_barrier[profit_barriers].mean() if profit_barriers.sum() > 0 else 0
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avg_return_profit = returns_pct[profit_barriers].mean() if profit_barriers.sum() > 0 else 0
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print(f"\n Quality Metrics:")
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print(f" Profit barriers hit: {profit_barriers.sum():6d} ({profit_barriers.sum()/n_total*100:5.2f}%)")
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print(f" Avg bars to profit: {avg_bars_profit:.1f}")
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print(f" Avg return (ATR): {avg_return_profit:.3f}")
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return df
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def apply_meta_labeling(
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self,
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df: pl.DataFrame,
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smc_signal_col: str = "smc_signal",
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smc_confidence_col: str = "smc_confidence",
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min_smc_confidence: float = 0.65,
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) -> pl.DataFrame:
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"""
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Meta-labeling: refine targets using SMC signal quality.
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If triple-barrier says BUY but SMC says SELL (or vice versa) with high confidence,
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flip to HOLD (conflicting signals = don't trade).
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Args:
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df: DataFrame with target column
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smc_signal_col: column with SMC signal ("BUY", "SELL", or "")
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smc_confidence_col: column with SMC confidence (0-1)
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min_smc_confidence: min confidence to trust SMC signal
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Returns:
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DataFrame with refined target column
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"""
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print(f"\n Applying Meta-Labeling (SMC signal quality)...")
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if smc_signal_col not in df.columns or smc_confidence_col not in df.columns:
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print(" SMC columns not found, skipping meta-labeling")
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return df
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# Count conflicts before
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conflicts_before = 0
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# Refine targets
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refined_targets = []
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for row in df.iter_rows(named=True):
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target = row["target"]
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target_label = row["target_label"]
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smc_signal = row.get(smc_signal_col, "")
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smc_conf = row.get(smc_confidence_col, 0.0)
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# If no strong SMC signal, keep original target
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if not smc_signal or smc_conf < min_smc_confidence:
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refined_targets.append(target)
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continue
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# Check for conflict
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if target_label == "BUY" and smc_signal == "SELL":
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conflicts_before += 1
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refined_targets.append(0) # HOLD (conflicting signals)
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elif target_label == "SELL" and smc_signal == "BUY":
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conflicts_before += 1
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refined_targets.append(0) # HOLD (conflicting signals)
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else:
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refined_targets.append(target) # Keep original
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df = df.with_columns([
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pl.Series("target", refined_targets)
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])
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# Recalculate target_label
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df = df.with_columns([
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pl.when(pl.col("target") == 1).then(pl.lit("BUY"))
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.when(pl.col("target") == -1).then(pl.lit("SELL"))
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.otherwise(pl.lit("HOLD"))
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.alias("target_label")
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])
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print(f" Conflicts resolved: {conflicts_before} (BUYSELL HOLD)")
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# New distribution
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n_buy = df.filter(pl.col("target") == 1).height
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n_sell = df.filter(pl.col("target") == -1).height
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n_hold = df.filter(pl.col("target") == 0).height
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n_total = n_buy + n_sell + n_hold
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print(f"\n Refined Target Distribution:")
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print(f" BUY: {n_buy:6d} ({n_buy/n_total*100:5.2f}%)")
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print(f" SELL: {n_sell:6d} ({n_sell/n_total*100:5.2f}%)")
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print(f" HOLD: {n_hold:6d} ({n_hold/n_total*100:5.2f}%)")
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return df
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def balance_classes(
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self,
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df: pl.DataFrame,
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target_buy_pct: float = 0.50,
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target_sell_pct: float = 0.50,
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random_seed: int = 42,
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) -> pl.DataFrame:
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"""
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Balance target classes via stratified downsampling (BINARY: BUY vs SELL).
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Args:
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df: DataFrame with target column (1=BUY, 0=SELL)
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target_buy_pct: desired % of BUY samples (default 50%)
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target_sell_pct: desired % of SELL samples (default 50%)
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random_seed: for reproducibility
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Returns:
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Balanced DataFrame
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"""
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print(f"\n Balancing Classes (BINARY)...")
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print(f" Target distribution: BUY={target_buy_pct*100:.0f}%, SELL={target_sell_pct*100:.0f}%")
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# Filter labeled data only (exclude -1 = unlabeled)
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df_labeled = df.filter(pl.col("target") >= 0)
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df_buy = df_labeled.filter(pl.col("target") == 1)
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df_sell = df_labeled.filter(pl.col("target") == 0)
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n_buy = df_buy.height
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n_sell = df_sell.height
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# Find minority class size
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min_count = min(n_buy, n_sell)
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# Calculate target counts to achieve desired distribution
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# Use minority class as anchor
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total_target = int(min_count / min(target_buy_pct, target_sell_pct))
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n_buy_target = int(total_target * target_buy_pct)
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n_sell_target = int(total_target * target_sell_pct)
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# Sample each class
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if n_buy > n_buy_target:
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df_buy = df_buy.sample(n=n_buy_target, seed=random_seed)
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if n_sell > n_sell_target:
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df_sell = df_sell.sample(n=n_sell_target, seed=random_seed)
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# Combine
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df_balanced = pl.concat([df_buy, df_sell])
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# Shuffle
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df_balanced = df_balanced.sample(fraction=1.0, seed=random_seed)
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print(f" Before: BUY={n_buy}, SELL={n_sell}")
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print(f" After: BUY={df_buy.height}, SELL={df_sell.height}")
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print(f" Total samples: {df_balanced.height}")
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return df_balanced
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if __name__ == "__main__":
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# Test on sample data
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from src.mt5_connector import MT5Connector
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from src.config import TradingConfig
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from src.feature_eng import FeatureEngineer
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config = TradingConfig()
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mt5 = MT5Connector(config)
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mt5.connect()
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# Fetch data
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df = mt5.get_market_data(symbol="XAUUSD", timeframe="M15", count=10000)
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print(f"Fetched {len(df)} bars")
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# Calculate features (need ATR)
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fe = FeatureEngineer()
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df = fe.calculate_all(df, include_ml_features=False)
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# Apply labeling
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labeler = TripleBarrierLabeling(
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profit_atr_mult=0.20,
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stoploss_atr_mult=0.15,
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max_holding_bars=8,
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min_move_threshold=0.10,
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)
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df = labeler.label_data(df)
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# Save
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output_path = Path("backtests/ml_v3/labeled_data_sample.csv")
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output_path.parent.mkdir(parents=True, exist_ok=True)
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df.write_csv(output_path)
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print(f"\n Saved to {output_path}")
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Reference in New Issue
Block a user