import logging import numpy as np import pandas as pd import pickle import warnings import sys from datetime import datetime from pathlib import Path from typing import Optional, Dict from importlib import metadata from functools import lru_cache import talib.abstract as ta from scipy.fft import fft, fftfreq from scipy.stats import skew, kurtosis import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.strategy import IStrategy, DecimalParameter, IntParameter, BooleanParameter from freqtrade.persistence import Trade # Suppress deprecation warnings globally warnings.filterwarnings("ignore", category=DeprecationWarning) warnings.filterwarnings("ignore", message=".*pkg_resources.*") warnings.simplefilter(action="ignore", category=pd.errors.PerformanceWarning) logger = logging.getLogger(__name__) try: from sklearn.ensemble import ( RandomForestClassifier, GradientBoostingClassifier, ExtraTreesClassifier, AdaBoostClassifier, VotingClassifier ) from sklearn.linear_model import LogisticRegression from sklearn.svm import SVC from sklearn.preprocessing import StandardScaler, RobustScaler from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score from sklearn.model_selection import cross_val_score, GridSearchCV from sklearn.feature_selection import SelectKBest, f_classif SKLEARN_AVAILABLE = True # Check sklearn version using modern approach try: sklearn_version = metadata.version("scikit-learn") logger.info(f"Using scikit-learn version: {sklearn_version}") except Exception as e: logger.debug(f"Could not get sklearn version: {e}") except ImportError as e: logger.warning(f"scikit-learn not available: {e}") SKLEARN_AVAILABLE = False # Modern PyWavelets import try: import pywt WAVELETS_AVAILABLE = True try: pywt_version = metadata.version("PyWavelets") logger.info(f"Using PyWavelets version: {pywt_version}") except Exception as e: logger.debug(f"Could not get PyWavelets version: {e}") except ImportError as e: logger.warning(f"PyWavelets not available: {e}") WAVELETS_AVAILABLE = False logger = logging.getLogger(__name__) # Define Murrey Math level names for consistency MML_LEVEL_NAMES = [ "[-3/8]P", "[-2/8]P", "[-1/8]P", "[0/8]P", "[1/8]P", "[2/8]P", "[3/8]P", "[4/8]P", "[5/8]P", "[6/8]P", "[7/8]P", "[8/8]P", "[+1/8]P", "[+2/8]P", "[+3/8]P" ] def calculate_minima_maxima(df, window): if df is None or df.empty: return np.zeros(0), np.zeros(0) minima = np.zeros(len(df)) maxima = np.zeros(len(df)) for i in range(window, len(df)): window_data = df['ha_close'].iloc[i - window:i + 1] if df['ha_close'].iloc[i] == window_data.min() and (window_data == df['ha_close'].iloc[i]).sum() == 1: minima[i] = -window if df['ha_close'].iloc[i] == window_data.max() and (window_data == df['ha_close'].iloc[i]).sum() == 1: maxima[i] = window return minima, maxima def calc_slope_advanced(series, period): """ Enhanced linear regression slope calculation with Wavelet Transform and FFT analysis for superior trend detection and noise filtering """ if len(series) < period: return 0 # Use only the last 'period' values for consistency y = series.values[-period:] # Enhanced data validation if np.isnan(y).any() or np.isinf(y).any(): return 0 # Check for constant values (no trend) if np.all(y == y[0]): return 0 try: # === 1. WAVELET DENOISING === if WAVELETS_AVAILABLE and len(y) >= 8: wavelet = 'db4' try: w = pywt.Wavelet(wavelet) max_level = pywt.dwt_max_level(len(y), w.dec_len) use_level = min(3, max_level) # cap at 3 but adapt if shorter series except Exception: use_level = 1 if use_level >= 1: coeffs = pywt.wavedec(y, wavelet, level=use_level, mode='periodization') threshold = 0.1 * np.std(coeffs[-1]) if len(coeffs) > 1 else 0.0 coeffs_thresh = list(coeffs) for i in range(1, len(coeffs_thresh)): coeffs_thresh[i] = pywt.threshold(coeffs_thresh[i], threshold, mode='soft') y_denoised = pywt.waverec(coeffs_thresh, wavelet, mode='periodization') if len(y_denoised) != len(y): y_denoised = y_denoised[:len(y)] else: y_denoised = y else: y_denoised = y # === 2. FFT FREQUENCY ANALYSIS === # Analyze dominant frequencies to identify trend components if len(y_denoised) >= 4: # Apply FFT fft_values = fft(y_denoised) freqs = fftfreq(len(y_denoised)) # Get magnitude spectrum magnitude = np.abs(fft_values) # Find dominant frequency (excluding DC component) non_dc_indices = np.where(freqs != 0)[0] if len(non_dc_indices) > 0: dominant_freq_idx = non_dc_indices[np.argmax(magnitude[non_dc_indices])] dominant_freq = freqs[dominant_freq_idx] # Calculate trend strength based on frequency content trend_frequency_weight = 1.0 / (1.0 + abs(dominant_freq) * 10) else: trend_frequency_weight = 1.0 else: trend_frequency_weight = 1.0 # === 3. MULTI-SCALE SLOPE CALCULATION === x = np.linspace(0, period-1, period) # Original slope calculation slope_original = np.polyfit(x, y, 1)[0] # Wavelet-denoised slope calculation slope_denoised = np.polyfit(x, y_denoised, 1)[0] # === 4. WAVELET-BASED TREND DECOMPOSITION === if WAVELETS_AVAILABLE and len(y) >= 8: # Extract trend component using wavelet approximation approx_coeffs = coeffs[0] # Approximation coefficients (trend) # Reconstruct trend component trend_component = pywt.upcoef( 'a', approx_coeffs, wavelet, level=3, take=len(y)) if len(trend_component) > len(y): trend_component = trend_component[:len(y)] elif len(trend_component) < len(y): # Pad with last value if needed pad_length = len(y) - len(trend_component) trend_component = np.pad(trend_component, (0, pad_length), mode='edge') # Calculate slope of trend component slope_trend = np.polyfit(x, trend_component, 1)[0] else: slope_trend = slope_denoised # === 5. FREQUENCY-WEIGHTED SLOPE COMBINATION === # Weight slopes based on signal characteristics weights = { 'original': 0.3, 'denoised': 0.4, 'trend': 0.3 } # Adjust weights based on noise level noise_level = np.std(y - y_denoised) / np.std(y) if np.std(y) > 0 else 0 if noise_level > 0.1: # High noise weights = {'original': 0.2, 'denoised': 0.5, 'trend': 0.3} elif noise_level < 0.05: # Low noise weights = {'original': 0.4, 'denoised': 0.3, 'trend': 0.3} # Combined slope calculation slope_combined = ( slope_original * weights['original'] + slope_denoised * weights['denoised'] + slope_trend * weights['trend'] ) # Apply frequency weighting final_slope = slope_combined * trend_frequency_weight # === 6. ENHANCED VALIDATION === if np.isnan(final_slope) or np.isinf(final_slope): return (slope_original if not (np.isnan(slope_original) or np.isinf(slope_original)) else 0) # Normalize extreme slopes max_reasonable_slope = np.std(y) / period if abs(final_slope) > max_reasonable_slope * 15: return np.sign(final_slope) * max_reasonable_slope * 15 return final_slope except Exception: # Fallback to enhanced simple method if advanced processing fails try: # Apply simple moving average smoothing as fallback if len(y) >= 3: y_smooth = ( pd.Series(y).rolling(window=3, center=True) .mean().bfill().ffill().values ) x = np.linspace(0, period-1, period) slope = np.polyfit(x, y_smooth, 1)[0] if not (np.isnan(slope) or np.isinf(slope)): return slope # Ultimate fallback: simple difference simple_slope = (y[-1] - y[0]) / (period - 1) return (simple_slope if not (np.isnan(simple_slope) or np.isinf(simple_slope)) else 0) except Exception: return 0 def calculate_advanced_trend_strength_with_wavelets(dataframe: pd.DataFrame) -> pd.DataFrame: """ Enhanced trend strength calculation using Wavelet Transform and FFT analysis """ try: # === WAVELET-ENHANCED SLOPE CALCULATION === dataframe['slope_5_advanced'] = dataframe['close'].rolling(5).apply( lambda x: calc_slope_advanced(x, 5), raw=False ) dataframe['slope_10_advanced'] = dataframe['close'].rolling(10).apply( lambda x: calc_slope_advanced(x, 10), raw=False ) dataframe['slope_20_advanced'] = dataframe['close'].rolling(20).apply( lambda x: calc_slope_advanced(x, 20), raw=False ) # === WAVELET TREND DECOMPOSITION === def wavelet_trend_analysis(series, window=20): """Analyze trend using adaptive wavelet (haar/db4), safe levels, symmetric mode, robust threshold.""" if not WAVELETS_AVAILABLE or len(series) < window: return pd.Series([0.0] * len(series), index=series.index) results: list[float] = [] for i in range(len(series)): if i < window: results.append(0.0) continue window_data = series.iloc[i-window+1:i+1].values n = len(window_data) if n < 12: results.append(0.0) continue wavelet_name = 'haar' if n < 24 else 'db4' try: w = pywt.Wavelet(wavelet_name) max_level = pywt.dwt_max_level(n, w.dec_len) except Exception: max_level = 1 if n < 48: max_level = min(max_level, 2) use_level = max(1, min(3, max_level)) try: coeffs = pywt.wavedec(window_data, wavelet_name, level=use_level, mode='symmetric') # Estimate sigma from finest detail if len(coeffs) > 1 and len(coeffs[-1]): detail = coeffs[-1] sigma = np.median(np.abs(detail - np.median(detail))) / 0.6745 thr = sigma * np.sqrt(2 * np.log(n)) if sigma > 0 else 0.0 else: thr = 0.0 for j in range(1, len(coeffs)): coeffs[j] = pywt.threshold(coeffs[j], thr, mode='soft') approx = coeffs[0] trend_strength = np.std(approx) / (np.std(window_data) + 1e-9) direction = 0 if len(approx) >= 2: direction = 1 if approx[-1] > approx[0] else -1 score = trend_strength * direction if not np.isfinite(score): score = 0.0 # Clamp extreme outliers results.append(float(np.clip(score, -5, 5))) except Exception: results.append(0.0) return pd.Series(results, index=series.index) # Apply wavelet trend analysis dataframe['wavelet_trend_strength'] = wavelet_trend_analysis(dataframe['close']) # === FFT-BASED CYCLE DETECTION === def fft_cycle_analysis(series, window=50): """Detect market cycles using FFT""" if len(series) < window: return (pd.Series([0] * len(series), index=series.index), pd.Series([0] * len(series), index=series.index)) cycle_strength = [] dominant_period = [] for i in range(len(series)): if i < window: cycle_strength.append(0) dominant_period.append(0) continue # Get window data window_data = series.iloc[i-window+1:i+1].values try: # Remove linear trend x = np.arange(len(window_data)) slope, intercept = np.polyfit(x, window_data, 1) detrended = window_data - (slope * x + intercept) # Apply FFT fft_values = fft(detrended) freqs = fftfreq(len(detrended)) magnitude = np.abs(fft_values) # Find dominant cycle (excluding DC component) positive_freqs = freqs[1:len(freqs)//2] positive_magnitude = magnitude[1:len(magnitude)//2] if len(positive_magnitude) > 0: max_idx = np.argmax(positive_magnitude) dominant_freq = positive_freqs[max_idx] dominant_per = 1.0 / (abs(dominant_freq) + 1e-8) # Cycle strength (normalized) cycle_str = positive_magnitude[max_idx] / ( np.sum(positive_magnitude) + 1e-8) else: dominant_per = 0 cycle_str = 0 cycle_strength.append(cycle_str) dominant_period.append(dominant_per) except Exception: cycle_strength.append(0) dominant_period.append(0) return (pd.Series(cycle_strength, index=series.index), pd.Series(dominant_period, index=series.index)) # Apply FFT cycle analysis (dataframe['cycle_strength'], dataframe['dominant_cycle_period']) = fft_cycle_analysis(dataframe['close']) # === ENHANCED TREND STRENGTH CALCULATION === # Normalize advanced slopes by price dataframe['trend_strength_5_advanced'] = ( dataframe['slope_5_advanced'] / dataframe['close'] * 100) dataframe['trend_strength_10_advanced'] = ( dataframe['slope_10_advanced'] / dataframe['close'] * 100) dataframe['trend_strength_20_advanced'] = ( dataframe['slope_20_advanced'] / dataframe['close'] * 100) # Wavelet-weighted combined trend strength dataframe['trend_strength_wavelet'] = ( dataframe['trend_strength_5_advanced'] * 0.4 + dataframe['trend_strength_10_advanced'] * 0.35 + dataframe['trend_strength_20_advanced'] * 0.25 ) # Incorporate wavelet trend analysis dataframe['trend_strength_combined'] = ( dataframe['trend_strength_wavelet'] * 0.7 + dataframe['wavelet_trend_strength'] * 0.3 ) # === CYCLE-ADJUSTED TREND STRENGTH === # Adjust trend strength based on cycle analysis dataframe['trend_strength_cycle_adjusted'] = dataframe['trend_strength_combined'].copy() # Boost trend strength when aligned with dominant cycle strong_cycle_mask = dataframe['cycle_strength'] > 0.3 dataframe.loc[strong_cycle_mask, 'trend_strength_cycle_adjusted'] *= ( 1 + dataframe.loc[strong_cycle_mask, 'cycle_strength']) # === FINAL TREND CLASSIFICATION WITH ADVANCED FEATURES === strong_threshold = 0.02 # Enhanced trend classification dataframe['strong_uptrend_advanced'] = ( (dataframe['trend_strength_cycle_adjusted'] > strong_threshold) & (dataframe['wavelet_trend_strength'] > 0) & (dataframe['cycle_strength'] > 0.1) ) dataframe['strong_downtrend_advanced'] = ( (dataframe['trend_strength_cycle_adjusted'] < -strong_threshold) & (dataframe['wavelet_trend_strength'] < 0) & (dataframe['cycle_strength'] > 0.1) ) dataframe['ranging_advanced'] = ( (dataframe['trend_strength_cycle_adjusted'].abs() < strong_threshold * 0.5) | (dataframe['cycle_strength'] < 0.05) # Very weak cycles indicate ranging ) # === TREND CONFIDENCE SCORE === # Calculate confidence based on agreement between methods methods_agreement = ( (np.sign(dataframe['trend_strength_5_advanced']) == np.sign(dataframe['trend_strength_10_advanced'])).astype(int) + (np.sign(dataframe['trend_strength_10_advanced']) == np.sign(dataframe['trend_strength_20_advanced'])).astype(int) + (np.sign(dataframe['trend_strength_wavelet']) == np.sign(dataframe['wavelet_trend_strength'])).astype(int) ) dataframe['trend_confidence'] = methods_agreement / 3.0 # High confidence trends dataframe['high_confidence_trend'] = ( (dataframe['trend_confidence'] >= 0.67) & (dataframe['cycle_strength'] > 0.2) & (dataframe['trend_strength_cycle_adjusted'].abs() > strong_threshold * 0.8) ) return dataframe except Exception as e: logger.warning(f"Advanced trend analysis failed: {e}. Using fallback method.") # Return dataframe with fallback values fallback_columns = [ 'slope_5_advanced', 'slope_10_advanced', 'slope_20_advanced', 'wavelet_trend_strength', 'cycle_strength', 'dominant_cycle_period', 'trend_strength_5_advanced', 'trend_strength_10_advanced', 'trend_strength_20_advanced', 'trend_strength_wavelet', 'trend_strength_combined', 'trend_strength_cycle_adjusted', 'strong_uptrend_advanced', 'strong_downtrend_advanced', 'ranging_advanced', 'trend_confidence', 'high_confidence_trend' ] for col in fallback_columns: if 'strength' in col: dataframe[col] = 0.0 else: dataframe[col] = False return dataframe # === ADVANCED PREDICTIVE ANALYSIS SYSTEM === class AdvancedPredictiveEngine: """ Advanced machine learning engine for high-precision trade entry prediction """ def __init__(self): # Model containers self.models = {} self.scalers = {} self.feature_importance = {} self.prediction_history = {} self.is_trained = {} # Cached training dataframe per pair for incremental extension self.training_cache: dict[str, pd.DataFrame] = {} # Retraining control self.last_train_time: dict[str, datetime] = {} self.last_train_index: dict[str, int] = {} # Periodic retraining interval (changed from 24h to 48h per latest requirement) self.retrain_interval_hours: int = 48 self.initial_train_candles: int = 1000 # initial window size self.min_new_candles_for_retrain: int = 50 # skip tiny updates # Strategy startup tracking for 48h retrain rule self.strategy_start_time: datetime = datetime.utcnow() self.retrain_after_startup_hours: int = 48 # Enable periodic retrain after startup period self.enable_startup_retrain: bool = True # Model persistence settings self.models_dir = Path("user_data/strategies/ml_models") self.models_dir.mkdir(parents=True, exist_ok=True) # Load existing models if available self._load_models_from_disk() # ----------------- ASSET EXISTENCE HELPERS ----------------- def _required_asset_paths(self, pair: str) -> list[Path]: """Return list of required core asset file paths for a pair.""" return [ self._get_model_filepath(pair, "model_random_forest"), self._get_model_filepath(pair, "model_gradient_boosting"), self._get_model_filepath(pair, "scaler"), self._get_model_filepath(pair, "metadata"), ] def _assets_exist(self, pair: str) -> bool: """Check if all required asset files exist for pair.""" return all(p.exists() for p in self._required_asset_paths(pair)) def mark_trained_if_assets(self, pair: str): """Mark pair as trained if asset files exist (called at startup).""" if self._assets_exist(pair): self.is_trained[pair] = True def _get_model_filepath(self, pair: str, model_type: str) -> Path: """Get the filepath for saving/loading models""" safe_pair = pair.replace('/', '_').replace(':', '_') return self.models_dir / f"{safe_pair}_{model_type}.pkl" def _save_models_to_disk(self, pair: str): """Save trained models to disk for persistence""" try: if pair not in self.models: return # Save models for model_name, model in self.models[pair].items(): filepath = self._get_model_filepath(pair, f"model_{model_name}") with open(filepath, 'wb') as f: pickle.dump(model, f) # Save scaler if pair in self.scalers: scaler_filepath = self._get_model_filepath(pair, "scaler") with open(scaler_filepath, 'wb') as f: pickle.dump(self.scalers[pair], f) # Save feature importance and metadata if pair in self.feature_importance: metadata_filepath = self._get_model_filepath(pair, "metadata") metadata = { 'feature_importance': self.feature_importance[pair], 'is_trained': self.is_trained.get(pair, False), 'timestamp': datetime.now().isoformat() } with open(metadata_filepath, 'wb') as f: pickle.dump(metadata, f) logger.info(f"Models saved to disk for {pair}") except Exception as e: logger.warning(f"Failed to save models for {pair}: {e}") def _load_models_from_disk(self): """Load existing models from disk""" try: if not self.models_dir.exists(): return # Find all model files model_files = list(self.models_dir.glob("*_model_*.pkl")) pairs_found = set() for model_file in model_files: # Extract pair name from filename filename = model_file.stem parts = filename.split('_model_') if len(parts) == 2: pair_safe = parts[0] pair = pair_safe.replace('_', '/') if ':' not in pair and len(parts[0].split('_')) > 1: # Handle cases like BTC_USDT_USDT -> BTC/USDT:USDT parts_pair = parts[0].split('_') if len(parts_pair) >= 3: pair = f"{parts_pair[0]}/{parts_pair[1]}:{parts_pair[2]}" pairs_found.add(pair) # Load models for each pair for pair in pairs_found: try: self._load_pair_models(pair) except Exception as e: logger.warning(f"Failed to load models for {pair}: {e}") if pairs_found: logger.info(f"Loaded ML models from disk for {len(pairs_found)} pairs: {list(pairs_found)}") except Exception as e: logger.warning(f"Failed to load models from disk: {e}") def _load_pair_models(self, pair: str): """Load models for a specific pair""" safe_pair = pair.replace('/', '_').replace(':', '_') # Load models models = {} for model_name in ['random_forest', 'gradient_boosting']: model_filepath = self._get_model_filepath(pair, f"model_{model_name}") if model_filepath.exists(): with open(model_filepath, 'rb') as f: models[model_name] = pickle.load(f) if models: self.models[pair] = models # Load scaler scaler_filepath = self._get_model_filepath(pair, "scaler") if scaler_filepath.exists(): with open(scaler_filepath, 'rb') as f: self.scalers[pair] = pickle.load(f) # Load metadata metadata_filepath = self._get_model_filepath(pair, "metadata") if metadata_filepath.exists(): with open(metadata_filepath, 'rb') as f: metadata = pickle.load(f) self.feature_importance[pair] = metadata.get('feature_importance', {}) self.is_trained[pair] = metadata.get('is_trained', False) def _cleanup_old_models(self, max_age_days: int = 7): """Remove models older than specified days""" try: cutoff_time = datetime.now() - pd.Timedelta(days=max_age_days) for model_file in self.models_dir.glob("*.pkl"): if model_file.stat().st_mtime < cutoff_time.timestamp(): model_file.unlink() logger.info(f"Removed old model file: {model_file.name}") except Exception as e: logger.warning(f"Failed to cleanup old models: {e}") def extract_advanced_features(self, dataframe: pd.DataFrame, lookback: int = 100) -> pd.DataFrame: """Extract sophisticated features for ML prediction with variance validation""" df = dataframe.copy() # === 1. ENHANCED PRICE ACTION FEATURES === # Multi-period price patterns with variance for period in [1, 2, 3, 5]: df[f'price_velocity_{period}'] = df['close'].pct_change(period) df[f'price_acceleration_{period}'] = df[f'price_velocity_{period}'].diff(1) # Add rolling statistics for variance df[f'price_velocity_std_{period}'] = df[f'price_velocity_{period}'].rolling(20).std() df[f'price_velocity_skew_{period}'] = df[f'price_velocity_{period}'].rolling(20).skew() # Volatility-adjusted momentum returns = df['close'].pct_change(1) vol_20 = returns.rolling(20).std() df['vol_adjusted_momentum'] = returns / (vol_20 + 1e-10) # Price position within recent range for window in [10, 20, 50]: high_window = df['high'].rolling(window).max() low_window = df['low'].rolling(window).min() range_size = high_window - low_window df[f'price_position_{window}'] = (df['close'] - low_window) / (range_size + 1e-10) # Support/Resistance with dynamic thresholds if 'minima_sort_threshold' in df.columns: support_distance = abs(df['low'] - df['minima_sort_threshold']) / (df['close'] + 1e-10) df['support_strength'] = (support_distance < 0.02).astype(int).rolling(20).mean() df['support_distance_norm'] = support_distance else: df['support_strength'] = np.random.uniform(0.3, 0.7, len(df)) # Random baseline df['support_distance_norm'] = np.random.uniform(0, 0.1, len(df)) if 'maxima_sort_threshold' in df.columns: resistance_distance = abs(df['high'] - df['maxima_sort_threshold']) / (df['close'] + 1e-10) df['resistance_strength'] = (resistance_distance < 0.02).astype(int).rolling(20).mean() df['resistance_distance_norm'] = resistance_distance else: df['resistance_strength'] = np.random.uniform(0.3, 0.7, len(df)) df['resistance_distance_norm'] = np.random.uniform(0, 0.1, len(df)) # === 2. VOLUME DYNAMICS === # Volume profile analysis df['volume_profile_score'] = self._calculate_volume_profile_score(df) df['volume_imbalance'] = self._calculate_volume_imbalance(df) df['smart_money_index'] = self._calculate_smart_money_index(df) # Volume-price correlation df['volume_price_correlation'] = df['volume'].rolling(20).corr(df['close']) df['volume_breakout_strength'] = self._calculate_volume_breakout_strength(df) # === 3. VOLATILITY CLUSTERING === df['volatility_regime'] = self._calculate_volatility_regime(df) df['volatility_persistence'] = self._calculate_volatility_persistence(df) df['volatility_mean_reversion'] = self._calculate_volatility_mean_reversion(df) # === 4. MOMENTUM DECOMPOSITION === for period in [3, 5, 8, 13, 21]: df[f'momentum_{period}'] = df['close'].pct_change(period) df[f'momentum_strength_{period}'] = abs(df[f'momentum_{period}']) df[f'momentum_consistency_{period}'] = ( np.sign(df[f'momentum_{period}']).rolling(5).mean() ) # Momentum regime detection df['momentum_regime'] = self._classify_momentum_regime(df) df['momentum_divergence_strength'] = self._calculate_momentum_divergence(df) # === 5. MICROSTRUCTURE FEATURES === df['spread_proxy'] = (df['high'] - df['low']) / df['close'] df['market_impact'] = df['volume'] * df['spread_proxy'] df['order_flow_imbalance'] = self._calculate_order_flow_imbalance(df) df['liquidity_index'] = self._calculate_liquidity_index(df) # === 6. STATISTICAL FEATURES === for window in [10, 20, 50]: returns = df['close'].pct_change(1) df[f'skewness_{window}'] = returns.rolling(window).apply( lambda x: skew(x.dropna()) if len(x.dropna()) > 3 else 0 ) df[f'kurtosis_{window}'] = returns.rolling(window).apply( lambda x: kurtosis(x.dropna()) if len(x.dropna()) > 3 else 0 ) df[f'entropy_{window}'] = self._calculate_entropy(df['close'], window) # === 7. REGIME DETECTION FEATURES === df['market_regime'] = self._detect_market_regime(df) df['regime_stability'] = self._calculate_regime_stability(df) df['regime_transition_probability'] = self._calculate_regime_transition_prob(df) return df def _calculate_volume_profile_score(self, df: pd.DataFrame, window: int = 50) -> pd.Series: """Calculate volume profile score""" def volume_profile(data): if len(data) < 10: return 0.5 prices = data['close'].values volumes = data['volume'].values # Create price bins price_min, price_max = prices.min(), prices.max() if price_max == price_min: return 0.5 bins = np.linspace(price_min, price_max, 10) # Calculate volume at each price level volume_at_price = [] for i in range(len(bins) - 1): mask = (prices >= bins[i]) & (prices < bins[i + 1]) vol_sum = volumes[mask].sum() volume_at_price.append(vol_sum) # Point of Control (POC) - price level with highest volume if sum(volume_at_price) == 0: return 0.5 poc_index = np.argmax(volume_at_price) current_price = prices[-1] poc_price = (bins[poc_index] + bins[poc_index + 1]) / 2 # Score based on distance from POC distance_ratio = abs(current_price - poc_price) / (price_max - price_min + 1e-10) score = 1 - distance_ratio # Closer to POC = higher score return max(0, min(1, score)) # Apply rolling calculation result = [] for i in range(len(df)): if i < window: result.append(0.5) else: window_data = df.iloc[i-window+1:i+1][['close', 'volume']] score = volume_profile(window_data) result.append(score) return pd.Series(result, index=df.index) def _calculate_volume_imbalance(self, df: pd.DataFrame) -> pd.Series: """Calculate volume imbalance between buying and selling""" up_volume = df['volume'].where(df['close'] > df['open'], 0) down_volume = df['volume'].where(df['close'] < df['open'], 0) total_volume = up_volume + down_volume imbalance = (up_volume - down_volume) / (total_volume + 1e-10) return imbalance.rolling(10).mean() def _calculate_smart_money_index(self, df: pd.DataFrame) -> pd.Series: """Calculate Smart Money Index (SMI)""" price_change = abs(df['close'].pct_change(1)) volume_norm = df['volume'] / df['volume'].rolling(20).mean() smi = volume_norm / (price_change + 1e-10) return smi.rolling(10).mean() def _calculate_volume_breakout_strength(self, df: pd.DataFrame) -> pd.Series: """Calculate volume breakout strength""" volume_ma = df['volume'].rolling(20).mean() volume_ratio = df['volume'] / volume_ma price_breakout = ( (df['close'] > df['close'].rolling(20).max().shift(1)) | (df['close'] < df['close'].rolling(20).min().shift(1)) ).astype(int) breakout_strength = volume_ratio * price_breakout return breakout_strength.rolling(5).mean() def _calculate_volatility_regime(self, df: pd.DataFrame) -> pd.Series: """Detect volatility regime""" returns = df['close'].pct_change(1) volatility = returns.rolling(20).std() vol_ma = volatility.rolling(50).mean() regime = pd.Series(1, index=df.index) # Default normal regime[volatility < vol_ma * 0.7] = 0 # Low volatility regime[volatility > vol_ma * 1.5] = 2 # High volatility return regime def _calculate_volatility_persistence(self, df: pd.DataFrame) -> pd.Series: """Calculate volatility persistence""" returns = df['close'].pct_change(1) volatility = returns.rolling(5).std() persistence = volatility.rolling(20).apply( lambda x: x.autocorr(lag=1) if len(x.dropna()) > 10 else 0 ) return persistence def _calculate_volatility_mean_reversion(self, df: pd.DataFrame) -> pd.Series: """Calculate volatility mean reversion tendency""" returns = df['close'].pct_change(1) volatility = returns.rolling(10).std() vol_ma = volatility.rolling(50).mean() vol_zscore = (volatility - vol_ma) / (volatility.rolling(50).std() + 1e-10) mean_reversion = -vol_zscore return mean_reversion def _classify_momentum_regime(self, df: pd.DataFrame) -> pd.Series: """Classify momentum regime""" mom_3 = df['close'].pct_change(3) mom_8 = df['close'].pct_change(8) mom_21 = df['close'].pct_change(21) regime = pd.Series(0, index=df.index) # Neutral strong_up = (mom_3 > 0.02) & (mom_8 > 0.05) & (mom_21 > 0.1) regime[strong_up] = 2 mod_up = (mom_3 > 0) & (mom_8 > 0) & (mom_21 > 0) & ~strong_up regime[mod_up] = 1 mod_down = (mom_3 < 0) & (mom_8 < 0) & (mom_21 < 0) & (mom_21 > -0.1) regime[mod_down] = -1 strong_down = (mom_3 < -0.02) & (mom_8 < -0.05) & (mom_21 < -0.1) regime[strong_down] = -2 return regime def _calculate_momentum_divergence(self, df: pd.DataFrame) -> pd.Series: """Calculate momentum divergence strength""" price_momentum = df['close'].pct_change(10) if 'rsi' in df.columns: rsi_momentum = df['rsi'].diff(10) else: rsi_momentum = pd.Series(0, index=df.index) volume_momentum = df['volume'].pct_change(10) # Normalize momentums using rolling z-score price_norm = (price_momentum - price_momentum.rolling(50).mean()) / ( price_momentum.rolling(50).std() + 1e-10) rsi_norm = (rsi_momentum - rsi_momentum.rolling(50).mean()) / ( rsi_momentum.rolling(50).std() + 1e-10) volume_norm = (volume_momentum - volume_momentum.rolling(50).mean()) / ( volume_momentum.rolling(50).std() + 1e-10) price_rsi_div = abs(price_norm - rsi_norm) price_volume_div = abs(price_norm - volume_norm) divergence_strength = (price_rsi_div + price_volume_div) / 2 return divergence_strength.rolling(5).mean() def _calculate_order_flow_imbalance(self, df: pd.DataFrame) -> pd.Series: """Calculate order flow imbalance""" price_impact = (df['close'] - df['open']) / df['open'] volume_impact = df['volume'] / df['volume'].rolling(20).mean() flow_imbalance = price_impact * volume_impact return flow_imbalance.rolling(5).mean() def _calculate_liquidity_index(self, df: pd.DataFrame) -> pd.Series: """Calculate market liquidity index""" spread = (df['high'] - df['low']) / df['close'] volume_norm = df['volume'] / df['volume'].rolling(50).mean() liquidity = volume_norm / (spread + 1e-10) return liquidity.rolling(10).mean() def _calculate_entropy(self, series: pd.Series, window: int) -> pd.Series: """Calculate information entropy""" def entropy(data): if len(data) < 5: return 0 returns = np.diff(data) / (data[:-1] + 1e-10) bins = np.histogram_bin_edges(returns, bins=10) hist, _ = np.histogram(returns, bins=bins) probs = hist / (hist.sum() + 1e-10) probs = probs[probs > 0] ent = -np.sum(probs * np.log2(probs + 1e-10)) return ent return series.rolling(window).apply(entropy, raw=False) def _detect_market_regime(self, df: pd.DataFrame) -> pd.Series: """Detect overall market regime""" if 'trend_strength' in df.columns: trend_regime = np.sign(df['trend_strength']) else: trend_regime = pd.Series(0, index=df.index) vol_regime = self._calculate_volatility_regime(df) - 1 momentum_regime = self._classify_momentum_regime(df) market_regime = ( trend_regime * 0.4 + vol_regime * 0.3 + momentum_regime * 0.3 ) return market_regime.rolling(5).mean() def _calculate_regime_stability(self, df: pd.DataFrame) -> pd.Series: """Calculate regime stability""" regime = self._detect_market_regime(df) regime_changes = abs(regime.diff(1)) stability = 1 / (regime_changes.rolling(20).mean() + 1e-10) return stability def _calculate_regime_transition_prob(self, df: pd.DataFrame) -> pd.Series: """Calculate probability of regime transition""" regime = self._detect_market_regime(df) transitions = [] for i in range(1, len(regime)): if not (pd.isna(regime.iloc[i]) or pd.isna(regime.iloc[i-1])): transition = abs(regime.iloc[i] - regime.iloc[i-1]) > 0.5 transitions.append(transition) else: transitions.append(False) transition_prob = pd.Series([False] + transitions, index=regime.index) prob_smooth = transition_prob.astype(int).rolling(20).mean() return prob_smooth def create_target_variable(self, df: pd.DataFrame, forward_periods: int = 5, profit_threshold: float | None = None, dynamic: bool = True, quantile: float = 0.85, k_atr: float = 1.2, k_vol: float = 1.5, min_abs: float = 0.003, max_abs: float = 0.05) -> pd.Series: """Create target variable with optional dynamic profit threshold. Dynamic threshold logic (if dynamic=True and profit_threshold not provided): 1. Compute ATR% (14) and rolling return volatility (20). 2. base_series = k_atr * ATR% + k_vol * vola20 3. base_scalar = median(base_series) 4. q_thr = 85th percentile of forward_returns (future move distribution) 5. blended = 0.5 * base_scalar + 0.5 * q_thr 6. final_thr = clip(blended, min_abs, max_abs) This produces a stable scalar threshold per training batch (reproducible) rather than per-row noise. """ # Forward returns used by several strategies forward_returns = df['close'].pct_change(forward_periods).shift(-forward_periods) # === DYNAMIC THRESHOLD CALCULATION === if dynamic and (profit_threshold is None): try: # ATR% calculation high = df['high'] low = df['low'] close = df['close'] prev_close = close.shift(1) tr = np.maximum(high - low, np.maximum((high - prev_close).abs(), (low - prev_close).abs())) atr = tr.rolling(14).mean() atr_pct = (atr / close).clip(lower=0) # Return volatility returns_1 = close.pct_change() vola20 = returns_1.rolling(20).std() base_series = k_atr * atr_pct + k_vol * vola20 base_scalar = float(np.nanmedian(base_series.tail(300))) if len(base_series.dropna()) > 30 else float(np.nanmedian(base_series)) # Distribution-based quantile of forward returns (future info okay for label construction stage) q_thr = float(forward_returns.quantile(quantile)) if forward_returns.notna().any() else min_abs if not np.isfinite(q_thr): q_thr = min_abs blended = 0.5 * base_scalar + 0.5 * q_thr profit_threshold = float(np.clip(blended, min_abs, max_abs)) except Exception as e: logger.warning(f"Dynamic threshold failed ({e}), falling back to default 0.015") profit_threshold = 0.015 elif profit_threshold is None: profit_threshold = 0.015 # === STRATEGY 1: SIMPLE FORWARD RETURNS === simple_target = (forward_returns > profit_threshold).astype(int) # === STRATEGY 2: MAXIMUM PROFIT POTENTIAL === forward_highs = df['high'].rolling(forward_periods).max().shift(-forward_periods) max_profit_potential = (forward_highs - df['close']) / df['close'] profit_target = (max_profit_potential > profit_threshold).astype(int) # === STRATEGY 3: RISK-ADJUSTED RETURNS === forward_lows = df['low'].rolling(forward_periods).min().shift(-forward_periods) max_loss_potential = (forward_lows - df['close']) / df['close'] risk_adjusted_return = forward_returns / (abs(max_loss_potential) + 1e-10) risk_target = ( (forward_returns > profit_threshold * 0.7) & (risk_adjusted_return > 0.5) ).astype(int) # === STRATEGY 4: VOLATILITY-ADJUSTED TARGET === returns_std = df['close'].pct_change().rolling(20).std() volatility_adjusted_threshold = profit_threshold * (1 + returns_std) vol_target = (forward_returns > volatility_adjusted_threshold).astype(int) # === ENSEMBLE VOTE === combined_target = simple_target + profit_target + risk_target + vol_target final_target = (combined_target >= 2).astype(int) positive_ratio = final_target.mean() logger.info( f"Target created (forward={forward_periods}) dynamic_thr={profit_threshold:.4f} " f"positives={final_target.sum()}/{len(final_target)} ratio={positive_ratio:.3f}") # Only log imbalance now; do not auto-alter labels (professional reproducibility) if positive_ratio < 0.05: logger.warning(f"Very low positive ratio ({positive_ratio:.3f}) at threshold {profit_threshold:.4f}") elif positive_ratio > 0.45: logger.warning(f"High positive ratio ({positive_ratio:.3f}) at threshold {profit_threshold:.4f}") return final_target def train_predictive_models(self, df: pd.DataFrame, pair: str) -> dict: """Train advanced ensemble of predictive models with hyperparameter optimization""" if not SKLEARN_AVAILABLE: return {'status': 'sklearn_not_available'} try: # Decide training slice: initial window or sliding window on retrain if pair not in self.is_trained or not self.is_trained[pair]: # First training: cut to last initial_train_candles if len(df) > self.initial_train_candles: base_df = df.iloc[-self.initial_train_candles:].copy() else: base_df = df.copy() else: # Incremental retrain: extend previous cached window with new rows since last_train_index prev_df = self.training_cache.get(pair) if prev_df is None: prev_df = df.iloc[-self.initial_train_candles:].copy() if len(df) > self.initial_train_candles else df.copy() # New rows (simple index-based diff). If dataframe has 'date', we could filter last 24h. new_rows = df.iloc[self.last_train_index.get(pair, 0):].copy() if len(new_rows) == 0: base_df = prev_df else: combined = pd.concat([prev_df, new_rows], ignore_index=True) # Keep only most recent window (rolling window behaviour) if len(combined) > self.initial_train_candles: base_df = combined.iloc[-self.initial_train_candles:].copy() else: base_df = combined feature_df = self.extract_advanced_features(base_df) target = self.create_target_variable(base_df) feature_columns = [] exclude_cols = ['open', 'high', 'low', 'close', 'volume', 'date', 'enter_long', 'enter_short', 'exit_long', 'exit_short'] for col in feature_df.columns: if (col not in exclude_cols and feature_df[col].dtype in ['float64', 'int64'] and not col.startswith('enter_') and not col.startswith('exit_')): feature_columns.append(col) X = feature_df[feature_columns].fillna(0) y = target.fillna(0) valid_mask = ~(pd.isna(y) | pd.isna(X).any(axis=1)) X = X[valid_mask] y = y[valid_mask] if len(X) < 100: return {'status': 'insufficient_data'} # === FEATURE QUALITY VALIDATION === # Remove constant features (zero variance) feature_variance = X.var() non_constant_features = feature_variance[feature_variance > 1e-10].index.tolist() logger.info(f"Removed {len(feature_columns) - len(non_constant_features)} " f"constant features out of {len(feature_columns)}") if len(non_constant_features) < 5: logger.warning(f"Too few variable features ({len(non_constant_features)})") return {'status': 'insufficient_features'} X = X[non_constant_features] feature_columns = non_constant_features # === CLASS BALANCE VALIDATION === positive_count = y.sum() negative_count = len(y) - positive_count positive_ratio = positive_count / len(y) logger.info(f"Class distribution: {positive_count} positive, " f"{negative_count} negative ({positive_ratio:.3f} ratio)") # Check for severe class imbalance if positive_count < 10: logger.warning(f"Too few positive examples ({positive_count}), " f"adjusting target variable") # Create more lenient target relaxed_target = self.create_target_variable(df, forward_periods=3, profit_threshold=0.01) y = relaxed_target[valid_mask].fillna(0) positive_count = y.sum() positive_ratio = positive_count / len(y) logger.info(f"Adjusted class distribution: {positive_count} positive " f"({positive_ratio:.3f} ratio)") if positive_count < 5: return {'status': 'insufficient_positive_examples'} # Advanced feature selection if len(feature_columns) > 30: selector = SelectKBest(score_func=f_classif, k=min(30, len(feature_columns))) X_selected = selector.fit_transform(X, y) selected_features = [feature_columns[i] for i in selector.get_support(indices=True)] X = pd.DataFrame(X_selected, columns=selected_features, index=X.index) feature_columns = selected_features logger.info(f"Selected {len(selected_features)} best features for {pair}") split_idx = int(len(X) * 0.7) X_train, X_test = X[:split_idx], X[split_idx:] y_train, y_test = y[:split_idx], y[split_idx:] # Use RobustScaler for better handling of outliers scaler = RobustScaler() X_train_scaled = scaler.fit_transform(X_train) X_test_scaled = scaler.transform(X_test) models = {} results = {} # === MODEL 1: OPTIMIZED RANDOM FOREST === # Full parameter grid for comprehensive optimization rf_params_full = { 'n_estimators': [150, 200, 250], 'max_depth': [15, 20, 25], 'min_samples_split': [5, 10, 15], 'min_samples_leaf': [2, 5, 8], 'max_features': ['sqrt', 'log2', 0.8] } # Quick parameter grid for faster training (used by default) rf_params_quick = { 'n_estimators': [150, 200], 'max_depth': [15, 20], 'min_samples_split': [5, 10], 'min_samples_leaf': [2, 5], 'max_features': ['sqrt', 0.8] } rf_base = RandomForestClassifier( random_state=42, n_jobs=-1, class_weight='balanced' ) # Use comprehensive grid search for datasets with sufficient data use_full_params = len(X_train) > 600 # More data = more thorough search selected_params = rf_params_full if use_full_params else rf_params_quick logger.info(f"Using {'full' if use_full_params else 'quick'} RF parameters " f"for {len(X_train)} training samples") # Adaptive grid search based on dataset size rf_grid = GridSearchCV( rf_base, param_grid=selected_params, cv=3, scoring='f1', n_jobs=-1 ) rf_grid.fit(X_train_scaled, y_train) models['random_forest'] = rf_grid.best_estimator_ # === MODEL 2: OPTIMIZED GRADIENT BOOSTING === gb_base = GradientBoostingClassifier( random_state=42, validation_fraction=0.1, n_iter_no_change=10, tol=1e-4 ) gb_grid = GridSearchCV( gb_base, param_grid={ 'n_estimators': [150, 200], 'max_depth': [6, 8, 10], 'learning_rate': [0.05, 0.1, 0.15], 'min_samples_split': [10, 20], 'min_samples_leaf': [5, 10] }, cv=3, scoring='f1', n_jobs=-1 ) gb_grid.fit(X_train_scaled, y_train) models['gradient_boosting'] = gb_grid.best_estimator_ # === MODEL 3: EXTRA TREES (EXTREMELY RANDOMIZED TREES) === et = ExtraTreesClassifier( n_estimators=150, max_depth=12, min_samples_split=8, min_samples_leaf=4, max_features='sqrt', random_state=42, n_jobs=-1, class_weight='balanced' ) et.fit(X_train_scaled, y_train) models['extra_trees'] = et # === MODEL 4: ADAPTIVE BOOSTING === ada = AdaBoostClassifier( n_estimators=80, learning_rate=0.8, random_state=42 ) ada.fit(X_train_scaled, y_train) models['ada_boost'] = ada # === MODEL 5: SUPPORT VECTOR MACHINE (for small datasets) === if len(X_train) < 2000: # Only for smaller datasets due to computational cost svm = SVC( kernel='rbf', C=1.0, gamma='scale', probability=True, random_state=42, class_weight='balanced' ) svm.fit(X_train_scaled, y_train) models['svm'] = svm # === MODEL 6: LOGISTIC REGRESSION (baseline) === lr = LogisticRegression( C=1.0, penalty='l2', solver='liblinear', random_state=42, class_weight='balanced', max_iter=1000 ) lr.fit(X_train_scaled, y_train) models['logistic_regression'] = lr # === EVALUATE ALL MODELS === for name, model in models.items(): y_pred = model.predict(X_test_scaled) y_pred_proba = model.predict_proba(X_test_scaled)[:, 1] if hasattr(model, 'predict_proba') else y_pred accuracy = accuracy_score(y_test, y_pred) precision = precision_score(y_test, y_pred, zero_division=0) recall = recall_score(y_test, y_pred, zero_division=0) f1 = f1_score(y_test, y_pred, zero_division=0) # Calculate AUC metrics using probabilities when available try: from sklearn.metrics import roc_auc_score, average_precision_score if hasattr(model, 'predict_proba') and len(np.unique(y_test)) > 1: auc_roc = roc_auc_score(y_test, y_pred_proba) auc_pr = average_precision_score(y_test, y_pred_proba) else: # Fallback for models without predict_proba auc_roc = roc_auc_score(y_test, y_pred) if len(np.unique(y_test)) > 1 else 0.5 auc_pr = average_precision_score(y_test, y_pred) if len(np.unique(y_test)) > 1 else 0.5 except ImportError: auc_roc = 0.5 auc_pr = 0.5 # Cross-validation score cv_scores = cross_val_score(model, X_train_scaled, y_train, cv=3, scoring='f1') cv_mean = cv_scores.mean() cv_std = cv_scores.std() results[name] = { 'model': model, 'accuracy': accuracy, 'precision': precision, 'recall': recall, 'f1_score': f1, 'auc_roc': auc_roc, 'auc_pr': auc_pr, 'cv_mean': cv_mean, 'cv_std': cv_std, 'probabilities': y_pred_proba, # Store probabilities for ensemble 'feature_importance': self._get_feature_importance(model, feature_columns) } logger.info(f"{pair} {name}: Acc={accuracy:.3f}, F1={f1:.3f}, " f"AUC={auc_roc:.3f}, CV={cv_mean:.3f}±{cv_std:.3f}") # === CREATE VOTING ENSEMBLE === # Select top 3 models based on F1 score sorted_models = sorted(results.items(), key=lambda x: x[1]['f1_score'], reverse=True) top_models = [(name, results[name]['model']) for name, _ in sorted_models[:3]] if len(top_models) >= 2: voting_classifier = VotingClassifier( estimators=top_models, voting='soft' # Use probability averaging ) voting_classifier.fit(X_train_scaled, y_train) models['voting_ensemble'] = voting_classifier # Evaluate ensemble y_pred_ensemble = voting_classifier.predict(X_test_scaled) ensemble_f1 = f1_score(y_test, y_pred_ensemble, zero_division=0) ensemble_accuracy = accuracy_score(y_test, y_pred_ensemble) results['voting_ensemble'] = { 'model': voting_classifier, 'accuracy': ensemble_accuracy, 'f1_score': ensemble_f1, 'feature_importance': {} # Ensemble doesn't have direct feature importance } logger.info(f"{pair} Voting Ensemble: Acc={ensemble_accuracy:.3f}, F1={ensemble_f1:.3f}") self.models[pair] = models self.scalers[pair] = scaler self.feature_importance[pair] = results self.is_trained[pair] = True # Update retrain metadata and cache self.last_train_time[pair] = datetime.utcnow() self.last_train_index[pair] = len(df) self.training_cache[pair] = base_df.copy() # Save models to disk for persistence self._save_models_to_disk(pair) # Find best model best_model_name = max(results.keys(), key=lambda k: results[k]['f1_score']) best_f1 = results[best_model_name]['f1_score'] return { 'status': 'success', 'results': results, 'feature_columns': feature_columns, 'n_samples': len(X), 'best_model': best_model_name, 'best_f1_score': best_f1, 'n_models': len(models) } except Exception as e: logger.warning(f"Model training failed for {pair}: {e}") return {'status': 'error', 'message': str(e)} def _get_feature_importance(self, model, feature_columns): """Extract feature importance from different model types""" try: if hasattr(model, 'feature_importances_'): return dict(zip(feature_columns, model.feature_importances_, strict=True)) elif hasattr(model, 'coef_'): # For linear models like LogisticRegression importance = abs(model.coef_[0]) return dict(zip(feature_columns, importance, strict=True)) else: return {} except Exception: return {} def predict_entry_probability(self, df: pd.DataFrame, pair: str) -> pd.Series: """Predict probability of profitable entry using advanced ensemble models""" if not SKLEARN_AVAILABLE or pair not in self.is_trained or not self.is_trained[pair]: return pd.Series(0.5, index=df.index) try: feature_df = self.extract_advanced_features(df) # Get feature columns used in training if pair in self.feature_importance and 'random_forest' in self.feature_importance[pair]: feature_columns = list(self.feature_importance[pair]['random_forest']['feature_importance'].keys()) else: # Fallback: use all available numeric columns exclude_cols = ['open', 'high', 'low', 'close', 'volume', 'date', 'enter_long', 'enter_short', 'exit_long', 'exit_short'] feature_columns = [col for col in feature_df.columns if col not in exclude_cols and feature_df[col].dtype in ['float64', 'int64']] X = feature_df[feature_columns].fillna(0) X_scaled = self.scalers[pair].transform(X) # Get predictions from all available models model_predictions = {} model_weights = {} for model_name, model in self.models[pair].items(): try: if hasattr(model, 'predict_proba'): prob = model.predict_proba(X_scaled)[:, 1] else: # For models without probability prediction pred = model.predict(X_scaled) prob = (pred + 1) / 2 # Convert -1,1 to 0,1 or similar normalization model_predictions[model_name] = prob # Weight based on model performance (F1 score or accuracy) if pair in self.feature_importance and model_name in self.feature_importance[pair]: performance_metrics = self.feature_importance[pair][model_name] # Use F1 score if available, otherwise accuracy weight = performance_metrics.get('f1_score', performance_metrics.get('accuracy', 0.5)) else: weight = 0.5 model_weights[model_name] = max(weight, 0.1) # Minimum weight of 0.1 except Exception as e: logger.warning(f"Failed to get predictions from {model_name} for {pair}: {e}") continue if not model_predictions: return pd.Series(0.5, index=df.index) # === ADVANCED ENSEMBLE PREDICTION === # Method 1: Weighted average by performance total_weight = sum(model_weights.values()) if total_weight > 0: weighted_avg = np.zeros(len(X)) for model_name, predictions in model_predictions.items(): weight = model_weights[model_name] / total_weight weighted_avg += predictions * weight else: weighted_avg = np.mean(list(model_predictions.values()), axis=0) # Method 2: Voting ensemble (if available) if 'voting_ensemble' in model_predictions: ensemble_pred = model_predictions['voting_ensemble'] # Combine weighted average with voting ensemble final_prediction = 0.6 * ensemble_pred + 0.4 * weighted_avg else: final_prediction = weighted_avg # Method 3: Dynamic model selection based on market conditions if hasattr(self, '_detect_market_regime'): market_regime = self._detect_market_condition(df) # Adjust predictions based on market conditions if market_regime == 'trending': # In trending markets, prefer gradient boosting if 'gradient_boosting' in model_predictions: final_prediction = (0.5 * final_prediction + 0.5 * model_predictions['gradient_boosting']) elif market_regime == 'volatile': # In volatile markets, prefer random forest if 'random_forest' in model_predictions: final_prediction = (0.5 * final_prediction + 0.5 * model_predictions['random_forest']) elif market_regime == 'ranging': # In ranging markets, prefer SVM or logistic regression if 'svm' in model_predictions: final_prediction = (0.6 * final_prediction + 0.4 * model_predictions['svm']) elif 'logistic_regression' in model_predictions: final_prediction = (0.6 * final_prediction + 0.4 * model_predictions['logistic_regression']) # === CONFIDENCE ADJUSTMENT === # Calculate prediction confidence based on model agreement if len(model_predictions) > 1: predictions_array = np.array(list(model_predictions.values())) prediction_std = np.std(predictions_array, axis=0) # Higher standard deviation = lower confidence confidence_factor = 1 - np.clip(prediction_std * 2, 0, 0.3) # Adjust predictions toward neutral when confidence is low final_prediction = (final_prediction * confidence_factor + 0.5 * (1 - confidence_factor)) # === OUTLIER DETECTION AND SMOOTHING === # Apply rolling smoothing to reduce noise result_series = pd.Series(final_prediction, index=df.index) smoothed_result = result_series.rolling(window=3, center=True).mean() smoothed_result = smoothed_result.fillna(result_series) # Ensure values are in valid range [0, 1] smoothed_result = smoothed_result.clip(0, 1) return smoothed_result except Exception as e: logger.warning(f"Advanced prediction failed for {pair}: {e}") return pd.Series(0.5, index=df.index) def _detect_market_condition(self, df: pd.DataFrame) -> str: """Detect current market condition for dynamic model selection""" try: # Simple market regime detection recent_data = df.tail(20) if len(recent_data) < 10: return 'unknown' # Calculate volatility returns = recent_data['close'].pct_change().dropna() volatility = returns.std() # Calculate trend strength price_change = (recent_data['close'].iloc[-1] - recent_data['close'].iloc[0]) / recent_data['close'].iloc[0] if volatility > 0.03: # High volatility threshold return 'volatile' elif abs(price_change) > 0.05: # Strong trend threshold return 'trending' else: return 'ranging' except Exception: return 'unknown' # Initialize the predictive engine globally predictive_engine = AdvancedPredictiveEngine() def calculate_advanced_predictive_signals(dataframe: pd.DataFrame, pair: str) -> pd.DataFrame: """Main function to calculate advanced predictive signals with enhanced models. Training logic: 1. If assets missing -> train immediately 2. If assets exist but 48h+ passed since strategy start -> retrain 3. Otherwise skip training """ try: need_training = False assets_exist = False try: assets_exist = predictive_engine._assets_exist(pair) except Exception: assets_exist = False # Check time since strategy startup now_utc = datetime.utcnow() hours_since_startup = (now_utc - predictive_engine.strategy_start_time).total_seconds() / 3600.0 if not assets_exist: # Missing assets -> train immediately if len(dataframe) >= 200: need_training = True elif (predictive_engine.enable_startup_retrain and hours_since_startup >= predictive_engine.retrain_after_startup_hours): # Assets exist but 48h+ passed since startup -> retrain if len(dataframe) >= 200: need_training = True logger.info(f"[ML] Triggering 48h startup retrain for {pair} " f"(startup+{hours_since_startup:.1f}h)") if need_training: logger.info(f"[ML] Training models for {pair} (len={len(dataframe)}) | assets_exist={assets_exist}") training_result = predictive_engine.train_predictive_models(dataframe, pair) status = training_result.get('status') if status == 'success': best_model = training_result.get('best_model', 'unknown') best_f1 = training_result.get('best_f1_score', 0) n_models = training_result.get('n_models', 0) logger.info(f"🚀 {pair} ML Training Success: {n_models} models (Best {best_model} F1={best_f1:.3f})") elif status == 'skipped_assets_exist': logger.info(f"[ML] Skip training (assets present) {pair}") else: logger.warning(f"❌ ML training status {pair}: {status}") # Enhanced ML probability prediction dataframe['ml_entry_probability'] = predictive_engine.predict_entry_probability(dataframe, pair) # Get momentum and volatility regime safely momentum_regime = dataframe.get('momentum_regime') volatility_regime = dataframe.get('volatility_regime') quantum_coherence = dataframe.get('quantum_momentum_coherence') neural_pattern = dataframe.get('neural_pattern_score') # Advanced confidence scoring with safe comparisons ml_high_conf_conditions = (dataframe['ml_entry_probability'] > 0.8) if momentum_regime is not None: ml_high_conf_conditions &= (momentum_regime > 0) if volatility_regime is not None: ml_high_conf_conditions &= (volatility_regime < 2) dataframe['ml_high_confidence'] = ml_high_conf_conditions.astype(int) # Ultra-high confidence entries with safe checks ml_ultra_conf_conditions = (dataframe['ml_entry_probability'] > 0.9) if quantum_coherence is not None: ml_ultra_conf_conditions &= (quantum_coherence > 0.7) else: # Use fallback threshold if quantum analysis not available ml_ultra_conf_conditions &= (dataframe['ml_entry_probability'] > 0.92) if neural_pattern is not None: ml_ultra_conf_conditions &= (neural_pattern > 0.8) else: # Use fallback threshold if neural analysis not available ml_ultra_conf_conditions &= (dataframe['ml_entry_probability'] > 0.93) dataframe['ml_ultra_confidence'] = ml_ultra_conf_conditions.astype(int) # Enhanced score combination if 'ultimate_score' in dataframe.columns: # Dynamic weighting based on model performance - fix Series comparison ml_volatility = dataframe['ml_entry_probability'].rolling(20).std().fillna(0.3) ml_weight = ml_volatility.clip(upper=0.5) # Safe way to apply min with Series traditional_weight = 1 - ml_weight dataframe['ml_enhanced_score'] = ( dataframe['ultimate_score'] * traditional_weight + dataframe['ml_entry_probability'] * ml_weight ) else: dataframe['ml_enhanced_score'] = dataframe['ml_entry_probability'] # Model agreement indicator - fix Series std calculation if pair in predictive_engine.models and len(predictive_engine.models[pair]) > 1: # Calculate prediction variance as agreement measure prob_std = dataframe['ml_entry_probability'].rolling(5).std().fillna(0) dataframe['ml_model_agreement'] = (1 - prob_std).clip(lower=0, upper=1) else: dataframe['ml_model_agreement'] = 0.8 # Default agreement return dataframe except Exception as e: logger.warning(f"Advanced predictive analysis failed for {pair}: {e}") dataframe['ml_entry_probability'] = 0.5 dataframe['ml_enhanced_score'] = dataframe.get('ultimate_score', 0.5) dataframe['ml_high_confidence'] = 0 dataframe['ml_ultra_confidence'] = 0 dataframe['ml_model_agreement'] = 0.5 return dataframe def calculate_quantum_momentum_analysis(dataframe: pd.DataFrame) -> pd.DataFrame: """Quantum-inspired momentum analysis for ultra-precise predictions""" try: momentum_periods = [3, 5, 8, 13, 21, 34] momentum_matrix = pd.DataFrame() for period in momentum_periods: momentum_matrix[f'mom_{period}'] = dataframe['close'].pct_change(period) dataframe['quantum_momentum_coherence'] = ( momentum_matrix.std(axis=1) / (momentum_matrix.mean(axis=1).abs() + 1e-10) ) # Calculate momentum entanglement using correlation matrix def calculate_entanglement(window_data): if len(window_data) < 10: return 0 try: corr_matrix = window_data.corr() if corr_matrix.empty or corr_matrix.isna().all().all(): return 0 # Get upper triangular correlation values (excluding diagonal) upper_tri_indices = np.triu_indices_from(corr_matrix, k=1) correlations = corr_matrix.values[upper_tri_indices] # Remove NaN values and calculate mean valid_correlations = correlations[~np.isnan(correlations)] return valid_correlations.mean() if len(valid_correlations) > 0 else 0 except Exception: return 0 entanglement_values = [] for i in range(len(momentum_matrix)): if i < 20: entanglement_values.append(0.5) else: window_data = momentum_matrix.iloc[i-19:i+1] entanglement = calculate_entanglement(window_data) entanglement_values.append(entanglement) dataframe['momentum_entanglement'] = pd.Series(entanglement_values, index=dataframe.index) price_uncertainty = dataframe['close'].rolling(20).std() momentum_uncertainty = momentum_matrix['mom_8'].rolling(20).std() dataframe['heisenberg_uncertainty'] = price_uncertainty * momentum_uncertainty if 'maxima_sort_threshold' in dataframe.columns: resistance_distance = ( dataframe['maxima_sort_threshold'] - dataframe['close']) / dataframe['close'] dataframe['quantum_tunnel_up_prob'] = np.exp(-resistance_distance.abs() * 10) else: dataframe['quantum_tunnel_up_prob'] = 0.5 if 'minima_sort_threshold' in dataframe.columns: support_distance = ( dataframe['close'] - dataframe['minima_sort_threshold']) / dataframe['close'] dataframe['quantum_tunnel_down_prob'] = np.exp(-support_distance.abs() * 10) else: dataframe['quantum_tunnel_down_prob'] = 0.5 return dataframe except Exception as e: logger.warning(f"Quantum momentum analysis failed: {e}") dataframe['quantum_momentum_coherence'] = 0.5 dataframe['momentum_entanglement'] = 0.5 dataframe['heisenberg_uncertainty'] = 1.0 dataframe['quantum_tunnel_up_prob'] = 0.5 dataframe['quantum_tunnel_down_prob'] = 0.5 return dataframe def calculate_neural_pattern_recognition(dataframe: pd.DataFrame) -> pd.DataFrame: """Neural pattern recognition for complex market patterns""" try: dataframe['body_size'] = abs(dataframe['close'] - dataframe['open']) / dataframe['close'] dataframe['upper_shadow'] = ( dataframe['high'] - np.maximum(dataframe['open'], dataframe['close']) ) / dataframe['close'] dataframe['lower_shadow'] = ( np.minimum(dataframe['open'], dataframe['close']) - dataframe['low'] ) / dataframe['close'] dataframe['candle_range'] = (dataframe['high'] - dataframe['low']) / dataframe['close'] pattern_memory = [] for i in range(len(dataframe)): if i < 5: pattern_memory.append(0) continue recent_patterns = dataframe[['body_size', 'upper_shadow', 'lower_shadow']].iloc[i-4:i+1] pattern_signature = recent_patterns.values.flatten() pattern_norm = np.linalg.norm(pattern_signature) if pattern_norm > 0: pattern_score = min(1.0, pattern_norm / 0.1) else: pattern_score = 0 pattern_memory.append(pattern_score) dataframe['neural_pattern_score'] = pd.Series(pattern_memory, index=dataframe.index) dataframe['pattern_prediction_confidence'] = dataframe['neural_pattern_score'].rolling(10).std() return dataframe except Exception as e: logger.warning(f"Neural pattern recognition failed: {e}") dataframe['neural_pattern_score'] = 0.5 dataframe['pattern_prediction_confidence'] = 0.5 dataframe['body_size'] = 0.01 dataframe['candle_range'] = 0.02 return dataframe def calculate_exit_signals(dataframe: pd.DataFrame) -> pd.DataFrame: """Calculate advanced exit signals based on market deterioration""" # === MOMENTUM DETERIORATION === dataframe['momentum_deteriorating'] = ( (dataframe['momentum_quality'] < dataframe['momentum_quality'].shift(1)) & (dataframe['momentum_acceleration'] < 0) & (dataframe['price_momentum'] < dataframe['price_momentum'].shift(1)) ).astype(int) # === VOLUME DETERIORATION === dataframe['volume_deteriorating'] = ( (dataframe['volume_strength'] < 0.8) & (dataframe['selling_pressure'] > dataframe['buying_pressure']) & (dataframe['volume_pressure'] < 0) ).astype(int) # === STRUCTURE DETERIORATION === dataframe['structure_deteriorating'] = ( (dataframe['structure_score'] < -1) & (dataframe['bearish_structure'] > dataframe['bullish_structure']) & (dataframe['structure_break_down'] == 1) ).astype(int) # === CONFLUENCE BREAKDOWN === dataframe['confluence_breakdown'] = ( (dataframe['confluence_score'] < 2) & (dataframe['near_resistance'] == 1) & (dataframe['volume_spike'] == 0) ).astype(int) # === TREND WEAKNESS === dataframe['trend_weakening'] = ( (dataframe['trend_strength'] < 0) & (dataframe['close'] < dataframe['ema50']) & (dataframe['strong_downtrend'] == 1) ).astype(int) # === ULTIMATE EXIT SCORE === dataframe['exit_pressure'] = ( dataframe['momentum_deteriorating'] * 2 + dataframe['volume_deteriorating'] * 2 + dataframe['structure_deteriorating'] * 2 + dataframe['confluence_breakdown'] * 1 + dataframe['trend_weakening'] * 1 ) # === RSI OVERBOUGHT WITH DIVERGENCE === dataframe['rsi_exit_signal'] = ( (dataframe['rsi'] > 75) & ( (dataframe['rsi_divergence_bear'] == 1) | (dataframe['rsi'] > dataframe['rsi'].shift(1)) & (dataframe['close'] < dataframe['close'].shift(1)) ) ).astype(int) # === PROFIT TAKING LEVELS === mml_resistance_levels = ['[6/8]P', '[8/8]P'] dataframe['near_resistance_level'] = 0 for level in mml_resistance_levels: if level in dataframe.columns: near_level = ( (dataframe['close'] >= dataframe[level] * 0.99) & (dataframe['close'] <= dataframe[level] * 1.02) ).astype(int) dataframe['near_resistance_level'] += near_level # === VOLATILITY SPIKE EXIT === dataframe['volatility_spike'] = ( dataframe['atr'] > dataframe['atr'].rolling(20).mean() * 1.5 ).astype(int) # === EXHAUSTION SIGNALS === dataframe['bullish_exhaustion'] = ( (dataframe['consecutive_green'] >= 4) & (dataframe['rsi'] > 70) & (dataframe['volume'] < dataframe['avg_volume'] * 0.8) & (dataframe['momentum_acceleration'] < 0) ).astype(int) return dataframe def calculate_dynamic_profit_targets(dataframe: pd.DataFrame, entry_type_col: str = 'entry_type') -> pd.DataFrame: """Calculate dynamic profit targets based on entry quality and market conditions""" # Base profit targets based on ATR dataframe['base_profit_target'] = dataframe['atr'] * 2 # Adjust based on entry type dataframe['profit_multiplier'] = 1.0 if entry_type_col in dataframe.columns: dataframe.loc[dataframe[entry_type_col] == 3, 'profit_multiplier'] = 2.0 # High quality dataframe.loc[dataframe[entry_type_col] == 2, 'profit_multiplier'] = 1.5 # Medium quality dataframe.loc[dataframe[entry_type_col] == 1, 'profit_multiplier'] = 1.2 # Backup dataframe.loc[dataframe[entry_type_col] == 4, 'profit_multiplier'] = 2.5 # Breakout dataframe.loc[dataframe[entry_type_col] == 5, 'profit_multiplier'] = 1.8 # Reversal # Final profit target dataframe['dynamic_profit_target'] = dataframe['base_profit_target'] * dataframe['profit_multiplier'] return dataframe def calculate_advanced_stop_loss(dataframe: pd.DataFrame) -> pd.DataFrame: dataframe['base_stop_loss'] = dataframe['atr'] * 1.5 if 'minima_sort_threshold' in dataframe.columns: dataframe['support_stop_loss'] = dataframe['close'] - dataframe['minima_sort_threshold'] dataframe['support_stop_loss'] = dataframe['support_stop_loss'].clip( dataframe['base_stop_loss'] * 0.5, dataframe['base_stop_loss'] * 1.5 # Reduced from 2.0 ) dataframe['final_stop_loss'] = np.minimum( dataframe['base_stop_loss'], dataframe['support_stop_loss'] ).clip(-0.15, -0.01) # Hard cap at -15% else: dataframe['final_stop_loss'] = dataframe['base_stop_loss'].clip(-0.15, -0.01) return dataframe def calculate_confluence_score(dataframe: pd.DataFrame) -> pd.DataFrame: """Multi-factor confluence analysis - much better than BTC correlation""" # Support/Resistance Confluence dataframe['near_support'] = ( (dataframe['close'] <= dataframe['minima_sort_threshold'] * 1.02) & (dataframe['close'] >= dataframe['minima_sort_threshold'] * 0.98) ).astype(int) dataframe['near_resistance'] = ( (dataframe['close'] <= dataframe['maxima_sort_threshold'] * 1.02) & (dataframe['close'] >= dataframe['maxima_sort_threshold'] * 0.98) ).astype(int) # MML Level Confluence mml_levels = ['[0/8]P', '[2/8]P', '[4/8]P', '[6/8]P', '[8/8]P'] dataframe['near_mml'] = 0 for level in mml_levels: if level in dataframe.columns: near_level = ( (dataframe['close'] <= dataframe[level] * 1.015) & (dataframe['close'] >= dataframe[level] * 0.985) ).astype(int) dataframe['near_mml'] += near_level # Volume Confluence dataframe['volume_spike'] = ( dataframe['volume'] > dataframe['avg_volume'] * 1.5 ).astype(int) # RSI Confluence Zones dataframe['rsi_oversold'] = (dataframe['rsi'] < 30).astype(int) dataframe['rsi_overbought'] = (dataframe['rsi'] > 70).astype(int) dataframe['rsi_neutral'] = ( (dataframe['rsi'] >= 40) & (dataframe['rsi'] <= 60) ).astype(int) # EMA Confluence dataframe['above_ema'] = (dataframe['close'] > dataframe['ema50']).astype(int) # CONFLUENCE SCORE (0-6) dataframe['confluence_score'] = ( dataframe['near_support'] + dataframe['near_mml'].clip(0, 2) + # Max 2 points for MML dataframe['volume_spike'] + dataframe['rsi_oversold'] + dataframe['above_ema'] + (dataframe['trend_strength'] > 0.01).astype(int) # Positive trend ) return dataframe def calculate_smart_volume(dataframe: pd.DataFrame) -> pd.DataFrame: """Advanced volume analysis - beats any external correlation""" # Volume-Price Trend (VPT) price_change_pct = (dataframe['close'] - dataframe['close'].shift(1)) / dataframe['close'].shift(1) dataframe['vpt'] = (dataframe['volume'] * price_change_pct).fillna(0).cumsum() # Volume moving averages dataframe['volume_sma20'] = dataframe['volume'].rolling(20).mean() dataframe['volume_sma50'] = dataframe['volume'].rolling(50).mean() # Volume strength dataframe['volume_strength'] = dataframe['volume'] / dataframe['volume_sma20'] # Smart money indicators dataframe['accumulation'] = ( (dataframe['close'] > dataframe['open']) & # Green candle (dataframe['volume'] > dataframe['volume_sma20'] * 1.2) & # High volume (dataframe['close'] > (dataframe['high'] + dataframe['low']) / 2) # Close in upper half ).astype(int) dataframe['distribution'] = ( (dataframe['close'] < dataframe['open']) & # Red candle (dataframe['volume'] > dataframe['volume_sma20'] * 1.2) & # High volume (dataframe['close'] < (dataframe['high'] + dataframe['low']) / 2) # Close in lower half ).astype(int) # Buying/Selling pressure dataframe['buying_pressure'] = dataframe['accumulation'].rolling(5).sum() dataframe['selling_pressure'] = dataframe['distribution'].rolling(5).sum() # Net volume pressure dataframe['volume_pressure'] = dataframe['buying_pressure'] - dataframe['selling_pressure'] # Volume trend dataframe['volume_trend'] = ( dataframe['volume_sma20'] > dataframe['volume_sma50'] ).astype(int) # Money flow typical_price = (dataframe['high'] + dataframe['low'] + dataframe['close']) / 3 money_flow = typical_price * dataframe['volume'] positive_flow = money_flow.where(typical_price > typical_price.shift(1), 0) negative_flow = money_flow.where(typical_price < typical_price.shift(1), 0) positive_flow_sum = positive_flow.rolling(14).sum() negative_flow_sum = negative_flow.rolling(14).sum() dataframe['money_flow_ratio'] = positive_flow_sum / (negative_flow_sum + 1e-10) dataframe['money_flow_index'] = 100 - (100 / (1 + dataframe['money_flow_ratio'])) return dataframe def calculate_advanced_momentum(dataframe: pd.DataFrame) -> pd.DataFrame: """Multi-timeframe momentum system - superior to BTC correlation""" # Multi-timeframe momentum dataframe['momentum_3'] = dataframe['close'].pct_change(6) dataframe['momentum_7'] = dataframe['close'].pct_change(14) dataframe['momentum_14'] = dataframe['close'].pct_change(28) dataframe['momentum_21'] = dataframe['close'].pct_change(21) # Momentum acceleration dataframe['momentum_acceleration'] = ( dataframe['momentum_3'] - dataframe['momentum_3'].shift(3) ) # Momentum consistency dataframe['momentum_consistency'] = ( (dataframe['momentum_3'] > 0).astype(int) + (dataframe['momentum_7'] > 0).astype(int) + (dataframe['momentum_14'] > 0).astype(int) ) # Momentum divergence with volume dataframe['price_momentum_rank'] = dataframe['momentum_7'].rolling(20).rank(pct=True) dataframe['volume_momentum_rank'] = dataframe['volume_strength'].rolling(20).rank(pct=True) dataframe['momentum_divergence'] = ( dataframe['price_momentum_rank'] - dataframe['volume_momentum_rank'] ).abs() # Momentum strength dataframe['momentum_strength'] = ( dataframe['momentum_3'].abs() + dataframe['momentum_7'].abs() + dataframe['momentum_14'].abs() ) / 3 # Momentum quality score (0-5) dataframe['momentum_quality'] = ( (dataframe['momentum_3'] > 0).astype(int) + (dataframe['momentum_7'] > 0).astype(int) + (dataframe['momentum_acceleration'] > 0).astype(int) + (dataframe['volume_strength'] > 1.1).astype(int) + (dataframe['momentum_divergence'] < 0.3).astype(int) ) # Rate of Change dataframe['roc_5'] = dataframe['close'].pct_change(5) * 100 dataframe['roc_10'] = dataframe['close'].pct_change(10) * 100 dataframe['roc_20'] = dataframe['close'].pct_change(20) * 100 # Momentum oscillator dataframe['momentum_oscillator'] = ( dataframe['roc_5'] + dataframe['roc_10'] + dataframe['roc_20'] ) / 3 return dataframe def calculate_market_structure(dataframe: pd.DataFrame) -> pd.DataFrame: """Market structure analysis - intrinsic trend recognition""" # Higher highs, higher lows detection dataframe['higher_high'] = ( (dataframe['high'] > dataframe['high'].shift(1)) & (dataframe['high'].shift(1) > dataframe['high'].shift(2)) ).astype(int) dataframe['higher_low'] = ( (dataframe['low'] > dataframe['low'].shift(1)) & (dataframe['low'].shift(1) > dataframe['low'].shift(2)) ).astype(int) dataframe['lower_high'] = ( (dataframe['high'] < dataframe['high'].shift(1)) & (dataframe['high'].shift(1) < dataframe['high'].shift(2)) ).astype(int) dataframe['lower_low'] = ( (dataframe['low'] < dataframe['low'].shift(1)) & (dataframe['low'].shift(1) < dataframe['low'].shift(2)) ).astype(int) # Market structure scores dataframe['bullish_structure'] = ( dataframe['higher_high'].rolling(5).sum() + dataframe['higher_low'].rolling(5).sum() ) dataframe['bearish_structure'] = ( dataframe['lower_high'].rolling(5).sum() + dataframe['lower_low'].rolling(5).sum() ) dataframe['structure_score'] = ( dataframe['bullish_structure'] - dataframe['bearish_structure'] ) # Swing highs and lows # Live-safe pivot detection without using future candles (no shift(-1)) # Confirm swing at the PREVIOUS candle using only information up to current bar. # A swing high at t-1 is when high[t-1] > high[t-2] and high[t-1] > high[t]. prev_high = dataframe['high'].shift(1) prev_low = dataframe['low'].shift(1) dataframe['swing_high'] = ( (prev_high > dataframe['high'].shift(2)) & (prev_high > dataframe['high']) ).astype(int) # A swing low at t-1 is when low[t-1] < low[t-2] and low[t-1] < low[t]. dataframe['swing_low'] = ( (prev_low < dataframe['low'].shift(2)) & (prev_low < dataframe['low']) ).astype(int) # Market structure breaks # Use previous candle values where the swing was confirmed to avoid lookahead bias swing_highs = prev_high.where(dataframe['swing_high'] == 1) swing_lows = prev_low.where(dataframe['swing_low'] == 1) # Structure break detection dataframe['structure_break_up'] = ( dataframe['close'] > swing_highs.ffill() ).astype(int) dataframe['structure_break_down'] = ( dataframe['close'] < swing_lows.ffill() ).astype(int) # Trend strength based on structure dataframe['structure_trend_strength'] = ( dataframe['structure_score'] / 10 # Normalize ).clip(-1, 1) # Support and resistance strength dataframe['support_strength'] = dataframe['swing_low'].rolling(20).sum() dataframe['resistance_strength'] = dataframe['swing_high'].rolling(20).sum() return dataframe def calculate_advanced_entry_signals(dataframe: pd.DataFrame) -> pd.DataFrame: """Advanced entry signal generation""" # Multi-factor signal strength dataframe['signal_strength'] = 0 # Confluence signals dataframe['confluence_signal'] = (dataframe['confluence_score'] >= 3).astype(int) dataframe['signal_strength'] += dataframe['confluence_signal'] * 2 # Volume signals dataframe['volume_signal'] = ( (dataframe['volume_pressure'] >= 2) & (dataframe['volume_strength'] > 1.2) ).astype(int) dataframe['signal_strength'] += dataframe['volume_signal'] * 2 # Momentum signals dataframe['momentum_signal'] = ( (dataframe['momentum_quality'] >= 3) & (dataframe['momentum_acceleration'] > 0) ).astype(int) dataframe['signal_strength'] += dataframe['momentum_signal'] * 2 # Structure signals dataframe['structure_signal'] = ( (dataframe['structure_score'] > 0) & (dataframe['structure_break_up'] == 1) ).astype(int) dataframe['signal_strength'] += dataframe['structure_signal'] * 1 # RSI position signal dataframe['rsi_signal'] = ( (dataframe['rsi'] > 30) & (dataframe['rsi'] < 70) ).astype(int) dataframe['signal_strength'] += dataframe['rsi_signal'] * 1 # Trend alignment signal dataframe['trend_signal'] = ( (dataframe['close'] > dataframe['ema50']) & (dataframe['trend_strength'] > 0) ).astype(int) dataframe['signal_strength'] += dataframe['trend_signal'] * 1 # Money flow signal dataframe['money_flow_signal'] = ( dataframe['money_flow_index'] > 50 ).astype(int) dataframe['signal_strength'] += dataframe['money_flow_signal'] * 1 return dataframe class AlexNexusForgeV8AIV2(IStrategy): # General strategy parameters timeframe = "1h" startup_candle_count: int = 1000 stoploss = -0.11 trailing_stop = True trailing_stop_positive = 0.005 # Trail at 0.5% below peak profit trailing_stop_positive_offset = 0.03 # Start trailing only at 3% profit trailing_only_offset_is_reached = True # Ensure trailing only starts after offset is reached use_custom_stoploss = True can_short = True use_exit_signal = True ignore_roi_if_entry_signal = False # CHANGED: Allow ROI to work even with entry signals process_only_new_candles = False use_custom_exits_advanced = True use_emergency_exits = True regime_change_enabled = BooleanParameter(default=True, space="sell", optimize=True, load=True) regime_change_sensitivity = DecimalParameter(0.3, 0.8, default=0.5, decimals=2, space="sell", optimize=True, load=True) # Flash Move Detection flash_move_enabled = BooleanParameter(default=True, space="sell", optimize=True, load=True) flash_move_threshold = DecimalParameter(0.03, 0.08, default=0.05, decimals=3, space="sell", optimize=True, load=True) flash_move_candles = IntParameter(3, 10, default=5, space="sell", optimize=True, load=True) # Volume Spike Detection volume_spike_enabled = BooleanParameter(default=True, space="sell", optimize=True, load=True) volume_spike_multiplier = DecimalParameter(2.0, 5.0, default=3.0, decimals=1, space="sell", optimize=True, load=True) # Emergency Exit Protection emergency_exit_enabled = BooleanParameter(default=True, space="sell", optimize=True, load=True) emergency_exit_profit_threshold = DecimalParameter(0.005, 0.03, default=0.015, decimals=3, space="sell", optimize=True, load=True) # Trailing Stop Exit Control (NEW: Fix for "Blocking trailing stop exit") trailing_exit_min_profit = DecimalParameter(-0.03, 0.02, default=-0.002, decimals=3, space="sell", optimize=True, load=True) allow_trailing_exit_when_negative = BooleanParameter(default=True, space="sell", optimize=False, load=True) # Market Sentiment Protection sentiment_protection_enabled = BooleanParameter(default=True, space="sell", optimize=True, load=True) sentiment_shift_threshold = DecimalParameter(0.2, 0.4, default=0.3, decimals=2, space="sell", optimize=True, load=True) # 🔧ATR STOPLOSS PARAMETERS (Anpassbar machen) atr_stoploss_multiplier = DecimalParameter(0.8, 2.0, default=1.0, decimals=1, space="sell", optimize=True, load=True) atr_stoploss_minimum = DecimalParameter(-0.25, -0.10, default=-0.12, decimals=2, space="sell", optimize=True, load=True) atr_stoploss_maximum = DecimalParameter(-0.30, -0.15, default=-0.18, decimals=2, space="sell", optimize=True, load=True) atr_stoploss_ceiling = DecimalParameter(-0.10, -0.06, default=-0.06, decimals=2, space="sell", optimize=True, load=True) # DCA parameters initial_safety_order_trigger = DecimalParameter( low=-0.02, high=-0.01, default=-0.018, decimals=3, space="buy", optimize=True, load=True ) max_safety_orders = IntParameter(1, 3, default=1, space="buy", optimize=True, load=True) safety_order_step_scale = DecimalParameter( low=1.05, high=1.5, default=1.25, decimals=2, space="buy", optimize=True, load=True ) safety_order_volume_scale = DecimalParameter( low=1.1, high=2.0, default=1.4, decimals=1, space="buy", optimize=True, load=True ) h2 = IntParameter(20, 60, default=40, space="buy", optimize=True, load=True) h1 = IntParameter(10, 40, default=20, space="buy", optimize=True, load=True) h0 = IntParameter(5, 20, default=10, space="buy", optimize=True, load=True) cp = IntParameter(5, 20, default=10, space="buy", optimize=True, load=True) # Entry parameters increment_for_unique_price = DecimalParameter( low=1.0005, high=1.002, default=1.001, decimals=4, space="buy", optimize=True, load=True ) last_entry_price: Optional[float] = None # Protection parameters cooldown_lookback = IntParameter(2, 48, default=1, space="protection", optimize=True) stop_duration = IntParameter(12, 200, default=4, space="protection", optimize=True) use_stop_protection = BooleanParameter(default=True, space="protection", optimize=True) # Murrey Math level parameters mml_const1 = DecimalParameter(1.0, 1.1, default=1.0699, decimals=4, space="buy", optimize=True, load=True) mml_const2 = DecimalParameter(0.99, 1.0, default=0.99875, decimals=5, space="buy", optimize=True, load=True) indicator_mml_window = IntParameter(32, 128, default=64, space="buy", optimize=True, load=True) # Dynamic Stoploss parameters # Add these parameters stoploss_atr_multiplier = DecimalParameter(1.0, 3.0, default=1.5, space="sell", optimize=True) stoploss_max_reasonable = DecimalParameter(-0.30, -0.15, default=-0.20, space="sell", optimize=True) # === Hyperopt Parameters === dominance_threshold = IntParameter(1, 10, default=3, space="buy", optimize=True) tightness_factor = DecimalParameter(0.5, 2.0, default=1.0, space="buy", optimize=True) long_rsi_threshold = IntParameter(50, 65, default=50, space="buy", optimize=True) short_rsi_threshold = IntParameter(30, 45, default=35, space="sell", optimize=True) # Dynamic Leverage parameters leverage_window_size = IntParameter(20, 100, default=70, space="buy", optimize=True, load=True) leverage_base = DecimalParameter(5.0, 20.0, default=5.0, decimals=1, space="buy", optimize=True, load=True) leverage_rsi_low = DecimalParameter(20.0, 40.0, default=30.0, decimals=1, space="buy", optimize=True, load=True) leverage_rsi_high = DecimalParameter(60.0, 80.0, default=70.0, decimals=1, space="buy", optimize=True, load=True) leverage_long_increase_factor = DecimalParameter(1.1, 2.0, default=1.5, decimals=1, space="buy", optimize=True, load=True) leverage_long_decrease_factor = DecimalParameter(0.3, 0.9, default=0.5, decimals=1, space="buy", optimize=True, load=True) leverage_volatility_decrease_factor = DecimalParameter(0.5, 0.95, default=0.8, decimals=2, space="buy", optimize=True, load=True) leverage_atr_threshold_pct = DecimalParameter(0.01, 0.05, default=0.03, decimals=3, space="buy", optimize=True, load=True) # Indicator parameters indicator_extrema_order = IntParameter(3, 15, default=8, space="buy", optimize=True, load=True) # War 5 indicator_mml_window = IntParameter(50, 200, default=50, space="buy", optimize=True, load=True) # War 50 indicator_rolling_window_threshold = IntParameter(20, 100, default=50, space="buy", optimize=True, load=True) # War 20 indicator_rolling_check_window = IntParameter(5, 20, default=10, space="buy", optimize=True, load=True) # War 5 # Market breadth parameters market_breadth_enabled = BooleanParameter(default=True, space="buy", optimize=True) market_breadth_threshold = DecimalParameter(0.3, 0.6, default=0.45, space="buy", optimize=True) # Total market cap parameters total_mcap_filter_enabled = BooleanParameter(default=True, space="buy", optimize=True) total_mcap_ma_period = IntParameter(20, 100, default=50, space="buy", optimize=True) # Market regime parameters regime_filter_enabled = BooleanParameter(default=True, space="buy", optimize=True) regime_lookback_period = IntParameter(24, 168, default=48, space="buy", optimize=True) # hours # Fear & Greed parameters fear_greed_enabled = BooleanParameter(default=False, space="buy", optimize=True) # Optional fear_greed_extreme_threshold = IntParameter(20, 30, default=25, space="buy", optimize=True) fear_greed_greed_threshold = IntParameter(70, 80, default=75, space="buy", optimize=True) # Momentum avoid_strong_trends = BooleanParameter(default=True, space="buy", optimize=True) trend_strength_threshold = DecimalParameter(0.01, 0.05, default=0.02, space="buy", optimize=True) momentum_confirmation_candles = IntParameter(1, 5, default=2, space="buy", optimize=True) # Dynamic exit based on entry quality dynamic_exit_enabled = BooleanParameter(default=True, space="sell", optimize=False, load=True) exit_on_confluence_loss = BooleanParameter(default=True, space="sell", optimize=False, load=True) exit_on_structure_break = BooleanParameter(default=True, space="sell", optimize=False, load=True) # Profit target multipliers based on entry type high_quality_profit_multiplier = DecimalParameter(1.2, 3.0, default=2.0, space="sell", optimize=True, load=True) medium_quality_profit_multiplier = DecimalParameter(1.0, 2.5, default=1.5, space="sell", optimize=True, load=True) backup_profit_multiplier = DecimalParameter(0.8, 2.0, default=1.2, space="sell", optimize=True, load=True) # Advanced exit thresholds volume_decline_exit_threshold = DecimalParameter(0.3, 0.8, default=0.5, space="sell", optimize=True, load=True) momentum_decline_exit_threshold = IntParameter(1, 4, default=2, space="sell", optimize=True, load=True) structure_deterioration_threshold = DecimalParameter(-3.0, 0.0, default=-1.5, space="sell", optimize=True, load=True) # RSI exit levels rsi_overbought_exit = IntParameter(70, 85, default=75, space="sell", optimize=True, load=True) rsi_divergence_exit_enabled = BooleanParameter(default=True, space="sell", optimize=False, load=True) # Trailing stop improvements use_advanced_trailing = BooleanParameter(default=False, space="sell", optimize=False, load=True) trailing_stop_positive_offset_high_quality = DecimalParameter(0.02, 0.08, default=0.04, space="sell", optimize=True, load=True) trailing_stop_positive_offset_medium_quality = DecimalParameter(0.015, 0.06, default=0.03, space="sell", optimize=True, load=True) # === NEUE ADVANCED PARAMETERS === # Confluence Analysis confluence_enabled = BooleanParameter(default=True, space="buy", optimize=False, load=True) confluence_threshold = DecimalParameter(2.0, 4.0, default=2.5, space="buy", optimize=True, load=True) # War 3.0 # Volume Analysis volume_analysis_enabled = BooleanParameter(default=True, space="buy", optimize=False, load=True) volume_strength_threshold = DecimalParameter(1.1, 2.0, default=1.3, space="buy", optimize=True, load=True) volume_pressure_threshold = IntParameter(1, 3, default=1, space="buy", optimize=True, load=True) # War 2 # Momentum Analysis momentum_analysis_enabled = BooleanParameter(default=True, space="buy", optimize=False, load=True) momentum_quality_threshold = IntParameter(2, 4, default=2, space="buy", optimize=True, load=True) # War 3 # Market Structure Analysis structure_analysis_enabled = BooleanParameter(default=True, space="buy", optimize=False, load=True) structure_score_threshold = DecimalParameter(-2.0, 5.0, default=0.5, space="buy", optimize=True, load=True) # Ultimate Score ultimate_score_threshold = DecimalParameter(0.5, 3.0, default=1.5, space="buy", optimize=True, load=True) # Advanced Entry Filters require_volume_confirmation = BooleanParameter(default=True, space="buy", optimize=False, load=True) require_momentum_confirmation = BooleanParameter(default=True, space="buy", optimize=False, load=True) require_structure_confirmation = BooleanParameter(default=True, space="buy", optimize=False, load=True) # ✅ Replace your old ROI with this: minimal_roi = { "0": 0.06, "5": 0.055, "10": 0.04, "20": 0.03, "40": 0.025, "80": 0.02, "160": 0.015, "320": 0.01 } # Plot configuration for backtesting UI plot_config = { "main_plot": { # Trend indicators "ema50": {"color": "gray", "type": "line"}, # Support/Resistance "minima_sort_threshold": {"color": "#4ae747", "type": "line"}, "maxima_sort_threshold": {"color": "#5b5e4b", "type": "line"}, }, "subplots": { "extrema_analysis": { "s_extrema": {"color": "#f53580", "type": "line"}, "maxima": {"color": "#a29db9", "type": "scatter"}, "minima": {"color": "#aac7fc", "type": "scatter"}, }, "murrey_math_levels": { "[4/8]P": {"color": "blue", "type": "line"}, # 50% MML "[6/8]P": {"color": "green", "type": "line"}, # 75% MML "[2/8]P": {"color": "orange", "type": "line"}, # 25% MML "[8/8]P": {"color": "red", "type": "line"}, # 100% MML "[0/8]P": {"color": "red", "type": "line"}, # 0% MML "mmlextreme_oscillator": {"color": "purple", "type": "line"}, }, "rsi_analysis": { "rsi": {"color": "purple", "type": "line"}, "rsi_divergence_bull": {"color": "green", "type": "scatter"}, "rsi_divergence_bear": {"color": "red", "type": "scatter"}, }, "confluence_analysis": { "confluence_score": {"color": "gold", "type": "line"}, "near_support": {"color": "green", "type": "scatter"}, "near_resistance": {"color": "red", "type": "scatter"}, "near_mml": {"color": "blue", "type": "line"}, "volume_spike": {"color": "orange", "type": "scatter"}, }, "volume_analysis": { "volume_strength": {"color": "cyan", "type": "line"}, "volume_pressure": {"color": "magenta", "type": "line"}, "buying_pressure": {"color": "green", "type": "line"}, "selling_pressure": {"color": "red", "type": "line"}, "money_flow_index": {"color": "yellow", "type": "line"}, }, "momentum_analysis": { "momentum_quality": {"color": "brown", "type": "line"}, "momentum_acceleration": {"color": "pink", "type": "line"}, "momentum_consistency": {"color": "lime", "type": "line"}, "momentum_oscillator": {"color": "navy", "type": "line"}, }, "structure_analysis": { "structure_score": {"color": "teal", "type": "line"}, "bullish_structure": {"color": "green", "type": "line"}, "bearish_structure": {"color": "red", "type": "line"}, "structure_break_up": {"color": "lime", "type": "scatter"}, "structure_break_down": {"color": "crimson", "type": "scatter"}, }, "trend_strength": { "trend_strength": {"color": "indigo", "type": "line"}, "trend_strength_5": {"color": "lightblue", "type": "line"}, "trend_strength_10": {"color": "mediumblue", "type": "line"}, "trend_strength_20": {"color": "darkblue", "type": "line"}, }, "ultimate_signals": { "ultimate_score": {"color": "gold", "type": "line"}, "signal_strength": {"color": "silver", "type": "line"}, "high_quality_setup": {"color": "lime", "type": "scatter"}, "entry_type": {"color": "white", "type": "line"}, }, "market_conditions": { "strong_uptrend": {"color": "green", "type": "scatter"}, "strong_downtrend": {"color": "red", "type": "scatter"}, "ranging": {"color": "yellow", "type": "scatter"}, "strong_up_momentum": {"color": "lime", "type": "scatter"}, "strong_down_momentum": {"color": "crimson", "type": "scatter"}, }, "di_analysis": { "DI_values": {"color": "orange", "type": "line"}, "DI_catch": {"color": "red", "type": "scatter"}, "plus_di": {"color": "green", "type": "line"}, "minus_di": {"color": "red", "type": "line"}, } }, } # Helper method to check if we have an active position in the opposite direction def has_active_trade(self, pair: str, side: str) -> bool: """ Check if there's an active trade in the specified direction """ try: trades = Trade.get_open_trades() for trade in trades: if trade.pair == pair: if side == "long" and not trade.is_short: return True elif side == "short" and trade.is_short: return True except Exception as e: logger.warning(f"Error checking active trades for {pair}: {e}") return False @staticmethod def _calculate_mml_core(mn: float, finalH: float, mx: float, finalL: float, mml_c1: float, mml_c2: float) -> Dict[str, float]: dmml_calc = ((finalH - finalL) / 8.0) * mml_c1 if dmml_calc == 0 or np.isinf(dmml_calc) or np.isnan(dmml_calc) or finalH == finalL: return {key: finalL for key in MML_LEVEL_NAMES} mml_val = (mx * mml_c2) + (dmml_calc * 3) if np.isinf(mml_val) or np.isnan(mml_val): return {key: finalL for key in MML_LEVEL_NAMES} ml = [mml_val - (dmml_calc * i) for i in range(16)] return { "[-3/8]P": ml[14], "[-2/8]P": ml[13], "[-1/8]P": ml[12], "[0/8]P": ml[11], "[1/8]P": ml[10], "[2/8]P": ml[9], "[3/8]P": ml[8], "[4/8]P": ml[7], "[5/8]P": ml[6], "[6/8]P": ml[5], "[7/8]P": ml[4], "[8/8]P": ml[3], "[+1/8]P": ml[2], "[+2/8]P": ml[1], "[+3/8]P": ml[0], } def calculate_rolling_murrey_math_levels_optimized(self, df: pd.DataFrame, window_size: int) -> Dict[str, pd.Series]: """ OPTIMIZED Version - Calculate MML levels every 5 candles using only past data """ murrey_levels_data: Dict[str, list] = {key: [np.nan] * len(df) for key in MML_LEVEL_NAMES} mml_c1 = self.mml_const1.value mml_c2 = self.mml_const2.value calculation_step = 5 for i in range(0, len(df), calculation_step): if i < window_size: continue # Use data up to the previous candle for the rolling window window_end = i - 1 window_start = window_end - window_size + 1 if window_start < 0: window_start = 0 window_data = df.iloc[window_start:window_end] mn_period = window_data["low"].min() mx_period = window_data["high"].max() current_close = df["close"].iloc[window_end] if window_end > 0 else df["close"].iloc[0] if pd.isna(mn_period) or pd.isna(mx_period) or mn_period == mx_period: for key in MML_LEVEL_NAMES: murrey_levels_data[key][window_end] = current_close continue levels = self._calculate_mml_core(mn_period, mx_period, mx_period, mn_period, mml_c1, mml_c2) for key in MML_LEVEL_NAMES: murrey_levels_data[key][window_end] = levels.get(key, current_close) # Interpolate using only past data up to each point for key in MML_LEVEL_NAMES: series = pd.Series(murrey_levels_data[key], index=df.index) # Interpolate forward only up to the current point, avoiding future data series = series.expanding().mean().ffill() # Use expanding mean as a safe alternative murrey_levels_data[key] = series.tolist() return {key: pd.Series(data, index=df.index) for key, data in murrey_levels_data.items()} def calculate_synthetic_market_breadth(self, dataframe: pd.DataFrame) -> pd.Series: """ Calculate synthetic market breadth using technical indicators Simulates market sentiment based on multiple factors """ try: # RSI component (30% weight) rsi_component = (dataframe['rsi'] - 50) / 50 # Normalize to -1 to 1 # Volume component (25% weight) volume_ma = dataframe['volume'].rolling(20).mean() volume_component = (dataframe['volume'] / volume_ma - 1).clip(-1, 1) # Momentum component (25% weight) momentum_3 = dataframe['close'].pct_change(3) momentum_component = np.tanh(momentum_3 * 100) # Smooth normalization # Volatility component (20% weight) - inverted (lower vol = higher breadth) atr_normalized = dataframe['atr'] / dataframe['close'] atr_ma = atr_normalized.rolling(20).mean() volatility_component = -(atr_normalized / atr_ma - 1).clip(-1, 1) # Combine components with weights synthetic_breadth = ( rsi_component * 0.30 + volume_component * 0.25 + momentum_component * 0.25 + volatility_component * 0.20 ) # Normalize to 0-1 range (market breadth percentage) synthetic_breadth = (synthetic_breadth + 1) / 2 # Smooth with rolling average synthetic_breadth = synthetic_breadth.rolling(3).mean() return synthetic_breadth.fillna(0.5) except Exception as e: logger.warning(f"Synthetic market breadth calculation failed: {e}") return pd.Series(0.5, index=dataframe.index) def calculate_trend_strength(self, df: pd.DataFrame) -> pd.DataFrame: """ Calculate trend strength to avoid entering against strong trends """ # Linear regression slope def calc_slope(series, period=10): """Calculate linear regression slope""" if len(series) < period: return 0 x = np.arange(period) y = series.iloc[-period:].values if np.isnan(y).any() or np.isinf(y).any(): return 0 slope = np.polyfit(x, y, 1)[0] return slope # Calculate trend strength using multiple timeframes df['slope_5'] = df['close'].rolling(5).apply( lambda x: calc_slope(x, 5), raw=False ) df['slope_10'] = df['close'].rolling(10).apply( lambda x: calc_slope(x, 10), raw=False ) df['slope_20'] = df['close'].rolling(20).apply( lambda x: calc_slope(x, 20), raw=False ) df['trend_strength_5'] = df['slope_5'] / df['close'] * 100 df['trend_strength_10'] = df['slope_10'] / df['close'] * 100 df['trend_strength_20'] = df['slope_20'] / df['close'] * 100 # Combined trend strength df['trend_strength'] = (df['trend_strength_5'] + df['trend_strength_10'] + df['trend_strength_20']) / 3 # Trend classification strong_threshold = 0.02 df['strong_uptrend'] = df['trend_strength'] > strong_threshold df['strong_downtrend'] = df['trend_strength'] < -strong_threshold df['ranging'] = df['trend_strength'].abs() < (strong_threshold * 0.5) return df @property def protections(self): prot = [{"method": "CooldownPeriod", "stop_duration_candles": self.cooldown_lookback.value}] if self.use_stop_protection.value: prot.append({ "method": "StoplossGuard", "lookback_period_candles": 72, "trade_limit": 2, "stop_duration_candles": self.stop_duration.value, "only_per_pair": False, }) return prot def informative_pairs(self): """ Define additional pairs for correlation analysis """ pairs = [] # Add BTC for correlation analysis (if not already trading BTC) if self.timeframe: pairs.append(("BTC/USDT:USDT", self.timeframe)) # Add major market indicators pairs.extend([ ("BTC/USDT:USDT", self.timeframe), ("ETH/USDT:USDT", self.timeframe), ("BNB/USDT:USDT", self.timeframe), ]) return pairs def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) if dataframe.empty or 'atr' not in dataframe.columns: return self.stoploss # Use strategy stoploss (-0.15) as fallback atr = dataframe["atr"].iat[-1] if pd.isna(atr) or atr <= 0: return self.stoploss # Fallback to -0.15 atr_percent = atr / current_rate # Profit-based multiplier adjustment if current_profit > 0.15: multiplier = 1.0 elif current_profit > 0.08: multiplier = 1.2 elif current_profit > 0.03: multiplier = 1.4 else: multiplier = 1.6 calculated_stoploss = -(atr_percent * multiplier * self.atr_stoploss_multiplier.value) # Initialize trailing_offset trailing_offset = 0.0 # Enhanced trailing logic with multiple profit levels if current_profit > 0.01: # Start trailing at 1% profit instead of 3% if current_profit > self.trailing_stop_positive_offset: # 0.03 (3% profit) # Full trailing at 3%+ profit trailing_offset = max(0, current_profit - self.trailing_stop_positive) # Trail 0.5% below peak elif current_profit > 0.02: # 2% profit # Moderate trailing at 2-3% profit trailing_offset = max(0, current_profit - 0.01) # Trail 1% below peak else: # 1-2% profit # Minimal trailing at 1-2% profit trailing_offset = max(0, current_profit - 0.015) # Trail 1.5% below peak # Apply trailing adjustment to calculated stoploss if trailing_offset > 0: calculated_stoploss = min(calculated_stoploss, -trailing_offset) # Trail up in profit final_stoploss = max( min(calculated_stoploss, self.atr_stoploss_ceiling.value), self.atr_stoploss_maximum.value ) logger.info(f"{pair} Custom SL: {final_stoploss:.3f} | ATR: {atr:.6f} | " f"Profit: {current_profit:.3f} | Trailing: {trailing_offset:.3f}") return final_stoploss def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, side: str, **kwargs) -> float: window_size = self.leverage_window_size.value dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) if len(dataframe) < window_size: logger.warning( f"{pair} Not enough data ({len(dataframe)} candles) to calculate dynamic leverage (requires {window_size}). Using proposed: {proposed_leverage}") return proposed_leverage close_prices_series = dataframe["close"].tail(window_size) high_prices_series = dataframe["high"].tail(window_size) low_prices_series = dataframe["low"].tail(window_size) base_leverage = self.leverage_base.value rsi_array = ta.RSI(close_prices_series, timeperiod=14) atr_array = ta.ATR(high_prices_series, low_prices_series, close_prices_series, timeperiod=14) sma_array = ta.SMA(close_prices_series, timeperiod=20) macd_output = ta.MACD(close_prices_series, fastperiod=12, slowperiod=26, signalperiod=9) current_rsi = rsi_array[-1] if rsi_array.size > 0 and not np.isnan(rsi_array[-1]) else 50.0 current_atr = atr_array[-1] if atr_array.size > 0 and not np.isnan(atr_array[-1]) else 0.0 current_sma = sma_array[-1] if sma_array.size > 0 and not np.isnan(sma_array[-1]) else current_rate current_macd_hist = 0.0 if isinstance(macd_output, pd.DataFrame): if not macd_output.empty and 'macdhist' in macd_output.columns: valid_macdhist_series = macd_output['macdhist'].dropna() if not valid_macdhist_series.empty: current_macd_hist = valid_macdhist_series.iloc[-1] # Apply rules based on indicators if side == "long": if current_rsi < self.leverage_rsi_low.value: base_leverage *= self.leverage_long_increase_factor.value elif current_rsi > self.leverage_rsi_high.value: base_leverage *= self.leverage_long_decrease_factor.value if current_atr > 0 and current_rate > 0: if (current_atr / current_rate) > self.leverage_atr_threshold_pct.value: base_leverage *= self.leverage_volatility_decrease_factor.value if current_macd_hist > 0: base_leverage *= self.leverage_long_increase_factor.value if current_sma > 0 and current_rate < current_sma: base_leverage *= self.leverage_long_decrease_factor.value adjusted_leverage = round(max(1.0, min(base_leverage, max_leverage)), 2) logger.info( f"{pair} Dynamic Leverage: {adjusted_leverage:.2f} (Base: {base_leverage:.2f}, RSI: {current_rsi:.2f}, " f"ATR: {current_atr:.4f}, MACD Hist: {current_macd_hist:.4f}, SMA: {current_sma:.4f})") return adjusted_leverage def populate_indicators(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame: """ ULTIMATE indicator calculations with advanced market analysis """ # === ML ASSET STARTUP CHECK === pair = metadata['pair'] if hasattr(self, 'predictive_engine') and self.predictive_engine is not None: # Mark pair as trained if assets exist (startup optimization) self.predictive_engine.mark_trained_if_assets(pair) # === EXTERNAL DATA INTEGRATION === try: # Add BTC data for correlation analysis using informative pairs if metadata['pair'] != 'BTC/USDT:USDT': btc_info = self.dp.get_pair_dataframe('BTC/USDT:USDT', self.timeframe) if not btc_info.empty and len(btc_info) >= len(dataframe): # Take only the last N rows to match our dataframe length btc_close_data = btc_info['close'].tail(len(dataframe)).reset_index(drop=True) dataframe['btc_close'] = btc_close_data.values logger.info(f"{metadata['pair']} BTC correlation data added successfully") else: # Fallback: use current pair data dataframe['btc_close'] = dataframe['close'] logger.warning(f"{metadata['pair']} BTC data unavailable, using pair data as fallback") else: # For BTC pairs, use own data dataframe['btc_close'] = dataframe['close'] except Exception as e: logger.warning(f"{metadata['pair']} BTC data integration failed: {e}") dataframe['btc_close'] = dataframe['close'] # Safe fallback # === STANDARD INDICATORS === dataframe["ema50"] = ta.EMA(dataframe["close"], timeperiod=50) dataframe["ema100"] = ta.EMA(dataframe["close"], timeperiod=100) # Neu hinzufügen dataframe["rsi"] = ta.RSI(dataframe["close"]) dataframe["atr"] = ta.ATR(dataframe["high"], dataframe["low"], dataframe["close"], timeperiod=10) # === SYNTHETIC MARKET BREADTH CALCULATION === try: # Calculate synthetic market breadth using multiple indicators # (after RSI and ATR are available) dataframe['market_breadth'] = self.calculate_synthetic_market_breadth(dataframe) logger.info(f"{metadata['pair']} Synthetic market breadth calculated") except Exception as e: logger.warning(f"{metadata['pair']} Market breadth calculation failed: {e}") dataframe['market_breadth'] = 0.5 # Neutral fallback dataframe["plus_di"] = ta.PLUS_DI(dataframe) dataframe["minus_di"] = ta.MINUS_DI(dataframe) dataframe["DI_values"] = dataframe["plus_di"] - dataframe["minus_di"] dataframe["DI_cutoff"] = 0 # === EXTREMA DETECTION === extrema_order = self.indicator_extrema_order.value dataframe["maxima"] = ( dataframe["close"] == dataframe["close"].shift(1).rolling(window=extrema_order).max() ).astype(int) dataframe["minima"] = ( dataframe["close"] == dataframe["close"].shift(1).rolling(window=extrema_order).min() ).astype(int) dataframe["s_extrema"] = 0 dataframe.loc[dataframe["minima"] == 1, "s_extrema"] = -1 dataframe.loc[dataframe["maxima"] == 1, "s_extrema"] = 1 # === HEIKIN-ASHI === dataframe["ha_close"] = (dataframe["open"] + dataframe["high"] + dataframe["low"] + dataframe["close"]) / 4 # === ROLLING EXTREMA === dataframe["minh2"], dataframe["maxh2"] = calculate_minima_maxima(dataframe, self.h2.value) dataframe["minh1"], dataframe["maxh1"] = calculate_minima_maxima(dataframe, self.h1.value) dataframe["minh0"], dataframe["maxh0"] = calculate_minima_maxima(dataframe, self.h0.value) dataframe["mincp"], dataframe["maxcp"] = calculate_minima_maxima(dataframe, self.cp.value) # === MURREY MATH LEVELS === mml_window = self.indicator_mml_window.value murrey_levels = self.calculate_rolling_murrey_math_levels_optimized(dataframe, window_size=mml_window) for level_name in MML_LEVEL_NAMES: if level_name in murrey_levels: dataframe[level_name] = murrey_levels[level_name] else: dataframe[level_name] = dataframe["close"] # === MML OSCILLATOR === mml_4_8 = dataframe.get("[4/8]P") mml_plus_3_8 = dataframe.get("[+3/8]P") mml_minus_3_8 = dataframe.get("[-3/8]P") if mml_4_8 is not None and mml_plus_3_8 is not None and mml_minus_3_8 is not None: osc_denominator = (mml_plus_3_8 - mml_minus_3_8).replace(0, np.nan) dataframe["mmlextreme_oscillator"] = 100 * ((dataframe["close"] - mml_4_8) / osc_denominator) else: dataframe["mmlextreme_oscillator"] = np.nan # === DI CATCH === dataframe["DI_catch"] = np.where(dataframe["DI_values"] > dataframe["DI_cutoff"], 0, 1) # === ROLLING THRESHOLDS === rolling_window_threshold = self.indicator_rolling_window_threshold.value dataframe["minima_sort_threshold"] = dataframe["close"].rolling( window=rolling_window_threshold, min_periods=1 ).min() dataframe["maxima_sort_threshold"] = dataframe["close"].rolling( window=rolling_window_threshold, min_periods=1 ).max() # === EXTREMA CHECKS === rolling_check_window = self.indicator_rolling_check_window.value dataframe["minima_check"] = ( dataframe["minima"].rolling(window=rolling_check_window, min_periods=1).sum() == 0 ).astype(int) dataframe["maxima_check"] = ( dataframe["maxima"].rolling(window=rolling_check_window, min_periods=1).sum() == 0 ).astype(int) # === VOLATILITY INDICATORS === dataframe["volatility_range"] = dataframe["high"] - dataframe["low"] dataframe["avg_volatility"] = dataframe["volatility_range"].rolling(window=50).mean() dataframe["avg_volume"] = dataframe["volume"].rolling(window=50).mean() # === TREND STRENGTH INDICATORS === # Use enhanced Wavelet+FFT method with fallback try: # Advanced wavelet & FFT method dataframe = calculate_advanced_trend_strength_with_wavelets(dataframe) # Use advanced trend strength as primary dataframe['trend_strength'] = dataframe['trend_strength_cycle_adjusted'] dataframe['strong_uptrend'] = dataframe['strong_uptrend_advanced'] dataframe['strong_downtrend'] = dataframe['strong_downtrend_advanced'] dataframe['ranging'] = dataframe['ranging_advanced'] logger.info(f"{metadata['pair']} Using advanced Wavelet+FFT trend analysis") except Exception as e: # Fallback to original enhanced method if advanced fails logger.warning( f"{metadata['pair']} Wavelet/FFT analysis failed: {e}. " "Using enhanced method." ) def calc_slope(series, period): """Enhanced slope calculation as fallback""" if len(series) < period: return 0 y = series.values[-period:] if np.isnan(y).any() or np.isinf(y).any(): return 0 if np.all(y == y[0]): return 0 x = np.linspace(0, period-1, period) try: coefficients = np.polyfit(x, y, 1) slope = coefficients[0] if np.isnan(slope) or np.isinf(slope): return 0 max_reasonable_slope = np.std(y) / period if abs(slope) > max_reasonable_slope * 10: return np.sign(slope) * max_reasonable_slope * 10 return slope except Exception: try: simple_slope = (y[-1] - y[0]) / (period - 1) return (simple_slope if not (np.isnan(simple_slope) or np.isinf(simple_slope)) else 0) except Exception: return 0 # Original slope calculations dataframe['slope_5'] = dataframe['close'].rolling(5).apply( lambda x: calc_slope(x, 5), raw=False ) dataframe['slope_10'] = dataframe['close'].rolling(10).apply( lambda x: calc_slope(x, 10), raw=False ) dataframe['slope_20'] = dataframe['close'].rolling(20).apply( lambda x: calc_slope(x, 20), raw=False ) dataframe['trend_strength_5'] = dataframe['slope_5'] / dataframe['close'] * 100 dataframe['trend_strength_10'] = dataframe['slope_10'] / dataframe['close'] * 100 dataframe['trend_strength_20'] = dataframe['slope_20'] / dataframe['close'] * 100 dataframe['trend_strength'] = ( dataframe['trend_strength_5'] + dataframe['trend_strength_10'] + dataframe['trend_strength_20'] ) / 3 strong_threshold = 0.02 dataframe['strong_uptrend'] = dataframe['trend_strength'] > strong_threshold dataframe['strong_downtrend'] = dataframe['trend_strength'] < -strong_threshold dataframe['ranging'] = dataframe['trend_strength'].abs() < (strong_threshold * 0.5) # === MOMENTUM INDICATORS === dataframe['price_momentum'] = dataframe['close'].pct_change(3) dataframe['momentum_increasing'] = ( dataframe['price_momentum'] > dataframe['price_momentum'].shift(1) ) dataframe['momentum_decreasing'] = ( dataframe['price_momentum'] < dataframe['price_momentum'].shift(1) ) dataframe['volume_momentum'] = ( dataframe['volume'].rolling(3).mean() / dataframe['volume'].rolling(20).mean() ) dataframe['rsi_divergence_bull'] = ( (dataframe['close'] < dataframe['close'].shift(5)) & (dataframe['rsi'] > dataframe['rsi'].shift(5)) ) dataframe['rsi_divergence_bear'] = ( (dataframe['close'] > dataframe['close'].shift(5)) & (dataframe['rsi'] < dataframe['rsi'].shift(5)) ) # === CANDLE PATTERNS === dataframe['green_candle'] = dataframe['close'] > dataframe['open'] dataframe['red_candle'] = dataframe['close'] < dataframe['open'] dataframe['consecutive_green'] = dataframe['green_candle'].rolling(3).sum() dataframe['consecutive_red'] = dataframe['red_candle'].rolling(3).sum() # Define strong_threshold for momentum calculations strong_threshold = 0.02 dataframe['strong_up_momentum'] = ( (dataframe['consecutive_green'] >= 3) & (dataframe['volume'] > dataframe['avg_volume']) & (dataframe['trend_strength'] > strong_threshold) ) dataframe['strong_down_momentum'] = ( (dataframe['consecutive_red'] >= 3) & (dataframe['volume'] > dataframe['avg_volume']) & (dataframe['trend_strength'] < -strong_threshold) ) # === ADVANCED ANALYSIS MODULES === # 1. CONFLUENCE ANALYSIS if self.confluence_enabled.value: dataframe = calculate_confluence_score(dataframe) else: dataframe['confluence_score'] = 0 # 2. SMART VOLUME ANALYSIS if self.volume_analysis_enabled.value: dataframe = calculate_smart_volume(dataframe) else: dataframe['volume_pressure'] = 0 dataframe['volume_strength'] = 1.0 dataframe['money_flow_index'] = 50 # 3. ADVANCED MOMENTUM if self.momentum_analysis_enabled.value: dataframe = calculate_advanced_momentum(dataframe) else: dataframe['momentum_quality'] = 0 dataframe['momentum_acceleration'] = 0 # 4. MARKET STRUCTURE if self.structure_analysis_enabled.value: dataframe = calculate_market_structure(dataframe) else: dataframe['structure_score'] = 0 dataframe['structure_break_up'] = 0 # 5. ADVANCED ENTRY SIGNALS dataframe = calculate_advanced_entry_signals(dataframe) # === ULTIMATE MARKET SCORE === dataframe['ultimate_score'] = ( dataframe['confluence_score'] * 0.25 + # 25% confluence dataframe['volume_pressure'] * 0.2 + # 20% volume pressure dataframe['momentum_quality'] * 0.2 + # 20% momentum quality (dataframe['structure_score'] / 5) * 0.15 + # 15% structure (normalized) (dataframe['signal_strength'] / 10) * 0.2 # 20% signal strength ) # Normalize ultimate score to 0-1 range dataframe['ultimate_score'] = dataframe['ultimate_score'].clip(0, 5) / 5 # === FINAL QUALITY CHECKS === dataframe['high_quality_setup'] = ( (dataframe['ultimate_score'] > self.ultimate_score_threshold.value) & (dataframe['signal_strength'] >= 5) & (dataframe['volume_strength'] > 1.1) & (dataframe['rsi'] > 30) & (dataframe['rsi'] < 70) ).astype(int) # === DEBUG INFO === if metadata['pair'] in ['BTC/USDT:USDT', 'ETH/USDT:USDT']: # Only log for major pairs latest_score = dataframe['ultimate_score'].iloc[-1] latest_signal = dataframe['signal_strength'].iloc[-1] logger.info(f"{metadata['pair']} Ultimate Score: {latest_score:.3f}, Signal Strength: {latest_signal}") # =========================================== # REGIME CHANGE DETECTION # =========================================== if self.regime_change_enabled.value: # =========================================== # FLASH MOVE DETECTION # =========================================== flash_candles = self.flash_move_candles.value flash_threshold = self.flash_move_threshold.value # Schnelle Preisbewegungen dataframe['price_change_fast'] = dataframe['close'].pct_change(flash_candles) dataframe['flash_pump'] = dataframe['price_change_fast'] > flash_threshold dataframe['flash_dump'] = dataframe['price_change_fast'] < -flash_threshold dataframe['flash_move'] = dataframe['flash_pump'] | dataframe['flash_dump'] # =========================================== # VOLUME SPIKE DETECTION # =========================================== volume_ma20 = dataframe['volume'].rolling(20).mean() volume_multiplier = self.volume_spike_multiplier.value dataframe['volume_spike'] = dataframe['volume'] > (volume_ma20 * volume_multiplier) # Volume + Bewegung kombiniert dataframe['volume_pump'] = dataframe['volume_spike'] & dataframe['flash_pump'] dataframe['volume_dump'] = dataframe['volume_spike'] & dataframe['flash_dump'] # =========================================== # MARKET SENTIMENT DETECTION # =========================================== # Market Breadth Change if 'market_breadth' in dataframe.columns: dataframe['market_breadth_change'] = dataframe['market_breadth'].diff(3) sentiment_threshold = self.sentiment_shift_threshold.value dataframe['sentiment_shift_bull'] = dataframe['market_breadth_change'] > sentiment_threshold dataframe['sentiment_shift_bear'] = dataframe['market_breadth_change'] < -sentiment_threshold else: dataframe['sentiment_shift_bull'] = False dataframe['sentiment_shift_bear'] = False # =========================================== # BTC CORRELATION MONITORING # =========================================== # BTC Flash Moves if 'btc_close' in dataframe.columns: dataframe['btc_change_fast'] = dataframe['btc_close'].pct_change(flash_candles) dataframe['btc_flash_pump'] = dataframe['btc_change_fast'] > flash_threshold dataframe['btc_flash_dump'] = dataframe['btc_change_fast'] < -flash_threshold # Correlation Break pair_movement = dataframe['price_change_fast'].abs() btc_movement = dataframe['btc_change_fast'].abs() dataframe['correlation_break'] = (btc_movement > flash_threshold) & (pair_movement < flash_threshold * 0.4) else: dataframe['btc_flash_pump'] = False dataframe['btc_flash_dump'] = False dataframe['correlation_break'] = False # =========================================== # REGIME CHANGE SCORE # =========================================== regime_signals = [ 'flash_move', 'volume_spike', 'sentiment_shift_bull', 'sentiment_shift_bear', 'btc_flash_pump', 'btc_flash_dump', 'correlation_break' ] dataframe['regime_change_score'] = 0 for signal in regime_signals: if signal in dataframe.columns: dataframe['regime_change_score'] += dataframe[signal].astype(int) # Normalisiere auf 0-1 max_signals = len(regime_signals) dataframe['regime_change_intensity'] = dataframe['regime_change_score'] / max_signals # Alert Level sensitivity = self.regime_change_sensitivity.value dataframe['regime_alert'] = dataframe['regime_change_intensity'] >= sensitivity else: # Falls Regime Detection deaktiviert dataframe['flash_pump'] = False dataframe['flash_dump'] = False dataframe['volume_pump'] = False dataframe['volume_dump'] = False dataframe['regime_alert'] = False dataframe['regime_change_intensity'] = 0.0 # === ADVANCED PREDICTIVE ANALYSIS === try: pair = metadata.get('pair', 'UNKNOWN') dataframe = calculate_advanced_predictive_signals(dataframe, pair) dataframe = calculate_quantum_momentum_analysis(dataframe) dataframe = calculate_neural_pattern_recognition(dataframe) logger.info(f"{pair} Advanced predictive analysis completed") except Exception as e: logger.warning(f"Advanced predictive analysis failed: {e}") dataframe['ml_entry_probability'] = 0.5 dataframe['ml_enhanced_score'] = dataframe.get('ultimate_score', 0.5) dataframe['ml_high_confidence'] = 0 dataframe['ml_ultra_confidence'] = 0 dataframe['quantum_momentum_coherence'] = 0.5 dataframe['momentum_entanglement'] = 0.5 dataframe['quantum_tunnel_up_prob'] = 0.5 dataframe['neural_pattern_score'] = 0.5 return dataframe def populate_entry_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame: """ AI-CENTRIC ENTRY LOGIC - Simplified and functional """ # =========================================== # INITIALIZE ENTRY COLUMNS # =========================================== dataframe["enter_long"] = 0 dataframe["enter_short"] = 0 dataframe["enter_tag"] = "" dataframe['entry_type'] = 0 # =========================================== # CORE AI SIGNALS (Primary Decision Makers) # =========================================== # Ensure AI probability exists and is valid ml_prob = dataframe.get('ml_entry_probability', pd.Series(0.5, index=dataframe.index)) ml_enhanced = dataframe.get('ml_enhanced_score', pd.Series(0.5, index=dataframe.index)) # =========================================== # BASIC SAFETY FILTERS (Minimal Requirements) # =========================================== basic_safety = ( (dataframe['rsi'] > 15) & (dataframe['rsi'] < 85) & # Not extreme RSI (dataframe['volume'] > dataframe['avg_volume'] * 0.3) & # Some volume (dataframe['atr'] < dataframe['close'] * 0.12) # Reasonable volatility ) # =========================================== # AI-DRIVEN LONG ENTRIES (Tiered System) # =========================================== # TIER 1: Ultra AI Confidence (Best threshold that actually works) ai_ultra_long = ( (ml_prob > 0.7) & # Reduced from 0.9 to 0.7 (ml_enhanced > 0.65) & # Reduced from 0.8 to 0.65 basic_safety ) # TIER 2: High AI Confidence ai_high_long = ( (ml_prob > 0.6) & # Reduced from 0.75 to 0.6 (ml_enhanced > 0.55) & # Reduced from 0.7 to 0.55 basic_safety & ~ai_ultra_long # Only if not ultra ) # TIER 3: Standard AI Confidence ai_standard_long = ( (ml_prob > 0.52) & # Reduced from 0.65 to 0.52 basic_safety & ~(ai_ultra_long | ai_high_long) # Only if not higher tier ) # TIER 4: Opportunistic AI (Market conditions favorable) ai_opportunistic_long = ( (ml_prob > 0.48) & # Close to neutral but favorable (dataframe['rsi'] < 35) & # Oversold opportunity (dataframe['volume'] > dataframe['avg_volume'] * 1.5) & # High volume basic_safety & ~(ai_ultra_long | ai_high_long | ai_standard_long) ) # =========================================== # AI-DRIVEN SHORT ENTRIES (Mirror Logic) # =========================================== # TIER 1: Ultra AI Short Confidence ai_ultra_short = ( (ml_prob < 0.3) & # Low probability = good for shorts (ml_enhanced < 0.35) & basic_safety & (dataframe['close'] < dataframe['ema50']) # Below EMA for shorts ) # TIER 2: High AI Short Confidence ai_high_short = ( (ml_prob < 0.4) & (ml_enhanced < 0.45) & basic_safety & (dataframe['close'] < dataframe['ema50']) & ~ai_ultra_short ) # TIER 3: Standard AI Short ai_standard_short = ( (ml_prob < 0.48) & basic_safety & (dataframe['close'] < dataframe['ema50']) & ~(ai_ultra_short | ai_high_short) ) # TIER 4: Opportunistic Short ai_opportunistic_short = ( (ml_prob < 0.52) & (dataframe['rsi'] > 65) & # Overbought opportunity (dataframe['volume'] > dataframe['avg_volume'] * 1.5) & basic_safety & (dataframe['close'] < dataframe['ema50']) & ~(ai_ultra_short | ai_high_short | ai_standard_short) ) # =========================================== # ENHANCED AI ENTRIES (Optional Boost) # =========================================== # Check for enhanced AI indicators safely quantum_coherence = dataframe.get('quantum_momentum_coherence', pd.Series(0.5, index=dataframe.index)) neural_pattern = dataframe.get('neural_pattern_score', pd.Series(0.5, index=dataframe.index)) ml_agreement = dataframe.get('ml_model_agreement', pd.Series(0.7, index=dataframe.index)) # Enhanced long entries (when advanced AI agrees) ai_enhanced_long = ( (ml_prob > 0.6) & (quantum_coherence > 0.6) & # Reduced threshold (neural_pattern > 0.6) & # Reduced threshold (ml_agreement > 0.6) & # Reduced threshold basic_safety ) # Enhanced short entries ai_enhanced_short = ( (ml_prob < 0.4) & (quantum_coherence < 0.4) & # Inverted for shorts (neural_pattern < 0.4) & # Inverted for shorts (ml_agreement > 0.6) & # Models agree on direction basic_safety & (dataframe['close'] < dataframe['ema50']) ) # =========================================== # FALLBACK TECHNICAL ENTRIES (If AI fails) # =========================================== # Simple technical long (backup when AI is neutral) technical_long = ( (ml_prob.between(0.45, 0.55)) & # AI neutral (dataframe['rsi'] < 30) & # Oversold (dataframe['close'] > dataframe['ema50']) & # Above EMA (dataframe['volume'] > dataframe['avg_volume'] * 2) & # High volume basic_safety ) # Simple technical short (backup when AI is neutral) technical_short = ( (ml_prob.between(0.45, 0.55)) & # AI neutral (dataframe['rsi'] > 70) & # Overbought (dataframe['close'] < dataframe['ema50']) & # Below EMA (dataframe['volume'] > dataframe['avg_volume'] * 2) & # High volume basic_safety ) # =========================================== # APPLY ENTRY SIGNALS (Hierarchical Priority) # =========================================== # LONG ENTRIES (Highest priority first) # Enhanced AI Long (Priority 1) dataframe.loc[ai_enhanced_long, "enter_long"] = 1 dataframe.loc[ai_enhanced_long, 'entry_type'] = 15 dataframe.loc[ai_enhanced_long, "enter_tag"] = "ai_enhanced_long" # Ultra AI Long (Priority 2) mask = ai_ultra_long & (dataframe["enter_long"] == 0) dataframe.loc[mask, "enter_long"] = 1 dataframe.loc[mask, 'entry_type'] = 14 dataframe.loc[mask, "enter_tag"] = "ai_ultra_long" # High AI Long (Priority 3) mask = ai_high_long & (dataframe["enter_long"] == 0) dataframe.loc[mask, "enter_long"] = 1 dataframe.loc[mask, 'entry_type'] = 13 dataframe.loc[mask, "enter_tag"] = "ai_high_long" # Standard AI Long (Priority 4) mask = ai_standard_long & (dataframe["enter_long"] == 0) dataframe.loc[mask, "enter_long"] = 1 dataframe.loc[mask, 'entry_type'] = 12 dataframe.loc[mask, "enter_tag"] = "ai_standard_long" # Opportunistic AI Long (Priority 5) mask = ai_opportunistic_long & (dataframe["enter_long"] == 0) dataframe.loc[mask, "enter_long"] = 1 dataframe.loc[mask, 'entry_type'] = 11 dataframe.loc[mask, "enter_tag"] = "ai_opportunistic_long" # Technical Long (Priority 6 - Fallback) mask = technical_long & (dataframe["enter_long"] == 0) dataframe.loc[mask, "enter_long"] = 1 dataframe.loc[mask, 'entry_type'] = 10 dataframe.loc[mask, "enter_tag"] = "technical_long" # SHORT ENTRIES (If shorting enabled) if self.can_short: # Enhanced AI Short (Priority 1) dataframe.loc[ai_enhanced_short, "enter_short"] = 1 dataframe.loc[ai_enhanced_short, 'entry_type'] = 25 dataframe.loc[ai_enhanced_short, "enter_tag"] = "ai_enhanced_short" # Ultra AI Short (Priority 2) mask = ai_ultra_short & (dataframe["enter_short"] == 0) dataframe.loc[mask, "enter_short"] = 1 dataframe.loc[mask, 'entry_type'] = 24 dataframe.loc[mask, "enter_tag"] = "ai_ultra_short" # High AI Short (Priority 3) mask = ai_high_short & (dataframe["enter_short"] == 0) dataframe.loc[mask, "enter_short"] = 1 dataframe.loc[mask, 'entry_type'] = 23 dataframe.loc[mask, "enter_tag"] = "ai_high_short" # Standard AI Short (Priority 4) mask = ai_standard_short & (dataframe["enter_short"] == 0) dataframe.loc[mask, "enter_short"] = 1 dataframe.loc[mask, 'entry_type'] = 22 dataframe.loc[mask, "enter_tag"] = "ai_standard_short" # Opportunistic AI Short (Priority 5) mask = ai_opportunistic_short & (dataframe["enter_short"] == 0) dataframe.loc[mask, "enter_short"] = 1 dataframe.loc[mask, 'entry_type'] = 21 dataframe.loc[mask, "enter_tag"] = "ai_opportunistic_short" # Technical Short (Priority 6 - Fallback) mask = technical_short & (dataframe["enter_short"] == 0) dataframe.loc[mask, "enter_short"] = 1 dataframe.loc[mask, 'entry_type'] = 20 dataframe.loc[mask, "enter_tag"] = "technical_short" # =========================================== # ENTRY DEBUGGING & MONITORING # =========================================== if metadata['pair'] in ['BTC/USDT:USDT', 'ETH/USDT:USDT']: # Count total entries in last 10 candles recent_long_entries = dataframe['enter_long'].tail(10).sum() recent_short_entries = dataframe['enter_short'].tail(10).sum() if recent_long_entries > 0 or recent_short_entries > 0: latest_ml_prob = ml_prob.iloc[-1] latest_enhanced = ml_enhanced.iloc[-1] latest_entry_type = dataframe['entry_type'].iloc[-1] latest_tag = dataframe['enter_tag'].iloc[-1] entry_types = { 10: 'Technical Long', 11: 'AI Opportunistic Long', 12: 'AI Standard Long', 13: 'AI High Long', 14: 'AI Ultra Long', 15: 'AI Enhanced Long', 20: 'Technical Short', 21: 'AI Opportunistic Short', 22: 'AI Standard Short', 23: 'AI High Short', 24: 'AI Ultra Short', 25: 'AI Enhanced Short' } logger.info(f"🎯 {metadata['pair']} ENTRY SIGNAL DETECTED!") logger.info(f" Type: {entry_types.get(latest_entry_type, 'Unknown')} ({latest_tag})") logger.info(f" 🤖 ML Probability: {latest_ml_prob:.3f}") logger.info(f" 📈 ML Enhanced Score: {latest_enhanced:.3f}") logger.info(f" 📊 RSI: {dataframe['rsi'].iloc[-1]:.1f}") logger.info(f" 💧 Volume Strength: {dataframe.get('volume_strength', pd.Series([1.0])).iloc[-1]:.2f}") logger.info(f" Recent Entries: {recent_long_entries} Long, {recent_short_entries} Short") # Alert if no AI indicators available if latest_ml_prob == 0.5 and latest_enhanced == 0.5: logger.warning(f"⚠️ {metadata['pair']} Using fallback - AI indicators may not be working!") # =========================================== # FINAL SAFETY CHECK # =========================================== # Ensure we don't have conflicting signals conflict_mask = (dataframe['enter_long'] == 1) & (dataframe['enter_short'] == 1) if conflict_mask.any(): logger.warning(f"{metadata['pair']} Resolving {conflict_mask.sum()} conflicting signals") # Resolve conflicts: prefer higher entry_type (more confident signal) long_priority = dataframe['entry_type'].where(dataframe['enter_long'] == 1, 0) short_priority = dataframe['entry_type'].where(dataframe['enter_short'] == 1, 0) # Keep the higher priority signal keep_long = long_priority >= short_priority dataframe.loc[conflict_mask & ~keep_long, 'enter_long'] = 0 dataframe.loc[conflict_mask & keep_long, 'enter_short'] = 0 return dataframe def populate_exit_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame: """ UNIFIED EXIT SYSTEM - Choose between Custom MML Exits or Simple Opposite Signal Exits """ # =========================================== # INITIALIZE EXIT COLUMNS # =========================================== dataframe["exit_long"] = 0 dataframe["exit_short"] = 0 dataframe["exit_tag"] = "" # =========================================== # CHOOSE EXIT SYSTEM # =========================================== if self.use_custom_exits_advanced: # Use Alex's Advanced MML-based Exit System return self._populate_custom_exits_advanced(dataframe, metadata) else: # Use Simple Opposite Signal Exit System return self._populate_simple_exits(dataframe, metadata) def _populate_custom_exits_advanced(self, df: pd.DataFrame, metadata: dict) -> pd.DataFrame: """ ALEX'S ADVANCED MML-BASED EXIT SYSTEM Profit-protecting exit strategy with better signal coordination """ # =========================================== # MML MARKET STRUCTURE FOR EXITS # =========================================== # Bullish/Bearish structure (same as entry) bullish_mml = ( (df["close"] > df["[6/8]P"]) | ((df["close"] > df["[4/8]P"]) & (df["close"].shift(5) < df["[4/8]P"].shift(5))) ) bearish_mml = ( (df["close"] < df["[2/8]P"]) | ((df["close"] < df["[4/8]P"]) & (df["close"].shift(5) > df["[4/8]P"].shift(5))) ) # MML resistance/support levels for exits at_resistance = ( (df["high"] >= df["[6/8]P"]) | # At 75% (df["high"] >= df["[7/8]P"]) | # At 87.5% (df["high"] >= df["[8/8]P"]) # At 100% ) at_support = ( (df["low"] <= df["[2/8]P"]) | # At 25% (df["low"] <= df["[1/8]P"]) | # At 12.5% (df["low"] <= df["[0/8]P"]) # At 0% ) # =========================================== # AI-BASED INTELLIGENT EXIT SIGNALS # =========================================== # Calculate AI prediction stability and direction indicators current_profit_signal = pd.Series([False] * len(df), index=df.index) ai_stability_signal = pd.Series([False] * len(df), index=df.index) ai_degradation_signal = pd.Series([False] * len(df), index=df.index) try: # 1. AI STABILITY ANALYSIS # Check if ML predictions are stable and maintain direction ml_prob = df.get('ml_entry_probability', pd.Series([0.5] * len(df))) ml_enhanced = df.get('ml_enhanced_score', pd.Series([0.5] * len(df))) # Calculate AI stability metrics ml_prob_sma_5 = ml_prob.rolling(5).mean().fillna(ml_prob) ml_prob_sma_10 = ml_prob.rolling(10).mean().fillna(ml_prob) ml_prob_std_5 = ml_prob.rolling(5).std().fillna(0.1) # For LONG positions: AI should maintain high probability (>0.6) for continuation ai_long_stable = ( (ml_prob > 0.6) & # Current prediction supports long (ml_prob_sma_5 > 0.6) & # Recent average supports long (ml_prob_std_5 < 0.15) & # Low volatility in predictions (stable) (ml_enhanced > 0.65) & # Enhanced score supports direction (ml_prob > ml_prob.shift(1)) # Predictions improving or stable ) # For SHORT positions: AI should maintain low probability (<0.4) for continuation ai_short_stable = ( (ml_prob < 0.4) & (ml_prob_sma_5 < 0.4) & # Recent average supports short (ml_prob_std_5 < 0.15) & # Low volatility in predictions (stable) (ml_enhanced < 0.35) & # Enhanced score supports direction (ml_prob < ml_prob.shift(1)) # Predictions improving toward short ) # 2. AI DEGRADATION DETECTION # Exit signals when AI predictions deteriorate # For LONG positions: Exit when AI confidence drops significantly ai_long_degradation = ( # Rapid confidence loss (ml_prob < 0.45) | # Prediction dropped below neutral (ml_prob < ml_prob.shift(2) - 0.2) | # 20% drop in 2 candles (ml_prob_sma_5 < ml_prob_sma_10 - 0.1) | # Trend deteriorating # High prediction volatility (uncertainty) (ml_prob_std_5 > 0.25) | # Enhanced score contradiction ((ml_prob > 0.6) & (ml_enhanced < 0.4)) # Conflicting signals ) # For SHORT positions: Exit when AI confidence drops for shorts ai_short_degradation = ( # Rapid confidence loss for shorts (ml_prob > 0.55) | # Prediction moved above neutral (ml_prob > ml_prob.shift(2) + 0.2) | # 20% rise in 2 candles (ml_prob_sma_5 > ml_prob_sma_10 + 0.1) | # Trend improving (bad for shorts) # High prediction volatility (uncertainty) (ml_prob_std_5 > 0.25) | # Enhanced score contradiction ((ml_prob < 0.4) & (ml_enhanced > 0.6)) # Conflicting signals ) # 3. COMBINED AI EXIT LOGIC # Exit LONG when AI shows degradation AND no stability ai_long_exit = ai_long_degradation & (~ai_long_stable) # Exit SHORT when AI shows degradation AND no stability ai_short_exit = ai_short_degradation & (~ai_short_stable) # Store signals for use in exit combinations ai_stability_signal = ai_long_stable | ai_short_stable ai_degradation_signal = ai_long_exit | ai_short_exit # Simple profit conditions (backup to AI logic) rolling_high = df['high'].rolling(20).max() current_drawdown = (rolling_high - df['close']) / rolling_high profit_exit_signal = ( (df['close'] > df['close'].shift(20) * 1.06) & # 6%+ gain from 20 candles ago (current_drawdown > 0.02) # But now dropped 2%+ from recent high ) resistance_profit_exit = ( at_resistance & (df['close'] > df['close'].shift(10) * 1.04) & # 4%+ gain from 10 candles ago (df['rsi'] > 65) & # Overbought (df['close'] < df['high']) # Didn't close at high ) # Combine AI exits with traditional profit-taking current_profit_signal = ( ai_degradation_signal | # AI degradation (primary) (profit_exit_signal & (~ai_stability_signal)) | # Profit exit only if AI unstable (resistance_profit_exit & (~ai_stability_signal)) # Resistance exit only if AI unstable ) except Exception as e: # If any error, continue with normal exit logic logger.warning(f"AI exit logic error for {metadata.get('pair', 'unknown')}: {e}") # Fallback to simple profit signals try: rolling_high = df['high'].rolling(20).max() current_drawdown = (rolling_high - df['close']) / rolling_high current_profit_signal = ( (df['close'] > df['close'].shift(20) * 1.06) & (current_drawdown > 0.02) ) except Exception: current_profit_signal = pd.Series([False] * len(df), index=df.index) # =========================================== # LONG EXIT SIGNALS (ADVANCED MML SYSTEM) # =========================================== # 1. Profit-Taking Exits long_exit_resistance_profit = ( at_resistance & (df["close"] < df["high"]) & # Failed to close at high (df["rsi"] > 65) & # Overbought (df["maxima"] == 1) & # Local top (df["volume"] > df["volume"].rolling(10).mean()) ) long_exit_extreme_overbought = ( (df["close"] > df["[7/8]P"]) & (df["rsi"] > 75) & (df["close"] < df["close"].shift(1)) & # Price turning down (df["maxima"] == 1) ) long_exit_volume_exhaustion = ( at_resistance & (df["volume"] < df["volume"].rolling(20).mean() * 0.6) & # Tightened from 0.8 (df["rsi"] > 70) & (df["close"] < df["close"].shift(1)) & (df["close"] < df["close"].rolling(3).mean()) # Added price confirmation ) # 2. Structure Breakdown (Improved with strong filters) long_exit_structure_breakdown = ( (df["close"] < df["[4/8]P"]) & (df["close"].shift(1) >= df["[4/8]P"].shift(1)) & bullish_mml.shift(1) & (df["close"] < df["[4/8]P"] * 0.995) & (df["close"] < df["close"].shift(1)) & (df["close"] < df["close"].shift(2)) & (df["rsi"] < 45) & # Tightened from 50 (df["volume"] > df["volume"].rolling(15).mean() * 2.0) & # Increased from 1.5 (df["close"] < df["open"]) & (df["low"] < df["low"].shift(1)) & (df["close"] < df["close"].rolling(3).mean()) & (df["momentum_quality"] < 0) # Added momentum check ) # 3. Momentum Divergence long_exit_momentum_divergence = ( at_resistance & (df["rsi"] < df["rsi"].shift(1)) & # RSI falling (df["rsi"].shift(1) < df["rsi"].shift(2)) & # RSI was falling (df["rsi"] < df["rsi"].shift(3)) & # 3-candle RSI decline (df["close"] >= df["close"].shift(1)) & # Price still up/flat (df["maxima"] == 1) & (df["rsi"] > 60) # Only in overbought territory ) # 4. Range Exit long_exit_range = ( (df["close"] >= df["[2/8]P"]) & (df["close"] <= df["[6/8]P"]) & # In range (df["high"] >= df["[6/8]P"]) & # HIGH touched 75%, not close (df["close"] < df["[6/8]P"] * 0.995) & # But closed below (df["rsi"] > 65) & # More conservative RSI (df["maxima"] == 1) & (df["volume"] > df["volume"].rolling(10).mean() * 1.2) # Volume confirmation ) # 5. Emergency Exit long_exit_emergency = ( (df["close"] < df["[0/8]P"]) & (df["rsi"] < 20) & # Changed from 15 (df["volume"] > df["volume"].rolling(20).mean() * 2.5) & # Reduced from 3 (df["close"] < df["close"].shift(1)) & (df["close"] < df["close"].shift(2)) & (df["close"] < df["open"]) ) if self.use_emergency_exits else pd.Series([False] * len(df), index=df.index) # =========================================== # AI-ENHANCED EXIT COMBINATION # =========================================== try: # Get ML prediction history for stability analysis ml_prob = df.get('ml_entry_probability', pd.Series([0.5] * len(df))) # Calculate AI trend and stability over longer period ml_prob_sma_20 = ml_prob.rolling(20).mean().fillna(ml_prob) ml_trend_strength = (ml_prob - ml_prob_sma_20).abs() # AI Override Logic: Don't exit if AI shows strong consistent signals ai_override_long = ( ai_stability_signal & # AI is stable (ml_prob > 0.7) & # High confidence for long (ml_trend_strength < 0.1) # Low deviation from 20-period average ) # Traditional MML exit signals traditional_mml_exits = ( long_exit_resistance_profit | long_exit_extreme_overbought | long_exit_volume_exhaustion | long_exit_structure_breakdown | long_exit_momentum_divergence | long_exit_range | long_exit_emergency ) # Final AI-enhanced exit decision any_long_exit = ( # AI degradation signal (highest priority - always exit) ai_degradation_signal | # Traditional profit-taking when AI not stable (current_profit_signal & (~ai_stability_signal)) | # Traditional MML exits unless AI strongly overrides (traditional_mml_exits & (~ai_override_long)) ) # Log AI decision for debugging (only when signal changes) if len(df) > 1: current_ai_exit = ai_degradation_signal.iloc[-1] if len(ai_degradation_signal) > 0 else False current_ai_stable = ai_stability_signal.iloc[-1] if len(ai_stability_signal) > 0 else False current_ml_prob = ml_prob.iloc[-1] if len(ml_prob) > 0 else 0.5 current_ai_override = ai_override_long.iloc[-1] if len(ai_override_long) > 0 else False # Initialize logging state if needed if not hasattr(self, '_last_ai_state'): self._last_ai_state = {} pair_key = metadata.get('pair', 'UNKNOWN') last_state = self._last_ai_state.get(pair_key, {}) # Log significant AI state changes if (abs(current_ml_prob - last_state.get('ml_prob', 0.5)) > 0.1 or current_ai_exit != last_state.get('ai_exit', False) or current_ai_stable != last_state.get('ai_stable', False) or current_ai_override != last_state.get('ai_override', False)): logger.info(f"AI Exit Analysis {pair_key}: " f"ML_Prob={current_ml_prob:.3f}, " f"AI_Exit={current_ai_exit}, " f"AI_Stable={current_ai_stable}, " f"AI_Override={current_ai_override}") # Store current state self._last_ai_state[pair_key] = { 'ml_prob': current_ml_prob, 'ai_exit': current_ai_exit, 'ai_stable': current_ai_stable, 'ai_override': current_ai_override } except Exception as e: logger.warning(f"AI exit combination error for {metadata.get('pair', 'unknown')}: {e}") # Fallback to traditional logic any_long_exit = ( current_profit_signal | long_exit_resistance_profit | long_exit_extreme_overbought | long_exit_volume_exhaustion | long_exit_structure_breakdown | long_exit_momentum_divergence | long_exit_range | long_exit_emergency ) # =========================================== # SHORT EXIT SIGNALS (if enabled) # =========================================== if self.can_short: # 1. Profit-Taking Exits short_exit_support_profit = ( at_support & (df["close"] > df["low"]) & # Failed to close at low (df["rsi"] < 35) & # Oversold (df["minima"] == 1) & # Local bottom (df["volume"] > df["volume"].rolling(10).mean()) ) short_exit_extreme_oversold = ( (df["close"] < df["[1/8]P"]) & (df["rsi"] < 25) & (df["close"] > df["close"].shift(1)) & # Price turning up (df["minima"] == 1) ) short_exit_volume_exhaustion = ( at_support & (df["volume"] < df["volume"].rolling(20).mean() * 0.6) & # Tightened from 0.8 (df["rsi"] < 30) & (df["close"] > df["close"].shift(1)) & (df["close"] > df["close"].rolling(3).mean()) # Added price confirmation ) # 2. Structure Breakout short_exit_structure_breakout = ( (df["close"] > df["[4/8]P"]) & (df["close"].shift(1) <= df["[4/8]P"].shift(1)) & bearish_mml.shift(1) & (df["close"] > df["[4/8]P"] * 1.005) & (df["close"] > df["close"].shift(1)) & (df["close"] > df["close"].shift(2)) & (df["rsi"] > 55) & # Tightened from 50 (df["volume"] > df["volume"].rolling(15).mean() * 2.0) & # Increased from 1.5 (df["close"] > df["open"]) & (df["high"] > df["high"].shift(1)) & (df["momentum_quality"] > 0) # Added momentum check ) # 3. Momentum Divergence short_exit_momentum_divergence = ( at_support & (df["rsi"] > df["rsi"].shift(1)) & # RSI rising (df["rsi"].shift(1) > df["rsi"].shift(2)) & # RSI was rising (df["rsi"] > df["rsi"].shift(3)) & # 3-candle RSI rise (df["close"] <= df["close"].shift(1)) & # Price still down/flat (df["minima"] == 1) & (df["rsi"] < 40) # Only in oversold territory ) # 4. Range Exit short_exit_range = ( (df["close"] >= df["[2/8]P"]) & (df["close"] <= df["[6/8]P"]) & # In range (df["low"] <= df["[2/8]P"]) & # LOW touched 25% (df["close"] > df["[2/8]P"] * 1.005) & # But closed above (df["rsi"] < 35) & # More conservative RSI (df["minima"] == 1) & (df["volume"] > df["volume"].rolling(10).mean() * 1.2) # Volume confirmation ) # 5. Emergency Exit short_exit_emergency = ( (df["close"] > df["[8/8]P"]) & (df["rsi"] > 80) & # Changed from 85 (df["volume"] > df["volume"].rolling(20).mean() * 2.5) & # Reduced from 3 (df["close"] > df["close"].shift(1)) & (df["close"] > df["close"].shift(2)) & (df["close"] > df["open"]) ) if self.use_emergency_exits else pd.Series([False] * len(df), index=df.index) # =========================================== # AI-ENHANCED SHORT EXIT COMBINATION # =========================================== try: # AI Override Logic for SHORT positions ai_override_short = ( ai_stability_signal & # AI is stable (ml_prob < 0.3) & # High confidence for short (ml_trend_strength < 0.1) # Low deviation from 20-period average ) # Traditional MML short exit signals traditional_short_exits = ( short_exit_support_profit | short_exit_extreme_oversold | short_exit_volume_exhaustion | short_exit_structure_breakout | short_exit_momentum_divergence | short_exit_range | short_exit_emergency ) # Final AI-enhanced SHORT exit decision any_short_exit = ( # AI degradation signal (always exit shorts too) ai_degradation_signal | # Traditional short exits unless AI strongly overrides (traditional_short_exits & (~ai_override_short)) ) except Exception as e: logger.warning(f"AI short exit error for {metadata.get('pair', 'unknown')}: {e}") # Fallback to traditional logic any_short_exit = ( short_exit_support_profit | short_exit_extreme_oversold | short_exit_volume_exhaustion | short_exit_structure_breakout | short_exit_momentum_divergence | short_exit_range | short_exit_emergency ) else: any_short_exit = pd.Series([False] * len(df), index=df.index) # =========================================== # COORDINATION WITH ENTRY SIGNALS # =========================================== # If we have new Entry signals, they override Exit signals has_long_entry = "enter_long" in df.columns and (df["enter_long"] == 1).any() has_short_entry = "enter_short" in df.columns and (df["enter_short"] == 1).any() if has_long_entry: long_entry_mask = df["enter_long"] == 1 any_long_exit = any_long_exit & (~long_entry_mask) if has_short_entry and self.can_short: short_entry_mask = df["enter_short"] == 1 any_short_exit = any_short_exit & (~short_entry_mask) # =========================================== # SET FINAL EXIT SIGNALS AND TAGS # =========================================== # Long Exits df.loc[any_long_exit, "exit_long"] = 1 # Tags for Long Exits (Priority: AI > Emergency > Structure > Profit) df.loc[any_long_exit & ai_degradation_signal, "exit_tag"] = "AI_Degradation_Exit" df.loc[any_long_exit & long_exit_emergency, "exit_tag"] = "MML_Emergency_Long_Exit" df.loc[any_long_exit & current_profit_signal & (df["exit_tag"] == ""), "exit_tag"] = "AI_Profit_Taking" df.loc[any_long_exit & long_exit_structure_breakdown & (df["exit_tag"] == ""), "exit_tag"] = "MML_Structure_Breakdown_Confirmed" df.loc[any_long_exit & long_exit_resistance_profit & (df["exit_tag"] == ""), "exit_tag"] = "MML_Resistance_Profit" df.loc[any_long_exit & long_exit_extreme_overbought & (df["exit_tag"] == ""), "exit_tag"] = "MML_Extreme_Overbought" df.loc[any_long_exit & long_exit_volume_exhaustion & (df["exit_tag"] == ""), "exit_tag"] = "MML_Volume_Exhaustion_Long" df.loc[any_long_exit & long_exit_momentum_divergence & (df["exit_tag"] == ""), "exit_tag"] = "MML_Momentum_Divergence_Long" df.loc[any_long_exit & long_exit_range & (df["exit_tag"] == ""), "exit_tag"] = "MML_Range_Exit_Long" # Short Exits if self.can_short: df.loc[any_short_exit, "exit_short"] = 1 # Tags for Short Exits (Priority: AI > Emergency > Structure > Profit) df.loc[any_short_exit & ai_degradation_signal, "exit_tag"] = "AI_Degradation_Exit" df.loc[any_short_exit & short_exit_emergency, "exit_tag"] = "MML_Emergency_Short_Exit" df.loc[any_short_exit & short_exit_structure_breakout & (df["exit_tag"] == ""), "exit_tag"] = "MML_Structure_Breakout_Confirmed" df.loc[any_short_exit & short_exit_support_profit & (df["exit_tag"] == ""), "exit_tag"] = "MML_Support_Profit" df.loc[any_short_exit & short_exit_extreme_oversold & (df["exit_tag"] == ""), "exit_tag"] = "MML_Extreme_Oversold" df.loc[any_short_exit & short_exit_volume_exhaustion & (df["exit_tag"] == ""), "exit_tag"] = "MML_Volume_Exhaustion_Short" df.loc[any_short_exit & short_exit_momentum_divergence & (df["exit_tag"] == ""), "exit_tag"] = "MML_Momentum_Divergence_Short" df.loc[any_short_exit & short_exit_range & (df["exit_tag"] == ""), "exit_tag"] = "MML_Range_Exit_Short" return df def _populate_simple_exits(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame: """ SIMPLE OPPOSITE SIGNAL EXIT SYSTEM - SYNTAX FIXED """ # Exit LONG when any SHORT signal appears long_exit_on_short = (dataframe["enter_short"] == 1) # Exit SHORT when any LONG signal appears short_exit_on_long = (dataframe["enter_long"] == 1) # Emergency exits (if enabled) if self.use_emergency_exits: emergency_long_exit = ( (dataframe['rsi'] > 85) & (dataframe['volume'] > dataframe['avg_volume'] * 3) & (dataframe['close'] < dataframe['open']) & (dataframe['close'] < dataframe['low'].shift(1)) ) | ( (dataframe.get('structure_break_down', 0) == 1) & (dataframe['volume'] > dataframe['avg_volume'] * 2.5) & (dataframe['atr'] > dataframe['atr'].rolling(20).mean() * 2) ) emergency_short_exit = ( (dataframe['rsi'] < 15) & (dataframe['volume'] > dataframe['avg_volume'] * 3) & (dataframe['close'] > dataframe['open']) & (dataframe['close'] > dataframe['high'].shift(1)) ) | ( (dataframe.get('structure_break_up', 0) == 1) & (dataframe['volume'] > dataframe['avg_volume'] * 2.5) & (dataframe['atr'] > dataframe['atr'].rolling(20).mean() * 2) ) else: emergency_long_exit = pd.Series([False] * len(dataframe), index=dataframe.index) emergency_short_exit = pd.Series([False] * len(dataframe), index=dataframe.index) # Apply exits dataframe.loc[long_exit_on_short, "exit_long"] = 1 dataframe.loc[long_exit_on_short, "exit_tag"] = "trend_reversal" dataframe.loc[short_exit_on_long, "exit_short"] = 1 dataframe.loc[short_exit_on_long, "exit_tag"] = "trend_reversal" # Emergency exits dataframe.loc[emergency_long_exit & ~long_exit_on_short, "exit_long"] = 1 dataframe.loc[emergency_long_exit & ~long_exit_on_short, "exit_tag"] = "emergency_exit" dataframe.loc[emergency_short_exit & ~short_exit_on_long, "exit_short"] = 1 dataframe.loc[emergency_short_exit & ~short_exit_on_long, "exit_tag"] = "emergency_exit" # DEBUGGING (FIXED THE ERROR HERE) if metadata['pair'] in ['BTC/USDT:USDT', 'ETH/USDT:USDT']: recent_exits = dataframe['exit_long'].tail(5).sum() + dataframe['exit_short'].tail(5).sum() if recent_exits > 0: exit_tag = dataframe['exit_tag'].iloc[-1] logger.info(f"{metadata['pair']} EXIT SIGNAL - Tag: {exit_tag}") # ✅ FIXED: Use the correct attribute name logger.info(f" Exit System: {'Custom MML' if self.use_custom_exits_advanced else 'Simple Opposite'}") logger.info(f" RSI: {dataframe['rsi'].iloc[-1]:.1f}") return dataframe def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs) -> bool: current_profit_ratio = trade.calc_profit_ratio(rate) trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600 # Hours always_allow = [ "stoploss", "stop_loss", "custom_stoploss", "roi", "trend_reversal", "emergency_exit" ] # Allow regime protection exits if any(char in exit_reason for char in ["⚡", "🔊", "🌊", "🎯", "₿"]): return True # Allow known good exits if exit_reason in always_allow: return True # Allow trailing stop only if in profit if exit_reason in ["trailing_stop_loss", "trailing_stop"]: if current_profit_ratio > 0: # Only allow trailing if trade is profitable logger.info(f"{pair} Allowing trailing stop exit. Profit: {current_profit_ratio:.2%}") return True else: logger.info(f"{pair} Blocking trailing stop exit. Trade not in profit: {current_profit_ratio:.2%}") return False # Allow timeout exit after 4 hours if trade_duration > 4: logger.info(f"{pair} Forcing exit due to max holding time (4h). Profit: {current_profit_ratio:.2%}") return True # Allow all other exits return True