# source: https://raw.githubusercontent.com/gilaigenttech/freqtrade-user_data/55fc4091f9f3acd4d4c049014dec19b5380bcc16/strategies/PerpSpotBasisStrategy_FreqAI_Debug.py #!/usr/bin/env python3 """ FreqAI Debug strategy - debugging FreqAI signal generation """ import pandas as pd from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import IntParameter, DecimalParameter from pandas import DataFrame import talib.abstract as ta import numpy as np import os class Github_gilaigenttech_freqtrade_user_data__PerpSpotBasisStrategy_FreqAI_Debug__20251008_112727(IStrategy): timeframe = "5m" can_short = False startup_candle_count = 200 # Disable FreqAI exit signals use_exit_signal = False # ROI and stoploss - use more conservative stoploss minimal_roi = { "0": 0.217, "31": 0.058, "65": 0.04, "142": 0 } stoploss = -0.05 # Less aggressive stoploss # Process only new candles process_only_new_candles = True freqai_label_period = 12 freqai_min_return = 0.003 def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: pair = metadata['pair'] print(f"\n=== DEBUG: Populating indicators for {pair} ===") print(f"Dataframe shape: {dataframe.shape}") print(f"Dataframe columns: {list(dataframe.columns)}") # Load futures data for basis calculations futures_data = self.load_futures_data(pair) if futures_data is not None: print(f"Futures data loaded successfully. Shape: {futures_data.shape}") dataframe = self.merge_futures_data(dataframe, futures_data) dataframe = self.calculate_basis_features(dataframe) else: print("No futures data loaded - proceeding without basis features") # Technical indicators dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["ema_20"] = ta.EMA(dataframe, timeperiod=20) dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50) dataframe["adx"] = ta.ADX(dataframe, timeperiod=14) macd = ta.MACD(dataframe) dataframe["macd"] = macd["macd"] dataframe["macdsignal"] = macd["macdsignal"] dataframe["macdhist"] = macd["macdhist"] # Volume features dataframe = self.calculate_volume_features(dataframe) # FreqAI-specific feature engineering (prefix with %-) dataframe["%-basis_zscore"] = dataframe.get("basis_zscore", 0).fillna(0).clip(-5, 5) dataframe["%-basis_ma_ratio"] = ( dataframe.get("basis_ma_10", 0) / dataframe.get("basis_ma_30", 1) ).replace([np.inf, -np.inf], 0).fillna(0).clip(-5, 5) dataframe["%-basis_roc"] = dataframe.get("basis_roc", 0).fillna(0).clip(-5, 5) dataframe["%-volume_ratio_ma"] = dataframe.get("volume_ratio_ma", 0).replace([np.inf, -np.inf], 0).fillna(0).clip(0, 5) dataframe["%-rsi"] = dataframe["rsi"].fillna(50) / 100.0 dataframe["%-adx"] = dataframe["adx"].fillna(20) / 100.0 dataframe["%-ema_ratio"] = ( dataframe["ema_20"].ffill().bfill() / dataframe["ema_50"].replace(0, np.nan).ffill().bfill() ).replace([np.inf, -np.inf], 1).clip(0.5, 1.5) dataframe["%-macd_norm"] = (dataframe["macd"].fillna(0) / dataframe["close"].replace(0, np.nan)).replace([np.inf, -np.inf], 0).fillna(0) dataframe["%-macdsignal_norm"] = (dataframe["macdsignal"].fillna(0) / dataframe["close"].replace(0, np.nan)).replace([np.inf, -np.inf], 0).fillna(0) dataframe["%-momentum"] = dataframe["close"].pct_change(periods=3).fillna(0) freqai_feature_columns = [col for col in dataframe.columns if col.startswith("%-")] print(f"FreqAI feature columns: {freqai_feature_columns}") # Trigger FreqAI pipeline (adds &- predictions) dataframe = self.freqai.start(dataframe, metadata, self) print(f"Final dataframe columns: {list(dataframe.columns)}") print(f"Final dataframe shape: {dataframe.shape}") # Check for FreqAI columns freqai_columns = [col for col in dataframe.columns if col.startswith('&-')] print(f"FreqAI columns detected: {freqai_columns}") return dataframe def load_futures_data(self, pair: str) -> pd.DataFrame: """Load futures data with multiple fallback paths""" pair_formatted = pair.replace('/', '_') possible_paths = [ f"/home/gil/freqtrade/user_data/data/binance/futures/{pair_formatted}-5m-futures.feather", f"/home/gil/freqtrade/user_data/data/binance/{pair_formatted}-5m-futures.feather", f"user_data/data/binance/futures/{pair_formatted}-5m-futures.feather", f"user_data/data/binance/{pair_formatted}-5m-futures.feather", ] for futures_path in possible_paths: if os.path.exists(futures_path): try: print(f"Loading futures data from: {futures_path}") futures_data = pd.read_feather(futures_path) print(f"Futures data loaded: {len(futures_data)} rows") return futures_data except Exception as e: print(f"Error loading from {futures_path}: {e}") continue print(f"No futures data found for {pair}") return None def merge_futures_data(self, spot_df: pd.DataFrame, futures_df: pd.DataFrame) -> pd.DataFrame: """Merge spot and futures data on timestamp""" try: # Ensure both dataframes have datetime index if not isinstance(spot_df.index, pd.DatetimeIndex): spot_df.index = pd.to_datetime(spot_df.index) if not isinstance(futures_df.index, pd.DatetimeIndex): futures_df.index = pd.to_datetime(futures_df.index) # Rename futures columns to avoid conflicts futures_columns = ['open_perp', 'high_perp', 'low_perp', 'close_perp', 'volume_perp'] futures_df_renamed = futures_df[['open', 'high', 'low', 'close', 'volume']].copy() futures_df_renamed.columns = futures_columns # Merge on index (timestamp) merged_df = spot_df.join(futures_df_renamed, how='left') print(f"Merged dataframe shape: {merged_df.shape}") return merged_df except Exception as e: print(f"Error merging futures data: {e}") return spot_df def calculate_basis_features(self, dataframe): """Calculate perpetual-spot basis features""" try: if 'close_perp' not in dataframe.columns: print("No perpetual data available for basis calculation") return dataframe # Basic basis calculation dataframe['basis'] = (dataframe['close_perp'] - dataframe['close']) / dataframe['close'] # Remove infinite or NaN values dataframe['basis'] = dataframe['basis'].replace([np.inf, -np.inf], np.nan) # Fill NaN with forward fill, then backward fill dataframe['basis'] = dataframe['basis'].ffill().bfill() # Basis moving averages dataframe['basis_ma_10'] = ta.SMA(dataframe['basis'], timeperiod=10) dataframe['basis_ma_30'] = ta.SMA(dataframe['basis'], timeperiod=30) # Basis rate of change dataframe['basis_roc'] = ta.ROC(dataframe['basis'], timeperiod=5) # Basis z-score (normalized basis) basis_mean = dataframe['basis'].rolling(window=100).mean() basis_std = dataframe['basis'].rolling(window=100).std() dataframe['basis_zscore'] = (dataframe['basis'] - basis_mean) / basis_std # Fill any remaining NaN values dataframe['basis_zscore'] = dataframe['basis_zscore'].fillna(0) print(f"Basis calculation completed") non_nan_basis = dataframe['basis'].dropna() if len(non_nan_basis) > 0: print(f"Basis stats - Mean: {non_nan_basis.mean():.6f}, Std: {non_nan_basis.std():.6f}") print(f"Basis Z-score range: {dataframe['basis_zscore'].min():.2f} to {dataframe['basis_zscore'].max():.2f}") return dataframe except Exception as e: print(f"Error calculating basis features: {e}") return dataframe def calculate_volume_features(self, dataframe): """Enhanced volume analysis features""" try: # Volume moving averages dataframe['volume_ma_10'] = ta.SMA(dataframe['volume'], timeperiod=10) dataframe['volume_ma_30'] = ta.SMA(dataframe['volume'], timeperiod=30) # Volume ratio (current vs moving average) dataframe['volume_ratio_ma'] = dataframe['volume'] / dataframe['volume_ma_30'] # On-balance volume (OBV) dataframe['obv'] = ta.OBV(dataframe['close'], dataframe['volume']) # Volume weighted average price (VWAP) vwap_period = 20 cumulative_price_volume = (dataframe['close'] * dataframe['volume']).rolling(vwap_period).sum() cumulative_volume = dataframe['volume'].rolling(vwap_period).sum() dataframe['vwap'] = cumulative_price_volume / cumulative_volume return dataframe except Exception as e: print(f"Error calculating volume features: {e}") return dataframe freqai_prediction_threshold = DecimalParameter(0.5, 0.9, default=0.75, space='buy', optimize=True, load=True) def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: print(f"\n=== DEBUG: Populating entry trend for {metadata['pair']} ===") print(f"Dataframe shape: {dataframe.shape}") # Initialize all entry signals to 0 dataframe.loc[:, 'enter_long'] = 0 # Check FreqAI columns freqai_columns = [col for col in dataframe.columns if col.startswith('&-')] print(f"Available FreqAI columns: {freqai_columns}") # Check if FreqAI enter_long signal exists if '&-enter_long' in dataframe.columns: enter_long_col = dataframe['&-enter_long'] print(f"FreqAI &-enter_long unique values: {enter_long_col.unique()}") proba_col = None for col in dataframe.columns: if not isinstance(col, str): continue if col.lower().endswith('enter') and not col.startswith('&-'): proba_col = col break if proba_col: predictions = dataframe[proba_col].fillna(0) freqai_condition = predictions > self.freqai_prediction_threshold.value print(f"FreqAI probability column '{proba_col}' used, signals above threshold: {freqai_condition.sum()}") dataframe.loc[freqai_condition, 'enter_long'] = 1 else: freqai_condition = enter_long_col.astype(str).str.lower() == 'enter' print(f"FreqAI class-based signals: {freqai_condition.sum()}") dataframe.loc[freqai_condition, 'enter_long'] = 1 if dataframe['enter_long'].sum() == 0: print(" - No FreqAI signals above threshold, using fallback") oversold = dataframe['rsi'] < 40 uptrend = dataframe['ema_20'] > dataframe['ema_50'] fallback_condition = oversold & uptrend print(f" - Fallback oversold signals: {oversold.sum()}") print(f" - Fallback uptrend signals: {uptrend.sum()}") print(f" - Combined fallback signals: {fallback_condition.sum()}") dataframe.loc[fallback_condition, 'enter_long'] = 1 else: print("No &-enter_long column found! Using fallback conditions") # More permissive fallback when no FreqAI oversold = dataframe['rsi'] < 40 uptrend = dataframe['ema_20'] > dataframe['ema_50'] fallback_condition = oversold & uptrend print(f"Fallback signals: {fallback_condition.sum()}") dataframe.loc[fallback_condition, 'enter_long'] = 1 total_signals = dataframe['enter_long'].sum() print(f"Total entry signals generated: {total_signals}") return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, 'exit_long'] = 0 # RSI overbought exit strong_overbought = dataframe['rsi'] > 75 dataframe.loc[strong_overbought, 'exit_long'] = 1 exit_signals = dataframe['exit_long'].sum() print(f"Total exit signals generated: {exit_signals}") return dataframe def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame: future_return = dataframe["close"].shift(-self.freqai_label_period) / dataframe["close"] - 1 dataframe["&-enter_long"] = np.where( future_return > self.freqai_min_return, "enter", "hold" ) dataframe["&-enter_long"] = dataframe["&-enter_long"].fillna("hold") print(f"Set FreqAI targets for {metadata['pair']} with threshold {self.freqai_min_return}") return dataframe # FreqAI configuration - match config.json identifier freqai_info = { "identifier": "MyFreqAIModel", "features": [ "%-basis_zscore", "%-basis_ma_ratio", "%-basis_roc", "%-volume_ratio_ma", "%-rsi", "%-adx", "%-ema_ratio", "%-macd_norm", "%-macdsignal_norm", "%-momentum", ], "label_period_candles": freqai_label_period, "data_split_parameters": {"test_size": 0.2, "shuffle": False}, "model_training_parameters": { "n_estimators": 50, "max_depth": 6, "min_samples_split": 10, "min_samples_leaf": 5, "random_state": 42, "n_jobs": -1 } }