# source: https://raw.githubusercontent.com/gilaigenttech/freqtrade-user_data/d16b7f5db829fb674151ed0894a239fe9ee6c3bd/strategies/PerpSpotBasisStrategy_Debug.py #!/usr/bin/env python3 """ Debug version of PerpSpotBasisStrategy to identify issues """ 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_Debug__20250926_111255(IStrategy): timeframe = "5m" can_short = False startup_candle_count = 200 # Disable FreqAI exit signals - they cause premature exits use_exit_signal = False # ROI and stoploss minimal_roi = { "0": 0.217, "31": 0.058, "65": 0.04, "142": 0 } stoploss = -0.274 # Process only new candles process_only_new_candles = True def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: pair = metadata['pair'] print(f"Processing pair: {pair}") print(f"Dataframe shape: {dataframe.shape}") print(f"Date range: {dataframe['date'].min()} to {dataframe['date'].max()}") # 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 for {pair}") dataframe = self.merge_futures_data(dataframe, futures_data) dataframe = self.calculate_basis_features(dataframe) else: print(f"No futures data found for {pair}") # Simplified feature set dataframe = self.calculate_volume_features(dataframe) # 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'] # Debug: Print available columns print(f"Available columns: {dataframe.columns.tolist()}") # Check for FreqAI columns freqai_cols = [col for col in dataframe.columns if col.startswith('&-')] print(f"FreqAI columns found: {freqai_cols}") if '&-enter_long' in dataframe.columns: print(f"FreqAI enter_long signal range: {dataframe['&-enter_long'].min()} to {dataframe['&-enter_long'].max()}") print(f"FreqAI enter_long signal last 10 values: {dataframe['&-enter_long'].tail(10).tolist()}") else: print("WARNING: No &-enter_long column found!") return dataframe def load_futures_data(self, pair): """Load corresponding futures data""" try: # Convert spot pair to futures pair (BTC/USDT -> BTC/USDT:USDT) futures_pair = pair.replace('/', '_') + '_USDT-5m-futures.feather' futures_path = f'/home/gil/freqtrade/user_data/data/binance/futures/{futures_pair}' print(f"Looking for futures data at: {futures_path}") if not os.path.exists(futures_path): print(f"Futures data file does not exist: {futures_path}") return None df = pd.read_feather(futures_path) print(f"Loaded futures data: {df.shape} rows") return df except Exception as e: print(f"Error loading futures data: {e}") return None def merge_futures_data(self, spot_df, futures_df): """Merge spot and futures data""" try: # Merge on date column merged = pd.merge(spot_df, futures_df[['date', 'close', 'volume']], left_on='date', right_on='date', how='left', suffixes=('', '_perp')) # Forward fill missing futures data merged[['close_perp', 'volume_perp']] = merged[['close_perp', 'volume_perp']].ffill() print(f"Merged data shape: {merged.shape}") print(f"Perp data coverage: {merged['close_perp'].notna().sum()}/{len(merged)} rows") return merged except Exception as e: print(f"Error merging futures data: {e}") return spot_df def calculate_basis_features(self, dataframe: DataFrame) -> DataFrame: """Calculate perp-spot basis features""" if 'close_perp' not in dataframe.columns: print("No perp data available for basis calculation") return dataframe # Basic basis calculation (perp - spot) / spot dataframe['basis'] = (dataframe['close_perp'] - dataframe['close']) / dataframe['close'] * 100 # 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) # Z-score of basis basis_mean = dataframe['basis'].rolling(window=50).mean() basis_std = dataframe['basis'].rolling(window=50).std() dataframe['basis_zscore'] = (dataframe['basis'] - basis_mean) / basis_std # Volume ratio dataframe['volume_ratio'] = dataframe['volume_perp'] / dataframe['volume'] # Basis momentum dataframe['basis_momentum'] = dataframe['basis'] - dataframe['basis_ma_30'] # Extreme basis conditions dataframe['basis_extreme_bull'] = (dataframe['basis_zscore'] > 2).astype(int) dataframe['basis_extreme_bear'] = (dataframe['basis_zscore'] < -2).astype(int) print(f"Basis stats - Mean: {dataframe['basis'].mean():.4f}, Std: {dataframe['basis'].std():.4f}") print(f"Basis Z-score range: {dataframe['basis_zscore'].min():.2f} to {dataframe['basis_zscore'].max():.2f}") 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 rate of change dataframe['volume_roc'] = ta.ROC(dataframe['volume'], timeperiod=5) # 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 # Volume extremes dataframe['high_volume'] = (dataframe['volume'] > dataframe['volume_ma_30'] * 1.5).astype(int) dataframe['low_volume'] = (dataframe['volume'] < dataframe['volume_ma_30'] * 0.5).astype(int) 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: pair = metadata['pair'] print(f"\n=== ENTRY LOGIC DEBUG for {pair} ===") print(f"Dataframe shape in entry: {dataframe.shape}") # Check for FreqAI signal if '&-enter_long' in dataframe.columns: freqai_signal = dataframe['&-enter_long'].iloc[-1] threshold = self.freqai_prediction_threshold.value print(f"FreqAI signal: {freqai_signal}, Threshold: {threshold}") if freqai_signal > threshold: print(f"FreqAI signal triggered! Setting enter_long=1") dataframe.loc[dataframe.index[-1], 'enter_long'] = 1 else: print(f"FreqAI signal below threshold") else: print("No FreqAI signal column found!") # Alternative entry logic for testing without FreqAI print("Using alternative entry logic...") # Simple RSI + EMA crossover entry rsi_condition = dataframe['rsi'] < 30 # Oversold ema_condition = dataframe['ema_20'] > dataframe['ema_50'] # Uptrend entry_condition = rsi_condition & ema_condition if entry_condition.iloc[-1]: print("Alternative entry condition met!") dataframe.loc[dataframe.index[-1], 'enter_long'] = 1 else: print("Alternative entry condition not met") # Debug: Check if any entry signals were set entry_signals = dataframe['enter_long'].sum() if 'enter_long' in dataframe.columns else 0 print(f"Total entry signals in dataframe: {entry_signals}") return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, 'exit_long'] = 0 # Basis normalization exit if 'basis_zscore' in dataframe.columns: long_basis_normalization = dataframe['basis_zscore'] > 1.0 dataframe.loc[long_basis_normalization, 'exit_long'] = 1 # RSI overbought exit strong_overbought = dataframe['rsi'] > 75 dataframe.loc[strong_overbought, 'exit_long'] = 1 return dataframe # FreqAI configuration - same as original freqai_info = { "identifier": "SKLearnRandomForestClassifier", "features": [ "rsi", "adx", "ema_20", "ema_50", "macd", "macdsignal", "macdhist", "volume_ratio_ma", "obv", "vwap", ], "label_period_candles": 12, # Predict 1 hour into the future "data_split_parameters": {"split_train": 0.8, "split_test": 0.2}, "model_training_parameters": {} }