a924328c90
- Introduced a new Strategy class to encapsulate trading strategies, including the Market Regime Strategy that adapts to different market conditions. - Refactored BollingerBands and RSI classes to accept configuration parameters for improved flexibility and maintainability. - Updated test_bbrsi.py to utilize the new strategy implementation and adjusted date ranges for testing. - Enhanced documentation to include details about the new Strategy class and its components.
131 lines
6.0 KiB
Python
131 lines
6.0 KiB
Python
import pandas as pd
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import numpy as np
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from cycles.Analysis.boillinger_band import BollingerBands
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class Strategy:
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def __init__(self, config = None, logging = None):
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if config is None:
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raise ValueError("Config must be provided.")
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self.config = config
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self.logging = logging
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def run(self, data, strategy_name):
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if strategy_name == "MarketRegimeStrategy":
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return self.MarketRegimeStrategy(data)
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else:
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if self.logging is not None:
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self.logging.warning(f"Strategy {strategy_name} not found. Using no_strategy instead.")
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return self.no_strategy(data)
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def no_strategy(self, data):
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"""No strategy: returns False for both buy and sell conditions"""
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buy_condition = pd.Series([False] * len(data), index=data.index)
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sell_condition = pd.Series([False] * len(data), index=data.index)
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return buy_condition, sell_condition
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def rsi_bollinger_confirmation(self, rsi, window=14, std_mult=1.5):
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"""Calculate RSI Bollinger Bands for confirmation
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Args:
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rsi (Series): RSI values
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window (int): Rolling window for SMA
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std_mult (float): Standard deviation multiplier
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Returns:
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tuple: (oversold condition, overbought condition)
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"""
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valid_rsi = ~rsi.isna()
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if not valid_rsi.any():
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# Return empty Series if no valid RSI data
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return pd.Series(False, index=rsi.index), pd.Series(False, index=rsi.index)
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rsi_sma = rsi.rolling(window).mean()
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rsi_std = rsi.rolling(window).std()
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upper_rsi_band = rsi_sma + std_mult * rsi_std
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lower_rsi_band = rsi_sma - std_mult * rsi_std
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return (rsi < lower_rsi_band), (rsi > upper_rsi_band)
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def MarketRegimeStrategy(self, data):
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"""Optimized Bollinger Bands + RSI Strategy for Crypto Trading (Including Sideways Markets)
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with adaptive Bollinger Bands
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This advanced strategy combines volatility analysis, momentum confirmation, and regime detection
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to adapt to Bitcoin's unique market conditions.
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Entry Conditions:
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- Trending Market (Breakout Mode):
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Buy: Price < Lower Band ∧ RSI < 50 ∧ Volume Spike (≥1.5× 20D Avg)
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Sell: Price > Upper Band ∧ RSI > 50 ∧ Volume Spike
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- Sideways Market (Mean Reversion):
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Buy: Price ≤ Lower Band ∧ RSI ≤ 40
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Sell: Price ≥ Upper Band ∧ RSI ≥ 60
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Enhanced with RSI Bollinger Squeeze for signal confirmation when enabled.
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"""
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# Initialize conditions as all False
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buy_condition = pd.Series(False, index=data.index)
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sell_condition = pd.Series(False, index=data.index)
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# Create masks for different market regimes
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sideways_mask = data['MarketRegime'] > 0
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trending_mask = data['MarketRegime'] <= 0
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valid_data_mask = ~data['MarketRegime'].isna() # Handle potential NaN values
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# Calculate volume spike (≥1.5× 20D Avg)
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if 'volume' in data.columns:
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volume_20d_avg = data['volume'].rolling(window=20).mean()
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volume_spike = data['volume'] >= 1.5 * volume_20d_avg
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# Additional volume contraction filter for sideways markets
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volume_30d_avg = data['volume'].rolling(window=30).mean()
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volume_contraction = data['volume'] < 0.7 * volume_30d_avg
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else:
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# If volume data is not available, assume no volume spike
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volume_spike = pd.Series(False, index=data.index)
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volume_contraction = pd.Series(False, index=data.index)
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if self.logging is not None:
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self.logging.warning("Volume data not available. Volume conditions will not be triggered.")
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# Calculate RSI Bollinger Squeeze confirmation
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if 'RSI' in data.columns:
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oversold_rsi, overbought_rsi = self.rsi_bollinger_confirmation(data['RSI'])
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else:
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oversold_rsi = pd.Series(False, index=data.index)
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overbought_rsi = pd.Series(False, index=data.index)
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if self.logging is not None:
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self.logging.warning("RSI data not available. RSI Bollinger Squeeze will not be triggered.")
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# Calculate conditions for sideways market (Mean Reversion)
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if sideways_mask.any():
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sideways_buy = (data['close'] <= data['LowerBand']) & (data['RSI'] <= 40)
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sideways_sell = (data['close'] >= data['UpperBand']) & (data['RSI'] >= 60)
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# Add enhanced confirmation for sideways markets
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if self.config.get("SqueezeStrategy", False):
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sideways_buy = sideways_buy & oversold_rsi & volume_contraction
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sideways_sell = sideways_sell & overbought_rsi & volume_contraction
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# Apply only where market is sideways and data is valid
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buy_condition = buy_condition | (sideways_buy & sideways_mask & valid_data_mask)
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sell_condition = sell_condition | (sideways_sell & sideways_mask & valid_data_mask)
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# Calculate conditions for trending market (Breakout Mode)
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if trending_mask.any():
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trending_buy = (data['close'] < data['LowerBand']) & (data['RSI'] < 50) & volume_spike
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trending_sell = (data['close'] > data['UpperBand']) & (data['RSI'] > 50) & volume_spike
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# Add enhanced confirmation for trending markets
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if self.config.get("SqueezeStrategy", False):
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trending_buy = trending_buy & oversold_rsi
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trending_sell = trending_sell & overbought_rsi
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# Apply only where market is trending and data is valid
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buy_condition = buy_condition | (trending_buy & trending_mask & valid_data_mask)
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sell_condition = sell_condition | (trending_sell & trending_mask & valid_data_mask)
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return buy_condition, sell_condition |