refactor to move inside strategy calculations
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Ajasra
parent
736b278ee2
commit
1107346594
@ -2,6 +2,8 @@ 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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from cycles.Analysis.rsi import RSI
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from cycles.utils.data_utils import aggregate_to_daily
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class Strategy:
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@ -65,45 +67,74 @@ class Strategy:
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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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Returns:
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DataFrame: A unified DataFrame containing original data, BB, RSI, and signals.
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"""
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data = aggregate_to_daily(data)
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# Calculate Bollinger Bands
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bb_calculator = BollingerBands(config=self.config)
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# Ensure we are working with a copy to avoid modifying the original DataFrame upstream
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data_bb = bb_calculator.calculate(data.copy())
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# Calculate RSI
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rsi_calculator = RSI(config=self.config)
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# Use the original data's copy for RSI calculation as well, to maintain index integrity
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data_with_rsi = rsi_calculator.calculate(data.copy(), price_column='close')
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# Combine BB and RSI data into a single DataFrame for signal generation
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# Ensure indices are aligned; they should be as both are from data.copy()
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if 'RSI' in data_with_rsi.columns:
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data_bb['RSI'] = data_with_rsi['RSI']
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else:
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# If RSI wasn't calculated (e.g., not enough data), create a dummy column with NaNs
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# to prevent errors later, though signals won't be generated.
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data_bb['RSI'] = pd.Series(index=data_bb.index, dtype=float)
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if self.logging:
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self.logging.warning("RSI column not found or not calculated. Signals relying on RSI may not be generated.")
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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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buy_condition = pd.Series(False, index=data_bb.index)
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sell_condition = pd.Series(False, index=data_bb.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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# MarketRegime is expected to be in data_bb from BollingerBands calculation
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sideways_mask = data_bb['MarketRegime'] > 0
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trending_mask = data_bb['MarketRegime'] <= 0
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valid_data_mask = ~data_bb['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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# 'volume' column should be present in the input 'data', and thus in 'data_bb'
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if 'volume' in data_bb.columns:
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volume_20d_avg = data_bb['volume'].rolling(window=20).mean()
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volume_spike = data_bb['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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volume_30d_avg = data_bb['volume'].rolling(window=30).mean()
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volume_contraction = data_bb['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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volume_spike = pd.Series(False, index=data_bb.index)
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volume_contraction = pd.Series(False, index=data_bb.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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# RSI column is now part of data_bb
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if 'RSI' in data_bb.columns and not data_bb['RSI'].isna().all():
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oversold_rsi, overbought_rsi = self.rsi_bollinger_confirmation(data_bb['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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oversold_rsi = pd.Series(False, index=data_bb.index)
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overbought_rsi = pd.Series(False, index=data_bb.index)
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if self.logging is not None and ('RSI' not in data_bb.columns or data_bb['RSI'].isna().all()):
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self.logging.warning("RSI data not available or all NaN. 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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sideways_buy = (data_bb['close'] <= data_bb['LowerBand']) & (data_bb['RSI'] <= 40)
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sideways_sell = (data_bb['close'] >= data_bb['UpperBand']) & (data_bb['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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@ -116,8 +147,8 @@ class Strategy:
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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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trending_buy = (data_bb['close'] < data_bb['LowerBand']) & (data_bb['RSI'] < 50) & volume_spike
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trending_sell = (data_bb['close'] > data_bb['UpperBand']) & (data_bb['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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@ -128,4 +159,8 @@ class Strategy:
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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
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# Add buy/sell conditions as columns to the DataFrame
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data_bb['BuySignal'] = buy_condition
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data_bb['SellSignal'] = sell_condition
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return data_bb
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@ -5,8 +5,6 @@ import pandas as pd
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from cycles.utils.storage import Storage
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from cycles.utils.data_utils import aggregate_to_daily
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from cycles.Analysis.boillinger_band import BollingerBands
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from cycles.Analysis.rsi import RSI
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from cycles.Analysis.strategies import Strategy
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logging.basicConfig(
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@ -59,44 +57,25 @@ if __name__ == "__main__":
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data = storage.load_data(config["data_file"], config["start_date"], config["stop_date"])
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if not IS_DAY:
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data_daily = aggregate_to_daily(data)
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storage.save_data(data, "btcusd_1-day_data.csv")
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df_to_plot = data_daily
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else:
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df_to_plot = data
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bb = BollingerBands(config=config_strategy)
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data_bb = bb.calculate(df_to_plot.copy())
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strategy = Strategy(config=config_strategy, logging=logging)
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processed_data = strategy.run(data.copy(), config_strategy["strategy_name"])
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rsi_calculator = RSI(config=config_strategy)
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data_with_rsi = rsi_calculator.calculate(df_to_plot.copy(), price_column='close')
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# Combine BB and RSI data into a single DataFrame for signal generation
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# Ensure indices are aligned; they should be as both are from df_to_plot.copy()
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if 'RSI' in data_with_rsi.columns:
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data_bb['RSI'] = data_with_rsi['RSI']
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else:
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# If RSI wasn't calculated (e.g., not enough data), create a dummy column with NaNs
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# to prevent errors later, though signals won't be generated.
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data_bb['RSI'] = pd.Series(index=data_bb.index, dtype=float)
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logging.warning("RSI column not found or not calculated. Signals relying on RSI may not be generated.")
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buy_condition = processed_data.get('BuySignal', pd.Series(False, index=processed_data.index)).astype(bool)
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sell_condition = processed_data.get('SellSignal', pd.Series(False, index=processed_data.index)).astype(bool)
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strategy = Strategy(config=config_strategy)
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buy_condition, sell_condition = strategy.run(data_bb, config_strategy["strategy_name"])
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buy_signals = data_bb[buy_condition]
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sell_signals = data_bb[sell_condition]
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buy_signals = processed_data[buy_condition]
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sell_signals = processed_data[sell_condition]
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# plot the data with seaborn library
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if df_to_plot is not None and not df_to_plot.empty:
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if processed_data is not None and not processed_data.empty:
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# Create a figure with two subplots, sharing the x-axis
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fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(16, 8), sharex=True)
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# Plot 1: Close Price and Bollinger Bands
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sns.lineplot(x=data_bb.index, y='close', data=data_bb, label='Close Price', ax=ax1)
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sns.lineplot(x=data_bb.index, y='UpperBand', data=data_bb, label='Upper Band (BB)', ax=ax1)
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sns.lineplot(x=data_bb.index, y='LowerBand', data=data_bb, label='Lower Band (BB)', ax=ax1)
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sns.lineplot(x=processed_data.index, y='close', data=processed_data, label='Close Price', ax=ax1)
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sns.lineplot(x=processed_data.index, y='UpperBand', data=processed_data, label='Upper Band (BB)', ax=ax1)
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sns.lineplot(x=processed_data.index, y='LowerBand', data=processed_data, label='Lower Band (BB)', ax=ax1)
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# Plot Buy/Sell signals on Price chart
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if not buy_signals.empty:
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ax1.scatter(buy_signals.index, buy_signals['close'], color='green', marker='o', s=20, label='Buy Signal', zorder=5)
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@ -108,8 +87,8 @@ if __name__ == "__main__":
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ax1.grid(True)
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# Plot 2: RSI
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if 'RSI' in data_bb.columns: # Check data_bb now as it should contain RSI
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sns.lineplot(x=data_bb.index, y='RSI', data=data_bb, label='RSI (' + str(config_strategy["rsi_period"]) + ')', ax=ax2, color='purple')
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if 'RSI' in processed_data.columns:
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sns.lineplot(x=processed_data.index, y='RSI', data=processed_data, label='RSI (' + str(config_strategy["rsi_period"]) + ')', ax=ax2, color='purple')
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ax2.axhline(config_strategy["trending"]["rsi_threshold"][1], color='red', linestyle='--', linewidth=0.8, label='Overbought (' + str(config_strategy["trending"]["rsi_threshold"][1]) + ')')
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ax2.axhline(config_strategy['trending']['rsi_threshold'][0], color='green', linestyle='--', linewidth=0.8, label='Oversold (' + str(config_strategy['trending']['rsi_threshold'][0]) + ')')
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# Plot Buy/Sell signals on RSI chart
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@ -126,8 +105,8 @@ if __name__ == "__main__":
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logging.info("RSI data not available for plotting.")
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# Plot 3: BB Width
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sns.lineplot(x=data_bb.index, y='BBWidth', data=data_bb, label='BB Width', ax=ax3)
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sns.lineplot(x=data_bb.index, y='MarketRegime', data=data_bb, label='Market Regime (Sideways: 1, Trending: 0)', ax=ax3)
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sns.lineplot(x=processed_data.index, y='BBWidth', data=processed_data, label='BB Width', ax=ax3)
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sns.lineplot(x=processed_data.index, y='MarketRegime', data=processed_data, label='Market Regime (Sideways: 1, Trending: 0)', ax=ax3)
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ax3.set_title('Bollinger Bands Width')
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ax3.set_ylabel('BB Width')
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ax3.legend()
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