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refactor to move inside strategy calculations

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This commit is contained in:
Ajasra 2025-05-22 17:15:51 +08:00
parent 736b278ee2
commit 1107346594
2 changed files with 73 additions and 59 deletions
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83
cycles/Analysis/strategies.py
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@ -2,6 +2,8 @@ import pandas as pd
import numpy as np import numpy as np
from cycles.Analysis.boillinger_band import BollingerBands from cycles.Analysis.boillinger_band import BollingerBands
from cycles.Analysis.rsi import RSI
from cycles.utils.data_utils import aggregate_to_daily
class Strategy: class Strategy:
@ -65,45 +67,74 @@ class Strategy:
Sell: Price ≥ Upper Band ∧ RSI ≥ 60 Sell: Price ≥ Upper Band ∧ RSI ≥ 60
Enhanced with RSI Bollinger Squeeze for signal confirmation when enabled. Enhanced with RSI Bollinger Squeeze for signal confirmation when enabled.
"""
Returns:
DataFrame: A unified DataFrame containing original data, BB, RSI, and signals.
"""
data = aggregate_to_daily(data)
# Calculate Bollinger Bands
bb_calculator = BollingerBands(config=self.config)
# Ensure we are working with a copy to avoid modifying the original DataFrame upstream
data_bb = bb_calculator.calculate(data.copy())
# Calculate RSI
rsi_calculator = RSI(config=self.config)
# Use the original data's copy for RSI calculation as well, to maintain index integrity
data_with_rsi = rsi_calculator.calculate(data.copy(), price_column='close')
# Combine BB and RSI data into a single DataFrame for signal generation
# Ensure indices are aligned; they should be as both are from data.copy()
if 'RSI' in data_with_rsi.columns:
data_bb['RSI'] = data_with_rsi['RSI']
else:
# If RSI wasn't calculated (e.g., not enough data), create a dummy column with NaNs
# to prevent errors later, though signals won't be generated.
data_bb['RSI'] = pd.Series(index=data_bb.index, dtype=float)
if self.logging:
self.logging.warning("RSI column not found or not calculated. Signals relying on RSI may not be generated.")
# Initialize conditions as all False # Initialize conditions as all False
buy_condition = pd.Series(False, index=data.index) buy_condition = pd.Series(False, index=data_bb.index)
sell_condition = pd.Series(False, index=data.index) sell_condition = pd.Series(False, index=data_bb.index)
# Create masks for different market regimes # Create masks for different market regimes
sideways_mask = data['MarketRegime'] > 0 # MarketRegime is expected to be in data_bb from BollingerBands calculation
trending_mask = data['MarketRegime'] <= 0 sideways_mask = data_bb['MarketRegime'] > 0
valid_data_mask = ~data['MarketRegime'].isna() # Handle potential NaN values trending_mask = data_bb['MarketRegime'] <= 0
valid_data_mask = ~data_bb['MarketRegime'].isna() # Handle potential NaN values
# Calculate volume spike (≥1.5× 20D Avg) # Calculate volume spike (≥1.5× 20D Avg)
if 'volume' in data.columns: # 'volume' column should be present in the input 'data', and thus in 'data_bb'
volume_20d_avg = data['volume'].rolling(window=20).mean() if 'volume' in data_bb.columns:
volume_spike = data['volume'] >= 1.5 * volume_20d_avg volume_20d_avg = data_bb['volume'].rolling(window=20).mean()
volume_spike = data_bb['volume'] >= 1.5 * volume_20d_avg
# Additional volume contraction filter for sideways markets # Additional volume contraction filter for sideways markets
volume_30d_avg = data['volume'].rolling(window=30).mean() volume_30d_avg = data_bb['volume'].rolling(window=30).mean()
volume_contraction = data['volume'] < 0.7 * volume_30d_avg volume_contraction = data_bb['volume'] < 0.7 * volume_30d_avg
else: else:
# If volume data is not available, assume no volume spike # If volume data is not available, assume no volume spike
volume_spike = pd.Series(False, index=data.index) volume_spike = pd.Series(False, index=data_bb.index)
volume_contraction = pd.Series(False, index=data.index) volume_contraction = pd.Series(False, index=data_bb.index)
if self.logging is not None: if self.logging is not None:
self.logging.warning("Volume data not available. Volume conditions will not be triggered.") self.logging.warning("Volume data not available. Volume conditions will not be triggered.")
# Calculate RSI Bollinger Squeeze confirmation # Calculate RSI Bollinger Squeeze confirmation
if 'RSI' in data.columns: # RSI column is now part of data_bb
oversold_rsi, overbought_rsi = self.rsi_bollinger_confirmation(data['RSI']) if 'RSI' in data_bb.columns and not data_bb['RSI'].isna().all():
oversold_rsi, overbought_rsi = self.rsi_bollinger_confirmation(data_bb['RSI'])
else: else:
oversold_rsi = pd.Series(False, index=data.index) oversold_rsi = pd.Series(False, index=data_bb.index)
overbought_rsi = pd.Series(False, index=data.index) overbought_rsi = pd.Series(False, index=data_bb.index)
if self.logging is not None: if self.logging is not None and ('RSI' not in data_bb.columns or data_bb['RSI'].isna().all()):
self.logging.warning("RSI data not available. RSI Bollinger Squeeze will not be triggered.") self.logging.warning("RSI data not available or all NaN. RSI Bollinger Squeeze will not be triggered.")
# Calculate conditions for sideways market (Mean Reversion) # Calculate conditions for sideways market (Mean Reversion)
if sideways_mask.any(): if sideways_mask.any():
sideways_buy = (data['close'] <= data['LowerBand']) & (data['RSI'] <= 40) sideways_buy = (data_bb['close'] <= data_bb['LowerBand']) & (data_bb['RSI'] <= 40)
sideways_sell = (data['close'] >= data['UpperBand']) & (data['RSI'] >= 60) sideways_sell = (data_bb['close'] >= data_bb['UpperBand']) & (data_bb['RSI'] >= 60)
# Add enhanced confirmation for sideways markets # Add enhanced confirmation for sideways markets
if self.config.get("SqueezeStrategy", False): if self.config.get("SqueezeStrategy", False):
@ -116,8 +147,8 @@ class Strategy:
# Calculate conditions for trending market (Breakout Mode) # Calculate conditions for trending market (Breakout Mode)
if trending_mask.any(): if trending_mask.any():
trending_buy = (data['close'] < data['LowerBand']) & (data['RSI'] < 50) & volume_spike trending_buy = (data_bb['close'] < data_bb['LowerBand']) & (data_bb['RSI'] < 50) & volume_spike
trending_sell = (data['close'] > data['UpperBand']) & (data['RSI'] > 50) & volume_spike trending_sell = (data_bb['close'] > data_bb['UpperBand']) & (data_bb['RSI'] > 50) & volume_spike
# Add enhanced confirmation for trending markets # Add enhanced confirmation for trending markets
if self.config.get("SqueezeStrategy", False): if self.config.get("SqueezeStrategy", False):
@ -128,4 +159,8 @@ class Strategy:
buy_condition = buy_condition | (trending_buy & trending_mask & valid_data_mask) buy_condition = buy_condition | (trending_buy & trending_mask & valid_data_mask)
sell_condition = sell_condition | (trending_sell & trending_mask & valid_data_mask) sell_condition = sell_condition | (trending_sell & trending_mask & valid_data_mask)
return buy_condition, sell_condition # Add buy/sell conditions as columns to the DataFrame
data_bb['BuySignal'] = buy_condition
data_bb['SellSignal'] = sell_condition
return data_bb

49
test_bbrsi.py
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@ -5,8 +5,6 @@ import pandas as pd
from cycles.utils.storage import Storage from cycles.utils.storage import Storage
from cycles.utils.data_utils import aggregate_to_daily from cycles.utils.data_utils import aggregate_to_daily
from cycles.Analysis.boillinger_band import BollingerBands
from cycles.Analysis.rsi import RSI
from cycles.Analysis.strategies import Strategy from cycles.Analysis.strategies import Strategy
logging.basicConfig( logging.basicConfig(
@ -59,44 +57,25 @@ if __name__ == "__main__":
data = storage.load_data(config["data_file"], config["start_date"], config["stop_date"]) data = storage.load_data(config["data_file"], config["start_date"], config["stop_date"])
if not IS_DAY:
data_daily = aggregate_to_daily(data)
storage.save_data(data, "btcusd_1-day_data.csv")
df_to_plot = data_daily
else:
df_to_plot = data
bb = BollingerBands(config=config_strategy) strategy = Strategy(config=config_strategy, logging=logging)
data_bb = bb.calculate(df_to_plot.copy()) processed_data = strategy.run(data.copy(), config_strategy["strategy_name"])
rsi_calculator = RSI(config=config_strategy) buy_condition = processed_data.get('BuySignal', pd.Series(False, index=processed_data.index)).astype(bool)
data_with_rsi = rsi_calculator.calculate(df_to_plot.copy(), price_column='close') sell_condition = processed_data.get('SellSignal', pd.Series(False, index=processed_data.index)).astype(bool)
# Combine BB and RSI data into a single DataFrame for signal generation
# Ensure indices are aligned; they should be as both are from df_to_plot.copy()
if 'RSI' in data_with_rsi.columns:
data_bb['RSI'] = data_with_rsi['RSI']
else:
# If RSI wasn't calculated (e.g., not enough data), create a dummy column with NaNs
# to prevent errors later, though signals won't be generated.
data_bb['RSI'] = pd.Series(index=data_bb.index, dtype=float)
logging.warning("RSI column not found or not calculated. Signals relying on RSI may not be generated.")
strategy = Strategy(config=config_strategy) buy_signals = processed_data[buy_condition]
buy_condition, sell_condition = strategy.run(data_bb, config_strategy["strategy_name"]) sell_signals = processed_data[sell_condition]
buy_signals = data_bb[buy_condition]
sell_signals = data_bb[sell_condition]
# plot the data with seaborn library # plot the data with seaborn library
if df_to_plot is not None and not df_to_plot.empty: if processed_data is not None and not processed_data.empty:
# Create a figure with two subplots, sharing the x-axis # Create a figure with two subplots, sharing the x-axis
fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(16, 8), sharex=True) fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(16, 8), sharex=True)
# Plot 1: Close Price and Bollinger Bands # Plot 1: Close Price and Bollinger Bands
sns.lineplot(x=data_bb.index, y='close', data=data_bb, label='Close Price', ax=ax1) sns.lineplot(x=processed_data.index, y='close', data=processed_data, label='Close Price', ax=ax1)
sns.lineplot(x=data_bb.index, y='UpperBand', data=data_bb, label='Upper Band (BB)', ax=ax1) sns.lineplot(x=processed_data.index, y='UpperBand', data=processed_data, label='Upper Band (BB)', ax=ax1)
sns.lineplot(x=data_bb.index, y='LowerBand', data=data_bb, label='Lower Band (BB)', ax=ax1) sns.lineplot(x=processed_data.index, y='LowerBand', data=processed_data, label='Lower Band (BB)', ax=ax1)
# Plot Buy/Sell signals on Price chart # Plot Buy/Sell signals on Price chart
if not buy_signals.empty: if not buy_signals.empty:
ax1.scatter(buy_signals.index, buy_signals['close'], color='green', marker='o', s=20, label='Buy Signal', zorder=5) ax1.scatter(buy_signals.index, buy_signals['close'], color='green', marker='o', s=20, label='Buy Signal', zorder=5)
@ -108,8 +87,8 @@ if __name__ == "__main__":
ax1.grid(True) ax1.grid(True)
# Plot 2: RSI # Plot 2: RSI
if 'RSI' in data_bb.columns: # Check data_bb now as it should contain RSI if 'RSI' in processed_data.columns:
sns.lineplot(x=data_bb.index, y='RSI', data=data_bb, label='RSI (' + str(config_strategy["rsi_period"]) + ')', ax=ax2, color='purple') sns.lineplot(x=processed_data.index, y='RSI', data=processed_data, label='RSI (' + str(config_strategy["rsi_period"]) + ')', ax=ax2, color='purple')
ax2.axhline(config_strategy["trending"]["rsi_threshold"][1], color='red', linestyle='--', linewidth=0.8, label='Overbought (' + str(config_strategy["trending"]["rsi_threshold"][1]) + ')') ax2.axhline(config_strategy["trending"]["rsi_threshold"][1], color='red', linestyle='--', linewidth=0.8, label='Overbought (' + str(config_strategy["trending"]["rsi_threshold"][1]) + ')')
ax2.axhline(config_strategy['trending']['rsi_threshold'][0], color='green', linestyle='--', linewidth=0.8, label='Oversold (' + str(config_strategy['trending']['rsi_threshold'][0]) + ')') ax2.axhline(config_strategy['trending']['rsi_threshold'][0], color='green', linestyle='--', linewidth=0.8, label='Oversold (' + str(config_strategy['trending']['rsi_threshold'][0]) + ')')
# Plot Buy/Sell signals on RSI chart # Plot Buy/Sell signals on RSI chart
@ -126,8 +105,8 @@ if __name__ == "__main__":
logging.info("RSI data not available for plotting.") logging.info("RSI data not available for plotting.")
# Plot 3: BB Width # Plot 3: BB Width
sns.lineplot(x=data_bb.index, y='BBWidth', data=data_bb, label='BB Width', ax=ax3) sns.lineplot(x=processed_data.index, y='BBWidth', data=processed_data, label='BB Width', ax=ax3)
sns.lineplot(x=data_bb.index, y='MarketRegime', data=data_bb, label='Market Regime (Sideways: 1, Trending: 0)', ax=ax3) sns.lineplot(x=processed_data.index, y='MarketRegime', data=processed_data, label='Market Regime (Sideways: 1, Trending: 0)', ax=ax3)
ax3.set_title('Bollinger Bands Width') ax3.set_title('Bollinger Bands Width')
ax3.set_ylabel('BB Width') ax3.set_ylabel('BB Width')
ax3.legend() ax3.legend()