Enhance strategy output standardization and improve plotting logic
- Introduced a new method to standardize output column names across different strategies, ensuring consistency in data handling and plotting. - Updated plotting logic in test_bbrsi.py to utilize standardized column names, improving clarity and maintainability. - Enhanced error handling for missing data in plots and adjusted visual elements for better representation of trading signals. - Improved the overall structure of strategy implementations to support additional indicators and metadata for better analysis.
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Ajasra
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3a9dec543c
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00873d593f
@ -16,14 +16,66 @@ class Strategy:
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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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result = self.MarketRegimeStrategy(data)
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return self.standardize_output(result, strategy_name)
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elif strategy_name == "CryptoTradingStrategy":
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return self.CryptoTradingStrategy(data)
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result = self.CryptoTradingStrategy(data)
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return self.standardize_output(result, strategy_name)
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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 standardize_output(self, data, strategy_name):
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"""
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Standardize column names across different strategies to ensure consistent plotting and analysis
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Args:
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data (DataFrame): Strategy output DataFrame
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strategy_name (str): Name of the strategy that generated this data
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Returns:
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DataFrame: Data with standardized column names
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"""
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if data.empty:
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return data
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# Create a copy to avoid modifying the original
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standardized = data.copy()
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# Standardize column names based on strategy
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if strategy_name == "MarketRegimeStrategy":
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# MarketRegimeStrategy already has standard column names for most fields
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# Just ensure all standard columns exist
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pass
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elif strategy_name == "CryptoTradingStrategy":
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# Map strategy-specific column names to standard names
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column_mapping = {
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'UpperBand_15m': 'UpperBand',
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'LowerBand_15m': 'LowerBand',
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'SMA_15m': 'SMA',
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'RSI_15m': 'RSI',
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'VolumeMA_15m': 'VolumeMA',
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# Keep StopLoss and TakeProfit as they are
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}
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# Add standard columns from mapped columns
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for old_col, new_col in column_mapping.items():
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if old_col in standardized.columns and new_col not in standardized.columns:
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standardized[new_col] = standardized[old_col]
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# Add additional strategy-specific data as metadata columns
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if 'UpperBand_1h' in standardized.columns:
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standardized['UpperBand_1h_meta'] = standardized['UpperBand_1h']
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if 'LowerBand_1h' in standardized.columns:
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standardized['LowerBand_1h_meta'] = standardized['LowerBand_1h']
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# Ensure all strategies have BBWidth if possible
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if 'BBWidth' not in standardized.columns and 'UpperBand' in standardized.columns and 'LowerBand' in standardized.columns:
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standardized['BBWidth'] = (standardized['UpperBand'] - standardized['LowerBand']) / standardized['SMA'] if 'SMA' in standardized.columns else np.nan
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return standardized
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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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@ -74,6 +126,7 @@ class Strategy:
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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_hourly(data, 4)
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data = aggregate_to_daily(data)
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# Calculate Bollinger Bands
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@ -33,7 +33,7 @@ config_strategy = {
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"rsi_threshold": [40, 60],
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"bb_std_dev_multiplier": 1.8,
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},
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"strategy_name": "MarketRegimeStrategy",
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"strategy_name": "MarketRegimeStrategy", # CryptoTradingStrategy
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"SqueezeStrategy": True
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}
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@ -65,18 +65,19 @@ if __name__ == "__main__":
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# Plot 1: Close Price and Strategy-Specific Bands/Levels
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sns.lineplot(x=processed_data.index, y='close', data=processed_data, label='Close Price', ax=ax1)
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if strategy_name == "MarketRegimeStrategy":
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if 'UpperBand' in processed_data.columns and 'LowerBand' in processed_data.columns:
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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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else:
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logging.warning("MarketRegimeStrategy: UpperBand or LowerBand not found for plotting.")
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elif strategy_name == "CryptoTradingStrategy":
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if 'UpperBand_15m' in processed_data.columns and 'LowerBand_15m' in processed_data.columns:
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sns.lineplot(x=processed_data.index, y='UpperBand_15m', data=processed_data, label='Upper Band (15m)', ax=ax1)
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sns.lineplot(x=processed_data.index, y='LowerBand_15m', data=processed_data, label='Lower Band (15m)', ax=ax1)
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else:
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logging.warning("CryptoTradingStrategy: UpperBand_15m or LowerBand_15m not found for plotting.")
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# Use standardized column names for bands
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if 'UpperBand' in processed_data.columns and 'LowerBand' in processed_data.columns:
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# Instead of lines, shade the area between upper and lower bands
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ax1.fill_between(processed_data.index,
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processed_data['LowerBand'],
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processed_data['UpperBand'],
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alpha=0.1, color='blue', label='Bollinger Bands')
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else:
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logging.warning(f"{strategy_name}: UpperBand or LowerBand not found for plotting.")
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# Add strategy-specific extra indicators if available
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if strategy_name == "CryptoTradingStrategy":
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if 'StopLoss' in processed_data.columns:
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sns.lineplot(x=processed_data.index, y='StopLoss', data=processed_data, label='Stop Loss', ax=ax1, linestyle='--', color='orange')
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if 'TakeProfit' in processed_data.columns:
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@ -84,54 +85,68 @@ if __name__ == "__main__":
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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=10, label='Buy Signal', zorder=5)
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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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if not sell_signals.empty:
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ax1.scatter(sell_signals.index, sell_signals['close'], color='red', marker='o', s=10, label='Sell Signal', zorder=5)
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ax1.scatter(sell_signals.index, sell_signals['close'], color='red', marker='o', s=20, label='Sell Signal', zorder=5)
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ax1.set_title(f'Price and Signals ({strategy_name})')
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ax1.set_ylabel('Price')
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ax1.legend()
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ax1.grid(True)
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# Plot 2: RSI and Strategy-Specific Thresholds
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rsi_col_name = 'RSI' if strategy_name == "MarketRegimeStrategy" else 'RSI_15m'
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if rsi_col_name in processed_data.columns:
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sns.lineplot(x=processed_data.index, y=rsi_col_name, data=processed_data, label=f'{rsi_col_name} (' + str(config_strategy.get("rsi_period", 14)) + ')', 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=f'RSI (' + str(config_strategy.get("rsi_period", 14)) + ')', ax=ax2, color='purple')
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if strategy_name == "MarketRegimeStrategy":
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# Assuming trending thresholds are what we want to show generally
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ax2.axhline(config_strategy.get("trending", {}).get("rsi_threshold", [30,70])[1], color='red', linestyle='--', linewidth=0.8, label=f'Overbought (' + str(config_strategy.get("trending", {}).get("rsi_threshold", [30,70])[1]) + ')')
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ax2.axhline(config_strategy.get("trending", {}).get("rsi_threshold", [30,70])[0], color='green', linestyle='--', linewidth=0.8, label=f'Oversold (' + str(config_strategy.get("trending", {}).get("rsi_threshold", [30,70])[0]) + ')')
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# Get threshold values
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upper_threshold = config_strategy.get("trending", {}).get("rsi_threshold", [30,70])[1]
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lower_threshold = config_strategy.get("trending", {}).get("rsi_threshold", [30,70])[0]
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# Shade overbought area (upper)
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ax2.fill_between(processed_data.index, upper_threshold, 100,
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alpha=0.1, color='red', label=f'Overbought (>{upper_threshold})')
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# Shade oversold area (lower)
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ax2.fill_between(processed_data.index, 0, lower_threshold,
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alpha=0.1, color='green', label=f'Oversold (<{lower_threshold})')
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elif strategy_name == "CryptoTradingStrategy":
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ax2.axhline(65, color='red', linestyle='--', linewidth=0.8, label='Overbought (65)') # As per Crypto strategy logic
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ax2.axhline(35, color='green', linestyle='--', linewidth=0.8, label='Oversold (35)') # As per Crypto strategy logic
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# Shade overbought area (upper)
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ax2.fill_between(processed_data.index, 65, 100,
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alpha=0.1, color='red', label='Overbought (>65)')
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# Shade oversold area (lower)
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ax2.fill_between(processed_data.index, 0, 35,
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alpha=0.1, color='green', label='Oversold (<35)')
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# Plot Buy/Sell signals on RSI chart
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if not buy_signals.empty and rsi_col_name in buy_signals.columns:
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ax2.scatter(buy_signals.index, buy_signals[rsi_col_name], color='green', marker='o', s=20, label=f'Buy Signal ({rsi_col_name})', zorder=5)
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if not sell_signals.empty and rsi_col_name in sell_signals.columns:
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ax2.scatter(sell_signals.index, sell_signals[rsi_col_name], color='red', marker='o', s=20, label=f'Sell Signal ({rsi_col_name})', zorder=5)
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ax2.set_title(f'Relative Strength Index ({rsi_col_name}) with Signals')
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ax2.set_ylabel(f'{rsi_col_name} Value')
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if not buy_signals.empty and 'RSI' in buy_signals.columns:
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ax2.scatter(buy_signals.index, buy_signals['RSI'], color='green', marker='o', s=20, label='Buy Signal (RSI)', zorder=5)
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if not sell_signals.empty and 'RSI' in sell_signals.columns:
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ax2.scatter(sell_signals.index, sell_signals['RSI'], color='red', marker='o', s=20, label='Sell Signal (RSI)', zorder=5)
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ax2.set_title('Relative Strength Index (RSI) with Signals')
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ax2.set_ylabel('RSI Value')
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ax2.set_ylim(0, 100)
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ax2.legend()
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ax2.grid(True)
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else:
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logging.info(f"{rsi_col_name} data not available for plotting.")
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logging.info("RSI data not available for plotting.")
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# Plot 3: Strategy-Specific Indicators
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ax3.clear() # Clear previous plot content if any
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if 'BBWidth' in processed_data.columns:
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sns.lineplot(x=processed_data.index, y='BBWidth', data=processed_data, label='BB Width', ax=ax3)
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if strategy_name == "MarketRegimeStrategy":
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if 'BBWidth' in processed_data.columns:
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sns.lineplot(x=processed_data.index, y='BBWidth', data=processed_data, label='BB Width', ax=ax3)
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if 'MarketRegime' in processed_data.columns:
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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 & Market Regime')
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ax3.set_ylabel('Value')
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elif strategy_name == "CryptoTradingStrategy":
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if 'VolumeMA_15m' in processed_data.columns:
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sns.lineplot(x=processed_data.index, y='VolumeMA_15m', data=processed_data, label='Volume MA (15m)', ax=ax3)
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if 'volume' in processed_data.columns: # Plot original volume for comparison
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sns.lineplot(x=processed_data.index, y='volume', data=processed_data, label='Volume (15m)', ax=ax3, alpha=0.5)
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ax3.set_title('Volume Analysis (15m)')
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if 'VolumeMA' in processed_data.columns:
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sns.lineplot(x=processed_data.index, y='VolumeMA', data=processed_data, label='Volume MA', ax=ax3)
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if 'volume' in processed_data.columns:
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sns.lineplot(x=processed_data.index, y='volume', data=processed_data, label='Volume', ax=ax3, alpha=0.5)
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ax3.set_title('Volume Analysis')
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ax3.set_ylabel('Volume')
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ax3.legend()
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