224 lines
8.6 KiB
Python
224 lines
8.6 KiB
Python
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"""
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Backtest Runner
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This module provides a high-level interface for running backtests with strategy
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management. It encapsulates the backtesting workflow and provides a clean
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interface for executing tests across different configurations.
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"""
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import pandas as pd
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import logging
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from typing import Dict, List, Tuple, Any, Optional
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from cycles.backtest import Backtest
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from cycles.charts import BacktestCharts
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from cycles.strategies import create_strategy_manager
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from .results_processor import ResultsProcessor
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class BacktestRunner:
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"""
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High-level backtest execution interface.
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Encapsulates the backtesting workflow, strategy management, and result
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processing into a clean, reusable interface.
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"""
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def __init__(self, results_processor: Optional[ResultsProcessor] = None):
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"""
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Initialize backtest runner.
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Args:
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results_processor: Optional results processor instance
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"""
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self.logger = logging.getLogger(__name__)
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self.results_processor = results_processor or ResultsProcessor()
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def run_single_timeframe(self, data_1min: pd.DataFrame, timeframe: str,
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config: Dict[str, Any], debug: bool = False) -> Tuple[Dict[str, Any], List[Dict[str, Any]]]:
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"""
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Run backtest for a single timeframe configuration.
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Args:
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data_1min: 1-minute OHLCV data
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timeframe: Timeframe identifier
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config: Configuration dictionary
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debug: Whether to enable debug mode
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Returns:
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Tuple[Dict, List]: (summary_row, trade_rows)
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"""
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try:
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# Create and initialize strategy manager
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strategy_manager = self._create_strategy_manager(config)
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# Setup backtester with appropriate data
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backtester = self._setup_backtester(data_1min, strategy_manager, config)
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# Run backtest
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results = self._execute_backtest(backtester, debug)
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# Process results
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strategy_summary = strategy_manager.get_strategy_summary()
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summary_row, trade_rows = self.results_processor.process_backtest_results(
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results, timeframe, config, strategy_summary
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)
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# Handle debug plotting
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if debug:
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self._handle_debug_plotting(backtester, results)
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return summary_row, trade_rows
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except Exception as e:
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self.logger.error(f"Backtest failed for timeframe {timeframe}: {e}")
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raise
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def _create_strategy_manager(self, config: Dict[str, Any]):
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"""Create and validate strategy manager from configuration."""
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strategy_config = {
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"strategies": config['strategies'],
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"combination_rules": config['combination_rules']
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}
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if not strategy_config['strategies']:
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raise ValueError("No strategy configuration provided")
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return create_strategy_manager(strategy_config)
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def _setup_backtester(self, data_1min: pd.DataFrame, strategy_manager, config: Dict[str, Any]) -> Backtest:
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"""Setup backtester with appropriate data and strategy manager."""
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# Get primary strategy for backtester setup
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primary_strategy = strategy_manager.strategies[0]
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# Determine working dataframe based on strategy type
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if primary_strategy.name == "bbrs":
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# BBRS strategy processes 1-minute data and handles internal resampling
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working_df = data_1min.copy()
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else:
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# Other strategies specify their preferred timeframe
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primary_strategy._resample_data(data_1min)
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working_df = primary_strategy.get_primary_timeframe_data()
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# Prepare working dataframe for backtester
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working_df_for_backtest = working_df.copy().reset_index()
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if 'index' in working_df_for_backtest.columns:
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working_df_for_backtest = working_df_for_backtest.rename(columns={'index': 'timestamp'})
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# Initialize backtest
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backtester = Backtest(
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config['initial_usd'],
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working_df_for_backtest,
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working_df_for_backtest,
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self._strategy_manager_init
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)
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# Store original data and attach strategy manager
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backtester.original_df = data_1min
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backtester.strategy_manager = strategy_manager
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# Initialize strategy manager
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strategy_manager.initialize(backtester)
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return backtester
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def _execute_backtest(self, backtester: Backtest, debug: bool = False) -> Dict[str, Any]:
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"""Execute the backtest using strategy manager functions."""
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return backtester.run(
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self._strategy_manager_entry,
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self._strategy_manager_exit,
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debug
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)
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def _handle_debug_plotting(self, backtester: Backtest, results: Dict[str, Any]) -> None:
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"""Handle debug plotting if enabled."""
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try:
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# Check if any strategy has processed_data for universal plotting
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processed_data = None
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for strategy in backtester.strategy_manager.strategies:
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if hasattr(backtester, 'processed_data') and backtester.processed_data is not None:
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processed_data = backtester.processed_data
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break
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if processed_data is not None and not processed_data.empty:
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# Format strategy data with actual executed trades for universal plotting
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formatted_data = BacktestCharts.format_strategy_data_with_trades(processed_data, results)
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# Plot using universal function
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BacktestCharts.plot_data(formatted_data)
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else:
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# Fallback to meta_trend plot if available
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if "meta_trend" in backtester.strategies:
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meta_trend = backtester.strategies["meta_trend"]
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working_df = backtester.df.set_index('timestamp')
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BacktestCharts.plot(working_df, meta_trend)
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else:
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self.logger.info("No plotting data available")
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except Exception as e:
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self.logger.warning(f"Plotting failed: {e}")
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# Strategy manager interface functions
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@staticmethod
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def _strategy_manager_init(backtester: Backtest):
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"""Strategy Manager initialization function."""
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# Actual initialization happens in strategy_manager.initialize()
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pass
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@staticmethod
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def _strategy_manager_entry(backtester: Backtest, df_index: int) -> bool:
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"""Strategy Manager entry function."""
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return backtester.strategy_manager.get_entry_signal(backtester, df_index)
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@staticmethod
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def _strategy_manager_exit(backtester: Backtest, df_index: int) -> Tuple[Optional[str], Optional[float]]:
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"""Strategy Manager exit function."""
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return backtester.strategy_manager.get_exit_signal(backtester, df_index)
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class TimeframeTask:
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"""Encapsulates a single timeframe backtest task."""
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def __init__(self, timeframe: str, data_1min: pd.DataFrame, config: Dict[str, Any]):
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"""
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Initialize timeframe task.
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Args:
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timeframe: Timeframe identifier
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data_1min: 1-minute OHLCV data
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config: Configuration for this task
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"""
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self.timeframe = timeframe
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self.data_1min = data_1min
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self.config = config
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def execute(self, debug: bool = False) -> Tuple[Dict[str, Any], List[Dict[str, Any]]]:
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"""
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Execute the timeframe task.
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Args:
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debug: Whether to enable debug mode
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Returns:
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Tuple[Dict, List]: (summary_row, trade_rows)
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"""
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runner = BacktestRunner()
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return runner.run_single_timeframe(self.data_1min, self.timeframe, self.config, debug)
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def create_timeframe_tasks(timeframes: List[str], data_1min: pd.DataFrame,
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config_manager) -> List[TimeframeTask]:
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"""
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Create timeframe tasks from configuration.
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Args:
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timeframes: List of timeframes to test
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data_1min: 1-minute OHLCV data
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config_manager: Configuration manager instance
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Returns:
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List[TimeframeTask]: List of timeframe tasks
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"""
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tasks = []
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for timeframe in timeframes:
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task_config = config_manager.get_timeframe_task_config(timeframe)
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tasks.append(TimeframeTask(timeframe, data_1min, task_config))
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return tasks
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