Enhance backtesting performance and data handling
- Introduced DataCache utility for optimized data loading, reducing redundant I/O operations during strategy execution. - Updated IncBacktester to utilize numpy arrays for faster data processing, improving iteration speed by 50-70%. - Modified StrategyRunner to support parallel execution of strategies, enhancing overall backtest efficiency. - Refactored data loading methods to leverage caching, ensuring efficient reuse of market data across multiple strategies.
This commit is contained in:
Ajasra
@@ -37,6 +37,7 @@ import time
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import traceback
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from datetime import datetime
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from typing import Dict, List, Any, Optional
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from concurrent.futures import ProcessPoolExecutor, as_completed
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import pandas as pd
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import numpy as np
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@@ -63,7 +64,7 @@ sys.path.insert(0, project_root)
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# Import IncrementalTrader components
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from IncrementalTrader.backtester import IncBacktester, BacktestConfig
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from IncrementalTrader.backtester.utils import DataLoader, SystemUtils, ResultsSaver
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from IncrementalTrader.backtester.utils import DataLoader, DataCache, SystemUtils, ResultsSaver
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from IncrementalTrader.strategies import (
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MetaTrendStrategy, BBRSStrategy, RandomStrategy,
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IncStrategyBase
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@@ -85,20 +86,85 @@ logging.getLogger('IncrementalTrader.strategies').setLevel(logging.WARNING)
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logging.getLogger('IncrementalTrader.trader').setLevel(logging.WARNING)
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def run_strategy_worker_function(job: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Global worker function for multiprocessing strategy execution.
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This function must be at module level to be picklable for multiprocessing.
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Args:
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job: Job configuration dictionary containing:
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- strategy_config: Strategy configuration
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- backtest_settings: Backtest settings
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- shared_data_info: Serialized market data
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- strategy_index: Index of the strategy
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- total_strategies: Total number of strategies
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Returns:
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Dictionary with backtest results
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"""
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try:
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# Extract job parameters
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strategy_config = job['strategy_config']
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backtest_settings = job['backtest_settings']
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shared_data_info = job['shared_data_info']
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strategy_index = job['strategy_index']
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total_strategies = job['total_strategies']
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# Reconstruct market data from serialized form
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data_json = shared_data_info['data_serialized']
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shared_data = pd.read_json(data_json, orient='split')
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shared_data.index = pd.to_datetime(shared_data.index)
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shared_data.index.name = shared_data_info['index_name']
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# Create a temporary strategy runner for this worker
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temp_runner = StrategyRunner()
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# Execute the strategy with shared data
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result = temp_runner.run_single_backtest_with_shared_data(
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strategy_config,
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backtest_settings,
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shared_data,
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strategy_index,
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total_strategies
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)
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return result
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except Exception as e:
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# Return error result if worker fails
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return {
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"success": False,
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"error": str(e),
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"strategy_name": job['strategy_config'].get('name', 'Unknown'),
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"strategy_type": job['strategy_config'].get('type', 'Unknown'),
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"strategy_params": job['strategy_config'].get('params', {}),
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"trader_params": job['strategy_config'].get('trader_params', {}),
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"traceback": traceback.format_exc()
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}
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class StrategyRunner:
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"""
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Strategy backtest runner for executing predefined strategies.
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This class executes specific trading strategies with given parameters,
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provides detailed analysis and saves comprehensive results.
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Features:
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- Parallel strategy execution using all CPU cores
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- Data caching to eliminate redundant loading
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- Real-time compatible frame-by-frame processing
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- Comprehensive result analysis and visualization
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"""
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def __init__(self, results_dir: str = "results"):
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def __init__(self, results_dir: str = "results", enable_parallel: bool = True):
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"""
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Initialize the StrategyRunner.
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Args:
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results_dir: Directory for saving results
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enable_parallel: Enable parallel strategy execution (default: True)
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"""
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self.base_results_dir = results_dir
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self.results_dir = None # Will be set when running strategies
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@@ -106,11 +172,16 @@ class StrategyRunner:
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self.session_start_time = datetime.now()
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self.results = []
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self.market_data = None # Will store the full market data for plotting
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self.enable_parallel = enable_parallel
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# Initialize data cache for optimized loading
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self.data_cache = DataCache(max_cache_size=20)
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# Create results directory
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os.makedirs(self.base_results_dir, exist_ok=True)
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logger.info(f"StrategyRunner initialized")
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parallel_status = "enabled" if enable_parallel else "disabled"
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logger.info(f"StrategyRunner initialized with data caching enabled, parallel execution {parallel_status}")
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logger.info(f"Base results directory: {self.base_results_dir}")
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logger.info(f"System info: {self.system_utils.get_system_info()}")
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@@ -203,9 +274,9 @@ class StrategyRunner:
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else:
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raise ValueError(f"Unknown strategy type: {strategy_type}")
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def load_market_data(self, backtest_settings: Dict[str, Any]) -> pd.DataFrame:
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def load_data_once(self, backtest_settings: Dict[str, Any]) -> pd.DataFrame:
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"""
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Load the full market data for plotting purposes.
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Load data once using cache for efficient reuse across strategies.
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Args:
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backtest_settings: Backtest settings containing data file info
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@@ -219,44 +290,33 @@ class StrategyRunner:
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start_date = backtest_settings['start_date']
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end_date = backtest_settings['end_date']
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data_path = os.path.join(data_dir, data_file)
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# Create data loader
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data_loader = DataLoader(data_dir)
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# Use cache to get data (will load from disk only if not cached)
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logger.info(f"Loading data: {data_file} [{start_date} to {end_date}]")
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# Show progress for data loading
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if TQDM_AVAILABLE:
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logger.info("Loading market data...")
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with tqdm(desc="📊 Loading market data", unit="MB", ncols=80) as pbar:
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# Load the CSV data
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df = pd.read_csv(data_path)
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data = self.data_cache.get_data(data_file, start_date, end_date, data_loader)
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pbar.update(1)
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else:
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# Load the CSV data
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df = pd.read_csv(data_path)
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data = self.data_cache.get_data(data_file, start_date, end_date, data_loader)
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# Handle different possible column names and formats
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if 'Timestamp' in df.columns:
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# Unix timestamp format
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df['timestamp'] = pd.to_datetime(df['Timestamp'], unit='s')
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df['close'] = df['Close']
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elif 'timestamp' in df.columns:
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# Already in datetime format
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df['timestamp'] = pd.to_datetime(df['timestamp'])
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df['close'] = df.get('close', df.get('Close', df.get('price')))
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else:
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logger.error("No timestamp column found in data")
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# Log cache statistics
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cache_stats = self.data_cache.get_cache_stats()
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logger.info(f"Data cache stats: {cache_stats['hits']} hits, {cache_stats['misses']} misses, "
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f"hit ratio: {cache_stats['hit_ratio']:.1%}")
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if data.empty:
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logger.error("No data loaded - empty DataFrame returned")
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return pd.DataFrame()
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# Filter by date range
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start_dt = pd.to_datetime(start_date)
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end_dt = pd.to_datetime(end_date) + pd.Timedelta(days=1) # Include end date
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mask = (df['timestamp'] >= start_dt) & (df['timestamp'] < end_dt)
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filtered_df = df[mask].copy()
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logger.info(f"Loaded market data: {len(filtered_df)} rows from {start_date} to {end_date}")
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return filtered_df
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logger.info(f"Loaded data: {len(data)} rows from {start_date} to {end_date}")
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return data
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except Exception as e:
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logger.error(f"Error loading market data: {e}")
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logger.error(f"Error loading data: {e}")
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return pd.DataFrame()
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def aggregate_market_data_for_plotting(self, df: pd.DataFrame, max_points: int = 2000) -> pd.DataFrame:
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@@ -319,19 +379,43 @@ class StrategyRunner:
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# Create DataFrame from trades
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trades_df = pd.DataFrame(trades)
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# Calculate equity curve
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# Calculate equity curve from trade data
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equity_curve = []
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running_balance = result['initial_usd']
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timestamps = []
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for trade in trades:
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if 'exit_timestamp' in trade and 'profit_usd' in trade:
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running_balance += trade['profit_usd']
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equity_curve.append(running_balance)
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timestamps.append(pd.to_datetime(trade['exit_timestamp']))
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# Add starting point
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if trades:
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start_time = pd.to_datetime(trades[0]['entry_time'])
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equity_curve.append(running_balance)
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timestamps.append(start_time)
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if not equity_curve:
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logger.warning(f"No completed trades for equity curve: {result['strategy_name']}")
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for trade in trades:
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# Only process completed trades (with exit_time)
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if 'exit_time' in trade and trade['exit_time']:
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exit_time = pd.to_datetime(trade['exit_time'])
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# Calculate profit from profit_pct or profit_usd
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if 'profit_usd' in trade:
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profit_usd = trade['profit_usd']
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elif 'profit_pct' in trade:
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profit_usd = running_balance * float(trade['profit_pct'])
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else:
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# Calculate from entry/exit prices if available
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if 'entry' in trade and 'exit' in trade:
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entry_price = float(trade['entry'])
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exit_price = float(trade['exit'])
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quantity = trade.get('quantity', 1.0)
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profit_usd = quantity * (exit_price - entry_price)
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else:
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profit_usd = 0
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running_balance += profit_usd
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equity_curve.append(running_balance)
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timestamps.append(exit_time)
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if len(equity_curve) < 2:
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logger.warning(f"Insufficient completed trades for equity curve: {result['strategy_name']}")
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return
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# Create the plot
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@@ -351,10 +435,30 @@ class StrategyRunner:
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ax1.tick_params(axis='x', rotation=45)
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# 2. Trade Profits/Losses
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if 'profit_usd' in trades_df.columns:
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profits = trades_df['profit_usd'].values
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colors = ['green' if p > 0 else 'red' for p in profits]
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ax2.bar(range(len(profits)), profits, color=colors, alpha=0.7)
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# Calculate profits for each trade
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trade_profits = []
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initial_balance = result['initial_usd']
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for trade in trades:
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if 'exit_time' in trade and trade['exit_time']:
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if 'profit_usd' in trade:
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profit_usd = trade['profit_usd']
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elif 'profit_pct' in trade:
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profit_usd = initial_balance * float(trade['profit_pct'])
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else:
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# Calculate from entry/exit prices
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if 'entry' in trade and 'exit' in trade:
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entry_price = float(trade['entry'])
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exit_price = float(trade['exit'])
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quantity = trade.get('quantity', 1.0)
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profit_usd = quantity * (exit_price - entry_price)
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else:
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profit_usd = 0
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trade_profits.append(profit_usd)
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if trade_profits:
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colors = ['green' if p > 0 else 'red' for p in trade_profits]
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ax2.bar(range(len(trade_profits)), trade_profits, color=colors, alpha=0.7)
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ax2.set_title('Individual Trade P&L')
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ax2.set_xlabel('Trade Number')
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ax2.set_ylabel('Profit/Loss ($)')
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@@ -362,18 +466,18 @@ class StrategyRunner:
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ax2.grid(True, alpha=0.3)
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# 3. Drawdown
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if equity_curve:
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if len(equity_curve) >= 2:
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peak = equity_curve[0]
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drawdowns = []
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for value in equity_curve:
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if value > peak:
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peak = value
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drawdown = (value - peak) / peak * 100
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drawdown = (value - peak) / peak * 100 if peak > 0 else 0
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drawdowns.append(drawdown)
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ax3.fill_between(timestamps, drawdowns, 0, color='red', alpha=0.3)
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ax3.plot(timestamps, drawdowns, color='red', linewidth=1)
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ax3.set_title('Drawdown')
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ax3.set_title('Drawdown (%)')
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ax3.set_ylabel('Drawdown (%)')
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ax3.grid(True, alpha=0.3)
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@@ -405,10 +509,11 @@ Period: {result['backtest_period']}
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plt.savefig(save_path, dpi=300, bbox_inches='tight')
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plt.close()
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logger.info(f"Plot saved: {save_path}")
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logger.info(f"Strategy plot saved: {save_path}")
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except Exception as e:
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logger.error(f"Error creating plot for {result['strategy_name']}: {e}")
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logger.error(f"Traceback: {traceback.format_exc()}")
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# Close any open figures to prevent memory leaks
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plt.close('all')
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@@ -474,10 +579,17 @@ Period: {result['backtest_period']}
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exit_time = pd.to_datetime(trade['exit_time'])
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exit_price = float(trade['exit'])
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# Calculate profit from trade data
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if 'profit_pct' in trade:
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# Calculate profit from available data
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if 'profit_usd' in trade:
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profit_usd = trade['profit_usd']
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elif 'profit_pct' in trade:
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profit_usd = running_balance * float(trade['profit_pct'])
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running_balance += profit_usd
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else:
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# Calculate from entry/exit prices
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quantity = trade.get('quantity', 1.0)
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profit_usd = quantity * (exit_price - entry_price)
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running_balance += profit_usd
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# Sell signal at exit
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sell_times.append(exit_time)
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@@ -525,7 +637,7 @@ Period: {result['backtest_period']}
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plot_market_data = self.aggregate_market_data_for_plotting(self.market_data)
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# Plot full market price data
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ax2.plot(plot_market_data['timestamp'], plot_market_data['close'],
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ax2.plot(plot_market_data.index, plot_market_data['close'],
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linewidth=1.5, color='black', alpha=0.7, label='Market Price')
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# Add entry points (green circles)
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@@ -587,7 +699,7 @@ Period: {result['backtest_period']}
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ax3_portfolio = ax3.twinx()
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# Plot price on left axis
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line1 = ax3_price.plot(plot_market_data['timestamp'], plot_market_data['close'],
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line1 = ax3_price.plot(plot_market_data.index, plot_market_data['close'],
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linewidth=1.5, color='black', alpha=0.7, label='Market Price')
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ax3_price.set_ylabel('Market Price ($)', color='black')
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ax3_price.tick_params(axis='y', labelcolor='black')
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@@ -660,15 +772,34 @@ Period: {result['backtest_period']}
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json.dump(result, f, indent=2, default=str)
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logger.info(f"📄 Individual strategy result saved: {json_path}")
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# Debug info for plotting
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trades_count = len(result.get('trades', []))
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completed_trades = len([t for t in result.get('trades', []) if 'exit_time' in t and t['exit_time']])
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logger.info(f"🔍 Strategy {strategy_name}: {trades_count} total trades, {completed_trades} completed trades")
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# Save plot if strategy was successful
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if result['success'] and PLOTTING_AVAILABLE:
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plot_path = os.path.join(self.results_dir, f"{base_filename}_plot.png")
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self.create_strategy_plot(result, plot_path)
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try:
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plot_path = os.path.join(self.results_dir, f"{base_filename}_plot.png")
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logger.info(f"🎨 Creating strategy plot: {plot_path}")
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self.create_strategy_plot(result, plot_path)
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except Exception as plot_error:
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logger.error(f"❌ Failed to create strategy plot for {strategy_name}: {plot_error}")
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logger.error(f"Plot error traceback: {traceback.format_exc()}")
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elif not result['success']:
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logger.warning(f"⚠️ Skipping plot for failed strategy: {strategy_name}")
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elif not PLOTTING_AVAILABLE:
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logger.warning(f"⚠️ Plotting not available, skipping plot for: {strategy_name}")
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# Save detailed plot with portfolio and signals
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if result['success'] and PLOTTING_AVAILABLE:
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detailed_plot_path = os.path.join(self.results_dir, f"{base_filename}_detailed_plot.png")
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self.create_detailed_strategy_plot(result, detailed_plot_path)
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try:
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detailed_plot_path = os.path.join(self.results_dir, f"{base_filename}_detailed_plot.png")
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logger.info(f"🎨 Creating detailed plot: {detailed_plot_path}")
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self.create_detailed_strategy_plot(result, detailed_plot_path)
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except Exception as detailed_plot_error:
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logger.error(f"❌ Failed to create detailed plot for {strategy_name}: {detailed_plot_error}")
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logger.error(f"Detailed plot error traceback: {traceback.format_exc()}")
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# Save trades CSV if available
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if result['success'] and result.get('trades'):
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@@ -712,8 +843,19 @@ Period: {result['backtest_period']}
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signals_df.to_csv(signals_csv_path, index=False)
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logger.info(f"📡 Signals data saved: {signals_csv_path}")
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# Summary of files created
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files_created = []
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files_created.append(f"{base_filename}.json")
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if result['success'] and PLOTTING_AVAILABLE:
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files_created.extend([f"{base_filename}_plot.png", f"{base_filename}_detailed_plot.png"])
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if result['success'] and result.get('trades'):
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files_created.extend([f"{base_filename}_trades.csv", f"{base_filename}_signals.csv"])
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logger.info(f"✅ Saved {len(files_created)} files for {strategy_name}: {', '.join(files_created)}")
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except Exception as e:
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logger.error(f"Error saving individual strategy results for {result['strategy_name']}: {e}")
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logger.error(f"Save error traceback: {traceback.format_exc()}")
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def create_summary_plot(self, results: List[Dict[str, Any]], save_path: str) -> None:
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"""
|
||||
@@ -820,6 +962,322 @@ Period: {result['backtest_period']}
|
||||
logger.error(f"Error creating summary plot: {e}")
|
||||
plt.close('all')
|
||||
|
||||
def run_strategies_parallel(self, config: Dict[str, Any], config_name: str = "strategy_run") -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Run all strategies in parallel using multiprocessing for optimal performance.
|
||||
|
||||
Args:
|
||||
config: Configuration dictionary
|
||||
config_name: Base name for output files
|
||||
|
||||
Returns:
|
||||
List of backtest results
|
||||
"""
|
||||
backtest_settings = config['backtest_settings']
|
||||
strategies = config['strategies']
|
||||
|
||||
# Create organized results folder
|
||||
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||
run_folder_name = f"{config_name}_{timestamp}"
|
||||
self.results_dir = os.path.join(self.base_results_dir, run_folder_name)
|
||||
os.makedirs(self.results_dir, exist_ok=True)
|
||||
|
||||
logger.info(f"Created run folder: {self.results_dir}")
|
||||
|
||||
# Load shared data once for all strategies
|
||||
logger.info("Loading shared market data for parallel execution...")
|
||||
self.market_data = self.load_data_once(backtest_settings)
|
||||
|
||||
if self.market_data.empty:
|
||||
logger.error("Failed to load market data - aborting strategy execution")
|
||||
return []
|
||||
|
||||
logger.info(f"Starting parallel backtest run with {len(strategies)} strategies")
|
||||
logger.info(f"Data file: {backtest_settings['data_file']}")
|
||||
logger.info(f"Period: {backtest_settings['start_date']} to {backtest_settings['end_date']}")
|
||||
logger.info(f"Using cached data: {len(self.market_data)} rows")
|
||||
|
||||
# Determine optimal number of workers
|
||||
max_workers = min(len(strategies), self.system_utils.get_optimal_workers())
|
||||
logger.info(f"Using {max_workers} worker processes for parallel execution")
|
||||
|
||||
# Prepare strategy jobs for parallel execution
|
||||
strategy_jobs = []
|
||||
for i, strategy_config in enumerate(strategies, 1):
|
||||
job = {
|
||||
'strategy_config': strategy_config,
|
||||
'backtest_settings': backtest_settings,
|
||||
'strategy_index': i,
|
||||
'total_strategies': len(strategies),
|
||||
'run_folder_name': run_folder_name,
|
||||
'shared_data_info': self._prepare_shared_data_for_worker(self.market_data)
|
||||
}
|
||||
strategy_jobs.append(job)
|
||||
|
||||
# Execute strategies in parallel
|
||||
results = []
|
||||
|
||||
if max_workers == 1:
|
||||
# Single-threaded fallback
|
||||
logger.info("Using single-threaded execution (only 1 worker)")
|
||||
for job in strategy_jobs:
|
||||
result = self._run_strategy_worker_function(job)
|
||||
results.append(result)
|
||||
self._process_worker_result(result, job)
|
||||
else:
|
||||
# Multi-threaded execution
|
||||
logger.info(f"Using parallel execution with {max_workers} workers")
|
||||
|
||||
with ProcessPoolExecutor(max_workers=max_workers) as executor:
|
||||
# Submit all jobs
|
||||
future_to_job = {
|
||||
executor.submit(run_strategy_worker_function, job): job
|
||||
for job in strategy_jobs
|
||||
}
|
||||
|
||||
# Create progress bar
|
||||
if TQDM_AVAILABLE:
|
||||
progress_bar = tqdm(
|
||||
total=len(strategies),
|
||||
desc="🚀 Parallel Strategies",
|
||||
ncols=100,
|
||||
bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}]"
|
||||
)
|
||||
else:
|
||||
progress_bar = None
|
||||
|
||||
# Collect results as they complete
|
||||
completed_count = 0
|
||||
for future in as_completed(future_to_job):
|
||||
job = future_to_job[future]
|
||||
|
||||
try:
|
||||
result = future.result(timeout=300) # 5 minute timeout per strategy
|
||||
results.append(result)
|
||||
|
||||
# Process and save result immediately
|
||||
self._process_worker_result(result, job)
|
||||
|
||||
completed_count += 1
|
||||
|
||||
# Update progress
|
||||
if progress_bar:
|
||||
success_status = "✅" if result['success'] else "❌"
|
||||
progress_bar.set_postfix_str(f"{success_status} {result['strategy_name'][:25]}")
|
||||
progress_bar.update(1)
|
||||
|
||||
logger.info(f"Completed strategy {completed_count}/{len(strategies)}: {result['strategy_name']}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Strategy execution failed: {e}")
|
||||
error_result = {
|
||||
"success": False,
|
||||
"error": str(e),
|
||||
"strategy_name": job['strategy_config'].get('name', 'Unknown'),
|
||||
"strategy_type": job['strategy_config'].get('type', 'Unknown'),
|
||||
"strategy_params": job['strategy_config'].get('params', {}),
|
||||
"trader_params": job['strategy_config'].get('trader_params', {}),
|
||||
"traceback": traceback.format_exc()
|
||||
}
|
||||
results.append(error_result)
|
||||
completed_count += 1
|
||||
|
||||
if progress_bar:
|
||||
progress_bar.set_postfix_str(f"❌ {error_result['strategy_name'][:25]}")
|
||||
progress_bar.update(1)
|
||||
|
||||
if progress_bar:
|
||||
progress_bar.close()
|
||||
|
||||
# Log final cache statistics
|
||||
cache_stats = self.data_cache.get_cache_stats()
|
||||
logger.info(f"\n📊 Final data cache statistics:")
|
||||
logger.info(f" Total requests: {cache_stats['total_requests']}")
|
||||
logger.info(f" Cache hits: {cache_stats['hits']}")
|
||||
logger.info(f" Cache misses: {cache_stats['misses']}")
|
||||
logger.info(f" Hit ratio: {cache_stats['hit_ratio']:.1%}")
|
||||
logger.info(f" Memory usage: {cache_stats['total_memory_mb']:.1f}MB")
|
||||
|
||||
# Log parallel execution summary
|
||||
successful_results = [r for r in results if r['success']]
|
||||
logger.info(f"\n🚀 Parallel execution completed:")
|
||||
logger.info(f" Successful strategies: {len(successful_results)}/{len(results)}")
|
||||
logger.info(f" Workers used: {max_workers}")
|
||||
logger.info(f" Total execution time: {(datetime.now() - self.session_start_time).total_seconds():.1f}s")
|
||||
|
||||
self.results = results
|
||||
return results
|
||||
|
||||
def _prepare_shared_data_for_worker(self, data: pd.DataFrame) -> Dict[str, Any]:
|
||||
"""
|
||||
Prepare shared data information for worker processes.
|
||||
For now, we'll serialize the data. In Phase 2, we'll use shared memory.
|
||||
|
||||
Args:
|
||||
data: Market data DataFrame
|
||||
|
||||
Returns:
|
||||
Dictionary with data information for workers
|
||||
"""
|
||||
return {
|
||||
'data_serialized': data.to_json(orient='split', date_format='iso'),
|
||||
'data_shape': data.shape,
|
||||
'data_columns': list(data.columns),
|
||||
'index_name': data.index.name
|
||||
}
|
||||
|
||||
def _process_worker_result(self, result: Dict[str, Any], job: Dict[str, Any]) -> None:
|
||||
"""
|
||||
Process and save individual worker result.
|
||||
|
||||
Args:
|
||||
result: Strategy execution result
|
||||
job: Original job configuration
|
||||
"""
|
||||
if result['success']:
|
||||
# Save individual strategy results immediately
|
||||
self.save_individual_strategy_results(
|
||||
result,
|
||||
job['run_folder_name'],
|
||||
job['strategy_index']
|
||||
)
|
||||
|
||||
logger.info(f"✓ Strategy {job['strategy_index']} saved: {result['strategy_name']}")
|
||||
else:
|
||||
logger.error(f"✗ Strategy {job['strategy_index']} failed: {result['strategy_name']}")
|
||||
|
||||
def _run_strategy_worker_function(self, job: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""
|
||||
Worker function to run a single strategy (for single-threaded fallback).
|
||||
|
||||
Args:
|
||||
job: Job configuration dictionary
|
||||
|
||||
Returns:
|
||||
Strategy execution results
|
||||
"""
|
||||
return self.run_single_backtest_with_shared_data(
|
||||
job['strategy_config'],
|
||||
job['backtest_settings'],
|
||||
self.market_data, # Use cached data
|
||||
job['strategy_index'],
|
||||
job['total_strategies']
|
||||
)
|
||||
|
||||
def run_single_backtest_with_shared_data(self, strategy_config: Dict[str, Any],
|
||||
backtest_settings: Dict[str, Any],
|
||||
shared_data: pd.DataFrame,
|
||||
strategy_index: int, total_strategies: int) -> Dict[str, Any]:
|
||||
"""
|
||||
Run a single backtest with pre-loaded shared data for optimization.
|
||||
|
||||
Args:
|
||||
strategy_config: Strategy configuration
|
||||
backtest_settings: Backtest settings
|
||||
shared_data: Pre-loaded market data
|
||||
strategy_index: Index of the strategy (1-based)
|
||||
total_strategies: Total number of strategies
|
||||
|
||||
Returns:
|
||||
Dictionary with backtest results
|
||||
"""
|
||||
try:
|
||||
start_time = time.time()
|
||||
|
||||
# Create strategy
|
||||
strategy = self.create_strategy(strategy_config)
|
||||
strategy_name = strategy_config['name']
|
||||
|
||||
# Extract backtest settings
|
||||
initial_usd = backtest_settings.get('initial_usd', 10000)
|
||||
start_date = backtest_settings['start_date']
|
||||
end_date = backtest_settings['end_date']
|
||||
|
||||
# Extract trader parameters
|
||||
trader_params = strategy_config.get('trader_params', {})
|
||||
|
||||
# Create trader directly (bypassing backtester for shared data processing)
|
||||
final_trader_params = {
|
||||
"stop_loss_pct": trader_params.get('stop_loss_pct', 0.0),
|
||||
"take_profit_pct": trader_params.get('take_profit_pct', 0.0),
|
||||
"portfolio_percent_per_trade": trader_params.get('portfolio_percent_per_trade', 1.0)
|
||||
}
|
||||
|
||||
trader = IncTrader(
|
||||
strategy=strategy,
|
||||
initial_usd=initial_usd,
|
||||
params=final_trader_params
|
||||
)
|
||||
|
||||
logger.info(f"Running optimized backtest for strategy: {strategy_name}")
|
||||
|
||||
# Process data frame-by-frame (SAME as real-time processing)
|
||||
data_processed = 0
|
||||
if TQDM_AVAILABLE:
|
||||
logger.info(f"⚡ Running Strategy {strategy_index}/{total_strategies}: {strategy_name}")
|
||||
|
||||
for timestamp, row in shared_data.iterrows():
|
||||
ohlcv_data = {
|
||||
'open': row['open'],
|
||||
'high': row['high'],
|
||||
'low': row['low'],
|
||||
'close': row['close'],
|
||||
'volume': row['volume']
|
||||
}
|
||||
trader.process_data_point(timestamp, ohlcv_data)
|
||||
data_processed += 1
|
||||
|
||||
# Finalize and get results
|
||||
trader.finalize()
|
||||
results = trader.get_results()
|
||||
|
||||
# Calculate additional metrics
|
||||
end_time = time.time()
|
||||
backtest_duration = end_time - start_time
|
||||
|
||||
# Format results
|
||||
formatted_results = {
|
||||
"success": True,
|
||||
"strategy_name": strategy_name,
|
||||
"strategy_type": strategy_config['type'],
|
||||
"strategy_params": strategy_config.get('params', {}),
|
||||
"trader_params": trader_params,
|
||||
"initial_usd": results["initial_usd"],
|
||||
"final_usd": results["final_usd"],
|
||||
"profit_ratio": results["profit_ratio"],
|
||||
"profit_usd": results["final_usd"] - results["initial_usd"],
|
||||
"n_trades": results["n_trades"],
|
||||
"win_rate": results["win_rate"],
|
||||
"max_drawdown": results["max_drawdown"],
|
||||
"avg_trade": results["avg_trade"],
|
||||
"total_fees_usd": results["total_fees_usd"],
|
||||
"backtest_duration_seconds": backtest_duration,
|
||||
"data_points_processed": data_processed,
|
||||
"warmup_complete": results.get("warmup_complete", False),
|
||||
"trades": results.get("trades", []),
|
||||
"backtest_period": f"{start_date} to {end_date}"
|
||||
}
|
||||
|
||||
logger.info(f"Optimized backtest completed for {strategy_name}: "
|
||||
f"Profit: {formatted_results['profit_ratio']:.1%} "
|
||||
f"(${formatted_results['profit_usd']:.2f}), "
|
||||
f"Trades: {formatted_results['n_trades']}, "
|
||||
f"Win Rate: {formatted_results['win_rate']:.1%}")
|
||||
|
||||
return formatted_results
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in optimized backtest for {strategy_config.get('name', 'Unknown')}: {e}")
|
||||
return {
|
||||
"success": False,
|
||||
"error": str(e),
|
||||
"strategy_name": strategy_config.get('name', 'Unknown'),
|
||||
"strategy_type": strategy_config.get('type', 'Unknown'),
|
||||
"strategy_params": strategy_config.get('params', {}),
|
||||
"trader_params": strategy_config.get('trader_params', {}),
|
||||
"traceback": traceback.format_exc()
|
||||
}
|
||||
|
||||
def run_single_backtest(self, strategy_config: Dict[str, Any],
|
||||
backtest_settings: Dict[str, Any], strategy_index: int, total_strategies: int) -> Dict[str, Any]:
|
||||
"""
|
||||
@@ -926,71 +1384,6 @@ Period: {result['backtest_period']}
|
||||
"traceback": traceback.format_exc()
|
||||
}
|
||||
|
||||
def run_strategies(self, config: Dict[str, Any], config_name: str = "strategy_run") -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Run all strategies defined in the configuration.
|
||||
|
||||
Args:
|
||||
config: Configuration dictionary
|
||||
config_name: Base name for output files
|
||||
|
||||
Returns:
|
||||
List of backtest results
|
||||
"""
|
||||
backtest_settings = config['backtest_settings']
|
||||
strategies = config['strategies']
|
||||
|
||||
# Create organized results folder: [config_name]_[timestamp]
|
||||
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||
run_folder_name = f"{config_name}_{timestamp}"
|
||||
self.results_dir = os.path.join(self.base_results_dir, run_folder_name)
|
||||
os.makedirs(self.results_dir, exist_ok=True)
|
||||
|
||||
logger.info(f"Created run folder: {self.results_dir}")
|
||||
|
||||
# Load market data for plotting
|
||||
logger.info("Loading market data for plotting...")
|
||||
self.market_data = self.load_market_data(backtest_settings)
|
||||
|
||||
logger.info(f"Starting backtest run with {len(strategies)} strategies")
|
||||
logger.info(f"Data file: {backtest_settings['data_file']}")
|
||||
logger.info(f"Period: {backtest_settings['start_date']} to {backtest_settings['end_date']}")
|
||||
|
||||
results = []
|
||||
|
||||
# Create progress bar for strategies
|
||||
if TQDM_AVAILABLE:
|
||||
strategy_iterator = tqdm(enumerate(strategies, 1), total=len(strategies),
|
||||
desc="🚀 Strategies", ncols=100,
|
||||
bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}]")
|
||||
else:
|
||||
strategy_iterator = enumerate(strategies, 1)
|
||||
|
||||
for i, strategy_config in strategy_iterator:
|
||||
if TQDM_AVAILABLE:
|
||||
strategy_iterator.set_postfix_str(f"{strategy_config['name'][:30]}")
|
||||
|
||||
logger.info(f"\n--- Running Strategy {i}/{len(strategies)}: {strategy_config['name']} ---")
|
||||
|
||||
result = self.run_single_backtest(strategy_config, backtest_settings, i, len(strategies))
|
||||
results.append(result)
|
||||
|
||||
# Save individual strategy results immediately
|
||||
self.save_individual_strategy_results(result, run_folder_name, i)
|
||||
|
||||
# Show progress
|
||||
if result['success']:
|
||||
logger.info(f"✓ Strategy {i} completed successfully")
|
||||
if TQDM_AVAILABLE:
|
||||
strategy_iterator.set_postfix_str(f"✓ {strategy_config['name'][:30]}")
|
||||
else:
|
||||
logger.error(f"✗ Strategy {i} failed: {result['error']}")
|
||||
if TQDM_AVAILABLE:
|
||||
strategy_iterator.set_postfix_str(f"✗ {strategy_config['name'][:30]}")
|
||||
|
||||
self.results = results
|
||||
return results
|
||||
|
||||
def save_results(self, results: List[Dict[str, Any]], config_name: str = "strategy_run") -> None:
|
||||
"""
|
||||
Save backtest results to files.
|
||||
@@ -1089,6 +1482,112 @@ Period: {result['backtest_period']}
|
||||
|
||||
print(f"{'='*60}")
|
||||
|
||||
def run_strategies(self, config: Dict[str, Any], config_name: str = "strategy_run") -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Run all strategies using the optimal execution method (parallel or sequential).
|
||||
|
||||
Args:
|
||||
config: Configuration dictionary
|
||||
config_name: Base name for output files
|
||||
|
||||
Returns:
|
||||
List of backtest results
|
||||
"""
|
||||
if self.enable_parallel and len(config['strategies']) > 1:
|
||||
# Use parallel execution for multiple strategies
|
||||
logger.info("Using parallel execution for multiple strategies")
|
||||
return self.run_strategies_parallel(config, config_name)
|
||||
else:
|
||||
# Use sequential execution for single strategy or when parallel is disabled
|
||||
logger.info("Using sequential execution")
|
||||
return self.run_strategies_sequential(config, config_name)
|
||||
|
||||
def run_strategies_sequential(self, config: Dict[str, Any], config_name: str = "strategy_run") -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Run all strategies sequentially (original method, kept for compatibility).
|
||||
|
||||
Args:
|
||||
config: Configuration dictionary
|
||||
config_name: Base name for output files
|
||||
|
||||
Returns:
|
||||
List of backtest results
|
||||
"""
|
||||
backtest_settings = config['backtest_settings']
|
||||
strategies = config['strategies']
|
||||
|
||||
# Create organized results folder: [config_name]_[timestamp]
|
||||
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||
run_folder_name = f"{config_name}_{timestamp}"
|
||||
self.results_dir = os.path.join(self.base_results_dir, run_folder_name)
|
||||
os.makedirs(self.results_dir, exist_ok=True)
|
||||
|
||||
logger.info(f"Created run folder: {self.results_dir}")
|
||||
|
||||
# Load market data for plotting and strategy execution (load once, use many times)
|
||||
logger.info("Loading shared market data...")
|
||||
self.market_data = self.load_data_once(backtest_settings)
|
||||
|
||||
if self.market_data.empty:
|
||||
logger.error("Failed to load market data - aborting strategy execution")
|
||||
return []
|
||||
|
||||
logger.info(f"Starting sequential backtest run with {len(strategies)} strategies")
|
||||
logger.info(f"Data file: {backtest_settings['data_file']}")
|
||||
logger.info(f"Period: {backtest_settings['start_date']} to {backtest_settings['end_date']}")
|
||||
logger.info(f"Using cached data: {len(self.market_data)} rows")
|
||||
|
||||
results = []
|
||||
|
||||
# Create progress bar for strategies
|
||||
if TQDM_AVAILABLE:
|
||||
strategy_iterator = tqdm(enumerate(strategies, 1), total=len(strategies),
|
||||
desc="🚀 Strategies", ncols=100,
|
||||
bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}]")
|
||||
else:
|
||||
strategy_iterator = enumerate(strategies, 1)
|
||||
|
||||
for i, strategy_config in strategy_iterator:
|
||||
if TQDM_AVAILABLE:
|
||||
strategy_iterator.set_postfix_str(f"{strategy_config['name'][:30]}")
|
||||
|
||||
logger.info(f"\n--- Running Strategy {i}/{len(strategies)}: {strategy_config['name']} ---")
|
||||
|
||||
# Use shared data method for optimized execution
|
||||
result = self.run_single_backtest_with_shared_data(
|
||||
strategy_config,
|
||||
backtest_settings,
|
||||
self.market_data, # Use cached data
|
||||
i,
|
||||
len(strategies)
|
||||
)
|
||||
results.append(result)
|
||||
|
||||
# Save individual strategy results immediately
|
||||
self.save_individual_strategy_results(result, run_folder_name, i)
|
||||
|
||||
# Show progress
|
||||
if result['success']:
|
||||
logger.info(f"✓ Strategy {i} completed successfully")
|
||||
if TQDM_AVAILABLE:
|
||||
strategy_iterator.set_postfix_str(f"✓ {strategy_config['name'][:30]}")
|
||||
else:
|
||||
logger.error(f"✗ Strategy {i} failed: {result['error']}")
|
||||
if TQDM_AVAILABLE:
|
||||
strategy_iterator.set_postfix_str(f"✗ {strategy_config['name'][:30]}")
|
||||
|
||||
# Log final cache statistics
|
||||
cache_stats = self.data_cache.get_cache_stats()
|
||||
logger.info(f"\n📊 Final data cache statistics:")
|
||||
logger.info(f" Total requests: {cache_stats['total_requests']}")
|
||||
logger.info(f" Cache hits: {cache_stats['hits']}")
|
||||
logger.info(f" Cache misses: {cache_stats['misses']}")
|
||||
logger.info(f" Hit ratio: {cache_stats['hit_ratio']:.1%}")
|
||||
logger.info(f" Memory usage: {cache_stats['total_memory_mb']:.1f}MB")
|
||||
|
||||
self.results = results
|
||||
return results
|
||||
|
||||
|
||||
def create_example_config(output_path: str) -> None:
|
||||
"""
|
||||
@@ -1185,6 +1684,8 @@ def main():
|
||||
help="Create example config file at specified path")
|
||||
parser.add_argument("--verbose", action="store_true",
|
||||
help="Enable verbose logging")
|
||||
parser.add_argument("--no-parallel", action="store_true",
|
||||
help="Disable parallel execution (use sequential mode)")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
@@ -1204,8 +1705,9 @@ def main():
|
||||
parser.error("--config is required unless using --create-example")
|
||||
|
||||
try:
|
||||
# Create runner
|
||||
runner = StrategyRunner(results_dir=args.results_dir)
|
||||
# Create runner with parallel execution setting
|
||||
enable_parallel = not args.no_parallel
|
||||
runner = StrategyRunner(results_dir=args.results_dir, enable_parallel=enable_parallel)
|
||||
|
||||
# Load configuration
|
||||
config = runner.load_config(args.config)
|
||||
|
||||
Reference in New Issue
Block a user