239 lines
9.0 KiB
Python
239 lines
9.0 KiB
Python
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"""
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Results Processor
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This module handles processing, aggregation, and analysis of backtest results.
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It provides utilities for calculating metrics, aggregating results across
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timeframes, and formatting output data.
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"""
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import pandas as pd
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import numpy as np
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import logging
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from typing import Dict, List, Tuple, Any, Optional
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from collections import defaultdict
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class BacktestMetrics:
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"""Container for backtest metrics calculation."""
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@staticmethod
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def calculate_trade_metrics(trades: List[Dict[str, Any]]) -> Dict[str, float]:
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"""Calculate trade-level metrics."""
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if not trades:
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return {
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"n_trades": 0,
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"n_winning_trades": 0,
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"win_rate": 0.0,
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"total_profit": 0.0,
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"total_loss": 0.0,
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"avg_trade": 0.0,
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"profit_ratio": 0.0,
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"max_drawdown": 0.0
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}
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n_trades = len(trades)
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wins = [t for t in trades if t.get('exit') and t['exit'] > t['entry']]
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n_winning_trades = len(wins)
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win_rate = n_winning_trades / n_trades if n_trades > 0 else 0
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total_profit = sum(trade['profit_pct'] for trade in trades)
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total_loss = sum(-trade['profit_pct'] for trade in trades if trade['profit_pct'] < 0)
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avg_trade = total_profit / n_trades if n_trades > 0 else 0
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profit_ratio = total_profit / total_loss if total_loss > 0 else float('inf')
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# Calculate max drawdown
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cumulative_profit = 0
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max_drawdown = 0
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peak = 0
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for trade in trades:
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cumulative_profit += trade['profit_pct']
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if cumulative_profit > peak:
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peak = cumulative_profit
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drawdown = peak - cumulative_profit
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if drawdown > max_drawdown:
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max_drawdown = drawdown
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return {
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"n_trades": n_trades,
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"n_winning_trades": n_winning_trades,
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"win_rate": win_rate,
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"total_profit": total_profit,
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"total_loss": total_loss,
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"avg_trade": avg_trade,
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"profit_ratio": profit_ratio,
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"max_drawdown": max_drawdown
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}
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@staticmethod
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def calculate_portfolio_metrics(trades: List[Dict[str, Any]], initial_usd: float) -> Dict[str, float]:
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"""Calculate portfolio-level metrics."""
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final_usd = initial_usd
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for trade in trades:
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final_usd *= (1 + trade['profit_pct'])
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total_fees_usd = sum(trade.get('fee_usd', 0.0) for trade in trades)
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return {
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"initial_usd": initial_usd,
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"final_usd": final_usd,
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"total_fees_usd": total_fees_usd,
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"total_return": (final_usd - initial_usd) / initial_usd
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}
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class ResultsProcessor:
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"""
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Processes and aggregates backtest results.
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Handles result processing, metric calculation, and aggregation across
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multiple timeframes and configurations.
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"""
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def __init__(self):
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"""Initialize results processor."""
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self.logger = logging.getLogger(__name__)
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def process_backtest_results(self, results: Dict[str, Any], timeframe: str,
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config: Dict[str, Any], strategy_summary: Dict[str, Any]) -> Tuple[Dict[str, Any], List[Dict[str, Any]]]:
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"""
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Process results from a single backtest run.
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Args:
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results: Raw backtest results
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timeframe: Timeframe identifier
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config: Configuration used for the test
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strategy_summary: Summary of strategies used
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Returns:
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Tuple[Dict, List]: (summary_row, trade_rows)
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"""
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trades = results.get('trades', [])
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initial_usd = config['initial_usd']
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# Calculate metrics
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trade_metrics = BacktestMetrics.calculate_trade_metrics(trades)
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portfolio_metrics = BacktestMetrics.calculate_portfolio_metrics(trades, initial_usd)
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# Get primary strategy info for reporting
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primary_strategy = strategy_summary['strategies'][0] if strategy_summary['strategies'] else {}
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primary_timeframe = primary_strategy.get('timeframes', ['unknown'])[0]
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stop_loss_pct = primary_strategy.get('params', {}).get('stop_loss_pct', 'N/A')
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# Create summary row
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summary_row = {
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"timeframe": f"{timeframe}({primary_timeframe})",
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"stop_loss_pct": stop_loss_pct,
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"n_stop_loss": sum(1 for trade in trades if trade.get('type') == 'STOP_LOSS'),
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**trade_metrics,
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**portfolio_metrics
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}
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# Create trade rows
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trade_rows = []
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for trade in trades:
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trade_rows.append({
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"timeframe": f"{timeframe}({primary_timeframe})",
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"stop_loss_pct": stop_loss_pct,
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"entry_time": trade.get("entry_time"),
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"exit_time": trade.get("exit_time"),
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"entry_price": trade.get("entry"),
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"exit_price": trade.get("exit"),
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"profit_pct": trade.get("profit_pct"),
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"type": trade.get("type"),
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"fee_usd": trade.get("fee_usd"),
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})
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# Log results
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strategy_names = [s['name'] for s in strategy_summary['strategies']]
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self.logger.info(
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f"Timeframe: {timeframe}({primary_timeframe}), Stop Loss: {stop_loss_pct}, "
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f"Trades: {trade_metrics['n_trades']}, Strategies: {strategy_names}"
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)
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return summary_row, trade_rows
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def aggregate_results(self, all_rows: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
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"""
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Aggregate results per timeframe and stop_loss_pct.
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Args:
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all_rows: List of result rows to aggregate
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Returns:
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List[Dict]: Aggregated summary rows
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"""
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grouped = defaultdict(list)
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for row in all_rows:
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key = (row['timeframe'], row['stop_loss_pct'])
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grouped[key].append(row)
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summary_rows = []
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for (timeframe, stop_loss_pct), rows in grouped.items():
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if not rows:
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continue
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# Aggregate metrics
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total_trades = sum(r['n_trades'] for r in rows)
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total_stop_loss = sum(r['n_stop_loss'] for r in rows)
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# Average metrics across runs
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avg_win_rate = np.mean([r['win_rate'] for r in rows])
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avg_max_drawdown = np.mean([r['max_drawdown'] for r in rows])
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avg_avg_trade = np.mean([r['avg_trade'] for r in rows])
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avg_profit_ratio = np.mean([r['profit_ratio'] for r in rows if r['profit_ratio'] != float('inf')])
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# Portfolio metrics
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initial_usd = rows[0]['initial_usd'] # Should be same for all
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avg_final_usd = np.mean([r['final_usd'] for r in rows])
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avg_total_fees_usd = np.mean([r['total_fees_usd'] for r in rows])
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summary_rows.append({
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"timeframe": timeframe,
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"stop_loss_pct": stop_loss_pct,
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"n_trades": total_trades,
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"n_stop_loss": total_stop_loss,
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"win_rate": avg_win_rate,
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"max_drawdown": avg_max_drawdown,
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"avg_trade": avg_avg_trade,
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"profit_ratio": avg_profit_ratio,
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"initial_usd": initial_usd,
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"final_usd": avg_final_usd,
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"total_fees_usd": avg_total_fees_usd,
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})
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return summary_rows
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def create_metadata_lines(self, config_manager, data_1min: pd.DataFrame) -> List[str]:
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"""
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Create metadata lines for result files.
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Args:
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config_manager: Configuration manager instance
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data_1min: 1-minute data for price lookups
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Returns:
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List[str]: Metadata lines
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"""
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start_date = config_manager.start_date
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stop_date = config_manager.stop_date
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initial_usd = config_manager.initial_usd
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def get_nearest_price(df: pd.DataFrame, target_date: str) -> Tuple[Optional[str], Optional[float]]:
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"""Get nearest price for a given date."""
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if len(df) == 0:
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return None, None
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target_ts = pd.to_datetime(target_date)
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nearest_idx = df.index.get_indexer([target_ts], method='nearest')[0]
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nearest_time = df.index[nearest_idx]
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price = df.iloc[nearest_idx]['close']
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return str(nearest_time), price
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nearest_start_time, start_price = get_nearest_price(data_1min, start_date)
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nearest_stop_time, stop_price = get_nearest_price(data_1min, stop_date)
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return [
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f"Start date\t{start_date}\tPrice\t{start_price}",
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f"Stop date\t{stop_date}\tPrice\t{stop_price}",
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f"Initial USD\t{initial_usd}"
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]
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