337 lines
13 KiB
Python
337 lines
13 KiB
Python
import pandas as pd
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import numpy as np
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import logging
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import concurrent.futures
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import os
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import datetime
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import argparse
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import json
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from cycles.utils.storage import Storage
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from cycles.utils.system import SystemUtils
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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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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s [%(levelname)s] %(message)s",
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handlers=[
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logging.FileHandler("backtest.log"),
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logging.StreamHandler()
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]
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)
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def strategy_manager_init(backtester: Backtest):
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"""Strategy Manager initialization function"""
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# This will be called by Backtest.__init__, but actual initialization
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# happens in strategy_manager.initialize()
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pass
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def strategy_manager_entry(backtester: Backtest, df_index: int):
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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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def strategy_manager_exit(backtester: Backtest, df_index: int):
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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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def process_timeframe_data(data_1min, timeframe, config, debug=False):
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"""Process a timeframe using Strategy Manager with configuration"""
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results_rows = []
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trade_rows = []
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# Extract values from config
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initial_usd = config['initial_usd']
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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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# Create and initialize strategy manager
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if not strategy_config:
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logging.error("No strategy configuration provided")
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return results_rows, trade_rows
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strategy_manager = create_strategy_manager(strategy_config)
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# Get the primary timeframe from the first strategy for backtester setup
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primary_strategy = strategy_manager.strategies[0]
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primary_timeframe = primary_strategy.get_timeframes()[0]
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# For BBRS strategy, it works with 1-minute data directly and handles internal resampling
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# For other strategies, use their preferred timeframe
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if primary_strategy.name == "bbrs":
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# BBRS strategy processes 1-minute data and outputs signals on its internal timeframes
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# Use 1-minute data for backtester working dataframe
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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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# Let the primary strategy resample the data to get the working dataframe
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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 (ensure timestamp column)
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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 with strategy manager initialization
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backtester = Backtest(initial_usd, working_df_for_backtest, working_df_for_backtest, strategy_manager_init)
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# Store original min1_df for strategy processing
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backtester.original_df = data_1min
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# Attach strategy manager to backtester and initialize
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backtester.strategy_manager = strategy_manager
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strategy_manager.initialize(backtester)
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# Run backtest with strategy manager functions
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results = backtester.run(
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strategy_manager_entry,
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strategy_manager_exit,
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debug
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)
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n_trades = results["n_trades"]
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trades = results.get('trades', [])
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wins = [1 for t in trades if t['exit'] is not None and t['exit'] > t['entry']]
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n_winning_trades = len(wins)
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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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win_rate = n_winning_trades / n_trades if n_trades > 0 else 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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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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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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# Get stop_loss_pct from the first strategy for reporting
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# In multi-strategy setups, strategies can have different stop_loss_pct values
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stop_loss_pct = primary_strategy.params.get("stop_loss_pct", "N/A")
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# Update row to include timeframe information
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row = {
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"timeframe": f"{timeframe}({primary_timeframe})", # Show actual timeframe used
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"stop_loss_pct": stop_loss_pct,
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"n_trades": n_trades,
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"n_stop_loss": sum(1 for trade in trades if 'type' in trade and trade['type'] == 'STOP_LOSS'),
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"win_rate": win_rate,
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"max_drawdown": max_drawdown,
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"avg_trade": avg_trade,
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"total_profit": total_profit,
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"total_loss": total_loss,
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"profit_ratio": profit_ratio,
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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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}
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results_rows.append(row)
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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 strategy summary
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strategy_summary = strategy_manager.get_strategy_summary()
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logging.info(f"Timeframe: {timeframe}({primary_timeframe}), Stop Loss: {stop_loss_pct}, "
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f"Trades: {n_trades}, Strategies: {[s['name'] for s in strategy_summary['strategies']]}")
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if debug:
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# Plot after each backtest run
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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 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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# Use the working dataframe for plotting
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BacktestCharts.plot(working_df, meta_trend)
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else:
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print("No plotting data available")
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except Exception as e:
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print(f"Plotting failed: {e}")
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return results_rows, trade_rows
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def process(timeframe_info, debug=False):
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"""Process a single timeframe with strategy config"""
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timeframe, data_1min, config = timeframe_info
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# Pass the essential data and full config
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results_rows, all_trade_rows = process_timeframe_data(
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data_1min, timeframe, config, debug=debug
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)
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return results_rows, all_trade_rows
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def aggregate_results(all_rows):
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"""Aggregate results per stop_loss_pct and per rule (timeframe)"""
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from collections import defaultdict
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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 (rule, stop_loss_pct), rows in grouped.items():
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n_months = len(rows)
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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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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])
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# Calculate final USD
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final_usd = np.mean([r.get('final_usd', initial_usd) for r in rows])
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total_fees_usd = np.mean([r.get('total_fees_usd') for r in rows])
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summary_rows.append({
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"timeframe": rule,
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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": final_usd,
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"total_fees_usd": total_fees_usd,
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})
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return summary_rows
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def get_nearest_price(df, target_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 nearest_time, price
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if __name__ == "__main__":
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debug = True
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parser = argparse.ArgumentParser(description="Run backtest with config file.")
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parser.add_argument("config", type=str, nargs="?", help="Path to config JSON file.")
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args = parser.parse_args()
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# Use config_default.json as fallback if no config provided
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config_file = args.config or "configs/config_default.json"
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try:
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with open(config_file, 'r') as f:
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config = json.load(f)
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print(f"Using config: {config_file}")
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except FileNotFoundError:
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print(f"Error: Config file '{config_file}' not found.")
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print("Available configs: configs/config_default.json, configs/config_bbrs.json, configs/config_combined.json")
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exit(1)
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except json.JSONDecodeError as e:
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print(f"Error: Invalid JSON in config file '{config_file}': {e}")
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exit(1)
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start_date = config['start_date']
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if config['stop_date'] is None:
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stop_date = datetime.datetime.now().strftime("%Y-%m-%d")
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else:
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stop_date = config['stop_date']
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initial_usd = config['initial_usd']
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timeframes = config['timeframes']
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timestamp = datetime.datetime.now().strftime("%Y_%m_%d_%H_%M")
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storage = Storage(logging=logging)
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system_utils = SystemUtils(logging=logging)
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data_1min = storage.load_data('btcusd_1-min_data.csv', start_date, stop_date)
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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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metadata_lines = [
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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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# Create tasks for each timeframe
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tasks = [
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(name, data_1min, config)
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for name in timeframes
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]
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if debug:
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all_results_rows = []
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all_trade_rows = []
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for task in tasks:
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results, trades = process(task, debug)
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if results or trades:
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all_results_rows.extend(results)
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all_trade_rows.extend(trades)
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else:
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workers = system_utils.get_optimal_workers()
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with concurrent.futures.ProcessPoolExecutor(max_workers=workers) as executor:
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futures = {executor.submit(process, task, debug): task for task in tasks}
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all_results_rows = []
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all_trade_rows = []
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for future in concurrent.futures.as_completed(futures):
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results, trades = future.result()
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if results or trades:
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all_results_rows.extend(results)
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all_trade_rows.extend(trades)
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backtest_filename = os.path.join(f"{timestamp}_backtest.csv")
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backtest_fieldnames = [
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"timeframe", "stop_loss_pct", "n_trades", "n_stop_loss", "win_rate",
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"max_drawdown", "avg_trade", "profit_ratio", "final_usd", "total_fees_usd"
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]
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storage.write_backtest_results(backtest_filename, backtest_fieldnames, all_results_rows, metadata_lines)
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trades_fieldnames = ["entry_time", "exit_time", "entry_price", "exit_price", "profit_pct", "type", "fee_usd"]
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storage.write_trades(all_trade_rows, trades_fieldnames)
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