125d4f7d52
- Introduced BacktestCharts class in charts.py to plot profit ratio vs stop loss and average trade vs stop loss for different timeframes. - Updated main.py to integrate new charting functionality and streamline data processing without monthly splits. - Enhanced backtesting logic in TrendDetectorSimple to include transaction costs and improved stop loss handling using 1-minute data for accuracy. - Added functionality to write results to individual CSV files for better organization and analysis.
198 lines
7.2 KiB
Python
198 lines
7.2 KiB
Python
import pandas as pd
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import numpy as np
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from trend_detector_simple import TrendDetectorSimple
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import os
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import datetime
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import csv
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def load_data(file_path, start_date, stop_date):
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"""Load and filter data by date range."""
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data = pd.read_csv(file_path)
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data['Timestamp'] = pd.to_datetime(data['Timestamp'], unit='s')
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data = data[(data['Timestamp'] >= start_date) & (data['Timestamp'] <= stop_date)]
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data.columns = data.columns.str.lower()
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return data.set_index('timestamp')
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def process_month_timeframe(min1_df, month_df, stop_loss_pcts, rule_name, initial_usd):
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"""Process a single month for a given timeframe with all stop loss values."""
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month_df = month_df.copy().reset_index(drop=True)
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trend_detector = TrendDetectorSimple(month_df, verbose=False)
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analysis_results = trend_detector.detect_trends()
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signal_df = analysis_results.get('signal_df')
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results_rows = []
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trade_rows = []
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for stop_loss_pct in stop_loss_pcts:
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results = trend_detector.backtest_meta_supertrend(
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min1_df,
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initial_usd=initial_usd,
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stop_loss_pct=stop_loss_pct
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)
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trades = results.get('trades', [])
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n_trades = results["n_trades"]
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n_winning_trades = sum(1 for trade in trades if trade['profit_pct'] > 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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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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# 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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# Final USD
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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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row = {
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"timeframe": rule_name,
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"month": str(month_df['timestamp'].iloc[0].to_period('M')),
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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'),
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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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"profit_ratio": profit_ratio,
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"initial_usd": initial_usd,
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"final_usd": final_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": rule_name,
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"month": str(month_df['timestamp'].iloc[0].to_period('M')),
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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_price"),
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"exit_price": trade.get("exit_price"),
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"profit_pct": trade.get("profit_pct"),
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"type": trade.get("type", ""),
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})
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return results_rows, trade_rows
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def process_timeframe(rule, data_1min, stop_loss_pcts, initial_usd):
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"""Process an entire timeframe sequentially."""
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if rule == "1T":
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df = data_1min.copy()
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else:
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df = data_1min.resample(rule).agg({
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'open': 'first',
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'high': 'max',
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'low': 'min',
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'close': 'last',
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'volume': 'sum'
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}).dropna()
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df = df.reset_index()
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df['month'] = df['timestamp'].dt.to_period('M')
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results_rows = []
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all_trade_rows = []
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for month, month_df in df.groupby('month'):
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if len(month_df) < 10:
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continue
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month_results, month_trades = process_month_timeframe(data_1min, month_df, stop_loss_pcts, rule, initial_usd)
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results_rows.extend(month_results)
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all_trade_rows.extend(month_trades)
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return results_rows, all_trade_rows
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def aggregate_results(all_rows, initial_usd):
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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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final_usd = np.mean([r.get('final_usd', initial_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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})
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return summary_rows
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def write_results(filename, fieldnames, rows):
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"""Write results to a CSV file."""
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with open(filename, 'w', newline="") as csvfile:
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writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
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writer.writeheader()
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for row in rows:
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writer.writerow(row)
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if __name__ == "__main__":
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# Config
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start_date = '2020-01-01'
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stop_date = '2025-05-15'
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initial_usd = 10000
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results_dir = "results"
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os.makedirs(results_dir, exist_ok=True)
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timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M")
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timeframes = ["6h", "1D"]
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stop_loss_pcts = [0.01, 0.02, 0.03, 0.05, 0.07, 0.10]
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data_1min = load_data('./data/btcusd_1-min_data.csv', start_date, stop_date)
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print(f"1min rows: {len(data_1min)}")
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filename = os.path.join(
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results_dir,
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f"{timestamp}_backtest_results_{start_date}_{stop_date}_multi_timeframe_stoploss.csv"
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)
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fieldnames = ["timeframe", "stop_loss_pct", "n_trades", "n_stop_loss", "win_rate", "max_drawdown", "avg_trade", "profit_ratio", "initial_usd", "final_usd"]
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all_results = []
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all_trades = []
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for name in timeframes:
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print(f"Processing timeframe: {name}")
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results, trades = process_timeframe(name, data_1min, stop_loss_pcts, initial_usd)
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all_results.extend(results)
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all_trades.extend(trades)
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summary_rows = aggregate_results(all_results, initial_usd)
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# write_results(filename, fieldnames, summary_rows)
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trades_filename = os.path.join(
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results_dir,
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f"{timestamp}_backtest_trades.csv"
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)
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trades_fieldnames = [
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"timeframe", "month", "stop_loss_pct", "entry_time", "exit_time",
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"entry_price", "exit_price", "profit_pct", "type"
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]
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# write_results(trades_filename, trades_fieldnames, all_trades)
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