| import pandas as pd |
| import glob |
| import os |
|
|
| pnl_files = glob.glob('backtest_results/*_daily_pnl.csv') |
| trades_files = glob.glob('backtest_results/*_trades.csv') |
|
|
| latest_pnl = max(pnl_files, key=os.path.getctime) |
| latest_trades = max(trades_files, key=os.path.getctime) |
|
|
| df_pnl = pd.read_csv(latest_pnl) |
| df_trades = pd.read_csv(latest_trades) |
|
|
| win_rate = (df_trades['pnl'] > 0).mean() * 100 |
| total_trades = len(df_trades) |
|
|
| max_profit_per_day = df_pnl['pnl'].max() |
| max_loss_per_day = df_pnl['pnl'].min() |
| avg_pnl_per_day = df_pnl['pnl'].mean() |
| win_days = (df_pnl['pnl'] > 0).sum() |
| loss_days = (df_pnl['pnl'] < 0).sum() |
|
|
| max_win_trade = df_trades['pnl'].max() |
| max_loss_trade = df_trades['pnl'].min() |
| avg_win_trade = df_trades[df_trades['pnl'] > 0]['pnl'].mean() |
| avg_loss_trade = df_trades[df_trades['pnl'] < 0]['pnl'].mean() |
|
|
| print(f"Total Trades: {total_trades}") |
| print(f"Win Rate: {win_rate:.2f}%") |
| print(f"Max Profit in a Day: ") |
| print(f"Max Loss in a Day: ") |
| print(f"Avg P&L per Day: ") |
| print(f"Winning Days: {win_days}") |
| print(f"Losing Days: {loss_days}") |
| print(f"Max Profit on a single trade: ") |
| print(f"Max Loss on a single trade: ") |
| print(f"Avg Win per Trade: ") |
| print(f"Avg Loss per Trade: ")
|
|
|
