| import pandas as pd |
| import numpy as np |
| import time |
|
|
| def simulate(): |
| print("==================================================") |
| print(" PYTHON QUANT LAB: FAST SIMULATE 'THE ONE' ") |
| print("==================================================") |
| |
| print("[1] Loading 100MB Parquet Tick Data...") |
| t0 = time.time() |
| df = pd.read_parquet(r"C:\Users\Black\Downloads\MT5EA\tick_data\ticks_100MB.parquet", columns=['time_msc', 'bid']) |
| |
| df['datetime'] = pd.to_datetime(df['time_msc'], unit='ms') |
| df.set_index('datetime', inplace=True) |
| print(f" -> Loaded {len(df):,} ticks in {time.time()-t0:.2f} seconds.") |
|
|
| print("\n[2] Resampling Ticks to M1 Candles...") |
| t1 = time.time() |
| ohlc = df['bid'].resample('1min').ohlc() |
| ohlc.dropna(inplace=True) |
| print(f" -> Generated {len(ohlc):,} M1 candles in {time.time()-t1:.2f} seconds.") |
|
|
| |
| min_bodies = [30, 50, 100, 150, 200, 300, 400] |
| tp_pts = 400 |
| sl_pts = 1000 |
|
|
| print("\n[3] Quét Ma Trận (Matrix Sweep) - Chờ MT5 36 tiếng, Python làm trong 5 giây!") |
| print(f"Cấu hình: TP = {tp_pts} pts | SL = {sl_pts} pts (Lưu ý: Chưa tính Nhồi Lưới - Chỉ test Naked Entry)") |
| print("-" * 75) |
| print(f"{'Min_Body':<10} | {'Tổng Lệnh':<12} | {'Win Lệnh':<10} | {'Lose Lệnh':<10} | {'WinRate':<10} | {'Net P/L (Pts)':<15}") |
| print("-" * 75) |
| |
| open_p = ohlc['open'].values |
| close_p = ohlc['close'].values |
| high_p = ohlc['high'].values |
| low_p = ohlc['low'].values |
| |
| for mb in min_bodies: |
| |
| mb_price = mb * 0.01 |
| tp_price = tp_pts * 0.01 |
| sl_price = sl_pts * 0.01 |
| |
| |
| buy_signals = (close_p - open_p) >= mb_price |
| sell_signals = (open_p - close_p) >= mb_price |
| |
| wins = 0 |
| losses = 0 |
| |
| for i in range(len(open_p)-1): |
| if buy_signals[i]: |
| entry = open_p[i+1] |
| |
| for j in range(i+1, min(len(open_p), i+120)): |
| if low_p[j] <= entry - sl_price: |
| losses += 1 |
| break |
| elif high_p[j] >= entry + tp_price: |
| wins += 1 |
| break |
| |
| elif sell_signals[i]: |
| entry = open_p[i+1] |
| for j in range(i+1, min(len(open_p), i+120)): |
| if high_p[j] >= entry + sl_price: |
| losses += 1 |
| break |
| elif low_p[j] <= entry - tp_price: |
| wins += 1 |
| break |
| |
| total = wins + losses |
| winrate = (wins/total*100) if total > 0 else 0 |
| pnl = (wins * tp_pts) - (losses * sl_pts) |
| |
| |
| pnl_str = f"+{pnl}" if pnl > 0 else f"{pnl}" |
| print(f"{mb:<10} | {total:<12} | {wins:<10} | {losses:<10} | {winrate:<8.2f}% | {pnl_str:<15}") |
|
|
| if __name__ == "__main__": |
| simulate() |
|
|