Upload simulate_v15_HuongA.py with huggingface_hub

simulate_v15_HuongA.py

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+import pandas as pd
+import numpy as np
+
+def calculate_adx(df, period=14):
+    df['h_l'] = df['high'] - df['low']
+    df['h_pc'] = (df['high'] - df['close'].shift(1)).abs()
+    df['l_pc'] = (df['low'] - df['close'].shift(1)).abs()
+    df['tr'] = df[['h_l', 'h_pc', 'l_pc']].max(axis=1)
+    
+    df['+dm'] = np.where((df['high'] - df['high'].shift(1)) > (df['low'].shift(1) - df['low']), 
+                         np.maximum(df['high'] - df['high'].shift(1), 0), 0)
+    df['-dm'] = np.where((df['low'].shift(1) - df['low']) > (df['high'] - df['high'].shift(1)), 
+                         np.maximum(df['low'].shift(1) - df['low'], 0), 0)
+    
+    alpha = 1 / period
+    df['tr_smooth'] = df['tr'].ewm(alpha=alpha, adjust=False).mean() * period
+    df['+dm_smooth'] = df['+dm'].ewm(alpha=alpha, adjust=False).mean() * period
+    df['-dm_smooth'] = df['-dm'].ewm(alpha=alpha, adjust=False).mean() * period
+    
+    df['+di'] = 100 * (df['+dm_smooth'] / df['tr_smooth'])
+    df['-di'] = 100 * (df['-dm_smooth'] / df['tr_smooth'])
+    
+    df['dx'] = 100 * (df['+di'] - df['-di']).abs() / (df['+di'] + df['-di']).replace(0, 1)
+    df['adx'] = df['dx'].ewm(alpha=alpha, adjust=False).mean()
+    return df
+
+def run_simulation():
+    print("--- V15.00 Huong A Causal Simulator ---")
+    print("Loading OHLC Parquet data...")
+    m1 = pd.read_parquet('native_rates_M1.parquet')
+    m5 = pd.read_parquet('native_rates_M5.parquet')
+    m15 = pd.read_parquet('native_rates_M15.parquet')
+    
+    print("Calculating Causal ADX...")
+    m1 = calculate_adx(m1, 14)
+    m5 = calculate_adx(m5, 14)
+    m15 = calculate_adx(m15, 14)
+    
+    m1['time_dt'] = pd.to_datetime(m1['time'], unit='s')
+    m5['time_dt'] = pd.to_datetime(m5['time'], unit='s')
+    m15['time_dt'] = pd.to_datetime(m15['time'], unit='s')
+    
+    m1.set_index('time_dt', inplace=True)
+    m5.set_index('time_dt', inplace=True)
+    m15.set_index('time_dt', inplace=True)
+    
+    df = m1[['open', 'high', 'low', 'close', 'adx', '+di', '-di']].copy()
+    df.rename(columns={'adx': 'adx_M1', '+di': 'di_plus_M1', '-di': 'di_minus_M1'}, inplace=True)
+    
+    m5_cols = m5[['adx', '+di', '-di']].rename(columns={'adx': 'adx_M5', '+di': 'di_plus_M5', '-di': 'di_minus_M5'})
+    m5_cols.index = m5_cols.index + pd.Timedelta(minutes=5)
+    df = df.join(m5_cols)
+    df[['adx_M5', 'di_plus_M5', 'di_minus_M5']] = df[['adx_M5', 'di_plus_M5', 'di_minus_M5']].ffill()
+    
+    m15_cols = m15[['adx', '+di', '-di']].rename(columns={'adx': 'adx_M15', '+di': 'di_plus_M15', '-di': 'di_minus_M15'})
+    m15_cols.index = m15_cols.index + pd.Timedelta(minutes=15)
+    df = df.join(m15_cols)
+    df[['adx_M15', 'di_plus_M15', 'di_minus_M15']] = df[['adx_M15', 'di_plus_M15', 'di_minus_M15']].ffill()
+    
+    df.dropna(inplace=True)
+    df['adx_M1'] = df['adx_M1'].shift(1)
+    
+    t_M1, t_M5, t_M15 = 18.0, 18.0, 18.0
+    
+    a1 = (df['adx_M1'] >= t_M1).astype(int)
+    a5 = (df['adx_M5'] >= t_M5).astype(int)
+    a15 = (df['adx_M15'] >= t_M15).astype(int)
+    
+    df['phase'] = 1
+    df.loc[(a15==0) & (a5==0) & (a1==0), 'phase'] = 1
+    df.loc[(a15==0) & (a5==0) & (a1==1), 'phase'] = 2
+    df.loc[(a15==0) & (a5==1) & (a1==0), 'phase'] = 3
+    df.loc[(a15==1) & (a5==0) & (a1==0), 'phase'] = 4
+    df.loc[(a15==0) & (a5==1) & (a1==1), 'phase'] = 5
+    df.loc[(a15==1) & (a5==0) & (a1==1), 'phase'] = 6
+    df.loc[(a15==1) & (a5==1) & (a1==0), 'phase'] = 7
+    df.loc[(a15==1) & (a5==1) & (a1==1), 'phase'] = 8
+    
+    sum_di = df['di_plus_M15'] + df['di_minus_M15']
+    df['dmi_dir'] = np.where(df['di_plus_M15'] > df['di_minus_M15'], 1, -1)
+    df['dmi_score'] = (df[['di_plus_M15', 'di_minus_M15']].max(axis=1) / sum_di) * 100
+    
+    # TRIGGER CALCULATION: Price Action Engulfing
+    df['body'] = df['close'] - df['open']
+    df['prev_body'] = df['body'].shift(1)
+    df['bull_engulf'] = (df['prev_body'] < 0) & (df['body'] > 0) & (df['close'] > df['open'].shift(1)) & (df['open'] < df['close'].shift(1))
+    df['bear_engulf'] = (df['prev_body'] > 0) & (df['body'] < 0) & (df['close'] < df['open'].shift(1)) & (df['open'] > df['close'].shift(1))
+
+    print("Executing Huong A Matrix (Filter + Engulfing Trigger) Causal...\n")
+    
+    stats = {
+        'Trend_Engulf_Buy': {'trades': 0, 'wins': 0, 'total_pips': 0.0},
+        'Trend_Engulf_Sell': {'trades': 0, 'wins': 0, 'total_pips': 0.0},
+        'Range_Engulf_Buy': {'trades': 0, 'wins': 0, 'total_pips': 0.0},
+        'Range_Engulf_Sell': {'trades': 0, 'wins': 0, 'total_pips': 0.0}
+    }
+    
+    for i in range(2, len(df)-60):
+        phase = df['phase'].iloc[i]
+        bullish = df['bull_engulf'].iloc[i]
+        bearish = df['bear_engulf'].iloc[i]
+        dir_mult = df['dmi_dir'].iloc[i]
+
+        gun = None
+        trade_dir = 0
+        tp, sl = 0, 0
+        
+        # 1. Trend Follow Filter: Phase 7 or 8 AND DMI alignment
+        if phase >= 7 and df['dmi_score'].iloc[i] > 60:
+            if bullish and dir_mult == 1:
+                gun = 'Trend_Engulf_Buy'
+                trade_dir = 1
+                tp, sl = 30.0, 20.0
+            elif bearish and dir_mult == -1:
+                gun = 'Trend_Engulf_Sell'
+                trade_dir = -1
+                tp, sl = 30.0, 20.0
+                
+        # 2. Range Scalp Filter: Phase 1 or 2
+        elif phase <= 2:
+            if bullish:
+                gun = 'Range_Engulf_Buy'
+                trade_dir = 1
+                tp, sl = 15.0, 15.0
+            elif bearish:
+                gun = 'Range_Engulf_Sell'
+                trade_dir = -1
+                tp, sl = 15.0, 15.0
+
+        if gun is None: continue
+            
+        entry_price = df['close'].iloc[i]
+        future_highs = df['high'].iloc[i+1:i+61]
+        future_lows = df['low'].iloc[i+1:i+61]
+        
+        pnl = 0
+        win = 0
+        
+        # CAUSAL AMBIGUOUS BAR HANDLING (Pessimistic)
+        if trade_dir == 1:
+            for h, l in zip(future_highs, future_lows):
+                if l <= entry_price - (sl * 0.01):
+                    pnl = -sl
+                    win = 0
+                    break
+                elif h >= entry_price + (tp * 0.01):
+                    pnl = tp
+                    win = 1
+                    break
+        else:
+            for h, l in zip(future_highs, future_lows):
+                if h >= entry_price + (sl * 0.01):
+                    pnl = -sl
+                    win = 0
+                    break
+                elif l <= entry_price - (tp * 0.01):
+                    pnl = tp
+                    win = 1
+                    break
+                    
+        if pnl == 0:
+            pnl = ((df['close'].iloc[i+60] - entry_price) / 0.01) * trade_dir
+            if pnl > 0: win = 1
+        
+        stats[gun]['trades'] += 1
+        stats[gun]['wins'] += win
+        stats[gun]['total_pips'] += pnl
+
+    print(f"|=================================================|")
+    for name, s in stats.items():
+        tr = s['trades']
+        if tr == 0:
+            print(f" * {name:<17} | Trades: 0")
+            continue
+        wr = (s['wins']/tr)*100
+        np_val = s['total_pips']
+        print(f" * {name:<17} | Trades: {tr:<4} | WinRate: {wr:>5.1f}% | Net Pips: {np_val:+.1f}")
+    print(f"|=================================================|")
+
+if __name__ == '__main__':
+    run_simulation()