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("--- V14.19 Quatum Pendulum 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') 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') print("Calculating ADX & DMI Vectors...") m1 = calculate_adx(m1, 14) m5 = calculate_adx(m5, 14) m15 = calculate_adx(m15, 14) 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) # Static thresholds based on average spread t_M1 = 18.0 t_M5 = 18.0 t_M15 = 18.0 print("Mapping 8 Quantum Phases...") a1 = (df['adx_M1'] >= t_M1).astype(int) a5 = (df['adx_M5'] >= t_M5).astype(int) a15 = (df['adx_M15'] >= t_M15).astype(int) # 8-Phase Mapping (Binary Logic) 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 df['phase_prev'] = df['phase'].shift(1) df['phase_transition'] = df['phase'] != df['phase_prev'] # DMI Score (using M15 as commanding direction) 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 # DMI Score M5 (for Death Zone check) sum_di_m5 = df['di_plus_M5'] + df['di_minus_M5'] df['dmi_dir_m5'] = np.where(df['di_plus_M5'] > df['di_minus_M5'], 1, -1) df['dmi_score_m5'] = (df[['di_plus_M5', 'di_minus_M5']].max(axis=1) / sum_di_m5) * 100 # Tick Phase Age age = [] current_age = 0 curr_p = df['phase'].iloc[0] for p in df['phase']: if p == curr_p: current_age += 1 else: current_age = 1 curr_p = p age.append(current_age) df['phase_age'] = age print("Executing FULL 4-Pendulum Trigger Matrix V14.19...\n") stats = { 'L0_Mega': {'trades': 0, 'wins': 0, 'total_pips': 0.0, 'mult': 3.0}, 'L0_Snipe': {'trades': 0, 'wins': 0, 'total_pips': 0.0, 'mult': 2.0}, 'L0_Scout': {'trades': 0, 'wins': 0, 'total_pips': 0.0, 'mult': 1.0}, 'D0_Grinder': {'trades': 0, 'wins': 0, 'total_pips': 0.0, 'mult': 1.0}, 'R0_Fade': {'trades': 0, 'wins': 0, 'total_pips': 0.0, 'mult': 1.5}, 'DeathZone_Trap': {'trades': 0, 'wins': 0, 'total_pips': 0.0, 'mult': 1.0} } stats_L0_Trap = {'dodges': 0} # Simulate entries tp_pips = 30.0 sl_pips = 30.0 tp_d0 = 15.0 # D0 takes smaller cuts sl_d0 = 15.0 tp_r0 = 20.0 # R0 (Fade) takes quick mean-reversion scalps sl_r0 = 20.0 for i in range(1, len(df)-60): if df['phase_transition'].iloc[i]: pf_prev = df['phase_prev'].iloc[i] pf_curr = df['phase'].iloc[i] dmi_score = df['dmi_score'].iloc[i] dir_mult = df['dmi_dir'].iloc[i] entry_price = df['close'].iloc[i] gun = None is_d0 = False is_r0 = False is_deathzone = False # --- Pendulum 4 (#5 -> #8): Mega & Snipe --- if pf_prev == 5 and pf_curr == 8: if dmi_score > 75: gun = 'L0_Mega' elif dmi_score > 65: gun = 'L0_Snipe' # --- The Return Stroke (#8 -> #7): R0 Fade --- elif pf_prev == 8 and pf_curr == 7: # Trạng thái rớt đài của siêu Trend (M1 rụng), nảy sinh nhịp Pullback ngược hướng gun = 'R0_Fade' is_r0 = True dir_mult = dir_mult * -1 # Bắn ngược lại với hướng Trend chính # --- Pendulum 2 (#4 -> #7) : Scout --- elif pf_prev == 4 and pf_curr == 7: if dmi_score > 55: gun = 'L0_Scout' # --- Pendulum 1 (#1 <-> #2) : D0 Grinder --- elif (pf_prev == 1 and pf_curr == 2) or (pf_prev == 2 and pf_curr == 1): if dmi_score < 60: # Neutral DMI gun = 'D0_Grinder' is_d0 = True dir_mult = 1 if df['close'].iloc[i] < df['open'].iloc[i] else -1 # --- Pendulum 3 (#3 <-> #6) : DEATH ZONE TRAP --- elif pf_prev == 3 and pf_curr == 6: gun = 'DeathZone_Trap' dir_mult = df['dmi_dir_m5'].iloc[i] is_deathzone = True if gun is None: continue future_highs = df['high'].iloc[i+1:i+61] future_lows = df['low'].iloc[i+1:i+61] pnl = 0 win = 0 # Dynamic TP/SL setup t_tp = tp_pips t_sl = sl_pips if is_d0: t_tp, t_sl = tp_d0, sl_d0 elif is_r0: t_tp, t_sl = tp_r0, sl_r0 if dir_mult == 1: for h, l in zip(future_highs, future_lows): if h >= entry_price + (t_tp * 0.01): pnl = t_tp win = 1 break elif l <= entry_price - (t_sl * 0.01): pnl = -t_sl win = 0 break else: for h, l in zip(future_highs, future_lows): if l <= entry_price - (t_tp * 0.01): pnl = t_tp win = 1 break elif h >= entry_price + (t_sl * 0.01): pnl = -t_sl win = 0 break # If no exit hit in 60 mins (closed by time) if pnl == 0: pnl = ((df['close'].iloc[i+60] - entry_price) / 0.01) * dir_mult if pnl > 0: win = 1 stats[gun]['trades'] += 1 stats[gun]['wins'] += win stats[gun]['total_pips'] += pnl # --- Trap Dodge Verification --- if df['phase'].iloc[i] == 8 and df['phase_age'].iloc[i] == 50: stats_L0_Trap['dodges'] += 1 # Print Dashboard print(f"|=================================================|") print(f"| FULL 4-PENDULUM MATRIX BACKTEST (10 DAYS) |") print(f"|=================================================|") for name, s in stats.items(): tr = s['trades'] if tr == 0: print(f" * {name:<14} | Trades: 0") continue wr = (s['wins']/tr)*100 net_profit = s['total_pips'] * s['mult'] if "DeathZone" in name: print(f" ☠️ {name:<14} | Trades: {tr:<4} | WinRate: {wr:>5.1f}% | Net Margin Delta: {net_profit:+.1f} units") else: print(f" * {name:<14} | Trades: {tr:<4} | WinRate: {wr:>5.1f}% | Net Margin Delta: {net_profit:+.1f} units") print(f"\\n - L0_Trap (Old L0 Top Execution Prevented): Avoided {stats_L0_Trap['dodges']} exact local tops/bottoms.") print(f"|=================================================|") if __name__ == '__main__': run_simulation()