import pandas as pd import numpy as np print("Loading 4-Months of tickflow data...") m1 = pd.read_parquet('tickflow_M1.parquet') m5 = pd.read_parquet('tickflow_M5.parquet') m15 = pd.read_parquet('tickflow_M15.parquet') 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 m1 = calculate_adx(m1, 14) m5 = calculate_adx(m5, 14) m15 = calculate_adx(m15, 14) df = m1[['open', 'high', 'low', 'close', 'adx', '+di', '-di', 'spread_avg']].copy() if 'spread_avg' not in df.columns: df['spread_avg'] = 15.0 else: df['spread_avg'] = df['spread_avg'].fillna(15.0) 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'}) df = df.join(m5_cols, how='left').ffill() m15_cols = m15[['adx', '+di', '-di']].rename(columns={'adx': 'adx_M15', '+di': 'di_plus_M15', '-di': 'di_minus_M15'}) df = df.join(m15_cols, how='left').ffill() df.dropna(inplace=True) t = 18.0 df['phase'] = 1 df.loc[(df['adx_M15'] < t) & (df['adx_M5'] < t) & (df['adx_M1'] >= t), 'phase'] = 2 df.loc[(df['adx_M15'] < t) & (df['adx_M5'] >= t) & (df['adx_M1'] < t), 'phase'] = 3 df.loc[(df['adx_M15'] >= t) & (df['adx_M5'] < t) & (df['adx_M1'] < t), 'phase'] = 4 df.loc[(df['adx_M15'] < t) & (df['adx_M5'] >= t) & (df['adx_M1'] >= t), 'phase'] = 5 df.loc[(df['adx_M15'] >= t) & (df['adx_M5'] < t) & (df['adx_M1'] >= t), 'phase'] = 6 df.loc[(df['adx_M15'] >= t) & (df['adx_M5'] >= t) & (df['adx_M1'] < t), 'phase'] = 7 df.loc[(df['adx_M15'] >= t) & (df['adx_M5'] >= t) & (df['adx_M1'] >= t), 'phase'] = 8 df['phase_prev'] = df['phase'].shift(1) df['phase_transition'] = df['phase'] != df['phase_prev'] df['dmi_dir_m15'] = np.where(df['di_plus_M15'] > df['di_minus_M15'], 1, -1) portfolio = { 'D0_Grinder': {'trades': 0, 'wins': 0, 'profit_usd': 0.0, 'tp': 15, 'sl': 15, 'lots': 1.0}, 'L0_Scout': {'trades': 0, 'wins': 0, 'profit_usd': 0.0, 'tp': 20, 'sl': 20, 'lots': 1.0}, 'L0_Snipe': {'trades': 0, 'wins': 0, 'profit_usd': 0.0, 'tp': 30, 'sl': 20, 'lots': 2.0}, 'L0_Mega': {'trades': 0, 'wins': 0, 'profit_usd': 0.0, 'tp': 40, 'sl': 20, 'lots': 3.0}, 'R0_Fade': {'trades': 0, 'wins': 0, 'profit_usd': 0.0, 'tp': 20, 'sl': 20, 'lots': 1.5}, 'R0_Mega_Fade': {'trades': 0, 'wins': 0, 'profit_usd': 0.0, 'tp': 30, 'sl': 20, 'lots': 2.0} } COMMISSION_PER_LOT = 7.0 PIP_VALUE = 10.0 SLIPPAGE_POINTS = 5.0 TRAIL_START_PIPS = 10.0 print("Simulating REAL BROKER + TRAILING LOCK SENTINEL...") active_trades = [] avoided_overlaps = 0 for i in range(1, len(df)): current_high = df['high'].iloc[i] current_low = df['low'].iloc[i] current_close = df['close'].iloc[i] current_spread = df['spread_avg'].iloc[i] / 10.0 # Process existing trades for trade in active_trades[:]: # copy to iterate and remove hit_tp = False hit_sl = False pnl = 0 # Check Trailing Activation if not trade['trailing_unlocked']: if trade['dir'] == 1: if current_high >= trade['entry'] + (TRAIL_START_PIPS * 0.01): trade['trailing_unlocked'] = True trade['sl_price'] = trade['entry'] # Move SL to breakeven else: if current_low <= trade['entry'] - (TRAIL_START_PIPS * 0.01): trade['trailing_unlocked'] = True trade['sl_price'] = trade['entry'] # Check SL/TP if trade['dir'] == 1: if current_high >= trade['tp_price']: hit_tp = True pnl = portfolio[trade['gun']]['tp'] elif current_low <= trade['sl_price']: hit_sl = True pnl = ((trade['sl_price'] - trade['entry']) / 0.01) - (SLIPPAGE_POINTS / 10.0) else: if current_low <= trade['tp_price'] - (current_spread * 0.01): hit_tp = True pnl = portfolio[trade['gun']]['tp'] elif current_high >= trade['sl_price'] - (current_spread * 0.01): hit_sl = True pnl = ((trade['entry'] - trade['sl_price']) / 0.01) - (SLIPPAGE_POINTS / 10.0) # Force close after 60 bars if not hit to prevent infinite holds in sim trade['age'] += 1 force_close = False if not hit_tp and not hit_sl and trade['age'] > 60: force_close = True raw_pnl = (current_close - trade['entry']) / 0.01 * trade['dir'] pnl = raw_pnl - (SLIPPAGE_POINTS / 10.0) if hit_tp or hit_sl or force_close: # Record Stats gun = trade['gun'] win = 1 if pnl > 0 else 0 net_profit_usd = (pnl * PIP_VALUE * portfolio[gun]['lots']) - (COMMISSION_PER_LOT * portfolio[gun]['lots']) portfolio[gun]['trades'] += 1 portfolio[gun]['wins'] += win portfolio[gun]['profit_usd'] += net_profit_usd active_trades.remove(trade) # Check Lock is_locked = any(not t['trailing_unlocked'] for t in active_trades) if df['phase_transition'].iloc[i]: if is_locked: avoided_overlaps += 1 continue p_prev = df['phase_prev'].iloc[i] p_curr = df['phase'].iloc[i] dir_m15 = df['dmi_dir_m15'].iloc[i] gun = None trade_dir = 1 if (p_prev == 1 and p_curr == 2) or (p_prev == 2 and p_curr == 1): gun = 'D0_Grinder' trade_dir = 1 if df['close'].iloc[i] < df['open'].iloc[i] else -1 elif (p_prev == 4 and p_curr in [7, 6]): gun = 'L0_Scout' trade_dir = dir_m15 elif (p_prev == 5 and p_curr == 8) or (p_prev == 2 and p_curr == 5) or (p_prev == 8 and p_curr == 5): gun = 'L0_Snipe' trade_dir = dir_m15 elif p_prev == 7 and p_curr == 8: gun = 'L0_Mega' trade_dir = dir_m15 elif p_prev == 8 and p_curr == 7: gun = 'R0_Fade' trade_dir = -dir_m15 elif p_prev == 8 and p_curr == 6 or p_prev == 6 and p_curr == 2 or p_prev == 7 and p_curr == 3: gun = 'R0_Mega_Fade' trade_dir = -dir_m15 if gun: real_entry = df['close'].iloc[i] + (current_spread * 0.01) if trade_dir == 1 else df['close'].iloc[i] real_entry = real_entry + (SLIPPAGE_POINTS * 0.01) if trade_dir == 1 else real_entry - (SLIPPAGE_POINTS * 0.01) tp_price = real_entry + (portfolio[gun]['tp'] * 0.01) if trade_dir == 1 else real_entry - (portfolio[gun]['tp'] * 0.01) sl_price = real_entry - (portfolio[gun]['sl'] * 0.01) if trade_dir == 1 else real_entry + (portfolio[gun]['sl'] * 0.01) active_trades.append({ 'gun': gun, 'dir': trade_dir, 'entry': real_entry, 'tp_price': tp_price, 'sl_price': sl_price, 'trailing_unlocked': False, 'age': 0 }) print("\n|=================================================|") print(f"| FULL LOCKED-PORTFOLIO (4 MONTHS XAUUSD) |") print(f"| (Trailing Sentinel ACTIVE | Spread/Slip ACTIVE)|") print(f"|=================================================|") total_usd = 0 for name, p in portfolio.items(): tr = p['trades'] if tr == 0: continue wr = (p['wins'] / tr) * 100 profit = p['profit_usd'] total_usd += profit print(f" * {name:<14} (Lot {p['lots']}) | Trades: {tr:<4} | WinRate: {wr:>5.1f}% | Net USD: ${profit:+.2f}") print(f"\\n [!] Trailing Sentinel Avoided {avoided_overlaps} overlapping/redundant risk exposures.") print(f"|=================================================|") print(f"| TOTAL NET PROFIT (4 MONTHS): ${total_usd:+.2f}") print(f"|=================================================|")