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 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') # Aligning Data 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() # Ensure spread exists, default to 15 points if missing 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 a1 = (df['adx_M1'] >= t).astype(int) a5 = (df['adx_M5'] >= t).astype(int) a15 = (df['adx_M15'] >= t).astype(int) # Exact Phase Mapping df['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'] df['dmi_dir_m15'] = np.where(df['di_plus_M15'] > df['di_minus_M15'], 1, -1) # Portfolio Setup 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} } # Costs COMMISSION_PER_LOT = 7.0 # $7 round turn PIP_VALUE = 10.0 # $10 per lot for 1 standard pip (Gold usually 100 points = 10 pips = $100. So 1 pip = $10) SLIPPAGE_POINTS = 5.0 # 0.5 pips slippage print("Simulating Full Broker Conditions (Spread + Comm + Slip) over 94,699 bars (4 Months)...") for i in range(1, len(df)-60): if df['phase_transition'].iloc[i]: p_prev = df['phase_prev'].iloc[i] p_curr = df['phase'].iloc[i] dir_m15 = df['dmi_dir_m15'].iloc[i] entry = df['close'].iloc[i] spread_pips = df['spread_avg'].iloc[i] / 10.0 # points to pips # Determine strategy 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 is None: continue # Execute Trade with Broker Costs # Effective Entry Price differs due to Spread (Long pays Ask, Short pays Bid) # Assuming Close is Bid. Ask = Bid + Spread. real_entry = entry + (spread_pips * 0.01) if trade_dir == 1 else entry # Add Entry Slippage 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) future_highs = df['high'].iloc[i+1:i+61].values future_lows = df['low'].iloc[i+1:i+61].values pnl_pips = 0 win = 0 if trade_dir == 1: for h, l in zip(future_highs, future_lows): # We need Bid to hit TP for Long. High is Bid High. if h >= tp_price: # Hit TP pnl_pips = portfolio[gun]['tp'] win = 1 break elif l <= sl_price: # Hit SL pnl_pips = -portfolio[gun]['sl'] # Add slippage to Stop loss (market order exit) pnl_pips -= (SLIPPAGE_POINTS / 10.0) break else: for h, l in zip(future_highs, future_lows): # For Short, we need Ask to hit TP. Ask = Bid + spread. # So Bid Low must be <= tp_price - spread. current_spread = df['spread_avg'].iloc[i] / 10.0 if l <= tp_price - (current_spread * 0.01): pnl_pips = portfolio[gun]['tp'] win = 1 break elif h >= sl_price - (current_spread * 0.01): pnl_pips = -portfolio[gun]['sl'] pnl_pips -= (SLIPPAGE_POINTS / 10.0) break # If timeout (60 bars), close at market if pnl_pips == 0: exit_price = df['close'].iloc[i+60] # Market close slippage exit_price = exit_price - (SLIPPAGE_POINTS * 0.01) if trade_dir == 1 else exit_price + (SLIPPAGE_POINTS * 0.01) raw_pnl = (exit_price - real_entry) / 0.01 * trade_dir pnl_pips = raw_pnl if pnl_pips > 0: win = 1 # Calculate Final Net USD # Swap ignored for 60-min scalps net_profit_usd = (pnl_pips * 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 print("\n|=================================================|") print(f"| FULL REAL-BROKER PORTFOLIO (4 MONTHS XAUUSD) |") print(f"| (Spread Dynamic, Slip 1Pip, Comm $7/Lot) |") 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"|=================================================|") print(f"| TOTAL NET PROFIT (4 MONTHS): ${total_usd:+.2f}") print(f"|=================================================|")