| import pandas as pd |
| import numpy as np |
|
|
| def run_simulation(): |
| print("--- U2Predator V4.1 Trailing Simulator ---") |
| df = pd.read_parquet('native_rates_M1.parquet') |
| |
| |
| df['dc_fast_h'] = df['high'].rolling(14).max() |
| df['dc_fast_l'] = df['low'].rolling(14).min() |
| df['dc_fast_mid'] = (df['dc_fast_h'] + df['dc_fast_l']) / 2 |
|
|
| df['dc_slow_h'] = df['high'].rolling(21).max() |
| df['dc_slow_l'] = df['low'].rolling(21).min() |
| df['dc_slow_mid'] = (df['dc_slow_h'] + df['dc_slow_l']) / 2 |
| |
| df['mid_gap'] = df['dc_fast_mid'] - df['dc_slow_mid'] |
| |
| |
| df['dc_f_mid_1'] = df['dc_fast_mid'].shift(1) |
| df['dc_s_mid_1'] = df['dc_slow_mid'].shift(1) |
| |
| df['mid_upper'] = df[['dc_f_mid_1', 'dc_s_mid_1']].max(axis=1) |
| df['mid_lower'] = df[['dc_f_mid_1', 'dc_s_mid_1']].min(axis=1) |
|
|
| |
| df['is_bull_cross'] = (df['open'].shift(1) < df['mid_upper']) & (df['close'].shift(1) > df['mid_upper']) |
| df['is_bear_cross'] = (df['open'].shift(1) > df['mid_lower']) & (df['close'].shift(1) < df['mid_lower']) |
| |
| df['gap_expansion'] = df['mid_gap'].abs() >= 1.00 |
| |
| |
| df['signal'] = 0 |
| df.loc[df['is_bull_cross'] & df['gap_expansion'] & (df['mid_gap'] > 0), 'signal'] = 1 |
| df.loc[df['is_bear_cross'] & df['gap_expansion'] & (df['mid_gap'] < 0), 'signal'] = -1 |
| |
| |
| trades = [] |
| in_trade = False |
| trade_dir = 0 |
| entry_price = 0 |
| hard_sl = 0 |
| locked_sl = 0 |
| max_float = 0 |
| |
| points = 0.01 |
| |
| for row in df.itertuples(): |
| if getattr(row, 'signal') == 1 and not in_trade: |
| in_trade = True |
| trade_dir = 1 |
| entry_price = getattr(row, 'open') |
| hard_sl = entry_price - 21.00 |
| locked_sl = hard_sl |
| max_float = 0 |
| continue |
| |
| if getattr(row, 'signal') == -1 and not in_trade: |
| in_trade = True |
| trade_dir = -1 |
| entry_price = getattr(row, 'open') |
| hard_sl = entry_price + 21.00 |
| locked_sl = hard_sl |
| max_float = 0 |
| continue |
| |
| if in_trade: |
| if trade_dir == 1: |
| current_high_float = (getattr(row, 'high') - entry_price) / points |
| current_low_float = (getattr(row, 'low') - entry_price) / points |
| close_float = (getattr(row, 'close') - entry_price) / points |
| |
| max_float = max(max_float, current_high_float) |
| |
| |
| if max_float >= 600: |
| locked_sl = max(locked_sl, entry_price + 5.00) |
| elif max_float >= 450: |
| locked_sl = max(locked_sl, entry_price + 3.50) |
| elif max_float >= 350: |
| locked_sl = max(locked_sl, entry_price + 3.00) |
| |
| |
| if getattr(row, 'low') <= locked_sl: |
| pnl = (locked_sl - entry_price) / points |
| pnl -= 50 |
| trades.append(pnl) |
| in_trade = False |
| |
| elif trade_dir == -1: |
| current_high_float = (entry_price - getattr(row, 'low')) / points |
| current_low_float = (entry_price - getattr(row, 'high')) / points |
| |
| max_float = max(max_float, current_high_float) |
| |
| if max_float >= 600: |
| locked_sl = min(locked_sl, entry_price - 5.00) |
| elif max_float >= 450: |
| locked_sl = min(locked_sl, entry_price - 3.50) |
| elif max_float >= 350: |
| locked_sl = min(locked_sl, entry_price - 3.00) |
| |
| if getattr(row, 'high') >= locked_sl: |
| pnl = (entry_price - locked_sl) / points |
| pnl -= 50 |
| trades.append(pnl) |
| in_trade = False |
|
|
| t_df = pd.Series(trades) |
| print(f"Total Trades: {len(t_df)}") |
| if len(t_df) > 0: |
| print(f"Wins: {len(t_df[t_df > 0])}") |
| print(f"Losses: {len(t_df[t_df < 0])}") |
| print(f"Win Rate: {(len(t_df[t_df > 0]) / len(t_df))*100:.2f}%") |
| print(f"Avg Win: {t_df[t_df > 0].mean():.2f} pts") |
| print(f"Avg Loss: {t_df[t_df < 0].mean():.2f} pts") |
| print(f"Net Profit Expected: {t_df.sum():.2f} pts") |
|
|
| if __name__ == '__main__': |
| run_simulation() |
|
|