tickdata / simulate_v4_1_trailing.py
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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')
# Calculate Donchian Channels
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']
# Shift to prevent lookahead
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)
# Simplified Entry: True Crossover + Expansion
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 # 100 pts
# Entry Signals
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
# Simulate Trades
trades = []
in_trade = False
trade_dir = 0
entry_price = 0
hard_sl = 0
locked_sl = 0
max_float = 0
points = 0.01 # Assuming 2 decimal pricing for Gold, 1 point = 0.01 (Wait, MT5 XAUUSD pnt = 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 # 2100 pts SL Mode A
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)
# Check Lock Updates (evaluate HIGH of candle)
if max_float >= 600:
locked_sl = max(locked_sl, entry_price + 5.00) # Stage 3 Lock 500pts minimum
elif max_float >= 450:
locked_sl = max(locked_sl, entry_price + 3.50) # Stage 2 Lock 350pts
elif max_float >= 350:
locked_sl = max(locked_sl, entry_price + 3.00) # Stage 1 Lock 300pts
# Check Exit (evaluate LOW of candle against SL)
if getattr(row, 'low') <= locked_sl:
pnl = (locked_sl - entry_price) / points
pnl -= 50 # 50 pts Spread penalty
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 # 50 pts Spread penalty
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()