104 lines
2.7 KiB
Python
104 lines
2.7 KiB
Python
import pandas as pd
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import numpy as np
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import os
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# Load 2022 EURUSD 1m data
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df = pd.read_excel("data/DAT_XLSX_EURUSD_M1_2021.xlsx", header=None, names=[
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'timestamp', 'open', 'high', 'low', 'close', 'volume'
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])
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df['timestamp'] = pd.to_datetime(df['timestamp'])
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df.set_index('timestamp', inplace=True)
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# Resample to 15-minute intervals
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df = df.resample('15min').agg({
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'open': 'first',
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'high': 'max',
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'low': 'min',
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'close': 'last',
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'volume': 'sum'
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}).dropna()
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# Create perfect actions
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lookahead = 4 # ~1 hour
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profit_threshold = 0.001
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loss_threshold = 0.001
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labels = []
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for i in range(len(df) - lookahead):
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current_close = df.iloc[i]['close']
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future_window = df.iloc[i + 1:i + 1 + lookahead]
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max_future_price = future_window['high'].max()
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min_future_price = future_window['low'].min()
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if max_future_price >= current_close * (1 + profit_threshold):
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labels.append(1)
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elif min_future_price <= current_close * (1 - loss_threshold):
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labels.append(-1)
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else:
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labels.append(0)
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labels += [0] * lookahead
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df['perfect_action'] = labels
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# Simulate perfect trader
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balance = 10000.0
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position = 0.0
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entry_price = 0.0
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win_count = 0
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loss_count = 0
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trades_count = 0
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trades = []
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for i in range(len(df)):
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price = df.iloc[i]['close']
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action = df.iloc[i]['perfect_action']
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if action == 1 and position == 0:
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position = balance / price
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entry_price = price
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trades.append({
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"timestamp": df.index[i],
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"action": "buy",
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"price": price,
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"balance": balance
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})
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balance = 0
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trades_count += 1
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elif action == -1 and position > 0:
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exit_value = position * price
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pnl = exit_value - (position * entry_price)
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if pnl > 0:
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win_count += 1
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else:
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loss_count += 1
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balance = exit_value
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trades.append({
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"timestamp": df.index[i],
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"action": "sell",
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"price": price,
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"balance": balance
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})
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position = 0
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entry_price = 0
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# Finalize any open position
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if position > 0:
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balance = position * df.iloc[-1]['close']
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# Results
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print("\n📊 Perfect Trader Simulation:")
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print(f"🟢 Starting Balance: $10,000.00")
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print(f"🔚 Ending Balance: ${balance:,.2f}")
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print(f"💼 Total Trades: {trades_count}")
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print(f"✅ Wins: {win_count} | ❌ Losses: {loss_count}")
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if trades_count > 0:
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print(f"📈 Win Rate: {win_count / trades_count * 100:.2f}%")
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# Save trade log
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output_path = "ml/perfect_trades.csv"
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os.makedirs("ml", exist_ok=True)
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trades_df = pd.DataFrame(trades)
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trades_df.to_csv(output_path, index=False)
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print(f"📁 Perfect trades saved to: {output_path}")
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