Scalping Robot with Machine Learning Signal Filter

This project integrates a scalping trading bot with an ML-based signal filter to improve trade accuracy.

Features

• The bot places trades based on high and low levels and trailing stop-loss logic.
• A Python ML model classifies signals as high-probability or not based on market data.

Requirements

• MetaTrader 5 platform installed
• Python 3.8+
• Required Python libraries: pandas, sklearn, joblib, MetaTrader5

Setup

1. Train the ML Model

   • Prepare your historical market data in market_data.csv.
   • Run the ml_signal_filter.py script to train and save the model:

     python ml_signal_filter.py

2. Run the Trading Bot

   • Place the scalping_robot.mq5 file in the Experts folder of your MetaTrader 5 platform.
   • Add the compiled .ex5 file to the chart.

3. Integrate ML with MT5

   • Use the MetaTrader5 Python API to fetch live market data:

     import MetaTrader5 as mt5
     
     mt5.initialize()
     rates = mt5.copy_rates_from_pos("EURUSD", mt5.TIMEFRAME_M1, 0, 100)
     # Use these rates to generate features and predict with your model

   • Predict signals using the saved model:

     from joblib import load
     
     model = load("scalping_signal_classifier.pkl")
     prediction = model.predict([[price_change, high_low_diff, rsi]])  # Replace with actual features
     if prediction[0] == 1:
         print("High-probability signal. Proceed with trading.")
     else:
         print("Signal filtered out.")

4. Monitor Trades

   • Check the trading bot's performance in MetaTrader 5 and optimize settings based on results.

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Feel free to tweak the ML model and trading parameters to better suit your trading strategy! 🚀