Moving Average Crossover Bot with Machine Learning Enhancements needed

Project Overview:

I would like you to develop a Moving Average Crossover Trading Bot for MetaTrader 5 (MT5), compatible with all trading instruments (Forex, Stocks, Indices, Commodities, etc.). The bot should be customizable, allowing users to adjust various parameters such as risk percentage, moving average periods, stop loss, take profit, and trade duration. The bot will leverage Long Short-Term Memory (LSTM) models to enhance trade decisions based on the Moving Average Crossover Strategy.

Trading Strategy:

The primary strategy will be based on the Moving Average Crossover Strategy, using two Exponential Moving Averages (EMA):

• Short-term EMA: Fast-moving average for short-term trends.
• Long-term EMA: Slow-moving average for long-term trend confirmation.

Buy/Sell Signals:

• Buy (Long) Signal: When the short-term EMA crosses above the long-term EMA (Bullish Crossover).
• Sell (Short) Signal: When the short-term EMA crosses below the long-term EMA (Bearish Crossover).

The exit strategy will include configurable Stop Loss (SL), Take Profit (TP), and optional trailing stops.

Machine Learning Enhancements (Using LSTM)

To enhance the performance and decision-making ability of the bot, we will integrate Long Short-Term Memory (LSTM), a type of recurrent neural network (RNN) capable of processing time-series data to capture long-term dependencies in price trends and volatility. This model will help improve the accuracy of the trade signals generated by moving average crossovers by analyzing past price data, trends, and market patterns.

How LSTM Enhancements Will Work:

1. Preprocessing:

   • Collect and preprocess historical price data, technical indicators (e.g., RSI, MACD, volume), and moving average crossovers.

2. Model Training:

   • The LSTM model will be trained on historical price data to predict:
     • Whether a crossover signal is likely to result in a successful trade (profitable outcome).
     • The probability of trend continuation after the crossover signal.
     • Market conditions (bullish, bearish) based on patterns in price action.

3. Decision-Making Process:

   • Trade Validation: Once a moving average crossover signal is triggered, the LSTM model will validate whether the signal is likely to be profitable based on historical patterns.
   • If the model predicts a high probability of success, the trade will be executed.
   • If the model predicts a low probability of success, the bot will ignore the crossover signal or wait for additional confirmation.
   • Adaptive Learning: The LSTM model will adapt to recent market data through periodic retraining to stay updated with current trends.

Key Features and Flexibility (Including LSTM Enhancements)

Moving Average Parameters:

• Short-term EMA Period: Adjustable by the user (default: 12).
• Long-term EMA Period: Adjustable by the user (default: 26).
• Option to select between Exponential Moving Average (EMA) or Simple Moving Average (SMA).

Risk Management Settings:

• Risk Percentage: User-defined risk percentage per trade (e.g., 1% or 2% of total balance).
• Stop Loss (SL): Adjustable based on fixed pips, ATR, or dynamic models (LSTM may help predict optimal stop-loss levels).
• Take Profit (TP): Adjustable based on fixed pips, Risk-to-Reward ratio, or trailing stops.

Customizable Trade Filters:

• LSTM Trade Validation: LSTM will validate trade signals generated by the moving average crossovers.
• Timeframes: User-defined timeframes (e.g., 1-minute, 5-minute, 1-hour, daily).
• Instruments/Pairs: The bot must work on all MT5 instruments, and users can select which trading pairs or instruments to trade.
• Trend Filter: Option to trade only in the direction of the larger trend (e.g., use the 200-period MA as a trend filter).
• Volume Filter: Option to take trades only if the volume exceeds a certain threshold.

Position Sizing:

• Automatic calculation of position sizes based on risk percentage and stop-loss settings.
• Option for manual lot size selection.

Trade Timing Options:

• Configure trading hours or days (e.g., only trade during London or New York sessions).
• Avoid trading during high-impact news events (option to disable trading during news releases).

Stop and Reverse:

• If a long trade is closed (e.g., bearish crossover), the bot should optionally reverse the position and open a short trade, and vice versa.

LSTM Model Flexibility:

The LSTM model should be easily customizable and adjustable:

• Training Period: Allow the user to define how much historical data to use for training the model.
• Retraining Frequency: Allow the bot to periodically retrain the LSTM model using recent data (e.g., once a week, or after a certain number of trades).

Backtesting & Optimization:

The bot must be compatible with MT5’s Strategy Tester, allowing users to backtest the performance of both the moving average strategy and the LSTM-enhanced decision-making.

• LSTM Model Metrics:

  • Accuracy: How well the model predicts successful trades.
  • Precision: The percentage of positive predictions that are correct.
  • Recall: The model’s ability to identify all profitable trades.

• Traditional Backtesting Metrics: Include win rate, drawdown, Sharpe ratio, and profit factor.

Final Deliverables:

• Completed Bot in MetaTrader 5 format (.ex5 or .mq5), including the LSTM model.
• Source code for future modifications.
• A detailed user guide or documentation explaining how to adjust parameters, train the LSTM model, and use the bot effectively.
• Initial testing on demo accounts to verify functionality.