MQL5 ADVANCED:Code Machine learning EAs with Neural Networks

Explore how machine learning, a subset of artificial intelligence, analyzes data, preprocesses it, trains and tests models, identifies patterns, and predicts future market trends to automate trading decisions.

Master the three machine learning categories: supervised learning, unsupervised learning, and reinforced learning, and see their trading applications from price prediction to risk management.

Learn seven steps to build a machine learning expert advisor, from defining the problem and data collection to training a neural network, backtesting, evaluation, and live deployment.

Explore how neural networks, inspired by the brain, learn from data through layers of neurons, backpropagation, and weight adjustments to recognize patterns and predict market trends in trading.

Define a neural network approach to identify buy and sell patterns in the RSI, using ten weighted inputs and historical RSI data to generate signals.

Learn how to clean and preprocess data for an MQL5 expert advisor by normalizing RSI values into a range of minus one to one for neural network inputs.

Learn to build a hidden layer by computing total weighted inputs from ten inputs and weights, apply the hyperbolic tangent activation, and compare it with ReLU and sigmoid options.

Learn to calculate the output layer with a linear activation for regression, using normalized inputs and hidden-layer results to generate buy or sell signals.

Apply a neural network to filter expert advisor entries and compare neural network enhanced EAs with standard versions in backtests, revealing improved drawdown, profit, win rate, and Sharpe ratio.

Teach cost averaging for Red Fairy expert advisor to convert losses into profit through staged entries at 20 pips with increasing lot sizes, controlled by cost averaging and lot multiplier.

Develop cost averaging signals by creating buy and sell templates, restrict neural network output to the first trade, and prep for grid PIs and grid cell traits.

Implement cost averaging in a MQL5 expert advisor by building grid buy and sell functions, using ask/bid prices, enforcing a 20-pip gap, and resetting targets after trades.

Test the expert advisor with cost averaging to evaluate performance, review backtest results, and assess equity drawdown risks that indicate the need for optimization.

Apply continuous learning to a MQL5 expert advisor by implementing a real-time backpropagation that adjusts neural network weights, using learning rate and buy/sell target outputs to adapt to market conditions.

Learn how backpropagation adjusts weight values in a supervisor for MQL5 neural network EAs, compare performance with and without cost averaging, and observe market-condition–driven weight updates.

Learn to implement machine learning models into your expert advisor using the Mql5 language, improving parameters and finding the best settings for expert advisors.