Discussing the article: "Example of Auto Optimized Take Profits and Indicator Parameters with SMA and EMA"

[MetaQuotes]

Check out the new article: Example of Auto Optimized Take Profits and Indicator Parameters with SMA and EMA.

This article presents a sophisticated Expert Advisor for forex trading, combining machine learning with technical analysis. It focuses on trading Apple stock, featuring adaptive optimization, risk management, and multiple strategies. Backtesting shows promising results with high profitability but also significant drawdowns, indicating potential for further refinement.

The EA's approach is multifaceted, utilizing price prediction from a machine learning model, trend following techniques, and adaptive parameter optimization. It's designed to operate primarily on the #AAPL stock, though it has the flexibility to be adapted for other instruments. With features like dynamic lot sizing, trailing stops, and automatic adjustment to market conditions, this EA represents a blend of cutting-edge technology and time-tested trading principles.

These indicators are combined in a sophisticated manner, with their parameters being dynamically optimized based on recent market conditions. The EA also includes features like trailing stops and moral expectation calculations to manage open positions effectively.

Explanation of Indicators Used:

1. Simple Moving Average (SMA): The EA uses a Simple Moving Average with an adaptively optimized period. The SMA helps identify the overall trend direction and is used in conjunction with price and other indicators to generate trading signals.
2. Exponential Moving Average (EMA): An Exponential Moving Average is also employed, with its period dynamically optimized. The EMA responds more quickly to recent price changes than the SMA, providing a different perspective on trend direction.
3. Average True Range (ATR): While not explicitly calculated in the code, the EA uses ATR-based calculations for setting stop-loss and take-profit levels. This allows for volatility-adjusted position sizing and risk management.
4. Machine Learning Model: The EA incorporates an ONNX (Open Neural Network Exchange) model for price prediction. This model takes in a series of recent price data and attempts to forecast the next price movement, adding a predictive element to the trading strategy.

The combination of these indicators, along with the machine learning component, allows the EA to adapt to changing market conditions and potentially identify trading opportunities across various market states.

Author: Javier Santiago Gaston De Iriarte Cabrera

[Arup Nag]

A step by step guide for auto optimization will be very helpful

[Javier Santiago Gaston De Iriarte Cabrera]

Arup Nag #:
A step by step guide for auto optimization will be very helpful

Thanks! I am making one just now, it would be finished in not much time.Please ask for anything else you want or need.

[Javier Santiago Gaston De Iriarte Cabrera]

Arup Nag #:
A step by step guide for auto optimization will be very helpful

Here is the article, I hope you like it:  How to Implement Auto Optimization in MQL5 Expert Advisors - MQL5 Articles