Project Description

This indicator is based on the Random Forest machine learning algorithm, and it has been created using a software package for developing artificial intelligence systems for trading – Hlaiman EA Generator. In this version, all calculations of price patterns and generation of the indicator trade signals are performed by means of 32 Decision Trees, each of which is implemented as a separate MQL function. The number of trees may be changed during the course of further studies. At the moment, it is dictated by the reasonable sufficiency and minimization of the source code, which currently comprises approximately 3 MB. The indicator predicts the reversal points and the price movement direction, and also displays the corresponding BUY and SELL signals. In addition, the indicator can display the statistics on the number of signals and the number of price points, which can be traded using the predicted signals.

Indicator parameters

  • Threshold BUY – threshold value for calculating buy signals in %.
  • Threshold SELL – threshold value for calculating sell signals in %.
  • Statistics – Mode for displaying statistics on predicted signals, available options:
    • Don’t Show – do not display statistics,
    • Current Day – display statistics for the current day,
    • Current Month – display statistics for the current month,
    • Current Year – display statistics for the current year,
    • All History – display statistics for all available chart data.

Tips on the Indicator Use

Before using the indicator signal in trading, try to configure their maximum possible statistically average size in points by selecting the optimal BUY and SELL threshold values. It is recommended to obtain these values for the nearest trading period.

In addition, do not forget to consider the spread losses and/or broker commission when predicting the potential profit factor of the trades opened according to the indicator’s signals. Also note that the point values displayed in the indicator statistics are calculated approximately, using the average bar prices.