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Lorentzian classification for mt5

MQL5 Indicators

Specification

Code Conversion from Pine Script(TradingView) to MQL5.

First of all we must define the general requirements to start this job:

Requirements for the code:

The code needs to be:

  • Object oriented.
  • Highly modular.
  • Comments in functions are required (preferably in English).

A Lorentzian Distance Classifier (LDC) is a Machine Learning classification algorithm capable of categorizing historical data from a multi-dimensional feature space. This indicator demonstrates how Lorentzian Classification can also be used to predict the direction of future price movements when used as the distance metric for a novel implementation of an Approximate Nearest Neighbors (ANN) algorithm.

General Settings:
  • Source - This has a default value of "hlc3" and is used to control the input data source.
  • Neighbors Count - This has a default value of 8, a minimum value of 1, a maximum value of 100, and a step of 1. It is used to control the number of neighbors to consider.
  • Max Bars Back - This has a default value of 2000.
  • Feature Count - This has a default value of 5, a minimum value of 2, and a maximum value of 5. It controls the number of features to use for ML predictions.
  • Color Compression - This has a default value of 1, a minimum value of 1, and a maximum value of 10. It is used to control the compression factor for adjusting the intensity of the color scale.
  • Show Exits - This has a default value of false. It controls whether to show the exit threshold on the chart.
  • Use Dynamic Exits - This has a default value of false. It is used to control whether to attempt to let profits ride by dynamically adjusting the exit threshold based on kernel regression.
Feature Engineering Settings:
Note: The Feature Engineering section is for fine-tuning the features used for ML predictions. The default values are optimized for the 4H to 12H timeframes for most charts, but they should also work reasonably well for other timeframes. By default, the model can support features that accept two parameters (Parameter A and Parameter B, respectively). Even though there are only 4 features provided by default, the same feature with different settings counts as two separate features. If the feature only accepts one parameter, then the second parameter will default to EMA-based smoothing with a default value of 1. These features represent the most effective combination I have encountered in my testing, but additional features may be added as additional options in the future.
  • Feature 1 - This has a default value of "RSI" and options are: "RSI", "WT", "CCI", "ADX".
  • Feature 2 - This has a default value of "WT" and options are: "RSI", "WT", "CCI", "ADX".
  • Feature 3 - This has a default value of "CCI" and options are: "RSI", "WT", "CCI", "ADX".
  • Feature 4 - This has a default value of "ADX" and options are: "RSI", "WT", "CCI", "ADX".
  • Feature 5 - This has a default value of "RSI" and options are: "RSI", "WT", "CCI", "ADX".

Filters Settings:
  • Use Volatility Filter - This has a default value of true. It is used to control whether to use the volatility filter.
  • Use Regime Filter - This has a default value of true. It is used to control whether to use the trend detection filter.
  • Use ADX Filter - This has a default value of false. It is used to control whether to use the ADX filter.
  • Regime Threshold - This has a default value of -0.1, a minimum value of -10, a maximum value of 10, and a step of 0.1. It is used to control the Regime Detection filter for detecting Trending/Ranging markets.
  • ADX Threshold - This has a default value of 20, a minimum value of 0, a maximum value of 100, and a step of 1. It is used to control the threshold for detecting Trending/Ranging markets.
Kernel Regression Settings:
  • Trade with Kernel - This has a default value of true. It is used to control whether to trade with the kernel.
  • Show Kernel Estimate - This has a default value of true. It is used to control whether to show the kernel estimate.
  • Lookback Window - This has a default value of 8 and a minimum value of 3. It is used to control the number of bars used for the estimation. Recommended range: 3-50
  • Relative Weighting - This has a default value of 8 and a step size of 0.25. It is used to control the relative weighting of time frames. Recommended range: 0.25-25
  • Start Regression at Bar - This has a default value of 25. It is used to control the bar index on which to start regression. Recommended range: 0-25

Display Settings:
  • Show Bar Colors - This has a default value of true. It is used to control whether to show the bar colors.
  • Show Bar Prediction Values - This has a default value of true. It controls whether to show the ML model's evaluation of each bar as an integer.
  • Use ATR Offset - This has a default value of false. It controls whether to use the ATR offset instead of the bar prediction offset.
  • Bar Prediction Offset - This has a default value of 0 and a minimum value of 0. It is used to control the offset of the bar predictions as a percentage from the bar high or close.

Backtesting Settings:
  • Show Backtest Results - This has a default value of true. It is used to control whether to display the win rate of the given configuration.

Files:

ZIP
machine_learning-lorentzian-classification.pine.zip
9.8 Kb

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Project information

Budget
30+ USD