Discussing the article: "Using Renko-bar Regression with Error Correction"

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Check out the new article: Using Renko-bar Regression with Error Correction.

The article presents a regression-based approach to forecasting Renko bars using CatBoost: the model estimates the logarithmic return of the next bar and the uncertainty of the forecast. We will examine a cascade of residual models with OOF validation using TimeSeriesSplit, shrinkage, and overall early stopping, as well as conditional bias correction. On EURUSD D1, a reduction in OOF-MAE was achieved, along with approximately 65% directional accuracy. A working script for MetaTrader 5 is provided that generates a signal, position size, SL, and TP in brick units.

The main problem with any regression model based on financial data is systematic error, or forecast bias. A model trained on historical data may systematically overestimate movements in one direction and underestimate them in the other. We will show how to identify and correct this error using a two-stage scheme. First, a multi-stage cascade of residual models is used, where each subsequent model learns from the errors of the previous one. Then a final conditional bias correction is performed. The result will be a production trading system that works with real daily EURUSD data from MetaTrader 5 and generates a trading signal with the calculated position size, stop-loss, and take-profit in units of the Renko brick size.

Renko charts have an important property: the size of each bar is fixed and equal to brick_size. This means that when forecasting the next bar, the target is effectively discrete. It takes three values: +log(1 + brick_size/close) when moving up by one brick; -log(1 - brick_size/close) when moving down; and a rare zero value at reversal points, when the close of the current bar coincides with the boundary. At first glance, this would seem to be an ideal task for classification.

Author: Yevgeniy Koshtenko