New article Finding seasonal patterns in the forex market using the CatBoost algorithm has been published:
The article considers the creation of machine learning models with time filters and discusses the effectiveness of this approach. The human factor can be eliminated now by simply instructing the model to trade at a certain hour of a certain day of the week. Pattern search can be provided by a separate algorithm.
You can set in the function a list of hours to be checked. In my example all 24 hours are set. For the purity of the experiment, I disabled sampling by setting 'min' and 'max' (minimum and maximum horizon of an open position) equal to 15. The 'iterations' variable is responsible for the number of retraining cycles for each hour. A more reliable statistics can be obtained by increasing this parameter. After completing operation, the function will display the following graph:
The X-axis features the ordinal numbers of the hours. The Y-axis represents R^2 scores for each iteration (10 iterations were used, which means model retraining for each hour). As you can see, passes for hours 4, 5 and 6 hours are located more closely, which gives more confidence in the quality of the found pattern. The selection principle is simple — the higher the position and density of the points, the better the model. For example, in the interval of 9-15, the graph shows a large dispersion of points, and the average quality of models drops to 0.6. You can further select the desired hours, retrain the model and view its results in the custom tester.
Author: Maxim Dmitrievsky
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