This morning (May 29th 2018) Trevor Trinkino presented the final part of his three-part Machine Learning webinar in co-operation with FXCM.
Part one is here. Part two is here.
In this series, quantitative trader Trevor Trinkino will walk you through a step-by-step introductory process for implementing machine learning and how you can turn this into a trading algorithm using Python. Plus he will show you the process of tuning your parameters for better performance of your trading system.
This week we look at some of the main hyper-parameters in the Random Forest and Gradient Boosted Decision Tree algorithms and cover how to quickly tune them. Then we also spend some time briefly looking over a LSTM neural network and the applicable code in Tensorflow. Finally, we will discuss how to import the tuned machine learning model into our back-testing or live-trading environments.
Files mentioned in the video are available from our Google Drive below..
Jupyter Notebook : Machine Learning Tutorial.ipynb
utils.py
ANN.py
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Trevor Trinkino presents the final part of his three-part Machine Learning webinar in co-operation with FXCM
