quantopian-ensemble-methods
Unverified ML strategy on Multi by KhaledSharif. BotFinder score 18 out of 100.
Assisting repository for the published paper investigating ensemble methods in algorithmic trading.
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quantopian-ensemble-methods
Investigating Algorithmic Stock Market Trading using Efficient Ensemble Techniques This is an assisting repository for the published paper investigating ensemble methods in algorithmic trading. It is available publicly at this link. It was written by Khaled Sharif and Mohammad Abu-Ghazaleh, and was supervised by Dr Ramzi Saifan. Below is a brief overview of the paper contents, in addition to a summary of the code and results of the paper. Abstract Recent advances in the machine learning field have given rise to efficient ensemble methods that accurately forecast time-series. In this paper, we will use the Quantopian algorithmic stock market trading simulator to assess ensemble method performance in daily prediction and trading; simulation results show significant returns relative to the benchmark and strengthen the role of machine learning in stock market trading. Figure 1: The graph above shows the cumulative returns of each of the three algorithms when working with 100 automatically selected stocks (selected at the start of each month) and using one classifier to predict the tradin
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