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Stefan Jansen. Hands-On Machine Learning for Algorithmic Trading: Design and implement smart investment strategies to analyze market behavior using the Python ecosystem

The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies.

We continue studying distributed Q-learning algorithms. Earlier we have already considered two algorithms. In the first one [4], our model learned the probabilities of receiving a reward in a given range of values. In the second algorithm [5], we used a different approach to solving the problem. We trained the model to predict the reward level with a given probability.

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