Can we actually predict the price of Google stock based on a dataset of price history? I’ll answer that question by building a Python demo that uses an underutilized technique in financial market prediction, reinforcement learning. The specific technique we'll use in this video is a subset of RL called Q learning. Using a combination of code, animations, and theory i'll explain how we can let our AI learn a policy for when to buy and sell google stock to maximize profit.

Code for this video:
https://github.com/llSourcell/Reinforcement_Learning_for_Stock_Prediction

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This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine_Learning_Journey

More Learning Resources:
http://cs229.stanford.edu/proj2006/Molina-StockTradingWithRecurrentReinforcementLearning.pdf
http://www.wildml.com/2018/02/introduction-to-learning-to-trade-with-reinforcement-learning/
https://medium.com/@ranko.mosic/predicting-price-movement-and-trading-using-reinforcement-learning-kearns-nevmyvaka-2013-b5a64daa34f0
https://hub.packtpub.com/develop-stock-price-predictive-model-using-reinforcement-learning-tensorflow/
https://iknowfirst.com/deep-reinforcement-learning-part-2-the-game-of-stock-trading
https://www.youtube.com/watch?v=v_L9jR8P-54&list=PLQVvvaa0QuDe6ZBtkCNWNUbdaBo2vA4RO

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