Applying Deep Reinforcement Learning To Algorithmic Trading

Abstract

The article presents an algorithm for trading long contracts with one asset in the financial market in the Python programming language using the LSTM neural network using the Keras library. The formalized LSTM model solves the vanishing gradient problem, which can hold the gradient of the objective function relative to the state signal. As applied to our problem, such an improvement in the model allows us to collect data on certain patterns of price changes. Sharpe Ratio is used to determine the optimal strategy and decision making at each time of application. The optimal minimum time period for the model operation has been determined; the signal transmission delay from the moment the market situation changes until the signal is received by the model, which will be infinitely small, and the computing power will be considered infinitely large. These assumptions give the right to say: when the market situation changes, the model is instantly ready to react and make a decision to sell, buy or hold an asset.