Machine Learning for Trading
The purpose of this paper is to discover whether it is possible to train a machine-learning algorithm to behave as a risk-adverse investor by using a dynamic model involving transaction costs. Transaction costs are frequently overlooked due to the complexity of integrating them into the learning algorithm used to train the trading system.
By specifying a series of mathematical and logical assumptions, it is concluded that machines can in fact learn an optimal total wealth function considering transaction costs while behaving like a risk-adverse investor. Total wealth in a portfolio can be identified at time t provided that a term for slippage is added into the equation to account for the fact that liquidating all open positions at the desired price is unlikely. Due to the complexity and non-linearity of a function of returns, the optimal-policy decision is best characterized using the mean-variance problem. It is assumed for the purposes of this study that the multivariate distribution p(r) is mean-variance equivalent.
Five components of reinforcement learning are explained as they are used in relation to creating a learning algorithm for trading—states, actions, value functions and policies, Q-learning, and the reward function. One limitation to this method is the need for millions of data observations for training steps, which may not be available or desirable. Therefore a simulation-based approach is introduced as a resolution, greatly simplifying this process. This paper contributes to the existing body of literature a way to estimate the cost function and optimal strategy by using only an asset return model. Read the full article by Gordon Ritter here.
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