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Deep Learning for Forex Trading

Many research papers cover the prediction of financial time series but only a small number of them speak about the application in a real trading strategy. Most of the time the research only gives the performance metrics of the model (accuracy, RMSE, …) but without trying to transform it into a profitable strategy. When we talk about financial time series, we talk about stochastic processes, meaning it deals with a lot of randomness. For this reason, it’s unrealistic to expect getting an accuracy similar to the ones obtained in many other applications of Deep Learning. So, don’t expect getting a 80% accuracy when predicting the market to go up or down for a given time horizon. Predicting Financial Time Series is known to be one of the hardest task in Machine Learning. The purpose is instead of trying to find a model that can give you a small edge when compared to a pure random guess, or to a pure Buy and Hold strategy when we speak about the stock market. There is also a lot of things to take care regarding the data processing when applying Machine Learning to Financial Time Series. If you are a beginner in this field of research or if you get good metrics from your model but poor real trading results, you can read our paper covering this subject, Financial Time Series Data Processing for Machine Learning, it can help you to understand what’s going wrong. For years now, and in collaboration with two French engineer schools, CentralSupelec and Polytechnique, Lusis AI Department is working on Financial Time Series predictions in the perspective of applicable trading strategies. In this article and the future ones, the reader will find some methods and results than can help him a lot with his own research. But because much more than in any other domain, an AI Based Trading Strategy that works can directly be transformed into money, so we will obiously never disclose the most important things enabling to reproduce our models. If you find a reproducible model with very good metrics anywhere in a paper, take care and do your own research and backtest before investing with it.