Trading Strategy Validation Using Forwardtesting with Deep Neural Networks

Traders commonly test their trading strategies by applying them on the historical market data (backtesting), and then reuse on their (future) trades the strategy that achieved the maximum profit on such past data. In this paper we propose a novel technique, that we shall call forwardtesting, that determines the strategy to apply by testing it on the possible future predicted by a deep neural network that has been designed to perform stock price forecasts and trained with the market historical data. Our results confirm that neural networks outperform classical statistical techniques when performing such forecasts, and their predictions allow to select a trading strategy that, when applied to the real future, results equally or more profitable than the strategy that would be selected through the traditional backtesting.