In my most recent project, I modify historical cryptocurrency market charts by simulating the behavior of commonly used trading bots. This process introduces predictable patterns that can be captured using an LSTM neural network.
The trading patterns of the bots create subtle, predictable price shifts, resulting in a correlated market. We demonstrate the neural network’s ability to capture this effect, as evidenced by a decreasing prediction error over training epochs.