This deep learning software was developed to forecast the most profitable trading signal for cryptocurrencies at any given point in time. The predictions are generated using artificial neural networks (CNN & RNN/LSTM hybrid) that were trained on historic price data. The predicted trading signals can be used for simulating trading on a historic time span or used for real trading in real-time.
The neural network part of this software started out as my master thesis and is written in Python, Tensorflow and Keras. The software that carries out the trades in real-time at an actual crypto exchange is written in TypeScript using Node.js. Both parts communicate via WebSockets.

  • Neural network predictions
  • Data aggregation of historic crypto data
  • Feature extraction, dimensionality reduction, preprocessing
  • Arbitrary intervals (1sec, 30sec, 1min, ...)
  • Real-life trading
  • Backtesting
  • Considering trading fees
  • Quick prototyping of new neural networks