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
- Considering trading fees
- Quick prototyping of new neural networks