AI-Powered Trading Bots

Retrain models during live deployments to self-adapt to the market in a supervised manner

Create large rich feature sets based on simple user-created strategies

 Threading allows for adaptive model retraining on a separate thread (or on GPU if available) from model inferencing (prediction) and bot trade operations

Emulate self-adaptive training on historic data with a backtesting module that automates retraining

The generalized and robust architecture allows for incorporating any machine learning library/method available in Python.

Remove outliers from training and prediction data sets using a variety of outlier detection techniques

Store trained models to disk to make reloading from a crash fast and easy, and purge obsolete files for sustained dry/live runs

Compute time ranges for data downloads and update historic data in live deployments.