Hobby project for showing off some Machine Learning skills in addition to software engineering.
Predicting the price of BTC/USD price using historical OHLCV (open, high, low, close and volume) data.
I might return to this project to add more transparency about the error metrics and some backtesting.
Update 2 August 2019: Scaling the output variable prior to fitting. Now fitting a stacked LSTM neural net outputing the next 24 hours of closing prices. Performance went up considerably with this method, and it's no longer reliant on the past movements of the closing price as much. Every hour I'm delivering predictions for the next 24 hours.
Update August 2019:
Currently predicting the percentage movement in BTC/USD 24 hours ahead. This proved to have some problems as the model mostly predicts small movements centered around 0. A better approach might be to try and predict in what percentile the percent change will be based on all earlier historical price movements. That way the model might also guess very high or very low values.
- Sign up to CoinApi and obtain an API token.
- Replace the dummy token in the
.envfile and replace the database credentials however you want. - Run
make runordocker-compose up -d --build
To run unit tests and type checking build the images then run
make tests
- Using InfluxDB to store time series.
- Serving the website with gunicorn
- Rate limit on the public api to prevent abuse
- Fetching hourly data from CoinApi and seeding the database with public cryptocurrency data in plain csv from a cryptocurrency data website.
- Charts drawn with Highstock
- Model is (currently) a Tensorflow one-layer recurrent neural network.
- Initial plan was to also utilize Tensorflow probability to get the confidence in the predictions to the front-end, but this first version does not include it.