Cryptolytic is a platform for beginners tinkering with cryptocurrency to the seasoned trader. It provides you with recommendations on when to buy and sell based on technical indicators and assesses the markets to predict arbitrage opportunities before they even happen.
| Stanley Kusmier |
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| Alfredo Quintana | Elizabeth Ter Sahakyan | Marvin A Davila | Nathan Van Wyck | Taylor Bickell |
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We gather the historical data from each exchange’s API into a database for all of our supported exchanges and trading pairs and implement Lambda functions in Cloud9 to collect live data from the cryptowat.ch API in the respective tables. This allows us to have the most up to date data in our databases for predictions.
Random forest classifier models were then trained on that dataset, stored in S3 buckets, and more Lambda functions were used to load those models and make predictions on the live data every 3 minutes. Those predictions are inserted into the database in a new table that stores all of the predictions.
The Flask app retrieves the most recent predictions from the database and can return them to a user or be available for a backend team to use via API. The API was deployed on Elastic Beanstalk.
Overall we created a data pipeline and a backend that makes use of 15 different models to generate and store predictions on a recurring basis, which can then be accessed via API endpoints.
Cryptolytic Product Canvas Notion
Python, SQL, Flask, AWS (Elastic Beanstalk, RDS, Lambda Functions, Cloud9, KMS, Sagemaker), PostgreSQL
The modeling/models/ folder contains two zip files, with a total of 15 models:
tr_pickles.zip contains 9 pickled trade recommender models.
arb_models.zip contains 6 pickled arbitrage models.
All 15 models use a Random Forest Classifier algorithm.
Each trade recommender model recommends trades for a particular trading pair on a particular exchange by predicting whether the closing price will increase by enough to cover the costs of executing a trade.
The arbitrage models predict arbitrage opportunities between two exchanges for a particular trading pair. Predictions are made ten minutes in advance. To count as an arbitrage opportunity, a price disparity between two exchanges must last for at least 30 minutes, and the disparity must be great enough to cover the costs of buying on one exchange and selling on the other.
Each of the nine trade recommender models is trained on 67 features. Of those 67 features, five are taken directly from the OHLCV data (open, high, low, close, base_volume), one indicates where gaps were present in the data (nan_ohlcv), three indicate the time (year, month, day), and the remainder are technical analysis features.
Each of the 6 arbitrage models is trained on 70 features. Of those 70 features, three features indicate the time (year, month, day), and four indicate the degree and length of price disparities between two exchanges (higher_closing_price, pct_higher, arbitrage_opportunity, window_length). The remaining 63 features are the original OHLCV features and technical analysis features specific to the two exchanges in a given arbitrage dataset and are labeled with the suffixes "exchange_1" and "exchange_2".
Technical analysis features were engineered with the Technical Analysis Library. They fall into five categories:
(1) Momentum indicators
(2) Volume indicators
(3) Volatility indicators
(4) Trend indicators
(5) Others indicators
Documentation for the technical analysis features features is available here:
Technical Analysis Library Documentation
We obtained all of our data from the Cryptowatch, Bitfinex, Coinbase Pro, and HitBTC APIs. Documentation for obtaining that data is listed below:
Cryptowatch API OHLCV Data Documentation
Bitfinex API OHLCV Data Documentation
Coinbase Pro API OHLCV Data Documentation
HitBTC OHLCV Data Documentation
Kraken OHLCV Data Documentation
Gemini OHLCV Data Documentation
Arbitrage Prediction Models
Trade Recommender Models
Data Processing and Modeling Notebook
http://www.cryptolyticapp.com/ (running on AWS but models outdated!)
Trade API /trade
Method: ["GET"]
Returns: {"results": "{('exchange', 'trading_pair'): [{ 'p_time': 'time', ‘period’: ‘minutes’, 'prediction': 'result'}], }"}
Arbitrage API /arbitrage
Note: Unavailable (AWS Cost High)
Method: ["GET"]
Returns: {"results":"{ ('exchange_1', 'exchange_2', 'trading_pair'): [ {'p_time': 'time', 'prediction': 'result'} ]}
We wrote article on how to do some of the technical aspects of this project:
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