You can find the project at Cryptolytic.
| Stanley Kusmier |
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| Alfredo Quintana | Elizabeth Ter Sahakyan | Marvin A Davila | Nathan Van Wyck | Taylor Bickell |
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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.
Python, AWS, PostgreSQL, SQL, Flask
The models folder contains two zip files, with a total of 30 models:
tr_pickles.zip contains nine pickled trade recommender models.
arb_models.zip contains 21 pickled arbitrage models.
All 30 models use a RandomForestClassifier 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 thirty 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 21 arbitrage models is trained on 91 features. Of those 91 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). Half of the remaining 84 features are specific to the first of the two exchanges in a given arbitrage dataset and are labelled with the suffix "exchange_1"; the other half are specific to the second of those two exchanges and are labelled with the suffix "exchange_2". In each of these two sets of 42 features, two are taken directly from the OHLCV data (close_exchange_#, base_volume_exchange_#), one indicates where gaps were present in the data (nan_ohlcv), and the remainder are technical analysis features.
Technical analysis features were engineered with the Technical Analysis Library; they fall into five types:
(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
http://www.cryptolyticapp.com/
Trade API /trade
Method: ["GET"]
Returns: {"results": "{('exchange', 'trading_pair'): [{ 'p_time': 'time', ‘period’: ‘minutes’, 'prediction': 'result'}], }"}
Arbitrage API /arbitrage
Method: ["GET"]
Returns: {"results":"{ ('exchange_1', 'exchange_2', 'trading_pair'): [ {'p_time': 'time', 'prediction': 'result'} ]}
When contributing to this repository, please first discuss the change you wish to make via issue, email, or any other method with the owners of this repository before making a change.
Please note we have a code of conduct. Please follow it in all your interactions with the project.
If you are having an issue with the existing project code, please submit a bug report under the following guidelines:
- Check first to see if your issue has already been reported.
- Check to see if the issue has recently been fixed by attempting to reproduce the issue using the latest master branch in the repository.
- Create a live example of the problem.
- Submit a detailed bug report including your environment & browser, steps to reproduce the issue, actual and expected outcomes, where you believe the issue is originating from, and any potential solutions you have considered.
We would love to hear from you about new features which would improve this app and further the aims of our project. Please provide as much detail and information as possible to show us why you think your new feature should be implemented.
If you have developed a patch, bug fix, or new feature that would improve this app, please submit a pull request. It is best to communicate your ideas with the developers first before investing a great deal of time into a pull request to ensure that it will mesh smoothly with the project.
Remember that this project is licensed under the MIT license, and by submitting a pull request, you agree that your work will be, too.
- Ensure any install or build dependencies are removed before the end of the layer when doing a build.
- Update the README.md with details of changes to the interface, including new plist variables, exposed ports, useful file locations and container parameters.
- Ensure that your code conforms to our existing code conventions and test coverage.
- Include the relevant issue number, if applicable.
- You may merge the Pull Request in once you have the sign-off of two other developers, or if you do not have permission to do that, you may request the second reviewer to merge it for you.
These contribution guidelines have been adapted from this good-Contributing.md-template.
See [Backend Documentation](link to your backend readme here) for details on the backend of our project.
See [Front End Documentation](link to your front end readme here) for details on the front end of our project.