Einstock Algo Trader
A stock trading simulation app that uses machine learning algorithms to predict equity price trends.
Team
- Product Owner: Kevin Kim
- Scrum Master: Lucas Hawes
- Lead Frontend Engineer: Aaron Stevens
- Lead Backend Engineer: Natasha Che
Table of Contents
- Features
- Requirements
- Development
- Installing Dependencies
- [Tasks] (#tasks)
- Contributing
Features
Multiple algorithms
Popular machine learning algorithms, including K Nearest Neighbors, Logistic regressions, Naive Bayes, Neural Networks, and Random Forests, are available for testing
Wide range of equity selections
Users can test algorithm performance with historical data for over 7000 stocks traded on NYSE and Nasdaq
Hassle-free trade simulation
Buying and selling decisions are automatically generated according to the price predictions from the selected algorithm
Comprehensive evaluation
Einstock reports various assessment metrics after a simulation run to evaluate an algorithm's performance against market benchmarks
Requirements
- Node 6.1.x
- Postgresql 9.1.x
Development
Installing Dependencies
From within the root directory:
npm install
bower install
Tasks
Start the local database:
postgres -D /usr/local/var/postgres
Start the server:
npm start
Contributing
See CONTRIBUTING.md for contribution guidelines.