Algorithmic Trading System with Machine Learning
- An Algorithmic Trading System connecting to Robinhood to execute various algo trading strategies
- Stocks Recommendation System based on Machine Learning Models
These instructions will enable you to run an algo trading system on your local machine
You need a robinhood account with a two factor code setup using your phone number. At the point of executing AlgoRobinhood, you will put the account id, password and the two-factor codes in terminal to login
$ python AutoTrade.py
Enter your account id: [email protected]
Do you want to run recommendation code (y/n): y
What recommendation model you want to use (LSTM/RF): LSTM
Enter your password: *****
Please Enter SMS Code: ******
2019-09-30 23:22:16,785 __main__ Successfully logged in account
2019-09-30 23:22:16,785 __main__ Running stocks recommendation for today...
...
2019-09-30 23:28:54,145 __main__ The code is outside execution period.
2019-09-30 23:28:54,202 __main__ Successfully logged out account.
Make sure you have below python packages installed:
- tensorflow
- xgboost
Git clone the AlgoRobinhood codebase and you should be ready to go:
git clone https://github.com/ChrisZheng1985/AlgoRobinhood.git
Simply git clone this codebase and execute the below command. After successfully login in, a log file will be generated under /log, with detailed information like recommendations made from a tensorflow model predction, as well as buy/sell operations being executed if any
python AutoTrade.py
Example of a log file
2019-10-02 22:35:17,385 __main__ Auto trading start at 2019-10-02 22:35:17
2019-10-02 22:35:17,385 __main__ The code is outside execution period.
2019-10-02 22:35:31,913 __main__ Successfully logged in account
2019-10-02 22:35:31,914 __main__ Running stocks recommendation for today...
2019-10-02 22:35:38,262 recommendation_system.recommendation Using Random Forest - RF to train and validate each stock price...
2019-10-02 22:35:39,102 recommendation_system.recommendation Symbol SNAP model accuracy is 79.73%, with prob of 7.5% to go up by 5.0% over the next 5 days
2019-10-02 22:35:39,412 recommendation_system.recommendation Symbol AAPL model accuracy is 89.19%, with prob of 5.99% to go up by 5.0% over the next 5 days
...
2019-10-02 22:35:51,735 recommendation_system.recommendation Symbol UBER model accuracy is 93.24%, with prob of 16.7% to go up by 5.0% over the next 5 days
2019-10-02 22:35:51,977 recommendation_system.recommendation Symbol COF model accuracy is 91.89%, with prob of 3.0% to go up by 5.0% over the next 5 days
2019-10-02 22:35:51,977 recommendation_system.recommendation Today's top 5 recommended stocks are:
2019-10-02 22:35:51,977 recommendation_system.recommendation Symbol FB: Rating 24.13% - Model Accuracy 94.59%
2019-10-02 22:35:51,977 recommendation_system.recommendation Symbol UBER: Rating 16.7% - Model Accuracy 93.24%
2019-10-02 22:35:51,977 recommendation_system.recommendation Symbol NFLX: Rating 15.93% - Model Accuracy 94.59%
2019-10-02 22:35:51,978 recommendation_system.recommendation Symbol SNE: Rating 13.67% - Model Accuracy 86.49%
2019-10-02 22:35:51,978 recommendation_system.recommendation Symbol TWTR: Rating 12.24% - Model Accuracy 82.43%
2019-10-02 22:35:51,978 __main__ The code is outside execution period.
2019-10-02 22:35:52,025 __main__ Successfully logged out account.
Example will be added about how to deploy this on a user-friendly web based system
Example to be added
- Python 3.6 - Codebase development and execution
- Django - The web framework development
- Version 1.0
This project is licensed under the MIT License - see the LICENSE.md file for details
- Build Github page with more information in README.md - done on 20190929;
- Optimize the data pipeline for modeling, and do codebase refactoring; - done on 20190930
- add modeling class, which can be extented to RF and XGBoost; - done on 20190930
- Extend machine leaning models by adding Random Forest to do forecast; - done on 20191001
- Extend machine leaning models by adding XGboost to do forecast; - in progress
- Create Python package;
- Build Web-based UI;
- Build NLP on financial news, take it as additional features in ML models to enhance model performance;
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent