Stock Price Prediction with BERT and XGBoost using Twitter data

pip install -r requirements.txt

• Tweet Scraping

python ./twint/twitter_scraper.py

• Feature Extraction w./ pre-processing
  • see 1. feature_generator + sentiment.ipynb
  • download the BERT model from here and place it according to the directory shown in the following command.

bert-serving-start -model_dir ./model/tmp/english_L-12_H-768_A-12/ -num_worker=1

• Feature Aggregation

  • see 2. feature_aggregator + sentiment.ipynb

• Classification Test with price-feature

  • see 3. EDA_stock + XGBoost.ipynb

• Regression Test with all features

  • see 4. XGBoost for Regression.ipynb

endpoints occupied:

• under ./api folder

python feature_api.py

python database_api.py

python xgboost_api.py

• under ./cors-anywhere folder

node server.js

• open ./visualize/index/html to see the web app

You can Contribute to this project with issues or pull requests.

See RELEASE NOTES file.

See MIT LICENSE file.

If you have any ideas, feedback, requests or bug reports, you can reach me at [email protected]