To examine a number of different forecasting techniques to predict future stock returns based on past returns and numerical news indicators to construct a portfolio of multiple stocks in order to diversify the risk. We do this by applying supervised learning methods for stock price forecasting by interpreting the seemingly chaotic market data.
$ workon myvirtualenv [Optional]
$ pip install -r requirements.txt
$ python scripts/Algorithms/regression_models.py <input-dir> <output-dir>
Download the Dataset needed for running the code from here.
- Preprocessing and Cleaning
- Feature Extraction
- Twitter Sentiment Analysis and Score
- Data Normalization
- Analysis of various supervised learning methods
- Conclusions
- Machine Learning in Stock Price Trend Forecasting. Yuqing Dai, Yuning Zhang
- Stock Market Forecasting Using Machine Learning Algorithms. Shunrong Shen, Haomiao Jiang. Department of Electrical Engineering. Stanford University
- How can machine learning help stock investment?, Xin Guo
- Slides: http://www.slideshare.net/SharvilKatariya/stock-price-trend-forecasting-using-supervised-learning
- Video: https://www.youtube.com/watch?v=z6U0OKGrhy0
- Report: https://github.com/scorpionhiccup/StockPricePrediction/blob/master/Report.pdf
- Recurrent Neural Networks - LSTM Models
- ARIMA Models
- https://github.com/dv-lebedev/google-quote-downloader
- Book Value
- http://www.investopedia.com/articles/basics/09/simplified-measuring-interpreting-volatility.asp
- Volatility
- https://github.com/dzitkowskik/StockPredictionRNN
- Scikit-Learn
- Theano
