Predicting Stable Portfolios using Machine Learning / Deep Learning

This repo contains notebooks for the 4 modules described in the report linked here.

Modules:

SEC Scraper

Note: We have already scraped S&P 500 SEC filings. For testing purpose we are scraping the filings for just 2 tickers here as an example.

• scraping_cleaning_sec.ipynb notebook is used to scrape all data from SEC EDGAR website. It also takes care of cleaning and parsing HTML into raw text files and checkpoints its progress so extraction can resume later.

Sentiment Analyzer

Notebooks for this module are located in EDGAR-reports-Text-Analysis.

• Sentiment_Analysis_SEC.ipynb contains all the code extract sentiment scores from SEC filings.
• Sentiment_Stocks_Visualization.ipynb is used to generate stock trends vs. the calculated sentiments.

Stock Predictor

Note that feature engineering code is common across these notebooks.

• lstmstocks-sentiments.ipynb is used to train models with and without sentiments. This notebook also performs all the feature engineering such as windowing, cascading, merging and interpolation.\

• lstmstocks-gww.ipynb notebook constructs, trains and saves three models (no sentiments, positive and negative sentiments) for a single stock which can be changed.

• generate-predictions-csv.ipynb generates predicted dataframes using saved models from earlier traing.

Portfolio Generator and Optimizer

• generating_portfolio.ipynb constructs stable portfolios using metrics such as correlation, covariance, Sharpe ratio and volatility. Also creates related visualizations that help select stable portfolios.

Libraries and requirements:

• Jupyter
• NLTK VADER
• Keras
• Scikit-learn
• fastai datepart
• Pandas
• Numpy
• BeautifulSoup
• Stocker
• Quandl
• Plotly
• Tensorboard