This is my capstone project for the Data Incubator Fellowship. My goal is to take this project from initial concept to a usable application.

1. I have found and scraped several investor news sites for past and upcoming FDA PDUFA (Prescription drug user fee act) action dates
2. I have munged the FDA announcement dates and joined them to stock price time-series for the relevant companies
3. I have sliced the relevant data into 120-day long preceding and following periods for data annotation and model training
4. I have annotated the data
5. I have calculated and selected important features from my training data
6. I have selected and trained a classification model (Support Vector Classifier)
7. I have cross validated the model, and verified it preforms better than random or majority case guessing
8. I have made a basic visualizaton showing how the model can trade better than a lay person

Check out the approximate portfolio values for my model's trading recomendations: SVM Advised Trades in Blue
Random Trades in Green
All Trades in Red

• Make a server (new repo) to constantly monitor the market and make trade decisions
• Learn webapp development the hard way
• Make a real project with a real repository on GitHub so I have something to demonstrate my skills beyond the lackluster scripts I wrote in graduate school