Coding Investment Strategies based on Sentiment with QuantConnect

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I created this project with a group of classmates as a final project in Introduction to Computational Investing (ECON3382). We programmed our project to simulate investing based on positive and negative sentiment in news articles published the day before trading. We traded the top ten most traded stocks by dollar volume and decided to buy/sell based on the sentiment of words in news articles using the TiingoNews API in QuantConnect. We also implemented stemming to be able to cover more variations of a type of word, to increase our calculated sentiment scores. This project used Python and was created within the QuantConnect project environment to simulate trading over 2020.

See the Python code in my GitHub!