lucaskienast/NLP-on-10Ks-from-EDGAR-DB
This is a sentiment trading strategy, written in Python, and applying NLP on 10-K's from the SEC EDGAR database.
This project helps quantitative traders and financial analysts incorporate insights from company financial filings into their trading strategies. It takes raw 10-K financial statements from the SEC EDGAR database and daily stock price data, applies natural language processing to extract sentiment scores, and outputs potential alpha factors for algorithmic trading models. The end-user persona for this project is a quantitative trader or a financial researcher.
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Use this if you are a quantitative trader looking to develop new alpha factors based on the sentiment extracted from 10-K filings for your algorithmic trading strategies.
Not ideal if you are looking for a ready-to-deploy trading bot or a tool to analyze individual stock sentiment without integrating it into a broader quantitative framework.
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Feb 21, 2022
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