Python tutorial builds sentiment-based investing strategy with yfinance and scikit-learn

It is written as a hands-on walkthrough rather than a report of a new tool or finding. It starts by noting that news and social media information matters for investment decisions, then lays out a six-step pipeline: collecting data, cleaning it, processing text, classifying sentiment, modeling a strategy, and backtesting.

The concrete example uses Apple stock data downloaded via yfinance from January 2020 through January 2023, alongside a placeholder news CSV. Text is cleaned by stripping URLs and special characters and lowercasing, then tokenized with nltk. A Naive Bayes classifier from scikit-learn is trained on labeled positive and negative sentiment, and its accuracy is printed. The strategy assigns a score of 1 for positive predictions and -1 for negative ones, multiplies the prior day's score by the stock's daily percentage change, and computes a cumulative return.

The closing section notes that results are sensitive to data source choices, model complexity, and backtest settings, and urges risk management and portfolio diversification. It also points readers to the author's profile for infrastructure details and further statistical validation data.