Predicting Tesla: Machine Learning Meets Stock Market Strategies Part 1

Опубликовано: 10 Май 2025
на канале: OnePageCode

Download the source code from here for entire project.
https://onepagecode.substack.com/p/pr...

In the world of financial markets, making informed decisions about when to buy or sell stocks is a complex task. One of the most common methods for predicting market movements involves the use of technical indicators, which are mathematical calculations based on the price, volume, or open interest of a stock. These indicators provide traders with valuable insights into market trends and allow them to develop strategies for maximizing profits or minimizing losses. In the context of stock trading, these technical indicators can also be considered features that are used in data analysis to better understand stock performance.

This article focuses on applying feature engineering techniques to stock market data, specifically Tesla stock, to create technical indicators such as Bollinger Bands, RSI, MACD, Moving Averages, Return, Momentum, Change, and Volatility. These indicators help investors and traders assess stock behavior and market trends, aiding in the prediction of stock prices. Additionally, sentiment analysis of financial news headlines is performed to further enhance stock market predictions by gauging market sentiment. Through the use of Python and various data science libraries, this article demonstrates how these features can be engineered and used for trading insights.