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Predict Stock Prices Using Technical Indicators and Machine Learning in Python

This code walkthrough shows you how to calculate financial technical indicators and use them to predict stock market prices with machine learning. It covers an example python workflow that pulls stock market prices directly from the yfinance API, feature engineers seven technical indicators using the Pandas-TA library, and then rigorously backtests the performance of these technical indicators on predicting future stock price values. It also includes code for how you can visualize model performance using the plotly library. This code can be adapted to your specific needs, whether it includes testing additional technical indicators on a specific stock you are researching, or whether you want to create a more comprehensive quantitative finance model that leverages machine learning techniques. Like, comment, and subscribe to the Deep Charts channel for more tutorials on how to leverage machine learning and data science techniques for financial market analysis! ***Important Note: This video is not financial or investing advice. It is an educational tutorial on how to use technical indicators within a machine learning pipeline.** Full Code: https://github.com/deepcharts/project... *Resources* yfinance library: https://pypi.org/project/yfinance/ pandas-ta library: https://github.com/twopirllc/pandas-ta *Chapters* 0:00 Introduction 1:28 Import Libraries and Pull Stock Ticker Data 1:57 Lagging Stock Price Data 2:35 Calculating Technical Indicators with the pandas-ta library 3:19 Modeling Process (Backtesting) 5:51 Model Performance Evaluation (MAE and MSE) 6:52 Visualization of Backtest Results (MAE)