Скачать или смотреть Feature Engineering for Stock Market AI | Log Returns, RSI, Volatility in Python (Part 3)

🔥 Welcome back to Part 3 of the AI-Powered Stock Market Analysis series!
In this episode, we get hands-on with data preprocessing and feature engineering – turning raw financial data into powerful signals for prediction models.

📊 Whether you're training ARIMA, Linear Regression, or LSTM, your model is only as good as the features you feed it. In this video, we build intelligent features like log returns, rolling volatility, max drawdown, momentum, and RSI – all coded in Python.

🔍 In this video, you’ll learn:

✅ How to clean and preprocess financial data
✅ How to compute Log Returns, Rolling Volatility, and Drawdown
✅ Technical indicators like RSI & Momentum explained
✅ Why feature engineering is critical in time series forecasting
✅ How these features improve financial model performance

📦 Tools Used: Python, Pandas, NumPy, yFinance

📢 This series is perfect for:

✔️ Machine Learning & AI Enthusiasts
✔️ Quant Developers & Data Scientists
✔️ Algo-Trading Builders
✔️ Python Finance Learners


👨‍💻 Code Repo: https://github.com/DhruvParmar18/YT-V...

📱 Connect With Me: 🔹 LinkedIn:   / dhruv-parmar18  
📝 Blogs:   / dhruv4218  

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