OHLCV Feature Engineering

Lesson Overview

In "Python Quant Trading Lab" → "Financial Data with Python", this lesson "OHLCV Feature Engineering" builds knowledge you can apply immediately.

Compute returns, log returns, rolling mean/volatility. Avoid look-ahead: shift(1) all rolling features before signals.

Core Concepts

These points align with the video walkthrough—take notes as you watch.

• ▸Compute returns, log returns, rolling mean/volatility.
• ▸Avoid look-ahead: shift(1) all rolling features before signals.

Practical Application

After studying, complete the companion practice lesson to move from understanding to doing.

• ▸Follow along in Jupyter or VS Code
• ▸Log how parameter changes affect backtest results
• ▸Submit a reproducible analysis notebook

FAQ

• ▸Q: How does this lesson fit the course? A: Part of "Financial Data with Python"—follow the curriculum order.
• ▸Q: What do I need? A: Demo account and notes; quant lessons need a Python environment.
• ▸Q: How do I know I've mastered it? A: Pass the quiz and explain three key points of OHLCV Feature Engineering on your own.