OHLCV 特征工程

Lesson Overview

In "Python Quant Trading Lab" → "Python 金融数据处理", this lesson "OHLCV 特征工程" builds knowledge you can apply immediately.

OHLCV特征应对应决策时点。收益率、滚动均值与波动率使用截至当时的数据；未来收益可作标签，但不能与特征混用。 先清洗→算close.pct_change→滚动窗口→标可用时间→分开特征与标签→用前缀不变性检测未来依赖。 本课作业：close为100/102/101，求两项有效收益；说明shift(-1)放入观察特征为什么危险。

Core Concepts

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

• ▸OHLCV特征应对应决策时点。收益率、滚动均值与波动率使用截至当时的数据；未来收益可作标签，但不能与特征混用。
• ▸先清洗→算close.pct_change→滚动窗口→标可用时间→分开特征与标签→用前缀不变性检测未来依赖。
• ▸本课作业：close为100/102/101，求两项有效收益；说明shift(-1)放入观察特征为什么危险。

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 "Python 金融数据处理"—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 特征工程 on your own.