数据质量检查

Lesson Overview

In "Trading Data Analysis" → "数据清洗", this lesson "数据质量检查" builds knowledge you can apply immediately.

数据质量检查包括schema、数值、时区、重复、缺失与账目平衡。发现异常时应隔离并解释，不用删除异常行让报告看起来漂亮。 校验必需列→解析时间→去重/冲突隔离→核方向和数量→检查净额=毛额−成本→记录覆盖率。 本课作业：trades-dirty.csv有重复和错误行。运行配套清洗并说明两种不应自动修复的情形。

Core Concepts

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

• ▸数据质量检查包括schema、数值、时区、重复、缺失与账目平衡。发现异常时应隔离并解释，不用删除异常行让报告看起来漂亮。
• ▸校验必需列→解析时间→去重/冲突隔离→核方向和数量→检查净额=毛额−成本→记录覆盖率。
• ▸本课作业：trades-dirty.csv有重复和错误行。运行配套清洗并说明两种不应自动修复的情形。

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 "数据清洗"—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 数据质量检查 on your own.