SMA 策略回测

Lesson Overview

In "Python Quant Trading Lab" → "Backtrader 策略开发", this lesson "SMA 策略回测" builds knowledge you can apply immediately.

SMA回测的交付包括输入哈希、参数、成交约定、成本、现金和持仓，不以净值上涨作为代码正确性标准。 先跑无依赖自检→跑两套引擎→核订单数量/费用/期末权益→检查前缀因果性→保留差异报告。 本课作业：对固定合成输入比较research_lab和Backtrader；允许1e−6浮点差，写二者共同未覆盖的三项。

Core Concepts

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

• ▸SMA回测的交付包括输入哈希、参数、成交约定、成本、现金和持仓，不以净值上涨作为代码正确性标准。
• ▸先跑无依赖自检→跑两套引擎→核订单数量/费用/期末权益→检查前缀因果性→保留差异报告。
• ▸本课作业：对固定合成输入比较research_lab和Backtrader；允许1e−6浮点差，写二者共同未覆盖的三项。

Practical Application

After studying, complete the practice tasks below 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 "Backtrader 策略开发"—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 SMA 策略回测 on your own.