backtesting-frameworks
Build robust backtesting systems for trading strategies with proper handling of look-ahead bias, survivorship bias, and transaction costs. Use when developing trading algorithms, validating strategies, or building backtesting infrastructure.
DeepseekModel
官方收录技能
质量 优秀 · 90
v1.0.0
获取
https://deepseekmodel.com/api/download.php?id=wshobson-agents-plugins-quantitative-trading-skills-backtesting-frameworks-skill-md&format=skill
下载 .skill
标准格式，含 system_prompt 与 model_config，导入任意 Agent 框架即可使用
.skill 文件中 system_prompt 字段的实际内容。
name backtesting-frameworks description Build robust backtesting systems for trading strategies with proper handling of look-ahead bias, survivorship bias, and transaction costs. Use when developing trading algorithms, validating strategies, or building backtesting infrastructure. Backtesting Frameworks Build robust, production-grade backtesting systems that avoid common pitfalls and produce reliable strategy performance estimates. When to Use This Skill Developing trading strategy backtests Building backtesting infrastructure Validating strategy performance Avoiding common backtesting biases Implementing walk-forward analysis Comparing strategy alternatives Core Concepts 1. Backtesting Biases Bias Description Mitigation Look-ahead Using future information Point-in-time data Survivorship Only testing on survivors Use delisted securities Overfitting Curve-fitting to history Out-of-sample testing Selection Cherry-picking strategies Pre-registration Transaction Ignoring trading costs Realistic cost models 2. Proper Backtest Structure Historical Data │ ▼ ┌─────────────────────────────────────────┐ │ Training Set │ │ (Strategy Development & Optimization) │ └─────────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────┐ │ Validation Set │ │ (Parameter Selection, No Peeking) │ └─────────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────┐ │ Test Set │ │ (Final Performance Evaluation) │ └─────────────────────────────────────────┘ 3. Walk-Forward Analysis Window 1: [Train──────][Test] Window 2: [Train──────][Test] Window 3: [Train──────][Test] Window 4: [Train──────][Test] ─────▶ Time Detailed worked examples and patterns Detailed sections (starting with ## Implementation Patterns ) live in references/details.md . Read that file when the navigation summary above is insufficient. Best Practices Do's Use point-in-time data - Avoid look-ahead bias Include transaction costs - Realistic estimates Test out-of-sample - Always reserve data Use walk-forward - Not just train/test Monte Carlo analysis - Understand uncertainty Don'ts Don't overfit - Limit parameters Don't ignore survivorship - Include delisted Don't use adjusted data carelessly - Understand adjustments Don't optimize on full history - Reserve test set Don't ignore capacity - Market impact matters
Agent 识别该技能的关键词，点击任意一个即可复制。
该技能未提供触发词。
下载的 .skill 包内含以下字段。
| 字段 | 说明 |
|---|---|
| format | 格式标识（skill/v1） |
| skill_id | 技能唯一 ID |
| name | 技能名称 |
| version | 版本号 |
| description | 技能描述 |
| category | 所属分类（数组） |
| trigger_words | 触发词列表 |
| tags | 标签列表 |
| source | 来源标识 |
| source_url | 来源链接（本页地址） |
| exported_at | 导出时间（每次下载生成） |
| system_prompt | 系统提示词正文 |
| model_config | 模型参数：provider / model / temperature / max_tokens / top_p |
| examples | 示例 |
| install_guide | 各平台导入说明（Coze / Dify / Claude / 自定义框架） |
