vectorbt-expert 是做什么的？
VectorBT回测专家。当用户要求回测策略、创建入场/出场信号、分析投资组合表现、优化参数、获取历史数据、使用VectorBT/vectorbt、比较策略、仓位管理、权益曲线、回撤图表或交易分析时使用。同时适用于openalgo.ta辅助函数（exrem、crossover、crossunder、flip、donchian、supertrend）。
VectorBT回测专家。当用户要求回测策略、创建入场/出场信号、分析投资组合表现、优化参数、获取历史数据、使用VectorBT/vectorbt、比较策略、仓位管理、权益曲线、回撤图表或交易分析时使用。同时适用于openalgo.ta辅助函数（exrem、crossover、crossunder、flip、donchian、supertrend）。
这是一个专门用于VectorBT回测的AI专家技能，适用于金融量化开发场景。它能够帮助用户快速实现策略回测、生成入场和出场信号、分析投资组合表现、优化参数以及获取历史数据。该技能支持多种主流平台，如Claude Code、Cursor等AI agent环境，并深度集成VectorBT库。此外，它还扩展支持openalgo.ta辅助函数，包括exrem（重复移除）、crossover（向上穿越）、crossunder（向下穿越）、flip（翻转）、donchian（唐奇安通道）和supertrend（超级趋势）等指标。其工作流程基于SKILL.md文档中定义的触发词和规则，用户只要涉及策略比较、仓位管理、权益曲线、回撤图表或交易分析，该技能就会被自动激活。开发者无需手动编写复杂代码，只需自然语言描述需求，即可获得专业的金融分析结果。
npx skills add https://github.com/marketcalls/vectorbt-backtesting-skills/tree/main/skills/vectorbt-expertInstalled? Explore more 研究与数据分析 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
VectorBT回测专家。当用户要求回测策略、创建入场/出场信号、分析投资组合表现、优化参数、获取历史数据、使用VectorBT/vectorbt、比较策略、仓位管理、权益曲线、回撤图表或交易分析时使用。同时适用于openalgo.ta辅助函数（exrem、crossover、crossunder、flip、donchian、supertrend）。
This repo contains 5 individual skills — each has its own dedicated page.
Quick backtest a strategy on a symbol. Creates a complete .py script with data fetch, signals, backtest, stats, and plots.
Optimize strategy parameters using VectorBT. Tests parameter combinations and generates heatmaps.
Quickly fetch data and print key backtest stats for a symbol with a default EMA crossover strategy. No file creation needed - runs inline in a notebook cell or prints to console.
Set up the Python backtesting environment. Detects OS, creates virtual environment, installs dependencies (openalgo, ta-lib, vectorbt, plotly), and creates the backtesting folder structure.
Compare multiple strategies or directions (long vs short vs both) on the same symbol. Generates side-by-side stats table.
USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Agent skill repository discovered by 10x-chat research.
Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.