vectorbt-expert 是做什麼的？
VectorBT 回測專家。當使用者要求回測策略、建立進出場訊號、分析投資組合績效、最佳化參數、獲取歷史資料、使用 VectorBT / vectorbt、比較策略、部位規模、權益曲線、最大回撤圖表或交易分析時使用。也可觸發 openalgo.ta 輔助函式（exrem、crossover、crossunder、flip、donchian、supertrend）。
VectorBT 回測專家。當使用者要求回測策略、建立進出場訊號、分析投資組合績效、最佳化參數、獲取歷史資料、使用 VectorBT / vectorbt、比較策略、部位規模、權益曲線、最大回撤圖表或交易分析時使用。也可觸發 openalgo.ta 輔助函式（exrem、crossover、crossunder、flip、donchian、supertrend）。
這是一項專為 VectorBT 與 openalgo 設計的 AI 技能，主要用於金融策略回測與技術指標輔助。它能夠根據使用者需求自動生成回測腳本，執行從歷史資料獲取、訊號建立、投資組合分析到參數最佳化的完整流程，並支援多策略比較、部位規模計算、權益曲線與最大回撤圖表繪製。此外，該技能亦整合了 openalgo.ta 模組，提供 exrem、crossover、crossunder、flip、donchian 與 supertrend 等技術分析輔助函式，適合在 Claude Code、Cursor 或 Codex 等開發環境中使用，讓開發者與量化交易者能夠快速迭代策略、驗證想法並視覺化回測結果。
npx skills add https://github.com/marketcalls/vectorbt-backtesting-skills/tree/main/skills/vectorbt-expertInstalled? Explore more 研究與資料分析 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
VectorBT 回測專家。當使用者要求回測策略、建立進出場訊號、分析投資組合績效、最佳化參數、獲取歷史資料、使用 VectorBT / vectorbt、比較策略、部位規模、權益曲線、最大回撤圖表或交易分析時使用。也可觸發 openalgo.ta 輔助函式（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.