quick-stats
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.
Packaged view
This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.
Install command
npx @skill-hub/cli install marketcalls-vectorbt-backtesting-skills-quick-stats
Repository
Skill path: .claude/skills/quick-stats
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.
Open repositoryBest for
Primary workflow: Analyze Data & AI.
Technical facets: Full Stack, Data / AI.
Target audience: everyone.
License: Unknown.
Original source
Catalog source: SkillHub Club.
Repository owner: marketcalls.
This is still a mirrored public skill entry. Review the repository before installing into production workflows.
What it helps with
- Install quick-stats into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
- Review https://github.com/marketcalls/vectorbt-backtesting-skills before adding quick-stats to shared team environments
- Use quick-stats for development workflows
Works across
Favorites: 0.
Sub-skills: 0.
Aggregator: No.
Original source / Raw SKILL.md
--- name: quick-stats description: 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. argument-hint: "[symbol] [exchange] [interval]" allowed-tools: Read, Bash, Glob, Grep --- Generate a quick inline backtest and print stats. Do NOT create a file - output code directly for the user to run or execute in a notebook. ## Arguments - `$0` = symbol (e.g., SBIN, RELIANCE). Default: SBIN - `$1` = exchange. Default: NSE - `$2` = interval. Default: D ## Instructions Generate a single code block the user can paste into a Jupyter cell or run as a script. The code must: 1. Fetch data from OpenAlgo (or DuckDB if user provides a DB path, or yfinance as fallback) 2. **Use TA-Lib** for EMA 10/20 crossover (never VectorBT built-in) 3. Clean signals with `ta.exrem()` (always `.fillna(False)` before exrem) 4. Use **Indian delivery fees**: `fees=0.00111, fixed_fees=20` 5. Fetch **NIFTY benchmark** via OpenAlgo (`symbol="NIFTY", exchange="NSE_INDEX"`) 6. Print a compact results summary: ``` Symbol: SBIN | Exchange: NSE | Interval: D Strategy: EMA 10/20 Crossover Period: 2023-01-01 to 2026-02-27 Fees: Delivery Equity (0.111% + Rs 20/order) ------------------------------------------- Total Return: 45.23% Sharpe Ratio: 1.45 Sortino Ratio: 2.01 Max Drawdown: -12.34% Win Rate: 42.5% Profit Factor: 1.67 Total Trades: 28 ------------------------------------------- Benchmark (NIFTY): 32.10% Alpha: +13.13% ``` 7. **Explain** key metrics in plain language for normal traders 8. Show equity curve plot using Plotly (`template="plotly_dark"`) ## Example Usage `/quick-stats RELIANCE` `/quick-stats HDFCBANK NSE 1h`