Quick Stats
Paste-ready VectorBT + TA-Lib EMA crossover backtest that prints Sharpe, drawdown, and NIFTY benchmark stats without creating project files.
Overview
Quick-stats is an agent skill for the Validate phase that generates inline VectorBT backtest code with TA-Lib EMA crossover and compact performance stats.
Install
npx skills add https://github.com/marketcalls/vectorbt-backtesting-skills --skill quick-statsWhat is this skill?
- Inline only: generates one runnable block—no new files— for Jupyter or console.
- Default EMA 10/20 crossover via TA-Lib (explicitly not VectorBT built-in indicators).
- Signal hygiene with ta.exrem() after .fillna(False).
- Indian delivery fee model: fees=0.00111 and fixed_fees=20 per order.
- Fetches NIFTY benchmark via OpenAlgo (NSE_INDEX) with OpenAlgo, DuckDB, or yfinance fallbacks.
- Default strategy: EMA 10/20 crossover with TA-Lib
- Indian delivery fees: 0.111% plus Rs 20 per order (fees=0.00111, fixed_fees=20)
- Compact stats block: return, Sharpe, Sortino, max drawdown, win rate, profit factor, total trades
Adoption & trust: 1.1k installs on skills.sh; 150 GitHub stars; 3/3 security scanners passed (skills.sh audits).
What problem does it solve?
You want key backtest metrics for an Indian equity symbol now, without creating files or wiring a full research project.
Who is it for?
Indie traders and builders iterating EMA crossover ideas on NSE symbols inside notebooks with OpenAlgo or yfinance data.
Skip if: Production execution systems, non-equity asset classes without adapting the template, or workflows that require persistent strategy modules on disk.
When should I use this skill?
Quick inline backtest and key stats for a symbol; optional args symbol, exchange, interval—do not create files.
What do I get? / Deliverables
You receive executable inline code that prints return, risk, win rate, trade count, and benchmark context for the default or specified symbol.
- Single inline Python code block
- Printed backtest summary stats
Recommended Skills
Journey fit
Validate/prototype fits rapid strategy smoke tests before committing to a full backtest repo or production signals. Prototype subphase matches inline notebook/console experiments rather than shipped trading infrastructure.
How it compares
One-cell smoke test—not a full vectorbt project generator or live trading integration skill.
Common Questions / FAQ
Who is quick-stats for?
Solo builders using VectorBT and TA-Lib who trade or research Indian equities and want fast console or Jupyter stats.
When should I use quick-stats?
During Validate prototyping when you need a default EMA 10/20 delivery backtest with NIFTY benchmark before writing a full strategy file.
Is quick-stats safe to install?
Review the Security Audits panel on this page; the skill allows Bash and data fetches—audit API keys, OpenAlgo endpoints, and any code before running.
SKILL.md
READMESKILL.md - Quick Stats
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`