How to install quick-stats
npx skills add https://github.com/marketcalls/vectorbt-backtesting-skills --skill quick-statsFull instructions (SKILL.md)
Source of truth, from marketcalls/vectorbt-backtesting-skills.
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:
- Fetch data from OpenAlgo (or DuckDB if user provides a DB path, or yfinance as fallback)
- Use TA-Lib for EMA 10/20 crossover (never VectorBT built-in)
- Clean signals with
ta.exrem()(always.fillna(False)before exrem) - Use Indian delivery fees:
fees=0.00111, fixed_fees=20 - Fetch NIFTY benchmark via OpenAlgo (
symbol="NIFTY", exchange="NSE_INDEX") - 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%
- Explain key metrics in plain language for normal traders
- Show equity curve plot using Plotly (
template="plotly_dark")
Example Usage
/quick-stats RELIANCE
/quick-stats HDFCBANK NSE 1h
Related skills
More from marketcalls/vectorbt-backtesting-skills and the wider catalog.
backtest
Quick backtest a strategy on a symbol. Creates a complete .py script with data fetch, signals, backtest, stats, and plots.
vectorbt-expert
VectorBT backtesting expert. Use when user asks to backtest strategies, create entry/exit signals, analyze portfolio performance, optimize parameters, fetch historical data, use VectorBT/vectorbt, compare strategies, position sizing, equity curves, drawdown charts, or trade analysis. Also triggers for openalgo.ta helpers (exrem, crossover, crossunder, flip, donchian, supertrend).
optimize
Optimize strategy parameters using VectorBT. Tests parameter combinations and generates heatmaps.
strategy-compare
Compare multiple strategies or directions (long vs short vs both) on the same symbol. Generates side-by-side stats table.
setup
Set up the Python backtesting environment. Detects OS, creates virtual environment, installs dependencies (openalgo, ta-lib, vectorbt, plotly), and creates the backtesting folder structure.