SkillsSKILLS
Frontend

quick-stats▌

marketcalls/vectorbt-backtesting-skills · updated Apr 8, 2026

$npx skills add https://github.com/marketcalls/vectorbt-backtesting-skills --skill quick-stats
summary

Inline backtest runner for Indian equities with EMA crossover strategy and benchmark comparison.

  • ›Fetches OHLC data from OpenAlgo (with yfinance fallback) and runs a TA-Lib EMA 10/20 crossover strategy without file creation
  • ›Applies Indian delivery fees (0.111% + Rs 20 per order) and automatically fetches NIFTY benchmark for alpha calculation
  • ›Prints compact results summary including total return, Sharpe/Sortino ratios, max drawdown, win rate, and profit factor with plain-language metri
skill.md

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%
  1. Explain key metrics in plain language for normal traders
  2. Show equity curve plot using Plotly (template="plotly_dark")

Example Usage

/quick-stats RELIANCE /quick-stats HDFCBANK NSE 1h