quick-stats▌
marketcalls/vectorbt-backtesting-skills · updated Apr 8, 2026
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
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
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★54 reviews- ★★★★★Ira Haddad· Dec 28, 2024
Solid pick for teams standardizing on skills: quick-stats is focused, and the summary matches what you get after install.
- ★★★★★Tariq Abebe· Dec 24, 2024
I recommend quick-stats for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Dhruvi Jain· Dec 20, 2024
We added quick-stats from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Layla Patel· Dec 20, 2024
quick-stats reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Chen Okafor· Dec 16, 2024
quick-stats is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Rahul Santra· Nov 27, 2024
quick-stats fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Amina Diallo· Nov 19, 2024
I recommend quick-stats for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Xiao Bhatia· Nov 15, 2024
Solid pick for teams standardizing on skills: quick-stats is focused, and the summary matches what you get after install.
- ★★★★★Oshnikdeep· Nov 11, 2024
Useful defaults in quick-stats — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Yusuf Shah· Nov 11, 2024
Registry listing for quick-stats matched our evaluation — installs cleanly and behaves as described in the markdown.
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