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quick-stats

Fetch inline EMA 10/20 crossover backtest stats for a symbol using TA-Lib and Plotly.

186|44|Updated Feb 25, 2026
Search Tags:#python#finance#backtesting#ema#notebook#ta-lib
One-click install
npx skills add https://github.com/marketcalls/vectorbt-backtesting-skills --skill quick-stats
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: quick-stats
Source: https://github.com/marketcalls/vectorbt-backtesting-skills/tree/main/.claude/skills/quick-stats
Command: npx skills add https://github.com/marketcalls/vectorbt-backtesting-skills --skill quick-stats

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Quickly fetch and print key backtest stats for a symbol using a default EMA 10/20 crossover, with no file creation.

Core Features & Use Cases

  • Inline backtesting in a notebook or script without creating files.
  • EMA 10/20 crossover analysis using TA-Lib (not VectorBT built-in).
  • Compact performance metrics: total return, Sharpe, Sortino, max drawdown, win rate, and profit factor; includes benchmark comparison (e.g., NIFTY) and alpha.
  • Equity curve visualization with Plotly (template="plotly_dark").

Quick Start

Run the quick-stats command with a symbol to generate an inline EMA crossover backtest and print a concise results summary in your notebook.

Frequently Asked Questions about quick-stats

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run an EMA crossover backtest inside a Python notebook without creating files?▼

You can run an EMA crossover backtest inline by passing a symbol, exchange, and interval to fetch data and print compact performance metrics directly in your notebook without generating any files.

What metrics are included in an inline EMA 10/20 crossover backtest?▼

An inline EMA 10/20 crossover backtest provides total return, Sharpe ratio, Sortino ratio, max drawdown, win rate, profit factor, alpha, and a benchmark comparison against an index like NIFTY.

Can I use TA-Lib and yfinance for EMA calculations and equity curve visualization?▼

Yes, the backtest uses TA-Lib for EMA calculations and can source data from yfinance or OpenAlgo, then generates a Plotly-based equity curve visualization using a dark theme template.

Does this inline backtesting approach support different exchanges and intervals?▼

Yes, inline backtesting accepts exchange and interval inputs alongside the trading symbol, allowing you to fetch and analyze data from OpenAlgo or yfinance across various market contexts.

What is the best way to visualize backtest equity curves in a Python script?▼

The best way is to generate an interactive Plotly-based equity curve using the plotly_dark template, which renders inline alongside your backtest performance metrics in your notebook or script.

Why use TA-Lib for EMA calculations instead of built-in backtesting library functions?▼

Using TA-Lib for EMA calculations ensures standardized technical analysis logic rather than relying on a backtesting library's built-in functions, providing consistent crossover signals across different scripts.

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