Setup

This handles the entire Python environment bootstrap for a VectorBT + OpenAlgo backtesting setup. It detects your OS, spins up a venv, wrestles with TA-Lib's C dependencies across macOS, Linux, and Windows, then installs the full stack (vectorbt, plotly, ta-lib, openalgo, quantstats, duckdb, and more). The nice touch is the interactive .env configuration that asks which markets you're trading, grabs your OpenAlgo or exchange API keys if needed, and sets up DuckDB paths for direct database access. It even auto-detects OpenAlgo Historify format. You end up with a clean backtesting folder and verified imports. Saves you 20 minutes of Stack Overflow tabs and pip install trial and error.

skills.sh installs: 2.1K
GitHub stars: 223
last updated: Aug 21, 2026

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Install

$ npx -y skills add marketcalls/vectorbt-backtesting-skills --skill setup --agent claude-code

Installs into .claude/skills of the current project.

Files

Set up the complete Python backtesting environment for VectorBT + OpenAlgo.

Arguments

$0 = Python version (optional, default: python3). Examples: python3.12, python3.13

Steps

Step 1: Detect Operating System

Run the following to detect the OS:

uname -s 2>/dev/null || echo "Windows"

Map the result:

Darwin = macOS
Linux = Linux
MINGW* or CYGWIN* or Windows = Windows

Print the detected OS to the user.

Step 2: Create Virtual Environment

Create a Python virtual environment in the current working directory:

macOS / Linux:

python3 -m venv venv
source venv/bin/activate
pip install --upgrade pip

Windows:

python -m venv venv
venv\Scripts\activate
pip install --upgrade pip

If the user specified a Python version argument, use that instead of python3:

$PYTHON_VERSION -m venv venv

Step 3: TA-Lib System Dependency (Optional)

OpenAlgo ta (from openalgo import ta) is the default indicator library for this project - it ships 100+ indicators and needs no separate system dependency. TA-Lib is only needed if the user wants to be able to say "use talib" for a specific backtest.

SKILL.md on GitHub