How to install setup
npx skills add https://github.com/marketcalls/vectorbt-backtesting-skills --skill setupFull instructions (SKILL.md)
Source of truth, from marketcalls/vectorbt-backtesting-skills.
name: setup description: Set up the Python backtesting environment. Detects OS, creates virtual environment, installs dependencies (openalgo, ta-lib, vectorbt, plotly), and creates the backtesting folder structure. argument-hint: "[python-version]" allowed-tools: Bash, Read, Write, Glob, AskUserQuestion
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= macOSLinux= LinuxMINGW*orCYGWIN*orWindows= 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: Install TA-Lib System Dependency
TA-Lib requires a C library installed at the OS level BEFORE pip install ta-lib.
macOS:
brew install ta-lib
Linux (Debian/Ubuntu):
sudo apt-get update
sudo apt-get install -y build-essential wget
wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz
tar -xzf ta-lib-0.4.0-src.tar.gz
cd ta-lib/
./configure --prefix=/usr
make
sudo make install
cd ..
rm -rf ta-lib ta-lib-0.4.0-src.tar.gz
Linux (RHEL/CentOS/Fedora):
sudo yum groupinstall -y "Development Tools"
wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz
tar -xzf ta-lib-0.4.0-src.tar.gz
cd ta-lib/
./configure --prefix=/usr
make
sudo make install
cd ..
rm -rf ta-lib ta-lib-0.4.0-src.tar.gz
Windows:
pip install ta-lib
If that fails, download the appropriate .whl file from https://github.com/cgohlke/talib-build/releases and install with:
pip install TA_Lib-0.4.32-cp312-cp312-win_amd64.whl
Step 4: Install Python Packages
Install all required packages (latest versions):
pip install openalgo vectorbt plotly anywidget nbformat ta-lib pandas numpy yfinance python-dotenv tqdm scipy numba nbformat ipywidgets quantstats ccxt duckdb psutil
Step 5: Create Backtesting Folder
Create only the top-level backtesting directory. Strategy subfolders are created on-demand when a backtest script is generated (by the /backtest skill).
mkdir -p backtesting
Do NOT pre-create strategy subfolders.
Step 6: Configure .env File
6a. Check if .env.sample exists at the project root. If it does, use it as a template.
6b. Ask the user which markets they will be backtesting using AskUserQuestion:
- Indian Markets (OpenAlgo) — requires OpenAlgo API key
- Indian Markets (DuckDB) — direct database loading, no API needed
- US Markets (yfinance) — no API key needed
- Crypto Markets (CCXT) — optional API key for private data
6c. If the user selected Indian Markets, ask for their OpenAlgo API key:
- Ask: "Enter your OpenAlgo API key (from the OpenAlgo dashboard):"
- If the user provides a key, store it in
.env - If the user skips, write a placeholder
6d. If the user selected Indian Markets (DuckDB), ask for the DuckDB database path:
- Ask: "Enter the path to your DuckDB database file (e.g., D:/data/market_data.duckdb):"
- Auto-detect format: If the database has a
market_datatable withsymbol, exchange, interval, timestampcolumns, it is OpenAlgo Historify format (store asHISTORIFY_DB_PATH). Otherwise store asDUCKDB_PATH. - If the user also has OpenAlgo Historify, ask: "Is this an OpenAlgo Historify database? (y/n)"
6e. If the user selected Crypto Markets, ask if they want to configure exchange API keys:
- Ask: "Do you have exchange API keys for authenticated data? (Optional — public OHLCV data works without keys)"
- If yes, ask for API key and secret key, store in
.env - If no, leave them blank in
.env
6f. Write the .env file in the project root directory. Use this template, filling in any keys/paths the user provided:
# Indian Markets (OpenAlgo)
OPENALGO_API_KEY={user_provided_key or "your_openalgo_api_key_here"}
OPENALGO_HOST=http://127.0.0.1:5000
# DuckDB Data Sources (direct database loading - fastest)
# Custom DuckDB (user-created with OHLCV table)
DUCKDB_PATH={user_provided_path or ""}
# OpenAlgo Historify DuckDB (market_data table with epoch timestamps)
HISTORIFY_DB_PATH={user_provided_path or ""}
# Crypto Markets (CCXT) - Optional
CRYPTO_API_KEY={user_provided_key or ""}
CRYPTO_SECRET_KEY={user_provided_key or ""}
6g. Add .env to .gitignore if it exists (never commit secrets):
Scripts use find_dotenv() to automatically walk up and find the single root .env, so no copies are needed in subdirectories.
grep -qxF '.env' .gitignore 2>/dev/null || echo '.env' >> .gitignore
Step 7: Verify Installation
Run a quick verification:
python -c "
import vectorbt as vbt
import openalgo
import plotly
import talib
import duckdb
import anywidget
import nbformat
import quantstats as qs
from dotenv import load_dotenv
print('All packages installed successfully')
print(f' vectorbt: {vbt.__version__}')
print(f' plotly: {plotly.__version__}')
print(f' duckdb: {duckdb.__version__}')
print(f' nbformat: {nbformat.__version__}')
print(f' quantstats: {qs.__version__}')
print(f' TA-Lib: available')
print(f' python-dotenv: available')
"
If TA-Lib import fails, inform the user that the C library needs to be installed first (see Step 3).
Step 8: Print Summary
Print a summary showing:
- Detected OS
- Python version used
- Virtual environment path
- Installed packages and versions
- Backtesting folder created (strategy subfolders created on-demand by
/backtest) .envfile status (configured with keys / placeholder) — single file at project root- Reminder: "Run
cp .env.sample .envand fill in API keys if you skipped configuration"
Important Notes
- Never install packages globally — always use the virtual environment
- TA-Lib C library installation requires admin/sudo privileges on Linux
- On macOS, Homebrew must be installed for
brew install ta-lib - If the user already has a virtual environment, ask before creating a new one
- The backtesting/ folder is where all generated backtest scripts will be saved
- NEVER commit
.envfiles — they contain secrets. Always use.gitignore. - If the user provides an API key during setup, write it directly to
.env— do not ask them to edit the file manually python-dotenvis included in the pip install and must be used by all scripts to load.env
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.
quick-stats
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.