setup
marketcalls/vectorbt-backtesting-skills
Set up a complete Python backtesting environment with VectorBT, OpenAlgo, and market data sources.
What is setup?
Automates OS detection, virtual environment creation, and installation of backtesting dependencies (vectorbt, openalgo, ta-lib, plotly, duckdb). Configures .env with API keys for Indian markets, US markets, and crypto exchanges, and creates the backtesting folder structure.
- Detects operating system (macOS, Linux, Windows) and configures environment accordingly
- Creates and activates a Python virtual environment with pip upgrade
- Optionally installs TA-Lib C library and Python package for advanced technical indicators
- Installs 13+ Python packages including vectorbt, openalgo, plotly, duckdb, openstatz, and ccxt
- Prompts user to select market data sources (OpenAlgo, DuckDB, yfinance, CCXT) and stores API keys in .env
- Creates backtesting/ folder structure and adds .env to .gitignore for security
How to install setup
npx skills add https://github.com/marketcalls/vectorbt-backtesting-skills --skill setup- Python 3.8 or later installed on system
- Homebrew installed (macOS only, for TA-Lib C library)
- sudo/admin access (Linux only, for TA-Lib C library system dependencies)
- Optional: OpenAlgo API key (from OpenAlgo dashboard) for Indian market data
- Optional: DuckDB database file path for direct market data loading
- Optional: CCXT exchange API credentials for authenticated crypto data
How to use setup
- 1.Run the skill with optional Python version argument (e.g., `setup python3.12`)
- 2.Select detected OS confirmation when prompted
- 3.Choose whether to install optional TA-Lib C library (recommended: skip unless explicitly needed)
- 4.Select which markets you will backtest: Indian Markets (OpenAlgo or DuckDB), US Markets (yfinance), or Crypto Markets (CCXT)
- 5.Provide API keys or database paths when prompted; skip to use placeholders and configure later
- 6.Verify installation completes successfully and review the summary output
- 7.Activate virtual environment before running backtest scripts: `source venv/bin/activate` (macOS/Linux) or `venv\Scripts\activate` (Windows)
Use cases
- Initialize a new backtesting project from scratch with all dependencies ready to use
- Configure market data connections for Indian equities via OpenAlgo API or DuckDB database
- Set up cryptocurrency backtesting with CCXT exchange integrations and optional API authentication
- Prepare environment for multi-market backtesting combining Indian, US, and crypto assets
- Ensure reproducible backtesting setup across team members with centralized .env configuration
- Quantitative traders and strategy developers using VectorBT
- Algo trading teams building backtesting pipelines with OpenAlgo
- Developers integrating multiple market data sources (equities, crypto, forex)
- Python developers new to backtesting who need guided environment setup
setup FAQ
No. OpenAlgo's built-in ta library includes 100+ indicators and requires no C library. Install TA-Lib only if you explicitly request it in a backtest script.
Yes. The skill writes placeholders to .env. You can edit .env later with your actual keys, or run the setup skill again to reconfigure.
The skill will ask before creating a new one. You can reuse an existing environment or create a fresh one.
Always at the project root. Scripts use find_dotenv() to automatically locate it. Never commit .env to version control.
Use OpenAlgo API for live/real-time Indian market data; DuckDB for fast local historical data; yfinance for free US market data; CCXT for crypto exchanges.
Full 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: 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.
Ask the user with AskUserQuestion:
- "Do you also want TA-Lib installed for when you explicitly request it in a backtest? (Optional - OpenAlgo ta already covers the same indicators plus 90+ more)"
- Yes, install TA-Lib too
- No, skip it (recommended - install it later if ever needed)
If the user skips it, skip this entire step and omit ta-lib from the Step 4 pip install. If the user wants it, 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). openstatz replaces QuantStats for tearsheets - always install it, never quantstats:
pip install openalgo vectorbt plotly anywidget nbformat pandas numpy yfinance python-dotenv tqdm scipy numba nbformat ipywidgets openstatz ccxt duckdb psutil
If the user opted into TA-Lib in Step 3, append ta-lib to this install command (after the C library is installed).
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
from openalgo import ta
import plotly
import duckdb
import anywidget
import nbformat
import openstatz
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' openstatz: {openstatz.__version__}')
print(f' OpenAlgo ta: available (default indicator library)')
print(f' python-dotenv: available')
"
If the user opted into TA-Lib, also verify with python -c "import talib; print('TA-Lib available')". If that 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
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