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: 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_data table with symbol, exchange, interval, timestamp columns, it is OpenAlgo Historify format (store as HISTORIFY_DB_PATH ). Otherwise store as DUCKDB_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 )

• .env file status (configured with keys / placeholder) — single file at project root

• Reminder: "Run cp .env.sample .env and 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 .env files — 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-dotenv is included in the pip install and must be used by all scripts to load .env