setup

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:

bash

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:

bash

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

Windows:

bash

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

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

python3

bash

$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:

bash

brew install ta-lib

Linux (Debian/Ubuntu):

bash

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):

bash

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:

bash

pip install TA_Lib-0.4.32-cp312-cp312-win_amd64.whl

Step 4: Install Python Packages

Install all required packages (latest versions):

bash

pip install openalgo vectorbt plotly anywidget nbformat ta-lib pandas numpy yfinance python-dotenv tqdm scipy numba nbformat ipywidgets quantstats ccxt

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).

bash

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
US Markets (yfinance) — no API key needed
Crypto Markets (Binance/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 Crypto Markets, ask if they want to configure Binance API keys:

Ask: "Do you have Binance 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
```

6e. Write the
.env
file in the project root directory. Use this template, filling in any keys 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

# Crypto Markets (Binance via CCXT) - Optional
BINANCE_API_KEY={user_provided_key or ""}
BINANCE_SECRET_KEY={user_provided_key or ""}

6f. 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.

bash

grep -qxF '.env' .gitignore 2>/dev/null || echo '.env' >> .gitignore

Step 7: Verify Installation

Run a quick verification:

bash

python -c "
import vectorbt as vbt
import openalgo
import plotly
import talib
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'  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
```

setup

NPX Install

Tags

SKILL.md Content

Arguments

Steps

Step 1: Detect Operating System

Step 2: Create Virtual Environment

Step 3: Install TA-Lib System Dependency

Step 4: Install Python Packages

Step 5: Create Backtesting Folder

Step 6: Configure .env File

Step 7: Verify Installation

Step 8: Print Summary

Important Notes