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algo-setup

algo-setup

github.com/marketcallsDevelopment941 installsUpdated 26w ago
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About algo-setup

A skill for setting up a local Python environment to build and run OpenAlgo dual-mode trading strategies. It guides developers through TA-Lib installation, dependencies, project folders, `.env` setup, and import checks.

Key features

  1. 01Create or reuse a virtual environment
  2. 02Install TA-Lib for the detected OS
  3. 03Install requirements.txt packages
  4. 04Create strategy and backtest folders
  5. 05Configure and verify the environment

Use cases

  1. 01Prepare an environment for new strategies
  2. 02Install project dependencies in an existing venv
  3. 03Troubleshoot TA-Lib or package installation

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/marketcalls/openalgo-execution-skills --skill algo-setup

After installing, type/algo-setupin Claude Code to confirm it loaded.

Details

From the project README

Set up the local Python environment for building and running OpenAlgo dual-mode trading strategies.

Arguments

$0 (optional) = python interpreter name (python, python3, python3.12). Default: python.

What to do

  1. Detect the OS (Windows / macOS / Linux) via uname -s or os.name. Use OS-specific install commands for the TA-Lib C library (which pip install ta-lib depends on).

  2. Create venv if not already present:

    python -m venv venv

  3. Install TA-Lib C library (required by Python ta-lib):

    • macOS: brew install ta-lib
    • Ubuntu/Debian: sudo apt install libta-lib-dev (may need sudo apt update)
    • Windows: pip install ta-lib uses pre-built wheels - no C library install needed

  4. Activate venv and install Python packages from requirements.txt:

    # Linux/macOS
source venv/bin/activate
pip install -r requirements.txt

# Windows
venv\Scripts\activate
pip install -r requirements.txt

  5. Scaffold folders:

    strategies/
    (empty - generated strategies will land here)
backtests/
    (empty - generated backtest CSV/HTML output lands here)

  6. Create .env by copying .env.sample and prompt the user to fill in OPENALGO_API_KEY:

    cp .env.sample .env

  7. Verify the install by running:

    from openalgo import api, ta
import vectorbt as vbt
import talib
import sklearn
import xgboost
print("OK")

  8. Print next steps:

    Setup complete. Next:
  1. Edit .env and set OPENALGO_API_KEY
  2. Make sure OpenAlgo is running at http://127.0.0.1:5000
  3. Generate a strategy: /algo-strategy ema-crossover SBIN NSE 5m

Avoid

  • Do not use icons/emojis in output
  • Do not auto-run OPENALGO_API_KEY=... in the shell - the user pastes it into .env manually
  • Do not assume pip exists outside the venv after step 4 - always activate first

When the user already has a venv

Skip step 2. Activate the existing venv, run pip install -r requirements.txt against it, and continue.

Failure modes

  • TA-Lib install fails on Linux: tell the user to run sudo apt update && sudo apt install build-essential libta-lib-dev. If that fails (older distros), suggest skipping TA-Lib and using only INDICATOR_LIB="openalgo" in strategies.
  • OpenAlgo not running: setup completes but the verify-install step's from openalgo import api works (SDK installs without network). Real connection is verified in /algo-strategy.

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