marketcalls/openalgo-execution-skills · Archived

algo-setup

Set up the Python environment for OpenAlgo execution skills - venv, openalgo[indicators], vectorbt, talib, scikit-learn, xgboost. Scaffolds strategies/ folder and .env.

First seen Apr 26, 2026

Installation

$ npx skills add marketcalls/openalgo-execution-skills --skill algo-setup

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More details

Agent compatibility

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Repository health

Stars 8
License LICENSE
Default branch main
Open issues 0
Status Archived

Skill metadata

Parsed from SKILL.md frontmatter.

Allowed toolsRead, Write, Edit, Bash, Glob, Grep

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 2,860 B
  • docs SUMMARY.md 186 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 30 installs

SKILL.md

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).
  1. Create venv if not already present:

``bash python -m venv venv ``

  1. 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

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

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

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

  1. Scaffold folders:

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

  1. Create .env by copying .env.sample and prompt the user to fill in OPENALGOAPIKEY:

``bash cp .env.sample .env ``

  1. Verify the install by running:

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

  1. Print next steps:

`` Setup complete. Next: 1. Edit .env and set OPENALGOAPIKEY 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 OPENALGOAPIKEY=... 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.