
indicator-setup
Set up the Python environment for OpenAlgo indicator analysis. Installs openalgo, plotly, dash, streamlit, yfinance, matplotlib, seaborn, and creates the project folder structure.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/marketcalls/openalgo-indicator-skills --skill indicator-setup- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads indicator-setup automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Set up the complete Python environment for OpenAlgo indicator analysis, charting, and dashboard development.
Arguments
$0= Python version (optional, default:python3). Examples:python3.12,python3.13
Note: openalgo 2.x requires Python 3.12 or newer (3.12 / 3.13 / 3.14). Check the version before creating the venv and abort with a clear message if it is older:
python3 --version # must be 3.12+
Steps
Step 1: Detect Operating System
uname -s 2>/dev/null || echo "Windows"
Map: Darwin = macOS, Linux = Linux, MINGW*/CYGWIN*/Windows = Windows.
Step 2: Create Virtual Environment
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 user specified a Python version argument, use that instead of python3.
Step 3: Install Python Packages
Install all required packages:
pip install openalgo yfinance plotly dash dash-bootstrap-components streamlit numpy pandas python-dotenv websocket-client httpx scipy nbformat matplotlib seaborn ipywidgets
Step 4: Create Project Folders
Create only the top-level directories. Subdirectories are created on-demand by other skills.
mkdir -p charts dashboards custom_indicators scanners
Step 5: Configure .env File
5a. Ask the user for their OpenAlgo API key using AskUserQuestion:
- "Enter your OpenAlgo API key (from the OpenAlgo dashboard at /apikey):"
5b. Ask for the OpenAlgo host URL:
- Default:
http://127.0.0.1:5000 - If user has a custom domain or ngrok URL, use that
5c. Optionally ask about WebSocket URL:
- Default: derived from host automatically
- Only needed if user has a custom WebSocket setup
5d. Write the .env file in the project root:
# OpenAlgo API Configuration
OPENALGO_API_KEY={user_provided_key or "your_openalgo_api_key_here"}
OPENALGO_HOST={user_provided_host or "http://127.0.0.1:5000"}
# WebSocket (optional - auto-derived from host if not set)
# OPENALGO_WS_URL=ws://127.0.0.1:8765
5e. Add .env to .gitignore:
grep -qxF '.env' .gitignore 2>/dev/null || echo '.env' >> .gitignore
Step 6: Verify Installation
python -c "
import openalgo
from openalgo import ta
import plotly
import dash
import streamlit
import numpy as np
import pandas as pd
import yfinance as yf
import matplotlib
import seaborn
import nbformat
from dotenv import load_dotenv
print('All packages installed successfully')
print(f' openalgo: {openalgo.__version__}')
print(f' plotly: {plotly.__version__}')
print(f' dash: {dash.__version__}')
print(f' streamlit: {streamlit.__version__}')
print(f' numpy: {np.__version__}')
print(f' pandas: {pd.__version__}')
print(f' matplotlib: {matplotlib.__version__}')
print(f' seaborn: {seaborn.__version__}')
# Quick indicator test
close = np.array([100.0, 101.0, 102.0, 103.0, 104.0, 105.0, 104.0, 103.0, 102.0, 101.0])
ema = ta.ema(close, 3)
rsi = ta.rsi(close, 5)
print(f' ta.ema test: {ema[-1]:.2f}')
print(f' ta.rsi test: {rsi[-1]:.2f}')
print('Indicator library ready')
"
Step 7: Print Summary
Print a summary showing:
- Detected OS
- Python version used
- Virtual environment path
- Installed packages and versions
- Project folders created
.envfile status- Available skills:
/indicator-chart,/custom-indicator,/indicator-dashboard,/indicator-scanner,/live-feed
Important Notes
- Never install packages globally — always use the virtual environment
- If the user already has a virtual environment, ask before creating a new one
- NEVER commit
.envfiles — they contain API keys python-dotenvis used by all scripts to load.envviafind_dotenv()- openalgo 2.x indicators run on a compiled Rust core inside the wheel — no JIT compilation or warmup; requires Python 3.12+
FAQ
What does the indicator-setup skill do?
Set up the Python environment for OpenAlgo indicator analysis. Installs openalgo, plotly, dash, streamlit, yfinance, matplotlib, seaborn, and creates the project folder structure.
How do I install indicator-setup?
Run npx skills add https://github.com/marketcalls/openalgo-indicator-skills --skill indicator-setup in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/indicator-setup/ (personal) or .claude/skills/indicator-setup/ (project).
Which AI agents can use indicator-setup?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is indicator-setup safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains indicator-setup?
It is published by marketcalls in marketcalls/openalgo-indicator-skills. It has 552 recorded installs.
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