OpenAlgo指标分析环境配置指南：Python虚拟环境、图表绘制与仪表盘开发

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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

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 numba 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 numba
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'  numba: {numba.__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
• .env file 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 .env files — they contain API keys
• python-dotenv is used by all scripts to load .env via find_dotenv()
• The openalgo library includes Numba-optimized indicators that compile on first use

Weekly Installs

90

Repository

marketcalls/ope…r-skills

GitHub Stars

5

First Seen

Feb 28, 2026

Security Audits

Gen Agent Trust HubPassSocketWarnSnykFail

Installed on

codex90

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gemini-cli89

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