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indicator-setup
// Set up the Python environment for OpenAlgo indicator analysis. Installs openalgo, plotly, dash, streamlit, numba, yfinance, matplotlib, seaborn, and creates the project folder structure.
// Set up the Python environment for OpenAlgo indicator analysis. Installs openalgo, plotly, dash, streamlit, numba, yfinance, matplotlib, seaborn, and creates the project folder structure.
OpenAlgo indicator expert. Use when user asks about technical indicators, charting, plotting indicators, creating custom indicators, building dashboards, real-time feeds, scanning stocks, indicator combinations, or using openalgo.ta. Also triggers for indicator functions (sma, ema, rsi, macd, supertrend, bollinger, atr, adx, ichimoku, stochastic, obv, vwap, crossover, crossunder, exrem).
Build a web dashboard for technical indicator analysis using Plotly Dash or Streamlit. Supports single-symbol, multi-symbol, and multi-timeframe layouts with real-time refresh.
Create a custom technical indicator using Numba JIT + NumPy. Generates production-grade, O(n) optimized indicator functions with charting and benchmarking.
Chart any technical indicator on a symbol using Plotly. Creates interactive dark-themed charts with candlestick, overlays, and subplots. Supports all 100+ openalgo.ta indicators.
Scan multiple symbols with indicator conditions. Find stocks matching RSI oversold, EMA crossovers, Supertrend signals, and custom filter combinations.
Set up real-time indicator computation on live WebSocket market data. Streams LTP/Quote/Depth and computes indicators in real-time with optional Plotly live charting.
| name | indicator-setup |
| description | Set up the Python environment for OpenAlgo indicator analysis. Installs openalgo, plotly, dash, streamlit, numba, yfinance, matplotlib, seaborn, and creates the project folder structure. |
| argument-hint | [python-version] |
| allowed-tools | Bash, Read, Write, Glob, AskUserQuestion |
Set up the complete Python environment for OpenAlgo indicator analysis, charting, and dashboard development.
$0 = Python version (optional, default: python3). Examples: python3.12, python3.13uname -s 2>/dev/null || echo "Windows"
Map: Darwin = macOS, Linux = Linux, MINGW*/CYGWIN*/Windows = Windows.
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.
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
Create only the top-level directories. Subdirectories are created on-demand by other skills.
mkdir -p charts dashboards custom_indicators scanners
5a. Ask the user for their OpenAlgo API key using AskUserQuestion:
5b. Ask for the OpenAlgo host URL:
http://127.0.0.1:50005c. Optionally ask about WebSocket URL:
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
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')
"
Print a summary showing:
.env file status/indicator-chart, /custom-indicator, /indicator-dashboard, /indicator-scanner, /live-feed.env files — they contain API keyspython-dotenv is used by all scripts to load .env via find_dotenv()