> For the complete documentation index, see [llms.txt](https://docs.openalgo.in/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.openalgo.in/skills/indicators.md). # Indicators ## OpenAlgo Indicator Skills for Agentic Coding Tools A comprehensive collection of technical indicator skills for charting, analysis, and custom indicator development using OpenAlgo. Works with **40+ AI coding agents** via [skills.sh](https://github.com/vercel-labs/skills) — including Claude Code, Cursor, Codex, OpenCode, Cline, Windsurf, GitHub Copilot, Gemini CLI, Roo Code, and more. Supports **Indian markets** via OpenAlgo and **US/Global markets** via yfinance. Includes 100+ Numba-optimized indicators, Plotly dark-themed charts, Dash and Streamlit web dashboards, real-time WebSocket feeds, multi-symbol scanners, and a custom indicator builder with Numba JIT + NumPy. ### Quick Install Install the skills into your project using [npx skills](https://github.com/vercel-labs/skills). The CLI auto-detects your AI coding agent and installs skills to the correct directory. ```bash # GitHub shorthand npx skills add marketcalls/openalgo-indicator-skills # Full GitHub URL npx skills add https://github.com/marketcalls/openalgo-indicator-skills ``` Install a specific skill only: ```bash npx skills add marketcalls/openalgo-indicator-skills -s indicator-chart npx skills add marketcalls/openalgo-indicator-skills -s custom-indicator npx skills add marketcalls/openalgo-indicator-skills -s indicator-dashboard npx skills add marketcalls/openalgo-indicator-skills -s indicator-scanner npx skills add marketcalls/openalgo-indicator-skills -s live-feed npx skills add marketcalls/openalgo-indicator-skills -s indicator-setup ``` List available skills before installing: ```bash npx skills add marketcalls/openalgo-indicator-skills -l ``` Install globally (available across all projects): ```bash npx skills add marketcalls/openalgo-indicator-skills -g ``` #### Supported AI Coding Agents Skills are installed via [skills.sh](https://github.com/vercel-labs/skills) which supports 40+ agents. Each agent reads skills from its own directory: | Agent | Skills Directory | | -------------- | ----------------------------- | | Claude Code | `.claude/skills/` | | Cursor | `.agents/skills/` | | Codex | `.agents/skills/` | | OpenCode | `.agents/skills/` | | Cline | `.agents/skills/` | | Windsurf | `.agents/skills/` | | GitHub Copilot | `.agents/skills/` | | Gemini CLI | `.agents/skills/` | | Roo Code | `.agents/skills/` | | + 30 more | Auto-detected by `npx skills` | The `npx skills add` command detects which agents you have installed and places the skill files in the correct paths automatically. ### Supported Markets | Market | Data Source | Method | Example Symbols | | ------------------ | ----------- | -------------------------------------- | -------------------- | | **India (Equity)** | OpenAlgo | `client.history()` | SBIN, RELIANCE, INFY | | **India (Index)** | OpenAlgo | `client.history(exchange="NSE_INDEX")` | NIFTY, BANKNIFTY | | **India (F\&O)** | OpenAlgo | `client.history(exchange="NFO")` | NIFTY30DEC25FUT | | **US/Global** | yfinance | `yf.download()` | AAPL, MSFT, SPY | > **Market detection**: If a symbol looks Indian (SBIN, RELIANCE, NIFTY), skills use OpenAlgo. If US (AAPL, MSFT), skills use yfinance. Automatic — no configuration needed. ### Capabilities #### Skills (User-Invocable Commands) | Command | What It Does | | ---------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | | `/indicator-setup` | Detects OS, creates venv, installs all packages (openalgo, plotly, dash, streamlit, numba, yfinance, matplotlib, seaborn), configures `.env` with API keys | | `/indicator-chart` | Charts any indicator on a symbol with Plotly dark theme — overlay or subplot, with signal markers and plain-language explanation | | `/custom-indicator` | Creates a custom indicator using Numba JIT + NumPy — generates `indicator.py` + `chart.py` + `benchmark.py` | | `/indicator-dashboard` | Builds a Plotly Dash or Streamlit web application — single-symbol, multi-symbol, multi-timeframe, or scanner dashboard | | `/indicator-scanner` | Scans multiple symbols (NIFTY 50, BANKNIFTY stocks) with indicator conditions — RSI, EMA crossover, Supertrend, volume spike | | `/live-feed` | Real-time indicator computation on WebSocket streaming data — LTP, quote, or depth mode with rolling buffer | #### Pre-Built Chart Templates (13) | Template | Type | Description | | ---------------- | ----------------- | ----------------------------------------------------------------------- | | EMA Chart | Overlay | EMA(10/20/50) overlay with crossover signal markers | | RSI Chart | Subplot | RSI(14) with overbought/oversold zones and color fills | | MACD Chart | Subplot | MACD line + signal + histogram with color coding | | Supertrend Chart | Overlay | Direction-colored Supertrend with buy/sell markers | | Bollinger Chart | Overlay + Subplot | Bollinger Bands + %B + Bandwidth (squeeze detection) | | Multi-Indicator | Multi-Panel | Candlestick + EMA + RSI + MACD + Volume with bias assessment | | Basic Dashboard | Web App | Single-symbol Plotly Dash app with indicator checkboxes and stats cards | | Multi Dashboard | Web App | Multi-timeframe Dash app (5m/15m/1h/D grid) with confluence detection | | Streamlit Basic | Web App | Single-symbol Streamlit app with sidebar, metrics, plotly charts | | Streamlit Multi | Web App | Multi-timeframe Streamlit app with confluence summary | | Custom Indicator | Numba | Z-Score example with `@njit` core + pandas wrapper + benchmark | | Live Feed | WebSocket | Real-time LTP feed with EMA/RSI computation on rolling buffer | | Scanner | Multi-Symbol | NIFTY 50 scanner with 5 scan types (RSI, EMA, Supertrend, Volume) | #### Knowledge Base (12 Rule Files) | Category | What's Covered | | -------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Indicators** | Complete 100+ indicator reference with signatures, parameters, return types. Trend (20), Momentum (9), Volatility (16), Volume (14), Oscillators (20+), Statistical (9), Hybrid (6+), Utilities | | **Data Fetching** | OpenAlgo history/quotes/depth/intervals, yfinance for US/Global, data normalization (datetime index, sort, strip timezone), option chain API | | **Plotting** | Plotly dark theme, candlestick overlays, multi-panel subplots, fill-between bands, color-coded direction, signal markers, save to HTML | | **Custom Indicators** | Numba `@njit(cache=True, nogil=True)` template patterns, single/multi-output, NaN handling, DO/DON'T rules, performance tips | | **WebSocket Feeds** | LTP/Quote/Depth subscription, polling stored data, unsubscribe/disconnect, real-time indicator computation with rolling buffer | | **Numba Optimization** | OpenAlgo numba\_shim config, decorator patterns, what works inside `@njit`, NaN handling (critical), cache management, warmup, algorithm complexity | | **Dash Dashboards** | Dash app structure, multi-indicator layout, dynamic subplot callbacks, stats cards, auto-refresh with `dcc.Interval` | | **Streamlit Dashboards** | Streamlit app structure, sidebar inputs, `st.plotly_chart()`, `st.metric()`, auto-refresh, scanner tables, dark theme | | **Multi-Timeframe** | Fetch multiple timeframes, same indicator across TFs, confluence detection (all bullish/bearish/mixed), MTF grid chart | | **Signal Generation** | Core 4-step pipeline, crossover/crossunder, `ta.exrem()` cleaning, common patterns (EMA, RSI, Supertrend, MACD, Bollinger, ADX) | | **Indicator Combinations** | Category mixing rules, 6 combination patterns (Trend+Momentum, Triple Screen, BB+Keltner Squeeze, ADX+DI, Multi-Indicator Scorecard) | | **Symbol Format** | OpenAlgo exchange codes (NSE, BSE, NFO, NSE\_INDEX, MCX), equity/futures/options format, common index symbols | ### Prerequisites #### 1. AI Coding Agent Install any supported AI coding agent. For example: * [Claude Code](https://docs.anthropic.com/en/docs/claude-code) — `npm install -g @anthropic-ai/claude-code` * [Cursor](https://cursor.com) — Desktop IDE with built-in AI * [Codex](https://github.com/openai/codex) — `npm install -g @openai/codex` * [OpenCode](https://github.com/opencode-ai/opencode) — `go install github.com/opencode-ai/opencode@latest` * [Cline](https://github.com/cline/cline) — VS Code extension * [Windsurf](https://windsurf.com) — Desktop IDE with AI * Or any of the [40+ supported agents](https://github.com/vercel-labs/skills) Then install the skills: ```bash npx skills add marketcalls/openalgo-indicator-skills ``` #### 2. Data Source Setup **Indian Markets** — requires [OpenAlgo](https://github.com/marketcalls/openalgo): ```bash git clone https://github.com/marketcalls/openalgo.git cd openalgo pip install -r requirements.txt python app.py ``` OpenAlgo runs locally at `http://127.0.0.1:5000`. You need a broker account connected via OpenAlgo and an API key from the dashboard. See [OpenAlgo documentation](https://docs.openalgo.in/). **US/Global Markets** — no setup needed. Uses yfinance (public Yahoo Finance data). #### 3. Python Environment Setup Use the `/indicator-setup` skill for automated setup, or manually: ```bash python -m venv venv source venv/bin/activate # Linux/Mac # venv\Scripts\activate # Windows pip install openalgo yfinance plotly dash dash-bootstrap-components streamlit numba numpy pandas python-dotenv websocket-client httpx scipy nbformat matplotlib seaborn ipywidgets ``` #### 4. Configure API Keys ```bash cp .env.sample .env # Edit .env with your API keys ``` ### Usage Examples #### `/indicator-setup` — Environment Setup Detects OS, creates venv, installs all dependencies, and collects API keys into `.env`. ``` /indicator-setup /indicator-setup python3.12 ``` #### `/indicator-chart` — Chart Any Indicator Create a Plotly chart with indicator overlays or subplots. Auto-detects overlay vs subplot positioning. ``` # Indian Markets /indicator-chart ema SBIN NSE D /indicator-chart rsi RELIANCE NSE D /indicator-chart supertrend NIFTY NSE_INDEX 15m /indicator-chart macd INFY NSE D /indicator-chart bbands HDFCBANK NSE D # US Markets /indicator-chart ema AAPL /indicator-chart rsi MSFT ``` #### `/custom-indicator` — Build Custom Indicators Create a Numba-optimized custom indicator with chart and benchmark. ``` /custom-indicator zscore /custom-indicator vwap-deviation /custom-indicator momentum-squeeze ``` #### `/indicator-dashboard` — Web Dashboards Build a Plotly Dash or Streamlit web application with live charts. ``` # Plotly Dash /indicator-dashboard single SBIN /indicator-dashboard multi-timeframe RELIANCE /indicator-dashboard scanner-dashboard # Streamlit /indicator-dashboard streamlit-single SBIN /indicator-dashboard streamlit-multi RELIANCE /indicator-dashboard streamlit-scanner ``` #### `/indicator-scanner` — Scan Stocks Screen multiple symbols with indicator conditions. ``` /indicator-scanner rsi-oversold /indicator-scanner rsi-overbought /indicator-scanner ema-crossover /indicator-scanner supertrend-buy /indicator-scanner volume-spike ``` #### `/live-feed` — Real-Time WebSocket Stream live prices with indicator computation. ``` /live-feed SBIN NSE /live-feed RELIANCE NSE quote /live-feed NIFTY NSE_INDEX ``` ### Key Features #### 100+ Numba-Optimized Indicators All indicators from the OpenAlgo `ta` library, compiled with Numba JIT for production-grade speed. ```python from openalgo import ta ema_20 = ta.ema(close, 20) # ~0.3ms on 100K bars rsi_14 = ta.rsi(close, 14) # ~1.8ms on 100K bars st, dir = ta.supertrend(high, low, close) # ~1.9ms on 100K bars macd, sig, hist = ta.macd(close, 12, 26, 9) # ~0.9ms on 100K bars ``` #### Plotly Dark Theme Charts All charts use `template="plotly_dark"` with `xaxis type="category"` for candlesticks (no weekend gaps). ```python import plotly.graph_objects as go fig = go.Figure() fig.update_layout(template="plotly_dark", xaxis_type="category") ``` #### Custom Indicators with Numba Build your own indicators with Numba `@njit(cache=True, nogil=True)` — never `fastmath=True` (breaks NaN handling). ```python from numba import njit import numpy as np @njit(cache=True, nogil=True) def _my_indicator(data, period): n = len(data) result = np.full(n, np.nan) # Your logic here return result ``` #### Signal Cleaning with EXREM Always use `ta.exrem()` after generating raw buy/sell signals — removes excess signals until the opposite occurs. ```python from openalgo import ta buy_raw = ta.crossover(ema_fast, ema_slow).fillna(False) sell_raw = ta.crossunder(ema_fast, ema_slow).fillna(False) buy_clean = ta.exrem(buy_raw, sell_raw) sell_clean = ta.exrem(sell_raw, buy_raw) ``` #### Real-Time WebSocket Feeds Live indicator computation on streaming market data with rolling buffer. ```python from openalgo import api, ta client = api(api_key=os.getenv("OPENALGO_API_KEY")) client.connect() client.subscribe_ltp( [{"exchange": "NSE", "symbol": "SBIN"}], on_data_received=on_data ) ``` #### Multi-Timeframe Confluence Analyze the same symbol across 4 timeframes (5m, 15m, 1h, D) with trend alignment detection. ``` STRONG BULLISH — All timeframes aligned STRONG BEARISH — All timeframes aligned MIXED — 2/4 bullish ``` #### OpenAlgo Data Methods | Method | Purpose | Returns | | -------------------------- | ------------------- | ------------- | | `client.history()` | OHLCV candles | DataFrame | | `client.quotes()` | Real-time snapshot | Dict | | `client.multiquotes()` | Multi-symbol quotes | List of dicts | | `client.depth()` | Market depth (L5) | Dict | | `client.intervals()` | Available intervals | Dict | | `client.connect()` | WebSocket connect | None | | `client.subscribe_ltp()` | Live LTP stream | Callback | | `client.subscribe_quote()` | Live quote stream | Callback | | `client.subscribe_depth()` | Live depth stream | Callback | #### Output Folder Structure Scripts go in appropriate directories, created on-demand. Each category folder is self-contained. ``` charts/ ├── sbin_ema_chart.py ├── reliance_rsi_chart.py └── nifty_supertrend_chart.py dashboards/ ├── sbin_dashboard/app.py └── multi_timeframe/app.py custom_indicators/ ├── zscore/ │ ├── indicator.py │ ├── chart.py │ └── benchmark.py └── momentum_squeeze/ └── ... scanners/ ├── rsi_oversold_scanner.py └── volume_spike_scanner.py ``` ### Project Structure ``` . ├── .claude/ │ └── skills/ │ ├── indicator-setup/ # /indicator-setup - Environment setup │ │ └── SKILL.md │ ├── indicator-chart/ # /indicator-chart - Chart any indicator │ │ └── SKILL.md │ ├── custom-indicator/ # /custom-indicator - Custom indicator builder │ │ └── SKILL.md │ ├── indicator-dashboard/ # /indicator-dashboard - Dash/Streamlit web apps │ │ └── SKILL.md │ ├── indicator-scanner/ # /indicator-scanner - Multi-symbol scanner │ │ └── SKILL.md │ ├── live-feed/ # /live-feed - WebSocket real-time feed │ │ └── SKILL.md │ └── indicator-expert/ # Knowledge base (auto-loaded) │ ├── SKILL.md # Main skill (modular reference hub) │ └── rules/ # 12 modular rule files │ ├── indicator-catalog.md │ ├── data-fetching.md │ ├── plotting.md │ ├── custom-indicators.md │ ├── websocket-feeds.md │ ├── numba-optimization.md │ ├── dashboard-patterns.md │ ├── streamlit-patterns.md │ ├── multi-timeframe.md │ ├── signal-generation.md │ ├── indicator-combinations.md │ ├── symbol-format.md │ └── assets/ # Production-ready templates │ ├── ema_chart/chart.py │ ├── rsi_chart/chart.py │ ├── macd_chart/chart.py │ ├── supertrend_chart/chart.py │ ├── bollinger_chart/chart.py │ ├── multi_indicator/chart.py │ ├── dashboard_basic/app.py │ ├── dashboard_multi/app.py │ ├── streamlit_basic/app.py │ ├── streamlit_multi/app.py │ ├── custom_indicator/template.py │ ├── live_feed/template.py │ └── scanner/template.py ├── .env.sample # Environment template (copy to .env) ├── .gitignore ├── requirements.txt └── README.md ``` ### Rule Files Reference | Rule File | Description | | --------------------------- | ------------------------------------------------------------------------------------- | | `indicator-catalog.md` | Complete 100+ indicator reference with signatures, parameters, return types | | `data-fetching.md` | OpenAlgo history/quotes/depth, yfinance for US, data normalization, option chain | | `plotting.md` | Plotly candlestick overlays, multi-panel subplots, signal markers, save to HTML | | `custom-indicators.md` | Numba template patterns, single/multi-output, NaN handling, DO/DON'T rules | | `websocket-feeds.md` | LTP/Quote/Depth subscription, rolling buffer, real-time indicator computation | | `numba-optimization.md` | `@njit` patterns, NaN handling, cache management, warmup, O(n) algorithms | | `dashboard-patterns.md` | Dash app structure, dynamic subplots, stats cards, auto-refresh | | `streamlit-patterns.md` | Streamlit app structure, sidebar inputs, `st.plotly_chart()`, metrics, scanner tables | | `multi-timeframe.md` | Multiple timeframes, confluence detection, MTF grid chart | | `signal-generation.md` | 4-step pipeline, crossover/crossunder, exrem cleaning, common patterns | | `indicator-combinations.md` | Category mixing, 6 combination patterns, confluence analysis | | `symbol-format.md` | Exchange codes, equity/futures/options format, NSE/BSE index symbols | ### Indicator Categories | Category | Count | Indicators | | --------------- | ----- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | **Trend** | 20 | SMA, EMA, WMA, DEMA, TEMA, HMA, VWMA, ALMA, KAMA, ZLEMA, T3, FRAMA, Supertrend, Ichimoku, Chande Kroll Stop, TRIMA, McGinley, VIDYA, Alligator, MA Envelopes | | **Momentum** | 9 | RSI, MACD, Stochastic, CCI, Williams %R, BOP, Elder Ray, Fisher Transform, Connors RSI | | **Volatility** | 16 | ATR, Bollinger Bands, Keltner, Donchian, Chaikin Volatility, NATR, RVI, Ultimate Oscillator, True Range, Mass Index, BB %B, BB Width, Chandelier Exit, Historical Volatility, Ulcer Index, STARC | | **Volume** | 14 | OBV, OBV Smoothed, VWAP, MFI, ADL, CMF, EMV, Force Index, NVI, PVI, Volume Oscillator, VROC, KVO, PVT | | **Oscillators** | 20+ | CMO, TRIX, UO, Awesome Oscillator, Accelerator, PPO, PO, DPO, Aroon Oscillator, Stochastic RSI, RVI Oscillator, Chaikin Oscillator, Choppiness, KST, TSI, Vortex, Gator, STC, Coppock, ROC | | **Statistical** | 9 | Linear Regression, LR Slope, Correlation, Beta, Variance, TSF, Median, Mode, Median Bands | | **Hybrid** | 6+ | ADX, DMI, Aroon, Pivot Points, Parabolic SAR, Williams Fractals, RWI | | **Utilities** | 11 | Crossover, Crossunder, Cross, Highest, Lowest, Change, ROC, StdDev, EXREM, FLIP, VALUEWHEN, Rising, Falling | ### Data Sources | Source | Use Case | Example Symbols | API Key Required | | -------- | ------------------------ | ----------------------------------------------------------------------- | ------------------------ | | OpenAlgo | Indian markets (primary) | NSE: SBIN, RELIANCE. NFO: NIFTY30DEC25FUT. NSE\_INDEX: NIFTY, BANKNIFTY | Yes (`OPENALGO_API_KEY`) | | yfinance | US markets, global | AAPL, MSFT, SPY, `^GSPC`, `^NSEI` | No | ### Configuration Copy the `.env.sample` and fill in your API keys: ```bash cp .env.sample .env ``` The `.env` file supports: ``` # Indian Markets (OpenAlgo) OPENALGO_API_KEY=your_openalgo_api_key_here OPENALGO_HOST=http://127.0.0.1:5000 ``` US market data via yfinance does not require an API key. ### License MIT