Indicators

Tradeboard Indicator Skills for Agentic Coding Tools

A comprehensive collection of technical indicator skills for charting, analysis, and custom indicator development using Tradeboard. Works with 40+ AI coding agents via skills.sh, including Claude Code, Cursor, Codex, OpenCode, Cline, Windsurf, GitHub Copilot, Gemini CLI, Roo Code, and more.

Supports Indian markets via Tradeboard 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. The CLI auto-detects your AI coding agent and installs skills to the correct directory.

bash

# GitHub shorthand
npx skills add wesoftcorp/tradeboard-docs-indicator-skills

# Full GitHub URL
npx skills add https://github.com/wesoftcorp/tradeboard-indicator-skills

Install a specific skill only:

bash

npx skills add wesoftcorp/tradeboard-docs-indicator-skills -s indicator-chart
npx skills add wesoftcorp/tradeboard-docs-indicator-skills -s custom-indicator
npx skills add wesoftcorp/tradeboard-docs-indicator-skills -s indicator-dashboard
npx skills add wesoftcorp/tradeboard-docs-indicator-skills -s indicator-scanner
npx skills add wesoftcorp/tradeboard-docs-indicator-skills -s live-feed
npx skills add wesoftcorp/tradeboard-docs-indicator-skills -s indicator-setup

List available skills before installing:

bash

npx skills add wesoftcorp/tradeboard-docs-indicator-skills -l

Install globally (available across all projects):

bash

npx skills add wesoftcorp/tradeboard-docs-indicator-skills -g

Supported AI Coding Agents

Skills are installed via skills.sh 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)	Tradeboard	`client.history()`	SBIN, RELIANCE, INFY
India (Index)	Tradeboard	`client.history(exchange="NSE_INDEX")`	NIFTY, BANKNIFTY
India (F&O)	Tradeboard	`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 Tradeboard. 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 (tradeboard, 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	Tradeboard 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	Tradeboard 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	Tradeboard 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: npm install -g @anthropic-ai/claude-code
Cursor: Desktop IDE with built-in AI
Codex: npm install -g @openai/codex
OpenCode: go install github.com/opencode-ai/opencode@latest
Cline: VS Code extension
Windsurf: Desktop IDE with AI
Or any of the 40+ supported agents

Then install the skills:

bash

npx skills add wesoftcorp/tradeboard-docs-indicator-skills

2. Data Source Setup

Indian Markets: requires Tradeboard:

bash

git clone https://github.com/wesoftcorp/tradeboard-docs.git
cd tradeboard
pip install -r requirements.txt
python app.py

Tradeboard runs locally at http://127.0.0.1:5000. You need a broker account connected via Tradeboard and an API key from the dashboard. See Tradeboard documentation.

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 tradeboard 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 Tradeboard ta library, compiled with Numba JIT for production-grade speed.

python

from tradeboard 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 tradeboard 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 tradeboard import api, ta

client = api(api_key=os.getenv("TRADEBOARD_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

Tradeboard 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`	Tradeboard 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
Tradeboard	Indian markets (primary)	NSE: SBIN, RELIANCE. NFO: NIFTY30DEC25FUT. NSE_INDEX: NIFTY, BANKNIFTY	Yes (`TRADEBOARD_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 (Tradeboard)
TRADEBOARD_API_KEY=your_tradeboard_api_key_here
TRADEBOARD_HOST=http://127.0.0.1:5000

US market data via yfinance does not require an API key.

License

MIT

Agent

Windows Installation

Mac OS Installation

Accounts API

Orders API

Data API

Utilities API

Strategy RMS API

Indicators

Visualization

Indicators ​

Tradeboard Indicator Skills for Agentic Coding Tools ​

Quick Install ​

Supported AI Coding Agents ​

Supported Markets ​

Capabilities ​

Skills (User-Invocable Commands) ​

Pre-Built Chart Templates (13) ​

Knowledge Base (12 Rule Files) ​

Prerequisites ​

1. AI Coding Agent ​

2. Data Source Setup ​

3. Python Environment Setup ​

4. Configure API Keys ​

Usage Examples ​

/indicator-setup: Environment Setup ​

/indicator-chart: Chart Any Indicator ​

/custom-indicator: Build Custom Indicators ​

/indicator-dashboard: Web Dashboards ​

/indicator-scanner: Scan Stocks ​

/live-feed: Real-Time WebSocket ​

Key Features ​

100+ Numba-Optimized Indicators ​

Plotly Dark Theme Charts ​

Custom Indicators with Numba ​

Signal Cleaning with EXREM ​

Real-Time WebSocket Feeds ​

Multi-Timeframe Confluence ​

Tradeboard Data Methods ​

Output Folder Structure ​

Project Structure ​

Rule Files Reference ​

Indicator Categories ​

Data Sources ​

Configuration ​

License ​

Indicators

Tradeboard Indicator Skills for Agentic Coding Tools

Quick Install

Supported AI Coding Agents

Supported Markets

Capabilities

Skills (User-Invocable Commands)

Pre-Built Chart Templates (13)

Knowledge Base (12 Rule Files)

Prerequisites

1. AI Coding Agent

2. Data Source Setup

3. Python Environment Setup

4. Configure API Keys

Usage Examples

`/indicator-setup`: Environment Setup

`/indicator-chart`: Chart Any Indicator

`/custom-indicator`: Build Custom Indicators

`/indicator-dashboard`: Web Dashboards

`/indicator-scanner`: Scan Stocks

`/live-feed`: Real-Time WebSocket

Key Features

100+ Numba-Optimized Indicators

Plotly Dark Theme Charts

Custom Indicators with Numba

Signal Cleaning with EXREM

Real-Time WebSocket Feeds

Multi-Timeframe Confluence

Tradeboard Data Methods

Output Folder Structure

Project Structure

Rule Files Reference

Indicator Categories

Data Sources

Configuration

License