indicator-expert

name	indicator-expert
description	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).
user-invocable	false

OpenAlgo Indicator Expert Skill

Environment

• Python with openalgo, pandas, numpy, plotly, dash, streamlit, numba
• Data sources: OpenAlgo (Indian markets via client.history(), client.quotes(), client.depth()), yfinance (US/Global)
• Real-time: OpenAlgo WebSocket (client.connect(), subscribe_ltp, subscribe_quote, subscribe_depth)
• Indicators: openalgo.ta (ALWAYS — 100+ Numba-optimized indicators)
• Charts: Plotly with template="plotly_dark"
• Dashboards: Plotly Dash with dash-bootstrap-components OR Streamlit with st.plotly_chart()
• Custom indicators: Numba @njit(cache=True, nogil=True) + NumPy
• API keys loaded from single root .env via python-dotenv + find_dotenv() — never hardcode keys
• Scripts go in appropriate directories (charts/, dashboards/, custom_indicators/, scanners/) created on-demand
• Never use icons/emojis in code or logger output

Critical Rules

1. ALWAYS use openalgo.ta for ALL technical indicators. Never reimplement what already exists in the library.
2. Data normalization: Always convert DataFrame index to datetime, sort, and strip timezone after fetching.
3. Signal cleaning: Always use ta.exrem() after generating raw buy/sell signals. Always .fillna(False) before exrem.
4. Plotly dark theme: All charts use template="plotly_dark" with xaxis type="category" for candlesticks.
5. Numba for custom indicators: Use @njit(cache=True, nogil=True) — never fastmath=True (breaks NaN handling).
6. Input flexibility: openalgo.ta accepts numpy arrays, pandas Series, or lists. Output matches input type.
7. WebSocket feeds: Use client.connect(), client.subscribe_ltp() / subscribe_quote() / subscribe_depth() for real-time data.
8. Environment: Load .env from project root via find_dotenv() — never hardcode API keys.
9. Market detection: If symbol looks Indian (SBIN, RELIANCE, NIFTY), use OpenAlgo. If US (AAPL, MSFT), use yfinance.
10. Always explain chart outputs in plain language so traders understand what the indicator shows.

Data Source Priority

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

OpenAlgo API Methods for Data

Method	Purpose	Returns
client.history(symbol, exchange, interval, start_date, end_date)	OHLCV candles	DataFrame (timestamp, open, high, low, close, volume)
client.quotes(symbol, exchange)	Real-time snapshot	Dict (open, high, low, ltp, bid, ask, prev_close, volume)
client.multiquotes(symbols=[...])	Multi-symbol quotes	List of quote dicts
client.depth(symbol, exchange)	Market depth (L5)	Dict (bids, asks, ohlc, volume, oi)
client.intervals()	Available intervals	Dict (minutes, hours, days, weeks, months)
client.connect()	WebSocket connect	None (sets up WS connection)
client.subscribe_ltp(instruments, callback)	Live LTP stream	Callback with {symbol, exchange, ltp}
client.subscribe_quote(instruments, callback)	Live quote stream	Callback with {symbol, exchange, ohlc, ltp, volume}
client.subscribe_depth(instruments, callback)	Live depth stream	Callback with {symbol, exchange, bids, asks}

Indicator Library Reference

All indicators accessed via from openalgo import ta:

Trend (20)

ta.sma, ta.ema, ta.wma, ta.dema, ta.tema, ta.hma, ta.vwma, ta.alma, ta.kama, ta.zlema, ta.t3, ta.frama, ta.supertrend, ta.ichimoku, ta.chande_kroll_stop, ta.trima, ta.mcginley, ta.vidya, ta.alligator, ta.ma_envelopes

Momentum (9)

ta.rsi, ta.macd, ta.stochastic, ta.cci, ta.williams_r, ta.bop, ta.elder_ray, ta.fisher, ta.crsi

Volatility (16)

ta.atr, ta.bbands, ta.keltner, ta.donchian, ta.chaikin_volatility, ta.natr, ta.rvi, ta.ultimate_oscillator, ta.true_range, ta.massindex, ta.bb_percent, ta.bb_width, ta.chandelier_exit, ta.historical_volatility, ta.ulcer_index, ta.starc

Volume (14)

ta.obv, ta.obv_smoothed, ta.vwap, ta.mfi, ta.adl, ta.cmf, ta.emv, ta.force_index, ta.nvi, ta.pvi, ta.volosc, ta.vroc, ta.kvo, ta.pvt

Oscillators (20+)

ta.cmo, ta.trix, ta.uo_oscillator, ta.awesome_oscillator, ta.accelerator_oscillator, ta.ppo, ta.po, ta.dpo, ta.aroon_oscillator, ta.stoch_rsi, ta.rvi_oscillator, ta.cho, ta.chop, ta.kst, ta.tsi, ta.vortex, ta.gator_oscillator, ta.stc, ta.coppock, ta.roc

Statistical (9)

ta.linreg, ta.lrslope, ta.correlation, ta.beta, ta.variance, ta.tsf, ta.median, ta.mode, ta.median_bands

Hybrid (6+)

ta.adx, ta.dmi, ta.aroon, ta.pivot_points, ta.sar, ta.williams_fractals, ta.rwi

Utilities

ta.crossover, ta.crossunder, ta.cross, ta.highest, ta.lowest, ta.change, ta.roc, ta.stdev, ta.exrem, ta.flip, ta.valuewhen, ta.rising, ta.falling

Modular Rule Files

Detailed reference for each topic is in rules/:

Rule File	Topic
indicator-catalog	Complete 100+ indicator reference with signatures and parameters
data-fetching	OpenAlgo history/quotes/depth, yfinance, data normalization
plotting	Plotly candlestick, overlay, subplot, multi-panel charts
custom-indicators	Building custom indicators with Numba + NumPy
websocket-feeds	Real-time LTP/Quote/Depth streaming via WebSocket
numba-optimization	Numba JIT patterns, cache, nogil, NaN handling
dashboard-patterns	Plotly Dash web applications with callbacks
streamlit-patterns	Streamlit web applications with sidebar, metrics, plotly charts
multi-timeframe	Multi-timeframe indicator analysis
signal-generation	Signal generation, cleaning, crossover/crossunder
indicator-combinations	Combining indicators for confluence analysis
symbol-format	OpenAlgo symbol format, exchange codes, index symbols

Chart Templates (in rules/assets/)

Template	Path	Description
EMA Chart	assets/ema_chart/chart.py	EMA overlay on candlestick
RSI Chart	assets/rsi_chart/chart.py	RSI with overbought/oversold zones
MACD Chart	assets/macd_chart/chart.py	MACD line, signal, histogram
Supertrend	assets/supertrend_chart/chart.py	Supertrend overlay with direction coloring
Bollinger	assets/bollinger_chart/chart.py	Bollinger Bands with squeeze detection
Multi-Indicator	assets/multi_indicator/chart.py	Candlestick + EMA + RSI + MACD + Volume
Basic Dashboard	assets/dashboard_basic/app.py	Single-symbol Plotly Dash app
Multi Dashboard	assets/dashboard_multi/app.py	Multi-symbol multi-timeframe dashboard
Streamlit Basic	assets/streamlit_basic/app.py	Single-symbol Streamlit app
Streamlit Multi	assets/streamlit_multi/app.py	Multi-timeframe Streamlit app
Custom Indicator	assets/custom_indicator/template.py	Numba custom indicator template
Live Feed	assets/live_feed/template.py	WebSocket real-time indicator
Scanner	assets/scanner/template.py	Multi-symbol indicator scanner

Quick Template: Standard Indicator Chart Script

import os
from datetime import datetime, timedelta
from pathlib import Path

import numpy as np
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from dotenv import find_dotenv, load_dotenv
from openalgo import api, ta

# --- Config ---
script_dir = Path(__file__).resolve().parent
load_dotenv(find_dotenv(), override=False)

SYMBOL = "SBIN"
EXCHANGE = "NSE"
INTERVAL = "D"

# --- Fetch Data ---
client = api(
    api_key=os.getenv("OPENALGO_API_KEY"),
    host=os.getenv("OPENALGO_HOST", "http://127.0.0.1:5000"),
)

end_date = datetime.now().date()
start_date = end_date - timedelta(days=365)

df = client.history(
    symbol=SYMBOL, exchange=EXCHANGE, interval=INTERVAL,
    start_date=start_date.strftime("%Y-%m-%d"),
    end_date=end_date.strftime("%Y-%m-%d"),
)
if "timestamp" in df.columns:
    df["timestamp"] = pd.to_datetime(df["timestamp"])
    df = df.set_index("timestamp")
else:
    df.index = pd.to_datetime(df.index)
df = df.sort_index()
if df.index.tz is not None:
    df.index = df.index.tz_convert(None)

close = df["close"]
high = df["high"]
low = df["low"]
volume = df["volume"]

# --- Compute Indicators ---
ema_20 = ta.ema(close, 20)
rsi_14 = ta.rsi(close, 14)

# --- Chart ---
fig = make_subplots(
    rows=2, cols=1, shared_xaxes=True,
    row_heights=[0.7, 0.3], vertical_spacing=0.03,
    subplot_titles=[f"{SYMBOL} Price + EMA(20)", "RSI(14)"],
)

# Candlestick
x_labels = df.index.strftime("%Y-%m-%d")
fig.add_trace(go.Candlestick(
    x=x_labels, open=df["open"], high=high, low=low, close=close,
    name="Price",
), row=1, col=1)

# EMA overlay
fig.add_trace(go.Scatter(
    x=x_labels, y=ema_20, mode="lines",
    name="EMA(20)", line=dict(color="cyan", width=1.5),
), row=1, col=1)

# RSI subplot
fig.add_trace(go.Scatter(
    x=x_labels, y=rsi_14, mode="lines",
    name="RSI(14)", line=dict(color="yellow", width=1.5),
), row=2, col=1)
fig.add_hline(y=70, line_dash="dash", line_color="red", row=2, col=1)
fig.add_hline(y=30, line_dash="dash", line_color="green", row=2, col=1)

fig.update_layout(
    template="plotly_dark", title=f"{SYMBOL} Technical Analysis",
    xaxis_rangeslider_visible=False, xaxis_type="category",
    xaxis2_type="category", height=700,
)
fig.show()