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

• ALWAYS use openalgo.ta for ALL technical indicators. Never reimplement what already exists in the library.

• Data normalization: Always convert DataFrame index to datetime, sort, and strip timezone after fetching.

• Signal cleaning: Always use ta.exrem() after generating raw buy/sell signals. Always .fillna(False) before exrem.

• Plotly dark theme: All charts use template="plotly_dark" with xaxis type="category" for candlesticks.

• Numba for custom indicators: Use @njit(cache=True, nogil=True) — never fastmath=True (breaks NaN handling).

• Input flexibility: openalgo.ta accepts numpy arrays, pandas Series, or lists. Output matches input type.

• WebSocket feeds: Use client.connect() , client.subscribe_ltp() / subscribe_quote() / subscribe_depth() for real-time data.

• Environment: Load .env from project root via find_dotenv() — never hardcode API keys.

• Market detection: If symbol looks Indian (SBIN, RELIANCE, NIFTY), use OpenAlgo. If US (AAPL, MSFT), use yfinance.

• 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()