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 ( )