| 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 3.12+ (required by openalgo 2.x) with openalgo, pandas, numpy, plotly, dash, streamlit
- 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+ indicators computed by a compiled Rust core, full speed from the first call)
- Charts: Plotly with
template="plotly_dark"
- Dashboards: Plotly Dash with
dash-bootstrap-components (default) OR Streamlit with st.plotly_chart() — use Streamlit only when the user explicitly asks for it
- Custom indicators: vectorized NumPy composed from openalgo.ta primitives (Rust core — no JIT, no warmup)
- 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.
- Custom indicators: Compose from openalgo.ta primitives (
ta.sma, ta.stdev, ta.bbands, ...) plus vectorized NumPy. Never reimplement built-ins; no JIT or warmup is needed.
- 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.optionchain(underlying, exchange, expiry_date, strike_count) | Option chain around ATM | Dict (underlying_ltp, atm_strike, chain with ce/pe per strike) |
client.optiongreeks(symbol, exchange, interest_rate, ...) | Option greeks + IV | Dict (greeks: delta/gamma/theta/vega/rho, implied_volatility, days_to_expiry) |
client.expiry(symbol, exchange, instrumenttype) | Expiry dates list | Dict (data: list of expiry dates) |
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 (15)
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, ta.rvol
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
TA-Lib Compatible (18, new in openalgo 2.0)
ta.mom, ta.rocp, ta.rocr, ta.rocr100, ta.apo, ta.midpoint, ta.midprice, ta.avgprice, ta.medprice, ta.typprice, ta.wclprice, ta.plus_dm, ta.minus_dm, ta.dx, ta.adxr, ta.stochf, ta.linregangle, ta.linregintercept
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 vectorized NumPy + ta primitives |
| websocket-feeds | Real-time LTP/Quote/Depth streaming via WebSocket |
| performance | Rust core performance, O(n) guarantees, benchmarking |
| 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 | NumPy custom indicator template (composes ta primitives) |
| 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
script_dir = Path(__file__).resolve().parent
load_dotenv(find_dotenv(), override=False)
SYMBOL = "SBIN"
EXCHANGE = "NSE"
INTERVAL = "D"
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"]
ema_20 = ta.ema(close, 20)
rsi_14 = ta.rsi(close, 14)
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)"],
)
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)
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)
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()
