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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(), / / for real-time data.

Data Source Priority

OpenAlgo API Methods for Data

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, , , , , , , ,

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, , , ,

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, ,

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, , , , , , , ,

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,

Modular Rule Files

Detailed reference for each topic is in rules/:

Chart Templates (in rules/assets/)

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()

Weekly Installs

125

Repository

marketcalls/ope…r-skills

GitHub Stars

5

First Seen

Feb 28, 2026

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