live-feed

Q: How do I install live-feed?

Run npx skills add https://github.com/marketcalls/openalgo-indicator-skills --skill live-feed in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/live-feed/ (personal) or .claude/skills/live-feed/ (project).

by marketcalls

Set up real-time indicator computation on live WebSocket market data. Streams LTP/Quote/Depth and computes indicators in real-time with optional Plotly live charting.

Installs: 557
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/marketcalls/openalgo-indicator-skills --skill live-feed

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads live-feed automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Create a real-time indicator feed using OpenAlgo WebSocket streaming.

Arguments

Parse $ARGUMENTS as: symbol exchange mode

$0 = symbol (e.g., SBIN, RELIANCE, NIFTY). Default: SBIN
$1 = exchange (e.g., NSE, NSE_INDEX). Default: NSE
$2 = mode (e.g., ltp, quote, depth, multi). Default: quote

If no arguments, ask user for symbol and what data they want.

Instructions

Read the indicator-expert rules, especially:
- rules/websocket-feeds.md — WebSocket connection and subscription
- rules/data-fetching.md — Historical data for buffer initialization
Create charts/live/ directory (on-demand)
Create {symbol}_live_feed.py
Use the template from rules/assets/live_feed/template.py

Feed Types

`ltp` — Last Traded Price + Indicators

Subscribe to LTP feed
Maintain rolling buffer (last 200 ticks)
Compute EMA, RSI on buffer
Print real-time indicator values

`quote` — Full Quote + Indicators

Subscribe to Quote feed
Display OHLC + LTP + Volume
Compute indicators on close buffer
Color-coded output (bullish/bearish)

`depth` — Market Depth Analysis

Subscribe to Depth feed
Display L5 bid/ask book
Compute bid-ask spread, order imbalance
Show total buy vs sell quantity

`multi` — Multi-Symbol Feed

Subscribe to multiple symbols
Display watchlist table with LTP and key indicator
Auto-refresh display

Script Structure

"""
Real-Time Indicator Feed for {SYMBOL}
Mode: {mode}
"""
import os
import time
import numpy as np
from datetime import datetime, timedelta
from dotenv import find_dotenv, load_dotenv
from openalgo import api, ta

load_dotenv(find_dotenv(), override=False)

SYMBOL = "{symbol}"
EXCHANGE = "{exchange}"

client = api(
    api_key=os.getenv("OPENALGO_API_KEY"),
    host=os.getenv("OPENALGO_HOST", "http://127.0.0.1:5000"),
    verbose=1,
)

# Pre-fetch historical data for buffer initialization
df = client.history(
    symbol=SYMBOL, exchange=EXCHANGE, interval="1m",
    start_date=(datetime.now() - timedelta(days=1)).strftime("%Y-%m-%d"),
    end_date=datetime.now().strftime("%Y-%m-%d"),
)
close_buffer = list(df["close"].values[-200:])

instruments = [{"exchange": EXCHANGE, "symbol": SYMBOL}]

def on_data(data):
    ltp = data["data"].get("ltp")
    if ltp is None:
        return

    close_buffer.append(float(ltp))
    if len(close_buffer) > 200:
        close_buffer.pop(0)

    if len(close_buffer) >= 20:
        arr = np.array(close_buffer, dtype=np.float64)
        ema_val = ta.ema(arr, 20)[-1]
        rsi_val = ta.rsi(arr, 14)[-1] if len(arr) >= 15 else float("nan")

        timestamp = datetime.now().strftime("%H:%M:%S")
        print(f"[{timestamp}] {SYMBOL} LTP:{ltp:>10.2f} | "
              f"EMA(20):{ema_val:>10.2f} | RSI(14):{rsi_val:>6.2f}")

# Connect and subscribe
client.connect()
client.subscribe_ltp(instruments, on_data_received=on_data)

print(f"Streaming {SYMBOL} on {EXCHANGE} — Press Ctrl+C to stop")
try:
    while True:
        time.sleep(1)
except KeyboardInterrupt:
    print("Stopping feed...")

client.unsubscribe_ltp(instruments)
client.disconnect()

Cleanup

The script must:

Handle Ctrl+C gracefully
Unsubscribe from all feeds
Disconnect WebSocket
Print summary of session duration and bars processed

Verbose Levels

Inform user about verbose options:

verbose=0: Silent mode (errors only)
verbose=1: Connection and subscription logs
verbose=2: All data updates (debug mode)

Example Usage

/live-feed SBIN NSE ltp /live-feed NIFTY NSE_INDEX quote /live-feed SBIN NSE depth /live-feed multi NSE

FAQ

What does the live-feed skill do?

Set up real-time indicator computation on live WebSocket market data. Streams LTP/Quote/Depth and computes indicators in real-time with optional Plotly live charting.

How do I install live-feed?

Run npx skills add https://github.com/marketcalls/openalgo-indicator-skills --skill live-feed in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/live-feed/ (personal) or .claude/skills/live-feed/ (project).

Which AI agents can use live-feed?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is live-feed safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains live-feed?

It is published by marketcalls in marketcalls/openalgo-indicator-skills. It has 557 recorded installs.