DEV Community: EmilyL

Stop Polling! Stream Real-Time Hong Kong Stock Connect Prices with Python and WebSockets

EmilyL — Tue, 26 May 2026 02:57:28 +0000

Ever timed how long your REST loop takes to grab quotes for all ~500 Hong Kong Stock Connect stocks? I did. It took 96 seconds, and the API throttled me halfway through. As an active intraday trader, I can’t afford my data arriving later than my edge.

So I ripped out the polling logic and piped everything through a single WebSocket connection. Now I get tick-by-tick updates in under 50ms, and I haven’t looked back. Let me show you how you can do the same, even if you’re a solo dev.

Why REST Polling is a Trap for Live Market Data

Polling 500+ stocks means 500+ HTTP round trips. Each trip costs connection overhead, and APIs will rate-limit you into oblivion. WebSocket lets you subscribe to hundreds of instruments in one go and passively receive updates. For my setup, I used the AllTick WebSocket API because it handles bulk HK Stock Connect subscriptions without hassle.

Architecture Overview

I like to keep things modular:

Symbol Service: Fetches Stock Connect components and appends .HK.
Connection Manager: Handles WebSocket lifecycle, batches subscriptions, and sends pings.
Tick Processor: Parses incoming JSON, maps fields to a standard schema, and queues them.
Storage Agent: Dumps queued ticks into a database asynchronously, plus a reconnection watchdog.

Module	Responsibility	Pro Tip
Symbol Service	Maintain a clean ticker list	Filter out suspended stocks
Connection Manager	Subscribe in batches, keep alive	Respect the max symbols per frame
Tick Processor	Deserialize and normalize	Use `.get()` to handle optional fields
Storage Agent	Async persistence + reconnect	Queue approach avoids back-pressure

Show Me the Code

import websocket
import json

def on_message(ws, message):
    # Parse pushed tick data
    data = json.loads(message)
    for tick in data.get("ticks", []):
        # Swap print with your queue/db writer in production
        print(f"{tick['symbol']} Price: {tick['price']} Volume: {tick['volume']}")

def on_open(ws):
    # Batch subscribe to HK Stock Connect symbols
    tickers = ["00700.HK", "09988.HK", "02628.HK"]
    ws.send(json.dumps({"action": "subscribe", "symbols": tickers}))

ws = websocket.WebSocketApp("wss://api.alltick.co/stock/ws",
                            on_message=on_message,
                            on_open=on_open)
ws.run_forever()

Life After the Migration

These days, I fire up my trading terminal, glance at the green connection indicator, and immediately start watching real-time heatmaps and volume anomaly dashboards. Debugging data gaps has dropped to near zero. If you’re still polling your market data, do yourself a favor: swap to WebSocket and reclaim both your latency and your sanity.

Stop Letting Multi-Crypto WebSocket Streams Mess Up Your Charts — Use a Timestamp Merge Queue

EmilyL — Mon, 18 May 2026 02:39:29 +0000

If you’ve ever built a real-time dashboard or a trading bot that watches BTC, ETH, and LTC simultaneously, you’ve likely seen it: the chart flickers, the latest trade jumps to a candle that should already be closed, and your alerts fire for events that seem to happen out of sequence. The root issue? Cross-symbol tick disorder. Let me show you how to fix it with a simple, battle-tested pattern.

🕵️ The problem: why your ticks are out of order

Every symbol’s WebSocket feed is an independent stream. Even when using a single API that supports multi-symbol subscriptions, the frames don’t wait for each other. Three things go wrong:

Network latency variance: a few milliseconds here and there are enough to shuffle the global sequence.
Shared queue + multiple workers: putting all ticks in one queue and processing with several threads? The OS decides the order, not you.
Timestamp mismatches: some APIs return local exchange time, some UTC, some with second precision, some with millisecond. Good luck sorting that reliably.

💡 The solution: per-symbol queues + a priority-based dispatcher

The fix is conceptually simple: separate reception from ordering.

Step 1: maintain an ordered list for each symbol. Since ticks usually arrive in order per symbol, you can just append them.
Step 2: use a min-heap (priority queue) that always looks at the first (earliest) tick of each symbol’s list. Pop the smallest timestamp, process it, then push the next tick from that same symbol back into the heap.

This gives you a perfectly time-ordered stream regardless of network jitter. Here’s a state table that explains the merge decision:

Symbol	Ordered Buffer	Head Timestamp	Next to process?
BTC	[tick1, tick2, tick3]	12:01:05	No
ETH	[tick1, tick2]	12:01:06	No
LTC	[tick1, tick2, tick3]	12:01:04	✅ Yes

LTC’s tick is the oldest, so it goes first, then BTC, then ETH. Even if LTC’s data arrived last, it’s processed in its correct time slot.

🛠️ Code snippet (ingestion side)

I use a data provider that gives consistent UTC timestamps for every tick — for example, AllTick’s WebSocket API. The Python ingestion code is dead simple:

import websocket
import json

queues = {}  # each symbol gets its own list

def on_message(ws, message):
    data = json.loads(message)
    symbol = data['symbol']
    timestamp = data['timestamp']
    queues[symbol].append(data)  # per-symbol queue, later sorted by timestamp

ws = websocket.WebSocketApp("wss://api.alltick.co/ws",
                            on_message=on_message)
ws.run_forever()

On the consumption side (not shown), you’d run a loop that uses heapq or your language’s priority queue to always pull the oldest tick across all buffers. If you’re using Node.js, a binary heap works just as well.

⚡ Performance and stability tweaks

Limit buffer sizes: set a maximum length (e.g., 1000 ticks per symbol) to avoid memory bloat during huge volume spikes.
Batch together: pop 10–50 ticks from the heap at once, sort that mini-batch (it’s nearly sorted already) and dispatch — cuts heap operations dramatically.
Reconnect wisely: on socket disconnect, clear all buffers and rebuild the heap. Stale data from a previous session will only confuse your strategy.

🧠 Final thoughts

Multi-symbol tick ordering isn’t a niche problem — it’s at the heart of any real-time crypto system. The per-symbol queue + timestamp merge pattern solves it elegantly without any heavy infrastructure. Try it in your next bot or dashboard, and you’ll immediately notice smoother charts and more reliable signals. If you have your own tricks for taming real-time streams, drop them in the comments!

Dev Perspective: Overcoming Dirty K-Line Data in Stock Backtesting Pipelines

EmilyL — Wed, 13 May 2026 02:57:29 +0000

Hey devs who venture into quant finance 👋. If you’re used to building APIs and microservices, stock market data might seem like “just another JSON”. I thought so too, until I built my first backtesting engine for US equities. Turns out, historical K-line (OHLCV) data is a swamp of edge cases. Let me walk you through how I tamed it.

The Wake-Up Call: A “Perfect” Backtest That Couldn’t Trade

I coded a simple breakout strategy in Python. Backtest result: +35% annually, drawdown 4%. I deployed a paper-trading version using the same data pipeline. Live results: -12% in three weeks. After days of debugging the algorithm, I found the issue: the minute bars I downloaded had timestamps in UTC but I had assumed they were US Eastern. The “breakouts” my strategy captured happened outside market hours — pure noise that couldn’t be traded. My entire evaluation was based on unexecutable signals.

Define Your Data Needs by Strategy Type

In software terms, choose the right data granularity for your use case:

Daily bars (1d): like a daily batch job. Sufficient for trend-following and end-of-day signals.
Minute bars (1m, 5m, etc.): analogous to real-time event streams. Critical for intraday logic.
Tick data: the raw event firehose. Requires Kafka-like throughput and careful state management.

Most quant developers will spend 80% of their time in minute bars. That’s also where data quality bites hardest.

Data Pain Points: A Quick Diagnostic Table

I keep this table in my project README:

Data Type	Frequent Bug	My Fix
Daily	Unadjusted for splits/dividends	Explicitly request adjusted data; prefer forward-adjusted for short-term tests
Minute	Timezone misalignment	Normalize all timestamps to EST; filter with official market calendar
Tick	Enormous storage size	Batch ingest, convert to Parquet, partition by symbol/date

Dividend and split adjustments are a common trap. A 2-for-1 split showing a price drop of 50% will trigger any momentum filter incorrectly, generating false short signals.

Building a Robust Data Pipeline

I now use a dedicated market data API to fetch clean data. A provider like AllTick returns structured OHLCV arrays. Example: pulling SPY daily klines.

import requests
import pandas as pd

# Fetch SPY daily kline from AllTick API
url = "https://api.alltick.co/stock/history/kline"
params = {
    "symbol": "SPY",
    "interval": "1d",
    "start_date": "2024-01-01",
    "end_date": "2024-12-01"
}

resp = requests.get(url, params=params)
data = resp.json()

df = pd.DataFrame(data['kline'])
df['time'] = pd.to_datetime(df['time'])
df.set_index('time', inplace=True)
print(df.head())

To avoid repeated API calls and network latency, I persist data locally using DuckDB — an embeddable OLAP database perfect for this job:

# Local persistent storage via DuckDB
import duckdb

conn = duckdb.connect('market_data.db')
conn.execute("""
    CREATE TABLE IF NOT EXISTS spy_daily (
        time DATE,
        open FLOAT,
        high FLOAT,
        low FLOAT,
        close FLOAT,
        volume BIGINT
    )
""")

# Incrementally append new data
conn.execute("INSERT INTO spy_daily SELECT * FROM new_data")

Four Engineering Best Practices

These are my non-negotiable rules for any backtesting data pipeline:

Time standardisation: Convert everything to America/New_York timezone and trim to 09:30–16:00. Use pandas_market_calendars to generate valid trading sessions.
Adjustment metadata: Store adjustment type in the schema (adj_type column). Let the backtest engine branch logic based on it. Never rely on “auto” adjustment.
Missing data handling: Forward-fill missing values and add an is_filled Boolean. In analysis, compute metrics both including and excluding these points.
Automated validation: Write a test that randomly picks several days and compares your OHLCV with a trusted reference (e.g., exchange bulk data). Run this weekly as a CI job.

Conclusion: Data First, Algorithms Second

As developers, we love elegant algorithms. But in quantitative finance, a robust data pipeline is the true differentiator. Clean K-line data makes simple strategies work; dirty data makes complex strategies fail. Invest the time to validate your historical bars, and your backtest will actually mean something when you go live.

How to Pick a Low-Latency US Stock API: Key Metrics & WebSocket Test

EmilyL — Tue, 12 May 2026 03:11:57 +0000

As a FinTech tech lead, I’ve onboarded several real-time market data feeds for US equities. The performance gap between a well-optimized API and a generic one can be over 200ms — enough to turn a profitable strategy into a loser. Here’s how I evaluate them, complete with code.

Breaking Down Latency

Latency comes from three main places:

Transport: HTTP adds overhead; WebSocket delivers push in real time.
Location: Servers near exchange data centers send data faster.
Serialization: JSON parsing slows you down at scale.

Mandatory Metrics

Metric	Target
End-to-end latency	< 80ms median
Throughput	≥ 1500 tick/s under load
Packet loss	< 0.001%

Messages must be sequenced and in order.

Test Script

I benchmark providers by running the same WebSocket client code during market open:

import websocket
import json

def on_message(ws, message):
    data = json.loads(message)
    # Display tick data
    print(f"Time: {data['ts']} Symbol: {data['symbol']} Price: {data['price']} Volume: {data['volume']}")

def on_open(ws):
    # Subscription payload
    sub_msg = {
        "action": "subscribe",
        "symbols": ["AAPL", "MSFT", "NVDA"]
    }
    ws.send(json.dumps(sub_msg))

ws = websocket.WebSocketApp(
    "wss://api.alltick.co/stock/ws",
    on_open=on_open,
    on_message=on_message
)
ws.run_forever()

In my tests, clean APIs like AllTick showed steady 60ms latency, while others varied wildly.

The Bigger Picture

In quantitative research, latency stability outweighs peak speed. A stream with low jitter delivers cleaner factor signals and reproducible backtests. Choose stability over hype.

Real-Time EURUSD Exchange Rates in Python: Let’s WebSocket!

EmilyL — Wed, 06 May 2026 02:57:21 +0000

Ever needed live forex rates for a side project and got stuck with sluggish polling? I was in the same boat until I rewired my data pipeline around WebSocket. Here’s the quick, pragmatic setup that now powers my market-monitoring toolkit.

The problem

REST APIs force you to ask for data. Even with short intervals you’ll get gaps between requests, and during volatile seconds those gaps hide price action you might want to reference in a blog post or tutorial. For anyone who writes about currency markets, that latency eats into the content’s perceived accuracy.

The fix

Use WebSocket. Once connected, the server pushes each tick immediately. I picked a provider (AllTick) with a straightforward subscribe/publish model. You send a JSON subscription for EURUSD and then just handle callbacks.

The code

Drop this into a script and run it — you’ll see ticks flowing right away.

import websocket
import json

prices = []

def on_message(ws, message):
    data = json.loads(message)
    prices.append(data['price'])
    print(f"Time: {data['time']}, EURUSD: {data['price']}")

def on_open(ws):
    sub_msg = {
        "action": "subscribe",
        "symbol": "EURUSD"
    }
    ws.send(json.dumps(sub_msg))

url = "wss://api.alltick.co/realtime"
ws = websocket.WebSocketApp(url, on_message=on_message, on_open=on_open)
ws.run_forever()

No polling loops, no timers. The connection stays open and feeds you prices as fast as the market moves.

Processing the data

For quick insights, I compute a rolling average inside the callback and log it. I also pipe the stream into a lightweight database so I can generate real-time charts for my dev-related articles. The improved data freshness has made my demos more engaging and credible.

My takeaways

Adding reconnection logic and JSON error handling turned this from a toy into a reliable tool. Now my forex-related content is backed by a stream I trust — and readers notice the difference when the chart moves in sync with their own trading platforms.

Is Your Real-Time Feed Lying to You? Streaming US Stock Tick Data with WebSockets

EmilyL — Tue, 05 May 2026 02:22:34 +0000

Every developer building a trading dashboard or a backtesting engine eventually stumbles on the same mismatch: the live price on your screen moved, but your recorded data doesn’t show a trade at that exact level. The culprit is almost always snapshot aggregation. Let's unpack this from a broker’s perspective and get you to a cleaner, tick-by-tick WebSocket pipeline.

The Data Integrity Problem: Snapshots Discard Microstructure

A snapshot—no matter how frequently polled—is a summary. It collapses potentially dozens of discrete trades into a single OHLCV tuple. The key information you lose includes the trade size distribution, the sequence of fills against the bid or ask, and the exact microsecond timestamps. For any kind of order flow or volume profile strategy, that’s a critical information loss. You’re effectively training your models on compressed JPEGs of the market rather than the RAW file.

The Efficiency Pitfall of HTTP Polling

Many of us start with a setInterval or a while True loop hitting a REST API. With cross-Atlantic latency and API rate limits, you’re lucky to get 5-10 samples per second. When Nvidia or Tesla prints 40+ trades in a second, your sampling rate becomes a joke. You’ll systematically miss the fat-tail events that move markets. Beyond data gaps, short-lived connections also introduce TLS handshake overhead that compounds under load.

WebSocket: A Single Connection, Zero Guessing

WebSocket solves this with a full-duplex, persistent channel. You perform one upgrade handshake, then the server streams messages to you. Each message is a tick. Because the protocol is event-driven, your application reacts only when real activity occurs. This dramatically lowers both latency and CPU usage compared to frantic polling loops.

Plugging Into a Real US Stock Tick Feed

Modern market data APIs expose WebSocket endpoints with JSON payloads that are easy to consume. In my current setup, I rely on AllTick’s stock WebSocket stream because it normalizes exchange feeds into a consistent schema. A minimal listener looks like this:

import websocket
import json

def on_message(ws, message):
    # Decode each tick from the stream
    data = json.loads(message)
    for trade in data.get("trades", []):
        symbol = trade.get("symbol")
        price = trade.get("price")
        volume = trade.get("volume")
        timestamp = trade.get("time")
        print(f"{symbol} price {price} volume {volume} time {timestamp}")

def on_open(ws):
    # Subscribe to the desired symbols
    subscribe = {
        "action": "subscribe",
        "symbols": ["AAPL", "MSFT", "GOOGL"]
    }
    ws.send(json.dumps(subscribe))

ws = websocket.WebSocketApp(
    "wss://api.alltick.co/stock/ws",
    on_open=on_open,
    on_message=on_message
)
ws.run_forever()

Once the connection is live, you’ll see a continuous log of prints. I recommend offloading message processing to a separate thread via a queue.Queue to keep the WebSocket callback non-blocking.

Transforming the Developer Workflow

With a true tick stream, you can build real-time micro analytics: a sliding window for recent volume surges, an alerting system when a trade exceeds N standard deviations of the rolling average, or a derived aggressor index when combined with the order book. Batch your UI updates—don’t attempt a DOM repaint on every message. Use a throttling mechanism (e.g., 4-5 times per second) to keep your application responsive while handling thousands of ticks per minute.

Who Needs This

If you’re coding a platform that depends on the most granular market activity—algorithmic trading, market surveillance, academic microstructure research—then snapshot data is simply insufficient. Swapping out a polling REST client for a WebSocket tick stream is one of those low-effort, high-impact refactors that pays dividend in data quality from day one. Make sure your foundation is solid, and the signal will follow.

From Zero to Live Silver Prices in 5 Minutes: A WebSocket Guide for Devs

EmilyL — Mon, 04 May 2026 03:34:42 +0000

How I Saved a Client’s Trading Dashboard with Just 20 Lines of Code

A fellow developer in the finance advisory space was asked to build a silver price alert system. The initial version used REST requests every second, but during high volatility, the delay was so bad the alerts fired after the move was over. After a quick pivot to WebSockets, the dashboard became truly real‑time — and the whole integration took under five minutes. Here’s how you can do the same.

Why Polling Feels Like Driving with the Brakes On

REST calls are great for most things, but for streaming price data they impose a minimum latency equal to your polling interval plus network round‑trip. For silver, a market that can see multiple ticks per second, polling turns a continuous process into a stuttered slideshow. Beyond the stale data, excessive requests can also get you throttled by the API provider.

The Better Way: A Persistent WebSocket Connection

WebSocket is a two‑way, long‑living connection that pushes data the moment it’s available. Instead of asking “what’s the price?” 60 times a minute, you say “let me know every time the price changes.” One of the easiest financial WebSocket APIs to start with is from AllTick. It offers direct access to precious metals like XAGUSD, with no complicated auth — just connect and subscribe.

Let’s Write Some Code

Here’s a minimal Python snippet that prints every new silver quote:

import websocket
import json

def when_message(ws, message):
    data = json.loads(message)
    # Show the latest silver price
    print("Silver:", data.get("price"), data.get("time"))

def when_open(ws):
    ws.send(json.dumps({"action": "subscribe", "symbols": ["XAGUSD"]}))

ws = websocket.WebSocketApp(
    "wss://ws.alltick.co/quote",
    on_message=when_message,
    on_open=when_open
)
ws.run_forever()

If you’re in a Node.js environment, the equivalent is:

const WebSocket = require('ws');
const ws = new WebSocket('wss://ws.alltick.co/quote');
ws.on('open', () => ws.send(JSON.stringify({
    action: 'subscribe', symbols: ['XAGUSD']
})));
ws.on('message', data => {
    const q = JSON.parse(data);
    console.log(`Silver: ${q.price}`);
});

Production Tips for a Stable Feed

Reconnect on close: Wrap your connection logic in a function and call it again when the socket closes.
Add a heartbeat: Send a ping every 30 seconds to keep the connection alive.
Filter duplicates: When the market is slow, you might receive the same price twice; a quick check on the sequence ID avoids noisy logs.
Use a queue: Offload heavy processing from the on_message callback to keep the socket responsive.

Wrap‑Up

Real‑time data isn’t a luxury—it’s a necessity for any serious financial tool. With just a few lines of code and a reliable WebSocket endpoint, you can give your apps the same sub‑100ms speed that professional traders rely on. Give it a try, and you’ll wonder why you ever stuck with polling.