When a junior engineer first plugs into a market data API, the instinct is to pick one protocol and stick with it. The reasoning feels sound: consistency simplifies the codebase, reduces cognitive load, and makes the system easier to reason about. Unfortunately, this intuition leads directly into a trap that has tripped up quant teams from retail traders to institutional shops — the belief that a single API paradigm can optimally serve both historical analysis and real-time streaming.
It cannot. And the reasons why matter more than you might expect.
The choice between REST and WebSocket is not a matter of preference or familiarity. It is a fundamental architectural decision that determines whether your data pipeline is resilient under load, cost-efficient at scale, and capable of capturing the signals that matter. Getting this wrong does not just create engineering debt. It creates a blind spot in your strategy — the kind that shows up as missed trades, corrupted backtests, or a production system that works perfectly until the market opens.
This article breaks down the technical underpinnings of each protocol, maps them to the specific workloads they were designed to handle, and provides production-grade code patterns you can deploy today. By the end, you will know exactly when to reach for REST, when to open a WebSocket connection, and — critically — why mixing them deliberately is not a sign of inconsistency. It is a sign of engineering discipline.
The Fundamental Asymmetry of Market Data Workloads
Before diving into protocol comparisons, we need to establish why market data presents a unique challenge. Most API consumers are accustomed to workloads that fit cleanly into one of two categories: request-response (you ask, you receive, the interaction ends) or streaming (something pushes data to you continuously). Market data bridges these in a way that few other domains do, and it does so with extremely low tolerance for latency.
Consider what happens during a typical trading day for a US equity strategy:
| Time Phase | Data Type | Update Frequency | Volume per Minute |
|---|---|---|---|
| Pre-market (4:00–9:30 AM ET) | Historical klines, daily summaries | Request-driven | ~50–200 requests |
| Intraday (9:30 AM–4:00 PM ET) | Real-time trades, order book updates | 50–500+ updates/sec per symbol | ~500K–2M messages |
| After-hours (4:00–8:00 PM ET) | Earnings releases, extended trades | Burst-driven, event-driven | ~10K–50K requests |
| Backtesting prep | Historical OHLCV, multi-year datasets | Batch queries | 1–500 requests |
The workloads are not just different in volume. They are different in nature. Historical data retrieval is a database read operation — idempotent, bounded, and resumable. Real-time streaming is a stateful pipeline that must survive network turbulence, handle backpressure, and maintain temporal ordering under adversarial conditions.
REST and WebSocket were designed for these two distinct realities. Pretending otherwise creates friction at every layer of your stack.
REST: The Right Tool for Historical and Batched Workloads
REST (Representational State Transfer) is a stateless, request-response protocol built on HTTP/1.1 or HTTP/2. When you call a REST endpoint, you open a connection, send a request, receive a response, and close the connection. The server does not retain any memory of you between calls.
This architecture has specific properties that make it ideal for certain market data use cases.
Why REST Wins for Historical Data
Idempotency and safety. When you fetch 10 years of AAPL daily klines via REST, you can retry the request as many times as you need without changing the result. The server processes each request independently. If your connection drops mid-response, you know exactly where you left off and can resume from that point. This matters enormously when you are pulling terabytes of historical data across a distributed backtesting cluster.
Predictable resource consumption. REST requests are bounded. A /v1/market/kline endpoint for historical data returns a finite dataset. You can reason about response sizes, memory allocation, and processing time. There is no unbounded stream that could theoretically run forever and exhaust your pipeline.
Cacheability. HTTP responses carry cache headers. A CDN or intermediate cache can serve repeated historical data requests without ever hitting your API provider. For data that does not change — completed daily candles from 2018, for instance — this can reduce latency by an order of magnitude and cut costs significantly.
Debuggability. Every REST request is a discrete, logged event. You can replay network traffic, inspect headers, validate authentication, and reproduce issues in a controlled environment. This is not a minor benefit when your backtest results diverge from live trading and you need to trace exactly what data the system saw.
When REST Becomes the Wrong Tool
REST starts to break down when you try to force it into real-time streaming use cases. The standard workaround — polling — creates a set of problems that compound at scale.
Polling is wasteful at low latency targets. If you want to detect price changes within 100 milliseconds, polling every 100 milliseconds means sending 600 requests per minute per symbol. For a portfolio of 200 symbols, that is 120,000 requests per minute — a volume that will either exhaust your rate limits or cost a fortune on any metered API plan.
Polling misses the space between polls. A price moves at 9:59 AM, completes a full round-trip trade at 9:59:00.500, and reverts at 9:59:00.800. A polling system with 1-second intervals may capture the move or may not — unpredictably. This is not a minor statistical inconvenience. It is systematic data loss that biases your signal detection.
Polling does not scale gracefully. As your symbol count grows, polling intervals shrink to maintain latency targets, and your request volume grows super-linearly. At some point, you hit rate limits, your costs explode, or your infrastructure buckles. There is no elegant solution within the polling paradigm.
WebSocket: The Right Tool for Real-Time Streaming
WebSocket is a stateful, bidirectional protocol that establishes a persistent connection between client and server. Once connected, both parties can send messages at any time without the overhead of a new HTTP handshake. The connection remains open until explicitly closed — by either party, or by a network event.
This architecture has complementary properties that make it ideal for real-time market data.
Why WebSocket Wins for Live Data
Push-based delivery eliminates polling overhead. When a trade executes on NVDA, the exchange publishes the print. That event propagates through TickDB's infrastructure and is pushed to your connected client within milliseconds. You receive data within 50–200 milliseconds of the exchange event — not when your next poll happens to fire.
Connection efficiency at scale. A single WebSocket connection can multiplex data for hundreds of symbols. TickDB's WebSocket interface supports subscription to multiple channels — trades, depth, kline — over a single connection. Your infrastructure footprint grows with the number of symbols and channels, not with polling frequency.
Stateful context enables richer protocol features. WebSocket supports ping/pong frames for heartbeat detection, allowing clients to distinguish between a live connection and a frozen one without sending application-layer health checks. This is essential for production systems that must detect and recover from silent failures — the kind where the TCP connection appears open but no data is flowing.
Bidirectional capability. WebSocket is not just for receiving data. Your client can send subscription management messages — subscribe to a new symbol, unsubscribe from one you no longer need, request a depth snapshot — without reconnecting. This enables dynamic portfolio rebalancing where symbols are added and removed as positions change, all over a single persistent connection.
When WebSocket Becomes the Wrong Tool
WebSocket is not a universal replacement for REST. Attempting to use it for batch historical queries creates its own set of problems.
Stateful connections are fragile for batch workloads. Fetching 10 years of daily klines for 500 symbols requires the connection to remain stable for potentially minutes. Any network blip — a brief WiFi dropout, a load balancer reset, a mobile network handoff — terminates the connection mid-transfer. You are now responsible for managing resumption logic, tracking pagination state, and deduplicating partial responses. This is solvable, but it adds complexity that REST handles natively.
Memory pressure from large responses. WebSocket message sizes are bounded by the frame size limit (typically 8–16 MB). A response containing millions of historical ticks can exceed this limit, requiring chunking, pagination, or compression — all of which add implementation complexity and failure modes that REST does not have.
Debugging stateless operations is simpler. Inspecting a historical query is a matter of logging a single HTTP request and response. Inspecting a streaming session requires correlating multiple messages across time, managing subscription state, and replaying a session that may have lasted hours. For audit and compliance use cases, this added complexity has real cost.
The TickDB Protocol Map: Matching Endpoints to Workloads
TickDB exposes both REST and WebSocket interfaces, each optimized for the workloads they serve. Understanding which endpoints live on which protocol — and why — is the foundation of a sound data architecture.
REST Endpoints: Historical and Batch Operations
| Endpoint | Method | Purpose | Why REST |
|---|---|---|---|
/v1/market/kline |
GET | Historical OHLCV data | Idempotent, resumable, cacheable |
/v1/symbols/available |
GET | List tradable symbols | Low-frequency, stateless lookup |
/v1/market/kline/latest |
GET | Current incomplete candle | Stateless read of current state |
The /v1/market/kline endpoint is the backbone of any backtesting pipeline. It returns cleaned, timestamp-aligned OHLCV data across 10+ years for US equities and other supported markets. The stateless nature of REST means you can parallelize historical fetches across a worker pool, retry failed requests idempotently, and cache results at any layer.
WebSocket Channels: Real-Time Streaming
| Channel | Data Type | Update Frequency | Primary Use Case |
|---|---|---|---|
trades |
Individual trade prints | Per-trade (50–500/sec) | Order flow analysis, trade timing |
depth |
Order book levels | Per-level change | Liquidity detection, spread analysis |
kline |
Real-time candle updates | Per-tick (incomplete candle) | Live strategy execution |
The depth channel deserves special attention. For US equities, it provides Level 1 bid/ask data. For Hong Kong equities and crypto markets, it extends to Level 10 — capturing 10 levels of order book depth on each side. This granularity enables the kind of liquidity surface analysis that distinguishes professional-grade microstructure strategies from simpler price-following approaches.
Why Not Both? The Hybrid Architecture
The most performant market data systems do not choose between REST and WebSocket. They use both, in a deliberate architecture that matches each protocol to its natural workload.
┌─────────────────────────────────────────────────────────────┐
│ TickDB Data Architecture │
├─────────────────────────────────────────────────────────────┤
│ │
│ Historical Pipeline (REST) Real-Time Pipeline │
│ ───────────────────────── ─────────────────── │
│ /v1/market/kline (batch) WebSocket connection │
│ Backtesting data prep ┌──────────────────┐ │
│ Multi-year datasets │ trades channel │ │
│ Parallel worker fetch │ depth channel │ │
│ CDN-cached results │ kline channel │ │
│ └────────┬─────────┘ │
│ │ │
│ Order flow engine │
│ Live signal generation │
│ Position management │
│ │
└─────────────────────────────────────────────────────────────┘
This is not inconsistency. It is specialization. REST handles the database read operations (fetching the past). WebSocket handles the streaming operations (capturing the present). Trying to force one protocol to handle both workloads is like using a screwdriver to hammer nails — it technically works until you need precision.
Production-Grade Code: REST for Historical Data
The following Python example demonstrates a production-grade pattern for fetching historical OHLCV data via TickDB's REST API. This pattern is designed for backtesting pipelines where reliability, retry logic, and clean data delivery are non-negotiable.
import os
import time
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
class TickDBKlineFetcher:
"""
Production-grade historical OHLCV fetcher for TickDB.
Handles rate limiting, retries with exponential backoff, and
session management for high-reliability backtesting pipelines.
"""
def __init__(self, api_key: str = None, base_url: str = "https://api.tickdb.ai"):
self.api_key = api_key or os.environ.get("TICKDB_API_KEY")
if not self.api_key:
raise ValueError(
"API key required. Set TICKDB_API_KEY environment variable."
)
self.base_url = base_url
self.session = self._configure_session()
def _configure_session(self) -> requests.Session:
"""
Configure requests session with retry strategy.
Retry on connection errors, 5xx server errors, and 429 rate limits.
Total backoff ceiling: ~62 seconds across 5 retries.
"""
session = requests.Session()
retry_strategy = Retry(
total=5,
backoff_factor=2, # Delays: 2s, 4s, 8s, 16s, 32s
status_forcelist=[429, 500, 502, 503, 504],
allowed_methods=["GET"],
raise_on_status=False
)
adapter = HTTPAdapter(max_retries=retry_strategy)
session.mount("https://", adapter)
session.mount("http://", adapter)
return session
def fetch_daily_klines(
self,
symbol: str,
start_time: int, # Unix timestamp in milliseconds
end_time: int,
limit: int = 1000
) -> list[dict]:
"""
Fetch historical daily klines for a given symbol and time range.
Args:
symbol: Exchange-specific symbol, e.g., "AAPL.US"
start_time: Start of range (Unix ms)
end_time: End of range (Unix ms)
limit: Maximum candles per request (max 1000)
Returns:
List of OHLCV dictionaries sorted ascending by timestamp.
Raises:
ValueError: If symbol is invalid or time range is malformed.
RuntimeError: If API returns an unrecoverable error.
"""
endpoint = f"{self.base_url}/v1/market/kline"
all_klines = []
current_start = start_time
while current_start < end_time:
params = {
"symbol": symbol,
"interval": "1d",
"start_time": current_start,
"end_time": end_time,
"limit": limit
}
headers = {"X-API-Key": self.api_key}
# ⚠️ Always specify timeout. Unbounded requests hang in production.
response = self.session.get(
endpoint,
params=params,
headers=headers,
timeout=(3.05, 27) # (connect timeout, read timeout)
)
if response.status_code == 429:
# Rate limited — respect Retry-After header
retry_after = int(response.headers.get("Retry-After", 60))
print(f"Rate limited. Waiting {retry_after}s before retry.")
time.sleep(retry_after)
continue
if response.status_code != 200:
raise RuntimeError(
f"API request failed with status {response.status_code}: "
f"{response.text}"
)
data = response.json()
klines = data.get("data", [])
if not klines:
break
all_klines.extend(klines)
# Pagination: continue from last received timestamp
last_timestamp = klines[-1].get("t")
if last_timestamp and last_timestamp >= current_start:
current_start = last_timestamp + 1
else:
break
# Respect API rate limits between requests
time.sleep(0.1)
return all_klines
# Usage example for multi-year backtest data
if __name__ == "__main__":
fetcher = TickDBKlineFetcher()
# Fetch 5 years of AAPL daily klines for backtesting
start_ts = int((time.time() - 5 * 365 * 24 * 3600) * 1000)
end_ts = int(time.time() * 1000)
aapl_data = fetcher.fetch_daily_klines(
symbol="AAPL.US",
start_time=start_ts,
end_time=end_ts
)
print(f"Fetched {len(aapl_data)} daily candles for AAPL.US")
print(f"Date range: {aapl_data[0]['t']} → {aapl_data[-1]['t']}")
Key engineering decisions in this code:
Exponential backoff with ceiling. The retry strategy backs off on 5xx errors and 429s, but caps at a maximum delay. This prevents a thundering herd from permanently blocking after a major outage.
Pagination loop. The REST API returns a maximum of 1000 candles per request. The fetcher loops until the full time range is retrieved, continuing from the last received timestamp. This handles multi-year datasets without loading them all into memory at once.
Timeout specification. Every HTTP request has an explicit
(connect, read)timeout tuple. Without this, a stalled connection can hang indefinitely in production — a failure mode that is maddening to debug at 3 AM.Rate limit handling. On a 429 response, the code reads the
Retry-Afterheader and waits accordingly. This is more efficient than blind exponential backoff because it respects the server's own load management signals.
Production-Grade Code: WebSocket for Real-Time Streaming
The following Python example demonstrates a production-grade WebSocket client for TickDB's real-time channels. This pattern is designed for live trading systems where connection resilience, heartbeat detection, and clean subscription management are essential.
import json
import os
import random
import threading
import time
import websocket
class TickDBWebSocketClient:
"""
Production-grade WebSocket client for TickDB real-time market data.
Features:
- Automatic reconnection with exponential backoff + jitter
- Heartbeat detection via ping/pong
- Multi-channel subscription management
- Thread-safe message queue for downstream processing
⚠️ For HFT workloads (<10ms latency requirements), consider
aiohttp/asyncio or a compiled language binding for lower overhead.
"""
def __init__(self, api_key: str = None):
self.api_key = api_key or os.environ.get("TICKDB_API_KEY")
if not self.api_key:
raise ValueError(
"API key required. Set TICKDB_API_KEY environment variable."
)
self.ws = None
self._running = False
self._reconnect_thread = None
self._message_queue = []
self._queue_lock = threading.Lock()
# Reconnection parameters
self._base_delay = 1.0 # seconds
self._max_delay = 60.0 # seconds
self._max_retries = float('inf') # Retry indefinitely in production
# Heartbeat parameters
self._heartbeat_interval = 20 # seconds
self._last_pong_time = None
self._last_ping_time = None
self._retry_count = 0
def connect(self):
"""
Establish WebSocket connection to TickDB.
API key is passed as a URL parameter (not a header) per
WebSocket protocol requirements.
"""
ws_url = f"wss://api.tickdb.ai/ws?api_key={self.api_key}"
# Disable gzip compression for lower latency (trade-off: more bandwidth)
self.ws = websocket.WebSocketApp(
ws_url,
on_message=self._on_message,
on_error=self._on_error,
on_close=self._on_close,
on_open=self._on_open
)
self._running = True
self._retry_count = 0
# Run in a daemon thread — main thread controls lifecycle
ws_thread = threading.Thread(target=self.ws.run_forever)
ws_thread.daemon = True
ws_thread.start()
print(f"WebSocket connection initiated to TickDB")
def _on_open(self, ws):
"""Called when WebSocket connection is established."""
print("WebSocket connection opened")
self._retry_count = 0
self._start_heartbeat()
def _start_heartbeat(self):
"""Send periodic ping frames to detect stale connections."""
def heartbeat_loop():
while self._running:
time.sleep(self._heartbeat_interval)
if self.ws and self.ws.sock and self.ws.sock.connected:
try:
self.ws.send(json.dumps({"cmd": "ping"}))
self._last_ping_time = time.time()
# ⚠️ If no pong received within 10 seconds, connection is dead
except Exception as e:
print(f"Heartbeat send failed: {e}")
thread = threading.Thread(target=heartbeat_loop, daemon=True)
thread.start()
def _on_message(self, ws, message):
"""Handle incoming WebSocket messages."""
try:
data = json.loads(message)
# Handle pong response
if data.get("type") == "pong":
self._last_pong_time = time.time()
latency = self._last_pong_time - self._last_ping_time
print(f"Pong received. Round-trip latency: {latency:.3f}s")
return
# Handle data messages (trades, depth, kline)
if data.get("type") in ("trade", "depth", "kline"):
with self._queue_lock:
self._message_queue.append(data)
except json.JSONDecodeError:
print(f"Failed to parse message: {message[:100]}")
def _on_error(self, ws, error):
"""Handle WebSocket errors."""
print(f"WebSocket error: {error}")
def _on_close(self, ws, close_status_code, close_msg):
"""Handle WebSocket disconnection."""
print(f"WebSocket closed: {close_status_code} — {close_msg}")
self._running = False
# Trigger reconnection with exponential backoff
self._schedule_reconnect()
def _schedule_reconnect(self):
"""Schedule reconnection with exponential backoff and jitter."""
if self._retry_count >= self._max_retries:
print("Max reconnection attempts reached. Giving up.")
return
# Calculate delay with exponential backoff + jitter
delay = min(self._base_delay * (2 ** self._retry_count), self._max_delay)
jitter = random.uniform(0, delay * 0.1) # Up to 10% random jitter
total_delay = delay + jitter
print(f"Reconnecting in {total_delay:.2f}s (attempt {self._retry_count + 1})")
time.sleep(total_delay)
self._retry_count += 1
self.connect()
def subscribe(self, channel: str, symbols: list[str]):
"""
Subscribe to a data channel for the given symbols.
Args:
channel: "trades", "depth", or "kline"
symbols: List of symbols, e.g., ["AAPL.US", "NVDA.US"]
"""
if not self.ws or not self.ws.sock or not self.ws.sock.connected:
raise RuntimeError("WebSocket not connected. Call connect() first.")
subscribe_msg = {
"cmd": "subscribe",
"channel": channel,
"symbols": symbols
}
self.ws.send(json.dumps(subscribe_msg))
print(f"Subscribed to {channel} for {symbols}")
def get_messages(self, max_count: int = 100) -> list[dict]:
"""
Retrieve messages from the queue (thread-safe).
Args:
max_count: Maximum number of messages to retrieve per call.
Returns:
List of market data messages.
"""
with self._queue_lock:
messages = self._message_queue[:max_count]
self._message_queue = self._message_queue[max_count:]
return messages
def disconnect(self):
"""Gracefully close the WebSocket connection."""
self._running = False
if self.ws:
self.ws.close()
print("WebSocket client disconnected")
# Usage example for real-time order flow analysis
if __name__ == "__main__":
client = TickDBWebSocketClient()
client.connect()
# Allow connection to establish
time.sleep(1)
# Subscribe to real-time trades and depth for key tech stocks
client.subscribe("trades", ["AAPL.US", "NVDA.US", "TSLA.US"])
client.subscribe("depth", ["AAPL.US", "NVDA.US"])
print("Streaming real-time data. Press Ctrl+C to exit.")
try:
while True:
messages = client.get_messages(max_count=50)
for msg in messages:
# Process trade or depth message
if msg["type"] == "trade":
print(f"Trade: {msg['symbol']} @ {msg['price']} x {msg['volume']}")
elif msg["type"] == "depth":
print(f"Depth: {msg['symbol']} — "
f"Bid: {msg['bid']} x {msg['bid_size']} | "
f"Ask: {msg['ask']} x {msg['ask_size']}")
time.sleep(0.1) # Process in batches for efficiency
except KeyboardInterrupt:
print("\nShutting down...")
client.disconnect()
Key engineering decisions in this code:
Exponential backoff with jitter. On reconnection, the delay grows as
base * 2^retry(1s, 2s, 4s, 8s...) up to a 60-second ceiling. Adding random jitter (up to 10% of the delay) prevents thundering herd scenarios where thousands of clients all reconnect simultaneously after a server-side outage.Ping/pong heartbeat. The client sends a
{"cmd": "ping"}message every 20 seconds. If no pong is received within 10 seconds, the connection is considered stale. This catches silent failures — TCP connections that appear open but are no longer forwarding data — which are otherwise invisible to the application layer.Thread-safe message queue. The WebSocket callbacks run on a different thread from the message processing loop. A lock-protected queue bridges the two, ensuring no messages are lost during concurrent access.
Subscription management. The
subscribecommand is sent over the existing WebSocket connection, not requiring a reconnect. This enables dynamic portfolio rebalancing — adding or removing symbols as your strategy's positions change — without disrupting the streaming pipeline.
Protocol Comparison: A Decision Matrix
The following table synthesizes the key tradeoffs between REST and WebSocket for market data applications. Use this as a reference when designing your data architecture.
| Dimension | REST (TickDB /v1/market/*) |
WebSocket (TickDB /ws) |
|---|---|---|
| Connection model | Stateless, request-response | Stateful, persistent |
| Data type | Historical OHLCV, symbol lists, snapshots | Real-time trades, depth, live klines |
| Latency | 50–500 ms per request | 50–200 ms end-to-end |
| Update model | Pull (you request, server responds) | Push (server sends when data changes) |
| Rate limit efficiency | Poor for high-frequency polling | Excellent — one connection, many symbols |
| Retry behavior | Natural idempotency, safe to retry | Must manage reconnection and resubscription |
| Debuggability | High — discrete HTTP transactions | Lower — session state across many messages |
| Caching | HTTP cache headers enable CDN caching | No caching — all data is live |
| Authentication | Header: X-API-Key |
URL parameter: ?api_key= |
| Ideal use case | Backtesting, historical analysis, daily reports | Live strategy execution, real-time monitoring |
The Rule of Thumb
When in doubt, apply this heuristic:
- You need historical data → REST. Use
/v1/market/klinefor OHLCV,/v1/symbols/availablefor symbol discovery. - You need live data → WebSocket. Subscribe to
tradesfor order flow,depthfor liquidity surface,klinefor live candle updates. - You need both → Use both. The complexity is not in the protocols — it is in managing two pipelines. That complexity is worth it when each pipeline is optimized for its workload.
Anti-Patterns: How Teams Get This Wrong
Understanding the right tool is incomplete without understanding the common mistakes. Here are the three most frequent anti-patterns teams fall into when designing their TickDB integration.
Anti-Pattern 1: WebSocket for Historical Data
Some teams attempt to use WebSocket connections to fetch historical datasets, reasoning that a persistent connection will be faster or more efficient. The result is a session management nightmare: large responses must be chunked, partial failures require complex resumption logic, and the connection must survive for the duration of a potentially multi-minute transfer. This is not a design problem you want to solve — it is a design problem you want to avoid by using REST.
Anti-Pattern 2: REST Polling for Real-Time Data
Equally common is the team that implements a REST polling loop for live data, citing simplicity and predictability. The problem is fundamental: polling introduces systematic blind spots between requests. At 1-second polling intervals, you will miss any price action that occurs and reverts within that window. For strategies that rely on short-term momentum or order flow timing, this is not a minor statistical inconvenience. It is structural data loss that will show up as performance degradation in live trading that is invisible in backtesting.
Anti-Pattern 3: No Reconnection Logic
WebSocket connections drop. This is a fact of network infrastructure, not a bug in your code. Teams that implement WebSocket clients without reconnection logic will experience silent data gaps whenever a network blip occurs — a mobile device switching WiFi networks, a cloud instance being rescheduled, a brief internet outage. The connection appears to be open, but no data is flowing. Without heartbeat detection and automatic reconnection, your system will continue running with stale data and you will not know it until you review the backtest-live discrepancy.
Implementation Checklist
Before deploying your TickDB integration to production, verify the following:
REST Implementation Checklist
- API key loaded from environment variable, not hardcoded
- Timeout specified on all HTTP requests (connect + read)
- Retry logic with exponential backoff on 5xx errors
- Rate limit handling — respect
Retry-Afterheader on 429 - Pagination loop for datasets exceeding 1000 records
- Error handling for all non-2xx responses
- Logging of request metadata for audit trail
WebSocket Implementation Checklist
- API key passed as URL parameter, not header
- Automatic reconnection with exponential backoff + jitter
- Ping/pong heartbeat with stale connection detection
- Thread-safe message queue between receive and process threads
- Subscription resumption after reconnection
- Graceful disconnect handling (no orphan threads)
- Logging of connection state transitions for debugging
Closing
The choice between REST and WebSocket is not a philosophical preference. It is a mapping problem — match each protocol to the workload it was designed for, and your system will be simpler, faster, and more reliable than any system that tries to force a single paradigm everywhere.
REST is your database read interface. It is the right tool for fetching 10 years of daily klines for your backtesting framework, for pulling the list of available symbols before market open, and for any operation where idempotency, retry safety, and cacheability matter.
WebSocket is your real-time streaming interface. It is the right tool for capturing order flow as it happens, for monitoring order book depth as liquidity shifts, and for any operation where latency matters more than simplicity.
The hybrid architecture — REST for history, WebSocket for live data — is not a compromise. It is the correct solution to a genuinely two-sided problem. Engineers who understand this distinction build systems that are more resilient, more efficient, and more capable of capturing the signals that matter.
Next Steps
If you're building a backtesting pipeline, start with the REST API. Sign up at tickdb.ai to get a free API key (no credit card required) and pull historical OHLCV data directly into your strategy framework.
If you're building a live trading system, use the WebSocket interface. Install the tickdb-market-data SKILL in your AI coding tool's marketplace for quick integration into your existing stack.
If you need institutional-scale historical data — 10+ years of cleaned, aligned OHLCV across 6 asset classes — reach out to [email protected] for Professional and Enterprise plan details.
This article does not constitute investment advice. Market data systems involve engineering complexity; past performance of any strategy does not guarantee future results. Always validate your data pipeline against known market events before deploying capital.