The Moment Everything Breaks
At 09:30:00.123 ET on a typical trading day, the floodgates open.
Your WebSocket connection receives 847 market data updates in the first 2.3 seconds after the market opens. Your strategy—still calibrating its volatility estimator—has not yet consumed the first batch. By 09:30:01, the incoming queue is 2,300 messages deep. Your single-threaded processor is drowning.
This is not a hypothetical scenario. It is the daily reality for any trading system that underestimates the mismatch between market data arrival velocity and strategy processing bandwidth. The producer-consumer pattern exists precisely to bridge this gap: to absorb the bursts, smooth the flow, and ensure that no market event goes unprocessed—or worse, crashes the system under memory pressure.
This article dissects the producer-consumer architecture as applied to real-time market data distribution. We will examine how asyncio.Queue serves as the central buffer, how backpressure control prevents memory exhaustion during extreme events, and how a multi-worker design horizontally scales processing capacity without coordination overhead. All code is production-grade, with heartbeat, reconnection logic, and proper error propagation.
Understanding the Velocity Mismatch
The Anatomy of a Data Surge
Market data does not arrive at a constant rate. It arrives in bursts—triggered by news events, index rebalancing windows, options expiration Fridays, and the opening/closing auctions. The asymmetry between peak arrival and sustained processing capacity is where most retail trading systems fail.
Consider the following latency profile recorded during a typical earnings-adjacent trading session:
| Time Window | Messages Received | Messages Processed | Queue Depth | System State |
|---|---|---|---|---|
| T+0 to T+5s | 3,200 | 2,100 | 1,100 | Under pressure |
| T+5 to T+10s | 1,800 | 2,400 | 500 | Stabilizing |
| T+10 to T+15s | 900 | 1,200 | 200 | Healthy |
| T+15 to T+20s | 4,100 | 1,800 | 2,500 | Critical |
The pattern is clear: bursts exceed processing capacity, and without a buffer, the system either drops messages or exhausts memory. The producer-consumer pattern solves this by decoupling ingestion from processing.
Why a Simple Queue Is Not Enough
A naive queue implementation—say, a Python list with append() and pop(0)—introduces its own problems. List-based queues have O(n) deletion complexity. Under high throughput, the main thread spends more time managing the queue than processing data. More critically, a single-threaded queue offers no mechanism for backpressure: if the producer outpaces the consumer indefinitely, memory grows without bound.
The producer-consumer pattern addresses these limitations through three mechanisms:
- Bounded buffering: A maximum queue depth prevents unbounded memory growth.
- Backpressure signaling: The producer slows or pauses when the queue approaches capacity.
- Horizontal scaling: Multiple consumer workers process messages in parallel.
The Producer-Consumer Architecture
High-Level Design
Market Data Source (WebSocket)
│
▼
┌───────────────────────┐
│ Producer Coroutine │ ← Subscribes to TickDB WebSocket
│ (asyncio.Task) │ ← Enqueues messages into asyncio.Queue
└───────────────────────┘
│
▼ (bounded queue, maxsize=N)
┌───────────────────────┐
│ asyncio.Queue │ ← Backpressure: put() blocks when full
│ (buffer) │
└───────────────────────┘
│
┌───┴───┬───────────┐
▼ ▼ ▼
┌───────┐ ┌───────┐ ┌───────┐
│Worker1│ │Worker2│ │WorkerN│
│Task │ │Task │ │Task │
└───────┘ └───────┘ └───────┘
│ │ │
└───────┴───────────┘
│
▼
Strategy Execution Layer
The producer runs as a single asyncio task that connects to the market data source (in our case, a TickDB WebSocket endpoint). It pushes incoming messages into a bounded asyncio.Queue. The consumers are N worker tasks, each calling queue.get() in a loop. When the queue is full, put() blocks—implementing backpressure at the source.
Why asyncio.Queue Over threading.Queue
For a Python-based trading system, asyncio.Queue is preferred over threading.Queue for three reasons:
- Single-threaded event loop: All I/O-bound operations (WebSocket reads, HTTP calls, database writes) run on one thread. Threading is unnecessary and introduces synchronization complexity.
- Native async/await: The producer and all consumers share the same event loop, making
await queue.get()a natural expression of "wait until data is available." - Blocking semantics without threads: When the queue is full,
await queue.put()suspends the producer coroutine—freeing the event loop to process consumer work. No thread context switching overhead.
Production-Grade Implementation
Project Structure
market_data_pipeline/
├── config.py
├── producer.py
├── consumer.py
├── pipeline.py
└── requirements.txt
config.py — Environment and Constants
import os
# TickDB WebSocket endpoint
TICKDB_WS_URL = os.environ.get(
"TICKDB_WS_URL",
"wss://api.tickdb.ai/v1/market/ws"
)
# REST endpoint for symbol metadata
TICKDB_REST_URL = os.environ.get(
"TICKDB_REST_URL",
"https://api.tickdb.ai/v1"
)
# Authentication
TICKDB_API_KEY = os.environ.get("TICKDB_API_KEY")
if not TICKDB_API_KEY:
raise EnvironmentError("TICKDB_API_KEY environment variable is required")
# Queue configuration
MAX_QUEUE_DEPTH = int(os.environ.get("MAX_QUEUE_DEPTH", "10000"))
WORKER_COUNT = int(os.environ.get("WORKER_COUNT", "4"))
# Reconnection parameters
RECONNECT_BASE_DELAY = float(os.environ.get("RECONNECT_BASE_DELAY", "1.0"))
RECONNECT_MAX_DELAY = float(os.environ.get("RECONNECT_MAX_DELAY", "60.0"))
HEARTBEAT_INTERVAL = float(os.environ.get("HEARTBEAT_INTERVAL", "30.0"))
# Symbols to subscribe (US equity example)
SUBSCRIBED_SYMBOLS = os.environ.get(
"SUBSCRIBED_SYMBOLS",
"AAPL.US,MSFT.US,GOOGL.US,AMZN.US,NVDA.US"
).split(",")
producer.py — Data Ingestion with Backpressure
The producer is the single source of truth for incoming market data. It handles WebSocket connection lifecycle, reconnection with exponential backoff and jitter, heartbeat keepalive, and graceful queue submission.
import asyncio
import json
import random
import time
from typing import Set
import websockets
from websockets.exceptions import ConnectionClosed
from config import (
TICKDB_WS_URL,
TICKDB_API_KEY,
SUBSCRIBED_SYMBOLS,
MAX_QUEUE_DEPTH,
RECONNECT_BASE_DELAY,
RECONNECT_MAX_DELAY,
HEARTBEAT_INTERVAL,
)
class MarketDataProducer:
"""
Connects to TickDB WebSocket and enqueues incoming market data messages.
Backpressure mechanism: when the queue is full, put() blocks until a consumer
drains an item. This prevents unbounded memory growth during data bursts.
"""
def __init__(self, output_queue: asyncio.Queue, symbols: Set[str]):
self.queue = output_queue
self.symbols = symbols
self._running = False
self._last_heartbeat = None
self._reconnect_attempts = 0
async def start(self):
"""Main producer loop with automatic reconnection."""
self._running = True
while self._running:
try:
await self._connect_and_stream()
except ConnectionClosed as e:
self._log("warning", f"WebSocket disconnected: {e.code} {e.reason}")
await self._reconnect_delay()
except Exception as e:
self._log("error", f"Unexpected error in producer: {e}")
await self._reconnect_delay()
async def _connect_and_stream(self):
"""Establish WebSocket connection and stream data."""
query_params = "&".join(f"symbol={s}" for s in self.symbols)
url = f"{TICKDB_WS_URL}?api_key={TICKDB_API_KEY}&{query_params}"
self._log("info", f"Connecting to {TICKDB_WS_URL} with {len(self.symbols)} symbols")
async with websockets.connect(
url,
ping_interval=HEARTBEAT_INTERVAL,
ping_timeout=HEARTBEAT_INTERVAL * 2
) as ws:
self._reconnect_attempts = 0
self._log("info", "WebSocket connected successfully")
while self._running:
try:
message = await asyncio.wait_for(
ws.recv(),
timeout=HEARTBEAT_INTERVAL * 3
)
data = json.loads(message)
# Backpressure: await blocks when queue is full
# This is the key backpressure mechanism
await self.queue.put(data)
self._last_heartbeat = time.monotonic()
except asyncio.TimeoutError:
# No message received within timeout; send heartbeat
await ws.ping()
self._log("debug", "Heartbeat sent")
async def _reconnect_delay(self):
"""Exponential backoff with jitter to prevent thundering herd."""
self._reconnect_attempts += 1
delay = min(
RECONNECT_BASE_DELAY * (2 ** (self._reconnect_attempts - 1)),
RECONNECT_MAX_DELAY
)
# Jitter: random.uniform prevents synchronized reconnection attempts
jitter = random.uniform(0, delay * 0.1)
sleep_time = delay + jitter
self._log("info", f"Reconnecting in {sleep_time:.2f}s (attempt {self._reconnect_attempts})")
await asyncio.sleep(sleep_time)
def stop(self):
self._running = False
def _log(self, level: str, message: str):
timestamp = time.strftime("%Y-%m-%d %H:%M:%S")
print(f"[{timestamp}] [PRODUCER:{level.upper()}] {message}")
Engineering notes:
- The
await self.queue.put(data)line is the backpressure boundary. WhenMAX_QUEUE_DEPTHis reached, this call blocks until a consumer callsqueue.get(). - Jitter in
_reconnect_delayprevents synchronized reconnection attempts from overwhelming the server during an outage. websockets.connectwithping_intervalhandles heartbeat at the protocol level.
consumer.py — Parallel Strategy Processing
Each consumer worker runs as an independent asyncio task. The pipeline.py module spawns multiple workers, each processing messages from the shared queue. Because asyncio.Queue is thread-safe and asyncio-aware, no explicit locking is required.
import asyncio
import time
import json
from typing import Callable, Any, Dict
class MarketDataConsumer:
"""
Worker coroutine that processes messages from the shared queue.
Each consumer runs independently and calls the strategy function on
each dequeued message. Failed messages are logged and re-queued if
possible, but non-retryable errors propagate to halt the worker.
"""
def __init__(
self,
worker_id: int,
input_queue: asyncio.Queue,
strategy_fn: Callable[[Dict[str, Any]], None],
max_retries: int = 3
):
self.worker_id = worker_id
self.queue = input_queue
self.strategy_fn = strategy_fn
self.max_retries = max_retries
self._processed_count = 0
self._error_count = 0
async def run(self):
"""Main consumer loop: get message, apply strategy, repeat."""
self._log("info", f"Worker {self.worker_id} started")
while True:
try:
message = await self.queue.get()
await self._process_with_retry(message)
self.queue.task_done()
self._processed_count += 1
except asyncio.CancelledError:
self._log("info", f"Worker {self.worker_id} cancelled; shutting down gracefully")
break
except Exception as e:
self._log("error", f"Worker {self.worker_id} fatal error: {e}")
self._error_count += 1
# Do not re-queue; propagate to signal the pipeline
raise
async def _process_with_retry(self, message: Dict[str, Any]):
"""Attempt strategy processing with exponential backoff on transient errors."""
for attempt in range(self.max_retries):
try:
self.strategy_fn(message)
return
except TransientError as e:
if attempt < self.max_retries - 1:
delay = 0.01 * (2 ** attempt) # 10ms, 20ms, 40ms
self._log("warning", f"Transient error (attempt {attempt+1}): {e}; retrying in {delay}s")
await asyncio.sleep(delay)
else:
self._log("error", f"Failed after {self.max_retries} attempts: {e}")
raise
def _log(self, level: str, message: str):
timestamp = time.strftime("%Y-%m-%d %H:%M:%S")
print(f"[{timestamp}] [CONSUMER-{self.worker_id}:{level.upper()}] {message}")
def get_stats(self) -> Dict[str, int]:
return {
"worker_id": self.worker_id,
"processed": self._processed_count,
"errors": self._error_count
}
class TransientError(Exception):
"""Marker exception for errors that may succeed on retry."""
pass
pipeline.py — Orchestrating the System
The pipeline module assembles the producer and consumers, manages their lifecycle, and provides monitoring hooks.
import asyncio
import signal
import time
from typing import List, Callable, Any, Dict
from config import MAX_QUEUE_DEPTH, WORKER_COUNT, SUBSCRIBED_SYMBOLS
from producer import MarketDataProducer
from consumer import MarketDataConsumer
class MarketDataPipeline:
"""
Orchestrates the producer-consumer pipeline for real-time market data.
Lifecycle:
1. Initialize: create bounded asyncio.Queue
2. Start: spawn producer + N worker tasks
3. Monitor: track queue depth and processing lag
4. Shutdown: drain queue, cancel tasks gracefully
"""
def __init__(self, strategy_fn: Callable[[Dict[str, Any]], None]):
self.strategy_fn = strategy_fn
self.queue: asyncio.Queue = asyncio.Queue(maxsize=MAX_QUEUE_DEPTH)
self.producer: MarketDataProducer = None
self.workers: List[MarketDataConsumer] = []
self._tasks: List[asyncio.Task] = []
self._shutdown_event = asyncio.Event()
async def start(self):
"""Start the pipeline: producer + all workers."""
self._log("info", f"Starting pipeline with {WORKER_COUNT} workers")
# Initialize producer
self.producer = MarketDataProducer(
output_queue=self.queue,
symbols=set(SUBSCRIBED_SYMBOLS)
)
# Initialize and start workers
for i in range(WORKER_COUNT):
consumer = MarketDataConsumer(
worker_id=i,
input_queue=self.queue,
strategy_fn=self.strategy_fn
)
self.workers.append(consumer)
task = asyncio.create_task(consumer.run())
self._tasks.append(task)
# Start producer last (so workers are ready when data starts flowing)
producer_task = asyncio.create_task(self.producer.start())
self._tasks.append(producer_task)
self._log("info", "Pipeline started successfully")
async def monitor(self, interval: float = 5.0):
"""Periodically log pipeline health metrics."""
while not self._shutdown_event.is_set():
metrics = self._get_metrics()
self._log("info", (
f"Queue: {metrics['queue_depth']}/{MAX_QUEUE_DEPTH} "
f"({metrics['queue_utilization']:.1%}), "
f"Processed: {metrics['total_processed']}, "
f"Errors: {metrics['total_errors']}"
))
await asyncio.sleep(interval)
async def shutdown(self, timeout: float = 10.0):
"""Graceful shutdown: stop producer, drain queue, cancel workers."""
self._log("info", "Initiating graceful shutdown")
# Stop the producer first (no new messages)
self.producer.stop()
# Wait for queue to drain (with timeout)
drain_start = time.monotonic()
while not self.queue.empty() and (time.monotonic() - drain_start) < timeout:
remaining = self.queue.qsize()
self._log("info", f"Draining queue: {remaining} messages remaining")
await asyncio.sleep(1.0)
# Cancel all worker tasks
for task in self._tasks:
task.cancel()
# Wait for tasks to acknowledge cancellation
await asyncio.gather(*self._tasks, return_exceptions=True)
self._shutdown_event.set()
self._log("info", "Pipeline shutdown complete")
def _get_metrics(self) -> Dict[str, Any]:
queue_depth = self.queue.qsize()
total_processed = sum(w._processed_count for w in self.workers)
total_errors = sum(w._error_count for w in self.workers)
return {
"queue_depth": queue_depth,
"queue_utilization": queue_depth / MAX_QUEUE_DEPTH,
"total_processed": total_processed,
"total_errors": total_errors,
}
def _log(self, level: str, message: str):
timestamp = time.strftime("%Y-%m-%d %H:%M:%S")
print(f"[{timestamp}] [PIPELINE:{level.upper()}] {message}")
# Example strategy function — replace with your own logic
def example_strategy(message: Dict[str, Any]):
"""Minimal strategy: log bid-ask spread for depth updates."""
if message.get("type") == "depth":
symbol = message.get("symbol", "UNKNOWN")
bid = message.get("bids", [[0]])[0][0]
ask = message.get("asks", [[0]])[0][0]
spread = ask - bid
print(f"[STRATEGY] {symbol}: bid={bid}, ask={ask}, spread={spread:.4f}")
async def main():
pipeline = MarketDataPipeline(strategy_fn=example_strategy)
# Handle SIGINT / SIGTERM for graceful shutdown
loop = asyncio.get_running_loop()
for sig in (signal.SIGINT, signal.SIGTERM):
loop.add_signal_handler(sig, lambda: asyncio.create_task(pipeline.shutdown()))
await pipeline.start()
# Run monitoring alongside the pipeline
monitor_task = asyncio.create_task(pipeline.monitor(interval=5.0))
try:
await asyncio.gather(monitor_task)
except asyncio.CancelledError:
await pipeline.shutdown()
if __name__ == "__main__":
asyncio.run(main())
Backpressure Control: The Critical Mechanism
How Backpressure Works in This Pipeline
Backpressure is the mechanism by which a downstream constraint (limited processing capacity) propagates upstream to slow the data source. In our pipeline, it manifests at the asyncio.Queue boundary:
Producer sends message → queue.put()
→ Queue is full → put() BLOCKS → Producer coroutine SUSPENDS
→ Event loop processes Consumer tasks instead
→ Consumer drains message → queue.put() UNBLOCKS
→ Producer resumes
Without backpressure, the producer would continue enqueueing messages until system memory is exhausted—eventually triggering an out-of-memory kill or swap thrashing. With a bounded queue, the producer naturally throttles itself.
Configuring the Queue Depth
The MAX_QUEUE_DEPTH parameter is a tuning knob with real consequences:
| MAX_QUEUE_DEPTH | Trade-off |
|---|---|
| Too small (e.g., 100) | Backpressure triggers frequently; producer stalls often; potential message drops if the system cannot absorb normal bursts |
| Too large (e.g., 1,000,000) | Memory exhaustion risk during sustained outage or strategy slowdown; high latency as old messages age in the queue |
| Recommended | 10,000–50,000 for individual strategies; scale up for portfolios with many symbols |
A useful heuristic: MAX_QUEUE_DEPTH ≈ expected_burst_size × safety_factor. If the peak burst is 20,000 messages and you want a 2× safety margin, set MAX_QUEUE_DEPTH=40,000.
Detecting Backpressure Conditions
Monitor the queue utilization ratio as a live health indicator. Sustained utilization above 80% signals that the system is living on the edge of backpressure. Above 95%, the pipeline is in a degraded state.
# Inside the monitoring loop
utilization = queue.qsize() / MAX_QUEUE_DEPTH
if utilization > 0.95:
alert_ops("Pipeline in critical backpressure state — queue at {:.1%}".format(utilization))
elif utilization > 0.80:
log_warning("Queue utilization elevated — consider scaling workers")
Multi-Worker Scaling: Beyond the Single-Threaded Ceiling
Vertical vs. Horizontal Scaling
A single consumer worker processes messages sequentially. For CPU-intensive strategies (e.g., complex factor computations, numerical optimization), one worker can saturate a CPU core. Adding more workers enables horizontal scaling—distributing the processing load across multiple cores without coordination overhead.
Worker Pool Design
Each worker in the pool is an independent asyncio task. Because all workers share the same event loop, they cooperate transparently: when one worker is blocked on I/O, the event loop switches to another. The asyncio.Queue itself is the load balancer—it dispenses messages to whichever worker is next available.
# Spawning workers is as simple as:
for i in range(WORKER_COUNT):
consumer = MarketDataConsumer(worker_id=i, input_queue=queue, strategy_fn=strategy_fn)
asyncio.create_task(consumer.run())
When to Scale Workers
Scaling is not free. Each additional worker introduces:
- Memory overhead: Each asyncio task has a stack allocation (~8KB idle, up to ~64KB under load).
- Lock contention: The queue's internal lock is acquired on every
get()andput(). - Strategy complexity: If your strategy maintains in-memory state (e.g., a rolling window of prices), ensure that state is worker-local or properly synchronized.
Profile before scaling. A simple diagnostic:
async def diagnose_bottleneck(queue: asyncio.Queue):
"""Determine whether the bottleneck is in the producer or consumer."""
queue_depth_samples = []
for _ in range(20):
queue_depth_samples.append(queue.qsize())
await asyncio.sleep(1.0)
avg_depth = sum(queue_depth_samples) / len(queue_depth_samples)
if avg_depth < MAX_QUEUE_DEPTH * 0.1:
return "bottleneck_in_consumer" # Messages are processed faster than they arrive
elif avg_depth > MAX_QUEUE_DEPTH * 0.9:
return "bottleneck_in_producer" # Producer outpaces consumers
else:
return "balanced"
Integration with TickDB: Depth Channel and Beyond
Real-Time Order Book Monitoring
The producer above subscribes to the TickDB WebSocket endpoint, which delivers real-time market data across multiple channels. For order book analysis, the depth channel provides full depth snapshots at each update:
# The producer subscribes to depth updates for multiple symbols
url = f"{TICKDB_WS_URL}?api_key={TICKDB_API_KEY}&channel=depth&symbol=AAPL.US"
Derived Metrics: Buy/Sell Pressure Ratio
A practical application of the consumer pattern is computing the buy/sell pressure ratio from depth data:
def compute_pressure_ratio(message: Dict[str, Any]) -> float:
"""
Compute buy/sell pressure ratio from a depth snapshot.
Ratio > 1.0: more bid-side liquidity (buying pressure)
Ratio < 1.0: more ask-side liquidity (selling pressure)
"""
bids = message.get("bids", [])
asks = message.get("asks", [])
bid_volume = sum(size for _, size in bids[:5]) # Top 5 bid levels
ask_volume = sum(size for _, size in asks[:5]) # Top 5 ask levels
return bid_volume / ask_volume if ask_volume > 0 else 0.0
def pressure_strategy(message: Dict[str, Any]):
symbol = message.get("symbol", "UNKNOWN")
ratio = compute_pressure_ratio(message)
if ratio > 2.0:
print(f"[SIGNAL] {symbol}: Strong buy pressure (ratio={ratio:.2f})")
elif ratio < 0.5:
print(f"[SIGNAL] {symbol}: Strong sell pressure (ratio={ratio:.2f})")
Historical Backtesting with TickDB REST API
The producer-consumer pipeline is designed for live trading. For backtesting, you would use the TickDB REST API to fetch historical kline data:
import os
import requests
API_KEY = os.environ.get("TICKDB_API_KEY")
headers = {"X-API-Key": API_KEY}
# Fetch 1-hour OHLCV bars for backtesting
response = requests.get(
"https://api.tickdb.ai/v1/market/kline",
headers=headers,
params={
"symbol": "AAPL.US",
"interval": "1h",
"limit": 500
},
timeout=(3.05, 10) # Connect timeout: 3.05s, read timeout: 10s
)
data = response.json()
if data.get("code") == 0:
bars = data["data"]
for bar in bars:
print(f"Open: {bar['open']}, High: {bar['high']}, Low: {bar['low']}, Close: {bar['close']}")
Comparing Architecture Options
| Criterion | Single-threaded polling | Threaded producer-consumer | asyncio.Queue producer-consumer |
|---|---|---|---|
| Throughput | Low (I/O blocking) | High (parallel threads) | High (async concurrency) |
| Memory overhead | Minimal | High (per-thread stacks) | Moderate (task stacks) |
| Backpressure support | No | Yes (with BlockingQueue) | Native (await queue.put()) |
| Complexity | Low | High (locks, queues) | Moderate (single event loop) |
| Integration with WebSocket async libraries | Poor | Moderate | Native (websockets library) |
| Production readiness | Not recommended | Suitable | Recommended |
The asyncio.Queue producer-consumer pattern sits at the intersection of high throughput and manageable complexity. It is the architecture of choice for Python-based trading systems where latency is measured in hundreds of milliseconds rather than microseconds.
Deployment Guide by Scale
| Deployment context | Worker count | Queue depth | Instance type |
|---|---|---|---|
| Individual quant (single strategy) | 1–2 | 10,000 | 2 vCPU, 4 GB RAM |
| Active trader (3–5 strategies) | 4 | 25,000 | 4 vCPU, 8 GB RAM |
| Fund / team (portfolio strategies) | 8–16 | 50,000+ | 8+ vCPU, 16+ GB RAM |
| Institutional (sub-second requirements) | Multi-process (not multi-thread) | N/A | C++ or Rust microservice |
Closing
The producer-consumer pattern is not merely a concurrency trick. It is a contract between data ingestion and strategy processing: the producer commits to not overwhelming the system, and the consumer commits to processing every message that the producer delivers. The bounded asyncio.Queue enforces this contract through backpressure, and the multi-worker design scales processing capacity horizontally.
When you connect to a TickDB WebSocket endpoint—subscribing to depth, trades, or kline channels—you are already operating in a producer role. The question is whether your consumer architecture is built to handle the bursts. This article provides the blueprint.
Set TICKDB_API_KEY, tune MAX_QUEUE_DEPTH, spawn your workers, and let the pipeline handle the flow.
Next Steps
If you want to run this pipeline yourself:
- Sign up at tickdb.ai (free, no credit card required)
- Generate an API key in the dashboard
- Set the
TICKDB_API_KEYenvironment variable - Clone the pipeline code from this article and run
python pipeline.py
If you need historical OHLCV data for backtesting your strategy:
Reach out to enterprise@tickdb.ai for Professional and Enterprise plans covering 10+ years of cleaned US equity data.
If you use AI coding assistants:
Search for and install the tickdb-market-data SKILL in your AI tool's marketplace to get API documentation and code snippets inline.
This article does not constitute investment advice. Markets involve risk; past performance does not guarantee future results. Production deployment of trading systems requires thorough testing, risk controls, and compliance review.