Why Your US Stock Strategy Might Not Need Tick Data After All | API Guide

At 3:47 AM on a Tuesday, a quant trader I'll call Marcus hit a wall. He had spent six weeks building a microstructure alpha model — order flow imbalance detection, queue position estimation, the works. Everything worked in backtesting. Then he tried to source live data for live deployment.

The quote he received for US equity tick data: $40,000 per month, minimum. For exchange-hosted深度 (depth) feed on top of that, another $15,000. His entire trading operation runs on margins thin enough to see through.

Marcus is not alone. Every month, developers discover that "market data" is not a monolithic product. The gap between what quant researchers expect and what market data vendors actually offer is wider — and more expensive — than most blog posts admit.

This article does not sell you on TickDB. It tells you exactly what TickDB supports for US equities, why tick-level data carries a structural cost barrier that most retail and even institutional quant shops cannot justify, and how to architect your strategy around the data you can actually afford.

What "Tick Data" Actually Means

Before discussing capability boundaries, the terminology must be precise.

Tick data refers to every individual market event: each trade print, each quote update, each order book change. For a heavily traded US equity like Apple (AAPL), this can mean tens of thousands of events per second during market hours. Over a trading day, that compounds to millions of raw records.

Compare this to OHLCV (candlestick) data, which condenses all activity within a time window into four numbers: Open, High, Low, Close, and Volume. A 1-minute kline for AAPL is a single record representing thousands of underlying ticks.

Data type	AAPL daily volume (approx.)	Storage per day	Storage per year
Tick-level trades	80–120 million events	8–12 GB raw	3–4 TB raw
1-minute OHLCV	~390 candles	~15 KB	~5 MB

The compression ratio is not incidental. It reflects the fundamental cost structure of market data licensing.

TickDB's Actual US Equity Capabilities

TickDB supports US equities through two data channels that are often conflated with tick data but serve different purposes.

What Is Supported

Historical OHLCV via the kline endpoint

TickDB provides 10+ years of cleaned, exchange-aligned US equity OHLCV data. This covers daily, hourly, and intraday intervals down to 1 minute. For strategy backtesting across full market cycles — including the 2020 COVID crash, the 2022 rate-hike bear market, and the 2023–2024 AI-driven rally — this dataset is complete.

Real-time kline via kline/latest

For live trading dashboards, the kline/latest endpoint delivers the current incomplete candle in real time. This is the correct endpoint for dashboards and intraday strategy signals — not the historical kline endpoint.

Authenticating requests

import os
import requests

API_KEY = os.environ.get("TICKDB_API_KEY")

# Fetch 1-year daily kline for AAPL
response = requests.get(
    "https://api.tickdb.ai/v1/market/kline",
    headers={"X-API-Key": API_KEY},
    params={
        "symbol": "AAPL.US",
        "interval": "1d",
        "limit": 365
    },
    timeout=(3.05, 10)
)
data = response.json()
if data.get("code") == 0:
    candles = data["data"]
    print(f"Fetched {len(candles)} daily candles for AAPL.US")
else:
    print(f"Error {data.get('code')}: {data.get('message')}")

What Is Not Supported

The trades endpoint does not cover US equities or A-shares. This is the key boundary. If your strategy requires individual trade prints — for order flow imbalance calculations, queue estimation, or quote-to-trade analysis — TickDB cannot supply that data.

Similarly, the depth channel for US equities is limited to Level 1 (best bid/best ask). Level 2 and Level 3 order book depth — the full bid/ask ladder — is not available for US stocks.

Capability	US Equities	HK Equities	Crypto
OHLCV (kline)	✅ Daily to 1m	✅ Daily to 1m	✅ Daily to 1m
Trade prints	❌ Not supported	✅ Supported	✅ Supported
Depth L1 (bid/ask)	✅	✅	✅
Depth L2–L10	❌ Not supported	✅ Up to L10	✅ Up to L10

Why Tick Data Carries a Structural Cost Barrier

The absence of US tick data in TickDB is not an oversight. It reflects a structural cost reality that affects every market data vendor.

Exchange Licensing Fees

US equity data is consolidated tape data, regulated by FINRA and distributed through exchanges and approved SIPs (Securities Information Processors). To redistribute trade-level data, vendors must pay exchange fees that scale with the number of symbols and the depth of data requested.

For individual exchange direct feeds — Nasdaq TotalView, Arca Book, or IEX Deep — licensing costs are negotiated per firm and can run $5,000–$50,000 per month depending on scope. The consolidated tape adds additional per-message fees.

Polygon, Databento, and other vendors who offer US tick data absorb these costs — and pass them through in subscription tiers that typically start at $1,000/month for limited symbol sets and scale into enterprise pricing for full market coverage.

Infrastructure Cost at Scale

Storing and serving tick-level data for US equities is not a static cost. AAPL alone generates approximately 100 million tick events per trading day. Multiply that across the top 500 US equities and you are managing 50 billion events per day — a storage and bandwidth problem that requires real-time data infrastructure with associated compute costs.

TickDB's architecture is optimized for OHLCV aggregation and streaming depth data. Supporting individual tick events for US equities would require a fundamentally different data pipeline, pricing model, and infrastructure investment.

Product Positioning Trade-offs

Every market data API makes trade-offs. TickDB chose to optimize for three capabilities that its users most frequently need:

Long-horizon OHLCV backtesting across 10+ years of clean data
Multi-asset coverage — forex, commodities, indices, crypto, HK equities — under a unified API
Real-time streaming for kline updates and depth data at accessible pricing

Pursuing full US equity tick data coverage would have required either raising prices across all tiers or compromising on one of these three axes. The product team chose not to.

What You Can Build Without Tick Data

The absence of tick data is not as limiting as it sounds — provided your strategy architecture is adapted accordingly.

OHLCV-Based Indicators

The vast majority of quantitative strategies do not require tick-level granularity. These indicators work perfectly well — and have decades of academic and practitioner validation — on kline data:

Moving average convergence/divergence (MACD)
Relative strength index (RSI)
Bollinger Bands
Average true range (ATR)
On-balance volume (OBV)
VWAP anchored to session open

import requests
import numpy as np

API_KEY = os.environ.get("TICKDB_API_KEY")

# Fetch 60-day hourly kline for backtesting
response = requests.get(
    "https://api.tickdb.ai/v1/market/kline",
    headers={"X-API-Key": API_KEY},
    params={
        "symbol": "AAPL.US",
        "interval": "1h",
        "limit": 1440  # 60 days at hourly granularity
    },
    timeout=(3.05, 10)
)

data = response.json()
candles = data["data"]

# Compute RSI(14) on hourly data
closes = np.array([c["close"] for c in candles])
deltas = np.diff(closes)
gain = np.where(deltas > 0, deltas, 0)[-15:]
loss = np.where(deltas < 0, -deltas, 0)[-15:]

avg_gain = np.mean(gain)
avg_loss = np.mean(loss)
rs = avg_gain / avg_loss if avg_loss > 0 else float('inf')
rsi = 100 - (100 / (1 + rs))

print(f"AAPL 14-period RSI on hourly data: {rsi:.2f}")

Event-Driven Strategies on OHLCV

Post-earnings drift, sector rotation, macro event responses — these strategies are fundamentally about where the price opens versus where it closes. The signal is in the candle, not in the individual trade that formed it.

TickDB's US equity kline data is fully sufficient for:

Earnings gap analysis and mean reversion testing
Intraday momentum strategies anchored to VWAP or session open
Cross-symbol correlation and sector rotation models
Backtesting over complete market cycles including 2020, 2022, and 2023–2024

Multi-Asset Strategies

TickDB's strongest advantage is its unified coverage across six asset classes. If your strategy involves US equities alongside forex, commodities, or crypto — for diversification, hedging, or cross-asset arbitrage signals — a single TickDB subscription covers the full universe.

# Multi-asset OHLCV fetch in a single session
symbols = {
    "US_Equity": "AAPL.US",
    "HK_Equity": "0700.HK",
    "Crypto": "BTC.USDT",
    "Forex": "EUR/USD"
}

for label, symbol in symbols.items():
    response = requests.get(
        "https://api.tickdb.ai/v1/market/kline",
        headers={"X-API-Key": API_KEY},
        params={"symbol": symbol, "interval": "1d", "limit": 30},
        timeout=(3.05, 10)
    )
    data = response.json()
    if data.get("code") == 0:
        print(f"{label} ({symbol}): {len(data['data'])} daily candles")

When You Actually Do Need Tick Data

Honesty requires acknowledging the strategies that genuinely require tick-level granularity.

Queue position estimation — knowing where your order sits relative to the queue ahead of it — requires Level 2 order book data and individual quote updates. This is the domain of high-frequency market makers, not retail or even most institutional quant strategies.

Quote-to-trade analysis — measuring the price impact of individual trades against the NBBO (National Best Bid and Offer) — requires tick-level trade prints matched to quote streams. This is critical for transaction cost analysis (TCA) at HFT scale.

Order flow imbalance (OFI) — measuring the net directional pressure from trade size asymmetry — requires individual trade prints with size and side information.

If your strategy falls into any of these three categories, you need a vendor that specifically offers US equity tick data. Polygon, Databento, and exchange direct feeds are the appropriate channels. Budget accordingly — realistic costs for full US equity tick coverage start at $3,000/month and scale rapidly with symbol count and history depth.

For everyone else — the vast majority of systematic traders — OHLCV data from TickDB is not a limitation. It is the appropriate data substrate for the strategy being built.

A Practical Decision Framework

Before selecting a market data vendor, answer three questions honestly:

1. What is the minimum time resolution your strategy can tolerate?

If your signals operate on 1-minute or longer candles, TickDB's kline data is the right tool. If you genuinely need sub-second resolution for order flow or queue analysis, you need tick data — and the budget to match.

2. What is your backtesting time horizon?

TickDB provides 10+ years of clean OHLCV data. This is sufficient for strategy validation across bull, bear, and sideways market regimes. If you need longer histories or higher-frequency tick replay, you are in enterprise data territory.

3. Do you need multi-asset coverage under a single API?

If your strategy universe spans US equities, HK equities, forex, and crypto, TickDB's unified API simplifies your data pipeline significantly. Consolidating data sources is a real engineering benefit that compounds over time.

When the answer to all three questions points toward OHLCV-based strategies over multi-year horizons across multiple asset classes, TickDB is the right choice. When the answer to question one points toward tick-level granularity, look elsewhere — and negotiate your licensing contract carefully.

Closing

Marcus, from the opening story, eventually restructured his model. He moved from order flow imbalance — which requires tick data — to a 5-minute candle momentum strategy anchored to VWAP, validated over 8 years of daily data. His backtesting showed a Sharpe of 1.22. His live trading costs dropped by $40,000 per month.

The constraint became the catalyst.

Not every strategy needs tick data. Not every quant shop can afford it. Understanding what your strategy actually requires — and selecting your data layer accordingly — is the first engineering decision in systematic trading.

TickDB supports what it supports: 10+ years of OHLCV for US equities and five other asset classes, real-time kline streaming, and Level 1 depth for HK and crypto markets. It does not support what it does not: US equity tick trades and Level 2 US depth. This boundary is not hidden. It is documented, and it is the right boundary for the product's target use case.

Next Steps

If you need long-horizon US equity backtesting, start with TickDB's free tier — no credit card required. Pull 10 years of AAPL daily kline and build your first strategy prototype.

If you need multi-asset coverage across equities, forex, and crypto, TickDB's unified API eliminates the data consolidation overhead. One API key, one data format, six asset classes.

If you genuinely need tick-level granularity for HFT or microstructure analysis, vendors like Polygon and Databento specialize in that tier. Allocate the budget, sign the licensing agreement, and plan for the infrastructure.

If you use AI coding assistants, search for and install the tickdb-market-data SKILL in your AI tool's marketplace for integrated market data access in your development workflow.

This article does not constitute investment advice. Markets involve risk; past performance does not guarantee future results.