Granular market data is the lifeblood of modern crypto trading. Without a clear, real-time view of the order book, down to every insert, cancel, and trade, your research, backtests, and trading signals are built on guesswork. Yet for most traders and quants, getting reliable, tick-by-tick order book data is still the hardest part of the job.

Anyone who’s tried to stitch together data from multiple exchange APIs knows the struggle: missing events, mismatched formats, and endless hours lost to cleanup. Most “crypto APIs” only provide partial trade prints or aggregated snapshots, leaving serious professionals without the full sequence of order book events they actually need.

This article breaks down what truly matters when working with order book data, showing you the difference between tick-by-tick and snapshots, the pitfalls to avoid, and how to make smarter choices for research, trading, or building your next product. Read on to make sure your data edge is real, not just a headline.

Tick by tick data vs Snapshot order book data: What’s the difference?

Tick-by-tick order book data captures every individual event that changes the order book, including new orders, cancellations, trades, and price or size updates, recorded in precise sequence. This type of granular data is essential for:

• Backtesting and validating trading strategies
• Studying market microstructure and order flow dynamics
• Detecting patterns of manipulative behavior (such as spoofing)

Snapshot order book data provides a complete “snapshot” or picture of the order book at a specific moment, typically showing the top N bid and ask levels (for example, the top 20 prices on each side). Order book snapshots are ideal for:

• Monitoring current market liquidity in real time
• Visualizing the full depth of the market at key intervals
• Supplying trading bots or analytics dashboards with up-to-date market states

Tick-by-tick data delivers every order book event as it happens, while snapshot data gives you the state of the order book at specific timestamps. Choose tick-by-tick for detailed research and backtesting, and snapshots for real-time monitoring and trading applications.

When should you use snapshots vs. Tick by tick data?

• Snapshots are far less storage- and bandwidth-intensive, making them useful for long-term studies or lightweight dashboards.
• However, they miss all the microstructure that happens between snapshots:
  • Order book flickers, spoofing, and fast-moving quote changes
  • Precise timing of liquidity appearing/disappearing

If you want to model slippage, simulate HFT strategies, or analyze queue dynamics, you’ll need the full tick-by-tick order book feed. But for trend analysis, liquidity heatmaps, or broad research, 1-minute snapshots are often the perfect balance.”

Snapshots vs. Tick by tick data

Where Snapshots shine and where they don’t

• Snapshots are great for:
  • Liquidity modeling over time
  • Historical research on depth/imbalance
  • Long-term studies, heatmaps, and risk dashboards
• But not for:
  • Backtesting exact fills
  • Studying rapid market events or manipulative order flow

How to use each data type?

• Use snapshots for:
  • Macro liquidity studies
  • Long-term trends
  • Low-frequency trading models
• Use tick-by-tick for:
  • Fill simulation
  • High-frequency or microstructure research
  • Slippage/queue analysis

Why snapshots matter (and when they don’t)

• Snapshots are useful for:
  • Initializing a local order book in your application
  • Visualizing the depth at a point in time
  • Research or audits that require a state at a known timestamp
• But they cannot capture how the book evolved between snapshots—that requires an event (tick) stream.

Order book snapshots are perfect for seeing ‘where the market is right now’—crucial for dashboards, liquidity analysis, or risk checks. However, to reconstruct market moves or simulate execution slippage, you need the event-by-event stream provided by tick-by-tick data.

What is the difference between tick data and aggregated data?

Tick data records every individual trade or order book event exactly as it happens, with millisecond or better precision. In contrast, aggregated data (such as 1-second bars) summarizes price and volume over fixed intervals, losing the fine details of market activity. True tick data gives you the full sequence of events needed for research and backtesting, while aggregated data is less precise and cannot capture microstructure, slippage, or real execution dynamics.

Aggregated data is useful, but not for serious backtesting

Aggregated 1-second bars are fine for simple charting or strategies, but useless for high-frequency or execution simulation. If you want to test execution or build a trading bot, you need the full tick stream, not just summary stats.

Aggregated data vs. Tick by tick data

Widespread confusion: Tick by tick data vs Level 2 data

Many traders mix up tick-by-tick, Level 2, and order book data. In reality, tick-by-tick usually refers to every individual trade or every order book update (each ‘tick’ in the data), while Level 2 refers to the visible ladder of resting limit orders beyond the best bid and ask. Level 2 does not always include every micro-event or trade tick, especially in crypto, where exchange data offerings differ.”

Level 2 is typically the aggregated order book, showing multiple bids/asks at different price levels (not just the top-of-book). Tick-by-tick (in equities/forex/crypto) is every individual trade (or sometimes every order book event, depending on the vendor/API). Level 2 can help you infer liquidity and trading interest, but it’s not the same as having every raw tick.

Order book data is even harder than trade data

Most exchanges only offer trade history, not full order book event deltas or snapshots. Users want true L2/L3 data (not just top-of-book or occasional snapshots). While exchanges like Bybit might let you fetch recent trades, true order book history, every insert, cancel, and change, is rarely available natively. CoinAPI delivers normalized, millisecond-precise order book event streams and snapshots for hundreds of venues, including Bybit.”

Pitfalls and pain points with tick by tick data

Multiple users share experiences that tick data from different vendors often does not match, either in trade count, timestamp, or sequencing. Some find that even “official” vendor feeds have missing ticks, small timing discrepancies, or different ways of handling corrections and cancellations.

• “Lots of ‘crypto APIs’ don’t really offer tick-by-tick.”
• “You’re often paying for aggregated, incomplete, or messy data.”
• “Even if you get L2, you have to do a ton of cleanup and reconciliation.”

Gaps, mismatched timestamps, and missing micro-events can skew your results or make your research non-reproducible. This is why CoinAPI invests so heavily in precise normalization, millisecond-level timestamps, and audit-ready completeness across all feeds.

Free APIs fall short; professional data is worth paying for

The quant community is clear: there’s no free lunch for tick-by-tick historical data. Free APIs may sound tempting, but they rarely offer the depth, precision, or reliability needed for serious research or trading. DIY solutions often lead to missing events, mismatched symbols, and endless hours of data cleanup, leaving your backtests fragile and results unreliable.

Serious traders and institutions don’t gamble on incomplete data—they invest in trusted, well-documented feeds with normalized schemas and full historical depth. The real challenge isn’t just finding tick data, but making it actionable. That’s why professionals turn to vendors like CoinAPI for research-grade, ready-to-use data that lets them skip the headaches and get straight to building strategies that work.

Everyone wants depth, but most providers only give surface-level data

Most APIs and free tools only provide spot prices, aggregated candles, or incomplete trade feeds. Several users explicitly mention that order book deltas, micro-events, and full tick data for the top 100 coins across multiple venues is either unavailable or paywalled. Serious researchers and quants aren’t satisfied with just top-of-book or aggregated candles. They’re seeking true tick-level and order book event data for the top 100 coins, across multiple exchanges—something most APIs don’t deliver. CoinAPI answers that need, with normalized, research-grade event streams and flat files spanning 370+ venues.”

Tick by tick data is a storage & engineering challenge, not just an API problem

Quant engineers point out that having terabytes of tick data isn’t helpful if you can’t efficiently search, compress, or transform it for your strategy. That’s why CoinAPI offers both normalized flat files for bulk download (ready for local or cloud storage) and API endpoints for dynamic queries, so you spend less time on ETL and more time modeling.

Raw tick data is enormous; even a single month for an active symbol can be tens or hundreds of GB. Institutions and serious quants rarely “just pull from the API,” they download, clean, compress, and warehouse data, often in time-series databases or custom flat files.

Data format & normalization are recurring nightmares

Managing historical tick data from multiple exchanges usually means wrangling dozens of formats and fixing broken symbols—one of the biggest time sinks in quant engineering, according to r/algotrading. CoinAPI’s plug-and-play flat files and streaming APIs are fully normalized: you get consistent schemas, unified timestamps, and clear asset mappings, no matter the source.”

Users want more than just price; they want the full story

What Actually Matters to Real Traders?

• Timestamp precision (millisecond-level)
• Schema consistency across exchanges
• Downloadable, reproducible files for backtesting
• Support for troubleshooting data issues

Users are specifically seeking APIs that provide not just trades, but true tick-level, order-by-order, event-by-event data (not aggregated candles).

• They want:
  • Full trade-by-trade (tick) data
  • Order book events (inserts, cancels, modifies)
  • Both real-time streams and historical archives

So what sets good providers apart?

• Data quality (true tick, not just trade prints or candle aggregates)
• Normalizing across venues (so you don’t have to remap symbols)
• Transparent and robust documentation
• Bulk access for historical research (not just “live” feeds)
• Developer experience (good docs, stable APIs, support)
• Clear, usage-based or flat pricing

Data transparency matters. CoinAPI provides not just normalized, timestamped feeds, but also public schemas, versioned documentation, and downloadable samples, so you can actually check and trust what you’re getting before building your pipeline. CoinAPI offers millisecond timestamps, normalized schema, historical flat files, and responsive support, no more trading blind.

Market microstructure: Why every micro-event matters

Serious quant/dev users want to see every event (inserts, cancels, modifications, trades) to reconstruct the order book or simulate fills accurately. The more granular the data, the less room for “vendor bias” or missed alpha.

If you care about real market microstructure—slippage, queue priority, or high-frequency signals, tiny gaps in data will sink your backtest. That’s why CoinAPI exposes every event, every microsecond, normalized and documented, so you’re modeling the real market, not a vendor’s incomplete version.”

Even for a single liquid instrument, the tick stream can be overwhelming (hundreds of MB or more per day).

Most users seriously underestimate the engineering effort required to just receive, parse, and store the stream, let alone analyze it in real time.

“Not all APIs are equal”: Some APIs batch, some drop micro-events, and some are prone to delays or throttling.

The real challenge with tick data starts after you subscribe. Streams from active markets are a firehose, hundreds of megabytes per day, with every insert, cancel, and trade event flowing in real time. CoinAPI helps you manage this scale, whether you need streaming APIs for live bots or flat files for long-term storage, and delivers the normalization that makes real-time analysis feasible.

Compression and storage - absolute musts

Always compress! Raw tick data is unwieldy. Many use gzip, zstd, or similar, sometimes compressing “on the fly” as they receive data. CoinAPI’s flat files are easily gzip-compressible and structured for storage in S3, cloud buckets, or local cold storage, perfect for years-long research projects.

Clear demand for BOTH real time and historical raw tick data

Quant traders frequently struggle to find sources for both live and historical tick-by-tick crypto data, especially with full order book events, not just trade prints. CoinAPI bridges this gap, offering unified, normalized feeds for both real-time and historical data, covering every insert, cancel, and trade across 370+ venues.”

What does “Raw tick by tick data” mean?

For real backtests, quants don’t want aggregates or summaries; they need the raw event stream, with every insert, cancel, and trade, timestamped for full market reconstruction. CoinAPI delivers this: you can replay or backtest the exact market microstructure as it unfolded, not just an after-the-fact chart.

Order book snapshots vs. Live market data: Clearing up the confusion

• Several users wonder about the difference between live/streaming market data (real-time feeds) and snapshots (a static picture of the order book at a moment in time).
• Some mistakenly assume snapshots update continuously or are as granular as live tick feeds.
• More advanced replies clarify:
  • A snapshot is a full view of the order book (or top N levels) at a particular instant—think of it as a photo, not a movie.
  • Snapshots are typically updated every few seconds (or on demand), and do not capture every single book event in between.

As the r/interactivebrokers community discusses, an order book snapshot is like a photograph—it captures the full state of the order book at one moment, but not the continuous flow of events. If you need to monitor real-time liquidity or initialize a trading engine, snapshots are essential for a quick start. For modeling microstructure or simulating fills, you’ll want to combine snapshots with tick-by-tick book updates.”

How to Access orderbook snapshots and tick by tick data with CoinAPI

Full-depth, historical tick-by-tick order book data is currently available via CoinAPI Flat Files only. The REST API supports snapshots of the top 20 levels for recent or historical order book data, but not the full event stream or all levels. For real-time tick-by-tick order book updates, use the WebSocket streaming API.

Order Book Snapshots (up to 20 levels, via REST Market Data API):

1bash
2
3GET /v1/orderbooks/{symbol_id}/latest        // Latest snapshot (top 20 levels)
4GET /v1/orderbooks/{symbol_id}/history       // Historical snapshots (top 20 levels, paginated)

Note: The REST API only returns up to 20 levels per snapshot, not full depth.

Full Historical Tick-by-Tick Order Book Data:

• Available via Flat Files only. Download these from CoinAPI’s Flat File Data Service for complete, research-grade historical coverage of the order book (all levels, all updates).

Real-Time Full Order Book Data:

• Available via WebSocket streaming API. Subscribe for the full tick-by-tick stream in real time (all events, all levels).

Sample order book snapshot (from CoinAPI’s REST Market Data API for BTC/USD on Coinbase):

1Smbol: COINBASE_SPOT_BTC_USD
2Timestamp: 2025-06-17T00:00:00.0025413Z
3
4Top 5 Asks:            Top 5 Bids:
565,100.67 @ 0.10       65,100.65 @ 0.18
665,100.71 @ 0.09       65,100.63 @ 0.11
765,100.73 @ 0.02       65,100.61 @ 0.05
865,100.75 @ 0.09       65,100.59 @ 0.07
965,100.80 @ 0.05       65,100.55 @ 0.06

TL;DR The real challenge is what you do with the data

Receiving tick data is just the start—the real edge is how you manage, store, and analyze it. The r/algotrading community is clear: invest in infrastructure and normalization, or risk drowning in your data. CoinAPI exists to take you from the firehose to real, actionable research.

What Should You Remember?

• Snapshots = efficient, point-in-time depth, great for trends and dashboards
• Tick-by-tick = every change, critical for serious backtesting and execution
• Free APIs rarely deliver what you need for research or production
• Normalization and completeness are the real edge—don’t skip them!

With CoinAPI, accessing tick-by-tick and snapshot order book data from 380+ exchanges is effortless. Whether you’re building trading algos, visualizing liquidity, or running analytics, the unified endpoints, rich docs, and scalable infrastructure give you everything you need.

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Sign up for a CoinAPI account and get your API key today!