tick data · historical tick data · stock tick data · trading tick data
Tick data
Tick data is high-resolution market data that records individual market events, commonly trades and quotes. It is used for intraday research, execution analysis, market microstructure, transaction-cost modelling and strategies where bar data is too coarse. Tick data is powerful, but it is larger, messier and more expensive to handle than daily or minute bars.
What this means
Tick data records market events rather than summarising them into fixed intervals. Trade ticks capture executed transactions. Quote ticks capture changes in bid and ask. Some datasets include venue, condition codes, correction flags and sequence information.
Tick data is useful when the order and timing of events matter. It is usually unnecessary when a daily or hourly bar captures enough information for the decision being tested.
Main data and source types
The core forms are trades, quotes and sometimes trade-and-quote combinations. Data may arrive as real-time feeds, historical archives, compressed files or vendor APIs.
- Trade ticks for executed price, size, venue and time.
- Quote ticks for bid, ask and spread changes.
- Condition and correction fields for filtering unusual records.
- Timestamps from source, vendor receipt or local capture systems.
Free or public sources
Free tick data is uncommon for traditional markets and often limited in depth, coverage or licence. Some crypto venues expose public trades and order book streams, but quality and historical completeness can vary.
Public examples are useful for learning event-driven data handling, but do not assume they match the constraints of regulated equity, options or futures feeds.
Paid or vendor sources
Paid tick archives may provide exchange-derived history, normalised fields, correction handling, bulk delivery and support. Costs can rise with asset class, history length, venue count and licence rights.
Check whether quotes and trades are synchronised, how corrections are represented and whether timestamps are exchange, SIP, vendor or receipt times.
API and infrastructure considerations
Tick data requires efficient storage, partitioning, compression, validation and replay. CSV downloads may work for samples, but serious workflows usually need columnar storage, query planning and deterministic cleaning logic.
Preserve raw condition codes and timestamps. Filtering them away early can make execution analysis impossible to audit later.
Common use cases
Tick data supports intraday research, spread modelling, transaction-cost analysis, market impact studies, execution quality review, volatility estimation and high-frequency feature generation.
Limitations and risks
Tick data can contain bad prints, corrections, out-of-sequence events, venue-specific quirks and timestamp ambiguity. Storage costs and query times can also dominate the project if the workflow is not designed around the data volume.
Selection checklist
Before buying or ingesting tick data, define instruments, venues, history length, timestamp precision, correction handling, condition-code rules, storage format, query patterns and licence rights.
FAQ
What is tick data?
Tick data records individual market events such as trades and quote updates rather than summarising activity into fixed time bars.
How is tick data different from OHLCV?
OHLCV summarises a period. Tick data records the underlying events, giving more detail but requiring more storage and cleaning.
Who needs tick data?
Tick data is mainly useful for intraday research, execution analysis, market microstructure and high-frequency or latency-sensitive workflows.
Is tick data always better than bar data?
No. Tick data is more detailed, but the extra detail is only useful when it changes the analysis or execution decision.
Ledgerstone is an independent financial-data research guide. It does not provide investment advice, trading advice, brokerage services, data vendor services or financial promotion.