AI × Quant Trader Series — Day 11¶
What is Market Data?¶
Reading time: ~15 minutes
Prerequisites: What is High Frequency Trading, What is Market Microstructure, What is an Order Book
Focus: understanding the data flowing through modern electronic trading systems
Part 1: Introduction¶
Every quantitative trading system begins with one thing.
Market Data.
Before a strategy can decide whether to buy or sell, it must first understand the current state of the market.
That information comes from market data.
Whether you are trading:
- Stocks
- Futures
- Options
- ETFs
- Cryptocurrencies
every trading decision ultimately depends on a continuous stream of market events.
For High Frequency Trading, market data is not just information.
It is the raw material from which every trading opportunity is created.
Part 2: What is Market Data?¶
Market Data is the real-time information published by an exchange describing everything happening in the market.
Typical market data includes:
- Best Bid
- Best Ask
- Trade Price
- Trade Size
- Order Book Updates
- Volume
- Market Status
- Instrument Information
Every update represents a new event occurring inside the exchange.
Unlike historical datasets, market data never stops arriving.
It is an infinite stream of events.
Part 3: Types of Market Data¶
Modern exchanges usually provide several categories of market data.
Trade Data¶
Trade data records completed transactions.
Example:
Trade data answers one question:
What actually traded?
Quote Data¶
Quote data describes the current market.
Typical information includes:
- Best Bid
- Bid Size
- Best Ask
- Ask Size
Example:
Most execution algorithms continuously monitor quote updates.
Order Book Data¶
Rather than publishing only the best prices,
many exchanges provide multiple price levels.
Example:
This information allows trading systems to reconstruct the entire local order book.
Part 4: Snapshot vs Incremental Updates¶
Exchanges generally publish market data in two formats.
Snapshot¶
A snapshot contains the complete market state.
Example:
Snapshots are simple but expensive to transmit frequently.
Incremental Updates¶
Incremental updates publish only changes.
Example:
Only the modified information is transmitted.
Nearly every modern HFT platform relies primarily on incremental updates because they minimize bandwidth and latency.
Part 5: Market Data Feed¶
Exchanges distribute market data through specialized data feeds.
A simplified architecture looks like:
The market data feed is responsible for delivering every market event to participants as quickly as possible.
For High Frequency Trading,
the market data feed is often the most latency-sensitive component of the entire system.
Part 6: Why Latency Matters¶
Imagine two trading firms receive the same market update.
Firm A processes the update in:
Firm B processes it in:
Both firms observe the same opportunity.
Only one is likely to execute first.
This is why HFT engineers spend enormous effort optimizing:
- Message parsing
- Memory allocation
- Cache locality
- Lock-free queues
- Network I/O
Every microsecond matters.
Part 7: Market Data Processing¶
Receiving market data is only the beginning.
A production trading system must also:
- Decode exchange protocols
- Validate messages
- Handle sequence numbers
- Detect packet loss
- Recover missing data
- Maintain synchronization
- Update the local order book
These operations occur continuously throughout the trading day.
For active markets, this may involve millions of messages every second.
Part 8: Local Market Data¶
Professional trading systems rarely query the exchange whenever market information is needed.
Instead, they maintain an in-memory representation of the market.
Strategies then read data directly from memory.
This architecture eliminates unnecessary network latency and dramatically improves performance.
Part 9: Market Data in High Frequency Trading¶
For long-term investors,
market data is simply information.
For HFT systems,
market data is an event stream.
Strategies react to:
- New trades
- Quote changes
- Order book updates
- Liquidity changes
- Spread changes
- Market imbalance
Many HFT strategies process thousands of events before placing a single order.
Understanding event flow is often more important than predicting future prices.
Part 10: Where godzilla.dev Fits¶
Efficient market data processing is one of the foundations of every ultra-low latency trading platform.
A production implementation must:
- Decode exchange messages
- Process incremental updates
- Maintain local market state
- Synchronize order books
- Distribute events across multiple strategies
- Minimize memory copies
- Maintain deterministic latency
These requirements define much of the architecture behind godzilla.dev.
Rather than rebuilding market data infrastructure for every project, developers can focus on strategy research while relying on a modular, high-performance framework designed for modern electronic markets.
Part 11: Key Takeaways¶
Market Data is the real-time information published by exchanges describing market activity.
It includes:
- Trades
- Quotes
- Order Book Updates
- Market Status
- Instrument Information
Professional trading systems transform this continuous stream of events into an in-memory representation of the market, allowing strategies to react with minimal latency.
Understanding market data is the first step toward building production-grade trading infrastructure.
What's Next?¶
The next article explores the component responsible for turning incoming orders into completed trades:
- How Matching Engines Work