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7 ways to use order book data (to improve trading)

7 ways to use order book data (to improve trading). So how can “normal” investors and traders use order book data to improve trading?

Order book data is usually used in high frequency trading (HFT) which most of us can’t do. So how can “normal” investors and traders use order book data to improve trading?

In this guest post from Databento, we find out.

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7 ways to use order book data (to improve trading)

7 ways to use order book data (to improve trading). So how can “normal” investors and traders use order book data to improve trading?

Order book data, also referred to as MBO (market by order), describes an order-based data feed that provides the ability to view individual queue position, full depth of book and the size of individual orders at each price level.

MBO provides the highest level of granularity of every individual order event, keyed by its order ID. This includes the real-time bid, ask, high, low, and last price of the day, as well as quote size.

1. Backtesting

It allows you to properly sequence the backtest or simulation of any strategy or execution algorithm that uses passive orders and more accurately simulate fill dynamics. Since a backtest seeks to simulate past market dynamics, this can be crucial for trading strategies that depend on depth of book.

2. Increased visibility

Almost all order book feeds provide visibility of every level in the book, which is useful for estimating things like capacity, sweep cost, and slippage. This is meaningful if you trade large sizes or illiquid instruments, regardless of the need for low latency.

3. Incremental deltas

Incremental deltas are a more efficient format than book-level snapshots, which is better for performance optimization. Incremental deltas are just messages that describe the difference from one state to another.

4. Specific venues

Some venues exclusively use an incremental order book feed as their main or only feed, so it’s required to use MBO data when working with one of these venues.

5. Software abstractions

Because order book feeds are ubiquitous, it’s easier to write software abstractions that purely consume order book data at any venue rather than a platform that has to handle a mix of price level feeds and MBO.

6. Liquidity insights

For US equities, trade and quote (TAQ) data leave out a lot of liquidity information compared to the MBO. Information like odd lots, non-top level at each venue, trade aggressor side, and order imbalances can play a vital role for traders.

7. Feature construction

There are a few widely-known classes of features (e.g. book pressue) that can’t be constructed from TAQ data. These features can improve your model fit. For certain mid-frequency strategies, using these features is necessary to get an edge over competitors that already use them.

Next steps

As a next step, check out Databento’s schemas doc which has information about what data is included in their MBO feed. You can also review their fields doc which provides information about the type and description for each field.