Understanding DOM, MBO, and MBP in Futures Trading

Futures Trading Platforms
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1

Hello,

I currently use Rithmic.

I trade ZN on the DOM on the Atas platform

I often notice that the data tells me that I am 1st in the queue, but in around 95% of cases, so almost all the time, when there are market orders while I am 1st in the queue position, I’m not executed.

It takes between 100 and 200 market orders, sometimes many more, before being executed on entry, and it’s the same on exit.

So my question is how is the queue calculated? Rithmic is an MBO flow, how is it possible to have a bad estimate of our position in the queue?

Thanks for reading, hoping to get an answer

Have a good day

Lebowski

2

Hi @Trading_Lebowski and welcome top the Optimus Futures community.
The answer below applies to Simulation data that we are pretty sure you are using.

Understanding your position in the order queue can indeed be perplexing, especially when using Market By Order (MBO) data. Let’s break down the key elements that could be affecting your order execution on the ATAS platform with Rithmic data, particularly if you are using simulation data.

How the Queue is Calculated:

  1. Market By Order (MBO) Data: Rithmic provides MBO data, which shows individual orders at each price level. This data allows traders to see the queue of orders and their relative position in that queue.

  2. Order Matching Engine: Exchanges use a matching engine that processes orders based on specific rules. The most common rule is price-time priority, meaning orders are filled based on the order in which they arrive at a given price level.

  3. Simulation Data: When using simulation data, the order matching process is replicated by the simulation engine rather than the actual exchange. This can introduce discrepancies because the simulation might not perfectly emulate the live market’s order processing and latency.

  4. Latency and Order Routing: In live trading, when you place an order, it goes through several steps:

    • Your trading platform (ATAS) sends the order to Rithmic.
    • Rithmic routes the order to the exchange.
    • The exchange processes the order and updates the order book.
      In a simulation, these steps are approximated, and the simulation engine tries to mimic the latency, but it may not always be accurate.

  5. Order Visibility: The position shown on your DOM (Depth of Market) in a simulation is an estimate based on the simulated data. The simulation might not capture all nuances of real-time changes and order flows in the live market.

Why You Might Not Be Executed as Expected:

  1. Simulation Engine Limitations: Simulated environments might not perfectly replicate the live market conditions, especially in terms of order matching and queue positioning. This can lead to differences in expected versus actual execution.

  2. Latency Simulation: While simulation platforms try to emulate latency, the approximation might not be perfect. This can result in your orders not being executed as they would be in the live market.

  3. Order Cancellation and Modification: Other simulated orders may be cancelled or modified in ways that do not fully represent live market behavior, impacting the perceived queue position.

  4. Matching Engine Priorities: Simulation platforms may use simplified rules for order matching compared to the live exchange, affecting your execution.

How to Improve Your Understanding:

  1. Understand Simulation Limitations: Be aware that simulation data can only approximate live market conditions. This understanding will help you set realistic expectations for order execution.

  2. Monitor Execution Discrepancies: Compare your simulation results with live market behavior when possible. This comparison can help you identify areas where the simulation might diverge from reality.

  3. Advanced Order Types: Even in simulations, explore advanced order types like “iceberg” or “hidden” orders if your platform supports them. These can help you manage your queue position more effectively.

  4. Platform Settings: Ensure that your ATAS platform settings are optimized for the best possible simulation accuracy. Sometimes, tweaking these settings can improve the fidelity of the simulation.

Conclusion:

While MBO data in a simulated environment provides valuable insights, it’s essential to recognize the limitations of simulation compared to live trading. Understanding these factors and continuously monitoring your performance can help you adapt and improve your trading strategy, both in simulation and live environments.

Hope this helps!

Best,
Matt Z
Optimus Futures

3

Hello Mattz,

Many thanks for your time and very detailed response. I understand better how queue position works in simulated data now.

Your answer opens up another question from me :

In a heavy DOM, like ZN, how can a MBP (market by price) data manage all these parameters ? Are users of MBP flow advantaged or disadvantaged on a heavy DOM like ZN ?

Cordially,

Lebowski

4

Hi again,

Your question about the effectiveness of Market By Price (MBP) data in managing the parameters on a heavy DOM like ZN (10-Year Treasury Note Futures) is an important one. Let’s delve into how MBP works and whether it provides advantages or disadvantages in such a context.

How MBP Data Manages Parameters:

  1. Aggregation of Orders: MBP data aggregates all orders at each price level rather than showing individual orders. This provides a summary of the total buy and sell interest at each price point without the granular detail of individual order sizes and positions.

  2. Simplicity and Clarity: MBP simplifies the order book by reducing the data complexity, making it easier to interpret the overall market depth and liquidity at each price level. This can be particularly useful in a heavy DOM like ZN, where there are many orders and significant market activity.

  3. Resource Efficiency: By aggregating data, MBP reduces the amount of data that needs to be processed and transmitted. This can lead to lower latency and faster updates, which is crucial in fast-moving markets.

Advantages of Using MBP in a Heavy DOM:

  1. Reduced Complexity: MBP provides a clearer and more straightforward view of market depth, which can be advantageous in highly liquid and active markets like ZN. Traders can quickly assess the overall supply and demand at different price levels without being overwhelmed by the sheer volume of individual orders.

  2. Faster Performance: Aggregated data requires less bandwidth and processing power, potentially resulting in quicker data updates and lower latency. This can be beneficial for making timely trading decisions.

  3. Easier Decision Making: For many traders, especially those not employing high-frequency trading strategies, the aggregated data of MBP is sufficient for making informed trading decisions. The simplicity of MBP can lead to more efficient decision-making processes.

Disadvantages of Using MBP in a Heavy DOM:

  1. Lack of Granular Detail: MBP does not provide information on individual orders, such as size, entry time, or order type. This lack of detail can be a disadvantage for traders who rely on precise order flow information to gain an edge.

  2. Hidden Orders and Order Types: MBP cannot show hidden orders or iceberg orders effectively, as it only displays visible aggregated order quantities. This might lead to an incomplete picture of the actual market depth.

  3. Queue Position Uncertainty: With MBP, it’s harder to estimate your exact position in the queue at a specific price level. This can be a disadvantage for traders who need precise information on their order’s position relative to others.

Balancing MBP and MBO:

  1. Hybrid Approach: Some trading platforms offer a hybrid approach, combining the simplicity of MBP with the detailed insights of MBO. This can provide a balanced view, leveraging the strengths of both data types.

  2. Use Case Specific: The choice between MBP and MBO often depends on your specific trading strategy. High-frequency traders and those who rely heavily on order flow analysis might prefer MBO, while those focusing on broader market trends might find MBP more useful.

Conclusion:

In a heavy DOM like ZN, MBP data offers the advantages of simplicity, faster performance, and easier decision-making. However, it also comes with the trade-off of reduced granularity and detail. Understanding these trade-offs and choosing the right data type based on your trading strategy can help you navigate the market more effectively.

Hope this helps clarify things!

Matt Z
Optimus Futures

PS. I changed the subject a bit in case other seek such data.

5

Hello Matt,

Your detailed and precise explanations on the difference between MBO and MBP were of great help to me, I thank you for taking the time to answer me.

I think this topic will help many people like me.

Cordially,

Lebowski

1 Like
6

@Trading_Lebowski, it’s my pleasure to help out a fellow trader. That’s what this forum is all about - sharing knowledge and experiences to help each other improve. If you come across any other questions, don’t hesitate to start new threads. The more quality discussions we have, the better this community becomes. And if you know other futures traders who could contribute or benefit from the collective wisdom here, definitely encourage them to join.

Now, regarding your questions about Market-by-Order (MBO) and Market-by-Price (MBP) data feeds, here’s my take: It’s easy to get caught up in the granularity of data and become obsessed with monitoring every little detail. But at the end of the day, you need to critically assess whether that level of granularity actually enhances your decision-making process.

There’s a difference between having general knowledge about these data feeds versus actively incorporating them into your trading strategy. My philosophy is to streamline my approach and only focus on the data points that demonstrably improve trading decisions. If something doesn’t add value to your process, consider minimizing its use or eliminating it altogether.

Your ultimate goal(IMHO) is to efficiently process relevant information to make optimal decisions in the markets. Anything that doesn’t serve that goal is essentially noise that can distract us from what really matters.

Matt Z
Optimus Futures

1 Like
7

That’s a very good advice,

Thank you Matt

1 Like
8

You very welcome! Keep asking good questions.

Best,
Matt Z
Optimus Futures