I've been slowly getting back through your Substacks. I have a question regarding your microstructure: are these all systems/data you are accounting for and processing yourself? Each seem to be very specific and "niche" style areas. Regardless, how much are you weighting your decision making off of each type of microstructure? Is it a temperature check or more of a compass?
Great question. So I have models running on everything I have noted in the educational articles. However this doesn’t mean I have incredibly in depth and comprehensive models on every single thing. This is a key way to frame things logically: for example I can know how to start a car and the basic things to maintain. However this doesn’t mean I have a comprehensive understanding of the engine. My lack of comprehensive understanding doesn’t imply my initial understanding is false, just the limitations of it. This also presupposes my initial understanding is correct.
This is a key way to start. Ensure you have a correct big picture view as a foundational model. Then you can build further sub models underneath these models providing further REFINEMENT. If you start right then you can know the intended range of implications that you have the capacity to make and when you need to withhold judgment. Determining the degree of refinement allows you to correctly frame the intended range of implications you can make. For example, I can have a basic VWAP script running identifying imbalances but this isn’t comprehensive in any way. This means I can only make specific implications about broad imbalances and withhold judgement in more specialized microstructure areas. This is key because you generally lose the most money in situations where you make implications when you should simply withhold judgment and have inaction.
Furthermore, most real order imbalance models for HFT rely on exchange data which most people don’t have access to. However, you don’t need that limit order book data from the exchange to make specific implications.
For microstructure specifically, how do you weight decisions ? First you need to eatables the intended range of implications you can make in determining or predicting causality. This can be different during different periods of time. Knowing the how and why behind a specific market microstructure refines how you are able to identify signal to noise and connect information with price.
Temperature or compass analogy is ok but I think more in terms of causality and complex system language.
Final thought, this whole idea of niche styles and specialization is key to identify because you can begin to identify silos of knowledge in peoples execution which can further connect to you knowing why you are extracting specific premia from the market and knowing your specific edge.
Let me know if this provides some clarity of if you have further questions. Maybe I will write an article going in a little more depth on this.
I've been slowly getting back through your Substacks. I have a question regarding your microstructure: are these all systems/data you are accounting for and processing yourself? Each seem to be very specific and "niche" style areas. Regardless, how much are you weighting your decision making off of each type of microstructure? Is it a temperature check or more of a compass?
Great question. So I have models running on everything I have noted in the educational articles. However this doesn’t mean I have incredibly in depth and comprehensive models on every single thing. This is a key way to frame things logically: for example I can know how to start a car and the basic things to maintain. However this doesn’t mean I have a comprehensive understanding of the engine. My lack of comprehensive understanding doesn’t imply my initial understanding is false, just the limitations of it. This also presupposes my initial understanding is correct.
This is a key way to start. Ensure you have a correct big picture view as a foundational model. Then you can build further sub models underneath these models providing further REFINEMENT. If you start right then you can know the intended range of implications that you have the capacity to make and when you need to withhold judgment. Determining the degree of refinement allows you to correctly frame the intended range of implications you can make. For example, I can have a basic VWAP script running identifying imbalances but this isn’t comprehensive in any way. This means I can only make specific implications about broad imbalances and withhold judgement in more specialized microstructure areas. This is key because you generally lose the most money in situations where you make implications when you should simply withhold judgment and have inaction.
Furthermore, most real order imbalance models for HFT rely on exchange data which most people don’t have access to. However, you don’t need that limit order book data from the exchange to make specific implications.
For microstructure specifically, how do you weight decisions ? First you need to eatables the intended range of implications you can make in determining or predicting causality. This can be different during different periods of time. Knowing the how and why behind a specific market microstructure refines how you are able to identify signal to noise and connect information with price.
Temperature or compass analogy is ok but I think more in terms of causality and complex system language.
Final thought, this whole idea of niche styles and specialization is key to identify because you can begin to identify silos of knowledge in peoples execution which can further connect to you knowing why you are extracting specific premia from the market and knowing your specific edge.
Let me know if this provides some clarity of if you have further questions. Maybe I will write an article going in a little more depth on this.