Here’s a quick thread on the essentials of a market making system that the pros use.
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Let’s start right off the bat with the most important part of all, the fair value. Your fair value is what you quote around. It’s what you think a fair price for that asset is. How do we get it?
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We start by trying to do a nowcast, this is simply trying to get what the market thinks the current price is. There are multiple prices across venues so this is important.
So perhaps aggregating multiple prices across exchanges, and weighting by volume or some other metric.
A thread on all the components of latency, optimizations, & assumptions with modelling it.
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This will primarily be for HFT, and focus on digital assets, but I will explain which parts are digital assets specific and which parts are not as much of it is generally applicable.
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So what are the 3 "components" to our latency:
1. Our compute 2. The network 3. The matching engine
It’s the gains in performance you accumulate over time from tuning your strategy and improving it.
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When it comes to non-HFT, accumulated improvement often leads to overfitting.
Continuing to tune the model once created often leads to decreases in performance other than simple re-fitting of the model on new data that has come out.
Let’s say a new trade has occurred on an exchange, if we have a latency edge we want to be one of the people incorporating it into price instead of one of the people reacting to price changes.
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As we can see based on this below Pepe, a trade will cause an initial spike before a much slower levelling off.
Where it levels off to (relative to starting point) is going to be important to know as well as both the spike up and return points.