I am very green when it comes to programming. I am doing a market microstructure study where I need to investigate how certain stock characteristics affect their liquidity. I have Nasdaq OMX ITCH Feed available, but in order to be able to analyze this data set, I need to figure out a way to turn this data into bid/ask quotes and get the volumes of executed trades.

I understand that Python is the way to go, and I need to build a code that constructs the limit order book from the feed. I have some familiarity with Python, and should be able to build the code that updates the book line by line, but being that the daily files are rather large (1-3gb), I understand that finding a way to efficiently do this is the main concern - I am going to need data for 100+ stocks, and would obviously prefer to have as long time window as possible. The final data doesn't have to be high frequency, minute frequency will be fine if it significantly reduces the processing time.

So, I was wondering if any of you guys could nudge me in the right direction, i.e. how should I approach this problem and where should I start in figuring this thing out?


1 Answer 1


Not sure if I understand your question, but the approach will be different if you are dealing with real time data, or if you want to study past data (simulation)

In the first case, you should keep the incoming quotes in an array (or dictionary) and then do your analysis.

In the other case, you need to simulate the incoming feed, and based on that do process the data.


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