Hobbyist ideas: what to do with level 2 data

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Hi,
I'm a data scientist by trade, don't work in the quant industry, but nonetheless enjoy exploring market data. I have young children so it's a brief bit of escapism for me.

I've recently purchased a small amount of level 2 data (for ES); circa 3.5GB per day worth of data.

I like the idea of monitoring the balance between volume of bids versus the asks (10 levels each side), to see if this provides any indication about future price movements. I am not trying to anticipate the next tick but rather, perhaps the path of price for the next few minutes or so. My instinct tells me the signals I am hoping to find will be rare (if exist at all), perhaps occurring a handful of times in a month.

With my limited spare time, so far I have been assigning a sliding scale of weights to each level (highest weight for bids or offers closest to the trading price), computing the weighted sum of each side, and then a rolling average of each side (eg rolling 15 second average to smooth the volatility).

From there I've computed an imbalance metric that varies between +100% and -100% (-100% here would indicate ALL of the volume on the inside 20 levels is on the sell (ask) side, 0% would indicate equally balanced bids Vs asks).

Then... I just eyeball the charts, imbalance versus price. Generally my imbalance metric bounces around between -20% and +20% but there are few periods where it may even exceed 40%.

I tend to think the best teacher is the data itself but nevertheless I felt it might be worthwhile posting these thoughts here incase any experienced members would be generous enough to nudge me in the right direction with my explorations.

Many thanks
 
These are my 5 cents

1. I believe you ought to also be able to obtain data like this from various crypto exchanges for free, if you wish to add more data to your pile.

2. Depending on the amount of data you have stored, I would start out by checking the long-term patterns. Being able to correctly understand long term behavior would be far more interesting to me. Your imbalance metric showing +40% in certain periods may be due to external events, mean reversion, outlier reversions or something like that.

3. The short-term behavior ought to be somewhat similar to supply-demand mechanics. With the twist of some market participants actively manipulating the market either directly or indirectly. It could of course be interesting to discern regular participant behavior from manipulations. ~ If you also had a corresponding list of "who send which order", you could reliably correlate the two. This is what I have been doing with crypto for a bit
 
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