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An Algo to Pocket the Bubble

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22 January 2018
Brad Bailey

Data Analysis, Machine Learning, and Bubbles

The search for bubbles is everywhere (Bitcoin, bonds, equities, housing).[1] Countless prognosticators are predicting where the next one will, well bubble up. Whether it is correlated cross assets, short volatility strategies, the rise in rates, a true end to QE, unwinding of treasuries, etc.-for the experienced trader talk of bubbles is just bathroom talk-or is it?

I am writing this from my own bubble-the one I live in from Dec 1 to Jan 31st each year. You probably are all familiar with it-the “review my year”- what was the most interesting things of 2017; preview the year-“look into the crystal ball” what will be important trend in 2018-all from the vantage point of that bubbly feeling one gets transitioning through the holidays to the future.

So I have spent a lot of time thinking about 2017 which, in the capital markets, requires going through the cryptocurrency trends, and the macro events-Trump and Brexit, and focus on fintech, where I come to AI and machine learning (ML).

We have seen what ML has done in the back and middle offices. In the front office, the growing importance of AI from a trading perspective hit home while attending the quantitative brokers’ client event just before the holidays. Quantitative Brokers (QB) has been building execution algos in the fixed income space for a decade and has brought the team’s expertise in market structure and algos to futures and liquid fixed income markets. (BTW, QB’s founders include Robert Almgren who is to algos what Satoshi Nakamoto is to bitcoin). The QB event was full of the best and brightest in quant trading. It became apparent the level of machine learning that is already being leveraged by quants for their trading and execution, and the awesome potential ahead.

Wait a minute-what does this have to do with bubbles-the original hook to this blog? Sorry, yes of course.

One of the presentations at the QB event was around capitalizing on micro, or intra-day price bubbles for trading. The example was in the SP500 given the robust data set. It was fascinating to see what the QB analysis yielded and the potential implications and cost save for traders. Now, that presentation has been released by Quantitative Brokers. For those thinking about algos across asset classes, or looking for another way to save or make some money is a worthwhile read. It is amazing the opportunities that readily available data, properly analysed can reveal.

So, whatever bubble you are kicking yourself for missing- bonds, stocks, Bitcoin, Ethereum, tulips, you can still take advantage of bubbles to facilitate better trading executions. It speaks to what the right data, combined with the right expertise and machine learning can yield for traders and investors of all types.

[1] BTW, real bubbles never occur where they are expected!

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