It never took off (as far as I know) but Q had an effort that presumably would have required some ML:
The motivating paper can be found here:
Pages 8-15 list all of the 101 alphas. For example, we have expressions like:
Alpha#59: (-1 * Ts_Rank(decay_linear(correlation(IndNeutralize(((vwap * 0.728317) + (vwap *
(1 - 0.728317))), IndClass.industry), volume, 4.25197), 16.2289), 8.19648))
Where did it come from? Is there a "strategic intent" along the lines of that required by the Q fund (https://www.quantopian.com/fund)? Or did a HAL 9000 computer (perhaps with a little human guidance) extract the expression from a database of financial data?
And suppose I then combine 101 of these alphas? How do I articulate a "strategic intent" then? "Uh, well, umm...the machine sorted it out, and it works!"
From the conclusion to the paper:
Technological advances nowadays allow automation of alpha mining. Quantitative trading
alphas are by far the most numerous of available trading signals that can be turned into trading
strategies/portfolios. There are myriad permutations of individual stock holdings in a (dollarneutral)
portfolio of, e.g., 2,000 most liquid U.S. stocks that can result in a positive return on
high- and mid-frequency time horizons. In addition, many of these alphas are ephemeral and
their universe is very fluid. It takes quantitatively sophisticated, technologically well-endowed
and ever-adapting trading operations to mine hundreds of thousands, millions and even billions
of alphas and combine them into a unified “mega-alpha”, which is then traded with an added
bonus of sizeable savings on execution costs due to automatic internal crossing of trades.
We're not talking humans formulating old-school hypotheses and then testing them: "Hmm...I wonder if the price of rice in China is an alpha?" A machine needs to formulate the hypotheses, test them, and then spit out the results. Additionally, if "many of these alphas are ephemeral and their universe is very fluid" again the effort would seem better suited to a machine.
On http://blog.quantopian.com/a-professional-quant-equity-workflow/, we hear mention of ML, in the context of combining alphas:
Lastly, modern machine learning techniques can capture complex relationships between alphas. Translating your alphas into features and feeding these into a machine learning classifier is a popular vein of research.
However, the use of ML in discovering alphas is not mentioned. The new Alphalens appears to be a manual tool for noodling on individual aphas, versus a function that would plug into a ML algo that would churn over Q datasets, but maybe I'm missing something (https://www.quantopian.com/posts/alphalens-a-new-tool-for-analyzing-alpha-factors).
It would be interesting to hear thoughts from the Q team on this topic. Within the proposed workflow, how would ML be implemented, potentially coupled with high-performance computing (HPC)? Is this combination viewed as a natural evolution for Q? If so, what might the platform look like?