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Artificial Intelligence in Trading Crypto Assets Might Not Be So Smart…
Traders Magazine Online News, November 22, 2017
There is an old expression in computer programming: “Garbage in, Garbage out.” The point of the expression is that if you program logic based on bad data or data derived from the wrong context, that the results will not be what you are looking for. In the world of trading cryptocurrencies, the data supplied to machine learning algorithms that determine where to price individual assets might be very unreliable at times, and the data for trading those assets might be even worse (witness the move in BitCoin Cash of 2.5 times higher then back down, pictured above).
With that said, there is a lot of buzz around the crypto universe about quantitative hedge funds entering the space and about the use of advanced techniques. One example is that Bitcoin News published a story about the use of Artificial Intelligence in crypto investing. It features an interview with Guy Zyskind, the CEO of Enigma, which offers a data marketplace which hopes to become the foundation for such endeavors.
While I agree wholeheartedly with Guy’s thesis that quantitative trading firms have helped improve liquidity and compress spreads in the equity markets, and also that the firms he mentioned (one of which I spent over 5 years at) have intelligently utilized machine learning and quantitative techniques. The problem with this article, however, is that it is wrong to conflate the use of such techniques in asset selection, portfolio construction and trading. Those are three separate disciplines which require very different quantitative tools and data, and Enigma, like most FinTech firms and Funds in Equities, is focused on asset selection using their alpha generation tools. To be clear, finding alpha is extremely important to funds and investors, but my point is that the tools are different and so is the data.
The article continues the confusion by alternatively discussing trading “bots” and hedge funds in a mashup. In reality, most of the automated market making firms which provide the liquidity that helped collapse spreads are brokers, not funds. Those firms do use machine learning and other quantitative tools for trading and risk management, but those are quite distinct from the type of data and techniques used for alpha generation. To understand these differences, readers can review a previous article I wrote, that went over the different elements of fund management that could benefit from quantitative techniques, “What is Quantamental Anyway?”.
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