In terms of the directional movement of bitcoin the currency, 2015 saw near 40% gains making it one of the best performing financial instruments out there. But often traders are seeking greater returns than that and don’t necessarily want the directional exposure, but just want to capture bitcoin’s volatility.
This means trading bitcoin at a higher frequency, balancing transaction costs and execution risk – and this can be facilitated by machine learning.
Arshak Navruzyan the founder of Startup.ML, who has been applying machine learning to quantitative finance problems, found that cryptocurrency is also interesting because it allows relatively small scale investors access to exchanges, where they can get full order book data and trade more cost effectively compared to going through a brokerage.
Navruzyan said: “This is actually one of the exciting things about cryptocurrency; why a lot of our modelling work is happening in this area is because you do get access to exchanges even as a little guy.”
A key thing for alpha traders is the concept of transaction costs. Volatility and transaction costs kind of go hand in hand, and if your transaction costs are high then your prediction has to be accurate for your alpha strategy to work.
Another key idea,