Xavier M made a quick overview of a project called Coindex at the December Un-hackathon, aimed at studying the array of cryptocurrencies with a market point of view. The study is done to devise quantitative systematic strategies that trading bots would execute.
After acknowledging that yes, Bitcoin was matching all criteria characterizing a bubble, Xavier refocused the challenge towards building something profitable out of it, whether it’s a bubble or not.
In a talk session that did not require or demand any programming, quantitative analyst Xavier proposed several applications that some research on cryptocurrencies could provide: trading cross-exchange arbitrages, identifying and following trends and investing in low-frequency trading strategies which provide a return similar to an individual trade while mitigating the risk.
Short-term horizon trading
The first category falls into trading with a short-term horizon, also known as day-trading, and Xavier showed a simple cross-market arbitrage monitor of real time opportunities for profit that could be made by buying cheap in one exchange and simultaneously selling high in another one, across six exchanges.
The app is elementary and aims simply to instruct, but could be extended easily to more complex real-time arbitrages, and also by adding trading functions for identified arbitrages.
Building more complex arbitrages or simply understanding the detailed working of the market, or microstructure, means a bit of data science has to come into play.
To do this, we can exploit the order books that each exchange publicly releases in real time. This kind of data allows study of the market microstructure and enables the design of high-frequency strategies. A full field of research can be explored (see for example Marco Avellaneda and Sasha Stoikov’s High-frequency trading in a limit order book or Rama Cont, Stoikov and Rishi Talreja’s A stochastic model for order book dynamics.
An example strategy would be to examine if one market is lagged compared to others. If this is the case, then other markets can surely be used as predictors.
Another example would be to study big orders, and see how to make a profit out of these.
If for example, as it is often heard, crypto markets are completely manipulated, then it could be interested to be able to identify manipulation, and based on the properties of such event, use it for profit.
Xavier provided a database made of order books of six exchanges retrieved every 30 seconds available to any data scientist wanting to design price prediction models or other strategies based on order book data.
Low-frequency investment strategies
The second category was about designing low-frequency investment strategies, where trading seldom occurs, but carries a lower risk than simply holding Bitcoins. Such risk reduction can classically be achieved through diversification, but as was shown in a study by R. Porsch, currencies recently tend to correlate to each other, reducing the benefit of diversification.
Nevertheless, other tactics are possible. For instance, systematic rebalancing with fixed weights for each currency, so every month, week or day the portfolio is rebalanced so that it holds the same value of every currency in USD equivalent. Following this while Bitcoin does an impressive 15x, the least performing portfolio does 30x, and with an equivalent volatility level.
These are extremely simple investment ideas, and many more can be designed to reduce risk (volatility) but not the return.
About the author
Xavier Mathieu developed his career as a quantitative team manager with BNP Paribas. He is now the CEO of Modwize limited. He co-organises the group data science Hong Kong.