Since the start of the electronic market, price prediction has always been a hot topic. With the advent of machine learning, the field is becoming more and more contested with mathematical methods leveraging these new tools. Stories from hedge funds such as Renaissance Capital have inspired financiers around the world to look towards mathematics as a means of generating alpha.
Two quants from DataSpartan were assigned to work on and analyse medium frequency and high frequency data respectively. They were asked to specifically investigate bond and option prices over a one hour period and a millisecond period to determine if there are any patterns that could be discerned from the data. The client had a proprietary data feed of tick data for each symbol which allowed the quants to conduct in depth research.
The client had a preexisting framework written in Java which could instantaneously switch between backtest and live. Ideas were gained mainly by reading research papers in the physics, biology and mathematics fields – the ideas were then explored and implemented on time series data. Strategies had to be run for a minimum amount of time and deemed profitable in a backtest before being put onto live servers. Concepts which had predictive characteristics such as entropy were explored (entropy has been used to predict the probability of cardiac arrest during open heart surgery by investigating the differences in systolic and diastolic pressure).
After four months of testing, a strategy was found that yielded a back tested Sharpe ratio (a measure of the reward over risk) of 1.5 inclusive of commissions. This strategy is now being run live on the platform and is currently generating profits for the client. Work is currently being done on the strategy to check if it works on other instruments in different asset classes. Due to the success of this strategy, the client has has DataSpartan to do some further work investigating strategies for portfolio allocation and diversification on a macroeconomic level. They want to investigate the automation of Warren Buffet’s trading style by analysing 10-K reports using machine learning and determining the optimal allocation of capital on an industry basis.