Emerging machine learning technologies for algorithmic trading
My name is Patrick Gabrielsson. My research project is entitled Emerging machine learning technologies for algorithmic trading, which focuses on improving profitability whilst reducing risk and costs within algorithmic trading through the adoption of novel machine learning methods and techniques.
Since the introduction of electronic exchanges in the late 20th century, there has been a proliferation of diverse trading algorithms within the trading community. The financial benefits associated with outperforming the market and gaining leverage over the competition has fueled the research of computational intelligence within financial information systems.
The most lucrative form of algorithmic trading is high-frequency trading. In 2008, 300 high-frequency trading firms netted a profit of $21 billion (TABB Group) and in 2009, 60-73% of all US equity trading volume was accounted for by high-frequency trading firms (Aite Group). Algorithmic trading is rapidly replacing human traders across other asset classes. This, coupled with the globalization of markets, is changing the nature of financial markets and obviating the need for research in order to better understand the effects of algorithmic trading and to protect against exceptional events, such as the Flash Crash of May 6, 2010, caused by a rouge trading algorithm (SEC).
Instead of relying on subjective interpretations of market behavior, machine learning techniques can be used to infer viable explanations of patterns in financial time series. One desirable property of such techniques is the creation of transparent and comprehensible trading models. Not only do such models provide insight into market behavior, but can be used to develop profitable trading strategies whilst minimizing risk and trading costs (a multi-objective optimization problem). This is especially valuable to fund managers in their quest to decipher the rationale behind bad trades.
It is widely known that different asset classes possess their own characteristic properties. This raises the question if some machine learning techniques are more appropriate for certain markets. Other factors that might influence such a decision, are contract types and trading frequencies. Trading algorithms also need to adapt to continuously changing markets and regime shifts. Many high-frequency trading algorithms are in demand of high performance computing (HPC). This necessitates the development of parallel and distributed machine learning techniques.
Lastly, algorithmic trading includes techniques for data acquisition, preprocessing, trade signal generation and trade execution. Algorithms for trade execution include the splitting and scheduling of large orders and finding pools of liquidity across different venues. This is especially important for institutional traders and brokers. The incorporation of machine readable news feeds and sentiment-based data feeds are highly relevant research areas.
The main aim with my research is to explore the diverse landscape of various machine learning techniques and to compare their performance in various algorithmic trading scenarios in order to gain an understanding of which machine learning techniques are most appropriate to use in various scenarios. The main goal with the research is to identify and improve novel, data-driven machine learning algorithms in order to increase profitability whilst reducing risk and costs in algorithmic trading. The research will cover most issues outlined in the motivation above.
So far, one cognitive machine learning technology, Hierarchical Temporal Memory (HTM), has been researched (and benchmarked against recurrent neural networks) in a high-frequency trading scenario for an equity index futures market using a trend-following strategy and an evolutionary optimization method (2 papers). One evolutionary machine learning technology, based on context-free grammars, Grammatical Evolution (GE), has also been researched in a high-frequency trading scenario for an equity index futures market, producing a risk-averse, mean-reverting trading strategy (1 paper). Both technologies created profitable trading models when back-tested and paper-traded on previously unseen data (trading costs were accounted for, but not slippage due to market impact and liquidity). GE also produces transparent, comprehensible trading models.
Supervisors and Mentors
- Primary Supervisor – Associate Professor Ulf Johansson, University of Borås
- Assistant Supervisor – Dr. Maria Riveiro, University of Skövde
- Assistant Supervisor – Dr. Joe Steinhauer, University of Skövde
- Board of directors of IPSI