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Master Thesis Defence by Filip Bruman

An evaluation of parallelized financial trading strategies on commodity, shared memory multi processors by Filip Bruman

External Examiner:

Morten Frank, Nordea

Supervisor:

Marcos Vaz Salles, DIKU

Abstract:

Quantitative trading utilizes trading strategies, which are computer programs that analyzes market data based on a set rules. Whenever a pattern matching the rules the strategy enters or exits a trade. Before make use of a trading strategy, traders test its settings on historic data to evaluate its performance and thereby project the future performance of the strategy. Being able to test strategies with a short turnaround time is useful for being able to try out many different settings.

In this thesis trading strategies are implemented in a general purpose programming language, parallelized utilizing coarse and fine-grained parallelization, running on commodity hardware, with contemporary multi-core processors enabling traders, with limited knowledge or experience of programming, to reduce the time to solution for testing of trading strategies.

Strategies are parallelized utilizing coarse and fine-grained parallelization, using threads and auto-vectorization respectively, and experimentally evaluated with a set of varying sizes of synthesized data simulating stock market data, as well as a dataset containing real market data.

Strategies were coarse and fine-grained parallelized with mixed results. One strategy got a speedup close to 20x on 32 cores. While another strategy had a merely 4x speedup on the same dataset size and 32 cores. Coarse-grained parallelization was beneficial to all strategies.

Fine-grained parallelization could in some cases have a significantly inhibiting effect on a strategy, close to 25% compared to a non-parallelized version.

To parallelize trading strategies enables traders to get a quicker response on testing workflow. Using commodity hardware, multi-core processors and a shared memory model can reduce testing significantly in some cases. Care should be taken in applying especially fine-grained parallelization to control its application particularly when external libraries are used and auto-vectorization is applied to all code.