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Publication Detail
Experimental Computational Simulation Environments for Algorithmic Trading
  • Publication Type:
    Thesis/Dissertation
  • Authors:
    Galas M
  • Date awarded:
    2014
  • Status:
    Unpublished
  • Awarding institution:
    UCL (University College London)
  • Language:
    English
Abstract
This thesis investigates experimental Computational Simulation Environments for Computational Finance that for the purpose of this study focused on Algorithmic Trading (AT) models and their risk. Within Computational Finance, AT combines different analytical techniques from statistics, machine learning and economics to create algorithms capable of taking, executing and administering investment decisions with optimal levels of profit and risk. Computational Simulation Environments are crucial for Big Data Analytics, and are increasingly being used by major financial institutions for researching algorithm models, evaluation of their stability, estimation of their optimal parameters and their expected risk and performance profiles. These large-scale Environments are predominantly designed for testing, optimisation and monitoring of algorithms running in virtual or real trading mode. The stateof-the-art Computational Simulation Environment described in this thesis is believed to be the first available for academic research in Computational Finance; specifically Financial Economics and AT. Consequently, the aim of the thesis was: 1) to set the operational expectations of the environment, and 2) to holistically evaluate the prototype software architecture of the system by providing access to it to the academic community via a series of trading competitions. Three key studies have been conducted as part of this thesis: a) an experiment investigating the design of Electronic Market Simulation Models; b) an experiment investigating the design of a Computational Simulation Environment for researching Algorithmic Trading; c) an experiment investigating algorithms and the design of a Portfolio Selection System, a key component of AT systems. Electronic Market Simulation Models (Experiment 1): this study investigates methods of simulating Electronic Markets (EMs) to enable computational finance experiments in trading. EMs are central hubs for bilateral exchange of securities in a well-defined, contracted and controlled manner. Such modern markets rely on electronic networks and are designed to replace Open Outcry Exchanges for the advantage of increased speed, reduced costs of transaction, and programmatic access. Study of simulation models of EMs is important from the point of view of testing trading paradigms, as it allows users to tailor the simulation to the needs of particular trading paradigms. This is a common practice amongst investment institutions to use EMs to fine-tune their algorithms before allowing the algorithms to trade with real funds. Simulations of EMs provide users with the ability to investigate the market micro-structure and to participate in a market, receive live data feeds and monitor their behaviour without bearing any of the risks associated with real-time market trading. Simulated EMs are used by risk managers to test risk characteristics and by quant developers to build and test quantitative financial systems against market behaviour. Computational Simulation Environments (Experiment 2): this study investigates the design, implementation and testing of an experimental Environment for Algorithmic Trading able to support a variety of AT strategies. The Environment consists of a set of distributed, multi-threaded, event-driven, real-time, Linux services communicating with each other via an asynchronous messaging system. The Environment allows multi-user real and virtual trading. It provides a proprietary application programming interface (API) to support research into algorithmic trading models and strategies. It supports advanced trading-signal generation and analysis in near real-time, with use of statistical and technical analysis as well as data mining methods. It provides data aggregation functionalities to process and store market data feeds. Portfolio Selection System (Experiment 3): this study investigates a key component of Computational Finance systems to discover exploitable relationships between financial time-series applicable amongst others to algorithmic trading; where the challenge lays in identification of similarities/dissimilarities in behaviour of elements within variable-size portfolios of tradable and non-tradable securities. Recognition of sets of securities characterized by a very similar/dissimilar behaviour over time, is beneficial from the perspective of risk management, recognition of statistical arbitrage and hedge opportunities, and can be also beneficial from the point of view of portfolio diversification. Consequently, a large-scale search algorithm enabling discovery of sets of securities with AT domain-specific similarity characteristics can be utilized in creation of better portfolio-based strategies, pairs-trading strategies, statistical arbitrage strategies, hedging and mean-reversion strategies. This thesis has the following contributions to science: Electronic Markets Simulation - identifies key features, modes of operation and software architecture of an electronic financial exchange for simulated (virtual) trading. It also identifies key exchange simulation models. These simulation models are crucial in the process of evaluation of trading algorithms and systemic risk. Majority of the proposed models are believed to be unique in the academia. Computational Simulation Environment - design, implementation and testing of a prototype experimental Computational Simulation Environment for Computational Finance research, currently supporting the design of trading algorithms and their associated risk. This is believed to be unique in the academia. Portfolio Selection System - defines what is believed to be a unique software system for portfolio selection containing a combinatorial framework for discovery of subsets of internally cointegrated time-series of financial securities and a graph-guided search algorithm for combinatorial selection of such time-series subsets.
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