Machine Learning Methods In Algorithmic Trading

In the context of the new trends in trading business, the aim of this thesis is to contribute in developing automated trading algorithms for acquiring explicit trading signals; Long or Short (Buy or Sell respectively) positions in Futures products with liquid underlying assets SP500, DAX 30 and German Bunds. The algorithms are based on two contemporary classification Machine Learning techniques:
SVM (Support Vector Machines)
Feed Forward Multilayer Perceptron (MLP) Neural Network (NN)
The parameters of the above methods are optimized using a heuristic method known as Differential Evolution (DE), which is a promising evolutionary algorithm able to produce an optimized classification method faster and more accurate than a classical grid search.
Raw high frequency data, also known as intraday data, are employed with the aim to generate normalised technical indicators which will be used as data set in our algorithms. The defined data set comprises to train and optimise (via DE) the SVM and NN classification methods, using Python as the appropriate programme language.
Out of sample data are used in order to calculate the profit and loss outcome and hence to evaluate the accuracy of the algorithmic outputs, comparing and contrasting the advantages and disadvantages of each method.

The current DTC in Financial Computing is acknowledged by the Department of Business Innovation & Skills as having had a major impact on our financial industry partners and on our academic partners. We and our Industry partners are also central to the forthcoming investments in Big Data from EPSRC and ESRC (e.g. Business Datasafe). The Pathways to Impact Attachment provides a comprehensive description of impact.

INDUSTRY
The Centre will continue to actively promote the placement of PhD and Masters students.
* PhD student placements- many of the Banks now have established formal PhD Internship programmes, in part due to the current DTC.
* Masters student placements - the Centre is actively involved in placing UCL Masters students in our partner companies, in part to address the shortage of PhD students.
* Utilising Industry Lecturers on Courses - UCL, LSE and Imperial College increasingly use industry professionals to enhance their finance-related courses and is popular with students.

REGULATORS AND GOVERNMENT
Collaboration with the financial Regulators and Government is a priority for the Centre (See letters of support from BoE, PRA and FCA).
*Financial regulators - currently we have 2 PhD students in the Bank of England, and we will be seeking to place more. In addition, with the new CDT we will be seeking to establish one or more major collaborative projects with the Regulators.
* Government - we have a PhD student collaborating with the Cabinet Office on their major cross-government benefit fraud programme, and we also support the MiData initiative.

SOCIETAL
The existing DTC and the proposed CDT's societal contribution focusses on supporting entrepreneurship.
* Entrepreneurship & start-ups - we will continue to encourage and support our PhD students in launching their own start-up.
* Professional (part-time) PhD students - we will continue to recruit young professionals who wish to pursue part-time PhDs.

ECONOMIC
* UK Services Sector - already the existing Centre is building research collaboration with companies such as Tesco, Sainsbury's, Alliance Boots, BUPA, Unilever, P&G, dunnhumby and SAS.
* Other sectors - we are promoting, but not funding, analytics in other sectors, a notable example being Sports Analytics.