A framework for intraday ensemble trading on the foreign exchange market
Date
2024-12
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Publisher
Stellenbosch University
Abstract
Financial trading consists of traders buying and selling financial assets in the hope of generating profit over time. These assets are traded in financial markets, an example of which is the liquid and volatile Foreign exchange (Forex) market. Generating profit when trading on the Forex market is not a trivial task. Many traders are, in fact, unsuccessful due to a variety of complicating factors such as the stochastic nature of the Forex market, market information inefficiencies, and trader cognitive biases. One might think that these problems can be conquered with enough trading experience, but research on the topic has shown that even highly skilled investment managers struggle with trading performance consistency in the long term. Modelling the behaviour of the Forex market in the light of this market complexity might, therefore, seem daunting to any novice trader. In an attempt to overcome the inefficiencies inherent to human traders, however, trading algorithms have been proposed as an alternative for automating parts of the trading process. Substantial amounts of time and resources have been committed by researchers to the design of new and innovative trading algorithms tailored to the pursuit of trading profitably and the establishment of a competitive edge over human and other algorithmic contenders. As a result, various frameworks for developing trading algorithms have been proposed in the literature, each enabling the establishment of new approaches toward the development of such a trading algorithm. These frameworks, however, typically conform to one of two extremes: They are either problem-specific (focusing on a particular portion of the trading pipeline) or lack enough depth to facilitate the inner workings of, and communication between, different framework constituent components adequately. Moreover, few frameworks emphasise the potential benefits of employing ensembling approaches during the trading process in the context of intraday trading, especially in respect of trading strategy ensembling. An intraday ensemble-based trading framework is proposed in this dissertation with the objective of addressing the shortcomings of current frameworks for this purpose in the literature. More specifically, the framework is tailored to provide a detailed (albeit holistic) road map for its users which may be used to design an ensemble-based Forex trading algorithm. Apart from the pre-processing of input data, the framework also facilitates processes such as forecasting future market behaviour and trading strategy development. Forecasting is conducted by invoking various time series forecasting methods from the realm of machine learning which are ultimately ensembled into a single forecast. This ensemble forecast is then incorporated into the trading strategies developed which are, in turn, also ensembled so as to strike a balance between risk mitigation and returns maximisation when executing Forex trades in real time. The practicality of the proposed framework is demonstrated via a computerised instantiation thereof. This framework instantiation is verified, after which it is validated by conducting two simulated real-world trading case studies.