Hybridizing data stream mining and technical indicators in automated trading systems

Automated trading systems for financial markets can use data mining techniques for future price movement prediction. However, classifier accuracy is only one important component in such a system: the other is a decision procedure utilizing the prediction in order to be long, short or out of the market. In this paper, we investigate the use of technical indicators as a means of deciding when to trade in the direction of a classifier’s prediction. We compare this “hybrid” technical/data stream mining-based system with a naive system that always trades in the direction of predicted price movement. We are able to show via evaluations across five financial market datasets that our novel hybrid technique frequently outperforms the naive system. To strengthen our conclusions, we also include in our evaluation several “simple” trading strategies without any data mining component that provide a much stronger baseline for comparison than traditional buy-and-hold or sell-and-hold strategies

SISTEM EVALUASI ROBOT TRADING DENGAN METODE ELECTRE BERBASIS REAL-TIME WEB SERVICE PADA PASAR VALAS

Penelitian ini bertujuan mengoptimalkan keuntungan perdagangan valas secara otomatis menggunakan robot trading namun tetap mempertimbangkan tingkat akurasi dan drawdown. Sistem evaluasi mengelompokkan kinerja robot trading berdasarkan sesi pasar perdagangan (Sydney, Tokyo, London dan New York) untuk menentukan robot trading yang tepat untuk digunakan pada sesi pasar tertentu. Sistem evaluasi ini berbasis web dengan perhitungan metode ELECTRE yang berinteraksi secara real-time dengan robot trading melalui web service dan mampu menyajikan grafik kinerja secara real-time pada dashboard dengan komunikasi protokol web socket. Aplikasi web diprogram menggunakan teknologi NodeJs. Pada periode pengujian, semua robot trading disimulasikan 24 jam di semua sesi pasar selama tiga bulan, robot trading terbaik dinilai berdasarkan kriteria laba, akurasi dan drawdown yang dihitung menggunakan metode ELECTRE berbasis web. Ide dari penelitian ini adalah membandingkan robot trading terbaik pada periode pengujian dengan kinerja kolaborasi empat robot trading terbaik di setiap sesi pasar. Penelitian ini menggunakan data historis pergerakan mata uang EURO terhadap USD sebagai periode pengujian dan 3 bulan berikutnya sebagai data validasi. Dari hasil penelitian, kinerja kolaborasi empat robot trading terbaik yang dikelompokkan berdasarkan sesi pasar dapat meningkatkan persentase keuntungan secara konsisten dengan tetap menjaga tingkat akurasi dan drawdown. Kata Kunci: Kinerja, Sistem Evaluasi, Valas, Robot Trading, ELECTRE This research aims to optimize forex trading profit automatically using EA but its still keep considering accuracy and drawdown levels. The evaluation system will classify EA performance based on trading market sessions (Sydney, Tokyo, London and New York) to determine the right EA to be used in certain market sessions. This evaluation system is a web-based ELECTRE methods that interact in real-time with EA through web service and are able to present real-time charts performance dashboard using web socket protocol communications. Web applications are programmed using NodeJs technology. In the testing period, all EAs had been simulated 24 hours in all market sessions for three months, the best EA is valued by its profit, accuracy and drawdown criterias that calculated using web-based ELECTRE method. The ideas of this research is to compare the best EA on testing period with collaboration performances of each best classified EA by market sessions. This research uses three months historical data of EUR against USD as testing period and other 3 months as validation period. As a result, performance of collaboration four best EA classified by market sessions can increase profits percentage consistently in testing and validation periods and keep securing accuracy and drawdown levels. Keywords: Performance, Evaluation System, Forex, EA, ELECTR

Forex Trading Signal Extraction with Deep Learning Models

The rise of AI technology has popularized deep learning models for financial trading prediction, promising substantial profits with minimal risk. Institutions like Westpac, Commonwealth Bank of Australia, Macquarie Bank, and Bloomberg invest heavily in this transformative technology. Researchers have also explored AI's potential in the exchange rate market. This thesis focuses on developing advanced deep learning models for accurate forex market prediction and AI-powered trading strategies. Three deep learning models are introduced: an event-driven LSTM model, an Attention-based VGG16 model named MHATTN-VGG16, and a pre-trained model called TradingBERT. These models aim to enhance signal extraction and price forecasting in forex trading, offering valuable insights for decision-making. The first model, an LSTM, predicts retracement points crucial for identifying trend reversals. It outperforms baseline models like GRU and RNN, thanks to noise reduction in the training data. Experiments determine the optimal number of timesteps for trend identification, showing promise for building a robotic trading platform. The second model, MHATTN-VGG16, predicts maximum and minimum price movements in forex chart images. It combines VGG16 with multi-head attention and positional encoding to effectively classify financial chart images. The third model utilizes a pre-trained BERT architecture to transform trading price data into normalized embeddings, enabling meaningful signal extraction from financial data. This study pioneers the use of pre-trained models in financial trading and introduces a method for converting continuous price data into categorized elements, leveraging the success of BERT. This thesis contributes innovative approaches to deep learning in algorithmic trading, offering traders and investors precision and confidence in navigating financial markets

IT service management: process capability, process performance, and business performance

As technology is at the core of almost every leading industry, organizations are increasingly scrutinizing their Information Technology (IT) group’s performance so that it is more in line with overall business performance and contributes to the business’ bottom line. Many IT departments are not equipped to meet these increasing IT service demands. They continue to operate as passive-reactive service providers, utilizing antiquated methods that do not adequately provide the quality, real-time solutions that organizations need to be competitive. Organizations need efficient Information Technology Service Management (ITSM) processes in order to cut costs, but ironically, in order to implement highly capable processes, there are significant costs involved, both in terms of time and resources. A potential way to achieve better performing and higher capable processes is to employ methods to compare an organization’s processes against best-practice standards to identify gaps and receive guidance to improve the processes. Many of the existing methods require large investments. Holding back progress towards best practice for financial benefit in the IT industry is the reluctance of many IT organizations to embrace the business side (specifically Service Portfolio Management and IT Financial Management) aspects of ITSM. Service Portfolio Management (SPM) is used to manage investments in Service Management across an organization, in terms of financial values. SPM enables managers to assess the quality requirements and associated costs. IT Financial Management aims to provide information on the IT assets and resources used to deliver IT services. Providing a Service Portfolio and practicing IT Financial Management requires a high level of maturity for an organization. It seems reasonable and logical that the organization’s Chief Information Officer should be able to articulate and justify the IT services provided, report the costs (by service) incurred in delivering these services, and can communicate the demand for those services, that is, how they are being consumed and projections on how they will be consumed in the future. However, a major investment in terms of time and resources may be needed to catalogue such information and report on it. The research problem that this paper addresses is the lack of a pragmatic model and method that associates ITSM process maturity (process capability and performance) with financial performance for organizations that lack mature ITSM processes. Previous studies have reported on cost savings, but there is currently no measurement model to associate ITSM maturity with financial profitability; which in turn prompts the research question: How can the association of ITSM process capability and process performance with financial performance of an organization be determined? This research iteratively develops and applies a measurement model that presents a pragmatic and cost-effective method to link ITSM process capability and process performance with business performance by operationalizing Key Performance Indicators (KPIs) to support Critical Success Factors (CSFs) and associating CSFs with business risks to determine business performance. This study employs a scholar-practitioner approach to changing/improving processes using action research and an adaptation of the Keys to IT Service Management Excellence Technique (KISMET) model to guide the process improvement initiative. This technique leads to the second research question: How can the ITSM measurement framework be demonstrated for CSI? The research was based on a single case study of a global financial services firm Company X that had implemented the ITIL® framework to improve the quality of its IT services. The study found that the measurement framework developed can be used as a starting point for self-improvement for businesses, identifying gaps in processes, benchmarking within an organization as well as guiding an organization’s process improvement efforts. The measurement model can be used to conduct What-If analyses to model the impacts of future business decisions on KPIs and CSFs. The measurement model presented in this study can be quickly implemented, adapted and evolved to meet the organization’s needs. The research offers an example from which other organizations can learn to measure their financial return on investment in ITSM improvement

Agents in the market place an exploratory study on using intelligent agents to trade financial instruments

Tese de doutoramento em InformáticaThis dissertation documents our exploratory research aimed at investigating the utilization of intelligent agents in the development of automated financial trading strategies. In order to demonstrate this potential use for agent technology, we propose a hybrid cognitive architecture meant for the creation of autonomous agents capable of trading different types of financial instruments. This architecture was used to implement 10 currency trading agents and 25 stock trading agents. Their overall performance, evaluated according to the cumulative return and the maximum drawdown metrics, was found to be acceptable in a reasonably long simulation period. In order to improve this performance, we defined negotiation protocols that allowed the integration of the 35 trading agents in a multi-agent system, which proved to be better suited for withstanding sudden market events, due to the diversification of the investments. This system obtained very promising results, and remains open to many obvious improvements. Our findings lead us to conclude that there is indeed a place for intelligent agents in the financial industry; in particular, they hold the potential to be employed in the establishment of investment companies where software agents make all the trading decisions, with human intervention being relegated to simple administrative tasks.Esta dissertação documenta um estudo exploratório destinado a investigar a utilização de agentes inteligentes no desenvolvimento de estratégias de investimento financeiro automatizadas. Para demonstrar este uso potencial para tecnologia de agentes, foi proposta uma arquitectura cognitiva híbrida destinada à criação de agentes autónomos capazes de negociar diferentes tipos de instrumentos financeiros. Esta arquitectura foi utilizada para implementar 10 agentes que negoceiam pares cambiais, e 25 agentes que negoceiam acções. A performance global destes agentes, avaliada de acordo com as métricas de retorno acumulado e drawdown máximo, foi considerada aceitável ao longo de um período de simulação relativamente longo. Para melhorar esta performance, foram definidos protocolos de negociação que permitiram a integração dos 35 agentes num sistema multi-agente, que demonstrou estar melhor preparado para enfrentar alterações súbitas nos mercados, devido à diversificação dos investimentos. Este sistema obteve resultados muito promissores, e pode ainda ser sujeito a diversos melhoramentos. Os nossos resultados indiciam que os agentes inteligentes podem ocupar um lugar de relevo na indústria financeira; em particular, aparentam ter potencial suficiente para serem aplicados na criação de fundos de investimento onde todas as decisões de negociação são efectuadas por agentes de software, sendo a intervenção humana relegada para tarefas administrativas básicas

Sequential Modelling and Inference of High-frequency Limit Order Book with State-space Models and Monte Carlo Algorithms

The high-frequency limit order book (LOB) market has recently attracted increasing research attention from both the industry and the academia as a result of expanding algorithmic trading. However, the massive data throughput and the inherent complexity of high-frequency market dynamics also present challenges to some classic statistical modelling approaches. By adopting powerful state-space models from the field of signal processing as well as a number of Bayesian inference algorithms such as particle filtering, Markov chain Monte Carlo and variational inference algorithms, this thesis presents my extensive research into the high-frequency limit order book covering a wide scope of topics. Chapter 2 presents a novel construction of the non-homogeneous Poisson process to allow online intensity inference of limit order transactions arriving at a central exchange as point data. Chapter 3 extends a baseline jump diffusion model for market fair-price process to include three additional model features taken from real-world market intuitions. In Chapter 4, another price model is developed to account for both long-term and short-term diffusion behaviours of the price process. This is achieved by incorporating multiple jump-diffusion processes each exhibiting a unique characteristic. Chapter 5 observes the multi-regime nature of price diffusion processes as well as the non-Markovian switching behaviour between regimes. As such, a novel model is proposed which combines the continuous-time state-space model, the hidden semi-Markov switching model and the non-parametric Dirichlet process model. Additionally, building upon the general structure of the particle Markov chain Monte Carlo algorithm, I further propose an algorithm which achieves sequential state inference, regime identification and regime parameters learning requiring minimal prior assumptions. Chapter 6 focuses on the development of efficient parameter-learning algorithms for state-space models and presents three algorithms each demonstrating promising results in comparison to some well-established methods. The models and algorithms proposed in this thesis not only are practical tools for analysing high-frequency LOB markets, but can also be applied in various areas and disciplines beyond finance