The Retail FX Trader: Random Trading and the Negative Sum Game

With the internet boom of early 2000 making access to trading the Foreign Exchange (FX) market far simpler for members of the general public, the growth of 'retail' FX trading continues, with daily transaction volumes as high as $200 billion. Potential new entrants to the retail FX trading world may come from the recent UK pension deregulations, further increasing the volumes. The attraction of FX trading is that it offers high returns and whilst it has been understood that it is high-risk in nature, the rewards are seen as being commensurately high for the 'skilled and knowledgeable' trader who has an edge over other market participants. This paper analyses a number of independent sources of data and previous research, to examine the profitability of the Retail FX trader and compares the results with that of a simulated random trading models. This paper finds evidence to suggest that whilst approximately 20% of traders can expect to end up with a profitable account, around 40% might expect their account to be subject to a margin call. This paper finds a strong correlation between the overall profitability of traders and impact of the cost of the bid-ask spread, whilst finding little if any evidence that retail FX traders, when viewed as a group, are achieving results better than that from random trading

WATTNet: Learning to Trade FX via Hierarchical Spatio-Temporal Representation of Highly Multivariate Time Series

Finance is a particularly challenging application area for deep learning models due to low noise-to-signal ratio, non-stationarity, and partial observability. Non-deliverable-forwards (NDF), a derivatives contract used in foreign exchange (FX) trading, presents additional difficulty in the form of long-term planning required for an effective selection of start and end date of the contract. In this work, we focus on tackling the problem of NDF tenor selection by leveraging high-dimensional sequential data consisting of spot rates, technical indicators and expert tenor patterns. To this end, we construct a dataset from the Depository Trust & Clearing Corporation (DTCC) NDF data that includes a comprehensive list of NDF volumes and daily spot rates for 64 FX pairs. We introduce WaveATTentionNet (WATTNet), a novel temporal convolution (TCN) model for spatio-temporal modeling of highly multivariate time series, and validate it across NDF markets with varying degrees of dissimilarity between the training and test periods in terms of volatility and general market regimes. The proposed method achieves a significant positive return on investment (ROI) in all NDF markets under analysis, outperforming recurrent and classical baselines by a wide margin. Finally, we propose two orthogonal interpretability approaches to verify noise stability and detect the driving factors of the learned tenor selection strategy.Comment: Submitted to the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 20

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