Exploring Trading Strategies and Their Effects in the Foreign Exchange Market

Aloud, Monira; Fasli, Maria

doi:10.1111/coin.12085

Cited by 10 publications

(10 citation statements)

References 38 publications

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“…One line of future research is to remove this feed and allow the market‐maker to generate artificial prices and explore and study the characteristics of the price time series generated. We have been experimenting with different agent strategies such as for instance the ZI and genetic programming‐based strategies as part of a study to assess the level of intelligence required to reproduce the stylized facts in the FX market (Aloud & Fasli, ). These results indicate that complex strategies may not be necessary for the reproduction of the stylized facts, but simpler strategies such as the ZI‐DCT0 used for this study may be more appropriate.…”

Section: Discussionmentioning

confidence: 99%

Modeling the High‐Frequency FX Market: An Agent‐Based Approach

Aloud

Fasli

Tsang

et al. 2017

Computational Intelligence

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1 AbstractThe development of computational-intelligence based strategies for electronic markets has been the focus of intense research. In order to be able to design efficient and effective automated trading strategies, one first needs to understand the workings of the market, the strategies that traders use and their interactions as well as the patterns emerging as a result of these interactions. In this paper, we develop an agent-based model of the FX market which is the market for the buying and selling of currencies. Our agent-based model of the FX market (ABFXM) comprises heterogeneous trading agents which employ a strategy that identifies and responds to periodic patterns in the price time series. We use the ABFXM to undertake a systematic exploration of its constituent elements and their impact on the stylized facts (statistical patterns) of transactions data. This enables us to identify a set of sufficient conditions which result in the emergence of the stylized facts similarly to the real market data, and formulate a model which closely approximates the stylized facts. We use a unique high frequency dataset of historical transactions data which enables us to run multiple simulation runs and validate our approach and draw comparisons and conclusions for each market setting.

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Section: Discussionmentioning

confidence: 99%

Modeling the High‐Frequency FX Market: An Agent‐Based Approach

Aloud

Fasli

Tsang

et al. 2017

Computational Intelligence

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“…The single‐threshold DC trading strategy is presented in (Aloud & Fasli, ). The B&H trading strategy is a common benchmark for trading strategies.…”

Section: Methodsmentioning

confidence: 99%

The role of attribute selection in Deep ANNs learning framework for high‐frequency financial trading

Aloud

2020

Intell Sys Acc Fin Mgmt

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SUMMARY In financial trading, technical and quantitative analysis tools are used for the development of decision support systems. Although these traditional tools are useful, new techniques in the field of machine learning have been developed for time‐series forecasting. This paper analyses the role of attribute selection on the development of a simple deep‐learning ANN (D‐ANN) multi‐agent framework to accomplish a profitable trading strategy in the course of a series of trading simulations in the foreign exchange market. The paper evaluates the performance of the D‐ANN multi‐agent framework over different time spans of high‐frequency (HF) intraday asset time‐series data and determines how a set of the framework attributes produces effective forecasting for profitable trading. The paper shows the existence of predictable short‐term price trends in the market time series, and an understanding of the probability of price movements may be useful to HF traders. The results of this paper can be used to further develop financial decision‐support systems and autonomous trading strategies for the financial market.

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“…The DC approach has shown its potential for studying and analysing financial time-series for the Foreign Exchange Market [1], [3], [7], [8], [9], [10], [11], [12], [13], and has the potential to be applied to other market data [14], [15], [16], [17]. Furthermore, the DC approach has been part of works related to trading strategies [5], [6], [13], [17] and forecasting [10], [12], [14], [18] studies. In addition, it has typically been applied with a fixed threshold, whereas a dynamic threshold may be more appropriate for markets whose nature is inherently dynamic.…”

Section: Directional Change Approachmentioning

confidence: 99%

A Directional Change Based Trading Strategy with Dynamic Thresholds

Alkhamees

Fasli

2017

2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

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Traders always seek a trading strategy that can increase and maximize their profits. However, given the current challenges in financial time-series streams-data elements (tick prices) arrive in real-time or almost real-time and at high velocity (at finer time scales)-it is difficult to identify and spot the best time and the most profitable price for trading. The Directional Change (DC) is an event-based approach for summarizing price movements based on a fixed given threshold value. An event in the DC approach is detected if the price change between two points satisfies the given threshold value. In this research, we aim to present a trading strategy based on the DC approach and a dynamic threshold to replace the fixed given one. We call this strategy, the Dynamic Threshold Trading Strategy (DT-TS). Thus, once a DC event is detected (a price change is identified) using the defined dynamic threshold, a trading action is triggered as prices continue to increase or decrease depending on the detected DC event. The trading action to be taken (buy or sell) depends on the previous day price transitions. An experiment was conducted on the FTSE 100 minute-by-minute prices stream to evaluate the DT-TS against different fixed threshold values and different trading strategies. Results showed that the DT-TS was the most profitable strategy among different fixed thresholds and all other examined trading strategies.

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Exploring Trading Strategies and Their Effects in the Foreign Exchange Market

Cited by 10 publications

References 38 publications

Modeling the High‐Frequency FX Market: An Agent‐Based Approach

Modeling the High‐Frequency FX Market: An Agent‐Based Approach

The role of attribute selection in Deep ANNs learning framework for high‐frequency financial trading

A Directional Change Based Trading Strategy with Dynamic Thresholds

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