Research Repository

A Directional Change Based Trading Strategy with Dynamic Thresholds

Alkhamees, N and Fasli, M (2018) A Directional Change Based Trading Strategy with Dynamic Thresholds. In: 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2017-10-19 - 2017-10-21, Tokyo, Japan.

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Abstract

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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: _not provided_
Uncontrolled Keywords: Directional Change, Trading Strategy, Financial time-series analysis
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 29 Jun 2018 11:39
Last Modified: 29 Jun 2018 11:39
URI: http://repository.essex.ac.uk/id/eprint/22336

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