Optimising Directional Changes trading strategies with different algorithms

Palsma, Jurgen; Adegboye, Adesola

doi:10.1109/cec.2019.8790222

Cited by 2 publications

(1 citation statement)

References 20 publications

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“…Early usage of genetic algorithm for optimization via DC can be seen in [24], where authors optimizes different thresholds by DC. Further work also captured two more optimizers as particle swarm optimization and continuous shuffled frog leaping algorithm, and again the research was scrutinized around multi-thresholds [25]. Moreover, DC has been seen under classification tasks too, these tasks have been evolved around finding the trend reversal in DC paradigm, by doing so, researchers were in a position of making informed trading decisions.…”

Section: Machine Learning Under DCmentioning

confidence: 99%

Trading Strategies Optimization by Genetic Algorithm under the Directional Changes Paradigm

Salman

Kampouridis

Jarchi

2022

2022 IEEE Congress on Evolutionary Computation (CEC)

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The subject of financial forecasting has been researched for decades, and the driver behind its measured data has been fuelled by the selection of physical time series, which summarize data using fixed time intervals. For instance, timeseries for daily stock data would be profiled at 252 points in one year. However, this episodic style neglects the important events, or price changes that occur between two intervals. Thus, we use Directional Changes (DC) as an event-based series, which is an alternative way to record price movements. In DC, unlike timeseries methods, time intervals are constituted by price changes. The unique feature that decides the price change to be considered as a significant is called a threshold θ. The objective of our paper is to create DC-based trading strategies, and then optimize them using a Genetic Algorithm (GA). To construct such strategies, we use DC-based indicators and scaling laws that have been empirically identified under DC summaries. We first propose four novel DC-based trading strategies and then combine them with existing DC-based strategies and finally optimize them via the GA. We conduct trading experiments over 44 stocks. Results show that the GA-optimized strategies are able to generate new and profitable trading strategies, significantly outperforming the individual DC-based strategies, as well as a buy and sell benchmark.

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Section: Machine Learning Under DCmentioning

confidence: 99%

Trading Strategies Optimization by Genetic Algorithm under the Directional Changes Paradigm

Salman

Kampouridis

Jarchi

2022

2022 IEEE Congress on Evolutionary Computation (CEC)

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A Deep Network-Based Trade and Trend Analysis System to Observe Entry and Exit Points in the Forex Market

et al. 2022

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In the Forex market, trend trading, where trend traders identify trends and attempt to capture gains through the analysis of an asset’s momentum in a particular direction, is a great way to profit from market movement. When the price of currency is moving in one either of the direction such as; up or down, it is known as trends. This trend analysis helps traders and investors find low risk entry points or exit points until the trend reverses. In this paper, empirical trade and trend analysis results are suggested by two-phase experimentations. First, considering the blended learning paradigm and wide use of deep-learning methodologies, the variants of long-short-term-memory (LSTM) networks such as Vanilla-LSTM, Stacked-LSTM, Bidirectional-LSTM, CNN-LSTM, and Conv-LSTM are used to build effective investing trading systems for both short-term and long-term timeframes. Then, a deep network-based system used to obtain the trends (up trends and down trends) of the predicted closing price of the currency pairs is proposed based on the best fit predictive networks measured using a few performance measures and Friedman’s non-parametric tests. The observed trends are compared and validated with a few readily available technical indicators such as average directional index (ADX), rate of change (ROC), momentum, commodity channel index (CCI), and moving average convergence divergence (MACD). The predictive ability of the proposed strategy for trend analysis can be summarized as follows: (a) with respect to the previous day for short-term predictions, AUD:INR achieves 99.7265% and GBP:INR achieves 99.6582% for long-term predictions; (b) considering the trend analysis strategy with respect to the determinant day, AUD:INR achieves 98.2906% for short-term predictive days and USD:INR achieves an accuracy of trend forecasting with 96.0342%. The significant outcome of this article is the proposed trend forecasting methodology. An attempt has been made to provide an environment to understand the average, maximum, and minimum unit up and/or downs observed during trend forecasting. In turn, this deep learning-based strategy will help investors and traders to comprehend the entry and exit points of this financial market.

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Optimising Directional Changes trading strategies with different algorithms

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 2 publications

References 20 publications

Trading Strategies Optimization by Genetic Algorithm under the Directional Changes Paradigm

Trading Strategies Optimization by Genetic Algorithm under the Directional Changes Paradigm

A Deep Network-Based Trade and Trend Analysis System to Observe Entry and Exit Points in the Forex Market

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