Intelligent Algorithmic Trading Strategy Using Reinforcement Learning and Directional Change

Aloud, Monira; Alkhamees, Nora

doi:10.1109/access.2021.3105259

Cited by 21 publications

(4 citation statements)

References 37 publications

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“…The trend of the real estate index and stock can be predicted and recognized by CNN. The end-to-end method eliminates the process of manual feature extraction and excessively extract its interior semantic characteristics [23,24]. The basic flow of CNN is drawn in…”

Section: Algorithm and Its Improvement Analysismentioning

confidence: 99%

Visual recognition and prediction analysis of China’s real estate index and stock trend based on CNN-LSTM algorithm optimized by neural networks

Chen

2023

PLoS ONE

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Today, with the rapid growth of Internet technology, the changing trend of real estate finance has brought great an impact on the progress of the social economy. In order to explore the visual identification (VI) effect of Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) algorithm based on neural network optimization on China’s real estate index and stock trend, in this study, artificial neural network (ANN) algorithm is introduced to predict its trend. Firstly, LSTM algorithm can effectively solve the problem of vanishing gradient, which is suitable for dealing with the problems related to time series. Secondly, CNN, with its unique fine-grained convolution operation, has significant advantages in classification problems. Finally, combining the LSTM algorithm with the CNN algorithm, and using the Bayesian Network (BN) layer as the transition layer for further optimization, the CNN-LSTM algorithm based on neural network optimization has been constructed for the VI and prediction model of real estate index and stock trend. Through the performance verification of the model, the results reveal that the CNN-LSTM optimization algorithm has a more accurate prediction effect, the prediction accuracy is 90.55%, and the prediction time is only 52.05s. At the same time, the significance advantage of CNN-LSTM algorithm is verified by statistical method, which can provide experimental reference for intelligent VI and prediction of trend of China real estate index and property company stocks.

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Section: Algorithm and Its Improvement Analysismentioning

confidence: 99%

Visual recognition and prediction analysis of China’s real estate index and stock trend based on CNN-LSTM algorithm optimized by neural networks

Chen

2023

PLoS ONE

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“…Since then, several other works have discovered new scaling laws. Some examples are Reference [15] which discovered 17 new scaling laws; Reference [10], which uncovered 12 additional laws; Reference [16], which added four more scaling laws; and Reference [17] which further contributed five scaling laws. Furthermore, Reference [11], presented four new DC indicators for profiling financial markets.…”

Section: Directional Changesmentioning

confidence: 99%

“…Another body of DC work has focused on estimating trend reversal and trading. For example, References [16,21] combined directional change with trend following and contrary trading technical indicators, and developed new trading strategies. Furthermore, Reference [22,23] created a DC‐based trading strategy, named “DBA,” and reported mean returns of around 14%.…”

Section: Directional Changesmentioning

confidence: 99%

Improving trend reversal estimation in forex markets under a directional changes paradigm with classification algorithms

Adegboye

Kampouridis

Otero

2021

Int J of Intelligent Sys

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“…As a result, an increasing number of works have been using the DC concept for trading purposes (e.g., Aloud 2020Aloud , 2021. Furthermore, Gypteau et al (2015) proposed a genetic programming (GP) based multi-threshold DC (MTDC) strategy, where the terminal nodes of the GP trees were composed of the trading actions (buy/sell/hold) recommended by each DC threshold, and the inner nodes were logical operators for combining the above recommendations.…”

Section: Introductionmentioning

confidence: 99%

Algorithmic trading with directional changes

2022

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Directional changes (DC) is a recent technique that summarises physical time data (e.g. daily closing prices, hourly data) into events, offering traders a unique perspective of the market to create novel trading strategies. This paper proposes the use of a genetic algorithm (GA) to optimize the recommendations of multiple DC-based trading strategies. Each trading strategy uses a novel framework that combines classification and regression techniques to predict when a trend will reverse. We evaluate the performance of the proposed multiple DC-strategy GA algorithm against nine benchmarks: five single DC-based trading strategies, three technical analysis indicators, as well as buy-and-hold, which is a popular financial benchmark. We perform experiments using 200 monthly physical time datasets from 20 foreign exchange markets—these datasets were created from snapshots of 10 min intervals. Experimental results show that our proposed algorithm is able to statistically significantly outperform all DC and non-DC benchmarks in terms of both return and risk, and establish multi-threshold DCs as an effective algorithmic trading technique.

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Intelligent Algorithmic Trading Strategy Using Reinforcement Learning and Directional Change

Cited by 21 publications

References 37 publications

Visual recognition and prediction analysis of China’s real estate index and stock trend based on CNN-LSTM algorithm optimized by neural networks

Visual recognition and prediction analysis of China’s real estate index and stock trend based on CNN-LSTM algorithm optimized by neural networks

Improving trend reversal estimation in forex markets under a directional changes paradigm with classification algorithms

Algorithmic trading with directional changes

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