Automatic trading method based on piecewise aggregate approximation and multi-swarm of improved self-adaptive particle swarm optimization with validation

Brasileiro, Rodrigo C.; Souza, Victor L. F.; Oliveira, Adriano L. I.

doi:10.1016/j.dss.2017.10.005

Cited by 17 publications

(6 citation statements)

References 16 publications

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“…Test= Dataset[(k+1):(k+n),1:P] (14) Model=ML Algorithm(Train) (15) Probability=Model(Test) (16) if (Probability>=0.5) { (17) Trading Signal0=1 (18) } else { (19) Trading Signal0=0 Mathematical Problems in Engineering In this paper, "UP" is the profit source of our trading strategies. The classification ability of ML algorithm is to evaluate whether the algorithms can recognize "UP".…”

Section: Evaluation Indicators Andmentioning

confidence: 99%

“…Over the years, traditional ML methods have shown strong ability in trend prediction of stock prices [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. In recent years, artificial intelligence computing methods represented by DNN have made a series of major breakthroughs in the fields of Natural Language Processing, image classification, voice translation, and so on.…”

Section: Introductionmentioning

confidence: 99%

“…or selecting a few stocks with limited features according to their own preferences ( [8-11, 14, 17, 20, 22, 26, 31], etc.) or not considering transaction cost ( [10,14,17,23], etc. ), or the period of backtesting is very short ( [2,8,9,11,17,20,22,27], etc.).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Empirical Study of Machine Learning Algorithms for Stock Daily Trading Strategy

Yuan

et al. 2019

Mathematical Problems in Engineering

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According to the forecast of stock price trends, investors trade stocks. In recent years, many researchers focus on adopting machine learning (ML) algorithms to predict stock price trends. However, their studies were carried out on small stock datasets with limited features, short backtesting period, and no consideration of transaction cost. And their experimental results lack statistical significance test. In this paper, on large-scale stock datasets, we synthetically evaluate various ML algorithms and observe the daily trading performance of stocks under transaction cost and no transaction cost. Particularly, we use two large datasets of 424 S&P 500 index component stocks (SPICS) and 185 CSI 300 index component stocks (CSICS) from 2010 to 2017 and compare six traditional ML algorithms and six advanced deep neural network (DNN) models on these two datasets, respectively. The experimental results demonstrate that traditional ML algorithms have a better performance in most of the directional evaluation indicators. Unexpectedly, the performance of some traditional ML algorithms is not much worse than that of the best DNN models without considering the transaction cost. Moreover, the trading performance of all ML algorithms is sensitive to the changes of transaction cost. Compared with the traditional ML algorithms, DNN models have better performance considering transaction cost. Meanwhile, the impact of transparent transaction cost and implicit transaction cost on trading performance are different. Our conclusions are significant to choose the best algorithm for stock trading in different markets.

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Section: Evaluation Indicators Andmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Empirical Study of Machine Learning Algorithms for Stock Daily Trading Strategy

Yuan

et al. 2019

Mathematical Problems in Engineering

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“…Moreover, data of time series are featured with large size, high dimensionality and great update frequency. They have been currently applied in multiple fields of transportation, communications, meteorology, medicine, finance stock, etc . Time series data simplification plays a role in substantially reducing data capacity, providing a strong technical support to the rapid acquisition of short‐interval driving behavior and capture of details concerned with long‐interval driving behavior.…”

Section: Introductionmentioning

confidence: 99%

A novel method of symbolic representation in diving data mining: A case study of highways in China

Sun

Liu

Chu

et al. 2018

Concurrency and Computation

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Summary Vehicle field test can be conducted smoothly because of the automobile‐mounted monitoring system and abundant diving data have been collected. Driving data mining is in an urgent need of new thoughts introduced to break through the original technical bottleneck. This paper presented a novel method of symbolic representation in diving data mining and applied the idea of time series symbolization to traffic engineering. The sample data is processed by normalization, dimensionality reduction, discretization, and symbolization based on the three steps of symbolic aggregate approximation (SAX) with driving data characteristics taken into adequate consideration. The results showed that the high‐dimensionality miscellaneous driving time series data was rationally converted into highly readable, easy to search and locate symbolic series after semantic encoding, and the main characteristics of time series data was preserved after a substantial reduction of data dimensionality. Finally, the paper demonstrated the positive effects of this method on the analysis of actual vehicle driving safety based on case study, and it explored the application of SAX to speed and acceleration data from driving data set.

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“…Therefore, it is needed a higher-level representation of data for efficient computation and extraction of higher-order features. A vast amount of methods exist for generating a difference between timeseries data; these methods include Discrete Fourier Transform (DFT) [14], Discrete Wavelet Transform (DWT) [15], piecewise aggregate approximation [16], 1-lag difference algorithm [17], to name a few.…”

Section: Introductionmentioning

confidence: 99%

Groundwater Level Prediction Model Using Correlation and Difference Mechanisms Based on Boreholes Data for Sustainable Hydraulic Resource Management

et al. 2021

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Drilling data for groundwater extraction incur changes over time due to variations in hydrogeological and weather conditions. At any time, if there is a need to deploy a change in drilling operations, drilling companies keep monitoring the time-series drilling data to make sure it is not introducing any changes or new errors. Therefore, a solution is needed to predict groundwater levels (GWL) and detect a change in boreholes data to improve drilling efficiency. The proposed study presents an ensemble GWL prediction (E-GWLP) model using boosting and bagging models based on stacking techniques to predict GWL for enhancing hydraulic resource management and planning. The proposed research study consists of two modules; descriptive analysis of boreholes data and GWL prediction model using ensemble model based on stacking. First, descriptive analysis techniques, such as correlation analysis and difference mechanisms, are applied to investigate boreholes log data for extracting underlying characteristics, which is critical for enhancing hydraulic resource management. Second, an ensemble prediction model is developed based on multiple hydrological patterns using robust machine learning (ML) techniques to predict GWL for enhancing drilling efficiency and water resource management. The architecture of the proposed ensemble model involves three boosting algorithms as base models (level-0) and a bagging algorithm as a meta-model that combines the base models predictions (level-1). The base models consist of the following boosting algorithms; eXtreme Gradient Boosting (XGBoost), AdaBoost, Gradient Boosting (GB). The meta-model includes Random Forest (RF) as a bagging algorithm referred to as a level-1 model. Furthermore, different evaluation metrics are used, including mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE), mean absolute percentage error (MAPE), and R2 score. The performance of the proposed E-GWLP model is compared with existing ensemble and baseline models. The experimental results reveal that the proposed model performed accurately in respect of MAE, MSE, and RMSE of 0.340, 0.564, and 0.751, respectively. The MAPE and R2 score of our proposed approach is 12.658 and 0.976, respectively, which signifies the importance of our work. Moreover, experimental results suggest that E-GWLP model is suitable for sustainable water resource management and improves reservoir engineering.

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Automatic trading method based on piecewise aggregate approximation and multi-swarm of improved self-adaptive particle swarm optimization with validation

Cited by 17 publications

References 16 publications

An Empirical Study of Machine Learning Algorithms for Stock Daily Trading Strategy

An Empirical Study of Machine Learning Algorithms for Stock Daily Trading Strategy

A novel method of symbolic representation in diving data mining: A case study of highways in China

Groundwater Level Prediction Model Using Correlation and Difference Mechanisms Based on Boreholes Data for Sustainable Hydraulic Resource Management

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