Detection of Buy and Sell Signals Using Technical Indicators with a Prediction Model Based on Neural Networks

Rodríguez, Francisco Solano López; López, José Manuel Zurita

doi:10.1007/978-3-030-97273-8_48

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…However, there are many technical indicators. The selection of appropriate TI for the stock remains a research topic (López Rodríguez and Zurita López, 2022). Most studies use single feature selection for stock price classification.…”

Section: Introductionmentioning

confidence: 99%

Stock Price Classification Based on Hybrid Feature Selection Method

Srivinay¹,

Manujakshi²,

Kabadi³

et al. 2023

Journal of Computer Science

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In recent years, investors and traders have used Technical Indicators (TIs) to forecast the stock market. An accurate classification model is required in the stock market to gain more profit. Selecting relevant TIs for the stock market remains a hot research topic. The proposed work aims to identify important technical indicators. Therefore, the proposed work considers a hybrid feature selection method to identify the relevant TIs. The hybrid feature selection combines two individual feature selection methods, such as Boruta and the Random Forest (RF) feature importance method. The work considers 20 TIs. The regression power of TIs is computed using the hybrid feature selection method. Using the hybrid feature selection method, the selected relevant TIs are given as input to the classification model, namely Naive Bayes (NB) and Deep Learning. The classification model aims to classify the stock price as up or down. The Hybrid Feature selection-based Deep Learning H2O model performs better than the hybrid feature selectionbased NB model in the experimental work. The accuracy of the hybrid feature selection of the Deep Learning H2O model is around 86 to 89%. The work considers the National Stock Exchange (NSE) in India for the experimental work.

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Section: Introductionmentioning

confidence: 99%

Stock Price Classification Based on Hybrid Feature Selection Method

Srivinay¹,

Manujakshi²,

Kabadi³

et al. 2023

Journal of Computer Science

View full text Add to dashboard Cite

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Algorithm-Based Low-Frequency Trading Using a Stochastic Oscillator, Williams%R, and Trading Volume for the S&P 500

Paik,

Choi,

Vaquero

2024

JRFM

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Recent research in algorithmic trading has primarily focused on ultra-high-frequency strategies and index estimation. In response to the need for a low-frequency, real-world trading model, we developed an enhanced algorithm that builds on existing models with high hit ratios and low maximum drawdowns. We utilized established price indicators, including the stochastic oscillator and Williams %R, while introducing a volume factor to improve the model’s robustness and performance. The refined algorithm achieved superior returns while maintaining its high hit ratio and low maximum drawdown. Specifically, we leveraged 2X and 3X signals, incorporating volume data, the 52-week average, standard deviation, and other variables. The dataset comprised SPY ETF price and volume data spanning from 2010 to 2023, over 13 years. Our enhanced algorithmic model outperformed both the benchmark and previous iterations, achieving a hit rate of over 90%, a maximum drawdown of less than 1%, an average of 1.5 trades per year, a total return of 519.3%, and an annualized return (AnnR) of 15.1%. This analysis demonstrates that the model’s simplicity, ease of use, and interpretability provide valuable tools for investors, although it is important to note that past performance does not guarantee future returns.

show abstract

Detection of Buy and Sell Signals Using Technical Indicators with a Prediction Model Based on Neural Networks

Cited by 2 publications

References 12 publications

Stock Price Classification Based on Hybrid Feature Selection Method

Stock Price Classification Based on Hybrid Feature Selection Method

Algorithm-Based Low-Frequency Trading Using a Stochastic Oscillator, Williams%R, and Trading Volume for the S&P 500

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