Utilizing Supervised Machine Learning and Technical Indicators for Quantitative Trading to Improve Stock Market Trading Decisions

This study investigates the use of a preceding day’s trading pattern to generate a LONG or SHORT signal for intraday stock trading the day before trading begins. Positions taken LONG or SHORT at the start and close of the trading day. Using stock time series data, we first identify key traits; then,...

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Veröffentlicht in:SN computer science Jg. 6; H. 5; S. 498
Hauptverfasser: Sahu, Santosh Kumar, Mokhade, Anil S., Agrawal, Pratik K., Kalyani, Kanak
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Singapore Springer Nature Singapore 01.06.2025
Springer Nature B.V
Schlagworte:
Algorithms
Artificial neural networks
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Data Structures and Information Theory
Decision trees
Deep learning
Information Systems and Communication Service
Investments
Investors
Kernel functions
Machine learning
Memory
Neural networks
Online securities trading
Original Research
Pattern Recognition and Graphics
Program trading
Research Advancements in Intelligent Computing
Research methodology
Securities markets
Software Engineering/Programming and Operating Systems
Stock exchanges
Stock prices
Supervised learning
Support vector machines
Time series
Trends
Vision
Wavelet transforms
Deep learning
Artificial intelligent
Support vector machine
Stock trading
Time series analysis
Machine learning
ISSN:2661-8907, 2662-995X, 2661-8907
Online-Zugang:Volltext
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Zusammenfassung:This study investigates the use of a preceding day’s trading pattern to generate a LONG or SHORT signal for intraday stock trading the day before trading begins. Positions taken LONG or SHORT at the start and close of the trading day. Using stock time series data, we first identify key traits; then, using a combination of machine learning and a deep learning algorithm, we predict traders’ actions for the next trading day. We applied decision tree (DT), random forest (RF), logistic regression (LR), support vector machine (SVM) classifier and an artificial neural network (ANN) for predicting the LONG or SHORT position. The following results are based on an experiment that was run on real market data from the website of the National Stock Exchange (NSE). We analyzed 2459 completed transactions spanning from January 2011 to November 2020. When compared to other classifiers, Support Vector Machine (SVM) with a 70% training data to 30% testing data ratio performed the best. The SVM classifier with kernel = ‘rbf’ provided the most accurate predictions (72.41 percent). We evaluate the performance of the BUY and HOLD strategy against the outcomes of our experiments. We also test our trading model with a 1% stop-loss, a 1.5% stop-loss, and a 2% stop-loss, and compare the results. The findings here might serve as a foundation for your intraday stock trading strategy. This forecast is useful information for a day trader to have.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2661-8907
2662-995X
2661-8907
DOI:10.1007/s42979-025-04025-x