Chandrakala Kuruba
@vignannirula.org
Assistant Professor
Vignan's Nirula Institute of Technology and Science for Women
RESEARCH INTERESTS
Machine Learning
Deep Learning
Image Processing
BioMedical Images
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Chandrakala Kuruba and N.P. Gopalan
Elsevier BV
Chandrakala Kuruba, N. Pushpalatha, Gandikota Ramu, I. Suneetha, M. Rudra Kumar, and P. Harish
Springer Science and Business Media LLC
G. Dharmaiah, Fateh Mebarek-Oudina, M. Sreenivasa Kumar, and K. Chandra Kala
Elsevier BV
S. Siamala Devi, Chandrakala Kuruba, Yunyoung Nam, and Mohamed Abouhawwash
Computers, Materials and Continua (Tech Science Press)
S. Siamala Devi, Chandrakala Kuruba, Yunyoung Nam, and Mohamed Abouhawwash
Computers, Materials and Continua (Tech Science Press)
G. Dharmaiah, W. Sridhar, K. S. Balamurugan, and K. Chandra Kala
Informa UK Limited
P Rajesh, Mansoor Alam, Mansour Tahernezhadi, K. Vamshikrishna Reddy, and K Chandrakala
IEEE
Stock trends prediction project is about designing a strategy that helps in predicting the stock trends in real time. To achieve it, we have considered Quantopian, one of the world’s leading financial marketing strategy analysis as our platform. Quantopian follows the 3-Step approach of Alpha coding, Code Optimization & Portfolio. Alpha Code is the main algorithm that harbours, our strategy in stock trend prediction. We consider a single factor that influences our code and gradually come up with two or more factors combination to advance efficiency of optimization strategy. Sentiment factor is used to give an efficiency of 9.3% at the end of back testing. When the combination of sentiment, operation ratio and revenue growth are passed to alpha lens, it is found that the efficiency is raised to 64%, making it much more reliable and stable. The accuracy of multiple alpha factors model attains more than 60%, contrast with earlier prediction algorithms with a single alpha factor have around 9% accuracy increases with 51%.