Options Trading using Artificial Neural Network and Algorithmic Trading

##plugins.themes.academic_pro.article.main##

Rashmi Welekar
Sayandeep Ghosh
Akshay Kurve
Sudhanshu Kumar
Atharva Deshmukh

Abstract

Options trading is a process of speculating the strike price of an underlying security or index on the expiration date. To finalize the options contract, a trader pays a small percentage as premium. This paper is to maximmize the profits of trader and minimize their loses which is generally manual based process, but this research paper helps integrates finance with technology. It fills the gap between the implementation of deep learning and algorithm trading, with option trading. We have named this model as Option Trading Prediction Model (OTP). This paper can be referred to develop an option trading tool or platform. The paper contains the information in the following manner, Introduction, describes an overview of Model Features, followed by an overview of Evaluation which includes data collection and preprocessing. Later on, discusses Model training and  about the modelling and prediction. 

##plugins.themes.academic_pro.article.details##

Author Biographies

Rashmi Welekar, a:1:{s:5:"en_US";s:53:"Shri Ramdeobaba College of Engineering and Management";}

Dr.Rashmi Welekar is working as an Assistant Professor in the Computer Science and Engineering Department at Shri Ramdeobaba College of Engineering and Management. She has done PhD in Computer Science Technology.She has 18 years of teaching experience and more than 30 research publications in refereed journals. Her areas of interest are Computer Networks, Evolutionary Computing and Cyber Security.

Sayandeep Ghosh, Shri Ramdeobaba College of Engineering and Management

Sayandeep Ghosh is student at Shri Ramdeobaba College Of Engineering and Management, Nagpur and pursuing his B.E. Degree in stream of Computer Science and Engineering (2019-2023).

Akshay Kurve, Shri Ramdeobaba College of Engineering and Management

Akshay Kurve is student at Shri Ramdeobaba College Of Engineering and Management, Nagpur and pursuing his B.E. Degree in stream of Computer Science and Engineering(2019-2023).

Sudhanshu Kumar, Shri Ramdeobaba College of Engineering and Management

Sudhanshu Kumar is student at Shri Ramdeobaba College Of Engineering and Management, Nagpur and pursuing his B.E. Degree in stream of Computer Science and Engineering(2019-2023).

Atharva Deshmukh, Shri Ramdeobaba College of Engineering and Management

Atharva Deshmukh is student at Shri Ramdeobaba College Of Engineering and Management, Nagpur and pursuing his B.E. Degree in stream of Computer Science and Engineering(2019-2023).

How to Cite
Welekar, R., Ghosh, S., Kurve, A., Kumar, S., & Deshmukh, A. (2022). Options Trading using Artificial Neural Network and Algorithmic Trading . International Journal of Next-Generation Computing, 13(5). https://doi.org/10.47164/ijngc.v13i5.924

References

  1. J. Wang, J. Wang, Z. Z. and Guo, S. 2011. Forecasting stock indices with back propagation neural network. Expert Systems with Apps. 38(11), pp.14346–14355. DOI: https://doi.org/10.1016/j.eswa.2011.04.222
  2. Araujo, R. and Ferreira, T. 2013. A morphological-rank-linear evolutionary method for stock market predictions. Information Sciences, 3–17. DOI: https://doi.org/10.1016/j.ins.2009.07.007
  3. F. Zarandi, B. Rezaee, I. T. and Neshat, E. 2009. Forecasting model of global stock index by stochastic time effective neural network. Expert Systems with Applications 36, pp.139–154. DOI: https://doi.org/10.1016/j.eswa.2007.09.034
  4. Jireh Yi-Le Chan, Seuk Wai Phoong, W. K. C. Y.-L. C. 2022. Support resistance levels towards profitability in intelligent algorithmic trading models.
  5. Kim., K. 2003. Financial time series forecasting using support vector machines. Neurocomputing 55(1–2), pp.307–319. DOI: https://doi.org/10.1016/S0925-2312(03)00372-2
  6. Liao, Z. andWang, J. 2010. Forecasting model of global stock index by stochastic time effective neural network. Expert Systems with Applications, 37(1)), pp.834–841. DOI: https://doi.org/10.1016/j.eswa.2009.05.086
  7. Park, K. and Shin, H. 2013. Stock price prediction based on a complex interrelation network of economic factor. Engineering Applications of Artificial Intelligence 26, pp.1550–1561. DOI: https://doi.org/10.1016/j.engappai.2013.01.009
  8. S Z Mahfooz, Iftikhar Ali, M. N. K. 2022. Improving stock trend prediction using lstm neural network trained on a complex trading strategy. Expert Systems with Applications. DOI: https://doi.org/10.22214/ijraset.2022.45961