Quantitative study of candlestick pattern & identifying candlestick patterns using deep learning for the Indian stock market

https://doi.org/10.53730/ijhs.v6nS3.7230

Authors

  • Animesh Upreti Undergraduate Student at Delhi Technological University
  • Ansh Agrawal Undergraduate Student at Delhi Technological University
  • Jai Kumar Joshi Undergraduate Student at Delhi Technological University
  • Sumedha Seniaray Assistant Professor at Delhi Technological University

Keywords:

candlesticks, NIFTY50, stop-loss , bullish reversal patterns, technical analysis

Abstract

The stock market is an integral aspect of any country’s economic infrastructure. Analyzing and attempting to play the markets to maximize profits is an endeavor a large fraction of the population aspires to. Candlestick patterns are the backbone of Technical Analysis, used for trading in the stock market. There are a number of candlestick patterns in the market, each with its own benefits and downsides. Due to this, the task that befalls the hands of analysts is deciding which patterns provide the most effective gauge of the current market situation. Due to the large level of noise and widely recognized semi-strong form of market efficiency, analyzing and forecasting the stock market is infamously difficult. For traders that use Technical Analysis to trade, it's critical to be able to recognize candlestick patterns quickly. We will be attempting to determine their respective effectiveness with respect to the Indian Stock Market via exploratory analysis conducted on real-world market data. Also, we'll use candlestick charts to train neural networks and subsequently find patterns. Deep Learning will be used to recognize candlestick patterns in large-cap Indian equities.

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References

Trang-Thi Ho, Yennun Huang “Stock Price Movement Prediction Using Sentiment Analysis and CandleStick Chart Representation”

Nison, S, (2001) Japanese Candlestick charting techniques: a contemporary guide to the ancient investment techniques of the Far East. (Penguin)

Jensen, Michael C. "Some anomalous evidence regarding market efficiency." Journal of financial economics 6.2/3 (1978): 95-101.

Lo, A. W., Mamaysky, H., & Wang, J. (2000) Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. The Journal of Finance, 55(4), 1705-1765.

Li, H., Ng, W. W., Lee, J. W., Sun, B., & Yeung, D. S, (2008) Quantitative study on candlestick pattern for Shenzhen Stock Market. In 2008 IEEE International Conference on Systems, Man and Cybernetic, October (pp. 54-59)

Duvinage, M., Mazza, P., & Petitjean, M. (2013) The intra-day performance of market timing strategies and trading systems based on Japanese candlesticks. Quantitative Finance, 13(7), 1059-1070.

Sharpe, W. F., (1994) The Sharpe Ratio, the Journal of Portfolio Management, Stanford University, Fall.

Gaurav Gupta, Geeta Gupta, (2014) An Analysis of Individual Investors Towards Investing in Stock Market, International Journal of Advanced Research in Management, 5(5), pp. 10-18.

Manoharan M and Dr Rajesh Mamilla, The Profitability of Bullish Reversal Candlestick Patterns – A Study on Select Indian Nifty50 Index Stocks, International Journal of Management, 11(6), 2020, pp. 1623-1631.

Yaohu Lin, Shancun Liu, Haijun Yang, Harris Wu, Bingbing Jiang “Improving stock trading decisions based on pattern recognition using machine learning technology”

Chen, Shi, Si Bao, and Yu Zhou. "The predictive power of Japanese candlestick charting in Chinese stock market." Physica A: Statistical Mechanics and its Applications 457 (2016): 148-165.

Yaohu Lin, Shancun Liu, Haijun Yang, Harris Wu “Stock Trend Prediction Using Candlestick Char ediction Using Candlestick Charting and Ensemble Machine Learning Techniques with a Novelty Feature Engineering scheme”

Rosdyana Mangir Irawan Kusuma, Trang-Thi Ho, Wei-Chun Kao, Yu-Yen Ou, Kai-Lung Hua “Using Deep Learning Neural Networks and Candlestick Chart Representation to Predict Stock Market”

Jun-Hao Chen, Yun-Cheng Tsai “Encoding candlesticks as images for pattern classification using convolutional neural networks"

Nti, Isaac Kofi, Adebayo Felix Adekoya, and Benjamin Asubam Weyori. "A systematic review of fundamental and technical analysis of stock market predictions." Artificial Intelligence Review 53.4 (2020): 3007-3057

Published

12-05-2022

How to Cite

Upreti, A., Agrawal, A., Joshi, J. K., & Seniaray, S. (2022). Quantitative study of candlestick pattern & identifying candlestick patterns using deep learning for the Indian stock market. International Journal of Health Sciences, 6(S3), 5739–5749. https://doi.org/10.53730/ijhs.v6nS3.7230

Issue

Section

Peer Review Articles