Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM47795
Volume:
9 |
5 |
May 2025
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Accurate stock market forecasting remains a challenging task due to the unpredictable and volatile nature of financial data. Traditional methods like candlestick chart analysis have helped traders interpret market trends visually, but they often lack predictive capability. In this study, we propose a hybrid model that integrates candlestick pattern recognition with the Prophet algorithm— a time-series forecasting tool developed by Facebook. The model is trained on historical OHLC (Open, High, Low, Close) data from the NIFTY 50 index, with an emphasis on capturing both short- term volatility and long-term seasonality. . By detecting key candlestick patterns (e.g., Doji, Engulfing, Hammer) and combining them with Prophet’s seasonality-aware forecasting engine, the system generates actionable buy/sell signals. The results indicate improved prediction accuracy compared to traditional pattern-only methods. This approach bridges the gap between classic technical analysis and modern AI forecasting, offering a more data-driven and adaptable tool for traders and investors operating in the Indian stock market.
KEYWORDS
Publication Date
5/13/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
3
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...