Stock Prediction Using AIML Based on Candlestick Chart Analysis

Abstract

This research presents a novel approach to stock market prediction through the application of Artificial Intelligence and Machine Learning (AIML), with a focus on candlestick chart analysis. Candlestick patterns, widely regarded as essential tools in technical analysis, provide key insights into market sentiment and potential price reversals. However, their manual interpretation can be subjective and limited by human error. In this paper, AIML techniques, particularly Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), are leveraged to automate the identification of key patterns such as doji, hammer, and engulfing, and subsequently predict future stock price movements. The models are trained on historical stock price data, incorporating various technical indicators like moving averages and RSI (Relative Strength Index) to enhance predictive power. Our results show that AIML can significantly improve prediction accuracy compared to traditional methods, making this approach valuable for traders and analysts. The research emphasizes the practicality of combining deep learning techniques with candlestick patterns for real-time stock market forecasting. This paper presents a stock prediction model utilizing Artificial Intelligence and Machine Learning (AIML) with a focus on candlestick chart analysis. Candlestick patterns such as doji, hammer, and engulfing are widely used in technical analysis for predicting stock price movements. By employing advanced AIML techniques, including Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), the model identifies key candlestick patterns from historical stock data and forecasts future price movements. The model is trained on historical stock prices, technical indicators, and trading volumes, achieving a prediction accuracy of 82% when applied to short-term stock trends. This approach offers improved accuracy over traditional time-series forecasting methods, making it a valuable tool for traders and analysts. The paper concludes by discussing the model\'s effectiveness and limitations, as well as potential applications in automated trading systems.

Introduction

This research focuses on using Artificial Intelligence (AI) and Machine Learning (ML)—specifically Support Vector Machines (SVM) and Convolutional Neural Networks (CNN)—to enhance stock market prediction through the automated analysis of candlestick chart patterns.

Key Objectives:

1. Automatically detect key candlestick patterns (e.g., doji, hammer, engulfing).

2. Use SVM and CNN to predict future stock price trends.

3. Evaluate the predictive performance of these models in real-market data.

Literature Review Highlights:

• Candlestick charts are widely used in technical analysis to interpret market sentiment and trend reversals.

• Traditional interpretation is often subjective and prone to bias.

• Machine learning models (like Random Forests, SVMs) have shown strong potential in price prediction tasks.

• CNNs, though image-focused, are effective for recognizing spatial patterns in candlestick charts.

• There is a research gap in directly applying AIML to candlestick pattern analysis, which this study addresses.

Methodology Overview:

• Dataset: Stock price data from Yahoo Finance (~900,000 records).

• Preprocessing: Data cleaned, normalized, and split (80% training, 20% testing).

• Feature Engineering: Included moving averages, RSI, and volume.

• Model Training:

  • SVM: Used to classify price trends as bullish or bearish based on patterns and indicators.

  • CNN: Used to identify candlestick shapes from chart-like input.

• Model Evaluation: Accuracy, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) were used as metrics.

Results:

• SVM Accuracy: 80% in classifying short-term trends.

• CNN Pattern Recognition Accuracy: 87% for key patterns.

• Prediction Quality: Close alignment with actual stock movement, especially during market volatility.

• Visualization: Line graphs and annotated candlestick charts showcased the model’s decision points.

Limitations & Future Scope:

• Models depend on historical data and lack integration with external factors like news or economic indicators.

• Potential for overfitting.

• Future work could include reinforcement learning or hybrid models combining technical and fundamental analysis.

Conclusion

This paper presents an AIML-based approach to stock prediction using candlestick chart analysis. By leveraging SVM for trend classification and CNN for pattern recognition, the models achieved high accuracy in predicting future stock price movements. The integration of technical analysis with machine learning provides traders and analysts with a powerful tool to enhance decision-making and improve trading strategies. This research underscores the importance of AIML in modern financial forecasting, offering practical benefits to both institutional and individual traders.

References

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Copyright

Copyright © 2025 Nikita Nikam, Vipul Pagar, Nikhil Jadhav, Dhruv Ramteke, Jayesh Nemade, Prof. Parag B. Patil. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.