Chart Scan AI

Abstract

1. Introduction

ChartScanAI is an advanced application designed for detecting patterns in stock and cryptocurrency charts using deep learning techniques. Inspired by the methodology detailed in the research article "YOLO Object Recognition Algorithm and “Buy-Sell Decision” Model Over 2D Candlestick Charts" by Serdar Birogul, Günay Temür, and Utku Kose, this project extends their approach by implementing the model with YOLOv8 and integrating it into a user-friendly Streamlit app. This application aims to automate the process of chart pattern recognition, providing traders and analysts with a powerful tool for making informed decisions.

ChartScanAI leverages the power of YOLOv8, a state-of-the-art object detection algorithm, to identify and classify patterns in financial charts. The model provides outputs in two classes: Buy and Sell, based on candlestick patterns. This application provides a robust solution for traders and analysts to quickly recognize significant chart formations, aiding in more informed decision-making.

In the financial market, timely and accurate identification of chart patterns is crucial for making profitable trading decisions. Manual detection is not only time-consuming but also prone to human error. There is a need for an automated system that can analyze charts in real-time, identify patterns with high accuracy, and present the results in an accessible format.

2. Objective

ChartScanAI is an advanced app for detecting patterns in stock and cryptocurrency charts using deep learning and YOLOv8. It automates chart pattern recognition, providing traders with a powerful tool for making informed decisions. Key features include real-time analysis, high accuracy for Buy/Sell signals, and support for various charts.

This project demonstrates how Python, Deep Learning, Streamlit can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/Omar-Karimov/ChartScanAI.git
cd ChartScanAI

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by Omar-Karimov and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.