Chart Analyzer AI

Mohsin AliMohsin Ali | 2025
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Introduction

Chart Analyzer AI is a cutting-edge AI-powered trading analysis platform designed to simplify chart interpretation for traders across stocks, forex, and cryptocurrency markets. The platform leverages advanced pattern recognition and Smart Money Concepts (SMC) to provide instant, data-driven insights with over 85% accuracy, helping traders make smarter and faster trading decisions.

Purpose and Objective

The goal of this project was to build a fully automated AI trading analysis tool that could process uploaded trading charts and generate real-time technical insights. Transitioning from traditional static web development to Next.js allowed me to create a scalable, dynamic, and high-performance platform. This project showcases my ability to combine AI-driven logic, API integration, and user-friendly UI/UX design to deliver a reliable and professional trading assistant.

Tools Utilized

  • [ ] Next.js – For building a fast, SEO-friendly, and dynamic web application

  • [ ] React.js – For an interactive and modern frontend experience

  • [ ] MongoDB – For secure and efficient data storage and management

  • [ ] Node.js – For backend processing and API handling

  • [ ] Tailwind CSS – For responsive and elegant UI design

  • [ ] Vercel – For smooth and efficient deployment with continuous integration

Highlight

What makes Chart Analyzer AI stand out is its AI-driven analysis pipeline. Users can upload any trading chart, and the system instantly detects patterns, liquidity zones, BOS, CHoCH, FVG, and order blocks using trained models. The results include bullish/bearish predictions, confidence scores, and market recommendations, providing traders with actionable insights within seconds.

The platform’s sleek design, optimized performance, and real-time AI integration highlight the balance between technical precision and user convenience.

Challenges Faced

One of the major challenges was ensuring accurate chart interpretation across different market types while maintaining fast response times. Optimizing image upload, analysis speed, and API performance was crucial. Additionally, deploying on Vercel required fine-tuning serverless functions for smooth interaction between the AI model and MongoDB. Overcoming these hurdles resulted in a reliable and efficient system with minimal downtime.

Key Learnings

This project deepened my understanding of full-stack AI integration, performance optimization, and deployment best practices. I learned the importance of designing with scalability in mind — ensuring both frontend responsiveness and backend stability. Building Chart Analyzer AI from concept to deployment reinforced my confidence in using Next.js and MongoDB for production-ready AI-powered applications.