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Building an Advanced Stock Tracker with Next.js and AI Predictions

Next.jsTypeScriptAIFinancial TechData Visualization

Building an Advanced Stock Tracker with Next.js and AI Predictions

In this blog post, I'll walk through the process of building an advanced stock tracker application with Next.js, TypeScript, and AI-powered predictions. This project combines modern web development practices with financial data analysis and machine learning to create a powerful tool for tracking and analyzing stocks.

Stock Tracker Preview

Project Overview

The Advanced Stock Tracker is a responsive web application that allows users to:

  • Search for stocks and view real-time price data
  • Add stocks to a persistent watchlist
  • View interactive charts with historical price data
  • Get AI-powered predictions for future stock prices
  • Analyze technical indicators like MACD, RSI, and Bollinger Bands
  • Receive market sentiment analysis from large language models

Technical Architecture

The application is built with:

  • Next.js 15 with App Router for routing and server-side rendering
  • TypeScript for type safety
  • Tailwind CSS for styling
  • Framer Motion for animations
  • Recharts for data visualization
  • React Query for data fetching and caching
  • Local Storage for maintaining user preferences
  • Mock Data API for generating realistic stock data

Development Journey

Phase 1: Setting Up the Project

The first challenge was setting up a modern Next.js project with TypeScript and Tailwind CSS. I created a clean, maintainable file structure following Next.js best practices, with components, lib utilities, and API services properly organized.

stock-tracker/
├── src/
│   ├── app/              # Next.js App Router
│   ├── components/       # Reusable React components
│   ├── lib/              # Utility functions
│   └── styles/           # Global styles
├── public/               # Static assets
└── ... configuration files

I focused on creating reusable components from the start, ensuring that the UI would be consistent and maintainable as the project grew.

Phase 2: Building the Core Stock Tracking Functionality

The core functionality was implemented in several stages:

  1. Stock Search: Created a search component that allows users to find stocks by symbol or name
  2. Watchlist Management: Built a persistent watchlist that saves to local storage
  3. Stock Charts: Implemented interactive charts for visualizing historical stock data
  4. Data Caching: Created a caching layer to reduce API calls and improve performance

A key challenge during this phase was managing state effectively across components. I implemented a clean state management approach using React hooks and context where appropriate.

Stock Chart Component

Phase 3: Implementing Mock Data

Initially, I planned to use Alpha Vantage API for stock data, but I encountered rate limiting issues during development. To solve this, I created a comprehensive mock data implementation:

// Generate realistic stock time series data
export function generateStockTimeSeries(symbol: string, days: number = 365): StockTimeSeriesData[] {
  // Find the base price for the symbol, or use a default
  const stockInfo = STOCK_LIST.find(s => s.symbol === symbol);
  const basePrice = stockInfo ? stockInfo.price : 100;
  
  const data: StockTimeSeriesData[] = [];
  const today = new Date();
  
  // Volatility parameters
  const dailyVolatility = 0.015; // 1.5% daily volatility
  const trendStrength = 0.001; // Slight upward trend
  
  // Generate data points with realistic price movements...
}

The mock data system simulates:

  • Realistic price movements with appropriate volatility
  • Daily, weekly, and monthly data aggregation
  • Network delays for realistic UX
  • Weekend skipping (no trading data on Saturdays/Sundays)
  • Gradual price trends reflecting real-world behavior

This turned out to be a blessing in disguise, as it allowed for unlimited development and testing without hitting API limits.

Phase 4: Adding AI Predictions

The most exciting phase was implementing AI-powered predictions:

Statistical Prediction Model

I built a statistical model that uses several technical analysis techniques:

  • Time series analysis with ARIMA-inspired methods
  • Moving averages (simple and exponential)
  • Support and resistance level detection
  • Trend analysis with confidence scoring
// ARIMA-inspired prediction (simplified)
function arimaInspiredPrediction(data: number[], daysToPredict: number): number[] {
  const predictions = [];
  const window = 5; // AR window size
  
  // Create differenced series for stationarity
  const differenced = [];
  for (let i = 1; i < data.length; i++) {
    differenced.push(data[i] - data[i - 1]);
  }
  
  // Predict using AR model and convert back to original scale
  // ...
}
AI Prediction Graph

Technical Indicators

I implemented advanced technical indicators to provide deeper insights:

  • Simple Moving Average (SMA)
  • Exponential Moving Average (EMA)
  • Moving Average Convergence Divergence (MACD)
  • Relative Strength Index (RSI)
  • Bollinger Bands
  • Average Directional Index (ADX)
  • Parabolic SAR

This required diving deep into financial mathematics and ensuring accurate calculations.

LLM-Based Market Analysis

The most ambitious feature was integrating large language models for market analysis:

/**
 * Generate a context for the AI model with historical stock data
 */
function generateContext(symbol: string, stockData: StockTimeSeriesData[]): string {
  // Extract the last 30 days of data
  const recentData = stockData.slice(-30);
  
  // Format the data for the LLM
  // ...
  
  return `
  STOCK ANALYSIS CONTEXT:
  Symbol: ${symbol}
  Current Price: $${lastPrice.toFixed(2)}
  30-Day Change: ${percentChange}%
  30-Day High: $${highestPrice.toFixed(2)}
  30-Day Low: $${lowestPrice.toFixed(2)}
  
  HISTORICAL DATA (Last 30 Days):
  ${dataPoints}
  `;
}

The LLM analyzes historical data to provide:

  • Market sentiment analysis (bullish, bearish, neutral)
  • Key factors influencing the stock's performance
  • Price targets for short-term horizons
  • Risk assessment and suggested actions

Development Challenges and Solutions

1. Data Reliability and API Limits

Challenge: Initially using Alpha Vantage API, I hit rate limits of 5 requests per minute and 25 per day.

Solution: Developed a sophisticated mock data system that generates realistic stock data with appropriate volatility and trends. This removed all API limitations and allowed for unlimited development and testing.

2. Performance Optimization

Challenge: The app became slow when handling large datasets and multiple charts.

Solution: Implemented data caching, memoization, and efficient rendering strategies. Used React's useMemo and useCallback hooks to prevent unnecessary re-renders.

3. Technical Indicator Accuracy

Challenge: Ensuring technical indicators like MACD and RSI were calculated correctly.

Solution: Studied financial mathematics papers and verified calculations against known good implementations. Created comprehensive test cases to validate results.

4. UI/UX for Complex Data

Challenge: Presenting complex financial data in an intuitive way for users of varying expertise levels.

Solution: Designed a tabbed interface that progressively reveals complexity. Used appropriate visualizations (line charts, bar charts, etc.) for different data types.

Technical Indicators

5. Responsive Design for Data-Heavy UI

Challenge: Making the data-rich UI work well on mobile devices.

Solution: Used Tailwind's responsive utilities and created mobile-specific layouts that prioritize the most important information first.

Future Improvements

Moving to Yahoo Finance API

A promising next step is integrating the Yahoo Finance API, which offers more comprehensive data:

npm install yahoo-finance2

This would provide additional benefits:

  • Real-time intraday data
  • Options chain information
  • Company fundamentals and financials
  • ESG scores and analyst recommendations

Additional Features Planned

  • Portfolio tracking with performance metrics
  • Stock screener with custom filters
  • News integration and sentiment analysis
  • Social sharing of predictions and analysis
  • Backtesting tools for prediction strategies

Lessons Learned

  1. Mock early, mock often: Creating realistic mock data early saved countless hours dealing with API limits.

  2. Progressive enhancement: Building the core functionality first, then adding advanced features incrementally, made the project manageable.

  3. State management matters: Carefully planning how state flows through the application prevented many potential bugs.

  4. TypeScript is worth it: The type safety provided by TypeScript caught many issues before they became problems, especially when handling complex data structures.

  5. Balancing AI and UX: Making AI predictions accessible required careful UI design to prevent overwhelming users.

Conclusion

Building the Advanced Stock Tracker was a rewarding journey that combined modern web development with financial technology and artificial intelligence. The project demonstrates how these technologies can work together to create powerful tools that were previously only available to financial professionals.

The most satisfying aspect was seeing how the mock data system and AI predictions came together to create a seamless experience that mimics real-world trading platforms. As financial APIs continue to improve and AI models become more sophisticated, I'm excited about the possibilities for enhancing this application further.

Explore the Advanced Stock Tracker

Disclaimer: This stock tracker application is for educational and informational purposes only. The predictions and analyses provided should not be considered as financial advice. Always conduct your own research before making investment decisions.