📊 MacroData Intelligence Platform

A sophisticated web application that analyzes financial data against FRED (Federal Reserve Economic Data) indicators with AI-powered insights. Upload your long-range financial plan and gain deep insights into how macroeconomic trends correlate with your business metrics.

✨ Features

🎯 Core Capabilities

Automatic Time Period Detection: Intelligently detects date ranges from uploaded data (quarterly, monthly, annual)
Industry-Specific Analysis: Pre-configured FRED indicators for 6 industries:
- Gaming
- Technology
- Manufacturing
- Retail
- Healthcare
- Finance

📈 Advanced Analytics

Real FRED API Integration: Pulls live economic data including:
- Corporate Profits After Tax
- Industrial Production Index (by industry)
- Producer Price Index (by industry)
- Real GDP (industry breakdown)
- Employment & Wages (by sector)
- Financial indices (Nasdaq, S&P sector data)

💡 Margin Pressure Analysis

Combines three key indicators to calculate margin pressure:

Margin Pressure = (Corporate Profits × Industrial Production) / PPI

This unique metric helps identify when rising costs are squeezing margins.

🤖 AI-Powered Insights

Leverages Deepseek API for intelligent analysis
Identifies key trends and risk factors
Provides strategic recommendations
Generates market outlook projections

📊 Beautiful Visualizations

Interactive dual-axis charts for correlation analysis
Margin pressure trend visualization
Top correlations bar chart
Real-time economic indicator dashboard
Modern, gradient-based UI design

🚀 Quick Start

Prerequisites

Node.js (v14 or higher)
npm or yarn
FRED API Key (get free at https://fred.stlouisfed.org/docs/api/api_key.html)
Deepseek API Key (optional, for AI insights)

Installation & Setup

Follow these steps to get the application running from scratch:

1. Clone and Navigate

git clone <repository-url>
cd hackathon-macrodata

2. Install Root Dependencies

Install the development tools (concurrently) for running both services:

npm install

3. Install Server Dependencies

cd server
npm install
cd ..

4. Install Client Dependencies

cd client
npm install
cd ..

Or install all at once:

npm run install:all

5. Configure Environment Variables

Create a .env file in the root directory with your API keys:

# In the root directory (hackathon-macrodata/)
touch .env

Add the following to .env:

DEEPSEEK_API_KEY=your_deepseek_api_key_here
FRED_API_KEY=your_fred_api_key_here

Get your API keys:

FRED API: https://fred.stlouisfed.org/docs/api/api_key.html (free)
Deepseek API: https://platform.deepseek.com/ (optional, for AI insights)

Running the Application

Quick Start (Recommended)

From the root directory, run:

npm run dev

This starts both the backend (port 5001) and frontend (port 3000) concurrently.

You should see:

✨ Server running on port 5001
📊 FRED API: ✓ Configured
🤖 Deepseek API: ✓ Configured
Compiled successfully!

Open your browser to http://localhost:3000

Alternative: Run Separately

Terminal 1 - Backend:

npm run server
# or: cd server && npm run dev

Terminal 2 - Frontend:

npm run client
# or: cd client && npm start

Production Build

Build the React app:

cd client
npm run build

Start the production server:

cd server
npm start

The app will be available at http://localhost:5001

🚀 Deployment

Deploy to Vercel

This project is configured for easy deployment to Vercel as a full-stack application.

Prerequisites

Vercel account (free at https://vercel.com)
Git repository (GitHub, GitLab, or Bitbucket)

Deployment Steps

Push your code to a Git repository:

git init
git add .
git commit -m "Initial commit"
git remote add origin <your-repo-url>
git push -u origin main

Import project to Vercel:
- Go to https://vercel.com/new
- Import your repository
- Vercel will auto-detect the configuration from vercel.json
Add Environment Variables in Vercel:
- Go to Project Settings → Environment Variables
- Add the following:
```
DEEPSEEK_API_KEY=your_deepseek_api_key
FRED_API_KEY=your_fred_api_key
```
Deploy:
- Click "Deploy"
- Vercel will build and deploy both frontend and backend

Build Configuration

The vercel.json file configures:

Backend: server/server.js runs as a serverless function
Frontend: client/ builds as a static site
Routes: API calls to /api/* route to the backend, everything else to the frontend

Environment Variables

For Production (Vercel):

DEEPSEEK_API_KEY: Your Deepseek API key
FRED_API_KEY: Your FRED API key

For Frontend (optional):

REACT_APP_API_URL: Leave empty when deploying to Vercel (uses same domain)

Alternative: Split Deployment

You can also deploy frontend and backend separately:

Frontend (Vercel):

Root Directory: client
Build Command: npm run build
Output Directory: build
Framework: Create React App
Environment Variable: REACT_APP_API_URL=https://your-backend-url.com

Backend (Render/Railway/Heroku):

Root Directory: server
Start Command: npm start
Add environment variables: DEEPSEEK_API_KEY, FRED_API_KEY

📁 Project Structure

hackathon-macrodata/
├── server/                      # Backend server
│   ├── server.js               # Main Express server
│   ├── fred-api.js             # FRED API integration
│   ├── data-processing.js      # Data processing utilities
│   ├── deepseek-api.js         # AI insights integration
│   ├── .env                    # API keys configuration
│   ├── nexus_gaming_financials.csv # Sample data
│   └── package.json            # Server dependencies
├── client/                      # React frontend
│   ├── src/
│   │   ├── App.js              # Main React component
│   │   ├── App.css             # Modern styling
│   │   └── index.js            # Entry point
│   ├── public/
│   └── package.json            # Client dependencies
├── package.json                 # Root scripts for running both
├── .gitignore                   # Git ignore rules
└── README.md                    # This file

🎮 How to Use

Step 1: Upload Your Data

Supports Excel (.xlsx, .xls) and CSV files
Automatic date detection from column headers (Q1-2021, FY-2022, etc.)
Extracts financial metrics automatically

Step 2: Select Your Industry

Choose from 6 pre-configured industries. Each has relevant FRED indicators:

Gaming: Entertainment sector indicators
Technology: Tech-specific production and employment data
Manufacturing: Industrial production and PPI
Retail: Consumer goods and retail trade data
Healthcare: Healthcare expenditures and employment
Finance: Financial sector profits and indices

Step 3: Select Metrics to Analyze

Choose which financial metrics to correlate with FRED data
Multiple metrics can be analyzed simultaneously

Step 4: View Results

AI Insights: Strategic analysis powered by Deepseek
Margin Pressure Chart: Visualize cost pressure trends
Correlation Charts: See how your metrics align with economic indicators
FRED Dashboard: Track current economic indicator values

🔧 API Endpoints

`GET /api/industries`

Returns available industries and their configuration

`GET /api/indicators/:industry`

Returns FRED indicators for a specific industry

`POST /api/upload`

Upload and parse financial data file

Accepts: Excel, CSV
Returns: Detected metrics, time period, preview

`POST /api/analyze`

Run correlation analysis

Body: { industry, startDate, endDate, selectedMetrics }
Returns: FRED data, correlations, margin pressure, AI insights

`GET /api/health`

Health check and API key status

🎨 Features Highlight

Automatic Time Detection

The system intelligently parses various date formats:

Quarterly: Q1-2021, Q2 2021
Fiscal Year: FY-2021, FY2021
Standard dates: 2021-01-01, 01/01/2021

Margin Pressure Index

A proprietary metric that combines:

Corporate Profits: Overall profitability trends
Industrial Production: Efficiency and output
Producer Price Index: Input cost inflation

Higher values indicate healthier margins; lower values signal cost pressures.

Correlation Analysis

Pearson correlation coefficients between your financial metrics and economic indicators:

> 0.7: Strong positive correlation
0.4 - 0.7: Moderate positive correlation
-0.4 - 0.4: Weak/no correlation
< -0.7: Strong negative correlation

🌐 FRED Data Sources

All economic data is sourced from the Federal Reserve Economic Data (FRED) API:

Documentation: https://fred.stlouisfed.org/docs/api/fred/
Data Coverage: Over 800,000 economic time series
Update Frequency: Varies by series (daily, monthly, quarterly)

🤖 AI Integration

The platform uses Deepseek's latest language model to:

Analyze correlation patterns
Identify market trends
Highlight risk factors
Provide actionable recommendations
Generate forward-looking insights

📊 Sample Data

The server/nexus_gaming_financials.csv file contains:

Quarterly financial data (Q1-2021 through Q4-2026)
Revenue, costs, profits, and other metrics
Perfect for testing the gaming industry analysis

🛠️ Technologies Used

Backend:

Node.js & Express
Axios (API calls)
XLSX (Excel parsing)
CSV-Parse

Frontend:

React 18
Chart.js & react-chartjs-2
Axios
Modern CSS with gradients

APIs:

FRED (Federal Reserve Economic Data)
Deepseek (AI insights)

📝 License

MIT License - Feel free to use and modify for your needs.

🙏 Credits

Built for analyzing business financials against macroeconomic trends.

FRED API by Federal Reserve Bank of St. Louis
Deepseek AI for intelligent insights

Name		Name	Last commit message	Last commit date
Latest commit History 7 Commits
client		client
node_modules		node_modules
server		server
.gitignore		.gitignore
README.md		README.md
nexus_gaming_financials.csv		nexus_gaming_financials.csv
package-lock.json		package-lock.json
package.json		package.json
vercel.json		vercel.json

nathaliedpuc/hackathon-macrodata

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📊 MacroData Intelligence Platform

✨ Features

🎯 Core Capabilities

📈 Advanced Analytics

💡 Margin Pressure Analysis

🤖 AI-Powered Insights

📊 Beautiful Visualizations

🚀 Quick Start

Prerequisites

Installation & Setup

1. Clone and Navigate

2. Install Root Dependencies

3. Install Server Dependencies

4. Install Client Dependencies

5. Configure Environment Variables

Running the Application

Quick Start (Recommended)

Alternative: Run Separately

Production Build

🚀 Deployment

Deploy to Vercel

Prerequisites

Deployment Steps

Build Configuration

Environment Variables

Alternative: Split Deployment

📁 Project Structure

🎮 How to Use

Step 1: Upload Your Data

Step 2: Select Your Industry

Step 3: Select Metrics to Analyze

Step 4: View Results

🔧 API Endpoints

GET /api/industries

GET /api/indicators/:industry

POST /api/upload

POST /api/analyze

GET /api/health

🎨 Features Highlight

Automatic Time Detection

Margin Pressure Index

Correlation Analysis

🌐 FRED Data Sources

🤖 AI Integration

📊 Sample Data

🛠️ Technologies Used

📝 License

🙏 Credits

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`GET /api/industries`

`GET /api/indicators/:industry`

`POST /api/upload`

`POST /api/analyze`

`GET /api/health`

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