NASDAQ Stock Agent

AI-powered stock analysis agent that provides investment recommendations for NASDAQ stocks using Claude AI.

📖 What is This?

The NASDAQ Stock Agent is an intelligent AI system that analyzes stocks and provides investment recommendations. It uses:

Claude AI (Anthropic) for intelligent analysis
Real-time market data from yfinance
Technical analysis (RSI, MACD, Moving Averages)
Fundamental analysis (P/E ratios, EPS, Revenue)
Natural language processing to understand your questions

What Can It Do?

Analyze any NASDAQ stock by ticker symbol (e.g., "AAPL", "TSLA")
Answer questions in plain English (e.g., "Should I buy Apple stock?")
Provide BUY/HOLD/SELL recommendations with confidence scores
Explain the reasoning behind each recommendation
Track analysis history in a database

Example

You ask: "What do you think about Apple stock?"

Agent responds:

{
  "symbol": "AAPL",
  "recommendation": "BUY",
  "confidence": 85,
  "current_price": 178.45,
  "reasoning": "Strong technical indicators with price above key moving averages.",
  "key_factors": [
    "Price above 50-day and 200-day moving averages",
    "Strong revenue growth of 8.5%",
    "Solid balance sheet"
  ],
  "risks": [
    "High valuation at 28.5x P/E ratio",
    "Potential regulatory headwinds"
  ]
}

🚀 Quick Start - Local Machine

Prerequisites

Docker and Docker Compose installed
Anthropic API key (Get one here)
2GB+ RAM available

Steps to Run Locally

1. Clone the repository

git clone https://github.com/YOUR_USERNAME/nasdaq-stock-agent.git
cd nasdaq-stock-agent

2. Configure environment

# Copy the template
cp .env.example .env

# Edit and add your API key
nano .env

Update this line in .env:

ANTHROPIC_API_KEY=your_actual_api_key_here

3. Start the application

docker-compose up -d

4. Test it

# Check if it's running
curl http://localhost:8000/

# Analyze a stock
curl -X POST http://localhost:8000/api/v1/analyze \
  -H "Content-Type: application/json" \
  -d '{"query": "AAPL"}'

5. View API documentation

Open in your browser: http://localhost:8000/docs

That's it! Your agent is running on http://localhost:8000

Stop the Application

docker-compose down

Using the Startup Script

The startup.sh script automatically detects your public IP and configures NEST:

# For development
./startup.sh

# For production (AWS EC2)
./startup.sh prod

What it does:

Detects public IP from AWS EC2 metadata or external services
Updates NEST_PUBLIC_URL in .env automatically
Optionally starts Docker services
Shows configuration summary

☁️ Deploy to AWS EC2

Prerequisites

AWS account with EC2 access
SSH key pair for EC2
Anthropic API key

Steps to Deploy on AWS EC2

1. Launch EC2 Instance

In AWS Console:

Go to EC2 Dashboard → Launch Instance
Name: nasdaq-stock-agent
AMI: Ubuntu Server 22.04 LTS
Instance Type: t3.medium (2 vCPU, 4GB RAM)
Key Pair: Select or create new
Storage: 20GB gp3
Security Group: Create with these rules:
- SSH (22) - Your IP
- HTTP (80) - 0.0.0.0/0
- Custom TCP (8000) - 0.0.0.0/0
- Custom TCP (6000) - 0.0.0.0/0

2. Connect to EC2

# Set key permissions
chmod 400 your-key.pem

# SSH into instance
ssh -i your-key.pem ubuntu@YOUR_EC2_PUBLIC_IP

3. Install Docker

# Update system
sudo apt-get update && sudo apt-get upgrade -y

# Install Docker
curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh get-docker.sh
sudo usermod -aG docker ubuntu

# Install Docker Compose
sudo curl -L "https://github.com/docker/compose/releases/latest/download/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose
sudo chmod +x /usr/local/bin/docker-compose

# Verify installations
docker --version
docker-compose --version

4. Deploy Application

# Clone repository
git clone https://github.com/YOUR_USERNAME/nasdaq-stock-agent.git
cd nasdaq-stock-agent

# Configure environment
cp .env.example .env
nano .env

Update these values in .env:

ANTHROPIC_API_KEY=your_actual_api_key_here
MONGO_ROOT_PASSWORD=your_secure_password_here
MONGO_PASSWORD=your_secure_password_here
# Note: NEST_PUBLIC_URL will be auto-detected by startup script

5. Start Services (Automatic IP Detection)

Option A: Quick Deploy (Easiest)

# One command deployment - handles everything
./quick-deploy.sh

# This will:
# 1. Create .env from template (if needed)
# 2. Detect your EC2 public IP automatically
# 3. Update NEST_PUBLIC_URL in .env
# 4. Start services in production mode

Option B: Using Startup Script

# If you already have .env configured
./startup.sh prod

# This will:
# 1. Detect your EC2 public IP
# 2. Update NEST_PUBLIC_URL in .env
# 3. Start services in production mode

Option B: Manual Start

# Manually update NEST_PUBLIC_URL with your EC2 IP
# Get your public IP
curl http://169.254.169.254/latest/meta-data/public-ipv4

# Update .env file
nano .env
# Set: NEST_PUBLIC_URL=http://YOUR_EC2_IP:6000

# Start with production configuration
docker-compose -f docker-compose.prod.yml up -d

# Check status
docker-compose -f docker-compose.prod.yml ps

# View logs
docker-compose -f docker-compose.prod.yml logs -f

6. Test Deployment

# From your local machine
curl http://YOUR_EC2_PUBLIC_IP:8000/

# Analyze a stock
curl -X POST http://YOUR_EC2_PUBLIC_IP:8000/api/v1/analyze \
  -H "Content-Type: application/json" \
  -d '{"query": "AAPL"}'

7. Access API Documentation

Open in browser: http://YOUR_EC2_PUBLIC_IP:8000/docs

Manage EC2 Deployment

# View logs
docker-compose -f docker-compose.prod.yml logs -f

# Restart services
docker-compose -f docker-compose.prod.yml restart

# Stop services
docker-compose -f docker-compose.prod.yml down

# Update application
git pull
docker-compose -f docker-compose.prod.yml up -d --build

📊 Usage Examples

Using curl

# Analyze by ticker
curl -X POST http://localhost:8000/api/v1/analyze \
  -H "Content-Type: application/json" \
  -d '{"query": "AAPL"}'

# Natural language
curl -X POST http://localhost:8000/api/v1/analyze \
  -H "Content-Type: application/json" \
  -d '{"query": "Should I buy Tesla stock?"}'

Using Python

import requests

response = requests.post(
    "http://localhost:8000/api/v1/analyze",
    json={"query": "AAPL"}
)

result = response.json()
print(f"Recommendation: {result['recommendation']}")
print(f"Confidence: {result['confidence']}%")

Using JavaScript

fetch('http://localhost:8000/api/v1/analyze', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify({ query: 'AAPL' })
})
.then(res => res.json())
.then(data => console.log(data));

🔧 Configuration

Docker Compose Files

This project includes two Docker Compose configurations:

`docker-compose.yml` (Development/Local)

Use for: Local development and testing
MongoDB: No authentication (simpler setup)
Nginx: Not included (direct access to port 8000)
Command: docker-compose up -d
Best for: Quick local testing and development

`docker-compose.prod.yml` (Production/AWS EC2)

Use for: Production deployments on AWS EC2 or other servers
MongoDB: Authentication required (secure)
Nginx: Included as reverse proxy (ports 80/443)
Resource Limits: CPU and memory limits configured
Command: docker-compose -f docker-compose.prod.yml up -d
Best for: Production environments requiring security and scalability

Key Differences:

Feature	docker-compose.yml	docker-compose.prod.yml
MongoDB Auth	❌ No	✅ Yes (secure)
Nginx Proxy	❌ No	✅ Yes
Resource Limits	❌ No	✅ Yes
SSL Support	❌ No	✅ Yes
Restart Policy	unless-stopped	always
Use Case	Local Dev	Production

Required Environment Variables

# Anthropic API (Required)
ANTHROPIC_API_KEY=your_api_key_here
ANTHROPIC_MODEL=claude-3-haiku-20240307

# MongoDB (Required)
MONGODB_URL=mongodb://mongodb:27017/
MONGODB_DATABASE=nasdaq_stock_agent

Production Variables (AWS EC2)

# MongoDB Authentication (Required for docker-compose.prod.yml)
MONGO_ROOT_PASSWORD=your_secure_password
MONGO_PASSWORD=your_secure_password

# NEST Integration (Optional)
NEST_ENABLED=true
NEST_PUBLIC_URL=http://YOUR_EC2_PUBLIC_IP:6000

See .env.example for all available configuration options.

🏗️ Architecture

┌─────────────┐
│   Client    │ (Browser, curl, API calls)
└──────┬──────┘
       │
       ▼
┌─────────────┐
│   Nginx     │ Port 80/443 (Production only)
└──────┬──────┘
       │
       ▼
┌─────────────┐
│   FastAPI   │ Port 8000 (Main API)
│   Agent     │
└──────┬──────┘
       │
       ├──────────┬──────────┬──────────┐
       ▼          ▼          ▼          ▼
   ┌────────┐ ┌────────┐ ┌────────┐ ┌────────┐
   │MongoDB │ │Claude  │ │yfinance│ │ NEST   │
   │Database│ │  AI    │ │ Market │ │ (A2A)  │
   └────────┘ └────────┘ └────────┘ └────────┘

🆘 Troubleshooting

Agent Won't Start

# Check logs
docker-compose logs nasdaq-agent

# Verify API key is set
docker exec nasdaq-stock-agent env | grep ANTHROPIC

# Restart
docker-compose restart nasdaq-agent

MongoDB Connection Issues

# Check MongoDB is running
docker-compose ps mongodb

# Restart MongoDB
docker-compose restart mongodb

Port Already in Use

# Check what's using port 8000
lsof -i :8000

# Stop the process or change PORT in .env

Out of Memory (EC2)

# Add swap space
sudo fallocate -l 2G /swapfile
sudo chmod 600 /swapfile
sudo mkswap /swapfile
sudo swapon /swapfile

📝 API Endpoints

GET / - Health check
GET /docs - Interactive API documentation
POST /api/v1/analyze - Analyze stock
GET /api/v1/status - System status
GET /health - Health check

🔐 Security Notes

Never commit .env file to Git (already in .gitignore)
Use strong passwords for MongoDB in production
Restrict EC2 security group to your IP for SSH
Consider enabling SSL/TLS for production (update nginx.conf)
Rotate API keys regularly

📞 Support

API Documentation: http://localhost:8000/docs
Issues: Open a GitHub issue
Logs: docker-compose logs -f nasdaq-agent

📄 License

This project is licensed under the MIT License.

Using Claude AI Model, FastAPI, and Docker

Name		Name	Last commit message	Last commit date
Latest commit History 11 Commits
src		src
tests		tests
.env.example		.env.example
.gitignore		.gitignore
AWS_EC2_DEPLOYMENT.md		AWS_EC2_DEPLOYMENT.md
Dockerfile		Dockerfile
README.md		README.md
agent_protocol_server.py		agent_protocol_server.py
docker-compose.prod.yml		docker-compose.prod.yml
docker-compose.yml		docker-compose.yml
main.py		main.py
mcp_server.py		mcp_server.py
mongo-init.js		mongo-init.js
nginx.conf		nginx.conf
pytest.ini		pytest.ini
quick-deploy.sh		quick-deploy.sh
requirements.txt		requirements.txt
setup.py		setup.py
startup.sh		startup.sh

Navigation Menu

Repository files navigation

NASDAQ Stock Agent

📖 What is This?

What Can It Do?

Example

🚀 Quick Start - Local Machine

Prerequisites

Steps to Run Locally

Stop the Application

Using the Startup Script

☁️ Deploy to AWS EC2

Prerequisites

Steps to Deploy on AWS EC2

Manage EC2 Deployment

📊 Usage Examples

Using curl

Using Python

Using JavaScript

🔧 Configuration

Docker Compose Files

`docker-compose.yml` (Development/Local)

`docker-compose.prod.yml` (Production/AWS EC2)

Required Environment Variables

Production Variables (AWS EC2)

🏗️ Architecture

🆘 Troubleshooting

Agent Won't Start

MongoDB Connection Issues

Port Already in Use

Out of Memory (EC2)

📝 API Endpoints

🔐 Security Notes

📞 Support

📄 License

About

Releases

Packages

Contributors

Languages

Folders and files

Latest commit

History

Repository files navigation

NASDAQ Stock Agent

📖 What is This?

What Can It Do?

Example

🚀 Quick Start - Local Machine

Prerequisites

Steps to Run Locally

Stop the Application

Using the Startup Script

☁️ Deploy to AWS EC2

Prerequisites

Steps to Deploy on AWS EC2

Manage EC2 Deployment

📊 Usage Examples

Using curl

Using Python

Using JavaScript

🔧 Configuration

Docker Compose Files

docker-compose.yml (Development/Local)

docker-compose.prod.yml (Production/AWS EC2)

Required Environment Variables

Production Variables (AWS EC2)

🏗️ Architecture

🆘 Troubleshooting

Agent Won't Start

MongoDB Connection Issues

Port Already in Use

Out of Memory (EC2)

📝 API Endpoints

🔐 Security Notes

📞 Support

📄 License

About

Resources

Stars

Watchers

Forks

Releases

Packages

Contributors

Languages

`docker-compose.yml` (Development/Local)

`docker-compose.prod.yml` (Production/AWS EC2)