📊 Stock-Analyzer

Stock-Analyzer is an application designed to analyze stock data retrieved from an external API and provide useful insights for investing. 💼

🎯 Application Purpose

Provide investment recommendations based on an automated analysis of broker activity, rating changes, and price projections. 📈

📱 User Interface

⚙️ Requirements

• Docker installed on your system 🐳
• .env file in the backend directory 🛠️

🚀 Setup

1. Configure the environment 🧪:

   • In the backend directory, make sure to include a .env file based on the provided .env.example.
   • You can copy and rename it using the following command:

     cp backend/.env.example backend/.env

   • Then, edit the .env file with the necessary configurations.

2. Start the project 🏁:

   • From the project root, run the following command:

     docker-compose --profile dev up

   • This will start the necessary containers to run the application.

3. Open the App Web UI 🔄:

   • Go to http://localhost:8082
   • Once there, click on the "Sync Stocks" button to fetch the latest stock data.

🖥️ Usage

Once the containers are up and running, you can access the application through the following URLs:

• CockroachDB Web UI: http://localhost:8080/
• API: http://localhost:8081/
• App Web UI: http://localhost:8082/

⚙️ How Does the Recommendation System Work?

1. Data Collection: It gathers all the stocks that had relevant movements on a specific date.
2. Recommendation Processing: Each stock is automatically evaluated based on three factors:
   • 📊 Rating changes.
   • 🧾 Actions taken by brokers (such as raising the target price).
   • 💰 Growth potential based on the target price change.
3. Score Calculation: Each factor is assigned a specific score. Stocks are sorted by this score and the top 5 are shown.
4. Result: The user receives a clear recommendation, with an explanation, score, and potential growth.

🧮 Algorithm Breakdown

// Stock evaluation:
score = ratingScore + actionScore + potentialGrowth

• RatingScore: Assigned based on rating improvement or deterioration.
• ActionScore: Based on broker actions (e.g., upgraded rating, raised target).
• PotentialGrowth: Percentage change between the previous and new normalized target price.

🧾 Table of Actions and Their Impact

🎯 Action	🔢 Points	📘 Meaning
✅ Upgraded by	+8	Rating of the stock was improved.
🟢 Target raised by	+7	Target price was increased.
🟡 Target set by	+6	A target price was set for the first time.
🟡 Initiated by	+5	Coverage of the stock was initiated.
🟡 Reiterated by	+4	Rating was reiterated.
🔴 Downgraded by	+3	Rating was lowered.
❌ Target lowered by	+1	Target price was decreased.

⭐ Rating Table and Its Meaning

🏷️ Rating	🔢 Points	📘 Interpretation
✅ Strong-Buy, Buy, etc.	+5	High expectation of price increase.
🟢 Outperform, Overweight	+4	Expected to perform better than the market.
🟡 Hold, Neutral, etc.	+3	Expected to remain stable.
🔴 Underperform, Negative	+2	Expected to perform below the market.
❌ Sell	+1	Recommendation to sell.

💡 Example Result for a Stock

Apple Inc. (AAPL)

• ⭐ Score: 14.3
• 🔍 Reasons:
  • Rating improvement from Hold to Buy (+2)
  • Action taken: Target raised by JP Morgan (+7)
  • Increase in target price by 52.86% (+5.3)
• 📈 Growth potential: 52.86%

Perfecto, aquí tienes el apartado para agregar al README.md sobre cómo desplegar la aplicación en AWS usando Terraform:

☁️ Deploying on AWS

To deploy this application on an AWS EC2 instance using Terraform, follow these steps:

🔧 Requirements

• Terraform installed.

• AWS credentials configured in your system (~/.aws/credentials or environment variables).

• An SSH key generated using ed25519.

  You can generate one with the following command:

  ssh-keygen -t ed25519

  Save the private key securely, as it will be required to access the instance.

🚀 Deployment Steps

1. Navigate to the Terraform folder:

   cd terraform

2. Initialize Terraform:

   terraform init

3. Apply the configuration:

   terraform apply

   Confirm when prompted. This will create an EC2 instance and set it up for deployment.

4. Upload your configured .env files to the corresponding folders in the instance:

   • backend/.env
   • frontend/.env

5. Access the EC2 instance via SSH, then navigate to the app directory and run:

   docker-compose --profile prod up -d

6. ✅ Your app should now be live and running on your EC2 instance!

⚠️ Disclaimer

This project is intended for educational and informational purposes only.

All investment decisions are the sole responsibility of the user. I am not responsible for any financial losses or outcomes resulting from the use of this tool. Please do your own research and consult a professional financial advisor before making any investment.