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SPY ETF Recommender

Requirement Document: ETF SPY Recommendation App

Objective:

I want to create an app that provides buy or not recommendations specifically for the ETF SPY, based on real-time data analysis and AI-driven insights.

Key Requirements:

  1. Data Sources:
  2. ETF SPY Data: The app should collect historical and real-time data for SPY, including prices, trading volume, dividend history, and expense ratios.
  3. Market Sentiment Data: The app should gather sentiment data from financial news, social media platforms like Twitter, and forums like Reddit to gauge market mood towards SPY.

  4. Macroeconomic Data: The app should also consider macroeconomic indicators like interest rates, inflation, and GDP growth that could impact SPY’s performance.

  5. Data Processing:

  6. Feature Engineering: Extract relevant features from the data, such as moving averages, RSI, sentiment scores, and other technical indicators.

  7. Data Storage: The app should store collected data in a database, ensuring it is updated in real-time.

  8. AI Model Development:

  9. Model Selection: The app should use machine learning models, like Random Forests or Neural Networks, to analyze the data and make predictions.

  10. Training and Evaluation: The model should be trained on historical data and regularly updated with new data. It should also be evaluated for accuracy using backtesting techniques.

  11. Recommendation Logic:

  12. Buy or Not Decision: Based on the AI model’s predictions and market sentiment analysis, the app should provide a simple buy or not recommendation.

  13. User Personalization: Allow users to input their risk tolerance and investment goals, which will tailor the recommendations.

  14. User Interface:

  15. Dashboard: The app should have a clean and user-friendly dashboard displaying SPY’s current status, AI-driven recommendations, and key metrics.

  16. Alerts: Users should receive notifications or alerts when a significant change in recommendation occurs.

  17. Deployment:

  18. Cloud Hosting: The app should be hosted on a cloud platform like AWS or Azure to ensure scalability and reliability.
  19. Real-Time Updates: The app should integrate with data sources via APIs to provide real-time updates and recommendations.

Solutioning Document: ETF SPY Recommendation App

1. Data Collection:

  • APIs: I will use Yahoo Finance API for fetching SPY’s financial data, including prices, volume, and dividends. For market sentiment, I’ll use the Twitter API and NewsAPI to gather social media and news sentiment on SPY.
  • Data Pipeline: I’ll set up a data pipeline using Apache Kafka to stream real-time data from these sources into my app’s backend.

2. Data Processing:

  • Feature Engineering: I’ll extract technical indicators like moving averages, RSI, and volatility from SPY’s historical data. Sentiment analysis will be performed on the collected news and social media data using NLP tools.
  • Database: I’ll store all processed data in a PostgreSQL database, ensuring real-time updates with proper indexing for quick access.

3. AI Model Development:

  • Model Selection: I’ll start with a Random Forest model for its robustness and ability to handle different types of data. I might also experiment with LSTM networks for better time-series predictions.
  • Training and Evaluation: I’ll use historical SPY data to train the model and backtest it against past market conditions to ensure accuracy.

4. Recommendation Logic:

  • Buy or Not Decision: The model’s output, combined with sentiment scores, will feed into a decision-making algorithm. If the model predicts positive returns and sentiment is high, the app will recommend a buy; otherwise, it will advise holding off.
  • User Personalization: I’ll add options for users to set their risk levels and investment preferences, which will adjust the final recommendation accordingly.

5. User Interface:

  • Dashboard Design: I’ll create a dashboard using React for the frontend, showing SPY’s current data, AI predictions, and a clear buy/not recommendation. I’ll also include charts and graphs for better visualization.
  • Alerts: I’ll implement real-time notifications using Firebase Cloud Messaging to alert users when the recommendation changes.

6. Deployment:

  • Cloud Hosting: I’ll deploy the app on AWS, using services like EC2 for hosting the application and RDS for the PostgreSQL database.
  • Real-Time Integration: The app will be connected to all data sources via APIs to ensure it provides up-to-the-minute recommendations.