https://github.com/cs2219/recommender_system

Recommendation system for Stock market
https://github.com/cs2219/recommender_system

Last synced: 20 days ago
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Recommendation system for Stock market

• Host: GitHub
• URL: https://github.com/cs2219/recommender_system
• Owner: CS2219
• Created: 2024-11-15T14:39:43.000Z (about 2 months ago)
• Default Branch: main
• Last Pushed: 2024-11-24T05:55:20.000Z (about 1 month ago)
• Last Synced: 2024-11-24T06:24:48.937Z (about 1 month ago)
• Size: 5.07 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Buy/Sell recommendation system

This project is an AI-powered Stock Recommendation System designed to provide actionable insights such as "Buy" or "Sell" recommendations for stocks. By integrating data from multiple sources, applying advanced machine learning techniques, and presenting a user-friendly interface, this system empowers users to make data-driven investment decisions.

**Features**

###### Smart Data Integration:

Yahoo Finance API: Provides historical stock data.

News API: Delivers news articles for sentiment analysis.

Twitter API: Captures real-time social sentiment about stocks.

Alpha Vantage API: Supplies additional technical indicators for deeper analysis.

Machine Learning for Predictions:

Predicts stock movements using a Random Forest Classifier trained on historical data and technical indicators.

Combines financial indicators (e.g., RSI, moving averages) with sentiment analysis for a holistic view.

Interactive UI with Streamlit:

User-friendly interface to explore stock trends, visualize technical indicators, and get "Buy" or "Sell" recommendations.

Offers interactive charts and insights to help users quickly analyze stock performance.

###### Data Storage:

Uses PostgreSQL to store fetched data, ensuring fast and scalable access to historical and real-time datasets.

**How It Works**

###### Data Gathering:

The system fetches stock prices, technical indicators, and sentiment data from news articles and tweets.

###### Data Processing & Feature Engineering:

Calculates essential indicators like moving averages, Bollinger Bands, and RSI.

Performs sentiment analysis to assess market mood.

###### Model Training:

Trains a Random Forest Classifier on historical stock data combined with sentiment metrics.

Optimized for accurate predictions of stock trends, with cross-validated accuracy of over 85%.

###### Recommendation & Visualization:

Provides "Buy" or "Sell" predictions for selected stocks.

Visualizes key data points like stock prices, moving averages, and sentiment trends in real time.

**Example Use Case**

A user can:

Enter a stock ticker (e.g., AAPL or TSLA).

View the system's recommendation: "Buy" or "Sell".

Explore visualized trends such as moving averages, RSI, and historical price data.

Gain confidence in decision-making by checking sentiment analysis from news and Twitter.

###### Why You'll Love This

This system is perfect for anyone who:

Wants data-driven insights for their stock trades.

Values an easy-to-use interface to understand complex stock trends.

Aims to combine technical analysis with market sentiment for smarter investment decisions.

Whether you're a seasoned trader or a curious investor, this recommendation system will elevate your stock analysis game!