Overview / Usage

India has a low participation rate in stock market investments, with only 3% of the population involved, far behind countries like the USA and China.
This is due to:

• Perceived complexity of stock markets.
• Limited awareness of investment options.
• Apprehension towards market volatility.

At the same time, India's renewable energy sector is in dire need of more investment to meet environmental and energy demands, but faces challenges such as:

• Regulatory uncertainties.
• Financial constraints.

We turn complex data into actionable insights. Empower yourself to make informed choices without being bogged down by complicated metrics.

Unlike traditional platforms that rely on a few expert opinions, Empower_AI leverages thousands of datasets to deliver more precise recommendations.

We specialize in uncovering hidden opportunities in renewable energy stocks, identifying promising investments that others might overlook.

Methodology / Approach

The stock analysis web app leverages modern frameworks to provide real-time insights into stock performance. Streamlit serves as the user-friendly interface, allowing for interactive and modular displays of technical and fundamental analysis. Yahoo Finance API is integrated for fetching real-time stock data, including key metrics like historical prices, P/E ratios, and earnings.

For data processing, Pandas and NumPy are used to handle large datasets and compute technical indicators like moving averages and RSI. Intel OneAPI AI Analytics Toolkit ensures efficient computation, optimizing performance, especially for handling historical stock data.

The app follows modular development standards, with distinct sections for technical and fundamental analysis, providing a clean and intuitive user experience. API error handling and response validation are built in to ensure robustness and reliability.

In future iterations, machine learning models using PyTorch could be introduced for predictive analysis, although the current app focuses on real-time data analysis and visual presentation. This approach ensures scalability, maintainability, and the ability to evolve into more complex tools for stock forecasting, making it an efficient solution for investors.

Technologies Used

• Intel OneAPI AI Analytics Toolkit: Optimizes machine learning and numerical computations, especially for large datasets and performance-intensive tasks like technical analysis.
• One DAAL: intel data analytics library for analysis of stocks
• Python: Core programming language used to build the entire backend logic and data handling of the web app.
• Streamlit: Provides the interactive and user-friendly interface, enabling the creation of a web app dashboard for stock analysis.
• Yahoo Finance API: Fetches real-time stock market data, such as price history, fundamental data (like P/E ratio, earnings), and other key metrics for analysis.
• Transformers (Hugging Face): Powers the app's natural language processing tasks, enabling advanced models to assist with sentiment analysis if needed.
• Pandas: Used for handling and manipulating stock data, providing efficient ways to clean, analyze, and visualize data.
• NumPy: Supports mathematical operations, including array manipulation and numerical analysis for technical stock metrics.
• Torch (PyTorch): Deep learning framework used for complex machine learning models, allowing for predictions and more advanced computations.
• virtualenv: Isolates the Python environment, ensuring that all dependencies and packages are managed cleanly, avoiding conflicts.