# Stock Analysis Package

The Stock Analysis Package is a Python library designed to simplify the process of analyzing stock market data. This package provides a range of tools that allow users to perform detailed trend analysis, calculate moving averages, examine trading volumes, assess volatility, and evaluate investment performance through various financial metrics.

## Features

- **Moving Averages**: Calculate and plot simple and exponential moving averages.
- **Performance Metrics**: Compute key metrics such as the Sharpe Ratio to assess the risk-adjusted returns of stocks.
- **Trend Analysis**: Visualize linear trends in stock price data to identify potential buying or selling opportunities.
- **Volatility Analysis**: Measure and visualize the volatility of stock prices, an important indicator of risk.
- **Volume Analysis**: Analyze and plot trading volumes to understand market activity levels.
- **Comparison Analysis**: Compare the performance of two stocks over a given period to help make informed investment decisions.

## Installation

To use the Stock Analysis Package, you will need Python 3.6 or later. The package can be installed via pip from PyPI:

```bash
pip install stock_analysis_package
```

Alternatively, if you have cloned or downloaded this GitHub repository, navigate to the root directory of the package and run:
```bash
pip install .
```
This command installs the package along with all required dependencies.

## Quick Start

Here is a quick example to get you started with the Stock Analysis Package:

```python
import yfinance as yf
from stock_analysis import moving_averages, performance_metrics, comparison_analysis

# Download historical data for Apple Inc. and Google LLC.
data_aapl = yf.download('AAPL', start='2021-01-01', end='2021-12-31')
data_googl = yf.download('GOOGL', start='2021-01-01', end='2021-12-31')

# Calculate 50-day and 200-day moving averages for Apple
ma50_aapl = moving_averages.calculate_moving_average(data_aapl, window_size=50)
ma200_aapl = moving_averages.calculate_moving_average(data_aapl, window_size=200)

# Calculate the Sharpe Ratio for Apple
returns_aapl = data_aapl['Adj Close'].pct_change().dropna()
sharpe_ratio_aapl = performance_metrics.sharpe_ratio(returns_aapl)
print(f"Apple Sharpe Ratio: {sharpe_ratio_aapl}")

# Compare Apple and Google
comparison_analysis.compare_two_stocks('AAPL', 'GOOGL', '2021-01-01', '2021-12-31')

```

## Documentation
For more detailed documentation on each module and its functions, please refer to the inline comments within the code or the official documentation.

## Contributing
Contributions to the Stock Analysis Package are welcome! Please refer to CONTRIBUTING.md for guidelines on how to make contributions.

## Support
If you encounter any issues or have questions about the package, please file an issue on the GitHub repository issue tracker.

## License
This project is licensed under the MIT License - see the LICENSE file for details.