💰 Financial Time-Series Analysis: Stock Comparison & Visualization

🎯 Project Overview & Business Value

This project provides a comprehensive framework for comparing the historical performance of multiple equity assets using advanced data visualization techniques. The goal is to derive actionable insights into market trends, volatility, and risk-adjusted returns, primarily for portfolio management.

Key Deliverable: An interactive visualization dashboard (built with Plotly) to track [List 1-2 Key Metrics, e.g., Cumulative Returns and Daily Volatility] over time.

⚙️ Technical Methodology & Metrics

1. Data Acquisition: Used [Specify Source, e.g., the yfinance library or API name] to fetch adjusted close prices for tickers [List 2-3 tickers analyzed, e.g., AAPL, GOOG, SPY].
2. Financial Metrics: Calculated key time-series metrics:
   • Daily Returns
   • Cumulative Returns (To track growth over the period)
   • Rolling Volatility (A measure of risk)
3. Interactive Visualization: Utilized Plotly to create interactive line plots and candlestick charts, enabling users to zoom and inspect specific periods.

📚 Project Files

• 01_Equity_Viz_Analysis.ipynb: Complete analysis covering data fetching, metric calculation, and visualization generation.
• requirements.txt: List of all necessary Python packages.

🛠️ Stack & Tools

• Language: Python
• Core Libraries: Pandas (for time-series data manipulation), NumPy
• Data Source: [e.g., yfinance / pandas-datareader]
• Visualization: Plotly

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📦 Setup and Installation

Follow these steps to set up the necessary environment and run the analysis notebook on your local machine.

1. Clone the Repository

git clone [https://github.com/pandakitty/Financial-Time-Series-Analysis.git](https://github.com/pandakitty/Financial-Time-Series-Analysis.git)
cd Financial-Time-Series-Analysis