Quantitative Equity Analysis Tool

A financial modeling engine built in Python to evaluate the risk-adjusted returns of large-cap tech stocks against the S&P 500 benchmark. This tool automates the retrieval of financial data, calculates key performance metrics, and visualizes the results through a comprehensive 3-panel dashboard.

📌 Overview

This project focuses on quantitative investment analysis by leveraging the Yahoo Finance API for historical price data and the Federal Reserve Economic Data (FRED) for risk-free rates. The core objective is to calculate the Sharpe Ratio and visualize the risk/reward profile, allowing for a direct comparison between individual stock performance (e.g., AAPL) and a market benchmark (S&P 500).

🚀 Key Features

• Automated Data Pipeline: Fetches monthly historical closing prices for user-defined tickers and benchmarks (default: AAPL vs. S&P 500).
• Dynamic Risk-Free Rate: Integrates the 3-Month Treasury Constant Maturity Rate (TB3MS) directly from FRED to calculate accurate excess returns.
• Robust Data Alignment: Uses date buffering and forward-filling (ffill) to ensure seamless alignment between monthly stock returns and economic data reporting dates.
• Performance Dashboard: A matplotlib-based visualization suite that displays:
  • Cumulative Returns: Tracks the growth of a hypothetical $1 investment over the analysis period.
  • Risk vs. Return Scatter: Visualizes the trade-off between Annualized Volatility and Return.
  • Sharpe Ratio Comparison: A bar chart ranking assets by their risk-adjusted efficiency.
• Performance Metrics: Calculates and prints:
  • Average Monthly & Annualized Returns (Logarithmic)
  • Monthly & Annualized Volatility (Standard Deviation using Square Root of Time rule)
  • Sharpe Ratio (Risk-Adjusted Return)

🛠️ Technologies Used

• Python 3.x
• Pandas: For time-series manipulation and data alignment (reindex, DateOffset).
• NumPy: For mathematical operations (log returns, square roots).
• Matplotlib & Seaborn: For plotting cumulative growth curves, scatter plots, and comparative bar charts.
• yfinance: For retrieving stock market data.
• pandas_datareader: For retrieving economic data (FRED).

📊 Methodology

1. Data Ingestion: Downloads monthly historical data for specified tickers.
2. Return Calculation: Computes Logarithmic Returns to ensure time-additivity: $$r_t = \ln(\frac{P_t}{P_{t-1}})$$
3. Risk-Free Adjustment: Fetches TB3MS data from FRED, aligns it to the stock data timeline, and calculates excess returns.
4. Annualization: Converts monthly metrics to annualized figures:
   • $Return_{ann} = Return_{monthly} \times 12$
   • $Volatility_{ann} = Volatility_{monthly} \times \sqrt{12}$
5. Sharpe Ratio: $$\text{Sharpe Ratio} = \frac{R_p - R_f}{\sigma_p}$$
6. Visualization: Generates a unified figure combining growth trends and risk metrics.

💻 Usage

Prerequisites

Ensure you have the required libraries installed:

pip install pandas numpy yfinance pandas_datareader matplotlib seaborn