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README ‑ Google Stock Data Analysis (2004 – 2024)

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1. Project Overview

This case study is a hands‑on time‑series analysis of Alphabet (GOOG) daily price data from 2004‑08‑19 through 2024‑12‑17.
It demonstrates a full workflow—from raw CSV ingestion to feature engineering, statistical profiling, resampling, and rich visualisation—implemented in a single Jupyter notebook.

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2. Data & Dependencies

Item	Details
Dataset	GoogleStockData2004To2024.csv (Kaggle download)
Key libraries	pandas, numpy, matplotlib
Notebook name	CaseStudy 02 : Google Stock Data Analysis

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3. Workflow Summary

Step	What Happens	Key Code / Output
Data prep	Convert Date column → datetime64, set as index for fast slicing.	Enables queries like df.loc['2022']
Enrich features	Adds month_name, weekday_name, Quarter plus Daily, Log, % and Intraday returns. &	Creates four return metrics for later stats
Data validity check	Confirms High ≥ Open/Close ≥ Low for every row—no anomalies found.	Data passes all rules
Exploratory slicing	Examples: Independence‑Day snapshot across years, full‑year slices, partial indexing.
Visualisations	Single‑series plots (Open, Close, High, Low), 2×2 dashboard, and dynamic helper functions:<br>visualize_quarter_values() & visualize_year_quarter_values() for any column/quarter. ,
Return statistics	display_return_stats() summarises mean, st‑dev, min, max, skew & kurtosis for each return measure. Example output: mean daily return ≈ 0.10 %, σ ≈ 1.9 %.
Comprehensive panel	plot_return_analysis() produces a 6‑panel figure (price path, daily/log returns, their distributions & 30‑day annualised volatility).
Resampling	Down‑sample to quarter/month/year means and up‑sample to 6‑hour intervals with spline interpolation. &
Smoothing & shift	Rolling window (10‑day) smoothing and shift() examples for lag features.

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4. Notable Findings

• Positive drift: Average daily arithmetic return ≈ 0.10 % with positive skew (≈ 0.62) indicating a long right tail.
• Volatility clusters: 30‑day rolling σ shows spikes during 2008‑09 and 2020 market shocks, calming post‑2023.
• Seasonality cues: Quick functions reveal quarter‑on‑quarter acceleration in closing prices since 2020, visible in visualize_quarter_values() plots.

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5. How to Reproduce

1. git clone / download this repo.
2. Place GoogleStockData2004To2024.csv in ./Data.
3. pip install -r requirements.txt (pandas ≥ 2.0, numpy, matplotlib).
4. Run CaseStudy_02_Google_Stock_Data_Analysis.ipynb.

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6. Repository Structure

text
│
├── Data/
│   └── GoogleStockData2004To2024.csv
├── notebooks/
│   └── CaseStudy_02_Google_Stock_Data_Analysis.ipynb
├── README.md  ← (this file)
└── requirements.txt

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7. Next Steps & Ideas

• Extend feature set with technical indicators (SMA, RSI, MACD).
• Apply ARIMA / Prophet forecasting on logarithmic returns.
• Compare Google with peer tech stocks for pair‑wise correlation & beta.
• Deploy notebook as an interactive dashboard (e.g., Streamlit).

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TL;DR – The notebook cleanses 20 years of Google stock data, engineers multiple return metrics, validates price integrity, visualises behaviour across time scales, computes rich descriptive stats and demonstrates resampling, smoothing and shifting techniques—offering a solid launch‑pad for deeper financial modelling.