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This project provides a comprehensive Technical and Risk Analysis of major technology stocks (Apple, Microsoft, Netflix, and Google) using Python's robust data science stack.
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Technical and Risk Analysis of major technology stocks (Apple, Microsoft, Netflix, and Google) using Python's robust data science stack.
The core goal is to leverage time-series analysis and statistical modeling to extract actionable financial metrics from the last three months of historical price data, offering insights into recent market behavior, asset momentum, and portfolio risk management.
Trend Identification: Analyze asset momentum using key technical indicators like Moving Averages.
Risk Quantification: Measure and compare the historical volatility (risk) of each stock using Rolling Volatility.
Relationship Analysis: Determine the correlation between stock returns, which is essential for a sound portfolio diversification strategy.
Analysis Module Metric/Technique Python Function Focus Strategic Insight Trend Analysis Simple Moving Average (SMA) df.rolling(window=N).mean() Identifies short-term and intermediate-term momentum signals.
Risk Analysis Rolling Volatility df_returns.rolling(window=20).std() Quantifies historical risk based on the standard deviation of daily returns.
Relationship Analysis Correlation Matrix df_returns.corr() Measures the linear relationship between returns for portfolio diversification.
Trend Visualization (Line Chart): Plots Adjusted Close prices overlaid with 20-day and 50-day SMAs to track trend changes and potential trading signals.
Volatility Comparison (Line Chart): Charts the 20-day Rolling Volatility across all four stocks to compare their relative risk profiles.
Correlation Heatmap: Uses Seaborn to visualize the Correlation Matrix, clearly highlighting pairs for risk-reduction benefits (low or negative correlation).
Cumulative Performance (Line Chart): Compares the Cumulative Return of each stock for overall performance efficiency.
A preliminary data exploration step also generated visualizations for:
Historical Closing Price Distribution (Histogram with KDE)
Total Trading Volume by Ticker (Bar Chart)
Volume vs. Closing Price (Scatter Plot)
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