The Market Regime & Risk Factor Analyzer is a quantitative research engine designed to deconstruct U.S. equity market behavior. By processing raw S&P 500 constituent data, the system identifies latent market regimes (low volatility/bull, high volatility/bear, transition) and quantifies shifting risk factors.
This tool bridges the gap between raw financial data and actionable risk insights, utilizing linear algebra and statistical modeling to track how correlation structures, volatility patterns, and factor dominance evolve over time.
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Data Transformation & Alignment
Convert raw price data into a clean synchronized return matrix suitable for quantitative analysis -
Risk Quantification
Measure market risk through rolling volatility, cross-sectional dispersion, and correlation dynamics -
Factor Analysis
Extract dominant risk factors using PCA and track their explanatory power over time -
Regime Identification
Detect structural changes in market behavior via statistical and linear-algebraic signals -
Extensible Architecture
Build a modular backend suitable for interactive visualization and user-facing analytical tools
- Core: Python 3.9+, NumPy, Pandas
- Statistical Analysis: SciPy, Scikit-Learn (PCA, Decomposition)
- Visualization: Matplotlib, Seaborn
- Data Processing: Vectorized operations for high-performance matrix manipulation
- Future: React/Next.js frontend for interactive dashboards
The project uses historical S&P 500 data consisting of:
- Individual stock prices (Adjusted Close, OHLC, volume)
- Index-level S&P 500 prices for benchmark comparison
- Company metadata (sector, industry, market cap classifications)
Raw data is reshaped into structured matrices:
P(t,i) ∈ ℝ^(T × N)
Where:
- T = number of time periods (trading days)
- N = number of assets (stocks)
- P(t,i) = price of asset i at time t
R(t,i) = ln(P(t,i)) - ln(P(t-1,i))
Where:
- R(t,i) = log return of asset i from time t-1 to t
- ln = natural logarithm
Why Log Returns?
This transformation ensures:
- Stationarity: Returns are more stationary than prices for time-series analysis
- Time-additivity: Multi-period returns can be summed: R(t₁→t₃) = R(t₁→t₂) + R(t₂→t₃)
- Scale normalization: Comparable across assets with different price levels
- Symmetry: A 10% gain followed by 10% loss returns approximately to origin
- Load raw CSV files (stocks, companies, index data)
- Validate data integrity and format consistency
- Report loading statistics and data dimensions
- Remove malformed and missing observations
- Handle edge cases (zero prices, gaps, outliers)
- Pivot stock prices into time × asset matrix
- Align timestamps across all assets
- Compute log returns for all assets
- Generate first-order return statistics
- Identify and report dropped observations
- Distribution Analysis: Mean returns, volatility, skewness, kurtosis
- Missing Data Patterns: Visualize data completeness across time and assets
- Summary Statistics: Generate comprehensive descriptive metrics
Compute time-varying metrics over multiple horizons:
- Volatility (annualized standard deviation)
- Correlation matrices (asset co-movement)
- Cross-sectional statistics (market-wide dispersion)
- Distribution plots (histograms, Q-Q plots)
- Volatility clustering detection
- Correlation spike analysis
- Rolling statistics dashboards
Rolling statistics are computed over standard market horizons:
| Window | Trading Days | Period | Use Case |
|---|---|---|---|
| 21 days | ~1 month | Short-term | Tactical risk management |
| 63 days | ~1 quarter | Medium-term | Earnings cycle analysis |
| 252 days | ~1 year | Long-term | Strategic positioning |
These windows enable analysis of:
- Volatility clustering: Periods of high/low market turbulence
- Correlation structure: Evolution of asset relationships
- Factor stability: Persistence of dominant risk drivers
To understand market structure, we compute the rolling covariance matrix Σ over a window W:
Σ_W = (1/(W-1)) × Σ(t=1 to W) [(R_t - R̄)(R_t - R̄)ᵀ]
Where:
- Σ_W = covariance matrix over window W
- R_t = return vector at time t
- R̄ = mean return vector
- ᵀ = matrix transpose
The corresponding correlation matrix provides scale-invariant co-movement metrics.
We solve the eigenvalue problem for the correlation matrix C to identify dominant risk factors:
C × v = λ × v
Where:
- C = correlation matrix
- v = eigenvector (factor loadings)
- λ = eigenvalue (variance explained)
Interpretation:
- High λ₁ (first eigenvalue) indicates a correlated "risk-on/risk-off" market regime
- The eigenvector v₁ defines the loadings of the dominant market factor
- A diversified portfolio has variance spread across multiple eigenvalues
The proportion of total variance explained by the first k components:
Explained Variance Ratio = (Σ(i=1 to k) λᵢ) / (Σ(i=1 to N) λᵢ)
Where:
- λᵢ = eigenvalue of the i-th principal component
- k = number of components considered
- N = total number of assets
A rising PC1 ratio suggests increasing market integration and systemic risk.
Market states are identified through:
- Volatility thresholds: Persistent high/low volatility periods
- Factor dominance: PC1 explanatory power exceeding historical norms
- Correlation breakpoints: Structural changes in asset relationships
QUANT-PROJECT-1/
├── data/ # Raw CSV datasets
│ ├── sp500_stocks.csv # Historical price data
│ ├── sp500_companies.csv # Company metadata
│ └── sp500_index.csv # Index-level data
├── src/ # Core application code
│ ├── analyze.py # Statistical computations & transformations
│ ├── display.py # Console output formatting
│ ├── visualize.py # Plotting and chart generation
│ └── main.py # Pipeline orchestration
├── archive/ # Legacy/backup files
├── notebooks/ # Jupyter notebooks for EDA
├── requirements.txt # Python dependencies
└── README.md # This filegit clone https://github.com/yourusername/market-regime-analyzer.git
cd market-regime-analyzer# Create virtual environment
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt# From project root
python src/main.py
# The pipeline will:
# - Load and clean data
# - Compute statistics
# - Generate visualizations
# - Display results in terminal# Modify src/main.py to adjust parameters
results = run_full_analysis(
base_path="data",
generate_plots=True, # Set False to skip visualizations
save_plots_dir="output" # Specify directory to save plots
)- Automated ETL pipeline with data validation
- Log-return transformation and cleaning
- Rolling volatility and correlation metrics
- Distribution analysis and summary statistics
- Visualization suite (distributions, rolling metrics)
- PCA Implementation: Extract principal components and track variance ratios over time
- Factor Analysis: Compute factor loadings and identify dominant risk drivers
- Regime Detection: Implement statistical tests for structural breaks
- Eigen-Portfolio Construction: Build portfolios based on principal components
- Hidden Markov Models (HMM): Automated regime labeling using probabilistic models
- K-Means Clustering: Unsupervised grouping of market states
- Interactive Web Dashboard: React/Next.js frontend for real-time exploration
- User-Uploaded Datasets: Support for custom equity universes
- Backtesting Framework: Test regime-based trading strategies
- API Development: RESTful endpoints for programmatic access
- Robust data ingestion with validation
- Intelligent handling of missing data
- Timestamp alignment across assets
- Outlier detection and treatment
- Volatility: Annualized standard deviation with multiple time horizons
- Correlation: Full correlation matrices and average pairwise correlation
- Skewness & Kurtosis: Tail risk and distribution shape metrics
- Cross-sectional Dispersion: Market-wide return variance
- Distribution Plots: Histograms with normal overlays
- Volatility Clustering: Time-series plots with regime highlighting
- Correlation Heatmaps: Dynamic relationship tracking
- Rolling Statistics Dashboard: Multi-metric overview charts
- Clean separation of concerns (ETL, analysis, visualization)
- Extensible design for adding new metrics
- Configurable parameters for flexible analysis
- Production-ready code structure
==================================================
Loading CSV files...
==================================================
✓ Loaded stocks data: (619,040 rows × 8 columns)
✓ Loaded companies data: (503 rows × 9 columns)
✓ Loaded index data: (1,259 rows × 7 columns)
==================================================
Cleaning and Pivoting Data...
==================================================
Initial rows: 619,040
Rows dropped: 12,384
Remaining rows: 606,656
Unique symbols: 503
Unique dates: 1,259
==================================================
Price Matrix P_{t,i} Summary
==================================================
Shape: (1,259 × 503)
Missing values: 2.3%
Date range: 2013-02-08 to 2018-02-07
...
This project applies concepts from:
- Modern Portfolio Theory (MPT): Markowitz mean-variance optimization
- Factor Models: Fama-French, APT (Arbitrage Pricing Theory)
- Time-Series Econometrics: GARCH models, structural breaks
- Multivariate Statistics: PCA, correlation analysis
- Machine Learning: Unsupervised clustering, dimensionality reduction
- Active Portfolio Management by Grinold & Kahn
- Quantitative Equity Portfolio Management by Qian, Hua & Sorensen
- Machine Learning for Asset Managers by Marcos López de Prado
- Advances in Financial Machine Learning by Marcos López de Prado
This project is for educational and research purposes only.
It utilizes historical data to explore mathematical and statistical concepts in quantitative finance. This tool is NOT:
- Investment advice or recommendations
- A trading signal generator
- A guarantee of future performance
- Suitable for live trading without extensive testing
Always consult with qualified financial professionals before making investment decisions.
Contributions are welcome! Please feel free to submit pull requests or open issues for:
- Bug fixes
- New statistical methods
- Additional visualizations
- Documentation improvements
- Performance optimizations
This project is licensed under the MIT License - see the LICENSE file for details.
Akishai
For questions or collaboration inquiries, please open an issue on GitHub.