Quantitative Alpha
Factor Research
Systematic signal discovery, rigorous backtesting, and risk-constrained portfolio construction. Research-grade tools for serious quantitative investors.
From Hypothesis to Portfolio
A disciplined, repeatable process for turning raw data into actionable trading signals.
Factor Discovery
Cross-sectional screening of hundreds of candidate signals across equity, options, and macro universes.
Statistical Validation
Rigorous statistical testing with multiple hypothesis correction, decay analysis, and regime filtering.
Out-of-Sample Validation
Walk-forward optimization with expanding and rolling windows. No lookahead bias. No curve fitting.
Portfolio Assembly
Risk-constrained optimization with sector, factor, and concentration limits. Transaction cost aware.
Research-Grade Tooling
Factor Universe Screening
Cross-sectional factor analysis across hundreds of candidate signals. Rank, sort, and filter the factor universe by information coefficient, turnover, and decay characteristics.
- Hundreds of pre-built factor definitions
- IC/IR analysis with significance testing
- Factor decay and half-life estimation
- Sector and market-cap neutralization
Backtesting Engine
Walk-forward optimization with expanding and rolling windows. Realistic modeling of transaction costs, slippage, market impact, and borrowing costs.
- Walk-forward and combinatorial purged CV
- Realistic transaction cost modeling
- Slippage and market impact estimation
- Multiple benchmark comparison
Portfolio Construction
Risk-constrained optimization with explicit sector, factor, and concentration exposure management. From signal weights to tradeable portfolios.
- Mean-variance and risk parity optimization
- Sector and factor exposure constraints
- Turnover and concentration limits
- Tax-aware rebalancing schedules
Research Replication
Academic paper replication framework. Verify published results against real market data before allocating capital. Trust, but verify.
- Structured replication methodology
- Out-of-sample and out-of-period testing
- Publication bias adjustment
- Reproducible research notebooks
Built on the Python Ecosystem
Production-grade research infrastructure built entirely in Python with open-source foundations.
- Polygon.io market data
- FRED economic indicators
- SEC EDGAR filings
- Options chain data
- AWS Lambda execution
- S3 data lake storage
- Jupyter research environment
- Git-versioned experiments
Get Alpha Lab research updates
Subscribe for factor research insights, backtesting results, and quantitative strategy analysis.
By clicking "Subscribe," you agree to our Terms of Use and acknowledge our Privacy Policy. You can unsubscribe at any time.
No spam. Unsubscribe anytime.