Methodology
A five-layer architecture designed to prevent the most common failures in quantitative research: lookahead bias, overfitting, and unrealistic cost assumptions.
05Execution
Cost simulation·Paper verification
04Risk
Position sizing·Circuit breakers·Regime detection
03Signal
ML ensemble·Factor composites·Validation
02Feature
Engineering·Causal windows·Cross-asset
01Data
Point-in-time·Survivorship-free·Gated access
Data flows up · Constraints flow down
01
Data Layer
Point-in-time universe construction
Survivorship-bias-free universe: only securities that existed at each historical point are included
Chronological gating enforced at the framework level — no future data can leak into past computations
Corporate actions adjusted using point-in-time records
Multiple data sources with automated quality checks and freshness monitoring
02
Feature Layer
Engineered features, strictly causal
Features computed using expanding or rolling windows only — no lookahead by construction
Technical, fundamental, and cross-asset feature families
Cross-sectional normalization to neutralize outlier effects
Automated feature importance monitoring and decay detection
03
Signal Layer
Ensemble models + factor composites
Machine learning ensemble methods with strict train/test separation
Factor composite signals using cross-sectional percentile ranks
Walk-forward validation with multiple-testing corrections on every candidate
Overfitting probability estimation before any strategy advances
04
Risk Layer
Position sizing + drawdown control
Disciplined position sizing with regime-conditional adjustment
Automated circuit breakers at strategy, portfolio, and system levels
Risk monitoring with configurable alert thresholds
Regime detection drives allocation scaling automatically
05
Execution Layer
Cost simulation + paper verification
Mandatory transaction cost model for every backtest — the engine will not run without one
Spread, market impact, and commission components calibrated to historical data
Paper trading on live market data before any strategy is considered validated
Execution quality monitoring: fill rates, slippage, and latency tracking
The architecture is the methodology.
Every bias prevention measure is enforced by the software, not by policy. Chronological gating, mandatory cost models, and out-of-sample validation are structural constraints built into the platform. The system prevents common research failures by design, not by discipline.
Factor Exposure Analysis
Illustrative factor decomposition showing exposure to standard equity risk factors. Not representative of any specific portfolio or time period.
Momentum
+0.65
Market β
+0.45
Quality
+0.32
Value
+0.28
Size
-0.12
Liquidity
-0.15
Volatility
-0.38
Illustrative factor exposures. Not representative of any specific portfolio or time period. See disclosures.