X-Quant
A production-ready algorithmic trading system that combines multi-layer machine learning ensembles with institutional risk management. The platform employs proprietary validation methodologies used by top-tier hedge funds to eliminate backtest overfitting and deliver consistent, risk-adjusted alpha across market regimes.
1.99
Sharpe Ratio (OOS)
-4.0%
Max Drawdown
68.7%
Win Rate
0.01
Overfitting Probability
Key Differentiators
- Multi-layer ML ensemble with 6 sub-models and proprietary signal generation
- Institutional-grade validation suite eliminating backtest overfitting
- Real-time risk management with regime-aware position sizing
- Production execution integrated with tier-1 brokerage infrastructure
The Problem
The vast majority of algorithmic trading strategies fail in live markets despite showing impressive backtest results. This "backtest overfitting" problem destroys capital and undermines confidence in systematic approaches. Even experienced quantitative teams struggle with execution risk, tail-risk management, and the gap between simulated and real-world performance. The industry needs rigorous validation methodologies that bridge this gap.
ML Pipeline Architecture
X-Quant employs a 4-layer machine learning architecture designed for robustness over raw performance. The system normalizes 137 raw features into a 411-dimensional space, applies alpha feature selection with information coefficient-based weighting, runs a 6-model ensemble (combining gradient-boosted trees with regularized linear models), and constructs portfolios with volatility scaling and regime-aware leverage. Signal decay mechanisms account for the time-decay of predictive power.
Validation Suite
The platform implements the same validation methodologies used by top-tier hedge funds: Combinatorial Purged Cross-Validation (CPCV) with embargo and purging, Probability of Backtest Overfitting (PBO) analysis, Hansen's Superior Predictive Ability test, and Model Confidence Sets. The system achieved a PBO of 0.01 on the deployed strategy — indicating near-zero probability that performance is due to data mining.
Risk Management
Institutional-grade risk controls operate at every level: multi-method VaR/CVaR calculation (Historical, Parametric, Cornish-Fisher, Monte Carlo), progressive drawdown de-risking across 5 severity levels, multi-trigger circuit breakers, and regime-aware position scaling across 4 detected market states. Kelly Criterion position sizing with risk parity ensures optimal capital allocation without excessive concentration.
Live Execution
The platform is integrated with tier-1 brokerage infrastructure for production execution. Real-time market data streaming feeds into the signal generation pipeline, with an Order Management System implementing smart order routing. Execution quality is monitored through implementation shortfall analysis and slippage tracking, with real-time P&L attribution across strategies and symbols.
Performance
Out-of-sample validation demonstrates a Sharpe ratio of 1.99 (top quintile of systematic strategies), maximum drawdown of -4.0%, win rate of 68.7%, and walk-forward efficiency of 71.18%. Parameter sensitivity analysis confirms stability across ±5% parameter variations, and zero lookahead bias has been verified across all 30 alpha signals. The deployed MetaEnsemble strategy maintains a net Sharpe of 1.24 with 16.97% CAGR.
Interested in learning more about this venture?
Get in Touch