The Fabless framework applies machine intelligence across three stages— Discovery, Enhancement, Distillation—to build ETFs that consistently outperform passive benchmarks on a risk-adjusted basis.
Passive indexing leaves alpha on the table. Our three-layer architecture compounds signal quality at every step.
Applying statistics, network analysis, and econometrics to measure how tickers move together—not just price dynamics—to identify asymmetric opportunities before they appear in price.
A proprietary stack of signal overlays applied in sequence. Each layer must independently improve risk-adjusted returns before it earns its place—so the pipeline compounds only genuine edge, never noise.
Reinforcement learning compresses a high-complexity strategy into a lean, deployable form—training across diverse market regimes until only the durable alpha survives. Simpler to operate, harder to decay.
Every strategy in the registry is validated with walk-forward testing, bootstrap sampling, and regime-consistency checks. No in-sample curve-fitting.
The Fabless architecture is market-agnostic. The same three-layer pipeline adapts to KRX, S&P 500, Nifty 50, Nikkei, CSI, and Hang Seng.
Universe 공백 발견 → 백테스트 완료 → 전략 제공 → 운용사는 출시만 하면 됩니다
WWAI의 Lifecycle Pipeline이 글로벌 ETF 시장에서 공백 테마를 자동으로 탐지합니다. Pre-Launch 단계 = 아직 아무도 출시하지 않은 투자 테마.
Space Economy 테마로 설계된 ETF의 2023-2026 백테스트. 실제 티커, 실제 월별 리밸런싱, 실제 거래비용 반영.
전용 ETF가 없는 테마에 먼저 진입하면 AUM 흡수 속도가 다릅니다. WWAI는 전략 설계만 합니다. 출시 주체는 운용사입니다.
실시간으로 새 테마를 입력하면 WWAI가 유사 종목을 탐색하고, 백테스트 가능한 Universe를 자동 구성합니다.
Lab 직접 사용해보기 scienceLive from the backtest registry. Full history metrics, cost-adjusted.
| Strategy | Market | Sharpe | Max DD | Ann. Return | vs Benchmark |
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