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.
Proprietary quantitative methods identify asymmetric opportunities across tickers before they appear in price—structural signals beyond simple momentum.
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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