1 레이어 1 · 집계
네 가지 소스: 클래식, 팩터, ML, AI 가설에서 나온 시그널.
Bring classic strategies, factors, ML models, and AI-assisted hypotheses into an inspectable candidate set. Candidate generation does not establish validity.
AI-Native Quant Research
StratCraft
Move from hypothesis to data, candidates, experiments, reproducible evidence, and the next research decision.
AI guides the workflow. You remain in control.
AI-Native Quant Research · human-controlled
Research evidence · evaluation and transparent baselines
Illustrative interface, not a performance claim500–1000×
workload-specific benchmarks
2000+/sess
reviewed candidates
7
LLM 제공업체
1000+
governed research set
Research Capabilities in Context
Product maturity is measured by evidence and governed decisions, not candidate volume.
0
AI helps frame, build, and explain research
0
Users approve operations and next decisions
0
Community execution, Artifacts, evidence, and lineage
0
Community, hosted inference, and Commercial boundaries
The Research Capability Chain
AI connects research work while the user controls operations and decisions.
2 레이어 2 · 속도
Local C++23 execution, with workload-specific evidence.
Run approved backtests locally and preserve inputs, Artifacts, lineage, and results. Performance claims remain benchmark-specific.
Python
(vectorbt)1×
(vectorbt)1×
Rust
(NautilusTrader)42×
(NautilusTrader)42×
StratCraft
C++23784×
C++23784×
3 레이어 3 · 구성
Transparent evaluation. Governance before promotion.
Compare candidates against appropriate baselines and retain acceptance or rejection evidence. Advanced fusion remains separately admitted.
∞ 완전 로컬 · 락인 없음
모두 로컬, 락인 없음
Community AI Studio supports loginless direct BYOK or a supported local model. Optional hosted inference is a separate, disclosed boundary.
Windows
macOS
Linux
Why Research Continuity Matters
Connected evidence and decisions matter more than generating a larger candidate count.
기존 접근법
StratCraft Research Workflow
시그널 소싱
3-5개 전략을 수작업으로 작성
pages.quantnexus.scaleComparison.row1Quantnexus
백테스트 처리량
Disconnected tools and incomplete run context
pages.quantnexus.scaleComparison.row2Quantnexus
포트폴리오 구성
최고의 전략을 선택하여 단독 운용
pages.quantnexus.scaleComparison.row3Quantnexus
구조적 접근법
집중 리스크, 하나의 전략 실패 시 전체 실패
pages.quantnexus.scaleComparison.row4Quantnexus
A larger search space creates more opportunities for false discoveries. StratCraft treats evidence, rejection, and research memory as first-class product responsibilities.
3계층 파이프라인
1
1계층: 네 가지 소스에서 집계
클래식 전략(오픈소스 라이브러리, 공개된 시스템, TradingView, 직접 만든 것), 퀀트 팩터, ML 모델, LLM이 생성한 가설을 한데 모읍니다. 하나의 아이디어가 손으로 코딩한 단일 전략이 아니라 후보 집단이 됩니다.
2
2계층: 전체 팩토리 백테스트
The local C++23 engine runs approved backtests and retains inspectable results. Named data routes and performance evidence are stated per workload.
3
3계층: 통계적 조합
Compare surviving candidates with transparent baselines. Community includes equal-weight combination and replay; advanced fusion and decision policies are separately admitted.
시그널 팩토리를 시작하세요
무료 티어에는 C++ 백테스트 엔진, Regime 감지, YFinance + Dukascopy 데이터가 포함됩니다: 스케일로 구축을 시작하는 데 필요한 모든 것.