1 第 1 层 · 聚合
信号来自四个来源：经典策略、因子、机器学习、AI 假设。
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 提供商
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 第 2 层 · 速度
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 第 3 层 · 组合
Transparent evaluation. Governance before promotion.
Compare candidates against appropriate baselines and retain acceptance or rejection evidence. Advanced fusion remains separately admitted.
∞ 全本地 · 无锁定
全本地，无锁定
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.
传统方法
StratCraft Research Workflow
信号来源
手动编写 3-5 个策略
pages.quantnexus.scaleComparison.row1Quantnexus
回测吞吐量
Disconnected tools and incomplete run context
pages.quantnexus.scaleComparison.row2Quantnexus
组合构建
选出最佳策略单独运行
pages.quantnexus.scaleComparison.row3Quantnexus
结构化方法
集中风险, 一个策略失败，全盘皆输
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.
三层流水线
1
第一层：从四个来源聚合
汇集经典策略（开源库、已发布的系统、TradingView、你自己的）、量化因子、机器学习模型和 LLM 生成的假设。一个想法变成一个候选群体，而非单个手写策略。
2
第二层: 回测整个工厂
The local C++23 engine runs approved backtests and retains inspectable results. Named data routes and performance evidence are stated per workload.
3
第三层: 机构级统计组合
Compare surviving candidates with transparent baselines. Community includes equal-weight combination and replay; advanced fusion and decision policies are separately admitted.