1 Capa 1 · Agregación
Señales desde cuatro fuentes: clásico · factor · ML · hipótesis IA.
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
proveedores LLM
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 Capa 2 · Velocidad
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 Capa 3 · Composición
Transparent evaluation. Governance before promotion.
Compare candidates against appropriate baselines and retain acceptance or rejection evidence. Advanced fusion remains separately admitted.
∞ Todo local · sin bloqueos
Todo local, sin bloqueo
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.
Enfoque convencional
StratCraft Research Workflow
Obtención de señales
Crear manualmente 3-5 estrategias
pages.quantnexus.scaleComparison.row1Quantnexus
Rendimiento de backtesting
Disconnected tools and incomplete run context
pages.quantnexus.scaleComparison.row2Quantnexus
Composición de portafolio
Elegir la mejor estrategia y operarla sola
pages.quantnexus.scaleComparison.row3Quantnexus
Enfoque estructural
Riesgo de concentración. Una estrategia falla, todo falla
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.
El pipeline de 3 capas
1
Capa 1: Agregar desde cuatro fuentes
Reúne estrategias clásicas (bibliotecas de código abierto, sistemas publicados, TradingView, las tuyas propias), factores cuantitativos, modelos ML e hipótesis generadas por LLM. Una idea se convierte en una población de candidatos, no en una única estrategia codificada a mano.
2
Capa 2: Backtest de toda la fábrica
The local C++23 engine runs approved backtests and retains inspectable results. Named data routes and performance evidence are stated per workload.
3
Capa 3: Componer y componer
Compare surviving candidates with transparent baselines. Community includes equal-weight combination and replay; advanced fusion and decision policies are separately admitted.
Profundizar
Research Modules, One Connected Workflow
Cada módulo del pipeline de 3 capas. Desde la agregación de cuatro fuentes hasta la ejecución C++23 y la composición estadística.
Explorar →Por qué la velocidad C++23 importa a escala
Architecture evolution, workload-specific benchmark evidence, and local execution boundaries.
Explorar →Inicia tu fábrica de señales
El nivel gratuito incluye el motor de backtest C++, detección de régimen y datos YFinance + Dukascopy: todo lo que necesitas para comenzar a construir a escala.