1 Ebene 1 · Aggregation
Signale aus vier Quellen: klassisch, Faktor, ML, KI-Hypothese.
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-Anbieter
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 Ebene 2 · Geschwindigkeit
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 Ebene 3 · Komposition
Transparent evaluation. Governance before promotion.
Compare candidates against appropriate baselines and retain acceptance or rejection evidence. Advanced fusion remains separately admitted.
∞ Alles lokal · kein Lock-in
Alles lokal, kein Lock-in
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.
Konventioneller Ansatz
StratCraft Research Workflow
Signalbeschaffung
3-5 Strategien manuell erstellen
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Backtest-Durchsatz
Disconnected tools and incomplete run context
pages.quantnexus.scaleComparison.row2Quantnexus
Portfolio-Komposition
Beste Strategie auswählen und allein betreiben
pages.quantnexus.scaleComparison.row3Quantnexus
Struktureller Ansatz
Konzentrationsrisiko. Eine Strategie scheitert, alles scheitert
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A larger search space creates more opportunities for false discoveries. StratCraft treats evidence, rejection, and research memory as first-class product responsibilities.
Die 3-Schichten-Pipeline
1
Schicht 1: Aggregation aus vier Quellen
Führen Sie klassische Strategien (Open-Source-Bibliotheken, veröffentlichte Systeme, TradingView, Ihre eigenen), Quant-Faktoren, ML-Modelle und LLM-generierte Hypothesen zusammen. Aus einer Idee wird eine Population von Kandidaten, nicht eine einzelne handcodierte Strategie.
2
Schicht 2: Die gesamte Fabrik backtesten
The local C++23 engine runs approved backtests and retains inspectable results. Named data routes and performance evidence are stated per workload.
3
Schicht 3: Statistische Komposition
Compare surviving candidates with transparent baselines. Community includes equal-weight combination and replay; advanced fusion and decision policies are separately admitted.
Tiefer eintauchen
Research Modules, One Connected Workflow
Jedes Modul in der 3-Schichten-Pipeline. Von Aggregation aus vier Quellen über C++23-Ausführung bis zur statistischen Komposition.
Erkunden →Warum C++23-Geschwindigkeit bei Skalierung wichtig ist
Architecture evolution, workload-specific benchmark evidence, and local execution boundaries.
Erkunden →Starten Sie Ihre Signal-Fabrik
Die kostenlose Version enthält die C++ Backtest-Engine, Regime-Erkennung und YFinance + Dukascopy-Daten: alles, was Sie für den Start im großen Maßstab brauchen.