1 Livello 1 · Aggregazione
Segnali da quattro fonti: classico · fattore · ML · ipotesi 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
provider 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 Livello 2 · Velocità
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 Livello 3 · Composizione
Transparent evaluation. Governance before promotion.
Compare candidates against appropriate baselines and retain acceptance or rejection evidence. Advanced fusion remains separately admitted.
∞ Tutto in locale · nessun lock-in
Tutto locale, nessun 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.
Approccio convenzionale
StratCraft Research Workflow
Reperimento dei segnali
Creare manualmente 3-5 strategie
pages.quantnexus.scaleComparison.row1Quantnexus
Throughput di backtest
Disconnected tools and incomplete run context
pages.quantnexus.scaleComparison.row2Quantnexus
Composizione portafoglio
Scegliere la migliore strategia e operarla da sola
pages.quantnexus.scaleComparison.row3Quantnexus
Approccio strutturale
Rischio di concentrazione. Una strategia fallisce, tutto fallisce
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.
Il pipeline a 3 livelli
1
Livello 1: Aggregare da quattro fonti
Riunisci strategie classiche (librerie open source, sistemi pubblicati, TradingView, le tue), fattori quantitativi, modelli ML e ipotesi generate dagli LLM. Un\'idea diventa una popolazione di candidati, non una singola strategia scritta a mano.
2
Livello 2: Backtest dell\'intera fabbrica
The local C++23 engine runs approved backtests and retains inspectable results. Named data routes and performance evidence are stated per workload.
3
Livello 3: Comporre e comporre
Compare surviving candidates with transparent baselines. Community includes equal-weight combination and replay; advanced fusion and decision policies are separately admitted.
Approfondisci
Research Modules, One Connected Workflow
Ogni modulo del pipeline a 3 livelli. Dall\'aggregazione da quattro fonti all\'esecuzione C++23 fino alla composizione statistica.
Esplora →Perché la velocità C++23 conta su scala
Architecture evolution, workload-specific benchmark evidence, and local execution boundaries.
Esplora →Avvia la tua fabbrica di segnali
Il livello gratuito include il motore di backtest C++, rilevamento regime e dati YFinance + Dukascopy: tutto ciò che serve per iniziare a costruire su scala.