1 レイヤー 1 · 集約
4つのソース: クラシック · ファクター · ML · AI 仮説からのシグナル。
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プロバイダー
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 レイヤー 2 · 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 レイヤー 3 · 構成
Transparent evaluation. Governance before promotion.
Compare candidates against appropriate baselines and retain acceptance or rejection evidence. Advanced fusion remains separately admitted.
∞ 完全ローカル · ロックインなし
全てローカル、ロックインなし
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.
従来のアプローチ
StratCraft Research Workflow
シグナルソーシング
3-5個の戦略を手作業で作成
pages.quantnexus.scaleComparison.row1Quantnexus
バックテスト処理量
Disconnected tools and incomplete run context
pages.quantnexus.scaleComparison.row2Quantnexus
ポートフォリオ構成
最良の戦略を選んで単独で運用
pages.quantnexus.scaleComparison.row3Quantnexus
構造的アプローチ
集中リスク. 1つの戦略が失敗すれば全て失う
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.
3層パイプライン
1
第1層: 4つのソースから集約
クラシック戦略（オープンソースライブラリ、公開システム、TradingView、自作）、クオンツファクター、MLモデル、LLMが生成する仮説を集約。1つのアイデアが、手書きの単一戦略ではなく、候補の母集団になります。
2
第2層: ファクトリー全体をバックテスト
The local C++23 engine runs approved backtests and retains inspectable results. Named data routes and performance evidence are stated per workload.
3
第3層: 機関投資家級統計的組合せ
Compare surviving candidates with transparent baselines. Community includes equal-weight combination and replay; advanced fusion and decision policies are separately admitted.
シグナルファクトリーを始めよう
無料版にはC++バックテストエンジン、Regime検出、YFinance + Dukascopyデータが含まれます: スケールでの構築開始に必要な全てが揃っています。