What is StratCraft?
StratCraft is an AI-native quantitative research application. Its Open Research Foundation supports human-controlled local research, while Commercial Quant Lab adds separately admitted advanced capabilities.
Answers about StratCraft, AI-assisted quantitative research, local execution, evidence, and capability boundaries. Read first. Then explore the Evolution page or compare platforms head-to-head.
StratCraft is an AI-native quantitative research application. Its Open Research Foundation supports human-controlled local research, while Commercial Quant Lab adds separately admitted advanced capabilities.
Community AI Studio supports loginless direct BYOK or a supported local model. Optional hosted inference is disclosed separately. Generated C++ returns to the desktop as an inspectable Artifact for user-approved validation and backtesting.
No. Users can work through Guide WebUI, typed MCP tools, SDKs, examples, and imports. C++23 runs local validation and backtesting, and generated C++ strategies remain inspectable.
TradingView is a charting-first platform centered on Pine Script, alerts, and community indicators. StratCraft focuses on AI-assisted authoring, local C++23 research execution, inspectable evidence, and a governed multi-candidate workflow.
StratCraft is local-first, not local-only. Community AI Studio supports loginless direct BYOK or a supported local model. Optional hosted inference and separately admitted Commercial capabilities remain explicit boundaries.
The research-library model keeps multiple candidates, experiments, evidence, and rejection reasons connected. Users compare transparent baselines, inspect evaluation evidence, and decide what to retain or test next; candidate volume does not establish quality.
StratCraft is preparing an independently useful Open Research Foundation. Commercial Quant Lab is delivered separately. Modules are classified by capability, not by whether they are written in C++23, TypeScript, or Python.
StratCraft represents an AI-assisted layer above earlier scripted, framework, and hosted-platform generations. AI helps create and explain research artifacts, while users define constraints, approve operations, review evidence, and decide the next iteration.
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