Explain your objective in plain language. AI creates strategy candidates as Python code, with no coding skills required.
AI strategy research
Describe an idea. Get a testable strategy.
Connect your LLM API and work in plain language. OryxQuant runs locally on your Windows computer and keeps the code, data, and results there. Use the workspace through the authenticated browser window opened by the application.
Research workflow
One loop from idea to portfolio
Run adjusted daily backtests with fees, slippage, FX, and integer shares.
Inspect portfolio lines, targets, metrics, and exported CSV results.
From plain language to working strategy.
Use your chosen LLM API to generate, evaluate, repair, compare, and save candidates. The resulting Python stays visible and auditable.
Keep accounting outside strategy code.
Target weights become integer positions with reference-currency cash, exposure, fees, slippage, and complete daily audit lines.
Control the research dataset.
Browse adjusted HistoData, instrument metadata, provider mappings, FX conversion, and exported results from the local application.