Describe a market making strategy. Watch it quote.

An agent from Orynero turns plain language into quoting parameters, runs them against a simulated order flow, and exports the result as Python you can take to your own stack.

PnL—
Sharpe (session)—
Fills—
Max drawdown—
Mean |inventory|—

Computed in your browser on a synthetic price path, updated every time you move a control.

No. 1

Build and test

EquityInventory
Agent
Python export
No. 2

How it works

Describe

Say what you want in your own words. The agent picks a model and sets every parameter within safe bounds.

Calibrate

Every control stays editable. Change one and the run repeats on the same price path, so differences are yours, not noise.

Backtest

Fills follow an exponential arrival model with a share of toxic flow that moves price against you after the trade.

Export

Download a Python quoting module with your parameters filled in. It returns bid and ask for a given mid and inventory.

No. 3

Limits

This is a research tool on simulated data. Real venues have queue position, latency, fees tiers and regime changes that this model does not capture. Nothing here is investment advice. Validate on your own historical data before risking capital.