Build and run Python trading strategies.

Write Python yourself or connect your AI client to help build and test a strategy. Read market data, calculate signals and submit execution requests, then deploy to a managed runtime when you are ready.

Open the script editor

strategy.pyPython v2

# Fetch the latest BTC candle.

script = {"name": "btc", "version": "2", "lookback": 1}

def on_data(ctx, history):
    btc = history.source(
        "btc@candles@hyperliquidf:timeframe=60", 0
    )
    return btc

Market data→Python→Structured results

Data-only example. No orders are placed.

Python scripting

What you can do.

Build with your AI client

Open AI connection in the editor and connect your AI client through MCP. Ask it to write or edit Python and run supported backtests. You review the code and decide when to deploy.

Connect data to execution

Fetch Hypercore candles and inspect trades from Marketlab-created Elysium pools and bonding markets. Return data-only results or route execution requests to a supported venue.

Inspect each run

Read formatted events, logs and generated artifacts. Historical backtests are available for supported data sources; Elysium raw trades are live-only.

From configuration
to execution.

Read the documentation

Start from an example, write Python or build with your AI client.
Select the account and run parameters.
Deploy, inspect events and stop from the run page.