In [1]: mlab.history(...)In [2]: history.source(CANDLES)Follow your curiosity.
Explore historical market data in Jupyter. Use your Python environment and the libraries you already know.
Explore researchResearch markets. Run Python strategies. Give your Elysium token an onchain market. From your terminal or in the cloud.
Deploy your token your way. Bring it to Marketlab to make it tradable on Elysium, with public liquidity and programmable trading.
Explore Elysium markets Build with the CLIGive a terminal-capable agent access to Marketlab. Research markets, write Python strategies, backtest ideas, and prepare execution from the tools you already use.
Explore Marketlab for agentsCodex · Claude Code · Gemini CLI · OpenCode & more
A connected workflow for traders who build. Explore the data, test your logic, and stay close to execution.
In [1]: mlab.history(...)In [2]: history.source(CANDLES)Explore historical market data in Jupyter. Use your Python environment and the libraries you already know.
Explore researchBacktest a plain Python file. Inspect its behavior before deciding what belongs in a live market.
Write a strategyRun a strategy or built-in bot. Follow job state and execution after your deploying terminal closes.
Meet the runtimeConnect market data and live execution through one engine, locally or in Cloud.
Market data
Live execution
Write your own Python strategy, work an order with an execution algorithm, or configure a market-making bot.
No. You can read supported public market data first. Private account data and live execution require the appropriate provider credentials.
Use Marketlab Cloud in your browser, or run the open-source CLI on your own machine. The CLI supports macOS Apple Silicon, Linux x64 and Linux arm64, with native, Docker and SSH runtime options.
Bring an existing ERC-20 token and create a public liquidity pool or a bonding market. Users can trade, liquidity providers can deposit into pools, and Python scripts can interact with Marketlab-created markets. The Elysium integration is currently on testnet; historical Elysium backtesting is not supported yet.
Jupyter is the research environment. Sequential backtesting and live deployment use plain Python strategy files. These are separate workflows.
A dry run previews and validates the execution plan without signing or submitting an order. It does not predict fills or guarantee how a live strategy will perform.
The daemon keeps deployed jobs and execution state running. Your host, daemon, network connection, and execution venue still need to remain available.
Install Marketlab. Read your first market.
Build from there.
curl -fsSL https://marketlab.sh/install.sh | bashmacOS Apple Silicon · Linux x64 / arm64