Almanak is a non‑custodial DeFi “smart agent” platform that lets builders and funds design Python‑based strategies, backtest them, and deploy them as on‑chain agents and vaults. Under the hood, it combines Safe‑secured wallets, Zodiac permissions, and a roadmap toward multi‑agent systems and trusted execution. The goal: turn quant logic into live, auditable DeFi automations.
How is the platform structured?
Almanak is organized around four primitives. Deployments execute actions on‑chain; Strategies encode the decision logic in Python; Wallets are non‑custodial and secured by Safe and Zodiac; and Vaults tokenize successful strategies so creators can raise capital and charge fees. It’s an assembly line from research to a live, investable product.
How do you build and test strategies?
The workflow runs through Almanak’s docs and app: write your Python strategy, simulate it with historical data, iterate on parameters, then ship to a Deployment with constrained permissions. Backtesting and how‑to guides are baked into the developer experience to shorten the path from idea to execution.
How are user funds protected?
Security is anchored in non‑custodial design. Users control Safe wallets; strategies only get the permissions they need via Zodiac modules. That minimizes blast radius and keeps custody with the user while allowing 24/7 automation.
What is on the roadmap?
Almanak’s roadmap calls for multi‑agent systems that coordinate across roles, integrations with centralized venues, and exploration of hardware‑backed trusted execution for sensitive logic—pointing to more autonomous, privacy‑preserving strategy execution.
Who is Almanak for?
Active DeFi traders, quant developers, and funds that want programmable VP trading without sacrificing custody. The platform abstracts wallet ops and permissions so teams can focus on models while keeping control of assets.
Conclusion
Almanak turns DeFi automation into an engineerable stack—code a strategy, test it, lock down permissions, and launch it as a vault others can back. If you’re evaluating it, start with a small, permission‑scoped Deployment, read the docs closely, and expand as your model proves itself.
