One sentence, and you've got an on-chain quant strategy
On September 1st, Almanak officially launched its Agentic DeFi strategy platform. Let's start with what it's selling, because the vision really is compelling:
You describe a trading idea in natural language—"I want a strategy that provides ETH/USDC liquidity on Base but hedges out impermanent loss"—and a team of AI agents behind the platform turns it into real, readable, editable Python code, then backtests it, simulates it, and finally deploys it directly on-chain to run.
They're not hiding the pitch. The official line is "your Agentic hedge fund," "your personal AI quant"—the goal is to open up quant capabilities that used to be exclusive to hedge funds and make them available to everyone. After digging in, these key design choices check out:
- A team of agents, split up like a quant research department: the official claim is 18 specialized AI agents (ideation, coding, backtesting, risk management...) working together, not a single model grinding through everything alone. (Though to be fair: more agents isn't automatically better—multi-agent collaboration has its own costs. A small error at one stage compounds as it moves down the chain, and latency and token costs pile up too. A clean division of labor is a good selling point, but whether it converges reliably is the real test, and that only gets proven with real-world use.)
- Serious backtesting: it can stress-test a strategy across more than 10,000 Monte Carlo scenarios (in plain terms: throw your strategy into ten thousand parallel-universe versions of the market and see if it's consistently profitable or just got lucky on this particular timeline), and it can do dry runs on a "mainnet fork" first—so you see if it blows up before a single dollar moves.
- Non-custodial: the strategy deploys to your own Safe smart wallet, and AI agents only get "scope-limited" permissions through Zodiac Role—they can only act within protocols you've pre-approved, and the private key stays in your hands the whole time.
- Multi-chain: this launch covers seven chains right out of the gate—Ethereum, Arbitrum, Base, BNB Chain, Polygon, Optimism, and Avalanche—with strategy types ranging from liquidity provision and yield optimization to delta-neutral and tokenized stocks and commodities.
Backers include familiar crypto names like Delphi Labs and HashKey Capital.
Why I'm not looking at the "natural language" part—I'm looking at something else
I've built my own multi-agent trading system myself—backtesting, testnet, live money, the whole pipeline, all hands-on. So when I look at a platform like Almanak, my eyes automatically skip past the flashiest part and go straight to whether it handles the things that actually blow up accounts.
Let me get straight to the point: AI "writing the strategy for you" was never the hard part. Getting a model to spit out professional-looking quant strategy Python code isn't impressive anymore in 2026. What actually determines whether you lose everything is the following two things—and, as it happens, they're exactly where Almanak put the most effort.
1. If an agent can touch your money, the "permission boundary" is a matter of life and death
This is the point I most want to flag for everyone.
If a chatbot-only AI gets something wrong, worst case it says something dumb. But the moment an agent that signs on-chain transactions and moves your wallet makes a mistake, the cost of being wrong jumps instantly from "wrong answer" to "your funds got sent somewhere you never agreed to." That's not fearmongering—it's a hard rule anyone who's actually built agents with real consequences already knows.
Almanak's answer is the Safe wallet plus a Zodiac Role modifier: the agent doesn't get your wallet's key, it gets a pass that's strictly limited in scope—it can only act within protocols and action ranges you've pre-approved. The private key and ultimate control stay with you, always. (An analogy: it's like handing over your keys for valet parking—they can start the car and drive it to the door, but they can't pop the trunk or drive off.)
But I want to draw a clear line here so nobody gets the wrong idea: Zodiac Role governs "permissions," not "profit and loss." It can stop an agent from recklessly moving your money or sending it somewhere unapproved—it cannot stop the strategy itself from being wrong, getting arbitraged, or having its profits eaten by slippage. Permission security ≠ strategy security. These are two separate layers, and you shouldn't let the reassurance of one bleed over onto the other.
Even so, this "permission box" design still matters far more than "writing a strategy in natural language." And its significance goes beyond DeFi: for any AI agent that acts on your behalf and touches something real, the first question shouldn't be how smart it is—it should be how small a box its permissions are boxed into, and whether things can be rolled back if it goes wrong. This applies to the agent booking your flights, the one filing your taxes, the one placing your orders—all of it.
2. A great backtest doesn't mean the strategy will make money
Almanak's 10,000 Monte Carlo scenarios plus mainnet-fork dry runs are solid engineering. But I want to be honest and pour a little cold water on this, because it's a lesson I paid for in blood myself:
Between "the backtest numbers look great" and "this strategy will actually make money with real capital," there's a gap that a lot of people fall into and don't climb back out of.
The most common trap is overfitting—you (or the AI) tune a strategy until it looks flawless on historical data, but it's really just memorized the past, and it falls apart the moment it hits market conditions it's never seen. My own system has been burned by this: a parameter that looked like a stroke of genius in the backtest turned out, once I replayed it candle by candle, to have just gotten a few lucky breaks that I mistook for a pattern. There's no shortcut around this—only the tedious work: out-of-sample testing, tightrope-walking walk-forward validation (in plain terms: let the strategy learn on the first half of the data and get tested on the second half, never letting it peek at the answers), and staying suspicious of results that look "too good to be real."
Almanak hands you a really good backtesting gun, but whether you pull the trigger, whether you trust that number—that judgment call is still yours. The platform can help you write a strategy quickly and cleanly, but it can't confirm whether that strategy will actually be profitable going forward—and that's something AI still can't take off anyone's plate, even today.
Three defensive designs from Almanak's playbook you can steal for any AI agent
Even if you never touch DeFi in your life, the way Almanak handles security is actually a general-purpose template for "how to safely let an AI act on your behalf." I've distilled it into three rules mapped to scenarios you'll actually run into:
1. Least privilege (Almanak's Safe + Zodiac → your API/account authorization): Don't hand an agent a master key. Almanak only gives the agent a pass scoped to "specific protocol, specific action." When you hook an agent up to your own Gmail, payments, or database, only open the narrowest scope that task actually requires. Over-granting permissions isn't a question of whether it'll be abused—it's a question of when it'll go wrong.
2. [REDACTED]-run first, then go live (Almanak's mainnet fork → your [REDACTED] testing): Almanak has strategies dry-run on a "mainnet fork" before real money moves. Same logic applies to any agent that touches something real: let it run first in an environment where nothing has real consequences (dry run, sandbox, [REDACTED] mode), confirm the behavior is correct, and only then connect it to real side effects. My own trading system goes through backtest → testnet → live capital as a strict gate sequence—skipping any one of those steps isn't an option.
3. A brake for when things go wrong (rollback/kill switch): Any agent that can touch something real needs a mechanism to "stop immediately and undo what's already been done." Almanak's non-custodial design lets you revoke permissions at any time; your own agent system needs that red button too.
One last dose of cold water
Platforms like Almanak will drastically lower the barrier to building your own quant strategy, and that's a good thing. But my deepest takeaway from doing this for a living is: lowering the barrier doesn't lower the risk.
And on-chain there's another reality that newcomers easily overlook—a strategy being perfect in backtest doesn't mean it'll run smoothly on the actual chain: gas costs, slippage, and profit eaten away by bots front-running you (MEV) all create a gap between "returns on paper" and "returns that actually land in your wallet." AI won't automatically factor these in when it writes your strategy for you.
The real moat was never about whose AI writes better code—it's about who understands more clearly where "AI can't help you" actually is, and then puts the discipline right there.
Sources (cross-verified across multiple outlets):
- TechFlow — Almanak officially launches Agentic DeFi strategy platform (techflowpost.com)
- Almanak official site almanak.co, SDK docs sdk.docs.almanak.co
- blocmates — Almanak: Your Personal AI Quant
- The Token Dispatch — Vibecoding DeFi strategies with Almanak's AI agents
- Phemex Academy / Gate Learn — Almanak AI-DeFi platform breakdown (non-custodial Safe + Zodiac Role permission controls)
References
- 零代码基础 2 小时搞定量化交易！揭秘 Vibe Coding 如何让我躺赚第一桶金-CSDN博客
- Almanak研报：链上量化金融的普惠之路
- 耗时1个月，我用 Vibe Coding 手搓了量化交易系统，但我发现了一个残酷的数学真相... - YouTube
Originally published at Judy AI Lab. Visit for more articles on AI engineering and development.
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