QuantPilot is an AI trading platform built by 3Commas. It lets you build and backtest crypto trading strategies by describing them in plain English. You type an idea in normal words, then AI agents turn it into code, run it on real market data, and improve it. This 3Commas QuantPilot AI review explains what the tool does, what it costs, what it cannot do yet, and who it is for.
Here is my verdict first. If you understand trading logic but do not write code, QuantPilot is a fast way to test your ideas. You go from one sentence to a running backtest within minutes. But there is one big point to remember: Right now, QuantPilot is a research and testing tool. Nothing you build here trades real money on its own yet.
What Is QuantPilot?
QuantPilot is an agentic platform. It does not just answer questions like a chatbot; it runs AI agents that do the work for you. It researches the market, writes the strategy, codes it, backtests it, and tunes it. They can even keep working in the cloud after you close the tab.
How Does QuantPilot Differ From 3Commas?
QuantPilot is a product built by 3Commas. 3Commas was founded in 2017, and for almost ten years, it has run trading bots on well-known exchanges like Binance, Bybit, OKX, and Coinbase. It is an established company adding a research layer on top of tools it already runs, and QuantPilot is one of the results. For the full picture of the parent platform, see our 3Commas review.
QuantPilot’s waiting list opened on April 2, 2026, and its public access opened on June 17, 2026. Before that, more than 5,000 early users helped shape the beta.
So, how is QuantPilot different from 3Commas? Think of it as the step before trading. 3Commas runs your strategies on live markets, while QuantPilot helps you build and test the strategy in the first place.
3Commas QuantPilot Core Features and Workflow
I have explained the basic logic of 3Commas QuantPilot AI, but here is a detailed overview of its core features and workflow.
AI Research and Strategy Building
This is the core of the product. You type an idea in plain words, then the agent repeats it back, draws the indicators on a chart, writes the code, and offers trading strategy backtesting. Behind the scenes, a World Model gives the agents lots of data and skills. This helps them reason more accurately.
MCP Servers and Market Data
QuantPilot pulls in outside market data through MCP (Model Context Protocol) servers. Through these, the agents can access data from CoinMarketCap, DefiLlama, CryptoQuant, the CryptoNews API, and Tavily. More sources are planned. This is what lets one prompt mix price data with on-chain, DeFi, and news signals.
Backtesting and Optimization
Using QuantPilot, crypto trading strategies run on real historical data with live charts. You watch the indicators plot as the agent works. There are two design choices in QuantPilot that make its backtests realistic: The engine runs on 1-minute candles, and it copies real liquidity using Hyperliquid order-book snapshots, instead of assuming every trade fills perfectly.
You can optimize in two ways:
- You can tell the agent to make specific changes.
- Or you can let it run long optimization on its own in the background and ping you on the app and Telegram when it is done.
Supported Exchanges
QuantPilot is Hyperliquid only today. Charts, strategy building, backtests, and the Terminal all run on Hyperliquid.
The team says more exchanges are coming, but they are not live yet. If your whole workflow is on Binance or Bybit, QuantPilot is a research tool you use next to your exchange, not a replacement.
If you are looking beyond QuantPilot for tools that can automate trades on Hyperliquid today, compare the best Hyperliquid trading bots. The guide covers ready-to-use platforms, AI-driven tools, and self-hosted options for different strategy and execution workflows.
QuantScript, and Why It Is Not Pine Script
The code the agents write is in QuantScript, QuantPilot’s own strategy language. QuantScript and Pine Script are not the same. Pine Script is TradingView’s language and lives in TradingView. QuantScript is a separate, Python-based language that runs in QuantPilot’s own engine.
Its main advantage is that trading tools come built in. Indicators, account management, trade execution, and market data are all there. You do not stitch libraries together. If you use TradingView as part of your research workflow, TradingView AI Copilot can also help you interpret chart conditions, explore strategy ideas, and refine the logic before turning it into code or a backtest.
Why care if you don’t touch the code? Two reasons. First, you can open and read what the agent wrote. Many black box tools do not let you. Second, the same code is meant to move into live trading once the Hyperliquid integration ships. So QuantScript is the bridge from backtested to live. If you prefer to build and run strategies independently rather than using a platform-specific language, see our guide to creating a Python trading bot.
QuantPilot Pricing and Token Economics
QuantPilot runs on QP tokens. These are usage credits inside the app, not a crypto coin or a wallet asset. Tokens get used when the AI does work: making ideas, editing strategies, running workflows, researching, writing QuantScript, and running long tasks. One key point: the cost per action is not fixed. It depends on the task, the model, the data, and the workflow.
Free QP Tokens
QuantPilot gives free access with free QP tokens. The onboarding rewards are clear on the dashboard. At the time of my test, verifying your email grants +500K QP daily, and starting your first strategy chat grants a one-time +500,000 QP.
Referral Program
There is also a referral program: each friend you refer gives +500K QP daily per invite (eligible for up to 3 referrals).
My test account showed a balance of 2,500,000 QP to start. Pay attention that the team may adjust these amounts often.
Top-Up Pricing
Paid top-ups are QP tokens you buy after your free daily tokens run out. They are priced per million tokens, with automatic volume discounts. Discounts start at higher amounts. For example, buying more than 21M unlocks the first discount, which is -2%.
VIP Badge
QuantPilot offers a VIP Badge for $5,000. It buys three things: a private VIP Telegram group, access to beta features, and lifetime account benefits.
The VIP badge is centered on status and access, like a permanent profile badge, immediate Arena access, the beta app, the private VIP Telegram group, and lifetime account benefits. AI tokens are managed separately in QuantPilot billing, through daily free tokens (with a weekly cap) and paid top-ups.
QuantPilot notes that if VIP ever changes how token limits apply to an account, that will be reflected in the account or billing settings.
Refunds
Since you buy tokens, a refund in QuantPilot refers to getting your money back for a token purchase you haven’t used. Here’s what the official QuantPilot refund policy actually says:
- First purchase: 15-day window, but only if untouched. You can withdraw from your token purchase package within 15 days of buying it and get a refund, but only if you have not used a single token from that package.
- Second and later purchases: 24 hours. For your second and every subsequent top-up, the window collapses to a 24-hour grace period, after which no refunds can be issued.
- Consumed tokens are gone. Paid tokens, once consumed, cannot under any circumstances be restored or refunded.
- One refund per disputed payment, and refunds are processed through Stripe (the billing is Stripe-hosted).
How to Use QuantPilot to Build a Strategy (Walkthrough)
With the features and workflow covered, let’s walk through how to use QuantPilot step by step.
Step 1: Sign Up
Create a free account at quantpilot.com. There is no waitlist anymore, so access is instant. The first thing you see is a dashboard with a short onboarding checklist: verify your email, start your first strategy chat, and run your first backtest.
Step 2: Describe Your Strategy
Open a new strategy agent and type your idea the way it is in your mind or how you would explain it to another trader. To keep my test consistent and easy to repeat, I used a simple test prompt:
“Build a BTC/USDC strategy on Hyperliquid. Go long on the 1H timeframe when RSI(14) crosses below 30 while price is above the 200 EMA. Exit when RSI(14) crosses above 70, or when price falls 3% below entry. Apply a 2% stop loss. Backtest the last 3 months.”
Step 3: Watch It Build
The agent restates your setup to confirm it is understood, selects the Hyperliquid BTC/USDC market, plots RSI and the 200 EMA on a live chart, and writes the strategy in QuantScript.
Step 4: Backtest
Once the strategy is built, let the agent run the backtest against real historical data. My “last 3 months” (mentioned in the prompt) ran as a 92-day window (early June to early September).
In my test, the initial backtest produced zero trades: across this 92-day window, RSI(14) rarely dropped below 30 while price was still above the 200 EMA, so the entry condition was not met.
My prompt contained two overlapping exits: “exit when price falls 3% below entry” and “apply a 2% stop loss.” For a long position, price reaches the 2% stop before the 3% level, so in typical bar-by-bar execution, the 3% exit never triggers. How the agent resolved that overlap is part of what I am testing.
The rules ran exactly as written; they just did not fire. The agent flagged the empty result and offered to loosen the entry thresholds to generate trades. At this point, when the agent adjusts your rules to produce trades, check that the revised version still reflects the idea you started with.
The entire workflow was somehow cheap in tokens. It consumed about 10,000 QP tokens, which is a tiny fraction of the free daily allowance.
Step 5: Optimize and Validate
Finally, let the agent improve the strategy. You can tell it to make specific changes, or let it run its own optimization across several variants.
In my run, as you can see, it did the latter. It tested looser thresholds and an ATR-stop version, then reported that none of the versions it tried showed a meaningful improvement over this 92-day window. At that point, it suggested these options:
- Continue optimizing
- Change approach – try a different strategy type or asset
- Revert to the original spec and finalize as-is
- Finalize the current best version
It also has another option titled: “None of these options match?”, which is for users who want their own optimization strategies.
I chose “finalize the current best version” to see what the result is. And here is the final result:
“BTC/USDC mean-reversion strategy on Hyperliquid (1h). The original strict spec (RSI(14) cross below 30 + price above 200 EMA) never fired a trade on 1h BTC, so the entry was relaxed to RSI(14) crossing below 40 with price above the 50 EMA.
Final backtest over the last 3 months: 18 orders (14 filled), profit factor 0.77, win rate 43%, Sortino -0.89, max drawdown -8.3%. The strategy has no demonstrated edge in this window — a strict dip-buy mean-reversion on 1h BTC was unprofitable across all 7 attempts. Finalized as-is per your request.”
3Commas QuantPilot AI Pros and Cons
Here are the main pros and cons I have found in my QuantPilot review that are worth weighing before you sign up.
Pros
- It delivers on its core promise. You describe a strategy in plain English and get a working, backtested version in minutes, with no coding at all. For a trader who understands logic but not Python, this removes the big barrier.
- It is not a black box. The agent writes its logic in readable QuantScript, so you can open it and trace the actual rules yourself.
- The backtesting is realistic and honest. The engine runs on 1-minute candles and models real liquidity from Hyperliquid order-book data.
- It is cheap to run. A full research loop used only about 10,000 tokens in my session, a small slice of the free daily allowance.
- It comes from a real company. 3Commas has almost a decade of trading-automation history, which is reassuring in a space full of anonymous launches.
Cons
- It is not a live trading engine yet. This is the single most important limitation. QuantPilot builds and backtests strategies, but automated live execution has not shipped yet.
- It is Hyperliquid only. Charts, strategy work, backtests, and the Terminal (that is coming soon) all run on Hyperliquid. If your trading lives on another exchange, you need to use another tool that supports it.
- It is not a shortcut to profit. It is not a signal service and not a source of guaranteed alpha. The thinking is still on you.
3Commas QuantPilot AI Security and Legal Fit
3Commas provides the software; it does not take custody of your money. You do not deposit funds into QuantPilot 3Commas. And once connected, the Terminal reads the required data from your own wallet address.
QuantPilot is made available to you by Quantpilot OÜ, a limited liability company established under the laws of Estonia, registered office at Laeva tn 2, 10111 Tallinn, Estonia, registration number 16238525 (“We”, “Us” or “Our”).
QuantPilot’s Terms of Use state that services may not be available in all jurisdictions, and that users in the EEA in particular may find certain features geo-restricted under MiCA and MiFID II rules.
How QuantPilot and Finestel Can Merge
QuantPilot helps you build a strategy and test it on past data. But it doesn’t actually trade for you. That means there’s one more step.
Once you have developed and tested a strategy in QuantPilot, you can put its rules into practice directly on Hyperliquid. For a single account, that may be enough. But coordinating the same trades across dozens or hundreds of accounts becomes harder through Hyperliquid’s own terminal. This is where Finestel can help.
Finestel’s multi-account, multi-venue trading terminal lets you place and manage trades across connected Hyperliquid accounts from one interface and apply the strategy’s entries and exits without repeating each action account by account.
You can also use advanced execution tools such as DCA orders to build positions through successive orders, or TWAP to spread larger orders over time. If you prefer to trade a master account, Finestel’s trade copier can replicate its trades across linked accounts.
For asset managers, professional traders, and trading desks, this means coordinating a strategy across client accounts while adjusting position sizes and risk settings for each account. The same workflow can extend beyond Hyperliquid to other supported exchanges, although the strategy should be revalidated for each venue’s fees, liquidity, and order constraints.
You can start by following the strategy manually in the terminal, then automate execution through a compatible signal source when needed. QuantPilot provides the research and backtesting environment; Finestel provides the infrastructure to manage the resulting trades at scale. Moving between them requires configuring the execution workflow, rather than importing QuantScript directly.
To connect your Hyperliquid account and start managing trades through Finestel, follow our step-by-step Finestel Hyperliquid trading tutorial.
Final Thoughts
So, is QuantPilot worth it? My 3Commas QuantPilot AI review shows that for the right person, yes.
If you understand trading but do not code, this is one of the fastest ways to test an idea I have seen. You type it in plain English, and minutes later you have a real backtest. That alone is a big deal. Try to go in with the right mindset. QuantPilot is a lab for finding ideas that work, not a machine that necessarily verifies your strategy.
FAQs
Do I need to know how to code to use QuantPilot?
No. You describe your idea in plain language, and the agents translate it into QuantScript, the platform’s strategy language. You can read that code to check it, but you don’t have to write it yourself.
Is 3Commas QuantPilot AI free?
There’s a free plan with a daily amount of AI tokens. If you need more, you can buy token top-ups at $1 per 1 million tokens.
Is QuantPilot 3Commas good for beginners?
It’s easy to start with since you just type your idea in plain English. But the strategies it builds are real trading strategies, so you still need to understand what you’re testing.
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