Core DeFi Primitives Behind Automated Market Makers Oracle-Free Design

In the last few years I’ve watched institutional traders and a handful of hedge funds try to wrestle with the world of automated market makers. Some of them have been fascinated by the elegance of a simple equation that lets anyone trade against a liquidity pool with zero counterparty risk. Others have been haunted by the way that the same equations, when mis‑understood, can lead to rug pulls or liquidity mining flash crashes. That tension—the push and pull between optimism and caution—has kept me up at night and fueled the articles I write for my readers.

Let’s start with a memory from nearly a decade ago. I was working in a big‑4 audit firm in London, staring at a spreadsheet that had grown to 200 rows and columns. My job was to reconcile a client’s asset book and flag any anomalies. Every row had a number attached to something the client claimed was a “real” asset. Behind that number, behind those labels, there was human capital, market sentiment, and the very real fear of falling into a bubble. A few months later, when the company that had been listed on the London Stock Exchange fell through a market crash, I watched the numbers on my screen collapse. The lesson learned that day was simple: money is a tool for freedom, but freedom ends up being more about being able to survive shocks than chasing gains.

Fast forward to today, and the tool I want to talk about now is the Automated Market Maker, or AMM. The most common AMM we now see is the constant product market maker (CPMM), the sort that powers Uniswap, SushiSwap, and many others. The equation is deceptively simple: reserve X times reserve Y equals k, a constant. That simple rule is the core of how the pool maintains an internal “market” that mirrors, to some degree, external price reality. No one has to tell it whether the price in the pool is correct; the product constraint does the heavy lifting.

But is that enough? What happens when the pool’s internal price diverges drastically from the external market? Some AMMs answer that question by adding price feeds from external oracles. But oracles bring a whole new set of problems: the risk of hack, the chance of manipulation, the cost of feeding the data. It would be simpler—and a more natural fit with the philosophy of decentralisation—to keep AMMs truly oracle‑free. How can we create a robust system that learns its own market signals without pulling in external data points? That problem is the heart of what I want to explore.

A Simple Primitives Map

When we talk about core primitives in a DeFi ecosystem, think of them as the building blocks of any larger structure. It’s like gardening: you have seeds, soil, water, sunlight. Without at least a few of these, a garden just can’t thrive. For AMMs, those blocks include:

• Liquidity provision: Users lock pairs of ERC‑20 tokens into a pool, providing the depth that others use to trade.
• Invariant enforcement: A mathematical rule (like the constant product) that anchors the pool’s internal price.
• Price discovery: Through trade execution, the invariant’s output becomes a market‑based price signal.
• Slippage calculation: The pool automatically tells traders how the price will move as they make a trade.
• Impermanent loss mitigation: Mechanisms to buffer providers when external prices move.

If you think of AMMs as ecosystems, each of these primitives can be seen as a species that must coexist to allow the whole system to thrive. Some AMMs tweak or add species—like concentrated liquidity or fee tiers—so that the ecosystem remains healthy under a wider range of weather patterns.

Let us walk through each primitive, noting how it keeps the ecosystem grounded and, importantly, how we can keep it free from external oracles.

Liquidity provision

When I first started explaining liquidity provision to traders in Portugal, I’d often use the metaphor of a city’s water supply. Imagine a pool as a public well. The pool’s owners deposit two types of tokens—say ETH and DAI—into that well so that anyone can draw a little from either side at a reasonably stable cost. The liquidity providers (LPs), in return for the risk they take, receive a slice of the trading fee that comes in each time someone withdraws or deposits tokens.

But there’s a subtle fear that’s always lurking: “I’ll get slippage, my capital will erode.” To address that, liquidity pools use the invariant as a safety net. The rule essentially says the product of reserves must stay constant after any trade. If a trader pulls the price out by too much, the invariant automatically forces the pool to adjust. For the LP, that means they will either keep their position without loss or lose some value; but that loss is impermanent—it disappears once external prices normalize.

Invariant enforcement

The constant product rule is a kind of equilibrium constraint. It’s simple enough to be transparent, yet robust enough to allow the pool to self‑correct. What feels magical is that the same rule that keeps a single pool balanced also allows it to interact with multiple pools through smart contracts. For instance, I once built a simple protocol that automatically swapped between Uniswap’s ETH‑DAI pool and SushiSwap’s ETH‑USDC pool whenever the swap fee for one dropped below the other. The invariant kept each pool balanced while the protocol hopped around, re‑balancing on each jump. No oracle or price checker—just the math.

The key benefit of an invariant is that it requires no external confirmation: the data it needs is everything that it already has. The reserves, the trade inputs, the fee structure—all are already on the chain. This is why invariant‑based AMMs are inherently oracle‑free.

Price discovery through trade execution

When someone trades in the pool, the resulting change in reserves automatically moves the internal price. This is a simple form of price discovery—when a lot of people want to buy A with B, the pool’s reserves shift, and the price naturally reflects that demand. This has a certain poetic honesty: the pool is a mirror of the collective will of traders without any bias, because the will is measured in tokens.

The speed of price discovery is limited to the depth of the pool; deeper pools provide more stable prices while thinner pools can produce large slippages for large orders. In practice, liquidity mining programs in many AMMs increase depth by giving back a portion of the fee to LPs.

Slippage calculation

To keep traders (and me) from being blindsided, AMMs automatically calculate slippage based on the size of the trade relative to the current depth. That slippage is a built‑in protection. It tells the user: “If you want this amount, expect this price.” The user can choose to refuse a trade, modify it, or adjust the slippage tolerance. In the end it is the user who decides how much volatility they can tolerate.

In an oracle‑free context, slippage is the only thing that exposes you to the risk of a mis‑priced trade. By keeping the slippage transparent, the system gives a clear, rational indicator of how much “market power” the trader actually needs.

Impermanent loss mitigation

One of the biggest fears for LPs is impermanent loss—when the external price moves, their deposited tokens lose value relative to holding them solo. There are many ways to mitigate that: concentrated liquidity, increasing fee tiers, AMM designs that use multiple invariants (like a mix of constant product and constant sum). The point is: the only way to give LPs a fair trade‑off is to make the system as honest about the risks as the price calculation is.

Because impermanent loss depends purely on how prices have evolved, it only uses data that has already emerged from on‑chain trades. No need to tell the system an oracle price; the math will say whether you’re losing or not.

Oracle‑free design: Why it matters

I remember the first time I tried to set up an oracle‑free AMM on a testnet. The idea was to see whether we could feed price information entirely from trade data and perhaps a small set of off‑chain data we verified ourselves. At the end of the day the protocol worked: the pool maintained its invariant and traders could buy and sell. The price drifted from external markets, but the drift was visible in the pool and could be corrected by arbitragers.

The advantage of eliminating oracles is twofold:

1. Security: Oracles add a layer of external trust. If they’re hacked or manipulated, every AMM that relies on them is compromised. Removing them eliminates that weak link.
2. Simplicity: For readers like us who focus on fundamental analysis, fewer moving parts mean fewer places where something can go wrong.

An early criticism of oracle‑free AMMs was that they would lag behind real prices, making them less useful for high‑frequency traders. In practice, if the pool depth is large enough, arbitrageurs will swoop in, correcting the price in a handful of transactions. The system, when correctly designed, doesn’t need an external oracle to keep up.

An oracle‑free AMM can still integrate small, trust‑minimized data sources. For example, we can tie a pool to a 48‑hour TWAP of a major exchange pair, but that’s more of a “price verification” than an oracle. The key point is that we’re not pulling an external price feed and hoping it stays honest at every instant.

Practical example: A minimal oracle‑free DEX

Consider a simple DEX built on Ethereum Layer 2, called “PureSwap.” When a user wants to trade, the contract first looks at the pool reserves. It calculates the price change by simply applying the constant product formula. Then it looks up the last 10 transactions on the pool and computes an average price. This micro‑TWAP is used to inform slippage calculations and to provide an indication of recent market trend. No external source is quoted.

PureSwap also offers a “volatility band” feature where liquidity providers can elect to hold a certain ratio of the two tokens that is not too far from the mid‑price. If the market drifts too far out of that band, liquidity providers can withdraw their funds without incurring significant slippage. In effect, the providers are taking a position in an automated hedging strategy that never relies on an external price.

All of this happens within a single transaction block. All the data needed to evaluate the trade is already on‑chain, making the system auditable, straightforward to reason about, and free from the attack surface of feeding data into a smart contract.

How is this useful for everyday investors?

If you’re not a day trader but you want to invest in DeFi, the concept of an oracle‑free AMM simplifies the risk profile of LP positions. You can:

• Provide liquidity to pools that use simple invariants, knowing the math will force a fair price.
• Track relative price drift by watching how your pool’s internal price deviates from a publicly observable market exchange. If it drifts beyond a certain threshold, you can decide to withdraw or rebalance.
• Use impermanent loss metrics to evaluate whether the fee income is truly compensating for exposure. Because the loss calculation uses internal data, it’s easier to trust.

The takeaway is that the absence of oracles makes the system more robust (no single point of failure) but requires careful design so that arbitrage still functions effectively. It also means that understanding the math behind the constant product formula, the invariant, and slippage is the most crucial thing you can do to manage risk.

A human‑centered reminder

I’ve met a lot of people who fear that the entire DeFi world is one big money pit. That fear is not unwarranted—there are indeed scams, rug pulls, and bugs. But every day I see portfolio managers and private individuals who, by keeping their investments simple and transparent, are building something that could outlast speculation. When you look at an oracle‑free AMM, you see a system that relies on common sense rather than sophisticated machinery. The only person who needs to be careful is you, the trader or LP, who must understand that the price you see inside the pool is derived from trades, not from a third‑party feed.

Final thought

Imagine walking through Lisbon’s old Alfama district. Each narrow street is like a price path. Some streets are straight and clear, some twist and turns. The rule that keeps the entire neighborhood organized is simple: every turn should be reachable by walking, not needing a detour onto the highway. In the same way, an oracle‑free AMM keeps its operations understandable; you walk from one side to another and the system automatically tells you how far you’ll travel.

Let’s zoom out and keep it simple:

• Liquidity is what people put in
• Invariant is the rule that keeps the balance
• Price comes from trades themselves
• Slippage tells us how much we’ll pay
• Impermanent loss is the risk we accept and, if we design correctly, can be managed

These are the primitives that can make DeFi accessible to everyday savers rather than only the fast‑moving. And that’s a good place to start, especially if we want to keep the financial system honest, simple, and, no matter how noisy the market, useful.

Takeaway
To make your DeFi experience trustworthy, look for protocols that use basic invariants and no external price oracles. Understand how slippage is calculated and how depth protects you. Monitor how your position in the pool diverges from the broader market and be ready to adjust when the price drift grows too large. In this way, you can enjoy the benefits of liquidity provision without becoming a casualty of oracle mis‑pricings or systemic hacks.