How to use liquidity pools in your decentralized exchange

Maciej Zieliński

27 Oct 2021
How to use liquidity pools in your decentralized exchange

Recently we summed up all you need to know about Automatic Market Makers. Get to know their key element- liquidity pools. How do they work and what do you need to know before you decide to implement them into your decentralized exchange? 

What will you find in the article?

  • Role of liquidity pools in AMM
  • Why liquidity pools are essential for DEXs
  • How does liquidity pool work?
  • LP tokens
  • How to use liquidity pools?


Liquidity pools are digital assets managed by smart contracts that enable trades between different tokens or cryptocurrencies on Decentralized Exchanges. Assets are deposited there by liquidity providers - investors and users of the platform. 

Liquidity pools are a backbone of Automatic Market Maker, which replaces one side of a trade with an individual liquidity pool. 

Decentralized Exchanges: Liquidity Pools

Liquidity pools are among the most robust solutions for contemporary DeFi ecosystems. Currently, most DEXs work on the Automatic Money Maker model, and liquidity pools are a crucial part of it.

To fully understand the importance of DeFi liquidity pools, we should first look at variable ways in which DEXs can handle trading. 

How do decentralized exchanges operate trading? 

  • On-chain order book
  • Off-chain order book
  • Automated Market Maker

Currently, the last of them seems to be the most effective. Therefore the vast majority of modern DEXs are based on it. Since liquidity pools are its backbone, their importance in the DeFi sector is undeniable. 

Problems with ordering books 

Before launching the first automated market makers, liquidity was a significant issue for decentralized exchanges, especially for new DEXs with a small number of buyers and sellers. Sometimes it was simply too difficult to find enough people willing to become a side in trading pair.

In those cases, the peer-to-peer model didn’t support liquidity on a sufficient level. The question was how to improve the situation without implementing a middle man, which would lead to losing the core value for the DeFi ecosystem - decentralization. The answer came with AMM.

Trading pairs 

Let’s use the example of Ether and Bitcoin to describe how trading pairs work in the order book model on DEX

If users want to trade their ETH for BTC, they need to find another trader willing to sell BTC for ETH. Furthermore, they need to agree on the same price. 

While in the case of popular cryptocurrencies and tokens, finding a trading pair shouldn’t be a problem, things get a bit more complicated when we want to trade more alternative assets. 

The vital difference between order books and automatic market makers is that the second one doesn’t require the existence of trading pairs to facilitate trade. All thanks to liquidity pools.

Role of liquidity pool in AMM

Automated Market Maker (AMM) is a decentralized exchange protocol that relies on smart contracts to set the price of tokens and provide liquidity. In an automated market makers' model, assets are priced according to a pricing algorithm and mathematical formula instead of the order book used by traditional exchanges.

We can say that liquidity pools are a crucial part of this system. In AMM trading pair that we know from traditional stock exchanges and order book models is replaced by a single liquidity pool. Hence users trade digital assets with a liquidity pool rather than other users.


Peer-to-peer is probably one of the best-known formulas from the DeFi ecosystem. For a long time, it was a core idea behind decentralized trading.

Yet blockchain technology improvement and the creativity of developers brought new possibilities. P2C - peer-to-contract model puts smart contracts as a side of the transaction. Because smart contract can’t be influenced by any central authority after it was started, P2C doesn’t compromise decentralization.

Essentially Automated Market Makers is peer-to-contract solutions because trades take place between users and a smart contract. 

Liquidity providers

Liquidity pools work as piles of funds deposited into a smart contract.  Yet, where do they come from?

The answer might sound quite surprising: pool tokens are added to liquidity pools by the exchange users. Or, more precisely, liquidity providers.

To provide the liquidity, you need to deposit both assets represented in the pool. Adding funds to the liquidity pool is not difficult and rewards are worth considering. The profits of liquidity providers differ depending on the platform. For instance, on Uniswap 0.3% of every transaction goes to liquidity providers.

Gaining profits in exchange for providing liquidity is often called liquidity mining.

How do liquidity pools work?

Essentially, the liquidity pool creates a market for a particular pair of assets, for example, Ethereum and Bitcoin. When a new pool is created, the first liquidity provider sets the initial price and equal supply of two assets. This concept of supply will remain the same for all the other liquidity providers that will eventually decide to stake their found in the pool. 

DeFi liquidity pools hold fair values for assets by implementing AMM algorithms, which maintain the price ratio between tokens in the particular pool.

Different AMMs use different algorithms. Uniswap, for example, uses the following formula:

a * b = k

Where 'a' and 'b' are the number of tokens traded in the DeFi liquidity pool. Since 'k' is constant, the total liquidity of the pool must always remain the same. Different AMMS use various formulas. However, all of them set the price algorithmically. 

Earning from trading fees

A good liquidity pool has to be designed to encourage users to stake their assets in it. Without it supplying liquidity on a sufficient level won't be possible.

Therefore most exchanges decide on sharing profits generated by trading fees with liquidity providers. In some cases (e. g., Uniswap), all the fees go to liquidity providers. If a user's deposit represents 5% of the assets locked in a pool, they will receive an equivalent of 5% of that pool’s accrued trading fees. The profit will be paid out in liquidity provider tokens. 

Liquidity provider token (LP token)

In exchange for depositing their tokens, liquidity providers get unique tokens, often called liquidity provider tokens. LP tokens reflect the value of assets deposited by investors. As mentioned above, those tokens are often also used to account for profits in exchange for liquidity. 

Normally when a token is staked or deposited somehow, it cannot be used or traded, which decreases liquidity in the whole system. That’s problematic, because as I mentioned, liquidity has a pivotal value in the DeFi space

LP tokens enable us to liquid assets that are staked and normally would be frozen until providers will decide to withdraw them. Thanks to LP tokens, each token can be used multiple times, despite being invested in one of the DeFi liquidity pools.

Furthermore, it opens new possibilities related to indirect forms of staking. 

Yield Farming

Yield farming refers to gaining profits from staking tokens in multiple DeFi liquidity pools. Essentially liquidity providers can stake their LP tokens in other protocols and get for it other liquidity tokens. 

How does it work?

Actually, from the user perspective, it's quite simple:

  • Deposit assets into a liquidity pool 
  • Collect LP tokens
  • Deposit or stake LP tokens into a 
  • Separate lending protocol
  • Earn profit from both protocols 

Note: You must exchange your LP tokens to withdraw your shares from the initial liquidity pool.

How to use Liquidity pools in your DEX?

Decentralized finance develops at tremendous speed, constantly bringing new possibilities. The number of people interested in DeFi investments increases every day; hence the popularity of options such as liquidity mining recently has grown significantly. While deciding to launch our DEX, you have to be aware of that.

As I mentioned, liquidity has pivotal importance for decentralized finance, particularly for exchanges. Liquidity pools can't exist without investors that will add liquidity to them. Their shortage will lead to low liquidity. In consequence, that will be a cause of the low competitiveness of the exchange. On the other hand, for new DEXs it's still easier than attracting enough buyers and sellers to support order book trading.

Implementing liquidity pools to your DEX requires not only experience of blockchain developers’ fluently using DeFi protocols but also a solid and well-planned business strategy. That's why choosing a technology partner with previous experience with both blockchain development and business consulting in the decentralized finance field might be the optimal solution.

Do you want to gain more first-hand knowledge regarding liquidity pools development and implementation? Don't hesitate to ask our professionals that will gladly answer your questions.

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Applying Game Theory in Token Design

Kajetan Olas

16 Apr 2024
Applying Game Theory in Token Design

Blockchain technology allows for aligning incentives among network participants by rewarding desired behaviors with tokens.
But there is more to it than simply fostering cooperation. Game theory allows for designing incentive-machines that can't be turned-off and resemble artificial life.

Emergent Optimization

Game theory provides a robust framework for analyzing strategic interactions with mathematical models, which is particularly useful in blockchain environments where multiple stakeholders interact within a set of predefined rules. By applying this framework to token systems, developers can design systems that influence the emergent behaviors of network participants. This ensures the stability and effectiveness of the ecosystem.

Bonding Curves

Bonding curves are tool used in token design to manage the relationship between price and token supply predictably. Essentially, a bonding curve is a mathematical curve that defines the price of a token based on its supply. The more tokens that are bought, the higher the price climbs, and vice versa. This model incentivizes early adoption and can help stabilize a token’s economy over time.

For example, a bonding curve could be designed to slow down price increases after certain milestones are reached, thus preventing speculative bubbles and encouraging steadier, more organic growth.

The Case of Bitcoin

Bitcoin’s design incorporates game theory, most notably through its consensus mechanism of proof-of-work (PoW). Its reward function optimizes for security (hashrate) by optimizing for maximum electricity usage. Therefore, optimizing for its legitimate goal of being secure also inadvertently optimizes for corrupting natural environment. Another emergent outcome of PoW is the creation of mining pools, that increase centralization.

The Paperclip Maximizer and the dangers of blockchain economy

What’s the connection between AI from the story and decentralized economies? Blockchain-based incentive systems also can’t be turned off. This means that if we design an incentive system that optimizes towards a wrong objective, we might be unable to change it. Bitcoin critics argue that the PoW consensus mechanism optimizes toward destroying planet Earth.

Layer 2 Solutions

Layer 2 solutions are built on the understanding that the security provided by this core kernel of certainty can be used as an anchor. This anchor then supports additional economic mechanisms that operate off the blockchain, extending the utility of public blockchains like Ethereum. These mechanisms include state channels, sidechains, or plasma, each offering a way to conduct transactions off-chain while still being able to refer back to the anchored security of the main chain if necessary.

Conceptual Example of State Channels

State channels allow participants to perform numerous transactions off-chain, with the blockchain serving as a backstop in case of disputes or malfeasance.

Consider two players, Alice and Bob, who want to play a game of tic-tac-toe with stakes in Ethereum. The naive approach would be to interact directly with a smart contract for every move, which would be slow and costly. Instead, they can use a state channel for their game.

  1. Opening the Channel: They start by deploying a "Judge" smart contract on Ethereum, which holds the 1 ETH wager. The contract knows the rules of the game and the identities of the players.
  2. Playing the Game: Alice and Bob play the game off-chain by signing each move as transactions, which are exchanged directly between them but not broadcast to the blockchain. Each transaction includes a nonce to ensure moves are kept in order.
  3. Closing the Channel: When the game ends, the final state (i.e., the sequence of moves) is sent to the Judge contract, which pays out the wager to the winner after confirming both parties agree on the outcome.

A threat stronger than the execution

If Bob tries to cheat by submitting an old state where he was winning, Alice can challenge this during a dispute period by submitting a newer signed state. The Judge contract can verify the authenticity and order of these states due to the nonces, ensuring the integrity of the game. Thus, the mere threat of execution (submitting the state to the blockchain and having the fraud exposed) secures the off-chain interactions.

Game Theory in Practice

Understanding the application of game theory within blockchain and token ecosystems requires a structured approach to analyzing how stakeholders interact, defining possible actions they can take, and understanding the causal relationships within the system. This structured analysis helps in creating effective strategies that ensure the system operates as intended.

Stakeholder Analysis

Identifying Stakeholders

The first step in applying game theory effectively is identifying all relevant stakeholders within the ecosystem. This includes direct participants such as users, miners, and developers but also external entities like regulators, potential attackers, and partner organizations. Understanding who the stakeholders are and what their interests and capabilities are is crucial for predicting how they might interact within the system.

Stakeholders in blockchain development for systems engineering

Assessing Incentives and Capabilities

Each stakeholder has different motivations and resources at their disposal. For instance, miners are motivated by block rewards and transaction fees, while users seek fast, secure, and cheap transactions. Clearly defining these incentives helps in predicting how changes to the system’s rules and parameters might influence their behaviors.

Defining Action Space

Possible Actions

The action space encompasses all possible decisions or strategies stakeholders can employ in response to the ecosystem's dynamics. For example, a miner might choose to increase computational power, a user might decide to hold or sell tokens, and a developer might propose changes to the protocol.

Artonomus, Github

Constraints and Opportunities

Understanding the constraints (such as economic costs, technological limitations, and regulatory frameworks) and opportunities (such as new technological advancements or changes in market demand) within which these actions take place is vital. This helps in modeling potential strategies stakeholders might adopt.

Artonomus, Github

Causal Relationships Diagram

Mapping Interactions

Creating a diagram that represents the causal relationships between different actions and outcomes within the ecosystem can illuminate how complex interactions unfold. This diagram helps in identifying which variables influence others and how they do so, making it easier to predict the outcomes of certain actions.

Artonomus, Github

Analyzing Impact

By examining the causal relationships, developers and system designers can identify critical leverage points where small changes could have significant impacts. This analysis is crucial for enhancing system stability and ensuring its efficiency.

Feedback Loops

Understanding feedback loops within a blockchain ecosystem is critical as they can significantly amplify or mitigate the effects of changes within the system. These loops can reinforce or counteract trends, leading to rapid growth or decline.

Reinforcing Loops

Reinforcing loops are feedback mechanisms that amplify the effects of a trend or action. For example, increased adoption of a blockchain platform can lead to more developers creating applications on it, which in turn leads to further adoption. This positive feedback loop can drive rapid growth and success.

Death Spiral

Conversely, a death spiral is a type of reinforcing loop that leads to negative outcomes. An example might be the increasing cost of transaction fees leading to decreased usage of the blockchain, which reduces the incentive for miners to secure the network, further decreasing system performance and user adoption. Identifying potential death spirals early is crucial for maintaining the ecosystem's health.

The Death Spiral: How Terra's Algorithmic Stablecoin Came Crashing Down
the-death-spiral-how-terras-algorithmic-stablecoin-came-crashing-down/, Forbes


The fundamental advantage of token-based systems is being able to reward desired behavior. To capitalize on that possibility, token engineers put careful attention into optimization and designing incentives for long-term growth.


  1. What does game theory contribute to blockchain token design?
    • Game theory optimizes blockchain ecosystems by structuring incentives that reward desired behavior.
  2. How do bonding curves apply game theory to improve token economics?
    • Bonding curves set token pricing that adjusts with supply changes, strategically incentivizing early purchases and penalizing speculation.
  3. What benefits do Layer 2 solutions provide in the context of game theory?
    • Layer 2 solutions leverage game theory, by creating systems where the threat of reporting fraudulent behavior ensures honest participation.

Token Engineering Process

Kajetan Olas

13 Apr 2024
Token Engineering Process

Token Engineering is an emerging field that addresses the systematic design and engineering of blockchain-based tokens. It applies rigorous mathematical methods from the Complex Systems Engineering discipline to tokenomics design.

In this article, we will walk through the Token Engineering Process and break it down into three key stages. Discovery Phase, Design Phase, and Deployment Phase.

Discovery Phase of Token Engineering Process

The first stage of the token engineering process is the Discovery Phase. It focuses on constructing high-level business plans, defining objectives, and identifying problems to be solved. That phase is also the time when token engineers first define key stakeholders in the project.

Defining the Problem

This may seem counterintuitive. Why would we start with the problem when designing tokenomics? Shouldn’t we start with more down-to-earth matters like token supply? The answer is No. Tokens are a medium for creating and exchanging value within a project’s ecosystem. Since crypto projects draw their value from solving problems that can’t be solved through TradFi mechanisms, their tokenomics should reflect that. 

The industry standard, developed by McKinsey & Co. and adapted to token engineering purposes by Outlier Ventures, is structuring the problem through a logic tree, following MECE.
MECE stands for Mutually Exclusive, Collectively Exhaustive. Mutually Exclusive means that problems in the tree should not overlap. Collectively Exhaustive means that the tree should cover all issues.

In practice, the “Problem” should be replaced by a whole problem statement worksheet. The same will hold for some of the boxes.
A commonly used tool for designing these kinds of diagrams is the Miro whiteboard.

Identifying Stakeholders and Value Flows in Token Engineering

This part is about identifying all relevant actors in the ecosystem and how value flows between them. To illustrate what we mean let’s consider an example of NFT marketplace. In its case, relevant actors might be sellers, buyers, NFT creators, and a marketplace owner. Possible value flow when conducting a transaction might be: buyer gets rid of his tokens, seller gets some of them, marketplace owner gets some of them as fees, and NFT creators get some of them as royalties.

Incentive Mechanisms Canvas

The last part of what we consider to be in the Discovery Phase is filling the Incentive Mechanisms Canvas. After successfully identifying value flows in the previous stage, token engineers search for frictions to desired behaviors and point out the undesired behaviors. For example, friction to activity on an NFT marketplace might be respecting royalty fees by marketplace owners since it reduces value flowing to the seller.


Design Phase of Token Engineering Process

The second stage of the Token Engineering Process is the Design Phase in which you make use of high-level descriptions from the previous step to come up with a specific design of the project. This will include everything that can be usually found in crypto whitepapers (e.g. governance mechanisms, incentive mechanisms, token supply, etc). After finishing the design, token engineers should represent the whole value flow and transactional logic on detailed visual diagrams. These diagrams will be a basis for creating mathematical models in the Deployment Phase. 

Token Engineering Artonomous Design Diagram
Artonomous design diagram, source: Artonomous GitHub

Objective Function

Every crypto project has some objective. The objective can consist of many goals, such as decentralization or token price. The objective function is a mathematical function assigning weights to different factors that influence the main objective in the order of their importance. This function will be a reference for machine learning algorithms in the next steps. They will try to find quantitative parameters (e.g. network fees) that maximize the output of this function.
Modified Metcalfe’s Law can serve as an inspiration during that step. It’s a framework for valuing crypto projects, but we believe that after adjustments it can also be used in this context.

Deployment Phase of Token Engineering Process

The Deployment Phase is final, but also the most demanding step in the process. It involves the implementation of machine learning algorithms that test our assumptions and optimize quantitative parameters. Token Engineering draws from Nassim Taleb’s concept of Antifragility and extensively uses feedback loops to make a system that gains from arising shocks.

Agent-based Modelling 

In agent-based modeling, we describe a set of behaviors and goals displayed by each agent participating in the system (this is why previous steps focused so much on describing stakeholders). Each agent is controlled by an autonomous AI and continuously optimizes his strategy. He learns from his experience and can mimic the behavior of other agents if he finds it effective (Reinforced Learning). This approach allows for mimicking real users, who adapt their strategies with time. An example adaptive agent would be a cryptocurrency trader, who changes his trading strategy in response to experiencing a loss of money.

Monte Carlo Simulations

Token Engineers use the Monte Carlo method to simulate the consequences of various possible interactions while taking into account the probability of their occurrence. By running a large number of simulations it’s possible to stress-test the project in multiple scenarios and identify emergent risks.

Testnet Deployment

If possible, it's highly beneficial for projects to extend the testing phase even further by letting real users use the network. Idea is the same as in agent-based testing - continuous optimization based on provided metrics. Furthermore, in case the project considers airdropping its tokens, giving them to early users is a great strategy. Even though part of the activity will be disingenuine and airdrop-oriented, such strategy still works better than most.

Time Duration

Token engineering process may take from as little as 2 weeks to as much as 5 months. It depends on the project category (Layer 1 protocol will require more time, than a simple DApp), and security requirements. For example, a bank issuing its digital token will have a very low risk tolerance.

Required Skills for Token Engineering

Token engineering is a multidisciplinary field and requires a great amount of specialized knowledge. Key knowledge areas are:

  • Systems Engineering
  • Machine Learning
  • Market Research
  • Capital Markets
  • Current trends in Web3
  • Blockchain Engineering
  • Statistics


The token engineering process consists of 3 steps: Discovery Phase, Design Phase, and Deployment Phase. It’s utilized mostly by established blockchain projects, and financial institutions like the International Monetary Fund. Even though it’s a very resource-consuming process, we believe it’s worth it. Projects that went through scrupulous design and testing before launch are much more likely to receive VC funding and be in the 10% of crypto projects that survive the bear market. Going through that process also has a symbolic meaning - it shows that the project is long-term oriented.

If you're looking to create a robust tokenomics model and go through institutional-grade testing please reach out to Our team is ready to help you with the token engineering process and ensure your project’s resilience in the long term.


What does token engineering process look like?

  • Token engineering process is conducted in a 3-step methodical fashion. This includes Discovery Phase, Design Phase, and Deployment Phase. Each of these stages should be tailored to the specific needs of a project.

Is token engineering meant only for big projects?

  • We recommend that even small projects go through a simplified design and optimization process. This increases community's trust and makes sure that the tokenomics doesn't have any obvious flaws.

How long does the token engineering process take?

  • It depends on the project and may range from 2 weeks to 5 months.