🔥Algorithmic Buy & Burn
Last updated
Last updated
The algorithmic buy & burn mechanism of Mangata enables it to maintain a capped supply while continuously minting new rewards for liquidity provision and block creation. As a bonus for token holders, it positively connects the success and activity of the network with token price appreciation.
DEXes are continually in need of issuing new tokens to distribute as incentives, and the rewards are often achieved with unbounded inflation by the protocol. Unbounded inflation leads to a problem that is widespread with DEX tokens today: Constant devaluation of the token.
Algorithmic buy & burn solves this dilemma by continually removing MGX from the supply with every trade. This allows Mangata to mint new tokens while maintaining a hard cap on the supply. Buy & burn creates demand for MGX and adds deflationary pressure, which leads to price appreciation. It also ensures that MGX holders are rewarded by trading activity in the system, irrespective of the pool that is used.
Buy & burn itself is not a new invention. It is already employed by other projects. The most prominent one is Binance, which burns parts of its profits in the BNB burn event every quarter. At Mangata, we will use a more dynamic method that burns MGX with every single trade: Algorithmic buy & burn.
Just as at other DEXes, the Mangata X exchange commission is 0.3% of every trade. Of that:
0.2% go to liquidity providers as LP fee
0.05% go to the Treasury and
0.05% are used for algorithmic buy & burn.
This means that for a million USD traded on the platform, 2,000 USD goes to liquidity providers, 500 USD goes to the Treasury, and 500 USD worth of MGX gets bought on the market and burned immediately.
The “algorithmic” in algorithmic buy & burn points to the fact that no human interaction is involved. It happens autonomously as an on-chain mechanism!
For the network, algorithmic buy & burn removes tokens from circulation which allows it to mint new incentives for collators and liquidity providers. This gives governance levers to control the growth of the ecosystem.
For token holders, buy & burn creates demand for MGX and adds deflationary pressure, which leads to price appreciation. Algorithmic buy & burn also ensures that MGX holders are rewarded by trading activity in the system, irrespective of the pool that is used.
For the team, it removes the need to intervene in the ecosystem to stimulate price activity. Algorithmic buy & burn ensures that the success of the token is not dependent on a central team, but is rather delegated to the users of the platform and a result of trading activity.
Given the right circumstances, it might even create enough deflationary pressure to balance out the creation of new tokens.
To develop a practical understanding of the effects of algorithmic buy & burn, we have simulated a year of trading activity for a hypothetical MGX-USDC pool on 4 different DEXes with different fee compositions and compared the results.
We have chosen 4 different exchange commission scenarios to compare against each other:
Mangata X: 0.2% LP fees, 0.05% to the Treasury, 0.05% to algorithmic buy & burn
Just LP fees: 0.3% LP fees.
Fees + Treasury: 0.2% LP fees, 0.1% Treasury
Fees + Buy & Burn: 0.2% LP fees, 0.1% algorithmic buy & burn
As parameters, we have chosen to simulate a present-day Top30 DEX:
TVL: 250m USD
Yearly volume: 8b USD
MGX liquidity to supply: 30%
MGX dominance: 30%
Following these parameters, we reduce complexity by assuming all liquid MGX exists in a single liquidity pool with USDC (150m USD pool value) and has an equivalent share of the total DEX volume (4.8b USD). These parameters would put MGX at an implied market cap of 250m USD.
To simulate the behavior of the pool over a year, we simulate 100,000 swaps USDC→MGX and back each, totaling 200,000 transactions. Each transaction has a volume of 24,000 USD (slippage 0.03%).
As shown in the table below, Mangata X offers the most sophisticated and calibrated model.
Algorithmic buy & burn adds upwards pressure to the price while removing tokens from the system as a reward for validators and liquidity providers. At the same time, the Treasury takes in revenue to fund the continued development of the ecosystem.
The Mangata X model shows better price performance than the “Just LP Fees” model and the “Fees + Treasury” model, which should create more interest for the DEX on a global scale. At the same time, the Mangata X model also does not sacrifice Treasury intake like “Fees + Buy & Burn”.
The DEX space is evolving and it is crucial for DEX Governance to be able to adapt to market conditions. While LP fees offer an incentive to liquidity providers and treasury fees generate revenue for the protocol to fund operations, marketing, and development, algorithmic buy & burn creates a new lever that leads to price appreciation for token holders and allows for the creation of new incentives for other stakeholders while still maintaining a hard cap.
Read out the blog post: Algorithmic Buy & Burn
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