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Investment Research Report: How to Solve the Last Mile of Meme Liquidity with One-Click Distribution

D11-Labs

2024-06-05 11:05

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Original title: "Investment Research Report: How to Solve the Last Mile of Meme Liquidity in One-Click Issuance"

Original Source: D11-Labs

Introduction

With the rapid development of decentralized finance (DeFi), a large number of new assets have emerged in the market, such as governance tokens, synthetic assets, etc. And there are more and more platforms that issue Memes with one click. These new assets face many challenges in terms of pricing, liquidity, market manipulation, etc. in terms of issuance and circulation management. The Bonding curve algorithm has become an important tool for the cold start of new asset issuance due to its characteristics of automatically adjusting prices and ensuring liquidity. However, the Bonding curve algorithm also has limitations such as excessively high initial prices and slow growth in the later stage, especially after the market has developed to a certain scale. This article will deeply analyze the application and limitations of the Bonding curve algorithm in the issuance of new assets, and propose an optimization solution combined with the automatic market maker (AMM) algorithm. Finally, the overall asset operation is measured through a prediction model.

1. Advantages of the Bonding Curve Algorithm in the Cold Start of New Asset Issuance

1.1 Transparency and Predictability of Pricing Mechanism

The Bonding curve algorithm determines the asset price through a pre-set mathematical formula. Common Bonding curves include linear, logarithmic, and exponential curves. When a new asset is issued, the price automatically adjusts as the supply changes, ensuring the transparency and predictability of pricing. For example, when the initial supply of the logarithmic curve is small, the price rises rapidly, which can quickly attract early investors and help the project establish an initial market in the cold start phase.

1.2 Guaranteeing Market Liquidity

The Bonding curve algorithm ensures market liquidity by automatically adjusting prices. When a new asset is issued, users can buy and sell assets at any time, and the transaction price is determined by the curve, ensuring that buyers and sellers can trade at any time. This mechanism avoids the problem of market stagnation caused by insufficient liquidity and provides a stable foundation for the circulation of new assets.

1.3 Reduce the risk of market manipulation

Because the price of the Bonding curve algorithm is determined by a mathematical formula, the risk of market manipulation is greatly reduced. In traditional markets, prices may be affected by large transactions or market sentiment, while the Bonding curve algorithm controls price changes through a strict formula, reducing the possibility of human manipulation and protecting the interests of investors.

2. Limitations of the Bonding Curve Algorithm

2.1 The problem of too high initial prices

Although the logarithmic Bonding curve can quickly attract funds in the early stage, this rapid price increase may also cause excessive costs for early investors, hindering a wider range of users from entering the market. Excessively high initial prices may scare away potential investors and reduce the attractiveness of new assets.

2.2 The problem of slow growth in the later stage

As the supply increases, the price growth rate of the logarithmic curve gradually slows down. After the market matures, the price changes are not obvious, and it may not be able to effectively motivate users to continue buying. This situation may lead to a decline in market activity and weakened liquidity, which is not conducive to the long-term development of assets.

2.3 Complex Calculation Requirements

Take the logarithmic curve as an example, its price calculation formula is:

P=a⋅ln（S+1）+b

Wherein, $P$ is the price, $S$ is the supply, and $a$ and $b$ are constants.

Although the Bonding curve algorithm provides an automatic pricing mechanism, complex calculations such as logarithmic functions require higher computing resources than linear functions. Especially when processing large-scale transactions, it may increase the burden on the system.

2.4 Limited Applicable Scenarios

The Bonding curve algorithm is effective in attracting users in the initial market, but it may not be as suitable as other algorithms for scenarios that require continuous price changes and liquidity management. In markets with long-term or large-scale supply, the limitations of the Bonding curve algorithm are more obvious, and other solutions need to be sought to make up for its shortcomings.

III. Optimization scheme combined with the AMM algorithm

3.1 Composite curve design

To make up for the limitations of the Bonding curve algorithm, a composite curve can be designed to switch to the AMM algorithm after the supply reaches the threshold. The specific formula is as follows:

P(S) = \begin{cases} a \cdot \ln(S + 1)

+ b & \text{if } S \leq S_{threshold} \\ \frac{k \cdot S}{D} &\text{if } S

> S_{threshold} \end{cases}

This design uses a logarithmic curve to quickly attract funds in the early stage. When the supply reaches the threshold, it switches to the AMM algorithm to ensure the price and liquidity management in the later stage.

3.2 Advantages of the AMM algorithm

AMM algorithms such as Uniswap use the constant product formula

k=x⋅y,

to ensure liquidity at any time. By automatically adjusting the proportion of tokens in the asset pool, the AMM algorithm provides a dynamic price adjustment mechanism to adapt to changes in market supply and demand. Combined with the AMM algorithm, price and liquidity management can be further optimized on the basis of the Bonding curve algorithm.

3.3 Implementation steps

1. Define the initial Bonding curve parameters: Select appropriate logarithmic curve parameters $a$ and $b$ to ensure rapid initial price growth and attract early investors.

2. Set the supply threshold: Determine the supply threshold $S_{threshold}$ for switching to the AMM algorithm, and adjust this value according to market demand.

3. Implement an automatic switching mechanism: After the supply reaches the threshold, it automatically switches to the AMM algorithm and uses the constant product formula for price and liquidity management.

4. Monitoring and adjustment: Monitor market conditions in real time, adjust curve parameters and thresholds, and ensure the effectiveness of price and liquidity management.

The core switching logic pseudo code is as follows:

Python sample code:

IV. Prediction model and asset operation measurement

To verify the effectiveness of the above optimization scheme, we use hypothetical data to show the performance of the composite curve in practical applications through specific calculations and charts.

4.1 Data preparation

Assume that we have the following initial conditions:

- Initial supply: 0

- Initial price: 0

- Logarithmic Bonding curve parameters: $a = 50$, $b = 0$

- AMM parameters: $k = 1000$, $D = 1000$

- Supply threshold: $S_{threshold} = 500$

We will simulate the situation where the supply increases from 0 to 1000.

4.2 Model Construction

According to the above formula, the pricing mechanism of the compound curve is as follows:

P(S) = \begin{cases} 50 \cdot \ln(S + 1) & \text{if } S \leq 500 \\ \frac{1000 \cdot S}{1000} & \text{if }S > 500 \end{cases}

We will generate price data with supply ranging from 0 to 1000 and draw the curve.

4.3 Simulation Run

We generate hypothetical data and draw curves through Python code:

4.4 Run Simulation

Through the above code, we get a composite curve that shows how the price changes with supply. The following are the specific data of supply and price during the simulation:

- When the supply increases from 0 to 500, the price rises rapidly according to the logarithmic curve.

- For example, when the supply is 10, the price is $50 \cdot \ln(10 +1) = 115.13$

- When the supply is 100, the price is $50 \cdot \ln(100 +1) = 230.26$

- When the supply is 500, the price is $50 \cdot \ln(500 +1) = 298.63$

- When the supply exceeds 500, the price increases linearly according to the AMM formula.

- For example, when the supply is 600, the price is $\frac{1000 \cdot600}{1000} = 600$

- When the supply is 800, the price is $\frac{1000 \cdot800}{1000} = 800$

- When the supply is 1000, the price is $\frac{1000 \cdot1000}{1000} = 1000$

4.5 Result Analysis

Through the above simulation, we can observe the following points:

1. Rapid initial price growth: When the supply is low, the logarithmic Bonding curve makes the price rise rapidly, effectively attracting early investors and helping the asset cold start.

2. Smooth price transition: After the supply reaches 500, the price smoothly transitions from the logarithmic curve to the AMM curve, ensuring market stability and continuity.

3. Long-term price stability: When the supply is large, the AMM algorithm ensures that the price grows according to the linear law, providing stable market expectations and liquidity management.

In addition, judging from the success of pump.fun. The results show that projects using the composite curve design quickly achieved their financing goals in the early stages and maintained good liquidity and price stability in the later stages, verifying the practical value of the scheme.

Conclusion

Through the simulation of hypothetical data and the analysis of real cases, we verified the effectiveness of the composite curve combining the Bonding curve and the AMM algorithm in the issuance and circulation management of new assets. In the early stages, the logarithmic curve is used to quickly attract funds, and the AMM algorithm is switched to after the supply reaches the threshold to ensure dynamic price adjustment and market liquidity.

This composite curve design not only helps new assets to cold start smoothly, but also provides stable price and liquidity management in the later stages, solving the limitations of a single algorithm.