How to Create a Stablecoin Liquidity Pool — Introduction to CLMM

Preface

Many projects have specific needs, such as stabilizing their token price within a certain range, but they may not know how to achieve this. Today, we’ll explain how to control a token’s price within a desired range.

Some might say, “Isn’t creating a stable pool with CLMM enough?” — but this is a misunderstanding.

First, let’s clarify the pricing mechanism and adjustment principles of CLMM (Concentrated Liquidity Market Maker):

Core Features of CLMM

CLMM allows liquidity providers (LPs) to concentrate their funds within a custom price range (instead of providing liquidity across all price ranges). This means:

When the market price is within your set range, your liquidity is utilized, and you earn trading fees.
When the market price moves outside your range, your liquidity becomes “inactive” (no longer participating in trades).

Price Limits and Adjustment Mechanisms

(1) Prices Are Not Automatically Fixed Within a Range

❌ Misconception: CLMM does not forcibly restrict prices to a fixed range.
✅ Correct Understanding: Prices are still determined by market supply and demand. CLMM simply lets LPs choose where to provide liquidity.

(2) Behavior When Prices Deviate

When the market price exceeds an LP’s set range:

That LP’s liquidity stops participating in trades.
Other LPs (whose ranges include the new price) continue providing liquidity.
The inactive liquidity converts to a single asset (e.g., if the price rises above your upper limit, your funds become entirely the quote token).

Of course, all of the above applies to LPs (Liquidity Providers). The essence of CLMM is to reduce LP losses under certain conditions.

However, for project teams, the token price is not truly fixed within a range. If large buy/sell orders occur and the price enters a zone with no liquidity, the price can still fluctuate—it just won’t execute trades.

Solution

To stabilize a token’s price within a range on a blockchain (e.g., Solana), active market intervention is typically required, usually via algorithmic trading bots (arbitrage/market-making bots). The exact approach depends on your goals (full price stability vs. guiding price trends) and resources (capital, technical capabilities). Below are detailed solutions:

1. Relying on CLMM Liquidity Design

Provide deep liquidity within the target price range (e.g., 1.0 USDC – 1.2 USDC) in CLMM pools (e.g., Orca Whirlpools or Raydium CLMM).
Effect:
- When the price deviates, liquidity decreases, and slippage spikes, naturally discouraging large trades.
- However, it cannot fully prevent price breaks (requires active intervention).
Drawback:
- Over-reliance on pool depth—if liquidity is insufficient, large orders may fail or cause significant price gaps.

2. Algorithmic Trading Bot Control

For stricter price control, an automated trading bot is needed. Common strategies include:

(1) Simple Limit Order Bot

Logic:

If price > upper limit (e.g., 1.2 USDC), the bot sells tokens (increasing supply).
If price < lower limit (e.g., 1.0 USDC), the bot buys tokens (reducing supply).

Pseudocode Example:

python

复制

下载

while (true) {  
    const currentPrice = fetchPriceFromDEX(); // Get current price  
    if (currentPrice > TARGET_MAX) {  
        sellToken(amountToSell); // Sell to push price down  
    } else if (currentPrice < TARGET_MIN) {  
        buyToken(amountToBuy); // Buy to push price up  
    }  
    sleep(10_000); // Check every 10 seconds  
}

Best for: Mid-to-small-cap tokens with sufficient capital.

(2) Dynamic Market Making (DMM)

Logic:

Acts like a traditional market maker, placing limit orders to earn fees while stabilizing price.
Uses TWAP (Time-Weighted Average Price) or VWAP (Volume-Weighted) for smoother adjustments.
Advantage:
Reduces market impact compared to simple limit orders.
Tools:
Bonfida or Serum API (Solana).

3. Hybrid Approach: CLMM + Bot Coordination

CLMM provides baseline liquidity (deep liquidity in the target range, e.g., 1.0–1.2 USDC).
Bot handles edge cases:
- Buys near the lower bound (1.0 USDC) for support.
- Sells near the upper bound (1.2 USDC) for resistance.

Example Architecture:

复制

下载

Price Monitoring (e.g., Pyth Network)  
       ↓  
Trading Bot (decides intervention)  
       ↓  
Execute Trade (Jupiter API / Orca SDK)  
       ↓  
Update CLMM Liquidity (if range adjustment is needed)

Summary

CLMM liquidity management helps stabilize prices but cannot enforce hard limits.
Algorithmic bots are essential for active control, combining limit orders, dynamic market-making, and arbitrage.
Best Practice = Deep CLMM Liquidity + Bot Boundary Intervention

For more details on CPBOX’s features and use cases, visit:
📄 https://docs.cpbox.io/

For collaboration or development inquiries, contact us at:
🌐 https://www.cpbox.io/cn/

CPBOX Original Content
Unauthorized reproduction prohibited. Attribution required.
Contact us via: https://www.cpbox.io/