As a lecturer of Production Engineering and a Computer Science Ph.D., I tend to view DeFi not just as "trading", but as a system that needs optimization.

I’ve been experimenting with Meteora’s DLMM (Dynamic Liquidity Market Maker) on Solana, specifically focusing on concentrated liquidity in stablecoin pairs like USD1-USDC. I like the predictability of stablecoins—much like a Just-in-Time (JIT) system—minimizing impermanent loss (IL) and fitting a passive income strategy, even if the nominal yield is lower than in volatile pairs.

The Setup:

Strategy: Spot mode with precise concentration (16 bins from $0.9988 to $1.0003).

Backtesting: Studying historical data, this range covered roughly 30% of the trading volume in the last 3 months.

Objective: Capture maximum fees with minimal capital by staying within a tight price range.

Recent Data: On a $121 trial, I generated $1.22 in fees over two weeks. I had zero IL and required no rebalancing. That is approximately a 1% return in two weeks (26% annualized). Interestingly, the volume was highly asymmetric: the first week saw an average volume of $600k for the USD1-USDC pair, while the second week dropped to $50k. Most of the fees were concentrated in that first high-volume window. This strategy proved its value during the recent market crash (BTC dropping from $98k to $60k), as I luckly avoided the drawdown while still collecting fees on the stable side.

The Engineering Perspective:

In production lines, we may have idle resources. In DLMM, "idle" is being out of range. The challenge is balancing the rebalancing frequency (Operational Cost) against the fee generation (Throughput).
Is anyone else applying either "Lean Manufacturing" or "Theory of Constraints" principles to their LP strategies? I'm curious if moving to volatile pairs (like BTC-SOL) justifies the increased "setup cost" of rebalancing in the current market.

#DeFi #Solana #Meteora #Engineering #Liquidity #Bitcoin