RangeScout Information

I built this quant engine as a personal experiment to better understand and operate within DeFi market structure — specifically concentrated liquidity. At its core, the system is designed to answer a simple but non-trivial question: where should capital be deployed within a liquidity pool to maximise yield, without taking on disproportionate risk? In concentrated liquidity environments, returns are highly sensitive to positioning. Small changes in price range selection can materially impact both fee generation and exposure to impermanent loss. Most participants either set static ranges or rely on intuition. This engine was built to move beyond that. The framework models optimal range placement across different market conditions, using a combination of historical price behaviour, volatility assumptions, and liquidity distribution. It attempts to identify where capital is most likely to be utilised, rather than simply spread, with the goal of improving capital efficiency. Practically, this means deploying liquidity in narrower, more deliberate ranges where trading activity is expected to occur, rather than passively providing across the full curve. The trade-off is clear: higher potential yield comes with increased sensitivity to price movement. The system exists to manage that balance more systematically. The engine is not fully automated. It is designed as a decision-support layer rather than a black-box trading system. Outputs inform where liquidity should be positioned, how wide ranges should be set, and when repositioning may be required as market conditions evolve. Risk is a central consideration. While concentrated liquidity can produce materially higher yields than traditional lending or staking, it introduces exposure to adverse price movement, inventory imbalance, and timing risk. The system incorporates basic hedging logic and scenario analysis to help mitigate some of these factors, but it does not eliminate them. This is not a product and is not of