Rebalancing Algorithm
Last updated
Last updated
Quadrat Research Team develops its own Highly Performance Quantum Rebalancing Strategies and auto-rebalancing on-chain executors for different Uniswap v3 Pools and Chains that you can find under the "" Tab on the Quadrat Strategy page.
Every Quadrat Rebalancing Strategy is automized with python algorithms and backtested on the historical data of the current Uniswap v3 Pool. The team is continuously optimizing these strategies to provide the best possible APY on each Uniswap v3 Pool.
The current Quadrat algorithms are privately held as an IP of the Quadrat Team.
The rebalancing strategy of algorithms and its actions are based on two factors:
1) Price Range Strategy. Calculation model and squeezing model of the current active price range for the strategy, based on the multiple factors:
Each underlying token price volatility
Pool Implied Volatility
ARCH/GARCH modeling of the pool
Monte Carlo Simulations for Asset Prices and Ranges.
TVL of Strategy compare to the Pool TVL
Current Virtual Liquidity in the ticks of ranges
Pool Trading Volume
Price Channel Trading Indicators
Historical backtest and modeling of the optimal parameters for a range rebalancing
Historical data on the price position at the price range
Swap Fees for rebalancing asset
Earned fees from the current price range
Economic Simulation of the strategy in different price ranges
Optimal and safe price ranges for rebalancing
2) Trading indicators for HODL positions. To protect the users' funds from impermanent losses in the volatile market, the rebalancing algorithm can decide which token and what amount to hold out of the Price Range. If the underlying token price goes rapidly down and out of the price range, assets will be rebalanced in the stablecoin, and if the price of a volatile asset rapidly goes up, the strategy can be rebalanced 100% in the growing asset, out of the current price range.
