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 "0xPlasma" 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.