Sr-AMM - Should be called DLMM(Tenetative)

Sandwich-Resistant Automated Market Maker (Sr-AMM) with dynamic fees. This combination should address both MEV issues and help mitigate LVR(DLMM). Here's a conceptual design:

1. Sr-AMM Core Mechanism:
   • Implement the Sr-AMM design as described in the "Sandwich-Resistant AMM" document:
     • Operate on slot windows (e.g., 4 slots on Solana, 1 on Ethereum).
     • Within a slot window, buys increase the offer price but keep the bid price constant.
     • Sells decrease the offer price but keep the bid price constant.
     • Reset the state to the equivalent xy=k state at the beginning of each slot window.
2. Dynamic Fee Structure:
   • Implement a fee model that adjusts based on market conditions:
     • Base fee: Start with a low base fee (e.g., 0.05%).
     • Volatility adjustment: Increase fees during high volatility periods.
     • Volume adjustment: Adjust fees based on recent trading volume.
     • Liquidity depth adjustment: Higher fees when liquidity is thin.
3. Fee Calculation Algorithm:
   • Fee = BaseFee + VolatilityFactor + VolumeFactor + LiquidityFactor
   • VolatilityFactor = f(price change over last N blocks)
   • VolumeFactor = g(trading volume over last M blocks)
   • LiquidityFactor = h(current liquidity depth)
   • Where f, g, and h are carefully calibrated functions.
4. Fee Update Mechanism:
   • Update fees at the beginning of each slot window.
   • Implement a maximum fee change per update to prevent drastic swings.
   • Use a moving average for smoother transitions.
5. Price Oracle Integration:
   • Use Time-Weighted Average Price (TWAP) to smooth out short-term fluctuations.(needed?)
6. Liquidity Provider Incentives:
   • Distribute a portion of the collected fees to LPs based on their share of the pool.
   • Implement a bonus structure for LPs who maintain their positions during high-volatility periods.
7. MEV Mitigation:
   • Leverage the Sr-AMM design to prevent atomic sandwich attacks.
   • Implement a minimum time delay between trades from the same address to further deter MEV.
8. Gas Optimization:
   • Optimize the fee calculation and update process to minimize gas costs.
   • Consider implementing batch updates for multiple pools if applicable.
9. User Interface:
   • Provide clear, real-time information about current fees and how they're calculated.
   • Offer predictive tools to help users estimate fees for upcoming trades.
10. Governance and Parameter Adjustment: ## I don't know why this is important but my co-founder claude thinks it is

• Allow for community governance to adjust key parameters like BaseFee or the functions f, g, and h.
• Implement a timelock for parameter changes to ensure transparency.

12. Safety Measures: # will see how Meteora does theirs but this what I thought of

• Create a fee cap to prevent potential exploitation of the dynamic fee system. Implementation Goals:
• Carefully test the interaction between the Sr-AMM mechanism and the dynamic fee structure.
• Ensure that the fee calculation is deterministic and can be predicted by users before they submit transactions.
• Consider the impact of the slot window reset on the dynamic fee calculation.
• Optimize the code to ensure that the added complexity doesn't significantly increase gas costs or introduce vulnerabilities. This design combines the MEV resistance of the Sr-AMM with the adaptability of dynamic fees, potentially creating a more efficient and fair trading environment. The dynamic fees should help address LVR by adjusting to market conditions, while the Sr-AMM structure mitigates sandwich attacks and other forms of MEV.