SLP Research
Uniswap Liquidity Simulation. Please note this is a simulation only, for educational purposes.
Model Design
The simulation model employs a straightforward design to evaluate the impact of various liquidity strategies. Key parameters and assumptions include:
Initial Conditions: Each strategy and simulation begins with identical starting parameters:
Total Supply: 1,000,000
Token1 Liquidity Pool (LP): 1,000,000 TOKEN
Token2 Liquidity Pool (LP): 15,000 USDC
Starting Price: $0.015
Tax Rate: 5/5 (buy/sell)
Assumptions
To ensure the model reflects real-world conditions to a certain degree, the following assumptions are incorporated:
➢ Buys and sells occur with a 50% equal random probability
➢ Sell orders are calculated as a maximum of 5% of the total holders balance (token1) amount. This Prevents sales without holdings and scaling sell size with holdings.
➢ Buy order size is determined by the (token1 LP) amount (max 5%). Ensuring size is limited by slippage and larger buys (token qty) when the price is lower .
➢ Excluding the above restrictions, order sizes are randomly generated to add variability to each simulation.
Methodology
A baseline simulation (Strategy 0) is generated without implementing any liquidity or tax adjustments.
Strategy 0's trade sequence is replicated, but with the defined tax strategies applied.
The results of the tax strategy simulations are compared against the baseline, revealing how each strategy would have potentially affected the outcomes.
Strategies
➢ Strategy 0 (Baseline): No liquidity management interventions.
➢ Strategy 1: 50% of taxes are used for buybacks and paired with ETH to provide liquidity following a 100% price increase.
➢ Strategy 2: 50% of taxes are used for buybacks and paired with ETH to provide liquidity following a 50% price decrease.
Results
Simulation Findings (1000 simulations, 1000 trades per simulation)
Visual analysis of the two liquidity strategies compared to the baseline scenario (Strategy 0) suggests a potential for outperformance in terms of price and liquidity pool value. However, as expected, these strategies also appear to result in a lower tax wallet value. This is attributed to the utilization of tax revenue for strategic buybacks and liquidity provision.
Overall Price Trend
The simulations demonstrate an initial price increase, followed by a general downward trend. This pattern is attributed to an early imbalance of buy/sell pressure, subsequently stabilizing as more tokens enter circulation.
Strategy Differences
A notable difference emerges between Strategy 1 (adding liquidity at highs) and Strategy 2 (adding liquidity at lows). While Strategy 1 may show an initial price advantage, Strategy 2 appears more effective in the long run.
Rationale
Strategy 2's success is likely due to purchasing more tokens per dollar spent when adding liquidity during price dips (more Token1 LP is added for each $) . This increased buying pressure, along with the timing of liquidity additions, contributes to overall higher price performance and a greater liquidity pool value.
While the results above give us an initial idea of what happens when applying the different liquidity strategies, it doesn’t give us an idea of what happens on a longer timeframe.
While the initial simulations provide valuable insights, they offer a limited perspective on longer timeframes. To explore potential long-term effects, we conducted 100 simulations, each with 10,000 trades.
It's important to note that the influence of edge cases might be amplified in this scenario, potentially reducing its direct reflection of real-world conditions. Nonetheless, these simulations should illuminate broader trends in liquidity strategy performance.
Key Observations
Extended simulations clearly demonstrate the long-term price outperformance of Strategy 2 compared to Strategy 1 and the baseline Strategy 0.
Without external catalysts, the baseline strategy (0) exhibits a gradual price decline towards 0.
While Strategy 0 initially maintains a higher tax wallet value due to no tax utilization, Strategy 2 surpasses it over time. This is likely driven by the combination of higher token prices, increased trading volume, and tax accumulation in Strategy 2.
Intermediary Summary
The simulation results strongly favor Strategy 2, which involves strategically providing liquidity during significant price drawdowns (-50% from local highs or the previous liquidity addition). This approach promote price stability and long-term value creation for the token.
Strategy 2 optimization
Further refinement of Strategy 2 is possible. We'll explore three variations focused on optimizing these key parameters:
Drawdown Trigger: Test the effectiveness of initiating liquidity provision at different drawdown levels (50% vs. 25% price declines).
Tax Allocation: Investigate the impact of using different percentages of collected taxes for buybacks and liquidity provision (50%, 25%, 10%).
Strategies
➢ Strategy 0 (Baseline): No liquidity management interventions.
➢ Strategy 1: 50% of taxes used for buybacks and providing liquidity (25% per token) , triggered by a 50% price drawdown from the local high or previous liquidity addition.
➢ Strategy 2: 25% of taxes used for buybacks and providing liquidity (12.5% per token), triggered by a 25% price drawdown from the local high or previous liquidity addition.
➢ Strategy 3: 10% of collected taxes used for buybacks and providing liquidity (12.5% per token), triggered by a 25% price drawdown from the local high or previous liquidity addition.
Our simulations suggest that both the percentage of taxes utilized and the drawdown percentage triggering liquidity provisions can potentially be optimized to improve outcomes. While decreasing both of these parameters appears to yield better results initially, there's a crucial trade-off to consider. Lowering the activation thresholds for these strategies leads to a significantly higher amount of taxes being used.
We must be cautious about overfitting the model. Hence, the ideal strategy likely lies in finding a balance between optimizing those parameters while carefully managing the frequency and size of tax-funded interventions.
Optimizing the parameters of Strategy 2 appears promising for enhancing token performance. However, pushing the optimization too far might tailor the results excessively to the specific simulation data, potentially hindering performance in real-world market conditions.
We can conclude that optimizing the different parameters for the second strategy can be useful. However, it's crucial to strike a careful balance between the amount of taxes used and the practical complexities of frequent liquidity additions. It's worth noting that similar results can be found with longer timeframes.
Conclusion
This research provides compelling evidence that strategic liquidity provision plays a significant role in token price stability and long-term market performance. Our simulation model, despite its inherent limitations, offers valuable insights into the efficacy of different liquidity management strategies.
Key Findings
Strategy 2 Outperformance: Strategy 2, involving the use of collected taxes for buybacks and liquidity provision during significant price drawdowns, consistently demonstrates superior price performance and liquidity pool value compared to both the baseline (Strategy 0) and Strategy 1.
Trade-off Considerations: Optimizing Strategy 2 by adjusting tax allocation and drawdown triggers can further improve results. However, a careful balance is necessary to avoid overfitting the model and to manage tax utilization responsibly.
Implications: These findings suggest that Ÿ could benefit from integrating dynamic liquidity provision strategies similar to Strategy 2. This approach has the potential to mitigate volatility and improve the overall attractiveness of tokens for investors.
Limitations and Future Research
Model Assumptions: While the simulation model incorporates several assumptions for realism, further refinements could enhance its accuracy. Future research could investigate the impact of relaxing certain assumptions or incorporating additional market dynamics.
Overall, this research establishes a strong foundation for data-driven decision for liquidity management. By exploring refinements, these strategies can be further honed to enhance the sustainability and success of Ÿ.
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