Strips AMM Deep Dive 2/3: TL;DR

TL;DR

  • STRIPS Automated Market Maker (AMM) model is an innovative new AMM model designed to trade interest rate swap derivatives.
  • We ran 216 combinations of AMM parameters to test profitability and robustness of staking in Strips
  • AMM ROI% averaged 314.7%
  • 5% average order rejection rate
  • 0.06% average DV01/TVL
  • 0.96% average daily VaR%

AMM Returns

Given an initial starting capital of $2,000,000, the chart above shows a time series of Strips ecosystem net income under high yield, high volatility market conditions. Each line represents the Strips net income under various trader style factor which is a probability density function with different trader risk preferences and action likelihood function given current market condition.

AMM Risk

We measure risk from three different angles:

  1. DV01: measures the $ dollar value change in profit/loss for every 0.01% change in interest rates
  2. DV01%: measure of profit/loss as a % of TVL for every 0.01% change in interest rates
  3. VaR: value at risk, measures the extent of financial losses at a certain probability

We plot daily returns% of Strips under each market condition for selected parameters, with total data sample size of 1530. We can find there are certain “fat tails” on the downside for momentum market conditions. However, the daily VaR (value at risk) was less than -1.3799% at the 99% percentile. Value at Risk measures the maximum possible loss at a certain probability taking into account of variance-covariance factors.

On a daily basis, Strips’ AMM across 5 different markets have an average DV01 of $3,411. DV01% of total staking liquidity is constantly below 0.03% and peaked at 0.06%. At 95% confidence level: Strips’ daily VaR% is 0.96%, which is lower than most hedge funds at 1–2%.

Dynamic Decision Making

In reality, these AMM parameters may not be fixed permanently, and could be changed by governance votes. Hence, we summarise a few broad findings to inform future decision makers.

  • If risk level is too high, then Strips should change parameters such as integrity check level to reject more trades.
  • If rejection rate is too high while we don’t see any pickup in risk level across all AMM markets or uptick in volatility level of floating and fixed rates, then we should increase bandwidth of integrity check level to on-board more trades.
  • In simulation, we linearly aggregate DV01 across all markets which is a naive assumption. In reality, we we also need to consider covariance matrix of all underlying markets. For example, market A moving 1% higher always corresponds with market B moving 1% lower, and then total DV01 will be lower because market A and B are negatively correlated.
  • In addition, for a single market, if we launch both fixed term IRS and perpetual IRS, total DV01 is also affected by term structure, such as term premium between 1yr-IRS and perpetual IRS.
  • We believe Strips’ risk measures should be reflected as a function of sigma of underlying market rates, both fixed and floating: f(σ(Yt , Ot)).
  • In conclusion, AMM parameters decides dynamic relations between rejection rate, risk level, and marginal income.

Appendix

Strips ecosystem model flow chart

Strips ecosystem model flow chart

Disclaimer

Any past performance, projection, forecast or simulation of results is not necessarily indicative of the future or likely performance of any investment.
The information and publications are not intended to be and do not constitute financial advice, investment advice, trading advice or any other advice or recommendation of any sort offered or endorsed by Strips Finance.

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Strips Finance

Strips Finance

To build the largest fixed income trading platform for DeFi

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