By | Danny

Introduction: The NEST prophecy machine is an on-chain game model: the offerer provides a two-way option that is validated by the arbitrageur. This mechanism ensures that the on-chain generated price is close to the market equilibrium price, and eventually outputs an on-chain price information flow through one round of the quote-validation game. This information flow has the smallest possible deviation from the market equilibrium price and the highest possible information density (density of prices in effect).

Price deviations and parameters

A closer look at the game (https://cofix.io/doc/CoFiX_White_Paper.pdf) reveals that the main variables that determine the deviation of the NEST price from the market price are: transaction costs, hedging costs, and two-way option costs (which have nothing to do with quoted commissions). We express the deviation of the NEST price from the market price in terms of bias.

The transaction fee is noted proportionally as g/E, where g is the gas fee per quote, E is the size of the quote, the hedging cost is noted as h, which can be interpreted as the transaction fee rate generated by exchange trading (plus the shock cost percentage, which is negligible for highly liquid assets), and the cost of a two-way option per unit size quote is noted as c, which is a function of the secular volatility σ which is essentially equivalent to the value of σ according to the calculations in the link. Taking the arbitrage idea of deviation, the price is validated when the bias exceeds the arbitrage cost of the validator, and thus under the assumption of effective arbitrage, it can be considered that bias < g/E+h+2c, where 2c is because the validator needs to double the quote, which can be approximated as bias<g/E+h+2σ.

Based on the actual data, the h for professional validators should be small, and if the offer size is small and there is no shock cost, the h is within 0.05%, and the σ is generally within 0.02% to 0.2% (very high fluctuations may reach 1%, as in the 312,519 line), so the cost of the last two items is generally about 0.1% to 0.5%.

However, the g/E item is extremely volatile, and gas consumption is determined by the congestion of the Ethernet ecosystem. According to the current prosperity of the Ethernet ecology, in the case of a very smooth network, the consumption of 10 gwei, burning 50,000 gas limit, can be traded successfully, that is, the consumption of

According to the data of the gas limit consumed by NEST offer in 8 – 100 thousand, we assume that the burning value is set to 100000 as a reference, then when the gas price is 1gwei, the gas fee is 0.0001 ETH, and according to the 30 ETH offer, this ratio g/E is 0.00033%, which is negligible, 0.0067% if it is 20 gwei, and 0.02% if the quote size is based on 10 ETH, which is a value to consider. Once the gas price rises to 200 gwei, g/E will have an impact of 0.067% at the base quote size of 30 ETH, and even 0.67% at 2000 gwei (of course, all kinds of transactions on the chain are out of order at this point). Therefore, all things considered, price volatility will have a 0.2% impact on price deviation in extreme cases, while congestion may cause a 0.67% deviation or even higher, because other traders in the Ether ecology may set the gas limit (burn value) higher when there is extreme congestion.

Suppose gas limit = 100000

Analyzing the above variables reveals that the cost of options affected by volatility is exogenous, while the percentage of deviation caused by congestion can be reduced by increasing the quote size. For example, when quoting with 300ETH, even with 2000gwei , the impact on price deviation is only 0.067%, while 3000ETH is negligible. But here the whole threshold of miners’ quotes is raised, while the h-cost increases sharply due to the rising size in a not very liquid underlying. So an offer size of 300ETH for the ETH/USDT track may be acceptable (miners turning into mining pools), but for other trading pairs, it is clearly not suitable, which is why we chose an offer size of 10-30. Put another way, if an asset is very illiquid, the deviation in validation may be slightly larger, because the price itself is not that efficient. Therefore, a deviation of 1%-5% is acceptable at 1ETH. The problem is that the liquidity parameter is not an endogenous variable, nor can we reflect the level of liquidity based on average volatility; such a setting can only be set by some convention (e.g., new assets can be quoted at a lower size, common assets at a higher one) or by DAO vote to revise it.

Price density and parameters

Price density is a parameter of the number of blocks, measured by how many blocks on average are in effect to generate an on-chain price. The smaller the number of blocks, the higher the price density of the offer. Therefore, there are two main metrics that affect the NEST price density: the NEST mining cost and the price volatility of the quote pair. This is because the mining cost determines the offer density, while the price volatility determines the probability of survival, and the product of the two is the price density (note the difference between offer density and price density).

The cost of mining NEST depends mainly on the NEST price and the quoting fee. Miners determine the cost of mining based on the NEST price, we can approximate that the two are linear, the higher the NEST price, the higher the cost of mining, or the unit cost = NEST price, and the cost of mining = (offer fee + g)/(offer block interval * unit block out), that is, the offer block interval = (offer fee + g)/(NEST price * unit block out), because the unit block out is relatively fixed, 240W blocks only decay once, so it is approximately constant, NEST price is a dynamic variable, once the price is lower, the higher the offer block interval, the lower the offer density. In the equation, the NEST price is determined by the market, the quoting fee can be determined by the contract, and g is determined by the ETH congestion level, which is generally an order of magnitude higher than g. For this reason, we can increase the quoting density by lowering the quoting fee, thus increasing the price density.

A more dynamic idea is to determine the offer fee based on the NEST price, so that the offer density is always dynamically maintained within a high availability range, this scheme can be written as, offer block interval = k/unit block out, and offer fee/NEST price = K, this design can be triggered once a day to correct (miners to a certain block height offer is triggered) This design can be corrected by triggering once a day (miner reaches a certain block height when the offer is triggered) and giving a certain NEST mining bonus each time it is triggered. This solution ensures that NEST is quoted steadily in most cases and also avoids redundancy.

However, there is a situation that may lock this adjustment, that is, when the NEST price is very low, the quoting fee is 0, but the g/NEST price > K, which will make the quoting block interval increase, if the NEST price is very low, may still quote the block interval is very large in the case of 0 handling fee, at this time, NEST is in the “bottom vortex”. The only way to get out of the vortex is for the NEST price to rise.

According to the calculations in the link, when the volatility of the asset price rises, some normal quotes are also eaten, and the ratio of the eaten price is called the probability of validation. In general, the limit of the probability of validation is 50%, i.e., if the volatility is even higher, half of the prices will remain under normal quotes, while in the regular case, the volatility is 0.01%-0.02% and the probability of validation is 5%-7%. You can calculate the validation probability based on the data in link 1. Quote block interval / (1 – probability of validation) = effective block interval, i.e. price density. So the extreme case can be further introduced by introducing volatility to the K value, i.e. dynamic K=K0*(1-validation probability), which corrects the offer density so that it is equal to the price density.

Finally, it is also necessary to design for extreme NEST prices, i.e., in the extreme case, this K value is no longer used, but the quoted handling fee is directly equal to 0. This treatment also needs to be reflected in the algorithm.

Conclusion

Based on the above analysis, we propose a liquidity-based dynamic sizing model and a dynamic handling fee model based on NEST price and quote pair volatility, thus improving the price density and quality of NEST. It is important to note that this dynamic adjustment has its cost: part of the value that originally belongs to the repo will be dissipated to ETH miners proportionally, reducing the repo value and requiring downstream applications to contribute more calls to reach the NEST value-add. This part can be analyzed in a more refined way.

Note: This does not constitute investment advice without full understanding of the project.

Posted by:CoinYuppie，Reprinted with attribution to:https://coinyuppie.com/nest-new-vision-dynamic-parametric-design/

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