# Risk model

**Liquidity Farming Model**

**Liquidity Farming Model**

We have developed a risk model to calculate the optimal range for LP positions in our Option Wheel Vault. This model is based on the concept that market conditions can transition between trending and accumulating phases. In other words, market volatility can experience significant fluctuations during trending periods and remain relatively stable during accumulating phases. Our model is designed to estimate this volatility to determine the optimal range for LP positions, with the ultimate goal of maximizing rewards and optimizing the duration of being in range.

Here is our first simplest version, we calculate SMA:

Where $P_i$ ā is the price for each period š, and š is the total number of periods.

Next, calculate the standard deviation of the price over the same š periods to measure volatility. The standard deviation tells you how much the price varies from the SMA.

Using the SMA and the volatility Ļ define the upper and lower bounds of this range can be calculated as follows:

- Upper Range (UR): $\text{SMA} + k_1 \times \sigma$

- Lower Range (LR): $\text{SMA} - k_1 \times \sigma$

Following market conditions, the values of $k_1$ā and $k_2$ will be dynamically adjusted, where $k_1$ represents the number of standard deviations for the upper band and $k_2$ā pertains to the number of standard deviations for the lower band. For instance, during an uptrending market scenario, we will set $k_1 = 2$ and $k_2 = 1$, thereby providing sufficient room for price appreciation while ensuring that positions remain within the acceptable range. Conversely, these values will be adapted differently in other market conditions as warranted.

**Conclusion: **We will continuously monitor the LP position in real-time. If the position goes out of the calculated optimal range after a specified duration, our model will automatically re-create a new LP position. Estimating the timeframe for allowing the position to go out of range before re-creating it helps reduce costs in cases where the market experiences sudden and significant volatility before returning to its normal range.

## Option OTM Model

**Risk Model Options Based on Mean-Reversion Principle**

**Risk Model Options Based on Mean-Reversion Principle**

**Introduction:** The risk model options have been developed based on the mean-reversion principle. We will take short positions when the price increases significantly above the moving average price and go long when the price decreases below the moving average.

**Model Development:** Based on this theory, the development team has created a model to determine suitable Long/Short points in the market:

**Definitions:**

$S$

**:**Residual Deviation determined by the model.$Q_1$

**:**Top positive quantile of the distribution of*S*.$Q_2$ā

**:**The quantile where the price is accumulating.$Q_3$ā

**:**Top negative quantile of the distribution of*S*.

**Weekly Procedure:** Every Friday, after the option's expiry time, the model will calculate S. Based on the value of S, if:

$S > Q_1$ā: Sell covered calls with a position size of 10% of the current holding value of ETH.

$Q_3 < S < Q_1$ā: Sell covered calls and covered puts with a position size of 7% for each position.

$S < Q_3$: Sell covered puts with a position size of 10%.

**Conclusion:** This model aims to utilize mean-reversion principles to make informed decisions regarding Long/Short positions in the market, thereby managing risks effectively.

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