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# DTV Sync Options

Create a Derivative of Any Product
As a team that consists of several professional investors, we wanted to further bring traditional finance into DeFi and vice versa. Through several formulas we use in our own portfolios to calculate option prices and volatility skew, we have applied and retrofitted them to encompass the utilities of our other exotic products. For the first time, we are removing all direct human influence in option pricing and assigning stringent yet dynamically oscillating values. Through this method - time, volume, and underlying volatility become the driving force of positional pricing. Therefore, pricing becomes more predictable, consistent, and less contingent on human emotions. By striping down option buying and selling to these core values - which are algorithmically enforced - we increase liquidity efficiency.
Every buy and sell order executed will be matched with an opposite order within a set range of drift - which will be displayed on the order screen. Once submitted, it will enter a queue to be filled either in full or by part, by the opposing end within the pre-determined drift.
Drift itself is similar to how bid-ask spreads function on traditional derivative products. However, rather than being set by direct human intervention, it is calculated as a change in time-to-expiry to trailing realized volatility of the underlying asset for the timeframe of the option. As a result, low liquidity strikes will theoretically clear more contracts based off of this algorithmic approach, thus driving more economic activity - which in turn allows for more complex portfolios to be built.
$\left( \dfrac{T_{c}-T_{e}}{\left| T_{c}\right| }\right) \left( \sigma _{yz}^{2}\right) =D$
We’ve built the overall algorithms around the focus of utilizing realized volatility instead of implied volatility for two reasons. First, by implementing an algorithmically controlled option price, the implied volatility entrant needs to be substituted by a suitable replacement. Secondly, realized-volatility oftentimes results in a lower value than implied volatility - as such, it becomes a seller's market. We wanted to find a balance between these two items to ensure unbiased influence into pricing which allows for accurate portfolio construction as you are no longer directly subjected to human control on derivative pricing.
Beyond this, we’ve eliminated the direct influence that option volume has on an underlying asset as we have replaced the traditional backing with a synthetic version of the asset. For example, with a purchased call option, rather than strictly implying a contract purchase of an underlying asset if the option is left to expire in the money (direct influence on the price of the underlying asset), the algorithm uses synthetic share counts instead and both the short and long ends execute amongst themselves for a profit or loss ratio. This format prevents manipulative events such as short squeezes, and therefore adds additional security to your portfolio.
This also allows for a nimble portfolio value as less capital is needed to secure each contract. Beyond this, we are able to implement non-standard synthetic share count contracts based off of realized volatility for the asset.
Unlike other platforms, we utilize American Options, which means you are able to take an active role in your portfolio management.

### Collateral Requirements and Additional Rules:

When selling short a call option, up to 2x the strike chosen worth of collateral is needed to secure margin requirements which will be backed in sRHO. When selling short a put 1x the strike chosen worth of collateral is needed to meet margin requirements - also secured by sRHO. If you already own a long option on the same asset you wish to sell short at an earlier date, it will count for 50% of the margin requirement when selling short. If you are short an option and the price of the underlying exceeds your margin requirements - the position will automatically be closed, and the collateral will be divided amongst the longs of the same asset and expiry, and the number of short contracts are subtracted from the entire long option positions of the same asset and expiry. Short sellers are able to add additional collateral to secure margin requirements using sRHO as time progresses.

### Derivatization of Any Product

Through our platform, investors can search for a contract of any token older than three weeks and will be presented with an algorithmically generated option chain. While not every chain will have open interest as not every token has volume, this allows for other platforms to give their investors additional tools for investing in their protocol movements. As we continue to develop our own host of synthetic products, we will continually update our option catalog to support ethere.al based products to allow our investors to create a more dynamic portfolio.

### Current Layout Exploration

The order flow consists of two main parts to prevent non-fill orders, reduce gas fees, and eliminate improper price fills. The forward-facing piece, the order screen, displays the drift price in live time, drawing from an off-chain compute server running the above calculations for the underlying asset. In the future, we are looking to decentralize this process to offer outside confirmation of pricing accuracy. Other information regarding margin requirements, volume, open interest per leg, and profitability potential is also displayed in this area and draws data from the host server to ensure accurate quotas. Once a trade is orchestrated, the required margin is collected in the order queue contract, but gas is not yet taken to execute the trade's legs. Once a counterparty is established - which goes through the same process above to establish an inverse trade - the front end matches the orders and establishes the option contract. At this time, gas is charged against the gas reserve deposited alongside the collateral to execute the legs.
The backend piece involves the option contract once a trade has been executed. The option contract is checked for pricing in three instances. The first instance is upon the expiry of the contract. The expiry pricing is calculated based on the underlying asset and its movements against or with the established position. On the expiry, the collateral deposited against the position is relinquished to the designated parties based on the expiry pricing. The second instance of price confirmation is during an early exercise. In this case, one party has decided to take off their position before expiration. There are two potential outcomes when that happens: selling or buying out the position with a third party or forcing an early exercise. In the first instance, instead of the option contract existing between investor 1 and investor 2, investor 2 relinquishes ownership to investor 3, so the contract is then between investor 1 and investor 3. However, this method is dependent on available liquidity. If liquidity is thin and investor 2 wants to force an early exercise, they may do so under a penalty proportional to the remainder of the position duration and underlying volatility. For example, an early exercise 3 weeks before expiry with a spike in volatility will be penalized more than an early exercise 2 days before expiration on a position that has experienced a volatility collapse. The third time the price is checked is when the underlying asset moves substantially against the option position, resulting in margin depletion. In that case, the trade between investor 1 and investor 2 is liquidated, with all collateral being liquidated to the non-called party.
To allow established positions and a user's portfolio to be reflected on the front end, the same pricing model flow used to display option leg prices initially is used to update the portfolio profit and loss. Then, each leg's profit and loss are calculated natively off of the individual leg prices and displayed as a quantifiable P/L.

### Option Pricing API Test Setup

post
https://polarorchid.ai
/optionchain