Methods, systems, apparatuses, and devices for facilitating trading operations for users

Abstract

A method of facilitating trading operations for users includes receiving, using a communication device, a request comprising an identifier of a user 112 and a number of tokens of a cryptocurrency for

Description

Field of the Invention

[0001] The present invention relates to a method for facilitating trading operations for users, the method comprising: receiving, using a communication device, a request for borrowing cryptocurrency tokens from a user device associated with a user, wherein the request comprises at least one identifier of the user and a number of tokens of at least one cryptocurrency; obtaining, using a smart contract module, a portfolio information of a portfolio associated with a margin account of the user based on the at least one identifier, wherein the margin account is maintained on a blockchain; analyzing, using a processing device, the portfolio information of the margin account; calculating, using the processing device, at least one value of at least one parameter based on the analyzing and the number of tokens of the at least one cryptocurrency; generating, using the processing device, an initial collateral portfolio for the user for the borrowing of the number of tokens of the at least one cryptocurrency based on the at least one value of the at least one parameter, wherein the initial collateral portfolio satisfies at least one initial margin requirement for the borrowing of the number of tokens of the at least one cryptocurrency; and transmitting, using the communication device, an initial collateral portfolio information of the initial collateral portfolio to the user device. Background of the Invention

[0002] The recent massive explosion of Decentralized Finance (DeFi) activity and innovation led to decentralized lending becoming vital to an emerging financial system. Early DeFi’s success has been driven by overcollateralized lending protocols such as Aave and Compound, which enable borrowers to deposit collateral in one token and withdraw part of its value in another. However, as the system becomes increasingly mature, on-chain market participants, especially asset managers, need access to undercollateralized loans, which provide more efficient capital allocation.

[0003] AAVE and Compound don’t restrict operations with leverage users receive through their platforms. As a result, numerous restrictions need to be imposed on the platform. This leads to an inefficient relative size of a loan in relation to the value of the collateral - overcollateralized loan. The undercollateralized loans, if liquidated incorrectly, impose serious risks on creditors, as a result sophisticated system is needed to define when a user’s loan should be liquidated. Further, the risks of several positions in a trader’s portfolio may offset each other. Margin calculations that account for total portfolio P&L (Cross Margin) instead of segregated risk calculation for each position (Isolated Margin) are needed to improve capital and leverage efficiency for professional asset managers as maintenance margin requirements in Cross Margin are much lower.

[0004] Therefore, there is a need for improved methods, systems, apparatuses, and devices for facilitating trading operations for users that may overcome one or more of the above-mentioned problems and / or limitations. Summary of the Invention

[0005] This summary is provided to introduce a selection of concepts in a simplified form, that are further described below in the Detailed Description. This summary is not intended to identify key features or essential features of the claimed subject matter. Nor is this summary intended to be used to limit the claimed subject matter’s scope.

[0006] Disclosed herein is a method of facilitating trading operations for users, in accordance with some embodiments. Accordingly, the method may include a step of receiving, using a communication device, a request for borrowing cryptocurrency tokens from a user device associated with a user. Further, the request may include at least one identifier of the user and a number of tokens of at least one cryptocurrency. Further, the method may include a step of obtaining, using a smart contract module, a portfolio information of a portfolio associated with a margin account of the user based on the at least one identifier. Further, the margin account may be maintained on a blockchain. Further, the method may include a step of analyzing, using a processing device, the portfolio information of the margin account. Further, the method may include a step of calculating, using the processing device, at least one value of at least one parameter based on the analyzing and the number of tokens of the at least one cryptocurrency. Further, the method may include a step of generating, using the processing device, an initial collateral portfolio for the user for the borrowing of the number of tokens of the at least one cryptocurrency based on the at least one value of the at least one parameter. Further, the initial collateral portfolio satisfies at least one initial margin requirement for the borrowing of the number of tokens of the at least one cryptocurrency. Further, the method may include a step of transmitting, using the communication device, an initial collateral portfolio information of the initial collateral portfolio to the user device.

[0007] Further disclosed herein is a system for facilitating trading operations for users, in accordance with some embodiments. Accordingly, the system may include a communication device, a smart contract module, and a processing device. Further, the communication device may be configured for receiving a request for borrowing cryptocurrency tokens from a user device associated with a user. Further, the request may include at least one identifier of the user and a number of tokens of at least one cryptocurrency. Further, the communication device may be configured for transmitting an initial collateral portfolio information of an initial collateral portfolio to the user device. Further, the smart contract module may be communicatively coupled with the communication device. Further, the smart contract module may be configured for obtaining a portfolio information of a portfolio associated with a margin account of the user based on the at least one identifier. Further, the margin account may be maintained on a blockchain. Further, the processing device may be communicatively coupled with the smart contract module and the communication device. Further, the processing device may be configured for analyzing the portfolio information of the margin account. Further, the processing device may be configured for calculating at least one value of at least one parameter based on the analyzing. Further, the processing device may be configured for generating the initial collateral portfolio for the user for the borrowing of the number of tokens of the at least one cryptocurrency based on the at least one value of the at least one parameter. Further, the initial collateral portfolio satisfies at least one initial margin requirement for the borrowing of the number of tokens of the at least one cryptocurrency.

[0008] Both the foregoing summary and the following detailed description provide examples and are explanatory only. Accordingly, the foregoing summary and the following detailed description should not be considered to be restrictive. Further, features or variations may be provided in addition to those set forth herein. For example, embodiments may be directed to various feature combinations and sub-combinations described in the detailed description. Brief Description of the Drawings

[0009] For The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various embodiments of the present disclosure. The drawings contain representations of various trademarks and copyrights owned by the Applicants. In addition, the drawings may contain other marks owned by third parties and are being used for illustrative purposes only. All rights to various trademarks and copyrights represented herein, except those belonging to their respective owners, are vested in and the property of the applicants. The applicants retain and reserve all rights in their trademarks and copyrights included herein, and grant permission to reproduce the material only in connection with reproduction of the granted patent and for no other purpose.

[0010] Furthermore, the drawings may contain text or captions that may explain certain embodiments of the present disclosure. This text is included for illustrative, non-limiting, explanatory purposes of certain embodiments detailed in the present disclosure. FIG. 1 is an illustration of an online platform consistent with various embodiments of the present disclosure. FIG. 2 is a flowchart of a method 200 for facilitating trading operations for users, in accordance with some embodiments. FIG. 3 is a flowchart of a method 300 of facilitating trading operations for users, in accordance with some embodiments. FIG. 4 is a flowchart of a method 400 of facilitating trading operations for users, in accordance with some embodiments. FIG. 5 is a flowchart of a method 500 of facilitating trading operations for users, in accordance with some embodiments. FIG. 6 is a flowchart of a method 600 of facilitating trading operations for users, in accordance with some embodiments. FIG. 7 is a flowchart of a method 700 of facilitating trading operations for users, in accordance with some embodiments. FIG. 8 is a flowchart of a method 800 of facilitating trading operations for users, in accordance with some embodiments. FIG. 9 is a flowchart of a method 900 of facilitating trading operations for users, in accordance with some embodiments. FIG. 10 is a block diagram of a system WOO for facilitating trading operations for users, in accordance with some embodiments. FIG. 11 is a block diagram of the system 1000, in accordance with some embodiments. FIG. 12 is a block diagram of the system 1000, in accordance with some embodiments. FIG. 13 is a block diagram of the smart contract module 1004 of the system 1000, in accordance with some embodiments. FIG. 14 is a block diagram of the decentralized data provider unit 1102 of the system 1000, in accordance with some embodiments. FIG. 15 is a schematic diagram of a platform 1500 for facilitating trading operations for users, in accordance with some embodiments. FIG. 16 is a schematic diagram of the platform 1500, in accordance with some embodiments. FIG. 17 is a schematic diagram of the order assembly system 1508 of the platform 1500, in accordance with some embodiments. FIG. 18 is a flow diagram of a platform 1800 for facilitating trading operations for users, in accordance with some embodiments. FIG. 19 is a block diagram of a computing device for implementing the methods disclosed herein, in accordance with some embodiments. Detailed Description of the Embodiments

[0011] As a preliminary matter, it will readily be understood by one having ordinary skill in the relevant art that the present disclosure has broad utility and application. As should be understood, any embodiment may incorporate only one or a plurality of the above-disclosed aspects of the disclosure and may further incorporate only one or a plurality of the above-disclosed features. Furthermore, any embodiment discussed and identified as being “preferred” is considered to be part of a best mode contemplated for carrying out the embodiments of the present disclosure. Other embodiments also may be discussed for additional illustrative purposes in providing a full and enabling disclosure. Moreover, many embodiments, such as adaptations, variations, modifications, and equivalent arrangements, will be implicitly disclosed by the embodiments described herein and fall within the scope of the present disclosure.

[0012] Accordingly, while embodiments are described herein in detail in relation to one or more embodiments, it is to be understood that this disclosure is illustrative and exemplary of the present disclosure, and are made merely for the purposes of providing a full and enabling disclosure. The detailed disclosure herein of one or more embodiments is not intended, nor is to be construed, to limit the scope of patent protection afforded in any claim of a patent issuing here from, which scope is to be defined by the claims and the equivalents thereof. It is not intended that the scope of patent protection be defined by reading into any claim limitation found herein and / or issuing here from that does not explicitly appear in the claim itself.

[0013] Thus, for example, any sequence(s) and / or temporal order of steps of various processes or methods that are described herein are illustrative and not restrictive. Accordingly, it should be understood that, although steps of various processes or methods may be shown and described as being in a sequence or temporal order, the steps of any such processes or methods are not limited to being carried out in any particular sequence or order, absent an indication otherwise. Indeed, the steps in such processes or methods generally may be carried out in various different sequences and orders while still falling within the scope of the present disclosure. Accordingly, it is intended that the scope of patent protection is to be defined by the issued claim(s) rather than the description set forth herein.

[0014] Additionally, it is important to note that each term used herein refers to that which an ordinary artisan would understand such term to mean based on the contextual use of such term herein. To the extent that the meaning of a term used herein—as understood by the ordinary artisan based on the contextual use of such term—differs in any way from any particular dictionary definition of such term, it is intended that the meaning of the term as understood by the ordinary artisan should prevail.

[0015] Furthermore, it is important to note that, as used herein, “a” and “an” each generally denotes “at least one,” but does not exclude a plurality unless the contextual use dictates otherwise. When used herein to join a list of items, “or” denotes “at least one of the items,” but does not exclude a plurality of items of the list. Finally, when used herein to join a list of items, “and” denotes “all of the items of the list.”

[0016] The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar elements. While many embodiments of the disclosure may be described, modifications, adaptations, and other implementations are possible. For example, substitutions, additions, or modifications may be made to the elements illustrated in the drawings, and the methods described herein may be modified by substituting, reordering, or adding stages to the disclosed methods. Accordingly, the following detailed description does not limit the disclosure. Instead, the proper scope of the disclosure is defined by the claims found herein and / or issuing here from. The present disclosure contains headers. It should be understood that these headers are used as references and are not to be construed as limiting upon the subjected matter disclosed under the header.

[0017] The present disclosure includes many aspects and features. Moreover, while many aspects and features relate to, and are described in the context of systems, methods, apparatuses, and devices for facilitating trading operations for users, embodiments of the present disclosure are not limited to use only in this context.

[0018] In general, the method disclosed herein may be performed by one or more computing devices. For example, in some embodiments, the method may be performed by a server computer in communication with one or more client devices over a communication network such as, for example, the Internet. In some other embodiments, the method may be performed by one or more of at least one server computer, at least one client device, at least one network device, at least one sensor and at least one actuator. Examples of the one or more client devices and / or the server computer may include, a desktop computer, a laptop computer, a tablet computer, a personal digital assistant, a portable electronic device, a wearable computer, a smart phone, an Internet of Things (loT) device, a smart electrical appliance, a video game console, a rack server, a super-computer, a mainframe computer, mini-computer, micro-computer, a storage server, an application server (e.g. a mail server, a web server, a real-time communication server, an FTP server, a virtual server, a proxy server, a DNS server etc.), a quantum computer, and so on. Further, one or more client devices and / or the server computer may be configured for executing a software application such as, for example, but not limited to, an operating system (e.g. Windows, Mac OS, Unix, Linux, Android, etc.) in order to provide a user interface (e.g. GUI, touch-screen based interface, voice based interface, gesture based interface etc.) for use by the one or more users and / or a network interface for communicating with other devices over a communication network. Accordingly, the server computer may include a processing device configured for performing data processing tasks such as, for example, but not limited to, analyzing, identifying, determining, generating, transforming, calculating, computing, compressing, decompressing, encrypting, decrypting, scrambling, splitting, merging, interpolating, extrapolating, redacting, anonymizing, encoding and decoding. Further, the server computer may include a communication device configured for communicating with one or more external devices. The one or more external devices may include, for example, but are not limited to, a client device, a third party database, a public database, a private database, and so on. Further, the communication device may be configured for communicating with the one or more external devices over one or more communication channels. Further, the one or more communication channels may include a wireless communication channel and / or a wired communication channel. Accordingly, the communication device may be configured for performing one or more of transmitting and receiving of information in electronic form. Further, the server computer may include a storage device configured for performing data storage and / or data retrieval operations. In general, the storage device may be configured for providing reliable storage of digital information. Accordingly, in some embodiments, the storage device may be based on technologies such as, but not limited to, data compression, data backup, data redundancy, deduplication, error correction, data finger-printing, role based access control, and so on.

[0019] Further, one or more steps of the method disclosed herein may be initiated, maintained, controlled, and / or terminated based on a control input received from one or more devices operated by one or more users such as, for example, but not limited to, an end user, an admin, a service provider, a service consumer, an agent, a broker and a representative thereof. Further, the user as defined herein may refer to a human, an animal or an artificially intelligent being in any state of existence, unless stated otherwise, elsewhere in the present disclosure. Further, in some embodiments, the one or more users may be required to successfully perform authentication in order for the control input to be effective. In general, a user of the one or more users may perform authentication based on the possession of a secret human readable secret data (e.g. username, password, passphrase, PIN, secret question, secret answer, etc.) and / or possession of a machine readable secret data (e.g. encryption key, decryption key, barcodes, etc.) and / or or possession of one or more embodied characteristics unique to the user (e.g. biometric variables such as, but not limited to, fingerprint, palm-print, voice characteristics, behavioral characteristics, facial features, iris pattern, heart rate variability, evoked potentials, brain waves, and so on) and / or possession of a unique device (e.g. a device with a unique physical and / or chemical and / or biological characteristic, a hardware device with a unique serial number, a network device with a unique IP / MAC address, a telephone with a unique phone number, a smartcard with an authentication token stored thereupon, etc.). Accordingly, the one or more steps of the method may include communicating (e.g. transmitting and / or receiving) with one or more sensor devices and / or one or more actuators in order to perform authentication. For example, the one or more steps may include receiving, using the communication device, the secret human readable data from an input device such as, for example, a keyboard, a keypad, a touch-screen, a microphone, a camera, and so on. Likewise, the one or more steps may include receiving, using the communication device, the one or more embodied characteristics from one or more biometric sensors.

[0020] Further, one or more steps of the method may be automatically initiated, maintained and / or terminated based on one or more predefined conditions. In an instance, the one or more predefined conditions may be based on one or more contextual variables. In general, the one or more contextual variables may represent a condition relevant to the performance of the one or more steps of the method. The one or more contextual variables may include, for example, but are not limited to, location, time, identity of a user associated with a device (e.g. the server computer, a client device etc.) corresponding to the performance of the one or more steps, environmental variables (e.g. temperature, humidity, pressure, wind speed, lighting, sound, etc.) associated with a device corresponding to the performance of the one or more steps, physical state and / or physiological state and / or psychological state of the user, physical state (e.g. motion, direction of motion, orientation, speed, velocity, acceleration, trajectory, etc.) of the device corresponding to the performance of the one or more steps and / or semantic content of data associated with the one or more users. Accordingly, the one or more steps may include communicating with one or more sensors and / or one or more actuators associated with the one or more contextual variables. For example, the one or more sensors may include, but are not limited to, a timing device (e.g. a real-time clock), a location sensor (e.g. a GPS receiver, a GLONASS receiver, an indoor location sensor etc.), a biometric sensor (e.g. a fingerprint sensor), an environmental variable sensor (e.g. temperature sensor, humidity sensor, pressure sensor, etc.) and a device state sensor (e.g. a power sensor, a voltage / current sensor, a switchstate sensor, a usage sensor, etc. associated with the device corresponding to performance of the or more steps).

[0021] Further, the one or more steps of the method may be performed one or more number of times. Additionally, the one or more steps may be performed in any order other than as exemplarily disclosed herein, unless explicitly stated otherwise, elsewhere in the present disclosure. Further, two or more steps of the one or more steps may, in some embodiments, be simultaneously performed, at least in part. Further, in some embodiments, there may be one or more time gaps between performance of any two steps of the one or more steps.

[0022] Further, in some embodiments, the one or more predefined conditions may be specified by the one or more users. Accordingly, the one or more steps may include receiving, using the communication device, the one or more predefined conditions from one or more devices operated by the one or more users. Further, the one or more predefined conditions may be stored in the storage device. Alternatively, and / or additionally, in some embodiments, the one or more predefined conditions may be automatically determined, using the processing device, based on historical data corresponding to performance of the one or more steps. For example, the historical data may be collected, using the storage device, from a plurality of instances of performance of the method. Such historical data may include performance actions (e.g. initiating, maintaining, interrupting, terminating, etc.) of the one or more steps and / or the one or more contextual variables associated therewith. Further, machine learning may be performed on the historical data in order to determine the one or more predefined conditions. For instance, machine learning on the historical data may determine a correlation between one or more contextual variables and performance of the one or more steps of the method. Accordingly, the one or more predefined conditions may be generated, using the processing device, based on the correlation.

[0023] Further, one or more steps of the method may be performed at one or more spatial locations. For instance, the method may be performed by a plurality of devices interconnected through a communication network. Accordingly, in an example, one or more steps of the method may be performed by a server computer. Similarly, one or more steps of the method may be performed by a client computer. Likewise, one or more steps of the method may be performed by an intermediate entity such as, for example, a proxy server. For instance, one or more steps of the method may be performed in a distributed fashion across the plurality of devices in order to meet one or more objectives. For example, one objective may be to provide load balancing between two or more devices. Another objective may be to restrict a location of one or more of an input data, an output data and any intermediate data therebetween corresponding to one or more steps of the method. For example, in a client-server environment, sensitive data corresponding to a user may not be allowed to be transmitted to the server computer. Accordingly, one or more steps of the method operating on the sensitive data and / or a derivative thereof may be performed at the client device.

[0024] Overview: The present disclosure describes systems, methods, apparatuses, and devices for facilitating trading operations for users. Further, the facilitating of the trading operations is based on calculating margin requirements for under-collateralized cross-chain leverage, considering DeFi-specific primitives such as liquidity pools, bridging, and concentrated liquidity.

[0025] Further, the present disclosure describes an Arkis Margin Engine which is a centralized system designed to calculate portfolio risk using complex scenario analysis of the underlying asset prices. Further, the engine is designed to provide portfolio risk analytics in a multi-chain environment. Further, the Arkis Margin Engine is designed to become a universal middleware and standard in DeFi margin calculation for trading operations. Further, the Arkis Margin Engine analyses the portfolio of whitelisted (supported) assets, tokens, and DEXs in real-time. The Arkis Margin Engine is designed to be configured for any universe / Liquidation Routing / Asset Price Information. Further, the Arkis Margin Engine determines the current portfolio Net Asset Value (NAV) relative to a borrowed asset, the stress-tested NAV, current risk-factor and maintenance margin value, the market impact / cost of liquidating token X for token Y on any supported DEX considering market impact, the gas / bridging cost of liquidating a portfolio using defined Liquidation Routing, the total liquidity pool position value relative to a borrowed asset considering fees to be claimed, and actions and trades need to be completed to liquidate a portfolio considering the Liquidation Routing scheme if a portfolio should be liquidated right now.

[0026] Further, the margin engine calculates the initial and maintenance margin at any given time. If the trader’s portfolio can not satisfy maintenance margin requirements - they get a margin call, and the portfolio is liquidated. As the liquidation process is quite specific in the DeFi space, especially in a multi-chain environment, market impact, gas, and bridging costs should also be included in margin calculations to detect liquidation levels. Firstly, the reference price - the price of an asset X relative to the Leverage Asset is defined. Further, the Arkis Margin Engine uses ChainLink Oracles to define the reference price. For example, if a trader took USDC leverage on Ethereum to open a leveraged position in ETH, then ETH / USDC (Ethereum) Chainlink Oracle is used to determine the Reference Price at any given time. Secondly, Liquidation Routing - the weight of each DEX in the liquidation process if a margin call happens is introduced. Taking the example above, in Arkis Protocol, for an open ETH position, if USDC is used as leverage, 70% of ETH will be converted back into LISDC on Uniswap, and 30% will be swapped using 1INCH. Swapping each token from the user’s portfolio during liquidation into a leverage asset to bring it back into the liquidity pool where it was taken. Reference Price and Liquidation Routing are used in Mark Price calculations. Mark Price is a proxy to the fair value of an asset taking into account current market liquidity, execution constraints, and other factors. For example, one may adjust the Reference Price for implied market impact if the trader’s position may be liquidated in the future. In the case of Arkis Protocol and Margin Engine, the Mark Price equals the Reference Price. Further, each portfolio position’s value is estimated separately for calculating a portfolio maintenance margin and liquidation risk factor. Further, the token price calculations include using Mark Price to estimate the price of a token relative to a borrowed asset for whitelisted tokens. For example, if the Mark Price of CRV / USDT is 1.088 and a user has 100 CRV in their portfolio in Arkis Finance, the value of the position will be 1.088 * 100 * (1 - 0.15) = 108.8. Liquidity pools consist of staked assets and fees (rewards) to be claimed. If a user claims fees -their price is calculated by the Token Price Calculations method only if a fee token is whitelisted inside Arkis Margin Engine. Non-whitelisted tokens are not included in portfolio value calculations. Staked assets are calculated using the Token Price Calculations methodology. For example, if a user has Uniswap V3 position with 5 ETH and 10 000 USDT and 0.7 ETH and 100 USDT in unclaimed rewards to be claimed with the borrowed asset is USDT, and ETH / USDT Mark Price is WOO then its position value is (5 * 1000 + 10000) + (0.7 * 1000 + 100) = 15800. For positions where a trader deposits collateral into a lending protocol (AAVE, Compound), a position value is calculated using Token Price Calculations by mapping the number of protocol tokens (aWETH, aUSDT, cUSDC, etc.) to collateral value. For borrowed assets, the position value is calculated as follows: Position Valuet = Collateral Deposited Valuet- Borrowed Asset Valuet - Accrued Interestt. For example, if a trader used Arkis Protocol USDT leverage to open a short position in 1 ETH through AAVE, the algorithm is following:

[0027] 1) Use borrowed from Arkis 1600 USDT as collateral to borrow 1 ETH from AAVE. (1 ETH = 1000 USDT).

[0028] 2) Sell immediately 1 ETH for 1000 USDT. At time to the value of the portfolio is: 1600 + 1000 -1 *1000. If the price of ETH at t1 becomes 800 the value of the portfolio is: 1600 + 1000 - 1 * 800 = 1800. On the other hand, an increased ETH price reduces a portfolio value, moving a trader closer to the liquidation point. The position value significantly influences the Market Impact Factor in Mark Price calculations, therefore portfolio positions are aggregated by tokens. So, the position value in a token is the sum of all tokens on all chains, tokens in liquidity pools, and fees to be claimed in this token. For example, a trader has a portfolio built by borrowing USDC: • 10 wETH, 5 wBTC on Ethereum. • 7 wETH, 10 000 USDT on BSC. • Uniswap V3 position (10 wETH x 6000 DAI) + fees (3wETH, 200 DAI) on Ethereum. • Curve LP position in (5000 USDT / 5000 USDC) and fees to be claimed (100 CRV, 200 USDT, 5000 USDC). As a result of aggregation, the portfolio has • wETH: 10+7+10+3 = 30 wETH. • wBTC: 5 wBTC. • USDT: 10000 + 5000 + 200 = 15200 USDT. • DAI: 6000 + 200 = 6200 DAI. • USDC: 5000 + 5000 = 10000 USDC. • CRV: 100 CRV. Further, the Portfolio Value is adjusted for Gas / Bridging Fees if the protocol / leverage entity intends to liquidate bad debt positions. In the Arkis Protocol, for example, the liquidation is done through Liquidation Routing without relying on external liquidators. The adjustment is needed as gas and bridging costs may significantly impact the overall liquidation cost. There are 4 types of actions that incur gas costs: • Asset swap (when we need to swap assets back to borrowed assets). • Extract liquidity from a pool / close debt on a lending protocol. • Bridging. • Return funds to LP. For tokens bridging fees arise if a token is not on the chain defined by the Liquidation Routing. For pools, gas fees for extracting liquidity / claiming fees are added. From the example above: • Bridge+gas fees: 7wETH, 10 000 USDT from BSC to Ethereum. • Gas fees: extracting liquidity from Uniswap V3, Curve. • Gas fees: swapping 30 wETH, 5wBTC, 15200 USDT, 6200 DAI, 100 CRV. • Gas fees: return USDC to Liquidity Pool. Finally, the process of Portfolio Value Calculation can be described as follows: 1) Aggregate portfolio positions by tokens. 2) Get tokens Mark Prices. 3) Get the sum of token prices / liquidity pools / fees prices using Mark Prices and apply haircuts where needed. 4) Get the sum of assets and adjust it for gas / bridging fees. Further, the Stress Tested Portfolio Value is calculated before calculating the Risk Factor which is used to determine if a user should be liquidated. Further, the Stress Tested Portfolio Value corresponds to a drop in the Mark price of each of the assets / LP positions held in the user’s portfolio, by X% relative to the Leverage Asset. Further, the assets are stress-testes based on either exchange rate risk or de-pegging (for stablecoins, ETH / stETH) risk. Based on a token risk and borrowed asset, we apply a different rate of change in the scenario matrix. Scenario Matrix, Q, is the matrix where each element represents a % change drop of the price of asset i relative to borrowed asset j. Scenario Matrix is a configurable parameter of Arkis Margin Engine. Based on borrowed assets and the Mark Price for each token from the user's portfolio, the token value using the Mark Price adjusted for the value from the Scenario Matrix is obtained. For LP positions, delta risk (change of an asset price) and gamma risk (Impermanent Loss) are considered. Taking the Impermanent Loss into consideration is extremely important, considering the non-linear nature of Liquidity Pools, especially those exposed to concentrated liquidity (Uniswap V3). When stress-testing, LP positions the algorithm in the following: 1) Calculate the updated token price ratio considering the updated exchange rate relative to a borrowed asset. 2) Get updated LP position quantities under the new token X / token Y price ratio. 3) Get an updated price of the LP position denominated in a borrowed asset. Further, position quantities for Uniswap V2 are estimated using the constant product formula x*y = k where x is the number of tokens X in the pool and y is the number of token Y, we can get the estimate of expected token quantities inside of LP as a function of k and the exchange rate pt between x and y:

[0029] Secondly, for each LP position, we keep the percentage stake, st, in a pool for a given position reflected in ’’liquidity tokens” provided to a user. If others subsequently add / withdraw coins, new liquidity tokens are minted / burned, so everyone's relative percentage share of the liquidity pool remains the same.

[0030] Finally, a stress-tested number of tokens for a user (x*,y*) is: F S; * (I* “

[0031] Where p* is the price ratio of stress-tested token X to token Y prices: p* = Q(borrowed asset, token X) / Q(borrowed asset, token Y)

[0032] For estimating values for Uniswap V3, for concentrated liquidity, price ranges may significantly impact updated token quantities under a stress-test scenario. As a result, Uniswap V3 LP positions are exposed to a bigger gamma risk than V2. Considering, x0,yd as the initial token amounts deposited into LP in a price range [u,l] where the price ratio is Vu y,>', liquidity values for each of the tokens need to be calculated. Further, token decimal constants (dx,dy) need to be set. The liquidity formulas for X and Y tokens are: ___ :¾ ■ • y / pf) ■ iff1* - y / po - ......... • 10’1'

[0033] When Lx, Ly are calculated, we get Lmin = min(Lx, Ly) the following formulas can be used to get the updated token amount amounts (x* y*) x ""x / J?* 1 ™ ------ .................. ■ IL' P'H ■ vpV YZ----------YU .

[0034] Where p* is the updated price ratio.

[0035] Further, stress tested fee values are obtained. Further, for token values, the tokens + haircut adjustment approach is used. After adjusting for stress test position values in borrowed assets, the total sum is determined and adjusted for the value of gas / bridging fees. Further, the Risk Factor is calculated which is used to determine if a trader's position should be liquidated. , StresaFori - LiamdaPanPef: odor# ™ ———-™---------------r;—n— -----------------* — LiqqAitiP&nJP&rmini -

[0036] Further, the Liquidation Premium is an extra margin of safety Arkis Protocol takes to liquidate a user for unpredicted events that may occur, while the Liquidation Fee is a fee that is deposited to populate Arkis Insurance Fund. In Arkis Protocol, Liquidation Premium and Liquidation Fee are set to 10%. Interest(t) refers to cumulative accrued interest for using leverage. A user is liquidated if their Risk Factor drops below 1. As a result, the maintenance margin is the amount of margin such that the Risk Factor equals 1.

[0037] The initial margin is calculated and needed to open the position corresponding to a value under which portfolio Risk Factor >1. However, as the trader decides which tokens to use as collateral (several tokens can be used), the initial margin calculation is a function of 1) Leverage asset and leverage amount. 2) Input tokens and token amounts. 3) Scenario analysis matrix values. For tokenagnostic, the initial margin (IM) value in the borrowed token is: IM = Borrowed Amount * (0.3 + 0.1 + 0.1), where 0.3 corresponds to the minimum value of scenario stress test in Scenario Analysis Matrix, Q, 0.1 and 0.1 correspond to LiquidationFee and Liquidationpremium values. However, if the collateral is in stable coins (USDT, FRAX, or DAI), the stress-tested value is 10% instead of 30%, which leads to a lower amount of initial margin to be deposited. Further, the Arkis Margin Engine provides the module to calculate the Initial Collateral Portfolio, which is the portfolio of assets used to open a leveraged position such that the initial margin requirements are satisfied (Risk Factor>1). Further, Initial Collateral Portfolio Calculation includes identifying whitelisted assets of a trader that can be potentially used as collateral defined as T1...TN. Additionally, for each asset Tj a trader sets maximum and minimum dollar amounts such that: d, / - 1., JV

[0038] Finally, a minimum value portfolio needs to be obtained that satisfies the above constraints, with Risk Factor >1. The resulting solution will give us token amounts that sum up in a portfolio that satisfies constraints and yields the most efficient leverage allocation for a trader. Denoting • pj as the Mark Price of asset j relative to a borrowed asset. • qj is the quantity of token j. • RF(qt,...qn|B) is a Risk Factor of the portfolio consisting of token of amounts qt,...qn, and borrowed amount B. • dj = pj * qj is the dollar value of asset j in the Initialcollateral Portfolio. In this case, the optimization problem is: minimize / -,- / . subject to djmin <pjq <dmjax, j = 1,...,N, RF(qi,...,qN|B) >1. Further, the Risk Factor (RF) is a function of stress tested prices from Q.

[0039] FIG. 1 is an illustration of an online platform 100 consistent with various embodiments of the present disclosure. By way of non-limiting example, the online platform 100 to facilitate trading operations for users may be hosted on a centralized server 102, such as, for example, a cloud computing service. The centralized server 102 may communicate with other network entities, such as, for example, a mobile device 106 (such as a smartphone, a laptop, a tablet computer, etc.), other electronic devices 110 (such as desktop computers, server computers, etc.), databases 114, and sensors 116 over a communication network 104, such as, but not limited to, the Internet. Further, users of the online platform 100 may include relevant parties such as, but not limited to, traders, end-users, administrators (admins), asset managers, service providers, service consumers, and so on. Accordingly, in some instances, electronic devices operated by the one or more relevant parties may be in communication with the platform.

[0040] A user 112, such as the one or more relevant parties, may access online platform 100 through a web based software application or browser. The web based software application may be embodied as, for example, but not be limited to, a website, a web application, a desktop application, and a mobile application compatible with a computing device 1900.

[0041] FIG. 2 is a flowchart of a method 200 for facilitating trading operations for users, in accordance with some embodiments. Accordingly, at 202, the method 200 may include receiving, using a communication device, a request for borrowing cryptocurrency tokens from a user device associated with a user. Further, the user device may include a computing device, a client device, etc. Further, the user may include a trader, an asset manager, etc. Further, the request may include at least one identifier of the user and a number of tokens of at least one cryptocurrency. Further, the at least one cryptocurrency may include wETH, wstETH, stETH, wBTC, DAI, USDT, USDC, FRAX, CRV, cvxCRV, FXS, LIDO, MATIC, etc.

[0042] Further, at 204, the method 200 may include obtaining, using a smart contract module, a portfolio information of a portfolio associated with a margin account of the user based on the at least one identifier. Further, the margin account may be maintained on a blockchain. Further, the blockchain may include Ethereum, Polygon, etc. Further, the at least one identifier may include at least one account identifier of the margin account. Further, the obtaining of the portfolio information may include interacting with a smart contract executed on the blockchain.

[0043] Further, at 206, the method 200 may include analyzing, using a processing device, the portfolio information of the margin account.

[0044] Further, at 208, the method 200 may include calculating, using the processing device, at least one value of at least one parameter based on the analyzing and the number of tokens of the at least one cryptocurrency. Further, the at least one parameter may include a leverage asset, a leverage amount, a scenario analysis matrix value, an initial margin, a liquidation fee, a liquidation premium, etc. Further, in an embodiment, the at least one parameter may include a reference price of at least one asset comprised in the margin account, a mark price, a portfolio value (such as a token price, a liquidity pool position, a lending protocol, a position value, etc.), a gas / bridging cost, a stress tested portfolio value, a risk factor, etc. Further, the calculating of the at least one value of the at least one parameter may be perfomed by an engine comprised in the processing device. Further, the engine may be an Arkis margin engine.

[0045] Further, at 210, the method 200 may include generating, using the processing device, an initial collateral portfolio for the user for the borrowing of the number of tokens of the at least one cryptocurrency based on the at least one value of the at least one parameter. Further, the initial collateral portfolio may include a portfolio of assets used to open a leveraged position of the number of tokens of the at least one cryptocurrency. Further, the initial collateral portfolio satisfies at least one initial margin requirement for the borrowing of the number of tokens of the at least one cryptocurrency. Further, the at least one initial margin requirement may be a risk factor being greater than 1.

[0046] Further, at 212, the method 200 may include transmitting, using the communication device, an initial collateral portfolio information of the initial collateral portfolio to the user device.

[0047] Further, in some embodiments, the portfolio information may include a number of assets owned by the user and a number of positions associated with at least one of the number of assets. Further, the number of assets may include one or more numbers of tokens of one or more cryptocurrencies, one or more positions on one or more decentralized exchanges (DEXs) pools, one or more positions on one or more liquidity pools, etc. Further, the analyzing of the portfolio information may include analyzing the number of assets and the number of positions associated with at least one of the number of assets using at least one machine learning model. Further, the at least one machine learning model may be configured for predicting the at least one value of the at least one parameter for at least one future time period. Further, the calculating of the at least one value of the at least one parameter may be further based on the predicting.

[0048] FIG. 3 is a flowchart of a method 300 of facilitating trading operations for users, in accordance with some embodiments. Accordingly, at 302, the method 300 may include determining, using the processing device, a number of collaterals for the borrowing of the number of tokens of the at least one cryptocurrency based on the initial collateral portfolio. Further, each of the number of collaterals may include at least one number of at least one leverageable token of at least one leverageable cryptocurrency. Further, the at least one number of the at least one leverageable token of the at least one leverageable cryptocurrency may be leveraged for the borrowing of the number of tokens of the at least one cryptocurrency.

[0049] Further, at 304, the method 300 may include identifying, using the processing device, at least one of the number of collaterals from the number of collaterals based on the number of collaterals.

[0050] Further, at 306, the method 300 may include generating, using the processing device, at least one order for allocating the number of tokens of the at least one cryptocurrency to the margin account based on the identifying of the collateral. Further, the at least one order may include an integration type, order details (such as token, poolld, amount, etc.), etc. Further, the at least one order may include trade parameters, asset details, execution instructions, etc.

[0051] Further, at 308, the method 300 may include executing, using the smart contract module, the at least one order on at least one blockchain associated with the at least one cryptocurrency. Further, the executing transfers the number of tokens of the at least one cryptocurrency to the margin account. Further, the executing of the at least one order may include accessing at least one smart contract deployed on the at least one blockchain, generating a smart contract transaction within the at least one smart contract, transmitting the smart contract transaction to the at least one blockchain for an execution of the smart contract transaction, wherein the execution triggers at least one trade operation on the blockchain. Further, the at least one blockchain may include a plurality of blockchains associated with a plurality of blockchain networks.

[0052] Further, in some embodiments, the identifying of at least one of the number of collaterals may include identifying at least one of the number of collaterals using at least one machine learning model based on the number of collaterals. Further, the generating of the at least one order may be further based on the identifying of at least one of the number of collaterals using the at least one machine learning model.

[0053] FIG. 4 is a flowchart of a method 400 of facilitating trading operations for users, in accordance with some embodiments. Accordingly, at 402, the method 400 may include obtaining, using the processing device, an input data comprising a number of features associated with each of the number of collaterals. Further, the number of features may include value stability, liquidity, smart contract integration, cross-chain compatibility, DeFi integrability, etc.

[0054] Further, at 404, the method 400 may include generating, using the processing device, a prediction score for each of the number of collaterals using the at least one machine learning model. Further, the at least one machine learning model may be trained for predicting a suitability of each of the number of collaterals based on the number of features. Further, the at least one machine learning model may include a plurality of decision trees. Further, the plurality of decision trees may include a random forest algorithm.

[0055] Further, at 406, the method 400 may include calculating, using the processing device, a relative ranking score for each of the number of collaterals based on the prediction score. Further, the identifying of at least one of the number of collaterals may be further based on the relative ranking score.

[0056] FIG. 5 is a flowchart of a method 500 of facilitating trading operations for users, in accordance with some embodiments. Accordingly, at 502, the method 500 may include transmitting, using the communication device, the number of collaterals to the user device.

[0057] Further, at 504, the method 500 may include receiving, using the communication device, a selection of at least one of the number of collaterals from the user device. Further, the identifying of at least one of the number of collaterals may be further based on the selection.

[0058] FIG. 6 is a flowchart of a method 600 of facilitating trading operations for users, in accordance with some embodiments. Accordingly, at 602, the method 600 may include obtaining, using the smart contract module, at least one blockchain data associated with at least one operation performed on the at least one blockchain.

[0059] Further, at 604, the method 600 may include training, using the processing device, at least one machine learning model for predicting optimal operation parameters for executing orders on the at least one blockchain based on the at least one blockchain data. Further, the at least one machine learning model may be comprised of at least one recurrent neural network.

[0060] Further, at 606, the method 600 may include predicting, using the processing device, at least one optimal parameter for the allocating of the number of tokens of the at least one cryptocurrency to the margin account. Further, the generating of the at least one order may be further based on the at least one optimal parameter.

[0061] FIG. 7 is a flowchart of a method 700 of facilitating trading operations for users, in accordance with some embodiments. Further, the portfolio information may include a number of assets owned by the user and a number of positions associated with at least one of the number of assets. Further, the analyzing of the portfolio information may include analyzing the number of assets and the number of positions associated with at least one of the number of assets.

[0062] Further, at 702, the method 700 may include calculating, using the processing device, at least one first value of at least one first parameter for each of the number of assets.

[0063] Further, at 704, the method 700 may include identifying, using the processing device, the at least one number of the at least one leverageable token of the at least one leverageable cryptocurrency in the number of assets based on the at least one first value of the at least one first parameter. Further, the determining of the number of collaterals for the borrowing of the number of tokens of the at least one cryptocurrency may be further based on the identifying of the at least one number of the at least one leverageable token of the at least one leverageable cryptocurrency in the number of assets.

[0064] FIG. 8 is a flowchart of a method 800 of facilitating trading operations for users, in accordance with some embodiments. Accordingly, at 802, the method 800 may include obtaining, using the processing device, an input data comprising a number of features associated with the at least one parameter for the at least one machine learning model.

[0065] Further, at 804, the method 800 may include generating, using the processing device, a number of enhanced features for the at least one parameter based on performing at least one feature engineering on the number of features.

[0066] Further, at 806, the method 800 may include training, using the processing device, the at least one machine learning model with the number of enhanced features using a training dataset. Further, the at least one machine learning model may include an artificial neural network comprising a plurality of layers.

[0067] Further, at 808, the method 800 may include tuning, using the processing device, at least one hyperparameter of the at least one machine learning model using at least one algorithm for optimizing a predicting performance of the at least one machine learning model. Further, the predicting of the at least one value of the at least one parameter may be further based on the tuning.

[0068] FIG. 9 is a flowchart of a method 900 of facilitating trading operations for users, in accordance with some embodiments. Accordingly, at 902, the method 900 may include obtaining, using a decentralized data provider unit, at least one asset information associated with at least one asset comprised in the portfolio of the user. Further, the at least one asset information may include a reference price of the at least one asset in real time.

[0069] Further, at 904, the method 900 may include analyzing, using the processing device, the at least one asset information.

[0070] Further, at 906, the method 900 may include determining, using the processing device, at least one value of at least one asset parameter based on the analyzing of the at least one asset information. Further, the determining of the at least one value of the at least one parameter may be further based on the at least one value of the at least one asset parameter.

[0071] FIG. 10 is a block diagram of a system 1000 for facilitating trading operations for users, in accordance with some embodiments. Accordingly, the system 1000 may include a communication device 1002, a smart contract module 1004, and a processing device 1006.

[0072] Further, the communication device 1002 may be configured for receiving a request for borrowing cryptocurrency tokens from a user device 1202, as shown in FIG. 12, associated with a user. Further, the request may include at least one identifier of the user and a number of tokens of at least one cryptocurrency. Further, the communication device 1002 may be configured for transmitting an initial collateral portfolio information of an initial collateral portfolio to the user device 1202.

[0073] Further, the smart contract module 1004 may be communicatively coupled with the communication device 1002. Further, the smart contract module 1004 may be configured for obtaining a portfolio information of a portfolio associated with a margin account of the user based on the at least one identifier. Further, the margin account may be maintained on a blockchain.

[0074] Further, the processing device 1006 may be communicatively coupled with the smart contract module 1004 and the communication device 1002. Further, the processing device 1006 may be configured for analyzing the portfolio information of the margin account. Further, the processing device 1006 may be configured for calculating at least one value of at least one parameter based on the analyzing and the number of tokens of the at least one cryptocurrency. Further, the processing device 1006 may be configured for generating the initial collateral portfolio for the user for the borrowing of the number of tokens of the at least one cryptocurrency based on the at least one value of the at least one parameter. Further, the initial collateral portfolio satisfies at least one initial margin requirement for the borrowing of the number of tokens of the at least one cryptocurrency.

[0075] Further, in some embodiments, the processing device 1006 may be configured for determining a number of collaterals for the borrowing of the number of tokens of the at least one cryptocurrency based on the initial collateral portfolio. Further, each of the number of collaterals may include at least one number of at least one leverageable token of at least one leverageable cryptocurrency. Further, the processing device 1006 may be configured for identifying at least one of the number of collaterals from the number of collaterals based on the number of collaterals. Further, the processing device 1006 may be configured for generating at least one order for allocating the number of tokens of the at least one cryptocurrency to the margin account based on the identifying of the collateral. Further, the smart contract module 1004 may be configured for executing the at least one order on at least one blockchain associated with the at least one cryptocurrency. Further, the executing transfers the number of tokens of the at least one cryptocurrency to the margin account.

[0076] Further, in an embodiment, the identifying of at least one of the number of collaterals may include identifying at least one of the number of collaterals using at least one machine learning model based on the number of collaterals. Further, the generating of the at least one order may be further based on the identifying of at least one of the number of collaterals using the at least one machine learning model. Further, in an embodiment, the processing device 1006 may be configured for obtaining an input data comprising a number of features associated with each of the number of collaterals. Further, the processing device 1006 may be configured for generating a prediction score for each of the number of collaterals using the at least one machine learning model. Further, the at least one machine learning model may be trained for predicting a suitability of each of the number of collaterals based on the number of features. Further, the at least one machine learning model may include a plurality of decision trees. Further, the processing device 1006 may be configured for calculating a relative ranking score for each of the number of collaterals based on the prediction score. Further, the identifying of at least one of the number of collaterals may be further based on the relative ranking score.

[0077] Further, in an embodiment, the communication device 1002 may be configured for transmitting the number of collaterals to the user device 1202. Further, the communication device 1002 may be configured for receiving a selection of at least one of the number of collaterals from the user device 1202. Further, the identifying of at least one of the number of collaterals may be further based on the selection.

[0078] Further, in an embodiment, the smart contract module 1004 may be configured for obtaining at least one blockchain data associated with at least one operation performed on the at least one blockchain. Further, the processing device -SI- 1006 may be configured for training at least one machine learning model for predicting optimal operation parameters for executing orders on the at least one blockchain based on the at least one blockchain data. Further, the at least one machine learning model may be comprised of at least one recurrent neural network. Further, the processing device 1006 may be configured for predicting at least one optimal parameter for the allocating of the number of tokens of the at least one cryptocurrency to the margin account. Further, the generating of the at least one order may be further based on the at least one optimal parameter.

[0079] Further, in an embodiment, the portfolio information may include a number of assets owned by the user and a number of positions associated with at least one of the number of assets. Further, the analyzing of the portfolio information may include analyzing the number of assets and the number of positions associated with at least one of the number of assets. Further, the processing device 1006 may be configured for calculating at least one first value of at least one first parameter for each of the number of assets. Further, the processing device 1006 may be configured for identifying the at least one number of the at least one leverageable token of the at least one leverageable cryptocurrency in the number of assets based on the at least one first value of the at least one first parameter. Further, the determining of the number of collaterals for the borrowing of the number of tokens of the at least one cryptocurrency may be further based on the identifying of the at least one number of the at least one leverageable token of the at least one leverageable cryptocurrency in the number of assets.

[0080] Further, in some embodiments, the portfolio information may include a number of assets owned by the user and a number of positions associated with at least one of the number of assets. Further, the analyzing of the portfolio information may include analyzing the number of assets and the number of positions associated with at least one of the number of assets using at least one machine learning model. Further, the at least one machine learning model may be configured for predicting the at least one value of the at least one parameter for at least one future time period. Further, the calculating of the at least one value of the at least one parameter may be further based on the predicting.

[0081] Further, in an embodiment, the processing device 1006 may be configured for obtaining an input data comprising a number of features associated with the at least one parameter for the at least one machine learning model. Further, the processing device 1006 may be configured for generating a number of enhanced features for the at least one parameter based on performing at least one feature engineering on the number of features. Further, the processing device 1006 may be configured for training the at least one machine learning model with the number of enhanced features using a training dataset. Further, the at least one machine learning model may include an artificial neural network comprising a plurality of layers. Further, the processing device 1006 may be configured for tuning at least one hyperparameter of the at least one machine learning model using at least one algorithm for optimizing a predicting performance of the at least one machine learning model. Further, the predicting of the at least one value of the at least one parameter may be further based on the tuning.

[0082] In further embodiments, the system 1000 may include a decentralized data provider unit 1102, as shown in FIG. 11, configured for obtaining at least one asset information associated with at least one asset comprised in the portfolio of the user. Further, the decentralized data provider unit 1102 may include a chainlink oracle. Further, the processing device 1006 may be communicatively coupled with the decentralized data provider unit 1102. Further, the processing device 1006 may be configured for analyzing the at least one asset information. Further, the processing device 1006 may be configured for determining at least one value of at least one asset parameter based on the analyzing of the at least one asset information. Further, the determining of the at least one value of the at least one parameter may be further based on the at least one value of the at least one asset parameter.

[0083] Further, in some embodiments, the processing device 1006 may execute a funds governance system and an order assembly system. Further, the funds governance system and the order assembly system may be a software system executed by the processing device.

[0084] Further, in some embodiments, the smart contract module 1004 may include blockchain trading protocols.

[0085] FIG. 11 is a block diagram of the system 1000, in accordance with some embodiments.

[0086] FIG. 12 is a block diagram of the system 1000, in accordance with some embodiments.

[0087] FIG. 13 is a block diagram of the smart contract module 1004 of the system 1000, in accordance with some embodiments. Further, the smart contract module 1004 may include a communication module 1302, a processing unit 1304, a hardware security module 1306, a communication interface 1308, and a crosschain adapter module 1310.

[0088] Further, the communication module 1302 may be configured for establishing a plurality of communication channels with a plurality of blockchain networks associated with a plurality of blockchains for accessing a plurality of smart contracts deployed on the plurality of blockchains. Further, each of the plurality of blockchain networks and each of the plurality of smart contracts operates on a distinct protocol. Further, the communication module 1302 may include at least one network interface such as Ethernet, Wi-Fi, etc.

[0089] Further, the processing unit 1304 may be communicatively coupled with the communication module 1302. Further, the processing unit 1304 may include a blockchain interaction engine programmed for generating and processing at least one smart contract transaction specific to each of the plurality of blockchain networks and each of the plurality of smart contracts.

[0090] Further, the hardware security module 1306 may be communicatively coupled with the processing unit 1304. Further, the hardware security module 1306 may be configured for storing a plurality of private keys. Further, the plurality of private keys may be used for securing the at least one smart contract transaction. Further, the communication module 1302 may be configured for transmitting the at least one smart contract transaction specific to each of the plurality of blockchain networks and each of the plurality of smart contracts for an execution of the at least one smart contract transaction.

[0091] Further, the communication interface 1308 may be communicatively coupled with the processing unit 1304. Further, the communication interface 1308 may be configured for communicatively coupling the smart contract module 1004 with the processing device 1006.

[0092] Further, the cross-chain adapter module 1310 may be communicatively coupled with the processing unit 1304. Further, the cross-chain adapter module 1310 may be configured for translating a transaction format of the at least one smart contract transaction for addressing a requirement of each of the plurality of blockchain networks and each of the plurality of smart contracts to facilitate cross-chain interoperability. Further, the transmitting of the at least one smart contract transaction may be further based on the translating.

[0093] FIG. 14 is a block diagram of the decentralized data provider unit 1102 of the system 1000, in accordance with some embodiments. Accordingly, the decentralized data provider unit 1102 may include a data aggregation module 1402 and a consensus module 1404.

[0094] Further, the data aggregation module 1402 may be configured for collecting a plurality of data associated with the at least one asset from a plurality of data sources across a plurality of blockchain networks. Further, the data aggregation module 1402 may include a specialized acceleration processor. Further, the specialized acceleration processor may be optimized for rapid aggregation and validation of the plurality of data from the plurality of data sources across the plurality of blockchain networks.

[0095] Further, the consensus module 1404 may be communicatively coupled with the data aggregation module 1402. Further, the consensus module 1404 may be a processor executing a distributed consensus algorithm. Further, the consensus module 1404 may be configured for establishing a consensus among the plurality of data from the plurality of data sources across the plurality of blockchain networks based on the executing of the distributed consensus algorithm. Further, the consensus module 1404 may be configured for generating the at least one asset information associated with the at least one asset based on the establishing of the consensus. Further, the obtaining of the at least one asset information associated with the at least one asset may be based on the generating of the at least one asset information associated with the at least one asset.

[0096] FIG. 15 is a schematic diagram of a platform 1500 for facilitating trading operations for users, in accordance with some embodiments. Further, the platform 1500 may include a Blink Trading system 1502, a fund governance system 1504, a blockchain trading protocol 1506, and an order assembly system 1508.

[0097] Further, the Blink Trading system 1502 manages clients' sessions and profiles as well as routes Trader's requests to the corresponding sub-systems. Further, the fund governance system 1504 controls Traders' collateral, allocates funds, manages the risks, and liquidates the positions. Further, the blockchain trading protocol 1506 may be a decentralized infrastructure that executes Smart Contract functions and automates value transfers. Further, the order assembly system 1508 accepts order requests in a standard format, transforms it depending on the trading protocol and the pool, validates its parameters, encodes call data to the underlying protocol, and returns raw data, that can be called in place. Further, a trader 1510 manages leverage and margin accounts using the Blink Trading system 1502 deposits funds in the margin account, and executes orders using the fund governance system 1504. Further, a liquidity provider 1512 deposits &withdraws funds in a liquidity pool using the fund governance system 1504. Further, the Blink Trading system 1502 performs funds allocation and release using the fund governance system 1504. Further, the fund governance system 1504 broadcasts encoded raw data received from OPS to the blockchain trading protocol 1506, and the blockchain trading protocol 1506 transmits traders’ position updates to the fund governance system 1504. Further, the fund governance system 1504 validates, transforms, builds, and encodes orders using the order assembly system 1508. Further, an admin 1514 manages approved protocols using the order assembly system 1508.

[0098] Further, the Blink Trading system 1502 may be a web portal, the fund governance system 1504 may be a software system, the blockchain trading protocol 1506 may be existing protocols, and the order assembly system 1508 may be a software system.

[0099] FIG. 16 is a schematic diagram of the platform 1500, in accordance with some embodiments. Further, the Blink Trading system 1502 may include a Blink Webapp and AP11602 and a profile 1604, the fund governance system 1504 may include a margin engine 1606, an insurance 1608, a liquidation engine 1610, and a portfolio administration 1612, the blockchain trading protocol 1506 may include ETHEREUM 1616, BSC 1618, and POLYGON 1620, and the order assembly system 1508 may include an order assembly and sandbox engine 1614. Further, the trader 1510 may perform funds withdraw, margin account control, and leverage control using the Blink Trading system 1502 and deposit funds and submit trading orders (integration type, order details, (token, poolld, amount, etc.)) through smart contracts using the fund governance system 1504. Further, the liquidity provider 1512 deposit funds into the liquidity pool and withdraw funds from the liquidity pool through smart contract using the fund governance system 1504. Further, the admin 1514 update withdraw and deposit thresholds for liquidity pools through smart contracts using the fund governance system 1504 and publish whitelists (liquidity pools &trading protocols) through smart contracts using the order assembly system 1504. Further, the Blink Trading system 1502 allocates / returns leverage close margin account through smart contract using the fund governance system 1504. Further, the fund governance system 1504 broadcast encoded raw data received from OPS through smart contracts to the blockchain trading protocol 1506. Further, the fund governance system 1504 builds orders (integration type, order details (token, poolld, amount, etc.)) through smart contracts for the order assembly system 1508. Further, the blockchain trading protocol 1506 provides view data for simulation using smart contracts to the order assembly system 1508.

[00100] FIG. 17 is a schematic diagram of the order assembly system 1508 of the platform 1500, in accordance with some embodiments. Further, the order assembly system 1508 may include a controller 1704, a repository 1706, and a state 1708.

[00101] Further, the controller 1704 may include an order simulator 1710, an order builder 1712, and a whitelisting controller 1714. Further, the order simulator 1710 may perform order validation &transformation based on selected protocol and order execution simulation (swap, add / remove liquidity, claim). Further, the order builder 1712 may perform order validation &transformation based on selected protocol and order construction (swap, add / remove liquidity, claim). Further, the repository 1706 may include a liquidity pools repository 1716 and a trading protocols repository 1718. Further, the liquidity pools repository 1716 modifies liquidity pools (encoding) and query liquidity pools (decoding). Further, the trading protocols repository 1718 modify trading protocols and query trading protocols. Further, the state 1708 may include whitelisted liquidity pools state 1720 and whitelisted trading protocols state 1722.

[00102] Further, the repository 1706 may access the order simulator 1710 to read liquidity pools and read trading protocols. Further, the repository 1706 may access the order builder 1712 to read liquidity pools and trading protocols. Further, the repository 1706 may communicate with the whitelisting controller 1714 to enable / suspend liquidity pools, enable / suspend trading protocols, read liquidity pools, and read trading protocols.

[00103] Further, the order simulator 1710 may provide an order for the simulation to the blockchain trading protocol 1506. Further, a margin account 1702 submits a constructed order (encoded raw data) to the blockchain trading protocol 1506 and provides data to build an order (integration type, order details (token, poolld, amount, etc.)) to the order builder 1712. Further, the whitelisting controller 1714 may receive modify and query whitelists.

[00104] FIG. 18 is a flow diagram of a platform 1800 for facilitating trading operations for users, in accordance with some embodiments. Further, the platform 1800 may include a Blink system 1802, a liquidity provider 1804, a trader 1806, and a Blink admin 1808.

[00105] Further, the trader 1806 deposits funds, request new leverage, return leverage, withdraw funds, close margin account which includes automatic return leverage, automatic close positions, automatic withdraw funds, and automatic commission &fee, and trade with balance (leverage &deposit).

[00106] Further, the Blink system 1802 signs a trader's risk factor with expiration that the trader will submit at withdrawal / account closing. Further, the Blink system 1802 performs funds allocation, builds a liquidation plan by automatic Liquidation DEXs priority calculation, automatic fee / com miss ion calculation, builds sequence of liquidation instructions, executes liquidation plan, manages priority queue for LP's withdraws (centralized vs decentralized).

[00107] Further, the platform 1800 may include a liquidity pool 1810, a funds allocator 1812, a margin account 1814, and an order executing processor 1816.

[00108] Further, at 1818, the trader 1802 initiates a close margin account, requests return leverage, and requests new leverage with the Blink system 1802. Further, at 1820, the trader 1802 deposits funds and executes trade with balance (leverage &deposit) with the margin account 1814. Further, at 1822, the liquidity provider 1804 put a withdraw request into the queue to the Blink system 1802. Further, at 1824, the liquidity provider 1804 deposit funds to the liquidity pool 1810. Further, at 1826, the Blink system 1802 withdraws not locked funds upon LP's request from the liquidity pool 1810. Further, at 1828, the Blink system 1802 requests new leverage return leverage &transfer commissions / fees using a crosschain from the funds allocator 1812. Further, at 1830, the Blink system 1802 submits an order (close positions) for a close / liquidation request from the margin account 1814. Further, at 1832, the funds allocator 1812 transfers funds to the margin account 1814 as collateral and transfers funds to margin account 1814 via funds allocator 1812. Further, at 1834, the funds allocator 1812 transfer funds from the margin account 1814. Further, at 1836, the funds allocator 1812 swaps on reference DEX with the order execution processor 1816. Further, at 1838, the Blink admin 1808 transfers protocols and pools whitelisting to the order execution processor 1816.

[00109] With reference to FIG. 19, a system consistent with an embodiment of the disclosure may include a computing device or cloud service, such as computing device 1900. In a basic configuration, computing device 1900 may include at least one processing unit 1902 and a system memory 1904. Depending on the configuration and type of computing device, system memory 1904 may comprise, but is not limited to, volatile (e.g. random-access memory (RAM)), non volatile (e.g. read-only memory (ROM)), flash memory, or any combination. System memory 1904 may include operating system 1905, one or more programming modules 1906, and may include a program data 1907. Operating system 1905, for example, may be suitable for controlling computing device 1900’s operation. In one embodiment, programming modules 1906 may include image-processing module, machine learning module. Furthermore, embodiments of the disclosure may be practiced in conjunction with a graphics library, other operating systems, or any other application program and is not limited to any particular application or system. This basic configuration is illustrated in FIG. 19 by those components within a dashed line 1908.

[00110] Computing device 1900 may have additional features or functionality. For example, computing device 1900 may also include additional data storage devices (removable and / or non-removable) such as, for example, magnetic disks, optical disks, or tape. Such additional storage is illustrated in FIG. 19 by a removable storage 1909 and a non-removable storage 1910. Computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules, or other data. System memory 1904, removable storage 1909, and non-removable storage 1910 are all computer storage media examples (i.e., memory storage.) Computer storage media may include, but is not limited to, RAM, ROM, electrically erasable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store information and which can be accessed by computing device 1900. Any such computer storage media may be part of device 1900. Computing device 1900 may also have input device(s) 1912 such as a keyboard, a mouse, a pen, a sound input device, a touch input device, a location sensor, a camera, a biometric sensor, etc. Output device(s) 1914 such as a display, speakers, a printer, etc. may also be included. The aforementioned devices are examples and others may be used.

[00111] Computing device 1900 may also contain a communication connection 1916 that may allow device 1900 to communicate with other computing devices 1918, such as over a network in a distributed computing environment, for example, an intranet or the Internet. Communication connection 1916 is one example of communication media. Communication media may typically be embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and includes any information delivery media. The term “modulated data signal” may describe a signal that has one or more characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media. The term computer readable media as used herein may include both storage media and communication media.

[00112] As stated above, a number of program modules and data files may be stored in system memory 1904, including operating system 1905. While executing on processing unit 1902, programming modules 1906 (e.g., application 1920 such as a media player) may perform processes including, for example, one or more stages of methods, algorithms, systems, applications, servers, databases as described above. The aforementioned process is an example, and processing unit 1902 may perform other processes. Other programming modules that may be used in accordance with embodiments of the present disclosure may include machine learning applications.

[00113] Generally, consistent with embodiments of the disclosure, program modules may include routines, programs, components, data structures, and other types of structures that may perform particular tasks or that may implement particular abstract data types. Moreover, embodiments of the disclosure may be practiced with other computer system configurations, including hand-held devices, general purpose graphics processor-based systems, multiprocessor systems, microprocessor-based or programmable consumer electronics, application specific integrated circuit-based electronics, minicomputers, mainframe computers, and the like. Embodiments of the disclosure may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[00114] Furthermore, embodiments of the disclosure may be practiced in an electrical circuit comprising discrete electronic elements, packaged or integrated electronic chips containing logic gates, a circuit utilizing a microprocessor, or on a single chip containing electronic elements or microprocessors. Embodiments of the disclosure may also be practiced using other technologies capable of performing logical operations such as, for example, AND, OR, and NOT, including but not limited to mechanical, optical, fluidic, and quantum technologies. In addition, embodiments of the disclosure may be practiced within a general-purpose computer or in any other circuits or systems.

[00115] Embodiments of the disclosure, for example, may be implemented as a computer process (method), a computing system, or as an article of manufacture, such as a computer program product or computer readable media. The computer program product may be a computer storage media readable by a computer system and encoding a computer program of instructions for executing a computer process. The computer program product may also be a propagated signal on a carrier readable by a computing system and encoding a computer program of instructions for executing a computer process. Accordingly, the present disclosure may be embodied in hardware and / or in software (including firmware, resident software, micro-code, etc.). In other words, embodiments of the present disclosure may take the form of a computer program product on a computer-usable or computer-readable storage medium having computer-usable or computer-readable program code embodied in the medium for use by or in connection with an instruction execution system. A computer-usable or computer-readable medium may be any medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

[00116] The computer-usable or computer-readable medium may be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. More specific computer-readable medium examples (a non-exhaustive list), the computer-readable medium may include the following: an electrical connection having one or more wires, a portable computer diskette, a random-access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CD-ROM). Note that the computer-usable or computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

[00117] Embodiments of the present disclosure, for example, are described above with reference to block diagrams and / or operational illustrations of methods, systems, and computer program products according to embodiments of the disclosure. The functions / acts noted in the blocks may occur out of the order as shown in any flowchart. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality / acts involved.

[00118] While certain embodiments of the disclosure have been described, other embodiments may exist. Furthermore, although embodiments of the present disclosure have been described as being associated with data stored in memory and other storage mediums, data can also be stored on or read from other types of 5 computer-readable media, such as secondary storage devices, like hard disks, solid state storage (e.g., USB drive), or a CD-ROM, a carrier wave from the Internet, or other forms of RAM or ROM. Further, the disclosed methods’ stages may be modified in any manner, including by reordering stages and / or inserting or deleting stages, without departing from the disclosure. io

[00119] Although the present disclosure has been explained in relation to its preferred embodiment, it is to be understood that many other possible modifications and variations can be made without departing from the spirit and scope of the disclosure. 15

Claims

1. A method for facilitating trading operations for users, the method comprising:receiving, using a communication device, a request for borrowing cryptocurrency tokens from a user device associated with a user, wherein the request comprises at least one identifier of the user and a number of tokens of at least one cryptocurrency;obtaining, using a smart contract module, a portfolio information of a portfolio associated with a margin account of the user based on the at least one identifier, wherein the margin account is maintained on a blockchain;analyzing, using a processing device, the portfolio information of the margin account;calculating, using the processing device, at least one value of at least one parameter based on the analyzing and the number of tokens of the at least one cryptocurrency;generating, using the processing device, an initial collateral portfolio for the user for the borrowing of the number of tokens of the at least one cryptocurrency based on the at least one value of the at least one parameter, wherein the initial collateral portfolio satisfies at least one initial margin requirement for the borrowing of the number of tokens of the at least one cryptocurrency; andtransmitting, using the communication device, an initial collateral portfolio information of the initial collateral portfolio to the user device.

2. The method of claim 1 further comprising:determining, using the processing device, a number of collaterals for the borrowing of the number of tokens of the at least one cryptocurrency based on the initial collateral portfolio, wherein each of the number of collaterals comprises at least one number of at least one leverageable token of at least one leverageable cryptocurrency;identifying, using the processing device, at least one of the number of collaterals from the number of collaterals based on the number of collaterals;generating, using the processing device, at least one order for allocating the number of tokens of the at least one cryptocurrency to the margin account based on the identifying of the collateral; andexecuting, using the smart contract module, the at least one order on at least one blockchain associated with the at least one cryptocurrency, wherein the executing transfers the number of tokens of the at least one cryptocurrency to the margin account.

3. The method of claim 2, wherein the identifying of at least one of the number of collaterals comprises identifying at least one of the number of collaterals using at least one machine learning model based on the number of collaterals, wherein the generating of the at least one order is further based on the identifying of at least one of the number of collaterals using the at least one machine learning model.

4. The method of claim 3 further comprising:obtaining, using the processing device, an input data comprising a number of features associated with each of the number of collaterals;generating, using the processing device, a prediction score for each of the number of collaterals using the at least one machine learning model, wherein the atleast one machine learning model is trained for predicting a suitability of each of the number of collaterals based on the number of features, wherein the at least one machine learning model comprises a plurality of decision trees; andcalculating, using the processing device, a relative ranking score for each of the number of collaterals based on the prediction score, wherein the identifying of at least one of the number of collaterals is further based on the relative ranking score.

5. The method of claim 2 further comprising:transmitting, using the communication device, the number of collaterals to the user device; andreceiving, using the communication device, a selection of at least one of the number of collaterals from the user device, wherein the identifying of at least one of the number of collaterals is further based on the selection.

6. The method of claim 2 further comprising:obtaining, using the smart contract module, at least one blockchain data associated with at least one operation performed on the at least one blockchain;training, using the processing device, at least one machine learning model for predicting optimal operation parameters for executing orders on the at least one blockchain based on the at least one blockchain data, wherein the at least one machine learning model is comprised of at least one recurrent neural network; andpredicting, using the processing device, at least one optimal parameter for the allocating of the number of tokens of the at least one cryptocurrency to the margin account, wherein the generating of the at least one order is further based on the at least one optimal parameter.

7. The method of claim 2, wherein the portfolio information comprises anumber of assets owned by the user and a number of positions associated with at least one of the number of assets, wherein the analyzing of the portfolio information comprises analyzing the number of assets and the number of positions associated with at least one of the number of assets, wherein the method further comprises:calculating, using the processing device, at least one first value of at least one first parameter for each of the number of assets; andidentifying, using the processing device, the at least one number of the at least one leverageable token of the at least one leverageable cryptocurrency in the number of assets based on the at least one first value of the at least one first parameter, wherein the determining of the number of collaterals for the borrowing of the number of tokens of the at least one cryptocurrency is further based on the identifying of the at least one number of the at least one leverageable token of the at least one leverageable cryptocurrency in the number of assets.

8. The method of claim 1, wherein the portfolio information comprises a number of assets owned by the user and a number of positions associated with at least one of the number of assets, wherein the analyzing of the portfolio information comprises analyzing the number of assets and the number of positions associated with at least one of the number of assets using at least one machine learning model, wherein the at least one machine learning model is configured for predicting the at least one value of the at least one parameter for at least one future time period, wherein the calculating of the at least one value of the at least one parameter is further based on the predicting.

9. The method of claim 8 further comprising:obtaining, using the processing device, an input data comprising a number of features associated with the at least one parameter for the at least one machine learning model;generating, using the processing device, a number of enhanced features for the at least one parameter based on performing at least one feature engineering on the number of features;training, using the processing device, the at least one machine learning model with the number of enhanced features using a training dataset, wherein the at least one machine learning model comprises an artificial neural network comprising a plurality of layers; andtuning, using the processing device, at least one hyperparameter of the at least one machine learning model using at least one algorithm for optimizing a predicting performance of the at least one machine learning model, wherein the predicting of the at least one value of the at least one parameter is further based on the tuning.

10. The method of claim 1 further comprising:obtaining, using a decentralized data provider unit, at least one asset information associated with at least one asset comprised in the portfolio of the user;analyzing, using the processing device, the at least one asset information; anddetermining, using the processing device, at least one value of at least one asset parameter based on the analyzing of the at least one asset information, wherein the determining of the at least one value of the at least one parameter is further based on the at least one value of the at least one asset parameter.

11. A system for facilitating trading operations for users, the system comprising:a communication device configured for:receiving a request for borrowing cryptocurrency tokens from a user device associated with a user, wherein the request comprises at least one identifier of the user and a number of tokens of at least one cryptocurrency; andtransmitting an initial collateral portfolio information of an initial collateral portfolio to the user device;a smart contract module communicatively coupled with the communication device, wherein the smart contract module is configured for obtaining a portfolio information of a portfolio associated with a margin account of the user based on the at least one identifier, wherein the margin account is maintained on a blockchain; anda processing device communicatively coupled with the smart contract module and the communication device, wherein the processing device is configured for:analyzing the portfolio information of the margin account;generating the initial collateral portfolio for the user for the borrowing of the number of tokens of the at least one cryptocurrency based on the at least one value of the at least one parameter, wherein the initial collateral portfolio satisfies at least one initial margin requirement for the borrowing of the number of tokens of the at least one cryptocurrency.

12. The system of claim 11, wherein the processing device is further configured for:determining a number of collaterals for the borrowing of the number of tokens of the at least one cryptocurrency based on the initial collateral portfolio, whereineach of the number of collaterals comprises at least one number of at least one leverageable token of at least one leverageable cryptocurrency;identifying at least one of the number of collaterals from the number of collaterals based on the number of collaterals; andgenerating at least one order for allocating the number of tokens of the at least one cryptocurrency to the margin account based on the identifying of the collateral, wherein the smart contract module is configured for executing the at least one order on at least one blockchain associated with the at least one cryptocurrency, wherein the executing transfers the number of tokens of the at least one cryptocurrency to the margin account.

13. The system of claim 12, wherein the identifying of at least one of the number of collaterals comprises identifying at least one of the number of collaterals using at least one machine learning model based on the number of collaterals, wherein the generating of the at least one order is further based on the identifying of at least one of the number of collaterals using the at least one machine learning model.

14. The system of claim 13, wherein the processing device is further configured for:obtaining an input data comprising a number of features associated with each of the number of collaterals;generating a prediction score for each of the number of collaterals using the at least one machine learning model, wherein the at least one machine learning model is trained for predicting a suitability of each of the number of collaterals based on the number of features, wherein the at least one machine learning model comprises a plurality of decision trees; andcalculating a relative ranking score for each of the number of collaterals based on the prediction score, wherein the identifying of at least one of the number of collaterals is further based on the relative ranking score.

15. The system of claim 12, wherein the communication device is further configured for:transmitting the number of collaterals to the user device; andreceiving a selection of at least one of the number of collaterals from the user device, wherein the identifying of at least one of the number of collaterals is further based on the selection.

16. The system of claim 12, wherein the smart contract module is configured for obtaining at least one blockchain data associated with at least one operation performed on the at least one blockchain, wherein the processing device is further configured for:training at least one machine learning model for predicting optimal operation parameters for executing orders on the at least one blockchain based on the at least one blockchain data, wherein the at least one machine learning model is comprised of at least one recurrent neural network; andpredicting at least one optimal parameter for the allocating of the number of tokens of the at least one cryptocurrency to the margin account, wherein the generating of the at least one order is further based on the at least one optimal parameter.

17. The system of claim 12, wherein the portfolio information comprises a number of assets owned by the user and a number of positions associated with at least one of the number of assets, wherein the analyzing of the portfolio information comprises analyzing the number of assets and the number of positionsassociated with at least one of the number of assets, wherein the processing device is further configured for:calculating at least one first value of at least one first parameter for each of the number of assets; andidentifying the at least one number of the at least one leverageable token of the at least one leverageable cryptocurrency in the number of assets based on the at least one first value of the at least one first parameter, wherein the determining of the number of collaterals for the borrowing of the number of tokens of the at least one cryptocurrency is further based on the identifying of the at least one number of the at least one leverageable token of the at least one leverageable cryptocurrency in the number of assets.

18. The system of claim 11, wherein the portfolio information comprises a number of assets owned by the user and a number of positions associated with at least one of the number of assets, wherein the analyzing of the portfolio information comprises analyzing the number of assets and the number of positions associated with at least one of the number of assets using at least one machine learning model, wherein the at least one machine learning model is configured for predicting the at least one value of the at least one parameter for at least one future time period, wherein the calculating of the at least one value of the at least one parameter is further based on the predicting.

19. The system of claim 18, wherein the processing device is further configured for:obtaining an input data comprising a number of features associated with the at least one parameter for the at least one machine learning model;generating a number of enhanced features for the at least one parameter based on performing at least one feature engineering on the number of features;training the at least one machine learning model with the number of enhanced features using a training dataset, wherein the at least one machine learning model comprises an artificial neural network comprising a plurality of layers; andtuning at least one hyperparameter of the at least one machine learning model using at least one algorithm for optimizing a predicting performance of the at least one machine learning model, wherein the predicting of the at least one value of the at least one parameter is further based on the tuning.

20. The system of claim 11 further comprising a decentralized data provider unit configured for obtaining at least one asset information associated with at least one asset comprised in the portfolio of the user, wherein the processing device is communicatively coupled with the decentralized data provider unit, wherein the processing device is further configured for:analyzing the at least one asset information; anddetermining at least one value of at least one asset parameter based on the analyzing of the at least one asset information, wherein the determining of the at least one value of the at least one parameter is further based on the at least one value of the at least one asset parameter.Your ref: LF / P04933 Examiner : David Kirwin Application No: GB2314931.3 Tel: 01633 813935 Applicant: PRM LBS Ltd Date of report: 4 April 2024 Latest date for reply: 29 September 2025 Page 1 / 3Patents Act 1977Combined Search Report under Section 17(5)(b) and Abbreviated Examination Report under Section 18(3)Excluded subject matter1. Your proposed invention relates to a method for doing business and / or a program for a computer as such, and is therefore excluded from patentability under Section 1(2) of the Patents Act 1977.

2. In Aerotel Ltd v Telco Holdings Ltd &Ors Rev 1 [2006] EWCA Civ 1371, [2007]RPC 7 the Court of Appeal set out the following four-step test for determining whether a proposed invention is excluded under Section 1(2):(1) properly construe the claims;(2) identify the actual or alleged contribution;(3) ask whether it falls solely within the excluded subject matter;(4) check whether the actual or alleged contribution is actually technical in nature.

3. In Symbian Ltd’s Application [2008] EWCA Civ 1066, [2009] RPC 1 the Court made it clear that when determining whether a proposed invention is excluded, the question “is the contribution technical?” must be asked, but that it does not matter whether it is asked at step (3) or (4). I will consider steps (3) and (4) together.Step 1 - Properly construe the claims4. Independent claim 1 defines a method for facilitating trading operations for users. Independent claim 11 defines a corresponding system. The claims will stand or fall together.

5. There are no major difficulties construing the claims in light of the description (see especially paragraphs 41-46). I note that the claims are non-specific as to what the parameter is. They set out that the value of the parameter is calculated from the analysis of the portfolio information of the user’s margin account and the number of cryptocurrency tokens in the borrowing request. The value of the parameter is then used to generate the initial collateral portfolio used for borrowing the number of cryptocurrency tokens. Paragraph 44 provides some detail of what the parameter may be (emphasis added):“[0044] Further, at 208, the method 200 may include calculating, using the processing device, at least one value of at least one parameter based on the 20 analyzing and the number of tokens of the at least one cryptocurrency.Further, the at least one parameter may include a leverage asset, a leverage amount, a scenario analysis matrix value, an initial margin, aYour ref: LF / P04933Application No : GB2314931.3[Examination Report contd.]Date of report: 4 April 2024Page 2 / 3liquidation fee, a liquidation premium, etc. Further, in an embodiment, the at least one parameter may include a reference price of at least one asset comprised in the margin account, a mark price, a portfolio 25 value (such as a token price, a liquidity pool position, a lending protocol, a position value, etc.), a gas / bridging cost, a stress tested portfolio value, a risk factor, etc. Further, the calculating of the at least one value of the at least one parameter may be perfomed by an engine comprised in the processing device. Further, the engine may be an Arkis margin engine.”Step 2 - Identify the actual or alleged contribution6. Paragraphs 2-4 set out the relevant background and problem addressed:“[0002] The recent massive explosion of Decentralized Finance (DeFi) activity and innovation led to decentralized lending becoming vital to an emerging financial system. Early DeFi’s success has been driven by overcollateralized lending protocols such as Aave and Compound, which enable borrowers to deposit collateral in one token and withdraw part of its value in another. However, as the system becomes increasingly mature, on-chain market participants, especially asset managers, need access to undercollateralized loans, which provide more efficient capital allocation.[0003] AAVE and Compound don’t restrict operations with leverage users receive through their platforms. As a result, numerous restrictions need to be imposed on the platform. This leads to an inefficient relative size of a loan in relation to the value of the collateral - overcollateralized loan. The undercollateralized loans, if liquidated incorrectly, impose serious risks on creditors, as a result sophisticated system is needed to define when a user’s loan should be liquidated. Further, the risks of several positions in a trader’s portfolio may offset each other. Margin calculations that account for total portfolio P&L (Cross Margin) instead of segregated risk calculation for each position (Isolated Margin) are needed to improve capital and leverage efficiency for professional asset managers as maintenance margin requirements in Cross Margin are much lower.[0004] Therefore, there is a need for improved methods, systems, apparatuses, and devices for facilitating trading operations for users that may overcome one or more of the above-mentioned problems and / or limitations.”7. The claimed invention would appear to address this by determining an optimal portfolio to be used as collateral when borrowing cryptocurrency. The portfolio is based on a parameter value (derived from the user’s portfolio information in their margin account) and also satisfies a margin requirement.Your ref: LF / P04933Application No : GB2314931.3[Examination Report contd.]Date of report: 4 April 2024Page 3 / 38. Therefore, the contribution would appear to be a method for facilitating a collateralized cryptocurrency loan where a borrower’s portfolio (associated with their margin account on a blockchain) is analysed to calculate a parameter value and, therefore, to determine and communicate an initial collateral portfolio for the loan that satisfies a margin requirement.Steps 3 A 4 Ask whether the contribution falls solely within the excluded subject matter and check whether the contribution is actually technical in nature9. The contribution relates entirely to a financial process of borrowing an amount of cryptocurrency and determining appropriate collateral for the loan. This falls squarely within the business method exclusion. It may well represent a better process for facilitating collateralized loans of cryptocurrency tokens (where the portfolio to be used as collateral is optimised in order to avoid over-collateralization or the risk of incorrectly liquidated undercollateralized loans) however this is no more than a better business method, which cannot escape the exclusion as per the Court of Appeal in Merrill Lynch's Application [1989] RPC 561.

10. The contribution plainly involves the use of a computer program. The program merely serves to implement the business method and so does not provide a technical contribution.

11. Therefore, the contribution falls solely within excluded matter as a business method and a computer program. Such a contribution is not technical in nature.

12. Regarding the dependent claims, there is nothing which takes the contribution outside of the business method exclusion (at least).Search would not serve any useful purpose - Section 17(5)(b)13. The contribution lies entirely within fields excluded from patentability and is not technical. I have also read through your specification in detail, but I have been unable to identify any subject matter that could form the basis of a patentable invention. Carrying out a search at this stage would therefore serve no useful purpose in relation to making an application for a UK patent. If you submit amended claims and / or a response to the points raised above, I will reconsider whether a search would serve a useful purpose.Deferred matters14. All other matters are deferred. Since I have not carried out a search, I am unable to assess the novelty and inventiveness of your proposed invention.

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