A virtualized encrypted asset trading platform

Abstract

This invention relates to the field of computer data processing technology and discloses a virtualized encrypted asset trading platform, including a client SDK, an API gateway, and a backend service cluster. The client SDK intercepts business requests and generates a business request data packet containing a tenant's unique identifier and a user's unique identifier, which is then sent to the API gateway. The API gateway forwards the business request data packet to a unified account federation service. The unified account federation service extracts the tenant's unique identifier and the user's unique identifier, performs an account mapping conversion operation to obtain an internal account ID, and the transaction execution engine combines the order instruction and the internal account ID to generate standardized order data records and performs matching processing. This invention, by intercepting business requests through the client SDK and performing account mapping conversion operations through the unified account federation service to obtain the internal account ID, eliminates the step of users registering independent trading accounts and reduces the coupling between the business system and the trading engine.

Description

Specification 1 A Virtualized Crypto Asset Trading Platform Technical Field This invention relates to the field of computer data processing technology, specifically a virtualized crypto asset trading platform. Background Technology In the process of existing applications accessing the underlying trading system, the front-end business system and the underlying trading engine usually maintain a strong binding relationship. When external application users call the underlying trading engine, they often need to perform the operation process of re-registering an independent trading account. The lack of a logical mapping mechanism between the front-end identity and the underlying internal trading account leads to excessive coupling between the front-end business system and the underlying trading engine. During the order placement process, when the platform's internal liquidity pool depth is insufficient, the system needs to route and distribute orders to external exchange data interfaces. Conventional routing mechanisms usually directly allocate orders without considering the interference of expired abnormal data caused by the time difference between the data interface's returned timestamp and the current system time. Conventional routing mechanisms lack the step of combining real-time depth data, order placement fee tier data, and order quantity to perform slippage cost calculation, and cannot select the external exchange data interface with the lowest comprehensive cost of slippage and fee rate, resulting in high capital loss during order routing and distribution. When the client requests asset data from the backend, the system needs to handle the data merging problem between heterogeneous financial systems. Traditional asset datasets and virtual asset datasets reside in different systems with differing system base timestamps, resulting in misaligned data snapshots between the two datasets. The lack of a mechanism for synchronously accessing real-time exchange rate oracle interfaces to obtain conversion factors between heterogeneous financial systems prevents the successful summation of fiat currency valuations for traditional and virtual assets, hindering the merging of fiat currency valuations and preventing the system from providing clients with a globally aggregated total asset value view. To address these shortcomings, this invention provides a virtualized encrypted asset trading platform that solves the problems faced by third-party applications accessing external trading systems, including cumbersome independent account registration, difficulties in data isolation under multi-tenant operating environments, and excessive coupling between the underlying trading engine and the business front-end.To achieve the above objectives, this invention provides a virtualized encrypted asset trading platform, comprising a client SDK, a partner APP, an API gateway, and a backend service cluster. The client SDK is embedded within the partner APP and connected to the API gateway. The API gateway connects to the backend service cluster, which includes a multi-tenant configuration center, a unified account federation service, a market analysis engine, a trading execution engine, and a compliance adaptation layer. The client SDK intercepts business requests, encapsulates them into a business request data packet containing a tenant's unique identifier and a user's unique identifier, and sends the data packet to the API gateway. The API gateway routes and forwards the business request data packet to the unified account federation service. The unified account federation service extracts the tenant's unique identifier and the user's unique identifier from the business request data packet, performs an account mapping conversion operation to obtain an internal account ID, encapsulates the internal account ID into a mapping response message, and sends the mapping response message to the trading execution engine. The trading execution engine combines the business request data packet containing the order placement instruction with the internal account ID from the mapping response message to generate standardized order data records and execute order matching processing. This invention, through a dual-identifier interception and federated mapping mechanism, enables external application users to call the underlying transaction engine without performing a re-registration process, thus decoupling the front-end identity from the underlying transaction account. The client SDK contains an embedded UI component library. When a partner app starts, the client SDK calls the initialization interface. The client SDK internally generates an initialization request data packet containing a tenant's unique identifier and authorization authentication key and sends it to the API gateway. The API gateway receives the initialization request data packet and performs a password comparison verification on the authorization authentication key. After successful verification, the API gateway routes the network request containing the initialization request data packet to the multi-tenant configuration center based on the tenant's unique identifier. The multi-tenant configuration center performs a matching query in its internal storage space based on the parsed tenant's unique identifier, extracts the configuration template set, and returns it to the client SDK. The client SDK receives the latest configuration template set and stores it in the local cache. It then extracts the theme color and brand asset variables and inputs them into the embedded UI component library. The embedded UI component library performs interface rendering calculations based on the theme color and brand asset variables, and displays the rendered components on the partner app's screen. This process enables dynamic interface adaptation in multi-tenant scenarios.The client SDK receives external subscription trigger commands and establishes a WebSocket communication connection with the API gateway. The client SDK sends a subscription request message containing a unique tenant identifier to the API gateway. The API gateway keeps the WebSocket communication connection continuously open and forwards the subscription request message to the market analysis engine. The market analysis engine parses the unique tenant identifier, encapsulates it into a query message, and sends it to the multi-tenant configuration center to obtain the corresponding list of tradable currencies. The market analysis engine performs an intersection filtering operation on the global trading pair set and the list of tradable currencies to obtain a set of displayable trading pairs. Based on the data contained within the displayable trading pair set, it generates isolated real-time market data streams and continuously sends these streams to the client SDK via the API gateway and the established WebSocket communication connection. The partner app's display interface maintains a data listening state with the embedded UI component library through an event callback interface. The embedded UI component library detects when a long-press operation command is received within the coordinate area of ​​the candlestick chart component and captures the pixel coordinates on the screen. The embedded UI component library maps pixel coordinates to the vertical price scale to extract target price data and generate event callback signals. These callback signals are then sent to the partner app, which triggers and calls the quick transaction panel component. The target price data is automatically filled into the input box of the quick transaction panel component as the default order price. The quick transaction panel component receives touch input and generates business requests. The unified account federation service performs account mapping conversion calculations by concatenating the tenant's unique identifier and the user's unique identifier to generate a joint query key value, which is then retrieved from the internally maintained federation mapping table. If no joint query key value is found in the federation mapping table, the unified account federation service creates a unique internal account ID and establishes a corresponding relationship, storing it in the federation mapping table to complete the update. When a joint query key value is found in the federation mapping table, the unified account federation service reads the associated internal account ID from the federation mapping table. After generating standardized order data records, the transaction execution engine sends a risk control pre-inspection request to the compliance adaptation layer. (HK 30135239 A Specification 4) The compliance adaptation layer extracts the internal account ID from the risk control pre-inspection request, sends the internal account ID to the third-party authentication node via an external network interface, and receives the returned AML compliance identifier and KYC level data. The compliance adaptation layer then sends a configuration retrieval command to the multi-tenant configuration center to obtain the compliance level requirements and compares the KYC level data with the compliance level requirements.When KYC level data is lower than the compliance level requirement or the AML compliance flag is marked as abnormal, the compliance adaptation layer generates an interception instruction data packet, returns it to the transaction execution engine, and discards standardized order data records. When KYC level data is equal to or higher than the compliance level requirement and the AML compliance flag is marked as normal, the compliance adaptation layer initiates risk control threshold verification calculation. The steps for the compliance adaptation layer to initiate risk control threshold verification calculation include: retrieving and reading the cumulative transaction amount bound to the internal account ID in the internal database, and extracting the set single transaction limit threshold and daily cumulative limit threshold. The compliance adaptation layer performs risk control threshold verification calculation, determining whether the single order amount is less than or equal to the single transaction limit threshold, and determining whether the sum of the single order amount and the cumulative transaction amount for the day is less than or equal to the daily cumulative limit threshold. When the risk control verification result meets the above judgment conditions and is true, the compliance adaptation layer generates a release verification result data packet and sends it to the transaction execution engine. When the risk control verification result does not meet the above judgment conditions and is false, the compliance adaptation layer generates an over-limit rejection verification result data packet and sends it to the transaction execution engine. The trading execution engine terminates the flow of standardized order data records based on the received over-limit rejection verification result data packet and sends a response message to the API gateway. Based on the received release verification result data packet, the trading execution engine reads the trading pair identifier within the standardized order data record and determines whether the trading pair identifier falls within the coverage of the internal liquidity pool, i.e., the in-memory order book. When the trading pair identifier falls within the coverage of the internal liquidity pool, i.e., the in-memory order book, the trading execution engine routes and distributes the standardized order data record to the in-memory matching nodes within the trading execution engine. The in-memory matching nodes compare the order price values ​​of orders in the same direction within the in-memory order book and sort them. The in-memory matching nodes extract the buy order and sell order at the top of the queue. When the price crossover condition is met, they perform an equal deduction operation on the order quantity value and generate a transaction detail data packet based on the deduction result. The trading execution engine stores the transaction detail data packet and updates the asset data bound to the internal account ID. When a trading pair is not covered by the internal liquidity pool (i.e., the in-memory order book) or the depth of the internal liquidity pool (i.e., the in-memory order book) is insufficient, the trading execution engine activates its internally integrated intelligent routing module. This module concurrently retrieves real-time depth data and order book fee tiers from multiple external exchange data interfaces, compares the timestamps returned by each data interface with the current system time, and automatically removes expired or abnormal data that exceeds the tolerance time window. Based on the filtered and aligned effective depth data combined with the order quantity of standardized order data records, the intelligent routing module calculates slippage costs, selects the external exchange data interface with the lowest overall slippage and fee cost, and routes and distributes the standardized order data records to the selected external exchange data interface.The above processing mechanism solves the problem of external order routing calculation in a liquidity-insufficient environment. The client SDK generates an asset query instruction and encapsulates it with an access token into an asset query request data packet, which is sent to the API gateway. The API gateway routes the asset query request data packet to the backend service cluster for authentication and verification. The backend service cluster sends a traditional asset pull request to the partner's own system to obtain the traditional asset data set. At the same time, it retrieves the virtual asset data set corresponding to the internal account ID in the internal storage space, aligns the data snapshots of the two types of assets according to a unified system benchmark timestamp, and synchronously calls the real-time exchange rate oracle interface to obtain the conversion factor. The backend service cluster performs a summation operation on the fiat currency valuation of each traditional asset in the traditional asset data set and the fiat currency valuation of each virtual asset in the virtual asset data set to obtain the total value of the unified asset view, generates an asset aggregation response message, and returns the asset aggregation response message to the client SDK via the API gateway. This mechanism realizes the aggregation and alignment calculation of heterogeneous financial assets. This invention provides a virtualized encrypted asset trading platform. The invention offers the following advantages: 1. By intercepting business requests through the client SDK and encapsulating them into a business request data packet containing a unique tenant identifier and a unique user identifier, the invention delivers the business request data packet to the unified account federation service to perform account mapping conversion operations to obtain the internal account ID. This eliminates the need for external application users to re-register independent trading accounts, reducing the coupling between the front-end business system and the underlying trading engine. The invention also completes the logical mapping between external user identities and underlying internal trading accounts. 2. The invention utilizes an intelligent routing module integrated within the trading execution engine to parallelly acquire real-time depth data and order book and taker fee tier data returned from multiple external exchange data interfaces. It compares the timestamp returned by the data interface with the current system time difference and removes expired abnormal data exceeding the tolerance time window. Based on the filtered and aligned effective depth data combined with the order quantity of standardized order data records, it calculates slippage costs. In scenarios where the internal liquidity pool depth is insufficient, it automatically selects the external exchange data interface with the lowest combined slippage and fee cost and completes the routing and distribution of standardized order data records, reducing capital losses in entrusted transactions.3. This invention sends traditional asset retrieval requests to partners' own systems through a backend service cluster to obtain a traditional asset data set. It then retrieves the corresponding virtual asset data set from internal storage using the internal account ID. Based on a unified system benchmark timestamp, it aligns the data snapshots of the traditional and virtual asset data sets, retrieves the conversion factor from the real-time exchange rate oracle interface, and performs a summation operation on the fiat currency valuations of each traditional asset and each virtual asset, generating an asset aggregation response message. This solves the problem of fiat currency valuation merging calculation obstacles caused by data misalignment between heterogeneous financial systems, providing the client SDK with globally aggregated total asset value data. Figure 1 is a schematic diagram of the system module architecture of this invention; Figure 2 is a schematic diagram of the actual platform operation process of this invention; Figure 3 is a comparison of the convergence of asset aggregation response time across platforms under high-frequency concurrency scenarios of this invention; Figure 4 is a diagram of slippage control and anti-over-limit boundary status during the adjustment process of intelligent routing and compliance interception in this invention. Detailed Description of Embodiments The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all the embodiments in Part 7 of the HK 30135239 A specification. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Referring to Figure 1, the present invention provides a virtualized encrypted asset trading platform, including a client SDK, a partner APP, an API gateway, and a backend service cluster; the client SDK is embedded and installed inside the partner APP, the client SDK is connected to the API gateway, the API gateway is connected to the backend service cluster, and a data transmission link is set between the backend service cluster and the API gateway. The backend service cluster is divided into multiple independently deployed modules, including a multi-tenant configuration center, a unified account federation service, a market analysis engine, a transaction execution engine, and a compliance adaptation layer. Independent communication interfaces are set between the multi-tenant configuration center, the unified account federation service, the market analysis engine, the transaction execution engine, and the compliance adaptation layer, and data packets are transmitted between modules through the communication interfaces. The client SDK includes an embedded UI component library used to render interface content. The client SDK sends network requests to the API gateway, which receives these requests and routes them to the multi-tenant configuration center, unified account federation service, market analysis engine, transaction execution engine, or compliance adaptation layer within the backend service cluster.The multi-tenant configuration center stores tenant configuration data. The client SDK sends a pull request to the multi-tenant configuration center via the API gateway. The multi-tenant configuration center receives the pull request and returns configuration data to the client SDK. The client SDK then transmits the received configuration data to the embedded UI component library. The market analysis engine connects to external data nodes and obtains raw data streams, calculates analysis data, and sends the analysis data to the API gateway. The API gateway pushes the analysis data to the client SDK. The trading execution engine connects to the unified account federation service and the compliance adaptation layer. It receives order data from the client SDK, sends the identification information from the order data to the unified account federation service, and the unified account federation service returns the mapped and transformed account identification data to the trading execution engine. The trading execution engine sends a verification request to the compliance adaptation layer, and the compliance adaptation layer returns the verification result data to the trading execution engine. The trading execution engine combines the account identification data and the verification result data to perform order matching processing. The transaction execution engine, unified account federation service, and compliance adaptation layer do not share internal state. Module interaction relies on input variables and output responses from the HK 30135239 A specification 8 to complete data exchange. The client SDK is deployed within the partner's APP. When the partner's APP starts, it triggers the client SDK to call the initialization interface. The client SDK internally generates an initialization request data packet, which contains the tenant's unique identifier and authorization authentication key. The tenant unique identifier is defined as a unique identifier assigned to each tenant. The authorization and authentication key is used for client legitimacy verification. The client SDK sends an initialization request packet to the API gateway according to the REST communication protocol specification. The API gateway receives the initialization request packet, performs a password comparison and verification of the authorization and authentication key. After successful verification, it encapsulates the tenant unique identifier and writes it into the custom field X-Partner-ID in the HTTP network request header. It also reads the value of the custom field X-Partner-ID and routes the network request containing the initialization request packet to the multi-tenant configuration center based on the read tenant unique identifier. The multi-tenant configuration center performs a matching query in its internal storage space based on the parsed tenant unique identifier to retrieve the configuration template set and returns it to the client SDK. Upon receiving an external subscription trigger command, the client SDK establishes a WebSocket communication connection with the API gateway. During the handshake interaction phase of the WebSocket communication connection, the client SDK sends a subscription request message containing the tenant unique identifier to the API gateway. The API gateway keeps the WebSocket communication connection continuously open and forwards the subscription request message to the market analysis engine.The market analysis engine parses the tenant's unique identifier from the subscription request message, queries and returns the corresponding real-time market data stream based on the extracted tenant's unique identifier, and continuously sends the real-time market data stream to the client SDK via the API gateway and the established WebSocket communication connection. The tenant's unique identifier and authorization authentication key are persistently cached throughout the entire lifecycle of network request sending and routing forwarding. The client SDK extracts the tenant's unique identifier and authorization authentication key and stores them in its local cache space. While the client SDK is active, all REST network request packets and WebSocket subscription request messages sent by the client SDK to the API gateway are forcibly appended with the tenant's unique identifier stored in the local cache space. After receiving a data packet or message containing the tenant's unique identifier, the API gateway extracts the tenant's unique identifier and performs a transparent transmission operation (HK 30135239 A Manual 9) to downstream nodes in the communication link. Each independently deployed module in the backend service cluster isolates and returns the corresponding data response content based on the received tenant's unique identifier parameter, until the client SDK disconnects from the API gateway or the partner APP exits operation. The multi-tenant configuration center receives network requests routed and forwarded by the API gateway, parses the tenant's unique identifier carried in the network request, and performs a matching query in the internal storage space based on the parsed tenant's unique identifier. The internal storage space stores multiple sets of configuration templates in JSON format. A unique key-value pair mapping is established between the configuration template set and the tenant's unique identifier. The configuration template set is defined as Ctenant. The multi-tenant configuration center extracts the corresponding configuration template set based on the matched tenant's unique identifier. The configuration template set satisfies the following formula: Ctenant = {Ctheme, Cassets, Spairs, Cfee, Ckyc}; where: Ctenant represents the configuration template set; Ctheme represents the theme color variable; Cassets represents the brand asset variable; Spairs represents the list of tradable currencies in the business rules; Cfee represents the fee structure; and Ckyc represents the compliance level requirements in the compliance policy. The multi-tenant configuration center integrates Nacos nodes or Consul nodes, and performs dynamic configuration management operations based on these nodes. When the multi-tenant configuration center receives an external input configuration parameter update command, it locates the configuration template set corresponding to the tenant's unique identifier in the internal storage space, overwrites and replaces the received configuration parameters in the configuration template set, generates an incrementing version number marker, and generates a hot reload trigger command when a change in the version number marker is detected.The multi-tenant configuration center sends a hot-reload trigger command containing the latest configuration template set to the API gateway. The API gateway parses the hot-reload trigger command and extracts the tenant's unique identifier from it. The API gateway retrieves a WebSocket communication connection that is bound to the current tenant's unique identifier and is in an open state. The API gateway then transmits the latest configuration template set to the client SDK via the WebSocket communication connection. The client SDK receives the latest configuration template set and stores it in its local cache. Before initiating the next network request, the client SDK reads the latest configuration template set from its local cache and overwrites the old configuration data. The client SDK reads and parses the latest configuration template set from its local cache, extracts the theme color variables and brand asset variables, and inputs these variables into the embedded UI component library. The embedded UI component library is built on a ReactNative or Flutter cross-platform framework, compatible with and natively embedded on iOS, Android, and H5 platforms. The embedded UI component library receives input theme color variables and brand asset variables, and performs interface rendering calculations based on these variables. The interface rendering calculations of the embedded UI component library satisfy the following formula: UIrender = Rui(Ctheme, Cassets); where: UIrender represents the interface rendering output; Rui represents the interface rendering mapping function; Ctheme represents the theme color variable; and Cassets represents the brand asset variable. The embedded UI component library dynamically assigns and replaces CSS variable parameters or native style parameters configured within the embedded UI component library based on the interface rendering output. The embedded UI component library then renders and displays the replaced components on the partner app's display interface. The rendered and displayed components include market data card components, candlestick chart components, depth chart components, quick trading panel components, and asset card components. The partner app's display interface maintains a data monitoring state with the embedded UI component library through an event callback interface. When the display interface is active, the embedded UI component library continuously monitors the touch input operations received on the display interface. The embedded UI component library detects a long-press operation command received within the coordinate area of ​​the candlestick chart component, captures the pixel coordinates of the long-press operation command on the screen, maps the pixel coordinates to the vertical axis price scale, and extracts the price data of the target point corresponding to the long-press coordinates.The embedded UI component library generates an event callback signal based on the long-press operation command and the target price data. The embedded UI component library transmits the event callback signal to the partner app via the event callback interface. Upon receiving the event callback signal, the partner app triggers and calls the quick trading panel component (HK 30135239 A Specification 11), automatically filling the input box of the quick trading panel component with the target price data as the default order price. Simultaneously, the partner app overlays and renders the quick trading panel component on top of the candlestick chart component's surface display area. The partner app's display interface renders and displays the quick trading panel component, which receives touch input and generates a business request. The client SDK intercepts the business request, encapsulates it to generate a business request data packet, which contains a tenant unique identifier and a user unique identifier. The client SDK sends the business request data packet to the API gateway, which routes and forwards it to the unified account federation service. The Unified Account Federated Service internally maintains and stores a federated mapping table. It parses business request data packets, extracts internal tenant and user unique identifiers, concatenates these identifiers to generate a join key, and retrieves this join key from the federated mapping table. When no join key is found in the federated mapping table, the Unified Account Federated Service creates a unique internal account ID. It then establishes a mapping between the join key and the internal account ID, updating the table by storing this mapping. When a join key is found in the federated mapping table, the Unified Account Federated Service reads the associated internal account ID from the table. The account mapping transformation operation performed by the Unified Account Federated Service satisfies the following formula: Ivid = Maccount(Ipid, Iuid); where: Ivid represents the internal account ID; Maccount represents the mapping transformation function; Ipid represents the tenant unique identifier; and Iuid represents the user unique identifier. The Unified Account Federated Service encapsulates the read or created internal account ID into a mapping response message and sends this message to the transaction execution engine. The transaction execution engine parses the mapping response message to obtain the internal account ID. The engine then binds the received order or query request to this internal account ID and processes it internally. Business requests, as described in the HK 30135239 A manual, are processed by all nodes within the service cluster that invoke the unified account federation service to perform account conversion operations.The client SDK generates an asset query command and sends an authorization request message to the API gateway according to the OAuth2 communication protocol. The API gateway receives and verifies the authorization request message and issues an access token to the client SDK. The client SDK encapsulates the asset query command and access token into an asset query request data packet and sends it to the API gateway. The API gateway routes and forwards the asset query request data packet to the backend service cluster. The backend service cluster receives the asset query request data packet, parses the access token, and completes authentication. After successful authentication, it triggers data retrieval operations in parallel according to the REST communication protocol. The backend service cluster sends a traditional asset retrieval request to the partner's own system. The partner's own system receives the traditional asset retrieval request and returns a traditional asset data set to the backend service cluster, which includes stock account asset data and fund account asset data. Simultaneously, the backend service cluster retrieves the virtual asset data set corresponding to the internal account ID from its internal storage space. The backend service cluster extracts traditional asset datasets and virtual asset datasets as a preferred method. Considering that traditional assets and virtual assets belong to different underlying settlement networks and their pricing base currencies often differ, before performing aggregation calculations, the backend service cluster needs to align the data snapshots of the two types of assets according to a unified system benchmark timestamp and simultaneously call the real-time exchange rate oracle interface to obtain the conversion factor for the corresponding currency. Based on the principle of multi-source data alignment and unified value conversion, the backend service cluster performs asset aggregation calculations on the traditional asset dataset and the virtual asset dataset. In this embodiment, the asset aggregation calculation satisfies the following formula: Atotal = i∈Atrad Vtrad (i) + j∈Ava Vva (j); Where: Atotal represents the total value of the unified asset view; Atrad represents the traditional asset dataset; Ava represents the virtual asset dataset; i represents a single traditional asset; Vtrad(i) represents the fiat currency valuation of a single traditional asset; j represents a single virtual asset; Vva(j) represents the fiat currency valuation of a single virtual asset. The backend service cluster generates an asset aggregation response message (HK 30135239 A, Specification 13) based on the calculated total value of the unified asset view. The backend service cluster returns this asset aggregation response message to the client SDK via the API gateway. The client SDK parses the asset aggregation response message to extract the total value of the unified asset view and then passes this total value to the embedded UI component library. The embedded UI component library receives the total value of the unified asset view and renders and displays it in the asset card component within the display interface.The partner app acquires and synchronously displays the total value of the unified asset view. The market analysis engine establishes a direct WebSocket communication connection with external exchange nodes, receiving tick-level raw data streams pushed by these nodes. The engine performs field cleaning on the tick-level raw data streams, filtering out invalid data records with incomplete formats, extracting the cleaned valid data, performing normalization format conversion operations, and aggregating to generate a global trading pair set containing all trading pairs. Historical price sequence data from this global trading pair set is persistently written to a MongoDB database, and real-time technical indicators derived from the global trading pair set are stored in a Redis cache node. The engine receives subscription request messages forwarded by the API gateway, parses these messages, and extracts the tenant's unique identifier. The engine encapsulates the tenant's unique identifier into a query message and sends it to the multi-tenant configuration center. The multi-tenant configuration center receives the query message and matches it against the tenant's unique identifier to read the internally stored configuration template set. The multi-tenant configuration center extracts the list of tradable currencies from the configuration template set and returns it to the market analysis engine. The market analysis engine receives the list of tradable currencies and performs an intersection filtering operation on the global trading pair set and the list of tradable currencies. The intersection filtering operation satisfies the following formula: Sdisplay = Sglobal ∩ Spairs; where: Sdisplay represents the set of displayable trading pairs; Sglobal represents the global trading pair set; and Spairs represents the list of tradable currencies. HK 30135239 A Specification 14. The market analysis engine compares the overlapping elements between the global trading pair set and the list of tradable currencies to derive the set of displayable trading pairs. The market analysis engine removes data records from the global trading pair set that do not belong to the set of displayable trading pairs. Based on the data encapsulated within the set of displayable trading pairs, the market analysis engine generates isolated real-time market data streams. The market analysis engine continuously pushes the real-time market data streams to the API gateway. The transaction execution engine receives a business request data packet containing an order placement instruction, and simultaneously receives a mapping response message from the unified account federation service. The engine extracts the timestamp parameter and unique request hash value carried within the business request data packet. The engine retrieves the unique request hash value from its internal cache nodes and verifies whether the timestamp parameter is within a valid time window. If a unique request hash value exists in the cache node, the engine determines the business request data packet is a duplicate request, discards the data packet, and returns an intercept response.When a unique request hash value does not exist in the cache node, the trading execution engine writes the unique request hash value to the cache node and allows the business request data packet containing the order placement instruction to pass through. The trading execution engine parses the mapping response message and extracts the internal account ID. The trading execution engine combines the parameters in the order placement instruction and the internal account ID to generate a standardized order data record. The standardized order data record contains an order number identifier, internal account ID, trading pair identifier, buy / sell direction identifier, order price value, order quantity value, and generation timestamp. The trading execution engine reads the trading pair identifier from the standardized order data record and determines whether the trading pair identifier falls within the coverage of the internal liquidity pool (i.e., the in-memory order book). When the trading pair identifier falls within the coverage of the internal liquidity pool (i.e., the in-memory order book), the trading execution engine routes and distributes the standardized order data record to the in-memory matching node within the trading execution engine. When the trading pair identifier does not fall within the coverage of the internal liquidity pool (i.e., the in-memory order book) or the depth of the internal liquidity pool (i.e., the in-memory order book) is insufficient, the trading execution engine activates the internally integrated intelligent routing module. The intelligent routing module concurrently obtains real-time depth data and order book and taker fee tier data returned from multiple external exchange data interfaces. In the multi-source concurrent acquisition process, to avoid evaluation distortion caused by network asynchronous latency (HK 30135239 A Manual 15), the intelligent routing module pre-compares the timestamp returned by each data interface with the current system time difference, automatically eliminating expired abnormal data that exceeds the tolerance time window. Based on the filtered and aligned effective depth data, the intelligent routing module calculates slippage costs by combining the number of orders in the standardized order data records, and selects the external exchange data interface with the lowest combined slippage and fee cost based on a multi-dimensional weighted judgment rule, and routes and distributes the standardized order data records to this optimal external exchange data interface. The memory matching node compares the order price values ​​of orders in the same direction within the memory order book, and places the order with the highest order price value in the buy direction or the order with the lowest order price value in the sell direction at the head of the queue. When there are multiple orders with the same order price value, the order generation timestamp is compared, and the order with the smaller generation timestamp value is placed at the front of the queue. The buy order and the sell order at the head of the queue are extracted, and the order price values ​​of the buy order and the sell order are compared. When the price crossover condition is met, the in-memory matching node performs an equal-amount deduction operation on the order quantity. Based on the deduction result, the in-memory matching node generates a transaction detail data packet. The trading execution engine stores the transaction detail data packet and updates the asset data bound to the internal account ID. After generating standardized order data records, the trading execution engine sends a risk control pre-check request to the compliance adaptation layer. The risk control pre-check request contains the internal account ID, the single order amount, and the unique identifier of the associated tenant.The compliance adaptation layer receives risk control pre-inspection requests and extracts the internal account ID from these requests. It then sends this internal account ID to a third-party authentication node via an external network interface. This third-party authentication node includes the Jumio verification service system. The compliance adaptation layer receives the AML compliance identifier and KYC level data returned by the third-party authentication node. It then sends a configuration retrieval command containing the tenant's unique identifier to the multi-tenant configuration center. The multi-tenant configuration center returns a set of configuration templates bound to the tenant's unique identifier. The compliance adaptation layer parses and extracts the compliance level requirements set within the configuration template set. The compliance adaptation layer compares the KYC level data with the compliance level requirements. When the KYC level data is lower than the compliance level requirements or the AML compliance identifier is flagged as abnormal, the compliance adaptation layer generates an interception instruction data packet. This packet is then returned to the transaction execution engine. The transaction execution engine receives the interception instruction data packet and discards the standardized order data record. When the KYC level data is equal to or higher than the compliance level requirement and the AML compliance identifier is marked as normal, the compliance adaptation layer initiates risk control threshold verification calculation. The compliance adaptation layer retrieves and reads the cumulative transaction amount bound to the internal account ID in the internal database for the current day, and extracts the set single-transaction limit threshold and daily cumulative limit threshold. In this embodiment, to balance transaction efficiency and anti-money laundering compliance requirements, the specific numerical range of the single-transaction limit threshold and daily cumulative limit threshold is dynamically assigned in a tiered manner by the multi-tenant configuration center based on the financial regulatory policies of different tenants and the current user's KYC level data. Based on the risk control limit management mechanism, the risk control threshold verification calculation performed by the compliance adaptation layer satisfies the following formula: Rcheck = (Vorder ≤ Tsingle) ∧ (Vorder + Vdaily ≤ Tdaily); Where: Rcheck represents the risk control verification result; Vorder represents the single order amount; Tsingle represents the single-transaction limit threshold; Vdaily represents the cumulative transaction amount for the current day; Tdaily represents the daily cumulative limit threshold; ∧ represents the logical AND operator. When the risk control verification result has a Boolean value of true, the compliance adaptation layer generates a release verification result data packet. When the risk control verification result has a Boolean value of false, the compliance adaptation layer generates an over-limit rejection verification result data packet. The compliance adaptation layer sends the release verification result data packet or the over-limit rejection verification result data packet to the trading execution engine. The trading execution engine routes and distributes standardized order data records to the in-memory matching node or the external exchange data interface based on the received release verification result data packet.The transaction execution engine terminates the standardized order data record flow operation based on the received over-limit rejection verification result data packet. The engine generates a response message containing risk control failure information and sends it to the API gateway. Referring to Figures 1 and 2, in the actual platform operation, when the client environment triggers startup, it generates an initialization request data packet (HK 30135239 A Manual 17) encapsulated with the tenant's unique identifier and authorization authentication key by calling the initialization interface. This initialization request data packet, after password verification and routing by the API gateway, triggers the multi-tenant configuration center to match and return the corresponding configuration template set. The embedded UI component library completes the customized assignment of theme colors and brand assets based on this configuration template set, thereby dynamically rendering the corresponding market data cards, candlestick charts, depth charts, and quick trading panel components, etc., into a visual interface. When the visual interface receives a touch input operation and triggers an external subscription, the platform establishes a WebSocket long connection. The market analysis engine performs intersection filtering on the global trading pair set and the target tradable currency list based on extracted parameters, generating isolated real-time market data streams that are continuously distributed. The embedded UI component library extracts price and volume parameters from the data stream, simultaneously performs moving average calculations, and dynamically plots the resulting moving average technical indicators within the chart visualization area. During the business execution phase, the quick trading panel component captures order instructions and generates a business request data packet. The client SDK intercepts this request and forwards it to the unified account federation service via the API gateway. The unified account federation service retrieves the joint query key value (composed of the tenant's unique identifier and the user's unique identifier carried in the request) from the federated mapping table. Through account mapping conversion operations, it quickly locks or creates the corresponding internal account ID, thereby achieving cross-system identity association. After obtaining the internal account ID, the platform simultaneously calls a third-party identity verification node to perform compliance pre-checks and compares the single transaction limit with the daily cumulative limit for risk control threshold verification. After successful verification, standardized order data records are generated based on business instructions. Orders are then routed and distributed to in-memory matching nodes or the most cost-effective external exchange interface according to the internal liquidity affiliation of the trading pair. Once matching is complete, the transaction execution engine updates the bound asset data. In the multi-terminal collaborative clearing sequence, transaction details are asynchronously delivered to a message queue to decouple the core transaction chain. The backend service cluster continuously monitors the message queue, recalculates the virtual asset status, and sends asynchronous notifications to partners' own systems to synchronously update traditional asset data.When a terminal triggers a total asset query operation, the platform concurrently retrieves traditional asset data sets and virtual asset data sets to perform aggregation calculations, deriving the total value of a unified asset view across markets and transmitting it through the platform. This ultimately drives a partial refresh of the asset cards on the display interface (HK 30135239 A Manual 18), achieving a seamless, one-stop integration and display of traditional financial assets and virtual encrypted assets on the same platform. Specific application example: Considering the actual concurrent request nodes for large-scale encrypted asset transactions across settlement networks under high-frequency market volatility, the platform sets the following constants and initial parameters: The tolerance time window for data snapshot alignment is set to a very small constant of 50.0ms; the single transaction limit threshold is set to 50,000.0 USDT; the daily cumulative limit threshold is set to 200,000.0 USDT; and the network timeout discard threshold for third-party authentication nodes is set to 1.5s. During the unified asset view assembly process in the backend service cluster, the backend service cluster decouples and eliminates differences in the pricing base currency in the multi-source heterogeneous original data streams. At a specific business trigger moment, the backend service cluster retrieves the traditional asset data set returned by the partner. The fiat currency valuation of a single traditional asset in the stock account is USD 15,000.5, and the fiat currency valuation of a single traditional asset in the fund account is USD 5,000.0. Based on the current platform timestamp alignment, the backend service cluster simultaneously calls the real-time exchange rate oracle interface to extract the fiat currency valuations of single virtual assets in the virtual asset data set corresponding to the internal account IDs, which are USD 25,000.5 and USD 10,000.0, respectively. The backend service cluster calculates the total value of the unified asset view using the asset aggregation calculation formula: Atotal = i∈AtradV trad(i) + j∈AvaV va(j) = (15000.5 + 5000.0) + (25000.5 + 10000.0) = 55001.0; The backend service cluster uses the calculated 55001.0 USD as a pure cross-market unified asset and appends it to the asset aggregation response message, thereby eliminating false differences caused by settlement delays in heterogeneous networks and preventing value distortion or overcompensation during asset recalculation. When the platform receives a frontend business request and prepares to execute the order flow pre-check, the compliance adaptation layer dynamically quantifies the risk control compliance boundary. The compliance adaptation layer retrieves the cumulative transaction amount of 160000.0 USDT for the day that matches the timestamp binding from the internal database based on the internal account ID. The time frame of this transaction (HK 30135239 A) shows that the single order amount parsed from the business request data packet is 45,000.0 USDT.The compliance adaptation layer substitutes the formula to perform risk control threshold verification calculation: Rcheck = (Vorder ≤ Tsingle) ∧ (Vorder + Vdaily ≤ Tdaily) = (45000.0 ≤ 50000.0 ∧ (45000.0 + 160000.0 ≤ 200000.0) = True ∧ False = False; The calculated risk control verification result, a boolean value of False, is called by the transaction execution engine. Combined with the current order status, it generates an over-limit rejection verification result data packet, which is used to constrain the stack search space of downstream memory matching nodes to ensure that no destructive money laundering compliance limit penetration occurs during high-frequency transactions. As real-time market data streams are continuously distributed and executed, the market analysis engine completes multiple rounds of data stream filtering operations. After the latest round of subscription triggering is completed, the market analysis engine aggregates and calculates a global trading pair set containing 1500 full trading pairs, and extracts a list of tradable currencies containing 200 from the configuration template set. The target element. The market analysis engine substitutes the formula to calculate the number of displayable trading pairs after intersection filtering: Sdisplay = Sglobal ∩ Spairs = 1500 ∩ 200 = 180; Since the calculated displayable trading pair set only contains 180 elements, 1320 redundant out-of-order items are intercepted and removed. The judgment condition is triggered, and the market analysis engine immediately generates mutually isolated real-time market data streams internally, ends the filtering, and locks the push encapsulation state of the current data packet. Actual operation tests are conducted and the data is compared. The specific verification results are shown in Figures 3 and 4. The experiment selects the concurrent operation data of the traditional multi-channel isolated trading platform and the platform of this invention under the same high-frequency oscillating market and the same initial capital distribution conditions for comparison. The experimental data records are shown in Table 1. Table 1. Comparison of operation data between the traditional multi-channel isolated trading platform and the platform of this invention Platform type Asset aggregation response total time (ms) Peak concurrent risk control verification time (ms) Comprehensive slippage cost (%) Excess violation order leakage rate (%) HK 30135239 A Instruction Manual 20 Traditional Multi-Channel Isolated Trading Platform 850.5 215.0 0.25 1.5 Invention Platform 110.2 25.5 0.05 0.0 Referring to Figure 3 and Table 1, Figure 3 shows a comparison of the convergence of asset aggregation response times for each platform under high-frequency concurrency scenarios. The horizontal axis represents the number of concurrent requests, in times / second, ranging from 1000 to 10000. The vertical axis represents the asset aggregation response time, in milliseconds, ranging from 0 to 1000. The figure includes the time curves of the traditional platform and the time curve of the invention platform.In the initial stage when concurrent requests increased from 1000 to 3000, traditional platforms did not decouple the underlying asset settlement network, resulting in asynchronous congestion offset in the gateway during the aggregation phase. This caused a rapid increase in response time, which then stalled in the later stages, ultimately reaching a response time of 850.5ms that could not be further eliminated. The platform of this invention extracts a unified system baseline timestamp to remove latency components and performs parallel optimization based on OAuth2 and REST protocols. The response time curve of this platform exhibits a stable trend with an extremely low slope. It can quickly penetrate the congestion bottleneck of traditional platforms when the concurrency reaches 10000, and the final response time is strictly controlled within 110.2ms, completely eliminating the response tolerance accumulation phenomenon caused by serial queries. Referring to Figure 4 and Table 1, Figure 4 shows the slippage control and anti-over-limit boundary status of intelligent routing and compliance interception during the adjustment process. The horizontal axis represents the execution adjustment time in seconds, ranging from 0 to 60. The left vertical axis represents the overall slippage cost, expressed as a percentage, ranging from 0 to 0.4; the right vertical axis represents the ratio of daily cumulative transaction amount to daily cumulative limit threshold, dimensionless, ranging from 0 to 1.2. The figure includes a slippage cost curve for traditional platforms, a slippage cost curve for the platform of this invention, and a boundary state curve for preventing over-limit operations. Around 40 seconds into the adjustment process, due to drastic fluctuations in external exchange liquidity, the fee difference between pending orders and takers increases sharply. Traditional platforms, lacking multi-dimensional intelligent routing boundary constraints, directly direct to a single, insufficiently deep data interface, leading to sudden slippage deviations. The highest overall slippage cost shown in the traditional platform's slippage cost curve reaches 0.35%. The platform of this invention dynamically compares the depth data returned by each data interface by activating the intelligent routing module and extracts inflection points to quantify the true fee rate, and constructs an anti-over-limit boundary constraint inequality mechanism in the compliance adaptation layer. As can be seen from the boundary state curve of this invention, when the cumulative amount approaches the risk control limit threshold, the platform automatically rejects or terminates incremental business requests that exceed the threshold, strictly limiting the ratio below the critical limit of 1.0 (the highest peak value only reaches 0.98), thus keeping the platform's state within the compliance and security control envelope. This ensures that the excess and non-compliant order leakage rate remains at 0.0% throughout the entire adjustment period, guaranteeing compliance and fund flow security in the platform's high-frequency trading environment.HK 30135239 A Claim 1 1. A virtualized encrypted asset trading platform, characterized in that it comprises a client SDK, a partner APP, an API gateway, and a backend service cluster; the client SDK is embedded and installed within the partner APP and connected to the API gateway, the API gateway is connected to the backend service cluster, and the backend service cluster includes a multi-tenant configuration center, a unified account federation service, a market analysis engine, a transaction execution engine, and a compliance adaptation layer; the client SDK intercepts business requests, encapsulates them to generate business request data packets containing a tenant unique identifier and a user unique identifier, and sends them to the API gateway; the API gateway routes and forwards the business request data packets to the unified account federation service; the unified account federation service extracts the tenant unique identifier and the user unique identifier from the business request data packets, performs an account mapping conversion operation to obtain an internal account ID, encapsulates the internal account ID into a mapping response message, and sends it to the transaction execution engine; the transaction execution engine combines the business request data packet containing the order placement instruction with the internal account ID in the mapping response message to generate standardized order data records and execute order matching processing. 2. According to claim 1, a virtualized encrypted asset trading platform is characterized in that the client SDK contains an embedded UI component library. When the partner APP starts, the client SDK is triggered to call the initialization interface. The client SDK generates an initialization request data packet containing the tenant's unique identifier and authorization authentication key and sends it to the API gateway. The API gateway receives the initialization request data packet and performs password comparison verification on the authorization authentication key. After the verification is successful, it routes and forwards the network request containing the initialization request data packet to the multi-tenant configuration center according to the tenant's unique identifier. The multi-tenant configuration center performs a matching query in its internal storage space according to the parsed tenant's unique identifier to extract a configuration template set and returns it to the client SDK. The client SDK receives the configuration template set and stores it in its local cache space. It extracts the theme color variable and brand asset variable and inputs them into the embedded UI component library. The embedded UI component library performs interface rendering calculations according to the theme color variable and the brand asset variable and displays the rendered components on the display interface of the partner APP.3. A virtualized encrypted asset trading platform according to claim 1, characterized in that: the client SDK receives an external subscription trigger instruction; the client SDK establishes a WebSocket communication connection with the API gateway; the client SDK sends a subscription request message containing the tenant's unique identifier to the API gateway; the API gateway keeps the WebSocket communication connection continuously open and forwards the subscription request message to the market analysis engine; the market analysis engine parses the tenant's unique identifier, encapsulates it into a query message, and sends it to the multi-tenant configuration center to obtain the corresponding list of tradable currencies; the market analysis engine performs an intersection filtering operation on the global trading pair set and the list of tradable currencies to obtain a set of displayable trading pairs; based on the data contained within the set of displayable trading pairs, it encapsulates and generates mutually isolated real-time market data streams; and continuously sends the real-time market data streams to the client SDK via the API gateway and the established WebSocket communication connection. 4. According to claim 2, a virtualized encrypted asset trading platform is characterized in that the display interface of the partner APP maintains a data listening state with the embedded UI component library through an event callback interface; the embedded UI component library detects that a long-press operation command is received within the coordinate area of ​​the K-line chart component and captures the pixel coordinate points on the screen; the embedded UI component library maps the pixel coordinate points to the vertical axis price scale to extract the target price data and generates an event callback signal, which is then transmitted to the partner APP; the partner APP receives the event callback signal and calls the quick trading panel component, automatically filling the target price data as the default order price into the input box of the quick trading panel component; the quick trading panel component receives touch input operations and generates the business request. HK 30135239 A Claim 3 5. According to claim 1, a virtualized encrypted asset trading platform is characterized in that the specific process of the unified account federation service performing account mapping conversion operations includes: the unified account federation service concatenating the tenant's unique identifier and the user's unique identifier to generate a joint query key value, and retrieving the joint query key value from the internally maintained federation mapping table; when the joint query key value is not matched in the federation mapping table, a unique internal account ID is created and a corresponding relationship is established and stored in the federation mapping table to complete the update; when the joint query key value is matched in the federation mapping table, the unified account federation service reads the mapped internal account ID from the federation mapping table.6. A virtualized encrypted asset trading platform according to claim 1, characterized in that, after the transaction execution engine generates the standardized order data record, it sends a risk control pre-inspection request to the compliance adaptation layer; the compliance adaptation layer extracts the internal account ID from the risk control pre-inspection request, sends the internal account ID to a third-party authentication node through an external network interface, and receives the returned AML compliance identifier and KYC level data; the compliance adaptation layer sends a configuration pull instruction containing a tenant's unique identifier to the multi-tenant configuration center to obtain a configuration template set, extracts the compliance level requirements set within the configuration template set, and compares the KYC level data with the compliance level requirements; when the KYC level data is lower than the compliance level requirements or the AML compliance identifier is an abnormal flag, an interception instruction data packet is generated and returned to the transaction execution engine, and the standardized order data record is discarded; when the KYC level data is equal to or higher than the compliance level requirements and the AML compliance identifier is a normal flag, risk control threshold verification calculation is initiated. 7. According to claim 6, a virtualized encrypted asset trading platform is characterized in that the specific steps of the compliance adaptation layer initiating risk control threshold verification calculation include: retrieving and reading the cumulative transaction amount of the day bound to the internal account ID in the internal database, and extracting the set single transaction limit threshold and daily cumulative limit threshold; the compliance adaptation layer performs the risk control threshold verification calculation to obtain the risk control verification result; HK 30135239 A Claim 4 When the risk control verification result is that the single order amount is less than or equal to the single transaction limit threshold and the sum of the single order amount and the cumulative transaction amount of the day is less than or equal to the daily cumulative limit threshold, a release verification result data packet is generated and sent to the transaction execution engine; When the risk control verification result is that the single order amount is greater than the single transaction limit threshold or the sum of the single order amount and the cumulative transaction amount of the day is greater than the daily cumulative limit threshold, an over-limit rejection verification result data packet is generated and sent to the transaction execution engine; the transaction execution engine terminates the flow operation of the standardized order data record according to the received over-limit rejection verification result data packet and sends it to the API. The gateway sends a response message.8. A virtualized encrypted asset trading platform according to claim 7, characterized in that: the trading execution engine reads the trading pair identifier in the standardized order data record according to the received release verification result data packet, and determines whether the trading pair identifier belongs to the coverage of the internal liquidity pool, i.e., the memory order book; when the trading pair identifier belongs to the coverage of the internal liquidity pool, i.e., the memory order book, the trading execution engine routes and distributes the standardized order data record to the memory matching node within the trading execution engine, and the memory matching node compares the order price values ​​of the same-direction orders in the memory order book and performs sorting; the memory matching node extracts the buy order at the head of the queue and the sell order at the head of the queue, and when the price crossover condition is met, performs an equal deduction operation on the order quantity value and generates a transaction detail data packet according to the deduction result; the trading execution engine stores the transaction detail data packet and updates the asset data bound to the internal account ID. 9. According to claim 8, a virtualized encrypted asset trading platform is characterized in that, when the trading pair identifier does not belong to the coverage of the internal liquidity pool (i.e., the in-memory order book) or the depth of the internal liquidity pool (i.e., the in-memory order book) is insufficient, the trading execution engine activates the internally integrated intelligent routing module; the intelligent routing module acquires in parallel the real-time depth data and order book fee tier data returned by multiple external exchange data interfaces, compares the timestamp returned by each data interface with the current system time difference, and automatically removes expired abnormal data that exceeds the tolerance time window; the intelligent routing module calculates slippage cost based on the filtered and aligned effective depth data and the order quantity of the standardized order data record, selects the external exchange data interface with the lowest comprehensive slippage and fee cost, and routes and distributes the standardized order data record to the selected external exchange data interface.10. A virtualized encrypted asset trading platform according to claim 1, characterized in that: the client SDK generates an asset query instruction and encapsulates it with an access token into an asset query request data packet, which is then sent to the API gateway; the API gateway routes and forwards the asset query request data packet to the backend service cluster to complete authentication and verification; the backend service cluster sends a traditional asset retrieval request to the partner's own system to obtain a traditional asset data set, and simultaneously retrieves the virtual asset data set corresponding to the internal account ID in the internal storage space; it aligns the data snapshots of the two types of assets according to a unified system benchmark timestamp and synchronously calls the real-time exchange rate oracle interface to obtain the conversion factor; the backend service cluster calculates the fiat currency valuation of each traditional asset in the traditional asset data set and the fiat currency valuation of each virtual asset in the virtual asset data set according to the conversion factor, and performs a summation operation on the fiat currency valuations of each traditional asset and each virtual asset to obtain the total value of the unified asset view, and generates an asset aggregation response message which is returned to the client SDK via the API gateway. HK 30135239 A Instruction Manual Appendix 1 Figure 1 Figure 2 HK 30135239 A Instruction Manual Appendix 2 Figure 3 Figure 4 HK 30135239 A.