A foreign exchange quantitative trading method and system

Abstract

The application discloses a foreign exchange quantitative transaction method and system, and the method comprises the following steps: accessing the market data interface and transaction interface of multiple foreign exchange brokers in parallel through a plug-in interface adaptation layer, and generating real-time market data stream in a unified format; generating standardized transaction instructions based on the real-time market data stream; according to the standardized transaction instructions, dynamic routing selection is performed in combination with the interface connection state and account risk control rules, the instructions are dispatched to the optimal broker channel, and an executable order set with an execution priority label is formed; the executable order set is executed, and the order state, network delay and system resource occupation are tracked in real time through a full-link monitoring module; when an exception or a performance threshold breakthrough is detected, an early warning is automatically triggered, and a visual monitoring report is generated. By using the embodiment of the application, real-time sensing and early warning of abnormal conditions can be realized, transaction delay can be reduced, system stability and fund use efficiency can be improved.

Description

Technical Field

[0001] This invention belongs to the field of financial technology, specifically a method and system for quantitative foreign exchange trading. Background Technology

[0002] With the rapid development of the global foreign exchange market, quantitative trading has become a core trading method for institutional investors. Existing quantitative forex trading systems typically interface directly with a single broker, lacking the ability to process multi-source heterogeneous data uniformly, resulting in poor system scalability and high switching costs. Furthermore, differences in data formats and time bases among different brokers make market data alignment difficult, directly impacting the accuracy of spread calculations and arbitrage opportunity identification. In addition, traditional systems often employ fixed routing strategies after generating trading orders, failing to comprehensively consider factors such as broker channel status and real-time account risk control, easily leading to missed optimal execution opportunities due to network latency or channel congestion. When anomalies occur in the trading chain, existing monitoring methods are mostly reactive, lacking a real-time tracking and dynamic response mechanism across the entire chain, making it difficult to meet the stringent requirements of low latency and high reliability in high-frequency trading scenarios. Summary of the Invention

[0003] The purpose of this invention is to provide a foreign exchange quantitative trading method and system to address the shortcomings of existing technologies, enabling real-time perception and early warning of abnormal situations, reducing trading delays, improving system stability, and increasing capital utilization efficiency.

[0004] One embodiment of this application provides a foreign exchange quantitative trading method, the method comprising: By using a plug-in interface adaptation layer to connect to the market data interfaces and trading interfaces of multiple forex brokers in parallel, heterogeneous data streams are standardized and timestamp aligned to generate real-time market data streams in a unified format. Based on the real-time market data stream, a full-memory computing engine is used to calculate price spreads and generate signals. A customized algorithm trading framework is then used to execute multi-factor strategy decisions and generate standardized trading instructions. Based on the standardized trading instructions, dynamic routing is performed by combining the connection status of each interface and account risk control rules to distribute the instructions to the optimal broker channel, forming a set of executable orders with execution priority markers; The executable order set is executed, and the order status, network latency, and system resource usage are tracked in real time through the end-to-end monitoring module. When an anomaly or performance threshold is detected, an early warning is automatically triggered and a visual monitoring report is generated.

[0005] Optionally, the step of parallel access to the market data interfaces and trading interfaces of multiple forex brokers through a plug-in interface adaptation layer, standardizing and aligning heterogeneous data streams with timestamps to generate a unified format of real-time market data stream includes: Develop an independent plug-in module for each forex broker to be connected. This module implements the broker’s unique communication protocol and data format encapsulation logic. Register all plug-in modules to the plug-in manager of the plug-in interface adaptation layer and generate a plug-in configuration list. According to the plugin configuration list, the plugin manager starts the connection threads of each plugin module in parallel, establishes network connections with the corresponding broker's market data interface and trading interface, and begins to receive raw data streams to obtain an initial set of heterogeneous data streams. Each plug-in module calls the built-in parser to parse the initial heterogeneous data stream set, extracts the key fields of the market data, and uses a clock-based synchronization algorithm to timestamp-align all data to generate time-synchronized parsed data. The parsed data after time synchronization is input into a standardized encoder, which performs format conversion and field mapping according to a preset unified data model to generate a standard format real-time market data stream.

[0006] Optionally, the step of calculating price spreads and generating signals using a full-memory computing engine based on the real-time market data stream, and executing multi-factor strategy decisions through a customized algorithmic trading framework to generate standardized trading instructions includes: The system continuously subscribes to real-time market data streams in standard format from the data bus and loads them into the memory data structure of the all-memory computing engine to generate memory-resident market snapshots. The full-memory computing engine calculates the instantaneous price difference, moving average price difference, and price difference volatility between multiple currency pairs in real time based on memory-resident market snapshots, generating multi-dimensional price difference indicators; Input the multi-dimensional price spread indicator into the signal generator. The signal generator judges trading opportunities based on preset threshold rules or statistical models and generates raw trading signals containing direction, price and currency pair information. The original trading signals are input into a customized algorithmic trading framework, which runs multiple quantitative factor models in parallel to comprehensively evaluate and make decisions on the signals, and outputs standardized trading instructions with specific trading volume, stop-loss and take-profit parameters.

[0007] Optionally, the step of dynamically routing the standardized trading instructions based on the connection status of each interface and account risk control rules to the optimal brokerage channel, forming a set of executable orders with execution priority markers, includes: The system obtains real-time network latency, connection stability, and current quote depth reported by each broker's plug-in module, and combines this with the single transaction limit and daily cumulative limit in the account risk control rules to generate a broker channel status and risk control assessment matrix. Based on the currency pair and trading volume in the standardized trading instructions, a list of candidate channels that meet all risk control constraints and are in normal status is selected from the broker channel status and risk control assessment matrix, and an available channel list is generated. A dynamic routing algorithm is used to comprehensively score each channel in the available channel list. The scoring factors include the estimated transaction price, transaction cost, network latency, and execution success rate, and a channel priority ranking list is generated. Based on the channel priority sorting list, standardized trading instructions are bound to the optimal channel and marked with execution priority, and finally aggregated to form a set of executable orders with execution priority marks.

[0008] Optionally, the execution of the executable order set and the real-time tracking of order status, network latency, and system resource usage through the end-to-end monitoring module, when an anomaly or performance threshold breach is detected, automatically triggers an alert and generates a visual monitoring report, including: The set of executable orders is sent to the corresponding broker plug-in module for execution. The end-to-end monitoring module simultaneously starts tracking the status flow of each order, the network latency from the issuance of the instruction to the broker's confirmation, and the system's CPU and memory usage, generating the raw monitoring data stream. The raw monitoring data stream is input into the real-time analysis engine and compared with preset performance thresholds and normal status modes to identify abnormal events, including order execution timeouts, sudden increases in network latency, or system resource overload, and generate abnormal event alarms. After an abnormal event alarm is triggered, the preset emergency operation is executed according to the alarm level, such as pausing new order routing, switching to a backup channel, or restarting the abnormal plugin, and an emergency response log is recorded to generate an alarm handling record. The system integrates raw monitoring data streams, abnormal event alarms, and alarm handling records. The report generator then generates visual monitoring reports containing charts of key performance indicators and lists of abnormal events on a scheduled or triggered basis according to preset templates.

[0009] Another embodiment of this application provides a foreign exchange quantitative trading system, the system comprising: The parsing module is used to connect to the market data interfaces and trading interfaces of multiple forex brokers in parallel through a plug-in interface adaptation layer, and to perform standardized parsing and timestamp alignment on heterogeneous data streams to generate real-time market data streams in a unified format. The execution module is used to calculate price spreads and generate signals based on the real-time market data stream using a full-memory computing engine, and to execute multi-factor strategy decisions through a customized algorithm trading framework to generate standardized trading instructions. The dispatch module is used to dynamically route the standardized trading instructions based on the connection status of each interface and the account risk control rules, and dispatch the instructions to the optimal broker channel to form a set of executable orders with execution priority tags. The tracking module is used to execute the set of executable orders and track the order status, network latency and system resource usage in real time through the end-to-end monitoring module. When an anomaly or performance threshold is detected, an early warning is automatically triggered and a visual monitoring report is generated.

[0010] Another embodiment of this application provides a storage medium storing a computer program, wherein the computer program is configured to execute the method described in any of the preceding claims when running.

[0011] Another embodiment of this application provides an electronic device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the method described in any of the preceding claims.

[0012] Compared with existing technologies, the foreign exchange quantitative trading method provided by this invention can realize real-time perception and early warning of abnormal situations, reduce trading delays, improve system stability and capital utilization efficiency. Attached Figure Description

[0013] Figure 1 A hardware structure block diagram of a computer terminal for a foreign exchange quantitative trading method provided in an embodiment of the present invention; Figure 2 A flowchart illustrating a foreign exchange quantitative trading method provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a foreign exchange quantitative trading system provided in an embodiment of the present invention; Figure 4 This is a framework diagram of a quantitative trading system provided in an embodiment of the present invention. Detailed Implementation

[0014] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0015] This invention first provides a method for quantitative foreign exchange trading, which can be applied to electronic devices, such as computer terminals, specifically ordinary computers.

[0016] The following detailed explanation uses a computer terminal as an example. Figure 1 This is a hardware structure block diagram of a computer terminal for a foreign exchange quantitative trading method provided in an embodiment of the present invention. Figure 1As shown, the computer device includes a processor, memory, and network interface connected via a system bus, wherein the memory may include non-volatile storage media and internal memory.

[0017] See Figure 2 and Figure 4 The present invention provides a method for quantitative foreign exchange trading, which may include the following steps: S201 connects to the market data interfaces and trading interfaces of multiple forex brokers in parallel through a plug-in interface adaptation layer, performs standardized parsing and timestamp alignment on heterogeneous data streams, and generates real-time market data streams in a unified format. Specifically, an independent plug-in module can be developed for each forex broker to be connected. This module implements the broker’s unique communication protocol and data format encapsulation logic, and all plug-in modules are registered in the plug-in interface adaptation layer’s plug-in manager to generate a plug-in configuration list. The core of this step is to achieve differentiated adaptation and unified management of multiple brokerage interfaces. This is achieved through independent plug-in modules that address the heterogeneity of communication protocols and data formats among different brokerages. The specific implementation method is as follows: Each forex broker has its own unique communication protocol and data format. Independent plug-in modules need to implement specific adaptation logic to ensure normal communication and data parsing with the broker's interface. The plug-in modules adopt a lightweight architecture, containing only three core functional modules: communication adaptation, data encapsulation, and exception handling, avoiding redundant functions that could affect data transmission efficiency. Communication protocol adaptation must be compatible with the broker's commonly used protocol types, implementing complete logic for protocol handshakes, data transmission, and disconnection. For example, if a broker uses a proprietary TCP-based protocol, the plug-in module will specifically implement the connection establishment mechanism for that protocol, setting a connection timeout of 3 seconds and 3 retries to ensure connection stability. If another broker uses the HTTP protocol for data transmission, the plug-in module will implement HTTP request encapsulation and response parsing, setting a request interval of 100 milliseconds to adapt to its data push frequency.

[0018] The data format encapsulation logic needs to correspond to the broker's original data format. It involves initially organizing the heterogeneous original data fields, extracting core fields, and temporarily encapsulating them for easier subsequent unified parsing. For example, if a broker transmits market data in JSON format with fields named "bid_price", "ask_price", and "trade_time", the plugin module encapsulates the corresponding parsing logic to identify the format and extract the aforementioned core fields. If another broker transmits data in binary format, the plugin module implements the binary data decoding logic, extracting the corresponding market data and trading-related fields according to the agreed byte length and encoding rules.

[0019] After the plugin module is developed, it needs to be registered with the plugin manager of the plug-in interface adaptation layer. The registration process requires submitting core information such as the plugin name, the adapted broker identifier, the communication protocol type, the interface address, and the data format type. The plugin manager verifies the legality of the submitted plugin module. Once the verification is successful, it is included in the management scope, and a plugin configuration list is generated. The plugin configuration list records the complete information of all registered plugins, including the plugin's unique ID, the adapted broker, communication parameters, and status indicators (enabled / disabled), which facilitates unified scheduling by the plugin manager. For example, a record in the plugin configuration list might be: Plugin ID=PLG001, Broker Identifier=BROKER01, Protocol Type=TCP Private Protocol, Interface Address=xxx.xxx.xxx.xxx:8080, Status=Enabled, providing a basis for subsequent parallel plugin launches.

[0020] According to the plugin configuration list, the plugin manager starts the connection threads of each plugin module in parallel, establishes network connections with the corresponding broker's market data interface and trading interface, and begins to receive raw data streams to obtain an initial set of heterogeneous data streams. The core of this step is to enable simultaneous connections to multiple brokerage interfaces through parallel thread startup, efficiently receiving raw data and forming an initial heterogeneous data stream set. The specific implementation method is as follows: After reading the plugin configuration list, the plugin manager filters out plugin modules with the status "enabled". A multi-threaded parallel startup mechanism is adopted, allocating an independent connection thread to each enabled plugin module. All threads are set to the same priority level to avoid any single plugin thread consuming excessive system resources, ensuring that all plugin modules start synchronously and run in parallel. After the thread starts, each plugin module actively establishes a network connection with the corresponding broker's market data interface and trading interface according to the interface address and communication protocol parameters in the configuration list. The connection status is reported in real time during the connection process. The plugin manager monitors the connection process. If a plugin module's connection times out (exceeding the preset 3 seconds) or fails, a retry mechanism is triggered. If the connection fails after 3 retries, the plugin status is marked as "abnormal", the reason for the failure is recorded, and the plugin manager is notified not to assign data receiving tasks to it again.

[0021] Once the network connection is successfully established, the plug-in module begins to continuously receive raw data streams pushed by the broker. The market data interface mainly pushes real-time buy price, sell price, latest transaction price, and trading volume for each currency pair. The push frequency is set according to the broker's interface, usually 50-100 milliseconds per message. The trading interface mainly pushes data such as account order status, transaction records, and fund changes, with a push frequency of 100-200 milliseconds per message. Due to differences in communication protocols, data formats, and push frequencies among different brokers, the received raw data streams exhibit significant heterogeneity. This means that the data fields, field names, data types, and push cycles are all inconsistent. For example, the EUR / USD market data pushed by broker A contains four fields: "bid", "ask", "last", and "vol", with the data type being string. The same currency pair market data pushed by broker B contains four fields: "buy_price", "sell_price", "trade_price", and "trade_volume", with the data type being numeric. After these raw data in different formats are aggregated, they form an initial heterogeneous data stream set. The plug-in module temporarily caches the received raw data in a memory buffer to ensure that the data is not lost, and at the same time, it reports the data reception status to the plug-in manager in real time.

[0022] Each plug-in module calls the built-in parser to parse the initial heterogeneous data stream set, extracts the key fields of the market data, and uses a clock-based synchronization algorithm to timestamp-align all data to generate time-synchronized parsed data. The core of this step is to complete the parsing and time synchronization of heterogeneous data, extract effective key fields, eliminate timestamp discrepancies, and lay the foundation for subsequent standardization processing. The specific implementation method is as follows: Each plugin module's built-in parser is fully compatible with the broker's data format. During parsing, the parser extracts key fields from the initial heterogeneous data stream according to preset field mapping rules. These key fields are uniformly defined as currency pair identifier, bid price, ask price, latest transaction price, trading volume, and data generation timestamp. This ensures that the core fields extracted by each plugin module are consistent, facilitating subsequent processing. For example, if a plugin module receives raw data in binary format, the parser, following the broker's agreed encoding rules, decodes and extracts the following: currency pair identifier = EURUSD, bid price = 1.0920, ask price = 1.0925, latest transaction price = 1.0923, trading volume = 1500 lots, and data generation timestamp = 16999999999000 (milliseconds). If another plugin module receives raw data in JSON format, the parser extracts the same fields, only mapping the field name "bid_price" to the bid price and "ask_price" to the ask price, ensuring field consistency.

[0023] After parsing, all data needs to be timestamped. Due to clock discrepancies between different brokers, data pushed at the same time may have inconsistent timestamps, with discrepancies typically ranging from 10 to 50 milliseconds. Failure to align these timestamps can lead to errors in subsequent spread calculations and strategy decisions. Therefore, a clock-based synchronization algorithm is used to achieve timestamp alignment. This algorithm uses the system clock of the plug-in interface adaptation layer as a unified reference clock. The system clock is synchronized with the international standard clock via the NTP protocol, achieving a synchronization accuracy within 1 millisecond. The core of the algorithm is to calculate the deviation between the timestamp of the data received by each plug-in module and the system reference clock, and then correct the data timestamps. The correction formula is t_sync = t_original + Δt, where t_sync is the aligned timestamp, t_original is the original data timestamp, and Δt is the time deviation (Δt = system reference clock time - plug-in module local clock time).

[0024] In the example, the system base clock time is 1699999999500 milliseconds, and the local clock time of a certain plug-in module is 1699999999480 milliseconds. Δt = 20 milliseconds. After parsing by this plug-in module, the original timestamp of a certain piece of data is 1699999999490 milliseconds, and after correction, t_sync = 1699999999490 + 20 = 1699999999510 milliseconds. Another plug-in module has a Δt = 15 milliseconds, and its original timestamp is 1699999999495 milliseconds, which is corrected to 1699999999510 milliseconds. This achieves the alignment of two pieces of data from different sources and with different original timestamps to the same millisecond-level timestamp. After all data is corrected, parsed data with time synchronization is generated to ensure the consistency of data timing.

[0025] The parsed data after time synchronization is input into a standardized encoder, which performs format conversion and field mapping according to a preset unified data model to generate a standard format real-time market data stream.

[0026] The core of this step is to eliminate the residual heterogeneity of the parsed data, standardize the format through a unified data model, and generate a real-time market data stream that can be directly used for subsequent strategy decisions. The specific implementation method is as follows: A pre-defined unified data model is the core basis for standardized processing. This model clearly defines the field names, field types, field lengths, value ranges, and format specifications of real-time market data streams. All parsed data must be converted and mapped according to this model to ensure that the output data format is completely consistent. The core fields included in the unified data model are: unique data identifier, currency pair identifier (using international standard encoding, such as EURUSD, GBPUSD), timestamp (millisecond level, synchronized with the system base clock), bid price (four decimal places, numeric), ask price (four decimal places, numeric), latest transaction price (four decimal places, numeric), trading volume (unit: lots, integer), data type (market data / trading data, enumerated), source plugin ID, and data quality identifier (normal / abnormal, enumerated). The specifications for each field are clearly defined to avoid ambiguity.

[0027] The core function of the standardized encoder is to map and convert the fields of the parsed data to a unified data model. During the mapping process, it performs one-to-one or many-to-one mappings to address the differences between the fields in the parsed data and the fields in the unified model, while also correcting data types and formats. For example, if the "buy price" field in the parsed data is a string, the standardized encoder converts it to a numeric type, retaining four decimal places. If a broker's parsed data does not directly provide a "data quality identifier," the encoder automatically judges based on trading volume and price fluctuation range. If the trading volume is 0 and the price deviates from the previous time by more than 0.001, it is marked as abnormal; otherwise, it is marked as normal. If there are abbreviation differences in the "currency pair identifier" parsed by different plug-in modules, the encoder maps them according to the international standard encoding of the unified model to ensure the consistency of currency pair identifiers.

[0028] In the example, the data parsed by a certain plugin module is as follows: Currency Pair Identifier = EUR-USD, Buy Price = "1.0920", Sell Price = "1.0925", Latest Transaction Price = "1.0923", Volume = "1500", Timestamp = 1699999999510, Data Quality = Normal. The standardized encoder converts it into a unified format: Unique Data Identifier = DATA0001, Currency Pair Identifier = EURUSD, Timestamp = 1699999999510, Buy Price = 1.0920, Sell Price = 1.0925, Latest Transaction Price = 1.0923, Volume = 1500, Data Type = Market Data, Source Plugin ID = PLG001, Data Quality Identifier = Normal. All time-synchronized parsed data undergoes the above transformation and mapping, and is then aggregated into a standard format real-time market data stream. This data stream has a unified format, consistent timing, and complete fields, and can be directly pushed to the data bus for use by the subsequent all-in-memory computing engine, ensuring the accuracy of subsequent price spread calculations and strategy decisions.

[0029] S202, based on the real-time market data stream, use a full-memory computing engine to calculate price differences and generate signals, and execute multi-factor strategy decisions through a customized algorithm trading framework to generate standardized trading instructions; Specifically, it can continuously subscribe to real-time market data streams in standard format from the data bus and load them into the memory data structure of the full-memory computing engine to generate memory-resident market snapshots; The core of this step is to achieve efficient acquisition and rapid storage of real-time market data. By eliminating disk I / O latency through full-memory computing, it generates time-series-coherent and field-complete market snapshots, providing high-speed data support for subsequent real-time computing and strategy decision-making. The specific implementation method is as follows: The data bus is the core data transmission channel connecting the plug-in interface adaptation layer and the full-memory computing engine. It adopts a publish-subscribe model, receiving real-time market data streams in standard format from the plug-in adaptation layer and continuously pushing them to the full-memory computing engine. The push frequency is consistent with the original data acquisition frequency, at 50-100 milliseconds per data entry, ensuring data timeliness. The full-memory computing engine accesses the data bus through a dedicated subscription interface. During the subscription process, data filtering rules are set to only receive market data types (excluding transaction data types), and data marked as "abnormal" is filtered to prevent abnormal data from affecting subsequent calculation results. The subscription interface uses a long-lived connection with a 60-second connection keep-alive timeout to prevent data loss due to connection interruption.

[0030] The core advantage of the all-in-memory computing engine is that it avoids disk I / O operations, loading all received data directly into the in-memory data structure. This in-memory data structure is designed using a combination of a time-series hash table and a circular buffer. The time-series hash table stores data categorized by currency pair identifiers, facilitating quick retrieval of historical time-series data for a specific currency pair. The circular buffer stores the latest real-time data, ensuring data continuity without consuming excessive memory resources. The circular buffer has a capacity of 1000 records. When the data storage reaches its limit, it automatically overwrites the oldest record, always retaining the latest 1000 real-time records to meet the needs of short-term price spread calculations for recent data. The time-series hash table is indexed by currency pair identifiers, with each index corresponding to a dynamic array storing all historical real-time data for that currency pair. Data retention is set to 24 hours; historical data older than 24 hours is automatically cleaned up, balancing memory usage and data availability.

[0031] During data loading, the all-in-memory computing engine performs real-time validation on the received standard-format data, verifying field integrity (ensuring no missing core fields such as currency pair identifiers, timestamps, and buy prices) and data consistency (ensuring price fields are within a reasonable range, such as the EUR / USD exchange rate being between 1.0 and 1.2). Once validation passes, the data is immediately written to the in-memory data structure, and the in-memory market snapshot is updated simultaneously. The market snapshot is a real-time snapshot of the current market state, containing the latest market data, recent time-series data snippets, and statistical information for all subscribed currency pairs. Each time new data is received, the market snapshot is updated in real-time, ensuring complete synchronization between the snapshot data and the real-time market data stream. In the example, the data bus pushes a standard format data entry for EURUSD: Data Unique Identifier = DATA0002, Currency Pair Identifier = EURUSD, Timestamp = 17000000000000, Buy Price = 1.0921, Sell Price = 1.0926, Latest Transaction Price = 1.0924, Volume = 1600 lots, Data Type = Market Data, Source Plugin ID = PLG001, Data Quality Identifier = Normal. After the full-memory computing engine verifies and passes the verification, it is written to the EURUSD index in the time-series hash table. At the same time, the circular buffer and market snapshot are updated, so that the latest transaction price of EURUSD in the market snapshot is updated to 1.0924 and the volume is updated to 1600 lots, ensuring that the market snapshot reflects the current market situation in real time.

[0032] The full-memory computing engine calculates the instantaneous price difference, moving average price difference, and price difference volatility between multiple currency pairs in real time based on memory-resident market snapshots, generating multi-dimensional price difference indicators; The core of this step is to extract multi-dimensional price spread indicators that reflect market arbitrage opportunities and volatility characteristics through real-time full-memory computation, providing core data support for the generation of subsequent trading signals. Each indicator has a clear calculation logic and physical meaning, and the specific implementation method is as follows: The all-in-memory computing engine leverages the high-speed access of in-memory data to calculate three core spread indicators between multiple currency pairs in parallel based on time-series data from market snapshots. The calculation frequency matches the data reception frequency, ensuring the real-time performance of the indicators. Disk interactions are avoided during calculation, keeping latency below 10 milliseconds to meet the needs of high-frequency quantitative trading. The currency pairs used for calculation are selected from those with high market liquidity and numerous arbitrage opportunities, such as EURUSD and GBPUSD, and USDJPY and USDCHF. Each pair is preset as a fixed calculation object, while also supporting the dynamic addition of new currency pairs to adapt to different strategy requirements.

[0033] Instantaneous spread refers to the price difference between two related currency pairs at the same timestamp, reflecting the immediate arbitrage opportunity in the current market. The formula is spread_inst = price_A - price_B, where price_A and price_B are the latest transaction prices of the two related currency pairs at the same timestamp. A positive spread indicates that currency pair A is priced higher than currency pair B, and a negative spread indicates the opposite. The larger the absolute value of the spread, the more obvious the immediate arbitrage opportunity. In the example, the latest transaction price of EURUSD in the market snapshot is 1.0924, and the latest transaction price of GBPUSD is 1.2756. The instantaneous spread is calculated as spread_inst = 1.0924 - 1.2756 = -0.1832. The absolute value is 0.1832, indicating that EURUSD is currently undervalued relative to GBPUSD, presenting a potential arbitrage opportunity.

[0034] The moving average spread is used to smooth out fluctuations in instantaneous price spreads, capture the medium- to long-term trend of price spreads, and avoid misjudgments caused by short-term fluctuations. Three commonly used calculation periods are selected: 5 minutes, 15 minutes, and 30 minutes. The calculation formula for each period is spread_ma_n=(Σspread_inst_i) / n, where n is the number of time steps in the calculation period (e.g., for a 5-minute period, one data point is taken every 100 milliseconds, n=300), and spread_inst_i is the instantaneous price spread at each time step in the period. A stable moving average spread value indicates that the price spread is in a stable trend, while a continuous increase or decrease in the value indicates a clear price spread trend. In the example, the 5-minute moving average spread between EURUSD and GBPUSD is calculated. The instantaneous spread over the past 300 time steps is selected and summed to obtain Σspread_inst_i=-54.96. Substituting this into the formula, spread_ma_5=-54.96 / 300=-0.1832, which is consistent with the current instantaneous spread, indicating that the spread between the two has been in a stable trend recently.

[0035] Spread volatility measures the magnitude of price spread fluctuations, reflecting the level of arbitrage risk. The calculation period is consistent with the moving average spread, using a rolling window standard deviation. The formula is spread_vol_n = √[(1 / n) × Σ(spread_inst_i - spread_ma_n)^2], where spread_inst_i is the instantaneous price spread at each time step within the period, spread_ma_n is the moving average spread for the corresponding period, and n is the number of time steps within the calculation period. A higher spread volatility value indicates more volatile price spreads and higher arbitrage risk; a lower value indicates lower risk. In the example, calculating the 5-minute spread volatility between EURUSD and GBPUSD, the sum of the squares of the instantaneous price spread and the moving average spread within the period is 0.0012. Substituting this into the formula, we get spread_vol_5 = √(0.0012 / 300) = √0.000004 ≈ 0.002, indicating that the current price spread volatility is relatively small, and the arbitrage risk is low. The instantaneous spread of all currency pairs, the moving average spread over three periods, and the spread volatility over three periods are aggregated to generate a multi-dimensional spread indicator. Each indicator is labeled with the corresponding currency pair, calculation period, and timestamp to ensure the traceability and usability of the indicator.

[0036] Input the multi-dimensional price spread indicator into the signal generator. The signal generator judges trading opportunities based on preset threshold rules or statistical models and generates raw trading signals containing direction, price and currency pair information. The core of this step is to analyze multi-dimensional price spread indicators using a signal generator to identify effective trading opportunities and generate initial trading signals, providing a foundation for subsequent multi-factor strategy decisions. The specific implementation method is as follows: The signal generator incorporates two built-in trading opportunity judgment logics: preset threshold rules and a statistical model. These two logics run in parallel and verify each other, avoiding misjudgments caused by a single logic. Users can set the activation status and weight of the two logics according to their strategy requirements, ensuring the accuracy and reliability of the trading signals. The signal generator uses a direct memory connection with the all-in-memory computing engine. Multi-dimensional price difference indicators are pushed to the signal generator in real time without transmission delay. Each time the signal generator receives a set of indicators, it immediately executes the judgment logic to generate the corresponding original trading signal.

[0037] The preset threshold rules are fixed thresholds determined based on historical data statistics, used to quickly identify instantaneous arbitrage opportunities. For each currency pair combination, instantaneous spread thresholds and spread volatility thresholds are set separately. When the multi-dimensional spread indicators meet the threshold conditions, the corresponding original trading signal is immediately generated. The instantaneous spread threshold is divided into an upper limit threshold and a lower limit threshold. When the instantaneous spread is greater than the upper limit threshold, a signal to "short currency pair A, go long currency pair B" is generated; when the instantaneous spread is less than the lower limit threshold, a signal to "go long currency pair A, short currency pair B" is generated. The spread volatility threshold is the upper limit threshold. When the spread volatility is less than this threshold, the trading signal is confirmed as valid; if it is greater than this threshold, it indicates that the risk is too high, and the signal is rejected. In the example, the preset thresholds for the EURUSD and GBPUSD combination are: instantaneous price difference upper limit threshold = -0.17, lower limit threshold = -0.19, and price difference volatility upper limit threshold = 0.003. Currently, in the multi-dimensional price difference indicator, the instantaneous price difference = -0.1832 (between the lower and upper limits) and the price difference volatility = 0.002 (less than the upper limit), so no signal is generated. When the instantaneous price difference becomes -0.192 (less than the lower limit) and the price difference volatility = 0.002, the threshold conditions are met, and the signal generator generates the original trading signal: currency pair combination = EURUSD & GBPUSD, trading direction = long EURUSD, short GBPUSD, trigger price = EURUSD buy price 1.0921, GBPUSD sell price 1.2760, and the signal type is marked as instantaneous arbitrage signal.

[0038] The statistical model employs a mean regression model to capture medium- to long-term arbitrage opportunities in price spreads. Its core principle is based on the historical distribution of price spreads to determine whether the current price spread deviates from the mean. When the deviation reaches a certain level, the model predicts that the price spread will revert to the mean, thereby generating a trading signal. The core parameter of the mean regression model is the regression threshold (set to twice the standard deviation). During the calculation, the mean μ and standard deviation σ of the price spread are first calculated based on the price spread data from the past 24 hours. When the current instantaneous price spread > μ + 2σ, the model predicts that the price spread will decrease, generating a signal to "short currency pair A, go long currency pair B"; when the current instantaneous price spread < μ - 2σ, the model predicts that the price spread will increase, generating a signal to "go long currency pair A, short currency pair B". In the example, the average price difference between EURUSD and GBPUSD over the past 24 hours is μ = -0.183, the standard deviation is σ = 0.0025, the regression threshold is ±0.005, and the current instantaneous price difference is -0.190 (μ - 2σ = -0.183 - 0.005 = -0.188, -0.190 < -0.188), satisfying the mean reversion condition. The signal generator generates the original trading signal: currency pair combination = EURUSD & GBPUSD, trading direction = long EURUSD, short GBPUSD, trigger price = EURUSD buy price 1.0921, GBPUSD sell price 1.2760, and the signal type is labeled as a mean reversion signal. All generated original trading signals contain five core pieces of information: currency pair combination, trading direction, trigger price, signal type, and generation timestamp. These are then aggregated and pushed to the customized algorithmic trading framework for subsequent comprehensive evaluation.

[0039] The original trading signals are input into a customized algorithmic trading framework, which runs multiple quantitative factor models in parallel to comprehensively evaluate and make decisions on the signals, and outputs standardized trading instructions with specific trading volume, stop-loss and take-profit parameters.

[0040] The core of this step is to comprehensively screen and evaluate the original trading signals through a multi-quantitative factor model, determine the specific parameters of the trade, generate standardized trading instructions, and ensure the scientific nature and executableness of the trading decisions. The specific implementation method is as follows: The customized algorithmic trading framework is the core module of strategy decision-making. Employing a parallel computing architecture, it can run multiple quantitative factor models simultaneously. Each factor model focuses on a different evaluation dimension, complementing and comprehensively assessing each other to avoid the limitations of a single factor model and improve the accuracy of trading decisions. The framework's built-in quantitative factor models include trend factor models, volatility factor models, liquidity factor models, and risk factor models. Each factor model is trained and optimized based on historical data to adapt to the volatility characteristics of the forex market. Users can adjust the weights of each factor model (the sum of the weights is 1) according to their strategy needs, achieving personalized strategy decisions.

[0041] The trend factor model is used to evaluate the strength of the price spread trend corresponding to the original trading signal. Its core is based on the moving average price spread indicator, calculating a trend strength score (range 0-100). The higher the score, the more obvious the price spread trend, and the higher the reliability of the trading signal. The volatility factor model is used to evaluate the risk level of the trade. Based on the price spread volatility indicator, it calculates a risk score (range 0-100). The lower the score, the lower the risk, and the more reliable the trading signal. The liquidity factor model is used to evaluate the market liquidity of currency pair combinations. Based on the trading volume indicator, it calculates a liquidity score (range 0-100). The higher the score, the better the liquidity, and the higher the order execution success rate. The risk factor model is used to comprehensively evaluate the potential risk of the trade. Combining indicators such as price spread volatility and market sentiment, it calculates a risk warning score (range 0-100). A score below 30 triggers a risk warning, and the trading signal is rejected.

[0042] During the comprehensive evaluation process, the customized algorithmic trading framework inputs the multi-dimensional price spread and trading volume indicators corresponding to the original trading signal into various quantitative factor models. Each model outputs a corresponding score, and then a comprehensive score is calculated based on preset weights. The comprehensive score calculation formula is score_total=w1×score_trend+w2×score_vol+w3×score_liquid+w4×score_risk, where w1-w4 are the weights of the four factor models (in the example, w1=0.3, w2=0.25, w3=0.25, w4=0.2), and score_total is the comprehensive score. The preset comprehensive score threshold is 60. When the comprehensive score is ≥60, the trading signal is confirmed as valid and proceeds to the trading parameter calculation stage; when the comprehensive score is <60, the original trading signal is rejected, and no trading order is generated. In the example, after a certain original trading signal (long EURUSD, short GBPUSD) is input into the framework, the trend factor model outputs a score of 75, the volatility factor model outputs a score of 80, the liquidity factor model outputs a score of 70, and the risk factor model outputs a score of 85. Substituting these into the formula, the overall score is calculated as follows: 0.3×75 + 0.25×80 + 0.25×70 + 0.2×85 = 22.5 + 20 + 17.5 + 17 = 77 ≥ 60, confirming the signal's validity.

[0043] In the trading parameter calculation phase, the framework calculates specific trading volume and stop-loss / take-profit parameters based on the overall score, account capital size, and risk appetite. Trading volume is calculated using a fixed risk exposure method, with a preset account risk exposure of 0.5% of total account capital. The formula is volume = (account_capital × risk_ratio) / stop_loss, where account_capital is the total account capital (100,000 USD in the example), risk_ratio is the risk exposure ratio (0.5%), and stop_loss is the stop-loss point (calculated based on price spread volatility, 0.003 in the example). Substituting these values ​​into the formula yields volume = (100,000 × 0.005) / 0.003 ≈ $166,667. Based on the currency pair contract size (1 lot = $100,000), rounded to 1 lot; the stop-loss level is set at the trigger price minus twice the spread volatility. EURUSD stop-loss price = 1.0921 - 2 × 0.002 = 1.0917, GBPUSD stop-loss price = 1.2760 + 2 × 0.002 = 1.2764; the take-profit level is set at the trigger price plus three times the spread volatility. EURUSD take-profit price = 1.0921 + 3 × 0.002 = 1.0927, GBPUSD take-profit price = 1.2760 - 3 × 0.002 = 1.2754. All trading parameters are organized into a pre-defined, unified format to generate standardized trading instructions. Each instruction includes core fields such as a unique identifier, currency pair combination, trading direction, trading volume, trigger price, stop-loss price, take-profit price, instruction generation timestamp, and validity period, ensuring a unified instruction format and complete parameters, which can be directly used for subsequent dynamic routing and order execution.

[0044] S203, Based on the standardized trading instructions, and combined with the connection status of each interface and account risk control rules, dynamic routing selection is performed to distribute the instructions to the optimal broker channel, forming a set of executable orders with execution priority markers; Specifically, it can obtain network latency, connection stability and current quote depth reported by each broker plug-in module in real time, and generate a broker channel status and risk control assessment matrix by combining the single transaction limit and daily cumulative limit in the account risk control rules; The core of this step is to comprehensively collect operational status data and account risk control constraints from brokerage channels, and to achieve multi-dimensional evaluation through a matrix format, providing a key decision-making basis for subsequent dynamic routing selection. The specific implementation method is as follows: Real-time data acquisition employs a combination of proactive reporting from plug-in modules and passive querying by the system to ensure data timeliness and completeness. The reporting frequency is set at 100 milliseconds per report, consistent with the market data push frequency, to avoid routing decision errors due to data lag. Each broker plug-in module reports three core channel status indicators to the dynamic routing module every 100 milliseconds: network latency, connection stability, and current quote depth. Each indicator has a clear definition and quantitative standard for easy subsequent evaluation.

[0045] Network latency refers to the time interval between the issuance of a trading instruction by the system and the confirmation of receipt by the broker's interface, measured in milliseconds (ms). It directly affects the order execution speed. The reported data is the average of the latency of the last 10 instruction transmissions, eliminating the impact of fluctuations in a single transmission. For example, if the latency of a certain plug-in module for the last 10 transmissions is 22ms, 21ms, 23ms, 22ms, 21ms, 22ms, 23ms, 22ms, 21ms, and 22ms respectively, the calculated average is 22ms, meaning that the current network latency of this broker's channel is 22ms. Connection stability is quantified by the duration of continuous normal operation and the number of abnormal interruptions. The core parameter is the stability score (range 0-100 points), calculated as stable_score = 100 - 10 × break_count + 0.01 × run_duration, where break_count is the number of connection interruptions in the past hour (10 points are deducted for each interruption), and run_duration is the current continuous normal operation time (in seconds, 1 point is added for every 100 seconds). The higher the score, the more stable the connection. For example, if a channel has been interrupted once in the past hour and is currently running continuously for 3600 seconds, the calculated stable_score is 100 - 10 × 1 + 0.01 × 3600 = 126. If the score exceeds the maximum of 100 points, it is counted as 100 points.

[0046] Current quote depth refers to the number of orders corresponding to the buy and sell quotes currently available through the brokerage interface. It reflects market liquidity and order execution success rate, and is quantified as a quote depth value. The calculation formula is depth_value = bid_volume + ask_volume, where bid_volume is the total trading volume (lots) corresponding to the current buy quote, and ask_volume is the total trading volume (lots) corresponding to the current sell quote. The larger the value, the deeper the quote depth and the higher the execution success rate. For example, if a brokerage's EURUSD buy quote of 1.0921 corresponds to a trading volume of 500 lots, and the sell quote of 1.0926 corresponds to a trading volume of 600 lots, its quote depth value = 500 + 600 = 1100 lots.

[0047] Account risk control rules are preset by the system and updated in real time. The core includes two limit indicators: single transaction limit and daily cumulative limit. The single transaction limit refers to the maximum trading volume (lots) that can be executed in a single trading order. It is set according to the account's capital size and risk appetite. For example, if the total account capital is $100,000, the preset single transaction limit is 5 lots (1 lot = $100,000 contract value). The daily cumulative limit refers to the maximum trading volume (lots) that can be executed in total for all trading orders within a single natural day. In the example, it is set to 20 lots. The system counts the executed trading volume in real time and dynamically updates the remaining trading quota.

[0048] After data collection, a brokerage channel status and risk control assessment matrix is ​​generated. This matrix uses brokerage channels as rows and assessment indicators as columns, integrating channel status indicators and risk control limit indicators. Each cell contains the specific value of the corresponding indicator and the assessment level (Excellent, Good, Average, Poor) to facilitate subsequent rapid filtering and scoring. In the example, the matrix contains three brokerage channels, with assessment indicators including network latency (ms), stability score, quote depth (lots), single transaction limit (lots), and daily cumulative remaining limit (lots). The indicators for channel 1 are: 22ms (Excellent), 100 points (Excellent), 1100 lots (Excellent), 5 lots, 18 lots; for channel 2: 35ms (Good), 80 points (Good), 800 lots (Good), 5 lots, 15 lots; and for channel 3: 50ms (Average), 60 points (Average), 500 lots (Average), 5 lots, 20 lots, forming a complete assessment matrix to provide a basis for subsequent filtering.

[0049] Based on the currency pair and trading volume in the standardized trading instructions, a list of candidate channels that meet all risk control constraints and are in normal status is selected from the broker channel status and risk control assessment matrix, and an available channel list is generated. The core of this step is to conduct an initial screening of brokerage channels based on trading order requirements and risk control rules, eliminating channels that do not meet the requirements and retaining available channels to ensure the rationality and compliance of subsequent routing selection. The specific implementation method is as follows: The screening process consists of two steps: first, screening currency pair compatibility, and then screening risk control constraints and status normality. The two steps are executed sequentially to ensure that the screening results are accurate. The screening frequency is consistent with the frequency of transaction instruction generation. Each time a standardized transaction instruction is received, a screening operation is immediately performed to avoid changes in channel status causing the screening results to become invalid.

[0050] The first step is currency pair compatibility screening. Standardized trading instructions contain explicit currency pair information (such as EURUSD, GBPUSD). The screening process involves identifying brokerage channels in the evaluation matrix that support this currency pair, and eliminating channels that do not support it. Different brokerage channels support different currency pairs. The evaluation matrix already lists the supported currency pairs for each channel. During screening, the currency pair in the instruction is directly compared with the channel's support list. If the channel supports the currency pair, the process proceeds to the next step; otherwise, it is eliminated. In the example, a standardized trading instruction uses EURUSD as the currency pair. All three channels in the evaluation matrix support EURUSD trading, so they all proceed to the next step. If the instruction's currency pair is GBPUSD, and channel 3 does not support it, channel 3 is eliminated, leaving only channels 1 and 2.

[0051] The second step is risk control constraint and status normality screening. The core is to ensure that the screened channels meet the account risk control rules and that the channels are operating normally, avoiding order execution failures or loss of risk control due to risk control violations or channel abnormalities. Risk control constraint screening mainly compares the trading volume in the trading order with the single transaction limit and daily cumulative remaining limit in the evaluation matrix. Two conditions must be met simultaneously: trading volume of the trading order ≤ single transaction limit, and trading volume of the trading order ≤ daily cumulative remaining limit. If either condition is not met, the channel is eliminated. In the example, the trading volume of the standardized trading order is 2 lots. The single transaction limit of the three channels in the evaluation matrix is ​​5 lots (2 ≤ 5, satisfied). The daily cumulative remaining limit of channel 1 is 18 lots (2 ≤ 18, satisfied), channel 2 has 15 lots remaining (satisfied), and channel 3 has 20 lots remaining (satisfied). All of these pass the risk control constraint screening.

[0052] The normality screening is based on two indicators in the evaluation matrix: network latency and stability score. Preset normality thresholds are: network latency ≤ 50ms, stability score ≥ 60 points, and no abnormal markers (such as connection interruption, data anomalies, etc.). If both indicators of a channel meet the threshold requirements and there are no abnormal markers, it is considered to be in normal condition. If either indicator fails to meet the requirements or an abnormal marker is present, the channel is removed. In the example, channel 1 has a network latency of 22ms ≤ 50ms and a stability score of 100 points ≥ 60 points, with no abnormalities; channel 2 has a latency of 35ms ≤ 50ms and a score of 80 points ≥ 60 points, with no abnormalities; channel 3 has a latency of 50ms (equal to the threshold) and a score of 60 points (equal to the threshold), with no abnormalities. All three channels pass the normality screening.

[0053] After all filtering is completed, the filtered broker channels will be aggregated to generate a list of available channels. This list includes core information such as channel identifier, network latency, stability score, quote depth, single-transaction limit, and daily cumulative remaining limit, facilitating subsequent comprehensive scoring. In the example, the available channel list includes three channels: Channel 1, Channel 2, and Channel 3, sorted by channel identifier, clearly presenting the core parameters of each channel and preparing for the next step of dynamic routing algorithm scoring.

[0054] A dynamic routing algorithm is used to comprehensively score each channel in the available channel list. The scoring factors include the estimated transaction price, transaction cost, network latency, and execution success rate, and a channel priority ranking list is generated. The core of this step is to use a dynamic routing algorithm to comprehensively score available channels from multiple dimensions, sort them according to their scores, determine the priority of each channel, provide a decision-making basis for transaction instruction allocation, and ensure that orders are executed under optimal conditions. The specific implementation method is as follows: The dynamic routing algorithm uses a weighted summation scoring method. The core of this method is to set reasonable weights for four scoring factors, with the total weights being 100%. Based on the core requirements of forex quantitative trading (optimal transaction price, lowest cost, fastest execution, and highest success rate), the weights for each factor are set as follows: estimated transaction price 30%, transaction cost 25%, network latency 25%, and execution success rate 20%. The weights can be dynamically adjusted according to strategy requirements, but the total weights always remain at 100% to ensure the rationality of the scoring.

[0055] Each scoring factor has a clear quantitative scoring standard (range 0-100 points). During scoring, the specific parameters of each channel in the available channel list are used to calculate the score for each factor against the standard. Then, a comprehensive score is calculated based on the weights. The higher the comprehensive score, the higher the channel priority. The specific scoring standards are as follows: Estimated Transaction Price Score: Based on the estimated transaction prices of all available channels, the deviation between the estimated price of each channel and the benchmark price is calculated. The smaller the deviation, the higher the score. The estimated transaction price is calculated based on the current quote depth and order volume of the channel. The formula is est_price=(bid_price×bid_volume+ask_price×ask_volume) / (bid_volume+ask_volume), where bid_price is the buy quote, ask_price is the sell quote, and bid_volume and ask_volume are the trading volumes for the corresponding quotes. In the example, the estimated transaction price for EURUSD using channel 1 is 1.0923, channel 2 is 1.0924, and channel 3 is 1.0925. The base price is (1.0923+1.0924+1.0925) / 3=1.0924. The deviation for channel 1 is 0, which scores 100 points; the deviation for channel 2 is 0, which scores 100 points; and the deviation for channel 3 is 0.0001, which scores 95 points.

[0056] Transaction Fee Cost Score: The lower the transaction fee cost, the higher the score. The transaction fee cost is calculated as a percentage of the transaction amount (unit: ten thousandths). In the example, the transaction fee for Channel 1 is 0.5‰, for Channel 2 it is 0.6‰, and for Channel 3 it is 0.7‰. Taking the lowest transaction fee (0.5‰) as the benchmark, the score calculation formula is fee_score=100-(current transaction fee-benchmark transaction fee) / benchmark transaction fee×100. Channel 1 scores 100 points, Channel 2 scores 100-(0.6-0.5) / 0.5×100=80 points, and Channel 3 scores 100-(0.7-0.5) / 0.5×100=60 points.

[0057] Network latency score: The lower the network latency, the higher the score. The lowest latency among the available channels is used as the benchmark. The score calculation formula is delay_score=100-(current latency-benchmark latency) / benchmark latency×100. In the example, channel 1 has a latency of 22ms (the lowest), so it gets 100 points; channel 2 has a latency of 35ms, so it gets 100-(35-22) / 22×100≈40.9 points (rounded to 41 points); channel 3 has a latency of 50ms, so it gets 100-(50-22) / 22×100≈-27.3 points (scores below 0 are counted as 0 points).

[0058] Success rate score: It is calculated based on the channel stability score and the quote depth value. The calculation formula is success_score=0.6×stable_score+0.4×(depth_value / max_depth)×100, where max_depth is the highest quote depth value in the available channels and stable_score is the channel stability score. In the example, max_depth=1100 hands (channel 1), channel 1 success_score=0.6×100+0.4×(1100 / 1100)×100=100 points; channel 2 success_score=0.6×80+0.4×(800 / 1100)×100≈56+29.1=85.1 points (rounded to 85 points); channel 3 success_score=0.6×60+0.4×(500 / 1100)×100≈36+18.2=54.2 points (rounded to 54 points).

[0059] The formula for calculating the overall score is: total_score = 0.3 × price_score + 0.25 × fee_score + 0.25 × delay_score + 0.2 × success_score. Substituting the example data, the calculations are as follows: Channel 1: total_score = 0.3 × 100 + 0.25 × 100 + 0.25 × 100 + 0.2 × 100 = 100 points; Channel 2: total_score = 0.3 × 100 + 0.25 × 80 + 0.25 × 41 + 0.2 × 85 = 30 + 20 + 10.25 + 17 = 77.25 points; Channel 3: total_score = 0.3 × 95 + 0.25 × 60 + 0.25 × 0 + 0.2 × 54 = 28.5 + 15 + 0 + 10.8 = 54.3 points. Based on the comprehensive scores, channels are sorted from highest to lowest, and a priority ranking list is generated: Channel 1 (100 points) > Channel 2 (77.25 points) > Channel 3 (54.3 points), which provides a basis for subsequent order binding.

[0060] Based on the channel priority sorting list, standardized trading instructions are bound to the optimal channel and marked with execution priority, and finally aggregated to form a set of executable orders with execution priority marks.

[0061] The core of this step is to bind trading instructions to the optimal brokerage channel. Order urgency is differentiated by execution priority markers, and the aggregated orders form a set of directly executable orders, ensuring efficient and orderly execution. The specific implementation is as follows: The binding logic between standardized trading instructions and optimal channels follows a "one-to-one priority, many-to-one supplement" approach. This means each standardized trading instruction is first bound to the top-ranked optimal channel in the channel priority list. If the optimal channel's daily cumulative remaining limit is insufficient, its connection status suddenly becomes abnormal, or it has reached its capacity limit, then the second- or third-ranked channel is bound in sequence, ensuring that each instruction is bound to a usable optimal channel. During the binding process, the latest channel status and remaining limit are queried in real time to prevent binding failure due to changes in channel status. After binding is complete, the channel identifier and channel parameters are written into the trading instruction, forming a preliminary executable order.

[0062] In the example, there are three standardized trading orders, all for the EURUSD currency pair, with a trading volume of 2 lots. The channel priority list is Channel 1 > Channel 2 > Channel 3. The first order is first bound to Channel 1, reducing the daily cumulative remaining limit on Channel 1 from 18 lots to 16 lots. The second order is also bound to Channel 1, with the remaining limit becoming 14 lots. The third order continues to be bound to Channel 1, with the remaining limit becoming 12 lots. All three orders are successfully bound to the optimal Channel 1, forming three initially executable orders. If a fourth order (trading volume of 2 lots) exists, Channel 1's remaining limit of 12 lots can still accommodate it, so it continues to be bound to Channel 1. If the remaining limit on Channel 1 becomes 1 lot (less than 2 lots), the fourth order is bound to Channel 2, which is ranked second, ensuring that the order is bound correctly.

[0063] The execution priority flag is set based on the urgency of standardized trading instructions. The urgency level is determined by market volatility and strategy requirements at the time the instruction is generated, and is divided into three levels: Level 1 (urgent), Level 2 (normal), and Level 3 (regular). The priority flag is independent of the channel priority and is only used to distinguish the order of order execution. Level 1 priority orders are executed first, followed by Level 2, and then Level 3. Orders of the same priority are executed according to the priority of the bound channel; the higher the channel priority, the earlier the order is executed. The urgency level is determined as follows: when the volatility of the multi-dimensional spread indicator exceeds a preset threshold (e.g., spread volatility > 0.005), the generated trading instruction is marked as Level 1 priority; when the volatility is within the threshold range, it is marked as Level 2 priority; and when the market is in a stable state (spread volatility < 0.002), it is marked as Level 3 priority.

[0064] In the example, of the three trading instructions, the first instruction was generated when the EURUSD price spread volatility was 0.006 (exceeding the threshold), and was marked as Level 1 priority; the second instruction was generated when the price spread volatility was 0.004 (within the threshold range), and was marked as Level 2 priority; the third instruction was generated when the price spread volatility was 0.001 (in a stable state), and was marked as Level 3 priority. All three instructions are bound to Channel 1, and the execution priority order is: Level 1 > Level 2 > Level 3. Within the same channel, they are executed in priority order to avoid order congestion.

[0065] After all standardized trading instructions have completed channel binding and execution priority marking, they are aggregated to form a set of executable orders with execution priority markings. During aggregation, they are sorted from high to low execution priority, and within the same priority level, they are sorted by channel priority. The set contains complete information for each executable order, including a unique instruction identifier, currency pair, trading direction, trading volume, trigger price, stop-loss and take-profit parameters, bound channel identifier, execution priority, generation timestamp, and validity period, ensuring the completeness and traceability of order information. Simultaneously, the order set is validated, removing orders with failed binding, missing parameters, or risk control violations to ensure all orders in the set can be executed normally. In the example, three instructions are aggregated to form an executable order set, sorted by first-level, second-level, and third-level priorities. Each order is marked with bound channel 1 and its corresponding priority, and can be directly sent to the broker plugin module for execution, completing the entire process for this step.

[0066] S204, execute the set of executable orders and track the order status, network latency and system resource usage in real time through the full-link monitoring module. When an anomaly or performance threshold is detected, an early warning is automatically triggered and a visual monitoring report is generated.

[0067] Specifically, the set of executable orders can be sent to the corresponding broker plug-in module for execution. The end-to-end monitoring module simultaneously starts tracking the status flow of each order, the network latency from the issuance of the instruction to the broker's confirmation, and the system's CPU and memory usage, generating the raw monitoring data stream. The core of this step is to achieve accurate issuance and real-time tracking of executable orders, comprehensively collecting raw data on order execution, network transmission, and system operation, providing a foundation for subsequent anomaly identification and report generation. The specific implementation method is as follows: The execution order set is issued using a "priority-based issuance" mechanism, issuing orders in the set according to their execution priority (Level 1 > Level 2 > Level 3). Orders with the same priority are issued according to the priority order of the bound brokerage channel, ensuring that urgent orders are executed first and avoiding order congestion. During the issuance process, the system assigns a unique tracking identifier to each order and binds it to the order's unique identifier, facilitating accurate association of order information and monitoring data by the end-to-end monitoring module. The issuance frequency is consistent with the order generation frequency, with batch issuance every 100 milliseconds, and the number of orders issued at one time not exceeding 50, to avoid excessive pressure on the brokerage interface and resulting execution delays.

[0068] Once the data is distributed, the end-to-end monitoring module synchronously starts the tracking process, covering the entire lifecycle of the order, network transmission performance, and system resource usage. The tracking frequency is set to 50 milliseconds per time to ensure the real-time and continuous nature of the monitoring data. All tracking data is aggregated in real time to generate the raw monitoring data stream.

[0069] Order status tracking is the core functionality, covering the entire process from order placement to termination. Preset order statuses include seven categories: Pending Execution, Submitted, Partially Executed, Fully Executed, Rejected, Cancelled, and Execution Timeout. The monitoring module receives real-time order status updates from the broker's plugin module, recording the timestamp of each status change, the reason for the change, and related parameters. In the example, after a first-priority order (EURUSD, long 1 lot, bound to channel 1) is placed, the monitoring module tracks it sequentially to: t1=1700000000000 (Pending Execution), t2=1700000000022 (Submitted, channel 1 confirmed receipt), and t3=1700000000050 (Fully Executed, execution price 1.0924, volume 1 lot), completely recording the status transition. If the order is rejected, the reason for rejection must be recorded (e.g., insufficient risk control limit, invalid quote, etc.).

[0070] Network latency tracking focuses on the end-to-end latency from "instruction issuance to broker confirmation," divided into two parts: instruction issuance latency and confirmation response latency. Total network latency = instruction issuance latency + confirmation response latency, measured in milliseconds (ms). Instruction issuance latency refers to the time interval between the system issuing an order instruction and the broker plug-in module receiving the instruction. Confirmation response latency refers to the time interval between the broker plug-in module receiving the instruction and returning confirmation information (status update). The monitoring module calculates the latency value for each transmission in real time and records the average and maximum latency over the last 10 transmissions for subsequent anomaly detection. In the example, a certain order instruction issuance latency is 8ms, the confirmation response latency is 15ms, the total network latency is 23ms, the average latency over the last 10 transmissions is 22ms, and the maximum latency is 25ms. All relevant data is included in the raw monitoring data stream.

[0071] System resource usage tracking primarily collects CPU and memory usage rates. CPU usage rate refers to the CPU utilization rate currently used for order execution, monitoring, and data processing. Memory usage rate refers to the ratio of currently used memory to total memory. The monitoring module collects data every 50 milliseconds, recording the instantaneous value and the average value over one minute to avoid misjudgments caused by single fluctuations. In the example, the total system memory is 16GB, and the currently used memory is 6.4GB, so the memory usage rate is 6.4 / 16 × 100% = 40%. The instantaneous CPU usage rate is 35%, and the average usage rate over one minute is 32%. Relevant data is written to the raw monitoring data stream in real time. The raw monitoring data stream uses a unified format and includes tracking identifiers, order information, status transition data, network latency data, system resource data, and collection timestamps to ensure data integrity and traceability.

[0072] The raw monitoring data stream is input into the real-time analysis engine and compared with preset performance thresholds and normal status modes to identify abnormal events, including order execution timeouts, sudden increases in network latency, or system resource overload, and generate abnormal event alarms. The core of this step is to quickly analyze the raw monitoring data stream through a real-time analysis engine, accurately identify various abnormal events, trigger corresponding alarms, and provide timely reminders for emergency response. The specific implementation method is as follows: The real-time analytics engine employs a streaming analytics architecture, eliminating the need for data caching. It directly parses and compares the raw monitoring data stream in real time, keeping the analysis latency below 10 milliseconds to ensure rapid identification of anomalies and avoid delays in response. The analysis process consists of two steps: first, single-indicator comparison, and then comprehensive judgment based on multiple indicators. This combination ensures the accuracy of anomaly identification and avoids false alarms caused by fluctuations in a single indicator.

[0073] The preset performance thresholds are the core basis for anomaly identification. Combining the real-time requirements of forex quantitative trading and the system's capacity, three types of core thresholds are preset. Each threshold has a clear value and judgment logic, and can be dynamically adjusted according to the system's operating status: First, there is an order execution timeout threshold, set at 500ms. This means that if no confirmation of execution, rejection, or cancellation is received from the broker within 500ms after an order is placed, and the order status is still "submitted", it is considered an order execution timeout exception. This threshold is set based on the average network latency (22ms) and the broker's execution response time (usually ≤300ms), allowing sufficient response buffer time.

[0074] Secondly, the network latency threshold is set at 50ms (instantaneous threshold) and 40ms (average threshold over 10 times). That is, if the total network latency of a single order is greater than 50ms, or the average network latency over the last 10 times is greater than 40ms, it is considered an abnormal increase in network latency. This threshold ensures that network transmission performance is within a reasonable range and avoids order execution delays caused by excessive latency.

[0075] Third, there are system resource overload thresholds. The CPU utilization threshold is 80% (average over 1 minute), and the memory utilization threshold is 85% (average over 1 minute). That is, if the average CPU utilization over 1 minute is greater than 80%, or the average memory utilization over 1 minute is greater than 85%, it is judged as an abnormal system resource overload, in order to prevent the system from crashing or running slowly due to insufficient resources.

[0076] The normal status mode is a baseline mode built based on historical normal operation data. It includes the normal duration range of order status transition (e.g., normal duration of pending execution → submitted ≤ 30ms, normal duration of submitted → all transactions ≤ 300ms), the normal fluctuation range of network latency (±10ms), and the normal range of system resource usage (CPU 20%-60%, memory 30%-70%). The real-time analysis engine compares the current monitoring data with the normal mode. If it deviates from the normal range and exceeds the performance threshold, it is judged as an abnormal event.

[0077] In the example, after an order is placed, it remains in the "submitted" state for 500ms without receiving any confirmation information. The real-time analysis engine compares this to the order execution timeout threshold (500ms) and determines it to be an order execution timeout anomaly. At the same time, the average network latency of a certain channel over the past 10 times is 42ms, exceeding the average threshold of 40ms, and is determined to be an abnormal network latency surge. The system's average CPU utilization rate over 1 minute is 82%, exceeding the threshold of 80%, and is determined to be an abnormal system resource overload.

[0078] When generating an abnormal event alarm, it must include a unique alarm identifier, alarm type, alarm level, trigger timestamp, anomaly details, and associated order / channel information. Alarm levels are divided into three levels: Level 1 alarm (urgent), corresponding to order execution timeout or system resource overload, requiring immediate action; Level 2 alarm (general), corresponding to a sudden increase in network latency, requiring close monitoring; and Level 3 alarm (notification), corresponding to minor fluctuations in a single indicator, requiring no immediate action but must be recorded. In the example, an order execution timeout anomaly generates a Level 1 alarm with the following content: Alarm ID=ALM001, Alarm Type=Order Execution Timeout, Alarm Level=Level 1, Trigger Time=1700000000500, Anomaly Details=Order ID=ORD001 (EURUSD, 1 lot) not confirmed within 500ms after being sent, Associated Channel=Channel 1. This ensures that the alarm information is clear, traceable, and facilitates subsequent emergency response.

[0079] After an abnormal event alarm is triggered, the preset emergency operation is executed according to the alarm level, such as pausing new order routing, switching to a backup channel, or restarting the abnormal plugin, and an emergency response log is recorded to generate an alarm handling record. The core of this step is to execute precise emergency actions for different levels of abnormal alarms, quickly resolve abnormal risks, and fully record the handling process to form a traceable alarm handling record. The specific implementation method is as follows: Emergency operations adopt the principle of "alarm level corresponding to handling strategy". Standardized emergency operation procedures for each alarm level are preset. No manual intervention is required. The system executes automatically with a handling response time of ≤100ms, ensuring that anomalies can be resolved quickly and minimizing the impact on order execution. At the same time, manual intervention is supported for handling special anomalies. Manual handling has a higher priority than automatic handling.

[0080] Emergency actions for Level 1 alarms (emergency) are divided into two types: First, if an order execution timeout occurs, the system immediately sends an order cancellation command to the corresponding broker plug-in module. At the same time, the order is re-bound to the next available channel (backup channel) in the priority sorting list and re-issued for execution. If all available channels are abnormal, the execution of the order is suspended, marked as "pending handling," and relevant personnel are notified. Second, if a system resource overload occurs, the system immediately suspends non-core tasks (such as historical data backup) to release system resources. At the same time, the frequency of new order generation is limited (from 100 milliseconds / time to 200 milliseconds / time). If the resource utilization rate still does not decrease, the core modules (monitoring module, analysis engine) are automatically restarted to ensure normal system operation.

[0081] In the example, after a Level 1 alarm (order execution timeout, order ID=ORD001, associated channel 1) is triggered, the system immediately sends a cancellation command to the channel 1 plugin module, and at the same time rebinds the order to channel 2 (second priority) and reissues it for execution. The entire emergency operation is completed within 80ms. If channel 2 also experiences an anomaly, the system suspends the execution of the order, marks it as "pending handling", and notifies relevant personnel through system messages.

[0082] A Level 2 alarm (general) corresponds to a sudden increase in network latency. The emergency procedure is to suspend new order routing for the affected channel, redirecting new orders originally bound to that channel to other available channels in the priority list. Simultaneously, the network latency of that channel is continuously monitored, with latency data collected every 50 milliseconds. If the latency is ≤40ms (average threshold) for three consecutive times, the order routing function for that channel is restored. If the latency remains consistently high (exceeding 100ms), the channel is marked as "abnormal," triggering a Level 3 alarm and notifying personnel for investigation. In the example, Channel 1's average latency over the past 10 times was 42ms, triggering a Level 2 alarm. The system suspended new order routing for Channel 1, redirecting new orders to Channels 2 and 3. After 100ms of continuous monitoring, the average latency of Channel 1 dropped to 38ms, and its order routing function was restored.

[0083] Level 3 alarms (prompts) correspond to minor fluctuations in a single indicator. No complex emergency actions are required; only abnormal data is recorded, and indicator changes are continuously monitored. If the indicator returns to normal, the alarm is automatically deactivated. If the indicator continues to deteriorate and exceeds a higher-level threshold, the alarm level is upgraded, and corresponding emergency actions are executed.

[0084] During emergency operations, the system synchronously records emergency response logs. These logs include a response timestamp, alarm ID, emergency operation type, operation details, execution result, and operator (automatic handling is marked as "System"). After the emergency operation is completed, the alarm information and response logs are integrated to generate an alarm handling record. This record includes alarm details, emergency operation process, execution result, indicator recovery status, and handling evaluation, ensuring the entire handling process is traceable and reproducible. In the example, the handling record for the order execution timeout alarm (ALM001) is as follows: Alarm Details = Order ORD001 execution timeout; Emergency Operation = Cancel the original order and rebind to channel 2 for issuance; Execution Result = Transaction completed 300ms after re-issuance; Indicator Recovery Status = Channel 1 delay returned to normal; Handling Evaluation = Timely handling, no loss, complete record of the entire handling process.

[0085] The system integrates raw monitoring data streams, abnormal event alarms, and alarm handling records. The report generator then generates visual monitoring reports containing charts of key performance indicators and lists of abnormal events on a scheduled or triggered basis according to preset templates.

[0086] The core of this step is to integrate all monitoring-related data and present the system's operational status and anomaly handling through visualization. This provides an intuitive and actionable reference for personnel monitoring and strategy optimization. The specific implementation method is as follows: The data integration process is automated by the report generator. The integration scope includes core statistical data from the original monitoring data stream, all abnormal event alarms (including unhandled and handled ones), and corresponding alarm handling records. During integration, the data is cleaned and summarized, invalid data (such as repeatedly collected resource usage data) is removed, and core information is extracted to ensure the accuracy and conciseness of the report data. The integrated data is categorized into four main types: order execution statistics, network performance statistics, system resource statistics, and abnormal event statistics. Each type of data includes instantaneous values, average values, maximum values, and trends for easy visualization later.

[0087] The report generator supports two generation modes: scheduled generation and triggered generation. The two modes run in parallel to ensure the timeliness and completeness of the reports. The scheduled generation mode has a preset generation cycle of 1 hour, generating a phased monitoring report every hour on the hour, summarizing all monitoring data and anomalies within that hour for daily system monitoring. The triggered generation mode generates a special alarm handling report immediately after a level 1 alarm is triggered and handled, highlighting the details, handling process, and results of the anomaly for anomaly review and problem investigation.

[0088] The pre-defined report templates clearly define the structure and content of the visual monitoring reports. The core consists of two parts: charts of key performance indicators and a list of abnormal events. The report format is standardized, making it easy for personnel to read and analyze quickly, and allowing them to grasp the system's operating status without complicated operations.

[0089] Key performance indicators (KPIs) are presented intuitively using line charts and bar charts, covering three core categories: First, the order execution indicator chart, which shows the order execution success rate (number of completed orders / total number of orders × 100%), average execution time, and the distribution of orders in each status within the period. In the example, there are 500 total orders in 1 hour, with 492 completed, resulting in an execution success rate of 492 / 500 × 100% = 98.4% and an average execution time of 280ms. The chart clearly shows the trend of the execution success rate and the distribution of order statuses. Second, the network performance indicator chart, which shows the average network latency, maximum latency, and latency fluctuation trends of each brokerage channel, intuitively reflecting the network transmission performance of each channel. Third, the system resource indicator chart, which shows the trend of CPU and memory usage, reflecting the system's operating load.

[0090] The list of abnormal events is sorted from highest to lowest alarm level. Each record includes the alarm ID, alarm type, trigger time, handling time, emergency operation, execution result, and handling evaluation. Unhandled abnormal events are marked as "Pending Handling" and highlighted to remind personnel to handle them promptly. In the example, the list of abnormal events includes one Level 1 alarm and two Level 2 alarms, all of which have been handled. Each record fully presents the alarm and handling details, allowing personnel to quickly understand the abnormal situation and the effectiveness of the handling.

[0091] After the visual monitoring report is generated, it is automatically pushed to the system monitoring interface and simultaneously saved to local storage (retained for 7 days for later review). The report supports zooming and filtering functions, allowing personnel to view specific indicator details and anomaly details as needed. In the example, after the 1-hour scheduled report is generated and pushed to the monitoring interface, the charts clearly show an order execution success rate of 98.4%, an average latency of 25ms for each channel, and an average CPU utilization of 35%. The list of abnormal events shows 3 handled anomalies and no pending alarms, indicating that the system is operating normally. This provides an intuitive reference for subsequent order execution and system optimization, completing the entire process for this step.

[0092] Another embodiment of the present invention provides a foreign exchange quantitative trading system, see [link to relevant documentation]. Figure 3 The system may include: The parsing module 301 is used to connect to the market data interfaces and trading interfaces of multiple forex brokers in parallel through a plug-in interface adaptation layer, and to perform standardized parsing and timestamp alignment on heterogeneous data streams to generate real-time market data streams in a unified format. The execution module 302 is used to perform price spread calculation and signal generation based on the real-time market data stream using a full-memory computing engine, and to execute multi-factor strategy decisions through a customized algorithm trading framework to generate standardized trading instructions. The dispatch module 303 is used to dynamically route the standardized trading instructions according to the connection status of each interface and the account risk control rules, and dispatch the instructions to the optimal broker channel to form a set of executable orders with execution priority markers. The tracking module 304 is used to execute the set of executable orders and track the order status, network latency and system resource usage in real time through the end-to-end monitoring module. When an anomaly or performance threshold is detected, an early warning is automatically triggered and a visual monitoring report is generated.

[0093] This invention also provides a storage medium storing a computer program, wherein the computer program is configured to execute the steps in any of the above method embodiments when running.

[0094] This invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0095] Specifically, the aforementioned electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the aforementioned processor, and the input / output device is connected to the aforementioned processor.

[0096] The above description, based on the embodiments shown in the figures, details the structure, features, and effects of the present invention. The above description is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the figures. Any changes made in accordance with the concept of the present invention, or equivalent embodiments modified to have equivalent changes, that do not exceed the spirit covered by the specification and figures, should be within the protection scope of the present invention.

Claims

1. A quantitative trading method for foreign exchange, characterized in that, The method includes: By using a plug-in interface adaptation layer to connect to the market data interfaces and trading interfaces of multiple forex brokers in parallel, heterogeneous data streams are standardized and timestamp aligned to generate real-time market data streams in a unified format. Based on the real-time market data stream, a full-memory computing engine is used to calculate price spreads and generate signals. A customized algorithm trading framework is then used to execute multi-factor strategy decisions and generate standardized trading instructions. Based on the standardized trading instructions, dynamic routing is performed by combining the connection status of each interface and account risk control rules to distribute the instructions to the optimal broker channel, forming a set of executable orders with execution priority markers; The executable order set is executed, and the order status, network latency, and system resource usage are tracked in real time through the end-to-end monitoring module. When an anomaly or performance threshold is detected, an early warning is automatically triggered and a visual monitoring report is generated.

2. The method according to claim 1, characterized in that, The method involves parallel access to the market data interfaces and trading interfaces of multiple forex brokers through a plug-in interface adaptation layer, standardizing and aligning heterogeneous data streams with timestamps to generate a unified format of real-time market data stream, including: Develop an independent plug-in module for each forex broker to be connected. This module implements the broker’s unique communication protocol and data format encapsulation logic. Register all plug-in modules to the plug-in manager of the plug-in interface adaptation layer and generate a plug-in configuration list. According to the plugin configuration list, the plugin manager starts the connection threads of each plugin module in parallel, establishes network connections with the corresponding broker's market data interface and trading interface, and begins to receive raw data streams to obtain an initial set of heterogeneous data streams. Each plug-in module calls the built-in parser to parse the initial heterogeneous data stream set, extracts the key fields of the market data, and uses a clock-based synchronization algorithm to timestamp-align all data to generate time-synchronized parsed data. The parsed data after time synchronization is input into a standardized encoder, which performs format conversion and field mapping according to a preset unified data model to generate a standard format real-time market data stream.

3. The method according to claim 2, characterized in that, Based on the real-time market data stream, a full-memory computing engine is used to calculate price spreads and generate signals. A customized algorithmic trading framework is then used to execute multi-factor strategy decisions and generate standardized trading instructions, including: The system continuously subscribes to real-time market data streams in standard format from the data bus and loads them into the memory data structure of the all-memory computing engine to generate memory-resident market snapshots. The full-memory computing engine calculates the instantaneous price difference, moving average price difference, and price difference volatility between multiple currency pairs in real time based on memory-resident market snapshots, generating multi-dimensional price difference indicators; Input the multi-dimensional price spread indicator into the signal generator. The signal generator judges trading opportunities based on preset threshold rules or statistical models and generates raw trading signals containing direction, price and currency pair information. The original trading signals are input into a customized algorithmic trading framework, which runs multiple quantitative factor models in parallel to comprehensively evaluate and make decisions on the signals, and outputs standardized trading instructions with specific trading volume, stop-loss and take-profit parameters.

4. The method according to claim 3, characterized in that, The process involves dynamically routing the standardized trading instructions based on the connection status of each interface and account risk control rules, assigning the instructions to the optimal brokerage channel, and forming a set of executable orders with execution priority markers, including: The system obtains real-time network latency, connection stability, and current quote depth reported by each broker's plug-in module, and combines this with the single transaction limit and daily cumulative limit in the account risk control rules to generate a broker channel status and risk control assessment matrix. Based on the currency pair and trading volume in the standardized trading instructions, a list of candidate channels that meet all risk control constraints and are in normal status is selected from the broker channel status and risk control assessment matrix, and an available channel list is generated. A dynamic routing algorithm is used to comprehensively score each channel in the available channel list. The scoring factors include the estimated transaction price, transaction cost, network latency, and execution success rate, and a channel priority ranking list is generated. Based on the channel priority sorting list, standardized transaction instructions are bound to the optimal channel and marked with execution priority, and finally aggregated to form a set of executable orders with execution priority marking.

5. The method according to claim 4, characterized in that, The executable order set is executed, and the order status, network latency, and system resource usage are tracked in real time through the end-to-end monitoring module. When an anomaly or performance threshold is detected, an alert is automatically triggered and a visual monitoring report is generated, including: The set of executable orders is sent to the corresponding broker plug-in module for execution. The end-to-end monitoring module simultaneously starts tracking the status flow of each order, the network latency from the issuance of the instruction to the broker's confirmation, and the system's CPU and memory usage, generating the raw monitoring data stream. The raw monitoring data stream is input into the real-time analysis engine and compared with preset performance thresholds and normal status modes to identify abnormal events, including order execution timeouts, sudden increases in network latency, or system resource overload, and generate abnormal event alarms. After an abnormal event alarm is triggered, the preset emergency operation is executed according to the alarm level, such as pausing new order routing, switching to a backup channel, or restarting the abnormal plugin, and an emergency response log is recorded to generate an alarm handling record. The system integrates raw monitoring data streams, abnormal event alarms, and alarm handling records. The report generator then generates visual monitoring reports containing charts of key performance indicators and lists of abnormal events on a scheduled or triggered basis according to preset templates.

6. A foreign exchange quantitative trading system, characterized in that, The system includes: The parsing module is used to connect to the market data interfaces and trading interfaces of multiple forex brokers in parallel through a plug-in interface adaptation layer, and to perform standardized parsing and timestamp alignment on heterogeneous data streams to generate real-time market data streams in a unified format. The execution module is used to calculate price spreads and generate signals based on the real-time market data stream using a full-memory computing engine, and to execute multi-factor strategy decisions through a customized algorithm trading framework to generate standardized trading instructions. The dispatch module is used to dynamically route the standardized trading instructions based on the connection status of each interface and the account risk control rules, and dispatch the instructions to the optimal broker channel to form a set of executable orders with execution priority tags. The tracking module is used to execute the set of executable orders and track the order status, network latency and system resource usage in real time through the end-to-end monitoring module. When an anomaly or performance threshold is detected, an early warning is automatically triggered and a visual monitoring report is generated.

7. The system according to claim 6, characterized in that, The parsing module is specifically used for: Develop an independent plug-in module for each forex broker to be connected. This module implements the broker’s unique communication protocol and data format encapsulation logic. Register all plug-in modules to the plug-in manager of the plug-in interface adaptation layer and generate a plug-in configuration list. According to the plugin configuration list, the plugin manager starts the connection threads of each plugin module in parallel, establishes network connections with the corresponding broker's market data interface and trading interface, and begins to receive raw data streams to obtain an initial set of heterogeneous data streams. Each plug-in module calls the built-in parser to parse the initial heterogeneous data stream set, extracts the key fields of the market data, and uses a clock-based synchronization algorithm to timestamp-align all data to generate time-synchronized parsed data. The parsed data after time synchronization is input into a standardized encoder, which performs format conversion and field mapping according to a preset unified data model to generate a standard format real-time market data stream.

8. The method according to claim 7, characterized in that, The execution module is specifically used for: The system continuously subscribes to real-time market data streams in standard format from the data bus and loads them into the memory data structure of the all-memory computing engine to generate memory-resident market snapshots. The full-memory computing engine calculates the instantaneous price difference, moving average price difference, and price difference volatility between multiple currency pairs in real time based on memory-resident market snapshots, generating multi-dimensional price difference indicators; Input the multi-dimensional price spread indicator into the signal generator. The signal generator judges trading opportunities based on preset threshold rules or statistical models and generates raw trading signals containing direction, price and currency pair information. The original trading signals are input into a customized algorithmic trading framework, which runs multiple quantitative factor models in parallel to comprehensively evaluate and make decisions on the signals, and outputs standardized trading instructions with specific trading volume, stop-loss and take-profit parameters.

9. A storage medium, characterized in that, The storage medium stores a computer program, wherein the computer program is configured to execute the method of any one of claims 1-5 when it is run.

10. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the method of any one of claims 1-5.

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