Multi-strategy parallel execution method for investment and research transaction

Abstract

The invention provides an investment and research transaction multi-strategy parallel execution method, and relates to the technical field of financial data analysis, and the method comprises the steps: accessing multi-source real-time data from a stock market, a future market and other financial markets, and obtaining a standardized multi-source heterogeneous data set; generating a multi-strategy transaction signal, outputting a strategy coordination instruction set, generating strategy instance operation state data, sending a transaction instruction generated by each strategy to a transaction system of a corresponding market for execution, obtaining transaction execution result data, generating risk open assessment data and a strategy consistency report, and performing risk open assessment based on an optimized strategy parameter set. And executing the steps of data access, factor analysis, strategy generation, independent execution and optimization in a loop iteration manner. Through the method and the device, the technical problems of high transaction delay and instable income caused by high coupling degree among multiple market strategies, poor execution isolation and difficulty in realizing cross-market strategy collaboration and risk unified management and control in the prior art are solved; and the transaction execution efficiency and the strategy stability are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of financial data analysis, and particularly relates to a multi-strategy parallel execution method for investment research and transaction. BACKGROUND

[0002] In the existing field of financial transaction and investment research, multi-market and multi-strategy quantitative transaction systems are gradually popularized. Traditional investment research methods usually rely on single market data analysis or manual strategy design, lack cross-market factor analysis and strategy linkage capability, and it is difficult to fully utilize the price, trading volume and other market factor information between different markets. Existing automated transaction systems mostly adopt sequential or single-strategy execution mode, and there are resource contention and execution conflict problems between different transaction strategies, and the strategy independence is insufficient, which leads to the performance of the strategy cannot be fully played. At the same time, due to the delay of market data processing, strategy calculation and transaction execution, there is a time difference between transaction instruction issuing and actual transaction, which leads to inaccurate strategy profit evaluation and makes it difficult to realize accurate strategy optimization.

[0003] In summary, in the prior art, due to the scattered sources of financial market data, the lag of information processing and the high complexity of transaction strategies, it is difficult to realize effective integration and timely analysis of cross-market information, which leads to low investment decision-making efficiency, insufficient risk control and difficult accurate evaluation of strategy execution effect. SUMMARY

[0004] The purpose of the present application is to provide a multi-strategy parallel execution method for investment research and transaction, which solves the technical problems in the prior art that due to the scattered sources of financial market data, the lag of information processing and the high complexity of transaction strategies, it is difficult to realize effective integration and timely analysis of cross-market information, which leads to low investment decision-making efficiency, insufficient risk control and difficult accurate evaluation of strategy execution effect.

[0005] In view of the above problems, the present application provides a multi-strategy parallel execution method for investment research and transaction, wherein the multi-strategy parallel execution method for investment research and transaction comprises the following steps: accessing multi-source real-time data from stock markets, futures markets and other financial markets, and performing standardized processing to form a multi-source heterogeneous data set; generating multi-strategy transaction signals based on factor analysis and cross-market linkage identification; forming transaction instructions through an automated strategy decision module and verifying the transaction instructions; realizing independent strategy execution and unified scheduling by using a containerization and distributed architecture; carrying out cross-market risk monitoring and strategy consistency evaluation based on transaction execution results; and updating strategy parameters through backtesting and dynamic optimization mechanism to realize end-to-end automation of cross-market investment research analysis and transaction execution.

[0006] One or more technical solutions provided in the present application have at least the following beneficial effects: By accessing multi-source real-time financial data and standardizing processing, the heterogeneous data format can be unified, and a standardized multi-source heterogeneous data set that can be directly used for analysis and modeling is constructed, providing a reliable data foundation for subsequent cross-market factor analysis and strategy generation. Based on the standardized multi-source heterogeneous data set, feature extraction and factor modeling can be performed to quantify the influence of each market factor on assets, identify potential cross-market linkage opportunities, and generate multi-strategy trading signals to provide executable strategy input for automated trading. By constructing a multi-market strategy coordinator, linkage control relationships between different markets can be established, factor analysis results can be converted into strategy coordination instructions, and unified management of cross-market strategy coordination and trading signal triggering can be achieved. Using containerization and distributed architecture to deploy and isolate trading strategy modules can ensure that strategies run independently at the physical level, and through unified scheduling and monitoring, the running state of strategy instances can be obtained to provide a safe and stable operating environment for subsequent trading execution. Based on the running state of strategy instances and trading logic, low-latency and high-performance trading processing can be achieved to efficiently and accurately send strategy instructions to each market trading system, ensuring the real-time and accuracy of trading execution, and generating traceable trading execution result data. Cross-market coordination management of trading execution results can monitor risk exposure and output strategy consistency reports to ensure the coordination and risk controllability of multi-market strategies during execution. Based on risk exposure evaluation data, historical market data and real-time feedback, backtesting and evaluation can be performed to dynamically optimize strategy parameters and decision models, improve the scientificity and adaptability of strategy execution, and generate optimized strategy parameter sets for the next round of trading decisions. Through the iterative execution of data access, factor analysis, strategy generation, independent execution and optimization steps, end-to-end automation of cross-market research and trading execution can be achieved to improve the efficiency, consistency and overall management level of trading strategy execution.

[0007] The above description is only a summary of the technical solutions of the present application. In order to more clearly understand the technical means of the present application, the following specific embodiments of the present application can be implemented according to the content of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only exemplary, and other drawings can be obtained by those skilled in the art without creating any creative labor on the basis of the provided drawings.

[0009] Figure 1 A flowchart of a multi-strategy parallel execution method for investment and research trading according to the present application. DETAILED DESCRIPTION

[0010] The present application provides a multi-strategy parallel execution method for investment and research trading, which solves the technical problems in the prior art that due to the scattered data sources and non-uniform formats of financial market data, it is difficult to realize multi-market factor analysis and strategy coordination execution, resulting in delayed trading decision-making, low strategy efficiency and imperfect risk control. By accessing standardized multi-source real-time data, constructing a cross-market strategy coordination mechanism, independently isolating the strategy execution environment and dynamically optimizing the trading signal, the execution efficiency of the trading strategy, the cross-market coordination capability and the overall risk management level are improved.

[0011] The technical solutions in the present application will be described clearly and completely below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited by the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application. In addition, it should be noted that, for convenience of description, only parts related to the present application are shown in the drawings, rather than all.

[0012] Embodiment, please refer to the accompanying Figure 1 The present application provides a multi-strategy parallel execution method for investment and research trading, which specifically comprises the following steps: S100: Access multi-source real-time data from stock markets, futures markets and other financial markets, perform format standardization processing on the multi-source real-time data, obtain a standardized multi-source heterogeneous data set, and store the standardized multi-source heterogeneous data set in a plurality of heterogeneous storage units.

[0013] Specifically, first, access multi-source real-time data from stock markets, futures markets and other financial markets. Specifically, the system collects real-time market data, historical K-line data, macroeconomic indicators and market event information provided by different exchanges or data service providers through a unified data access gateway. The data access gateway includes a data collection submodule and a data verification submodule, wherein the data collection submodule is used to receive real-time data streams of each market through API interface, message queue or subscription push; the data verification submodule is used to check the consistency of the received data, the accuracy of the timestamp and the missing fields, so as to ensure the reliability of subsequent processing.

[0014] Secondly, the multi-source data is subjected to format standardization processing. Due to the differences in data field structure, time granularity and symbol system of different financial markets, the embodiment adopts a format adaptation mechanism based on a mapping rule library to uniformly convert the heterogeneous data sources into a standardized structured format. The mapping rule library presets the field correspondence and data cleaning strategies for stock markets, futures markets and other financial markets, for example, the "last_price" field in the stock market is corresponded to the unified standard field "close_price", and the data is resampled to a unified time interval according to a time synchronization strategy. The standardization processing further includes data unit conversion, timestamp alignment, missing value filling, outlier removal and field type correction operations to ensure the comparability and fusion of data from different markets. After the format standardization is completed, the system generates a standardized multi-source heterogeneous data set. The data set includes the market data table, the trading volume data table and the derived indicator table after cleaning and alignment, each table contains a unique identification field, which is used to quickly associate cross-market data in the subsequent strategy execution stage.

[0015] Finally, the standardized multi-source heterogeneous data set is stored in a plurality of heterogeneous storage units. In order to balance data access speed and analysis computing performance, the embodiment adopts a hierarchical heterogeneous storage architecture, including a relational database, a time series database and a distributed file system. The relational database is used to store structured transaction information and strategy metadata; the time series database is used for fast query and playback of high-frequency market data; the distributed file system is used to save large-scale historical data and model training samples. The system maintains the reference relationship between different storage units through a data index management module to realize a unified data access interface.

[0016] S200: Based on the standardized multi-source heterogeneous data set, a cross-market factor analysis engine is designed to extract features and model factors from the standardized multi-source heterogeneous data set, and generate multi-strategy transaction signals, including multi-market factor analysis results, cross-market linkage opportunity identification results and automatic strategy decision results Further, the application further comprises the following steps: S201: obtaining a standardized multi-source financial market data set, the multi-source financial market data set comprising real-time transaction data of stock markets, futures markets and other financial markets; S202: performing feature extraction on the multi-source financial market data set, comprising calculating market price volatility, volume indicators, yield sequences and other related market factors, and the feature extraction result serving as input for factor modeling; S203: performing factor modeling on the extracted market factors, comprising constructing a factor weight matrix, a factor correlation matrix and a factor exposure matrix to quantify the influence degree of each factor on different market assets, and generating a factor model; S204: performing multi-market factor analysis, performing statistical testing, correlation analysis and stability evaluation on the performance of each factor in different markets, identifying a high-quality factor set, and obtaining a multi-market factor analysis result; S205: identifying cross-market linkage opportunities based on the high-quality factor set, comprising detecting price synchronicity, arbitrage opportunities and potential risk exposure between different markets, and generating a cross-market linkage opportunity identification result; S206: constructing an automated strategy decision module based on the cross-market linkage opportunities, comprising generating buy and sell signals, setting transaction thresholds and risk control rules, and obtaining an automated strategy decision result; S207: integrating the multi-market factor analysis result, the cross-market linkage opportunity identification result and the automated strategy decision result to generate a comprehensive multi-strategy trading signal set; S208: verifying the multi-strategy trading signal set, comprising historical data backtesting, strategy conflict detection and execution feasibility evaluation, and outputting verified multi-strategy trading signals.

[0017] Further, the application further comprises the following steps: calculating market price volatility , volume indicators , yield sequences ; in a certain time window of each market m, a plurality of basic factors are defined as components of a factor vector, and the yield sequence is calculated according to the following formula: = - ; wherein is the asset price at time t, is the asset price at the previous time point, is the yield at time t, and the yield values of a plurality of consecutive time points form a yield time sequence; the yield sequence is extracted over a time window T as a factor yield = ; the price volatility is calculated according to the following formula: = ; wherein T-1 is a degree of freedom correction term for unbiased estimation of sample standard deviation; the volume indicator The calculation formula is: = ;in, Let be the trading volume at time point t; factor modeling R is performed based on the extracted market factors, including constructing the factor weight matrix B, the factor correlation matrix C, and the factor exposure matrix F, where C = Cov After fitting the factor model, the portion of each asset's return not explained by the factor is represented by the residual matrix ε, which is the portion of the return that the factor model failed to explain. The formula for calculating the residual matrix ε is: ε = RF × B; Calculate the residual variance matrix D, D = Var The residual variance matrix D measures the idiosyncratic risk distribution; a mathematical model structure is established to characterize the relationship between the returns and risk characteristics of different market assets, generating a multi-factor model: = + × + ;in, Let i be the rate of return in the i-th market. This is a unique income item for the asset. Let j be the value of the j-th market factor. Let be the exposure coefficient of the i-th asset to the j-th factor. This represents the model residuals.

[0018] Specifically, the system accesses a standardized multi-source heterogeneous dataset generated in the previous stage via a data access interface. This dataset includes real-time trading data from the stock market, futures market, and other financial markets. Each market's data includes fields such as timestamp, opening price, closing price, highest price, lowest price, trading volume, open interest, and relevant macroeconomic indicators. To ensure the timeliness and synchronization of subsequent calculations, the data access module caches and updates the market data according to a unified time-series index. The raw market data is then transformed into feature variables with analytical value. The feature extraction engine calculates feature parameters for each market's data, including price volatility, volume change rate, return series, price-volume ratio, price spread series, open interest change rate, and market breadth indicator. Simultaneously, a statistical sliding window and index-weighted update mechanism are introduced to dynamically capture changes in market microstructure. The feature extraction results form a feature matrix, which serves as input data for factor modeling. The system then calculates the price volatility for each market. Trading volume indicators , return series Within a certain time window of each market m, several basic factors are defined as components of a factor vector, and the return series... The calculation formula is: = - ;in, Let be the asset price at time t. The asset price at the previous point in time. For the return at time t, the return values ​​at multiple consecutive time points constitute a return time series; the return series is extracted over time window T as the factor return. = Price volatility The calculation formula is: = Where T-1 is the degree-of-freedom correction term, used for unbiased estimation of the sample standard deviation; trading volume indicator The calculation formula is: = ;in, Let be the trading volume at time point t; factor modeling R is performed based on the extracted market factors, including constructing the factor weight matrix B, the factor correlation matrix C, and the factor exposure matrix F, where C = Cov After fitting the factor model, the portion of each asset's return not explained by the factor is represented by the residual matrix ε, which is the portion of the return that the factor model failed to explain. The formula for calculating the residual matrix ε is: ε = RF × B; Calculate the residual variance matrix D, D = Var The residual variance matrix D measures the idiosyncratic risk distribution; a mathematical model structure is established to characterize the relationship between the returns and risk characteristics of different market assets, generating a multi-factor model: = + × + ;in, Let i be the rate of return in the i-th market. This is a unique income item for the asset. Let j be the value of the j-th market factor. Let be the exposure coefficient of the i-th asset to the j-th factor. The model residuals receive the feature matrix output by the feature extraction module in the factor analysis engine. In this feature matrix, each row corresponds to a time window or trading day, and each column corresponds to a candidate market factor, such as return series, price momentum indicators, volume change rate, volatility factor, price-volume co-movement indicators, and fund flow indicators. The system normalizes and denoises this feature matrix to eliminate interference caused by different market factors due to varying dimensions or noise. Normalization includes Z-score standardization or interval scaling, and denoising can be achieved through moving average filtering or principal component analysis (PCA). The system constructs three core matrices using statistical modeling and machine learning methods: a factor weight matrix, which characterizes the importance of each market factor in return prediction or risk assessment; and a regression analysis of the correlation between each factor and the target variable, such as future returns or volatility, using historical data samples to calculate the weight coefficient of each factor. The weights can be estimated using least squares regression, ridge regression, or LASSO regression to suppress multicollinearity and highlight significant factors. The weight matrix is ​​updated using a sliding window mechanism; whenever new time-series data is added, the system re-estimates the weights to reflect market changes. The factor correlation matrix characterizes the linear or non-linear correlations between different factors. The system calculates the correlation coefficients between factors and determines the degree of dependence through Pearson correlation, Spearman rank correlation, or mutual information analysis. When a high correlation between two factors exceeds a set threshold, the system automatically executes a redundant factor compression mechanism, merging or removing redundant factors to reduce model complexity and improve factor independence. The factor exposure matrix quantifies the sensitivity of different market assets to various factors. The system calculates the exposure value of each asset to each factor through linear regression or Bayesian estimation of asset returns and factor time series, thus establishing a mapping relationship between assets and factors. Each element of the exposure matrix represents the degree of response of an asset to a certain factor, used for subsequent multi-market factor analysis and risk decomposition. After constructing the above matrices, the system combines the factor weights, correlations, and exposure matrices to form a complete factor model structure. The core functions of the factor model include: factor impact quantification: assessing the strength of the impact of various market factors on changes in the prices of different assets; factor importance ranking: determining key driving factors based on weights and stability indicators; risk decomposition and return attribution: decomposing asset returns through an exposure matrix to identify the sources of returns; and factor screening and update mechanism: periodically updating the factor set based on model performance and removing ineffective or noisy factors. In engineering implementation, the factor model achieves efficient computation through a parallel computing framework. The system utilizes a distributed in-memory database to cache historical feature data and uses a matrix operation acceleration engine (such as BLAS or CUDA modules) to complete the calculation of weights and exposure values, enabling rapid updates and real-time responses to the factor model.Finally, the system outputs the modeled and screened factor model results, including the set of key factors, corresponding weight coefficients, exposure matrix, and correlation structure.

[0019] The multi-market factor analysis step is used to analyze the statistical characteristics, correlations, and stability of factors in different markets based on the results of the aforementioned factor model. It identifies a high-quality set of factors that demonstrate stable predictive and explanatory power across multiple market conditions, providing input for subsequent cross-market strategy identification and automated decision-making. The system reads the factor return sequences and exposure matrices for each market from the factor model. For each market, such as the stock market, futures market, and foreign exchange market, the system extracts the representative asset set and its corresponding factor exposure vectors, and constructs a factor performance dataset by combining historical return data. The system performs statistical tests on the factor performance dataset to determine the significance and effectiveness of each factor. Statistical tests include, but are not limited to, the following steps: Significance test: using t-tests or F-tests to determine whether factor returns significantly deviate from zero statistically; significant factors are considered to have stable return contributions. Robustness test: using rolling regression or sliding time window methods to evaluate the temporal stability of factor returns and detect fluctuations in their effectiveness over different time periods. Explanatory power assessment: calculating the explanatory power (R²) of each factor on asset returns using a regression model, and selecting factors with high explanatory power based on preset thresholds. Based on the statistical tests, the system further performs correlation analysis.

[0020] The interdependence between different factors is analyzed using Pearson correlation coefficient, Spearman rank correlation coefficient, or mutual information index among factor return sequences. If a high correlation is detected between certain factors (e.g., correlation coefficient exceeding 0.8), a factor deduplication and aggregation mechanism is triggered: principal component analysis (PCA) is used to extract principal components, retaining representative orthogonal factors; or factor clustering algorithms (such as K-means or hierarchical clustering) are used to group highly correlated factors into the same cluster, and the factor with the highest explanatory power in that cluster is selected as the representative factor. Through the above processing, it is ensured that the final retained factor set is informationally independent and without significant redundancy. Then, the system performs a stability assessment on the screened factor set.

[0021] Stability assessment is used to determine the sustained performance of factors under different market environments and volatility cycles. The system constructs a rolling window time series, dynamically calculating the mean, standard deviation, and information ratio (IR) of each factor's returns, and analyzing its performance differences in different market cycles (such as bull markets, sideways markets, and bear markets). If a factor exhibits a high information ratio and low volatility in most market conditions, it is considered to have good stability. Furthermore, the system introduces a cross-market consistency analysis mechanism to measure the consistency of the same factor's performance across different markets. The system calculates the return sign consistency rate and return correlation of the factor in each market to identify general factors that exhibit similar influence directions and return characteristics across multiple markets. For example, when a momentum factor shows positive returns and a high correlation coefficient in both the stock and futures markets, it is identified as a "cross-market efficient factor." After the above statistical tests, correlation analysis, and stability assessment, the system forms a high-quality factor set. This set includes factors that meet the following conditions: statistical significance is higher than a set confidence threshold (e.g., 95% confidence interval); correlation with other factors is lower than a correlation threshold; stability indicators (e.g., rolling information ratio) are higher than a preset standard; and cross-market consistency is higher than a preset ratio.

[0022] Finally, the system generates multi-market factor analysis results based on a high-quality factor set. The results include: a market fitness score for each factor; the factor's return contribution rate and risk exposure coefficient in each market; a structural correlation graph between factors; and a ranking table of factor importance in different markets. The system stores the multi-market factor analysis results in a strategy database and uses them as input data for subsequent cross-market linkage opportunity identification, used to identify inter-market resonance effects, profit potential, and risk propagation paths. At the engineering implementation level, the system can execute factor testing and analysis tasks in parallel based on a distributed data computing framework (such as Spark or Ray), utilizing an in-memory computing module to improve the efficiency of factor correlation and rolling stability calculations, achieving real-time and scalable multi-market factor analysis.

[0023] The system selects core factors with cross-market consistency characteristics from a high-quality factor set as driving variables for linkage analysis. These factors typically include price momentum factors, volume momentum factors, risk premium factors, and macroeconomic sensitive factors. The system matches the corresponding factor return series with asset price series in each market to form a cross-market factor time-aligned dataset to ensure consistency in the time dimension across different markets. The system performs price synchronicity detection on the time-aligned dataset. The system identifies synchronicity characteristics between markets through the following two mechanisms: Statistical correlation detection: Calculates the correlation coefficient and covariance matrix of the returns of the same or similar factors in different markets. If the correlation exceeds a set threshold (e.g., 0.75), the market pair is determined to be linked. Cointegration relationship test: Uses the Engle-Granger or Johansen cointegration test to determine whether there is a long-term equilibrium relationship between asset price series in different markets. If the cointegration residuals are stable within the confidence interval, the system identifies a long-term linkage structure. The system identifies arbitrage opportunities for market pairs that pass the synchronicity detection. The specific process of arbitrage identification includes: calculating the cross-market spread sequence and its standardized Z-value; determining the magnitude and duration of the spread deviation from the long-term equilibrium range; if the spread deviation exceeds a preset threshold (e.g., 2σ), it is identified as a potential arbitrage signal, and the trigger time, direction, and scale are recorded; if the spread recovers to the equilibrium range, an arbitrage closing signal is generated. Simultaneously with arbitrage identification, the system performs risk exposure detection to identify potential risk propagation caused by inter-market linkages. The system uses factor exposure matrices and market volatility parameters to calculate risk transmission coefficients between markets and establish a risk impact map. For example, when the volatility factor in the futures market rises significantly and its transmission coefficient to stock market returns is high, the system determines that the risk exposure in the stock market has increased and generates a corresponding risk warning record. Furthermore, the system performs cross-market signal fusion. The system integrates the results of synchronization detection, arbitrage identification, and risk detection to form a preliminary linkage signal set. To reduce interference from false signals, the system employs a signal confidence assessment mechanism: signal confidence is calculated based on the number of signal triggers, historical success rate, and the stability of relevant factors. When the signal confidence exceeds a preset threshold (e.g., 0.85), the system includes the signal in the "effective linkage signal set." If multiple market pairs generate resonance signals for the same factor, the system uses a logical weighting mechanism to fuse them, outputting a single cross-market trigger signal. Ultimately, the system generates cross-market linkage opportunity identification results, including: linkage strength indicators for each market pair; potential arbitrage range and direction; risk transmission path and impact coefficient; and effective linkage signal set and trigger conditions. The cross-market linkage opportunity identification results are linked and stored with the aforementioned multi-market factor analysis results, uniformly written into the strategy database. This data is then used as input to the subsequent automated strategy decision-making module to generate specific trading signals, threshold settings, and risk control rules.At the implementation level, the system can achieve real-time comparison of factor data streams and market price streams based on a distributed stream processing architecture (such as Apache Flink). Through in-memory computing and low-latency network communication mechanisms, the system ensures millisecond-level real-time response capabilities for cross-market linkage detection, thereby supporting rapid signal linkage identification in high-frequency and multi-strategy collaborative scenarios. Based on the identified cross-market linkage opportunities, the system constructs an automated strategy decision-making module. This module generates specific trading instructions and execution constraints without manual intervention, mainly including the following steps: Buy / Sell Signal Generation: Based on the expected return, risk exposure level, and linkage strength indicators output by the aforementioned factor model, combined with real-time market data, the system uses a threshold-triggered and dynamically weighted decision-making mechanism to generate buy or sell signals. For example, when the expected return of the target asset exceeds a dynamically set threshold and the linkage strength with the reference market is higher than a set correlation threshold, a buy signal is automatically triggered; conversely, when the expected return is lower than the lower limit threshold or the linkage weakens, a sell signal is generated. Trading Threshold Setting: The system dynamically calculates the risk tolerance and expected volatility of different asset classes and sets the trading threshold range based on historical volatility distribution and real-time liquidity levels. Trading thresholds may include price fluctuation thresholds, trading volume thresholds, and open interest thresholds, which are used to constrain the frequency of signal triggering and the intensity of trading to prevent overtrading.

[0024] The risk control rules are defined and configured by the system based on market category and strategy type. These rules include stop-loss lines for single trades, maximum drawdown limits for portfolios, position concentration constraints, and cross-market risk alert mechanisms. The risk control rules are dynamically adjusted through real-time monitoring of execution status to ensure that the strategy execution process meets the predetermined risk tolerance. Finally, the module outputs automated strategy decision results containing information such as trading direction, target asset, execution threshold, and risk constraint parameters. After obtaining the automated strategy decision results, the system performs a multi-source signal integration step to generate a comprehensive multi-strategy trading signal set. The system uniformly encodes and standardizes the results of multi-market factor analysis, cross-market linkage opportunity identification, and automated strategy decision results to form comparable signal vectors. Using a weighted fusion algorithm (such as a hierarchical weighted fusion model), a comprehensive signal score is calculated based on factor effectiveness, strategy stability, and linkage strength weights. When different strategies generate opposite signals for the same asset, the system mediates the conflict based on strategy priority, historical win rate, and risk exposure, retaining signals with higher credibility or better risk-reward ratios. This coordination mechanism can be automatically executed by setting a set of signal conflict resolution rules without manual intervention. The generated multi-strategy trading signal set includes unified outputs from multiple markets, asset classes, and strategy types, forming a comprehensive trading instruction set. To ensure the effectiveness and reliability of the generated signals, the system performs a verification process before signal output, specifically including: Historical data backtesting: Backtesting the generated trading signals using historical market data to evaluate their returns, risk ratios, and transaction costs under different market cycles. The system automatically calculates core indicators such as annualized return, maximum drawdown, and Sharpe ratio to determine the historical feasibility of the strategy. Strategy conflict detection: Checking for potential conflicts between different strategies in the signal set, including issues such as duplicate holdings of target assets, directional hedging, or risk superposition. The system identifies and automatically removes incompatible signals based on predefined conflict detection rules. Execution feasibility assessment: Based on real-time market depth, liquidity levels, and trading channel availability, the feasibility of signal execution is quantitatively assessed. Signals with expected large slippage or trading delays exceeding thresholds are downweighted or their execution is temporarily suspended. Verified multi-strategy trading signals serve as the final output, available for scheduling by the strategy execution engine or for automatic order placement by the trading terminal. By combining multi-market factor results, interconnected opportunities, and strategy decisions, and introducing signal verification and risk control processes, the system achieves automated, collaborative, and robust execution of multi-strategy trading, significantly improving the response efficiency and strategy reliability of the integrated investment research and trading system.

[0025] S300: Based on the multi-strategy trading signals, a multi-market strategy coordinator is constructed to establish a linkage control relationship between different markets and output a strategy coordination instruction set to support the execution of futures market trading signals triggered by stock market factor analysis results and other cross-market linkage strategies.

[0026] Furthermore, this application also includes the following steps: S301: Constructing a multi-market strategy coordinator based on multi-strategy trading signals, wherein the multi-market strategy coordinator is used to manage the strategy coordination and triggering logic between different markets; S302: Establishing a linkage control relationship between different markets in the multi-market strategy coordinator, wherein the linkage control relationship is used to define the interactive triggering conditions between the stock market, futures market and other financial markets; S303: When the stock market factor analysis result meets the preset triggering conditions, the multi-market strategy coordinator automatically triggers the futures market trading signal and executes the corresponding cross-market linkage strategy; S304: Dynamically adjusting the weight parameters and triggering priorities between each market according to the execution result of the cross-market linkage strategy to achieve adaptive coordination and optimization of the multi-market strategy.

[0027] Furthermore, this application also includes the following steps: receiving and aggregating the aforementioned verified multi-strategy trading signals; classifying the multi-strategy trading signals according to their source markets to form a stock market signal set, a futures market signal set, and other financial market signal sets; performing time-series alignment and feature synchronization on the classified multi-strategy trading signals to obtain a cross-market signal sequence with a unified time benchmark; establishing a linkage control relationship between different markets based on historical trading data and factor correlation information, wherein the linkage control relationship includes trigger thresholds, response delay parameters, and risk weights; constructing a cross-market trigger logic model based on the linkage control relationship to describe the trigger causal relationship between stock market factor analysis results and futures market trading signals; executing a cross-market strategy coordination process; and generating corresponding futures market trading instructions or other market collaborative trading strategies when a stock market signal is detected to meet the conditions in the trigger logic model; and providing feedback evaluation on the executed strategy results, updating the linkage control relationship based on indicators such as return performance, risk exposure, and signal accuracy.

[0028] Specifically, the system first receives verified multi-strategy trading signals through a unified signal access gateway. These signals are generated by the aforementioned factor analysis and automated strategy module and originate from the stock market, futures market, and other financial markets. The received trading signals include fields such as signal type (buy, sell, position adjustment, etc.), trigger time, target asset, expected return range, and risk constraints. The system categorizes the trading signals by their source market, automatically dividing them into three signal sets: Stock Market Signal Set: containing trading instructions generated by stock market factor analysis and price behavior models; Futures Market Signal Set: containing trading strategies generated based on derivative price trends and hedging logic; Other Financial Market Signal Set: including auxiliary signals from markets such as foreign exchange, interest rates, and commodities. After classification, the system establishes a unified signal index table for subsequent time synchronization and feature comparison operations. Due to differences in data update frequencies and trading time windows across different markets, directly using signals from different sources may lead to trigger timing misalignments. To address this issue, the system uses a high-precision clock synchronization mechanism and a unified timestamp calibration strategy to align the timing of each signal set. The specific steps include: attaching a standardized timestamp to each signal (using Coordinated Universal Time (UTC) or exchange benchmark time) to ensure comparability of multi-market signals within the same timeframe; employing interpolation and resampling strategies for data from different time zones or frequencies to achieve time alignment of key factors (such as returns and volatility); and synchronizing the execution characteristics of multiple signals within the same time window to ensure consistency in market states, factor values, and trading decision parameters. After signal synchronization, the system constructs a linkage control relationship between different markets based on historical trading data and factor correlation information. This relationship describes the signal response logic between the dominant market (such as the stock market) and subordinate markets (such as the futures market). The linkage control relationship consists of the following core parameters: Trigger threshold: defines the boundary conditions for triggering events, such as triggering a futures market opening strategy when the rate of change of a factor's return in the stock market exceeds a preset threshold; Response delay parameter: describes the delay compensation between signal triggering and actual execution, used to coordinate the trading execution cycles of different markets; Risk weight: used to quantify the impact of different market strategies on the overall risk exposure to guide subsequent weight adjustments and risk balancing. The model is implemented through the following process: taking stock market factor signals as input variables and futures market trading behavior as output targets; analyzing the leading nature of factor signals in time series and their response strength to changes in futures market prices; defining multi-layered logical conditions based on trigger thresholds, delay parameters, and risk weights, for example, "when the stock market momentum factor exceeds the threshold and volatility is in a stable range, a futures market buy signal is triggered"; storing these conditions in the trigger logic engine in the form of a set of logical rules to form a dynamically executable causal mapping model.During trading, the system continuously monitors stock market signals. When an event that meets the trigger logic model conditions is detected, it automatically generates corresponding futures market trading instructions or collaborative trading strategies from other markets, achieving automated cross-market strategy response. For example, when the stock price fluctuation in the dominant market exceeds a set threshold and the corresponding factor return changes significantly, hedging or arbitrage strategies in the futures market can be triggered. The coordinator also has a strategy synchronization update mechanism, enabling time-series alignment and consistency processing of cross-market signals even when data refresh cycles differ across markets. After coordinator initialization, the system further establishes linkage control relationships between different markets. These linkage control relationships clarify the signal triggering conditions and response mechanisms between different markets, typically including: triggering condition definition, using market factors, price fluctuations, sudden changes in trading volume, or changes in risk indicators as triggering conditions; for example, when the expected return of a selected factor in the stock market rises above a threshold, a futures market opening signal can be defined; and response logic configuration, where the system configures corresponding response actions for each triggering condition, such as buying, selling, closing positions, or adjusting positions. The response logic can also nest multiple layers of condition judgments, such as requiring simultaneous satisfaction of volatility and liquidity constraints before execution. Execution Path Definition: Signal linkage between different markets can be divided into two categories: direct linkage and indirect linkage. Direct linkage refers to stock market signals directly triggering futures market trading orders; indirect linkage requires confirmation of signal validity from an intermediate market (such as the foreign exchange or interest rate market) before execution. Through this step, the system establishes clear logical relationships and signal response paths between multiple markets, realizing a multi-level linkage control system. When the factor analysis results of the stock market meet the preset trigger conditions, the coordinator automatically executes the corresponding linkage logic, including: signal matching, where the coordinator first determines whether the signal generated by the stock market matches any trigger template in the trigger rule base; if the match is successful, a corresponding trigger event object is generated; linkage execution, where the trigger event object is passed to the corresponding subordinate market module (such as the futures market module), and the system generates specific trading signals, such as opening, adding to, or reverse hedging orders, based on the pre-configured response logic; and feedback recording, where the execution results are recorded in real time to the coordinator database for subsequent weight adjustments and trigger optimization. Through this mechanism, factor fluctuations in the stock market can directly drive trading behavior in the futures market or other markets, realizing automated linkage and response execution of cross-market strategies. To enhance the adaptability and execution efficiency of strategy coordination, the coordinator dynamically updates the weight parameters and trigger priorities of each market after the coordinated strategy is executed, based on the execution results and market feedback. For execution result evaluation, the system calculates the execution benefits, slippage impact, and risk exposure changes of each coordinated strategy to measure the effectiveness of the coordination. A weight adjustment mechanism automatically reduces the weight of a market in the coordination model when its strategy contribution or signal accuracy continues to decline, and vice versa. The weight adjustment process can smoothly transition over multiple periods, avoiding abrupt changes that could lead to strategy instability.Trigger priority optimization: Based on the historical performance and real-time stability of the strategy, the system rearranges the priority order of each market in the triggering chain, so that the market signals with better performance have higher trigger priority. This embodiment realizes unified management, automatic triggering and adaptive optimization of cross-market strategy signals by constructing a multi-market strategy coordinator, which solves the problems of market segmentation, response lag and insufficient coordination in traditional strategy systems, thereby significantly improving the linkage and execution efficiency of the investment research and trading system.

[0029] S400: Based on the strategy coordination instruction set, containerization technology and distributed architecture are used to achieve strategy isolation. Different trading strategy modules are deployed and run on different physical servers. The containerized strategy instances are uniformly scheduled and monitored through a resource orchestration system. Each trading strategy module runs independently in a physically isolated execution environment, generating strategy instance running status data.

[0030] Furthermore, this application also includes the following steps: S401: Based on the trading strategy configuration file output by the multi-market strategy coordinator, parse the computing resources, data interfaces, and execution permissions required by each strategy, and generate a strategy deployment description file; S402: Based on the strategy deployment description file, create corresponding containerized strategy instances, each strategy instance corresponding to an independent trading strategy module; S403: Distribute the containerized strategy instances to different physical server nodes according to a preset isolation strategy, forming a strategy isolation deployment structure; S404: After the strategy deployment is completed, register each containerized strategy instance as a node. S405: Generate an instance topology mapping table containing node addresses, port information, and communication permission identifiers; S406: Based on the instance topology mapping table, establish communication channels between policy instances through a distributed architecture management component, collect communication status data of each policy instance, including network latency, bandwidth utilization, and connection stability indicators, and synchronize the communication status data to the resource orchestration system in real time; S407: Use the resource orchestration system to uniformly schedule and monitor containerized policy instances. The scheduling is dynamically executed based on communication status data and resource usage, including instance startup, scaling up and down, load balancing, and anomaly recovery.

[0031] Specifically, upon receiving the strategy coordination instruction set output by the multi-market strategy coordinator, the system first parses the trading strategy configuration file. This configuration file contains information such as the execution logic, input data source, computational intensity, memory requirements, and network communication requirements for each strategy module. Based on the configuration file content, the system automatically generates a strategy deployment descriptor. Key fields include: strategy identifier, data input interface and API call path, required CPU, GPU, and memory resource specifications, security isolation level and access permission configuration, container image version, and startup parameters. The generated deployment descriptor serves as a template for container instance creation, which is then used by the containerization engine. Based on the aforementioned deployment descriptor, a corresponding containerized strategy instance is created in the container engine (such as Docker or Podman). Each strategy instance corresponds to an independent trading strategy module, which encapsulates its algorithm logic, runtime environment dependencies, and data interfaces. During container instance creation, the following sub-operations are performed: The image management service is invoked to load the corresponding version of the policy image file from the image repository; an independent network namespace and storage volume are allocated to the container; an access control list (ACL) is set to restrict the scope of interaction between the container and external systems; the container is started and a health check is performed to verify that the policy algorithm module and communication ports are functioning correctly. After creation, the system records the unique instance ID, host server node identifier, and runtime timestamp for each container instance. After successful policy instance creation, the system allocates each instance to different physical server nodes according to the preset isolation policy. Isolation policies include the following categories: resource isolation: ensuring that policies with high computational load are not deployed on the same node as low-latency policies; security isolation: preventing policies with different data access permissions from sharing the same host; network isolation: controlling the communication path between policies to prevent unauthorized data transmission. The system calculates the node allocation result based on node load information and policy priority through the scheduling control module, generating a policy isolation deployment structure table. This table guides subsequent instance registration and communication topology establishment. After the containerized policy instance is deployed, the node registration process is executed.

[0032] During registration, a unique communication identifier is assigned to each policy instance, and the instance's node address, listening port, authorized communication channel, and encryption key are recorded. Registration information is integrated into an instance topology mapping table. This table describes the physical location, logical relationships, and reachability information of all policy instances in the system, forming the structural foundation of the distributed policy network. The topology mapping table also includes status fields to record node heartbeats, load indices, and real-time operating status, providing data for subsequent communication channel management and resource scheduling. Based on the instance topology mapping table, a distributed architecture management component establishes communication channels between policy instances. This component uses message queues and asynchronous transmission mechanisms to support low-latency data interaction and command synchronization between instances. After the communication channel is established, the system continuously collects communication status data for each instance, mainly including: network round-trip time, bandwidth utilization, connection stability (packet loss rate and retransmission rate), message transmission rate, and queue backlog. The collected communication status data is synchronized in real-time to the resource orchestration system to evaluate the operating quality and communication performance of each node, providing a basis for dynamic scheduling. After communication status and resource usage data are aggregated into the resource orchestration system, the system performs unified scheduling and monitoring operations. The scheduling and monitoring process includes: instance startup and shutdown management: automatically starting or stopping corresponding container instances according to the policy execution cycle; scaling management: automatically replicating container instances to achieve horizontal scaling when the computational load of a policy exceeds a threshold; load balancing scheduling: dynamically allocating tasks to the optimal nodes based on communication status and resource usage; and anomaly recovery: automatically triggering migration and recovery processes when container or node anomalies are detected to ensure continuous policy execution. During scheduling, the system continuously updates the runtime status data of each policy instance, including CPU utilization, memory consumption, I / O latency, and execution duration. All status data is stored in the policy runtime monitoring database for subsequent performance analysis and policy stability assessment.

[0033] S500: Based on the running status data of the strategy instance and the corresponding trading logic, through high-performance programming, in-memory computing, hardware acceleration and low-latency network technology, millisecond-level or microsecond-level trading processing is achieved, and the trading instructions generated by each strategy are sent to the trading system of the corresponding market for execution to obtain trading execution result data.

[0034] Furthermore, this application also includes the following steps: S501: Obtain the running status data of multiple strategy instances and their corresponding trading logic information, wherein the running status data includes strategy identifier, computing resource occupancy status, signal generation frequency, and execution delay information; S502: Based on the running status data, construct an association mapping table between strategy instances and trading logic to determine the real-time execution priority and target market type of each strategy instance; Parallelize the computational tasks of the strategy instances using a high-performance programming framework, and implement cache sharing of strategy signal computation data through in-memory computing technology to reduce disk read / write latency; S503: Based on the hardware acceleration module, perform core computational processing in the trading signal generation process... The computation process is accelerated, and the hardware acceleration module includes an FPGA acceleration unit, a GPU parallel computing unit, or a smart network card processing unit to shorten the response time of signal generation and trading instruction issuance; S504: The trading instructions generated by high-performance processing are sent to the corresponding market trading system for execution in real time through a low-latency network communication mechanism, which includes a zero-copy transmission protocol, a kernel bypass network stack, and a TCP / UDP hybrid transmission optimization mechanism; S505: The trading execution result data from the market trading system is received, and based on the matching relationship between the trading instructions and the execution results, a trading execution feedback record is generated for subsequent strategy effect evaluation and dynamic optimization.

[0035] Specifically, based on the operational status data of the strategy instances and the corresponding trading logic information, a high-performance trading execution module is constructed to achieve millisecond-level or microsecond-level strategy execution and trading instruction issuance. This module employs high-performance programming, in-memory computing, hardware acceleration, and low-latency network technologies for collaborative optimization. Through the design of parallel computing and fast communication mechanisms for each strategy instance, it ensures efficient transmission and consistent execution of cross-market multi-strategy trading signals. The system collects operational status data of multiple strategy instances in real time from the containerized execution environment. This data includes strategy identifiers, computing resource utilization, signal generation frequency, latency indicators, and task execution time. Simultaneously, it reads the corresponding trading logic information from the strategy management module, including trading strategy type (such as arbitrage, trend, quantitative timing, etc.), signal generation model parameters, and target market identifier. Trading logic information refers to the rules, conditions, and algorithms followed by each trading strategy in actual execution, guiding the strategy instance to generate trading instructions. Signal generation rules determine the logic under which buy, sell, or hold operations are triggered, such as based on factor values, technical indicators, thresholds, or cross-market linkage conditions.

[0036] This includes: execution conditions, such as trading time windows, maximum acceptable slippage, and order batch execution strategies; risk control rules, such as stop-loss, take-profit, position limits, capital allocation, and risk exposure constraints; parameter settings, such as factor weights, threshold parameters, decision window length, and trigger delay, which affect trading decisions; and cross-market triggering logic, which triggers strategies or instructions in another market when a signal in one market meets specific conditions.

[0037] By integrating runtime status data and trading logic information, a multi-dimensional state description dataset is formed that can be used for subsequent performance optimization. Based on this data, the system establishes a mapping table between strategy instances and trading logic. This mapping table defines the execution priority, computational task dependencies, and target market classification for each strategy instance. To improve execution efficiency, a high-performance programming framework (such as C++ / Rust / Go or a JIT-based computing engine) is used to decompose and parallelize the strategy logic, distributing the computationally intensive signal generation process to multi-core CPU or GPU nodes. Simultaneously, the system utilizes in-memory computing technologies (such as Redis, MemTable, or shared memory pools) to cache and share intermediate strategy data, reducing latency caused by disk I / O operations and improving the trading signal generation rate. For key stages in trading signal generation and feature calculation, the system introduces hardware acceleration modules to shorten the computation and transmission paths. The hardware acceleration module includes an FPGA acceleration unit (for high-speed execution of fixed logic, such as signal threshold judgment and Boolean logic matching), a GPU parallel computing unit (for batch vector computation and deep model inference), and a SmartNIC processing unit (for network layer data preprocessing and kernel bypass transmission). Through hardware coordination mechanisms, the system can control the average response time from signal generation to trading instruction issuance within the microsecond range. To achieve synchronous execution of trading signals from multiple markets, the system employs a low-latency network communication mechanism for trading instruction transmission. The communication module supports zero-copy transmission protocols, kernel bypass network stacks (DPDK, RDMA), and TCP / UDP hybrid transmission optimization mechanisms to reduce system call overhead and protocol stack latency. During instruction issuance, the system prioritizes and batches trading requests from different markets using message queue middleware (such as Kafka, NATS, or ZeroMQ) to ensure that cross-market trading signals can be synchronously delivered to the target trading system with microsecond-level network latency. The system receives real-time trading execution result data from the trading systems of each target market, including transaction status, price slippage, latency feedback, and execution confirmation information. By matching trading instructions with execution results, a trading execution feedback record is generated and stored in the strategy feedback database. This feedback data serves as input for strategy performance evaluation and dynamic optimization, used to update signal generation parameters, execution priorities, and resource allocation strategies, enabling adaptive iteration and continuous performance improvement.

[0038] S600: Based on the transaction execution result data, coordinate and manage the execution of different market strategies, monitor cross-market risk exposure, and generate risk exposure assessment data and strategy consistency reports.

[0039] Furthermore, this application also includes the following steps: S601: Obtaining transaction execution result data from different markets, wherein the transaction execution result data includes transaction instruction identifier, transaction price, transaction quantity, transaction time, and account position information; S602: Establishing cross-market strategy correlation based on the transaction instruction identifier, matching and summarizing the execution results of different markets belonging to the same trading strategy or linked strategy, and generating a multi-market strategy execution set; S603: Performing statistical analysis on the multi-market strategy execution set, calculating position changes, open interest risk, and return deviation in each market, and forming basic data on cross-market risk exposure; S604: Based on a preset risk monitoring model, The basic data on cross-market risk exposure is calculated and classified to generate risk exposure assessment data. The risk monitoring model includes a position concentration analysis model, a market volatility sensitivity analysis model, and a net risk exposure model. S605: The execution deviations between different markets are compared. Based on indicators such as transaction timing, price deviation, and execution delay, the consistency between strategy instances is evaluated, and a strategy consistency report is generated. S606: The risk exposure assessment data and the strategy consistency report are stored in the risk monitoring database, and the data stored in the database is fed back to the multi-strategy trading signal generation module for dynamically adjusting trading weights or triggering strategy pause and optimization mechanisms.

[0040] Specifically, based on transaction execution result data, the system coordinates and manages the execution status of different market strategies, monitors cross-market risk exposure, and generates risk exposure assessment data and strategy consistency reports. This module achieves unified monitoring and a closed-loop risk feedback mechanism for multi-market trading activities by constructing a cross-market execution correlation mechanism and risk assessment model, providing real-time decision-making basis for subsequent trading signal adjustments and strategy optimization. Transaction execution result data is acquired from various market trading systems, including core fields such as transaction instruction identifier, transaction price, transaction quantity, transaction time, and account position information. The data acquisition module adopts a distributed data stream interface access mechanism, supporting the parsing of heterogeneous data formats from the stock market, futures market, and other derivatives markets. To ensure data timeliness and consistency, the system performs timestamp alignment and duplicate record removal upon receiving data, forming a unified format transaction execution result dataset. Cross-market strategy correlations are established based on transaction instruction identifiers and strategy number information. These correlations identify execution instances of the same strategy in different markets and their corresponding transaction relationships. By matching the execution results of different markets belonging to the same or linked strategies, the system merges and summarizes the transaction data from each market to form a multi-market strategy execution set. During data aggregation, the system also records the start and end times of each strategy instance, the order of transactions, and market linkage indicators, providing basic data support for subsequent risk exposure analysis. Statistical analysis is performed on the multi-market strategy execution set to calculate indicators such as changes in open positions, open interest risk, and return deviation in each market. The position analysis module compares account positions with transaction data to identify the direction and exposure ratio of cross-market net positions; the return deviation calculation module assesses the difference in expected returns of strategy execution based on the deviation between transaction prices and theoretical model predicted prices; finally, a basic dataset of cross-market risk exposure containing risk indicators for each market is generated as input for subsequent risk assessment. Based on a preset risk monitoring model, the basic data of cross-market risk exposure is calculated and classified to generate risk exposure assessment data. The risk monitoring model comprises three sub-models: a position concentration analysis model, used to identify risks associated with excessively high position ratios in specific markets or asset classes; a market volatility sensitivity analysis model, which assesses the strategy's sensitivity to market fluctuations by calculating the correlation coefficient between positions and volatility; and a net risk exposure model, which calculates the overall risk exposure level based on net positions across multiple markets, hedging ratios, and directional risk parameters. Through comprehensive calculations by these models, the system generates a risk exposure assessment data table, marking high-risk strategies and potential sources of interconnected risk. It also compares and quantifies trade execution deviations across different markets, focusing on analyzing indicators such as order timing, price deviation, and execution delays.The consistency analysis module uses a time-series comparison algorithm to synchronize and match the execution trajectories of strategies across different markets. The price deviation analysis module calculates the relative deviation rate between the actual transaction price and the target price to measure the accuracy of trade execution. The latency analysis module utilizes a high-precision clock synchronization mechanism to evaluate the latency distribution characteristics between signal issuance and trade confirmation. Based on the above multi-dimensional evaluation results, the system generates a strategy consistency report to determine the collaborative stability and execution synchronization between multi-market strategies. The system stores risk exposure assessment data and strategy consistency reports in a risk monitoring database and establishes a data feedback channel. The feedback module sends the evaluation results to the multi-strategy trading signal generation module in real time to dynamically adjust trading weights, update risk parameters, or trigger a strategy suspension mechanism. When the risk exposure of a strategy exceeds a threshold, the system can automatically execute risk control actions, including: suspending the signal issuance of the strategy; adjusting cross-market weight allocation; reducing the trading frequency in related markets; and initiating a strategy optimization task to update model parameters. Through this closed-loop mechanism, the system achieves dynamic linkage control from execution monitoring to risk assessment to feedback adjustment, ensuring the robustness and risk controllability of multi-market strategy operation.

[0041] S700: Based on the aforementioned risk exposure assessment data, historical market data, and real-time trading feedback, backtesting and evaluation of the strategy execution results are performed, and the parameters and decision-making models of multi-strategy trading signals are dynamically optimized according to the evaluation results to generate an optimized set of strategy parameters.

[0042] Furthermore, this application also includes the following steps: S701: Obtaining risk exposure assessment data, historical market data, and real-time trading feedback data, wherein the risk exposure assessment data includes single-market risk exposure value, cross-market net risk exposure, and volatility trend indicators; S702: Based on the risk exposure assessment data and historical market data, constructing a strategy backtesting model, replaying and simulating the execution process of existing strategies, calculating the return rate, drawdown rate, and signal accuracy of each strategy under different market conditions, and generating strategy performance evaluation data, wherein the strategy backtesting model is implemented based on a sliding time window and a composite factor weighting mechanism; S703: Combining the real-time trading feedback data, performing deviation correction and timeliness weighting on the strategy performance evaluation data to generate a comprehensive... The evaluation results, including deviation corrections such as transaction delay correction, signal trigger lag correction, and profit attribution correction; S704: Based on the comprehensive evaluation results, identify key parameters affecting trading performance, including factor weights, threshold parameters, decision windows, and linkage trigger conditions; S705: Apply dynamic optimization algorithms to the key parameters for adaptive adjustment, update the parameter configuration and decision model structure of the multi-strategy trading signals, and form an optimized strategy parameter set. The dynamic optimization algorithm includes reinforcement learning algorithms, Bayesian optimization algorithms, or parameter search mechanisms based on genetic algorithms; S706: Feed back the optimized strategy parameter set to the multi-market strategy coordinator and trading signal generation module, and complete parameter synchronization through an asynchronous data channel.

[0043] Furthermore, this application also includes the following steps: S703-1: Obtaining strategy performance evaluation data and real-time transaction feedback data The strategy performance evaluation data includes strategy returns. pullback Signal accuracy and risk indicators The real-time transaction feedback data includes the transaction time. Transaction price Transaction volume and strategy trigger time S703-2: Based on real-time transaction feedback data The delay Δ between the calculation strategy instruction and the actual transaction. Δ = - And adjust the strategy returns. To obtain the benefits after delay correction ;in, = ,in, The function is used to adjust the revenue based on the delay; S703-3: Correct the difference between the actual trigger time and the expected trigger time of the strategy signal to obtain the revenue after signal lag correction. , = ,in, This is the signal hysteresis. The function is a lag correction function; S703-4: Combining market conditions and multi-strategy interaction data, attribution processing is performed on the corrected strategy returns to generate attribution-corrected strategy return data. ,in, For market conditions and multi-strategy interaction factors; S703-5: Weight the attribution-corrected strategy returns based on transaction timestamps to generate weighted strategy returns. , × ,in, The weights are calculated based on the distance between the transaction time and the current time. Where T is the current time, S703-6: The attenuation coefficient; S703-6: The weighted strategy return pullback Signal accuracy and risk indicators By integrating the data, we obtain a comprehensive strategy evaluation dataset E, where E = Each record The execution status of a corresponding strategy. S703-7: Output the above comprehensive strategy evaluation data set for subsequent strategy optimization and risk management.

[0044] Specifically, based on risk exposure assessment data, historical market data, and real-time trading feedback, the system backtests and evaluates the strategy execution results, and dynamically optimizes the parameters and decision models of multi-strategy trading signals based on the evaluation results, generating an optimized strategy parameter set. By introducing a dynamic optimization algorithm and a multi-source feedback fusion mechanism, the system achieves adaptive evolution and continuous performance improvement of the strategy model, effectively enhancing the return stability and risk control capabilities of cross-market strategies. First, risk exposure assessment data, historical market data, and real-time trading feedback data are acquired. The risk exposure assessment data comes from a risk monitoring database, including single-market risk exposure values, cross-market net risk exposure, and volatility trend indicators; historical market data includes trading prices, trading volumes, opening prices, closing prices, and macroeconomic indicator sequences; real-time trading feedback data includes strategy execution delays, trading deviation rates, and slippage distribution. The data processing module performs unified formatting, timestamp alignment, and missing value repair on the above data, forming a standardized multi-dimensional input dataset, providing a consistent data foundation for strategy backtesting and dynamic optimization. Based on the risk exposure assessment data and historical market data, a strategy backtesting model is constructed to simulate the execution process of existing strategies under different historical time intervals and market conditions. The strategy backtesting model employs a sliding time window mechanism, segmenting historical data by time series to evaluate the strategy's performance in short-term, medium-term, and long-term market fluctuations. Simultaneously, the model introduces a composite factor weighting mechanism, comprehensively weighting indicators such as market volatility, trading activity, and risk exposure to correct the confidence interval for strategy return calculation.

[0045] During backtesting, the system calculates the return rate, maximum drawdown rate, and signal accuracy of each strategy under different market conditions, forming a strategy performance evaluation data table. This data table serves as input for the subsequent dynamic optimization module, used to quantify the actual performance and stability of each strategy. Real-time trading feedback data is integrated with the strategy performance evaluation data to correct static biases in historical backtesting and introduce real-time dynamic weights. Bias correction includes: trade delay correction, adjusting the timing of backtest signal triggers based on the trade confirmation delay time in real-time feedback; signal trigger lag correction, correcting the time lag in strategy signal response to market events to optimize causal matching accuracy; and return attribution correction, eliminating return biases caused by non-strategy factors such as network latency and differences in matching mechanisms. After correction, the system assigns higher weights to recent feedback data using a timeliness weighting function, generating a comprehensive evaluation result to reflect the latest performance and adaptability of the strategy. S703-1: Obtaining Strategy Performance Evaluation Data and real-time transaction feedback data The strategy performance evaluation data includes strategy returns. pullback Signal accuracy and risk indicators The real-time transaction feedback data includes the transaction time. Transaction price Transaction volume and strategy trigger time S703-2: Based on real-time transaction feedback data The delay Δ between the calculation strategy instruction and the actual transaction. Δ = - And adjust the strategy returns. To obtain the benefits after delay correction ;in, = ,in, The function is used to adjust the revenue based on the delay; S703-3: Correct the difference between the actual trigger time and the expected trigger time of the strategy signal to obtain the revenue after signal lag correction. , = ,in, This is the signal hysteresis. The function is a lag correction function; S703-4: Combining market conditions and multi-strategy interaction data, attribution processing is performed on the corrected strategy returns to generate attribution-corrected strategy return data. ,in, For market conditions and multi-strategy interaction factors; S703-5: Weight the attribution-corrected strategy returns based on transaction timestamps to generate weighted strategy returns. , × ,in, The weights are calculated based on the distance between the transaction time and the current time. Where T is the current time, S703-6: The attenuation coefficient; S703-6: The weighted strategy return pullback Signal accuracy and risk indicators By integrating the data, we obtain a comprehensive strategy evaluation dataset E, where E = Each record The execution status of a corresponding strategy. The number of strategy instructions; S703-7: Output the above comprehensive strategy evaluation data set for subsequent strategy optimization and risk management. Based on the comprehensive evaluation results, the system identifies a set of key parameters affecting trading performance, including but not limited to: factor weight parameters, affecting the strength of strategy signal generation; threshold parameters, determining the sensitivity of trading signal triggering; decision window parameters, controlling the observation period length of strategy signals; and linkage trigger condition parameters, defining the strategy response logic between different markets. The parameter identification module uses a combination of correlation analysis and sensitivity analysis to calculate the impact of each parameter on return, risk control, and execution stability to determine the optimal parameter adjustment direction. Dynamic optimization algorithms are applied to the identified key parameters for adaptive adjustment, updating the parameter configuration and decision model structure of multi-strategy trading signals, and generating an optimized strategy parameter set. The dynamic optimization algorithm includes three optional implementation methods: reinforcement learning algorithm, which gradually updates strategy parameters to maximize cumulative returns through interactive learning with the virtual market environment; Bayesian optimization algorithm, which uses a Gaussian process model in the parameter space to estimate the distribution of the objective function for efficient searching of the optimal parameter combination; and genetic algorithm parameter search mechanism, which uses selection, crossover, and mutation operations to iterate the population evolution of the parameter set to achieve an approximate global optimum. The dynamic optimization process is executed in a distributed computing environment, utilizing a parallel task scheduling mechanism to achieve synchronous updates and performance monitoring of multiple strategy parameters. The generated optimized strategy parameter set is fed back to the multi-market strategy coordinator and the trading signal generation module. Feedback is achieved through an asynchronous data channel to avoid blocking the current trade execution. The parameter synchronization module registers the updated parameters based on the strategy identifier and version number, and records metadata such as optimization time, algorithm type, and evaluation score. In subsequent trading cycles, the trading signal generation module automatically loads the latest parameter configuration, thereby achieving continuous iterative optimization and self-learning evolution of strategy performance.

[0046] S800: Based on the optimized strategy parameter set, the data access, factor analysis, strategy generation, independent execution and optimization steps are executed iteratively to achieve end-to-end automation of cross-market investment research analysis and transaction execution.

[0047] Specifically, after completing a full trade execution and risk monitoring cycle, the risk exposure assessment data and strategy consistency report are input into the strategy optimization module. Combining historical return data and market environment characteristics, the strategy parameters are adaptively adjusted to form an optimized strategy parameter set. This parameter set includes, but is not limited to, factor weight parameters, trading signal trigger thresholds, capital allocation ratios, risk exposure limits, and trade execution priority parameters. A unified data access gateway is used to re-acquire multi-source real-time data streams from the stock, futures, bond, and foreign exchange markets. New data is standardized in format, synchronized in time, and imputed to generate a standardized multi-source heterogeneous dataset. This data access process supports real-time updates and scrolling window-style data refreshes to ensure that factor calculations and model training are based on the latest market conditions. Based on the updated dataset, the core trading factors for each market and instrument are recalculated, including momentum factors, value factors, volatility factors, and liquidity factors. These are then combined with the weight system defined in the strategy parameter set to generate a factor score matrix. The system further uses factor correlation matrix detection and principal component analysis to automatically select a set of significant factors to improve the stability and interpretability of strategy signal generation. Based on the new factor analysis results, and according to the constraints and weights set in the optimized strategy parameter set, a comprehensive strategy signal for multiple markets and instruments is generated. The signal fusion engine unifies the encoding of signals from different markets to form an executable strategy set. Each strategy set includes a strategy ID, market type, directional indicator, target position size, and risk control parameters. Each market's trading execution engine independently receives the corresponding strategy instructions and places orders and tracks transactions according to preset priorities and market liquidity conditions. Execution results are transmitted back to the trading result processing module in real time for subsequent risk exposure assessment and strategy consistency verification. After the current round of trading execution and monitoring is completed, the system automatically summarizes return performance, risk volatility, and execution deviation indicators, and calculates the strategy return rate, Sharpe ratio, and drawdown indicators. Based on these evaluation results, the strategy optimization module uses adaptive gradient update or reinforcement learning optimization algorithms to retrain and adjust the strategy parameter set, generating a new round of optimized parameter set. Through this iterative mechanism, the system achieves a fully automated closed loop from data access, factor analysis, strategy generation, trading execution to optimization feedback, enabling continuous self-optimization and evolution of multi-market investment research and trading activities. This end-to-end automation process can significantly improve strategy response speed and execution consistency, reduce the need for manual intervention, and enhance the stability of returns and risk control capabilities in cross-market trading.

[0048] In summary, the parallel execution method for multiple investment research and trading strategies provided in this application has the following beneficial effects: A closed-loop system covering data access, factor analysis, strategy generation, independent execution, and dynamic optimization has been constructed, realizing an integrated automated process for cross-market investment research and trading execution. Through the design of a multi-strategy independent execution engine, strategies from different markets and instruments can run in parallel without interference, significantly improving trading execution efficiency and system stability. Combined with cross-market strategy correlations and risk monitoring models, real-time monitoring and dynamic control of position concentration, market volatility sensitivity, and net risk exposure can be achieved. An adaptive parameter optimization mechanism based on historical performance and real-time feedback is introduced, enabling the system to continuously correct strategy parameters and decision-making models, improving return stability and risk-reward ratio. Through a strategy consistency reporting mechanism, the transaction timing, price deviation, and execution delay of different market strategy instances are quantitatively evaluated, improving the consistency and interpretability of strategy execution. The overall methodology possesses good scalability and modular deployment capabilities, supporting multi-data source access, parallel operation of multiple algorithms, and hierarchical management of multi-account strategies, making it suitable for intelligent investment research and trading systems in various financial markets such as stocks, futures, and foreign exchange.

[0049] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0050] Obviously, those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A method for parallel execution of multiple investment research and trading strategies, characterized in that, include: S100: Access multi-source real-time data from the stock market, futures market and other financial markets, perform format standardization processing on the multi-source real-time data to obtain a standardized multi-source heterogeneous dataset, and store the standardized multi-source heterogeneous dataset in multiple heterogeneous storage units; S200: Based on the standardized multi-source heterogeneous dataset, design a cross-market factor analysis engine to perform feature extraction and factor modeling on the standardized multi-source heterogeneous dataset, and generate multi-strategy trading signals. The multi-strategy trading signals include multi-market factor analysis results, cross-market linkage opportunity identification results, and automated strategy decision-making results. S300: Based on the multi-strategy trading signals, a multi-market strategy coordinator is constructed to establish a linkage control relationship between different markets and output a strategy coordination instruction set to support the execution of futures market trading signals triggered by stock market factor analysis results and other cross-market linkage strategies. S400: Based on the strategy coordination instruction set, containerization technology and distributed architecture are used to achieve strategy isolation. Different trading strategy modules are deployed and run on different physical servers. The containerized strategy instances are uniformly scheduled and monitored through a resource orchestration system. Each trading strategy module runs independently in a physically isolated execution environment, generating strategy instance running status data. S500: Based on the running status data of the strategy instance and the corresponding trading logic, through high-performance programming, in-memory computing, hardware acceleration and low-latency network technology, millisecond-level or microsecond-level trading processing is achieved, and the trading instructions generated by each strategy are sent to the trading system of the corresponding market for execution to obtain trading execution result data; S600: Based on the transaction execution result data, coordinate and manage the execution of different market strategies, monitor cross-market risk exposure, and generate risk exposure assessment data and strategy consistency reports; S700: Based on the aforementioned risk exposure assessment data, historical market data, and real-time trading feedback, backtest and evaluate the strategy execution results, and dynamically optimize the parameters and decision-making model of multi-strategy trading signals according to the evaluation results, generating an optimized strategy parameter set; S800: Based on the optimized strategy parameter set, the data access, factor analysis, strategy generation, independent execution and optimization steps are executed iteratively to achieve end-to-end automation of cross-market investment research analysis and transaction execution.

2. The method for parallel execution of multiple investment research and trading strategies as described in claim 1, characterized in that, Based on the standardized multi-source heterogeneous dataset, a cross-market factor analysis engine is designed to extract features and model factors from the dataset, generating multi-strategy trading signals. These signals include multi-market factor analysis results, cross-market linkage opportunity identification results, and automated strategy decision-making results, including: S201: Obtain a standardized multi-source financial market dataset, which includes real-time transaction data from the stock market, futures market and other financial markets; S202: Extract features from the multi-source financial market dataset, including calculating price volatility, trading volume indicators, return series and other relevant market factors for each market. The feature extraction results are used as input for factor modeling. S203: Perform factor modeling on the extracted market factors, including constructing factor weight matrices, factor correlation matrices, and factor exposure matrices to quantify the impact of each factor on different market assets and generate factor models; S204: Perform multi-market factor analysis, conduct statistical tests, correlation analysis and stability assessments on the performance of each factor in different markets, identify high-quality factor sets and obtain multi-market factor analysis results; S205: Identify cross-market linkage opportunities based on high-quality factor sets, including detecting price synchronicity, arbitrage opportunities and potential risk exposures between different markets, and generating cross-market linkage opportunity identification results; S206: Construct an automated strategy decision-making module based on the aforementioned cross-market linkage opportunities, including generating buy and sell signals, setting trading thresholds and risk control rules, and obtaining automated strategy decision-making results; S207: Integrates multi-market factor analysis results, cross-market linkage opportunity identification results, and automated strategy decision-making results to generate a comprehensive set of multi-strategy trading signals; S208: Verify the multi-strategy trading signal set, including historical data backtesting, strategy conflict detection and execution feasibility assessment, and output the verified multi-strategy trading signal.

3. The method for parallel execution of multiple investment research and trading strategies as described in claim 2, characterized in that, Factor modeling is performed on the extracted market factors, including constructing factor weight matrices, factor correlation matrices, and factor exposure matrices to quantify the impact of each factor on different market assets, generating factor models, including: Calculate price volatility in each market Trading volume indicators , return series ; Within a certain time window of each market m, several basic factors are defined as components of a factor vector, and the return series... The calculation formula is: = - ； in, Let be the asset price at time t. The asset price at the previous point in time. Let t be the rate of return at time t, and the rate of return values ​​at multiple consecutive time points constitute a time series of returns; Extract the return series over time window T as the factor return. = ； Price volatility The calculation formula is: = ； Where T-1 is the degree of freedom correction term, used for unbiased estimation of the sample standard deviation; Trading volume indicator The calculation formula is: = ； in, Let be the transaction volume at time point t; Factor modeling R is performed based on the extracted market factors, including constructing the factor weight matrix B, the factor correlation matrix C, and the factor exposure matrix F, where C = Cov ; After fitting the factor model, the portion of each asset's return not explained by the factor is represented by the residual matrix ε, which is the portion of the return that the factor model failed to explain. The formula for calculating the residual matrix ε is as follows: ε = RF × B; Calculate the residual variance matrix D, D = Var The residual variance matrix D measures the idiosyncratic risk distribution; Establish a mathematical model structure that can characterize the relationship between the returns and risk characteristics of different market assets, and generate a multi-factor model: = + × + ； in, Let i be the rate of return in the i-th market. This is a unique income item for the asset. Let j be the value of the j-th market factor. Let be the exposure coefficient of the i-th asset to the j-th factor. This represents the model residuals.

4. The method for parallel execution of multiple investment research and trading strategies as described in claim 1, characterized in that, Based on the aforementioned multi-strategy trading signals, a multi-market strategy coordinator is constructed to establish inter-market linkage control relationships and output a strategy coordination instruction set. This set supports the execution of futures market trading signals triggered by stock market factor analysis results and other cross-market linkage strategies, including: S301: Construct a multi-market strategy coordinator based on multi-strategy trading signals. The multi-market strategy coordinator is used to manage the strategy coordination and triggering logic between different markets. S302: Establish a linkage control relationship between different markets in the multi-market strategy coordinator. The linkage control relationship is used to define the interactive triggering conditions between the stock market, futures market and other financial markets. S303: When the stock market factor analysis results meet the preset trigger conditions, the multi-market strategy coordinator automatically triggers the futures market trading signal and executes the corresponding cross-market linkage strategy. S304: Based on the execution results of the cross-market linkage strategy, dynamically adjust the weight parameters and trigger priorities among the markets to achieve adaptive coordination and optimization of the multi-market strategy.

5. The method for parallel execution of multiple investment research and trading strategies as described in claim 4, characterized in that, A multi-market strategy coordinator is constructed based on multi-strategy trading signals. This coordinator manages strategy coordination and triggering logic across different markets, including: Receive and aggregate the aforementioned verified multi-strategy trading signals, and classify the multi-strategy trading signals according to their source markets to form stock market signal sets, futures market signal sets, and other financial market signal sets; The classified multi-strategy trading signals are time-series aligned and feature-synchronized to obtain cross-market signal sequences with a unified time base; Based on historical trading data and factor correlation information, a linkage control relationship is established between different markets. The linkage control relationship includes trigger thresholds, response delay parameters, and risk weights. Based on the aforementioned linkage control relationship, a cross-market triggering logic model is constructed to describe the triggering causal relationship between stock market factor analysis results and futures market trading signals. The process of coordinating cross-market strategies is executed. When a stock market signal is detected that meets the conditions in the triggering logic model, corresponding futures market trading instructions or coordinated trading strategies in other markets are generated. Feedback evaluation is performed on the results of the executed strategy, and the linkage control relationship is updated based on indicators such as profit performance, risk exposure, and signal accuracy.

6. The method for parallel execution of multiple investment research and trading strategies as described in claim 1, characterized in that, Based on the aforementioned strategy coordination instruction set, containerization technology and a distributed architecture are used to achieve strategy isolation. Different trading strategy modules are deployed and run on different physical servers. A resource orchestration system is used to uniformly schedule and monitor containerized strategy instances. Each trading strategy module runs independently in a physically isolated execution environment, generating strategy instance runtime status data, including: S401: Based on the trading strategy configuration file output by the multi-market strategy coordinator, parse the computing resources, data interfaces and execution permissions required by each strategy, and generate a strategy deployment description file; S402: Based on the strategy deployment description file, create a corresponding containerized strategy instance, with each strategy instance corresponding to an independent trading strategy module; S403: Distribute the containerized policy instances to different physical server nodes according to the preset isolation policy to form a policy isolation deployment structure; S404: After the policy deployment is completed, each containerized policy instance is registered as a node, and an instance topology mapping table containing node addresses, port information and communication permission identifiers is generated. S405: Based on the instance topology mapping table, establish a communication channel between policy instances through the distributed architecture management component, collect communication status data of each policy instance, including network latency, bandwidth utilization and connection stability indicators, and synchronize the communication status data to the resource orchestration system in real time; S406: The resource orchestration system is used to uniformly schedule and monitor containerized policy instances. The scheduling is dynamically executed based on communication status data and resource usage, including instance startup, scaling up and down, load balancing, and anomaly recovery.

7. The method for parallel execution of multiple investment research and trading strategies as described in claim 1, characterized in that, Based on the operational status data of the strategy instances and the corresponding trading logic, millisecond-level or microsecond-level transaction processing is achieved through high-performance programming, in-memory computing, hardware acceleration, and low-latency network technologies. The trading instructions generated by each strategy are then sent to the corresponding market's trading system for execution, obtaining transaction execution result data, including: S501: Obtain the running status data of multiple strategy instances and their corresponding transaction logic information. The running status data includes strategy identifier, computing resource occupancy status, signal generation frequency, and execution delay information. S502: Based on the aforementioned operational status data, construct an association mapping table between strategy instances and trading logic to determine the real-time execution priority and target market type of each strategy instance; The computational tasks of the policy instance are parallelized using a high-performance programming framework, and cache sharing of policy signal computation data is achieved through in-memory computing technology to reduce disk read / write latency. S503: Accelerate the core computational process in the trading signal generation process based on the hardware acceleration module. The hardware acceleration module includes an FPGA acceleration unit, a GPU parallel computing unit, or a smart network card processing unit to shorten the response time of signal generation and trading instruction issuance. S504: Through a low-latency network communication mechanism, the trading instructions generated by high-performance processing are sent to the corresponding market trading system for execution in real time. The low-latency network communication mechanism includes a zero-copy transmission protocol, a kernel bypass network stack, and a TCP / UDP hybrid transmission optimization mechanism. S505: Receive transaction execution result data from the market trading system, and generate a transaction execution feedback record based on the matching relationship between the transaction instruction and the execution result, for subsequent strategy effect evaluation and dynamic optimization.

8. The method for parallel execution of multiple investment research and trading strategies as described in claim 1, characterized in that, Based on the transaction execution results data, the execution of different market strategies is coordinated and managed, cross-market risk exposure is monitored, and risk exposure assessment data and strategy consistency reports are generated, including: S601: Obtain transaction execution result data from different markets, including transaction instruction identifier, transaction price, transaction quantity, transaction time, and account position information; S602: Based on the trading instruction identifier, establish a cross-market strategy association relationship, match and summarize the execution results of different markets belonging to the same trading strategy or linked strategy, and generate a multi-market strategy execution set; S603: Perform statistical analysis on the multi-market strategy execution set, calculate position changes, open interest risk and return deviation in each market, and form basic data on cross-market risk exposure; S604: Based on a preset risk monitoring model, calculate and classify the basic data of cross-market risk exposure to generate risk exposure assessment data. The risk monitoring model includes a position concentration analysis model, a market volatility sensitivity analysis model, and a net risk exposure model. S605: Compare the execution deviations of trades across different markets, and evaluate the consistency between strategy instances based on indicators such as transaction timing, price deviation, and execution delay, and generate a strategy consistency report. S606: Store the risk exposure assessment data and strategy consistency report in the risk monitoring database, and feed the data stored in the database back to the multi-strategy trading signal generation module for dynamically adjusting trading weights or triggering strategy suspension and optimization mechanisms.

9. The method for parallel execution of multiple investment research and trading strategies as described in claim 1, characterized in that, Based on the aforementioned risk exposure assessment data, historical market data, and real-time trading feedback, the strategy execution results are backtested and evaluated. Based on the evaluation results, the parameters and decision-making models of the multi-strategy trading signals are dynamically optimized to generate an optimized strategy parameter set, including: S701: Obtain risk exposure assessment data, historical market data, and real-time transaction feedback data. The risk exposure assessment data includes single-market risk exposure value, cross-market net risk exposure, and volatility trend indicators. S702: Based on the risk exposure assessment data and historical market data, construct a strategy backtesting model, replay and simulate the execution process of existing strategies, calculate the return rate, drawdown rate and signal accuracy of each strategy under different market conditions, and generate strategy performance evaluation data. The strategy backtesting model is implemented based on a sliding time window and a composite factor weighting mechanism. S703: Combining the real-time transaction feedback data, the strategy performance evaluation data is corrected for deviation and weighted for timeliness to generate a comprehensive evaluation result. The deviation correction includes transaction delay correction, signal trigger lag correction and profit attribution correction. S704: Based on the comprehensive evaluation results, identify the key parameters that affect trading performance, including factor weights, threshold parameters, decision windows, and linkage triggering conditions; S705: Apply a dynamic optimization algorithm to the key parameters for adaptive adjustment, update the parameter configuration and decision model structure of the multi-strategy trading signal, and form an optimized set of strategy parameters. The dynamic optimization algorithm includes reinforcement learning algorithm, Bayesian optimization algorithm or parameter search mechanism based on genetic algorithm. S706: The optimized strategy parameter set is fed back to the multi-market strategy coordinator and trading signal generation module, and parameter synchronization is completed through an asynchronous data channel.

10. The method for parallel execution of multiple investment research and trading strategies as described in claim 9, characterized in that, Based on the real-time trading feedback data, the strategy performance evaluation data is adjusted for bias and weighted by timeliness to generate a comprehensive evaluation result. The bias adjustment includes transaction delay correction, signal trigger lag correction, and profit attribution correction, including: S703-1: Obtaining Strategy Performance Evaluation Data and real-time transaction feedback data The strategy performance evaluation data includes strategy returns. pullback Signal accuracy and risk indicators The real-time transaction feedback data includes the transaction time. Transaction price Transaction volume and strategy trigger time ; S703-2: Based on real-time transaction feedback data The delay Δ between the calculation strategy instruction and the actual transaction. Δ = - And adjust the strategy returns. To obtain the benefits after delay correction ; in, = ,in, This is a function that adjusts revenue based on delay. S703-3: Correct the difference between the actual trigger time and the expected trigger time of the strategy signal to obtain the benefit after signal lag correction. , = ,in, This is the signal hysteresis. It is a lag correction function; S703-4: Combining market conditions and multi-strategy interaction data, attribution processing is performed on the corrected strategy returns to generate attribution-corrected strategy return data. ,in, Factors influencing market conditions and multiple strategies; S703-5: Weight the attribution-corrected strategy returns based on the transaction timestamps to generate the weighted strategy returns. , × ,in, The weights are calculated based on the distance between the transaction time and the current time. Where T is the current time, The attenuation coefficient; S703-6: Weighted strategy returns pullback Signal accuracy and risk indicators By integrating the data, we obtain a comprehensive strategy evaluation dataset E, where E = Each record The execution status of a corresponding strategy. The number of policy instructions; S703-7: Output the above comprehensive strategy evaluation data set for subsequent strategy optimization and risk management.

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