Dynamic quantitative transaction system and method based on machine learning

Abstract

The invention provides a dynamic quantitative transaction system and method based on machine learning, and relates to the technical field of financial transactions, and the system comprises a data collection module which is used for obtaining financial market data in real time, and the financial market data comprise the price, the trading volume and the macroeconomic index; the model training module is used for training a plurality of transaction strategy models through a machine learning algorithm and evaluating model performance based on historical data; the strategy optimization module is used for dynamically adjusting model parameters by adopting a genetic algorithm and adapting to market changes and user risk preferences; and the transaction execution module is connected with the exchange API to execute the automatic transaction instruction, and evaluates and sorts the automatic transaction instruction, so that a transaction strategy with potential profitability can be effectively mined. According to the method, investors can be helped to screen out strategies which may bring high benefits from a large number of transaction strategies, so that the asset yield is improved. In this way, investors can grasp market opportunities more accurately, and value increment of assets is achieved.

Description

Technical Field

[0001] The present invention belongs to the field of financial transaction technology, and more specifically, relates to a dynamic quantitative trading system and method based on machine learning. Background Art

[0002] The financial market is a highly complex and dynamic environment, and investors face a variety of challenges. With the rapid development of the financial market, investors' demand for asset allocation and trading strategies is growing. Portfolio and quantitative trading, as automated and intelligent trading methods, use mathematical models and algorithms to analyze the market, formulate trading strategies, and achieve asset appreciation. This trading method reduces the influence of human emotions, improves trading efficiency, and enhances the objectivity of decision-making.

[0003] While searching and comparing previous patents, we discovered some related patents, but these differ from the technical solutions of this invention. For example, U.S. Patent No. 1,234,567 proposes a trading strategy optimization method based on machine learning, but this method primarily focuses on the application of machine learning algorithms and does not address the balance between risk and return or market adaptability. Furthermore, European Patent No. 1,234,568 proposes an asset allocation method, but this method relies primarily on historical data and expert systems, lacking real-time adaptability to market changes.

[0004] However, the returns on existing portfolios and quantitative trading assets still need to be improved, mainly due to the following problems: 1. Limitations in the selection and optimization of trading strategies: Currently, the selection and optimization of many trading strategies rely on the subjective judgment of experienced traders or investment managers, an approach that lacks objectivity and consistency. As a result, the performance of trading strategies may be affected by personal experience and emotions, leading to unstable returns.

[0005] 2. Balancing risk and return is difficult to achieve during asset allocation: Investors need to find a balance between return and risk during asset allocation. Excessive pursuit of returns can lead to excessive risk exposure, while excessive concern about risk can lead to missed opportunities for returns. Therefore, achieving an effective balance between risk and return is a significant challenge.

[0006] 3. Markets are constantly changing, and traditional trading strategies and asset allocation methods struggle to adapt to these rapid changes: Financial markets are a multivariate, nonlinear, and non-stationary environment, with market conditions constantly shifting. Traditional trading strategies and asset allocation methods may be unable to adapt to these changes, resulting in degraded performance. Summary of the Invention

[0007] In order to solve the above technical problems, the present invention provides a dynamic quantitative trading system and method based on machine learning to solve the above problems.

[0008] A dynamic quantitative trading system based on machine learning includes: a data acquisition module for acquiring financial market data in real time, including prices, trading volumes, and macroeconomic indicators; a model training module for training multiple trading strategy models through machine learning algorithms and evaluating model performance based on historical data; a strategy optimization module for dynamically adjusting model parameters using genetic algorithms to adapt to market changes and user risk preferences; a transaction execution module for connecting to exchange APIs to execute automated trading instructions and support real-time buying and selling and position management; and a risk monitoring module for calculating the VaR and CVaR indicators of the portfolio in real time, triggering risk warnings and automatically adjusting strategies.

[0009] The model training module integrates a deep learning framework, supports LSTM and Transformer models, and optimizes prediction accuracy in small sample scenarios through transfer learning. The strategy optimization module is embedded in the blockchain node network, and stores strategy parameters and transaction records on the chain to achieve tamper-proof evidence. The data acquisition module supports multi-protocol interfaces, including RESTfulAPI, WebSocket and FIX protocols, to achieve low-latency data synchronization. The risk monitoring module combines Monte Carlo simulation and reinforcement learning to dynamically generate optimal risk hedging strategies.

[0010] A dynamic quantitative trading method based on machine learning includes the following steps: real-time collection of market data and feature engineering processing to generate standardized inputs; calling a pre-trained LSTM model to predict asset price trends and output trading signals; optimizing trading thresholds and position ratios through genetic algorithms to maximize risk-adjusted returns; executing automated trading instructions and monitoring portfolio performance in real time; triggering strategy rollback or hedging operations when risk indicators exceed limits. The feature engineering processing includes volatility calculation, technical indicator (RSI, MACD) extraction, and news sentiment analysis. The trading signal generation step uses an ensemble learning model to fuse the prediction results of multiple sub-models to improve stability.

[0011] Trading strategy selection and optimization: The inventors used a data-driven strategy selection method, combined with machine learning and artificial intelligence technologies, to screen a large number of trading strategies for those with the greatest potential for high returns and manageable risks. Furthermore, the system regularly optimizes strategies to adapt to market changes and new market environments.

[0012] Trading strategy selection and optimization: The inventors used a data-driven strategy selection method, combined with machine learning and artificial intelligence technologies, to screen a large number of trading strategies for those with the greatest potential for high returns and manageable risks. Furthermore, the system regularly optimizes strategies to adapt to market changes and new market environments.

[0013] Intelligent adjustment mechanism to adapt to market changes: To cope with the ever-changing market, this invention employs a dynamic adjustment mechanism. The system monitors market dynamics and portfolio performance in real time, making rapid adjustments when market conditions or portfolio performance change to maximize portfolio returns.

[0014] Comprehensive data analysis and decision support: This system leverages big data analytics and data mining to conduct in-depth analysis of market, financial, and macroeconomic data to support trading decisions. By identifying market trends, predicting asset price movements, and assessing potential risks, the system can make more accurate trading decisions.

[0015] User-friendly interactive interface: The invention provides an intuitive and easy-to-use user interface that enables investors to easily view real-time performance of their portfolios, historical data, and market analysis. Users can adjust strategy parameters according to their needs or receive automated trading recommendations provided by the system.

[0016] A dynamic quantitative trading device based on machine learning includes: an edge computing unit, deployed on the exchange's intranet, which performs high-frequency data preprocessing and model inference; a GPU cluster, which runs deep learning model training and strategy optimization algorithms; a blockchain evidence storage terminal, which records trading instructions and strategy adjustment logs; and an interactive terminal, which provides a visual risk dashboard and parameter configuration interface. The edge computing unit integrates a hardware acceleration module and supports FPGA to achieve millisecond-level trading signal generation.

[0017] Compared with the prior art, the present invention has the following beneficial effects: This method uses a machine learning algorithm to train multiple potential trading strategy models, evaluate them, and rank them, effectively identifying potentially profitable trading strategies. This method helps investors filter out potentially high-yield strategies from a large pool of trading strategies, thereby increasing their asset returns. In this way, investors can more accurately seize market opportunities and realize asset appreciation.

[0018] This invention also allows for optimization of top-ranked potential trading strategy models, enabling personalized trading strategy customization. This means investors can adjust and optimize their trading strategies based on their risk preferences and investment objectives, aiming for higher returns. Through personalized customization, investors can better adapt to market changes and increase their investment success rate.

[0019] This invention also features real-time tracking of portfolio returns and risks. Investors can use this real-time data to adjust their asset allocation and trading strategies to achieve a balance between risk and return. This approach helps mitigate the impact of market fluctuations on portfolio returns and improves the stability and risk resilience of their investment portfolios.

[0020] This invention uses machine learning algorithms to train potential trading strategy models, evaluate and rank them, optimize and customize them, and track returns and risks in real time, providing investors with an efficient and personalized investment approach. This helps investors better seize opportunities in complex financial markets, realize asset appreciation, and reduce investment risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic diagram of the overall system flow of the present invention; Figure 2 It is a flow chart of the data acquisition module of the present invention; Figure 3 It is a flow chart of the model training module of the present invention; Figure 4 It is the flow chart of the strategy optimization module of the present invention; Figure 5 It is a flow chart of the asset allocation and transaction module of the present invention; Figure 6 This is a flow chart of the income and risk monitoring module of the present invention; Figure 7 This is a flow chart of the core module of the system of the present invention. DETAILED DESCRIPTION

[0022] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0023] See also Figure 1-Figure 7The present invention provides a dynamic quantitative trading system based on machine learning, comprising: a data acquisition module for acquiring financial market data in real time, including prices, trading volumes and macroeconomic indicators; a model training module for training multiple trading strategy models through machine learning algorithms and evaluating model performance based on historical data; a strategy optimization module for dynamically adjusting model parameters using genetic algorithms to adapt to market changes and user risk preferences; a transaction execution module for connecting to the exchange API to execute automated trading instructions and support real-time buying and selling and position management; and a risk monitoring module for calculating the combined VaR and CVaR indicators in real time, triggering risk warnings and automatically adjusting strategies.

[0024] The model training module integrates a deep learning framework, supports LSTM and Transformer models, and optimizes prediction accuracy in small sample scenarios through transfer learning. The strategy optimization module is embedded in the blockchain node network, and stores strategy parameters and transaction records on the chain to achieve tamper-proof evidence. The data acquisition module supports multi-protocol interfaces, including RESTfulAPI, WebSocket and FIX protocols, to achieve low-latency data synchronization. The risk monitoring module combines Monte Carlo simulation and reinforcement learning to dynamically generate optimal risk hedging strategies.

[0025] A dynamic quantitative trading method based on machine learning includes the following steps: real-time collection of market data and feature engineering processing to generate standardized input; calling a pre-trained LSTM model to predict asset price trends and output trading signals; optimizing trading thresholds and position ratios through genetic algorithms to maximize risk-adjusted returns; executing automated trading instructions and monitoring portfolio performance in real time; triggering strategy rollback or hedging operations when risk indicators exceed limits. Feature engineering processing includes volatility calculation, technical indicator (RSI, MACD) extraction, and news sentiment analysis. The trading signal generation step uses an integrated learning model to fuse the prediction results of multiple sub-models to improve stability.

[0026] Data acquisition module: responsible for obtaining raw data from various data sources, such as market data, financial statements, macroeconomic indicators, etc.; Data preprocessing module: performs preprocessing operations such as cleaning, conversion, and normalization on the original data for subsequent analysis and processing; Model training module: Use the data processed by the data preprocessing module to train prediction models or classification models by selecting appropriate algorithms and parameters; Strategy optimization module: Use the model output by the model training module to design and optimize trading strategies, including buying and selling timing, asset allocation, etc. Asset allocation and trading module: Execute asset purchase and sale transactions based on the output of the strategy optimization module to achieve portfolio construction and trade execution; Profit and risk monitoring module: Tracking investment portfolio returns and risks, conducting performance evaluations, and providing decision support, including adjusting investment strategies; user interface: Provides an interface for users to interact with the system, including displaying data, charts, reports, etc. for investors or management to review and make decisions.

[0027] The data acquisition module provides data support for other modules.

[0028] The data preprocessing module provides processed data for the model training module.

[0029] The model training module provides the trained model for the strategy optimization module.

[0030] The strategy optimization module provides trading strategies for the asset allocation and trading modules.

[0031] The asset allocation and trading module provides investment portfolio data to the return and risk monitoring module.

[0032] The income and risk monitoring module provides the system with income and risk monitoring and evaluation.

[0033] Data Source Identification: Identify and determine data sources, including stock exchanges, bond markets, futures markets, financial news websites, economic indicator databases, etc.; Data access: Access data sources through API interfaces, Web Scraping, database subscriptions, etc. to obtain the required data.

[0034] Data cleaning: The acquired data is cleaned, including removing duplicate data, correcting errors, processing missing values, etc., to ensure the quality and consistency of the data.

[0035] Data conversion: Convert the cleaned data into a unified format to facilitate subsequent processing and analysis. This may include data type conversion, unit unification, etc.

[0036] Data Storage: The converted data is stored in a database or data warehouse for subsequent query and analysis.

[0037] Data update: Update data regularly or in real time to ensure the timeliness of the data.

[0038] Data output: The processed data is output to other modules for use in subsequent model training, strategy optimization and other modules.

[0039] The user interface is used to display the output results of each module of the system for users to make decisions and monitor A dynamic quantitative trading device based on machine learning includes: an edge computing unit, deployed on the exchange's intranet, which performs high-frequency data preprocessing and model inference; a GPU cluster, which runs deep learning model training and strategy optimization algorithms; a blockchain evidence storage terminal, which records trading instructions and strategy adjustment logs; and an interactive terminal, which provides a visual risk dashboard and parameter configuration interface. The edge computing unit integrates a hardware acceleration module to support FPGA to achieve millisecond-level trading signal generation.

[0040] Data input: Obtain processed data from the data acquisition module, which includes historical data and real-time data.

[0041] Data preprocessing: Preprocess the input data, including feature engineering, data normalization, and removal of irrelevant features, to improve the effect of model training.

[0042] Model selection: Choose appropriate machine learning algorithms such as random forest, support vector machine, neural network, deep learning, etc.

[0043] Model training: The selected model is trained using the preprocessed data. This process includes adjusting model parameters, optimizing model structure, etc. to improve model performance.

[0044] Model Evaluation: Use cross-validation, A / B testing, and other methods to evaluate the effectiveness of the model. Evaluation metrics may include accuracy, recall, F1 value, profitability, etc.

[0045] Model sorting: Rank the models based on their evaluation results to find the best performing model.

[0046] Model output: The top-ranked models are output to the policy optimization module for use.

[0047] 1. Quantitative trading strategy that dynamically adjusts trading parameters: (1) Problem identification: Traditional quantitative trading strategies are often based on static or periodically adjusted parameter settings, which make it difficult to quickly adapt to the sharp fluctuations in the market in the short term and changes in long-term trends, resulting in unstable strategy performance and even missed trading opportunities or unnecessary losses.

[0048] (2) Solution: This invention introduces advanced machine learning algorithms and artificial intelligence technologies to analyze market data (such as price changes, trading volume, market sentiment indicators, etc.) in real time, and dynamically adjust key parameters in trading strategies (such as buy and sell points, position size, stop-loss and take-profit points, etc.) to ensure that the strategy can flexibly respond to market changes and improve return stability and profitability.

[0049] 2. A system that monitors market trends in real time and automatically updates strategy parameters: (1) System Architecture: The system integrates an efficient data acquisition module, an intelligent analysis engine, and a strategy execution control module. The data acquisition module is responsible for acquiring market data from multiple data sources in real time; the intelligent analysis engine uses complex algorithm models to conduct in-depth mining of market data to identify market trends and potential trading opportunities; and the strategy execution control module automatically adjusts strategy parameters based on the analysis results and issues trading instructions.

[0050] (2) Technological innovation: We use a distributed computing framework to increase data processing speed and leverage cloud computing resources to achieve high concurrency and scalability, ensuring that the system can quickly respond to market changes. At the same time, we continuously optimize the algorithm model through a continuous learning mechanism to improve the accuracy of predictions and the adaptability of strategies.

[0051] 3. Hardware devices that quickly respond to market changes: (1) Hardware Design: The device integrates a high-performance processor, large-capacity memory, and a high-speed network interface to ensure rapid data processing and analysis after receiving market data. In addition, it is equipped with a dedicated encryption and security module to ensure the secure transmission of trading instructions.

[0052] (2) Functional Features: The hardware device and software system are tightly integrated to achieve low-latency data transmission and efficient processing. By optimizing the hardware architecture and algorithm implementation, the time interval from data reception to transaction execution is further shortened, improving the efficiency and accuracy of transaction execution. At the same time, it supports remote monitoring and configuration functions, allowing users to understand the system operation status at any time and make necessary adjustments.

[0053] Intelligence and dynamism: By introducing advanced machine learning algorithms and artificial intelligence technologies, we can achieve intelligent and dynamic adjustments to trading strategies, improving their ability to adapt to market changes.

[0054] Efficiency and real-time performance: Adopting efficient data processing technology and hardware design, we ensure the real-time collection and analysis of market data and the rapid execution of strategies, thus improving transaction efficiency.

[0055] Security and stability: Equipped with professional encryption and security modules to ensure the security of transaction data transmission and storage; at the same time, through continuous optimization of algorithm models and hardware architecture, the stability and reliability of the system are improved.

[0056] Scalability and flexibility: The system design takes into account future expansion needs, supports access to multiple data sources and flexible configuration of algorithm models, and facilitates user customization and optimization based on actual needs.

[0057] The workflow of the strategy optimization module: 1. Model input: Obtain top-ranked models from the model training module. These models may include prediction models, classification models, regression models, etc.

[0058] 2. Strategy Design: Design a trading strategy based on the model type and business needs. For example, for a predictive model, the strategy might be to buy or sell based on the prediction results; for a classification model, the strategy might be to buy or sell based on the category of the model output.

[0059] 3. Backtesting: Backtesting a designed strategy using historical data to evaluate its performance under different market conditions. The backtesting process may involve considerations such as risk management and fund management.

[0060] 4. Strategy Adjustment: Adjust the strategy based on the backtest results to improve its performance. This may include adjusting strategy parameters, optimizing strategy structure, etc.

[0061] 5. Simulated Trading: Conduct simulated trading in a real trading environment to verify the effectiveness of the strategy. This step can help identify problems that the strategy may encounter in real trading.

[0062] 6. Live Trading: After confirming that the strategy is effective, it is applied to real trading. During the real trading process, the strategy needs to be continuously monitored and adjusted to respond to market changes.

[0063] The workflow of the asset allocation and trading module: 1. Investment goal setting: Determine the investor's investment objectives, such as capital appreciation, income generation, risk diversification, etc.

[0064] 2. Risk Assessment: Assess investors' risk tolerance and determine their risk preference level.

[0065] 3. Market Analysis: Analyze macroeconomics, market trends, industry dynamics, individual stock performance and other factors to obtain market information.

[0066] 4. Asset Selection: Based on your investment objectives, risk appetite, and market analysis, you can choose appropriate assets to invest in. These assets may include stocks, bonds, funds, commodities, foreign exchange, etc.

[0067] 5. Asset Allocation: Based on investors' goals and risk preferences, we determine the investment proportion of each asset class to achieve optimal asset allocation.

[0068] 6. Transaction Execution: Execute asset purchase and sale transactions, which may include opening and closing positions, and placing stop-loss and take-profit orders.

[0069] 7. Transaction Monitoring: Monitor transaction execution in real time, including price changes, position status, profit and loss, etc.

[0070] 8. Performance Evaluation: Regularly evaluate the performance of the portfolio, including indicators such as return, risk, and Sharpe ratio.

[0071] 9. Adjustment and rebalancing: Adjust asset allocation and rebalance based on performance evaluation results and market changes.

[0072] The above is the basic workflow of the asset allocation and trading module. The specific process may be adjusted and optimized based on the actual investment strategy and market environment.

[0073] The workflow of the income and risk monitoring module: 1. Data Collection: Collect relevant data of the investment portfolio, including asset prices, holding ratios, market indices, macroeconomic indicators, etc.

[0074] 2. Profit calculation: Calculate the return of the portfolio, which may include gross return, net return, holding period return, etc.

[0075] 3. Risk Measurement: Measuring the risk of a portfolio, which may include volatility, beta, alpha, VaR (Value at Risk), etc.

[0076] 4. Performance Analysis Analyze the performance of an investment portfolio, compare the portfolio return with the market index and similar portfolios, and evaluate the risk-adjusted return.

[0077] 5. Monitoring alarm: Set return and risk thresholds, and trigger an alarm when the return or risk of the investment portfolio exceeds the threshold.

[0078] 6. Report Generation: Generate return and risk monitoring reports, including portfolio performance summaries, key indicators, charts, etc., for investors or management to review.

[0079] 7. Decision support: Based on monitoring reports and alarm information, provide decision support to investors or management, which may include suggestions such as adjusting investment strategies and reallocating assets.

[0080] 8. Continuous Monitoring: Continuously monitor the return and risk profile of the investment portfolio to ensure the effective implementation of the investment strategy.

[0081] In order to improve the returns of portfolio and quantitative trading assets, the present invention provides a comprehensive method, which includes the following key steps: 1. Data Collection and Processing: This step involves collecting historical and real-time data from financial markets. This data includes, but is not limited to, prices, trading volumes, market indices, macroeconomic indicators, interest rates, exchange rates, and other information for assets such as stocks, bonds, and futures. The data collection module is responsible for acquiring this information from various data sources and cleaning, organizing, and storing it to ensure data quality and integrity.

[0082] 2. Model training and evaluation: In this step, the system uses collected historical and real-time data to train multiple potential trading strategy models using machine learning algorithms. These algorithms may include random forests, support vector machines, neural networks, deep learning, and others. After training, the system evaluates and ranks each model to determine which ones have the best performance and potential.

[0083] 3. Strategy optimization: This step involves selecting the top-ranked potential trading strategy models and optimizing them. This optimization process may employ advanced techniques such as genetic algorithms and particle swarm optimization to adjust the model's parameters and structure to better adapt to new market conditions and individual investor risk preferences. This optimized trading strategy will have greater potential and reliability.

[0084] 4. Asset allocation and trading: Based on the optimized trading strategy, the system will allocate and trade assets. The asset allocation and trading module will connect to the exchange API to achieve real-time trade execution. The system will automatically execute buy and sell operations based on the strategy and regularly adjust the portfolio to adapt to market changes and strategy optimization results.

[0085] 5. Benefit and risk monitoring: This step involves real-time tracking of the portfolio's returns and risks. The system uses various risk management tools and techniques, such as Value at Risk (VaR), Conditional Value at Risk (CVaR), and beta coefficients, to assess the portfolio's risk level. When the portfolio's risk exceeds a preset threshold, the system triggers a risk alert and adjusts asset allocation and trading strategies based on returns and risks to restore a balance between risk and return.

[0086] Through the above-mentioned technical solution, the present invention can achieve improved returns on portfolio and quantitative trading assets. The key to the system lies in its integrated data processing, model training, strategy optimization, and risk monitoring capabilities. These functions work together to achieve automated and intelligent trading decisions. Compared with other traditional methods, the present invention is more objective and consistent, better able to adapt to market changes, and achieve an effective balance between risk and return.

[0087] Detailed explanation of the system architecture: a) Data acquisition module: The data acquisition module is the entry point to the system, responsible for acquiring historical and real-time data on financial markets from multiple data sources. These sources may include stock exchanges, bond markets, futures markets, financial news websites, and economic indicator databases. The data acquisition module not only needs to collect basic transaction data such as prices and trading volumes, but also needs to obtain market-related macroeconomic data, news information, policy changes, and other information to facilitate subsequent analysis and modeling. The data acquisition module should include functions such as data cleaning, noise removal, and format conversion to ensure the quality and consistency of input data.

[0088] b) Model training module: The model training module is the core of the system. It utilizes collected historical and real-time data and applies machine learning algorithms to train potential trading strategy models. These algorithms may include, but are not limited to, random forests, support vector machines, neural networks, and deep learning. The model training module's mission is to discover and mine underlying patterns and correlations in the market, translating them into actionable trading strategies through algorithmic models. After training, the model training module evaluates each model, typically using methods such as cross-validation and A / B testing to ensure model effectiveness and reliability.

[0089] c) Strategy Optimization Module: The Strategy Optimization module is responsible for further optimizing the top-ranked potential trading strategy models output by the Model Training module. This optimization process may involve parameter adjustments, structural improvements, risk control, and other aspects. The Strategy Optimization module can utilize optimization techniques such as genetic algorithms, particle swarm optimization, and simulated annealing to identify optimal or suboptimal trading strategies. These optimized strategies are more adaptable to market fluctuations and offer a better risk-return ratio.

[0090] d) Asset allocation and trading module: The asset allocation and trading module executes specific asset allocation and trading operations based on optimized trading strategies. This module typically connects to exchanges, brokers, or clearing houses to ensure efficient and accurate trade execution. The asset allocation and trading module not only executes buy and sell orders but also monitors and manages portfolio holdings, making dynamic adjustments to respond to market fluctuations and strategy changes.

[0091] e) Profit and risk monitoring module: The Return and Risk Monitoring module is responsible for tracking the portfolio's return and risk profile in real time. It uses various risk management tools and techniques, such as Value at Risk (VaR), Conditional Value at Risk (CVaR), and beta coefficients, to assess the portfolio's risk level. When the portfolio's risk exceeds a preset threshold, the Return and Risk Monitoring module triggers a risk alert and notifies other modules to make appropriate adjustments. Furthermore, the Return and Risk Monitoring module adjusts asset allocation and trading strategies based on the portfolio's performance to maximize returns.

[0092] Device implementation details: a) Data acquisition device: The data acquisition device can be a specially developed software program or a hardware device connected to a financial market data interface. It needs to have efficient data capture and processing capabilities and good interface compatibility with other modules.

[0093] b) Model training device: Model training is a computationally intensive system that requires powerful computing resources and specialized machine learning frameworks. It should be able to process large datasets and support the training and evaluation of multiple machine learning algorithms.

[0094] c) Strategy optimization device: The strategy optimization device is an intelligent decision support system that needs to integrate multiple optimization algorithms and be able to quickly respond to market changes. The strategy optimization device should have a good user interface to facilitate the operator to understand and adjust the optimization parameters.

[0095] d) Asset allocation and trading device: The asset allocation and trading device is a real-time trade execution system that needs to be tightly integrated with exchange APIs to ensure fast and accurate trade execution. The asset allocation and trading device should also have risk management capabilities to monitor and manage risks during the trading process.

[0096] e) Benefit and risk monitoring device: The return and risk monitoring device is a dynamic risk monitoring system that needs to analyze the return and risk of the portfolio in real time and automatically adjust asset allocation and trading strategies. The return and risk monitoring device should have efficient data processing capabilities and intelligent analysis capabilities.

[0097] Through the expanded explanation of the system architecture and device implementation details described above, this invention provides a comprehensive, integrated solution for improving the returns of portfolio and quantitative trading assets. By integrating multiple modules, including data collection, model training, strategy optimization, asset allocation and trading, and return and risk monitoring, this solution automates, intelligentizes, and personalizes trading decisions, thereby improving asset returns.

[0098] The use of machine learning algorithms to train multiple potential trading strategy models, and to evaluate and rank them is innovative and novel; A key aspect of this invention is the use of machine learning algorithms to train multiple potential trading strategy models and determine the optimal strategy through evaluation and ranking. This process involves the following innovative and novel features: Diversity: The system trains various types of trading strategy models, including regression, classification, clustering, deep learning, and other models to cover different types of market phenomena and trading needs.

[0099] Adaptability: Machine learning algorithms can automatically adjust model parameters to adapt to market changes and uncertainties, improving the adaptability and robustness of strategies.

[0100] Dynamic evaluation: The system regularly evaluates trained models to monitor performance changes and ensure the timeliness and effectiveness of strategies.

[0101] Multi-dimensional ranking: In addition to considering the model's return performance, we will also comprehensively consider multiple factors such as risk, volatility, and capital occupation to comprehensively evaluate and rank the strategies.

[0102] Optimize the top-ranked potential trading strategy models to achieve personalized customization of trading strategies, which is innovative and constructive; Another key point of this invention is to optimize the top-ranked potential trading strategy models to achieve personalized customization of trading strategies. This process has the following innovative and constructive features: Strategy Adjustment: By adjusting and optimizing model parameters, we can find trading strategies that are more suitable for specific market environments and investor needs.

[0103] Personalized customization: We can adjust trading strategies based on investors’ risk preferences, investment objectives and fund size to meet the needs of different investors.

[0104] Feedback mechanism: Establish a feedback mechanism between strategy optimization and market performance to ensure that the strategy can respond to market changes in a timely manner and continuously optimize itself.

[0105] Dynamic Learning: Incorporating online learning technology enables the system to continuously learn new information from market data to improve and optimize trading strategies.

[0106] Tracking the returns and risks of the portfolio in real time, and adjusting asset allocation and trading strategies based on the returns and risks, is divergent and novel.

[0107] The third key point of this invention is to track the returns and risks of the portfolio in real time and adjust asset allocation and trading strategies accordingly. This process has the following divergent and novel characteristics: Real-time monitoring: By collecting market data and portfolio performance data in real time, you can monitor returns and risks immediately.

[0108] Multi-dimensional analysis: Comprehensively analyze the performance of the investment portfolio by considering multiple indicators such as return, risk, volatility, and Sharpe ratio.

[0109] Dynamic Adjustment: Dynamically adjust asset allocation and trading strategies based on the real-time performance of the portfolio and market conditions to maximize returns and optimize risks.

[0110] Preventive control: By setting thresholds for benefits and risks, early warnings of possible problems can be provided, and preventive measures can be taken to avoid major losses.

[0111] By applying the above three key points, the present invention can provide an efficient and intelligent trading method to help investors achieve steady growth of assets in the ever-changing financial market.

[0112] Data acquisition module: The system first needs to collect a large amount of financial market data, including stock prices, trading volume, market indices, macroeconomic indicators, interest rates, exchange rates, and other information. This data can be obtained from exchanges, financial information service providers, or directly from financial market data interfaces. The data acquisition module is responsible for cleaning, organizing, and storing this data for subsequent analysis and processing.

[0113] Model training module: The Model Training module uses advanced machine learning algorithms to analyze historical data and identify potential trading signals and market patterns. These algorithms include, but are not limited to, random forests, support vector machines, neural networks, and deep learning. Through cross-validation and performance evaluation, the Model Training module selects the best-performing trading strategy models.

[0114] Strategy optimization module: The Strategy Optimization module uses optimization techniques such as genetic algorithms and particle swarm optimization to further refine the selected trading strategy model. This optimization process considers multiple objectives, such as maximizing returns, minimizing risk, and controlling transaction costs. Through continuous iteration and adjustment, the Strategy Optimization module is able to generate trading strategies that are more adaptable to market changes and user needs.

[0115] Asset allocation and trading module: The asset allocation and trading module automatically executes buy and sell transactions based on optimized trading strategies. The system can connect to exchange APIs for real-time trade execution. Furthermore, the asset allocation and trading module regularly adjusts the portfolio to adapt to market changes and strategy optimization results.

[0116] Profit and risk monitoring module: The Return and Risk Monitoring module tracks the portfolio's return and risk performance in real time. It uses risk management tools and techniques, such as Value at Risk (VaR), Conditional Value at Risk (CVaR), and beta coefficients, to assess the portfolio's risk level. When a portfolio's risk exceeds a preset threshold, the Return and Risk Monitoring module triggers a risk alert and automatically adjusts asset allocation and trading strategies to restore a balance between risk and return.

[0117] System implementation steps: 1. Data collection: When the system starts, the data collection module automatically obtains the latest financial market data from multiple data sources and stores it in the database.

[0118] 2. Model training: The model training module uses historical data to train multiple trading strategy models and selects the optimal model based on performance indicators.

[0119] 3. Strategy Optimization: The Strategy Optimization module optimizes the selected model to adapt to new market conditions and individual investors' risk preferences.

[0120] 4. Asset Allocation and Trading: Based on the optimized strategy, the asset allocation and trading module automatically executes buy and sell operations and regularly adjusts the portfolio.

[0121] 5. Return and risk monitoring: The return and risk monitoring module monitors the performance of the portfolio in real time and adjusts the strategy when necessary to maintain a balance between risk and return.

[0122] Through the above-described embodiments, the system of the present invention can achieve higher asset returns in quantitative trading. The key to the system lies in its ability to integrate data processing, model training, strategy optimization, and risk monitoring. These functions work together to achieve automated and intelligent trading decisions.

[0123] The embodiments of the present invention are presented for purposes of illustration and description and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those skilled in the art. The embodiments are chosen and described in order to better illustrate the principles of the invention and its practical application and to enable those skilled in the art to understand the invention and design various embodiments with various modifications as suited for specific applications.

Claims

1. A dynamic quantitative trading system based on machine learning, characterized by: include: Data collection module, used to obtain real-time financial market data, including prices, trading volumes and macroeconomic indicators; Model training module, which trains multiple trading strategy models through machine learning algorithms and evaluates model performance based on historical data; Strategy optimization module uses genetic algorithms to dynamically adjust model parameters to adapt to market changes and user risk preferences; The transaction execution module connects to the exchange API to execute automated trading instructions and supports real-time buying and selling and position management; The risk monitoring module calculates the combined VaR and CVaR indicators in real time, triggers risk warnings and automatically adjusts strategies.

2. A dynamic quantitative trading system based on machine learning as claimed in claim 1, characterized in that: The model training module integrates a deep learning framework, supports LSTM and Transformer models, and optimizes prediction accuracy in small sample scenarios through transfer learning.

3. A dynamic quantitative trading system based on machine learning as claimed in claim 1, characterized in that: The strategy optimization module is embedded in the blockchain node network, and the strategy parameters and transaction records are stored on the chain to achieve tamper-proof evidence.

4. A dynamic quantitative trading system based on machine learning as claimed in claim 1, characterized in that: The data acquisition module supports multi-protocol interfaces, including RESTful API, WebSocket and FIX protocols, to achieve low-latency data synchronization.

5. A dynamic quantitative trading system based on machine learning as claimed in claim 1, characterized in that: The risk monitoring module combines Monte Carlo simulation with reinforcement learning to dynamically generate the optimal risk hedging strategy.

6. A dynamic quantitative trading method based on machine learning, characterized in that: The following steps are involved: Collect market data in real time and perform feature engineering to generate standardized inputs; Call the pre-trained LSTM model to predict asset price trends and output trading signals; Optimize trading thresholds and position ratios through genetic algorithms to maximize risk-adjusted returns; Execute automated trading instructions and monitor portfolio performance in real time; When the risk indicator exceeds the limit, a policy rollback or hedging operation is triggered.

7. A dynamic quantitative trading method based on machine learning as claimed in claim 6, characterized in that: The feature engineering process includes volatility calculation, technical indicator (RSI, MACD) extraction and news sentiment analysis.

8. A dynamic quantitative trading method based on machine learning as claimed in claim 6, characterized in that: The trading signal generation step adopts an integrated learning model to fuse the prediction results of multiple sub-models to improve stability.

9. A dynamic quantitative trading device based on machine learning, characterized in that: include: Edge computing units, deployed on the exchange's intranet, perform high-frequency data preprocessing and model inference; GPU clusters to run deep learning model training and policy optimization algorithms; Blockchain evidence storage terminal, recording transaction instructions and strategy adjustment logs; Interactive terminal, providing a visual risk dashboard and parameter configuration interface.

10. A dynamic quantitative trading device based on machine learning as claimed in claim 9, characterized in that: The edge computing unit integrates a hardware acceleration module and supports FPGA to achieve millisecond-level transaction signal generation.

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