Aggregator transaction strategy based on langchain and agent technologies

Abstract

The invention relates to the technical field of power markets, and discloses an aggregator transaction strategy based on langchain and agent technologies, comprising the following steps: designing a decision framework of an intelligent agent according to transaction strategy requirements, and making a transaction decision; obtaining a preset number of market transaction data and market emotion data; performing emotion analysis and recognition on the obtained market emotion data by using an emotion recognition algorithm; according to the market transaction data and the corresponding emotion analysis result, dynamically adjusting transaction strategy parameters; and the intelligent agent generates a transaction signal according to the adjusted transaction strategy, and executes an actual transaction order converted from the transaction signal. According to the method, rapid processing of large-scale real-time data and deep analysis of complex market emotions can be realized, so that more accurate and more dynamic transaction decision support can be provided for a load aggregator, the market trend can be predicted more accurately, risks can be managed more effectively, and finally advantages are obtained in the market with intense competition.

Description

Technical Field

[0001] The present invention relates to the technical field of power market, and in particular to an aggregator trading strategy based on langchain and agent technology. Background Art

[0002] With the continuous advancement of e-commerce and financial technology, traditional market trading strategies have gradually shown their shortcomings, especially in the high-speed development and highly dynamic environment of modern power and financial markets. Market participants, especially load aggregators who are key nodes in energy dispatch, face the challenge of growing demand. They must be able to respond to market changes quickly and accurately, and process and analyze huge data sets in real time in order to formulate effective trading strategies and execute decisions. This demand has driven the pursuit of more efficient and intelligent trading systems.

[0003] However, the design of most traditional trading systems has failed to keep up with the changes in demand. These systems often lack sufficient real-time data processing capabilities and fail to achieve instant data updates and rapid decision support, which limits their efficiency and effectiveness in a rapidly changing market environment. In addition, these systems usually rely only on preset static models and rules, which are often clumsy and slow in responding to rapid changes in the market, and cannot provide flexible and timely trading decisions, resulting in the loss of trading opportunities and potential economic losses.

[0004] More importantly, market sentiment plays a vital role in influencing price fluctuations and predicting market trends. Emotional factors, such as investor confidence, market panic, or optimism, can significantly affect market movements in a short period of time. Despite the growing importance of market sentiment, most existing trading strategies fail to effectively integrate and analyze these sentiment data, especially those from unstructured texts such as social media posts, news reports, and analyst comments. These unstructured texts contain key sentiment signals, which, if properly captured and analyzed, will greatly enhance the predictability and adaptability of trading strategies.

[0005] Therefore, the present invention proposes an aggregator transaction strategy based on langchain and agent technology. Summary of the invention

[0006] In response to the problems in the related technology, the present invention proposes an aggregator transaction strategy based on langchain and agent technology to overcome the above-mentioned technical problems existing in the existing related technology.

[0007] To this end, the specific technical solution adopted by the present invention is as follows:

[0008] The aggregator trading strategy based on langchain and agent technology includes the following steps:

[0009] S1. Design the decision framework of the intelligent agent according to the trading strategy requirements, select the deep learning model to analyze the market data, and make trading decisions based on the analysis results;

[0010] S2. Based on different data sources, the aggregator obtains a preset amount of market transaction data and market sentiment data, and performs data cleaning and normalization;

[0011] S3, using the emotion recognition algorithm to perform emotion analysis and recognition on the acquired market emotion data to obtain the corresponding emotion analysis results;

[0012] S4. Dynamically adjust trading strategy parameters according to market trading data and corresponding sentiment analysis results to obtain an adjusted trading strategy;

[0013] S5. The intelligent agent generates trading signals according to the adjusted trading strategies and executes actual trading orders converted from the trading signals through exchanges or other financial platforms.

[0014] Preferably, the decision framework of the intelligent agent is designed according to the trading strategy requirements, and a deep learning model is selected to analyze the market data. The trading decision is made based on the analysis results, and the following steps are included:

[0015] S11. Determine the basic requirements of the trading strategy and the type of market data to be analyzed, where the basic requirements of the trading strategy include risk appetite, investment objectives and trading frequency;

[0016] S12. Integrate natural language processing capabilities into decision-making systems using langchain technology and design intelligent agents that integrate market data analysis;

[0017] S13. Obtain historical transaction data in real time through the langchain connector, and use the historical transaction data to train the pre-built deep learning model;

[0018] S14. Use the trained deep learning model to predict market trends and trading opportunities, and formulate corresponding trading strategies based on the analyzed market trends and trading opportunities.

[0019] Preferably, based on different data sources, the aggregator obtains a preset amount of market transaction data and market sentiment data, and performs data cleaning and normalization processing, including the following steps:

[0020] S21. The aggregator obtains a preset amount of market transaction data and market sentiment data based on different data sources, wherein the data sources include financial market data, exchanges, financial news websites and social media platforms;

[0021] S22. Clean the acquired data, remove erroneous and incomplete data, and perform normalization.

[0022] Preferably, the method of using an emotion recognition algorithm to perform emotion analysis and recognition on the acquired market emotion data to obtain a corresponding emotion analysis result comprises the following steps:

[0023] S31. Use a word segmentation tool to segment the text in the market sentiment data, perform part-of-speech tagging on the text after word segmentation, and identify the dependency relationship between words based on dependency syntactic analysis;

[0024] S32, extracting emotional words, part-of-speech information, and word collocations in dependency relationships in the market sentiment data as basic features;

[0025] S33, analyzing the deep semantic information in phrases or sentences composed of word combinations according to the theory of combinational semantics, and constructing word vectors and phrase vectors to describe and utilize subtle differences in semantics;

[0026] S34, constructing an emotion classification model based on a semi-Markov conditional random field, and training it using pre-labeled training data to obtain a trained emotion classification model;

[0027] S35. Use the trained sentiment classification model to classify the unlabeled words in the market sentiment data, combine the sentiment classification results of each word, and determine the sentiment category of the entire text based on the text structure and semantic flow.

[0028] Preferably, the identifying dependency relationship between words based on dependency syntactic analysis comprises the following steps:

[0029] Use the dependency parser to perform dependency syntactic analysis on words;

[0030] Construct dependency relationships between words based on the results of dependency syntactic analysis to form a dependency tree;

[0031] Extract the dependency relationship of each word based on the dependency tree, including core words and subordinate words;

[0032] Label the relationship type for each pair of dependency relationships, including subject-predicate relationship, verb-object relationship, and attributive-predicate relationship.

[0033] Preferably, the method of analyzing the deep semantic information in the phrases or sentences composed of vocabulary combinations according to the combinatorial semantics theory and constructing word vectors and phrase vectors to describe and utilize the subtle differences in semantics includes the following steps:

[0034] S331, labeling the semantic role of each word in the sentence and identifying the semantic relationship between words;

[0035] S332, using a word vector training method to map the words into dense vector representations, and selecting corresponding word vectors according to the semantic role of the words in the sentence and context information;

[0036] S333, analyzing the deep semantic structure of the phrase or sentence formed by the word combination according to the combination semantic theory, and combining the word vectors into a phrase vector by weighted average method;

[0037] S334. The subtle semantic differences are described by calculating the differences between phrase vectors, and the similarity between phrase vectors is measured using the cosine similarity method.

[0038] Preferably, the calculation formula for the difference between the phrase vectors is:

[0039]

[0040] The calculation formula for the similarity between the phrase vectors is:

[0041]

[0042] Where d(v1,v2) represents the difference between the phrase vector v1 and the phrase vector v2; n represents the total spatial dimension; v 1i 、v 2i Respectively represent the components of phrase vector v1 and phrase vector v2 in the i-th dimension; cos(v1,v2) represents the similarity between phrase vector v1 and phrase vector v2; v1·v2 represents the dot product of phrase vector v1 and phrase vector v2; ||v1||, ||v2|| represent the Euclidean norm of phrase vector v1 and phrase vector v2 respectively.

[0043] Preferably, the method of using the trained emotion classification model to perform emotion classification on unlabeled words in the market emotion data, combining the emotion classification results of each word, and determining the emotion category of the entire text according to the text structure and semantic flow includes the following steps:

[0044] S351, using the trained sentiment classification model to perform sentiment classification on all unlabeled words in the market sentiment data, and labeling each unlabeled word with a corresponding sentiment label;

[0045] S352, assigning different emotion weights to each word according to its importance in the sentence, and weightedly accumulating the emotion classification results of all words to form the emotion tendency of the sentence;

[0046] S353. Analyze the overall structure of the text and the changing trend of emotions in the text;

[0047] S354. Based on the emotional tendency of the sentence, the overall structure of the text and the changing trend of the emotions in the text, the dominant emotion of the entire text is comprehensively judged to obtain the emotional category of the entire text.

[0048] Preferably, dynamically adjusting the trading strategy parameters according to the market trading data and the corresponding sentiment analysis results to obtain the adjusted trading strategy comprises the following steps:

[0049] S41. Evaluate the performance of real-time trading strategies under current market conditions and identify the potential impact of market sentiment fluctuations on trading strategies based on sentiment analysis results;

[0050] S42. Adjust the trading strategy parameters based on the evaluation results and the identified potential impacts to obtain an adjusted trading strategy.

[0051] Preferably, the intelligent agent generates a trading signal according to the adjusted trading strategy, and executes the actual trading order converted from the trading signal through an exchange or other financial platform, including the following steps:

[0052] S51. Based on the adjusted trading strategy, the intelligent agent evaluates market trends and trading opportunities, and generates corresponding trading signals based on the trading strategy parameters;

[0053] S52, converting the generated trading signal into a trading order format, and ensuring that the order format complies with the requirements of the exchange or financial platform;

[0054] S53. Send orders through the API interface of exchanges or other financial platforms, and monitor the execution status of orders in real time, where the execution status includes transaction status and potential market impact.

[0055] Compared with the prior art, the present invention provides an aggregator trading strategy based on langchain and agent technology, which has the following beneficial effects:

[0056] 1) By combining natural language processing technology and deep learning models, the present invention can achieve rapid processing of large-scale real-time data and in-depth analysis of complex market sentiment, thereby providing load aggregators with more accurate and dynamic trading decision support, enabling them to more accurately predict market trends, more effectively manage risks, and ultimately gain an advantage in a highly competitive market.

[0057] 2) The present invention can not only analyze historical and real-time market data through deep learning models, so that intelligent agents can predict market trends and trading opportunities and improve the accuracy and timeliness of decision-making, but also combine the results of market sentiment analysis to enable intelligent agents to dynamically adjust trading strategy parameters to cope with market sentiment fluctuations, optimize trading strategies to adapt to market changes, and reduce decision-making delays and uncertainties.

[0058] 3) The intelligent agent of the present invention can not only generate trading signals, automatically convert these signals into trading orders, and quickly respond to market opportunities, but also execute them through exchanges or financial platforms, ensuring that trading strategies are quickly and accurately executed under appropriate market conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0060] Figure 1 It is a flow chart of an aggregator transaction strategy based on langchain and agent technology according to an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] According to an embodiment of the present invention, an aggregator transaction strategy based on langchain and agent technology is provided.

[0063] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to one embodiment of the present invention, a transaction strategy for an aggregator based on langchain and agent technology is provided, including the following steps:

[0064] S1. Design the decision framework of the intelligent agent according to the trading strategy requirements, select the deep learning model to analyze the market data, and make trading decisions based on the analysis results;

[0065] The design of the intelligent agent decision framework according to the trading strategy requirements, the selection of a deep learning model to analyze market data, and the formulation of trading decisions based on the analysis results include the following steps:

[0066] S11. Determine the basic requirements of the trading strategy and the type of market data to be analyzed;

[0067] Specifically, in order to develop an effective trading strategy, it is first necessary to clarify the basic requirements of the trading strategy and the type of market data to be analyzed. This is a multi-level and multi-dimensional process involving the following key aspects:

[0068] 1) Determine the basic requirements of the trading strategy

[0069] Risk appetite: This is one of the most critical considerations in a trading strategy. Depending on the investor's risk tolerance, the strategy will be adjusted between conservative, moderate or aggressive. Risk appetite will directly affect the selection of potential investments, as well as the corresponding money management and stop-loss strategy.

[0070] Investment objectives: Having clear investment objectives is the basis for developing a trading strategy. These objectives include, but are not limited to, capital appreciation, stable returns, or the achievement of specific financial goals, such as the accumulation of retirement funds. Investment objectives will affect the strategy's time frame, asset selection, and risk management.

[0071] Trading frequency: Trading frequency determines how active the strategy is. It can be high frequency trading, where you trade multiple times a day, or low frequency trading, where you hold for the long term. Trading frequency affects transaction costs, market exposure, and the complexity of implementing the strategy.

[0072] 2) Determine the type of market data to be analyzed

[0073] Price data: This includes historical and real-time prices for stocks, bonds, commodities, or any other financial asset. This is the most basic type of data in trading decisions and is used for technical analysis and pattern recognition.

[0074] Volume data: Volume can provide important clues about market activity and potential price movements. High volume indicates a strong reaction to a certain price level.

[0075] Fundamental data: For fundamental-based trading strategies, it is necessary to analyze financial statements, industry data, economic indicators and other information.

[0076] By carefully determining these needs and data types, load aggregators or any other market participants can design more precise and effective trading strategies to adapt to changing market conditions. The key to this process is to ensure that all decisions are based on sufficient information and appropriate risk management strategies.

[0077] S12. Integrate natural language processing capabilities into decision-making systems using langchain technology and design intelligent agents that integrate market data analysis;

[0078] Specifically, integrating Langchain technology with natural language processing capabilities into decision-making systems and designing intelligent agents that integrate market data analysis can greatly enhance the flexibility and efficiency of trading strategies. This process involves several key steps:

[0079] 1) Integrate natural language processing technology

[0080] Technology selection and integration: Choose appropriate natural language processing (NLP) frameworks and tools, such as Langchain, to support complex text analysis and sentiment recognition. These tools can extract valuable trend information from unstructured data sources (such as financial news, market comments, social media, etc.).

[0081] Data preprocessing: Implement preprocessing steps such as data cleaning, standardization, and tokenization to prepare text data for deeper analysis. This includes identifying and extracting relevant financial terms and expressions from large amounts of text.

[0082] 2) Designing Intelligent Agents

[0083] Strategy Definition and Algorithm Development: Define the strategy framework of intelligent agents based on market data analysis needs. Develop algorithms to parse and understand market data, while using machine learning techniques to identify patterns and predict market trends.

[0084] Functional integration: Combine NLP capabilities with market data analysis tools to create a comprehensive decision-making system that can understand market language and digital data. For example, the system can combine sentiment trends analyzed from news reports or social media with real-time market price and volume data.

[0085] S13. Obtain historical transaction data in real time through the langchain connector, and use the historical transaction data to train the pre-built deep learning model;

[0086] Specifically, using the Langchain connector to obtain historical transaction data in real time and using this data to train pre-built deep learning models is an effective way to optimize and enhance trading strategies. This process can be divided into the following key steps:

[0087] 1) Set up data access

[0088] Langchain connector configuration: First, you need to configure the Langchain connector to access historical transaction data in real time. This includes determining the data source, such as exchanges, financial information service providers, etc., and setting up API calls to pull the required historical data.

[0089] Data access verification: Ensure that the connector can stably and securely obtain data, including key information such as price, trading volume, market opening and closing times, etc.

[0090] 2) Data preprocessing

[0091] Data cleaning: Clean the received historical transaction data to remove any outliers or missing data to ensure the quality of the input data.

[0092] Feature Engineering: Extract useful features from raw trading data, including price changes, volume changes, historical volatility, etc. These features are critical for training deep learning models.

[0093] 3) Deep learning model training

[0094] Choose the model architecture: According to the needs of the trading strategy, choose a suitable deep learning model, such as convolutional neural network (CNN), recurrent neural network (RNN) or long short-term memory network (LSTM). These models are good at processing sequence data and are suitable for financial time series analysis.

[0095] Model training: Use historical trading data to train the model. This usually involves defining a loss function, selecting an optimizer, and setting an appropriate training cycle. The purpose of model training is to enable the model to learn the ability to predict future market behavior from historical data.

[0096] 4) Model validation and optimization

[0097] Cross-validation: Apply cross-validation techniques to evaluate the stability and generalization ability of the model. This can be done by dividing the historical dataset into multiple subsets and then repeatedly training and validating the model on these subsets.

[0098] Performance optimization: Adjust and optimize the model based on the verification results, such as adjusting the network structure, optimizing hyperparameters, etc., to improve prediction accuracy and response speed.

[0099] 5) Implementation and monitoring

[0100] Model implementation: Deploy the trained model into an actual trading system to predict market trends and generate trading signals in real time.

[0101] Performance monitoring: Continuously monitor the performance of the model and make necessary fine-tuning based on market changes and model performance.

[0102] Through this process, using langchain technology and deep learning, the intelligence level of trading strategies can be greatly improved, enabling market participants such as load aggregators to grasp market dynamics more accurately and improve the speed and efficiency of trading decisions.

[0103] S14. Use the trained deep learning model to predict market trends and trading opportunities, and formulate corresponding trading strategies based on the analyzed market trends and trading opportunities.

[0104] Specifically, using trained deep learning models to predict market trends and identify trading opportunities, and then formulating corresponding trading strategies based on these analysis results, is an efficient way to optimize the decision-making process of market participants. This process usually includes the following key steps:

[0105] 1) Market trend forecast

[0106] Model deployment: Deploy the trained deep learning model in an actual trading system to analyze market data in real time.

[0107] Trend analysis: The model will analyze historical data and real-time market dynamics to identify market trends. This includes determining whether the market is rising, falling, or in a stable state.

[0108] 2) Identify trading opportunities

[0109] Signal Generation: Based on the model's predictions, the system generates specific buy or sell signals. These signals indicate potential profit opportunities based on the predicted market movement.

[0110] Risk Assessment: At the same time, the system assesses the risks associated with these trading opportunities to ensure that the trading strategy complies with the predetermined risk management parameters.

[0111] 3) Develop a trading strategy

[0112] Strategy formulation: Develop specific trading strategies based on the market trends and trading opportunities provided by the model, including setting entry and exit points for transactions, selecting appropriate trading tools (such as stocks, options, futures, etc.), and determining the size and frequency of transactions.

[0113] Strategy Adjustment: Continuously adjust and optimize strategies based on changing market conditions. For example, if market volatility increases, you may need to adjust your stop loss or reduce your trade size to manage risk.

[0114] 4) Execution and monitoring

[0115] Automated Execution: Utilize the trading platform’s automation tools to automatically execute trading orders based on your strategy. This includes automatic triggering of buy and sell orders.

[0116] Results monitoring: Monitor trading results and model performance to ensure that the strategy execution is in line with expectations. This also includes continuous backtesting and adjustment of the model to cope with changes in the market environment.

[0117] Through such a comprehensive process, deep learning models can not only help market participants capture short-term and long-term market trends, but also provide specific trading recommendations to help them remain competitive and profitable in the highly competitive financial markets. This approach is particularly suitable for load aggregators and other market traders who want to optimize their decision-making through advanced data analysis techniques.

[0118] S2. Based on different data sources, the aggregator obtains a preset amount of market transaction data and market sentiment data, and performs data cleaning and normalization;

[0119] Wherein, based on different data sources, the aggregator obtains a preset amount of market transaction data and market sentiment data, and performs data cleaning and normalization processing, including the following steps:

[0120] S21. Based on different data sources, the aggregator obtains a preset amount of market transaction data (including market data, price information, trading volume, etc.) and market sentiment data, where the data sources include financial market data, exchanges, financial news websites and social media platforms;

[0121] S22. Clean the acquired data, remove erroneous and incomplete data, and perform normalization.

[0122] S3, using the emotion recognition algorithm to perform emotion analysis and recognition on the acquired market emotion data to obtain the corresponding emotion analysis results;

[0123] The method of using the emotion recognition algorithm to perform emotion analysis and recognition on the acquired market emotion data to obtain the corresponding emotion analysis results includes the following steps:

[0124] S31. Use a word segmentation tool to segment the text in the market sentiment data, perform part-of-speech tagging on the text after word segmentation, and identify the dependency relationship between words based on dependency syntactic analysis;

[0125] Specifically, using a word segmentation tool to segment the text in the market sentiment data and performing part-of-speech tagging on the segmented text includes the following steps:

[0126] 1) Word segmentation

[0127] Select a word segmentation tool: Select a suitable word segmentation tool, such as Jieba, HanLP, etc. These tools are specially designed for processing Chinese text and can effectively identify word boundaries in Chinese sentences.

[0128] Perform word segmentation: The text in the market sentiment data is processed through the word segmentation tool to break down the continuous text string into meaningful vocabulary units. This step is the basis for subsequent text analysis and can help to more accurately understand the text content and structure.

[0129] 2) Part-of-speech tagging

[0130] Choose a part-of-speech tagging tool: You can also use tools such as Jieba and HanLP, which not only support word segmentation, but also part-of-speech tagging.

[0131] Perform part-of-speech tagging: After word segmentation, perform part-of-speech tagging on each independent word, such as noun, verb, adjective, etc. Part-of-speech tagging helps to further analyze the grammatical role of the word and its function in the sentence.

[0132] 3) Application to sentiment analysis

[0133] Text parsing: Use the results of word segmentation and part-of-speech tagging to parse the text in market sentiment data and identify the emotional words and expressions therein.

[0134] Emotional word recognition: Pay special attention to words that are often associated with emotional expression, such as adjectives and some verbs. These words can express emotional colors, such as "rise", "panic", etc.

[0135] The process of identifying the dependency relationship between words based on dependency syntactic analysis comprises the following steps:

[0136] 1) Use the dependency parser to perform dependency syntactic analysis on words;

[0137] 2) Construct the dependency relationship between words based on the results of dependency syntactic analysis to form a dependency tree;

[0138] 3) Extract the dependency relationship of each word based on the dependency tree, including core words and subordinate words;

[0139] 4) Label the relationship type for each pair of dependency relationships, including subject-predicate relationship, verb-object relationship, and attributive-predicate relationship.

[0140] S32, extracting emotional words, part-of-speech information, and word collocations in dependency relationships in the market sentiment data as basic features;

[0141] S33, analyzing the deep semantic information in phrases or sentences composed of word combinations according to the theory of combinational semantics, and constructing word vectors and phrase vectors to describe and utilize subtle differences in semantics;

[0142] Specifically, the method of analyzing the deep semantic information in phrases or sentences composed of vocabulary combinations according to the combinational semantics theory and constructing word vectors and phrase vectors to describe and utilize the subtle differences in semantics includes the following steps:

[0143] S331, labeling the semantic role of each word in the sentence and identifying the semantic relationship between words;

[0144] S332, using a word vector training method to map the words into dense vector representations, and selecting corresponding word vectors according to the semantic role of the words in the sentence and context information;

[0145] Specifically, using word vector training to map words into dense vector representations can effectively capture the semantic information of words and be used in various natural language processing tasks. When processing market sentiment data, selecting the corresponding word vector based on the semantic role of the word in the sentence and the context information is a key step. This process can be achieved through the following steps:

[0146] 1) Choose a word vector training method

[0147] Method selection: Choose a suitable word vector training method, such as Word2Vec, GloVe, or FastText. These methods generate word vectors by analyzing the co-occurrence information of words, which can express the similarity and semantic relationship between words.

[0148] Training data preparation: Prepare text data for training word embeddings. For market sentiment analysis, this usually includes financial news, market comments, social media posts, etc.

[0149] 2) Training word vectors

[0150] Model configuration: Set the parameters of the word vector model, such as vector size, window size, minimum word frequency, etc.

[0151] Perform training: Use the selected method to train word vectors on the prepared text data. During the training process, the model will learn dense vector representations of words.

[0152] 3) Apply word vectors

[0153] Vector selection: Select the corresponding word vector based on the semantic role of the word in the sentence and the context information. For example, for the different meanings of the same word in different contexts, the context can be used to decide which word vector to use.

[0154] Semantic role identification: Use tools such as dependency parsing to identify the semantic roles of words, such as subject, object, etc., to ensure that the best word vector matching its semantic role is selected.

[0155] 4) Integrate word vectors for analysis

[0156] Feature construction: Combine the selected word vectors into higher-level feature representations, such as sentence vectors or document vectors, which can be used for further machine learning tasks.

[0157] Sentiment analysis application: Use the integrated vector features to perform sentiment analysis and identify the sentiment tendency and intensity in market texts.

[0158] This process not only enhances the understanding of word semantics, but also provides accurate data support for complex sentiment analysis and market forecasting. Through this method, the nuances in market sentiment data can be captured more accurately, thereby improving the responsiveness and efficiency of trading strategies.

[0159] S333, analyzing the deep semantic structure of the phrase or sentence formed by the word combination according to the combination semantic theory, and combining the word vectors into a phrase vector by weighted average method;

[0160] Specifically, according to the Compositionality Theory, analyzing the deep semantic structure of phrases or sentences formed by word combinations and combining word vectors into phrase vectors by weighted average method is a common method to deal with semantic combination problems. The key to this process is how to effectively extract the meaning of the entire phrase or sentence from the vector representation of a single word. The following are the detailed steps to achieve this process:

[0161] 1) Acquisition of word vectors

[0162] Get word vectors: First, you need to get the vector representation of each word from the trained word vector model. These vectors usually contain the semantic characteristics and contextual information of the word.

[0163] 2) Applying Compositional Semantics Theory

[0164] Theoretical framework: Combinatorial semantics theory claims that the meanings of words can be systematically constructed into the semantic structure of phrases or sentences through their combination. For example, the meaning of "red apple" is composed of the meanings of "red" and "apple" through a specific semantic function.

[0165] Semantic role analysis: Analyze the semantic role of each word in a phrase or sentence, such as modifier, noun core, etc., to ensure that their semantic contribution is correctly reflected when the vectors are combined.

[0166] 3) Weighted average method

[0167] Determine weights: Assign weights to words based on their importance and semantic contribution in a sentence or phrase. For example, a core noun might be assigned a higher weight than an adjective.

[0168] Vector combination: Use weighted average to combine the word vectors to form a vector representation of the entire phrase or sentence. This step takes into account the importance of each word and its contribution to the overall semantics.

[0169] 4) Vector Regularization

[0170] Vector regularization: In order to ensure that the combined vector remains within a reasonable size range, vector regularization processing, such as normalization, is performed to avoid problems in numerical calculations.

[0171] 5) Application and analysis

[0172] Semantic analysis: Use the generated phrase or sentence vectors for further semantic analysis, such as sentiment analysis, topic classification, etc.

[0173] Model validation: Test the effectiveness of the combined vector in real applications to ensure that it can correctly express the semantic intent of the phrase or sentence.

[0174] This method is not only applicable to understanding the semantics at the phrase level, but also has important value for analyzing text at the sentence or even paragraph level. Through such technology, the semantic information in text data can be captured and utilized more accurately, providing support for various natural language processing applications.

[0175] S334. The subtle semantic differences are described by calculating the differences between phrase vectors, and the similarity between phrase vectors is measured using the cosine similarity method.

[0176] The difference between the phrase vectors is calculated as:

[0177]

[0178] The calculation formula for the similarity between the phrase vectors is:

[0179]

[0180] Where d(v1,v2) represents the difference between the phrase vector v1 and the phrase vector v2; n represents the total spatial dimension; v 1i 、v 2i Respectively represent the components of phrase vector v1 and phrase vector v2 in the i-th dimension; cos(v1,v2) represents the similarity between phrase vector v1 and phrase vector v2; v1·v2 represents the dot product of phrase vector v1 and phrase vector v2; ||v1||, ||v2|| represent the Euclidean norm of phrase vector v1 and phrase vector v2 respectively.

[0181] S34, constructing an emotion classification model based on a semi-Markov conditional random field, and training it using pre-labeled training data to obtain a trained emotion classification model;

[0182] Semi-Markov Conditional Random Field (Semi-CRF) is an effective sequence annotation method, especially suitable for processing text data with complex structures. Semi-CRF is a sequence annotation model used to annotate paragraphs or fragments in sequence data. Compared with the traditional CRF model, Semi-CRF can process variable-length data blocks and is more suitable for identifying patterns with complex structures such as emotional expressions in text.

[0183] S35. Use the trained sentiment classification model to classify the unlabeled words in the market sentiment data, combine the sentiment classification results of each word, and determine the sentiment category of the entire text based on the text structure and semantic flow.

[0184] Specifically, the method of using the trained emotion classification model to classify the unlabeled words in the market emotion data, combining the emotion classification results of each word, and determining the emotion category of the entire text according to the text structure and semantic flow includes the following steps:

[0185] S351, using the trained sentiment classification model to perform sentiment classification on all unlabeled words in the market sentiment data, and labeling each unlabeled word with a corresponding sentiment label;

[0186] S352, assigning different emotion weights to each word according to its importance in the sentence, and weightedly accumulating the emotion classification results of all words to form the emotion tendency of the sentence;

[0187] Specifically, in order to assign different emotional weights to each word according to its importance in the sentence, and to perform weighted accumulation of the emotional classification results of all words to form the emotional tendency of the sentence, the following steps can be adopted:

[0188] 1) Word sentiment classification

[0189] Sentiment labeling: First, each word needs to be classified to determine the emotion category (such as positive, negative, neutral, etc.) to which it belongs.

[0190] Sentiment weighting: Assign a sentiment weight to each word based on its importance in the sentence. The weight can be determined based on factors such as the word's semantic importance, part of speech, position in the sentence, contextual information, etc.

[0191] 2) Weight calculation

[0192] Word importance analysis: Analyze the semantic importance of words. The semantic contribution of words can be evaluated through word vector models.

[0193] Contextual analysis: Consider the context of the word in the sentence, such as modification relations, verb-noun collocation, etc., to adjust the weight.

[0194] Weight assignment: Assign weights to each word based on the analysis results. For example, core verbs and adjectives may be given higher weights.

[0195] 3) Weighted accumulation

[0196] Weighted sum: Perform weighted summation on the sentiment classification results of all words.

[0197] Normalization: In order to make the results easier to compare and understand, it may be necessary to normalize the weighted summation results.

[0198] 4) Emotional tendency analysis

[0199] Tendency determination: The emotional tendency of the entire sentence is determined based on the weighted cumulative results. If the sum is positive, it can be considered that the sentence as a whole has a positive emotional tendency; if the sum is negative, it has a negative emotional tendency.

[0200] 5) Model evaluation and optimization

[0201] Evaluation: The accuracy of the model is evaluated by comparing it with manually annotated sentiment tendencies.

[0202] Optimization: Adjust the weight distribution strategy and model parameters based on the evaluation results to improve the accuracy and robustness of sentiment tendency analysis.

[0203] In this way, the emotional tendencies at the sentence level can be captured more accurately, providing strong support for text analysis and sentiment computing.

[0204] S353. Analyze the overall structure of the text (including the logical relationship between sentences and the structure of the text) and the changing trend of emotions in the text (such as the transition from positive to negative, or the fluctuation of emotional intensity);

[0205] Specifically, analyzing the overall structure of the text and the changing trends of emotions in the text is a complex and detailed text analysis process. This analysis can help to more fully understand the text content and the author's emotional expression. The following are the detailed analysis steps:

[0206] 1) Text structure analysis

[0207] Logical relationship identification: Analyze the logical relationship between sentences, such as cause and effect, contrast, parallel, etc. This can be achieved by using natural language processing tools to identify conjunctions (such as "therefore", "but", "and", etc.) and sentence structures.

[0208] Discourse Structure Analysis: Analyze the text’s discourse structure, such as introduction, body, conclusion, etc. Understanding discourse structure helps capture the overall layout and logical flow of the text.

[0209] 2) Analysis of emotional change trends

[0210] Emotion recognition: First, identify the emotional expressions in the text, which usually involves the recognition and classification of emotion words (positive, negative, neutral).

[0211] Emotion intensity quantification: Quantify the intensity of each emotion expression, which can be achieved using an emotion intensity dictionary or machine learning-based methods.

[0212] Emotional change tracking: Tracking the changing trend of emotions in text, such as the shift from positive to negative or the rise and fall of emotional intensity. This requires analyzing the location and context of emotional expression in the text.

[0213] 3) Apply time series analysis

[0214] Time Annotations: If the text is time-annotated (e.g. logs, comment timestamps, etc.), use this information to analyze how sentiment changes over time.

[0215] Time Series Modeling: Build time series models to analyze patterns and periodicity of sentiment changes.

[0216] 4) Comprehensive analysis

[0217] Comprehensive analysis: Combine the results of structural and sentiment analysis to conduct a comprehensive evaluation of the text and determine the overall sentiment tendency of the text and its changes.

[0218] 5) Visualization and reporting

[0219] Data visualization: Use charts and timelines to visualize sentiment trends and text structure relationships to present analysis results more intuitively.

[0220] Generate Report: Generate a detailed report based on the analysis results, including key findings and recommendations.

[0221] This approach allows for an in-depth text analysis perspective that not only captures the sentiment of a single sentence or paragraph, but also understands the emotional flow and structural logic of the entire text, providing a basis for in-depth content analysis and decision-making.

[0222] S354. Based on the emotional tendency of the sentence, the overall structure of the text and the changing trend of the emotions in the text, the dominant emotion of the entire text is comprehensively judged to obtain the emotional category of the entire text.

[0223] Specifically, the emotions of each sentence are weighted and synthesized according to the importance and emotional intensity of the sentence to obtain the overall emotional tendency of the text. Based on the results of the comprehensive analysis, the dominant emotional category of the entire text is determined. Factors considered include the frequency of emotional occurrence, emotional intensity, and the emotions of key sentences.

[0224] S4. Dynamically adjust trading strategy parameters according to market trading data and corresponding sentiment analysis results to obtain an adjusted trading strategy;

[0225] The dynamically adjusting the trading strategy parameters according to the market trading data and the corresponding sentiment analysis results to obtain the adjusted trading strategy comprises the following steps:

[0226] S41. Evaluate the performance of real-time trading strategies under current market conditions and identify the potential impact of market sentiment fluctuations on trading strategies based on sentiment analysis results;

[0227] Specifically, evaluating the performance of real-time trading strategies under current market conditions and identifying the potential impact of market sentiment fluctuations on trading strategies based on sentiment analysis results is a complex task that requires comprehensive data analysis and sentiment recognition. The following are the steps to achieve this goal:

[0228] 1) Collect and integrate market data

[0229] Market data collection: First, you need to collect relevant market data, including price fluctuations, trading volumes, market news, social media sentiment, etc.

[0230] Data aggregation: Integrate this data into a single platform for real-time analysis. This may include time series analysis of price data, sentiment analysis of news and social media, etc.

[0231] 2) Real-time trading strategy analysis

[0232] Strategy Evaluation: Use historical data to test the performance of your trading strategy, as well as its adaptability and profitability under current market conditions.

[0233] Risk Assessment: Identify and evaluate the potential risks of a trading strategy, such as how the strategy will perform in situations of high market volatility and low liquidity.

[0234] 3) Market sentiment analysis

[0235] Sentiment Data Extraction: Extract sentiment data from social media, news reports, and professional market analysis.

[0236] Sentiment quantification: Quantifying sentiment data, such as through sentiment analysis tools to assess whether the overall market sentiment is positive or negative.

[0237] 4) Identify the impact of emotions on strategy

[0238] Correlation analysis between sentiment and market behavior: Analyze the relationship between changes in market sentiment and market behavior (such as price fluctuations and changes in trading volume).

[0239] Emotional impact assessment: Based on the results of sentiment analysis, predict the impact that emotional fluctuations may have on trading strategies, such as affecting buying and selling timing, adjusting risk management strategies, etc.

[0240] Through this comprehensive analysis and adjustment method, market sentiment data can be better utilized to optimize trading strategies, thereby improving trading efficiency and success rate in a dynamically changing market environment.

[0241] S42. Adjust the trading strategy parameters based on the evaluation results and the identified potential impacts to obtain an adjusted trading strategy.

[0242] Specifically, it is a systematic and meticulous process to adjust the trading strategy parameters based on the evaluation results and the potential impacts identified, and to obtain the adjusted trading strategy. This requires making reasonable adjustments based on market sentiment analysis and the real-time performance of the trading strategy. The following are the detailed steps:

[0243] 1) Analyze and evaluate the results

[0244] Performance Analysis: Get detailed analysis of your trading strategy’s performance under current market conditions, including profit and loss, risk-adjusted returns, and more.

[0245] Impact Identification: Based on the results of sentiment analysis, identify the specific impact of market sentiment fluctuations on trading strategies, such as certain strategies perform poorly under specific sentiments.

[0246] 2) Parameter adjustment plan formulation

[0247] Key Parameter Identification: Identify strategy parameters that need to be adjusted, such as stop-loss levels, entry and exit points, position sizing, etc.

[0248] Adjustment basis: Develop a specific parameter adjustment plan based on the changing trend of market sentiment and the performance analysis results of the strategy.

[0249] 3) Adjust trading strategy

[0250] Parameter update: Adjust the key parameters of the trading strategy according to the formulated plan.

[0251] Strategy optimization: It may also be necessary to introduce new trading indicators or modify existing trading logic to better adapt to changes in market sentiment.

[0252] 4) Strategy backtesting

[0253] Backtesting verification: Backtest the adjusted trading strategy with historical data to verify the effectiveness of the adjustment and the robustness of the strategy.

[0254] Performance comparison: Compare the performance of the strategy before and after the adjustment to ensure that the new strategy can provide better risk-adjusted returns.

[0255] 5) Implementation and monitoring

[0256] Strategy implementation: deploying the adjusted strategy in actual trading.

[0257] Continuous Monitoring: Continuously monitor the performance of the strategy and changes in market sentiment, and further adjust the strategy when necessary.

[0258] 6) Continuous Optimization

[0259] Data Feedback Loop: Leverage the latest trading data and market feedback to continuously optimize trading strategies.

[0260] Strategy iteration: Regularly iterate and update trading strategies based on market development and technological progress.

[0261] Through this systematic approach, you can ensure that your trading strategy always adapts to changes in market conditions and effectively use sentiment analysis results to improve trading results. This dynamic adjustment and optimization process helps improve the adaptability and profitability of the strategy and reduce unnecessary risks.

[0262] S5. The intelligent agent generates trading signals according to the adjusted trading strategies and executes actual trading orders converted from the trading signals through exchanges or other financial platforms.

[0263] The intelligent agent generates a trading signal according to the adjusted trading strategy, and executes the actual trading order converted from the trading signal through an exchange or other financial platform, including the following steps:

[0264] S51. Based on the adjusted trading strategy, the intelligent agent evaluates market trends and trading opportunities, and generates corresponding trading signals based on the trading strategy parameters;

[0265] S52, converting the generated trading signal into a trading order format, and ensuring that the order format complies with the requirements of the exchange or financial platform;

[0266] S53. Send orders through the API interface of exchanges or other financial platforms, and monitor the execution status of orders in real time, where the execution status includes transaction status and potential market impact.

[0267] In summary, with the help of the above technical solutions of the present invention, the present invention can realize the rapid processing of large-scale real-time data and the in-depth analysis of complex market sentiment by combining natural language processing technology and deep learning models, so as to provide more accurate and dynamic trading decision support for load aggregators, so that they can more accurately predict market trends, manage risks more effectively, and ultimately gain advantages in the fiercely competitive market. In addition, the present invention can not only analyze historical and real-time market data through deep learning models, so that intelligent agents can predict market trends and trading opportunities, improve the accuracy and timeliness of decision-making, but also combine the results of market sentiment analysis, so that intelligent agents can dynamically adjust trading strategy parameters to cope with market sentiment fluctuations, optimize trading strategies to adapt to market changes, and reduce decision delays and uncertainties. In addition, the intelligent agent of the present invention can not only generate trading signals, automatically convert these signals into trading orders, and respond quickly to market opportunities, but also execute through exchanges or financial platforms to ensure that trading strategies are quickly and accurately executed under appropriate market conditions.

[0268] The technical features of the above-mentioned embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification. A person of ordinary skill in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program. The program can be stored in a computer-readable storage medium. When the program is executed, it includes the steps described in the above method, and the storage medium, such as ROM / RAM, a disk, an optical disk, etc.

[0269] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. The aggregator trading strategy based on langchain and agent technology is characterized by: The following steps are involved: S1. Design the decision framework of the intelligent agent according to the trading strategy requirements, select the deep learning model to analyze the market data, and make trading decisions based on the analysis results; S2. Based on different data sources, the aggregator obtains a preset amount of market transaction data and market sentiment data, and performs data cleaning and normalization; S3, using the emotion recognition algorithm to perform emotion analysis and recognition on the acquired market emotion data to obtain the corresponding emotion analysis results; S4. Dynamically adjust trading strategy parameters according to market trading data and corresponding sentiment analysis results to obtain an adjusted trading strategy; S5. The intelligent agent generates trading signals according to the adjusted trading strategies and executes actual trading orders converted from the trading signals through exchanges or other financial platforms.

2. The aggregator transaction strategy based on langchain and agent technology according to claim 1 is characterized in that: The design of the decision framework of the intelligent agent according to the trading strategy requirements, the selection of a deep learning model to analyze market data, and the formulation of trading decisions based on the analysis results include the following steps: S11. Determine the basic requirements of the trading strategy and the type of market data to be analyzed, where the basic requirements of the trading strategy include risk appetite, investment objectives and trading frequency; S12. Integrate natural language processing capabilities into decision-making systems using langchain technology and design intelligent agents that integrate market data analysis; S13. Obtain historical transaction data in real time through the langchain connector, and use the historical transaction data to train the pre-built deep learning model; S14. Use the trained deep learning model to predict market trends and trading opportunities, and formulate corresponding trading strategies based on the analyzed market trends and trading opportunities.

3. The aggregator transaction strategy based on langchain and agent technology according to claim 1 is characterized in that: Based on different data sources, the aggregator obtains a preset amount of market transaction data and market sentiment data, and performs data cleaning and normalization processing, including the following steps: S21. The aggregator obtains a preset amount of market transaction data and market sentiment data based on different data sources, wherein the data sources include financial market data, exchanges, financial news websites and social media platforms; S22. Clean the acquired data, remove erroneous and incomplete data, and perform normalization.

4. The aggregator transaction strategy based on langchain and agent technology according to claim 1 is characterized in that: The method of using the emotion recognition algorithm to perform emotion analysis and recognition on the acquired market emotion data to obtain the corresponding emotion analysis results comprises the following steps: S31. Use a word segmentation tool to segment the text in the market sentiment data, perform part-of-speech tagging on the text after word segmentation, and identify the dependency relationship between words based on dependency syntactic analysis; S32, extracting emotional words, part-of-speech information, and word collocations in dependency relationships in the market sentiment data as basic features; S33, analyzing the deep semantic information in phrases or sentences composed of word combinations according to the theory of combinational semantics, and constructing word vectors and phrase vectors to describe and utilize subtle differences in semantics; S34, constructing an emotion classification model based on a semi-Markov conditional random field, and training it using pre-labeled training data to obtain a trained emotion classification model; S35. Use the trained sentiment classification model to classify the unlabeled words in the market sentiment data, combine the sentiment classification results of each word, and determine the sentiment category of the entire text based on the text structure and semantic flow.

5. The aggregator transaction strategy based on langchain and agent technology according to claim 4 is characterized in that: The process of identifying the dependency relationship between words based on dependency syntactic analysis comprises the following steps: Use the dependency parser to perform dependency syntactic analysis on words; Construct dependency relationships between words based on the results of dependency syntactic analysis to form a dependency tree; Extract the dependency relationship of each word based on the dependency tree, including core words and subordinate words; Label the relationship type for each pair of dependency relationships, including subject-predicate relationship, verb-object relationship, and attributive-predicate relationship.

6. The aggregator transaction strategy based on langchain and agent technology according to claim 4 is characterized in that: The method of analyzing the deep semantic information in phrases or sentences composed of vocabulary combinations according to the compositional semantics theory and constructing word vectors and phrase vectors to describe and utilize the subtle differences in semantics includes the following steps: S331, marking the semantic role of each word in the sentence and identifying the semantic relationship between words; S332, using a word vector training method to map the words into dense vector representations, and selecting corresponding word vectors according to the semantic role of the words in the sentence and context information; S333, analyzing the deep semantic structure of the phrase or sentence formed by the word combination according to the combination semantic theory, and combining the word vectors into a phrase vector by weighted average method; S334. The subtle semantic differences are described by calculating the differences between phrase vectors, and the similarity between phrase vectors is measured using the cosine similarity method.

7. The aggregator transaction strategy based on langchain and agent technology according to claim 6 is characterized in that: The difference between the phrase vectors is calculated as: The calculation formula for the similarity between the phrase vectors is: Where d(v1,v2) represents the difference between the phrase vector v1 and the phrase vector v2; n represents the total spatial dimension; v 1i 、v 2i Respectively represent the components of phrase vector v1 and phrase vector v2 in the i-th dimension; cos(v1,v2) represents the similarity between phrase vector v1 and phrase vector v2; v1·v2 represents the dot product of the phrase vector v1 and the phrase vector v2; ||v1||, ||v2|| represent the Euclidean norms of phrase vector v1 and phrase vector v2 respectively.

8. The aggregator transaction strategy based on langchain and agent technology according to claim 4 is characterized in that: The method of using the trained emotion classification model to classify the unlabeled words in the market emotion data, combining the emotion classification results of each word, and determining the emotion category of the entire text according to the text structure and semantic flow includes the following steps: S351, using the trained sentiment classification model to perform sentiment classification on all unlabeled words in the market sentiment data, and labeling each unlabeled word with a corresponding sentiment label; S352, assigning different emotion weights to each word according to its importance in the sentence, and weightedly accumulating the emotion classification results of all words to form the emotion tendency of the sentence; S353. Analyze the overall structure of the text and the changing trend of emotions in the text; S354. Based on the emotional tendency of the sentence, the overall structure of the text and the changing trend of the emotions in the text, the dominant emotion of the entire text is comprehensively judged to obtain the emotional category of the entire text.

9. The aggregator transaction strategy based on langchain and agent technology according to claim 1 is characterized in that: The dynamically adjusting the trading strategy parameters according to the market trading data and the corresponding sentiment analysis results to obtain the adjusted trading strategy comprises the following steps: S41. Evaluate the performance of real-time trading strategies under current market conditions and identify the potential impact of market sentiment fluctuations on trading strategies based on sentiment analysis results; S42. Adjust the trading strategy parameters based on the evaluation results and the identified potential impacts to obtain an adjusted trading strategy.

10. The aggregator transaction strategy based on langchain and agent technology according to claim 1 is characterized in that: The intelligent agent generates trading signals according to the adjusted trading strategy, and executes the actual trading orders converted from the trading signals through the exchange or other financial platform, including the following steps: S51. Based on the adjusted trading strategy, the intelligent agent evaluates market trends and trading opportunities, and generates corresponding trading signals based on the trading strategy parameters; S52, converting the generated trading signal into a trading order format, and ensuring that the order format complies with the requirements of the exchange or financial platform; S53. Send orders through the API interface of exchanges or other financial platforms, and monitor the execution status of orders in real time, where the execution status includes transaction status and potential market impact.

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