10 results about "Stock forecasting" patented technology

A stock portfolio recommendation method based on complex network multi-source information fusion, comprising: collecting transaction data and text data of N stocks; data preprocessing; constructing a relationship matrix between the N stocks; constructing a multi-source information fusion neural network model based on a gated recurrent unit and a gated graph convolution, which is composed of a tensor fusion module, a gated recurrent unit neural network layer, a gated graph convolution network layer and an output layer in series; model iterative training, the cross loss value of the model is optimized in the iterative training, the trainable parameters of the model are updated; the stock prediction model considers the momentum spillover effect. Improve the prediction accuracy of the target stock price change, improve the robustness and reliability of the recommendation system, and provide a reference direction for the selection of stock portfolio for investors.

The invention relates to a stock screening and predicting method and system based on theme comprehensive scores. The method comprises the following steps: acquiring a text, and performing data cleaning and word segmentation processing to obtain vocabularies in the text; performing theme extraction on the text to obtain vocabularies under each theme and the occurrence frequency of the vocabularies; calculating the similarity between the vocabularies, performing topic classification on the vocabularies according to the similarity to obtain the total number of times of occurrence of the related vocabularies in the text under each topic, and taking the total number of times as the topic popularity of the topic; predicting the sentiment polarity of the text, and calculating the sentiment polarity score of each theme; obtaining a theme comprehensive score according to the theme popularity and the emotion polarity score; selecting a theme with a top comprehensive score ranking, adding related stocks under the theme into a stock pool, and screening in the stock pool to obtain a stock target; and predicting rising and falling of the stock target through the stock prediction model to obtain a prediction result. Compared with the prior art, the accuracy of quantitative bid selection and stock rising and falling prediction is improved.

The invention discloses a stock prediction method and system based on time constraint comparative learning, and the method comprises the steps: obtaining the historical daily frequency transaction data of a stock, including an opening price, a closing price, a highest price, a lowest price, and a trading volume; preprocessing the data and dividing the data into a training set, a verification set and a test set; constructing a time sequence prediction model with independent fragments; constructing positive and negative samples based on time constraints; designing a loss function and training the model; and the model predicts and outputs the stock yield. Through the time sequence encoder with independent segments, the problem of data offset existing in the stock market can be effectively solved, generalization on a test set is improved, positive and negative samples are constructed through comparative learning, stock modes with similar incomes can be effectively captured, and through time constraint, the influence of big trend on stock incomes in different periods can be eliminated.

The invention discloses a stock prediction model based on a multi-head cross-attention mechanism and an improved GRU, and the model comprises an improved GRU model, the improved GRU model comprises an attention mechanism, and the attention mechanism is used for replacing a reset gate; the graph attention network is used for capturing the relation between different stocks to extract the cross section characteristics of the data, the graph attention network comprises an attention mechanism, and different weights are distributed to different nodes according to the relation between the stocks through the attention mechanism; and a multi-head cross attention mechanism, wherein the multi-head cross attention mechanism is used for interaction between the improved GRU model and the output of the graph attention network. According to the method, the potential state interacts with the time characteristics and the cross section characteristics, so that the expression ability of the model and the ability of capturing complex market dynamics are further enhanced.

The invention discloses a stock prediction system and method based on a CEEMDAN-CNN-BiLSTM-Attention hybrid algorithm, in the system, a data acquisition module is used for acquiring stock data to be predicted; the data preprocessing module preprocesses the acquired stock data; the model building module is used for building a CEEMDAN-CNN-BiLSTM-Attention model, and training and adjusting the built CEEMDAN-CNN-BiLSTM-Attention model by using the stock data, so as to learn the characteristics and rules of the change of the stock price; the trained CEEMDAN-CNN-BiLSTM-Attention model is used for stock prediction, and the model is used for stock prediction; and the prediction output module is used for outputting a prediction result of the CEEMDAN-CNN-BiLSTM-Attention model. Through signal decomposition and recombination and deep learning model collaborative optimization, the problems of nonlinearity, non-stationarity and multi-scale noise interference of the stock price sequence are solved, and high-precision end-to-end prediction is realized.

The application discloses a stock analysis method based on order transaction volume, and steps include: 1) determining a first price and a second price, wherein the first price is a price which is more than 1h from real time and order transaction volume is more than 80 hands for the first time; 2) when the first price is higher than the second price, if the stock real-time price is between the first price and the second price, the first index is stable; 3) when the first price is lower than the second price, if the stock real-time price is between the first price and the second price, the first index is stable, and the trend of the stock is estimated to be stable; the technical scheme brings at least the following beneficial effects: the history data of transaction orders and the stock order information in a short period are used to estimate the trend of the stock, and the success rate of the stock prediction method and system for estimating the stock trend is high.

A stock prediction method based on multi-modal fusion and momentum spillover effect, comprising: collecting transaction data and text data of a target stock and N-1 stocks related to the target stock; data preprocessing; constructing a relationship matrix among N stocks; extracting features from the text data of the N stocks and converting them into text feature vectors for each trading day; setting input samples of a stock prediction model; establishing a stock prediction model; iteratively training and predicting the stock prediction model using the input samples, and updating the trainable parameters of the stock prediction model during training. The stock prediction method based on multi-modal fusion and momentum spillover effect mainly designs suitable and effective deep learning modules from the aspects of multi-modal data feature fusion and market momentum spillover effect to improve the accuracy and reliability of the stock prediction model.

The invention discloses a stock prediction method based on two-stage causal-industry graph fusion, and is applied to the technical field of financial time series prediction. Comprising the following steps: acquiring feature vectors of stocks, and constructing original input features; performing trend-fluctuation decomposition on the original input features to obtain trend components and fluctuation components; constructing a static industry graph and a dynamic causal graph; fusing the static industry graph and the dynamic causal graph through a two-stage cause-fruit industry graph fusion mechanism, and constructing a heterogeneous propagation graph; extracting trend component features and fluctuation component features; trend component coding and fluctuation component coding are carried out based on heterogeneous time sequence coding, and the yield relative ranking of the stock at the future target moment is expressed and predicted through additive fusion integration coding. The prediction method provided by the invention can adapt to market state changes, and is excellent in prediction performance and investment portfolio income.

The invention provides a stock price frequency domain collaborative prediction method based on biological nerve inspiration, and aims to solve the problems that an existing stock prediction method is sensitive to high-frequency noise, poor in extreme market adaptability and low in multi-source information fusion efficiency, and frequency domain collaborative prediction is carried out by simulating a human brain auditory center processing mechanism. Specifically, a bionic cochlea frequency domain decomposition module is designed, and a stock price time sequence is converted into a nonlinear sub-band component; an emotion and price frequency domain cooperation engine is created, and physical fusion of news emotion signals and price fluctuation is achieved through a coherence function; developing a chaos state recognition mechanism, and dynamically switching a prediction mode based on a Lyapunov exponent; constructing a dual-mode prediction engine of the gated chaotic network and the frequency domain residual network, and generating a minute-level price fluctuation direction decision; according to the method, the problems of high-frequency noise suppression, extreme market response and multi-mode collaborative prediction are effectively solved, and the accuracy and robustness of stock price direction prediction are remarkably improved.

The invention relates to a stock prediction and transaction method based on abnormal jump behavior analysis, and belongs to the technical field of data processing. The method comprises the following steps: step 1, signal identification: identifying a stock with an abnormal jump behavior through a statistical analysis method, and screening out an abnormal signal for positioning stock data of the abnormal jump behavior; step 2, constructing a data set: extracting related data based on abnormal signals, constructing the data set containing labels and training features, and analyzing a fund relationship before and after an abnormal jump behavior; step 3, selecting a deep learning prediction model: selecting an appropriate deep learning model based on given transaction data, and setting parameters such as batches, network layers, input and output dimensions and the like; step 4, evaluating a prediction model: evaluating the model through accuracy, and checking the prediction effect and effectiveness of the fitting model; and 5, constructing a transaction method: constructing a time-selecting transaction strategy, setting a control bin, designing an interference and loss stopping mechanism, and testing a yield rate based on a classification result of the prediction model. According to the method, the potential relationship between funds and abnormal jump behaviors in the stock market can be better revealed, so that decision support with higher reference value is provided for investors.