CN111489259A - Stock market risk prediction intelligent implementation method based on deep learning - Google Patents
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Abstract
The invention discloses an intelligent realization method for stock market risk prediction based on deep learning, which forms more abstract high-feature representation by combining shallow features, thereby discovering a deep implicit relationship of data, and can better obtain a compound function by stacking a plurality of layers of neural networks and selecting a Sigmoid activation function.
Description
Technical Field
The invention relates to an intelligent stock market risk prediction realization method based on deep learning, and belongs to the technical field of communication.
Background
The stock market plays an important role in the financial market, and whether to accurately predict the fluctuation of the future whole stock market is also a key point for obtaining income in the stock market. Investors in recent years have tried to choose to predict stocks using expert analysis, combinatorial analysis, strategic research, and other related methods. Most of the methods adopt related contents of probability and statistics, and simultaneously use various mathematical models to finally obtain a prediction result. However, the trend of stock prices is influenced by various factors such as national policies, national economic conditions, international environments and enterprise management conditions, so that a single mathematical model cannot accurately describe the future trend of the stock market. Meanwhile, because the stock investment is accompanied by high risk and the stabilization of the income cannot be realized only by subjective experience of people and a mathematical model, the rising and falling characteristics of the stock market need to be mined according to historical data of a large number of stock trades, a certain reference is provided for stock prediction in a short period in the future, and the method has great significance for improving the income rate of stocks and is also an improvement part of the technology.
With the development and application of artificial intelligence methods in recent years, more artificial intelligence technologies are applied to stock market forecasting, and some Machine learning-based methods and data mining-based methods, such as Neural Network (NN) and Support Vector Machine (SVM) technologies, are more common, and are commonly applied to stock market forecasting. However, the SVM technique is difficult to implement for large-scale training samples, and the data in the stock market is large and various, and when the data size becomes large, the training time of the SVM becomes long, thereby bringing more time-consuming cost to the prediction. In addition, the classical support vector machine algorithm only gives out an algorithm of a two-class classification method, and in the practical application of stock data mining, multiple classes of problems are generally solved. In addition, for the SVM technology, the method is sensitive to missing data and to selection of parameters and kernel parameters, and the selection of the parameters is generally artificial and has certain randomness and unrepresentability, so that domain knowledge should be introduced in the stock market field, but no good method is available for solving the selection problem of kernel functions at present.
Disclosure of Invention
The invention aims to provide an intelligent realization method for stock market risk prediction based on deep learning, aiming at the defects of the prior art, the method forms more abstract high-feature representation by combining shallow features, thereby discovering the implicit relationship of data deep level, and through the stacking of multilayer neural networks and the selection of Sigmoid activation functions:
where f (x) represents the output layer, the output range is (0, 1), e represents a natural constant in mathematics, x represents the input value, and the function is to transform real values within one (— infinity, + ∞) to the interval [0,1 ].
The technical scheme adopted by the invention for solving the technical problems is as follows: the invention provides an intelligent realization method for stock market risk prediction based on deep learning, which comprises the following steps:
step 1: the historical trading data of the stock market is subjected to data preprocessing to obtain daily opening price, closing price, highest price, lowest price, bargaining price and bargaining volume of the stock, namely information data influencing the stock price, so that a long-term data sequence is obtained. And the data is normalized, and the influence of larger data difference is reduced.
And 2, selecting historical trading data of the stock in the past 10 years, and training the data by applying an L STM (L ong Short-Term Memory) model in deep learning to obtain a training set, wherein the training set mainly comprises the fluctuation condition of the past stock data.
And step 3: selecting the latest real data as a test set, predicting the stock closing price situation 15 days later by using a trained training model according to the transaction data 40 days before a certain day, comparing the closing price with the price on the day, making a data rule, regarding 2% of the price exceeding the price as rising and 2% of the price lower than the date as falling, and thus obtaining the situation of predicting the rising and falling of the stock in the future.
And 4, step 4: and adjusting different reasonable parameters, and respectively changing the number of the past test data, the future prediction days and the rise-fall evaluation value so as to obtain various different data prediction results.
And 5: the multiple data results are sorted and integrated by a time series method, so that the result can be accurately predicted every day in the future. And different fluctuation results caused by different parameters adopt a few principle of obeying most, and the obtained prediction precision is ensured to be higher.
Step 6: after the final prediction result is obtained, according to the rise and fall prediction conditions in the future period, when the rise and fall conditions in the future period are the same, buying or selling can be selected at the first rise point or fall point; when two successive changes and falls are opposite in a future period, buying (or selling) is carried out at the change and fall point in time.
The invention carries out risk prediction on the whole stock market based on a deep learning method, carries out prediction on the whole market of the financial stock market, and particularly adopts the combination of the deep learning and the financial stock, carries out self-learning self-adaptation through an L STM model in the deep learning, and obtains more accurate prediction on the whole stock market in the future according to the training of past historical data.
Has the advantages that:
1. the method carries out self-learning self-adaptation through a long-Short Term Memory network model (L ong Short-Term Memory, L STM) in deep learning, and well obtains accurate prediction of the future whole stock market according to training of past historical data.
2. According to the invention, a more abstract high-feature representation is formed by combining shallow features to discover a deep implicit relation of data, and a composite function can be better obtained by stacking a plurality of layers of neural networks and selecting a reasonable activation function.
3. The method has good technical effects on multiple indexes such as RMSE, error value, self-designed profit value and the like.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings.
As shown in FIG. 1, the invention provides an intelligent method for predicting risk of stock market based on deep learning, which comprises the following steps:
step 1: carrying out data preprocessing on historical trading data of stocks: the stock history data is obtained by first obtaining daily opening price, closing price, highest price, lowest price, bargaining price and bargaining volume (the bargaining volume is the volume ratio of the current day to the last day), namely information data influencing the stock price, thereby obtaining a string of long-term data sequences. And then defining interval data, and unitizing the data according to the price difference range of the highest price and the lowest price, thereby obtaining each parameter data capable of being used for training and reducing the influence of larger data difference.
Step 2, selecting historical trading data of the stock in the past 10 years, applying a L STM (L ong Short-Term Memory) model in deep learning, setting a time span of 40 days after removing holidays and rest days of stock trading, training the data by parameters of 2% of fluctuation amplitude and 3000 times of iteration after predicting the time span of 10 days, and obtaining a training model by a deep learning self-training method, wherein the training set takes a closing price as a reference and roughly comprises the fluctuation condition of the past stock data.
And step 3: selecting the latest real data as a test set, predicting the stock closing price situation 15 days later by using a trained training model according to the transaction data 40 days before a certain day, comparing the closing price with the price on the day, making a data rule, regarding 2% of the price exceeding the price as rising and 2% of the price lower than the date as falling, and thus obtaining the situation of predicting the rising and falling of the stock in the future.
And 4, step 4: adjusting different reasonable parameters: the following parameters (1) the number of past test data (selected to be 40.50.60 days in the past), (2) the number of future prediction days (selected to be 10.15.20 days in the future), and (3) the rise and fall evaluation values (with 0.5%, 1%, and 2% as standards) are respectively changed, so that various data prediction results are obtained.
And 5: and displaying various data results in a two-dimensional coordinate chart form, and superposing and integrating different graphs obtained by different parameters by a time sequence method to ensure that the results can be accurately predicted every day in the future. And different fluctuation results caused by different parameters adopt a few principle of obeying most, and the obtained prediction precision is ensured to be higher.
Step 6: according to the different prediction result graphs under different parameters, overlapping to obtain a final prediction result graph, and according to the rise and fall prediction conditions in the future period in the graph, when the rise and fall conditions in a period of time in the future are the same, buying (or selling) can be selected at the first rise (or fall) point; when two successive changes and falls are opposite in a future period, buying (or selling) is carried out at the change and fall point in time. After screening and listing each transaction time point obtained under different fluctuation conditions, the investment selection time for obtaining more stable income in the future stock market can be obtained.
For the stock market, a series of contents such as market price fluctuation, market rule change, enterprise decision and the like can change future trends in real time, so that the problems cannot be solved by only using the traditional methods of mathematical modeling and operation research model statistical analysis, and for a simple artificial intelligence method, L STM model can process and train a large amount of data, and meanwhile, the use of the STM model can be greatly improved in accuracy compared with the traditional SVM and BP model, and the regression prediction method has a plurality of profit values in the Root mean square Error (RMsquared), the network model (Back prediction, BP) compared with the traditional linear regression algorithm (L initial regression), and the like.
Claims (3)
1. A stock market risk prediction intelligent implementation method based on deep learning is characterized by comprising the following steps:
step 1: carrying out data preprocessing on historical trading data of the stock to obtain daily opening price, closing price, highest price, lowest price, bargaining price and bargaining volume of the stock, namely information data influencing the stock price, thereby obtaining a string of long-term data sequences;
selecting historical trading data of the stock in the past 10 years, and training the data by applying an L STM model in deep learning to obtain a training set, wherein the training set comprises the fluctuation condition of the stock data in the past;
and step 3: selecting the latest real data as a test set, predicting the stock closing price situation 15 days later by using a trained training model according to the transaction data 40 days before a certain day, comparing the closing price with the price on the day, making a data rule, regarding 2% of the price exceeding the price as rising and 2% of the price lower than the date as falling, and thus obtaining a situation of predicting the rising and falling of the stock in the future;
and 4, step 4: adjusting different reasonable parameters, and respectively changing the number of past test data, the future prediction days and the rise-fall evaluation value so as to obtain various different data prediction results;
and 5: the data results are sorted and integrated by a time series method, so that the result can be accurately predicted for each day in the future, wherein different fluctuation results caused by different parameters are ensured;
step 6: after the final prediction result is obtained, according to the rise and fall prediction conditions in the future period, when the rise and fall conditions in the future period are the same, buying or selling is selected at the first rise point or fall point; when two successive changes and falls are opposite in a future period, buying or selling is carried out at the change and fall point.
2. The method for intelligently predicting risk in stock market based on deep learning as claimed in claim 1, wherein the method combines features of shallow layer to form moreAdding abstract high-feature representation to discover data deep-level implicit relations, and through stacking of multilayer neural networks and selecting activation functions:
where f (x) denotes the output layer, the output range is (0, 1), e denotes the natural constants in mathematics, x denotes the input values, and the function is the transformation of real values within one (- ∞, + ∞) into the interval [0,1 ∞ ]]。
3. The intelligent realization method for risk prediction of stock market based on deep learning as claimed in claim 1, wherein the method is used for predicting the whole market of financial stock market, and the self-learning self-adaptation is carried out by L STM model in deep learning by combining deep learning and financial stock.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111932379A (en) * | 2020-09-23 | 2020-11-13 | 北京口袋财富信息科技有限公司 | Data processing method and device, electronic equipment and readable storage medium |
| CN112884211A (en) * | 2021-02-02 | 2021-06-01 | 上海卡方信息科技有限公司 | Stock price prediction system and method based on deep learning |
| CN113159941A (en) * | 2021-02-02 | 2021-07-23 | 上海卡方信息科技有限公司 | Intelligent streaming transaction execution method and device |
| WO2023185125A1 (en) * | 2022-04-02 | 2023-10-05 | 富途网络科技(深圳)有限公司 | Product resource data processing method and apparatus, electronic device and storage medium |
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- 2020-04-16 CN CN202010298282.3A patent/CN111489259A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111932379A (en) * | 2020-09-23 | 2020-11-13 | 北京口袋财富信息科技有限公司 | Data processing method and device, electronic equipment and readable storage medium |
| CN112884211A (en) * | 2021-02-02 | 2021-06-01 | 上海卡方信息科技有限公司 | Stock price prediction system and method based on deep learning |
| CN113159941A (en) * | 2021-02-02 | 2021-07-23 | 上海卡方信息科技有限公司 | Intelligent streaming transaction execution method and device |
| WO2023185125A1 (en) * | 2022-04-02 | 2023-10-05 | 富途网络科技(深圳)有限公司 | Product resource data processing method and apparatus, electronic device and storage medium |
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