TWI768265B - Intelligent investment assistance system and method thereof - Google Patents

Abstract

An intelligent investment assistance system and method thereof are provided. The method includes the steps of: receiving an input condition and an investor preference from a user device; setting an relevant environment parameter according to the input condition; and generating a backtest result corresponding to the input condition according to the relevant environment parameter, the input condition and the historical database; analyzing the backtest result by the investment market model and the investor preference to generate an investment suggestion, and transmitting the investment suggestion to the user device, wherein the investment suggestion is related to adjusting the input condition; receiving from the user device a user feedback; and based on the user feedback, a machine learning is performed on the investment market model to update the investment market model.

Description

Intelligent investment assistance system and method thereof

The present invention relates to an intelligent investment assistance system and method thereof. Specifically, the present invention relates to an intelligent investment assistance system and a method thereof capable of providing investment advice and updating the system using a mass feedback mechanism.

In financial markets, backtesting is a fairly common way of validating investment strategies. In short, backtesting refers to setting certain investment strategies and investment targets, based on the real market data that has occurred in history, starting from a certain point in history, according to the set investment strategies and investment targets, simulate real financial Trading according to the rules of market trading, verifying the investment strategy and the performance of the investment target.

However, due to the complex combination of investment targets and investment strategies, for non-professional investors, when faced with so many choices, investors usually have no way to judge which investment targets and investment strategies produce backtest results. Relatively effective, so it is difficult to efficiently use these backtesting tools to obtain useful investment strategies. In addition, the existing technology lacks to analyze and provide suggestions for the generated backtest results, so the backtest results cannot objectively provide investors' evaluation criteria, and the existing technology does not provide community backtest results sharing, community analysis and comparison Therefore, it cannot effectively play the community function, nor can it provide investors with diversified investment assistance.

In addition, since the existing technology only uses historical data for various statistical analysis analysis and lack of interaction with investors. Therefore, the existing technology does not have a feedback mechanism for the intelligent system, and cannot further allow the intelligent system to learn, so that the intelligent system can better understand the needs of investors.

In view of this, how to provide investment advice and community analysis and comparison based on investors' backtest results, and further update the intelligent system based on investors' feedback, is an urgent goal for the industry.

One object of the present invention is to provide an intelligent investment assistance system. The intelligent investment assistance system is connected with a user device through a network, and the user device is operated by a first user. The intelligent investment assistance system includes a transceiver interface, a storage and a processor, and the processor is electrically connected to the transceiver interface and the storage. The storage stores a historical data database and an investment market model related to the historical data database, wherein the historical database stores a plurality of historical data corresponding to a plurality of targets, and each historical data includes a corresponding time A plurality of historical prices and a plurality of signal indicators for the series.

The processor receives an input condition and an investor preference from the user device, the input condition and the investor preference being determined by the first user. The processor sets a relevant environmental parameter according to the input condition. According to the relevant environmental parameters, the input conditions and the historical database, the processor performs a historical back-test operation simulating market price changes for the input conditions, so as to generate a back-test result corresponding to the input conditions. The processor analyzes the back-test result through the investment market model and the investor's preference to generate an investment suggestion, and transmits the investment suggestion to the user device, wherein the investment suggestion is related to adjusting the input condition. The processor receives a user feedback from the user device. The processor performs a machine learning on the investment market model according to the user feedback, so as to update the investment market model.

Another object of the present invention is to provide an intelligent investment assistance method. The intelligent investment assistance method is suitable for an electronic device. The electronic device is connected to a user device through a network, and the user device is connected by a first user. Operation, the electronic device includes a transceiver interface, a storage and a processor, the storage stores a historical data database and an investment market model related to the historical data database, wherein the historical database stores a plurality of corresponding A plurality of historical data data of the target, each of the historical data data includes a plurality of historical prices and a plurality of signal indicators corresponding to a time series.

The intelligent investment assistance method is executed by the processor and includes the following steps: receiving an input condition and an investor preference from the user device, the input condition and the investor preference being determined by the first user; according to the Input conditions, and set a relevant environmental parameter; according to the relevant environmental parameters, the input conditions and the historical database, perform a historical back-test operation simulating market price changes for the input conditions to generate a back-test result corresponding to the input conditions ; analyze the backtest results through the investment market model and the investor preferences to generate an investment recommendation, and transmit the investment recommendation to the user device, wherein the investment recommendation is related to adjusting the input conditions; from the user The device receives a user feedback; and according to the user feedback, performs a machine learning on the investment market model to update the investment market model.

The intelligent investment assistance technology (at least including the system and the method) provided by the present invention generates corresponding back-test results by receiving back-test conditions designed by the user. Then, through the intelligent system (ie, the intelligent investment assistance system) and investors' preferences, the back-test results are analyzed and investment recommendations are generated. Then, according to the feedback suggestions of users, the investment market model of the intelligent investment assistance system is updated to learn new backtest cases to strengthen the intelligent system. Since the investment back-test case utilized by each user can generate a feedback, in the present invention, the intelligent system (ie, Intelligent investment assistance system), so that the generated recommendations can be closer to the needs of users. In addition, the present invention can also provide statistical analysis of the community for the user's reference. Therefore, the present invention addresses the shortcomings of the prior art and provides effective investment assistance.

The detailed techniques and embodiments of the present invention are described below with reference to the drawings, so that those with ordinary knowledge in the technical field to which the present invention pertains can understand the technical features of the claimed invention.

1‧‧‧Intelligent investment assistance system

3‧‧‧Users

5‧‧‧User Device

11‧‧‧Transceiver interface

13‧‧‧Storage

15‧‧‧Processor

101‧‧‧Input Conditions

102‧‧‧Investor preferences

103‧‧‧Investment advice

105‧‧‧User Feedback

S401~S411‧‧‧Steps

FIG. 1 is a schematic diagram illustrating the structure of the intelligent investment assistance system according to the first embodiment;

Figure 2 depicts a specific example of the visualized backtest results;

Figure 3 depicts a specific example of visualized community statistics; and

FIG. 4 is a partial flow chart depicting the intelligent investment assistance method of the second embodiment.

The following will explain an intelligent investment assistance system and method thereof provided by the present invention through embodiments. However, these embodiments are not intended to limit the implementation of the present invention in any environment, application or manner as described in these embodiments. Therefore, the description of the embodiments is only for the purpose of explaining the present invention, rather than limiting the scope of the present invention. It should be understood that, in the following embodiments and drawings, elements not directly related to the present invention have been omitted and not shown, and the dimensions of each element and the dimension ratio among the elements are only examples, not intended to limit the present invention range.

The first embodiment of the present invention is an intelligent investment assistance system 1 , the schematic diagram of which is depicted in FIG. 1 . As shown in FIG. 1 , the intelligent investment assistance system 1 is connected to the user device 5 through a wired network or a wireless network, and the user 3 can use the application program installed on the user device 5 The platform (ie, the front-end platform) interacts with the intelligent investment assistance system 1 (ie, the back-end system). For example, the user device 5 may be a mobile device, a personal computer, or the like having a device capable of transmitting or receiving data. It should be noted that FIG. 1 is only for illustration, and the present invention does not limit the number of user devices connected to the intelligent investment assistance system 1 . In other words, in other embodiments of the present invention, the intelligent investment assistance system 1 can be connected to a plurality of user devices through a network, depending on the scale and actual needs of the intelligent investment assistance system 1 .

In this embodiment, the intelligent investment assistance system 1 includes a transceiver interface 11 , a storage 13 and a processor 15 , and the processor 15 is electrically connected to the transceiver interface 11 and the storage 13 . The transceiver interface 11 is an interface capable of receiving and transmitting data or other interfaces capable of receiving and transmitting data known to those skilled in the art to which the present invention pertains.

The storage 13 can be a memory, a Universal Serial Bus (USB) disk, a hard disk, an optical disk, a flash disk, or the same as those known to those skilled in the art to which the present invention pertains. function of any other storage medium or circuit. The processor 15 may be various processors, central processing units, microprocessors, digital signal processors, or other computing devices known to those of ordinary skill in the art to which the present invention pertains. In some embodiments, the intelligent investment assistance system 1 can be set up separately (eg, an independent back-end platform system), or the intelligent investment assistance system 1 can be integrated into other electronic devices with computing capabilities (eg, a mobile device) , personal computer, etc.), the content of which is not limited by the present invention.

In the present embodiment, the storage 13 stores a historical data database and an investment market model related to the historical data database, wherein the historical database stores a plurality of historical data corresponding to a plurality of targets, and each historical data The data includes a plurality of historical prices and a plurality of signal indicators corresponding to a time series. It should be noted that the historical data database is used to store the history The actual market data and related information that have already occurred, the present invention does not limit the number and types of historical data data stored in the intelligent investment assistance system 1 , which depends on the scale and actual needs of the intelligent investment assistance system 1 . For example, the historical data such as that stored in the intelligent investment assistance system 1 may be the Taiwan stock market listing over the past 20 years, the historical price information of each target (ie, each stock) at various time points and the corresponding signal indicators, and The intelligent investment assistance system 1 can also store the data of the Taiwan futures market for the past 20 years.

It should be noted that the signal indicators can be various scoring indicators, technical indicators or relevant important information, etc., various signals that can be used to evaluate the operation or risk of the target. For example, the signal indicator can include the fundamental data, quarterly earnings report, annual earnings report, quarterly average, annual average, news events related to the target, major events, revenue change indicators, exponentially smoothed moving average (MACD) corresponding to the target. ), Relative Strength Index (RSI), Stochastic Oscillator, etc. The present invention does not limit the number and types of signal indicators, which depend on the scale and actual needs of the intelligent investment assistance system 1 .

It should be noted that the investment market model is a model that describes the investment market through the Ito process. It is generated based on a large amount of learning data and repeated machine learning operations. It can use a single or multivariate random process. Calculate past parameters for individual assets, individual strategies, or entire portfolios and strategies, and predict future performance and risk. Specifically, the stochastic process f ( t,W ( t )) model can describe the behavior of N individual assets, individual strategies, or portfolios and strategies, and can take the following differential form (1):

df ( t,W ( t )) = mu ( t,W ( t )) dt + sigma ( t,W ( t )) dW ( t ) (1)

In the above differential form (1), mu and sigma are non-random functions (Deterministic Functions), mu is used to represent the corresponding rate of return, and sigma is used to represent the corresponding risk level. W ( t ) is the Standard Brownian Motion of K dimension, and is described by calculating the correlation Rho between different assets (ie, the Instantaneous Correlation Matrix of Standard Brownian Motion). Through the above differential form (1), the investment market model learns to judge the functional form of mu , sigma , and Rho to describe the investment market after a large amount of learning materials and multiple machine learning.

For example, taking the securities market as an example, the initial learning materials can be learned using at least the following materials: (1) information on securities prices and trading volumes, or derivative information generated by them; (2) important information about various companies, industries, etc. Financial information, including income statement, balance sheet, cash flow statement or others, and derivative information generated therefrom; (3) market media information or community information, or derivative information generated by it; and (4) National government policy information, national central bank policy information, or information from other national government departments, as well as derivative information generated.

It should be noted that the investment market model can be built and trained by the intelligent investment assistance system 1 itself, or the trained investment market model can also be directly received from an external device, and subsequently updated and trained by the intelligent investment assistance system 1 itself. Those who are usually knowledgeable should be able to understand how the investment market model is generated and its operation contents based on the above content, so I won't go into details here.

In order to facilitate understanding of the present invention, this paragraph first briefly describes the operation flow of this embodiment. This embodiment can be divided into a back-testing stage, a result reporting stage, and a system feedback stage according to the purpose of the operation contents of each stage. First, in the back-testing stage of the intelligent investment assistance system 1 , the intelligent investment assistance system 1 receives from the user device 5 the input conditions and investor preferences for the back-test set by the user 3 . Next, the intelligent investment assistance system 1 sets relevant environmental parameters according to the input conditions. Subsequently, the intelligent investment assistance system 1 performs historical back-testing operations based on relevant environmental parameters, input conditions of the user 3 and historical data to generate back-test results.

Then, in the result reporting stage of the intelligent investment assistance system 1, the intelligent investment assistance system 1 analyzes the back-test results through the investment market model and the investor's preference to generate investment advice, and transmits the investment advice to the user device 5, And receive user feedback from the user device 5 . Finally, in the system feedback stage of the intelligent investment assistance system 1, the intelligent investment assistance system 1 performs further machine learning on the investment market model through the feedback information of the user to update the investment market model. It should be noted that the operation of the intelligent investment assistance system 1 still includes other relevant details, but the focus of the present invention lies in the operation of data analysis and intelligent investment assistance, so the following paragraphs will only describe the implementation details related to the present invention in detail.

First, the operation performed by the intelligent investment assistance system 1 in the back-testing stage of the present invention will be described in detail, please refer to FIG. 1 . In this embodiment, the intelligent investment assistance system 1 receives input conditions 101 and investor preferences 102 from the user device 5 through the transceiver interface 11 , and the input conditions 101 and investor preferences 102 are determined by the first user. Specifically, the input condition 101 includes at least one investment target, a back-test period corresponding to the at least one investment target, and a starting condition. Investor preferences 102 may include a target expected return, an acceptable risk, a current asset allocation and One or a combination of future funding needs.

It should be noted that, taking the securities market as an example, the at least one investment target may be a single stock or a combination of multiple stocks, and the back-test period is the time when the back-test is desired (for example, nearly 5 years). The starting condition is the allocation of starting capital. For example, when an investor selects 3 targets A, B and C, the initial condition set by the investor can be 1 million initial capital and allocates 50%, 30% and 20% of the capital to target A, respectively , B and C.

In some embodiments, in order to provide more diverse back-test conditions, the input condition 101 may further include an entry condition and an exit condition. Specifically, the entry conditions and exit conditions can be They are the signal (investment strategy) or the stop-loss and stop-profit conditions on which to buy or sell, respectively. When the signal is satisfied, buy or sell. Specifically, the signal can be the aforementioned various scoring indicators, technical indicators, or relevant important information, etc., which can be used to evaluate the operation or risk of the target. For example, the entry condition can be set to execute buying when the quarterly moving average exceeds a threshold. The exit condition can be set to sell when the stochastic indicator crosses.

In addition, the entry conditions and exit conditions can also be set as multiple conditions, which will be executed when multiple conditions are met at the same time, and the entry conditions and exit conditions can also be set to execute when at least one of the multiple conditions is met. The target objects may also have their own entry and exit conditions, and the present invention does not limit their contents.

Next, the processor 15 sets a relevant environmental parameter according to the input condition 101 . Specifically, due to different investment targets (for example, securities markets, futures, etc.), there will be different relevant environmental parameters. For example, the relevant environmental parameters may include one or a combination of a transaction fee, a price sliding formula corresponding to the transaction, and a transaction method (eg, the market's transaction rules).

It should be noted that, taking the securities market as an example, although the price at a certain point in time can be known during backtesting. However, the operation will affect the market. When buying or selling a lot, it will affect the price fluctuation of the actual market (for example, if you buy 10 shares of the target A at the same time and buy 1,000 shares of the target A at the same time, the actual transaction price will be slightly different). Therefore, in order to make the backtest result closer to reality, the present invention also considers the price slippage formula generated by the corresponding transaction, and calculates the price slippage ratio according to the price and the transaction volume.

Next, the processor 15 performs a historical back-test operation simulating market price changes for the input conditions 101 according to the relevant environmental parameters, the input conditions 101 and the historical database, so as to generate Generate a backtest result corresponding to the input condition. Specifically, the backtest result includes one or a combination of a detailed transaction process report, a performance report, and a transaction risk level report.

Next, the operation performed by the intelligent investment assistance system 1 in the result reporting stage of the present invention will be described. In this embodiment, the processor 15 analyzes the backtest results through the investment market model and investor preferences 102 to generate investment recommendations 103, and transmit the investment recommendations 103 to the user device 5, where the investment recommendations 103 and the adjustment of the Input conditions are relevant. Specifically, since the aforementioned investment market model has been pre-established by a large amount of learning data and repeated machine learning to build a model describing the investment market, the processor 15 can analyze the backtest results through the investment market model and investor preferences 102 , Input conditions 101 to suggest how to adjust the user.

For example, the investment advice 103 may include adjusting the capital allocation ratio of the starting conditions, adjusting the combination of investment targets, adding a signal of the quarterly moving average in the entry conditions, and so on. As another example, the investment recommendation 103 may include a recommendation to "allocate the lowest risk in the same investment target and return tier", or a recommendation to "allocate 20% U.S. Treasuries to increase the rate of return per unit of risk." It should be noted that the intelligent investment assistance system 1 can integrate the back-test results and investment suggestions 103 and then transmit them to the user device 5 for subsequent user feedback mechanism.

Next, the operation performed by the intelligent investment assistance system 1 in the system feedback stage of the present invention will be described. In this embodiment, the processor 15 receives the user feedback 105 from the user device 5 . Next, the processor 15 performs a machine learning process on the investment market model according to the user feedback 105 to update the investment market model. It should be noted that the user can comprehensively judge the correctness, reliability and value of the investment suggestion 103 and then generate the user feedback 105. The user feedback 105 can include the user's evaluation of the various contents of the investment suggestion 103 (for example, a numerical score). ), for example: the user's score for "remove the exit indicator with the monthly average falling below the annual average" in investment advice 103, the user's investment advice The "recommended 20% increase in U.S. Treasuries" score at 103.

Therefore, the user can judge the investment advice result of the intelligent investment assistance system 1 based on the user feedback 105, and the processor 15 can use the score to adjust the investment market model based on the evaluation and a supervised learning for this back-test case The analysis weights are used to further learn new backtest cases, and update the investment market model to reduce the error value, so that the generated recommendations can be closer to the needs of users.

In some embodiments, when many users use the intelligent investment assistance system 1, the intelligent investment assistance system 1 can further aggregate the data of multiple users and multiple back-test information and perform community analysis to generate relevant information. community statistics. For example, the intelligent investment assistance system 1 can provide relevant community statistical results such as "popular investment targets for people living in Taipei City", "targets preferred by the same age group", "investment rate ranking", etc. refer to. Specifically, the storage 13 further stores a community database, and the community database stores one public information of each of the plurality of second users and a plurality of historical back-test results, and the processor further performs the following operations: according to The back-test results and the historical back-test results generate a community statistic result and transmit the community statistic result to the user device.

In some embodiments, the community statistical result comparison result may include a community statistical analysis, a community investment comparison result, a return rate ranking, an investment ranking, a popular signal index ranking, and a popular investment target ranking. one or a combination of them.

In some embodiments, the processor 15 further transmits one or a combination of the backtest result, the investment suggestion and the community statistics result to the user device through a visualization technique. For example, Figure 2 and Figure 3 illustrate the visual backtest results and the community statistics results, respectively.

The intelligent investment assistance system 1 provided by the present invention generates corresponding back-test results by receiving back-test conditions designed by the user. Then, through the intelligent system (ie, the intelligent investment assistance system) and investors' preferences, the back-test results are analyzed and investment recommendations are generated. Then, according to the feedback suggestions of users, the investment market model of the intelligent investment assistance system is updated to learn new backtest cases to strengthen the intelligent system. Since each user's investment back-test case can generate a feedback, in the present invention, the intelligent system (ie, the intelligent investment assistance system 1) is updated by the feedback force of the mass users, so that the generated suggestions can be closer to the user needs. In addition, the present invention can also provide statistical analysis of the community for the user's reference. Therefore, the present invention solves the shortcomings of the prior art and provides effective investment assistance.

The second embodiment of the present invention is an intelligent investment assistance method, the flowchart of which is depicted in FIG. 4 . The intelligent investment assistance method is applied to an electronic device (for example, the intelligent investment assistance system 1 described in the first embodiment), the electronic device is connected to a user device through a network, and the user device is used by a first user operator operation. The electronic device includes a transceiver interface, a storage and a processor, the storage stores a historical data database and an investment market model related to the historical data database (for example, the investment market described in the first embodiment) model), wherein the historical database stores a plurality of historical data data corresponding to a plurality of targets, each of the historical data data includes a plurality of historical prices and a plurality of signal indicators corresponding to a time series, and the intelligent investment assistance method is provided by the processor performed. The intelligent investment assistance method generates investment suggestions through steps S401 to S411, and updates the investment market model according to user feedback.

In step S401, an input condition and an investor preference are received by the electronic device from the user device, and the input condition and the investor preference are determined by the first user. Next, in step S403, the electronic device sets a relevant environmental parameter according to the input condition. follow Then, in step S405, the electronic device performs a historical back-test operation simulating market price changes for the input conditions according to the relevant environmental parameters, the input conditions and the historical database, so as to generate a back-test corresponding to the input conditions result.

Then, in step S407, the electronic device analyzes the back-test result through the investment market model and the investor's preference to generate an investment suggestion, and transmits the investment suggestion to the user device, wherein the investment suggestion and adjustment This input condition is relevant. Next, in step S409, the electronic device receives a user feedback from the user device.

Finally, in step S411, the electronic device performs a machine learning on the investment market model according to the user feedback to update the investment market model.

In some embodiments, the input condition includes at least one investment target, a back-test period corresponding to the at least one investment target, and a starting condition.

In certain embodiments, the investor preferences include one or a combination of a target expected return, a risk tolerance, a current asset allocation, and a future funding requirement.

In some embodiments, the relevant environmental parameters include one or a combination of a transaction fee, a price slide formula corresponding to the transaction, and a transaction method.

In some embodiments, the back-test result includes one or a combination of a detailed transaction process report, a performance report and a transaction risk level report.

In some embodiments, the storage further stores a community database, and the community database stores one public information of each of the plurality of second users and a plurality of historical back-test results, and the intelligent investment assistance method The method further includes the following steps: generating a community statistics result according to the backtest results and the historical backtest results, and transmitting the community statistics results to the user device.

In some embodiments, the community statistical result comparison result may include a One or a combination of community statistical analysis, a community investment comparison result, a return rate ranking, an investment ranking, a popular signal indicator ranking and a popular investment target ranking.

In some embodiments, the intelligent investment assistance method further includes the following step: transmitting one or a combination of the backtest result, the investment suggestion and the community statistical result to the user through a visualization technique device.

In addition to the above steps, the second embodiment can also execute all the operations and steps of the intelligent investment assistance system 1 described in the first embodiment, has the same functions, and achieves the same technical effects. Those with ordinary knowledge in the technical field to which the present invention pertains can directly understand how the second embodiment performs these operations and steps based on the above-mentioned first embodiment, has the same functions, and achieves the same technical effects, so it is not repeated here.

It should be noted that in the patent specification and the scope of the patent application of the present invention, some terms (including: user) are prefixed with "first", and the "first" is only used to distinguish different terms. For example: the first user is only used to indicate that it is different from the second user.

To sum up, the intelligent investment assistance technology (at least including the system and method) provided by the present invention generates corresponding back-test results by receiving back-test conditions designed by users. Then, through the intelligent system (ie, the intelligent investment assistance system), the backtest results and investor preferences are analyzed and investment suggestions are generated. Then, according to the feedback suggestions of users, the investment market model of the intelligent investment assistance system is updated to learn new backtest cases to strengthen the intelligent system. Since each user's investment back-test case can generate a feedback, the present invention uses the feedback power of the mass users to update the intelligent system (ie, the intelligent investment assistance system), so that the generated suggestions can be closer to use. users' needs. In addition, the present invention can also provide statistical analysis of the community for the user's reference. Therefore, the present invention solves the shortcomings of the prior art and provides effective investment assistance.

The above-mentioned embodiments are only used to illustrate some embodiments of the present invention and to illustrate the technical characteristics of the present invention, but are not used to limit the protection scope and scope of the present invention. Any changes or equivalent arrangements that can be easily accomplished by those with ordinary knowledge in the technical field to which the present invention pertains belong to the claimed scope of the present invention, and the scope of protection of the present invention is subject to the scope of the patent application.

S401~S411‧‧‧Steps

Claims (14)

An intelligent investment assistance system, the intelligent investment assistance system and a user device are connected through a network, the user device is operated by a first user, the intelligent investment assistance system comprises: a transceiver interface; a storage, storing a community database, a historical data database, and an investment market model related to the historical data database, wherein the community database stores respective public information and a plurality of historical backtests of a plurality of second users As a result, the historical data database stores a plurality of historical data data corresponding to a plurality of targets, each of the historical data data includes a plurality of historical prices and a plurality of signal indicators corresponding to a time series; and a processor electrically connected to the A transceiver interface and the storage device, and perform the following operations: receive an input condition and an investor preference from the user device, the input condition and the investor preference are determined by the first user; set an input condition according to the input condition Relevant environmental parameters; according to the relevant environmental parameters, the input conditions and the historical data database, perform a historical back-test operation simulating market price changes for the input conditions to generate a back-test result corresponding to the input conditions; through the investment Analyzing the back-test results by market model and the investor's preferences to generate an investment suggestion, and transmitting the investment suggestion to the user device, wherein the investment suggestion is related to adjusting the input condition; receiving an application from the user device User feedback; according to the user feedback, perform a machine learning on the investment market model to update the investment market model, wherein the input data of the machine learning at least include the user feedback and the output data of the machine learning include at least one supervised learning result; and according to the back-test result and the historical back-test results, generate a community statistical result, and transmit the community statistical result to the user device. The intelligent investment assistance system according to claim 1, wherein the input condition includes at least one investment target, a back-test period corresponding to the at least one investment target, and a starting condition. The intelligent investment assistance system of claim 1, wherein the investor preference comprises one or a combination of a target expected return, an acceptable risk, a current asset allocation and a future capital requirement. The intelligent investment assistance system according to claim 1, wherein the relevant environmental parameters include one or a combination of a transaction fee, a price sliding formula corresponding to a transaction, and a transaction method. The intelligent investment assistance system as claimed in claim 1, wherein the back-test result includes one or a combination of a detailed transaction process report, a performance report and a transaction risk level report. The intelligent investment assistance system according to claim 1, wherein the community statistical result comparison result may include a community statistical analysis, a community investment comparison result, a return rate ranking, an investment ranking, and a popular signal index ranking and one or a combination of the rankings of a popular investment target. The intelligent investment assistance system of claim 1, wherein the processor further transmits one or a combination of the backtest result, the investment advice and the community statistical result to the user device through a visualization technology . An intelligent investment assistance method, which is suitable for an electronic device, the electronic device and a user device are connected through a network, the user device is operated by a first user, the electronic device comprises a transceiver interface, a storage and a processor, the memory stores a community data database, a historical data database and an investment market model related to the historical data database, wherein the community database stores respective public information of a plurality of second users and a plurality of historical back-test results, the historical data The database stores a plurality of historical data data corresponding to a plurality of targets, each of the historical data data includes a plurality of historical prices and a plurality of signal indicators corresponding to a time series, the intelligent investment assistance method is executed by the processor, and includes the following Steps: receive an input condition and an investor preference from the user device, the input condition and the investor preference are determined by the first user; set a relevant environmental parameter according to the input condition; according to the relevant environmental parameter, The input condition and the historical data database are subjected to historical back-testing operations simulating market price changes for the input condition to generate a back-test result corresponding to the input condition; the back-test result is analyzed through the investment market model and the investor's preference. measuring results to generate an investment suggestion, and transmitting the investment suggestion to the user device, wherein the investment suggestion is related to adjusting the input condition; receiving a user feedback from the user device; according to the user feedback, for The investment market model performs a machine learning to update the investment market model, wherein the input data of the machine learning at least include the user feedback, and the output data of the machine learning at least include a supervised learning result; and according to the The back-test results and the historical back-test results generate a community statistic result and transmit the community statistic result to the user device. The intelligent investment assistance method as claimed in claim 8, wherein the input condition includes at least one investment The target, a back-test period and a starting condition corresponding to at least one investment target. The intelligent investment assistance method as claimed in claim 8, wherein the investor preference comprises one or a combination of a target expected return, an acceptable risk, a current asset allocation and a future capital requirement. The intelligent investment assistance method according to claim 8, wherein the relevant environmental parameter comprises one of a transaction fee, a price slip formula corresponding to a transaction, a transaction method, or a combination thereof. The intelligent investment assistance method as claimed in claim 8, wherein the back-test result includes one or a combination of a detailed transaction process report, a performance report and a transaction risk level report. The intelligent investment assistance method according to claim 8, wherein the community statistical result comparison result may include a community statistical analysis, a community investment comparison result, a return rate ranking, an investment ranking, and a popular signal index ranking and one or a combination of the rankings of a popular investment target. The intelligent investment assistance method as claimed in claim 8, wherein the investment assistance method further comprises the following step: transmitting one or a combination of the backtest result, the investment suggestion and the community statistical result through a visualization technology to the user device.

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