Stock transaction simulation method and system

Abstract

The invention discloses a stock transaction simulation method and system, and the method comprises the steps: obtaining the stock data and forum index of each stock in the current day every day, calculating the current-day final weight of each stock, and carrying out the smooth weighting calculation of the current-day final weight and the historical weight of each stock to obtain the current-day updated weight; comparing the single-day update weight with a preset score threshold to screen stock pools with different difficulties; respectively acquiring stock K lines of stocks in each stock pool on the same day, and calculating KDJ indexes and MACD indexes of each stock for a user to operate and view; the user starts K-line simulation stock investment practice; the K lines of the corresponding stocks are displayed in batches, and the user carries out virtual stock transaction operation; and counting the income and the total income of the user in each batch interval, and comparing the income and the total income with the income of other players in the community and the index of the security exchange at the corresponding time to form training result data so as to calculate and obtain user ranking information and income statistics. The method simulates the real stock market environment, and helps the user to improve the understanding ability of the K-line technology.

Description

Technical Field

[0001] The present invention relates to the field of information processing technology, and in particular to a simulated stock trading method and system. Background Art

[0002] Stock investing is a hot topic in the current financial market, and ordinary people hope to grow their wealth through stock investment. However, not everyone can succeed in stock investing. To improve your chances of success, you often need to conduct stock research, focusing on fundamentals, policies, funding, news, and technical aspects. Technical analysis refers to indicators that reflect stock price fluctuations, such as candlestick chart patterns and trend patterns. Investors can identify market price trends through candlestick analysis and identify suitable trading opportunities by observing support and resistance levels and candlestick patterns.

[0003] However, learning the stock market requires real money and time to validate investors' ideas. Investors who rush into the stock market without any prior knowledge often face significant disappointment. By practicing K-line stock trading simulations, investors can more quickly validate their models and ideas, improving their understanding of different K-line patterns. Summary of the Invention

[0004] The purpose of the present invention is to provide a simulated stock trading method and system, which aims to assist users in performing simulated practice through K-line on selected real stocks and improve users' understanding of the technical aspects of stock K-line.

[0005] The technical solution adopted in the present invention is:

[0006] A simulated stock trading method comprises the following steps:

[0007] Step 1: Obtain the stock data and forum index of each stock every day, and calculate the final weight of each stock on that day;

[0008] Step 2: Perform a smooth weighted calculation on the final weight of each stock on the day and the historical weight of each stock to obtain the updated weight of each stock on the day;

[0009] Step 3: Compare the daily updated weight with the preset scoring threshold to screen stock pools of different difficulty levels;

[0010] Step 4: Get the stock K-line of each stock in the stock pool on that day, and calculate the KDJ indicator and MACD indicator of each stock for the user to operate and view;

[0011] Step 5: The user selects a corresponding practice mode from different practice modes to start the K-line simulated stock trading practice;

[0012] Step 6: Display the K-line charts of a specified number of stocks in the corresponding difficulty stock pool in batches for users to view. Users can perform virtual stock trading operations by observing different indicators of the stock K-line charts. The user's stock trading profit for each batch and the total profit of all batches are calculated.

[0013] Step 7: Compare the user's total earnings across all batches with the earnings of other players in the community and the stock exchange index at the corresponding time to form training result data;

[0014] Step 8: After the user completes the training, the data is retained and the user ranking information and revenue statistics are calculated.

[0015] Furthermore, in step 1, the final weight of the day is regularly reassessed and the weight distribution is adjusted based on market dynamics and user behavior data.

[0016] Furthermore, the specific calculation steps for the final weight of each stock on the day in step 1 are as follows:

[0017] Step 1-1, obtain the discussion ranking, search ranking and stock ranking of each stock as three independent subjective factor indicators;

[0018] Step 1-2: assign corresponding ranking weights to each stock based on its ranking in each subjective factor indicator. The higher the ranking, the greater the corresponding ranking weight.

[0019] Steps 1-3: Calculate the ranking weights of each subjective factor indicator for each stock and add them up to form the subjective weight of the corresponding stock;

[0020] Specifically, the calculation formula of subjective weight is:

[0021]

[0022] Among them, W 主观 Indicates subjective weight; I i, The top ten indicates that the subjective factor index i is in the top ten. When the stock is ranked in the top ten in the subjective factor index i, I i, The first ten values ​​are 1, otherwise, I i, The first ten values ​​are 0; I i, The top five indicates that the subjective factor index i is in the top five. When the stock is in the top five in the subjective factor index i, I i, The first five values ​​are 1, otherwise, I i, The first five values ​​are 0.

[0023] Steps 1-4: Obtain the price fluctuation, turnover rate, and transaction volume of each stock as three independent objective factor indicators;

[0024] Steps 1-5: Compare each objective factor indicator of each stock with its respective preset numerical threshold. For any stock whose objective factor indicator is greater than the corresponding numerical threshold, assign the corresponding objective factor weight; otherwise, assign a value of 0.

[0025] Steps 1-6: Accumulate the objective factor weights assigned to each objective factor indicator of each stock to form the objective weight of the corresponding stock;

[0026] Specifically, the calculation formula of objective weight is:

[0027]

[0028] Among them, J j Indicates whether the stock meets the indicator parameter of the jth objective factor. If it meets the indicator parameter, then J j =1, otherwise 0;

[0029] Steps 1-7: The subjective weight and the objective weight are weighted together to obtain the final weight for the day. The formula for calculating the final weight for the day is:

[0030] W 最终 =(K s ×W 主观 )+(K o ×W 客观 )

[0031] Among them, K s is the coefficient of subjective weight, K s =0.3; K o is the coefficient of objective weight, K o =0.7;

[0032] W 主观 W 客观 Substituting the summation formula into the equation, we get:

[0033]

[0034] Furthermore, in step 1-2, when a stock ranks in the top ten in any subjective factor index, it is given a ranking weight of 0.3; when a stock ranks in the top five in any subjective factor index, the ranking weight is increased to 0.7.

[0035] Furthermore, the preset numerical thresholds of the objective factor indicators in steps 1-5 are respectively a price fluctuation greater than 5%, a turnover rate greater than 5%, and a transaction volume greater than 100 million; objective factor indicators greater than the corresponding numerical thresholds are assigned an objective factor weight of 0.3.

[0036] Furthermore, the calculation formula for the updated weight of the stock on that day in step 2 is as follows:

[0037] W 更新=α×W 当日 +(1-α)×W 历史 ;

[0038] Among them, W 更新 is the update weight of the day; α is the smoothing coefficient; W 当日 is the final weight of the stock on the day; W 历史 The historical weight of the corresponding stock in the stock pool. The initial historical weight is calculated by calculating community data from the previous 90 days. Only stocks older than 90 days will be included in the stock pool. Stocks younger than 90 days will have their weight accumulated daily.

[0039] Specifically, the smoothing coefficient α (e.g., 0.6 or 0.7) determines the ratio of the current day's weight to the historical weight. A higher α value (e.g., 0.7) places more emphasis on the current day's weight, allowing for a quicker reflection of the latest market information. A lower α value (e.g., 0.3) places more emphasis on the historical weight, minimizing the impact of short-term fluctuations.

[0040] Furthermore, in step 3, different scoring thresholds are set for different risk and return levels so as to screen and form stock pools with different risk and return levels.

[0041] As a preferred embodiment, further, the difficulty of the stocks screened in step 3 is divided into: bull market, bear market and balanced market. The difficulty is set by calculating the increase or decrease of the stock within the selected range, and at the same time comparing the increase or decrease of the selected stock exchange index within the current range.

[0042] As a preferred embodiment, the practice mode in step 5 further includes single practice, long-term practice, and online PK. The user logs in for the first time to obtain virtual initial funds for long-term practice; the user initiates a PK invitation or accepts a PK invitation from another user in the interface provided by the simulated stock trading system.

[0043] Specifically, in step 5, users can choose a difficulty level for a single practice session, or use the system to grant initial assets for long-term practice. Assets accumulated during long-term practice demonstrate the user's ability. Users can also compete with online users in real time. After both parties have completed their practice, they can compare their progress points. This allows users to exchange and discuss with others, accelerating their experience accumulation.

[0044] As a preferred embodiment, further, the stock trading operations in step 6 include buying, selling, and skipping.

[0045] As a preferred embodiment, further, step 8 provides user winning rate, historical records, rankings and a communication area.

[0046] Furthermore, the user ranking information in step 8 includes the user's ranking in the corresponding stock simulation trading community, personal ability ranking, and income statistics;

[0047] Specifically, Personal Ability is a statistic that measures individual performance, including profit, drawdown, return, success rate in different markets, and game win rate in simulations. Community Ranking refers to the ranking of an individual's win rate within the community.

[0048] Furthermore, in step 8, the user can view his or her own historical simulation operations, and can also view the operations of others, and communicate with other users in the community by leaving messages.

[0049] Furthermore, the present invention also discloses a simulated stock trading system, which adopts a simulated stock trading method. The system includes a selected stock screening module, an interactive user module, and a win rate ranking calculation module;

[0050] Selected stock screening module: select a specified number of stocks that meet the set requirements from the daily market to form stock pools of different difficulties.

[0051] Specifically, requirements are set such as stocks with a price fluctuation of more than 5% or a trading volume of 100 million, and more than 5 times in a month, to enter the candidate pool;

[0052] Interactive User Module: Provides K-line stock trading simulation exercises in different practice modes, including single practice, long-term practice, and online PK. Users log in for the first time to receive virtual initial funds for long-term practice. Users can initiate PK invitations or accept PK invitations from other users through the interface provided by the simulated stock trading system.

[0053] Specifically, online PK is implemented through websoket. When a user initiates a PK request, a soket message is sent to all online users in the game. When the user agrees to the request, the game begins. The party that completes the game first waits for the other party to complete the game, and then the results are calculated and the winner is counted. In the communication area, users can communicate and interact by leaving comments under the posts;

[0054] Win rate ranking calculation module: counts all players' real-time rankings, points and win rates, and displays user win rates, historical records and rankings.

[0055] Furthermore, the interactive user module provides a communication area for user communication.

[0056] Specifically, the system features a rich social module, allowing users to share their thoughts and experiences after each operation. The system generates rankings within the community based on average win rates for profit and drawdown, win rates for each model, and win rates in different market conditions for user communication.

[0057] The present invention adopts the above technical solution and has the following advantages compared with the existing technology: 1. By carefully selecting stocks, it solves the problem that the stock fluctuations in the K-line are small, and players randomly choose stocks with small fluctuations, which affects the player experience; 2. Through special gameplay, it solves the current online stand-alone game environment and increases interactivity; 3. By building a communication area, it facilitates users to exchange and share experiences. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments;

[0059] Figure 1 The figure is a flow chart of a simulated stock trading method of the present invention. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0061] like Figure 1 As shown, the present invention discloses a simulated stock trading method, which includes the following steps:

[0062] Step 1: Obtain the stock data and forum index of each stock every day, and calculate the final weight of each stock on that day;

[0063] Specifically, comprehensive stock market data after the close of the day is collected, including but not limited to stock data such as price changes, turnover rate, transaction amount, trading volume, as well as forum indices such as the number of discussions on the stock in the forum on that day and search index.

[0064] Step 2: Perform a smooth weighted calculation on the final weight of each stock on the day and the historical weight of each stock to obtain the updated weight of each stock on the day;

[0065] Step 3: Compare the daily updated weight with the preset scoring threshold to screen stock pools of different difficulty levels;

[0066] Step 4: Get the stock K-line of each stock in the stock pool on that day, and calculate the KDJ indicator and MACD indicator of each stock for the user to operate and view;

[0067] Step 5: The user selects a corresponding practice mode from different practice modes to start the K-line simulated stock trading practice;

[0068] Step 6: Display the K-line charts of a specified number of stocks in the corresponding difficulty stock pool in batches for users to view. Users can perform virtual stock trading operations by observing different indicators of the stock K-line charts. The user's stock trading profit for each batch and the total profit of all batches are calculated.

[0069] Step 7: Compare the user's total earnings across all batches with the earnings of other players in the community and the stock exchange index at the corresponding time to form training result data;

[0070] Step 8: After the user completes the training, the data is retained and the user ranking information and revenue statistics are calculated.

[0071] Furthermore, in step 1, the final weight of the day is regularly reassessed and the weight distribution is adjusted based on market dynamics and user behavior data.

[0072] Specifically, the final weighting for the day is assigned based on the importance of each metric. For example, price volatility and price fluctuations may be assigned a higher weight because they directly impact the potential profit and risk of a trade. Weighting can be subjective or based on objective analysis of historical data. For example, stock quotes are objectively weighted higher, while forum data are subjectively weighted lower.

[0073] Furthermore, the specific calculation steps for the final weight of each stock on the day in step 1 are as follows:

[0074] Step 1-1, obtain the discussion ranking, search ranking and stock ranking of each stock as three independent subjective factor indicators;

[0075] Step 1-2: assign corresponding ranking weights to each stock based on its ranking in each subjective factor indicator. The higher the ranking, the greater the corresponding ranking weight.

[0076] Specifically, if a stock ranks in the top ten in any one metric, it is given a weight of 0.3; if it ranks in the top five, the weight increases to 0.7 (0.3 top ten weight plus 0.4 top five weight). These weights are cumulative, meaning a stock that performs well in multiple metrics will receive a higher subjective weight.

[0077] Steps 1-3: Calculate the ranking weights of each subjective factor indicator for each stock and add them up to form the subjective weight of the corresponding stock;

[0078] Specifically, the calculation formula of subjective weight is:

[0079]

[0080] Among them, W 主观 Indicates subjective weight; I i, The top ten indicates that the subjective factor index i is in the top ten. When the stock is ranked in the top ten in the subjective factor index i, I i, The first ten values ​​are 1, otherwise, I i, The first ten values ​​are 0; I i,The top five indicates that the subjective factor index i is in the top five. When the stock is in the top five in the subjective factor index i, I i, The first five values ​​are 1, otherwise, I i, The first five values ​​are 0.

[0081] Steps 1-4: Obtain the price fluctuation, turnover rate, and transaction volume of each stock as three independent objective factor indicators;

[0082] Steps 1-5: Compare each objective factor indicator of each stock with its respective preset numerical threshold. For any stock whose objective factor indicator is greater than the corresponding numerical threshold, assign the corresponding objective factor weight; otherwise, assign a value of 0.

[0083] Steps 1-6: Accumulate the objective factor weights assigned to each objective factor indicator of each stock to form the objective weight of the corresponding stock;

[0084] Specifically, the calculation formula of objective weight is:

[0085]

[0086] Among them, J j Indicates whether the stock meets the indicator parameter of the jth objective factor. If it meets the indicator parameter, then J j =1, otherwise 0;

[0087] Steps 1-7: The subjective weight and the objective weight are weighted together to obtain the final weight for the day. The formula for calculating the final weight for the day is:

[0088] W 最终 =(K s ×W 主观 )+(K o ×W 客观 )

[0089] Among them, K s is the coefficient of subjective weight, K s =0.3; K o is the coefficient of objective weight, K o =0.7;

[0090] W 主观 W 客观 Substituting the summation formula into the equation, we get:

[0091]

[0092] Furthermore, in step 1-2, when a stock ranks in the top ten in any subjective factor index, it is given a ranking weight of 0.3; when a stock ranks in the top five in any subjective factor index, the ranking weight is increased to 0.7.

[0093] Furthermore, the preset numerical thresholds of the objective factor indicators in steps 1-5 are: price fluctuation greater than 5%, turnover rate greater than 5%, and transaction volume greater than 100 million; objective factor indicators greater than the corresponding numerical thresholds are assigned an objective factor weight of 0.3.

[0094] Furthermore, the calculation formula for the updated weight of the stock on that day in step 2 is as follows:

[0095] W 更新 =α×W 当日 +(1-α)×W 历史 ;

[0096] Among them, W 更新 is the update weight of the day; α is the smoothing coefficient; W 当日 is the final weight of the stock on the day; W 历史 The historical weight of the corresponding stock in the stock pool. The initial historical weight is calculated by calculating community data from the previous 90 days. Only stocks older than 90 days will be included in the stock pool. Stocks younger than 90 days will have their weight accumulated daily.

[0097] Specifically, the smoothing coefficient α (e.g., 0.6 or 0.7) determines the ratio of the current day's weight to the historical weight. A higher α value (e.g., 0.7) places more emphasis on the current day's weight, allowing for a quicker reflection of the latest market information. A lower α value (e.g., 0.3) places more emphasis on the historical weight, minimizing the impact of short-term fluctuations.

[0098] Furthermore, in step 3, different scoring thresholds are set for different risk and return levels so as to screen and form stock pools with different risk and return levels.

[0099] As a preferred embodiment, further, the difficulty of the stocks screened in step 3 is divided into: bull market, bear market and balanced market. The difficulty is set by calculating the increase or decrease of the stock within the selected range, and at the same time comparing the increase or decrease of the selected stock exchange index within the current range.

[0100] As a preferred implementation, further, the practice mode in step 5 includes single practice, long-term practice and online PK.

[0101] Specifically, in step 5, users can choose the difficulty level for a single practice, or use the initial assets issued by the system for long-term practice, and can also compete with online users in real time.

[0102] Long-term practice: Players obtain initial virtual funds and perform buying and selling operations in a simulated environment. The system calculates profits and losses based on actual market data.

[0103] Single Practice: Players perform a single transaction, and the system only calculates the profit and loss results of this transaction.

[0104] Online PK: Initiation and response: Players initiate PK requests through WebSocket, and the system broadcasts the requests to all online players.

[0105] Game matching: When a player responds to a PK request, the system will match two players to start the game.

[0106] Game Progress and Settlement: Successfully matched players conduct trades within a specified timeframe. The player who completes the trade first must wait for the other player to complete the trade. After the game ends, the system determines the winner based on the trade results.

[0107] Specifically, after all operations of both parties in online PK are completed, the operation points of both parties in this practice can be compared, allowing users to communicate and discuss with others in PK and accumulate experience more quickly.

[0108] As a preferred embodiment, further, the stock trading operations in step 6 include buying, selling, and skipping.

[0109] As a preferred embodiment, further, step 8 provides user winning rate, historical records, rankings and a communication area.

[0110] Furthermore, the user ranking information in step 8 includes the user's ranking within the corresponding stock trading simulation community, personal ability ranking, and profit statistics. Specifically, personal ability refers to individual ability statistics, including individual profits, drawdowns, returns, success rates in different markets, and game win rates during simulation exercises. Community ranking refers to the individual's win rate ranking within the community.

[0111] Furthermore, in step 8, the user can view his or her own historical simulation operations, and can also view the operations of others, and communicate with other users in the community by leaving messages.

[0112] Furthermore, the present invention also discloses a simulated stock trading system, which adopts a simulated stock trading method. The system includes a selected stock screening module, an interactive user module, and a win rate ranking calculation module;

[0113] Selected stock screening module: select a specified number of stocks that meet the set requirements from the daily market to form stock pools of different difficulties.

[0114] Specifically, requirements are set such as stocks with a price fluctuation of more than 5% or a trading volume of 100 million, and more than 5 times in a month, to enter the candidate pool;

[0115] Interactive User Module: Provides K-line stock trading simulation exercises in different practice modes, including single practice, long-term practice, and online PK. Users log in for the first time to receive virtual initial funds for long-term practice. Users can initiate PK invitations or accept PK invitations from other users through the interface provided by the simulated stock trading system.

[0116] Online PK is achieved through websoket. When a user initiates a PK request, a soket message is sent to all online users in the game. When the user agrees to the request, the game begins. The party that completes the game first waits for the other party to complete the game, and then the results are calculated and the winner is determined. In the communication area, users can communicate and interact by leaving comments under the posts.

[0117] Win rate ranking calculation module: counts all players' real-time rankings, points and win rates, and displays user win rates, historical records and rankings.

[0118] Furthermore, the interactive user module provides a communication area for users to communicate

[0119] Specifically, the system features a rich social module, allowing users to share their thoughts and experiences after each operation. The system generates rankings within the community based on average win rates for profit and drawdown, win rates for each model, and win rates in different market conditions for user communication.

[0120] Experimental Results and Effects: To verify the effectiveness of the interactive K-line stock trading simulation system presented in this invention, we conducted an experiment in which we invited users to try out the interactive K-line stock trading simulation system. User feedback indicated that the stock selection in the K-line game better reflects the forum atmosphere, while the newly added online PK function adds significant fun to the interactions. Furthermore, the interactive communication area facilitates communication and learning. After the official launch, user activity in the K-line game increased by approximately 40%.

[0121] The present invention has the following technical advantages: (1) Dynamic and flexible: By calculating the weight of the day every day, it can quickly respond to market changes and adjust the weight of each stock in time. At the same time, the smoothing coefficient α is introduced to balance the historical weight and the weight of the day. In this way, the cumulative effect of historical data can be retained, while being able to integrate new market changes and avoid over-reliance on outdated information. (2) Reducing excessive fluctuations: By updating the weights by weighted average (W update), the excessive influence of a single factor (such as extreme rise and fall on a certain day) on the stock weight is avoided, achieving a smoothing effect. Setting the smoothing coefficient α controls the ratio of historical and daily weights to ensure that the weight update is not a drastic fluctuation. Preventing excessive chasing of rising and falling prices: The weighting of historical data makes the weight update more robust and reduces the interference of short-term market fluctuations on decision-making. (3) Comprehensively considering the interaction of subjective factors (such as forum discussions, search rankings, etc.) and objective factors (such as price fluctuations, turnover rate, etc.), it can fully reflect the multi-dimensional information of the market, rather than relying solely on a single data source. (4) Quantifying subjective factors into weights through ranking avoids the subjective bias of relying solely on human judgment, making decision-making more systematic and transparent. (5) By adjusting the smoothing coefficient α, users can flexibly adjust the proportion of historical data and current day data according to actual needs. For example, if the market is very dynamic, the proportion of current day weights can be increased; if the market is relatively stable, the proportion of historical weights can be increased. Dynamic update of stock pool: For stocks newly entering the stock pool, the weights calculated on the same day are directly used, so that the system can quickly adapt to new stocks and include them in the calculation. (6) Advantages of historical data accumulation: Over time, historical weights will gradually reflect the long-term performance of stocks. The system will not adjust frequently due to short-term market fluctuations, making the weights more stable in the long term. Gradually adapt to market trends: Due to the existence of historical data, the changes in stock weights are gradual, helping the system gradually adapt to the long-term trend of the market rather than blindly following the short-term fluctuations of the market. (7) By combining subjective indicators (such as market discussion heat and search rankings) with objective indicators (such as price increase and decrease, turnover rate), the potential of stocks can be measured more comprehensively, avoiding over-reliance on a single indicator. Subjective factors can sometimes provide additional information about market sentiment, while objective factors help objectively reflect the actual performance of stocks. Multiple sources of information reduce bias: Different indicator sources and weighting methods help avoid misleading results from a single indicator, making the weights more accurate and reliable. (8) Easy to implement and maintain: The algorithm has a simple structure, and the calculation mainly relies on two core steps: daily weight calculation and historical weight update, which is easy to implement and maintain. Parameters such as the smoothing coefficient and the weights of subjective and objective factors can be flexibly adjusted according to the specific situation of the stock pool, so that the algorithm can adapt to different investment strategies or market environments.

[0122] The present invention adopts the above technical solution and has the following advantages compared with the existing technology: 1. By carefully selecting stocks, it solves the problem that the stock fluctuations in the K-line are small, and players randomly choose stocks with small fluctuations, which affects the player experience; 2. Through special gameplay, it solves the current online stand-alone game environment and increases interactivity; 3. By building a communication area, it facilitates users to exchange and share experiences.

[0123] Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. In the absence of conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the detailed description of the embodiments of the present application is not intended to limit the scope of the application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

Claims

1. A simulated stock trading method, characterized by: It includes the following steps: Step 1: Obtain the stock data and forum index of each stock every day, and calculate the final weight of each stock on that day; Step 2: Perform a smooth weighted calculation on the final weight of each stock on the day and the historical weight of each stock to obtain the updated weight of each stock on the day; Step 3: Compare the daily updated weight with the preset scoring threshold to screen stock pools of different difficulty levels; Step 4: Get the stock K-line of each stock in the stock pool on that day, and calculate the KDJ indicator and MACD indicator of each stock for the user to operate and view; Step 5: The user selects a corresponding practice mode from different practice modes to start the K-line simulated stock trading practice; Step 6: Display the K-line charts of a specified number of stocks in the corresponding difficulty stock pool in batches for users to view. Users can perform virtual stock trading operations by observing different indicators of the stock K-line charts. The user's stock trading profit for each batch and the total profit of all batches are calculated. Step 7: Compare the user's total earnings across all batches with the earnings of other players in the community and the stock exchange index at the corresponding time to form training result data; Step 8: After the user completes the training, the data is retained and the user ranking information and revenue statistics are calculated.

2. A simulated stock trading method according to claim 1, characterized in that: The specific steps for calculating the final weight of each stock on the day in step 1 are as follows: Step 1-1: Obtain the discussion ranking, search ranking, and stock ranking of each stock as three independent subjective factor indicators; Step 1-2: Assign a corresponding ranking weight to each stock based on its ranking in each subjective factor indicator, with the higher the ranking, the greater the corresponding ranking weight; Steps 1-3 calculate the ranking weights of each subjective factor indicator for each stock and add them up to form the subjective weight of the corresponding stock; Step 1-4 obtains the price change, turnover rate, and transaction volume of each stock as three independent objective factor indicators; Steps 1-5: Compare each objective factor indicator of each stock with its respective preset numerical threshold. For any stock whose objective factor indicator is greater than the corresponding numerical threshold, assign the corresponding objective factor weight; otherwise, assign a value of 0. Steps 1-6: Accumulate the objective factor weights assigned to each objective factor indicator of each stock to form the objective weight of the corresponding stock; Steps 1-7: Add the subjective weight and objective weight to obtain the final weight for the day.

3. A simulated stock trading method according to claim 2, characterized in that: In steps 1-2, when a stock ranks in the top ten in any subjective factor index, it is given a ranking weight of 0.3; when a stock ranks in the top five in any subjective factor index, the ranking weight is increased to 0.

7.

4. A simulated stock trading method according to claim 2, characterized in that: The preset numerical thresholds of the objective factor indicators in steps 1-5 are respectively a price fluctuation greater than 5%, a turnover rate greater than 5%, and a transaction volume greater than 100 million; objective factor indicators greater than the corresponding numerical thresholds are assigned an objective factor weight of 0.

3.

5. A simulated stock trading method according to claim 1 or 2, characterized in that: The calculation formula for the updated weight of the stock on that day in step 2 is as follows; W 更新 =α×W 当日 +(1-a)×W 历史 ; Among them, W 更新 is the update weight of the day; α is the smoothing coefficient; W 当日 is the final weight of the stock on the day; W 历史 It is the historical weight value of the corresponding stock in the stock pool.

6. A simulated stock trading method according to claim 1, characterized in that: In step 3, different scoring thresholds are set for different risk and return levels to screen and form stock pools with different risk and return levels; the difficulty of the screened stocks is divided into: bull market, bear market and balanced market. The difficulty is set by calculating the increase or decrease of the stock within the selected range, and at the same time comparing the increase or decrease of the selected stock exchange index within the current range.

7. A simulated stock trading method according to claim 1, characterized in that: The practice modes in step 5 include single practice, long-term practice and online PK.

8. A simulated stock trading method according to claim 1, characterized in that: The user ranking information in step 8 includes the user's ranking in the corresponding stock simulation trading community, personal ability ranking, and income statistics.

9. A simulated stock trading system, using a simulated stock trading method according to any one of claims 1 to 8, characterized in that: The system includes a stock selection screening module, an interactive user module and a win rate ranking calculation module; Selected stock screening module: select a specified number of stocks that meet the set requirements from the daily market to form stock pools of different difficulties. Interactive user module: provides K-line stock trading simulation exercises in different practice modes, including single practice, long-term practice and online PK; The user will be given virtual initial funds for long-term practice upon first login; the user can initiate a PK invitation or accept a PK invitation from other users in the interface provided by the simulated stock trading system; Win rate ranking calculation module: counts all players' real-time rankings, points and win rates, and displays user win rates, historical records and rankings.

10. A simulated stock trading system according to claim 9, characterized in that: The interactive user module provides a communication area for user communication.

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