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CN-122023000-A - Calculator system and method for predicting stock buying and selling strategies

CN122023000ACN 122023000 ACN122023000 ACN 122023000ACN-122023000-A

Abstract

The invention relates to a calculator system and a method for predicting stock buying and selling strategies. The core processing unit executes a bidirectional closed-loop prediction algorithm based on initial price, principal, number of times, price difference, nonlinear growth multiplying power, complete transaction cost and financing and melting coupon parameters input by a user, wherein the buying sequence with decreasing price and nonlinear increasing quantity is generated in the buying direction, the total cost containing the financing cost and an accurate flat selling price are synchronously calculated, the selling sequence with increasing price and nonlinear increasing quantity is generated in the selling direction, and the total profit containing the financing cost and the re-investment potential are synchronously calculated. All the calculation is integrated with an accurate cost and interest model in real time, the interface supports the parameter linkage modification and the result real-time visual update, and the risk boundary of the buying and selling decision is preposed and quantized by combining a bidirectional closed-loop prediction algorithm with a nonlinear transaction model, so that the dynamic controllability of the transaction strategy is realized.

Inventors

  • LIU PINGHUA

Assignees

  • 刘平华

Dates

Publication Date
20260512
Application Date
20260127

Claims (10)

  1. 1. A calculator system for predicting a stock buying and selling policy, comprising: The system comprises a parameter input unit, a transaction parameter set and a transaction parameter set, wherein the parameter input unit is used for receiving a transaction parameter set by a user, and the transaction parameter set at least comprises an initial principal (P 0 ), an initial price (S 0 ), a number of times of fractional transactions (N), a fractional price difference (delta), a fractional multiplying power (r), a transaction cost parameter set (F), a transaction direction indication (D) and a financing coupon parameter set (M); the core processing unit is connected with the parameter input unit and is configured to execute the following operations: in response to the transaction direction indication (D) being a purchase direction, performing a purchase strategy prediction algorithm: generating a purchase price sequence { P 1 , P 2 , …, P n } decreasing in an arithmetic progression based on (S 0 ), (N), and (Δ), where Pi=S 0 (i-1) ×Δ; Calculating a first purchase quantity (Q 1 ) based on (P 0 ）、（S 0 ), (N), (r), (F) and (M), and generating a purchase quantity sequence { Q 1 , Q 2 , …, Q n } presenting an increasing trend according to a growth model; Based on the price sequence and the quantity sequence, successively calculating transaction amount, transaction cost and financing interest of each buying, and accumulating to obtain total input cost (C_total) and total stock number (S_total); Calculating a tie-off price (BEP) of the overall holding warehouse according to the selling cost rate contained in (C_total), (S_total) and (F) and the coupon interest rate contained in (M), and simultaneously, predicting reverse selling transaction based on a preset price difference rule and estimating the reverse selling cost; in response to the transaction direction indication (D) being a sell direction, executing a sell strategy prediction algorithm: Based on (S 0 ), (N), and (Δ), generating a selling price sequence { P ' 1 , P′ 2 , …, P′ n }, in arithmetic progression, where P' i = S 0 + (i-1) x Δ; Based on the initial sell quantities (Q ' 1 ), (N), (r), (F), and (M), a sequence of sell quantities { Q' 1 , Q′ 2 , …, Q′ n } presenting increasing trends is generated; based on the price sequence and the quantity sequence, successively calculating the transaction amount, the transaction cost, the printing tax and the coupon interest sold each time, and accumulating to obtain total sale net income (I_net); Calculating the maximum stock number (M_max) and the remaining cash (R_cash) available when buying again with the total income according to the buying rate contained in (I_net), (F) and the financing rate contained in (M), and simultaneously, predicting reverse buying transaction based on a preset price difference rule and estimating the reverse buying rate; The result output unit is connected with the core processing unit and used for displaying the classified transaction detail list, the summarized data, the prediction index and the bidirectional transaction prediction result generated by the buying strategy prediction algorithm or the selling strategy prediction algorithm in a visual mode.
  2. 2. The computer system of claim 1, wherein the transaction fee parameter set (F) includes a full commission rate, a net commission rate, a manager rate, a surcharge rate, a tax stamp rate, and a minimum commission, and wherein the core processing unit, when calculating the single transaction fee, performs: calculate commission = max (transaction amount x current direction corresponds to commission rate, lowest commission); Calculate the gauge fee = transaction amount x (manager rate + manager rate); calculating the fee = transaction amount x fee rate; If the current direction is the selling direction, additionally calculating tax stamp = transaction amount x tax stamp rate; single transaction fee = commission + regulation fee + spending fee + tax on printing.
  3. 3. The computer system of claim 1, wherein the first bid amount (Q 1 ) or first sell amount (Q' 1 ) is determined by: calculating an estimated cost rate (F) of the current direction according to (F); Calculating the actual funds available for the transaction (a) =p 0 /(1+f) (buy) or P 0 × (1 f) (sell); calculate theoretical number of purchases/sales (T) =a/S 0 ; Rounding (T) according to a preset transaction unit, rounding (Q 1 ) downwards when buying and rounding (Q' 1 ) upwards when selling.
  4. 4. A calculator system according to claim 3, wherein the predetermined transaction unit is 100 shares, and the rounding operation is: When buying, Q 1 =floor (T/100) ×100; when sold, Q' 1 =ceil (T/100) ×100; where floor () is a downward rounding function and ceil () is an upward rounding function.
  5. 5. The computer system of claim 1, wherein the generation of the buy quantity sequence { Q 1 , Q 2 , …, Q n } or the sell quantity sequence { Q' 1 , Q′ 2 , …, Q′ n } conforms to a non-linear growth model, wherein the determination of the ith quantity (Qi) includes: When i=1, it is the first number (Q 1 ) or (Q ' 1 ), when i=2, qi=floor (Q 1 ×r/100) ×100 or ceil (Q' 1 ×r/100) ×100; When i is not less than 3, qi=floor ([ Σ (Q 1 to Qi- 1 ) ]×r× [ 1+k× (i+1)/N ]/100) ×100, where k is the adjustment coefficient.
  6. 6. The calculator system of claim 5, wherein the division ratio (r) is related to the golden ratio (Φ), where Φ≡1.618 or 0.618, in particular r=Φ or r=1/Φ.
  7. 7. The computer system of claim 1, wherein the core processing unit, when calculating a flat-present-sell-price (BEP), employs the formula: bep= (c_total/s_total)/[ 1 (commission rate sold + household rate + tax stamp + coupon rate x double melting days/360) ].
  8. 8. The computer system of claim 1, wherein the parameter input unit is dynamically coupled with the core processing unit and configured to: The method comprises the steps of monitoring modification operation of a user on any transaction parameter in real time, automatically triggering the core processing unit to execute a corresponding prediction algorithm again based on a modified parameter set, and driving the result output unit to synchronously update all display contents within a preset time threshold so as to realize interaction experience of what you see is what you get.
  9. 9. A method for predicting a stock exchange strategy, comprising: receiving a transaction parameter set input by a user, wherein the set comprises an initial principal, an initial price, a number of times, a number of times price difference, a number of times multiplying power, a transaction cost parameter, a financing coupon parameter and a transaction direction; if the transaction direction is buy, then: Generating a decreasing sequence of purchase prices; calculating a first purchase quantity and generating an incremental purchase quantity sequence; Successively calculating buying cost, expense and financing interest to obtain total cost and total share number; Calculating the cost selling price, the expected income interval and the reverse selling estimated cost; If the transaction direction is sell, then: generating an incremental sequence of selling prices; generating an incremental sequence of sell quantities based on the first sell quantity; Gradually calculating sales income, expense, printing tax and coupon interest to obtain total net income; Calculating the maximum number of available shares, the residual cash and the estimated reverse buying cost; And visually outputting the classified transaction details, the summarized data, the prediction index and the bidirectional transaction prediction result.
  10. 10. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, implements the stock exchange strategy prediction method of claim 9.

Description

Calculator system and method for predicting stock buying and selling strategies Technical Field The invention relates to the technical field of financial science and technology, in particular to a calculator system and a method for predicting stock buying and selling strategies. Background In the stock market, for smooth holding costs and management of investment risk, the stepwise trading strategies of "buying in batch" and "selling out batch" are widely adopted. The core of the strategy is that through executing multiple transactions at different price points, a warehouse-holding or selling structure with the prices and transaction quantity distributed in a specific rule is formed, so that better average cost is obtained or better income is realized in market fluctuation. However, in practice, investors are faced with significant challenges in formulating and optimizing such strategies, and the assistance tools provided by the prior art have various limitations. Currently, investors mainly rely on three types of modes to assist policy calculation, but all have obvious defects. First, simulations were performed using manual calculations or general purpose spreadsheet software (e.g., microsoft Excel). This approach requires the investor to design and maintain complex calculation formulas at his own discretion to calculate the price, quantity, individual transaction fees (including commissions, tax prints, surcharges, etc.) for each transaction, as well as total cost, average cost and expected earnings and losses after the total. The process is tedious and very easy to make mistakes, and the tiny adjustment of strategy parameters (such as changing the fractional price difference, increasing the multiplying power or considering the cost of financing and melting the coupon) needs to repeatedly calculate a large number of times, so that the efficiency is low, and the quick iteration and dynamic optimization of the strategy cannot be supported. More importantly, such methods typically involve a buy and sell decision splitting process, which makes it difficult to synchronously and accurately predict the minimum future sell price (flat price) required to achieve a warranty when making a buy plan, and also makes it impossible to visually evaluate the potential and remaining cash of the funds invested in selling the plan when selling, resulting in a lag and blurry risk control, lacking a full chain of vision. The second type relies on basic computing functions built into a general-purpose financial calculator or existing stock exchange software. These tools, while providing simple cost calculation or revenue estimation modules, tend to be stand alone, static in their functional design. They generally lack two-way closed-loop prediction logic of "buy-in-time, i.e., synchronous prediction of sell-out conditions" and "sell-in-time, synchronous planning, and re-buy-in", and cannot provide consistent risk boundary previews for users. Meanwhile, the transaction cost is usually too simplified (for example, flat rate is adopted, lowest commission limit is ignored, and the buying and selling cost difference is not distinguished), and credit transaction cost such as financing interest, coupon interest and the like is not generally included, so that a deviation exists between a calculation result and actual delivery cost, and the calculation precision of key indexes such as profit and loss balance points, safety margin and the like is insufficient, so that the reliability of strategies and actual combat reference value are weakened. And thirdly, adopting a professional quantitative transaction platform or a strategy return system. The system has powerful functions and can process complex models and a large amount of historical data, but the design initially focuses on the historical data feedback and the automatic transaction execution, and is not a flexible and visual simulation calculation facing the strategy conception stage. The algorithm is usually packaged into a black box, so that a common investor cannot easily understand, customize or verify core parameters (such as a specific nonlinear quantity growth model), is complex in operation and high in learning cost, and generally lacks interactive simulation experience aiming at single strategy planning, wherein the parameters are linked in real time and the results are visible in real time, so that strategy previewing and quick sensitivity analysis of 'what you see is what you get' cannot be provided for the investor. In view of the foregoing, the prior art lacks a stock ladder trading strategy aid that can simultaneously meet the following requirements. Disclosure of Invention The invention aims to solve the problems of the prior art, and provides a visual, accurate and efficient calculator tool for overcoming the defects of the prior art so as to reduce the threshold for policy making and improve the scientificity and safety of investment decisions. In order to solve the