A method and system for automated identification of major market trend and closed-loop trading decision

Abstract

The application discloses a financial market main wave trend automatic identification and closed-loop transaction decision method and system, and belongs to the technical field of financial quantification and computer automatic decision. The application constructs an original three-layer progressive special technical architecture: six core basic principles are arranged at the bottom layer, two general standardized judgment criteria are arranged at the middle layer, and a whole-link closed-loop execution process covering the complete life cycle of the main wave is designed at the outer layer. The application solves the industry pain points that the existing trend analysis is highly subjective, the judgment criteria are not unified, and cannot be stably and automatically landed, can realize standardized trend judgment without manual intervention in the whole process, has a rigorous architecture, high consistency in judgment, and strong adaptability, can be directly realized through computer software programming, and has strong practicality and cross-scene expansibility.

Description

Technical Field

[0001] This invention relates to the fields of financial data analysis, quantitative trading, and computer-automated decision-making, specifically to a method and implementation system for automated closed-loop determination of the main wave trend throughout its entire lifecycle based on a layered architecture. Background Technology

[0002] Financial market trend analysis and trading decision-making techniques are widely used in quantitative trading, asset allocation, and market analysis. Current mainstream analytical methods are mainly divided into three categories: fundamental analysis, traditional technical pattern analysis, and quantitative analysis.

[0003] Fundamental analysis relies on publicly available, albeit delayed, data such as macroeconomic data and financial reports. This approach suffers from limitations such as delayed assessments, excessively long timeframes, and difficulty in capturing short- to medium-term trend inflection points. In contrast, traditional technical analysis methods often rely on fragmented, subjective judgments based on single patterns and indicators. They lack a unified underlying benchmark and fail to form a complete, closed-loop logic covering the entire lifecycle of a trend, from its inception, development, evolution, level transition, to its conclusion. Consequently, they suffer from poor consistency in judgments, lack of standardization, and difficulty in achieving stable, automated implementation through computer programs. These methods generally suffer from the industry pain points of strong subjectivity, weak reproducibility, and high error rates.

[0004] Meanwhile, most existing quantitative analysis frameworks lack a layered and progressive standardized technical architecture. Their underlying logic and execution processes are severely disconnected, making it impossible to achieve a complete and systematic design from the underlying technical foundation and unified judgment criteria to fully automated execution. This hinders the formation of stable, reusable, and highly deterministic automated decision-making capabilities. Currently, there is no complete technical solution for determining the main wave trend that possesses a three-layer native architecture, a full-cycle multi-node closed loop, and can be automated by computers. Summary of the Invention

[0005] (a) The technical problem to be solved by the present invention

[0006] In view of the shortcomings of existing technologies and common defects in the industry, the core technical problem to be solved by this invention is:

[0007] 1. Overcome the inherent shortcomings of existing trend judgments, such as strong subjectivity and inconsistent judgment standards;

[0008] 2. Provide a complete, closed-loop standardized judgment process covering the entire life cycle of the main wave;

[0009] 3. Build a robust, layered, and progressive underlying architecture to unify and standardize the judgment rules throughout the entire process;

[0010] 4. Provide trend identification and trading decision-making solutions that can be directly programmed into computer software and run fully automatically;

[0011] 5. Significantly improves the stability, consistency, and batch execution capability of trend evolution prediction.

[0012] (II) The technical solution adopted in this invention

[0013] To address the aforementioned technical issues, this invention provides an automated method for identifying and making closed-loop decisions regarding the main trend of financial markets, accompanied by a dedicated layered architecture system.

[0014] This invention features a unique three-layer progressive technical architecture:

[0015] 1. Foundational Principles: Based on six core fundamental principles, the entire system is supported by a unified underlying technology.

[0016] 2. Intermediate benchmark layer: Two top-level general judgment criteria are set, namely the spatial benchmark judgment criterion and the trend initiation judgment criterion, which provide a unified and fixed standardized judgment scale for all judgment nodes in the whole process;

[0017] 3. Outer Execution Implementation Layer: Construct a full lifecycle execution judgment layer, designing 11 progressively advancing, closed-loop standard execution steps to fully cover the entire lifecycle of the main wave from the start of the cycle, stage evolution, level switching, ultimate high point termination to the restart of the new cycle;

[0018] It also features a dedicated internal probability support module to provide auxiliary verification and probability support for critical level switching nodes and cycle closing nodes.

[0019] Based on the above architecture, this invention achieves fully automated trend judgment and decision output without human intervention through a closed-loop process of initial state standby, benchmark starting point locking, trend resonance confirmation, two-stage main wave operation, resistance high point prediction, level switching identification, reverse structure deduction, and new cycle initiation.

[0020] (III) Beneficial Effects of the Invention

[0021] Compared with the prior art, the present invention has the following outstanding substantive features and beneficial effects:

[0022] 1. Unique and Rigorous Architecture: It features a pioneering three-layer progressive nested architecture, with deep binding of the underlying foundation, general principles, and execution flow, resulting in rigorous logic and clear hierarchy;

[0023] 2. Zero subjective bias in judgment: A unified standard is established throughout the entire process through two general judgment criteria, completely eliminating the subjectivity and arbitrariness of traditional manual judgment;

[0024] 3. Seamless full-cycle coverage: For the first time, a complete closed loop of the main wave's life cycle, from its birth, growth, switching, and termination to the start of a new cycle, is achieved without any omissions in the process;

[0025] 4. Naturally Automatable: The entire process has clear steps and fixed logic. Ordinary technical personnel in this field do not need to know the internal core algorithms to directly develop computer software systems and achieve 24 / 7 unattended, fully automated and stable operation.

[0026] 5. Significantly improved stability: Through multi-layer node verification and branch-assisted verification, the probability of trend misjudgment is greatly reduced, and the consistency between trend tracking and prediction is significantly improved;

[0027] 6. Highly adaptable and scalable: The overall modular design allows for rapid adaptation to different financial markets and time periods, making it suitable for a wide range of applications. Attached Figure Description

[0028] Figure 1 This is a diagram illustrating the underlying rule module architecture of the system provided in this embodiment of the invention.

[0029] Figure 2 The main process fully automatic closed-loop determination flowchart provided in the embodiments of the present invention;

[0030] Figure 3 The main process fully automatic closed-loop determination flowchart provided for the embodiments of the present invention (with attached figure).

[0031] Figure 1 In the diagram, thick arrows represent the support relationship between the six core fundamental principles and the two top-level general judgment criteria; thin arrows represent the output relationship between the two top-level general judgment criteria and the full lifecycle execution judgment layer; and dashed arrows represent the derivation relationship between the six core fundamental principles and the internal probability support module. Detailed Implementation

[0032] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings:

[0033] This embodiment provides an automated method for identifying and making decisions regarding the main trend of financial markets, which relies on... Figure 1 The three-layer underlying architecture shown is based on Figure 2 The closed-loop process shown is executed as follows:

[0034] S1: The system is powered on and initialized, entering the market main wave cycle benchmark starting point state and waiting, continuously accessing the target's real-time market data;

[0035] S2: At time node T1, determine and lock the starting point of the main wave cycle benchmark, and at the same time, based on the spatial benchmark determination criteria, delineate the operating space of the first stage of the original main wave;

[0036] S3: After locking the operating space, continuously observe and track the trend evolution. At time node T2, complete the trend resonance confirmation based on the trend initiation judgment criteria.

[0037] S4: The trend has been confirmed and the first stage of the primary main wave energy has been initiated. The upward evolution of the primary wave will be continuously and dynamically tracked.

[0038] S5: During operation, the high point of the primary main wave resistance in the first stage is predicted at time node T3 by verifying the critical resistance of the stage resistance.

[0039] S6: After the high point is reached, continuously monitor the level switching signal and identify the main wave level upgrade switching node at time node T4;

[0040] S7: After the level switch is confirmed, the second stage of extended main wave energy burst will begin;

[0041] S8: Dynamically track the entire upward movement of the second-stage extended main wave;

[0042] S9: By verifying the critical resistance level, the high point of the ultimate resistance of the second-stage extended main wave is identified at time node T5, and the main wave operation ends.

[0043] S10: After the main wave ends, the reverse rebound structure is deduced and tracked to complete the overall end of this main wave.

[0044] S11: After the rebound cycle ends, at time node T6, a new benchmark starting point is determined, and the process jumps to the initial steps to start a new main wave cycle.

[0045] Those skilled in the art can build corresponding software automation programs based on the process framework described in this specification. Without knowing the internal core judgment algorithm, they can completely replicate the process logic and automated operation of the entire system, fully meeting the requirements for sufficient patent disclosure.

[0046] The above process includes branch execution paths such as the second outbreak of the first main wave and the determination of rebound structure conditions, all of which fall within the protection scope of this invention.

Claims

1. A method for automated identification of the main trend in financial markets and closed-loop trading decision-making, characterized in that, Includes the following steps: (1) The system enters the initial standby state, continuously monitors the market data of the target, and identifies and locks the starting point of the main wave cycle benchmark; (2) After the benchmark starting point is locked, the trend status is continuously observed and tracked to complete the trend resonance confirmation stage status identification; (3) After the trend is confirmed, the first stage of the primary main wave energy initiation judgment is executed; (4) Dynamically determine the evolution of the primary wave during the first stage of the upward movement; (5) Predict the resistance high point of the first stage primary wave; (6) Identify the main wave level upgrade switching node; (7) Confirm the start of the second phase of the extended main wave's large-scale energy burst; (8) Dynamically determine the evolution of the second-stage extended main wave during its upward movement; (9) Identify the ultimate resistance high point of the second-stage extended main wave; (10) After the main wave ends, deduce and track the evolution of the reverse rebound structure to complete the end of this cycle; (11) Determine and initiate a new round of benchmark starting point formation, and enter the next cycle.

2. The method according to claim 1, characterized in that: The entire methodology operates on a three-layer architecture: the bottom layer consists of six core fundamental principles, the middle layer consists of two top-level general judgment criteria, and the outer layer consists of a full lifecycle execution judgment process.

3. The method according to claim 2, characterized in that: The two top-level general judgment criteria include the spatial benchmark judgment criterion for delineating the periodic space and the trend initiation judgment criterion for verifying the trend initiation.

4. The method according to claim 1, characterized in that: For critical level switching nodes and cycle closing nodes, a dedicated internal probability support module is configured for auxiliary judgment and verification.

5. An automated decision-making system for implementing the method of any one of claims 1-4, characterized in that, include: The data access module is used to execute the corresponding steps in the method of any one of claims 1-4, the benchmark starting point locking module is used to execute the corresponding steps in the method of any one of claims 1-4, the trend determination module is used to execute the corresponding steps in the method of any one of claims 1-4, the dual-stage main wave tracking module is used to execute the corresponding steps in the method of any one of claims 1-4, the level switching identification module is used to execute the corresponding steps in the method of any one of claims 1-4, the cycle closing and loop restart module is used to execute the corresponding steps in the method of any one of claims 1-4, and the modules work together to achieve fully automated closed-loop decision-making.

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