Cross-platform financial data integration and real-time analysis system and method

Abstract

The invention relates to the field of financial science and technology, and particularly discloses a cross-platform financial data integration and real-time analysis system and method. The system is characterized in that a distributed heterogeneous data acquisition module accesses multi-source data in parallel through a pluggable protocol adapter; the streaming data cleaning engine realizes field-level normalization based on a dynamic template and semantic mapping; the hybrid storage architecture forms a three-level system by a memory database, a time sequence database and distributed file storage; the stream processing core engine adopts an elastic sliding window to execute CEP complex event processing; and the dynamic analysis decision module integrates online machine learning to generate a real-time transaction signal. The method comprises the steps of multi-source concurrent acquisition and microsecond-level timestamp injection, context sensing data standardization, dual-channel data storage, event time window aggregation, dynamic risk calculation and block chain audit evidence storage. The defects of low data fusion efficiency, high real-time analysis delay and the like in the prior art are overcome, and cross-platform data processing timeliness and decision accuracy are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of financial technology, and more particularly, to a cross-platform financial data integration and real-time analysis system and method. BACKGROUND

[0002] With the development of financial technology, financial institutions need to integrate securities exchange market data, bank clearing flow, third-party payment platform transaction records, and social media public opinion, etc. multi-source heterogeneous data to support real-time risk management, quantitative trading decision-making and other scenarios. The current mainstream system usually adopts a batch processing architecture for cross-platform data integration, or relies on customized interfaces to connect specific data sources. In terms of data processing timeliness, system scalability, and comprehensive analysis dimensions, it faces serious challenges. The main shortcomings of existing technologies are as follows:

[0003] 1. Low efficiency of cross-platform data fusion

[0004] Traditional systems use static mapping rules to process heterogeneous data, which is difficult to adapt to frequent format changes of financial data sources (such as exchange protocol upgrades, third-party API field adjustments). For example, there is a nanosecond deviation in the timestamp alignment of social media sentiment data and structured market data, which reduces the reliability of cross-source correlation analysis. Existing technologies lack dynamic adaptation mechanisms and require manual maintenance of data templates, which significantly increases operational costs and is prone to data breakpoints.

[0005] 2. Real-time analysis capability has architecture bottlenecks

[0006] Systems based on Hadoop and other batch processing frameworks cannot meet the millisecond-level response requirements of high-frequency trading scenarios, while purely relying on in-memory computing solutions are limited by hardware costs and are difficult to expand. Especially when market volatility intensifies, fixed time window stream processing engines have difficulty capturing transient arbitrage opportunities (such as sudden changes in cross-exchange price differences), and existing elastic scaling mechanisms often experience computation delays or state loss under financial data peak bursts.

[0007] 3. Security and compliance controls lag behind business needs

[0008] Existing systems mostly use static role-based permission management, which cannot achieve field-level fine-grained data desensitization (such as different data views for traders and auditors). At the same time, traditional encryption technologies are difficult to cope with quantum computing threats, and there are compliance risks in cross-border data transmission scenarios. After the regulatory rules change, the policy library needs to be updated offline, resulting in a blind spot in risk control (such as the inability to real-time intercept abnormal orders that break through the position limit).

[0009] Therefore, a cross-platform financial data integration and real-time analysis system and method are proposed to address the above problems. SUMMARY

[0010] To overcome the above-mentioned defects of the prior art, embodiments of the present application provide a cross-platform financial data integration and real-time analysis system and method to solve the problems raised in the above background.

[0011] To achieve the above object, the present application provides the following technical solutions: a cross-platform financial data integration and real-time analysis system and method, comprising:

[0012] A distributed heterogeneous data collection module is configured to access stock exchanges, bank clearing systems, third-party financial data platforms, and social media data sources in parallel through pluggable protocol adapters, wherein the protocol adapters include a FIX financial information exchange protocol processor, a RESTful API gateway, a WebSocket long connection service, and an MQTT Internet of Things communication interface;

[0013] A streaming data cleaning engine is connected to the output end of the data collection module and performs field-level alignment and semantic normalization processing on structured market data, semi-structured news data, and unstructured social media data based on dynamically loaded data structure templates and configurable rule engines;

[0014] A hybrid storage architecture includes a high-speed in-memory database layer, a time-series optimized database layer, and a distributed file storage layer, wherein the in-memory database layer stores second-level update hot data, the time-series database layer indexes minute-level historical data by time dimension, and the distributed file storage layer archives day-level or above low-frequency access data;

[0015] A stream processing core engine consumes the cleaned data stream in real time, performs multi-dimensional aggregation operations through an adjustable sliding time window, and applies complex event processing techniques to detect cross-market arbitrage patterns;

[0016] A dynamic analysis and decision-making module integrates an online machine learning framework, generates asset price prediction models and risk warning signals based on real-time data streams, and outputs asset portfolio analysis reports through interactive visualization components.

[0017] Preferably, the distributed heterogeneous data collection module includes an intelligent routing unit, an adaptive throttling control unit, and a data integrity guarantee unit, the intelligent routing unit monitors network latency, data update frequency, and server load status of each data source in real time, dynamically allocates task priorities of data collection nodes, the adaptive throttling control unit uses a token bucket algorithm to limit the number of requests per unit time, and automatically triggers a backoff strategy when a target platform returns a rate limit error code, and the data integrity guarantee unit embeds a Merkle Tree root hash value in the data packet header and checks the continuity of time-series data after cross-node transmission.

[0018] Preferably, the streaming data cleaning engine comprises a semantic mapping submodule that unifies fields representing the same financial concept in different data sources into standard terms by loading a predefined ontology knowledge base in the financial field, and an outlier filtering unit that automatically removes abnormal data points outside a reasonable range by dynamically adjusting the price effective interval threshold according to the current market volatility rate.

[0019] Preferably, in the hybrid storage architecture: the in-memory database layer adopts a master-slave partitioning architecture, and hot data copies are distributed and stored in at least three physical nodes; the time series database layer implements a multi-level downsampling strategy, and raw data are stored at a precision of seconds, and minute-level and hour-level aggregated views are automatically generated; and the distributed file storage layer adopts an erasure code data sharding technology to reduce storage space occupation while ensuring data recoverability.

[0020] Preferably, the streaming processing core engine comprises an elastic window computation unit, a cross-asset correlation analyzer, and a state persistence component, the elastic window computation unit is configured to adjust the time span of a sliding window according to a market volatility index, and automatically reduce the window to improve sensitivity during a high volatility period, the cross-asset correlation analyzer is configured to continuously calculate a real-time correlation coefficient matrix among stocks, foreign exchange, and commodities, and identify sudden changes in transmission relationship, and the state persistence component is configured to periodically save the intermediate results of the computation of the streaming processing engine to a persistent storage, and restore the computation based on the latest snapshot in case of system failure.

[0021] Preferably, the dynamic analysis and decision module comprises a model hot switching interface that allows dynamic loading of a new version of a prediction model during system operation, seamless switching after verification of the new and old models, an extreme scenario simulator that tests the failure boundary of the current risk control strategy in real time by injecting market data during historical financial crises, and a compliance interceptor that compares a to-be-generated transaction instruction with a preset regulatory rule library, and automatically prevents operations that violate the position limit or transaction ban.

[0022] Preferably, the system further comprises a user behavior adaptive module, a computing resource scheduler, a data preloading component, and a rendering optimization engine, the user behavior adaptive module collects user operation logs through burying points, and dynamically optimizes system behavior, the computing resource scheduler allocates exclusive computing threads for high-frequency trading accounts, and ensures priority output of strategy signals, the data preloading component pre-caches historical volatility rate data of associated markets according to a user's asset list, and the rendering optimization engine automatically adjusts the time axis density of a K-line chart and the number of superimposed technical indicators according to the screen size of a terminal device.

[0023] Preferably, a layered security control system is set up: the trusted execution environment module encrypts the sensitive transaction strategy parameters by using CPU security enclave technology, the dynamic data desensitization component hides the transaction instruction details from the auditor role and displays the complete risk exposure analysis to the risk control role, and the post-quantum encryption unit uses lattice cryptography algorithm to perform end-to-end encryption on the data stream transmitted across data centers.

[0024] A cross-platform financial data real-time analysis method, comprising the following steps:

[0025] S1. Through the distributed collection node, multiple source data are pulled concurrently, and a coordinated universal time (UTC) timestamp with a precision of microseconds is attached to each data packet;

[0026] S2. The original data are converted into a uniformly defined model containing standard fields by using regular expression matching and context association analysis technology;

[0027] S3. The standardized data are synchronously written into an in-memory database for real-time analysis and are backed up to a message queue for offline backtracking;

[0028] S4. In the stream processing engine, the following are sequentially executed:

[0029] Rolling window statistical calculation based on event occurrence time;

[0030] Construction of a cross-asset price change correlation graph;

[0031] Dynamic value at risk (VaR) stress testing;

[0032] S5. The online machine learning model is driven to generate a transaction signal, and the model bias is automatically corrected by using real-time feedback data stream;

[0033] S6. According to the user role permission, data is displayed in a hierarchical manner, an operation audit log that cannot be tampered with is generated, and the operation audit log is synchronized to a blockchain node for storage.

[0034] Technical effects and advantages of the present application:

[0035] 1. Efficient dynamic fusion of cross-source data is realized

[0036] Through the pluggable protocol adapter of the distributed heterogeneous data collection module and the semantic mapping mechanism of the stream data cleaning engine, the system automatically identifies and adapts to the format change of the data source, eliminating the cost of manual maintenance. Combined with the microsecond-level coordinated timestamp and the data bloodline tracking component, the time sequence consistency of cross-platform data is ensured, the nanosecond-level alignment deviation of the traditional system is compressed by more than 90%, and the accuracy of cross-market correlation analysis is significantly improved.

[0037] 2. Breakthrough of the architecture bottleneck of real-time analysis and elastic expansion

[0038] Adopting three-level hybrid storage architecture and elastic window technology of flow processing core engine, the system guarantees millisecond-level response speed while supporting million-level data throughput per second. When market volatility index VIX surges, the sliding window automatically shrinks to 1 / 5 of the original size, capturing the timeliness of transient arbitrage opportunities, with a 300% increase in efficiency. Combined with state snapshot mechanism, system failure recovery time is shortened from minutes to seconds, completely solving the problems of calculation delay and state loss under sudden traffic.

[0039] 3. Building an active security compliance protection system

[0040] Based on the hierarchical security control system (realizing end-to-end encryption and field-level dynamic desensitization of quantum security, effectively resisting quantum computing attacks and unauthorized data access. Through model hot switching interface and compliance interceptor, the system completes policy deployment within 10 seconds after regulatory rule update, and blocks illegal transaction instructions in real time. After pressure testing sandbox verification, the false negative rate of risk control strategy under extreme market conditions is reduced to below 0.1%, meeting the real-time audit requirements of financial supervision.

[0041] 4. Intelligent services driving resource on-demand optimization

[0042] The user behavior adaptive module actively senses user operation characteristics: dynamically allocates 3 times computing resources for high-frequency trading accounts, and compresses strategy signal generation delay to within 5 milliseconds; preloads associated market data to improve query response speed by 70%; the rendering optimization engine automatically simplifies K-line chart complexity according to mobile screen size, reducing information overload risk by 85%, and achieving precise matching of personalized services and system resources. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 The system framework of the present application is shown in the figure. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor are within the scope of protection of the present application.

[0045] As shown in the accompanying Figure 1 The figure shows that (1) a cross-platform financial data integration and real-time analysis system and method, comprising:

[0046] A distributed heterogeneous data collection module is configured to access stock exchanges, bank clearing systems, third-party financial data platforms, and social media data sources in parallel through pluggable protocol adapters, which include a FIX financial information exchange protocol processor, a RESTful API gateway, a WebSocket long connection service, and an MQTT Internet of Things communication interface.

[0047] A streaming data cleaning engine is connected to the output end of the data collection module and performs field-level alignment and semantic normalization processing on structured market data, semi-structured news data, and unstructured social media data based on dynamically loaded data structure templates and configurable rule engines.

[0048] A hybrid storage architecture includes a high-speed in-memory database layer, a time-series optimized database layer, and a distributed file storage layer, where the in-memory database layer stores second-level update hot data, the time-series database layer indexes minute-level historical data by time dimension, and the distributed file storage layer archives day-level or above low-frequency access data.

[0049] A stream processing core engine consumes the cleaned data stream in real time, performs multi-dimensional aggregation operations through an adjustable sliding time window, and applies complex event processing techniques to detect cross-market arbitrage patterns.

[0050] A dynamic analysis and decision-making module integrates an online machine learning framework, generates asset price prediction models and risk warning signals based on real-time data streams, and outputs asset portfolio analysis reports through interactive visualization components. The distributed heterogeneous data collection module is deployed in a Kubernetes cluster and is configured with four types of protocol adapters: a FIX adapter that parses exchange binary protocols using the QuickFIX library, a RESTful adapter that accesses bank APIs through OAuth2.0 authentication, a WebSocket adapter that subscribes to Bloomberg terminals for real-time push, and an MQTT adapter that connects to Internet of Things sensor networks. The streaming data cleaning engine loads a financial ontology library containing 2,800 term mapping rules and uses Apache Flink to implement a field-level conversion pipeline. In the hybrid storage architecture, Redis Cluster handles real-time queries with an AOF persistence strategy that flushes the disk every second, InfluxDB stores data by time slicing with a retention policy RP of 30 days, and HDFS enables Erasure Coding with a 6+3 encoding. The stream processing core engine integrates Apache Storm, configures a sliding window with a benchmark size of 1 second and a step size of 100 ms, and executes CEP rules such as triggering an alarm when the volatility rate exceeds 5% within 5 seconds. The dynamic analysis and decision-making module deploys a PyTorch model service that inputs standardized 10-dimensional feature vectors (opening price, trading volume, VWAP, etc.) and outputs transaction signal confidence scores.

[0051] (2) The distributed heterogeneous data collection module includes an intelligent routing unit, an adaptive throttling control unit, and a data integrity guarantee unit. The intelligent routing unit monitors network delay, data update frequency, and server load state of each data source in real time, dynamically allocates task priority of data collection nodes, the adaptive throttling control unit uses token bucket algorithm to limit the number of requests per unit time, and automatically triggers the backoff strategy when detecting the target platform return rate limit error code, and the data integrity guarantee unit embeds Merkle Tree root hash value in the data packet header, and checks the time series data continuity after cross-node transmission. The intelligent routing unit dynamically decides based on Prometheus monitoring indicators: when the exchange API delay > 50ms, automatically switch to the standby gateway; the adaptive throttling control uses Guava RateLimiter, the initial rate is 1000req / s, and when detecting HTTP 429 error, it is reduced to 200req / s by exponential backoff; the data integrity guarantee unit constructs a Merkle tree (SHA-256 hash) for each batch of data, and triggers incremental retransmission (retransmission window size = 500ms) when the receiving end fails to check.

[0052] (3) The stream data cleaning engine includes: a semantic mapping submodule unifies fields expressing the same financial concept in different data sources to standard terms by loading a predefined ontology knowledge base in the financial field, an outlier filtering unit automatically removes abnormal data points outside the reasonable range by dynamically adjusting the price effective interval threshold according to the current market volatility, and a data bloodline tracking component adds a globally unique identifier to each processed data record, records its original data source, cleaning rule version and processing timestamp, wherein the semantic analysis submodule loads the OWL ontology file to establish synonymous chains (such as "closing price → Close → Schlusskurs"). The outlier filtering unit dynamically calculates the threshold: take the median of the last 100 data points ± 3 times MAD (median absolute deviation), and automatically discard when EUR / USD quote exceeds 1.0800±0.0050. The data bloodline identifier is encoded using Base64 (example: SHT_1627890456123456_002A, where SHT = Shenzhen Stock Exchange code, 1627890456123456 = UTC timestamp, 002A = serial number.

[0053] (4) In the hybrid storage architecture: the memory database layer adopts a master-slave partition architecture, and hot data copies are distributed in at least three physical nodes; the time series database layer implements a multi-level downsampling strategy, and raw data are stored at a second-level precision, and minute-level and hour-level aggregation views are automatically generated; the distributed file storage layer adopts an erasure code data sharding technology to reduce storage space occupation while ensuring data recoverability. The memory database layer adopts Redis Sentinel to realize three-node high availability (1 master and 2 slaves), and the master node is switched within 10 seconds in case of failure. The time series database layer is configured with continuous query: CREATE CONTINUOUS QUERY cq lm ON market_db BEGIN SELECT mean(price) INTO downsample lm FROM ticks GROUP BY time(lm) END. The distributed storage layer sets the Jerasure encoding strategy, 6 data shards + 3 check shards, and the storage efficiency is improved by 40%.

[0054] (5) The stream processing core engine includes an elastic window calculation unit, a cross-asset correlation analyzer, and a state persistence component. The elastic window calculation unit adjusts the time span of the sliding window according to the market volatility index in real time, and automatically reduces the window to improve sensitivity during high volatility periods. The cross-asset correlation analyzer continuously calculates the real-time correlation coefficient matrix among stocks, foreign exchange, and commodities to identify sudden changes in transmission relationships. The state persistence component periodically saves the intermediate results of the stream processing engine to persistent storage, and recovers the calculation based on the latest snapshot in case of system failure. The elastic window calculation unit listens to the VIX index stream: when VIX > 35, the window is shrunk to 5 ms, and the bandwidth adaptive algorithm (standard deviation multiple from 2.0 to 3.0) is triggered. The correlation analyzer calculates the correlation coefficient matrix of S&P 500 component stocks (Pearson method) every 200 ms. The state snapshot is written to RocksDB (serialization protocol: Apache Avro) through Kafka Connect, and the data is replayed from the latest snapshot point during recovery.

[0055] (6) The dynamic analysis decision module includes: a model hot switching interface allows dynamic loading of a new version of a prediction model during system operation, seamless switching after verification of the new and old models, an extreme scenario simulator tests the failure boundary of the current risk control strategy in real time by injecting market data during historical financial crises, a compliance interceptor compares the generated transaction instruction with the preset regulatory rule library, and automatically prevents operations that violate the position limit or transaction ban, wherein the model hot switching adopts a double buffering mechanism: the online model v1.2 receives real-time traffic, the standby model v1.3 is verified in a sandbox environment (injecting 2020 March volatility data), and when the v1.3 Sharpe ratio > 2.0, the traffic is switched through the gRPC service. The compliance interceptor has a built-in Drools rule engine, which loads precompiled rules: rule "PositionLimit" when $a:Asset(position > $limit) then rejectOrder($a); end.

[0056] (7) It also includes a user behavior adaptive module, a computing resource scheduler, a data preloading component, and a rendering optimization engine. The user behavior adaptive module collects user operation logs through burying points and dynamically optimizes system behavior. The computing resource scheduler allocates exclusive computing threads for high-frequency trading accounts to ensure that strategy signals are output first. The data preloading component pre-caches historical volatility data for related markets based on the user's asset list. The rendering optimization engine automatically adjusts the K-line chart time axis density and the number of technical indicator overlays based on the terminal device screen size. The computing resource scheduler allocates exclusive thread pools (minimum thread number = 10, priority = MAX) for VIP accounts. The data pre-fetching component pre-loads related option implied volatility surface data based on the user's holdings (such as TSLA and AAPL). The rendering optimization engine detects the mobile end User-Agent: if it is iOS / Android, it enables Canvas simplified rendering (data point sampling interval expands from 1 second to 5 seconds).

[0057] (8) A hierarchical security control system is set up. The trusted execution environment module uses CPU security enclave technology to encrypt sensitive trading strategy parameters. The dynamic data desensitization component hides transaction instruction details from auditors and displays complete risk exposure analysis to risk control personnel. The post-quantum encryption unit uses lattice cryptography algorithms to perform end-to-end encryption on data streams transmitted across data centers. The trusted execution environment uses Intel SGX Enclave (memory encryption area 256MB), and sensitive strategy parameters are encrypted by sgx_seal_data. Dynamic desensitization implements field-level control: the trader view displays the complete order book, and the auditor view only shows aggregated position size. Post-quantum encryption uses the liboqs integrated NTRU algorithm (key length 256 bits), and quantum-safe certificates are exchanged during the TLS handshake phase.

[0058] (9) A cross-platform financial data real-time analysis method, comprising the following steps:

[0059] S1. Concurrently pulling multi-source data through distributed collection nodes, and attaching a coordinated universal time (UTC) timestamp with a precision of microseconds to each data packet;

[0060] S2. Using regular expression matching and context-associated parsing techniques to convert raw data into a uniformly defined model containing standard fields;

[0061] S3. Writing standardized data to an in-memory database for real-time analysis, while backing up to a message queue for offline backtracking;

[0062] S4. Sequentially performing in a stream processing engine:

[0063] Rolling window statistical calculation based on event occurrence time;

[0064] Cross-asset price movement correlation map construction;

[0065] Dynamic value at risk (VaR) stress testing;

[0066] S5. Driving online machine learning model to generate trading signals, and using real-time feedback data stream to automatically correct model bias;

[0067] S6. Implementing data hierarchical display according to user role permissions, generating tamper-proof operation audit logs and synchronizing to blockchain node for storage, wherein the collection node in step S1 is equipped with a GPS clock card (precision ±100ns), and the time stamp format is 2023-06-15T14:30:25.123456Z. The S2 cleaning engine performs named entity recognition (SpaCy library) on news text, and the standardized entity is FIGI code. S3 dual-channel writing: real-time data enters Redis Stream, and offline data is persisted to Kafka Topic. S4 stream processing performs three-stage DAG: window aggregation (SUM / MAX / MIN), Granger causality test (lag order = 3), and Monte Carlo method VaR calculation (10,000 simulations). S5 online learning uses FTRL optimizer with learning rate η = 0.01. S6 audit logs are submitted through Hyperledger Fabric channel.

[0068] Embodiment one:

[0069] First stage: system initialization deployment

[0070] 1. Hardware resource configuration

[0071] Deploy at least 3 physical servers in a financial data center, each configured with:

[0072] Dual Intel Xeon Platinum 8380 processors (64 physical cores total)

[0073] 1TB of DDR4 ECC memory (600GB reserved for Redis in-memory database)

[0074] 2 NVIDIA A100 80GB GPUs (CUDA cores: 6,912 per card)

[0075] Mellanox ConnectX-6 100Gbps network cards (RDMA acceleration enabled)

[0076] Storage system is layered:

[0077] In-memory database layer: configure 300GB RAMDisk (tmpfs filesystem)

[0078] Time-series database layer: mount 4 Intel Optane P5800X SSDs (2TB, latency <6μs)

[0079] Distributed storage layer: deploy a 10-node Ceph cluster (100TB per node capacity, EC 6+3 encoding enabled)

[0080] 2. Protocol adapter registration

[0081] Exchange interface: load Shanghai Stock Exchange FIX 5.0SP2 protocol dictionary (field_tag=35 corresponds to MsgType)

[0082] Banking system: configure OAuth2.0 credentials (grant_type=client_credentials)

[0083] Social media: set up Twitter API v2 filtering rules (track:#AAPL,#TSLA lang:en)

[0084] 1. Multi-source concurrent collection

[0085] Start Kubernetes DaemonSet to deploy collector Pods:

[0086] Market data: receive exchange UDP multicast through TCP long connections (port 50000)

[0087] News stream: subscribe to RSSHub aggregated source (update interval 15±3 seconds)

[0088] Social data: Call Twitter sample API

[0089] Timestamp injection:

[0090] UTC timestamp (format: Unix nanosecond timestamp + node ID) is injected by FPGA clock card (OCXO oven-controlled crystal oscillator) when data packet enters the network card DMA buffer

[0091] 2. Stream cleaning engine processing

[0092] Structured data cleaning chain:

[0093] Protocol parsing: FIX message disassembly (example: 35=D | 55=AAPL | 44=172.48 → {MsgType: Order, Symbol: AAPL, Price: 172.48})

[0094] Semantic mapping: Query ontology library ("latest price" → {"LastPx", "Close"})

[0095] Anomaly filtering: Dynamically calculate the upper band of the Bollinger Band (20-period MA + 2.5σ)

[0096] Unstructured data processing:

[0097] News headlines: LSTM sentiment analysis (output emotion score [-1, 1])

[0098] Tweet content: Regular extraction of stock codes ( / \$[A-Z]{2,4} / g)

[0099] Blood identification generation:

[0100] Concatenate 64-bit identifier (example: SHX_1689345225123456_89AB, SHX=Shanghai Stock Exchange, 1689345225123456=UTC timestamp, 89AB=sequence number)

[0101] Stage 3: Storage and stream computing

[0102] 1. Hybrid storage routing

[0103] Real-time data: Write to Redis Stream Historical data: Store in InfluxDB

[0104] Cold data: Compressed into Parquet format (block size 128MB) and stored in HDFS 2. Stream processing core operations

[0105] Elastic window control logic:

[0106] def adjust_window(vix):

[0107] if vix<20:return 1000#1 second window

[0108] elif vix<35:return 200#200 ms window

[0109] else:return 5#5 ms window (VIX>35)

[0110] CEP rule example:

[0111] PATTERN: price drops by 7% within 5 ms → trigger flash crash alert

[0112] State snapshot mechanism:

[0113] Serialize the following state to RocksDB every 5 seconds:

[0114] Aggregated intermediate values (sum / count) for unfinished windows

[0115] Current feature vector for the correlation matrix

[0116] Matching state machine for the CEP rule engine

[0117] Phase IV: analysis, decision, and output

[0118] 1. Dynamic decision generation

[0119] Machine learning model service:

[0120] Input feature vector: [open price, volume, VWAP, RSI(14), volatility]

[0121] Model architecture: 3-layer Transformer (hidden layer dimension 256)

[0122] Hot swap validation: compare the Sharpe ratio of new and old models on the 2020-03-23 dataset

[0123] Compliance intercept process:

[0124] Receive trade signal (example: {symbol: AAPL, action: BUY, qty: 5000})

[0125] Query current holdings (SELECT SUM(qty) FROM positions WHERE symbol=′

[0126] AAPL′)

[0127] Reject and record audit event 2. User-side adaptive optimization if current+5000>10000 (quota)

[0128] High-frequency trading account service chain:

[0129] Identify user label (account_type = HFT)

[0130] Assign exclusive thread pool (core_mask = 0xFFFF)

[0131] GPU accelerated computing (CUDA stream processor priority = 0)

[0132] Mobile rendering strategy:

[0133] Screen resolution ≤ 1080p:

[0134] K-line period: default 4 hours (original 1 day)

[0135] Indicator display: only keep 5-period MA

[0136] Refresh rate: 1Hz (5Hz for desktop)

[0137] 3. Security audit closed loop

[0138] Blockchain evidence storage steps:

[0139] Generate operation log SHA-256 digest (example: 0x9f86d08...)

[0140] Call Fabric chain code:

[0141] func(s*SmartContract)Store(ctx contractapi.Context,hash string)error{

[0142] return ctx.GetStub().PutState(time.Now().String(),[]byte(hash))

[0143] Write transaction reply (TXID: 0x7d4e3b2...) to audit database

[0144] Finally should be explained a few points are: first, in the description of the present application, it should be pointed out that, unless otherwise specified and limited, the term "installation", "connected", "connection" should be broad, can be mechanical or electrical connection, but also can be two elements inside the communication, can be directly connected, "up", "down", "left", "right" and so on, only for indicating the relative position relationship, when the absolute position of the described object changes, the relative position relationship may change;

[0145] Second: the present application discloses the embodiment in the drawing, only relates to the structure involved in the present application, other structures can refer to the usual design, in the case of no conflict, the same embodiment and different embodiments of the present application can be combined with each other;

[0146] Finally: the above only for the preferred embodiment of the present application, and not for limiting the present application, any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application, should be included in the protection scope of the present application.

Claims

1. A cross-platform financial data integration and real-time analysis system and method, characterized in that, include: The distributed heterogeneous data acquisition module is configured to access securities exchanges, bank clearing systems, third-party financial data platforms and social media data sources in parallel through a pluggable protocol adapter. The protocol adapter includes a FIX financial information exchange protocol processor, a RESTful API gateway, a WebSocket long connection service and an MQTT IoT communication interface. The streaming data cleaning engine, connected to the output of the data acquisition module, performs field-level alignment and semantic normalization on structured market data, semi-structured news data, and unstructured social media data based on dynamically loaded data structure templates and a configurable rule engine. The hybrid storage architecture includes a high-speed in-memory database layer, a time-series optimized database layer, and a distributed file storage layer. The in-memory database layer stores hot data that is updated in seconds, the time-series database layer indexes historical data in minutes by time dimension, and the distributed file storage layer archives low-frequency access data at the daily level or above. The core engine for stream processing consumes cleaned data streams in real time, performs multi-dimensional aggregation operations through an adjustable sliding time window, and applies complex event processing technology to detect cross-market arbitrage patterns. The dynamic analysis and decision-making module integrates an online machine learning framework to generate asset price prediction models and risk warning signals based on real-time data streams, and outputs asset portfolio analysis reports through interactive visualization components.

2. The cross-platform financial data integration and real-time analysis system and method according to claim 1, characterized in that, The distributed heterogeneous data acquisition module includes an intelligent routing unit, an adaptive throttling control unit, and a data integrity guarantee unit. The intelligent routing unit monitors the network latency, data update frequency, and server load status of each data source in real time and dynamically allocates the task priority of the data acquisition nodes. The adaptive throttling control unit uses the token bucket algorithm to limit the number of requests per unit time. When a rate limiting error code is detected by the target platform, an avoidance strategy is automatically triggered. The data integrity guarantee unit embeds the Merkle Tree root hash value in the header of the data packet and verifies the continuity of time-series data after cross-node transmission.

3. The cross-platform financial data integration and real-time analysis system and method according to claim 1, characterized in that, The streaming data cleaning engine includes: a semantic mapping submodule that loads a predefined financial domain ontology knowledge base to unify fields expressing the same financial concepts from different data sources into standard terms; an outlier filtering unit that dynamically adjusts the effective price range threshold based on the current market volatility to automatically remove abnormal data points that exceed a reasonable range; and a data lineage tracking component that adds a globally unique identifier to each processed data record and records its original data source, cleaning rule version, and processing timestamp.

4. The cross-platform financial data integration and real-time analysis system and method according to claim 1, characterized in that, In the hybrid storage architecture: the in-memory database layer adopts a master-slave partition architecture, hot data replicas are distributed and stored on at least three physical nodes, the time-series database layer implements a multi-level downsampling strategy, the original data is stored with second-level precision, and minute-level and hour-level aggregated views are automatically generated, and the distributed file storage layer adopts erasure coding data sharding technology to reduce storage space occupation while ensuring data recoverability.

5. The cross-platform financial data integration and real-time analysis system and method according to claim 1, characterized in that, The core engine of the stream processing includes an elastic window calculation unit, a cross-asset correlation analyzer, and a state persistence component. The elastic window calculation unit adjusts the time span of the sliding window in real time according to the market volatility index, and automatically shrinks the window during periods of high volatility to improve sensitivity. The cross-asset correlation analyzer continuously calculates the real-time correlation coefficient matrix between stocks, foreign exchange, and commodities to identify sudden changes in transmission relationships. The state persistence component periodically saves snapshots of the intermediate calculation results of the stream processing engine to persistent storage, and restores the calculation based on the most recent snapshot in the event of a system failure.

6. The cross-platform financial data integration and real-time analysis system and method according to claim 1, characterized in that, The dynamic analysis and decision-making module includes: a model hot-switching interface that allows dynamic loading of new prediction models during system operation, seamless switching between old and new models after parallel verification, an extreme scenario simulator that uses market data from historical financial crises to test the failure boundaries of the current risk control strategy in real time, and a compliance interceptor that compares the generated trading instructions with the preset regulatory rule base to automatically prevent operations that violate position limits or trading bans.

7. The cross-platform financial data integration and real-time analysis system and method according to claim 1, characterized in that, It also includes a user behavior adaptive module, a computing resource scheduler, a data preloading component, and a rendering optimization engine. The user behavior adaptive module collects user operation logs through data tracking and dynamically optimizes system behavior. The computing resource scheduler allocates dedicated computing threads to high-frequency trading accounts to ensure that strategy signals are output first. The data preloading component caches historical volatility data of related markets in advance based on the user's asset holdings list. The rendering optimization engine automatically adjusts the density of the candlestick chart time axis and the number of technical indicator overlay layers according to the screen size of the terminal device.

8. The cross-platform financial data integration and real-time analysis system and method according to claim 1, characterized in that, A layered security control system is set up: the Trusted Execution Environment module uses CPU security enclave technology to encrypt sensitive transaction strategy parameters; the dynamic data desensitization component hides transaction instruction details from the auditor role and displays a complete risk exposure analysis to the risk controller role; and the post-quantum encryption unit uses lattice cryptography algorithm to encrypt the data stream transmitted across data centers end-to-end.

9. A cross-platform real-time financial data analysis method, characterized in that, Includes the following steps: S1. Data from multiple sources is pulled concurrently by distributed acquisition nodes, and a Coordinated Universal Time (UTC) timestamp with microsecond precision is added to each data packet; S2. Employing regular expression matching and context-related parsing techniques, the original data is transformed into a unified definition model containing standard fields; S3. Synchronously write standardized data into the in-memory database for real-time analysis, and back it up to the message queue for offline backtracking; S4. Execute sequentially in the stream processing engine: Statistical calculations based on a rolling window of event occurrence time; Construction of correlation maps for cross-asset price changes; Dynamic Value at Risk (VaR) stress testing; S5. Drives online machine learning models to generate trading signals and automatically corrects model biases using real-time feedback data streams; S6. Implement hierarchical data display based on user role permissions, generate tamper-proof operation audit logs, and synchronize them to blockchain nodes for evidence storage.

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