Parrot Trade
  • Parrot Trade
  • Introduction
  • Background of the project
  • Project Overview
  • Technical Overview
  • Technical architecture overview
  • Core functions and features
  • Data collection and preprocessing
  • Data cleaning and denoising
  • Data standardization and normalization
  • Data transformation and feature engineering
  • AI intelligent learning and training model
  • Neural network model architecture
  • Supervised and unsupervised learning algorithms
  • Model evaluation and optimization strategies
  • Intelligent Trading Strategy
  • Risk Management and Money Management
  • Real-time decision-making and trade execution
  • Technical implementation and system architecture
  • Data storage and processing
  • Real-time data stream processing
  • Distributed computing and high availability
  • Security and Privacy Protection
  • Development Roadmap
  • Commercial Application and Value
  • Conclusion and Outlook
  • Appendix
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Real-time data stream processing

Real-time data stream processing refers to the process of quickly and instantly processing and analyzing data streams generated in real time. In the Parrot platform, real-time data stream processing is mainly used to process market data, including price data, transaction volume data, etc., in order to generate trading signals and decision support in a timely manner. The following is the general process of parrot real-time data stream processing:

  1. Data source access: Real-time market data is usually obtained from exchanges or third-party data providers through API, WebSocket, etc. The Parrot platform establishes connections to these data sources and receives real-time data streams.

  2. Data distribution and routing: The received real-time data stream may contain multiple data sources and different types of data, which needs to be distributed and routed to send the data to the corresponding processing module.

  3. Data preprocessing: The received real-time data may have various problems, such as outliers, noise, etc. Data preprocessing operations, such as denoising, filtering, smoothing, etc., are required to ensure the quality and stability of the data.

  4. Feature extraction and calculation: Extract effective feature information from real-time data streams, such as technical indicators, volatility, etc. Derived indicators can be calculated based on these characteristic information for subsequent model building and decision making.

  5. Real-time data analysis: Perform real-time data analysis and model prediction based on preprocessed and feature extracted data. Machine learning algorithms, deep learning models, etc. can be used for data analysis and identification of market trends and patterns.

  6. Real-time decision generation: Generate corresponding trading signals and decision-making suggestions based on the results of real-time data analysis. These decisions can be based on predefined trading strategies and models such as technical indicator crossovers, trend analysis, etc.

  7. Trade Execution and Monitoring: The real-time trading signals generated will be executed through the trade execution system. At the same time, it is necessary to monitor the execution of transactions in real time, including transaction status, changes in account funds, etc., and record logs for subsequent auditing and analysis.

Through the above real-time data stream processing process, the Parrot platform can quickly respond to market changes, generate trading signals and decision support in a timely manner, and provide investors with efficient, accurate and stable trading services.

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Last updated 1 year ago