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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Data storage and processing

Parrot data storage and processing is a crucial part of the platform, which involves obtaining data from various data sources, storing data, processing and analyzing data, etc. The following is the general process of Parrot data storage and processing:

  1. Data acquisition: The Parrot platform obtains data from various data sources through API, third-party data providers or web crawlers, including market price data, transaction volume data, technical indicator data, fundamental data, etc.

  2. Data storage: The acquired data needs to be stored for subsequent analysis and processing. The Parrot platform can select appropriate database technologies for data storage, such as relational databases (such as MySQL, PostgreSQL), NoSQL databases (such as MongoDB, Redis), etc.

  3. Data cleaning and preprocessing: The original data obtained may have various problems, such as missing values, outliers, duplicate values, etc., which requires data cleaning and preprocessing operations. This includes operations such as removing invalid data, filling in missing values, and smoothing data to ensure the quality and integrity of the data.

  4. Feature extraction and feature engineering: Based on data preprocessing, feature extraction and feature engineering operations can be performed to extract effective feature information from the original data to build trading models and generate trading signals.

  5. Model training and optimization: Based on the data after preprocessing and feature engineering, model training and optimization can be performed. The Parrot platform can use historical data to train trading models and optimize and adjust them based on the performance of the model.

  6. Real-time data updates: As the market changes, real-time data needs to be constantly updated and adjusted. The Parrot platform can set up scheduled tasks or real-time data streams to update data regularly or in real time to ensure the timeliness and accuracy of the data.

  7. Data analysis and decision generation: After data processing and model training, data analysis and transaction decision generation can be performed. The Parrot platform generates corresponding trading signals and decision-making suggestions based on real-time market data and pre-defined trading strategies.

  8. Trade Execution and Monitoring: The generated trading signals 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 data storage and processing processes, the Parrot platform can effectively acquire, store, process and analyze market data, providing investors with efficient, accurate and stable trading decision support.

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Last updated 1 year ago