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 transformation and feature engineering

In the data preprocessing stage of the Parrot platform, data transformation and feature engineering are crucial steps. They can help extract effective features and appropriately transform the data to facilitate subsequent modeling and analysis.

  1. Feature extraction: Feature extraction is to extract representative and distinguishing features from the original data. In digital currency trading, features such as price, trading volume, technical indicators (such as moving averages, relative strength indicators, etc.), as well as fundamental data (such as market capitalization, trading volume, news sentiment indicators, etc.) can be extracted from market data. wait.

  2. Feature transformation: Feature transformation is the appropriate transformation of the extracted features to facilitate model training and optimization. Common feature transformation methods include polynomial feature transformation, logarithmic transformation, exponential transformation, etc.

  3. Feature selection: Feature selection is to select the most influential features for the target task from the extracted features. The Parrot platform can use statistical methods, machine learning methods or domain knowledge for feature selection to reduce feature dimensions and improve model efficiency.

  4. Feature construction: Feature construction is to build new features based on business needs and domain knowledge. In digital currency trading, features related to market trends, trading volumes, etc. can be constructed to help the model better capture market changes and trends.

  5. Data transformation: Data transformation is the appropriate transformation of original data to facilitate subsequent modeling and analysis. Common data transformation methods include data standardization, data normalization, data smoothing, etc.

Through the processing of data conversion and feature engineering, the Parrot platform can extract representative and distinguishing features and perform appropriate conversion of the data, laying a good foundation for subsequent modeling and analysis work.

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Last updated 1 year ago