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  • Whitepaper
    • Abstract
    • C-Mizar
      • Problem
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      • Opportunity
      • Product
        • Marketplace
        • DCA Bots
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        • Contract Sniffer
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      • Use Cases
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  • Mizar AI (on hold)
    • Mizar AI (on hold)
    • Data Sources
    • Model
      • Downsampling with CUSUM Filter
      • Average Uniqueness
      • Sample Weights
      • Sequentially Bootstrapped Bagging Classifier
      • Metalabeling
      • Bet Sizing
      • Combinatorial Purged Cross Validation
    • Structural Breaks
    • Transformations
      • Labeling Methods
      • Technical Analysis Features
      • Microstructural Features
    • Strategy Backtesting
    • Strategy Deployment
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  1. Mizar AI (on hold)

Transformations

Transforming the data such that one can build targets and predictive features to fit the model.

To build a machine learning model for a trading strategy we need to transform the data from ticks and bars into features and targets. The following sections describe how targets can be constructed using the triple barrier labeling method or the trend scanning method. The other sections describe which type of features the user can generate. The user can construct more traditional technical analysis features such as moving averages, but it is also possible to construct more advanced features that analyse the microstructure of the market.

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Last updated 4 years ago

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