Yo-Yo.ai Docs
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  • Prediction: Accuracy
    • Overview
    • Technical
  • Model Output
  • Case Study
  • Conclusion
  • Prediction: Alpha
    • Overview
  • Methodology
  • Feature Engineering
  • Model Ensemble Implementation
  • Risk Management
  • Model Testing and Backtesting
    • Testing Methodology
    • Backtesting Framework
    • Hybrid Strategy Implementation
    • Results
    • Key Findings
    • APPENDIX A:
  • Vaults
    • How To
  • Product Road Map
  • FAQ
  • YOYO Token
    • Tokenomics
      • The YOYO Token
      • Token Utility
      • Token Metrics
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Model Output

Yo-Yo’s prediction model outputs a probability distribution across three potential classes: upward, downward, or stationary; offering a nuanced assessment of market direction rather than a simple categorical prediction. It presents a probabilistic breakdown via a confidence measure, giving users insights into the likelihood of each class.

2.1 Entropy-Based Confidence Measure

Entropy is used to assess the prediction’s confidence level. Lower entropy signals a higher confidence level, while higher entropy suggests a less certain forecast, giving users an objective measure of forecast reliability.

By delivering a predicted price movement and its associated confidence level, Yo-Yo’s model supports more informed trading decisions, integrating quantitative insights on both expected movement and prediction reliability.

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Last updated 2 months ago