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Entropy-Assisted Quality Pattern Identification in Finance.

Rishabh Gupta1, Shivam Gupta2, Jaskirat Singh3

  • 1Department of Chemistry, Purdue University, West Lafayette, IN 47907, USA.

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Summary
This summary is machine-generated.

This study introduces an entropy-assisted framework to identify reliable short-term trading patterns in financial data. The method enhances algorithmic trading by focusing on informative patterns with consistent behavior, outperforming traditional clustering techniques.

Keywords:
algorithmic tradingentropyfinancepattern identification

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Area of Science:

  • Quantitative Finance
  • Computational Finance
  • Time Series Analysis

Background:

  • Algorithmic trading strategies heavily rely on identifying short-term patterns in financial time series.
  • Extracting reliable patterns from noisy market data presents a significant challenge for existing methods.
  • Conventional clustering techniques like K-means and Gaussian Mixture Models (GMMs) can produce biased or unbalanced pattern groupings.

Purpose of the Study:

  • To propose an entropy-assisted framework for identifying high-quality, non-overlapping financial patterns.
  • To enhance the predictive power of historical patterns for short-term price movements in algorithmic trading.
  • To develop a method that emphasizes pattern balance and predictive purity over forced visual segmentation.

Main Methods:

  • Incorporation of an entropy-based measure as a proxy for information gain in pattern identification.
  • Utilizing historical data to identify patterns exhibiting high one-sided movements and low local entropy.
  • Employing a novel clustering approach that prioritizes balance and quality over strict visual boundaries, contrasting with K-means and GMMs.

Main Results:

  • The entropy-assisted framework successfully identifies informative patterns with consistent behavior over time.
  • The proposed method demonstrates superior performance in achieving balanced representations of Buy and Sell patterns compared to conventional clustering.
  • Case studies on Gold vs. USD and GBPUSD illustrate the framework's potential for extracting high-quality trading patterns.

Conclusions:

  • The entropy-assisted framework offers a robust approach for extracting high-quality, predictive patterns in financial time series.
  • This method is well-suited for short-term algorithmic trading strategies due to its emphasis on pattern purity and historical profitability.
  • The framework provides a valuable tool for quantitative traders seeking to improve the reliability of their algorithmic strategies.