Paper: arXiv 2503.06251

Abstract

Short-term patterns in financial time series form the cornerstone of many algorithmic trading strategies, yet extracting these patterns reliably from noisy market data remains a formidable challenge. In this paper, we propose an entropy-assisted framework for identifying high-quality, non-overlapping patterns that exhibit consistent behavior over time. We ground our approach in the premise that historical patterns, when accurately clustered and pruned, can yield substantial predictive power for short-term price movements. To achieve this, we incorporate an entropy-based measure as a proxy for information gain. Patterns that lead to high one-sided movements in historical data, yet retain low local entropy, are more informative in signaling future market direction. Compared to conventional clustering techniques such as K-means and Gaussian Mixture Models (GMM), which often yield biased or unbalanced groupings, our approach emphasizes balance over a forced visual boundary, ensuring that quality patterns are not lost due to over-segmentation. By emphasizing both predictive purity (low local entropy) and historical profitability, our method achieves a balanced representation of Buy and Sell patterns, making it better suited for short-term algorithmic trading strategies.

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 4.0/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper employs advanced information theory concepts like Shannon entropy and features a custom algorithm with normalization and distance metrics, indicating high math density. However, the empirical section is largely conceptual, describing the methodology without showing backtest performance, statistical metrics, or code, which limits its current backtest readiness.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Reliable short-term pattern identification<br>in financial time series"] --> B["Data Inputs<br>Historical price data"]
  B --> C["Key Methodology Step 1<br>Pattern extraction &amp; clustering"]
  C --> D["Computational Process<br>Entropy-based quality scoring<br>Low local entropy + high movement"]
  D --> E["Key Outcome 1<br>Balanced Buy/Sell pattern set<br>No forced visual boundaries"]
  D --> F["Key Outcome 2<br>Superior predictive purity<br>vs K-means/GMM"]
  E --> G["Application<br>Short-term algorithmic trading strategies"]
  F --> G

Cited References (35)

  1. The pricing of options and corporate liabilities (1973)
  2. Exact distributions of the maximum and range of random diffusivity processes (2021)
  3. A closed-form solution for options with stochastic volatility with applications to bond and currency options (1993)
  4. Stochastic Integral (1944)
  5. Multiple W iener integral (1951)
  6. On stochastic differential equations (1951)
  7. On information and sufficiency (1951)
  8. On the validity of the geometric B rownian motion assumption (2005)
and 27 more
  1. Option pricing when underlying stock returns are discontinuous (1976)
  2. A mathematical theory of communication (1948)
  3. Cooperation dynamics in networked geometric B rownian motion (2019)
  4. Generalised geometric B rownian motion: theory and applications to option pricing (2020)
  5. Entropy of financial time series due to the shock of war — → 2303.16155
  6. Information Theory and Statistical Mechanics (1957)
  7. Maximal entropy approach for quantum state tomography (2021)
  8. The inefficiency of Bitcoin revisited: A high-frequency analysis with alternative currencies (2019)
  9. Entropy corrected geometric Brownian motion — → 2403.06253
  10. Variational approach to quantum state tomography based on maximal entropy formalism (2022)
  11. Time resolved quantum tomography in molecular spectroscopy by the maximal entropy approach (2024)
  12. The large deviation approach to statistical mechanics (2009)
  13. Single-cell entropy for accurate estimation of differentiation potency from a cell’s transcriptome (2017)
  14. A maximum entropy method to assess the predictability of financial and commodity prices (2015)
  15. Stock market uncertainty and economic fundamentals: an entropy-based approach (2019)
  16. Applications of entropy in finance: A review (2013)
  17. Trading strategies based on K-means clustering and regression models (2007)
  18. Testing the convergence and the divergence in five Asian countries: from a GMM model to a new Machine Learning algorithm (2022)
  19. Forex Daytrading Strategy: An Application of the Gaussian Mixture Model to Marginalized Currency pairs in Africa (2023)
  20. Principal component analysis: a review and recent developments (2016)
  21. A new approach to global minimization (1997)
  22. Pivot method for global optimization (1997)
  23. Comparison study of pivot methods for global optimization (1997)
  24. Symmetry breaking and stability of binary clusters (1997)
  25. Pivot method for global optimization: a study of water clusters (H2O) N with 2⩽ N⩽ 33 (1999)
  26. Study of phase changes of the water octamer using parallel tempering and multihistogram methods (2001)
  27. 0 (1)

2 of 35 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.