DNA-Inspired Time Series Encoding:
A Glimpse Into The Next 4-Hour Timeframe
Abstract
In this work, we introduce a bio‑inspired encoding framework for forecasting the direction of financial time series. Motivated by the limitations of linear models and the opacity of many deep learning approaches, we draw an analogy to genetics: observable micro‑patterns are encoded into symbolic "Financial DNA" sequences. These sequences are then analyzed using a probabilistic state‑transition mechanism to estimate the likelihood of subsequent market directions. We evaluate the approach on Bitcoin hourly OHLCV data with a rolling backtest. Among the horizons considered, modeling transitions from current Financial DNA patterns to the 4‑hour‑ahead price direction yields the strongest results, achieving a win ratio of 0.729. The findings suggest that compact, interpretable symbolic representations can capture salient, recurring structures in noisy, non‑stationary markets and support effective directional forecasts.
Open access
In alignment with the author's commitment to open-access science [01↗, 02↗, 03↗], this research will not be submitted to closed-publication journals [04↗, 05↗, 06↗, 07↗, 08↗]. The paper will be publicly available through science presses, preprint servers, and open repositories.
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BibTeX
author = {Bao Bui-Quang},
publisher = {Cambridge University Press - Open Engage},
title = {{DNA-Inspired Time Series Encoding: A Glimpse Into The Next 4-Hour Timeframe}},
year = {2025},
doi = {10.33774/COE-2025-JMMNX},
url = {https://doi.org/10.33774/COE-2025-JMMNX}
}