DNA-Inspired Time Series Encoding: A Glimpse Into The Next 4-Hour Timeframe

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.