Cryptol improves cryptocurrency price forecasting with scale and structure fixes
CryptoL: Towards Scale Dominance and Physics Constraints Mitigation in Financial Multivariate Time Series Forecasting
Artificial IntelligenceComputational Engineering, Finance, and ScienceMachine Learning
Summary
Predicting cryptocurrency prices is hard because different coins vary greatly in size, and the price data points are linked in special ways. The authors created CryptoL, a method that adjusts the data and error calculations to treat each coin fairly and keep important price relationships intact. They also added checks to keep predictions realistic and stable across many different coins. Tests show CryptoL is more accurate and reliable than some other approaches.
What this means in practice
- •For quantitative trading teams: Use CryptoL to improve price predictions for a wide range of cryptocurrencies by balancing scale effects and respecting price relationships.
- •For financial data platform developers: Integrate CryptoL to enhance the stability and accuracy of multivariate crypto price forecasts in analytic services.
Authors
Yalda Taheri, Mohammad Hassan Heydari, Armon Rasooli, Maryam Amirshahkarami, Mohammad Ebrahim Mahdavi, Hossein Karshenas
Abstract
Cryptocurrency forecasting presents a distinctive combination of extreme cross-asset scale heterogeneity, non-stationary dynamics, and structural dependencies among Open, High, Low, and Close (OHLC) variables. We present CryptoL, a unified framework designed to address these challenges within multivariate time-series forecasting. CryptoL evaluates forecasting error in context-normalized coordinates within the RevIN pipeline, preventing inverse normalization from introducing an additional squared-scale weighting into the MSE objective. We formally characterize this effect through the empirical risk and parameter-gradient geometry, establishing the conditions under which large-scale assets can disproportionately influence shared-model optimization. Beyond loss-space normalization, CryptoL examines channel-independent and channel-dependent normalization for OHLC data, showing that a shared channel-dependent affine transformation preserves candle-order relations that independent channel transformations need not preserve. The framework further incorporates scale-adaptive numerical stabilization to reduce distortions caused by a fixed normalization constant across assets spanning many orders of magnitude, together with a soft feasibility loss that penalizes violations of the defining OHLC inequalities. Experiments across heterogeneous cryptocurrency assets evaluate these components through controlled ablations and demonstrate improvements in forecasting accuracy, training stability, and the frequency of financially valid OHLC predictions relative to the considered baselines. CryptoL therefore provides an integrated approach to scale-balanced optimization, structure-preserving normalization, numerical stabilization, and constraint-aware cryptocurrency forecasting.