A Hybrid SVR-Based Framework for Cryptocurrency Price Forecasting and Strategy Backtesting

Cryptocurrency price forecasting has gained increasing attention due to the market’s high volatility and structural complexity. While many recent studies have explored deep learning architectures, including attention- and transformer-based models, existing research still faces notable limitations: (i) inconsistent feature engineering choices, (ii) limited examination of hybrid machine-learning models, and (iii) a lack of transparent trading evaluation using realistic backtesting assumptions. To address these gaps, this study develops a hybrid forecasting and trading framework based on Support Vector Regression (SVR) combined with a set of rule-based technical strategies. Using four major cryptocurrencies – BTC, ETH, XRP, and LTC – from 2018 to 2020, the proposed framework integrates thirteen technical indicators with a sliding-window scheme and compares SVR against Random Forest (RF) and Long Short-Term Memory (LSTM) benchmarks. Empirical results show that SVR offers a competitive balance between predictive accuracy and computational efficiency, particularly in moderate-volatility regimes. The strategy backtesting further demonstrates that SVR-driven signals can outperform traditional technical rules under certain market conditions, although limitations remain for highly volatile assets such as Bitcoin. The study contributes to the literature by clarifying feature-design choices, evaluating SVR within a multi-asset setting, and providing reproducible code and datasets through an open-access repository.