Forecasting the Dynamics of Cryptocurrency Rates by Inspecting Technical Patterns

Abstract

The unprecedented growth and extreme volatility of the cryptocurrency market have created an urgent and persistent need for reliable and precise forecasting models that can assist investors, traders, and financial analysts in making well-informed and timely decisions. Cryptocurrency prices are influenced by a variety of factors, including market sentiment, macroeconomic trends, trading volume, and technological developments, making prediction a complex and multidimensional task. Traditional statistical models often fall short in capturing the nonlinear dependencies and temporal patterns inherent in such data, highlighting the necessity for advanced approaches. This research introduces a novel hybrid deep learning framework that com- bines the strengths of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks to accurately forecast price movements for ten major cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Dogecoin (DOGE), Shiba Inu (SHIB), Ripple (XRP), NEAR Protocol (NEAR), SUI20947, PEPE24478, and Solana (SOL). By integrating both short-term high-frequency intervals (five-minute price data) and long-term daily closing prices, the proposed methodology is designed to capture a diverse range of market dynamics, seasonal trends, and high-volatility fluctuations. To enhance predictive accuracy and practical usability, the framework incorporates widely used technical indicators, including the Relative Strength Index (RSI), Simple Moving Average (SMA) crossovers, and Bollinger Bands. These features are merged with deep learning outputs to produce predictions that are both statistically sound and aligned with real-world trading strategies. The dataset, sourced from Yahoo Finance, undergoes preprocessing steps such as normalization and trans- formation using a sliding window approach to enable the generation of multi-step forecasts. Model performance is rigorously evaluated using the Mean Absolute Percent- age Error (MAPE) metric. When applied to long-term (daily) price data, the LSTM- GRU hybrid model achieves a forecast accuracy in the range of 95% to 96%. In contrast, when tested on short-term (five-minute) interval data, the GRU-based implementation alone yields an exceptional accuracy of approximately 99%. These results underscore the adaptability and robustness of the proposed approach across multiple timeframes. The findings demonstrate that the integration of deep learning architectures with domain-specific technical indicators results in a more reliable, generalized, and scalable solution for cryptocurrency price forecasting. The cross-asset, cross- timeframe experimental design offers a practical blueprint for real-time algorithmic trading systems and provides a foundation for the development of adaptive financial models. Furthermore, the study emphasizes the importance of leveraging temporal features, technical patterns, and advanced neural architectures to improve decision making capabilities in the inherently volatile and unpredictable digital asset markets.