This paper presents a novel approach to cryptocurrency trading by introducing a hybrid deep learning architecture that combines state-of-the-art sequence modeling techniques with reinforcement learning. Our model integrates Mamba State Space Models (SSM), Temporal Convolution Networks (TCN), and multi-head attention mechanisms to capture complex temporal dependencies in market data, while leveraging Deep Q-Network variants for optimal decision making. We implement a sophisticated signal processing pipeline with adaptive smoothing and feature fusion mechanisms, followed by a reinforcement learning framework for trading strategy optimization. The proposed architecture demonstrates superior performance in capturing market dynamics and generating robust trading signals, as validated through comprehensive backtesting on high-frequency cryptocurrency data.
The large price changes in Bitcoin have led to increased interest in predicting its future prices. This study uses a Deep learning approach to forecast Bitcoin values based on past data and technical indicators. Several models, including LSTM and Gradient Boosting, are used to find patterns in Bitcoin's price trends. Results show that the LSTM model reaches an accuracy of 98%. It also achieves a 97% accuracy rate for daily price predictions using logistic regression and linear discriminant analysis methods. The use of LSTM for predicting Bitcoin prices over time is more effective than traditional methods, especially regarding the importance of sample size in Deep learning. The analysis looks at Bitcoin price charts to project future changes, primarily through time series analysis that utilizes historical data. Deep learning algorithms are applied to identify complex patterns in Bitcoin pricing data. The dataset for these predictions is from KAGGLE, covering eight years from 2014 to 2022, which aids in developing more accurate forecasting techniques.
Senior Data Engineer - Lead, Citibank, USA, Pradeep Rao Vennamaneni
The financial services industry is transforming batch processing to real-time, AI-driven architectures. This article looks at how the frameworks Apache Kafka and Apache Spark are used as bases for building scalable and low-latency, fault-tolerant data pipelines, meeting the special requirements of the financial sector. These real-time applications include high-frequency trading, fraud detection, compliance monitoring, and customer engagement. They are made possible through these open-source platforms that publicly ingest, process, and make decisions. Integrating cloud-native infrastructure—using Kubernetes, service mesh, and container orchestration—ensures elasticity, security, and regulatory alignment. Large language models (LLMs) are now being entrenched into micro services for decision support, regulatory reporting automation, and the automation of client interactions. The article also contains detailed architectural guidance on how to integrate Kafka and Spark, tips for improving Kafka Spark performance, and best practices around observability and DevSecOps. Real-time stream processing combined with AI-driven analysis serves as a real-world use case for trade surveillance. The future impact of emerging trends such as edge-native computing, federated learning, and decentralized finance is also examined. Strategic recommendations to CTOs and architects for developing secure, AI-native, and future-proof financial systems are presented to close.
Cryptocurrency is a new type of asset that has emerged with the advancement of financial technology, creating significant opportunities for research. bitcoin is the most valuable cryptocurrency and holds significant research value. However, due to the significant fluctuations in bitcoin's value in recent years, predicting its value and ensuring the reliability of these predictions, which have become crucial, have gained increasing importance. A method that combines Long Short-term Memory (LSTM) with conformal prediction is proposed in this paper. Initially, the high-dimensional features in the dataset are divided using the Spearman correlation coefficient method, and features below 0.75 and above 0.95 are excluded. Subsequently, an LSTM model is built, and data are fed into it and the data is used to train the model to generate predictions. Finally, the predicted values generated by the LSTM are fed into the conformal prediction model, and confidence intervals for these values are generated to verify their reliability. In the conformal prediction model, the quantile loss of the loss function is defined, and an Average Coverage Interval (ACI) predictor is designed to improve the accuracy of the results. The experiments are conducted using data from CoinGecko, which is a publicly available data. The results show that the LSTM-conformal prediction (LSTM-CP) combination improves reliability.
The initial driving force behind the development of the cryptocurrency market is the Bitcoin currency. The emergence and expansion of specialized exchanges were essential for trading this cryptocurrency. Consequently, experts in the field recognized the necessity of developing econometric models to forecast Bitcoin’s exchange rate. Proposals presented by econometricians at scientific conferences demonstrated that such models could help minimize risks and potential losses during the stages of investing in Bitcoin and selecting financial instruments, as well as forecast future returns. In this article, we attempt to present methods for constructing predictive econometric models to automate cryptocurrency trading and analyze Bitcoin’s price fluctuations using econometric modeling techniques. The economic development of the cryptocurrency market, the technological architecture of Bitcoin, and the principles of econometric modeling have been systematically examined, and the data were analyzed based on real statistical information. The article is structured in logical order, and the conclusions and recommendations are presented with scientific justification. The statistical methods, analytical charts, and forecasting models used in the study were selected according to the research topic, and the results are expressed clearly and in a scientific manner. The article’s plagiarism index is below 5%, which confirms its status as a fully original scientific work.
Maryam Sarmad Mohammed Ali, Majid M. Manhosh, Ahmed Bahaaulddin A. Alwahhab, Faez Hlail Srayyih · 9 authors
This project examines the use of federated learning for financial forecasting, which focuses on a better prediction with privacy. Data aggregation in centralized models can break confidentiality, notably in finance. Our study offers a federated learning (FL) paradigm utilizing long short-term memory (LSTM) networks whereby diverse financial institutions collectively train strong forecasting models without data sharing. We employed NASDAQ-100 and S&P 500 datasets and utilized a differentially private LSTM network leveraging secure multiparty computing. The data reveal that performing an averaging federated (FedAvg) model was much superior to centralized and decentralized models with lower MAE and RMSE. The model's R2values of 0.92 show its ability to capture the market's complexity and perform well. This framework secures privacy and enables scalability for realtime financial forecasting. According to our findings, federated learning has the ability to substantially impact the banking industry and give an accurate and secure alternative to the existing approaches. Future studies will aim at including sophisticated privacy-preserving approaches and increasing model applications across varied financial datasets.
Bitcoin's high volatility poses significant challenges for short-term price prediction, making it a critical area of study for financial forecasting. Traditional models such as Long Short-Term Memory (LSTM) networks often encounter difficulties in handling long-range dependencies and non-stationary data, limiting their predictive accuracy under volatile conditions. This study introduces the Time-Series Transformer (TST) as a novel approach to predict Bitcoin's short-term prices. By leveraging self-attention mechanisms, TST effectively captures complex temporal patterns in historical Bitcoin data, including prices and trading volume. The data was segmented into fixed-length windows to facilitate model training and testing. Evaluation metrics such as Mean Squared Error (MSE), Mean Absolute Scaled Error (MASE), and R-squared (R²) demonstrated TST’s superior performance over LSTM, particularly during periods of high market fluctuation. Furthermore, TST exhibited notable computational efficiency when working with large datasets, underscoring its scalability. These findings not only highlight TST’s potential for enhancing cryptocurrency price prediction but also pave the way for future research integrating external data sources and exploring further model enhancements for more robust financial forecasting.
In recent years, cryptocurrencies have attracted growing attention from both private investors and institutions. Among them, Bitcoin stands out for its impressive volatility and widespread influence. This paper explores the predictability of Bitcoin's price movements, drawing a parallel with traditional financial markets. We examine whether the cryptocurrency market operates under the efficient market hypothesis (EMH) or if inefficiencies still allow opportunities for arbitrage. Our methodology combines theoretical reviews, empirical analyses, machine learning approaches, and time series modeling to assess the extent to which Bitcoin's price can be predicted. We find that while, in general, the Bitcoin market tends toward efficiency, specific conditions, including information asymmetries and behavioral anomalies, occasionally create exploitable inefficiencies. However, these opportunities remain difficult to systematically identify and leverage. Our findings have implications for both investors and policymakers, particularly regarding the regulation of cryptocurrency brokers and derivatives markets.
The integration of Artificial Intelligence (AI) in finance is transforming digital economics by enhancing decision-making, automating processes, and optimizing financial strategies. This book chapter explores AI-driven learning techniques, including machine learning, deep learning, and reinforcement learning, and their applications in financial markets, risk management, fraud detection, and algorithmic trading. We analyze the impact of AI on financial institutions, digital banking, and decentralized finance (DeFi), highlighting how AI enhances predictive analytics, customer experience, and regulatory compliance. Additionally, the chapter discusses the ethical and regulatory challenges of AI adoption in finance, emphasizing the need for transparency and fairness in AI-driven financial systems. By examining real-world case studies and emerging trends, this chapter provides a comprehensive overview of AI's role in shaping the future of digital economics.
Esam Mahdi, Carlos Martín-Barreiro, Xavier Cabezas
In this article, we introduce a novel deep learning hybrid model that integrates attention Transformer and Gated Recurrent Unit (GRU) architectures to improve the accuracy of cryptocurrency price predictions. By combining the Transformer's strength in capturing long-range patterns with the GRU's ability to model short-term and sequential trends, the hybrid model provides a well-rounded approach to time series forecasting. We apply the model to predict the daily closing prices of Bitcoin and Ethereum based on historical data that include past prices, trading volumes, and the Fear and Greed index. We evaluate the performance of our proposed model by comparing it with four other machine learning models: two are non-sequential feedforward models: Radial Basis Function Network (RBFN) and General Regression Neural Network (GRNN), and two are bidirectional sequential memory-based models: Bidirectional Long-Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU). The performance of the model is assessed using several metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), along with statistical validation through the nonparametric Friedman test followed by a post hoc Wilcoxon signed rank test. The results demonstrate that our hybrid model consistently achieves superior accuracy, highlighting its effectiveness for financial prediction tasks. These findings provide valuable insights for improving real-time decision making in cryptocurrency markets and support the growing use of hybrid deep learning models in financial analytics.
Recently, with the gradual development of machine learning technology, more and more people are trying to apply machine learning technology in various fields, and finance is one of the important fields. This work investigates the optimization of cryptocurrency portfolios by combining Long Short-Term Memory (LSTM) time series forecasting with traditional portfolio optimization methods. The focus of the paper is on using the historical price data from the past six years of Bitcoin (BTC), Ethereum (ETH), and Litecoin (LTC) to train LSTM models, which are then used to predict the prices of these cryptocurrencies for the period from January to June 2024. These predictions are subsequently incorporated into an extended Markowitz framework to optimize the portfolio on a monthly basis. The results indicate that the LSTM-enhanced portfolio optimization method yields higher returns and better risk management compared to traditional methods. This finding could prove that it is feasible and effective to apply machine learning methods, especially time series forecasting methods, to cryptocurrency portfolios.
Proshanta Kumar Bhowmik, Faiaz Rahat Chowdhury, Md Sumsuzzaman, Rejon Kumar Ray · 9 authors
Cryptocurrency markets in the USA, especially that of Bitcoin, are plagued by extreme volatility fueled by a dynamic intersection of macroeconomic forces, speculator behavior, and sentiment of investors. Conventional financial models cannot keep pace with the high-frequency price changes typical of digital assets, prompting the need for novel methodologies that can better account for unstructured data like social media sentiment, news reports, and discussion forum postings. The central aim of this study was to establish a strong model that integrates sentiment analysis and machine learning methods to forecast the price movements of Bitcoin. The dataset used included multi-source sentiment data and cryptocurrency market indicators, which allow for in-depth analysis of public emotion on cryptocurrency volatility. Sentiment was sourced from Twitter (tweet text with Bitcoin hashtags and keyword mentions), Reddit (r/Bitcoin and r/Crypto Currency subreddits), and financial headlines (Bloomberg, CoinDesk, Reuters), covering the timeframe of 2019–2024 to ensure the inclusion of various market cycles. Textual data was pre-cleaned to remove noise signals (bots, spam, non-English text) and annotated for sentiment polarity (positive, negative, neutral) using both VADER (Valence Aware Dictionary for sEntiment Reasoner) and fine-tuned BERT models for contextual relevance. In analyzing how sentiment affects the volatility of the Bitcoin market, we used various modeling methods such as Logistic Regression, Random Forest Classifier, and Support Vector Machines. Support Vector Machines stands slightly ahead in terms of accuracy, implying that it might be the strongest among the three for this particular task. Logistic Regression and Random Forest both show similar levels of accuracy, which means that both of them are also strong, though less optimal compared to the Random Forest model. The use of sentiment analysis in financial markets, especially in the cryptocurrency market, provides U.S.-based investors and traders with a valuable means of risk protection. Through the use of sentiment-aware forecasts, investors can make predictions of market trends and probable price movements based on public sentiment. Crypto-fintech platforms can leverage sentiment analysis to build real-time alert systems that update users on important market movements. Through social media and news channels, the platforms can issue alerts on impending price volatility or impending trends, allowing the user to react quickly to market forces. The capability to bring in real-time social media APIs for live predictions marks a critical leap for sentiment analysis in the cryptocurrency market. Through APIs like Twitter, Reddit, and other social media platforms, investors can get instant readings on public sentiment, which in turn will allow them to make real-time and better-informed trading decisions.
The dynamic crypto-assets business is famous for its highly changing prices, quick price swings, and the influence of global socio-economic factors on the prices. The workflows of traders and investors have to deal with these complications because they are looking for safe ways to manage risk and maximize profits. This research investigates how Artificial Intelligence (AI) for predictive analytics could be used to improve decision-making in cryptocurrency markets. Using the state-of-the-art machine learning techniques, model deep learning strategies, and assessment of the opinion of the audiences and sentiments, the research is verifying the feasibility of the synthesis of the structured data such as the historical price movement and the unstructured data comprising social media and news sources. By building and comparing different predictive models, this research shows that AI-powered methods can be effectively used for predicting the movements of cryptocurrency prices and the discovery of irregularities in the market. Aspects identified in this research include the fact that AI is successful in providing conclusions that can be acted on and automating time-consuming research in such a capricious and unpredictable environment.
Mohammad Inairat, Nema Abuhelou, Mohammed A. Afifi, Nizar Sahawneh · 6 authors
The rapid transformation of technology in financial services has greatly highlighted the need for precise and secure financial forecasting models. Nevertheless, the centralized analysis of financial data is being increasingly limited by privacy legislation and the possibility of data infringement. Federated Learning (FL) appears as a groundbreaking concept, allowing for decentralized model training over various data sources without losing the privacy of the data. The paper investigates the implementation of FL in the decentralized financial forecasting while addressing important issues such as data diversity, communication overload, and non-IID financial dataset model optimization. Using the real-world datasets we assess the efficiency of FL frameworks against the existing centralized methods, thus exposing the higher precision, safety, and ability to scale in forecast viability. The results show the promise of FL in changing the process of financial forecasting, issuing solid estimates of future events while protecting sensitive financial information. This study could be seen as an initial step towards a more widespread application of FL in finance which could lead to the promotion of innovations in secure and decentralized analysis of data.
Dalia Elbanna, Ema Izati Zull Kepili, Nik Hadiyan Nik Azman
Ethereum's anonymity and uncontrolled cryptocurrency attraction have attracted investors.Ethereum's price dynamic inspired this study's prediction analyses.Previous study has focused on either technical analysis or on-chain analysis, leaving investors without the synergistic effects of integrating the two.This study addresses missed insights and lack of cross-comparisons by identifying variable relationships and dependencies and comparing a classical model (ARIMA), a supervised deep learning model (LSTM), and an ensemble machine learning model (XGBoost) in Ethereum price prediction.The dependent variable is Ethereum price and the independent variables are opening, high, low, closing, adjusted closing, volume traded, market capitalization, cumulative return, transactions, blocks, and gas utilized.Prices and market capitalization, traded volume, and volume are strongly correlated, and the LSTM model is the most promising due to its greater prediction accuracy and generality.The analysis reveals the bitcoin market's complexity, affecting investing and risk management.
Cryptocurrencies have attracted significant attention from investors, regulators and the media since their emergence. In a world where digital advancements are increasingly included in everyday relations, studying the behaviour of cryptocurrencies and their impact on financial markets becomes a necessity. This paper introduces a comparative analysis towards a hybrid model combining classical and modern methods for predicting cryptocurrency prices. This study deals with everyday recordings of 10 cryptocurrencies that represent different technological innovations and use cases. Studying these cryptocurrencies can help understand volatility, volumes and price movements. We aim to develop a time series statistical model and to study the effectiveness of deep learning (DL) models, specifically long short-term memory (LSTM) model and the autoregressive integrated moving average (ARIMA) model, for predicting cryptocurrency prices accurately and forecasting stationary data. Combining ARIMA and LSTM, we managed to obtain a high value of R² for Binance Coin (BNB) cryptocurrency (0.936) with an average R² for all evaluated cryptocurrencies of 0.6555.
Volatility in the cryptocurrency market poses a significant challenge to traders' ability to identify patterns while using it with all those uncertainties. The traditional methods that have been the starting point now increasingly become inadequate because they depend on manual analysis, man's emotional biases, and lack the capacity for real-time data processing. This paper suggests an innovative automated trading system based on sentiment analysis and advanced AI technologies to overcome these deficiencies. The system combines natural language processing with robust backend architecture to process unstructured sentiment data from various sources for trading strategy decisionmaking. This proposed method would be seamless, efficient, and scalable to empower traders to make real-time data-driven decisions. The architecture and methodology are described with a critical analysis of traditional trading practices and the advantages offered by automation. This paper will explore the potential of how AI-driven trading systems can be used to revamp the spectrum of cryptocurrencies, so that it may be accurate, adaptable, and usercentred.
Cryptocurrencies have become a significant asset class, attracting considerable attention from investors and researchers due to their potential for high returns despite inherent price volatility. Traditional forecasting methods often fail to accurately predict price movements as they do not account for the non-linear and non-stationary nature of cryptocurrency data. In response to these challenges, this study introduces the Helformer model, a novel deep learning approach that integrates Holt-Winters exponential smoothing with Transformer-based deep learning architecture. This integration allows for a robust decomposition of time series data into level, trend, and seasonality components, enhancing the model’s ability to capture complex patterns in cryptocurrency markets. To optimize the model’s performance, Bayesian hyperparameter tuning via Optuna, including a pruner callback, was utilized to efficiently find optimal model parameters while reducing training time by early termination of suboptimal training runs. Empirical results from testing the Helformer model against other advanced deep learning models across various cryptocurrencies demonstrate its superior predictive accuracy and robustness. The model not only achieves lower prediction errors but also shows remarkable generalization capabilities across different types of cryptocurrencies. Additionally, the practical applicability of the Helformer model is validated through a trading strategy that significantly outperforms traditional strategies, confirming its potential to provide actionable insights for traders and financial analysts. The findings of this study are particularly beneficial for investors, policymakers, and researchers, offering a reliable tool for navigating the complexities of cryptocurrency markets and making informed decisions.
Bitcoin is a decentralised digital currency that has been in existence for some time now. Its value has been volatile, with most of its prices fluctuating significantly, making it difficult to predict its prices when investing. This paper employed a Neural Basis Expansion Analysis Time Serie (N-BEATS) deep learning architecture to predict Bitcoin prices. The model was chosen because of its proven capabilities of modelling intricate patterns in time series data. An hourly Bitcoin price data of 729 days collected from Yahoo Finance extensively assesses the N-BEATS model's performance while comparing it with other machine learning models like the Linear Regression and the long-short-term-memory (LSTM) networks. Mean Absolute Error (MAE) and R-squared (R²) were utilised as performance evaluation metrics. N-BEATS surpassed the others by providing an R² score of 0.00240 and an MAE score of 0.9998. These findings are significant and shed light on how deep learning models can be used for financial forecasting. The result shows that the N-BEATS model is more accurate and reliable for predicting cryptocurrencies' prices, which may be very useful for investors in making decisions and managing risks.
Brij B. Gupta, Akshat Gaurav, Juan Piñeiro Chousa, Ángeles López Cabarcos · 5 authors
This study suggests a method for forecasting Decentralised Finance (DeFi). In order to represent DeFi cryptocurrencies, we have used DeFi Pulse Index (DPI) that tracks the performance of some of the largest protocols in the DeFi. To get prices and sentiment analysis from social media, we used the LunarCRUSH dataset. We then conducted a time series study of DPI price using a Bi-LSTM and Long Short-Term Memory (LSTM) model and compared it withwith LSTM, BiLSTM, and GRU models. The mean square error of our proposed model is 0.0005745, and the mean absolute error is 0.01891.