In the rapidly evolving domain of cryptocurrency trading, accurate market data analysis is crucial for informed decision making. Candlestick patterns, a cornerstone of technical analysis, serve as visual representations of market sentiment and potential price movements. However, the sheer volume and complexity of cryptocurrency price time-series data presents a significant challenge to traders and analysts alike. This paper introduces an innovative rule-based methodology for recognizing candlestick patterns in cryptocurrency markets using Python. By focusing on Ethereum, Bitcoin, and Litecoin, this study demonstrates the effectiveness of the proposed methodology in identifying key candlestick patterns associated with significant market movements. The structured approach simplifies the recognition process while enhancing the precision and reliability of market analysis. Through rigorous testing, this study shows that the automated recognition of these patterns provides actionable insights for traders. This paper concludes with a discussion on the implications, limitations, and potential future research directions that contribute to the field of computational finance by offering a novel tool for automated analysis in the highly volatile cryptocurrency market.

This article presents an analysis of the historical dynamics of the cryptocurrency market based on time series data using the OHLCV dataset. The study presents the results of calculations of the main cryptocurrency market momentum technical indicators. Using intelligent computational methods, the paper assesses patterns and trends in the data of major cryptocurrencies. The study emphasizes the importance of technical analysis in understanding the volatile landscape of digital currencies. Key words: time-series, data analysis, cryptocurrency market, momentum indicators, technical analysis indicators, OHLCV.

Streaming decision support systems frequently face two coupled operational constraints: multimodal input streams are non-stationary and periodically exhibit modality degradation (missing values, additive noise, gain or scale shifts), while alarm frequency must be limited by an explicit false alarm rate (FAR) budget. Naive multimodal residual scoring then inflates anomaly scores during degraded segments, pushing FAR-constrained thresholds upward and reducing detector sensitivity to genuine anomaly events. The aim is to design and empirically validate a residual scoring policy that retains anomaly sensitivity under alternating modality degradation at a fixed FAR budget. The research tasks are to formalise the streaming multimodal anomaly detection problem under a FAR budget in a causal setting, to design an online modality reliability estimator from cheap causal degradation cues (missingness and feature-energy inflation), to construct a reliability-gated rule for selecting the source of the residual evidence, and to validate the approach on a controlled stream and on the UCI Air Quality dataset. The paper proposes RC-AD, a reliability-constrained residual scoring policy that combines standardised residual scores from modality-specific and early-fusion multimodal predictors, online modality reliability weights estimated from short causal windows using the missingness and feature-energy inflation, and a winner-takes-all rule: in clean windows the early-fusion residual score is used, otherwise the residual score of the currently more reliable modality. RC-AD is a control policy on top of an arbitrary forecasting backbone, which makes it lightweight, auditable, and easy to integrate into existing monitoring pipelines. The empirical study uses a causal protocol with ten fixed seeds and 95% confidence intervals: on a controlled benchmark with alternating modality degradation and injected event anomalies, RC-AD improves Recall@FAR from 0.103 to 0.335 at FAR 0.05 and from 0.182 to 0.426 at FAR 0.10, outperforming naive multimodal, equal-weight fusion and single-modality baselines; a demonstration on UCI Air Quality at FAR 0.01 confirms the same trend against the multimodal baselines. The scientific novelty consists in formulating a multimodal anomaly detection policy with an explicit online reliability constraint that, for the first time, combines gating of early-fusion evidence by causal degradation cues with selection of the most reliable single modality at a prescribed FAR budget. The practical significance is supported by reproducible Recall@FAR improvements on both controlled and real sensor data and by the lightness and interpretability of the method, which suits auditable decision support systems Keywords: machine learning, data analysis, information systems, decision support systems, multimodal time series, non-stationary time series, streaming anomaly detection, data quality degradation, modality reliability estimation, false alarm rate budget.

This paper examines rollover activity in European government bond futures through a rollover triangle comprising the nearest contract, the second-nearest contract, and the calendar spread linking the two. While the rollover literature has largely focused on the migration of activity from the expiring contract to the second-nearest contract, we investigate whether the spread market develops liquidity characteristics of its own rather than simply reflecting liquidity in the outright contracts. Using ultra-high-frequency limit order book data from EUREX, comprising more than 127 million updates across nine sovereign bond futures, we analyse market behaviour during the 10 trading days preceding contract expiry. We document a systematic reallocation of liquidity within the rollover triangle as expiry approaches. Order-book activity and update intensity migrate from the nearest contract to the second-nearest contract, while the spread book attracts a substantial share of displayed liquidity. Direct execution in the spread book is typically cheaper than constructing the same position synthetically through the two outright contracts and, in the final stages of the rollover, often exhibits tighter quoted spreads than the expiring contract itself. Evidence from activity, update intensity, displayed liquidity, liquidity quality, and execution efficiency suggests that the spread market functions as an autonomous liquidity pool rather than a passive combination of its constituent legs. The findings challenge the conventional view of rollovers as a bilateral transition between two futures contracts and instead highlight the spread book as a distinct liquidity venue within the rollover process. Cross-country and maturity differences further suggest that sovereign risk, investor composition, and duration exposure influence rollover behaviour.