Academic literature on the topic 'Bitcoin trend App'

Objective.Analyze the utility of an algorithmic trading system based on artificial intelligence models that uses Google Trends as predictor of dozens of financial terms, to predict the evolution of S&P 500 index and Bitcoin. Methodology. A trading algorithmic system has been developed that opens a weekly long or short position in S&P 500 and Bitcoin, following the signals issued by an artificial intelligence model that uses Google Tends as predictor for next week market trend. The artificial intelligence models were trained using weekly data from 2013 to 2018 and have been tested in a prospective way from February 2018 to December 2021. Results. Google Trends is a good predictor for global investors’ mood. The artificial intelligence algorithmic trading systems tested in a prospective way has been profitable. Trading strategies based on investors’ mood have been more accurate and profitable for Bitcoin (beating the evolution of the cryptocurrency) than for S&P 500 (not beating the index). Conclusions. This evidence opens a new field for the investigation of trading systems based on big data instead of Chartism. Although there are many trading systems based on Chartism, there are no artificial intelligence trading syste

In this paper we analyze the existence of cointegrating relationships between Bitcoin, S&P 500, and the quantity of money M2. We perform our analysis with and without applying time warping pre-processing. In all cases we find strong evidence that, in the period 2016-2021 the three time series show two cointegrating relationships and therefore share a common stochastic trend. In addition, a low correlation between Bitcoin and S&P 500 is detected. These finding justify the increased interest of investors in Bitcoin as an alternative asset class. The economic interpretation is that the stock valuation is primarily determined by financial phenomena, in particular the availability of large quantity of money. Money supporting investment is due both to the actions of Quantitative Easing and to the exchange of creditor/debtor role that took place between households and firms. The price of both Bitcoin and stocks is increasingly influenced by the amount of money in circulation and follows the same stochastic trend.

Bitcoin’s market structure underwent a fundamental and irreversible transformation following the 2024 regulatory approval and launch of spot Exchange-Traded Funds (ETFs) in the United States. This event catalyzed an unprecedented wave of institutional adoption, signaling the asset's maturation from a fringe, retail-driven speculative vehicle into an emergent institutional-grade macro-asset. This study moves beyond traditional cyclical models, which are predicated on historical, pre-institutional market dynamics, to analyze Bitcoin's valuation within this profoundly evolved landscape. The primary objective is to project the potential price apex for Bitcoin in the 2024-2025 market cycle by developing and applying a transparent, replicable, and comprehensive multi-factor analytical framework. A multi-factorial, longitudinal analysis was conducted using a combination of publicly available data and simulated datasets from Q1 2022 to Q2 2025. The model is built upon a structured, semi-quantitative framework designed to synthesize three core analytical pillars: (1) Macroeconomic Environment, quantitatively assessing the impact of Federal Reserve interest rate policy, US Dollar Index (DXY) dynamics, and inflation trends through correlation analysis and sensitivity modeling. (2) On-Chain Intelligence, utilizing a suite of metrics from primary sources like Glassnode, including MVRV Z-Score, LTH-SOPR, and Illiquid Supply growth, while critically evaluating the continued validity of their historical thresholds. (3) Market & Flow Dynamics, which integrates technical analysis with a rigorous, quantitative assessment of spot ETF demand versus daily new supply, moving beyond subjective interpretations of price charts. A transparent weighting rubric was developed to integrate the findings from each pillar, mitigating subjective bias and ensuring the analytical synthesis is replicable. The synthesis of the model's components revealed a powerful confluence of bullish factors projected to intensify through late 2024 and into 2025. The Macroeconomic pillar scored moderately positive, forecasting a probable shift to monetary easing. The On-Chain pillar registered a strongly positive score, driven by a profound and persistent supply shock, evidenced by record illiquid supply growth and sustained exchange outflows, indicating strong holder conviction. The Market & Flow Dynamics pillar also scored strongly positive, with institutional demand via ETFs consistently outstripping newly mined supply by a significant multiple. The model's base-case scenario, derived from the weighted synthesis of these pillars, projects a Bitcoin price apex in the range of $150,000 to $200,000, with the most probable timing for this peak occurring between Q4 2024 and Q2 2025. In conclusion, the findings indicate that the 2024-2025 Bitcoin market cycle is fundamentally distinct from its predecessors, primarily driven by a structural, institutional-led demand shock that interacts with, and is amplified by, traditional macroeconomic tailwinds and established cyclical patterns. The projected price apex reflects a market structure that has matured, with future cycles likely to be more influenced by global liquidity conditions than the halving event alone. This research provides a robust, transparent, and theoretically grounded framework for valuing Bitcoin in its new role within the global financial system and offers a template for future analysis of digital assets as they integrate with traditional finance.

This research is the first attempt to customize a trading system that is based on second order stochastic dominance (SSD) to five known cryptocurrencies’ daily data: Bitcoin, Ethereum, XRP, Binance Coin, and Cardano. Results show that our system can predict price trends of cryptocurrencies, trade them profitably, and in most cases outperform the buy and hold (B&H) simple strategy. Our system’s best performance was achieved trading XRP, Binance Coin, Ethereum, and Bitcoin. Although our system has also generated a positive net profit (NP) for Cardano, it failed to outperform the B&H strategy. For all currencies, the system better predicted long trends than short trends.

In this paper, we use Analytic Hierarchy Process (AHP) to determine the appropriate weights and establish a bull market and bear market judgment indicator and investment risk models. Secondly, the Autoregressive Integrated Moving Average model (ARIMA) is constructed to make the expected trend for the next five years, and the optimal asset portfolio of gold assets and bitcoin assets is constructed through quadratic programming, and the Dynamic Programming (DP) model is used to compare strategies between two trades. Finally, the rationality of the model calculation is verified by the test of Long-Short-Term Memory (LSTM).

This paper explores the growing perception of Bitcoin as “digital gold” by examining its economic characteristics, historical evolution, and role in modern investment strategies. While gold has long served as a safe-haven asset and store of value, Bitcoin, launched in 2009, is a relatively new and volatile digital asset. However, both share several core attributes: limited supply, difficulty of production, and their appeal in times of economic uncertainty. The study conducts a comparative analysis of Bitcoin and gold, focusing on shared traits such as scarcity, production effort, and store-of-value properties, while also highlighting key differences in tangibility, regulatory environment, volatility, and energy consumption. Empirical research from recent years suggests a shift in how investors perceive Bitcoin, particularly after 2017. Studies such as Zwick & Syed (2019) identify a structural transformation in the correlation between Bitcoin and gold, with the two assets now increasingly moving in parallel, especially during periods of financial turmoil. Despite these convergences, Bitcoin faces significant challenges. Its scalability limitations, legal uncertainties, high energy demands, and dependency on market-driven mining incentives raise questions about its long-term viability and security. Moreover, the fragmented regulatory landscape across jurisdictions hinders its adoption as a fully mature financial instrument. Yet, there are promising signs of market maturation, such as institutional adoption, improved financial infrastructure, and regulatory clarity in certain regions. Ultimately, the paper concludes that Bitcoin has not yet fully attained the status of digital gold, but current trends suggest a gradual convergence in that direction. If it succeeds in stabilizing its volatility, reducing environmental impact, and achieving global regulatory coherence, Bitcoin could become a complementary store of value to gold, playing an integral role in the evolving architecture of the digital financial economy.

<abstract> <p>This research attempts to fit a polynomial auto regression (PAR) model to intraday price data of four major cryptocurrencies and convert the model into a real-time profitable automated trading system. A PAR model was constructed to fit cryptocurrencies' behavior and to attempt to predict their short-term trends and trade them profitably. We used machine learning (ML) procedures enabling our system to train using minutes' data for six months and perform actual trading and reporting for the next six months. Results have shown that our system has dramatically outperformed the naive buy and hold (B & H) strategy for all four examined cryptocurrencies. Results show that our system's best performances were achieved trading Ethereum and Bitcoin and worse trading Cardano. The highest net profit (NP) for Bitcoin trades was 15.58%, achieved by using 67 minutes bars to form the prediction model, compared to −44.8% for the B & H strategy. Trading Ethereum, the system generated 16.98% NP, compared to −33.6% for the B & H strategy, 61 minutes bars. Moreover, the highest NPs achieved trading Binance Coin (BNB) and Cardano were 9.33% and 4.26%, compared to 0.28% and −41.8% for the B & H strategy, respectively. Furthermore, the system better predicted Ethereum and Cardano uptrends than downtrends while it better predicted Bitcoin and BNB downtrends than uptrends.</p> </abstract>

E-currency is a form of digital currency that employs encryption to safeguard transactions, limit the manufacture of new units, and verify asset transfers. Bitcoin exchange rates and returns are the primary subjects of this study. In order to measure volatility, the standard deviation of logarithmic returns is determined. This study used a special test to determine whether or not the data were normal. Findings of high volatility were also made using a plot, a statistical process control chart, and other methods. Normality test (casual test) has been investigated accordingly to approve and validate the results. The F-test has been considered as the main indicator for the validity of the results. It has been based on the F-value of 9.3. As well as the financial performance has been done using the time and currency with upper and lower limits. The maximum limit is 34 with a G-value of 0.34. Furthermore, market return-based e-currency has been investigated and analyzed using free and fixed limits for both main variables time and currency. According to these data, the greatest value is 23 in fixed limit circumstances, while it is 18.4 in broad trend cases. The financial performance-based ANP method has been examined using the ANP approach with return values for the currency. The upper limit reached 544 with 0.43 as a G-value. An increasing number of people are valuing volatility. Because of the present high level of volatility, investing in Bitcoin is seen as a high-risk endeavor. The purpose of this study is to assist investors in developing a strategy that maximizes returns while minimizing risk

This study aims to analyze the impact of news related to Central Bank Digital Currency (CBDC) on the stock and cryptocurrency markets in the United States. Using the Time-Varying Parameter Vector Autoregression (TVP-VAR) method, this study examines the responses of the S&P 500 index, the CBOE Volatility Index (VIX), Bitcoin trading volume, and Ethereum trading volume to CBDC news from January 2020 to December 2023. The results indicate that CBDC news has a positive effect on stock market prices, but its impact on market volatility is negligible. Furthermore, Bitcoin and Ethereum trading volumes exhibit a declining trend in response to the rapid development of CBDC news. Although CBDC development is still in its early stages, these findings provide insights into the potential influence of CBDC news on financial market behavior, particularly in shaping investor sentiment and digital asset trading patterns. The study suggests that policymakers and investors should closely monitor CBDC developments, as they may gradually affect the financial ecosystem. Future research should further explore the long-term effects of CBDCs on financial market stability and cryptocurrency adoption.

Research background: As humanity evolves, so do the new payment options. Today, one of the most popular and powerful electronic money is Bitcoin. Over time, virtual currency is becoming one of the most widely available and used means of payment. Purpose of the article: The aim of this paper is to analyse ATMs in post offices, focusing on virtual currency ATMs. The paper also includes an analysis of available ATMs for virtual currencies and a comparison of their capabilities. Methods: We compare three most important ATM manufacturers and their most used models. It examines the use of those ATMs in post offices in Slovakia and abroad. Paper also analyses the attitude of customers of Slovak Post towards the deployment of virtual currency ATMs at its branches (on a sample of 400 respondents). Findings & Value added: In the Slovak Republic postal services are offered by several companies (19 out of 24 providers responded to our questionnaire). Slovak Post is leading provider of distribution, communication, and payment services in Slovakia. Czech Post is an example of country where virtual currency ATMs were introduced but subsequently withdrawn from post offices. In turn, Austrian Post Office is an example of a country where this system still operates and expands. While Slovak market still lacks such a service in the postal business, these trends will adapt over time. The virtual currency ATMs we have in Slovakia, are not operated by postal enterprises.

This article examines three key issues related to cryptocurrencies, with a primary focus on bitcoins, the most prominent cryptocurrency. The first issue addressed is the contention that cryptocurrencies lack qualifications to be classified as money due to the absence of legal tender recognition by the United States government. The study also evaluates the procedural frameworks employed by the Federal Reserve and the central bank of Sweden to assess the feasibility of introducing a digital currency. Additionally, the article explores the feasibility of blockchain technology as an independent and prosperous innovation, originally introduced in the bitcoin framework. The subject matter centers on digital currencies, particularly prominent examples like Bitcoin, Ethereum, and Ripple, known for their decentralized nature and blockchain technology. The study also explores blockchain wallets as digital repositories for managing various cryptocurrencies, using cryptographic techniques to secure and verify transactions while controlling new unit creation. Overall, the research aims to provide comprehensive insights into the world of digital currencies and blockchain technology.