A visualization analysis tool for price bubble of Bitcoin, including basic price information, 60-days accumulative increase, hot keywords index, and bubble index.

Predicts real-time bitcoin price using twitter and reddit sentiment, and sends out notifications via SMS.

[NOT MAINTAINED] Predicting Bit coin price using Time series analysis and sentiment analysis of tweets on bitcoin

AI multi-agent system for comprehensive Bitcoin (BTC) analysis, combining financial news, market performance, and AI-driven price predictions for investment recommendations.

Bitcoin price prediction using both traditonal machine learning and deep learning techniques, based on historical price and sentiment extracted from Twitter posts. Fear of missing out analysis after Elon Musk tweeted about Dogecoin.

Bitcoin price prediction using twitter sentiment analysis

Bitcoin dashboard with real-time price data, TradingView chart, order book, market analysis, Twitter/X insights, and halving countdown. Clean, ad-free, responsive design.

💰 Coin Tracker 🚁 Real Time 🛸 Cryptocurrency 🚢 Market Tracker 🚀data Bitcoin 🚟 Ethereum and ⛴ Interactive charts 🛼 and graphs ☂price volume 🎳 and market 🏩 cap analysis 🏪 Secure storage 🏰 with local 🚅 caching Firebase 🕌 integration Support 🚋 light themes 🏯 fiat currency 🚒 conversion and 🍑 multi language 🍏

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Time Series analysis with Python and ARIMA model to forecast Bitcoin price

Identify the software project, create business case, arrive at a problem statement. REQUIREMENT: Window XP, Internet, MS Office, etc. Problem Description: - 1. Introduction of AI and Machine Learning: - Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems. Artificial intelligence (AI) brings the genuine human-to-machine interaction. Simply, Machine Learning is the algorithm that give computers the ability to learn from data and then make decisions and predictions, AI refers to idea where machines can execute tasks smartly. It is a faster process in learning the risk factors, and profitable opportunities. They have a feature of learning from their mistakes and experiences. When Machine learning is combined with Artificial Intelligence, it can be a large field to gather an immense amount of information and then rectify the errors and learn from further experiences, developing in a smarter, faster and accuracy handling technique. The main difference between Machine Learning and Artificial Intelligence is , If it is written in python then it is probably machine learning, If it is written in power point then it is artificial intelligence. As there are many existing projects that are implemented using AI and Machine Learning , And one of the project i.e., Bitcoin Price Prediction :- Bitcoin (₿ ) (founder - Satoshi Nakamoto , Ledger start: 3 January 2009 ) is a digital currency, a type of electronic money. It is decentralized advanced cash without a national bank or single chairman that can be sent from client to client on the shared Bitcoin arrange without middle people's requirement. Machine learning models can likely give us the insight we need to learn about the future of Cryptocurrency. It will not tell us the future but it might tell us the general trend and direction to expect the prices to move. These machine learning models predict the future of Bitcoin by coding them out in Python. Machine learning and AI-assisted trading have attracted growing interest for the past few years. this approach is to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. the application of machine learning algorithms to the cryptocurrency market has been limited so far to the analysis of Bitcoin prices, using random forests , Bayesian neural network , long short-term memory neural network , and other algorithms. 2. Applications/Scope of AI and Machine Learning :- a) Sentiment Analysis :- It is the classification of subjective opinions or emotions (positive, negative, and neutral) within text data using natural language processing. b) It is Characterized as a use of computerized reasoning where accessible data is utilized through calculations to process or help the handling of factual information. BITCOIN PRICE PREDICTION USING AI AND MACHINE LEARNING: - The main aim of this is to find the actual Bitcoin price in US dollars can be predicted. The chance to make a model equipped for anticipating digital currencies fundamentally Bitcoin. # It works the prediction by taking the coinMarkup cap. # CoinMarketCap provides with historical data for Bitcoin price changes, keep a record of all the transactions by recording the amount of coins in circulation and the volume of coins traded in the last 24-hours. # Quandl is used to filter the dataset by using the MAT Lab properties. 3. Problem statement: - Some AI and Machine Learning problem statements are: - a) Data Privacy and Security: Once a company has dug up the data, privacy and security is eye-catching aspect that needs to be taken care of. b) Data Scarcity: The data is a very important aspect of AI, and labeled data is used to train machines to learn and make predictions. c) Data acquisition: In the process of machine learning, a large amount of data is used in the process of training and learning. d) High error susceptibility: In the process of artificial intelligence and machine learning, the high amount of data is used. Some problem statements of Bitcoin Price Prediction using AI and Machine Learning: - a) Experimental Phase Risk: It is less experimental than other counterparts. In addition, relative to traditional assets, its level can be assessed as high because this asset is not intended for conservative investors. b) Technology Risks: There is a technological risk to other cryptocurrencies in the form of the potential appearance of a more advanced cryptocurrency. Investors may simply not notice the moment when their virtual assets lose their real value. c) Price Variability: The variability of the value of cryptocurrency are the large volumes of exchange trading, the integration of Bitcoin with various companies, legislative initiatives of regulatory bodies and many other, sometimes disregarded phenomena. d) Consumer Protection: The property of the irreversibility of transactions in itself has little effect on the risks of investing in Bitcoin as an asset. e) Price Fluctuation Prediction: Since many investors care more about whether the sudden rise or fall is worth following. Bitcoin price often fluctuates by more than 10% (or even more than 30%) at some times. f) Lacks Government Regulation: Regulators in traditional financial markets are basically missing in the field of cryptocurrencies. For instance, fake news frequently affects the decisions of individual investors. g) It is difficult to use large interval data (e.g., day-level, and month-level data) . h) The change time of mining difficulties is much longer. Moreover, do not consider the news information since it is hard to determine the authenticity of a news or predict the occurrence of emergencies.

Ethereum has brought us tools like Smart Contract, Dapp and DAO creation, deployment, and management. We can easily pay someone without ever hitting the send button, access decentralized applications that cannot be censored or shut down and we can be part of Decentralized Autonomous Organizations. Mistakes were made, bugs were found, and recently, millions were lost. Some are calling The DAO hack the most expensive bug bounty ever held, but whoever said this certainly didn’t have his Ether invested in The DAO, as the situation regarding the seizure of the stolen funds doesn’t seem to be improving. The DAO happened, it failed, all we can do now is move on and learn from our mistakes. The problem is that if we keep learning from $50m errors, we’ll be the wisest and poorest people on the planet. That’s why it’s good to have training wheels sometimes. Ethereum is the perfect playground for skilled developers, but with its 700% value increase since creation, it has made Solidity, one of the programming languages in Ethereum, a very expensive toy. That’s why Krypton has launched an open invitation to all developers to poke around the Krypton blockchain and see what it has to offer. Krypton (KR) is an Ethereum-based cryptocurrency that allows users all the same features and perks (Smart Contracts, Dapps, DAOs, DACs) but for a lower “price.” Ethereum transaction fees, which are known as “Gas” are spent according to computational costs, which means that the higher the price of Ether, the higher those costs will be. covertress, the Krypton founder, and project manager said: We’ve contacted several faculties at major universities and invited them to use the KR chain for this purpose. All of this means that developers have a testbed for smart contracts and Dapps, which are less expensive to deploy in the KR blockchain, before moving on to a more mainstream environment like Ethereum. Krypton can now be considered as a “gateway” into Ethereum. The team isn’t planning to stay humble forever but will, however, take their time before deploying anything and becoming a direct competitor to Ethereum, allowing them to tighten up security and functionality before moving on to providing smart contracts and Dapp solutions for companies. If you liked this article follow us on Twitter @themerklenews and make sure to subscribe to our newsletter to receive the latest bitcoin and altcoin price analysis and the latest cryptocurrency news. Krypton – Smart Contracts and DAPPs Development for Business Systems & IoT Ticker: KR Algorithm: Dagger-Hashimoto Block Reward: 0.25 KR Block Target: 15 Seconds Listen Port: 17171 RPC Port: 8888 Total KR: ~2.669 Million Real-Time Total KR Ethereum-Based: Utilizes Smart Contracts, DAOs, DACs and DAPPs Block Explorer: http://explorer.krypton.rocks After years in the tech sector, for engineering, entertainment, travel & finance companies, I’ve turned my focus to blockchain and building a startup, Krypton, to help companies realize their distributed applications. $KR is my vision for an ultra-fast blockchain that can realize all of the features of Ethereum with fewer initial coins, faster speed and lower inflation. Krypton can do the same things as Ethereum. However, with Ethereum’s codebase being updated to safely deploy DAOs, DACs, and DAPPs, there will be an explosion of practical-use cases, especially in the Internet of Things field. Companies will be actively seeking experienced developers. KR is an alternative platform on which to deploy these new technologies and Krypton developers are ready to build these systems. Join me in connecting Ðapps devs with real-life applications. Let’s code the future. — covertress, Founder & Project Manager Krypton is now hiring Smart Contracts and Ðapps developers with experience in Solidity, JS, and node.js. Please join Krypton’s Slack to apply: http://slack.krypton.rocks

Basic time series analysis and trading strategy with Bitcoin price data

Sentiment analysis of Reddit comments to predict bitcoin price movement

Jupyter notebook used to write the medium post: https://medium.com/@gerardmartnez/statistical-analysis-of-bitcoin-price-manipulation-by-tether-issuance-in-late-2017-b3342b8915a0

:mortar_board: Social media text data scraping for sentiment analysis (Vader, Bert), correlation study, and Bitcoin price prediction using LSTM and XGBoost, with a comparative performance analysis across various media sources like forums and group chats.

Project shows that real-time Twitter data can be used to predict market movement of Bitcoin Price. The goal of this project is to prove whether Twitter data relating to cryptocurrencies can be utilized to develop advantageous crypto coin trading strategies. By way of supervised machine learning techniques, have outlined several machine learning pipelines with the objective of identifying cryptocurrency market movement. The prominent alternative currency ex- amined in this paper is Bitcoin (BTC). Our approach to cleaning data and applying supervised learning algorithms such as logistic regression, Decision Tree Classifier, and LDA leads to a final prediction accuracy exceeding 70%. In order to achieve this result, rigorous error analysis is employed in order to ensure that accurate inputs are utilized at each step of the model.

Bitcoin pricing prediction and trading simulation through time series and sentiment analysis.

2 Hidden Markov Models, Monte Carlo and Hierarchical Clustering for Bitcoin price analysis

Codebase for paper titled: "Using Machine and Deep Learning Models, On-Chain Data, and Technical Analysis for Predicting Bitcoin Price Direction and Magnitude"

Explore this comprehensive API for Bitcoin price analysis, featuring historical data and real-time updates from major exchanges. Built with FastAPI, Python and PostgreSQL, this open-source project is optimised for efficiency and scalability catering to developers, traders and analysts. Contribute and collaborate to enhance functionality.

Term Paper on Financial Econometrics exploring the volatility in Bitcoin prices as measured using a bayesian stochastic volatility analysis and using GARCH and rolling window GARCH estimates. These methods are compared and tested for the best way forward.

[Python] Adopted Neural Network (LSTM), Time Series (ARIMA) and Sentiment Analysis (NLP) to predict Bitcoin prices

Predicted the Price of the Cryptocurrency(Bitcoin) using the past time-series data, Twitter Sentiments(Polarity and Sensitivity), Currency's Fundamentals and Technical Indicators like RSI and SMA on LSTM. The Notebook contains the Exploratory data analysis(with important links) and the astounding result at the end of it

💸 Bitcoin trading analysis

Predict Bitcoin stock prices based on Twitter tweets using sentiment analysis and Bitcoin historical stock prices

In this project, analysis and prediction of the bitcoin price was carried out as part of a project to research artificial intelligence in finance in the scope of Interactive ML course at Augsburg University

This repository is for visualize and analyze financial trading data using python. In building, we learn about stocks and commodities from different industries, such as education 教育 , technology 科技 and cybersecurity 网路安全, oil companies and oil prices 油价, and crypto currency Bitcoin 比特币. We build basic time series data analysis techniques using Python that can be applied to any stock or other time series data

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A technical analysis of price volatility in bitcoins for a over a year using 6 hour intervals