Processes bitcoin binary data to flat file formats suitable for import to other development/analysis tools. Licensed under standard apache open source license. Attribution is appreciated.

The Bitcoin currency is a publicly available, transparent, large scale network in which every single transaction can be analysed. Multiple tools are used to extract binary information, pre-process data and train machine learning models from the decentralised blockchain. As Bitcoin popularity increases both with consumers and businesses alike, this paper looks at the threat to privacy faced by users through commercial adoption by deriving user attributes, transaction properties and inherent idioms of the network. We define the Bitcoin network protocol, describe heuristics for clustering, mine the web for publicly available user information and finally train supervised learning models. We show that two machine learning algorithms perform successfully in clustering the Bitcoin transactions based on only graphical metrics measured from the transaction network. The Logistic Regression algorithm achieves an F1 score of 0.731 and the Support Vector Machines achieves an F1 score of 0.727. This work demonstrates the value of machine learning and network analysis for business intelligence; on the other hand it also reveals the potential threats to user privacy.

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 🍏

ادات جلب معلوماتUSES SpiderFoot can be used offensively (e.g. in a red team exercise or penetration test) for reconnaissance of your target or defensively to gather information about what you or your organisation might have exposed over the Internet. You can target the following entities in a SpiderFoot scan: IP address Domain/sub-domain name Hostname Network subnet (CIDR) ASN E-mail address Phone number Username Person's name Bitcoin address SpiderFoot's 200+ modules feed each other in a publisher/subscriber model to ensure maximum data extraction to do things like: Host/sub-domain/TLD enumeration/extraction Email address, phone number and human name extraction Bitcoin and Ethereum address extraction Check for susceptibility to sub-domain hijacking DNS zone transfers Threat intelligence and Blacklist queries API integration with SHODAN, HaveIBeenPwned, GreyNoise, AlienVault, SecurityTrails, etc. Social media account enumeration S3/Azure/Digitalocean bucket enumeration/scraping IP geo-location Web scraping, web content analysis Image, document and binary file meta data analysis Dark web searches Port scanning and banner grabbing Data breach searches So much more... INSTALLING & RUNNING To install and run SpiderFoot, you need at least Python 3.6 and a number of Python libraries which you can install with pip. We recommend you install a packaged release since master will often have bleeding edge features and modules that aren't fully tested. Stable build (packaged release): $ wget https://github.com/smicallef/spiderfoot/archive/v3.3.tar.gz $ tar zxvf v3.3.tar.gz $ cd spiderfoot ~/spiderfoot$ pip3 install -r requirements.txt ~/spiderfoot$ python3 ./sf.py -l 127.0.0.1:5001 Development build (cloning git master branch): $ git clone https://github.com/smicallef/spiderfoot.git $ cd spiderfoot $ pip3 install -r requirements.txt ~/spiderfoot$ python3 ./sf.py -l 127.0.0.1:5001 Check out the documentation and our asciinema videos for more tutorials. COMMUNITY Whether you're a contributor, user or just curious about SpiderFoot and OSINT in general, we'd love to have you join our community! SpiderFoot now has a Discord server for chat, and a Discourse server to serve as a more permanent knowledge base.

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lil' data analysis on the block chain

Artifical neural network that executes trades on the BTC-e bitcoin exchange upon analysis of historical trade data.

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.

📉📈Bitcoin orderbook data collection and analysis. Just some fun data science stuff

Basic time series analysis and trading strategy with Bitcoin price data

: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.

Tools for analyzing data on Bitcoin and Lightning Network

Deciphering Bitcoin Blockchain Data by Cohort Analysis

Analysis of speculative/bubble-like behaviour using LPPL model (exponential accelerations with oscillations). Fitted to Bitcoin data. Submitted for my university thesis at Cambridge, 1st class awarded.

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

Data Analysis for Elliptic Datasets in Bitcoin

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

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.

Bitcoin Transaction Data Analysis System.

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

In these Python and R scripts, markdown and notebooks, I provide a basis for understanding how the cryptocurrencies market works, as well as how we can divide it into categories, supported by a hierarchical clustering. Other contributions regard the accurate prediction of Bitcoin and Ether Prices one day ahead and nice data bases with which to play around and test my results, if wanted.

The on-chain data crawler supports the collection and analysis of data on Bitcoin, Eth, and Polkadot chain.

The data used in the experiment in Analysis of Bitcoin Price Prediction Using Machine Learning (jrfm-2156907).

A predictive model for forecasting the hourly Bitcoin price movements using sentiment analysis drawn from extracted from Twitter data and tree based algorithms. The resulting predictive model utilises an XGBoost gradient boosted random forest model which demonstrates a 63.16% accuracy in predicting the hourly price movements of Bitcoin.

A PHP script that ingests Bitcoin/Altcoin blockchain data into MongoDB for analysis

Data analysis for bitcoin and other cryptocurrencies

Analysis of Bitcoin mining economics using publically available data in R.

Analysing Bitcoin's price movements pre-2024 halving using Python, data analysis and machine learning to forecast future trends in cryptocurrency markets.