LSTM-RNN Tutorial with LSTM and RNN Tutorial with Demo with Demo Projects such as Stock/Bitcoin Time Series Prediction, Sentiment Analysis, Music Generation using Keras-Tensorflow

Golang bitcoin trading bot, it features multiple trading methods using technical analysis

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

Analysis of High Frequency Trading on Bitcoin exchanges

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

Analysis of Bitcoin's current and future transaction throughput bottlenecks.

Analysis of Twitter Sentiment to discover correlations with Bitcoin and other cryptocurrencies

API for predicting the next Bitcoin and Ethereum with machine learning and technical analysis

Real-time analysis of bitcoin markets with Kafka and Tensorflow Serving

[HackIllinois 17 Winner] Bitcoin Trading Algorithm Backtesting and Analysis Toolkit

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

Python Bitcoin is widely used cryptocurrency for digital market. It is decentralised that means it is not own by government or any other company.Transactions are simple and easy as it doesn’t belong to any country.Records data are stored in Blockchain.Bitcoin price is variable and it is widely used so it is important to predict the price of it for making any investment.This project focuses on the accurate prediction of cryptocurrencies price using neural networks. We’re implementing a Long Short Term Memory (LSTM) model using keras; it’s a particular type of deep learning model that is well suited to time series data (or any data with temporal/spatial/structural order e.g. movies, sentences, etc.).We have used different activation function for analysing the efficiency of the system.Instead of historical data we are using live streaming data for better accuracy.

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.

Bithoven is a smart contract language for composing powerful and secure instruments on Bitcoin. LR(1) parser with static analysis for compile-time safety. Formal verification paper: https://arxiv.org/abs/2601.01436

Lightweight Bitcoin node connection and analysis CLI tool

Onchain security and performance analysis of Bitcoin's second-layer protocols

A collection of cryptocurrency news. Day to day financial charts of top 10 cryptocurrencies (Bitcoin, litecoin, monero ...). Google trend analysis and geographical interests. Bitcoin timeline. News collection from major cryptocurrency news sites such as CoinDesk, Bitcoin Magazine, etc.

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

A Cryptoeconomic Traffic Analysis of Bitcoin's Lightning Network

比特币源代码分析

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 🍏

This repository contains code for BABD, as detailed in our paper published in IEEE TIFS

Analysis of LSTM and Deep-Learning for machine-learning guided Bitcoin Trading.

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.

Bitcoin blockchain graph analysis with the GraphBLAS.

Blockchain graph analysis tools for Bitcoin. In collaboration w. Aurora Guo.