Ethereum Solidity Development Overview
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Updated
Jun 21, 2017
A cryptocurrency is a digital currency that only has value dependent on those who back it. For security, cryptocurrencies rely on blockchaining: a database organized in such a way that records are kept secure through peer-to-peer networks. Each record is kept within a block, and each block holds a timestamp and link to the block before it. The first cryptocurrency was Bitcoin, implemented in 2009 by Satoshi Nakamoto.
Ethereum Solidity Development Overview
Jupyter Notebook with examples of useful CryptoCompare API calls
Notebooks to poke around the blockchain for codebreaker 2018 challenge walkthrough
Collection of data science scripts and notebooks from :glass_of_milk:
Two Jupyter Notebooks written in Python, treating of time series analysis with ARIMA and its seasonal counterpart.
Notebooks with Time series analysis and ML algorithms applied to cryptocurrencies
A jupyter notebook with step-by-step details on how to build Nano blocks and interact with the network.
An analysis on Bitocin Prices, it includes two price prediction models to compare the sensibility of Bitcoin to underlying factors.
Jupyter Notebook that calculates a rough estimate of how much e-waste is generated per transaction by the Bitcoin main network, based on the expected lifespan and hash rate of the ASIC Antminer S19J Pro.
A repository containing a jupyter notebook meant to act as a report that includes what cryptocurrencies are on the trading market and how they could be grouped to create a classification system for investing.
Notebook to scrape 4channel imageboards and perform NLP analysis. Expanding use to identify and track emerging/new cryptocurrencies.
A python notebook which simulates the functioning of a cryptocurrency.
A python jupyter notebook using Time Series Analysis to predict the price of Solana crypto currency for the next 30 days
A Jupyter notebook that clusters cryptocurrencies by their performance in different time periods. Visualize results!
A Python Jupyter Notebook that gives a good overview of how to connect to the new Coinbase Cloud Rest API. It also shows how to add advanced trading indicators to the public data and how to visualize the data.
A Python Jupyter Notebook that gives a good overview of how to establish an authenticated connection with the Coinbase Cloud Rest API.
Jupyter notebook which connects to Coinbase pro and downloads market data of crypto currencies. Data is then transformed via Pandas. Various trading indicators are added (SMA, EMA, MACD). Data is then visualised via Plotly.