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Educational Resources
Gemini educational articles with live python code examples to demonstrate how to access different aspects of the Gemini API or perform a particular analysis are listed below:Historical OHLC Price Data includes Volume
We track and produce files for ~100+ assets in Daily, Hourly, and Minute(!) time intervals data for the spot/physical market for the US Dollar (USD) pairs. Each file is easily downloadable in CSV format and can be consumed automatically by Python scripts or other automated processes. In each file, you will find the below/following fields. This OHLC (Open/High/Low/Close) pricing data is updated each day. We are proud to offer granular minute data for FREE going back all the way to 2015 for select pairs.Symbol List for Gemini Daily and Hourly Timeframes
Symbol | Timeframe | First Date Available | Last Date Available | File Link |
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Symbol | Timeframe | First Date Available | Last Date Available | File Link |
Gemini Minute Data Broken Down by Year
Historical Trade Prints
Every transaction, regardless of size, takes place between a buyer and a seller and is recorded with the timestamp at which it occurred. These are actual historical trades; and these "trade prints" are made available below. Due to the sheer amount and size of the files, we compress the files across quarterly timeframes. Please reach out to us if you are looking for access on a more frequently updated basis.
Historical Order Books
Orderbooks are captured by taking a snapshot of the market limit orders for both buyers (bid) and sellers (ask) ~ 10 minute intervals. Please simply register FREE acct to access this sectionPredicting Zombie Cryptocurrency Assets Using Machine Learning Models
Applying Logistic Regression to Make Predictions About the Next Day in Python
Python Example to Calculate Bitcoin Supply for any Block Height
Tracking Ethereum Block Transaction Complexity and Gas Usage With On-Chain Data
How to Calculate the Bitcoin Network Hashrate for Any Date in Python
Working Example for Accessing Bitcoin or Ethereum Public On-Chain Blockchain Data in Python
Differences between On-Chain Data and Exchange Data and Related Use Cases
Predictive Power of Cryptocurrency Exchange Inflows and Our Comments
Foundational Properties of Market Volatility for Strategy Development
Combining NR4 Inside Days & Historical Volatility to Produce Trading Signals
Basana Backtesting library with CryptoDataDownload Data Sources
Modified Whiplash Trading Strategy with Buy & Sell Signals: Python