Crypto Price Monitoring Dataset for On-chain Derivatives Research

# Crypto Price Monitoring Repository

This repository contains two CSV data files that were created to support the research titled "Price Arbitrage for DeFi Derivatives." This research is to be presented at the IEEE International Conference on Blockchain and Cryptocurrencies, taking place on 5th May 2023 in Dubai, UAE. The data files include monitoring prices for various crypto assets from several sources. The data files are structured with five columns, providing information about the symbol, unified symbol, time, price, and source of the price.

## Data Files

There are two CSV data files in this repository (one for each date):

1.  `Pricemon_results_2023_01_13.csv`
2.  `Pricemon_results_2023_01_14.csv`

## Data Format

Both data files have the same format and structure, with the following five columns:

1.  `symbol`: The trading symbol for the crypto asset (e.g., BTC, ETH).
2.  `unified_symbol`: A standardized symbol used across different platforms.
3.  `time`: Timestamp for when the price data was recorded (in UTC format).
4.  `price`: The price of the crypto asset at the given time (in USD).
5.  `source`: The name of the price source for the data.

## Price Sources

The `source` column in the data files refers to the provider of the price data for each record. The sources include:

-   `chainlink`: Chainlink Price Oracle
-   `mycellium`: Built-in oracle of the Mycellium platform
-   `bitfinex`: Bitfinex cryptocurrency exchange
-   `ftx`: FTX cryptocurrency exchange
-   `binance`: Binance cryptocurrency exchange

## Usage

You can use these data files for various purposes, such as analyzing price discrepancies across different sources, identifying trends, or developing trading algorithms. To use the data, simply import the CSV files into your preferred data processing or analysis tool.

### Example

Here's an example of how you can read and display the data using Python and the pandas library:

       import pandas as pd

       # Read the data from CSV file
       data = pd.read_csv('Pricemon_results_2023_01_13.csv')

       # Display the first 5 rows of the data
       print(data.head())` 

## Acknowledgements

These datasets were recorded and supported by Datamint company (value-added on-chain data provider) and its team.

## Contributing

If you have any suggestions or find any issues with the data, please feel free to contact authors.