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Dataset . 2023
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Crypto Price Monitoring Dataset for On-chain Derivatives Research

Authors: Ivan Vakhmyanin; Yana Volkovich;

Crypto Price Monitoring Dataset for On-chain Derivatives Research

Abstract

# 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_2022_11_01.csv` 2. `Pricemon_results_2022_11_08.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_2022_11_01.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.

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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