CryptoSlam!

NFT Sales

Introduction

The NFT Sales dataset by CryptoSlam! provides Non-Fungible Tokens (NFT) sales volume data in various blockchain marketplaces. This dataset covers 11 blockchains that have their own native Cryptocurrencies. The data starts in June 2017 and is delivered on a daily frequency. This dataset fetches the number of transactions, unique buyers, unique sellers, and the dollar volume of NFT transactions on all secondary marketplaces tracked by CryptoSlam, which includes owner-to-owner sales only (not initial sales from the product directly to the owners).

For more information about the NFT Sales dataset, including CLI commands and pricing, see the dataset listing.

About the Provider

CryptoSlam! is an NFT industry data aggregator backed by Mark Cuban. Features project analytics, NFT values, rarity, scarcity, most popular collections, activity history & more.

Getting Started

The following snippet demonstrates how to request data from the NFT Sales dataset:

from QuantConnect.DataSource import *

self.ethusd = self.add_crypto("ETHUSD", Resolution.DAILY, Market.BITFINEX).symbol
self.dataset_symbol = self.add_data(CryptoSlamNFTSales, "ETH").symbol
using QuantConnect.DataSource;

_symbol = AddCrypto("ETHUSD", Resolution.Daily, Market.Bitfinex).Symbol;
_datasetSymbol = AddData<CryptoSlamNFTSales>("ETH").Symbol;

Data Summary

The following table describes the dataset properties:

PropertyValue
Start DateJune 2017
Asset Coverage11 blockchains
Data DensitySparse
ResolutionDaily
TimezoneUTC

Example Applications

The NFT Sales dataset enables you to incorporate NFT sales information into your strategies. Examples include the following strategies:

  • Studying the correlation between the supply-demand trend of NFTs and the price changes of the underlying Cryptocurrencies.
  • Measuring the activity/popularity blockchains to provide insight on the future price movements of the underlying Cryptocurrencies.

Data Point Attributes

The NFT Sales dataset provides CryptoSlamNFTSales objects, which have the following attributes:

Supported Blockchains and Symbols

The following table shows the blockchains tracked in the NFT Sales dataset:

SymbolBlockchain RepresentedStart Date (yyyy-mm-dd)
AVAXAvalanche2021-09-01
CROCronos2021-12-18
ETHEthereum2017-06-23
FLOWFlow2020-07-28
FTMFantom2021-09-15
MATICPolygon2021-03-01
SOLSolana2021-08-05
THETATheta2021-06-24
WAVEWaves2021-06-03
WAXWax2020-03-16
XTZTezos2021-03-01

Requesting Data

To add NFT Sales data to your algorithm, call the AddDataadd_data method. Save a reference to the dataset Symbol so you can access the data later in your algorithm.

class CryptoSlamNFTSalesAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2019, 1, 1)
        self.set_end_date(2020, 6, 1)
        self.set_cash(100000)

        self.ethusd = self.add_crypto("ETHUSD", Resolution.DAILY, Market.BITFINEX).symbol
        self.dataset_symbol = self.add_data(CryptoSlamNFTSales, "ETH").symbol
namespace QuantConnect
{
    public class CryptoSlamNFTSalesAlgorithm: QCAlgorithm
    {
        private Symbol _symbol, _datasetSymbol;

        public override void Initialize()
        {
            SetStartDate(2019, 1, 1);
            SetEndDate(2020, 6, 1);
            SetCash(100000);

            _symbol = AddCrypto("ETHUSD", Resolution.Daily, Market.Bitfinex).Symbol;
            _datasetSymbol = AddData<CryptoSlamNFTSales>("ETH").Symbol;
        }
    }
}

Accessing Data

To get the current NFT Sales data, index the current Slice with the dataset Symbol. Slice objects deliver unique events to your algorithm as they happen, but the Slice may not contain data for your dataset at every time step. To avoid issues, check if the Slice contains the data you want before you index it.

def on_data(self, slice: Slice) -> None:
    if slice.contains_key(self.dataset_symbol):
        data_point = slice[self.dataset_symbol]
        self.log(f"{self.dataset_symbol} unique buyers at {slice.time}: {data_point.unique_buyers}")
public override void OnData(Slice slice)
{
    if (slice.ContainsKey(_datasetSymbol))
    {
        var dataPoint = slice[_datasetSymbol];
        Log($"{_datasetSymbol} unique buyers at {slice.Time}: {dataPoint.UniqueBuyers}");
    }
}

To iterate through all of the dataset objects in the current Slice, call the Getget method.

def on_data(self, slice: Slice) -> None:
    for dataset_symbol, data_point in slice.get(CryptoSlamNFTSales).items():
        self.log(f"{dataset_symbol} unique buyers at {slice.time}: {data_point.unique_buyers}")

public override void OnData(Slice slice)
{
    foreach (var kvp in slice.Get<CryptoSlamNFTSales>())
    {
        var datasetSymbol = kvp.Key;
        var dataPoint = kvp.Value;
        Log($"{datasetSymbol} unique buyers at {slice.Time}: {dataPoint.UniqueBuyers}");
    }
}

Historical Data

To get historical NFT Sales data, call the Historyhistory method with the dataset Symbol. If there is no data in the period you request, the history result is empty.

# DataFrame 
history_df = self.history(self.dataset_symbol, 100, Resolution.DAILY)

# Dataset objects
history_bars = self.history[CryptoSlamNFTSales](self.dataset_symbol, 100, Resolution.DAILY)
var history = History<CryptoSlamNFTSales>(_datasetSymbol, 100, Resolution.Daily);

For more information about historical data, see History Requests.

Remove Subscriptions

To remove a subscription, call the RemoveSecurityremove_security method.

self.remove_security(self.dataset_symbol)
RemoveSecurity(_datasetSymbol);

Example Applications

The NFT Sales dataset enables you to incorporate NFT sales information into your strategies. Examples include the following strategies:

  • Studying the correlation between the supply-demand trend of NFTs and the price changes of the underlying Cryptocurrencies.
  • Measuring the activity/popularity blockchains to provide insight on the future price movements of the underlying Cryptocurrencies.

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