The_Dark_Side_of_NFTs: ALarge-Scale Empirical Study of Wash Trading

source: https://drive.google.com/drive/folders/1yzLbQURsv-bc9EU4q5BR9cmDmGCodzlY?usp=sharing

data

• collectionRaw.zip: the raw data of event sequences of 285 NFT collections with address inconsistency, timestamp error and fake events collected by OpenSea API
• collectionPreprocessed.zip: the preprocessed data of event sequences of 285 NFT collections with improved consisRate
• ERC-20Price.zip: historical market price of 2,982 ERC-20 tokens collected by CoinGecko API
• pickle_files.zip:
  • wins_timeWindowInfo.pickle: segmented time windows for event sequences
  • Txn_relatedNormalTransactions.pickle: selected block transactions
  • Txn20_relatedERC20Transactions.pickle: selected ERC-20 transactions
  • real_cir_circlesofTransactionSequences.pickle: details of all cycles in each time window
  • mr_mergeTransactionSequences.pickle: details of the merged edges
  • g_graphTransactionSequences.pickle: directed graph constructed by merged edges.
  • fs_validfile.pickle: names of NFT collections
  • flow_relatedNormalTransactions.pickle: details for selected block transactions
  • flow20_relatedERC20Transactions.pickle: details for selected ERC-20 transactions
  • eth20P_erc20TokenHistoricalPrice.pickle: historical price for ERC-2O tokens

code

• collection_slug.py: to run collection_slug.py to retrieve event sequences of NFT collections, firstly change the file name with the collection_slug of the NFT collection (See example in the following figure / retrieve collecton_slug via OpenSea API).
  python3 -u collection_slug.py -s start_time -e end_time >collection_slug.log 2>&1
• preprocess&overview.py：python3 preprocess&overview.py