mirror of
https://github.com/FlipsideCrypto/nft-deal-score.git
synced 2026-02-06 10:56:58 +00:00
104 lines
3.4 KiB
Python
104 lines
3.4 KiB
Python
import os
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import json
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import psycopg2
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import pandas as pd
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import requests
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os.chdir('/Users/kellenblumberg/git/nft-deal-score')
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COLLECTION = 'Stoned Ape Crew'
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def thorchain():
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r = requests.get('https://thornode.ninerealms.com/thorchain/pool/TERRA.LUNA/liquidity_providers?height=5461281')
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df_luna = pd.DataFrame(r.json())
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df_luna['pre_attack_luna_value'] = df_luna.asset_deposit_value
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r = requests.get('https://thornode.ninerealms.com/thorchain/pool/TERRA.UST/liquidity_providers?height=5461281')
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df_ust = pd.DataFrame(r.json())
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df_ust['pre_attack_ust_value'] = df_ust.asset_deposit_value
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df = df_luna[['asset_address','pre_attack_luna_value']].dropna().merge(df_ust[['asset_address','pre_attack_ust_value']].dropna(), how='outer').fillna(0)
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for c in [ 'pre_attack_luna_value','pre_attack_ust_value' ]:
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df[c] = df[c].astype(int)
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df = df[ (df.pre_attack_luna_value > 0) | (df.pre_attack_ust_value > 0) ]
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df.to_csv('~/Downloads/pre_attack_terra.csv', index=False)
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def f():
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conn = psycopg2.connect("dbname=suppliers user=postgres password=postgres")
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conn = psycopg2.connect("dbname=suppliers user=postgres password=postgres")
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conn = psycopg2.connect(
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host="vic5o0tw1w-repl.twtim97jsb.tsdb.cloud.timescale.com",
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user="tsdbadmin",
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password="yP4wU5bL0tI0kP3k"
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)
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query = '''
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SELECT from_addr
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, to_addr
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, asset
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, amount_e8
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, block_timestamp
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, COUNT(1) AS n
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FROM midgard.transfer_events
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WHERE block_timestamp < 1650000000000000000
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AND block_timestamp >= 1640000000000000000
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GROUP BY 1, 2, 3, 4, 5
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HAVING COUNT(1) > 1
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'''
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df = pd.read_sql_query(query, conn)
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cur.execute(query)
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it = 0
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qs = []
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for i in range(1618000000000000000, 1657000000000000000, 3000000000000000):
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print(i)
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it += 1
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query = '''
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SELECT from_addr
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, to_addr
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, asset
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, amount_e8
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, block_timestamp
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, COUNT(1) AS n
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FROM midgard.transfer_events
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WHERE block_timestamp >= {}
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AND block_timestamp < {}
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GROUP BY 1, 2, 3, 4, 5
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HAVING COUNT(1) > 1
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'''.format(i, i + 3000000000000000)
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with open('/Users/kellenblumberg/Downloads/query_{}.txt'.format(it), 'w') as f:
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f.write(query)
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def read_tokenlist():
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with open('./data/solana_tokenlist.json', 'r') as f:
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j = json.loads(f.read())
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j.keys()
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df = pd.DataFrame(j['tokens'])
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df[[ 'chainId','address','symbol','name','decimals','logoURI' ]].to_csv('~/Downloads/tokenlist.csv', index=False)
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def idk():
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sales = pd.read_csv('./data/sales.csv')
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sales = pd.read_csv('./data/model_sales.csv')
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sorted(sales.collection.unique())
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# sales = sales[sales.exclude == 0]
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metadata = pd.read_csv('./data/metadata.csv')
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metadata = metadata[metadata.collection == COLLECTION]
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metadata[metadata.feature_name=='Role'].groupby('feature_value').token_id.count()
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sorted(metadata.feature_name.unique())
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sales = sales[sales.collection == COLLECTION]
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features = sorted(metadata.feature_name.unique())
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metadata = metadata.pivot( ['collection','token_id'], ['feature_name'], ['feature_value'] ).reset_index()
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metadata.columns = [ 'collection','token_id' ] + features
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sales['token_id'] = sales.token_id.astype(int)
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metadata['token_id'] = metadata.token_id.astype(int)
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df = sales.merge(metadata)
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# df.sort_values('sale_date', ascending=0).head()
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df = df.fillna('None')
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df['id'] = len(df)
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df['rel_price_0'] = (df.price - df.mn_20).apply(lambda x: max(0, x))
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df['rel_price_1'] = (df.price / df.mn_20).apply(lambda x: max(0, x-1))
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df[ (df.rel_price_0.notnull()) ].to_csv('./data/tableau_data.csv', index=False)
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