Entrepot NFT floor price (ICP)
SHOGAKU
2 years ago
Google Colaboratory で実施した例です
Google Colaboratory → https://colab.research.google.com/?hl=ja#
Entrepot NFT floor price (ICP)
データ取得
!rm -r nfts_data
!git clone https://github.com/lukasvozda/nfts_data.git
!pip install -r nfts_data/requirements.txt
from ic.agent import *
from ic.identity import *
from ic.client import *
from ic.candid import Types, encode, decode
# Inicialisation
client = Client(url = "https://ic0.app")
iden = Identity()
agent= Agent(iden, client)
# Endpoint that we want to pull data from
function_to_call = 'transactions'
# List of NFTs canisters we want to pull data from
# You can find it on Entrepot clicking on NFTs
canisters = [
{
'name': 'Poked bots',
'id': 'bzsui-sqaaa-aaaah-qce2a-cai'
},
{
'name': 'OG MEDALS',
'id': 'rw623-hyaaa-aaaah-qctcq-cai'
},
{
'name': 'ICPuppies',
'id': '5movr-diaaa-aaaak-aaftq-cai'
},
{
'name': 'Dfinity Space Apes',
'id': '3mttv-dqaaa-aaaah-qcn6q-cai'
},
{
'name': 'IC Dinos',
'id': 'yrdz3-2yaaa-aaaah-qcvpa-cai'
},
{
'name': 'ICPCS',
'id': 'j3dqa-byaaa-aaaah-qcwfa-cai'
},
{
'name': 'D-City',
'id': 'gtb2b-tiaaa-aaaah-qcxca-cai'
},
{
'name': 'Meme Cake',
'id': 'txr2a-fqaaa-aaaah-qcmkq-cai'
},
{
'name': 'Pineapple Punks',
'id': 'skjpp-haaaa-aaaae-qac7q-cai'
},
{
'name': 'BTC Flower',
'id': 'pk6rk-6aaaa-aaaae-qaazq-cai'
},
{
'name': 'ETH Flower',
'id': 'dhiaa-ryaaa-aaaae-qabva-cai'
},
{
'name': 'ICP Flower',
'id': '4ggk4-mqaaa-aaaae-qad6q-cai'
},
{
'name': 'Motoko',
'id': 'oeee4-qaaaa-aaaak-qaaeq-cai'
},
{
'name': 'Motoko Mechs',
'id': 'ugdkf-taaaa-aaaak-acoia-cai'
},
{
'name': 'Cubetopia Islands',
'id': '3vdxu-laaaa-aaaah-abqxa-cai'
},
{
'name': 'ICKitties',
'id': 'rw7qm-eiaaa-aaaak-aaiqq-cai'
},
{
'name': 'Cubetopia Islands',
'id': '3vdxu-laaaa-aaaah-abqxa-cai'
},
{
'name': 'Poked Bots Mutant Army',
'id': 'jv55j-riaaa-aaaal-abvnq-cai'
},
{
'name': 'ICPunks',
'id': 'bxdf4-baaaa-aaaah-qaruq-cai'
},
{
'name': 'Pet bots',
'id': 't2mog-myaaa-aaaal-aas7q-cai'
},
{
'name': 'IC BUCKS',
'id': '6wih6-siaaa-aaaah-qczva-cai'
},
{
'name': 'MoonWalkers',
'id': 'er7d4-6iaaa-aaaaj-qac2q-cai'
},
{
'name': 'Finterest',
'id': '4fcza-biaaa-aaaah-abi4q-cai'
},
{
'name': 'ICmojis',
'id': 'gevsk-tqaaa-aaaah-qaoca-cai'
},
{
'name': 'ICTuTs',
'id': 'ahl3d-xqaaa-aaaaj-qacca-cai'
},
{
'name': 'Starverse',
'id': 'nbg4r-saaaa-aaaah-qap7a-cai'
},
{
'name': 'ICP.DOG',
'id': '3bqt5-gyaaa-aaaah-qcvha-cai'
},
{
'name': 'ICApes',
'id': 'zvycl-fyaaa-aaaah-qckmq-cai'
},
{
'name': 'Boxy Dude',
'id': 's36wu-5qaaa-aaaah-qcyzq-cai'
},
{
'name': 'ICPets',
'id': 'unssi-hiaaa-aaaah-qcmya-cai'
},
{
'name': 'Pet bots',
'id': 't2mog-myaaa-aaaal-aas7q-cai'
},
{
'name': 'CrowdFund NFT',
'id': '2glp2-eqaaa-aaaak-aajoa-cai'
},
{
'name': 'Astronauts',
'id': 'sr4qi-vaaaa-aaaah-qcaaq-cai'
},
]
# File to write results to
f = open('result.txt','w')
# Header row
f.write(f"collection,index,token,icp,time_updated,timestamp,seller,buyer")
for c in canisters:
params = []
params = encode(params)
response = agent.query_raw(c["id"], function_to_call, params)
print("Getting collection:", c["name"])
trans = response[0]["value"]
for i,t in enumerate(trans):
price = str(t['_3364572809']/100000000)
timestamp = t['_1291635725']
token = t['_338395897']
seller = t['_1782082687']
buyer = t['_3136747827']
f.write(f"\n{c['name']},{i},{token},{price},,{timestamp},{seller},{buyer}")
f.close()取得したデータ確認
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv("result.txt", header=0)
df['timestamp'] = pd.to_datetime(df['timestamp'])
sns.scatterplot(x='timestamp', y='icp', hue='collection', legend=True, data=df)
plt.grid(linestyle='dotted')
#レジェンド
#plt.legend(loc='upper left')
#X軸のラベルを変更
import matplotlib.dates as mdates
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%y/%m'))
plt.gca().xaxis.set_major_locator(mdates.MonthLocator())
plt.gca().xaxis.set_major_locator(mdates.MonthLocator(interval=3))
plt.gcf().autofmt_xdate()
#Y軸 Max1000
import matplotlib.pyplot as plt
# Place legend outside of the plot
plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
#plt.ylim(0, 1000)
plt.show()
df = df.sort_values(by='timestamp')
フロア価格に変更
日付ごとの最低取引価格をフロア価格とする
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df['day'] = df['timestamp'].dt.date
df_min = df.groupby(['day', 'collection'])['icp'].min()
# ffillメソッドを使用して、前日のデータで空欄を埋める
df_min = df_min.ffill()
# プロットする
sns.scatterplot(x=df_min.index.get_level_values(0), y=df_min, hue=df_min.index.get_level_values(1), legend=True)
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%y/%m'))
plt.gca().xaxis.set_major_locator(mdates.MonthLocator())
plt.gca().xaxis.set_major_locator(mdates.MonthLocator(interval=3))
plt.gcf().autofmt_xdate()
# Place legend outside of the plot
plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
plt.grid(linestyle='dotted')
plt.show()
保存
# CSV形式で保存する
df_min.to_csv("min_icp.csv")Bar Chart Race作成
ピポットテーブルして空欄を埋める作業実施して、グラフ作成
!pip install bar_chart_race
import pandas as pd
import bar_chart_race as bcr
# CSVファイルを読み込む
df = pd.read_csv('min_icp.csv')
# ピボットテーブルを作成する
df = df.pivot_table(index='day',
columns='collection',
values='icp')
# ffillメソッドを使用して、前日のデータで空欄を埋める
df = df.ffill()
# バーグラフを作成する
bcr.bar_chart_race(df=df, n_bars=10)コメント
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SHOGAKU
2 years ago
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