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chore(Linter): reformatted code with black (#211)

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* chore(Linter): reformatted code with black

* Create black.yaml
This commit is contained in:
Mark Aron Szulyovszky
2022-02-17 19:22:17 +01:00
committed by GitHub
parent f3fee4a4e1
commit 8dd2d88740
101 changed files with 2596 additions and 2320 deletions
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run_fetch_data_glassnode.py
+34 -31
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@@ -3,60 +3,63 @@ import pandas as pd
from tqdm import tqdm
from utils.glassnode import GlassnodeClient, Indicators, Mining
path = 'data/daily_glassnode/'
client = GlassnodeClient(asset='BTC', since='2014-01-01', until='2021-12-17')
path = "data/daily_glassnode/"
client = GlassnodeClient(asset="BTC", since="2014-01-01", until="2021-12-17")
print("Client initiated")
indicator_client = Indicators(client)
indicator_names = ['rhodl_ratio',
'cvdd',
'difficulty_ribbon_compression',
'nvt_ratio',
'nvt_signal',
'velocity',
'supply_adjusted_cdd',
'binary_cdd',
'supply_adjusted_dormancy',
'puell_multiple',
'asopr',
'reserve_risk',
'sopr',
'cdd',
'asol',
'msol',
'dormancy',
'liveliness',
'relative_unrealized_profit',
'relative_unrealized_loss',
'nupl',
indicator_names = [
"rhodl_ratio",
"cvdd",
"difficulty_ribbon_compression",
"nvt_ratio",
"nvt_signal",
"velocity",
"supply_adjusted_cdd",
"binary_cdd",
"supply_adjusted_dormancy",
"puell_multiple",
"asopr",
"reserve_risk",
"sopr",
"cdd",
"asol",
"msol",
"dormancy",
"liveliness",
"relative_unrealized_profit",
"relative_unrealized_loss",
"nupl",
# 'sth_nupl',
# 'lth_nupl',
'ssr',
'bvin',
"ssr",
"bvin",
]
def process_df(df: pd.DataFrame) -> pd.DataFrame:
df.index.rename('time', inplace=True)
df.index.rename("time", inplace=True)
if len(df.columns) != 1:
df = df[['v']]
df.rename(columns={df.columns[0]: 'close'}, inplace=True)
df = df[["v"]]
df.rename(columns={df.columns[0]: "close"}, inplace=True)
df.sort_index(inplace=True)
return df
for name in tqdm(indicator_names):
method_to_call = getattr(indicator_client, name)
df = method_to_call()
df = process_df(df)
df.to_csv(path + name + '.csv')
df.to_csv(path + name + ".csv")
# Mining data
mining_names = ['hash_rate']
mining_names = ["hash_rate"]
mining_client = Mining(client)
for name in tqdm(mining_names):
method_to_call = getattr(mining_client, name)
df = method_to_call()
df = process_df(df)
df.to_csv(path + name + '.csv')
df.to_csv(path + name + ".csv")