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feat(Data): added day_month, month, day_week columns as features

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Mark Aron Szulyovszky
2021-11-14 11:59:59 +01:00
parent 1429051c4c
commit 9d342d6039
4 changed files with 35 additions and 13 deletions
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load_data.py
+32 -10
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@@ -1,18 +1,31 @@
#%%
import pandas as pd
import os
import numpy as np
#%%
def load_files(path, add_features):
dfs = [__load_df(os.path.join(path,f), f.split('.')[0], add_features) for f in os.listdir(path) if os.path.isfile(os.path.join(path,f))]
def load_files(path: str, add_features: bool, log_returns: bool) -> pd.DataFrame:
dfs = [__load_df(os.path.join(path,f), f.split('.')[0], add_features, log_returns) for f in os.listdir(path) if os.path.isfile(os.path.join(path,f))]
dfs = pd.concat(dfs, axis=1).fillna(0.)
dfs.index = pd.DatetimeIndex(dfs.index)
if add_features:
dfs['day_month'] = dfs.index.day
dfs['day_week'] = dfs.index.dayofweek
dfs['month'] = dfs.index.month
return dfs.drop(index=dfs.index[0], axis=0)
def __load_df(path, prefix, add_features):
def __load_df(path: str, prefix: str, add_features: bool, log_returns: bool) -> pd.DataFrame:
df = pd.read_csv(path, header=0, index_col=0).fillna(0)
df['returns'] = df['close'].pct_change()
if log_returns:
df['returns'] = np.log(df['close']).diff(1)
else:
df['returns'] = df['close'].pct_change()
if add_features:
# volatility (10, 20, 30 days)
df['vol_10'] = df['returns'].rolling(10).std()*(252**0.5)
@@ -20,12 +33,21 @@ def __load_df(path, prefix, add_features):
df['vol_30'] = df['returns'].rolling(30).std()*(252**0.5)
# momentum (10, 20, 30, 60, 90 days)
df['mom_10'] = df['close'].pct_change(10)
df['mom_20'] = df['close'].pct_change(20)
df['mom_30'] = df['close'].pct_change(30)
df['mom_60'] = df['close'].pct_change(60)
df['mom_90'] = df['close'].pct_change(90)
if log_returns:
df['mom_10'] = np.log(df['close']).diff(10)
df['mom_20'] = np.log(df['close']).diff(20)
df['mom_30'] = np.log(df['close']).diff(30)
df['mom_60'] = np.log(df['close']).diff(60)
df['mom_90'] = np.log(df['close']).diff(90)
else:
df['mom_10'] = df['close'].pct_change(10)
df['mom_20'] = df['close'].pct_change(20)
df['mom_30'] = df['close'].pct_change(30)
df['mom_60'] = df['close'].pct_change(60)
df['mom_90'] = df['close'].pct_change(90)
df = df.drop(columns=['open', 'high', 'low', 'close'])
df.columns = [prefix + "_" + c for c in df.columns]
return df
# %%