GeezerVison is showing github.com — SimpleStockAnalysisPython/Python_02_Modify_Date.py at master · qalpha/SimpleStockAnalysisPython

Expand file tree

Python_02_Modify_Date.py

Copy path

More file actions

Latest commit

History

150 lines (107 loc) · 3.35 KB

Python_02_Modify_Date.py

Copy path

File metadata and controls

150 lines (107 loc) · 3.35 KB

Raw

Copy raw file

Download raw file

Open symbols panel

Edit and raw actions

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

# -*- coding: utf-8 -*-

"""

Created on Fri Nov 27 08:09:11 2020

@author: Tin

"""

# Modify Yahoo Dataframe Date

import pandas as pd # Dataframe Library

pd.set_option('max_columns', None) # To show all columns

import yfinance as yf

yf.pdr_override()

# input

symbol = 'AAPL'

start = '2014-01-01'

end = '2018-01-01'

# dataframe

data = yf.download(symbol,start,end)

# View the first 5 rows

print('First 5 Rows')

print(data.head())

print('-'*80)

# Date becomes a columns

df = data.copy() # Copy the original data

dfn = df.reset_index()

print(dfn.head())

print('-'*80)

# Add Year, Month, Day

df['Year'] = df.index.year

df['Month'] = df.index.month

df['Day'] = df.index.day

print('Year, Month, & Day')

print(df.head())

print('-'*80)

# Convert Daily to Weekly

weekly = data.copy()

weekly = weekly.resample('W').last()

print('Weekly Data')

print(weekly.head())

print('-'*80)

# Convert Daily to Monthly

monthly = data.copy()

monthly = monthly.resample('1M').mean()

print('Monthly Data')

print(monthly.head())

print('-'*80)

# Choose Particular Year to analyze

monthly = data.copy()

monthly = monthly.reset_index()

y2017 = monthly[monthly['Date'].dt.year==2017]

print("Analyze Particular Year in Historical Data")

print(y2017)

print('-'*80)

month_name = data.copy()

# Convert Daily to Monthly

# 'BMS', which stands for "business month start frequency"

# 'BM', which stands for "business month end frequency"

month_name = month_name.asfreq('BM')

print('Number of the Month')

print(month_name.head())

print('-'*80)

import calendar

month_name['Month_Number'] = month_name.index.month

month_name['Month_ABBR'] = month_name['Month_Number'].apply(lambda x: calendar.month_abbr[x])

print('Abbreviation for Months')

print(month_name.head())

print('-'*80)

print('Month Name')

month_name['Month_Name'] = month_name['Month_Number'].apply(lambda x: calendar.month_name[x])

print(month_name.head())

print('-'*80)

# Pivot Table Date

df_months = pd.pivot_table(df, index=df.index.month, columns=df.index.year, values = 'Adj Close') # each months

print('Year by Year')

print(df_months)

print('-'*80)

df_days = pd.pivot_table(df, index=df.index.day, columns=df.index.year, values = 'Adj Close') # daily for one whole months

print('Year by Year in daily rows')

print(df_days)

print('-'*80)

df_all_columns = pd.pivot_table(df, index=df.index.month, columns=df.index.year)

print('All columns in yearly')

print(df_all_columns)

print('-'*80)

stock_data = df.copy()

stock_data['Year'] = df.index.year

stock_data['Month'] = df.index.month

stock_data['Day'] = df.index.day

stock_data['Week_Day'] = df.index.dayofweek

stock_data['Week_Day_Name'] = df.index.strftime('%A')

print('Number of day with M-F')

print(stock_data.tail(10))

print('-'*80)

approach1 = stock_data.groupby(['Year', 'Month']).first()['Adj Close']

print('# of Month')

print(approach1.tail(12))

print('-'*80)

approach2 = stock_data.groupby(['Year', 'Day']).first()['Adj Close']

print('# of Day')

print(approach2.tail(12))

print('-'*80)

print('Convert Date to String')

string_date = data.copy()

string_date['Date'] = string_date.index

print(string_date.head())

print('-'*80)

string_date['Date'] = string_date['Date'].dt.strftime("%Y%m%d").astype(int)

print('Convert Date to Numbers')

print(string_date.head())