[pull] master from LastAncientOne:master

LICENSE

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+MIT License
+
+Copyright (c) 2020 LastAncientOne
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

Python_01_Dowload__YahooData.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Fri Nov 27 08:09:11 2020
+
+@author: Tin
+"""
+# Download stock historical data from Yahoo Finance
+
+import yfinance as yf
+yf.pdr_override()
+
+
+# input
+symbol = 'AAPL'
+start = '2014-01-01'
+end = '2018-01-01'
+
+
+# dataframe
+df = yf.download(symbol,start,end)
+
+# View the first 5 rows
+print('First 5 Rows')
+print(df.head())
+print('-'*80)
+
+# View the last 5 rows
+print('Last 5 Rows')
+print(df.tail())

Python_02_Modify_Date.py

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+# -*- 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())
+
+
+
+

Python_03_save_csv.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Fri Dec  4 18:55:42 2020
+
+@author: Tin
+"""
+# Save Data to CSV
+import warnings
+warnings.filterwarnings("ignore")
+
+import yfinance as yf
+yf.pdr_override()
+
+# input
+symbol = 'AMD'
+start = '2014-01-01'
+end = '2019-01-01'
+
+# Read data 
+data = yf.download(symbol,start,end)
+
+# Output data into CSV
+# To save in your certain folder, change the Users name
+data.to_csv("C:/Users/Finance/Desktop/AMD.csv")
+
+
+
+symbols = ['PFE','TGT','MA','UNH','VZ','V','WMT','GS']
+start = '2014-01-11' 
+end = '2019-01-01'
+stocks_info = yf.download(symbols, start, end)['Adj Close']
+stocks_data = stocks_info.iloc[::]
+print(stocks_data)
+
+# Output data into CSV
+stocks_data.to_csv("C:/Users/Finance/Desktop/stocks_data.csv")

Python_04_Plot_Charts.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Fri Nov 27 08:09:11 2020
+
+@author: Tin
+"""
+# Plot Line Charts
+import pandas as pd # Dataframe Library
+import matplotlib.pyplot as plt # Plot Chart 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)
+
+print('Line Chart')
+plt.figure(figsize=(12,8))
+plt.plot(data['Adj Close'])
+plt.title("Stock Line Chart")
+plt.legend(loc='best')
+plt.xlabel("Date")
+plt.ylabel("Price")
+plt.show()
+print('-'*80)
+
+
+print('Line Chart with Grid')
+plt.figure(figsize=(12,8))
+plt.plot(data['Adj Close'])
+plt.title("Stock Line Chart")
+plt.grid()
+plt.legend(loc='best')
+plt.xlabel("Date")
+plt.ylabel("Price")
+plt.show()
+print('-'*80)
+
+
+print('Render the grid')
+fig, ax = plt.subplots()
+data.plot(kind='line', y= 'Adj Close', ax=ax)
+# Turn on the grid
+ax.grid()
+plt.title("Stock Line Chart")
+plt.legend(loc='best')
+plt.xlabel("Date")
+plt.ylabel("Price")
+plt.show()
+print('-'*80)
+
+
+print('Customize the grid')
+fig, ax = plt.subplots()
+data.plot(kind='line', y= 'Adj Close', ax=ax)
+# Don't allow the axis to be on top of your data
+ax.set_axisbelow(True)
+# Customize the grid
+ax.grid(linestyle='-', linewidth='0.5', color='red')
+plt.title("Stock Line Chart")
+plt.xlabel("Date")
+plt.ylabel("Price")
+plt.show()
+print('-'*80)
+
+
+print('Major grid & Minor Grid')
+plt.figure(figsize=(12,8))
+plt.plot(data['Adj Close'])
+plt.minorticks_on()
+plt.grid(b=True, which='major', color='b', linestyle='-')
+plt.grid(b=True, which='minor', color='r', linestyle='--')
+plt.title("Stock Line Chart")
+plt.xlabel("Date")
+plt.ylabel("Price")
+plt.show()
+print('-'*80)
+
+
+import seaborn as sns # Plot Library 0.9.0 Version
+# conda install -c anaconda seaborn=0.9.0
+plt.figure(figsize=(10,5))
+sns.lineplot(data=data, x=data.index, y='Adj Close')
+print('-'*80)
+
+plt.figure(figsize=(10,5))
+top = plt.subplot2grid((4,4), (0, 0), rowspan=3, colspan=4)
+bottom = plt.subplot2grid((4,4), (3,0), rowspan=1, colspan=4)
+top.plot(data.index, data['Adj Close']) 
+bottom.bar(data.index, data['Volume']) 
+
+# set the labels
+top.axes.get_xaxis().set_visible(False)
+top.set_title('Stock Price and Volume')
+top.set_ylabel('Adj Closing Price')
+bottom.set_ylabel('Volume')
+print('-'*80)
+
+
+# Candlestick
+from mpl_finance import candlestick_ohlc
+from matplotlib import dates as mdates
+
+# Converting date to pandas datetime format
+dfc = data.copy()
+dfc = dfc.reset_index()
+dfc['Date'] = pd.to_datetime(dfc['Date'])
+dfc['Date'] = dfc['Date'].apply(mdates.date2num)
+# dfc.head()
+
+fig = plt.figure(figsize=(14,10))
+ax1 = plt.subplot(2, 1, 1)
+candlestick_ohlc(ax1,dfc.values, width=0.5, colorup='g', colordown='r', alpha=1.0)
+ax1.xaxis_date()
+ax1.xaxis.set_major_formatter(mdates.DateFormatter('%d-%m-%Y'))
+ax1.set_title('Stock '+ symbol +' Closing Price')
+ax1.set_ylabel('Price')
+
+
+
+
+import plotly.graph_objs as go
+# from plotly.offline import init_notebook_mode, iplot
+
+df = data.copy()
+# Plot OHLC Bar Chart
+trace = go.Ohlc(x=df['12-2016'].index,
+                open=df['12-2016'].Open,
+                high=df['12-2016'].High,
+                low=df['12-2016'].Low,
+                close=df['12-2016'].Close)
+data = [trace]
+iplot(data, filename='simple_ohlc')