Stock Market and Portfolio Anaylsis with pandas_datareader and quandl
This post includes code adapted from python for finance and trading algorithms udemy course and python for finance and trading algorithms udemy course notebooks.
import pandas as pd
from pandas_datareader import data, wb
import datetime
start = pd.to_datetime('2020-02-04')
end = pd.to_datetime('today')
MSFT_stock = web.DataReader('MSFT', 'yahoo', start, end)
MSFT_stock.head()
ZOOM_stock = web.DataReader('ZM', 'yahoo', start, end)
ZOOM_stock.head()
SNOW_stock = web.DataReader('SNOW', 'yahoo', start, end)
SNOW_stock.head()
fig = plt.figure(figsize=(12, 6))
plt.title('Open')
MSFT_stock['Open'].plot(label='Microsoft')
ZOOM_stock['Open'].plot(label='Zoom')
SNOW_stock['Open'].plot(label='Snowflake')
plt.legend()
fig = plt.figure(figsize=(12, 6))
plt.title('Volume')
MSFT_stock['Volume'].plot(label='Microsoft')
ZOOM_stock['Volume'].plot(label='Zoom')
SNOW_stock['Volume'].plot(label='Snowflake')
plt.legend()
import pandas as pd
import quandl
start = pd.to_datetime('2010-01-01')
end = pd.to_datetime('today')
# Grabbing a bunch of tech stocks for our portfolio
COST = quandl.get('WIKI/COST.11',
start_date = start,
end_date = end)
NLSN = quandl.get('WIKI/NLSN.11',
start_date = start,
end_date = end)
NKE = quandl.get('WIKI/NKE.11',
start_date = start,
end_date = end)
DIS = quandl.get('WIKI/DIS.11',
start_date = start,
end_date = end)
# Example
COST.iloc[0]['Adj. Close']
for stock_df in (COST, NLSN, NKE, DIS):
stock_df['Normed Return'] = stock_df['Adj. Close'] / stock_df.iloc[0]['Adj. Close']
COST.head()
COST.tail()
## Allocations
for stock_df,allo in zip([COST,NLSN,NKE,DIS],[.2, .1, .4, .3]):
stock_df['Allocation'] = stock_df['Normed Return'] * allo
COST.head()
## Investment
for stock_df in [COST,NLSN,NKE,DIS]:
stock_df['Position Values'] = stock_df['Allocation'] * 1000000
## Total Portfolio Value
portfolio_val = pd.concat([COST['Position Values'],
NLSN['Position Values'],
NKE['Position Values'],
DIS['Position Values']],
axis = 1)
portfolio_val.head()
portfolio_val.columns = ['COST Pos', 'NLSN Pos', 'NKE Pos', 'DIS Pos']
portfolio_val.head()
portfolio_val['Total Pos'] = portfolio_val.sum(axis = 1)
portfolio_val.head()
import matplotlib.pyplot as plt
%matplotlib inline
portfolio_val['Total Pos'].plot(figsize = (12, 8))
plt.title('Total Portfolio Value')
portfolio_val.drop('Total Pos',
axis = 1).plot(kind = 'line', figsize = (12, 8))
portfolio_val.tail()
