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Fundamental Market Analysis with Python

Fundamental Market Analysis with Python - Find Your Own Answers On What Is Going on in the Financial Markets

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The Little Book of Fundamental Market Indicators

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The Little Book of Fundamental Market Indicators

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Real Estate: Is It Time to Unload? Hands-On Market Analysis with Python

Introduction

Indicators are pointing to a bubble once again. Let's roll up our sleeves and find out what is going on.

Code

ViralML-Hands-On-Real-Estate-Time-To-Sell?
In [95]:
from IPython.display import Image
Image(filename='viralml-book.png')
Out[95]:

Fundamental and Technical Indicators - Hands-On Market Analysis

Companion book: "The Little Book of Fundamental Market Indicators":

https://amzn.to/2DERG3d

More at:

https://www.viralml.com/

Data

S&P 500 (^GSPC) https://finance.yahoo.com/quote/%5EGSPC/history?p=%5EGSPC

S&P/Case-Shiller U.S. National Home Price Index (CSUSHPINSA) https://fred.stlouisfed.org/series/CSUSHPINSA

Average Hourly Earnings of Production and Nonsupervisory Employees: Total Private https://fred.stlouisfed.org/series/AHETPI

Consumer Price Index for All Urban Consumers: Owners' equivalent rent of residences https://fred.stlouisfed.org/series/CUSR0000SEHC

In [96]:
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import io, base64, os, json, re 
import pandas as pd
import numpy as np
import datetime
import warnings
warnings.filterwarnings('ignore')
In [97]:
path_to_market_data = '/Users/manuel/Documents/financial-research/market-data/2019-07-20/'

# create an MACD indicators
def MACD(df, feature, fast=9, medium=12, long=26):
    # WARNING - Feed data in ascending order only (i.e. first row should be your oldest print)
    
    tmp_df = df.copy()
    
    
    tmp_df['EXP1'] = tmp_df[feature].ewm(span=medium, adjust=False).mean()
    tmp_df['EXP2'] = tmp_df[feature].ewm(span=long, adjust=False).mean()
    tmp_df['MACD'] = tmp_df['EXP1']-tmp_df['EXP2']
    tmp_df['EXP3'] = tmp_df['MACD'].ewm(span=fast, adjust=False).mean()
    tmp_df['MACD_Hist'] = tmp_df['MACD'] - tmp_df['EXP3']
    
    return tmp_df

Data

In [57]:
# S&P 500
sp500_df = pd.read_csv(path_to_market_data + '^GSPC.csv')
sp500_df['Date'] = pd.to_datetime(sp500_df['Date'])

sp500_df = sp500_df[['Date','Adj Close']]
sp500_df.columns = ['Date', 'SP500_Close']
print(np.min(sp500_df['Date'] ),np.max(sp500_df['Date'] ))
sp500_df = sp500_df.sort_values('Date', ascending=True) # sort in ascending date order
sp500_df.tail()
1950-01-03 00:00:00 2019-07-17 00:00:00
Out[57]:
Date SP500_Close
17492 2019-07-11 2999.909912
17493 2019-07-12 3013.770020
17494 2019-07-15 3014.300049
17495 2019-07-16 3004.040039
17496 2019-07-17 2984.419922
In [58]:
# Case-Shiller

caseshill_df = pd.read_csv(path_to_market_data + 'CSUSHPINSA.csv')
caseshill_df['DATE'] = pd.to_datetime(caseshill_df['DATE'])
caseshill_df.columns = ['Date', 'Case-Shiller']
caseshill_df = caseshill_df.sort_values('Date')
print('caseshill_df:', np.min(caseshill_df['Date']), np.max(caseshill_df['Date']))
caseshill_df.tail()
caseshill_df: 1987-01-01 00:00:00 2019-04-01 00:00:00
Out[58]:
Date Case-Shiller
383 2018-12-01 204.971
384 2019-01-01 204.487
385 2019-02-01 204.748
386 2019-03-01 206.056
387 2019-04-01 207.970
In [59]:
# Average Hourly Earnings of Production and Nonsupervisory Employees

avghourly_df = pd.read_csv(path_to_market_data + 'AHETPI.csv')
avghourly_df['DATE'] = pd.to_datetime(avghourly_df['DATE'])
avghourly_df.columns = ['Date', 'AHETPI']
avghourly_df = avghourly_df.sort_values('Date')
print('avghourly_df:', np.min(avghourly_df['Date']), np.max(avghourly_df['Date']))
avghourly_df.tail()
avghourly_df: 1964-01-01 00:00:00 2019-06-01 00:00:00
Out[59]:
Date AHETPI
661 2019-02-01 23.17
662 2019-03-01 23.25
663 2019-04-01 23.30
664 2019-05-01 23.39
665 2019-06-01 23.43
In [60]:
# Consumer Price Index for All Urban Consumers

cpi_df = pd.read_csv(path_to_market_data + 'CUSR0000SEHC.csv')
cpi_df['DATE'] = pd.to_datetime(cpi_df['DATE'])
cpi_df.columns = ['Date', 'CPI']
cpi_df = cpi_df.sort_values('Date')
print('cpi_df:', np.min(cpi_df['Date']), np.max(cpi_df['Date']))
cpi_df.tail()
cpi_df: 1983-01-01 00:00:00 2019-06-01 00:00:00
Out[60]:
Date CPI
433 2019-02-01 321.566
434 2019-03-01 322.599
435 2019-04-01 323.669
436 2019-05-01 324.513
437 2019-06-01 325.518

Let's Plot it All

In [98]:
cut_off_date = '1988-01-01'

sp500_df_tmp = sp500_df.copy()
caseshill_df_tmp = caseshill_df.copy()
avghourly_df_tmp = avghourly_df.copy()
cpi_df_tmp = cpi_df.copy()

sp500_df_tmp = sp500_df_tmp[sp500_df_tmp['Date'] >= cut_off_date]
caseshill_df_tmp = caseshill_df_tmp[caseshill_df_tmp['Date'] >= cut_off_date]
avghourly_df_tmp = avghourly_df_tmp[avghourly_df_tmp['Date'] >= cut_off_date]
cpi_df_tmp = cpi_df_tmp[cpi_df_tmp['Date'] >= cut_off_date]
In [99]:
# join both datasets together
fig, ax = plt.subplots(figsize=(16, 8))
plt.plot(sp500_df_tmp['Date'], sp500_df_tmp['SP500_Close'] , color='blue', label='SP500_Close')
plt.title('SP500, Case-Shiller, AHETPI, CPI')
plt.grid()

# Get new axis
ax2 = ax.twinx()
plt.plot(caseshill_df_tmp['Date'], caseshill_df_tmp['Case-Shiller'] , color='red', label='Case-Shiller')

# Get new axis
ax3 = ax.twinx()
plt.plot(avghourly_df_tmp['Date'], avghourly_df_tmp['AHETPI'] , color='green', label='AHETPI')

# Get new axis
ax4 = ax.twinx()
plt.plot(cpi_df_tmp['Date'], cpi_df_tmp['CPI'] , color='brown', label='CPI')
 


plt.show()

Adjustting for 2012 = 100

In [100]:
sp500_df_tmp[sp500_df_tmp['Date'] == '2012-01-03']
Out[100]:
Date SP500_Close
15601 2012-01-03 1277.060059
In [101]:
print(100/1277.060059)
0.07830485284952445
In [102]:
sp500_df_tmp['SP500_Close-adj'] = sp500_df_tmp['SP500_Close'] * 0.07830485284952445
In [103]:
caseshill_df_tmp[caseshill_df_tmp['Date'] == '2012-01-01']
Out[103]:
Date Case-Shiller
300 2012-01-01 134.164
In [104]:
print(100/134.164)
0.7453564294445605
In [105]:
caseshill_df_tmp['Case-Shiller-adj'] = caseshill_df_tmp['Case-Shiller'] * 0.7453564294445605
In [106]:
avghourly_df[avghourly_df['Date'] == '2012-01-01']
Out[106]:
Date AHETPI
576 2012-01-01 19.58
In [107]:
print(100/19.58)
5.107252298263535
In [108]:
avghourly_df_tmp['AHETPI-adj'] = avghourly_df_tmp['AHETPI'] * 5.107252298263535
In [109]:
cpi_df[cpi_df['Date'] == '2012-01-01']
Out[109]:
Date CPI
348 2012-01-01 262.366
In [110]:
print(100/262.366)
0.3811469473940983
In [111]:
cpi_df_tmp['CPI-adj'] = cpi_df_tmp['CPI'] * 0.3811469473940983
In [116]:
# join both datasets together
fig, ax = plt.subplots(figsize=(16, 8))
plt.plot(sp500_df_tmp['Date'], sp500_df_tmp['SP500_Close-adj'] , color='blue', label='SP500_Close')
plt.title('SP500, Case-Shiller, AHETPI, CPI - Ajusted to 2012=100')
 
plt.plot(caseshill_df_tmp['Date'], caseshill_df_tmp['Case-Shiller-adj'] , color='red', label='Case-Shiller')
 
plt.plot(avghourly_df_tmp['Date'], avghourly_df_tmp['AHETPI-adj'] , color='green', label='AHETPI')
 
plt.plot(cpi_df_tmp['Date'], cpi_df_tmp['CPI-adj'] , color='brown', label='CPI')

plt.grid()
plt.show()
In [117]:
# VNQ
vnq_df = pd.read_csv(path_to_market_data + 'VNQ.csv')
vnq_df['Date'] = pd.to_datetime(vnq_df['Date'])

vnq_df = vnq_df[['Date','Adj Close']]
vnq_df.columns = ['Date', 'VNQ_Close']
print(np.min(vnq_df['Date'] ),np.max(vnq_df['Date'] ))
vnq_df = vnq_df.sort_values('Date', ascending=True) # sort in ascending date order
vnq_df.tail()
2004-09-29 00:00:00 2019-07-17 00:00:00
Out[117]:
Date VNQ_Close
3720 2019-07-11 89.580002
3721 2019-07-12 89.459999
3722 2019-07-15 89.389999
3723 2019-07-16 89.180000
3724 2019-07-17 88.879997
In [118]:
vnq_df[vnq_df['Date'] == '2012-01-03']
Out[118]:
Date VNQ_Close
1829 2012-01-03 42.986839
In [119]:
print(100/42.986839)
2.326293403429826
In [120]:
vnq_df['VNQ_Close-adj'] = vnq_df['VNQ_Close'] * 2.326293403429826
In [122]:
# join both datasets together
fig, ax = plt.subplots(figsize=(16, 8))
plt.plot(vnq_df['Date'], vnq_df['VNQ_Close-adj'] , color='blue', label='VNQ_Close-adj')
plt.title('SP500, Case-Shiller, AHETPI, CPI - Ajusted to 2012=100')
 
plt.plot(caseshill_df_tmp['Date'], caseshill_df_tmp['Case-Shiller-adj'] , color='red', label='Case-Shiller')
 
plt.plot(avghourly_df_tmp['Date'], avghourly_df_tmp['AHETPI-adj'] , color='green', label='AHETPI')
 
plt.plot(cpi_df_tmp['Date'], cpi_df_tmp['CPI-adj'] , color='brown', label='CPI')

plt.grid()
plt.show()

Show Notes

(pardon typos and formatting -
these are the notes I use to make the videos)

Indicators are pointing to a bubble once again. Let's roll up our sleeves and find out what is going on.