Market Bottom Analysis¶

This script attempts a basic data comparison of previous bottoms in the stock market durig various well known periods (ie .com bubble, subprime mortgage crash, 2020 pandemic crash). It compares data from the St. Louis Fed (FRED) with stock market data.

Last Updated: 2022-06-12¶

In [1]:
import numpy as np
from scipy import signal
import yfinance as yf
import pandas as pd
import requests
import json
from datetime import datetime
In [2]:
%matplotlib inline
import matplotlib.pyplot as plt
import mplcursors
plt.rcParams["figure.figsize"] = (20,4)
In [3]:
# Instead of sharing my API key with the world, I'll hide it here
from fred_api_key import FRED_API_KEY

Let's first define some useful functions to normalize the data so it's easier to visually compare.

In [4]:
def normalize( series ):
    """ 
    Returns the normalized input series
    
    return (series - series.min()) / series.max()
    """
    min = series.min()
    max = series.max()
    return (series - min) / max
In [5]:
def normalize_merge( series_array, start_date=None, end_date=None ):
    """
    Normalize each series in the input array and merge them all together
    """
    merge_series = normalize( series_array[0][ start_date : end_date ] )
    merge_change_series = merge_series.pct_change()
    for series in series_array[1:]:
        norm_series = normalize( series[ start_date : end_date ] )
        norm_change_series = norm_series.pct_change()
        merge_series = pd.merge( merge_series, norm_series, on=['date'] )
        merge_change_series = pd.merge( merge_change_series, norm_change_series, on=['date'] )
        
    return merge_series, merge_change_series

Now we define a couple functions to download data from the FRED

In [6]:
def downloadFredSeriesInfo( series_id, api_key ):
    """
    Download series info from St. Louis Fed (FRED).

    Parameters:
        series_id : str
            string used to denote series on St. Louis Fed (FRED) website
        api_key : str
            API key -- A new key can be requested here: https://fred.stlouisfed.org/docs/api/api_key.html
    """
    json_out = None
    url = f"https://api.stlouisfed.org/fred/series?series_id={series_id}&api_key={api_key}&file_type=json"
    print( f"GET {url.replace(api_key,'???')}")
    res = requests.get( url )
    
    if res.status_code == 200:
        json_out = json.loads( res.text )
    else:
        print(f"{res.status_code}: {series_id} not found!")
              
    return json_out
In [7]:
def downloadFredSeries( series_id, api_key, start="", end='9999-12-31' ):
    """
    Download series from St. Louis Fed (FRED).

    Parameters:
        series_id : str
            A string used to denote series on St. Louis Fed (FRED) website
        api_key : str
            The API key needed to access the series data.
                A new key can be requested here: https://fred.stlouisfed.org/docs/api/api_key.html
        start : str
            The start date of the series data
        end : str
            The end date of the series data
    """

    # The resulting pandas dataframe to convert and store the series data into
    df_out = None

    # The data frequency paramter (ie yearly, monthly, weekly)
    frequency = ""
    #unrate_info = downloadFredSeriesInfo( "UNRATE", FRED_API )
    #if not unrate_info is None:
    #    frequency = unrate_info['seriess'][0]['frequency_short'].lower()
   
    if len(start) > 0:
        start = f"&observation_start={start}"
        
    if len(frequency) > 0:
        frequency = f"&frequency={frequency}"

    url = f"https://api.stlouisfed.org/fred/series/observations?series_id={series_id}{start}&observation_end={end}&api_key={api_key}{frequency}&file_type=json"
    print( f"GET {url.replace(api_key,'???')}")
    res = requests.get( url )
    
    if res.status_code == 200:
        # Read the data as JSON and convert to a pandas dataframe
        json_out = json.loads( res.text )
        df_out = pd.DataFrame.from_dict( json_out["observations"] )
        #filt = df_out['realtime_end'] == end
        
        # pull out just the date and value data from the downloaded series
        df_out = df_out[['date','value']]
        df_out['date'] = pd.to_datetime( df_out['date'] )
        df_out = df_out.set_index('date')
        df_out['value'] = pd.to_numeric( df_out['value'] )
    else:
        print(f"{res.status_code}: {series_id} not found!")
        
    return df_out

And finally we define some functions to normalize, merge and plot the data

In [8]:
def normalize_merge_plot_range( series_array, start_date=None, end_date=None ):
    """
    Normalize and merge series in array and plot them
    """
    merge_series = normalize( series_array[0][ start_date : end_date ] )
    for series in series_array[1:]:
        norm_series = normalize( series[ start_date : end_date ] )
        merge_series = pd.merge( merge_series, norm_series, on=['date'] )
        
    merge_series.plot()
    merge_series.pct_change().plot()
In [9]:
def plot_peaks_and_valleys( frame, name, start=None, stop=None ):
    """
    Locates peaks and valleys (relative extrema) of column {name} in pandas dataframe between start and stop dates.
    Plot the data with the peaks and valleys marked.
        peaks => Yellow
        valleys => Red

    Parameters:
        frame : DataFrame
            The pandas dataframe
        name : str
            The name of the column in the pandas dataframe
        start : str
            The start date
        stop : str
            The stop date
    """
    peaks = []
    valleys = []
    frame_idx = 0
    cur_idx = 0

    values = frame[start:stop][name].values
    # dates = frame[start:stop].values

    if start is None:
        start = frame.index[0]
    if stop is None:
        stop = frame.index[-1]

    start = pd.Timestamp( start )
    stop = pd.Timestamp( stop )

    date_str = f"{start.date():%Y-%m-%d} - {stop.date():%Y-%m-%d}"

    while( frame_idx < len(values) ):
    #     frame_peaks_idx, _ = signal.find_peaks( data_butter[ frame_idx : ], distance=14, prominence=7 )
        frame_peaks_idx = signal.argrelextrema( values[ frame_idx : ], np.greater, order=18 )[0]
        if len(frame_peaks_idx) > 0:
            cur_idx = frame_peaks_idx[0]
            frame_idx += cur_idx
            peaks.append( frame_idx )
        
    #     frame_valleys_idx, _ = signal.find_peaks( -1 * data_butter[ frame_idx : ], distance=14, prominence=7 )
        frame_valleys_idx = signal.argrelextrema( values[ frame_idx : ], np.less, order=18 )[0]
        if len(frame_valleys_idx) > 0:
            cur_idx = frame_valleys_idx[0]
            frame_idx += cur_idx
            valleys.append( frame_idx )
        
        if len(frame_peaks_idx) == 0 or len(frame_valleys_idx) == 0:
            break


    r = np.array(range(len(frame[start:stop][name])))
    plt.plot( frame[start:stop][name], zorder=0 )
    plt.plot( frame[start:stop][name][ np.array(peaks) ], 'y*', zorder=0 )
    plt.plot( frame[start:stop][name][ np.array(valleys) ], 'r*', zorder=0 )
    plt.title( name+f" (Peaks & Valleys) ({date_str})", fontsize=25 )
    plt.show()

    plt.plot( frame[start:stop][name][ np.array(peaks) ], 'y', zorder=0 )
    plt.plot( frame[start:stop][name][ np.array(valleys) ], 'r', zorder=0 )
    plt.title( name+f" (Peaks & Valleys Range) ({date_str})", fontsize=25 )
    plt.show()

    # ret_peaks = zip( dates[ peaks ], vix_closes[ np.array(peaks) ] )
    # ret_valleys = zip( dates[ valleys ], vix_closes[ np.array(valleys) ] )
    # ret_peaks_dates = dates[ peaks ]
    # ret_valleys_dates = dates[ valleys ]
    # ret_current_date = dates[ -1 ]

    # return (ret_peaks, ret_valleys, ret_peaks_dates, ret_valleys_dates, ret_current_date)
In [10]:
def plot_range_with_marker( frame, name, start=None, stop=None ):
    """
    Plot a column of a pandas dataframe between start and stop dates.
    Draw a green vertical line to denote the max value of the column data.
    Draw a red vertical line to denote the min value of the column data.
    Print the min/max and min/max percent change.

    Parameters:
        frame : DataFrame
            The pandas dataframe to plot
        name : str
            The column within the dataframe to plot
        start : str
            The start date
        stop : str
            The stop date
    """
    segment = frame[start:stop][name]
    segment_change = segment.pct_change()
    seg_min = segment.min()
    seg_max = segment.max()
    seg_change_min = segment_change.min()
    seg_change_max = segment_change.max()
    seg_min_idx = segment <= seg_min
    seg_max_idx = segment >= seg_max
    seg_change_min_idx = segment_change <= seg_change_min
    seg_change_max_idx = segment_change >= seg_change_max
    
    if start is None:
        start = frame.index[0]
    if stop is None:
        stop = frame.index[-1]

    start = pd.Timestamp( start )
    stop = pd.Timestamp( stop )

    date_str = f"{start.date():%Y-%m-%d} - {stop.date():%Y-%m-%d}"

    plt.figure(1)
    plt.title( name+f" ({date_str})", fontsize=25 )
    plt.plot( segment )
    plt.vlines( segment[ seg_min_idx ].index, seg_min, seg_max, color='red' )
    plt.vlines( segment[ seg_max_idx ].index, seg_min, seg_max, color='green' )
    
    plt.figure(2)
    plt.plot( segment_change )
    plt.vlines( segment_change[ seg_change_min_idx ].index, seg_change_min, seg_change_max, color='red' )
    plt.vlines( segment_change[ seg_change_max_idx ].index, seg_change_min, seg_change_max, color='green' )
    
    plt.interactive(True)
    plt.title( name+f" % Change  ({date_str})", fontsize=25 )
    plt.show()

    plot_peaks_and_valleys( frame, name, start, stop )
    
    print( f"Value Min:    ({segment[ seg_min_idx ].index[0].date()}) => {segment[ seg_min_idx ][0]:12.3f}" )
    print( f"Value Max:    ({segment[ seg_max_idx ].index[0].date()}) => {segment[ seg_max_idx ][0]:12.3f}" )
    print( f"% Change Min: ({segment_change[ seg_change_min_idx].index[0].date()}) => {segment_change[ seg_change_min_idx][0]:12.3f}" )
    print( f"% Change Max: ({segment_change[ seg_change_max_idx].index[0].date()}) => {segment_change[ seg_change_max_idx][0]:12.3f}" )
In [11]:
def plot_ranges_with_marker( frame_dict, start=None, stop=None ):
    """
    Plot columns from data frames contained in dictionary from start date to stop date.
    The dictionary should contain keys with string values representing the column in the dataframe.

    Parameters:
        frame_dict : {str:DataFrame}
            A dictionary containing key/value pairs with column names and dataframes
        start : str
            The start date
        stop : str
            The stop date
    """
    for name,frame in frame_dict.items():
        plot_range_with_marker( frame, name, start, stop )
In [12]:
# Create a new API key at:  https://fred.stlouisfed.org/docs/api/api_key.html
# Replace FRED_API_KEY with your api key in quotes (ie FRED_API="4fd8asdf8sadf8asdf8")
FRED_API=FRED_API_KEY
In [13]:
# Dictionary to store downloaded FRED data
fred_dict = {}

Unemployment Rate¶

In [14]:
# Downlaod the unemployment data from the FRED
unrate_df = downloadFredSeries( "UNRATE", FRED_API )
unrate_df = unrate_df.rename(columns={'value':'UE Rate'})
fred_dict['UNRATE'] = unrate_df

GET https://api.stlouisfed.org/fred/series/observations?series_id=UNRATE&observation_end=9999-12-31&api_key=???&file_type=json
In [15]:
# Downlaod the unemployment data from the FRED for the 1980s only
print("* Download just the 80's...\n")
unrate80_df = downloadFredSeries( "UNRATE", FRED_API, '1980-01-01', '1989-01-01' ).rename(columns={'value':'UE Rate'})
unrate80_df.plot()
unrate80_df
type(unrate80_df)

* Download just the 80's...

GET https://api.stlouisfed.org/fred/series/observations?series_id=UNRATE&observation_start=1980-01-01&observation_end=1989-01-01&api_key=???&file_type=json
Out[15]:
pandas.core.frame.DataFrame

Federal Funds Rate (Interest Rate)¶

In [16]:
# Download the Federal Funds Rata date from the FRED
ffr_df = downloadFredSeries( "FEDFUNDS", FRED_API )
ffr_df = ffr_df.rename(columns={'value':'FF Rate'})
fred_dict['FEDFUNDS']=ffr_df

GET https://api.stlouisfed.org/fred/series/observations?series_id=FEDFUNDS&observation_end=9999-12-31&api_key=???&file_type=json

Consumer Price Index (Inflation)¶

In [17]:
# Download the Consumer Price Index, and Consumer Price Index Less Food And Energy data from the FRED
cpi_df = downloadFredSeries( "CPIAUCSL", FRED_API )
cpi_less_food_energy_df = downloadFredSeries( "CPILFESL", FRED_API )
fred_dict['CPIAUCSL']=cpi_df
fred_dict['CPILFESL']=cpi_less_food_energy_df

GET https://api.stlouisfed.org/fred/series/observations?series_id=CPIAUCSL&observation_end=9999-12-31&api_key=???&file_type=json
GET https://api.stlouisfed.org/fred/series/observations?series_id=CPILFESL&observation_end=9999-12-31&api_key=???&file_type=json
In [18]:
# Rename columns and merge both CPI data sets
cpi_df = cpi_df.rename( columns={'value':'CPI'})
cpi_less_food_energy_df = cpi_less_food_energy_df.rename( columns={'value':'CPI-FE'})
cpi_merged_df = pd.merge( cpi_df, cpi_less_food_energy_df, on=['date'] )
fred_dict['CPI']=cpi_merged_df

# mplcursors.cursor(hover=True)
# mplcursors.cursor().connect(
#     "add", lambda sel: sel.annotation.set_text(cpi_merged_df["value"][sel.index]))

# cpi_merged_df.pct_change(12).plot()
# cpi_merged_df.pct_change(12)*100
In [19]:
# Display the data as year over year percent change for the past 2 years
(cpi_merged_df[-48:].pct_change(12)*100)[-36:]
Out[19]:
CPI CPI-FE
date
2019-06-01 1.689644 2.134792
2019-07-01 1.821162 2.188499
2019-08-01 1.759689 2.366797
2019-09-01 1.727330 2.353146
2019-10-01 1.757085 2.325123
2019-11-01 2.030816 2.314852
2019-12-01 2.261721 2.234413
2020-01-01 2.460490 2.258438
2020-02-01 2.319711 2.375759
2020-03-01 1.530638 2.117208
2020-04-01 0.364865 1.455577
2020-05-01 0.242436 1.252283
2020-06-01 0.726814 1.195307
2020-07-01 1.032825 1.541229
2020-08-01 1.327587 1.713896
2020-09-01 1.400645 1.724946
2020-10-01 1.184198 1.627430
2020-11-01 1.137757 1.640542
2020-12-01 1.278154 1.596285
2021-01-01 1.359971 1.387294
2021-02-01 1.675244 1.287615
2021-03-01 2.658377 1.660073
2021-04-01 4.151991 2.971731
2021-05-01 4.944441 3.806013
2021-06-01 5.341016 4.447401
2021-07-01 5.276105 4.201800
2021-08-01 5.205332 3.961855
2021-09-01 5.389907 4.036752
2021-10-01 6.236941 4.593958
2021-11-01 6.828372 4.952634
2021-12-01 7.096542 5.476621
2022-01-01 7.525934 6.040783
2022-02-01 7.912024 6.415744
2022-03-01 8.557586 6.436039
2022-04-01 8.224139 6.133687
2022-05-01 8.516413 6.011280

Real Gross Domestic Product (+ Recession Markers)¶

In [20]:
# Download Real GDP data from FRED
gdp_df = downloadFredSeries( 'GDPC1', FRED_API )
fred_dict['GDPC1']=gdp_df

GET https://api.stlouisfed.org/fred/series/observations?series_id=GDPC1&observation_end=9999-12-31&api_key=???&file_type=json
In [21]:
# Calculate Real GDP percent change for 2-month periods and check if it is negative (recession marker)
gdp_neg = gdp_df.pct_change(2)['value'] < 0.0
gdp_neg = gdp_df.pct_change(2)[ gdp_neg ]
In [22]:
# Plot Real GDP and recession markers
plt.axhline(y=0, color='red', linestyle='--')
plt.plot( gdp_df.pct_change() )
plt.vlines( gdp_neg.index, gdp_df.pct_change().min(), gdp_df.pct_change().max(), color='grey'  )
plt.show()
In [23]:
# Display the data as month over month percent change for last 3 years
(gdp_df.pct_change()*100)[-36:]
Out[23]:
value
date
2013-04-01 0.139385
2013-07-01 0.791653
2013-10-01 0.711171
2014-01-01 -0.350110
2014-04-01 1.284129
2014-07-01 1.164962
2014-10-01 0.448993
2015-01-01 0.813314
2015-04-01 0.580001
2015-07-01 0.323373
2015-10-01 0.146234
2016-01-01 0.589180
2016-04-01 0.302389
2016-07-01 0.601115
2016-10-01 0.496889
2017-01-01 0.471931
2017-04-01 0.559765
2017-07-01 0.719150
2017-10-01 0.940400
2018-01-01 0.762854
2018-04-01 0.833911
2018-07-01 0.481953
2018-10-01 0.223142
2019-01-01 0.597790
2019-04-01 0.792924
2019-07-01 0.685499
2019-10-01 0.469098
2020-01-01 -1.303583
2020-04-01 -8.937251
2020-07-01 7.547535
2020-10-01 1.115277
2021-01-01 1.533890
2021-04-01 1.640747
2021-07-01 0.570948
2021-10-01 1.680778
2022-01-01 -0.379531

Stock Market¶

In [24]:
# Get start date of CPI data
start_date = cpi_merged_df.index[0]
print( "CPI Start Date:", start_date )

CPI Start Date: 1957-01-01 00:00:00
In [25]:
# Create frame_dict to store downloaded Stock Market data
frame_dict = {}

Volatiltiy Index¶

In [26]:
# Download VIX data
vix = yf.Ticker("^VIX")
#start_date = "2020-01-01"
vix_df = yf.download("^VIX", start=start_date)
vix_df.index.rename('date',inplace=True)

[*********************100%***********************]  1 of 1 completed
In [27]:
# Rename column and store
vix_df = vix_df.rename(columns={'Close':'VIX'})
frame_dict[ 'VIX' ] = vix_df

Apple Inc.¶

In [28]:
# Download AAPL data
aapl = yf.Ticker("AAPL")
aapl_df = yf.download("AAPL", start=start_date)
aapl_df.index.rename('date',inplace=True)

[*********************100%***********************]  1 of 1 completed
In [29]:
# Rename column and store
aapl_df = aapl_df.rename(columns={'Close':'AAPL'})
frame_dict['AAPL'] = aapl_df

Dow Jones Industrial¶

In [30]:
# Download DJI data
dji = yf.Ticker("^DJI")
dji_df = yf.download("^DJI", start=start_date)
dji_df.index.rename('date',inplace=True)

[*********************100%***********************]  1 of 1 completed
In [31]:
# Rename column and store
dji_df = dji_df.rename(columns={'Close':'DJI'})
frame_dict['DJI'] = dji_df

NASDAQ¶

In [32]:
# Download NASDAQ data
nasdaq = yf.Ticker("^IXIC")
nasdaq_df = yf.download("^IXIC", start=start_date)
nasdaq_df.index.rename('date',inplace=True)

[*********************100%***********************]  1 of 1 completed
In [33]:
# Rename column and store
nasdaq_df = nasdaq_df.rename(columns={'Close':'NASDAQ'})
frame_dict['NASDAQ'] = nasdaq_df

S&P 500¶

In [34]:
# Download S&P500 data
snp500 = yf.Ticker("^GSPC")
snp500_df = yf.download("^GSPC", start=start_date)
snp500_df.index.rename('date',inplace=True)

[*********************100%***********************]  1 of 1 completed
In [35]:
# Rename column and store
snp500_df = snp500_df.rename(columns={'Close':'SP500'})
frame_dict['SP500'] = snp500_df

Russell 2000¶

In [36]:
# Download Russell 2000 data
rus2000 = yf.Ticker("^RUT")
rus2000_df = yf.download("^RUT", start=start_date)
rus2000_df.index.rename('date',inplace=True)

[*********************100%***********************]  1 of 1 completed
In [37]:
# Rename column and store
rus2000_df = rus2000_df.rename(columns={'Close':'RUS2K'})
frame_dict['RUS2K'] = rus2000_df
In [38]:
plot_ranges_with_marker( frame_dict )

Value Min:    (2017-11-03) =>        9.140
Value Max:    (2020-03-16) =>       82.690
% Change Min: (2010-05-10) =>       -0.296
% Change Max: (2018-02-05) =>        1.156

Value Min:    (1982-07-08) =>        0.049
Value Max:    (2022-01-03) =>      182.010
% Change Min: (2000-09-29) =>       -0.519
% Change Max: (1997-08-06) =>        0.332

Value Min:    (1992-10-09) =>     3136.600
Value Max:    (2022-01-04) =>    36799.648
% Change Min: (2020-03-16) =>       -0.129
% Change Max: (2020-03-24) =>        0.114

Value Min:    (1974-10-03) =>       54.870
Value Max:    (2021-11-19) =>    16057.440
% Change Min: (2020-03-16) =>       -0.123
% Change Max: (2001-01-03) =>        0.142

Value Min:    (1957-10-22) =>       38.980
Value Max:    (2022-01-03) =>     4796.560
% Change Min: (1987-10-19) =>       -0.205
% Change Max: (2008-10-13) =>        0.116

Value Min:    (1987-10-28) =>      106.080
Value Max:    (2021-11-08) =>     2442.740
% Change Min: (2020-03-16) =>       -0.143
% Change Max: (2020-03-24) =>        0.094

All Indices¶

In [39]:
# Normalize all indices data (Russell 2000, Dow Jones, NASDAQ, S&P500) and plot value and percent chamge
norm, norm_change = normalize_merge( [rus2000_df['RUS2K'], dji_df['DJI'], nasdaq_df['NASDAQ'], snp500_df['SP500']])
norm.plot()
norm_change.plot()
Out[39]:
<AxesSubplot:xlabel='date'>

S&P 500 vs CPI¶

In [40]:
# Normalize CPI percent change over 12 months, along with S&P 500 data and plot both value and change
norm, norm_change = normalize_merge( [cpi_df.pct_change(12), cpi_less_food_energy_df.pct_change(12), snp500_df['SP500']])
norm.plot()
norm_change.plot()
Out[40]:
<AxesSubplot:xlabel='date'>

S&P 500 vx CPI vs Federal Funds Rate¶

In [41]:
# Normalize and plot CPI vs S&P500 vs Fed Funds Rate both value and change
normalize_merge_plot_range( [cpi_df, snp500_df, ffr_df] )

S&P 500 vs CPI vs Federal Funds Rate vs Unemployment Rate (normalized)¶

In [42]:
# Normalize and plot CPI vs S&P500 vs Fed Funds Rate vs Unemployment Rate both value and change
normalize_merge_plot_range( [cpi_df, cpi_less_food_energy_df, snp500_df, ffr_df, unrate_df] )

.com bubble¶

In [43]:
# A date range encompasing the dot com bubble
start, stop = ('1999-01-01', '2004-01-01')

CPI vs CPI Less Food & Energy vs Federal Funds Rate vs Unemployment (normalized)¶

In [44]:
# Normalize and plot CPI percent change over 12-months vs Fed Funds Rate vs Unemployment rate both value and change
normalize_merge_plot_range( [cpi_df.pct_change(12), cpi_less_food_energy_df.pct_change(12), ffr_df, unrate_df ], start, stop )
Stock Market Indices (normalized)¶
In [45]:
# Plot market indices normalized over .com bubble both value and change
normalize_merge_plot_range( [rus2000_df['RUS2K'], snp500_df['SP500'], dji_df['DJI'], aapl_df['AAPL']], start, stop )
In [46]:
# Plot Stock Market data over .com bubble (value, change, peaks/valleys, peaks/valleys range)
plot_ranges_with_marker( frame_dict, start, stop )

Value Min:    (2003-12-17) =>       15.580
Value Max:    (2002-08-05) =>       45.080
% Change Min: (2000-01-07) =>       -0.156
% Change Max: (2001-09-17) =>        0.312

Value Min:    (2003-04-17) =>        0.234
Value Max:    (2000-03-22) =>        1.287
% Change Min: (2000-09-29) =>       -0.519
% Change Max: (1999-10-14) =>        0.143

Value Min:    (2002-10-09) =>     7286.270
Value Max:    (2000-01-14) =>    11722.980
% Change Min: (2001-09-17) =>       -0.071
% Change Max: (2002-07-24) =>        0.063

Value Min:    (2002-10-09) =>     1114.110
Value Max:    (2000-03-10) =>     5048.620
% Change Min: (2000-04-14) =>       -0.097
% Change Max: (2001-01-03) =>        0.142

Value Min:    (2002-10-09) =>      776.760
Value Max:    (2000-03-24) =>     1527.460
% Change Min: (2000-04-14) =>       -0.058
% Change Max: (2002-07-24) =>        0.057

Value Min:    (2002-10-09) =>      327.040
Value Max:    (2000-03-09) =>      606.050
% Change Min: (2000-04-14) =>       -0.073
% Change Max: (2000-04-18) =>        0.058

Subprime mortgage collapse¶

In [47]:
# A date range encompasing the subprime mortgage collapse
start, stop = ('2006-01-01', '2012-01-01')

CPI vs CPI Less Food & Energy vx Feder Funds Rate vs Unemployment (normalized)¶

In [48]:
# Normalize and plot CPI percent change over 12-months vs Fed Funds Rate vs Unemployment rate both value and change
normalize_merge_plot_range( [cpi_df.pct_change(12), cpi_less_food_energy_df.pct_change(12), ffr_df, unrate_df ], start, stop )

Stock Market Indices (normalized)¶

In [49]:
# Plot market indices normalized over subprime mortgage collapse both value and change
normalize_merge_plot_range( [rus2000_df['RUS2K'], snp500_df['SP500'], dji_df['DJI'], aapl_df['AAPL']], start, stop )
In [50]:
# Plot Stock Market data over subprime mortgage collapse (value, change, peaks/valleys, peaks/valleys range)
plot_ranges_with_marker( frame_dict, start, stop )

Value Min:    (2007-01-24) =>        9.890
Value Max:    (2008-11-20) =>       80.860
% Change Min: (2010-05-10) =>       -0.296
% Change Max: (2007-02-27) =>        0.642

Value Min:    (2006-07-14) =>        1.810
Value Max:    (2011-10-18) =>       15.080
% Change Min: (2008-09-29) =>       -0.179
% Change Max: (2008-10-13) =>        0.139

Value Min:    (2009-03-09) =>     6547.050
Value Max:    (2007-10-09) =>    14164.530
% Change Min: (2008-10-15) =>       -0.079
% Change Max: (2008-10-13) =>        0.111

Value Min:    (2009-03-09) =>     1268.640
Value Max:    (2011-04-29) =>     2873.540
% Change Min: (2008-09-29) =>       -0.091
% Change Max: (2008-10-13) =>        0.118

Value Min:    (2009-03-09) =>      676.530
Value Max:    (2007-10-09) =>     1565.150
% Change Min: (2008-10-15) =>       -0.090
% Change Max: (2008-10-13) =>        0.116

Value Min:    (2009-03-09) =>      343.260
Value Max:    (2011-04-29) =>      865.290
% Change Min: (2008-12-01) =>       -0.119
% Change Max: (2008-10-13) =>        0.093

Pandemic (2018-Present)¶

In [51]:
# A date range encompasing the COVID-19 pandemic
start, stop = ('2018-01-01', None)
In [52]:
# Normalize and plot CPI percent change over 12-months vs Fed Funds Rate vs Unemployment rate both value and change
normalize_merge_plot_range( [cpi_df.pct_change(12), cpi_less_food_energy_df.pct_change(12), ffr_df, unrate_df ], start, stop )
In [53]:
# Plot market indices normalized over pandemic both value and change
normalize_merge_plot_range( [rus2000_df['RUS2K'], snp500_df['SP500'], dji_df['DJI'], aapl_df['AAPL']], start, stop )
In [54]:
# Plot Stock Market data over pandamic (value, change, peaks/valleys, peaks/valleys range)
plot_ranges_with_marker( frame_dict, start, stop )

Value Min:    (2018-01-03) =>        9.150
Value Max:    (2020-03-16) =>       82.690
% Change Min: (2020-03-13) =>       -0.234
% Change Max: (2018-02-05) =>        1.156

Value Min:    (2019-01-03) =>       35.548
Value Max:    (2022-01-03) =>      182.010
% Change Min: (2020-03-16) =>       -0.129
% Change Max: (2020-03-13) =>        0.120

Value Min:    (2020-03-23) =>    18591.930
Value Max:    (2022-01-04) =>    36799.648
% Change Min: (2020-03-16) =>       -0.129
% Change Max: (2020-03-24) =>        0.114

Value Min:    (2018-12-24) =>     6192.920
Value Max:    (2021-11-19) =>    16057.440
% Change Min: (2020-03-16) =>       -0.123
% Change Max: (2020-03-13) =>        0.093

Value Min:    (2020-03-23) =>     2237.400
Value Max:    (2022-01-03) =>     4796.560
% Change Min: (2020-03-16) =>       -0.120
% Change Max: (2020-03-24) =>        0.094

Value Min:    (2020-03-18) =>      991.160
Value Max:    (2021-11-08) =>     2442.740
% Change Min: (2020-03-16) =>       -0.143
% Change Max: (2020-03-24) =>        0.094

Pandemic Aftermath (2021-04-01 - Present)¶

In [55]:
# A somewht abritrary date range depicting pandemic aftermath
start, stop = ('2021-04-01',None)
In [56]:
# Normalize and plot CPI percent change over 12-months vs Fed Funds Rate vs Unemployment rate both value and change
normalize_merge_plot_range( [cpi_df.pct_change(12), cpi_less_food_energy_df.pct_change(12), ffr_df, unrate_df ], start, stop )
In [57]:
# Plot market indices normalized over post-pandemic both value and change
normalize_merge_plot_range( [rus2000_df['RUS2K'], snp500_df['SP500'], dji_df['DJI'], aapl_df['AAPL']], start, stop )
In [58]:
# Plot Stock Market data over post-pandamic (value, change, peaks/valleys, peaks/valleys range)
plot_ranges_with_marker( frame_dict, start, stop )

Value Min:    (2021-10-21) =>       15.010
Value Max:    (2022-03-07) =>       36.450
% Change Min: (2021-11-29) =>       -0.198
% Change Max: (2021-11-26) =>        0.540

Value Min:    (2021-05-12) =>      122.770
Value Max:    (2022-01-03) =>      182.010
% Change Min: (2022-05-18) =>       -0.056
% Change Max: (2022-01-28) =>        0.070

Value Min:    (2022-05-19) =>    31253.131
Value Max:    (2022-01-04) =>    36799.648
% Change Min: (2022-05-18) =>       -0.036
% Change Max: (2022-05-04) =>        0.028

Value Min:    (2022-05-24) =>    11264.450
Value Max:    (2021-11-19) =>    16057.440
% Change Min: (2022-05-05) =>       -0.050
% Change Max: (2022-05-13) =>        0.038

Value Min:    (2022-05-19) =>     3900.790
Value Max:    (2022-01-03) =>     4796.560
% Change Min: (2022-05-18) =>       -0.040
% Change Max: (2022-05-04) =>        0.030

Value Min:    (2022-05-11) =>     1718.140
Value Max:    (2021-11-08) =>     2442.740
% Change Min: (2022-05-09) =>       -0.042
% Change Max: (2022-05-17) =>        0.032

.com plus subprime¶

In [59]:
# A date range encompasing the .com bubble and the subprime mortgage collapse
start, stop = ('1999-01-01', '2012-01-01')
In [60]:
normalize_merge_plot_range( [cpi_df.pct_change(12), cpi_less_food_energy_df.pct_change(12), snp500_df['SP500'], ffr_df, unrate_df ], start, stop )
In [61]:
plot_ranges_with_marker( frame_dict, start, stop )

Value Min:    (2007-01-24) =>        9.890
Value Max:    (2008-11-20) =>       80.860
% Change Min: (2010-05-10) =>       -0.296
% Change Max: (2007-02-27) =>        0.642

Value Min:    (2003-04-17) =>        0.234
Value Max:    (2011-10-18) =>       15.080
% Change Min: (2000-09-29) =>       -0.519
% Change Max: (1999-10-14) =>        0.143

Value Min:    (2009-03-09) =>     6547.050
Value Max:    (2007-10-09) =>    14164.530
% Change Min: (2008-10-15) =>       -0.079
% Change Max: (2008-10-13) =>        0.111

Value Min:    (2002-10-09) =>     1114.110
Value Max:    (2000-03-10) =>     5048.620
% Change Min: (2000-04-14) =>       -0.097
% Change Max: (2001-01-03) =>        0.142

Value Min:    (2009-03-09) =>      676.530
Value Max:    (2007-10-09) =>     1565.150
% Change Min: (2008-10-15) =>       -0.090
% Change Max: (2008-10-13) =>        0.116

Value Min:    (2002-10-09) =>      327.040
Value Max:    (2011-04-29) =>      865.290
% Change Min: (2008-12-01) =>       -0.119
% Change Max: (2008-10-13) =>        0.093