Consumer Credit Card Rates¶

A look at consumer credit card rates, according to the US Federal Reserve.

import pandas as pd
import altair as alt
import numpy as np

import re

df_cc = pd.read_html("https://www.federalreserve.gov/releases/g19/HIST/cc_hist_tc_levels.html")[0]

df_cc = df_cc.droplevel(0, axis=1)

df_cc['dt'] = df_cc['Date'].map(lambda v: pd.to_datetime(v.replace(" ", "-")) + pd.DateOffset(months=3))

#df_cc.head()

alt.Chart(df_cc).mark_line().encode(
    alt.X('dt:T', axis=alt.Axis(title="")),
    alt.Y('All creditcard amounts 1:Q', axis=alt.Axis(title="Interest Rate [%]"))
).properties(
    title='Average Consumer Credit Card Rate',
    width=750,
    height=400
)

from os import environ

try:
    # for local execution
    apiKeyFromFile = open("/Users/kyledunn/fredApiKey.txt", "r").read().strip()
except FileNotFoundError:
    apiKeyFromFile = None
    pass
# for CI
apiKey = environ.get("FRED_API_KEY", apiKeyFromFile)

from io import BytesIO
from zipfile import ZipFile
from urllib.request import urlopen
    
def getSeries(series="", description=None, apiKey=apiKey):
    """
    Fetch a data series from the St. Louis FED system (FRED)
    and return it as a Pandas dataframe indexed by date
    """
    baseUrl = "https://api.stlouisfed.org"
    endpoint = "/fred/series/observations?series_id={s}&realtime_end=9999-12-31&api_key={k}&file_type=txt" 
    
    response = urlopen(baseUrl + endpoint.format(s=series, k=apiKey))
    zipfile = ZipFile(BytesIO(response.read()))
    
    filesInZip = zipfile.namelist()
    
    data = zipfile.open(filesInZip[1])

    if description is None:
        description = series
        
    df = pd.read_csv(data, sep="\t", header=None, skiprows=1,
                       names=["date", description, "rt_start", "rt_end"], na_values=".")
    
    df['date'] = pd.to_datetime(df.date)
    
    return df.set_index("date")

df_ff = getSeries('FEDFUNDS', 'prime')

How does the Fed Funds Rate affect consumer credit rates?¶

df_m = df_cc.set_index('dt')['All creditcard amounts 1'].reset_index()\
       .merge(df_ff.reset_index()[['date', 'prime']], left_on='dt', right_on='date')

df_m['decade'] = df_m['dt'].apply(lambda v: str(v.year)[:-1] + "0's")

hist = alt.Chart(df_m).mark_line(strokeDash=[4], strokeJoin='round').encode(
    alt.X('prime:Q', sort=df_m.sort_values('dt')['All creditcard amounts 1'].unique().tolist()),
    alt.Y('All creditcard amounts 1:Q', scale=alt.Scale(zero=False)),
    alt.Color('decade:N', scale=alt.Scale(range=["purple", "#ff0000", "teal", "blue"]))
)

now = alt.Chart(df_m[df_m['dt'].isin([pd.to_datetime('2019-10-01')])]).mark_point(color='black').encode(
    alt.X('prime:Q', axis=alt.Axis(title='Fed Funds Rate [%]')),
    alt.Y('All creditcard amounts 1:Q', axis=alt.Axis(title='Average Rate [%]'))   
)
nowText = now.mark_text(dy=-15).encode(
    alt.Text('dt')
)

oh_eight = alt.Chart(df_m[df_m['dt'].isin([pd.to_datetime('2007-10-01')])]).mark_point(color='black').encode(
    alt.X('prime:Q'),
    alt.Y('All creditcard amounts 1:Q')   
)
oh_eightText = oh_eight.mark_text(dy=-15, dx=-5).encode(
    alt.Text('dt')
)
oh_eightPost = alt.Chart(df_m[df_m['dt'].isin([pd.to_datetime('2008-10-01')])]).mark_point(color='black').encode(
    alt.X('prime:Q'),
    alt.Y('All creditcard amounts 1:Q')   
)
oh_eightPostText = oh_eightPost.mark_text(dy=15).encode(
    alt.Text('dt')
)

dotcom = alt.Chart(df_m[df_m['dt'].isin([pd.to_datetime('2000-10-01')])]).mark_point(color='black').encode(
    alt.X('prime:Q'),
    alt.Y('All creditcard amounts 1:Q')   
)
dotcomtText = dotcom.mark_text(dy=-15).encode(
    alt.Text('dt')
)
dotcomPost = alt.Chart(df_m[df_m['dt'].isin([pd.to_datetime('2003-10-01')])]).mark_point(color='black').encode(
    alt.X('prime:Q'),
    alt.Y('All creditcard amounts 1:Q')   
)
dotcomtPostText = dotcomPost.mark_text(dy=25, dx=-10).encode(
    alt.Text('dt')
)

c = (hist + now + nowText\
 + oh_eight + oh_eightText + oh_eightPost + oh_eightPostText\
 + dotcom + dotcomtText + dotcomPost + dotcomtPostText).properties(
    title='Path of historical credit card rates according to the Fed Funds Rate',
    width=750,
    height=400,
    background='white'
)

c.save('creditcards.png')
c.display()