from IPython.display import Image
Image(filename='viralml-book.png')

Fundamental and Technical Indicators - Hands-On Market Analysis

Get the book "The Little Book of Fundamental Market Indicators":

https://amzn.to/2DERG3d

More at:

https://www.viralml.com/

Data from the Federal Reserve Bank of St. Louis

Treasury 30-Year Treasury Constant Maturity Rate (DGS30)

https://fred.stlouisfed.org/series/DGS30

3-Month Treasury Constant Maturity Rate (DGS3MO)

https://fred.stlouisfed.org/series/DGS3MO

%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')

path_to_market_data = '/Users/manuel/Documents/financial-research/market-data/2019-08-30/'

Load 30-Year Treasury Constant Maturity Rate (DGS30)

In a recession, short-term interest rates can rise above long-term ones, which is the opposite of the more conventional pattern of longer-term interest rates being higher than shorter-term interest rates in normal economic times.

# Get the rates 30 year
# Treasury Yield Curve Rates
rates_30_df = pd.read_csv(path_to_market_data + 'DGS30.csv')
rates_30_df.columns = ['Date', '30yearT']
rates_30_df['Date'] = pd.to_datetime(rates_30_df['Date']) 
print(np.min(rates_30_df['Date'] ),np.max(rates_30_df['Date'] ))
rates_30_df = rates_30_df.sort_values('Date', ascending=True) 
rates_30_df.tail()

1977-02-15 00:00:00 2019-08-29 00:00:00

	Date	30yearT
11093	2019-08-23	2.02
11094	2019-08-26	2.04
11095	2019-08-27	1.97
11096	2019-08-28	1.94
11097	2019-08-29	1.97

rates_30_df.describe()

	Date	30yearT
count	11098	11098
unique	11098	1223
top	1989-12-27 00:00:00	.
freq	1	1461
first	1977-02-15 00:00:00	NaN
last	2019-08-29 00:00:00	NaN

# remove NaN and text characters
rates_30_df = rates_30_df[rates_30_df['30yearT'] != '.']
rates_30_df['30yearT'] = rates_30_df['30yearT'].astype(float)
rates_30_df.head()

	Date	30yearT
0	1977-02-15	7.70
1	1977-02-16	7.67
2	1977-02-17	7.67
3	1977-02-18	7.76
5	1977-02-22	7.77

cut_off_date = '1977-01-01'
rates_30_df_tmp = rates_30_df.copy()

fig, ax = plt.subplots(figsize=(16, 10))
plt.plot(rates_30_df_tmp['Date'], rates_30_df_tmp['30yearT'], color='black', label='Rates')

plt.grid()
plt.title("30 Year Rates")
plt.legend(loc='upper left')
plt.axhline(2)

<matplotlib.lines.Line2D at 0x1218d4470>

# Get the 3 months rates
# Treasury Yield Curve Rates
rates_3MO_df = pd.read_csv(path_to_market_data + 'DGS3MO.csv')
rates_3MO_df.columns = ['Date', '3MOT']
rates_3MO_df['Date'] = pd.to_datetime(rates_3MO_df['Date']) 
print(np.min(rates_3MO_df['Date'] ),np.max(rates_3MO_df['Date'] ))
rates_3MO_df = rates_3MO_df.sort_values('Date', ascending=True) 

rates_3MO_df = rates_3MO_df[rates_3MO_df['3MOT'] != '.']
rates_3MO_df['3MOT'] = rates_3MO_df['3MOT'].astype(float)

rates_3MO_df.tail()

1982-01-04 00:00:00 2019-08-29 00:00:00

	Date	3MOT
9819	2019-08-23	1.97
9820	2019-08-26	2.01
9821	2019-08-27	1.98
9822	2019-08-28	1.99
9823	2019-08-29	1.99

cut_off_date = '2008-01-01 00:00:00'
rates_30_df_tmp = rates_30_df.copy()
rates_30_df_tmp = rates_30_df_tmp[rates_30_df_tmp['Date'] >= cut_off_date]
rates_3MO_df_tmp = rates_3MO_df.copy()
rates_3MO_df_tmp = rates_3MO_df_tmp[rates_3MO_df_tmp['Date'] >= cut_off_date]

fig, ax = plt.subplots(figsize=(16, 8))
plt.plot(rates_30_df_tmp['Date'], rates_30_df_tmp['30yearT'], color='black', label='30 Year Rates')
plt.plot(rates_3MO_df_tmp['Date'], rates_3MO_df_tmp['3MOT'], color='blue', label='3MO Rates')

plt.grid()
plt.title("30 Year Rates")
plt.legend(loc='upper left')
plt.axhline(2)

<matplotlib.lines.Line2D at 0x11e1d32b0>