While the dollar has depreciated against most major currencies over the past month (notably 4.4% against the Canadian dollar, 3.3% against the Yen, 4.2% against the Real, and 8.6% against the Rand), the Fed H.10 release provides measures of exactly how much the value of the greenback has fallen relative to a weighted set of its trading partners. The latest data show that the U.S. dollar has fallen on April 12 to its lowest level since June 2015 compared to other major currencies, and its lowest level since October 2015 compared to a broad index of currencies.
This post covers the trade-weighted dollar and is split into two segments: a description of the index and its recent behavior, and a short python script showing how you can use the pandas and matplotlib libraries to retrieve the time series and plot it.
Two trade-weighted dollar indices, described
The Fed’s two most common trade-weighted indices of the foreign exchange value of the U.S. dollar are the major-currencies index and the broad-index. Both measure the value of the dollar relative to other countries’ currencies and intend to be aligned, through their weighting system, with the currencies closely related to U.S. trade patterns. The broad index, however, covers more currencies, and especially more emerging market currencies.
Discrepancies between even the broad index and the way the dollar is actually traded are inevitable. For example, the broad index weight in 2016 for Canada is 12.664 percent (broad index weights are updated yearly), whereas year-t0-date, 15.2 percent of total U.S. trade has been with Canada. From a share-of-total-U.S.-trade perspective, the broad index is somewhat overweight the Yuan, Euro, and Yen, and underweight the Canadian dollar and Mexican peso.
From 2005 through 2015, the major currencies index remained largely within a range of 70-85. After a steep climb through 2015, in January, the measure peaked at 95.8, to the frustration of U.S. exporters whose customers essentially pay more for the same goods (and as a result buy fewer). On April 12, the major currencies index hit its lowest level since June 2015.
Meanwhile, the broad index has moved in the same direction, hitting a five month low on April 12. The plot of the broad index is below, including how to obtain it. You can substitute DTWEXB with DTWEXM if you are interested in the major currencies index.
Python: Retrieve and plot the trade-weighted dollar
Next we show how Python can be used to gather and plot data on the Fed’s broad index of the foreign exchange value of the dollar. The script gathers data from Fred and plots each business day’s index value since the start of 2014.
Gathering data
First, we import pandas, numpy, and matplotlib and give them conventional short names. We will also use datetime and date.
# Import libraries
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
import datetime
from datetime import date
Next, we use the pandas.io.data package to request the data from Fred. I’ve found the code for our series of interest, DTWEXB, by searching, but you can also find it on the Fred site by source (Board of Governors of the Federal Reserve System), or by release (H.10). We paste the series code into the datareader and provide start and end dates. Pandas retrieves the data into a dataframe.
import pandas_datareader.data as webdata
tstart = datetime.datetime(2014, 1, 1)
#retrieve trade-weighted dollar data from fred
dtwexb = webdata.DataReader("DTWEXB", "fred", tstart);
#display five most recent observations
dtwexb.tail(5)
When was the last time the measure was as low as its April 12 value?
print dtwexb[119.29>=dtwexb].dropna().tail(2)
Line plot of data
Lastly, we can use matplotlib to plot the data.
#Create figure and plot dtwexb
fig = plt.figure(figsize=[7,5])
ax1 = plt.subplot(111)
line = dtwexb.DTWEXB.plot(color='blue',linewidth=2)
#Add a title
ax1.set_title('Trade-Weighted U.S. Dollar, Broad Index (1997=100)', fontsize=15)
#Add y label and no x-label since it is dates
ax1.set_ylabel('Index')
ax1.set_xlabel('')
#Axis options
ax1.spines["top"].set_visible(False)
ax1.spines["right"].set_visible(False)
ax1.get_xaxis().tick_bottom()
ax1.get_yaxis().tick_left()
ax1.tick_params(axis='x', which='major', labelsize=8)
ax1.yaxis.grid(True)
#Annotate with text
fig.text(0.15, 0.85,'Last: ' + str(dtwexb.DTWEXB[-1]) \
+ ' (as of: ' + str(dtwexb.index[-1].strftime('%Y-%m-%d'))\
+ ')');
url = 'https://research.stlouisfed.org/fred2/series/TWEXB'
fig.text(0.05, 0.025, 'Source: ' + url)
fig.text(0.65, 0.16, 'briandew.wordpress.com')
#Save as png
plt.savefig('dtwexb.png', dpi=1000)
BD Economics 