File:M1Usd.svg
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Summary
| Description |
English: M1 for U.S.dollar, plotted using the Python code from Plotting code section. |
| Date | |
| Source | Own work |
| Author | Dan Polansky |
Licensing
I, the copyright holder of this work, hereby publish it under the following license:
This file is licensed under the Creative Commons Attribution 4.0 International license.
- You are free:
- to share – to copy, distribute and transmit the work
- to remix – to adapt the work
- Under the following conditions:
- attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
Plotting code
from matplotlib import pyplot as plt
import datetime
plotVolumes = True
plot5YRates = True
# Data source: https://fred.stlouisfed.org/series/MANMM101USM189S
# Monetary Aggregates and Their Components: Narrow Money and Components: M1 and Components: M1 for United States
inputData = [
"1960-01-01:140000000000",
"1961-01-01:141100000000",
"1962-01-01:145200000000",
"1963-01-01:148300000000",
"1964-01-01:153700000000",
"1965-01-01:160700000000",
"1966-01-01:169100000000",
"1967-01-01:171900000000",
"1968-01-01:184300000000",
"1969-01-01:198700000000",
"1970-01-01:206200000000",
"1971-01-01:215500000000",
"1972-01-01:230100000000",
"1973-01-01:251500000000",
"1974-01-01:263800000000",
"1975-01-01:273900000000",
"1976-01-01:288400000000",
"1977-01-01:308300000000",
"1978-01-01:334400000000",
"1979-01-01:358600000000",
"1980-01-01:385800000000",
"1981-01-01:411300000000",
"1982-01-01:442700000000",
"1983-01-01:477200000000",
"1984-01-01:525100000000",
"1985-01-01:557000000000",
"1986-01-01:621400000000",
"1987-01-01:730200000000",
"1988-01-01:756200000000",
"1989-01-01:785700000000",
"1990-01-01:795400000000",
"1991-01-01:827200000000",
"1992-01-01:910400000000",
"1993-01-01:1030400000000",
"1994-01-01:1131600000000",
"1995-01-01:1151400000000",
"1996-01-01:1123500000000",
"1997-01-01:1081200000000",
"1998-01-01:1074000000000",
"1999-01-01:1098100000000",
"2000-01-01:1122100000000",
"2001-01-01:1096700000000",
"2002-01-01:1190700000000",
"2003-01-01:1227300000000",
"2004-01-01:1306000000000",
"2005-01-01:1367100000000",
"2006-01-01:1379500000000",
"2007-01-01:1371700000000",
"2008-01-01:1377700000000",
"2009-01-01:1582800000000",
"2010-01-01:1674700000000",
"2011-01-01:1846300000000",
"2012-01-01:2201800000000",
"2013-01-01:2476700000000",
"2014-01-01:2697800000000",
"2015-01-01:2939700000000",
"2016-01-01:3095000000000",
"2017-01-01:3389300000000",
"2018-01-01:3650700000000",
"2019-01-01:3745000000000",
"2020-01-01:3978600000000"]
#"2021-01-01:18098900000000" The last years are for a changed definition
#"2022-01-01:20506300000000"
#"2023-01-01:19555000000000"
dates = []
volumes = []
calcRates = []
for dataRowStr in inputData:
dataRow = dataRowStr.split(":")
date = datetime.datetime.strptime(dataRow[0], "%Y-%m-%d").date()
volume = float(dataRow[1])
dates.append(date)
volumes.append(volume)
if len(volumes) > 5:
calcRates.append((float(volumes[-1]) / volumes[-1 - 5]) ** (1/5.0))
else:
calcRates.append(float("nan"))
if plotVolumes:
fig, biax = plt.subplots()
figSize = fig.get_size_inches()
fig.set_size_inches(figSize[0] * 4/3, figSize[1])
biax.set(xlabel="Date (Label on 1 January)", ylabel="M1 (U.S.Dollar)",
title="U.S.Dollar M1 (per stlouisfed.org)")
xtickDates, xtickDatesStr = [], []
for idx, date in enumerate(dates):
if date.month == 1 and date.day == 1:
xtickDates.append(date)
xtickDatesStr.append(str(date.year) if date.year % 2 == 0 else "")
plt.xticks(xtickDates, xtickDatesStr, rotation=70)
plt.plot(dates, volumes, color="#6699CC")
plt.grid(linewidth=0.25, color="#CCCCCC")
plt.tight_layout()
plt.savefig("M1Usd.svg")
if plot5YRates:
# Plot 5Y average annual rate of change
fig, biax = plt.subplots()
figSize = fig.get_size_inches()
fig.set_size_inches(figSize[0] * 4/3, figSize[1])
title="U.S.Dollar M1\n5-year average of annual rate of change (base data per stlouisfed.org)"
biax.set(xlabel="Date (label on 1 January)", ylabel="Rate of change", title=title)
xtickDates = []
xtickDatesStr = []
for idx, date in enumerate(dates):
xtickDates.append(date)
xtickDatesStr.append(str(date.year) if date.year % 2 == 0 else "")
plt.xticks(xtickDates, xtickDatesStr, rotation=70)
plt.axhline(1, color='#BBBBBB', linewidth=0.75)
plt.plot(dates, calcRates, color="#6699CC")
plt.grid(linewidth=0.25, color="#DDDDDD")
values = biax.get_yticks()
biax.set_yticklabels(['{:,.1%}'.format(x - 1) for x in values])
plt.tight_layout()
plt.savefig("M1Usd5YAvgChangeRate.svg")
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| Date/Time | Thumbnail | Dimensions | User | Comment | |
|---|---|---|---|---|---|
| current | 15:47, 10 April 2024 | | 768 × 432 (72 KB) | Dan Polansky | Uploaded own work with UploadWizard |
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