File:GoldPriceUSD.svg
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Summary
DescriptionGoldPriceUSD.svg |
English: Gold price in USD per troy ounce plotted using the Python code in the separate section. |
Date | |
Source | Own work |
Author | Dan Polansky |
Licensing
I, the copyright holder of this work, hereby publish it under the following license:
![w:en:Creative Commons](https://upload.wikimedia.org/wikipedia/commons/thumb/7/79/CC_some_rights_reserved.svg/90px-CC_some_rights_reserved.svg.png)
![attribution](https://upload.wikimedia.org/wikipedia/commons/thumb/1/11/Cc-by_new_white.svg/24px-Cc-by_new_white.svg.png)
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
plotPrices = True
plot5YRates = True
plot10YRates = True
plot20YRates = True
plot30YRates = True
# from https://www.macrotrends.net/1333/historical-gold-prices-100-year-chart
# from its frame https://www.macrotrends.net/assets/php/chart_iframe_comp.php?id=1333&url=historical-gold-prices-100-year-chart
inputDataPre1971 = [ # For reference only: not used for plotting
"1915-01-01:19.250",
"1915-02-01:19.250",
"1915-03-01:19.250",
"1915-04-01:19.250",
"1915-05-01:19.250",
"1915-06-01:19.250",
"1915-07-01:19.250",
"1915-08-01:19.250",
"1915-09-01:19.250",
"1915-10-01:19.250",
"1915-11-01:19.250",
"1915-12-01:19.250",
"1916-01-01:19.470",
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"1918-01-01:19.840",
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"1932-01-01:20.780",
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"2013-12-01:1204.500",
"2014-01-01:1251.000",
"2014-02-01:1326.500",
"2014-03-01:1291.750",
"2014-04-01:1288.500",
"2014-05-01:1250.500",
"2014-06-01:1315.000",
"2014-07-01:1285.250",
"2014-08-01:1285.750",
"2014-09-01:1216.500",
"2014-10-01:1164.250",
"2014-11-01:1182.750",
"2014-12-01:1206.000",
"2015-01-01:1260.250",
"2015-02-01:1214.000",
"2015-03-01:1187.000",
"2015-04-01:1180.250",
"2015-05-01:1191.400",
"2015-06-01:1171.000",
"2015-07-01:1098.400",
"2015-08-01:1135.000",
"2015-09-01:1114.000",
"2015-10-01:1142.350",
"2015-11-01:1061.900",
"2015-12-01:1069.250",
"2016-01-01:1114.700",
"2016-02-01:1122.000",
"2016-03-01:1240.000",
"2016-04-01:1230.850",
"2016-05-01:1217.500",
"2016-06-01:1320.600",
"2016-07-01:1357.500",
"2016-08-01:1311.400",
"2016-09-01:1317.100",
"2016-10-01:1273.100",
"2016-11-01:1173.900",
"2016-12-01:1151.700",
"2017-01-01:1211.400",
"2017-02-01:1253.900",
"2017-03-01:1251.200",
"2017-04-01:1268.300",
"2017-05-01:1275.400",
"2017-06-01:1242.300",
"2017-07-01:1273.400",
"2017-08-01:1322.200",
"2017-09-01:1284.800",
"2017-10-01:1270.500",
"2017-11-01:1282.150",
"2017-12-01:1296.500",
"2018-01-01:1343.350",
"2018-02-01:1320.300",
"2018-03-01:1323.900",
"2018-04-01:1316.250",
"2018-05-01:1303.500",
"2018-06-01:1250.550",
"2018-07-01:1219.200",
"2018-08-01:1206.850",
"2018-09-01:1183.500",
"2018-10-01:1217.700",
"2018-11-01:1220.450",
"2018-12-01:1281.650",
"2019-01-01:1322.500",
"2019-02-01:1325.450",
"2019-03-01:1291.150",
"2019-04-01:1285.150",
"2019-05-01:1296.000",
"2019-06-01:1413.200",
"2019-07-01:1430.550",
"2019-08-01:1526.550",
"2019-09-01:1487.600",
"2019-10-01:1506.400",
"2019-11-01:1456.350",
"2019-12-01:1523.000",
"2020-01-01:1580.850",
"2020-02-01:1626.350",
"2020-03-01:1604.650",
"2020-04-01:1716.750",
"2020-05-01:1725.650",
"2020-06-01:1770.700",
"2020-07-01:1971.680",
"2020-08-01:1970.500",
"2020-09-01:1893.900",
"2020-10-01:1879.900",
"2020-11-01:1779.860",
"2020-12-01:1895.100",
"2021-01-01:1849.700",
"2021-02-01:1728.800",
"2021-03-01:1715.240",
"2021-04-01:1767.700",
"2021-05-01:1904.740",
"2021-06-01:1771.600",
"2021-07-01:1814.120",
"2021-08-01:1815.800",
"2021-09-01:1756.660",
"2021-10-01:1783.900",
"2021-11-01:1775.920",
"2021-12-01:1828.600",
"2022-01-01:1796.120",
"2022-02-01:1900.700",
"2022-03-01:1953.040",
"2022-04-01:1911.700",
"2022-05-01:1847.260",
"2022-06-01:1829.338",
"2022-07-01:1766.431",
"2022-08-01:1708.580",
"2022-09-01:1660.880",
"2022-10-01:1632.380",
"2022-11-01:1768.610",
"2022-12-01:1824.323",
"2023-01-01:1928.120",
"2023-02-01:1826.740",
"2023-03-01:1969.546",
"2023-04-01:1990.600",
"2023-05-01:1965.200",
"2023-06-01:1919.569",
"2023-07-01:1964.339",
"2023-08-01:1939.828",
"2023-09-01:1848.490",
"2023-10-01:1984.011",
"2023-11-01:2001.900",
"2023-12-01:2062.923",
"2024-01-01:2039.154",
"2024-02-01:2044.200",
"2024-03-01:2190.777"]
dates = []
prices = []
calc5YRates = []
calc10YRates = []
calc20YRates = []
calc30YRates = []
for dataRowStr in inputData:
dataRow = dataRowStr.split(":")
#date = datetime.datetime.strptime("1 1 " + dataRow[0], "%d %m %Y").date()
date = datetime.datetime.strptime(dataRow[0], "%Y-%m-%d").date()
price = float(dataRow[1])
dates.append(date)
prices.append(price)
mInY = 12 # Month count in year
if len(prices) > 5 * mInY:
calc5YRates.append((float(prices[-1]) / prices[-1 - 5 * mInY]) ** (1/5.0))
else:
calc5YRates.append(float("nan"))
if len(prices) > 10 * mInY:
calc10YRates.append((float(prices[-1]) / prices[-1 - 10 * mInY]) ** (1/10.0))
else:
calc10YRates.append(float("nan"))
if len(prices) > 20 * mInY:
calc20YRates.append((float(prices[-1]) / prices[-1 - 20 * mInY]) ** (1/20.0))
else:
calc20YRates.append(float("nan"))
if len(prices) > 30 * mInY:
calc30YRates.append((float(prices[-1]) / prices[-1 - 30 * mInY]) ** (1/30.0))
else:
calc30YRates.append(float("nan"))
if plotPrices:
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="Gold Price (USD)", title="Gold price in USD per troy ounce, monthly step (per macrotrends.net)")
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.yticks([x * 250 for x in range(0, 10)])
plt.plot(dates, prices, color="#6699CC")
plt.grid(linewidth=0.25, color="#CCCCCC")
plt.tight_layout()
plt.savefig("GoldPriceUSD.svg")
if plot5YRates:
fig, biax = plt.subplots()
figSize = fig.get_size_inches()#_
fig.set_size_inches(figSize[0] * 4/3, figSize[1])
biax.set(xlabel="Withdrawal date (label on 1 January)", ylabel="Gold Price (USD) Change Rate",
title="5-year change rate of gold price in USD, annualized (base data per macrotrends.net)\n" +
"The date of withdrawal, the date of deposit being 5 years before")
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.axhline(1, color='#BBBBBB', linewidth=0.75)
plt.plot(dates, calc5YRates, color="#6699CC")
plt.grid(linewidth=0.25, color="#CCCCCC")
values = biax.get_yticks()
biax.set_yticklabels(['{:,.1%}'.format(x - 1) for x in values])
plt.tight_layout()
plt.savefig("GoldPrice5YYield.svg")
if plot10YRates:
fig, biax = plt.subplots()
figSize = fig.get_size_inches()#_
fig.set_size_inches(figSize[0] * 4/3, figSize[1])
biax.set(xlabel="Withdrawal date (label on 1 January)", ylabel="Gold Price (USD) Change Rate",
title="10-year change rate of gold price in USD, annualized (base data per macrotrends.net)\n" +
"The date of withdrawal, the date of deposit being 10 years before")
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.axhline(1, color='#BBBBBB', linewidth=0.75)
plt.plot(dates, calc10YRates, color="#6699CC")
plt.grid(linewidth=0.25, color="#CCCCCC")
values = biax.get_yticks()
biax.set_yticklabels(['{:,.1%}'.format(x - 1) for x in values])
plt.tight_layout()
plt.savefig("GoldPrice10YYield.svg")
if plot20YRates:
fig, biax = plt.subplots()
figSize = fig.get_size_inches()#_
fig.set_size_inches(figSize[0] * 4/3, figSize[1])
biax.set(xlabel="Withdrawal date (label on 1 January)", ylabel="Gold Price (USD) Change Rate",
title="20-year change rate of gold price in USD, annualized (base data per macrotrends.net)\n" +
"The date of withdrawal, the date of deposit being 20 years before")
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.axhline(1, color='#BBBBBB', linewidth=0.75)
plt.plot(dates, calc20YRates, color="#6699CC")
plt.grid(linewidth=0.25, color="#CCCCCC")
values = biax.get_yticks()
biax.set_yticklabels(['{:,.1%}'.format(x - 1) for x in values])
plt.tight_layout()
plt.savefig("GoldPrice20YYield.svg")
if plot30YRates:
fig, biax = plt.subplots()
figSize = fig.get_size_inches()#_
fig.set_size_inches(figSize[0] * 4/3, figSize[1])
biax.set(xlabel="Withdrawal date (label on 1 January)", ylabel="Gold Price (USD) Change Rate",
title="30-year change rate of gold price in USD, annualized (base data per macrotrends.net)\n" +
"The date of withdrawal, the date of deposit being 30 years before")
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.axhline(1, color='#BBBBBB', linewidth=0.75)
plt.plot(dates, calc30YRates, color="#6699CC")
plt.grid(linewidth=0.25, color="#CCCCCC")
values = biax.get_yticks()
biax.set_yticklabels(['{:,.1%}'.format(x - 1) for x in values])
plt.tight_layout()
plt.savefig("GoldPrice30YYield.svg")
![]() |
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Captions
Gold price in USD per troy ounce
Items portrayed in this file
depicts
some value
29 March 2024
image/svg+xml
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Date/Time | Thumbnail | Dimensions | User | Comment | |
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current | 07:53, 29 March 2024 | ![]() | 768 × 432 (84 KB) | Dan Polansky | Uploaded own work with UploadWizard |
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