In [1]:

from IPython.core.display import HTML
(""" <style>
.output_png {
    display: table-cell;
    text-align: center;
    vertical-align: middle;
}
</style> """)
display(HTML("<style>.container { width:70% !important; }</style>"))

The Euro on Forex¶

Exploring Twenty Years of Volatility¶

History¶

The currency exchange market has been around since coins were first used. At the end of the 1800s the development of the gold standard fixed currency values to a certain amount of gold, allowing for an internationally stable monetary system (1). Eventually the amount of gold was not enough to keep up with the demand for currency. After World War II the Bretton Woods Accord set the US Dollar as the global fixed currency. This was an adjustable rate, but still determined by central banks and state actors (2). The US dollar was pegged to gold at the time, but during the 1970s the same problem (i.e. amount of gold vs currency in circulation) occurred with the dollar. In August of 1971 President Richard Nixon unilaterally canceled direct international convertibility of the US dollar to gold, inadvertently beginning a regime of free-floating currencies (3).

Today¶

The modern foreign exchange market (Forex, FX, or currency market) is a direct broker to dealer global marketplace that determines the market value and exchange rate of currencies all over the world. It is open 24 hours a day, 5 days a week. Currencies are always traded in pairs, and the value expressed is of one currency relative to another. For example, at the end of the day on 11/19/21 the Euro to US dollar rate was 1.13, or $1.13 equals €1. All of the twenty most traded currencies are free-floating, their value is determined by political conditions, economic factors and market psychology.

Forex is by far the largest financial market in the world, in 2019 having a daily volume of over $6.6 trillion (4). The Euro to US dollar (EURUSD) is the most traded currency pair, taking about 30% of the multi-billion dollar Forex turnover (5).

Methodology¶

This study examines the volatility of the value of the Euro to eight other currencies. It uses the 2019 Triennial Central Bank Survey by the Bank for International Settlements to determine four currencies that represent large portions of the market and four currencies that represent small portions of the market. The highly traded currencies are the US dollar, Japanese yen, UK pound, and Australian dollar. The Israeli shekel, Phillipine peso, Malaysian ringgit, and Romanian leu comprise the group of least traded currencies.

The data set used is based on daily closing values provided by the European Central Bank and compiled by Daria Chemkaeva. It is updated weekly, the version used for this study was downloaded from Kaggle in November 2021.

Summary¶

Text with conclusion.

Initial Look¶

The first steps are to import the basic libraries, open the file, and take an initial look at the dataset.

In [2]:

# import libraries and set display options
import pandas as pd
import pprint

pd.options.display.float_format = '{:20,.4f}'.format
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 3000)
pd.set_option('display.colheader_justify', 'center')
pd.set_option('display.precision', 3)
# import json
# s = json.load( open("bmh_matplotlibrc.json") )  #edit path to json file
# matplotlib.rcParams.update(s)

# open file
x_rates = pd.read_csv("euro-daily-hist_1999_2020.csv")

# head, tail, and basic info
display(x_rates.head())
display(x_rates.tail())
display(x_rates.info())

	Period\Unit:	[Australian dollar ]	[Bulgarian lev ]	[Brazilian real ]	[Canadian dollar ]	[Swiss franc ]	[Chinese yuan renminbi ]	[Cypriot pound ]	[Czech koruna ]	[Danish krone ]	[Estonian kroon ]	[UK pound sterling ]	[Greek drachma ]	[Hong Kong dollar ]	[Croatian kuna ]	[Hungarian forint ]	[Indonesian rupiah ]	[Israeli shekel ]	[Indian rupee ]	[Iceland krona ]	[Japanese yen ]	[Korean won ]	[Lithuanian litas ]	[Latvian lats ]	[Maltese lira ]	[Mexican peso ]	[Malaysian ringgit ]	[Norwegian krone ]	[New Zealand dollar ]	[Philippine peso ]	[Polish zloty ]	[Romanian leu ]	[Russian rouble ]	[Swedish krona ]	[Singapore dollar ]	[Slovenian tolar ]	[Slovak koruna ]	[Thai baht ]	[Turkish lira ]	[US dollar ]	[South African rand ]
0	2021-01-08	1.5758	1.9558	6.5748	1.5543	1.0827	7.9184	NaN	26.163	7.4369	NaN	0.90128	NaN	9.4982	7.5690	359.62	17247.33	3.8981	89.7975	155.5000	127.26	1337.90	NaN	NaN	NaN	24.4718	4.9359	10.2863	1.6883	58.947	4.5113	4.8708	90.8000	10.0510	1.6228	NaN	NaN	36.8480	9.0146	1.2250	18.7212
1	2021-01-07	1.5836	1.9558	6.5172	1.5601	1.0833	7.9392	NaN	26.147	7.4392	NaN	0.90190	NaN	9.5176	7.5660	357.79	17259.99	3.9027	90.0455	155.3000	127.13	1342.29	NaN	NaN	NaN	24.2552	4.9570	10.3435	1.6907	59.043	4.4998	4.8712	91.2000	10.0575	1.6253	NaN	NaN	36.8590	8.9987	1.2276	18.7919
2	2021-01-06	1.5824	1.9558	6.5119	1.5640	1.0821	7.9653	NaN	26.145	7.4393	NaN	0.90635	NaN	9.5659	7.5595	357.86	17168.20	3.9289	90.2040	156.3000	127.03	1339.30	NaN	NaN	NaN	24.3543	4.9482	10.3810	1.6916	59.296	4.5160	4.8720	90.8175	10.0653	1.6246	NaN	NaN	36.9210	9.0554	1.2338	18.5123
3	2021-01-05	1.5927	1.9558	6.5517	1.5651	1.0803	7.9315	NaN	26.227	7.4387	NaN	0.90333	NaN	9.5136	7.5588	360.27	17075.10	3.9277	89.8670	156.1000	126.25	1335.85	NaN	NaN	NaN	24.5860	4.9293	10.4713	1.7036	59.020	4.5473	4.8721	91.6715	10.0570	1.6180	NaN	NaN	36.7760	9.0694	1.2271	18.4194
4	2021-01-04	1.5928	1.9558	6.3241	1.5621	1.0811	7.9484	NaN	26.141	7.4379	NaN	0.90160	NaN	9.5330	7.5565	361.32	17062.67	3.9430	89.7890	156.1000	126.62	1332.03	NaN	NaN	NaN	24.3031	4.9264	10.4440	1.7065	59.058	4.5475	4.8713	90.3420	10.0895	1.6198	NaN	NaN	36.7280	9.0579	1.2296	17.9214

	Period\Unit:	[Australian dollar ]	[Bulgarian lev ]	[Brazilian real ]	[Canadian dollar ]	[Swiss franc ]	[Chinese yuan renminbi ]	[Cypriot pound ]	[Czech koruna ]	[Danish krone ]	[Estonian kroon ]	[UK pound sterling ]	[Greek drachma ]	[Hong Kong dollar ]	[Croatian kuna ]	[Hungarian forint ]	[Indonesian rupiah ]	[Israeli shekel ]	[Indian rupee ]	[Iceland krona ]	[Japanese yen ]	[Korean won ]	[Lithuanian litas ]	[Latvian lats ]	[Maltese lira ]	[Mexican peso ]	[Malaysian ringgit ]	[Norwegian krone ]	[New Zealand dollar ]	[Philippine peso ]	[Polish zloty ]	[Romanian leu ]	[Russian rouble ]	[Swedish krona ]	[Singapore dollar ]	[Slovenian tolar ]	[Slovak koruna ]	[Thai baht ]	[Turkish lira ]	[US dollar ]	[South African rand ]
5694	1999-01-08	1.8406	NaN	NaN	1.7643	1.6138	NaN	0.58187	34.938	7.4433	15.6466	0.70940	324.00	9.0302	NaN	250.15	9321.63	NaN	NaN	80.9900	130.09	1366.73	4.66430	0.6654	0.4419	11.4414	4.4295	8.5900	2.1557	44.295	4.0363	1.3143	27.2075	9.1650	1.9537	188.8400	42.560	42.5590	0.3718	1.1659	6.7855
5695	1999-01-07	1.8474	NaN	NaN	1.7602	1.6165	NaN	0.58187	34.886	7.4431	15.6466	0.70585	324.40	9.0131	NaN	250.09	9218.77	NaN	NaN	81.0600	129.43	1337.16	4.65480	0.6627	0.4413	11.5511	4.4203	8.6295	2.1531	44.436	4.0165	1.3092	26.9876	9.1800	1.9436	188.8000	42.765	42.1678	0.3701	1.1632	6.8283
5696	1999-01-06	1.8820	NaN	NaN	1.7711	1.6116	NaN	0.58200	34.850	7.4452	15.6466	0.70760	324.72	9.1010	NaN	250.67	9337.68	NaN	NaN	81.5400	131.42	1359.54	4.69940	0.6649	0.4420	11.4705	4.4637	8.7335	2.1890	44.872	4.0065	1.3168	27.4315	9.3050	1.9699	188.7000	42.778	42.6949	0.3722	1.1743	6.7307
5697	1999-01-05	1.8944	NaN	NaN	1.7965	1.6123	NaN	0.58230	34.917	7.4495	15.6466	0.71220	324.70	9.1341	NaN	250.80	9314.51	NaN	NaN	81.5300	130.96	1373.01	4.71740	0.6657	0.4432	11.5960	4.4805	8.7745	2.2011	44.745	4.0245	1.3168	26.5876	9.4025	1.9655	188.7750	42.848	42.5048	0.3728	1.1790	6.7975
5698	1999-01-04	1.9100	NaN	NaN	1.8004	1.6168	NaN	0.58231	35.107	7.4501	15.6466	0.71110	327.15	9.1332	NaN	251.48	9433.61	NaN	NaN	81.4800	133.73	1398.59	4.71700	0.6668	0.4432	11.6446	4.4798	8.8550	2.2229	45.510	4.0712	1.3111	25.2875	9.4696	1.9554	189.0450	42.991	42.6799	0.3723	1.1789	6.9358

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5699 entries, 0 to 5698
Data columns (total 41 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   Period\Unit:              5699 non-null   object 
 1   [Australian dollar ]      5699 non-null   object 
 2   [Bulgarian lev ]          5297 non-null   object 
 3   [Brazilian real ]         5431 non-null   object 
 4   [Canadian dollar ]        5699 non-null   object 
 5   [Swiss franc ]            5699 non-null   object 
 6   [Chinese yuan renminbi ]  5431 non-null   object 
 7   [Cypriot pound ]          2346 non-null   object 
 8   [Czech koruna ]           5699 non-null   object 
 9   [Danish krone ]           5699 non-null   object 
 10  [Estonian kroon ]         3130 non-null   object 
 11  [UK pound sterling ]      5699 non-null   object 
 12  [Greek drachma ]          520 non-null    object 
 13  [Hong Kong dollar ]       5699 non-null   object 
 14  [Croatian kuna ]          5431 non-null   object 
 15  [Hungarian forint ]       5699 non-null   object 
 16  [Indonesian rupiah ]      5699 non-null   object 
 17  [Israeli shekel ]         5431 non-null   object 
 18  [Indian rupee ]           5431 non-null   object 
 19  [Iceland krona ]          3292 non-null   float64
 20  [Japanese yen ]           5699 non-null   object 
 21  [Korean won ]             5699 non-null   object 
 22  [Lithuanian litas ]       4159 non-null   object 
 23  [Latvian lats ]           3904 non-null   object 
 24  [Maltese lira ]           2346 non-null   object 
 25  [Mexican peso ]           5699 non-null   object 
 26  [Malaysian ringgit ]      5699 non-null   object 
 27  [Norwegian krone ]        5699 non-null   object 
 28  [New Zealand dollar ]     5699 non-null   object 
 29  [Philippine peso ]        5699 non-null   object 
 30  [Polish zloty ]           5699 non-null   object 
 31  [Romanian leu ]           5637 non-null   float64
 32  [Russian rouble ]         5699 non-null   object 
 33  [Swedish krona ]          5699 non-null   object 
 34  [Singapore dollar ]       5699 non-null   object 
 35  [Slovenian tolar ]        2085 non-null   object 
 36  [Slovak koruna ]          2608 non-null   object 
 37  [Thai baht ]              5699 non-null   object 
 38  [Turkish lira ]           5637 non-null   float64
 39  [US dollar ]              5699 non-null   object 
 40  [South African rand ]     5699 non-null   object 
dtypes: float64(3), object(38)
memory usage: 1.8+ MB

None

Initial Look Continued¶

The dataset covers the time period from January 1999 to January 2021. It has 5699 rows and 41 columns. Some of these columns have less than half the number of entries as other columns. In the original data set, cells with missing entries have a hyphen (-). This causes problems with a lot of the functions that will be used. For the time being the hyphens will be converted to the value NaN, which preserves the row of information.

Data Cleaning¶

The first steps will be:

Clean and modify column names
Convert to datetime data type
Sort the values with oldest first and reset the index
Clean currency columns
Convert exchange rates to floats

In [3]:

# strip brackets and trailing space from country names
# replace remaining space with underscore
# lower case all column names
x_rates.columns = x_rates.columns.str.replace("[","").str.replace("]","")
x_rates.columns = x_rates.columns.str.rstrip()
x_rates.columns = x_rates.columns.str.replace(" ","_")
x_rates.columns = x_rates.columns.str.lower()

# rename columns
x_rates.rename(columns={"period\\unit:":"date",
                        "chinese_yuan_renminbi":"chinese_yuan",
                        "uk_pound_sterling":"uk_pound"}, inplace=True)

# convert datetime
x_rates["date"] = pd.to_datetime(x_rates["date"])

# resort and reindex
x_rates.sort_values("date", inplace=True)
x_rates.reset_index(drop=True, inplace=True)

# convert hyphens in currency columns to NaN
import numpy as np
x_rates = x_rates.replace("-", np.nan)

# convert exchange rate values to float
x_rates.iloc[:,1:] = x_rates.iloc[:,1:].astype(float)

# set style for cells with centered output
from IPython.core.display import HTML
HTML("""
<style>
.output_png {
    display: table-cell; text-align: center; vertical-align: middle}
</style>
""")

display(x_rates.head())

/var/folders/bz/5cj1tzbj5xn319rpqrw6gpmh0000gn/T/ipykernel_13006/2256657528.py:4: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.
  x_rates.columns = x_rates.columns.str.replace("[","").str.replace("]","")

	date	australian_dollar	bulgarian_lev	brazilian_real	canadian_dollar	swiss_franc	chinese_yuan	cypriot_pound	czech_koruna	danish_krone	estonian_kroon	uk_pound	greek_drachma	hong_kong_dollar	croatian_kuna	hungarian_forint	indonesian_rupiah	israeli_shekel	indian_rupee	iceland_krona	japanese_yen	korean_won	lithuanian_litas	latvian_lats	maltese_lira	mexican_peso	malaysian_ringgit	norwegian_krone	new_zealand_dollar	philippine_peso	polish_zloty	romanian_leu	russian_rouble	swedish_krona	singapore_dollar	slovenian_tolar	slovak_koruna	thai_baht	turkish_lira	us_dollar	south_african_rand
0	1999-01-04	1.9100	NaN	NaN	1.8004	1.6168	NaN	0.5823	35.1070	7.4501	15.6466	0.7111	327.1500	9.1332	NaN	251.4800	9,433.6100	NaN	NaN	81.4800	133.7300	1,398.5900	4.7170	0.6668	0.4432	11.6446	4.4798	8.8550	2.2229	45.5100	4.0712	1.3111	25.2875	9.4696	1.9554	189.0450	42.9910	42.6799	0.3723	1.1789	6.9358
1	1999-01-05	1.8944	NaN	NaN	1.7965	1.6123	NaN	0.5823	34.9170	7.4495	15.6466	0.7122	324.7000	9.1341	NaN	250.8000	9,314.5100	NaN	NaN	81.5300	130.9600	1,373.0100	4.7174	0.6657	0.4432	11.5960	4.4805	8.7745	2.2011	44.7450	4.0245	1.3168	26.5876	9.4025	1.9655	188.7750	42.8480	42.5048	0.3728	1.1790	6.7975
2	1999-01-06	1.8820	NaN	NaN	1.7711	1.6116	NaN	0.5820	34.8500	7.4452	15.6466	0.7076	324.7200	9.1010	NaN	250.6700	9,337.6800	NaN	NaN	81.5400	131.4200	1,359.5400	4.6994	0.6649	0.4420	11.4705	4.4637	8.7335	2.1890	44.8720	4.0065	1.3168	27.4315	9.3050	1.9699	188.7000	42.7780	42.6949	0.3722	1.1743	6.7307
3	1999-01-07	1.8474	NaN	NaN	1.7602	1.6165	NaN	0.5819	34.8860	7.4431	15.6466	0.7058	324.4000	9.0131	NaN	250.0900	9,218.7700	NaN	NaN	81.0600	129.4300	1,337.1600	4.6548	0.6627	0.4413	11.5511	4.4203	8.6295	2.1531	44.4360	4.0165	1.3092	26.9876	9.1800	1.9436	188.8000	42.7650	42.1678	0.3701	1.1632	6.8283
4	1999-01-08	1.8406	NaN	NaN	1.7643	1.6138	NaN	0.5819	34.9380	7.4433	15.6466	0.7094	324.0000	9.0302	NaN	250.1500	9,321.6300	NaN	NaN	80.9900	130.0900	1,366.7300	4.6643	0.6654	0.4419	11.4414	4.4295	8.5900	2.1557	44.2950	4.0363	1.3143	27.2075	9.1650	1.9537	188.8400	42.5600	42.5590	0.3718	1.1659	6.7855

Euro to Dollar¶

The change over time in Euro exchange rate can be visualized in a line graph. It and the US dollar are the first currency pair to be examined.

In [4]:

# create a us dollar to euro dataframe and add 30 day rolling mean column
us_dollar = x_rates[["date","us_dollar"]].copy()
us_dollar["rolling_mean_30"] = us_dollar["us_dollar"].rolling(30).mean()

# line graph for dollar to euro exchange rate
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
%matplotlib inline
fig, ax = plt.subplots(figsize=(10,3.75))
ax.plot(us_dollar["date"], us_dollar["rolling_mean_30"])
ax.set(xlabel="Year",
       ylabel="US dollar",
       title="US Dollar to Euro Exhcange Rates")
ax.set_xticks(["2000-01-04 00:00:00", "2005-01-04 00:00:00", "2010-01-04 00:00:00",
               "2015-01-04 00:00:00", "2020-01-04 00:00:00"])
ax.set_xticklabels([2000,2005,2010,2015,2020])
plt.show()

In [5]:

%%html
<style>
table {float:center}
</style>

Euro to Dollar Continued¶

The line graph shows that the exchange rate between the Euro and US dollar is pretty variable. There are also a lot of gaps in the graph. These represent points in the data frame where information for a particular day is missing. This is the most accurate representation of the data, but it's not very easy to look at. A quick visual check can illustrate this overlap and then compare the original data frame with one where the missing dates are removed.

In [6]:

# create nan group and get dates
nan_dollar_group = us_dollar[us_dollar["us_dollar"].isnull()]
nan_days = nan_dollar_group["date"]

# line graph showing dollar to euro showing NaN values with and without highlight
fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(10,7.5))
ax1.plot(us_dollar["date"], us_dollar["rolling_mean_30"])
ax1.set(title="dataset w/ NaN values")
ax2.plot(us_dollar["date"], us_dollar["rolling_mean_30"])
ax2.set(title="dataset w/ corresponding dates in light blue")
for ax in ax1, ax2:
    ax.set_xticks(["2000-01-04 00:00:00", "2005-01-04 00:00:00", "2010-01-04 00:00:00",
               "2015-01-04 00:00:00", "2020-01-04 00:00:00"])
    ax.set_xticklabels([2000,2005,2010,2015,2020])
for xc in nan_days:
    plt.axvline(x=xc, color="lightcyan")
fig.suptitle("US dollar to Euro exchange rates", fontsize=20)
fig.tight_layout(pad=3.0)
plt.show()

# create a dollar to euro dataframe with NaN dates removed
US_dollar_nonull = x_rates[x_rates["us_dollar"].notna()].copy()
US_dollar_nonull["rolling_mean_30"] = US_dollar_nonull["us_dollar"].rolling(30).mean()

# line graph showing 30 day rolling mean with and without NaN rows
fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(10,7.5))
ax1.plot(us_dollar["date"], us_dollar["rolling_mean_30"])
ax1.set(title="NaN rows included")
ax2.plot(US_dollar_nonull["date"], US_dollar_nonull["rolling_mean_30"])
ax2.set(title="NaN rows removed")
for ax in ax1, ax2:
    ax.set_xticks(["2000-01-04 00:00:00", "2005-01-04 00:00:00", "2010-01-04 00:00:00",
               "2015-01-04 00:00:00", "2020-01-04 00:00:00"])
    ax.set_xticklabels([2000,2005,2010,2015,2020])
fig.suptitle("US dollar to Euro exchange rates", fontsize=20)
fig.tight_layout(pad=3.0)
plt.show()

Euro to Dollar Conclusion¶

The third graph shows vertical blue stripes for all dates with missing information. These correspond perfectly with the gaps in the original chart. The final chart, with these rows removed, appears to be good match and is easier to look at. This also demonstrates how the rolling mean handles NaN values within a data frame. In either case the shape of the line is identical where the information exists, therefor both are similarly accurate representations of the available data. All further currency data frames will be handled the same.

Euro & Traded Pairs¶

Euro to Highly Traded Currencies¶

The line graphs represent the exchange rates for the Euro to the US dollar, Japanese yen, UK pound, and Australian dollar. These four currencies, in addition to the Euro, are the top five currencies in the Forex market. All five together comprise over 75% of daily trades (4).

Euro to Least Traded Currencies¶

The line graphs represent the exchange rates for the Euro to the Israeli shekel, Philippine peso, Malaysian ringgit, and Romanian leu. These four currencies have low trade volumes, they comprise less than 0.4% of daily trades (4).

In [7]:

# create a list of data frame names
# not sure this is neccessary but trying df = df_list.index(currency) in the framer function doesn't seem to work
# create a list of data frames for each currency with log rate of the exchange rate, 30 day rolling mean, and year
col_names = []
df_dict = {}
for currency in x_rates.columns[1:]:
    df_name = currency
    col_names.append(df_name)
    df = x_rates[["date", currency]].copy()
    df = df[df[currency].notna()]
    df["log_rate"] = np.log(df.iloc[:,1]/df.iloc[:,1].shift()) # getting the log of the exchange rate # double check this is the correct way to get log
    df["rolling_mean_30"] = df[currency].rolling(30).mean()
    df["year"] = df["date"].dt.year
    df_dict[currency] = df

# list of countries for comparisson    
c_list = ["us_dollar", "japanese_yen", "uk_pound", "australian_dollar",
          "israeli_shekel", "philippine_peso", "malaysian_ringgit", "romanian_leu"]

# line graph showing Euro to each high frequency trading pairs
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize=(12,8))
fig.tight_layout(pad=3)
for ax, currency in zip((ax1, ax2, ax3, ax4), (df_dict["us_dollar"], df_dict["japanese_yen"], df_dict["uk_pound"], df_dict["australian_dollar"])):
    ax.plot(currency["date"], currency["rolling_mean_30"])
for ax in ax1, ax2, ax3, ax4:
    ax.set_xticks(["2000-01-04 00:00:00", "2005-01-04 00:00:00", "2010-01-04 00:00:00",
               "2015-01-04 00:00:00", "2020-01-04 00:00:00"])
    ax.set_xticklabels([2000,2005,2010,2015,2020])
    ax.set(yticks=[],
           xlabel="Year")
#   ax.set_ylabel(str(currency)) # why not, i can get it to change to a string "currency", but not a variable
ax1.set(ylabel="US dollar")
ax2.set(ylabel="Japanese yen")
ax3.set(ylabel="UK pound")
ax4.set(ylabel="Australian dollar")
fig.suptitle("Euro to High Frequency Pairs", fontsize=20)
plt.show()

# line graph showing Euro to each low frequency trading pairs
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize=(12,8))
fig.tight_layout(pad=3)
for ax, currency in zip((ax1, ax2, ax3, ax4), (df_dict["israeli_shekel"], df_dict["philippine_peso"], df_dict["malaysian_ringgit"], df_dict["romanian_leu"])):
    ax.plot(currency["date"], currency["rolling_mean_30"])
for ax in ax1, ax2, ax3, ax4:
    ax.set_xticks(["2000-01-04 00:00:00", "2005-01-04 00:00:00", "2010-01-04 00:00:00",
               "2015-01-04 00:00:00", "2020-01-04 00:00:00"])
    ax.set_xticklabels([2000,2005,2010,2015,2020])
    ax.set(yticks=[],
           xlabel="Year")
#   ax.set_ylabel(str(currency)) # why not, i can get it to change to a string "currency", but not a variable
ax1.set(ylabel="Israeli shekel")
ax2.set(ylabel="Philippine peso")
ax3.set(ylabel="Malaysian ringgit")
ax4.set(ylabel="Romanian leu")
fig.suptitle("Euro to Low Frequency Pairs", fontsize=20)
plt.show()

Euro & Traded Pairs Conclusion¶

High Frequency Pairs¶

The US dollar, Japanese yen, and UK pound all show a tight ratio with the Euro in the early 2000s.
Both the Japanese yen and Australian dollar have a period in late 2011 where the exchange ratio gets tight and then rebounds. Is it really at the same time? Are there any other countries with the same pattern and what could the cause have been?

Low Frequency Pairs¶

The Israeli shekel appears to have increased in value and the Romanian leu is definitely loosing value.
The shekel and leu appear to be fairly stable, or have low volatility.
The Israeli shekel and Malaysian ringgit also show the tight ratio of the early 2000s. This is seen to a lesser extent in the Phillipine peso.

The Early Struggle and Other Alignments¶

An overlay of all currencies focused on the first decade of the Euro shows it's initial struggle and eventual success.

In [8]:

x_values1 = df_dict["us_dollar"]["date"]
y_values1 = df_dict["us_dollar"]["rolling_mean_30"]

x_values2 = df_dict["japanese_yen"]["date"]
y_values2 = df_dict["japanese_yen"]["rolling_mean_30"]

x_values3 = df_dict["uk_pound"]["date"]
y_values3 = df_dict["uk_pound"]["rolling_mean_30"]

x_values4 = df_dict["australian_dollar"]["date"]
y_values4 = df_dict["australian_dollar"]["rolling_mean_30"]

fig = plt.figure(figsize=(12,8))
ax = fig.add_subplot(111, label = "1")
ax2 = fig.add_subplot(111, label = "2", frame_on = False)
ax3 = fig.add_subplot(111, label = "3", frame_on = False)
ax4 = fig.add_subplot(111, label = "4", frame_on = False)
# for ax in ax, ax2, ax3, ax4:
#     ax.xaxis.set_major_locator(years) # format the x ticks
#     ax.xaxis.set_major_formatter(years_fmt)
#     fmt_three_years = mdates.MonthLocator(interval = 36) # Major ticks every 3 years
#     ax.xaxis.set_major_locator(fmt_three_years)
#     ax.set(yticks=[],
#            xlabel="Year")
ax.plot(x_values1, y_values1, color = "blue")
ax.set_yticks([])
# ax.set_xlabel("x label 1", color = "C0")
# ax.set_ylabel("y label 1", color = "C0")
# ax.tick_params(axis = 'x', colors = "C0")
# ax.tick_params(axis = 'y', colors = "C0")
ax2.plot(x_values2, y_values2, color = "green")
ax2.set_yticks([])
# ax2.xaxis.tick_top()
# ax2.yaxis.tick_right()
# ax2.set_xlabel('x label 2', color = "C1")
# ax2.set_ylabel('y label 2', color = "C1")
# ax2.xaxis.set_label_position('top')
# ax2.yaxis.set_label_position('right')
# ax2.tick_params(axis = 'x', colors = "C1")
# ax2.tick_params(axis = 'y', colors = "C1")
ax3.plot(x_values3, y_values3, color = "yellow")
ax3.set_yticks([])
ax4.plot(x_values4, y_values4, color = "purple")
ax4.set_yticks([])

plt.show()

In [9]:

x_values1 = df_dict["israeli_shekel"]["date"]
y_values1 = df_dict["israeli_shekel"]["rolling_mean_30"]

x_values2 = df_dict["philippine_peso"]["date"]
y_values2 = df_dict["philippine_peso"]["rolling_mean_30"]

x_values3 = df_dict["malaysian_ringgit"]["date"]
y_values3 = df_dict["malaysian_ringgit"]["rolling_mean_30"]

x_values4 = df_dict["romanian_leu"]["date"]
y_values4 = df_dict["romanian_leu"]["rolling_mean_30"]

fig = plt.figure(figsize=(12,8))
ax = fig.add_subplot(111, label = "1")
ax2 = fig.add_subplot(111, label = "2", frame_on = False)
ax3 = fig.add_subplot(111, label = "3", frame_on = False)
ax4 = fig.add_subplot(111, label = "4", frame_on = False)
# for ax in ax, ax2, ax3, ax4:
#     ax.xaxis.set_major_locator(years) # format the x ticks
#     ax.xaxis.set_major_formatter(years_fmt)
#     fmt_three_years = mdates.MonthLocator(interval = 36) # Major ticks every 3 years
#     ax.xaxis.set_major_locator(fmt_three_years)
#     ax.set(yticks=[],
#            xlabel="Year")
ax.plot(x_values1, y_values1, color = "blue")
ax.set_yticks([])
# ax.set_xlabel("x label 1", color = "C0")
# ax.set_ylabel("y label 1", color = "C0")
# ax.tick_params(axis = 'x', colors = "C0")
# ax.tick_params(axis = 'y', colors = "C0")
ax2.plot(x_values2, y_values2, color = "green")
ax2.set_yticks([])
# ax2.xaxis.tick_top()
# ax2.yaxis.tick_right()
# ax2.set_xlabel('x label 2', color = "C1")
# ax2.set_ylabel('y label 2', color = "C1")
# ax2.xaxis.set_label_position('top')
# ax2.yaxis.set_label_position('right')
# ax2.tick_params(axis = 'x', colors = "C1")
# ax2.tick_params(axis = 'y', colors = "C1")
ax3.plot(x_values3, y_values3, color = "yellow")
ax3.set_yticks([])
ax4.plot(x_values4, y_values4, color = "purple")
ax4.set_yticks([])

plt.show()

The Early Struggle and Other Alignments Conclusion¶

Stuff¶

Stuff
Stuff

Volatility¶

Stuff

In [10]:

# function to create df with year and annual volatility for every row
volatility_df_list = []
def volatizer(currency):
    annual_df_list = [currency[currency['year'] == y] for y in currency['year'].unique()] # list of annual dfs
    c_name = currency.columns[1]
    row_dict = {} # dictionary with year:annual_volatility as key:value 
    for frame in annual_df_list:
        year_name = frame.iat[0,4] # first cell of the "year" column, becomes the "year" key for row_dict
        annual_volatility = frame["log_rate"].std()*253**.5 # volatility measured by standard deviation * 253 trading days per year raised to the 0.5 power
        row_dict[year_name] = annual_volatility # dictionary with year:annual_volatility as key:value 
    df = pd.DataFrame.from_dict(row_dict, orient="index", columns=[c_name+"_annual_vol"]) # new df from dictionary indexing on year
    volatility_df_list.append(df)

# apply volatizer to each currency df
for key in df_dict:
    df_dict[key] = volatizer(df_dict[key])
    
# create an annual volatility data frame with all 40 currencies
annual_volatility = pd.concat(volatility_df_list, axis=1)

In [11]:

display(annual_volatility.head())
annual_volatility["year"] = annual_volatility.index
display(annual_volatility.head())

	australian_dollar_annual_vol	bulgarian_lev_annual_vol	brazilian_real_annual_vol	canadian_dollar_annual_vol	swiss_franc_annual_vol	chinese_yuan_annual_vol	cypriot_pound_annual_vol	czech_koruna_annual_vol	danish_krone_annual_vol	uk_pound_annual_vol	greek_drachma_annual_vol	hong_kong_dollar_annual_vol	croatian_kuna_annual_vol	hungarian_forint_annual_vol	indonesian_rupiah_annual_vol	israeli_shekel_annual_vol	indian_rupee_annual_vol	iceland_krona_annual_vol	japanese_yen_annual_vol	korean_won_annual_vol	lithuanian_litas_annual_vol	latvian_lats_annual_vol	maltese_lira_annual_vol	mexican_peso_annual_vol	malaysian_ringgit_annual_vol	norwegian_krone_annual_vol	new_zealand_dollar_annual_vol	philippine_peso_annual_vol	polish_zloty_annual_vol	romanian_leu_annual_vol	russian_rouble_annual_vol	swedish_krona_annual_vol	singapore_dollar_annual_vol	slovenian_tolar_annual_vol	slovak_koruna_annual_vol	thai_baht_annual_vol	turkish_lira_annual_vol	us_dollar_annual_vol	south_african_rand_annual_vol
1999	0.1287	NaN	NaN	0.1184	0.0256	NaN	0.0096	0.0694	0.0020	0.0718	0.0245	0.0913	NaN	0.0513	0.3246	NaN	NaN	0.0489	0.1445	0.1130	0.0914	0.0669	0.0784	0.2085	0.0914	0.0569	0.1293	0.1208	0.1142	0.2024	0.1963	0.0556	0.0966	0.0456	0.0569	0.1250	0.0614	0.0924	0.1311
2000	0.1270	0.0098	0.2621	0.1507	0.0441	0.1376	0.0116	0.0452	0.0032	0.1112	0.0066	0.1374	0.0169	0.0159	0.2169	0.1479	0.1448	0.0811	0.1650	0.1640	0.1376	0.1035	0.0640	0.1662	0.1373	0.0533	0.1461	0.1688	0.1251	0.1414	0.1533	0.0618	0.1391	0.0207	0.0471	0.1597	0.0832	0.1376	0.1273
2001	0.1273	0.0058	0.2217	0.1149	0.0456	0.1115	0.0170	0.0468	0.0030	0.0755	NaN	0.1116	0.0498	0.0784	0.2555	0.1212	0.1124	0.1161	0.1371	0.1467	0.1115	0.0818	0.0426	0.1575	0.1122	0.0522	0.1206	0.2043	0.1275	0.1119	0.1129	0.0752	0.1132	0.0129	0.0347	0.1201	0.6486	0.1115	0.2148
2002	0.0990	0.0075	0.3007	0.1037	0.0297	0.0962	0.0081	0.0760	0.0023	0.0592	NaN	0.0962	0.0278	0.0508	0.1363	0.1299	0.0961	0.0831	0.0897	0.0982	0.0363	0.0600	0.0335	0.1533	0.0957	0.0499	0.1091	0.0980	0.1016	0.0993	0.0975	0.0677	0.0810	0.0321	0.0504	0.0851	0.1908	0.0962	0.1996
2003	0.0843	0.0047	0.2003	0.0947	0.0387	0.1031	0.0084	0.0530	0.0024	0.0709	NaN	0.1030	0.0344	0.1017	0.1114	0.1256	0.1010	0.0798	0.0941	0.1110	0.0014	0.0599	0.0316	0.1627	0.1024	0.0680	0.0903	0.1068	0.0934	0.0597	0.1011	0.0509	0.0869	0.0082	0.0412	0.0946	0.1606	0.1031	0.1846

	australian_dollar_annual_vol	bulgarian_lev_annual_vol	brazilian_real_annual_vol	canadian_dollar_annual_vol	swiss_franc_annual_vol	chinese_yuan_annual_vol	cypriot_pound_annual_vol	czech_koruna_annual_vol	danish_krone_annual_vol	uk_pound_annual_vol	greek_drachma_annual_vol	hong_kong_dollar_annual_vol	croatian_kuna_annual_vol	hungarian_forint_annual_vol	indonesian_rupiah_annual_vol	israeli_shekel_annual_vol	indian_rupee_annual_vol	iceland_krona_annual_vol	japanese_yen_annual_vol	korean_won_annual_vol	lithuanian_litas_annual_vol	latvian_lats_annual_vol	maltese_lira_annual_vol	mexican_peso_annual_vol	malaysian_ringgit_annual_vol	norwegian_krone_annual_vol	new_zealand_dollar_annual_vol	philippine_peso_annual_vol	polish_zloty_annual_vol	romanian_leu_annual_vol	russian_rouble_annual_vol	swedish_krona_annual_vol	singapore_dollar_annual_vol	slovenian_tolar_annual_vol	slovak_koruna_annual_vol	thai_baht_annual_vol	turkish_lira_annual_vol	us_dollar_annual_vol	south_african_rand_annual_vol	year
1999	0.1287	NaN	NaN	0.1184	0.0256	NaN	0.0096	0.0694	0.0020	0.0718	0.0245	0.0913	NaN	0.0513	0.3246	NaN	NaN	0.0489	0.1445	0.1130	0.0914	0.0669	0.0784	0.2085	0.0914	0.0569	0.1293	0.1208	0.1142	0.2024	0.1963	0.0556	0.0966	0.0456	0.0569	0.1250	0.0614	0.0924	0.1311	1999
2000	0.1270	0.0098	0.2621	0.1507	0.0441	0.1376	0.0116	0.0452	0.0032	0.1112	0.0066	0.1374	0.0169	0.0159	0.2169	0.1479	0.1448	0.0811	0.1650	0.1640	0.1376	0.1035	0.0640	0.1662	0.1373	0.0533	0.1461	0.1688	0.1251	0.1414	0.1533	0.0618	0.1391	0.0207	0.0471	0.1597	0.0832	0.1376	0.1273	2000
2001	0.1273	0.0058	0.2217	0.1149	0.0456	0.1115	0.0170	0.0468	0.0030	0.0755	NaN	0.1116	0.0498	0.0784	0.2555	0.1212	0.1124	0.1161	0.1371	0.1467	0.1115	0.0818	0.0426	0.1575	0.1122	0.0522	0.1206	0.2043	0.1275	0.1119	0.1129	0.0752	0.1132	0.0129	0.0347	0.1201	0.6486	0.1115	0.2148	2001
2002	0.0990	0.0075	0.3007	0.1037	0.0297	0.0962	0.0081	0.0760	0.0023	0.0592	NaN	0.0962	0.0278	0.0508	0.1363	0.1299	0.0961	0.0831	0.0897	0.0982	0.0363	0.0600	0.0335	0.1533	0.0957	0.0499	0.1091	0.0980	0.1016	0.0993	0.0975	0.0677	0.0810	0.0321	0.0504	0.0851	0.1908	0.0962	0.1996	2002
2003	0.0843	0.0047	0.2003	0.0947	0.0387	0.1031	0.0084	0.0530	0.0024	0.0709	NaN	0.1030	0.0344	0.1017	0.1114	0.1256	0.1010	0.0798	0.0941	0.1110	0.0014	0.0599	0.0316	0.1627	0.1024	0.0680	0.0903	0.1068	0.0934	0.0597	0.1011	0.0509	0.0869	0.0082	0.0412	0.0946	0.1606	0.1031	0.1846	2003

In [20]:

# horizontal bar chart showing volatility
from IPython.display import display, HTML
plt.barh(y=annual_volatility.iloc[:,40], width=annual_volatility.iloc[:,2])
plt.show()
# CSS = """
# .output {
#     align-items: center;
# }
# """
# HTML('<style>{}</style>'.format(CSS))

Volatility Conclusion¶

Stuff
Stuff

Volatility Map¶

Stuff

In [13]:

# build and animate the map

Volatility Map Conclusion¶

Stuff
Stuff

Further Research¶

Stuff

Conclusion¶

Stuff

In [14]:

# # need this to correctly get year ticks in DQ server
# years = mdates.YearLocator() # every year
# years_fmt = mdates.DateFormatter('%Y')
# for ax in ax1, ax2, ax3, ax4:
#     ax.xaxis.set_major_locator(years) # format the x ticks
#     ax.xaxis.set_major_formatter(years_fmt)
#     fmt_three_years = mdates.MonthLocator(interval = 36) # Major ticks every 3 years
#     ax.xaxis.set_major_locator(fmt_three_years)
#     ax.set(yticks=[],
#            xlabel="Year")