In [1]:

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

# set frame width and center output
from IPython.core.display import display, HTML
display(HTML("""
<style>
.container {width:84% !important;
}
</style>
"""))

# pretty print options
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)

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

In [2]:

# 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)

display(x_rates.head())

/var/folders/bz/5cj1tzbj5xn319rpqrw6gpmh0000gn/T/ipykernel_10747/1632861684.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

In [3]:

import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib.pyplot import cm
colors = iter([plt.cm.Set3(i) for i in range(20)])

# 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

# currencies for comparison
high_freq = [df_dict["us_dollar"], df_dict["japanese_yen"], df_dict["uk_pound"], df_dict["australian_dollar"]]
low_freq = [df_dict["israeli_shekel"], df_dict["philippine_peso"], df_dict["malaysian_ringgit"], df_dict["romanian_leu"]]

In [4]:

# line graph showing Euro to four high frequency trading pairs overlapped
# widget can be turned on and off to discover specific dates
%matplotlib widget
fig, ax = plt.subplots(figsize=(7.5,5))
ax1 = ax.twinx()
ax2 = ax.twinx()
ax3 = ax.twinx()
line = ax.plot(df_dict["us_dollar"]["date"], df_dict["us_dollar"]["rolling_mean_30"], 'tab:orange')
line_1 = ax1.plot(df_dict["us_dollar"]["date"], df_dict["japanese_yen"]["rolling_mean_30"], 'tab:blue')
line_2 = ax2.plot(df_dict["us_dollar"]["date"], df_dict["uk_pound"]["rolling_mean_30"], 'tab:green')
line_3 = ax3.plot(df_dict["us_dollar"]["date"], df_dict["australian_dollar"]["rolling_mean_30"], 'tab:red')
lines = [line, line_1, line_2, line_3]
for ax in ax, ax1, ax2, ax3:
    ax.set(yticks=[])
plt.show()

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
/var/folders/bz/5cj1tzbj5xn319rpqrw6gpmh0000gn/T/ipykernel_10747/3736899207.py in <module>
      1 # line graph showing Euro to four high frequency trading pairs overlapped
      2 # widget can be turned on and off to discover specific dates
----> 3 get_ipython().run_line_magic('matplotlib', 'widget')
      4 fig, ax = plt.subplots(figsize=(7.5,5))
      5 ax1 = ax.twinx()

/opt/anaconda3/envs/geo_env/lib/python3.9/site-packages/IPython/core/interactiveshell.py in run_line_magic(self, magic_name, line, _stack_depth)
   2349                 kwargs['local_ns'] = self.get_local_scope(stack_depth)
   2350             with self.builtin_trap:
-> 2351                 result = fn(*args, **kwargs)
   2352             return result
   2353 

/opt/anaconda3/envs/geo_env/lib/python3.9/site-packages/decorator.py in fun(*args, **kw)
    230             if not kwsyntax:
    231                 args, kw = fix(args, kw, sig)
--> 232             return caller(func, *(extras + args), **kw)
    233     fun.__name__ = func.__name__
    234     fun.__doc__ = func.__doc__

/opt/anaconda3/envs/geo_env/lib/python3.9/site-packages/IPython/core/magic.py in <lambda>(f, *a, **k)
    185     # but it's overkill for just that one bit of state.
    186     def magic_deco(arg):
--> 187         call = lambda f, *a, **k: f(*a, **k)
    188 
    189         if callable(arg):

/opt/anaconda3/envs/geo_env/lib/python3.9/site-packages/IPython/core/magics/pylab.py in matplotlib(self, line)
     97             print("Available matplotlib backends: %s" % backends_list)
     98         else:
---> 99             gui, backend = self.shell.enable_matplotlib(args.gui.lower() if isinstance(args.gui, str) else args.gui)
    100             self._show_matplotlib_backend(args.gui, backend)
    101 

/opt/anaconda3/envs/geo_env/lib/python3.9/site-packages/IPython/core/interactiveshell.py in enable_matplotlib(self, gui)
   3531                 gui, backend = pt.find_gui_and_backend(self.pylab_gui_select)
   3532 
-> 3533         pt.activate_matplotlib(backend)
   3534         configure_inline_support(self, backend)
   3535 

/opt/anaconda3/envs/geo_env/lib/python3.9/site-packages/IPython/core/pylabtools.py in activate_matplotlib(backend)
    357     from matplotlib import pyplot as plt
    358 
--> 359     plt.switch_backend(backend)
    360 
    361     plt.show._needmain = False

/opt/anaconda3/envs/geo_env/lib/python3.9/site-packages/matplotlib/pyplot.py in switch_backend(newbackend)
    265     backend_name = cbook._backend_module_name(newbackend)
    266 
--> 267     class backend_mod(matplotlib.backend_bases._Backend):
    268         locals().update(vars(importlib.import_module(backend_name)))
    269 

/opt/anaconda3/envs/geo_env/lib/python3.9/site-packages/matplotlib/pyplot.py in backend_mod()
    266 
    267     class backend_mod(matplotlib.backend_bases._Backend):
--> 268         locals().update(vars(importlib.import_module(backend_name)))
    269 
    270     required_framework = _get_required_interactive_framework(backend_mod)

/opt/anaconda3/envs/geo_env/lib/python3.9/importlib/__init__.py in import_module(name, package)
    125                 break
    126             level += 1
--> 127     return _bootstrap._gcd_import(name[level:], package, level)
    128 
    129 

/opt/anaconda3/envs/geo_env/lib/python3.9/importlib/_bootstrap.py in _gcd_import(name, package, level)

/opt/anaconda3/envs/geo_env/lib/python3.9/importlib/_bootstrap.py in _find_and_load(name, import_)

/opt/anaconda3/envs/geo_env/lib/python3.9/importlib/_bootstrap.py in _find_and_load_unlocked(name, import_)

/opt/anaconda3/envs/geo_env/lib/python3.9/importlib/_bootstrap.py in _call_with_frames_removed(f, *args, **kwds)

/opt/anaconda3/envs/geo_env/lib/python3.9/importlib/_bootstrap.py in _gcd_import(name, package, level)

/opt/anaconda3/envs/geo_env/lib/python3.9/importlib/_bootstrap.py in _find_and_load(name, import_)

/opt/anaconda3/envs/geo_env/lib/python3.9/importlib/_bootstrap.py in _find_and_load_unlocked(name, import_)

ModuleNotFoundError: No module named 'ipympl'