#!/usr/bin/env python
# coding: utf-8

# In[1]:


get_ipython().run_cell_magic('javascript', '', 'IPython.OutputArea.prototype._should_scroll = function(lines) {\n    return false;\n}\n')


# In[2]:


# import libraries and set display options
import pandas as pd
import pprint
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib.pyplot import cm
import matplotlib.dates as mdates
plt.style.use('ggplot') # set style for graphs

# 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")

# 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)

# create a list of data frames for each currency with
# the log rate of the exchange rate, 30 day rolling mean, and year
df_dict = {}
for currency in x_rates.columns[1:]:
    df_name = currency
    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"]]

print("x-rates head:")
display(x_rates.head())


# In[3]:


# widget can be turned on and off to discover specific dates
# %matplotlib widget
# line graph showing Euro to four high frequency trading pairs overlapped
chart_date_high = (df_dict["us_dollar"]["date"])
colors = iter([plt.cm.tab10(i) for i in range(0,4)])
fig, ax = plt.subplots(figsize=(18,10))
ax1 = ax.twinx()
ax2 = ax.twinx()
ax3 = ax.twinx()
for ax, currency in zip ((ax, ax1,ax2,ax3), high_freq):
    ax.plot(chart_date_high, currency["rolling_mean_30"], linewidth=3, c=next(colors))
# for ax in ax, ax1, ax2, ax3:
    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], fontsize=18)
    ax.set(yticks=[])
# Sept 11, 2001
ax.axvline(x=chart_date_high.iloc[701], alpha=0.8, color="gold", linewidth=2)
ax.axvspan(xmin=chart_date_high.iloc[11], xmax=chart_date_high.iloc[701],
           ymax=0.97,  ymin=0.86, alpha=0.4, color='gold')
ax.text(0.165, 0.82, "9/11 attacks", color="black", fontsize=16,
        transform=fig.transFigure)
ax.text(0.165, 0.8, "in US", color="black", fontsize=16,
        transform=fig.transFigure)
# EESA
# https://en.wikipedia.org/wiki/Emergency_Economic_Stabilization_Act_of_2008
ax.axvline(x=chart_date_high.iloc[2544], color="gold", linewidth=2)
ax.axvspan(xmin=chart_date_high.iloc[1644], xmax=chart_date_high.iloc[2544],
           ymax=0.19,  ymin=0.10, alpha=0.4, color='gold')
ax.text(0.365, 0.23, "E.E.S.A. enacted", color="black", fontsize=16,
        transform=fig.transFigure)
ax.text(0.365, 0.21, "in US", color="black", fontsize=16,
        transform=fig.transFigure)
# European Debt Crisis
# https://www.businessinsider.com/s?q=euro+forex+2012
# https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1532.pdf
ax.axvline(x=chart_date_high.iloc[3568], color="gold", linewidth=2)
ax.axvspan(xmin=chart_date_high.iloc[3568], xmax=chart_date_high.iloc[4488],
           ymax=0.97,  ymin=0.86, alpha=0.4, color='gold')
ax.text(0.60, 0.82, "ECB announces", color="black", fontsize=16,
        transform=fig.transFigure)
ax.text(0.60, 0.8, "unlimited support", color="black", fontsize=16,
        transform=fig.transFigure)
fig.suptitle(" "*12 + "Alignment of Euro/High Frequency Pairs" + " "*12,
             color = "#f0f0f0", backgroundcolor = "#4d4d4d", fontsize=35, weight="bold")
ax.text(0.75, 0.24, "US dollar", color="tab:blue", fontsize=18, weight="bold",
        transform=fig.transFigure)
ax.text(0.75, 0.21, "Japanese yen", color="tab:orange", fontsize=18, weight="bold",
        transform=fig.transFigure)
ax.text(0.75, 0.17, "UK pound", color="tab:green", fontsize=18, weight="bold",
        transform=fig.transFigure)
ax.text(0.75, 0.14, "Australian dollar", color="tab:red", fontsize=18, weight="bold",
        transform=fig.transFigure)
ax.text(0.08, 0.0, "© B McMinn" + " "*174 + "Source: European Central Bank",
        color = "#f0f0f0", backgroundcolor = "#4d4d4d", fontsize=14,
        transform=fig.transFigure)
plt.show()


# In[4]:


# *****
# Here is all the same stuff but with NaN excluded


# In[5]:


# create a list of data frames for each currency with
# the log rate of the exchange rate, 30 day rolling mean, and year
df_dict = {}
for currency in x_rates.columns[1:]:
    df_name = currency
    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[6]:


# the rows for the three vlines
sept11 = df_dict["us_dollar"]["date"] == "2001-09-11"
sept11 = df_dict["us_dollar"][sept11]
eesa = df_dict["us_dollar"]["date"] == "2008-10-03"
eesa = df_dict["us_dollar"][eesa]
edc = df_dict["us_dollar"]["date"] == "2012-09-06"
edc = df_dict["us_dollar"][edc]

print("the rows for the three vlines")
display(sept11)
display(eesa)
display(edc)


# In[7]:


# widget can be turned on and off to discover specific dates
# %matplotlib widget
# line graph showing Euro to four high frequency trading pairs overlapped
chart_date_high = (df_dict["us_dollar"]["date"])
colors = iter([plt.cm.tab10(i) for i in range(0,4)])
fig, ax = plt.subplots(figsize=(18,10))
ax1 = ax.twinx()
ax2 = ax.twinx()
ax3 = ax.twinx()
for ax, currency in zip ((ax, ax1,ax2,ax3), high_freq):
    ax.plot(chart_date_high, currency["rolling_mean_30"], linewidth=3, c=next(colors))
    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], fontsize=18)
    ax.set(yticks=[])
# Sept 11, 2001
sept11 = df_dict["us_dollar"]["date"] == "2001-09-11"
sept11 = df_dict["us_dollar"][sept11]
sept11 = sept11["date"]
ax.axvline(x=sept11, alpha=0.8, color="gold", linewidth=2)
ax.axvspan(xmin=chart_date_high.iloc[11], xmax=chart_date_high.iloc[701],
           ymax=0.97,  ymin=0.86, alpha=0.4, color='gold')
ax.text(0.165, 0.82, "9/11 attacks", color="black", fontsize=16,
        transform=fig.transFigure)
ax.text(0.165, 0.8, "in US", color="black", fontsize=16,
        transform=fig.transFigure)
# EESA
# https://en.wikipedia.org/wiki/Emergency_Economic_Stabilization_Act_of_2008
eesa = df_dict["us_dollar"]["date"] == "2008-10-03"
eesa = df_dict["us_dollar"][eesa]
eesa = eesa["date"]
ax.axvline(x=eesa, color="gold", linewidth=2)
ax.axvspan(xmin=chart_date_high.iloc[1644], xmax=chart_date_high.iloc[2544],
           ymax=0.19,  ymin=0.10, alpha=0.4, color='gold')
ax.text(0.365, 0.23, "E.E.S.A. enacted", color="black", fontsize=16,
        transform=fig.transFigure)
ax.text(0.365, 0.21, "in US", color="black", fontsize=16,
        transform=fig.transFigure)
# European Debt Crisis
# https://www.businessinsider.com/s?q=euro+forex+2012
# https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1532.pdf
edc = df_dict["us_dollar"]["date"] == "2012-09-06"
edc = df_dict["us_dollar"][edc]
edc = edc["date"]
ax.axvline(x=edc, color="gold", linewidth=2)
ax.axvspan(xmin=chart_date_high.iloc[3568], xmax=chart_date_high.iloc[4488],
           ymax=0.97,  ymin=0.86, alpha=0.4, color='gold')
ax.text(0.60, 0.82, "ECB announces", color="black", fontsize=16,
        transform=fig.transFigure)
ax.text(0.60, 0.8, "unlimited support", color="black", fontsize=16,
        transform=fig.transFigure)
# other text
fig.suptitle(" "*12 + "Alignment of Euro/High Frequency Pairs" + " "*12,
             color = "#f0f0f0", backgroundcolor = "#4d4d4d", fontsize=35, weight="bold")
ax.text(0.75, 0.24, "US dollar", color="tab:blue", fontsize=18, weight="bold",
        transform=fig.transFigure)
ax.text(0.75, 0.21, "Japanese yen", color="tab:orange", fontsize=18, weight="bold",
        transform=fig.transFigure)
ax.text(0.75, 0.17, "UK pound", color="tab:green", fontsize=18, weight="bold",
        transform=fig.transFigure)
ax.text(0.75, 0.14, "Australian dollar", color="tab:red", fontsize=18, weight="bold",
        transform=fig.transFigure)
ax.text(0.08, 0.0, "© B McMinn" + " "*174 + "Source: European Central Bank",
        color = "#f0f0f0", backgroundcolor = "#4d4d4d", fontsize=14,
        transform=fig.transFigure)
plt.show()