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

# In[1]:


# import libraries and set display options
import pandas as pd
import pprint
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates

# 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

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())


# In[2]:


# create a list of data frames for each currency with 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["rolling_mean_30"] = df[currency].rolling(30).mean()
    df_dict[currency] = df

# create a stand-alone date series
chart_date = (x_rates["date"])


# In[3]:


# line graph showing Euro to four high frequency trading pairs overlapped
get_ipython().run_line_magic('matplotlib', 'widget')
fig, ax = plt.subplots(figsize=(8,6))
ax1 = ax.twinx()
ax2 = ax.twinx()
ax3 = ax.twinx()
line = ax.plot(chart_date, df_dict["us_dollar"]["rolling_mean_30"],
               'tab:blue')
line_1 = ax1.plot(chart_date, df_dict["japanese_yen"]["rolling_mean_30"],
                  'tab:orange')
line_2 = ax2.plot(chart_date, df_dict["uk_pound"]["rolling_mean_30"],
                  'tab:green')
line_3 = ax3.plot(chart_date, df_dict["australian_dollar"]["rolling_mean_30"],
                  'tab:red')
ax.axvline(x=chart_date.iloc[3560], color="gold", linewidth=2) # vline to highlight point on chart
for ax in ax, ax1, ax2, ax3:
    ax.set(yticks=[])
    ax.xaxis.set_major_formatter(mdates.DateFormatter('%m/%d/%y'))
plt.show()


# In[4]:


display(chart_date.iloc[0])
display(chart_date.iloc[701])
display(chart_date.iloc[2499])
display(chart_date.iloc[3560])


# In[5]:


display(chart_date)