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

# In[1]:


from IPython.core.display import display, HTML
display(HTML("<style>.container { width:80% !important; }</style>"))


# In[2]:


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', 1000)
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)

display(x_rates.head(2))
display(x_rates.tail(2))


# In[3]:


# miniaturize x_rates
x_rates = x_rates.iloc[:,:6]

# array with names of currencies
currency_names = x_rates.columns.values
currency_names = currency_names[1:]

# create a list & dictionary of data frames for each currency with log rate of the exchange rate, 30 day rolling mean, and year
col_names = [] # list with names of currencies
df_list = [] # list of 40 dataframes
df_dict = {} # dictionary with 40 currency:currency_dataframe pairs
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
    df_list.append(df)


# In[4]:


# function to create df with year and annual volatility for every row # i think i could use aggregation here but don't know how
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 = {}
    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
    df = pd.DataFrame.from_dict(row_dict, orient="index", columns=[c_name+"_annual_vol"]) # indexing on year, not sure if this is cool
    return df

# apply volatizer to each currency df
for key in df_dict:
    df_dict[key] = volatizer(df_dict[key])

display(df_dict["bulgarian_lev"])