#!/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)

mini = x_rates.iloc[:5000]
display(mini.head(2))
display(mini.tail(2))


# In[3]:


# 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_list = []
df_dict = {}
for column in mini.columns[1:]:
    df_name = column
    col_names.append(df_name)
    df = mini[["date", column]].copy()
    df = df[df[column].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[column].rolling(30).mean()
    df["year"] = df["date"].dt.year
#     df_dict[column] = df
    df_list.append(df)


# In[4]:


# functions to build annual volatility given string of currency name
# function to assign dataframe to variable name
# i could include this in volatizer, but for now keep separate because these are frames of all the original data
def framer(currency):
    index = col_names.index(currency)
    df = df_list[index] # this is a dataframe containing a single currency and the columns built in cell 3
    return df

# 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):
    all_the_years = [currency[currency['year'] == y] for y in currency['year'].unique()] # list of dataframes for each year
    c_name = currency.columns[1]
    df_dict = {}
    for frame in all_the_years:
        year_name = frame.iat[0,4] # the year for each df, becomes the "year" cell for annual volatility df
        annual_volatility = frame["log_rate"].std()*253**.5 # volatility measured by standard deviation * 253 trading days per year raised to the 0.5 power
        df_dict[year_name] = annual_volatility
    df = pd.DataFrame.from_dict(df_dict, orient="index", columns=[c_name+"_annual_vol"]) # indexing on year, not sure if this is cool
    return df
        
brazilian_real = framer("brazilian_real")
br_vol = volatizer(brazilian_real)
# now i need to run this on all 40 df's and get a new df with all of them joined
# then build an hbar chart with the eight from above
# maybe two hbar charts for cell phones?


# In[5]:


display(br_vol)


# In[10]:


# use string of currency to get index position and assign it to variable name
index = col_names.index("chinese_yuan")
print(index)
chinese_yuan = df_list[5]

# function to create df for each year in currency df
def split_years(df):
    return [df[df['year'] == y] for y in df['year'].unique()]

all_the_years = split_years(chinese_yuan)
# see if I can make split_years split and heads


# # determine annual volatility for euro to currency
# for frame in all_the_years:
#     year_name = frame.iat[0,4]
#     annual_volatility = frame["log_rate"].std()*253**.5 # volatility measured by standard deviation * 253 trading days per year
#     print("The Euro to Chinese yuan volatility for", year_name, "is" ,annual_volatility)


# In[11]:


display(all_the_years[0])