import os
import numpy as np
import pandas as pd
import pickle
import quandl
from datetime import datetime
import matplotlib.pyplot as plt
from scipy.optimize import minimize



import plotly.offline as py
import plotly.graph_objs as go
import plotly.figure_factory as ff
py.init_notebook_mode(connected=True)



# Choose the number of portfolios to investigate
num_ports = 100
altcoins = ['ETH']



#altcoins = ['ETH','XRP','LTC','XMR','DCR']

def get_quandl_data(quandl_id):
    '''Download and cache Quandl dataseries'''
    cache_path = '{}.pkl'.format(quandl_id).replace('/','-')
    try:
        f = open(cache_path, 'rb')
        df = pickle.load(f)   
        print('Loaded {} from cache'.format(quandl_id))
    except (OSError, IOError) as e:
        print('Downloading {} from Quandl'.format(quandl_id))
        df = quandl.get(quandl_id, returns="pandas")
        df.to_pickle(cache_path)
        print('Cached {} at {}'.format(quandl_id, cache_path))
    return df

# Pull Kraken BTC price exchange data
btc_usd_price_kraken = get_quandl_data('BCHARTS/KRAKENUSD')

# Pull pricing data for 3 more BTC exchanges
exchanges = ['BITSTAMP']

exchange_data = {}

exchange_data['KRAKEN'] = btc_usd_price_kraken

for exchange in exchanges:
    exchange_code = 'BCHARTS/{}USD'.format(exchange)
    btc_exchange_df = get_quandl_data(exchange_code)
    exchange_data[exchange] = btc_exchange_df

def merge_dfs_on_column(dataframes, labels, col):
    '''Merge a single column of each dataframe into a new combined dataframe'''
    series_dict = {}
    for index in range(len(dataframes)):
        series_dict[labels[index]] = dataframes[index][col]
        
    return pd.DataFrame(series_dict)


# Merge the BTC price dataseries' into a single dataframe
btc_usd_datasets = merge_dfs_on_column(list(exchange_data.values()), list(exchange_data.keys()), 'Weighted Price')

# Remove "0" values
btc_usd_datasets.replace(0, np.nan, inplace=True)