btc_usd_price_kraken = ctl.get_quandl_data('BCHARTS/KRAKENUSD', dataDir)
# Pull pricing data for 3 more BTC exchanges
exchanges = ['COINBASE', 'BITSTAMP', 'ITBIT']
# Storing Pandas DF in dict (data from KRAKEN + other exchanges):
exchange_data = {} # dict
exchange_data['KRAKEN'] = btc_usd_price_kraken # Pandas DF
for exchange in exchanges:
exchange_code = 'BCHARTS/{}USD'.format(exchange)
btc_exchange_df = ctl.get_quandl_data(exchange_code, dataDir) # Pandas DF
exchange_data[exchange] = btc_exchange_df
# Now we will merge all of the dataframes together on their "Weighted
# Price" column (merge the BTC price dataseries' into a single dataframe)
btc_usd_datasets = ctl.merge_dfs_on_column( # def merge_dfs_on_column(dataframes, labels, col):
list(exchange_data.values()), list(exchange_data.keys()), 'Weighted Price')
# Remove "0" values
btc_usd_datasets.replace(0, np.nan, inplace=True)
# We can now calculate a new column, containing the average daily
# Bitcoin price across all of the exchanges.
btc_usd_datasets['avg_btc_price_usd'] = btc_usd_datasets.mean(axis=1)
# Step 3 - Retrieve Altcoin Pricing Data
# --------------------------------------
# Step 3.2 - Download Trading Data From Poloniex
# ----------------------------------------------
# We'll download exchange data for nine of the top cryptocurrencies -
# Ethereum, Litecoin, Ripple, Ethereum Classic, Stellar, Dashcoin,
exchanges = ['COINBASE', 'BITSTAMP', 'ITBIT']
exchange_data = {}
exchange_data['KRAKEN'] = btc_usd_price_kraken
for exchange in exchanges:
exchange_code = 'BCHARTS/{}USD'.format(exchange)
btc_exchange_df = ctl.get_quandl_data(exchange_code, dataDir)
exchange_data[exchange] = btc_exchange_df
# Now we will merge all of the dataframes together on their "Weighted
# Price" column (merge the BTC price dataseries' into a single dataframe)
btc_usd_datasets = ctl.merge_dfs_on_column( \
list(exchange_data.values()), \
list(exchange_data.keys()), 'Weighted Price' )
#print("btc_usd_datasets = ")
#print( btc_usd_datasets.tail() )
# Step 2.5 - Visualize The Pricing Datasets
# -----------------------------------------
# For this, we'll define a helper function to provide a single-line
# command to generate a graph from the dataframe.
# We can now easily generate a graph for the Bitcoin pricing data.
# # Plot all of the BTC exchange prices
ctl.df_scatter(btc_usd_datasets, 'Bitcoin Price (USD) By Exchange')
# Step 2.6 - Clean and Aggregate the Pricing Data
# -----------------------------------------------