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 # -----------------------------------------------