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Tutorial for cryptos

Author: PyDataBlog

Tutorials/cryptos.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+
+# load data of various cryptos from kaggle datasets:
+# https://www.kaggle.com/sudalairajkumar/cryptocurrencypricehistory/downloads/cryptocurrencypricehistory.zip
+
+# if you run in jupyter, simply make sure that all CSV files are in the same folder as the (.ipynb) notebook file
+# other IDEs should load the CSV filepath accordingly
+
+
+def create_csv(name):
+    file_path = f'{name}_price.csv'
+    return pd.read_csv(file_path, parse_dates=True, index_col='Date')
+
+
+dataset_to_create = ['bitcoin', 'bitcoin_cash', 'dash', 'ethereum_classic',
+                     'bitconnect', 'litecoin', 'monero', 'nem',
+                     'neo', 'numeraire', 'omisego',
+                     'qtum', 'ripple', 'stratis', 'waves']
+
+cryptos = [create_csv(currency) for currency in dataset_to_create]
+
+bitcoin = cryptos[0]
+bitcoin_cash = cryptos[1]
+dash = cryptos[2]
+ethereum_classic = cryptos[3]
+bitconnect = cryptos[4]
+litecoin = cryptos[5]
+monero = cryptos[6]
+nem = cryptos[7]
+neo = cryptos[8]
+numeraire = cryptos[9]
+omisego = cryptos[10]
+qtum = cryptos[11]
+ripple = cryptos[12]
+stratis = cryptos[13]
+waves = cryptos[14]
+
+dataset = [bitcoin, bitcoin_cash, dash,
+           ethereum_classic, bitconnect,
+           litecoin, monero, nem, neo,
+           numeraire, omisego, qtum,
+           ripple,stratis, waves]
+
+for item in dataset:
+    item.sort_index(inplace=True)
+    item['30_day_mean'] = item['Close'].rolling(window=20).mean()
+    item['30_day_volatility'] = item['Close'].rolling(window=20).std()
+
+ethereum_classic[['Close','30_day_mean', '30_day_volatility']].plot(figsize=(10,8));
+plt.title('Ethereum Closing Price with 30 Day Mean & Volatility')
+plt.ylabel('Price')
+plt.show()
+
+print(bitcoin['Close'].resample('A').mean())
+print(bitcoin['Close'].resample('A').apply(lambda x: x[-1]))