├── crypto_candle.py
└── crypto_correlation.py


/crypto_candle.py:
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 1 | import pandas_datareader as web
 2 | import datetime as dt
 3 | import mplfinance as mpf
 4 | 
 5 | start = dt.datetime(2020, 1, 1)
 6 | end = dt.datetime.now()
 7 | 
 8 | # Altcoins also work
 9 | data = web.DataReader("BTC-USD", "yahoo", start, end)
10 | 
11 | mpf.plot(data, type="candle", volume=True, style="yahoo")
12 | 


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/crypto_correlation.py:
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 1 | import pandas_datareader as web
 2 | import mplfinance as mpf
 3 | import datetime as dt
 4 | import matplotlib.pyplot as plt
 5 | import seaborn as sns
 6 | 
 7 | currency = "USD"
 8 | metric = "Close"
 9 | 
10 | start = dt.datetime(2018,1,1)
11 | end = dt.datetime.now()
12 | 
13 | crypto = ['BTC', 'ETH', 'LTC', 'XRP', 'DASH', 'SC']
14 | colnames = []
15 | 
16 | first = True
17 | 
18 | for ticker in crypto:
19 |     data = web.DataReader(f"{ticker}-{currency}", "yahoo", start, end)
20 |     if first:
21 |         combined = data[[metric]].copy()
22 |         colnames.append(ticker)
23 |         combined.columns = colnames
24 |         first = False
25 |     else:
26 |         combined = combined.join(data[metric])
27 |         colnames.append(ticker)
28 |         combined.columns = colnames
29 | 
30 | plt.yscale('log') # first show linear
31 | 
32 | for ticker in crypto:
33 |     plt.plot(combined[ticker], label=ticker)
34 | 
35 | plt.legend(loc="upper right")
36 | 
37 | plt.show()
38 | 
39 | # # Correlation Heat Map
40 | 
41 | print(combined)
42 | 
43 | combined = combined.pct_change().corr(method='pearson')
44 | 
45 | sns.heatmap(combined, annot=True, cmap="coolwarm")
46 | plt.show()
47 | 


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