<jupyter_start><jupyter_text>code adapted from [Analyzing Cryptocurrencies in Python](https://youtu.be/HqGlkACB3rg)  import dependencies<jupyter_code>import matplotlib.pyplot as plt
import mplfinance as mpf
import seaborn as sns
import datetime as dt

from yahoo_fin.stock_info import get_data<jupyter_output><empty_output><jupyter_text>Load data<jupyter_code>currency = "USD"
metric = "close"

start = dt.datetime(2019, 1, 1)
end = dt.datetime.now()

crypto = ['BTC', 'ETH','USDT','BNB','ADA','DOGE', 'XRP',
          'USDC', 'DOT1','UNI3','BCH', 'LTC','SOL1','LINK',
          'MATIC','XLM','XMR','EOS','CRO','ALGO','AAVE',
          'MIOTA','XTZ','ATOM1']

colnames = []<jupyter_output><empty_output><jupyter_text>Prepare Data<jupyter_code>first = True

for ticker in crypto:
    data = get_data(f'{ticker}-{currency}', start, end, index_as_date = True, interval = '1d')
    if first:
        combined = data[[metric]].copy()
        colnames.append(ticker)
        combined.columns = colnames
        first = False
    else:
        combined = combined.join(data[metric])
        colnames.append(ticker)
        combined.columns = colnames
plt.figure(figsize=(17, 10))
plt.yscale('log')

for ticker in crypto:
    plt.plot(combined[ticker], label=ticker)

plt.legend(loc="upper left")
plt.show()
# plot heatmap
combined = combined.pct_change().corr(method="pearson")
fig, ax = plt.subplots(figsize=(15, 12))

sns.heatmap(combined,
            annot=True,
            cmap="RdYlGn",
            #robust=True,
            center=0.4)
plt.show()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15545685279187818, 'fraction_numerical': 0.013959390862944163, 'mean_word_length': 4.12012987012987, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 13, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '271537', 'license': 'MIT', 'n_tokens_mistral': 599, 'n_tokens_neox': 578, 'n_words': 104, 'path': 'notebooks/experimentation/cryptocurrency_correlation_analysis_from_yahoo.ipynb', 'repo_name': 'ArtTucker/crypto_prices_ML', 'size': '1576'}