Selecting the top most correlated markets.
top_10 = df_transpose.corr()['BTC-GBP'].sort_values(ascending=False).keys()[0:20]
print (top_10)Index(['BTC-GBP', 'BTC-USD', 'WBTC-USD', 'BTC-EUR', 'BTC-USDT', 'BTC-USDC', 'ADA-USDC', 'ADA-USD', 'YFI-USD', 'ADA-EUR', 'ADA-GBP', 'OXT-USD', 'ETH-GBP', 'WBTC-BTC', 'ETH-USD', 'ETH-DAI', 'ETH-EUR', 'ETH-USDT', 'ETH-USDC', 'STORJ-USD'], dtype='object')
Selecting the least correlated markets.
bottom_10 = df_transpose.corr()['BTC-GBP'].sort_values(ascending=True).keys()[0:20]
print (bottom_10)Index(['MIR-GBP', 'USDT-EUR', 'USDC-EUR', 'ZEC-BTC', 'DAI-USD', 'CRV-BTC', 'ADA-ETH', 'DAI-USDC', 'UMA-BTC', 'COMP-BTC', 'USDC-GBP', 'MIR-BTC', 'USDT-GBP', 'REP-BTC', 'FIL-BTC', 'ICP-BTC', 'SUSHI-ETH', 'MIR-EUR', 'MIR-USD', 'BAT-ETH'], dtype='object')
What would be of most interest to us is the least correlated markets. I don’t think it’s such a surprise that the stable coins like Tether (USDT) and USD Coin (USDC) are there. I’m inclined to remove those as they are by design supposed to be stable.
df_filtered = df[~df_transpose.keys().str.contains('USD[TC]', regex=True)]
df_filtered_transpose = df_filtered.T
And re-create our bottom list 10 without USDT and USDC.
Index(['MIR-GBP', 'ZEC-BTC', 'DAI-USD', 'CRV-BTC', 'ADA-ETH', 'UMA-BTC', 'COMP-BTC', 'MIR-BTC', 'REP-BTC', 'FIL-BTC', 'ICP-BTC', 'SUSHI-ETH', 'MIR-EUR', 'MIR-USD', 'BAT-ETH', 'ZEC-USD', 'FORTH-BTC', 'CRV-EUR', 'SUSHI-BTC', 'RLC-BTC'], dtype='object')
Summary
So there you have it. I would be interested to hear your thoughts. I can see ADA in the list which is what I would expect to some degree. It’s not totally resistant to Bitcoin and Ethereum crashes but tends to be affected less. I’m trading ADA-GBP but according to this ADA-ETH is a better option. The problem here is often if Bitcoin crashes the same happens for Ethereum. I would be inclined to ignore the ETH options above.
The next step would be to open TradingView.com and to compare these markets against BTC-GBP.
Google Colab
I’ve provided all the code for you already above but if you would like the source code I’ve created a notebook which can be easily run in Google Colab.
- Go to “https://colab.research.google.com”
- Click the GitHub tab
- For “Enter a GitHub URL or search by organisation or user” enter “https://github.com/whittlem/colabnotebooks” and press enter
- Repository: “whittlem/colabnotebooks”, Branch: “main”
- Click on “CoinbaseProMarketAnalysis.ipynp”
- Click on “Runtime” from the menu, then “Run all”
Good luck and I hope you have found this useful.