Created
April 28, 2024 19:52
-
-
Save gaugau3000/cbd745ce840eabcac4a8a48f6b6a0a73 to your computer and use it in GitHub Desktop.
rolling betas
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| import ccxt | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| # Initialize exchange | |
| exchange = ccxt.binance({ | |
| 'enableRateLimit': True, | |
| 'options': { | |
| 'defaultType': 'future' | |
| } | |
| }) | |
| # Fetch daily candles for BTC/USDT and UNI/USDT | |
| btc_data = exchange.fetch_ohlcv("BTC/USDT", timeframe='1h', limit=1000) | |
| uni_data = exchange.fetch_ohlcv("UNI/USDT", timeframe='1h', limit=1000) | |
| # Convert data to DataFrame | |
| btc_df = pd.DataFrame(btc_data, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume']) | |
| uni_df = pd.DataFrame(uni_data, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume']) | |
| # Convert timestamp to readable dates | |
| btc_df['timestamp'] = pd.to_datetime(btc_df['timestamp'], unit='ms') | |
| uni_df['timestamp'] = pd.to_datetime(uni_df['timestamp'], unit='ms') | |
| # Set the timestamp as the index | |
| btc_df.set_index('timestamp', inplace=True) | |
| uni_df.set_index('timestamp', inplace=True) | |
| # Compute daily returns | |
| btc_df['returns'] = btc_df['close'].pct_change() | |
| uni_df['returns'] = uni_df['close'].pct_change() | |
| # Compute rolling beta of UNI to BTC | |
| rolling_covariance = uni_df['returns'].rolling(window=40).cov(btc_df['returns']) | |
| rolling_variance = btc_df['returns'].rolling(window=40).var() | |
| rolling_beta = rolling_covariance / rolling_variance | |
| # Compute rolling correlation of UNI to BTC | |
| rolling_correlation = uni_df['returns'].rolling(window=40).corr(btc_df['returns']) | |
| # Plotting the prices, rolling beta, and rolling correlation | |
| fig, ax = plt.subplots(3, 1, figsize=(12, 15)) | |
| # Prices plot with different scales | |
| ax0 = ax[0] | |
| ax0.plot(btc_df['close'], label='BTC/USDT', color='blue') | |
| ax0.set_ylabel('BTC/USDT Price', color='blue') | |
| ax0.tick_params(axis='y', labelcolor='blue') | |
| ax0.legend(loc='upper left') | |
| # Create a second y-axis for the UNI/USDT prices | |
| ax1 = ax0.twinx() | |
| ax1.plot(uni_df['close'], label='UNI/USDT', color='red') | |
| ax1.set_ylabel('UNI/USDT Price', color='red') | |
| ax1.tick_params(axis='y', labelcolor='red') | |
| ax1.legend(loc='upper right') | |
| ax[0].set_title('BTC/USDT and UNI/USDT Prices on Different Scales') | |
| # Rolling beta plot | |
| ax[1].plot(rolling_beta, color='green', label='Rolling Beta UNI/BTC') | |
| ax[1].set_title('Rolling Beta of UNI to BTC on 40 periods') | |
| ax[1].legend() | |
| # Rolling correlation plot | |
| ax[2].plot(rolling_correlation, color='purple', label='Rolling Correlation UNI/BTC') | |
| ax[2].set_title('Rolling Correlation of UNI to BTC on 40 periods') | |
| ax[2].legend() | |
| plt.tight_layout() | |
| plt.show() |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment