In [8]:

# import man modules
import yfinance as yf
import matplotlib.pyplot as plt
import seaborn as sns

In [10]:

# Currency pairs to analyse
currency_pairs = ['EURUSD=X', 'EURCHF=X', 'EURJPY=X', 'EURCAD=X', 'EURGBP=X',
                 'GBPUSD=X', 'GBPCHF=X', 'GBPJPY=X', 'GBPCAD=X',
                 'USDCHF=X', 'USDJPY=X','USDCAD=X',
                 'CADCHF=X', 'CADJPY=X', 
                 'CHFJPY=X']

# Download historical forex data from Yahoo finance
data = yf.download(currency_pairs, period='1y', interval='1d')

[*********************100%%**********************]  15 of 15 completed

In [12]:

# Calculate the correlation matrix between the currency pairs
correlation = data['Adj Close'].corr()

correlation

Out[12]:

	CADCHF=X	CADJPY=X	CHFJPY=X	EURCAD=X	EURCHF=X	EURGBP=X	EURJPY=X	EURUSD=X	GBPCAD=X	GBPCHF=X	GBPJPY=X	GBPUSD=X	USDCAD=X	USDCHF=X	USDJPY=X
CADCHF=X	1.000000	0.146840	-0.570674	-0.926703	0.317325	0.286145	-0.597370	-0.892600	-0.941544	0.066657	-0.548551	-0.887658	0.201108	0.953216	0.212857
CADJPY=X	0.146840	1.000000	0.728234	-0.422460	-0.682597	-0.554867	0.683256	-0.303501	-0.136864	-0.052272	0.712798	-0.053794	-0.209035	0.068519	0.955807
CHFJPY=X	-0.570674	0.728234	1.000000	0.290026	-0.789389	-0.657114	0.979984	0.364166	0.537094	-0.094058	0.970635	0.567901	-0.309463	-0.602271	0.646915
EURCAD=X	-0.926703	-0.422460	0.290026	1.000000	0.061373	-0.124578	0.372838	0.943475	0.932272	0.134686	0.318147	0.866441	-0.160260	-0.875930	-0.481119
EURCHF=X	0.317325	-0.682597	-0.789389	0.061373	1.000000	0.437999	-0.651874	0.015116	-0.146147	0.526453	-0.657038	-0.164467	0.113172	0.316189	-0.660304
EURGBP=X	0.286145	-0.554867	-0.657114	-0.124578	0.437999	1.000000	-0.670907	-0.215398	-0.432377	-0.421029	-0.754033	-0.474687	0.310892	0.350118	-0.470356
EURJPY=X	-0.597370	0.683256	0.979984	0.372838	-0.651874	-0.670907	1.000000	0.450776	0.611936	0.055478	0.986979	0.645242	-0.347809	-0.637934	0.589409
EURUSD=X	-0.892600	-0.303501	0.364166	0.943475	0.015116	-0.215398	0.450776	1.000000	0.916995	0.183974	0.404730	0.948837	-0.477928	-0.943228	-0.455000
GBPCAD=X	-0.941544	-0.136864	0.537094	0.932272	-0.146147	-0.432377	0.611936	0.916995	1.000000	0.271922	0.596908	0.956783	-0.259496	-0.919121	-0.220275
GBPCHF=X	0.066657	-0.052272	-0.094058	0.134686	0.526453	-0.421029	0.055478	0.183974	0.271922	1.000000	0.147821	0.297155	-0.192418	-0.000010	-0.111455
GBPJPY=X	-0.548551	0.712798	0.970635	0.318147	-0.657038	-0.754033	0.986979	0.404730	0.596908	0.147821	1.000000	0.634517	-0.355918	-0.596876	0.617149
GBPUSD=X	-0.887658	-0.053794	0.567901	0.866441	-0.164467	-0.474687	0.645242	0.948837	0.956783	0.297155	0.634517	1.000000	-0.528706	-0.953960	-0.216169
USDCAD=X	0.201108	-0.209035	-0.309463	-0.160260	0.113172	0.310892	-0.347809	-0.477928	-0.259496	-0.192418	-0.355918	-0.528706	1.000000	0.487539	0.087426
USDCHF=X	0.953216	0.068519	-0.602271	-0.875930	0.316189	0.350118	-0.637934	-0.943228	-0.919121	-0.000010	-0.596876	-0.953960	0.487539	1.000000	0.218839
USDJPY=X	0.212857	0.955807	0.646915	-0.481119	-0.660304	-0.470356	0.589409	-0.455000	-0.220275	-0.111455	0.617149	-0.216169	0.087426	0.218839	1.000000

In [13]:

# Create a heatmap plot for the correlation matrix
plt.figure(figsize=(10, 6))
sns.heatmap(correlation, annot=True, cmap='coolwarm', fmt=".2f")
plt.title("Forex Pairs Correlation Heatmap")
plt.show()

In [ ]:

Forex correlation