Trace_eigen_stats.py

# Create a data frame df to store the two-time series
data = yf.download(stock_list, start=start_date, end=end_date)['Adj Close']

# Perform the Johansen Cointegration Test with a specified number of zero
specified_number = 0  # Testing for zero cointegrating relationships
coint_test_result = coint_johansen(data, specified_number, 1)

# Extract the trace statistics and eigen statistics
trace_stats = coint_test_result.lr1
eigen_stats = coint_test_result.lr2

# Print the test results
print("Johansen Cointegration Test Results (Testing for Zero Cointegrating Relationships):")
print(f"Trace Statistics: {coint_test_result.lr1}")
print(f"Critical Values: {coint_test_result.cvt}")

# Define stock pairs
stock_pairs = [('AAPL', 'AMZN'), ('MSFT', 'AAPL'), ('AMZN', 'MSFT')]

# Separate the output sections
print("\n" + "-" * 50 + "\n")

# Interpret the results for each pair
for i, (stock1, stock2) in enumerate(stock_pairs):
    trace_statistic = trace_stats[i]
    eigen_statistic = eigen_stats[i]
    print(f"Pair {i + 1} ({stock1} and {stock2}):")
    print(f"Trace Statistic: {trace_statistic}")
    print(f"Eigen Statistic: {eigen_statistic}")
    print("\n" + "-" * 50 + "\n")

# Determine cointegration based on critical values or other criteria
# Add your cointegration assessment logic here
print("Cointegration Assessment: Testing for Zero Cointegrating Relationships (Null Hypothesis)\n")