In this code, you will check whether the non-stationary independent variables are cointegrated or not

cointegration_check_gold_price_prediction.py

S_3 = Df['S_3'].dropna()
S_9 = Df['S_9'].dropna()

# Since S_3 and S_9 are moving averages, we could check their cointegration with the original Close series
close_price = Df['next_day_price']

# Conduct Engle-Granger Cointegration Test between S_3 and Close price
coint_result_3 = coint(S_3, close_price)

# Conduct Engle-Granger Cointegration Test between S_9 and Close price
coint_result_9 = coint(S_9, close_price)

# Extract p-values
p_value_3 = coint_result_3[1]
p_value_9 = coint_result_9[1]

# Print the results
print(f"Cointegration p-value between S_3 and next_day_price: {p_value_3}")
print(f"Cointegration p-value between S_9 and next_day_price: {p_value_9}")

# Interpretation
alpha = 0.05
if p_value_3 < alpha:
    print("S_3 and next_day_price are cointegrated.")
else:
    print("S_3 and next_day_price are not cointegrated.")

if p_value_9 < alpha:
    print("S_9 and next_day_price are cointegrated.")
else:
    print("S_9 and next_day_price are not cointegrated.")