!pip install pandas seaborn statsmodels

import pandas as pd import seaborn as sns import matplotlib.pyplot as plt

gold_price = pd.read_csv('gold price(1968-2023).csv') gold_price['Date'] = pd.to_datetime(gold_price['Date'], format="%d/%m/%Y") gold_price.rename(columns={'Close': 'Gold Price'}, inplace=True) print(gold_price.head())

cpi = pd.read_csv('US CPI monthly.csv') cpi['Date'] = pd.to_datetime(cpi['Date'], format="%d/%m/%Y") cpi.rename(columns={'Close': 'CPI'}, inplace=True) print(cpi.head())

money_supply = pd.read_csv('US Money Supply (M2) monthly.csv') money_supply['Date'] = pd.to_datetime(money_supply['Date'], format="%d/%m/%Y") def convert_to_float(value): if 'T' in value: return float(value.rstrip('T')) * 1e12 if 'B' in value: return float(value.rstrip('B')) * 1e9 money_supply['Close'] = money_supply['Close'].apply(convert_to_float) money_supply.rename(columns={'Close': 'Money Supply'}, inplace=True) print(money_supply.head())

interest_rate = pd.read_csv('US OVERNIGHT FED FUNDS EFFECT.csv') interest_rate['Date'] = pd.to_datetime(interest_rate['Date'], format="%d/%m/%Y") interest_rate.rename(columns={'Close': 'Interest Rate %'}, inplace=True) print(interest_rate.head())

sp500 = pd.read_csv('S&P 500 Index.csv') sp500['Date'] = pd.to_datetime(sp500['Date'], format="%d-%b-%Y") sp500.rename(columns={'Close': 'S&P 500'}, inplace=True) sp500['S&P 500'] = sp500['S&P 500'].str.replace(',', '').astype(float) print(sp500.head())

world_gdp = pd.read_csv('World GDP yearly.csv', skiprows = 0) world_gdp['Date'] = pd.to_datetime(world_gdp['Date'], format="%d/%m/%Y") world_gdp['Close'] = world_gdp['Close'].apply(convert_to_float) world_gdp.rename(columns={'Close': 'World GDP'}, inplace=True) print(world_gdp.head())

world_uncertainty = pd.read_csv('uncertainty.csv') # Convert 'Year' to datetime format world_uncertainty['Year'] = pd.to_datetime(world_uncertainty['Year'].str.replace('q', '-Q')) # Rename the 'Year' column to 'Date' world_uncertainty = world_uncertainty.rename(columns={'Year': 'Date'}) # Identify string type columns (excluding 'Date' column) string_columns = world_uncertainty.select_dtypes(include=['object']).columns.difference(['Date']) world_uncertainty = world_uncertainty.drop(columns=['Year.1'], errors='ignore') # Sort dataframe in descending order based on 'Date' world_uncertainty = world_uncertainty.sort_values(by='Date', ascending=False).reset_index(drop=True) # Columns to convert columns_to_convert = ['Global (simple average)', 'Global (GDP weighted average)', 'Advanced economies', 'Emerging economies', 'Low-income economies'] # Iterate through each column and remove commas, then convert to float for column in columns_to_convert: world_uncertainty[column] = world_uncertainty[column].str.replace(',', '').astype(float) print(world_uncertainty.head())

# Convert to monthly averages gold_price_monthly = gold_price.resample('M', on='Date').mean() dataframes = [gold_price_monthly, sp500, cpi, money_supply, interest_rate, world_gdp, world_uncertainty] for df in dataframes: # Convert to datetime index if 'Date' is a column if 'Date' in df.columns: df['Date'] = pd.to_datetime(df['Date']) df.set_index('Date', inplace=True) # Convert all columns except 'Date' to numeric for col in df.columns: df[col] = pd.to_numeric(df[col], errors='coerce') # Resample each dataframe to monthly frequency for i, df in enumerate(dataframes): # Use mean for resampling df_resampled = df.resample('M').mean() # Forward fill the missing values dataframes[i] = df_resampled.fillna(method='ffill') # Merge all dataframes on the date index df_combined = pd.concat(dataframes, axis=1) df_cleaned_and_combined = df_combined.dropna() print(df_cleaned_and_combined.tail(24))

ax = df_cleaned_and_combined['Interest Rate %'].plot(figsize=(10, 6), color='blue', label='Interest Rates') ax2 = ax.twinx() df_cleaned_and_combined['Gold Price'].plot(ax=ax2, color='gold', label='Gold Price USD/oz') ax.set_xlabel("Date") ax2.set_ylabel("Gold Prices USD/oz") ax.set_title("Interest Rates and Gold Prices") # Adding a legend for both axes lines, labels = ax.get_legend_handles_labels() lines2, labels2 = ax2.get_legend_handles_labels() ax.legend(lines + lines2, labels + labels2, loc='upper left') plt.tight_layout() plt.show()

# Plotting the data to visualize trends df_cleaned_and_combined.plot(subplots=True, figsize=(10, 15)) plt.tight_layout() # Set x-axis label plt.xlabel("Date") plt.show()

# Calculate the correlation matrix correlation_matrix = df_cleaned_and_combined.corr() correlation_matrix = correlation_matrix.round(2) print(correlation_matrix) # Create a correlation heatmap plt.figure(figsize=(12, 10)) sns.heatmap(correlation_matrix, annot=True, cmap="coolwarm", fmt=".2f", linewidths=.5) plt.title("Correlation Heatmap") plt.show()

# Plotting the data to visualize trends df_cleaned_and_combined['Money Supply'].plot(subplots=True, figsize=(8, 5)) plt.tight_layout() # Set x-axis label plt.xlabel("Date") plt.show()

import statsmodels.api as sm # Defining the dependent variable and independent variables Y = df_cleaned_and_combined['Gold Price'] X = df_cleaned_and_combined[['S&P 500', 'CPI', 'Money Supply', 'Interest Rate %', 'World GDP', 'Global (simple average)', 'Global (GDP weighted average)', 'Advanced economies', 'Emerging economies', 'Low-income economies']] # Adding a constant to the model (it's a best practice!) X = sm.add_constant(X) # Constructing the model model = sm.OLS(Y, X).fit() # Printing the summary print(model.summary())

from statsmodels.stats.outliers_influence import variance_inflation_factor # Calculate VIF for each independent variable vif_data = pd.DataFrame() vif_data["Variable"] = X.columns vif_data["VIF"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])] print(vif_data)

# Defining the dependent variable and independent variables Y = df_cleaned_and_combined['Gold Price'] X = df_cleaned_and_combined[['CPI', 'Money Supply', 'Interest Rate %', 'Global (GDP weighted average)']] #'Advanced economies', 'Emerging economies', 'Low-income economies']] # Adding a constant to the model (it's a best practice!) X = sm.add_constant(X) # Constructing the model model = sm.OLS(Y, X).fit() # Printing the summary print(model.summary())

vif_data = pd.DataFrame() vif_data["Variable"] = X.columns vif_data["VIF"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])] print(vif_data)

#Testin for heteroscedasticity from statsmodels.stats.diagnostic import het_breuschpagan # Assuming you have already fit your regression model as 'model' residuals = model.resid exog = model.model.exog bp_test = het_breuschpagan(residuals, exog) labels = ['LM Statistic', 'LM-Test p-value', 'F-Statistic', 'F-Test p-value'] print(dict(zip(labels, bp_test)))

columns_to_drop = ['S&P 500', 'World GDP', 'Advanced economies', 'Emerging economies', 'Low-income economies', 'Global (simple average)'] # Create the new dataframe df_new = df_cleaned_and_combined.drop(columns=columns_to_drop) #Can change the threshold threshold = 1.5

# Splitting data based on the threshold df_high_interest = df_new[df_new['Interest Rate %'] > threshold] df_low_interest = df_new[df_new['Interest Rate %'] <= threshold] # Regression for high interest rate period X_high = df_high_interest.drop('Gold Price', axis=1) X_high = sm.add_constant(X_high) model_high = sm.OLS(df_high_interest['Gold Price'], X_high).fit() print("High Interest Rate Period Regression Results:") print(model_high.summary()) # Regression for low interest rate period X_low = df_low_interest.drop('Gold Price', axis=1) X_low = sm.add_constant(X_low) model_low = sm.OLS(df_low_interest['Gold Price'], X_low).fit() print("\nLow Interest Rate Period Regression Results:") print(model_low.summary())