import pandas as pd import sklearn as sl import numpy as np import matplotlib.pyplot as plt import statsmodels.api as sm from statsmodels.tsa.arima.model import ARIMA from statsmodels.graphics.tsaplots import plot_acf, plot_pacf from statsmodels.tsa.stattools import adfuller from statsmodels.tsa.seasonal import seasonal_decompose from statsmodels.tsa.holtwinters import ExponentialSmoothing from statsmodels.tsa.statespace.sarimax import SARIMAX from statsmodels.tools.eval_measures import rmse from arch import arch_model

import yfinance as yf

data = yf.download("SN.L", start="2015-01-01", end="2022-12-31")

CLOSEPRICE = data["Close"].dropna()

print(CLOSEPRICE.describe())

plt.figure(figsize=(10, 6)) plt.plot(CLOSEPRICE) plt.title("Stock Close Price, 2015-2022") plt.xlabel("Days") plt.ylabel("Price") plt.show()

train = CLOSEPRICE[:1518] test = CLOSEPRICE[1518:]

plt.figure(figsize=(10, 6)) plt.plot(CLOSEPRICE, label="Close Price") plt.plot(train, label="Training Data", color="blue") plt.plot(test, label="Testing Data", color="green") plt.legend(loc="lower right") plt.title("Stock Close Price, 2015-2022") plt.xlabel("Days") plt.ylabel("Price") plt.show()

adf_result = adfuller(train) print("ADF Statistic:", adf_result[0]) print("p-value:", adf_result[1])

fig, ax = plt.subplots(2, 1, figsize=(10, 8)) plot_acf(train, ax=ax[0]) plot_pacf(train, ax=ax[1]) plt.show()

diff_train = train.diff().dropna()

adf_result_diff = adfuller(diff_train) print("ADF Statistic (After Differencing):", adf_result_diff[0]) print("p-value (After Differencing):", adf_result_diff[1])

fig, ax = plt.subplots(2, 1, figsize=(10, 8)) plot_acf(diff_train, ax=ax[0]) plot_pacf(diff_train, ax=ax[1]) plt.show()

model = ARIMA(train, order=(2, 1, 2)) model_fit = model.fit() print(model_fit.summary())

residuals = model_fit.resid fig, ax = plt.subplots(2, 1, figsize=(10, 8)) plot_acf(residuals, ax=ax[0]) plot_pacf(residuals, ax=ax[1]) plt.show()

forecast = model_fit.forecast(steps=503) forecast_values = forecast[0]

plt.figure(figsize=(10, 6)) plt.plot(forecast_values, label="Forecasted Values") plt.title("Forecasted Stock Close Price") plt.xlabel("Days") plt.ylabel("Price") plt.legend() plt.show()

rmse_value = rmse(test, forecast_values) print("RMSE:", rmse_value)

model_garch = arch_model(returnss, vol='Garch', p=1, q=1) model_garch_fit = model_garch.fit() print(model_garch_fit.summary())

forecast_garch = model_garch_fit.forecast(start=len(returnss), horizon=503) forecast_values_garch = forecast_garch.variance.values[-1, :]

plt.figure(figsize=(10, 6)) plt.plot(forecast_values_garch, label="Forecasted Values (GARCH)") plt.title("Forecasted Stock Close Price (GARCH)") plt.xlabel("Days") plt.ylabel("Price") plt.legend() plt.show()

rmse_value_garch = rmse(test, forecast_values_garch) print("RMSE (GARCH):", rmse_value_garch)