siner-Python

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

# Install and import required libraries
import yfinance as yf

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

# Extract close price
CLOSEPRICE = data["Close"].dropna()

# Summary statistics
print(CLOSEPRICE.describe())

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

# Train-test split
train = CLOSEPRICE[:1518]
test = CLOSEPRICE[1518:]

# Plot train and test data
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()

# Augmented Dickey-Fuller test
adf_result = adfuller(train)
print("ADF Statistic:", adf_result[0])
print("p-value:", adf_result[1])

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

# Differencing
diff_train = train.diff().dropna()

# Augmented Dickey-Fuller test after differencing
adf_result_diff = adfuller(diff_train)
print("ADF Statistic (After Differencing):", adf_result_diff[0])
print("p-value (After Differencing):", adf_result_diff[1])

# Autocorrelation and Partial Autocorrelation plots after differencing
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()

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

# Residual analysis
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()

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

# Plot forecast
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()

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

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

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

# Plot forecast using GARCH model
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()

# Accuracy evaluation using GARCH model
rmse_value_garch = rmse(test, forecast_values_garch)
print("RMSE (GARCH):", rmse_value_garch)