#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 28 11:11:06 2021
@author: sadrachpierre
"""
import pandas as pd
import pandas_datareader as web
import datetime

import matplotlib.pyplot as plt
import seaborn as sns

from statsmodels.tsa.statespace.sarimax import SARIMAX
from statsmodels.tsa.arima.model import ARIMA
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)




# btc = web.get_data_yahoo(['BTC-USD'], start=datetime.datetime(2018, 1, 1), end=datetime.datetime(2020, 12, 2))

# btc = btc['Close']

# btc.to_csv("btc.csv")

btc = pd.read_csv("btc.csv")


btc.index = pd.to_datetime(btc['Date'], format='%Y-%m-%d')
del btc['Date']

print(btc.head())
sns.set()
plt.ylabel('BTC Price')
plt.xlabel('Date')
plt.xticks(rotation=45)
plt.plot(btc.index, btc['BTC-USD'], )
plt.show()

train = btc[btc.index < pd.to_datetime("2020-11-01", format='%Y-%m-%d')]
test = btc[btc.index >= pd.to_datetime("2020-11-01", format='%Y-%m-%d')]
print(test)
plt.plot(train, color = "black", label = 'Training')
plt.plot(test, color = "red", label = 'Testing')
plt.ylabel('BTC Price')
plt.xlabel('Date')
plt.xticks(rotation=45)
plt.title("Train/Test split for BTC Data")

y = train['BTC-USD']

ARMAmodel = SARIMAX(y, order = (1, 0, 1))
ARMAmodel = ARMAmodel.fit()

y_pred = ARMAmodel.get_forecast(len(test.index))
y_pred_df = y_pred.conf_int(alpha = 0.05)
y_pred_df["Predictions"] = ARMAmodel.predict(start = y_pred_df.index[0], end = y_pred_df.index[-1])
y_pred_df.index = test.index
y_pred_out = y_pred_df["Predictions"]
plt.plot(y_pred_out, color='green', label = 'ARMA Predictions')
plt.legend()


import numpy as np
from sklearn.metrics import mean_squared_error

arma_rmse = np.sqrt(mean_squared_error(test["BTC-USD"].values, y_pred_df["Predictions"]))
print("ARMA RMSE: ",arma_rmse)




ARIMAmodel = ARIMA(y, order = (5, 4, 2))
ARIMAmodel = ARIMAmodel.fit()

y_pred = ARIMAmodel.get_forecast(len(test.index))
y_pred_df = y_pred.conf_int(alpha = 0.05)
y_pred_df["Predictions"] = ARIMAmodel.predict(start = y_pred_df.index[0], end = y_pred_df.index[-1])
y_pred_df.index = test.index
y_pred_out = y_pred_df["Predictions"]
plt.plot(y_pred_out, color='Yellow', label = 'ARIMA Predictions')
plt.legend()


import numpy as np
from sklearn.metrics import mean_squared_error

arma_rmse = np.sqrt(mean_squared_error(test["BTC-USD"].values, y_pred_df["Predictions"]))
print("ARIMA RMSE: ",arma_rmse)



SARIMAXmodel = SARIMAX(y, order = (5, 4, 2), seasonal_order=(2,2,2,12))
SARIMAXmodel = SARIMAXmodel.fit()

y_pred = SARIMAXmodel.get_forecast(len(test.index))
y_pred_df = y_pred.conf_int(alpha = 0.05)
y_pred_df["Predictions"] = SARIMAXmodel.predict(start = y_pred_df.index[0], end = y_pred_df.index[-1])
y_pred_df.index = test.index
y_pred_out = y_pred_df["Predictions"]
plt.plot(y_pred_out, color='Blue', label = 'SARIMA Predictions')
plt.legend()


import numpy as np
from sklearn.metrics import mean_squared_error

arma_rmse = np.sqrt(mean_squared_error(test["BTC-USD"].values, y_pred_df["Predictions"]))
print("SARIMA RMSE: ",arma_rmse)