# -*- coding: utf-8 -*-
"""Master Card&VisaStockData.159

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/127-oS8O1T914B2Fx1z0r0JAfHc3RJ8NB
"""

import pandas as pd

data = pd.read_csv('MVR.csv')

print(data.head())

print(data.isnull().sum())

data['Date'] = pd.to_datetime(data['Date'])

data.set_index('Date', inplace=True)

print(data.dtypes)

print(data.info())

print(data.describe())

import matplotlib.pyplot as plt

plt.figure(figsize=(14, 7))
plt.plot(data.index, data['Close_M'], label='MasterCard Close')
plt.plot(data.index, data['Close_V'], label='Visa Close')
plt.title('Stock Prices of MasterCard and Visa')
plt.xlabel('Date')
plt.ylabel('Stock Price')
plt.legend()
plt.show()

data['MA_Close_M'] = data['Close_M'].rolling(window=30).mean()
data['MA_Close_V'] = data['Close_V'].rolling(window=30).mean()

plt.figure(figsize=(14, 7))
plt.plot(data['Close_M'], label='MasterCard Close Price')
plt.plot(data['MA_Close_M'], label='MasterCard 30-Day MA')
plt.title('Moving Averages of Stock Prices')
plt.xlabel('Date')
plt.ylabel('Price')
plt.legend()
plt.show()

plt.figure(figsize=(14, 7))
plt.plot(data['Volume_M'], label='MasterCard Volume')
plt.plot(data['Volume_V'], label='Visa Volume')
plt.title('Volume of Stocks Traded')
plt.xlabel('Date')
plt.ylabel('Volume')
plt.legend()
plt.show()

data['SMA50_M'] = data['Close_M'].rolling(window=50).mean()
data['SMA200_M'] = data['Close_M'].rolling(window=200).mean()

data['SMA50_V'] = data['Close_V'].rolling(window=50).mean()
data['SMA200_V'] = data['Close_V'].rolling(window=200).mean()

plt.figure(figsize=(14, 7))
plt.plot(data.index, data['Close_M'], label='MasterCard Close')
plt.plot(data.index, data['SMA50_M'], label='MasterCard SMA50')
plt.plot(data.index, data['SMA200_M'], label='MasterCard SMA200')
plt.title('MasterCard Stock Price and Moving Averages')
plt.xlabel('Date')
plt.ylabel('Stock Price')
plt.legend()
plt.show()

plt.figure(figsize=(14, 7))
plt.plot(data.index, data['Close_V'], label='Visa Close')
plt.plot(data.index, data['SMA50_V'], label='Visa SMA50')
plt.plot(data.index, data['SMA200_V'], label='Visa SMA200')
plt.title('Visa Stock Price and Moving Averages')
plt.xlabel('Date')
plt.ylabel('Stock Price')
plt.legend()
plt.show

data['Volatility_M'] = data['Close_M'].rolling(window=30).std()
data['Volatility_V'] = data['Close_V'].rolling(window=30).std()

plt.figure(figsize=(14, 7))
plt.plot(data.index, data['Volatility_M'], label='MasterCard Volatility')
plt.plot(data.index, data['Volatility_V'], label='Visa Volatility')
plt.title('Stock Price Volatility of MasterCard and Visa')
plt.xlabel('Date')
plt.ylabel('Volatility')
plt.legend()
plt.show()

data['Return_M'] = data['Close_M'].pct_change()
data['Return_V'] = data['Close_V'].pct_change()

data['Cumulative_Return_M'] = (1 + data['Return_M']).cumprod()
data['Cumulative_Return_V'] = (1 + data['Return_V']).cumprod()

plt.figure(figsize=(14, 7))
plt.plot(data.index, data['Cumulative_Return_M'], label='MasterCard Cumulative Return')
plt.plot(data.index, data['Cumulative_Return_V'], label='Visa Cumulative Return')
plt.title('Cumulative Returns of MasterCard and Visa')
plt.xlabel('Date')
plt.ylabel('Cumulative Return')
plt.legend()
plt.show()

correlation = data[['Close_M', 'Close_V']].corr()
print(correlation)

from statsmodels.tsa.seasonal import seasonal_decompose

decomposition_M = seasonal_decompose(data['Close_M'], model='multiplicative', period=365)
fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, figsize=(15, 12))

ax1.plot(decomposition_M.observed)
ax1.set_title('Observed - MasterCard')
ax2.plot(decomposition_M.trend)
ax2.set_title('Tren - MasterCard')
ax3.plot(decomposition_M.seasonal)
ax3.set_title('Seasonal - MasterCard')
ax4.plot(decomposition_M.resid)
ax4.set_title('Residual - MasterCard')

plt.tight_layout()
plt.show

decomposition_V = seasonal_decompose(data['Close_V'], model='multiplicative', period=365)
fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, figsize=(15, 12))

ax1.plot(decomposition_V.observed)
ax1.set_title('Observed - Visa')
ax2.plot(decomposition_V.trend)
ax2.set_title('Trend - Visa')
ax3.plot(decomposition_V.seasonal)
ax3.set_title('Seasonal - Visa')
ax4.plot(decomposition_V.resid)
ax4.set_title('Residual - Visa')

plt.tight_layout()
plt.show()

from statsmodels.tsa.stattools import adfuller

def adf_test(series):
  result = adfuller(series.dropna())
  print('ADF Statistic:', result[0])
  print('p-value:', result[1])
  for key, value in result[4].items():
    print('Critial Values:')
    print(f' {key}, {value}')

print("ADF Test for MasterCard Close Price:")
adf_test(data['Close_M'])

print("\ADF Test for Visa Close Price:")
adf_test(data['Close_V'])

import numpy as np
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import LSTM, Dense, Input
from sklearn.metrics import mean_squared_error

scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data_M = scaler.fit_transform(data[['Close_M']])
scaled_data_V = scaler.fit_transform(data[['Close_V']])

train_len_M = int(len(scaled_data_M) * 0.8)
train_len_V = int(len(scaled_data_V) * 0.8)

train_data_M = scaled_data_M[:train_len_M]
test_data_M = scaled_data_M[train_len_M:]

train_data_V = scaled_data_V[:train_len_V]
test_data_V = scaled_data_V[train_len_V:]

def create_sequences(data, seq_length):
  x = []
  y = []
  for i in range(seq_length, len(data)):
    x.append(data[i-seq_length:i, 0])
    y.append(data[i, 0])
  return np.array(x), np.array(y)

seq_length = 60
x_train_M, y_train_M = create_sequences(train_data_M, seq_length)
x_test_M, y_test_M = create_sequences(test_data_M, seq_length)

x_train_V, y_train_V = create_sequences(train_data_V, seq_length)
x_test_V, y_test_V = create_sequences(test_data_V, seq_length)

x_train_M = np.reshape(x_train_M, (x_train_M.shape[0], x_train_M.shape[1], 1))
x_test_M = np.reshape(x_test_M, (x_test_M.shape[0], x_test_M.shape[1], 1))

x_train_V = np.reshape(x_train_V, (x_train_V.shape[0], x_train_V.shape[1], 1))
x_test_V = np.reshape(x_test_V, (x_test_V.shape[0], x_test_V.shape[1], 1))

model_M = Sequential()
model_M.add(Input(shape=(x_train_M.shape[1], 1)))
model_M.add(LSTM(units=50, return_sequences=True))
model_M.add(LSTM(units=50, return_sequences=False))
model_M.add(Dense(units=25))
model_M.add(Dense(units=1))

model_M.compile(optimizer='adam', loss='mean_squared_error')

model_V = Sequential()
model_V.add(Input(shape=(x_train_V.shape[1], 1)))
model_V.add(LSTM(units=50, return_sequences=True))
model_V.add(LSTM(units=50, return_sequences=False))
model_V.add(Dense(units=25))
model_V.add(Dense(units=1))

model_V.compile(optimizer ='adam', loss='mean_squared_error')

model_M.fit(x_train_M, y_train_M, batch_size=32, epochs=100)
model_V.fit(x_train_V, y_train_V, batch_size=32, epochs=100)

predictions_M = model_M.predict(x_test_M)
predictions_M = scaler.inverse_transform(predictions_M)

predictions_V = model_V.predict(x_test_V)
predictions_V = scaler.inverse_transform(predictions_V)

rmse_M = np.sqrt(mean_squared_error(y_test_M, predictions_M))
rmse_V = np.sqrt(mean_squared_error(y_test_V, predictions_V))

print(f'RMSE for MasterCard: {rmse_M}')
print(f'RMSE for Visa: {rmse_V}')

train_M = data[:train_len_M]['Close_M']
valid_M = data[train_len_M:train_len_M + len(predictions_M)]['Close_M']
valid_M = valid_M.to_frame()
valid_M['Predictions'] = predictions_M

train_V = data[:train_len_V]['Close_V']
valid_V = data[train_len_V:train_len_V + len(predictions_V)]['Close_V']
valid_V = valid_V.to_frame()
valid_V['Predictions'] = predictions_V

plt.figure(figsize=(14, 7))
plt.plot(train_M, label='Train - MasterCard')
plt.plot(valid_M['Close_M'], label='Valid - MasterCard')
plt.plot(valid_M['Predictions'], label='Predictions - MasterCard')
plt.legend()
plt.show()

plt.figure(figsize=(14, 7))
plt.plot(train_V, label ='Train -Visa')
plt.plot(valid_V['Close_V'], label='Valid -Visa')
plt.plot(valid_V['Predictions'], label='Predictions - Visa')
plt.legend()
plt.show()

from statsmodels.tsa.arima.model import ARIMA

data = data.asfreq('B')

train_size = int(len(data) * 0.8)
train, test = data['Close_M'][:train_size], data['Close_M'][train_size:]

model = ARIMA(train, order=(5, 1, 0))
model_fit = model.fit()
print(model_fit.summary())

predictions = model_fit.forecast(steps=len(test))
predictions = pd.Series(predictions, index=test.index)

plt.figure(figsize=(14, 7))
plt.plot(train, label='Training Data')
plt.plot(test, label='Test Data')
plt.plot(predictions, label='Predicted Data')
plt.title('ARIMA Model Predictions for MasterCard')
plt.xlabel('Date')
plt.ylabel('Price')
plt.legend()
plt.show()

data = data.asfreq('B')

train_size = int(len(data) * 0.8)
train_V, test_V = data['Close_V'][:train_size], data['Close_V'][train_size:]

model_V = ARIMA(train_V, order=(5, 1, 0))
model_fit_V = model_V.fit()
print(model_fit_V.summary())

predictions_V = model_fit_V.forecast(steps=len(test_V))
predictions_V = pd.Series(predictions_V, index=test_V.index)

plt.figure(figsize=(14, 7))
plt.plot(train_V, label='Training Data')
plt.plot(test_V, label='Test Data')
plt.plot(predictions_V, label='Predicted Data'),
plt.title('ARIMA Model Predictions for Visa')
plt.xlabel('Date')
plt.ylabel('Price')
plt.legend()
plt.show()

import warnings
warnings.filterwarnings('ignore')
import plotly.graph_objects as go

def predict_stock_price(data, column_name, forecast_periods):
    train_size = int(len(data) * 0.8)
    train, test = data[column_name][:train_size], data[column_name][train_size:]

    model = ARIMA(train, order=(5, 1, 0))
    model_fit = model.fit()

    future_dates = pd.date_range(start=data.index[-1], periods=forecast_periods, freq='B')
    forecast = model_fit.forecast(steps=forecast_periods)
    forecast_series = pd.Series(forecast, index=future_dates)

    return forecast_series

forecast_periods = 3 * 252
forecast_M = predict_stock_price(data, 'Close_M', forecast_periods)
forecast_V = predict_stock_price(data, 'Close_V', forecast_periods)

extended_data_M = pd.concat([data['Close_M'], forecast_M])
extended_data_V = pd.concat([data['Close_V'], forecast_V])

candlestick_data_M = pd.DataFrame({
    'Date': extended_data_M.index,
    'Open': extended_data_M.shift(1).fillna(method='bfill'),
    'High': extended_data_M.rolling(2).max(),
    'Low': extended_data_M.rolling(2).min(),
    'Close': extended_data_M
}).reset_index(drop=True)

candlestick_data_V = pd.DataFrame({
    'Date': extended_data_V.index,
    'Open': extended_data_V.shift(1).fillna(method='bfill'),
    'High': extended_data_V.rolling(2).max(),
    'Low': extended_data_V.rolling(2).min(),
    'Close': extended_data_V
}).reset_index(drop=True)

fig = go.Figure()

fig.add_trace(go.Candlestick(
    x=candlestick_data_M['Date'],
    open=candlestick_data_M['Open'],
    high=candlestick_data_M['High'],
    low=candlestick_data_M['Low'],
    close=candlestick_data_M['Close'],
    name='MasterCard',
     increasing_line_color='blue', decreasing_line_color='red'
))

fig.add_trace(go.Candlestick(
    x=candlestick_data_V['Date'],
    open=candlestick_data_V['Open'],
    high=candlestick_data_V['High'],
    low=candlestick_data_V['Low'],
    close=candlestick_data_V['Close'],
    name='Visa',
     increasing_line_color='green', decreasing_line_color='orange'
))

fig.update_layout(
    title='MasterCard and Visa Stock Prices (Historical and Predicted)',
    xaxis_title='Date',
    yaxis_title='Price',
    xaxis_rangeslider_visible=False
)

fig.show()