Upload master_card&visastockdata_159.py

MasterCard & Visa Stock Data (2008-2024)

master_card&visastockdata_159.py

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+# -*- 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()