Delete master_card_stock_data_159_(2008_2024).py

master_card_stock_data_159_(2008_2024).py

@@ -1,376 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Master Card Stock Data.159 (2008-2024)
-
-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()