Upload bayburtanalysis_159.py

bayburtanalysis_159.py

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+# -*- coding: utf-8 -*-
+"""bayburtanalysis.159
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1i3xf37d6YszBy480hNM0EGmK3u-RtMJB
+"""
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from datetime import datetime
+
+from statsmodels.tsa.seasonal import seasonal_decompose
+from statsmodels.tsa.arima.model import ARIMA
+import prophet
+
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error, r2_score
+from sklearn.ensemble import RandomForestRegressor
+
+from textblob import TextBlob
+import nltk
+from nltk.sentiment.vader import SentimentIntensityAnalyzer
+nltk.download('vader_lexicon')
+
+import plotly.express as px
+import plotly.graph_objs as go
+import plotly.figure_factory as ff
+
+import warnings
+warnings.filterwarnings('ignore')
+
+print("Very well you may continue")
+
+big_tech_companies = pd.read_csv('big_tech_companies.csv')
+big_tech_stock_prices = pd.read_csv('big_tech_stock_prices.csv')
+
+print("Big Tech Companies Dataset:")
+print(big_tech_companies.head())
+
+print("\nBig Tech Stock Prices Dataset:")
+print(big_tech_stock_prices.head())
+
+print("\nBig Tech Companies Dataset Info:")
+print(big_tech_companies.info())
+
+print("\nBig Tech Stock Prices Dataset Info:")
+print(big_tech_stock_prices.info())
+
+print("\nBig Tech Companies Dataset Description:")
+print(big_tech_companies.describe())
+
+print("\nBig Tech Stock Prices Dataset Description:")
+print(big_tech_stock_prices.describe())
+
+print("\nUnique Companies in Big Tech Companies Dataset:")
+print(big_tech_companies['company'].nunique())
+
+print("\nUnique Stock Symbols in Big Tech Stock Prices Dataset:")
+print(big_tech_stock_prices['stock_symbol'].nunique())
+
+print("\nMissing Values in Big Tech Companies Dataset:")
+print(big_tech_companies.isnull().sum())
+
+print("\nMissing Values in Big Tech Stock Prices Dataset:")
+print(big_tech_stock_prices.isnull().sum())
+
+print("\nStock Symbol Counts in Big Tech Stock Prices Dataset:")
+print(big_tech_stock_prices['stock_symbol'].value_counts())
+
+big_tech_stock_prices['date'] = pd.to_datetime(big_tech_stock_prices['date'])
+
+plt.figure(figsize=(14, 7))
+sns.lineplot(data=big_tech_stock_prices, x='date', y='close', hue='stock_symbol')
+plt.title('Stock Prices Over Time')
+plt.xlabel('Date')
+plt.ylabel('Close Price')
+plt.legend(title='Stock Symbol')
+plt.show()
+
+plt.figure(figsize=(14, 7))
+sns.lineplot(data=big_tech_stock_prices, x='date', y ='volume', hue='stock_symbol')
+plt.title('Trading Volume Over Time')
+plt.xlabel('Data')
+plt.ylabel('Volume')
+plt.legend(title='Stock Symbol')
+plt.show()
+
+plt.figure(figsize=(14,7))
+sns.boxplot(data=big_tech_stock_prices, x='stock_symbol', y='close')
+plt.title('Distribution of Closing Prices by Stock Symbol')
+plt.xlabel('Stock Symbol')
+plt.ylabel('Close Price')
+plt.show()
+
+apple_stock = big_tech_stock_prices[big_tech_stock_prices['stock_symbol'] == 'AAPL']
+apple_stock.set_index('date', inplace=True)
+
+decompostiion = seasonal_decompose(apple_stock['close'], model='multiplicative', period=365)
+fig = decompostiion.plot()
+fig.set_size_inches(14, 10)
+plt.show()
+
+plt.figure(figsize=(14, 7))
+apple_stock['close'].plot()
+plt.title('Apple Closing Prices')
+plt.xlabel('Date')
+plt.ylabel('Close Price')
+plt.show()
+
+apple_stock['rolling_mean'] = apple_stock['close'].rolling(window=30).mean()
+
+plt.figure(figsize=(14, 7))
+apple_stock[['close', 'rolling_mean']].plot()
+plt.title('Apple Closing Prices and 30-Day Moving Average')
+plt.xlabel('Date')
+plt.ylabel('Close Price')
+plt.show()
+
+pivot_table = big_tech_stock_prices.pivot(index='date', columns='stock_symbol', values='close')
+correlation_matrix = pivot_table.corr()
+
+plt.figure(figsize=(12, 8))
+sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=0.5)
+plt.title('Correlation Matrix of Stock Closing Prices')
+plt.show()
+
+big_tech_stock_prices_2020 = big_tech_stock_prices
+[(big_tech_stock_prices['date'] >= '2020-01-01') &
+ (big_tech_stock_prices['date'] <= '2020-12-31')]
+
+plt.figure(figsize=(14, 7))
+sns.lineplot(data=big_tech_stock_prices_2020, x='date', y='close', hue='stock_symbol')
+plt.title('Stock Prices During 2020')
+plt.xlabel('Date')
+plt.ylabel('Close Price')
+plt.legend(title='Stock Symbol')
+plt.show()
+
+big_tech_stock_prices['year'] = big_tech_stock_prices['date'].dt.year
+
+yearly_avg_prices = big_tech_stock_prices.groupby(['year', 'stock_symbol']).mean().reset_index()
+
+plt.figure(figsize=(14, 7))
+sns.lineplot(data=yearly_avg_prices, x='year', y='close', hue='stock_symbol')
+plt.title('Yearly Average Closing Prices')
+plt.xlabel('Year')
+plt.ylabel('Average Close Price')
+plt.legend(title='Stock Symbol')
+plt.show()
+
+big_tech_stock_prices['price_change'] = big_tech_stock_prices.groupby('stock_symbol')['close'].pct_change()
+
+plt.figure(figsize=(14, 10))
+
+sns.histplot(big_tech_stock_prices['price_change']. dropna(), bins=100, kde=True)
+plt.title('Histogram of Daily Price Changes for All Stocks')
+plt.xlabel('Daily Price Change')
+plt.ylabel('Frequency')
+plt.show()
+
+unique_symbols = big_tech_stock_prices['stock_symbol'].unique()
+
+for symbol in unique_symbols:
+    plt.figure(figsize=(14, 7))
+    sns.histplot(big_tech_stock_prices[big_tech_stock_prices['stock_symbol'] == symbol]['price_change'].dropna(), bins=100, kde=True)
+    plt.title(f'Histogram of Daily Price Changes for {symbol}')
+    plt.xlabel('Daily Price Change')
+    plt.ylabel('Frequency')
+    plt.show()
+
+volatility = big_tech_stock_prices.groupby('stock_symbol')['price_change'].std().reset_index()
+volatility.columns = ['stock_symbol', 'volatility']
+
+plt.figure(figsize=(14, 7))
+sns.barplot(data=volatility, x='stock_symbol', y='volatility')
+plt.title('Stock Price Volatility')
+plt.xlabel('Stock Symbol')
+plt.ylabel('Volatility(Standard Deviation of Daily Price Changes)')
+plt.show()
+
+yearly_price_change = big_tech_stock_prices.groupby(['year', 'stock_symbol'])['close'].mean().pct_change().reset_index()
+yearly_price_change = yearly_price_change.dropna()
+
+plt.figure(figsize=(14, 7))
+sns.lineplot(data=yearly_price_change, x='year', y='close', hue='stock_symbol', marker='o')
+plt.title('Yearly Percentage Change in Average Closing Prices')
+plt.xlabel('Year')
+plt.ylabel('Percentage Change in Average Close Price')
+plt.legend(title='Stock Symbol')
+plt.show()
+
+model = ARIMA(apple_stock['close'], order=(5, 1, 0))
+
+model_fit = model.fit()
+print(model_fit.summary())
+
+plt.figure(figsize=(14, 7))
+plt.plot(apple_stock['close'], label='Original')
+plt.plot(model_fit.fittedvalues, color='red', label='Fitted Values')
+plt.title('ARIMA Model Fit')
+plt.xlabel('Date')
+plt.ylabel('Close Price')
+plt.legend()
+plt.show()
+
+forecast = model_fit.get_forecast(steps=30)
+forecast_index = pd.date_range(start=apple_stock.index[-1], periods=30, freq='D')
+forecast_mean = forecast.predicted_mean
+forecast_conf_int = forecast.conf_int()
+
+plt.figure(figsize=(14, 7))
+plt.plot(apple_stock['close'], label='Original')
+plt.plot(forecast_index, forecast_mean, color='red', label='Forecast')
+plt.fill_between(forecast_index, forecast_conf_int.iloc[:, 0], forecast_conf_int.iloc[:, 1], color='pink', alpha=0.3)
+plt.title('ARIMA Model Forecast')
+plt.xlabel('Date')
+plt.ylabel('Close Price')
+plt.legend()
+plt.show()
+
+unique_symbols = big_tech_stock_prices['stock_symbol'].unique()
+
+for symbol in unique_symbols:
+  stock_data = big_tech_stock_prices[big_tech_stock_prices['stock_symbol'] == symbol]
+  stock_data.set_index('date', inplace=True)
+
+  print(f"\n### {symbol} ###")
+
+  model = ARIMA(stock_data['close'], order=(5, 1, 0))
+  model_fit = model.fit()
+  print(model_fit.summary())
+
+  plt.figure(figsize=(14, 7))
+  plt.plot(stock_data['close'], label='Original')
+  plt.plot(model_fit.fittedvalues, color='red', label='Fitted Values')
+  plt.title(f'{symbol} ARIMA Model Fit')
+  plt.xlabel('Date')
+  plt.ylabel('Close Price')
+  plt.legend()
+  plt.show()
+
+  forecast = model_fit.get_forecast(steps=30)
+  forecast_index = pd.date_range(start=stock_data.index[-1], periods=30, freq='D')
+  forecast_mean = forecast.predicted_mean
+  forecast_conf_int = forecast.conf_int()
+
+  plt.figure(figsize=(14, 7))
+  plt.plot(stock_data['close'], label='Original')
+  plt.plot(forecast_index, forecast_mean, color='red', label='Forecast')
+  plt.fill_between(forecast_index, forecast_conf_int.iloc[:, 0], forecast_conf_int.iloc[:, 1], color='pink', alpha=0.3)
+  plt.title(f'{symbol} ARIMA Model Forecast')
+  plt.xlabel('Date')
+  plt.ylabel('Close Price')
+  plt.legend()
+  plt.show()
+
+big_tech_stock_prices['daily_return'] = big_tech_stock_prices.groupby('stock_symbol')['close'].pct_change()
+
+mean_returns = big_tech_stock_prices.groupby('stock_symbol')['daily_return'].mean()
+volatilties = big_tech_stock_prices.groupby('stock_symbol')['daily_return'].std()
+
+risk_return_df = pd.DataFrame({'mean_return': mean_returns, 'volatility': volatilties})
+print(risk_return_df)
+
+mean_returns = big_tech_stock_prices.groupby('stock_symbol')['daily_return'].mean()
+cov_matrix = big_tech_stock_prices.pivot_table(index='date', columns='stock_symbol', values='daily_return').cov()
+
+num_portfolios = 10000
+results = np.zeros((4, num_portfolios))
+weights_record = []
+
+np.random.seed(42)
+
+for i in range(num_portfolios):
+  weights = np.random.random(len(mean_returns))
+  weights /= np.sum(weights)
+  weights_record.append(weights)
+  portfolio_return = np.dot(weights, mean_returns)
+  portfolio_stddev = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights)))
+  results[0, i] = portfolio_return
+  results[1, i] = portfolio_stddev
+  results[2, i] = results[0, i] / results[1, i]
+
+results_frame = pd.DataFrame(results.T, columns=['Return', 'Risk', 'Sharpe Ratio', 'Index'])
+
+max_sharpe_idx = results_frame['Sharpe Ratio'].idxmax()
+max_sharpe_portfolio = results_frame.iloc[max_sharpe_idx]
+max_sharpe_weights = weights_record[int(max_sharpe_portfolio[3])]
+
+min_risk_idx = results_frame['Risk'].idxmin()
+min_risk_portfolio = results_frame.iloc[min_risk_idx]
+min_risk_weights = weights_record[int(min_risk_portfolio[3])]
+
+plt.figure(figsize=(10, 6))
+plt.scatter(results_frame['Risk'], results_frame['Return'], c=results_frame['Sharpe Ratio'], cmap='viridis')
+plt.colorbar(label='Sharpe Ratio')
+plt.scatter(max_sharpe_portfolio[1], max_sharpe_portfolio[0], marker='*', color='r', s=200, label='Max Sharpe Ratio')
+plt.scatter(min_risk_portfolio[1], min_risk_portfolio[0], marker='*', color='b', s=200, label= 'Min Risk')
+plt.title('Portfolio Optimization based on Efficient Frontier')
+plt.xlabel('Risk (Standard Deviation)')
+plt.ylabel('Return')
+plt.legend()
+plt.show
+
+print("Maximum Sharpe Ratio Portfolio Allocation\n")
+print("Return:", max_sharpe_portfolio[0])
+print("Risk:", max_sharpe_portfolio[1])
+print("Sharpe Ratio:", max_sharpe_portfolio[2])
+print("\nWeights:\n")
+for i, txt in enumerate(mean_returns.index):
+  print(f"{txt}: {max_sharpe_weights[i]}")
+
+print("\nMinimum Risk Portfolio Allocation\n")
+print("Return:", min_risk_portfolio[0])
+print("Risk:", min_risk_portfolio[1])
+print("\nWeights:\n")
+for i, txt in enumerate(mean_returns.index):
+  print(f"{txt}: {min_risk_weights[i]}")
+
+big_tech_stock_price = pd.read_csv('big_tech_stock_prices.csv')
+macro_data = pd.read_csv('DATA.csv')
+
+print(macro_data.columns)
+
+macro_data = macro_data.rename(columns={
+    'UNRATE(%)': 'unemployment_rate',
+    'CPIALLITEMS': 'cpi',
+    'INFLATION(%)': 'inflation_rate',
+    'MORTGAGE INT. MONTHLY AVG(%)': 'mortgage_interest_rate',
+    'CORP. BOND YIELD(%)': 'corporate_bond_yield'
+})
+
+macro_data['DATE'] = pd.to_datetime(macro_data['DATE'])
+
+macro_data.rename(columns={'DATE': 'date'}, inplace=True)
+
+big_tech_stock_price['date'] = pd.to_datetime(big_tech_stock_price['date'])
+
+merged_data = pd.merge(big_tech_stock_prices, macro_data, on='date', how='inner')
+
+print(merged_data.head())
+print(merged_data.columns)
+
+correlation_matrix = merged_data[['close', 'unemployment_rate', 'cpi', 'inflation_rate', 'mortgage_interest_rate', 'corporate_bond_yield']].corr()
+print(correlation_matrix)
+
+plt.figure(figsize=(10, 6))
+sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=0.5)
+plt.title('Correlation Matrix of Stock Prices and Macro-Economic Indicators')
+plt.show()
+
+plt.figure(figsize=(14, 7))
+sns.lineplot(data=merged_data, x='date', y='close', hue='stock_symbol')
+plt.title('Stock Prices Over Time')
+plt.xlabel('Date')
+plt.ylabel('Close Price')
+plt.show()
+
+X = merged_data[['unemployment_rate', 'cpi', 'inflation_rate', 'mortgage_interest_rate', 'corporate_bond_yield']]
+y = merged_data['close']
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+model = LinearRegression()
+model.fit(X_train, y_train)
+
+y_pred = model.predict(X_test)
+r2_score = model.score(X_test, y_test)
+
+print(f"R^2 Score: {r2_score}")
+
+coefficients = pd.DataFrame(model.coef_, X.columns, columns=['Coefficient'])
+print(coefficients)
+
+for symbol in unique_symbols:
+  stock_data = big_tech_stock_prices[big_tech_stock_prices['stock_symbol'] == symbol]
+  stock_data.set_index('date', inplace=True)
+
+stock_data['z_score'] = (stock_data['close'] - stock_data['close'].mean()) / stock_data['close'].std()
+
+stock_data['anomaly'] = np.where(stock_data['z_score'].abs() > 3, True, False)
+
+plt.figure(figsize=(14, 7))
+plt.plot(stock_data.index, stock_data['close'], label='Close Price')
+plt.scatter(stock_data[stock_data['anomaly']]. index, stock_data[stock_data['anomaly']]['close'], color='red', label='Anomaly')
+plt.title(f'{symbol} Stock Price with Anomalies')
+plt.xlabel('Date')
+plt.ylabel('Close Price')
+plt.legend()
+plt.show()
+
+anomalies = stock_data[stock_data['anomaly']]
+print(f"Anomalies for {symbol}:")
+print(anomalies[['close', 'z_score']])
+print("\n")
+
+pip install arch
+
+from arch import arch_model
+
+for symbol in unique_symbols:
+  stock_data = big_tech_stock_prices[big_tech_stock_prices['stock_symbol'] == symbol]
+  stock_data.set_index('date', inplace=True)
+
+  stock_data['return'] = stock_data['close'].pct_change().dropna()
+
+  model = arch_model(stock_data['return'].dropna(), vol='Garch', p=1, q=1)
+  model_fit = model.fit(disp='off')
+  print(f"Summary for {symbol}:")
+  print(model_fit.summary())
+
+  volatility = model_fit.conditional_volatility
+
+  plt.figure(figsize=(14, 7))
+  plt.plot(volatility)
+  plt.title(f'{symbol} Stock Volatility')
+  plt.xlabel('Date')
+  plt.ylabel('Volatility')
+  plt.show()
+
+  forecast_horizon = 30
+  forecast = model_fit.forecast(horizon=forecast_horizon)
+  forecast_volatility = np.sqrt(forecast.variance.values[-1, :])
+
+  plt.figure(figsize=(14, 7))
+  plt.plot(range(1, forecast_horizon+1), forecast_volatility)
+  plt.title(f'{symbol} Forecasted Volatility for Next 30 Days')
+  plt.xlabel('Days')
+  plt.ylabel('Volatility')
+  plt.show()
+
+for symbol in unique_symbols:
+  stock_data = big_tech_stock_prices[big_tech_stock_prices['stock_symbol'] == symbol]
+  stock_data.set_index('date', inplace=True)
+
+  stock_data['SMA50'] = stock_data['close'].rolling(window=50).mean()
+  stock_data['SMA200'] = stock_data['close'].rolling(window=200).mean()
+
+  stock_data['Signal'] = 0.0
+  stock_data['Signal'][50:] = np.where(stock_data['SMA50'][50:] > stock_data['SMA200'][50:], 1.0, 0.0)
+  stock_data['Position'] = stock_data['Signal'].diff()
+
+for symbol in unique_symbols:
+  stock_data = big_tech_stock_prices[big_tech_stock_prices['stock_symbol'] == symbol]
+  stock_data.set_index('date', inplace=True)
+
+  stock_data['SMA50'] = stock_data['close'].rolling(window=50).mean()
+  stock_data['SMA200'] = stock_data['close'].rolling(window=200).mean()
+
+  stock_data['Signal'] = 0.0
+  stock_data['Signal'][50:] = np.where(stock_data['SMA50'][50:] > stock_data['SMA200'][50:], 1.0, 0.0)
+  stock_data['Position'] = stock_data['Signal'].diff()
+
+  # The following lines were incorrectly indented
+  plt.figure(figsize=(14, 7))
+  plt.plot(stock_data['close'], label='Close Price')
+  plt.plot(stock_data['SMA50'], label='50-day SMA', alpha=0.7)
+  plt.plot(stock_data['SMA200'], label='200-day SMA', alpha=0.7)
+  plt.plot(stock_data[stock_data['Position'] == 1].index, stock_data['SMA50'][stock_data['Position'] == 1], '^', markersize=10, color='g', lw=0, label='Buy Signal')
+  plt.plot(stock_data[stock_data['Position'] == -1].index, stock_data['SMA50'][stock_data['Position'] == -1], 'v', markersize=10, color='r', lw=0, label='Sell Signal')
+  plt.title(f'{symbol} - SMA Crossover Strategy')
+  plt.xlabel('Date')
+  plt.ylabel('Close Price')
+  plt.legend()
+  plt.show()
+
+X = merged_data[['unemployment_rate', 'cpi', 'inflation_rate', 'mortgage_interest_rate', 'corporate_bond_yield']]
+y = merged_data['close']
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+model = RandomForestRegressor()
+model.fit(X_train, y_train)
+
+!pip install shap
+import shap
+
+explainer = shap.TreeExplainer(model)
+shap_values = explainer.shap_values(X_test)
+
+shap.summary_plot(shap_values, X_test)