# -*- 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)