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import requests |
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import pandas as pd |
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from bs4 import BeautifulSoup |
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import io |
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import yfinance as yf |
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from datetime import datetime |
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import numpy as np |
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from statsmodels.tsa.api import Holt |
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from statsmodels.tsa.arima.model import ARIMA |
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from statsmodels.graphics.tsaplots import plot_predict |
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import matplotlib.pyplot as plt, mpld3 |
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from statistics import covariance |
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import warnings |
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warnings.filterwarnings("ignore") |
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def dowanload_data(): |
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url = 'https://stockmarketmba.com/stocksinthedjia.php' |
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req = requests.get(url).text |
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soup = BeautifulSoup(req, 'lxml') |
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table = soup.find('table', attrs={'id': 'ipos'}) |
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df = pd.read_html(str(table))[0] |
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tickers = df['Symbol'].tolist()+['^DJI'] |
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stock_final = pd.DataFrame() |
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for i in tickers: |
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print(i) |
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try: |
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stock = [] |
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stock = yf.download(i, start=datetime(2017, 10, 31), end=datetime(2022, 10, 31), progress=False) |
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if len(stock) == 0: |
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None |
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else: |
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stock['Name'] = i |
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stock_final = stock_final.append(stock, sort=False) |
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except: |
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pass |
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stock_final.to_csv('djia_2017-2022.csv') |
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def main(ticker): |
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df = pd.read_csv('djia_2017-2022.csv') |
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df = df[df['Name']==ticker].dropna(how='any') |
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x = np.array(df.dropna()['Date']) |
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y = np.array(df.dropna()['Close']) |
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model = ARIMA(y, order=(1,1,2)) |
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model_fit = model.fit() |
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print(model_fit.summary()) |
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plot_predict(model_fit, dynamic=False) |
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data = pd.Series(y, x) |
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fit1 = Holt(data).fit(smoothing_level=0.1, smoothing_slope=0.1, optimized=False) |
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plt.plot(x, y, label='actual') |
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plt.legend(loc="upper left") |
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step = 10 |
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plt.xticks([x[i+step] for i in range(-step, len(x), step) if i+step < len(x)], rotation=90) |
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plt.title('ARIMA forecast model vs. actual for {}'.format(ticker)) |
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mpld3.show() |
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def stock_covariance(stocks): |
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df = pd.read_csv('djia_2017-2022.csv') |
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x = df[df['Name']==stocks[0]].dropna(how='any')['Close'] |
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y = df[df['Name']==stocks[0]].dropna(how='any')['Close'] |
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cov = covariance(x, y) |
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return cov |
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def dji_covariance(stock): |
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df = pd.read_csv('djia_2017-2022.csv') |
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x = df[df['Name']=='^DJI'].dropna(how='any')['Close'] |
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y = df[df['Name']==stock].dropna(how='any')['Close'] |
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cov = covariance(x, y) |
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return cov |
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if __name__ == "__main__": |
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main('IBM') |
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