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| from configparser import ParsingError | |
| from logging import raiseExceptions | |
| import yfinance as yf | |
| import requests | |
| import pandas as pd | |
| from bs4 import BeautifulSoup | |
| class Stock_Data(object): | |
| ''' | |
| This class contains 5 methods responsible for choosing a stock's ticker, then checking whether the | |
| stock exchange it is listed in is open or not, and in case it is, it gets data for the last 6 months | |
| from "yfinance" module of Yahoo Inc. which will be fed to the models. | |
| ''' | |
| def Ticker(self, tick): | |
| ''' | |
| This method will "carry" the company's ticker, and it will also be used as a placeholder. | |
| ''' | |
| global ticker | |
| ticker = tick | |
| return ticker | |
| def status_getter(self, Ticker): | |
| ''' | |
| This method gets the company ticker the user chooses, creates a www.marketwatch.com | |
| link, then scraps the HTML code of the corresponding company page in marketwatch website, | |
| and gets the current market status of the exchange this stock is listed in. Possible values are: | |
| After Hours, Open, and Market Closed. | |
| ''' | |
| global company_ticker | |
| company_ticker = Ticker | |
| link_1 = 'https://www.marketwatch.com/investing/stock/' | |
| link_2 = '?mod=search_symbol' | |
| # Pasting the above 3 parts to create the URL | |
| global final_link | |
| final_link = link_1 + company_ticker + link_2 | |
| page = requests.get(final_link) | |
| global soup | |
| soup = BeautifulSoup(page.text, "lxml") | |
| if soup is None: | |
| raise ParsingError("HTML code of MarketWatch website was not scraped and current status can not be found") | |
| else: | |
| current_status = soup.find("div", class_="status").text # Finding the market status | |
| return current_status | |
| def current_price_getter(self, Ticker): | |
| ''' | |
| This method will get the current price only if the market is open. | |
| ''' | |
| current_price = None | |
| if self.status_getter(Ticker) == "Open": | |
| current_price = float(soup.find("bg-quote", class_="value").text.replace(',','')) | |
| return current_price | |
| else: | |
| return "Market Closed" | |
| def stock_data_getter(self, Ticker): | |
| ''' | |
| This method will return a dataframe containing Stock data from the Yahoo's "yfinance" | |
| library in case the market is open. | |
| ''' | |
| if self.status_getter(Ticker) == "Open": | |
| data = yf.download(tickers = str(Ticker), period = "6mo", interval = "1d") | |
| df = pd.DataFrame(data) | |
| return df | |
| else: | |
| return "Market Closed" | |
| def LSTM_stock_data_getter(self, Ticker): | |
| ''' | |
| This method will return a dataframe containing Stock data from the Yahoo's "yfinance" | |
| library regardrless of whether the market is open or not, and will feed the LSTM model. | |
| ''' | |
| data = yf.download(tickers = str(Ticker), period = "2y", interval = "1d") | |
| df = pd.DataFrame(data) | |
| # Prediction in the data we evaluate the model | |
| # If the user wants to run the model with the data that has been evaluated and predicted for , uncomment the 2 lines below | |
| # Setting the start = 2022-08-26 and end = 2020-08-26 Yahoo Finance will return data from 25-8-2020 to 25-8-2022 (2 years period). | |
| # In those data our model has been evaluated. | |
| #data = yf.download(tickers = str(Ticker),end="2022-08-26", start="2020-08-26") | |
| #df = pd.DataFrame(data) | |
| return df | |
| def article_parser(self, ticker): | |
| ''' | |
| This method gets as input a stock ticker, creates the www.marketwatch.com link of this stock | |
| and returns a dataframe with the last 17 articles' headers. | |
| ''' | |
| company_ticker = self.Ticker(tick=ticker) | |
| link_1 = 'https://www.marketwatch.com/investing/stock/' | |
| link_2 = '?mod=search_symbol' | |
| # Pasting the above 3 parts to create the URL | |
| final_link = link_1 + company_ticker + link_2 | |
| page = requests.get(final_link) | |
| soup = BeautifulSoup(page.content, "html.parser") | |
| results = soup.find("div", class_="tab__pane is-active j-tabPane") | |
| articles = results.find_all("a", class_="link") | |
| headerList = ["ticker", "headline"] | |
| rows = [] | |
| counter = 1 | |
| df_headers = pd.DataFrame() | |
| for art in articles: | |
| if counter <= 17: | |
| ticker = company_ticker | |
| title = art.text.strip() | |
| if title is None: | |
| break | |
| rows.append([ticker, title]) | |
| counter = counter + 1 | |
| df_headers = pd.DataFrame(rows, columns=headerList) | |
| return df_headers | |