Upload stocks.py

stocks.py

@@ -0,0 +1,122 @@
+from asyncio.windows_events import NULL
+from configparser import ParsingError
+from logging import raiseExceptions
+from sympy import comp
+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 = NULL
+                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)
+                # If the user wants to run the model to have a General Prediction of stocks with data of the last 2 years , uncomment the 2 lines below
+                # data = yf.download(tickers = str(Ticker),period = "2y", interval = "1d") #period = "2y", interval = "1d"
+                # 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 NULL:
+                                        break
+                                rows.append([ticker, title])
+                                counter = counter + 1
+
+                        df_headers = pd.DataFrame(rows, columns=headerList)
+
+                return df_headers
+