Delete DataLoader.py

DataLoader.py

@@ -1,108 +0,0 @@
-#Class to fetch news and stock data from the web for a specific ticker and combine them into a dataframe.
-
-import pandas as pd
-import yfinance as yf
-from pygooglenews import GoogleNews
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-from transformers import pipeline
-class DataLoader:
-    def __init__(self, ticker, time_period_news, time_period_stock, news_decay_rate = 0):
-        self.ticker = ticker
-        self.time_period_news = time_period_news
-        self.time_period_stock = time_period_stock
-        self.news_decay_rate = news_decay_rate
-
-    def get_data(self):
-        stock_data = self.get_stock_data()
-        news_data = self.get_news_data()
-        news_sentiment = self.get_sentiment(news_data)
-        combined_data = self.combine_data(stock_data, news_sentiment)
-
-        if self.news_decay_rate != 0:
-            combined_data = self.news_decay(combined_data, self.news_decay_rate)
-
-        return combined_data
-
-
-    def get_stock_data(self):
-        data = yf.download(self.ticker , period = self.time_period_stock)
-        df = pd.DataFrame()
-        df['Open'] = data['Open']
-        df['Close'] = data['Close']
-        df['High'] = data['High']
-        df['Low'] = data['Low']
-        df['Volume'] = data['Volume']
-
-        return df
-
-    def get_news_data(self):
-        googlenews = GoogleNews()
-        news_data = googlenews.search(self.ticker, when=self.time_period_news)
-        news_data = pd.DataFrame(news_data['entries'])
-        return news_data
-
-    def get_sentiment(self, news_data):
-        tokenizer = AutoTokenizer.from_pretrained("ProsusAI/finbert")
-        model = AutoModelForSequenceClassification.from_pretrained("ProsusAI/finbert")
-        classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
-
-        news_sentiment = []
-        for i in range(len(news_data)):
-            sentiment = classifier(news_data['title'][i], top_k=None)
-            postive_score = sentiment[0]['score']
-            negative_score = sentiment[1]['score']
-            neutral_score = sentiment[2]['score']
-            reformmated_time_stamp = pd.to_datetime(news_data['published'][i]).date()
-            news_sentiment.append({'Date': reformmated_time_stamp, 'positive_score': postive_score, 'negative_score': negative_score, 'neutral_score': neutral_score})
-        return pd.DataFrame(news_sentiment)
-
-    def combine_data(self, stock_data, news_sentiment):
-        news_sentiment = (
-            news_sentiment
-            .groupby('Date')
-            .mean()
-            .fillna(0)
-            .reset_index()
-            .set_index('Date')
-            .sort_index()
-        )
-
-        common_index = pd.date_range(
-            start=pd.Timestamp(min(pd.Timestamp(stock_data.index[0]), pd.Timestamp(news_sentiment.index[0]))),
-            end=pd.Timestamp(max(pd.Timestamp(stock_data.index[-1]), pd.Timestamp(news_sentiment.index[-1]))),
-            freq='D'
-        )
-        stock_data = stock_data.reindex(common_index).fillna(-1)
-
-        news_sentiment = news_sentiment.reindex(common_index).fillna(0)
-
-        #Ensure stock_data and news_sentiment have combatile indices
-        stock_data.index = pd.to_datetime(stock_data.index).normalize()
-        news_sentiment.index = pd.to_datetime(news_sentiment.index).normalize()
-
-        combined_data = pd.merge(
-            stock_data,
-            news_sentiment,
-            how='left',
-            left_index=True,
-            right_index=True
-        )
-
-        #Drop all close values that are -1
-        combined_data = combined_data[combined_data['Close'] != -1]
-        #fill all missing values with 0
-        combined_data = combined_data.fillna(0)
-
-        return combined_data
-
-    def news_decay(self, Combined_data, decay_rate):
-        #We have lots of days in the data with no news. We will fill these days with the previous days news * decay_rate
-        #This will allow us to have a more continuous news data
-        combined_data = Combined_data.copy()
-        news_columns = ['positive_score', 'negative_score', 'neutral_score']
-        #We want to start from the oldest date and work our way to the newest date
-        for i in range(1, len(combined_data)):
-            for column in news_columns:
-                if combined_data[column][i] == 0 and combined_data[column][i-1] != 0:
-                    combined_data[column][i] = combined_data[column][i-1] * decay_rate
-        return combined_data