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| #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 InferenceDataPipeline: | |
| def __init__(self, ticker, time_period_news, time_period_stock): | |
| self.ticker = ticker | |
| self.time_period_news = time_period_news | |
| self.time_period_stock = time_period_stock | |
| 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) | |
| 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 |