Spaces:
Sleeping
Sleeping
File size: 10,108 Bytes
38b6b6d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 |
from transformers import pipeline
from alpaca_trade_api import REST
import os
from dotenv import load_dotenv
from datetime import datetime
import pandas as pd
import matplotlib.pyplot as plt
from datetime import date, timedelta
from pydantic.v1 import BaseModel, Field
from langchain.tools import BaseTool
from typing import Optional, Type
from langchain.tools import StructuredTool
def sentimental_analysis_tools():
class AlpacaNewsFetcher:
"""
A class for fetching news articles related to a specific stock from Alpaca API.
Attributes:
- api_key (str): Alpaca API key for authentication.
- api_secret (str): Alpaca API secret for authentication.
- rest_client (alpaca_trade_api.REST): Alpaca REST API client.
"""
def __init__(self):
"""
Initializes the AlpacaNewsFetcher object.
Args:
- api_key (str): Alpaca API key for authentication.
- api_secret (str): Alpaca API secret for authentication.
"""
load_dotenv()
self.api_key = os.environ["ALPACA_API_KEY"]
self.api_secret = os.environ["ALPACA_SECRET"]
self.rest_client = REST(self.api_key, self.api_secret)
#No of news articles to fetch for the input stock ticker.
self.no_of_newsarticles_to_fetch = os.environ["NO_OF_NEWSARTICLES_TO_FETCH"]
#No of days to fetch news articles for
self.no_of_days = os.environ["NO_OF_DAYS_TO_FETCH_NEWS_ARTICLES"]
def fetch_news(self, stockticker):
"""
Fetches news articles for a given stock symbol within a specified date range.
Args:
- stockticker (str): Stock symbol for which news articles are to be fetched (e.g., "AAPL").
Returns:
- list: A list of dictionaries containing relevant information for each news article.
"""
#Date range for which to get the news
start_date = date.today()
end_date = date.today() - timedelta(self.no_of_days)
news_articles = self.rest_client.get_news(stockticker, start_date, end_date, limit=self.no_of_newsarticles_to_fetch )
formatted_news = []
for article in news_articles:
summary = article.summary
title = article.headline
timestamp = article.created_at
relevant_info = {
'timestamp': timestamp,
'title': title,
'summary': summary
}
formatted_news.append(relevant_info)
return formatted_news
class NewsSentimentAnalysis:
"""
A class for sentiment analysis of news articles using the Transformers library.
Attributes:
- classifier (pipeline): Sentiment analysis pipeline from Transformers.
"""
def __init__(self):
"""
Initializes the NewsSentimentAnalysis object.
"""
self.classifier = pipeline('sentiment-analysis')
def analyze_sentiment(self, news_article):
"""
Analyzes the sentiment of a given news article.
Args:
- news_article (dict): Dictionary containing 'summary', 'headline', and 'created_at' keys.
Returns:
- dict: A dictionary containing sentiment analysis results.
"""
summary = news_article['summary']
title = news_article['title']
timestamp = news_article['timestamp']
relevant_text = summary + title
sentiment_result = self.classifier(relevant_text)
analysis_result = {
'timestamp': timestamp,
'title': title,
'summary': summary,
'sentiment': sentiment_result
}
return analysis_result
def plot_sentiment_graph(self, sentiment_analysis_result):
"""
Plots a sentiment analysis graph
Args:
- sentiment_analysis_result): (dict): Dictionary containing 'summary', 'headline', and 'created_at' keys.
Returns:
- dict: A dictionary containing sentiment analysis results.
"""
df = pd.DataFrame(sentiment_analysis_result)
df['Timestamp'] = pd.to_datetime(df['Timestamp'])
df['Date'] = df['Timestamp'].dt.date
#Group by Date, sentiment value count
grouped = df.groupby(by='Date')['Sentiment'].value_counts()
grouped.plot.pie()
def get_dominant_sentiment (self, sentiment_analysis_result):
"""
Returns overall sentiment, negative or positive or neutral depending on the count of negative sentiment vs positive sentiment
Args:
- sentiment_analysis_result): (dict): Dictionary containing 'summary', 'headline', and 'created_at' keys.
Returns:
- dict: A dictionary containing sentiment analysis results.
"""
df = pd.DataFrame(sentiment_analysis_result)
df['Timestamp'] = pd.to_datetime(df['Timestamp'])
df['Date'] = df['Timestamp'].dt.date
#Group by Date, sentiment value count
grouped = df.groupby(by='Date')['Sentiment'].value_counts()
df = pd.DataFrame(list(grouped.items()), columns=['Sentiment', 'count'])
df['date'] = df['Sentiment'].apply(lambda x: x[0])
df['sentiment'] = df['Sentiment'].apply(lambda x: x[1])
df.drop('Sentiment', axis=1, inplace=True)
result = df.groupby('sentiment')['count'].sum().reset_index()
# Determine the sentiment with the most count
dominant_sentiment = result.loc[result['count'].idxmax()]
return dominant_sentiment
#Function to get the stock sentiment
def get_stock_sentiment(stockticker: str):
#Initialize AlpacaNewsFetcher, a class for fetching news articles related to a specific stock from Alpaca API.
news_fetcher = AlpacaNewsFetcher()
# Fetch news (contains - title of the news, timestamp and summary) for specified stocksticker
news_data = news_fetcher.fetch_news(stockticker)
# Initialize the NewsSentimentAnalysis object
news_sentiment_analyzer = NewsSentimentAnalysis()
analysis_result = []
# Assume 'news_data' is a list of news articles (each as a dictionary), analyze sentiment of each news
for article in news_data:
sentiment_analysis_result = news_sentiment_analyzer.analyze_sentiment(article)
# Display sentiment analysis results
print(f'Timestamp: {sentiment_analysis_result["timestamp"]}, '
f'Title: {sentiment_analysis_result["title"]}, '
f'Summary: {sentiment_analysis_result["summary"]}')
print(f'Sentiment: {sentiment_analysis_result["sentiment"]}', '\n')
result = {
'Timestamp': sentiment_analysis_result["timestamp"],
'News- Title:Summar': sentiment_analysis_result["title"] + sentiment_analysis_result["summary"],
'Sentiment': sentiment_analysis_result["sentiment"][0]['label']
}
analysis_result.append(result)
#Extracting timestamp of article and sentiment of article for graphing
""" result_for_graph = {
'Timestamp': sentiment_analysis_result["timestamp"],
'Sentiment': sentiment_analysis_result["sentiment"][0]['label']
}
analysis_result.append(result_for_graph)
"""
#Get dominant sentiment
dominant_sentiment = news_sentiment_analyzer.get_dominant_sentiment(sentiment_analysis_result)
#Build response string for news sentiment
output_string = ""
for result in analysis_result:
output_string = output_string + f'{result["Timestamp"]} : {result["News- Title:Summary"]} : {result["Sentiment"]}' + '\n'
final_result = {
'Sentiment-analysis-result' : output_string,
'Dominant-sentiment' : dominant_sentiment['sentiment']
}
return final_result
class StockSentimentCheckInput(BaseModel):
"""Input for Stock price check."""
stockticker: str = Field(..., description="Ticker symbol for stock or index")
class StockSentimentAnalysisTool(BaseTool):
name = "get_stock_sentiment"
description = """Useful for finding sentiment of stock, based on published news articles.
Fetches configured number of news items for the sentiment,
determines sentiment of each news items and then returns
List of sentiment analysit result & domainant sentiment of the news
"""
"""Input for Stock sentiment analysis."""
stockticker: str = Field(..., description="Ticker symbol for stock or index")
def _run(self, stockticker: str):
# print("i'm running")
sentiment_response = get_stock_sentiment(stockticker)
print("++++++++++++++++++++++++++++++++++++++++++++++++++++++")
print(str(sentiment_response))
print("++++++++++++++++++++++++++++++++++++++++++++++++++++++")
return sentiment_response
def _arun(self, stockticker: str):
raise NotImplementedError("This tool does not support async")
args_schema: Optional[Type[BaseModel]] = StockSentimentCheckInput
tools_sentiment_analyst = [StructuredTool.from_function(
func=StockSentimentAnalysisTool,
args_schema=StockSentimentCheckInput,
description="Function to get stock sentiment.",
)
]
return tools_sentiment_analyst |