mirror of
https://github.com/B-Wear/QuantumEdge.git
synced 2026-08-12 22:58:04 +00:00
B-Wear
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import numpy as np
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import pandas as pd
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from typing import Dict, List, Optional
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import logging
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from datetime import datetime, timedelta
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import requests
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from textblob import TextBlob
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import tweepy
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from newsapi import NewsApiClient
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import yfinance as yf
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from concurrent.futures import ThreadPoolExecutor
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import json
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import os
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logger = logging.getLogger(__name__)
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class SentimentAnalyzer:
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def __init__(self, config: Dict):
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self.config = config
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self.news_api = NewsApiClient(api_key=config['news_api_key'])
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self.twitter_auth = tweepy.OAuthHandler(
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config['twitter_api_key'],
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config['twitter_api_secret']
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)
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self.twitter_auth.set_access_token(
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config['twitter_access_token'],
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config['twitter_access_token_secret']
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)
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self.twitter_api = tweepy.API(self.twitter_auth)
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# Initialize sentiment cache
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self.sentiment_cache = {}
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self.cache_duration = timedelta(hours=1)
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def analyze_text(self, text: str) -> float:
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"""
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Analyze sentiment of a single text using TextBlob
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"""
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try:
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analysis = TextBlob(text)
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# Normalize sentiment score to [-1, 1]
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return analysis.sentiment.polarity
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except Exception as e:
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logger.error(f"Error analyzing text: {str(e)}")
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return 0.0
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def get_news_sentiment(self, symbol: str) -> Optional[float]:
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"""
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Get sentiment from news articles
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"""
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try:
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# Check cache first
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cache_key = f"news_{symbol}"
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if cache_key in self.sentiment_cache:
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cached_data = self.sentiment_cache[cache_key]
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if datetime.now() - cached_data['timestamp'] < self.cache_duration:
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return cached_data['sentiment']
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# Get news articles
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news = self.news_api.get_everything(
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q=symbol,
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language='en',
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from_param=(datetime.now() - timedelta(days=1)).strftime('%Y-%m-%d'),
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sort_by='relevancy'
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)
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if not news['articles']:
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return None
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# Analyze sentiment of each article
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sentiments = []
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for article in news['articles']:
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title_sentiment = self.analyze_text(article['title'])
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if article['description']:
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desc_sentiment = self.analyze_text(article['description'])
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sentiments.append((title_sentiment + desc_sentiment) / 2)
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else:
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sentiments.append(title_sentiment)
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# Calculate weighted average sentiment
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avg_sentiment = np.mean(sentiments)
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# Cache the result
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self.sentiment_cache[cache_key] = {
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'sentiment': avg_sentiment,
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'timestamp': datetime.now()
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}
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return avg_sentiment
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except Exception as e:
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logger.error(f"Error getting news sentiment: {str(e)}")
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return None
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def get_twitter_sentiment(self, symbol: str) -> Optional[float]:
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"""
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Get sentiment from Twitter
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"""
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try:
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# Check cache first
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cache_key = f"twitter_{symbol}"
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if cache_key in self.sentiment_cache:
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cached_data = self.sentiment_cache[cache_key]
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if datetime.now() - cached_data['timestamp'] < self.cache_duration:
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return cached_data['sentiment']
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# Get tweets
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tweets = self.twitter_api.search_tweets(
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q=f"${symbol}",
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lang="en",
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count=100
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)
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if not tweets:
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return None
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# Analyze sentiment of each tweet
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sentiments = []
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for tweet in tweets:
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sentiment = self.analyze_text(tweet.text)
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sentiments.append(sentiment)
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# Calculate weighted average sentiment
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avg_sentiment = np.mean(sentiments)
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# Cache the result
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self.sentiment_cache[cache_key] = {
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'sentiment': avg_sentiment,
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'timestamp': datetime.now()
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}
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return avg_sentiment
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except Exception as e:
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logger.error(f"Error getting Twitter sentiment: {str(e)}")
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return None
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def get_market_sentiment(self, symbol: str) -> Optional[float]:
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"""
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Get market sentiment indicators
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"""
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try:
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# Check cache first
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cache_key = f"market_{symbol}"
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if cache_key in self.sentiment_cache:
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cached_data = self.sentiment_cache[cache_key]
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if datetime.now() - cached_data['timestamp'] < self.cache_duration:
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return cached_data['sentiment']
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# Get market data
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ticker = yf.Ticker(symbol)
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info = ticker.info
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# Calculate various sentiment indicators
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sentiment_indicators = []
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# RSI sentiment
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if 'RSI' in info:
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rsi = info['RSI']
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rsi_sentiment = (rsi - 50) / 50 # Normalize to [-1, 1]
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sentiment_indicators.append(rsi_sentiment)
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# Volume sentiment
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if 'volume' in info and 'averageVolume' in info:
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volume_ratio = info['volume'] / info['averageVolume']
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volume_sentiment = (volume_ratio - 1) / volume_ratio # Normalize to [-1, 1]
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sentiment_indicators.append(volume_sentiment)
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# Price momentum sentiment
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if 'regularMarketChangePercent' in info:
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momentum_sentiment = info['regularMarketChangePercent'] / 100
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sentiment_indicators.append(momentum_sentiment)
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if not sentiment_indicators:
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return None
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# Calculate weighted average sentiment
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avg_sentiment = np.mean(sentiment_indicators)
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# Cache the result
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self.sentiment_cache[cache_key] = {
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'sentiment': avg_sentiment,
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'timestamp': datetime.now()
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}
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return avg_sentiment
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except Exception as e:
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logger.error(f"Error getting market sentiment: {str(e)}")
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return None
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def get_combined_sentiment(self, symbol: str) -> Optional[float]:
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"""
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Get combined sentiment from all sources
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"""
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try:
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# Check cache first
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cache_key = f"combined_{symbol}"
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if cache_key in self.sentiment_cache:
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cached_data = self.sentiment_cache[cache_key]
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if datetime.now() - cached_data['timestamp'] < self.cache_duration:
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return cached_data['sentiment']
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# Get sentiment from all sources
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sentiments = []
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weights = []
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# News sentiment
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news_sentiment = self.get_news_sentiment(symbol)
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if news_sentiment is not None:
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sentiments.append(news_sentiment)
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weights.append(self.config['sentiment_weights']['news'])
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# Twitter sentiment
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twitter_sentiment = self.get_twitter_sentiment(symbol)
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if twitter_sentiment is not None:
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sentiments.append(twitter_sentiment)
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weights.append(self.config['sentiment_weights']['twitter'])
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# Market sentiment
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market_sentiment = self.get_market_sentiment(symbol)
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if market_sentiment is not None:
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sentiments.append(market_sentiment)
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weights.append(self.config['sentiment_weights']['market'])
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if not sentiments:
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return None
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# Calculate weighted average sentiment
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avg_sentiment = np.average(sentiments, weights=weights)
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# Cache the result
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self.sentiment_cache[cache_key] = {
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'sentiment': avg_sentiment,
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'timestamp': datetime.now()
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}
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return avg_sentiment
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except Exception as e:
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logger.error(f"Error getting combined sentiment: {str(e)}")
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return None
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def clear_cache(self):
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"""
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Clear the sentiment cache
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"""
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self.sentiment_cache.clear()
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def get_sentiment_signal(self, symbol: str) -> int:
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"""
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Convert sentiment to trading signal
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"""
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sentiment = self.get_combined_sentiment(symbol)
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if sentiment is None:
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return 0
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# Define sentiment thresholds
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positive_threshold = self.config['sentiment_thresholds']['positive']
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negative_threshold = self.config['sentiment_thresholds']['negative']
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if sentiment > positive_threshold:
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return 1 # Buy signal
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elif sentiment < negative_threshold:
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return -1 # Sell signal
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else:
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return 0 # Neutral signal
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