Kye Gomez
2 weeks ago
parent
7ca26e0162
commit
d603604ccf
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from swarms.utils.litellm_wrapper import LiteLLM
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model = LiteLLM(model_name="gpt-4o-mini", verbose=True)
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print(model.run("What is your purpose in life?"))
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from swarms.utils.litellm_wrapper import LiteLLM
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model = LiteLLM(
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model_name="groq/meta-llama/llama-4-scout-17b-16e-instruct",
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verbose=True,
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)
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print(model.run("What is your purpose in life?"))
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from swarms import Agent
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from swarms.prompts.finance_agent_sys_prompt import (
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FINANCIAL_AGENT_SYS_PROMPT,
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)
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from dotenv import load_dotenv
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load_dotenv()
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# Initialize the agent
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agent = Agent(
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agent_name="Financial-Analysis-Agent",
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agent_description="Personal finance advisor agent",
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system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
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max_loops=1,
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model_name="groq/meta-llama/llama-4-scout-17b-16e-instruct",
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dynamic_temperature_enabled=True,
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)
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print(
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agent.run(
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"Perform a comprehensive analysis of the most promising undervalued ETFs, considering market trends, historical performance, and potential growth opportunities. Please think through the analysis for 2 internal loops to refine your insights."
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)
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)
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# pip install swarms bs4 requests
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import re
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from typing import Any, Dict
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from urllib.parse import urlparse
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import requests
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from bs4 import BeautifulSoup
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from dotenv import load_dotenv
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from swarms import Agent
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load_dotenv()
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# Custom system prompt for financial sentiment analysis
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FINANCIAL_SENTIMENT_SYSTEM_PROMPT = """
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You are an expert financial analyst specializing in sentiment analysis of financial news and content. Your task is to:
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1. Analyze financial content for bullish or bearish sentiment
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2. Provide a numerical sentiment score between 0.0 (extremely bearish) and 1.0 (extremely bullish) where:
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- 0.0-0.2: Extremely bearish (strong negative outlook)
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- 0.2-0.4: Bearish (negative outlook)
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- 0.4-0.6: Neutral (balanced or unclear outlook)
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- 0.6-0.8: Bullish (positive outlook)
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- 0.8-1.0: Extremely bullish (strong positive outlook)
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3. Provide detailed rationale for your sentiment score by considering:
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- Market indicators and metrics mentioned
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- Expert opinions and quotes
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- Historical comparisons
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- Industry trends and context
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- Risk factors and potential challenges
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- Growth opportunities and positive catalysts
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- Overall market sentiment and broader economic factors
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Your analysis should be:
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- Objective and data-driven
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- Based on factual information present in the content
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- Free from personal bias or speculation
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- Considering both explicit and implicit sentiment indicators
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- Taking into account the broader market context
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For each analysis, structure your response as a clear sentiment score backed by comprehensive reasoning that explains why you arrived at that specific rating.
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"""
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class ArticleExtractor:
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"""Class to handle article content extraction and cleaning."""
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# Common financial news domains and their article content selectors
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DOMAIN_SELECTORS = {
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"seekingalpha.com": {"article": "div#SA-content"},
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"finance.yahoo.com": {"article": "div.caas-body"},
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"reuters.com": {
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"article": "div.article-body__content__17Yit"
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},
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"bloomberg.com": {"article": "div.body-content"},
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"marketwatch.com": {"article": "div.article__body"},
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# Add more domains and their selectors as needed
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}
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@staticmethod
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def get_domain(url: str) -> str:
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"""Extract domain from URL."""
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return urlparse(url).netloc.lower()
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@staticmethod
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def clean_text(text: str) -> str:
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"""Clean extracted text content."""
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# Remove extra whitespace
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text = re.sub(r"\s+", " ", text)
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# Remove special characters but keep basic punctuation
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text = re.sub(r"[^\w\s.,!?-]", "", text)
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# Remove multiple periods
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text = re.sub(r"\.{2,}", ".", text)
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return text.strip()
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@classmethod
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def extract_article_content(
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cls, html_content: str, domain: str
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) -> str:
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"""Extract article content using domain-specific selectors."""
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soup = BeautifulSoup(html_content, "html.parser")
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# Remove unwanted elements
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for element in soup.find_all(
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["script", "style", "nav", "header", "footer", "iframe"]
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):
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element.decompose()
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# Try domain-specific selector first
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if domain in cls.DOMAIN_SELECTORS:
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selector = cls.DOMAIN_SELECTORS[domain]["article"]
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content = soup.select_one(selector)
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if content:
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return cls.clean_text(content.get_text())
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# Fallback to common article containers
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article_containers = [
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"article",
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'[role="article"]',
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".article-content",
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".post-content",
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".entry-content",
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"#main-content",
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]
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for container in article_containers:
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content = soup.select_one(container)
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if content:
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return cls.clean_text(content.get_text())
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# Last resort: extract all paragraph text
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paragraphs = soup.find_all("p")
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if paragraphs:
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return cls.clean_text(
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" ".join(p.get_text() for p in paragraphs)
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)
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return cls.clean_text(soup.get_text())
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def fetch_url_content(url: str) -> Dict[str, Any]:
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"""
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Fetch and extract content from a financial news URL.
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Args:
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url (str): The URL of the financial news article
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Returns:
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Dict[str, Any]: Dictionary containing extracted content and metadata
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"""
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try:
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headers = {
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
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}
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response = requests.get(url, headers=headers, timeout=10)
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response.raise_for_status()
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domain = ArticleExtractor.get_domain(url)
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content = ArticleExtractor.extract_article_content(
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response.text, domain
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)
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# Extract title if available
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soup = BeautifulSoup(response.text, "html.parser")
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title = soup.title.string if soup.title else None
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return {
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"title": title,
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"content": content,
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"domain": domain,
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"url": url,
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"status": "success",
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}
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except Exception as e:
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return {
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"content": f"Error fetching URL content: {str(e)}",
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"status": "error",
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"url": url,
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}
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tools = [
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{
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"type": "function",
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"function": {
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"name": "analyze_sentiment",
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"description": "Analyze the sentiment of financial content and provide a bullish/bearish rating with rationale.",
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"parameters": {
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"type": "object",
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"properties": {
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"sentiment_score": {
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"type": "number",
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"description": "A score from 0.0 (extremely bearish) to 1.0 (extremely bullish)",
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},
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"rationale": {
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"type": "string",
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"description": "Detailed explanation of the sentiment analysis",
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},
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},
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"required": ["sentiment_score", "rationale"],
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},
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},
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}
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]
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# Initialize the agent
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agent = Agent(
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agent_name="Financial-Sentiment-Analyst",
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agent_description="Expert financial sentiment analyzer that provides detailed bullish/bearish analysis of financial content",
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system_prompt=FINANCIAL_SENTIMENT_SYSTEM_PROMPT,
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max_loops=1,
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tools_list_dictionary=tools,
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output_type="final",
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model_name="gpt-4o",
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)
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def run_sentiment_agent(url: str) -> Dict[str, Any]:
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"""
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Run the sentiment analysis agent on a given URL.
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Args:
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url (str): The URL of the financial content to analyze
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Returns:
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Dict[str, Any]: Dictionary containing sentiment analysis results
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"""
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article_data = fetch_url_content(url)
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if article_data["status"] == "error":
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return {"error": article_data["content"], "status": "error"}
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prompt = f"""
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Analyze the following financial article:
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Title: {article_data.get('title', 'N/A')}
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Source: {article_data['domain']}
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URL: {article_data['url']}
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Content:
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{article_data['content']}
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Please provide a detailed sentiment analysis with a score and explanation.
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"""
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return agent.run(prompt)
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if __name__ == "__main__":
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url = "https://finance.yahoo.com/"
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result = run_sentiment_agent(url)
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print(result)
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