【GPT入门】第71课 autogen介绍与代码实现股票分析汇报多智能体
AutoGen是微软公司发布的一个开源编程框架，旨在通过多智能体协作来构建基于大语言模型的下一代应用程序。以下是其详细介绍和特点：
1. autogen介绍
1.1 autogen介绍
AutoGen的核心在于支持多个Agent之间的互动合作，以解决复杂的任务。每个Agent都可以被定制，以扮演不同的角色，例如程序员、公司高管或设计师等。通过这种方式，AutoGen使得大模型能够模拟人类间的对话和协作，从而高效地处理工作流。
除了AutoGen框架本身，微软还推出了AutoGen Studio，这是一个更为直观的工具，它为用户提供了一个可视化界面，用以定义、修改智能体参数，与智能体交互，添加技能，并管理会话。AutoGen的应用场景非常广泛，包括自动化文档生成、多智能体协作的客户服务、数据分析和报告，以及个性化学习等。
AUTOGEN 架构
1.2 特点
- 可定制的智能体：开发者可以定义不同角色和能力的智能体，如AssistantAgent、UserProxyAgent、TeachableAgent等，每个智能体都能根据需求被赋予特定的功能和行为模式。
- 多智能体协作：支持多种交互模式，如双人对话、顺序聊天、群聊等，群聊由管理者协调，能够模拟人类团队的协作方式，将复杂任务分解为多个子任务，由不同智能体分别处理，提高任务处理效率。
- 工具集成：智能体可以调用外部工具，如代码执行、API调用、数据库查询等，从而扩展了智能体的功能，使其能够更好地处理各种实际任务。
- 人类参与：支持“人在回路”，允许人类提供反馈或干预，使得系统在运行过程中能够结合人类的智慧和判断，提高结果的准确性和可靠性。
- 异步与可扩展架构：最新版本采用异步、事件驱动架构，支持分布式系统和跨语言开发，如Python和.NET，这使得AutoGen能够适应不同的开发环境和需求，具有良好的扩展性和性能表现。
- 低代码接口：通过AutoGen Studio提供无代码或低代码界面，降低了开发门槛，即使是非专业的开发者也能够轻松上手，快速构建基于智能体的应用程序。
2. 安装
参考官网：https://github.com/microsoft/autogen
官网建议python版本3.10及以上， 这里使用3.11，参考前面的文章安装conda环境
# Install AgentChat and OpenAI client from Extensions
pip install -U "autogen-agentchat" "autogen-ext[openai]"
# Install AutoGen Studio for no-code GUI
pip install -U "autogenstudio"
3. 股票分析的多agent代码实践
3.1 agent设计思路
设计思路： 设计一个team: team由3个agent组成，分别是 股票分析agent、google搜索agent、股票分析汇报agent，执行顺序是依次。
3.2 代码实现
- 导包
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.conditions import TextMentionTermination
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_agentchat.ui import Console
from autogen_core.tools import FunctionTool
from autogen_ext.models.openai import OpenAIChatCompletionClient
import os
os.environ["GOOGLE_API_KEY"] = "AIzaSyChLbcLjXwSSssuzXzjchqA5E_t4aEDWp4"
os.environ["GOOGLE_SEARCH_ENGINE_ID"] = "961e69d5fd62145b2"
- 编写function
#!pip install yfinance matplotlib pytz numpy pandas python-dotenv requests bs4
def google_search(query: str, num_results: int = 2, max_chars: int = 500) -> list: # type: ignore[type-arg]
import os
import time
import requests
from bs4 import BeautifulSoup
from dotenv import load_dotenv
load_dotenv()
api_key = os.getenv("GOOGLE_API_KEY")
search_engine_id = os.getenv("GOOGLE_SEARCH_ENGINE_ID")
if not api_key or not search_engine_id:
raise ValueError("API key or Search Engine ID not found in environment variables")
url = "https://customsearch.googleapis.com/customsearch/v1"
params = {"key": str(api_key), "cx": str(search_engine_id), "q": str(query), "num": str(num_results)}
response = requests.get(url, params=params)
if response.status_code != 200:
print(response.json())
raise Exception(f"Error in API request: {response.status_code}")
results = response.json().get("items", [])
def get_page_content(url: str) -> str:
try:
response = requests.get(url, timeout=10)
soup = BeautifulSoup(response.content, "html.parser")
text = soup.get_text(separator=" ", strip=True)
words = text.split()
content = ""
for word in words:
if len(content) + len(word) + 1 > max_chars:
break
content += " " + word
return content.strip()
except Exception as e:
print(f"Error fetching {url}: {str(e)}")
return ""
enriched_results = []
for item in results:
body = get_page_content(item["link"])
enriched_results.append(
{"title": item["title"], "link": item["link"], "snippet": item["snippet"], "body": body}
)
time.sleep(1) # Be respectful to the servers
return enriched_results
def analyze_stock(ticker: str) -> dict: # type: ignore[type-arg]
import os
from datetime import datetime, timedelta
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import yfinance as yf
from pytz import timezone # type: ignore
stock = yf.Ticker(ticker)
# Get historical data (1 year of data to ensure we have enough for 200-day MA)
end_date = datetime.now(timezone("UTC"))
start_date = end_date - timedelta(days=365)
hist = stock.history(start=start_date, end=end_date)
# Ensure we have data
if hist.empty:
return {"error": "No historical data available for the specified ticker."}
# Compute basic statistics and additional metrics
current_price = stock.info.get("currentPrice", hist["Close"].iloc[-1])
year_high = stock.info.get("fiftyTwoWeekHigh", hist["High"].max())
year_low = stock.info.get("fiftyTwoWeekLow", hist["Low"].min())
# Calculate 50-day and 200-day moving averages
ma_50 = hist["Close"].rolling(window=50).mean().iloc[-1]
ma_200 = hist["Close"].rolling(window=200).mean().iloc[-1]
# Calculate YTD price change and percent change
ytd_start = datetime(end_date.year, 1, 1, tzinfo=timezone("UTC"))
ytd_data = hist.loc[ytd_start:] # type: ignore[misc]
if not ytd_data.empty:
price_change = ytd_data["Close"].iloc[-1] - ytd_data["Close"].iloc[0]
percent_change = (price_change / ytd_data["Close"].iloc[0]) * 100
else:
price_change = percent_change = np.nan
# Determine trend
if pd.notna(ma_50) and pd.notna(ma_200):
if ma_50 > ma_200:
trend = "Upward"
elif ma_50 < ma_200:
trend = "Downward"
else:
trend = "Neutral"
else:
trend = "Insufficient data for trend analysis"
# Calculate volatility (standard deviation of daily returns)
daily_returns = hist["Close"].pct_change().dropna()
volatility = daily_returns.std() * np.sqrt(252) # Annualized volatility
# Create result dictionary
result = {
"ticker": ticker,
"current_price": current_price,
"52_week_high": year_high,
"52_week_low": year_low,
"50_day_ma": ma_50,
"200_day_ma": ma_200,
"ytd_price_change": price_change,
"ytd_percent_change": percent_change,
"trend": trend,
"volatility": volatility,
}
# Convert numpy types to Python native types for better JSON serialization
for key, value in result.items():
if isinstance(value, np.generic):
result[key] = value.item()
# Generate plot
plt.figure(figsize=(12, 6))
plt.plot(hist.index, hist["Close"], label="Close Price")
plt.plot(hist.index, hist["Close"].rolling(window=50).mean(), label="50-day MA")
plt.plot(hist.index, hist["Close"].rolling(window=200).mean(), label="200-day MA")
plt.title(f"{ticker} Stock Price (Past Year)")
plt.xlabel("Date")
plt.ylabel("Price ($)")
plt.legend()
plt.grid(True)
# Save plot to file
os.makedirs("coding", exist_ok=True)
plot_file_path = f"coding/{ticker}_stockprice.png"
plt.savefig(plot_file_path)
print(f"Plot saved as {plot_file_path}")
result["plot_file_path"] = plot_file_path
return result
- 定义function tool
google_search_tool = FunctionTool(
google_search, description="Search Google for information, returns results with a snippet and body content"
)
stock_analysis_tool = FunctionTool(analyze_stock, description="Analyze stock data and generate a plot")
- 定义client
from autogen_core.models import ModelFamily
model_client = OpenAIChatCompletionClient(model="deepseek-chat",
base_url="https://api.deepseek.com/v1",
api_key="sk-改为自己的key",
model_info={
"vision": False,
"function_calling": True,
"json_output": False,
"family": ModelFamily.R1,
},
)
- 组建team
# model_client = OpenAIChatCompletionClient(model="gpt-4o")
search_agent = AssistantAgent(
name="Google_Search_Agent",
model_client=model_client,
tools=[google_search_tool],
description="Search Google for information, returns top 2 results with a snippet and body content",
system_message="You are a helpful AI assistant. Solve tasks using your tools.",
)
stock_analysis_agent = AssistantAgent(
name="Stock_Analysis_Agent",
model_client=model_client,
tools=[stock_analysis_tool],
description="Analyze stock data and generate a plot",
system_message="Perform data analysis.",
)
report_agent = AssistantAgent(
name="Report_Agent",
model_client=model_client,
description="Generate a report based the search and results of stock analysis",
system_message="You are a helpful assistant that can generate a comprehensive report on a given topic based on search and stock analysis. When you done with generating the report, reply with TERMINATE.",
)
team = RoundRobinGroupChat([stock_analysis_agent, search_agent, report_agent], max_turns=3)
- 启动agent
stream = team.run_stream(task="Write a financial report on American airlines")
await Console(stream)
await model_client.close()
4. autogen studio
可以使用可视化页面做上面代码的功能
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