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stock_analysis/.env

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+SERPER_API_KEY="88540dcf1210ca7031a57df5b6236ab609bb2a32"
+BROWSERLESS_API_KEY=NA
+SEC_API_API_KEY="ed5cb8aae17cbe13ca0594919300b4f3f1f6badfef3b85cde47066d7a29af703"
+OPENAI_API_KEY=NA
+GROQ_API_KEY="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"

stock_analysis/README.md

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+# Stock Analysis
+
+This project is an example using the CrewAI framework to automate the process of analyzing a stock. CrewAI orchestrates autonomous AI agents, enabling them to collaborate and execute complex tasks efficiently.

stock_analysis/content.txt

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+SERPER_API_KEY=KEY
+BROWSERLESS_API_KEY=KEY
+SEC_API_API_KEY=KEY
+OPENAI_API_KEY=NA
+GROQ_API_KEY=KEY

stock_analysis/main.py

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+from crewai import Crew
+from textwrap import dedent
+
+from stock_analysis_agents import StockAnalysisAgents
+from stock_analysis_tasks import StockAnalysisTasks
+
+from dotenv import load_dotenv
+load_dotenv()
+
+
+class FinancialCrew:
+  def __init__(self, company):
+    self.company = company
+
+  def run(self):
+    agents = StockAnalysisAgents()
+    tasks = StockAnalysisTasks()
+
+    research_analyst_agent = agents.research_analyst()
+    financial_analyst_agent = agents.financial_analyst()
+    investment_advisor_agent = agents.investment_advisor()
+
+    research_task = tasks.research(research_analyst_agent, self.company)
+    financial_task = tasks.financial_analysis(financial_analyst_agent)
+    filings_task = tasks.filings_analysis(financial_analyst_agent)
+    recommend_task = tasks.recommend(investment_advisor_agent)
+
+
+    crew = Crew(
+      agents=[
+        research_analyst_agent,
+        financial_analyst_agent,
+        investment_advisor_agent
+      ],
+      tasks=[
+        research_task,
+        financial_task,
+        filings_task,
+        recommend_task
+      ],
+      verbose=True
+    )
+
+    result = crew.kickoff()
+    return result
+
+if __name__ == "__main__":
+  print("## Welcome to Financial Analysis Crew")
+  print('-------------------------------')
+  company = input(
+    dedent("""
+      What is the company you want to analyze?
+    """))
+  
+  financial_crew = FinancialCrew(company)
+  result = financial_crew.run()
+  print("\n\n########################")
+  print("## Here is the Report")
+  print("########################\n")
+  print(result)
+  with open("report.txt", "w+") as f:
+    f.write(result)

stock_analysis/pyproject.toml

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+[tool.poetry]
+name = "stock-analysis-crew"
+version = "0.1.0"
+description = ""
+authors = ["Your Name <[email protected]>"]
+
+[tool.poetry.dependencies]
+python = ">=3.10.0,<3.12"
+crewai = "^0.11.0"
+unstructured = '==0.10.25'
+pyowm = '3.3.0'
+tools = "^0.1.9"
+wikipedia = "1.4.0"
+yfinance = "0.2.35"
+sec-api = "1.0.17"
+tiktoken = "0.5.2"
+faiss-cpu = "1.7.4"
+python-dotenv = "1.0.0"
+langchain-community = "0.0.10"
+langchain-core = "0.1.8"
+openai = "1.7.1"
+
+[tool.pyright]
+# https://github.com/microsoft/pyright/blob/main/docs/configuration.md
+useLibraryCodeForTypes = true
+exclude = [".cache"]
+
+[tool.ruff]
+# https://beta.ruff.rs/docs/configuration/
+select = ['E', 'W', 'F', 'I', 'B', 'C4', 'ARG', 'SIM']
+ignore = ['W291', 'W292', 'W293']
+
+[build-system]
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"

stock_analysis/stock_analysis_agents.py

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+from crewai import Agent
+from tools.browser_tools import BrowserTools
+from tools.calculator_tools import CalculatorTools
+from tools.search_tools import SearchTools
+from tools.sec_tools import SECTools
+# from langchain.llms.ollama import Ollama
+from langchain_groq import ChatGroq
+from dotenv import load_dotenv
+import os
+load_dotenv()
+from langchain.tools.yahoo_finance_news import YahooFinanceNewsTool
+llm = ChatGroq(temperature=0, groq_api_key=os.getenv("GROQ_API_KEY"), model_name="mixtral-8x7b-32768")
+# llm = Ollama(model="crew-mistral")
+
+class StockAnalysisAgents():
+  def financial_analyst(self):
+    return Agent(
+      role='The Best Financial Analyst',
+      goal="""Impress all customers with your financial data 
+      and market trends analysis""",
+      backstory="""The most seasoned financial analyst with 
+      lots of expertise in stock market analysis and investment
+      strategies that is working for a super important customer.""",
+      verbose=True,
+      tools=[
+        BrowserTools.scrape_and_summarize_website,
+        SearchTools.search_internet,
+        CalculatorTools.calculate,
+        SECTools.search_10q,
+        SECTools.search_10k
+      ],
+      llm=llm,
+      max_iter=30
+    )
+
+  def research_analyst(self):
+    return Agent(
+      role='Staff Research Analyst',
+      goal="""Being the best at gather, interpret data and amaze
+      your customer with it""",
+      backstory="""Known as the BEST research analyst, you're
+      skilled in sifting through news, company announcements, 
+      and market sentiments. Now you're working on a super 
+      important customer""",
+      verbose=True,
+      tools=[
+        BrowserTools.scrape_and_summarize_website,
+        SearchTools.search_internet,
+        SearchTools.search_news,
+        YahooFinanceNewsTool(),
+        SECTools.search_10q,
+        SECTools.search_10k
+      ],
+      llm=llm,
+      max_iter=30
+  )
+
+  def investment_advisor(self):
+    return Agent(
+      role='Private Investment Advisor',
+      goal="""Impress your customers with full analyses over stocks
+      and completer investment recommendations""",
+      backstory="""You're the most experienced investment advisor
+      and you combine various analytical insights to formulate
+      strategic investment advice. You are now working for
+      a super important customer you need to impress.""",
+      verbose=True,
+      tools=[
+        BrowserTools.scrape_and_summarize_website,
+        SearchTools.search_internet,
+        SearchTools.search_news,
+        CalculatorTools.calculate,
+        YahooFinanceNewsTool()
+      ],
+      llm=llm,
+      max_iter=30
+    )

stock_analysis/stock_analysis_tasks.py

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+from crewai import Task
+from textwrap import dedent
+
+class StockAnalysisTasks():
+  def research(self, agent, company):
+    return Task(description=dedent(f"""
+        Collect and summarize recent news articles, press
+        releases, and market analyses related to the stock and
+        its industry.
+        Pay special attention to any significant events, market
+        sentiments, and analysts' opinions. Also include upcoming 
+        events like earnings and others.
+  
+        Your final answer MUST be a report that includes a
+        comprehensive summary of the latest news, any notable
+        shifts in market sentiment, and potential impacts on 
+        the stock.
+        Also make sure to return the stock ticker.
+        
+        {self.__tip_section()}
+  
+        Make sure to use the most recent data as possible.
+  
+        Selected company by the customer: {company}
+      """),
+      expected_output="A detailed report covering the specified analysis points",
+      agent=agent
+    )
+    
+  def financial_analysis(self, agent): 
+    return Task(description=dedent(f"""
+        Conduct a thorough analysis of the stock's financial
+        health and market performance. 
+        This includes examining key financial metrics such as
+        P/E ratio, EPS growth, revenue trends, and 
+        debt-to-equity ratio. 
+        Also, analyze the stock's performance in comparison 
+        to its industry peers and overall market trends.
+
+        Your final report MUST expand on the summary provided
+        but now including a clear assessment of the stock's
+        financial standing, its strengths and weaknesses, 
+        and how it fares against its competitors in the current
+        market scenario.{self.__tip_section()}
+
+        Make sure to use the most recent data possible.
+      """),
+      expected_output="A detailed report covering the specified analysis points",
+      agent=agent
+    )
+
+  def filings_analysis(self, agent):
+    return Task(description=dedent(f"""
+        Analyze the latest 10-Q and 10-K filings from EDGAR for
+        the stock in question. 
+        Focus on key sections like Management's Discussion and
+        Analysis, financial statements, insider trading activity, 
+        and any disclosed risks.
+        Extract relevant data and insights that could influence
+        the stock's future performance.
+
+        Your final answer must be an expanded report that now
+        also highlights significant findings from these filings,
+        including any red flags or positive indicators for
+        your customer.
+        {self.__tip_section()}        
+      """),
+      expected_output="A detailed report covering the specified analysis points",
+      agent=agent
+    )
+
+  def recommend(self, agent):
+    return Task(description=dedent(f"""
+        Review and synthesize the analyses provided by the
+        Financial Analyst and the Research Analyst.
+        Combine these insights to form a comprehensive
+        investment recommendation. 
+        
+        You MUST Consider all aspects, including financial
+        health, market sentiment, and qualitative data from
+        EDGAR filings.
+
+        Make sure to include a section that shows insider 
+        trading activity, and upcoming events like earnings.
+
+        Your final answer MUST be a recommendation for your
+        customer. It should be a full super detailed report, providing a 
+        clear investment stance and strategy with supporting evidence.
+        Make it pretty and well formatted for your customer.
+        {self.__tip_section()}
+      """),
+      expected_output="A detailed report covering the specified analysis points",
+      agent=agent
+    )
+
+  def __tip_section(self):
+    return "If you do your BEST WORK, I'll give you a $10,000 commission!"

stock_analysis/tools/browser_tools.py

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+import json
+import os
+
+import requests
+from crewai import Agent, Task
+from langchain.tools import tool
+from unstructured.partition.html import partition_html
+
+
+class BrowserTools():
+
+  @tool("Scrape website content")
+  def scrape_and_summarize_website(website):
+    """Useful to scrape and summarize a website content"""
+    url = f"https://chrome.browserless.io/content?token={os.environ['BROWSERLESS_API_KEY']}"
+    payload = json.dumps({"url": website})
+    headers = {'cache-control': 'no-cache', 'content-type': 'application/json'}
+    response = requests.request("POST", url, headers=headers, data=payload)
+    elements = partition_html(text=response.text)
+    content = "\n\n".join([str(el) for el in elements])
+    content = [content[i:i + 8000] for i in range(0, len(content), 8000)]
+    summaries = []
+    for chunk in content:
+      agent = Agent(
+          role='Principal Researcher',
+          goal=
+          'Do amazing research and summaries based on the content you are working with',
+          backstory=
+          "You're a Principal Researcher at a big company and you need to do research about a given topic.",
+          allow_delegation=False)
+      task = Task(
+          agent=agent,
+          description=
+          f'Analyze and summarize the content below, make sure to include the most relevant information in the summary, return only the summary nothing else.\n\nCONTENT\n----------\n{chunk}'
+      )
+      summary = task.execute()
+      summaries.append(summary)
+    return "\n\n".join(summaries)

stock_analysis/tools/calculator_tools.py

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+from langchain.tools import tool
+
+
+class CalculatorTools():
+
+  @tool("Make a calculation")
+  def calculate(operation):
+    """Useful to perform any mathematical calculations, 
+    like sum, minus, multiplication, division, etc.
+    The input to this tool should be a mathematical 
+    expression, a couple examples are `200*7` or `5000/2*10`
+    """
+    return eval(operation)

stock_analysis/tools/search_tools.py

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+import json
+import os
+
+import requests
+from langchain.tools import tool
+
+
+class SearchTools():
+  @tool("Search the internet")
+  def search_internet(query):
+    """Useful to search the internet 
+    about a a given topic and return relevant results"""
+    top_result_to_return = 4
+    url = "https://google.serper.dev/search"
+    payload = json.dumps({"q": query})
+    headers = {
+        'X-API-KEY': os.environ['SERPER_API_KEY'],
+        'content-type': 'application/json'
+    }
+    response = requests.request("POST", url, headers=headers, data=payload)
+    results = response.json()['organic']
+    string = []
+    for result in results[:top_result_to_return]:
+      try:
+        string.append('\n'.join([
+            f"Title: {result['title']}", f"Link: {result['link']}",
+            f"Snippet: {result['snippet']}", "\n-----------------"
+        ]))
+      except KeyError:
+        next
+
+    return '\n'.join(string)
+
+  @tool("Search news on the internet")
+  def search_news(query):
+    """Useful to search news about a company, stock or any other
+    topic and return relevant results"""""
+    top_result_to_return = 4
+    url = "https://google.serper.dev/news"
+    payload = json.dumps({"q": query})
+    headers = {
+        'X-API-KEY': os.environ['SERPER_API_KEY'],
+        'content-type': 'application/json'
+    }
+    response = requests.request("POST", url, headers=headers, data=payload)
+    results = response.json()['news']
+    string = []
+    for result in results[:top_result_to_return]:
+      try:
+        string.append('\n'.join([
+            f"Title: {result['title']}", f"Link: {result['link']}",
+            f"Snippet: {result['snippet']}", "\n-----------------"
+        ]))
+      except KeyError:
+        next
+
+    return '\n'.join(string)

stock_analysis/tools/sec_tools.py

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+import os
+
+import requests
+
+from langchain.tools import tool
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings import OpenAIEmbeddings
+from langchain_community.vectorstores import FAISS
+
+from sec_api import QueryApi
+from unstructured.partition.html import partition_html
+
+class SECTools():
+  @tool("Search 10-Q form")
+  def search_10q(data):
+    """
+    Useful to search information from the latest 10-Q form for a
+    given stock.
+    The input to this tool should be a pipe (|) separated text of
+    length two, representing the stock ticker you are interested and what
+    question you have from it.
+		For example, `AAPL|what was last quarter's revenue`.
+    """
+    stock, ask = data.split("|")
+    queryApi = QueryApi(api_key=os.environ['SEC_API_API_KEY'])
+    query = {
+      "query": {
+        "query_string": {
+          "query": f"ticker:{stock} AND formType:\"10-Q\""
+        }
+      },
+      "from": "0",
+      "size": "1",
+      "sort": [{ "filedAt": { "order": "desc" }}]
+    }
+
+    fillings = queryApi.get_filings(query)['filings']
+    if len(fillings) == 0:
+      return "Sorry, I couldn't find any filling for this stock, check if the ticker is correct."
+    link = fillings[0]['linkToFilingDetails']
+    answer = SECTools.__embedding_search(link, ask)
+    return answer
+
+  @tool("Search 10-K form")
+  def search_10k(data):
+    """
+    Useful to search information from the latest 10-K form for a
+    given stock.
+    The input to this tool should be a pipe (|) separated text of
+    length two, representing the stock ticker you are interested, what
+    question you have from it.
+    For example, `AAPL|what was last year's revenue`.
+    """
+    stock, ask = data.split("|")
+    queryApi = QueryApi(api_key=os.environ['SEC_API_API_KEY'])
+    query = {
+      "query": {
+        "query_string": {
+          "query": f"ticker:{stock} AND formType:\"10-K\""
+        }
+      },
+      "from": "0",
+      "size": "1",
+      "sort": [{ "filedAt": { "order": "desc" }}]
+    }
+
+    fillings = queryApi.get_filings(query)['filings']
+    if len(fillings) == 0:
+      return "Sorry, I couldn't find any filling for this stock, check if the ticker is correct."
+    link = fillings[0]['linkToFilingDetails']
+    answer = SECTools.__embedding_search(link, ask)
+    return answer
+
+  def __embedding_search(url, ask):
+    text = SECTools.__download_form_html(url)
+    elements = partition_html(text=text)
+    content = "\n".join([str(el) for el in elements])
+    text_splitter = CharacterTextSplitter(
+        separator = "\n",
+        chunk_size = 1000,
+        chunk_overlap  = 150,
+        length_function = len,
+        is_separator_regex = False,
+    )
+    docs = text_splitter.create_documents([content])
+    retriever = FAISS.from_documents(
+      docs, OpenAIEmbeddings()
+    ).as_retriever()
+    answers = retriever.get_relevant_documents(ask, top_k=4)
+    answers = "\n\n".join([a.page_content for a in answers])
+    return answers
+
+  def __download_form_html(url):
+    headers = {
+      'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7',
+      'Accept-Encoding': 'gzip, deflate, br',
+      'Accept-Language': 'en-US,en;q=0.9,pt-BR;q=0.8,pt;q=0.7',
+      'Cache-Control': 'max-age=0',
+      'Dnt': '1',
+      'Sec-Ch-Ua': '"Not_A Brand";v="8", "Chromium";v="120"',
+      'Sec-Ch-Ua-Mobile': '?0',
+      'Sec-Ch-Ua-Platform': '"macOS"',
+      'Sec-Fetch-Dest': 'document',
+      'Sec-Fetch-Mode': 'navigate',
+      'Sec-Fetch-Site': 'none',
+      'Sec-Fetch-User': '?1',
+      'Upgrade-Insecure-Requests': '1',
+      'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36'
+    }
+
+    response = requests.get(url, headers=headers)
+    return response.text

tree

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+