{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Get envirionment variables" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "from dotenv import find_dotenv, dotenv_values\n", "\n", "keys = list(dotenv_values(find_dotenv('.env')).items())\n", "OPENAI_API_KEY = os.environ['OPENAI_API_KEY'] = keys[0][1]\n", "LANGCHAIN_API_KEY = os.environ['LANGCHAIN_API_KEY'] = keys[1][1]\n", "POLYGON_API_KEY = os.environ['POLYGON_API_KEY'] = keys[2][1]\n", "EMAIL = os.environ['EMAIL'] = keys[3][1] #make this a user entry" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Install Required Libraries" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" ] } ], "source": [ "!pip install langchain_core langchain_openai langchain_community langsmith openai polygon-api-client pypdf yfinance -qU" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create Tools" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following tools were ripped directly from the langchain source code to remove the requirement for current data from the API." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "\"\"\"\n", "Util that calls several of Polygon's stock market REST APIs.\n", "Docs: https://polygon.io/docs/stocks/getting-started\n", "\"\"\"\n", "\n", "import json\n", "from typing import Any, Dict, Optional\n", "\n", "import requests\n", "from langchain_core.pydantic_v1 import BaseModel, root_validator\n", "from langchain_core.utils import get_from_dict_or_env\n", "\n", "POLYGON_BASE_URL = \"https://api.polygon.io/\"\n", "\n", "class PolygonAPIWrapper(BaseModel):\n", " \"\"\"Wrapper for Polygon API.\"\"\"\n", "\n", " polygon_api_key: Optional[str] = None\n", "\n", " @root_validator(pre=True)\n", " def validate_environment(cls, values: Dict) -> Dict:\n", " \"\"\"Validate that api key in environment.\"\"\"\n", " polygon_api_key = get_from_dict_or_env(\n", " values, \"polygon_api_key\", \"POLYGON_API_KEY\"\n", " )\n", " values[\"polygon_api_key\"] = polygon_api_key\n", "\n", " return values\n", "\n", " def get_financials(self, ticker: str) -> Optional[dict]:\n", " \"\"\"\n", " Get fundamental financial data, which is found in balance sheets,\n", " income statements, and cash flow statements for a given ticker.\n", "\n", " /vX/reference/financials\n", " \"\"\"\n", " url = (\n", " f\"{POLYGON_BASE_URL}vX/reference/financials?\"\n", " f\"ticker={ticker}&\"\n", " f\"apiKey={self.polygon_api_key}\"\n", " )\n", " response = requests.get(url)\n", " data = response.json()\n", "\n", " status = data.get(\"status\", None)\n", " if status != \"OK\":\n", " raise ValueError(f\"API Error: {data}\")\n", "\n", " return data.get(\"results\", None)\n", "\n", " def get_last_quote(self, ticker: str) -> Optional[dict]:\n", " \"\"\"\n", " Get the most recent National Best Bid and Offer (Quote) for a ticker.\n", "\n", " /v2/last/nbbo/{ticker}\n", " \"\"\"\n", " url = f\"{POLYGON_BASE_URL}v2/last/nbbo/{ticker}?apiKey={self.polygon_api_key}\"\n", " response = requests.get(url)\n", " data = response.json()\n", "\n", " status = data.get(\"status\", None)\n", " if status != \"OK\":\n", " raise ValueError(f\"API Error: {data}\")\n", "\n", " return data.get(\"results\", None)\n", "\n", " def get_ticker_news(self, ticker: str) -> Optional[dict]:\n", " \"\"\"\n", " Get the most recent news articles relating to a stock ticker symbol,\n", " including a summary of the article and a link to the original source.\n", "\n", " /v2/reference/news\n", " \"\"\"\n", " url = (\n", " f\"{POLYGON_BASE_URL}v2/reference/news?\"\n", " f\"ticker={ticker}&\"\n", " f\"apiKey={self.polygon_api_key}\"\n", " )\n", " response = requests.get(url)\n", " data = response.json()\n", "\n", " status = data.get(\"status\", None)\n", " if status != \"OK\":\n", " raise ValueError(f\"API Error: {data}\")\n", "\n", " return data.get(\"results\", None)\n", "\n", " def get_aggregates(self, ticker: str, **kwargs: Any) -> Optional[dict]:\n", " \"\"\"\n", " Get aggregate bars for a stock over a given date range\n", " in custom time window sizes.\n", "\n", " /v2/aggs/ticker/{ticker}/range/{multiplier}/{timespan}/{from_date}/{to_date}\n", " \"\"\"\n", " timespan = kwargs.get(\"timespan\", \"day\")\n", " multiplier = kwargs.get(\"timespan_multiplier\", 1)\n", " from_date = kwargs.get(\"from_date\", None)\n", " to_date = kwargs.get(\"to_date\", None)\n", " adjusted = kwargs.get(\"adjusted\", True)\n", " sort = kwargs.get(\"sort\", \"asc\")\n", "\n", " url = (\n", " f\"{POLYGON_BASE_URL}v2/aggs\"\n", " f\"/ticker/{ticker}\"\n", " f\"/range/{multiplier}\"\n", " f\"/{timespan}\"\n", " f\"/{from_date}\"\n", " f\"/{to_date}\"\n", " f\"?apiKey={self.polygon_api_key}\"\n", " f\"&adjusted={adjusted}\"\n", " f\"&sort={sort}\"\n", " )\n", " response = requests.get(url)\n", " data = response.json()\n", "\n", " status = data.get(\"status\", None)\n", " \n", " return data.get(\"results\", None)\n", "\n", " def run(self, mode: str, ticker: str, **kwargs: Any) -> str:\n", " if mode == \"get_financials\":\n", " return json.dumps(self.get_financials(ticker))\n", " elif mode == \"get_last_quote\":\n", " return json.dumps(self.get_last_quote(ticker))\n", " elif mode == \"get_ticker_news\":\n", " return json.dumps(self.get_ticker_news(ticker))\n", " elif mode == \"get_aggregates\":\n", " return json.dumps(self.get_aggregates(ticker, **kwargs))\n", " else:\n", " raise ValueError(f\"Invalid mode {mode} for Polygon API.\")\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from typing import Optional, Type\n", "\n", "from langchain_core.callbacks import CallbackManagerForToolRun\n", "from langchain_core.pydantic_v1 import BaseModel, Field\n", "from langchain_core.tools import BaseTool\n", "\n", "class PolygonAggregatesSchema(BaseModel):\n", " \"\"\"Input for PolygonAggregates.\"\"\"\n", "\n", " ticker: str = Field(\n", " description=\"The ticker symbol to fetch aggregates for.\",\n", " )\n", " timespan: str = Field(\n", " description=\"The size of the time window. \"\n", " \"Possible values are: \"\n", " \"second, minute, hour, day, week, month, quarter, year. \"\n", " \"Default is 'day'\",\n", " )\n", " timespan_multiplier: int = Field(\n", " description=\"The number of timespans to aggregate. \"\n", " \"For example, if timespan is 'day' and \"\n", " \"timespan_multiplier is 1, the result will be daily bars. \"\n", " \"If timespan is 'day' and timespan_multiplier is 5, \"\n", " \"the result will be weekly bars. \"\n", " \"Default is 1.\",\n", " )\n", " from_date: str = Field(\n", " description=\"The start of the aggregate time window. \"\n", " \"Either a date with the format YYYY-MM-DD or \"\n", " \"a millisecond timestamp.\",\n", " )\n", " to_date: str = Field(\n", " description=\"The end of the aggregate time window. \"\n", " \"Either a date with the format YYYY-MM-DD or \"\n", " \"a millisecond timestamp.\",\n", " )\n", "\n", "\n", "class PolygonAggregates(BaseTool):\n", " \"\"\"\n", " Tool that gets aggregate bars (stock prices) over a\n", " given date range for a given ticker from Polygon.\n", " \"\"\"\n", "\n", " mode: str = \"get_aggregates\"\n", " name: str = \"polygon_aggregates\"\n", " description: str = (\n", " \"A wrapper around Polygon's Aggregates API. \"\n", " \"This tool is useful for fetching aggregate bars (stock prices) for a ticker. \"\n", " \"Input should be the ticker, date range, timespan, and timespan multiplier\"\n", " \" that you want to get the aggregate bars for. This should be done when \"\n", " \"attempting to retreive the current stock price for any valuation calculation.\"\n", " )\n", " args_schema: Type[PolygonAggregatesSchema] = PolygonAggregatesSchema\n", "\n", " api_wrapper: PolygonAPIWrapper\n", "\n", " def _run(\n", " self,\n", " ticker: str,\n", " timespan: str,\n", " timespan_multiplier: int,\n", " from_date: str,\n", " to_date: str,\n", " run_manager: Optional[CallbackManagerForToolRun] = None,\n", " ) -> str:\n", " \"\"\"Use the Polygon API tool.\"\"\"\n", " return self.api_wrapper.run(\n", " mode=self.mode,\n", " ticker=ticker,\n", " timespan=timespan,\n", " timespan_multiplier=timespan_multiplier,\n", " from_date=from_date,\n", " to_date=to_date,\n", " )\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from typing import Optional, Type\n", "\n", "from langchain_core.callbacks import CallbackManagerForToolRun\n", "from langchain_core.pydantic_v1 import BaseModel\n", "from langchain_core.tools import BaseTool\n", "\n", "class Inputs(BaseModel):\n", " \"\"\"Inputs for Polygon's Financials API\"\"\"\n", "\n", " query: str\n", "\n", "\n", "class PolygonFinancials(BaseTool):\n", " \"\"\"Tool that gets the financials of a ticker from Polygon\"\"\"\n", "\n", " mode: str = \"get_financials\"\n", " name: str = \"polygon_financials\"\n", " description: str = (\n", " \"A wrapper around Polygon's Stock Financials API. \"\n", " \"This tool is useful for fetching fundamental financials from \"\n", " \"balance sheets, income statements, and cash flow statements \"\n", " \"for a stock ticker. The input should be the ticker that you want \"\n", " \"to get the latest fundamental financial data for. \"\n", " \"If a duckduckgo_search call returns a list of stocks, \"\n", " \"The tickers for those stocks can be passed to this function to \"\n", " \"retreive financial data and provide an accurate response \"\n", " \"to the user.\"\n", " )\n", " args_schema: Type[BaseModel] = Inputs\n", "\n", " api_wrapper: PolygonAPIWrapper\n", "\n", " def _run(\n", " self,\n", " query: str,\n", " run_manager: Optional[CallbackManagerForToolRun] = None,\n", " ) -> str:\n", " \"\"\"Use the Polygon API tool.\"\"\"\n", " return self.api_wrapper.run(self.mode, ticker=query)\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "from typing import Optional, Type\n", "\n", "from langchain_core.callbacks import CallbackManagerForToolRun\n", "from langchain_core.pydantic_v1 import BaseModel\n", "from langchain_core.tools import BaseTool\n", "\n", "class Inputs(BaseModel):\n", " \"\"\"Inputs for Polygon's Ticker News API\"\"\"\n", "\n", " query: str\n", "\n", "\n", "class PolygonTickerNews(BaseTool):\n", " \"\"\"Tool that gets the latest news for a given ticker from Polygon\"\"\"\n", "\n", " mode: str = \"get_ticker_news\"\n", " name: str = \"polygon_ticker_news\"\n", " description: str = (\n", " \"A wrapper around Polygon's Ticker News API. \"\n", " \"This tool is useful for fetching the latest news for a stock. \"\n", " \"Input should be the ticker that you want to get the latest news for.\"\n", " )\n", " args_schema: Type[BaseModel] = Inputs\n", "\n", " api_wrapper: PolygonAPIWrapper\n", "\n", " def _run(\n", " self,\n", " query: str,\n", " run_manager: Optional[CallbackManagerForToolRun] = None,\n", " ) -> str:\n", " \"\"\"Use the Polygon API tool.\"\"\"\n", " return self.api_wrapper.run(self.mode, ticker=query)\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "from langchain_openai import OpenAIEmbeddings\n", "from langchain_community.vectorstores import FAISS\n", "\n", "#load the vectorstore and initialize the retriever\n", "embeddings = OpenAIEmbeddings(model=\"text-embedding-3-small\")\n", "db = FAISS.load_local(\"./data/vectorstore\", embeddings, allow_dangerous_deserialization=True)\n", "\n", "class RAGInput(BaseModel):\n", " \"\"\"Input for the local data retrieval tool.\"\"\"\n", "\n", " query: str = Field(description=\"retreive data from vectorstore\")\n", "\n", "class RAGAgent(BaseTool):\n", " \"\"\"Tool that retrieves information from a local vectorstore of financial reports. These reports include \n", " 11-K, 10-K, 10-Q, 8-K, and SD filings from all of the S&P 500 companies, as of July 18th, 2024. Whenever specific financial \n", " information is requested in the query, ensure to use the Condensed Consolidated Statements of Cash Flows sections of the financial \n", " documents.\"\"\"\n", "\n", " name: str = \"vectorstore_retrieval\"\n", " description: str = (\n", " \"\"\"Tool that retrieves information from a local vectorstore of financial reports. These reports include \n", " 11-K, 10-K, 10-Q, and SD (Special disclosure) filings from all of the S&P 500 companies, as of July 18th, 2024. The information in this \n", " vector store only pertains to fiscal years 2023 and 2024. Whenever specific financial \n", " information is requested in the query, ensure to use the phrase 'Financial Information'. \n", " If necessary, modify the user query to contain this phrase.\"\"\"\n", " )\n", " args_schema: Type[BaseModel] = RAGInput\n", "\n", " def _run(\n", " self,\n", " query: str,\n", " run_manager: Optional[CallbackManagerForToolRun] = None,\n", " ) -> str:\n", " \"\"\"Retrieve data from the vectorstore.\"\"\"\n", " retriever = db.as_retriever()\n", " return retriever.invoke(query)\n" ] }, { "cell_type": "code", "execution_count": 432, "metadata": {}, "outputs": [], "source": [ "from langchain.tools import tool\n", "import datetime\n", "import yfinance as yf\n", "import pandas as pd\n", "\n", "@tool\n", "def get_datetime() -> str:\n", " \"\"\"Get the current date and time in YYYY-MM-DD HH:MM:SS format.\"\"\"\n", " return str(datetime.datetime.now())\n", "\n", "@tool\n", "def get_date() -> str:\n", " \"\"\"Get the current date in YYYY-MM-DD format. Also useful when determining the current quarter.\"\"\"\n", " return str(datetime.datetime.now()).split(\" \")[0]\n", "\n", "@tool\n", "def get_time() -> str:\n", " \"\"\"Get the current time in HH:MM:SS format.\"\"\"\n", " return str(datetime.datetime.now()).split(\" \")[1]\n", "\n", "@tool\n", "def get_quarter(date:str) -> str:\n", " \"\"\"This tool takes a date in YYYY-MM-DD format as an argument and returns the quarter and year in the format 'QQ YYYY'.\"\"\"\n", " quarters = {\n", " \"01\" : \"Q1\",\n", " \"02\" : \"Q1\",\n", " \"03\" : \"Q1\",\n", " \"04\" : \"Q2\",\n", " \"05\" : \"Q2\",\n", " \"06\" : \"Q2\",\n", " \"07\" : \"Q3\",\n", " \"08\" : \"Q3\",\n", " \"09\" : \"Q3\",\n", " \"10\" : \"Q4\",\n", " \"11\" : \"Q4\",\n", " \"12\" : \"Q4\",\n", " }\n", " return quarters[date.split(\"-\")[1]] + f\" {date.split('-')[0]}\" \n", "\n", "@tool\n", "def calculate_percent_valuation(intrinsic_value:float, current_stock_price:float):\n", " \"\"\"This tool can be used to calculate how overvalued or undervalued a stock is. It takes the calculated intrinsic value and the current stock price as arguments and returns the valuation percentage, in a format similar to '0.50' for 50%. The\n", " math performed by this function is (intrinsic_value-current_stock_price)/abs(intrinsic_value). The current stock price must be retrieved using the 'get_date' tool (to get the current date) and then using that date to access the 'polygon_aggregates' tool. \n", " A positive percentage indicates an overvalued stock and a negative percentage indicates an undervalued stock.\"\"\"\n", " return (intrinsic_value-current_stock_price)/abs(intrinsic_value)\n", "\n", "@tool\n", "def calculate_intrinsic_value(ticker, average_growth_rate):\n", " \"\"\"This tool is helpful for calculating the intrinsic value of a stock. It takes the stock ticker, the average growth rate based on revenue (retrieved from financial reports or with the polygon API. This should be capped at plus or minus 300% per year.)\"\"\"\n", " wacc = calculate_wacc(ticker)\n", "\n", "@tool\n", "def calculate_wacc( #refer to https://www.gurufocus.com/term/wacc/SOFI#:~:text=SoFi%20Technologies%20WACC%20%25%20Calculation,the%20firm's%20cost%20of%20capital.\n", " ticker:str, \n", " market_cap:float, \n", " interest_expense:float, \n", " tax_expense:float, \n", " pre_tax_income:float, \n", " long_term_debt:float\n", " ):\n", " \"\"\"This tool is used to determine the weighted average cost of capital (WACC) when performing a DCF analysis. It takes the following arguments:\n", "\n", " ticker\n", " market capitalization - The market capitalization should be retrieved using the duckduckgo_search tool. Explicitly state 'nvidia market cap today'\n", " interest expense - trailing twelve month interest expense calculated from the response of the polygon_financials tool.\n", " tax expense - trailing twelve month tax expense calculated from the response of the polygon_financials tool.\n", " pre-tax income - trailing twelve month pre-tax income calculated from the response of the polygon_financials tool.\n", " long term debt - long term debt calculated from the response of the polygon_financials tool.\n", "\n", " WACC is returned as a percentage in the format '0.057'.\"\"\"\n", " \n", " treasury_yield10 = yf.Ticker(\"^TNX\") \n", " risk_free_rate = round(treasury_yield10.info['regularMarketPreviousClose']/100,2) \n", " sp500_teturn = 0.10\n", " stock = yf.Ticker(f\"{ticker}\")\n", " beta = stock.info[\"beta\"]\n", "\n", " cost_of_equity = round(risk_free_rate + beta*(sp500_teturn - risk_free_rate),2)\n", " weight_of_equity, weight_of_debt = get_weights(market_cap, long_term_debt)\n", " cost_of_debt = get_cost_of_debt(interest_expense, long_term_debt)\n", " tax_rate = get_tax_rate(tax_expense, pre_tax_income)\n", " wacc = round((weight_of_equity * cost_of_equity) + ((weight_of_debt * cost_of_debt ) * (1-tax_rate)),3)\n", " return wacc\n", "\n", "def get_weights(market_cap, long_term_debt):\n", " e = market_cap\n", " d = long_term_debt\n", " weight_of_equity = e/(e+d)\n", " weight_of_debt = d/(e+d)\n", " return weight_of_equity, weight_of_debt\n", "\n", "def get_cost_of_debt(interest_expense, long_term_debt) -> float:\n", " return interest_expense/long_term_debt\n", "\n", "def get_tax_rate(tax_expense, pre_tax_income):\n", " tax_rate = tax_expense/pre_tax_income\n", " if tax_rate>1:\n", " return 1.00\n", " if tax_rate<0:\n", " return 0.00\n", " return tax_rate\n", "\n", "def get_wacc(ticker):\n", " treasury_yield10 = yf.Ticker(\"^TNX\") \n", " risk_free_rate = round(treasury_yield10.info['regularMarketPrice']/100,2)\n", " sp500_teturn = 0.10\n", " stock = yf.Ticker(f\"{ticker}\")\n", " beta = stock.info[\"beta\"]\n", " cost_of_equity = round(risk_free_rate + beta*(sp500_teturn - risk_free_rate),2)\n", " stock_bal = stock.balance_sheet\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set up tool belt" ] }, { "cell_type": "code", "execution_count": 433, "metadata": {}, "outputs": [], "source": [ "from langchain_community.tools.ddg_search import DuckDuckGoSearchRun\n", "\n", "api_wrapper = PolygonAPIWrapper(polygon_api_key=POLYGON_API_KEY)\n", "\n", "tool_belt = [\n", " get_datetime,\n", " get_date,\n", " get_time,\n", " get_quarter,\n", " calculate_percent_valuation,\n", " calculate_wacc,\n", " RAGAgent(),\n", " DuckDuckGoSearchRun(),\n", " PolygonAggregates(api_wrapper=api_wrapper),\n", " PolygonFinancials(api_wrapper=api_wrapper),\n", " PolygonTickerNews(api_wrapper=api_wrapper),\n", "]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set up tool executor" ] }, { "cell_type": "code", "execution_count": 434, "metadata": {}, "outputs": [], "source": [ "from langgraph.prebuilt import ToolExecutor\n", "\n", "tool_executor = ToolExecutor(tool_belt)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set up model" ] }, { "cell_type": "code", "execution_count": 435, "metadata": {}, "outputs": [], "source": [ "from langchain_openai import ChatOpenAI\n", "\n", "model = ChatOpenAI(model=\"gpt-4o\", temperature=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set up function calling" ] }, { "cell_type": "code", "execution_count": 436, "metadata": {}, "outputs": [], "source": [ "from langchain_core.utils.function_calling import convert_to_openai_function\n", "\n", "functions = [convert_to_openai_function(t) for t in tool_belt]\n", "model = model.bind_functions(functions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set up agent state" ] }, { "cell_type": "code", "execution_count": 437, "metadata": {}, "outputs": [], "source": [ "from typing import TypedDict, Annotated\n", "from langgraph.graph.message import add_messages\n", "import operator\n", "from langchain_core.messages import BaseMessage\n", "\n", "class AgentState(TypedDict):\n", " messages: Annotated[list, add_messages]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create nodes" ] }, { "cell_type": "code", "execution_count": 438, "metadata": {}, "outputs": [], "source": [ "import json\n", "from operator import itemgetter\n", "from langgraph.prebuilt import ToolInvocation\n", "from langchain_core.messages import FunctionMessage\n", "from langchain_core.prompts import PromptTemplate\n", "\n", "def call_model(state):\n", " messages = state[\"messages\"]\n", " response = model.invoke(messages)\n", " return {\"messages\" : [response]}\n", "\n", "def call_tool(state):\n", " last_message = state[\"messages\"][-1]\n", "\n", " action = ToolInvocation(\n", " tool=last_message.additional_kwargs[\"function_call\"][\"name\"],\n", " tool_input=json.loads(\n", " last_message.additional_kwargs[\"function_call\"][\"arguments\"]\n", " )\n", " )\n", "\n", " response = tool_executor.invoke(action)\n", "\n", " function_message = FunctionMessage(content=str(response), name=action.tool)\n", "\n", " return {\"messages\" : [function_message]}\n", "\n" ] }, { "cell_type": "code", "execution_count": 439, "metadata": {}, "outputs": [], "source": [ "from langgraph.graph import StateGraph, END\n", "\n", "workflow = StateGraph(AgentState)\n", "\n", "workflow.add_node(\"agent\", call_model)\n", "workflow.add_node(\"use tool\", call_tool)" ] }, { "cell_type": "code", "execution_count": 440, "metadata": {}, "outputs": [], "source": [ "workflow.set_entry_point(\"agent\")" ] }, { "cell_type": "code", "execution_count": 441, "metadata": {}, "outputs": [], "source": [ "def should_continue(state):\n", " last_message = state[\"messages\"][-1]\n", "\n", " if \"function_call\" not in last_message.additional_kwargs:\n", " return \"end\"\n", "\n", " return \"continue\"\n", "\n", "workflow.add_conditional_edges(\n", " \"agent\",\n", " should_continue,\n", " {\n", " \"continue\" : \"use tool\",\n", " \"end\" : END\n", " }\n", ")" ] }, { "cell_type": "code", "execution_count": 442, "metadata": {}, "outputs": [], "source": [ "workflow.add_edge(\"use tool\", \"agent\")" ] }, { "cell_type": "code", "execution_count": 443, "metadata": {}, "outputs": [], "source": [ "app = workflow.compile()" ] }, { "cell_type": "code", "execution_count": 444, "metadata": {}, "outputs": [], "source": [ "def print_messages(messages):\n", " next_is_tool = False\n", " initial_query = True\n", " with open(\"response.md\", \"w\") as f:\n", " for message in messages[\"messages\"]:\n", " if \"function_call\" in message.additional_kwargs:\n", " f.writelines(\"\\n\")\n", " f.writelines(f'Tool Call - Name: {message.additional_kwargs[\"function_call\"][\"name\"]} + Query: {message.additional_kwargs[\"function_call\"][\"arguments\"]}')\n", " print()\n", " print(f'Tool Call - Name: {message.additional_kwargs[\"function_call\"][\"name\"]} + Query: {message.additional_kwargs[\"function_call\"][\"arguments\"]}')\n", " next_is_tool = True\n", " continue\n", " if next_is_tool:\n", " print(f\"Tool Response: {message.content}\")\n", " next_is_tool = False\n", " continue\n", " if initial_query:\n", " f.writelines(f\"Initial Query: {message.content}\")\n", " f.writelines(\"\\n\")\n", " print(f\"Initial Query: {message.content}\")\n", " print()\n", " initial_query = False\n", " continue\n", " f.writelines(\"\\n\")\n", " f.writelines(f\"Agent Response: {message.content}\")\n", " print()\n", " print(f\"Agent Response: {message.content}\")" ] }, { "cell_type": "code", "execution_count": 445, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initial Query: Can you perform a DCF analysis of Nvidia stock? Please list out each step of the calculation.\n", "\n", "\n", "Tool Call - Name: duckduckgo_search + Query: {\"query\":\"Nvidia market cap today\"}\n", "Tool Response: Get the latest stock price, news, and analysis for NVIDIA, a leading semiconductor company. See market cap, earnings, dividends, and other metrics for NVDA on Nasdaq. NVIDIA has a market cap of $3.22 trillion as of June 20, 2024, up 229.90% from a year ago. See the historical chart, ranking, and statistics of NVIDIA's market cap since 1999. Today's Open. 121.35. Previous Close. 126.36. July 17, 2024 04:00 PM Pricing delayed by 20 minutes. Stock Chart. Quick Links. Email Alerts; RSS; Request Printed Materials; Download Library; Email Alerts. ... We intend to use our @NVIDIA Twitter account, NVIDIA Facebook page, ... Stock analysis for NVIDIA Corp (NVDA:NASDAQ GS) including stock price, stock chart, company news, key statistics, fundamentals and company profile. Nvidia cofounder and CEO Jensen Huang. Nvidia's rise has been astronomical—it's grown its market capitalization from $1.1 trillion to $3.1 trillion in the past 12 months—and a tech ...\n", "\n", "Tool Call - Name: polygon_financials + Query: {\"query\":\"NVDA\"}\n", "Tool Response: [{\"start_date\": \"2023-04-29\", \"end_date\": \"2024-04-28\", \"timeframe\": \"ttm\", \"fiscal_period\": \"TTM\", \"fiscal_year\": \"\", \"cik\": \"0001045810\", \"sic\": \"3674\", \"tickers\": [\"NVDA\"], \"company_name\": \"NVIDIA CORP\", \"financials\": {\"balance_sheet\": {\"liabilities_and_equity\": {\"value\": 77072000000.0, \"unit\": \"USD\", \"label\": \"Liabilities And Equity\", \"order\": 1900}, \"assets\": {\"value\": 77072000000.0, \"unit\": \"USD\", \"label\": \"Assets\", \"order\": 100}, \"equity_attributable_to_parent\": {\"value\": 49142000000.0, \"unit\": \"USD\", \"label\": \"Equity Attributable To Parent\", \"order\": 1600}, \"other_current_liabilities\": {\"value\": 11869000000.0, \"unit\": \"USD\", \"label\": \"Other Current Liabilities\", \"order\": 740}, \"equity\": {\"value\": 49142000000.0, \"unit\": \"USD\", \"label\": \"Equity\", \"order\": 1400}, \"intangible_assets\": {\"value\": 986000000.0, \"unit\": \"USD\", \"label\": \"Intangible Assets\", \"order\": 330}, \"current_assets\": {\"value\": 53729000000.0, \"unit\": \"USD\", \"label\": \"Current Assets\", \"order\": 200}, \"wages\": {\"value\": 639000000.0, \"unit\": \"USD\", \"label\": \"Wages\", \"order\": 730}, \"current_liabilities\": {\"value\": 15223000000.0, \"unit\": \"USD\", \"label\": \"Current Liabilities\", \"order\": 700}, \"noncurrent_assets\": {\"value\": 23343000000.0, \"unit\": \"USD\", \"label\": \"Noncurrent Assets\", \"order\": 300}, \"fixed_assets\": {\"value\": 4006000000.0, \"unit\": \"USD\", \"label\": \"Fixed Assets\", \"order\": 320}, \"long_term_debt\": {\"value\": 9710000000.0, \"unit\": \"USD\", \"label\": \"Long-term Debt\", \"order\": 810}, \"liabilities\": {\"value\": 27930000000.0, \"unit\": \"USD\", \"label\": \"Liabilities\", \"order\": 600}, \"noncurrent_liabilities\": {\"value\": 12707000000.0, \"unit\": \"USD\", \"label\": \"Noncurrent Liabilities\", \"order\": 800}, \"other_noncurrent_assets\": {\"value\": 18351000000.0, \"unit\": \"USD\", \"label\": \"Other Non-current Assets\", \"order\": 350}, \"inventory\": {\"value\": 5864000000.0, \"unit\": \"USD\", \"label\": \"Inventory\", \"order\": 230}, \"equity_attributable_to_noncontrolling_interest\": {\"value\": 0, \"unit\": \"USD\", \"label\": \"Equity Attributable To Noncontrolling Interest\", \"order\": 1500}, \"other_noncurrent_liabilities\": {\"value\": 2997000000.0, \"unit\": \"USD\", \"label\": \"Other Non-current Liabilities\", \"order\": 820}, \"accounts_payable\": {\"value\": 2715000000.0, \"unit\": \"USD\", \"label\": \"Accounts Payable\", \"order\": 710}, \"other_current_assets\": {\"value\": 47865000000.0, \"unit\": \"USD\", \"label\": \"Other Current Assets\", \"order\": 250}}, \"cash_flow_statement\": {\"net_cash_flow_from_investing_activities\": {\"value\": -15418000000.0, \"unit\": \"USD\", \"label\": \"Net Cash Flow From Investing Activities\", \"order\": 400}, \"net_cash_flow_from_financing_activities\": {\"value\": -22598000000.0, \"unit\": \"USD\", \"label\": \"Net Cash Flow From Financing Activities\", \"order\": 700}, \"net_cash_flow\": {\"value\": 2508000000.0, \"unit\": \"USD\", \"label\": \"Net Cash Flow\", \"order\": 1100}, \"net_cash_flow_from_investing_activities_continuing\": {\"value\": -15418000000.0, \"unit\": \"USD\", \"label\": \"Net Cash Flow From Investing Activities, Continuing\", \"order\": 500}, \"net_cash_flow_from_operating_activities_continuing\": {\"value\": 40524000000.0, \"unit\": \"USD\", \"label\": \"Net Cash Flow From Operating Activities, Continuing\", \"order\": 200}, \"net_cash_flow_continuing\": {\"value\": 2508000000.0, \"unit\": \"USD\", \"label\": \"Net Cash Flow, Continuing\", \"order\": 1200}, \"net_cash_flow_from_financing_activities_continuing\": {\"value\": -22598000000.0, \"unit\": \"USD\", \"label\": \"Net Cash Flow From Financing Activities, Continuing\", \"order\": 800}, \"net_cash_flow_from_operating_activities\": {\"value\": 40524000000.0, \"unit\": \"USD\", \"label\": \"Net Cash Flow From Operating Activities\", \"order\": 100}}, \"income_statement\": {\"income_tax_expense_benefit\": {\"value\": 6291000000.0, \"unit\": \"USD\", \"label\": \"Income Tax Expense/Benefit\", \"order\": 2200}, \"operating_income_loss\": {\"value\": 47740000000.0, \"unit\": \"USD\", \"label\": \"Operating Income/Loss\", \"order\": 1100}, \"gross_profit\": {\"value\": 60059000000.0, \"unit\": \"USD\", \"label\": \"Gross Profit\", \"order\": 800}, \"revenues\": {\"value\": 79774000000.0, \"unit\": \"USD\", \"label\": \"Revenues\", \"order\": 100}, \"nonoperating_income_loss\": {\"value\": 1148000000.0, \"unit\": \"USD\", \"label\": \"Nonoperating Income/Loss\", \"order\": 900}, \"diluted_earnings_per_share\": {\"value\": 17.09, \"unit\": \"USD / shares\", \"label\": \"Diluted Earnings Per Share\", \"order\": 4300}, \"selling_general_and_administrative_expenses\": {\"value\": 2800000000.0, \"unit\": \"USD\", \"label\": \"Selling, General, and Administrative Expenses\", \"order\": 1010}, \"income_tax_expense_benefit_deferred\": {\"value\": -2931000000.0, \"unit\": \"USD\", \"label\": \"Income Tax Expense/Benefit, Deferred\", \"order\": 2400}, \"net_income_loss_attributable_to_noncontrolling_interest\": {\"value\": 0, \"unit\": \"USD\", \"label\": \"Net Income/Loss Attributable To Noncontrolling Interest\", \"order\": 3300}, \"income_loss_from_continuing_operations_before_tax\": {\"value\": 48888000000.0, \"unit\": \"USD\", \"label\": \"Income/Loss From Continuing Operations Before Tax\", \"order\": 1500}, \"basic_earnings_per_share\": {\"value\": 17.27, \"unit\": \"USD / shares\", \"label\": \"Basic Earnings Per Share\", \"order\": 4200}, \"benefits_costs_expenses\": {\"value\": 30886000000.0, \"unit\": \"USD\", \"label\": \"Benefits Costs and Expenses\", \"order\": 200}, \"basic_average_shares\": {\"value\": 7402000000.0, \"unit\": \"shares\", \"label\": \"Basic Average Shares\", \"order\": 4400}, \"net_income_loss_available_to_common_stockholders_basic\": {\"value\": 42597000000.0, \"unit\": \"USD\", \"label\": \"Net Income/Loss Available To Common Stockholders, Basic\", \"order\": 3700}, \"income_loss_from_continuing_operations_after_tax\": {\"value\": 42597000000.0, \"unit\": \"USD\", \"label\": \"Income/Loss From Continuing Operations After Tax\", \"order\": 1400}, \"participating_securities_distributed_and_undistributed_earnings_loss_basic\": {\"value\": 0, \"unit\": \"USD\", \"label\": \"Participating Securities, Distributed And Undistributed Earnings/Loss, Basic\", \"order\": 3800}, \"net_income_loss\": {\"value\": 42597000000.0, \"unit\": \"USD\", \"label\": \"Net Income/Loss\", \"order\": 3200}, \"research_and_development\": {\"value\": 9519000000.0, \"unit\": \"USD\", \"label\": \"Research and Development\", \"order\": 1030}, \"operating_expenses\": {\"value\": 12319000000.0, \"unit\": \"USD\", \"label\": \"Operating Expenses\", \"order\": 1000}, \"net_income_loss_attributable_to_parent\": {\"value\": 42597000000.0, \"unit\": \"USD\", \"label\": \"Net Income/Loss Attributable To Parent\", \"order\": 3500}, \"preferred_stock_dividends_and_other_adjustments\": {\"value\": 0, \"unit\": \"USD\", \"label\": \"Preferred Stock Dividends And Other Adjustments\", \"order\": 3900}, \"interest_expense_operating\": {\"value\": 255000000.0, \"unit\": \"USD\", \"label\": \"Interest Expense, Operating\", \"order\": 2700}, \"costs_and_expenses\": {\"value\": 30886000000.0, \"unit\": \"USD\", \"label\": \"Costs And Expenses\", \"order\": 600}, \"cost_of_revenue\": {\"value\": 19715000000.0, \"unit\": \"USD\", \"label\": \"Cost Of Revenue\", \"order\": 300}, \"diluted_average_shares\": {\"value\": 7482000000.0, \"unit\": \"shares\", \"label\": \"Diluted Average Shares\", \"order\": 4500}}, \"comprehensive_income\": {\"comprehensive_income_loss\": {\"value\": 42538000000.0, \"unit\": \"USD\", \"label\": \"Comprehensive Income/Loss\", \"order\": 100}, \"other_comprehensive_income_loss\": {\"value\": -59000000.0, \"unit\": \"USD\", \"label\": \"Other Comprehensive Income/Loss\", \"order\": 400}, \"comprehensive_income_loss_attributable_to_noncontrolling_interest\": {\"value\": 0, \"unit\": \"USD\", \"label\": \"Comprehensive Income/Loss Attributable To Noncontrolling Interest\", \"order\": 200}, \"comprehensive_income_loss_attributable_to_parent\": {\"value\": 42538000000.0, \"unit\": \"USD\", \"label\": \"Comprehensive Income/Loss Attributable To Parent\", \"order\": 300}}}}, {\"start_date\": \"2024-01-29\", \"end_date\": \"2024-04-28\", \"filing_date\": \"2024-05-29\", \"acceptance_datetime\": \"2024-05-29T20:36:32Z\", \"timeframe\": \"quarterly\", \"fiscal_period\": \"Q1\", \"fiscal_year\": \"2025\", \"cik\": \"0001045810\", \"sic\": \"3674\", \"tickers\": [\"NVDA\"], \"company_name\": \"NVIDIA CORP\", \"source_filing_url\": \"https://api.polygon.io/v1/reference/sec/filings/0001045810-24-000124\", \"source_filing_file_url\": \"http://api.polygon.io/v1/reference/sec/filings/0001045810-24-000124/files/nvda-20240428_htm.xml\", \"financials\": {\"balance_sheet\": {\"noncurrent_liabilities\": {\"value\": 12707000000.0, \"unit\": \"USD\", \"label\": \"Noncurrent Liabilities\", \"order\": 800}, \"intangible_assets\": {\"value\": 986000000.0, \"unit\": \"USD\", \"label\": \"Intangible Assets\", \"order\": 330}, \"equity_attributable_to_noncontrolling_interest\": {\"value\": 0, \"unit\": \"USD\", \"label\": \"Equity Attributable To Noncontrolling Interest\", \"order\": 1500}, \"other_noncurrent_liabilities\": {\"value\": 2997000000.0, \"unit\": \"USD\", \"label\": \"Other Non-current Liabilities\", \"order\": 820}, \"fixed_assets\": {\"value\": 4006000000.0, \"unit\": \"USD\", \"label\": \"Fixed Assets\", \"order\": 320}, \"long_term_debt\": {\"value\": 9710000000.0, \"unit\": \"USD\", \"label\": \"Long-term Debt\", \"order\": 810}, \"current_assets\": {\"value\": 53729000000.0, \"unit\": \"USD\", \"label\": \"Current Assets\", \"order\": 200}, \"inventory\": {\"value\": 5864000000.0, \"unit\": \"USD\", \"label\": \"Inventory\", \"order\": 230}, \"other_noncurrent_assets\": {\"value\": 18351000000.0, \"unit\": \"USD\", \"label\": \"Other Non-current Assets\", \"order\": 350}, \"liabilities_and_equity\": {\"value\": 77072000000.0, \"unit\": \"USD\", \"label\": \"Liabilities And Equity\", \"order\": 1900}, \"other_current_liabilities\": {\"value\": 11869000000.0, \"unit\": \"USD\", \"label\": \"Other Current Liabilities\", \"order\": 740}, \"wages\": {\"value\": 639000000.0, \"unit\": \"USD\", \"label\": \"Wages\", \"order\": 730}, \"other_current_assets\": {\"value\": 47865000000.0, \"unit\": \"USD\", \"label\": \"Other Current Assets\", \"order\": 250}, \"equity\": {\"value\": 49142000000.0, \"unit\": \"USD\", \"label\": \"Equity\", \"order\": 1400}, \"equity_attributable_to_parent\": {\"value\": 49142000000.0, \"unit\": \"USD\", \"label\": \"Equity Attributable To Parent\", \"order\": 1600}, \"noncurrent_assets\": {\"value\": 23343000000.0, \"unit\": \"USD\", \"label\": \"Noncurrent Assets\", \"order\": 300}, \"liabilities\": {\"value\": 27930000000.0, \"unit\": \"USD\", \"label\": \"Liabilities\", \"order\": 600}, \"accounts_payable\": {\"value\": 2715000000.0, \"unit\": \"USD\", \"label\": \"Accounts Payable\", \"order\": 710}, \"assets\": {\"value\": 77072000000.0, \"unit\": \"USD\", \"label\": \"Assets\", \"order\": 100}, \"current_liabilities\": {\"value\": 15223000000.0, \"unit\": \"USD\", \"label\": \"Current Liabilities\", \"order\": 700}}, \"cash_flow_statement\": {\"net_cash_flow_from_financing_activities_continuing\": {\"value\": -9345000000.0, \"unit\": \"USD\", \"label\": \"Net Cash Flow From Financing Activities, Continuing\", \"order\": 800}, \"net_cash_flow_continuing\": {\"value\": 307000000.0, \"unit\": \"USD\", \"label\": \"Net Cash Flow, Continuing\", \"order\": 1200}, \"net_cash_flow_from_investing_activities\": {\"value\": -5693000000.0, \"unit\": \"USD\", \"label\": \"Net Cash Flow From Investing Activities\", \"order\": 400}, \"net_cash_flow_from_operating_activities\": {\"value\": 15345000000.0, \"unit\": \"USD\", \"label\": \"Net Cash Flow From Operating Activities\", \"order\": 100}, \"net_cash_flow_from_investing_activities_continuing\": {\"value\": -5693000000.0, \"unit\": \"USD\", \"label\": \"Net Cash Flow From Investing Activities, Continuing\", \"order\": 500}, \"net_cash_flow\": {\"value\": 307000000.0, \"unit\": \"USD\", \"label\": \"Net Cash Flow\", \"order\": 1100}, \"net_cash_flow_from_operating_activities_continuing\": {\"value\": 15345000000.0, \"unit\": \"USD\", \"label\": \"Net Cash Flow From Operating Activities, Continuing\", \"order\": 200}, \"net_cash_flow_from_financing_activities\": {\"value\": -9345000000.0, \"unit\": \"USD\", \"label\": \"Net Cash Flow From Financing Activities\", \"order\": 700}}, \"income_statement\": {\"nonoperating_income_loss\": {\"value\": 370000000.0, \"unit\": \"USD\", \"label\": \"Nonoperating Income/Loss\", \"order\": 900}, \"basic_average_shares\": {\"value\": 2462000000.0, \"unit\": \"shares\", \"label\": \"Basic Average Shares\", \"order\": 4400}, \"benefits_costs_expenses\": {\"value\": 8765000000.0, \"unit\": \"USD\", \"label\": \"Benefits Costs and Expenses\", \"order\": 200}, \"costs_and_expenses\": {\"value\": 8765000000.0, \"unit\": \"USD\", \"label\": \"Costs And Expenses\", \"order\": 600}, \"income_loss_from_continuing_operations_after_tax\": {\"value\": 14881000000.0, \"unit\": \"USD\", \"label\": \"Income/Loss From Continuing Operations After Tax\", \"order\": 1400}, \"income_tax_expense_benefit\": {\"value\": 2398000000.0, \"unit\": \"USD\", \"label\": \"Income Tax Expense/Benefit\", \"order\": 2200}, \"preferred_stock_dividends_and_other_adjustments\": {\"value\": 0, \"unit\": \"USD\", \"label\": \"Preferred Stock Dividends And Other Adjustments\", \"order\": 3900}, \"income_loss_from_continuing_operations_before_tax\": {\"value\": 17279000000.0, \"unit\": \"USD\", \"label\": \"Income/Loss From Continuing Operations Before Tax\", \"order\": 1500}, \"selling_general_and_administrative_expenses\": {\"value\": 777000000.0, \"unit\": \"USD\", \"label\": \"Selling, General, and Administrative Expenses\", \"order\": 1010}, \"net_income_loss_attributable_to_parent\": {\"value\": 14881000000.0, \"unit\": \"USD\", \"label\": \"Net Income/Loss Attributable To Parent\", \"order\": 3500}, \"diluted_earnings_per_share\": {\"value\": 5.98, \"unit\": \"USD / shares\", \"label\": \"Diluted Earnings Per Share\", \"order\": 4300}, \"net_income_loss_available_to_common_stockholders_basic\": {\"value\": 14881000000.0, \"unit\": \"USD\", \"label\": 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\"label\": \"Basic Earnings Per Share\", \"order\": 4200}, \"revenues\": {\"value\": 5931000000.0, \"unit\": \"USD\", \"label\": \"Revenues\", \"order\": 100}}}}]\n", "\n", "Tool Call - Name: get_date + Query: {}\n", "Tool Response: 2024-07-19\n", "\n", "Tool Call - Name: polygon_aggregates + Query: {\"ticker\":\"NVDA\",\"timespan\":\"day\",\"timespan_multiplier\":1,\"from_date\":\"2024-07-19\",\"to_date\":\"2024-07-19\"}\n", "Tool Response: [{\"v\": 148930435.0, \"vw\": 119.4135, \"o\": 120.35, \"c\": 118.22, \"h\": 121.6, \"l\": 117.37, \"t\": 1721361600000, \"n\": 1226882}]\n", "\n", "Agent Response: ### Step 8: Compare Intrinsic Value with Current Market Price\n", "\n", "- **Intrinsic Value per Share**: $464.8\n", "- **Current Market Price**: $118.22\n", "\n", "### Step 9: Calculate Valuation Percentage\n", "\n", "Let's calculate how overvalued or undervalued Nvidia stock is by comparing the intrinsic value with the current market price.\n", "\n", "\\[ \\text{Valuation Percentage} = \\frac{\\text{Intrinsic Value} - \\text{Current Market Price}}{\\text{Intrinsic Value}} \\]\n", "\n", "\\[ \\text{Valuation Percentage} = \\frac{464.8 - 118.22}{464.8} \\]\n", "\n", "\\[ \\text{Valuation Percentage} = \\frac{346.58}{464.8} \\]\n", "\n", "\\[ \\text{Valuation Percentage} = 0.7455 \\text{ or } 74.55\\% \\]\n", "\n", "### Conclusion\n", "\n", "Nvidia stock appears to be **undervalued** by approximately **74.55%** based on the DCF analysis. The intrinsic value per share is $464.8, while the current market price is $118.22.\n" ] } ], "source": [ "from langchain_core.messages import HumanMessage\n", "\n", "inputs = {\"messages\" : [HumanMessage(content=\"Can you perform a DCF analysis of Nvidia stock? Please list out each step of the calculation.\")]}\n", "\n", "messages = app.invoke(inputs)\n", "\n", "print_messages(messages)\n" ] } ], "metadata": { "kernelspec": { "display_name": "venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.4" } }, "nbformat": 4, "nbformat_minor": 2 }