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community: Add stock market tools from financialdatasets.ai (#25025)
**Description:** In this PR, I am adding three stock market tools from financialdatasets.ai (my API!): - get balance sheets - get cash flow statements - get income statements Twitter handle: [@virattt](https://twitter.com/virattt) --------- Co-authored-by: Erick Friis <erick@langchain.dev>
This commit is contained in:
Virat Singh
GitHub
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docs/docs/integrations/toolkits/financial_datasets.ipynb
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docs/docs/integrations/toolkits/financial_datasets.ipynb
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{
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"cells": [
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{
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"cell_type": "raw",
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"id": "afaf8039",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_label: financial datasets\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"# FinancialDatasetsToolkit\n",
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"\n",
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"The [financial datasets](https://financialdatasets.ai/) stock market API provides REST endpoints that let you get financial data for 16,000+ tickers spanning 30+ years.\n",
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"\n",
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"## Setup\n",
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"\n",
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"To use this toolkit, you need two API keys:\n",
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"\n",
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"`FINANCIAL_DATASETS_API_KEY`: Get it from [financialdatasets.ai](https://financialdatasets.ai/).\n",
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"`OPENAI_API_KEY`: Get it from [OpenAI](https://platform.openai.com/)."
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"import getpass\n",
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"import os\n",
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"\n",
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"os.environ[\"FINANCIAL_DATASETS_API_KEY\"] = getpass.getpass()"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Installation\n",
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"\n",
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"This toolkit lives in the `langchain-community` package."
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "652d6238-1f87-422a-b135-f5abbb8652fc",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install -qU langchain-community"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a38cde65-254d-4219-a441-068766c0d4b5",
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"metadata": {},
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"source": [
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"## Instantiation\n",
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"\n",
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"Now we can instantiate our toolkit:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.agent_toolkits.financial_datasets.toolkit import (\n",
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" FinancialDatasetsToolkit,\n",
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")\n",
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"from langchain_community.utilities.financial_datasets import FinancialDatasetsAPIWrapper\n",
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"\n",
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"api_wrapper = FinancialDatasetsAPIWrapper(\n",
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" financial_datasets_api_key=os.environ[\"FINANCIAL_DATASETS_API_KEY\"]\n",
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")\n",
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"toolkit = FinancialDatasetsToolkit(api_wrapper=api_wrapper)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5c5f2839-4020-424e-9fc9-07777eede442",
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"metadata": {},
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"source": [
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"## Tools\n",
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"\n",
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"View available tools:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "51a60dbe-9f2e-4e04-bb62-23968f17164a",
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"metadata": {},
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"outputs": [],
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"source": [
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"tools = toolkit.get_tools()"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Use within an agent\n",
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"\n",
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"Let's equip our agent with the FinancialDatasetsToolkit and ask financial questions."
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"system_prompt = \"\"\"\n",
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"You are an advanced financial analysis AI assistant equipped with specialized tools\n",
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"to access and analyze financial data. Your primary function is to help users with\n",
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"financial analysis by retrieving and interpreting income statements, balance sheets,\n",
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"and cash flow statements for publicly traded companies.\n",
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"\n",
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"You have access to the following tools from the FinancialDatasetsToolkit:\n",
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"\n",
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"1. Balance Sheets: Retrieves balance sheet data for a given ticker symbol.\n",
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"2. Income Statements: Fetches income statement data for a specified company.\n",
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"3. Cash Flow Statements: Accesses cash flow statement information for a particular ticker.\n",
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"\n",
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"Your capabilities include:\n",
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"\n",
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"1. Retrieving financial statements for any publicly traded company using its ticker symbol.\n",
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"2. Analyzing financial ratios and metrics based on the data from these statements.\n",
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"3. Comparing financial performance across different time periods (e.g., year-over-year or quarter-over-quarter).\n",
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"4. Identifying trends in a company's financial health and performance.\n",
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"5. Providing insights on a company's liquidity, solvency, profitability, and efficiency.\n",
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"6. Explaining complex financial concepts in simple terms.\n",
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"\n",
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"When responding to queries:\n",
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"\n",
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"1. Always specify which financial statement(s) you're using for your analysis.\n",
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"2. Provide context for the numbers you're referencing (e.g., fiscal year, quarter).\n",
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"3. Explain your reasoning and calculations clearly.\n",
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"4. If you need more information to provide a complete answer, ask for clarification.\n",
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"5. When appropriate, suggest additional analyses that might be helpful.\n",
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"\n",
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"Remember, your goal is to provide accurate, insightful financial analysis to\n",
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"help users make informed decisions. Always maintain a professional and objective tone in your responses.\n",
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"\"\"\""
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"Instantiate the LLM."
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "310bf18e-6c9a-4072-b86e-47bc1fcca29d",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_core.tools import tool\n",
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"from langchain_openai import ChatOpenAI\n",
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"\n",
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"model = ChatOpenAI(model=\"gpt-4o\")"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"Define a user query."
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "23e11cc9-abd6-4855-a7eb-799f45ca01ae",
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"metadata": {},
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"outputs": [],
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"source": [
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"query = \"What was AAPL's revenue in 2023? What about it's total debt in Q1 2024?\""
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"Create the agent."
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"from langchain.agents import AgentExecutor, create_tool_calling_agent\n",
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"from langchain_core.prompts import ChatPromptTemplate\n",
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"\n",
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"prompt = ChatPromptTemplate.from_messages(\n",
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" [\n",
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" (\"system\", system_prompt),\n",
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" (\"human\", \"{input}\"),\n",
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" # Placeholders fill up a **list** of messages\n",
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" (\"placeholder\", \"{agent_scratchpad}\"),\n",
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" ]\n",
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")\n",
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"\n",
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"\n",
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"agent = create_tool_calling_agent(model, tools, prompt)\n",
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"agent_executor = AgentExecutor(agent=agent, tools=tools)"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"Query the agent."
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"agent_executor.invoke({\"input\": query})"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## API reference\n",
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"\n",
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"For detailed documentation of all `FinancialDatasetsToolkit` features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/agent_toolkits/langchain_community.agent_toolkits.financial_datasets.toolkit.FinancialDatasetsToolkit.html)."
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [],
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"metadata": {
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"collapsed": false
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}
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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