mirror of
https://github.com/OpenBB-finance/OpenBB.git
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langchain agent using openbb functions (#7131)
* langchain agent using openbb functions A simple notebook that shows how to leverage openbb functions by building an agent via langchain * added prompt for strong buy in utilities * added prompt for strong buy in utilities * Simplified notebook --------- Co-authored-by: Danglewood <85772166+deeleeramone@users.noreply.github.com> Co-authored-by: Theodore Aptekarev <aptekarev@gmail.com>
This commit is contained in:
mmistroni
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examples/openbb_vs_langchain.ipynb
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examples/openbb_vs_langchain.ipynb
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "0PSrErguxVcn"
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},
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"source": [
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"### Brief Description\n",
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"\n",
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"This notebook shows few examples on how to leverage OpenBB functionality via an Agent built with Langchain.\n",
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"It requires the user to have\n",
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"- an OpenAI Key. This is required as OpenAI is used as LLM\n",
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"\n",
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"\n",
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"For help on how to configure Colab Secrets, please refer to this article\n",
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"https://margaretmz.medium.com/use-colab-secrets-to-store-kaggle-api-key-b57c7464f9fa\n",
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"\n",
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"This work was inspired by examples from this repo https://github.com/AlgoTrading101/Magentic-AlgoTrading101\n",
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"\n",
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"Functionality shown in this notebook is purely an example of what can be done.\n",
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"\n",
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"### Author\n",
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"Marco Mistroni"
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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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"### Installing dependencies"
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],
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"metadata": {
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"id": "pw_DrWCXVijQ"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"!pip install openbb\n",
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"!pip install openbb-yfinance\n",
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"!pip install openbb-finviz\n",
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"!pip install langchain\n",
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"!pip install langchain_core\n",
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"!pip install langchain_openai\n"
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],
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"metadata": {
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"id": "sNhm_dyvVlI1"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Getting keys"
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],
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"metadata": {
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"id": "Ua8Hmj2lWI6R"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"from google.colab import userdata\n",
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"OPENAI_KEY = userdata.get('OPENAI_KEY')"
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],
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"metadata": {
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"id": "fTUdeQpdWL81"
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},
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"execution_count": null,
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"outputs": []
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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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"metadata": {
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"id": "TEBnI33fxVcr"
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},
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"outputs": [],
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"source": [
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"import os\n",
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"from openbb import obb\n",
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"from langchain_openai import ChatOpenAI\n",
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"import logging\n",
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"from langchain.agents import tool\n",
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"from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
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"\n",
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"llm = ChatOpenAI(model=\"gpt-4.1\", temperature=0, openai_api_key=OPENAI_KEY)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "6zNCxSmOxVct"
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},
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"source": [
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"### OpenBB Useful functions"
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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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"metadata": {
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"id": "28N-T7aIxVcu"
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},
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"outputs": [],
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"source": [
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"@tool\n",
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"def get_industry_performance() -> list:\n",
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" \"\"\" Return performance by industry for last week, last month, last quarter, last half year and last year\"\"\"\n",
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" return obb.equity.compare.groups(group='industry', metric='performance').to_llm()\n",
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"\n",
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"@tool\n",
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"def get_strong_buy_for_sector(sector : str) -> list :\n",
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" \"\"\" Return the strong buy recommendation for a given sector\"\"\"\n",
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" new_sector = '_'.join(sector.lower().split()).lower()\n",
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" data = obb.equity.screener(provider='finviz', sector=new_sector, recommendation='buy')\n",
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" return data.to_llm()\n",
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"\n",
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"@tool\n",
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"def get_strong_buy_for_industry(industry : str) -> list :\n",
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" \"\"\" Return the strong buy recommendation for a given industry\"\"\"\n",
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" data = obb.equity.screener(provider='finviz', industry=industry, recommendation='buy')\n",
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" return data.to_llm()\n",
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"\n",
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"@tool\n",
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"def get_best_stock_performers_for_sector(sector:str) -> list :\n",
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" \"\"\" Return the best 5 stock performers for last week and last month for a given sector\"\"\"\n",
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" data = obb.equity.screener(provider='finviz', filters_dict={'Sector' : sector, 'Performance' : 'Week Up', 'Performance 2' : 'Month Up'}, limit=5)\n",
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" return data.to_llm()\n",
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"\n",
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"@tool\n",
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"def get_best_stock_performers_for_industry(industry:str) -> list :\n",
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" \"\"\" Return the best 5 stock performers for last week and last month for an industry\"\"\"\n",
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" data = obb.equity.screener(provider='finviz', filters_dict={'Industry' : industry, 'Performance' : 'Week Up', 'Performance 2' : 'Month Up'}, limit=3)\n",
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" return data.to_llm()\n",
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"\n",
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"@tool\n",
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"def get_candidate_stocks_to_invest_relaxed(industry:str) -> list:\n",
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" ''' Use relaxed criteria to find best companies in an industry which are worth investing into'''\n",
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" desc_filters = {\n",
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" 'Market Cap.': '+Small (over $300mln)',\n",
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" 'Average Volume': 'Over 200K',\n",
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" }\n",
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" fund_filters = {\n",
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" 'InstitutionalOwnership': 'Under 60%',\n",
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" 'Current Ratio' : 'Over 1.5',\n",
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" 'Debt/Equity' : 'Over 0.3',\n",
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" #'EPS growthnext 5 years' : 'Positive (>0%)',\n",
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" }\n",
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"\n",
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" desc_filters.update(fund_filters)\n",
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"\n",
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" try:\n",
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" data = obb.equity.screener(provider='finviz', industry='semiconductors',\n",
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" filters_dict=desc_filters\n",
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" )\n",
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" return data.to_llm()\n",
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" except Exception as e:\n",
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" logging.info(f'No data found:{str(e)}')\n",
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" return []\n",
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"\n",
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"\n",
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"@tool\n",
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"def get_valuation_for_industries(input:str) -> list:\n",
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" \"\"\" Return valuation metrics for the industry provided as input\"\"\"\n",
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" data = obb.equity.compare.groups(group='industry', metric='valuation', provider='finviz').to_df()\n",
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" filtered = data[data.name == input]\n",
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" return filtered.to_json(\n",
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" orient=\"records\",\n",
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" date_format=\"iso\",\n",
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" date_unit=\"s\",\n",
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" )\n",
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"\n",
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"@tool\n",
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"def get_consensus(ticker:str) -> list:\n",
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" \"\"\" Return analyst consensus for the ticker provided\n",
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" It returns the following fields:\n",
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" - target_high: float, High target of the price target consensus.\n",
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" - target_low: float Low target of the price target consensus.\n",
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" - target_consensus: float Consensus target of the price target consensus.\n",
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" - target_median: float Median target of the price target consensus\n",
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"\n",
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"\n",
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" \"\"\"\n",
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" data = obb.equity.estimates.consensus(symbol=ticker, limit=3, provider='yfinance').to_df()\n",
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" return data.to_json(\n",
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" orient=\"records\",\n",
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" date_format=\"iso\",\n",
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" date_unit=\"s\",\n",
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" )\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "1hXQnSGFxVcv"
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},
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"source": [
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"### Chat Memory"
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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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"metadata": {
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"id": "YBF6S6DxxVcv"
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},
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"outputs": [],
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"source": [
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"from langchain_core.prompts import MessagesPlaceholder\n",
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"from langchain.memory import ConversationTokenBufferMemory\n",
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"from langchain.agents.format_scratchpad.openai_tools import (\n",
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" format_to_openai_tool_messages,\n",
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")\n",
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"from langchain.agents.output_parsers.openai_tools import OpenAIToolsAgentOutputParser\n",
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"from langchain_core.output_parsers import StrOutputParser, CommaSeparatedListOutputParser\n",
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"from langchain.agents import AgentExecutor\n",
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"from langchain_core.messages import AIMessage, HumanMessage\n",
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"\n",
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"MEMORY_KEY = \"chat_history\"\n",
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"prompt = ChatPromptTemplate.from_messages(\n",
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" [\n",
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" (\n",
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" \"system\",\n",
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" \"\"\" You are very powerful stock financial researcher.\n",
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" You will take the user questions and answer using the tools available.\n",
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" Once you have the information you need, you will answer user's questions using the data returned.\n",
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" Use the following tools to answer user queries:\n",
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" - get_strong_buy_for_sector to find strong buy recommendations for a sector\n",
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" - get_strong_buy_for_industry to find strong buy recommendations for an industry\n",
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" - get_industry_performance to find the performance for an industry\n",
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" - get_valuation_for_industries to find valuation metrics for industries\n",
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" - get_candidate_stocks_to_invest_relaxed to fetch all companies using relaxed criteria\n",
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" - def get_consensus(ticker:str) - to find analyst consensus for a company\n",
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" You should call each function only once, and you should not call the function if you already have the information you need.\n",
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" \"\"\",\n",
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" ),\n",
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" MessagesPlaceholder(variable_name=MEMORY_KEY),\n",
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" (\"user\", \"{input}\"),\n",
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" MessagesPlaceholder(variable_name=\"agent_scratchpad\"),\n",
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" ]\n",
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")"
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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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"metadata": {
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"id": "1yrlv9jUxVcw"
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},
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"outputs": [],
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"source": [
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"tools = [get_industry_performance, get_strong_buy_for_sector, get_strong_buy_for_industry, get_best_stock_performers_for_industry, get_valuation_for_industries,\n",
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" get_candidate_stocks_to_invest_relaxed, get_consensus]\n",
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"llm_with_tools = llm.bind_tools(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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"metadata": {
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"id": "ToVLUWZfxVcw"
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},
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"outputs": [],
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"source": [
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"chat_history = []\n",
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"chat_history.append(HumanMessage(content=\"Your question here\"))\n",
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"chat_history.append(AIMessage(content=\"AI response here\"))\n",
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"memory = ConversationTokenBufferMemory(\n",
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" llm=llm, # Required for token counting\n",
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" max_token_limit=16000, # Leave buffer for functions + responses\n",
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" memory_key=\"chat_history\", # Must match your prompt's key\n",
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" return_messages=True\n",
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")\n",
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"\n",
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"agent = (\n",
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" {\n",
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" \"input\": lambda x: x[\"input\"],\n",
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" \"agent_scratchpad\": lambda x: format_to_openai_tool_messages(\n",
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" x[\"intermediate_steps\"]\n",
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" ),\n",
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" \"chat_history\": lambda x: memory.load_memory_variables(x)[\"chat_history\"],\n",
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" }\n",
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" | prompt\n",
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" | llm_with_tools\n",
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" | OpenAIToolsAgentOutputParser()\n",
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")\n",
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"agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "kbFXeyMUxVcy"
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},
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"source": [
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"### Let's try a chain of thought approach. We start with the industry with best performance.\n",
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"Then find the best performing company and check some metrics"
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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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"metadata": {
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"id": "MlAvYHU0xVcy"
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},
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"outputs": [],
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"source": [
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"input1 = '''\n",
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"First, find an industry that has consistently shown positive performance across quarterly, monthly, and weekly timeframes.\n",
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"Second, once you have identified the industry, extract its relevant valuation metrics (e.g., P/E, P/B, EV/EBITDA).\n",
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"Third, extract companies from the selected industry using relaxed criteria.\n",
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"Fourth, for the best performing companies get the analyst consensus\n",
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"Finally, summarize your findings in no more than 80 words detailing:\n",
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"- Best performing industry\n",
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"- Best performing companies in industry\n",
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"- A table displaying the analyst consensus for each of the companies you found at previous step'''\n",
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"result = agent_executor.invoke({\"input\": input1, \"chat_history\": chat_history})\n",
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"print(result['output'])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "au-Qbu9KxVcy"
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},
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"source": [
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"### Finding strong buys in the Utilities sector "
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]
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},
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{
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"cell_type": "code",
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"source": [
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"input1 = '''\n",
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"First, find the stocks recommended fro strong buy in the Utilities Sector\n",
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"Second, find the valuation metrics for this stock.\n",
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"Third, summarize your findings in a short paragraph.\n",
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"'''\n",
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"result = agent_executor.invoke({\"input\": input1, \"chat_history\": chat_history})\n",
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"print(result['output'])"
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],
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"metadata": {
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"id": "doUw61HPU6fR"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [],
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"metadata": {
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"id": "rc7X4Ep4VNbS"
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},
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"execution_count": null,
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"outputs": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "myenv",
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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": "3.12.1"
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},
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"colab": {
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"provenance": []
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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Block a user