mirror of
https://github.com/hwchase17/langchain.git
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cafffe8a21
**Description:** In this PR, I am adding a `PolygonAggregates` tool, which can be used to get historical stock price data (called aggregates by Polygon) for a given ticker. Polygon [docs](https://polygon.io/docs/stocks/get_v2_aggs_ticker__stocksticker__range__multiplier___timespan___from___to) for this endpoint. **Twitter**: [@virattt](https://twitter.com/virattt)
409 lines
17 KiB
Plaintext
409 lines
17 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "245a954a",
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"metadata": {
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"id": "245a954a"
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},
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"source": [
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"# Polygon Stock Market API Tools\n",
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"\n",
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">[Polygon](https://polygon.io/) The Polygon.io Stocks API provides REST endpoints that let you query the latest market data from all US stock exchanges.\n",
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"\n",
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"This notebook uses tools to get stock market data like the latest quote and news for a ticker from Polygon."
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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": 1,
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"id": "34bb5968",
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"metadata": {
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"id": "34bb5968",
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"is_executing": true,
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stdin",
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"output_type": "stream",
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"text": [
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" ········\n"
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]
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}
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],
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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[\"POLYGON_API_KEY\"] = getpass.getpass()"
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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": 2,
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"id": "ac4910f8",
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"metadata": {
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"id": "ac4910f8",
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"is_executing": true
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},
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"outputs": [],
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"source": [
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"from langchain_community.tools.polygon.aggregates import PolygonAggregates\n",
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"from langchain_community.tools.polygon.financials import PolygonFinancials\n",
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"from langchain_community.tools.polygon.last_quote import PolygonLastQuote\n",
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"from langchain_community.tools.polygon.ticker_news import PolygonTickerNews\n",
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"from langchain_community.utilities.polygon import PolygonAPIWrapper"
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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": 3,
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"id": "8660b910-905b-46f3-9541-920b9fc3d4d6",
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"metadata": {},
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"outputs": [],
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"source": [
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"api_wrapper = PolygonAPIWrapper()\n",
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"ticker = \"AAPL\""
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]
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},
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{
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"cell_type": "markdown",
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"id": "347f6951-b383-4675-b116-9b7d16c1f505",
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"metadata": {},
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"source": [
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"### Get latest quote for ticker"
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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": 4,
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"id": "84b8f773",
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"metadata": {
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"id": "84b8f773"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Tool output: {\"P\": 170.5, \"S\": 2, \"T\": \"AAPL\", \"X\": 11, \"i\": [604], \"p\": 170.48, \"q\": 106666224, \"s\": 1, \"t\": 1709945992614283138, \"x\": 12, \"y\": 1709945992614268948, \"z\": 3}\n"
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]
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}
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],
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"source": [
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"# Get the last quote for ticker\n",
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"last_quote_tool = PolygonLastQuote(api_wrapper=api_wrapper)\n",
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"last_quote = last_quote_tool.run(ticker)\n",
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"print(f\"Tool output: {last_quote}\")"
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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": 5,
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"id": "068991a6",
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"metadata": {
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"id": "068991a6",
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"outputId": "c5cdc6ec-03cf-4084-cc6f-6ae792d91d39"
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},
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"outputs": [],
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"source": [
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"import json\n",
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"\n",
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"# Convert the last quote response to JSON\n",
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"last_quote = last_quote_tool.run(ticker)\n",
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"last_quote_json = json.loads(last_quote)"
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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": 6,
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"id": "174e2556-eb3e-48a4-bde6-9a3309fae9c9",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Latest price for AAPL is $170.48\n"
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]
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}
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],
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"source": [
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"# Print the latest price for ticker\n",
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"latest_price = last_quote_json[\"p\"]\n",
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"print(f\"Latest price for {ticker} is ${latest_price}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1f478364-f41b-47f2-ac4b-d3154f1c7faa",
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"metadata": {},
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"source": [
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"### Get aggregates (historical prices) for ticker"
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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": 20,
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"id": "5e14e091-3150-4bd5-bfd3-de17caa75ee1",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.tools.polygon.aggregates import PolygonAggregatesSchema\n",
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"\n",
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"# Define param\n",
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"params = PolygonAggregatesSchema(\n",
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" ticker=ticker,\n",
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" timespan=\"day\",\n",
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" timespan_multiplier=1,\n",
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" from_date=\"2024-03-01\",\n",
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" to_date=\"2024-03-08\",\n",
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")\n",
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"# Get aggregates for ticker\n",
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"aggregates_tool = PolygonAggregates(api_wrapper=api_wrapper)\n",
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"aggregates = aggregates_tool.run(tool_input=params.dict())\n",
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"aggregates_json = json.loads(aggregates)"
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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": 25,
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"id": "a01f3888-d233-400d-b8c4-298d27c8f793",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Total aggregates: 6\n",
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"Aggregates: [{'v': 73450582.0, 'vw': 179.0322, 'o': 179.55, 'c': 179.66, 'h': 180.53, 'l': 177.38, 't': 1709269200000, 'n': 911077}, {'v': 81505451.0, 'vw': 174.8938, 'o': 176.15, 'c': 175.1, 'h': 176.9, 'l': 173.79, 't': 1709528400000, 'n': 1167166}, {'v': 94702355.0, 'vw': 170.3234, 'o': 170.76, 'c': 170.12, 'h': 172.04, 'l': 169.62, 't': 1709614800000, 'n': 1108820}, {'v': 68568907.0, 'vw': 169.5506, 'o': 171.06, 'c': 169.12, 'h': 171.24, 'l': 168.68, 't': 1709701200000, 'n': 896297}, {'v': 71763761.0, 'vw': 169.3619, 'o': 169.15, 'c': 169, 'h': 170.73, 'l': 168.49, 't': 1709787600000, 'n': 825405}, {'v': 76267041.0, 'vw': 171.5322, 'o': 169, 'c': 170.73, 'h': 173.7, 'l': 168.94, 't': 1709874000000, 'n': 925213}]\n"
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]
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}
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],
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"source": [
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"print(f\"Total aggregates: {len(aggregates_json)}\")\n",
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"print(f\"Aggregates: {aggregates_json}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "04f1b612-f91f-471c-8264-9cc8c14bdaef",
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"metadata": {},
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"source": [
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"### Get latest news for ticker"
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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": "024982db-1402-4bd7-9788-6cb369a9565d",
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"metadata": {},
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"outputs": [],
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"source": [
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"ticker_news_tool = PolygonTickerNews(api_wrapper=api_wrapper)\n",
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"ticker_news = ticker_news_tool.run(ticker)"
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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": 9,
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"id": "dfd26ef6-2d92-483e-9780-484091bd3774",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Total news items: 10\n"
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]
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}
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],
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"source": [
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"# Convert the news response to JSON array\n",
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"ticker_news_json = json.loads(ticker_news)\n",
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"print(f\"Total news items: {len(ticker_news_json)}\")"
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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": 10,
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"id": "dbbb4b43-1096-45f3-8000-45538b3c73ee",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Title: An AI surprise could fuel a 20% rally for the S&P 500 in 2024, says UBS\n",
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"Description: If Gen AI causes a big productivity boost, stocks could see an unexpected rally this year, say UBS strategists.\n",
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"Publisher: MarketWatch\n",
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"URL: https://www.marketwatch.com/story/an-ai-surprise-could-fuel-a-20-rally-for-the-s-p-500-in-2024-says-ubs-1044d716\n"
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]
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}
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],
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"source": [
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"# Inspect the first news item\n",
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"news_item = ticker_news_json[0]\n",
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"print(f\"Title: {news_item['title']}\")\n",
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"print(f\"Description: {news_item['description']}\")\n",
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"print(f\"Publisher: {news_item['publisher']['name']}\")\n",
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"print(f\"URL: {news_item['article_url']}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "675cbae0-a754-45b0-be01-738333c3255e",
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"metadata": {},
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"source": [
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"### Get financials for ticker"
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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": 28,
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"id": "f46a8c88-8793-470d-8fce-31e8d4b1f77c",
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"metadata": {},
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"outputs": [],
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"source": [
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"financials_tool = PolygonFinancials(api_wrapper=api_wrapper)\n",
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"financials = financials_tool.run(ticker)"
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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": 30,
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"id": "3495021c-a31b-4dba-8daf-811c43e24bd9",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Total reporting periods: 10\n"
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]
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}
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],
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"source": [
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"# Convert the financials response to JSON\n",
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"financials_json = json.loads(financials)\n",
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"print(f\"Total reporting periods: {len(financials_json)}\")"
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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": 31,
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"id": "86215a03-a927-4334-a82b-54cb9574ed05",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Company name: APPLE INC\n",
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"CIK: 0000320193\n",
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"Fiscal period: TTM\n",
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"End date: 2023-12-30\n",
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"Start date: 2022-12-31\n"
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]
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}
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],
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"source": [
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"# Print the latest reporting period's financials metadata\n",
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"financial_data = financials_json[0]\n",
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"print(f\"Company name: {financial_data['company_name']}\")\n",
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"print(f\"CIK: {financial_data['cik']}\")\n",
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"print(f\"Fiscal period: {financial_data['fiscal_period']}\")\n",
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"print(f\"End date: {financial_data['end_date']}\")\n",
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"print(f\"Start date: {financial_data['start_date']}\")"
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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": 32,
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"id": "2665d48b-fd44-4279-a2fe-e42836d3acdb",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Income statement: {'diluted_earnings_per_share': {'value': 6.42, 'unit': 'USD / shares', 'label': 'Diluted Earnings Per Share', 'order': 4300}, 'costs_and_expenses': {'value': 267270000000.0, 'unit': 'USD', 'label': 'Costs And Expenses', 'order': 600}, 'net_income_loss_attributable_to_noncontrolling_interest': {'value': 0, 'unit': 'USD', 'label': 'Net Income/Loss Attributable To Noncontrolling Interest', 'order': 3300}, 'net_income_loss_attributable_to_parent': {'value': 100913000000.0, 'unit': 'USD', 'label': 'Net Income/Loss Attributable To Parent', 'order': 3500}, 'income_tax_expense_benefit': {'value': 17523000000.0, 'unit': 'USD', 'label': 'Income Tax Expense/Benefit', 'order': 2200}, 'income_loss_from_continuing_operations_before_tax': {'value': 118436000000.0, 'unit': 'USD', 'label': 'Income/Loss From Continuing Operations Before Tax', 'order': 1500}, 'operating_expenses': {'value': 55013000000.0, 'unit': 'USD', 'label': 'Operating Expenses', 'order': 1000}, 'benefits_costs_expenses': {'value': 267270000000.0, 'unit': 'USD', 'label': 'Benefits Costs and Expenses', 'order': 200}, 'diluted_average_shares': {'value': 47151996000.0, 'unit': 'shares', 'label': 'Diluted Average Shares', 'order': 4500}, 'cost_of_revenue': {'value': 212035000000.0, 'unit': 'USD', 'label': 'Cost Of Revenue', 'order': 300}, 'operating_income_loss': {'value': 118658000000.0, 'unit': 'USD', 'label': 'Operating Income/Loss', 'order': 1100}, 'net_income_loss_available_to_common_stockholders_basic': {'value': 100913000000.0, 'unit': 'USD', 'label': 'Net Income/Loss Available To Common Stockholders, Basic', 'order': 3700}, 'preferred_stock_dividends_and_other_adjustments': {'value': 0, 'unit': 'USD', 'label': 'Preferred Stock Dividends And Other Adjustments', 'order': 3900}, 'research_and_development': {'value': 29902000000.0, 'unit': 'USD', 'label': 'Research and Development', 'order': 1030}, 'revenues': {'value': 385706000000.0, 'unit': 'USD', 'label': 'Revenues', 'order': 100}, 'participating_securities_distributed_and_undistributed_earnings_loss_basic': {'value': 0, 'unit': 'USD', 'label': 'Participating Securities, Distributed And Undistributed Earnings/Loss, Basic', 'order': 3800}, 'selling_general_and_administrative_expenses': {'value': 25111000000.0, 'unit': 'USD', 'label': 'Selling, General, and Administrative Expenses', 'order': 1010}, 'nonoperating_income_loss': {'value': -222000000.0, 'unit': 'USD', 'label': 'Nonoperating Income/Loss', 'order': 900}, 'income_loss_from_continuing_operations_after_tax': {'value': 100913000000.0, 'unit': 'USD', 'label': 'Income/Loss From Continuing Operations After Tax', 'order': 1400}, 'basic_earnings_per_share': {'value': 6.46, 'unit': 'USD / shares', 'label': 'Basic Earnings Per Share', 'order': 4200}, 'basic_average_shares': {'value': 46946265000.0, 'unit': 'shares', 'label': 'Basic Average Shares', 'order': 4400}, 'gross_profit': {'value': 173671000000.0, 'unit': 'USD', 'label': 'Gross Profit', 'order': 800}, 'net_income_loss': {'value': 100913000000.0, 'unit': 'USD', 'label': 'Net Income/Loss', 'order': 3200}}\n"
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]
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}
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],
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"source": [
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"# Print the latest reporting period's income statement\n",
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"print(f\"Income statement: {financial_data['financials']['income_statement']}\")"
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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": 34,
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"id": "9e24d339-c0d5-4b6a-9ba6-3f97e46ff6df",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Balance sheet: {'equity_attributable_to_noncontrolling_interest': {'value': 0, 'unit': 'USD', 'label': 'Equity Attributable To Noncontrolling Interest', 'order': 1500}, 'other_noncurrent_liabilities': {'value': 39441000000.0, 'unit': 'USD', 'label': 'Other Non-current Liabilities', 'order': 820}, 'equity': {'value': 74100000000.0, 'unit': 'USD', 'label': 'Equity', 'order': 1400}, 'liabilities': {'value': 279414000000.0, 'unit': 'USD', 'label': 'Liabilities', 'order': 600}, 'noncurrent_assets': {'value': 209822000000.0, 'unit': 'USD', 'label': 'Noncurrent Assets', 'order': 300}, 'equity_attributable_to_parent': {'value': 74100000000.0, 'unit': 'USD', 'label': 'Equity Attributable To Parent', 'order': 1600}, 'liabilities_and_equity': {'value': 353514000000.0, 'unit': 'USD', 'label': 'Liabilities And Equity', 'order': 1900}, 'other_current_liabilities': {'value': 75827000000.0, 'unit': 'USD', 'label': 'Other Current Liabilities', 'order': 740}, 'inventory': {'value': 6511000000.0, 'unit': 'USD', 'label': 'Inventory', 'order': 230}, 'other_noncurrent_assets': {'value': 166156000000.0, 'unit': 'USD', 'label': 'Other Non-current Assets', 'order': 350}, 'other_current_assets': {'value': 137181000000.0, 'unit': 'USD', 'label': 'Other Current Assets', 'order': 250}, 'current_liabilities': {'value': 133973000000.0, 'unit': 'USD', 'label': 'Current Liabilities', 'order': 700}, 'noncurrent_liabilities': {'value': 145441000000.0, 'unit': 'USD', 'label': 'Noncurrent Liabilities', 'order': 800}, 'fixed_assets': {'value': 43666000000.0, 'unit': 'USD', 'label': 'Fixed Assets', 'order': 320}, 'long_term_debt': {'value': 106000000000.0, 'unit': 'USD', 'label': 'Long-term Debt', 'order': 810}, 'current_assets': {'value': 143692000000.0, 'unit': 'USD', 'label': 'Current Assets', 'order': 200}, 'assets': {'value': 353514000000.0, 'unit': 'USD', 'label': 'Assets', 'order': 100}, 'accounts_payable': {'value': 58146000000.0, 'unit': 'USD', 'label': 'Accounts Payable', 'order': 710}}\n"
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]
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}
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],
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"source": [
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"# Print the latest reporting period's balance sheet\n",
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"print(f\"Balance sheet: {financial_data['financials']['balance_sheet']}\")"
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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": 33,
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"id": "4bfb47f1-8b9e-4293-be18-c4e9ab945d51",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
|
|
"Cash flow statement: {'net_cash_flow_continuing': {'value': 20000000000.0, 'unit': 'USD', 'label': 'Net Cash Flow, Continuing', 'order': 1200}, 'net_cash_flow_from_investing_activities_continuing': {'value': 7077000000.0, 'unit': 'USD', 'label': 'Net Cash Flow From Investing Activities, Continuing', 'order': 500}, 'net_cash_flow_from_investing_activities': {'value': 7077000000.0, 'unit': 'USD', 'label': 'Net Cash Flow From Investing Activities', 'order': 400}, 'net_cash_flow_from_financing_activities_continuing': {'value': -103510000000.0, 'unit': 'USD', 'label': 'Net Cash Flow From Financing Activities, Continuing', 'order': 800}, 'net_cash_flow_from_operating_activities': {'value': 116433000000.0, 'unit': 'USD', 'label': 'Net Cash Flow From Operating Activities', 'order': 100}, 'net_cash_flow_from_financing_activities': {'value': -103510000000.0, 'unit': 'USD', 'label': 'Net Cash Flow From Financing Activities', 'order': 700}, 'net_cash_flow_from_operating_activities_continuing': {'value': 116433000000.0, 'unit': 'USD', 'label': 'Net Cash Flow From Operating Activities, Continuing', 'order': 200}, 'net_cash_flow': {'value': 20000000000.0, 'unit': 'USD', 'label': 'Net Cash Flow', 'order': 1100}}\n"
|
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]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Print the latest reporting period's cash flow statement\n",
|
|
"print(f\"Cash flow statement: {financial_data['financials']['cash_flow_statement']}\")"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"colab": {
|
|
"provenance": []
|
|
},
|
|
"kernelspec": {
|
|
"display_name": "Python (langchain)",
|
|
"language": "python",
|
|
"name": "langchain"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.9.18"
|
|
},
|
|
"vscode": {
|
|
"interpreter": {
|
|
"hash": "53f3bc57609c7a84333bb558594977aa5b4026b1d6070b93987956689e367341"
|
|
}
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|