Polygon 股票市场 API 工具
Polygon Polygon.io 股票 API 提供 REST 接口，让您查询所有美国股票交易所的最新市场数据。
本笔记本使用工具获取股票市场数据，如来自 Polygon 的最新报价和某个股票代码的新闻。
import getpass
import os
os.environ["POLYGON_API_KEY"] = getpass.getpass()
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from langchain_community.tools.polygon.aggregates import PolygonAggregates
from langchain_community.tools.polygon.financials import PolygonFinancials
from langchain_community.tools.polygon.last_quote import PolygonLastQuote
from langchain_community.tools.polygon.ticker_news import PolygonTickerNews
from langchain_community.utilities.polygon import PolygonAPIWrapper
api_wrapper = PolygonAPIWrapper()
ticker = "AAPL"
获取股票代码的最新报价
# Get the last quote for ticker
last_quote_tool = PolygonLastQuote(api_wrapper=api_wrapper)
last_quote = last_quote_tool.run(ticker)
print(f"Tool output: {last_quote}")
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}
import json
# Convert the last quote response to JSON
last_quote = last_quote_tool.run(ticker)
last_quote_json = json.loads(last_quote)
# Print the latest price for ticker
latest_price = last_quote_json["p"]
print(f"Latest price for {ticker} is ${latest_price}")
Latest price for AAPL is $170.48
获取指定股票的聚合数据（历史价格）
from langchain_community.tools.polygon.aggregates import PolygonAggregatesSchema
# 定义参数
params = PolygonAggregatesSchema(
ticker=ticker,
timespan="day",
timespan_multiplier=1,
from_date="2024-03-01",
to_date="2024-03-08",
)
# 获取指定股票的聚合数据
aggregates_tool = PolygonAggregates(api_wrapper=api_wrapper)
aggregates = aggregates_tool.run(tool_input=params.dict())
aggregates_json = json.loads(aggregates)
print(f"总聚合数据数量: {len(aggregates_json)}")
print(f"聚合数据: {aggregates_json}")
Total aggregates: 6
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}]
获取最新的股票新闻
ticker_news_tool = PolygonTickerNews(api_wrapper=api_wrapper)
ticker_news = ticker_news_tool.run(ticker)
# 将新闻响应转换为 JSON 数组
ticker_news_json = json.loads(ticker_news)
print(f"总新闻项目数: {len(ticker_news_json)}")
总新闻项目数: 10
# 检查第一条新闻项目
news_item = ticker_news_json[0]
print(f"标题: {news_item['title']}")
print(f"描述: {news_item['description']}")
print(f"发布者: {news_item['publisher']['name']}")
print(f"网址: {news_item['article_url']}")
标题: An AI surprise could fuel a 20% rally for the S&P 500 in 2024, says UBS
描述: If Gen AI causes a big productivity boost, stocks could see an unexpected rally this year, say UBS strategists.
发布者: MarketWatch
网址: https://www.marketwatch.com/story/an-ai-surprise-could-fuel-a-20-rally-for-the-s-p-500-in-2024-says-ubs-1044d716
获取股票代码的财务数据
financials_tool = PolygonFinancials(api_wrapper=api_wrapper)
financials = financials_tool.run(ticker)
# 将财务响应转换为 JSON
financials_json = json.loads(financials)
print(f"报告期总数: {len(financials_json)}")
报告期总数: 10
# 打印最新报告期的财务元数据
financial_data = financials_json[0]
print(f"公司名称: {financial_data['company_name']}")
print(f"CIK: {financial_data['cik']}")
print(f"财务周期: {financial_data['fiscal_period']}")
print(f"结束日期: {financial_data['end_date']}")
print(f"开始日期: {financial_data['start_date']}")
公司名称: APPLE INC
CIK: 0000320193
财务周期: TTM
结束日期: 2023-12-30
开始日期: 2022-12-31
# 打印最新报告期的损益表
print(f"损益表: {financial_data['financials']['income_statement']}")
损益表: {'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}}
# 打印最新报告期的资产负债表
print(f"资产负债表: {financial_data['financials']['balance_sheet']}")
资产负债表: {'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}}
# 打印最新报告期的现金流量表
print(f"现金流量表: {financial_data['financials']['cash_flow_statement']}")
现金流量表: {'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}}