Finance

Use this skill when the user asks about stock prices, options, dividends, splits, ticker details, cryptocurrency prices, market caps, or any financial market data. Triggers include mentions of 'stock price', 'ticker', 'market data', 'options chain', 'dividends', 'crypto price', 'bitcoin price', or any request for financial market information.

Financial Market Data

Overview

Python libraries for querying financial market data are pre-installed with API keys already configured in the environment. Use them via the bash tool.

• Polygon.io — US equities, options, dividends, splits, ticker metadata
• CoinGecko — Cryptocurrency prices, market caps, historical data

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Polygon.io (Stocks & Options)

Setup

python

from polygon import RESTClient
import pandas as pd

client = RESTClient()  # API key is pre-configured via environment

Aggregate Bars (OHLCV)

python

# get_aggs returns a list directly
bars = client.get_aggs(
    ticker="AAPL",
    multiplier=1,
    timespan="day",       # "minute", "hour", "day", "week", "month"
    from_="2024-01-01",
    to="2024-12-31",
    adjusted=True,
    sort="asc",
    limit=50000,
)

# Convert to DataFrame
df = pd.DataFrame(bars)
df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms")
print(df[["timestamp", "open", "high", "low", "close", "volume"]].head())

# list_aggs is the paginated iterator version (preferred for large date ranges)
for agg in client.list_aggs("AAPL", 1, "day", "2024-01-01", "2024-12-31", limit=50000):
    print(f"{agg.timestamp}: O={agg.open} H={agg.high} L={agg.low} C={agg.close} V={agg.volume}")

Ticker Details

python

details = client.get_ticker_details("AAPL")
print(f"Name: {details.name}")
print(f"Ticker: {details.ticker}")
print(f"Market Cap: {details.market_cap}")
print(f"Description: {details.description}")
print(f"SIC Description: {details.sic_description}")

# Historical snapshot
details = client.get_ticker_details("AAPL", date="2024-01-01")

Last Quote (NBBO)

python

quote = client.get_last_quote(ticker="AAPL")
print(f"Bid: {quote.bid_price} x {quote.bid_size}")
print(f"Ask: {quote.ask_price} x {quote.ask_size}")

Options Contracts

python

# list_options_contracts returns a paginated iterator
contracts = list(client.list_options_contracts(
    underlying_ticker="AAPL",
    contract_type="call",           # "call", "put"
    expiration_date="2024-12-20",   # exact date, or use _gte/_lte
    expired=False,
    order="asc",
    sort="ticker",
    limit=100,
))

for c in contracts[:5]:
    print(f"{c.ticker}: strike={c.strike_price}, exp={c.expiration_date}, type={c.contract_type}")

Stock Splits

python

for s in client.list_splits(ticker="NVDA"):
    print(f"{s.ticker} split on {s.execution_date}: {s.split_from}:{s.split_to}")

Dividends

python

for d in client.list_dividends(ticker="MSFT"):
    print(f"{d.ticker} ex-date={d.ex_dividend_date}: ${d.cash_amount}")

Polygon Quick Reference

Task	Method	Key Parameters
Daily/intraday bars	get_aggs / list_aggs	ticker, multiplier, timespan, from_, to
Ticker metadata	get_ticker_details	ticker, date (optional)
Last NBBO quote	get_last_quote	ticker
Options chain	list_options_contracts	underlying_ticker, contract_type, expiration_date
Stock splits	list_splits	ticker
Dividends	list_dividends	ticker

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CoinGecko (Cryptocurrency)

Setup

python

import requests
import os

BASE_URL = os.environ.get("COINGECKO_BASE_URL", "https://api.coingecko.com/api/v3")

Current Prices

python

# Simple price lookup for one or more coins
r = requests.get(f"{BASE_URL}/simple/price", params={
    "ids": "bitcoin,ethereum",
    "vs_currencies": "usd,eur",
    "include_market_cap": "true",
    "include_24hr_vol": "true",
    "include_24hr_change": "true",
    "include_last_updated_at": "true",
})
data = r.json()
print(f"BTC: ${data['bitcoin']['usd']}")
print(f"ETH: ${data['ethereum']['usd']}")

Market Data (Top Coins)

python

r = requests.get(f"{BASE_URL}/coins/markets", params={
    "vs_currency": "usd",
    "ids": "bitcoin,ethereum,solana",
    "order": "market_cap_desc",
    "per_page": 10,
    "page": 1,
    "sparkline": "false",
})
for coin in r.json():
    print(f"{coin['name']} ({coin['symbol'].upper()}): "
          f"${coin['current_price']:,.2f}, "
          f"mcap=${coin['market_cap']:,.0f}, "
          f"24h_change={coin['price_change_percentage_24h']:.2f}%")

Historical Price Chart

python

import pandas as pd

r = requests.get(f"{BASE_URL}/coins/bitcoin/market_chart", params={
    "vs_currency": "usd",
    "days": "30",
    "interval": "daily",
})
data = r.json()
prices = data["prices"]           # [[timestamp_ms, price], ...]
market_caps = data["market_caps"]  # [[timestamp_ms, mcap], ...]
volumes = data["total_volumes"]    # [[timestamp_ms, vol], ...]

df = pd.DataFrame(prices, columns=["timestamp", "price"])
df["date"] = pd.to_datetime(df["timestamp"], unit="ms")
print(df[["date", "price"]].tail())

Coin Details

python

r = requests.get(f"{BASE_URL}/coins/bitcoin", params={
    "localization": "false",
    "tickers": "false",
    "community_data": "false",
    "developer_data": "false",
})
coin = r.json()
print(f"Name: {coin['name']}")
print(f"Symbol: {coin['symbol']}")
print(f"Current price: ${coin['market_data']['current_price']['usd']:,.2f}")
print(f"ATH: ${coin['market_data']['ath']['usd']:,.2f}")

List All Coins

python

r = requests.get(f"{BASE_URL}/coins/list")
coins = r.json()  # [{"id": "bitcoin", "symbol": "btc", "name": "Bitcoin"}, ...]

CoinGecko Quick Reference

Task	Endpoint	Key Parameters
Current prices	/simple/price	ids, vs_currencies, include_market_cap
Market data	/coins/markets	vs_currency, ids, order, per_page
Historical chart	/coins/{id}/market_chart	vs_currency, days, interval
Coin details	/coins/{id}	localization, tickers
All coin IDs	/coins/list	none

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Important Notes

• No general internet access is available; only the Polygon and CoinGecko API proxies are accessible.
• API keys are pre-configured; do not set them manually.
• The Polygon list_* methods return paginated iterators; wrap in list() to get all results.
• For CoinGecko, all endpoints use GET requests; no authentication is required.
• For time-sensitive queries, data may be gated to a specific date via the TIME_GATE environment variable.