Yahoo

Fetch equity option chains from Yahoo Finance.

The client is intentionally minimal: it fetches a full option chain via the public v7/finance/options endpoint and exposes a helper to build a VolSurfaceLoader from it.

You can import the module via

from quantflow.data.yahoo import Yahoo

Authentication

Yahoo Finance requires a session cookie and a crumb token for the options endpoint. The client fetches both on the first request and caches the crumb for the lifetime of the instance.

quantflow.data.yahoo.Yahoo dataclass

Yahoo(
    url="https://query2.finance.yahoo.com/v7/finance",
    content_type="text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7",
    default_headers=(
        lambda: {
            "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/148.0.0.0 Safari/537.36"
        }
    )(),
    _crumb=None,
)

Bases: HttpxClient

Yahoo Finance API client

Minimal client for fetching historical prices and option chains.

Examples

Fetch daily prices for a symbol:

from quantflow.data.yahoo import Yahoo

async with Yahoo() as yahoo:
    df = await yahoo.prices("AAPL", range="1y")

Build a volatility surface from the option chain:

async with Yahoo() as yahoo:
    loader = await yahoo.volatility_surface_loader("AAPL")
    surface = loader.surface()

url class-attribute instance-attribute

url = 'https://query2.finance.yahoo.com/v7/finance'

content_type class-attribute instance-attribute

content_type = "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7"

default_headers class-attribute instance-attribute

default_headers = field(
    default_factory=lambda: {
        "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/148.0.0.0 Safari/537.36"
    }
)

freq

Bases: StrEnum

Yahoo Finance chart intervals

ONE_MIN class-attribute instance-attribute

ONE_MIN = '1m'

TWO_MIN class-attribute instance-attribute

TWO_MIN = '2m'

FIVE_MIN class-attribute instance-attribute

FIVE_MIN = '5m'

FIFTEEN_MIN class-attribute instance-attribute

FIFTEEN_MIN = '15m'

THIRTY_MIN class-attribute instance-attribute

THIRTY_MIN = '30m'

ONE_HOUR class-attribute instance-attribute

ONE_HOUR = '1h'

ONE_DAY class-attribute instance-attribute

ONE_DAY = '1d'

FIVE_DAY class-attribute instance-attribute

FIVE_DAY = '5d'

ONE_WEEK class-attribute instance-attribute

ONE_WEEK = '1wk'

ONE_MONTH class-attribute instance-attribute

ONE_MONTH = '1mo'

THREE_MONTH class-attribute instance-attribute

THREE_MONTH = '3mo'

option_chain async

option_chain(symbol)

Return the full option chain for symbol

PARAMETER	DESCRIPTION
symbol	Underlying ticker symbol TYPE: str

Source code in quantflow/data/yahoo.py

async def option_chain(
    self,
    symbol: Annotated[str, Doc("Underlying ticker symbol")],
) -> dict:  # pragma: no cover
    """Return the full option chain for `symbol`"""
    params = dict(getAllData="true", crumb=await self._get_crumb())
    data = await self.get(f"{self.url}/options/{symbol}", params=params)
    return data["optionChain"]["result"][0]

volatility_surface_loader async

volatility_surface_loader(
    symbol,
    *,
    ref_date=None,
    exclude_volume=None,
    exclude_open_interest=None
)

Build a VolSurfaceLoader by fetching the option chain for symbol and passing it to loader_from_chain.

PARAMETER	DESCRIPTION
symbol	Underlying ticker symbol TYPE: str
ref_date	Reference date for the yield curves; defaults to now TYPE: datetime | None DEFAULT: None
exclude_volume	Drop contracts with volume at or below this threshold TYPE: int | None DEFAULT: None
exclude_open_interest	Drop contracts with open interest at or below this threshold TYPE: int | None DEFAULT: None

Source code in quantflow/data/yahoo.py

async def volatility_surface_loader(
    self,
    symbol: Annotated[str, Doc("Underlying ticker symbol")],
    *,
    ref_date: Annotated[
        datetime | None,
        Doc("Reference date for the yield curves; defaults to now"),
    ] = None,
    exclude_volume: Annotated[
        int | None, Doc("Drop contracts with volume at or below this threshold")
    ] = None,
    exclude_open_interest: Annotated[
        int | None,
        Doc("Drop contracts with open interest at or below this threshold"),
    ] = None,
) -> VolSurfaceLoader:
    """Build a [VolSurfaceLoader][quantflow.options.surface.VolSurfaceLoader]
    by fetching the option chain for `symbol` and passing it to
    [loader_from_chain][quantflow.data.yahoo.Yahoo.loader_from_chain]."""
    return self.loader_from_chain(
        await self.option_chain(symbol),
        ref_date=ref_date,
        exclude_volume=exclude_volume,
        exclude_open_interest=exclude_open_interest,
    )

loader_from_chain classmethod

loader_from_chain(
    chain,
    *,
    ref_date=None,
    exclude_volume=None,
    exclude_open_interest=None
)

Build a VolSurfaceLoader from a Yahoo option chain dictionary.

US equity options are non-inverse: prices are in the quote currency and the spot is taken from the underlying quote. Forwards are not provided by Yahoo, so they are recovered from put-call parity by the loader.

PARAMETER	DESCRIPTION
chain	Yahoo option chain payload TYPE: dict
ref_date	Reference date for the yield curves; defaults to now TYPE: datetime | None DEFAULT: None
exclude_volume	Drop contracts with volume at or below this threshold TYPE: int | None DEFAULT: None
exclude_open_interest	Drop contracts with open interest at or below this threshold TYPE: int | None DEFAULT: None

Source code in quantflow/data/yahoo.py

@classmethod
def loader_from_chain(
    cls,
    chain: Annotated[dict, Doc("Yahoo option chain payload")],
    *,
    ref_date: Annotated[
        datetime | None,
        Doc("Reference date for the yield curves; defaults to now"),
    ] = None,
    exclude_volume: Annotated[
        int | None, Doc("Drop contracts with volume at or below this threshold")
    ] = None,
    exclude_open_interest: Annotated[
        int | None,
        Doc("Drop contracts with open interest at or below this threshold"),
    ] = None,
) -> VolSurfaceLoader:
    """Build a [VolSurfaceLoader][quantflow.options.surface.VolSurfaceLoader]
    from a Yahoo option chain dictionary.

    US equity options are non-inverse: prices are in the quote currency and
    the spot is taken from the underlying quote. Forwards are not provided
    by Yahoo, so they are recovered from put-call parity by the loader.
    """
    symbol = chain.get("underlyingSymbol", "")
    ref = ref_date or utcnow()
    loader = VolSurfaceLoader(
        asset=symbol,
        exclude_volume=to_decimal(exclude_volume) if exclude_volume else None,
        exclude_open_interest=(
            to_decimal(exclude_open_interest) if exclude_open_interest else None
        ),
        quote_curve=NoDiscountCurve(ref_date=ref),
        asset_curve=NoDiscountCurve(ref_date=ref),
    )
    quote = chain.get("quote") or {}
    bid = quote.get("bid") or quote.get("regularMarketPrice")
    ask = quote.get("ask") or quote.get("regularMarketPrice")
    if bid and ask:
        loader.add_spot(
            DefaultVolSecurity.spot(),
            bid=to_decimal(bid),
            ask=to_decimal(ask),
        )
    for expiry in chain.get("options", []):
        maturity = (
            pd.to_datetime(expiry["expirationDate"], unit="s", utc=True)
            .to_pydatetime()
            .replace(hour=20, tzinfo=timezone.utc)
        )
        for option_type, contracts in (
            (OptionType.CALL, expiry.get("calls", [])),
            (OptionType.PUT, expiry.get("puts", [])),
        ):
            for c in contracts:
                bid_ = c.get("bid")
                ask_ = c.get("ask")
                if not bid_ or not ask_:
                    continue
                loader.add_option(
                    DefaultVolSecurity.option(),
                    strike=to_decimal(c["strike"]),
                    maturity=maturity,
                    option_type=option_type,
                    bid=to_decimal(bid_),
                    ask=to_decimal(ask_),
                    open_interest=to_decimal(c.get("openInterest") or 0),
                    volume=to_decimal(c.get("volume") or 0),
                    inverse=False,
                )
    return loader

prices async

prices(
    symbol,
    *,
    interval=ONE_DAY,
    from_date=None,
    to_date=None,
    range=None
)

Historical OHLCV prices for symbol.

Returns a DataFrame with columns timestamp, open, high, low, close, volume, and adj_close (when available).

Pass from_date / to_date for a specific window, or range for a shorthand period. When neither is given Yahoo defaults to one month.

PARAMETER	DESCRIPTION
symbol	Ticker symbol TYPE: str
interval	Bar interval — use Yahoo.freq members or a raw string TYPE: str | freq DEFAULT: ONE_DAY
from_date	Start date (inclusive) TYPE: date | None DEFAULT: None
to_date	End date (inclusive) TYPE: date | None DEFAULT: None
range	Shorthand period when dates are omitted: '1mo', '3mo', '6mo', '1y', '2y', '5y', 'ytd', 'max', etc. TYPE: str | None DEFAULT: None

Source code in quantflow/data/yahoo.py

async def prices(
    self,
    symbol: Annotated[str, Doc("Ticker symbol")],
    *,
    interval: Annotated[
        str | freq, Doc("Bar interval — use Yahoo.freq members or a raw string")
    ] = freq.ONE_DAY,
    from_date: Annotated[date | None, Doc("Start date (inclusive)")] = None,
    to_date: Annotated[date | None, Doc("End date (inclusive)")] = None,
    range: Annotated[
        str | None,
        Doc(
            "Shorthand period when dates are omitted: '1mo', '3mo', '6mo', "
            "'1y', '2y', '5y', 'ytd', 'max', etc."
        ),
    ] = None,
) -> pd.DataFrame:
    """Historical OHLCV prices for `symbol`.

    Returns a DataFrame with columns `timestamp`, `open`, `high`, `low`,
    `close`, `volume`, and `adj_close` (when available).

    Pass `from_date` / `to_date` for a specific window, or `range` for a
    shorthand period. When neither is given Yahoo defaults to one month.
    """
    params: dict = {"interval": str(interval)}
    if from_date:
        params["period1"] = int(as_utc(from_date).timestamp())
    if to_date:
        params["period2"] = int(as_utc(to_date).timestamp())
    if range and not from_date and not to_date:
        params["range"] = range
    data = await self.get(
        f"https://query2.finance.yahoo.com/v8/finance/chart/{symbol}",
        params=params,
    )
    result = data["chart"]["result"][0]
    timestamps = result.get("timestamps") or result.get("timestamp", [])
    quote = result["indicators"]["quote"][0]
    adj = result["indicators"].get("adjclose", [{}])[0]
    df = pd.DataFrame(
        {
            "timestamp": pd.to_datetime(timestamps, unit="s", utc=True),
            "open": quote.get("open"),
            "high": quote.get("high"),
            "low": quote.get("low"),
            "close": quote.get("close"),
            "volume": quote.get("volume"),
        }
    )
    if "adjclose" in adj:
        df["adj_close"] = adj["adjclose"]
    return df

save_fixture async

save_fixture(symbol, path)

Fetch the option chain for symbol and save it as a JSON fixture.

Only the fields read by volatility_surface_loader are kept, so the fixture stays small enough to commit.

If path ends with .gz, the output is gzipped.

PARAMETER	DESCRIPTION
symbol	Underlying ticker symbol TYPE: str
path	File path where to save the fixture TYPE: str | Path

Source code in quantflow/data/yahoo.py

async def save_fixture(
    self,
    symbol: Annotated[str, Doc("Underlying ticker symbol")],
    path: Annotated[str | Path, Doc("File path where to save the fixture")],
) -> Path:
    """Fetch the option chain for `symbol` and save it as a JSON fixture.

    Only the fields read by
    [volatility_surface_loader]
    [quantflow.data.yahoo.Yahoo.volatility_surface_loader]
    are kept, so the fixture stays small enough to commit.

    If `path` ends with `.gz`, the output is gzipped.
    """
    chain = await self.option_chain(symbol)
    contract_keys = ("strike", "bid", "ask", "openInterest", "volume")
    quote_keys = ("bid", "ask", "regularMarketPrice")
    quote = chain.get("quote") or {}
    stripped = {
        "underlyingSymbol": chain.get("underlyingSymbol", symbol),
        "quote": {k: quote[k] for k in quote_keys if k in quote},
        "options": [
            {
                "expirationDate": expiry["expirationDate"],
                "calls": [
                    {k: c[k] for k in contract_keys if k in c}
                    for c in expiry.get("calls", [])
                ],
                "puts": [
                    {k: c[k] for k in contract_keys if k in c}
                    for c in expiry.get("puts", [])
                ],
            }
            for expiry in chain.get("options", [])
        ],
    }
    out = Path(path)
    payload = json.dumps(stripped, indent=2).encode()
    if out.suffix == ".gz":
        out.write_bytes(gzip.compress(payload))
    else:
        out.write_bytes(payload)
    return out

response_data async classmethod

response_data(response)

Source code in quantflow/data/yahoo.py

@classmethod
async def response_data(
    cls, response: HttpResponse
) -> ResponseType:  # pragma: no cover
    if (
        "text/plain" in response.headers["content-type"]
        or "text/html" in response.headers["content-type"]
    ):
        return await response.text()
    return await response.json()