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Python SDK Reference

Install: pip install hypquant — Python ≥ 3.10, polars ≥ 0.20.

For pandas output: pip install "hypquant[pandas]" (requires pyarrow).

MarketData (synchronous)

from hypquant import MarketData

Constructor

MarketData(
    api_key: str,
    base_url: str = "https://api.hypquant.com",
    timeout: float = 30.0,
)

Use as a context manager (recommended):

with MarketData(api_key="qp_...") as md:
    df = md.ohlcv(...)

Or call .close() explicitly when done.

md.ohlcv()

md.ohlcv(
    symbol: str,
    exchange: str,
    timeframe: str,
    start: str | datetime,
    end: str | datetime | None = None,
    limit: int = 1000,
    as_pandas: bool = False,
) -> pl.DataFrame | pd.DataFrame

Returns OHLCV candles. Columns: time (Datetime UTC), open, high, low, close, volume (all Float64).

Parameters:

Parameter Description
symbol Trading pair, e.g. "BTC-USDC"
exchange "hyperliquid" or "binance"
timeframe "1m", "5m", "15m", "1h", "4h", "1d"
start Start time — ISO8601 string or timezone-aware datetime
end End time. Defaults to now
limit Max rows (1–10,000). Default: 1,000
as_pandas Return pd.DataFrame instead of pl.DataFrame

md.funding()

md.funding(
    symbol: str,
    exchange: str,
    start: str | datetime,
    end: str | datetime | None = None,
    normalized: bool = False,
    as_pandas: bool = False,
) -> pl.DataFrame | pd.DataFrame

Returns funding rates. Columns: time, rate, and optionally rate_normalized, premium.

Parameter Description
normalized If True, add rate_normalized (z-score) and premium (spot/perp basis)

md.features()

md.features(
    symbol: str,
    exchange: str,
    timeframe: str,
    features: list[str],
    start: str | datetime,
    end: str | datetime | None = None,
    as_pandas: bool = False,
) -> pl.DataFrame | pd.DataFrame

Returns wide-format feature values. Columns: time plus one column per requested feature.

Parameter Description
timeframe "1m", "5m", "15m", "1h", "4h", "1d", or "funding"
features List of feature names, e.g. ["rsi_14", "macd", "bb_upper"]

md.multi_asset()

md.multi_asset(
    symbols: list[str],
    exchange: str,
    timeframe: str,
    field: str,
    start: str | datetime,
    end: str | datetime | None = None,
    as_pandas: bool = False,
) -> pl.DataFrame | pd.DataFrame

Returns wide-format data for multiple symbols. Columns: time plus one column per symbol.

Parameter Description
symbols List of trading pairs
field "open", "high", "low", "close", "volume", "funding_rate", or any feature name

md.available_features()

md.available_features() -> list[dict]

Returns the full feature catalogue. Each dict has keys name, description, category.

md.quality()

md.quality(
    symbol: str,
    exchange: str,
    timeframe: str | None = None,
) -> dict

Returns DQS breakdown. See Data Quality API for the response schema.

AsyncMarketData (async)

from hypquant import AsyncMarketData

Constructor

Same signature as MarketData. Use as an async context manager:

async with AsyncMarketData(api_key="qp_...") as md:
    df = await md.ohlcv(...)

Or call await md.aclose() explicitly.

Methods

All methods mirror MarketData with await:

await md.ohlcv(...)
await md.funding(...)
await md.features(...)
await md.multi_asset(...)
await md.available_features()
await md.quality(...)

Exceptions

Exception When Extra attributes
hypquant.HypQuantError Base class — any other HTTP error status_code, request_id
hypquant.AuthenticationError Invalid or missing API key (HTTP 401) —
hypquant.TierLimitError Request exceeds tier limits (HTTP 403) upgrade_url
hypquant.RateLimitError Daily or per-minute quota exhausted (HTTP 429) retry_after_secs
hypquant.SymbolNotFoundError Symbol or resource not found (HTTP 404) —
hypquant.InvalidParameterError Invalid request parameter (HTTP 400) —
hypquant.ServiceUnavailableError Backend temporarily unavailable (HTTP 503) —
ImportError as_pandas=True but pandas/pyarrow not installed — 
from hypquant import MarketData, TierLimitError, RateLimitError, AuthenticationError

try:
    with MarketData(api_key="qp_...") as md:
        df = md.ohlcv("BTC-USDC", exchange="hyperliquid", timeframe="1m", start="2024-01-01")
except TierLimitError as e:
    print(f"Upgrade needed: {e}")
    print(f"Upgrade at: {e.upgrade_url}")
except RateLimitError as e:
    print(f"Rate limited, retry after: {e.retry_after_secs}s")
except AuthenticationError:
    print("Invalid API key — check your credentials")