hqg-algorithms 0.1.1

Algorithmic trading research utilities for quant teams

hqg-algorithms

Interfaces and helper types for writing HQG trading strategies.

Install

python3 -m pip install --upgrade pip setuptools wheel
pip install hqg-algorithms

Quick start

Subclass Strategy and implement three methods:

from hqg_algorithms import Strategy, Cadence, Slice, PortfolioView, BarSize, CallPhase


class BuyAndRebalance(Strategy):
    def universe(self) -> list[str]:
        return ["SPY", "IEF"]

    def cadence(self) -> Cadence:
        return Cadence(bar_size=BarSize.DAILY, call_phase=CallPhase.ON_BAR_CLOSE)

    def on_data(self, data: Slice, portfolio: PortfolioView) -> dict[str, float] | None:
        return {"SPY": 0.6, "IEF": 0.4}

Method	Purpose
universe()	Symbols the platform loads for this strategy
cadence()	Bar resolution, trigger timing, and execution delay
on_data()	Return target portfolio weights, {} for all cash, or None to skip an update

Slice exposes helpers like data.close("SPY") to read prices. PortfolioView gives read-only access to current equity, cash, positions, and weights.

Example — SMA crossover

from hqg_algorithms import Strategy, Cadence, Slice, PortfolioView, BarSize, CallPhase
from collections import deque


class SimpleSMA(Strategy):
    """Go risk-on when SPY is above its 21-day mean, otherwise hold bonds."""

    def __init__(self):
        self._window = 21
        self._q: deque[float] = deque(maxlen=self._window)

    def universe(self) -> list[str]:
        return ["SPY", "BND"]

    def cadence(self) -> Cadence:
        return Cadence(bar_size=BarSize.DAILY, call_phase=CallPhase.ON_BAR_CLOSE)

    def on_data(self, data: Slice, portfolio: PortfolioView) -> dict[str, float] | None:
        spy_close = data.close("SPY")
        if spy_close is None:
            return None

        self._q.append(spy_close)

        if len(self._q) < self._window:
            return {"BND": 1.0}  # hold bonds while warming up

        sma = sum(self._q) / len(self._q)

        if spy_close > sma:
            return {"SPY": 0.5, "BND": 0.5}  # uptrend
        return {"BND": 1.0}                  # downtrend

Additional docs

• Publishing workflow and release checklist: docs/publishing.md