hqg-algorithms 0.1.0

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

Implement a strategy

Subclass Strategy and implement the three abstract methods the backtester calls.

Example:

from datetime import timedelta
from hqg_algorithms import Strategy, Cadence, Slice, PortfolioView


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

    def cadence(self) -> Cadence:
        return Cadence( 
            bar_size=timedelta(days=1), # default
            call_phase="on_bar_close", # default
            exec_lag_bars=1, # default
        )

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

Key lifecycle methods:

• universe() tells the platform which symbols to load.
• cadence() specifies call frequency, trigger phase, and execution lag.
• on_data(data, portfolio) returns target portfolio weights, {} for all cash, or None to skip an update.

Slice exposes helper methods like slice.close(symbol) to inspect prices, while PortfolioView gives read-only access to current holdings and weights.

Additional docs

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