okx-codex-trader/tests/test_search_cross_symbol_high_frequency_portfolios.py

import importlib.util
import sys
from pathlib import Path

import pytest


def load_module():
    path = Path(__file__).resolve().parents[1] / "scripts" / "search_cross_symbol_high_frequency_portfolios.py"
    spec = importlib.util.spec_from_file_location("search_cross_symbol_high_frequency_portfolios", path)
    assert spec is not None
    module = importlib.util.module_from_spec(spec)
    assert spec.loader is not None
    sys.modules[spec.name] = module
    spec.loader.exec_module(module)
    return module


def test_add_cost_columns_records_net_metrics():
    module = load_module()
    row = {
        "total_return": 0.12,
        "annualized_return": 0.04,
        "max_drawdown": 0.20,
        "calmar": 0.20,
    }

    module.add_cost_columns(row, "maker_taker", 0.0021)

    assert row["cost_model"] == "maker_taker"
    assert row["roundtrip_cost_on_margin"] == pytest.approx(0.0021)
    assert row["net_total_return"] == pytest.approx(0.12)
    assert row["net_annualized_return"] == pytest.approx(0.04)
    assert row["net_max_drawdown"] == pytest.approx(0.20)
    assert row["net_calmar"] == pytest.approx(0.20)


def test_risk_qualified_keeps_recent_horizons_per_cost_model():
    module = load_module()
    pandas = pytest.importorskip("pandas")
    rows = []
    for cost_model, ret_1y in (("maker_taker", 0.11), ("taker_taker", 0.07)):
        for horizon, total_return in (("full", 0.30), ("3y", 0.20), ("1y", ret_1y), ("6m", 0.08), ("3m", 0.06)):
            rows.append(
                {
                    "cost_model": cost_model,
                    "portfolio": "test-portfolio",
                    "horizon": horizon,
                    "total_return": total_return,
                    "annualized_return": 0.05,
                    "max_drawdown": 0.10,
                    "calmar": 0.50,
                    "trades_per_month": 20.0,
                }
            )

    qualified = module.risk_qualified(pandas.DataFrame(rows)).sort_values("cost_model").reset_index(drop=True)

    assert qualified["cost_model"].tolist() == ["maker_taker", "taker_taker"]
    assert qualified.loc[0, "ret_1y"] == pytest.approx(0.11)
    assert qualified.loc[1, "ret_1y"] == pytest.approx(0.07)


def test_robust_survivors_requires_positive_all_horizons_and_calmar():
    module = load_module()
    pandas = pytest.importorskip("pandas")
    rows = []
    for portfolio, ret_3m in (("survivor", 0.04), ("reject", -0.01)):
        for horizon, total_return in (("full", 0.30), ("3y", 0.20), ("1y", 0.10), ("6m", 0.08), ("3m", ret_3m)):
            rows.append(
                {
                    "cost_model": "maker_taker",
                    "portfolio": portfolio,
                    "horizon": horizon,
                    "total_return": total_return,
                    "annualized_return": 0.05,
                    "max_drawdown": 0.10,
                    "calmar": 0.50,
                    "trades_per_month": 20.0,
                }
            )

    survivors = module.robust_survivors(pandas.DataFrame(rows))

    assert survivors["portfolio"].tolist() == ["survivor"]