103 lines
2.8 KiB
Python
103 lines
2.8 KiB
Python
from __future__ import annotations
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import zipfile
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from scripts import run_fundamentals_research as runner
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def _window(name, sharpe, drawdown):
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return {
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"window": name,
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"sharpe": sharpe,
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"sharpe_se": 0.2,
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"dsr": 0.8,
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"cagr_pct": 10.0,
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"max_drawdown_pct": drawdown,
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"calmar": 1.0,
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"trades": 20,
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}
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def test_arm_matrix_is_bounded_and_pre_registered():
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assert runner.N_TRIALS == 13
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assert runner.ARMS[0]["id"] == "control_w00"
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assert {arm["weight"] for arm in runner.ARMS[1:]} == {0.1, 0.2, 0.3, 0.4}
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assert {arm["composite"] for arm in runner.ARMS[1:]} == {
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"quality",
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"growth",
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"balanced",
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}
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def test_split_safe_matrix_is_smaller_and_trial_corrected():
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assert runner.SPLIT_SAFE_N_TRIALS == 10
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assert runner.SPLIT_SAFE_ARMS[0]["id"] == "control_w00"
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assert {arm["weight"] for arm in runner.SPLIT_SAFE_ARMS[1:]} == {
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0.05,
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0.10,
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0.15,
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}
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assert {arm["composite"] for arm in runner.SPLIT_SAFE_ARMS[1:]} == {
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"quality",
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"growth",
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"balanced",
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}
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def test_split_safe_output_has_a_distinct_name():
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assert runner._default_out(runner.SPLIT_SAFE_PROTOCOL).name.startswith(
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"fundamentals-splitsafe-"
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)
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def test_development_grade_does_not_read_test_window():
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control = {
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"windows": [
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_window("train", 1.0, 10.0),
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_window("validation", 1.0, 10.0),
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_window("test", 9.0, 1.0),
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]
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}
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arm = {
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"windows": [
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_window("train", 1.1, 10.0),
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_window("validation", 1.2, 11.0),
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_window("test", -9.0, 90.0),
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]
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}
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assert runner._development_grade(control, arm)["pass"] is True
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def test_output_bundle_is_self_contained(tmp_path):
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report = {
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"generated_at": "2026-07-23T00:00:00Z",
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"splits": {"train_end": "2024-01-01", "test_start": "2025-01-01"},
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"n_trials": 13,
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"warnings": ["survivorship bias"],
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"factor_ic": {"full": []},
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"arms": [],
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"development_selection": None,
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"final_check": None,
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}
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output = tmp_path / "result.json"
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runner._write_outputs(report, output, bundle=True)
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with zipfile.ZipFile(output.with_suffix(".zip")) as archive:
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names = set(archive.namelist())
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assert {
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"result.json",
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"result.md",
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"result-arms.csv",
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"result-factor-ic.csv",
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"result-trades.csv",
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} <= names
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def test_winner_concentration_exposes_top_five_dependence():
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details = [
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{"pnl": value, "r": value / 10} for value in (100, 90, 80, 70, 60, -10, -20)
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]
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result = runner._winner_concentration(details)
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assert result["top5_pnl"] == 400
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assert result["net_pnl_ex_top5"] == -30
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assert result["avg_r_ex_top5"] == -1.5
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