Raise coverage threshold to 95% and expand test coverage
- pyproject.toml: fail_under 80 → 95 - test_alpha: +79 lines - test_cli_workflow: +226 lines - test_derived: +121 lines - test_downloader_contracts: +169 lines - test_features: +16 lines - test_minute_downloader: +81 lines - test_portfolio: +208 lines
This commit is contained in:
Yuxuan Yan
@@ -5,10 +5,13 @@ from __future__ import annotations
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import textwrap
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from pathlib import Path
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import click
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import pandas as pd
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from click.testing import CliRunner
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from cli import cli
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import pipeline.derived.cli as derived_cli
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import pipeline.features.cli as features_cli
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from tests.helpers import (
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make_generated_daily_bars,
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make_generated_derived_features,
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@@ -426,6 +429,61 @@ def test_cli_error_paths_are_clear_for_bad_user_inputs(tmp_path):
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])
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assert "Symbol 'sh999999' not found" in alphaview_missing_symbol.output
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alphaview_missing_column = _invoke_error(runner, [
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"alphaview",
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"--data-path", str(daily_path),
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"--alpha-path", str(positions_path),
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"--symbol", "sh600000",
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"--columns", "close,missing_bar_col",
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])
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assert "Bar columns not found: missing_bar_col" in alphaview_missing_column.output
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alphaview_alpha = pd.DataFrame({
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"symbol_id": ["sh600000"],
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"date": [daily_bars["date"].min()],
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"alpha_name": ["toy_alpha"],
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"weight": [1.0],
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})
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alphaview_alpha_path = tmp_path / "alphaview_alpha.pq"
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alphaview_alpha.to_parquet(alphaview_alpha_path, index=False)
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alphaview_empty_range = _invoke_error(runner, [
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"alphaview",
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"--data-path", str(daily_path),
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"--alpha-path", str(alphaview_alpha_path),
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"--symbol", "sh600000",
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"--start-date", "2030-01-01",
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])
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assert "No rows in the requested date range" in alphaview_empty_range.output
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empty_combo_paths = _invoke_ok(runner, [
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"combo", "combine",
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"--alpha-paths", " , ",
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"--combo-name", "empty",
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"--output-dir", str(tmp_path / "combos"),
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])
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assert "requires at least 1 path" in empty_combo_paths.output
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def test_cli_parser_helpers_cover_string_coercion_and_bad_params():
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assert derived_cli._parse_params(("n=7", "scale=2.5", "label=demo")) == {
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"n": 7,
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"scale": 2.5,
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"label": "demo",
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}
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assert features_cli._parse_params(("n=7", "scale=2.5", "label=demo")) == {
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"n": 7,
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"scale": 2.5,
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"label": "demo",
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}
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for module in (derived_cli, features_cli):
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try:
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module._parse_params(("not-an-assignment",))
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except click.BadParameter as exc:
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assert "--param must be name=value" in str(exc)
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else:
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raise AssertionError("expected BadParameter")
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def test_cli_list_and_legacy_feature_paths(tmp_path):
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runner = CliRunner()
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@@ -519,6 +577,174 @@ def test_cli_list_and_legacy_feature_paths(tmp_path):
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assert "Unknown feature-type" in unknown_feature.output
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def test_cli_shortcuts_and_external_module_loading(tmp_path):
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runner = CliRunner()
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daily_bars = make_generated_daily_bars(n_sessions=8, include_missing=False)
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minute_bars = make_generated_minute_bars(daily_bars)
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daily_path = tmp_path / "daily_bars.pq"
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minute_path = tmp_path / "minute_bars.pq"
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daily_bars.to_parquet(daily_path, index=False)
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minute_bars.to_parquet(minute_path, index=False)
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alpha_module = tmp_path / "listed_alpha.py"
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alpha_module.write_text(textwrap.dedent("""
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import pandas as pd
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from pipeline.alpha.base import BaseAlpha
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from pipeline.alpha.registry import register_alpha
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@register_alpha
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class ListedAlpha(BaseAlpha):
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name = "listed_alpha_cli"
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def __init__(self, scale: float = 1.0, label: str = "x"):
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self.scale = scale
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self.label = label
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def signal(self, close: pd.DataFrame) -> pd.DataFrame:
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return close.pct_change(1, fill_method=None) * self.scale
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"""))
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derived_module = tmp_path / "listed_derived.py"
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derived_module.write_text(textwrap.dedent("""
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import pandas as pd
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from pipeline.derived.base import BaseDerivedData
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from pipeline.derived.registry import register_derived
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@register_derived
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class ListedDerived(BaseDerivedData):
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name = "listed_derived_cli"
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def __init__(self, scale: float = 1.0):
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self.scale = scale
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def compute(self, daily=None, minute=None) -> pd.DataFrame:
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out = daily[["symbol_id", "date", "close"]].copy()
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out["listed_value"] = out.pop("close") * self.scale
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return out
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"""))
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derived_compute_module = tmp_path / "computed_derived.py"
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derived_compute_module.write_text(textwrap.dedent("""
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import pandas as pd
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from pipeline.derived.base import BaseDerivedData
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from pipeline.derived.registry import register_derived
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@register_derived
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class ComputedDerived(BaseDerivedData):
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name = "computed_derived_cli"
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def __init__(self, scale: float = 1.0):
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self.scale = scale
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def compute(self, daily=None, minute=None) -> pd.DataFrame:
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out = daily[["symbol_id", "date", "close"]].copy()
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out["computed_value"] = out.pop("close") * self.scale
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return out
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"""))
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feature_module = tmp_path / "listed_feature.py"
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feature_module.write_text(textwrap.dedent("""
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import pandas as pd
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from pipeline.features.base import BaseFeature
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from pipeline.features.registry import register_feature
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@register_feature
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class ListedFeature(BaseFeature):
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name = "listed_feature_cli"
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def compute(self, daily=None, minute=None) -> pd.DataFrame:
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out = minute[["symbol_id", "date", "close"]].copy()
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out["date"] = pd.to_datetime(out["date"]).dt.normalize()
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out = out.groupby(["symbol_id", "date"], as_index=False)["close"].mean()
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return out.rename(columns={"close": "listed_feature_value"})
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"""))
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feature_compute_module = tmp_path / "computed_feature.py"
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feature_compute_module.write_text(textwrap.dedent("""
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import pandas as pd
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from pipeline.features.base import BaseFeature
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from pipeline.features.registry import register_feature
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@register_feature
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class ComputedFeature(BaseFeature):
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name = "computed_feature_cli"
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def compute(self, daily=None, minute=None) -> pd.DataFrame:
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out = minute[["symbol_id", "date", "close"]].copy()
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out["date"] = pd.to_datetime(out["date"]).dt.normalize()
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out = out.groupby(["symbol_id", "date"], as_index=False)["close"].mean()
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return out.rename(columns={"close": "computed_feature_value"})
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"""))
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alpha_list = _invoke_ok(runner, [
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"alpha", "list",
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"--alpha-module", str(alpha_module),
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])
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assert "listed_alpha_cli" in alpha_list.output
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alpha_dir = tmp_path / "alphas"
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reversal = _invoke_ok(runner, [
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"alpha", "reversal",
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"--data-path", str(daily_path),
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"--output-dir", str(alpha_dir),
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"--lookback", "3",
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])
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reversal_vol = _invoke_ok(runner, [
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"alpha", "reversal-vol",
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"--data-path", str(daily_path),
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"--output-dir", str(alpha_dir),
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"--lookback", "3",
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"--vol-window", "3",
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])
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external_alpha = _invoke_ok(runner, [
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"alpha", "compute",
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"--data-path", str(daily_path),
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"--alpha-type", "listed_alpha_cli",
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"--alpha-name", "listed_alpha_run",
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"--param", "scale=2.5",
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"--param", "label=demo",
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"--output-dir", str(alpha_dir),
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])
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assert "Saved alpha:" in reversal.output
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assert "Saved alpha:" in reversal_vol.output
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assert "Saved alpha:" in external_alpha.output
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assert (alpha_dir / "reversal_3d.pq").exists()
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assert (alpha_dir / "reversal_vol_3d_3d.pq").exists()
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assert (alpha_dir / "listed_alpha_run.pq").exists()
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derived_list = _invoke_ok(runner, [
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"derived", "list",
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"--derived-module", str(derived_module),
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])
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assert "listed_derived_cli" in derived_list.output
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derived_dir = tmp_path / "derived_external"
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derived_compute = _invoke_ok(runner, [
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"derived", "compute",
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"--daily-path", str(daily_path),
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"--derived-module", str(derived_compute_module),
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"--derived-type", "computed_derived_cli",
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"--derived-name", "listed_derived_run",
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"--param", "scale=3",
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"--output-dir", str(derived_dir),
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])
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assert "Saved derived data:" in derived_compute.output
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assert (derived_dir / "listed_derived_run.pq").exists()
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feature_list = _invoke_ok(runner, [
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"feature", "list",
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"--feature-module", str(feature_module),
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])
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assert "listed_feature_cli" in feature_list.output
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feature_dir = tmp_path / "features_external"
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feature_compute = _invoke_ok(runner, [
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"feature", "compute",
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"--minute-path", str(minute_path),
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"--feature-module", str(feature_compute_module),
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"--feature-type", "computed_feature_cli",
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"--feature-name", "listed_feature_run",
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"--output-dir", str(feature_dir),
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])
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assert "Saved feature:" in feature_compute.output
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assert (feature_dir / "listed_feature_run.pq").exists()
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def test_cli_pqcat_row_modes(tmp_path):
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runner = CliRunner()
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daily_bars = make_generated_daily_bars(n_sessions=3, include_missing=False)
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