Yuxuan Yan
99 lines
3.0 KiB
Python
99 lines
3.0 KiB
Python
"""Malformed parquet/input tests for phase boundary contracts."""
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from __future__ import annotations
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import pandas as pd
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import pytest
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from pipeline.alpha.compute import compute_alpha
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from pipeline.combo.combine import combine_alphas
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from pipeline.derived.compute import validate_derived_frame
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from pipeline.portfolio.construct import construct_positions
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from pipeline.portfolio.simulator import ReferenceSimulator
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from tests.helpers import make_generated_daily_bars
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def test_alpha_compute_rejects_daily_data_without_close():
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daily = make_generated_daily_bars().drop(columns=["close"])
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with pytest.raises(KeyError, match="close"):
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compute_alpha(daily, "bad", "reversal", lookback=3)
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def test_alpha_feature_path_rejects_duplicate_symbol_dates(tmp_path):
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daily = make_generated_daily_bars()
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feature = pd.DataFrame({
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"symbol_id": ["sh600000", "sh600000"],
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"date": ["2024-01-02 09:30:00", "2024-01-02 15:00:00"],
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"toy_feature": [1.0, 2.0],
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})
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feature_path = tmp_path / "duplicate_feature_keys.pq"
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feature.to_parquet(feature_path, index=False)
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with pytest.raises(ValueError, match="duplicate symbol_id,date"):
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compute_alpha(
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daily,
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"bad_features",
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"reversal",
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lookback=3,
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feature_paths=[str(feature_path)],
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)
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def test_derived_validation_rejects_bool_value_columns():
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derived = pd.DataFrame({
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"symbol_id": ["sh600000"],
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"date": [pd.Timestamp("2024-01-02")],
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"is_good": [True],
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})
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with pytest.raises(ValueError, match="numeric"):
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validate_derived_frame(derived)
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def test_combo_combine_rejects_missing_weight_column(tmp_path):
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bad_alpha = pd.DataFrame({
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"symbol_id": ["sh600000"],
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"date": [pd.Timestamp("2024-01-02")],
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"alpha_name": ["bad"],
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})
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bad_alpha_path = tmp_path / "bad_alpha.pq"
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bad_alpha.to_parquet(bad_alpha_path, index=False)
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with pytest.raises(KeyError, match="weight"):
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combine_alphas([str(bad_alpha_path)], "bad_combo")
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def test_portfolio_build_rejects_weights_without_symbol_id():
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daily = make_generated_daily_bars()
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bad_weights = pd.DataFrame({
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"date": [pd.Timestamp("2024-01-02")],
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"combo_name": ["bad"],
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"weight": [1.0],
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})
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with pytest.raises(KeyError, match="symbol_id"):
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construct_positions(
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bad_weights,
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daily,
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booksize=1_000_000.0,
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portfolio_name="bad_portfolio",
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)
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def test_portfolio_simulate_rejects_positions_without_position_shares():
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daily = make_generated_daily_bars()
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bad_positions = pd.DataFrame({
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"symbol_id": ["sh600000"],
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"date": [pd.Timestamp("2024-01-02")],
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"portfolio_name": ["bad"],
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"target_weight": [1.0],
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"target_value": [1000.0],
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"target_shares": [100.0],
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"position_value": [1000.0],
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"price": [10.0],
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})
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with pytest.raises(KeyError, match="position_shares"):
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ReferenceSimulator().run(bad_positions, daily)
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