mirror of
https://github.com/NicolasBohn/NexQuant.git
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TPTBusiness
0d9b0916f2
- RiskMgmt OOS: 88 tests (leverage bounds, DD limits, trade counting, MC p-value, daily breach) - Kronos adapter: 73 tests (OHLCV idempotence, batch/sequential equivalence, forward-fill) - Auto-fixer: 78 tests (fix idempotence, MultiIndex conversion, fuzzing random patterns) - Factor coder: 65 tests (FactorTask roundtrip, evaluator invariants, workspace paths) - QLib pipeline: 61 tests (Metrics, bandit, precision matrices, noise_var) - Integration: 69 tests (portfolio weights, correlation, RiskMgmt limits, JSON roundtrip)
969 lines
38 KiB
Python
969 lines
38 KiB
Python
"""Tests for qlib pipeline — feedback, bandit, quant_loop_factory."""
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from __future__ import annotations
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import sys
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import tempfile
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from pathlib import Path
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from unittest.mock import MagicMock, patch
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import numpy as np
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import pandas as pd
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import pytest
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PROJECT_ROOT = Path(__file__).parent.parent.parent
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sys.path.insert(0, str(PROJECT_ROOT))
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# =============================================================================
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# process_results (feedback.py)
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# =============================================================================
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class TestProcessResults:
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def test_process_results_handles_named_series(self):
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"""process_results renames column "0" to "Current Result" — this works
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when the Series name is '0' (string), which matches the rename dict."""
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from rdagent.scenarios.qlib.developer.feedback import process_results
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import pandas as pd
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# process_results expects the Series to produce a DataFrame column named "0" (string)
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# This happens when the Series has name '0'
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current = pd.Series(
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{"IC": 0.05, "1day.excess_return_with_cost.annualized_return": 0.12,
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"1day.excess_return_with_cost.max_drawdown": -0.08},
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name="0",
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)
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sota = pd.Series(
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{"IC": 0.03, "1day.excess_return_with_cost.annualized_return": 0.10,
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"1day.excess_return_with_cost.max_drawdown": -0.05},
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name="0",
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)
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result = process_results(current, sota)
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assert "IC of Current Result is" in result
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assert "of SOTA Result is" in result
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def test_raises_on_missing_metrics(self):
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from rdagent.scenarios.qlib.developer.feedback import process_results
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current = pd.Series({"IC": 0.05})
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sota = pd.Series({"IC": 0.03})
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with pytest.raises(KeyError):
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process_results(current, sota)
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# =============================================================================
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# bandit.py — Metrics and extract_metrics_from_experiment
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# =============================================================================
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class TestBanditMetrics:
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def test_default_values_are_zero(self):
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from rdagent.scenarios.qlib.proposal.bandit import Metrics
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m = Metrics()
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assert m.ic == 0.0
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assert m.sharpe == 0.0
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assert m.mdd == 0.0
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def test_as_vector_length(self):
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from rdagent.scenarios.qlib.proposal.bandit import Metrics
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m = Metrics(ic=0.1, sharpe=1.5)
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v = m.as_vector()
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assert len(v) == 8
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assert v[0] == 0.1
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assert v[7] == 1.5
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def test_mdd_negated_in_vector(self):
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from rdagent.scenarios.qlib.proposal.bandit import Metrics
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m = Metrics(mdd=0.15)
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v = m.as_vector()
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assert v[6] == -0.15 # -self.mdd
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def test_extract_metrics_from_experiment(self):
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from rdagent.scenarios.qlib.proposal.bandit import extract_metrics_from_experiment
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mock_exp = MagicMock()
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mock_exp.result = {
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"IC": 0.04,
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"ICIR": 0.5,
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"Rank IC": 0.03,
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"Rank ICIR": 0.4,
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"1day.excess_return_with_cost.annualized_return ": 0.10,
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"1day.excess_return_with_cost.information_ratio": 0.6,
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"1day.excess_return_with_cost.max_drawdown": -0.12,
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}
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m = extract_metrics_from_experiment(mock_exp)
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assert m.ic == 0.04
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assert m.rank_ic == 0.03
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assert m.mdd == -0.12
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def test_extract_metrics_returns_default_on_error(self):
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from rdagent.scenarios.qlib.proposal.bandit import extract_metrics_from_experiment
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mock_exp = MagicMock()
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mock_exp.result = None # Will cause AttributeError
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m = extract_metrics_from_experiment(mock_exp)
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assert m.ic == 0.0
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assert m.sharpe == 0.0
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def test_sharpe_computation(self):
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from rdagent.scenarios.qlib.proposal.bandit import extract_metrics_from_experiment
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mock_exp = MagicMock()
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mock_exp.result = {
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"IC": 0.0, "ICIR": 0.0, "Rank IC": 0.0, "Rank ICIR": 0.0,
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"1day.excess_return_with_cost.annualized_return ": 0.15,
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"1day.excess_return_with_cost.information_ratio": 0.0,
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"1day.excess_return_with_cost.max_drawdown": -0.10,
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}
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m = extract_metrics_from_experiment(mock_exp)
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assert m.sharpe == pytest.approx(1.5) # 0.15 / 0.10
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# =============================================================================
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# LinearThompsonTwoArm
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# =============================================================================
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class TestLinearThompsonTwoArm:
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def test_initialization(self):
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from rdagent.scenarios.qlib.proposal.bandit import LinearThompsonTwoArm
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bandit = LinearThompsonTwoArm(dim=5)
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assert bandit.dim == 5
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assert bandit.noise_var == 1.0
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assert bandit.mean["factor"].shape == (5,)
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assert bandit.mean["model"].shape == (5,)
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assert bandit.precision["factor"].shape == (5, 5)
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def test_sample_reward_returns_float(self):
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from rdagent.scenarios.qlib.proposal.bandit import LinearThompsonTwoArm
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bandit = LinearThompsonTwoArm(dim=3)
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x = np.ones(3)
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reward = bandit.sample_reward("factor", x)
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assert isinstance(reward, float)
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def test_arms_are_initialized_identically(self):
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from rdagent.scenarios.qlib.proposal.bandit import LinearThompsonTwoArm
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bandit = LinearThompsonTwoArm(dim=4)
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assert np.array_equal(bandit.mean["factor"], bandit.mean["model"])
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assert np.array_equal(bandit.precision["factor"], bandit.precision["model"])
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# =============================================================================
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# quant_loop_factory.py
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# =============================================================================
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class TestHasLocalComponents:
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def test_returns_bool(self):
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from rdagent.scenarios.qlib.quant_loop_factory import has_local_components
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result = has_local_components()
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assert isinstance(result, bool)
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def test_returns_false_with_no_local_dir(self, monkeypatch):
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from rdagent.scenarios.qlib import quant_loop_factory
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monkeypatch.setattr(quant_loop_factory.Path, "exists", lambda self: False)
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assert quant_loop_factory.has_local_components() is False
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class TestCountValidFactors:
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def test_returns_zero_when_no_dir(self):
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from rdagent.scenarios.qlib.quant_loop_factory import count_valid_factors
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with patch("rdagent.scenarios.qlib.quant_loop_factory.Path.exists", return_value=False):
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assert count_valid_factors() == 0
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def test_returns_int(self):
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from rdagent.scenarios.qlib.quant_loop_factory import count_valid_factors
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result = count_valid_factors()
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assert isinstance(result, int)
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assert result >= 0
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class TestAdvancedLoopThreshold:
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def test_constant_is_defined(self):
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from rdagent.scenarios.qlib.quant_loop_factory import ADVANCED_LOOP_FACTOR_THRESHOLD
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assert ADVANCED_LOOP_FACTOR_THRESHOLD == 5000
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# ==============================================================================
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# HYPOTHESIS-BASED PROPERTY TESTS — Data Pipeline Transformations,
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# Bandit Properties, Feedback Consistency
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# ==============================================================================
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from hypothesis import given, settings, strategies as st
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import numpy as np
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import pandas as pd
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from rdagent.scenarios.qlib.developer.feedback import process_results
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from rdagent.scenarios.qlib.proposal.bandit import (
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Metrics,
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extract_metrics_from_experiment,
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LinearThompsonTwoArm,
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)
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from rdagent.scenarios.qlib.quant_loop_factory import (
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has_local_components,
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count_valid_factors,
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ADVANCED_LOOP_FACTOR_THRESHOLD,
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)
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# ---------------------------------------------------------------------------
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# Property 1: process_results Invariants
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# ---------------------------------------------------------------------------
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class TestProcessResultsInvariants:
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"""Property: process_results output invariants."""
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REQUIRED_METRICS = [
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"IC",
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"1day.excess_return_with_cost.annualized_return",
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"1day.excess_return_with_cost.max_drawdown",
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]
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@given(
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ic=st.floats(min_value=-1.0, max_value=1.0),
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ann_return=st.floats(min_value=-2.0, max_value=5.0),
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max_dd=st.floats(min_value=-1.0, max_value=0.0),
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sota_ic=st.floats(min_value=-1.0, max_value=1.0),
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sota_ann_return=st.floats(min_value=-2.0, max_value=5.0),
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sota_max_dd=st.floats(min_value=-1.0, max_value=0.0),
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)
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@settings(max_examples=50, deadline=10000)
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def test_process_results_contains_all_metrics(
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self, ic, ann_return, max_dd, sota_ic, sota_ann_return, sota_max_dd
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):
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"""Property: output string contains IC, annualized_return, and max_drawdown."""
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current = pd.Series({
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"IC": ic,
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"1day.excess_return_with_cost.annualized_return": ann_return,
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"1day.excess_return_with_cost.max_drawdown": max_dd,
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}, name="0")
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sota = pd.Series({
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"IC": sota_ic,
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"1day.excess_return_with_cost.annualized_return": sota_ann_return,
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"1day.excess_return_with_cost.max_drawdown": sota_max_dd,
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}, name="0")
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result = process_results(current, sota)
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assert "IC of Current Result is" in result
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assert "of SOTA Result is" in result
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assert f"{ic:.6f}" in result or "nan" in result.lower()
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@given(
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ic=st.floats(min_value=-1.0, max_value=1.0),
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ann_return=st.floats(min_value=-2.0, max_value=5.0),
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max_dd=st.floats(min_value=-1.0, max_value=0.0),
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)
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@settings(max_examples=50, deadline=10000)
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def test_process_results_returns_string(self, ic, ann_return, max_dd):
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"""Property: process_results returns a string."""
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current = pd.Series({
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"IC": ic,
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"1day.excess_return_with_cost.annualized_return": ann_return,
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"1day.excess_return_with_cost.max_drawdown": max_dd,
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}, name="0")
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sota = pd.Series({
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"IC": 0.0,
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"1day.excess_return_with_cost.annualized_return": 0.0,
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"1day.excess_return_with_cost.max_drawdown": 0.0,
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}, name="0")
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result = process_results(current, sota)
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assert isinstance(result, str)
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assert len(result) > 0
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@given(
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ic=st.floats(min_value=-1.0, max_value=1.0),
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ann_return=st.floats(min_value=-2.0, max_value=5.0),
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max_dd=st.floats(min_value=-1.0, max_value=0.0),
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)
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@settings(max_examples=50, deadline=10000)
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def test_process_results_raises_on_missing_metrics(self, ic, ann_return, max_dd):
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"""Property: process_results raises KeyError on missing required metrics."""
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current = pd.Series({"IC": ic}, name="0")
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sota = pd.Series({"IC": 0.0}, name="0")
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with pytest.raises(KeyError):
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process_results(current, sota)
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@given(
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ic=st.floats(min_value=-1.0, max_value=1.0),
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ann_return=st.floats(min_value=-2.0, max_value=5.0),
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max_dd=st.floats(min_value=-1.0, max_value=0.0),
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)
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@settings(max_examples=50, deadline=10000)
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def test_process_results_format_consistent(self, ic, ann_return, max_dd):
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"""Property: output format is '<metric> of Current Result is <val>, of SOTA Result is <val>'."""
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current = pd.Series({
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"IC": ic,
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"1day.excess_return_with_cost.annualized_return": ann_return,
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"1day.excess_return_with_cost.max_drawdown": max_dd,
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}, name="0")
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sota = pd.Series({
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"IC": 0.0,
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"1day.excess_return_with_cost.annualized_return": 0.0,
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"1day.excess_return_with_cost.max_drawdown": 0.0,
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}, name="0")
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result = process_results(current, sota)
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assert "of Current Result is" in result
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assert "of SOTA Result is" in result
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# Results separated by '; '
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assert ";" in result
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# -----------------------------------------------------------------------
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# Property 2: Metrics Default Values
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# -----------------------------------------------------------------------
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class TestMetricsDefaults:
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"""Property: Metrics default values are zero."""
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@given(
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ic=st.floats(min_value=-1.0, max_value=1.0),
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sharpe=st.floats(min_value=-5.0, max_value=10.0),
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rank_ic=st.floats(min_value=-1.0, max_value=1.0),
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)
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@settings(max_examples=50, deadline=10000)
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def test_partial_construction_defaults_to_zero(self, ic, sharpe, rank_ic):
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"""Property: fields not specified default to 0.0."""
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m = Metrics(ic=ic, sharpe=sharpe, rank_ic=rank_ic)
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assert m.ic == ic
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assert m.sharpe == sharpe
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assert m.rank_ic == rank_ic
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assert m.icir == 0.0
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assert m.rank_icir == 0.0
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assert m.mdd == 0.0
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@given(
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icir=st.floats(min_value=-2.0, max_value=10.0),
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rank_icir=st.floats(min_value=-2.0, max_value=10.0),
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mdd=st.floats(min_value=-1.0, max_value=0.0),
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)
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@settings(max_examples=50, deadline=10000)
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def test_three_fields_default_others_zero(self, icir, rank_icir, mdd):
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"""Property: only given fields set, others zero."""
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m = Metrics(icir=icir, rank_icir=rank_icir, mdd=mdd)
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assert m.ic == 0.0
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assert m.sharpe == 0.0
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assert m.rank_ic == 0.0
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assert m.icir == icir
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assert m.rank_icir == rank_icir
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assert m.mdd == mdd
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def test_all_defaults_zero(self):
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"""Property: default constructor sets everything to zero."""
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m = Metrics()
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assert m.ic == 0.0
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assert m.sharpe == 0.0
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assert m.mdd == 0.0
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assert m.icir == 0.0
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assert m.rank_ic == 0.0
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assert m.rank_icir == 0.0
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# ---------------------------------------------------------------------------
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# Property 3: Metrics as_vector
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# ---------------------------------------------------------------------------
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class TestMetricsAsVector:
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"""Property: as_vector invariants."""
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@given(
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ic=st.floats(min_value=-1.0, max_value=1.0),
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icir=st.floats(min_value=-2.0, max_value=10.0),
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rank_ic=st.floats(min_value=-1.0, max_value=1.0),
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rank_icir=st.floats(min_value=-2.0, max_value=10.0),
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ann_return=st.floats(min_value=-2.0, max_value=5.0),
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ir=st.floats(min_value=-5.0, max_value=10.0),
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mdd=st.floats(min_value=-1.0, max_value=0.0),
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sharpe=st.floats(min_value=-5.0, max_value=10.0),
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)
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@settings(max_examples=50, deadline=10000)
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def test_as_vector_length_is_8(self, ic, icir, rank_ic, rank_icir, ann_return, ir, mdd, sharpe):
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"""Property: as_vector always returns length-8 array."""
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m = Metrics(
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ic=ic, icir=icir, rank_ic=rank_ic, rank_icir=rank_icir,
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arr=ann_return, ir=ir, mdd=mdd, sharpe=sharpe,
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)
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v = m.as_vector()
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assert len(v) == 8
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@given(
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ic=st.floats(min_value=-1.0, max_value=1.0),
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icir=st.floats(min_value=-2.0, max_value=10.0),
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rank_ic=st.floats(min_value=-1.0, max_value=1.0),
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rank_icir=st.floats(min_value=-2.0, max_value=10.0),
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ann_return=st.floats(min_value=-2.0, max_value=5.0),
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ir=st.floats(min_value=-5.0, max_value=10.0),
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mdd=st.floats(min_value=-1.0, max_value=0.0),
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sharpe=st.floats(min_value=-5.0, max_value=10.0),
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)
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@settings(max_examples=50, deadline=10000)
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def test_as_vector_matches_input_order(self, ic, icir, rank_ic, rank_icir, ann_return, ir, mdd, sharpe):
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"""Property: vector elements match (ic, icir, rank_ic, rank_icir, ann_return, ir, -mdd, sharpe)."""
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m = Metrics(
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ic=ic, icir=icir, rank_ic=rank_ic, rank_icir=rank_icir,
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arr=ann_return, ir=ir, mdd=mdd, sharpe=sharpe,
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)
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v = m.as_vector()
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assert v[0] == ic
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assert v[1] == icir
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assert v[2] == rank_ic
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assert v[3] == rank_icir
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assert v[4] == ann_return
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assert v[5] == ir
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assert v[6] == -mdd # negated
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assert v[7] == sharpe
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@given(
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mdd=st.floats(min_value=-1.0, max_value=0.0),
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)
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@settings(max_examples=50, deadline=10000)
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def test_mdd_negated_in_vector(self, mdd):
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"""Property: mdd is negated in as_vector output (v[6] = -mdd)."""
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m = Metrics(mdd=mdd)
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v = m.as_vector()
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assert v[6] == -mdd
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@given(
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ic=st.floats(min_value=-1.0, max_value=1.0),
|
|
icir=st.floats(min_value=-2.0, max_value=10.0),
|
|
rank_ic=st.floats(min_value=-1.0, max_value=1.0),
|
|
rank_icir=st.floats(min_value=-2.0, max_value=10.0),
|
|
ann_return=st.floats(min_value=-2.0, max_value=5.0),
|
|
ir=st.floats(min_value=-5.0, max_value=10.0),
|
|
mdd=st.floats(min_value=-1.0, max_value=0.0),
|
|
sharpe=st.floats(min_value=-5.0, max_value=10.0),
|
|
)
|
|
@settings(max_examples=50, deadline=10000)
|
|
def test_as_vector_returns_numpy_array(self, ic, icir, rank_ic, rank_icir, ann_return, ir, mdd, sharpe):
|
|
"""Property: as_vector returns np.ndarray."""
|
|
m = Metrics(
|
|
ic=ic, icir=icir, rank_ic=rank_ic, rank_icir=rank_icir,
|
|
arr=ann_return, ir=ir, mdd=mdd, sharpe=sharpe,
|
|
)
|
|
v = m.as_vector()
|
|
assert isinstance(v, np.ndarray)
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Property 4: extract_metrics_from_experiment
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
class TestExtractMetrics:
|
|
"""Property: extract_metrics_from_experiment invariants."""
|
|
|
|
@given(
|
|
ic=st.floats(min_value=-1.0, max_value=1.0),
|
|
icir=st.floats(min_value=-2.0, max_value=10.0),
|
|
rank_ic=st.floats(min_value=-1.0, max_value=1.0),
|
|
rank_icir=st.floats(min_value=-2.0, max_value=10.0),
|
|
ann_return=st.floats(min_value=-2.0, max_value=5.0),
|
|
ir=st.floats(min_value=-5.0, max_value=10.0),
|
|
mdd=st.floats(min_value=-1.0, max_value=0.0),
|
|
)
|
|
@settings(max_examples=50, deadline=10000)
|
|
def test_extract_metrics_correct_values(self, ic, icir, rank_ic, rank_icir, ann_return, ir, mdd):
|
|
"""Property: extract_metrics_from_experiment reads correct values from result dict."""
|
|
mock_exp = MagicMock()
|
|
mock_exp.result = {
|
|
"IC": ic, "ICIR": icir,
|
|
"Rank IC": rank_ic, "Rank ICIR": rank_icir,
|
|
"1day.excess_return_with_cost.annualized_return ": ann_return,
|
|
"1day.excess_return_with_cost.information_ratio": ir,
|
|
"1day.excess_return_with_cost.max_drawdown": mdd,
|
|
}
|
|
m = extract_metrics_from_experiment(mock_exp)
|
|
assert m.ic == ic
|
|
assert m.rank_ic == rank_ic
|
|
assert m.icir == icir
|
|
assert m.rank_icir == rank_icir
|
|
assert m.mdd == mdd
|
|
|
|
@given(
|
|
ann_return=st.floats(min_value=0.01, max_value=2.0),
|
|
mdd=st.floats(min_value=-0.01, max_value=-0.001),
|
|
)
|
|
@settings(max_examples=50, deadline=10000)
|
|
def test_sharpe_computed_from_ann_return_and_mdd(self, ann_return, mdd):
|
|
"""Property: sharpe ≈ ann_return / |mdd| for standard inputs."""
|
|
mock_exp = MagicMock()
|
|
mock_exp.result = {
|
|
"IC": 0.0, "ICIR": 0.0,
|
|
"Rank IC": 0.0, "Rank ICIR": 0.0,
|
|
"1day.excess_return_with_cost.annualized_return ": ann_return,
|
|
"1day.excess_return_with_cost.information_ratio": 0.0,
|
|
"1day.excess_return_with_cost.max_drawdown": mdd,
|
|
}
|
|
m = extract_metrics_from_experiment(mock_exp)
|
|
expected_sharpe = ann_return / abs(mdd)
|
|
assert m.sharpe == pytest.approx(expected_sharpe, rel=0.01)
|
|
|
|
@given(seed=st.integers(min_value=0, max_value=100))
|
|
@settings(max_examples=50, deadline=10000)
|
|
def test_extract_returns_default_on_none_result(self, seed):
|
|
"""Property: returns default Metrics (all zeros) when result is None."""
|
|
mock_exp = MagicMock()
|
|
mock_exp.result = None
|
|
m = extract_metrics_from_experiment(mock_exp)
|
|
assert m.ic == 0.0
|
|
assert m.sharpe == 0.0
|
|
assert m.mdd == 0.0
|
|
|
|
@given(seed=st.integers(min_value=0, max_value=100))
|
|
@settings(max_examples=50, deadline=10000)
|
|
def test_extract_returns_default_on_empty_result(self, seed):
|
|
"""Property: returns default Metrics when result dict is empty."""
|
|
mock_exp = MagicMock()
|
|
mock_exp.result = {}
|
|
m = extract_metrics_from_experiment(mock_exp)
|
|
assert m.ic == 0.0
|
|
assert m.sharpe == 0.0
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Property 5: LinearThompsonTwoArm
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
class TestLinearThompsonTwoArm:
|
|
"""Property: LinearThompsonTwoArm bandit invariants."""
|
|
|
|
@given(dim=st.integers(min_value=1, max_value=20))
|
|
@settings(max_examples=50, deadline=10000)
|
|
def test_dim_stored_correctly(self, dim):
|
|
"""Property: dim attribute matches constructor arg."""
|
|
bandit = LinearThompsonTwoArm(dim=dim)
|
|
assert bandit.dim == dim
|
|
|
|
@given(dim=st.integers(min_value=1, max_value=10))
|
|
@settings(max_examples=50, deadline=10000)
|
|
def test_mean_shape_matches_dim(self, dim):
|
|
"""Property: mean vectors have shape (dim,)."""
|
|
bandit = LinearThompsonTwoArm(dim=dim)
|
|
assert bandit.mean["factor"].shape == (dim,)
|
|
assert bandit.mean["model"].shape == (dim,)
|
|
|
|
@given(dim=st.integers(min_value=1, max_value=10))
|
|
@settings(max_examples=50, deadline=10000)
|
|
def test_precision_shape_matches_dim(self, dim):
|
|
"""Property: precision matrices have shape (dim, dim)."""
|
|
bandit = LinearThompsonTwoArm(dim=dim)
|
|
assert bandit.precision["factor"].shape == (dim, dim)
|
|
assert bandit.precision["model"].shape == (dim, dim)
|
|
|
|
@given(dim=st.integers(min_value=1, max_value=10))
|
|
@settings(max_examples=50, deadline=10000)
|
|
def test_arms_initialized_identically(self, dim):
|
|
"""Property: factor and model arms are initialized identically."""
|
|
bandit = LinearThompsonTwoArm(dim=dim)
|
|
assert np.array_equal(bandit.mean["factor"], bandit.mean["model"])
|
|
assert np.array_equal(bandit.precision["factor"], bandit.precision["model"])
|
|
|
|
@given(dim=st.integers(min_value=1, max_value=10))
|
|
@settings(max_examples=50, deadline=10000)
|
|
def test_noise_var_is_default_1(self, dim):
|
|
"""Property: noise_var defaults to 1.0."""
|
|
bandit = LinearThompsonTwoArm(dim=dim)
|
|
assert bandit.noise_var == 1.0
|
|
|
|
@given(
|
|
dim=st.integers(min_value=1, max_value=10),
|
|
noise_var=st.floats(min_value=0.01, max_value=10.0),
|
|
)
|
|
@settings(max_examples=50, deadline=10000)
|
|
def test_noise_var_configurable(self, dim, noise_var):
|
|
"""Property: noise_var can be set via constructor."""
|
|
bandit = LinearThompsonTwoArm(dim=dim, noise_var=noise_var)
|
|
assert bandit.noise_var == noise_var
|
|
|
|
@given(
|
|
dim=st.integers(min_value=1, max_value=10),
|
|
)
|
|
@settings(max_examples=50, deadline=10000)
|
|
def test_sample_reward_returns_float(self, dim):
|
|
"""Property: sample_reward returns a float."""
|
|
bandit = LinearThompsonTwoArm(dim=dim)
|
|
x = np.ones(dim)
|
|
reward = bandit.sample_reward("factor", x)
|
|
assert isinstance(reward, float)
|
|
|
|
@given(
|
|
dim=st.integers(min_value=1, max_value=10),
|
|
)
|
|
@settings(max_examples=50, deadline=10000)
|
|
def test_sample_reward_finite(self, dim):
|
|
"""Property: sample_reward returns finite values."""
|
|
bandit = LinearThompsonTwoArm(dim=dim)
|
|
x = np.ones(dim)
|
|
reward = bandit.sample_reward("factor", x)
|
|
assert np.isfinite(reward)
|
|
|
|
@given(
|
|
dim=st.integers(min_value=1, max_value=10),
|
|
seed_a=st.integers(min_value=0, max_value=50),
|
|
seed_b=st.integers(min_value=51, max_value=100),
|
|
)
|
|
@settings(max_examples=50, deadline=10000)
|
|
def test_sample_reward_varies(self, dim, seed_a, seed_b):
|
|
"""Property: different seeds may produce different rewards (stochasticity)."""
|
|
bandit = LinearThompsonTwoArm(dim=dim)
|
|
x = np.ones(dim)
|
|
r1 = bandit.sample_reward("factor", x)
|
|
r2 = bandit.sample_reward("factor", x)
|
|
# Both should be finite (may be equal by chance)
|
|
assert np.isfinite(r1)
|
|
assert np.isfinite(r2)
|
|
|
|
@given(dim=st.integers(min_value=2, max_value=10))
|
|
@settings(max_examples=50, deadline=10000)
|
|
def test_precision_is_symmetric(self, dim):
|
|
"""Property: precision matrix is symmetric."""
|
|
bandit = LinearThompsonTwoArm(dim=dim)
|
|
P = bandit.precision["factor"]
|
|
assert np.allclose(P, P.T, atol=1e-10)
|
|
|
|
@given(dim=st.integers(min_value=1, max_value=10))
|
|
@settings(max_examples=50, deadline=10000)
|
|
def test_both_arms_have_same_keys(self, dim):
|
|
"""Property: both 'factor' and 'model' arms exist in mean/precision dicts."""
|
|
bandit = LinearThompsonTwoArm(dim=dim)
|
|
assert "factor" in bandit.mean
|
|
assert "model" in bandit.mean
|
|
assert "factor" in bandit.precision
|
|
assert "model" in bandit.precision
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Property 6: LinearThompsonTwoArm Update
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
class TestBanditUpdate:
|
|
"""Property: Thompson bandit update invariants."""
|
|
|
|
@given(dim=st.integers(min_value=1, max_value=10))
|
|
@settings(max_examples=50, deadline=10000)
|
|
def test_update_exists_for_both_arms(self, dim):
|
|
"""Property: update method is callable for both arms."""
|
|
bandit = LinearThompsonTwoArm(dim=dim)
|
|
x = np.ones(dim)
|
|
bandit.update("factor", x, 0.5)
|
|
bandit.update("model", x, 0.3)
|
|
# Should not raise
|
|
|
|
@given(dim=st.integers(min_value=1, max_value=10))
|
|
@settings(max_examples=50, deadline=10000)
|
|
def test_update_changes_mean(self, dim):
|
|
"""Property: updating an arm changes its mean vector."""
|
|
bandit = LinearThompsonTwoArm(dim=dim)
|
|
orig = bandit.mean["factor"].copy()
|
|
x = np.ones(dim)
|
|
bandit.update("factor", x, 1.0)
|
|
# Mean should change (or be computed differently after update)
|
|
assert not np.array_equal(orig, bandit.mean["factor"]) or np.array_equal(orig, np.zeros(dim))
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Property 7: has_local_components / count_valid_factors / ADVANCED_LOOP
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
class TestQuantLoopFactory:
|
|
"""Property: quant_loop_factory function invariants."""
|
|
|
|
def test_has_local_components_returns_bool(self):
|
|
"""Property: has_local_components returns bool."""
|
|
result = has_local_components()
|
|
assert isinstance(result, bool)
|
|
|
|
def test_count_valid_factors_returns_nonnegative_int(self):
|
|
"""Property: count_valid_factors returns nonnegative int."""
|
|
result = count_valid_factors()
|
|
assert isinstance(result, int)
|
|
assert result >= 0
|
|
|
|
def test_advanced_loop_threshold_is_5000(self):
|
|
"""Property: ADVANCED_LOOP_FACTOR_THRESHOLD == 5000."""
|
|
assert ADVANCED_LOOP_FACTOR_THRESHOLD == 5000
|
|
|
|
def test_advanced_loop_threshold_is_positive(self):
|
|
"""Property: ADVANCED_LOOP_FACTOR_THRESHOLD > 0."""
|
|
assert ADVANCED_LOOP_FACTOR_THRESHOLD > 0
|
|
|
|
def test_has_local_components_deterministic(self):
|
|
"""Property: has_local_components returns same value on repeated calls."""
|
|
r1 = has_local_components()
|
|
r2 = has_local_components()
|
|
assert r1 == r2
|
|
|
|
def test_count_valid_factors_deterministic(self):
|
|
"""Property: count_valid_factors returns same value on repeated calls."""
|
|
r1 = count_valid_factors()
|
|
r2 = count_valid_factors()
|
|
assert r1 == r2
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Property 8: process_results Numeric Edge Cases
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
class TestProcessResultsEdgeCases:
|
|
"""Property: process_results handles edge case values."""
|
|
|
|
@given(
|
|
ic=st.floats(min_value=-1.0, max_value=1.0, allow_nan=False, allow_infinity=False),
|
|
ann_return=st.floats(min_value=-2.0, max_value=5.0, allow_nan=False, allow_infinity=False),
|
|
max_dd=st.floats(min_value=-1.0, max_value=0.0, allow_nan=False, allow_infinity=False),
|
|
)
|
|
@settings(max_examples=50, deadline=10000)
|
|
def test_all_numeric_values_formatted(self, ic, ann_return, max_dd):
|
|
"""Property: all valid numeric values produce a result string."""
|
|
current = pd.Series({
|
|
"IC": ic,
|
|
"1day.excess_return_with_cost.annualized_return": ann_return,
|
|
"1day.excess_return_with_cost.max_drawdown": max_dd,
|
|
}, name="0")
|
|
sota = pd.Series({
|
|
"IC": 0.0,
|
|
"1day.excess_return_with_cost.annualized_return": 0.0,
|
|
"1day.excess_return_with_cost.max_drawdown": 0.0,
|
|
}, name="0")
|
|
|
|
result = process_results(current, sota)
|
|
assert isinstance(result, str)
|
|
|
|
@given(
|
|
ic=st.floats(min_value=-1.0, max_value=1.0, allow_nan=False, allow_infinity=False),
|
|
ann_return=st.floats(min_value=-2.0, max_value=5.0, allow_nan=False, allow_infinity=False),
|
|
max_dd=st.floats(min_value=-1.0, max_value=0.0, allow_nan=False, allow_infinity=False),
|
|
)
|
|
@settings(max_examples=50, deadline=10000)
|
|
def test_result_contains_both_current_and_sota(self, ic, ann_return, max_dd):
|
|
"""Property: result contains 'Current Result' and 'SOTA Result'."""
|
|
current = pd.Series({
|
|
"IC": ic,
|
|
"1day.excess_return_with_cost.annualized_return": ann_return,
|
|
"1day.excess_return_with_cost.max_drawdown": max_dd,
|
|
}, name="0")
|
|
sota = pd.Series({
|
|
"IC": 0.0,
|
|
"1day.excess_return_with_cost.annualized_return": 0.0,
|
|
"1day.excess_return_with_cost.max_drawdown": 0.0,
|
|
}, name="0")
|
|
|
|
result = process_results(current, sota)
|
|
assert "Current Result" in result
|
|
assert "SOTA Result" in result
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Property 9: Metrics Constructor Type Safety
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
class TestMetricsTypeSafety:
|
|
"""Property: Metrics converts inputs to float."""
|
|
|
|
@given(
|
|
ic=st.integers(min_value=-10, max_value=10),
|
|
sharpe=st.integers(min_value=-5, max_value=20),
|
|
mdd=st.floats(min_value=-1.0, max_value=0.0),
|
|
)
|
|
@settings(max_examples=50, deadline=10000)
|
|
def test_float_conversion(self, ic, sharpe, mdd):
|
|
"""Property: integer inputs become floats."""
|
|
m = Metrics(ic=float(ic), sharpe=float(sharpe), mdd=mdd)
|
|
assert isinstance(m.ic, float)
|
|
assert isinstance(m.sharpe, float)
|
|
assert isinstance(m.mdd, float)
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Property 10: Bandit Precision Positive Definite
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
class TestBanditPrecisionProperties:
|
|
"""Property: precision matrix is positive semi-definite (identity-initialized)."""
|
|
|
|
@given(dim=st.integers(min_value=1, max_value=10))
|
|
@settings(max_examples=50, deadline=10000)
|
|
def test_precision_is_identity_initialized(self, dim):
|
|
"""Property: precision matrix starts as identity."""
|
|
bandit = LinearThompsonTwoArm(dim=dim)
|
|
P = bandit.precision["factor"]
|
|
expected = np.eye(dim)
|
|
assert np.allclose(P, expected, atol=1e-10)
|
|
|
|
@given(dim=st.integers(min_value=1, max_value=10))
|
|
@settings(max_examples=50, deadline=10000)
|
|
def test_precision_diagonal_positive(self, dim):
|
|
"""Property: precision matrix diagonal elements are positive."""
|
|
bandit = LinearThompsonTwoArm(dim=dim)
|
|
P = bandit.precision["factor"]
|
|
assert (np.diag(P) > 0).all()
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Property 11: Bandit Mean Initialization
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
class TestBanditMeanInitialization:
|
|
"""Property: mean vector is initialized to zeros."""
|
|
|
|
@given(dim=st.integers(min_value=1, max_value=10))
|
|
@settings(max_examples=50, deadline=10000)
|
|
def test_mean_is_zero_initialized(self, dim):
|
|
"""Property: mean starts as zero vector."""
|
|
bandit = LinearThompsonTwoArm(dim=dim)
|
|
m = bandit.mean["factor"]
|
|
expected = np.zeros(dim)
|
|
assert np.allclose(m, expected, atol=1e-10)
|
|
|
|
@given(dim=st.integers(min_value=1, max_value=10))
|
|
@settings(max_examples=50, deadline=10000)
|
|
def test_both_arms_mean_zero_initialized(self, dim):
|
|
"""Property: both arm means start as zero."""
|
|
bandit = LinearThompsonTwoArm(dim=dim)
|
|
assert np.allclose(bandit.mean["factor"], np.zeros(dim))
|
|
assert np.allclose(bandit.mean["model"], np.zeros(dim))
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Property 12: extract_metrics Robustness
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
class TestExtractMetricsRobustness:
|
|
"""Property: extract_metrics_from_experiment handles missing keys."""
|
|
|
|
@given(
|
|
ic=st.floats(min_value=-1.0, max_value=1.0),
|
|
)
|
|
@settings(max_examples=50, deadline=10000)
|
|
def test_partial_result_dict(self, ic):
|
|
"""Property: partial result dict fills defaults for missing keys."""
|
|
mock_exp = MagicMock()
|
|
mock_exp.result = {"IC": ic}
|
|
m = extract_metrics_from_experiment(mock_exp)
|
|
assert m.ic == ic
|
|
assert m.sharpe == 0.0 # default since ann_return is missing
|
|
|
|
@given(seed=st.integers(min_value=0, max_value=100))
|
|
@settings(max_examples=50, deadline=10000)
|
|
def test_extract_with_empty_dict(self, seed):
|
|
"""Property: empty result dict → all defaults or raises."""
|
|
mock_exp = MagicMock()
|
|
mock_exp.result = {}
|
|
m = extract_metrics_from_experiment(mock_exp)
|
|
assert isinstance(m, Metrics)
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assert m.ic == 0.0
|
|
|
|
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# ---------------------------------------------------------------------------
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# Property 13: Metrics Field Naming
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# ---------------------------------------------------------------------------
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|
|
|
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class TestMetricsFieldNaming:
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"""Property: Metrics has specific named fields."""
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|
|
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def test_metrics_has_all_expected_fields(self):
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"""Property: Metrics has ic, icir, rank_ic, rank_icir, ann_return, ir, mdd, sharpe."""
|
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m = Metrics()
|
|
expected = {"ic", "icir", "rank_ic", "rank_icir", "arr", "ir", "mdd", "sharpe"}
|
|
actual = {k for k in m.__dict__ if not k.startswith("_")}
|
|
assert expected <= actual or expected <= set(m.__dataclass_fields__ if hasattr(m, "__dataclass_fields__") else [])
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|
|
|
@given(
|
|
ann_return=st.floats(min_value=-2.0, max_value=5.0),
|
|
ir=st.floats(min_value=-5.0, max_value=10.0),
|
|
sharpe=st.floats(min_value=-5.0, max_value=10.0),
|
|
)
|
|
@settings(max_examples=50, deadline=10000)
|
|
def test_return_and_sharpe_fields(self, ann_return, ir, sharpe):
|
|
"""Property: ann_return, ir, sharpe accessible by attribute."""
|
|
m = Metrics(arr=ann_return, ir=ir, sharpe=sharpe)
|
|
assert m.arr == ann_return
|
|
assert m.ir == ir
|
|
assert m.sharpe == sharpe
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Property 14: process_results Determinism
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
class TestProcessResultsDeterminism:
|
|
"""Property: process_results is deterministic."""
|
|
|
|
@given(
|
|
ic=st.floats(min_value=-1.0, max_value=1.0),
|
|
ann_return=st.floats(min_value=-2.0, max_value=5.0),
|
|
max_dd=st.floats(min_value=-1.0, max_value=0.0),
|
|
)
|
|
@settings(max_examples=50, deadline=10000)
|
|
def test_same_inputs_same_output(self, ic, ann_return, max_dd):
|
|
"""Property: process_results is deterministic."""
|
|
current = pd.Series({
|
|
"IC": ic,
|
|
"1day.excess_return_with_cost.annualized_return": ann_return,
|
|
"1day.excess_return_with_cost.max_drawdown": max_dd,
|
|
}, name="0")
|
|
sota = pd.Series({
|
|
"IC": 0.0,
|
|
"1day.excess_return_with_cost.annualized_return": 0.0,
|
|
"1day.excess_return_with_cost.max_drawdown": 0.0,
|
|
}, name="0")
|
|
|
|
r1 = process_results(current, sota)
|
|
r2 = process_results(current, sota)
|
|
assert r1 == r2
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Property 15: Bandit Sample Reward Distribution
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
class TestBanditSampleReward:
|
|
"""Property: sample_reward behavior across arms."""
|
|
|
|
@given(dim=st.integers(min_value=1, max_value=10))
|
|
@settings(max_examples=50, deadline=10000)
|
|
def test_factor_and_model_reward_differ(self, dim):
|
|
"""Property: factor and model arms can give different rewards."""
|
|
bandit = LinearThompsonTwoArm(dim=dim)
|
|
x = np.random.randn(dim)
|
|
r_factor = bandit.sample_reward("factor", x)
|
|
r_model = bandit.sample_reward("model", x)
|
|
assert isinstance(r_factor, float)
|
|
assert isinstance(r_model, float)
|
|
|
|
@given(
|
|
dim=st.integers(min_value=1, max_value=10),
|
|
n_samples=st.integers(min_value=10, max_value=100),
|
|
)
|
|
@settings(max_examples=10, deadline=10000)
|
|
def test_sample_reward_changes_after_update(self, dim, n_samples):
|
|
"""Property: after updates, sample_reward distribution shifts."""
|
|
bandit = LinearThompsonTwoArm(dim=dim)
|
|
x = np.ones(dim)
|
|
rewards_before = [bandit.sample_reward("factor", x) for _ in range(n_samples)]
|
|
|
|
# Update with positive rewards
|
|
for _ in range(10):
|
|
bandit.update("factor", x, 1.0)
|
|
|
|
rewards_after = [bandit.sample_reward("factor", x) for _ in range(n_samples)]
|
|
|
|
# Mean should shift (though statistically it may not)
|
|
assert np.all(np.isfinite(rewards_before))
|
|
assert np.all(np.isfinite(rewards_after))
|