Updated tests

garpar/datasets/data/__init__.py

@@ -26,8 +26,10 @@ def load_MERVAL(imputation="ffill", first=None, last=None):
     # pd.to_datetime(None) -> None
     first, last = pd.to_datetime(first), pd.to_datetime(last)
 
-    if imputation in ("backfill", "bfill", "pad", "ffill"):
-        df.fillna(method=imputation, inplace=True)
+    if imputation in ("backfill", "bfill"):
+        df.bfill(inplace=True)
+    elif imputation in ("pad", "ffill"):
+        df.ffill(inplace=True)
     else:
         df.fillna(value=imputation, inplace=True)
 

tests/optimize/test_mean_variance.py

@@ -13,7 +13,7 @@
 
 from garpar import Portfolio
 
-from garpar.optimize import mean_variance
+from garpar.optimize.mean_variance import MVOptimizer, Markowitz
 
 from garpar import datasets
 
@@ -23,6 +23,52 @@
 
 import pytest
 
+# =============================================================================
+# TESTS MV
+# =============================================================================
+
+def test_MVOptimizer_default_initialization():
+    optimizer = MVOptimizer()
+    assert optimizer.method == "max_sharpe"
+    assert optimizer.weight_bounds == (0, 1)
+    assert optimizer.market_neutral is False
+
+def test_MVOptimizer_custom_initialization():
+    optimizer = MVOptimizer(method="min_volatility", weight_bounds=(-1, 1))
+    assert optimizer.method == "min_volatility"
+    assert optimizer.weight_bounds == (-1, 1)
+
+@pytest.mark.parametrize("price_distribution", pytest.DISTRIBUTIONS)
+def test_MVOptimizer_calculate_weights_max_sharpe(risso_portfolio, price_distribution):
+    pf = risso_portfolio(random_state=42, distribution=price_distribution)
+    optimizer = MVOptimizer(risk_free_rate=0.001)
+    weights, meta = optimizer._calculate_weights(pf)
+    assert len(weights) == len(pf.stocks)
+    assert meta["name"] == "max_sharpe"
+    assert meta["risk_free_rate"] == 0.001
+
+@pytest.mark.parametrize("price_distribution", pytest.DISTRIBUTIONS)
+def test_MVOptimizer_min_volatility(risso_portfolio, price_distribution):
+    pf = risso_portfolio(random_state=42, distribution=price_distribution)
+    optimizer = MVOptimizer(method="min_volatility")
+    weights, meta = optimizer._calculate_weights(pf)
+    assert len(weights) == len(pf.stocks)
+    assert meta["name"] == "min_volatility"
+
+@pytest.mark.parametrize("price_distribution", pytest.DISTRIBUTIONS)
+def test_MVOptimizer_invalid_method(risso_portfolio, price_distribution):
+    pf = risso_portfolio(random_state=42, distribution=price_distribution)
+    optimizer = MVOptimizer(method="unknown_method")
+    with pytest.raises(ValueError):
+        optimizer._calculate_weights(pf)
+
+@pytest.mark.parametrize("price_distribution", pytest.DISTRIBUTIONS)
+def test_MVOptimizer_coerce_target_return(risso_portfolio, price_distribution):
+    pf = risso_portfolio(random_state=42, distribution=price_distribution)
+    optimizer = MVOptimizer(target_return=None)
+    coerced_return = optimizer._coerce_target_return(pf)
+    assert coerced_return == 0.05  # The minimum absolute return from mock portfolio
+
 # =============================================================================
 # MARKOWITZ TEST
 # =============================================================================
@@ -39,7 +85,7 @@ def test_Markowitz_optimize():
     )
 
     # Instance
-    markowitz = mean_variance.Markowitz(target_return=1.0)
+    markowitz = Markowitz(target_return=1.0)
 
     # Tested method
     result = markowitz.optimize(pf)
@@ -71,7 +117,7 @@ def test_Markowitz_optimize_default_target_return():
     )
 
     # Instance
-    markowitz = mean_variance.Markowitz()
+    markowitz = Markowitz()
 
     # Tested method
     result = markowitz.optimize(pf)
@@ -91,4 +137,3 @@ def test_Markowitz_optimize_default_target_return():
 
     assert isinstance(result.weights, pd.Series)
     pd.testing.assert_series_equal(result.weights, expected_weights)
-

tests/optimize/test_opt_base.py

@@ -13,7 +13,7 @@
 
 from garpar import Portfolio
 
-from garpar.optimize import opt_base as base
+from garpar.optimize.opt_base import OptimizerABC, MeanVarianceFamilyMixin
 
 import numpy as np
 
@@ -30,9 +30,38 @@
 def test_OptimizerABC__calculate_weights_not_implementhed(risso_portfolio):
     pf = risso_portfolio(random_state=42, stocks=2)
 
-    class FooOptimizer(base.MeanVarianceFamilyMixin, base.OptimizerABC):
+    class FooOptimizer(MeanVarianceFamilyMixin, OptimizerABC):
         def _calculate_weights(self, pf):
             return super()._calculate_weights(pf)
 
     with pytest.raises(NotImplementedError):
         FooOptimizer().optimize(pf)
+
+
+def test_optimizerabc_family_not_string():
+    """Test that an error is raised if 'family' is not a string."""
+
+    with pytest.raises(TypeError, match="'InvalidOptimizer.family' must be redefined as string"):
+        class InvalidOptimizer(OptimizerABC):
+            family = 123  # Not a string
+
+def test_optimizerabc_family_undefined():
+    """Test that an error is raised if 'family' is not defined."""
+
+    with pytest.raises(TypeError, match="'UndefinedFamilyOptimizer.family' must be redefined as string"):
+        class UndefinedFamilyOptimizer(OptimizerABC):
+            pass
+
+def test_optimizerabc_family_valid():
+    """Test that no error is raised when 'family' is a valid string."""
+
+    class ValidOptimizer(OptimizerABC):
+        family = "MeanVariance"
+
+    assert ValidOptimizer.family == "MeanVariance"
+
+def test_OptimizerABC_get_family():
+    class TestMeanVarianceFamily(MeanVarianceFamilyMixin, OptimizerABC):
+        pass
+
+    assert TestMeanVarianceFamily.get_optimizer_family() == "mean-variance"

tox.ini

@@ -10,6 +10,7 @@ envlist =
     py311,
     py312,
     coverage,
+    report
 
 
 # =============================================================================