From 1543f111cb662f17733f8a96ed77c8306d448b25 Mon Sep 17 00:00:00 2001
From: Divasco <diego.gimenez@unc.edu.ar>
Date: Wed, 9 Oct 2024 00:15:17 -0300
Subject: [PATCH] More testing

---
 garpar/optimize/mean_variance.py     |  8 ++++----
 tests/optimize/test_mean_variance.py | 22 ++++++++++++++++++++++
 2 files changed, 26 insertions(+), 4 deletions(-)

diff --git a/garpar/optimize/mean_variance.py b/garpar/optimize/mean_variance.py
index 8a02d51..399e187 100644
--- a/garpar/optimize/mean_variance.py
+++ b/garpar/optimize/mean_variance.py
@@ -62,13 +62,13 @@ def _coerce_target_return(self, pf):
 
     def _coerce_target_volatility(self, pf):
         if self.target_risk is None:
-            return np.min(np.std(pf.as_prices()))
+            return np.min(np.std(pf.as_prices(), axis=0))
         return self.target_risk
 
     def _calculate_weights(self, pf):
         optimizer = self._get_optimizer(pf)
         method = self.method
-        
+
         optimization_methods = {
             "min_volatility": self._min_volatility,
             "max_sharpe": self._max_sharpe,
@@ -77,10 +77,10 @@ def _calculate_weights(self, pf):
             "efficient_return": self._efficient_return,
             "portfolio_performance": self._portfolio_performance,
         }
-        
+
         if method not in optimization_methods:
             raise ValueError(f"Unknown optimization method: {method}")
-        
+
         return optimization_methods[method](optimizer, pf)
 
     def _min_volatility(self, optimizer, pf):
diff --git a/tests/optimize/test_mean_variance.py b/tests/optimize/test_mean_variance.py
index da19720..e4e12a0 100644
--- a/tests/optimize/test_mean_variance.py
+++ b/tests/optimize/test_mean_variance.py
@@ -15,6 +15,8 @@
 
 from garpar.optimize.mean_variance import MVOptimizer, Markowitz
 
+import pypfopt
+
 from garpar import datasets
 
 import numpy as np
@@ -69,6 +71,26 @@ def test_MVOptimizer_coerce_target_return(risso_portfolio, price_distribution):
     coerced_return = optimizer._coerce_target_return(pf)
     assert coerced_return == 0.05  # The minimum absolute return from mock portfolio
 
+@pytest.mark.parametrize("price_distribution", pytest.DISTRIBUTIONS)
+def test_MVOptimizer_get_optimizer(risso_portfolio, price_distribution):
+    pf = risso_portfolio(random_state=42, distribution=price_distribution)
+    optimizer = MVOptimizer()
+    assert type(optimizer._get_optimizer(pf)) == pypfopt.efficient_frontier.EfficientFrontier
+
+@pytest.mark.parametrize(
+    "volatiliy, price_distribution",
+    [
+        (0.03313144315467211, pytest.DISTRIBUTIONS['levy-stable']),
+        (0.8509377578214843, pytest.DISTRIBUTIONS['normal']),
+        (13.383798092382262, pytest.DISTRIBUTIONS['uniform'])
+    ]
+)
+def test_MVOptimizer_coerce_volatiliy(volatiliy, price_distribution):
+    pf = price_distribution(random_state=43)
+    optimizer = MVOptimizer(method="max-sharpe")
+    coerced_volatility = optimizer._coerce_target_volatility(pf)
+    assert coerced_volatility == pytest.approx(volatiliy, 1e-9)
+
 # =============================================================================
 # MARKOWITZ TEST
 # =============================================================================