def testPortfolioReturnsWithRandomWeightsWithRebalance(self):
weights = np.random.uniform(0, 1.0, len(self.navData.columns))
weights = weights / weights.sum()
pReturns = portfolio_returns(weights, self.navData, rebalance=True)
returnsTable = self.return_cal(self.navData.values)
pReturns_ben = np.dot(returnsTable, weights)
self.assertTrue(np.allclose(pReturns, pReturns_ben))
def testPortfolioReturnsWithRandomWeightsWithRebalance(self):
weights = np.random.uniform(0, 1.0, len(self.navData.columns))
weights = weights / weights.sum()
pReturns = portfolio_returns(weights, self.navData, rebalance=True)
returnsTable = self.return_cal(self.navData.values)
pReturns_ben = np.dot(returnsTable, weights)
self.assertTrue(np.allclose(pReturns, pReturns_ben))
def testPortfolioReturnsWithUnitWeight(self):
weights = [0.0] * len(self.navData.columns)
for i, col in enumerate(self.navData.columns):
tmpWeights = weights[:]
tmpWeights[i] = 1.0
pReturns = portfolio_returns(tmpWeights, self.navData, rebalance=False)
pReturns_ben = self.return_cal(navs=self.navData[col].values)
self.assertTrue(np.allclose(pReturns, pReturns_ben))
def testPortfolioReturnsWithUnitWeight(self):
weights = [0.0] * len(self.navData.columns)
for i, col in enumerate(self.navData.columns):
tmpWeights = weights[:]
tmpWeights[i] = 1.0
pReturns = portfolio_returns(tmpWeights,
self.navData,
rebalance=False)
pReturns_ben = self.return_cal(navs=self.navData[col].values)
self.assertTrue(np.allclose(pReturns, pReturns_ben))
