def setUp(self):
ensemble_returns = Returns(buildNumericDataFrame(['all'], 10))
returns1 = Returns(buildNumericDataFrame(['a'], 10))
returns2 = Returns(buildNumericDataFrame(['b'], 10))
returns3 = Returns(buildNumericDataFrame(['c'], 10))
sub_returns = [returns1, returns2, returns3]
self.strategy = EnsembleStrategy("CC", "C", [])
self.strategy.indexer.positions = Mock()
self.strategy.market = Mock()
lagged_positions = Mock(spec = Position)
lagged_positions.applied_to.return_value = ensemble_returns
self.strategy.indexer.positions.return_value = lagged_positions
sub_strat1 = Mock(spec = Strategy)
sub_strat1.returns = Mock(spec = Returns)
sub_strat1.returns.collapse_by.return_value = returns1
sub_strat2 = Mock(spec = Strategy)
sub_strat2.returns = Mock(spec = Returns)
sub_strat2.returns.collapse_by.return_value = returns2
sub_strat3 = Mock(spec = Strategy)
sub_strat3.returns = Mock(spec = Returns)
sub_strat3.returns.collapse_by.return_value = returns3
self.strategy.strategies = [sub_strat1, sub_strat2, sub_strat3]
self.expected_result = ensemble_returns
for returns in sub_returns:
self.expected_result.data[returns.columns[0]] = returns.data
def setUp(self):
ensemble_returns = Returns(buildNumericDataFrame(['all'], 10))
returns1 = Returns(buildNumericDataFrame(['a'], 10))
returns2 = Returns(buildNumericDataFrame(['b'], 10))
returns3 = Returns(buildNumericDataFrame(['c'], 10))
sub_returns = [returns1, returns2, returns3]
self.strategy = EnsembleStrategy("CC", "C", [])
self.strategy.indexer.positions = Mock()
self.strategy.market = Mock()
lagged_positions = Mock(spec=Position)
lagged_positions.applied_to.return_value = ensemble_returns
self.strategy.indexer.positions.return_value = lagged_positions
sub_strat1 = Mock(spec=Strategy)
sub_strat1.returns = Mock(spec=Returns)
sub_strat1.returns.collapse_by.return_value = returns1
sub_strat2 = Mock(spec=Strategy)
sub_strat2.returns = Mock(spec=Returns)
sub_strat2.returns.collapse_by.return_value = returns2
sub_strat3 = Mock(spec=Strategy)
sub_strat3.returns = Mock(spec=Returns)
sub_strat3.returns.collapse_by.return_value = returns3
self.strategy.strategies = [sub_strat1, sub_strat2, sub_strat3]
self.expected_result = ensemble_returns
for returns in sub_returns:
self.expected_result.data[returns.columns[0]] = returns.data
def setUp(self):
self.strategy = Strategy("OO", "C")
self.position_series = buildNumericDataFrame(["ASX", "BHP", "CBA"], 20)
self.positions = Position(self.position_series)
self.returns = buildNumericDataFrame(["ASX", "BHP", "CBA"], 20) / 10
self.strategy.measure = Mock()
self.strategy.positions = self.positions
self.strategy.market = Mock()
self.strategy.market.returns = Mock()
self.strategy.market.returns.return_value = Returns(self.returns)
def setUp(self):
self.strategy = Strategy("OO", "C")
self.position_series = buildNumericDataFrame(["ASX", "BHP", "CBA"], 20)
self.positions = Position(self.position_series)
self.returns = buildNumericDataFrame(["ASX", "BHP", "CBA"], 20) / 10
self.strategy.measure = Mock()
self.strategy.positions = self.positions
self.strategy.market = Mock()
self.strategy.market.returns = Mock()
self.strategy.market.returns.return_value = Returns(self.returns)
def testReturnsCombinesTogether(self):
data2 = buildNumericDataFrame(['a', 'b'], len(self.data))
expected_data = self.returns.data
expected_data[data2.columns] = data2
returns2 = Returns(data2)
self.returns.append(returns2)
self.assertTrue(self.returns.data.equals(expected_data))
def testReturnsCombinesTogether(self):
data2 = buildNumericDataFrame(['a', 'b'], len(self.data))
expected_data = self.returns.data
expected_data[data2.columns] = data2
returns2 = Returns(data2)
self.returns.append(returns2)
self.assertTrue(self.returns.data.equals(expected_data))
def setUp(self):
self.strategy = Mock(spec = Strategy)
self.model = BlockMeanReturnsForecaster(window = 4)
self.tickers = ["ASX", "BHP", "CBA"]
self.returns = AverageReturns(buildNumericDataFrame(self.tickers, 10))
self.strategy.market_returns = self.returns
self.strategy.forecasts = None
def setUp(self):
self.strategy = Mock(spec=Strategy)
self.model = BlockMeanReturnsForecaster(window=4)
self.tickers = ["ASX", "BHP", "CBA"]
self.returns = AverageReturns(buildNumericDataFrame(self.tickers, 10))
self.strategy.market_returns = self.returns
self.strategy.forecasts = None
def setUp(self):
self.tickers = ['ASX', 'BHP', 'CBA']
self.forecast_data = buildNumericDataFrame(self.tickers, 10)
self.forecasts = Mock(spec=Forecast)
self.forecasts.optF.return_value = self.forecast_data
self.strategy = Strategy("OO", "C")
self.strategy.forecasts = self.forecasts
self.model = SingleLargestF()
self.model_results = self.model(self.strategy)
def setUp(self):
self.tickers = ['ASX', 'BHP', 'CBA']
self.forecast_data = buildNumericDataFrame(self.tickers, 10)
self.forecasts = Mock(spec = Forecast)
self.forecasts.optF.return_value = self.forecast_data
self.strategy = Strategy("OO", "C")
self.strategy.forecasts = self.forecasts
self.model = SingleLargestF()
self.model_results = self.model(self.strategy)
def testStrategyCallsIndexerForPositions(self):
position_series = buildNumericDataFrame(["ASX", "BHP"], 10)
positions = Position(position_series)
self.strategy.indexer.positions = Mock()
self.strategy.indexer.positions.return_value = positions
self.strategy.positions = positions
result = self.strategy.lagged_positions
self.assertIsInstance(result, Position)
expected_calls = [call(positions)]
self.strategy.indexer.positions.assert_has_calls(expected_calls)
def setUp(self):
self.tickers = ['ASX', 'BHP', 'CBA', 'MOC', 'QBE']
self.forecast_data = buildNumericDataFrame(self.tickers, 10)
self.forecasts = Mock(spec=Forecast)
self.forecasts.optF.return_value = self.forecast_data
self.strategy = Strategy("OO", "C")
self.strategy.forecasts = self.forecasts
self.num_positions = 3
self.select_positions = HighestRankedFs(self.num_positions)
self.positions = self.select_positions(self.strategy)
def testStrategyCallsIndexerForPositions(self):
position_series = buildNumericDataFrame(["ASX", "BHP"], 10)
positions = Position(position_series)
self.strategy.indexer.positions = Mock()
self.strategy.indexer.positions.return_value = positions
self.strategy.positions = positions
result = self.strategy.lagged_positions
self.assertIsInstance(result, Position)
expected_calls = [call(positions)]
self.strategy.indexer.positions.assert_has_calls(expected_calls)
def setUp(self):
self.tickers = ['ASX', 'BHP', 'CBA', 'MOC', 'QBE']
self.forecast_data = buildNumericDataFrame(self.tickers, 10)
self.forecasts = Mock(spec = Forecast)
self.forecasts.optF.return_value = self.forecast_data
self.strategy = Strategy("OO", "C")
self.strategy.forecasts = self.forecasts
self.num_positions = 3
self.select_positions = HighestRankedFs(self.num_positions)
self.positions = self.select_positions(self.strategy)
def testModelProducesResultsForAllData(self):
tickers = ["ASX", "BHP", "CBA"]
indicator_data = buildTextDataFrame(tickers, 10)
self.strategy.lagged_indicator = Indicator(indicator_data)
self.strategy.market_returns = buildNumericDataFrame(tickers, 10)
forecast = self.model(self.strategy)
self.assertEqual(indicator_data.shape, forecast.mean.shape)
self.assertEqual(indicator_data.shape, forecast.sd.shape)
self.assertTrue(all(indicator_data.index == forecast.mean.index))
self.assertTrue(all(indicator_data.index == forecast.sd.index))
self.assertEqual(tickers, list(forecast.mean.columns))
self.assertEqual(tickers, list(forecast.sd.columns))
def testModelProducesResultsForAllData(self):
tickers = ["ASX", "BHP", "CBA"]
indicator_data = buildTextDataFrame(tickers, 10)
self.strategy.lagged_indicator = Indicator(indicator_data)
self.strategy.market_returns = buildNumericDataFrame(tickers, 10)
forecast = self.model(self.strategy)
self.assertEqual(indicator_data.shape, forecast.mean.shape)
self.assertEqual(indicator_data.shape, forecast.sd.shape)
self.assertTrue(all(indicator_data.index == forecast.mean.index))
self.assertTrue(all(indicator_data.index == forecast.sd.index))
self.assertEqual(tickers, list(forecast.mean.columns))
self.assertEqual(tickers, list(forecast.sd.columns))
def setUp(self):
self.prices = buildNumericDataFrame(["ASX", "BHP", "CBA"], 10).abs()
self.strategy = Mock()
self.strategy.get_indicator_prices = Mock(return_value = self.prices)
self.crossover = Crossover(5, 3)
self.indicator = self.crossover(self.strategy)
def setUp(self):
self.trade_timing = "OO"
self.ind_timing = "C"
self.strategy = Strategy(self.trade_timing, self.ind_timing)
self.dummy_timeSeries = buildNumericDataFrame(["ASX", "BHP", "CBA"],
10)
def setUp(self):
self.tickers = ['ASX', 'BHP', 'CBA']
self.position_data = buildNumericDataFrame(self.tickers, 10)
self.position = Position(self.position_data)
def testStrategyContainerElementImplementsShift(self):
time_series = buildNumericDataFrame(["ASX", "BHP", "CBA"], 10)
indicator = StrategyContainerElement()
indicator.data = time_series
lagged_indicator = indicator.shift(2)
self.assertTrue(time_series.shift(2).equals(lagged_indicator.data))
def setUp(self):
self.data = buildNumericDataFrame(["ASX", "BHP", "CBA"], 20) / 10
self.returns = Returns(self.data)
def setUp(self):
self.prices = buildNumericDataFrame(["ASX", "BHP", "CBA"], 10).abs()
self.strategy = Mock()
self.strategy.get_indicator_prices = Mock(return_value=self.prices)
self.crossover = Crossover(5, 3)
self.indicator = self.crossover(self.strategy)
def testStrategyContainerElementImplementsShift(self):
time_series = buildNumericDataFrame(["ASX", "BHP", "CBA"], 10)
indicator = StrategyContainerElement()
indicator.data = time_series
lagged_indicator = indicator.shift(2)
self.assertTrue(time_series.shift(2).equals(lagged_indicator.data))
def setUp(self):
self.trade_timing = "OO"
self.ind_timing = "C"
self.strategy = Strategy(self.trade_timing, self.ind_timing)
self.dummy_timeSeries = buildNumericDataFrame(["ASX", "BHP", "CBA"], 10)
def setUp(self):
self.data = buildNumericDataFrame(["ASX", "BHP", "CBA"], 20) / 10
self.returns = Returns(self.data)
def setUp(self):
self.tickers = ['ASX', 'BHP', 'CBA']
self.position_data = buildNumericDataFrame(self.tickers, 10)
self.position = Position(self.position_data)
