def test_pcm_fixed_weight_optimiser_fixed_alpha_weights_call_end_to_end(
helpers):
"""
Tests the full portfolio base class logic for carrying out
rebalancing.
TODO: DataHandler is mocked. A non-disk based data source
should be utilised instead.
"""
first_dt = pd.Timestamp('2019-01-01 15:00:00', tz=pytz.utc)
asset_list = ['EQ:SPY', 'EQ:AGG', 'EQ:TLT', 'EQ:GLD']
initial_funds = 1e6
account_id = '1234'
port_id = '1234'
cash_buffer_perc = 0.05
exchange = SimulatedExchange(first_dt)
universe = StaticUniverse(asset_list)
mock_asset_prices_first = {
'EQ:SPY': 56.87,
'EQ:AGG': 219.45,
'EQ:TLT': 178.33,
'EQ:GLD': 534.21
}
data_handler = Mock()
data_handler.get_asset_latest_ask_price.side_effect = \
lambda self, x: mock_asset_prices_first[x]
broker = SimulatedBroker(first_dt,
exchange,
data_handler,
account_id,
initial_funds=initial_funds)
broker.create_portfolio(port_id, 'Portfolio')
broker.subscribe_funds_to_portfolio(port_id, initial_funds)
order_sizer = DollarWeightedCashBufferedOrderSizeGeneration(
broker, port_id, data_handler, cash_buffer_perc)
optimiser = FixedWeightPortfolioOptimiser(data_handler)
alpha_weights = {
'EQ:SPY': 0.345,
'EQ:AGG': 0.611,
'EQ:TLT': 0.870,
'EQ:GLD': 0.0765
}
alpha_model = FixedSignalsAlphaModel(alpha_weights)
pcm = PortfolioConstructionModel(broker, port_id, universe, order_sizer,
optimiser, alpha_model)
result_first = pcm(first_dt)
expected_first = [
Order(first_dt, 'EQ:AGG', 1390),
Order(first_dt, 'EQ:GLD', 71),
Order(first_dt, 'EQ:SPY', 3029),
Order(first_dt, 'EQ:TLT', 2436)
]
helpers.assert_order_lists_equal(result_first, expected_first)
def test_create_zero_target_weight_vector(description, full_assets, expected):
"""
Tests the _create_zero_target_weight_vector method of the
PortfolioConstructionModel base class.
"""
port_id = '1234'
broker = Mock()
universe = Mock()
order_sizer = Mock()
optimiser = Mock()
pcm = PortfolioConstructionModel(broker, port_id, universe, order_sizer,
optimiser)
result = pcm._create_zero_target_weight_vector(full_assets)
assert result == expected
def test_generate_rebalance_orders(helpers, description, target_portfolio,
current_portfolio, expected):
"""
Tests the _generate_rebalance_orders method of the
PortfolioConstructionModel base class.
"""
port_id = '1234'
broker = Mock()
universe = Mock()
order_sizer = Mock()
optimiser = Mock()
pcm = PortfolioConstructionModel(broker, port_id, universe, order_sizer,
optimiser)
result = pcm._generate_rebalance_orders(SENTINEL_DT, target_portfolio,
current_portfolio)
helpers.assert_order_lists_equal(result, expected)
def test_obtain_full_asset_list(description, port_dict, uni_assets, expected):
"""
Tests the _obtain_full_asset_list method of the
PortfolioConstructionModel base class.
"""
port_id = '1234'
broker = Mock()
broker.get_portfolio_as_dict.return_value = port_dict
universe = Mock()
universe.get_assets.return_value = uni_assets
order_sizer = Mock()
optimiser = Mock()
pcm = PortfolioConstructionModel(broker, port_id, universe, order_sizer,
optimiser)
result = pcm._obtain_full_asset_list(SENTINEL_DT)
assert result == expected
def _initialise_models(self):
"""
Initialise the various models for the quantitative
trading strategy. This includes the portfolio
construction and the execution.
TODO: Add TransactionCostModel
TODO: Ensure this is dynamically generated from config.
"""
# Portfolio Construction
order_sizer = DollarWeightedCashBufferedOrderSizeGeneration(
self.broker,
self.broker_portfolio_id,
self.data_handler,
cash_buffer_percentage=self.cash_buffer_percentage)
optimiser = FixedWeightPortfolioOptimiser(
data_handler=self.data_handler)
self.portfolio_construction_model = PortfolioConstructionModel(
self.broker,
self.broker_portfolio_id,
self.universe,
order_sizer,
optimiser,
alpha_model=self.alpha_model,
risk_model=self.risk_model,
data_handler=self.data_handler)
# Execution
execution_algo = MarketOrderExecutionAlgorithm()
self.execution_handler = ExecutionHandler(
self.broker,
self.broker_portfolio_id,
self.universe,
submit_orders=self.submit_orders,
execution_algo=execution_algo,
data_handler=self.data_handler)
def _initialise_models(self, **kwargs):
"""
Initialise the various models for the quantitative
trading strategy. This includes the portfolio
construction and the execution.
TODO: Add TransactionCostModel
TODO: Ensure this is dynamically generated from config.
"""
# Determine the appropriate order sizing mechanism
order_sizer = self._create_order_sizer(**kwargs)
# TODO: Allow optimiser to be generated from config
optimiser = FixedWeightPortfolioOptimiser(
data_handler=self.data_handler)
# Generate the portfolio construction
self.portfolio_construction_model = PortfolioConstructionModel(
self.broker,
self.broker_portfolio_id,
self.universe,
order_sizer,
optimiser,
alpha_model=self.alpha_model,
risk_model=self.risk_model,
data_handler=self.data_handler)
# Execution
execution_algo = MarketOrderExecutionAlgorithm()
self.execution_handler = ExecutionHandler(
self.broker,
self.broker_portfolio_id,
self.universe,
submit_orders=self.submit_orders,
execution_algo=execution_algo,
data_handler=self.data_handler)