wehub-resource-sync
91 lines
4.1 KiB
Python
91 lines
4.1 KiB
Python
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from cmath import isclose
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from AlgorithmImports import *
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### <summary>
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### Demonstration of how to use custom security properties.
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### In this algorithm we trade a security based on the values of a slow and fast EMAs which are stored in the security itself.
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### </summary>
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class SecurityCustomPropertiesAlgorithm(QCAlgorithm):
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'''Demonstration of how to use custom security properties.
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In this algorithm we trade a security based on the values of a slow and fast EMAs which are stored in the security itself.'''
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def initialize(self):
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self.set_start_date(2013,10, 7)
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self.set_end_date(2013,10,11)
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self.set_cash(100000)
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self.spy = self.add_equity("SPY", Resolution.MINUTE)
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# Using the dynamic interface to store our indicator as a custom property.
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self.spy.slow_ema = self.ema(self.spy.symbol, 30, Resolution.MINUTE)
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# Using the generic interface to store our indicator as a custom property.
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self.spy.add("fast_ema", self.ema(self.spy.symbol, 60, Resolution.MINUTE))
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# Using the indexer to store our indicator as a custom property
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self.spy["bb"] = self.bb(self.spy.symbol, 20, 1, MovingAverageType.SIMPLE, Resolution.MINUTE)
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# Fee factor to be used by the custom fee model
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self.spy.fee_factor = 0.00002
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self.spy.set_fee_model(CustomFeeModel())
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# This property will be used to store the prices used to calculate the fees in order to assert the correct fee factor is used.
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self.spy.orders_fees_prices = {}
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def on_data(self, data):
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if not self.spy.fast_ema.is_ready:
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return
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if not self.portfolio.invested:
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# Using the property and the generic interface to access our indicator
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if self.spy.slow_ema > self.spy.fast_ema:
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self.set_holdings(self.spy.symbol, 1)
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else:
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if self.spy.get[ExponentialMovingAverage]("slow_ema") < self.spy.get[ExponentialMovingAverage]("fast_ema"):
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self.liquidate(self.spy.symbol)
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# Using the indexer to access our indicator
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bb: BollingerBands = self.spy["bb"]
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self.plot("bb", bb.upper_band, bb.middle_band, bb.lower_band)
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def on_order_event(self, order_event):
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if order_event.status == OrderStatus.FILLED:
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fee = order_event.order_fee
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expected_fee = self.spy.orders_fees_prices[order_event.order_id] * order_event.absolute_fill_quantity * self.spy.fee_factor
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if not isclose(fee.value.amount, expected_fee, rel_tol=1e-15):
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raise AssertionError(f"Custom fee model failed to set the correct fee. Expected: {expected_fee}. Actual: {fee.value.amount}")
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def on_end_of_algorithm(self):
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if self.transactions.orders_count == 0:
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raise AssertionError("No orders executed")
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class CustomFeeModel(FeeModel):
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'''This custom fee is implemented for demonstration purposes only.'''
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def get_order_fee(self, parameters):
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security = parameters.security
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# custom fee math using the fee factor stored in security instance
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fee_factor = security.fee_factor
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if fee_factor is None:
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fee_factor = 0.00001
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# Store the price used to calculate the fee for this order
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security["orders_fees_prices"][parameters.order.id] = security.price
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fee = max(1.0, security.price * parameters.order.absolute_quantity * fee_factor)
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return OrderFee(CashAmount(fee, "USD"))
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