wehub-resource-sync
59 lines
2.7 KiB
Python
59 lines
2.7 KiB
Python
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
|
|
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
from AlgorithmImports import *
|
|
from Alphas.RsiAlphaModel import RsiAlphaModel
|
|
from Portfolio.EqualWeightingPortfolioConstructionModel import EqualWeightingPortfolioConstructionModel
|
|
from Execution.VolumeWeightedAveragePriceExecutionModel import VolumeWeightedAveragePriceExecutionModel
|
|
|
|
### <summary>
|
|
### Regression algorithm for the VolumeWeightedAveragePriceExecutionModel.
|
|
### This algorithm shows how the execution model works to split up orders and
|
|
### submit them only when the price is on the favorable side of the intraday VWAP.
|
|
### </summary>
|
|
### <meta name="tag" content="using data" />
|
|
### <meta name="tag" content="using quantconnect" />
|
|
### <meta name="tag" content="trading and orders" />
|
|
class VolumeWeightedAveragePriceExecutionModelRegressionAlgorithm(QCAlgorithm):
|
|
'''Regression algorithm for the VolumeWeightedAveragePriceExecutionModel.
|
|
This algorithm shows how the execution model works to split up orders and
|
|
submit them only when the price is on the favorable side of the intraday VWAP.'''
|
|
|
|
def initialize(self):
|
|
|
|
self.universe_settings.resolution = Resolution.MINUTE
|
|
|
|
self.set_start_date(2013,10,7)
|
|
self.set_end_date(2013,10,11)
|
|
self.set_cash(1000000)
|
|
|
|
self.set_universe_selection(ManualUniverseSelectionModel([
|
|
Symbol.create('AIG', SecurityType.EQUITY, Market.USA),
|
|
Symbol.create('BAC', SecurityType.EQUITY, Market.USA),
|
|
Symbol.create('IBM', SecurityType.EQUITY, Market.USA),
|
|
Symbol.create('SPY', SecurityType.EQUITY, Market.USA)
|
|
]))
|
|
|
|
# using hourly rsi to generate more insights
|
|
self.set_alpha(RsiAlphaModel(14, Resolution.HOUR))
|
|
self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
|
|
self.set_execution(VolumeWeightedAveragePriceExecutionModel())
|
|
|
|
self.insights_generated += self.on_insights_generated
|
|
|
|
def on_insights_generated(self, algorithm, data):
|
|
self.log(f"{self.time}: {', '.join(str(x) for x in data.insights)}")
|
|
|
|
def on_order_event(self, orderEvent):
|
|
self.log(f"{self.time}: {orderEvent}")
|