AlexCatarino
87 lines
4.5 KiB
Python
87 lines
4.5 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 clr import AddReference
|
|
AddReference("QuantConnect.Common")
|
|
AddReference("QuantConnect.Algorithm.Framework")
|
|
|
|
from QuantConnect.Algorithm.Framework.Alphas import *
|
|
from Portfolio.EqualWeightingPortfolioConstructionModel import *
|
|
|
|
class AccumulativeInsightPortfolioConstructionModel(EqualWeightingPortfolioConstructionModel):
|
|
'''Provides an implementation of IPortfolioConstructionModel that allocates percent of account
|
|
to each insight, defaulting to 3%.
|
|
For insights of direction InsightDirection.Up, long targets are returned and
|
|
for insights of direction InsightDirection.Down, short targets are returned.
|
|
By default, no rebalancing shall be done.
|
|
Rules:
|
|
1. On active Up insight, increase position size by percent
|
|
2. On active Down insight, decrease position size by percent
|
|
3. On active Flat insight, move by percent towards 0
|
|
4. On expired insight, and no other active insight, emits a 0 target'''
|
|
|
|
def __init__(self, rebalance = None, portfolioBias = PortfolioBias.LongShort, percent = 0.03):
|
|
'''Initialize a new instance of AccumulativeInsightPortfolioConstructionModel
|
|
Args:
|
|
rebalance: Rebalancing parameter. If it is a timedelta, date rules or Resolution, it will be converted into a function.
|
|
If None will be ignored.
|
|
The function returns the next expected rebalance time for a given algorithm UTC DateTime.
|
|
The function returns null if unknown, in which case the function will be called again in the
|
|
next loop. Returning current time will trigger rebalance.
|
|
portfolioBias: Specifies the bias of the portfolio (Short, Long/Short, Long)
|
|
percent: percent of portfolio to allocate to each position'''
|
|
super().__init__(rebalance)
|
|
self.portfolioBias = portfolioBias
|
|
self.percent = abs(percent)
|
|
self.sign = lambda x: -1 if x < 0 else (1 if x > 0 else 0)
|
|
|
|
def DetermineTargetPercent(self, activeInsights):
|
|
'''Will determine the target percent for each insight
|
|
Args:
|
|
activeInsights: The active insights to generate a target for'''
|
|
percentPerSymbol = {}
|
|
|
|
insights = sorted(self.InsightCollection.GetActiveInsights(self.currentUtcTime), key=lambda insight: insight.GeneratedTimeUtc)
|
|
|
|
for insight in insights:
|
|
targetPercent = 0
|
|
if insight.Symbol in percentPerSymbol:
|
|
targetPercent = percentPerSymbol[insight.Symbol]
|
|
if insight.Direction == InsightDirection.Flat:
|
|
# We received a Flat
|
|
# if adding or subtracting will push past 0, then make it 0
|
|
if abs(targetPercent) < self.percent:
|
|
targetPercent = 0
|
|
else:
|
|
# otherwise, we flatten by percent
|
|
targetPercent += (-self.percent if targetPercent > 0 else self.percent)
|
|
targetPercent += self.percent * insight.Direction
|
|
|
|
# adjust to respect portfolio bias
|
|
if self.portfolioBias != PortfolioBias.LongShort and self.sign(targetPercent) != self.portfolioBias:
|
|
targetPercent = 0
|
|
|
|
percentPerSymbol[insight.Symbol] = targetPercent
|
|
|
|
return dict((insight, percentPerSymbol[insight.Symbol]) for insight in activeInsights)
|
|
|
|
def CreateTargets(self, algorithm, insights):
|
|
'''Create portfolio targets from the specified insights
|
|
Args:
|
|
algorithm: The algorithm instance
|
|
insights: The insights to create portfolio targets from
|
|
Returns:
|
|
An enumerable of portfolio targets to be sent to the execution model'''
|
|
self.currentUtcTime = algorithm.UtcTime
|
|
return super().CreateTargets(algorithm, insights)
|