Files
quantconnect--lean/Algorithm.Framework/Alphas/ConstantAlphaModel.py
Martin Molinero 1983f36792 Allow Python selection to return unchanged
- We will now check if python selection method returned `Universe.Unchanged`
- Removing `ToList()` call on fine and coarse data before sending it to
the python algorithm
- Adding regression algorithms
2019-07-31 15:36:07 -03:00

109 lines
4.6 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")
AddReference("QuantConnect.Algorithm.Framework")
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Algorithm.Framework import *
from QuantConnect.Algorithm.Framework.Alphas import AlphaModel, Insight, InsightType, InsightDirection
class ConstantAlphaModel(AlphaModel):
''' Provides an implementation of IAlphaModel that always returns the same insight for each security'''
def __init__(self, type, direction, period, magnitude = None, confidence = None):
'''Initializes a new instance of the ConstantAlphaModel class
Args:
type: The type of insight
direction: The direction of the insight
period: The period over which the insight with come to fruition
magnitude: The predicted change in magnitude as a +- percentage
confidence: The confidence in the insight'''
self.type = type
self.direction = direction
self.period = period
self.magnitude = magnitude
self.confidence = confidence
self.securities = []
self.insightsTimeBySymbol = {}
typeString = Extensions.GetEnumString(type, InsightType)
directionString = Extensions.GetEnumString(direction, InsightDirection)
self.Name = '{}({},{},{}'.format(self.__class__.__name__, typeString, directionString, strfdelta(period))
if magnitude is not None:
self.Name += ',{}'.format(magnitude)
if confidence is not None:
self.Name += ',{}'.format(confidence)
self.Name += ')';
def Update(self, algorithm, data):
''' Creates a constant insight for each security as specified via the constructor
Args:
algorithm: The algorithm instance
data: The new data available
Returns:
The new insights generated'''
insights = []
for security in self.securities:
# security price could be zero until we get the first data point. e.g. this could happen
# when adding both forex and equities, we will first get a forex data point
if security.Price != 0 and self.ShouldEmitInsight(algorithm.UtcTime, security.Symbol):
insights.append(Insight(security.Symbol, self.period, self.type, self.direction, self.magnitude, self.confidence))
return insights
def OnSecuritiesChanged(self, algorithm, changes):
''' Event fired each time the we add/remove securities from the data feed
Args:
algorithm: The algorithm instance that experienced the change in securities
changes: The security additions and removals from the algorithm'''
for added in changes.AddedSecurities:
self.securities.append(added)
# this will allow the insight to be re-sent when the security re-joins the universe
for removed in changes.RemovedSecurities:
if removed in self.securities:
self.securities.remove(removed)
if removed.Symbol in self.insightsTimeBySymbol:
self.insightsTimeBySymbol.pop(removed.Symbol)
def ShouldEmitInsight(self, utcTime, symbol):
generatedTimeUtc = self.insightsTimeBySymbol.get(symbol)
if generatedTimeUtc is not None:
# we previously emitted a insight for this symbol, check it's period to see
# if we should emit another insight
if utcTime - generatedTimeUtc < self.period:
return False
# we either haven't emitted a insight for this symbol or the previous
# insight's period has expired, so emit a new insight now for this symbol
self.insightsTimeBySymbol[symbol] = utcTime
return True
def strfdelta(tdelta):
d = tdelta.days
h, rem = divmod(tdelta.seconds, 3600)
m, s = divmod(rem, 60)
return "{}.{:02d}:{:02d}:{:02d}".format(d,h,m,s)