wehub-resource-sync
103 lines
4.4 KiB
Python
103 lines
4.4 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 AlgorithmImports import *
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class ConstantAlphaModel(AlphaModel):
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''' Provides an implementation of IAlphaModel that always returns the same insight for each security'''
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def __init__(self, type, direction, period, magnitude = None, confidence = None, weight = None):
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'''Initializes a new instance of the ConstantAlphaModel class
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Args:
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type: The type of insight
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direction: The direction of the insight
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period: The period over which the insight with come to fruition
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magnitude: The predicted change in magnitude as a +- percentage
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confidence: The confidence in the insight
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weight: The portfolio weight of the insights'''
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self.type = type
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self.direction = direction
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self.period = period
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self.magnitude = magnitude
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self.confidence = confidence
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self.weight = weight
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self.securities = []
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self.insights_time_by_symbol = {}
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self.Name = '{}({},{},{}'.format(self.__class__.__name__, type, direction, strfdelta(period))
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if magnitude is not None:
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self.Name += ',{}'.format(magnitude)
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if confidence is not None:
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self.Name += ',{}'.format(confidence)
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self.Name += ')'
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def update(self, algorithm, data):
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''' Creates a constant insight for each security as specified via the constructor
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Args:
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algorithm: The algorithm instance
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data: The new data available
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Returns:
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The new insights generated'''
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insights = []
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for security in self.securities:
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# security price could be zero until we get the first data point. e.g. this could happen
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# when adding both forex and equities, we will first get a forex data point
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if security.price != 0 and self.should_emit_insight(algorithm.utc_time, security.symbol):
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insights.append(Insight(security.symbol, self.period, self.type, self.direction, self.magnitude, self.confidence, weight = self.weight))
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return insights
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def on_securities_changed(self, algorithm, changes):
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''' Event fired each time the we add/remove securities from the data feed
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Args:
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algorithm: The algorithm instance that experienced the change in securities
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changes: The security additions and removals from the algorithm'''
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for added in changes.added_securities:
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self.securities.append(added)
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# this will allow the insight to be re-sent when the security re-joins the universe
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for removed in changes.removed_securities:
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if removed in self.securities:
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self.securities.remove(removed)
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if removed.symbol in self.insights_time_by_symbol:
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self.insights_time_by_symbol.pop(removed.symbol)
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def should_emit_insight(self, utc_time, symbol):
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if symbol.is_canonical():
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# canonical futures & options are none tradable
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return False
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generated_time_utc = self.insights_time_by_symbol.get(symbol)
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if generated_time_utc is not None:
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# we previously emitted a insight for this symbol, check it's period to see
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# if we should emit another insight
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if utc_time - generated_time_utc < self.period:
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return False
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# we either haven't emitted a insight for this symbol or the previous
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# insight's period has expired, so emit a new insight now for this symbol
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self.insights_time_by_symbol[symbol] = utc_time
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return True
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def strfdelta(tdelta):
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d = tdelta.days
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h, rem = divmod(tdelta.seconds, 3600)
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m, s = divmod(rem, 60)
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return "{}.{:02d}:{:02d}:{:02d}".format(d,h,m,s)
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