AlexCatarino
181 lines
7.9 KiB
Python
181 lines
7.9 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 clr import AddReference
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AddReference("System")
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AddReference("QuantConnect.Algorithm")
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AddReference("QuantConnect.Algorithm.Framework")
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AddReference("QuantConnect.Common")
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AddReference("QuantConnect.Indicators")
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from System import *
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from QuantConnect import *
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from QuantConnect.Indicators import *
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from QuantConnect.Algorithm import *
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from QuantConnect.Algorithm.Framework import *
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from QuantConnect.Algorithm.Framework.Portfolio import *
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from Portfolio.MinimumVariancePortfolioOptimizer import MinimumVariancePortfolioOptimizer
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from datetime import timedelta
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import numpy as np
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import pandas as pd
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### <summary>
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### Provides an implementation of Mean-Variance portfolio optimization based on modern portfolio theory.
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### The default model uses the MinimumVariancePortfolioOptimizer that accepts a 63-row matrix of 1-day returns.
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### </summary>
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class MeanVarianceOptimizationPortfolioConstructionModel(PortfolioConstructionModel):
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def __init__(self,
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rebalance = Resolution.Daily,
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portfolioBias = PortfolioBias.LongShort,
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lookback = 1,
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period = 63,
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resolution = Resolution.Daily,
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targetReturn = 0.02,
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optimizer = None):
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"""Initialize the model
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Args:
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rebalance: Rebalancing parameter. If it is a timedelta, date rules or Resolution, it will be converted into a function.
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If None will be ignored.
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The function returns the next expected rebalance time for a given algorithm UTC DateTime.
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The function returns null if unknown, in which case the function will be called again in the
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next loop. Returning current time will trigger rebalance.
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portfolioBias: Specifies the bias of the portfolio (Short, Long/Short, Long)
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lookback(int): Historical return lookback period
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period(int): The time interval of history price to calculate the weight
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resolution: The resolution of the history price
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optimizer(class): Method used to compute the portfolio weights"""
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self.lookback = lookback
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self.period = period
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self.resolution = resolution
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self.portfolioBias = portfolioBias
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self.sign = lambda x: -1 if x < 0 else (1 if x > 0 else 0)
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lower = 0 if portfolioBias == PortfolioBias.Long else -1
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upper = 0 if portfolioBias == PortfolioBias.Short else 1
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self.optimizer = MinimumVariancePortfolioOptimizer(lower, upper, targetReturn) if optimizer is None else optimizer
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self.symbolDataBySymbol = {}
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# If the argument is an instance of Resolution or Timedelta
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# Redefine rebalancingFunc
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rebalancingFunc = rebalance
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if isinstance(rebalance, int):
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rebalance = Extensions.ToTimeSpan(rebalance)
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if isinstance(rebalance, timedelta):
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rebalancingFunc = lambda dt: dt + rebalance
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if rebalancingFunc:
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self.SetRebalancingFunc(rebalancingFunc)
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def ShouldCreateTargetForInsight(self, insight):
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if len(PortfolioConstructionModel.FilterInvalidInsightMagnitude(self.Algorithm, [insight])) == 0:
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return False
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symbolData = self.symbolDataBySymbol.get(insight.Symbol)
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if insight.Magnitude is None:
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self.algorithm.SetRunTimeError(ArgumentNullException('MeanVarianceOptimizationPortfolioConstructionModel does not accept \'None\' as Insight.Magnitude. Please checkout the selected Alpha Model specifications.'))
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return False
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symbolData.Add(self.Algorithm.Time, insight.Magnitude)
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return True
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def DetermineTargetPercent(self, activeInsights):
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"""
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Will determine the target percent for each insight
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Args:
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Returns:
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"""
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targets = {}
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symbols = [insight.Symbol for insight in activeInsights]
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# Create a dictionary keyed by the symbols in the insights with an pandas.Series as value to create a data frame
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returns = { str(symbol) : data.Return for symbol, data in self.symbolDataBySymbol.items() if symbol in symbols }
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returns = pd.DataFrame(returns)
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# The portfolio optimizer finds the optional weights for the given data
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weights = self.optimizer.Optimize(returns)
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weights = pd.Series(weights, index = returns.columns)
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# Create portfolio targets from the specified insights
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for insight in activeInsights:
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weight = weights[str(insight.Symbol)]
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# don't trust the optimizer
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if self.portfolioBias != PortfolioBias.LongShort and self.sign(weight) != self.portfolioBias:
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weight = 0
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targets[insight] = weight
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return targets
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def OnSecuritiesChanged(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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# clean up data for removed securities
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super().OnSecuritiesChanged(algorithm, changes)
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for removed in changes.RemovedSecurities:
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symbolData = self.symbolDataBySymbol.pop(removed.Symbol, None)
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symbolData.Reset()
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# initialize data for added securities
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symbols = [ x.Symbol for x in changes.AddedSecurities ]
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history = algorithm.History(symbols, self.lookback * self.period, self.resolution)
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if history.empty: return
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tickers = history.index.levels[0]
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for ticker in tickers:
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symbol = SymbolCache.GetSymbol(ticker)
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if symbol not in self.symbolDataBySymbol:
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symbolData = self.MeanVarianceSymbolData(symbol, self.lookback, self.period)
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symbolData.WarmUpIndicators(history.loc[ticker])
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self.symbolDataBySymbol[symbol] = symbolData
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class MeanVarianceSymbolData:
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'''Contains data specific to a symbol required by this model'''
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def __init__(self, symbol, lookback, period):
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self.symbol = symbol
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self.roc = RateOfChange(f'{symbol}.ROC({lookback})', lookback)
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self.roc.Updated += self.OnRateOfChangeUpdated
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self.window = RollingWindow[IndicatorDataPoint](period)
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def Reset(self):
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self.roc.Updated -= self.OnRateOfChangeUpdated
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self.roc.Reset()
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self.window.Reset()
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def WarmUpIndicators(self, history):
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for tuple in history.itertuples():
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self.roc.Update(tuple.Index, tuple.close)
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def OnRateOfChangeUpdated(self, roc, value):
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if roc.IsReady:
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self.window.Add(value)
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def Add(self, time, value):
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item = IndicatorDataPoint(self.symbol, time, value)
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self.window.Add(item)
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@property
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def Return(self):
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return pd.Series(
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data = [(1 + float(x.Value))**252 - 1 for x in self.window],
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index = [x.EndTime for x in self.window])
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@property
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def IsReady(self):
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return self.window.IsReady
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def __str__(self, **kwargs):
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return '{}: {:.2%}'.format(self.roc.Name, (1 + self.window[0])**252 - 1) |