chore: import upstream snapshot with attribution
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wehub-resource-sync
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# 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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from Portfolio.MinimumVariancePortfolioOptimizer import MinimumVariancePortfolioOptimizer
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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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portfolio_bias = PortfolioBias.LONG_SHORT,
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lookback = 1,
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period = 63,
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resolution = Resolution.DAILY,
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target_return = 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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portfolio_bias: 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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super().__init__()
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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.portfolio_bias = portfolio_bias
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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 portfolio_bias == PortfolioBias.LONG else -1
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upper = 0 if portfolio_bias == PortfolioBias.SHORT else 1
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self.optimizer = MinimumVariancePortfolioOptimizer(lower, upper, target_return) if optimizer is None else optimizer
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self.symbol_data_by_symbol = {}
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# If the argument is an instance of Resolution or Timedelta
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# Redefine rebalancing_func
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rebalancing_func = rebalance
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if isinstance(rebalance, Resolution):
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rebalance = Extensions.to_time_span(rebalance)
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if isinstance(rebalance, timedelta):
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rebalancing_func = lambda dt: dt + rebalance
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if rebalancing_func:
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self.set_rebalancing_func(rebalancing_func)
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def should_create_target_for_insight(self, insight):
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if len(PortfolioConstructionModel.filter_invalid_insight_magnitude(self.algorithm, [insight])) == 0:
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return False
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symbol_data = self.symbol_data_by_symbol.get(insight.symbol)
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if insight.magnitude is None:
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self.algorithm.set_run_time_error(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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symbol_data.add(self.algorithm.time, insight.magnitude)
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return True
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def determine_target_percent(self, active_insights):
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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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# If we have no insights just return an empty target list
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if len(active_insights) == 0:
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return targets
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symbols = [insight.symbol for insight in active_insights]
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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.id) : data.return_ for symbol, data in self.symbol_data_by_symbol.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 active_insights:
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weight = weights[str(insight.symbol.id)]
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# don't trust the optimizer
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if self.portfolio_bias != PortfolioBias.LONG_SHORT and self.sign(weight) != self.portfolio_bias:
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weight = 0
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targets[insight] = weight
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return targets
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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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# clean up data for removed securities
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super().on_securities_changed(algorithm, changes)
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for removed in changes.removed_securities:
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symbol_data = self.symbol_data_by_symbol.pop(removed.symbol, None)
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symbol_data.reset()
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# initialize data for added securities
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symbols = [x.symbol for x in changes.added_securities]
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for symbol in [x for x in symbols if x not in self.symbol_data_by_symbol]:
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self.symbol_data_by_symbol[symbol] = self.MeanVarianceSymbolData(symbol, self.lookback, self.period)
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history = algorithm.history[TradeBar](symbols, self.lookback * self.period, self.resolution)
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for bars in history:
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for symbol, bar in bars.items():
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symbol_data = self.symbol_data_by_symbol.get(symbol).update(bar.end_time, bar.value)
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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.on_rate_of_change_updated
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self.window = RollingWindow(period)
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def reset(self):
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self.roc.updated -= self.on_rate_of_change_updated
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self.roc.reset()
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self.window.reset()
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def update(self, time, value):
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return self.roc.update(time, value)
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def on_rate_of_change_updated(self, roc, value):
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if roc.is_ready:
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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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# Get symbols' returns, we use simple return according to
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# Meucci, Attilio, Quant Nugget 2: Linear vs. Compounded Returns – Common Pitfalls in Portfolio Management (May 1, 2010).
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# GARP Risk Professional, pp. 49-51, April 2010 , Available at SSRN: https://ssrn.com/abstract=1586656
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@property
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def return_(self):
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return pd.Series(
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data = [x.value for x in self.window],
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index = [x.end_time for x in self.window])
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@property
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def is_ready(self):
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return self.window.is_ready
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def __str__(self, **kwargs):
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return '{}: {:.2%}'.format(self.roc.name, self.window[0])
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