wehub-resource-sync
159 lines
7.0 KiB
Python
159 lines
7.0 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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from Portfolio.RiskParityPortfolioOptimizer import RiskParityPortfolioOptimizer
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### <summary>
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### Risk Parity Portfolio Construction Model
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### </summary>
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### <remarks>Spinu, F. (2013). An algorithm for computing risk parity weights. Available at SSRN 2297383.
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### Available at https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=2297383</remarks>
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class RiskParityPortfolioConstructionModel(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 = 252,
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resolution = Resolution.DAILY,
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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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if portfolio_bias == PortfolioBias.SHORT:
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raise ArgumentException("Long position must be allowed in RiskParityPortfolioConstructionModel.")
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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.sign = lambda x: -1 if x < 0 else (1 if x > 0 else 0)
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self.optimizer = RiskParityPortfolioOptimizer() 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, int):
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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 determine_target_percent(self, active_insights):
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"""Will determine the target percent for each insight
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Args:
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active_insights: list of active insights
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Returns:
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dictionary of insight and respective target weight
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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) : 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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targets[insight] = weights[str(insight.symbol)]
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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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algorithm.unregister_indicator(symbol_data.roc)
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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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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.get_symbol(ticker)
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if symbol not in self._symbol_data_by_symbol:
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symbol_data = self.RiskParitySymbolData(symbol, self.lookback, self.period)
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symbol_data.warm_up_indicators(history.loc[ticker])
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self._symbol_data_by_symbol[symbol] = symbol_data
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algorithm.register_indicator(symbol, symbol_data.roc, self.resolution)
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class RiskParitySymbolData:
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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 warm_up_indicators(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 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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@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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