Mean-Variance Portfolio Rebalancing

Article Strategy library · Author: QuantConnect

Summary

This example assembles a portfolio algorithm from separate framework components. A historical-returns alpha model supplies investment insights, and a mean-variance portfolio construction model turns those insights into portfolio weights intended to follow modern portfolio theory. The algorithm uses minute-resolution universe settings and an immediate execution model, with risk management set to a null model.

The example selects a small set of US equities through a coarse universe selector and removes one symbol after a specified calendar day. It disables portfolio rebalancing on insight changes and sets a short historical run window with starting cash. These details illustrate framework wiring rather than a documented investment study: there are no reported returns, benchmark comparisons, transaction-cost analysis, or explanation of the optimizer’s assumptions. Mean-variance allocations depend on estimated returns and risk inputs, so the example alone does not establish the quality or stability of its resulting portfolio.

Key ideas

Historical returns generate alpha insights that feed a mean-variance portfolio construction model.
A framework combines universe selection, signal generation, portfolio construction, execution, and risk management.
The sample universe consists of selected US equities and changes during the brief configured run.
No performance results or optimizer diagnostics are presented, so the example demonstrates implementation structure rather than validated performance.

Tags

Equities US markets Portfolio construction Statistics Execution

Full text

# MeanVarianceOptimizationFrameworkAlgorithm


# MeanVarianceOptimizationFrameworkAlgorithm









Mean Variance Optimization algorithm.

Mean Variance Optimization algorithm Uses the HistoricalReturnsAlphaModel and the MeanVarianceOptimizationPortfolioConstructionModel to create an algorithm that rebalances the portfolio according to modern portfolio theory

## Source (Apache-2.0)

```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 AlgorithmImports import *
from Portfolio.MeanVarianceOptimizationPortfolioConstructionModel import *

### <summary>
### Mean Variance Optimization algorithm
### Uses the HistoricalReturnsAlphaModel and the MeanVarianceOptimizationPortfolioConstructionModel
### to create an algorithm that rebalances the portfolio according to modern portfolio theory
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="using quantconnect" />
### <meta name="tag" content="trading and orders" />
class MeanVarianceOptimizationFrameworkAlgorithm(QCAlgorithm):
    '''Mean Variance Optimization algorithm.'''

    def initialize(self):

        # Set requested data resolution
        self.universe_settings.resolution = Resolution.MINUTE

        self.settings.rebalance_portfolio_on_insight_changes = False

        self.set_start_date(2013,10,7)   #Set Start Date
        self.set_end_date(2013,10,11)    #Set End Date
        self.set_cash(100000)           #Set Strategy Cash

        self._symbols = [ Symbol.create(x, SecurityType.EQUITY, Market.USA) for x in [ 'AIG', 'BAC', 'IBM', 'SPY' ] ]

        # set algorithm framework models
        self.set_universe_selection(CoarseFundamentalUniverseSelectionModel(self.coarse_selector))
        self.set_alpha(HistoricalReturnsAlphaModel(resolution = Resolution.DAILY))
        self.set_portfolio_construction(MeanVarianceOptimizationPortfolioConstructionModel())
        self.set_execution(ImmediateExecutionModel())
        self.set_risk_management(NullRiskManagementModel())

    def coarse_selector(self, coarse):
        # Drops SPY after the 8th
        last = 3 if self.time.day > 8 else len(self._symbols)

        return self._symbols[0:last]

    def on_order_event(self,  order_event):
        if order_event.status == OrderStatus.FILLED:
            self.log(str(order_event))

```

Shown in full with attribution under the source's licence. Licence: Apache-2.0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.

Mean-Variance Portfolio Rebalancing with Historical Return Signals

Summary

Key ideas

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