Mean-Reversion Portfolio Model Example

Article Strategy library · Author: QuantConnect

Summary

This short QuantConnect Lean example shows how to assemble an algorithm around a mean-reversion portfolio construction model. It initializes a historical simulation with daily data for SPY and AAPL, sets cash and a test window, and registers a security initializer that supplies each security with its last known price. The portfolio construction model is then attached to the algorithm.

The example also adds a constant one-day upward price insight. That signal does not itself demonstrate a mean-reversion entry rule; the mean-reversion behavior is delegated to the named portfolio construction component, whose internals are not included here. The source therefore illustrates framework wiring rather than a complete strategy specification. It provides no performance results, trading rationale, risk controls, or comparison against alternatives, so it is useful primarily as a minimal integration example for researchers already familiar with Lean's insight and portfolio model abstractions.

Key ideas

The example attaches a mean-reversion portfolio construction model to a Lean algorithm.
It subscribes to daily data for SPY and AAPL over a defined historical test period.
The algorithm emits a constant one-day upward price insight rather than a described mean-reversion signal.
The portfolio model's internal allocation rules are not shown in the document.
No performance results or risk management approach are provided.

Tags

Equities Mean reversion Portfolio construction Backtesting

Full text

# MeanReversionPortfolioAlgorithm


# MeanReversionPortfolioAlgorithm









Example algorithm of using MeanReversionPortfolioConstructionModel

## 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.MeanReversionPortfolioConstructionModel import *

class MeanReversionPortfolioAlgorithm(QCAlgorithm):
    '''Example algorithm of using MeanReversionPortfolioConstructionModel'''

    def initialize(self):
        # Set starting date, cash and ending date of the backtest
        self.set_start_date(2020, 9, 1)
        self.set_end_date(2021, 2, 28)
        self.set_cash(100000)

        self.set_security_initializer(lambda security: security.set_market_price(self.get_last_known_price(security)))
        
        # Subscribe to data of the selected stocks
        self._symbols = [self.add_equity(ticker, Resolution.DAILY).symbol for ticker in ["SPY", "AAPL"]]

        self.add_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(1)))
        self.set_portfolio_construction(MeanReversionPortfolioConstructionModel())

```

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.

Using a Mean-Reversion Portfolio Model in a Lean Example Algorithm

Summary

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