Tactical Portfolio Descriptions
Below are brief descriptions for each of the tactical (dynamic) Portfolio Recipes that we track at RecipeInvesting. Click on the ID (e.g., "t.aaaa") for any of the Portfolio Recipes to view graphs and detailed risk and return metrics.
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Adaptive Portfolio Recipes
These Portfolio Recipes use a combination of allocation methods to adapt to market conditions.
Adaptive Allocation A (t.aaaa) ranks 9 ETFs (DBC, EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM) based on total return over the past month. Then the algorithm picks the top 5 ETFs. Then it chooses a weight (percentage allocation) for each of the chosen 5 ETFs in such a way that the overall portfolio's volatility is minimized using a minimum variance algorithm based on standard deviation. See the detailed risk and return metrics for this Portfolio Recipe.
Adaptive Allocation B (t.aaab) ranks 9 ETFs (DBC, EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM) based on total return over the past month. Then the algorithm picks the top 4 ETFs. Then it chooses a weight (percentage allocation) for each of the 5 chosen ETFs in such a way that the overall portfolio's volatility is minimized using a minimum variance algorithm based on downside deviation. See the detailed risk and return metrics for this Portfolio Recipe.
Adaptive Allocation C (t.aaac) ranks 9 ETFs (DBC, EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM) based on total return over the past month. Then the algorithm picks the top 4 ETFs. Then it chooses a weight (percentage allocation) for each of the 4 chosen ETFs in such a way that the overall portfolio's volatility is minimized using a minimum variance algorithm based on standard deviation. See the detailed risk and return metrics for this Portfolio Recipe.
Adaptive Allocation D (t.aaad) ranks 9 ETFs (DBC, EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM) based on total return over the past few months. Then the algorithm picks the top 3 ETFs. Then it chooses the a weight (percentage allocation) for each of the 3 chosen ETFs based on the inverse of each asset's risk as defined by standard deviation. See the detailed risk and return metrics for this Portfolio Recipe.
Adaptive Allocation E (t.aaae) ranks 9 ETFs (DBC, EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM) based on total return over the past month. Then the algorithm picks the top 4 ETFs. Then it chooses the a weight (percentage allocation) for each of the 4 chosen ETFs based on the inverse of each asset's risk as defined by standard deviation. See the detailed risk and return metrics for this Portfolio Recipe.
Adaptive Allocation F (t.aaaf) ranks 9 ETFs (DBC, EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM) based on total return over several months. Then the algorithm picks the top 5 ETFs. Then it chooses a weight (percentage allocation) for each of the 5 chosen ETFs in such a way that the overall portfolio's volatility is minimized using a minimum variance algorithm. See the detailed risk and return metrics for this Portfolio Recipe.
Correlation & Diversification Portfolio Recipes
Minimum Correlation (t.coco) creates a correlation matrix that calculates the correlation of the assets in a set of 8 asset class ETFs (EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM). The lookback period for calculating the correlation is 100 trading days. The algorithm then solves to find the combination of assets that gives the weighted portfolio the lowest overall correlation. See the detailed risk and return metrics for this Portfolio Recipe.
Maximum Diversification (t.mdiv) identifies the most risky asset in the set of 8 asset class ETFs (EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM), based on standard deviation of daily returns over the past 90 trading days. Then the algorithm applies a Minimum Variance approach, excluding the riskiest asset and choosing a portfolio allocation that maximizes diversification of the portfolio. See the detailed risk and return metrics for this Portfolio Recipe.
Equal Weight Portfolio (t.eqwt) allocates 12.5% to each of 8 asset class ETFs (EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM). While this is technically not a tactical portfolio recipe, since the recipe never changes, t.eqwt is used as a benchmark for comparing the various recipes that also use the same set of 8 asset class ETFs. See the detailed risk and return metrics for this Portfolio Recipe.
Equal Weight With Cluster (t.dist) identifies clusters of correlated assets within the group of 8 asset class ETFs (EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM) over the past 95 trading days. Then the algorithm allocates an equal weight to each cluster. Within each cluster the allocation to each asset within the cluster is also divided evenly. See the detailed risk and return metrics for this Portfolio Recipe.
Active Combined Asset Portfolio (t.acap) chooses an allocation from a set of 5 asset class ETFs (SPY, IEF, GLD, TLT, VNQ). This recipe allocates each third of the portfolio by choosing from a different pair of asset class ETFs. Each pair of ETFs is analyzed based on price momentum (adjusted for distributions) over the past year and then one ETF is chosen for each third of the portfolio's allocation, as follows:
- For the first 1/3, t.acap chooses SPY or IEF.
- For the next 1/3, t.acap chooses GLD or TLT.
- For the last 1/3, t.acap chooses VNQ or IEF.
Quarterly Asset Rotation Portfolio (t.qaro) choose from a set of 4 asset class ETFs (VTI, GLD, TLT, SHY) based on their 3-month total return. This ranking is done at the end of March, June, September, and December. See the detailed risk and return metrics for this Portfolio Recipe.
Then t.qaro chooses the Top 2 asset class ETFs and allocates 50% to each chosen ETF for the upcoming quarter. The Top 2 asset class ETFs will remain as the chosen assets for three months, until the next quarterly asset rotation. Although a new target allocation is chosen only quarterly, this recipe rebalances monthly to match the most recent quarterly allocation.
Defensive Bond Portfolio (t.dbnd) chooses from a set of 4 asset class ETFs (HYS, HYMB, MBB, SHY) based on 3-month total return. This recipe chooses a 100% allocation to the top-ranked ETF, as long as that ETF's 3-month return is greater than SHY, the short-term bond ETF. If the top-ranked ETF has underperformed SHY, then the recipe's asset allocation is 100% SHY for the coming month. See the detailed risk and return metrics for this Portfolio Recipe.
Dynamic Harry Browne Portfolio (t.dyhb) chooses from a set of 4 asset class ETFs (VTI, TLT, GLD, SHY) based on 3-month total return. This recipe chooses the top two ranked asset class ETFs (from VTI, TLT, GLD). Then this recipe allocates 50% to each of the two ETFs as long as 1) each ETF's 3-month total return greater than zero and 2) each ETF's latest adjusted closing price exceeds the 21-day moving average. If an ETF fails one of these tests, then its 50% allocation goes to SHY instead. See the detailed risk and return metrics for this Portfolio Recipe.
Faber Relative Strength: Top 1 (t.frs1) ranks a set of 6 global asset class ETFs (SPY, EFA, DBC, IEF, VNQ, SHY) based on a weighted moving average of returns over the past 1, 3, 6, 9, and 12 months. Then t.frs1 chooses the Top 1 asset class ETF. An additional test is applied based on each asset's 10-month simple moving average. This methodology is described in Mebane Faber's paper entitled "Relative Strength Strategic for Investing" (April 2010). See the detailed risk and return metrics for this Portfolio Recipe.
Faber Relative Strength: Top 2 (t.frs2) is similar to t.frs1, except that the Top 2 asset classes are chosen each month. See the detailed risk and return metrics for this Portfolio Recipe.
Faber Relative Strength: Top 3 (t.frs3) is similar to t.frs1, except that the Top 3 asset classes are chosen each month. See the detailed risk and return metrics for this Portfolio Recipe.
Faber Relative Strength: Top 4 (t.frs4) is similar to t.frs1, except that the Top 4 asset classes are chosen each month. See the detailed risk and return metrics for this Portfolio Recipe.
Pure Momentum (t.pure) ranks a set of 6 asset class ETFs (SPY, TLT, QQQ, MDY, IWM, EEM) based on a weighted moving average of returns over the past 3 months. Then the algorithm chooses the top ranked ETF and applies a 100% allocation to that ETF for the upcoming month. See the detailed risk and return metrics for this Portfolio Recipe.
Risk-Driven Portfolio Recipes
Minimum CDaR (t.cdar) chooses an allocation from a set of 8 asset class ETFs (EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM) based on the combination that will minimize the portfolio's Conditional Drawdown at Risk (CdaR) based is the past 80 trading days. See the detailed risk and return metrics for this Portfolio Recipe.
Minimum CVaR (t.cvar) chooses an allocation from a set of 8 asset class ETFs (EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM) based on the combination that will minimize the portfolio's expected shortfall as defined by Conditional Value at Risk (CVaR). The algorithm uses a lookback period of 30 trading days. See the detailed risk and return metrics for this Portfolio Recipe.
Equal Risk Contribution (t.eqrc) chooses an allocation from a set of 8 asset class ETFs (EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM) based on the combination that will result in an equal contribution of risk from each asset based on each asset's standard deviation. The algorithm uses a lookback period of 80 trading days. See the detailed risk and return metrics for this Portfolio Recipe.
Minimum Drawdown (t.loss) chooses an allocation from a set of 8 asset class ETFs (EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM) based on the combination that will minimize the maximum drawdown of the portfolio. The algorithm uses a lookback period of 70 trading days. See the detailed risk and return metrics for this Portfolio Recipe.
Minimum Downside MAD: Mean Absolute Deviation (t.madd) chooses an allocation from a set of 8 asset class ETFs (EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM) based on the combination that will minimize the downside Mean Absolute Deviation (MAD) of the portfolio. The algorithm uses a lookback period of 80 trading days. See the detailed risk and return metrics for this Portfolio Recipe.
Minimum Mean Absolute Deviation (t.madm) chooses an allocation from a set of 8 asset class ETFs (EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM) based on the combination that will minimize the Mean Absolute Deviation (MAD) of the portfolio. The algorithm uses a lookback period of 80 trading days. See the detailed risk and return metrics for this Portfolio Recipe.
Minimum Correlation A (t.mca1) creates a correlation matrix that calculates the correlation of the assets in a set of 8 asset class ETFs (EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM). The lookback period for calculating the correlation is 80 trading days. t.mca1 then solves to find the combination of assets that gives the weighted portfolio the lowest overall correlation. See the detailed risk and return metrics for this Portfolio Recipe.
Minimum Correlation B (t.mca2) creates a correlation matrix that calculates the correlation of the assets in a set of 8 asset class ETFs (EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM). The lookback period for calculating the correlation is 85 trading days. t.mca2 then solves to find the combination of assets that gives the weighted portfolio the lowest overall correlation. See the detailed risk and return metrics for this Portfolio Recipe.
Minimum Variance A (t.mvar) creates a covariance matrix that includes the covariance of each possible pair of assets from a set of 8 asset class ETFs (EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM). The lookback period for calculating the covariance is 80 trading days. t.mvar then solves to find the combination of asset weights (i.e., percentage allocations) that gives the portfolio the lowest overall covariance. See the detailed risk and return metrics for this Portfolio Recipe.
Minimum Variance B (t.mva2) creates a covariance matrix that calculates the covariance of the assets in a set of 8 asset class ETFs (EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM). The lookback period for calculating the covariance is 70 trading days. t.mva2 then solves to find the combination of asset weights (i.e., percentage allocations) that gives the portfolio the lowest overall covariance. See the detailed risk and return metrics for this Portfolio Recipe.
Minimum Variance C (t.mva3) creates a covariance matrix that includes the covariance of each possible pair of assets from a set of 8 asset class ETFs (EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM). The lookback period for calculating the covariance is 60 trading days. t.mva3 then solves to find the combination of asset weights (i.e., the percentage allocation to each asset) that gives the portfolio the lowest overall covariance. See the detailed risk and return metrics for this Portfolio Recipe.
Min Downside Deviation (t.risd) chooses an allocation from a set of 8 asset class ETFs (EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM). The algorithm minimizes the downside deviation of the portfolio based on the most recent 80 trading days. To measure risk, the algorithm uses the downside deviation of daily returns over the past 80 trading days. For an explanation of the Downside Deviation, see the Risk vs. Return Metrics page.
Risk Parity Portfolio A (t.rpba) calculates a weighting for each of 8 asset class ETFs (EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM) in inverse proportion to its risk as measured by standard deviation of daily returns over the past 60 trading days. See the detailed risk and return metrics for this Portfolio Recipe.
Risk Parity With Cluster (t.rpcl) first creates clusters of assets for the set of 8 asset class ETFs (EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM) using the k-means algorithm. Then the algorithm calculates a weighting for each cluster of asset classes in inverse proportion to their risk as measured by standard deviation of daily returns over the past 85 trading days. See the detailed risk and return metrics for this Portfolio Recipe.
Risk Parity Portfolio B (t.rsop) calculates a weighting for each of 8 asset class ETFs (EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM) in inverse proportion to its risk as measured by standard deviation of daily returns over the past 80 trading days. See the detailed risk and return metrics for this Portfolio Recipe.
Target Risk 10% (t.tris) chooses an allocation based on a targeted level of risk, as measured by standard deviation. This portfolio algorithm first plots an "efficient frontier" of optimal portfolios (return vs. standard deviation) using the historical returns from the set of 8 ETFs (EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM). Then it chooses a portfolio on the frontier with a target allocation that results in 10% standard deviation over the period that was used to construct the frontier. The portfolio is rebalanced at the end of each month using a new efficient frontier created from the last 85 trading days of historical returns for each ETF. See the detailed risk and return metrics for this Portfolio Recipe.
Risk/Reward & Target Return Portfolio Recipes
Maximum Sharpe Portfolio (t.shar) chooses an allocation from a set of 8 asset class ETFs (EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM). The algorithm maximizes the Sharpe ratio of the entire portfolio based on the most recent 80 trading days. The Sharpe ratio uses standard deviation to measure risk. For an explanation of the Sharpe Ratio, see the Risk vs. Return Metrics page.
Maximum Sortino Portfolio (t.sort) chooses an allocation from a set of 8 asset class ETFs (EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM). The algorithm maximizes the Sortino ratio of the entire portfolio based on the most recent 80 trading days. The Sortino ratio uses downside deviation to measure risk. For an explanation of the Sortino Ratio, see the Risk vs. Return Metrics page.
Target Return 12% Portfolio (t.tret) chooses an allocation from a set of 8 asset class ETFs (EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM) based on a targeted level of total annual return. This portfolio algorithm first plots an "efficient frontier" of optimal portfolios (return vs. standard deviation) using the historical returns from the set of 8 ETFs. Then it chooses a portfolio on the frontier with a target allocation that results in a 12% annual return (or as close as possible) over the period that was used to construct the frontier. The portfolio is rebalanced at the end of each month using a new efficient frontier created from the last 80 trading days of historical returns for each ETF. See the detailed risk and return metrics for this Portfolio Recipe.
Target Return Post-Modern Portfolio (t.trdd) is similiar to t.tret except instead of using standard deviation as the risk measure, this algorithm uses downside deviation. The algorithm targets a total return of 12% while working to minimize risk as measured by downside deviation. This has the benefit of not penalizing the portfolio for favorable, upside variation. See the detailed risk and return metrics for this Portfolio Recipe.
Sector Rotation Portfolio Recipes
Quartile Sector Rotation (t.srqr) ranks a set of 117 sector funds from various fund companies based on a relative strength using returns from the last 12 months. Then t.srqr buys the one top-ranked asset, and continues to hold that asset as long as it remains in the top quartile. When the asset falls out of the top quartile, t.srqr sells the asset and replaces it with the current #1 ranked fund. See the detailed risk and return metrics for this Portfolio Recipe.
Relative Strength Sector Rotation (t.srrs) looks at 9 sector ETFs (XLY, XLP, XLE, XLF, XLV, XLI, XLB, XLK, XLU) and buys each asset whose 10-month total return is above its 10-month simple moving average total return. This portfolio recipe sells an asset when that asset's 10-month total return falls below its 10-month simple moving average total return. This Portfolio Recipe can also invest in short-term treasury bonds (SHY) according to the following rule: if only 3 assets are above their 10-month simple moving average (SMA), then invest 25% in SHY; if only 2 assets above SMA, then invest 50% in SHY; if only 1 asset is above its SMA then invest 75% in SHY; if none of the 9 ETFs are above their SMA, then invest 100% in SHY. See the detailed risk and return metrics for this Portfolio Recipe.
Top 3 Sector Rotation (t.srt3) ranks 38 Fidelity Select sector funds based on the sum of each funds 3-, 6-, and 12-month return. Then based on the ranking, this Portfolio Recipe buys the top 3 assets. This Portfolio Recipe sells an asset when it falls out of Top 3 ranking. This Portfolio Recipe can also invest in cash if SPY (an ETF indexed to the S&P 500) is below its 10-month simple moving average. If that is the case, then the portfolio invests 100% in SHY (as a proxy for cash). See the detailed risk and return metrics for this Portfolio Recipe.
Top 5 Sector Rotation (t.srt5) ranks 38 Fidelity Select sector funds based on their 12-month return. Then based on the ranking, this Portfolio Recipe buys the top 5 funds in equal amounts (20% allocation to each). This Portfolio Recipe sells an asset when it falls out of Top 5 ranking. This Portfolio Recipe can also invest in cash if a Top-5 asset has a negative 12-month return. In that case, the algorithm holds SHY (as a proxy for cash) in place of that fund. Any of the Top-5 funds can be replaced with a cash allocation if its 12-month return is negative. See the detailed risk and return metrics for this Portfolio Recipe.