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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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 20 trading days. 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. The minimum variance algorithm uses standard deviation with a
lookback period of 125 trading days.

**Adaptive Allocation B** (t.aaab) ranks 9 ETFs
(DBC, EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM) based on total return over the past 20 trading days. 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. The minimum variance algorithm uses downside deviation with a
lookback period of 220 trading days.

**Adaptive Allocation C** (t.aaac) ranks 9 ETFs
(DBC, EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM) based on total return over the past 20 trading days. 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. The minimum variance algorithm uses standard deviation with a
lookback period of 240 trading days.

**Adaptive Allocation D** (t.aaad) ranks 9 ETFs
(DBC, EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM) based on total return over the past 75 trading days. 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 over the past 65 days.

**Adaptive Allocation E** (t.aaae) ranks 9 ETFs
(DBC, EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM) based on total return over the past 20 trading days. 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 over the past 80 days.

**Adaptive Allocation F** (t.aaaf) ranks 9 ETFs
(DBC, EFA, EEM, GLD, TLT, SPY, QQQ, IYR, IWM) based on total return over the past 180 trading days. 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. The minimum variance algorithm uses standard deviation with a
lookback period of 20 trading days.

**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.

**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.

**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.

**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.

**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).

**Faber Relative Strength: Top 2** (t.frs2) is similar to t.frs1, except that the Top 2 asset classes are chosen each month.

**Faber Relative Strength: Top 3** (t.frs3) is similar to t.frs1, except that the Top 3 asset classes are chosen each month.

**Faber Relative Strength: Top 4** (t.frs4) is similar to t.frs1, except that the Top 4 asset classes are chosen each month.

Pure Momentum (t.pure ranks a set of 6 asset class ETFs (SPY, TLT, QQQ, MDY, IWM, EEM) based on 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.

**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.

**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.

**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.

**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.

**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.

**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.

**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.

**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.

**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.

**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.

**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.

**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.

**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.

**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.

**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.

**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.

**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.

**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.

**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.

**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.

**Quartile Sector Rotation** (t.srqr) ranks a set
of 117 sector funds from various fund companies based on a relative strength calculation which is the sum of 1, 3, 6, and 12-month returns. 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.

**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 cash according to the following
rule: if only 3 assets are above their 10-month simple moving average (SMA), then invest 25% in cash using the SHY ETF; if only 2 assets above SMA, then invest 50% cash; if only 1 asset is above
its SMA then invest 75% in cash; if none of the 9 ETFs are above their SMA, then invest 100% cash.

**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).

**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.

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