General FAQs:
- What is Tactical Asset Allocation (TAA)?
- What assets do you trade?
- When will the allocation for my Model Portfolio (or strategy) change? When are trades executed?
- What is a custom “Model Portfolio”?
- What strategies do you track?
- Can your platform be used by international (non-US) investors?
- What if the ETFs shown are not available to me?
- Why are your results different than I’ve seen elsewhere?
- Interest rates are at historic lows. How do you account for a future of rising rates?
- Do you track strategies that use leverage/margin?
- Do you track strategies that go short or trade inverse ETFs?
- Can you test my custom idea?
- Can I modify the strategy rules (assets traded, parameters, etc.)?
- Are strategy rules fully disclosed? Do you include programming code?
- What is “Cash”?
- Upgrading from a monthly to annual subscription
About our Backtests (the Nuts & Bolts):
What is Tactical Asset Allocation (TAA)?
Tactical asset allocation strategies invest in broad asset classes like stock indices (ex. SPY), bond indices (AGG), and gold (GLD). TAA strategies tend to trade once a month or less, capturing major market trends and ignoring day-to-day noise. That makes them easier to follow for traders who refuse to be glued to a monitor all day.
Unlike traditional buy & hold, TAA aims to maximize returns and minimize losses by dynamically increasing/decreasing allocation to assets expected to outperform/underperform. TAA differs from other active strategies in that the concepts underpinning TAA (mostly long-term momentum and trend-following) have proven effective for decades, and in some cases, centuries.
What assets do you trade?
All of the strategies that you’ll find here trade ETFs that track broad asset classes such as stock indices (ex. SPY), bond indices (AGG), or gold (GLD). We represent each asset class using the largest, most liquid ETF available (read why).
For example, when testing a strategy that trades the S&P 500, we apply the strategy to the ETF SPY. In most cases, there is at least one ETF that has performed similarly. In the case of SPY, the ETF IVV has been a close substitute.
TAA is designed to take advantage of broad market trends, and over the long-term, the difference between specific assets within each asset class (ex. SPY vs IVV) can usually be considered “noise”.
Members: Click for a list of multiple ETF alternatives representing each asset class covered on our site.
European members: Click for a list of UCITS funds representing each asset class.
In some instances, vehicles besides ETFs, such as mutual funds or futures, would have also performed similarly. We do not list every possible alternative asset, but key considerations would include: (1) How well the asset tracks the underlying asset class or index. (2) Liquidity and ease of trading. (3) Any additional costs associated with the asset, such as a significant increase in the expense ratio, penalties for over-trading, and transaction fees or slippage beyond what we’ve accounted for in our tests. See Backtest Assumptions.
Additional FAQs: What if none of the ETFs shown are available to me?
When will the allocation for my Model Portfolio (or strategy) change? When are trades executed?
Skip ahead to: Model Portfolios | Individual Strategies | Additional Info
For Model Portfolios:
Your “Model Portfolio” is your own custom combination of individual asset allocation strategies (learn more).
Most of the individual strategies that we track trade once per month. When you configure your Model Portfolio, you determine what day(s) of the month you would like each strategy to trade. It could be the last day of the month (the default), the first day of the month, any day in between, or you can even spread strategies across multiple days (aka “tranching”).
Click for a handy reference table for 2024 to convert days of the month to calendar days.
When one of the days you’ve selected arrives each month:
- Starting at the market open (09:30 am ET) you’ll begin seeing the day’s new expected allocation. We do this to give members as much forewarning as possible about any upcoming changes.
- That new allocation could potentially change at any point in the day as asset prices change, but is usually stable throughout the day.
- Our results assume that an investor executed the final allocation of the day shown at the market close (04:00 pm ET except on half days) at the closing price.
Daily strategies in your Model Portfolio could potentially trade on any trading day, but will otherwise follow the process described above.
Many members choose to execute trades at some point other than the same day’s close, either out of convenience or in order to improve execution price. That’s okay too. The strategies that we track are designed to capture broad market trends, and have been robust to reasonable delays in execution. Trades could have been delayed by up to a full day with a negligible impact on performance. Read more.
For Individual Strategies:
Our platform is designed to be followed in real-time using members’ Model Portfolios (as described above). Individual strategy backtests (i.e. the kind found here) are designed purely for research. Having said that, we know that members often use these strategy backtests for real-time tracking. When can you expect individual strategies to signal a change in allocation?
For monthly strategies, asset allocations will only change on the last trading day of the month. To see the current asset allocation for any other trading day, you can either add the strategy to a Model Portfolio or click the link reading “Alt. Trading Days: Current Allocation”.
When the last trading day of the month arrives, we follow the same process as we do with Model Portfolios:
- Starting at the market open (09:30 am ET) you’ll begin seeing the day’s new expected allocation. We do this to give members as much forewarning as possible about any upcoming changes.
- That new allocation could potentially change at any point in the day as asset prices change, but is usually stable throughout the day.
- Our results assume that an investor executed the final allocation of the day shown at the market close (04:00 pm ET except on half days) at the closing price.
Daily strategies could potentially trade on any trading day, but will otherwise follow the process described above.
Many members choose to execute trades at some point other than the same day’s close, either out of convenience or in order to improve execution price. That’s okay too. The strategies that we track are designed to capture broad market trends, and have been robust to reasonable delays in execution. Trades could have been delayed by up to a full day with a negligible impact on performance. Read more.
Additional info:
The fact that we don’t add a lag between when the final official allocation is issued and when the trade is executed, raises the possibility that a strategy could change position in the final moments of the trading day, and that an investor might not have time to execute the correct trade. In practice however, that rarely happens. The allocation shown mid-day usually remains unchanged into the close.
The strategies that we track have been robust to reasonable delays in execution, so correcting for occasional incorrect trades the next day or even delaying execution altogether by up to a full day, would have had a negligible impact on returns over the long run (read more). Further, by combining multiple strategies together in your Model Portfolio, we reduce the impact of any last-minute shift in an individual strategy.
Why do we assume trades are executed at the close, as opposed to the next open or at some other point during the day? Because it allows us to extend our backtests much further into history (see Simulated Asset Data). All things being equal, having more historical data to test is better than having less, as it allows us to see how the strategy has performed during a broader variety of market conditions.
What is a custom “Model Portfolio”?
Members are able to create up to three custom Model Portfolios.
A Model Portfolio is like any other portfolio, but rather than combining individual assets, the member is combining entire strategies. Members can backtest how the Model Portfolio would have performed historically, and then follow the portfolio in near real-time. Combining strategies in this way reduces the risk of any single strategy going off the rails and helps to provide smoother, more consistent investment returns.
When following their Model Portfolio, members see the weighted asset allocation of the underlying strategies.
For example, consider a portfolio with just two strategies: half of the portfolio is allocated to Strategy A and half to Strategy B. If today Strategy A is 100% long SPY (S&P 500), and Strategy B is 100% in cash, the Model Portfolio would call for 50% SPY and 50% cash.
This is an extremely oversimplified example. Imagine a more realistic scenario, with a portfolio of a dozen individual strategies, each trading a diverse array of assets. Keeping track of all those disparate strategies and diverse assets without the benefit of a tool like ours, would be unmanageable.
Note: Custom Model Portfolios are only available to paid members. Free members are provided with sample Model Portfolios.
What strategies do you track?
Click for the full list of strategies that we track.
We are continuously improving Allocate Smartly for our members, so expect this list to continue to grow in the months and years ahead.
Can your platform be used by international (non-US) investors?
Absolutely. A large percentage of our user base is outside of the US.
This question usually comes from European investors:
- Most European investors can’t access US ETFs. Instead, they trade UCITS funds listed on non-US exchanges, often denominated in currencies other than USD.
- We did a deep dive on this subject, which we encourage you to read. There are four approaches a European investor could take in trading US TAA strategies with UCITS funds.
- The most straightforward approach is trading USD-denominated UCITS funds at the next market close in Europe. This would have provided similar results to those shown on the platform over the long-term, assuming an investor traded a diversified portfolio of TAA strategies. TAA is designed to capture broad market trends and has been robust to reasonable delays in execution.
- Trading UCITS funds denominated in currencies other than USD introduces additional currency risk, which may help or hurt returns. Investors would need to either accept that risk as the price of trading US-centric strategies, hedge it – which bears some inherent costs, or attempt to time it to their advantage. See the blog link above for further discussion and data.
- Click for a list of UCITS Fund Alternatives for each asset covered on our site.
For all other non-US investors, here is a more general discussion of three issues related to replicating our results:
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When trades are executed: Other markets function on a different schedule than the US, and may not be open at the US market close.Fortunately, because TAA is designed to capture broad market trends, it has been robust to reasonable delays in the timing of trades. Here’s a study looking at delaying execution by an entire day (i.e. the next day’s close) that found little impact on long-term results.
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Limited asset choices: Non-US investors may not have access to the more “niche” asset classes that some strategies employ (ex. US small cap value stocks).Tactical strategies have been robust to shifting this niche exposure to higher level, more generic asset classes. For example, here’s another study looking at executing tactical strategies using just 10 high level asset classes. Like we saw when delaying execution by a day, this had little impact on long-term results.
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Currency risk: Obviously, even if an investor could perfectly match our results, changes in exchange rates between USD and the investor’s home currency will impact the investor’s final return.That risk is beyond our purview. Investors would need to either accept that risk as the price of trading US-centric strategies, hedge it – which bears some inherent costs, or attempt to time it to their advantage.
The key takeaway from all the above is that international users shouldn’t get overly anxious about perfectly mirroring the results we show on our platform. Tactical Asset Allocation is a hammer, not a scalpel, and has been robust to reasonable differences in execution, both in terms of when trades are executed, and the specific assets chosen.
A final closing thought: shop around. Many non-US members have reported that they’ve found brokerages in their home country that could trade US ETFs like those we model on our platform. We don’t share those resources because they vary so widely by country and over time, but it’s an option worth investigating.
Why are your results different than I’ve seen elsewhere?
Generally speaking, our results are more pessimistic (realistic) than you’ll find elsewhere because our test assumptions tend to be stricter. Some examples:
- We account for transaction costs/slippage, which developers often do not. See Backtest Assumptions.
- We represent each asset class using the largest, most liquid ETF in that space. We do not allow for niche or less commonly traded assets for both practical reasons (it makes combining multiple strategies together more manageable), as well as analytical reasons (it’s a guard against overfitting). We discuss further on our blog: Why We Conform Strategies to a Common Set of Assets.
- When simulating historical asset data (read more), we only use indices or other data sources that are closely related to a large, liquid ETF trading today. Developers will often use data from French, Ibbotson, etc., and while those data sources have value in terms of strategy development, they often do not translate in to large, liquid ETFs that can be traded in the real-world today. We discuss further on our blog: The Perils of Backtesting with Unrealistic Data
These types of strict assumptions mean that our tests are often more pessimistic than you’ll find elsewhere. We’re okay with that. We believe that it’s in our members’ best interest to see these strategies through as realistic of a lens as possible.
Backtest Assumptions
In order to provide an apples-to-apples comparison between strategies, we make certain simplifying assumptions that all backtests share, unless specifically noted otherwise. We assume that:
- All strategies trade at the market close (4 pm ET). Note however that the strategies we track are designed to capture broad market trends, and have been robust to reasonable delays in execution. That means trades could have been delayed by up to a full day with a negligible impact on performance. Read more.
- Transaction fees plus slippage total 0.10% per trade (0.20% round-trip). This may be too high or too low based on the size of the portfolio and the broker used, but it should be in the ballpark for most investors.
- Both dividends and gains are reinvested.
- Return on cash (i.e. the return on any portion of the portfolio not invested) is assumed to equal the 3-month US Treasury rate. Read more: What is “cash”?
- We do not account for taxes as they’re highly specific to the individual, but we do model the historical tax efficiency of every strategy that we track.
Simulated Asset Data
In order to extend our backtests as far into the past as possible, we often make use of simulated data.
For example, a strategy trading the S&P 500 ETF SPY could only be tested back to 1993, when SPY began trading, using actual data. But using alternate data sources, such as the underlying index on which the ETF is based, we’re able to extend that data much further into the past.
This is inappropriate for hyperactive strategies that rely on small price changes to generate return, because this simulated data is not sufficiently accurate. It is also inappropriate for very illiquid assets where, had traders actually been trading the asset, it would have significantly moved the price. In our case, neither is true. The strategies that we track are relatively slow moving, capturing broad trends rather than quickly capturing small price changes. And the assets those strategies trade tend to be very broad in nature, like stock indices, bond indices, or gold.
In short, we believe that the benefit of having more data to consider far outweighs the potential drawbacks of using simulated data.
Note that when creating simulated data, we apply an expense ratio equal to that of the most liquid similar ETF. So for example, when simulating data for an S&P 500 ETF, we apply the same expense ratio as SPY, the most liquid ETF in that space.
Also note that we tend to be much stricter when simulating asset data than you’ll see elsewhere. We discuss this issue further on our blog: The Perils of Backtesting with Unrealistic Data
Monthly vs Daily Asset Data
Many of the strategies that we track were designed to only trade on the last day of the month. In these instances, we usually show two versions of our backtests, one performed on monthly data (i.e. using month-end values only) and the other on daily data. Each has unique benefits.
We use monthly data because it often allows us to extend our test farther back into history (see Simulated Asset Data). For example, the real estate ETF VNQ began trading in 2004. The underlying index that VNQ tracks, the MSCI US REIT Index (Bloomberg: RMS/RMZ), is available as daily data from 1995. But the FTSE NAREIT Index (Bloomberg: FNER/FNERTR), a very close proxy, is available as monthly data from 1972.
Even when backtesting with monthly data, we provide a second test over daily data. The daily data backtest may not extend as far back into history, but it allows us to show how the strategy has performed when trading on other days of the month. If a strategy has performed poorly on other days of the month, it could be a sign that the strategy is overfit and unlikely to perform as expected in the future.
Note that when adding these monthly trading strategies to your custom Model Portfolio, you have the option of either trading at month-end (the default), or choosing an alternate trading day.
Also see: Normalizing the Days of the Month
Raw vs Dividend-Adjusted Data
Prices that you see quoted in the financial press are often actually raw prices (also known as cash or nominal prices), meaning they don’t account for an important driver of total return: dividends. The backtested returns that we show on this site are always adjusted for dividends.
A more murky issue is whether to use raw or dividend-adjusted data when calculating the indicators that each strategy employs. To illustrate, consider a strategy that goes long asset X when asset X closes above its 200-day moving average (MA). To truly capture what the 200-day MA is intended to capture (i.e. the average value of asset X over the last 200 days), we should use dividend-adjusted data. The problem is that there is often a delay in that dividend adjustment being reflected in past and current prices by data providers. So if we were to assume that, historically, the 200-day MA had always accounted for all of the dividends that we know about today, we might be introducing look-ahead bias, meaning our test is based on data that wasn’t easily available at that moment in time.
Yes, an investor could spend a lot time manually monitoring and adjusting historical data in real-time, but practically speaking, this is difficult to do when trading quantitative strategies across a broad range of assets as we do on this site.
To control for this, when calculating historical indicator values, we assume that we did not know about a dividend until 3-days after the ex-dividend date. This is a very pessimistic assumption to be sure, but one that we believe more than controls for any potential look-ahead bias.
The good news is that this approach has had a negligible affect on long-term performance for the vast majority of strategies that we track. That’s because tactical asset allocation strategies, by their very nature, tend not to be overly sensitive to small differences in price.
Trades vs Rebalances
All of the strategies that we track include both trades and rebalances.
A “trade” is a change in the optimal allocation of a strategy. Over time though, the strategy will drift from that optimal allocation due to differences in each asset’s performance. A “rebalance” is done to bring the strategy back to that optimal allocation, even if the optimal allocation remains unchanged.
When tracking a strategy in near real-time, only trades (i.e. changes in optimal allocation) are signaled to members. Rebalance assumptions are explained in the strategy description and included in results (including transaction costs), but are not signaled.
That’s because signaling rebalances would be impossible. We do not know when a member entered a strategy or the price at which they purchased assets. We leave it to members’ discretion to determine if and when to rebalance.
Normalizing the Days of the Month
Click for a list of normalized days of the month for 2024.
Many of the strategies that we track were designed to only trade on the last day of the month. In most cases though, there’s nothing particularly special about the last day of the month, so we also show the results of trading on other days of the month as well.
The problem of course is that every month has a different number of trading days. For example, a member might opt to trade a strategy on day 20, but not every month has 20 trading days.
To account for this, we normalize the number of trading days each month to 21 (21 being the average number of trading days per month). Shorter months get stretched, and longer months get compressed to fit that number. We normalize the day of the month as follows (in Excel parlance for simplicity):
= Round ( ( Trading Day of Month / Total Trading Days in Month ) * 21, 0 )
Note that day 1 will always be the first trading day of the month, and day 21 will always be the last.
We understand that this might be a bit confusing initially, but it’s a necessary evil to ensure that we’re comparing trading day performance across months accurately.
Normalized Trading Days and Month-End Indicators
Backtests are further complicated when calculating month-end indicators (ex. a 10-month moving average) for other days of the month.
Consider a strategy that was originally designed to trade on the last day of the month using a 10-month moving average. If the member opted to instead trade that strategy on day 15 of the month, we would use the last 10 day 15 values to calculate that moving average.
In essence, we’re remaining as closely as possible to the original intent of the developer, while still allowing members to trade the strategy on alternate trading days.
For a more in-depth discussion of alternate trading days, and how they can be used to better assess a strategy, please see our blog: Alternate Trading Days: An Important Analytical Tool
The 60/40 Benchmark and Other Alternatives
We compare strategy performance throughout our platform to the US 60/40 benchmark. We define the 60/40 as a 60% investment in the S&P 500 (via SPY), 40% investment in intermediate-term US Treasuries (IEF), rebalanced monthly.
Alternative benchmarks, such as the S&P 500, Dow Jones Industrial Average and global stocks (ACWI) are available to members via the Compare Strategies tool.
Why do we use the 60/40 as our default benchmark?
First, it’s ubiquitous. The 60/40 is the most commonly used benchmark in the US asset management industry. Second, it’s been a tough hurdle to beat, especially on a risk-adjusted basis. That’s due to (a) the long-term outperformance of US stocks over the rest of the world, and (b) strong US Treasury performance over the last 40+ years due to consistently declining interest rates.
To compare the performance of the 60/40 to other alternative benchmarks, please refer to the aforementioned Compare Strategies tool.
Backtesting Your Custom Model Portfolio
A member’s Model Portfolio is like any other portfolio, but rather than combining individual assets, the member is combining entire strategies. Learn more: What is a Model Portfolio?
When following their Model Portfolio, members see the weighted asset allocation of the underlying strategies. For example, consider a portfolio with just two strategies: half of the portfolio is allocated to Strategy A and half to Strategy B. If today Strategy A is 100% long SPY (S&P 500), and Strategy B is 100% in cash, the Model Portfolio would call for 50% SPY and 50% cash.
When backtesting the historical performance of Model Portfolios, we make all of the same assumptions that we do when testing individual strategies, including transaction fees and slippage, dividends, return on cash, and taxes (see Backtest Assumptions). In addition, there are two unique aspects to backtesting Model Portfolios: portfolio rebalancing and the backtest start date.
Portfolio Rebalancing:
We assume that a trader rebalanced between the individual strategies in the Model Portfolio at the close on the last trading day of the calendar month. This is in addition to any rebalancing done within the individual strategies themselves.
For example, assume a trader’s Model Portfolio consisted of 50% allocated to Strategy A and 50% to Strategy B. Over the course of a calendar month, because of differences in how each of those strategies performed, the portfolio is now 52% allocated to Strategy A and 48% to Strategy B. Our backtest assumes that at the close on the last trading day of the month, the trader rebalanced the portfolio back to 50/50%. We make the same assumptions about transaction costs and slippage for this special monthly rebalance as we do when testing individual strategies.
This special rebalance is a simplifying assumption to allow for an apples-to-apples comparison between portfolios, but practically speaking, very similar results would be achieved rebalancing much less frequently (quarterly, annually, etc.)
Backtest Start Date:
Consider a Model Portfolio that’s 99% invested in Strategy A, which began trading in 1970, and 1% invested in Strategy B, which began trading in 2010. It wouldn’t make sense to begin our portfolio backtest in 2010, losing 40 years of historical data, because Strategy B’s small allocation likely had little practical impact on our results.
All things being equal, more data is preferable to less. Our solution is to begin our Model Portfolio backtests when at least 80% of the required strategy data (weighted by the user’s allocation) is available.
For example, if a Model Portfolio were split evenly between five individual strategies (i.e. 20% invested in each) we would begin the backtest when at least four of those strategies were available (20% x 4 = 80%). Until all five strategies became available, the unallocated portion of the portfolio (20%) would be split proportionally between the other four strategies.
To determine the start date on an individual static, daily or monthly strategy trading at month-end (day 21), refer to the “primary” backtest. Using the Protective Asset Allocation strategy as an example, the primary backtest can be found here and begins in 1973. For a monthly strategy trading on any other day (days 1-20) refer to the “alt. trading day” backtest. For the PAA strategy, that alt. trading day backtest can be found here and begins in 1989.
Filtering Strategies by Category
To help members better distinguish the quantitative and qualitative differences between strategies, we’ve provided the ability to filter strategies by category. Below we detail the criteria used in those strategy categories.
Estimated Risk:
Categorizes each strategy as Conservative, Moderate or Aggressive, based on historical volatility relative to the 60/40 benchmark. Intended as an estimate of long-term volatility, and may not accurately reflect risk at any single moment of time. Of course, past performance is not necessarily indicative of future results.
Strategy Type:
- Momentum & Trend-Following
- Portfolio Optimization: Strategies that use correlation, volatility and/or covariance to improve asset allocation. Includes complex techniques like “Equal Risk Contribution”, as well as simple techniques like naïve “Risk Parity”.
- Economic Data: Strategies that trade based on economic data, such as the unemployment rate.
- Four Seasons: Strategies based on the “Four Seasons” theory of investing, where portfolios are built to weather four potential economic conditions: prosperity (growth), recession, inflation and deflation.
Recent (10 Year) Best:
Shows top performers by various metrics over just the previous 10 years:
- Highest return
- Highest risk-adjusted return: Based on a weighted average of the Sharpe Ratio, Sortino Ratio and Ulcer Performance Index.
- Lowest drawdown: Based on a weighted average of the Max Drawdown and Ulcer Index.
Interest rates are at historic lows. How do you account for a future of rising rates?
In the long-term, there are mathematical limits on how well Treasuries and other highly rate-sensitive assets can perform. We’ve modeled these limitations extensively on our blog. In the short-term however, these assets can still generate big returns no matter where rates stand.
As a result, we handle the issue differently for TAA versus buy & hold:
For tactical asset allocation, which has the ability to rotate out of underperforming asset classes, we model interest rate exposure for each strategy. Investors concerned with rising rates can avoid strategies with excessive exposure, especially when the strategy doesn’t have the ability to turn that exposure off. Members: See your Exposure to Rising Rates report.
For buy & hold, which does not have the ability to rotate out underperforming asset classes, current interest rates are one of the most important drivers in how we should create portfolios.
In short, we fully embrace that we are approaching a historic turning point in interest rates. Our response to that is simply different depending on whether we are trading nimble TAA or stay-the-course buy & hold.
Do you track strategies that use leverage/margin?
As a rule, we do not. We don’t think that there’s anything inherently wrong with leverage, but we do not track strategies that employ it (via leveraged ETFs or margin) for three reasons:
- It’s easy to fool less experienced investors with sexy backtests. By their nature, leveraged strategies tend to generate big returns in backtests, but less experienced investors may not fully understand that those big returns come with increased risk. We would never want to be responsible for an investor taking on more risk than they are prepared for.
- Most leveraged ETF products magnify daily returns, but TAA tends to hold positions for a month or longer. Holding daily leveraged instruments for long periods of time often leads to unexpected results due to volatility decay.
- Any strategy can be leveraged via margin (assuming the investor’s account supports it). The goal when selecting strategies should be to identify those that maximize return relative to risk. Investors can then scale up or down exposure, using leverage when appropriate, to meet their desired risk tolerance. This is investing 101. Focusing on leverage at step one (selecting strategies) rather than step two (scaling risk up/down) short-circuits that process. First, determine your strategy, then determine your exposure to it, not vice-versa.
Note: There are approaches to trading that involve leverage without a commensurate increase in risk, such as 130-30 long/short strategies. Those fall outside of what is usually considered tactical asset allocation.
Do you track strategies that go short or trade inverse ETFs?
We do not. Don’t get us wrong, we’d love to. Our system is built for it, and we’d like nothing more than to add strategies that provide short exposure. The problem is that those types of tactical strategies don’t work. Going short in the longer timeframes that TAA trades, with the types of broad assets that TAA trades, just hasn’t performed reliably through history.
There’s a place for going short, but it generally involves narrower assets like individual stocks, or alternative assets like volatility products, or much shorter timeframes. Trying to short broad asset classes like US stocks or bonds over longer timeframes hasn’t worked consistently because of the long-term positive bias in financial asset classes.
We’re very open to being proven wrong.
Can you test my custom idea?
We receive a lot of requests for custom analysis. We really appreciate curiosity from members, and we think it’s great to see members thinking about ways to improve on TAA. Unfortunately however, given the low cost of our memberships and how frequently we receive these requests, we have to limit this type of bespoke analysis – otherwise, it would literally be all that we do.
What is your best resource for custom analysis?
- The simplest choice is the original strategy author. For all of the strategies that we track, we include the source material where the strategy originated (a book, paper, etc.) You could start there. Perhaps the author has already considered your variation.
- If that didn’t bear fruit, you could reach out to the broader quantitative analysis community. Ilya Kipnis of QuantStrat TradeR, a trusted resource that has contributed to multiple strategies that we track, offers bespoke TAA backtesting. There are other great resources at Quantocracy that might be able to help.
- We understand that neither of these options is going to have access to the testing environment, data quality, etc. that our members are accustomed to. If you are dead set on Allocate Smartly testing out your idea, please contact us for a custom quote.
Can I modify the strategy rules (assets traded, parameters, etc.)?
We track each strategy as closely as possible to the author’s original intent, within the confines of some basic standards that we apply uniformly sitewide (read more). We are not a custom backtesting site, and do not allow users to tweak strategy parameters, change the assets traded, etc.
This allows us to do two things that custom backtesters cannot:
- Perform a much deeper level of analysis. Examples include tax analysis, sustainable withdrawal rates, aggregate allocation, etc.
- Provide access to higher quality, longer historical asset data. The data that we use to simulate asset class performance prior to the launch of each ETF is usually not publicly available, and in most cases, very expensive. We’ve made this investment in order to bring you the most realistic historical results possible (read more). Custom backtesters cannot because users could suss out that data (a violation of the data providers’ terms of service).
There are a lot of custom backtesting sites out there (ex. QuantConnect) and we would encourage users who want that level of control to check them out, but that’s not what we do.
We believe that perpetually tweaking strategy parameters to achieve “optimal” historical results is simply a recipe for overfitting and disappointing future results. A much more effective approach is to understand the broad concepts available in tactical asset allocation, and then to combine them together to reduce the risk of any one of them underperforming.
Are strategy rules fully disclosed? Do you include programming code?
The short answer: Yes, rules are usually fully disclosed, but no, we do not provide programming code.
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In nearly all cases, we provide a link to source material describing the strategy rules (ex. book, academic paper, etc.)The only exceptions to this are the handful of proprietary strategies we track. In those cases, we know the rules, but are prevented from sharing them publicly.
- In most cases, we also provide a blog post where we lay out the rules as simply as possible and share our own thoughts on the strategy.
In no cases do we provide programming code.
What is “Cash”?
Some of the strategies we track will at times allocate to what we simply call “cash” (in all other cases, we use an actual ETF to represent each asset class). What is “cash” in the real-world?
Practically speaking, cash is usually whatever cash sweep vehicle your broker offers, i.e. where your broker automatically places your funds when not invested (ex. money market, mutual fund, etc.)
We can’t know the return offered by your specific broker’s cash sweep vehicle, so for simplicity’s sake, we assume the return on cash to be equal to the 3-month US Treasury Bill rate. Historically, cash has only accounted for about 2% of the median strategy’s total return, so in most cases, investors shouldn’t get overly anxious about perfectly matching that rate.
Alternatively, for investors that don’t have a quality cash sweep investment available to them, there are ETF solutions that provide very short-term Treasury exposure such as BIL, SHV or GBIL.
Disclaimer: Under no circumstances does this information represent an offer to sell, a solicitation to buy, or a recommendation regarding any securities transaction.
Upgrading from a monthly to annual subscription
You can upgrade from a monthly to an annual subscription at any time by visiting your Account page, clicking “Change Plan”, and selecting Annual Subscription.
You do not need to wait until your current monthly subscription expires.
Upon upgrading to an annual subscription, the time remaining on the current monthly gets prorated at the new cheaper annual rate. In other words, extra day(s) are tacked on to the monthly. After the monthly + extra days end, your new annual subscription begins.
That means you can start taking advantage of the cheaper annual rate immediately. All other benefits, including both our risk-free money back guarantee and price lock, apply to annual subscriptions as well. There’s really no reason not to start enjoying the cost savings today.
Glossary of Statistics
This is a brief glossary of statistics used on this site.
Annualized Return: The average annual return earned by the portfolio over the period tested. Note that this is a geometric average (i.e. CAGR), not a simple arithmetic average.
Drawdown: For any given date, shows how much the strategy was down relative to its previous all-time high. A value of -10% would mean that the strategy was down 10% from its previous all-time high. A value of 0% would mean that the strategy was at a new all-time high. Throughout this site we show end-of-month (EOM) drawdowns to allow for apples-to-apples comparisons regardless of whether monthly or daily asset data is used (learn more). EOM drawdowns are a common standard in the asset management industry.
Longest Drawdown: The longest drawdown ever suffered by the strategy, measured from the start of the drawdown (i.e. the day of the previous all-time high) until the end of the drawdown (the day a new all-time high was recorded).
Max Drawdown: The worst loss ever suffered by the strategy, relative to a previous all-time high. A value of -50% would mean that, at some point in the test, the strategy lost 50% of it’s value relative to its previous all-time high.
Sharpe Ratio: A measure of a strategy’s historical return relative to volatility. Higher values are better than lower values. This is the most common measure of a strategy’s risk-adjusted performance. It’s often criticized for considering both upside and downside volatility equally.
Sortino Ratio: A measure of a strategy’s historical return relative to downside volatility (i.e. the volatility exhibited on just losing months). Higher values are better than lower values. It’s considered by some to be superior to the Sharpe Ratio because it excludes upside volatility.
Ulcer Performance Index (UPI, aka the “Martin Ratio”): A measure of a strategy’s historical return relative to the length and depth of drawdowns. Higher values are better than lower values. This is the least commonly used of the risk-adjusted performance stats we provide, but we think it’s just as important, if not more. Read more about UPI.
Annual Turnover: The rate at which a strategy replaces its holdings. 100% annual turnover would mean that, on average, the strategy replaces 100% of the value of its holdings in any given year. Read more on our blog.