When the Inflation Adjusted toggle is enabled, every monthly return shown is converted from a nominal return to a real return - what you actually gained in purchasing power after consumer prices rose that month.
The conversion uses the standard formula:
real return = (1 + nominal return) / (1 + monthly CPI change) - 1
Inflation data comes from the BLS CPI-U (Consumer Price Index for All Urban Consumers, not seasonally adjusted), measured month over month.
How recent and missing months are handled
The CPI is published with roughly a one-month lag, so the newest month in the data usually has no official print yet. Rather than treating those months as having no inflation at all, DMS carries the last known CPI value forward. This is conservative - month-over-month CPI is small and slow-moving - and it keeps the inflation-adjusted view from visibly diverging from nominal returns at the right edge of every chart. When the official figure is published, it replaces the carried-forward value automatically.
The same carry-forward fills any gap in the historical record, though those are rare. The one case where returns are left nominal is a month that precedes the earliest CPI value on file, since there is nothing to carry forward from.
The current month
Mid-month, there is no CPI figure for a month still in progress, so the month-to-date return is deflated using the most recent CPI available as a stand-in. The day-over-day figure is left nominal - a single trading day is too short a horizon for an inflation adjustment to mean anything.
A few other things to know
Why use it?
Long backtests can flatter a strategy. A 10% nominal year during 8% inflation only grew your purchasing power about 2%. Real returns are the honest yardstick for long-horizon planning, especially for withdrawal-rate analysis.
Strategy results which ignore trading costs overstate what you would have actually earned. The Trading Friction toggle (on by default) deducts an estimated cost from each month's return based on how much the strategy actually traded that month.
The model is turnover-weighted. Each month, the strategy's new target ETF weights are compared against what it was already holding as the month opened. The sum of the absolute weight changes is the turnover, and each traded slice is charged a one-way cost based on the ETF's leverage:
| ETF type | One-way cost |
|---|---|
| Non-leveraged (1×) | 10 bps (0.10%) |
| 2× leveraged | 15 bps (0.15%) |
| 3× leveraged | 20 bps (0.20%) |
So a month that rotates 50% of the portfolio out of one 1× ETF and into another costs roughly 0.10% (10 bps on the 50% sold + 10 bps on the 50% bought). Leveraged ETFs are charged more because their wider spreads and higher trading impact make them costlier to trade in practice.
Holdings that are left alone still move. This is the subtle part, and it is why the comparison is against what the strategy was holding rather than against last month's published percentages. If you hold 50% stocks and 50% bonds and stocks gain 10% while bonds are flat, you are holding roughly 52.4% / 47.6% a month later without having placed a single trade. Those percentages changed, but nothing was bought or sold and nothing was owed to a broker.
Two consequences follow:
Other points
Where the estimate is approximate
If you want to see the "frictionless" academic version of a backtest, switch the toggle off - just remember nobody earns those returns in a real account.
Monthly rotation strategies generate realized capital gains, and in a taxable account those gains get taxed. The Taxable Account toggle estimates after-tax returns so you can see how a strategy holds up once the IRS takes its share.
When you enable it, a settings dialog collects:
From your filing status and income the model looks up your marginal ordinary-income rate and your long-term capital-gains rate using the 2026 federal brackets (IRS Revenue Procedure 2025-32), then adds the 3.8% Net Investment Income Tax if your income exceeds the NIIT threshold ($200K single or head of household, $250K married filing jointly). Those NIIT thresholds are fixed by statute and have never been indexed for inflation.
If you use the manual override, the rates you enter are used exactly as typed. No NIIT is added on top of them. If you are over the threshold and entering your own rates to capture state tax, include the 3.8% yourself.
How the tax is applied
The model charges each calendar year the tax it would actually have owed, built from what the strategy realized rather than what it earned on paper.
Gains you have not sold are not taxed. A position held across a year boundary is not a taxable event, so its gain carries forward untaxed and keeps compounding on the full balance. This is the entire tax argument for low turnover, and it is why a buy-and-hold allocation can show almost no tax drag while a monthly rotator shows a great deal.
The flip side: after-tax figures carry an embedded liability for anything still held. The Tax Profile panel reports that as Deferred (unsold). It is not charged, because you do not owe it until you sell, and under current law a step-up in basis at death may mean it is never owed at all.
Yields and distributions are taxed too
Interest and dividends arrive in cash whether or not you sell anything, and they get no holding-period benefit. Each ticker carries a published income character:
This matters most for defensive sleeves. A strategy parked in BIL or TLT during Risk Off is earning interest, not capital appreciation, and is taxed accordingly. Rate-sensitive holdings track the prevailing short rate through history rather than a fixed yield, so cash sleeves are correctly shown earning almost nothing in the ZIRP years.
Gold is taxed differently
Physical bullion trusts such as GLD and SGOL are collectibles under IRC 408(m). Their long-term gains are taxed at the lesser of 28% and your ordinary rate, not at the 15% or 20% long-term rates. Short-term gains on them are unaffected.
This is not a flat 28%. At a $150K married-filing-jointly income it works out to 22%; only at higher incomes does the 28% cap actually bind. Where it does bind it is a meaningful penalty: 31.8% including NIIT against 23.8% for ordinary long-term gains.
SHNY is not treated this way. It is an exchange-traded note, so its holder owns an obligation of the issuer rather than an interest in bullion, and it blends at the ordinary long-term rates.
Why one month each year looks unusually bad
The tax for an entire year is applied to that year's last month, not spread across it. December absorbs the full annual bill in one figure.
This is worth knowing before you scan a monthly returns table with the toggle enabled. A December showing a steep loss after a strong year is not a data error, and it is not what that month actually returned. Annual and longer-period figures are unaffected by where the charge lands, so CAGR, drawdown, and every multi-year statistic remain correct.
Comparing against a benchmark
Only benchmarks with a known composition can be taxed. The 60/40 benchmark is modeled as 60% SPY and 40% BND, rebalanced each January, and is taxed accordingly - including ordinary-income tax on BND's interest. Index benchmarks such as the S&P 500 and QQQ have no underlying allocation to derive trades from, so they are left untaxed.
That means a strategy compared against one of those benchmarks with the toggle on is being shown after tax against a benchmark shown before tax. The gap is understated. Use the 60/40 benchmark when you want a like-for-like after-tax comparison.
A few other caveats worth knowing
Use the ⚙ button next to the toggle to revisit your settings at any time.
The Tax Profile (Est.) panel in the Strategy View's Return Statistics shows how tax-friendly a strategy's trading actually is - how much of what it earns gets turned into taxable gain, how much of that qualifies for the favorable long-term rate, and how much is simply left to compound untaxed.
Rather than guessing from turnover, we run a realization-based simulation of what a real account would have done, using FIFO tax lots:
What the panel reports
For the date range you have selected:
A higher LT Gains % means more of the strategy's gains get the favorable long-term rate. Lower ST gains / yr means less is being handed to the IRS each year in the first place.
Reading the numbers
How this relates to the Taxable Account toggle
The toggle uses the same simulation, but it runs over the strategy's entire history, not the date range selected here.
The panel is window-scoped; the toggle is not. So the LT Gains % displayed here is often not the exact split being applied to your after-tax returns, and narrowing the date range will change this panel without changing the tax being charged.
Limitations to be aware of
Every number on this site traces back to a small set of outside sources. There is no proprietary data feed and nothing is hand-entered. Knowing what feeds what makes it easier to judge how much weight any given figure deserves.
Monthly returns: Tiingo
The monthly return series behind every strategy comes from Tiingo's end-of-day data. This is the authoritative source. When a strategy's performance is computed, recomputed, or published, it is Tiingo's month-end closing prices doing the work.
Returns are total return, adjusted for dividends and distributions. This matters more than it sounds. For bond and commodity positions the income component is most of the return, and a price-only series would understate those assets badly rather than slightly.
Inflation: the Bureau of Labor Statistics, via FRED
The CPI series used by the Inflation Adjusted toggle is CPI-U, All Urban Consumers, not seasonally adjusted. It is pulled from the St. Louis Fed's FRED service, which redistributes the BLS figures. FRED also supplies several market indicators shown elsewhere on the site, including the high-yield credit spread.
The CPI is published with about a one-month lag, so the newest month usually has no official print yet. See the Inflation Adjusted FAQ for how that gap is handled.
Live quotes: Twelve Data and Yahoo Finance
Intraday numbers come from a different place than the historical record. The market strip above the navigation and the month-to-date figures during an open month are built from live quotes, refreshed through the trading day. Historical performance never depends on them.
This is a deliberate separation. Live quote feeds are fast and occasionally wrong. End-of-day data is slower and much more reliable. The site uses each for what it is good at, and nothing in a strategy's published track record is ever sourced from an intraday quote.
History before an ETF existed
Where a fund is younger than the track record shown, the earlier history is reconstructed from other sources. That reconstruction has its own rules and its own limitations, covered in full in the extended-returns FAQ.
What this means for the numbers you see
A few consequences worth carrying around:
Where errors would come from
Being honest about the failure modes: the most likely source of a wrong number on this site is not the price data itself, which is well-tested and widely used. It is the reconstruction layer for pre-ETF history, and it is the transformation code that turns raw prices into strategy results. Both get audited, and both have had real defects found and fixed. The price feeds themselves have not been a meaningful source of trouble.
Most of the ETFs a DMS strategy trades are younger than the track record shown for that strategy. A broad commodity ETF may have launched in 2014, an international equity ETF in 2011, a Nasdaq-100 fund in 1999. If a backtest could only use the period where every holding actually existed, the entire test would be truncated to the youngest fund in the lineup - and a strategy tested only on the post-2014 era has never been shown a rate-shock, a commodity boom, or a 1970s-style inflation regime.
To avoid that, DMS uses extended return series: each asset class is reconstructed backwards to January 1979 from the best available source for each stretch of history. The strategy is then tested against that longer record.
The waterfall
Every asset class is built from a prioritized chain of sources. The engine walks the chain from best to worst and takes each date's return from the highest-ranked source that actually has data for that date. In order of preference:
The chain is a splice, not a blend. Two adjacent tiers are never averaged together; each date is sourced from exactly one place.
Substitutions are held to a standard
A fund only earns a place in a chain if it is genuinely tracking the same thing. This is where most of the judgment sits, and it cuts both ways: a corporate-bond fund is not a proxy for a broad aggregate bond index even though both are "bonds," and a high-yield fund is a different asset class entirely rather than a longer-history version of investment grade. Tiers get removed when they fail that test, even when removing them shortens the history.
Everything is total return
Price history alone is not usable. Every source in every chain is dividend- and distribution-adjusted, so the series represents what an investor would actually have earned holding the position. For bond and commodity series in particular, the income component is most of the return, and a price-only source would be badly wrong rather than slightly wrong.
Expenses are handled tier by tier
Each source in a chain carries its own expense ratio, and the reconstruction accounts for that source's actual fee for the dates it supplies - not a single blanket fee applied across the whole history. A 1990s mutual fund charging 0.90% and a modern ETF charging 0.05% are not interchangeable on cost, and using one fee across both eras would leave a small permanent drag or boost baked into the older half of the record.
Leveraged series are rebuilt, not borrowed
For 2x and 3x series, no leveraged fund existed before roughly 2006-2010, so those histories are constructed rather than sourced. Each day's unlevered return is multiplied by the leverage factor, then charged a financing cost derived from the prevailing overnight rate plus a spread, then charged the real leveraged fund's own published expense ratio. This is daily-reset leverage, matching how the actual products work - which means the reconstruction reproduces volatility decay in choppy markets rather than papering over it.
Monthly history often reaches further back than daily history
Daily price history and monthly return history come from different places and stop at different points. A fund may have thirty years of published monthly returns but only twenty years of usable daily prices. Where that's true, the monthly series is extended using the monthly data while the daily series stops at the last date with trustworthy daily prices.
The practical consequence: for some asset classes, monthly-resolution results start earlier than daily-resolution results. A strategy rebalancing monthly may therefore show a longer track record than the same strategy tested at daily granularity.
Regression-based tiers
Where an asset class has no fund and no clean index reaching far enough back, one remaining option is to estimate its returns from the assets it is statistically related to - regressing the target series on factors or on other markets with longer histories, then using that relationship to project the missing period.
This produces a plausible series, not a real one, and it is treated with more suspicion than every tier above it. A regression fit on one era's correlations can behave badly in an era where those correlations broke down, which is precisely the period a long backtest is meant to stress. Regression tiers are used sparingly, sit at the bottom of the chain, and are retired when a better source appears or when the reconstruction fails review.
Real data always wins
Where the actual ETF has traded, its own returns are used - always. Synthetic history exists only to fill the space before that, and the moment real data begins it takes over completely. The two are stitched at the fund's inception, not blended across it.
What synthetic history is and isn't
It is a careful reconstruction of what an asset class returned. It is not a record of what an investor experienced.
Specifically, it excludes: bid-ask spreads and market impact, tracking error against the index, securities-lending revenue, borrow availability, and the practical reality that many of these strategies would have been difficult or impossible to run in 1979. Reconstructed leveraged series in particular assume idealized daily rebalancing at zero cost, which real leveraged funds do not achieve.
It is also worth remembering that the deepest tiers of any chain are the least reliable, and they sit in the oldest and most unusual part of the record - the stretch doing the most work in a long backtest. Treat the earliest years as directionally informative about how a strategy behaves in an unfamiliar regime, not as a precise measurement.
How to read results that use it
Results built on extended history are best used to answer "does this approach survive conditions the last fifteen years never produced?" rather than "what exactly would I have made?" Where a strategy's edge depends heavily on the pre-ETF era, that is worth knowing and worth discounting.
DMS recomputes every strategy once a day, in the evening, after the US market has closed and the day's closing prices have settled. Nothing changes during the trading day except live quotes.
The daily cycle
All times below are US Eastern. Phoenix runs three hours behind Eastern in summer.
Closing price data is fetched in several passes through the evening, starting shortly before 6 PM and continuing into the late evening. Multiple passes exist because data providers do not publish everything at once, and a ticker that is not ready on the first attempt gets picked up on a later one.
Strategy results are then computed twice:
The after-midnight pass is the authoritative one. The 8:15 PM run is a useful early look, but it can be working from an incomplete picture.
The practical consequence: a figure you check at 9 PM Eastern can differ from the same figure the next morning. That is not an error being corrected. It is the early pass being superseded by the complete one. If a number matters, read it after midnight Eastern, or simply read it the following day.
FINAL versus "through" on the allocation table
The most recent row of a strategy's allocation table carries a small tag telling you how settled it is:
The tag reports the actual state of the underlying data rather than the calendar. A month that has ended on the calendar is not automatically final; it becomes final when the data confirming it has arrived and been verified.
The current month
While a month is in progress, two different things are shown, and they behave differently.
The allocation is published daily. You can see what a strategy is holding right now. This updates as the month goes on.
The return is not published until the month closes. Instead, during the month you see a month-to-date figure, labeled MTD rather than Month Return. MTD is built from live quotes and moves during the trading day. It is a running estimate of an unfinished month, not a result.
When the month closes, the MTD figure is replaced by a settled monthly return computed from month-end closing prices, and it becomes a permanent part of the record.
"Provisional" means something different
There is also a row labeled Provisional, and it is easy to confuse with the freshness tag above. They are unrelated.
The Provisional row is a projection of next month's allocation, showing what the strategy would hold if the current month ended today. It is a forward look, not a status label, and it is a Premium feature.
It also moves. A projection made on the 8th of the month is built on eight days of incomplete data, and the signals driving it can reverse before month-end. Treat it as a preview of where things are heading, not as an instruction.
A note for free accounts
Current-month allocations for Premium strategies unlock for free users on the 11th of each month. Everything else on the daily cycle above applies the same way regardless of account type.
What to do with all this
CAGR stands for Compound Annual Growth Rate. It answers a simple question: if a strategy grew at a perfectly steady rate every year, what would that annual rate be? It is the standard way to compare returns across strategies with different track record lengths, because raw cumulative return is misleading - a strategy with 20 years of history and a strategy with 3 years of history need a common yardstick.
The formula compounds the total return over the full period and then scales it to one year:
CAGR = (Ending Value / Starting Value) ^ (1 / Years) - 1
A strategy that turned $10,000 into $18,000 over 5 years has a CAGR of about 12.5%, regardless of how bumpy or smooth the ride was along the way.
How the current month is handled
The most recent month shown on the site is usually still in progress. Its allocation is published daily, but its return is not final until the month closes, and you will see it labeled accordingly.
When a date range ends in an in-progress month, DMS counts that month as a fraction of a month rather than a whole one, based on how many days have elapsed. This matters more than it might sound. On the third day of a month, only a sliver of that month's return exists. Annualizing it as though it represented a full month would badly distort the result - a small partial gain would be scaled up as if it had taken a month to earn, inflating CAGR, and a small partial loss would do the reverse.
The practical effect:
A consequence worth knowing: a CAGR quoted for a range ending in the current month is a moving number. It will shift day to day as the month accumulates, and it will settle once the month closes. That is the calculation working correctly, not a data problem.
The same fractional logic applies at the other end of the track record. If a strategy went live partway through a month, its first month is scaled to the portion of the month it was actually invested, so it is never credited with a full month of return it did not earn.
Why the number on your screen may differ from one you noted earlier
Three global toggles change the CAGR displayed:
If a figure does not match what you recorded previously, check these first - the underlying data has not changed, only the lens.
What CAGR doesn't tell you
CAGR is a two-point measurement. It sees only where the strategy started and where it ended, and it is blind to everything in between. Two strategies can post an identical CAGR while one lost half its value and clawed back while the other barely dipped. The ride is invisible to this number.
For a fuller picture, pair it with Max Drawdown, Ulcer Index, and the Sortino ratio.
Maximum Drawdown (Max DD) is the largest peak-to-trough decline a strategy has experienced, measured from a peak (high-water mark) down to the lowest point that followed it. It is expressed as a negative percentage. A Max DD of -15% means the strategy fell 15% from its peak before recovering.
It answers a pointed question: what is the worst loss an investor in this strategy would have had to endure, and how bad did it get before things turned around?
How it is calculated
DMS computes Max DD on a month-end basis. Walking forward through the equity curve, it tracks the highest value reached so far and measures how far below that high-water mark each subsequent month falls. The deepest of those declines is the Max DD.
Because DMS strategies are evaluated monthly, the figure reflects month-end to month-end moves. Intra-month, a strategy will have dipped lower than any month-end close shows, so a daily-measured drawdown would be deeper. Every strategy and benchmark on the site is measured the same way, so comparisons between them remain fair even though all of them understate the intra-month extreme.
The global toggles feed into it. Turning on Inflation Adjusted Returns, Include Trading Friction, or Taxable Account changes the return stream the curve is built from, and the Max DD moves with it.
What it is useful for
Max DD is one of the most honest stress-tests available for a strategy. It tells you: this is the real-world pain that an investor would have experienced at the worst moment in the strategy's recorded history. Paired with the Max DD Recovery metric, which shows how long it took to get back to the prior peak, it gives a sense of both depth and duration of the worst episode.
For comparison, the S&P 500 has experienced drawdowns exceeding 50%, and a traditional 60/40 portfolio has seen drawdowns around 32%. DMS strategies are designed with low drawdowns as a central goal, not an afterthought.
What it does not tell you
This is the most important thing to understand about Max DD: it is a historical figure, not a guarantee of maximum future drawdowns.
The Max DD shown is the worst drawdown the strategy has experienced to date, based on the specific market environments in the backtest and live history. It is not a promise, a guarantee, or a prediction of the worst that could ever happen. As Meb Faber so aptly has said: your largest drawdown is still to come.
Max DD and the date range selector
The Max DD shown reflects the currently selected date range, not the full history. Narrow the range to the last five years and you will see the worst drawdown within those five years, which may be far smaller than the all-time figure if the worst episode fell outside the window. For the all-time number, use the maximum available range for the strategy.
There is a subtlety here worth knowing. The high-water mark resets at the start of your selected range. The calculation has no memory of anything before it, so the opening month is treated as the first peak.
If your range begins partway into a decline, the real peak that preceded it is invisible, and the drawdown gets measured from an already-depressed starting value. A range beginning at a market bottom will make almost any strategy look serene. When you want to understand risk rather than study a specific episode, start from the full range.
Pairing Max DD with other risk metrics
Max DD captures the single worst episode but says nothing about how often or how persistently a strategy draws down. Two strategies can share the same Max DD while feeling very different to hold - one might recover quickly, another might grind sideways for years. For a fuller picture of drawdown behavior, pair Max DD with:
Two of the detailed metrics answer a retirement question rather than an investment one: given this strategy's actual history, how much could you have pulled out every year without running out of money?
Both follow the method William Bengen introduced in 1994, which is stricter than it first sounds. The number you see is not what one retirement would have supported. It is the worst outcome across every 30-year retirement the strategy's history contains.
How the cohorts work
Take a strategy with 46 years of monthly returns. A retirement beginning in January 1985 and running 30 years is one cohort. February 1985 is another. March 1985 is another. Roll that window forward one month at a time and a 46-year record yields roughly 200 distinct 30-year retirements, each with its own sequence of good and bad years.
Every one of them is tested. The published figure is the worst of them.
That distinction matters more than it might appear. A single run starting at the beginning of a strategy's record tests exactly one entry point, and an early-1980s start happens to be close to the most favourable moment in the modern record. Reporting that number would describe a lucky retirement rather than a safe rate. The minimum across all cohorts is what the word "safe" is doing.
Because a 30-year cohort needs 30 years of data, and because a minimum is only meaningful if there are enough cohorts to take a minimum of, both metrics require at least 35 years of history. Strategies with less show N/A rather than a number built from too few retirements.
Safe Withdrawal Rate (SWR)
SWR is the largest annual withdrawal, as a percentage of your starting balance, that would have carried a full 30-year retirement through to the end without the account reaching zero, beginning in any month on record.
The withdrawal is held constant in real terms. A withdrawal fixed in dollars shrinks every year in what it actually buys, so holding it constant in purchasing power is what makes the figure describe a standard of living rather than a dollar amount.
Perpetual Withdrawal Rate (PWR)
PWR asks a stricter question over the same cohorts: what could you have withdrawn while leaving the account, in real terms, at least as large at the end of 30 years as it was at the start? SWR permits you to spend the balance down toward zero. PWR does not touch the principal.
PWR is therefore always at or below SWR. The gap is usually small, often a few tenths of a percentage point, and it is smaller for strategies that compound faster. That is not a rounding artefact: when a portfolio grows a great deal over 30 years, the extra draw that spending down the principal would buy you is small next to what the growth itself already supports.
They ignore the date range
This is the one behaviour that surprises people, and it is deliberate.
Every other figure in Detailed Metrics answers "over the period you selected." These two do not. They always use the strategy's full history, whatever range is on screen. A sustainable withdrawal rate is a property of the strategy, not of the window you happen to be looking at, and tying it to the range would mean the number vanished on any view shorter than 35 years. The row labels carry "(full history)" so the exception is visible rather than silent.
What the toggles do
Where they appear
How to read them
What these numbers are not
They are not a retirement plan and not a recommendation. They describe a strategy's historical capacity to support withdrawals across the retirements its own record contains.
A real retirement introduces everything the model leaves out: a specific horizon rather than a fixed 30 years, taxes on the withdrawals themselves, Social Security and other income, spending that is lumpy rather than smooth, and the near certainty that you would change your behaviour after a bad year rather than mechanically withdrawing the same amount into a falling account.
An equity curve shows one path: the particular sequence of months that happened. It cannot tell you how much of the result came from the strategy and how much came from the order those months arrived in.
Range of Outcomes answers that. Switch the equity chart to it using the toggle above the chart, and instead of one line you get a spread of paths the same strategy could plausibly have produced, with the actual result drawn over the top.
How it is built
The strategy's own monthly returns are resampled 2,000 times to produce 2,000 alternative 46-year histories, each using the same pool of months in a different arrangement. The percentile bands show where those 2,000 paths sit at each point in time.
The resampling is done in blocks of consecutive months, not one month at a time, and that detail is the difference between a useful chart and a misleading one.
Real market declines are made of bad months arriving in a row. Draw months independently and those runs get scattered apart, so simulated portfolios recover between shocks and never experience a proper crash. The effect is not subtle. On a test series containing one sustained decline, independent-month resampling reported a typical worst drawdown of 34% where block resampling on the identical data reported 88%. Independent draws would have understated the risk by a factor of more than two.
Block resampling keeps those runs intact. Block lengths are random, averaging up to 24 months on a full history and scaling down on shorter ranges so there are always enough distinct blocks for genuine variety. The applied block length is shown beneath the chart.
Reading the chart
The bands start narrow and fan out. That is the point of the picture. Early on, sequence has had little chance to matter; over decades it compounds into an enormous spread.
The four figures below the chart
Median Outcome is the ending value of a $10,000 starting balance at the 50th percentile, with the 5th and 95th beneath it. The gap between those two is usually startling, and it is worth sitting with. Every path used the same returns.
Median CAGR is the same idea in annualised terms.
Median Max Drawdown is the deepest peak-to-trough fall at the 50th percentile, with the 95th shown as the unlucky case. Expect this to be worse than the strategy's actual historical drawdown. The realised figure is one draw; the simulation asks what the same months could have done in a crueller order, and the answer is usually "quite a bit worse."
Actual vs Range is where the real backtest landed among the 2,000. This one is routinely misread, so it is worth being explicit: a middling number here is the correct and expected result. The simulation is built from the strategy's own returns, so it is centred on them by construction. A figure near the 50th percentile means the machinery is working. It is not a measure of skill, and a high number would not be good news, it would be a sign something was wrong upstream.
What responds to what
Range of Outcomes uses the selected date range, so narrowing the range changes both the bands and the figures. In this it differs from the Safe and Perpetual Withdrawal Rates, which always use full history.
The Inflation Adjusted, Trading Friction and Taxable Account toggles all feed the return series being resampled, so the bands respond to them as the ordinary equity chart does.
The view needs at least 60 months in the selected range.
The bands do not move between visits. The simulation uses a fixed starting point for its random number generator, so the same strategy over the same range always produces the same picture. Without that, the bands would shift slightly every time you touched a toggle and the chart would look untrustworthy for no reason.
What this tells you
That the strategy's historical result was, or was not, heavily dependent on the order in which its months arrived. If the actual path sits comfortably inside the bands and the median lands near it, sequence luck is not what produced the backtest.
It also gives you a realistic sense of dispersion. A strategy with a 15% historical CAGR whose 5th-to-95th band spans 8% to 22% is telling you something a single number cannot.
What it does not tell you
It is not a forecast. The distribution is centred on returns the strategy has already earned. It assumes those returns keep coming from the same process. If markets change, nothing in this simulation would know.
Two thousand paths are not two thousand pieces of evidence. They are one dataset rearranged 2,000 times. The apparent precision is real in the sense that the arithmetic is exact, and misleading in the sense that it all rests on one historical record. If that record is optimistic, every percentile shown is optimistic by the same amount, and nothing inside the method can detect it.
It cannot validate the strategy. Resampling takes the edge as given and only reshuffles it. It answers "was this sequence luck," which is a different and easier question than "does this strategy work." Establishing the second requires a test where the strategy is allowed to fail, which resampling is not.
It does not correct for having chosen this strategy. DMS publishes many strategies. Looking at a strong one in isolation, however rigorously, does not account for the fact that it stands out partly because it performed well.
Why it is not called "Monte Carlo"
It is a Monte Carlo simulation, and the tooltip says so. But the label invites a particular misreading, that thousands of simulations amount to thousands of independent observations, when they are one dataset restated many times. "Range of Outcomes" describes what is actually on screen: the spread of results consistent with this strategy's historical behaviour, including the unfavourable ones.
Maximum Drawdown tells you how bad the single worst moment was. It says nothing about whether a strategy spent one month underwater or eleven years. The Ulcer Index closes that gap.
Ulcer Index
The Ulcer Index measures the depth and the duration of every drawdown across a period, not just the deepest one.
It is computed by walking the equity curve month by month, recording how far below the previous high-water mark each month sits, squaring that figure, and taking the square root of the average across all months. Months at a new high contribute zero. Months deep underwater contribute a great deal, because squaring them punishes depth disproportionately.
The name is literal. It is meant to approximate how much stress holding the strategy would have caused. Lower is better, and unlike most statistics on this site there is no theoretical maximum, only comparisons between strategies over the same window.
Two things follow from the construction:
Ulcer Performance Index (UPI)
UPI turns the Ulcer Index into a risk-adjusted return measure:
UPI = (CAGR - risk-free rate) / Ulcer Index
The numerator is what the strategy earned above cash. The denominator is how much discomfort it caused getting there. Higher is better.
The structure is the same idea as the Sharpe ratio, with one substitution that matters. Sharpe divides excess return by standard deviation, which treats upside volatility as risk. UPI divides by drawdown pain, which counts only the downside and counts prolonged recoveries as worse than quick ones. For a strategy designed around limiting drawdowns rather than limiting volatility, UPI is usually the more informative of the two.
Why UPI is our headline risk-adjusted number
Standard deviation punishes a strategy for a strong upside month exactly as hard as for a weak one. Nobody experiences those two months the same way. Drawdown-based measures line up much more closely with what actually causes an investor to abandon a strategy, which is the failure mode that destroys more returns than any market decline does.
An important caveat: UPI is not leverage-invariant
It is sometimes assumed that a risk-adjusted ratio like UPI stays roughly constant when you scale a position up or down, so that a 3x version of an asset would show similar UPI to the unlevered version. Our own measurement says otherwise, decisively.
Measured against daily data for an unlevered S&P 500 fund and its 2x and 3x counterparts, buy-and-hold UPI at 3x falls to roughly 27% of the unlevered figure. The Ulcer Index does not scale linearly with leverage; across seven leveraged asset-class families it scales at approximately the leverage factor raised to the power 1.3, and that exponent was stable across sub-periods.
The practical consequence: do not compare the UPI of a leveraged strategy against an unleveraged one and conclude the leverage was free. Leverage degrades UPI structurally, before any question of skill or timing enters. A leveraged strategy that holds its UPI near its unleveraged parent's has done something genuinely difficult, and the comparison to make is against the same strategy at the same leverage, not across leverage levels.
How to read these numbers
What they do not tell you
Neither statistic knows anything about why a strategy drew down, whether the conditions that caused it are likely to repeat, or what a drawdown outside the historical record might look like. A strategy with an excellent Ulcer Index has been comfortable to hold across the period tested. That is a real and useful thing to know, and it is not a forecast.
Both answer the same question in slightly different ways: how much return did this strategy earn for the risk it took? Raw return alone cannot distinguish a strategy that earned 12% smoothly from one that earned 12% through violent swings.
Sharpe ratio
Sharpe = (average monthly return above cash / standard deviation of those excess returns) x the square root of 12
The numerator strips out what you could have earned sitting in cash. The denominator measures how much the monthly returns scattered around their own average. The square root of 12 annualizes a figure computed from monthly data.
Higher is better. As a rough guide, above 1.0 is good and above 2.0 is unusual over a long period, though these rules of thumb depend heavily on the era and the asset class.
DMS uses the arithmetic mean of monthly returns here, not the compound annual growth rate. This is the standard construction and it is what makes the figure comparable to Sharpe ratios published elsewhere. A version built on CAGR runs systematically lower, by roughly half the annualized variance, purely as an artifact of the formula rather than anything about the strategy.
Sortino ratio
Sortino keeps the same shape and changes what counts as risk. Instead of the standard deviation of all returns, it uses the deviation of only the losing months.
Sortino = (average monthly return / deviation of months below zero) x the square root of 12
The reasoning is straightforward. Standard deviation treats a surprise gain as risk, identical to a surprise loss of the same size. Nobody experiences it that way. Sortino counts only the outcomes that actually hurt.
The threshold here is zero, not the risk-free rate, and it is zero on both sides of the calculation. The minimum acceptable return is "do not lose money," and the numerator is measured against that same standard. Using one threshold in the numerator and a different one in the denominator produces a figure that is not comparable to anything, which is worth knowing if you are checking our numbers against another source.
Sortino is essentially always higher than Sharpe for the same strategy, since the denominator is built from a subset of the same months and the numerator is not reduced by the cash rate. The two are not comparable to each other in absolute terms. Compare Sharpe against Sharpe and Sortino against Sortino.
The gap between them is informative in itself. A strategy whose Sortino greatly exceeds its Sharpe has volatility concentrated on the upside, which is exactly what you want. A strategy where the two sit close together has volatility distributed evenly in both directions.
The risk-free rate
Sharpe needs a risk-free rate. DMS uses the actual return on cash over the period you have selected, taken from the cash series in the return data rather than from a fixed assumption. When cash data is unavailable it falls back to 4% per year.
This is the honest approach, and it has a consequence worth understanding: the bar moves across eras. In the early 1980s cash yielded close to 10%, so a strategy needed to earn well into double digits before its excess return was even positive. Through the 2010s cash yielded nearly nothing and almost any positive return counted as excess. A Sharpe ratio from the 1980s and one from the 2010s are not measuring against the same standard.
Sortino, using a zero threshold, is unaffected by this. That is one of its advantages when comparing across long periods with very different interest rate regimes.
When the Inflation Adjusted toggle is on, the risk-free rate is deflated along with the strategy returns, so the Sharpe comparison stays internally consistent rather than measuring a real return against a nominal benchmark.
Comparing our figures against other sites
If a ratio here differs from one you have seen elsewhere for a similar strategy, the cause is usually one of these rather than a disagreement about the underlying returns:
Which one to use
For DMS strategies specifically, we lead with the Ulcer Performance Index rather than either of these. UPI divides excess return by drawdown pain instead of by volatility, and for strategies designed to limit drawdowns rather than to limit volatility, it captures the design goal more directly.
Sharpe and Sortino are here because they are the standard vocabulary of the field, and because a strategy that looks good on one measure and poor on another is telling you something worth investigating.
Limitations shared by both
Almost every statistic on this site is computed over the date range you have selected, not over the strategy's full history. Move the range and the numbers move with it. This is intended, but a few of the behaviors surprise people, and one of them can be genuinely misleading if you do not know about it.
The general rule
CAGR, Max Drawdown, Ulcer Index, UPI, Sharpe, Sortino, standard deviation, alpha, beta, and the withdrawal rates are all window-scoped. Each is recomputed from the months inside your selection and nothing outside it.
So a strategy showing a 14% CAGR over its full history and 9% over the last five years is not contradicting itself. Those are two different measurements of two different periods.
The drawdown clock restarts
This one deserves particular attention.
Max Drawdown measures the decline from a running high-water mark. That high-water mark resets to the first month of your selected range. The calculation has no memory of anything before your window opens.
If your range begins partway into a decline, the true peak that preceded it is invisible, and the drawdown is measured from an already-depressed value. A range that starts at a market bottom will make nearly any strategy look serene, because the calculation never sees the fall that created the bottom.
The practical consequence: when you are trying to understand risk rather than study a particular episode, use the full available range. Narrow windows are for examining a specific period, not for judging how much pain a strategy can inflict.
The risk-free rate changes with the window
Sharpe, Sortino, and UPI all measure return above cash, and DMS uses the actual cash return over your selected period rather than a fixed assumption.
Cash yielded close to 10% in the early 1980s and nearly nothing through the 2010s. A window covering the first is holding the strategy to a far higher standard than a window covering the second. Risk-adjusted figures from very different eras are not directly comparable, even for the same strategy.
Some things do not change with the window
A few figures are computed over the strategy's entire history regardless of your selection:
Short windows are noisy
A statistic computed over 24 months is a much weaker claim than the same statistic over 300 months, and the site does not visually distinguish between them.
Some measures refuse to compute below a minimum. The withdrawal rates require ten years. The Tax Profile requires twelve months of allocation data. Most others will happily return a number from a very short window, and that number deserves proportionally less trust.
Comparing strategies fairly
When comparing two strategies, make sure the window covers a period both actually lived through. A strategy that launched in 2015 compared against one going back to 1979 over the maximum range is not a comparison of strategies. It is a comparison of eras.
The comparison views handle the common cases by aligning the period, but it is worth checking the start dates yourself when a result looks surprising.
Quick checklist when a number looks wrong
Most surprises resolve at one of those five.
The published track record assumes a specific and quite simple execution: you hold each month's target allocation for the whole month, and you move to the next month's targets at the month-end close. Everything on this site is built on that assumption.
Nobody trades exactly that way. Understanding where your execution differs from the model is most of what determines whether your account tracks the published curve.
The monthly rhythm
The gap between step 1 and step 3 is where reality enters. The model buys at the previous close; you buy the next morning at whatever the market opens at. Sometimes that helps you, sometimes it hurts, and over many months it mostly washes out. It does not disappear, though, and it is the main reason two people running the same strategy get slightly different results.
Wait for the month to close
The Provisional row projects what next month's allocation would be if the current month ended today. It is genuinely useful for anticipating a change and getting mentally prepared for it.
It is not a trade instruction. Signals computed on a partial month can and do reverse in the final days. Trading a projection early means occasionally taking a position the strategy never actually called for, and then paying again to undo it.
Wait for the month to close and the allocation to settle.
Not every month requires a trade
Some months the targets are unchanged and your holdings have drifted only slightly. Some strategies deliberately let positions run and only trade when a holding wanders outside a set band.
Do not manufacture activity. The published results already account for holding without trading, and adding trades the strategy did not call for only adds cost. See the Trading Friction FAQ for how the model distinguishes drift from an actual trade.
Costs the model assumes
Trading Friction charges 10 basis points one way for a non-leveraged ETF, 15 for a 2x fund, and 20 for a 3x fund. That covers spread and market impact under normal conditions in liquid funds.
You can do better or worse. Limit orders in calm conditions cost less. Market orders at the open, when spreads are at their widest, cost more. Small accounts trading illiquid leveraged funds can cost considerably more. If your execution is habitually worse than the model assumes, your results will drift below the published curve for reasons that have nothing to do with the strategy.
Account type matters
These strategies rotate, and rotation realizes gains. In a tax-deferred account that is irrelevant. In a taxable account it is a real and recurring cost.
The Taxable Account toggle and the Tax Profile panel exist to let you see the size of that difference for a given strategy before you commit to running it somewhere it will be taxed.
AutoPilot
Some strategies are available through AutoPilot, which handles execution rather than leaving it to you. The strategy page indicates whether a given strategy is supported. It removes the timing and discipline problems described above, which for many people are the largest source of tracking difference.
Where real accounts diverge from the published curve
Ranked roughly by how much damage each one does:
Note that the first three are behavioral, not mechanical. The published record assumes a level of discipline that is genuinely hard to sustain through a stretch where the strategy is behind.
One caution
None of this is personal financial advice. It describes how the published signals are constructed and how they were assumed to be executed. Whether any strategy is appropriate for you, and how much of your money belongs in it, are questions this site cannot answer.
Tactical Asset Allocation (TAA) is an active approach to investing that shifts a portfolio's mix of assets in response to changing market conditions. Rather than holding fixed weights forever - say, always 60% stocks and 40% bonds - a TAA strategy adjusts those weights month to month based on rules designed to favor what is working and reduce exposure to what isn't.
The goal is to capture meaningful upside during strong market environments while pulling back during serious downturns. Most TAA strategies use some form of momentum (leaning toward assets that have been rising) or trend-following (staying invested when an asset is above a long-term average, stepping aside when it falls below).
Reacting, not predicting
This is the point most often misunderstood. A TAA strategy does not forecast. Every signal it uses is computed from returns that have already happened, and the rules are fixed in advance. When a strategy moves to a defensive position, it is not because anything predicted a decline - it is because the trend it was following has already turned.
That distinction sets realistic expectations. A rules-based strategy will never exit at the top or re-enter at the bottom. It gives up some of the peak on the way out and some of the recovery on the way back in, in exchange for not sitting through the whole descent.
How it differs from buy-and-hold
A buy-and-hold investor accepts whatever the market delivers, including the full depth of bear markets. A TAA strategy attempts to earn competitive long-term returns with smaller, shorter drawdowns by rotating out of falling assets and into rising ones. The trade-off is that it won't always be fully invested in the best-performing asset, and it can lag during sharp, fast recoveries.
How it differs from strategic (static) allocation
Strategic allocation sets target weights - such as 40% US stocks, 30% international, 20% bonds, 10% real assets - and rebalances back to those targets periodically. The weights themselves don't change with market conditions. TAA goes a step further: the weights themselves are driven by signals, so the portfolio can look very different from one month to the next.
Whipsaw: the characteristic cost
The recurring frustration with any trend-following approach is the whipsaw - a signal that moves the portfolio defensive just as the market turns back up, or back into risk just as it rolls over again. A choppy, directionless market can produce several of these in a row, each one a small loss, with nothing to show for the trading.
Whipsaw cannot be eliminated without also giving up the protection that makes the approach worth running. It can only be managed. Several DMS strategies do this by locking a defensive decision in place for the remainder of a risk-off period rather than reconsidering it every month, accepting a missed rebound in exchange for not being repeatedly shaken in and out of position.
Monthly decisions, not necessarily monthly trades
Most strategies tracked on DMS evaluate their rules once a month using end-of-month data, and produce a target allocation for the month ahead. Whether that produces a trade is a separate question, and it varies by strategy:
One consequence is worth knowing when reading the Allocations view: a change in published weights does not by itself mean a trade happened. A portfolio left completely alone will still show different weights next month, because the holdings grew and shrank at different rates. Trading costs on the site are measured against last month's allocation after that drift, so a month spent holding costs nothing even though the numbers on screen moved.
Turnover isn't free
Every trade carries a spread and, in a taxable account, potentially a tax bill. A strategy that trades often needs to earn enough to cover that. This is why the site offers the Include Trading Friction toggle, on by default, and the Taxable Account toggle - a TAA strategy's headline return before costs and after costs can be meaningfully different numbers.
Why TAA strategies vary widely
The universe of assets, the signals used, the lookback period, and the rules for switching between risk-on and risk-off positions all differ across strategies. Some rotate among a small set of broad ETFs; others select from a larger menu. Some go to cash or short-term bonds when conditions look poor; others rotate into defensive assets. These differences produce very different return and risk profiles, which is why comparing them across the same historical periods is useful.
Risk On means the conditions a strategy requires for holding equities are being met, so it holds them. Risk Off means they are not, so it holds something defensive instead: treasuries, short-duration bonds, or cash equivalents depending on the strategy.
The terms describe a state the rules produce, not a forecast anyone is making. A strategy is Risk Off because a measurable condition failed, not because a decline is expected.
The site-wide Risk On / Risk Off indicator
The indicator shown on the site is a single, simple comparison:
Risk Off when the weighted momentum of US large-cap equities is at or below the weighted momentum of cash. Risk On otherwise.
In plain terms: if broad US equities have not been outperforming cash on a blended measure of recent returns, the market is in a Risk Off state.
The momentum figure here is the standard DMS weighted average, which blends the trailing 1, 3, and 6-month returns with half the weight on the 6-month leg. See the momentum FAQ for why it is built that way.
This indicator is a general market read. It is deliberately simple, it is stateless, and it is the same measure regardless of which strategy you happen to be looking at.
Individual strategies use their own gates
Here is the part worth understanding clearly: a strategy's own Risk On / Risk Off decision is not necessarily the same as the site-wide indicator.
Global Navigator, for example, considers both US and international equities. It goes Risk Off only when both fail to beat cash. So there are months where the site-wide indicator reads Risk Off, because US equities are lagging cash, while Global Navigator is fully Risk On and holding international equities, because those are beating cash comfortably.
Other strategies differ more still. Some use moving-average gates rather than momentum comparisons. Some have multiple sleeves that can be in different states simultaneously, so the strategy as a whole is neither wholly Risk On nor wholly Risk Off. Some use a canary asset, where an unrelated instrument acts as the trigger.
The site-wide indicator tells you the general weather. The strategy's allocation tells you what that strategy is actually doing, and the allocation is authoritative.
The signal is lagged deliberately
Signals are computed from the prior month's completed data, then applied to the month ahead. No strategy uses data from the month it is trading in, because that data does not exist yet when the decision has to be made.
This is a correctness requirement rather than a design preference. A backtest that decides January's allocation using January's returns is reporting results nobody could have achieved.
What being Risk Off does and does not mean
Why the equity-versus-cash comparison
The comparison is not "are equities rising" but "are equities beating cash." Those are different questions, and the second is the one that matters to someone deciding where to put money.
Equities grinding out 1% a year while cash pays 5% are rising and are also the wrong place to be. Using cash as the reference point builds that judgment into the rule automatically, and it is what makes the same rule sensible across eras with wildly different interest rates.
Momentum in these strategies means something specific and mechanical: a number computed from an asset's own recent returns, used to rank it against other assets or to test it against cash. Different strategy families use different formulas, and the differences are deliberate.
The DMS weighted average
The formula used by Global Navigator, LT Gain, Smart Leverage, and the site-wide Risk On / Risk Off indicator is:
wa = 0.25 x (1-month return) + 0.25 x (3-month return) + 0.50 x (6-month return)
Each component is the cumulative return over that trailing window, through the end of the most recently completed month. Half the total weight sits on the 6-month leg.
Why six months carries half the weight
The instinct is usually the opposite. Recent data feels more relevant, so weighting the most recent month heavily seems more responsive and therefore more protective.
We tested exactly that. Reversing the emphasis to put half the weight on the 1-month leg was run across the full history of the Global Navigator family. It was worse. The reason is instructive: a signal dominated by the most recent month is easily flipped by a single counter-trend bounce, and counter-trend bounces are a defining feature of the exact market episodes a defensive rule exists to catch. In both October 1987 and the 1994 bond selloff, a one-month rebound would have vetoed a defensive move that the slower weighting correctly took.
The current weighting also sits on a wide plateau. Varying the 1-month weight anywhere from zero to 0.40 produces identical results, which is the signature of a robust setting rather than a tuned one. A parameter that only works at one precise value is usually fitted to history rather than measuring anything real.
Other formulas in use
Not every strategy on the site uses the DMS weighted average. The third-party strategies published here use the formulas their original authors specified, because reproducing someone else's strategy means reproducing their rules, not substituting ours.
What all of these have in common
Why any of this works
The honest answer is that nobody knows with certainty, and the strategies do not depend on knowing. Momentum's persistence across a century of data, across asset classes, and across markets is one of the most heavily documented effects in finance, and it has survived publication, which most claimed anomalies do not.
The common explanations involve investors reacting to news gradually rather than instantly, and then overreacting once a trend is established. Whether that is the true mechanism is not something a backtest can settle.
What we can say is narrower and more useful: over the full record available, ranking assets by these formulas and holding the leaders has produced better risk-adjusted outcomes than holding everything through everything. That is an empirical claim about the past, and it is the only kind of claim any of this can support.
Where to see the numbers
Each strategy's page shows the signal values driving its current allocation, using whichever formula that strategy actually employs. If a strategy is Risk Off and you want to know how close the call was, that is where to look.
Smart Leverage is a rules-based overlay used in several DMS strategies that selectively deploys leveraged ETFs during market recovery windows - when the odds are in your favor of capturing additional gains on the upside without large downside risk. It is not the same as being permanently leveraged. The base strategy operates unleveraged; leverage is an occasional, conditional event triggered by a market drawdown.
Arming, then deploying
Smart Leverage works in two steps, and the distinction matters when you are watching a live allocation.
Step one: arming. Smart Leverage watches the month-end drawdown of IWB (the iShares Russell 1000 ETF) from its highest month-end close on record. When that drawdown reaches 10% or greater, Smart Leverage arms. Nothing changes in the portfolio yet.
Step two: deploying. Once armed, Smart Leverage waits for a month where all of the following are true:
Only then does the substitution happen:
The wait can be long. In the spec's worked example the drawdown armed Smart Leverage in March 1980, but momentum did not turn until June, so June is when leverage was actually deployed.
The drawdown is measured from month-end close to month-end close; intraday swings do not trigger Smart Leverage. It can accumulate across multiple months of declining markets, so it is not a single-month measure.
If the strategy rotates to international instead
For strategies that can hold international equities, there is an important exception. If the strategy's Risk On choice turns out to be international rather than IWB while Smart Leverage is armed, the arm is cancelled rather than deployed.
The reasoning: the drawdown fired during a stretch where the strategy preferred international markets. Coming back to IWB later is a different environment, not a continuation of the recovery that armed it.
A cancelled arm also blocks re-arming until the strategy passes through a Risk Off month. Without that block, the drawdown still sitting on the books would simply re-arm Smart Leverage the following month and undo the cancellation.
The exit rule
A live deployment ends on whichever of these comes first:
When the position closes, the strategy returns to its unleveraged default.
One trigger, one deployment
This is the rule most often misread. Once a deployment ends, for any of the three reasons above, the original drawdown is spent. If momentum flips favorable again the very next month, Smart Leverage does not redeploy. Only a fresh 10% drawdown in IWB can arm it again.
The spec's diagnostic case is April 1981: IWB was beating cash, the strategy was in equities, and Smart Leverage stayed on the sidelines because the drawdown that had armed it earlier was already used up.
A note on taxes
The 12-month cap is deliberately set where it is partly with taxable accounts in mind, since a longer hold is more likely to reach favorable long-term treatment than rapid in-and-out trading. Treat that as a design leaning, not a promise - the IRS long-term test requires holding more than one year, and a position closed at the twelve-month cap sits right at that boundary. Your own treatment depends on actual trade dates and your tax situation.
How often does it trigger?
Smart Leverage triggers infrequently and selectively. The goal is not to be leveraged most of the time, but to concentrate leverage in high-conviction recovery setups - periods where a meaningful market pullback has already occurred and momentum signals a return to equities.
Historical track record
The historical results have been compelling. For Global Navigator, only one of its Smart Leverage periods produced a worse outcome than staying unleveraged would have.
What Smart Leverage is not
Smart Leverage is not a guarantee. Leverage amplifies both gains and losses - if the market continues to fall after deployment, the impact is magnified compared to holding the unleveraged fund. The historical win rate is high, but no rule works every time. Anyone using a leveraged strategy variant should be comfortable with the possibility of outsized drawdowns during the periods when leverage is active.
To date, the Smart Leverage variants have not recorded deeper maximum drawdowns than their unleveraged parents. That is a historical observation, not a property of the design, and it may not hold in future.
Which strategies use Smart Leverage?
Six: Global Navigator 200 and 300, LT Gain 200 and 300, and Triad 135 and 170.
The three-digit number in a strategy's name indicates its maximum total notional leverage - Triad 135 can reach 135%, Global Navigator 300 can reach 300%. Some strategies carry such a number without running the overlay themselves. Calculated Risk 229 and Calculated Risk 288 are portfolios of other strategies, and their leverage comes from the leveraged components they hold rather than from their own Smart Leverage instance.
If you pull up a strategy on the Strategy View page, you can see the maximum and average leverage positions by strategy at the bottom of the ALLOCATIONS & CONTRIBUTIONS section.
Treasury Duration Limiter, TDL. A protective overlay built into several DMS strategies that steers the Risk Off holding into short-duration treasuries when long-duration treasuries look hazardous.
Why it exists
Several DMS strategies hold long-duration treasuries as their Risk Off asset. Historically, when equities fall, investors flee to long-duration treasuries, which drives their prices up and helps cushion market drawdowns. That relationship held reliably for decades, but it is not guaranteed. In early 2022, rising interest rates caused long-duration treasuries to fall at the same time as equities - one of the worst years on record for long-duration treasury returns. Strategies that rotated defensively into long-term treasuries in that environment found that the expected safe harbor was also under water.
TDL was developed in response to that reality. The goal: if long-duration treasuries look likely to be hazardous, steer into short-duration treasuries instead and avoid compounding a bad equity period with a bad treasury period.
How the signal works
TDL uses a momentum signal on long-duration treasuries themselves. It applies a weighted lookback to long-duration treasury returns, blending the trailing 1, 3, and 6-month results with half the weight on the 6-month leg. If that weighted result is negative - long duration is losing money on its own terms - TDL fires and the Risk Off allocation moves to short-duration treasuries.
The deliberate emphasis on the slower 6-month leg matters. The episodes TDL exists to catch are ones where long duration has been damaged for months but has just bounced. A faster, more recent-weighted formula would let a single flight-to-quality month veto the signal. Testing across 44 years of history confirmed this: weighting the most recent month more heavily caused TDL to miss both October 1987 and the 1994 bond massacre. The current weighting also sits on a wide plateau, meaning small changes to it produce identical results - a sign the setting is robust rather than tuned.
Once TDL fires, it stays fired
TDL is not re-evaluated every month. Once it fires within a Risk Off run, the short-duration position is locked and held for the remainder of that run. The lock clears only when the strategy returns to Risk On.
This is intentional. It prevents whipsawing back into long duration on a single-month reversal, and it produces a smoother path with fewer trades. The tradeoff is real: if long-duration treasuries rally later in the same Risk Off run, the strategy sits it out. That cost was measured and accepted.
What TDL does not do
TDL is only active when a strategy is already in its Risk Off, defensive position. It has no effect on equity allocations and does not determine when to enter or exit equities - that remains the exclusive domain of the strategy's momentum rules. TDL is purely a safety layer within the treasury sleeve.
Which strategies use it
TDL is built into the Global Navigator family (Global Navigator, 200, and 300) and the LT Gain family (LT Gain, 200, and 300). It is part of those strategies, not a separately configurable option.
Not every DMS strategy needs it. Triad, for example, was tested with four different TDL variants on its defensive sleeve and every one performed worse than leaving the sleeve alone. Global Navigator needs a TDL because it goes fully Risk Off for extended runs. Triad's defensive weight is much smaller and is already conditioned by three separate trend gates, so a second timing layer adds whipsaw rather than protection.
Every DMS strategy makes a decision once a month. Whether that decision produces a trade is a separate question, and the answer varies enormously across the lineup.
Two different rebalancing philosophies
Signal-driven strategies trade whenever their rules point somewhere new. A momentum strategy that rotates from equities to treasuries acts on that immediately and completely. If the signal is unchanged, the position is generally left alone.
Band-based strategies hold fixed target weights and only trade when a holding drifts far enough away from its target to matter. Between those breaches they do nothing at all, sometimes for years.
Many strategies combine both: signals decide what to hold, bands decide when it is worth trading to get back to precise weights.
How drift bands work
Suppose a strategy targets 25% in an asset. Rather than restoring exactly 25% every month, it defines a band around that target, and trades only when the position leaves the band.
The Permanent Portfolio strategies use a wide band on their four equal sleeves, rebalancing a sleeve only when it falls below 15% or rises above 35%. They also rebalance every January regardless. In practice this means long stretches with no trading at all.
Other strategies use tighter bands. Triad allows its sleeves to drift within a 5% band before pulling them back. GPMv uses a similar tolerance.
Why not just rebalance every month?
Because precision is not free and it is not obviously better.
The tradeoff is that a band-based strategy carries somewhat different weights than its stated targets most of the time. The bands are set so that difference stays within a range that does not change the strategy's character.
What this looks like on the site
Two consequences you will notice in the Allocations view:
Published weights change even when nothing was traded. A portfolio left completely alone still shows different percentages next month, because the holdings grew and shrank at different rates. That is drift, not activity.
Trading costs do not track changes in published weights. The friction model compares each month's targets against what the strategy was actually holding after drift, not against last month's published percentages. A month spent holding costs nothing even though the numbers moved. A rebalance back to unchanged targets does cost something, because real money moved. The Trading Friction FAQ covers this in detail.
Why the difference matters when choosing a strategy
Turnover is a real consideration, not a technicality:
None of this makes low turnover better in the abstract. A strategy that trades often because its rules genuinely call for it is doing its job. But two strategies with similar returns and very different turnover are not equally attractive in every account.
Where to check
Each strategy's page reports its historical turnover and trading costs, and the Allocations view shows exactly what changed month to month. If you want to know what running a strategy would actually involve month to month, that is the place to look before committing to it.