BTS Zones — Major Indexes Backtest

BTS Zones — Major Indexes is a long-only tactical allocation strategy built to pursue targeted equity participation from the premise that recurring calendar windows can identify favorable periods for major-index exposure, using published BTS Strength Zones and a portfolio-level VIX 50/35 admission regime across QQQ, MDY, IJR, and IYT, with SPY or SHY used when no zone is entered.

Jump to >> Rules and mechanics · Full backtest history · Advanced insights and diagnostics

Strategy summary

BTS Zones — Major Indexes is a long-only, event-driven tactical equity allocation model. The BTS heat map supplies eight published BTS Strength Zone windows across four major-index ETFs, while a portfolio-level VIX 50/35 regime determines whether an active zone may enter.

The strategy uses recurring calendar windows to seek targeted equity exposure. The VIX regime controls admission, scheduled zone endings control exits, and a blocked zone may enter later if the regime returns to ON while the zone remains active.

The traded set combines four offensive source-index mappings with two fallback ETFs:

• Nasdaq-100: QQQ
• S&P MidCap 400: MDY
• S&P SmallCap 600: IJR
• Dow Jones Transportation Average: IYT
• Fallback assets: SPY / SHY

Once admitted, each ETF remains held through its scheduled BTS Strength Zone and exits at the Close of its final trading day. At target-decision events, all entered BTS Strength Zone ETFs are equal-weighted.

The portfolio remains long-only and targets full investment whenever its allocation is set. Entered BTS Strength Zone ETFs receive the full allocation; with zero entered zones, SPY carries the ON state and SHY carries the OFF state. The backtest evaluates whether combining the published BTS Strength Zones with VIX-gated admission adds value relative to passive SPY ownership.

What this strategy is not

• Not a VIX exit strategy: a later OFF signal does not terminate or resize a BTS Strength Zone that has already entered.
• Not a relative-strength ranking model: the strategy does not select only the highest-ranked ETF or rotate into a single winner.
• Not a fixed four-ETF basket or daily equal-weight portfolio: only entered zones receive weight, and routine market drift does not trigger rebalancing.
• Instead: it is an event-driven allocation model that admits published BTS Strength Zones while the portfolio-level VIX regime is ON, holds admitted zones to their scheduled ending, and uses SPY or SHY when no zone is entered.

Report summary

Item	Value
Strategy	BTS Zones — Major Indexes
Category	Tactical allocation / VIX-regime BTS Strength Zones
Universe	QQQ, MDY, IJR, IYT, SPY, SHY; $VIX is a non-traded reference
Trade Direction	Long-only allocation
Free Preview Window	2021–2025 (5 years); BTS uses the five most recent whole calendar years for free previews.
Full Backtest Period	2004–2025 (22 years); BTS uses the available whole-calendar-year window supported by required ETF history and methodology rules.
Window Start Rule	The last valid pre-window $VIX Close establishes the opening regime; active BTS Strength Zones and the fallback allocation are then evaluated at the first strategy Open.
Starting Capital	$10,000
Primary Benchmark	SPY buy-and-hold
Methodology Version	BTS-3377
Publication Date	June 27, 2026
Source / Credit	Brian Ernest Metzger; BTS Zones methodology and Major Indexes Heatmap

Benchmark summary

The primary benchmark is buy-and-hold SPY. It preserves broad U.S. equity-market exposure through continuous passive ownership of SPY.

The strategy adds published BTS Strength Zone timing, VIX-gated admission, equal allocation across entered zones, and an SPY/SHY fallback when zero zones are entered.

The comparison therefore tests whether the strategy’s calendar-zone and admission decisions added value relative to passive SPY ownership.

For the benchmark-selection framework, see How to Choose the Right Benchmark.

• Primary Benchmark: buy-and-hold SPY.
• Preserves: broad U.S. equity-market exposure through continuous passive SPY ownership.
• Removes: published BTS Strength Zone timing, VIX-gated admission, equal allocation across entered zones, and the SPY/SHY fallback.
• Excludes: other tactical allocation, ranking, and market-timing rules that are not part of this strategy.

Key metrics: 2021–2025 free preview

• The free preview is a recent-window orientation tool, not the complete evidence set.
• A five-year free-preview window can be useful, but it can also overstate or understate the full historical tradeoff.
• The full report expands the scorecard across the complete report window and adds the path-level interpretation behind the headline numbers.

The recent window was positive but mixed. BTS Zones produced 16.2% CAGR and $21,144 of ending capital versus 14.7% and $19,791 for buy-and-hold SPY. Strategy volatility was higher at 18.5% versus 17.1% for SPY, and strategy max drawdown was deeper at -26.1% versus -24.5%. The preview therefore supports a modest return edge, not a clean recent-window risk advantage.

Category	Metric	Strategy	Benchmark
Activity	Time in Market	98.8%	100.0%
Activity	Trades per Year	35.8	—
Activity	Win Rate	72.1%	—
Risk	Volatility	18.5%	17.1%
Risk	Max Drawdown	-26.1%	-24.5%
Risk	Sharpe Ratio	0.9	0.9
Risk	Calmar Ratio	0.6	0.6
Result	CAGR	16.2%	14.7%
Result	Ending Capital	$21,144	$19,791

---

Pro level unlocked

Full backtest history

Full backtest history shows the complete scorecard, path behavior, caution flags, failure modes, and portfolio-role framing behind the tested result.

Key metrics: 2004–2025 full backtest

Category	Metric	Strategy	Benchmark
Activity	Time in Market	98.8%	100.0%
Activity	Trades per Year	34.4	—
Activity	Win Rate	68.4%	—
Risk	Volatility	17.6%	18.8%
Risk	Max Drawdown	-26.1%	-55.2%
Risk	Sharpe Ratio	0.9	0.6
Risk	Calmar Ratio	0.6	0.2
Result	CAGR	14.5%	10.6%
Result	Ending Capital	$198,004	$91,344

What the scorecard shows

Key takeaways

• Stronger long-run compounding: the strategy finished the full report window with a higher CAGR and substantially more ending capital than buy-and-hold SPY.
• Shallower full-window losses: volatility was modestly lower and maximum drawdown was materially smaller than the benchmark.
• A less decisive recent window: the 2021–2025 preview retained a return edge but did not show the same risk advantage.

Over the full report window, the backtest shows a favorable combination of compounding and downside containment relative to passive SPY ownership. The strategy’s activity level was much higher than the benchmark’s, so the result depends on the tested calendar windows, admission rules, event timing, and implementation conventions rather than on passive market exposure alone.

Caution flags

Flag	Why it matters	Where to review the evidence
Recent-window risk edge was absent	In 2021–2025, strategy volatility was 18.5% versus 17.1% for SPY and max drawdown was -26.1% versus -24.5%.	Free-preview scorecard
Material losses still occurred	A -26.1% maximum drawdown remains a large portfolio loss even though it was much smaller than SPY’s full-window drawdown.	Full scorecard; drawdown profile
Near-continuous exposure is not cash-like defense	Time in Market was 98.8%; market exposure remained high outside brief transition gaps.	Key metrics: 2004–2025 full backtest; Equity curve
Active implementation burden remains	The strategy averaged 34.4 modeled executions per year over the 2004–2025 full window. Trades per Year excludes the terminal reporting close.	Key metrics: 2004–2025 full backtest
Relative lags still occurred	The largest calendar-year lag was 12.9 percentage points in 2006; the worst three-year rolling lag was 3.8 points per year.	Annual and rolling diagnostics

Equity curve

BTS Zones finished far ahead of buy-and-hold SPY, but the path was still equity-like rather than defensive.

What the equity curve shows

The strategy’s advantage accumulated across multiple cycles rather than from one isolated crisis trade. BTS Zones finished at $198,004 versus $91,344 for buy-and-hold SPY, while experiencing a much smaller decline during 2008–2009.

The curve was not uniformly defensive. The strategy reached a peak on Nov. 25, 2024, fell 26.1% by Apr. 21, 2025, and did not recover that peak until Dec. 10, 2025. It then reached a new high on Dec. 11 before ending the year 3.3% below that peak.

The endpoint gap supports a strong long-run compounding result, but the 2025 drawdown shows that the strategy still accepted material equity risk. The path is better described as active downside containment than as continuous low-volatility defense.

Drawdown profile

The drawdown profile isolates each series’ decline from its own prior equity peak and shows how loss depth, recovery, and time underwater differed between the strategy and buy-and-hold SPY.

Metric	Strategy	Benchmark
Max drawdown	-26.1%	-55.2%
Peak-to-trough window	Nov. 25, 2024 to Apr. 21, 2025	Oct. 9, 2007 to Mar. 9, 2009
Recovery from max drawdown	Dec. 10, 2025	Aug. 16, 2012
Longest drawdown period	Oct. 31, 2007 to Sept. 15, 2009 (685 calendar days)	Oct. 9, 2007 to Aug. 16, 2012 (1,773 calendar days)
Ending drawdown	-3.3%	-1.3%

The drawdown profile shows a materially shallower worst loss and shorter recovery burden than buy-and-hold SPY.

The strategy’s worst episode occurred from late 2024 into 2025, while its longest underwater period remained tied to the 2007–2009 cycle. Reduced drawdown depth did not eliminate long recovery periods or ending drawdown.

Failure modes and tradeoffs

The strategy did not eliminate timing risk. Its worst loss remained substantial, and the recent preview shows that the model can carry more volatility and drawdown than SPY even when it retains a return advantage.

The second tradeoff is implementation burden. The tested result depends on an active, event-driven allocation process with recurring admissions, exits, and fallback changes rather than passive equity ownership.

The third tradeoff is opportunity cost. Some calendar years and rolling windows favored SPY, so the full-window advantage required tolerating periods of relative underperformance.

Where this strategy may fit

Based on this test, BTS Zones — Major Indexes is best read as a tactical major-index allocation model whose value depends on the interaction between published calendar windows, VIX-gated admission, and event-driven portfolio construction.

Benefit

The full report window showed stronger compounding and materially shallower losses than passive SPY ownership.

Cost

The tested design required active implementation and still experienced substantial drawdowns, uneven recent-window risk, and periods of benchmark-relative underperformance.

Role

In portfolio-context terms, the evidence supports evaluating the strategy as an active equity-allocation sleeve rather than as a cash substitute, a continuously defensive allocation, or a strategy expected to lead in every market period.

---

Ultra Pro level unlocked

Advanced insights and diagnostics

Advanced insights and diagnostics show how results behave by calendar period, regime context, rolling window, and implementation logic.

Monthly and annual returns

Strategy returns by calendar year. Monthly columns use month-end equity versus the prior month-end equity, with the first displayed report month anchored to starting capital. The Annual column uses year-end equity versus prior year-end equity, with the first displayed report year anchored to starting capital. Calculations use unrounded source values; displayed values are rounded to one decimal place. The Avg row shows arithmetic averages of the displayed observations; the Annual value in the Avg row is not CAGR.

Year	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec	Annual
2004	1.4%	2.4%	-0.9%	-3.0%	2.1%	3.7%	-4.2%	0.2%	1.0%	4.3%	6.0%	2.4%	16.0%
2005	-0.7%	3.6%	-1.9%	-3.7%	6.5%	-1.8%	8.9%	-0.9%	0.8%	-0.8%	7.0%	-1.1%	16.0%
2006	4.1%	-0.3%	1.3%	0.8%	-5.3%	0.5%	-7.5%	2.2%	2.7%	3.9%	1.5%	-0.3%	2.9%
2007	2.7%	1.1%	0.9%	1.9%	3.5%	0.4%	-1.2%	1.3%	3.9%	3.0%	-4.6%	-1.7%	11.5%
2008	-4.5%	-1.1%	-0.2%	4.6%	3.9%	-9.0%	2.9%	1.5%	-9.3%	-7.7%	1.1%	0.6%	-17.1%
2009	-0.4%	-0.1%	0.5%	4.2%	1.6%	4.1%	9.2%	3.7%	3.5%	-3.5%	5.5%	7.2%	40.8%
2010	-3.8%	5.0%	6.6%	2.6%	-7.0%	-4.9%	8.6%	-4.5%	8.9%	6.0%	2.5%	8.4%	30.0%
2011	2.6%	4.5%	0.5%	3.5%	-1.7%	-3.2%	-1.6%	-5.5%	-6.9%	11.7%	0.0%	-0.6%	2.1%
2012	4.5%	4.5%	3.0%	-0.4%	-5.9%	4.1%	-0.9%	2.5%	2.5%	-1.2%	1.9%	2.8%	18.2%
2013	6.2%	0.8%	2.4%	3.2%	4.6%	-1.8%	6.4%	-3.0%	3.2%	5.7%	3.0%	2.3%	37.9%
2014	-2.5%	4.9%	1.1%	-1.0%	0.7%	2.9%	-0.3%	3.9%	-1.4%	4.2%	2.9%	0.9%	17.3%
2015	-1.1%	5.0%	-1.9%	-1.7%	2.2%	-1.8%	2.3%	-6.1%	-2.5%	7.8%	1.3%	-4.4%	-1.9%
2016	-5.1%	1.2%	7.7%	2.0%	1.3%	-2.5%	3.8%	0.1%	0.0%	-1.3%	9.1%	2.7%	19.8%
2017	1.9%	2.6%	0.2%	2.8%	-1.3%	0.2%	-0.1%	0.3%	2.0%	2.1%	4.1%	-0.8%	14.9%
2018	2.9%	-4.4%	-0.6%	0.0%	6.8%	-0.7%	5.3%	3.2%	0.6%	-8.5%	3.4%	-11.8%	-5.3%
2019	9.4%	4.3%	1.8%	3.5%	-8.4%	10.1%	1.8%	-1.7%	1.9%	3.8%	2.9%	3.0%	36.1%
2020	-1.8%	-9.4%	-5.7%	0.8%	2.5%	7.4%	7.1%	7.0%	-3.7%	-2.3%	12.4%	5.8%	19.5%
2021	-2.2%	6.8%	2.8%	4.6%	1.6%	5.9%	0.3%	3.0%	-4.6%	8.6%	-4.0%	3.8%	28.7%
2022	-7.6%	1.1%	3.1%	-8.4%	2.8%	-8.1%	10.0%	-4.1%	-9.2%	5.9%	5.8%	-6.4%	-16.2%
2023	7.3%	-1.9%	3.2%	-1.1%	1.0%	9.6%	6.3%	-1.6%	-4.7%	-4.4%	9.8%	10.5%	37.3%
2024	0.0%	5.9%	3.1%	-2.2%	5.2%	6.8%	2.2%	2.3%	2.1%	1.3%	8.8%	-7.6%	30.2%
2025	4.0%	-4.2%	-6.4%	-8.3%	5.9%	5.7%	2.6%	2.0%	3.6%	2.3%	1.6%	0.2%	8.1%
Avg	0.8%	1.5%	0.9%	0.2%	1.0%	1.3%	2.8%	0.3%	-0.3%	1.9%	3.7%	0.7%	15.8%

Calendar-return summary

Observation	Strategy result
Positive calendar years	18 of 22
Negative calendar years	4 of 22
Flat calendar years	0
Best calendar year	2009: 40.8%
Worst calendar year	2008: -17.1%
Arithmetic average displayed annual return	15.8%
Largest relative lead	2008: 19.7 pts vs SPY
Largest relative lag	2006: 12.9 pts lag vs SPY

The calendar pattern was positive in 18 of 22 years, with the best result in 2009 at 40.8% and the worst in 2008 at -17.1%. The strategy’s largest annual lead over SPY also came in 2008, while its largest annual lag came in 2006. The sequence shows broad participation with meaningful year-to-year timing risk rather than a return path driven by one isolated period.

Regime-filtered results

Benchmark up years are whole calendar years in which the primary benchmark had a positive full-year return. Benchmark down years are whole calendar years in which the primary benchmark had a negative full-year return. These are filtered-year diagnostics, not continuous-window backtest results.

Read this section as an alignment test. It shows whether the strategy added value only when SPY was weak, or whether it also participated during favorable SPY years.

Definitions

• CAGR and Ending Capital: compound only the included calendar years from a rebased $10,000 starting value.
• Max Drawdown: worst within-calendar-year drawdown among the included years, not a stitched non-contiguous drawdown.
• Volatility and Sharpe: use daily returns from the included years.
• Trade stats: use strategy trade records that exclude the terminal reporting close with execution dates inside the included years and FIFO closed-position observations that exclude the terminal reporting close with exit dates inside the included years. Buy-and-hold SPY does not have recurring benchmark trade statistics, so benchmark Trades per Year and Win Rate are not reported.

Benchmark up years: filtered key metrics

Included years: 2004, 2005, 2006, 2007, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2019, 2020, 2021, 2023, 2024, and 2025.

Category	Metric	Strategy	Benchmark
Activity	Time in Market	98.8%	100.0%
Activity	Trades per Year	35.2	—
Activity	Win Rate	70.7%	—
Risk	Volatility	16.6%	16.5%
Risk	Max Drawdown	-25.4%	-33.7%
Risk	Sharpe Ratio	1.2	1.0
Risk	Calmar Ratio	0.8	0.5
Result	CAGR	19.6%	16.6%
Result	Ending Capital	$300,963	$185,134

Benchmark down years: filtered key metrics

Included years: 2008, 2018, and 2022.

Category	Metric	Strategy	Benchmark
Activity	Time in Market	98.9%	100.0%
Activity	Trades per Year	29.3	—
Activity	Win Rate	50.0%	—
Risk	Volatility	22.8%	29.4%
Risk	Max Drawdown	-23.0%	-47.1%
Risk	Sharpe Ratio	-0.5	-0.7
Risk	Calmar Ratio	-0.6	-0.4
Result	CAGR	-13.0%	-21.0%
Result	Ending Capital	$6,579	$4,934

The filtered-year view shows that the strategy participated in favorable SPY years and limited losses in the three benchmark down years. In up years, strategy CAGR was 19.6% versus 16.6% for SPY, with a smaller worst within-year drawdown. In down years, strategy CAGR was -13.0% versus -21.0% for SPY and max drawdown was -23.0% versus -47.1%. The result is better downside containment, not loss avoidance.

Rolling-window diagnostics

Rolling-window diagnostics show how the strategy and benchmark behaved across overlapping 3-year and 5-year windows. The 3-year rolling windows use 36 complete calendar months; the 5-year rolling windows use 60 complete calendar months. They summarize the same return paths over many starting and ending points rather than relying only on the full-period result.

Each rolling window ends monthly inside the 2004–2025 report period. CAGR is annualized from the rolling-window start value to the window end value. Volatility and Sharpe use daily returns inside each rolling window with a 0% risk-free rate. Max drawdown uses the daily equity path inside each window.

3-year rolling windows

Metric	Strategy	Benchmark
Median rolling CAGR	14.4%	11.7%
Worst rolling CAGR	-3.3%	-15.1%
Median rolling volatility	18.7%	17.3%
Median rolling Sharpe	0.9	0.8
Deepest rolling max drawdown	-26.1%	-55.2%

Diagnostic	Result
CAGR wins vs benchmark	209/229 windows (91.3%)
Lower volatility	76/229 windows (33.2%)
Smaller max drawdown	114/229 windows (49.8%)
Higher Sharpe	169/229 windows (73.8%)

Across 3-year windows, the strategy beat SPY on CAGR in 209 of 229 windows and had a higher Sharpe in 169. Lower volatility was less frequent, appearing in 76 windows, while smaller max drawdown appeared in 114. The three-year evidence therefore shows broad return leadership with less consistent day-to-day volatility control.

5-year rolling windows

Metric	Strategy	Benchmark
Median rolling CAGR	14.2%	12.2%
Worst rolling CAGR	4.2%	-6.7%
Median rolling volatility	17.9%	18.9%
Median rolling Sharpe	0.8	0.8
Deepest rolling max drawdown	-26.1%	-55.2%

Diagnostic	Result
CAGR wins vs benchmark	191/205 windows (93.2%)
Lower volatility	121/205 windows (59.0%)
Smaller max drawdown	123/205 windows (60.0%)
Higher Sharpe	173/205 windows (84.4%)

Across 5-year windows, the strategy beat SPY on CAGR in 191 of 205 windows and had a higher Sharpe in 173. Unlike the three-year results, lower volatility and smaller max drawdown were also more common than not. The worst strategy five-year CAGR remained positive at 4.2%, while SPY’s worst five-year CAGR was -6.7%.

Rolling return extremes

Window	Series	Window dates	CAGR
3-year	Strategy	Apr. 2009–Mar. 2012	28.1%
3-year	Strategy	Apr. 2006–Mar. 2009	-3.3%
3-year	Benchmark	Jan. 2019–Dec. 2021	26.0%
3-year	Benchmark	Mar. 2006–Feb. 2009	-15.1%
5-year	Strategy	Apr. 2009–Mar. 2014	25.8%
5-year	Strategy	Mar. 2004–Feb. 2009	4.2%
5-year	Benchmark	Mar. 2009–Feb. 2014	22.8%
5-year	Benchmark	Mar. 2004–Feb. 2009	-6.7%

Rolling relative extremes

Note: pts/yr means annualized percentage-point CAGR difference versus the benchmark over the same rolling window.

Window	Diagnostic	Result
3-year	Largest Strategy Lead	May 2008 to Apr. 2011: 18.0 pts/yr lead
3-year	Largest Strategy Lag	May 2017 to Apr. 2020: 3.8 pts/yr lag
5-year	Largest Strategy Lead	Aug. 2006 to Jul. 2011: 12.0 pts/yr lead
5-year	Largest Strategy Lag	Jun. 2015 to May 2020: 1.7 pts/yr lag

The rolling-window evidence shows broad return leadership across both horizons, while risk advantages were more frequent in the 5-year windows. The remaining caution is timing: the strategy still had lag windows, including the 3-year period ending in April 2020 and the 5-year period ending in May 2020.

Pseudocode

The pseudocode below translates the strategy rules into an implementation-ready logic map. It focuses on the strategy’s required inputs, signal timing, state handling, eligibility gates, and target output, while leaving standardized BTS Methodology mechanics — including data alignment, execution pricing, trading costs, portfolio accounting, cash and dividend handling, short-selling treatment, benchmark conventions, reporting windows, and performance metric calculations — to the BTS Methodology framework.

Output: Event target weights for QQQ, MDY, IJR, IYT, SPY, and SHY, plus scheduled final-zone Close exits.

offensive_etfs = ["QQQ", "MDY", "IJR", "IYT"]
fallback_on = "SPY"
fallback_off = "SHY"
regime_reference = "$VIX"
off_threshold = 50
on_threshold = 35

Use the BTS heat map as the authoritative public source for the eight published BTS Strength Zone windows.
Map each window to the corresponding ETF's first true Open and final true Close.
Load enough pre-window $VIX history to establish the regime before the first strategy Open.

Initialize the portfolio-level regime:
    If the last valid pre-window $VIX Close is greater than 50, regime = OFF.
    Else, regime = ON.

After each true $VIX observation with a valid Close(t):
    Ignore padded or invalid rows; they neither update the regime nor become the prior observation.
    Let Close(prev) be the immediately preceding true $VIX observation with a valid Close.
    For the first valid in-window observation, Close(prev) is the last valid pre-window $VIX Close.
    If Close(t) > 50 and Close(prev) <= 50, set the regime effective at the next strategy true Open to OFF.
    Else if Close(t) < 35 and Close(prev) >= 35, set the regime effective at the next strategy true Open to ON.
    Else, retain the prior regime.

At each target-decision Open:
    Carry forward every entered zone that has not reached its final-zone Close.
    Admit every active, unentered zone when the current regime is ON.

    If one or more BTS Strength Zone ETFs are entered:
        Target equal weights across all entered ETFs.
        Target 0% in SPY and SHY.
    Else if regime is ON:
        Target 100% in SPY.
    Else:
        Target 100% in SHY.

Target-decision Opens occur at:
    The first strategy Open.
    A BTS Strength Zone admission Open at its first true Open when the regime is ON.
    The first true Open after a regime change to ON while an active zone remains unentered.
    The first true Open after one or more entered zones exit at their final-zone Close.
    A regime-change Open when zero zones are entered and the SPY/SHY fallback changes.

At all other Opens:
    Carry the existing positions forward without rebalancing.

At each BTS Strength Zone's final true Close:
    Exit each entered ETF whose zone ends at that Close.
    Rebuild the complete target portfolio at the next true Open.

Implementation guardrails

Small implementation differences can materially change this strategy’s signals, holdings, or timing. The notes below highlight the choices most likely to produce a different result.

Issue	Why it matters	Implementation note
Published BTS Strength Zone schedule	The exact eight windows determine which calendar opportunities can become eligible.	Use the BTS heat map as the authoritative public source and apply each published window to its corresponding ETF trading calendar.
Pre-window VIX state	The opening regime can change the first allocation and whether an active zone is admitted.	Use the last valid $VIX Close before the first strategy Open to initialize the persistent ON/OFF state.
Strict threshold equality	Treating 50 or 35 as a crossing changes regime-transition dates.	Switch OFF only on a strict cross above 50 and ON only on a strict cross below 35; equality retains the prior state.
Next-Open regime use	Using the same day’s closing VIX value at that day’s Open introduces look-ahead.	Apply a valid $VIX Close transition beginning at the next true trading-day Open.
Blocked-zone admission	A zone that begins while OFF may still become eligible before its scheduled ending.	Admit every active, unentered zone at the first true Open after the regime returns to ON, provided that Open remains inside the zone.
Hold-to-schedule treatment	Using later VIX changes as exits converts the strategy into a different timing model.	Once a zone is admitted, hold it through its scheduled final true Close; later OFF signals do not terminate or resize it.
Event-driven allocation	Daily rebalancing or ranking changes holdings and turnover relative to the tested design.	Equal-weight entered zones only at target-decision events, use SPY or SHY when none are entered, and allow ordinary weight drift between events.

The main implementation risk is changing the strategy’s regime timing, zone-admission rules, scheduled exits, or event-driven allocation while still treating the result as the same strategy. Those alternatives may be useful, but they should be presented separately from this version.

Further research

• BTS Methodology
• What Makes a Backtest Reliable?
• Why a Standardized Methodology Matters in Backtesting
• How to Choose the Right Benchmark
• Strategy Library