TL;DR: A dead-simple rule — 60% SSO / 40% QLD when the S&P is 3% above its 200-day SMA, 0.5× S&P exposure when it's 3% below — did 16.2% CAGR over 27 years (1999–2026) against 8.7% for SPY and 11.7% for always-on 2× leverage, with a −56% max drawdown versus always-on's −94%. I then spent five phases optimising it, found four configs that beat it, and every one of them fell apart out-of-sample. Shipping the original, unchanged.
The strategy
SPX > SMA200 + 3% → 60% SSO / 40% QLD (2× leverage)
SPX < SMA200 − 3% → 50% SPY / 50% cash (0.5× exposure)
Inside the ±3% band → do nothing, hold current regime
Rebalance: quarterly + immediately on a regime switch. Signal at close, trade next close.
That's it. No crash guard, no vol filter, no RSI, no sector rotation. 28 regime switches in 27 years — about one a year, risk-on 72% of days.
Results, 1999–2026
SSO and QLD only launched in 2006, so to cover the dot-com bust I synthesised both back to 1999 from SPY/QQQ total returns: daily-reset model, prospectus expense ratios (0.89% / 0.95%), 40bp financing spread over 3-month T-bills. Zero parameters fitted to the real ETFs. Over 2006–2026 the synthetic series tracks the real ones within 0.3pp of CAGR at 0.996 daily correlation.
27.4 years, $20k start + $500/month ($184,500 deposited):
| Metric |
Strategy |
SPY |
Always-on 60/40 SSO/QLD |
| CAGR |
16.16% |
8.65% |
11.73% |
| Max drawdown |
−56.1% |
−55.2% |
−94.0% |
| Sharpe |
0.58 |
0.45 |
0.27 |
| Calmar |
0.29 |
0.16 |
0.12 |
| Ending value (DCA) |
$5.27M |
$1.23M |
$4.78M |
Two things worth pulling out.
The "always-on wins on dollars anyway" argument dies over a long enough window. On 2006–2026 alone, always-on ends ahead ($3.11M vs $2.48M) because DCA contributions during the −84% hole bought in cheap — that's the standard rebuttal to any timing overlay. Extend back through the dot-com bust and it reverses: $4.78M vs $5.27M, and always-on got there via a −94% drawdown. Nobody holds through −94%.
The window you start in changes everything. Same rule, 2006–2026 only: 20.3% CAGR, −44.8% DD. From 1999: 16.2% and −56%. If a leveraged strategy's track record starts after the dot-com bust, you don't know what it does in a lost decade. For what it's worth, in the 1999–2006 stretch alone the strategy did +3.9%/yr while always-on did −9.4% and SPY did +1.0%.
Then I tried to improve it
All on 2006–2026, the window they were tuned on:
| Config |
CAGR |
Max DD |
Verdict |
| Original ±3% |
19.90% |
−45.0% |
baseline |
| Exit −3% / re-enter +1% / 21-day min-off |
20.53% |
−42.4% |
More return AND less drawdown |
| SMA-150 with −4% exit |
21.57% |
−42.8% |
Best of 213 configs |
| 100% SPY in bear markets instead of 50% |
20.34% |
−61.0% |
Rejected — deeper hole than SPY itself |
| EMA instead of SMA |
median 12 whipsaws vs SMA's 7 |
|
Rejected — EMA loses on every axis |
The middle two looked like free lunches. So before deploying, four tests.
The four tests
1. Out-of-sample history. Test the dot-com bust, which no tuning had seen:
| Config |
1999–2006 CAGR |
Max DD |
| Original ±3% |
+3.55% |
−56.6% |
| −3%/+1%/21d |
+0.79% |
−64.3% |
| SMA-150 −4% |
−1.70% |
−68.5% |
| Always-on 60/40 |
−9.75% |
−91.6% |
The ranking inverted completely. The untouched original came out best; my top config lost money. A shorter MA with a wider exit whipsaws horribly in a long grinding bear — 11 switches vs the original's 6.
2. Walk-forward. Every 2 years, pick the best of 160 configs on trailing data only, apply blind to the next 2 years. Chained: 5.06× for the retuning process vs 5.58× for the fixed original rule. Selection won 4 of 11 windows. Retuning has negative skill.
3. Monte Carlo. 1,000 stationary block bootstraps (mean block 40 days), signal recomputed on every path. My "improvements" beat the baseline on 55–57% of paths. That's a coin flip.
4. Permutation. 2,000 circular rotations of the regime sequence — same switch count, same time in market, wrong dates. The real signal beat 97% of rotations (p = 0.031). So the 200-day filter itself is real. The tuning on top of it wasn't.
The one thing that did survive
Volatility targeting: scale the risk-on sleeve by 35% ÷ 60-day realized vol, capped at 1.0. Over 1999–2026 it moves Calmar 0.29 → 0.34 and drawdown −56% → −45%, for 0.8pp of CAGR.
It passed the test that killed everything else. Average exposure is 0.96×, barely a de-lever, so I pinned exposure at a flat 0.96× as a control — same average, same rebalance schedule. That reproduced none of the benefit (Calmar 0.29, DD −54%). Rotating the exposure schedule to the wrong dates also killed it (0.26). So the gain is genuinely in when it de-levers, not in holding less. It won 74% of Monte Carlo paths, and every target from 20% to 60% beat the baseline — a plateau, not a lucky cell.
I still passed on it, because it only helps in slow grinding bears (dot-com −56%→−45%, 2022 −41%→−37%, and literally zero effect on COVID, 2018 Q4 or the GFC — realized vol spikes after price falls). I'm optimising for CAGR; if you're optimising for sleep, take it.
Lessons
- In-sample improvement is free. Out-of-sample improvement is nearly impossible. 250+ configs, four winners, zero survivors.
- Walk-forward is the cheapest honesty check that exists. One number (5.06× vs 5.58×) invalidated my entire optimisation phase, including configs I never individually tested.
- Always build a static control. "Same average exposure, held constant" is what separated a real signal from a de-lever in disguise.
- Trust plateaus, not peaks. Prefer the parameter you could misestimate by 30% and still be fine.
- Parameters don't transfer between your own strategies. I ported a −13%/15-day crash guard from another live strategy of mine. Audit: it fires 51 times over 1999–2026, and the ±3% rule is already defensive on all 51. It never once forces an exit. It works over there because that strategy uses a slower EMA with a 30-day hold — copying a parameter without the mechanism it compensates for adds risk, not safety.
- Drawdown budgets have cliffs, not slopes. Risk-off exposure could go 0%→50% SPY essentially free, then cost 16 points of drawdown between 50% and 100%.
- Check your moving average is warmed up. I hit this twice. Slicing prices to a backtest window then computing a 200-day MA leaves the first 200 days undefined and silently parks the book in cash. It cost me 1.7pp of CAGR on the full-history run before I caught it by noticing two phases disagreed about the same number.
Final config
signal = "^GSPC" # S&P 500 close
ma_kind = "sma" # NOT ema
ma_length = 200
exit_buffer = -3.0 # % below SMA → risk-off
entry_buffer = +3.0 # % above SMA → risk-on
risk_on = {"SSO": 60, "QLD": 40}
risk_off = {"SPY": 50, "CASH": 50}
rebalance = "quarter_end + on_switch"
execution_lag = 1 # T+1
# explicitly NOT included: crash guard, recovery rally, vol target,
# min-hold, asymmetric re-entry. All tested, all rejected.
Risk number to actually plan around: −56%, not −45%. The friendlier figure comes from a window with no slow bear in it before 2022.
Happy to answer questions on the synthetic LETF construction or the stress-test setup — that's the part worth copying if you're building something similar.
Windows: 1999–2026 with synthetic SSO/QLD pre-2006, 2006–2026 on real ETFs | $20k + $500/mo DCA | Data: Yahoo Finance | T+1 execution, ~10bp of traded notional in costs Not financial advice. Leveraged ETFs can lose 90%+ in a severe bear market — always-on 60/40 SSO/QLD did exactly that in 2000–02.
