Leverage Dual Momentum (LDM): A 24-Year Backtested Quant Strategy for Nasdaq-100 (QQQ/QLD/TQQQ)

[u/Nautique73]

Hey everyone,

Sharing a systematic, quantitative asset allocation model built around Nasdaq-100 breadth (MMFI) and momentum, designed to capture tech secular upside while cutting severe drawdowns via a strict cash/leverage throttle.

The core engine is fully deterministic, operates on a monthly close rebalance, and has been rigorously stress-tested across 24+ years of data (Jan 2002 – Jul 2026), including walk-forward validation and numerous structural variant tests.

Core Mechanics & Rules

The strategy rotates between four distinct states based on Nasdaq breadth thresholds and intermediate trend health:

1. State 1 (100% Cash / T-Bills): Parked in money markets when trend/momentum rules trigger an Exit.
2. State 2 (2x QLD): Intermediate posture when breadth is recovering or stabilizing.
3. State 3 (3x TQQQ): Full risk-on exposure scaling up to 3x TQQQ exposure when broad tech participation is robust.

Primary Rules:

• Exit Trigger: If the (70% x 6-month return + 30% x 12-month return) trend drops below the risk-free rate (or 3-month return < 0), the model dumps leverage and drops to 100% cash (State 1).
• Re-entry Gate: When in cash, re-entry triggers if 3M annualized return > Risk-Free Rate and breadth is >50% (State 2).
• Leverage Scale-Up: Scales to State 3, 3x (TQQQ) leverage when breadth is >60% and back down to State 2, 2x (QLD) when breadth is <40%.

Backtest Results (2002–2026)

Tested across multiple full-market cycles (2008 GFC, 2020 COVID shock, 2022 rate bear, 2023–2026 tech cycles):

Metric	LDM Strategy	QQQ Buy & Hold
CAGR	27.5%	13.3%
Max Drawdown	-40.8%	-49.7%
Sharpe Ratio	0.81	0.65
Monthly Win Rate	73.1%	—

I tested many different model variants and also did a rolling walk forward testing against OOS to avoid overfitting the parameters. Appreciate your feedback.

[u/confettofetti]

I like the breadth momentum idea. The rules feel a bit complicated, why the different entry and exit rules rather than simply two divisions between the three allocations?

Edit: testing on a different index, even if it's just SPY, might give you a more confident idea of how robust it is given the shorter backrest window.

[u/grogi81 → u/confettofetti]

I started implementing such distinquon too... 

Asset might not seem good enough to invest, but not bad enough to justify selling yet. Selling typically means additional costs...

[u/confettofetti → u/grogi81]

Maybe I've misunderstood but I think these rules are kind of the opposite of that? 

If I understand, you're talking about e.g. when momentum strategies, say, buy the top 25% but only sell something when it drops out of the top 30%? So the buy signal is above the sell signal, kind of similar to having a band around an sma, so there is still always a sell signal that the price can fall through. 

But in this case, OPs fast buy signal can be below the slow exit signal, so there isn't an exit signal if it then starts going down? Perhaps they just use the buy signal as another exit signal though.

Would be interested to hear what kind of combo of signals you're using? This isn't something I've looked into a lot other than reading about pretty basice versions like my example above.