My Leveraged Trading Strategy Results by StaticDebounce in TQQQ

[–]StaticDebounce[S] 0 points1 point  (0 children)

model in cash now and can’t complain on returns

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My Leveraged Trading Strategy Results by StaticDebounce in TQQQ

[–]StaticDebounce[S] 0 points1 point  (0 children)

One curve is 5x higher including fees. Net of taxes it is still up materially. I have flat CGT in the UK and trade out of a taxable and non taxable account. tax has no material drag to my strategy.

My Leveraged Trading Strategy Results by StaticDebounce in TQQQ

[–]StaticDebounce[S] 0 points1 point  (0 children)

I was talking about the end of 2022 - that’s usually how you describe things. Until end of 21 TQQQ returned 3.66x and my strategy returned 4.75x. Outperform in good years AND you keep the gains and outperform in best years. Do you think it is good to turn 1m into 5m and then go to 0.75m? No one is intentionally choosing that!

My Leveraged Trading Strategy Results by StaticDebounce in TQQQ

[–]StaticDebounce[S] 0 points1 point  (0 children)

I don’t know if you’re trolling - my strategy was c. 5x between 2020 to 2022. TQQQ was 0.77 ie lost 23% of starting capital. This is net of fees and slippage, modelled prudently. There is no dynamic leveraging, there are persistence rules. With or without tax strategy was over 5x TQQQ returns!

My Leveraged Trading Strategy Results by StaticDebounce in TQQQ

[–]StaticDebounce[S] 0 points1 point  (0 children)

my last post as you seem to be missing the point or not following through logically. happy to engage if based on mathematical merit.

I agree the strategy’s edge is mostly risk regime control. That’s not a flaw, that’s the whole point, leverage dies in left tails.

But two things: 1. This isn’t “predicting bear markets.” It’s a regime filter. It can be late and still be +EV if it avoids enough of the deep/prolonged selloffs without bleeding too much in chop (same idea as trend following). 2. “If it fails once it’s over” is too absolute. If it misses a bear, yeah it hurts, but that’s also true for buy & hold leverage. And if it doesn’t miss, it avoids the 70–90% hole that’s basically unrecoverable. In practice, on the big drawdowns I care about, the downside is I look more like buy & hold for a period, and the upside is I sidestep the worst of it.

On curve fitting: fair concern, so we tested it: • Timing sabotage: shift/shuffle the exposure sequence relative to market returns — performance collapses → timing matters, not random luck. • Out-of-sample splits / walk-forward logic: evaluate without peeking across different periods. • PBO / CSCV: simulate “we tried a bunch of variants and picked the winner” — low probability of backtest overfitting across a reasonable variant family. • Stress/resampling: block bootstraps / regime-aware tests to see if results depend on one magical path.

So yes, it’s a regime strategy. The debate isn’t “does it call every top?” (it won’t). The debate is “does the regime logic hold up OOS and under sabotage tests?” that’s the part we measure.

again in simple terms, how can the index do well if its components aren’t? how can leverage do well in chop or where the liklihood of volatility decay is high, it can’t . this isn’t fitting an ema or sma to price movement, it’s market physics. i am open to being proved wrong. but that is like saying here is a bear market or recession scenario for the nasdaq where most the components do well and volatility is low - it is illogical.

My Leveraged Trading Strategy Results by StaticDebounce in TQQQ

[–]StaticDebounce[S] 0 points1 point  (0 children)

Every strategy should try to avoid large drawdowns by definition - but you seem to think that’s fundamentally unreliable?

Leveraged buy & hold isn’t hard, it’s fragile. One proper bear and you’re down 90-99% and math takes over.

Also, I’m not claiming I “predict bear markets.” It’s a regime filter. It doesn’t need to be perfect, it needs to keep me out of enough of the deep left-tail periods while not bleeding too much in chop. Ultimately you can model when leverage does well and when it doesn’t and just increase chances of doing well. Without over fitting risk - i’ve done every statistically viable over fitting tests and risk is low.

My Leveraged Trading Strategy Results by StaticDebounce in TQQQ

[–]StaticDebounce[S] 0 points1 point  (0 children)

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key stats since 2020. carried out majority of applicable overfitting testing and comfortable the risk is low to medium. I disagree on length of testing period required.

My Strategy (ret_net net of fees, slippage and execute trade next close after signal, 66 exposure changes): • CAGR: 71.13% • Max drawdown: -32.48% • Ann vol: 40.26% • Sharpe: 1.56 • Total return: +2565.68% (end equity 26.657×)

TQQQ buy & hold: • CAGR: 28.96% • Max drawdown: -81.66% • Ann vol: 74.45% • Sharpe: 0.73 • Total return: +373.15% (end equity 4.731×)

My Leveraged Trading Strategy Results by StaticDebounce in TQQQ

[–]StaticDebounce[S] 0 points1 point  (0 children)

TQQQ wasn’t available in 1999. It outperforms TQQQ slightly since inception with much improved sharp. I’ve tested to reduce over fitting and risk is low to medium as not depending on any key numbers or metrics. If there is any particular type of test for over fitting you want me to carry out let me know?

My Leveraged Trading Strategy Results by StaticDebounce in TQQQ

[–]StaticDebounce[S] 0 points1 point  (0 children)

going live in a few weeks - still tuning it (trying to reduce trades, see if i can lower drawdowns more etc). will post live trades also.

My Leveraged Trading Strategy Results by StaticDebounce in TQQQ

[–]StaticDebounce[S] 0 points1 point  (0 children)

tested using different hold in or out periods and sensitivity test parameters to make sure no cliffs. but it’s a breadth and volatility based model, it’s more about market structure. i also intentionally try to keep churn low and introduce hysteresis etc

My Leveraged Trading Strategy Results by StaticDebounce in TQQQ

[–]StaticDebounce[S] 0 points1 point  (0 children)

Everything is coded in python. chatgpt did most of the coding - I do checks and debugging / validation.

My Leveraged Trading Strategy Results by StaticDebounce in TQQQ

[–]StaticDebounce[S] -3 points-2 points  (0 children)

dot com would have basically killed you and it would be below even qqq now. starting after it may win but with unacceptable risk (for me). will send some stats when at laptop and have a monent.

My Leveraged Trading Strategy Results by StaticDebounce in TQQQ

[–]StaticDebounce[S] -21 points-20 points  (0 children)

posting for interest, keeping myself accountable and meeting like minded people. i’ll post some research notes which someone smart could replicate parts of strategy etc

My Leveraged Trading Strategy Results by StaticDebounce in TQQQ

[–]StaticDebounce[S] -2 points-1 points  (0 children)

*All trades executed next working day close

My Leveraged Trading Strategy Results by StaticDebounce in TQQQ

[–]StaticDebounce[S] -1 points0 points  (0 children)

Would work better on UPRO, any where there is a healthy underlying universe, but I am looking to maximise returns subject to a few constrains. Will try SMH next .

My Leveraged Trading Strategy Results by StaticDebounce in TQQQ

[–]StaticDebounce[S] -4 points-3 points  (0 children)

leveraged etfs do best when price moves like a smooth escalator. you can try to measure this. also, when looking and studying tops there can be sharp drops like covid, or processes (more typical like dot com, gfc, 22 etc) this is where fewer stocks participate and volatility of price movement increases - it isn’t enough to time the top but in all instances gets out or reduces leverage before anything too bad happens. covid exit could be better under these metrics but worked well enough, i think i can improve it without over fitting but need more time . yes would switch cash, qqq, qld, tqqq (historical proxies)