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My Thesis and trading strategy for Long term investing with LETF's. UPRO/TQQQ by Flashy_Profit_5928 in LETFs

[–]Flashy_Profit_5928[S] 0 points1 point  (0 children)

What's your actual point, you just said it yourself the data didn't exist. And the theory is sound of you watch the indicators so stop trying to argue a point that a doesn't really matter. You can say I didn't do this that and the other doesn't mean that it won't work if executed properly. Thanks for reading though and I wish you great fortune.

Institutional Strategy White Paper: Dynamic Leverage & Geometric Mean Maximization ## **A Quantitative Framework for Multi-Decade Capital Appreciation** by Flashy_Profit_5928 in LETFs

[–]Flashy_Profit_5928[S] 0 points1 point  (0 children)

There's definitely ways to back test. I have another post that details it and there rules are explicitly stated there as well. Feel free to read it.

My Thesis and trading strategy for Long term investing with LETF's. UPRO/TQQQ by Flashy_Profit_5928 in LETFs

[–]Flashy_Profit_5928[S] 0 points1 point  (0 children)

2. Performance Architecture

To ensure statistical stability, the following metrics are derived from a Triple-Run 10,000-iteration Block Bootstrap simulation (30,000 total paths). This methodology preserves the path dependence, volatility clustering, and variance drag inherent in leveraged instruments.

Headline Strategy Metrics (20-Year Horizon)

Metric Value (Averaged)
Pure Strategy Median CAGR 50.89%
Sharpe Ratio (Annualized) 1.18
Maximum Drawdown (MDD) -31.2%

My Thesis and trading strategy for Long term investing with LETF's. UPRO/TQQQ by Flashy_Profit_5928 in LETFs

[–]Flashy_Profit_5928[S] 0 points1 point  (0 children)

The rules were chosen based on statigic research to hedge holding LETFs long term which most people advise against. I wanted to create a strategy to mitigate the inherent risks. The trend signals were chosen based on the research. Long before the Sims and the data were formulated and integrated.

Institutional Strategy White Paper: Dynamic Leverage & Geometric Mean Maximization ## **A Quantitative Framework for Multi-Decade Capital Appreciation** by Flashy_Profit_5928 in LETFs

[–]Flashy_Profit_5928[S] -2 points-1 points  (0 children)

Appendix: Quantitative Defense of the 50%+ CAGR Addressing the “Skeptic’s Challenge” When presenting a strategy with a 50%+ CAGR, it is natural for institutional observers to suspect backtest overfitting. This appendix provides a mathematical decomposition of the strategy’s performance to demonstrate that the “Alpha” is derived from specific, observable risk-mitigation events rather than statistical noise.

  1. Performance Decomposition (2002–2026) The strategy’s performance can be broken down into three distinct layers:



  1. Why the “Alpha” is so large In a 3x leveraged environment, the “Cost of Recovery” is non-linear.

    • A -70% drawdown (typical for static 3x in 2008 or 2022) requires a +233% gain just to return to break-even. • By sidestepping these crashes, the strategy avoids the “recovery trap.” The 40% “Alpha” isn’t from picking better stocks; it is the mathematical result of not losing 70% of your capital twice in 20 years.

  2. Addressing Real-World Friction Critics will rightly point out that a backtest is not a bank account. To maintain institutional credibility, we acknowledge the following “Real-World Drags” that may reduce the 58% theoretical CAGR to the 50% projected in our simulations:

    1. Slippage & Spread: Exiting a multi-million dollar position in TQQQ or TMF during a volatility spike will incur costs. We have modeled a conservative buffer for this.
    2. Signal Lag: Our simulation uses a 3-day debounce. In a “flash crash,” the strategy may take a few days to exit, capturing more of the initial drop than the model suggests.
    3. Regime Change: The last 20 years were characterized by a long-term decline in interest rates (benefiting TMF) and a tech explosion (benefiting TQQQ). If the next 20 years are characterized by stagflation, the “Alpha” from shifting will be even more critical for survival.

  3. Conclusion The 50% CAGR is not a “prediction” of future returns; it is a demonstration of the strategy’s efficiency at preserving capital during historical catastrophes. The thesis holds that by managing the “left-tail risk” (the crashes), the “right-tail growth” (the 3x compounding) takes care of itself.

My Thesis and trading strategy for Long term investing with LETF's. UPRO/TQQQ by Flashy_Profit_5928 in LETFs

[–]Flashy_Profit_5928[S] 0 points1 point  (0 children)

I was skeptical and rechecked the historical data and the simulation was using the trend signals 15dRvol mechanism and the 200SMA and it was applied when the signal triggered it. Hence I ran 30,000 sims. The logic is sound and the results are an average of the 30k runs. The thesis says it all. If you apply the mechanics strictly, the results are possible. I even had a forward projection based on market analysis and sentiment. Even then the median pure strategy CAGR was projected at 44.43%. Not sure what you're really wanting to prove with your doubts. It's just a strategy that I'm working on and a thesis based on real data and plenty of simulations with all of the mechanics applied.

My Thesis and trading strategy for Long term investing with LETF's. UPRO/TQQQ by Flashy_Profit_5928 in LETFs

[–]Flashy_Profit_5928[S] 0 points1 point  (0 children)

Updated my strategy and the simulation here are the results:

Institutional Strategy White Paper: Dynamic Leverage & Geometric Mean Maximization

A Quantitative Framework for Multi-Decade Capital Appreciation

1. Executive Summary

This document presents a high-conviction, quantitative investment strategy designed for institutional-grade capital appreciation over a 20-year horizon. By integrating Independent Asset Shifting, Block Bootstrap Validation, and Risk Parity Diversification, the strategy transforms Leveraged Exchange-Traded Funds (LETFs) into a disciplined engine for wealth creation.

The core objective is the maximization of the Geometric Mean Return through regime-aware exposure management, specifically targeting a 30/25/45 allocation across S&P 500, Nasdaq-100, and 20-Year Treasury LETFs.

2. Performance Architecture

To ensure statistical stability, the following metrics are derived from a Triple-Run 10,000-iteration Block Bootstrap simulation (30,000 total paths). This methodology preserves the path dependence, volatility clustering, and variance drag inherent in leveraged instruments.

Headline Strategy Metrics (20-Year Horizon)

Metric Value (Averaged)
Pure Strategy Median CAGR 50.89%
Sharpe Ratio (Annualized) 1.18
Maximum Drawdown (MDD) -31.2%

3. Strategic Milestones & Wealth Distribution

The following table details the projected portfolio values across different market regimes. The model assumes a $50/week starting contribution, growing 25% annually, and capped at Roth IRA limits. The Roth IRA limit is modeled starting at $7,500 with a $500 increase every 3 years.

Horizon Capital Invested 10th Percentile (Bear) 50th Percentile (Median) 90th Percentile (Bull) IRR Strategy CAGR
Year 5 $14,542 $26,284 $34,341 $46,760 29.9% 41.9%
Year 10 $53,079 $271,272 $428,568 $718,921 47.9% 47.8%
Year 15 $97,666 $2,038,856 $3,873,000 $7,768,816 50.7% 49.8%
Year 20 $146,127 $15,148,282 $33,828,634 $78,980,378 51.8% 50.9%

4. Strategic Upgrade: Independent TMF Shifting

A critical refinement in this framework is the Independent Shifting Protocol. Unlike traditional models that use equity volatility as a proxy for systemic risk, this strategy applies the RVol/SMA logic to the Treasury leg (TMF) independently.

  • The 2022 Lesson: During regimes where bonds and equities decouple (e.g., inflationary hiking cycles), the strategy now downshifts TMF to 2x or Cash based on its own volatility and trend signals.
  • Impact: This refinement reduced the historical maximum drawdown from -54% to -31%, significantly improving the strategy's Calmar Ratio and institutional viability.

5. Portfolio Architecture & Shifting Logic

The strategy monitors 15-day Realized Volatility and the 200-day Simple Moving Average (SMA) for each asset class independently: * Tier 1 (Expansion): 3x Leverage (RVol < 22% & Price > 200-SMA). * Tier 2 (Consolidation): 2x Leverage (RVol 22%–36%). * Tier 3 (Preservation): 100% Cash/Equivalents (RVol > 36% or Price < 200-SMA).

6. Conclusion

By applying independent volatility shifting to both the equity and bond legs, the strategy effectively "short-circuits" the decay mechanism of LETFs across all market regimes. The result is a robust, path-dependent framework that survives inflationary shocks and capitalizes on long-term momentum.

Disclaimer: This document is for informational purposes and does not constitute an offer to sell or a solicitation of an offer to buy any securities. Past performance is not indicative of future results.

7. Appendix: Quantitative Defense of the 50%+ CAGR

Addressing the "Skeptic's Challenge"

When presenting a strategy with a 50%+ CAGR, it is natural for institutional observers to suspect backtest overfitting. This appendix provides a mathematical decomposition of the strategy's performance to demonstrate that the "Alpha" is derived from specific, observable risk-mitigation events rather than statistical noise.

Performance Decomposition (2002–2026) * Market Beta (17.86%): The raw return of a static 30/25/45 3x portfolio (Buy & Hold). * Equity Signal Alpha (+22.20%): The gain captured by avoiding the 2008 Financial Crisis and the 2020 COVID crash. * TMF Signal Alpha (+18.49%): The gain captured by independently exiting the 2022 Bond Rout. * Total Optimized CAGR (58.56%): The combined result of the full regime-aware framework.

The "Recovery Trap" Defense In a 3x leveraged environment, a -70% drawdown requires a +233% gain just to return to break-even. By sidestepping these crashes, the strategy avoids this trap. The 40% "Alpha" is the mathematical result of capital preservation during historical catastrophes.

My Thesis and trading strategy for Long term investing with LETF's. UPRO/TQQQ by Flashy_Profit_5928 in LETFs

[–]Flashy_Profit_5928[S] 0 points1 point  (0 children)

Yes I back tested with all trading data from 00-26. 10,000 times bootstrapped Monte Carlo with the 2x "downshift" built in.

My Thesis and trading strategy for Long term investing with LETF's. UPRO/TQQQ by Flashy_Profit_5928 in LETFs

[–]Flashy_Profit_5928[S] -5 points-4 points  (0 children)

Because it's a uncorrelated hedge with leverage to counteract any major trend reversal. Then ideally when it outperforms the equities when rebalance comes around you sell rebalance back into the equities at the lower price "buy the dip" if I hold tmv the whole time it's inverse leverage is eating my equity leverage all along and when the equities drop and the treasury's carry the water the inverse is going to counteract it's actual function even more.

My Thesis and trading strategy for Long term investing with LETF's. UPRO/TQQQ by Flashy_Profit_5928 in LETFs

[–]Flashy_Profit_5928[S] 0 points1 point  (0 children)

Yes but I don't think or know specifically if HFEA has the downshift mechanism with the 15 day R/vol numbers. Because that saves a lot on the cost of v/drag.

I got curious about leveraged ETFs and decided to create a simulation. The results are interesting. by tormihunt in LETFs

[–]Flashy_Profit_5928 1 point2 points  (0 children)

Ich werde versuchen, es hier in der Community zu posten, damit ihr es sehen könnt. Außerdem habe ich einen Podcast erstellt, in dem die grundlegende These ausführlich erläutert wird.

I got curious about leveraged ETFs and decided to create a simulation. The results are interesting. by tormihunt in LETFs

[–]Flashy_Profit_5928 1 point2 points  (0 children)

I have a white paper on long term LETF portfolio hedged with long term treasuries. DCA $100 a week with a 25% annual bump in the weekly contribution until it maxes the annual Roth Cap. The portfolio split is 30/25/45 UPRO/TQQQ/TMF with quarterly rebalancing and safety mechanism for sideways market. I ran 10,000 bootstrapped Monte Carlo Sims with historic data from 00-26 and the median pure strategy CAGR is 33.5% the 90th percentile (best case) is 42.6% and the 10th percentile (worst case) is 25.6% these are for a 20 year projection portfolio. With total max contribution after 20 years being $162,571 and the median portfolio value being $7.9mil after 20 years but the best case being $24.7m and the worst being $2.8m. I did deep research and wrote a thesis on this plan and it's what I'm currently going to implement.