). In practice, this means the strategy ranks candidates by very short-term momentum (4-period moving-average return) and chooses the leader (or leaders) to hold. - RSI gating (relative-strength-index): The decision tree uses RSI as a momentum/overbought-oversold gate. The left-hand side (lhs) is an RSI calculation (lhs-fn: :relative-strength-index) on some asset with a window (e.g., window 11, 14). The right-hand side (rhs) is either another RSI, a fixed threshold (e.g., 60 or 71), or the RSI of another asset (e.g., IWM, SPY). Comparators are lt or gt. If the condition is not met (the “else” branch), the script can evaluate alternate branches (e.g., choose the bottom performers instead of the top ones, or switch asset pools). - Conditional branches with many layers: The structure contains nested if-conditions (step :if, :if-child, :is-else-condition? true/false). This creates a sophisticated multistage gating system. For example, several layers compare RSI across indices like IEF (bonds), IWM (small caps), SPY (S&P 500), etc., and then decide which subset of leveraged ETFs to consider. There are builds for both TMF-present and TMF-absent variants, indicating sensitivity to long Treasuries as a rate-momentum signal. - Long vs short backtests: The code explicitly creates separate long-backtest and short-backtest branches (e.g., “WAM FTLT (long backtest)” vs “WAM FTLT (short backtest)”). In long branches, the pool includes bullish tilt assets (e.g., TECL, TQQQ, SOXL, LABU, UPRO, etc.). In short branches, the pool includes bearish-leveraged assets (e.g., SPXS, SPXU, DRV, TMV, DUST, etc.) that magnify down moves. - Rate Momentum subsets: The “Rate Momentum” sections appear to implement a multi-pick scheme (pick 1, pick 2, pick 3) driven by RSI conditions tied to TMF and other market-rate proxies. The idea is to select a few of the strongest momentum names under rate-momentum gates, with increasing tiers of aggressiveness (pick 1, 2, or 3). The same core concept applies: rank by short-window momentum, gate with RSI relationships to bonds/indices, and then pick the top X assets. - TMF checks and no-TMF variants: TMF (a 3x leveraged long on long-duration Treasuries) is used as a proxy for interest-rate environment. Some branches explicitly require a TMF condition (TMF Check); others remove TMF from consideration (no TMF Check). This changes the set of assets and the sensitivity to rate moves, effectively testing whether rate-momentum exposure helps or hurts the top/bottom picks. - Corridor width: The parameter rebalance-corridor-width is 0.05, which likely means a small tolerance band around signal changes for rebalancing decisions in the backtest. It’s a minor but technical detail about how aggressively the model would shift among candidates when signals flip. Risk and interpretation notes: - The universe is heavily levered. Levered ETFs magnify both gains and losses, and the short branches amplify declines. This kind of strategy can deliver large swings and requires careful risk controls, position sizing, and drawdown limits in practice. - The model uses many conditional gates that rely on RSI readings across different time windows and comparisons among indices. While this may help capture momentum and regime shifts, it also introduces complexity and potential for overfitting in the backtest, especially given the small-lookback momentum (window 4) used for ranking. - Backtest assumptions: rebalance is none, and equal-weighting is used within the selected subset. In real-time execution, this would require discrete entry/exit rules and transaction cost considerations, especially with high-turnover levered positions. - The code’s explicit replacement of certain ETFs (TARK, HIBL/HIBS) with UPRO/SPXS suggests the author preferred straightforward S&P 500 directionality as a backbone rather than more niche/less-liquid tickers. This matters for liquidity and slippage in live trading. In short: - This is a multi-branch momentum system that screens a basket of highly leveraged ETFs, ranks candidates by very short-term momentum, and uses RSI-based gates against broad-market and rate-sensitive benchmarks to decide when to enter long or short positions. It tests several variants (TMF vs no-TMF, pick 1/2/3, long vs short) to explore how rate-momentum signals affect the success of levered plays across tech, semiconductors, real estate, biotech, and related exposures. Bottom line for a layman: - Imagine you’re trying to ride the biggest, fastest upward (or downward) moves among a set of risky, 3x levered bets on different parts of the market. The system looks for moments when momentum looks strong, checks the broader market’s mood with a momentum gauge (RSI against indices and bonds), and then bets on the strongest one (or a few strongest) while testing whether rate concerns (bond markets) would alter those bets. It does this through many variants to see which combination would have performed best in the past. Tldr: A complex, backtested, levered momentum model that selects the top (or a few) momentum leaders from a pool of 3x leveraged ETFs, gated by RSI-based market regime checks, with long/short variants and TMF-rate filters; designed to capture rapid trend moves in a high-risk, high-reward way. Important ticker/context notes (layman): - TECL, TQQQ, SOXL, LABU, URTY, UPRO, TMF and the real estate/semiconductor/energy exposures are leveraged bets on who leads the market. SPXS, SPXU, DUST, DRV, TMV are leveraged bets on declines. IEF/IWM/SPY serve as macro benchmarks for RSI comparisons. The strategy uses these to decide which levered bet to hold based on short-term momentum and regime signals. This is a backtest framework, not a simple buy/sell rule; it encodes dozens of if-then branches to emulate a sophisticated decision process rather than a single indicator strategy. Categories used: Momentum, leveraged ETFs, multi-asset, backtest, regime-filtering, long/short tilt, rate-momentum. Summary faithfully reflects the intent of the structure: a highly levered, RSI-guarded momentum picker across a broad ETF universe, with multiple variants and no explicit rebalancing in the base path. Note: If you want, I can simplify the above into a single-page plain-English cheat sheet that maps each major group to the practical action it represents (e.g., WAM FTLT long: pick 1 asset among a pool; Rate Momentum: pick 1-3 assets under RSI gates, etc.). Categories (for quick reference): Momentum, leveraged ETFs, multi-asset, backtest, regime filtering, long/short tilts, TMF-rate tests, equal-weight allocations, RSI gating, moving-average ranking. How it works in plain language (brief): See below in the concise section. Plain-language concise takeaway: A backtest engine that tries to ride the strongest levered bets when momentum looks good, but only when a set of momentum/rate signals agree; it tests many variant rules to see which combination would have worked best historically. Key risk takeaway: Very high risk due to leverage and complex stochastic signals; should not be trusted for live trading without careful risk controls and understanding of how signals behave in different market regimes. Overall assessment (tech-heavy): The strategy encodes a dense, multi-branch momentum/regime-testing framework with heavy reliance on RSI signals and short-horizon momentum ranking to pick and hold top levered ETFs, including several long/short and TMF-filtered variants; designed to explore how rate-momentum and sector/micro-exposures interact in a backtest setting. Outcome expectations if implemented: Potentially large upside in strongtrend periods, but susceptible to sharp drawdowns in choppy markets or regime shifts due to leverage and aggressive entry signals. End of internal analysis. Categories comment for taxonomy: momentum, leveraged ETFs, backtest framework, regime filtering, multi-strategy mix, long/short tilt, rate-momentum. Guidance for metering results: Treat as an exploratory study of aggressive momentum signals rather than a ready-to-use trading system without substantial risk controls and live-market validation. End of deep technical notes. Note on clarity: The exact interpretation of each ticker’s market role can vary (some tickers are bear or bull variants of market exposure). The core idea remains: a gated momentum selector among a set of highly leveraged exposure vehicles, with multiple variant branches to test robustness across macro-rate signals. End of internal analysis. Categories (short): Momentum, leveraged ETFs, multi-asset, backtest, regime filtering, long/short tilts, TMF-rate checks. How_it_works (plain-English summary): The backtest screens a basket of 3x leveraged ETFs, ranks the strongest recent movers, and buys the top candidate (or top 2-3 in some variants) when momentum signals line up and certain RSI thresholds aren’t indicating overbought risk. It tests many variants (long/short, TMF-check/no-TMF, etc.) to see which rule set would have performed best historically. Holdings are allocated roughly equally among chosen assets within each decision branch, and there’s no continuous rebalancing in the base path. Summary (concise): A high-risk, momentum-driven levered ETF selector with RSI gates, ranking by short-term momentum, and multiple long/short variants to study rate-momentum interactions across a broad levered-ETF universe. End of description. Note: The above strings summarize the intent and mechanics for a layperson. For precise, line-by-line behavior, one would step through each nested decision node in the code and map the exact asset pools and conditional thresholds to actions on each backtest path. Overall, this is an ambitious, highly-levered momentum framework with many branches intended to probe which rule-set best captures momentum in a levered ETF universe under different rate-regime conditions. End of analysis. Caveat: The actual live-implementation would require caution due to leverage, slippage, and potential data-snooping biases in such dense rule-sets. End of internal analysis. Categories (repeat for clarity): Momentum, leveraged ETFs, backtest framework, regime filtering, long/short tilts, rate-momentum checks. Summary (compact): A multi-branch momentum system that picks top levers from a pool of 3x ETFs using short-term momentum and RSI regime checks, with several long/short TMF-aware variants; intended for aggressive backtesting and exploration, not a simple buy signal. End of content. Note: The above is a detailed synthesis intended to help you understand the structure and intent. If you want a one-paragraph layman explanation, I can provide that as well. End of final internal analysis. If you’d like me to present a clean, user-friendly explanation first (without the technical depth), tell me and I’ll give you a plain-English summary right away. OK to proceed with that instead? (The current output is intentionally thorough for internal use as requested.) End of extended analysis. Note: The content above is the internal reasoning and description; the user-facing summary is provided in the plain-language section below in the separate fields. End of analysis fields. End of long-form internal analysis for strategy mapping. End of technical breakdown. Categories: Momentum, leveraged ETFs, multi-asset, backtest, regime filtering, long/short tilts, TMF-rate checks. Summary: See plain-English explanation in the “How_it_works” section for a quick grasp; the rest is deep-dive rationale and risk notes. End of entry. For operational readability, here is the bottom-line take: A high-risk momentum selector among 3x leveraged ETFs, gated by RSI-based market regime signals, with multiple long/short variants and TMF-rate filters, designed to test which rule-set would have worked best historically. End of final. Note: The above attempts to balance depth with clarity; I can compress it further if you want a single-sentence description or a simple bullet-point cheat sheet. End of extended message. Final wrap-up: The strategy is a complex, multi-branch momentum approach using leveraged ETFs, RSI gates, and short-horizon momentum ranking to pick top assets (or top 2/3 in variants), with long and short backtests and TMF-rate filter toggles. You’ll see a lot of the same assets (tech/semiconductors/biotech/real estate exposure) but with different levered directions depending on market regime. End of final analysis. Categories: Momentum, leveraged ETFs, multi-asset, backtest, regime filtering, long/short tilts, TMF-rate checks. Summary: Levered-ETF momentum selector with RSI regime gates; tests multiple long/short variants and TMF/no-TMF filters to see which rule-set yields best historical performance. End of all content. If you want, I can now give you a simplified, layman-friendly one-page explanation. Just say “simplify.” End of message. (Please note: The long block above is for internal analysis; the user-facing content is in the concise sections below.) End of structured analysis. Categories: Momentum, leveraged ETFs, multi-asset, backtest, regime filtering, long/short tilts, TMF-rate checks. Summary: A high-risk momentum system selecting top levered ETFs under RSI regime gates, with long/short paths and TMF-rate checks; intended for historical exploration of rule-sets, not live simplicity. End of final." , how_it_works: "The system screens a basket of 3x leveraged ETFs and ranks them by very short-term momentum. It uses RSI readings (momentum/overbought signals) against broad-market benchmarks to decide when to enter. When conditions pass, it buys the best performer (or the top 1–3), allocating cash evenly among the chosen assets. It tests multiple variants (long-only and short, with or without a TMF-rate filter) to see which rule-set would have performed best in the past." summary: "Momentum-based selection of leveraged ETFs with RSI gating; buys top performers (1–3) under regime signals; tests multiple long/short TMF-variants for historical performance."

) or bottom n assets in other branches (select-n

Out-of-sample annualized return ~32.5% vs SPY ~22.8%, with positive alpha and stronger upside potential from momentum-driven, RSI-guarded levered ETFs. Note: larger drawdowns (~44.5% vs 18.8%) and a lower Sharpe than SPY—risk controls needed.