Weights are often expressed as a fraction of 100 (per 100%), sometimes collected under a group-level "weight"). The overall framework includes a cash or hedging cushion and sometimes “wt-cash-specified” which assigns a fixed cash-like weight to certain paths or assets. - Risk controls and defensive signals: The structure includes many conditional branches that act as risk guards. Examples of risk-management themes embedded in the tree: - Overbought/oversold filters (RSI-based) to time entries/exits on indices like SPY or levered proxies. - Trend-following/mean-reversion style checks using long-horizon moving averages (e.g., 210/360-day moving averages) and comparisons to SPY. - Defensive hedges or safety nets when long-term trends weaken (e.g., shifts toward bonds, cash, or inverse/defensive funds like SHY, IEF, TMV, USDU, etc., in several branches with checks on relative momentum vs risk measures). - Drawdown/risk-aware sorting blocks (e.g., “Drawdown Correlated” blocks that attempt to pick assets with smaller max drawdowns or favorable risk metrics). - Dynamic, multi-regime mindset: The multiple named themes (e.g., “BIOTECH + TECH (Long BT)”, “WAM Core”, “Defense | Modified”) suggest the system attempts to maintain exposure across several market regimes (growth tech strength, defense/risk-off periods, and drawdown scenarios). The engine then blends these into a composite allocation with overall weights that sum to 100% (or close to it) across holdings, while also occasionally holding cash or hedges for risk mitigation. - Practical implications for an investor: this is a highly active, complex, levered, and diversified rule-set. Daily rebalancing paired with leveraged ETFs means daily compounding and path-dependency risk. The model trades many instruments that magnify daily moves, which can lead to large short-term swings but potentially large upside over time if trends persist. Investors should have a high tolerance for volatility, consider trading costs/slippage, and understand that performance in the backtest may not translate to future results. - What’s being optimized: At its core, the system is trying to capture momentum across multiple market themes (technology, biotech, defense, etc.), while using risk-checks and hedges to limit drawdowns. It does this by ranking a wide ETF universe on a combination of momentum and trend metrics, selecting the best performers within each theme, assigning weights, and rebalancing regularly. The end result is a basket of several levered or hedged ETFs designed to deliver outsized exposure when the market is favorable, while preserving capital when conditions deteriorate. - Important caveats for understanding this approach: leverage magnifies both gains and losses; the sheer number of moving parts and signals can make the system sensitive to data quality, costs, and market regime shifts; backtesting assumptions (e.g., daily rebalancing, exact fill prices, and dividend handling) can materially affect results versus live trading. Nevertheless, the structure provides a vivid illustration of a highly diversified, rule-based, momentum-oriented multi-asset approach that seeks to adapt to different parts of the market on a daily basis. In short: this is a sophisticated, multi-theme, rule-driven momentum portfolio that uses a large set of ETFs (including levered and inverse funds) and a web of filters to decide which assets to own and how much of each to hold each day, with risk controls intended to guard against bad markets and to shift toward hedges or cash when needed. Categories of exposure you’re getting (high level): broad U.S. equities, technology/biotech-heavy exposures, defense/market-safety plays, fixed income/defensive hedges, and cash hedges; overall risk management is built into the decision tree through RSI, moving averages, and other trend/volatility signals. Overall investment thesis (layman): A big, automated decision tree tries to pick a few bets each day across different market themes, using momentum and trend measures to decide what to own, and uses hedges and cash to keep risk in check. It’s like a committee of small, theme-based momentum strategies all voting on a diversified mix, updated every trading day.

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Out-of-sample returns are near-par with the S&P 500 (≈19.09% vs 19.07%), powered by a multi-theme, rule-based momentum system with hedges. It adds diversification and potential upside in trending regimes, but comes with higher drawdown risk than SPY.