A complex, rules-based ETF-only strategy that rotates among cash, dollar-strong bets, Treasuries, semiconductors, and healthcare/defensive themes using RSI, moving averages, and relative strength signals, with some leveraged bets and hedges to boost growth while limiting downside.

In plain terms, the strategy operates like a very structured decision tree that selects ETFs to own and how much to own based on a sequence of tests. Think of the system as a planner that looks at several pools of ETFs (cash-like assets, dollar-strength bets, short‑term bonds, semiconductors, broad market and sector themes, and hedges) and then uses rules to decide which pool to fund at any moment. The rules are built around four ideas: 1) momentum and price strength (is an asset moving up vs. its own past or vs peers?), 2) price highs/lows and trend signals (is it overbought or oversold, is it above/below certain moving averages?), 3) how an asset is performing relative to another (is semiconductor exposure stronger than a bond/defensive alternative?), and 4) risk safeguards (is recent volatility or drawdown too large?). Thresholds like RSI values (a number that measures momentum) and moving-average checks are used to trigger buys or sells. The system also uses specific groupings with fixed or percentage weights, so the portfolio tilts among multiple themes rather than putting everything into a single bet. Some parts use leveraged ETFs (betting that a sector moves 2–3x in a direction), which can magnify gains but also magnify losses. The overall aim is to capture trends and favorable rotations across sectors and market regimes while keeping risk in check via hedges and short-term risk rules. A quick tour of the kinds of signals you’ll see: buy/sell signals based on RSI thresholds (overbought/oversold), relative strength comparisons (is one ETF stronger than another), and simple momentum checks like moving-average dominance. The philosophy is to be opportunistic, switching exposure among semiconductors, healthcare, cash/bonds, and hedges depending on what the rules say is most favorable at the moment. Important caveats: the strategy is data- and rule-heavy, relies on backtested performance that may look better in-sample than out-of-sample, uses leveraged ETFs that can explode risk, and rebalancing is not a simple annual tweak but a cascade of conditional decisions that can lead to rapid position changes.

Out-of-sample, this ETF-only strategy rotates across cash, bonds, semis, and healthcare with hedges, delivering ~43% annualized return and Calmar ~1.73 vs SPY ~28% return with ~19% drawdown—better risk-adjusted upside, albeit higher drawdown.