Leveraged-ETF regime strategy
A systematic strategy that shifts exposure across leveraged ETFs based on a hidden-Markov regime signal: leveraged products are punishing in choppy, mean-reverting markets and generous in trending ones, so most of the edge lives in knowing which regime you're standing in, not in the entry rule. Every configuration below shares the same regime detector, the same weekly-Friday rebalance, and the same performance methodology — the only thing that differs is each config's own risk-sizing and gating logic, so the numbers are directly comparable rather than four separate stories.
The engine
All four configurations run on the same regime detector, the same weekly-Friday rebalance loop, and the same fee/slippage model. What actually differs between them is layered on top of that shared core — not a uniform five-layer stack, but two genuinely different approaches to risk sizing and drawdown protection that this page's numbers let you compare directly.
Regime & signal
- Macro score reads SPY/QQQ/DIA trend, semiconductor leadership, EM stress, dollar strength, rate direction, credit spread, and choppiness into one of six regimes (RISK_ON, RISK_ON_EXTENDED, NEUTRAL, RISK_OFF, CRISIS, OVERSOLD_REBOUND)
- Per-ticker score: trend + volume-weighted momentum + intraday momentum + relative strength vs SPY/QQQ − a volatility penalty, then regime-adjusted
- 3-state Gaussian HMM on rolling SPY returns feeds a confidence read into position sizing
- Alpha 2 adds one thing on top: when SPY is above its 200-day average, a would-be risk-off read floors to neutral instead — the only config with this context filter
Risk sizing
- RiskBalance & Conservative size the entire book off one global risk number — a single risk posture chosen from 10 discrete states, applied across every position at once
- Alpha 1 & Alpha 2 replace that with a per-group engine: aggressive leverage, sector leverage, commodities, and defensive income each get their own independently-computed budget
- The practical difference: a global budget can't send commodities to full deployment while aggressive leverage sits at zero — they're fighting over the same number. A per-group budget can do exactly that
Drawdown protection
- RiskBalance & Conservative hard-block all trading past a drawdown threshold, then reanchor the peak-equity high-water mark once de-risked — without this they can lock into cash permanently, since a 100%-cash book never moves and drawdown-from-peak never shrinks
- Alpha 1 & Alpha 2 never built this failure mode in the first place — a per-group budget can independently de-risk one group without forcing the whole book to zero, so there's nothing to get stuck in
Backtests
Pick one to see how it's built and its full result — including a button to re-run it fresh, right now.
Side by side
The development arc
RiskBalance and Conservative are two parallel experiments on the same global-budget architecture, not early steps in a straight improvement chain. Alpha 1 is the real architectural break from both of them, and Alpha 2 is one further refinement on Alpha 1.
- RiskBalance — the widest tradable universe, no structural pre-filter on any position. The question it answers: does casting a wide net generate better risk-adjusted return than a narrower, more defensive one? Here, yes — RiskBalance edges out Conservative on CAGR, Sharpe, and Sortino despite having neither the rate hedge nor the sticky regime read.
- Conservative — RiskBalance's universe plus a leveraged rate hedge (TMF) and a deliberately stickier regime read (slower to change its mind about which state the market is in). The intent was a smoother ride; in this window it costs return without actually reducing max drawdown versus RiskBalance.
- Alpha 1 — the per-group Nexis engine described above. Best Calmar ratio, shortest drawdown recovery, and highest win rate of any configuration at this point in the arc.
- Alpha 2 — Alpha 1 plus one addition: a 200-day trend filter. When the market is in a confirmed uptrend, a brief pullback no longer gets misread as a regime reversal and doesn't zero out the highest-conviction positions for no reason. The result is a further, incremental improvement on every headline metric — CAGR, Sharpe, Calmar, and win rate all move up again, at the cost of a slightly deeper max drawdown than Alpha 1's.
A note on methodology
These four numbers are directly comparable because they're computed the same way: same offline weekly-rebalance harness, same Sharpe/Sortino/Calmar formulas, same fee and slippage model, same 2022-to-present window. That wasn't always true of this project's earlier public materials, which mixed a QuantConnect daily-resolution backtester (for the earlier configurations) with this offline weekly-resolution one (for Alpha 1/2) — a real methodology difference that inflated the apparent gap between generations. Recomputing everything on one methodology also surfaced a real bug: an earlier version of this page's RiskBalance/Conservative numbers reflected a permanent cash-lockout state that the original strategy code had actually already fixed historically. Both are corrected here.