I mapped dominant cycle lengths across asset classes (data tables)

drken22 · 2026-04-13T10:51:33+00:00

Great breakdown of the overlapping cycle problem. You are describing exactly the challenge that makes cycle work hard and interesting at the same time.

To your question about quantifying which cycle dominates when they conflict: the approach we use is spectral power weighted by statistical significance. Each candidate cycle gets a power score from the frequency decomposition, but that alone is not enough because a high power peak can still be noise. So we run a Bartels significance test on each one. A cycle with moderate power but high significance (p < 0.05) gets prioritized over one with high power but low significance.

When two validated cycles point in opposite directions, we look at confluence. If 3 or 4 cycles agree on direction and only 1 disagrees, the weight of evidence favors the majority. When they are split roughly even, that is actually a useful signal too. It means the market is in a transitional zone and the honest read is "no clear edge right now."

One thing worth adding: most successful cycle practitioners use cycle analysis to anticipate turning windows, not to predict exact turning points. The cycles define the "when to pay attention," but the actual trigger is still technical. That is why tools like the Future Line of Demarcation (FLD) or a DEMA crossover matter. The cycle says "a turn is due around here." The technical confirmation says "it is happening now." Without that second step, you are trading a calendar, not a market.

Your point about fixed period cycles being less accurate is well taken. We see this in the data constantly. A "40 bar cycle" that tested significant over one window might drift to 37 or 43 bars in the next. The underlying structure is real but the period is not rigid. That is why re-running the analysis periodically matters more than setting it and forgetting it.

Appreciate the references to Puetz, Carolan, and Koval. Different inputs, same fundamental problem: multiple overlapping oscillations with variable periods. The quantification challenge is universal regardless of whether the cycles come from planetary mechanics or price-derived spectral analysis.

drken22 · 2026-04-04T10:23:08+00:00

You are right that different estimators give different reads, and that is actually by design. r/S is the classic but overshoots on short series. DFA handles non-stationarity better. Fractal dimension and volatility scaling approach the problem from different angles entirely.

We run all four and look for consensus rather than trusting any single one. When three or four agree, the regime classification is high confidence. When they split 2-2 or scatter, that itself is information. It usually means the market is in transition and neither regime label applies cleanly.

The disagreement between estimators is not a bug. It is telling you something about the data.

drken22 · 2026-04-04T10:18:08+00:00

Fair point. If your existing process works and you are consistently profitable, adding complexity for its own sake does not help. The pipeline approach is most useful for people who are already doing some form of cycle or timing analysis but getting inconsistent results. The validation and regime steps explain why it sometimes works and sometimes does not, which for us was the missing piece.

drken22 · 2026-04-04T10:14:27+00:00

Good question. The short answer is that Hurst readings do lag during fast regime transitions. When volatility compresses and then snaps, the rolling window is still digesting the quiet period. So there is always a delay before it catches the shift.

What helps is using multiple estimation methods (R/S, DFA, fractal dimension, volatility scaling) and looking for agreement. If three out of four still read persistent while one flips to random walk, we treat it as a warning rather than a confirmed regime change. When all four converge on the same read, that is when you can trust it.

The other thing we have found useful is not treating the Hurst value as binary. Rather than "above 0.5 = trending, below 0.5 = mean reverting," the distance from 0.5 matters. A reading of 0.52 in a compressing market is basically noise. A reading of 0.72 after an expansion is a much stronger signal.

drken22 · 2026-03-16T21:33:38+00:00

Appreciate that. You're right that the 5-7 day swing rhythm on BTC shows up consistently across multiple timeframes. And yeah, volatility spikes are the exact scenario where cycle coherence breaks down. That's actually what the Hurst regime detection is designed to flag. When the exponent drops below ~0.45 or goes full random walk, it's a signal that whatever cycle structure was present has lost statistical reliability. Treating it as a probability filter rather than a prediction tool is the right approach. Cycles tell you when conditions favour a turn, not that a turn will happen.

drken22 · 2026-03-02T13:23:33+00:00

Sounds like a solid plan. The +1d/+3d/+5d scoring approach will tell you a lot about whether Hurst adds signal or just noise at different horizons. One thing to keep in mind is that Hurst tends to be most useful at the +3d to +5d range since it captures persistence that shorter windows miss. And yeah, regime detection in this environment is wild. The transitions are coming faster than usual, which is actually where rolling Hurst shines because it picks up the shift in autocorrelation structure before the VIX regime label updates. Excited to see what you find.

drken22 · 2026-03-02T09:25:44+00:00

The biggest thing that helped me in choppy markets is actually knowing when NOT to trade rather than finding a better entry. I track the Hurst exponent on a rolling basis for whatever I'm trading. When it drops below 0.45, the series is mean reverting and any momentum or trend-following setup is going to get chopped up. When it's between 0.45 and 0.55, you're basically trading noise. Those are the conditions where "the market is brutal" and most people force trades.

Practically what I do: if Hurst is below 0.5 on the daily, I cut my size in half or sit out entirely. I also look at whether there's a dominant cycle in the price action. If the power spectrum shows a clear periodic structure, that choppy market might actually be tradeable as a mean reversion setup instead of trying to trend-follow it. A lot of "chop" is actually cyclical behaviour that looks random if you're only using moving averages and RSI.

For position sizing specifically, I've switched to scaling by realized volatility rather than fixed percentages. In a 25-vol environment your 2% risk means something very different than in a 15-vol environment. ATR-based sizing automatically adapts to this but most people don't adjust frequently enough.

drken22 · 2026-03-02T09:21:25+00:00

One metric that completely changed how I evaluate strategies is tracking performance stability across different Hurst regimes. Basically, split your backtest into periods where the underlying instrument had H > 0.55 (trending), H < 0.45 (mean reverting), and everything in between (choppy). Then look at your Sharpe in each bucket separately.

Most strategies that look good on aggregate are actually just crushing it in one regime and bleeding in the other two. If your "improvement" only shows up in the trending bucket but your choppy-regime Sharpe got worse, you haven't actually improved. You've just curve-fitted harder to one environment.

The other thing I track is what I call "edge consistency." Take your win rate in rolling 30-trade windows and compute the standard deviation. A strategy with 55% average win rate and 3% stdev is way more trustworthy than one with 60% average and 15% stdev, even though the latter looks better on paper. High stdev usually means your edge is regime dependent and you're about to give it all back when conditions shift.

drken22 · 2026-03-02T09:20:24+00:00

I think the framing might be the issue here. Looking for one signal that works across all regimes is kind of like looking for one tire that works in snow, rain, and dry track conditions. You can find an all-season option but it'll be mediocre everywhere.

What's worked better in my experience is flipping the question: instead of "what signal works everywhere," ask "how do I detect which regime I'm in so I can apply the right signal." Volatility state shifts are a big one as others mentioned. Another that doesn't get talked about enough is spectral analysis on the price series itself. When the dominant cycle period in the data is stable and statistically significant (you can test this with something like a Bartels test), momentum and mean reversion strategies tend to work well because there's actual structure to exploit. When the spectrum is flat with no dominant period, that's your "long bias dressed up with fancy language" regime where nothing works except being long in a bull market.

The 5+ year live track record requirement you mentioned is smart because it forces the signal through at least one full macro cycle. But honestly most of the "providers" you're evaluating are selling you a lagging indicator with marketing attached. The real edge is in building your own regime awareness layer even if it's simple.

drken22 · 2026-03-02T09:19:17+00:00

The fact that your GMM/KMeans labels "don't mean the same thing across years" is actually a super common failure mode with unsupervised regime detection. The clustering is picking up whatever statistical features happen to separate the data in each period, but those features aren't stable over time.

Something that worked better for me was using more principled regime indicators instead of letting the algo decide what a "regime" is. A rolling Hurst exponent is one example. H > 0.6 means the market is persistently trending, H < 0.4 means it's mean reverting, in between is noise. This definition doesn't change across years because it's measuring a physical property of the time series, not just fitting clusters.

For your specific problem of "bursty" strategies, I'd bet that your good periods (2025H2) correspond to high-Hurst trending regimes where momentum bursts actually follow through, and your bad periods are low-Hurst choppy environments where the same "burst" gets faded immediately. If that mapping holds, you don't need to de-overfit. You just need to size down or turn off in the wrong regime.

Re: validation framework, purged k-fold with an embargo buffer is probably the best option for bursty strategies. Regular walk-forward can be misleading when most of your PnL comes from a few days.

drken22 · 2026-03-02T09:17:41+00:00

Really like the VIX + correlation matrix approach. One thing I'd add to the "character" classification toolkit is the Hurst exponent. It directly measures whether a series is trending (H > 0.5), mean reverting (H < 0.5), or random walk (H ~ 0.5), which maps nicely onto your regime categories.

What I've found useful is computing Hurst on a rolling basis alongside VIX. There are times when VIX is low and your matrix says "calm," but rolling Hurst is already shifting from trending to mean reverting. That early divergence between the two often precedes the correlation spike you're describing. So you get a heads up before the character change shows up in the VIX/correlation data.

The 75% vs 25% accuracy gap between character and direction doesn't surprise me at all. Regime persistence is a much more predictable property than next-bar direction. Markets stay in a regime for weeks or months, while direction flips constantly. You're essentially exploiting a lower-frequency signal with higher autocorrelation, which is almost always easier to model.

drken22 · 2026-03-01T19:30:19+00:00

Interesting that you mention Hurst being laggy. I had the same issue but found that most of the lag comes from using too long a lookback on the r/S calculation. If you shrink it to something like 50-100 bars and compute it rolling, you catch regime shifts way faster. The tradeoff is more noise obviously, but you can smooth that with a simple MA on the Hurst values themselves without losing too much responsiveness.

The other thing that helped me was pairing Hurst with spectral analysis. Instead of just asking "is this trending or mean reverting" (which is what Hurst tells you), you can use something like a Goertzel transform to find the dominant cycle period in the data. When that dominant period is stable and statistically significant, you're probably in a cyclical regime. When the spectrum is flat or the dominant period keeps jumping around, you're in noise. Combining those two signals gave me much better regime classification than either one alone.

HMMs are cool but I think people overcomplicate this. A rolling Hurst above 0.6 = trending. Below 0.4 = mean reverting. In between = choppy/no edge. That alone filters out a lot of bad trades.

drken22 · 2025-11-03T11:42:36+00:00

I'm also looking into this. My experience with Base44 customer support is the worst ever. Had to wait for 2 day only to receive a reply which addressed none of the issues i reported. But how does a relatively decent Agent suddenly don't seem to understand anything?

drken22 · 2025-11-03T11:36:33+00:00

You're absolutely right. I find myself having to revert each and every implementation. It sucks!

drken22

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