Kijun Equilibria [by Oberlunar]

The “story” starts with Ichimoku Kinkō Hyō, created by the Japanese journalist Goichi Hosoda in the 1930s and published in the 1960s; its literal meaning is often rendered as a “one-glance equilibrium chart” because it aims to show balance, trend direction, and dynamic support/resistance at a glance.

In that tradition, the Kijun-sen (“base line”) is not just a moving average: it is a reference equilibrium level, classically computed as the midpoint of the high–low range over 26 periods.

Kijun Equilibria [by Oberlunar] keeps that Japanese “equilibrium” idea, but modernises it in two ways. First, it turns the Kijun concept into an adaptive equilibrium line: instead of assuming a fixed market tempo (like the classic 26), it estimates a dominant cycle length using an Ehlers-style Hilbert/cycle approach, then scales internal lengths and smoothers so the equilibrium line responds differently in trending vs choppy regimes.

Second, it makes equilibrium explicitly multi-timeframe: you compute the adaptive Kijun on the chart TF (in this example 30 m) plus three lower TFs (in this example 1, 3, 5 m), then build a “cloud” between the highest and lowest of those equilibria, which becomes a practical map of where timeframes disagree and where price is most likely to “snap back” toward balance.

Bearish bias
This is a signal that the trend may shift into a bearish bias.



Due to this graphical setup, “cloud fog” is a meaningful meta-word here. In classic Ichimoku, the thickness and shape of the cloud provide a visual way to reason about strength and uncertainty.
In my indicator, the “cloud fog fills” reinterpret that same visual principle, but instead of Senkou spans, they shade the space between equilibria across timeframes, making dispersion (and compression) immediately visible.

The Ornstein–Uhlenbeck part then adds a quantitative “pullback detector” that fits the Ichimoku philosophy rather than replacing it. OU was introduced by Ornstein and Uhlenbeck as a mean-reverting stochastic process; in modern terms, it is a canonical model for a variable that is continuously pulled back toward a mean.

Bullish bias
In this case, we have a bullish bias, and the pullback detector based on Ornstein–Uhlenbeck mean-reversion calculations has signalled that the price is re-entering the green cloud, suggesting a potential bullish continuation after the bounce.


In my indicator, the mean is not an arbitrary moving average: it is the Kijun equilibrium itself. I apply OU to the deviation x = price − kijun, estimate a reversion strength (κ/kappa), and convert the deviation into a z-like score.

The result is very “Japanese” in spirit: the model isn’t saying “price is random”; it’s saying “price departs from balance, but balance pulls back”, and you only trust that pullback when κ is strong enough and the deviation is meaningfully stretched.

Bearish bias and Pull-Back idea
In this case, there are multiple pullbacks that may offer short opportunities, but eventually price breaks strongly through the TF baseline—at that point, it’s time to stop treating the trend as bearish-biased.


Finally, ATR is the glue that makes the bias logic practical and comparable across regimes. ATR (popularised by J. Welles Wilder in 1978) is fundamentally a volatility yardstick. Here it becomes, coupled with biased signals, the unit of measure for everything that should scale with volatility: how far price must be outside the cloud to count as “stretched”, how much spacing you require between stacked Kijuns to accept a true long/short bias, and even how far above/below price you place bias labels. In other words, the “Long Bias / Short Bias” is not just alignment across timeframes; it is alignment with enough ATR-separated structure to reduce false signals when all lines are compressed.

This isn’t one of the most advanced tools in my collection, but it can help newcomers. Be careful: despite the safeguards added, it may or may not produce consistently reliable signals. Risk management is central.

However, given its history, I wanted it to be part of my own collection of scripts with my personal mods, and I’m releasing it for free to the community.

by Oberlunar 👁★