MMD CP 1pay

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The MMD CP (Cloud Passages) indicator is another key component of the MMD methodology, focusing on moments when a shorter MMD-based cloud crosses through a longer-period MMD-based cloud. This is not merely about overlapping areas, but precisely capturing the actual intersection of two clouds to better understand the relationship between different time horizons and the market’s price equilibrium.

12-period cloud passing through the 48-period cloud.
12-period cloud passing through the 48-period cloud.

To grasp the essence of CP passages, imagine two MMD clouds—for example, a red (12-period) cloud and an orange (48-period) cloud—each constructed from a pair of SMA/EMA averages. A cloud is defined as the area between the SMA and EMA of the same period. A CP passage occurs when the shorter cloud starts to “enter” the longer cloud and then fully passes through it. In practice, this can be visualized by drawing two horizontal lines at the points where each of the shorter cloud’s averages breaks through the area defined by the longer cloud.

48-period cloud passing through the 288-period cloud.
48-period cloud passing through the 288-period cloud.

These identified CP zones reflect changes in market structure, highlighting critical moments when the market may transition from one price regime to another. They can signal potential trend slowdowns, accelerations, or pauses before important turning points. As a result, CP passages become valuable reference points for traders seeking signals reinforced by MMD logic—whether within custom strategies or established approaches built on the MMD methodology.

288-period cloud passing through the 1140-period cloud.
288-period cloud passing through the 1140-period cloud.

The MMD CP indicator, especially when combined with other MMD tools (such as MMD Cloud, MMD Diamond, or MMD BTZ) and knowledge gained from educational materials, including videos on the MagicOnCharts YouTube channel, allows for a more in-depth analysis of market context. This, in turn, enables more informed decision-making, taking into account the relationships between various time horizons and the statistical dependencies considered within the MMD methodology.