Bitcoin's PriceTime Continuum III (UPDATED)

Jan 8, 2021

UPD: Added an equally important downward headed line to make pricetime fabric more complete.
This sums up to 4 fib channel lines of different key angles of the fractal to form a solid matrix of targets.
I assume so far this is the best system of coverage of all support/resistance levels for the decade.
Values of red and orange fib channels correspond to top fib values.

Jan 8, 2021

Note

Original work looks like this when all fib channels colored identically

Feb 14, 2023

Note

I'd believe in continuation of bullish incentive after a breakout out of yellow dotted fib line at which the price recently felt resistance.

Jun 6, 2024

Note

Price established the encounter with red fib 2.236

Oct 18

Trade closed: target reached

Closing Post (no trading bias!)

Stochastic Nature of Market Movements
Market dynamics can be formally represented through Brownian motion, an example of a stochastic process. In such systems, individual price movements are inherently unpredictable, yet the process itself is governed by a defined probabilistic structure. The key characteristic of stochasticity is that while each incremental movement is random, the aggregate behavior remains statistically consistent within a prescribed framework.
This formulation immediately exposes a conceptual tension with traditional technical analysis. Technical analysis rests on the implicit assumption that markets exhibit recurrent, deterministic patterns — geometrical formations that allegedly repeat across different temporal scales. These formations are treated as self-reinforcing prophecies, where recognition of a pattern influences collective trader behavior, thereby bringing the expected outcome into existence.
However, a stochastic process such as Brownian motion actually does not produce reproducible geometric patterns. It is path-dependent but non-deterministic, possessing no graphical memory of its prior configurations. Instead, it retains a probabilistic memory, where future states depend on transition probabilities rather than on fixed shapes or cycles. Consequently, the evolution of price is constrained not by deterministic trajectories but by stochastic transitions that continuously adapt as the underlying environment evolves.
An illustrative analogy is found in atomic motion. Consider an electron orbiting a nucleus: its instantaneous position is uncertain, yet it remains confined within quantized energy levels. The electron’s motion is random within a defined structural boundary. Similarly, market prices fluctuate unpredictably within probabilistic constraints — an uncertainty bounded by structure.
From this perspective, the essence of market analysis shifts from prediction to probabilistic framing. The objective is not to forecast the next discrete movement but to delineate the space of possible states. By defining this probabilistic envelope, one can estimate the likelihood of future trajectories, quantify risk, and infer the statistical boundaries within which random fluctuations occur.

In this sense, stochastic modeling does not eliminate uncertainty; rather, it formalizes uncertainty into a measurable and analyzable domain.