NVIDIA: All Fractal Patterns - You decide the direction
Patterns create a framework for understanding market behavior, helping you organize chaotic price action into more predictable structures.
In this report I'm prepared to go through most Patterns I can spot across NVIDIA Chart to be able to interpret bigger emerging picture.
REGULARITIES
"Think not of what you see, but what it took to produce what you see." ~ Benoit Mandelbrot
Fractal Cyclicality
Sub-cycles are smaller composite cycles recurring within larger ones, showing periodic patterns of price oscillations that collectively shape the rhythm of the full cycle.
In NVIDIA's chart, these sub-cycles typically consist of three final peaks, each representing the market's effort to sustain bullish momentum while gradually approaching a point of inevitable bullish exhaustion.
The peak of the 3rd composite sub-cycle is critical decision-making period for bulls, indicating last chances for the profitable exit points before major trend reversals take hold.
Fractal Validation Through Scaling
This particular fractal, starting from 2015, caught my attention due to its consistency and proportional alignment with the current market cycle.
According to EW, fractal matches really well from 1 to 4 wave. The 5th wave, being too prolonged. Either it played out faster because oh higher frequency of reversals.
Assessing:
Expansion with observed part of pattern Final Peaks Scaled with derived top of cycle:
Another progression nicely curved that could match with smaller scale cycles as building blocks
Alignment with 1st systematic cycle:
This means that next single-cycled consolidation confirms bearish exhaustion by matching proportions within a cycle.
"Reactive" Patterns to after heavy drops, like this often contain compressed fractals with higher frequency or reversals.
Witnessing how even single-cycled bullish “consolidation after drop” contains undeformed proportions of fractal, at this point there is no need to look for another fractal.
This approach illustrates how dynamics of smaller cycle evolve into larger market movements, maintaining their core proportions across price and time scales.
The ability of these patterns to mirror both micro (next one) and macro (overall shape) levels indicates that the metrics defining these fractals are consistent and scalable across timeframes and price scales.
This scalability hints at a deeper, intrinsic market behavior rooted in fractal geometry. The fact that all patterns seem to "abide by each other's metrics" implies a self-referential system, where smaller cycles influence larger ones, and vice versa.
This aligns with the theory of self-similarity, a core principle of fractals, suggesting that markets are not random but governed by a structured, recursive mechanism.
Viewing the chart in logarithmic scale amplifies this universal quality, as it normalizes the exponential growth of markets and reveals the proportionality between fractal patterns.
Will do Fractal Mapping with Fibs in Part II
In this report I'm prepared to go through most Patterns I can spot across NVIDIA Chart to be able to interpret bigger emerging picture.
REGULARITIES
"Think not of what you see, but what it took to produce what you see." ~ Benoit Mandelbrot
Fractal Cyclicality
Sub-cycles are smaller composite cycles recurring within larger ones, showing periodic patterns of price oscillations that collectively shape the rhythm of the full cycle.
In NVIDIA's chart, these sub-cycles typically consist of three final peaks, each representing the market's effort to sustain bullish momentum while gradually approaching a point of inevitable bullish exhaustion.
The peak of the 3rd composite sub-cycle is critical decision-making period for bulls, indicating last chances for the profitable exit points before major trend reversals take hold.
Fractal Validation Through Scaling
This particular fractal, starting from 2015, caught my attention due to its consistency and proportional alignment with the current market cycle.
According to EW, fractal matches really well from 1 to 4 wave. The 5th wave, being too prolonged. Either it played out faster because oh higher frequency of reversals.
Assessing:
Expansion with observed part of pattern Final Peaks Scaled with derived top of cycle:
Another progression nicely curved that could match with smaller scale cycles as building blocks
Alignment with 1st systematic cycle:
This means that next single-cycled consolidation confirms bearish exhaustion by matching proportions within a cycle.
"Reactive" Patterns to after heavy drops, like this often contain compressed fractals with higher frequency or reversals.
Witnessing how even single-cycled bullish “consolidation after drop” contains undeformed proportions of fractal, at this point there is no need to look for another fractal.
This approach illustrates how dynamics of smaller cycle evolve into larger market movements, maintaining their core proportions across price and time scales.
The ability of these patterns to mirror both micro (next one) and macro (overall shape) levels indicates that the metrics defining these fractals are consistent and scalable across timeframes and price scales.
This scalability hints at a deeper, intrinsic market behavior rooted in fractal geometry. The fact that all patterns seem to "abide by each other's metrics" implies a self-referential system, where smaller cycles influence larger ones, and vice versa.
This aligns with the theory of self-similarity, a core principle of fractals, suggesting that markets are not random but governed by a structured, recursive mechanism.
Viewing the chart in logarithmic scale amplifies this universal quality, as it normalizes the exponential growth of markets and reveals the proportionality between fractal patterns.
Will do Fractal Mapping with Fibs in Part II
Unlock exclusive tools: fractlab.com
ᴀʟʟ ᴄᴏɴᴛᴇɴᴛ ᴘʀᴏᴠɪᴅᴇᴅ ʙʏ ꜰʀᴀᴄᴛʟᴀʙ ɪꜱ ɪɴᴛᴇɴᴅᴇᴅ ꜰᴏʀ ɪɴꜰᴏʀᴍᴀᴛɪᴏɴᴀʟ ᴀɴᴅ ᴇᴅᴜᴄᴀᴛɪᴏɴᴀʟ ᴘᴜʀᴘᴏꜱᴇꜱ ᴏɴʟʏ.
ᴘᴀꜱᴛ ᴘᴇʀꜰᴏʀᴍᴀɴᴄᴇ ɪꜱ ɴᴏᴛ ɪɴᴅɪᴄᴀᴛɪᴠᴇ ᴏꜰ ꜰᴜᴛᴜʀᴇ ʀᴇꜱᴜʟᴛꜱ.
ᴀʟʟ ᴄᴏɴᴛᴇɴᴛ ᴘʀᴏᴠɪᴅᴇᴅ ʙʏ ꜰʀᴀᴄᴛʟᴀʙ ɪꜱ ɪɴᴛᴇɴᴅᴇᴅ ꜰᴏʀ ɪɴꜰᴏʀᴍᴀᴛɪᴏɴᴀʟ ᴀɴᴅ ᴇᴅᴜᴄᴀᴛɪᴏɴᴀʟ ᴘᴜʀᴘᴏꜱᴇꜱ ᴏɴʟʏ.
ᴘᴀꜱᴛ ᴘᴇʀꜰᴏʀᴍᴀɴᴄᴇ ɪꜱ ɴᴏᴛ ɪɴᴅɪᴄᴀᴛɪᴠᴇ ᴏꜰ ꜰᴜᴛᴜʀᴇ ʀᴇꜱᴜʟᴛꜱ.
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