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Fractal

XRP Market Cap With 2017 FractalStriking similarities between early 2017 price action on XRP market cap and our present situation. I've overlayed the fracal from early 2017 and scaled it to fit our top of $2.92. If we follow the same pattern our bottom will be around $1.68. Back in 2017 we consolidated for about month before we broke out from that local top. From there it took rougly another 15 days to reach the ultimate top. The scaled fractal points exactly to a 5.236 fibonacci extension. By today's supply that would amount to $8.50 per XRP. Keep in mind I didn't take into account that there is XRP released from ESCROW every month and the supply is therefore increasing. Currently 57B in circulation and the maximum supply ever will be 100B. In conclusion, the longer it takes, the more likely it is that these price targets have to be adjusted (lowered). Will we follow this fractal? Let's see how it plays out!
CRYPTOCAP:XRP
by butwhowasmoon
AMD Chart-Based Probabilistic TargetsFRACTAL SCALING For a start I'll use the monthly timeframe that captures the broader market cycles and observes the structural trends to understand the scale and distribution of randomness over time. We need a solid foundation before diving into finer timeframes for more detailed analysis. Capturing critical points of a cycle with Fibonacci channels, especially when aligned with the direction of the trend, reveals hidden non-linear dynamics due to the following reasons: Fibonacci ratios reveal fractal structures that align with key reversal points in cycles, reflecting inherent market patterns. Directionality highlights trend asymmetries, showing where price reacts differently in bullish or bearish conditions. Cycles map the rhythm of reversals, exposing non-random patterns in market transitions. Price reacts disproportionately at Fibonacci levels, reflecting non-linear market forces like supply and demand. Hidden symmetry emerges, revealing harmonic relationships within price swings. Integration of time and price uncovers rhythm, where significant moves align with Fibonacci projections. Historical patterns anticipate future reactions, showing the underlying structure of market behavior. Justified Shift This version of the wave metrics aligns the top of the Fibonacci channel with a more recent cycle high, allowing it to better reflect the current price structure. By anchoring the top cycle closer to the present price action, the analysis enhances the accuracy of the underlying frequency dynamics and non-linear relationships. This adjustment also highlights a clearer transition between past and current cycles, capturing how momentum evolves within the channel. The updated metrics likely improve the identification of potential reversal zones or continuation points relative to the new cycle top. Curves Curves are essential in fractal analysis because they reveal the non-linear dynamics and self-similar structures that govern market behavior. Unlike straight lines, curves accurately model the natural rhythm of price movements, capturing how trends accelerate or decelerate over time and oscillate between key levels. By connecting critical price points such as highs, lows, and retracement levels, curves expose the proportional relationships that link fractals, often aligning with natural laws like the Fibonacci sequence. They also define boundaries like "Full Capacity," highlighting where price tends to exhaust momentum and reverse, offering a roadmap for identifying turning points. Furthermore, curves integrate time and price, capturing the dynamic relationship between the two and providing deeper insights into how cycles evolve and repeat. In fractal analysis, they bridge the gap between mathematical models and real market behavior, making them invaluable for interpreting and anticipating price action. Weekly Timeframe AMD’s remarkable growth of 14,018.01% from $1.61 to its all-time high signals an impressive rally, but it also raises the likelihood of growing bearish pressure as the market enters an overheated condition. Such parabolic moves are rarely sustainable, and they often lead to exhaustion, where natural resistance levels, such as the upper bounds of the Fibonacci channel, come into play. These levels, particularly the "Full Capacity" threshold, often signal overbought conditions, triggering profit-taking by institutional investors and traders. As price approaches these critical thresholds, momentum typically begins to slow, with indicators like RSI or volume divergence potentially signaling weakening bullish sentiment. The natural cyclical behavior of markets, combined with extended valuations, creates a favorable environment for bearish reversals. Additionally, macroeconomic risks, declining earnings growth, or broader fundamental concerns can further amplify selling pressure. If price fails to maintain upward momentum or begins forming bearish reversal patterns such as lower highs or rising wedges, it may confirm that the market is entering a corrective phase. Monitoring technical indicators, such as volume trends and momentum divergences, alongside fundamental triggers, will be essential in assessing whether bearish pressure will dominate in the near term. Repetitive Patterns The repetitive pattern circled in yellow represents a critical cyclical phase in AMD's price movement. Each time this pattern completes, it is immediately followed by an "off-the-range" move that resembles the beginning of a super cycle. This phenomenon suggests that the yellow-circled phase acts as a precursor to a significant shift in the market's dynamics, where price transitions into a larger, more powerful trend. That fractal may indicate consolidation or accumulation, where price oscillates within a confined range before breaking out. This breakout initiates a super cycle, marked by a rapid and sustained directional move beyond the previous range. The repetitive nature of this sequence highlights the fractal behavior of the market, where similar patterns recur at different scales, providing opportunities to anticipate major market movements. Fractal I Fractal II
NASDAQ:AMD
by fract
4
SMCI, the worst is likely behind usSMCI has crashed from this years highs, a good 80%. To me it sounds like the worst has happened. And while we may see some positivity this EOY that can help us reach new ATHs, we must remain aware of the risk the broad economy poses. Target is 130+ short term, with one more 50%+ drop coming right after. I would make sure I have the funds ready to scoop up shares if such a scenario happens. As the second dip doesn't look as bad as the first one. After that SMCI will resume its lifetime bullish climb, and keep on going for as long as the bull market lasts.
NASDAQ:SMCILong
by Ilyassou
Liquidity targetsIn an alternate reality: I'm a billionaire. I'm watching a very public asset called CoinBit linger below 100K - a very unusual and exciting situation. So I sell! I DO NOT let it break above 100K, and I sell so much that it pushes price all the way down to break into a liquidity pool below the previous trading range. Then I buy-buy-buy all the way up to squeeze the shorts with stops around 100K. In terms of unbiased odds based on TA alone, this prediction is wack. But I think this is a unique window of time where something like this could happen, and if it does happen, this is what it might look like.
BITMEX:BTCUSD.P
by ThousandDollarBitcoin
Reality & FibonacciParallels between Schrödinger’s wave function and Fibonacci ratios in financial markets Just as the electron finds its position within the interference pattern, price respects Fibonacci levels due to their harmonic relationship with the market's fractal geometry. Interference Pattern ⚖️ Fibonacci Ratios In the double-slit experiment, particles including photons behave like a wave of probability, passing through slits and landing at specific points within the interference pattern . These points represent zones of higher probability where the electron is most likely to end up. Interference Pattern (Schrodinger's Wave Function) Similarly, Fractal-based Fibonacci ratios act as "nodes" or key zones where price is more likely to react. Here’s the remarkable connection: the peaks and troughs of the interference pattern align with Fibonacci ratios, such as 0.236, 0.382, 0.618, 0.786. These ratios emerge naturally from the mathematics of the wave function, dividing the interference pattern into predictable zones. The ratios act as nodes of resonance, marking areas where probabilities are highest or lowest—mirroring how Fibonacci levels act in financial markets. Application In markets, price action often behaves like a wave of probabilities, oscillating between levels of support and resistance. Just as an electron in the interference pattern is more likely to land at specific points, price reacts at Fibonacci levels due to their harmonic relationship with the broader market structure. This connection is why tools like Fibonacci retracements work so effectively: Fibonacci ratios predict price levels just as they predict the high-probability zones in the wave function. Timing: Market cycles follow wave-like behavior, with Fibonacci ratios dividing these cycles into phase zones. Indicators used in illustrations: Exponential Grid Fibonacci Time Periods Have you noticed Fibonacci ratios acting as critical levels in your trading? Share your insights in the comments below!
BITMEX:BTCUSD.PEducation
by fract
18
AMC, December 2024Close one, with the timing of the Debt for Equity at 5.66, GO plan, big box office movies and a peaking stock market, there's a bullish lotto play short term with at least a 100% returns. Up to the $9 - $13 range. Could last until January, but we all know AMC runs quickly, then falls as quick. The reason for that fall would likely be economic conditions getting worse and the market finally falling. ---- It is safe to assume that AMC will also feel the effect of a recession although it has proven in the past that it could care less (check out defunct symbol : AEN, October 2000). ---- Still it is wise to remain cautious and expect rejection near $11 and be ready to catch the dip. As AMC is poised to recover along with the movie business through 2025 - 2029. ---- If their mission is successful and AMC can survive through harsh months coming up, then this ticker will play a major role in a potential movie bubble that is brewing. ---- Nothing is guaranteed, there is always a lot of risks investing in non-profitable and debt ridden companies. Thankfully AMC has seen a slow but solid return to balance sheet cleanliness. Less expenses, more streams of revenues and debt is being pushed out and actively paid. There are probably more rounds of dilution coming up along the way, this is when you should have your cash ready. Because when the box-office numbers start popping up again and resume their pre COVID climb, AMC won't spend much more time down there. ______ This is not financial advice.
NYSE:AMCLong
by Ilyassou
2
Natural Patterns & Fractal GeometryIn my previous research publication, I explored the parallels between the randomness and uncertainty of financial markets and Quantum Mechanics, highlighting how markets operate within a probabilistic framework where outcomes emerge from the interplay of countless variables. At this point, It should be evident that Fractal Geometry complements Chaos Theory. While CT explains the underlying unpredictability, FG reveals the hidden order within this chaos. This transition bridges the probabilistic nature of reality with their geometric foundations. ❖ WHAT ARE FRACTALS? Fractals are self-replicating patterns that emerge in complex systems, offering structure and predictability amidst apparent randomness. They repeat across different scales, meaning smaller parts resemble the overall structure. By recognizing these regularities across different scales, whether in nature, technology, or markets, self-similarity provides insights into how systems function and evolve. Self-Similarity is a fundamental characteristic of fractals, exemplified by structures like the Mandelbrot set, where infinite zooming continuously reveals smaller versions of the same intricate pattern. It's crucial because it reveals the hidden order within complexity, allowing us to understand and anticipate its behavior. ❖ Famous Fractals List of some of the most iconic fractals, showcasing their unique properties and applications across various areas. Mandelbrot Set Generated by iterating a simple mathematical formula in the complex plane. This fractal is one of the most famous, known for its infinitely detailed, self-similar patterns. The edges of the Mandelbrot set contain infinite complexity. Zooming into the set reveals smaller versions of the same structure, showing exact self-similarity at different scales. Models chaos and complexity in natural systems. Used to describe turbulence, market behavior, and signal processing. Julia Set Closely related to the Mandelbrot set, the Julia set is another fractal generated using complex numbers and iterations. Its shape depends on the starting parameters. It exhibits a diverse range of intricate, symmetrical patterns depending on the formula used. Shares the same iterative principles as the Mandelbrot set but with more artistic variability. Explored in graphics, simulations, and as an artistic representation of mathematical complexity. Koch Snowflake Constructed by repeatedly dividing the sides of an equilateral triangle into thirds and replacing the middle segment with another equilateral triangle pointing outward. A classic example of exact self-similarity and infinite perimeter within a finite area. Visualizes how fractals can create complex boundaries from simple recursive rules. Models natural phenomena like snowflake growth and frost patterns. Sierpinski Triangle Created by recursively subdividing an equilateral triangle into smaller triangles and removing the central one at each iteration. Shows perfect self-similarity; each iteration contains smaller versions of the overall triangle. Highlights the balance between simplicity and complexity in fractal geometry. Found in antenna design, artistic patterns, and simulations of resource distribution. Sierpinski Carpet A two-dimensional fractal formed by repeatedly subdividing a square into smaller squares and removing the central one in each iteration. A visual example of how infinite complexity can arise from a simple recursive rule. Used in image compression, spatial modeling, and graphics. Barnsley Fern A fractal resembling a fern leaf, created using an iterated function system (IFS) based on affine transformations. Its patterns closely resemble real fern leaves, making it a prime example of fractals in nature. Shows how simple rules can replicate complex biological structures. Studied in biology and used in graphics for realistic plant modeling. Dragon Curve A fractal curve created by recursively replacing line segments with a specific geometric pattern. Exhibits self-similarity and has a branching, winding appearance. Visually similar to the natural branching of rivers or lightning paths. Used in graphics, artistic designs, and modeling branching systems. Fractal Tree Represents tree-like branching structures generated through recursive algorithms or L-systems. Mimics the structure of natural trees, with each branch splitting into smaller branches that resemble the whole. Demonstrates the efficiency of fractal geometry in resource distribution, like water or nutrients in trees. Found in nature, architecture, and computer graphics. ❖ FRACTALS IN NATURE Before delving into their most relevant use cases, it's crucial to understand how fractals function in nature. Fractals are are the blueprint for how nature organizes itself efficiently and adaptively. By repeating similar patterns at different scales, fractals enable natural systems to optimize resource distribution, maintain balance, and adapt to external forces. Tree Branching: Trees grow in a hierarchical branching structure, where the trunk splits into large branches, then into smaller ones, and so on. Each smaller branch resembles the larger structure. The angles and lengths follow fractal scaling laws, optimizing the tree's ability to capture sunlight and distribute nutrients efficiently. Rivers and Tributaries: River systems follow a branching fractal pattern, where smaller streams (tributaries) feed into larger rivers. This structure optimizes water flow and drainage, adhering to fractal principles where the system's smaller parts mirror the larger layout. Lightning Strikes: The branching paths of a lightning bolt are determined by the path of least resistance in the surrounding air. These paths are fractal because each smaller branch mirrors the larger discharge pattern, creating self-similar jagged structures which ensures efficient distribution of resources (electrical energy) across space. Snowflakes: Snowflakes grow by adding water molecules to their crystal structure in a symmetrical, self-similar pattern. The fractal nature arises because the growth process repeats itself at different scales, producing intricate designs that look similar at all levels of magnification. Blood Vessels and Lungs: The vascular system and lungs are highly fractal, with large arteries branching into smaller capillaries and bronchi splitting into alveoli. This maximizes surface area for nutrient delivery and oxygen exchange while maintaining efficient flow. ❖ FRACTALS IN MARKETS Fractal Geometry provides a unique way to understand the seemingly chaotic behavior of financial markets. While price movements may appear random, beneath this surface lies a structured order defined by self-similar patterns that repeat across different timeframes. Fractals reveal how smaller trends often replicate the behavior of larger ones, reflecting the nonlinear dynamics of market behavior. These recurring structures allow to uncover the hidden proportions that influence market movements. Mandelbrot’s work underscores the non-linear nature of financial markets, where patterns repeat across scales, and price respects proportionality over time. Fractals in Market Behavior: Mandelbrot argued that markets are not random but exhibit fractal structures—self-similar patterns that repeat across scales. Power Laws and Scaling: He demonstrated that market movements follow power laws, meaning extreme events (large price movements) occur more frequently than predicted by standard Gaussian models. Turbulence in Price Action: Mandelbrot highlighted how market fluctuations are inherently turbulent and governed by fractal geometry, which explains the clustering of volatility. 🔹 @fract's Version of Fractal Analysis I've always used non-generic Fibonacci ratios on a logarithmic scale to align with actual fractal-based time scaling. By measuring the critical points of a significant cycle from history, Fibonacci ratios uncover the probabilistic fabric of price levels and project potential targets. The integration of distance-based percentage metrics ensures that these levels remain proportional across exponential growth cycles. Unlike standard ratios, the modified Fibonacci Channel extends into repeating patterns, ensuring it captures the full scope of market dynamics across time and price. For example, the ratios i prefer follow a repetitive progression: 0, 0.236, 0.382, 0.618, 0.786, 1, (starts repeating) 1.236 , 1.382, 1.618, 1.786, 2, 2.236, and so on. This progression aligns with fractal time-based scaling, allowing the Fibonacci Channel to measure market cycles with exceptional precision. The repetitive nature of these ratios reflects the self-similar and proportional characteristics of fractal structures, which are inherently present in financial markets. Key reasons for the tool’s surprising accuracy include: Time-Based Scaling: By incorporating repeating ratios, the Fibonacci Channel adapts to the temporal dynamics of market trends, mapping critical price levels that align with the natural flow of time and price. Fractal Precision: The repetitive sequence mirrors the proportionality found in fractal systems, enabling to decode the recurring structure of market movements. Enhanced Predictability: These ratios identify probabilistic price levels and turning points with a level of detail that generic retracement tools cannot achieve. By aligning Fibonacci ratios with both trend angles and fractal time-based scaling, the Fibonacci Channel becomes a powerful predictive tool. It uncovers not just price levels but also the temporal rhythm of market movements, offering a method to navigate the interplay between chaos and hidden order. This unique blend of fractal geometry and repetitive scaling underscores the tool’s utility in accurately predicting market behavior.
BITSTAMP:BTCUSDEducation
by fract
17