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Fractal

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
ALTCOINS | FRACTAL | Can ARB DOUBLE ?ARB has only recently established a clear bottom pattern, and unlike other alts it hasn't quite yet seen the usual parabolic increases. Previously, ARB established a similar W-Bottom patter, which led to a new ATH. Is it possible that after forming a similar patter, ARB could make a new ATH again? Don't miss yesterdays update on BTC and ALTSEASON , and why we can still see rallies across the alt market: ___________________ BINANCE:ARBUSDT
Long
by CryptoCheck-
2
TELCOIN GEM 4NAME: TELCOIN (TEL) CATEGORY: DEFI MARKET CAPITALIZATION: $157,580,000 USD (LOW CAPITALIZATION) FULLY DILUTED VALUATION: $174,106,000 USD TOTAL TOKEN SUPPLY: 100,000 M CIRCULATING TOKEN SUPPLY: 90,000 M (90.65% of Total Supply) NETWORK: ETHEREUM AND POLYGON (MATIC) The Telcoin project was born in 2017, and its legal entity (Telcoin Pte. Ltd) was established in Singapore, although the company is currently headquartered in Japan. It was founded by Paul Neuner. Its business model was established from the beginning: to partner with telecommunications operators worldwide to ensure a way to send money through mobile devices. Telcoin has a platform or application called Telcoin (available on both Google Play and the App Store), which acts as a cryptocurrency wallet and provides a simple way to send money. Telcoin focuses on fast mobile payments that can be easily sent from one user to another, similar to systems like Venmo and Western Union. The blockchain-based solution offers greater speed and lower transaction fees, both crucial features for this use case. The Telcoin platform doesn’t seek to replace such payment providers or compete with them but instead offers a bridge between fiat currencies and blockchain. Some available exchanges: KuCoin Bybit Bitget Uniswap on the Ethereum network and Polygon (Matic) network Quickswap on the Polygon (Matic) network Upcoming Fundamental Updates: 1.Digital Cash will be launched in the Telcoin app, offering a new fully backed and 1:1 redeemable stablecoin product that will enable next-generation remittances and multi-currency payments. 2.Implementation of the Telcoin Platform V4: The next version of the Telcoin app (V4) will introduce an improved user experience, expanding functionality beyond fiat remittances and DeFi trading, integrating Digital Cash, and broadening access to financial services. 3.Launch of the Telcoin Network 4.Banking Project Update: Telcoin is working to become a regulated bank in the United States and aims to be the first regulated bank issuer of stablecoins, creating connections between digital assets and traditional banking. Technical Analysis: Telcoin is currently within a descending wedge since its all-time high reached in May 2021, and it hit a low in December 2023. Using a fractal from 2020 to May 2021, it seems to be following a similar pattern up to this point. The RSI indicates levels similar to November 2020, around 43. Recently, DeFi has not experienced significant movements, so it’s possible that it could enter a trend in the coming months. I hope this is valuable to you. If you liked it, support me with a Like. If you didn’t like it, I welcome criticisms and comments.
Long
by Crypto_girl028
7
A Bearish OpportunityThe market has been forming a series of higher highs and higher lows, but with weaker broken highs. An hourly liquidity sweep led to a swift bearish movement, clearing the swing low at 1.47490. Anticipating a flip to level 1.48000 for a bearish entry. Trade Plan: - Entry: 1.48000 - Stop Loss: 1.48500 (50 pips) - Target: 1.45912 (over 200 pips) - Risk-Reward Ratio: Market Analysis: - Market structure: Higher highs and higher lows, with weaker broken highs - Liquidity sweep: Hourly sweep led to a swift bearish movement - Swing low: 1.47490
by tokelohomi