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Economic Cycles

the midwit perspectiveThe +4std and the -2std iq crowd are currently shaking hands in agreement that the cycle is right translated and will top in Nov-Dec 2025. These people are most probably correct, however... Here's the +1std iq (midwit) take: last top to bottom was left translated by over a year 117 months from Dec 2017 top to Mar 2020 bottom 52 months from Nov 2021 top to Nov 2022 bottom last bottom to top was left translated by 35 weeks 122 months from Aug 2015 bottom to Dec 2017 top 87 months from the Mar 2020 bottom to the Nov 2021 cycle top If we were to see the same type of left translation of 35-60 months after the Nov 2022 bottom, we would have seen the top already between Jul 2024 and Dec 2024 meaning either: December 2024 could still be the December of dreams or $99.9k was the left translated top in Nov 2024 and we're about to enter the downtrend part of the cycle.. ..OR.. it's right translated and is going to top next Nov. p.s. just to validate my credentials here as a midwit, I meant to use the btcusd coinbase chart but accidentally used bitfinex and didn't want to redraw all the lines. Happy Thanksgiving
BITFINEX:BTCUSDLong
by herblock
Trade Recaps: GBPJPY-SHORT, EURUSD-SHORT, 28/11/2024GJ Bias Analysis: The bearish pro-trend was clear with price trading lower from a premium. After printing the weekly low and beginning to correct higher, price mitigated the 50% equilibrium during New York Killzone where I waited for further confirmation by way of a LTF TBL sweep before executing a short position. EU Bias Analysis: The 4H had established a counter trend as price pulled back into the 1D OB and while short entries were not in line with the bullish range, IPDA was clear. Liquidity was being generated to the upside as price corrected and after a TBL sweep to the upside during New York killzone, a short entry was executed. Grade: - UJ: High Risk - EU: Low Risk
Short
by The_Modern_Day_Trader
Bitcoin's Elliott Wave and Cylce AnalysisBitcoin's price has surged close to the $100,000 level, fueled by increasing optimism and hype, particularly following Trump’s victory in the US elections. While this milestone has sparked renewed bullish sentiment, it's critical to remember that Bitcoin is now in uptrend for only last four week, but it has been in an moving up since December 2022, marking nearly two years of consistent gains. So actually, we may now be in the late stages of this bull run. Keep in mind that when optimism and positive news dominate, like the prospect of Bitcoin being added to US reserves under Trump, it often aligns with the peak of a trend. While this kind of news could potentially drive one last strong upward move (FOMO / Blow-off), it might also signal the end of this cycle. Historically, extreme optimism is a warning sign, and the Elliott wave analysis supports this view. Bitcoin is currently in its fifth wave, with potential for slightly more upside, possibly extending late into January time projection. Its going to be the 20 or 26 weeks of a recovery from August low. Smart investors will consider taking some parts of profits now rather than risking missing an exit, especially now when we approach the year end. The market's shift from "optimism to pessimism", can be swift and surprising. Greed often leads to losses, so prudence is key at this stage. On another note, we have a Black Friday offer on our Elliott Wave service. It’s a great opportunity to stay aligned with market rhythm and sentiment during this critical period. If you are celebrating, I wish you a happy Thanksgiving! Grega
BITSTAMP:BTCUSD
by ew-forecast
Hello to QNT As always, we start with cycles hwc=uptrend mwc = upward but weak trend lwc= rang According to the cycles, I give priority to the long position🧐 Tips for long positions 📈 1,Our cycle tends to go up 2.The volume is decreasing when it falls That's enough for me for a long position By breaking the trend line And the failure of 96.3, we can take a long position, definitely 96.3 is the safe entry point And the trend line is more risky For shorts position📉 For shorts, the only thing we see is the weakness of MWC, which could change the trend I wait for the short position and wait for it to reach the safe entry point High risk entry point 91.5 And 83.8 is safe If you want me to analyze a coin, tell me in the comment🫡 ⚠️ Do capital management to survive ⚠️
by parsa_xa
AMD Harmonic Elliott Wave and Hurst Cyclic AnalysisIt’s been a while since my last update on AMD. Looking at the wave structure, I believe we are currently in a complex pullback, correcting the bullish move from October 2022 to March 2024. This correction appears to be a double zigzag, and we’re in the process of completing wave A of the second zigzag (labeled 'y'). Analyzing the cycles, we can see that the trough from October 2023 aligns with an 80-week (80W) cycle trough and the trough in August 2024 aligns with a 40-week (40W) cycle. Currently, I’m expecting the first 20-week (20W) cycle of this 40W cycle, which is actually the second 40W cycle in the 80W cycle, to occur around December 18th. This would correspond to wave A of the y leg of the larger wave II in the wave analysis. Following this, I anticipate a rally that should take us higher before the extreme bearish phase begins as we approach the 80W cycle trough, which is expected in early April 2025. There is an alternative (less likely) bullish scenario to keep in mind. If wave B becomes more strongly bullish, I would shift my bias to that scenario. However, until that happens, my preferred view remains as shown in the chart.
NASDAQ:AMD
by bamdadsalarieh
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
$AKT Cup & HandleSince September 2021, we’ve been witnessing a massive Cup and Handle pattern taking shape, signaling potential bullish momentum ahead. With the Fed’s recent 50bps cut acting as a catalyst for risk-on assets, now is the time to pay attention! As BTC dominance (BTC.D) shows signs of topping, select altcoins with genuine adoption and utility are poised for significant rallies, possibly reaching new all-time highs. Keep your eyes peeled on these emerging stars in the crypto space!
CRYPTO:AKTUSDLong
by httpz
1
Breakout Signals via Asymmetrical AveragingSpecial Application of Average Bullish & Bearish Percentage Change Indicator INDICATOR AVERAGES BULLISH AND BEARISH VOLATILITY SEPARATELY THROUGH THEIR NATIVE PAST CANDLE COUNT. NOT PERIODICALLY! Asymmetrical averaging is a versatile technique that involves assigning different lengths for independent averaging of opposite market forces. This adaptability uncovers high-probability breakout signals by establishing a threshold that filters out irrelevant fluctuations. Below, I illustrated 2 practical examples of the method applied to bullish and bearish breakout scenarios: Bullish Breakout Example: Set the bullish averaging to 30 and the bearish averaging to 1000. If the bullish average consistently surpasses the bearish threshold, it indicates robust buying momentum and a potential breakout to the upside. The extreme bearish average establishes a consistent baseline, filtering out short-term fluctuations and focusing on significant upward momentum to deliver reliable bullish breakout signals. Bearish Breakout Example: Set the bearish averaging to 30 and the bullish averaging to 1000. If the bearish average rises above the bullish threshold, it signals growing selling pressure and a potential breakout to the downside. The extreme bullish average provides a steady reference point, eliminating minor fluctuations and isolating significant downward momentum for dependable bearish breakout signals. LINK TO THE INDICATOR:
BITSTAMP:ETHUSDEducation
by fract
1
BLX curves worth more than long speechesHistorically this indicator provided a good overview of the range of BTC price since its early days. Obviously, in the long term it will become depreciated. However, so far, it seems to be pretty accurate. This is the result of 2 curve lines on both ends. Noting that tops are difficult to draw accurately and predict since we are on a logarithmic scale and fewer tops are touched (a small change puts the price to 195k$). Yet, it provides a pretty accurate insight of what the bottom price could be. For people who don't own BTC, a good strategy would be to be patient and start an aggressive DCA strategy close to the bottom curve.
BNC:BLX
by princealadin888
1