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Economic Cycles

Quarterly Theory "QT" Introduction to Quarterly Theory (QT) Time must be divided into quarters for a proper interpretation of market cycles. Combining QT (Quarterly Theory) concepts with basic ICT concepts leads to greater accuracy. Understanding QT allows you to be flexible. It adapts to any trading style as it is universal across all time frames. QT eliminates ambiguity by providing specific time-based reference points to look for when entering trades . THE CYCLE Annual Cycle - 3 quarters each Monthly Cycle - 1 week each Weekly Cycle - 1 day each* Daily Cycle - 6 hours each Session Cycle - 90 minutes each *Monday to Thursday, Friday has its own specific function . Annual Cycle: Q1 JANUARY - MARCH Q2 APRIL - JUNE Q3 JULY - SEPTEMBER Q4 OCT - DECEMBER Monthly Cycle**: Q1 FIRST WEEK Q2 SECOND WEEK Q3 THIRD WEEK Q4 FOURTH WEEK Weekly Cycle*: Q1 MONDAY Q2 TUESDAY Q3 WEDNESDAY Q4 THURSDAY Daily Cycle: Q1 ASIA Q2 LONDON Q3 NEW YORK Q4 AFTERNOON **Monthly Cycle starts with the first full week of the month. *Friday has its own cycle, which is why it is not listed. Q1 indicates the quarters that follow. If Q1 expands, Q2 is likely to consolidate. If Q1 consolidates, Q2 is likely to expand. TRUE OPENS True price opens are the beginning of Q2 in each cycle. It validates key levels. What are the true opens? Yearly: First Monday of April (Q2) Monthly: Second Monday of the month (Q2) Weekly: Second daily candle of the week Daily: Start of the London session (6 hours after the open of the daily candle) Asia - London - NY - Evening: 90 minutes after the open of the 6-hour candle. DIAGRAM: Q1 (A) Accumulation - Consolidation. Q2 (M) Manipulation - Judas Swing (Trade this). Q3 (D) Distribution (Trade this). Q4 (X) Continuation - Reversal of the previous quarter. Q1 (X) Continuation - Reversal of the previous quarter. Q2 (A) Accumulation - Consolidation. Q3 (M) Manipulation - Judas Swing (Trade this). Q4 (D) Distribution (Trade this). ANNUAL CYCLE: MONTHLY CYCLE: WEEKLY CYCLE: DAILY CYCLE:
Education
by Ed_Ale
Probabilistic RealmI remember taking the CMT exam, where one question referenced the Efficient Market Hypothesis (EMH), which asserts that price action is purely random. To avoid losing points, I had to select “random” as the correct answer, despite knowing that market behavior is far more structured than EMH suggests. Despite of passing I still won't ever agree that market is random. Prices are neither random nor deterministic. Market fluctuations follow a chaotic structure, but chaos is not the same as randomness. Chaos operates within underlying patterns and scaling, whereas randomness lacks any order or predictability. Although chaos makes predictions difficult, keep in mind that the universe is not random— effects still follow causes in continuity . No matter how chaotic a system may seem, it always follows a trajectory toward a certain point. For example, in Lorenz’s model of chaos, the trajectory formed a pattern resembling the wings of a butterfly. Understanding these patterns of chaos has practical applications. In the market, even a slight fluctuation can trigger irreversible changes, reinforcing the idea that we cannot rely on absolute forecasts— only probabilities . The market is not necessarily a reflection of the economy; rather, it reflects participants’ feelings about the “economy.” The human emotional component drives the uncertainty and chaos, making it essential to visualize price dynamics exclusively through "systematic" lens. Market Structure Is Self-Referential Markets move in proportion to their own size, not in fixed amounts. Price is arbitrary, but percentage is universal – A $10 move on Bitcoin at $100 is not the same as a $10 move at $100,000. Percentage metrics reflects this natural scaling and allows comparability across assets and timeframes – A 50% swing in 2011 holds similar structural significance to a 50% swing in 2024, despite price differences. Using log scale is a must in unified fractal analysis. Percentage swings quantify the intensity of collective emotions—fear, panic, euphoria—within market cycles. Since markets are driven by crowd psychology, percentage changes act as a unit of measurement for emotional extremes rather than just price fluctuations. After all it's the % that make people worry.. The magnitude of percentage swings encodes emotional energy, shaping the complexity of future market behavior. This means that larger past emotional extremes leave deeper imprints on market structure, influencing the trajectories future trends. The inverse relationship between liquidity and psychology of masses partially explains the market’s fractured movements leading to reversals. In bullish trends, abundant liquidity fosters structured price behavior, allowing trends to develop smoothly. In contrast, during bearish conditions, fear-driven liquidity contraction disrupts market stability, resulting in erratic price swings. This dynamic highlights how shifting sentiment can amplify price distortions, causing reactions that are often disproportionate to fundamental changes. PROBABILISTIC REALM Rather than viewing fluctuations as a sequence of independent events, price action unfolds as a probabilistic wave shaped by market emotions. Each oscillation (outcome) is relative to historical complexity, revealing the deep interconnectedness of the entire chart that embodies the “2-Polar Gravity of Prices.” Fibonacci numbers found in the Mandelbrot set emphasizes a concept of order in chaos. The golden ratio (Phi) acts as a universal constant, imposing order on what appears to be a chaotic. This maintains fractal coherence across all scales, proving that price movements do not follow arbitrary patterns but instead move relative to historic rhythm. The reason why I occasionally have been referring to concepts from Quantum Mechanics because it best illustrates the wave of probability and probabilistic realm of chaos in general. Particularly the Schrodinger's wave equation that shows probability distributions. Key intersections in Fibonacci-based structures function as "quantum" nodes, areas of market confluence where probability densities increase. These intersections act as attractors or (and) repellers, influencing price movement based on liquidity and market sentiment. Similar to Probability Distribution in QM. Intersections of Fibonacci channels reveal the superposition of real psychological levels, where collective market perception aligns with structural price dynamics. These points act as probabilistic zones where traders’ decisions converge, influencing reversals, breakouts, or trend continuations. Don’t expect an immediate reversal at a Fibonacci level—expect probability of reversal to increase with each crossing. To prove that Efficient Market Hypothesis is wrong about prices being random, I'd go back to a very distant past from current times. For example, price fell 93% from 2011 ATH, reversed and established 2013 ATH. Using a tool "Fibonacci Channels" to interconnect those 3 coordinates reveals that markets move within its fractal-based timing derived from direction. If prices were random, this would have never happened. The bottomline is that viewing current price relative to history is crucial because markets operate within a structured, evolving framework where proportions of past movements shape future probabilities. Price action is not isolated—it emerges from a continuous interaction between historical trends as phases of cycles, and liquidity shifts. By analyzing price within its full historical context , we can differentiate between temporary fluctuations and meaningful structural shifts justified by the fractal hierarchy. This approach helps identify whether price is expanding, contracting, or aligning with larger fractal cycles. Without referencing historical complexity, there is a risk misinterpreting patterns from regular TA, overreacting to short-term noise, and overlooking the deeper probabilistic structure that governs price behavior.
BITSTAMP:BTCUSDEducation
by fract
20
Stockholm Syndrome in Crypto Trading: Why We Stay LoyalLet’s be honest: altcoins haven’t been performing as well as many would like. As I’ve started pointing this out through posts and videos, I’ve received a fair share of criticism. Whenever I mention the possibility of a market decline, I’m met with hate, while others who claim the market is heading to the moon are celebrated. What’s baffling is that no one seems to ask, “Hey, you’ve been saying ‘altcoin season’ is coming for a year, yet we’re still stuck around the same prices. What’s going on?” This got me thinking: Could this be a form of Stockholm Syndrome in trading? ________________________________________ What is Stockholm Syndrome in Trading? Stockholm Syndrome is a psychological phenomenon where hostages develop positive feelings towards their captors. In trading, it’s a bit like this: traders grow emotionally attached to a losing market, even when all signs point to the fact that things aren’t going well. Instead of cutting losses and accepting reality, they keep holding on, hoping things will change – just like a hostage hoping for their captor's kindness. In trading, this manifests as traders continuing to support a market (like coins or certain stocks) that isn’t performing, even when the evidence suggests it’s time to move on. They become attached to the idea that a specific asset will turn around and deliver massive profits – even when the price action doesn’t back that up. ________________________________________ The Comfort of Familiarity Many traders are caught in the cycle of constant hope and “what ifs.” It’s much easier to stay attached to the narrative that specific coins will eventually “take off” than to admit that their portfolios might be stuck sideways or even bear market. It's also easy to get drawn into the excitement of “moonshots” and grand promises of big returns. The altcoin season, the bull run, the new innovations – these ideas are comforting, even when the market isn’t cooperating. But here’s the catch: sticking with a market that’s not performing well out of loyalty is dangerous. It stops you from adapting, from making the necessary moves to protect your capital, and from taking advantage of more promising opportunities elsewhere. ________________________________________ The Reality of the Market Altcoins have been on a rollercoaster. The hope for altcoin season has been building up for over a year now, yet many traders are still facing stagnant or even declining prices. When faced with this reality, we often see two types of responses: 1. The Blind Optimist: Some traders will continue to hold and buy into altcoins, even when it’s clear the market isn’t moving in their favor. They believe that the next big move is just around the corner, and they refuse to let go of the dream. 2. The Critic: Others, like me, will point out the slow or negative price action, urging caution and suggesting that a pullback or continued consolidation is more likely. But when we do, we’re met with anger, disbelief, or even accusations of “fear-mongering.” It’s frustrating to see those who remain hopeful get so emotionally attached to a failing asset, while others who try to see things more clearly get met with hostility. ________________________________________ The Dangers of Stockholm Syndrome in Trading When traders fall into this “Stockholm Syndrome,” they stop questioning their strategies and beliefs. They become too emotionally involved with a market that isn’t giving them the results they want. This prevents them from making the tough decisions they need to make to protect their portfolios – whether that’s cutting losses or re-allocating capital to more promising assets. It’s also a trap that keeps you stuck in an echo chamber of hope and denial, rather than facing the market with logic and clear-headed analysis. The longer you stay loyal to an asset that’s underperforming, the more you risk watching your portfolio sink further. ________________________________________ Breaking Free: A Rational Approach to Trading The key to successful trading is learning to let go of emotional attachment. Don’t hold onto an asset simply because you’ve been told it will perform or because you’ve invested a lot of time and money into it. Here are a few ways to break free from the Stockholm Syndrome in trading: 1. Focus on the facts: Look at the actual price action and market conditions, not the narrative you’ve built around it. If the market isn’t moving, don’t force a belief that it will soon. 2. Admit when it’s time to move on: It’s not about being right or wrong – it’s about protecting your capital. If an asset isn’t performing, consider cutting your losses and finding new opportunities that align with your trading strategy. 3. Stay flexible: The market is dynamic, and you need to be able to adjust your strategy based on current conditions. Don’t get stuck in a “one-size-fits-all” approach. 4. Let go of the need to be loyal: Trading isn’t about loyalty; it’s about profits and risk management. Sometimes, moving on is the best decision for your financial health. ________________________________________ Conclusion If you’ve been stuck in the cycle of hoping that altcoins will suddenly surge, or waiting for the long-awaited altcoin season, it might be time to reconsider your approach. It’s important to recognize when you’re emotionally attached to a market that isn’t performing, and break free from that attachment. By focusing on logical analysis, cutting losses when necessary, and staying flexible in your approach, you can avoid the dangers of Stockholm Syndrome in trading and move towards more profitable opportunities. Remember: Trading isn’t about loyalty to a coin or a narrative – it’s about making smart, objective decisions that will help you grow your capital.
BITSTAMP:BTCUSDEducation
by Mihai_Iacob
22
Fractal Phenomenon Proves Simulation Hypothesis?The humanity is accelerating towards the times when virtual worlds will get so realistic that their inhabitants gain consciousness without realizing they exist in a simulation. The idea that we might be living in a simulation was widely introduced in 2003 by philosopher Nick Bostrom. He argued that if the civilization can create realistic simulations, the probability that we are living in one is extremely high. Modern games only render areas that the player is observing, much like how reality might function in a simulation. Similarly, texture of game environments update as soon as they are viewed, reinforcing the idea that observation determines what is rendered. QUANTUM MECHANICS: The Ultimate Clue Quantum Mechanics challenges our fundamental understanding of reality, revealing a universe that behaves more like a computational process than a physical construct. The wave function (Ψ) describes a probability distribution, defining where a particle might be found. However, upon measurement, the particle’s position collapses into a definite state, raising a paradox: why does the smooth evolution of the wave function lead to discrete outcomes? This behavior mirrors how digital simulations optimize resources by rendering only what is observed, suggesting that reality itself may function as an information-processing system. The Born Rule reinforces this perspective by asserting that the probability of finding a particle at a given location is determined by the square of the wave function’s amplitude (|Ψ|²). This principle introduced probability into the very foundations of physics, replacing classical determinism with a probabilistic framework. Einstein famously resisted this notion, declaring, “God does not play dice,” yet Quantum Mechanics has since revealed that randomness and structure are not opposing forces but intertwined aspects of reality. If probability governs the fabric of our universe, it aligns with how simulations generate dynamic outcomes based on algorithmic rules rather than fixed physical laws. One of the most striking paradoxes supporting the Simulation Hypothesis is Schrödinger’s Cat, which illustrates the conflict between quantum superposition and observation. In a sealed box, a cat is both alive and dead until an observer opens the box, collapsing the wave function into a single state. This suggests that reality does not exist in a definite form until it is observed—just as digital environments in a simulation are rendered only when needed. Similarly, superposition demonstrates that a particle exists in multiple states until measured, while entanglement reveals that two particles can be instantaneously correlated across vast distances, defying classical locality. These phenomena hint at an underlying informational structure, much like a networked computational system where data is processed and linked instantaneously. Hugh Everett’s Many-Worlds Interpretation (MWI) takes this concept further by suggesting that reality does not collapse into a single outcome but instead branches into parallel universes, where each possible event occurs. Rather than a singular, objective reality, MWI posits that we exist within a constantly expanding system of computational possibilities—much like a simulation running countless parallel computations. Sean Carroll supports this view, arguing that the wave function itself is the fundamental reality, and measurements merely reveal different branches of an underlying universal structure. If our reality behaves like a quantum computational system—where probability governs outcomes, observation dictates existence, and parallel computations generate multiple possibilities—then the Simulation Hypothesis becomes a compelling explanation. The universe’s adherence to mathematical laws, discrete quantum states, and non-local interactions mirrors the behavior of an advanced simulation, where data is processed and rendered in real-time based on observational inputs. In this view, consciousness itself may act as the observer that dictates what is “rendered,” reinforcing the idea that we exist not in an independent, physical universe, but within a sophisticated computational framework indistinguishable from reality. Fractals - Another Blueprint of the MATRIX? Price movements wired by multi-cycles shaping market complexity. Long-term cycles define the broader trend, while short-term fluctuations create oscillations within that structure. Bitcoin’s movement influencing Altcoins exemplifies market entanglement—assets affecting each other, much like quantum particles. A single event in a correlated market can ripple across the entire system like in Butterfly effect. Just as a quantum particle exists in multiple states until observed, price action is a probability field—potential breakouts and breakdowns coexist until liquidity shifts. Before a definite major move, the market, like Schrödinger’s cat, remains both bullish and bearish until revealed by Fractal Hierarchy. (Model using Weierstrass Function ) A full fractal cycle consists of multiple oscillations that repeat in a structured yet complex manner. These cycles reflect the inherent scale-invariance of market movements—where the same structural patterns appear.. By visualizing the full fractal cycle: • We observe the relationship between micro-movements and macro-structures. • We track the transformation of price behavior as the fractal unfolds across time. • We avoid misleading interpretations that come from looking at an incomplete cycle, which may appear random or noisy From Wave of Probability to Reality 1. Fractal Probability Waves – The market does not move in a straight line but rather follows a probabilistic fractal wave, where past structures influence future movements. 2. Emerging Reality – As the price action unfolds, these probability waves materialize, turning potential fractal paths into actual price trends. 3. Scaling Effect – The same cyclical behavior repeats at different scales (6H vs. 1W in this case), reinforcing the concept that price movements are self-similar and probabilistically driven. If psychology of masses that shapes price dynamics is governed by mathematical sequences found in nature, it strongly supports the Simulation Hypothesis Do you think we live in a simulation? Let’s discuss in comments!
INDEX:BTCUSDEducation
by fract
21
Bitcoin: Entering New Presidential CycleCharts are essential, but it’s equally important to stay aware of major events that can significantly impact markets. Alongside this, I’ll share some theoretical insights. Market During Presidencies: The chart tracks the S&P 500’s growth on a logarithmic scale, highlighting U.S. presidential terms by party since 1933. Blue areas represent Democrat presidencies, and red areas indicate Republican presidencies. It shows that the market has grown steadily over time, despite fluctuations tied to economic cycles, policies, and global events. Key trends include significant growth during Clinton and Obama presidencies (dot-com boom, post-2008 recovery) and slower growth during Nixon and Carter presidencies. The chart also reflects recent market gains under Trump and Biden, despite challenges like the COVID-19 pandemic. Overall, it demonstrates consistent long-term market growth under both political parties, driven by a mix of policies and external factors. PRESIDENTIAL CYCLE "Presidential Cycle" in trading refers to a theory that financial markets tend to follow a recurring pattern tied to the four-year term of U.S. presidential administrations. This cycle is based on the idea that government policies and political events during a president’s term can influence economic conditions and market behavior in predictable ways. PHASES: Post-Election Year Stock Market: New or re-elected presidents introduce reforms that may unsettle markets. Slower growth and higher volatility are common as policies stabilize. ₿ Market: Historically, Bitcoin has experienced significant growth following U.S. presidential elections. For instance, after the 2016 election, Bitcoin’s price increased by over 2,500% in the subsequent year. Potential Impact: The resolution of electoral uncertainty typically restores market stability. Additionally, newly introduced policies can foster investor confidence, making alternative assets like Bitcoin more appealing. If these policies are crypto-friendly, they could accelerate Bitcoin adoption and drive price appreciation. Midterm Year Stock Market: Midterm elections create political uncertainty, often causing market corrections. The second half of the year typically sees recovery as clarity improves. ₿ Market: Bitcoin may experience corrections or slower growth during midterm years. For example, in 2018, Bitcoin’s price declined significantly, aligning with the midterm election period. Potential Impact: Midterm elections can lead to shifts in political power, creating regulatory uncertainty for the crypto market. This could deter institutional investors or slow Bitcoin’s momentum. However, as the political landscape becomes clearer, the market could stabilize, potentially paving the way for future growth. Pre-Election Year Stock Market: Historically the strongest year, with administrations boosting the economy. Market-friendly policies lead to stronger performance and public support. ₿ Market: Pre-election years have often been bullish for Bitcoin. In 2019, Bitcoin’s price saw substantial gains, rising from around $3,700 in January to over $13,000 by June. Potential Impact: Increased government spending and the anticipation of policy changes often stimulate economic activity, benefiting risk-on assets like Bitcoin. This optimism can lead to higher investor participation and significant price increases as the market factors in favorable policy expectations. Election Year Stock Market: Election uncertainty heightens volatility, but clarity post-election boosts markets. Performance depends on the perceived business-friendliness of leading candidates. ₿ Market: Bitcoin has shown mixed reactions during election years. In 2020, despite initial volatility, Bitcoin reached a new all-time high post-election, suggesting that the resolution of political uncertainty can positively influence its price. Potential Impact: The election outcome often dictates the regulatory direction for cryptocurrencies. A pro-crypto administration could fuel optimism and attract new investors, while stricter regulations could introduce headwinds. Regardless, the post-election clarity often drives market confidence, benefiting Bitcoin’s valuation. Chronological Flow of Events Fueling Bitcoin’s Exponential Growth Shift to CFTC Regulation Trump proposed moving crypto regulation from the SEC to the CFTC, creating a friendlier environment to foster innovation and boost investor confidence. Institutional and Retail Adoption Bitcoin became accessible through retirement accounts and ETFs, driving demand from both institutions and retail investors. Market Sentiment and Musk’s Influence Endorsements from Elon Musk (Trump's circle) sparked optimism, fueling rallies and increasing crypto adoption. Geopolitical Competition The U.S. aimed to lead the crypto space, countering China’s dominance and stabilizing Bitcoin’s market. Trump’s Bitcoin Strategic Reserve A proposed U.S. Bitcoin reserve would position it alongside gold, boosting demand and global legitimacy. J.D. Vance’s Proposal to Devalue the U.S. Dollar Vance’s plan to weaken the dollar to boost exports contrasts sharply with Bitcoin’s fixed supply of 21m coins, which makes it an inflation-resistant alternative to fiat currencies. Bitcoin’s finite supply and decentralized nature make it a strong hedge during monetary policy uncertainty, further solidifying its role as a store of value. Vance’s proposal inadvertently highlights the vulnerabilities of fiat currencies, positioning Bitcoin as a compelling alternative in a volatile economic landscape. Holiday Effect Bitcoin’s performance is influenced by alignment of market sentiment, economic factors, and geopolitical events with holiday seasonality known as the “holiday effect” during major holidays like Christmas and New Year. 🏛️ FEDERAL RESERVE The Federal Reserve operates independently of the President and Congress, focusing on economic goals like controlling inflation, maintaining employment, and ensuring stability. While the President appoints members to the Board of Governors, these appointments require Senate confirmation and fixed terms, insulating monetary policy from political influence. This structure safeguards long-term economic stability and credibility. Donald Trump’s pro-crypto stance faces significant challenges due to the Federal Reserve’s autonomy and cautious approach to cryptocurrencies. The Fed has historically expressed skepticism about decentralized assets, citing concerns over financial stability, regulatory risks, and potential misuse. Instead, it prioritizes initiatives like Central Bank Digital Currencies (CBDCs), such as a digital dollar, which could compete with cryptocurrencies like Bitcoin. This divergence underscores a conflict of goals: pro-crypto policies encourage innovation and adoption, while the Fed views decentralized cryptocurrencies as a challenge to its control over monetary policy and the U.S. dollar’s global reserve currency status. Additionally, the Fed collaborates with other regulatory agencies, like the SEC and Treasury, which have traditionally taken a cautious stance on cryptocurrencies. Ultimately, while Trump’s policies may boost private crypto adoption and innovation, the Federal Reserve’s focus on financial stability and its own priorities, like CBDCs, limits the broader impact of these policies. This highlights the difficulty of aligning political aspirations with the Fed’s institutional priorities.
INDEX:BTCUSDEducation
by fract
14
Beyond Basic Candlestick Pattern AnalysisLearning to Recognize Who Is Controlling the Stock Price There is a plethora of training on Candlestick Pattern Analysis and interpretation, and yet this remains one of the most problematic areas for Technical Traders who want to trade at the expert level. Once the basics of Japanese Candlestick Patterns are understood, it is time to move up to the next tier of analysis. That is being able to recognize not only where a pattern is, but also who forms that pattern, why they are capable of creating that pattern, what automated orders generate that pattern, and which Market Participant Groups react or chase that pattern. Nowadays it has become critical to include Volume with Candlestick Analysis, because this provides the basis for recognizing which Market Participant Group created that candle pattern. Candlestick Pattern Analysis at the expert level involves more than just one to three candles. Instead it includes a larger group of candles in the near term. This is what I call "Relational Analysis." This is especially useful for Swing Traders, Momentum Traders, Velocity Traders, Swing Options Traders, and Day Traders using Swing Style Intraday action. The NYSE:RAMP chart is an excellent example of a Candlestick Pattern for Swing Style Trading. See where High Frequency Traders (HFTs) took control of price, and gapped the stock down for one day on extreme volume. Selling did not continue the following two days, and Volume was above the Moving Average, but much lower than the High Frequency Traders' spiking Volume pattern. This was the first accumulation level for this stock. Dark Pools started buying the stock even though High Frequency Traders were selling, since they typically miss this initial buy mode of the giant Institutions. High Frequency Traders typically create the final gap down to the low which, if it reverses quickly, indicates a Buy Zone area for the Dark Pools. These patterns are what I call "Shifts of Sentiment." They happen in bottom formations where buying is generally dominated by the Largest Institutions' quiet accumulation. The next phase will be when Professional Traders and then High Frequency Traders discover the Dark Pool accumulation. The bottom is not complete, but it shifts sideways if more Dark Pools decide to buy.
NYSE:RAMPEducation
by MarthaStokesCMT-TechniTrader
1
4-Year Cycles [jpkxyz]Brief Introduction why Crypto moves in Cycles. "Crypto is an expression of Macro." The 2007-2008 global financial crisis was a pivotal moment that fundamentally transformed monetary policy, particularly in how central banks manage economic cycles through liquidity manipulation. Before the crisis, central banks primarily used interest rates as a blunt instrument for economic management. The 2008 financial crisis exposed deep vulnerabilities in the global financial system, particularly the interconnectedness of financial institutions and the risks of unregulated credit markets. In response, central banks, led by the Federal Reserve, developed a more sophisticated approach to economic management: 1. Quantitative Easing (QE) The Federal Reserve introduced large-scale asset purchases, essentially creating money to buy government bonds and mortgage-backed securities. This unprecedented monetary intervention: - Prevented a complete economic collapse - Provided liquidity to frozen credit markets - Kept interest rates artificially low - Supported asset prices and prevented a deeper recession 2. Synchronized Global Monetary Policy Central banks worldwide began coordinating their monetary policies more closely, creating a more interconnected approach to economic management: - Coordinated interest rate decisions - Shared information about economic interventions - Created global liquidity pools 3. Cyclical Liquidity Management The new approach involves deliberately creating and managing economic cycles through: - Periodic liquidity injections - Strategic interest rate adjustments - Using monetary policy as a proactive economic tool rather than a reactive one The 4-year cycle emerged as a pattern of: - 2-3 years of expansionary policy - Followed by a contraction or normalization period This cycle typically involves: - Expanding money supply - Lowering interest rates - Supporting asset prices - Then gradually withdrawing support to prevent overheating The 2007-2008 crisis essentially forced central banks to become more active economic managers, moving from a passive regulatory role to an interventionist approach that continuously adjusts monetary conditions. This approach represents a significant departure from previous monetary policy, where central banks now see themselves as active economic architects rather than passive observers.
BITSTAMP:BTCUSDEducation
by jpkxyz
GANN TRADING LESSON: TIME IS MORE IMPORTANT THAN PRICEGANN TRADING LESSON: TIME IS MORE IMPORTANT THAN PRICE – THE CORE OF W.D. GANN’S METHODOLOGY William Delbert Gann, one of the most enigmatic figures in trading history, built his legendary status on a profound understanding of market movements. Among his many revolutionary insights, none resonate more than his assertion: “TIME is more important than PRICE.” Gann's studies reveal that markets are governed by cyclical laws where TIME dictates market behavior, and PRICE merely reflects the outcomes. This article delves deeply into Gann’s philosophy, integrating examples, methodologies, and references from his works, to illuminate why mastering TIME can give traders a significant edge. Understanding the Superiority of TIME in Trading 1. The Foundation of Gann’s Philosophy: - In his book “The Tunnel Thru the Air”, Gann states, “The future is but a repetition of the past; cycles can be studied and predicted with mathematical precision.” - This emphasizes that TIME controls market events. Price, on the other hand, is secondary—a mere result of the unfolding TIME cycles. 2. Why TIME is More Important Than PRICE: - PRICE is Reactive: Price changes happen as a result of events, but those events themselves are determined by TIME cycles. Without the correct timing, price predictions are speculative at best. - TIME is Predictive: Understanding TIME cycles allows traders to foresee when significant price movements are likely to occur, providing a roadmap for market behavior. 3. The Illusion of PRICE: - Traders often fall into the trap of chasing prices—buying highs or selling lows—without realizing that markets move within predetermined TIME windows. Gann showed that price breakouts or breakdowns are unsustainable if they occur outside critical TIME cycles. Key Concepts from Gann’s Methodology on TIME 1. The Law of Vibration: Gann believed that every market has its unique vibration, influenced by TIME cycles. In “The Law of Vibration”, Gann explains that market movements align with natural and cosmic vibrations, which repeat over TIME. 2. Cyclicality of Markets: Markets move in cycles determined by TIME. Gann’s studies revealed major cycles such as: - The 20-Year Cycle: Markets often exhibit significant highs or lows every 20 years. - The 60-Year Cycle: This aligns with major economic booms and depressions. - Planetary Cycles: Gann tied TIME cycles to planetary movements, including the 11.86-year Jupiter cycle and Saturn’s 29.5-year orbit. 3.The Square of Nine and TIME Projections: Gann’s Square of Nine is one of his most famous tools. While often used to predict price levels, it is equally powerful for determining TIME turning points. Example: The Square of Nine can map out important dates when markets are likely to reverse, based on the angle of price and TIME. 4. Geometry in TIME: In “The Geometry of Stock Market Profits”, Gann emphasized the relationship between price and TIME through angles. A 1x1 angle (45 degrees) represents the ideal balance between price and TIME. Any deviation from this angle signals acceleration or deceleration in the trend. 5. Astrological Influence on TIME: Gann’s work integrates astrology to predict TIME cycles. He studied planetary aspects, transits, and lunar phases to determine when markets would experience significant changes. Example: Gann highlighted the importance of eclipses, retrogrades, and planetary conjunctions in marking market highs and lows. Practical Applications of TIME in Trading 1. Time-Price Symmetry: Gann believed that price movements often mirror TIME durations. Example: If a market drops 100 points over 10 days, it is likely to recover 100 points over a similar TIME interval. 2. Repetition of Historical Cycles: Gann showed that the 1929 crash followed a similar TIME pattern to earlier financial crises. By studying historical TIME intervals, traders can predict future market events. Timing Highs and Lows: 3. Use Fibonacci TIME zones to identify when markets are likely to peak or bottom. Combine this with Gann’s techniques, such as using the Square of Nine, for precise predictions. Seasonality and TIME Cycles: 4. Markets are influenced by seasonal and cyclical TIME patterns. Gann demonstrated that major market reversals often coincide with solstices, equinoxes, and other seasonal turning points. Examples of TIME’s Importance in Gann’s Predictions 1. The 1929 Stock Market Crash: Gann predicted the crash using TIME cycles, noting that it occurred 60 years after the Panic of 1869 and 30 years after the 1899 bear market. 2. The 1987 Crash: Gann’s methods, when applied to long-term TIME cycles, also align with the 1987 crash. It occurred exactly 58 years after the 1929 collapse, reflecting the repetitive nature of TIME cycles. The Interplay Between TIME and PRICE While PRICE is easier to track and analyze, Gann believed that the greatest trading success comes from aligning PRICE movements with TIME predictions. He illustrated this in his “Master Forecasting Course”, where he taught students to: - Map out major TIME cycles. - Identify the angles and relationships between TIME and PRICE. - Use TIME as a framework to validate PRICE movements. Steps to Master Gann’s TIME Methodology Study Historical Cycles: - Identify significant market events and analyze the TIME intervals between them. Use Tools Like the Square of Nine: - Plot critical TIME intervals to predict market reversals. Combine TIME Analysis with Price Patterns: - Validate price movements with TIME projections to confirm trends or reversals. Incorporate Natural and Planetary Cycles: - Use planetary ephemerides and lunar calendars to enhance TIME forecasts. Conclusion: Why TIME is the Ultimate Edge Gann’s timeless wisdom teaches us that focusing solely on PRICE is like chasing shadows. TIME is the true master, dictating when markets turn, rally, or crash. By mastering TIME, traders can move from being reactive to predictive, seizing opportunities before they manifest. As Gann said, “When TIME is up, price will reverse.” This simple yet profound truth encapsulates the essence of his methodology. Focus on TIME, and the illusion of PRICE will reveal its secrets. Join the Discussion: Do you agree with Gann that TIME is the most critical factor in trading? Share your thoughts and experiences below!
CAPITALCOM:GOLDEducation
by GannAstroTrader
When is a stock too high to buy? (Example: IHG)How do you know when you’ve missed the boat? A stock has already gone up a tonne, so bascally you are too late! Sometimes, you just have to let go, right? Sometimes yes, but not always - let’s look at an example. International Hotels Group (IHG) Back in 2020, LSE:IHG IHG shares were trading down at ~2000 GBX, now they are a hairs breadth from 10,000 - that’s 5X in about 4 years. Not bad. Can you really even think about buying shares at 10,000 that were 2,000 only 4 years ago. 🤔 We’re saying YES.. if you follow some guidelines. Clearly this is not a value investment - this is a momentum trade. To be buying IHG shares up here, one is basically arguing that the price at new highs indicates and buyers are in charge and the price is going to keep going up for the time being. This helps define the trade risk very well. If the trade is that IHG has broken out over the previous peak at ~8,800. We don’t want to be owning shares below this level - if they’re back below 8,800 the momentum has stalled and we need to be out. To put it another way, we are not buying just under 10,000 and willing to hold the shares all the way back down to 2,000 again - no. We want to ride the momentum up - not down ! From here there’s a pretty good chance that momentum takes the price up to the 10,000 level. As a big round number, there is also a good chance that profit taking takes place here too. That creates our buy zone between 8,800 and the current market price (9,750). So what might a trading strategy look like to capture this situation? The following is a way to have: An intial risk of £1000 to test the waters A total risk £3000 if/when the trade starts working A 2X profit potential (with the opportunity to capture more) Spread Betting Strategy: Target £6000+ Profit with £1000 Initial Risk Entry Points and Stops 9000 GBX Entry: Stop Loss: 8600 GBX. Bet Size: £2.50 per point. Risk: £1000. 9200 GBX Entry: Stop Loss: 8800 GBX. Bet Size: £2.50 per point. Risk: £1000. 9400 GBX Entry: Stop Loss: Trailing 400 points. Bet Size: £2.50 per point. Initial Risk: £1000. Profit Targets First Position (9000): Gain: 1000 points. Profit: £2500. Second Position (9200): Gain: 800 points. Profit: £2000. Third Position (9400): Trailing Stop Profit Example: 10,400 GBX: Profit = £2500. 11,000 GBX: Profit = £4000 or more. Summary Total Risk: £3000. Fixed Profit (First Two Positions): £4500. Potential Profit (Third Position): Variable, based on trailing stop. Reward-to-Risk Ratio: 2:1 or higher, depending on trend continuation.
LSE:IHGEducation
by jasperlawler
5
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
19
Crypto Money Flow CycleHello, The Crypto Money Flow Cycle is a flow model that discusses the route of investments from fiat to Bitcoin, from Bitcoin to altcoins, and backward into fiat, booking profit at every step. The model theorizes that most Bitcoins in circulation aren't mined but are bought for fiat. Before every bull run, investors don't necessarily buy mining equipment but purchase Bitcoins from their fiat money. As more and more money flows from fiat into Bitcoin, Bitcoin price rallies. At this phase, Bitcoin usually pumps more than most altcoins. At the end of the phase, investors buy altcoins from their Bitcoins. They prioritize large caps like Ethereum. So, the price of large caps rallies compared to fiat and Bitcoin. Usually, these rallies outperform Bitcoin because the investors can afford to invest not only the initial fiat value but all the profits so far. That is Bitcoin's performance on fiat compounded by the large caps' performance compared to Bitcoin. Over time, investors move the value from large caps to medium caps and from medium caps to small caps, pumping the markets in this order. Since the investment in medium caps is larger with the profit than the large caps, medium caps usually pump more, and similarly, small caps pump even more when money from medium caps flows into them. To realize all the profit so far, investors can exchange small-cap altcoins back into Bitcoin, which means Bitcoin will pump once again. Then all the money so far, which is the initial fiat value compounded by the profit from each phase can return into fiat. Usually, this is when Bitcoin suffers correction and drags altcoins with itself. That's how the Crypto Money Flow Cycle usually works. It's a model, which might or might not be true. However, I can say AI could trade the estimated phases with a success rate of over 71.23%, which means there might be more to this model than luck. Regards, Ely
COINBASE:ETHUSDEducation
by Elysian_Mind
9
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
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
Election Year Cycle & Stock Market Returns - VisualisedIn this chart, we're analysing the open value of the week the US election took place and comparing it to the open of the following election, showing the gain (or loss) in value between each election cycle. Historically we can see prices in the Dow Jones Industrials Index tend to appreciate the week the election is held. Only twice has the return between the cycles produced a negative return. Buying stocks on election day, 8 out of 10 times has yielded a profitable return between the election cycles. 80% of the time in the past 40 years returning a profit, has so far been a good strategy to take. The typical cycle starts with the election results, an immediate positive movement and continued growth before finishing positive. The Outliers 2000-2004 was the only year which ended negative without prices going higher than the election day. 2004-2008 increased 41.84% before ending negative. 2008-2012 began the cycle falling 30.62% before finishing positive. The names of presidents who won their respective elections is to visualise who had the presidential term during that specific cycle.
TVC:DJIEducation
by Mt.BFX
What Is Money Flow In & Out of a Stock? And Why Should You Care?Professionals often speak of money flowing in or out of a stock, but how can that be if there is an equal number of buyers and sellers? It is because “Money Flow” comes from the balance of the lot sizes. There are four possible positions in any one stock: Buy Buy to Cover Sell Sell Short Each investor and trader in the stock has their own separate agenda. Each may come from a different Market Participant Group. There are now 9 Stock Market Participant Groups, starting from those who buy first, at the bottom of a new upward cycle: The giant Buy Side Institutions who invest Mutual and Pension Funds and/or create ETFs and other kinds of stock market derivatives. The Sell Side Institutions, aka the big banks and major market makers Wealthy Individual Investors Corporations Institutional/ Pro Traders High Frequency Traders (HFTs) Small Funds Individual Small-Lot Investors, Investment Groups and Individual Retail Traders Odd-Lot Investors Buyers are anticipating that the stock is going to move up. Their stock order types span the spectrum, for example: Market Orders, Limit Orders, Stop Orders. Buy to Cover Orders are placed by traders who sold short and are now taking profits. Those who are selling the stock are anticipating that the stock is going to move down. In an uptrending stock, this is profit-taking near the top of the run. It can also be similar in a downtrending stock because the seller is afraid that the stock is going to move down more, and they have been holding through what they thought was a short retracement. Most of these stock order types will be “Sell at Market” (SAM). Sell Short Traders are anticipating that the stock is going to move down, and they can place a variety of orders just like the buyers. Both Buyers and Sell Shorters are entering the trade, while Buy to Covers and Sellers are exiting the trade. It is the mix of these different types of buying and selling coupled with the kind of investor or trader and the size of their share lots that causes money to flow in or out of a stock. If the buyers are mostly large lots and the sellers are mostly small lots, who is in control? The buyers purchasing large lots . This is because, at some point, there will not be enough small-lot sellers, and those who are Selling Short will turn and start Buying to Cover, creating more of a shortage of sellers. Consequently, this will put more pressure on the buy side. There are always latecomers to a stock run, and they are usually small-lot buyers. As the stock moves up in price, more of the small-lot buyers will step in, pushing the price up even further. Most small-lot buyers typically use a “Buy at Market” Order, which is the worst kind to use to control the entry price. As the stock moves up further in price, the last of the Short Sellers will panic and Buy to Cover, causing the stock to gap up or jump even higher. This then triggers the large-lot buyers to start selling for profit. As profit-taking begins, the stock dips in price. This causes the odd-lot buyer, who is the last in the market participant cycle to buy, to rush into the stock and buy because they have been told to “Buy the Dip.” By now, the news media has been talking about this stock and its great run. Consequently, the odd-lot uninformed investor finds the dip irresistible and buys on pure emotion without any analysis of the stock. This causes the final gap up and exhaustion pattern. Now, while all of those odd-lot latecomers are buying, who is selling to balance the equation? Market Makers are Selling Short and the Smart Money, who were the first to enter, are selling to take profits. Suddenly, the large lots are now shifting to the downside, and what happens? The control switches to the sellers who are moving larger lots. Now, money is flowing out of the stock, yet the price may go up briefly before a downtrend develops. Large lots are usually wiser investors and traders who know more than the other investors and traders. So the giant Buy Side Institutions investing Mutual and Pension Funds, who have access to information often not yet available to Individual Investors and Retail Traders, are called the Smart Money. It can be assumed that the smaller the lot size, the less the investor or trader knows and understands about the market. As smaller lots move in, a shift of power occurs due to the large lots moving to the sell side, and thus money shifts to flowing out of the stock. As the stock collapses and reaches a price or equilibrium near a base or bottom, those smaller lots who held through the collapse reach an emotional point of extreme pain of loss and begin to sell in panic. In response, the Smart Money and Market Makers switch roles again, Buying to Cover their profitable shorts and buying to hold as the stock moves up again. Summary: Every time you take a position in a stock, there are also three other positions in that same stock. You need to be aware of each of these and make sure that you are with the right group. Most of the time, traders who are having problems with their trades are simply trading with the wrong group. It is important, then, to learn about today's stock market structure and what I call the "Cycle of Market Participants." When traders can trade with the flow of the Smart Money, they have a decided advantage.
NASDAQ:AAPLEducation
by MarthaStokesCMT-TechniTrader
What is Structure Mapping in Gold Trading XAUUSD? Structure mapping is essential for day trading, scalping and swing trading gold. It is applied for trend analysis, pattern recognition, reversal and trend-following trading. In this article, I will teach you how to execute structure mapping on Gold chart and how to apply that for making accurate predictions and forecasts. Take notes and let's get started. Let's discuss first, what is structure mapping? With structure mapping, we perceive the price chart as the set of impulse and retracement legs. Structure mapping can be executed on any time frame and on any financial market. Look at a Gold chart on a 4H time frame. What I did, I underlined significant price movements. Each point where every leg of a movement completes will have a specific name and meaning. On a gold chart, I underlined all such points. These points are very important because it determines the market trend and show the patterns. When you execute structure mapping, the first thing that you should start with the identification of a starting point - the initial point of analysis. On a price chart, such a point should be the highest high that you see or the lowest low. If you start structure mapping with a high, that high will be called Initial High. A completion point of a bearish movement from the Initial High will be called Lower Low LL. A bullish movement that completes BELOW the level of the Initial High or Any Other High will be called Lower High LH. A bullish movement that completes on the level of the Initial High or Any Other High will be called Equal High. A bullish movement that completes above the level of the Initial High or Any Other High will be called Higher High HH. If you start with the low, such point will be called Initial Low. A completion point of a bullish movement from the Initial Low will be called Higher High HH. A bearish movement that completes ABOVE the level of the Initial Low or Any Other Low will be called Higher Low HL. A bearish movement that completes on the level of the Initial Low or Any Other Low will be called Equal Low. A bearish movement that completes below the level of the Initial Low or Any Other Low will be called Lower Low LL. Look how I executed structure mapping on Gold chart. Starting with the lowest low, I underlined all significant price movements and its lows and highs. You should learn to recognize these points because it is the foundation of gold structure mapping. Combinations of these points will be applied for the identification of the market trend, trend reversal and patterns. According to the rules, 2 lower lows and a lower high between them are enough to confirm that the market is trading in a bearish trend. While 2 higher highs and a higher low between them confirm that the trend is bullish . In a bullish trend, a bullish violation of the level of the last Higher High will be called a Break of Structure BoS. That event signifies the strength of the buyers and a bullish trend continuation. A bearish violation of the level of the last Higher Low will be called Change of Character CHoCH . It will mean the violation of a current bullish trend. In a bearish trend, a bearish violation of the level of the last Lower Low will be called a Break of Structure BoS . It is an important event that signifies the strength of the sellers and a bearish trend continuation. While a bullish violation of the level of the last lower high will be called Change of Character CHoCH. That even will signify a violation of a bearish trend. That's how a complete structure mapping should look on Gold chart. With the identification of the legs of the move, highs and lows, BoS and ChoCh you can clearly understand what is happening with the market. Gold was trading in a bearish trend. Once the level of our Initial Low was tested, the market started a correctional movement and started to trade in a bullish trend. Once some important resistance was reached, the market reversed. We saw a confirmed CHoCH and the market returned to a bearish trend. Structure mapping is the foundation of technical analysis. It is the basis of various trading strategies and trading styles. It is the first thing that you should start your trading education with. I hope that my guide helped you to understand how to execute structure mapping in Gold trading. ❤️Please, support my work with like, thank you!❤️
OANDA:XAUUSDEducation
by VasilyTrader
2
These Market Structures Are Crucial for EveryoneIn this article, we will simplify complex market structures by breaking them down into easy-to-understand patterns. Recognizing market structure can enhance your trading strategy, increase your pattern recognition skills in various market conditions. Let’s dive into some essential chart patterns that every trader should know. Double Bottom / Double Top A double bottom is a bullish reversal pattern that occurs when the price tests a support level twice without breaking lower, indicating strong buying interest. This pattern often suggests that the downtrend is losing momentum and a potential uptrend may follow. Conversely, a double top signals a bearish reversal, formed when the price tests a resistance level twice without breaking through. This pattern indicates selling pressure and suggests that the uptrend may be coming to an end. Bull Flag / Bear Flag A bull flag is a continuation pattern that appears after a strong upward movement. It typically involves a slight consolidation period before the trend resumes, providing a potential entry point for traders looking to capitalize on the ongoing bullish momentum. On the other hand, a bear flag forms during a downtrend, signaling a brief consolidation before the price continues its downward movement. Recognizing these flags can help traders identify potential breakout opportunities. Bull Pennant / Bear Pennant A bull pennant is a continuation pattern that forms after a sharp price increase, followed by a period of consolidation where the price moves within converging trendlines. This pattern often indicates that the upward trend is likely to continue after the breakout. Conversely, a bear pennant forms after a sharp decline, with the price consolidating within converging lines. This pattern suggests that the downtrend may resume after the breakout. Ascending Wedge / Descending Wedge An ascending wedge is a bearish reversal pattern that often forms during a weakening uptrend. It indicates that buying pressure is slowing down, and a reversal may be imminent. Traders should be cautious as this pattern suggests a potential downtrend ahead. In contrast, a descending wedge appears during a downtrend and indicates that selling pressure is weakening. This pattern may signal a bullish reversal, suggesting a possible upward breakout in the near future. Triple Top / Triple Bottom A triple top is a bearish reversal pattern that forms after the price tests a resistance level three times without breaking through, indicating strong selling pressure. This pattern can help traders anticipate a potential downtrend. Conversely, a triple bottom is a bullish reversal pattern where the price tests support three times before breaking higher. This pattern highlights strong buying interest and can signal a significant upward move. Cup and Handle / Inverted Cup and Handle The cup and handle pattern is a bullish continuation pattern resembling a rounded bottom, followed by a small consolidation phase (the handle) before a breakout. This pattern often indicates strong bullish sentiment and can provide a solid entry point. The inverted cup and handle is the bearish counterpart, signaling potential downward movement after a rounded top formation, suggesting that a reversal may occur. Head and Shoulders / Inverted Head and Shoulders The head and shoulders pattern is a classic bearish reversal signal characterized by a peak (head) flanked by two smaller peaks (shoulders). This formation indicates a potential downtrend ahead, helping traders to identify possible selling opportunities. The inverted head and shoulders pattern serves as a bullish reversal indicator, suggesting that an uptrend may follow after the price forms a trough (head) between two smaller troughs (shoulders). Expanding Wedge An expanding wedge is formed when price volatility increases, characterized by higher highs and lower lows. This pattern often indicates market uncertainty and can precede a breakout in either direction . Traders should monitor this pattern closely, as it can signal potential trading opportunities once a breakout occurs. Falling Channel / Rising Channel / Flat Channel A falling channel is defined by a consistent downtrend, with price movement contained within two parallel lines. This pattern often suggests continued bearish sentiment. Conversely, a rising channel indicates an uptrend, with price moving between two upward-sloping parallel lines, signaling bullish momentum. A flat channel represents sideways movement, indicating consolidation with no clear trend direction, often leading to a breakout once the price escapes the channel. P.S. It's essential to remember that market makers, whales, smart investors, and Wall Street are well aware of these structures. Sometimes, these patterns may not work as expected because these entities can manipulate the market to pull money from unsuspecting traders. Therefore, always exercise caution, and continuously practice and hone your trading skills. What are your thoughts on these patterns? Have you encountered any of them in your trading? I’d love to hear your experiences and insights in the comments below! If you found this breakdown helpful, please give it a like and follow for more technical insights. Stay tuned for more content, and feel free to suggest any specific patterns you’d like me to analyze next!
Education
by ArShevelev
5
Here's How You CONSOLIDATE Your Portfolio Into WinnersGoing through my entire portfolio to judge performance vs Solana, which has been my golden goose this cycle. I bought TSX:FIL in October 2023. If I put that money in Solana instead, I’d be up 345% vs breakeven right now. Obviously I’m selling that position here and flipping it into CRYPTOCAP:SOL How to compare: Jump onto TradingView and on the chart name type: BINANCE:SOLUSDT/BINANCE:FILUSDT You can swap out tickers and exchanges to compare your own portfolio.
Education
by jonnieking