Learning#02 : Fractals⛰️ Learning#02 : Fractals
The Cleanest Clue on a Cluttered Chart
If you like clean charts and smart price behaviour, Fractals are one of those tools that give subtle but powerful signals. They’re not magic. They simply reflect what price is telling you—if you’re willing to listen.
Let’s unpack the concept and learn how to use Fractals like a pro.
🔍 What Is a Fractal in Trading?
In technical analysis, a Fractal is a five-candle pattern that marks a local top or bottom in price. It’s a pure price-action signal that doesn’t rely on lagging indicators.
There are two types of Fractals:
Bearish Fractal (Top): The 3rd candle has the highest high, surrounded by two lower highs on each side.
Bullish Fractal (Bottom): The 3rd candle has the lowest low, flanked by two higher lows on each side.
These formations are Price's way of saying: *"I tried to go further, but couldn't."
📊 What Do Fractals Indicate?
A shift in short-term control (bulls vs. bears)
Minor support or resistance zones
Useful markers for entries, exits, or trailing stop levels
They don't guarantee reversals but are excellent at highlighting where price momentum may pause, reverse, or build structure.
📈 How to Use Fractals – A Practical Guide
Let’s be clear: Fractals are not trade signals by themselves.
Instead, they work best when used in confluence with your strategy. Think of them as tools that:
Help confirm breakout levels
Refine pullback entries
Guide you in drawing cleaner trendlines, fib zones, and support/resistance levels
Assist in identifying swing highs and lows for Dow Theory-style trend analysis
🔗 Fractals + Strategy = Smart Trading
Whether you trade breakouts or mean reversion, Fractals help clarify:
Which highs or lows matter
Where to place stop losses with structure-based logic
How to trail SL as the trade progresses
They quietly organize your chart into readable, tradeable levels.
🚀 Practical Uses of Fractals
Fractals are the first tool I add to any chart—they instantly reveal structure and guide every step of my analysis.
1. Breakout Confirmation
Wait for a candle to close above a bullish fractal high or below a bearish fractal low.
Useful when the market is trending or forming structures like double bottoms/tops.
2. Pullback with Confirmation
Use the fractal zone as a short-term S/R level. If price returns and shows signs of rejection (like an inside bar, wick rejections, or low volume), consider entries based on confirmation.
Great in sideways or swing environments.
3. Trend Structure Validation
Fractals reveal clear pivot highs/lows, helping:
Confirm higher highs/higher lows
Mark structure for trendline drawing
Validate Fib levels or S/R zones
4. Trailing Stop Loss
Update your SL to trail behind the most recent opposite-side fractals.
In longs: SL below new bullish fractals
In shorts: SL above new bearish fractals
This lets you stay in the move while managing risk like a pro.
How it’s Look Like on Chart
snapshot
⚠️ Common Mistakes to Avoid
Trading every fractal blindly
Ignoring price context or trend
Relying on fractals in low-volume, choppy markets
📝 Final Thoughts
Fractals are like breadcrumbs left by price action. They quietly point to areas where the market faced resistance or found support. Alone, they’re not enough. But in the hands of a price-action trader, they’re incredibly useful.
Used alongside market structure, confirmation signals, and clean charting habits, Fractals become:
Trend identifiers
Entry enhancers
Stop loss trail markers
⭐ Bonus Tip
Next time you mark a level, Fibonacci or draw a trendline, check if a Fractal confirms it. You’ll be surprised how often it does.
Trade simple. Trade clean.
— Kiran Zatakia
Fractal
Understanding SFP In Trading1. What is a Swing Failure Pattern (SFP)?
A Swing Failure Pattern (SFP) occurs when the price temporarily breaks a key swing high or low but fails to continue in that direction, leading to a sharp reversal.
This pattern is often driven by liquidity grabs, where price manipulates traders into taking positions before reversing against them.
An SFP typically consists of:
A false breakout beyond a previous swing high/low.
A sharp rejection back within the prior range.
A liquidity grab, triggering stop-loss orders and fueling a reversal.
SFPs provide powerful trade opportunities, signaling potential reversals and the exhaustion of trends.
2. Understanding Liquidity Grabs & Stop Hunts
The financial markets are structured around liquidity. Large institutions and algorithmic traders require liquidity to execute their large orders efficiently.
One way they achieve this is by triggering liquidity grabs and stop hunts.
Liquidity Grab:
Occurs when price moves beyond a key level (e.g., swing high/low), activating orders from breakout traders and stop-losses of trapped traders.
Smart money absorbs this liquidity before pushing the price in the opposite direction.
Stop Hunt:
A deliberate price movement designed to trigger stop-loss orders of retail traders before reversing.
Often seen near major support and resistance levels.
These events are crucial for understanding SFPs because they explain why false breakouts occur before significant reversals.
3. Why Smart Money Uses SFPs
Institutions, market makers, and algorithmic traders use SFPs to:
Fill large orders: By grabbing liquidity at key levels, they ensure they can enter large positions without causing excessive price slippage.
Manipulate retail traders: Many retail traders place stop-losses at obvious swing points. Smart money exploits this by pushing the price beyond these levels before reversing.
Create optimal trade entries: SFPs often align with high-probability reversal zones, allowing smart money to enter positions at better prices.
Understanding how institutions operate gives traders an edge in identifying manipulative moves before major price reversals.
4. Market Structure & SFPs
Market structure is built upon a series of swing highs and swing lows. Identifying these key points is crucial because they represent areas where liquidity accumulates and where price is likely to react.
Swing High (SH): A peak where price makes a temporary high before reversing downward.
Swing Low (SL): A trough where price makes a temporary low before reversing upward.
Types of Swing Points in Market Structure
Higher Highs (HH) & Higher Lows (HL) – Bullish Trend
Lower Highs (LH) & Lower Lows (LL) – Bearish Trend
Equal Highs & Equal Lows – Range-Bound Market
5. Liquidity Pools: Where Traders Get Trapped
Liquidity pools refer to areas where traders' stop-loss orders, pending orders, and breakout entries accumulate. Smart money uses these liquidity zones to execute large orders.
Common Liquidity Pool Zones:
Above swing highs: Retail traders place breakout buy orders and stop-losses here.
Below swing lows: Stop-losses of long positions and breakout sell orders accumulate.
Trendline & Range Liquidity:
Multiple touches of a trendline encourage traders to enter positions based on trendline support/resistance.
Smart money may engineer a fake breakout before reversing price.
6. Identifying Bullish SFPs
SFPs can occur in both bullish and bearish market conditions. The key is to identify when a liquidity grab has occurred and whether the rejection is strong enough to confirm a reversal.
Bullish SFP (Swing Low Failure in a Downtrend)
Price sweeps a key low, triggering stop-losses of long traders.
A strong rejection wick forms, pushing price back above the previous low.
A shift in order flow (bullish market structure) confirms a potential reversal.
Traders look for bullish confirmation, such as a higher low forming after the SFP.
Best bullish SFP setups occur:
At strong support levels
Below previous swing lows with high liquidity
After a liquidity grab with momentum confirmation
7. Identifying Bearish SFPs
Bearish SFP (Swing High Failure in an Uptrend)
Price takes out a key high, triggering stop-losses of short traders.
A sharp rejection forms, pushing the price back below the previous high.
A bearish shift in order flow confirms downside continuation.
Traders look for bearish confirmation, such as a lower high forming after the SFP.
Best bearish SFP setups occur:
At strong resistance levels
Above previous swing highs where liquidity is concentrated
With clear rejection wicks and momentum shift
8. How SFPs Signal Reversals
SFPs provide early warning signs of trend reversals because they expose areas where liquidity has been exhausted.
Once liquidity is taken and the price fails to continue in that direction, it often results in a strong reversal.
Key Signs of a Strong SFP Reversal
Long wick rejection (indicating absorption of liquidity).
Close back inside the previous range (invalidating the breakout).
Increased volume on the rejection candle (confirming institutional activity).
Break of short-term market structure (trend shifting).
Divergences with indicators (e.g., RSI divergence at the SFP).
9. Identifying High-Probability SFPs
One of the most critical aspects of a valid SFP is how the price reacts after a liquidity grab. The candle’s wick and close determine whether an SFP is strong or weak.
A. Wick Rejections & Candle Closes
Key Features of a Strong SFP Wick Rejection
Long wick beyond a key swing high/low (indicating a liquidity grab).
Candle closes back inside the previous range (invalidating the breakout).
Engulfing or pin bar-like structure (showing aggressive rejection).
Minimal body size relative to wick length (e.g., wick is 2–3x the body).
Bullish SFP (Swing Low Failure)
Price sweeps below a key low, triggering stop-losses of buyers.
A long wick forms below the low, but the candle closes back above the level.
This signals that smart money absorbed liquidity and rejected lower prices.
Best bullish SFPs occur at major support zones, previous swing lows, or untested demand areas.
Bearish SFP (Swing High Failure)
Price sweeps above a key high, triggering stop-losses of short sellers.
A long wick forms above the high, but the candle closes back inside the range.
This signals that smart money absorbed liquidity and rejected higher prices.
Best bearish SFPs occur at resistance levels, previous swing highs, or untested supply areas.
❌ Weak SFPs (Avoid These)
❌ Wick is too small, meaning the liquidity grab wasn’t significant.
❌ Candle closes above the swing high (for a bearish SFP) or below the swing low (for a bullish SFP).
❌ Lack of strong momentum after rejection.
B. Volume Confirmation in SFPs
Volume plays a crucial role in validating an SFP. Institutional traders execute large orders during liquidity grabs, which often results in spikes in trading volume.
How to Use Volume for SFP Confirmation
High volume on the rejection wick → Indicates smart money absorption.
Low volume on the breakout move → Suggests a lack of real buying/selling pressure.
Increasing volume after rejection → Confirms a strong reversal.
Spotting Fake SFPs Using Volume
If volume is high on the breakout but low on the rejection wick, the move may continue trending rather than reversing.
If volume remains low overall, it suggests weak market participation and a higher chance of chop or consolidation instead of a clean reversal.
Best tools for volume analysis:
Volume Profile (VPVR)
Relative Volume (RVOL)
Footprint Charts
10. Key Takeaways
SFPs are Liquidity Grabs – Price temporarily breaks a key high/low, triggers stop losses, and then reverses, signaling smart money absorption.
Wick Rejection & Close Matter – A strong SFP has a long wick beyond a swing point but closes back inside the range, invalidating the breakout.
Volume Confirms Validity – High volume on rejection wicks indicates smart money involvement, while low-volume breakouts often fail.
Higher Timeframes = Stronger SFPs – 1H, 4H, and Daily SFPs are more reliable than lower timeframe setups, reducing false signals.
Confluence Increases Probability – SFPs are most effective when aligned with order blocks, imbalances (FVGs), and major liquidity zones.
Optimal Entry Methods Vary – Aggressive entries capitalize on immediate rejection, while confirmation and retracement entries improve accuracy.
Proper Stop Loss Placement Prevents Fakeouts – Placing SL just beyond the rejection wick or using structure-based stops reduces premature exits.
Take Profit at Key Liquidity Levels – Secure profits at previous swing highs/lows, order blocks, or imbalance zones to maximize returns.
NASDAQ Bread and Butter & Turtle Soup Example XIIaight, so im gonna break down a trade i took on nasdaq today using a setup i picked out myself from the ict concepts. just my own flavor of it, ya know
before i knock out at night, i open up the charts real quick — just tryna see if there's any clean liquidity chillin’ nearby. if there aint, i shut it down and catch some solid sleep. but if there is... bingo baby
this basically means i might just wake up rich tomorrow, bro. on the daily, im seeing two strong green days back to back, and right above that boom some equal highs just sitting there, begging to get run. they are even cleaner on the 1h. bias locked in. im waking up tomorrow and hunting longs, simple as that.
i mark up the daily open first thing. if im lookin for longs, i wanna see some turtle soup under the open. if im hunting shorts, i need that setup above the open. thats just how i roll.
if there is a swing low, trend liquidity, or some equal lows carryin over from yesterday, im locked in on those levels for turtle soup. if not, im just chillin, waitin for price to build some fresh liquidity during the day and then snatch it.
in this setup, i got some leftover liquidity from yesterday plus a clean 4h fvg sittin there like a neon sign.
next, i check the time. liquidity grabs usually hit during one of the killzones depends on the pair, but im watchin asia, london, or new york sessions.
then i scope out if there is any news droppin around that time, especially stuff that could move the pair. no point in getting blindsided.
and yeah, i always peep correlated pairs too sometimes they snitch before your chart even says a word.
when all the stars and planets line up just right, that is when I drop down to the 15m and wait for a clean csd to show up. but here is the thing i dont jump in the second i see it. i wanna see price actually leave the liquidity zone.
yeah, it might lower my rr a bit, but the win rate goes way up. It keeps me outta those fake-ass turtle soups that look good at first but just wanna wreck your stop.
once im in the trade, i usually try to close out half the position the same day take profits where the chance of price reversing is damn near zero. then i let the other half ride toward my target liquidity. just lettin it breathe, do its thing.
thats it, peace out
Macromics Group: Market Trends Overview (June 2025)Global Economic Landscape: What Has Changed?
June 2025 marks significant shifts in the global economy. After several years of instability caused by the pandemic, inflation, and geopolitical tensions, markets are gradually stabilizing. However, new challenges are emerging: rising risks in Asia, digital transformation in Europe, and strategy shifts in the U.S.
China and India continue to show strong growth rates—5.8% and 6.5% respectively. Europe, by contrast, is lagging behind due to slow recovery and persistent inflation. The U.S. maintains a steady course driven by consumer spending and innovation, reporting 2.1% GDP growth.
Macromics Group continues to deliver in-depth analytics and strategies for clients seeking to understand and capitalize on these changes. We analyze trends across more than 120 industries, helping companies adapt and thrive.
Macroeconomics and Monetary Policy: A Shift Toward Stabilization
Financial regulators have begun cautiously lowering interest rates after the peaks of 2024. The U.S. Federal Reserve has dropped its rate to 4.5%, while the ECB has reduced its rate to 3.75%. This is made possible by a decline in inflation: 2.7% in the U.S. and 3.1% in the EU.
Meanwhile, developing nations like Turkey and Argentina are still grappling with high inflation. These countries risk falling behind the global recovery unless decisive steps are taken.
Overall, the global course is toward soft stabilization: interest rates remain high but steady. This creates favorable conditions for investment and long-term planning.
Financial Markets: From Caution to Moderate Optimism
Stock markets in June 2025 show mixed performance. U.S. indexes such as the S&P 500 and Nasdaq hit new highs, thanks to the booming tech sector. Stocks of companies involved in AI, quantum computing, and cybersecurity are particularly strong.
European markets are less active but relatively stable. Growth is limited by high costs, demographic issues, and the transition to ESG standards. In Russia and CIS countries, markets are under pressure due to sanctions, currency restrictions, and reduced investment.
On the currency front, the U.S. dollar and Chinese yuan dominate. The ruble is volatile, the euro is stable, and the yen is strengthening as a safe haven asset.
Technology: The Engine of New Markets
The main trend in 2025 is AI and automation. Companies are deploying neural networks in logistics, marketing, finance, and HR to cut costs and boost efficiency. Demand for AI professionals and developers is surging.
5G infrastructure has matured in most developed countries, unlocking new potential in IoT, telemedicine, and remote work. At the same time, quantum computing is advancing rapidly, with commercial solutions expected by 2026.
Macromics Group invests in next-generation analytical platforms, enabling clients to access real-time insights and forecast trends before they go mainstream.
Energy and Sustainability: ESG and the “Green” Shift
Energy markets have stabilized after the turbulence of 2024. Oil prices remain between $70–$85 per barrel—comfortable for both producers and consumers. Meanwhile, renewable energy—solar, wind, and hydrogen—is seeing record investment.
Corporations are increasingly reporting according to ESG standards. It’s not just a trend, but a new business reality. Investors demand transparency, consumers prefer socially responsible brands, and regulators impose mandatory reporting.
Macromics Group supports clients in transitioning to sustainable models by developing ESG strategies, assessing risks, and offering financial solutions.
Conclusion: Outlook for the Second Half of 2025
The first half of 2025 showed that markets are learning to operate in a new reality. The global economy is no longer chasing rapid growth, but adapting to volatility. Key focus areas are technology, sustainability, and smart resource management.
For businesses, this means quick adaptation, innovative thinking, and reliance on data-driven decisions. In this context, Macromics Group serves not just as an analyst but as a strategic partner.
Our recommendation: act proactively. In times of uncertainty, those who plan years ahead and use quality data will win.
Seeds in Chaos, Petals in Profit -A trader's guideSeeds in Chaos, Petals in Profit
A trader's guide to reading the market through nature's lens.
By: Masterolive
Intro:
This trader's guide is not another cookie-cutter trading system.
Instead, it focuses on building a long-term mindset and a way to read the market's chaos through nature's lens. This guide is grounded in real success but is not for the daily trader; it works for long-term swings using hourly price moves.
Over seven years of trading, I developed a unique way to view the market, which led to a practical trading mindset. The technique comes from simplifying the chart after experiencing endless combinations of indicators to no avail. It wasn't until I had to explain my concept to someone else that I found a way to use a garden analogy that fits the mindset well to see the market as a natural system: planting in chaos, thriving through storms.
Later, I read two books: "The Alchemy of Finance" by George Soros and "The Misbehavior of Markets" by Benoit Mandelbrot and Richard L. Hudson. Surprisingly, these two books validated my approach and inspired me to share it. Previously, I would tell no one because I thought it was silly.
The overall goal is to plant a garden, watch it grow, and understand how the weather affects the plants. This guide walks you through determining what flowers you want to plant and how to read the weather after you have made your choice.
It uses a garden and planting flowers as an analogy to choosing the right stocks and interpreting an EMA indicator to determine the market's direction. This guide also works well for Bitcoin.
This guide will help you understand how to read and interpret the chart. It will also give you accurate future context so you react less to the market moves and see the bigger picture: Plant while they panic.
This guide is not financial advice.
Part One - Planting.
Some traders focus on various companies based on technicals or fundamentals, some short-term and some long-term. Other traders will focus on a few stocks or diversify across many.
For this guide, we pick and diversify a sector with roles that thrive together. The industry can be broad or small, but we will use 10 assets, including nine stocks and Bitcoin, and explain how they correlate and grow into a weather-worthy garden.
In this garden, we will focus on Tech and Finance and explain how to plant and organize the garden. First, we must look back at the broadest picture in finance. We will choose a stock exchange and a crypto exchange in this garden. (1 and 2 out of 10 flowers)
Why an exchange? Simply put, traders will always look for stocks and crypto to buy. They will look for the best companies and the best opportunities. Therefore, stock exchanges will benefit from the revenue they generate. If a stock goes parabolic, the exchange still profits from that price move.
Choosing the exchange skips the hassle of finding companies in a haystack. The same is true for the crypto exchange. Our garden has two flowers: one stock exchange and one crypto exchange, representing those two sectors.
Next, what else can correlate with our garden from a zoomed-out view?
Let's choose a Bank and a payment processor. (3 and 4 out of 10 flowers)
Traders will need the bank to on and offramp their cash profits to and from the stock and crypto exchange. Meanwhile, they will need to process those electronic payments.
The bank and payment processors benefit from trading surges; if everyone piles in for a parabolic price move of a particular stock, the bank and payment processors benefit from the action, and the exchanges offering the stock get revenue from the surge.
Once again, this choice skips the need to hunt for specific stocks. It takes advantage of all stocks since traders need cash, banks, electronic payments, and exchanges to buy those company stocks or bitcoin.
Our garden now has Four flowers, a bank and payment processor, and two exchanges for this sector. The correlation? Exchanges, banks, and processors all thrive when traders move money.
The fifth is a pivot flower before we discuss the tech company sector. This pivot flower is a gambling company (5 out of 10).
How does this correlate? Some traders and other users gamble with their cash and profits; even in a recession or a depression, people will still gamble. Plus, users might take their gambling winnings and invest them in a stock or buy bitcoin. They need a bank, an exchange, and a payment method.
In this case, the flowers are self-reinforcing: gambling winnings or losses, stock booms or busts; it doesn't matter in the big picture because, once again, exchanges, banks, and processors all thrive when people move money. Our garden now has five flowers with a broad but strong correlation.
Now, on to the tech sector with the last five flowers.
You will hone in on specific tech roles at this point, but remember that your choices will be self-reinforcing.
If your choice booms, the exchange benefits, and you benefit again from the exchange stock. You will electronically transfer your profits to your bank, which you benefit from by owning the bank stock and payment processor. But if you're smart, you will skip the gambling and let the crowd roll the dice while you plant the profits.
We will focus on two more flowers (6 and 7 out of 10) for tech, so we need to find companies exposed to the popular and relevant tech we want. For tech company 1, you could expose yourself to AI, EVs, and ROBOTS. For tech company 2, Semiconductors (or graphics cards).
In this section of our garden, graphic cards and AI rely on one another, while EVs and robots use AI to operate. Eventually, people will buy or sell the robot and EV, and some may use the profits to buy stock (or Bitcoin), requiring a bank and payment processor.
Meanwhile, people use LLMs, log into their bank, or exchange daily on a computer that requires a graphics card.
Our garden now has seven flowers out of 10, 3 more to go!
We want to diversify (but stay correlated with our garden), so next, we will look at a real estate company or ETF—but not just any company or ETF, one that develops in tech hub areas. How does this correlate?
Robots, AI, EVs, and graphics cards all need workers to operate the companies; young talent will want to move to places where they can work in AI or Robotics or factory EV workers, so the real estate in those areas will be in high demand, so now we own the real estate for our Ai, EV, Robots, and graphic card workers.
As tech grows, real estate booms, driving more money through exchanges, banks, and processors.
We now have eight flowers in our weather-worthy garden.
For the 9th flower, we turn to a wildflower: none other than Bitcoin. Bitcoin is not just a crypto coin but a capital asset, a store of value for your currency when it debases.
People, especially tech workers, will buy, trade, and sell Bitcoin.
As people learn and turn to the asset, global capital will flow through Bitcoin as people around the world save their cash value,
whether it be from gambling winnings, selling a car, selling real estate, selling a stock, or simply putting part of their income from their tech job into it regularly. All of this requires Exchanges, Banks, and payment processors to move.
Bitcoin correlates with that, as exchanges profit off bitcoin, which you own stock in the exchange company. You still need a bank to land on and a payment processor to move the money electronically.
We now have nine flowers in our garden, and it's almost complete.
How can we diversify even more? We can use industrial metal for our last flower, but how does an industrial metal correlate with our tech and finance garden?
Copper is the metal that conducts electricity, and electricity is needed to move money, send Bitcoin, power a growing network of EV superchargers, and power the factories that produce EVs, graphics cards, robots, and more. Copper's the most vigorous root, tying every flower, from tech to finance, into a weather-worthy bed. Meanwhile, the crowds go for gold and sleep on copper.
That completes our garden with 10 flowers. It's a diversified flowerbed, but the flowers correlate in the big picture: Tech drives money movement, which benefits exchanges, banks, and processors; copper powers tech, which drives Bitcoin adoption.
Your goal is to find and build your garden. Think up different bigger pictures with other sectors and roles. Correlating these assets keeps the garden strong through chaos and self-reinforces one another.
To review, we have the following:
Stock exchange
Crypto Exchange
Bank
Payment processor
Gambling
Ai / EV / Robots
Semiconductors (Graphics cards)
Real estate
Bitcoin
Copper
Now that we have planted our garden, let's examine the weather and its meaning. We will learn to read the weather and see when storms are coming or clearing.
In part 2, you will set a simple EMA indicator, learn how to interpret the weather, and tend to the flowers in our garden.
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.
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!
$BTC Cheat Sheet They Don't Want You To See!THE CRYPTO CHEAT SHEET
After seeing this, don't let anyone tell you that trading the market is hard.
All you need is a 4-year mindset.
Sell in November (the latest) post-halving year, ie 2025
Buy in November the year after, ie 2026
It really is that simple.
CRYPTOCAP:BTC 👑
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!
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.
Quantum Mechanics & Market Behavior At this stage of my research, I would like to share the primary inspirations behind my style of analysis. As you've already noticed, I don’t create forecasts, as they are subjective and inherently disconnected from the objective nature of markets. Instead, I focus on predictions grounded in the captured dynamics of market behavior in order to actually get closer to its causality.
"QUANTUM MARKET"
In the unpredictable world of trading, price action often mirrors the strange principles of quantum mechanics. Concepts like wave function collapse, entanglement, chaos theory, the multiverse, and even the double-slit experiment provide a unique lens to understand why markets behave as they do—particularly when they defy the majority of forecasts and move in unexpected directions.
The Collapse of the Market Wave Function
In quantum mechanics, a particle exists in a state of possibilities described by its wave function until it is measured. When observed, the wave function "collapses" into one definite outcome. Similarly, in markets, price exists as a spectrum of probabilities, influenced by fundamental data, sentiment, and technical levels. These probabilities reflect the collective forecasts of traders, analysts, and institutions.
The "collapse" of the market wave function can be likened to the moments when price unexpectedly moves against the prevailing sentiment, proving the majority wrong. For instance, when experts predict a bullish breakout, only for the market to reverse sharply, it resembles the moment a quantum system resolves into a state that surprises its observers.
This metaphor highlights the fragile relationship between market expectations and actual outcomes. Just as the act of measurement influences a quantum system, the collective observation and positioning of traders directly impact market movements.
The Multiverse of Price Action
The Many-Worlds Interpretation (MWI) of quantum mechanics posits that every possible outcome of a quantum event occurs, creating branching universes for each scenario. This offers a useful metaphor for the multiverse of market possibilities, where price action simultaneously holds countless potential paths. Each decision by traders, institutions, and external forces influences which path the market ultimately "chooses," much like the branching of quantum states into separate realities.
When the market takes an unexpected turn, it can be thought of as moving into a "branch" of the multiverse that was previously considered improbable by the majority. For example:
A widely anticipated bullish breakout may fail, with the price collapsing into a bearish reversal. This outcome corresponds to a "parallel universe" of price action where the market follows a path contrary to the consensus. When they say market has its on path, chances are they're definitely referring to approach from Fractal Market Hypothesis.
The moment traders observe the market defy expectations, their reality shifts into this new "branch," leaving the discarded probabilities as theoretical relics.
While traders only experience one "reality" of the market—the observed price movement—the multiverse perspective reminds us that all potential outcomes coexist until resolved by market forces.
Chaos Theory: The Hidden Order Behind Market Behavior
Markets may appear chaotic, but their movements are not entirely random. Instead, they follow principles reminiscent of chaos theory, where complex systems display patterns that arise from underlying order.
In trading, this hidden order emerges from the entanglement of price action—the intricate relationship between buyers, sellers, sentiment, and external events. Counter-oscillations of opposing forces, such as bullish and bearish sentiment that has stake in patterns. When these forces reach a critical point, they can produce dramatic reversals or breakouts.
A fascinating aspect of this hidden order lies in the measurement of cycle intervals, which can decrypt the path and stops of price action. These intervals, often influenced by Fibonacci ratios, reflect the inherent chaos of the market while maintaining a surprising consistency. In chaotic systems, the ratios of results inherit the domestic chaos properties of the system itself. This means the measured intervals not only explain past behavior but also project future movements, where price has no option but to adhere to the golden ratio in its path, regardless of direction.
Tools like Fibonacci Channels on TradingView combine these ratios with the angle of the trend, revealing fractal-based timing measurements that highlight potential trend shifts. These tools demonstrate how price action, driven by the chaotic yet structured forces of the market, aligns with these self-similar patterns over time.
Entanglement and the Double-Slit Experiment in Markets
Einstein described quantum entanglement as "spooky action at a distance," where the state of one particle instantaneously influences another, no matter how far apart they are. Markets also mirror another iconic quantum experiment: the double-slit experiment, which demonstrates how particles behave as waves when unobserved but collapse into definitive points when measured.
In the double-slit experiment, an electron passes through two slits, existing as a wave of probabilities until observed. Without observation, it creates an interference pattern, suggesting it travels through both slits simultaneously. However, when measured, the electron collapses into a single state, taking a definitive path through one slit and landing at a specific spot on the detector.
Price action behaves in a strikingly similar way. Just as an electron "feels" it is being observed and alters its behavior, ongoing price action appears to respond to the collective observation of millions of traders. Despite this intense scrutiny, price action frequently surprises both bulls and bears, defying expectations as if reflecting the duality of probability and definitiveness.
When unobserved or in a state of uncertainty, markets exhibit wave-like behavior, oscillating between potential paths. Trends consolidate, creating a balance of opposing forces. However, as traders act on their observations—placing bets, setting stop losses, or predicting breakouts—price "collapses" into a definitive state, choosing a path that often defies the collective expectations of the market.
Logical Deductions
Understanding the market through the lens of quantum mechanics, chaos theory, and the multiverse offers valuable insights for traders:
Expect the Unexpected: Just as a quantum particle's state cannot be precisely predicted, markets are inherently probabilistic. Even the most widely expected outcomes can collapse under the weight of unforeseen variables or simply change of incentive during overheat volatility.
Beware of Herd Mentality: When the majority aligns behind a forecast, the market becomes entangled in their collective assumptions. This might create conditions for a dramatic reversal, much like how a quantum system shifts into an unanticipated state.
Recognize Counter-Oscillations: Price action is driven by the push and pull of opposing forces. Trends often mask the tension beneath, and understanding these dynamics can help traders anticipate critical turning points.
Measure Cycles with Ratios: Fibonacci-based tools, when combined with trend angles, reveal fractal rhythms and the frequency of reversals. These measurements help traders predict price shifts with greater accuracy.
Embrace the Multiverse: Just as the Many-Worlds Interpretation suggests all outcomes coexist until resolved, traders should recognize that multiple possibilities are always present in the market. Being prepared for alternative scenarios helps mitigate risk and improve decision-making.
General Interconnectedness:
Markets are a dynamic interplay of order and chaos, shaped by the entanglement of opposing forces and the constant tension between consensus and contrarian dynamics. The collapse of the wave function—those moments when price defies expert predictions—reminds us of the deep complexities underlying actual behavior of masses.
Through the lens of the multiverse, every market outcome can be seen as a branching reality, where the price action we observe is just one of many potential paths. By embracing this perspective, traders can better navigate the intricate dance of probabilities and entanglement, understanding that markets are not linear systems but ever-changing, interconnected realities. This mindset empowered me to thrive in the environment of duality, where adaptability and probabilistic thinking are the actual keys to understanding price mechanism in Financial Markets.
Disclaimer:
You don’t have to accept these observations as true. Always trust your own judgment and cultivate independent thinking. Personally, I find that the behavior of particles at the quantum scale is the closest phenomenon that mirrors the chaos of the market.
YOU ONLY NEED 3 TIMEFRAME TO BE PROFITABLE !!!most of the time people on the internet bombard us with so many information when it comes to trading. like use this use that you have to use 5 or 6 timeframes, but in fact using this much could make you even more confused . so in this post I will share the easiest way for you you can to capitalize on timeframe analysis.
THE HIGHER TIMEFRAME - for bias which tells us in what way the price is going.
( up, down, range)
THE MIDDLE TIMEFRAME - to identify our zone for example if your trading system uses FVG you can locate your zone their. i personally use supply and demand so at this time i zone out my i will draw my supply or demand.
THE LOW TIMEFRAME - in this stage use it for entry confirmation.
this multi timeframe analysis can work on every time which means you can scalp , day trade or swing trade .
for example you can use
1 HOUR FOR BIAS
15 MIN ZONE IDENTIFICATION
5 CONFIRMATION
thanks for taking your time and read this post.
tell us your thought in the comment.
Ultimate guide on Williams Fractals in crypto tradingIn today’s article we will reveal one of the most powerful tools in cryptocurrency trading which often ignored even by top crypto traders. We are talking about fractals. Best crypto traders just use fractal levels to find support and resistance and trade bounces and breakouts. Spoiler - the fractal breakout is a right way to use it, but support and resistances aren’t. A lot of people are using fractals even for their algorithmic trading bots. We remember when encoding our first automated crypto trading bot the fractals were used for support and resistance detection. It’s not surprise that it was not one of the profitable crypto trading strategies.
Now we researched a different ways to use fractals and assume that we have the great expertise in it to share our knowledge with you. It’s not a top secret that even Skyrex ai trading bot is using fractals in detecting potential trading opportunities. Please, read this article carefully and you will know build your own cryptocurrency trading strategy or even apply it to automated cryptocurrency trading. Let’s go!
Initiating fractal
First of all let’s understand what is the fractal and how it looks like. Fractal is not just a sequence of candles like it can seems on the first look. This is the change in behavior of traders on the market. When you see the fractal on the chart, this is the turning point where a lot of traders were too worried that current trend can be stopped here.
Technically fractal is very simple. If we talk about upfractal it’s just a consequence of bars where the central bar have the highest high than two preceding and two following bars. On the chart below you can see different fractal’s shapes. Don’t worry about it that much because on the TradingView you can find an indicator which find all fractals. Even if you build automated trading bots you can just copy the code of this indicator.
How to trade using fractal
Let’s go to the most interesting part of an article. How to execute trades using fractal? You will be surprised but it’s super easy. Let’s take a look at the picture and try to understand the concept of fractal start, signal and stop.
Fractal start is the fractal which precedes the another one fractal in the opposite direction
Fractal signal is the fractal which follows the fractal in the opposite direction
Now when we have this two fractal combination we can place our sell stop one tick below the fractal signal and go short if market reach this level. Now it’s time to place the stop loss. When your trade is open you shall chose the highest fractal from the last two and place stop loss order one tick above. Here you can have two cases A and B. Look carefully and try to find these formations on the real charts.
Conclusion
Next time we will look inside the fractal and try to understand how to trade during sideways. I think today you could understand that fractal trading is good in trend markets, but it’s not profitable during sideways. Price will hit your order every time and hit stop loss many times before the true trend move. For sure fractal breakout trade guarantees that you will not miss the big trend move, but you will have multiple losing trades in the range bounded market. Next time we will discuss how to avoid it.
The ultimate guide on Elliott waves in crypto tradingMost of you have probably heard about Elliott waves and we are sure that you don’t use it in cryptocurrency trading strategy because it’s very complicated and subjective approach. Crypto trading for beginners is very challenging and stressful even without Elliott waves. To be honest when we first time tried to implement it to my crypto trading strategies it was a complete disappointment. We were sure that it does not suit for both trading bot and manual trades. Elliott waves were thrown into a garbage bin for almost two years and we developed our crypto trading algorithm using only linear programming approaches.
While we have been trying to invent the best automated trading bot using only indicators and support and resistance levels, best crypto traders have been successfully using Elliott waves in their analysis. Finally we make a decision to have a deep dive in this popular crypto trading tool and studied in details all available literature. As a result we found that Elliott waves will ruin your trading if you use it without special indicators for confirmation. Now we have 2 years of experience in trading with waves and almost one year ago we implemented them into our algorithmic trading bot. Today we prepared the best ultimate guide ever on Elliott waves using best practices and our unique experience how to use them in developing your own profitable crypto trading strategies. Let’s go!
Why it’s vital to use Elliott waves?
Before answer this question, let me ask another one! Why is important to use map to reach the final destination? I think here is the obvious answer! Talking about Elliott waves it’s almost the same reason. This is the only one approach which gives you a map for a price chart. I think you agree that technical indicators or support and resistance levels will not give you the answer which direction the price will choose. When you have, for example Stochastic Oscillator crossover or RSI oversold area hit you just open long because this is the most common strategy. You buy asset like a blind kitten. We are not criticize this approach, because using proper risk and money management you will earn with almost every strategy, but understanding the Elliott waves concept will dramatically increase your profit even if you combine them with your ordinary strategy. Why it’s happening? The answer is easy, because Elliott waves in the underlying structure of the market. You will be aware when you shall use your signals and when it’s better to skip trade. Now let’s dive into the Elliott waves to understand how to find them on the price chart. In the first part we will give you all needed theory and after that we will show in the real charts how it works.
Elliott waves
In general, Elliott waves concept is pretty easy. All markets are globally moving up with the five waves formations and then show the pullback with at the reactive waves. On the Bitcoin price chart above you can see the most common picture for Elliott waves. We had the bull run which consists of five waves and then was the bear market represented with the ABC correction.
Waves can be divided into two groups: impulsive and reactive. On the bullish phase waves 1, 3 and 5 are impulsive, 2 and 4 reactive. Impulsive waves consists also with five sub waves, while reactive have usually three waves (exception the triangle correction, will be covered later). On the bearish phase we have the opposite situation: waves A and C are impulsive, while wave B is reactive. Now let’s discuss each wave in details.
What will stop every wave in 90% of cases?
Before we will observe the wave it’s very important to understand what are the early signs that current wave is about to be finished. This is really crucial concept because without it almost impossible to use Elliott waves for profitable trading. We need four tools to make sure that our counting is correct. In this article we will not spend to much time for these indicators, we just show you in practice how to use them. These tools are: Awesome Oscillator, Market Facilitation Index (MFI), Fibonacci retracement and extension and Fractals. These four indicators produce five wave’s end conditions.
Divergence with Awesome Oscillator. If you found five sub waves inside any wave and you can see that price set the higher high (or lower low for bearish case), while AO set lower high (or higher low) it’s divergence between wave 3 and 5. This is the most powerful signal that trend is over.
Fractal at the top or bottom. When you see the divergence it’s just the first sign of trend weakness, we need confirmation with the fractal forming at the top or bottom. You can easily find this indicator in TradingView, it will show you all fractals.
MFI squat bar. We will cover MFI in one of the next educational articles, now you just need to know that it has squat state - the last battle between bulls and bears. One of the three top bars will be the squat in 80% of waves end. You can also find this indicator in TradingView.
AO momentum change. Another one confirmation that trend is over is when AO histogram changes color. It’s better to wait three consecutive columns of the other color or when AO will cross back the signal line, 5 period MA of the AO.
Target area. Using Fibonacci extension and retracement we can find the area where the reversal is the most likely. We will show you this targets when talking about waves.
Now you know the five basic rules and we are ready to discuss every wave using this concept.
Wave 1
When the previous trend is over the impulsive wave 1 begins. We can define the wave 1 start only establishing the previous wave end. It could be wave 5, C or E. It does not matter. You just need to apply our five rules: divergence, momentum change, target area, squat bar and fractal. On the chart you can see how in theory wave 1 can be looks like.
Wave 1 always consists of five waves. That’s why we can wait for the same five rules to complete between wave 3 and 5 inside the wave 1. When you anticipate the wave 1 finish you have two options: close trade and re-enter at the wave 2 bottom or hold for the entire cycle.
Wave 2
When wave 1 ends, you will see pull back in wave 2. It’s important to catch wave 2 bottom because wave 3 will bring you a lot of profit. Wave 2 can be classical ABC zigzag, flat or irregular correction. 70% probability it will be ended inside 0.38 and 0.62 Fibonacci retracement range of wave 1, in rare cases it can ends higher or lower. That’s why it’s better t count waves inside wave 2 and do not miss when all five trend killing conditions are met in wave C inside 2.
Wave 3
The most impulsive wave in the entire cycle is obligatory for trading. Here you can have the less risky and the most easy trading. Wave 3 has the great fundamental factors as a price drivers. For example, Bitcoin spot ETF triggered a huge pump recently. Let’s imagine you correctly entered at the wave 2 end. Now we have to define wave 3 targets. The target area using fibonacci extension can be found between 1 and 1.61. This is the most likely case. In crypto it’s very often when waves 3 are extended.
To have the most precise target it’s highly recommended to count waves inside wave 3. Found five waves? Check our favorite trend killing rules to exit a trade at the top. We know it sounds fantastic, but we managed to buy the exact bottom and sell at the top many times, but to be honest, we have never caught the top of the extended wave 3. Need more experience for that.
Wave 4
Wave 4 can be the most complicated because it has a lot of different variants: zigzag, flat, irregular or even triangle. But at the same time in wave 4 we can have the easiest setup. When you predicted wave 3 top, it’s time to setup the target for the wave 4. The most reliable one is between 0.38 and 0.5. This wave is not so rapid as wave 2 and takes much more time (up to 70% of all cycle).
The very important tip here is to look at the price where wave 4 inside wave 3 has been ended. If this level coincides with the 0.38-0.5 zone it can give you much more confidence. We have never made a mistake using this technique. As usual you have to look for the five trend killing rules in wave C inside wave 4 as well.
Another one thing we want to point out. You know the axiom, that wave 4 has not overlap wave 1 top. This rule can be slightly violated and we will show you the case. Don’t pay attention that much to this rule.
Wave 5
Finally we are in wave 5. This is really vital to define it’s top because bear market will follow this wave and can destroy your deposits. The target area for the wave 5 is defined as the distance between wave 1 bottom and wave 3 top, measured from wave 4 bottom. Area between 0.61 of this distance and 1 Fibonacci level is our target. There you have to find trend killing rules as usual but this time for all cycle, not subwaves.
Corrections
The most dangerous place for trading is the correction. From our experience only wave C in zigzag is tradable. You would better to skip corrections and try to catch it’s end. We have four types of corrections, but the most important knowledges is that wave C and E are always consists of five waves. It means you can use the rules how to catch wave 5 end inside these waves.
Zigzag ABC. If wave A consists of 5 waves the most like we will see zigzag. Wait when wave B reach 0.5-0.61 Fibonacci of wave A and be ready to trade in wave C.
Flat. Wave A has 5 waves inside. Waves A, B and C are almost equal to each other.
Irregular. Wave B top is higher that the previous impulsive wave. Wave A consists of 3 waves.
Triangle. Consists of A, B, C, D and E waves. Wave E consists of five waves. Usually occurs inside waves 4 and B of higher degree.
Now you have a theoretical description. It’s time to trade!
Trend Trading Strategy for the Heiken Ashi Algo v6Knowing when the RSI and price are in a ranging phase even in the short term can be a difficult process.
You are either #Ranging #bullish or #bearish. At least in the Algo v6 you can get a clear vision of exactly whats happening.
In this video im going to give you a VERY simple strategy on:
1. How to know if the RSI and price are ranging
2. When do i break away from Ranges
3. Am I trending
4. Im trending but whats my confluence to take a long or short
5. Is my range getting bigger or smaller
Enjoy this quick vid and ask questions below.
Thanks everyone.
Bitcoin - Probabilistic MapSince traders are literally made of particles, it's vital to know the principles of their behavior in micro scale. Some people even use planetary cycles to implement into charting. But I believe the answer is deep in quantum world of probabilities - the fabric of reality itself.
Reference to Quantum Mechanics
The universe itself prohibits 100% prediction accuracy. This is called Heisenberg Uncertainty Principle, and it's the fundamental building blocks of Quantum Mechanics. In order to predict particles behavior, all you need are just 2 quantities/data/features:
1) Position of the particle
2) Momentum of the particles.
If you know it's position and it's momentum, you can easily predict it's trajectory. So if you have position and momentum data of all particles in the universe, and you have unlimited computational power, you can predict their behavior (interaction, movement, etc.), and basically predict the future (stock market, weather, natural disaster, etc).
However, the Heisenberg Uncertainty Principle states that it is impossible to collect information of particles's position and momentum with 100% certainty. The more certain you know about particle's position, the less certain it's momentum" and vice versa.
So if somehow with the unlimited computational power you can predict particle's position at time with 100% accuracy, then your prediction error for its velocity will be infinity, which prevent you for making accurate further predictions, rendering your model useless.
Hence, it's theoretically impossible to make 100% accurate prediction even with unlimited data and unlimited computational power.
So Is The Universe deterministic or probabilistic?
100% prediction accuracy also means the universe is deterministic - there's only one possible outcome of the future. Einstein was on this side, citing "God doesn't play with dice". On the other hand, folks like Heisenberg, Max Born, Schrodinger, Oppenheimer, etc.., the founding fathers of Quantum Mechanics, viewed the future as set of possible outcomes each having it's own probability.
Since market couldn't care less about anyone's subjective forecasts, I do predictions solely based on historic price dynamics in macro scale to stay objective and true with the market pulse rather than be bared with my endless interpretations of patterns. I don't need my consciousness to interpret because we already have a data derived from collective consciousnesses to work with. Chart is already a reflection of reality that captures the emotions of participants. In other words, it's a time fractal that exposes the essence of the market across timeframes. In turn the market itself is a function of trading time . These basis justify linking systematic fragments of cycles to work out the capacity of price action. Basically in Fractal Analysis, the question is how can direct metrics of the historic waves geometrically explain current and future price levels.
The Fibonacci sequence is a mathematical concept that appears in various aspects of nature. This connection between mathematics and the natural world is a fascinating example of how patterns and structures found in abstract concepts like numbers can manifest in physical reality . Particularly, using Golden Ratio as a key rule that governs order in chaos.
In TradingView, the "Fibonacci Channels" is a great tool to capture the waves (domestic certainty) and turn them into a probabilistic interconnected structure that captures the uncertainty of the market - the entanglement of price action.
To start with it's vital to use log scale where percentages are equally captured in distances. So a 100% a growth, say a vertical distance from $40 to $80 measures the same distance as from $1000 to $2000. Besides, percentages are what drives people to feel emotions which affect market behavior (collective executions). Finding geometric relationship between waves, the use of log scale is a must.
As I've done this before I want to show how market deviates near fibs.
A Direction of 2013 HIGH ⇨ 2017 HIGH with bottom of 2011 gives next bottom 2015 at 0.618 after -86% drop.
And also predicts the COVID bottom in 2019 after -72% drop as well as current level where price has cooled down locally.
We can note that previous ATHs are explained with logarithmic curve.
That's why we'd need another fib channel to connect 2017 HIGH ⇨ 2021 HIGH direction with previous bottom of -86% drop in 2015. FC of that direction predicts bottoms of 2018 (-84%) and covid 2019 (-72%) at 0.618 again.
Together they produce an interference pattern covers significant historic price changes.
To further interpret current levels though the chart itself, we can use line with angle of direction connecting 2021 double tops:
This shows the capacity of how high the market might still grow before next significant correction, if the local fib to the price hasn't yet dimmed the bullish incentive.
Another straight line can be used to connect 2019 COVID LOW (-72%) with 2022 LOW, because we might probably never see such price levels in the nearest future as price has broken out with high rate of change.
Now it needs more time and bearish capacity to go there. This line can indicate the bottom of hypothetical correction, if it happens now. Other than that it's a clear trendline with almost 4Y wavelength.
Since straight lines doesn't exist in nature, I didn't extend them to the right. Now we need a more adaptive version of it to connect recent local bottoms of the trend.
That would be a logarithmic trendline, in other words curves to mimic the function of exponential growth. Therefore falling below it, might indicate a possibility of correction and even reversal. Each day if it fails to grow with the curve, the bears will get depleted. A cross below the logarithmic curve of spreading information would be a confirmation of new bearish incentive. This is simply done to work out boundaries as limits of the function that explains the market.
Corrective wave has a timing of 15 days in respect to its domestic volatility properties, before it becomes bearish impulsive or continues the impulsive bullish wave.
Curves as a function of trading time explain pretty much all historic bullrun growths.
As if there is some kind of gravity that governs the trend or it's the PriceTime that curves with the emerging trend.
Individual cycles can be too curved accordingly.
So the more the price fails to break out that function, the more predictive curve becomes.
ICT Unicorn Model - The powerful ModelThe Unicorn entry model in the ICT method combines the concepts of the Breaker Block and the Fair Value Gap, providing a unique approach to identifying trade opportunities. This combination highlights a future area of support/resistance.
A Bullish Unicorn Pattern consists out of:
A Lower Low (LL), followed by a Higher High (HH)
A Fair Value Gap (FVG), overlapping the established Breaker Block
A successful re-test of the FVG which confirms the pattern.
A Bearish Unicorn Pattern consists of:
A Higher High (HH), followed by a Lower Low (LL)
A Fair Value Gap (FVG), overlapping the established Breaker Block
A successful re-test of the FVG which confirms the pattern
In this trading idea, I would combine the movement of DXY and GU/EU to explain the correlation and divergence (ICT SMT). Futhermore, I want to share how powerful the ICT Unicorn Entry Model is.
ICT Kill Zones Time Asia London New YorkIn the fast-paced world of forex trading, timing is everything. While the forex market operates 24 hours a day, not all hours offer the same trading opportunities. That’s where ICT Kill Zones Times come into play. Forex kill zones are the time when high probability trading setup formed
These strategic time frames can open up a world of possibilities for traders who know how to leverage them. In this post, we’ll explore the concept of ICT Kill Zones ‘ times and how they can lead to high-probability trade setups and potential profits.
The ICT Asian Kill Zone Times: The Dawn of Opportunities
The Asian Kill Zone is the first of the strategic periods in the forex market. It is particularly relevant for traders dealing with the Australian dollar, New Zealand dollar, and Japanese yen pairs, as these markets are most active during this time.
What makes the Asian Kill Zone special is its volatility, driven by economic news releases that occur during this session. Traders who keep an eye on these news releases and their impact on the market can make the most of this period.
Main Characteristics of Asian Kill Zone
-During the Asian Kill Zone, traders can often find optimal trade entry patterns, offering potential gains of 15 to 20 pips for scalp trades.
-NZD, and JPY pairs are ideal for this time of the day.
-The Asian Open can sometimes set up an Optimal Trade Entry Pattern that can offer a 15 – 20 pip scalp.
-The Higher frame bias is helpful here – but short-term retracements in either Bull or Bear
-Markets can offer similar OTE Setups.
Asian Kill Zone Time
ICT Asian Kill Zone Times lies in between 8:00 PM Eastern to10:00 PM Eastern
ICT London Kill Zone Time
The ICT London Kill Zone takes center stage during the London trading session, witnessing the highest volume of order execution compared to other sessions. It is an opportune time for those trading the EUR and GBP pairs. Notably, the London Open often presents opportunities for traders to enter positions with the potential for gains ranging from 25 to 50 pips.
Main Characteristics of London Kill Zone
One of the distinctive characteristics of the London Kill Zone is its tendency to create the low of the day in bullish markets and the high of the day in bearish markets.
Time of ICT London Kill Zone
London Kill Zone of ICT lies between 2:00 AM to 5:00 AM Eastern Time
Traders should monitor the key times between 2:00 AM to 5:00 AM New York time to capitalize on the price action during the London session.
The New York Kill Zone Time: The Land of Opportunities
For traders dealing with major pairs coupled with the dollar index, the New York Kill Zone is an essential timeframe to watch.
Similar to other Kill Zones, this period often sets up optimal trade entry patterns, providing potential gains of 20 to 30 pips for scalp trades.
Time of New York Kill Zone
The New York Kill Zone occurs between 8:00 AM to 11:00 AM Eastern Time. This time is favorable for major pairs and benefits from the overlap with the London session, making it a golden opportunity for traders.
New York Kill Zone lies between 8:00 AM to 1:00 AM Eastern Time
The London Close Kill Zone: The Final Countdown
The London Close Kill Zone is a specific time frame that can create continuation points for swings that extend well into New York afternoon hours. It’s the last chance for traders to make their moves before the market closes for the day, making accurate predictions during this period potentially profitable.
Between approximately 8:00 AM to 9:00 AM Eastern Time (adjusted for daylight savings) , traders can find optimal trade entry patterns, offering opportunities for 10 to 20 pips of profit on scalp trades. Monitoring the key times from 10:00 AM to Noon NY time can yield valuable insights during the London Close Kill Zone.
ICT Kill Zone on During Daylight Saving Time (DST)
Now, let’s talk about Daylight Saving Time (DST), which starts on the second Sunday in March and ends on the first Sunday in November. During this period, Eastern Time is shifted one hour ahead to Eastern Daylight Time (EDT), which is UTC-4.
For example, let’s consider April 10th, and the time is 11:30 AM in Eastern Time (ET) during Daylight Saving Time. To convert this to Coordinated Universal Time (UTC), you add 4 hours to the local time:
11:30 AM ET (UTC-4) + 4 hours = 3:30 PM UTC
During Daylight Saving Time, the clocks are adjusted forward by one hour, giving us an extra hour of daylight in the evenings. When Daylight Saving Time ends, we set the clocks back by one hour to return to Eastern Standard Time.
ICT Kill Zone Setting on Trading View
On the TradingView chart, you’ll find the time zone option at the bottom right corner. To set the correct time zone, click on it, and choose “UTC-5” during regular days (Standard Time) and “UTC-4” during daylight saving time, which typically occurs from the second Sunday in March to the first Sunday in November.
ICT Kill Zones Indicator Trading view & MT4
A number of indicator are available on the trading view that automatically highlights the ICT kill zones on your chart.
ICT Kill Zone LuxAlgo is one of the best indicators available on trading view.
To Add ICT Kill Zone indicator you adopt the following steps:
Step1:Click on the indicator icon on top of the trading view
Step2 write LuxAlgo ICT Kill Zone
Understanding and effectively utilizing ICT Kill Zones can significantly enhance a trader’s success in the forex market. Each Kill Zone represents a unique opportunity with its own set of potential gains.
Wyckoff simplified + entries & exitsI'm going to explain Wyckoff to you in a simplified manner and show you how you can use it for entries & exits.
What is Wyckoff?
Large market orders by huge entities come in gradually. If the market only consisted of buying and selling, it would be too easy to make money as it would be too predictable. So instead, orders are injected into the market via an accumulation process (i.e. Wyckoff schematic)
Basically, the big players of the market try to take out the retail traders’ stoplosses by injecting orders into the market (to move price toward the stoplosses and hit them). They inject these orders gradually (to avoid being predictable and to trick the retail traders).
Basic Wyckoff schematic
This is a bearish Wyckoff schematic:
Let’s break this down.
BC - This stands for Buying Climax. The Buying Climax marks the end of buying and is confirmed by an Automatic Rally.
AR - This stands for Automatic Rally. This is when price goes in the opposite direction of the climax. In this case, the AR was to the downside. This confirms that it is the end of buying because it shoots straight down (indicating strong selling pressure). This confirms the Buying Climax by going into the Discount level (bottom 25%) and by being bigger than all the other downward pullbacks which happened before.
Test - Price goes close to the Climax point and re-tests it. Then, traders take sells because they think that because of the AR, price would go down. The traders think that price went up for the last time and will finally go down. Because of their sell orders, price falls a little.
Purge - The big players try to take out the traders’ sell orders by moving price up to the Climax point. They push price a little higher than the Climax point to take out all the stoplosses.
RTO - This stands for Return to Origin. Because of the purge, traders think that price broke structure to the upside. So, they buy which makes price form the RTO. They’re trying to make price revisit the Climax point. Then, price moves lower and they get stopped out again.
SOW - This stands for Sign of Weakness. When structure breaks to the downside after the RTO, this shows that selling pressure is coming in.
LPS - Last Point of Support. This is the consolidation which must happen before price breaks out of the consolidation to convince you that price is bearish and no longer bullish.
Here is how a bullish Wyckoff structure looks like:
Let me explain this once more so that you understand it.
The main trend was a down trend on the left side of the chart. Then, price had a strong bull move up (the AR) which means that there were buy trades (i.e. Automatic Rally). That confirms that there was a Selling Climax (i.e. SC) and that it’s the end of selling (because if it wasn't the end of selling, the AR wouldn't go so high)
After that, price came down to re-test the Selling Climax zone (which is called the Test). Then, traders took a buy because they thought that because of the AR, price would be going up.
Then the big players pushed price down a little lower than the Selling Climax to hit the buy orders' stoplosses which forms the Purge.
After that, because the Purge happened, it made traders think that price broke structure to the downside which led them to sell. Then, price went down because of those sell orders (forming the RTO) and rejected from the Selling Climax (price went up).
Price rejected from that level because there were buy orders from the big players which made price go up. Since price went up, those sell trades got taken out. Because price went up, it formed an SOS (i.e. Sign of Strength). It means that the selling pressure had weakened, and the buying pressure had strengthened.
Finally, price formed a consolidation (i.e. LPS) which tricked traders again into thinking that price will go down. The traders sold and the big players pushed prices up to hit their stoplosses one last time.
This is a basic Wyckoff pattern in a nutshell.
You’ll be more likely to predict the Wyckoff pattern in its later stages when some parts of it have formed. The earlier it is, the riskier it’ll be.
Advanced Wyckoff schematic
Let’s talk about the 2nd variation of the Wyckoff pattern. This is the same as the basic Wyckoff schematic except that the Test will go beyond the BC/SC. It will look like a purge, but it won’t be. It will be a fake purge. Then, after the Test, the actual Purge will happen.
This is to trick most of the Smart Money Concept traders into thinking that the purge has already happened and that price will form an RTO and go lower (in case of a bearish schematic). The traders will then sell. The big players will then push price up to break the Test and form the actual Purge. All the traders will get wiped out because price has hit their stoplosses.
In case of a bullish schematic, the traders will think that the purge has already happened and that price will form an RTO and go higher. They’ll buy. The big players will then push price down to form the actual Purge and take out the buy orders.
Here is how it looks like:
Structures
Before I explain how you can use this to trade, let’s first understand market structures. There are 2 types of market structures which I’ll be talking about: Support & Resistance and Supply & Demand.
There’s also 1 more thing to understand: ranges. A range is the area between the latest swing high and swing low.
👉 Supply & Demand Structure
This is when price forms a new range by forming a new high or a new low. Then, it comes back into the old range.
When price comes back into the range, it finds more buy orders to push it up again.
When price comes back into the range, it finds more sell orders to push it down again.
👉 Support & Resistance Structure
This is the same thing as the Supply and Demand structure except that price will not come back into the range but instead bounce off of the highs/lows.
Let’s see how we can use structures with Wyckoff to take entries and exits. We’re first going to use the Supply & Demand structure. Then, we’ll see how we can use the Support & Resistance structure.
Supply & Demand Entry
We’re going to take entries using the Supply & Demand structure. This strategy uses 2 timeframes to take entries (Macro & Micro). We’re going to look at a buy example. For a sell, simply use the opposite logic.
The main idea is to trade with the trend. So, first go to a higher timeframe and find a Supply & Demand structure. Then, look for when price forms a new low/high. We can see that, in this case, price formed the first lower low.
Now, we know that because this is a Supply & Demand structure, price will go back up into the range. So, to take advantage of this up move, we can take a buy.
We first have to know where to buy. So, go down to a lower timeframe. Then, look for a bullish Wyckoff schematic. Look for the Selling Climax (i.e. SC). This means that it is the end of the downtrend. Then, wait for price to form the AR, Test, Purge and RTO. You can buy when the RTO or LPS happens.
You can exit when you see a bearish schematic. This bearish schematic has to reach the Premium level. First, find the Premium level by going back to the higher timeframe and taking the upper 25% of the down leg. Then wait for price to form a bearish schematic and reach that premium level.
The Premium level will be reached when price forms a Purge (during a bearish schematic). We can see (in the picture below) that during the bearish schematic, price did Purge and break into the Premium level. Exit your buy here.
There’s also another way you can take a trade (look at the picture below). You can sell during the bearish schematic. Sell when you see the RTO or LPS (during the bearish schematic). You can exit at the Purge of the next bullish schematic.
It is more preferable to sell than to buy, in this case, because the larger trend on the higher timeframe is a bearish Supply & Demand structure. So, price is going down on the larger trend. When you trade with the trend, the probability of your trade giving profits is higher.
This was in case of a sell. If the larger trend was bullish, a buy would’ve been taken at the RTO or LPS of a bullish schematic. Then it can be exited at the Purge of the next bearish schematic.
Support & Resistance Entry
To trade a Support & Resistance structure, we do the exact same things we did for the Supply and Demand structure. The only difference is that instead of looking for a Purge near the upper 25%/bottom 25%, look for it where price will react (near the red line).
After you’ve found it, you can enter your trade when the RTO, SOW or LPS comes.
This is in case of a buy. For a sell, use the opposite logic.
Like I’ve said before, you can also take a sell to trade with the trend on the higher timeframe. You can sell during the bearish schematic. Sell when you see the RTO or LPS (during the bearish schematic). You can exit at the Purge of the next bullish schematic.
If the larger trend was bullish, a buy would’ve been taken at the RTO or LPS of a bullish schematic. Then it can be exited at the Purge of the next bearish schematic.
I hope you found this useful!
SP500 Entanglement of Price Action IIThe price touched the line with specific angle that covers ATH and (current) Lower High.
I consider it as a point of reference because current observable price can be explained with that vector.
The line separates 2 outcomes:
Continuation of the uptrend
Rejection
Significant reversals that caused the structure to look the way it looks are:
"ATH" 4 JAN'22
13 OCT'22 Lowest (> 2 years)
27 JUL'23 Lower High
Those dates initiated longer term movements, hence defining the entanglement.
The angle of general direction can be defined by the Fibonacci Channels of macro-fractal which emerged from Covid Low:
It kinda exposes their domestic "spin to the side".
Another example:
Since the angle of -27.47% drop (ATH and Lowest >2yrs) are more perpendicular to the direction of time scale, the derived fibonacci would define periods of waves.
Matches angle of -10.93% drop from 27 JUL'23 to 0.382 fib of the domestic structure.
But, since after such drop, it didn't fall further but in reverse grew back, it must be defined with upward direction vector, the fibs of which would cover that low with cold colors. The fact of growing at higher levels after just 10% drop, deserve to get filtered with upward fibs.
In respect to 31% growth the current price resides at 0.618.
Further interconnectedness of points:
1.236 fib confirms that price is indeed at crossroad and in case of violating it, the price would set its tendency to move to next (1.382) fib line and reverse there under heavier pressure.
Currently price is still under pressure because the market has grown to levels of domestic resistance. The curve shows mathematical function that mimics highs before reversing.
Hence, it can be used to refer deviation where the price can end up after escaping ATH-LH-Current_Price vector.
Otherwise, with failing to breakout now, it might go for correction in short-term perspective as soon as players notice that market is at already saturated levels.
INTRODUCTION TO TOP-DOWN ANALYSIS
Top-down analysis is a comprehensive strategy that begins with a broad picture of the market and progressively focuses in on details. It incorporates a number of time frames, economic indicators, and analytical tools to give traders a comprehensive grasp of the market environment.
In my few years of trading the financial markets, I have found that trading is one of the most inclusive career of all. When I say inclusive, I am not talking about it's absorption in terms of gender or race. I am talking about the strategies traders use. The strategies I have seen in my life, both profitable and unprofitable, are so many that I almost drowned because I wanted to learn every single one of them.
Now let's dive deeper and get an understanding of what top down analysis encompasses.
1. Weekly and Monthly chart (Long term analysis)
The weekly and monthly charts, which offer a macro view of the market, are at the top of the analysis pyramid. Significant price patterns, important support and resistance levels, and big trends are all easier to spot on these longer time frames for traders.
2. Daily and 4 hr chart (Medium term analysis)
As you proceed down the pyramid, traders examine daily and 4-hour charts in greater detail to learn more about the intermediate-term dynamics of the market. Here, you can spot possible patterns like trend line breaks or head and shoulders formations that could indicate a reversal or continuation.
3. 1 hr to 5 min chart ( Short term analysis)
The last level of top-down analysis looks at charts with shorter time frames, like the one-hour and five-minute charts. Traders can determine exact entry and exit points for their trades with this fine-grained view which helps to increase the accuracy of your entries.
Top-down Analysis and the fractal nature of price
In the context of forex trading, the term "fractals" refers to the recurrence of similar price patterns over different time frames. This fractal nature is articulately shown by top-down analysis, which shows how patterns found on higher time frames repeat themselves on lower ones. A weekly chart showing a double top formation, for example, could also show up as a lower time frame double top on a 4-hour chart and a smaller version on a 5-minute chart.
In conclusion:
Top-down analysis is a great tool which reveals the fractal nature of price movements and offers an in-depth view of the market. Traders can make well-informed decisions that take into account both the short-term dynamics and the broader market trends by integrating insights from weekly to 5-minute charts.
Gaining insight into how these various time frames interact improves a trader's flexibility in the face of shifting market conditions and raises the probability of profitable trades.
N/B: The chart image shows a EUR/USD chart outlining High time frame and Low time frame levels that would be utilized as you branch out/narrow down.
PS: The next release in this series will be out soon.
SP500 Entanglement of Price ActionFibonacci interconnectedness of impulsive and corrective waves.
Impulsive.
Since Time is taken into account in terms of angles, the Fibonacci channels derived from multi fractals simulate phenomenon of the order in chaotic price action.
More like projection of Levels of Probability like in QM, where Interference Pattern derived from waves of probability in Double Slit Experiment.
Nevertheless, I would never accept that price unfolds because of the very act of measurement that assumably collapses the wave function and makes it behave accordingly. I'm implying that price formation just like fabric of reality itself is not deterministic but probabilistic.
In charts, the fabric of PriceTime is continuously curved by the price action itself. That's why even after dramatic rise of volatility the price would end up at certain random levels but distinctive to domestic chaos and frequency of reversals.
How To Find Strongest Altcoins : TutorialNavigating the world of cryptocurrencies can be like embarking on a treasure hunt, and today, we'll discuss the art of finding robust altcoins. AVAX and INJ serve as excellent examples of how to identify strong performers.
Comparing AVAX with Bitcoin:
When searching for strong altcoins, it's crucial to compare their performance against the market leader, Bitcoin. A compelling example is AVAX, which, during a specific period, saw a decline of 21% while Bitcoin surged by 108%. This discrepancy highlights AVAX's relative weakness during that time.
INJ's Remarkable Ascent:
On the other hand, INJ paints a different picture. When we compare its performance with Bitcoin, we witness an incredible 973% increase. INJ not only kept pace with Bitcoin but outpaced it significantly. This type of performance makes INJ a prime candidate for those seeking strong altcoins.
The Takeaway:
When hunting for strong altcoins, it's crucial to perform relative strength assessments against Bitcoin. While Bitcoin remains the benchmark, the altcoins that can surpass it or at least keep up with its pace are often the ones to watch.
Trading Strategy:
Comparison is Key: Continually compare altcoins with Bitcoin and monitor their relative strength over time.
Risk Management: Implement sound risk management practices, especially when dealing with the crypto market's volatility.
Stay Informed: Stay updated on the fundamentals and developments related to the altcoins you're considering.
Conclusion:
The cryptocurrency market is a dynamic landscape filled with opportunities, and identifying strong altcoins is a skill worth honing. The performance of altcoins concerning Bitcoin can provide valuable insights into their potential.
As you embark on your quest for strong altcoins, remember that the crypto world is ever-evolving. Stay informed, trade wisely, and may your search lead to success.
❗️Get my 3 crypto trading indicators for FREE! Link below🔑