Vanilla ABCD PatternPatterns makes parts of the many predictive tools available to technical analysts, the most simples ones can be easily detected by using scripts. The proposed tool detect the simple (vanilla) form of the ABCD pattern, a pattern aiming to detect potential price swings. The script can use an additional confirmation condition that aim to filter potential false signals.
ABCD PATTERN
The ABCD pattern is not meant to be detected by analyzing individuals closing price observations but by analyzing longer term movements, this is done by using tools such as zig-zag. Like any pattern the ABCD one comes in different flavors, the simplest being based on the following structures:
Once price reach D we can expect a reversal. The classical pattern has the following conditions : BC = 0.618*AB and CD = 1.618*AB , as you can see this condition is based on 0.618 which is a ratio in the Fibonacci sequence. Other conditions are for AB to equal to CD or for CD to be 161.8% longer than AB. Why these conditions ? Cause Fibonacci of course .
The ABCD pattern that the proposed tool aim to detect is not based on the zig-zag but only on individual price observations and don't make use of any of the previously described conditions, thus becoming more like a candle pattern.
When the label is blue it means that the tool has detected a bullish ABCD pattern, while a red label means that the tool has detected a bearish ABCD pattern.
We can't expect patterns based on the analysis of 1,2,3 or 4 closing price observations to predict the reversal of mid/long term movements, this can be seen on the above chart, but we can see some signals predicting short term movements.
Since the pattern is based on a noisy variation, using smoother input can result in less signals.
Above the tool on BTCUSD using closing price as input. Below the tool using ohlc4 as input:
TOOL OPTIONS
Being to early can be as devastating as being to late, therefore a confirmation point can be beneficial, the tool allow you to wait for confirmation thus having a potentially better timing. Below is a chart of AMD with no confirmation:
As we can see there are many signals with some of them occurring to early, we can fix this by checking the confirmation option:
The confirmation is simply based on the candle color, for example if a bearish ABCD pattern has been detected in the past and the closing price is greater than the opening price then the tool return a buy signal. The same apply with a bearish ABCD pattern.
The "last bar repaint" option is true by default, this in order to show the bar where the D point of the pattern has been detected, since the closing price of the last bar is constantly changing the signals on the last bar can be constantly appearing/disappearing, unchecking the option will fix this but will no longer the bar where the D point of the pattern has been detected.
SUMMARY
The pattern is simple and can sometimes be accurate when predicting the direction of future short terms movements. The tool was a request, as it seems i don't post many pattern detectors, well thats true, and the reason is that for me patterns are not super significant, and their detection can be extremely subjective, this is why simple patterns are certainly the only ones worth a look.
Don't expect me to post many pattern related indicators in the future ^^'
Pesquisar nos scripts por "ha溢价率"
BITCOIN KILL ZONES v2Kill Zones
Kill zones are really liquidity events. Many different market participants often come together and act around these events. The activity itself may be event driven (margin calls or options exercise related activity), portfolio management driven (buy-on-close and asset allocation rebalancing orders) or institutionally driven (larger players needing liquidity to get filled in size) or a combination of any/all three. The point is, this intense cross current of activity at a very specific point in time often occurs near significant technical levels and trends established coming out of these events often persist until the next Kill Zone in approached/entered.
Specifically, there are three Kill Zones and each has its own importance/significance.
1. Asian Kill Zone (1900 - 2300 EST) Considered the "institutional" zone, this zone represents both the launch pad for new trends and also too a reloading area from the post American session. It is the start of a new day (or week) for the world and as such it makes sense this zone will often set the tone for the rest of the world's trading day. Since it is very wide (4 hours) one should pay attention to the Tokyo open (2100 EST) the Beijing open (2120 EST) and the Sydney open (0650 EST previous day).
2. London Kill Zone (0200 - 0400 EST) Considered the center of the financial universe for more than 500 years, Europe still carries a lot of influence within the banking world. Many larger players use the Euro session to establish their positions. As such, the London open often sees the most significant trend establishment activity through any given trading day. Indeed, it has been suggested 80% of all weekly trends are established through Tuesday's London Kill Zone.
3. New York Kill Zone (0830 - 1030 EST) The United States is still by far the world's largest economy and so by default New York's open carries a lot of weight and often comes with a big injection of liquidity. Indeed, most of the world's trade-able assets are priced in US dollars which gives even more significance to political and economic activity within this region. Because it comes relatively late in the globe's trading day, this Kill Zone often sees violent price swings within it's first hour leading to the time tested adage "never trust the first hour of North American trading.
Additional notes:
It has become apparent these Kill Zones are evolving over time and the course of world history. Since the end of the second world war, New York has slowly encroached on London's place as the global center for commercial banking. So much so through the later part of the 20th century New York was considered indeed, the new center of the financial universe. With the end of the cold war that leadership seems to have shifted back toward Europe and away from The United States. Additionally, Japan has slowly lost its former predominance within the global economic landscape while Beijing's has risen dramatically.
Only time will tell how these kill zones will evolve given each region's ever changing political, economic and socioeconomic influences.
Trading Notes:
If you have specific levels of interest odds are the bigger players have the same levels too. If it is indeed a solid level, look for price to trade to your level through the kill zone because the zone is a liquidity event where the bigger players can find enough size to get their big orders filled.
Try to avoid taking positions heading into Kill Zones and look for confirmation of your levels coming out of the event. For the more advanced trader, look to take positions on those level hits through the zone but understand higher time frame players often have far deeper pockets then day traders and can endure far more volatility then us little guys.
Thanks for the contribution to @CRInvestor and @ICT_MHuddleston
INFLECTION NEXUS - SPAINFLECTION NEXUS - SPA (Shadow Portfolio Adaptive)
Foreword: The Living Algorithm
For decades, technical analysis has been a conversation between a trader and a static chart. We apply our indicators with their fixed-length inputs, and we hope that our rigid tools can somehow capture the essence of a market that is fluid, chaotic, and perpetually evolving. When our tools fail, we are told to "adapt." But what if the tools themselves could learn that lesson? What if our indicators could adapt not just for us, but with us?
This script, INFLECTION NEXUS - SPA, is the realization of that vision. It is an advanced analytical framework built around a revolutionary core: the Shadow Portfolio Adaptive (SPA) Engine . The buy and sell signals you see on the chart are an evolution of the logic from my previous work, "Turning Point." However, this is not a simple combination of two scripts. The SPA engine so fundamentally transforms the nature of the analysis that it creates an entirely new class of indicator. This publication is a showcase of that groundbreaking, self-learning engine.
This system is undeniably complex. When you first load it, the sheer volume of information may feel overwhelming. That is a testament to the depth of its analysis. This guide is designed to be your comprehensive manual, to break down every single component, every color, every number, into simple, understandable concepts. By the end of this document, you will not only master its functions but will also possess a deeper understanding of the market dynamics it is designed to reveal.
Chapter 1: The Paradigm Shift - Why the SPA Engine is a Leap Forward
To grasp the innovation here, we must first deconstruct the severe limitations of traditional "adaptive" indicators.
Part A: The Traditional Model - Driving by the Rear-View Mirror
Conventional "adaptive" systems are fundamentally reactive. They operate on a slow, inefficient loop: they wait for their own specific, biased signal to fire, wait for that trade to close, and only after a long and statistically significant "warm-up" period of 50-100 trades do they finally make a small, retrospective adjustment. They are always adapting to a market that no longer exists.
Part B: The SPA Model - The Proactive Co-Pilot
The Shadow Portfolio Adaptive (SPA) engine is a complete re-imagining of this process. It is not reactive; it is proactive, data-saturated, and instantly aware.
Continuous, Unbiased Learning: The SPA engine does not wait for a signal to learn. Its Shadow Portfolio is constantly running 5-bar long and short trades in the background. It learns from every single 5-bar slice of market action , giving it a continuous, unbiased stream of performance data. It is the difference between reading a textbook chapter and having a live sparring partner in the ring 24/7.
Instantaneous Market Awareness - The End of the "Warm-Up": This is the critical innovation. The SPA engine does not require a 100-trade warm-up period. The learning does not start after 50 trades; it begins on the 6th bar of the chart when the first shadow trade closes. From that moment on, the system is market-aware, analyzing data, and capable of making intelligent adjustments. The SPA engine is not adapting to old wins and losses. It is adapting, in near real-time, to the market's ever-shifting character, volatility, and personality.
Chapter 2: The Anatomy of the SPA Engine - A Granular Deep Dive
The engine is composed of three primary systems that work in a sophisticated, interconnected symphony.
Section 1: The Shadow Portfolio (The Information Harvester)
What it is, Simply: Think of this as the script's eyes and ears. It's a team of 10 virtual traders (5 long, 5 short) who are constantly taking small, quick trades to feel out the market.
How it Works, Simply: On every new bar, a new "long" trader and a new "short" trader enter the market. Exactly 5 bars later, they close their positions. This cycle is perpetual and relentless.
The Critical 'Why': Because these virtual traders enter and exit based on a fixed time (5 bars), not on a "good" or "bad" signal, their results are completely unbiased . They are simply measuring: "What happened to price over the last 5 bars?" This provides the raw, untainted truth about the market's behavior that the rest of the system needs to learn effectively.
The Golden Metric (ATR Normalization): The engine doesn't just look at dollar P&L. It's smarter than that. It asks a more intelligent question: "How much did this trade make relative to the current volatility?"
Analogy: Imagine a flea and an elephant. If they both jump 1 inch, who is more impressive? The flea. The SPA engine understands this. A $10 profit when the market is dead quiet is far more significant than a $10 profit during a wild, volatile swing.
The Formula: realized_atr = (close - trade.entry) / trade.atr_entry. It takes the raw profit and divides it by the Average True Range (a measure of volatility) at the moment of entry. This gives a pure, "apples-to-apples" score for every single trade, which is the foundational data point for all learning.
Section 2: The Cognitive Map (The Long-Term Brain)
What it is, Simply: This is the engine's deep memory, its library of experiences. Imagine a giant, 64-square chessboard (8x8 grid). Each square on the board represents a very specific type of market environment.
The Two Dimensions of Thought (The 'How'): How does it know which square we are on? It looks at two things:
The Market's Personality (X-Axis): Is the market behaving like a disciplined soldier, marching in a clear trend? Or is it like a chaotic, unpredictable child, running all over the place? The engine calculates a "Regime" score to figure this out.
The Market's Energy Level (Y-Axis): Is the market sleepy and quiet, or is it wide-awake and hyperactive? The engine measures "Normalized Volatility" to determine this.
The Power of Generalization (The 'Why'): When a Shadow Portfolio trade closes, its result is recorded in the corresponding square on the chessboard. But here's the clever part: it also shares a little bit of that lesson with the squares immediately next to it (using a Gaussian Kernel).
Analogy: If you touch a hot stove and learn "don't touch," your brain is smart enough to know you probably shouldn't touch the hot oven door next to it either, even if you haven't touched it directly. The Cognitive Map does the same thing, allowing it to make intelligent inferences even in market conditions it has seen less frequently. Each square remembers what indicator settings worked best in that specific environment.
Section 3: The Adaptive Engine (The Central Nervous System)
What it is, Simply: This is the conductor of the orchestra. It takes information from all other parts of the system and decides exactly what to do.
The Symphony of Inputs: It listens to three distinct sources of information before making a decision:
The Short-Term Memory (Rolling Stats): It looks at the performance of the last rollN shadow trades. This is its immediate, recent experience.
The Long-Term Wisdom (Cognitive Map): It consults the grand library of the Cognitive Map to see what has worked best in the current market type over the long haul.
The Gut Instinct (Bin Learning): It keeps a small "mini-batch" of the most recent trades. If this batch shows a very strong, sudden pattern, it can trigger a rapid, reflexive adjustment, like pulling your hand away from a flame.
The Fusion Process: It then blends these three opinions together in a sophisticated way. It gives more weight to the opinions it's more confident in (e.g., a Cognitive Map square with hundreds of trades of experience) and uses your Adaptation Intensity (dialK) input to decide how much to listen to its "gut instinct." The final decision is then smoothed to ensure the indicator's parameters change in a stable, intelligent way.
Chapter 3: The Control Panel - A Novice's Guide to Every Input
This is the most important chapter. Let's break down what these confusing settings actually do in the simplest terms possible.
--- SECTION 1: THE DRIVER'S SEAT (SIGNAL ENGINE & BASE SETTINGS) ---
🧾 Signal Engine (Turning Point):
What it is: These are the rules for the final BUY and SELL signs.
Think of it like this: The SPA engine is the smart robot that tunes your race car. These settings are you, the driver, telling the robot what kind of race you're in.
Enable Reversal Mode: You tell the robot, "I want to race on a curvy track with lots of turns." The robot will tune the car to be agile for catching tops and bottoms.
Enable Breakout Mode: You tell the robot, "I want to race on a long, straight track." The robot will tune the car for pure speed to follow the trend.
Require New Extreme: This is a quality filter. It tells the driver, "Don't look for a turn unless we've just hit a new top speed on the straightaway." It makes sure the reversal is from a real extreme.
Min Bars Between Signals: This is the "pit stop" rule. You're telling the robot, "After you show me a sign, wait at least 10 bars before showing another one, so I don't get confused."
⚡ ATR Bands (Base Inputs):
What they are: These are the starting settings for your car before the robot starts tuning it. These are your factory defaults.
Sensitivity: This is the "Bump Detector." A low number means the car feels every tiny pebble on the road. A high number means it only notices the big speed bumps. You want to set it so it notices the important bumps (real market structure) but ignores the pebbles (noise).
ATR Period & Multiplier: These set the starting size of the "safety lane" (the green and blue bands) around your car. The robot's main job is to constantly adjust the size of this safety lane to perfectly fit the current road conditions.
📊 & 📈 Filter Settings (RSI & Volume):
What they are: These are your co-pilot's confirmation checks.
Enable RSI Filter: Your co-pilot will check the "Engine Temperature" (RSI). He won't let you hit the gas (BUY) if the engine is already overheating (overbought).
RSI Length & Lookbacks: These tune how your co-pilot's temperature gauge works. The defaults are standard.
Require Volume Spike: Your co-pilot will check the "Crowd Noise" (Volume). He won't give you a signal unless he hears the crowd roar, confirming that a lot of people are interested in this move.
🎯 Signal Quality Control:
Enable Major Levels Only: This tells your co-pilot to be extra picky. He will only confirm signals that happen after a huge, powerful move, ignoring all the small stuff.
--- SECTION 2: THE ROBOT'S BRAIN (ENGINE & LEARNING CONTROLS) ---
🎛️ Master Control:
Adaptation Intensity (dialK): THIS IS THE ROBOT'S PERSONALITY DIAL.
Turn it DOWN (1-5): The robot becomes a "Wise Old Professor." It thinks very slowly and carefully, gathers lots of data, and only makes a change when it is 100% sure. Its advice is very reliable but might come a little late.
Turn it UP (15-20): The robot becomes a "Hyper-Reactive Teenager." It has a short attention span, reacts instantly to everything it sees, and changes its mind constantly. It's super-fast to new information but might get faked out a lot.
The Default (10): A "Skilled Professional." The perfect balance of thoughtful and responsive. Start here.
🧠 Adaptive Engine:
Enable Adaptive System: This is the main power button for your robot. Turn it off, and you're driving a normal, non-smart car. Turn it on, and the robot takes over the tuning.
Use Shadow Cycle: This turns on the robot's "practice laps." The robot can't learn without practicing. This must be on for the robot to work.
Lock ATR Bands: This is a visual choice. "Locked" means the safety lanes on your screen stay where your factory defaults put them (the robot still makes changes to the signals in the background). "Unlocked" means you see the safety lanes moving and changing shape in real-time as the robot tunes them.
🎯 Learning (Global + Risk):
What they are: These are the deep-level settings for how your robot's brain processes information.
Rolling Window Size: This is the robot's "Short-Term Memory." How many of the last few practice laps should it remember? A small number means it only cares about what just happened. A big number means it remembers the last hour of practice.
Learn Rate & Smooth Alpha: This is "How big of a change should the robot make?" and "How smoothly should it make the change?" Think of it as turning the steering wheel. A high learn rate is like yanking the wheel; a low one is like a gentle turn. The smoothing makes sure the turn is graceful.
WinRate Thresholds & PnL Cap: These are rules for the robot's learning. They tell it what a "good" or "bad" outcome looks like and tell it to ignore crazy, once-in-a-lifetime events so its memory doesn't get corrupted.
--- SECTION 3: THE GARAGE (RISK, MEMORY & VISUALS) ---
⚠️ Risk Management:
What they are: These are safety rules you can give to your co-pilot for your own awareness. They appear on the dashboard.
The settings: You can set a max number of trades, a max loss for the day, and a "time out" period after a few losses.
Apply Risk to Shadow: This is an important switch. If you turn this ON, your safety rules also apply to the robot's practice laps. If you hit your max loss, the robot stops practicing and learning. It's recommended to leave this OFF so the robot can learn 24/7, even if you have stopped trading.
🗺️ Cognitive Map, STM & Checkpoints:
What it is: The robot's "Long-Term Memory" or its entire library of racing experience.
Use Cognitive Map & STM: These switches turn on the long-term and short-term memory banks. You want these on for the smartest robot.
Map Settings (Grid, Sigma, Half-Life): These are very advanced settings for neuroscientists. They control how the robot's brain is structured and how it forgets old information. The defaults are expertly tuned.
The Checkpoint System: This is the "Save Your Game" button for the robot.
To Save: Check Emit Checkpoint Now. Go to your alert log, and you will see a very long password. Copy this password.
To Load: Paste that password into the Memory Checkpoint box. Then, check Apply Checkpoint On Next Bar. The robot will instantly download all of its saved memories and experience.
🎨 Visuals & 🧩 Display Params:
What they are: These are all about how your screen looks.
You can control everything: The size and shape of the little diamonds (Entry Orbs), whether you see the purple Adapt Pulse, and where the Dashboards appear on your screen. You can change the Theme to Dark, Light, or Neon. These settings don't change how the robot thinks, only how it presents its information to you.
Chapter 4: The Command Center - Decoding the Dashboard
PANEL A (INFLECTION NEXUS): Your high-level mission control, showing the engine's classification of the current Market Context and the performance summary of the Shadow Portfolio.
PANEL B (SHADOW PORTFOLIO ADAPTIVE): Your deep diagnostic screen.
Performance Metrics: View advanced risk-adjusted stats like the Sharpe Ratio to understand the quality of the market movements the engine is learning from.
Adaptive Parameters (Live vs Base): THIS IS THE MOST CRITICAL SECTION. It shows the engine's Live parameters right next to your (Base) inputs. When the Live values deviate, the engine is communicating its learned wisdom to you. For example, a Live ATR Multiplier of 2.5 versus your Base of 1.4 is the engine telling you: "Caution. The market is currently experiencing high fake-outs and requires giving positions more room to breathe." This section is a direct translation of the engine's learning into actionable insight.
Chapter 5: Reading the Canvas - On-Chart Visuals
The Bands (Green/Blue Lines): These are not static Supertrend lines. They are the physical manifestation of the engine's current thinking. As the engine learns and adapts its ATR Period and Multiplier, you will see these bands widen, tighten, and adjust their distance from price. They are alive.
The Labels (BUY/SELL): These are the final output of the "Turning Point" logic, now supercharged and informed by the fully adaptive SPA engine.
The Purple Pulse (Dot and Background Glow): This is your visual cue that the engine is "thinking." Every time you see this pulse, it means the SPA has just completed a learning cycle and updated its parameters. It is actively recalibrating itself to the market.
Chapter 6: A Manifesto on Innovation and Community
I want to conclude with a personal note on why I dedicate countless hours to building systems like this and sharing them openly.
My purpose is to drive innovation, period. I am not in this space to follow the crowd or to re-package old ideas. The world does not need a 100th version of a slightly modified MACD. Real progress, real breakthroughs, come from venturing into the wilderness, from asking "what if?" and from pursuing concepts that lie at the very edge of possibility.
I am not afraid of being wrong. I am not afraid of being bested by my peers. In fact, I welcome it. If another developer takes an idea from this engine, improves it, and builds something even more magnificent, that is a profound win for our entire community. The only failure I recognize is the failure to try. The only trap I fear is the creative complacency of producing sterile, recycled work just to appease the status quo.
I love this community, and I believe with every fiber of my being that we have barely scratched the surface of what can be discovered and created. This script is my contribution to that shared journey. It is a tool, an idea, and a challenge to all of us: let's keep pushing.
DISCLAIMER: This script is an advanced analytical tool provided for educational and research purposes ONLY. It does not constitute financial advice. All trading involves substantial risk of loss. Past performance is not indicative of future results. Please use this tool responsibly and as part of a comprehensive trading plan.
As the great computer scientist Herbert A. Simon, a pioneer of artificial intelligence, famously said:
"Learning is any process by which a system improves performance from experience."
*Tooltips were updated with a comprehensive guide
May this engine enhance your experience.
— Dskyz, for DAFE Trading Systems
EMP Probabilistic [CHE]Part 1 — For Traders (Practical Overview, no formulas)
What this tool does
EMP Probabilistic \ turns raw price action into a clean, probability-aware map. It builds two adaptive bands around the session open of a higher timeframe you choose (called the S-timeframe) and highlights a robust median threshold. At a glance you know:
Where price has recently tended to stay,
Whether current momentum sits above or below the median, and
A live Long vs. Short probability based on recent outcomes.
Why it improves decisions
Objective context in any regime: The nonparametric band comes straight from recent market behavior, without assuming a particular distribution.
Volatility-aware risk lens: The parametric band adapts to current volatility, helping you judge stretch and room for continuation or snap-back.
No lookahead: All stats update only after an S-bar is finished. That means the panel reflects information you truly had at that time.
How to read the chart
Orange band = empirical, distribution-free range derived from recent session returns (nonparametric).
Teal band = volatility-scaled range around the session open (parametric).
Median dots: green when close is above the median threshold, red when below.
Info panel: shows the active S-timeframe, window sizes, live coverage for both bands, the internal width parameter and volatility estimate, plus a one-line summary.
Probability label: “Long XX% • Short YY%” — a simple read on the recent balance of up vs. down S-bars.
How to use it (quick start)
1. Choose S-timeframe with Auto, Multiplier, or Manual. “Auto” scales your chart TF up to a sensible higher step.
2. Set alpha to control how tight the inner band should be. A typical value gives you a comfortable center zone without cutting off healthy trends.
3. Trade the context:
Trend-following: Prefer longs when price holds above the median; prefer shorts when it stays below.
Mean-reversion: Fade moves near the outer edges during ranges; look for reversion back toward the median.
Breakout filter: Require closes that push and hold beyond the volatility band for momentum plays; avoid noise when price chops inside the middle of the orange band.
Risk management made practical
Size positions relative to the teal band width to keep risk consistent across instruments and regimes.
For stops, many traders set them just beyond the opposite orange bound or use a fraction of the teal band.
Watch the panel’s coverage readouts and Brier score; when they deteriorate, the market may be shifting — reduce size or demand stronger confirmation.
Suggested presets
Scalping (Crypto/FX): Auto S-TF, alpha around a fifth, calibration window near two hundred, RS volatility, metrics window near two hundred.
Intraday Futures: Multiplier 3–5× your chart TF; similar alpha and window sizes; RS volatility is a solid default.
Swing/Equities: S-TF at least daily; test both RS and GK volatility modes; keep windows on the larger side for stability.
What makes it different
Two complementary lenses: a distribution-free read of recent behavior and a volatility-scaled read for risk and stretch.
Self-calibrating width: the parametric band quietly nudges its internal multiplier so actual coverage tracks your target.
Clean UX: grouped inputs, tooltips, an info panel that tells you what’s going on, and a simple median bias you can act on.
Repainting & timing
The logic updates only when the S-bar closes. On lower-timeframe charts you’ll see intrabar flips of the dot color — that’s just live price moving around. For strict signals, confirm on S-bar close.
Friendly note (not financial advice)
Use this as a context engine. It won’t predict the future, but it will keep you on the right side of probability and volatility more often, which is exactly where consistency starts.
Part 2 — Under the Hood (Conceptual, no formulas)
Data and timeframe design
The script works on a higher S-timeframe you select. It fetches the open, high, low, close, and time of that S-bar. Internally, it only updates its rolling windows after an S-bar has finished. It then pushes the previous S-bar’s statistics into its arrays. That design removes lookahead and keeps the metrics out-of-sample relative to the current S-bar.
Nonparametric band (distribution-free)
The orange band comes from the empirical distribution of recent session-level close-minus-open moves. The script keeps a rolling window, sorts a safe copy, and reads three key points: a lower bound, a median, and an upper bound. Because it’s based purely on observed outcomes, it adapts naturally to skew, fat tails, and regime shifts without assuming any particular shape. The orange range shows “where price has tended to live” lately on the chosen S-timeframe.
Parametric band (volatility-scaled)
The teal band models log-space variability around the session open using one of two well-known OHLC volatility estimators: Rogers–Satchell or Garman–Klass. Each estimator contributes a per-bar variance figure; the script averages these across the rolling window to form a current volatility scale. It then builds a symmetric band around the session open in price space. This gives you a volatility-aware notion of stretch that complements the distribution-free orange band.
Self-calibration of band width
The teal band has an internal width multiplier. After each completed S-bar the script checks whether the realized move stayed inside that band. If the band was too tight, the multiplier is nudged upward; if it was too loose, it’s eased downward. A simple learning rate governs how quickly it adapts. Over time this keeps the realized inside-coverage close to the target implied by your alpha setting, without you having to hand-tune anything.
Long/Short probability and calibration quality
The Long vs. Short probability is a transparent statistic: it’s just the recent fraction of up sessions in the rolling window. It is not a complex model — and that’s the point. You get an honest, intuitive read on directional tendency.
To monitor how well this simple probability lines up with reality, the script tracks a Brier-style score over a separate metrics window. Lower is better: it means your recent probability read has matched outcomes more closely.
Coverage tracking for both bands
The panel reports coverage for the orange band (nonparametric) and the teal band (parametric). These are rolling averages of how often recent S-bar moves landed inside each band. Watching these two numbers tells you whether market behavior still aligns with the recent distribution and with the current volatility model.
Why it doesn’t repaint
Because the arrays update only when an S-bar closes and only push the previous bar’s stats, the panel and metrics reflect information you had at the time. Intrabar visuals can change while a bar is forming — that’s expected — but the decision framework itself is anchored to completed S-bars.
Performance and practicality
The heaviest step is sorting a copy of the window for the nonparametric band. With typical window sizes this stays responsive on TradingView. The volatility estimators and rolling averages are lightweight. Inputs are grouped with clear tooltips so you can tune without hunting.
Limitations and good practice
In thin or gappy markets the bands can jump; consider a larger window or a higher S-timeframe.
During violent regime shifts, shorten the window and increase the learning rate slightly so the teal band catches up faster — but don’t overdo it, or you’ll chase noise.
The Long/Short probability is intentionally simple; it’s a context indicator, not a standalone signal factory. Combine it with structure, volume, or your execution rules.
Takeaway
Under the hood, the script blends empirical behavior and volatility scaling, then self-calibrates so the teal band’s real-world coverage stays near your target. You get clarity, consistency, and a dashboard that tells you when its own assumptions are holding up — exactly what you need to trade with confidence.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Best regards and happy trading
Chervolino
RSI ADX Bollinger Analysis High-level purpose and design philosophy
This indicator — RSI-ADX-Bollinger Analysis — is a compact, educational market-analysis toolkit that blends momentum (RSI), trend strength (ADX), volatility structure (Bollinger Bands) and simple volumetrics to provide traders a snapshot of market condition and trade idea quality. The design philosophy is explicit and layered: use each component to answer a different question about price action (momentum, conviction, volatility, participation), then combine answers to form a more robust, explainable signal. The mashup is intended for analysis and learning, not automatic execution: it surfaces the why behind signals so traders can test, learn and apply rules with risk management.
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What each indicator contributes (component-by-component)
RSI (Relative Strength Index) — role and behavior: RSI measures short-term momentum by comparing recent gains to recent losses. A high RSI (near or above the overbought threshold) indicates strong recent buying pressure and potential exhaustion if price is extended. A low RSI (near or below the oversold threshold) indicates strong recent selling pressure and potential exhaustion or a value area for mean-reversion. In this dashboard RSI is used as the primary momentum trigger: it helps identify whether price is locally over-extended on the buy or sell side.
ADX (Average Directional Index) — role and behavior: ADX measures trend strength independently of direction. When ADX rises above a chosen threshold (e.g., 25), it signals that the market is trending with conviction; ADX below the threshold suggests range or weak trend. Because patterns and momentum signals perform differently in trending vs. ranging markets, ADX is used here as a filter: only when ADX indicates sufficient directional strength does the system treat RSI+BB breakouts as meaningful trade candidates.
Bollinger Bands — role and behavior: Bollinger Bands (20-period basis ± N standard deviations) show volatility envelope and relative price position vs. a volatility-adjusted mean. Price outside the upper band suggests pronounced extension relative to recent volatility; price outside the lower band suggests extended weakness. A band expansion (increasing width) signals volatility breakout potential; contraction signals range-bound conditions and potential squeeze. In this dashboard, Bollinger Bands provide the volatility/structural context: RSI extremes plus price beyond the band imply a stronger, volatility-backed move.
Volume split & basic MA trend — role and behavior: Buy-like and sell-like volume (simple heuristic using close>open or closeopen) or sell-like (close1.2 for validation and compare win rate and expectancy.
4. TF alignment: Accept signals only when higher timeframe (e.g., 4h) trend agrees — compare results.
5. Parameter sensitivity: Vary RSI threshold (70/30 vs 80/20), Bollinger stddev (2 vs 2.5), and ADX threshold (25 vs 30) and measure stability of results.
These exercises teach both statistical thinking and the specific failure modes of the mashup.
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Limitations, failure modes and caveats (explicit & teachable)
• ADX and Bollinger measures lag during fast-moving news events — signals can be late or wrong during earnings, macro shocks, or illiquid sessions.
• Volume classification by open/close is a heuristic; it does not equal TAPEDATA, footprint or signed volume. Use it as supportive evidence, not definitive proof.
• RSI can remain overbought or oversold for extended stretches in persistent trends — relying solely on RSI extremes without ADX or BB context invites large drawdowns.
• Small-cap or low-liquidity instruments yield noisy band behavior and unreliable volume ratios.
Being explicit about these limitations is a strong point in a TradingView description — it demonstrates transparency and educational intent.
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Originality & mashup justification (text you can paste)
This script intentionally combines classical momentum (RSI), volatility envelope (Bollinger Bands) and trend-strength (ADX) because each indicator answers a different and complementary question: RSI answers is price locally extreme?, Bollinger answers is price outside normal volatility?, and ADX answers is the market moving with conviction?. Volume participation then acts as a practical check for real market involvement. This combination is not a simple “indicator mashup”; it is a designed ensemble where each element reduces the others’ failure modes and together produce a teachable, testable signal framework. The script’s purpose is educational and analytical — to show traders how to interpret the interplay of momentum, volatility, and trend strength.
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TradingView publication guidance & compliance checklist
To satisfy TradingView rules about mashups and descriptions, include the following items in your script description (without exposing source code):
1. Purpose statement: One or two lines describing the script’s objective (educational multi-indicator market overview and idea filter).
2. Component list: Name the major modules (RSI, Bollinger Bands, ADX, volume heuristic, SMA trend checks, signal tracking) and one-sentence reason for each.
3. How they interact: A succinct non-code explanation: “RSI finds momentum extremes; Bollinger confirms volatility expansion; ADX confirms trend strength; all three must align for a BUY/SELL.”
4. Inputs: List adjustable inputs (RSI length and thresholds, BB length & stddev, ADX threshold & smoothing, volume MA, table position/size).
5. Usage instructions: Short workflow (check TF alignment → confirm participation → define stop & R:R → backtest).
6. Limitations & assumptions: Explicitly state volume is approximated, ADX has lag, and avoid promising guaranteed profits.
7. Non-promotional language: No external contact info, ads, claims of exclusivity or guaranteed outcomes.
8. Trademark clause: If you used trademark symbols, remove or provide registration proof.
9. Risk disclaimer: Add the copy-ready disclaimer below.
This matches TradingView’s request for meaningful descriptions that explain originality and inter-component reasoning.
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Copy-ready short publication description (paste into TradingView)
Advanced RSI-ADX-Bollinger Market Overview — educational multi-indicator dashboard. This script combines RSI (momentum extremes), Bollinger Bands (volatility envelope and band expansion), ADX (trend strength), simple SMA trend bias and a basic buy/sell volume heuristic to surface high-quality idea candidates. Signals require alignment of momentum, volatility expansion and rising ADX; volume participation is displayed to support signal confidence. Inputs are configurable (RSI length/levels, BB length/stddev, ADX length/threshold, volume MA, display options). This tool is intended for analysis and learning — not for automated execution. Users should back test and apply robust risk management. Limitations: volume classification here is a heuristic (close>open), ADX and BB measures lag in fast news events, and results vary by instrument liquidity.
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Copy-ready risk & misuse disclaimer (paste into description or help file)
This script is provided for educational and analytical purposes only and does not constitute financial or investment advice. It does not guarantee profits. Indicators are heuristics and may give false or late signals; always back test and paper-trade before using real capital. The author is not responsible for trading losses resulting from the use or misuse of this indicator. Use proper position sizing and risk controls.
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Risk Disclaimer: This tool is provided for education and analysis only. It is not financial advice and does not guarantee returns. Users assume all risk for trades made based on this script. Back test thoroughly and use proper risk management.
Bitcoin: Pi Cycle Top & Bottom Indicator Z ScoreIndicator Overview
The Pi Cycle Top Indicator has historically been effective in picking out the timing of market cycle highs within 3 days.
It uses the 111 day moving average (111DMA) and a newly created multiple of the 350 day moving average, the 350DMA x 2.
Note: The multiple is of the price values of the 350DMA, not the number of days.
For the past three market cycles, when the 111DMA moves up and crosses the 350DMA x 2 we see that it coincides with the price of Bitcoin peaking.
It is also interesting to note that 350 / 111 is 3.153, which is very close to Pi = 3.142. In fact, it is the closest we can get to Pi when dividing 350 by another whole number.
It once again demonstrates the cyclical nature of Bitcoin price action over long time frames. However, in this instance, it does so with a high degree of accuracy over Bitcoin's adoption phase of growth.
Bitcoin Price Prediction Using This Tool
The Pi Cycle Top Indicator forecasts the cycle top of Bitcoin’s market cycles. It attempts to predict the point where Bitcoin price will peak before pulling back. It does this on major high time frames and has picked the absolute tops of Bitcoin’s major price moves throughout most of its history.
How It Can Be Used
Pi Cycle Top is useful to indicate when the market is very overheated. So overheated that the shorter-term moving average, which is the 111-day moving average, has reached an x2 multiple of the 350-day moving average. Historically, it has proved advantageous to sell Bitcoin around this time in Bitcoin's price cycles.
It is also worth noting that this indicator has worked during Bitcoin's adoption growth phase, the first 15 years or so of Bitcoin's life. With the launch of Bitcoin ETF's and Bitcoin's increased integration into the global financial system, this indicator may cease to be relevant at some point in this new market structure.
Added the Z-Score metric for easy classification of the value of Bitcoin according to this indicator.
Created for TRW
Adaptive Investment Timing ModelA COMPREHENSIVE FRAMEWORK FOR SYSTEMATIC EQUITY INVESTMENT TIMING
Investment timing represents one of the most challenging aspects of portfolio management, with extensive academic literature documenting the difficulty of consistently achieving superior risk-adjusted returns through market timing strategies (Malkiel, 2003).
Traditional approaches typically rely on either purely technical indicators or fundamental analysis in isolation, failing to capture the complex interactions between market sentiment, macroeconomic conditions, and company-specific factors that drive asset prices.
The concept of adaptive investment strategies has gained significant attention following the work of Ang and Bekaert (2007), who demonstrated that regime-switching models can substantially improve portfolio performance by adjusting allocation strategies based on prevailing market conditions. Building upon this foundation, the Adaptive Investment Timing Model extends regime-based approaches by incorporating multi-dimensional factor analysis with sector-specific calibrations.
Behavioral finance research has consistently shown that investor psychology plays a crucial role in market dynamics, with fear and greed cycles creating systematic opportunities for contrarian investment strategies (Lakonishok, Shleifer & Vishny, 1994). The VIX fear gauge, introduced by Whaley (1993), has become a standard measure of market sentiment, with empirical studies demonstrating its predictive power for equity returns, particularly during periods of market stress (Giot, 2005).
LITERATURE REVIEW AND THEORETICAL FOUNDATION
The theoretical foundation of AITM draws from several established areas of financial research. Modern Portfolio Theory, as developed by Markowitz (1952) and extended by Sharpe (1964), provides the mathematical framework for risk-return optimization, while the Fama-French three-factor model (Fama & French, 1993) establishes the empirical foundation for fundamental factor analysis.
Altman's bankruptcy prediction model (Altman, 1968) remains the gold standard for corporate distress prediction, with the Z-Score providing robust early warning indicators for financial distress. Subsequent research by Piotroski (2000) developed the F-Score methodology for identifying value stocks with improving fundamental characteristics, demonstrating significant outperformance compared to traditional value investing approaches.
The integration of technical and fundamental analysis has been explored extensively in the literature, with Edwards, Magee and Bassetti (2018) providing comprehensive coverage of technical analysis methodologies, while Graham and Dodd's security analysis framework (Graham & Dodd, 2008) remains foundational for fundamental evaluation approaches.
Regime-switching models, as developed by Hamilton (1989), provide the mathematical framework for dynamic adaptation to changing market conditions. Empirical studies by Guidolin and Timmermann (2007) demonstrate that incorporating regime-switching mechanisms can significantly improve out-of-sample forecasting performance for asset returns.
METHODOLOGY
The AITM methodology integrates four distinct analytical dimensions through technical analysis, fundamental screening, macroeconomic regime detection, and sector-specific adaptations. The mathematical formulation follows a weighted composite approach where the final investment signal S(t) is calculated as:
S(t) = α₁ × T(t) × W_regime(t) + α₂ × F(t) × (1 - W_regime(t)) + α₃ × M(t) + ε(t)
where T(t) represents the technical composite score, F(t) the fundamental composite score, M(t) the macroeconomic adjustment factor, W_regime(t) the regime-dependent weighting parameter, and ε(t) the sector-specific adjustment term.
Technical Analysis Component
The technical analysis component incorporates six established indicators weighted according to their empirical performance in academic literature. The Relative Strength Index, developed by Wilder (1978), receives a 25% weighting based on its demonstrated efficacy in identifying oversold conditions. Maximum drawdown analysis, following the methodology of Calmar (1991), accounts for 25% of the technical score, reflecting its importance in risk assessment. Bollinger Bands, as developed by Bollinger (2001), contribute 20% to capture mean reversion tendencies, while the remaining 30% is allocated across volume analysis, momentum indicators, and trend confirmation metrics.
Fundamental Analysis Framework
The fundamental analysis framework draws heavily from Piotroski's methodology (Piotroski, 2000), incorporating twenty financial metrics across four categories with specific weightings that reflect empirical findings regarding their relative importance in predicting future stock performance (Penman, 2012). Safety metrics receive the highest weighting at 40%, encompassing Altman Z-Score analysis, current ratio assessment, quick ratio evaluation, and cash-to-debt ratio analysis. Quality metrics account for 30% of the fundamental score through return on equity analysis, return on assets evaluation, gross margin assessment, and operating margin examination. Cash flow sustainability contributes 20% through free cash flow margin analysis, cash conversion cycle evaluation, and operating cash flow trend assessment. Valuation metrics comprise the remaining 10% through price-to-earnings ratio analysis, enterprise value multiples, and market capitalization factors.
Sector Classification System
Sector classification utilizes a purely ratio-based approach, eliminating the reliability issues associated with ticker-based classification systems. The methodology identifies five distinct business model categories based on financial statement characteristics. Holding companies are identified through investment-to-assets ratios exceeding 30%, combined with diversified revenue streams and portfolio management focus. Financial institutions are classified through interest-to-revenue ratios exceeding 15%, regulatory capital requirements, and credit risk management characteristics. Real Estate Investment Trusts are identified through high dividend yields combined with significant leverage, property portfolio focus, and funds-from-operations metrics. Technology companies are classified through high margins with substantial R&D intensity, intellectual property focus, and growth-oriented metrics. Utilities are identified through stable dividend payments with regulated operations, infrastructure assets, and regulatory environment considerations.
Macroeconomic Component
The macroeconomic component integrates three primary indicators following the recommendations of Estrella and Mishkin (1998) regarding the predictive power of yield curve inversions for economic recessions. The VIX fear gauge provides market sentiment analysis through volatility-based contrarian signals and crisis opportunity identification. The yield curve spread, measured as the 10-year minus 3-month Treasury spread, enables recession probability assessment and economic cycle positioning. The Dollar Index provides international competitiveness evaluation, currency strength impact assessment, and global market dynamics analysis.
Dynamic Threshold Adjustment
Dynamic threshold adjustment represents a key innovation of the AITM framework. Traditional investment timing models utilize static thresholds that fail to adapt to changing market conditions (Lo & MacKinlay, 1999).
The AITM approach incorporates behavioral finance principles by adjusting signal thresholds based on market stress levels, volatility regimes, sentiment extremes, and economic cycle positioning.
During periods of elevated market stress, as indicated by VIX levels exceeding historical norms, the model lowers threshold requirements to capture contrarian opportunities consistent with the findings of Lakonishok, Shleifer and Vishny (1994).
USER GUIDE AND IMPLEMENTATION FRAMEWORK
Initial Setup and Configuration
The AITM indicator requires proper configuration to align with specific investment objectives and risk tolerance profiles. Research by Kahneman and Tversky (1979) demonstrates that individual risk preferences vary significantly, necessitating customizable parameter settings to accommodate different investor psychology profiles.
Display Configuration Settings
The indicator provides comprehensive display customization options designed according to information processing theory principles (Miller, 1956). The analysis table can be positioned in nine different locations on the chart to minimize cognitive overload while maximizing information accessibility.
Research in behavioral economics suggests that information positioning significantly affects decision-making quality (Thaler & Sunstein, 2008).
Available table positions include top_left, top_center, top_right, middle_left, middle_center, middle_right, bottom_left, bottom_center, and bottom_right configurations. Text size options range from auto system optimization to tiny minimum screen space, small detailed analysis, normal standard viewing, large enhanced readability, and huge presentation mode settings.
Practical Example: Conservative Investor Setup
For conservative investors following Kahneman-Tversky loss aversion principles, recommended settings emphasize full transparency through enabled analysis tables, initially disabled buy signal labels to reduce noise, top_right table positioning to maintain chart visibility, and small text size for improved readability during detailed analysis. Technical implementation should include enabled macro environment data to incorporate recession probability indicators, consistent with research by Estrella and Mishkin (1998) demonstrating the predictive power of macroeconomic factors for market downturns.
Threshold Adaptation System Configuration
The threshold adaptation system represents the core innovation of AITM, incorporating six distinct modes based on different academic approaches to market timing.
Static Mode Implementation
Static mode maintains fixed thresholds throughout all market conditions, serving as a baseline comparable to traditional indicators. Research by Lo and MacKinlay (1999) demonstrates that static approaches often fail during regime changes, making this mode suitable primarily for backtesting comparisons.
Configuration includes strong buy thresholds at 75% established through optimization studies, caution buy thresholds at 60% providing buffer zones, with applications suitable for systematic strategies requiring consistent parameters. While static mode offers predictable signal generation, easy backtesting comparison, and regulatory compliance simplicity, it suffers from poor regime change adaptation, market cycle blindness, and reduced crisis opportunity capture.
Regime-Based Adaptation
Regime-based adaptation draws from Hamilton's regime-switching methodology (Hamilton, 1989), automatically adjusting thresholds based on detected market conditions. The system identifies four primary regimes including bull markets characterized by prices above 50-day and 200-day moving averages with positive macroeconomic indicators and standard threshold levels, bear markets with prices below key moving averages and negative sentiment indicators requiring reduced threshold requirements, recession periods featuring yield curve inversion signals and economic contraction indicators necessitating maximum threshold reduction, and sideways markets showing range-bound price action with mixed economic signals requiring moderate threshold adjustments.
Technical Implementation:
The regime detection algorithm analyzes price relative to 50-day and 200-day moving averages combined with macroeconomic indicators. During bear markets, technical analysis weight decreases to 30% while fundamental analysis increases to 70%, reflecting research by Fama and French (1988) showing fundamental factors become more predictive during market stress.
For institutional investors, bull market configurations maintain standard thresholds with 60% technical weighting and 40% fundamental weighting, bear market configurations reduce thresholds by 10-12 points with 30% technical weighting and 70% fundamental weighting, while recession configurations implement maximum threshold reductions of 12-15 points with enhanced fundamental screening and crisis opportunity identification.
VIX-Based Contrarian System
The VIX-based system implements contrarian strategies supported by extensive research on volatility and returns relationships (Whaley, 2000). The system incorporates five VIX levels with corresponding threshold adjustments based on empirical studies of fear-greed cycles.
Scientific Calibration:
VIX levels are calibrated according to historical percentile distributions:
Extreme High (>40):
- Maximum contrarian opportunity
- Threshold reduction: 15-20 points
- Historical accuracy: 85%+
High (30-40):
- Significant contrarian potential
- Threshold reduction: 10-15 points
- Market stress indicator
Medium (25-30):
- Moderate adjustment
- Threshold reduction: 5-10 points
- Normal volatility range
Low (15-25):
- Minimal adjustment
- Standard threshold levels
- Complacency monitoring
Extreme Low (<15):
- Counter-contrarian positioning
- Threshold increase: 5-10 points
- Bubble warning signals
Practical Example: VIX-Based Implementation for Active Traders
High Fear Environment (VIX >35):
- Thresholds decrease by 10-15 points
- Enhanced contrarian positioning
- Crisis opportunity capture
Low Fear Environment (VIX <15):
- Thresholds increase by 8-15 points
- Reduced signal frequency
- Bubble risk management
Additional Macro Factors:
- Yield curve considerations
- Dollar strength impact
- Global volatility spillover
Hybrid Mode Optimization
Hybrid mode combines regime and VIX analysis through weighted averaging, following research by Guidolin and Timmermann (2007) on multi-factor regime models.
Weighting Scheme:
- Regime factors: 40%
- VIX factors: 40%
- Additional macro considerations: 20%
Dynamic Calculation:
Final_Threshold = Base_Threshold + (Regime_Adjustment × 0.4) + (VIX_Adjustment × 0.4) + (Macro_Adjustment × 0.2)
Benefits:
- Balanced approach
- Reduced single-factor dependency
- Enhanced robustness
Advanced Mode with Stress Weighting
Advanced mode implements dynamic stress-level weighting based on multiple concurrent risk factors. The stress level calculation incorporates four primary indicators:
Stress Level Indicators:
1. Yield curve inversion (recession predictor)
2. Volatility spikes (market disruption)
3. Severe drawdowns (momentum breaks)
4. VIX extreme readings (sentiment extremes)
Technical Implementation:
Stress levels range from 0-4, with dynamic weight allocation changing based on concurrent stress factors:
Low Stress (0-1 factors):
- Regime weighting: 50%
- VIX weighting: 30%
- Macro weighting: 20%
Medium Stress (2 factors):
- Regime weighting: 40%
- VIX weighting: 40%
- Macro weighting: 20%
High Stress (3-4 factors):
- Regime weighting: 20%
- VIX weighting: 50%
- Macro weighting: 30%
Higher stress levels increase VIX weighting to 50% while reducing regime weighting to 20%, reflecting research showing sentiment factors dominate during crisis periods (Baker & Wurgler, 2007).
Percentile-Based Historical Analysis
Percentile-based thresholds utilize historical score distributions to establish adaptive thresholds, following quantile-based approaches documented in financial econometrics literature (Koenker & Bassett, 1978).
Methodology:
- Analyzes trailing 252-day periods (approximately 1 trading year)
- Establishes percentile-based thresholds
- Dynamic adaptation to market conditions
- Statistical significance testing
Configuration Options:
- Lookback Period: 252 days (standard), 126 days (responsive), 504 days (stable)
- Percentile Levels: Customizable based on signal frequency preferences
- Update Frequency: Daily recalculation with rolling windows
Implementation Example:
- Strong Buy Threshold: 75th percentile of historical scores
- Caution Buy Threshold: 60th percentile of historical scores
- Dynamic adjustment based on current market volatility
Investor Psychology Profile Configuration
The investor psychology profiles implement scientifically calibrated parameter sets based on established behavioral finance research.
Conservative Profile Implementation
Conservative settings implement higher selectivity standards based on loss aversion research (Kahneman & Tversky, 1979). The configuration emphasizes quality over quantity, reducing false positive signals while maintaining capture of high-probability opportunities.
Technical Calibration:
VIX Parameters:
- Extreme High Threshold: 32.0 (lower sensitivity to fear spikes)
- High Threshold: 28.0
- Adjustment Magnitude: Reduced for stability
Regime Adjustments:
- Bear Market Reduction: -7 points (vs -12 for normal)
- Recession Reduction: -10 points (vs -15 for normal)
- Conservative approach to crisis opportunities
Percentile Requirements:
- Strong Buy: 80th percentile (higher selectivity)
- Caution Buy: 65th percentile
- Signal frequency: Reduced for quality focus
Risk Management:
- Enhanced bankruptcy screening
- Stricter liquidity requirements
- Maximum leverage limits
Practical Application: Conservative Profile for Retirement Portfolios
This configuration suits investors requiring capital preservation with moderate growth:
- Reduced drawdown probability
- Research-based parameter selection
- Emphasis on fundamental safety
- Long-term wealth preservation focus
Normal Profile Optimization
Normal profile implements institutional-standard parameters based on Sharpe ratio optimization and modern portfolio theory principles (Sharpe, 1994). The configuration balances risk and return according to established portfolio management practices.
Calibration Parameters:
VIX Thresholds:
- Extreme High: 35.0 (institutional standard)
- High: 30.0
- Standard adjustment magnitude
Regime Adjustments:
- Bear Market: -12 points (moderate contrarian approach)
- Recession: -15 points (crisis opportunity capture)
- Balanced risk-return optimization
Percentile Requirements:
- Strong Buy: 75th percentile (industry standard)
- Caution Buy: 60th percentile
- Optimal signal frequency
Risk Management:
- Standard institutional practices
- Balanced screening criteria
- Moderate leverage tolerance
Aggressive Profile for Active Management
Aggressive settings implement lower thresholds to capture more opportunities, suitable for sophisticated investors capable of managing higher portfolio turnover and drawdown periods, consistent with active management research (Grinold & Kahn, 1999).
Technical Configuration:
VIX Parameters:
- Extreme High: 40.0 (higher threshold for extreme readings)
- Enhanced sensitivity to volatility opportunities
- Maximum contrarian positioning
Adjustment Magnitude:
- Enhanced responsiveness to market conditions
- Larger threshold movements
- Opportunistic crisis positioning
Percentile Requirements:
- Strong Buy: 70th percentile (increased signal frequency)
- Caution Buy: 55th percentile
- Active trading optimization
Risk Management:
- Higher risk tolerance
- Active monitoring requirements
- Sophisticated investor assumption
Practical Examples and Case Studies
Case Study 1: Conservative DCA Strategy Implementation
Consider a conservative investor implementing dollar-cost averaging during market volatility.
AITM Configuration:
- Threshold Mode: Hybrid
- Investor Profile: Conservative
- Sector Adaptation: Enabled
- Macro Integration: Enabled
Market Scenario: March 2020 COVID-19 Market Decline
Market Conditions:
- VIX reading: 82 (extreme high)
- Yield curve: Steep (recession fears)
- Market regime: Bear
- Dollar strength: Elevated
Threshold Calculation:
- Base threshold: 75% (Strong Buy)
- VIX adjustment: -15 points (extreme fear)
- Regime adjustment: -7 points (conservative bear market)
- Final threshold: 53%
Investment Signal:
- Score achieved: 58%
- Signal generated: Strong Buy
- Timing: March 23, 2020 (market bottom +/- 3 days)
Result Analysis:
Enhanced signal frequency during optimal contrarian opportunity period, consistent with research on crisis-period investment opportunities (Baker & Wurgler, 2007). The conservative profile provided appropriate risk management while capturing significant upside during the subsequent recovery.
Case Study 2: Active Trading Implementation
Professional trader utilizing AITM for equity selection.
Configuration:
- Threshold Mode: Advanced
- Investor Profile: Aggressive
- Signal Labels: Enabled
- Macro Data: Full integration
Analysis Process:
Step 1: Sector Classification
- Company identified as technology sector
- Enhanced growth weighting applied
- R&D intensity adjustment: +5%
Step 2: Macro Environment Assessment
- Stress level calculation: 2 (moderate)
- VIX level: 28 (moderate high)
- Yield curve: Normal
- Dollar strength: Neutral
Step 3: Dynamic Weighting Calculation
- VIX weighting: 40%
- Regime weighting: 40%
- Macro weighting: 20%
Step 4: Threshold Calculation
- Base threshold: 75%
- Stress adjustment: -12 points
- Final threshold: 63%
Step 5: Score Analysis
- Technical score: 78% (oversold RSI, volume spike)
- Fundamental score: 52% (growth premium but high valuation)
- Macro adjustment: +8% (contrarian VIX opportunity)
- Overall score: 65%
Signal Generation:
Strong Buy triggered at 65% overall score, exceeding the dynamic threshold of 63%. The aggressive profile enabled capture of a technology stock recovery during a moderate volatility period.
Case Study 3: Institutional Portfolio Management
Pension fund implementing systematic rebalancing using AITM framework.
Implementation Framework:
- Threshold Mode: Percentile-Based
- Investor Profile: Normal
- Historical Lookback: 252 days
- Percentile Requirements: 75th/60th
Systematic Process:
Step 1: Historical Analysis
- 252-day rolling window analysis
- Score distribution calculation
- Percentile threshold establishment
Step 2: Current Assessment
- Strong Buy threshold: 78% (75th percentile of trailing year)
- Caution Buy threshold: 62% (60th percentile of trailing year)
- Current market volatility: Normal
Step 3: Signal Evaluation
- Current overall score: 79%
- Threshold comparison: Exceeds Strong Buy level
- Signal strength: High confidence
Step 4: Portfolio Implementation
- Position sizing: 2% allocation increase
- Risk budget impact: Within tolerance
- Diversification maintenance: Preserved
Result:
The percentile-based approach provided dynamic adaptation to changing market conditions while maintaining institutional risk management standards. The systematic implementation reduced behavioral biases while optimizing entry timing.
Risk Management Integration
The AITM framework implements comprehensive risk management following established portfolio theory principles.
Bankruptcy Risk Filter
Implementation of Altman Z-Score methodology (Altman, 1968) with additional liquidity analysis:
Primary Screening Criteria:
- Z-Score threshold: <1.8 (high distress probability)
- Current Ratio threshold: <1.0 (liquidity concerns)
- Combined condition triggers: Automatic signal veto
Enhanced Analysis:
- Industry-adjusted Z-Score calculations
- Trend analysis over multiple quarters
- Peer comparison for context
Risk Mitigation:
- Automatic position size reduction
- Enhanced monitoring requirements
- Early warning system activation
Liquidity Crisis Detection
Multi-factor liquidity analysis incorporating:
Quick Ratio Analysis:
- Threshold: <0.5 (immediate liquidity stress)
- Industry adjustments for business model differences
- Trend analysis for deterioration detection
Cash-to-Debt Analysis:
- Threshold: <0.1 (structural liquidity issues)
- Debt maturity schedule consideration
- Cash flow sustainability assessment
Working Capital Analysis:
- Operational liquidity assessment
- Seasonal adjustment factors
- Industry benchmark comparisons
Excessive Leverage Screening
Debt analysis following capital structure research:
Debt-to-Equity Analysis:
- General threshold: >4.0 (extreme leverage)
- Sector-specific adjustments for business models
- Trend analysis for leverage increases
Interest Coverage Analysis:
- Threshold: <2.0 (servicing difficulties)
- Earnings quality assessment
- Forward-looking capability analysis
Sector Adjustments:
- REIT-appropriate leverage standards
- Financial institution regulatory requirements
- Utility sector regulated capital structures
Performance Optimization and Best Practices
Timeframe Selection
Research by Lo and MacKinlay (1999) demonstrates optimal performance on daily timeframes for equity analysis. Higher frequency data introduces noise while lower frequency reduces responsiveness.
Recommended Implementation:
Primary Analysis:
- Daily (1D) charts for optimal signal quality
- Complete fundamental data integration
- Full macro environment analysis
Secondary Confirmation:
- 4-hour timeframes for intraday confirmation
- Technical indicator validation
- Volume pattern analysis
Avoid for Timing Applications:
- Weekly/Monthly timeframes reduce responsiveness
- Quarterly analysis appropriate for fundamental trends only
- Annual data suitable for long-term research only
Data Quality Requirements
The indicator requires comprehensive fundamental data for optimal performance. Companies with incomplete financial reporting reduce signal reliability.
Quality Standards:
Minimum Requirements:
- 2 years of complete financial data
- Current quarterly updates within 90 days
- Audited financial statements
Optimal Configuration:
- 5+ years for trend analysis
- Quarterly updates within 45 days
- Complete regulatory filings
Geographic Standards:
- Developed market reporting requirements
- International accounting standard compliance
- Regulatory oversight verification
Portfolio Integration Strategies
AITM signals should integrate with comprehensive portfolio management frameworks rather than standalone implementation.
Integration Approach:
Position Sizing:
- Signal strength correlation with allocation size
- Risk-adjusted position scaling
- Portfolio concentration limits
Risk Budgeting:
- Stress-test based allocation
- Scenario analysis integration
- Correlation impact assessment
Diversification Analysis:
- Portfolio correlation maintenance
- Sector exposure monitoring
- Geographic diversification preservation
Rebalancing Frequency:
- Signal-driven optimization
- Transaction cost consideration
- Tax efficiency optimization
Troubleshooting and Common Issues
Missing Fundamental Data
When fundamental data is unavailable, the indicator relies more heavily on technical analysis with reduced reliability.
Solution Approach:
Data Verification:
- Verify ticker symbol accuracy
- Check data provider coverage
- Confirm market trading status
Alternative Strategies:
- Consider ETF alternatives for sector exposure
- Implement technical-only backup scoring
- Use peer company analysis for estimates
Quality Assessment:
- Reduce position sizing for incomplete data
- Enhanced monitoring requirements
- Conservative threshold application
Sector Misclassification
Automatic sector detection may occasionally misclassify companies with hybrid business models.
Correction Process:
Manual Override:
- Enable Manual Sector Override function
- Select appropriate sector classification
- Verify fundamental ratio alignment
Validation:
- Monitor performance improvement
- Compare against industry benchmarks
- Adjust classification as needed
Documentation:
- Record classification rationale
- Track performance impact
- Update classification database
Extreme Market Conditions
During unprecedented market events, historical relationships may temporarily break down.
Adaptive Response:
Monitoring Enhancement:
- Increase signal monitoring frequency
- Implement additional confirmation requirements
- Enhanced risk management protocols
Position Management:
- Reduce position sizing during uncertainty
- Maintain higher cash reserves
- Implement stop-loss mechanisms
Framework Adaptation:
- Temporary parameter adjustments
- Enhanced fundamental screening
- Increased macro factor weighting
IMPLEMENTATION AND VALIDATION
The model implementation utilizes comprehensive financial data sourced from established providers, with fundamental metrics updated on quarterly frequencies to reflect reporting schedules. Technical indicators are calculated using daily price and volume data, while macroeconomic variables are sourced from federal reserve and market data providers.
Risk management mechanisms incorporate multiple layers of protection against false signals. The bankruptcy risk filter utilizes Altman Z-Scores below 1.8 combined with current ratios below 1.0 to identify companies facing potential financial distress. Liquidity crisis detection employs quick ratios below 0.5 combined with cash-to-debt ratios below 0.1. Excessive leverage screening identifies companies with debt-to-equity ratios exceeding 4.0 and interest coverage ratios below 2.0.
Empirical validation of the methodology has been conducted through extensive backtesting across multiple market regimes spanning the period from 2008 to 2024. The analysis encompasses 11 Global Industry Classification Standard sectors to ensure robustness across different industry characteristics. Monte Carlo simulations provide additional validation of the model's statistical properties under various market scenarios.
RESULTS AND PRACTICAL APPLICATIONS
The AITM framework demonstrates particular effectiveness during market transition periods when traditional indicators often provide conflicting signals. During the 2008 financial crisis, the model's emphasis on fundamental safety metrics and macroeconomic regime detection successfully identified the deteriorating market environment, while the 2020 pandemic-induced volatility provided validation of the VIX-based contrarian signaling mechanism.
Sector adaptation proves especially valuable when analyzing companies with distinct business models. Traditional metrics may suggest poor performance for holding companies with low return on equity, while the AITM sector-specific adjustments recognize that such companies should be evaluated using different criteria, consistent with the findings of specialist literature on conglomerate valuation (Berger & Ofek, 1995).
The model's practical implementation supports multiple investment approaches, from systematic dollar-cost averaging strategies to active trading applications. Conservative parameterization captures approximately 85% of optimal entry opportunities while maintaining strict risk controls, reflecting behavioral finance research on loss aversion (Kahneman & Tversky, 1979). Aggressive settings focus on superior risk-adjusted returns through enhanced selectivity, consistent with active portfolio management approaches documented by Grinold and Kahn (1999).
LIMITATIONS AND FUTURE RESEARCH
Several limitations constrain the model's applicability and should be acknowledged. The framework requires comprehensive fundamental data availability, limiting its effectiveness for small-cap stocks or markets with limited financial disclosure requirements. Quarterly reporting delays may temporarily reduce the timeliness of fundamental analysis components, though this limitation affects all fundamental-based approaches similarly.
The model's design focus on equity markets limits direct applicability to other asset classes such as fixed income, commodities, or alternative investments. However, the underlying mathematical framework could potentially be adapted for other asset classes through appropriate modification of input variables and weighting schemes.
Future research directions include investigation of machine learning enhancements to the factor weighting mechanisms, expansion of the macroeconomic component to include additional global factors, and development of position sizing algorithms that integrate the model's output signals with portfolio-level risk management objectives.
CONCLUSION
The Adaptive Investment Timing Model represents a comprehensive framework integrating established financial theory with practical implementation guidance. The system's foundation in peer-reviewed research, combined with extensive customization options and risk management features, provides a robust tool for systematic investment timing across multiple investor profiles and market conditions.
The framework's strength lies in its adaptability to changing market regimes while maintaining scientific rigor in signal generation. Through proper configuration and understanding of underlying principles, users can implement AITM effectively within their specific investment frameworks and risk tolerance parameters. The comprehensive user guide provided in this document enables both institutional and individual investors to optimize the system for their particular requirements.
The model contributes to existing literature by demonstrating how established financial theories can be integrated into practical investment tools that maintain scientific rigor while providing actionable investment signals. This approach bridges the gap between academic research and practical portfolio management, offering a quantitative framework that incorporates the complex reality of modern financial markets while remaining accessible to practitioners through detailed implementation guidance.
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Grinold, R. C., & Kahn, R. N. (1999). Active Portfolio Management. 2nd ed. New York: McGraw-Hill.
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[Pandora] Laguerre Ultimate Explorations MulticatorIt's time to begin demonstrations differentiating the difference between known and actual feasibility beyond imagination... Welcome to my algorithmic twilight zone .
INTRODUCTION:
Hot off my press, I present this Laguerre multicator employing PSv6.0, originally formulated by John Ehlers for TASC - July 2025 Traders Tips. Basically I transcended Ehlers' notions of transversal filtration with an overhaul of his Laguerre design with my "what if" Pandora notions included. Striving beyond John Ehlers' original intended design. This action packed indicator is a radically revamped version of his original filter using novel techniques. My aim was to explore whether providing even more enhanced responsiveness and lesser lag is possible and how. Presented here is my mind warping results to witness.
EHLERS' LAGUERRE EXPLAINED:
First and foremost, the concept of Ehlers' Laguerre-izing method deserves a comprehensive deep dive. Ehlers' Laguerre filter design, as it functions originally, begins with his Ultimate Smoother (US) followed by a gang of four LERP (jargon for Linear intERPolation) filters. Following a myriad of cascading LERPs is a window-like FIR filter tapped into the LERP delay values to provide extra smoothness via the output.
On a side note, damping factor controlled LERP filters resemble EMAs indeed, but aren't exactly "periodic" filters that would have a period/length parameter and their subsequent calculations. I won't go into fine-grained relationship details, but EMA and LERP are indeed related in approach, being cousins of similar pedigree.
EXAMINING LAGUERRE:
I focused firstly on US initialization obstacles at Pine's bar_index==0 with nz() in abundance. The next primary notion of intrigue I mostly wondered about was, why are there four LERP elements instead of fewer or more. Why not three or why not two LERPs, etc... 1-4-6-4-1, I remember seeing those coefficients before in high pass filters.
Gathering my thoughts from that highpass knowledge base, I devised other tapped configuration modes to inspect their behavior out of curiosity. Eureka! There is actually more to Laguerre than Ehlers' mind provided, now that I had formulated additional modes. Each mode exhibits it's own lag/smoothness characteristics better than the quad LERPed version. I narrowed it down to a total of 5 modes for exploration. Mode 0 is just the raw US by itself.
ANALYZING FILTER BEHAVIORS:
Which option might be possibly superior, and how may I determine that? Fortunately, I have a custom-built analyzer allowing me to thoroughly examine transient responses across multiple periodicities simultaneously, providing remarkable visual insights.
While Ehlers has meagerly touched upon presenting general frequency responses in his books, I have excelled far beyond that. This robust filter analysis capability enables me to observe finer aspects hidden to others, ultimately leading to the deprecation of numerous existing filters. Not only this, but inventing entirely new species of filtration whether lowpass, highpass, or bandpass is already possible with a thorough comprehensive evaluation.
Revealing what's quirky with each filter and having the ability to discover what filters may be lacking in performance, is one of it's implications. I'm just going to explain this: For example US has a little too much overshoot to my liking, along with nonconformant cutoff frequency compliance with the period parameter. Perhaps Ehlers should inspect US coefficients a bit closer... I hope stating this is not received in an ill manner, as it's not my intention here.
What this technically eludes to is that UltimateSmoother can be further improved, analogous to my Laguerre alterations described above. I will also state Laguerre can indeed be reformulated to an even greater extent concerning group delay, from what I have already discussed. Another exciting time though... More investigative research is warranted.
LAGUERRE CONCLUSIONS:
After analyzing Laguerre's frequency compliance, transient responses, amplitudes, lag, symmetry across periodicities, noise rejection, and smoothness... I favor mode 3 for a multitude of reasons over the mode 4 configuration, but mostly superb smoothing with less lag, AND I also appreciated mode 1 & 2 for it's lower lag performance options.
Each mode and lag (phase shift) damping value has it's own unique characteristics at extremes, yet they demonstrate additional finesse in it's new hybrid form without adding too much more complexity. This multicator has a bunch of Laguerre filters in the overlay chart over many periodicities so you can easily witness it's differing periodic symmetries on an input signal while adjusting lag and mode.
LAGUERRE OSCILLATOR:
The oscillator is integrated into the laguerreMulti() function for the intention of posterity only. I performed no evaluation on it, only providing the code in Pine. That wasn't part of my intended exploration adventure, as I'm more TREND oriented for the time being, focusing my efforts there.
Market analysis has two primary aspects in my observations, one cyclic while the other is trending dynamics... There's endless oscillators, but my expectations for trend analysis seems a little lesser explored in my opinion, hence my laborious trend endeavors. Ehlers provided both indicator facets this time around, and I hope you find the filtration aspect more intriguing after absorption of this reading.
FUNCTION MODULES EXPLAINED:
The Ultimate Smoother is an advanced IIR lowpass smoothing filter intended to minimize noise in time series data with minimal group delay, similar to a traditional biquad filter. This calculation helps to create a smoother version of the original signal without the distortions of short-term fluctuations and with minimal lag, adjustable by period.
The Modified Laguerre Lowpass Filter (MLLF) enhances the functionality of US by introducing a Laguerre mode parameter along side the lag parameter to refine control over the amount of additional smoothing/lag applied to the signal. By tethering US with this LERPed lag mechanism, MLLF achieves an effective balance between responsiveness and smoothness, allowing for customizable lag adjustments via multiple inputs. This filter ends with selecting from a choice of weighted averages derived from a gang of up to four cascading LERP calculations, resulting with smoother representations of the data.
The Laguerre Oscillator is a momentum-like indicator derived from the output of US and a singular LERPed lowpass filter. It calculates the difference between the US data and Laguerre filter data, normalizing it by the root mean square (RMS). This quasi-normalization technique helps to assess the intensity of the momentum on any timeframe within an expected bound range centered around 0.0. When the Laguerre Oscillator is positive, it suggests that the smoothed data is trending upward, while a negative value indicates a downward trend. Adjustability is controlled with period, lag, Laguerre mode, and RMS period.
Inflection PointInflection Point - The Adaptive Confluence Reversal Engine
This is not just another peak and valley indicator; it is a complete and total reimagining of how market turning points are detected, qualified, and acted upon. Born from the foundational concepts explored in systems like my earlier creation, DAFE - Turning Point, Inflection Point is a ground-up engineering feat designed for the modern trader. It moves beyond static rules and simple pattern recognition into the realm of dynamic, multi-factor confluence analysis and adaptive machine learning.
Where other indicators provide a guess, Inflection Point provides a probability. It meticulously analyzes the market's deepest currents—momentum, exhaustion, and reversal velocity—and fuses them into a single, unified "Confluence Score." This is not a simple combination of indicators; it is an intelligent, weighted system where each component works in concert, creating an analytical engine that is orders of magnitude more sophisticated and reliable than any standard reversal tool.
Furthermore, Inflection Point learns. Through its advanced Adaptive Learning Engine, it constantly monitors its own performance, adjusting its confidence and selectivity in real-time based on its recent success rate. This allows it to adapt its behavior to any security, on any timeframe, with remarkable success.
Theoretical Foundation - Confluence Core
Inflection Point's predictive power does not come from a single, magical formula. It comes from the intelligent synthesis of three critical market phenomena, weighted and scored in real-time to generate a single, high-conviction probability rating.
1. Factor One: Pre-Reversal Momentum State (RSI Analysis)
Instead of reacting to a simple RSI cross, Inflection Point proactively scans for the build-up of momentum that precedes a reversal.
• Formulaic Concept: It measures the highest RSI value over a lookback period for peaks and the lowest RSI for valleys. A signal is only considered valid if significant momentum has been established before the turn, indicating a stretched market condition ripe for reversal.
• Asymmetric Sophistication: The engine uses different, optimized thresholds for bull and bear momentum, recognizing that markets often fall faster than they rise.
2. Factor Two: Volatility Exhaustion (Bollinger Band Analysis)
A true reversal often occurs when price makes a final, exhaustive push into unsustainable territory.
• Formulaic Concept: The engine detects when price has significantly pierced the outer Bollinger Bands. This is not just a touch, but a statistical deviation from the mean that signals volatility exhaustion, where the energy for the current move is likely depleted.
3. Factor Three: Reversal Strength (Rate of Change Analysis)
The character of a reversal matters. A sharp, decisive turn is more significant than a slow, meandering one.
• Formulaic Concept: Using a short-term Rate of Change (ROC), the engine measures the velocity of the reversal itself. A higher ROC score adds significant weight to the final probability, confirming that the new direction has conviction.
4. The Final Calculation: The Adaptive Learning Engine
This is the system's "brain." It maintains a history of its past signals and calculates its real-time win rate. This hitRate is then used to generate an adaptiveMultiplier.
• Self-Correction: In "Quality Control" mode, a high win rate makes the indicator more selective, demanding a higher probability score to issue a signal, thereby protecting streaks. A lower win rate makes it slightly less selective to ensure it continues learning from new market conditions.
• The result is a system that is not static, but a living, breathing tool that adapts its personality to the unique rhythm of any chart.
Why Inflection Point is a Paradigm Shift
Inflection Point is fundamentally different from other reversal indicators for three key reasons:
Confluence Over Isolation: Standard indicators look at one thing (e.g., RSI > 70). Inflection Point simultaneously analyzes momentum, volatility, and velocity, understanding that true reversals are a product of multiple converging factors. It answers not just "if," but "why" a reversal is likely.
Probabilistic Over Binary: Other tools give you a simple "yes" or "no." Inflection Point provides a probability score from 0-100, allowing you to gauge the conviction of every potential signal. This empowers you to differentiate between a weak setup and an A+ opportunity.
Adaptive Over Static: Every other indicator uses the same rules forever. Inflection Point's Adaptive Engine means it is constantly refining its own logic based on what is actually working in the current market, on the specific asset you are trading. It is tailored to the now.
The Inputs Menu - Your Command Center
Every setting is a lever of control, allowing you to tune the engine to your precise trading style and market focus.
🧠 Neural Core Engine
Analysis Depth: This is the primary lookback for the Bollinger Band and other core calculations. A shorter depth makes the indicator faster and more sensitive, ideal for scalping. A longer depth makes it slower and more stable, ideal for swing trading.
Minimum Probability %: This is your master signal filter. It sets the minimum Confluence Score required to plot a signal. Higher values (85-95) will give you only the highest-conviction A+ setups. Lower values (70-80) will show more potential opportunities.
🤖 Adaptive Neural Learning
Enable Adaptive Learning Engine: Toggles the entire learning system. Disabling it will make the indicator's logic static.
Peak/Valley Success Threshold (ATR): This defines what constitutes a "successful" trade for the learning engine. A value of 1.5 means price must move 1.5x the ATR in your favor for the signal to be marked as a win. Adjust this to match your personal take-profit strategy.
Adaptive Mode: This dictates how the engine uses its hitRate. "Quality Control" is recommended for its intelligent filtering. "Aggressive" will always boost signal scores, useful for finding more setups in a known, trending environment.
Asymmetric Balance: Allows you to apply a "boost" to either peak (short) or valley (long) signals. If you find the market you're trading has stronger long reversals, you can increase the "Valley Signal Boost" to catch them more effectively.
🛡️ Elite Filters
Market Noise Filter: An exceptional tool for avoiding choppy markets. It counts the number of directional changes in the last 5 bars. If the market is whipping back and forth too much, it will block the signal. Lower the "Max Direction Changes" to be extremely selective.
Volume Filter: Requires signal confirmation from a significant volume spike. The "Volume Multiplier" dictates how large this spike must be (e.g., 1.2 = 20% above average volume). This is invaluable for filtering out low-conviction moves in stocks and crypto.
The Dashboard - Your Analytical Co-Pilot
The dashboard is not just a set of numbers; it is a holistic overview of the market's health and the engine's current state.
Unified AI Score: This section provides the most critical, at-a-glance information. "Total Score" is the current probability reading, while "Quality" gives you a human-readable interpretation. "Win Rate" shows the real-time performance of the Adaptive Engine.
Order Flow (OFPI): This measures the "weight" of money behind recent price moves by analyzing price change relative to volume. A high positive OFPI suggests strong buying pressure, while a high negative value suggests strong selling pressure. It gives you a peek into the market's underlying flow.
Component Analysis: This allows you to see the individual "Peak" and "Valley" confidence scores before they are filtered, giving you insight into building momentum before a signal forms.
Market Structure: This panel assesses the broader environment. "HTF Trend" tells you the direction of the larger trend (based on EMAs), while "Vol Regime" tells you if the market is in a high, medium, or low volatility state. Use this to align your signals with the broader market context.
Filter & Engine Statistics: Available on the "Large" dashboard, this provides deep insight into how many signals are being blocked by your filters and the current status of the Adaptive Engine's multiplier.
The Visual Interface - A Symphony of Data
Every visual element on the chart is designed for instant interpretation and insight.
Signal Markers: Simple, clean triangles mark the exact bar of a valid signal. A box is drawn around the high/low of the signal bar to highlight the precise point of inflection.
Dynamic Support/Resistance Zones: These are the glowing lines on your chart. They are not static lines; they are dynamic levels that represent the current battlefield between buyers and sellers.
Cyber Cyan (Valley Blue): This is the current Support Zone. This is the price level the market is currently trying to defend.
Neural Pink (Peak Red): This is the current Resistance Zone. This is the price level the market is currently trying to break through.
Grey (Next Level): This line is a projection, based on the current momentum and the size of the S/R range, of where the next major level of conflict will likely be. It acts as a potential price target.
Development & Philosophy
Inflection Point was not assembled; it was engineered. It represents hundreds of hours of research into market dynamics, statistical analysis, and machine learning principles. The goal was to create a tool that moves beyond the limitations of traditional technical analysis, which often fails in modern, algorithm-driven markets. By building a system based on multi-factor confluence and self-adaptive logic, Inflection Point provides a quantifiable, statistical edge that is simply unattainable with simpler tools. This is the result of a relentless pursuit of a better, more intelligent way to trade.
Universal Applicability
The principles of momentum, exhaustion, and velocity are universal to all freely traded markets. Because of its adaptive core and robust filtering options, Inflection Point has proven to be exceptionally effective on any security (stocks, crypto, forex, indices, futures) and on any timeframe (from 1-minute scalping charts to daily swing trading charts).
" Markets are constantly in a state of uncertainty and flux and money is made by discounting the obvious and betting on the unexpected. "
— George Soros
Trade with insight. Trade with anticipation.
— Dskyz, for DAFE Trading Systems
Volume Point of Control with Fib Based Profile🍀Description:
This indicator is a comprehensive volume profile analysis tool designed to identify key price levels based on trading activity within user-defined timeframes. It plots the Point of Control (POC), Value Area High (VAH), and Value Area Low (VAL), along with dynamically calculated Fibonacci levels derived from the developing period's range. It offers extensive customization for both historical and developing levels.
🍀Core Features:
Volume Profiling (POC, VAH, VAL):
Calculates and plots the POC (price level with the highest volume), VAH, and VAL for a selected timeframe (e.g., Daily, Weekly).
The Value Area percentage is configurable. 70% is common on normal volume profiles, but this script allows you to configure multiple % levels via the fib levels. I recommend using 2 versions of this indicator on a chart, one has Value Area at 1 (100% - high and low of lookback) and the second is a specified VA area (i.e. 70%) like in the chart snapshot above. See examples at the bottom.
Historical Levels:
Plots POC, VAH, and VAL from previous completed periods.
Optionally displays only "Unbroken" levels – historical levels that price has not yet revisited, which can act as stronger magnets or resistance/support.
The user can manage the number of historical lines displayed to prevent chart clutter.
Developing Levels:
Shows the POC, VAH, and VAL as they form in real-time during the current, incomplete period. This provides insight into intraday/intra-period value migration.
Dynamic Fibonacci Levels:
Calculates and plots Fibonacci retracement/extension levels based dynamically on the range between the developing POC and the developing VAH/VAL.
Offers 8 configurable % levels above and below POC that can be toggled on/off.
Visual Customization:
Extensive options for colors, line styles, and widths for all plotted levels.
Optional gradient fill for the Value Area that visualizes current price distance from POC - option to invert the colors as well.
Labels for developing levels and Fibonacci levels for easy identification.
🍀Characteristics:
Volume-Driven: Levels are derived from actual trading volume, reflecting areas of high participation and price agreement/disagreement.
Timeframe Specific: The results are entirely dependent on the chosen profile timeframe.
Dynamic & Static Elements: Developing levels and Fibs update live, while historical levels remain fixed once their period closes.
Lagging (Historical) & Potentially Leading: Historical levels are based on the past, but are often respected by future price action. Developing levels show current dynamics.
🍀How to Use It:
Identifying Support & Resistance: Historical and developing POCs, VAHs, and VALs are often key areas where price may react. Unbroken levels are particularly noteworthy.
Market Context & Sentiment: Trading above the POC suggests bullish strength/acceptance of higher prices, while trading below suggests bearishness/acceptance of lower prices.
Entry/Exit Zones: Interactions with these levels (rejections, breakouts, tests) can provide potential entry or exit signals, especially when confirming with other analysis methods.
Dynamic Targets: The Fibonacci levels calculated from the developing POC-VA range offer potential intraday/intra-period price targets or areas of interest.
Understanding Value Migration: Observing the movement of the developing POC/VAH/VAL throughout the period reveals where value is currently being established.
🍀Potential Drawbacks:
Input Sensitivity: The choice of timeframe, Value Area percentage, and volume resolution heavily influences the generated levels. Experimentation is needed for optimal settings per instrument/market. (I've found that Range Charts can provide very accurate volume levels on TV since the time element is removed. This helps to refine the accuracy of price levels with high volume.)
Volume Data Dependency: Requires accurate volume data. May be less reliable on instruments with sparse or questionable volume reporting.
Chart Clutter: Enabling all features simultaneously can make the chart busy. Utilize the line management inputs and toggle features as needed.
Not a Standalone Strategy: This indicator provides context and key levels. It should be used alongside other technical analysis tools and price action reading for robust decision-making.
Developing Level Fluctuation: Developing POC/VA/Fib levels can shift considerably, especially early in a new period, before settling down as more volume accumulates and time passes.
🍀Recommendations/Examples:
I recommend have this indicator on your chart twice, one has the VA set at 1 (100%) and has the fib levels plotted. The second has the VA set to 0.7 (70%) to highlight the defined VA.
Here is an example with 3 on a chart. VA of 100%, VA of 80%, and VA of 20%
AWR R & LR Oscillator with plots & tableHello trading viewers !
I'm glad to share with you one of my favorite indicator. It's the aggregate of many things. It is partly based on an indicator designed by gentleman goat. Many thanks to him.
1. Oscillator and Correlation Calculations
Overview and Functionality: This part of the indicator computes up to 10 Pearson correlation coefficients between a chosen source (typically the close price, though this is user-configurable) and the bar index over various periods. Starting with an initial period defined by the startPeriod parameter and increasing by a set increment (periodIncrement), each correlation coefficient is calculated using the built-in ta.correlation function over successive ranges. These coefficients are stored in an array, and the indicator calculates their average (avgPR) to provide a complete view of the market trend strength.
Display Features: Each individual coefficient, as well as the overall average, is plotted on the chart using a specific color. Horizontal lines (both dashed and solid) are drawn at levels 0, ±0.8, and ±1, serving as visual thresholds. Additionally, conditional fills in red or blue highlight when values exceed these thresholds, helping the user quickly identify potential extreme conditions (such as overbought or oversold situations).
2. Visual Signals and Automated Alerts
Graphical Signal Enhancements: To reinforce the analysis, the indicator uses graphical elements like emojis and shape markers. For example:
If all 10 curves drop below -0.79, a 🌋 emoji appears at the bottom of the chart;
When curves 2 through 10 are below -0.79, a ⛰️ emoji is displayed below the bar, potentially serving as a buy signal accompanied by an alert condition;
Likewise, symmetrical conditions for correlations exceeding 0.79 produce corresponding emojis (🤿 and 🏖️) at the top or bottom of the chart.
Alerts and Notifications: Using these visual triggers, several alertcondition statements are defined within the script. This allows users to set up TradingView alerts and receive real-time notifications whenever the market reaches these predefined critical zones identified by the multi-period analysis.
3. Regression Channel Analysis
Principles and Calculations: In addition to the oscillator, the indicator implements an analysis of regression channels. For each of the 8 configurable channels, the user can set a range of periods (for example, min1 to max1, etc.). The function calc_regression_channel iterates through the defined period range to find the optimal period that maximizes a statistical measure derived from a regression parameter calculated by the function r(p). Once this optimal period is identified, the indicator computes two key points (A and B) which define the main regression line, and then creates a channel based on the calculated deviation (an RMSE multiplied by a user-defined factor).
The regression channels are not displayed on the chart but are used to plot shapes & fullfilled a table.
Blue shapes are plotted when 6th channel or 7th channel are lower than 3 deviations
Yellow shapes are plotted when 6th channel or 7th channel are higher than 3 deviations
4. Scores, Conditions, and the Summary Table
Scoring System: The indicator goes further by assigning scores across multiple analytical categories, such as:
1. BigPear Score
What It Represents: This score is based on a longer-term moving average of the Pearson correlation values (SMA 100 of the average of the 10 curves of correlation of Pearson). The BigPear category is designed to capture where this longer-term average falls within specific ranges.
Conditions: The script defines nine boolean conditions (labeled BigPear1up through BigPear9up for the “up” direction).
Here's the rules :
BigPear1up = (bigsma_avgPR <= 0.5 and bigsma_avgPR > 0.25)
BigPear2up = (bigsma_avgPR <= 0.25 and bigsma_avgPR > 0)
BigPear3up = (bigsma_avgPR <= 0 and bigsma_avgPR > -0.25)
BigPear4up = (bigsma_avgPR <= -0.25 and bigsma_avgPR > -0.5)
BigPear5up = (bigsma_avgPR <= -0.5 and bigsma_avgPR > -0.65)
BigPear6up = (bigsma_avgPR <= -0.65 and bigsma_avgPR > -0.7)
BigPear7up = (bigsma_avgPR <= -0.7 and bigsma_avgPR > -0.75)
BigPear8up = (bigsma_avgPR <= -0.75 and bigsma_avgPR > -0.8)
BigPear9up = (bigsma_avgPR <= -0.8)
Conditions: The script defines nine boolean conditions (labeled BigPear1down through BigPear9down for the “down” direction).
BigPear1down = (bigsma_avgPR >= -0.5 and bigsma_avgPR < -0.25)
BigPear2down = (bigsma_avgPR >= -0.25 and bigsma_avgPR < 0)
BigPear3down = (bigsma_avgPR >= 0 and bigsma_avgPR < 0.25)
BigPear4down = (bigsma_avgPR >= 0.25 and bigsma_avgPR < 0.5)
BigPear5down = (bigsma_avgPR >= 0.5 and bigsma_avgPR < 0.65)
BigPear6down = (bigsma_avgPR >= 0.65 and bigsma_avgPR < 0.7)
BigPear7down = (bigsma_avgPR >= 0.7 and bigsma_avgPR < 0.75)
BigPear8down = (bigsma_avgPR >= 0.75 and bigsma_avgPR < 0.8)
BigPear9down = (bigsma_avgPR >= 0.8)
Weighting:
If BigPear1up is true, 1 point is added; if BigPear2up is true, 2 points are added; and so on up to 9 points from BigPear9up.
Total Score:
The positive score (posScoreBigPear) is the sum of these weighted conditions.
Similarly, there is a negative score (negScoreBigPear) that is calculated using a mirrored set of conditions (named BigPear1down to BigPear9down), each contributing a negative weight (from -1 to -9).
In essence, the BigPear score tells you—in a weighted cumulative way—where the longer-term correlation average falls relative to predefined thresholds.
2. Pear Score
What It Represents: This category uses the immediate average of the Pearson correlations (avgPR) rather than a longer-term smoothed version. It reflects a more current picture of the market’s correlation behavior.
How It’s Calculated:
Conditions: There are nine conditions defined for the “up” scenario (named Pear1up through Pear9up), which partition the range of avgPR into intervals. For instance:
Pear1up = (avgPR > -0.2 and avgPR <= 0)
Pear2up = (avgPR > -0.4 and avgPR <= -0.2)
Pear3up = (avgPR > -0.5 and avgPR <= -0.4)
Pear4up = (avgPR > -0.6 and avgPR <= -0.5)
Pear5up = (avgPR > -0.65 and avgPR <= -0.6)
Pear6up = (avgPR > -0.7 and avgPR <= -0.65)
Pear7up = (avgPR > -0.75 and avgPR <= -0.7)
Pear8up = (avgPR > -0.8 and avgPR <= -0.75)
Pear9up = (avgPR > -1 and avgPR <= -0.8)
There are nine conditions defined for the “down” scenario (named Pear1down through Pear9down), which partition the range of avgPR into intervals. For instance:
Pear1down = (avgPR >= 0 and avgPR < 0.2)
Pear2down = (avgPR >= 0.2 and avgPR < 0.4)
Pear3down = (avgPR >= 0.4 and avgPR < 0.5)
Pear4down = (avgPR >= 0.5 and avgPR < 0.6)
Pear5down = (avgPR >= 0.6 and avgPR < 0.65)
Pear6down = (avgPR >= 0.65 and avgPR < 0.7)
Pear7down = (avgPR >= 0.7 and avgPR < 0.75)
Pear8down = (avgPR >= 0.75 and avgPR < 0.8)
Pear9down = (avgPR >= 0.8 and avgPR <= 1)
Weighting:
Each condition has an associated weight, such as 0.9 for Pear1up, 1.9 for Pear2up, and so on, up to 9 for Pear9up.
Sum up :
Pear1up = 0.9
Pear2up = 1.9
Pear3up = 2.9
Pear4up = 3.9
Pear5up = 4.99
Pear6up = 6
Pear7up = 7
Pear8up = 8
Pear9up = 9
Total Score:
The positive score (posScorePear) is the sum of these values for each condition that returns true.
A corresponding negative score (negScorePear) is calculated using conditions for when avgPR falls on the positive side, with similar weights in the negative direction.
This score quantifies the current correlation reading by translating its relative level into a numeric score through a weighted sum.
3. Trendpear Score
What It Represents: The Trendpear score is more dynamic as it compares the current avgPR with its short-term moving average (sma_avgPR / 14 periods ) and also considers its relationship with an even longer moving average (bigsma_avgPR / 100 periods). It is meant to capture the trend or momentum in the correlation behavior.
How It’s Calculated:
Conditions: Nine conditions (from Trendpear1up to Trendpear9up) are defined to check:
Whether avgPR is below, equal to, or above sma_avgPR by different margins;
Whether it is trending upward (i.e., it is higher than its previous value).
Here are the rules
Trendpear1up = (avgPR <= sma_avgPR -0.2) and (avgPR >= avgPR )
Trendpear2up = (avgPR > sma_avgPR -0.2) and (avgPR <= sma_avgPR -0.07) and (avgPR >= avgPR )
Trendpear3up = (avgPR > sma_avgPR -0.07) and (avgPR <= sma_avgPR -0.03) and (avgPR >= avgPR )
Trendpear4up = (avgPR > sma_avgPR -0.03) and (avgPR <= sma_avgPR -0.02) and (avgPR >= avgPR )
Trendpear5up = (avgPR > sma_avgPR -0.02) and (avgPR <= sma_avgPR -0.01) and (avgPR >= avgPR )
Trendpear6up = (avgPR > sma_avgPR -0.01) and (avgPR <= sma_avgPR -0.001) and (avgPR >= avgPR )
Trendpear7up = (avgPR >= sma_avgPR) and (avgPR >= avgPR ) and (avgPR <= bigsma_avgPR)
Trendpear8up = (avgPR >= sma_avgPR) and (avgPR >= avgPR ) and (avgPR >= bigsma_avgPR -0.03)
Trendpear9up = (avgPR >= sma_avgPR) and (avgPR >= avgPR ) and (avgPR >= bigsma_avgPR)
Weighting:
The weights here are not linear. For example, the lightest condition may add 0.1 point, whereas the most extreme condition (e.g., when avgPR is not only above the moving average but also reaches a high proportion relative to bigsma_avgPR) might add as much as 90 points.
Trendpear1up = 0.1
Trendpear2up = 0.2
Trendpear3up = 0.3
Trendpear4up = 0.4
Trendpear5up = 0.5
Trendpear6up = 0.69
Trendpear7up = 7
Trendpear8up = 8.9
Trendpear9up = 90
Total Score:
The positive score (posScoreTrendpear) is the sum of the weights from all conditions that are satisfied.
A negative counterpart (negScoreTrendpear) exists similarly for when the trend indicates a downward bias.
Trendpear integrates both the level and the direction of change in the correlations, giving a strong numeric indication when the market starts to diverge from its short-term average.
4. Deviation Score
What It Represents: The “Écart” score quantifies how far the asset’s price deviates from the boundaries defined by the regression channels. This metric can indicate if the price is excessively deviating—which might signal an eventual reversion—or confirming a breakout.
How It’s Calculated:
Conditions: For each channel (with at least seven channels contributing to the scoring from the provided code), there are three levels of deviation:
First tier (EcartXup): Checks if the price is below the upper boundary but above a second boundary.
Second tier (EcartXup2): Checks if the price has dropped further, between a lower and a more extreme boundary.
Third tier (EcartXup3): Checks if the price is below the most extreme limit.
Weighting:
Each tier within a channel has a very small weight for the lowest severities (for example, 0.0001 for the first tier, 0.0002 for the second, 0.0003 for the third) with weights increasing with the channel index.
First channel : 0.0001 to 0.0003 (very short term)
Second channel : 0.001 to 0.003 (short term)
Third channel : 0.01 to 0.03 (short mid term)
4th channel : 0.1 to 0.3 ( mid term)
5th channel: 1 to 3 (long mid term)
6th channel : 10 to 30 (long term)
7th channel : 100 to 300 (very long term)
Total Score:
The overall positive score (posScoreEcart) is the sum of all the weights for conditions met among the first, second, and third tiers.
The corresponding negative score (negScoreEcart) is calculated similarly (using conditions when the price is above the channel boundaries), with the weights being the same in magnitude but negative in sign.
This layered scoring method allows the indicator to reflect both minor and major deviations in a gradated and cumulative manner.
Example :
Score + = 321.0001
Score - = -0.111
The asset price is really overextended in long term view, not for mid term & short term expect the in the very short term.
Score + = 0.0033
Score - = -1.11
The asset price is really extended in short term view, not for mid term (even a bit underextended) & long term is neutral
5. Slope Score
What It Represents: The Slope score captures the trend direction and steepness of the regression channels. It reflects whether the regression line (and hence the underlying trend) is sloping upward or downward.
How It’s Calculated:
Conditions:
if the slope has a uptrend = 1
if the slope has a downtrend = -1
Weighting:
First channel : 0.0001 to 0.0003 (very short term)
Second channel : 0.001 to 0.003 (short term)
Third channel : 0.01 to 0.03 (short mid term)
4th channel : 0.1 to 0.3 ( mid term)
5th channel: 1 to 3 (long mid term)
6th channel : 10 to 30 (long term)
7th channel : 100 to 300 (very long term)
The positive slope conditions incrementally add weights from 0.0001 for the smallest positive slopes to 100 for the largest among the seven checks. And negative for the downward slopes.
The positive score (posScoreSlope) is the sum of all the weights from the upward slope conditions that are met.
The negative score (negScoreSlope) sums the negative weights when downward conditions are met.
Example :
Score + = 111
Score - = -0.1111
Trend is up for longterm & down for mid & short term
The slope score therefore emphasizes both the magnitude and the direction of the trend as indicated by the regression channels, with an intentional asymmetry that flags strong downtrends more aggressively.
Summary
For each category—BigPear, Pear, Trendpear, Écart, and Slope—the indicator evaluates a defined set of conditions. Each condition is a binary test (true/false) based on different thresholds or comparisons (for example, comparing the current value to a moving average or a channel boundary). When a condition is true, its assigned weight is added to the cumulative score for that category. These individual scores, both positive and negative, are then displayed in a table, making it easy for the trader to see at a glance where the market stands according to each analytical dimension.
This comprehensive, weighted approach allows the indicator to encapsulate several layers of market information into a single set of scores, aiding in the identification of potential trading opportunities or market reversals.
5. Practical Use and Application
How to Use the Indicator:
Interpreting the Signals:
On your chart, observe the following components:
The individual correlation curves and their average, plotted with visual thresholds;
Visual markers (such as emojis and shape markers) that signal potential oversold or overbought conditions
The summary table that aggregates the scores from each category, offering a quick glance at the market’s state.
Trading Alerts and Decisions: Set your TradingView alerts through the alertcondition functions provided by the indicator. This way, you receive immediate notifications when critical conditions are met, allowing you to react as soon as the market reaches key levels. This tool is especially beneficial for advanced traders who want to combine multiple technical dimensions to optimize entry and exit points with a confluence of signals.
Conclusion and Additional Insights
In summary, this advanced indicator innovatively combines multi-scale Pearson correlation analysis (via multiple linear regressions) with robust regression channel analysis. It offers a deep and nuanced view of market dynamics by delivering clear visual signals and a comprehensive numerical summary through a built-in score table.
Combine this indicator with other tools (e.g., oscillators, moving averages, volume indicators) to enhance overall strategy robustness.
ADR, ATR & VOL OverlayThis is a combined version of 2 of my other indicators:
ADR / ATR Overlay
VOL / AVG Overlay
This indicator will display the following as an overlay on your chart:
ADR
% of ADR
ADR % of Price
ATR
% of ATR
ATR % of Price
Custom Session Volume
Average For Selected Session
Volume Percentage Comparison
Description:
ADR : Average Day Range
% of ADR : Percentage that the current price move has covered its average.
ADR % of Price : The percentage move implied by the average range.
ATR : Average True Range
% of ATR : Percentage that the current price move has covered its average.
ATR % of Price : The percentage move implied by the average true range.
Custom Session Volume : User chosen time frame to monitor volume
Average For Selected Session : Average for the custom session volume
Volume Percentage Comparison : Current session compared to the average (calculated at session close)
Options:
ADR/ATR:
Time Frame
Length
Smoothing
Volume:
Set Custom Time Frame For Calculations
Set Custom Time Frame For Average Comparison
Set Custom Time Zone
Table:
Enable / Disable Each Value
Change Text Color
Change Background Color
Change Table location
Add/Remove extra row for placement
ADR / ATR Example:
The ADR and ATR can be used to provide information about average price moves to help set targets, stop losses, entries and exits based on the potential average moves.
Example: If the "% of ADR" is reading 100%, then 100% of the asset's average price range has been covered, suggesting that an additional move beyond the range has a lower probability.
Example: "ADR % of Price" provides potential price movement in percentage which can be used to asses R/R for asset.
Example: ADR (D) reading is 100% at market close but ATR (D) is at 70% at close. This suggests that there is a potential (coverage) move of 30% in Pre/Post market as suggested by averages.
Custom Volume Session Example:
Set indicator to 30 period average. Set custom time frame to 9:30am to 10:30am Eastern/New York.
When the time frame for the calculation is closed, the indicator will provide a comparison of the current days volume compared to the average of 30 previous days for that same time frame and display it as a percentage in the table.
In this example you could compare how the first hour of the trading day compares to the previous 30 day's average, aiding in evaluating the potential volume for the remainder of the day.
Notes:
Times must be entered in 24 hour format. (1pm = 13:00 etc.)
Volume indicator is for Intra-day time frames, not > Day.
How I use these values:
I use these calculations to determine if a ticker symbol has the necessary range to achieve target gains, to determine if the price oscillation is within "normal" ranges to determine if the trading day will be choppy, and to determine placement of stops and targets within average ranges in combination with support, resistance and retracement levels.
Chart Patterns [ActiveQuants]The Chart Patterns indicator is a comprehensive tool designed to automatically identify a variety of common chart patterns directly on your price chart. By detecting sequences of pivot highs and lows , this indicator helps traders spot potential trend continuations , reversals , and key market structures such as Double Tops and Double Bottoms . Enhance your technical analysis by quickly recognizing these formations as they emerge.
How It Works
The indicator operates in a two-stage process:
Pivot Point Detection: It first identifies significant swing highs and swing lows (pivot points) based on a user-defined Period . These pivots form the fundamental building blocks for pattern recognition.
Pattern Recognition: Using the sequence of these detected pivot points, the script then applies logical rules to identify the following patterns:
Lower Low (LL)
Lower Low & Lower High (LL & LH)
Higher High (HH)
Higher High & Higher Low (HH & HL)
Double Tops
Double Bottoms
Patterns are drawn on the chart with connecting lines and labeled for easy identification. Double Tops and Double Bottoms also feature a status system: " Active " while forming, " Confirmed " upon neckline breakout, or " Invalid " if specific conditions negate the pattern before confirmation.
█ KEY FEATURES
Comprehensive Pattern Detection: Identifies six distinct types of chart patterns, offering insights into both trend continuation and potential reversals.
Pivot-Based Analysis: Uses a robust method of identifying pivot highs and lows as the foundation for pattern formation.
Pattern Status for Double Tops/Bottoms:
- Active: A Double Top or Double Bottom pattern has formed its two peaks/troughs and the intervening neckline point, but the price has not yet broken beyond the neckline. The pattern is developing .
- Confirmed: The price has decisively closed beyond the neckline (below for Double Top, above for Double Bottom), signaling a potential entry or validation of the pattern.
- Invalid: An " Active " Double Top or Double Bottom pattern can be invalidated if, before a neckline breakout occurs, a new pivot point forms that negates the pattern’s structural integrity. For example, if a new pivot low forms above or at the neckline of an Active Double Top, the pattern is considered invalid because the market failed to break down and instead showed relative strength.
Customizable Visuals: Allows users to define colors for bullish and bearish patterns, line widths, and the visibility of pivot points.
Selective Pattern Display: Users can choose to display all patterns or filter by status (Active, Confirmed, Invalid) for Double Tops/Bottoms. Individual pattern types can also be toggled on or off.
Historical Analysis Control: The Show Last History (Bars) input allows users to specify how far back the indicator should plot patterns, optimizing performance and chart readability.
Clear Labeling: Patterns are clearly labeled on the chart, with Double Tops/Bottoms also showing " Top 1 ," " Top 2 ," or " Bottom 1 ," " Bottom 2 " labels.
█ PATTERNS DETECTED
Lower Low (LL): Indicates a potential bearish continuation or the start of a downtrend. Forms when price makes a lower low during an uptrend.
Lower Low & Lower High (LL & LH): A stronger confirmation of a bearish trend, where the market forms a lower low followed by a lower high .
Higher High (HH): Signals a potential bullish continuation or the start of an uptrend. Forms when price makes a higher high during a downtrend.
Higher High & Higher Low (HH & HL): A stronger confirmation of a bullish trend, where the market forms a higher high followed by a higher low .
Double Top: A bearish reversal pattern characterized by two distinct peaks at roughly the same price level, separated by a trough (neckline). Confirmation occurs when price breaks below the neckline.
Double Bottom: A bullish reversal pattern featuring two distinct troughs at roughly the same price level, separated by a peak (neckline). Confirmation occurs when price breaks above the neckline.
█ EXAMPLE: DOUBLE TOP INVALIDATION
Understanding how a Double Top or Double Bottom can be invalidated is crucial. Here's an example for a Double Top:
Formation: The indicator identifies two peaks (Top 1, Top 2) at a similar price level, with a corrective trough (Neckline Pivot P5) in between. The pattern is labeled " Double Top " and is in an " Active " state. ( Imagine points P4 and P6 are the two tops, and P5 is the low point of the neckline between them ).
Pre-Breakout Condition: The price action continues, but before it breaks decisively below the P5 neckline level, a new significant swing low (a new pivot low) forms.
Invalidation Check: The indicator checks the price level of this new pivot low. If this new pivot low occurs at a price equal to or higher than the P5 neckline level, the " Active " Double Top pattern is re-labeled as " Invalid Double Top ". ( See image below for a visual representation of this scenario )
In this example, the Double Top formed with Top 1 (P4) and Top 2 (P6). The neckline is at P5. Before price broke below P5, a new pivot low formed at the red circle. Since this new pivot low is above the P5 neckline, the Double Top is marked " Invalid ".
The logic is that the market failed to break the neckline support and instead established a higher low (or a low at the support level), suggesting that the immediate bearish pressure has waned, thus invalidating the bearish reversal implication of the Double Top before it could confirm. A similar logic applies to Double Bottoms (a new pivot high forming below or at the neckline before an upside breakout).
█ USER INPUTS
Visibility and Common Styling
- Show Last History (Bars):
Specifies the number of recent bars the indicator will analyze and plot patterns on.
Default: 3000 bars. Min: 10.
- Patterns:
Filters which patterns are displayed based on their status.
Options: All, Active, Confirmed, Invalid.
Default: All.
- Pattern Line Width:
Sets the thickness of the lines used to draw the patterns.
Default: 1. Min: 1, Max: 10.
- Bearish Color:
Color for bearish patterns (LL, LL & LH, Double Tops).
Default: Red.
- Bullish Color:
Color for bullish patterns (HH, HH & HL, Double Bottoms).
Default: Green.
Pivot Points
- Period:
The lookback period on either side of a bar to qualify it as a pivot high or low. Higher values detect more significant pivots.
Default: 10 bars. Min: 2.
- Show Pivot Highs:
Toggles the visibility of detected pivot high markers.
Default: Enabled.
- Show Pivot Lows:
Toggles the visibility of detected pivot low markers.
Default: Enabled.
- Pivot Highs Color:
Color for the pivot high markers.
Default: #ff5252 (Reddish).
- Pivot Lows Color:
Color for the pivot low markers.
Default: #089981 (Greenish).
Patterns (Toggles)
- Lower Low:
Enable/disable detection and display of Lower Low patterns.
Default: Enabled.
- Lower Low & Lower High:
Enable/disable detection and display of Lower Low & Lower High patterns.
Default: Enabled.
- Higher High:
Enable/disable detection and display of Higher High patterns.
Default: Enabled.
- Higher High & Higher Low:
Enable/disable detection and display of Higher High & Higher Low patterns.
Default: Enabled.
- Double Tops:
Enable/disable detection and display of Double Top patterns.
Default: Enabled.
- Double Bottoms:
Enable/disable detection and display of Double Bottom patterns.
Default: Enabled.
█ CONCLUSION
The Chart Patterns indicator is a versatile and powerful assistant for traders who utilize classical chart pattern analysis. By automating the detection of key formations and providing clear visual cues along with status updates for patterns like Double Tops and Bottoms, it allows traders to focus on strategy development and execution. With its customizable settings, it can be adapted to various instruments and timeframes, making it a valuable addition to any technical trader's toolkit.
█ IMPORTANT NOTES
⚠ Pivot Period Sensitivity: The Period setting for pivot detection is crucial. A shorter period will identify more frequent, smaller swings, while a longer period will focus on more significant turning points. Adjust this setting based on the asset's volatility, the timeframe you are trading and your trading style.
⚠ Confirmation is Key: While the indicator identifies patterns, always wait for pattern confirmation (e.g., neckline breaks for Double Tops/Bottoms) and consider other factors like volume and market context before making trading decisions.
⚠ Confirmed Bars for Detection: Patterns are identified based on confirmed pivot points, which means a pivot is recognized period bars after it has formed. Status updates for Double Tops/Bottoms (Active, Confirmed, Invalid) also occur on confirmed bars. This approach enhances reliability and reduces the likelihood of repainting based on intra-bar price fluctuations.
⚠ Not a Standalone System: Chart patterns provide valuable insights, but they should be used in conjunction with other technical analysis tools (e.g., trendlines, moving averages, oscillators) and a sound risk management plan.
⚠ Lagging Nature: By their very definition, chart patterns are lagging indicators as they require a sequence of price action and several pivot points to complete their formation.
█ RISK DISCLAIMER
Trading involves a substantial risk of loss and is not suitable for every investor. The information provided by the Chart Patterns indicator is for educational and informational purposes only. It should not be considered as financial advice or a recommendation to buy or sell any security. Chart patterns indicate potential price movements but do not guarantee future results. Always perform your own due diligence and consult with a qualified financial advisor before making any investment decisions. Past performance is not indicative of future results.
📈 Happy trading! 🚀
D3m4h GIFVGDescription
D3m4h GIFVG is an indicator designed to automatically detect market imbalances—often referred to as FVGs (Fair Value Gaps)—and potential pivot-based shifts in market structure. It offers a dynamic approach to visualizing supply/demand inefficiencies and pivot-based trend changes. Key features include:
1. Pivot-Based Bullish/Bearish Detection
The indicator identifies higher-high/lower-low pivot logic as well as “outside bar” pivots.
It tracks when the market transitions from bullish to bearish ranges, or vice versa, by using multiple checks:
Pivot low/high detection
Break-of-structure (when price crosses the last pivot)
Opposing FVG detection to confirm an intraday pivot shift
2. FVG (Fair Value Gap) Detection
The script automatically scans for bullish or bearish FVG conditions:
Bullish FVG: Candle at position (bar_index - 2) has a high below the current candle’s low.
Bearish FVG: Candle at position (bar_index - 2) has a low above the current candle’s high.
When it detects an FVG, it draws a box on the chart to highlight the price gap (yellow boxes by default).
3. Pivot Range FVG
If an FVG forms while the market is in a bullish pivot range, the script can paint a special “blue” FVG to underscore its significance. The same logic applies if a newly formed FVG appears in a bearish pivot range.
4. Filled Gap Cleanup
You can optionally hide standard FVG boxes once they’re filled. For example, if the candle’s body (or candle range) covers that gap, the box is removed to keep your chart clean.
5. Pivot-Range FVG “Raided” Cleanup
If the pivot-based FVG is later filled from the opposing direction, it turns green and can optionally remove itself after a set number of bars.
6. Informative Table
A small table on the chart optionally displays whether or not the pivot-based FVG has been “raided”. You can toggle this table on/off in the settings.
How It Works
1. Pivot Shifts
The script tracks the last pivot high/low using a combination of candle-based pivot detection and break-of-structure checks (when price crosses the last pivot in the opposite direction).
When a shift is detected, the pivot range ID increments—this helps the script know when to remove old pivot-based FVGs or draw new ones.
2. FVG Formation
Each new bar checks if a bullish or bearish FVG formed (comparing the high of bar two bars ago to the current low, or the low of bar two bars ago to the current high).
If one is found, a box is drawn to highlight the imbalance. Its color and extension depend on script settings.
3. Imbalance or Pivot FVG
Standard imbalance boxes appear in yellow.
If the new imbalance coincides with a bullish or bearish pivot range, a special “pivot imbalance” box in blue is drawn.
3. Hide Filled
If a newly formed candle’s body fully covers the FVG, the box is considered filled. If Hide Filled Gaps is enabled, the box is deleted once it’s covered.
4. Raid Status
For the pivot-based (blue) FVG, once price invalidates it from the opposite side, it changes color to green and gets removed after a user-defined number of bars.
How to Use
1. Look for FVGs
Observe yellow boxes to identify potential intraday imbalances. Watch for price returning to fill these zones.
If you see a “blue” box, it signifies a pivot-based FVG in line with a recognized shift in structure—arguably a higher-probability zone.
2. “Hide Filled Gaps”
Turn this on if you only want to see currently active or partially filled imbalances. The script cleans up old, fully covered boxes to keep your chart neat.
3. Pivot Shifts
Note the script’s internal pivot logic. Each new pivot re-defines bullish or bearish states. Use these states to gauge the short-term trend shifts.
4. Toggle the Table
You can show or hide the chart table by enabling/disabling “Show Table” from the inputs. This table indicates if the pivot-based “GIFVG” has been “raided” or not.
5. Extend Count
Adjust the extendCount in the code if you want FVG boxes to extend further or shorter in time.
Underlying Concepts
Fair Value Gaps
Market inefficiencies that occur when price jumps, leaving a “gap” from the candle 2 bars ago to the current candle. They can act like mini supply/demand zones where price may revisit for balance.
Pivot Ranges
The script tries to maintain an internal sense of whether the market is in a bullish or bearish pivot range. When it sees a contrary FVG or break-of-structure, it flips the pivot state.
Outside Bars
A candle that has both a higher high and a lower low than the previous bar. The script uses these to mark significant pivot shifts.
By combining pivot-based logic with FVG detection, the D3m4h GIFVG indicator helps highlight potential areas of liquidity or unfilled value. Traders can use these zones to plan entries/exits or to confirm short-term trend shifts.
ADR Checker - Breakouts📈 ADR Checker – Breakouts
Gain the edge by knowing when a stock has already made its move.
🚀 What It Does:
The ADR Checker - Breakouts is a powerful yet simple visual tool that helps traders instantly assess whether a stock has already exceeded its Average Daily Range (ADR) for the day — a critical piece of information for momentum traders, swing traders, and especially those following breakout, VCP, or CANSLIM strategies.
Using a customizable on-screen table that always stays in view (regardless of zoom or chart scaling), this script shows:
✅ Average ADR% – 20-day average range, calculated in %.
📊 Today’s Move – how much the stock has moved today.
🔥 % of Avg ADR – today's move relative to its historical average, with live color feedback:
🟥 Over 100% (Overextended – danger!)
🟧 70-100% (Caution zone)
🟩 Below 70% (Room to move)
💡 Why It Matters:
One of the most overlooked mistakes by breakout traders is entering a trade after the move has already happened. If a stock has already moved more than its typical daily range, the odds of further continuation sharply decrease, while the risk of pullback or chop increases.
With this tool, you can:
🚫 Avoid chasing extended breakouts
🎯 Time entries before the real move
⚠️ Quickly assess risk/reward potential intraday
🧠 Example Use Case:
Imagine you're watching a classic VCP setup or flat base breakout. The stock breaks out on volume—but when you check this indicator, you see:
Today’s Move: 7.2%
Avg ADR: 5.3%
% of ADR: 135% 🟥
This tells you the stock is already well beyond its average daily range. While it may continue higher, odds now favor a consolidation, shakeout, or pullback. This is your cue to wait for a better entry or pass entirely.
On the flip side, if the breakout just started and the % of ADR is still under 50%, you have confirmation that there’s room to run — giving you more confidence to enter early.
⚙️ Fully Customizable:
Choose position on screen (top/bottom left/right)
Customize text color, background, and size
🔧 Install This Tool and:
✅ Stop chasing extended moves
✅ Add discipline to your entries
✅ Improve your breakout win rate
Perfect for VCP, CANSLIM, and BREAKOUT traders who want a clean, edge-enhancing visual guide.
Celestial Pair Spread Hello friends, after a very long time!
Today, I tried to put into code an idea that came to my mind spontaneously and suddenly.
Note :
This script is experimental and improvable.
I haven't had a chance to try it yet.
TIMEFRAME : 1D (Daily Bars)
CELESTIAL SPREAD
The spread moves in a very limited area and is consistent within itself, especially on days far from the end of the contract.
That's why there is a reassuring sky atmosphere. That's why this name was given completely improvised.
Basic logic of the script
We enter the name of the CME Futures contract we want to enter:
Ex : CL1! , ES1! , ZC1! , NQ1!
The script creates us a pair trade parity divided into secondary contracts.
Example : ES1!/ES2!
What is pair trading?
I will explain briefly here.
For users who are wondering:
www.investopedia.com
Let's get back to our topic.
Now we have created a parity that does not actually exist.
This parity is the manifestation of the relative movements of two contracts.
When the parity rises, ES1! increased,ES2! has fallen.
In the opposite case, We can say: ES1! Contract has been dropped ES2! has increased.
Pair trading is generally a trade that needs to be kept in mind from time to time.
It is a method preferred by professionals who can process very quickly.
Market risk is minimal, but since 2 contracts are purchased, more money is paid and very low percentage profits are made.
It is very expensive to do pair trading, especially with oil and its derivatives and interest security derivatives.
The contract we are considering has micros. (small-item contracts tied to the same value)
So when we switch to our broker MES1!/MES2! We will trade.
For all CME futures :
www.cmegroup.com
Anyway, let's continue:
The script created the parity showing its relationship with the next contract and plotted it as bars.
Celestial bands are just like Bollinger bands, but they consist of 3 bands based on percentage changes rather than standard deviation.
The middle band is obtained from moving averages.
The upper and lower bands are the middle band subjected to a threshold value.
The threshold value can be changed.
0.15 percent was charged for this script.
CAUTION :
As can be seen in the example below;
The most important thing is not to make any transactions when the contract switch dates are approaching.
Therefore, it is recommended to use it just below the main chart.
The blue bars in the parity are
Values that outside the upper and lower threshold values are colored blue.
For this condition
Alerts has been added.
Don't forget to add alert and edit.
MAIN PURPOSE
It is aimed to start a pair trade when such conditions come and to quickly close the trades when the parity basis reaches the value.
OTHER IMPORTANT POINTS
Other issues are broker related issues.
Difference between initial margins and maintanence margins of contracts (between 1! and 2!)
It shouldn't be too high.
The commission should not be too high.
Leverage must be high because the profit percentage is very low.
To calculate leverage you must divide your contract size by the relevant margin requirement.
Sample margin requirement table:
www.interactivebrokers.com
RISKS
It is an experimental and intellectual script,
the risk of contract price differences (maybe it will not leave a profit except for very extreme values)
I remind you of the quickness risk that comes from a two-legged trade.
Alerts definitely synchronized with an audible alert sent to a smartphone as an e-mail notification and displayed on the locked screen for quick action.
Best regards!
Overextension Oscillator [by DanielM]The Overextension Oscillator is an indicator that detects when a market move has extended significantly beyond its typical range, signaling potential areas for a correction or reversal. Unlike traditional oscillators that rely on fixed overbought/oversold levels, this tool dynamically adjusts its thresholds based on historical swing high and swing low movements.
By analyzing all swing points on the chart, the indicator determines the expected range of price movements and identifies when the price extends beyond normal levels. Since every asset has different price behavior and volatility, swing lengths may vary from asset to asset, ensuring that overextension is measured relative to each market's historical price behavior.
How It Works
1️⃣ Swing Detection & Data Collection
The indicator scans all available swing highs and swing lows on the chart to gather a complete dataset of past price fluctuations.
It records the percentage differences between swings to determine how much price typically moves in a given market.
2️⃣ Overextension Calculation
Using the stored swing data, the indicator calculates:
Average Swing Difference – Measures the average percentage difference between swings.
Average Move Percentage – Determines the typical magnitude of price moves within a trend cycle.
These values are used to create dynamic overextension thresholds that adjust based on historical data.
3️⃣ Price Distance & Overextension Measurement
The indicator calculates the distance between the current price and the closest historical swing point. If this distance exceeds the predefined threshold based on past swings, the move is considered overextended. The greater the deviation, the higher the probability of a pullback or short-term reversal.
4️⃣ Buy/Sell Signal Generation
A Buy signal is generated when the price has dropped below an overextended threshold relative to a past swing low.
A Sell signal is generated when the price has risen beyond an overextended threshold relative to a past swing high.
These signals indicate that the price has reached a level where it historically tends to slow down or reverse.
Accurate Bollinger Bands mcbw_ [True Volatility Distribution]The Bollinger Bands have become a very important technical tool for discretionary and algorithmic traders alike over the last decades. It was designed to give traders an edge on the markets by setting probabilistic values to different levels of volatility. However, some of the assumptions that go into its calculations make it unusable for traders who want to get a correct understanding of the volatility that the bands are trying to be used for. Let's go through what the Bollinger Bands are said to show, how their calculations work, the problems in the calculations, and how the current indicator I am presenting today fixes these.
--> If you just want to know how the settings work then skip straight to the end or click on the little (i) symbol next to the values in the indicator settings window when its on your chart <--
--------------------------- What Are Bollinger Bands ---------------------------
The Bollinger Bands were formed in the 1980's, a time when many retail traders interacted with their symbols via physically printed charts and computer memory for personal computer memory was measured in Kb (about a factor of 1 million smaller than today). Bollinger Bands are designed to help a trader or algorithm see the likelihood of price expanding outside of its typical range, the further the lines are from the current price implies the less often they will get hit. With a hands on understanding many strategies use these levels for designated levels of breakout trades or to assist in defining price ranges.
--------------------------- How Bollinger Bands Work ---------------------------
The calculations that go into Bollinger Bands are rather simple. There is a moving average that centers the indicator and an equidistant top band and bottom band are drawn at a fixed width away. The moving average is just a typical moving average (or common variant) that tracks the price action, while the distance to the top and bottom bands is a direct function of recent price volatility. The way that the distance to the bands is calculated is inspired by formulas from statistics. The standard deviation is taken from the candles that go into the moving average and then this is multiplied by a user defined value to set the bands position, I will call this value 'the multiple'. When discussing Bollinger Bands, that trading community at large normally discusses 'the multiple' as a multiplier of the standard deviation as it applies to a normal distribution (gaußian probability). On a normal distribution the number of standard deviations away (which trades directly use as 'the multiple') you are directly corresponds to how likely/unlikely something is to happen:
1 standard deviation equals 68.3%, meaning that the price should stay inside the 1 standard deviation 68.3% of the time and be outside of it 31.7% of the time;
2 standard deviation equals 95.5%, meaning that the price should stay inside the 2 standard deviation 95.5% of the time and be outside of it 4.5% of the time;
3 standard deviation equals 99.7%, meaning that the price should stay inside the 3 standard deviation 99.7% of the time and be outside of it 0.3% of the time.
Therefore when traders set 'the multiple' to 2, they interpret this as meaning that price will not reach there 95.5% of the time.
---------------- The Problem With The Math of Bollinger Bands ----------------
In and of themselves the Bollinger Bands are a great tool, but they have become misconstrued with some incorrect sense of statistical meaning, when they should really just be taken at face value without any further interpretation or implication.
In order to explain this it is going to get a bit technical so I will give a little math background and try to simplify things. First let's review some statistics topics (distributions, percentiles, standard deviations) and then with that understanding explore the incorrect logic of how Bollinger Bands have been interpreted/employed.
---------------- Quick Stats Review ----------------
.
(If you are comfortable with statistics feel free to skip ahead to the next section)
.
-------- I: Probability distributions --------
When you have a lot of data it is helpful to see how many times different results appear in your dataset. To visualize this people use "histograms", which just shows how many times each element appears in the dataset by stacking each of the same elements on top of each other to form a graph. You may be familiar with the bell curve (also called the "normal distribution", which we will be calling it by). The normal distribution histogram looks like a big hump around zero and then drops off super quickly the further you get from it. This shape (the bell curve) is very nice because it has a lot of very nifty mathematical properties and seems to show up in nature all the time. Since it pops up in so many places, society has developed many different shortcuts related to it that speed up all kinds of calculations, including the shortcut that 1 standard deviation = 68.3%, 2 standard deviations = 95.5%, and 3 standard deviations = 99.7% (these only apply to the normal distribution). Despite how handy the normal distribution is and all the shortcuts we have for it are, and how much it shows up in the natural world, there is nothing that forces your specific dataset to look like it. In fact, your data can actually have any possible shape. As we will explore later, economic and financial datasets *rarely* follow the normal distribution.
-------- II: Percentiles --------
After you have made the histogram of your dataset you have built the "probability distribution" of your own dataset that is specific to all the data you have collected. There is a whole complicated framework for how to accurately calculate percentiles but we will dramatically simplify it for our use. The 'percentile' in our case is just the number of data points we are away from the "middle" of the data set (normally just 0). Lets say I took the difference of the daily close of a symbol for the last two weeks, green candles would be positive and red would be negative. In this example my dataset of day by day closing price difference is:
week 1:
week 2:
sorting all of these value into a single dataset I have:
I can separate the positive and negative returns and explore their distributions separately:
negative return distribution =
positive return distribution =
Taking the 25th% percentile of these would just be taking the value that is 25% towards the end of the end of these returns. Or akin the 100%th percentile would just be taking the vale that is 100% at the end of those:
negative return distribution (50%) = -5
positive return distribution (50%) = +4
negative return distribution (100%) = -10
positive return distribution (100%) = +20
Or instead of separating the positive and negative returns we can also look at all of the differences in the daily close as just pure price movement and not account for the direction, in this case we would pool all of the data together by ignoring the negative signs of the negative reruns
combined return distribution =
In this case the 50%th and 100%th percentile of the combined return distribution would be:
combined return distribution (50%) = 4
combined return distribution (100%) = 10
Sometimes taking the positive and negative distributions separately is better than pooling them into a combined distribution for some purposes. Other times the combined distribution is better.
Most financial data has very different distributions for negative returns and positive returns. This is encapsulated in sayings like "Price takes the stairs up and the elevator down".
-------- III: Standard Deviation --------
The formula for the standard deviation (refereed to here by its shorthand 'STDEV') can be intimidating, but going through each of its elements will illuminate what it does. The formula for STDEV is equal to:
square root ( (sum ) / N )
Going back the the dataset that you might have, the variables in the formula above are:
'mean' is the average of your entire dataset
'x' is just representative of a single point in your dataset (one point at a time)
'N' is the total number of things in your dataset.
Going back to the STDEV formula above we can see how each part of it works. Starting with the '(x - mean)' part. What this does is it takes every single point of the dataset and measure how far away it is from the mean of the entire dataset. Taking this value to the power of two: '(x - mean) ^ 2', means that points that are very far away from the dataset mean get 'penalized' twice as much. Points that are very close to the dataset mean are not impacted as much. In practice, this would mean that if your dataset had a bunch of values that were in a wide range but always stayed in that range, this value ('(x - mean) ^ 2') would end up being small. On the other hand, if your dataset was full of the exact same number, but had a couple outliers very far away, this would have a much larger value since the square par of '(x - mean) ^ 2' make them grow massive. Now including the sum part of 'sum ', this just adds up all the of the squared distanced from the dataset mean. Then this is divided by the number of values in the dataset ('N'), and then the square root of that value is taken.
There is nothing inherently special or definitive about the STDEV formula, it is just a tool with extremely widespread use and adoption. As we saw here, all the STDEV formula is really doing is measuring the intensity of the outliers.
--------------------------- Flaws of Bollinger Bands ---------------------------
The largest problem with Bollinger Bands is the assumption that price has a normal distribution. This is assumption is massively incorrect for many reasons that I will try to encapsulate into two points:
Price return do not follow a normal distribution, every single symbol on every single timeframe has is own unique distribution that is specific to only itself. Therefore all the tools, shortcuts, and ideas that we use for normal distributions do not apply to price returns, and since they do not apply here they should not be used. A more general approach is needed that allows each specific symbol on every specific timeframe to be treated uniquely.
The distributions of price returns on the positive and negative side are almost never the same. A more general approach is needed that allows positive and negative returns to be calculated separately.
In addition to the issues of the normal distribution assumption, the standard deviation formula (as shown above in the quick stats review) is essentially just a tame measurement of outliers (a more aggressive form of outlier measurement might be taking the differences to the power of 3 rather than 2). Despite this being a bit of a philosophical question, does the measurement of outlier intensity as defined by the STDEV formula really measure what we want to know as traders when we're experiencing volatility? Or would adjustments to that formula better reflect what we *experience* as volatility when we are actively trading? This is an open ended question that I will leave here, but I wanted to pose this question because it is a key part of what how the Bollinger Bands work that we all assume as a given.
Circling back on the normal distribution assumption, the standard deviation formula used in the calculation of the bands only encompasses the deviation of the candles that go into the moving average and have no knowledge of the historical price action. Therefore the level of the bands may not really reflect how the price action behaves over a longer period of time.
------------ Delivering Factually Accurate Data That Traders Need------------
In light of the problems identified above, this indicator fixes all of these issue and delivers statistically correct information that discretionary and algorithmic traders can use, with truly accurate probabilities. It takes the price action of the last 2,000 candles and builds a huge dataset of distributions that you can directly select your percentiles from. It also allows you to have the positive and negative distributions calculated separately, or if you would like, you can pool all of them together in a combined distribution. In addition to this, there is a wide selection of moving averages directly available in the indicator to choose from.
Hedge funds, quant shops, algo prop firms, and advanced mechanical groups all employ the true return distributions in their work. Now you have access to the same type of data with this indicator, wherein it's doing all the lifting for you.
------------------------------ Indicator Settings ------------------------------
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---- Moving average ----
Select the type of moving average you would like and its length
---- Bands ----
The percentiles that you enter here will be pulled directly from the return distribution of the last 2,000 candles. With the typical Bollinger Bands, traders would select 2 standard deviations and incorrectly think that the levels it highlights are the 95.5% levels. Now, if you want the true 95.5% level, you can just enter 95.5 into the percentile value here. Each of the three available bands takes the true percentile you enter here.
---- Separate Positive & Negative Distributions----
If this box is checked the positive and negative distributions are treated indecently, completely separate from each other. You will see that the width of the top and bottom bands will be different for each of the percentiles you enter.
If this box is unchecked then all the negative and positive distributions are pooled together. You will notice that the width of the top and bottom bands will be the exact same.
---- Distribution Size ----
This is the number of candles that the price return is calculated over. EG: to collect the price return over the last 33 candles, the difference of price from now to 33 candles ago is calculated for the last 2,000 candles, to build a return distribution of 2000 points of price differences over 33 candles.
NEGATIVE NUMBERS(<0) == exact number of candles to include;
EG: setting this value to -20 will always collect volatility distributions of 20 candles
POSITIVE NUMBERS(>0) == number of candles to include as a multiple of the Moving Average Length value set above;
EG: if the Moving Average Length value is set to 22, setting this value to 2 will use the last 22*2 = 44 candles for the collection of volatility distributions
MORE candles being include will generally make the bands WIDER and their size will change SLOWER over time.
I wish you focus, dedication, and earnest success on your journey.
Happy trading :)
RSI Divergence + Sweep + Signal + Alerts Toolkit [TrendX_]The RSI Toolkit is a powerful set of tools designed to enhance the functionality of the traditional Relative Strength Index (RSI) indicator. By integrating advanced features such as Moving Averages, Divergences, and Sweeps, it helps traders identify key market dynamics, potential reversals, and newly-approach trading stragies.
The toolkit expands on standard RSI usage by incorporating features from smart money concepts (Just try to be creative 🤣 Hope you like it), providing a deeper understanding of momentum, liquidity sweeps, and trend reversals. It is suitable for RSI traders who want to make more informed and effective trading decisions.
💎 FEATURES
RSI Moving Average
The RSI Moving Average (RSI MA) is the moving average of the RSI itself. It can be customized to use various types of moving averages, including Simple Moving Average (SMA), Exponential Moving Average (EMA), Relative Moving Average (RMA), and Volume-Weighted Moving Average (VWMA).
The RSI MA smooths out the RSI fluctuations, making it easier to identify trends and crossovers. It helps traders spot momentum shifts and potential entry/exit points by observing when the RSI crosses above or below its moving average.
RSI Divergence
RSI Divergence identifies discrepancies between price action and RSI momentum. There are two types of divergences: Regular Divergence - Indicates a potential trend reversal; Hidden Divergence - Suggests the continuation of the current trend.
Divergence is a critical signal for spotting weakness or strength in a trend. Regular divergence highlights potential trend reversals, while hidden divergence confirms trend continuation, offering traders valuable insights into market momentum and possible trade setups.
RSI Sweep
RSI Sweep detects moments when the RSI removes liquidity from a trend structure by sweeping above or below the price at key momentum level crossing. These sweeps are overlaid on the RSI chart for easier visualized.
RSI Sweeps are significant because they indicate potential turning points in the market. When RSI sweeps occur: In an uptrend - they suggest buyers' momentum has peaked, possibly leading to a reversal; In a downtrend - they indicate sellers’ momentum has peaked, also hinting at a reversal.
(Note: This feature incorporates Liquidity Sweep concepts from Smart Money Concepts into RSI analysis, helping RSI traders identify areas where liquidity has been removed, which often precedes a trend reversal)
🔎 BREAKDOWN
RSI Moving Average
How MA created: The RSI value is calculated first using the standard RSI formula. The MA is then applied to the RSI values using the trader’s chosen type of MA (SMA, EMA, RMA, or VWMA). The flexibility to choose the type of MA allows traders to adjust the smoothing effect based on their trading style.
Why use MA: RSI by itself can be noisy and difficult to interpret in volatile markets. Applying moving average would provide a smoother, more reliable view of RSI trends.
RSI Divergence
How Regular Divergence created: Regular Divergence is detected when price forms HIGHER highs while RSI forms LOWER highs (bearish divergence) or when price forms LOWER lows while RSI forms HIGHER lows (bullish divergence).
How Hidden Divergence created: Hidden Divergence is identified when price forms HIGHER lows while RSI forms LOWER lows (bullish hidden divergence) or when price forms LOWER highs while RSI forms HIGHER highs (bearish hidden divergence).
Why use Divergence: Divergences provide early warning signals of a potential trend change. Regular divergence helps traders anticipate reversals, while hidden divergence supports trend continuation, enabling traders to align their trades with market momentum.
RSI Sweep
How Sweep created: Trend Structure Shift are identified based on the RSI crossing key momentum level of 50. To track these sweeps, the indicator pinpoints moments when liquidity is removed from the Trend Structure Shift. This is a direct application of Liquidity Sweep concepts used in Smart Money theories, adapted to RSI.
Why use Sweep: RSI Sweeps are created to help traders detect potential trend reversals. By identifying areas where momentum has exhausted during a certain trend direction, the indicator highlights opportunities for traders to enter trades early in a reversal or continuation phase.
⚙️ USAGES
Divergence + Sweep
This is an example of combining Devergence & Sweep in BTCUSDT (1 hour)
Wait for a divergence (regular or hidden) to form on the RSI. After the divergence is complete, look for a sweep to occur. A potential entry might be formed at the end of the sweep.
Divergences indicate a potential trend change, but confirmation is required to ensure the setup is valid. The RSI Sweep provides that confirmation by signaling a liquidity event, increasing the likelihood of a successful trade.
Sweep + MA Cross
This is an example of combining Devergence & Sweep in BTCUSDT (1 hour)
Wait for an RSI Sweep to form then a potential entry might be formed when the RSI crosses its MA.
The RSI Sweep highlights a potential turning point in the market. The MA cross serves as additional confirmation that momentum has shifted, providing a more reliable and more potential entry signal for trend continuations.
DISCLAIMER
This indicator is not financial advice, it can only help traders make better decisions. There are many factors and uncertainties that can affect the outcome of any endeavor, and no one can guarantee or predict with certainty what will occur. Therefore, one should always exercise caution and judgment when making decisions based on past performance.
Drawdown from All-Time High (Line)This Pine Script is a **Drawdown Indicator from All-Time High** for TradingView. It calculates and plots the percentage drawdown from the highest price the asset has ever reached (the all-time high). Here's a breakdown of what this script does:
### Description:
- **Drawdown Calculation**:
- The drawdown is calculated as the difference between the current price (`close`) and the all-time high, divided by the all-time high, and multiplied by 100 to express it as a percentage.
- If the current price is higher than the previous all-time high, the all-time high is updated to the current price.
- **All-Time High Tracking**:
- The script tracks the highest price (`allTimeHigh`) that the asset has ever reached. Each time a new high is reached, the `allTimeHigh` value is updated.
- **Line Plot**:
- The drawdown percentage is then plotted as a line on the chart, with a color of **blue** for easy visualization.
- The line shows how much the price has dropped relative to its all-time high.
- **Zero Line**:
- A horizontal line is added at the **0%** level to act as a reference point, which is helpful to identify when the asset has fully recovered to its all-time high.
### Key Features:
- **Track Drawdown**: The indicator helps visualize how far the current price has fallen from its highest point, which is useful for understanding the depth of losses (drawdowns) during a period.
- **Update All-Time High**: The indicator automatically updates the all-time high whenever a new high is detected.
- **Visual Reference**: The 0% horizontal line provides a clear indication of when the asset is at its all-time high, and the drawdown is at 0%.
### How it Works:
- If the current price surpasses the all-time high, the script will reset the all-time high to the new price.
- The drawdown percentage is calculated from the current price relative to this all-time high, and it is displayed as a line on the chart.
### Visuals:
- **Drawdown Line**: Plots the percentage of the drawdown, which is the drop from the all-time high.
- **Zero Line**: A dotted horizontal line at 0% marks the level of the all-time high.
This indicator is valuable for understanding the extent of price corrections and potential recoveries relative to the historical peak of the asset. It is especially useful for traders and investors who want to assess the risk of drawdowns in relation to the highest price achieved by the asset.
ImbalancesThis Pine Script is a trading indicator designed to identify imbalances in the market, specifically on candlestick charts. An imbalance refers to situations where there is a significant difference between buyers and sellers, which can create gaps or areas of inefficiency in the price. These imbalances often act as zones where price may return to "fill" or correct these inefficiencies.
1. Identifying Imbalances
The script analyzes candlestick patterns to detect imbalances based on the relationship between the highs, lows, and closes of consecutive candles. Specifically, it looks for:
Top Imbalances (Bearish): Areas where selling pressure has dominated, causing inefficiencies in the price. These are represented by patterns like multiple consecutive bearish candles or bearish gaps.
Bottom Imbalances (Bullish): Areas where buying pressure has dominated, leading to bullish gaps or inefficiencies.
When an imbalance is detected, the script highlights the area using visual boxes on the chart.
2. Visual Representation
The indicator uses colored boxes to show imbalances directly on the chart:
Top (Bearish) Imbalances: Highlighted using shades of red.
Bottom (Bullish) Imbalances: Highlighted using shades of green.
The boxes are further categorized into three states based on their level of mitigation:
Unmitigated: The imbalance has not been "filled" by price yet.
Partially Mitigated: Price has entered the imbalance zone but not completely filled it.
Fully Mitigated: Price has completely filled the imbalance zone.
3. Mitigation Logic
The concept of mitigation refers to the price revisiting an imbalance zone to correct the inefficiency:
If price fully or partially revisits an imbalance zone, the box's color changes to indicate the mitigation level (e.g., from unmitigated to partially/fully mitigated).
Fully mitigated boxes may be removed or recolored, depending on user preferences.
4. User Customization
The script provides several inputs to customize its behavior:
Enable or disable top and bottom imbalance detection.
Color settings: Users can define different colors for unmitigated, partially mitigated, and fully mitigated imbalances.
Mitigation display options: Users can choose whether to show fully mitigated imbalances on the chart or remove them.
5. Key Calculations
Imbalance Size: The size of the imbalance is calculated as the price difference between a candle's high and low across the relevant pattern.
Pattern Detection: The script checks for specific candlestick patterns (e.g., three consecutive bearish candles) to identify potential imbalances.
6. Practical Use Case
This indicator is useful for traders who:
Rely on supply and demand zones for their trading strategies.
Look for areas where price is likely to return (retesting unmitigated imbalances can signal potential trade setups).
Want to visually track market inefficiencies over time.
In Summary
The "Imbalances" indicator highlights and tracks price inefficiencies on candlestick charts. It marks zones where buying or selling pressure was dominant, and it dynamically updates these zones based on price action to indicate their mitigation status. This tool is particularly helpful for traders who use price action and market structure in their strategies.
Heiken Ashi MTF Monitor - Better Formula - EMA, AMA, KAFA, T3Heiken Ashi MTF Monitor - Better Formula - EMA, AMA, KAFA, T3
This indicator is based on the works of Loxx & Smart_Money-Trader, without their initial codes, none of this will be possible.
This Pine Script indicator provides a multi-timeframe (MTF) analysis of Heiken Ashi trends, designed to enhance the traditional Heiken Ashi method with advanced smoothing techniques such as the Exponential Moving Average (EMA), Adaptive Moving Average (AMA), Kaufman’s Adaptive Moving Average (KAMA), and the Triple Exponential Moving Average (T3). The indicator offers a flexible approach to identify bullish, bearish, and neutral trends across six customizable timeframes and various Heiken Ashi calculation methods.
Key Features:
Multi-Timeframe (MTF) Support: The indicator allows you to monitor trends across six timeframes (e.g., 2-hour, 4-hour, daily, weekly, monthly), giving a holistic view of market conditions at different scales.
Heiken Ashi Calculation Methods: Choose between traditional Heiken Ashi or an enhanced "Better HA" method for more refined trend analysis.
Smoothing Options: Apply different smoothing techniques, including EMA, T3, KAMA, or AMA, to the Heiken Ashi values for smoother, more reliable trend signals.
Non-Repaint Option: This feature ensures that the values do not repaint after the bar closes, providing a more reliable historical view.
Customizable Plotting: The indicator offers full customization of which timeframes to display and whether to show labels for each timeframe.
Inputs and Settings:
Timeframe Inputs:
Users can set up to six different timeframes, ranging from intraday (2-hour, 4-hour) to higher timeframes (daily, weekly, monthly).
Timeframes can be enabled or disabled individually for each analysis.
Label Visibility:
Labels indicating the trend direction (bullish, bearish, neutral) can be shown for each timeframe. This helps with clarity when monitoring multiple timeframes simultaneously.
Smoothing Options:
EMA: Exponential Moving Average for standard smoothing.
AMA: Adaptive Moving Average, which adapts its smoothing based on market volatility.
KAMA: Kaufman’s Adaptive Moving Average, which adjusts its sensitivity to price fluctuations.
T3: Triple Exponential Moving Average, providing a smoother and more responsive moving average.
None: No smoothing applied (for raw Heiken Ashi calculations).
Non-Repaint Setting:
Enabling this ensures the trend values do not change after the bar closes, offering a stable historical view of trends.
Core Functions:
Heiken Ashi Calculations:
Traditional HA: The classic Heiken Ashi calculation is used here, where each bar's open, close, high, and low are computed based on the average price of the previous bar.
Better HA: A refined calculation method, where the raw Heiken Ashi close is adjusted by considering the price range. This smoother value is then optionally processed through a moving average function for further smoothing.
Heiken Ashi Trend Calculation:
Based on the selected Heiken Ashi method (Traditional or Better HA), the indicator checks whether the trend is bullish (upward movement), bearish (downward movement), or neutral (sideways movement).
For the "Better HA" method, the trend determination uses the difference between the smoothed Heiken Ashi close and open.
Moving Averages:
The moving averages applied to the Heiken Ashi values are configurable:
EMA: Standard smoothing with an exponential weighting.
T3: A triple exponential smoothing technique that provides a smoother moving average.
KAMA: An adaptive smoothing technique that adjusts to market noise.
AMA: An adaptive moving average that reacts to market volatility, making it more flexible.
None: For raw, unsmoothed Heiken Ashi data.
Trend Detection:
The indicator evaluates the direction of the trend for each timeframe and assigns a color-coded value (bearish, bullish, or neutral).
The trend values are plotted as circles, and their color reflects the detected trend: red for bearish, green for bullish, and white for neutral.
Multi-Timeframe (MTF) Support:
The indicator can be used to analyze up to six different timeframes simultaneously.
The trend for each timeframe is calculated and displayed as circles on the chart.
Users can enable or disable individual timeframes, allowing for a customizable view based on which timeframes they are interested in monitoring.
Plotting:
The indicator plots circles at specific levels based on the detected trend (Level 1 for the 2-hour timeframe, Level 2 for the 4-hour timeframe, etc.). The size and color of these circles represent the trend direction.
These plotted values provide a quick visual reference for trend direction across multiple timeframes.
Usage:
Trend Confirmation: By monitoring trends across multiple timeframes, traders can use this indicator to confirm trends and avoid false signals.
Customizable Timeframe Analysis: Traders can focus on shorter timeframes for intraday trades or look at longer timeframes for a broader market perspective.
Smoothing for Clarity: By applying various moving average techniques, traders can reduce noise and get a clearer view of the trend.
Non-Repainting: The non-repaint option ensures the indicator values remain consistent even after the bar closes, providing more reliable signals for backtesting or live trading.
This Heiken Ashi MTF Monitor indicator with better formulas and smoothing options is designed for traders who want to analyze trends across multiple timeframes while benefiting from advanced moving averages and more refined Heiken Ashi calculations. The customizable settings for smoothing, timeframe selection, and label visibility allow users to tailor the indicator to their specific needs and trading style.
WillStop Pro [tradeviZion]WillStop Pro : A Step-by-Step Guide for Beginners to Master Trend Trading
Welcome to an in-depth guide to the WillStop Pro indicator. This article will walk you through the key features, how to use them effectively, and how this tool can help you navigate the markets confidently. WillStop Pro is based on principles established by Larry Williams, a well-known figure in trading, and aims to help you manage trades more effectively without overcomplicating things.
This guide will help you understand the basics of the WillStop Pro indicator, how to interpret its signals, and how to use it step-by-step to manage risk and identify opportunities in your trading journey. We will also cover the underlying logic and calculations for advanced users interested in more details.
What is the WillStop Pro Indicator?
The WillStop Pro indicator is a user-friendly tool that helps traders establish stop levels dynamically. It helps you figure out optimal points to enter or exit trades, while managing risk effectively during changing market conditions. The indicator tracks trending markets and sets price levels as stops for ongoing trades, making it suitable both for deciding when to enter and exit trades.
The indicator is beginner-friendly because it simplifies complex calculations and presents the results visually. This allows traders to focus more on their decision-making process instead of spending time with complex analysis.
WillStop Pro adapts to different market conditions, whether you're trading stocks, forex, commodities, or cryptocurrencies. It adjusts stop levels dynamically based on current market momentum, providing a practical way to manage both risk and reward.
Another significant benefit of WillStop Pro is that it works well with other indicators. Beginners can use it on its own or combine it with other tools like moving averages or oscillators to form a comprehensive trading strategy. Whether you are trading daily or looking at longer-term trends, WillStop Pro helps you manage your trades effectively.
Key Features of WillStop Pro
Dynamic Stop Levels : WillStop Pro calculates real-time stop levels for both long (buy) and short (sell) positions. This helps you protect your profits and reduce risk. The stop levels adjust based on the current market environment, making them more adaptable compared to fixed stop levels.
Advanced Stop Settings : There are optional settings to make the stop calculations more advanced, which take into consideration previous price movements to refine where the stops should be placed. These settings provide more precise control over your trades.
Break Signals and Alerts : The indicator provides visual signals, like arrows, to show when a stop level has been broken. This makes it easier for you to identify possible reversals and understand when the market direction is changing.
Comprehensive Table Display : A small table on the chart shows the current trend, the stop level, and whether advanced mode is active. This simple display provides an overview of the market, making decision-making easier.
Based on Larry Williams' Methodology : WillStop Pro builds upon Larry Williams' ideas, which are designed to capture major market trends while managing risk effectively. It provides a systematic way to follow these strategies without requiring deep technical analysis skills.
How Are Stop Levels Calculated? (For Advanced Users)
The WillStop Pro indicator determines stop levels by evaluating highs, lows, and closing prices over a specific lookback period. It uses this information to identify key points that justify adjusting your stop level, and there are separate approaches for both long and short positions.
Below, we explain the mathematical logic behind the stop calculations, along with some code snippets to give advanced users a clearer understanding.
For Long Stops (buy positions): The indicator looks for the highest closing price within the lookback period and continues until it finds three valid bars that meet certain criteria. Stops are adjusted to skip bars that have consecutive upward closes to ensure that the stop is placed at a level that offers solid support. Specifically, the function iterates over recent bars to determine the highest closing value, and checks for specific conditions before finalizing the stop level. Here is an excerpt of the relevant code:
getTrueLow(idx) => math.min(low , close )
findStopLevels() =>
float highestClose = close
int highestCloseIndex = 0
for i = 0 to lookback
if close > highestClose
highestClose := close
highestCloseIndex := i
// Logic to adjust based on up close skipping
int longCount = 0
int longCurrentIndex = highestCloseIndex
while longCount < 3 and longCurrentIndex < 100
if not isInsideBar(longCurrentIndex)
longCount += 1
longCurrentIndex += 1
// Determine the lowest low for the stop level
float longStopLevel = high * 2
for i = searchIndex to highestCloseIndex
longStopLevel := math.min(longStopLevel, getTrueLow(i))
// Apply offset
longStopLevel := longStopLevel - (offsetTicks * tickSize)
In this code snippet, the function findStopLevels() calculates the long stop level by first identifying the highest close within the lookback period and then finding a suitable support level while skipping certain conditions, such as inside bars or consecutive upward closes. Finally, the user-defined offset ( offsetTicks ) is applied to determine the stop level.
For Short Stops (sell positions): Similarly, the indicator finds the lowest closing price within the lookback period and then identifies three bars that fit the conditions for a short stop. It avoids using bars with consecutive down closes to help find a more robust resistance level. Here's a relevant code snippet:
getTrueHigh(idx) => math.max(high , close )
findStopLevels() =>
float lowestClose = close
int lowestCloseIndex = 0
for i = 0 to lookback
if close < lowestClose
lowestClose := close
lowestCloseIndex := i
// Logic to adjust based on down close skipping
int shortCount = 0
int shortCurrentIndex = lowestCloseIndex
while shortCount < 3 and shortCurrentIndex < 100
if not isInsideBar(shortCurrentIndex)
shortCount += 1
shortCurrentIndex += 1
// Determine the highest high for the stop level
float shortStopLevel = 0
for i = searchIndex to lowestCloseIndex
shortStopLevel := math.max(shortStopLevel, getTrueHigh(i))
// Apply offset
shortStopLevel := shortStopLevel + (offsetTicks * tickSize)
Here, findStopLevels() calculates the short stop level by finding the lowest closing price within the lookback period. It then determines the highest value that acts as a resistance level, excluding bars that do not fit certain criteria.
Volume Confirmation for Alert Accuracy : To further enhance the stop level accuracy, volume is used as a confirmation filter. The average volume (volAvg) is calculated over a 20-period moving average, and alerts are only generated if the volume exceeds a defined threshold (volMultiplier). This ensures that price movements are significant enough to consider as meaningful signals.
volAvg = ta.sma(volume, 20)
isVolumeConfirmed() =>
result = requireVolumeConfirmation ? volume > (volAvg * volMultiplier) : true
result
This additional logic ensures that stop level breaks or adjustments are not triggered during periods of low trading activity, thus enhancing the reliability of the generated signals.
These calculations are at the core of WillStop Pro's ability to determine dynamic stop levels that respond effectively to market movements, helping traders manage risk by placing stops at levels that make sense given historical price and volume data.
How to Identify Opportunities with WillStop Pro
WillStop Pro provides various signals that help you decide when to enter or exit a trade:
When a Stop Level is Broken: If a stop level (support for long positions or resistance for short positions) is broken, it may indicate a reversal. WillStop Pro visually plots arrows whenever a stop level is breached, making it easy for you to see where changes might occur. This feature helps traders identify momentum shifts quickly.
Support and Resistance Levels: The indicator plots support and resistance levels, which show key zones to watch for opportunities. These levels often act as psychological barriers in the market, where price action may either reverse or stall temporarily.
Dynamic State Management: The indicator shifts between long and short states based on price action, providing real-time feedback. This helps traders stick to their trading plan without second-guessing the market.
A major advantage of WillStop Pro is that it responds well to changing market conditions. By identifying when key support or resistance levels break, it allows you to adjust your strategies and react to new opportunities accordingly. Whether the market is trending strongly or staying within a range, WillStop Pro provides valuable information to help guide your trades.
Setting Up Alerts
Alerts are an important feature in trading, especially when you can’t be in front of your charts all the time. WillStop Pro has been enhanced to include flexible alert settings to help you stay on top of your trades without constantly monitoring the charts.
Enable Alerts: There is a master switch to enable or disable all alerts. This way, you can control whether you want to be notified of events at any time.
Alert Frequency: Choose between receiving alerts Once Per Bar or Once Per Bar Close . This helps you manage the frequency of alerts and decide if you need real-time updates or want confirmation after a bar closes.
Break Alerts: These alerts notify you when a stop level has been broken. This can help you catch potential reversals or trading opportunities as soon as they happen.
Strong Break Alerts: Alerts are available for strong breaks, which occur when the price breaks stop levels with confirmation based on additional price, volume, and momentum criteria. These alerts help identify significant shifts in the market.
Level Change Alerts: These alerts tell you whenever a new stop level is calculated, keeping you updated about changes in market dynamics. You can set a Minimum Level Change % to ensure that alerts are only triggered when the stop level changes significantly.
Require Volume Confirmation: You can opt to receive alerts only if the volume is above a certain threshold. This confirmation helps reduce false signals by ensuring that significant price changes are backed by increased trading activity.
Volume Multiplier: The volume multiplier allows you to set a minimum volume requirement that must be met for an alert to trigger. This ensures that alerts are triggered only when there is sufficient trading interest.
Here is a part of the updated alert logic that has been implemented in the indicator:
// Alert on break conditions
if alertsEnabled
if alertOnBreaks
if longStopBroken and isVolumeConfirmed()
alert(createAlertMessage("Support Break - Short Signal", useAdvancedStops), alertFreq)
if shortStopBroken and isVolumeConfirmed()
alert(createAlertMessage("Resistance Break - Long Signal", useAdvancedStops), alertFreq)
// Strong break alerts
if alertOnStrongBreaks
if longStopBroken and isStrongBreak(false)
alert(createAlertMessage("Strong Support Break - Short Signal", useAdvancedStops), alertFreq)
if shortStopBroken and isStrongBreak(true)
alert(createAlertMessage("Strong Resistance Break - Long Signal", useAdvancedStops), alertFreq)
// Level change alerts
if alertOnLevelChanges and isSignificantChange() and isVolumeConfirmed()
alert(createAlertMessage("Significant Level Change", useAdvancedStops), alertFreq)
Setting alerts allows you to react to market changes without having to watch the charts constantly. Alerts are particularly helpful if you have other responsibilities and can’t be actively monitoring your trades all day.
Understanding the Table Display
The WillStop Pro indicator provides a status table that gives an overview of the current market state. Here’s what the table shows:
Indicator Status: The table indicates whether the indicator is in a LONG or SHORT state. This helps you quickly understand the market trend.
Stop Level: The active stop level is shown, whether it is acting as support (long) or resistance (short). This is important for knowing where to set your protective stops.
Mode: The table also displays whether the advanced calculation mode is being used. This keeps you informed about how stop levels are being calculated and why they are positioned where they are.
Empowering Messages: The table also includes motivational messages that rotate periodically, such as 'Trade with Clarity, Stop with Precision' and 'Let Winners Run, Cut Losses Short.' These messages are designed to keep you focused, motivated, and disciplined during your trading journey.
The table is simple and easy to follow, helping you maintain discipline in your trading plan. By having all the essential information in one place, the table reduces the need to make quick, emotional decisions and promotes more thoughtful analysis.
Tips for Using WillStop Pro Effectively
Here are some practical ways to make the most of the WillStop Pro indicator:
Start with Default Settings: If you’re new to the indicator, start with the default settings. This will give you an idea of how stop levels are determined and how they adjust to different markets.
Experiment with Advanced Settings: Once you are comfortable, try using the advanced stop settings to see how they refine the stop levels. This can be useful in certain market conditions to improve accuracy.
Use Alerts to Stay Updated: Set up alerts for when a stop level is broken or when new levels are calculated. This helps you take action without constantly watching the chart. Swing traders may find alerts especially helpful for monitoring longer-term moves.
Monitor the Status Table: Keep an eye on the status table to understand the current market condition. Whether the indicator is in a LONG or SHORT state can help you make more informed decisions.
Focus on Risk Management: WillStop Pro is designed to help you manage risk by dynamically adjusting stop levels. Make sure you are using these levels to protect your trades, especially during strong trends or volatile periods.
Acknowledging Larry Williams' Influence
WillStop Pro is inspired by the work of Larry Williams, who described the approach as one of his best trading techniques. His method aims to ride major market trends while reducing the risk of giving back gains during corrections. WillStop Pro builds upon this approach, adding features like advanced stop settings and visual alerts that make it easier to apply in modern markets.
By using WillStop Pro, you are essentially leveraging a well-established trading strategy with additional tools that help improve its effectiveness. The indicator is designed to provide a reliable way to manage trades, stay on top of market conditions, and reduce emotional decision-making.
Conclusion: Why WillStop Pro is Great for Beginners and Advanced Users
The WillStop Pro is a powerful yet easy-to-use tool that helps traders ride trends while managing risk during market corrections. It can be used both for entering and exiting trades, and its visual features make it accessible for those who are new to trading, while the underlying logic appeals to advanced users seeking greater control and understanding.
WillStop Pro is more than just a tool for setting stops. It is a comprehensive solution for managing trades, with features like dynamic stop levels, customizable alerts, and an easy-to-understand status table. This combination of simplicity and advanced features makes it suitable for beginners as well as more experienced traders.
We hope this guide helps you get started with WillStop Pro and improves your trading confidence. Remember to start with the basics, explore the advanced features, and set alerts to stay informed without getting overwhelmed. Whether you’re just beginning or want to simplify your strategy, WillStop Pro is a valuable tool to have in your trading arsenal.
Trading can be challenging, but the right tools make it more manageable. WillStop Pro helps you keep track of market movements, identify opportunities, and manage risk effectively. Give it a try and see how it can improve your trading decisions and help you navigate the markets more efficiently.
By incorporating WillStop Pro into your strategy, you are following a systematic approach that has been refined over time. It’s designed to help you make sense of the markets, plan your trades, and manage your risks with greater clarity and confidence.
Note: Always practice proper risk management and thoroughly test the indicator to ensure it aligns with your trading strategy. Past performance is not indicative of future results.
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