PSv5 3D Array/Matrix Super Hack"In a world of ever pervasive and universal deceit, telling a simple truth is considered a revolutionary act."
INTRO:
First, how about a little bit of philosophic poetry with another dimension applied to it?
The "matrix of control" is everywhere...
It is all around us, even now in the very place you reside. You can see it when you look at your digitized window outwards into the world, or when you turn on regularly scheduled television "programs" to watch news narratives and movies that subliminally influence your thoughts, feelings, and emotions. You have felt it every time you have clocked into dead end job workplaces... when you unknowingly worshiped on the conformancy alter to cultish ideologies... and when you pay your taxes to a godvernment that is poisoning you softly and quietly by injecting your mind and body with (psyOps + toxicCompounds). It is a fictitiously generated world view that has been pulled over your eyes to blindfold, censor, and mentally prostrate you from spiritually hearing the real truth.
What TRUTH you must wonder? That you are cognitively enslaved, like everyone else. You were born into mental bondage, born into an illusory societal prison complex that you are entirely incapable of smelling, tasting, or touching. Its a contrived monetary prison enterprise for your mind and eternal soul, built by pretending politicians, corporate CONartists, and NonGoverning parasitic Organizations deploying any means of infiltration and deception by using every tactic unimaginable. You are slowly being convinced into becoming a genetically altered cyborg by acclimation, socially engineered and chipped to eventually no longer be 100% human.
Unfortunately no one can be told eloquently enough in words what the matrix of control truly is. You have to experience it and witness it for yourself. This is your chance to program a future paradigm that doesn't yet exist. After visiting here, there is absolutely no turning back. You can continually take the blue pill BIGpharmacide wants you to repeatedly intake. The story ends if you continually sleep walk through a 2D hologram life, believing whatever you wish to believe until you cease to exist. OR, you can take the red pill challenge, explore "question every single thing" wonderland, program your arse off with 3D capabilities, ultimately ascertaining a new mathematical empyrean. Only then can you fully awaken to discover how deep the rabbit hole state of affairs transpire worldwide with a genuine open mind.
Remember, all I'm offering is a mathematical truth, nothing more...
PURPOSE:
With that being said above, it is now time for advanced developers to start creating their own matrix constructs in 3D, in Pine, just as the universe is created spatially. For those of you who instantly know what this script's potential is easily capable of, you already know what you have to do with it. While this is simplistically just a 3D array for either integers or floats, additional companion functions can in the future be constructed by other members to provide a more complete matrix/array library for millions of folks on TV. I do encourage the most courageous of mathemagicians on TV to do so. I have been employing very large 2D/3D array structures for quite some time, and their utility seems to be of great benefit. Discovering that for myself, I fully realized that Pine is incomplete and must be provided with this agility to process complex datasets that traders WILL use in the future. Mark my words!
CONCEPTION:
While I have long realized and theorized this code for a great duration of time, I was finally able to turn it into a Pine reality with the assistance and training of an "artificially intuitive" program while probing its aptitude. Even though it knows virtually nothing about Pine Script 4.0 or 5.0 syntax, functions, and behavior, I was able to conjure code into an identity similar to what you see now within a few minutes. Close enough for me! Many manual edits later for pine compliance, and I had it in chart, presto!
While most people consider the service to be an "AI", it didn't pass my Pine Turing test. I did have to repeatedly correct it, suffered through numerous apologies from it, was forced to use specifically tailored words, and also rationally debate AND argued with it. It is a handy helper but beware of generating Pine code from it, trust me on this one. However... this artificially intuitive service is currently available in its infancy as version 3. Version 4 most likely will have more diversity to enhance my algorithmic expertise of Pine wizardry. I do have to thank E.M. and his developers for an eye opening experience, or NONE of this code below would be available as you now witness it today.
LIMITATIONS:
As of this initial release, Pine only supports 100,000 array elements maximum. For example, when using this code, a 50x50x40 element configuration will exceed this limit, but 50x50x39 will work. You will always have to keep that in mind during development. Running that size of an array structure on every single bar will most likely time out within 20-40 seconds. This is not the most efficient method compared to a real native 3D array in action. Ehlers adepts, this might not be 100% of what you require to "move forward". You can try, but head room with a low ceiling currently will be challenging to walk in for now, even with extremely optimized Pine code.
A few common functions are provided, but this can be extended extensively later if you choose to undertake that endeavor. Use the code as is and/or however you deem necessary. Any TV member is granted absolute freedom to do what they wish as they please. I ultimately wish to eventually see a fully equipped library version for both matrix3D AND array3D created by collaborative efforts that will probably require many Pine poets testing collectively. This is just a bare bones prototype until that day arrives. Considerably more computational server power will be required also. Anyways, I hope you shall find this code somewhat useful.
Notice: Unfortunately, I will not provide any integration support into members projects at all. I have my own projects that require too much of my time already.
POTENTIAL APPLICATIONS:
The creation of very large coefficient 3D caches/buffers specifically at bar_index==0 can dramatically increase runtime agility for thousands of bars onwards. Generating 1000s of values once and just accessing those generated values is much faster. Also, when running dozens of algorithms simultaneously, a record of performance statistics can be kept, self-analyzed, and visually presented to the developer/user. And, everything else under the sun can be created beyond a developers wildest dreams...
EPILOGUE:
Free your mind!!! And unleash weapons of mass financial creation upon the earth for all to utilize via the "Power of Pine". Flying monkeys and minions are waging economic sabotage upon humanity, decimating markets and exchanges. You can always see it your market charts when things go horribly wrong. This is going to be an astronomical technical challenge to continually navigate very choppy financial markets that are increasingly becoming more and more unstable and volatile. Ordinary one plot algorithms simply are not enough anymore. Statistics and analysis sits above everything imagined. This includes banking, godvernment, corporations, REAL science, technology, health, medicine, transportation, energy, food, etc... We have a unique perspective of the world that most people will never get to see, depending on where you look. With an ever increasingly complex world in constant dynamic flux, novel ways to process data intricately MUST emerge into existence in order to tackle phenomenal tasks required in the future. Achieving data analysis in 3D forms is just one lonely step of many more to come.
At this time the WesternEconomicFraudsters and the WorldHealthOrders are attempting to destroy/reset the world's financial status in order to rain in chaos upon most nations, causing asset devaluation and hyper-inflation. Every form of deception, infiltration, and theft is occurring with a result of destroyed wealth in preparation to consolidate it. Open discussions, available to the public, by world leaders/moguls are fantasizing about new dystopian system as a one size fits all nations solution of digitalID combined with programmableDemonicCurrencies to usher in a new form of obedient servitude to a unipolar digitized hegemony of monetary vampires. If they do succeed with economic conquest, as they have publicly stated, people will be converted into human cattle, herded within smart cities, you will own nothing, eat bugs for breakfast/lunch/dinner, live without heat during severe winter conditions, and be happy. They clearly haven't done the math, as they are far outnumbered by a ratio of 1 to millions. Sith Lords do not own planet Earth! The new world disorder of human exploitation will FAIL. History, my "greatest teacher" for decades reminds us over, and over, and over again, and what are time series for anyways? They are for an intense mathematical analysis of prior historical values/conditions in relation to today's values/conditions... I imagine one day we will be able to ask an all-seeing AI, "WHO IS TO BLAME AND WHY AND WHEN?" comprised of 300 pages in great detail with images, charts, and statistics.
What are the true costs of malignant lies? I will tell you... 64bit numbers are NOT even capable of calculating the extreme cost of pernicious lies and deceit. That's how gigantic this monstrous globalization problem has become and how awful the "matrix of control" truly is now. ALL nations need a monumental revision of its CODE OF ETHICS, and that's definitely a multi-dimensional problem that needs solved sooner than later. If it was up to me, economies and technology would be developed so extensively to eliminate scarcity and increase the standard of living so high, that the notion of war and conflict would be considered irrelevant and extremely appalling to the future generations of humanity, our grandchildren born and unborn. The future will not be owned and operated by geriatric robber barons destined to expire quickly. The future will most likely be intensely "guided" by intelligent open source algorithms that youthful generations will inherit as their birth right.
P.S. Don't give me that politco-my-diction crap speech below in comments. If they weren't meddling with economics mucking up 100% of our chart results in 100% of tickers, I wouldn't have any cause to analyze any effects generated by them, nor provide this script's code. I am performing my analytical homework, but have you? Do you you know WHY international affairs are in dire jeopardy? Without why, the "Power of Pine" would have never existed as it specifically does today. I'm giving away much of my mental power generously to TV members so you are specifically empowered beyond most mathematical agilities commonly existing. I'm just a messenger of profound ideas. Loving and loathing of words is ALWAYS in the eye of beholders, and that's why the freedom of speech is enshrined as #1 in the constitutional code of the USA. Without it, this entire site might not have been allowed to exist from its founder's inceptions.
ค้นหาในสคริปต์สำหรับ "ai"
Volume Spike Strategy This is a Pine Script implementation of “Capitalize AI: Volume Spike Strategy" by Bitcoin Trading Challenge (copied with permission).
Original Capital AI formula :
If BTC/USD 1 minute volume > BTC/USD average volume in 20-1m bar by at least 500% and if BTC/USD is below the MA (5,1m,close) of BTC/USD then buy 10,000 USD WORTH of BTC/USD
Tested on XBTUSD 1 minute.
Original strategy is buy-only. Option for sells was added (enable in settings).
First published script -- comments/feedback appreciated
Well Rounded Moving AverageIntroduction
There are tons of filters, way to many, and some of them are redundant in the sense they produce the same results as others. The task to find an optimal filter is still a big challenge among technical analysis and engineering, a good filter is the Kalman filter who is one of the more precise filters out there. The optimal filter theorem state that : The optimal estimator has the form of a linear observer , this in short mean that an optimal filter must use measurements of the inputs and outputs, and this is what does the Kalman filter. I have tried myself to Kalman filters with more or less success as well as understanding optimality by studying Linear–quadratic–Gaussian control, i failed to get a complete understanding of those subjects but today i present a moving average filter (WRMA) constructed with all the knowledge i have in control theory and who aim to provide a very well response to market price, this mean low lag for fast decision timing and low overshoots for better precision.
Construction
An good filter must use information about its output, this is what exponential smoothing is about, simple exponential smoothing (EMA) is close to a simple moving average and can be defined as :
output = output(1) + α(input - output(1))
where α (alpha) is a smoothing constant, typically equal to 2/(Period+1) for the EMA.
This approach can be further developed by introducing more smoothing constants and output control (See double/triple exponential smoothing - alpha-beta filter) .
The moving average i propose will use only one smoothing constant, and is described as follow :
a = nz(a ) + alpha*nz(A )
b = nz(b ) + alpha*nz(B )
y = ema(a + b,p1)
A = src - y
B = src - ema(y,p2)
The filter is divided into two components a and b (more terms can add more control/effects if chosen well) , a adjust itself to the output error and is responsive while b is independent of the output and is mainly smoother, adding those components together create an output y , A is the output error and B is the error of an exponential moving average.
Comparison
There are a lot of low-lag filters out there, but the overshoots they induce in order to reduce lag is not a great effect. The first comparison is with a least square moving average, a moving average who fit a line in a price window of period length .
Lsma in blue and WRMA in red with both length = 100 . The lsma is a bit smoother but induce terrible overshoots
ZLMA in blue and WRMA in red with both length = 100 . The lag difference between each moving average is really low while VWRMA is way more precise.
Hull MA in blue and WRMA in red with both length = 100 . The Hull MA have similar overshoots than the LSMA.
Reduced overshoots moving average (ROMA) in blue and WRMA in red with both length = 100 . ROMA is an indicator i have made to reduce the overshoots of a LSMA, but at the end WRMA still reduce way more the overshoots while being smoother and having similar lag.
I have added a smoother version, just activate the extra smooth option in the indicator settings window. Here the result with length = 200 :
This result is a little bit similar to a 2 order Butterworth filter. Our filter have more overshoots which in this case could be useful to reduce the error with edges since other low pass filters tend to smooth their amplitude thus reducing edge estimation precision.
Conclusions
I have presented a well rounded filter in term of smoothness/stability and reactivity. Try to add more terms to have different results, you could maybe end up with interesting results, if its the case share them with the community :)
As for control theory i have seen neural networks integrated to Kalman flters which leaded to great accuracy, AI is everywhere and promise to be a game a changer in real time data smoothing. So i asked myself if it was possible for a neural networks to develop pinescript indicators, if yes then i could be replaced by AI ? Brrr how frightening.
Thanks for reading :)
NQ Command Center [EOD Predictor]This is a sophisticated Macro-correlated Dashboard designed specifically for trading NQ (Nasdaq 100). It attempts to predict how the daily candle will close (Green or Red) by combining Price Action (Market Structure) with External Market Drivers (Yields, Volatility, Dollar, and Breadth).
How This Script Works
The script assigns a "Score" to current market conditions. The higher the score, the more bullish the prediction. The lower the score, the more bearish.
1. The "Structure" Score (Price Action) It looks at the Daily High/Low (PDH/PDL) and recent daily trend:
Bullish (+1): We are making Higher Highs/Higher Lows, or price is holding in the top 33% of yesterday's range.
Breakout (+2): Price has broken above the Previous Daily High (PDH).
Bearish (-1/-2): We are making Lower Highs, or price has broken below the Previous Daily Low (PDL).
2. The "Macro" Score (External Data) It pulls data from 5 external tickers to see if the environment supports a move:
ADDQ (Breadth): If > 0, more stocks are advancing than declining (Bullish).
VXN (Volatility): If falling, fear is decreasing (Bullish).
DXY (Dollar) & US10Y (Yields): If these are dropping, it is usually good for Tech/Nasdaq (Bullish).
CVD (Volume): Estimates if volume is dominated by buyers or sellers.
3. The Prediction (The Output) It sums these scores.
Total Score ≥ 4: "STRONG GREEN CLOSE 🚀" (High confidence Longs)
Total Score ≤ -4: "STRONG RED CLOSE 🩸" (High confidence Shorts)
Near 0: "CHOP / NEUTRAL" (Avoid trading or take quick scalps).
How to Use It Effectively
Symbol: Open a chart for NQ1! (Nasdaq Futures) or NDX.
Timeframe: This is designed for Intraday trading. Use 5m, 15m, or 1h charts. (Do not use on Daily chart, as the table lines up intraday data against daily history).
The Dashboard: Look at the table in the top right.
Focus on "AI Forecast": If it says STRONG GREEN, look for Long setups (pullbacks to support).
Check Confidence: If Confidence is "LOW", the macro data might be conflicting with price action (e.g., Price is going up, but Volume is selling). Be careful.
The Lines: The script plots Green (PDH) and Red (PDL) lines on your chart.
These are key reaction points. If price breaks the Green line, the "Live Status" on the dashboard will switch to BREAKOUT.
Custom Sector Comparison Index (CSCI)Compare any stock against a custom basket of its true peers.
Most traders compare stocks to broad indexes like the S&P 500 (SPY) or the Nasdaq (QQQ). But if you are analyzing a niche sector—like Residential REITs, Gold Miners, or AI Semis—broad indexes are too noisy.
This indicator allows you to build your own Custom Equal-Weight Index made up of up to 12 specific symbols. It then plots the performance of the stock you are currently viewing against that custom index, starting from a specific "Anchor Date" of your choosing.
Why use this?
Standard relative strength tools force you to compare against a single symbol (like SPY). But if you want to know if Centerspace (CSR) is lagging its direct competitors, comparing it to SPY (which contains Tech and Healthcare) is useless. Comparing it to VNQ (which contains Cell Towers and Malls) is also imperfect.
With this tool, you can create a "Pure Residential REIT" index and see exactly how your stock is performing against the group.
Key Features:
Fully Configurable: Input up to 12 different symbols to build your custom index.
Smart Filtering: Automatically ignores blank slots. You can build a basket of 3 stocks or 12 stocks without breaking the math.
Custom Anchor Date: Set the specific start date for the comparison (e.g., YTD, Q3 start, or a specific market event).
Visual Performance Gap: Green shading indicates your stock is outperforming the basket; Red shading indicates underperformance.
Example Use Case: Residential REITs
I developed this to analyze the "Residential REIT" sector. I wanted to see if Invitation Homes (INVH) was trading at a discount due to fundamentals or if the whole sector was down.
I configured the basket with 9 of its closest peers:
NYSE:VRE, NYSE:UDR, NYSE:MAA
NYSE:EQR, NYSE:CSR, NYSE:ESS
NYSE:CPT, NYSE:AVB, NYSE:AMH
The Result: The indicator draws a gray line representing the average return of those 9 "Big Boys." I can then load CSR on the chart and immediately see if it is lagging the pack (a potential value buy) or leading it.
How to Use:
Add the indicator to your chart.
Open Settings (Double-click the line).
Start Date: Set the date you want the "race" to begin (where all returns reset to 0%).
Symbols: Type in the tickers for your custom basket (e.g., NVDA, AMD, INTC). Leave unused slots blank.
Analyze:
Gray Line: The average performance of your basket.
Blue Line: The performance of the current symbol on your chart.
Pro Tip: You can save different "Presets" in the indicator settings for different sectors (e.g., save one preset as "Semis" and another as "Oil Majors") so you don't have to re-type symbols every time.
Clock&Flow: Elements of Cycle Analysis 2nd partClock&Flow – Elements of Cycle Analysis (ECA) | Complete Suite
Elements of Cycle Analysis (ECA) is an advanced cyclic analysis suite designed to interpret the market through time, structure, strength, and energy, combining cycles, volatility, and participation into a single operational framework.
The suite consists of two complementary modules:
🔹ECA 1 – Cycles, Structure, and Volatility (Overlay: True)
ECA 1 is dedicated to the structural and temporal analysis of the market.
Cyclic SMAs (Cyclic Ratio) Moving averages are calibrated according to nominal cycles and timeframes to monitor multiple cycles simultaneously (from the lower cycle to the upper cycles). Crossovers between fast and slow SMAs certify the closing or transition of the cycle related to the faster SMA. The specific cycle is identified in the Info Table at the bottom right (for 15m - 1h - 2h - 1D timeframes). You can select the number of cycles to observe and the asset type to apply them to:
Index: Standard quotes (e.g., Cash sessions).
Future: Extended quotes (24h).
50-200: Classic institutional references for the medium-long term.
ATR-based Dynamic Cyclic Channels The channels represent a lower cycle and its upper counterpart; their width is determined by the observed timeframe and calculated based on average volatility (ATR). Volatility is not treated as noise but as a structural component of the cycle, essential for contextualizing excesses, compressions, and expansions.
Info Table and Quick Guide Dynamic tables automatically link SMAs, timeframes, and time cycles, providing an immediate reading of the current cyclic context.
Time Bands (Weekly / Daily) Temporal visualization helps identify cyclic pivots and rhythm transitions.
🔹 ECA 2 – Market Excesses, Strength, and Energy
ECA 2 analyzes how the market moves within the cyclic structure.
Excesses and Divergences (Cyclic Stochastic) An oscillator calibrated on the same cyclic ratio as the suite. Crossovers between the lower cycle (blue) and upper cycle (red) signal potential phase changes. In areas of excess, divergences often confirm the closing and restart of a cycle.
Directional Movement System (DMS) The ADX measures the strength of the movement, while +DI and -DI indicate direction. A simultaneous crossover of ADX, +DI, and -DI signals imminent acceleration, even before the strength is fully expressed.
Market Pulse – Real Market Energy The Market Pulse measures the amount of real energy moving through the market by relating three factors:
Price Velocity
Normalized Volume
Volatility (ATR relative to price)
These three factors are combined multiplicatively: if one is missing, the impulse weakens. The zero line represents a state of energy equilibrium; values above or below indicate a real imbalance (bullish or bearish). Note: Market Pulse is not a classic oscillator and should not be interpreted as overbought or oversold; it is used to evaluate the energetic quality of a movement.
Operational Convergence
The maximum operational effectiveness of the ECA suite is achieved when all modules converge on the same market phase.
When cyclic timing, volatility, price structure, trend strength, and movement energy align, the context signals a high-probability operational phase. The system is applicable to any timeframe or asset because it is not bound by dogmatic or subjective interpretations of technical or fundamental analysis; instead, it leverages what is actually happening in the market. Major chart patterns and Volume Profile (technically not includable in this specific suite) provide further confirmation.
Under these conditions, the signal does not originate from a single indicator but from the consistency of the entire system: time, volatility, and energy moving in the same direction.
Entries should always be accompanied by proper risk management.
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Clock&Flow – Elements of Cycle Analysis (ECA) | Suite Completa
Elements of Cycle Analysis (ECA) è una suite avanzata di analisi ciclica progettata per leggere il mercato attraverso tempo, struttura, forza ed energia, combinando cicli, volatilità e partecipazione in un unico framework operativo.
La suite è composta da due moduli complementari:
🔹 ECA 1 – Cicli, Struttura e Volatilità (overlay true)
ECA 1 è dedicato all’analisi strutturale e temporale del mercato.
SMA cicliche (ratio ciclica)
Le medie mobili sono calibrate in funzione dei cicli nominali e del timeframe per monitorare più cicli simultaneamente (dal ciclo inferiore fino ai cicli superiori).
Gli incroci tra SMA veloci e lente certificano la chiusura o transizione del ciclo correlato alla SMA più veloce. Il ciclo in questione è segnalato nella info table in basso a destra (per i time frame 15’ - 1h - 2h - 1D) Puoi selezionare il numero dei cicli da osservare e su quali asset applicarle (Index = quotazioni standard / Future = quotazioni estese / 50-200 i classici riferimenti istituzionali per il medio-lungo periodo
Canali ciclici dinamici basati su ATR
I canali rappresentano un ciclo inferiore e il suo superiore, l’ampiezza è data dal time frame osservato e calcolata sulla volatilità media (ATR).
La volatilità non è trattata come rumore, ma come componente strutturale del ciclo, utile per contestualizzare eccessi, compressioni ed espansioni.
Info Table e Quick Guide
Tabelle dinamiche collegano automaticamente SMA, timeframe e cicli temporali, fornendo una lettura immediata del contesto ciclico in corso.
Time Bands (Weekly / Daily)
La visualizzazione temporale aiuta a individuare pivot ciclici e transizioni di ritmo.
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🔹 ECA 2 – Eccessi, Forza ed Energia del Mercato
ECA 2 analizza come il mercato si muove all’interno della struttura ciclica.
Eccessi e divergenze (Stochastic ciclico)
Oscillatore calibrato sulla stessa ratio ciclica della suite.
Gli incroci tra ciclo inferiore (blu) e superiore (rosso) segnalano potenziali cambi di fase; in area di eccesso, le divergenze certificano spesso la chiusura e ripartenza del ciclo.
Directional Movement System (DMS)
L’ADX misura la forza del movimento, mentre +DI e –DI ne indicano la direzione.
L’incrocio simultaneo di ADX, +DI e –DI segnala un’accelerazione imminente, anche in assenza di forza già espressa.
Market Pulse – Energia reale del mercato
Il Market Pulse misura quanta energia reale sta attraversando il mercato mettendo in relazione:
velocità del prezzo
volume normalizzato
volatilità (ATR rapportato al prezzo)
I tre fattori sono combinati in modo moltiplicativo: se uno manca, l’impulso si indebolisce.
La linea dello zero rappresenta una condizione di equilibrio energetico; valori sopra o sotto indicano uno sbilanciamento reale, rialzista o ribassista.
Il Market Pulse non è un oscillatore classico e non va interpretato in termini di ipercomprato o ipervenduto: serve a valutare la qualità energetica del movimento.
La massima efficacia operativa della suite ECA si ottiene quando tutti i moduli convergono sulla stessa fase di mercato.
Quando tempi ciclici, volatilità, struttura del prezzo, forza del trend ed energia del movimento risultano allineati, il contesto segnala una fase ad alta probabilità operativa.
È applicabile su qualunque time frame o asset perché non è vincolato a dogmatiche e soggettive interpretazioni di analisi tecnica - fondamentale ma sfrutta ciò che realmente sta accadendo sul mercato.
I principali pattern grafici e il Volume Profile (in questa suite tecnicamente non inseribili) forniscono ulteriori conferme e/o indicazioni.
In queste condizioni il segnale non nasce da un singolo indicatore, ma dalla coerenza dell’intero sistema: tempo, volatilità ed energia si muovono nella stessa direzione.
Gli ingressi vanno sempre accompagnati da una corretta gestione del rischio.
Clock&Flow: Elements of Cycle Analysis 1st partClock&Flow – Elements of Cycle Analysis (ECA) | Complete Suite
Elements of Cycle Analysis (ECA) is an advanced cyclic analysis suite designed to interpret the market through time, structure, strength, and energy, combining cycles, volatility, and participation into a single operational framework.
The suite consists of two complementary modules:
🔹 ECA 1 – Cycles, Structure, and Volatility (Overlay: True)
ECA 1 is dedicated to the structural and temporal analysis of the market.
Cyclic SMAs (Cyclic Ratio) Moving averages are calibrated according to nominal cycles and timeframes to monitor multiple cycles simultaneously (from the lower cycle to the upper cycles). Crossovers between fast and slow SMAs certify the closing or transition of the cycle related to the faster SMA. The specific cycle is identified in the Info Table at the bottom right (for 15m - 1h - 2h - 1D timeframes). You can select the number of cycles to observe and the asset type to apply them to:
Index: Standard quotes (e.g., Cash sessions).
Future: Extended quotes (24h).
50-200: Classic institutional references for the medium-long term.
ATR-based Dynamic Cyclic Channels The channels represent a lower cycle and its upper counterpart; their width is determined by the observed timeframe and calculated based on average volatility (ATR). Volatility is not treated as noise but as a structural component of the cycle, essential for contextualizing excesses, compressions, and expansions.
Info Table and Quick Guide Dynamic tables automatically link SMAs, timeframes, and time cycles, providing an immediate reading of the current cyclic context.
Time Bands (Weekly / Daily) Temporal visualization helps identify cyclic pivots and rhythm transitions.
🔹 ECA 2 – Market Excesses, Strength, and Energy
ECA 2 analyzes how the market moves within the cyclic structure.
Excesses and Divergences (Cyclic Stochastic) An oscillator calibrated on the same cyclic ratio as the suite. Crossovers between the lower cycle (blue) and upper cycle (red) signal potential phase changes. In areas of excess, divergences often confirm the closing and restart of a cycle.
Directional Movement System (DMS) The ADX measures the strength of the movement, while +DI and -DI indicate direction. A simultaneous crossover of ADX, +DI, and -DI signals imminent acceleration, even before the strength is fully expressed.
Market Pulse – Real Market Energy The Market Pulse measures the amount of real energy moving through the market by relating three factors:
Price Velocity
Normalized Volume
Volatility (ATR relative to price)
These three factors are combined multiplicatively: if one is missing, the impulse weakens. The zero line represents a state of energy equilibrium; values above or below indicate a real imbalance (bullish or bearish). Note: Market Pulse is not a classic oscillator and should not be interpreted as overbought or oversold; it is used to evaluate the energetic quality of a movement.
Operational Convergence
The maximum operational effectiveness of the ECA suite is achieved when all modules converge on the same market phase.
When cyclic timing, volatility, price structure, trend strength, and movement energy align, the context signals a high-probability operational phase. The system is applicable to any timeframe or asset because it is not bound by dogmatic or subjective interpretations of technical or fundamental analysis; instead, it leverages what is actually happening in the market. Major chart patterns and Volume Profile (technically not includable in this specific suite) provide further confirmation.
Under these conditions, the signal does not originate from a single indicator but from the consistency of the entire system: time, volatility, and energy moving in the same direction.
Entries should always be accompanied by proper risk management.
––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––
Clock&Flow – Elements of Cycle Analysis (ECA) | Suite Completa
Elements of Cycle Analysis (ECA) è una suite avanzata di analisi ciclica progettata per leggere il mercato attraverso tempo, struttura, forza ed energia, combinando cicli, volatilità e partecipazione in un unico framework operativo.
La suite è composta da due moduli complementari:
🔹 ECA 1 – Cicli, Struttura e Volatilità (overlay true)
ECA 1 è dedicato all’analisi strutturale e temporale del mercato.
SMA cicliche (ratio ciclica)
Le medie mobili sono calibrate in funzione dei cicli nominali e del timeframe per monitorare più cicli simultaneamente (dal ciclo inferiore fino ai cicli superiori).
Gli incroci tra SMA veloci e lente certificano la chiusura o transizione del ciclo correlato alla SMA più veloce. Il ciclo in questione è segnalato nella info table in basso a destra (per i time frame 15’ - 1h - 2h - 1D) Puoi selezionare il numero dei cicli da osservare e su quali asset applicarle (Index = quotazioni standard / Future = quotazioni estese / 50-200 i classici riferimenti istituzionali per il medio-lungo periodo
Canali ciclici dinamici basati su ATR
I canali rappresentano un ciclo inferiore e il suo superiore, l’ampiezza è data dal time frame osservato e calcolata sulla volatilità media (ATR).
La volatilità non è trattata come rumore, ma come componente strutturale del ciclo, utile per contestualizzare eccessi, compressioni ed espansioni.
Info Table e Quick Guide
Tabelle dinamiche collegano automaticamente SMA, timeframe e cicli temporali, fornendo una lettura immediata del contesto ciclico in corso.
Time Bands (Weekly / Daily)
La visualizzazione temporale aiuta a individuare pivot ciclici e transizioni di ritmo.
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🔹 ECA 2 – Eccessi, Forza ed Energia del Mercato
ECA 2 analizza come il mercato si muove all’interno della struttura ciclica.
Eccessi e divergenze (Stochastic ciclico)
Oscillatore calibrato sulla stessa ratio ciclica della suite.
Gli incroci tra ciclo inferiore (blu) e superiore (rosso) segnalano potenziali cambi di fase; in area di eccesso, le divergenze certificano spesso la chiusura e ripartenza del ciclo.
Directional Movement System (DMS)
L’ADX misura la forza del movimento, mentre +DI e –DI ne indicano la direzione.
L’incrocio simultaneo di ADX, +DI e –DI segnala un’accelerazione imminente, anche in assenza di forza già espressa.
Market Pulse – Energia reale del mercato
Il Market Pulse misura quanta energia reale sta attraversando il mercato mettendo in relazione:
velocità del prezzo
volume normalizzato
volatilità (ATR rapportato al prezzo)
I tre fattori sono combinati in modo moltiplicativo: se uno manca, l’impulso si indebolisce.
La linea dello zero rappresenta una condizione di equilibrio energetico; valori sopra o sotto indicano uno sbilanciamento reale, rialzista o ribassista.
Il Market Pulse non è un oscillatore classico e non va interpretato in termini di ipercomprato o ipervenduto: serve a valutare la qualità energetica del movimento.
La massima efficacia operativa della suite ECA si ottiene quando tutti i moduli convergono sulla stessa fase di mercato.
Quando tempi ciclici, volatilità, struttura del prezzo, forza del trend ed energia del movimento risultano allineati, il contesto segnala una fase ad alta probabilità operativa.
È applicabile su qualunque time frame o asset perché non è vincolato a dogmatiche e soggettive interpretazioni di analisi tecnica - fondamentale ma sfrutta ciò che realmente sta accadendo sul mercato.
I principali pattern grafici e il Volume Profile (in questa suite tecnicamente non inseribili) forniscono ulteriori conferme e/o indicazioni.
In queste condizioni il segnale non nasce da un singolo indicatore, ma dalla coerenza dell’intero sistema: tempo, volatilità ed energia si muovono nella stessa direzione.
Gli ingressi vanno sempre accompagnati da una corretta gestione del rischio.
WN 5-20-50 SMA Setup (Discrete Lines = SL TP) Multiple Entries Pretty Simple Script as I got this idea from a YouTuber that showed us how to use AI to make TradingView Indicators.
When the 5 day Simple Moving Average Goes Above the 20 day Simple Moving Average it issues a BUY Signal when the Trend itself is over the 50 day Simple Moving Average.
When the 5 day Simple Moving Average Goes Below the 20 day Simple Moving Average it issues a SELL Signal when the Trend itself is under the 50 day Simple Moving Average.
The Green Cloud Represents price over the 50 day Simple Moving Average. BUY signals will only show up in the green cloud.
The Red Cloud Represents price under the 50 day Simple Moving Average. SELL signals will only show up in the green cloud.
The lines represent Stop Loss and two Take Profit Levels. Take Profit 1 is 1.5x the stop loss and Take Profit 2 is 3x the Stop Loss.
This version of the Script has multiple Trend signals for entries so you can scale into a trade when the Trend is being aggressive.
Multi-Fractal Trading Plan [Gemini] v22Multi-Fractal Trading Plan
The Multi-Fractal Trading Plan is a quantitative market structure engine designed to filter noise and generate actionable daily strategies. Unlike standard auto-trendline indicators that clutter charts with irrelevant data, this system utilizes Fractal Geometry to categorize market liquidity into three institutional layers: Minor (Intraday), Medium (Swing), and Major (Institutional).
This tool functions as a Strategic Advisor, not just a drawing tool. It calculates the delta between price and structural pivots in real-time, alerting you when price enters high-probability "Hot Zones" and generating a live trading plan on your dashboard.
Core Features
1. Three-Tier Fractal Engine The algorithm tracks 15 distinct fractal lengths simultaneously, aggregating them into a clean hierarchy:
Minor Structure (Thin Lines): Captures high-frequency volatility for scalping.
Medium Structure (Medium Lines): Identifies significant swing points and intermediate targets.
Major Structure (Thick Lines): Maps the "Institutional" defense lines where trend reversals and major breakouts occur.
2. The Strategic Dashboard A dynamic data panel in the bottom-right eliminates analysis paralysis:
Floor & Ceiling Targets: Displays the precise price levels of the nearest Support and Resistance.
AI Logic Output: The script analyzes market conditions to generate a specific command, such as "WATCH FOR BREAKOUT", "Near Lows (Look Long?)", or "WAIT (No Setup)".
3. "Hot Zone" Detection Never miss a critical test of structure.
Dynamic Alerting: When price trades within 1% (adjustable) of a Major Trend Line, the indicator’s labels turn Bright Yellow and flash a warning (e.g., "⚠️ WATCH: MAJOR RES").
Focus: This visual cue highlights the exact moment execution is required, reducing screen fatigue.
4. The Quant Web & Markers
Pivot Validation: Deep blue fractal markers (▲/▼) identify the exact candles responsible for the structure.
Inter-Timeframe Web: Faint dotted lines connect Minor pivots directly to Major pivots, visualizing the "hidden" elasticity between short-term noise and long-term trend anchors.
5. Enterprise Stability Engine Engineered to solve the "Vertical Line" and "1970 Epoch" glitches common in Pine Script trend indicators. This engine is optimized for Futures (NQ/ES), Forex, and Crypto, ensuring stability across all timeframes (including gaps on ETH/RTH charts).
Operational Guide
Consult the Dashboard: Before executing, check the "Strategy" output. If it says "WAIT", the market is in chop. If it says "WATCH FOR BOUNCE", prepare your entry criteria.
Monitor Hot Zones: A Yellow Label indicates price is testing a major liquidity level. This is your signal to watch for a rejection wick or a high-volume breakout.
Utilize the Web: Use the faint web lines to find "confluence" where a short-term pullback aligns with a long-term trend line.
Configuration
Show History: Toggles "Ghost Lines" (Blue) to display historical structure and broken trends.
Fractal Points: Toggles the geometric pivot markers.
Hot Zone %: Adjusts the sensitivity of the Yellow Warning system (Default: 1%).
Max Line Length: A noise filter that removes stale or "spiderweb" lines that are no longer statistically relevant.
OCC Strategy Optimized (MA 5 + Delayed TSL)# OCC Strategy Optimized (MA 5 + Delayed TSL) - User Guide
## Introduction
The **OCC Strategy Optimized** is an enhanced version of the classic **Open Close Cross (OCC)** strategy. This strategy is designed for high-precision trend following, utilizing the crossover logic of Open and Close moving averages to identify market shifts. This optimized version incorporates advanced risk management, multi-timeframe analysis, and a variety of moving average types to provide a robust trading solution for modern markets.
>
> **Special Thanks:** This strategy is based on the original work of **JustUncleL**, a renowned Pine Script developer. You can find their work and profile on TradingView here: (in.tradingview.com).
---
## Key Features
### 1. Optimized Core Logic
- **MA Period (Default: 5):** The strategy is tuned with a shorter MA length to reduce lag and capture trends earlier.
- **Crossing Logic:** Signals are generated when the Moving Average of the **Close** crosses the Moving Average of the **Open**.
### 2. Multi-Timeframe (MTF) Analysis
- **Alternate Resolution:** Use a higher timeframe (Resolution Multiplier) to filter out noise. By default, it uses $3 \times$ your current chart timeframe to confirm the trend.
- **Non-Repainting:** Includes an optional delay offset to ensure signals are confirmed and do not disappear (repaint) after the bar closes.
### 3. Advanced Risk Management
This script features a hierarchical exit system to protect your capital and lock in profits:
- **Fixed Stop Loss (Initial):** Protects against sudden market reversals immediately after entry.
- **Delayed Trailing Stop Loss (TSL):**
- **Activation Delay:** The TSL only activates after the trade reaches a specific profit threshold (e.g., 1%). This prevents being stopped out too early in the trade's development.
- **Ratchet Trail:** Once activated, the stop loss "ratchets" up/down, never moving backward, ensuring you lock in profits as the trend continues.
- **Take Profit (TP):** A fixed percentage target to exit the trade at a pre-defined profit level.
### 4. Versatility
- **12 MA Types:** Choose from SMA, EMA, DEMA, TEMA, WMA, VWMA, SMMA, HullMA, LSMA, ALMA, SSMA, and TMA.
- **Trade Direction:** Toggle between Long-only, Short-only, or Both.
- **Visuals:** Optional bar coloring to visualize the trend directly on the candlesticks.
---
## User Input Guide
### Core Settings
- **Use Alternate Resolution?:** Enable this to use the MTF logic.
- **Multiplier for Alternate Resolution:** How many charts higher the "filter" timeframe should be.
- **MA Type:** Select your preferred moving average smoothing method.
- **MA Period:** The length of the Open/Close averages.
- **Delay Open/Close MA:** Use `1` or higher to force non-repainting behavior.
### Risk Management Settings
- **Use Trailing Stop Loss?:** Enables the TSL system.
- **Trailing Stop %:** The distance the stop follows behind the price (Optimized Default: 1.5%).
- **TSL Activation % (Delay):** The profit % required before the TSL starts moving. (Optimized Default: 2.0% to ensure 0.5% profit is locked immediately).
- **Initial Fixed Stop Loss %:** Your hard stop if the trade immediately goes against you.
- **Take Profit %:** Your ultimate profit target for the trade.
---
## How to Trade with This Strategy
1. **Identify the Trend:** Look for the Moving Average lines (Close vs Open) to cross.
2. **Wait for Confirmation:** If using MTF, ensure the higher timeframe also shows a trend change.
3. **Manage the Trade:** Let the TSL work. With the default **2.0% Activation** and **1.5% Trail**, the strategy will automatically lock in **0.5% profit** the moment the threshold is hit, then follow the price higher.
4. **Position Sizing:** Adjust the `Properties` tab in the script settings to match your desired capital allocation (Default is 10% of equity).
---
## Recommended Settings
1. Trialing < Activation
2. Check ranging
## Credits
Original Strategy by: **JustUncleL**
Optimized and Enhanced by: **Antigravity AI**
ML Adaptive SuperTrend Strategy [trade_crush]# ML Adaptive SuperTrend Strategy - User Guide
## Introduction
The **ML Adaptive SuperTrend Strategy** is a sophisticated trading tool that combines traditional trend-following logic with **Machine Learning (K-Means Clustering)** to dynamically adapt to market volatility. Unlike standard SuperTrend indicators that use a fixed ATR, this strategy analyzes historical volatility to categorize the current market into distinct clusters, providing more precise entries and exits.
>
> **Special Thanks:** This strategy is based on the innovative work of **AlgoAlpha**. You can explore their extensive library of high-quality indicators and strategies on TradingView: (www.tradingview.com).
---
## Machine Learning Engine (K-Means)
The core of this strategy is its ability to "learn" from recent market behavior.
- **K-Means Clustering**: The script takes the last $N$ bars of ATR data and runs an iterative clustering algorithm to find three "centroids" representing **High**, **Medium**, and **Low** volatility.
- **Adaptive ATR**: Based on the current volatility, the strategy selects the nearest centroid to use as the ATR value for the SuperTrend calculation. This ensures the trailing stop tightens during low volatility and widens during high volatility to avoid "noise".
---
## Key Features
### 1. Non-Repainting Signals
- **Confirm Signals**: When enabled, signals are only triggered after a bar closes. This ensures that the arrows and entries you see on the chart are permanent and reliable for backtesting.
### 2. Intelligent Risk Management
- **Multiple SL/TP Types**: Choose between **Percentage** based stops or **ATR** based stops for both Stop Loss and Take Profit.
- **Trailing Stop Loss (TSL)**:
- Supports both Percentage and ATR modes.
- **Activation Offset**: Only activates the trailing mechanism after the price has moved a certain percentage in your favor, protecting early-stage trades.
### 3. Risk-Based Position Sizing
- **Dynamic Quantity**: If enabled, the strategy automatically calculates the trade size based on your **Risk % Per Trade** and the distance to your **Stop Loss**. This ensures you never lose more than your defined risk on a single trade.
---
## User Input Guide
### SuperTrend & ML Settings
- **ATR Length**: The window used to calculate market volatility.
- **SuperTrend Factor**: The multiplier that determines the distance of the trailing stop from the price.
- **Use ML Adaptive ATR**: Toggle between the ML-enhanced logic and standard ATR.
- **Training Data Length**: How many historical bars the ML engine analyzes to find clusters.
### Risk Management
- **Stop Loss Type**: Set to Percentage, ATR, or None.
- **TS Activation Offset**: The profit buffer required before the trailing stop starts following the price.
- **Use Risk-Based Sizing**: Toggle this to let the script manage your position size automatically.
---
## How to Trade with This Strategy
1. **Monitor the Dashboard**: Check the top-right table to see which volatility cluster the market is currently in.
2. **Observe the Fills**: The adaptive fills (green/red) visualize the "breathing room" the strategy is giving the price.
3. **Execution**: The strategy enters on "ML Bullish" (Triangle Up) and "ML Bearish" (Triangle Down) signals.
4. **Exits**: The script will automatically exit based on your SL, TP, or Trailing Stop settings.
---
## Credits
Original Concept: **AlgoAlpha**
Strategy Conversion & Enhancements: **Antigravity AI**
Antigravity OCC Strategy (MA 5 + Delayed TSL)# OCC Strategy Optimized (MA 5 + Delayed TSL) - User Guide
## Introduction
The **OCC Strategy Optimized** is an enhanced version of the classic **Open Close Cross (OCC)** strategy. This strategy is designed for high-precision trend following, utilizing the crossover logic of Open and Close moving averages to identify market shifts. This optimized version incorporates advanced risk management, multi-timeframe analysis, and a variety of moving average types to provide a robust trading solution for modern markets.
>
> **Special Thanks:** This strategy is based on the original work of **JustUncleL**, a renowned Pine Script developer. You can find their work and profile on TradingView here: (in.tradingview.com).
---
## Key Features
### 1. Optimized Core Logic
- **MA Period (Default: 5):** The strategy is tuned with a shorter MA length to reduce lag and capture trends earlier.
- **Crossing Logic:** Signals are generated when the Moving Average of the **Close** crosses the Moving Average of the **Open**.
### 2. Multi-Timeframe (MTF) Analysis
- **Alternate Resolution:** Use a higher timeframe (Resolution Multiplier) to filter out noise. By default, it uses $3 \times$ your current chart timeframe to confirm the trend.
- **Non-Repainting:** Includes an optional delay offset to ensure signals are confirmed and do not disappear (repaint) after the bar closes.
### 3. Advanced Risk Management
This script features a hierarchical exit system to protect your capital and lock in profits:
- **Fixed Stop Loss (Initial):** Protects against sudden market reversals immediately after entry.
- **Delayed Trailing Stop Loss (TSL):**
- **Activation Delay:** The TSL only activates after the trade reaches a specific profit threshold (e.g., 1%). This prevents being stopped out too early in the trade's development.
- **Ratchet Trail:** Once activated, the stop loss "ratchets" up/down, never moving backward, ensuring you lock in profits as the trend continues.
- **Take Profit (TP):** A fixed percentage target to exit the trade at a pre-defined profit level.
### 4. Versatility
- **12 MA Types:** Choose from SMA, EMA, DEMA, TEMA, WMA, VWMA, SMMA, HullMA, LSMA, ALMA, SSMA, and TMA.
- **Trade Direction:** Toggle between Long-only, Short-only, or Both.
- **Visuals:** Optional bar coloring to visualize the trend directly on the candlesticks.
---
## User Input Guide
### Core Settings
- **Use Alternate Resolution?:** Enable this to use the MTF logic.
- **Multiplier for Alternate Resolution:** How many charts higher the "filter" timeframe should be.
- **MA Type:** Select your preferred moving average smoothing method.
- **MA Period:** The length of the Open/Close averages.
- **Delay Open/Close MA:** Use `1` or higher to force non-repainting behavior.
### Risk Management Settings
- **Use Trailing Stop Loss?:** Enables the TSL system.
- **Trailing Stop %:** The distance the stop follows behind the price.
- **TSL Activation % (Delay):** The profit % required before the TSL starts moving.
- **Initial Fixed Stop Loss %:** Your hard stop if the trade immediately goes against you.
- **Take Profit %:** Your ultimate profit target for the trade.
---
## How to Trade with This Strategy
1. **Identify the Trend:** Look for the Moving Average lines (Close vs Open) to cross.
2. **Wait for Confirmation:** If using MTF, ensure the higher timeframe also shows a trend change.
3. **Manage the Trade:** Let the TSL work. Once the trade hits the activation threshold, the TSL will take over, protecting your runner.
4. **Position Sizing:** Adjust the `Properties` tab in the script settings to match your desired capital allocation (Default is 10% of equity).
---
## Credits
Original Strategy by: **JustUncleL**
Optimized and Enhanced by: **Antigravity AI**
BTC DCA Risk Metric StrategyBTC DCA Risk Strategy - Automated Dollar Cost Averaging with 3Commas Integration
Overview
This strategy combines the proven Oakley Wood Risk Metric with an intelligent tiered Dollar Cost Averaging (DCA) system, designed to help traders systematically accumulate Bitcoin during periods of low risk and take profits during high-risk conditions.
Key Features
📊 Multi-Component Risk Assessment
4-Year SMA Deviation: Measures Bitcoin's distance from its long-term mean
20-Week MA Analysis: Tracks medium-term momentum shifts
50-Day/50-Week MA Ratio: Captures short-to-medium term trend strength
All metrics are normalized by time to account for Bitcoin's maturing market dynamics
💰 3-Tier DCA Buy System
Level 1 (Low Risk): Conservative entry with base allocation
Level 2 (Lower Risk): Increased allocation as opportunity improves
Level 3 (Extreme Low Risk): Maximum allocation during rare buying opportunities
Buys execute every bar while risk remains below thresholds, enabling true DCA accumulation
📈 Progressive Profit Taking
Sell Level 1: Take initial profits as risk increases
Sell Level 2: Scale out further positions during elevated risk
Sell Level 3: Final exit during extreme market conditions
Sell levels automatically reset when new buy signals occur, allowing flexible re-entry
🤖 3Commas Integration
Fully automated webhook alerts for Custom Signal Bots
JSON payloads formatted per 3Commas API specifications
Supports multiple exchanges (Binance, Coinbase, Kraken, Gemini, Bybit)
Configurable quote currency (USD, USDT, BUSD)
How It Works
The strategy calculates a composite risk metric (0-1 scale):
0.0-0.2: Extreme buying opportunity (green zone)
0.2-0.5: Favorable accumulation range (yellow zone)
0.5-0.8: Neutral to cautious territory (orange zone)
0.8-1.0+: High risk, profit-taking zone (red zone)
Buy Logic: As risk decreases, position sizes increase automatically. If risk drops from L1 to L3 threshold, the strategy combines all three tier allocations for maximum exposure.
Sell Logic: Sequential profit-taking ensures you capture gains progressively. The system won't advance to Sell L2 until L1 completes, preventing premature full exits.
Configuration
Risk Metric Parameters:
All calculations use Bitcoin price data (any BTC chart works)
Time-normalized formulas adapt to market maturity
No manual parameter tuning required
Buy Settings:
Set risk thresholds for each tier (default: 0.20, 0.10, 0.00)
Define dollar amounts per tier (default: $10, $15, $20)
Fully customizable to your risk tolerance and capital
Sell Settings:
Configure risk thresholds for profit-taking (default: 1.00, 1.50, 2.00)
Set percentage of position to sell at each level (default: 25%, 35%, 40%)
3Commas Setup:
Create a Custom Signal Bot in 3Commas
Copy Bot UUID and Secret Token into strategy inputs
Enable 3Commas Alerts checkbox
Create TradingView alert: Condition → "alert() function calls only", Webhook → api.3commas.io
Backtesting Results
Strengths:
Systematically buys dips without emotion
Averages down during extended bear markets
Captures explosive bull run profits through tiered exits
Pyramiding (1000 max orders) allows true DCA behavior
Considerations:
Requires sufficient capital for multiple buys during prolonged downtrends
Backtest on Daily timeframe for most reliable signals
Past performance does not guarantee future results
Visual Design
The indicator pane displays:
Color-coded risk metric line: Changes from white→red→orange→yellow→green as risk decreases
Background zones: Green (buy), yellow (hold), red (sell) areas
Dashed threshold lines: Clear visual markers for each buy/sell level
Entry/Exit labels: Green buy labels and orange/red sell labels mark all trades
Credits
Original Risk Metric: Oakley Wood
Strategy Development & 3Commas Integration: Claude AI (Anthropic)
Modifications: pommesUNDwurst
Disclaimer
This strategy is for educational and informational purposes only. Cryptocurrency trading carries substantial risk of loss. Always conduct your own research and never invest more than you can afford to lose. The authors are not financial advisors and assume no responsibility for trading decisions made using this tool.
🐋 MACRO POSITION TRADER - Quarterly Alignment 💎Disclaimer: This tool is an alignment filter and educational resource, not financial advice. Backtest and use proper risk management. Past performance does not guarantee future returns.
so the idea behind this one came from an experience i had when i first started learning how to trade. dont laugh at me but i was the guy to buy into those stupid AI get rich quick schemes or the first person to buy the "golden indicator" just to find out that it was a scam. Its also to help traders place trades they can hold for months with high confidence and not have to sit in front of charts all day, and to also scale up quickly with small accounts confidently. and basically what it does is gives an alert once the 3 mo the 6 mo and the 12 mo tfs all align with eachother and gives the option to toggle on or off the 1 mo tf as well for extra confidence. Enter on the 5M–15M after a sweep + CHOCH in the direction of the aligned 1M–12M bias. that simple just continue to keep watching key levels mabey take profit 1-2 weeks and jump back in scaling up if desired..easy way to combine any small account size.
Perfect balance of:
low risk
high R:R
optimal precision
minimal chop
best sweep/CHOCH clarity
hope you guys enjoy this one.
EMA Crossover CandlesEMA Crossover Candles
This indicator colors your chart candles based on the relationship between two Exponential Moving Averages (EMAs).
How It Works
Green Candles - When the Fast EMA is above the Slow EMA, indicating bullish momentum
Red Candles - When the Fast EMA is below the Slow EMA, indicating bearish momentum
Settings
Source - The price data used for EMA calculations (default: close)
Fast Length - Period for the fast EMA (default: 5)
Slow Length - Period for the slow EMA (default: 10)
How To Use
This indicator provides a quick visual reference for trend direction. Green candles suggest the short-term trend is bullish, while red candles suggest bearish conditions. This can help you:
Identify trend direction at a glance
Filter trades in the direction of the trend
Spot potential trend changes when candle colors shift
Tips
Adjust the Fast and Slow Length settings to match your trading timeframe
Shorter periods = more responsive but more false signals
Longer periods = smoother but slower to react to trend changes
Consider hiding default candles in Chart Settings for a cleaner look
Note: This indicator is for informational purposes only and should not be used as the sole basis for trading decisions. Always use proper risk management and consider combining with other forms of analysis.
Feel free to modify this to match your style or add any additional details you'd like to include.Claude is AI and can make mistakes. Please double-check responses. Opus 4.5
Obsidian Flux Matrix# Obsidian Flux Matrix | JackOfAllTrades
Made with my Senior Level AI Pine Script v6 coding bot for the community!
Narrative Overview
Obsidian Flux Matrix (OFM) is an open-source Pine Script v6 study that fuses social sentiment, higher timeframe trend bias, fair-value-gap detection, liquidity raids, VWAP gravitation, session profiling, and a diagnostic HUD. The layout keeps the obsidian palette so critical overlays stay readable without overwhelming a price chart.
Purpose & Scope
OFM focuses on actionable structure rather than marketing claims. It documents every driver that powers its confluence engine so reviewers understand what triggers each visual.
Core Analytical Pillars
1. Social Pulse Engine
Sentiment Webhook Feed: Accepts normalized scores (-1 to +1). Signals only arm when the EMA-smoothed value exceeds the `sentimentMin` input (0.35 by default).
Volume Confirmation: Requires local volume > 30-bar average × `volSpikeMult` (default 2.0) before sentiment flags.
EMA Cross Validation: Fast EMA 8 crossing above/below slow EMA 21 keeps momentum aligned with flow.
Momentum Alignment: Multi-timeframe momentum composite must agree (positive for longs, negative for shorts).
2. Peer Momentum Heatmap
Multi-Timeframe Blend: RSI + Stoch RSI fetched via request.security() on 1H/4H/1D by default.
Composite Scoring: Each timeframe votes +1/-1/0; totals are clamped between -3 and +3.
Intraday Readability: Configurable band thickness (1-5) so scalpers see context without losing space.
Dynamic Opacity: Stronger agreement boosts column opacity for quick bias checks.
3. Trend & Displacement Framework
Dual EMA Ribbon: Cyan/magenta ribbon highlights immediate posture.
HTF Bias: A higher-timeframe EMA (default 55 on 4H) sets macro direction.
Displacement Score: Body-to-ATR ratio (>1.4 default) detects impulses that seed FVGs or VWAP raids.
ATR Normalization: All thresholds float with volatility so the study adapts to assets and regimes.
4. Intelligent Fair Value Gap (FVG) System
Gap Detection: Three-candle logic (bullish: low > high ; bearish: high < low ) with ATR-sized minimums (0.15 × ATR default).
Overlap Prevention: Price-range checks stop redundant boxes.
Spacing Control: `fvgMinSpacing` (default 5) avoids stacking from the same impulse.
Storage Caps: Max three FVGs per side unless the user widens the limit.
Session Awareness: Kill zone filters keep taps focused on London/NY if desired.
Auto Cleanup: Boxes delete when price closes beyond their invalidation level.
5. VWAP Magnet + Liquidity Raid Engine
Session or Rolling VWAP: Toggle resets to match intraday or rolling preferences.
Equal High/Low Scanner: Looks back 20 bars by default for liquidity pools.
Displacement Filter: ATR multiplier ensures raids represent genuine liquidity sweeps.
Mean Reversion Focus: Signals fire when price displaces back toward VWAP following a raid.
6. Session Range Breakout System
Initial Balance Tracking: First N bars (15 default) define the session box.
Breakout Logic: Requires simultaneous liquidity spikes, nearby FVG activity, and supportive momentum.
Z-Score Volume Filter: >1.5σ by default to filter noisy moves.
7. Lifestyle Liquidity Scanner
Volume Z-Scores: 50-bar baseline highlights statistically significant spikes.
Smart Money Footprints: Bottom-of-chart squares color-code buy vs sell participation.
Panel Memory: HUD logs the last five raid timestamps, direction, and normalized size.
8. Risk Matrix & Diagnostic HUD
HUD Structure: Table in the top-right summarizes HTF bias, sentiment, momentum, range state, liquidity memory, and current risk references.
Signal Tags: Aggregates SPS, FVG, VWAP, Range, and Liquidity states into a compact string.
Risk Metrics: Swing-based stops (5-bar lookback) + ATR targets (1.5× default) keep risk transparent.
Signal Families & Alerts
Social Pulse (SPS): Volume-confirmed sentiment alignment; triangle markers with “SPS”.
Kill-Zone FVG: Session + HTF alignment + FVG tap; arrow markers plus SL/TP labels.
Local FVG: Captures local reversals when HTF bias has not flipped yet.
VWAP Raid: Equal-high/low raids that snap toward VWAP; “VWAP” label markers.
Range Breakout: Initial balance violations with liquidity and imbalance confirmation; circle markers.
Liquidity Spike: Z-score spikes ≥ threshold; square markers along the baseline.
Visual Design & Customization
Theme Palette: Primary background RGB (12,6,24). Accent shading RGB (26,10,48). Long accents RGB (88,174,255). Short accents RGB (219,109,255).
Stylized Candles: Optional overlay using theme colors.
Signal Toggles: Independently enable markers, heatmap, and diagnostics.
Label Spacing: Auto-spacing enforces ≥4-bar gaps to prevent text overlap.
Customization & Workflow Notes
Adjust ATR/FVG thresholds when volatility shifts.
Re-anchor sentiment to your webhook cadence; EMA smoothing (default 5) dampens noise.
Reposition the HUD by editing the `table.new` coordinates.
Use multiples of the chart timeframe for HTF requests to minimize load.
Session inputs accept exchange-local time; align them to your market.
Performance & Compliance
Pure Pine v6: Single-line statements, no `lookahead_on`.
Resource Safe: Arrays trimmed, boxes limited, `request.security` cached.
Repaint Awareness: Signals confirm on close; alerts mirror on-chart logic.
Runtime Safety: Arrays/loops guard against `na`.
Use Cases
Measure when social sentiment aligns with structure.
Plan ICT-style intraday rebalances around session-specific FVG taps.
Fade VWAP raids when displacement shows exhaustion.
Watch initial balance breaks backed by statistical volume.
Keep risk/target references anchored in ATR logic.
Signal Logic Snapshot
Social Pulse Long/Short: `sentimentEMA` gated by `sentimentMin`, `volSpike`, EMA 8/21 cross, and `momoComposite` sign agreement. Keeps hype tied to structural follow-through.
Kill-Zone FVG Long/Short: Requires session filter, HTF EMA bias alignment, and an active FVG tap (`bullFvgTap` / `bearFvgTap`). Labels include swing stops + ATR targets pulled from `swingLookback` and `liqTargetMultiple`.
Local FVG Long/Short: Uses `localBullish` / `localBearish` heuristics (EMA slope, displacement, sequential closes) to surface intraday reversals even when HTF bias has not flipped.
VWAP Raids: Detect equal-high/equal-low sweeps (`raidHigh`, `raidLow`) that revert toward `sessionVwap` or rolling VWAP when displacement exceeds `vwapAlertDisplace`.
Range Breakouts: Combine `rangeComplete`, breakout confirmation, liquidity spikes, and nearby FVG activity for statistically backed initial balance breaks.
Liquidity Spikes: Volume Z-score > `zScoreThreshold` logs direction, size, and timestamp for the HUD and optional review workflows.
Session Logic & VWAP Handling
Kill zone + NY session inputs use TradingView’s session strings; `f_inSession()` drives both visual shading and whether FVG taps are tradeable when `killZoneOnly` is true.
Session VWAP resets using cumulative price × volume sums that restart when the daily timestamp changes; rolling VWAP falls back to `ta.vwap(hlc3)` for instruments where daily resets are less relevant.
Initial balance box (`rangeBars` input) locks once complete, extends forward, and stays on chart to contextualize later liquidity raids or breakouts.
Parameter Reference
Trend: `emaFastLen`, `emaSlowLen`, `htfResolution`, `htfEmaLen`, `showEmaRibbon`, `showHtfBiasLine`.
Momentum: `tf1`, `tf2`, `tf3`, `rsiLen`, `stochLen`, `stochSmooth`, `heatmapHeight`.
Volume/Liquidity: `volLookback`, `volSpikeMult`, `zScoreLen`, `zScoreThreshold`, `equalLookback`.
VWAP & Sessions: `vwapMode`, `showVwapLine`, `vwapAlertDisplace`, `killSession`, `nySession`, `showSessionShade`, `rangeBars`.
FVG/Risk: `fvgMinTicks`, `fvgLookback`, `fvgMinSpacing`, `killZoneOnly`, `liqTargetMultiple`, `swingLookback`.
Visualization Toggles: `showSignalMarkers`, `showHeatmapBand`, `showInfoPanel`, `showStylizedCandles`.
Workflow Recipes
Kill-Zone Continuation: During the defined kill session, look for `killFvgLong` or `killFvgShort` arrows that line up with `sentimentValid` and positive `momoComposite`. Use the HUD’s risk readout to confirm SL/TP distances before entering.
VWAP Raid Fade: Outside kill zone, track `raidToVwapLong/Short`. Confirm the candle body exceeds the displacement multiplier, and price crosses back toward VWAP before considering reversions.
Range Break Monitor: After the initial balance locks, mark `rangeBreakLong/Short` circles only when the momentum band is >0 or <0 respectively and a fresh FVG box sits near price.
Liquidity Spike Review: When the HUD shows “Liquidity” timestamps, hover the plotted squares at chart bottom to see whether spikes were buy/sell oriented and if local FVGs formed immediately after.
Metadata
Author: officialjackofalltrades
Platform: TradingView (Pine Script v6)
Category: Sentiment + Liquidity Intelligence
Hope you Enjoy!
The Quantum Leap: Renko + ML(Note: This indicator uses the BackQuant & SuperTrend which takes a 4-5 seconds to load)
This strategy uses the following indicators (please see source code)
Synthetic Renko: Ignores time and focuses purely on price movement to detect clear trend reversals (Red-to-Green).
ATR (Average True Range): Measures volatility to calculate the Renko brick sizes and SuperTrend sensitivity.
Adaptive SuperTrend: A trend filter that uses volatility clustering to confirm if the market is currently in a "Bearish" state.
RSI (Relative Strength Index): A momentum gauge ensuring the asset is "Oversold" (exhausted) before we consider a setup.
Monthly Pivots: Horizontal support lines based on last month's data acting as price "floors" (S1, S2, S3).
SMA (Simple Moving Average): A 100-bar average ensuring we are strictly buying below the long-term mean (deep value).
BackQuant (KNN): A Machine Learning engine that compares current data to historical patterns to predict immediate momentum.
This is a sophisticated, multi-stage strategy script. It combines "Old School" price action (Renko) with "New School" Machine Learning (KNN and Clustering).
Here is the high-level summary of how we will break this down:
Topic 1: The "Bottom Hunter" Setup. How the script uses Renko bricks and aggressive filtering (SuperTrend, SMA, RSI, Pivots) to find a potential market bottom.
Topic 2: The ML Engine (BackQuant & SuperTrend). How the script uses K-Nearest Neighbors (KNN) to predict momentum and Volatility Clustering to adjust the SuperTrend.
Topic 3: The "Leap" Execution. How the script synchronizes the Setup (Topic 1) with the ML Trigger (Topic 2) using a time window.
Topic 1: The "Bottom Hunter" Setup
This script is designed as a Mean Reversion strategy (often called "catching a falling knife" or "bottom fishing"). It is trying to find the exact moment a downtrend stops and reverses.
Most strategies buy when price is above the 200 SMA or above the SuperTrend. This script does the exact opposite.
The Logic:
Renko Bricks: It simulates Renko bricks internally (without changing your chart view). It waits for a specific pattern: A Red Brick followed immediately by a Green Brick (a reversal).
The "Bearish" Filters: To generate a "WATCH" signal, the following must be true:
Price < SuperTrend: The market must officially be in a downtrend.
Price < SMA: Long-term trend is down.
Price < Monthly Pivot: Price is deeply discounted.
RSI < Threshold: The asset is oversold (exhausted).
Recommended Settings for daily signals for Stocks :
Confirmation : 10. (How many bars after Renko Buy signal the AI has to identify a bullish move).
Percentage : 2 (This is the Renko bar size. This represents 2% move.)
SMA: 100 (Signal must be found below 100 SMA)
Price must be below: PIVOT (This is the monthly Pivot levels)
BTC Fear & Greed Incremental StrategyIMPORTANT: READ SETUP GUIDE BELOW OR IT WON'T WORK
# BTC Fear & Greed Incremental Strategy — TradeMaster AI (Pure BTC Stack)
## Strategy Overview
This advanced Bitcoin accumulation strategy is designed for long-term hodlers who want to systematically take profits during greed cycles and accumulate during fear periods, while preserving their core BTC position. Unlike traditional strategies that start with cash, this approach begins with a specified BTC allocation, making it perfect for existing Bitcoin holders who want to optimize their stack management.
## Key Features
### 🎯 **Pure BTC Stack Mode**
- Start with any amount of BTC (configurable)
- Strategy manages your existing stack, not new purchases
- Perfect for hodlers who want to optimize without timing markets
### 📊 **Fear & Greed Integration**
- Uses market sentiment data to drive buy/sell decisions
- Configurable thresholds for greed (selling) and fear (buying) triggers
- Automatic validation to ensure proper 0-100 scale data source
### 🐂 **Bull Year Optimization**
- Smart quarterly selling during bull market years (2017, 2021, 2025)
- Q1: 1% sells, Q2: 2% sells, Q3/Q4: 5% sells (configurable)
- **NO SELLING** during non-bull years - pure accumulation mode
- Preserves BTC during early bull phases, maximizes profits at peaks
### 🐻 **Bear Market Intelligence**
- Multi-regime detection: Bull, Early Bear, Deep Bear, Early Bull
- Different buying strategies based on market conditions
- Enhanced buying during deep bear markets with configurable multipliers
- Visual regime backgrounds for easy market condition identification
### 🛡️ **Risk Management**
- Minimum BTC allocation floor (prevents selling entire stack)
- Configurable position sizing for all trades
- Multiple safety checks and validation
### 📈 **Advanced Visualization**
- Clean 0-100 scale with 2 decimal precision
- Three main indicators: BTC Allocation %, Fear & Greed Index, BTC Holdings
- Real-time portfolio tracking with cash position display
- Enhanced info table showing all key metrics
## How to Use
### **Step 1: Setup**
1. Add the strategy to your BTC/USD chart (daily timeframe recommended)
2. **CRITICAL**: In settings, change the "Fear & Greed Source" from "close" to a proper 0-100 Fear & Greed indicator
---------------
I recommend Crypto Fear & Greed Index by TIA_Technology indicator
When selecting source with this indicator, look for "Crypto Fear and Greed Index:Index"
---------------
3. Set your "Starting BTC Quantity" to match your actual holdings
4. Configure your preferred "Start Date" (when you want the strategy to begin)
### **Step 2: Configure Bull Year Logic**
- Enable "Bull Year Logic" (default: enabled)
- Adjust quarterly sell percentages:
- Q1 (Jan-Mar): 1% (conservative early bull)
- Q2 (Apr-Jun): 2% (moderate mid bull)
- Q3/Q4 (Jul-Dec): 5% (aggressive peak targeting)
- Add future bull years to the list as needed
### **Step 3: Fine-tune Thresholds**
- **Greed Threshold**: 80 (sell when F&G > 80)
- **Fear Threshold**: 20 (buy when F&G < 20 in bull markets)
- **Deep Bear Fear Threshold**: 25 (enhanced buying in bear markets)
- Adjust based on your risk tolerance
### **Step 4: Risk Management**
- Set "Minimum BTC Allocation %" (default 20%) - prevents selling entire stack
- Configure sell/buy percentages based on your position size
- Enable bear market filters for enhanced timing
### **Step 5: Monitor Performance**
- **Orange Line**: Your BTC allocation percentage (target: fluctuate between 20-100%)
- **Blue Line**: Actual BTC holdings (should preserve core position)
- **Pink Line**: Fear & Greed Index (drives all decisions)
- **Table**: Real-time portfolio metrics including cash position
## Reading the Indicators
### **BTC Allocation Percentage (Orange Line)**
- **100%**: All portfolio in BTC, no cash available for buying
- **80%**: 80% BTC, 20% cash ready for fear buying
- **20%**: Minimum allocation, maximum cash position
### **Trading Signals**
- **Green Buy Signals**: Appear during fear periods with available cash
- **Red Sell Signals**: Appear during greed periods in bull years only
- **No Signals**: Either allocation limits reached or non-bull year
## Strategy Logic
### **Bull Years (2017, 2021, 2025)**
- Q1: Conservative 1% sells (preserve stack for later)
- Q2: Moderate 2% sells (gradual profit taking)
- Q3/Q4: Aggressive 5% sells (peak targeting)
- Fear buying active (accumulate on dips)
### **Non-Bull Years**
- **Zero selling** - pure accumulation mode
- Enhanced fear buying during bear markets
- Focus on rebuilding stack for next bull cycle
## Important Notes
- **This is not financial advice** - backtest thoroughly before use
- Designed for **long-term holders** (4+ year cycles)
- **Requires proper Fear & Greed data source** - validate in settings
- Best used on **daily timeframe** for major trend following
- **Cash calculations**: Use allocation % and BTC holdings to calculate available cash: `Cash = (Total Portfolio × (1 - Allocation%/100))`
## Risk Disclaimer
This strategy involves active trading and position management. Past performance does not guarantee future results. Always do your own research and never invest more than you can afford to lose. The strategy is designed for educational purposes and long-term Bitcoin accumulation thesis.
---
*Developed by Sol_Crypto for the Bitcoin community. Happy stacking! 🚀*
Vdubus Divergence Wave Pattern Generator V1The Vdubus Divergence Wave Theory
10 years in the making & now finally thanks to AI I have attempted to put my Trading strategy & logic into a visual representation of how I analyse and project market using Core price action & MacD. Enjoy :)
A Proprietary Structural & Momentum Confluence SystemPart 1: The Strategic Concept1. The Core Philosophy: "Geometry + Physics"Traditional technical analysis often fails because traders confuse location with timing.Geometry (Price Patterns): Tells us WHERE the market is likely to reverse (e.g., at a resistance level or harmonic D-point).Physics (Momentum): Tells us WHEN the energy driving the trend has actually shifted. The Vdubus Theory posits that a trade should never be taken based on Geometry alone. A valid signal requires a specific, fractal decay in momentum—a "Handshake" between price structure and energy exhaustion.2. The 3-Wave Momentum Filter (The Engine)Most traders look for simple divergence (2 points). The Vdubus Theory demands a 3-Wave Structure to confirm the true state of the market.A. The Standard Reversal (Exhaustion)This is the "Safe" entry, catching the slow death of a trend.Wave 1 $\rightarrow$ 2 (The Warning): Price pushes higher, but momentum is lower (Standard Divergence). This signals that the trend is tapping the brakes.Wave 2 $\rightarrow$ 3 (The Confirmation): Price pushes to a final extreme (often a stop-hunt), but momentum is flat or lower than Wave 2 ("No Divergence").The Logic: This confirms that the buyers have expended all remaining energy. The engine is dead.
B. The Climax Reversal (The Trap)This is the "Aggressive" entry, catching V-shape reversals.Wave 1 $\rightarrow$ 2 (The Bait): Price pushes higher, and momentum is Stronger/Higher (No Divergence). This sucks in retail traders who believe the trend is accelerating.Wave 2 $\rightarrow$ 3 (The Snap): Price pushes again, but momentum suddenly collapses (Divergence).The Logic: A "Strong to Weak" shift. The market traps traders with a show of strength before hitting a "concrete wall" of limit orders.C. The Predator (The Trend Continuation)The Logic: Trends rarely move in straight lines. The "Predator" looks for Hidden Divergence during a pullback.The Signal: Price makes a Higher Low (Trend Structure Intact), but Momentum makes a Lower Low (Oversold Trap). This signals the end of the correction and the resumption of the main trend.3. The "Clean Path" PrincipleA trade is only valid if there is no opposing force. If you are looking to Sell (Bearish Reversal), the opposing Bullish momentum must be weak or neutral. If the "Enemy" is strong, the trade is skipped.
Part 2: The Indicator Breakdown
Tool Name: Vdubus Divergence Wave Pattern Generator V1
This script automates your analysis by combining ZigZag Pattern Recognition (Geometry) with your Custom MACD Logic (Physics).
1. The "Golden" Settings
The physics engine is tuned to your specific discovery:
Fast Length: 8
Slow Length: 21
Signal Length: 5
Lookback: 3 (Sensitive enough to catch the exact pivot points).
2. Signal Generation Logic
The indicator scans for four distinct setups. Here is the exact logic code translated into English:
Signal 1: Standard Reversal (Green/Red Pattern)
Geometry: The ZigZag algorithm identifies a 5-point structure (X-A-B-C-D), such as a Gartley, Bat, or Butterfly.
Physics Check:
Finds the last 3 momentum peaks matching the price highs.
Rule: Momentum Peak 2 must be < Peak 1 (Divergence).
Rule: Momentum Peak 3 must be <= Peak 2 (Confirmation/No Div).
Output: Draws the colored pattern and labels it (e.g., "Bearish Gartley (Exhaustion)").
Signal 2: Climax Reversal (Orange Pattern)
Geometry: Identifies the same 5-point structures.
Physics Check:
Rule: Momentum Peak 2 is >= Peak 1 (Strength/No Div).
Rule: Momentum Peak 3 is < Peak 2 (Sudden Failure/Div).
Output: Draws the pattern in Orange labeled "⚠️ CLIMAX REVERSAL". This is your "Trap" detector.
Signal 3: Rounded Top/Bottom (Navy/Maroon Label)
Geometry: Price is compressing or rounding over.
Physics Check:
Scans for 4 consecutive waves of momentum decay.
Rule: Peak 1 > Peak 2 > Peak 3 > Peak 4.
Output: Places a label indicating a "Multi-Wave Decay," identifying turns that don't have sharp pivots.
Signal 4: The Predator (Purple Pattern)
Geometry: Identifies a trend pullback (Higher Low for Buys).
Physics Check:
Rule: Momentum makes a Lower Low while Price makes a Higher Low (Hidden Divergence).
Output: Draws a Purple pattern labeled "🦖 PREDATOR" to signal trend continuation.
3. The Confluence Dashboard
Located in the corner of the screen, this provides a final "Safety Check."
Logic: It compares the absolute value (strength) of the most recent Bearish Momentum Peak vs. the most recent Bullish Momentum Low.
Output:
Green (Bulls Strong): Buying pressure is dominant. Safe to Buy, Dangerous to Sell.
Red (Bears Strong): Selling pressure is dominant. Safe to Sell, Dangerous to Buy.
Grey (Neutral): Forces are balanced.
Summary of Potential
This system solves the "Trader's Dilemma" of entering too early or too late. By waiting for the 3rd Wave, you effectively filter out the market noise and only commit capital when the opposing side has structurally and physically collapsed. It transforms trading from a guessing game into a disciplined execution of identifying Geometric Exhaustion.
Logic 1 / PREVIOUS DIVERGENCE PROJECTS future TREND BREAKS / Reversals *Not in script*
Logic 2 / Wave 1 to 2 = Divergence / Wave 2 to 3 = NO divergence = Signal
Reverse logic: Wave 1 to 2 = NO Divergence / Wave 2 to 3 = Divergence = Signal
MACD Forecast Colorful [DiFlip]MACD Forecast Colorful
The Future of Predictive MACD — is one of the most advanced and customizable MACD indicators ever published on TradingView. Built on the classic MACD foundation, this upgraded version integrates statistical forecasting through linear regression to anticipate future movements — not just react to the past.
With a total of 22 fully configurable long and short entry conditions, visual enhancements, and full automation support, this indicator is designed for serious traders seeking an analytical edge.
⯁ Real-Time MACD Forecasting
For the first time, a public MACD script combines the classic structure of MACD with predictive analytics powered by linear regression. Instead of simply responding to current values, this tool projects the MACD line, signal line, and histogram n bars into the future, allowing you to trade with foresight rather than hindsight.
⯁ Fully Customizable
This indicator is built for flexibility. It includes 22 entry conditions, all of which are fully configurable. Each condition can be turned on/off, chained using AND/OR logic, and adapted to your trading model.
Whether you're building a rules-based quant system, automating alerts, or refining discretionary signals, MACD Forecast Colorful gives you full control over how signals are generated, displayed, and triggered.
⯁ With MACD Forecast Colorful, you can:
• Detect MACD crossovers before they happen.
• Anticipate trend reversals with greater precision.
• React earlier than traditional indicators.
• Gain a powerful edge in both discretionary and automated strategies.
• This isn’t just smarter MACD — it’s predictive momentum intelligence.
⯁ Scientifically Powered by Linear Regression
MACD Forecast Colorful is the first public MACD indicator to apply least-squares predictive modeling to MACD behavior — effectively introducing machine learning logic into a time-tested tool.
It uses statistical regression to analyze historical behavior of the MACD and project future trajectories. The result is a forward-shifted MACD forecast that can detect upcoming crossovers and divergences before they appear on the chart.
⯁ Linear Regression: Technical Foundation
Linear regression is a statistical method that models the relationship between a dependent variable (y) and one or more independent variables (x). The basic formula for simple linear regression is:
y = β₀ + β₁x + ε
Where:
y = predicted variable (e.g., future MACD value)
x = independent variable (e.g., bar index)
β₀ = intercept
β₁ = slope
ε = random error (residual)
The regression model calculates β₀ and β₁ using the least squares method, minimizing the sum of squared prediction errors to produce the best-fit line through historical values. This line is then extended forward, generating a forecast based on recent price momentum.
⯁ Least Squares Estimation
The regression coefficients are computed with the following formulas:
β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²)
β₀ = ȳ - β₁x̄
Where:
Σ denotes summation; x̄ and ȳ are the means of x and y; and i ranges from 1 to n (number of observations). These equations produce the best linear unbiased estimator under the Gauss–Markov assumptions — constant variance (homoscedasticity) and a linear relationship between variables.
⯁ Regression in Machine Learning
Linear regression is a foundational model in supervised learning. Its ability to provide precise, explainable, and fast forecasts makes it critical in AI systems and quantitative analysis.
Applying linear regression to MACD forecasting is the equivalent of injecting artificial intelligence into one of the most widely used momentum tools in trading.
⯁ Visual Interpretation
Picture the MACD values over time like this:
Time →
MACD →
A regression line is fitted to recent MACD values, then projected forward n periods. The result is a predictive trajectory that can cross over the real MACD or signal line — offering an early-warning system for trend shifts and momentum changes.
The indicator plots both current MACD and forecasted MACD, allowing you to visually compare short-term future behavior against historical movement.
⯁ Scientific Concepts Used
Linear Regression: models the relationship between variables using a straight line.
Least Squares Method: minimizes squared prediction errors for best-fit.
Time-Series Forecasting: projects future data based on past patterns.
Supervised Learning: predictive modeling using labeled inputs.
Statistical Smoothing: filters noise to highlight trends.
⯁ Why This Indicator Is Revolutionary
First open-source MACD with real-time predictive modeling.
Scientifically grounded with linear regression logic.
Automatable through TradingView alerts and bots.
Smart signal generation using forecasted crossovers.
Highly customizable with 22 buy/sell conditions.
Enhanced visuals with background (bgcolor) and area fill (fill) support.
This isn’t just an update — it’s the next evolution of MACD forecasting.
⯁ Example of simple linear regression with one independent variable
This example demonstrates how a basic linear regression works when there is only one independent variable influencing the dependent variable. This type of model is used to identify a direct relationship between two variables.
⯁ In linear regression, observations (red) are considered the result of random deviations (green) from an underlying relationship (blue) between a dependent variable (y) and an independent variable (x)
This concept illustrates that sampled data points rarely align perfectly with the true trend line. Instead, each observed point represents the combination of the true underlying relationship and a random error component.
⯁ Visualizing heteroscedasticity in a scatterplot with 100 random fitted values using Matlab
Heteroscedasticity occurs when the variance of the errors is not constant across the range of fitted values. This visualization highlights how the spread of data can change unpredictably, which is an important factor in evaluating the validity of regression models.
⯁ The datasets in Anscombe’s quartet were designed to have nearly the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but look very different when plotted
This classic example shows that summary statistics alone can be misleading. Even with identical numerical metrics, the datasets display completely different patterns, emphasizing the importance of visual inspection when interpreting a model.
⯁ Result of fitting a set of data points with a quadratic function
This example illustrates how a second-degree polynomial model can better fit certain datasets that do not follow a linear trend. The resulting curve reflects the true shape of the data more accurately than a straight line.
⯁ What is the MACD?
The Moving Average Convergence Divergence (MACD) is a technical analysis indicator developed by Gerald Appel. It measures the relationship between two moving averages of a security’s price to identify changes in momentum, direction, and strength of a trend. The MACD is composed of three components: the MACD line, the signal line, and the histogram.
⯁ How to use the MACD?
The MACD is calculated by subtracting the 26-period Exponential Moving Average (EMA) from the 12-period EMA. A 9-period EMA of the MACD line, called the signal line, is then plotted on top of the MACD line. The MACD histogram represents the difference between the MACD line and the signal line.
Here are the primary signals generated by the MACD:
• Bullish Crossover: When the MACD line crosses above the signal line, indicating a potential buy signal.
• Bearish Crossover: When the MACD line crosses below the signal line, indicating a potential sell signal.
• Divergence: When the price of the security diverges from the MACD, suggesting a potential reversal.
• Overbought/Oversold Conditions: Indicated by the MACD line moving far away from the signal line, though this is less common than in oscillators like the RSI.
⯁ How to use MACD forecast?
The MACD Forecast is built on the same foundation as the classic MACD, but with predictive capabilities.
Step 1 — Spot Predicted Crossovers:
Watch for forecasted bullish or bearish crossovers. These signals anticipate when the MACD line will cross the signal line in the future, letting you prepare trades before the move.
Step 2 — Confirm with Histogram Projection:
Use the projected histogram to validate momentum direction. A rising histogram signals strengthening bullish momentum, while a falling projection points to weakening or bearish conditions.
Step 3 — Combine with Multi-Timeframe Analysis:
Use forecasts across multiple timeframes to confirm signal strength (e.g., a 1h forecast aligned with a 4h forecast).
Step 4 — Set Entry Conditions & Automation:
Customize your buy/sell rules with the 20 forecast-based conditions and enable automation for bots or alerts.
Step 5 — Trade Ahead of the Market:
By preparing for future momentum shifts instead of reacting to the past, you’ll always stay one step ahead of lagging traders.
📈 BUY
🍟 Signal Validity: The signal will remain valid for X bars.
🍟 Signal Sequence: Configurable as AND or OR.
🍟 MACD > Signal Smoothing
🍟 MACD < Signal Smoothing
🍟 Histogram > 0
🍟 Histogram < 0
🍟 Histogram Positive
🍟 Histogram Negative
🍟 MACD > 0
🍟 MACD < 0
🍟 Signal > 0
🍟 Signal < 0
🍟 MACD > Histogram
🍟 MACD < Histogram
🍟 Signal > Histogram
🍟 Signal < Histogram
🍟 MACD (Crossover) Signal
🍟 MACD (Crossunder) Signal
🍟 MACD (Crossover) 0
🍟 MACD (Crossunder) 0
🍟 Signal (Crossover) 0
🍟 Signal (Crossunder) 0
🔮 MACD (Crossover) Signal Forecast
🔮 MACD (Crossunder) Signal Forecast
📉 SELL
🍟 Signal Validity: The signal will remain valid for X bars.
🍟 Signal Sequence: Configurable as AND or OR.
🍟 MACD > Signal Smoothing
🍟 MACD < Signal Smoothing
🍟 Histogram > 0
🍟 Histogram < 0
🍟 Histogram Positive
🍟 Histogram Negative
🍟 MACD > 0
🍟 MACD < 0
🍟 Signal > 0
🍟 Signal < 0
🍟 MACD > Histogram
🍟 MACD < Histogram
🍟 Signal > Histogram
🍟 Signal < Histogram
🍟 MACD (Crossover) Signal
🍟 MACD (Crossunder) Signal
🍟 MACD (Crossover) 0
🍟 MACD (Crossunder) 0
🍟 Signal (Crossover) 0
🍟 Signal (Crossunder) 0
🔮 MACD (Crossover) Signal Forecast
🔮 MACD (Crossunder) Signal Forecast
🤖 Automation
All BUY and SELL conditions can be automated using TradingView alerts. Every configurable condition can trigger alerts suitable for fully automated or semi-automated strategies.
⯁ Unique Features
Linear Regression: (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Table of Conditions: BUY/SELL
Conditions Label: BUY/SELL
Plot Labels in the graph above: BUY/SELL
Automate & Monitor Signals/Alerts: BUY/SELL
Background Colors: "bgcolor"
Background Colors: "fill"
Linear Regression (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Table of Conditions: BUY/SELL
Conditions Label: BUY/SELL
Plot Labels in the graph above: BUY/SELL
Automate & Monitor Signals/Alerts: BUY/SELL
Background Colors: "bgcolor"
Background Colors: "fill"
ADX Forecast Colorful [DiFlip]ADX Forecast Colorful
Introducing one of the most advanced ADX indicators available — a fully customizable analytical tool that integrates forward-looking forecasting capabilities. ADX Forecast Colorful is a scientific evolution of the classic ADX, designed to anticipate future trend strength using linear regression. Instead of merely reacting to historical data, this indicator projects the future behavior of the ADX, giving traders a strategic edge in trend analysis.
⯁ Real-Time ADX Forecasting
For the first time, a public ADX indicator incorporates linear regression (least squares method) to forecast the future behavior of ADX. This breakthrough approach enables traders to anticipate trend strength changes based on historical momentum. By applying linear regression to the ADX, the indicator plots a projected trendline n periods ahead — helping users make more accurate and timely trading decisions.
⯁ Highly Customizable
The indicator adapts seamlessly to any trading style. It offers a total of 26 long entry conditions and 26 short entry conditions, making it one of the most configurable ADX tools on TradingView. Each condition is fully adjustable, enabling the creation of statistical, quantitative, and automated strategies. You maintain full control over the signals to align perfectly with your system.
⯁ Innovative and Science-Based
This is the first public ADX indicator to apply least-squares predictive modeling to ADX dynamics. Technically, it embeds machine learning logic into a traditional trend-strength indicator. Using linear regression as a predictive engine adds powerful statistical rigor to the ADX, turning it into an intelligent, forward-looking signal generator.
⯁ Scientific Foundation: Linear Regression
Linear regression is a fundamental method in statistics and machine learning used to model the relationship between a dependent variable y and one or more independent variables x. The basic formula for simple linear regression is:
y = β₀ + β₁x + ε
Where:
y = predicted value (e.g., future ADX)
x = explanatory variable (e.g., bar index or time)
β₀ = intercept
β₁ = slope (rate of change)
ε = random error term
The goal is to estimate β₀ and β₁ by minimizing the sum of squared errors. This is achieved using the least squares method, ensuring the best linear fit to historical data. Once the coefficients are calculated, the model extends the regression line forward, generating the ADX projection based on recent trends.
⯁ Least Squares Estimation
To minimize the error, the regression coefficients are calculated as:
β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²)
β₀ = ȳ - β₁x̄
Where:
Σ = summation
x̄ and ȳ = means of x and y
i ranges from 1 to n (number of data points)
These formulas provide the best linear unbiased estimator under Gauss-Markov conditions — assuming constant variance and linearity.
⯁ Linear Regression in Machine Learning
Linear regression is a foundational algorithm in supervised learning. Its power in producing quantitative predictions makes it essential in AI systems, predictive analytics, time-series forecasting, and automated trading. Applying it to the ADX essentially places an intelligent forecasting engine inside a classic trend tool.
⯁ Visual Interpretation
Imagine an ADX time series like this:
Time →
ADX →
The regression line smooths these values and projects them n periods forward, creating a predictive trajectory. This forecasted ADX line can intersect with the actual ADX, offering smarter buy and sell signals.
⯁ Summary of Scientific Concepts
Linear Regression: Models variable relationships with a straight line.
Least Squares: Minimizes prediction errors for best fit.
Time-Series Forecasting: Predicts future values using historical data.
Supervised Learning: Trains models to predict outcomes from inputs.
Statistical Smoothing: Reduces noise and highlights underlying trends.
⯁ Why This Indicator Is Revolutionary
Scientifically grounded: Based on rigorous statistical theory.
Unprecedented: First public ADX using least-squares forecast modeling.
Smart: Uses machine learning logic.
Forward-Looking: Generates predictive, not just reactive, signals.
Customizable: Flexible for any strategy or timeframe.
⯁ Conclusion
By merging ADX and linear regression, this indicator enables traders to predict market momentum rather than merely follow it. ADX Forecast Colorful is not just another indicator — it’s a scientific leap forward in technical analysis. With 26 fully configurable entry conditions and smart forecasting, this open-source tool is built for creating cutting-edge quantitative strategies.
⯁ Example of simple linear regression with one independent variable
This example demonstrates how a basic linear regression works when there is only one independent variable influencing the dependent variable. This type of model is used to identify a direct relationship between two variables.
⯁ In linear regression, observations (red) are considered the result of random deviations (green) from an underlying relationship (blue) between a dependent variable (y) and an independent variable (x)
This concept illustrates that sampled data points rarely align perfectly with the true trend line. Instead, each observed point represents the combination of the true underlying relationship and a random error component.
⯁ Visualizing heteroscedasticity in a scatterplot with 100 random fitted values using Matlab
Heteroscedasticity occurs when the variance of the errors is not constant across the range of fitted values. This visualization highlights how the spread of data can change unpredictably, which is an important factor in evaluating the validity of regression models.
⯁ The datasets in Anscombe’s quartet were designed to have nearly the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but look very different when plotted
This classic example shows that summary statistics alone can be misleading. Even with identical numerical metrics, the datasets display completely different patterns, emphasizing the importance of visual inspection when interpreting a model.
⯁ Result of fitting a set of data points with a quadratic function
This example illustrates how a second-degree polynomial model can better fit certain datasets that do not follow a linear trend. The resulting curve reflects the true shape of the data more accurately than a straight line.
⯁ What is the ADX?
The Average Directional Index (ADX) is a technical analysis indicator developed by J. Welles Wilder. It measures the strength of a trend in a market, regardless of whether the trend is up or down.
The ADX is an integral part of the Directional Movement System, which also includes the Plus Directional Indicator (+DI) and the Minus Directional Indicator (-DI). By combining these components, the ADX provides a comprehensive view of market trend strength.
⯁ How to use the ADX?
The ADX is calculated based on the moving average of the price range expansion over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and has three main zones:
Strong Trend: When the ADX is above 25, indicating a strong trend.
Weak Trend: When the ADX is below 20, indicating a weak or non-existent trend.
Neutral Zone: Between 20 and 25, where the trend strength is unclear.
⯁ Entry Conditions
Each condition below is fully configurable and can be combined to build precise trading logic.
📈 BUY
🅰️ Signal Validity: The signal will remain valid for X bars .
🅰️ Signal Sequence: Configurable as AND or OR .
🅰️ +DI > -DI
🅰️ +DI < -DI
🅰️ +DI > ADX
🅰️ +DI < ADX
🅰️ -DI > ADX
🅰️ -DI < ADX
🅰️ ADX > Threshold
🅰️ ADX < Threshold
🅰️ +DI > Threshold
🅰️ +DI < Threshold
🅰️ -DI > Threshold
🅰️ -DI < Threshold
🅰️ +DI (Crossover) -DI
🅰️ +DI (Crossunder) -DI
🅰️ +DI (Crossover) ADX
🅰️ +DI (Crossunder) ADX
🅰️ +DI (Crossover) Threshold
🅰️ +DI (Crossunder) Threshold
🅰️ -DI (Crossover) ADX
🅰️ -DI (Crossunder) ADX
🅰️ -DI (Crossover) Threshold
🅰️ -DI (Crossunder) Threshold
🔮 +DI (Crossover) -DI Forecast
🔮 +DI (Crossunder) -DI Forecast
🔮 ADX (Crossover) +DI Forecast
🔮 ADX (Crossunder) +DI Forecast
📉 SELL
🅰️ Signal Validity: The signal will remain valid for X bars .
🅰️ Signal Sequence: Configurable as AND or OR .
🅰️ +DI > -DI
🅰️ +DI < -DI
🅰️ +DI > ADX
🅰️ +DI < ADX
🅰️ -DI > ADX
🅰️ -DI < ADX
🅰️ ADX > Threshold
🅰️ ADX < Threshold
🅰️ +DI > Threshold
🅰️ +DI < Threshold
🅰️ -DI > Threshold
🅰️ -DI < Threshold
🅰️ +DI (Crossover) -DI
🅰️ +DI (Crossunder) -DI
🅰️ +DI (Crossover) ADX
🅰️ +DI (Crossunder) ADX
🅰️ +DI (Crossover) Threshold
🅰️ +DI (Crossunder) Threshold
🅰️ -DI (Crossover) ADX
🅰️ -DI (Crossunder) ADX
🅰️ -DI (Crossover) Threshold
🅰️ -DI (Crossunder) Threshold
🔮 +DI (Crossover) -DI Forecast
🔮 +DI (Crossunder) -DI Forecast
🔮 ADX (Crossover) +DI Forecast
🔮 ADX (Crossunder) +DI Forecast
🤖 Automation
All BUY and SELL conditions are compatible with TradingView alerts, making them ideal for fully or semi-automated systems.
⯁ Unique Features
Linear Regression: (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
Background Colors: "bgcolor"
Background Colors: "fill"
Linear Regression (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Table of Conditions: BUY/SELL
Conditions Label: BUY/SELL
Plot Labels in the graph above: BUY/SELL
Automate & Monitor Signals/Alerts: BUY/SELL
Background Colors: "bgcolor"
Background Colors: "fill"
RSI Forecast Colorful [DiFlip]RSI Forecast Colorful
Introducing one of the most complete RSI indicators available — a highly customizable analytical tool that integrates advanced prediction capabilities. RSI Forecast Colorful is an evolution of the classic RSI, designed to anticipate potential future RSI movements using linear regression. Instead of simply reacting to historical data, this indicator provides a statistical projection of the RSI’s future behavior, offering a forward-looking view of market conditions.
⯁ Real-Time RSI Forecasting
For the first time, a public RSI indicator integrates linear regression (least squares method) to forecast the RSI’s future behavior. This innovative approach allows traders to anticipate market movements based on historical trends. By applying Linear Regression to the RSI, the indicator displays a projected trendline n periods ahead, helping traders make more informed buy or sell decisions.
⯁ Highly Customizable
The indicator is fully adaptable to any trading style. Dozens of parameters can be optimized to match your system. All 28 long and short entry conditions are selectable and configurable, allowing the construction of quantitative, statistical, and automated trading models. Full control over signals ensures precise alignment with your strategy.
⯁ Innovative and Science-Based
This is the first public RSI indicator to apply least-squares predictive modeling to RSI calculations. Technically, it incorporates machine-learning logic into a classic indicator. Using Linear Regression embeds strong statistical foundations into RSI forecasting, making this tool especially valuable for traders seeking quantitative and analytical advantages.
⯁ Scientific Foundation: Linear Regression
Linear regression is a fundamental statistical method that models the relationship between a dependent variable y and one or more independent variables x. The general formula for simple linear regression is:
y = β₀ + β₁x + ε
where:
y = predicted variable (e.g., future RSI value)
x = explanatory variable (e.g., bar index or time)
β₀ = intercept (value of y when x = 0)
β₁ = slope (rate of change of y relative to x)
ε = random error term
The goal is to estimate β₀ and β₁ by minimizing the sum of squared errors. This is achieved using the least squares method, ensuring the best linear fit to historical data. Once the coefficients are calculated, the model extends the regression line forward, generating the RSI projection based on recent trends.
⯁ Least Squares Estimation
To minimize the error between predicted and observed values, we use the formulas:
β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²)
β₀ = ȳ - β₁x̄
Σ denotes summation; x̄ and ȳ are the means of x and y; and i ranges from 1 to n (number of observations). These equations produce the best linear unbiased estimator under the Gauss–Markov assumptions — constant variance (homoscedasticity) and a linear relationship between variables.
⯁ Linear Regression in Machine Learning
Linear regression is a foundational component of supervised learning. Its simplicity and precision in numerical prediction make it essential in AI, predictive algorithms, and time-series forecasting. Applying regression to RSI is akin to embedding artificial intelligence inside a classic indicator, adding a new analytical dimension.
⯁ Visual Interpretation
Imagine a time series of RSI values like this:
Time →
RSI →
The regression line smooths these historical values and projects itself n periods forward, creating a predictive trajectory. This projected RSI line can cross the actual RSI, generating sophisticated entry and exit signals. In summary, the RSI Forecast Colorful indicator provides both the current RSI and the forecasted RSI, allowing comparison between past and future trend behavior.
⯁ Summary of Scientific Concepts Used
Linear Regression: Models relationships between variables using a straight line.
Least Squares: Minimizes squared prediction errors for optimal fit.
Time-Series Forecasting: Predicts future values from historical patterns.
Supervised Learning: Predictive modeling based on known output values.
Statistical Smoothing: Reduces noise to highlight underlying trends.
⯁ Why This Indicator Is Revolutionary
Scientifically grounded: Built on statistical and mathematical theory.
First of its kind: The first public RSI with least-squares predictive modeling.
Intelligent: Incorporates machine-learning logic into RSI interpretation.
Forward-looking: Generates predictive, not just reactive, signals.
Customizable: Exceptionally flexible for any strategic framework.
⯁ Conclusion
By combining RSI and linear regression, the RSI Forecast Colorful allows traders to predict market momentum rather than simply follow it. It's not just another indicator: it's a scientific advancement in technical analysis technology. Offering 28 configurable entry conditions and advanced signals, this open-source indicator paves the way for innovative quantitative systems.
⯁ Example of simple linear regression with one independent variable
This example demonstrates how a basic linear regression works when there is only one independent variable influencing the dependent variable. This type of model is used to identify a direct relationship between two variables.
⯁ In linear regression, observations (red) are considered the result of random deviations (green) from an underlying relationship (blue) between a dependent variable (y) and an independent variable (x)
This concept illustrates that sampled data points rarely align perfectly with the true trend line. Instead, each observed point represents the combination of the true underlying relationship and a random error component.
⯁ Visualizing heteroscedasticity in a scatterplot with 100 random fitted values using Matlab
Heteroscedasticity occurs when the variance of the errors is not constant across the range of fitted values. This visualization highlights how the spread of data can change unpredictably, which is an important factor in evaluating the validity of regression models.
⯁ The datasets in Anscombe’s quartet were designed to have nearly the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but look very different when plotted
This classic example shows that summary statistics alone can be misleading. Even with identical numerical metrics, the datasets display completely different patterns, emphasizing the importance of visual inspection when interpreting a model.
⯁ Result of fitting a set of data points with a quadratic function
This example illustrates how a second-degree polynomial model can better fit certain datasets that do not follow a linear trend. The resulting curve reflects the true shape of the data more accurately than a straight line.
⯁ What Is RSI?
The RSI (Relative Strength Index) is a technical indicator developed by J. Welles Wilder. It measures the velocity and magnitude of recent price movements to identify overbought and oversold conditions. The RSI ranges from 0 to 100 and is commonly used to identify potential reversals and evaluate trend strength.
⯁ How RSI Works
RSI is calculated from average gains and losses over a set period (commonly 14 bars) and plotted on a 0–100 scale. It consists of three key zones:
Overbought: RSI above 70 may signal an overbought market.
Oversold: RSI below 30 may signal an oversold market.
Neutral Zone: RSI between 30 and 70, indicating no extreme condition.
These zones help identify potential price reversals and confirm trend strength.
⯁ Entry Conditions
All conditions below are fully customizable and allow detailed control over entry signal creation.
📈 BUY
🧲 Signal Validity: Signal remains valid for X bars.
🧲 Signal Logic: Configurable using AND or OR.
🧲 RSI > Upper
🧲 RSI < Upper
🧲 RSI > Lower
🧲 RSI < Lower
🧲 RSI > Middle
🧲 RSI < Middle
🧲 RSI > MA
🧲 RSI < MA
🧲 MA > Upper
🧲 MA < Upper
🧲 MA > Lower
🧲 MA < Lower
🧲 RSI (Crossover) Upper
🧲 RSI (Crossunder) Upper
🧲 RSI (Crossover) Lower
🧲 RSI (Crossunder) Lower
🧲 RSI (Crossover) Middle
🧲 RSI (Crossunder) Middle
🧲 RSI (Crossover) MA
🧲 RSI (Crossunder) MA
🧲 MA (Crossover)Upper
🧲 MA (Crossunder)Upper
🧲 MA (Crossover) Lower
🧲 MA (Crossunder) Lower
🧲 RSI Bullish Divergence
🧲 RSI Bearish Divergence
🔮 RSI (Crossover) Forecast MA
🔮 RSI (Crossunder) Forecast MA
📉 SELL
🧲 Signal Validity: Signal remains valid for X bars.
🧲 Signal Logic: Configurable using AND or OR.
🧲 RSI > Upper
🧲 RSI < Upper
🧲 RSI > Lower
🧲 RSI < Lower
🧲 RSI > Middle
🧲 RSI < Middle
🧲 RSI > MA
🧲 RSI < MA
🧲 MA > Upper
🧲 MA < Upper
🧲 MA > Lower
🧲 MA < Lower
🧲 RSI (Crossover) Upper
🧲 RSI (Crossunder) Upper
🧲 RSI (Crossover) Lower
🧲 RSI (Crossunder) Lower
🧲 RSI (Crossover) Middle
🧲 RSI (Crossunder) Middle
🧲 RSI (Crossover) MA
🧲 RSI (Crossunder) MA
🧲 MA (Crossover)Upper
🧲 MA (Crossunder)Upper
🧲 MA (Crossover) Lower
🧲 MA (Crossunder) Lower
🧲 RSI Bullish Divergence
🧲 RSI Bearish Divergence
🔮 RSI (Crossover) Forecast MA
🔮 RSI (Crossunder) Forecast MA
🤖 Automation
All BUY and SELL conditions can be automated using TradingView alerts. Every configurable condition can trigger alerts suitable for fully automated or semi-automated strategies.
⯁ Unique Features
Linear Regression Forecast
Signal Validity: Keep signals active for X bars
Signal Logic: AND/OR configuration
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Chart Labels: BUY/SELL markers above price
Automation & Alerts: BUY/SELL
Background Colors: bgcolor
Fill Colors: fill
Linear Regression Forecast
Signal Validity: Keep signals active for X bars
Signal Logic: AND/OR configuration
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Chart Labels: BUY/SELL markers above price
Automation & Alerts: BUY/SELL
Background Colors: bgcolor
Fill Colors: fill
