Weighted Burg AR Spectral Estimate Extrapolation of Price [Loxx]Weighted Burg AR Spectral Estimate Extrapolation of Price is an indicator that uses an autoregressive spectral estimation called the Weighted Burg Algorithm. This method is commonly used in speech modeling and speech prediction engines. This method also includes Levinson–Durbin algorithm. As was already discussed previously in the following indicator:
Levinson-Durbin Autocorrelation Extrapolation of Price
What is Levinson recursion or Levinson–Durbin recursion?
In many applications, the duration of an uninterrupted measurement of a time series is limited. However, it is often possible to obtain several separate segments of data. The estimation of an autoregressive model from this type of data is discussed. A straightforward approach is to take the average of models estimated from each segment separately. In this way, the variance of the estimated parameters is reduced. However, averaging does not reduce the bias in the estimate. With the Burg algorithm for segments, both the variance and the bias in the estimated parameters are reduced by fitting a single model to all segments simultaneously. As a result, the model estimated with the Burg algorithm for segments is more accurate than models obtained with averaging. The new weighted Burg algorithm for segments allows combining segments of different amplitudes.
The Burg algorithm estimates the AR parameters by determining reflection coefficients that minimize the sum of for-ward and backward residuals. The extension of the algorithm to segments is that the reflection coefficients are estimated by minimizing the sum of forward and backward residuals of all segments taken together. This means a single model is fitted to all segments in one time. This concept is also used for prediction error methods in system identification, where the input to the system is known, like in ARX modeling
Data inputs
Source Settings: -Loxx's Expanded Source Types. You typically use "open" since open has already closed on the current active bar
LastBar - bar where to start the prediction
PastBars - how many bars back to model
LPOrder - order of linear prediction model; 0 to 1
FutBars - how many bars you want to forward predict
BurgWin - weighing function index, rectangular, hamming, or parabolic
Things to know
Normally, a simple moving average is calculated on source data. I've expanded this to 38 different averaging methods using Loxx's Moving Avreages.
This indicator repaints
Included
Bar color muting
Further reading
Performance of the weighted burg methods of ar spectral estimation for pitch-synchronous analysis of voiced speech
The Burg algorithm for segments
Techniques for the Enhancement of Linear Predictive Speech Coding in Adverse Conditions
Related Indicators
Cerca negli script per "algo"
Auto Fibonacci Retracement - Real-Time (Expo)█ Fibonacci retracement is a popular technical analysis method to draw support and resistance levels. The Fibonacci levels are calculated between 2 swing points (high/low) and divided by the key Fibonacci coefficients equal to 23.6%, 38.2%, 50%, 61.8%, and 100%. The percentage represents how much of a prior move the price has retraced.
█ Our Auto Fibonacci Retracement indicator analyzes the market in real-time and draws Fibonacci levels automatically for you on the chart. Real-time fib levels use the current swing points, which gives you a huge advantage when using them in your trading. You can always be sure that the levels are calculated from the correct swing high and low, regardless of the current trend. The algorithm has a trend filter and shifts the swing points if there is a trend change.
The user can set the preferred swing move to scalping, trend trading, or swing trading. This way, you can use our automatic fib indicator to do any trading. The auto fib works on any market and timeframe and displays the most important levels in real-time for you.
█ This Auto Fib Retracement indicator for TradingView is powerful since it does the job for you in real-time. Apply it to the chart, set the swing move to fit your trading style, and leave it on the chart. The indicator does the rest for you. The auto Fibonacci indicator calculates and plots the levels for you in any market and timeframe. In addition, it even changes the swing points based on the current trend direction, allowing traders to get the correct Fibonacci levels in every trend.
█ How does the Auto Fib Draw the levels?
The algorithm analyzes the recent price action and examines the current trend; based on the trend direction, two significant swings (high and low) are identified, and Fibonacci levels will then be plotted automatically on the chart. If the algorithm has identified an uptrend, it will calculate the Fibonacci levels from the swing low and up to the swing high. Similarly, if the algorithm has identified a downtrend, it will calculate the Fibonacci levels from the swing high and down to the swing low.
█ HOW TO USE
The levels allow for a quick and easy understanding of the current Fibonacci levels and help traders anticipate and react when the price levels are tested. In addition, the levels are often used for entries to determine stop-loss levels and to set profit targets. It's also common for traders to use Fibonacci levels to identify resistance and support levels.
Traders can set alerts when the levels are tested.
-----------------
Disclaimer
Copyright by Zeiierman.
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
SIVE 2.0 - [Soldi]SIVE 2.0 IS FINALLY HERE, after the long awaited update we are finally able to bring to you SIVE 2.0!
SIVE 1.0 (Systematic Institutional Volatility Expansion) brought a whole new approach to the algorithm and retail trading game on TradingView. Never before have you had access to a quantitative institutional approach like this, after years in development and testing we finally brought SIVE 1.0 to market. With very very high demand, support and so much positive feedback we knew that what we've created really hit the mark for so many traders!
What is SIVE?
SIVE as stated above stands for, Systematic Institutional Volatility Expansion. What this means is we have a highly effective system that reads what institutional algorithms are proven to be looking at. While only providing alerts during periods where Volatility is Expanding
We don't shy away from volatility here, that is where the bread and butter lays. volatility is a double edged sword that not many people know how to effectively use to their advantage. Simply put, because they are told in their retail trading that volatility is risky, and that you should stay away from volatile products. I say embrace it with the right tools.
What Has Changed?
At the core, SIVE 2.0 brings more efficient calculations to the volatility modelling as well as the triggering of trades!
Trend Scalper - This is a sub-set strategy we have included, what it measures is 'Super Trend' with a deviation of 7 and the MTI ribbon crosses. This is to be used as a way to scalp and trade the momentum of the market. I am aware that another brand/community has put this out as a paid feature to their algorithm. Since they didn't want to credit me for my contribution I decided to release it for free and also add it here. This is listed in my scripts as a free to use access.
Volatility Confluence - We have now also added a feature where you can choose how many volatility models you want to be aligned before SIVE calls an alert. There are a total of 9 models we have included, example. You choose 3 'Volatility Confluence', this means that SIVE will only call alerts when 3 of those 9 models agree. This can be very effective if you want to have more refined volatility trades, giving you more confidence that an expansion will take place.
Low Volatility Flashes - You now have the ability to control the low volatility back ground flash feature that was included with SIVE 1.0
Volatility Candles - You can now plot the volatility strength as candles! before you weren't able to see the actual underlaying volatility . Till now, turn this on and watch it turn you candles into a colourful array of the rainbow based on the volatility . Note - You will either need to *bring to front* or turn off the price data to see it
Take Profit System (beta) - Before on SIVE 1.0 and in the beta versions we had an early version of the money management. Where based on the ATR on the trade it would give you a suggested Stop Loss and Take Profit area. Now we have completely over hauled that and re calculated how we approach this also giving the trader 2 different options to choose from for suggested Stop Loss placement. We also included a 'Dynamic Take Profit' system that's based on the MTI to give you momentum based Take Profits. These are still in beta stages so any feedback is much appreciated and as always will be reviewed and considered.
RSI bands - Reverse plot the RSI onto your chart. Plot the over sold and over Bought static lines to price!
Moving Average Filter ( Multi Time Frame ) - Introducing a way further refine the trade alerts and give more power into the traders hands. We know that many many traders like to only trade if example. price is Greater Than 200 EMA. We wanted to give traders a choice to refine the trade alerts based on this information. You can choose between 'Price vs MA' - which is explained in that example just provided. The other option is 'MA vs MA', this allows you to filter out trades based on if a Moving Average of your choice(MA1) is Greater than MA2. With all this we also provided Multi Time Frame accessibility to just further give the trader more control and range. You also have the ability to just plot the Moving averages and not filter the trades at all!
Kill Zone / Time Sessions - Including another free script that has already been posted to my account. This script is also unique as it plots the specified time zones 24 hours in advanced . If you trade example. 'New York Session', Instead of using an indicator that only shows you after the fact it happens. You can now plot that time zone 24 Hours in advanced and watch how price trades to it and interacts with it. It has 4 completely customizable Time Zone slots. Please adjust to your time zone and desired sessions.
Here are some examples of SIVE working across various charts with the different features
USDCAD - 1 Hour
Take Profit System
XAUUSD ( Gold ) - 15 min
Trend Scalper System
US30(Dow Jones) - 15 min
Volatility Candles + Low Volatility Flashes
BTCUSD ( Bitcoin ) - 1 hour
Support / Resistance + Dashboard + Multi Time Frame MTI
USOIL (WTI Crude Oil ) - 5m
Kill Zones + Moving Average Filter
APPL( Apple ) - 1 hour
Moving Average Filter
Technimentals RobotThis robot includes multiple trade signal algorithms and technical overlays. With tools for all markets and trading styles, access original and beautiful charting tools that work for you.
Flexible and robust trend detection & confirmation
Maverick mean reversion signals
Immensely customizable settings for all markets
Indicator prediction zones, perfect for options traders
The most beautiful bands in the world
As of this update, Technimentals Robot includes:
Queen Mary - A powerful mean reversion algorithm which compares the performance of the chart against the performance of a user-chosen benchmark. She uses short term mean reversion optionally combined with longer term trend following logic to detect possible deviations and thus unique pivot points which may lead to short term reversals or continuations of trend.
Brian - An agile and fully customizable trend following algorithm which uses various filtering systems to hone signals.
Prediction Zones - Projections of future price levels and indicator levels, currently featuring RSI and MFI.
Volume Weighted Filtered Bands - The most beautiful bands in the world.
...and much more! Check the change log below for new features.
Technimentals Robot is an all-in-one suite of indicators designed to be used as a standalone trading system. The backbone of this indicator is the trade signal generation. However, blindly following trade signals without context is unwise and that's where the supplementary bands and Prediction Zones come in. The signals are designed to be used primarily for entry signals; the bands can be used to determine whether or not a chart is overextended (and worth stopping out or profit taking) or not. The Prediction Zones are built in particular for those wishing to trade these signals via options due to the quantifiable nature of their predictions, but they too can be used to add an extra data point for knowing which areas of support & resistance to use when determining take profit and stop loss locations.
Sub-Component Descriptions:
Queen Mary
Queen Mary is a versatile trading signal generator which uses another symbol as a benchmark to build trading signals for your chart.
Queen Mary works by detecting whether or not there are sustained divergences and alerts the user via trade signals for when reversions to the mean are expected.
A typical use case for Queen Mary would be to set her to run on a technology stock with a technology ETF as the benchmark, but you use any pair of your choice.
Buy signals on the chart simultaneously indicate sell signals in the benchmark.
This can be used for pairs trading and long/short portfolio strategies.
Suppose the following; you’ve applied Queen Mary to a chart of AAPL and are using XLK as the benchmark. A buy signal for AAPL would also indicate a sell signal for the XLK. The user could then long AAPL and short XLK the same dollar amount, expecting a reversion to the mean.
Brian
Brian is a flexible trend following algorithm which uses multiple filtering techniques to hone entries and exits.
Brian has been designed to catch every major trend without fail. The inevitable problem all breakout or trend following algorithms face is their propensity to get chopped up during sideways market action. Brian addresses this problem via the ‘Risk’ setting which allows the user to determine the market conditions via a risk/reward standpoint.
During periods of sideways action, the risk setting should be increased. This will reduce the number of signals Brian gives and increase the odds of the breakout leading to a continuation.
Brian signals profit taking signals via blue flags. These always occur at a user defined risk to reward ratio. Partial profits should always be taken as soon as these flags occur. It is advised to use a user-defined trailing stop loss on the remaining position which suits the user’s own risk preferences.
Prediction Zones
Prediction Zones predict zones of indicator and price levels into the future.
Currently featuring the Relative Strength Index and the Money Flow Index, Prediction Zones will display at what future prices these indicators will reach user defined outputs.
A classic use-case example of this would be for options traders as these zones can be used as support and resistance areas. For example, if you believe the daily RSI won’t reach below 30 before the end of the week, you can use this zone as another data point for deciding where to put your short strike.
The zones can be shown into the past too so you can see how they behaved on historical data.
Volume Weighted Filtered Bands
These bands are built by firstly using a volatility and short term range detection algorithm and plugging this into three different lengths of smoothing filters. The output is then combined and filtered one last time before being coloured and plotted as multiple bands.
They can be customized to suit any trading style, but were built with scalp traders particularly in mind. It’s rare for prices to deviate from these bands for long.
A typical use case for these bands would be to trade with the trend while price is trading cleanly inside and in the same direction as the bands. Profit taking should typically be considered when price exceeds the bands, although this will depend on the settings chosen by the user.
The bands can also be used to gauge volatility (with an unusual increase in width) and volume (increased brightness).
The brightness of the bands are determined by volume, the brighter the bands are, the greater the volume.
Queen Mary
Brian
Most of the above images were carefully chosen, others were not. No indicator or strategy is perfect. Trend following algorithms will inevitably experience chop. Mean reversion algorithms will inevitably miss out on the big moves. Our tools aim to give you the data to help you determine which signals to act upon.
You are responsible for your own trading decisions. Trading signals are worthless if you do not have a clear plan, including exit targets and risk management. If you do not have these, you should study them seriously before considering fancy indicators. This indicator is probably unsuitable for beginners.
SIVE 2.0SIVE 2.0 IS FINALLY HERE, after the long awaited update we are finally able to bring to you SIVE 2.0!
SIVE 1.0 (Systematic Institutional Volatility Expansion) brought a whole new approach to the algorithm and retail trading game on TradingView. Never before have you had access to a quantitative institutional approach like this, after years in development and testing we finally brought SIVE 1.0 to market. With very very high demand, support and so much positive feedback we knew that what we've created really hit the mark for so many traders!
What is SIVE?
SIVE as stated above stands for, Systematic Institutional Volatility Expansion. What this means is we have a highly effective system that reads what institutional algorithms are proven to be looking at. While only providing alerts during periods where Volatility is Expanding
We don't shy away from volatility here, that is where the bread and butter lays. volatility is a double edged sword that not many people know how to effectively use to their advantage. Simply put, because they are told in their retail trading that volatility is risky, and that you should stay away from volatile products. I say embrace it with the right tools.
What Has Changed?
At the core, SIVE 2.0 brings more efficient calculations to the volatility modelling as well as the triggering of trades!
Trend Scalper - This is a sub-set strategy we have included, what it measures is 'Super Trend' with a deviation of 7 and the MTI ribbon crosses. This is to be used as a way to scalp and trade the momentum of the market. I am aware that another brand/community has put this out as a paid feature to their algorithm. Since they didn't want to credit me for my contribution I decided to release it for free and also add it here. This is listed in my scripts as a free to use access.
Volatility Confluence - We have now also added a feature where you can choose how many volatility models you want to be aligned before SIVE calls an alert. There are a total of 9 models we have included, example. You choose 3 'Volatility Confluence', this means that SIVE will only call alerts when 3 of those 9 models agree. This can be very effective if you want to have more refined volatility trades, giving you more confidence that an expansion will take place.
Low Volatility Flashes - You now have the ability to control the low volatility back ground flash feature that was included with SIVE 1.0
Volatility Candles - You can now plot the volatility strength as candles! before you weren't able to see the actual underlaying volatility. Till now, turn this on and watch it turn you candles into a colourful array of the rainbow based on the volatility. Note - You will either need to *bring to front* or turn off the price data to see it
Take Profit System (beta) - Before on SIVE 1.0 and in the beta versions we had an early version of the money management. Where based on the ATR on the trade it would give you a suggested Stop Loss and Take Profit area. Now we have completely over hauled that and re calculated how we approach this also giving the trader 2 different options to choose from for suggested Stop Loss placement. We also included a 'Dynamic Take Profit' system that's based on the MTI to give you momentum based Take Profits. These are still in beta stages so any feedback is much appreciated and as always will be reviewed and considered.
RSI bands - Reverse plot the RSI onto your chart. Plot the over sold and over Bought static lines to price!
Moving Average Filter (Multi Time Frame) - Introducing a way further refine the trade alerts and give more power into the traders hands. We know that many many traders like to only trade if example. price is Greater Than 200 EMA . We wanted to give traders a choice to refine the trade alerts based on this information. You can choose between 'Price vs MA' - which is explained in that example just provided. The other option is 'MA vs MA', this allows you to filter out trades based on if a Moving Average of your choice(MA1) is Greater than MA2. With all this we also provided Multi Time Frame accessibility to just further give the trader more control and range. You also have the ability to just plot the Moving averages and not filter the trades at all!
Kill Zone / Time Sessions - Including another free script that has already been posted to my account. This script is also unique as it plots the specified time zones 24 hours in advanced. If you trade example. 'New York Session', Instead of using an indicator that only shows you after the fact it happens. You can now plot that time zone 24 Hours in advanced and watch how price trades to it and interacts with it. It has 4 completely customizable Time Zone slots. Please adjust to your time zone and desired sessions.
Here are some examples of SIVE working across various charts with the different features
USDCAD - 1 Hour
Take Profit System
XAUUSD(Gold) - 15 min
Trend Scalper System
US30(Dow Jones) - 15 min
Volatility Candles + Low Volatility Flashes
BTCUSD(Bitcoin) - 1 hour
Support / Resistance + Dashboard + Multi Time Frame MTI
USOIL(WTI Crude Oil) - 5m
Kill Zones + Moving Average Filter
APPL(Apple) - 1 hour
Moving Average Filter
Please report any bugs, feedback or suggestions you have about SIVE to myself or in the Discord so we can review it and take action!
If it isn't Soldi, it isn't money
Synthetic Price Action GeneratorNOTICE:
First thing you need to know, it "DOES NOT" reflect the price of the ticker you will load it on. THIS IS NOT AN INDICATOR FOR TRADING! It's a developer tool solely generating random values that look exactly like the fractals we observe every single day. This script's generated candles are as fake as the never ending garbage news cycles we are often force fed and expected to believe by using carefully scripted narratives peddled as hypnotic truth to psychologically and emotionally influence you to the point of control by coercion and subjugation. I wanted to make the script's synthetic nature very clear using that analogy, it's dynamically artificial. Do not accidentally become disillusioned by this scripts values, make trading decisions from it, and lastly don't become victim to predatory media magic ministry parrots with pretty, handsome smiles, compelling you to board their ferris wheel of fear. Now, on to the good stuff...
BACKSTORY:
Occasionally I find myself in situations where I have to build analyzers in Pine to actually build novel quantitative analytic indicators and tools worthy of future use. These analyzers certainly don't exist on this platform, but usually are required to engineer and tweak algorithms of the highest quality with the finest computational caliber. I have numerous other synthesizers to publish besides this one.
For many reasons, I needed a synthetic environment to utilize the analyzers I built in Pine, to even pursue building some exotic indicators and algorithms. Pine doesn't allow sourcing of tuples. Not to mention, I required numerous Pine advancements to make long held dreams into tangible realities. Many Pine upgrades have arrived and MANY, MANY more are in need of implementation for all. Now that I have this, intending to use it in the future often when in need, you can now use it too. I do anticipate some skilled Pine poets will employ this intended handy utility to design and/or improved indicators for trading.
ORIGIN:
This was inspired by the brilliance from the world renowned ALGOmist John F. Ehlers, but it's taken on a completely alien form from its original DNA. Browsing on the internet for something else, I came across an article with a small code snippet, and I remembered an old wish of mine. I have long known that by flipping back and forth on specific tickers and timeframes in my Watchlist is not the most efficient way to evaluate indicators in multiple theatres of price action. I realized, I always wanted to possess and use this sort of tool, so... I put it into Pine form, but now have decided to inject it with Pine Script steroids. The outcome is highly mutable candle formations in a reusable mutagenic package, observable above and masquerading as genuine looking price candles.
OVERVIEW:
I guess you could call it a price action synthesizer, but I entitled it "Synthetic Price Action Generator" for those who may be searching for such a thing. You may find this more useful on the All or 5Y charts initially to witness indication from beginning (barstate.isfirst === barindex==0) to end (last_bar_index), but you may also use keyboard shortcuts + + to view the earliest plottable bars on any timeframe. I often use that keyboard shortcut to qualify an indicator through the entirety of it's runtime.
A lot can go wrong unexpectedly with indicator initialization, and you will never know it if you don't inspect it. Many recursively endowed Infinite Impulse Response (IIR) Filters can initialize with unintended results that minutely ring in slightly erroneous fashion for the entire runtime, beginning to end, causing deviations from "what should of been..." values with false signals. Looking closely at spg(), you will recognize that 3 EMAs are employed to manage and maintain randomness of CLOSE, HIGH, and LOW. In fact, any indicator's barindex==0 initialization can be inspected with the keyboard shortcuts above. If you see anything obviously strange in an authors indicator, please contact the developer if possible and respectfully notify them.
PURPOSE:
The primary intended application of this script, is to offer developers from advanced to even novice skill levels assistance with building next generation indicators. Mostly, it's purpose is for testing and troubleshooting indicators AND evaluating how they perform in a "manageable" randomized environment. Some times indicators flake out on rare but problematic price fluctuations, and this may help you with finding your issues/errata sooner than later. While the candles upon initial loading look pristine, by tweaking it to the minval/maxval parameters limits OR beyond with a few code modifications, you can generate unusual volatility, for instance... huge wicks. Limits of minval= and maxval= of are by default set to a comfort zone of operation. Massive wicks or candle bodies will undoubtedly affect your indication and often render them useless on tickers that exhibit that behavior, like WGMCF intraday currently.
Copy/paste boundaries are provided for relevant insertion into another script. Paste placement should happen at the very top of a script. Note that by overwriting the close, open, high, etc... values, your compiler will give you generous warnings of "variable shadowing" in abundance, but this is an expected part of applying it to your novel script, no worries. plotcandle() can be copied over too and enabled/disabled in Settings->Style. Always remember to fully remove this scripts' code and those assignments properly before actual trading use of your script occurs, AND specifically when publishing. The entirety of this provided code should never, never exist in a published indicator.
OTHER INTENTIONS:
Even though these are 100% synthetic generated price points, you will notice ALL of the fractal pseudo-patterns that commonly exist in the markets, are naturally occurring with this generator too. You can also swiftly immerse yourself in pattern recognition exercises with increased efficiency in real time by clicking any SPAG Setting in focus and then using the up/down arrow keys. I hope I explained potential uses adequately...
On a personal note, the existence of fractal symmetry often makes me wonder, do we truly live in a totality chaotic universe or is it ordered mathematically for some outcomes to a certain extent. I think both. My observations, it's a pre-deterministic reality completely influenced by infinitesimal amounts of sentient free will with unimaginable existing and emerging quantities. Some how an unknown mysterious mechanism governing the totality of universal physics and mathematics counts this 100.0% flawlessly and perpetually. Anyways, you can't change the past that long existed before your birth or even yesterday, but you can choose to dream, create, and forge the future into your desires and hopes. As always, shite always happens when your not looking for it. What you choose to do after stepping in it unintentionally... is totally up to you. :) Maybe this tool and tips provided will aid you in not stepping in an algo cachucha up to your ankles somehow.
SCRIPTING LESSONS PORTRAYED IN THIS SCRIPT:
Pine etiquette and code cleanliness
Overwrite capabilities of built-in Pine variables for testing indicators
Various techniques to organize Settings panel while providing ease of adjustment utility
Use of tooltip= to provide users adequate valuable information. Most people want to trade with indicators, not blindly make adjustments to them without any knowledge of their intended operation/effects
When available time provides itself, I will consider your inquiries, thoughts, and concepts presented below in the comments section, should you have any questions or comments regarding this indicator. When my indicators achieve more prevalent use by TV members , I may implement more ideas when they present themselves as worthy additions. Have a profitable future everyone!
SB Master Chart v5 (Public)SB Master Chart v5 is the latest progression of the SB Master Chart series of charts.
The original SB Master Chart and its successors was designed to be a visual aid for the savvy investor. The original concept was designed to provide valuable information so decisions could be made at a glance with utmost confidence.
As the chart progressed through versions, it has slowly shifted the responsibility of decision making from the trader to the indicator. In this version of the script, we have updated the backend decision code. The script has 3 distinct personalities coded to compliment each other, as well as keep the others in check..
The first personality is the buy algorithm. The buy personality is based on two conditions. The first algorithm first determines a trend, then it waits for a confirmation. The personality is comprised of the following indicators.
EMA 7
EMA 14
MACD
Stochastic
RSI
By default, the first personality has its visual settings disabled. Its still working, its just not displayed on the chart. It can be enabled in the settings. The background colors designate trend and confirmation.
The second personality is stubborn and its committed to making a profit. Its a hard line in the sand that configurable by you the user. Its the take profit/trailing take profit setting. It will not let other personalities sell for less than these configured values. The visual component of personality two is represented by black dots. This serves to showcase its minimum profit target when opening a trade and a trailing stop loss when the price exceeds the minimum profit target.
The third personality is the guy that does the dirty work that nobody wants to admit they do. This personality is based on the original SB Master Chart algorithm. This personality takes over when the first personality is unable to turn a profit. This personality goes to work finding appropriate places to dollar cost average. There are two settings that affect this personality.
DCA %
Risk Multiplier (use extreme caution, this could cause a margin call if used inappropriately).
DCA percent setting restricts this algorithm from buying when the price has not fallen below this threshold.
Risk Multiplier instructs this algorithm how much positions/qty to buy when it buys. At 2x, the algorithm will buy enough shares to double its current position, at 3x the algorithm will buy enough shares to triple its current position.
The visual representations of the third personality are that of red, orange, yellow and green. Red means overbought. When an orange appears just prior to a red, that orange means overbought with volume. Green means oversold and an orange preceding a green is an oversold with volume. Both the red and green represent an possible trend reversal and that's the signal to buy when its green.
This personality is comprised of the following indicators:
RSI
Stochastic
MACD
Bollinger
Volume
The code also features 3 modes. Altering the mode setting changes the way the personalities work together (or do not work together).
Normal
Aggressive
Buy the Dip
Mode Normal works exactly like described above. Each personality has its own duty and they do not interfere with each others work.
Aggressive mode adjusts the dynamic and both the first personality and the third personality share an equal part in opening starter positions.
Buy the Dip mode prevents personality one from buying. Since personality one only buys uptrends, you will never see it buying a dip. This mode puts personality 3 in the spotlight. All position are typically opened during a fast/quick market decline. Personality three is still bound by the rules of personality 2, but its responsible for buying and dollar cost averaging.
I have also included labels for every buy/sell. A green label is the script making its first purchase, yellow is points where it decided to dollar cost average and the red is where it chose to deleverage by closing out all its positions. Nothing prevents the algorithm from buying immediately after a sell, this is by design because we do not want to miss out on an uptrend, but we also do not want to be caught with too much leverage.
Also included vital statistics on the top right of the chart.
Open Positions
Cost Basis
Current Gain/Loss
Minimum Profit Target
Trailing Stop Loss
Total Trades to Date
Maximum Positions/Qty to Date
In the bottom right of the chart, I have the user configurable settings. This is important so a user can at a glance see the settings of the chart without having to open the options menu.
Together, all three personalities form a COMPLETE trading system. The system tracks purchase quantity, cost basis from the first buy, adjust with each new buy and calculates the running profit from the begining of the date set in the settings if it were to have bought and sold at every signal. The public version of the script requires the trader to use the script in real time watching for buy and sell opportunities. The private subscription version of the script has custom alerts that can be configured to alert the user on when to buy and sell and also gives the user appropriate trailing stop loss settings to automate the trading process.
I want to name the personalities at some point in time for the novelty factor, but I wanted to release the script as soon as possible for others to enjoy, so they are nameless at this point. If you have suggestions, please contact me with your suggestion. I will credit the person with the best personality with a free subscription to the private version of this indicator.
As always, understand the risks of trading and trade responsibly. Nothing in this script can predict the future. Past results do not guarantee future performance
JD's Apollo ConfirmationsJD Apollo Confirmations Indicator is used as the confirmation indicators for a number of other algorithms.
This has been specifically designed for Indicies, namely the US30.
How to use;
When the bars align, it means the price is heading in the direction of alignment.
This indicator is intended to be used as a confirmation indicator for other algorithms for best effect.
This algorithm combines a number of indicators with specifically tested and chosen settings that have shown to work on a number of timeframes.
How to Access
Gain access to JD Apollo Confirmations for your TradingView account through our website, links below.
7 day paid trials, subscriptions and lifetime access are all available.
JD Progress ConfirmationsJD Progress Confirmations Indicator is used as the confirmation indicators for a number of other algorithms.
This can be applied to Forex, Stocks and Crypto.
How to use;
When the bars align, it means the price is heading in the direction of alignment.
This indicator is intended to be used as a confirmation indicator for other algorithms for best effect.
This algorithm combines a number of indicators with specifically tested and chosen settings that have shown to work on a number of timeframes.
How to Access
Gain access to JD Progress Confirmations for your TradingView account through our website, links below.
7 day paid trials, subscriptions and lifetime access are all available.
All tiers give you full instructions on how to trade this strategy.
JD ConfirmationsJD Confirmations Indicator is used as the confirmation indicators for a number of other algorithms.
This can be applied to Forex, Stocks and Crypto.
How to use;
When the bars align, it means the price is heading in the direction of alignment.
This indicator is intended to be used as a confirmation indicator for other algorithms for best effect.
This algorithm combines a number of indicators with specifically tested and chosen settings that have shown to work on a number of timeframes.
How to Access
Gain access to JD Core for your TradingView account through our website, links below.
Both 7 day paid trials and lifetime access are available.
Both tiers give you full instructions on how to trade this strategy.
the "fasle" hull moving averageThere is a little different between my "fasle hull moving average" the "correct one".
the correct algorithm:
hma = wma((2*wma(close,n/2) - wma(close,n),sqrt(n))
the "fasle" algorithm:
=wma((2*wma(close,n/4) - wma(close,n),sqrt(n))
Amazing! Why the "fasle" describe the trend so accurate!?
FIAfirecrest Trading System(INVITE ONLY indicator. TRIAL ONLY indicator Delay by 15 candles available here .)
To access real time indicators click here for subscription.
Please don't post comment to ask for invitation. This indicator based on our own smart signal algorithm:
FIAfirecrest IS BASED ON OUR OWN ALGORITHM:
FIAfirecrest is actually a trading system based on ‘trend following’ strategy. The system consists of several indicators which can give users trading signals as well as shows the validity of the trading signals.
Since FIAfirecrest are very concerned about ‘trend following’, therefore we urge our trader to also focus on ‘trend following’ only. Besides thats, FIAfirecrest also features several important qualities:
• The usage of colours improve trading clarity
• Easily to determine the market structure
• Enable to translate trading signal in many angle
• Includes take profit and stop loss level
• Early alert on any trend changes possibilities
FIAfirecrest trading system originally based on 3 smart signal indicators: FIAbreakout, FIAmist and FIApierce. FIAbreakout initial calculation based on higher lower high/low price, FIAmist originally based on momentum thus forming the trend, while FIApierce use as filter based on non-lag adaptive moving averages.
How to use:
FIAbreakout | area Grey, Yellow & Blue
FIAmist | area Green & Red
FIApierce | cross Green & Red
FIAosc | triangle Green & Red
FIAbreakout and FIAmist condition:
• LONG when price cross above Grey area and forming Yellow above Grey. Entry within FIAmist Green. Set your stop loss at lowest Grey.
• SHORT when price cross below Grey area and forming Blue below Grey. Entry within FIAmist Red. Set your stop loss at highest Grey.
FIApierce and FIAosc condition:
• FIApierce cross Green or sharp turn formation from Grey area. Is early signal price reverse from long to short. Entry short within FIAmist Red.
• FIApierce cross Red or sharp turn formation from Grey area. Is early signal price reverse from short to long. Entry short within FIAmist Green.
• Use FIAosc to filter whether both setup are valid or not.
That’s all for our FIAfirecrest Trading System.
FIAfirecrest Trading System Trial(TRIAL ONLY indicator Delay by 15 candles.)
To access real time indicators click here for subscription.
Please don't post comment to ask for remove delay. This indicator based on our own smart signal algorithm:
FIAfirecrest IS BASED ON OUR OWN ALGORITHM:
FIAfirecrest is actually a trading system based on ‘trend following’ strategy. The system consists of several indicators which can give users trading signals as well as shows the validity of the trading signals.
Since FIAfirecrest are very concerned about ‘trend following’, therefore we urge our trader to also focus on ‘trend following’ only. Besides thats, FIAfirecrest also features several important qualities:
• The usage of colours improve trading clarity
• Easily to determine the market structure
• Enable to translate trading signal in many angle
• Includes take profit and stop loss level
• Early alert on any trend changes possibilities
FIAfirecrest trading system originally based on 3 smart signal indicators: FIAbreakout, FIAmist and FIApierce. FIAbreakout initial calculation based on higher lower high/low price, FIAmist originally based on momentum thus forming the trend, while FIApierce use as filter based on non-lag adaptive moving averages.
How to use:
FIAbreakout | area Grey, Yellow & Blue
FIAmist | area Green & Red
FIApierce | cross Green & Red
FIAosc | triangle Green & Red
FIAbreakout and FIAmist condition:
• LONG when price cross above Grey area and forming Yellow above Grey. Entry within FIAmist Green. Set your stop loss at lowest Grey.
• SHORT when price cross below Grey area and forming Blue below Grey. Entry within FIAmist Red. Set your stop loss at highest Grey.
FIApierce and FIAosc condition:
• FIApierce cross Green or sharp turn formation from Grey area. Is early signal price reverse from long to short. Entry short within FIAmist Red.
• FIApierce cross Red or sharp turn formation from Grey area. Is early signal price reverse from short to long. Entry short within FIAmist Green.
• Use FIAosc to filter whether both setup are valid or not.
That’s all for our FIAfirecrest Trading System.
PRICE SATURATION INDEX / FİYAT YOĞUNLUK ENDEKSİEN: PRICE SATURATION INDEX is a momentum algorithm that measures price intensity. It helps us to determine the times when the price reaches intensity and calculates the latency in those moving averages. Moving averages have lag. The lag is necessary because the smoothing is done using past data. It shows you how to filtered a selected amount of lag from an exponential moving average (ema) and price movements. Removing all the lag is not necessarily a good thing, because with no lag, the indicator would just track out the price we were filtering, just as it is the moving average of 1 period; the amount of lag removed is a tradeoff with the amount of smoothing we are willing to forgo with golden ratio and multiline function. We show you the effects of lag removal in an indicator and then use the filter in an effective trading strategy with multiline function. The multiline function is inspired by Jhon Ehlers' zero lag formule, smooth moving average strategy and Schrödinger equation. The Schrödinger equation is a wave function based on quantum mechanics
TR: FİYAT YOĞUNLUK ENDEKSİ, fiyat yoğunluğunu ölçen bir momentum algoritmasıdır. Fiyatın yoğunluğa ulaştığı zamanları belirlememize ve hareketli ortalamalardaki gecikmeyi hesaplamamıza yardımcı olur. Hareketli ortalamalar daima gecikir. Gecikme gereklidir çünkü yumuşatma geçmiş veriler kullanılarak yapılır. Bu algoritma hem fiyat hareketlerindeki hemde üstel hareketli ortalamadaki gecikme miktarının nasıl filtreleneceğini gösterir. Tüm gecikmenin kaldırılması iyi bir şey değildir, çünkü gecikme olmadığında gösterge sadece 1 periyodun hareketli ortalaması gibi davranacağı için filtrelediğimiz fiyatı izleyecektir; filtrelenen gecikme miktarı, terk etmek istediğimiz yumuşatma miktarına alternatif bir multiline fonksiyon ve altın orana uyarlanan frekans değirinden oluşur. Bu göstergede gecikmenin ortadan kaldırılmasının etkilerini gösteriyoruz ve daha sonra filtreyi multiline fonksiyona sahip etkili bir trading stratejisi olarak kullanıyoruz. Multiline fonksiyon, Jhon Ehler'in zero lag formülü, smooth hareketli ortalama stratejisi ve Schrödinger denkleminden esinlenmiştir. Schrödinger denklemi ise kuantum mekaniğini temel alan bir dalga fonksiyonudur.
Thiru 369 Labels"Thiru 369 Labels - Advanced Time Sum Market Timing System
🔢 PROPRIETARY TIME SUM ALGORITHM:
This indicator implements a unique mathematical model that converts time (HH:MM) into digital sums and identifies when the sum equals your target values (default: 3, 6, 9). The algorithm uses advanced digit reduction techniques to find market timing opportunities.
📊 SESSION-BASED FILTERING:
• London Session (02:30-07:00 GMT-4): Purple labels for European market timing
• NY AM Session (07:00-11:30 GMT-4): Green labels for US morning momentum
• NY PM Session (11:30-16:00 GMT-4): Blue labels for US afternoon activity
⏰ MULTI-CYCLE DETECTION SYSTEM:
• 90-minute cycles: Major session phases and momentum shifts
• 30-minute cycles: Intraday trend changes and reversals
• 10-minute cycles: Precise entry/exit timing for scalping
🎯 CUSTOMIZABLE FEATURES:
• Set any target sums (3,6,9 or custom values like 1,2,4,5,7,8)
• Choose which trading sessions to monitor
• Select specific cycle timeframes for your trading style
• Custom label colors and sizes for visual clarity
• Drawing limits (current day, last 2-5 days, all days)
• Real-time debug table for advanced monitoring and analysis
💡 TRADING APPLICATIONS:
• Identify high-probability reversal points based on time patterns
• Time entries during optimal market sessions and cycles
• Multi-timeframe analysis for different trading strategies
• Session-specific market behavior pattern recognition
• Scalping opportunities with 10-minute cycle precision
🔧 TECHNICAL SPECIFICATIONS:
• Uses GMT-4 timezone for consistency with US markets
• Advanced time sum calculation with mathematical reduction
• Session overlap detection and midnight handling
• Real-time cycle analysis and status monitoring
• Customizable alert system for time sum matches
• Professional debug interface with real-time monitoring table
📈 MATHEMATICAL FOUNDATION:
The time sum calculation follows this process:
1. Extract individual digits from hour and minute (e.g., 09:51 → 0,9,5,1)
2. Calculate hour sum (0+9=9) and minute sum (5+1=6)
3. Add totals (9+6=15) and reduce to single digit (1+5=6)
4. Compare against target sums (3,6,9) for signal generation
📊 DEBUG TABLE FEATURES:
• Real-time monitoring: Shows current time, sum, session, and cycle
• Status indicators: Match/No Match status with color coding
• Session tracking: Visual session identification (London/NY AM/NY PM)
• Cycle analysis: Current cycle type (90min/30min/10min)
• Target display: Shows configured target sums
• Next prediction: Displays next potential sum match
• Professional interface: Clean, organized table for advanced users
This proprietary algorithm is not available in any open-source form and provides unique market timing insights based on mathematical time analysis with professional-grade monitoring tools."
Metallic Retracement ToolI made a version of the Metallic Retracement script where instead of using automatic zig-zag detection, you get to place the points manually. When you add it to the chart, it prompts you to click on two points. These two points become your swing range, and the indicator calculates all the metallic retracement levels from there and plots them on your chart. You can drag the points around afterwards to adjust the range, or just add the indicator to the chart again to place a completely new set of points.
The mathematical foundation is identical to the original Metallic Retracement indicator. You're still working with metallic means, which are the sequence of constants that generalize the golden ratio through the equation x² = kx + 1. When k equals 1, you get the golden ratio. When k equals 2, you get silver. Bronze is 3, and so on forever. Each metallic number generates its own set of retracement ratios by raising alpha to various negative powers, where alpha equals (k + sqrt(k² + 4)) / 2. The script algorithmically calculates these levels instead of hardcoding them, which means you can pick any metallic number you want and instantly get its complete retracement sequence.
What's different here is the control. Automatic zig-zag detection is useful when you want the indicator to find swings for you, but sometimes you have a specific price range in mind that doesn't line up with what the zig-zag algorithm considers significant. Maybe you're analyzing a move that's still developing and hasn't triggered the zig-zag's reversal thresholds yet. Maybe you want to measure retracements from an arbitrary high to an arbitrary low that happened weeks apart with tons of noise in between. Manual placement lets you define exactly which two points matter for your analysis without fighting with sensitivity settings or waiting for confirmation.
The interactive placement system uses TradingView's built-in drawing tools, so clicking the two points feels natural and works the same way as drawing a trendline or fibonacci retracement. First click sets your starting point, second click sets your ending point, and the indicator immediately calculates the range and draws all the metallic levels extending from whichever point you chose as the origin. If you picked a swing low and then a swing high, you get retracement levels projecting upward. If you went from high to low, they project downward.
Moving the points after placement is as simple as grabbing one of them and dragging it to a new location. The retracement levels recalculate in real-time as you move the anchor points, which makes it easy to experiment with different range definitions and see how the levels shift. This is particularly useful when you're trying to figure out which swing points produce retracement levels that line up with other technical features like previous support or resistance zones. You can slide the points around until you find a configuration that makes sense for your analysis.
Adding the indicator to the chart multiple times lets you compare different metallic means on the same price range, or analyze multiple ranges simultaneously with different metallic numbers. You could have golden ratio retracements on one major swing and silver ratio retracements on a smaller correction within that swing. Since each instance of the indicator is independent, you can mix and match metallic numbers and ranges however you want without one interfering with the other.
The settings work the same way as the original script. You select which metallic number to use, control how many power ratios to display above and below the 1.0 level, and adjust how many complete retracement cycles you want drawn. The levels extend from your manually placed swing points just like they would from automatically detected pivots, showing you where price might react based on whichever metallic mean you've selected.
What this version emphasizes is that retracement analysis is subjective in terms of which swing points you consider significant. Automatic detection algorithms make assumptions about what constitutes a meaningful reversal, but those assumptions don't always match your interpretation of the price action. By giving you manual control over point placement, this tool lets you apply metallic retracement concepts to exactly the price ranges you care about, without requiring those ranges to fit someone else's definition of a valid swing. You define the context, the indicator provides the mathematical framework.
Chronos Reversal Labs🧬 Chronos Reversal Lab - Machine Learning Market Structure Analysis
OVERVIEW
Chronos Reversal Lab (CRL) is an advanced market structure analyzer that combines computational intelligence kernels with classical technical analysis to identify high-probability reversal opportunities. The system integrates Shannon Entropy analysis, Detrended Fluctuation Analysis (DFA), Kalman adaptive filtering, and harmonic pattern recognition into a unified confluence-based signal engine.
WHAT MAKES IT ORIGINAL
Unlike traditional reversal indicators that rely solely on oscillators or pattern recognition, CRL employs a multi-kernel machine learning approach that analyzes market behavior through information theory, statistical physics, and adaptive state-space estimation. The system combines these computational methods with geometric pattern analysis and market microstructure to create a comprehensive reversal detection framework.
HOW IT WORKS (Technical Methodology)
1. COMPUTATIONAL KERNELS
Shannon Entropy Analysis
Measures market uncertainty using information theory:
• Discretizes price returns into bins (user-configurable 5-20 bins)
• Calculates probability distribution entropy over lookback window
• Normalizes entropy to 0-1 scale (0 = perfectly predictable, 1 = random)
• Low entropy states (< 0.3 default) indicate algorithmic clarity phases
• When entropy drops, directional moves become statistically more probable
Detrended Fluctuation Analysis (DFA)
Statistical technique measuring long-range correlations:
• Analyzes price series across multiple box sizes (4 to user-set maximum)
• Calculates fluctuation scaling exponent (Alpha)
• Alpha > 0.5: Trend persistence (momentum regime)
• Alpha < 0.5: Mean reversion tendency (reversal regime)
• Alpha range 0.3-1.5 mapped to trading strategies
Kalman Adaptive Filter
State-space estimation for lag-free trend tracking:
• Maintains separate fast and slow Kalman filters
• Process noise and measurement noise are user-configurable
• Tracks price state with adaptive gain adjustments
• Calculates acceleration (second derivative) for momentum detection
• Provides cleaner trend signals than traditional moving averages
2. HARMONIC PATTERN DETECTION
Identifies geometric reversal patterns:
• Gartley: 0.618 AB/XA, 0.786 AD/XA retracement
• Bat: 0.382-0.5 AB/XA, 0.886 AD/XA retracement
• Butterfly: 0.786 AB/XA, 1.272-1.618 AD/XA extension
• Cypher: 0.382-0.618 AB/XA, 0.786 AD/XA retracement
Pattern Validation Process:
• Requires alternating swing structure (XABCD points)
• Fibonacci ratio tolerance: 0.02-0.20 (user-adjustable precision)
• Minimum 50% ratio accuracy score required
• PRZ (Potential Reversal Zone) calculated around D point
• Zone size: ATR-based with pattern-specific multipliers
• Active pattern tracking with 100-bar invalidation window
3. MARKET STRUCTURE ANALYSIS
Swing Point Detection:
• Pivot-based swing identification (3-21 bars configurable)
• Minimum swing size: ATR multiples (0.5-5.0x)
• Adaptive filtering: volatility regime adjustment (0.7-1.3x)
• Swing confirmation tracking with RSI and volume context
• Maintains structural history (up to 500 swings)
Break of Structure (BOS):
• Detects price crossing previous swing highs/lows
• Used for trend continuation vs reversal classification
• Optional requirement for signal validation
Support/Resistance Detection:
• Identifies horizontal levels from swing clusters
• Touch counting algorithm (price within ATR×0.3 tolerance)
• Weighted by recency and number of tests
• Dynamic updating as structure evolves
4. CONFLUENCE SCORING SYSTEM
Multi-factor analysis with regime-aware weighting:
Hierarchical Kernel Logic:
• Entropy gates advanced kernel activation
• Only when entropy < threshold do DFA and Kalman accelerate scoring
• Prevents false signals during chaotic (high entropy) conditions
Scoring Components:
ML Kernels (when entropy low):
• Low entropy + trend alignment: +3.0 points × trend weight
• DFA super-trend (α>1.5): +4.0 points × trend weight
• DFA persistence (α>0.65): +2.5 points × trend weight
• DFA mean-reversion (α<0.35): +2.0 points × mean-reversion weight
• Kalman acceleration: up to +3.0 points (scaled by magnitude)
Classical Technical Analysis:
• RSI oversold (<30) / overbought (>70): +1.5 points
• RSI divergence (bullish/bearish): +2.5 points
• High relative volume (>1.5x): +0-2.0 points (scaled)
• Volume impulse (>2.0x): +1.5 points
• VWAP extremes: +1.0 point
• Trend alignment (Kalman fast vs slow): +1.5 points
• MACD crossover/momentum: +1.0 point
Structural Factors:
• Near support (within 0.5 ATR): +0-2.0 points (inverse distance)
• Near resistance (within 0.5 ATR): +0-2.0 points (inverse distance)
• Harmonic PRZ zone: +3.0 to +6.0 points (pattern score dependent)
• Break of structure: +1.5 points
Regime Adjustments:
• Trend weight: 1.5× in trend regime, 0.5× in mean-reversion
• Mean-reversion weight: 1.5× in MR regime, 0.5× in trend
• Volatility multiplier: 0.7-1.3× based on ATR regime
• Theory mode multiplier: 0.8× (Conservative) to 1.2× (APEX)
Final Threshold:
Base threshold (default 3.5) adjusted by:
• Theory mode: -0.3 (APEX) to +0.8 (Conservative)
• Regime: +0.5 (high vol) to -0.3 (low vol or strong trend)
• Filter: +0.2 if regime filter enabled
5. SIGNAL GENERATION ARCHITECTURE
Five-stage validation process:
Stage 1 - ML Kernel Analysis:
• Entropy threshold check
• DFA regime classification
• Kalman acceleration confirmation
Stage 2 - Structural Confirmation:
• Market structure supports directional bias
• BOS alignment (if required)
• Swing point validation
Stage 3 - Trigger Validation:
• Engulfing candle (if required)
• HTF bias confirmation (if strict HTF enabled)
• Harmonic PRZ alignment (if confirmation enabled)
Stage 4 - Consistency Check:
• Anticipation depth: checks N bars back (1-13 configurable)
• Ensures Kalman acceleration direction persists
• Filters whipsaw conditions
Stage 5 - Structural Soundness (Critical Filter):
• Verifies adequate room before next major swing level
• Long signals: must have >0.25 ATR clearance to last swing high
• Short signals: must have >0.25 ATR clearance to last swing low
• Prevents trades directly into obvious structural barriers
Dynamic Risk Management:
• Stop-loss: Placed beyond last structural swing ± 2 ticks
• Take-profit 1: Risk × configurable R1 multiplier (default 1.5R)
• Take-profit 2: Risk × configurable R2 multiplier (default 3.0R)
• Confidence score: Calibrated 0-99% based on confluence + kernel boost
6. ADAPTIVE REGIME SYSTEM
Continuous market state monitoring:
Trend Regime:
• Kalman fast vs slow positioning
• Multi-timeframe alignment (optional HTF)
• Strength: ATR-normalized fast/slow spread
Volatility Regime:
• Current ATR vs 100-bar average
• Regime ratio: 0.7-1.3 typical range
• Affects swing size filtering and cooldown periods
Signal Cooldown:
• Base: User-set bars (1-300)
• High volatility (>1.5): cooldown × 1.5
• Low volatility (<0.5): cooldown × 0.7
• Post-BOS: minimum 20-bar cooldown enforced
FOUR OPERATIONAL MODES
CONSERVATIVE MODE:
• Threshold adjustment: +0.8
• Mode multiplier: 0.8×
• Strictest filtering for highest quality
• Recommended for: Beginners, large accounts, swing trading
• Expected signals: 3-5 per week (typical volatile instrument)
BALANCED MODE:
• Threshold adjustment: +0.3
• Mode multiplier: 1.0×
• Standard operational parameters
• Recommended for: General trading, learning phase
• Expected signals: 5-10 per week
APEX MODE:
• Threshold adjustment: -0.3
• Mode multiplier: 1.2×
• Maximum sensitivity, reduced cooldowns
• Recommended for: Scalping, high volatility, experienced traders
• Expected signals: 10-20 per week
INSTITUTIONAL MODE:
• Threshold adjustment: +0.5
• Mode multiplier: 1.1×
• Enhanced structural weighting, HTF emphasis
• Recommended for: Professional traders, swing positions
• Expected signals: 4-8 per week
VISUAL COMPONENTS
1. Fibonacci Retracement Levels
• Auto-calculated from most recent swing structure
• Standard levels: 0%, 23.6%, 38.2%, 50%, 61.8%, 78.6%, 100%, 127.2%, 161.8%, 200%, 261.8%
• Key levels emphasized (50%, 61.8%, 100%, 161.8%)
• Color gradient from bullish to bearish based on level
• Automatic cleanup when levels are crossed
• Label intensity control (None/Fib only/All)
2. Support and Resistance Lines
• Dynamic horizontal levels from swing clusters
• Width: 2px solid lines
• Colors: Green (support), Red (resistance)
• Labels show price and level type
• Touch-based validation (minimum 2 touches)
• Real-time updates and invalidation
3. Harmonic PRZ Boxes
• Displayed around pattern completion (D point)
• Pattern-specific colors (Gartley: purple, Bat: orange, etc.)
• Box height: ATR-based zone sizing
• Score-dependent transparency
• 100-bar active window before removal
4. Confluence Boxes
• Appear when confluence ≥ threshold
• Yellow/orange gradient based on score strength
• Height: High to low of bar
• Width: 1 bar on each side
• Real-time score-based transparency
5. Kalman Filter Lines
• Fast filter: Bullish color (green default)
• Slow filter: Bearish color (red default)
• Width: 2px
• Transparency adjustable (0-90%)
• Optional display toggle
6. Signal Markers
• Long: Green triangle below bar (tiny size)
• Short: Red triangle above bar (tiny size)
• Appear only on confirmed signals
• Includes alert generation
7. Premium Dashboard
Features real-time metrics with visual gauges:
Layout Options:
• Position: 4 corners selectable
• Size: Small (9 rows) / Normal (12 rows) / Large (14 rows)
• Themes: Supreme, Cosmic, Vortex, Heritage
Metrics Displayed:
• Gamma (DFA - 0.5): Shows trend persistence vs mean-reversion
• TCI (Trend Strength): ATR-normalized Kalman spread with gauge
• v/c (Relative Volume): Current vs average with color coding
• Entropy: Market predictability state with gauge
• HFL (High-Frequency Line): Kalman fast/slow difference / ATR
• HFL_acc (Acceleration): Second derivative momentum
• Mem Bias: Net bullish-bearish confluence (-1 to +1)
• Assurance: Confidence × (1-entropy) metric
• Squeeze: Bollinger Band / Keltner Channel squeeze detection
• Breakout P: Probability estimate from DFA + trend + acceleration
• Score: Final confluence vs threshold (normalized)
• Neighbors: Active harmonic patterns count
• Signal Strength: Strong/Moderate/Weak classification
• Signal Banner: Current directional bias with emoji indicators
Gauge Visualization:
• 10-bar horizontal gauges (█ filled, ░ empty)
• Color-coded: Green (strong) / Gold (moderate) / Red (weak)
• Real-time updates every bar
HOW TO USE
Step 1: Configure Mode and Resolution
• Select Theory Mode based on trading style (Conservative/Balanced/APEX/Institutional)
• Set Structural Resolution (Standard for fast markets, High for balanced, Ultra/Institutional for swing)
• Enable Adaptive Filtering (recommended for all volatile assets)
Step 2: Enable Desired Kernels
• Shannon Entropy: Essential for predictability detection (recommended ON)
• DFA Analysis: Critical for regime classification (recommended ON)
• Kalman Filter: Provides lag-free trend tracking (recommended ON)
• All three work synergistically; disabling reduces effectiveness
Step 3: Configure Confluence Factors
• Enable desired technical factors (RSI, MACD, Volume, Divergence)
• Enable Liquidity Mapping for support/resistance proximity scoring
• Enable Harmonic Detection if trading pattern-based setups
• Adjust base confluence threshold (3.5 default; higher = fewer, cleaner signals)
Step 4: Set Trigger Requirements
• Require Engulfing: Adds precision, reduces frequency (recommended for Conservative)
• Require BOS: Ensures structural alignment (recommended for trend-following)
• Require Structural Soundness: Critical filter preventing traps (highly recommended)
• Strict HTF Bias: For multi-timeframe traders only
Step 5: Adjust Visual Preferences
• Enable/disable Fibonacci levels, S/R lines, PRZ boxes, confluence boxes
• Set label intensity (None/Fib/All)
• Adjust transparency (0-90%) for overlay clarity
• Configure dashboard position, size, and theme
Step 6: Configure Alerts
• Enable master alerts toggle
• Select alert types: Anticipation, Confirmation, High Confluence, Low Entropy
• Enable JSON details for automated trading integration
Step 7: Interpret Signals
• Wait for triangle markers (green up = long, red down = short)
• Check dashboard for confluence score, entropy, DFA regime
• Verify signal aligns with higher timeframe bias (if using HTF setting)
• Confirm adequate space to take-profit levels (no nearby structural barriers)
Step 8: Execute and Manage
• Enter at close of signal candle (or next bar open)
• Set stop-loss at calculated level (visible in alert if JSON enabled)
• Scale out at TP1 (1.5R default), trail remaining to TP2 (3.0R default)
• Exit early if entropy spikes >0.7 or DFA regime flips against position
CUSTOMIZATION GUIDE
Timeframe Optimization:
Scalping (1-5 minutes):
• Theory Mode: APEX
• Anticipation Depth: 3-5
• Structural Resolution: STANDARD
• Signal Cooldown: 8-12 bars
• Enable fast kernels, disable HTF bias
Day Trading (15m-1H):
• Theory Mode: BALANCED
• Anticipation Depth: 5-8
• Structural Resolution: HIGH
• Signal Cooldown: 12-20 bars
• Standard configuration
Swing Trading (4H-Daily):
• Theory Mode: INSTITUTIONAL
• Anticipation Depth: 8-13
• Structural Resolution: ULTRA or INSTITUTIONAL
• Signal Cooldown: 20-50 bars
• Enable HTF bias, strict confirmations
Market Type Optimization:
Forex Majors:
• All kernels enabled
• Harmonic patterns effective
• Balanced or Institutional mode
• Standard settings work well
Stock Indices:
• Emphasis on volume analysis
• DFA critical for regime detection
• Conservative or Balanced mode
• Enable liquidity mapping
Cryptocurrencies:
• Adaptive filtering essential
• Higher volatility regime expected
• APEX mode for active trading
• Wider ATR multiples for swing sizing
IMPORTANT DISCLAIMERS
• This indicator does not predict future price movements
• Computational kernels calculate probabilities, not certainties
• Past confluence scores do not guarantee future signal performance
• Always backtest on YOUR specific instruments and timeframes before live trading
• Machine learning kernels require calibration period (minimum 100 bars of data)
• Performance varies significantly across market conditions and regimes
• Signals are suggestions for analysis, not automated trading instructions
• Proper risk management (stops, position sizing) is mandatory
• Complex calculations may impact performance on lower-end devices
• Designed for liquid markets; avoid illiquid or gap-prone instruments
PERFORMANCE CONSIDERATIONS
Computational Intensity:
• DFA analysis: Moderate (scales with length and box size parameters)
• Entropy calculation: Moderate (scales with lookback and bins)
• Kalman filtering: Low (efficient state-space updates)
• Harmonic detection: Moderate to High (pattern matching across swing history)
• Overall: Medium computational load
Optimization Tips:
• Reduce Structural Analysis Depth (144 default → 50-100 for faster performance)
• Increase Calc Step (2 default → 3-4 for lighter load)
• Reduce Pattern Analysis Depth (8 default → 3-5 if harmonics not primary focus)
• Limit Draw Window (150 bars default prevents visual clutter on long charts)
• Disable unused confluence factors to reduce calculations
Best Suited For:
• Liquid instruments: Major forex, stock indices, large-cap crypto
• Active timeframes: 5-minute through daily (avoid tick/second charts)
• Trending or ranging markets: Adapts to both via regime detection
• Pattern traders: Harmonic integration adds geometric confluence
• Multi-timeframe analysts: HTF bias and regime detection support this approach
Not Recommended For:
• Illiquid penny stocks or micro-cap altcoins
• Markets with frequent gaps (stocks outside regular hours without gap adjustment)
• Extremely fast timeframes (tick, second charts) due to calculation overhead
• Pure mean-reversion systems (unless using CONSERVATIVE mode with DFA filters)
METHODOLOGY NOTE
The computational kernels (Shannon Entropy, DFA, Kalman Filter) are established statistical and signal processing techniques adapted for financial time series analysis. These are deterministic mathematical algorithms, not predictive AI models. The term "machine learning" refers to the adaptive, data-driven nature of the calculations, not neural networks or training processes.
Confluence scoring is rule-based with regime-dependent weighting. The system does not "learn" from historical trades but adapts its sensitivity to current volatility and trend conditions through mathematical regime classification.
SUPPORT & UPDATES
• Questions about configuration or usage? Send me a message on TradingView
• Feature requests are welcome for consideration in future updates
• Bug reports appreciated and addressed promptly
• I respond to messages within 24 hours
• Regular updates included (improvements, optimizations, new features)
FINAL REMINDERS
• This is an analytical tool for confluence analysis, not a standalone trading system
• Combine with your existing strategy, risk management, and market analysis
• Start with paper trading to learn the system's behavior on your markets
• Allow 50-100 signals minimum for performance evaluation
• Adjust parameters based on YOUR timeframe, instrument, and trading style
• No indicator guarantees profitable trades - proper risk management is essential
— Dskyz, Trade with insight. Trade with anticipation.
Kalman Filter [DCAUT]█ Kalman Filter
📊 ORIGINALITY & INNOVATION
The Kalman Filter represents an important adaptation of aerospace signal processing technology to financial market analysis. Originally developed by Rudolf E. Kalman in 1960 for navigation and guidance systems, this implementation brings the algorithm's noise reduction capabilities to price trend analysis.
This implementation addresses a common challenge in technical analysis: the trade-off between smoothness and responsiveness. Traditional moving averages must choose between being smooth (with increased lag) or responsive (with increased noise). The Kalman Filter improves upon this limitation through its recursive estimation approach, which continuously balances historical trend information with current price data based on configurable noise parameters.
The key advancement lies in the algorithm's adaptive weighting mechanism. Rather than applying fixed weights to historical data like conventional moving averages, the Kalman Filter dynamically adjusts its trust between the predicted trend and observed prices. This allows it to provide smoother signals during stable periods while maintaining responsiveness during genuine trend changes, helping to reduce whipsaws in ranging markets while not missing significant price movements.
📐 MATHEMATICAL FOUNDATION
The Kalman Filter operates through a two-phase recursive process:
Prediction Phase:
The algorithm first predicts the next state based on the previous estimate:
State Prediction: Estimates the next value based on current trend
Error Covariance Prediction: Calculates uncertainty in the prediction
Update Phase:
Then updates the prediction based on new price observations:
Kalman Gain Calculation: Determines the weight given to new measurements
State Update: Combines prediction with observation based on calculated gain
Error Covariance Update: Adjusts uncertainty estimate for next iteration
Core Parameters:
Process Noise (Q): Represents uncertainty in the trend model itself. Higher values indicate the trend can change more rapidly, making the filter more responsive to price changes.
Measurement Noise (R): Represents uncertainty in price observations. Higher values indicate less trust in individual price points, resulting in smoother output.
Kalman Gain Formula:
The Kalman Gain determines how much weight to give new observations versus predictions:
K = P(k|k-1) / (P(k|k-1) + R)
Where:
K is the Kalman Gain (0 to 1)
P(k|k-1) is the predicted error covariance
R is the measurement noise parameter
When K approaches 1, the filter trusts new measurements more (responsive).
When K approaches 0, the filter trusts its prediction more (smooth).
This dynamic adjustment mechanism allows the filter to adapt to changing market conditions automatically, providing an advantage over fixed-weight moving averages.
📊 COMPREHENSIVE SIGNAL ANALYSIS
Visual Trend Indication:
The Kalman Filter line provides color-coded trend information:
Green Line: Indicates the filter value is rising, suggesting upward price momentum
Red Line: Indicates the filter value is falling, suggesting downward price momentum
Gray Line: Indicates sideways movement with no clear directional bias
Crossover Signals:
Price-filter crossovers generate trading signals:
Golden Cross: Price crosses above the Kalman Filter line, suggests potential bullish momentum development, may indicate a favorable environment for long positions, filter will naturally turn green as it adapts to price moving higher
Death Cross: Price crosses below the Kalman Filter line, suggests potential bearish momentum development, may indicate consideration for position reduction or shorts, filter will naturally turn red as it adapts to price moving lower
Trend Confirmation:
The filter serves as a dynamic trend baseline:
Price Consistently Above Filter: Confirms established uptrend
Price Consistently Below Filter: Confirms established downtrend
Frequent Crossovers: Suggests ranging or choppy market conditions
Signal Reliability Factors:
Signal quality varies based on market conditions:
Higher reliability in trending markets with sustained directional moves
Lower reliability in choppy, range-bound conditions with frequent reversals
Parameter adjustment can help adapt to different market volatility levels
🎯 STRATEGIC APPLICATIONS
Trend Following Strategy:
Use the Kalman Filter as a dynamic trend baseline:
Enter long positions when price crosses above the filter
Enter short positions when price crosses below the filter
Exit when price crosses back through the filter in the opposite direction
Monitor filter slope (color) for trend strength confirmation
Dynamic Support/Resistance:
The filter can act as a moving support or resistance level:
In uptrends: Filter often provides dynamic support for pullbacks
In downtrends: Filter often provides dynamic resistance for bounces
Price rejections from the filter can offer entry opportunities in trend direction
Filter breaches may signal potential trend reversals
Multi-Timeframe Analysis:
Combine Kalman Filters across different timeframes:
Higher timeframe filter identifies primary trend direction
Lower timeframe filter provides precise entry and exit timing
Trade only in direction of higher timeframe trend for better probability
Use lower timeframe crossovers for position entry/exit within major trend
Volatility-Adjusted Configuration:
Adapt parameters to match market conditions:
Low Volatility Markets (Forex majors, stable stocks): Use lower process noise for stability, use lower measurement noise for sensitivity
Medium Volatility Markets (Most equities): Process noise default (0.05) provides balanced performance, measurement noise default (1.0) for general-purpose filtering
High Volatility Markets (Cryptocurrencies, volatile stocks): Use higher process noise for responsiveness, use higher measurement noise for noise reduction
Risk Management Integration:
Use filter as a trailing stop-loss level in trending markets
Tighten stops when price moves significantly away from filter (overextension)
Wider stops in early trend formation when filter is just establishing direction
Consider position sizing based on distance between price and filter
📋 DETAILED PARAMETER CONFIGURATION
Source Selection:
Determines which price data feeds the algorithm:
OHLC4 (default): Uses average of open, high, low, close for balanced representation
Close: Focuses purely on closing prices for end-of-period analysis
HL2: Uses midpoint of high and low for range-based analysis
HLC3: Typical price, gives more weight to closing price
HLCC4: Weighted close price, emphasizes closing values
Process Noise (Q) - Adaptation Speed Control:
This parameter controls how quickly the filter adapts to changes:
Technical Meaning:
Represents uncertainty in the underlying trend model
Higher values allow the estimated trend to change more rapidly
Lower values assume the trend is more stable and slow-changing
Practical Impact:
Lower Values: Produces very smooth output with minimal noise, slower to respond to genuine trend changes, best for long-term trend identification, reduces false signals in choppy markets
Medium Values: Balanced responsiveness and smoothness, suitable for swing trading applications, default (0.05) works well for most markets
Higher Values: More responsive to price changes, may produce more false signals in ranging markets, better for short-term trading and day trading, captures trend changes earlier, adjust freely based on market characteristics
Measurement Noise (R) - Smoothing Control:
This parameter controls how much the filter trusts individual price observations:
Technical Meaning:
Represents uncertainty in price measurements
Higher values indicate less trust in individual price points
Lower values make each price observation more influential
Practical Impact:
Lower Values: More reactive to each price change, less smoothing with more noise in output, may produce choppy signals
Medium Values: Balanced smoothing and responsiveness, default (1.0) provides general-purpose filtering
Higher Values: Heavy smoothing for very noisy markets, reduces whipsaws significantly but increases lag in trend change detection, best for cryptocurrency and highly volatile assets, can use larger values for extreme smoothing
Parameter Interaction:
The ratio between Process Noise and Measurement Noise determines overall behavior:
High Q / Low R: Very responsive, minimal smoothing
Low Q / High R: Very smooth, maximum lag reduction
Balanced Q and R: Middle ground for most applications
Optimization Guidelines:
Start with default values (Q=0.05, R=1.0)
If too many false signals: Increase R or decrease Q
If missing trend changes: Decrease R or increase Q
Test across different market conditions before live use
Consider different settings for different timeframes
📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES
Comparison with Traditional Moving Averages:
Versus Simple Moving Average (SMA):
The Kalman Filter typically responds faster to genuine trend changes
Produces smoother output than SMA of comparable length
Better noise reduction in ranging markets
More configurable for different market conditions
Versus Exponential Moving Average (EMA):
Similar responsiveness but with better noise filtering
Less prone to whipsaws in choppy conditions
More adaptable through dual parameter control (Q and R)
Can be tuned to match or exceed EMA responsiveness while maintaining smoothness
Versus Hull Moving Average (HMA):
Different noise reduction approach (recursive estimation vs. weighted calculation)
Kalman Filter offers more intuitive parameter adjustment
Both reduce lag effectively, but through different mechanisms
Kalman Filter may handle sudden volatility changes more gracefully
Response Characteristics:
Lag Time: Moderate and configurable through parameter adjustment
Noise Reduction: Good to excellent, particularly in volatile conditions
Trend Detection: Effective across multiple timeframes
False Signal Rate: Typically lower than simple moving averages in ranging markets
Computational Efficiency: Efficient recursive calculation suitable for real-time use
Optimal Use Cases:
Markets with mixed trending and ranging periods
Assets with moderate to high volatility requiring noise filtering
Multi-timeframe analysis requiring consistent methodology
Systematic trading strategies needing reliable trend identification
Situations requiring balance between responsiveness and smoothness
Known Limitations:
Parameters require adjustment for different market volatility levels
May still produce false signals during extreme choppy conditions
No single parameter set works optimally for all market conditions
Requires complementary indicators for comprehensive analysis
Historical performance characteristics may not persist in changing market conditions
USAGE NOTES
This indicator is designed for technical analysis and educational purposes. The Kalman Filter's effectiveness varies with market conditions, tending to perform better in markets with clear trending phases interrupted by consolidation. Like all technical indicators, it has limitations and should not be used as the sole basis for trading decisions, but rather as part of a comprehensive trading approach.
Algorithm performance varies with market conditions, and past characteristics do not guarantee future results. Always test thoroughly with different parameter settings across various market conditions before using in live trading. No technical indicator can predict future price movements with certainty, and all trading involves risk of loss.
Session Volume Profile HVN210
Session Volume Profile HVN - Comprehensive Indicator Description
Overview
The Session Volume Profile HVN is an advanced volume analysis indicator that provides traders with a visual representation of volume distribution across price levels within defined trading sessions. This powerful tool combines traditional volume profile analysis with High Volume Node (HVN) detection and Volume Point of Control (VPOC) tracking to help identify key support and resistance areas based on trading activity.
Key Features
1. Dynamic Volume Profile Visualization
Creates a comprehensive volume profile for each trading session (daily, weekly, or custom timeframes)
Displays volume distribution as a horizontal histogram, showing where the most trading activity occurred
Automatically scales to fit the price range of each session
Customizable number of price levels (rows) for granular or broad analysis
Profile extension capability to project volume areas into subsequent sessions
2. Volume Point of Control (VPOC)
Automatically identifies and marks the price level with the highest volume in each session
Displays VPOC as a prominent horizontal line that can extend into future sessions
Tracks multiple historical VPOCs with customizable extension limits
Optional date labels for easy identification of when each VPOC was formed
Particularly useful for identifying potential support/resistance levels based on peak trading activity
3. High Volume Node (HVN) Detection
Sophisticated algorithm that identifies significant volume clusters within the profile
Validates HVNs based on customizable strength criteria
Two display options:
Levels: Shows HVNs as horizontal lines (solid for VPOC, dotted for other nodes)
Areas: Displays HVNs as shaded boxes covering the full price range of the node
Color-coded based on price position relative to previous close:
Bullish color for HVNs below the previous close (potential support)
Bearish color for HVNs above the previous close (potential resistance)
4. Multi-Timeframe Analysis
Profile Timeframe: Defines the session boundaries (e.g., daily, weekly, monthly)
Resolution Timeframe: Uses lower timeframe data for more accurate volume distribution
Automatically adjusts to ensure compatibility with chart timeframe
Enables precise volume analysis even on higher timeframe charts
Practical Applications
Support and Resistance Identification
VPOCs and HVNs often act as significant support/resistance levels
Multiple confluent HVNs can indicate strong price zones
Historical VPOC levels provide context for potential price reactions
Trading Strategy Development
Entry/exit points near HVN boundaries
Stop loss placement beyond significant volume nodes
Trend continuation or reversal signals when price breaks through HVN areas
Market Structure Analysis
Identify accumulation/distribution zones
Recognize price acceptance or rejection at specific levels
Understand market participant behavior through volume concentration
Customization Options
Visual Settings
Adjustable colors for profile, VPOC lines, and HVN areas
Line width controls for better visibility
Label size options from tiny to huge
Profile transparency for chart clarity
Technical Parameters
Number of price levels (rows) for profile resolution
HVN detection strength for sensitivity adjustment
VPOC extension count for historical reference
Profile extension percentage for future projection
Display Preferences
Toggle VPOC visibility
Enable/disable HVN display
Choose between line or area representation for HVNs
Control date label display based on timeframe
Best Practices
Timeframe Selection: Choose profile timeframes that align with your trading style (day traders might use hourly profiles, swing traders daily or weekly)
HVN Strength Calibration: Adjust the HVN strength parameter based on market volatility and desired sensitivity
Multiple Timeframe Confirmation: Use different profile timeframes to identify confluence zones
Combination with Other Indicators: Enhance analysis by combining with trend indicators, momentum oscillators, or price action patterns
Performance Considerations
The indicator is optimized for smooth performance while maintaining accuracy through:
Efficient data processing algorithms
Smart memory management for historical data
Automatic cleanup of old visual elements
Scalable architecture supporting up to 500 visual elements
Ideal For
Day Traders: Identifying intraday support/resistance levels
Swing Traders: Finding multi-day accumulation zones
Position Traders: Analyzing longer-term volume structures
Market Analysts: Understanding market participant behavior
Algorithmic Traders: Incorporating volume-based levels into automated strategies
ICT SIlver Bullet Trading Windows UK times🎯 Purpose of the Indicator
It’s designed to highlight key ICT “macro” and “micro” windows of opportunity, i.e., time ranges where liquidity grabs and algorithmic setups are most likely to occur. The ICT Silver Bullet concept is built on the idea that institutions execute in recurring intraday windows, and these often produce high-probability setups.
🕰️ Windows
London Macro Window
10:00 – 11:00 UK time
This aligns with a major liquidity window after the London equities open settles and London + EU traders reposition.
You’re looking for setups like liquidity sweeps, MSS (market structure shift), and FVG entries here.
New York Macro Window
15:00 – 16:00 UK time (10:00 – 11:00 NY time)
This is right after the NY equities open, a key ICT window for volatility and liquidity grabs.
Power Hour
Usually 20:00 – 21:00 UK time (3pm–4pm NY time), the last trading hour of NY equities.
ICT often refers to this as another manipulation window where setups can form before the daily close.
🔍 What the Indicator Does
Draws session boxes or shading: so you can visually see the London/NY/Power Hour windows directly on your chart.
Macro vs. Micro time frames:
Macro windows → The ones you set (London & NY) are the major daily algo execution windows.
Micro windows → Within those boxes, ICT expects smaller intraday setups (like a Silver Bullet entry from a sweep + FVG).
Guides your trade selection: it tells you when not to hunt trades everywhere, but instead to wait for price action confirmation inside those boxes.
🧩 How This Fits ICT Silver Bullet Trading
The ICT Silver Bullet strategy says:
Wait for one of the macro windows (London or NY).
Look for liquidity sweep → market structure shift → FVG.
Enter with defined risk inside that hour.
This indicator essentially does step 1 for you: it makes those high-probability windows visually obvious, so you don’t waste time trading random hours where algos aren’t active.
Auto-Fit Growth Trendline# **Theoretical Algorithmic Principles of the Auto-Fit Growth Trendline (AFGT)**
## **🎯 What Does This Algorithm Do?**
The Auto-Fit Growth Trendline is an advanced technical analysis system that **automates the identification of long-term growth trends** and **projects future price levels** based on historical cyclical patterns.
### **Primary Functionality:**
- **Automatically detects** the most significant lows in regular periods (monthly, quarterly, semi-annually, annually)
- **Constructs a dynamic trendline** that connects these historical lows
- **Projects the trend into the future** with high mathematical precision
- **Generates Fibonacci bands** that act as dynamic support and resistance levels
- **Automatically adapts** to different timeframes and market conditions
### **Strategic Purpose:**
The algorithm is designed to identify **fundamental value zones** where price has historically found support, enabling traders to:
- Identify optimal entry points for long positions
- Establish realistic price targets based on mathematical projections
- Recognize dynamic support and resistance levels
- Anticipate long-term price movements
---
## **🧮 Core Mathematical Foundations**
### **Adaptive Temporal Segmentation Theory**
The algorithm is based on **dynamic temporal partition theory**, where time is divided into mathematically coherent uniform intervals. It uses modular transformations to create bijective mappings between continuous timestamps and discrete periods, ensuring each temporal point belongs uniquely to a specific period.
**What does this achieve?** It allows the algorithm to automatically identify natural market cycles (annual, quarterly, etc.) without manual intervention, adapting to the inherent periodicity of each asset.
The temporal mapping function implements a **discrete affine transformation** that normalizes different frequencies (monthly, quarterly, semi-annual, annual) to a space of unique identifiers, enabling consistent cross-temporal comparative analysis.
---
## **📊 Local Extrema Detection Theory**
### **Multi-Point Retrospective Validation Principle**
Local minima detection is founded on **relative extrema theory with sliding window**. Instead of using a simple minimum finder, it implements a cross-validation system that examines the persistence of the extremum across multiple historical periods.
**What problem does this solve?** It eliminates false minima caused by temporal volatility, identifying only those points that represent true historical support levels with statistical significance.
This approach is based on the **statistical confirmation principle**, where a minimum is only considered valid if it maintains its extremum condition during a defined observation period, significantly reducing false positives caused by transitory volatility.
---
## **🔬 Robust Interpolation Theory with Outlier Control**
### **Contextual Adaptive Interpolation Model**
The mathematical core uses **piecewise linear interpolation with adaptive outlier correction**. The key innovation lies in implementing a **contextual anomaly detector** that identifies not only absolute extreme values, but relative deviations to the local context.
**Why is this important?** Financial markets contain extreme events (crashes, bubbles) that can distort projections. This system identifies and appropriately weights them without completely eliminating them, preserving directional information while attenuating distortions.
### **Implicit Bayesian Smoothing Algorithm**
When an outlier is detected (deviation >300% of local average), the system applies a **simplified Kalman filter** that combines the current observation with a local trend estimation, using a weight factor that preserves directional information while attenuating extreme fluctuations.
---
## **📈 Stabilized Extrapolation Theory**
### **Exponential Growth Model with Dampening**
Extrapolation is based on a **modified exponential growth model with progressive dampening**. It uses multiple historical points to calculate local growth ratios, implements statistical filtering to eliminate outliers, and applies a dampening factor that increases with extrapolation distance.
**What advantage does this offer?** Long-term projections in finance tend to be exponentially unrealistic. This system maintains short-to-medium term accuracy while converging toward realistic long-term projections, avoiding the typical "exponential explosions" of other methods.
### **Asymptotic Convergence Principle**
For long-term projections, the algorithm implements **controlled asymptotic convergence**, where growth ratios gradually converge toward pre-established limits, avoiding unrealistic exponential projections while preserving short-to-medium term accuracy.
---
## **🌟 Dynamic Fibonacci Projection Theory**
### **Continuous Proportional Scaling Model**
Fibonacci bands are constructed through **uniform proportional scaling** of the base curve, where each level represents a linear transformation of the main curve by a constant factor derived from the Fibonacci sequence.
**What is its practical utility?** It provides dynamic resistance and support levels that move with the trend, offering price targets and profit-taking points that automatically adapt to market evolution.
### **Topological Preservation Principle**
The system maintains the **topological properties** of the base curve in all Fibonacci projections, ensuring that spatial and temporal relationships are consistently preserved across all resistance/support levels.
---
## **⚡ Adaptive Computational Optimization**
### **Multi-Scale Resolution Theory**
It implements **automatic multi-resolution analysis** where data granularity is dynamically adjusted according to the analysis timeframe. It uses the **adaptive Nyquist principle** to optimize the signal-to-noise ratio according to the temporal observation scale.
**Why is this necessary?** Different timeframes require different levels of detail. A 1-minute chart needs more granularity than a monthly one. This system automatically optimizes resolution for each case.
### **Adaptive Density Algorithm**
Calculation point density is optimized through **adaptive sampling theory**, where calculation frequency is adjusted according to local trend curvature and analysis timeframe, balancing visual precision with computational efficiency.
---
## **🛡️ Robustness and Fault Tolerance**
### **Graceful Degradation Theory**
The system implements **multi-level graceful degradation**, where under error conditions or insufficient data, the algorithm progressively falls back to simpler but reliable methods, maintaining basic functionality under any condition.
**What does this guarantee?** That the indicator functions consistently even with incomplete data, new symbols with limited history, or extreme market conditions.
### **State Consistency Principle**
It uses **mathematical invariants** to guarantee that the algorithm's internal state remains consistent between executions, implementing consistency checks that validate data structure integrity in each iteration.
---
## **🔍 Key Theoretical Innovations**
### **A. Contextual vs. Absolute Outlier Detection**
It revolutionizes traditional outlier detection by considering not only the absolute magnitude of deviations, but their relative significance within the local context of the time series.
**Practical impact:** It distinguishes between legitimate market movements and technical anomalies, preserving important events like breakouts while filtering noise.
### **B. Extrapolation with Weighted Historical Memory**
It implements a memory system that weights different historical periods according to their relevance for current prediction, creating projections more adaptable to market regime changes.
**Competitive advantage:** It automatically adapts to fundamental changes in asset dynamics without requiring manual recalibration.
### **C. Automatic Multi-Timeframe Adaptation**
It develops an automatic temporal resolution selection system that optimizes signal extraction according to the intrinsic characteristics of the analysis timeframe.
**Result:** A single indicator that functions optimally from 1-minute to monthly charts without manual adjustments.
### **D. Intelligent Asymptotic Convergence**
It introduces the concept of controlled asymptotic convergence in financial extrapolations, where long-term projections converge toward realistic limits based on historical fundamentals.
**Added value:** Mathematically sound long-term projections that avoid the unrealistic extremes typical of other extrapolation methods.
---
## **📊 Complexity and Scalability Theory**
### **Optimized Linear Complexity Model**
The algorithm maintains **linear computational complexity** O(n) in the number of historical data points, guaranteeing scalability for extensive time series analysis without performance degradation.
### **Temporal Locality Principle**
It implements **temporal locality**, where the most expensive operations are concentrated in the most relevant temporal regions (recent periods and near projections), optimizing computational resource usage.
---
## **🎯 Convergence and Stability**
### **Probabilistic Convergence Theory**
The system guarantees **probabilistic convergence** toward the real underlying trend, where projection accuracy increases with the amount of available historical data, following **law of large numbers** principles.
**Practical implication:** The more history an asset has, the more accurate the algorithm's projections will be.
### **Guaranteed Numerical Stability**
It implements **intrinsic numerical stability** through the use of robust floating-point arithmetic and validations that prevent overflow, underflow, and numerical error propagation.
**Result:** Reliable operation even with extreme-priced assets (from satoshis to thousand-dollar stocks).
---
## **💼 Comprehensive Practical Application**
**The algorithm functions as a "financial GPS"** that:
1. **Identifies where we've been** (significant historical lows)
2. **Determines where we are** (current position relative to the trend)
3. **Projects where we're going** (future trend with specific price levels)
4. **Provides alternative routes** (Fibonacci bands as alternative targets)
This theoretical framework represents an innovative synthesis of time series analysis, approximation theory, and computational optimization, specifically designed for long-term financial trend analysis with robust and mathematically grounded projections.
RISK ROTATION MATRIX ║ BullVision [3.0]🔍 Overview
The Risk Rotation Matrix is a comprehensive market regime detection system that analyzes global market conditions across four critical domains: Liquidity, Macroeconomic, Crypto/Commodities, and Risk/Volatility. Through proprietary algorithms and advanced statistical analysis, it transforms 20+ diverse market metrics into a unified framework for identifying regime transitions and risk rotations.
This institutional-grade system aims to solve a fundamental challenge: how to synthesize complex, multi-domain market data into clear, actionable trading intelligence. By combining proprietary liquidity calculations with sophisticated cross-asset analysis.
The Four-Domain Architecture
1. 💧 LIQUIDITY DOMAIN
Our liquidity analysis combines standard metrics with proprietary calculations:
Proprietary Components:
Custom Global Liquidity Index (GLI): Unique formula aggregating central bank assets, credit spreads, and FX dynamics through our weighted algorithm
Federal Reserve Balance Proxy: Advanced calculation incorporating reverse repos, TGA fluctuations, and QE/QT impacts
China Liquidity Proxy: First-of-its-kind metric combining PBOC operations with FX-adjusted aggregates
Global M2 Composite: Custom multi-currency M2 aggregation with proprietary FX normalization
2. 📈 MACRO DOMAIN
Sophisticated integration of global economic indicators:
S&P 500: Momentum and trend analysis with custom z-score normalization
China Blue Chips: Asian market sentiment with correlation filtering
MBA Purchase Index: Real estate market health indicator
Emerging Markets (EEMS): Risk appetite measurement
Global ETF (URTH): Worldwide equity exposure tracking
Each metric undergoes proprietary transformation to ensure comparability and regime-specific sensitivity.
3. 🪙 CRYPTO/COMMODITIES DOMAIN
Unique cross-asset analysis combining:
Total Crypto Market Cap: Liquidity flow indicator with custom smoothing
Bitcoin SOPR: On-chain profitability analysis with adaptive periods
MVRV Z-Score: Advanced implementation with multiple MA options
BTC/Silver Ratio: Novel commodity-crypto relationship metric
Our algorithms detect when crypto markets lead or lag traditional assets, providing crucial timing signals.
4. ⚡ RISK/VOLATILITY DOMAIN
Advanced volatility regime detection through:
MOVE Index: Bond volatility with inverse correlation analysis
VVIX/VIX Ratio: Volatility-of-volatility for regime extremes
SKEW Index: Tail risk measurement with custom normalization
Credit Stress Composite: Proprietary combination of credit spreads
USDT Dominance: Crypto flight-to-safety indicator
All risk metrics are inverted and normalized to align with the unified scoring system.
🧠 Advanced Integration Methodology
Multi-Stage Processing Pipeline
Data Collection: Real-time aggregation from 20+ sources
Normalization: Custom z-score variants accounting for regime-specific volatility
Domain Scoring: Proprietary weighting within each domain
Cross-Domain Synthesis: Advanced correlation matrix between domains
Regime Detection: State-transition model identifying four market phases
Signal Generation: Composite score with adaptive smoothing
🔁 Composite Smoothing & Signal Generation
The user can apply smoothing (ALMA, EMA, etc.) to highlight trends and reduce noise. Smoothing length, type, and parameters are fully customizable for different trading styles.
🎯 Color Feedback & Market Regimes
Visual dynamics (color gradients, labels, trails, and quadrant placement) offer an at-a-glance interpretation of the market’s evolving risk environment—without forecasting or forward-looking assumptions.
🎯 The Quadrant Visualization System
Our innovative visual framework transforms complex calculations into intuitive intelligence:
Dynamic Ehlers Loop: Shows current position and momentum
Trailing History: Visual path of regime transitions
Real-Time Animation: Immediate feedback on condition changes
Multi-Layer Information: Depth through color, size, and positioning
🚀 Practical Applications
Primary Use Cases
Multi-Asset Portfolio Management: Optimize allocation across asset classes based on regime
Risk Budgeting: Adjust exposure dynamically with regime changes
Tactical Trading: Time entries/exits using regime transitions
Hedging Strategies: Implement protection before risk-off phases
Specific Trading Scenarios
Domain Divergence: When liquidity improves but risk metrics deteriorate
Early Rotation Detection: Crypto/commodity signals often lead broader markets
Volatility Regime Trades: Position for mean reversion or trend following
Cross-Asset Arbitrage: Exploit temporary dislocations between domains
⚙️ How It Works
The Composite Score Engine
The system's intelligence emerges from how it combines domains:
Each domain produces a normalized score (-2 to +2 range)
Proprietary algorithms weight domains based on market conditions
Composite score indicates overall market regime
Smoothing options (ALMA, EMA, etc.) optimize for different timeframes
Regime Classification
🟢 Risk-On (Green): Positive composite + positive momentum
🟠 Weakening (Orange): Positive composite + negative momentum
🔵 Recovery (Blue): Negative composite + positive momentum
🔴 Risk-Off (Red): Negative composite + negative momentum
Signal Interpretation Framework
The indicator provides three levels of analysis:
Composite Score: Overall market regime (-2 to +2)
Domain Scores: Identify which factors drive regime
Individual Metrics: Granular analysis of specific components
🎨 Features & Functionality
Core Components
Risk Rotation Quadrant: Primary visual interface with Ehlers loop
Data Matrix Dashboard: Real-time display of all 20+ metrics
Domain Aggregation: Separate scores for each domain
Composite Calculation: Unified score with multiple smoothing options
Customization Options
Selective Metrics: Enable/disable individual components
Period Adjustment: Optimize lookback for each metric
Smoothing Selection: 10 different MA types including ALMA
Visual Configuration: Quadrant scale, colors, trails, effects
Advanced Settings
Pre-smoothing: Reduce noise before final calculation
Adaptive Periods: Automatic adjustment during volatility
Correlation Filters: Remove redundant signals
Regime Memory: Hysteresis to prevent whipsaws
📋 Implementation Guide
Setup Process
Add to chart (optimized for daily, works on all timeframes)
Review default settings for your market focus
Adjust domain weights based on trading style
Configure visual preferences
Optimization by Trading Style
Position Trading: Longer periods (60-150), heavy smoothing
Swing Trading: Medium periods (20-60), balanced smoothing
Active Trading: Shorter periods (10-40), minimal smoothing
Best Practices
Monitor domain divergences for early signals
Use extreme readings (-1.5/+1.5) for high-conviction trades
Combine with price action for confirmation
Adjust parameters during major events (FOMC, earnings)
💎 What Makes This Unique
Beyond Traditional Indicators
Multi-Domain Integration: Only system combining liquidity, macro, crypto, and volatility
Proprietary Calculations: Custom formulas for GLI, Fed, China, and M2 proxies
Adaptive Architecture: Dynamically adjusts to market regimes
Institutional Depth: 20+ integrated metrics vs typical 3-5
Technical Innovation
Statistical Normalization: Custom z-score variants for cross-asset comparison
Correlation Management: Prevents double-counting related signals
Regime Persistence: Algorithms to identify sustainable vs temporary shifts
Visual Intelligence: Information-dense display without overwhelming
🔢 Performance Characteristics
Strengths
Early regime detection (typically 1-3 weeks ahead)
Robust across different market environments
Clear visual feedback reduces interpretation errors
Comprehensive coverage prevents blind spots
Optimal Conditions
Most effective with 100+ bars of history
Best on daily timeframe (4H minimum recommended)
Requires liquid markets for accurate signals
Performance improves with more enabled components
⚠️ Risk Considerations & Limitations
Important Disclaimers
Probabilistic system, not predictive
Requires understanding of macro relationships
Signals should complement other analysis
Past regime behavior doesn't guarantee future patterns
Known Limitations
Black swan events may cause temporary distortions
Central bank interventions can override signals
Requires active management during regime transitions
Not suitable for pure technical traders
💎 Conclusion
The Risk Rotation Matrix represents a new paradigm in market regime analysis. By combining proprietary liquidity calculations with comprehensive multi-domain monitoring, it provides institutional-grade intelligence previously available only to large funds. The system's strength lies not just in its individual components, but in how it synthesizes diverse market information into clear, actionable trading signals.
⚠️ Access & Intellectual Property Notice
This invite-only indicator contains proprietary algorithms, custom calculations, and years of quantitative research. The mathematical formulations for our liquidity proxies, cross-domain correlation matrices, and regime detection algorithms represent significant intellectual property. Access is restricted to protect these innovations and maintain their effectiveness for serious traders who understand the value of comprehensive market regime analysis.
IME's Community First Presented FVGsIME's Community First Presented FVGs v1.5 - Advanced Implementation
ORIGINALITY & INNOVATION
This indicator advances beyond basic Fair Value Gap detection by implementing a sophisticated 24-hour FVG lifecycle management system aligned with institutional trading patterns. While many FVG indicators simply detect gaps and extend them indefinitely, this implementation introduces temporal intelligence that mirrors how institutional algorithms actually manage these inefficiencies.
Key Innovations that set this apart:
- 24-Hour Lifecycle Management: FVGs extend dynamically until 16:59, then freeze until removal at 17:00 next day
- Institutional Day Alignment: Recognizes 18:00-16:59 trading cycles vs standard calendar days
- Multi-Session Detection: Simultaneous monitoring of Midnight, London, NY AM, and NY PM sessions
- Advanced Classification System: A.FVG detection with volume imbalance analysis vs classic FVG patterns
- Volatility Settlement Logic: Blocks contamination from opening mechanics (3:01+, 0:01+, 13:31+ rules)
- Visual Enhancement System: C.E. lines, contamination warnings, dark mode support with proper transparency handling
BASED ON ICT CONCEPTS
This indicator implements First Presented Fair Value Gap methodology taught by ICT (Inner Circle Trader). The original F.P. FVG concepts, timing rules, and session-based detection are credited to ICT's educational material. This implementation extends those foundational concepts with advanced lifecycle management and institutional alignment features.
ICT's Core F.P. FVG Rules Implemented:
- First clean FVG after session opening (avoids opening contamination)
- 3-candle pattern requirement for valid detection
- Session-specific timing windows and volatility settlement
- Consequent Encroachment level identification
IME's Advanced Enhancements:
- Automated lifecycle management with institutional day recognition
- Multi-session simultaneous monitoring with proper isolation
- Advanced visual system with transparency states for aged FVGs
- A.FVG classification with volume imbalance detection algorithms
HOW IT WORKS
Core Detection Engine
The indicator monitors four key institutional sessions using precise timing windows:
- Midnight Session: 00:01-00:30 (blocks 00:00 contamination)
- London Session: 03:01-03:30 (blocks 03:00 contamination)
- NY AM Session: 09:30-10:00 (configurable 9:30 detection)
- NY PM Session: 13:31-14:00 (blocks 13:30 contamination)
During each session window, the algorithm scans for the first valid FVG pattern using ICT's 3-candle rule while applying volatility settlement principles to avoid false signals from opening mechanics.
Advanced Classification System
Classic FVG Detection:
Standard 3-candle wick-to-wick gap where candle 1 and 3 don't overlap, creating an inefficiency that institutions must eventually fill.
A.FVG (Advanced FVG) Detection:
Enhanced pattern recognition that includes volume imbalance analysis (deadpool detection) to identify more significant institutional inefficiencies. A.FVGs incorporate both the basic gap plus additional price imbalances between candle bodies, creating larger, more significant levels.
24-Hour Lifecycle Management
Phase 1 - Dynamic Extension (Creation Day):
From detection until 16:59 of creation day, FVGs extend in real-time as new bars form, maintaining their relevance as potential support/resistance levels.
Phase 2 - Freeze Period (Next Day):
At 16:59, FVGs stop extending and "freeze" at their final size, remaining visible as reference levels but no longer growing. This prevents outdated levels from contaminating fresh analysis.
Phase 3 - Cleanup (17:00 Next Day):
Exactly 24+ hours after creation, FVGs are automatically removed to maintain chart clarity. This timing aligns with institutional trading cycle completion.
Institutional Day Logic
The algorithm recognizes that institutional trading days run from 18:00-16:59 (not midnight-midnight). This alignment ensures FVGs are managed according to institutional timeframes rather than arbitrary calendar boundaries.
Contamination Avoidance System
Volatility Settlement Principle:
Opening mechanics create artificial volatility that can produce false FVG signals. The indicator automatically blocks detection during exact session opening times (X:00) and requires settlement time (X:01+) before identifying clean institutional inefficiencies.
Special NY AM Handling:
Provides configurable 9:30 detection for advanced users who want to capture potential opening range FVGs, with clear visual warnings about contamination risk.
VISUAL SYSTEM
Color Intelligence
- Current Day FVGs: Full opacity with session-specific colors
- Previous Day FVGs: 70% transparency for historical reference
- Special Timing (9:30): Dedicated warning color with alert labels
- Dark Mode Support: Automatic text/line color adaptation
Enhanced Visual Elements
C.E. (Consequent Encroachment) Lines:
Automatically calculated 50% levels within each FVG, representing the most likely fill point based on institutional behavior patterns. These levels extend and freeze with their parent FVG.
Contamination Warnings:
Visual alerts when FVGs are detected during potentially contaminated timing, helping traders understand signal quality.
Session Identification:
Clear labeling system showing FVG type (FVG/A.FVG), session origin (NY AM, London, etc.), and creation date for easy reference.
HOW TO USE
Basic Setup
1. Session Selection: Enable/disable specific sessions based on your trading strategy
2. FVG Type: Choose between Classic FVGs or A.FVGs depending on your analysis preference
3. Visual Preferences: Adjust colors, text size, and enable dark mode if needed
Trading Applications
Intraday Reference Levels:
Use current day FVGs as potential support/resistance for price action analysis. The dynamic extension ensures levels remain relevant throughout the trading session.
Multi-Session Analysis:
Monitor how price interacts with FVGs from different sessions to understand institutional flow and market structure.
C.E. Level Trading:
Focus on the 50% consequent encroachment levels for high-probability entry points when price approaches FVG zones.
Historical Context:
Previous day FVGs (shown with transparency) provide context for understanding market structure evolution across multiple trading days.
Advanced Features
9:30 Special Detection:
For experienced traders, enable 9:30 FVG detection to capture opening range inefficiencies, but understand the contamination risks indicated by warning labels.
A.FVG vs Classic Toggle:
Switch between detection modes based on market conditions - A.FVGs for trending environments, Classic FVGs for ranging conditions.
Best Practices
- Use on 1-minute to 15-minute timeframes for optimal session detection
- Combine with other institutional concepts (order blocks, liquidity levels) for comprehensive analysis
- Pay attention to transparency states - current day FVGs are more actionable than previous day references
- Consider C.E. levels as primary targets rather than full FVG fills
TECHNICAL SPECIFICATIONS
Platform: Pine Script v6 for optimal performance and reliability
Timeframe Compatibility: All timeframes (optimized for 1M-15M)
Market Compatibility: 24-hour markets (Forex, Crypto, Futures)
Session Management: Automatic trading day detection with weekend handling
Memory Management: Intelligent capacity limits with automatic cleanup
Performance: Optimized algorithms for smooth real-time operation
CLOSED SOURCE JUSTIFICATION
This indicator is published as closed source to protect the proprietary algorithms that enable:
- Precise 24-hour lifecycle timing calculations with institutional day alignment
- Advanced A.FVG classification with sophisticated volume imbalance detection
- Complex multi-session coordination with contamination filtering
- Optimized memory management preventing performance degradation
- Specialized visual state management for transparency and extension logic
The combination of these advanced systems creates a unique implementation that goes far beyond basic FVG detection, warranting protection of the underlying computational methods while providing full transparency about functionality and usage.
PERFORMANCE CHARACTERISTICS
Real-Time Operation: Smooth performance with minimal resource usage
Accuracy: Precise session detection with timezone consistency
Reliability: Robust error handling and edge case management
Scalability: Supports multiple simultaneous FVGs without performance impact
This advanced implementation represents significant evolution beyond basic FVG indicators, providing institutional-grade analysis tools for serious traders while maintaining the clean visual presentation essential for effective technical analysis.
IMPORTANT DISCLAIMERS
Past performance does not guarantee future results. This indicator is an educational tool based on ICT's Fair Value Gap concepts and should be used as part of a comprehensive trading strategy. Users should understand the risks involved in trading and consider their risk tolerance before making trading decisions. The indicator identifies potential support/resistance levels but does not predict market direction with certainty.
2 days ago
Release Notes
IME's Community First Presented FVGs v1.5.2 - Critical Bug Fixes
Bug Fixes:
v1.5.1 - Fixed 9:30 Contamination Blocking:
Issue: When 9:30 detection toggle was OFF, script still detected 9:30 candles as F.P. FVGs
Fix: Added proper contamination blocking logic that prevents 9:30 middle candle detection when toggle is OFF
Result: Toggle OFF now correctly shows clean F.P. FVGs at 9:31+ (proper ICT volatility settlement)
v1.5.2 - Fixed A.FVG Box Calculation Accuracy:
Issue: A.FVG boxes incorrectly included ALL body levels even when no actual deadpool existed between specific candles
Fix: Implemented selective body level inclusion - only adds body prices where actual volume imbalances exist
Result: A.FVG boxes now accurately represent only areas with real institutional volume imbalances
Impact:
More Accurate Detection: 9:30 contamination properly blocked when disabled
Precise A.FVG Zones: Boxes only include levels with actual deadpools/volume imbalances
Institutional Accuracy: Both fixes align detection with true institutional trading principles
Technical Details:
Enhanced contamination blocking checks middle candle timing in normal mode
A.FVG calculation now selectively includes body levels based on individual deadpool existence
Maintains backward compatibility with all existing features and settings
These fixes ensure the indicator provides institutionally accurate FVG detection and sizing for professional trading analysis.
