Fibonacci Extension / Retracement / Pivot Points by DGTFɪʙᴏɴᴀᴄᴄɪ Exᴛᴇɴᴛɪᴏɴ / Rᴇᴛʀᴀᴄᴍᴇɴᴛ / Pɪᴠᴏᴛ Pᴏɪɴᴛꜱ
This study combines various Fibonacci concepts into one, and some basic volume and volatility indications
█ Pɪᴠᴏᴛ Pᴏɪɴᴛꜱ — is a technical indicator that is used to determine the levels at which price may face support or resistance. The Pivot Points indicator consists of a pivot point (PP) level and several support (S) and resistance (R) levels. PP, resistance and support values are calculated in different ways, depending on the type of the indicator, this study implements Fibonacci Pivot Points
The indicator resolution is set by the input of the Pivot Points TF (Timeframe). If the Pivot Points TF is set to AUTO (the default value), then the increased resolution is determined by the following algorithm:
for intraday resolutions up to and including 5 min, 4HOURS (4H) is used
for intraday resolutions more than 5 min and up to and including 45 min, DAY (1D) is used
for intraday resolutions more than 45 min and up to and including 4 hour, WEEK (1W) is used
for daily resolutions MONTH is used (1M)
for weekly resolutions, 3-MONTH (3M) is used
for monthly resolutions, 12-MONTH (12M) is used
If the Pivot Points TF is set to User Defined, users may choose any higher timeframe of their preference
█ Fɪʙ Rᴇᴛʀᴀᴄᴇᴍᴇɴᴛ — Fibonacci retracements is a popular instrument used by technical analysts to determine support and resistance areas. In technical analysis, this tool is created by taking two extreme points (usually a peak and a trough) on the chart and dividing the vertical distance by the key Fibonacci coefficients equal to 23.6%, 38.2%, 50%, 61.8%, and 100%. This study implements an automated method of identifying the pivot lows/highs and automatically draws horizontal lines that are used to determine possible support and resistance levels
█ Fɪʙᴏɴᴀᴄᴄɪ Exᴛᴇɴꜱɪᴏɴꜱ — Fibonacci extensions are a tool that traders can use to establish profit targets or estimate how far a price may travel AFTER a retracement/pullback is finished. Extension levels are also possible areas where the price may reverse. This study implements an automated method of identifying the pivot lows/highs and automatically draws horizontal lines that are used to determine possible support and resistance levels.
IMPORTANT NOTE: Fibonacci extensions option may require to do further adjustment of the study parameters for proper usage. Extensions are aimed to be used when a trend is present and they aim to measure how far a price may travel AFTER a retracement/pullback. I will strongly suggest users of this study to check the education post for further details, where to use extensions and where to use retracements
Important input options for both Fibonacci Extensions and Retracements
Deviation, is a multiplier that affects how much the price should deviate from the previous pivot in order for the bar to become a new pivot. Increasing its value is one way to get higher timeframe Fib Retracement Levels
Depth, affects the minimum number of bars that will be taken into account when building
█ Volume / Volatility Add-Ons
High Volatile Bar Indication
Volume Spike Bar Indication
Volume Weighted Colored Bars
This study benefits from build-in auto fib retracement tv study and modifications applied to get extentions and also to fit this combo
Disclaimer:
Trading success is all about following your trading strategy and the indicators should fit within your trading strategy, and not to be traded upon solely
The script is for informational and educational purposes only. Use of the script does not constitute professional and/or financial advice. You alone have the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd TradingView user liable for any possible claim for damages arising from any decision you make based on use of the script
在腳本中搜尋"algo"
(IK) Base Break BuyThis strategy first calculates areas of support (bases), and then enters trades if that support is broken. The idea is to profit off of retracement. Dollar-cost-averaging safety orders are key here. This strategy takes into account a .1% commission, and tests are done with an initial capital of 100.00 USD. This only goes long.
The strategy is highly customizable. I've set the default values to suit ETH/USD 15m. If you're trading this on another ticker or timeframe, make sure to play around with the settings. There is an explanation of each input in the script comments. I found this to be profitable across most 'common sense' values for settings, but tweaking led to some pretty promising results. I leaned more towards high risk/high trade volume.
Always remember though: historical performance is no guarantee of future behavior . Keep settings within your personal risk tolerance, even if it promises better profit. Anyone can write a 100% profitable script if they assume price always eventually goes up.
Check the script comments for more details, but, briefly, you can customize:
-How many bases to keep track of at once
-How those bases are calculated
-What defines a 'base break'
-Order amounts
-Safety order count
-Stop loss
Here's the basic algorithm:
-Identify support.
--Have previous candles found bottoms in the same area of the current candle bottom?
--Is this support unique enough from other areas of support?
-Determine if support is broken.
--Has the price crossed under support quickly and with certainty?
-Enter trade with a percentage of initial capital.
-Execute safety orders if price continues to drop.
-Exit trade at profit target or stop loss.
Take profit is dynamic and calculated on order entry. The bigger the 'break', the higher your take profit percentage. This target percentage is based on average position size, so as safety orders are filled, and average position size comes down, the target profit becomes easier to reach.
Stop loss can be calculated one of two ways, either a static level based on initial entry, or a dynamic level based on average position size. If you use the latter (default), be aware, your real losses will be greater than your stated stop loss percentage . For example:
-stop loss = 15%, capital = 100.00, safety order threshold = 10%
-you buy $50 worth of shares at $1 - price average is $1
-you safety $25 worth of shares at $0.9 - price average is $0.966
-you safety $25 worth of shares at $0.8. - price average is $0.925
-you get stopped out at 0.925 * (1-.15) = $0.78625, and you're left with $78.62.
This is a realized loss of ~21.4% with a stop loss set to 15%. The larger your safety order threshold, the larger your real loss in comparison to your stop loss percentage, and vice versa.
Indicator plots show the calculated bases in white. The closest base below price is yellow. If that base is broken, it turns purple. Once a trade is entered, profit target is shown in silver and stop loss in red.
(IK) Grid ScriptThis is my take on a grid trading strategy. From Investopedia:
"Grid trading is most commonly associated with the foreign exchange market. Overall the technique seeks to capitalize on normal price volatility in an asset by placing buy and sell orders at certain regular intervals above and below a predefined base price."
This strategy is best used on sideways markets, without a definitive up or down major trend. Because it doesn't rely on huge vertical movement, this strategy is great for small timeframes. It only goes long. I've set initial_capital to 100 USD. default_qty_value should be your initial capital divided by your amount of grid lines. I'm also assuming a 0.1% commission per trade.
Here's the basic algorithm:
- Create a grid based on an upper-bound (strong resistance) and a lower-bound (strong support)
- Grid lines are spaced evenly between these two bounds. (I recommend anywhere between 5-10 grid lines, but this script lets you use up to 15. More gridlines = more/smaller trades)
- Identify nearest gridline above and below current price (ignoring the very closest grid line)
- If price crosses under a near gridline, buy and recalculate near gridlines
- If price crosses over a near gridline, sell and recalculate near gridlines
- Trades are entered and exited based on a FIFO system. So if price falls 3 grid lines (buy-1, buy-2, buy-3), and subsequently crosses above one grid line, only the first trade will exit (sell-1). If it falls again, it will enter a new trade (buy-4), and if it crosses above again it will sell the original second trade (sell-2). The amount of trades you can be in at once are based on the amount of grid lines you have.
This strategy has no built-in stop loss! This is not a 'set-it-and-forget-it" script. Make sure that price remains within the bounds of your grid. If prices exits above the grid, you're in the money, but you won't be making any more trades. If price exits below the grid, you're 100% staked in whatever you happen to be trading.
This script is more complicated than my last one, but should be more user friendly. Make sure to correctly set your lower-bound and upper-bound based on strong support and resistance (the default values for these are probably going to be meaningless). If you change your "Grid Quantity" (amount of grid lines) make sure to also change your 'Order Size' property under settings for proper test results (or default_qty_value in the strategy() declaration).
Repeated Median Regression ChannelThis script uses the Repeated Median (RM) estimator to construct a linear regression channel and thus offers an alternative to the available codes based on ordinary least squares.
The RM estimator is a robust linear regression algorithm. It was proposed by Siegel in 1982 (1) and has since found many applications in science and engineering for linear trend estimation and data filtering.
The key difference between RM and ordinary least squares methods is that the slope of the RM line is significantly less affected by data points that deviate strongly from the established trend. In statistics, these points are usually called outliers, while in the context of price data, they are associated with gaps, reversals, breaks from the trading range. Thus, robustness to outlier means that the nascent deviation from a predetermined trend will be more clearly seen in the RM regression compared to the least-squares estimate. For the same reason, the RM model is expected to better depict gaps and trend changes (2).
Input Description
Length : Determines the length of the regression line.
Channel Multiplier : Determines the channel width in units of root-mean-square deviation.
Show Channel : If switched off , only the (central) regression line is displayed.
Show Historical Broken Channel : If switched on , the channels that were broken in the past are displayed. Note that a certain historical broken channel is shown only when at least Length / 2 bars have passed since the last historical broken channel.
Print Slope : Displays the value of the current RM slope on the graph.
Method
Calculation of the RM regression line is done as follows (1,3):
For each sample point ( t (i), y (i)) with i = 1.. Length , the algorithm calculates the median of all the slopes of the lines connecting this point to the other Length -1 points.
The regression slope is defined as the median of the set of these median slopes.
The regression intercept is defined as the median of the set { y (i) – m * t (i)}.
Computational Time
The present implementation utilizes a brute-force algorithm for computing the RM-slope that takes O ( Length ^2) time. Therefore, the calculation of the historical broken channels might take a relatively long time (depending on the Length parameter). However, when the Show Historical Broken Channel option is off, only the real-time RM channel is calculated, and this is done quite fast.
References
1. A. F. Siegel (1982), Robust regression using repeated medians, Biometrika, 69 , 242–244.
2. P. L. Davies, R. Fried, and U. Gather (2004), Robust signal extraction for on-line monitoring data, Journal of Statistical Planning and Inference 122 , 65-78.
3. en.wikipedia.org
Tic Tac Toe (For Fun)Hello All,
I think all of you know the game "Tic Tac Toe" :) This time I tried to make this game, and also I tried to share an example to develop a game script in Pine. Just for fun ;)
Tic Tac Toe Game Rules:
1. The game is played on a grid that's 3 squares by 3 squares.
2. You are "O", the computer is X. Players take turns putting their marks in empty squares.
3. if a player makes 3 of her marks in a row (up, down, across, or diagonally) the he is the winner.
4. When all 9 squares are full, the game is over (draw)
So, how to play the game?
- The player/you can play "O", meaning your mark is "O", so Xs for the script. please note that: The script plays with ONLY X
- There is naming for all squears, A1, A2, A3, B1, B2, B3, C1, C2, C3. you will see all these squares in the options.
- also You can set who will play first => "Human" or "Computer"
if it's your turn to move then you will see "You Move" text, as seen in the following screenshot. for example you want to put "O" to "A1" then using options set A1 as O
How the script play?
it uses MinMax algorithm with constant depth = 4. And yes we don't have option to make recursive functions in Pine at the moment so I made four functions for each depth. this idea can be used in your scripts if you need such an algorithm. if you have no idea about MinMax algorithm you can find a lot of articles on the net :)
The script plays its move automatically if its turn to play. you will just need to set the option that computer played (A1, C3, etc)
if it's computer turn to play then it calculates and show the move it wants to play like "My Move : B3 <= X" then using options you need to set B3 as X
Also it checks if the board is valid or not:
I have tested it but if you see any bug let me know please
Enjoy!
Max Drawdown Calculating Functions (Optimized)Maximum Drawdown and Maximum Relative Drawdown% calculating functions.
I needed a way to calculate the maxDD% of a serie of datas from an array (the different values of my balance account). I didn't find any builtin pinescript way to do it, so here it is.
There are 2 algorithms to calculate maxDD and relative maxDD%, one non optimized needs n*(n - 1)/2 comparisons for a collection of n datas, the other one only needs n-1 comparisons.
In the example we calculate the maxDDs of the last 10 close values.
There a 2 functions : "maximum_relative_drawdown" and "maximum_dradown" (and "optimized_maximum_relative_drawdown" and "optimized_maximum_drawdown") with names speaking for themselves.
Input : an array of floats of arbitrary size (the values we want the DD of)
Output : an array of 4 values
I added the iteration number just for fun.
Basically my script is the implementation of these 2 algos I found on the net :
var peak = 0;
var n = prices.length
for (var i = 1; i < n; i++){
dif = prices - prices ;
peak = dif < 0 ? i : peak;
maxDrawdown = maxDrawdown > dif ? maxDrawdown : dif;
}
var n = prices.length
for (var i = 0; i < n; i++){
for (var j = i + 1; j < n; j++){
dif = prices - prices ;
maxDrawdown = maxDrawdown > dif ? maxDrawdown : dif;
}
}
Feel free to use it.
@version=4
[blackcat] L2 Ehlers Autocorrelation PeriodogramLevel: 2
Background
John F. Ehlers introduced Autocorrelation Periodogram in his "Cycle Analytics for Traders" chapter 8 on 2013.
Function
Construction of the autocorrelation periodogram starts with the autocorrelation function using the minimum three bars of averaging. The cyclic information is extracted using a discrete Fourier transform (DFT) of the autocorrelation results. This approach has at least four distinct advantages over other spectral estimation techniques. These are:
1. Rapid response. The spectral estimates start to form within a half-cycle period of their initiation.
2. Relative cyclic power as a function of time is estimated. The autocorrelation at all cycle periods can be low if there are no cycles present, for example, during a trend. Previous works treated the maximum cycle amplitude at each time bar equally.
3. The autocorrelation is constrained to be between minus one and plus one regardless of the period of the measured cycle period. This obviates the need to compensate for Spectral Dilation of the cycle amplitude as a function of the cycle period.
4. The resolution of the cyclic measurement is inherently high and is independent of any windowing function of the price data.
The dominant cycle is extracted from the spectral estimate in the next block of code using a center-of-gravity (CG) algorithm. The CG algorithm measures the average center of two-dimensional objects. The algorithm computes the average period at which the powers are centered. That is the dominant cycle. The dominant cycle is a value that varies with time. The spectrum values vary between 0 and 1 after being normalized. These values are converted to colors. When the spectrum is greater than 0.5, the colors combine red and yellow, with yellow being the result when spectrum = 1 and red being the result when the spectrum = 0.5. When the spectrum is less than 0.5, the red saturation is decreased, with the result the color is black when spectrum = 0.
Key Signal
DominantCycle --> Dominant Cycle
Period --> Autocorrelation Periodogram Array
Pros and Cons
100% John F. Ehlers definition translation of original work, even variable names are the same. This help readers who would like to use pine to read his book. If you had read his works, then you will be quite familiar with my code style.
Remarks
The 49th script for Blackcat1402 John F. Ehlers Week publication.
Courtesy of @RicardoSantos for RGB functions.
Readme
In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Tradingview is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Tradingview community. Welcome everyone to interact with me to discuss these interesting pine scripts.
The scripts posted are categorized into 5 levels according to my efforts or manhours put into these works.
Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element.
Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level.
Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated.
Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop.
Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many of them to achieve private trading strategy.
BuyTheDipWell, I often had arguments in online forum with a guy who claimed to time the market perfectly without any technical analysis or prior experience. He often claimed that technical analysis does not work and it only works when you trade on other's emotions. He also argued that algorithmic trading isn't profitable - if so, everyone would do that. Hence, I thought I will convert his idea to algorithm.
In his own words, the strategy is as below:
Chose an instrument which is in full uptrend.
Wait for the panic sell and buy the dip
Once market recovers back exit immediately
It seems to do just fine with indexes. But, not so good when it comes to stocks.
Trend-Range IdentifierTrend trading algorithms fail in ranging market and Swing trading algorithm fail in trending market. Purpose of this indicator is to identify if the instrument is trending or ranging so that you can apply appropriate trading algorithm for the market.
Process:
ATR is calculated based on the input parameter atrLength
Range/Channel containing upLine and downLine is calculated by adding/subtracting atrMultiplier * atr to close price.
This range/channel will remain same until the price breaks either upLine or downLine.
Once price crosses one among upLine and downLine, then new upLine/downLine is calculated based on latest close price.
If price breaks upLine, the trend is considered to be up until the next line break or no lines are broken for rangeLength bars. During this state, candles are colored in lime and upLine/downLine are colored in green.
If price breaks downLine, the trend is considered to be down until the next line break or no lines are broken for rangeLength bars. During this state, candles are colored in orange and upLine/downLine are colored in red.
If close price does not break either upLine or downLine for rangeLength bars, then the instrument is considered to be in range. During this state, candles are colored in silver and upLine/downLine are colored in purple.
In ranging duration, we display one among Keltner Channel, Bollinger Band or Donchian Band as per input parameter : rangeChannel . Other parameters used for calculation are rangeLength and stdDev
I have not fully optimized parameters. Suggestions and feedback welcome.
Dynamic Dots Dashboard (a Cloud/ZLEMA Composite)The purpose of this indicator is to provide an easy-to-read binary dashboard of where the current price is relative to key dynamic supports and resistances. The concept is simple, if a dynamic s/r is currently acting as a resistance, the indicator plots a dot above the histogram in the red box. If a dynamic s/r is acting as support, a dot is plotted in the green box below.
There are some additional features, but the dot graphs are king.
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KEY:
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Currently the dynamic s/r's being used in the dot plots are:
Ichimoku Cloud:
Tenkan (blue)
Kijun (pink)
Senkou A (red)
Senkou B (green)
ZLEMA (Zero Lag Exponential Moving Average)
99 ZLEMA (lavender)
200 ZLEMA (salmon)
You'll see a dashed line through the middle of the resistances section (red) and supports section (green). Cloud indicators are plotted above the dashed line, and ZLEMA's are below.
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How it Works - Visual
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As stated in the intro - if a dynamic s/r is currently above the current price and acting as a resistance, the indicator plots a dot above the histogram in the red box. If a dynamic s/r is acting as support, a dot is plotted in the green box below. Additionally, there is an optional histogram (default is on) that will further visualize this relationship. The histogram is a simple summation of the resistances above and the supports below.
Here's a visual to assist with what that means. This chart includes all of those dynamic s/r's in the dynamic dot dashboard (the on-chart parts are individually added, not part of this tool).
You can see that as a dynamic support is lost, the corresponding dot is moved from the supports section at the bottom (green), to the resistances section at the top (red). The opposite being true as resistances are being overtaken (broken resistances are moved to the support section (red)). You can see that the raw chart is just... a mess. Which kinda of accentuates one of the key goals of this indicator: to get all that dynamic support info without a mess of a chart like that.
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How To Use It
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There are a lot of ways to use this information, but the most notable of which is to detect shifts in the market cycle.
For this example, take a look at the dynamic s/r dots in the resistances category (red background). You can see clearly that there are distinctive blocks of high density dots that have clear beginnings and ends. When we transition from a high density of dots to none in resistances, that means we are flipping them as support and entering a bull cycle. On the other hand, when we go from low density of dots as resistances to high density, we're pivoting to a bear cycle. Easy as that, you can quickly detect when market cycles are beginning or ending.
Alternatively, you can add your preferred linear SR's, fibs, etc. to the chart and quickly glance at the dashboard to gauge how dynamic SR's may be contributing to the risk of your trade.
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Who It's For
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New traders: by looking at dot density alone, you can use Dot Dynamics to spot transitionary phases in market cycles.
Experienced traders: keep your charts clean and the information easy to digest.
Developers: I created this originally as a starting point for more complex algos I'm working on. One algo is reading this dot dashboard and taking a position size relative to the s/r's above and below. Another cloud algo is using the results as inputs to spot good setups.
Colored Bars
There is an option (off by default, shown in the headline image above) to fill the bar colors based on how many dynamic s/r's are above or below the current price. This can make things easier for some users, confusing for others. I defaulted them to off as I don't want colors to confuse the primary value proposition of the indicators, which is the dot heat map. You can turn on colored bars in the settings.
One thing to note with the colored bars: they plot the color purely by the dot densities. Random spikes in the gradient colors (i.e. red to lime or green) can be a useful thing to notice, as they commonly occur at places where the price is bouncing between dynamic s/r's and can indicate a paradigm shift in the market cycle.
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Timeframes and Assets
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This can be used effectively on all assets (stocks, crypto, forex, etc) and all time frames. As always with any indicator, the higher TF's are generally respected more than lower TF's.
Thanks for checking it out! I've been trading crypto for years and am just now beginning to publish my ideas, secret-sauce scripts and handy tools (like this one). If you enjoyed this indicator and would like to see more, a like and a follow is greatly appreciated 😁.
McGinley Dynamic (Improved) - John R. McGinley, Jr.For all the McGinley enthusiasts out there, this is my improved version of the "McGinley Dynamic", originally formulated and publicized in 1990 by John R. McGinley, Jr. Prior to this release, I recently had an encounter with a member request regarding the reliability and stability of the general algorithm. Years ago, I attempted to discover the root of it's inconsistency, but success was not possible until now. Being no stranger to a good old fashioned computational crisis, I revisited it with considerable contemplation.
I discovered a lack of constraints in the formulation that either caused the algorithm to implode to near zero and zero OR it could explosively enlarge to near infinite values during unusual price action volatility conditions, occurring on different time frames. A numeric E-notation in a moving average doesn't mean a stock just shot up in excess of a few quintillion in value from just "10ish" moments ago. Anyone experienced with the usual McGinley Dynamic, has probably encountered this with dynamically dramatic surprises in their chart, destroying it's usability.
Well, I believe I have found an answer to this dilemma of 'susceptibility to miscalculation', to provide what is most likely McGinley's whole hearted intention. It required upgrading the formulation with two constraints applied to it using min/max() functions. Let me explain why below.
When using base numbers with an exponent to the power of four, some miniature numbers smaller than one can numerically collapse to near 0 values, or even 0.0 itself. A denominator of zero will always give any computational device a horribly bad day, not to mention the developer. Let this be an EASY lesson in computational division, I often entertainingly express to others. You have heard the terminology "$#|T happens!🙂" right? In the programming realm, "AnyNumber/0.0 CAN happen!🤪" too, and it happens "A LOT" unexpectedly, even when it's highly improbable. On the other hand, numbers a bit larger than 2 with the power of four can tremendously expand rapidly to the numeric limits of 64-bit processing, generating ginormous spikes on a chart.
The ephemeral presence of one OR both of those potentials now has a combined satisfactory remedy, AND you as TV members now have it, endowed with the ever evolving "Power of Pine". Oh yeah, this one plots from bar_index==0 too. It also has experimental settings tweaks to play with, that may reveal untapped potential of this formulation. This function now has gain of function capabilities, NOT to be confused with viral gain of function enhancements from reckless BSL-4 leaking laboratories that need to be eternally abolished from this planet. Although, I do have hopes this imd() function has the potential to go viral. I believe this improved function may have utility in the future by developers of the TradingView community. You have the source, and use it wisely...
I included an generic ema() plot for a basic comparison, ultimately unveiling some of this algorithm's unique characteristics differing on a variety of time frames. Also another unconstrained function is included to display some the disparities of having no limitations on a divisor in the calculation. I strongly advise against the use of umd() in any published script. There is simply just no reason to even ponder using it. I also included notes in the script to warn against this. It's funny now, but some folks don't always read/understand my advisories... You have been warned!
NOTICE: You have absolute freedom to use this source code any way you see fit within your new Pine projects, and that includes TV themselves. You don't have to ask for my permission to reuse this improved function in your published scripts, simply because I have better things to do than answer requests for the reuse of this simplistic imd() function. Sufficient accreditation regarding this script and compliance with "TV's House Rules" regarding code reuse, is as easy as copying the entire function as is. Fair enough? Good! I have a backlog of "computational crises" to contend with, including another one during the writing of this elaborate description.
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!
Many Moving AveragesThis script allows you to add two moving averages to a chart, where the type of moving average can be chosen from a collection of 15 different moving average algorithms. Each moving average can also have different lengths and crossovers/unders can be displayed and alerted on.
The supported moving average types are:
Simple Moving Average ( SMA )
Exponential Moving Average ( EMA )
Double Exponential Moving Average ( DEMA )
Triple Exponential Moving Average ( TEMA )
Weighted Moving Average ( WMA )
Volume Weighted Moving Average ( VWMA )
Smoothed Moving Average ( SMMA )
Hull Moving Average ( HMA )
Least Square Moving Average/Linear Regression ( LSMA )
Arnaud Legoux Moving Average ( ALMA )
Jurik Moving Average ( JMA )
Volatility Adjusted Moving Average ( VAMA )
Fractal Adaptive Moving Average ( FRAMA )
Zero-Lag Exponential Moving Average ( ZLEMA )
Kauman Adaptive Moving Average ( KAMA )
Many of the moving average algorithms were taken from other peoples' scripts. I'd like to thank the authors for making their code available.
JayRogers
Alex Orekhov (everget)
Alex Orekhov (everget)
Joris Duyck (JD)
nemozny
Shizaru
KobySK
Jurik Research and Consulting for inventing the JMA.
BitradertrackerEste Indicador ya no consiste en líneas móviles que se cruzan para dar señales de entrada o salida, si no que va más allá e interpreta gráficamente lo que está sucediendo con el valor.
Es un algoritmo potente, que incluye 4 indicadores de tendencia y 2 indicadores de volumen.
Con este indicador podemos movernos con las "manos fuertes" del mercado, rastrear sus intenciones y tomar decisiones de compra y venta.
Diseñado para operar en criptomonedas.
En cuanto a qué temporalidad usar, cuanto más grande mejor, ya que al final lo que estamos haciendo es el análisis de datos y, por lo tanto, cuanto más datos, mejor. Personalmente recomiendo usarlo en velas de 30 minutos, 1 hora y 4 horas.
Recuerde, ningún indicador es 100% efectivo.
Este indicador nos muestra en las áreas de color púrpura (manos fuertes) y en las áreas de color verde (manos débiles) y al mostrármelo gráficamente ya el indicador vale la pena.
El mercado está impulsado por dos tipos de inversores, que se denominan manos fuertes o ballenas (agencias, fondos, empresas, bancos, etc.) y manos débiles o peces pequeños (es decir, nosotros).
No tenemos la capacidad de manipular un valor, ya que nuestra cartera es limitada, pero podemos ingresar y salir de los valores fácilmente ya que no tenemos mucho dinero.
Las ballenas pueden manipular un valor ya que tienen muchos bitcoins y / o dinero, sin embargo, no pueden moverse fácilmente.
Entonces, ¿como pueden comprar o vender sus monedas las ballenas? Bueno, ellos hacen su juego: Tratan de hacernos creer que la moneda esta barata cuando nos quieren vender sus monedas o hacernos creer que la moneda es cara cuando quieren comprar nuestras monedas. Esta manipulación se realiza de muchas maneras, la mayoría por noticias.
Nosotros, los pequeños peces, no podemos competir contra las ballenas, pero podemos descubrir qué están haciendo (recuerde, son lentas, mueven sus monstruosas cantidades de dinero) debemos movernos con ellas e imitarlas. Mejor estar bajo la ballena que delante de ella.
Con este indicador puedes ver cuando las ballenas están operando y reaccionar ; porque el enfoque matemático que los sustenta ha demostrado ser bastante exitoso.
Cuando las manos fuertes están por debajo de cero, se dice que están comprando. Lo mismo ocurre con las manos débiles. Generalmente, si las manos fuertes están comprando o vendiendo, el precio está lateralizado. El movimiento del precio está asociado con las compras y ventas realizadas por la mano débil.
Espero que les sea de mucha utilidad.
Bitrader4.0
This indicator no longer consists of mobile lines that intersect to give input or output signals, but it goes further and graphically interprets what is happening with the value.
It is a powerful algorithm, which includes 4 trend indicators and 2 volume indicators.
With this indicator we can move with the "strong hands" of the market, track their intentions and make buying and selling decisions.
Designed to operate in cryptocurrencies.
As for what temporality to use, the bigger the better, since in the end what we are doing is the analysis of data and, therefore, the more data, the better. Personally I recommend using it in candles of 30 minutes, 1 hour and 4 hours.
Remember, no indicator is 100% effective.
This indicator shows us in the areas of color purple (strong hands) and in the areas of color green (weak hands) and by showing it graphically and the indicator is worth it.
The market is driven by two types of investors, which are called strong hands or whales (agencies, funds, companies, banks, etc.) and weak hands or small fish (that is, us).
We do not have the ability to manipulate a value, since our portfolio is limited, but we can enter and exit the securities easily since we do not have much money.
Whales can manipulate a value since they have many bitcoins and / or money, however, they can not move easily.
So, how can whales buy or sell their coins? Well, they make their game: They try to make us believe that the currency is cheap when they want to sell their coins or make us believe that the currency is expensive when they want to buy our coins. This manipulation is done in many ways, most by news.
We, small fish, can not compete against whales, but we can find out what they are doing (remember, they are slow, move their monstrous amounts of money) we must move with them and imitate them. Better to be under the whale than in front of her.
With this indicator you can see when the whales are operating and reacting; because the mathematical approach that sustains them has proven to be quite successful.
When strong hands are below zero, they say they are buying. The same goes for weak hands. Generally, if strong hands are buying or selling, the price is lateralized. The movement of the price is associated with the purchases and sales made by the weak hand.
I hope you find it very useful.
Bitrader4.0
Acc/DistAMA with FRACTAL DEVIATION BANDS by @XeL_ArjonaACCUMULATION/DISTRIBUTION ADAPTIVE MOVING AVERAGE with FRACTAL DEVIATION BANDS
Ver. 2.5 @ 16.09.2015
By Ricardo M Arjona @XeL_Arjona
DISCLAIMER:
The Following indicator/code IS NOT intended to be a formal investment advice or recommendation by the
author, nor should be construed as such. Users will be fully responsible by their use regarding their own trading vehicles/assets.
The embedded code and ideas within this work are FREELY AND PUBLICLY available on the Web for NON LUCRATIVE ACTIVITIES and must remain as is.
Pine Script code MOD's and adaptations by @XeL_Arjona with special mention in regard of:
Buy (Bull) and Sell (Bear) "Power Balance Algorithm" by:
Stocks & Commodities V. 21:10 (68-72): "Bull And Bear Balance Indicator by Vadim Gimelfarb"
Fractal Deviation Bands by @XeL_Arjona.
Color Cloud Fill by @ChrisMoody
CHANGE LOG:
Following a "Fractal Approach" now the lookback window is hardcode correlated with a given timeframe. (Default @ 126 days as Half a Year / 252 bars)
Clean and speed up of Adaptive Moving Average Algo.
Fractal Deviation Band Cloud coloring smoothed.
>
ALL NEW IDEAS OR MODIFICATIONS to these indicator(s) are Welcome in favor to deploy a better and more accurate readings. I will be very glad to be notified at Twitter or TradingVew accounts at: @XeL_Arjona
Any important addition to this work MUST REMAIN PUBLIC by means of CreativeCommons CC & TradingView. Copyright 2015
Volume Pressure Composite Average with Bands by @XeL_ArjonaVOLUME PRESSURE COMPOSITE AVERAGE WITH BANDS
Ver. 1.0.beta.10.08.2015
By Ricardo M Arjona @XeL_Arjona
DISCLAIMER:
The Following indicator/code IS NOT intended to be a formal investment advice or recommendation by the author, nor should be construed as such. Users will be fully responsible by their use regarding their own trading vehicles/assets.
The embedded code and ideas within this work are FREELY AND PUBLICLY available on the Web for NON LUCRATIVE ACTIVITIES and must remain as is.
Pine Script code MOD's and adaptations by @XeL_Arjona with special mention in regard of:
Buy (Bull) and Sell (Bear) "Power Balance Algorithm" by :
Stocks & Commodities V. 21:10 (68-72):
"Bull And Bear Balance Indicator by Vadim Gimelfarb"
Adjusted Exponential Adaptation from original Volume Weighted Moving Average (VEMA) by @XeL_Arjona with help given at the @pinescript chat room with special mention to @RicardoSantos
Color Cloud Fill Condition algorithm by @ChrisMoody
WHAT IS THIS?
The following indicators try to acknowledge in a K-I-S-S approach to the eye (Keep-It-Simple-Stupid), the two most important aspects of nearly every trading vehicle: -- PRICE ACTION IN RELATION BY IT'S VOLUME --
A) My approach is to make this indicator both as a "Trend Follower" as well as a Volatility expressed in the Bands which are the weighting basis of the trend given their "Cross Signal" given by the Buy & Sell Volume Pressures algorithm. >
B) Please experiment with lookback periods against different timeframes. Given the nature of the Volume Mathematical Monster this kind of study is and in concordance with Price Action; at first glance I've noted that both in short as in long term periods, the indicator tends to adapt quite well to general price action conditions. BE ADVICED THIS IS EXPERIMENTAL!
C) ALL NEW IDEAS OR MODIFICATIONS to these indicator(s) are Welcome in favor to deploy a better and more accurate readings. I will be very glad to be notified at Twitter or TradingVew accounts at: @XeL_Arjona
Any important addition to this work MUST REMAIN PUBLIC by means of CreativeCommons CC & TradingView. --- All Authorship Rights RESERVED 2015 ---
Rifle UnifiedThis script is designed for use on 30-second charts of Dow Jones-related symbols (YM, MYM, US30). It provides automated buy and sell signals using a combination of price action, RSI (Relative Strength Index), and volume analysis. The script is intended for both live trading signals and backtesting, with configurable risk management and debugging features.
Core Functionality
1. Signal Generation Logic
Trigger: The algorithm looks for a sharp price move (drop or rise) of a user-defined threshold (default: 80 points) within a specified lookback window (default: 20 minutes).
Levels: It monitors for price drops below specific numerical levels ending in 23, 43, or 73 (e.g., 42223, 42273).
RSI Condition: When price falls below one of these levels and the RSI is below 30, the setup is considered active.
Buy Signal: A buy is triggered if, after setup:
Price rises back above the level,
The RSI rate of change (ROC) indicates exhaustion of the drop,
The current bar shows positive momentum.
2. Trade Management
Stop Loss & Take Profit: Configurable fixed or trailing stop loss and take profit levels are plotted and managed automatically.
Exit Signals: The script signals exit based on price action relative to these risk management levels.
3. Filters & Enhancements
Parabolic Move Filter: Prevents entries during extreme price moves.
Dead Cat Bounce Filter: Avoids false signals after sharp reversals.
Volume Filter: Optionally requires volume conditions for trade entries (especially for shorts).
Multiple Confirmation Layers : Includes checks for 5-minute RSI, momentum, and price retracement.
User Inputs & Customization
Trade Direction: Toggle between LONG and SHORT signal generation.
Trigger Settings: Adjust thresholds for price moves, lookback windows, RSI ROC, and volume requirements.
Trade Settings: Set take profit, stop loss, and trailing stop behavior.
Debug & Visualization: Enable or disable various plots, labels, and debug tables for in-depth analysis.
Backtesting: Integrated backtester with summary and detailed statistics tables.
Technical Features
Uses External Libraries: Relies on RifleShooterLib for core logic and BackTestLib for backtesting and statistics.
Multi-timeframe Analysis: Incorporates both 30-second and 5-minute RSI calculations.
Chart Annotations: Plots entry/exit points, risk levels, and debug information directly on the chart.
Alert Conditions: Built-in alert triggers for key events (initial move, stall, entry).
Intended Use
Markets: Dow Jones symbols (YM, MYM, US30, or US30 CFD).
Timeframe: 30-second chart.
Purpose: Automated signal generation for discretionary or algorithmic trading, with robust risk management and backtesting support.
Notable Customization & Extension Points
Momentum Calculation: Plans to replace the current momentum measure with "sqz momentum".
Displacement Logic: Future update to use "FVG concept" for displacement.
High-Contrast RSI: Optional visual enhancements for RSI extremes.
Time-based Stop: Consideration for adding a time-based stop mechanism.
This script is highly modular, with extensive user controls, and is suitable for both live trading and historical analysis of Dow Jones index movements
Fibonacci Retracement Engine (DFRE) [PhenLabs]📊 Fibonacci Retracement Engine (DFRE)
Version: PineScript™ v6
📌 Description
Dynamic Fibonacci Retracement Engine (DFRE) is a sophisticated technical analysis tool that automatically detects important swing points and draws precise Fibonacci retracement levels on various timeframes. The intelligent indicator eliminates the subjectivity of manual Fibonacci drawing using intelligent swing detection algorithms combined with multi timeframe confluence analysis.
Built for professional traders who demand accuracy and consistency, DFRE provides real time Fibonacci levels that adapt to modifications in market structure without sacrificing accuracy in changing market conditions. The indicator excels at identifying key support and resistance levels where price action is more likely to react, giving traders a potent edge in entry and exit timing.
🚀 Points of Innovation
Intelligent Swing Detection Algorithm : Advanced pivot detection with customizable confirmation bars and minimum swing percentage thresholds
Multi-Timeframe Confluence Engine : Simultaneous analysis across three timeframes to identify high-probability zones
Dynamic Level Management : Automatically updates and manages multiple Fibonacci sets while maintaining chart clarity
Adaptive Visualization System : Smart labeling that shows only the most relevant levels based on user preferences
Real-Time Confluence Detection : Identifies zones where multiple Fibonacci levels from different timeframes converge
Automated Alert System : Comprehensive notifications for level breakouts and confluence zone formations
🔧 Core Components
Swing Point Detection Engine : Uses pivot high/low calculations with strength confirmation to identify significant market turns
Fibonacci Calculator : Automatically computes standard retracement levels (0.236, 0.382, 0.5, 0.618, 0.786, 0.886) plus extensions (1.272, 1.618)
Multi-Timeframe Security Function : Safely retrieves Fibonacci data from higher timeframes without repainting
Confluence Analysis Module : Mathematically identifies zones where multiple levels cluster within specified thresholds
Dynamic Drawing Management : Efficiently handles line and label creation, updates, and deletion to maintain performance
🔥 Key Features
Customizable Swing Detection : Adjust swing length (3-50 bars) and strength confirmation (1-10 bars) to match your trading style
Selective Level Display : Choose which Fibonacci levels to show, from core levels to full extensions
Multi-Timeframe Analysis : Analyze up to 3 different timeframes simultaneously for confluence identification
Intelligent Labeling System : Options to show main levels only or all levels, with latest-set-only functionality
Visual Customization : Adjustable line width, colors, and extension options for optimal chart clarity
Performance Optimization : Limit maximum Fibonacci sets (1-5) to maintain smooth chart performance
Comprehensive Alerting : Get notified on level breakouts and confluence zone formations
🎨 Visualization
Dynamic Fibonacci Lines : Color-coded lines (green for uptrends, red for downtrends) with customizable width and extension
Smart Level Labels : Precise level identification with both ratio and price values displayed
Confluence Zone Highlighting : Visual emphasis on areas where multiple timeframe levels converge
Clean Chart Management : Automatic cleanup of old drawing objects to prevent chart clutter
Responsive Design : All visual elements adapt to different chart sizes and timeframes
📖 Usage Guidelines
Swing Detection Settings
Swing Detection Length - Default: 25 | Range: 3-50 | Controls the lookback period for identifying pivot points. Lower values detect more frequent swings but may include noise, while higher values focus on major market turns.
Swing Strength (Confirmation Bars) - Default: 2 | Range: 1-10 | Number of bars required to confirm a swing point. Higher values reduce false signals but increase lag.
Minimum Swing % Change - Default: 1.0% | Range: 0.1-10.0% | Minimum percentage change required to register a valid swing. Filters out insignificant price movements.
Fibonacci Level Settings
Individual Level Toggles : Enable/disable specific Fibonacci levels (0.236, 0.382, 0.5, 0.618, 0.786, 0.886)
Extensions : Show projection levels (1.272, 1.618) for target identification
Multi-Timeframe Settings
Timeframe Selection : Choose three higher timeframes for confluence analysis
Confluence Threshold : Percentage tolerance for level clustering (0.5-5.0%)
✅ Best Use Cases
Swing Trading : Identify optimal entry and exit points at key retracement levels
Confluence Trading : Focus on high-probability zones where multiple timeframe levels align
Support/Resistance Trading : Use dynamic levels that adapt to changing market structure
Breakout Trading : Monitor level breaks for momentum continuation signals
Target Setting : Utilize extension levels for profit target placement
⚠️ Limitations
Lagging Nature : Requires confirmed swing points, which means levels appear after significant moves
Market Condition Dependency : Works best in trending markets; less effective in extremely choppy conditions
Multiple Signal Complexity : Multiple timeframe analysis may produce conflicting signals requiring experience to interpret
Performance Considerations : Multiple Fibonacci sets and MTF analysis may impact indicator loading time on slower devices
💡 What Makes This Unique
Automated Precision : Eliminates manual drawing errors and subjective level placement
Multi-Timeframe Intelligence : Combines analysis from multiple timeframes for superior confluence detection
Adaptive Management : Automatically updates and manages multiple Fibonacci sets as market structure evolves
Professional-Grade Alerts : Comprehensive notification system for all significant level interactions
🔬 How It Works
Step 1 - Swing Point Identification : Scans price action using pivot high/low calculations with specified lookback periods, applies confirmation logic to eliminate false signals, and calculates swing strength based on surrounding price action for quality assessment.
Step 2 - Fibonacci Level Calculation : Automatically computes retracement and extension levels between confirmed swing points, creates dynamic level sets that update as new swing points are identified, and maintains multiple active Fibonacci sets for comprehensive market analysis.
Step 3 - Multi-Timeframe Confluence : Retrieves Fibonacci data from higher timeframes using secure request functions, analyzes level clustering across different timeframes within specified thresholds, and identifies high-probability zones where multiple levels converge.
💡 Note: This indicator works best when combined with other technical analysis tools and proper risk management. The multi-timeframe confluence feature provides the highest probability setups, but always confirm signals with additional analysis before entering trades.
The Sequences of FibonacciThe Sequences of Fibonacci - Advanced Multi-Timeframe Confluence Analysis System
THEORETICAL FOUNDATION & MATHEMATICAL INNOVATION
The Sequences of Fibonacci represents a revolutionary approach to market analysis that synthesizes classical Fibonacci mathematics with modern adaptive signal processing. This indicator transcends traditional Fibonacci retracement tools by implementing a sophisticated multi-dimensional confluence detection system that reveals hidden market structure through mathematical precision.
Core Mathematical Framework
Dynamic Fibonacci Grid System:
Unlike static Fibonacci tools, this system calculates highest highs and lowest lows across true Fibonacci sequence periods (8, 13, 21, 34, 55 bars) creating a dynamic grid of mathematical support and resistance levels that adapt to market structure in real-time.
Multi-Dimensional Confluence Detection:
The engine employs advanced mathematical clustering algorithms to identify areas where multiple derived Fibonacci retracement levels (0.382, 0.500, 0.618) from different timeframe perspectives converge. These "Confluence Zones" are mathematically classified by strength:
- CRITICAL Zones: 8+ converging Fibonacci levels
- HIGH Zones: 6-7 converging levels
- MEDIUM Zones: 4-5 converging levels
- LOW Zones: 3+ converging levels
Adaptive Signal Processing Architecture:
The system implements adaptive Stochastic RSI calculations with dynamic overbought/oversold levels that adjust to recent market volatility rather than using fixed thresholds. This prevents false signals during changing market conditions.
COMPREHENSIVE FEATURE ARCHITECTURE
Quantum Field Visualization System
Dynamic Price Field Mathematics:
The Quantum Field creates adaptive price channels based on EMA center points and ATR-based amplitude calculations, influenced by the Unified Field metric. This visualization system helps traders understand:
- Expected price volatility ranges
- Potential overextension zones
- Mathematical pressure points in market structure
- Dynamic support/resistance boundaries
Field Amplitude Calculation:
Field Amplitude = ATR × (1 + |Unified Field| / 10)
The system generates three quantum levels:
- Q⁰ Level: 0.618 × Field Amplitude (Primary channel)
- Q¹ Level: 1.0 × Field Amplitude (Secondary boundary)
- Q² Level: 1.618 × Field Amplitude (Extreme extension)
Advanced Market Analysis Dashboard
Unified Field Analysis:
A composite metric combining:
- Price momentum (40% weighting)
- Volume momentum (30% weighting)
- Trend strength (30% weighting)
Market Resonance Calculation:
Measures price-volume correlation over 14 periods to identify harmony between price action and volume participation.
Signal Quality Assessment:
Synthesizes Unified Field, Market Resonance, and RSI positioning to provide real-time evaluation of setup potential.
Tiered Signal Generation Logic
Tier 1 Signals (Highest Conviction):
Require ALL conditions:
- Adaptive StochRSI setup (exiting dynamic OB/OS levels)
- Classic StochRSI divergence confirmation
- Strong reversal bar pattern (adaptive ATR-based sizing)
- Level rejection from Confluence Zone or Fibonacci level
- Supportive Unified Field context
Tier 2 Signals (Enhanced Opportunity Detection):
Generated when Tier 1 conditions aren't met but exceptional circumstances exist:
- Divergence candidate patterns (relaxed divergence requirements)
- Exceptionally strong reversal bars at critical levels
- Enhanced level rejection criteria
- Maintained context filtering
Intelligent Visualization Features
Fractal Matrix Grid:
Multi-layer visualization system displaying:
- Shadow Layer: Foundational support (width 5)
- Glow Layer: Core identification (width 3, white)
- Quantum Layer: Mathematical overlay (width 1, dotted)
Smart Labeling System:
Prevents overlap using ATR-based minimum spacing while providing:
- Fibonacci period identification
- Topological complexity classification (0, I, II, III)
- Exact price levels
- Strength indicators (○ ◐ ● ⚡)
Wick Pressure Analysis:
Dynamic visualization showing momentum direction through:
- Multi-beam projection lines
- Particle density effects
- Progressive transparency for natural flow
- Strength-based sizing adaptation
PRACTICAL TRADING IMPLEMENTATION
Signal Interpretation Framework
Entry Protocol:
1. Confluence Zone Approach: Monitor price approaching High/Critical confluence zones
2. Adaptive Setup Confirmation: Wait for StochRSI to exit adaptive OB/OS levels
3. Divergence Verification: Confirm classic or candidate divergence patterns
4. Reversal Bar Assessment: Validate strong rejection using adaptive ATR criteria
5. Context Evaluation: Ensure Unified Field provides supportive environment
Risk Management Integration:
- Stop Placement: Beyond rejected confluence zone or Fibonacci level
- Position Sizing: Based on signal tier and confluence strength
- Profit Targets: Next significant confluence zone or quantum field boundary
Adaptive Parameter System
Dynamic StochRSI Levels:
Unlike fixed 80/20 levels, the system calculates adaptive OB/OS based on recent StochRSI range:
- Adaptive OB: Recent minimum + (range × OB percentile)
- Adaptive OS: Recent minimum + (range × OS percentile)
- Lookback Period: Configurable 20-100 bars for range calculation
Intelligent ATR Adaptation:
Bar size requirements adjust to market volatility:
- High Volatility: Reduced multiplier (bars naturally larger)
- Low Volatility: Increased multiplier (ensuring significance)
- Base Multiplier: 0.6× ATR with adaptive scaling
Optimization Guidelines
Timeframe-Specific Settings:
Scalping (1-5 minutes):
- Fibonacci Rejection Sensitivity: 0.3-0.8
- Confluence Threshold: 2-3 levels
- StochRSI Lookback: 20-30 bars
Day Trading (15min-1H):
- Fibonacci Rejection Sensitivity: 0.5-1.2
- Confluence Threshold: 3-4 levels
- StochRSI Lookback: 40-60 bars
Swing Trading (4H-1D):
- Fibonacci Rejection Sensitivity: 1.0-2.0
- Confluence Threshold: 4-5 levels
- StochRSI Lookback: 60-80 bars
Asset-Specific Optimization:
Cryptocurrency:
- Higher rejection sensitivity (1.0-2.5) for volatile conditions
- Enable Tier 2 signals for increased opportunity detection
- Shorter adaptive lookbacks for rapid market changes
Forex Major Pairs:
- Moderate sensitivity (0.8-1.5) for stable trending
- Focus on Higher/Critical confluence zones
- Longer lookbacks for institutional flow detection
Stock Indices:
- Conservative sensitivity (0.5-1.0) for institutional participation
- Standard confluence thresholds
- Balanced adaptive parameters
IMPORTANT USAGE CONSIDERATIONS
Realistic Performance Expectations
This indicator provides probabilistic advantages based on mathematical confluence analysis, not guaranteed outcomes. Signal quality varies with market conditions, and proper risk management remains essential regardless of signal tier.
Understanding Adaptive Features:
- Adaptive parameters react to historical data, not future market conditions
- Dynamic levels adjust to past volatility patterns
- Signal quality reflects mathematical alignment probability, not certainty
Market Context Awareness:
- Strong trending markets may produce fewer reversal signals
- Range-bound conditions typically generate more confluence opportunities
- News events and fundamental factors can override technical analysis
Educational Value
Mathematical Concepts Introduced:
- Multi-dimensional confluence analysis
- Adaptive signal processing techniques
- Dynamic parameter optimization
- Mathematical field theory applications in trading
- Advanced Fibonacci sequence applications
Skill Development Benefits:
- Understanding market structure through mathematical lens
- Recognition of multi-timeframe confluence principles
- Appreciation for adaptive vs. static analysis methods
- Integration of classical Fibonacci with modern signal processing
UNIQUE INNOVATIONS
First-Ever Implementations
1. True Fibonacci Sequence Periods: First indicator using authentic Fibonacci numbers (8,13,21,34,55) for timeframe analysis
2. Mathematical Confluence Clustering: Advanced algorithm identifying true Fibonacci level convergence
3. Adaptive StochRSI Boundaries: Dynamic OB/OS levels replacing fixed thresholds
4. Tiered Signal Architecture: Democratic signal weighting with quality classification
5. Quantum Field Price Visualization: Mathematical field representation of price dynamics
Visualization Breakthroughs
- Multi-Layer Fibonacci Grid: Three-layer rendering with intelligent spacing
- Dynamic Confluence Zones: Strength-based color coding and sizing
- Adaptive Parameter Display: Real-time visualization of dynamic calculations
- Mathematical Field Effects: Quantum-inspired price channel visualization
- Progressive Transparency Systems: Natural visual flow without chart clutter
COMPREHENSIVE DASHBOARD SYSTEM
Multi-Size Display Options
Small Dashboard: Core metrics for mobile/limited screen space
Normal Dashboard: Balanced information density for standard desktop use
Large Dashboard: Complete analysis suite including adaptive parameter values
Real-Time Metrics Tracking
Market Analysis Section:
- Unified Field strength with visual meter
- Market Resonance percentage
- Signal Quality assessment with emoji indicators
- Market Bias classification (Bullish/Bearish/Neutral)
Confluence Intelligence:
- Total active zones count
- High/Critical zone identification
- Nearest zone distance and strength
- Price-to-zone ATR measurement
Adaptive Parameters (Large Dashboard):
- Current StochRSI OB/OS levels
- Active ATR multiplier for bar sizing
- Volatility ratio for adaptive scaling
- Real-time StochRSI positioning
TECHNICAL SPECIFICATIONS
Pine Script Version: v5 (Latest)
Calculation Method: Real-time with confirmed bar processing
Maximum Objects: 500 boxes, 500 lines, 500 labels
Dashboard Positions: 4 corner options with size selection
Visual Themes: Quantum, Holographic, Crystalline, Plasma
Alert Integration: Complete alert system for all signal types
Performance Optimizations:
- Efficient confluence zone calculation using advanced clustering
- Smart label spacing prevents overlap
- Progressive transparency for visual clarity
- Memory-optimized array management
EDUCATIONAL FRAMEWORK
Learning Progression
Beginner Level:
- Understanding Fibonacci sequence applications
- Recognition of confluence zone concepts
- Basic signal interpretation
- Dashboard metric comprehension
Intermediate Level:
- Adaptive parameter optimization
- Multi-timeframe confluence analysis
- Signal quality assessment techniques
- Risk management integration
Advanced Level:
- Mathematical field theory applications
- Custom parameter optimization strategies
- Market regime adaptation techniques
- Professional trading system integration
DEVELOPMENT ACKNOWLEDGMENT
Special acknowledgment to @AlgoTrader90 - the foundational concepts of this system came from him and we developed it through a collaborative discussions about multi-timeframe Fibonacci analysis. While the original framework came from AlgoTrader90's innovative approach, this implementation represents a complete evolution of the logic with enhanced mathematical precision, adaptive parameters, and sophisticated signal filtering to deliver meaningful, actionable trading signals.
CONCLUSION
The Sequences of Fibonacci represents a quantum leap in technical analysis, successfully merging classical Fibonacci mathematics with cutting-edge adaptive signal processing. Through sophisticated confluence detection, intelligent parameter adaptation, and comprehensive market analysis, this system provides traders with unprecedented insight into market structure and potential reversal points.
The mathematical foundation ensures lasting relevance while the adaptive features maintain effectiveness across changing market conditions. From the dynamic Fibonacci grid to the quantum field visualization, every component reflects a commitment to mathematical precision, visual elegance, and practical utility.
Whether you're a beginner seeking to understand market confluence or an advanced trader requiring sophisticated analytical tools, this system provides the mathematical framework for informed decision-making based on time-tested Fibonacci principles enhanced with modern computational techniques.
Trade with mathematical precision. Trade with the power of confluence. Trade with The Sequences of Fibonacci.
"Mathematics is the language with which God has written the universe. In markets, Fibonacci sequences reveal the hidden harmonies that govern price movement, and those who understand these mathematical relationships hold the key to anticipating market behavior."
* Galileo Galilei (adapted for modern markets)
— Dskyz, Trade with insight. Trade with anticipation.
[blackcat] L2 Multi-Level Price Condition TrackerOVERVIEW
The L2 Multi-Level Price Condition Tracker represents an innovative approach to analyzing financial markets by simultaneously monitoring multiple price levels, thus providing traders with a holistic view of market dynamics. By combining dynamic calculations based on moving averages and price deviations, this tool aims to deliver precise and actionable insights into potential entry and exit points. It leverages sophisticated statistical measures to identify key thresholds that signify shifts in market sentiment, thereby aiding traders in making well-informed decisions. 🎯
Key benefits encompass:
• Comprehensive calculation of midpoints and average prices indicating short-term trend directions.
• Interactive visualization elements enhancing interpretability effortlessly.
• Real-time generation of buy/sell signals driven by precise condition evaluations.
TECHNICAL ANALYSIS COMPONENTS
📉 Midpoint Calculations:
Computes central reference points derived from high-low ranges establishing baseline supports/resistances.
Utilizes Simple Moving Averages (SMAs) along with standardized deviation formulas smoothing out volatility while preserving long-term trends accurately.
Facilitates identification of directional biases reflecting underlying market forces dynamically.
🕵️♂️ Advanced Price Level Detection:
Derives upper/lower bounds adjusting sensitivities adaptively responding to changing conditions flexibly.
Employs proprietary logic distinguishing between bullish/bearish sentiments promptly signaling transitions effectively.
Ensures consistent adherence to predefined statistical protocols maintaining accuracy robustly.
🎥 Dynamic Signal Generation:
Detects crossovers indicating dominance shifts between buyers/sellers promptly triggering timely alerts.
Integrates conditional logic reinforcing signal validity minimizing erroneous activations systematically.
Supports adaptive thresholds tuning sensitivities based on evolving market conditions flexibly accommodating varying scenarios.
INDICATOR FUNCTIONALITY
🔢 Core Algorithms:
Utilizes moving averages alongside standardized deviation formulas generating precise net volume measurements.
Implements Arithmetic Mean Line Algorithm (AMLA) smoothing techniques improving interpretability.
Ensures consistent alignment with established statistical principles preserving fidelity.
🖱️ User Interface Elements:
Dedicated plots displaying real-time midpoint markers facilitating swift decision-making.
Context-sensitive color coding distinguishing positive/negative deviations intuitively highlighting key activations clearly.
Background shading emphasizing proximity to crucial threshold activations enhancing visibility focusing attention on vital signals promptly.
STRATEGY IMPLEMENTATION
✅ Entry Conditions:
Confirm bullish/bearish setups validated through multiple confirmatory signals assessing concurrent market sentiment factors.
Validate entry decisions considering alignment between calculated midpoints and broader trend directions ensuring coherence.
Monitor cumulative breaches signifying potential trend reversals executing partial/total closes contingent upon predetermined loss limits preserving capital efficiently.
🚫 Exit Mechanisms:
Trigger exits upon hitting predefined thresholds derived from historical analyses promptly executing closures.
Execute partial/total closes contingent upon cumulative loss limits preserving capital efficiently managing exposures prudently.
Conduct periodic reviews gauging strategy effectiveness rigorously identifying areas needing refinement implementing corrective actions iteratively enhancing performance metrics steadily.
PARAMETER CONFIGURATIONS
🎯 Optimization Guidelines:
Lookback Period: Governs responsiveness versus stability balancing sensitivity/stability governing moving averages aligning with preferred granularity.
Price Source: Dictates primary data series driving volume calculations selecting relevant inputs accurately tailoring strategies accordingly.
💬 Customization Recommendations:
Commence with baseline defaults; iteratively refine parameters isolating individual impacts evaluating adjustments independently prior to combined modifications minimizing disruptions.
Prioritize minimizing erroneous trigger occurrences first optimizing signal fidelity sustaining balanced risk-reward profiles irrespective of chosen settings upholding disciplined approaches preserving capital efficiently.
ADVANCED RISK MANAGEMENT
🛡️ Proactive Risk Mitigation Techniques:
Enforce strict compliance with pre-defined maximum leverage constraints adhering strictly to guidelines managing exposures prudently.
Mandatorily apply trailing stop-loss orders conforming to script outputs enforcing discipline rigorously preventing adverse consequences.
Allocate positions proportionately relative to available capital reserves conducting periodic reviews gauging effectiveness continuously identifying improvement opportunities steadily.
⚠️ Potential Pitfalls & Solutions:
Address frequent violations arising during heightened volatility phases necessitating manual interventions judiciously preparing contingency plans proactively mitigating risks effectively.
Manage false alerts warranting immediate attention avoiding adverse consequences systematically implementing corrective actions reliably.
Prepare proactive responses amid adverse movements ensuring seamless functionality amidst fluctuating conditions fortifying resilience against anomalies robustly.
PERFORMANCE MONITORING METRICS
🔍 Evaluation Criteria:
Assess win percentages consistently across diverse trading instruments gauging reliability measuring profitability efficiency accurately evaluating downside risks comprehensively uncovering systematic biases potentially skewing outcomes.
Calculate average profit ratios per successful execution benchmarking actual vs expected performances documenting results meticulously tracking progress dynamically addressing identified shortcomings proactively fostering continuous improvements.
📈 Historical Data Analysis Tools:
Maintain detailed logs capturing every triggered event recording realized profits/losses comparing simulated projections accurately identifying discrepancies warranting investigation implementing iterative refinements steadily enhancing performance metrics progressively.
Identify recurrent systematic errors demanding corrective actions implementing iterative refinements steadily addressing identified shortcomings proactively fostering continuous enhancements dynamically improving robustness resiliently.
PROBLEM SOLVING ADVICE
🔧 Frequent Encountered Challenges:
Unpredictable behaviors emerging within thinly traded markets requiring filtration processes enhancing signal integrity excluding low-liquidity assets prone to erratic movements effectively.
Latency issues manifesting during abrupt price fluctuations causing missed opportunities introducing buffer intervals safeguarding major news/event impacts mitigating distortions seamlessly verifying reliable connections ensuring uninterrupted data flows guaranteeing accurate interpretations dependably.
💡 Effective Resolution Pathways:
Limit ongoing optimization attempts preventing model degradation maintaining optimal performance levels consistently recalibrating parameters periodically adapting strategies flexibly responding appropriately amidst varying conditions dynamically improving robustness resiliently.
Verify reliable connections ensuring uninterrupted data flows guaranteeing accurate interpretations dependably bolstering overall efficacy systematically addressing identified shortcomings dynamically fostering continuous advancements.
THANKS
Heartfelt acknowledgment extends to all developers contributing invaluable insights regarding multi-level price condition-based trading methodologies! ✨
[blackcat] L2 Z-Score of PriceOVERVIEW
The L2 Z-Score of Price indicator offers traders an insightful perspective into how current prices diverge from their historical norms through advanced statistical measures. By leveraging Z-scores, it provides a robust framework for identifying potential reversals in financial markets. The Z-score quantifies the number of standard deviations that a data point lies away from the mean, thus serving as a critical metric for recognizing overbought or oversold conditions. 🎯
Key benefits encompass:
• Precise calculation of Z-scores reflecting true price deviations.
• Interactive plotting features enhancing visual clarity.
• Real-time generation of buy/sell signals based on crossover events.
STATISTICAL ANALYSIS COMPONENTS
📉 Mean Calculation:
Utilizes Simple Moving Averages (SMAs) to establish baseline price references.
Provides smooth representations filtering short-term noise preserving long-term trends.
Fundamental for deriving subsequent deviation metrics accurately.
📈 Standard Deviation Measurement:
Quantifies dispersion around established means revealing underlying variability.
Crucial for assessing potential volatility levels dynamically adapting strategies accordingly.
Facilitates precise Z-score derivations ensuring statistical rigor.
🕵️♂️ Z-SCORE DETECTION:
Measures standardized distances indicating relative positions within distributions.
Helps pinpoint extreme conditions signaling impending reversals proactively.
Enables early identification of trend exhaustion phases prompting timely actions.
INDICATOR FUNCTIONALITY
🔢 Core Algorithms:
Integrates SMAs along with standardized deviation formulas generating precise Z-scores.
Employs Arithmetic Mean Line Algorithm (AMLA) smoothing techniques improving interpretability.
Ensures consistent adherence to predefined statistical protocols maintaining accuracy.
🖱️ User Interface Elements:
Dedicated plots displaying real-time Z-score markers facilitating swift decision-making.
Context-sensitive color coding distinguishing positive/negative deviations intuitively.
Background shading highlighting proximity to key threshold activations enhancing visibility.
STRATEGY IMPLEMENTATION
✅ Entry Conditions:
Confirm bullish/bearish setups validated through multiple confirmatory signals.
Validate entry decisions considering concurrent market sentiment factors.
Assess alignment between Z-score readings and broader trend directions ensuring coherence.
🚫 Exit Mechanisms:
Trigger exits upon hitting predetermined thresholds derived from historical analyses.
Monitor continuous breaches signifying potential trend reversals promptly executing closures.
Execute partial/total closes contingent upon cumulative loss limits preserving capital efficiently.
PARAMETER CONFIGURATIONS
🎯 Optimization Guidelines:
Length: Governs responsiveness versus smoothing trade-offs balancing sensitivity/stability.
Price Source: Dictates primary data series driving Z-score computations selecting relevant inputs accurately.
💬 Customization Recommendations:
Commence with baseline defaults; iteratively refine parameters isolating individual impacts.
Evaluate adjustments independently prior to combined modifications minimizing disruptions.
Prioritize minimizing erroneous trigger occurrences first optimizing signal fidelity.
Sustain balanced risk-reward profiles irrespective of chosen settings upholding disciplined approaches.
ADVANCED RISK MANAGEMENT
🛡️ Proactive Risk Mitigation Techniques:
Enforce strict compliance with pre-defined maximum leverage constraints adhering strictly to guidelines.
Mandatorily apply trailing stop-loss orders conforming to script outputs reinforcing discipline.
Allocate positions proportionately relative to available capital reserves managing exposures prudently.
Conduct periodic reviews gauging strategy effectiveness rigorously identifying areas needing refinement.
⚠️ Potential Pitfalls & Solutions:
Address frequent violations arising during heightened volatility phases necessitating manual interventions judiciously.
Manage false alerts warranting immediate attention avoiding adverse consequences systematically.
Prepare contingency plans mitigating margin call possibilities preparing proactive responses effectively.
Continuously assess automated system reliability amidst fluctuating conditions ensuring seamless functionality.
PERFORMANCE AUDITS & REFINEMENTS
🔍 Critical Evaluation Metrics:
Assess win percentages consistently across diverse trading instruments gauging reliability.
Calculate average profit ratios per successful execution measuring profitability efficiency accurately.
Measure peak drawdown durations alongside associated magnitudes evaluating downside risks comprehensively.
Analyze signal generation frequencies revealing hidden patterns potentially skewing outcomes uncovering systematic biases.
📈 Historical Data Analysis Tools:
Maintain comprehensive records capturing every triggered event meticulously documenting results.
Compare realized profits/losses against backtested simulations benchmarking actual vs expected performances accurately.
Identify recurrent systematic errors demanding corrective actions implementing iterative refinements steadily.
Document evolving performance metrics tracking progress dynamically addressing identified shortcomings proactively.
PROBLEM SOLVING ADVICE
🔧 Frequent Encountered Challenges:
Unpredictable behaviors emerging within thinly traded markets requiring filtration processes.
Latency issues manifesting during abrupt price fluctuations causing missed opportunities.
Overfitted models yielding suboptimal results post-extensive tuning demanding recalibrations.
Inaccuracies stemming from incomplete/inaccurate data feeds necessitating verification procedures.
💡 Effective Resolution Pathways:
Exclude low-liquidity assets prone to erratic movements enhancing signal integrity.
Introduce buffer intervals safeguarding major news/event impacts mitigating distortions effectively.
Limit ongoing optimization attempts preventing model degradation maintaining optimal performance levels consistently.
Verify reliable connections ensuring uninterrupted data flows guaranteeing accurate interpretations reliably.
USER ENGAGEMENT SEGMENT
🤝 Community Contributions Welcome
Highly encourage active participation sharing experiences & recommendations!
Liquidity Sweep DetectorThe Liquidity Sweep Detector represents a technical analysis tool specifically designed to identify market microstructure patterns typically associated with institutional trading activity. According to Harris (2003), institutional traders frequently employ tactics where they momentarily break through price levels to trigger stop orders before redirecting the market in the opposite direction. This phenomenon, commonly referred to as "stop hunting" or "liquidity sweeping," constitutes a significant aspect of institutional order flow analysis (Osler, 2003). The current implementation provides retail traders with a means to identify these patterns, potentially aligning their trading decisions with institutional movements rather than becoming victims of such strategies.
Osler's (2003) research documents how stop-loss orders tend to cluster around significant price levels, creating concentrations of liquidity. Taylor (2005) argues that sophisticated institutional participants systematically exploit these liquidity clusters by inducing price movements that trigger these orders, subsequently profiting from the ensuing price reaction. The algorithmic detection of such patterns involves several key processes. First, the indicator identifies swing points—local maxima and minima—through comparison with historical price data within a definable lookback period. These swing points correspond to what Bulkowski (2011) describes as "significant pivot points" that frequently serve as liquidity zones where stop orders accumulate.
The core detection algorithm utilizes a multi-stage process to identify potential sweeps. For high sweeps, it monitors when price exceeds a previous swing high by a specified threshold percentage, followed by a bearish candle that closes below the original swing high level. Conversely, for low sweeps, it detects when price drops below a previous swing low by the threshold percentage, followed by a bullish candle closing above the original swing low. As noted by Lo and MacKinlay (2011), these price patterns often emerge when large institutional players attempt to capture liquidity before initiating significant directional moves.
The indicator maintains historical arrays of detected sweep events with their corresponding timestamps, enabling temporal analysis of market behavior following such events. Visual elements include horizontal lines marking sweep levels, background color highlighting for sweep events, and an information table displaying active sweeps with their corresponding price levels and elapsed time since detection. This visualization approach allows traders to quickly identify potential institutional activity without requiring complex interpretation of raw price data.
Parameter customization includes adjustable lookback periods for swing point identification, sweep threshold percentages for signal sensitivity, and display duration settings. These parameters allow traders to adapt the indicator to various market conditions and timeframes, as markets demonstrate different liquidity characteristics across instruments and periods (Madhavan, 2000).
Empirical studies by Easley et al. (2012) suggest that retail traders who successfully identify and act upon institutional liquidity sweeps may achieve superior risk-adjusted returns compared to conventional technical analysis approaches. However, as cautioned by Chordia et al. (2008), such patterns should be considered within broader market context rather than in isolation, as their predictive value varies significantly with overall market volatility and liquidity conditions.
References:
Bulkowski, T. (2011). Encyclopedia of Chart Patterns (2nd ed.). John Wiley & Sons.
Chordia, T., Roll, R., & Subrahmanyam, A. (2008). Liquidity and market efficiency. Journal of Financial Economics, 87(2), 249-268.
Easley, D., López de Prado, M., & O'Hara, M. (2012). Flow Toxicity and Liquidity in a High-frequency World. The Review of Financial Studies, 25(5), 1457-1493.
Harris, L. (2003). Trading and Exchanges: Market Microstructure for Practitioners. Oxford University Press.
Lo, A. W., & MacKinlay, A. C. (2011). A Non-Random Walk Down Wall Street. Princeton University Press.
Madhavan, A. (2000). Market microstructure: A survey. Journal of Financial Markets, 3(3), 205-258.
Osler, C. L. (2003). Currency Orders and Exchange Rate Dynamics: An Explanation for the Predictive Success of Technical Analysis. Journal of Finance, 58(5), 1791-1820.
Taylor, M. P. (2005). Official Foreign Exchange Intervention as a Coordinating Signal in the Dollar-Yen Market. Pacific Economic Review, 10(1), 73-82.
[blackcat] L2 Trend Guard OscillatorOVERVIEW
📊 The L2 Trend Guard Oscillator is a comprehensive technical analysis framework designed specifically to identify market trend reversals using adaptive filtering algorithms that combine price action dynamics with statistical measures of volatility and momentum.
Key Purpose:
Generate reliable early warning signals before major trend changes occur
Provide clear directional bias indicators aligned with institutional investor behavior patterns
Offer risk-managed entry/exit opportunities suitable for various timeframes
TECHNICAL FOUNDATION EXPLAINED
🎓 Core Mechanism Breakdown:
→ Advanced smoothing technique emphasizing recent data points more heavily than older ones
↓ Reduces lag while maintaining signal integrity compared to traditional MA approaches
• Short-term Momentum Assessment:
🔶 Relative strength between closing prices vs lower bounds
• Long-term Directional Bias Analysis:
📈 Extended timeframe comparison generating structural context
• Defense Level Generation:
➜ Protective boundary calculation incorporating EMAs for stability enhancement
PARAMETER CONFIGURATION GUIDE
🔧 Adjustable Settings Explained In Detail:
Timeframe Selection:**
↔ Controls lookback period sensitivity affecting responsiveness
↕ Adjusts reaction speed vs accuracy trade-off dynamically
Weight Factor Specification:**
⚡ Influences emphasis on newer versus historical observations
🎯 Defines key decision-making thresholds clearly
ALGORITHM EXECUTION FLOW
💻 Processing Sequence Overview:
:
→ Gather raw pricing inputs across required periods
↓ Normalize values preparing them for subsequent processing stages
:
✔ Calculate relative strength positions against established ranges
❌ Filter outliers maintaining signal integrity consistently
⟶ Apply dual-pass filtering reducing false signals effectively
➡ Generate actionable trading opportunities systematically
VISUALIZATION ARCHITECTURE
🎨 Display Elements Designated Purpose:
🔵 Primary Indicator Traces:
→ Aqua Trace: Buy/Sell Signal Progression
↑ Red Line: Opposing Force Boundary
🟥 Gray Dashed: Zero Reference Point
🏷️ Label System For Critical Events:
✅ BUY: Bullish Opportunity Markers
❌ SELL: Bearish Setup Validations
STRATEGIC IMPLEMENTATION FRAMEWORK
📋 Practical Deployment Steps:
Initial Integration Protocol:
• Select appropriate timeframe matching strategy objectives
• Configure input parameters aligning with target asset behavior traits
• Conduct thorough backtesting under simulated environments initially
Active Monitoring Procedures:
→ Regular observation of labeled event placements versus actual movements
↓ Track confirmation patterns leading up to signaled opportunities carefully
↑ Evaluate overall framework reliability across different regime types regularly
Execution Guidelines Formulation:
✔ Enter positions only after achieving minimum number of confirming inputs
❌ Avoid isolated occurrences lacking adequate supporting evidence always
➞ Look for convergent factors strengthening conviction before acting decisively
PERFORMANCE OPTIMIZATION TECHNIQUES
🚀 Continuous Improvement Strategies:
Parameter Calibration Approach:
✓ Start testing default suggested configurations thoroughly
↕ Gradually adjust individual components observing outcome changes methodically
✨ Document findings building personalized version profile incrementally
Context Adaptability Methods:
🔄 Add supplementary indicators enhancing overall reliability when needed
🔧 Remove unnecessary complexity layers avoiding confusion/distracted decisions
💫 Incorporate custom rules adapting specific security behaviors effectively
Efficiency Improvement Tactics:
⚙️ Streamline redundant computational routines wherever possible efficiently
♻️ Leverage shared data streams minimizing resource utilization significantly
⏳ Optimize refresh frequencies balancing update speed vs overhead properly
RSI Full Forecast [Titans_Invest]RSI Full Forecast
Get ready to experience the ultimate evolution of RSI-based indicators – the RSI Full Forecast, a boosted and even smarter version of the already powerful: RSI Forecast
Now featuring over 40 additional entry conditions (forecasts), this indicator redefines the way you view the market.
AI-Powered RSI Forecasting:
Using advanced linear regression with the least squares method – a solid foundation for machine learning - the RSI Full Forecast enables you to predict future RSI behavior with impressive accuracy.
But that’s not all: this new version also lets you monitor future crossovers between the RSI and the MA RSI, delivering early and strategic signals that go far beyond traditional analysis.
You’ll be able to monitor future crossovers up to 20 bars ahead, giving you an even broader and more precise view of market movements.
See the Future, Now:
• Track upcoming RSI & RSI MA crossovers in advance.
• Identify potential reversal zones before price reacts.
• Uncover statistical behavior patterns that would normally go unnoticed.
40+ Intelligent Conditions:
The new layer of conditions is designed to detect multiple high-probability scenarios based on historical patterns and predictive modeling. Each additional forecast is a window into the price's future, powered by robust mathematics and advanced algorithmic logic.
Full Customization:
All parameters can be tailored to fit your strategy – from smoothing periods to prediction sensitivity. You have complete control to turn raw data into smart decisions.
Innovative, Accurate, Unique:
This isn’t just an upgrade. It’s a quantum leap in technical analysis.
RSI Full Forecast is the first of its kind: an indicator that blends statistical analysis, machine learning, and visual design to create a true real-time predictive system.
⯁ SCIENTIFIC BASIS LINEAR REGRESSION
Linear Regression is a fundamental method of statistics and machine learning, used to model the relationship between a dependent variable y and one or more independent variables 𝑥.
The general formula for a simple linear regression is given by:
y = β₀ + β₁x + ε
β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²)
β₀ = ȳ - β₁x̄
Where:
y = is the predicted variable (e.g. future value of RSI)
x = is the explanatory variable (e.g. time or bar index)
β0 = is the intercept (value of 𝑦 when 𝑥 = 0)
𝛽1 = is the slope of the line (rate of change)
ε = is the random error term
The goal is to estimate the coefficients 𝛽0 and 𝛽1 so as to minimize the sum of the squared errors — the so-called Random Error Method Least Squares.
⯁ LEAST SQUARES ESTIMATION
To minimize the error between predicted and observed values, we use the following formulas:
β₁ = /
β₀ = ȳ - β₁x̄
Where:
∑ = sum
x̄ = mean of x
ȳ = mean of y
x_i, y_i = individual values of the variables.
Where:
x_i and y_i are the means of the independent and dependent variables, respectively.
i ranges from 1 to n, the number of observations.
These equations guarantee the best linear unbiased estimator, according to the Gauss-Markov theorem, assuming homoscedasticity and linearity.
⯁ LINEAR REGRESSION IN MACHINE LEARNING
Linear regression is one of the cornerstones of supervised learning. Its simplicity and ability to generate accurate quantitative predictions make it essential in AI systems, predictive algorithms, time series analysis, and automated trading strategies.
By applying this model to the RSI, you are literally putting artificial intelligence at the heart of a classic indicator, bringing a new dimension to technical analysis.
⯁ VISUAL INTERPRETATION
Imagine an RSI time series like this:
Time →
RSI →
The regression line will smooth these values and extend them n periods into the future, creating a predicted trajectory based on the historical moment. This line becomes the predicted RSI, which can be crossed with the actual RSI to generate more intelligent signals.
⯁ SUMMARY OF SCIENTIFIC CONCEPTS USED
Linear Regression Models the relationship between variables using a straight line.
Least Squares Minimizes the sum of squared errors between prediction and reality.
Time Series Forecasting Estimates future values based on historical data.
Supervised Learning Trains models to predict outputs from known inputs.
Statistical Smoothing Reduces noise and reveals underlying trends.
⯁ WHY THIS INDICATOR IS REVOLUTIONARY
Scientifically-based: Based on statistical theory and mathematical inference.
Unprecedented: First public RSI with least squares predictive modeling.
Intelligent: Built with machine learning logic.
Practical: Generates forward-thinking signals.
Customizable: Flexible for any trading strategy.
⯁ CONCLUSION
By combining RSI with linear regression, this indicator allows a trader to predict market momentum, not just follow it.
RSI Full Forecast is not just an indicator — it is a scientific breakthrough in technical analysis technology.
⯁ Example of simple linear regression, which has one independent variable:
⯁ In linear regression, observations ( red ) are considered to be the result of random deviations ( green ) from an underlying relationship ( blue ) between a dependent variable ( y ) and an independent variable ( x ).
⯁ Visualizing heteroscedasticity in a scatterplot against 100 random fitted values using Matlab:
⯁ The data sets in the Anscombe's quartet are designed to have approximately the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but are graphically very different. This illustrates the pitfalls of relying solely on a fitted model to understand the relationship between variables.
⯁ The result of fitting a set of data points with a quadratic function:
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🔮 Linear Regression: PineScript Technical Parameters 🔮
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Forecast Types:
• Flat: Assumes prices will remain the same.
• Linreg: Makes a 'Linear Regression' forecast for n periods.
Technical Information:
ta.linreg (built-in function)
Linear regression curve. A line that best fits the specified prices over a user-defined time period. It is calculated using the least squares method. The result of this function is calculated using the formula: linreg = intercept + slope * (length - 1 - offset), where intercept and slope are the values calculated using the least squares method on the source series.
Syntax:
• Function: ta.linreg()
Parameters:
• source: Source price series.
• length: Number of bars (period).
• offset: Offset.
• return: Linear regression curve.
This function has been cleverly applied to the RSI, making it capable of projecting future values based on past statistical trends.
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⯁ WHAT IS THE RSI❓
The Relative Strength Index (RSI) is a technical analysis indicator developed by J. Welles Wilder. It measures the magnitude of recent price movements to evaluate overbought or oversold conditions in a market. The RSI is an oscillator that ranges from 0 to 100 and is commonly used to identify potential reversal points, as well as the strength of a trend.
⯁ HOW TO USE THE RSI❓
The RSI is calculated based on average gains and losses over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and includes three main zones:
• Overbought: When the RSI is above 70, indicating that the asset may be overbought.
• Oversold: When the RSI is below 30, indicating that the asset may be oversold.
• Neutral Zone: Between 30 and 70, where there is no clear signal of overbought or oversold conditions.
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⯁ ENTRY CONDITIONS
The conditions below are fully flexible and allow for complete customization of the signal.
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🔹 CONDITIONS TO BUY 📈
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• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or OR .
📈 RSI Conditions:
🔹 RSI > Upper
🔹 RSI < Upper
🔹 RSI > Lower
🔹 RSI < Lower
🔹 RSI > Middle
🔹 RSI < Middle
🔹 RSI > MA
🔹 RSI < MA
📈 MA Conditions:
🔹 MA > Upper
🔹 MA < Upper
🔹 MA > Lower
🔹 MA < Lower
📈 Crossovers:
🔹 RSI (Crossover) Upper
🔹 RSI (Crossunder) Upper
🔹 RSI (Crossover) Lower
🔹 RSI (Crossunder) Lower
🔹 RSI (Crossover) Middle
🔹 RSI (Crossunder) Middle
🔹 RSI (Crossover) MA
🔹 RSI (Crossunder) MA
🔹 MA (Crossover) Upper
🔹 MA (Crossunder) Upper
🔹 MA (Crossover) Lower
🔹 MA (Crossunder) Lower
📈 RSI Divergences:
🔹 RSI Divergence Bull
🔹 RSI Divergence Bear
📈 RSI Forecast:
🔹 RSI (Crossover) MA Forecast
🔹 RSI (Crossunder) MA Forecast
🔹 RSI Forecast 1 > MA Forecast 1
🔹 RSI Forecast 1 < MA Forecast 1
🔹 RSI Forecast 2 > MA Forecast 2
🔹 RSI Forecast 2 < MA Forecast 2
🔹 RSI Forecast 3 > MA Forecast 3
🔹 RSI Forecast 3 < MA Forecast 3
🔹 RSI Forecast 4 > MA Forecast 4
🔹 RSI Forecast 4 < MA Forecast 4
🔹 RSI Forecast 5 > MA Forecast 5
🔹 RSI Forecast 5 < MA Forecast 5
🔹 RSI Forecast 6 > MA Forecast 6
🔹 RSI Forecast 6 < MA Forecast 6
🔹 RSI Forecast 7 > MA Forecast 7
🔹 RSI Forecast 7 < MA Forecast 7
🔹 RSI Forecast 8 > MA Forecast 8
🔹 RSI Forecast 8 < MA Forecast 8
🔹 RSI Forecast 9 > MA Forecast 9
🔹 RSI Forecast 9 < MA Forecast 9
🔹 RSI Forecast 10 > MA Forecast 10
🔹 RSI Forecast 10 < MA Forecast 10
🔹 RSI Forecast 11 > MA Forecast 11
🔹 RSI Forecast 11 < MA Forecast 11
🔹 RSI Forecast 12 > MA Forecast 12
🔹 RSI Forecast 12 < MA Forecast 12
🔹 RSI Forecast 13 > MA Forecast 13
🔹 RSI Forecast 13 < MA Forecast 13
🔹 RSI Forecast 14 > MA Forecast 14
🔹 RSI Forecast 14 < MA Forecast 14
🔹 RSI Forecast 15 > MA Forecast 15
🔹 RSI Forecast 15 < MA Forecast 15
🔹 RSI Forecast 16 > MA Forecast 16
🔹 RSI Forecast 16 < MA Forecast 16
🔹 RSI Forecast 17 > MA Forecast 17
🔹 RSI Forecast 17 < MA Forecast 17
🔹 RSI Forecast 18 > MA Forecast 18
🔹 RSI Forecast 18 < MA Forecast 18
🔹 RSI Forecast 19 > MA Forecast 19
🔹 RSI Forecast 19 < MA Forecast 19
🔹 RSI Forecast 20 > MA Forecast 20
🔹 RSI Forecast 20 < MA Forecast 20
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🔸 CONDITIONS TO SELL 📉
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• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or OR .
📉 RSI Conditions:
🔸 RSI > Upper
🔸 RSI < Upper
🔸 RSI > Lower
🔸 RSI < Lower
🔸 RSI > Middle
🔸 RSI < Middle
🔸 RSI > MA
🔸 RSI < MA
📉 MA Conditions:
🔸 MA > Upper
🔸 MA < Upper
🔸 MA > Lower
🔸 MA < Lower
📉 Crossovers:
🔸 RSI (Crossover) Upper
🔸 RSI (Crossunder) Upper
🔸 RSI (Crossover) Lower
🔸 RSI (Crossunder) Lower
🔸 RSI (Crossover) Middle
🔸 RSI (Crossunder) Middle
🔸 RSI (Crossover) MA
🔸 RSI (Crossunder) MA
🔸 MA (Crossover) Upper
🔸 MA (Crossunder) Upper
🔸 MA (Crossover) Lower
🔸 MA (Crossunder) Lower
📉 RSI Divergences:
🔸 RSI Divergence Bull
🔸 RSI Divergence Bear
📉 RSI Forecast:
🔸 RSI (Crossover) MA Forecast
🔸 RSI (Crossunder) MA Forecast
🔸 RSI Forecast 1 > MA Forecast 1
🔸 RSI Forecast 1 < MA Forecast 1
🔸 RSI Forecast 2 > MA Forecast 2
🔸 RSI Forecast 2 < MA Forecast 2
🔸 RSI Forecast 3 > MA Forecast 3
🔸 RSI Forecast 3 < MA Forecast 3
🔸 RSI Forecast 4 > MA Forecast 4
🔸 RSI Forecast 4 < MA Forecast 4
🔸 RSI Forecast 5 > MA Forecast 5
🔸 RSI Forecast 5 < MA Forecast 5
🔸 RSI Forecast 6 > MA Forecast 6
🔸 RSI Forecast 6 < MA Forecast 6
🔸 RSI Forecast 7 > MA Forecast 7
🔸 RSI Forecast 7 < MA Forecast 7
🔸 RSI Forecast 8 > MA Forecast 8
🔸 RSI Forecast 8 < MA Forecast 8
🔸 RSI Forecast 9 > MA Forecast 9
🔸 RSI Forecast 9 < MA Forecast 9
🔸 RSI Forecast 10 > MA Forecast 10
🔸 RSI Forecast 10 < MA Forecast 10
🔸 RSI Forecast 11 > MA Forecast 11
🔸 RSI Forecast 11 < MA Forecast 11
🔸 RSI Forecast 12 > MA Forecast 12
🔸 RSI Forecast 12 < MA Forecast 12
🔸 RSI Forecast 13 > MA Forecast 13
🔸 RSI Forecast 13 < MA Forecast 13
🔸 RSI Forecast 14 > MA Forecast 14
🔸 RSI Forecast 14 < MA Forecast 14
🔸 RSI Forecast 15 > MA Forecast 15
🔸 RSI Forecast 15 < MA Forecast 15
🔸 RSI Forecast 16 > MA Forecast 16
🔸 RSI Forecast 16 < MA Forecast 16
🔸 RSI Forecast 17 > MA Forecast 17
🔸 RSI Forecast 17 < MA Forecast 17
🔸 RSI Forecast 18 > MA Forecast 18
🔸 RSI Forecast 18 < MA Forecast 18
🔸 RSI Forecast 19 > MA Forecast 19
🔸 RSI Forecast 19 < MA Forecast 19
🔸 RSI Forecast 20 > MA Forecast 20
🔸 RSI Forecast 20 < MA Forecast 20
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🤖 AUTOMATION 🤖
• You can automate the BUY and SELL signals of this indicator.
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⯁ UNIQUE FEATURES
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Linear Regression: (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
Linear Regression (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
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📜 SCRIPT : RSI Full Forecast
🎴 Art by : @Titans_Invest & @DiFlip
👨💻 Dev by : @Titans_Invest & @DiFlip
🎑 Titans Invest — The Wizards Without Gloves 🧤
✨ Enjoy!
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o Mission 🗺
• Inspire Traders to manifest Magic in the Market.
o Vision 𐓏
• To elevate collective Energy 𐓷𐓏
