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Support & Resistance AI (K means/median) [ThinkLogicAI]█ OVERVIEW K-means is a clustering algorithm commonly used in machine learning to group data points into distinct clusters based on their similarities. While K-means is not typically used directly for identifying support and resistance levels in financial markets, it can serve as a tool in a broader analysis approach. Support and resistance levels are price levels in financial markets where the price tends to react or reverse. Support is a level where the price tends to stop falling and might start to rise, while resistance is a level where the price tends to stop rising and might start to fall. Traders and analysts often look for these levels as they can provide insights into potential price movements and trading opportunities. █ BACKGROUND The K-means algorithm has been around since the late 1950s, making it more than six decades old. The algorithm was introduced by Stuart Lloyd in his 1957 research paper "Least squares quantization in PCM" for telecommunications applications. However, it wasn't widely known or recognized until James MacQueen's 1967 paper "Some Methods for Classification and Analysis of Multivariate Observations," where he formalized the algorithm and referred to it as the "K-means" clustering method. So, while K-means has been around for a considerable amount of time, it continues to be a widely used and influential algorithm in the fields of machine learning, data analysis, and pattern recognition due to its simplicity and effectiveness in clustering tasks. █ COMPARE AND CONTRAST SUPPORT AND RESISTANCE METHODS 1) K-means Approach: Cluster Formation: After applying the K-means algorithm to historical price change data and visualizing the resulting clusters, traders can identify distinct regions on the price chart where clusters are formed. Each cluster represents a group of similar price change patterns. Cluster Analysis: Analyze the clusters to identify areas where clusters tend to form. These areas might correspond to regions of price behavior that repeat over time and could be indicative of support and resistance levels. Potential Support and Resistance Levels: Based on the identified areas of cluster formation, traders can consider these regions as potential support and resistance levels. A cluster forming at a specific price level could suggest that this level has been historically significant, causing similar price behavior in the past. Cluster Standard Deviation: In addition to looking at the means (centroids) of the clusters, traders can also calculate the standard deviation of price changes within each cluster. Standard deviation is a measure of the dispersion or volatility of data points around the mean. A higher standard deviation indicates greater price volatility within a cluster. Low Standard Deviation: If a cluster has a low standard deviation, it suggests that prices within that cluster are relatively stable and less likely to exhibit sudden and large price movements. Traders might consider placing tighter stop-loss orders for trades within these clusters. High Standard Deviation: Conversely, if a cluster has a high standard deviation, it indicates greater price volatility within that cluster. Traders might opt for wider stop-loss orders to allow for potential price fluctuations without getting stopped out prematurely. Cluster Density: Each data point is assigned to a cluster so a cluster that is more dense will act more like gravity and 2) Traditional Approach: Trendlines: Draw trendlines connecting significant highs or lows on a price chart to identify potential support and resistance levels. Chart Patterns: Identify chart patterns like double tops, double bottoms, head and shoulders, and triangles that often indicate potential reversal points. Moving Averages: Use moving averages to identify levels where the price might find support or resistance based on the average price over a specific period. Psychological Levels: Identify round numbers or levels that traders often pay attention to, which can act as support and resistance. Previous Highs and Lows: Identify significant previous price highs and lows that might act as support or resistance. The key difference lies in the approach and the foundation of these methods. Traditional methods are based on well-established principles of technical analysis and market psychology, while the K-means approach involves clustering price behavior without necessarily incorporating market sentiment or specific price patterns. It's important to note that while the K-means approach might provide an interesting way to analyze price data, it should be used cautiously and in conjunction with other traditional methods. Financial markets are influenced by a wide range of factors beyond just price behavior, and the effectiveness of any method for identifying support and resistance levels should be thoroughly tested and validated. Additionally, developments in trading strategies and analysis techniques could have occurred since my last update. █ K MEANS ALGORITHM The algorithm for K means is as follows: Initialize cluster centers assign data to clusters based on minimum distance calculate cluster center by taking the average or median of the clusters repeat steps 1-3 until cluster centers stop moving █ LIMITATIONS OF K MEANS There are 3 main limitations of this algorithm: Sensitive to Initializations: K-means is sensitive to the initial placement of centroids. Different initializations can lead to different cluster assignments and final results. Assumption of Equal Sizes and Variances: K-means assumes that clusters have roughly equal sizes and spherical shapes. This may not hold true for all types of data. It can struggle with identifying clusters with uneven densities, sizes, or shapes. Impact of Outliers: K-means is sensitive to outliers, as a single outlier can significantly affect the position of cluster centroids. Outliers can lead to the creation of spurious clusters or distortion of the true cluster structure. █ LIMITATIONS IN APPLICATION OF K MEANS IN TRADING Trading data often exhibits characteristics that can pose challenges when applying indicators and analysis techniques. Here's how the limitations of outliers, varying scales, and unequal variance can impact the use of indicators in trading: Outliers are data points that significantly deviate from the rest of the dataset. In trading, outliers can represent extreme price movements caused by rare events, news, or market anomalies. Outliers can have a significant impact on trading indicators and analyses:    Indicator Distortion: Outliers can skew the calculations of indicators, leading to   misleading signals. For instance, a single extreme price spike could cause indicators like moving averages or RSI (Relative Strength Index) to give false signals.    Risk Management: Outliers can lead to overly aggressive trading decisions if not  properly accounted for. Ignoring outliers might result in unexpected losses or missed opportunities to adjust trading strategies. Different Scales: Trading data often includes multiple indicators with varying units and scales. For example, prices are typically in dollars, volume in units traded, and oscillators have their own scale. Mixing indicators with different scales can complicate analysis:   Normalization: Indicators on different scales need to be normalized or standardized to ensure they contribute equally to the analysis. Failure to do so can lead to one indicator dominating the analysis due to its larger magnitude.   Comparability: Without normalization, it's challenging to directly compare the significance of indicators. Some indicators might have a larger numerical range and could overshadow others. Unequal Variance: Unequal variance in trading data refers to the fact that some indicators might exhibit higher volatility than others. This can impact the interpretation of signals and the performance of trading strategies:   Volatility Adjustment: When combining indicators with varying volatility, it's essential to adjust for their relative volatilities. Failure to do so might lead to overemphasizing or underestimating the importance of certain indicators in the trading strategy.   Risk Assessment: Unequal variance can impact risk assessment. Indicators with higher volatility might lead to riskier trading decisions if not properly taken into account. █ APPLICATION OF THIS INDICATOR This indicator can be used in 2 ways: 1) Make a directional trade: If a trader thinks price will go higher or lower and price is within a cluster zone, The trader can take a position and place a stop on the 1 sd band around the cluster. As one can see below, the trader can go long the green arrow and place a stop on the one standard deviation mark for that cluster below it at the red arrow. using this we can calculate a risk to reward ratio. Calculating risk to reward: targeting a risk reward ratio of 2:1, the trader could clearly make that given that the next resistance area above that in the orange cluster exceeds this risk reward ratio. 2) Take a reversal Trade: We can use cluster centers (support and resistance levels) to go in the opposite direction that price is currently moving in hopes of price forming a pivot and reversing off this level. Similar to the directional trade, we can use the standard deviation of the cluster to place a stop just in case we are wrong. In this example below we can see that shorting on the red arrow and placing a stop at the one standard deviation above this cluster would give us a profitable trade with minimal risk. Using the cluster density table in the upper right informs the trader just how dense the cluster is. Higher density clusters will give a higher likelihood of a pivot forming at these levels and price being rejected and switching direction with a larger move. █ FEATURES & SETTINGS General Settings: Number of clusters: The user can select from 3 to five clusters. A good rule of thumb is that if you are trading intraday, less is more (Think 3 rather than 5). For daily 4 to 5 clusters is good. Cluster Method: To get around the outlier limitation of k means clustering, The median was added. This gives the user the ability to choose either k means or k median clustering. K means is the preferred method if the user things there are no large outliers, and if there appears to be large outliers or it is assumed there are then K medians is preferred. Bars back To train on: This will be the amount of bars to include in the clustering. This number is important so that the user includes bars that are recent but not so far back that they are out of the scope of where price can be. For example the last 2 years we have been in a range on the sp500 so 505 days in this setting would be more relevant than say looking back 5 years ago because price would have to move far to get there. Show SD Bands: Select this to show the 1 standard deviation bands around the support and resistance level or unselect this to just show the support and resistance level by itself. Features: Besides the support and resistance levels and standard deviation bands, this indicator gives a table in the upper right hand corner to show the density of each cluster (support and resistance level) and is color coded to the cluster line on the chart. Higher density clusters mean price has been there previously more than lower density clusters and could mean a higher likelihood of a reversal when price reaches these areas. █ WORKS CITED Victor Sim, "Using K-means Clustering to Create Support and Resistance", 2020, towardsdatascience.com Chris Piech, "K means", stanford.edu █ ACKNOLWEDGMENTS @jdehorty- Thanks for the publish template. It made organizing my thoughts and work alot easier.
Penunjuk Pine Script®
oleh ThinkLogicAI
40
Recursive Micro Zigzag🎲 Overview Zigzag is basic building block for any pattern recognition algorithm. This indicator is a research-oriented tool that combines the concepts of Micro Zigzag and Recursive Zigzag to facilitate a comprehensive analysis of price patterns. This indicator focuses on deriving zigzag on multiple levels in more efficient and enhanced manner in order to support enhanced pattern recognition. The Recursive Micro Zigzag Indicator utilises the Micro Zigzag as the foundation and applies the Recursive Zigzag technique to derive higher-level zigzags. By integrating these techniques, this indicator enables researchers to analyse price patterns at multiple levels and gain a deeper understanding of market behaviour. 🎲 Concept: Micro Zigzag Base : The indicator utilises the Micro Zigzag concept to capture detailed price movements within each candle. It allows for the visualisation of the sequential price action within the candle, aiding in pattern recognition at a micro level. Basic implementation of micro zigzag can be found in this link - Micro-Zigzag Recursive Zigzag Expansion : Building upon the Micro Zigzag base, the indicator applies the Recursive Zigzag concept to derive higher-level zigzags. Through recursive analysis of the Micro Zigzag's pivots, the indicator uncovers intricate patterns and trends that may not be evident in single-level zigzags. Earlier implementations of recursive zigzag can be found here: Recursive Zigzag Recursive Zigzag - Trendoscope And the libraries rZigzag ZigzagMethods The major differences in this implementation are Micro Zigzag Base - Earlier implementation made use of standard zigzag as base whereas this implementation uses Micro Zigzag as base Not cap on Pivot depth - Earlier implementation was limited by the depth of level 0 zigzag. In this implementation, we are trying to build the recursive algorithm progressively so that there is no cap on the depth of level 0 zigzag. But, if we go for higher levels, there is chance of program timing out due to pine limitations. These algorithms are useful in automatically spotting patterns on the chart including Harmonic Patterns, Chart Patterns, Elliot Waves and many more.
Penunjuk Pine Script®
oleh Trendoscope
10
cbndLibrary "cbnd" Description: A standalone Cumulative Bivariate Normal Distribution (CBND) functions that do not require any external libraries. This includes 3 different CBND calculations: Drezner(1978), Drezner and Wesolowsky (1990), and Genz (2004) Comments: The standardized cumulative normal distribution function returns the probability that one random variable is less than a and that a second random variable is less than b when the correlation between the two variables is p. Since no closed-form solution exists for the bivariate cumulative normal distribution, we present three approximations. The first one is the well-known Drezner (1978) algorithm. The second one is the more efficient Drezner and Wesolowsky (1990) algorithm. The third is the Genz (2004) algorithm, which is the most accurate one and therefore our recommended algorithm. West (2005b) and Agca and Chance (2003) discuss the speed and accuracy of bivariate normal distribution approximations for use in option pricing in ore detail. Reference: The Complete Guide to Option Pricing Formulas, 2nd ed. (Espen Gaarder Haug) CBND1(A, b, rho)   Returns the Cumulative Bivariate Normal Distribution (CBND) using Drezner 1978 Algorithm   Parameters:      A : float,      b : float,      rho : float,   Returns: float. CBND2(A, b, rho)   Returns the Cumulative Bivariate Normal Distribution (CBND) using Drezner and Wesolowsky (1990) function   Parameters:      A : float,      b : float,      rho : float,   Returns: float. CBND3(x, y, rho)   Returns the Cumulative Bivariate Normal Distribution (CBND) using Genz (2004) algorithm (this is the preferred method)   Parameters:      x : float,      y : float,      rho : float,   Returns: float.
Perpustakaan Pine Script®
oleh loxx
STD-Stepped Fast Cosine Transform Moving Average [Loxx]STD-Stepped Fast Cosine Transform Moving Average is an experimental moving average that uses Fast Cosine Transform to calculate a moving average. This indicator has standard deviation stepping in order to smooth the trend by weeding out low volatility movements. What is the Discrete Cosine Transform? A discrete cosine transform (DCT) expresses a finite sequence of data points in terms of a sum of cosine functions oscillating at different frequencies. The DCT, first proposed by Nasir Ahmed in 1972, is a widely used transformation technique in signal processing and data compression. It is used in most digital media, including digital images (such as JPEG and HEIF, where small high-frequency components can be discarded), digital video (such as MPEG and H.26x), digital audio (such as Dolby Digital, MP3 and AAC), digital television (such as SDTV, HDTV and VOD), digital radio (such as AAC+ and DAB+), and speech coding (such as AAC-LD, Siren and Opus). DCTs are also important to numerous other applications in science and engineering, such as digital signal processing, telecommunication devices, reducing network bandwidth usage, and spectral methods for the numerical solution of partial differential equations. The use of cosine rather than sine functions is critical for compression, since it turns out (as described below) that fewer cosine functions are needed to approximate a typical signal, whereas for differential equations the cosines express a particular choice of boundary conditions. In particular, a DCT is a Fourier-related transform similar to the discrete Fourier transform (DFT), but using only real numbers. The DCTs are generally related to Fourier Series coefficients of a periodically and symmetrically extended sequence whereas DFTs are related to Fourier Series coefficients of only periodically extended sequences. DCTs are equivalent to DFTs of roughly twice the length, operating on real data with even symmetry (since the Fourier transform of a real and even function is real and even), whereas in some variants the input and/or output data are shifted by half a sample. There are eight standard DCT variants, of which four are common. The most common variant of discrete cosine transform is the type-II DCT, which is often called simply "the DCT". This was the original DCT as first proposed by Ahmed. Its inverse, the type-III DCT, is correspondingly often called simply "the inverse DCT" or "the IDCT". Two related transforms are the discrete sine transform (DST), which is equivalent to a DFT of real and odd functions, and the modified discrete cosine transform (MDCT), which is based on a DCT of overlapping data. Multidimensional DCTs (MD DCTs) are developed to extend the concept of DCT to MD signals. There are several algorithms to compute MD DCT. A variety of fast algorithms have been developed to reduce the computational complexity of implementing DCT. One of these is the integer DCT (IntDCT), an integer approximation of the standard DCT, : ix, xiii, 1, 141–304  used in several ISO/IEC and ITU-T international standards. Notable settings windowper = period for calculation, restricted to powers of 2: "16", "32", "64", "128", "256", "512", "1024", "2048", this reason for this is FFT is an algorithm that computes DFT (Discrete Fourier Transform) in a fast way, generally in 𝑂(𝑁⋅log2(𝑁)) instead of 𝑂(𝑁2). To achieve this the input matrix has to be a power of 2 but many FFT algorithm can handle any size of input since the matrix can be zero-padded. For our purposes here, we stick to powers of 2 to keep this fast and neat. read more about this here: Cooley–Tukey FFT algorithm smthper = smoothing count, this smoothing happens after the first FCT regular pass. this zeros out frequencies from the previously calculated values above SS count. the lower this number, the smoother the output, it works opposite from other smoothing periods Included Alerts Signals Loxx's Expanded Source Types Additional reading A Fast Computational Algorithm for the Discrete Cosine Transform by Chen et al. Practical Fast 1-D DCT Algorithms With 11 Multiplications by Loeffler et al. Cooley–Tukey FFT algorithm
Penunjuk Pine Script®
oleh loxx
9
Weighted Burg AR Spectral Estimate Extrapolation of Price [Loxx]Weighted Burg AR Spectral Estimate Extrapolation of Price is an indicator that uses an autoregressive spectral estimation called the Weighted Burg Algorithm. This method is commonly used in speech modeling and speech prediction engines. This method also includes Levinson–Durbin algorithm. As was already discussed previously in the following indicator: Levinson-Durbin Autocorrelation Extrapolation of Price What is Levinson recursion or Levinson–Durbin recursion? In many applications, the duration of an uninterrupted measurement of a time series is limited. However, it is often possible to obtain several separate segments of data. The estimation of an autoregressive model from this type of data is discussed. A straightforward approach is to take the average of models estimated from each segment separately. In this way, the variance of the estimated parameters is reduced. However, averaging does not reduce the bias in the estimate. With the Burg algorithm for segments, both the variance and the bias in the estimated parameters are reduced by fitting a single model to all segments simultaneously. As a result, the model estimated with the Burg algorithm for segments is more accurate than models obtained with averaging. The new weighted Burg algorithm for segments allows combining segments of different amplitudes. The Burg algorithm estimates the AR parameters by determining reflection coefficients that minimize the sum of for-ward and backward residuals. The extension of the algorithm to segments is that the reflection coefficients are estimated by minimizing the sum of forward and backward residuals of all segments taken together. This means a single model is fitted to all segments in one time. This concept is also used for prediction error methods in system identification, where the input to the system is known, like in ARX modeling Data inputs Source Settings: -Loxx's Expanded Source Types. You typically use "open" since open has already closed on the current active bar LastBar - bar where to start the prediction PastBars - how many bars back to model LPOrder - order of linear prediction model; 0 to 1 FutBars - how many bars you want to forward predict BurgWin - weighing function index, rectangular, hamming, or parabolic Things to know Normally, a simple moving average is calculated on source data. I've expanded this to 38 different averaging methods using Loxx's Moving Avreages. This indicator repaints Included Bar color muting Further reading Performance of the weighted burg methods of ar spectral estimation for pitch-synchronous analysis of voiced speech The Burg algorithm for segments Techniques for the Enhancement of Linear Predictive Speech Coding in Adverse Conditions Related Indicators
Penunjuk Pine Script®
oleh loxx
3
Synthetic Price Action GeneratorNOTICE: First thing you need to know, it "DOES NOT" reflect the price of the ticker you will load it on. THIS IS NOT AN INDICATOR FOR TRADING! It's a developer tool solely generating random values that look exactly like the fractals we observe every single day. This script's generated candles are as fake as the never ending garbage news cycles we are often force fed and expected to believe by using carefully scripted narratives peddled as hypnotic truth to psychologically and emotionally influence you to the point of control by coercion and subjugation. I wanted to make the script's synthetic nature very clear using that analogy, it's dynamically artificial. Do not accidentally become disillusioned by this scripts values, make trading decisions from it, and lastly don't become victim to predatory media magic ministry parrots with pretty, handsome smiles, compelling you to board their ferris wheel of fear. Now, on to the good stuff... BACKSTORY: Occasionally I find myself in situations where I have to build analyzers in Pine to actually build novel quantitative analytic indicators and tools worthy of future use. These analyzers certainly don't exist on this platform, but usually are required to engineer and tweak algorithms of the highest quality with the finest computational caliber. I have numerous other synthesizers to publish besides this one. For many reasons, I needed a synthetic environment to utilize the analyzers I built in Pine, to even pursue building some exotic indicators and algorithms. Pine doesn't allow sourcing of tuples. Not to mention, I required numerous Pine advancements to make long held dreams into tangible realities. Many Pine upgrades have arrived and MANY, MANY more are in need of implementation for all. Now that I have this, intending to use it in the future often when in need, you can now use it too. I do anticipate some skilled Pine poets will employ this intended handy utility to design and/or improved indicators for trading. ORIGIN: This was inspired by the brilliance from the world renowned ALGOmist John F. Ehlers, but it's taken on a completely alien form from its original DNA. Browsing on the internet for something else, I came across an article with a small code snippet, and I remembered an old wish of mine. I have long known that by flipping back and forth on specific tickers and timeframes in my Watchlist is not the most efficient way to evaluate indicators in multiple theatres of price action. I realized, I always wanted to possess and use this sort of tool, so... I put it into Pine form, but now have decided to inject it with Pine Script steroids. The outcome is highly mutable candle formations in a reusable mutagenic package, observable above and masquerading as genuine looking price candles. OVERVIEW: I guess you could call it a price action synthesizer, but I entitled it "Synthetic Price Action Generator" for those who may be searching for such a thing. You may find this more useful on the All or 5Y charts initially to witness indication from beginning (barstate.isfirst === barindex==0) to end (last_bar_index), but you may also use keyboard shortcuts + + to view the earliest plottable bars on any timeframe. I often use that keyboard shortcut to qualify an indicator through the entirety of it's runtime. A lot can go wrong unexpectedly with indicator initialization, and you will never know it if you don't inspect it. Many recursively endowed Infinite Impulse Response (IIR) Filters can initialize with unintended results that minutely ring in slightly erroneous fashion for the entire runtime, beginning to end, causing deviations from "what should of been..." values with false signals. Looking closely at spg(), you will recognize that 3 EMAs are employed to manage and maintain randomness of CLOSE, HIGH, and LOW. In fact, any indicator's barindex==0 initialization can be inspected with the keyboard shortcuts above. If you see anything obviously strange in an authors indicator, please contact the developer if possible and respectfully notify them. PURPOSE: The primary intended application of this script, is to offer developers from advanced to even novice skill levels assistance with building next generation indicators. Mostly, it's purpose is for testing and troubleshooting indicators AND evaluating how they perform in a "manageable" randomized environment. Some times indicators flake out on rare but problematic price fluctuations, and this may help you with finding your issues/errata sooner than later. While the candles upon initial loading look pristine, by tweaking it to the minval/maxval parameters limits OR beyond with a few code modifications, you can generate unusual volatility, for instance... huge wicks. Limits of minval= and maxval= of are by default set to a comfort zone of operation. Massive wicks or candle bodies will undoubtedly affect your indication and often render them useless on tickers that exhibit that behavior, like WGMCF intraday currently. Copy/paste boundaries are provided for relevant insertion into another script. Paste placement should happen at the very top of a script. Note that by overwriting the close, open, high, etc... values, your compiler will give you generous warnings of "variable shadowing" in abundance, but this is an expected part of applying it to your novel script, no worries. plotcandle() can be copied over too and enabled/disabled in Settings->Style. Always remember to fully remove this scripts' code and those assignments properly before actual trading use of your script occurs, AND specifically when publishing. The entirety of this provided code should never, never exist in a published indicator. OTHER INTENTIONS: Even though these are 100% synthetic generated price points, you will notice ALL of the fractal pseudo-patterns that commonly exist in the markets, are naturally occurring with this generator too. You can also swiftly immerse yourself in pattern recognition exercises with increased efficiency in real time by clicking any SPAG Setting in focus and then using the up/down arrow keys. I hope I explained potential uses adequately... On a personal note, the existence of fractal symmetry often makes me wonder, do we truly live in a totality chaotic universe or is it ordered mathematically for some outcomes to a certain extent. I think both. My observations, it's a pre-deterministic reality completely influenced by infinitesimal amounts of sentient free will with unimaginable existing and emerging quantities. Some how an unknown mysterious mechanism governing the totality of universal physics and mathematics counts this 100.0% flawlessly and perpetually. Anyways, you can't change the past that long existed before your birth or even yesterday, but you can choose to dream, create, and forge the future into your desires and hopes. As always, shite always happens when your not looking for it. What you choose to do after stepping in it unintentionally... is totally up to you. :) Maybe this tool and tips provided will aid you in not stepping in an algo cachucha up to your ankles somehow. SCRIPTING LESSONS PORTRAYED IN THIS SCRIPT: Pine etiquette and code cleanliness Overwrite capabilities of built-in Pine variables for testing indicators Various techniques to organize Settings panel while providing ease of adjustment utility Use of tooltip= to provide users adequate valuable information. Most people want to trade with indicators, not blindly make adjustments to them without any knowledge of their intended operation/effects When available time provides itself, I will consider your inquiries, thoughts, and concepts presented below in the comments section, should you have any questions or comments regarding this indicator. When my indicators achieve more prevalent use by TV members , I may implement more ideas when they present themselves as worthy additions. Have a profitable future everyone!
Penunjuk Pine Script®
oleh ImmortalFreedom
18
RSI-Adaptive T3 [ChartPrime]The RSI-Adaptive T3 is a precision trend-following tool built around the legendary T3 smoothing algorithm developed by Tim Tillson , designed to enhance responsiveness while reducing lag compared to traditional moving averages. Current implementation takes it a step further by dynamically adapting the smoothing length based on real-time RSI conditions — allowing the T3 to “breathe” with market volatility. This dynamic length makes the curve faster in trending moves and smoother during consolidations. To help traders visualize volatility and directional momentum, adaptive volatility bands are plotted around the T3 line, with visual crossover markers and a dynamic info panel on the chart. It’s ideal for identifying trend shifts, spotting momentum surges, and adapting strategy execution to the pace of the market. HOIW IT WORKS At its core, this indicator fuses two ideas: The T3 Moving Average — a 6-stage recursively smoothed exponential average created by Tim Tillson , designed to reduce lag without sacrificing smoothness. It uses a volume factor to control curvature. A Dynamic Length Engine — powered by the RSI. When RSI is low (market oversold), the T3 becomes shorter and more reactive. When RSI is high (overbought), the T3 becomes longer and smoother. This creates a feedback loop between price momentum and trend sensitivity. // Step 1: Adaptive length via RSI rsi = ta.rsi(src, rsiLen) rsi_scale = 1 - rsi / 100 len = math.round(minLen + (maxLen - minLen) * rsi_scale) pine_ema(src, length) => alpha = 2 / (length + 1) sum = 0.0 sum := na(sum ) ? src : alpha * src + (1 - alpha) * nz(sum ) sum // Step 2: T3 with adaptive length e1 = pine_ema(src, len) e2 = pine_ema(e1, len) e3 = pine_ema(e2, len) e4 = pine_ema(e3, len) e5 = pine_ema(e4, len) e6 = pine_ema(e5, len) c1 = -v * v * v c2 = 3 * v * v + 3 * v * v * v c3 = -6 * v * v - 3 * v - 3 * v * v * v c4 = 1 + 3 * v + v * v * v + 3 * v * v t3 = c1 * e6 + c2 * e5 + c3 * e4 + c4 * e3 The result: an evolving trend line that adapts to market tempo in real-time. KEY FEATURES ⯁ RSI-Based Adaptive Smoothing The length of the T3 calculation dynamically adjusts between a Min Length and Max Length , based on the current RSI. When RSI is low → the T3 shortens, tracking reversals faster. When RSI is high → the T3 stretches, filtering out noise during euphoria phases. Displayed length is shown in a floating table, colored on a gradient between min/max values. ⯁ T3 Calculation (Tim Tillson Method) The script uses a 6-stage EMA cascade with a customizable Volume Factor (v) , as designed by Tillson (1998) . Formula: T3 = c1 * e6 + c2 * e5 + c3 * e4 + c4 * e3 This technique gives smoother yet faster curves than EMAs or DEMA/Triple EMA. ⯁ Visual Trend Direction & Transitions The T3 line changes color dynamically: Color Up (default: blue) → bullish curvature Color Down (default: orange) → bearish curvature Plot fill between T3 and delayed T3 creates a gradient ribbon to show momentum expansion/contraction. Directional shift markers (“🞛”) are plotted when T3 crosses its own delayed value — helping traders spot trend flips or pullback entries. ⯁ Adaptive Volatility Bands Optional upper/lower bands are plotted around the T3 line using a user-defined volatility window (default: 100). Bands widen when volatility rises, and contract during compression — similar to Bollinger logic but centered on the adaptive T3. Shaded band zones help frame breakout setups or mean-reversion zones. ⯁ Dynamic Info Table A live stats panel shows: Current adaptive length Maximum smoothing (▲ MaxLen) Minimum smoothing (▼ MinLen) All values update in real time and are color-coded to match trend direction. HOW TO USE Use T3 crossovers to detect trend transitions, especially during periods of volatility compression. Watch for volatility contraction in the bands — breakouts from narrow band periods often precede trend bursts. The adaptive smoothing length can also be used to assess current market tempo — tighter = faster; wider = slower. CONCLUSION RSI-Adaptive T3 modernizes one of the most elegant smoothing algorithms in technical analysis with intelligent RSI responsiveness and built-in volatility bands. It gives traders a cleaner read on trend health, directional shifts, and expansion dynamics — all in a visually efficient package. Perfect for scalpers, swing traders, and algorithmic modelers alike, it delivers advanced logic in a plug-and-play format.
Penunjuk Pine Script®
oleh ChartPrime
7
[blackcat] L3 Smart Money FlowCOMPREHENSIVE ANALYSIS OF THE L3 SMART MONEY FLOW INDICATOR 🌐 OVERVIEW: The L3 Smart Money Flow indicator represents a sophisticated multi-dimensional analytics tool combining traditional momentum measurements with advanced institutional investor tracking capabilities. It's particularly effective at identifying large-scale capital movement dynamics that often precede significant price shifts. Core Objectives: • Detect subtle but meaningful price action anomalies indicating major player involvement • Provide clear entry/exit markers based on multiple validated criteria • Offer risk-managed positioning strategies suitable for various account sizes • Maintain operational efficiency even during high volatility regimes THEORETICAL BACKDROP AND METHODOLOGY 🎓 Conceptual Foundation Principles: Utilizes Time-Varying Moving Averages (TVMA) responding adaptively to changing market states Implements Extended Smoothing Algorithm (XSA) providing enhanced filtration characteristics Employs asymmetric weight distribution favoring recent price observations over historical ones → Analyzes price-weighted closing prices incorporating volume influence indirectly ← Applies Asymmetric Local Maximum (ALMA) filters generating institution-specific trends ⟸ Combines multiple temporal perspectives producing robust directional assessments ✓ Calculates normalized momentum ratios comparing current state against extended range extremes ✗ Filters out insignificant fluctuations via double-stage verification process ⤾ Generates actionable alerts upon exceeding predefined significance boundaries CONFIGURABLE PARAMETERS IN DEPTH ⚙️ Input Customization Options Detailed Explanation: Temporal Resolution Control: → TVMA Length Setting: Minimum value constraint ensuring mathematical validity Higher numbers increase smoothing effect reducing reaction velocity Lower intervals enhance responsiveness potentially increasing noise exposure Validation Threshold Definition: ↓ Bull-Bear Boundary Level: Establishes fundamental acceptance/rejection zones Typically set near extreme values reflecting rare occurrence probability Can be adjusted per instrument liquidity profiles if necessary ADVANCED ALGORITHMIC PROCEDURES BREAKDOWN 💻 Internal Operation Architecture: Base Calculations Infrastructure: ☑ Raw Data Preparation and Normalization ☐ High/Low/Closing Aggregation Processes ☒ Range Estimation Algorithms Intermediate Transform Engine: 📈 Momentum Ratio Computation Workflow ↔ First Pass XSA Application Details ➖ Second Stage Refinement Mechanics Final Output Synthesis Framework: ➢ Composite Reading Compilation Logic ➣ Validation Status Determination Process ➤ Alert Trigger Decision Making Structure INTERACTIVE VISUAL INTERFACE COMPONENTS 🎨 User Experience Interface Elements: 🔵 Plotting Series Hierarchy: → Primary FundFlow Signal: White trace marking core oscillator progression ↑ Secondary Confirmation Overlay: Orange/Yellow highlighting validation status 🟥 Risk/Reward Boundaries: Aqua line delineating strategic areas requiring attention 🏷️ Interactive Marker System: ✔ "BUY": Green upward-pointing labels denoting confirmed long entries ❌ "SELL": Red downward-facing badges signaling short setups PRACTICAL APPLICATION STRATEGY GUIDE 📋 Operational Deployment Instructions: Strategic Planning Initiatives: • Define precise profit targets considering realistic reward/risk scenarios → Set maximum acceptable loss thresholds protecting available resources adequately ↓ Develop contingency plans addressing unexpected adverse developments promptly Live Trading Engagement Protocols: → Maintaining vigilant monitoring of label placement activities continuously ↓ Tracking order fill success rates across implemented grids regularly ↑ Evaluating system effectiveness compared alternative methodologies periodically Performance Optimization Techniques: ✔ Implement incremental improvements iteratively throughout lifecycle ❌ Eliminate ineffective component variations systematically ⟹ Ensure proportional growth capability matching user needs appropriately EFFICIENCY ENHANCEMENT APPROACHES 🚀 Ongoing Development Strategy: Resource Management Focus Areas: → Minimizing redundant computation cycles through intelligent caching mechanisms ↓ Leveraging parallel processing capabilities where feasible efficiently ↑ Optimizing storage access patterns improving response times substantially Scalability Consideration Factors: ✔ Adapting to varying account sizes/market capitalizations seamlessly ❌ Preventing bottlenecks limiting concurrent operation capacity ⟹ Ensuring balanced growth capability matching evolving requirements accurately Maintenance Routine Establishment: ✓ Regular codebase updates incorporation keeping functionality current ↓ Periodic performance audits conducting verifying continued effectiveness ↑ Documentation refinement updating explaining any material modifications made SYSTEMATIC RISK CONTROL MECHANISMS 🛡️ Comprehensive Protection Systems: Position Sizing Governance: ∅ Never exceed predetermined exposure limitations strictly observed ± Scale entries proportionally according to available resources carefully × Include slippage allowances within planning stages realistically Emergency Response Procedures: ↩ Well-defined exit strategies including trailing stops activation logic 🌀 Contingency plan formulation covering worst-case scenario contingencies ⇄ Recovery procedure documentation outlining restoration steps methodically
Penunjuk Pine Script®
oleh blackcat1402
Finite Difference - Backward (mcbw_)In calculus there exists a 'derivative', which simply just measures the difference between two points on a curve. For well behaved mathematical functions there are infinitely many points and so there exists a derivative at every point. Where there are infinitely many points in a curve that curve is called 'continuous'. Continuous curves are very nice to deal with since each point on it exists almost exactly where its neighbors are. However, if the curve does not have infinitely many points on it, but instead has a finite number of points on it, that curve is called 'discrete' instead of continuous. Taking the derivative of discrete curves is much trickier business since there are none of the mathematical conveniences that a continuous offers. In the real world everything we measure is a discrete curve, including Price (since we measure it a finite number of times, aka each candlestick)! The branch of Discrete Mathematics has found an approach to measure the derivative along a discrete curve, that approach is aptly called " Finite Difference ". To get a more accurate approximation of a discrete derivative, the finite difference approach uses weighted combinations of neighboring points. The most common type of finite difference is a 'central' difference, this uses a combination of points before and after the point of interest to approximate the discrete derivative. This is great for historical analysis but is not of much use for trading algorithms since it technically means using future prices to calculate the derivative of the current point. Instead we can use a less common variant called a ' Backwards Difference ' that only uses a combination of points before the current one to help approximate the current derivative. In this script you can choose the " Order " of your derivative and the " Accuracy " of its approximation. This script is for educational purposes for folks building trading algorithms. Many trading algorithms often have an element of seeing how much Price has changed from the previous candle to the current candle. This approach is the lowest accuracy derivative possible, and using the backwards finite differences, made available for the first time on TradingView (!!), algorithms that use derivatives can now have higher orders of accuracy! Happy Trading/Developing!
Penunjuk Pine Script®
oleh mcbw_
AiTrend Pattern Matrix for kNN Forecasting (AiBitcoinTrend)The AiTrend Pattern Matrix for kNN Forecasting (AiBitcoinTrend) is a cutting-edge indicator that combines advanced mathematical modeling, AI-driven analytics, and segment-based pattern recognition to forecast price movements with precision. This tool is designed to provide traders with deep insights into market dynamics by leveraging multivariate pattern detection and sophisticated predictive algorithms. 👽 Core Features Segment-Based Pattern Recognition At its heart, the indicator divides price data into discrete segments, capturing key elements like candle bodies, high-low ranges, and wicks. These segments are normalized using ATR-based volatility adjustments to ensure robustness across varying market conditions. AI-Powered k-Nearest Neighbors (kNN) Prediction The predictive engine uses the kNN algorithm to identify the closest historical patterns in a multivariate dictionary. By calculating the distance between current and historical segments, the algorithm determines the most likely outcomes, weighting predictions based on either proximity (distance) or averages. Dynamic Dictionary of Historical Patterns The indicator maintains a rolling dictionary of historical patterns, storing multivariate data for: Candle body ranges, High-low ranges, Wick highs and lows. This dynamic approach ensures the model adapts continuously to evolving market conditions. Volatility-Normalized Forecasting Using ATR bands, the indicator normalizes patterns, reducing noise and enhancing the reliability of predictions in high-volatility environments. AI-Driven Trend Detection The indicator not only predicts price levels but also identifies market regimes by comparing current conditions to historically significant highs, lows, and midpoints. This allows for clear visualizations of trend shifts and momentum changes. 👽 Deep Dive into the Core Mathematics 👾 Segment-Based Multivariate Pattern Analysis The indicator analyzes price data by dividing each bar into distinct segments, isolating key components such as: Body Ranges: Differences between the open and close prices. High-Low Ranges: Capturing the full volatility of a bar. Wick Extremes: Quantifying deviations beyond the body, both above and below. Each segment contributes uniquely to the predictive model, ensuring a rich, multidimensional understanding of price action. These segments are stored in a rolling dictionary of patterns, enabling the indicator to reference historical behavior dynamically. 👾 Volatility Normalization Using ATR To ensure robustness across varying market conditions, the indicator normalizes patterns using Average True Range (ATR). This process scales each component to account for the prevailing market volatility, allowing the algorithm to compare patterns on a level playing field regardless of differing price scales or fluctuations. 👾 k-Nearest Neighbors (kNN) Algorithm The AI core employs the kNN algorithm, a machine-learning technique that evaluates the similarity between the current pattern and a library of historical patterns. Euclidean Distance Calculation: The indicator computes the multivariate distance across four distinct dimensions: body range, high-low range, wick low, and wick high. This ensures a comprehensive and precise comparison between patterns. Weighting Schemes: The contribution of each pattern to the forecast is either weighted by its proximity (distance) or averaged, based on user settings. 👾 Prediction Horizon and Refinement The indicator forecasts future price movements (Y_hat) by predicting logarithmic changes in the price and projecting them forward using exponential scaling. This forecast is smoothed using a user-defined EMA filter to reduce noise and enhance actionable clarity. 👽 AI-Driven Pattern Recognition Dynamic Dictionary of Patterns: The indicator maintains a rolling dictionary of N multivariate patterns, continuously updated to reflect the latest market data. This ensures it adapts seamlessly to changing market conditions. Nearest Neighbor Matching: At each bar, the algorithm identifies the most similar historical pattern. The prediction is based on the aggregated outcomes of the closest neighbors, providing confidence levels and directional bias. Multivariate Synthesis: By combining multiple dimensions of price action into a unified prediction, the indicator achieves a level of depth and accuracy unattainable by single-variable models. Visual Outputs Forecast Line (Y_hat_line): A smoothed projection of the expected price trend, based on the weighted contribution of similar historical patterns. Trend Regime Bands: Dynamic high, low, and midlines highlight the current market regime, providing actionable insights into momentum and range. Historical Pattern Matching: The nearest historical pattern is displayed, allowing traders to visualize similarities 👽 Applications Trend Identification: Detect and follow emerging trends early using dynamic trend regime analysis. Reversal Signals: Anticipate market reversals with high-confidence predictions based on historically similar scenarios. Range and Momentum Trading: Leverage multivariate analysis to understand price ranges and momentum, making it suitable for both breakout and mean-reversion strategies. Disclaimer: This information is for entertainment purposes only and does not constitute financial advice. Please consult with a qualified financial advisor before making any investment decisions.
Penunjuk Pine Script®
oleh aibitcointrend
5
Bayesian Price Projection Model [Pinescriptlabs]📊 Dynamic Price Projection Algorithm 📈 This algorithm combines **statistical calculations**, **technical analysis**, and **Bayesian theory** to forecast a future price while providing **uncertainty ranges** that represent upper and lower bounds. The calculations are designed to adjust projections by considering market **trends**, **volatility**, and the historical probabilities of reaching new highs or lows. Here’s how it works: 🚀 Future Price Projection A dynamic calculation estimates the future price based on three key elements: 1. **Trend**: Defines whether the market is predisposed to move up or down. 2. **Volatility**: Quantifies the magnitude of the expected change based on historical fluctuations. 3. **Time Factor**: Uses the logarithm of the projected period (`proyeccion_dias`) to adjust how time impacts the estimate. 🧠 **Bayesian Probabilistic Adjustment** - Conditional probabilities are calculated using **Bayes' formula**: \ This models future events using conditional information: - **Probability of reaching a new all-time high** if the price is trending upward. - **Probability of reaching a new all-time low** if the price is trending downward. - These probabilities refine the future price estimate by considering: - **Higher volatility** increases the likelihood of hitting extreme levels (highs/lows). - **Market trends** influence the expected price movement direction. 🌟 **Volatility Calculation** - Volatility is measured using the **ATR (Average True Range)** indicator with a 14-period window. This reflects the average amplitude of price fluctuations. - To express volatility as a percentage, the ATR is normalized by dividing it by the closing price and multiplying it by 200. - Volatility is then categorized into descriptive levels (e.g., **Very Low**, **Low**, **Moderate**, etc.) for better interpretation. --- 🎯 **Deviation Limits (Upper and Lower)** - The upper and lower limits form a **projected range** around the estimated future price, providing a framework for uncertainty. - These limits are calculated by adjusting the ATR using: - A user-defined **multiplier** (`factor_desviacion`). - **Bayesian probabilities** calculated earlier. - The **square root of the projected period** (`proyeccion_dias`), incorporating the principle that uncertainty grows over time. 🔍 **Interpreting the Model** This can be seen as a **dynamic probabilistic model** that: - Combines **technical analysis** (trends and ATR). - Refines probabilities using **Bayesian theory**. - Provides a **visual projection range** to help you understand potential future price movements and associated uncertainties. ⚡ Whether you're analyzing **volatile markets** or confirming **bullish/bearish scenarios**, this tool equips you with a robust, data-driven approach! 🚀 Español : 📊 Algoritmo de Proyección de Precio Dinámico 📈 Este algoritmo combina **cálculos estadísticos**, **análisis técnico** y **la teoría de Bayes** para proyectar un precio futuro, junto con rangos de **incertidumbre** que representan los límites superior e inferior. Los cálculos están diseñados para ajustar las proyecciones considerando la **tendencia del mercado**, **volatilidad** y las probabilidades históricas de alcanzar nuevos máximos o mínimos. Aquí se explica su funcionamiento: 🚀 **Proyección de Precio Futuro** Se realiza un cálculo dinámico del precio futuro estimado basado en tres elementos clave: 1. **Tendencia**: Define si el mercado tiene predisposición a subir o bajar. 2. **Volatilidad**: Determina la magnitud del cambio esperado en función de las fluctuaciones históricas. 3. **Factor de Tiempo**: Usa el logaritmo del período proyectado (`proyeccion_dias`) para ajustar cómo el tiempo afecta la estimación. 🧠 **Ajuste Probabilístico con la Teoría de Bayes** - Se calculan probabilidades condicionales mediante la fórmula de **Bayes**: \ Esto permite modelar eventos futuros considerando información condicional: - **Probabilidad de alcanzar un nuevo máximo histórico** si el precio sube. - **Probabilidad de alcanzar un nuevo mínimo histórico** si el precio baja. - Estas probabilidades ajustan la estimación del precio futuro considerando: - **Mayor volatilidad** aumenta la probabilidad de alcanzar niveles extremos (máximos/mínimos). - **La tendencia del mercado** afecta la dirección esperada del movimiento del precio. 🌟 **Cálculo de Volatilidad** - La volatilidad se mide usando el indicador **ATR (Average True Range)** con un período de 14 velas. Este indicador refleja la amplitud promedio de las fluctuaciones del precio. - Para obtener un valor porcentual, el ATR se normaliza dividiéndolo por el precio de cierre y multiplicándolo por 200. - Además, se clasifica esta volatilidad en categorías descriptivas (e.g., **Muy Baja**, **Baja**, **Moderada**, etc.) para facilitar su interpretación. 🎯 **Límites de Desviación (Superior e Inferior)** - Los límites superior e inferior representan un **rango proyectado** en torno al precio futuro estimado, proporcionando un marco para la incertidumbre. - Estos límites se calculan ajustando el ATR según: - Un **multiplicador** definido por el usuario (`factor_desviacion`). - Las **probabilidades condicionales** calculadas previamente. - La **raíz cuadrada del período proyectado** (`proyeccion_dias`), lo que incorpora el principio de que la incertidumbre aumenta con el tiempo. --- 🔍 **Interpretación del Modelo** Este modelo se puede interpretar como un **modelo probabilístico dinámico** que: - Integra **análisis técnico** (tendencias y ATR). - Ajusta probabilidades utilizando **la teoría de Bayes**. - Proporciona un **rango de proyección visual** para ayudarte a entender los posibles movimientos futuros del precio y su incertidumbre. ⚡ Ya sea que estés analizando **mercados volátiles** o confirmando **escenarios alcistas/bajistas**, ¡esta herramienta te ofrece un enfoque robusto y basado en datos! 🚀
Penunjuk Pine Script®
oleh PINE_LABS
3
Market Trades PinescriptlabsThis algorithm is designed to emulate the true order book of exchanges by showing the quantity of transactions of an asset in real-time, while identifying patterns of high activity and volatility in the market through the analysis of volume and price movements. 📈 Below, I explain how to understand and use the information provided by the chart, along with the trades table: Identification of High Activity Zones 🚀 The algorithm calculates the average volume and the rate of price change to detect areas with spikes in activity. This is visualized on the chart with labels "Volatility Spike Buy" and "Volatility Spike Sell": Volatility Spike Buy: Indicates an unusual increase in volatility in the buying market, suggesting a potential surge in buying interest. 🟢 Volatility Spike Sell: Signals an increase in volatility in the selling market, which may indicate selling pressure or a sudden massive sell-off. 🔴 Market Trades Table 📋 The table provides a detailed view of the latest trades: Price: Displays the price at which each trade was executed. 💵 Quantity (Traded): Indicates the amount of the asset traded. 💰 Type of Trade (Buy/Sell): Differentiates between buy (Buy) and sell (Sell) operations based on volume and strength. 🔄 Date and Time: Refers to the start of the calculated trading candle. ⏰ Recency: Identifies the most recent trade to facilitate tracking of current activity. 🔍 Analysis of Trade Imbalance ⚖️ The imbalance between buys and sells is calculated based on the volume of both. This indicator helps to understand whether the market has a tendency toward buying or selling, showing if there is greater strength on one side of the market. A positive imbalance suggests more buying pressure. 📊 A negative imbalance indicates greater selling pressure. 📉 Volume Presentation Visualizes the volume of buying and selling in the market, allowing the identification of buying or selling strength through the size of the volume candle. 🔍 Español : "Este algoritmo está diseñado para emular el verdadero libro de órdenes de los intercambios al mostrar la cantidad de transacciones de un activo en tiempo real, mientras identifica patrones de alta actividad y volatilidad en el mercado a través del análisis de volumen y movimientos de precios. 📈 A continuación, explico cómo entender y usar la información proporcionada por el gráfico, junto con la tabla de operaciones:" Identificación de Zonas de Alta Actividad 🚀 El algoritmo calcula el volumen promedio y la velocidad de cambio de precio para detectar zonas con picos de actividad. Esto se visualiza en el gráfico con etiquetas de "Volatility Spike Buy" y "Volatility Spike Sell": Volatility Spike Buy: Indica un incremento inusual de volatilidad en el mercado de compra, sugiriendo un posible interés de compra elevado. 🟢 Volatility Spike Sell: Señala un incremento de volatilidad en el mercado de venta, lo cual puede indicar presión de venta o una venta masiva repentina. 🔴 Tabla de Operaciones en el Mercado (Market Trades) 📋 La tabla proporciona una vista detallada de las últimas operaciones: Precio: Muestra el precio al cual se realizó cada operación. 💵 Cantidad (Transaccionada): Indica la cantidad del activo transaccionada. 💰 Tipo de operación (Buy/Sell): Diferencia entre operaciones de compra (Buy) y de venta (Sell), dependiendo del volumen y fuerza. 🔄 Fecha y Hora: Refleja el inicio de la vela de negociación calculada. ⏰ Recency: Identifica la operación más reciente para facilitar el seguimiento de la actividad actual. 🔍 Análisis de Desequilibrio de Operaciones (Imbalance) ⚖️ El desequilibrio entre compras y ventas se calcula con base en el volumen de ambas. Este indicador ayuda a entender si el mercado tiene una tendencia hacia la compra o venta, mostrando si hay una mayor fuerza en uno de los lados del mercado. Un desequilibrio positivo sugiere más presión de compra. 📊 Un desequilibrio negativo indica mayor presión de venta. 📉 Presentación en Volumen Visualiza el volumen de compra y venta en el mercado, permitiendo identificar mediante el tamaño de la vela de volumen la fuerza, ya sea compradora o vendedora. 🔍
Penunjuk Pine Script®
oleh PINE_LABS
2
RSI (Kernel Optimized) | Flux Charts💎 GENERAL OVERVIEW Introducing our new KDE Optimized RSI Indicator! This indicator adds a new aspect to the well-known RSI indicator, with the help of the KDE (Kernel Density Estimation) algorithm, estimates the probability of a candlestick will be a pivot or not. For more information about the process, please check the "HOW DOES IT WORK ?" section. Features of the new KDE Optimized RSI Indicator : A New Approach To Pivot Detection Customizable KDE Algorithm Realtime RSI & KDE Dashboard Alerts For Possible Pivots Customizable Visuals ❓ HOW TO INTERPRET THE KDE % The KDE % is a critical metric that reflects how closely the current RSI aligns with the KDE (Kernel Density Estimation) array. In simple terms, it represents the likelihood that the current candlestick is forming a pivot point based on historical data patterns. a low percentage suggests a lower probability of the current candlestick being a pivot point. In these cases, price action is less likely to reverse, and existing trends may continue. At moderate levels, the possibility of a pivot increases, indicating potential trend shifts or consolidations.Traders should start monitoring closely for confirmation signals. An even higher KDE % suggests a strong likelihood that the current candlestick could form a pivot point, which could lead to a reversal or significant price movement. These points often align with overbought or oversold conditions in traditional RSI analysis, making them key moments for potential trade entry or exit. 📌 HOW DOES IT WORK ? The RSI (Relative Strength Index) is a widely used oscillator among traders. It outputs a value between 0 - 100 and gives a glimpse about the current momentum of the price action. This indicator then calculates the RSI for each candlesticks, and saves them into an array if the candlestick is a pivot. The low & high pivot RSIs' are inserted into two different arrays. Then the a KDE array is calculated for both of the low & high pivot RSI arrays. Explaining the KDE might be too much for this write-up, but for a brief explanation, here are the steps : 1. Define the necessary options for the KDE function. These are : Bandwidth & Nº Steps, Array Range (Array Max - Array Min) 2. After that, create a density range array. The array has (steps * 2 - 1) elements and they are calculated by (arrMin + i * stepCount), i being the index. 3. Then, define a kernel function. This indicator has 3 different kernel distribution modes : Uniform, Gaussian and Sigmoid 4. Then, define a temporary value for the current element of KDE array. 5. For each element E in the pivot RSI array, add "kernel(densityRange.get(i) - E, 1.0 / bandwidth)" to the temporary value. 6. Add 1.0 / arrSize * to the KDE array. Then the prefix sum array of the KDE array is calculated. For each candlestick, the index closest to it's RSI value in the KDE array is found using binary search. Then for the low pivot KDE calculation, the sum of KDE values from found index to max index is calculated. For the high pivot KDE, the sum of 0 to found index is used. Then if high or low KDE value is greater than the activation threshold determined in the settings, a bearish or bullish arrow is plotted after bar confirmation respectively. The arrows are drawn as long as the KDE value of current candlestick is greater than the threshold. When the KDE value is out of the threshold, a less transparent arrow is drawn, indicating a possible pivot point. 🚩 UNIQUENESS This indicator combines RSI & KDE Algorithm to get a foresight of possible pivot points. Pivot points are important entry, confirmation and exit points for traders. But to their nature, they can be only detected after more candlesticks are rendered after them. The purpose of this indicator is to alert the traders of possible pivot points using KDE algorithm right away when they are confirmed. The indicator also has a dashboard for realtime view of the current RSI & Bullish or Bearish KDE value. You can fully customize the KDE algorithm and set up alerts for pivot detection. ⚙️ SETTINGS 1. RSI Settings RSI Length -> The amount of bars taken into account for RSI calculation. Source -> The source value for RSI calculation. 2. Pivots Pivot Lengths -> Pivot lengths for both high & low pivots. For example, if this value is set to 21; 21 bars before AND 21 bars after a candlestick must be higher for a candlestick to be a low pivot. 3. KDE Activation Threshold -> This setting determines the amount of arrows shown. Higher options will result in more arrows being rendered. Kernel -> The kernel function as explained in the upper section. Bandwidth -> The bandwidth variable as explained in the upper section. The smoothness of the KDE function is tied to this setting. Nº Bins -> The Nº Steps variable as explained in the upper section. It determines the precision of the KDE algorithm.
Penunjuk Pine Script®
oleh fluxchart
23
Uptrick: Dynamic AMA RSI Indicator### **Uptrick: Dynamic AMA RSI Indicator** **Overview:** The **Uptrick: Dynamic AMA RSI Indicator** is an advanced technical analysis tool designed for traders who seek to optimize their trading strategies by combining adaptive moving averages with the Relative Strength Index (RSI). This indicator dynamically adjusts to market conditions, offering a nuanced approach to trend detection and momentum analysis. By leveraging the Adaptive Moving Average (AMA) and Fast Adaptive Moving Average (FAMA), along with RSI-based overbought and oversold signals, traders can better identify entry and exit points with higher precision and reduced noise. **Key Components:** 1. **Source Input:** - The source input is the price data that forms the basis of all calculations. Typically set to the closing price, traders can customize this to other price metrics such as open, high, low, or even the output of another indicator. This flexibility allows the **Uptrick** indicator to be tailored to a wide range of trading strategies. 2. **Adaptive Moving Average (AMA):** - The AMA is a moving average that adapts its sensitivity based on the dominant market cycle. This adaptation allows the AMA to respond swiftly to significant price movements while smoothing out minor fluctuations, making it particularly effective in trending markets. The AMA adjusts its responsiveness dynamically using a calculated phase adjustment from the dominant cycle, ensuring it remains responsive to the current market environment without being overly reactive to market noise. 3. **Fast Adaptive Moving Average (FAMA):** - The FAMA is a more sensitive version of the AMA, designed to react faster to price changes. It serves as a signal line in the crossover strategy, highlighting shorter-term trends. The interaction between the AMA and FAMA forms the core of the signal generation, with crossovers between these lines indicating potential buy or sell opportunities. 4. **Relative Strength Index (RSI):** - The RSI is a momentum oscillator that measures the speed and change of price movements, providing insights into whether an asset is overbought or oversold. In the **Uptrick** indicator, the RSI is used to confirm the validity of crossover signals between the AMA and FAMA, adding an additional layer of reliability to the trading signals. **Indicator Logic:** 1. **Dominant Cycle Calculation:** - The indicator starts by calculating the dominant market cycle using a smoothed price series. This involves applying exponential moving averages to a series of price differences, extracting cycle components, and determining the instantaneous phase of the cycle. This phase is then adjusted to provide a phase adjustment factor, which plays a critical role in determining the adaptive alpha. 2. **Adaptive Alpha Calculation:** - The adaptive alpha, a key feature of the AMA, is computed based on the fast and slow limits set by the trader. This alpha is clamped within these limits to ensure the AMA remains appropriately sensitive to market conditions. The dynamic adjustment of alpha allows the AMA to be highly responsive in volatile markets and more conservative in stable markets. 3. **Crossover Detection:** - The indicator generates trading signals based on crossovers between the AMA and FAMA: - **CrossUp:** When the AMA crosses above the FAMA, it indicates a potential bullish trend, suggesting a buy opportunity. - **CrossDown:** When the AMA crosses below the FAMA, it signals a potential bearish trend, indicating a sell opportunity. 4. **RSI Confirmation:** - To enhance the reliability of these crossover signals, the indicator uses the RSI to confirm overbought and oversold conditions: - **Buy Signal:** A buy signal is generated only when the AMA crosses above the FAMA and the RSI confirms an oversold condition, ensuring that the signal aligns with a momentum reversal from a low point. - **Sell Signal:** A sell signal is triggered when the AMA crosses below the FAMA and the RSI confirms an overbought condition, indicating a momentum reversal from a high point. 5. **Signal Management:** - To prevent signal redundancy during strong trends, the indicator tracks the last generated signal (buy or sell) and ensures that the next signal is only issued when there is a genuine reversal in trend direction. 6. **Signal Visualization:** - **Buy Signals:** The indicator plots a "BUY" label below the bar when a buy signal is generated, using a green color to clearly mark the entry point. - **Sell Signals:** A "SELL" label is plotted above the bar when a sell signal is detected, marked in red to indicate an exit or shorting opportunity. - **Bar Coloring (Optional):** Traders have the option to enable bar coloring, where green bars indicate a bullish trend (AMA above FAMA) and red bars indicate a bearish trend (AMA below FAMA), providing a visual representation of the market’s direction. **Customization Options:** - **Source:** Traders can select the price data input that best suits their strategy (e.g., close, open, high, low, or custom indicators). - **Fast Limit:** Adjustable sensitivity for the fast response of the AMA, allowing traders to tailor the indicator to different market conditions. - **Slow Limit:** Sets the slower boundary for the AMA’s sensitivity, providing stability in less volatile markets. - **RSI Length:** The period for the RSI calculation can be adjusted to fit different trading timeframes. - **Overbought/Oversold Levels:** These thresholds can be customized to define the RSI levels that trigger buy or sell confirmations. - **Enable Bar Colors:** Traders can choose whether to enable bar coloring based on the AMA/FAMA relationship, enhancing visual clarity. **How Different Traders Can Use the Indicator:** 1. **Day Traders:** - **Uptrick: Dynamic AMA RSI Indicator** is highly effective for day traders who need to make quick decisions in fast-moving markets. The adaptive nature of the AMA and FAMA allows the indicator to respond rapidly to intraday price swings. Day traders can use the buy and sell signals generated by the crossover and RSI confirmation to time their entries and exits with greater precision, minimizing exposure to false signals often prevalent in high-frequency trading environments. 2. **Swing Traders:** - Swing traders can benefit from the indicator’s ability to identify and confirm trend reversals over several days or weeks. By adjusting the RSI length and sensitivity limits, swing traders can fine-tune the indicator to catch longer-term price movements, helping them to ride trends and maximize profits over medium-term trades. The dual confirmation of crossovers with RSI ensures that swing traders enter trades that have a higher probability of success. 3. **Position Traders:** - For position traders who hold trades over longer periods, the **Uptrick** indicator offers a reliable method to stay in trades that align with the dominant trend while avoiding premature exits. By adjusting the slow limit and extending the RSI length, position traders can smooth out the indicator’s sensitivity, allowing them to focus on major market shifts rather than short-term volatility. The bar coloring feature also provides a clear visual indication of the overall trend, aiding in trade management decisions. 4. **Scalpers:** - Scalpers, who seek to profit from small price movements, can use the fast responsiveness of the FAMA in conjunction with the RSI to identify micro-trends within larger market moves. The indicator’s ability to adapt quickly to changing conditions makes it a valuable tool for scalpers looking to execute numerous trades in a short period, capturing profits from minor price fluctuations while avoiding prolonged exposure. 5. **Algorithmic Traders:** - Algorithmic traders can incorporate the **Uptrick** indicator into automated trading systems. The precise crossover signals combined with RSI confirmation provide clear and actionable rules that can be coded into algorithms. The adaptive nature of the indicator ensures that it can be used across different market conditions and timeframes, making it a versatile component of algorithmic strategies. **Usage:** The **Uptrick: Dynamic AMA RSI Indicator** is a versatile tool that can be integrated into various trading strategies, from short-term day trading to long-term investing. Its ability to adapt to changing market conditions and provide clear buy and sell signals makes it an invaluable asset for traders seeking to improve their trading performance. Whether used as a standalone indicator or in conjunction with other technical tools, **Uptrick** offers a dynamic approach to market analysis, helping traders to navigate the complexities of financial markets with greater confidence. **Conclusion:** The **Uptrick: Dynamic AMA RSI Indicator** offers a comprehensive and adaptable solution for traders across different styles and timeframes. By combining the strengths of adaptive moving averages with RSI confirmation, it delivers robust signals that help traders capitalize on market trends while minimizing the risk of false signals. This indicator is a powerful addition to any trader’s toolkit, enabling them to make informed decisions with greater precision and confidence. Whether you're a day trader, swing trader, or long-term investor, the **Uptrick** indicator can enhance your trading strategy and improve your market outcomes.
Penunjuk Pine Script®
oleh Uptrick
1
ICT Power Of Three | Flux Charts💎 GENERAL OVERVIEW Introducing our new ICT Power Of Three Indicator! This indicator is built around the ICT's "Power Of Three" strategy. This strategy makes use of these 3 key smart money concepts : Accumulation, Manipulation and Distribution. Each step is explained in detail within this write-up. For more information about the process, check the "HOW DOES IT WORK" section. Features of the new ICT Power Of Three Indicator : Implementation of ICT's Power Of Three Strategy Different Algorithm Modes Customizable Execution Settings Customizable Backtesting Dashboard Alerts for Buy, Sell, TP & SL Signals 📌 HOW DOES IT WORK ? The "Power Of Three" comes from these three keywords "Accumulation, Manipulation and Distribution". Here is a brief explanation of each keyword : Accumulation -> Accumulation phase is when the smart money accumulate their positions in a fixed range. This phase indicates price stability, generally meaning that the price constantly switches between up & down trend between a low and a high pivot point. When the indicator detects an accumulation zone, the Power Of Three strategy begins. Manipulation -> When the smart money needs to increase their position sizes, they need retail traders' positions for liquidity. So, they manipulate the market into the opposite direction of their intended direction. This will result in retail traders opening positions the way that the smart money intended them to do, creating liquidity. After this step, the real move that the smart money intended begins. Distribution -> This is when the real intention of the smart money comes into action. With the new liquidity thanks to the manipulation phase, the smart money add their positions towards the opposite direction of the retail mindset. The purpose of this indicator is to detect the accumulation and manipulation phases, and help the trader move towards the same direction as the smart money for their trades. Detection Methods Of The Indicator : Accumulation -> The indicator detects accumulation zones as explained step-by-step : 1. Draw two lines from the lowest point and the highest point of the latest X bars. 2. If the (high line - low line) is lower than Average True Range (ATR) * accumulationConstant 3. After the condition is validated, an accumulation zone is detected. The accumulation zone will be invalidated and manipulation phase will begin when the range is broken. Manipulation -> If the accumulation range is broken, check if the current bar closes / wicks above the (high line + ATR * manipulationConstant) or below the (low line - ATR * manipulationConstant). If the condition is met, the indicator detects a manipulation zone. Distribution -> The purpose of this indicator is to try to foresee the distribution zone, so instead of a detection, after the manipulation zone is detected the indicator automatically create a "shadow" distribution zone towards the opposite direction of the freshly detected manipulation zone. This shadow distribution zone comes with a take-profit and stop-loss layout, customizable by the trader in the settings. The X bars, accumulationConstant and manipulationConstant are subject to change with the "Algorithm Mode" setting. Read the "Settings" section for more information. This indicator follows these steps and inform you step by step by plotting them in your chart. 🚩UNIQUENESS This indicator is an all-in-one suite for the ICT's Power Of Three concept. It's capable of plotting the strategy, giving signals, a backtesting dashboard and alerts feature. Different and customizable algorithm modes will help the trader fine-tune the indicator for the asset they are currently trading. The backtesting dashboard allows you to see how your settings perform in the current ticker. You can also set up alerts to get informed when the strategy is executable for different tickers. ⚙️SETTINGS 1. General Configuration Algorithm Mode -> The indicator offers 3 different detection algorithm modes according to your needs. Here is the explanation of each mode. a) Small Manipulation This mode has the default bar length for the accumulation detection, but a lower manipulation constant, meaning that slighter imbalances in the price action can be detected as manipulation. This setting can be useful on tickers that have lower liquidity, thus can be manipulated easier. b) Big Manipulation This mode has the default bar length for the accumulation detection, but a higher manipulation constant, meaning that heavier imbalances on the price action are required in order to detect manipulation zones. This setting can be useful on tickers that have higher liquidity, thus can be manipulated harder. c) Short Accumulation This mode has a ~70% lower bar length requirement for accumulation zone detection, and the default manipulation constant. This setting can be useful on tickers that are highly volatile and do not enter accumulation phases too often. Breakout Method -> If "Close" is selected, bar close price will be taken into calculation when Accumulation & Manipulation zone invalidation. If "Wick" is selected, a wick will be enough to validate the corresponding zone. 2. TP / SL TP / SL Method -> If "Fixed" is selected, you can adjust the TP / SL ratios from the settings below. If "Dynamic" is selected, the TP / SL zones will be auto-determined by the algorithm. Risk -> The risk you're willing to take if "Dynamic" TP / SL Method is selected. Higher risk usually means a better winrate at the cost of losing more if the strategy fails. This setting is has a crucial effect on the performance of the indicator, as different tickers may have different volatility so the indicator may have increased performance when this setting is correctly adjusted. 3. Visuals Show Zones -> Enables / Disables rendering of Accumulation (yellow) and Manipulation (red) zones.
Penunjuk Pine Script®
oleh fluxchart
33
KNN OscillatorOverview The KNN Oscillator is an advanced technical analysis tool designed to help traders identify potential trend reversals and market momentum. Using the K-Nearest Neighbors (KNN) algorithm, this oscillator normalizes KNN values to create a dynamic and responsive indicator. The oscillator line changes color to reflect the market sentiment, providing clear visual cues for trading decisions. Key Features Dynamic Color Oscillator: The line changes color based on the oscillator value – green for positive, red for negative, and grey for neutral. Advanced KNN Algorithm: Utilizes the K-Nearest Neighbors algorithm for precise trend detection. Normalized Values: Ensures the oscillator values are normalized to align with the stock price range, making it applicable to various assets. Easy Integration: Can be easily added to any TradingView chart for enhanced analysis. How It Works The KNN Oscillator leverages the K-Nearest Neighbors algorithm to calculate the average distance of the nearest neighbors over a specified period. These values are then normalized to match the stock price range, ensuring they are comparable across different assets. The oscillator value is derived by taking the difference between the normalized KNN values and the source price. The line's color changes dynamically to provide an immediate visual indication of the market's state: Green: Positive values indicate upward momentum. Red: Negative values indicate downward momentum. Grey: Neutral values indicate a stable or consolidating market. Usage Instructions Trend Reversal Detection: Use the color changes to identify potential trend reversals. A shift from red to green suggests a bullish reversal, while a shift from green to red indicates a bearish reversal. Momentum Analysis: The oscillator's value and color help gauge market momentum. Strong positive values (green) indicate strong upward momentum, while strong negative values (red) indicate strong downward momentum. Market Sentiment: The dynamic color changes provide an easy-to-understand visual representation of market sentiment, helping traders make informed decisions quickly. Confirmation Tool: Use the KNN Oscillator in conjunction with other technical indicators to confirm signals and improve the accuracy of your trades. Scalability: Applicable to various timeframes and asset classes, making it a versatile tool for all types of traders.
Penunjuk Pine Script®
oleh PuzzlerTrades
Tri-State SupertrendTri-State Supertrend: Buy, Sell, Range ( Credits: Based on "Pivot Point Supertrend" by LonesomeTheBlue.) Tri-State Supertrend incorporates a range filter into a supertrend algorithm. So in addition to the Buy and Sell states, we now also have a Range state. This avoids the typical "whipsaw" problem: During a range, a standard supertrend algorithm will fire Buy and Sell signals in rapid succession. These signals are all false signals as they lead to losing positions when acted on. In this case, a tri-state supertrend will go into Range mode and stay in this mode until price exits the range and a new trend begins. I used Pivot Point Supertrend by LonesomeTheBlue as a starting point for this script because I believe LonesomeTheBlue's version is superior to the classic Supertrend algorithm. This indicator has two additional parameters over Pivot Point Supertrend: A flag to turn the range filter on or off A range size threshold in percent With that last parameter, you can define what a range is. The best value will depend on the asset you are trading. Also, there are two new display options. "Show (non-) trendline for ranges" - determines whether to draw the "trendline" inside of a range. Seeing as there is no trend in a range, this is usually just visual noise. "Show suppressed signals" - allows you to see the Buy/Sell signals that were skipped by the range filter. How to use Tri-State Supertrend in a strategy You can use the Buy and Sell signals to enter positions as you would with a normal supertrend. Adding stop loss, trailing stop etc. is of course encouraged and very helpful. But what to do when the Range signal appears? I currently run a strategy on LDO based on Tri-State Supertrend which appears to be profitable. (It will quite likely be open sourced at some point, but it is not released yet.) In that strategy, I experimented with different actions being taken when the Range state is entered: Continue: Just keep last position open during the range Close: Close the last position when entering range Reversal: During the range, execute the OPPOSITE of each signal (sell on "buy", buy on "sell") In the backtest, it transpired that "Continue" was the most profitable option for this strategy. How ranges are detected The mechanism is pretty simple: During each Buy or Sell trend, we record price movement, specifically, the furthest move in the trend direction that was encountered (expressed as a percentage). When a new signal is issued, the algorithm checks whether this value (for the last trend) is below the range size set by the user. If yes, we enter Range mode. The same logic is used to exit Range mode. This check is performed on every bar in a range, so we can enter a buy or sell as early as possible. I found that this simple logic works astonishingly well in practice. Pros/cons of the range filter A range filter is an incredibly useful addition to a supertrend and will most likely boost your profits. You will see at most one false signal at the beginning of each range (because it takes a bit of time to detect the range); after that, no more false signals will appear over the range's entire duration. So this is a huge advantage. There is essentially only one small price you have to pay: When a range ends, the first Buy/Sell signal you get will be delayed over the regular supertrend's signal. This is, again, because the algorithm needs some time to detect that the range has ended. If you select a range size of, say, 1%, you will essentially lose 1% of profit in each range because of this delay. In practice, it is very likely that the benefits of a range filter outweigh its cost. Ranges can last quite some time, equating to many false signals that the range filter will completely eliminate (all except for the first one, as explained above). You have to do your own tests though :)
Penunjuk Pine Script®
oleh StefanReich
13
Smart Money Concepts Probability (Expo)█ Overview The Smart Money Concept Probability (Expo) is an indicator developed to track the actions of institutional investors, commonly known as "smart money." This tool calculates the likelihood of smart money being actively engaged in buying or selling within the market, referred to as the "smart money order flow." The indicator measures the probability of three key events: Change of Character ( CHoCH ), Shift in Market Structure ( SMS ), and Break of Structure ( BMS ). These probabilities are displayed as percentages alongside their respective levels, providing a straightforward and immediate understanding of the likelihood of smart money order flow. Finally, the backtested results are shown in a table, which gives traders an understanding of the historical performance of the current order flow direction. █ Calculations The algorithm individually computes the likelihood of the events ( CHoCH , SMS , and BMS ). A positive score is assigned for events where the price successfully breaks through the level with the highest probability, and a negative score when the price fails to do so. By doing so, the algorithm determines the probability of each event occurring and calculates the total profitability derived from all the events. █ Example In this case, we have an 85% probability that the price will break above the upper range and make a new Break Of Structure and only a 16.36% probability that the price will break below the lower range and make a Change Of Character. █ Settings The Structure Period sets the pivot period to use when calculating the market structure. The Structure Response sets how responsive the market structure should be. A low value returns a more responsive structure. A high value returns a less responsive structure. █ How to use This indicator is a perfect tool for anyone that wants to understand the probability of a Change of Character ( CHoCH ), Shift in Market Structure ( SMS ), and Break of Structure ( BMS ) The insights provided by this tool help traders gain an understanding of the smart money order flow direction, which can be used to determine the market trend. █ Any Alert function call An alert is sent when the price breaks the upper or lower range, and you can select what should be included in the alert. You can enable the following options: Ticker ID Timeframe Probability percentage ----------------- Disclaimer The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs. My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Penunjuk Pine Script®
oleh Zeiierman
110
Breakout Probability (Expo)█ Overview Breakout Probability is a valuable indicator that calculates the probability of a new high or low and displays it as a level with its percentage. The probability of a new high and low is backtested, and the results are shown in a table— a simple way to understand the next candle's likelihood of a new high or low. In addition, the indicator displays an additional four levels above and under the candle with the probability of hitting these levels. The indicator helps traders to understand the likelihood of the next candle's direction, which can be used to set your trading bias. █ Calculations The algorithm calculates all the green and red candles separately depending on whether the previous candle was red or green and assigns scores if one or more lines were reached. The algorithm then calculates how many candles reached those levels in history and displays it as a percentage value on each line. █ Example In this example, the previous candlestick was green; we can see that a new high has been hit 72.82% of the time and the low only 28.29%. In this case, a new high was made. █ Settings Percentage Step The space between the levels can be adjusted with a percentage step. 1% means that each level is located 1% above/under the previous one. Disable 0.00% values If a level got a 0% likelihood of being hit, the level is not displayed as default. Enable the option if you want to see all levels regardless of their values. Number of Lines Set the number of levels you want to display. Show Statistic Panel Enable this option if you want to display the backtest statistics for that a new high or low is made. (Only if the first levels have been reached or not) █ Any Alert function call An alert is sent on candle open, and you can select what should be included in the alert. You can enable the following options: Ticker ID Bias Probability percentage The first level high and low price █ How to use This indicator is a perfect tool for anyone that wants to understand the probability of a breakout and the likelihood that set levels are hit. The indicator can be used for setting a stop loss based on where the price is most likely not to reach. The indicator can help traders to set their bias based on probability. For example, look at the daily or a higher timeframe to get your trading bias, then go to a lower timeframe and look for setups in that direction. ----------------- Disclaimer The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs. My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Penunjuk Pine Script®
oleh Zeiierman
286
FATL, SATL, RFTL, & RSTL Digital Signal Filter Smoother [Loxx]FATL, SATL, RFTL, & RSTL Digital Signal Filter (DSP) Smoother is is a baseline indicator with DSP processed source inputs What are digital indicators: distinctions from standard tools, types of filters. To date, dozens of technical analysis indicators have been developed: trend instruments, oscillators, etc. Most of them use the method of averaging historical data, which is considered crude. But there is another group of tools - digital indicators developed on the basis of mathematical methods of spectral analysis. Their formula allows the trader to filter price noise accurately and exclude occasional surges, making the forecast more effective in comparison with conventional indicators. In this review, you will learn about their distinctions, advantages, types of digital indicators and examples of strategies based on them. Two non-standard strategies based on digital indicators Basic technical analysis indicators built into most platforms are based on mathematical formulas. These formulas are a reflection of market behavior in past periods. In other words, these indicators are built based on patterns that were discovered as a result of statistical analysis, which allows one to predict further trend movement to some extent. But there is also a group of indicators called digital indicators. They are developed using mathematical analysis and are an algorithmic spectral system called ATCF (Adaptive Trend & Cycles Following). In this article, I will tell you more about the components of this system, describe the differences between digital and regular indicators, and give examples of 2 strategies with indicator templates. ATCF - Market Spectrum Analysis Method There is a theory according to which the market is chaotic and unpredictable, i.e. it cannot be accurately analyzed. After all, no one can tell how traders will react to certain news, or whether some large investor will want to play against the market like George Soros did with the Bank of England. But there is another theory: many general market trends are logical, and have a rationale, causes and effects. The economy is undulating, which means it can be described by mathematical methods. Digital indicators are defined as a group of algorithms for assessing the market situation, which are based exclusively on mathematical methods. They differ from standard indicators by the form of analysis display. They display certain values: price, smoothed price, volumes. Many standard indicators are built on the basis of filtering the minute significant price fluctuations with the help of moving averages and their variations. But we can hardly call the MA a good filter, because digital indicators that use spectral filters make it possible to do a more accurate calculation. Simply put, digital indicators are technical analysis tools in which spectral filters are used to filter out price noise instead of moving averages. The display of traditional indicators is lines, areas, and channels. Digital indicators can be displayed both in the form of lines and in digital form (a set of numbers in columns, any data in a text field, etc.). The digital display of the data is more like an additional source of statistics; for trading, a standard visual linear chart view is used. All digital models belong to the category of spectral analysis of the market situation. In conventional technical indicators, price indications are averaged over a fixed period of time, which gives a rather rough result. The use of spectral analysis allows us to increase trading efficiency due to the fact that digital indicators use a statistical data set of past periods, which is converted into a “frequency” of the market (period of fluctuations). Fourier theory provides the following spectral ranging of the trend duration: low frequency range (0-4) - a reflection of a long trend of 2 months or more medium frequency range (5-40) - the trend lasts 10-60 days, thus it is referred to as a correction high frequency range (41-130) - price noise that lasts for several days The ATCF algorithm is built on the basis of spectral analysis and includes a set of indicators created using digital filters. Its consists of indicators and filters: FATL: Built on the basis of a low-frequency digital trend filter SATL: Built on the basis of a low-frequency digital trend filter of a different order RFTL: High frequency trend line RSTL: Low frequency trend line Inclucded: 4 DSP filters Bar coloring Keltner channels with variety ranges and smoothing functions Bollinger bands 40 Smoothing filters 33 souce types Variable channels
Penunjuk Pine Script®
oleh loxx
3
Ehlers Autocorrelation Periodogram [Loxx]Ehlers Autocorrelation Periodogram contains two versions of Ehlers Autocorrelation Periodogram Algorithm. This indicator is meant to supplement adaptive cycle indicators that myself and others have published on Trading View, will continue to publish on Trading View. These are fast-loading, low-overhead, streamlined, exact replicas of Ehlers' work without any other adjustments or inputs. Versions: - 2013, Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers - 2016, TASC September, "Measuring Market Cycles" Description The Ehlers Autocorrelation study is a technical indicator used in the calculation of John F. Ehlers’s Autocorrelation Periodogram. Its main purpose is to eliminate noise from the price data, reduce effects of the “spectral dilation” phenomenon, and reveal dominant cycle periods. The spectral dilation has been discussed in several studies by John F. Ehlers; for more information on this, refer to sources in the "Further Reading" section. As the first step, Autocorrelation uses Mr. Ehlers’s previous installment, Ehlers Roofing Filter, in order to enhance the signal-to-noise ratio and neutralize the spectral dilation. This filter is based on aerospace analog filters and when applied to market data, it attempts to only pass spectral components whose periods are between 10 and 48 bars. Autocorrelation is then applied to the filtered data: as its name implies, this function correlates the data with itself a certain period back. As with other correlation techniques, the value of +1 would signify the perfect correlation and -1, the perfect anti-correlation. Using values of Autocorrelation in Thermo Mode may help you reveal the cycle periods within which the data is best correlated (or anti-correlated) with itself. Those periods are displayed in the extreme colors (orange) while areas of intermediate colors mark periods of less useful cycles. What is an adaptive cycle, and what is the Autocorrelation Periodogram Algorithm? From his Ehlers' book mentioned above, page 135: "Adaptive filters can have several different meanings. For example, Perry Kaufman’s adaptive moving average ( KAMA ) and Tushar Chande’s variable index dynamic average ( VIDYA ) adapt to changes in volatility . By definition, these filters are reactive to price changes, and therefore they close the barn door after the horse is gone.The adaptive filters discussed in this chapter are the familiar Stochastic , relative strength index ( RSI ), commodity channel index ( CCI ), and band-pass filter.The key parameter in each case is the look-back period used to calculate the indicator.This look-back period is commonly a fixed value. However, since the measured cycle period is changing, as we have seen in previous chapters, it makes sense to adapt these indicators to the measured cycle period. When tradable market cycles are observed, they tend to persist for a short while.Therefore, by tuning the indicators to the measure cycle period they are optimized for current conditions and can even have predictive characteristics. The dominant cycle period is measured using the Autocorrelation Periodogram Algorithm. That dominant cycle dynamically sets the look-back period for the indicators. I employ my own streamlined computation for the indicators that provide smoother and easier to interpret outputs than traditional methods. Further, the indicator codes have been modified to remove the effects of spectral dilation.This basically creates a whole new set of indicators for your trading arsenal." How to use this indicator The point of the Ehlers Autocorrelation Periodogram Algorithm is to dynamically set a period between a minimum and a maximum period length. While I leave the exact explanation of the mechanic to Dr. Ehlers’s book, for all practical intents and purposes, in my opinion, the punchline of this method is to attempt to remove a massive source of overfitting from trading system creation–namely specifying a look-back period. SMA of 50 days? 100 days? 200 days? Well, theoretically, this algorithm takes that possibility of overfitting out of your hands. Simply, specify an upper and lower bound for your look-back, and it does the rest. In addition, this indicator tells you when its best to use adaptive cycle inputs for your other indicators. Usage Example 1 Let's say you're using "Adaptive Qualitative Quantitative Estimation (QQE) ". This indicator has the option of adaptive cycle inputs. When the "Ehlers Autocorrelation Periodogram " shows a period of high correlation that adaptive cycle inputs work best during that period. Usage Example 2 Check where the dominant cycle line lines, grab that output number and inject it into your other standard indicators for the length input.
Penunjuk Pine Script®
oleh loxx
5
Bogdan Ciocoiu - Code runnerDescription The Code Runner is a hybrid indicator that leverages other pre-configured, integrated open-source algorithms to help traders spot regular and continuation divergences. The Code Runner specialises in integrating some of the most popular oscillators well known for their accuracy when scalping using divergence strategies. Uniqueness The Code Runner stands out as a one-stop-shop pack of oscillator algorithms that traders can further customise to spot divergences. The indicator's uniqueness stands from its capability to recast each algorithm to apply to the same scale. This feature is achieved by manually adjusting the outputs of each algorithm to fit on a scale between +100 and -100. Another benefit of the Code Runner comes from its standardisation of outputs, mainly consisting of lines. Showing lines enables traders to draw potential regular and continuation divergences quickly. The indicator has been pre-configured to support scalping at 1-5 minutes. Open-source The Code Runner uses the following open-source scripts and algorithms: www.tradingview.com www.tradingview.com www.tradingview.com www.tradingview.com www.tradingview.com www.tradingview.com www.tradingview.com www.tradingview.com These algorithms are available in the public domain either in TradingView space or outside (given their popularity in the financial markets industry).
Penunjuk Pine Script®
oleh BogdanCiocoiu