Quick scan for signal🙏🏻 Hey TV, this is QSFS, following:
^^ Quick scan for drift (QSFD)
^^ Quick scan for cycles (QSFC)
As mentioned before, ML trading is all about spotting any kind of non-randomness, and this metric (along with 2 previously posted) gonna help ya'll do it fast. This one will show you whether your time series possibly exhibits mean-reverting / consistent / noisy behavior, that can be later confirmed or denied by more sophisticated tools. This metric is O(n) in windowed mode and O(1) if calculated incrementally on each data update, so you can scan Ks of datasets w/o worrying about melting da ice.
^^ windowed mode
Now the post will be divided into several sections, and a couple of things I guess you’ve never seen or thought about in your life:
1) About Efficiency Ratios posted there on TV;
Some of you might say this is the Efficiency Ratio you’ve seen in Perry's book. Firstly, I can assure you that neither me nor Perry, just as X amount of quants all over the world and who knows who else, would say smth like, "I invented it," lol. This is just a thing you R&D when you need it. Secondly, I invite you (and mods & admin as well) to take a lil glimpse at the following screenshot:
^^ not cool...
So basically, all the Efficiency Ratios that were copypasted to our platform suffer the same bug: dudes don’t know how indexing works in Pine Script. I mean, it’s ok, I been doing the same mistakes as well, but loxx, cmon bro, you... If you guys ever read it, the lines 20 and 22 in da code are dedicated to you xD
2) About the metric;
This supports both moving window mode when Length > 0 and all-data expanding window mode when Length < 1, calculating incrementally from the very first data point in the series: O(n) on history, O(1) on live updates.
Now, why do I SQRT transform the result? This is a natural action since the metric (being a ratio in essence) is bounded between 0 and 1, so it can be modeled with a beta distribution. When you SQRT transform it, it still stays beta (think what happens when you apply a square root to 0.01 or 0.99), but it becomes symmetric around its typical value and starts to follow a bell-shaped curve. This can be easily checked with a normality test or by applying a set of percentiles and seeing the distances between them are almost equal.
Then I noticed that on different moving window sizes, the typical value of the metric seems to slide: higher window sizes lead to lower typical values across the moving windows. Turned out this can be modeled the same way confidence intervals are made. Lines 34 and 35 explain it all, I guess. You can see smth alike on an autocorrelogram. These two match the mean & mean + 1 stdev applied to the metric. This way, we’ve just magically received data to estimate alpha and beta parameters of the beta distribution using the method of moments. Having alpha and beta, we can now estimate everything further. Btw, there’s an alternative parameterization for beta distributions based on data length.
Now what you’ll see next is... u guys actually have no idea how deep and unrealistically minimalistic the underlying math principles are here.
I’m sure I’m not the only one in the universe who figured it out, but the thing is, it’s nowhere online or offline. By calculating higher-order moments & combining them, you can find natural adaptive thresholds that can later be used for anomaly detection/control applications for any data. No hardcoded thresholds, purely data-driven. Imma come back to this in one of the next drops, but the truest ones can already see it in this code. This way we get dem thresholds.
Your main thresholds are: basis, upper, and lower deviations. You can follow the common logic I’ve described in my previous scripts on how to use them. You just register an event when the metric goes higher/lower than a certain threshold based on what you’re looking for. Then you take the time series and confirm a certain behavior you were looking for by using an appropriate stat test. Or just run a certain strategy.
To avoid numerous triggers when the metric jitters around a threshold, you can follow this logic: forget about one threshold if touched, until another threshold is touched.
In general, when the metric gets higher than certain thresholds, like upper deviation, it means the signal is stronger than noise. You confirm it with a more sophisticated tool & run momentum strategies if drift is in place, or volatility strategies if there’s no drift in place. Otherwise, you confirm & run ~ mean-reverting strategies, regardless of whether there’s drift or not. Just don’t operate against the trend—hedge otherwise.
3) Flex;
Extension and limit thresholds based on distribution moments gonna be discussed properly later, but now you can see this:
^^ magic
Look at the thresholds—adaptive and dynamic. Do you see any optimizations? No ML, no DL, closed-form solution, but how? Just a formula based on a couple of variables? Maybe it’s just how the Universe works, but how can you know if you don’t understand how fundamentally numbers 3 and 15 are related to the normal distribution? Hm, why do they always say 3 sigmas but can’t say why? Maybe you can be different and say why?
This is the primordial power of statistical modeling.
4) Thanks;
I really wanna dedicate this to Charlotte de Witte & Marion Di Napoli, and their new track "Sanctum." It really gets you connected to the Source—I had it in my soul when I was doing all this ∞
Noise
Noise Footprint ImbalanceNoise Footprint Imbalance Indicator
The Noise Footprint Imbalance Indicator highlights areas of imbalance in price action, marking potential zones of support and resistance. This indicator helps traders visualize "footprints" of imbalance on the chart, allowing for better identification of areas where price moves significantly away from equilibrium. This can help traders pinpoint potential reversal points or zones where buyers or sellers may step in.
Features
Customizable Box Count: Choose the maximum number of imbalance zones displayed on the chart, keeping your workspace clear and focused.
Imbalance Detection: Highlights both top and bottom imbalances, identifying them based on price discrepancies between open/close and high/low levels.
Dynamic Zone Boxes: Draws boxes around imbalance zones with customizable colors and transparency, providing visual clarity without overwhelming the chart.
Usage
This indicator is beneficial for traders who:
Use imbalance zones as potential areas of interest for entries or exits.
Want to combine it with other indicators or price action analysis to improve trade setups.
Customization Options
Maximum Imbalance Zones: Adjusts the maximum number of imbalance boxes shown.
Imbalance Box Color: Customize the color and transparency of the imbalance zones to suit your chart's theme.
Add this script to your chart to enhance your technical analysis and bring more structure to your trading approach with the Noise Footprint Imbalance Indicator.
Total Death and Golden Crosses Calculator The Indicator calculates the total number of the death and golden crosses in the total chart which can help the moving average user to compare the number of signals generated by the moving average pair in the given timeframe.
All you need is to plot any two moving average then change the source of the indicator to get the total number of crosses.
If Indicator is not plotting anything then right click on the indicator's scale and click on "Auto(data fits the screen" option.
Signal to Noise TrendSignal to Noise Ratio
The Signal to Noise Ratio or SNR is used to assess the quality of information or data by comparing the strength of a useful signal to the presence of background noise or random variations.
In Finance the SNR refers to the ratio of strength of a trading signal to the background noise. A high SNR suggest a clear and reliable signal, meanwhile a low SNR indicates more noise (random fluctuations, volatility, or randomness).
Signal To Noise Trend
This indicator basically calculates the signal to noise of returns and then gets the Z-Score of the signal to noise ratio to find extremes levels of signal and noise. The Lines basically are standard deviations from the mean. 1,2,3 Are standard deviations same with the -1,-2,-3 Lines.
The signal is expressed as the positive Z-Score value, and the Noise is the negative Z-Score Value.
The moving average enhances the indicator ability to display the trend of returns and the trend strength. It provides a smooth representation of the Signal to Nose Ratio values.
There are more trending conditions when there is a higher signal, and there is more "ranging" conditions when there is more noise present in the markets.
The Standard deviations help find extreme levels of signal and noise. If the noise reaches the standard deviation of -3 then that means that there is a extreme negative deviation from the mean, and this would be a rare occurrence, with a lot of noise. This could indicate a potential reversion in market states, and could be followed by a trending move.
Another example is that if the Z-Score value reaches a Standard deviation of 3, this could mean that there is extremely strong and rare signal, and could potentially mean a change to a more noisy environment soon.
White NoiseThe "White Noise" indicator is designed to visualize the dispersion of price movements around a moving average, providing insights into market noise and potential trend changes. It highlights periods of increased volatility or noise compared to the underlying trend.
Code Explanation:
Inputs:
mlen: Input for the length of the noise calculation.
hlen: Input for the length of the Hull moving average.
col_up: Input for the color of the up movement.
col_dn: Input for the color of the down movement.
Calculations:
ma: Calculate the simple moving average of the high, low, and close prices (hlc3) over the specified mlen period.
dist: Calculate the percentage distance between the hlc3 and the moving average ma, then scale it by 850. This quantifies the deviation from the moving average as a value.
sm: Smooth the calculated dist values using a weighted moving average (WMA) twice, with different weights, and subtract one from the other. This provides a smoothed representation of the dispersion.
Coloring:
col_wn: Determine the color of the bars based on whether dist is positive or negative and whether it's greater or less than the smoothed sm value. This creates color-coded columns indicating upward or downward movements with varying opacity.
col_switch: Define the color for the current trend state. It switches color when the smoothed sm crosses above or below its previous value, indicating potential trend changes.
col_switch2: Define the color for the horizontal line that separates the two trend states. It switches color based on the same crossover and crossunder conditions as col_switch.
Plots:
plot(dist): Plot the dispersion values as columns with color defined by col_wn.
plot(sm): Plot the smoothed dispersion line with a white color and thicker linewidth.
plot(sm ): Plot the previous smoothed dispersion value with a lighter white color to create a visual distinction.
Usage:
This indicator can help traders identify periods of increased market noise, visualize potential trend reversals, and assess the strength of price movements around the moving average. The colored columns and smoothed line offer insights into the ebb and flow of market sentiment, aiding in decision-making.
ps. This can be used as a long-term TPI component if you dabble in Modern Portfolio Theory (MPT)
Recommended for timeframes on the 1D or above:
NET on Variety Moving Averages [Loxx]NET (Noise Elimination Technology) on Variety Moving Averages is a moving average indicator that applies John Ehlers' NET (Noise Elimination Technology) to your choice of 36 different moving averages.
█ What is NET (Noise Elimination Technology)?
Noise Elimination Technology (NET) is a method introduced by John Ehlers to enhance the clarity of technical indicators by removing noise without resorting to filtering. Here's a more detailed explanation:
Purpose of Technical Indicators: Technical indicators aim to provide insights into market inefficiencies, assisting traders in making informed decisions. However, many indicators are inherently noisy due to their reliance on a limited amount of data.
Traditional Noise Removal: Noise in indicators is typically removed using smoothing filters. While these filters can reduce noise, they introduce lag, leading to potentially delayed trading decisions which can be costly.
NET's Approach: NET offers a solution to this problem by using the nonlinearity of a rank-ordered Kendall correlation. Instead of filtering, NET clarifies indicators by focusing on their main direction and stripping out noise components.
Kendall Correlation: This is a statistical method that compares the ranked order of two sets of random variables. These pairs of ranked variables can be either concordant or discordant. In the context of NET:
The "y" variable represents a straight line with a positive slope.
The "x" variable is the output of the technical indicator.
When applied, the Kendall correlation in this configuration removes noise components that don't align with the primary direction of the indicator.
NET's Mechanism:
The "y" variable (a straight line with a positive slope) and the "x" variable (indicator output) are used in the Kendall correlation.
This correlation essentially removes noise components not aligned with the main direction of the indicator in a nonlinear manner.
The effectiveness of NET lies in its ability to reduce noise without introducing lag.
Flexibility: NET is designed to be versatile and can be applied to various technical indicators. It doesn't necessarily replace traditional smoothing filters but can complement them to provide a clearer visual representation of the indicator's behavior.
In essence, NET offers a novel approach to refining technical indicators by removing noise using the principles of Kendall correlation, without the drawbacks associated with traditional smoothing filters.
█ Moving Average Types
ADXvma - Average Directional Volatility Moving Average
Ahrens Moving Average
Alexander Moving Average - ALXMA
Double Exponential Moving Average - DEMA
Double Smoothed Exponential Moving Average - DSEMA
Exponential Moving Average - EMA
Fast Exponential Moving Average - FEMA
Fractal Adaptive Moving Average - FRAMA
Hull Moving Average - HMA
IE/2 - Early T3 by Tim Tilson
Integral of Linear Regression Slope - ILRS
Instantaneous Trendline
Laguerre Filter
Leader Exponential Moving Average
Linear Regression Value - LSMA (Least Squares Moving Average)
Linear Weighted Moving Average - LWMA
McGinley Dynamic
McNicholl EMA
Non-Lag Moving Average
Parabolic Weighted Moving Average
Recursive Moving Trendline
Simple Moving Average - SMA
Sine Weighted Moving Average
Smoothed Moving Average - SMMA
Smoother
Super Smoother
Three-pole Ehlers Butterworth
Three-pole Ehlers Smoother
Triangular Moving Average - TMA
Triple Exponential Moving Average - TEMA
Two-pole Ehlers Butterworth
Two-pole Ehlers smoother
Volume Weighted EMA - VEMA
Zero-Lag DEMA - Zero Lag Double Exponential Moving Average
Zero-Lag Moving Average
Zero Lag TEMA - Zero Lag Triple Exponential Moving Average
█ Included
Bar coloring
Alerts
Channels fill
Loxx's Expanded Source Types
█ Libraries included
loxxmas - moving averages used in Loxx's indis & strats
loxxexpandedsourcetypes
Opal - Aggr.Crypto█ OVERVIEW
The Multi-Exchange Crypto Aggregator is a unique concept ticker that gathers up to 10 tickers into one. A new OPAL Chart is created as an indicator, with its own candles and information. This information is meant to be interpreted as average information in order to reduce noise from a single ticker only. Everything is automated between assets. Our script will always check and ensure that data is received for calculations; otherwise, invalid tickers are ignored. This version is designed for Crypto Perpetual markets.
█ HOW DATA SLIPPAGE/DIFFERENCE IMPACT NOISE
This new average ticker aims to reduce noise in your candles and their live movements, avoiding most of the minor/last-second spikes, especially when they don't happen on every desired exchange at the same time. Our candles have different behaviors and highlight close-open slippage/gaps, as it seems to provide a strong reaction. Those gaps represent average slippage.
█ HOW TO USE
This should help you visualize market behaviors. Volume pressures are the origin of a lot of misunderstood things. Data analysis and observations show that makers target liquidity on both sides. Time and sessions have their own logic and will always need experience, as it is basically a gigantic Tetris game. Anyway, this should help with timing confirmations or bring confidence.
█ FEATURES
Aggregated (Tickers) Candles ▸ Aggregated OHLC candles, the idea behind the script. Set desired tickers to automate in settings. Value and Var% are displayed right next to the current candle.
Aggr. Dynamics/Levels ▸ Plot some strong levels as landmarks calculated on modified price, from Volume Weighted Average Price (VWAP) to Daily aggregated Open Price. The previous day's key level is included.
Aggr. Data Markers ▸ Plot some key markers on the chart, such as Open Pressure gaps, or estimated 3-scale liquidation bubbles with 2 confirmation modes (using different filters).
Aggr. Averages ▸ Plot up to 3 averages or HLC channels for visual ease.
█ SIGN
All of our contents are shared for educational purposes only.
Wishing you success;
OPAL - Strive for Greatness
Variety MA Cluster Filter Crosses [Loxx]What is a Cluster Filter?
One of the approaches to determining a useful signal (trend) in stream data. Small filtering (smoothing) tests applied to market quotes demonstrate the potential for creating non-lagging digital filters (indicators) that are not redrawn on the last bars.
Standard Approach
This approach is based on classical time series smoothing methods. There are lots of articles devoted to this subject both on this and other websites. The results are also classical:
1. The changes in trends are displayed with latency;
2. Better indicator (digital filter) response achieved at the expense of smoothing quality decrease;
3. Attempts to implement non-lagging indicators lead to redrawing on the last samples (bars).
And whereas traders have learned to cope with these things using persistence of economic processes and other tricks, this would be unacceptable in evaluating real-time experimental data, e.g. when testing aerostructures.
The Main Problem
It is a known fact that the majority of trading systems stop performing with the course of time, and that the indicators are only indicative over certain intervals. This can easily be explained: market quotes are not stationary. The definition of a stationary process is available in Wikipedia:
A stationary process is a stochastic process whose joint probability distribution does not change when shifted in time.
Judging by this definition, methods of analysis of stationary time series are not applicable in technical analysis. And this is understandable. A skillful market-maker entering the market will mess up all the calculations we may have made prior to that with regard to parameters of a known series of market quotes.
Even though this seems obvious, a lot of indicators are based on the theory of stationary time series analysis. Examples of such indicators are moving averages and their modifications. However, there are some attempts to create adaptive indicators. They are supposed to take into account non-stationarity of market quotes to some extent, yet they do not seem to work wonders. The attempts to "punish" the market-maker using the currently known methods of analysis of non-stationary series (wavelets, empirical modes and others) are not successful either. It looks like a certain key factor is constantly being ignored or unidentified.
The main reason for this is that the methods used are not designed for working with stream data. All (or almost all) of them were developed for analysis of the already known or, speaking in terms of technical analysis, historical data. These methods are convenient, e.g., in geophysics: you feel the earthquake, get a seismogram and then analyze it for few months. In other words, these methods are appropriate where uncertainties arising at the ends of a time series in the course of filtering affect the end result.
When analyzing experimental stream data or market quotes, we are focused on the most recent data received, rather than history. These are data that cannot be dealt with using classical algorithms.
Cluster Filter
Cluster filter is a set of digital filters approximating the initial sequence. Cluster filters should not be confused with cluster indicators.
Cluster filters are convenient when analyzing non-stationary time series in real time, in other words, stream data. It means that these filters are of principal interest not for smoothing the already known time series values, but for getting the most probable smoothed values of the new data received in real time.
Unlike various decomposition methods or simply filters of desired frequency, cluster filters create a composition or a fan of probable values of initial series which are further analyzed for approximation of the initial sequence. The input sequence acts more as a reference than the target of the analysis. The main analysis concerns values calculated by a set of filters after processing the data received.
In the general case, every filter included in the cluster has its own individual characteristics and is not related to others in any way. These filters are sometimes customized for the analysis of a stationary time series of their own which describes individual properties of the initial non-stationary time series. In the simplest case, if the initial non-stationary series changes its parameters, the filters "switch" over. Thus, a cluster filter tracks real time changes in characteristics.
Cluster Filter Design Procedure
Any cluster filter can be designed in three steps:
1. The first step is usually the most difficult one but this is where probabilistic models of stream data received are formed. The number of these models can be arbitrary large. They are not always related to physical processes that affect the approximable data. The more precisely models describe the approximable sequence, the higher the probability to get a non-lagging cluster filter.
2. At the second step, one or more digital filters are created for each model. The most general condition for joining filters together in a cluster is that they belong to the models describing the approximable sequence.
3. So, we can have one or more filters in a cluster. Consequently, with each new sample we have the sample value and one or more filter values. Thus, with each sample we have a vector or artificial noise made up of several (minimum two) values. All we need to do now is to select the most appropriate value.
An Example of a Simple Cluster Filter
For illustration, we will implement a simple cluster filter corresponding to the above diagram, using market quotes as input sequence. You can simply use closing prices of any time frame.
1. Model description. We will proceed on the assumption that:
The aproximate sequence is non-stationary, i.e. its characteristics tend to change with the course of time.
The closing price of a bar is not the actual bar price. In other words, the registered closing price of a bar is one of the noise movements, like other price movements on that bar.
The actual price or the actual value of the approximable sequence is between the closing price of the current bar and the closing price of the previous bar.
The approximable sequence tends to maintain its direction. That is, if it was growing on the previous bar, it will tend to keep on growing on the current bar.
2. Selecting digital filters. For the sake of simplicity, we take two filters:
The first filter will be a variety filter calculated based on the last closing prices using the slow period. I believe this fits well in the third assumption we specified for our model.
Since we have a non-stationary filter, we will try to also use an additional filter that will hopefully facilitate to identify changes in characteristics of the time series. I've chosen a variety filter using the fast period.
3. Selecting the appropriate value for the cluster filter.
So, with each new sample we will have the sample value (closing price), as well as the value of MA and fast filter. The closing price will be ignored according to the second assumption specified for our model. Further, we select the МА or ЕМА value based on the last assumption, i.e. maintaining trend direction:
For an uptrend, i.e. CF(i-1)>CF(i-2), we select one of the following four variants:
if CF(i-1)fastfilter(i), then CF(i)=slowfilter(i);
if CF(i-1)>slowfilter(i) and CF(i-1)slowfilter(i) and CF(i-1)>fastfilter(i), then CF(i)=MAX(slowfilter(i),fastfilter(i)).
For a downtrend, i.e. CF(i-1)slowfilter(i) and CF(i-1)>fastfilter(i), then CF(i)=MAX(slowfilter(i),fastfilter(i));
if CF(i-1)>slowfilter(i) and CF(i-1)fastfilter(i), then CF(i)=fastfilter(i);
if CF(i-1)<slowfilter(i) and CF(i-1)<fastfilter(i), then CF(i)=MIN(slowfilter(i),fastfilter(i)).
Where:
CF(i) – value of the cluster filter on the current bar;
CF(i-1) and CF(i-2) – values of the cluster filter on the previous bars;
slowfilter(i) – value of the slow filter
fastfilter(i) – value of the fast filter
MIN – the minimum value;
MAX – the maximum value;
What is Variety MA Cluster Filter Crosses?
For this indicator we calculate a fast and slow filter of the same filter and then we run a cluster filter between the fast and slow filter outputs to detect areas of chop/noise. The output is the uptrend is denoted by green color, downtrend by red color, and chop/noise/no-trade zone by white color. As a trader, you'll likely want to avoid trading during areas of chop/noise so you'll want to avoid trading when the color turns white.
Extras
Bar coloring
Alerts
Loxx's Expanded Source Types, see here:
Loxx's Moving Averages, see here:
An example of filtered chop, see the yellow circles. The cluster filter identifies chop zones so you don't get stuck in a sideways market.
KERPD Noise Filter - Kaufman Efficiency Ratio and Price DensityThis indicator combines Kaufman Efficiency Ratio (KER) and Price Density theories to create a unique market noise filter that is 'right on time' compared to using KER or Price Density alone. All data is normalized and merged into a single output. Additionally, this indicator provides the ability to consider background noise and background noise buoyancy to allow dynamic observation of noise level and asset specific calibration of the indicator (if desired).
The basic theory surrounding usage is that: higher values = lower noise, while lower values = higher noise in market.
Notes: NON-DIRECTIONAL Kaufman Efficiency Ratio used. Threshold period of 30 to 40 applies to Kaufman Efficiency Ratio systems if standard length of 20 is applied; maintained despite incorporation of Price Density normalized data.
TRADING USES:
-Trend strategies, mean reversion/reversal/contrarian strategies, and identification/avoidance of ranging market conditions.
-Trend strategy where KERPD is above a certain value; generally a trend is forming/continuing as noise levels fall in the market.
-Mean reversion/reversal/contrarian strategies when KERPD exits a trending condition and falls below a certain value (additional signal confluence confirming for a strong reversal in price required); generally a reversal is forming as noise levels increase in the market.
-A filter to screen out ranging/choppy conditions where breakouts are frequently fake-outs and or price fails to move significantly; noise level is high, in addition to the background buoyancy level.
-In an adaptive trading systems to assist in determining whether to apply a trend following algorithm or a mean reversion algorithm.
THEORY / THOUGHT SPACE:
The market is a jungle. When apex predators are present it often goes quiet (institutions moving price), when absent the jungle is loud.
There is always background noise that scales with the anticipation of the silence, which has features of buoyancy that act to calibrate the beginning of the silence and return to background noise conditions.
Trend traders hunt in low noise conditions. Reversion traders hunt in the onset of low noise into static conditions. Ranges can be avoided during high noise and buoyant background noise conditions.
Distance between the noise line and background noise can help inform decision making.
CALIBRATION:
- Set the Noise Threshold % color change line so that the color cut off is where your trend/reversion should begin.
- Set the Background Noise Buoyancy Calibration Decimal % to match the beginning/end of the color change Noise Threshold % line. Match the Background Noise Baseline Decimal %' to the number set for buoyancy.
- Additionally, create your own custom settings; 33/34 and 50 length also provides interesting results.
- A color change tape option can be enabled by un-commenting the lines at the bottom of this script.
Market Usage:
Stock, Crypto, Forex, and Others
Excellent for: NDQ, J225, US30, SPX
Market Conditions:
Trend, Reversal, Ranging
Adaptive ATR Keltner Channels [Loxx]Adaptive ATR Channels are adaptive Keltner channels. ATR is calculated using a rolling signal-to-noise ratio making this indicator flex more to changes in price volatility than the fixed Keltner Channels.
What is Average True Range (ATR)?
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.1
The true range is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
What are Keltner Channel (ATR)?
Keltner Channels are volatility-based bands that are placed on either side of an asset's price and can aid in determining the direction of a trend.
The Keltner channel uses the average-true range (ATR) or volatility, with breaks above or below the top and bottom barriers signaling a continuation.
Parabolic SAR of KAMA [Loxx]Parabolic SAR of KAMA attempts to reduce noise and volatility from regular Parabolic SAR in order to derive more accurate trends. In addition, and to further reduce noise and enhance trend identification, PSAR of KAMA includes two calculations of efficiency ratio: 1) price change adjusted for the daily volatility; or, 2) Jurik Fractal Dimension Adaptive (explained below)
What is PSAR?
The parabolic SAR indicator, developed by J. Wells Wilder, is used by traders to determine trend direction and potential reversals in price. The indicator uses a trailing stop and reverse method called "SAR," or stop and reverse, to identify suitable exit and entry points. Traders also refer to the indicator as to the parabolic stop and reverse, parabolic SAR, or PSAR.
What is KAMA?
Developed by Perry Kaufman, Kaufman's Adaptive Moving Average (KAMA) is a moving average designed to account for market noise or volatility. KAMA will closely follow prices when the price swings are relatively small and the noise is low. KAMA will adjust when the price swings widen and follow prices from a greater distance. This trend-following indicator can be used to identify the overall trend, time turning points and filter price movements.
What is the efficiency ratio?
In statistical terms, the Efficiency Ratio tells us the fractal efficiency of price changes. ER fluctuates between 1 and 0, but these extremes are the exception, not the norm. ER would be 1 if prices moved up 10 consecutive periods or down 10 consecutive periods. ER would be zero if price is unchanged over the 10 periods.
What is Jurik Fractal Dimension?
There is a weak and a strong way to measure the random quality of a time series.
The weak way is to use the random walk index (RWI). You can download it from the Omega web site. It makes the assumption that the market is moving randomly with an average distance D per move and proposes an amount the market should have changed over N bars of time. If the market has traveled less, then the action is considered random, otherwise it's considered trending.
The problem with this method is that taking the average distance is valid for a Normal (Gaussian) distribution of price activity. However, price action is rarely Normal, with large price jumps occuring much more frequently than a Normal distribution would expect. Consequently, big jumps throw the RWI way off, producing invalid results.
The strong way is to not make any assumption regarding the distribution of price changes and, instead, measure the fractal dimension of the time series. Fractal Dimension requires a lot of data to be accurate. If you are trading 30 minute bars, use a multi-chart where this indicator is running on 5 minute bars and you are trading on 30 minute bars.
Conclusion from the combined efforts explained above:
-PSAR is a tool that identifies trends
-To reduce noise and identify trends during periods of low volatility, we calculate a PSAR on KAMA
-To enhance noise and reduction and trend identification, we attempt to derive an efficiency ratio that is less reliant on a Normal (Gaussian) distribution of price
Included:
-Customization of all variables
-Select from two different ER calculation styles
-Multiple timeframe enabled
FiboBars ExtendedA trend indicator FiboBars Extended , the main purpose of which is to confirm the trend and cut off market noise. In his logic, he uses the Fibonacci sequence.
Two settings are used to account for noise suppression accuracy:
Period - number of calculation bars
Level - Fibonacci number selection
Trend-Quality IndicatorBINANCE:BTCUSDT
Open source version of the Trend-Quality Indicator as described by David Sepiashvili in [ Stocks & Commodities V. 22:4 (14-20) ]
Q-Indicator and B-Indicator are available both separately or together
█ OVERVIEW
The Trend-Quality indicator is a trend detection and estimation tool that is based on a two-step filtering technique. It measures cumulative price changes over term-oriented semicycles and relates them to “noise”. The approach reveals congestion and trending periods of the price movement and focuses on the most important trends, evaluating their strength in the process. The indicator is presented in a centered oscillator (Q-Indicator) and banded oscillator format (B-Indicator).
Semicycles are determined by using a short term and a longer term EMAs. The starting points for the cycles are determined by the moving averages crossover.
Cumulative price change (CPC) indicator measures the amount that the price has changed from a fixed starting point within a given semicycle. The CPC indicator is calculated as a cumulative sum of differences between the current and previous prices over the period from the fixed starting point.
The trend within the given semicycle can be found by calculating the moving average of the cumulative price change.
The noise can be defined as the average deviation of the cumulative price change from the trend. To determine linear noise, we calculate the absolute value of the difference between CPC and trend, and then smooth it over the n-point period. The root mean square noise, similar to the conventional standard deviation, can be derived by summing the squares of the difference between CPC and trend over each of the preceding n-point periods, dividing the sum by n, and calculating the square root of the result.
█ Q-INDICATOR
The Q-Indicator is a centered oscillator that fluctuates around a zero line with no upper or lower limits, is calculated by dividing trend by noise.
The Q-Indicator is intended to measure trend activity. The further the Q is from 0, the less the risk of trading with a trend, and the more reliable the trading opportunity. Values exceeding +2 or -2 can be qualified as promising
Values:
in the -1 to +1 range (GRAY) indicate that the trend is buried beneath noise. It is preferable to stay out of this zone
in the +1 to +2 or -1 to -2 range (YELLOW) indicate weak trending
in the +2 to +5 range (BLUE) or -2 to -5 range (ORANGE) indicate moderate trending
above +5 range (GREEN) or below -5 (RED) indicate strong trending
Readings exceeding strong trending levels can indicate overbought or oversold conditions and signal that price action should be monitored closely.
█ B-INDICATOR
The B-Indicator is a banded oscillator that fluctuates between 0 and 100, is calculated by dividing the absolute value of trend by noise added to absolute value of trend, and scaling the result appropriately.
The B-indicator doesn’t show the direction of price movement, but only the existence of the trend and its strength. It requires additional tools for reversal manifestations.
The indicator’s interpretation is simple. The central line suggests that the trend and noise are in equilibrium (trend is equal to noise).
Values:
below 50 (GRAY) indicate ranging market
in the 50 to 65 range (YELLOW) indicate weak trending
in the 65 to 80 range (BLUE) indicate moderate trending
above 80 (GREEN) indicate strong trending
The 65 level can be thought of as the demarcation line of trending and ranging markets and can help determine which type of technical analysis indicator (lagging or leading) is better suited to current market conditions. Readings exceeding strong trending levels can indicate overbought or oversold conditions.
Cyclic RSI High Low With Noise Filter█ OVERVIEW
This indicator displays Cyclic Relative Strength Index based on Decoding the Hidden Market Rhythm, Part 1 written by Lars von Thienen.
To determine true or false for Overbought / Oversold are unnecessary, therefore these should be either strong or weak.
Noise for weak Overbought / Oversold can be filtered, especially for smaller timeframe.
█ FEATURES
Display calculated Cyclic Relative Strength Index.
Zigzag high low based on Cyclic Relative Strength Index.
Able to filter noise for high low.
█ LEGENDS
◍ Weak Overbought / Oversold
OB ▼ = Strong Overbought
OS ▲ = Strong Oversold
█ USAGE / TIPS
Recommend to be used for Harmonic Patterns such as XABCD and ABCD.
Condition 1 (XABCD) : When ▼ and ▲ exist side by side, usually this outline XA, while the next two ◍ can be BC.
Condition 2 (ABCD) : When ▼ and ▲ exist side by side, usually this outline AB, while the next one ◍ can be BC, strong ABCD.
Condition 3 (ABCD) : When ▼ or ▲ exist at Point A, the next two ◍ can be Point B and Point C, medium ABCD.
Condition 4 (ABCD) : When ◍ exist at Point a, the next two ◍ can be Point b and Point c, weak ABCD usually used as lower case as abcd.
█ CREDITS
LoneSomeTheBlue
WhenToTrade
Rebalance OscillatorRebalancing is a common strategy to reduce risk and achieve a constant portfolio ratio between two tokens. It shifts between two values in order to keep a static ratio between them as their value oscillates.
However what is less known about rebalancing is that it provides a way to remove the noise from a signal, effectively showing us the points where to buy and to sell.
This works best in highly volatile tickers, between two tokens that are not correlated and show a high std deviation between them (i.e. XTZUSD is a better signal for this than XTZBTC).
The buying bars are marked in blue, and the selling bars are marked in red, in a similar fashion to Trading View strategy orders.
Efficiency RatioThe efficiency ratio (ER) is described by Perry Kaufman in his book, Trading Systems and Methods.
It works by measuring the momentum of the market, that is, the absolute change from the current price to a past price, and divides it by the volatility, which is the sum of the absolute changes of each bar. That makes this a bounded indicator, going from 0 to 100, like an oscillator. Higher values mean less noise, while lower values mean more.
Eg.: if the market moves from 10.0 to 15.0 in a directional manner, with every bar up, the ER is going to be at 100. However, if it moves up and down, and goes all over the place until finally reaching 15.0, the ER is going to be at around 20. It is very difficult for the ER to be at zero, because that would require 0 volatility, which is almost impossible to occur.
This indicator is useful when planning for trades. If you notice the ER being higher than average, you may choose to increase the position size, because that would mean that the market is directional and has less chance of a whipsaw.
Ehlers Noise Elimination Technology [CC]The Noise Elimination Technology Indicator was created by John Ehlers (Stocks and Commodities Dec 2020 pg 17) and he created this indicator to be used with his version of RSI but I think it works well with any price data or any indicator really.
I'm trying a new signal system due to a request from @luckyCamel58789 so let me know what you think. I now differentiate between a buy and a strong buy when the indicator increases over itself twice and vice versa. Dark green is a strong buy and light green is a regular buy. Dark red is a strong sell and light red is a regular sell.
Let me know what indicators you would like to see me publish!
Combo Backtest 123 Reversal & Signal To Noise This is combo strategies for get a cumulative signal.
First strategy
This System was created from the Book "How I Tripled My Money In The
Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies.
The strategy buys at market, if close price is higher than the previous close
during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50.
The strategy sells at market, if close price is lower than the previous close price
during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50.
Second strategy
The signal-to-noise (S/N) ratio.
And Simple Moving Average.
WARNING:
- For purpose educate only
- This script to change bars colors.
Backtest Signal To Noise This source code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
© HPotter 05/01/2021
The signal-to-noise (S/N) ratio.
And Simple Moving Average.
Thank you for idea BlockchainYahoo
WARNING:
- For purpose educate only
- This script to change bars colors.
Signal To Noise This source code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
© HPotter 05/01/2021
The signal-to-noise (S/N) ratio.
Thank you for idea BlockchainYahoo
Institutional PivotsToday I propose a novel idea of plotting pivots, this can be also considered as Value areas/Noise areas.
*What is it?
Its a simple concept of gauging price action with respect to its most time spent in a particular range, this is usually denoted as Value area in the Market profile concept, where that "most" word is represented by 70% of the price action.
*What's different from the Market Profile?
Market profile is dependent on real time price movement to complete to see the value area or noise area to plot a static area, there is always a possibility of it shifting as price may move outside of it, and hence its called "developing value area", till of course session is closed and plot is finalized.
While that method is solid indication of "actual price profile" development, it lacks when it comes to offering traders a more stable view to enable them to make decisions. And therefor, when traders trade MP they are usually limited by the number of trades they can take.
This is the main reason as to why traders prefer to use other methods like Pivots/ORB/Range-breakouts over pure MP charts, even though latter reduces the possibility of wrong estimations of "support/resistance working/holding".
*Why the name Institutional Pivots?
In my research I've found that these Pivots/Value area/Noise area ranges are often the areas watched by the big players who trade breakouts or mean reversion strategies, so while that name may sound dubiously clickbaity, it is indeed intended to represent an observation. I know how that sounds, but you can choose to ignore it if you do not agree or see good results after using it. After all, its free to use for everyone.
*Nomenclature/colors and settings?
Noise area/Value area/Central Pivot area - Designated with Yellow labels, in which NU represents the Upper level and ND represents the Lower level
Targets - Target calculation mechanism is based on " today's price action" and today's Open, D1 and D2 represent down targets for the day and U1 and U2 represent upper targets. Please note that this is independent of the "Noise area/Value area/Central Pivot area", so overlap of levels is possible. AND if overlap happens, that's an indication of more strength at S/R line/area for the price.
One more thing to note here is that if there is formation of the new low or new high in the day, those levels will change as their calculation gets influenced by the same. This is NOT a repaint issue, this is SHIFT/FORMATION OF THE NEW levels and it's an "intended" behavior.
Open and POpen - Open represents open of the time-frame selected and POpen represents previous open of the time-frame.
Lable's starting with "H" are indicating higher Timeframe levels, levels which are same as above.
ATR based targets - When you enable this in settings, you will get target calculation based on ATR (self explanatory)
Full ATR mode - When you enable this option, you will get both Noise area as well as targets based on ATR, please don't forgot to turn off the ATR based targets when you enable this, as they conflict with each other.
I've not kept lines, area plots or even price levels as I feel it's just noise and takes away from the indicators main focus, please don't ask to add them, I'll not.
This is meant as purely educational idea, if you use this to trade, it is at your discretion and responsibility will be yours alone.
Past performance is not assurance of the future performance.
More example chart/s
Combined MA Trend FilterToday I propose a simple but an effective tool to use as a trend identifier.
It is simple because it doesn't require user to tinker with it and it works on all scripts and all time frames.
It is effective because it's based on what I believe to be the most used ma's by the traders who are successful and usually trade with large qty.
So, what's under the hood?
-It's a combination of MA's and its alpha multiplier to replicate effect of higher TF MA without producing the weird square shapes.
-We are utilizing the range between the two as a way to identify "noise areas" or "ranging areas" for the price action, where taking a trade might not be the best decision.
-As soon as bar starts closing above the both MA and its alpha multiplier, it is in strong bullish zone
-And as soon as bar start closing below the same we have a strong bearish zone.
-Bar Colour coding
Lime - Strong Bullish sentiment
Yellow - Weak Sentiment (Ranged area)
Red - Strong bearish sentiment
-This indicator works in two modes, one is noise mode and one is noiseless mode.
When we select noiseless mode, we are utilizing here a filter to reduce noise, which can be also plotted on chart and option for doing so is given in settings.
Some examples?
I've used alpha of 5 in above examples (You can change it to anything you want, depending on your script and TF)
As you can see, it produces far better filtering and keeps you out from possible "noise areas" when trading, it is also good at working as scaling in and out tool for purpose of maximizing the profits when you do catch the trend.
Please note that higher the alpha you use, you will be shifting to higher TF MA, while its difficult to have a set number of set TimeFrame effect replication, its best to keep the alpha multiplier value around 5.
Authors note:
This indicator is free to use for all, I'm only protecting the code to avoid people selling it to unsuspecting new users. It happens a lot on TV.
Past performance does not mean future profit and trader is responsible for his own losses or profits, author does not take any responsibility to wrong application of the tool provided here.
Have a profitable trading journey and enjoy~
[NLX-L1] Noise Filter- NLX Modular Trading Framework -
This Noise Filter is build upon a logic of Hurst Exponent and MA-ATR %-Distance to Price and does a great job at filtering choppy trades and noise.
The Hurst Exponent will analyze a time series and determine whether it is a geometric Brownian motion, mean reverting or trending and effective at filtering out whipsaws.
- Getting Started -
1. Add the Noise Filter to your Chart
2. Add one of my Indicator Modules to your Chart, such as the QQE++ Indicator
3. Select the Noise Filter in the Indicator Settings
2. Add the Backtest Module to your Chart
3. Select the QQE Indicator in the Backtest Settings
- Alerts for Automated Trading -
This module is coming soon and you will be able to create alerts for the QQE Signals as part of my framework.
See my signature below for more information.
