Real-Fast Fourier Transform of Price w/ Linear Regression [Loxx]Real-Fast Fourier Transform of Price w/ Linear Regression is a indicator that implements a Real-Fast Fourier Transform on Price and modifies the output by a measure of Linear Regression. The solid line is the Linear Regression Trend of the windowed data, The green/red line is the Real FFT of price.
What is the Discrete Fourier Transform?
In mathematics, the discrete Fourier transform (DFT) converts a finite sequence of equally-spaced samples of a function into a same-length sequence of equally-spaced samples of the discrete-time Fourier transform (DTFT), which is a complex-valued function of frequency. The interval at which the DTFT is sampled is the reciprocal of the duration of the input sequence. An inverse DFT is a Fourier series, using the DTFT samples as coefficients of complex sinusoids at the corresponding DTFT frequencies. It has the same sample-values as the original input sequence. The DFT is therefore said to be a frequency domain representation of the original input sequence. If the original sequence spans all the non-zero values of a function, its DTFT is continuous (and periodic), and the DFT provides discrete samples of one cycle. If the original sequence is one cycle of a periodic function, the DFT provides all the non-zero values of one DTFT cycle.
What is the Complex Fast Fourier Transform?
The complex Fast Fourier Transform algorithm transforms N real or complex numbers into another N complex numbers. The complex FFT transforms a real or complex signal x in the time domain into a complex two-sided spectrum X in the frequency domain. You must remember that zero frequency corresponds to n = 0, positive frequencies 0 < f < f_c correspond to values 1 ≤ n ≤ N/2 −1, while negative frequencies −fc < f < 0 correspond to N/2 +1 ≤ n ≤ N −1. The value n = N/2 corresponds to both f = f_c and f = −f_c. f_c is the critical or Nyquist frequency with f_c = 1/(2*T) or half the sampling frequency. The first harmonic X corresponds to the frequency 1/(N*T).
The complex FFT requires the list of values (resolution, or N) to be a power 2. If the input size if not a power of 2, then the input data will be padded with zeros to fit the size of the closest power of 2 upward.
What is Real-Fast Fourier Transform?
Has conditions similar to the complex Fast Fourier Transform value, except that the input data must be purely real. If the time series data has the basic type complex64, only the real parts of the complex numbers are used for the calculation. The imaginary parts are silently discarded.
Inputs:
src = source price
uselreg = whether you wish to modify output with linear regression calculation
Windowin = windowing period, restricted to powers of 2: "4", "8", "16", "32", "64", "128", "256", "512", "1024", "2048"
Treshold = to modified power output to fine tune signal
dtrendper = adjust regression calculation
barsback = move window backward from bar 0
mutebars = mute bar coloring for the range
Further reading:
Real-valued Fast Fourier Transform Algorithms IEEE Transactions on Acoustics, Speech, and Signal Processing, June 1987
Related indicators utilizing Fourier Transform
Fourier Extrapolator of Variety RSI w/ Bollinger Bands
Fourier Extrapolation of Variety Moving Averages
Fourier Extrapolator of Price w/ Projection Forecast
Trend
Ultimate IndicatorThis is a combination of all the price chart indicators I frequently switch between. It contains my day time highlighter (for day trading), multi-timeframe long-term trend indicator for current commodity in the bottom right, customizable trend EMA which also has multi-timeframe drawing capabilities, VWAP, customizable indicators with separate settings from the trend indicator including: EMA, HL2 over time, Donchian Channels, Keltner Channels, Bollinger Bands, and Super Trend. The settings for these are right below the trend settings and can have their length and multiplier adjusted. All of those also have multi-timeframe capabilities separate from the trend multi-time settings.
The Day Trade Highlight option will draw faint yellow between 9:15-9:25, red between 9:25-9:45, yellow between 9:45-10:05. There will be one white background at 9:30am to show the opening of the market. while the market is open there will be a very faint blue background. For the end of the day there will be yellow between 15:45-15:50, red between 15:50-16:00, and yellow between 16:00-16:05. During the night hours, there is no coloring. The purpose of this highlight is to show the opening / closing times of the market and the hot times for large moves.
The indicators can also be colored in the following ways:
1. Simple = Makes all colors for the indicator Gray
2. Trend = Will use the Donchian Channels to get the short-trend direction and by default will color the short-term direction as Blue or Red. Unless using Super Trend, the Donchian Channel is used to find short-term trend direction.
3. Trend Adv = Will use the Donchian Channels to get the short-trend direction and by default will color the short-term direction as Blue or Red. Unless using Super Trend, the Donchian Channel is used to find short-term trend direction. If there is a short-term up-trend during a long-term down-trend, the Blue will become Navy. If short-term down-trend during long-term up-trend, the Red will be Brown.
4. Squeeze = Compares the Bollinger Bands width to the Keltner Channels width and will color based on relative squeeze of the market: Teal = no squeeze. Yellow = little squeeze. Red = decent squeeze. White = huge squeeze. if you do not understand this one, try drawing the Bollinger Bands while using the Squeeze color option and it should become more apparent how this works. I also recommend leaving the length and multiplier to the default 20 and 2 if using this setting and only changing the timeframe to get longer/shorter lengths as I've seen that changing the length or multiplier can more or less make it not work at all.
Along with the indicator settings are options to draw lines/labels/fills for the indicator. I enjoy having only fills for a cleaner look.
The Labels option will show Buy/Sell signals when the short-term trend flips to agree with the long-term trend.
The Trend Bars option will do the same as the Labels option but instead will color the bars white when a Buy/Sell option is given.
The Range Bars option shows will color a bar white when the Close of a candle is outside of a respective ranging indicator option (Bollinger or Keltner).
The Trend Bars will draw white candles no matter which indicator selection you make (even "Off"). However, Range Bars will only draw white when either Bollinger or Keltner are selected.
The Donchian Channels and Super Trend are trending indicators and should be used during trending markets. I like to use the MACD in conjunction with these indicators for possibly earlier entries.
The Bollinger Bands and Keltner Channel are ranging indicators and should be used during ranging markets. I like to use the RSI in conjunction with these indicators and will use 60/40 for overbought and oversold areas rather than 70/30. During a range, I wait for an overbought or oversold indication and will buy/sell when it crosses back into the middle area and close my position when it touches the opposite band.
I have a MACD/RSI combination indicator if you'd like that as well :D
As always, trade at your own risk. This is not some secret indicator that will 100% win. As always, the trades you see in the picture use a 1:1.5 or 1:2 risk to reward ratio, for today (August 8, 2022) it won 5/6 times with one trade still open at the end of the day. Manage your account correctly and you'll win in the long term. Hit me up with any questions or suggestions. Happy Trading!
Refracted EMARefracted EMA is a price based indicator with bands that is built on moving average.
The price range between the bands directly depends on relationship of Average True Range to Moving Average. This gives us very valuable variable constant that changes with the market moves.
So the bands expand and contract due to changes in volatility of the market, which makes this tool very flexible exposing psychological levels.
TF Segmented Polynomial Regression [LuxAlgo]This indicator displays polynomial regression channels fitted using data within a user selected time interval.
The model is fitted using the same method described in our previous script:
Settings
Degree: Degree of the fitted polynomial
Width: Multiplicative factor of the model RMSE. Controls the width of the polynomial regression's channels
Timeframe: Fits the polynomial regression using data within the selected timeframe interval
Show fit for new bars: If selected, will fit the regression model for newly generated bars, else the previous fitted value is displayed.
Src: Input source
Usage
Segmented (or piecewise) models yield multiple fits by first partitioning the data into multiple intervals from specific partitioning conditions. In this script this partitioning condition is for a user selected timeframe to change.
Segmented models can be particularly pertinent for market prices, which often describes a series of local trends.
Segmented polynomial regressions can describe the nature of underlying trends in the price from their fit, such as if an underlying trend is more linear (trending) or constant (ranging), and if a trend is monotonic.
The above chart shows a monthly partitioning on SPX 15m, using a polynomial regression of degree 3. Channel extremities allows highlighting local tops/bottoms.
For real time applications users can choose to fit a current model to incoming price data using the Show fit for new bars settings.
Details
The script does not make use of line.new to display the segmented linear regressions, which allows showing a higher number of historical fits. Each channel extremity as well as the model fit is displayed from the plot function, as such user can more easily set alerts on them.
It is important to note that achieving this requires accessing future price data, as such this script is subject to lookahead bias, historical results differ from the results one could have obtained in real-time.
ADXVMA iTrend [Loxx]ADXVMA iTrend is an iTrend indicator with ADXVMA smoothing. Trend is used to determine where the trend starts and ends. Adjust the period inputs accordingly to suit your backtest requirements. This is also useful for scalping lower timeframes.
What is the ADXvma - Average Directional Volatility Moving Average?
Linnsoft's ADXvma formula is a volatility-based moving average, with the volatility being determined by the value of the ADX indicator.The ADXvma has the SMA in Chande's CMO replaced with an EMA , it then uses a few more layers of EMA smoothing before the "Volatility Index" is calculated.
Included
Bar coloring
Alerts
Signals
Loxx's Expanded Source Types
VHF-Adaptive T3 iTrend [Loxx]VHF-Adaptive T3 iTrend is an iTrend indicator with T3 smoothing and Vertical Horizontal Filter Adaptive period input. iTrend is used to determine where the trend starts and ends. You'll notice that the noise filter on this one is extreme. Adjust the period inputs accordingly to suit your take and your backtest requirements. This is also useful for scalping lower timeframes. Enjoy!
What is VHF Adaptive Period?
Vertical Horizontal Filter (VHF) was created by Adam White to identify trending and ranging markets. VHF measures the level of trend activity, similar to ADX DI. Vertical Horizontal Filter does not, itself, generate trading signals, but determines whether signals are taken from trend or momentum indicators. Using this trend information, one is then able to derive an average cycle length.
What is the T3 moving average?
Better Moving Averages Tim Tillson
November 1, 1998
Tim Tillson is a software project manager at Hewlett-Packard, with degrees in Mathematics and Computer Science. He has privately traded options and equities for 15 years.
Introduction
"Digital filtering includes the process of smoothing, predicting, differentiating, integrating, separation of signals, and removal of noise from a signal. Thus many people who do such things are actually using digital filters without realizing that they are; being unacquainted with the theory, they neither understand what they have done nor the possibilities of what they might have done."
This quote from R. W. Hamming applies to the vast majority of indicators in technical analysis . Moving averages, be they simple, weighted, or exponential, are lowpass filters; low frequency components in the signal pass through with little attenuation, while high frequencies are severely reduced.
"Oscillator" type indicators (such as MACD , Momentum, Relative Strength Index ) are another type of digital filter called a differentiator.
Tushar Chande has observed that many popular oscillators are highly correlated, which is sensible because they are trying to measure the rate of change of the underlying time series, i.e., are trying to be the first and second derivatives we all learned about in Calculus.
We use moving averages (lowpass filters) in technical analysis to remove the random noise from a time series, to discern the underlying trend or to determine prices at which we will take action. A perfect moving average would have two attributes:
It would be smooth, not sensitive to random noise in the underlying time series. Another way of saying this is that its derivative would not spuriously alternate between positive and negative values.
It would not lag behind the time series it is computed from. Lag, of course, produces late buy or sell signals that kill profits.
The only way one can compute a perfect moving average is to have knowledge of the future, and if we had that, we would buy one lottery ticket a week rather than trade!
Having said this, we can still improve on the conventional simple, weighted, or exponential moving averages. Here's how:
Two Interesting Moving Averages
We will examine two benchmark moving averages based on Linear Regression analysis.
In both cases, a Linear Regression line of length n is fitted to price data.
I call the first moving average ILRS, which stands for Integral of Linear Regression Slope. One simply integrates the slope of a linear regression line as it is successively fitted in a moving window of length n across the data, with the constant of integration being a simple moving average of the first n points. Put another way, the derivative of ILRS is the linear regression slope. Note that ILRS is not the same as a SMA ( simple moving average ) of length n, which is actually the midpoint of the linear regression line as it moves across the data.
We can measure the lag of moving averages with respect to a linear trend by computing how they behave when the input is a line with unit slope. Both SMA (n) and ILRS(n) have lag of n/2, but ILRS is much smoother than SMA .
Our second benchmark moving average is well known, called EPMA or End Point Moving Average. It is the endpoint of the linear regression line of length n as it is fitted across the data. EPMA hugs the data more closely than a simple or exponential moving average of the same length. The price we pay for this is that it is much noisier (less smooth) than ILRS, and it also has the annoying property that it overshoots the data when linear trends are present.
However, EPMA has a lag of 0 with respect to linear input! This makes sense because a linear regression line will fit linear input perfectly, and the endpoint of the LR line will be on the input line.
These two moving averages frame the tradeoffs that we are facing. On one extreme we have ILRS, which is very smooth and has considerable phase lag. EPMA has 0 phase lag, but is too noisy and overshoots. We would like to construct a better moving average which is as smooth as ILRS, but runs closer to where EPMA lies, without the overshoot.
A easy way to attempt this is to split the difference, i.e. use (ILRS(n)+EPMA(n))/2. This will give us a moving average (call it IE /2) which runs in between the two, has phase lag of n/4 but still inherits considerable noise from EPMA. IE /2 is inspirational, however. Can we build something that is comparable, but smoother? Figure 1 shows ILRS, EPMA, and IE /2.
Filter Techniques
Any thoughtful student of filter theory (or resolute experimenter) will have noticed that you can improve the smoothness of a filter by running it through itself multiple times, at the cost of increasing phase lag.
There is a complementary technique (called twicing by J.W. Tukey) which can be used to improve phase lag. If L stands for the operation of running data through a low pass filter, then twicing can be described by:
L' = L(time series) + L(time series - L(time series))
That is, we add a moving average of the difference between the input and the moving average to the moving average. This is algebraically equivalent to:
2L-L(L)
This is the Double Exponential Moving Average or DEMA , popularized by Patrick Mulloy in TASAC (January/February 1994).
In our taxonomy, DEMA has some phase lag (although it exponentially approaches 0) and is somewhat noisy, comparable to IE /2 indicator.
We will use these two techniques to construct our better moving average, after we explore the first one a little more closely.
Fixing Overshoot
An n-day EMA has smoothing constant alpha=2/(n+1) and a lag of (n-1)/2.
Thus EMA (3) has lag 1, and EMA (11) has lag 5. Figure 2 shows that, if I am willing to incur 5 days of lag, I get a smoother moving average if I run EMA (3) through itself 5 times than if I just take EMA (11) once.
This suggests that if EPMA and DEMA have 0 or low lag, why not run fast versions (eg DEMA (3)) through themselves many times to achieve a smooth result? The problem is that multiple runs though these filters increase their tendency to overshoot the data, giving an unusable result. This is because the amplitude response of DEMA and EPMA is greater than 1 at certain frequencies, giving a gain of much greater than 1 at these frequencies when run though themselves multiple times. Figure 3 shows DEMA (7) and EPMA(7) run through themselves 3 times. DEMA^3 has serious overshoot, and EPMA^3 is terrible.
The solution to the overshoot problem is to recall what we are doing with twicing:
DEMA (n) = EMA (n) + EMA (time series - EMA (n))
The second term is adding, in effect, a smooth version of the derivative to the EMA to achieve DEMA . The derivative term determines how hot the moving average's response to linear trends will be. We need to simply turn down the volume to achieve our basic building block:
EMA (n) + EMA (time series - EMA (n))*.7;
This is algebraically the same as:
EMA (n)*1.7-EMA( EMA (n))*.7;
I have chosen .7 as my volume factor, but the general formula (which I call "Generalized Dema") is:
GD (n,v) = EMA (n)*(1+v)-EMA( EMA (n))*v,
Where v ranges between 0 and 1. When v=0, GD is just an EMA , and when v=1, GD is DEMA . In between, GD is a cooler DEMA . By using a value for v less than 1 (I like .7), we cure the multiple DEMA overshoot problem, at the cost of accepting some additional phase delay. Now we can run GD through itself multiple times to define a new, smoother moving average T3 that does not overshoot the data:
T3(n) = GD ( GD ( GD (n)))
In filter theory parlance, T3 is a six-pole non-linear Kalman filter. Kalman filters are ones which use the error (in this case (time series - EMA (n)) to correct themselves. In Technical Analysis , these are called Adaptive Moving Averages; they track the time series more aggressively when it is making large moves.
Included
Bar coloring
Alerts
Signals
Loxx's Expanded Source Types
Donchian Channels+Standard Donchian Channels, this includes a Trend EMA which can be configured and the channels will use to give buy and sell signals. The channels are also colored in such a way to indicate a trend reversals. My favorite part that sets this apart from others is, if using the bClose option (which I recommend you do) the trend will not reverse until there is a close above/below the previous channel high/low. i.e. a reversal into an uptrend requires the current bar to close above the previous upper channel rather than just a new high.
The coloring of the current trend allows for a quick read of current market conditions and I use this on any timeframe across all forms of charts (forex stock, futures , crypto, etc). When taking one of the signals I use the opposite channel as the stop loss (when buying use bottom, and selling use top channel)
Update 1:
- Added my name to the code
- Updated the picture to show off the indicator better
- Raised the brightness of the fill color a bit, looks nicer I think
As a warning: I have obviously hand picked this picture to really show off this indicator's power to work in a trending market. Donchian Channels are a trending indicator and work best in trending markets with decent pull backs. If there are multiple signals going in each direction, it is a choppy market and you should stay out until it starts trending again. I generally use a 1:1.5 risk to reward ratio but sometimes will stretch it to 1:2 if it is a nice trade and I feel the market is in my favor as you can see from the trades placed in the picture (and I use that method for any commodity / chart and timeframe). I use the close of the white candle and then (since they are trades going short) I use the upper Donchian Channel as my stop loss.
I personally like to have the lines and labels turned off and enable all other options. Labels are just there to really stand out so you don't miss the white candle indication for a trade. OH! Also, I've had comments from friends that the white candles are hard to see, I turn off my candle borders in settings; makes it super easy to see them then. I turn lines off as the fill does the job well enough and it seems cleaner in my eyes.
If you'd like to see this picture on your chart, it is Micro E-Mini S&P 500 Futures from July 10, 2022 @ 18:00 on the 1-min chart.
just now
Update 2:
- Added day trading time highlights
- Added ranging market indicator (This I'm still working on so use at your own discretion, it's intended purpose is to tell when the Donchian Channels are not going to give great signals and to use a ranging indicator instead)
- Added multi-timeframe trend screener (handy if you want to quickly see the trend direction (based on the Trend EMA you set) of several time frames without having to change charts). It will draw in the bottom right of the chart.
Mark MinerviniHi everyone,
Just sharing a script that I made when I began to be interested in Mark Minervini, Wiliam O'Neil, Nicolas Darvas,.. trading style.
This script displays :
- 10 EMA (orange)
- 20 EMA (blue light)
- 50 SMA (blue)
-150 SMA (green)
-200 SMA (red)
- Shows when the stock is "extended" from EMA10 to high of the candle (Works in Daily, you can adjust the % to make it match with the stock's volatility)
(Shows a red area between price and 10 EMA)
- Shows when Mark Minervini's trend template is respected by highlighting green between 150 & 200 SMA.
I Although added bollinger bands and 5 EMA for very strong stocks. (I never use them)
Have fun
EMA Cross CloudSimple ema cross trading alerts, completely customizable fast and slow crossovers with rendered cloud.
This cross indicator attempts to filter counter trend trades by added additional ema market and if crossunder/over occurs opposite of ema market the cloud will turn gray, otherwise if it's with the overall trend then red/green.
The last ema is a trailing stop guideline that is rendered with yellow crosses, could also utilize other stops such as the top or bottom of the cloud.
Alerts are baked in for ema crosses, trend breaks into stops.
Nyquist Moving Average (NMA) MACD [Loxx]Nyquist Moving Average (NMA) MACD is a MACD indicator using Nyquist Moving Average for its calculation.
What is the Nyquist Moving Average?
A moving average outlined originally developed by Dr . Manfred G. Dürschner in his paper "Gleitende Durchschnitte 3.0".
In signal processing theory, the application of a MA to itself can be seen as a Sampling procedure. The sampled signal is the MA (referred to as MA.) and the sampling signal is the MA as well (referred to as MA). If additional periodic cycles which are not included in the price series are to be avoided sampling must obey the Nyquist Criterion.
It can be concluded that the Moving Averages 3.0 on the basis of the Nyquist Criterion bring about a significant improvement compared with the Moving Averages 2.0 and 1.0. Additionally, the efficiency of the Moving Averages 3.0 can be proven in the result of a trading system with NWMA as basis.
What is the MACD?
Moving average convergence divergence (MACD) is a trend-following momentum indicator that shows the relationship between two moving averages of a security’s price. The MACD is calculated by subtracting the 26-period exponential moving average (EMA) from the 12-period EMA.
The result of that calculation is the MACD line. A nine-day EMA of the MACD called the "signal line," is then plotted on top of the MACD line, which can function as a trigger for buy and sell signals. Traders may buy the security when the MACD crosses above its signal line and sell—or short—the security when the MACD crosses below the signal line. Moving average convergence divergence (MACD) indicators can be interpreted in several ways, but the more common methods are crossovers, divergences, and rapid rises/falls.
Included
Bar coloring
2 types of signal output options
Alerts
Loxx's Expanded Source Types
RSI Precision Trend Candles [Loxx]RSI Precision Trend Candles is a candle coloring indicator that uses an average range algorithm to determine trend direction. The precision trend algorithm can be used on any calculated output to tease out interesting trend information.
What is RSI?
The relative strength index (RSI) is a momentum indicator used in technical analysis. RSI measures the speed and magnitude of a security's recent price changes to evaluate overvalued or undervalued conditions in the price of that security.
The RSI is displayed as an oscillator (a line graph) on a scale of zero to 100. The indicator was developed by J. Welles Wilder Jr. and introduced in his seminal 1978 book, New Concepts in Technical Trading Systems.
Included
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
MACD + RSI with Trade SignalsThis indicator by default comes with the MACD shown but can be switched to show the RSI instead. Settings for each indicator can also be customized as well as Buy/Sell signals given based on pull back crossovers that follow the 200 EMA of the price Chart. There's an above/below middle fill option you can use but I tend not to but I know some traders like to see when an oscillator is above/below the middle and use it as a trend diretion. By the way, the fourth setting for the MACD (which is 2 by default) is the size of the histogram.
Buy Signal = Price is above the 200 EMA. Current or previous MACD or RSI line is/was below middle line and now crossed above the signal line.
Sell Signal = Price is below the 200 EMA. Current or previous MACD or RSI line is/was above middle line and now crossed below the signal line.
There are alerts for each signal as well (MACD and RSI, both buy and sell).
Feel free to leave a comment regarding issues or suggestions for this indicator or ideas for the next one I should do :)
Price-Filtered Spearman Rank Correl. w/ Floating Levels [Loxx]Price-Filtered Spearman Rank Correl. w/ Floating Levels is a Spearman Rank Correlation indicator with optional source filtering and floating levels.
What is Spearman rank correlation?
Spearman rank correlation, also known as Spearman coefficient is a formula used to identify the strength of the link between two datasets. This coefficient is a method that can be used to assess the strength of a relationship apart from the direction it takes. The formula, named after Charles Spearman, a mathematician, can only be used in circumstances where data can be categorized or put in order, for instance, the highest to the lowest.
For a better understanding of Spearman coefficient, it helps to get a sense of what monotonic function means. There’s a monotonic relationship under these circumstances:
– When the variable values rise together.
– When one variable value rises the other variable value lowers.
– The rate of movement of the variables need not necessarily be constant.
The Spearman correlation coefficient or rs, between +1 and -1, where +1 indicates a perfect strength between variables, while zero shows no association and -1 shows a perfect negative strength.
Spearman rank correlation theory:
A nonparametric (distribution-free) rank statistic proposed by Spearman in 1904 as a measure of the strength of the associations between two variables (Lehmann and D'Abrera 1998). The Spearman rank correlation coefficient can be used to give an R-estimate, and is a measure of monotone association that is used when the distribution of the data make Pearson's correlation coefficient undesirable or misleading.
Included:
Zero-line and signal cross options for bar coloring, signals, and alerts
Alerts
3 Signal types
Loxx's Expanded Source Types
Dynamic Zones Polychromatic Momentum Candles [Loxx]Dynamic Zones Polychromatic Momentum Candles is a candle coloring, momentum indicator that uses Jurik Filtering and Dynamic Zones to calculate the monochromatic color between two colors.
What is Jurik Volty used in the Juirk Filter?
One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive.
What is the Jurik Moving Average?
Have you noticed how moving averages add some lag (delay) to your signals? ... especially when price gaps up or down in a big move, and you are waiting for your moving average to catch up? Wait no more! JMA eliminates this problem forever and gives you the best of both worlds: low lag and smooth lines.
What are Dynamic Zones?
As explained in "Stocks & Commodities V15:7 (306-310): Dynamic Zones by Leo Zamansky, Ph .D., and David Stendahl"
Most indicators use a fixed zone for buy and sell signals. Here’ s a concept based on zones that are responsive to past levels of the indicator.
One approach to active investing employs the use of oscillators to exploit tradable market trends. This investing style follows a very simple form of logic: Enter the market only when an oscillator has moved far above or below traditional trading lev- els. However, these oscillator- driven systems lack the ability to evolve with the market because they use fixed buy and sell zones. Traders typically use one set of buy and sell zones for a bull market and substantially different zones for a bear market. And therein lies the problem.
Once traders begin introducing their market opinions into trading equations, by changing the zones, they negate the system’s mechanical nature. The objective is to have a system automatically define its own buy and sell zones and thereby profitably trade in any market — bull or bear. Dynamic zones offer a solution to the problem of fixed buy and sell zones for any oscillator-driven system.
An indicator’s extreme levels can be quantified using statistical methods. These extreme levels are calculated for a certain period and serve as the buy and sell zones for a trading system. The repetition of this statistical process for every value of the indicator creates values that become the dynamic zones. The zones are calculated in such a way that the probability of the indicator value rising above, or falling below, the dynamic zones is equal to a given probability input set by the trader.
To better understand dynamic zones, let's first describe them mathematically and then explain their use. The dynamic zones definition:
Find V such that:
For dynamic zone buy: P{X <= V}=P1
For dynamic zone sell: P{X >= V}=P2
where P1 and P2 are the probabilities set by the trader, X is the value of the indicator for the selected period and V represents the value of the dynamic zone.
The probability input P1 and P2 can be adjusted by the trader to encompass as much or as little data as the trader would like. The smaller the probability, the fewer data values above and below the dynamic zones. This translates into a wider range between the buy and sell zones. If a 10% probability is used for P1 and P2, only those data values that make up the top 10% and bottom 10% for an indicator are used in the construction of the zones. Of the values, 80% will fall between the two extreme levels. Because dynamic zone levels are penetrated so infrequently, when this happens, traders know that the market has truly moved into overbought or oversold territory.
Calculating the Dynamic Zones
The algorithm for the dynamic zones is a series of steps. First, decide the value of the lookback period t. Next, decide the value of the probability Pbuy for buy zone and value of the probability Psell for the sell zone.
For i=1, to the last lookback period, build the distribution f(x) of the price during the lookback period i. Then find the value Vi1 such that the probability of the price less than or equal to Vi1 during the lookback period i is equal to Pbuy. Find the value Vi2 such that the probability of the price greater or equal to Vi2 during the lookback period i is equal to Psell. The sequence of Vi1 for all periods gives the buy zone. The sequence of Vi2 for all periods gives the sell zone.
In the algorithm description, we have: Build the distribution f(x) of the price during the lookback period i. The distribution here is empirical namely, how many times a given value of x appeared during the lookback period. The problem is to find such x that the probability of a price being greater or equal to x will be equal to a probability selected by the user. Probability is the area under the distribution curve. The task is to find such value of x that the area under the distribution curve to the right of x will be equal to the probability selected by the user. That x is the dynamic zone.
Included
Loxx's Expanded Source Types
Larry Williams Large Trade Index (LWTI) [Loxx]Larry Williams Large Trade Index (LWTI) is an indicatory by Larry Williams as explained in his book "Trade Stocks and Commodities with the Insiders: Secrets of the COT Report". I've added optional smoothing if you wish to smooth the output.
What is the Larry Williams Large Trade Index (LWTI)?
The original concept was specifically based on Trader (or Market) Sentiment and predicting market reversals. It's calculated as follows:
MovingAvg(Close - Close , bars used in average)/MovingAvg(Range,bars used in average)*50 + 50
Included
Bar coloring
Signals
Alerts
Larry Williams Proxy Index (LWPI) [Loxx]Larry Williams Proxy Index (LWPI) is an indicatory by Larry Williams as explained in his book "Trade Stocks and Commodities with the Insiders: Secrets of the COT Report", pages 129-135. This is an INVERSE indicator, so follow the signals and colors to understand what it's doing. I've added optional smoothing if you wish to smooth the output.
What is the Larry Williams Proxy Index (LWPI)?
The original concept was specifically based on Trader (or Market) Sentiment and predicting market reversals. It's calculated as follows:
MovingAvg(Open-Close, bars used in average)/MovingAvg(Range, bars used in average)*50+50
Included
Bar coloring
Signals
Alerts
pickle financialversion 1.0
A multi-use indicator
FEATURES:
1) Robust VWAP with up to 3 deviations that can be adjusted for Session, Week, Month, Quarter, Year, Decade, Century, Earnings , Dividend, & Split intervals
2) 20 customizable Moving Averages that can be changed to EMAs, SMAs , WMAs, SMMAs, and VWMAs; can also be adjusted to Candle Closes, Highs, Lows, Opens, HLC3, HL2 , OHLC4, & HLCC4
-Praise be lady VWAP
Smoothed Repulse w/ Floating Levels [Loxx]Smoothed Repulse w/ Floating Levels indicator measures and displays the bullish or bearish pressure associated with each price candlestick in the form of a curve.
It is more relevant when compared to price and offers valuable additional information on the feeling and confidence that traders have about the markets.
This version can use one of the 4 basic averages types for smoothing.
Coloring can be chosen depending on :
slope
outer levels cross
middle ("zero") level cross
Since the "repulse" indicator is not limited to known bounds, levels are dynamic — the "zero" value too. That makes it more responsive in the times of elevated volatility. Alerts are triggered based on the color change.
Included:
Bar coloring
Signals
Alerts
Your choice of moving average for smoothing
Auto Trendline Indicator (based on fractals)A tool that automatically draws out trend lines by connecting the most recent fractals.
Description:
The process of manual drawing out trend lines is highly subjective. Many times, we don’t trade what we see, but what we “want to see”. As a result, we draw lines pointing to the direction that we wishfully want price to move towards. While there are no right/wrong ways to draw trend lines, there are, however, systematic/unsystematic ways to draw trend lines. This tool will systematically draw out trend lines based on fractals.
Additional feature:
This tool will also plot out symbols (default symbol “X”) to signify points of crossings. This can be useful for traders considering to use trend lines as part of their trading strategies.
Here is an interesting observation on the price actions of NASDAQ futures on a 5 second chart during regular trading hours on July 14, 2022.
It’s a phenomenon. People like to see straight lines connecting HL/LH, etc., so it's possible for the market as a whole to psychologically react to these lines. However, it is important to note that is is impossible to predict the direction of price. In the case above, price could have tanked below auto-drawn trend line. Fractal based trend lines should only be taken as references and regarded as price levels. No studies have ever proven that the slope of trend lines can indicate price's future direction.
More about fractals:
To understand more about fractals:
www.investopedia.com
www.tradingview.com
Contrary to what it sounds like, fractal in "technical analysis" does not refer to the recursive self-repeating patterns that appear in nature, such as the mesmerizing patterns found in snowflakes. The Fractal Markets Hypothesis claims that market prices exhibit fractal properties over time. Assuming this assertion to be true, then fractals can be used a tool to represent the chaotic movements of price is a simplified manner.
The purpose of this exercise is to take a tool that is readily available (ie. in this case, TradingView’s built-in fractals tool), and to create a newer tool based on it.
Parameters:
Fractal period (denoted as ‘n’ in code): It is the number of bars bounding a high/low point that must be lower/higher than it, respectively, in order for fractal to be considered valid. Period ‘n’ can be adjusted in this tool. Traditionally, chartists pick the value of 5. The longer it is, the less noise seen on the chart, and the pivot point may also be exhibited in higher timeframes. The drawback is that it will increase the period of lag, and it will take more bars to confirm the printed fractal.
Others: Intuitive parameters such as whether to draw historical trend lines, what color to use, which way to extend the lines, and whether or not to show points of crossings.
God Number Channel v2(GNC v2)GNC got a little update:
1) Logic changed a bit.
I tried to calculate MAs based on the power(high - low of previous bars).You can see it the M-variables, as new statements were added in calculation section of MAs. I don't really know if I did right, because I didn't go too much in Pine Script. I just wanted to make a Bollinger-bands-like bands, which could predict the levels at which might reverse, using legendary fibonacci and Tesla's harmonic number 432. It's might sound as a joke, but as you can see, it works pretty good.
2) Customization :
No need to change Fibonacci ratios in code. Now you can do it in the GNC settings. Also MAs' names were made obvious, just check it out. Time of million similar "MA n1" has passed :)
3) Trade-entry advices :
I didn't tell you exactly the trade-entry advices, as I haven't explored this script fully yet :) But you probably understood something intuitively, when added GNC on the chart. Now I made things way more obvious:
1. Zones between Fib ratios show you how aware you should be of price movements. Basically, here are the rules, but you probably understand them already:
1.1 Red zone(RZ) : high awareness, very likly for price to be reversed, but if there is a clear trend and you know, than it might be a time for price to shoot up/down.
1.2 Orange zone(OZ) : medium awareness, not so obvious, as price might go between boundaries of OZ and continue the trend movement if such followed before entering the OZ. If price go below lower boundary of OZ and the next bar opens below this boundary, it might be a signal for SHORY, BUT(!) please consider confirmation of any sort to be more sure. Think of going beyond the upper boundary by analogy.
1.3 Green Zone(GZ) : if the price hits any boundary of green zone, it is usually a good oppurtunity to open a position against the movement(hit lower boundary -> open LONG, hit upper boundary -> open SHORT).
1.4 Middle Zone(Harmonic Zone)(MZ) : same rules from Green Zone.
IMPORTANT RECCOMENDATION : Use trend indicator to trend all signals from zones to follow the trend, 'cause counter-trending with this thing without stop loss might very quickly wipe you out , might if you will counter-trend strategy with GNC, I will be glad if you share it with the community :)
Reccomendation for better entries :
1) if the price hits the lower(or high) boundaries(LB or HB) zone after zone(hit LB or HB of RZ, then of OZ, then of GZ), it is a very good signal to either LONG, if price was hitting LBs , or SHORT, if hitting HBs .
2) Consider NOT to place trades when in MZ, as price in this zone gets tricky often enough. By the way, if you dont the see the harmonic MAs(which go with plot(ma1+(0.432*avg1)) ), then set the transparency of zone to 20% or a bit more and then it will be ok.
I will continue to develop the GNC and any help or feedback from you, guys, will be very helpful for me, so you welcome for any of those, but please be precise in your critics.
Thank you for using my stuff, hope you found it usefull. Good luck :)
Directional BiasA Directional Bias to stop me trading against the trend
Utilising EMA'S - I personally view on the 15M TF but it can be set on any
40/50/60 15Minute STF
and 13/35/50 - 30M 1H and 4H HTF
Mixing them together in direction and location to each other Gives a 6 colour system for keeping away from trading against trend
Dark Red Both Align - Sells Only do not take Buy Trades
Medium Red HTF and Dark Red Mix - Sells Only do not take Buy Trades
Light Red STF - if in a mix with Blanks and greens - Trade Both Directions - if 8 hours of solid Light Red ? Darker Red - do not take Buy Trades - Sells Only
Orange - Trading Both ways - No Directional Bias
Dark Green Both Align - Buys Only do not take Sell Trades
Medium Green HTF and Dark Green Mix - Buys Only do not take Sell Trades
Light Red STF - if in a mix with Blanks and greens - Trade Both Directions - if 8 hours of solid Light Green ? Darker Green - do not take Sell Trades - Buys Only
Alert Setting for Change of Direction included
I find this useful - to at least give me a pause for thought when I am about to trade against the trend - I hope you do to
Andean OscillatorThe following script is an original creation originally posted on the blog section of the broker Alpaca.
The proposed indicator aims to measure the degree of variations of individual up-trends and down-trends in the price, thus allowing to highlight the direction and amplitude of a current trend.
Settings
Length : Determines the significance of the trends degree of variations measured by the indicator.
Signal Length : Moving average period of the signal line.
Usage
The Andean Oscillator can return multiple information to the user, with its core interpretation revolving around the bull and bear components.
A rising bull component (in green) indicates the presence of bullish price variations while a rising bear component (in red) indicates the presence of bearish price variations.
When the bull component is over the bear component market is up-trending, and the user can expect new higher highs. When the bear component is over the bull component market is down-trending, and the user can expect new lower lows.
The signal line (in orange) allows a more developed interpretation of the indicator and can be used in several ways.
It is possible to use it to filter out potential false signals given by the crosses between the bullish and bearish components. As such the user might want to enter a position once the bullish or bearish component crosses over the signal line instead.
Details
Measuring the degree of variations of trends in the price by their direction (up-trend/down-trend) can be done in several way.
The approach taken by the proposed indicator makes use of exponential envelopes and the naive computation of standard deviation.
First, exponential envelopes are obtained from both the regular prices and squared prices, thus giving two upper extremities, and two lower extremities.
The bullish component is obtained by first subtracting the upper extremity of the squared prices with the squared upper extremity of regular prices, the square root is then applied to this result.
The bearish component is obtained in the same way, but makes use of the lower extremities of the exponential envelopes.
God Number Channel V1 (GNC V1)Channel, made of 5 MAs, which a made this way: High of N-period SMA - Low of N-period SMA + X-period SMA (check the code), where N and X are defined by your input.
Main purpose: helps you understand in what range price can move.
WARNING!
HAS TO BE USED WITH OTHER INDICATORS TO HAVE MORE ACCURATE ENTRIES!!!
If the price is above or below the channel, it means that the movement is very strong and you count it as a trend, but be careful then the price returns to the channel, as correction will follow very soon. Use fib correction tool to understand the approximate depth of correction, works pretty good.
Recommendation: consider using the Vortex Indicator( len 21 and 14 are fine; for trend) and "Vumanchu Divergencies + B"(for anything, but calibrate for accuracy, otherwise there will be too much false signals). If you want to see more options where the price might go, just add new MA and add/substract to/from its value avg1*(any of fibonacci correction levels, I personally use 1.618 and 2.618 and for me it is ok): plot(show_ma1 ? ma1+( [ [ ]]]*avg1) : na, color = ma1_color, title="MA №1")
Recommendations and feedback are welcome(!)
Take your wins
