Zero-lag TEMA Crosses [Loxx]Zero-lag TEMA Crosses is a spinoff of a the Zero-lag MA as described by David Stendahl in the April 2000 issue of the journal "Technical Analysis of Stocks and Commodities". This indicator uses TEMA calculation mode in order to make the lag lesser compared to the original Zero-lag MA, and that makes this version even faster than the Zero-lag DEMA too. This indicator is the difference between a Fast and Slow Zero-lag TEMA. This indicator is very useful for lower timeframe scalping.
What is the Zero-lag MA?
The Zero-lag MA (Zero-Lag Moving Average) is a technical indicator that was introduced in the April 2000 issue of the journal "Technical Analysis of Stocks and Commodities" by David Stendahl.
The Zero-lag MA is a type of moving average (MA) that is designed to reduce or eliminate the lag that is typically associated with traditional moving averages. Moving averages are a widely used technical analysis tool that helps traders to identify trends and potential trading opportunities. They work by calculating the average price of a security over a given period of time, and then plotting that average on a chart. The most commonly used moving averages are simple moving averages (SMAs) and exponential moving averages (EMAs).
The problem with traditional moving averages is that they can be slow to respond to changes in market conditions. This lag can cause traders to miss out on potential trading opportunities, or to enter or exit trades at the wrong time. The Zero-lag MA was developed as a solution to this problem.
The Zero-lag MA is calculated using a combination of two EMAs and a subtraction formula. The first step in calculating the Zero-lag MA is to calculate two exponential moving averages: a fast EMA and a slow EMA. The fast EMA is calculated over a shorter period of time than the slow EMA. The exact period lengths will depend on the trader's preferences and the security being analyzed.
Once the two EMAs have been calculated, the next step is to take the difference between them. This difference represents the current market trend, with a positive value indicating an uptrend and a negative value indicating a downtrend. However, this difference alone is not enough to create a useful indicator, as it can still suffer from lag.
To further reduce lag, the difference between the two EMAs is multiplied by a factor derived from a third, slower EMA. This slower EMA acts as a smoothing factor, helping to reduce noise and make the indicator more accurate. The exact period length of the slower EMA will depend on the trader's preferences and the security being analyzed.
The final step in calculating the Zero-lag MA is to add the result of the multiplication to the fast EMA. This produces a final value that represents the current market trend with reduced lag. The Zero-lag MA can be plotted on a chart like any other moving average, and can be used to identify trends, potential trading opportunities, and support and resistance levels.
Overall, the Zero-lag MA is designed to provide traders with a more accurate representation of current market conditions by reducing the lag time between price changes and the moving average. By doing so, it can help traders to make more informed trading decisions and improve their overall profitability.
What is the TEMA?
The triple exponential moving average (TEMA) is a technical analysis indicator that was developed to reduce the lag of traditional moving averages, such as the simple moving average (SMA) or the exponential moving average (EMA). The TEMA was first introduced by Patrick Mulloy in the January 1994 issue of the "Technical Analysis of Stocks and Commodities" magazine.
The TEMA is a type of moving average that is calculated by applying multiple exponential smoothing techniques to price data. Unlike traditional moving averages, which apply a single smoothing factor to price data, the TEMA applies three smoothing factors to produce a more responsive and accurate indicator.
To calculate the TEMA, the following steps are taken:
Calculate the single exponential moving average (SMA) of the price data over a given period.
Calculate the double exponential moving average (DEMA) of the SMA over the same period.
Calculate the triple exponential moving average (TEMA) of the DEMA over the same period.
The formula for calculating the TEMA is:
TEMA = 3 * EMA(SMA) - 3 * EMA(EMA(SMA)) + EMA(EMA(EMA(SMA)))
where EMA is the exponential moving average and SMA is the simple moving average.
The TEMA is designed to reduce the lag associated with traditional moving averages by applying multiple smoothing factors to the price data. This helps to filter out short-term price fluctuations and provide a smoother indicator of the underlying trend. The TEMA is also less susceptible to whipsaws, which occur when a security's price moves in one direction and then quickly reverses, causing false trading signals.
The TEMA can be used in a variety of ways in technical analysis. It can be used to identify trends, determine support and resistance levels, and generate trading signals. When the TEMA is rising, it is generally interpreted as a bullish signal, indicating that the price is trending higher. When the TEMA is falling, it is generally interpreted as a bearish signal, indicating that the price is trending lower.
In summary, the TEMA is a more responsive and accurate indicator than traditional moving averages, designed to reduce lag and provide a smoother representation of the underlying trend. It is a useful tool for technical analysts and traders looking to identify trends, support and resistance levels, and potential trading opportunities.
Extras
Alerts
Bar coloring
Signals
Loxx's Expanded Source Types, see here:
Lagreduction
STD/Clutter Filtered, One-Sided, N-Sinc-Kernel, EFIR Filt [Loxx]STD/Clutter Filtered, One-Sided, N-Sinc-Kernel, EFIR Filt is a normalized Cardinal Sine Filter Kernel Weighted Fir Filter that uses Ehler's FIR filter calculation instead of the general FIR filter calculation. This indicator has Kalman Velocity lag reduction, a standard deviation filter, a clutter filter, and a kernel noise filter. When calculating the Kernels, the both sides are calculated, then smoothed, then sliced to just the Right side of the Kernel weights. Lastly, blackman windowing is used for our purposes here. You can read about blackman windowing here:
Blackman window
Advantages of Blackman Window over Hamming Window Method for designing FIR Filter
The Kernel amplitudes are shown below with their corresponding values in yellow:
This indicator is intended to be used with Heikin-Ashi source inputs, specially HAB Median. You can read about this here:
Moving Average Filters Add-on w/ Expanded Source Types
What is a Finite Impulse Response Filter?
In signal processing, a finite impulse response (FIR) filter is a filter whose impulse response (or response to any finite length input) is of finite duration, because it settles to zero in finite time. This is in contrast to infinite impulse response (IIR) filters, which may have internal feedback and may continue to respond indefinitely (usually decaying).
The impulse response (that is, the output in response to a Kronecker delta input) of an Nth-order discrete-time FIR filter lasts exactly {\displaystyle N+1}N+1 samples (from first nonzero element through last nonzero element) before it then settles to zero.
FIR filters can be discrete-time or continuous-time, and digital or analog.
A FIR filter is (similar to, or) just a weighted moving average filter, where (unlike a typical equally weighted moving average filter) the weights of each delay tap are not constrained to be identical or even of the same sign. By changing various values in the array of weights (the impulse response, or time shifted and sampled version of the same), the frequency response of a FIR filter can be completely changed.
An FIR filter simply CONVOLVES the input time series (price data) with its IMPULSE RESPONSE. The impulse response is just a set of weights (or "coefficients") that multiply each data point. Then you just add up all the products and divide by the sum of the weights and that is it; e.g., for a 10-bar SMA you just add up 10 bars of price data (each multiplied by 1) and divide by 10. For a weighted-MA you add up the product of the price data with triangular-number weights and divide by the total weight.
Ultra Low Lag Moving Average's weights are designed to have MAXIMUM possible smoothing and MINIMUM possible lag compatible with as-flat-as-possible phase response.
Ehlers FIR Filter
Ehlers Filter (EF) was authored, not surprisingly, by John Ehlers. Read all about them here: Ehlers Filters
What is Normalized Cardinal Sine?
The sinc function sinc (x), also called the "sampling function," is a function that arises frequently in signal processing and the theory of Fourier transforms.
In mathematics, the historical unnormalized sinc function is defined for x ≠ 0 by
sinc x = sinx / x
In digital signal processing and information theory, the normalized sinc function is commonly defined for x ≠ 0 by
sinc x = sin(pi * x) / (pi * x)
What is a Clutter Filter?
For our purposes here, this is a filter that compares the slope of the trading filter output to a threshold to determine whether to shift trends. If the slope is up but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. If the slope is down but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. Alternatively if either up or down slope exceeds the threshold then the trend turns green for up and red for down. Fro demonstration purposes, an EMA is used as the moving average. This acts to reduce the noise in the signal.
What is a Dual Element Lag Reducer?
Modifies an array of coefficients to reduce lag by the Lag Reduction Factor uses a generic version of a Kalman velocity component to accomplish this lag reduction is achieved by applying the following to the array:
2 * coeff - coeff
The response time vs noise battle still holds true, high lag reduction means more noise is present in your data! Please note that the beginning coefficients which the modifying matrix cannot be applied to (coef whose indecies are < LagReductionFactor) are simply multiplied by two for additional smoothing .
Included
Bar coloring
Loxx's Expanded Source Types
Signals
Alerts
