STD-Filtered, Regularized EMA/RMA [Loxx]STD-Filtered, Regularized EMA/RMA calculates and visualizes a standard deviation (STD) filtered, regularized version of the Exponential Moving Average (EMA) or Regular Moving Average (RMA) on a trading chart.
█ Understanding the Regularized Moving Average
The Regularized Moving Average, as conceptualized by Chris Satchwell, offers a more responsive interpretation compared to traditional moving averages. By incorporating a smoothing mechanism using "Lambda", this approach reduces lag without compromising the data's integrity.
In the realm of technical analysis, many regard it as a preferred alternative to the standard Moving Average and Exponential Moving Average.
█ How Does It Stand Out from Other Moving Averages?
While analysts traditionally shorten an indicator's length or period to minimize lag, the Regularized Moving Average uses a unique approach. By embedding "Regularization" within its computation, this method introduces Lambda (often symbolized as λ-calculus). This mathematical factor tames the moving average's undue fluctuations, offering more stability through its Lambda adjustments.
Pro Tip: For those analyzing smaller intraday timeframes, consider ramping up the Lambda setting to 6.0 or even higher. When tweaking these settings, always remember to backtest and observe how it impacts signal accuracy and noise filtering.
█ Standard Deviation Filtering:
This filtering mechanism is designed to smoothen price data by eliminating minor fluctuations that might be considered "noise". Here's how the process works:
For every data point, the standard deviation of prices over a specified period is calculated.
This standard deviation is then multiplied by a user-defined value to determine a threshold. This threshold defines the magnitude of change required in the price for it to be considered significant.
For each price, if the absolute difference between its current and previous value is less than this threshold, the price is kept unchanged (considered insignificant and thus filtered). If the difference exceeds the threshold, the price is considered significant and remains as is.
By applying this filter, minor price variations within the threshold are disregarded, resulting in a smoother representation of the price data.
█ Moving Average Calculation:
The script provides an option to calculate either a regularized Exponential Moving Average (EMA) or a Regular Moving Average (RMA). Here's how these are approached:
If the EMA option is selected: A weighted formula is used where more recent prices have a higher influence on the average than older prices. This is achieved by applying a fraction that's inversely related to the chosen period. The outcome is an average that reacts more quickly to recent price changes.
If the RMA option is selected: The average is computed by giving equal weight to all prices within the chosen period.
Both these averages then undergo a regularization process. Regularization, in this context, refers to adjusting the moving average using a factor to make it potentially more sensitive or responsive to price changes.
This regularized moving average can offer a refined perspective on price trends by being more adaptive to recent changes, potentially highlighting turning points or trend continuations more effectively.
█ Extras
Signals
Alerts
Bar coloring

# Stepping

Step-MA Filtered Stochastic [Loxx]Step-MA Filtered Stochastic is a stochastic indicator with step moving average filtering. This smooths the signal by filtering out noise.
What is the Stochastic Indicator?
The stochastic oscillator, also known as stochastic indicator, is a popular trading indicator that is useful for predicting trend reversals. It also focuses on price momentum and can be used to identify overbought and oversold levels in shares, indices, currencies and many other investment assets.
The stochastic oscillator measures the momentum of price movements. Momentum is the rate of acceleration in price movement. The idea behind the stochastic indicator is that the momentum of an instrument’s price will often change before the price movement of the instrument actually changes direction. As a result, the indicator can be used to predict trend reversals.
The stochastic indicator can be used by experienced traders and those learning technical analysis. With the help of other technical analysis tools such as moving averages, trendlines and support and resistance levels, the stochastic oscillator can help to improve trading accuracy and identify profitable entry and exit points.
Included:
Bar coloring
3 signal variations w/ alerts
Loxx's Expanded Source Types

Stepped Moving Average of CCI [Loxx]Stepped Moving Average of CCI is a CCI that applies a stepping algorithm to smooth CCI. This allows for noice reduction and better identification of breakouts/breakdowns/reversals.
What is CCI?
The Commodity Channel Index ( CCI ) measures the current price level relative to an average price level over a given period of time. CCI is relatively high when prices are far above their average. CCI is relatively low when prices are far below their average. Using this method, CCI can be used to identify overbought and oversold levels.
Included:
Bar coloring
4 signal variations w/ alerts
Loxx's Expanded Source Types
Loxx's Moving Averages

Pips-Stepped PDFMA [Loxx]Pips-Stepped PDFMA is and Pips-stepped moving average that uses a probability density function moving average. This is tuned for Forex. You must adjust the step size to extreme levels for this to work for crypto or stocks. Try 30000 for BTC on the daily chart, for example.
What is Probability Density Function?
Probability density function based MA is a sort of weighted moving average that uses probability density function to calculate the weights.
Included:
Bar coloring
Alerts
Expanded source types
Signals
Flat-level coloring for scalping

Pips Stepped VHF-Adaptive VMA w/ Expanded Source Types [Loxx]Pips Stepped VHF-Adaptive VMA w/ Expanded Source Types is a volatility adaptive Variable Moving Average (VMA) with stepping by pips.
What is Variable Moving Average (VMA)?
VMA (Variable Moving Average) is often mistakenly confused with the VIDYA (Volatility Index Dynamic Average) which is not strange since Tushar Chande took part in developing both. But the VMA was preceding the VIDYA and should not be mistaken for it.
What is Vertical Horizontal Filter (VHF)?
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 in the Directional Movement System. Trend indicators can then be employed in trending markets and momentum indicators in ranging markets.
VMA, as is, is a "good candidate" for this type of filtering since it tends to produce prolonged periods of nearly horizontal values when the volatility of the market is low, so, when the step filtering is applied to it, the small slope changes that are happening as a results of the semi EMA calculation are filtered out, and signals are becoming more usable.
Included:
-Color bars
-Show signals
-Long/short alerts

STD Stepped Ehlers Optimal Tracking Filter MTF w/ Alerts [Loxx]STD Stepped Ehlers Optimal Tracking Filter MTF w/ Alerts is the traditional Ehlers Optimal Tracking Filter but with stepped price levels, access to multiple time frames, and alerts.
What is Ehlers Optimal Tracking Filter?
From "OPTIMAL TRACKING FILTERS" by John Ehlers:
"Dr. R.E. Kalman introduced his concept of optimum estimation in 1960. Since that time, his technique has proven to be a powerful and practical tool. The approach is particularly well suited for optimizing the performance of modern terrestrial and space navigation systems. Many traders not directly involved in system analysis have heard about Kalman filtering and have expressed an interest in learning more about it for market applications. Although attempts have been made to provide simple, intuitive explanations, none has been completely successful. Almost without exception, descriptions have become mired in the jargon and state-space notation of the “cult”.
Surprisingly, in spite of the obscure-looking mathematics (the most impenetrable of which can be found in Dr. Kalman’s original paper), Kalman filtering is a fairly direct and simple concept. In the spirit of being pragmatic, we will not deal with the full-blown matrix equations in this description and we will be less than rigorous in the application to trading. Rigorous application requires knowledge of the probability distributions of the statistics. Nonetheless we end with practically useful results. We will depart from the classical approach by working backwards from Exponential Moving Averages. In this process, we introduce a way to create a nearly zero lag moving average. From there, we will use the concept of a Tracking Index that optimizes the filter tracking for the given uncertainty in price movement and the uncertainty in our ability to measure it."
Included:
-Standard deviation stepping filter, price is required to exceed XX deviations before the moving average line shifts direction
-Selection of filtering based on source price, the moving average, or both; you can also set the Filter deviations to 0 for no filtering at all
-Toggle on/off bar coloring
-Toggle on/off signals
-Long/Short alerts