Oma — Indicators and Strategies

Flow Optimized Moving Average Overview The Flow Optimized Moving Average (Flow OMA) is an advanced adaptive moving average designed to dynamically adjust smoothing factors based on market efficiency and volatility. By integrating the Efficiency Ratio (ER) with an Adaptive Moving Average (AMA) and leveraging ATR-based bands, this indicator provides traders with a refined tool for identifying trend direction, strength, and potential reversal zones. Key Features Adaptive Moving Average (AMA) Adjusts to price action based on the Efficiency Ratio (ER), reducing lag in trending markets while smoothing noise in ranging conditions. Efficiency Ratio (ER) Measures the effectiveness of price movement over a defined lookback period. Helps in dynamically adjusting the smoothing constant of the AMA. ATR-Based Volatility Bands Creates upper and lower dynamic bands based on the Average True Range (ATR). Expands in high volatility and contracts in low volatility, providing traders with a contextual understanding of price action. Slope-Based Trend Strength Normalizes the moving average slope relative to ATR. Generates a trend strength score, which influences band opacity, making strong trends visually distinguishable. Dynamic Color Coding Bullish Trends: Cyan/Turquoise (#00e2ff) Bearish Trends: Blue (#003ff5) Neutral Trends: Gray The transparency of the bands dynamically adjusts based on trend strength. Fill Zone Effect The area between the ATR bands is filled with a gradient-like effect, giving a clear visual representation of trend strength and transitions. Indicator Components Inputs (User Settings) ER Lookback Period: Defines how many bars are used in the Efficiency Ratio calculation (default: 10). Fast & Slow Periods: Control the sensitivity of the Adaptive Moving Average (default: 2 & 30). ATR Period: Defines the lookback for Average True Range (default: 14). Band Multiplier: Determines the width of ATR-based bands (default: 1.5). Slope Average Period: Smooths trend slope for more stable trend assessment (default: 5). Efficiency Ratio Calculation Measures how effectively price moves in a straight line compared to its total movement. A higher ER value suggests strong trend momentum, while a lower value implies consolidation. Adaptive Moving Average (AMA) Dynamically adjusts its smoothing factor based on ER. Uses a smoothing constant that ranges between the fastest and slowest specified values. Volatility-Based Bands Constructed using the ATR multiplier. Expand and contract dynamically in response to market volatility. Trend Strength & Direction Computed using the normalized slope of AMA against ATR. Positive slope = Bullish trend, Negative slope = Bearish trend. Visual Enhancements Colored Adaptive MA Line: Changes based on trend direction. ATR Bands with Gradient Fill: Visual representation of market conditions. Dynamic Opacity: Highlights trend strength through transparency. How to Use the Flow OMA Indicator Trend Identification When the Adaptive MA is rising and colored cyan, a bullish trend is in play. When the Adaptive MA is falling and colored blue, a bearish trend is present. Trend Strength Assessment A stronger trend results in more opaque band fills, indicating a clear directional bias. Weaker trends or consolidations result in fainter fills, signaling a loss of momentum. Reversal Signals If price touches the upper band in a bullish move and starts reversing, it can indicate potential profit-taking areas. If price approaches the lower band in a bearish move and rebounds, a short-term reversal may be imminent. Volatility Insights Narrow bands indicate low volatility and possible breakout conditions. Wider bands suggest increased volatility, warning traders of potential price swings. Best Practices ✅ Combine with Other Indicators Use RSI, MACD, or Volume Profile for confirmation before executing trades. ✅ Apply to Multiple Timeframes Works effectively in higher timeframes (1H, 4H, Daily) for trend trading. Can be utilized in lower timeframes (5m, 15m) for scalping setups. ✅ Adjust Parameters Based on Asset Volatility Increase ATR Period for stocks with high volatility. Reduce ATR Multiplier for forex pairs to avoid excessive band width. The Flow Optimized Moving Average (Flow OMA) is a powerful trend-following tool designed for both swing and intraday traders. Its adaptive nature allows it to efficiently track trends while minimizing false signals. By incorporating dynamic volatility bands and trend-sensitive color coding, this indicator enhances traders' ability to read price action effectively. Whether used standalone or in combination with other indicators, Flow OMA provides a significant edge in trend analysis.

RSIOMA with Volume Index Confirmation This indicator is called "RSIOMA with Volume Index Confirmation". It is a technical analysis tool that plots buy and sell signals on a chart based on the Relative Strength Index (RSI) and the Negative Volume Index (NVI) and Positive Volume Index (PVI) indicators. The indicator has the following input parameters: - RSI Length: determines the number of periods used to calculate the RSI. Default value is 14. - Overbought Level: determines the RSI level at which a security is considered overbought. Default value is 70. - Oversold Level: determines the RSI level at which a security is considered oversold. Default value is 30. - NVI Length: determines the number of periods used to calculate the Negative Volume Index. Default value is 255. - PVI Length: determines the number of periods used to calculate the Positive Volume Index. Default value is 255. The indicator calculates the RSI using the RSI Length input parameter and the close price of the security. It also calculates the NVI and PVI by looping through the volume data and the close price data of the security over the specified periods. The indicator then uses the RSI, NVI, and PVI to determine buy and sell signals. A bearish divergence signal is generated when the RSI from one period ago is greater than the Overbought Level, the current RSI is less than the Overbought Level, and the close price from one period ago is greater than the current close price. A bullish divergence signal is generated when the RSI from one period ago is less than the Oversold Level, the current RSI is greater than the Oversold Level, and the close price from one period ago is less than the current close price. A sell signal is generated when a bearish divergence signal occurs and the current NVI is less than the previous NVI value. A buy signal is generated when a bullish divergence signal occurs and the current PVI is greater than the previous PVI value. The indicator plots the buy and sell signals on the chart as green and red triangles, respectively. The "overlay=true" parameter in the indicator function indicates that the signals are plotted on top of the security's price chart.

CFB-Adaptive Velocity Histogram [Loxx]CFB-Adaptive Velocity Histogram is a velocity indicator with One-More-Moving-Average Adaptive Smoothing of input source value and Jurik's Composite-Fractal-Behavior-Adaptive Price-Trend-Period input with Dynamic Zones. All Juirk smoothing allows for both single and double Jurik smoothing passes. Velocity is adjusted to pips but there is no input value for the user. This indicator is tuned for Forex but can be used on any time series data. What is Composite Fractal Behavior ( CFB )? All around you mechanisms adjust themselves to their environment. From simple thermostats that react to air temperature to computer chips in modern cars that respond to changes in engine temperature, r.p.m.'s, torque, and throttle position. It was only a matter of time before fast desktop computers applied the mathematics of self-adjustment to systems that trade the financial markets. Unlike basic systems with fixed formulas, an adaptive system adjusts its own equations. For example, start with a basic channel breakout system that uses the highest closing price of the last N bars as a threshold for detecting breakouts on the up side. An adaptive and improved version of this system would adjust N according to market conditions, such as momentum, price volatility or acceleration. Since many systems are based directly or indirectly on cycles, another useful measure of market condition is the periodic length of a price chart's dominant cycle, (DC), that cycle with the greatest influence on price action. The utility of this new DC measure was noted by author Murray Ruggiero in the January '96 issue of Futures Magazine. In it. Mr. Ruggiero used it to adaptive adjust the value of N in a channel breakout system. He then simulated trading 15 years of D-Mark futures in order to compare its performance to a similar system that had a fixed optimal value of N. The adaptive version produced 20% more profit! This DC index utilized the popular MESA algorithm (a formulation by John Ehlers adapted from Burg's maximum entropy algorithm, MEM). Unfortunately, the DC approach is problematic when the market has no real dominant cycle momentum, because the mathematics will produce a value whether or not one actually exists! Therefore, we developed a proprietary indicator that does not presuppose the presence of market cycles. It's called CFB (Composite Fractal Behavior) and it works well whether or not the market is cyclic. CFB examines price action for a particular fractal pattern, categorizes them by size, and then outputs a composite fractal size index. This index is smooth, timely and accurate Essentially, CFB reveals the length of the market's trending action time frame. Long trending activity produces a large CFB index and short choppy action produces a small index value. Investors have found many applications for CFB which involve scaling other existing technical indicators adaptively, on a bar-to-bar basis. 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. Ideally, you would like a filtered signal to be both smooth and lag-free. Lag causes delays in your trades, and increasing lag in your indicators typically result in lower profits. In other words, late comers get what's left on the table after the feast has already begun. 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: Bar coloring 3 signal variations w/ alerts Divergences w/ alerts Loxx's Expanded Source Types

Dynamic Zone Range on OMA [Loxx]Dynamic Zone Range on OMA is an One More Moving Average oscillator with Dynamic Zones. What is the One More Moving Average (OMA)? The usual story goes something like this : which is the best moving average? Everyone that ever started to do any kind of technical analysis was pulled into this "game". Comparing, testing, looking for new ones, testing ... The idea of this one is simple: it should not be itself, but it should be a kind of a chameleon - it should "imitate" as much other moving averages as it can. So the need for zillion different moving averages would diminish. And it should have some extra, of course: The extras: it has to be smooth it has to be able to "change speed" without length change it has to be able to adapt or not (since it has to "imitate" the non-adaptive as well as the adaptive ones) The steps: Smoothing - compared are the simple moving average (that is the basis and the first step of this indicator - a smoothed simple moving average with as little lag added as it is possible and as close to the original as it is possible) Speed 1 and non-adaptive are the reference for this basic setup. Speed changing - same chart only added one more average with "speeds" 2 and 3 (for comparison purposes only here) Finally - adapting : same chart with SMA compared to one more average with speed 1 but adaptive (so this parameters would make it a "smoothed adaptive simple average") Adapting part is a modified Kaufman adapting way and this part (the adapting part) may be a subject for changes in the future (it is giving satisfactory results, but if or when I find a better way, it will be implemented here) Some comparisons for different speed settings (all the comparisons are without adaptive turned on, and are approximate. Approximation comes from a fact that it is impossible to get exactly the same values from only one way of calculation, and frankly, I even did not try to get those same values). speed 0.5 - T3 (0.618 Tilson) speed 2.5 - T3 (0.618 Fulks/Matulich) speed 1 - SMA , harmonic mean speed 2 - LWMA speed 7 - very similar to Hull and TEMA speed 8 - very similar to LSMA and Linear regression value Parameters: Length - length (period) for averaging Source - price to use for averaging Speed - desired speed (i limited to -1.5 on the lower side but it even does not need that limit - some interesting results with speeds that are less than 0 can be achieved) Adaptive - does it adapt or not Variety Moving Averages w/ Dynamic Zones contains 33 source types and 35+ moving averages with double dynamic zones levels. 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 4 signal types Bar coloring Alerts Channels fill

Pips-Stepped, OMA-Filtered, Ocean NMA [Loxx]Pips-Stepped, OMA-Filtered, Ocean NMA is an Ocean Natural Moving Average Filter that is pre-filtered using One More Moving Average (OMA) and then post-filtered using stepping by pips. This indicator is quadruple adaptive depending on the settings used: OMA adaptive Hiekin-Ashi Better Source Input Adaptive (w/ AMA of Kaufman smoothing) Ocean NMA adaptive Pips adaptive What is the One More Moving Average (OMA)? The usual story goes something like this : which is the best moving average? Everyone that ever started to do any kind of technical analysis was pulled into this "game". Comparing, testing, looking for new ones, testing ... The idea of this one is simple: it should not be itself, but it should be a kind of a chameleon - it should "imitate" as much other moving averages as it can. So the need for zillion different moving averages would diminish. And it should have some extra, of course: The extras: it has to be smooth it has to be able to "change speed" without length change it has to be able to adapt or not (since it has to "imitate" the non-adaptive as well as the adaptive ones) The steps: Smoothing - compared are the simple moving average (that is the basis and the first step of this indicator - a smoothed simple moving average with as little lag added as it is possible and as close to the original as it is possible) Speed 1 and non-adaptive are the reference for this basic setup. Speed changing - same chart only added one more average with "speeds" 2 and 3 (for comparison purposes only here) Finally - adapting : same chart with SMA compared to one more average with speed 1 but adaptive (so this parameters would make it a "smoothed adaptive simple average") Adapting part is a modified Kaufman adapting way and this part (the adapting part) may be a subject for changes in the future (it is giving satisfactory results, but if or when I find a better way, it will be implemented here) Some comparisons for different speed settings (all the comparisons are without adaptive turned on, and are approximate. Approximation comes from a fact that it is impossible to get exactly the same values from only one way of calculation, and frankly, I even did not try to get those same values). speed 0.5 - T3 (0.618 Tilson) speed 2.5 - T3 (0.618 Fulks/Matulich) speed 1 - SMA , harmonic mean speed 2 - LWMA speed 7 - very similar to Hull and TEMA speed 8 - very similar to LSMA and Linear regression value Parameters: Length - length (period) for averaging Source - price to use for averaging Speed - desired speed (i limited to -1.5 on the lower side but it even does not need that limit - some interesting results with speeds that are less than 0 can be achieved) Adaptive - does it adapt or not What is the Ocean Natural Moving Average? Created by Jim Sloman, the NMA is a moving average that automatically adjusts to volatility without being programed to do so. For more info, read his guide "Ocean Theory, an Introduction" What's the difference between this indicator and Sloan's original NMA? Sloman's original calculation uses the natural log of price as input into the NMA , here we use moving averages of price as the input for NMA . As such, this indicator applies a certain level of Ocean theory adaptivity to moving average filter used. Included: Bar coloring Alerts Expanded source types Signals Flat-level coloring for scalping

OMA-Filtered, Gann HiLo Activator [Loxx]OMA-Filtered, Gann HiLo Activator is a Gann HiLo activator that uses OMA filtering instead SMA filtering. This GHA calculation also includes a variable for close period to further tune the indicator. What is Gann HiLo? The HiLo Activator study is a trend-following indicator introduced by Robert Krausz as part of the Gann Swing trading strategy. In addition to indicating the current trend direction, this can be used as both entry signal and trailing stop. Here is how the HiLo Activator is calculated: 1. The system calculates the moving averages of the high and low prices over the last several candles. By default, the average is calculated using the last three candles. 2. If the close price falls below the average low or rises above the average high, the system plots the opposite moving average. For example, if the price crosses above the average high, the system will plot the average low. If the price crosses below the average low afterward, the system will stop plotting the average low and will start plotting the average high, and so forth . The plot of the HiLo Activator thus consists of sections on the top and bottom of the price plot. The sections on the bottom signify bullish trending conditions. Vice versa, those on the top signify the bearish conditions. Included -Toggle bar color on/off

OMA (One More Average) RSI Histogram [Loxx]OMA (One More Average) RSI Histogram is an RSI histogram built using the OMA adaptive moving average. This is meant to filter out the noise from regular RSI. You'll notice that it rarely signals, but the signals that do show up are perfectly set up for mean reversion/trend ATR-based trading. What is the One More Moving Average (OMA)? The usual story goes something like this : which is the best moving average? Everyone that ever started to do any kind of technical analysis was pulled into this "game". Comparing, testing, looking for new ones, testing ... The idea of this one is simple: it should not be itself, but it should be a kind of a chameleon - it should "imitate" as much other moving averages as it can. So the need for zillion different moving averages would diminish. And it should have some extra, of course: How to use Green means buy Red means sell Included Bar coloring

Adaptive, One More Moving Average (OMA) [Loxx]Adaptive, One More Moving Average (OMA) is an adaptive moving average created by Mladen Rakic that changes shape with volatility and momentum What is the One More Moving Average (OMA)? The usual story goes something like this : which is the best moving average? Everyone that ever started to do any kind of technical analysis was pulled into this "game". Comparing, testing, looking for new ones, testing ... The idea of this one is simple: it should not be itself, but it should be a kind of a chameleon - it should "imitate" as much other moving averages as it can. So the need for zillion different moving averages would diminish. And it should have some extra, of course: The extras: it has to be smooth it has to be able to "change speed" without length change it has to be able to adapt or not (since it has to "imitate" the non-adaptive as well as the adaptive ones) The steps: Smoothing - compared are the simple moving average (that is the basis and the first step of this indicator - a smoothed simple moving average with as little lag added as it is possible and as close to the original as it is possible) Speed 1 and non-adaptive are the reference for this basic setup. Speed changing - same chart only added one more average with "speeds" 2 and 3 (for comparison purposes only here) Finally - adapting : same chart with SMA compared to one more average with speed 1 but adaptive (so this parameters would make it a "smoothed adaptive simple average") Adapting part is a modified Kaufman adapting way and this part (the adapting part) may be a subject for changes in the future (it is giving satisfactory results, but if or when I find a better way, it will be implemented here) Some comparisons for different speed settings (all the comparisons are without adaptive turned on, and are approximate. Approximation comes from a fact that it is impossible to get exactly the same values from only one way of calculation, and frankly, I even did not try to get those same values). speed 0.5 - T3 (0.618 Tilson) speed 2.5 - T3 (0.618 Fulks/Matulich) speed 1 - SMA, harmonic mean speed 2 - LWMA speed 7 - very similar to Hull and TEMA speed 8 - very similar to LSMA and Linear regression value Parameters: Length - length (period) for averaging Source - price to use for averaging Speed - desired speed (i limited to -1.5 on the lower side but it even does not need that limit - some interesting results with speeds that are less than 0 can be achieved) Adaptive - does it adapt or not

BBMA : Bollinger Band & Moving Average Based on trading technique by Mr Oma Ally, Technical Analysis Guru. 1. Bollinger Band - standard setting, MA20, Dev = 2 2. EMA 50 ( Line Color = Aqua ) 3. WMA High ( wma5 High and wma10 High ) Area Pink : WMA5 High < WMA10 High Area Gray : WMA5 High > WMA10 High 4. WMA Low ( wma5 Low and wma10 Low ) Area Green : WMA5 Low > WMA10 Low Area Gray : WMA5 Low < WMA10 Low 5. If you wish to see wma lines, adjust the transparency at the menu. This is just an indicator setting. For technique and how to apply for trading, kindly please refer to web/youtube/class/etc :)