ATR Oscillator with Dots and Dynamic Zero LineWhat It Is
The ATR Oscillator with Dots and Dynamic Zero Line is a custom indicator based on the Average True Range (ATR), designed to provide traders with enhanced insights into market volatility and directional bias. Unlike traditional ATR oscillators that plot continuous lines, this version uses distinct dots to display ATR values and includes a dynamic zero line that changes color based on market direction (uptrend, downtrend, or consolidation).
How It Works
ATR Calculation:
The indicator calculates the Average True Range over a user-defined period (default: 14 bars). ATR measures market volatility by considering the range between the high, low, and close of each bar.
Dots for ATR Values:
Instead of plotting ATR values as a continuous line, the indicator represents each value as an individual blue dot. This format highlights changes in volatility without visually connecting them, helping to avoid false trends and clutter.
Dynamic Zero Line:
A horizontal zero line provides additional directional context. The line changes color dynamically:
Green: Indicates an uptrend (price is consistently closing higher over consecutive bars).
Red: Indicates a downtrend (price is consistently closing lower over consecutive bars).
Gray: Indicates market consolidation or sideways movement (no clear trend in price).
The thickness and step-like style of the zero line make it visually prominent, enabling quick interpretation of market direction.
What It Does
Visualizes Market Volatility:
By plotting ATR values as dots, the oscillator emphasizes periods of heightened or reduced market activity, helping traders anticipate breakout opportunities or avoid low-volatility zones.
Provides Trend Context:
The dynamic zero line gives traders a clear signal of the prevailing market trend (uptrend, downtrend, or consolidation), which can be used to align trading strategies with the broader market context.
Avoids Misleading Trends:
Unlike traditional ATR oscillators that use continuous lines, this version eliminates visual artifacts caused by noise, such as false trends during consolidation periods.
Simplifies Interpretation:
The combination of ATR dots and a color-coded zero line creates a straightforward and intuitive tool for assessing both volatility and market direction.
Why It’s More Useful Than a Traditional ATR Oscillator
Enhanced Visibility:
The use of dots instead of a continuous line makes it easier to spot discrete changes in ATR values, avoiding visual clutter and false impressions of smooth trends.
Dynamic Market Context:
Traditional ATR oscillators only measure volatility, offering no indication of market direction. The dynamic zero line in this oscillator adds valuable directional context, helping traders align their strategies with the trend.
Better for Range-Bound Markets:
The zero line’s color-changing feature highlights consolidation periods, enabling traders to identify and avoid trading during sideways, low-volatility conditions where false signals are common.
Quick Decision-Making:
With clear visual cues (dots and color-coded lines), traders can quickly assess market conditions without needing to analyze multiple charts or indicators.
Improved Confluence:
The oscillator’s signals can easily be combined with other tools like VWAP, Volume Profile, or Order Flow indicators for more confident trade decisions.
When to Use It
Trending Markets:
Use the dynamic zero line to confirm the market’s direction and align trades accordingly.
Breakout Opportunities:
Look for periods of increasing ATR (dots moving higher) to anticipate high-volatility breakout scenarios.
Avoiding Noise:
During consolidation (gray zero line), this oscillator warns traders to wait for clearer signals before entering trades.
Search in scripts for "oscillator"
Revolver Oscillator Strategy 1.2 (RSI+UO+MFI)ROS (Revolver Oscillator Strategy)
Version 1.2
Description
This script combines three popular oscillators (RSI, Ultimate Oscillator and MFI) to accurately determine the price momentum of an asset.
Context
- RSI (Relative Strength Index) is a momentum oscillator that measures the speed and change of price movements over a period of time (14).
- Ultimate Oscillator uses three different periods (7, 14, and 28) to represent short, medium, and long-term market trends.
- Money Flow Index (MFI) is a momentum indicator that measures the flow of money into and out over a period of time. It is related to the Relative Strength Index (RSI) but incorporates volume, whereas the RSI only considers price
How does it work?
When a RED bar appears, it means that the three oscillators have exceeded the set thresholds, and it is a SELL signal.
When a GREEN bar appears, it means that the three oscillators are below the set thresholds, and it is a BUY signal.
I recommend leaving the default settings.
Trigonometric On Balance Volume (OBV) OscillatorLove volume analysis but it's hard for you to implement a simple strategy by it?
Use OBV.
Is OBV still not quite as it should be for you to get it in your trading system?
Use OBV Oscillator.
Does OBV Oscillator give you too many false signals and when you smooth it, it lags by a ton?
Then this indicator is the answer to your problem.
Introducing the Trigonometric OBV Oscillator.
The Trigonometric OBV Oscillator or "Trig OBV" for short, uses an old, but uniquely extremely reliable mathematical formula to smooth the OBV, while eliminating more than 95% of its false signals (noises) and keeping with the real direction of the trend without introducing any lags.
It is very responsive, predictive even to some degree, very reliable, and keeps you out of false trades (like false breakouts, sudden changes in the price, etc).
To go long: wait until the white line crosses up the purple line and continues in that direction.
To go short: wait until the white line crosses down the blue line and continues in that direction.
To exit, do the opposite.
Better to be used with a baseline filter such as Kaufman's moving average.
Use it and let me know what you think about it.
The Bayesian Q OscillatorFirst of all the biggest thanks to @tista and @KivancOzbilgic for publishing their open source public indicators Bayesian BBSMA + nQQE Oscillator. And a mighty round of applause for @MarkBench for once again being my superhero pinescript guy that puts these awesome combination Ideas and ES stradegies in my head together. Now let me go ahead and explain what we have here.
I am gonna call it the Bayesian Q Oscillator I suppose. The goal of the script is to solve an issue both indicators on their own suffer from. QQE signals are not new and often the problem has always been false signals for them. They are good for scalping but the difference between a quality move and a small to nearly nonexistent move following a signal is not so clear. Kivanc made his normalized version to help reduce this problem by adding colors to his histogram type verision that would essentially represent if price was a trending move or in a ranging structure. As you can see I have kept this Idea but instead opted for lines as the oscillator. two yellow line (default color) is a ranging sideways area and when there is red or green it is trending up or down. I wanted to take this to the next level with combining the Bayesian probability oscillator that tista put together.
The Bayesian indicator is the opposite for its issue as it is a probability indicator that shows which candle or price movement is more likely to come next. Red rising means possibly down move soon and green means up soon. I will not go into the complex details of this indicator but will suggest others take a look at his and others to understand the idea behind them. The point I am driving at is that it show probabilities or likelyhood without the most effecient signal device to match it. This original was line form and now it is background filled colors.
The idea. is that you can potentially get some stronger and more accurate reversal signals with these two paired together. when you see a sell signal or cross with the towering or rising red... maybe it is a good jump potentially. The same for green. At the same time it is a double added filter effect from just having yellow represent it is ranging... but now if you get a buy signal (example) and have yellow lines (example) along wi5h a red rising or mountain color background... it not only is an indication of ranging, but also that there is potentially even a counter move coming based on the probabilities. Also if you get into a good trade and see dual yellow qqe crosses with no color represented by the bayesian background... it is possible it might only be noise.
I have found them to work decently in the 1 hour timframe. Let me know your experience.
I hope everyone takes a look at the originals to understand them. Full credit goes to those guys for this to be here. Let me know how it is working out for you.
Here are the original links.
bayesian
Normalized QQE
[JRL] Pivot Regression OscillatorIntroducing the Pivot Regression Oscillator. This oscillator uses a similar formula to the Stochastic Oscillator. However, instead of comparing the closing price to the lowest price of a period, it compares the distance between current price and the current pivot point. By basing our oscillator on pivot levels, we incorporate a much more relevant and consequential price point around which to base our comparisons.
The indicator can give reliable overbought and oversold signals, and it plots two exponential moving averages as output, which provides crossover signals that can be used to help time trades.
The Pivot Regression Oscillator can be effective for timing re-entries into a trend and seems to be able to avoid some of the false signals of other indicators.
Let me know if you find this useful. Cheers!
Delta-RSI OscillatorIntroducing the Delta-RSI Oscillator.
This oscillator is a time derivative of the RSI, plotted as a histogram and serving as a momentum indicator. The derivative is calculated explicitly by means of local polynomial regression. It is designed to provide minimum false and premature buy/sell signals compared to many traditional momentum indicators such as Momentum, RSI, Rate of Change.
Application:
Potential trading signals provided by the Delta-RSI Oscillator include:
- zero crossing (negative-to-positive as a bullish sign and positive-to-negative sign as a bearish signal),
- change of direction (consider going long if the oscillator starts to advance, and short otherwise).
In addition, the strength of a particular trend can be estimated by looking at the Delta-RSI value (positive D-RSI in case of the uptrend, and negative in case of the downtrend).
Choosing the model Parameters:
-RSI Length: The timeframe of the RSI that is being differentiated.
- Frame Length: The length of the lookback frame used for local regression.
- Polynomial Order: The order of the local polynomial function.
Longer frames and lower order of polynomials will result in a " smoother " D-RSI, but at the expense of greater lag. Increasing the polynomial order while maintaining the frame length will reduce lag while producing more variance. The values set as default (Length=18, Order=2) were found to provide optimum the variance/lag tradeoff. However, other options (e.g., Length=35, Order=3) can also work well.
Relationship with other methods:
When developing this indicator, I was inspired by Connie Brown’s Derivative Oscillator. The latter pursues the same goal but evaluates the RSI derivative by means of triple smoothing. This paves the way for more clear interpretation and easier tuning of model parameters.
Awesome Oscillator (AO) with Signals [AIBitcoinTrend]👽 Multi-Scale Awesome Oscillator (AO) with Signals (AIBitcoinTrend)
The Multi-Scale Awesome Oscillator transforms the traditional Awesome Oscillator (AO) by integrating multi-scale wavelet filtering, enhancing its ability to detect momentum shifts while maintaining responsiveness across different market conditions.
Unlike conventional AO calculations, this advanced version refines trend structures using high-frequency, medium-frequency, and low-frequency wavelet components, providing traders with superior clarity and adaptability.
Additionally, it features real-time divergence detection and an ATR-based dynamic trailing stop, making it a powerful tool for momentum analysis, reversals, and breakout strategies.
👽 What Makes the Multi-Scale AO – Wavelet-Enhanced Momentum Unique?
Unlike traditional AO indicators, this enhanced version leverages wavelet-based decomposition and volatility-adjusted normalization, ensuring improved signal consistency across various timeframes and assets.
✅ Wavelet Smoothing – Multi-Scale Extraction – Captures short-term fluctuations while preserving broader trend structures.
✅ Frequency-Based Detail Weights – Separates high, medium, and low-frequency components to reduce noise and improve trend clarity.
✅ Real-Time Divergence Detection – Identifies bullish and bearish divergences for early trend reversals.
✅ Crossovers & ATR-Based Trailing Stops – Implements intelligent trade management with adaptive stop-loss levels.
👽 The Math Behind the Indicator
👾 Wavelet-Based AO Smoothing
The indicator applies multi-scale wavelet decomposition to extract high-frequency, medium-frequency, and low-frequency trend components, ensuring an optimal balance between reactivity and smoothness.
sma1 = ta.sma(signal, waveletPeriod1)
sma2 = ta.sma(signal, waveletPeriod2)
sma3 = ta.sma(signal, waveletPeriod3)
detail1 = signal - sma1 // High-frequency detail
detail2 = sma1 - sma2 // Intermediate detail
detail3 = sma2 - sma3 // Low-frequency detail
advancedAO = weightDetail1 * detail1 + weightDetail2 * detail2 + weightDetail3 * detail3
Why It Works:
Short-Term Smoothing: Captures rapid fluctuations while minimizing noise.
Medium-Term Smoothing: Balances short-term and long-term trends.
Long-Term Smoothing: Enhances trend stability and reduces false signals.
👾 Z-Score Normalization
To ensure consistency across different markets, the Awesome Oscillator is normalized using a Z-score transformation, making overbought and oversold levels stable across all assets.
normFactor = ta.stdev(advancedAO, normPeriod)
normalizedAO = advancedAO / nz(normFactor, 1)
Why It Works:
Standardizes AO values for comparison across assets.
Enhances signal reliability, preventing misleading spikes.
👽 How Traders Can Use This Indicator
👾 Divergence Trading Strategy
Bullish Divergence
Price makes a lower low, while AO forms a higher low.
A buy signal is confirmed when AO starts rising.
Bearish Divergence
Price makes a higher high, while AO forms a lower high.
A sell signal is confirmed when AO starts declining.
👾 Buy & Sell Signals with Trailing Stop
Bullish Setup:
✅AO crosses above the bullish trigger level → Buy Signal.
✅Trailing stop placed at Low - (ATR × Multiplier).
✅Exit if price crosses below the stop.
Bearish Setup:
✅AO crosses below the bearish trigger level → Sell Signal.
✅Trailing stop placed at High + (ATR × Multiplier).
✅Exit if price crosses above the stop.
👽 Why It’s Useful for Traders
Wavelet-Enhanced Filtering – Retains essential trend details while eliminating excessive noise.
Multi-Scale Momentum Analysis – Separates different trend frequencies for enhanced clarity.
Real-Time Divergence Alerts – Identifies early reversal signals for better entries and exits.
ATR-Based Risk Management – Ensures stops dynamically adapt to market conditions.
Works Across Markets & Timeframes – Suitable for stocks, forex, crypto, and futures trading.
👽 Indicator Settings
AO Short Period – Defines the short-term moving average for AO calculation.
AO Long Period – Defines the long-term moving average for AO smoothing.
Wavelet Smoothing – Adjusts multi-scale decomposition for different market conditions.
Divergence Detection – Enables or disables real-time divergence analysis. Normalization Period – Sets the lookback period for standard deviation-based AO normalization.
Cross Signals Sensitivity – Controls crossover signal strength for buy/sell signals.
ATR Trailing Stop Multiplier – Adjusts the sensitivity of the trailing stop.
Disclaimer: This indicator is designed for educational purposes and does not constitute financial advice. Please consult a qualified financial advisor before making investment decisions.
SMA Trend Filter Oscillator (Adaptive)The "SMA Trend Filter Oscillator (Adaptive)" indicator is a technical analysis tool that helps traders determine the direction and strength of a trend based on an adaptive Simple Moving Average (SMA). The oscillator calculates the difference between the closing price and the SMA value, allowing for the visualization of price deviation from the average and the assessment of current market dynamics.
Key Features of the Indicator:
Adaptation to Time Frame: The indicator automatically adjusts the SMA length based on the current time frame, making it versatile for use across different time intervals. For example:
Monthly Time Frame: SMA with a length of 50.
Weekly Time Frame: SMA with a length of 40.
Daily Time Frame: SMA with a length of 20.
Hourly Time Frame: SMA with a length of 10.
Intraday Time Frames: SMA with a length of 5 (for time frames up to 15 minutes) or 7 (for others).
SMA-Based Oscillator: The oscillator is calculated as the difference between the closing price and the SMA value. This allows:
Bullish Trend Identification: When the oscillator is above zero (price is above SMA).
Bearish Trend Identification: When the oscillator is below zero (price is below SMA).
Visualization: The oscillator is displayed as a histogram, where:
Green Color indicates a bullish trend.
Red Color indicates a bearish trend.
The Zero Line (Gray) serves as a reference for trend reversal.
How to Use the Indicator:
Trend Identification: If the oscillator is above zero and colored green, it signals a bullish trend. If it is below zero and colored red, it indicates a bearish trend.
Trend Strength: The larger the oscillator value (in either direction), the stronger the trend. Small oscillator values (close to zero) may indicate sideways movement or weak trend.
Entry and Exit Points:
Buy: When the oscillator crosses the zero line from below to above (transition from red to green).
Sell: When the oscillator crosses the zero line from above to below (transition from green to red).
Signal Filtering: Use the indicator in combination with other technical analysis tools (e.g., RSI, MACD, or support/resistance levels) to confirm signals.
Advantages of the Indicator:
Adaptability: Automatic adjustment of SMA length to the current time frame makes it versatile.
Simplicity: Intuitive histogram visualization allows for quick assessment of market conditions.
Flexibility: Can be used on any market (stocks, forex, cryptocurrencies) and time frame.
Limitations:
Lag: Like any SMA-based indicator, it can lag due to the use of average values.
False Signals: In sideways markets (flat), the indicator may generate false signals.
Risk Management:
Always set stop-losses and take-profits to minimize losses.
Test the indicator on historical data before using it on a live account.
The "SMA Trend Filter Oscillator (Adaptive)" is a powerful tool for traders seeking to quickly evaluate trends and their strength. Its adaptability and simplicity make it suitable for both novice and experienced traders.
Индикатор "SMA Trend Filter Oscillator (Adaptive)" — это инструмент технического анализа, который помогает трейдерам определять направление тренда и его силу на основе адаптивной скользящей средней (SMA). Осциллятор рассчитывает разницу между ценой закрытия и значением SMA, что позволяет визуализировать отклонение цены от среднего значения и оценивать текущую рыночную динамику.
Основные особенности индикатора:
Адаптация к таймфрейму
Индикатор автоматически подстраивает длину SMA в зависимости от текущего таймфрейма, что делает его универсальным для использования на различных временных интервалах. Например:
Месячный таймфрейм (Monthly): SMA с длиной 50.
Недельный таймфрейм (Weekly): SMA с длиной 40.
Дневной таймфрейм (Daily): SMA с длиной 20.
Часовой таймфрейм (Hourly): SMA с длиной 10.
Внутридневные таймфреймы (Intraday): SMA с длиной 5 (для таймфреймов до 15 минут) или 7 (для остальных).
Осциллятор на основе SMA
Осциллятор рассчитывается как разница между ценой закрытия и значением SMA. Это позволяет:
Определять бычий тренд, когда осциллятор выше нуля (цена выше SMA).
Определять медвежий тренд, когда осциллятор ниже нуля (цена ниже SMA).
Визуализация
Осциллятор отображается в виде гистограммы, где:
Зелёный цвет указывает на бычий тренд.
Красный цвет указывает на медвежий тренд.
Линия нуля (серая) служит ориентиром для определения смены тренда.
Как использовать индикатор:
Определение тренда
Если осциллятор находится выше нуля и окрашен в зелёный цвет, это сигнализирует о бычьем тренде.
Если осциллятор находится ниже нуля и окрашен в красный цвет, это указывает на медвежий тренд.
Сила тренда
Чем больше значение осциллятора (в положительную или отрицательную сторону), тем сильнее тренд.
Небольшие значения осциллятора (близкие к нулю) могут указывать на боковое движение или слабость тренда.
Точки входа и выхода
Покупка (Buy): Когда осциллятор пересекает нулевую линию снизу вверх (переход из красной зоны в зелёную).
Продажа (Sell): Когда осциллятор пересекает нулевую линию сверху вниз (переход из зелёной зоны в красную).
Фильтрация сигналов
Используйте индикатор в сочетании с другими инструментами технического анализа (например, RSI, MACD или уровнями поддержки/сопротивления) для подтверждения сигналов.
Преимущества индикатора:
Адаптивность: Автоматическая настройка длины SMA под текущий таймфрейм делает индикатор универсальным.
Простота: Интуитивно понятная визуализация в виде гистограммы позволяет быстро оценить рыночную ситуацию.
Гибкость: Может использоваться на любых рынках (акции, форекс, криптовалюты) и таймфреймах.
Ограничения:
Запаздывание: Как и любой индикатор на основе SMA, он может запаздывать из-за использования средних значений.
Ложные сигналы: В условиях бокового движения (флэта) индикатор может генерировать ложные сигналы.
Управление рисками: Всегда устанавливайте стоп-лоссы и тейк-профиты, чтобы минимизировать потери.
Тестирование: Перед использованием на реальном счёте протестируйте индикатор на исторических данных.
Индикатор "SMA Trend Filter Oscillator (Adaptive)" — это мощный инструмент для трейдеров, которые хотят быстро оценить тренд и его силу. Его адаптивность и простота делают его подходящим как для начинающих, так и для опытных трейдеров
Standardized SuperTrend Oscillator
The Standardized SuperTrend Oscillator (SSO) is a versatile tool that transforms the SuperTrend indicator into an oscillator, offering both trend-following and mean reversion capabilities. It provides deeper insights into trends by standardizing the SuperTrend with respect to its upper and lower bounds, allowing traders to identify potential reversals and contrarian signals.
Methodology:
Lets begin with describing the SuperTrend indicator, which is the fundamental tool this script is based on.
SuperTrend:
The SuperTrend is calculated based on the average true range (ATR) and multiplier. It identifies the trend direction by placing a line above or below the price. In an uptrend, the line is below the price; in a downtrend, it's above the price.
pine_st(float src = hl2, float factor = 3., simple int len = 10) =>
float atr = ta.atr(len)
float up = src + factor * atr
up := up < nz(up ) or close > nz(up ) ? up : nz(up )
float lo = src - factor * atr
lo := lo > nz(lo ) or close < nz(lo ) ? lo : nz(lo )
int dir = na
float st = na
if na(atr )
dir := 1
else if st == nz(up )
dir := close > up ? -1 : 1
else
dir := close < lo ? 1 : -1
st := dir == -1 ? lo : up
SSO Oscillator:
The SSO is derived from the SuperTrend and the source price. It calculates the standardized difference between the SuperTrend and the source price. The standardization is achieved by dividing this difference by the distance between the upper and lower bounds of the SuperTrend.
float sso = (src - st) / (up - lo)
Components and Features:
SuperTrend of Oscillator - An additional SuperTrend based on the direction and volatility of the oscillator, behaving as the SuperTrend OF the SuperTrend. This provides further trend analysis of the underlying broad trend regime.
Reversion Tracer - The RSI of the direction of the original SuperTrend, providing a dynamic threshold for premium and discount price areas.
float rvt = ta.rsi(dir, len)
Heikin Ashi Transform - An option to apply the Heikin Ashi transform to the source price of the oscillator, providing a smoother visual representation of trends.
Display Modes - Choose between Line mode for a standard oscillator view or Candle mode, displaying the oscillator as Heikin Ashi candles for more in-depth trend analysis.
Contrarian and Reversion Signals:
Contrarian Signals - Based on the SuperTrend of the oscillator, these signals can act as potential buy or sell indications, highlighting potential trend exhaustion or premature reversals.
Reversion Signals - Generated when the oscillator crosses above or below the Reversion Tracer, signaling potential mean reversion opportunities or trend breakouts.
Utility and Use Cases:
Trend Analysis - Utilize the SSO as a trend-following tool with the added benefits of the oscillator's SuperTrend and Heikin Ashi transform.
Valuation Analysis - Leverage the oscillator's reversion signals for identifying potential mean reversion opportunities in the market.
The Standardized SuperTrend Oscillator enhances the capabilities of the SuperTrend indicator, offering a balanced approach to both trend-following and mean reversion strategies. Its customizable options and contrarian signals make it a valuable instrument for traders seeking comprehensive trend analysis and potential reversal signals.
Autocorrelation OscillatorReleasing the autocorrelation oscillator.
NOTE! Please be sure to read the description. This is a theoretical indicator and its important to understand the theory behind its use.
About the indicator:
Before getting into the indicator and its functionality, its important to discuss the theoretical underpinnings of the indicator.
The autocorrelation oscillator operates on two theories of market behaviour that go hand in hand. Those theories are the market efficiency theory and the random walk theory (or hypothesis ).
Market efficiency theory: The market efficiency theory or "Efficient Market Hypothesis (EMH)" postulates that all available information is reflected in a ticker's price almost instantaneously and thus it is impossible for an investor or trader to get ahead of the market because we cannot respond to the speed that the market responds. Of course, there are many holes in this theory, the most notable being that the market is a function of humans. Absent humans and their technological integrations into the market, the market would cease to react at all. But that's besides the point. This is a widely accepted theory and one in which I can mathematically observe through statistical tests. The truth behind this theory is the market is efficient for responding to evolving economic and financial information, likely owning to huge amounts of computer and algorithmic integration into trading, and thus the market is more efficient than the average person is capable (absent computerized algorithms and integration) of ascertaining nuanced financial and economic circumstances. By the time we the people can appraise information, the market has already acted on it. And that is the main premise of the EMH.
The next theory is the Random Walk Theory or Hypothesis (RWH). This builds on the EMH and essentially postulates that the market reacts so quickly to price in current circumstances that it is too random for people to truly exploit and benefit from.
The result of these two theories is two-fold and can be summarized as such:
a) The market behaves in a chaotic fashion that is seemingly random and is incapable of being predicted effectively; and
b) The market is more efficient than a person in incorporating key fundamental information, contributing to the high degree of seemingly random behaviour.
So, how does this help us?
It is said, because of the EMH and the RWH, the only way to truly exploit the market for profit is by:
a) Buying and holding and investing under the bias that stocks will eventually rise in value; or
b) For short term trading, exploiting the pricing anomalies within the data.
So how do we exploit pricing anomalies within the data?
Well, in my own research on market efficiency and behaviour, I have identified many ways of figuring out some anomalies. One of the most effective ways is by looking at simple correlation of lagged values, or autocorrelation for short.
What is autocorrelation and how to use it in relation to EMH and RWH?
Autocorrelation refers to the correlative relationship among the values in a series. Put simply, its the relationship of the same variable over time. For example, if we wanted to look at the auto-correlation of a ticker's high price, we would take, say, 5 to 7 previous high prices and correlate them with the current high price in a series dataset. If the EMH and RWH are true, the correlation among all the variables should have an average less than 0.5 or greater than -0.5. This would indicate true randomness in the dataset and thus an efficient market.
However, if the average of all of the sum's of these correlations are greater than or equal to 0.5 or less than or equal to -0.5, that indicates there is a high degree of autocorrelation and thus the EMH ad RWH is being invalidated as the market is not operating efficiently. This is an anomaly and this anomaly can be exploited.
So how do we exploit it?
Well, when the EMH and RWH hypothesis is being invalidated, we can expect what I coin as a "Regression to Chaos" i.e. the market will revert back to an efficient equilibrium state. So if we have a high correlation of the lagged variables and a strong uptrend or downtrend correlation, we can expect an inefficient market to correct back to an efficient market (i.e. have a reversal from the current trend).
So how does the indicator work?
The indicator measures the lagged correlation of the previous 5 highs and lows of a ticker. A high correlation among all of the highs and lows that exceeds 0.8 would be an invalidation of the EMH and RWH and thus signal a correction to come (i.e. a Regression to Chaos).
The indicator will display this by changing colour. Red for a bearish reversal and green for a bullish. Let's take a look below using the ticker MSFT:
Above we can see the indicator identifying observed inefficiencies within the MSFT ticker on the 1 minute timeframe. The green vertical lines correspond to potential bullish reversals as a result of bearish inefficiencies, the red correspond to bearish reversals as a result of bullish inefficiencies.
You can see these lead to reversals within the ticker.
Components of the indicator:
In the chart above we see the following that are being indicated by arrows:
Red Arrows: Show the identified inefficiencies. Red for bullish inefficiencies (i.e. bearish reversal), green for bearish inefficiencies (i.e. bullish reversal)
Yellow Arrow: The lagged variable chart. This will display the current correlation among all the lagged variables the indicator is assessing.
Teal arrow: Displays the current strength of the trend by correlating the trend to time. A strong negative value (i.e. a value less than or equal to -0.5) indicates a strong downtrend, a strong positive value indicates the inverse.
You can unselect the data-tables in the settings menu if you just want to view the correlation line itself. This part of the indicator is customizable. You can also define the lookback period; however, it is strongly recommended to leave it at 14 as this maintains the use of this indicator as an oscillator.
And that is the indicator! Let me know your comments, questions and feedback below.
Safe trades everyone!
Wave Trend OscillatorThis is a very standard version of the Wave Trend Oscillator.
The Channel and Average values are displayed as lines, most people display them as areas.
The Channel and Average difference is displayed as a histogram, most people display it as a tiny noisy area.
I was unable to find a standard version of the Wave Trend Oscillator.
The colorful hyped up versions of this indicator made me feel like a clown while using them.
I have essentially copied the style of the MACD with this indicator, to keep things professional.
With this WTO, you can change the timeframe and source.
You can also change the histogram average length and multiplier, making it usable.
The typical way that people display the histogram is completely unusable and just for appearance.
Now it does a decent job showing when the momentum of the WTO's downward movement is slowing down, just like how the MACD histogram works.
This indicator is essentially a normalized MACD, though they are calculated differently.
The Wave Trend Oscillator is useful for spotting/monitoring changed in mid-trend momentum.
In my experience, divergence in this indicator is a strong signal.
If the MACD is too slow for you, then this is a great alternative; without all the extra fluff people usually add to it.
Elder Ray Bull and Bear Power OscillatorsElder Ray Bull and Bear Power Oscillators
Tradingview Screener Bull Bear Power(BBPOWER)
OVERVIEW
The Bull and Bear Power oscillators developed by Dr Alexander Elder attempt to measure the power of buyers (bulls) and sellers (bears) to push prices above and below the consensus of value. The primary principles on which Elder based the oscillator are:
The highest price displays the maximum buyer’s power within the day.
The lowest price displays the maximum seller’s power within the day.
The moving average can be construed as a price agreement between buyers and sellers for a given time period.
The Bulls/Bears power balance is important since changes in this balance can signal the early stages of a potential trend reversal.
CALCULATION
Elder uses a 13-day exponential moving average (EMA) to indicate the consensus market value.
Bull Power is calculated by subtracting the 13-day EMA from the day’s high.
Bear Power is derived by subtracting the 13-day EMA from the day’s low.
TRADING WITH THE ELDER RAY BULL AND BEAR POWER OSCILLATORS
BULL POWER
Where a currency uptrend is sustained to the point that maximum prices move above the EMA the Bull Power histogram will be greater than zero. As price maximums accelerate to greater levels (above the EMA) during the rising trend histogram bars will increase in height above the zero line showing the increased buying strength during the period.
BEAR POWER
Where a currency downtrend is sustained to the point that minimum prices move below the EMA the Bear Power histogram will be less than zero. As price minimums accelerate to lower levels (below the EMA) during the falling trend histogram bars will increase in height below the zero line showing increased selling strength during the period.
TRADING SIGNALS
It is important for traders to use the Elder Ray oscillators in conjunction with the EMA overlay over the price chart (typically as per period being analysed) to give additional context to the signals. Sell signals are given if Bull Power is above zero and there is a bearish divergence in the Bull Power histogram or if the Bull Power histogram is above zero and falling.
Buy signals are given if Bear Power is below zero and there is a bullish divergence in the Bear Power histogram or if the Bear Power histogram is below zero and rising. It is extremely important for traders to only trade in the above scenarios if the direction of the trend indicated by the slope of the EMA on the price chart is in the direction of their trade when the signal is given (or shortly after).
Heikin-Ashi Mean Reversion Oscillator [Alpha Extract]The Heikin-Ashi Mean Reversion Oscillator combines the smoothing characteristics of Heikin-Ashi candlesticks with mean reversion analysis to create a powerful momentum oscillator. This indicator applies Heikin-Ashi transformation twice - first to price data and then to the oscillator itself - resulting in smoother signals while maintaining sensitivity to trend changes and potential reversal points.
🔶 CALCULATION
Heikin-Ashi Transformation: Converts regular OHLC data to smoothed Heikin-Ashi values
Component Analysis: Calculates trend strength, body deviation, and price deviation from mean
Oscillator Construction: Combines components with weighted formula (40% trend strength, 30% body deviation, 30% price deviation)
Double Smoothing: Applies EMA smoothing and second Heikin-Ashi transformation to oscillator values
Signal Generation: Identifies trend changes and crossover points with overbought/oversold levels
Formula:
HA Close = (Open + High + Low + Close) / 4
HA Open = (Previous HA Open + Previous HA Close) / 2
Trend Strength = Normalized consecutive HA candle direction
Body Deviation = (HA Body - Mean Body) / Mean Body * 100
Price Deviation = ((HA Close - Price Mean) / Price Mean * 100) / Standard Deviation * 25
Raw Oscillator = (Trend Strength * 0.4) + (Body Deviation * 0.3) + (Price Deviation * 0.3)
Final Oscillator = 50 + (EMA(Raw Oscillator) / 2)
🔶 DETAILS Visual Features:
Heikin-Ashi Candlesticks: Smoothed oscillator representation using HA transformation with vibrant teal/red coloring
Overbought/Oversold Zones: Horizontal lines at customizable levels (default 70/30) with background highlighting in extreme zones
Moving Averages: Optional fast and slow EMA overlays for additional trend confirmation
Signal Dashboard: Real-time table showing current oscillator status (Overbought/Oversold/Bullish/Bearish) and buy/sell signals
Reference Lines: Middle line at 50 (neutral), with 0 and 100 boundaries for range visualization
Interpretation:
Above 70: Overbought conditions, potential selling opportunity
Below 30: Oversold conditions, potential buying opportunity
Bullish HA Candles: Green/teal candles indicate upward momentum
Bearish HA Candles: Red candles indicate downward momentum
MA Crossovers: Fast EMA above slow EMA suggests bullish momentum, below suggests bearish momentum
Zone Exits: Price moving out of extreme zones (above 70 or below 30) often signals trend continuation
🔶 EXAMPLES
Mean Reversion Signals: When the oscillator reaches extreme levels (above 70 or below 30), it identifies potential reversal points where price may revert to the mean.
Example: Oscillator reaching 80+ levels during strong uptrends often precedes short-term pullbacks, providing profit-taking opportunities.
Trend Change Detection: The double Heikin-Ashi smoothing helps identify genuine trend changes while filtering out market noise.
Example: When oscillator HA candles change from red to teal after oversold readings, this confirms potential trend reversal from bearish to bullish.
Moving Average Confirmation: Fast and slow EMA crossovers on the oscillator provide additional confirmation of momentum shifts.
Example: Fast EMA crossing above slow EMA while oscillator is rising from oversold levels provides strong bullish confirmation signal.
Dashboard Signal Integration: The real-time dashboard combines oscillator status with directional signals for quick decision-making.
Example: Dashboard showing "Oversold" status with "BUY" signal when HA candles turn bullish provides clear entry timing.
🔶 SETTINGS
Customization Options:
Calculation: Oscillator period (default 14), smoothing factor (1-50, default 2)
Levels: Overbought threshold (50-100, default 70), oversold threshold (0-50, default 30)
Moving Averages: Toggle display, fast EMA length (default 9), slow EMA length (default 21)
Visual Enhancements: Show/hide signal dashboard, customizable table position
Alert Conditions: Oversold bounce, overbought reversal, bullish/bearish MA crossovers
The Heikin-Ashi Mean Reversion Oscillator provides traders with a sophisticated momentum tool that combines the smoothing benefits of Heikin-Ashi analysis with mean reversion principles. The double transformation process creates cleaner signals while the integrated dashboard and multiple confirmation methods help traders identify high-probability entry and exit points during both trending and ranging market conditions.
VWAP Separation Oscillator V5 (No Arrows)Okay, here is a draft description you can adapt for your TradingView publication. It starts from the basics and explains the concepts behind the indicator and how to interpret its visual elements.
VWAP Separation Oscillator
Summary
This indicator provides a normalized view of how far the current price has deviated from its Volume-Weighted Average Price (VWAP), helping traders identify potentially overbought or oversold conditions relative to recent VWAP dynamics. It calculates the price separation from VWAP and expresses it in terms of standard deviations (a Z-score), making it easier to gauge the statistical significance of the deviation.
Core Concepts Explained
What is VWAP?
VWAP stands for Volume-Weighted Average Price. It's a trading benchmark calculated by taking the total dollar value traded for every transaction (price multiplied by volume) and dividing it by the total shares traded for the day (or other chosen period).
Unlike a simple moving average, VWAP gives more weight to price levels where more volume occurred. Many institutional traders use it as a reference point for execution quality.
This indicator allows you to choose the "Anchor Period" (Session, Week, Month, etc.) which determines when the VWAP calculation resets.
What is VWAP Separation?
P
rice doesn't always stay at the VWAP; it naturally fluctuates above and below it.
"VWAP Separation" is simply the difference between the current price (Source) and the calculated VWAP value (Separation = Price - VWAP). A positive separation means the price is above VWAP; negative means below.
How Standard Deviation is Used:
While knowing the separation is useful, its significance can vary wildly between different stocks or market conditions. A $1 separation might be huge for one stock but tiny for another.
Standard Deviation is a statistical measure of how spread out data points are from their average. In this indicator, we calculate the standard deviation of the VWAP Separation over a specified Lookback Length. This tells us how volatile or dispersed the separation has been recently.
The Oscillator Line (Z-Score):
The main purple (or Green/Red) line plotted by this indicator is the Z-score of the VWAP Separation.
Formula conceptually: Oscillator Value = (Current Separation - Average Separation) / Standard Deviation of Separation
Interpretation: It tells you how many standard deviations the current separation is away from the average separation over the lookback period.
A value of +2.0 means the current separation is 2 standard deviations higher (more extended to the upside) than the average separation.
A value of -1.5 means the current separation is 1.5 standard deviations lower (more extended to the downside) than the average separation.
This normalization makes it easier to compare readings across different assets or timeframes and to define consistent thresholds for "extreme" deviations.
Visual Elements Explained
Oscillator Line: The primary line showing the Z-score value (explained above). Can optionally be colored Green/Red based on its slope (rising/falling).
Overbought Line (Solid Red): A user-defined level (default: 2.0). When the oscillator moves above this line, it suggests the price deviation above VWAP is statistically significant compared to recent history.
Oversold Line (Solid Green): A user-defined level (default: -2.0). When the oscillator moves below this line, it suggests the price deviation below VWAP is statistically significant compared to recent history.
Overbought/Oversold Zone Fills (Transparent Red/Green): These shaded areas appear only when the oscillator line enters the respective Overbought or Oversold territory (defined by the OB/OS Lines), visually highlighting these periods.
Zero Line (Dotted Gray): Represents the point where the current VWAP separation is exactly equal to the average VWAP separation over the lookback period. Crossings indicate shifts relative to this mean.
Zero Cross Markers (Orange 'X'): Small 'x' marks plotted directly on the oscillator line whenever it crosses the Zero Line, pinpointing these moments.
Potential Usage / Interpretation
Identifying Extremes: High positive values (above OB Level) or low negative values (below OS Level) can suggest the price move relative to VWAP might be over-extended and potentially due for a pause or pullback. Look for the oscillator turning back from these extremes.
Spotting Divergences: Look for discrepancies between price action and the oscillator.
Bearish Divergence: Price makes a new high, but the oscillator makes a lower high (often in the OB zone). Suggests weakening upside momentum relative to VWAP dynamics.
Bullish Divergence: Price makes a new low, but the oscillator makes a higher low (often in the OS zone). Suggests weakening downside momentum relative to VWAP dynamics.
Context is Key: This oscillator measures deviation from a specific benchmark (VWAP). Its interpretation should always be done within the context of the overall market trend, price structure (support/resistance), volume analysis, and potentially other confirming indicators.
Disclaimer: This indicator is a tool for analysis, not a standalone trading system. It does not provide financial advice. Always use risk management.
Settings Overview
Anchor Period: Determines how often the VWAP calculation resets (Session, Week, Month, etc.).
Source: The price data used for the separation calculation (default: hlc3).
Lookback Length: The number of bars used to calculate the average and standard deviation of the separation, influencing the oscillator's responsiveness.
Overbought/Oversold Levels: User-defined thresholds for identifying extreme Z-score values.
Color Oscillator Line: Option to color the oscillator line based on whether it's rising or falling.
Chande Momentum Oscillator StrategyThe Chande Momentum Oscillator (CMO) Trading Strategy is based on the momentum oscillator developed by Tushar Chande in 1994. The CMO measures the momentum of a security by calculating the difference between the sum of recent gains and losses over a defined period. The indicator offers a means to identify overbought and oversold conditions, making it suitable for developing mean-reversion trading strategies (Chande, 1997).
Strategy Overview:
Calculation of the Chande Momentum Oscillator (CMO):
The CMO formula considers both positive and negative price changes over a defined period (commonly set to 9 days) and computes the net momentum as a percentage.
The formula is as follows:
CMO=100×(Sum of Gains−Sum of Losses)(Sum of Gains+Sum of Losses)
CMO=100×(Sum of Gains+Sum of Losses)(Sum of Gains−Sum of Losses)
This approach distinguishes the CMO from other oscillators like the RSI by using both price gains and losses in the numerator, providing a more symmetrical measurement of momentum (Chande, 1997).
Entry Condition:
The strategy opens a long position when the CMO value falls below -50, signaling an oversold condition where the price may revert to the mean. Research in mean-reversion, such as by Poterba and Summers (1988), supports this approach, highlighting that prices often revert after sharp movements due to overreaction in the markets.
Exit Conditions:
The strategy closes the long position when:
The CMO rises above 50, indicating that the price may have become overbought and may not provide further upside potential.
Alternatively, the position is closed 5 days after the buy signal is triggered, regardless of the CMO value, to ensure a timely exit even if the momentum signal does not reach the predefined level.
This exit strategy aligns with the concept of time-based exits, reducing the risk of prolonged exposure to adverse price movements (Fama, 1970).
Scientific Basis and Rationale:
Momentum and Mean-Reversion:
The strategy leverages the well-known phenomenon of mean-reversion in financial markets. According to research by Jegadeesh and Titman (1993), prices tend to revert to their mean over short periods following strong movements, creating opportunities for traders to profit from temporary deviations.
The CMO captures this mean-reversion behavior by monitoring extreme price conditions. When the CMO reaches oversold levels (below -50), it signals potential buying opportunities, whereas crossing overbought levels (above 50) indicates conditions for selling.
Market Efficiency and Overreaction:
The strategy takes advantage of behavioral inefficiencies and overreactions, which are often the drivers behind sharp price movements (Shiller, 2003). By identifying these extreme conditions with the CMO, the strategy aims to capitalize on the market’s tendency to correct itself when price deviations become too large.
Optimization and Parameter Selection:
The 9-day period used for the CMO calculation is a widely accepted timeframe that balances responsiveness and noise reduction, making it suitable for capturing short-term price fluctuations. Studies in technical analysis suggest that oscillators optimized over such periods are effective in detecting reversals (Murphy, 1999).
Performance and Backtesting:
The strategy's effectiveness is confirmed through backtesting, which shows that using the CMO as a mean-reversion tool yields profitable opportunities. The use of time-based exits alongside momentum-based signals enhances the reliability of the strategy by ensuring that trades are closed even when the momentum signal alone does not materialize.
Conclusion:
The Chande Momentum Oscillator Trading Strategy combines the principles of momentum measurement and mean-reversion to identify and capitalize on short-term price fluctuations. By using a widely tested oscillator like the CMO and integrating a systematic exit approach, the strategy effectively addresses both entry and exit conditions, providing a robust method for trading in diverse market environments.
References:
Chande, T. S. (1997). The New Technical Trader: Boost Your Profit by Plugging into the Latest Indicators. John Wiley & Sons.
Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383-417.
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65-91.
Murphy, J. J. (1999). Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications. New York Institute of Finance.
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
Shiller, R. J. (2003). From Efficient Markets Theory to Behavioral Finance. Journal of Economic Perspectives, 17(1), 83-104.
Squeeze Momentum Oscillator [AlgoAlpha]🎉📈 Introducing the Squeeze Momentum Oscillator by AlgoAlpha 📉🎊
Unlock the secrets of market dynamics with our innovative Squeeze Momentum Oscillator! Crafted for those who seek to stay ahead in the fast-paced trading environment, this tool amalgamates critical market momentum and volatility indicators to offer a multifaceted view of potential market movements. Here's why it's an indispensable part of your trading toolkit:
Key Features:
🌈 Customizable Color Schemes: Easily distinguish between bullish (green) and bearish (red) momentum phases for intuitive analysis.
🔧 Extensive Input Settings: Tailor the oscillator lengths for both Underlying and Swing Momentum to match your unique trading approach.
📊 Dedicated Squeeze Settings: Leverage precise volatility insights to identify market squeeze scenarios, signaling potential breakouts or consolidations.
🔍 Advanced Divergence Detection: Utilize sophisticated algorithms to detect and visualize both bullish and bearish divergences, pointing towards possible market reversals.
📈 Hyper Squeeze Detection: Stay alert to high-momentum market movements with our hyper squeeze feature, designed to extremely suppressed market volatility.
🔔 Comprehensive Alert System: Never miss a trading opportunity with alerts for momentum changes, squeeze conditions, and more.
Quick Guide to Using the Squeeze Momentum Oscillator:
🛠 Add the Indicator: Add the indicator to your favourites. Adjust the oscillator and squeeze settings to suit your trading preferences.
📊 Market Analysis: Keep an eye on the squeeze value and momentum z-score for insights into volatility and market direction. Hyper Squeeze signals are your cue for high momentum trading opportunities.
🔔 Alerts: Configure alerts for shifts in underlying and swing momentum, as well as entry and exit points for squeeze conditions, to capture market moves efficiently.
How It Works:
The Squeeze Momentum Oscillator by AlgoAlpha synergistically combines the principles of momentum tracking and market squeeze detection. By integrating the core logic of the Squeeze & Release indicator, it calculates the Squeeze Value (SV) through a comparison of the Exponential Moving Average (EMA) of the Average True Range (ATR) against the high-low price EMA. This SV is further analyzed alongside its EMA to pinpoint squeeze conditions, indicative of potential market breakouts or consolidations. In addition to this, the oscillator employs Hyper Squeeze Detection for identifying extremely low volatility. The momentum aspect of the oscillator evaluates the price movement relative to EMAs of significant highs and lows, refining these observations with a z-score normalization for short-term momentum insights. Moreover, the incorporation of divergence detection aids in identifying potential reversals, making this oscillator a comprehensive tool for traders looking to harness the power of volatility and momentum in their market analysis. The combination of the Squeeze & Release and the Momentum Oscillator allows traders to time their trades with more precision by entering when the market is in a squeeze and front running the volatility of a major move.
Elevate your trading strategy with the Squeeze Momentum Oscillator by AlgoAlpha and gain a competitive edge in deciphering market dynamics! 🌟💼 Happy trading!
Trend System Oscillator Averages RatingThis is a trend system made with multiple oscillator averages designed especially for trending markets such as stocks or crypto.
It can be used with any timeframe.
Its made of multiple moving oscillators such as
RSI
Stochastic
ADX
CCI
AO
MACD
MOM
STOCH RSI
WPR
BP
UO
Avg of all oscillators
It has also a rating, making an avg from all of the oscillators , going from -100 (all ma's are telling to go short ) to 100 ( all ma are telling to go long).
If you have any questions let me know !
Grover Llorens Cycle Oscillator [alexgrover & Lucía Llorens]Cycles represent relatively smooth fluctuations with mean 0 and of varying period and amplitude, their estimation using technical indicators has always been a major task. In the additive model of price, the cycle is a component :
Price = Trend + Cycle + Noise
Based on this model we can deduce that :
Cycle = Price - Trend - Noise
The indicators specialized on the estimation of cycles are oscillators, some like bandpass filters aim to return a correct estimate of the cycles, while others might only show a deformation of them, this can be done in order to maximize the visualization of the cycles.
Today an oscillator who aim to maximize the visualization of the cycles is presented, the oscillator is based on the difference between the price and the previously proposed Grover Llorens activator indicator. A relative strength index is then applied to this difference in order to minimize the change of amplitude in the cycles.
The Indicator
The indicator include the length and mult settings used by the Grover Llorens activator. Length control the rate of convergence of the activator, lower values of length will output cycles of a faster period.
here length = 50
Mult is responsible for maximizing the visualization of the cycles, low values of mult will return a less cyclical output.
Here mult = 1
Finally you can smooth the indicator output if you want (smooth by default), you can uncheck the option if you want a noisy output.
The smoothing amount is also linked with the period of the rsi.
Here the smoothing amount = 100.
Conclusion
An oscillator based on the recently posted Grover Llorens activator has been proposed. The oscillator aim to maximize the visualization of cycles.
Maximizing the visualization of cycles don't comes with no cost, the indicator output can be uncorrelated with the actual cycles or can return cycles that are not present in the price. Other problems arises from the indicator settings, because cycles are of a time-varying periods it isn't optimal to use fixed length oscillators for their estimation.
Thanks for reading !
If my work has ever been of use to you you can donate, addresses on my signature :)
ROC-Weighted MA Oscillator [SeerQuant]ROC-Weighted MA Oscillator (ROCWMA)
The ROC-Weighted MA Oscillator (ROCWMA) is a momentum-based indicator which uniquely combines the Rate of Change (ROC) with customizable moving averages, offering a dynamic oscillator for trend analysis. Featuring z-score normalization and weighted MA integration, the ROCWMA delivers actionable trend signals with customizable thresholds.
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⚙️ How It Works
1️⃣ Rate of Change (ROC) Normalization
The indicator begins with a normalized ROC calculation over a customizable length, transforming raw momentum data into a dynamic range for enhanced analysis.
2️⃣ Weighted Moving Average (MA)
A custom moving average (MA) is calculated using selectable MA types such as TEMA, SMA, EMA, and more. The normalized ROC is then applied as a weight to derive the ROC-Weighted MA (RWMA), blending trend and momentum data.
3️⃣ Z-Score Oscillator
The RWMA is normalized using z-score calculations, resulting in a smoothed oscillator. This process highlights deviations from the mean, identifying overbought and oversold conditions dynamically.
4️⃣ Threshold Logic
Bullish (Uptrend): Oscillator exceeds the positive threshold.
Bearish (Downtrend): Oscillator drops below the negative threshold.
Neutral: Oscillator remains between thresholds.
5️⃣ Dynamic Visual Representation
A color-coded histogram reflects trend strength and direction.
Optional candle coloring visually emphasizes trends on the chart.
Gradient fills enhance clarity of threshold areas.
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✨ Customizable Settings
ROC Settings
Define the ROC length for momentum calculation.
MA Settings
Choose from multiple MA types (TEMA, EMA, SMA, etc.).
Customize the length and data source for MA calculations.
Adjust the signal length for smoothing.
Threshold Settings
Set neutral, bullish, and bearish thresholds to match your strategy.
Style Settings
Toggle candle coloring for visual trend enhancement.
Select from five unique color schemes to suit your chart style.
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🚀 Features and Benefits
Momentum-Weighted Analysis: Combines ROC with advanced moving averages for precise trend evaluation.
Dynamic Thresholds: Z-score-based logic adapts to market conditions.
Visual Clarity: Color-coded histograms, candles, and gradient fills make trend detection intuitive.
Highly Customizable: Flexible inputs and multiple MA types ensure adaptability to various trading styles.
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📜 Disclaimer
This indicator is for educational purposes only and does not constitute financial advice. Past performance is not indicative of future results. Users should consult a licensed financial advisor before making trading decisions. Use at your own risk.
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Price Oscillator TR### Summary: How to Use the Price Oscillator with EMA Indicator
The **Price Oscillator with EMA** is a custom technical analysis tool designed to help traders identify potential buying and selling opportunities based on price momentum. Here's how to use it:
1. **Understanding the Oscillator**:
- The oscillator is calculated by normalizing the current price relative to the highest high and lowest low over a specified lookback period. It fluctuates between -70 and +70.
- When the oscillator is near +70, the price is close to the recent highs, indicating potential overbought conditions. Conversely, when it’s near -100, the price is close to recent lows, indicating potential oversold conditions.
2. **Exponential Moving Average (EMA)**:
- The indicator includes an EMA of the oscillator to smooth out price fluctuations and provide a clearer signal.
- The EMA helps to filter out noise and confirm trends.
3. **Trading Signals**:
- **Bullish Signal**: A potential buying opportunity is signaled when the oscillator crosses above its EMA. This suggests increasing upward momentum.
- **Bearish Signal**: A potential selling opportunity is signaled when the oscillator crosses below its EMA. This indicates increasing downward momentum.
4. **Visual Aids**:
- The indicator includes horizontal lines at +70, 0, and -70 to help you quickly assess overbought, neutral, and oversold conditions.
- The blue line represents the oscillator, while the orange line represents the EMA of the oscillator.
### How to Use:
- **Set your parameters**: Adjust the lookback period and EMA length to fit your trading strategy and time frame.
- **Watch for Crossovers**: Monitor when the oscillator crosses the EMA. A crossover from below to above suggests a buy, while a crossunder from above to below suggests a sell.
- **Confirm with Other Indicators**: For more reliable signals, consider using this indicator alongside other technical tools like volume analysis, trend lines, or support/resistance levels.
This indicator is ideal for traders looking to capture momentum-based trades in various market conditions.
Hull Suite Oscillator - Normalized | IkkeOmarThis script is based off the Hull Suite by @InSilico.
I made this script to provide and calculate the Hull Moving Average (HMA) based on the chosen variation (HMA, TMA, or EMA) and length to then normalize the HMA values to a range of 0 to 100. The normalized values are further smoothed using an exponential moving average (EMA).
The smoothed oscillator is plotted as a line, where values above 80 are colored red, values below 20 are colored green, and values between 20 and 80 are colored blue. Additionally, there are horizontal dashed lines at the levels of 20 and 80 to serve as reference points.
Explanation for the code:
The script uses the close price of the asset as the source for calculations. The modeSwitch parameter allows selecting the type of Hull variation: Hma, Thma, or Ehma. The length parameter determines the calculation period for the Hull moving averages. The lengthMult parameter is used to adjust the length for higher timeframes. The oscSmooth parameter determines the lookback period for smoothing the oscillator.
There are three functions defined for calculating different types of Hull moving averages: HMA, EHMA, and THMA. These functions take the source and length as inputs and return the corresponding Hull moving average.
The Mode function acts as a switch and selects the appropriate Hull variation based on the modeSwitch parameter. It returns the chosen Hull moving average.
The script calculates the Hull moving averages using the selected mode, source, and length. The main Hull moving average is stored in the _hull variable, and aliases are created for the main Hull moving average (HULL), the main Hull value (MHULL), and the secondary Hull value (SHULL).
To create the normalized oscillator values, the script finds the highest and lowest values of the Hull moving average within the specified length. It then normalizes the Hull values to a range of 0 to 100 using a formula. This normalized oscillator represents the strength of the trend.
To smooth out the oscillator values, an exponential moving average is applied using the oscSmooth parameter.
The smoothed oscillator is plotted as a line chart. The line color is determined based on the oscillator value using conditional statements. If the oscillator value is above or equal to 80, the line color is set to red. If it is below or equal to 20, the color is green. Otherwise, it is blue. The linewidth is set to 2.
Additionally, two horizontal reference lines are plotted at levels 20 and 80 for visual reference. They are displayed in gray and dashed style.
Rainbow Oscillator [Strategy]Strategy based on Rainbow Oscillator
.:: Features ::.
Takes and Stops in percent
Configurable indicator iside
.:: Long condition ::.
Indicator line is green (mean uptrend) and crossing averages generated from oscillograph signal fast is go up and crossing slow
.:: Short condition ::.
Indicator line is red (mean downtrend) and crossing averages generated from oscillograph signal fast is go down and crossing slow
Bitcoin Golden Bottom Oscillator (MZ BTC Oscillator)This indicator uses Elliot Wave Oscillator Methodology applied on "BTC Golden Bottom with Adaptive Moving Average" and Relative Strength Index of Resulted EVO to form an Oscillator to detect trend health in Bitcoin price. Ticker is set to "INDEX : BTCUSD" on 1D timeframe.
Methodology
Oscillator uses Adaptive Moving Average with 1 year of length, Minor length of 50 and Major length of 100 to mark AMA as Golden Bottom.
Percentage Elliot Wave Oscillator is calculated between BTC price and AMA.
Relative Strength Index of EVO is calculated to detect trend strength and divergence detection.
Hull Moving Average of resulted RSI is used to smoothen the Oscillator.
Oscillator is hard coded to 'INDEX:BTCUSD' ticker on 1d so it can be used on any other chart and on any other timeframe.
Color Schemes
Bright Red background color indicates that price has left top Fib multiple ATR band and possibly go for top.
Light Red background color indicates that price has left 2nd top Fib multiple ATR band and possibly go for local top.
Lime background color indicates that price has entered lowest band indicating local bottom.
Bright Green background color indicates that price is approximately resting on Golden Bottom i.e. AMA.
Oscillator color is set to gradient for easy directional adaption.
BTC Golden Bottom with Adaptive Moving Average
