Rolling Volatility Indicator
Description :
The Rolling Volatility indicator calculates the volatility of an asset's price movements over a specified period. It measures the degree of variation in the price series over time, providing insights into the market's potential for price fluctuations.
This indicator utilizes a rolling window approach, computing the volatility by analyzing the logarithmic returns of the asset's price. The user-defined length parameter determines the timeframe for the volatility calculation.
How to Use :
Adjust the "Length" parameter to set the rolling window period for volatility calculation.
Ajust "trading_days" for the sampling period, this is the total number of trading days (usually 252 days for stocks and 365 for crypto)
Higher values for the length parameter will result in a smoother, longer-term view of volatility, while lower values will provide a more reactive, shorter-term perspective.
Volatility levels can assist in identifying periods of increased market activity or potential price changes. Higher volatility may suggest increased risk and potential opportunities, while lower volatility might indicate periods of reduced market activity.
Key Features :
Customizable length parameter for adjusting the calculation period and trading days such that it can also be applied to stock market or any markets.
Visual representation of volatility with a plotted line on the chart.
The Rolling Volatility indicator can be a valuable tool for traders and analysts seeking insights into market volatility trends, aiding in decision-making processes and risk management strategies.
Волатильность
Logarithmic CVD [IkkeOmar]The LCVD is another Mean-Reversion Indicator. it doesn't detect trends and does not give a signal per se. However the logarithmic transformation is made to visualize the direction of the trend for the volume. This allows you to see if money is flowing in or out of an asset.
What it does is tell you if we have a flashcrash based on the difference in volume.
Think of this indicator like a form of a volatility index.
Smoothing input:
The only input is an input for the smoothing length of the logDelta.
Volume Calculation:
// @IkkeOmar
//@version=5
indicator('Logarithmic CVD', shorttitle='CVD', overlay=false)
smooth = input.int(defval = 25, title = "Smoothing Distance")
// Calculate buying and selling volume
askVolume = volume * (close > open ? 1 : 0) // Assuming higher close than open indicates buying
bidVolume = volume * (close < open ? 1 : 0) // Assuming lower close than open indicates selling
// Delta is the difference between buying and selling volume
delta = askVolume - bidVolume
// Apply logarithmic transformation to delta
// Adding a check to ensure delta is not zero as log(0) is undefined
logDelta = delta > 0 ? math.log(math.abs(delta)) * math.sign(delta) : - math.log(math.abs(delta)) * math.sign(delta)
// use the the ta lib for calculating the sma of the logDelta
smoothLogDelta = ta.sma(logDelta, smooth)
// Create candlestick plot
plot(logDelta, color= color.green, title='Logarithmic CVD')
plot(smoothLogDelta, color= color.rgb(145, 37, 1), title='Smooth CVD')
These lines calculate the buying and selling volumes. askVolume is calculated as the total volume when the closing price is higher than the opening price, assuming this indicates buying pressure. bidVolume is calculated as the total volume when the closing price is lower than the opening price, assuming selling pressure.
The Delta is simply the difference between buying and selling volumes.
Logarithmic Transformation:
logDelta = delta > 0 ? math.log(math.abs(delta)) * math.sign(delta) : - math.log(math.abs(delta)) * math.sign(delta)
Applies a logarithmic transformation to delta. The math.log function is used to calculate the natural logarithm of the absolute value of delta. The sign of delta is preserved to differentiate between positive and negative values. This transformation helps in scaling the delta values, especially useful when dealing with large numbers.
This script essentially provides a visual representation of the buying and selling pressures in a market, transformed logarithmically for better scaling and smoothed for trend analysis.
Hope it makes sense!
Stay safe everyone!
Don't hesitate to ask any questions if you have any!
Tick Volume Direction IndicatorTick Volume Direction Indicator
This indicator captures:
• tick volume
• tick direction
The settings are as follows:
• volume or base currency value selection.
• label distance (away from the low of the candle).
• Tick volume - on/off switch for tick volume.
• label size.
• Up tick move color.
• tick move absorbed - when the tick doesn't change position.
• Down tick move.
On the first initial load, it will have the existing volume data as "?" as tradingview doesn't have a history of each tick.
Be aware, any settings change you make will refresh the tick data from start.
This indicator is one of the best real-time ways of seeing buying and selling pressure.
Logarithmic Volatility Direction Index [IkkeOmar]The LVDI is a Mean-Reversion Indicator. it doesn't detect trends and does not give a signal per se.
What it does is tell you if we have a flashcrash based on the price action and volume that is available. It is not always easy to see with the naked eye, so this indicator can help you DCA into an asset in a smarter way, if you couple it with other trend systems.
Think of this indicator like a form of a volatility index.
Inputs:
len and lenWMA are integers representing different lengths for calculations, and src is the data source
Keep in mind that "Length" is the lookback for the WMA, and the Length smooting is the lookback for the SMA of the "volume_weighted".
WMA Calculation
wma_basic = math.log10(ta.wma(src, len))
This calculates the logarithm (base 10) of the Weighted Moving Average (WMA) of the source data over len periods. WMA is a type of moving average giving more importance to recent data. The reason I use log10, is to make it transformative over a longer timeframe. This makes it easier to see the growth direction. I like to use this for crypto, since there is asymetric upside.
Volume Filter:
average_volume = ta.sma(volume, lenWMA)
volume_weighted = math.log10(wma_basic * (volume / math.log10(average_volume)))
Here, the script first calculates the Simple Moving Average (SMA) of the trading volume over lenWMA periods. Then, it computes a volume-weighted value of the WMA, adjusted by the logarithmic ratio of current volume to average volume.
Distance and Score Calculation:
distance = math.log10(src) - math.log10(volume_weighted)
score = math.sign(distance) * math.pow(math.abs(distance), 2)
The script calculates the logarithmic difference between the source data and the volume-weighted WMA. The score is determined by the sign of this distance multiplied by its square. This potentially amplifies the impact of larger distances.
Plotting:
plot(volume_weighted, title="Volume Weighted WMA", color=color.blue, linewidth = 2)
plot(ta.sma(volume_weighted, lenWMA), title="Volume Weighted WMA", color=color.rgb(189, 160, 0))
Mathematical concepts
Weighted Moving Average (WMA):
WMA is a moving average that assigns more weight to recent data points. The idea is that recent prices are more relevant to the current trend than older prices.
Logarithms:
The use of log10 (logarithm base 10) is interesting. Logarithms help in normalizing data and can make certain patterns more visible, especially when dealing with exponential growth or decay.
Volume Weighting:
Multiplying the WMA by the ratio of current volume to average volume (both logarithmic) integrates volume into the analysis. High trading volume can signify stronger market interest and can thus validate price movements.
Distance and Score:
The distance measures how far the current price is from the volume-weighted WMA on a logarithmic scale. The score squares this distance, potentially highlighting large divergences.
Case example
In the case above (which is a low timeframe that shouldn't be your main system) we see the blue line going up before going below the moving average line (orange). This indicates a local bottom zone. Does that mean that we wont go lower? No! What you can do is calculate a zone range.
We have an average line, you can get that from the POC with the VRVP.
Then you take the low and high of that zone and take the average:
(3.17% + 2.33%) / 2 = 2.75%
This means that we expect that the price can fall an additional 2.75%! Low and behold. When you check the same chart as above:
Hope it makes sense!
Stay safe everyone!
Don't hesitate to ask any questions if you have any!
Channel CorridorOVERVIEW
The Channel Corridor indicator is designed to operate on a log chart of asset prices (e.g., BTCUSD), specifically on a weekly timeframe.
The intent of the indicator is to provide a visual representation of market dynamics, focusing on a dynamically adjusted corridor around a Simple Moving Average (SMA) of an asset's price. The corridor adapts to changing market conditions. The indicator includes channels within the corridor for additional reference points.
PURPOSE
Trend Identification: The channel corridor can aid in visualising the overall trend, as it dynamically adjusts the corridor based on an SMA and user-defined parameters.
Volatility Assessment: The width of the channel corridor can may act as a gauge of market volatility.
Reversal Points: The channel corridor may signal potential trend reversals or corrections when an asset price approaches the upper or lower bounds of the corridor.
Long-Term Trend Analysis: The channel corridor may aid in longer-term trend analysis.
CONSIDERATIONS
Validation: It's recommended that careful back-testing over historical data be done before acting on any identified opportunities.
User Discretion: Trading decisions should not rely solely on this script. Users should exercise judgment and consider market conditions.
CREDIT
Ideation: Thanks @Sw1ngTr4der for the idea and corridor seed code
Historical Volatility StudyThe goal of this script it to provide you an idea to forecast the future momentum by looking at historical volatility.
This chart has basically three parts.
1. Three lines are there. The multi color line represents the historical annualized volatility in terms of minimum look back period . The white line represents the historical annualized volatility in terms of medium term look back period . The green line represents the historical annualized volatility in terms of longer term look back period .
2. The back ground color has three components. Green zone is the zone where overall volatility is on the lower side. Red zone is the zone where overall volatility is on the higher side. Purple zone means fluctuating volatility.
3. The multi color line has three colors. Red color means volatility moving towards extreme low. Yellow means it is moving towards extreme high. Purple means it is in normal course of action.
This tool can be used as a confirmation tool with other studies to aid you to make better decisions. For example- look at the diagram below.
Make your thorough study before making any trading decision. Thanks.
Fibonacci Bollinger Volume Weighted DeviationDiscover market dynamics with the 'Fibonacci Bollinger Volume Weighted Deviation' indicator – a unique tool blending Fibonacci ratios, Bollinger Bands, and volume-weighted analysis. Ideal for spotting overbought/oversold conditions and potential market turnarounds, this indicator is a must-have for traders seeking nuanced insights into price behavior and volatility.
Description:
"The 'Fibonacci Bollinger Volume Weighted Deviation' indicator presents a novel approach to market trend analysis by integrating Fibonacci ratios with the classic concept of Bollinger Bands. Designed for traders who incorporate Fibonacci levels in their market analysis, this indicator adapts Bollinger Bands to a user-defined Fibonacci ratio. It creates dynamic upper and lower bands around a Simple Moving Average (SMA), offering insights into price deviations and potential overbought or oversold market states.
Incorporating volume data, this indicator provides a volume-weighted perspective of price deviations. This feature is crucial in gauging the market sentiment, as significant volumes linked with price deviations can signal strong market moves. By plotting these deviations and emphasizing those that significantly diverge from the volume-weighted average, it aids in pinpointing potential turning points or key support and resistance zones.
Versatile in nature, the 'Fibonacci Bollinger Volume Weighted Deviation' indicator is adaptable to various trading styles and market conditions. It proves especially valuable in markets where Fibonacci levels are a key factor. Traders can explore long positions when prices fall below the lower band and consider short positions when prices breach the upper band. The addition of volume-weighted deviation analysis refines these trading signals, offering a more sophisticated and nuanced decision-making process for entries and exits.
As a standalone tool or in conjunction with other technical instruments, this indicator is an invaluable addition to any technical analyst's toolkit. It not only enhances traditional Fibonacci and Bollinger Band methodologies but also integrates volume analysis to provide a comprehensive view of market trends and movements."
DNA GRAVITY PRICE V1 PINESCRIPTLABSWe can observe that this indicator displays the range within which the asset fluctuates around the average price, and its behavior depends on the parameters of amplitude and angular frequency. "price_mas" is a measure calculated as part of the indicator. It is derived by adding an adjusted amplitude (A_mas) multiplied by the cosine of the combination of angular frequency (w), time, and a phase shift (phi) to the average price (P0). This calculated value oscillates around the actual asset price and is used to identify potential turning points and the range where the price has established itself within the specified lookback period.
2.- At its core, the indicator utilizes the innovative concept of 'price_mas,' a calculated metric visualized in three essential colors: green to indicate low levels, blue for medium levels, and red for high levels. These colors reflect the position of the price in relation to a range determined by historical highs and lows.
In the context of the "DNA GRAVITY PRICE V1 " indicator, low, medium, and high levels specifically refer to the calculated value of 'price_mas,' which is a derived measure within the indicator. They do not directly refer to the actual asset price but rather to a calculated value that the indicator uses to analyze and predict the behavior of the asset's price.
This algorithm stands out for its ability to capture the 'strength' of the price through the 'price_mas' zones. Once the price exits the zones marked by the 'price_mas' (red, blue, and green plots), it tends to return with significant force.
Buy & Sell Signals:
Buy Signal: If the price and the Donchian lines cross above the high threshold, visually represented by red diamonds, it indicates a strong bullish momentum. This not only shows that the price is rising but also that the trend is strong enough to push the Donchian lines, which represent price extremes over a certain period, above the threshold. This convergence of movements, marked by the crossing over the red diamonds, suggests a higher probability of the bullish trend continuing.
Sell Signal: Similarly, if the price and the Donchian lines fall below the low threshold, visualized as green diamonds, this signals a significant bearish momentum. The simultaneous decline of the price and the Donchian lines below this threshold, marked by the green diamonds, indicates that not only is the price decreasing, but the bearish trend is strong enough to influence the price extremes calculated by the Donchian lines.
Configuration:
-The "Initial Dynamic Length of MAS Price" parameter controls the smoothness and sensitivity of the indicator. A high value smooths the Simple Moving Average (SMA), making the indicator less responsive to short-term price fluctuations. On the other hand, a low value makes the indicator more sensitive to short-term price fluctuations, generating faster and more volatile signals
-This parameter, "MAS Amplitude Percentage," determines the amplitude as a percentage. Increasing the Initial Dynamic Price will result in a larger amplitude relative to the price, leading to wider ranges for the indicator. Decreasing this value will have the opposite effect, reducing the amplitude relative to the price. Increasing "A_mas_pct" can make signals more extreme and less frequent, while decreasing it will make signals smoother and more frequent.
-This parameter, "Angular Frequency of MAS," affects the frequency of oscillations in the calculation of the "Initial Dynamic Price." A higher value of "w" will make the oscillations faster and more frequent, which means that the indicator will be more responsive to abrupt price changes. Conversely, a lower value will make the oscillations slower and smoother, making the indicator less sensitive to rapid price changes. Modifying ""Angular Frequency of MAS,"" directly impacts the frequency of oscillations in the indicator.
Español:
Podemos observar que este indicador muestra el rango en el cual el activo fluctúa alrededor del precio promedio y su comportamiento depende de los parámetros de amplitud y frecuencia angular. "price_mas" es una medida calculada como parte del indicador. Se deriva al sumar una amplitud ajustada (A_mas) multiplicada por el coseno de la combinación de frecuencia angular (w), tiempo y un desplazamiento de fase (phi) al precio promedio (P0). Este valor calculado oscila alrededor del precio real del activo y se utiliza para identificar posibles puntos de giro y el rango donde el precio se ha establecido dentro del período de búsqueda especificado.
En su núcleo, el indicador utiliza el innovador concepto de 'price_mas', una métrica calculada visualizada en tres colores esenciales: verde para indicar niveles bajos, azul para niveles medios y rojo para niveles altos. Estos colores reflejan la posición del precio en relación con un rango determinado por los máximos y mínimos históricos.
En el contexto del indicador "DNA GRAVITY PRICE V1", los niveles bajos, medios y altos se refieren específicamente al valor calculado de 'price_mas', que es una medida derivada dentro del indicador. No se refieren directamente al precio real del activo, sino a un valor calculado que el indicador utiliza para analizar y predecir el comportamiento del precio del activo.
Este algoritmo se destaca por su capacidad para capturar la 'fortaleza' del precio a través de las zonas de 'price_mas'. Una vez que el precio sale de las zonas marcadas por 'price_mas' (trazas rojas, azules y verdes), tiende a regresar con una fuerza significativa. Este comportamiento es crucial para los operadores, ya que proporciona oportunidades tanto para capitalizar las retracciones de precios como para anticipar posibles cambios de tendencia.
Señales de Compra y Venta:
Señal de Compra: Si el precio y las líneas Donchian cruzan por encima del umbral alto, visualmente representado por diamantes rojos, indica un fuerte impulso alcista. Esto no solo muestra que el precio está aumentando, sino que la tendencia es lo suficientemente fuerte como para empujar las líneas Donchian, que representan los extremos de precio durante un período determinado, por encima del umbral. Esta convergencia de movimientos, marcada por el cruce sobre los diamantes rojos, sugiere una mayor probabilidad de que la tendencia alcista continúe.
Señal de Venta: De manera similar, si el precio y las líneas Donchian caen por debajo del umbral bajo, visualizado como diamantes verdes, esto señala un fuerte impulso bajista. La caída simultánea del precio y las líneas Donchian por debajo de este umbral, marcada por los diamantes verdes, indica que no solo el precio está disminuyendo, sino que la tendencia bajista es lo suficientemente fuerte como para influir en los extremos de precio calculados por las líneas Donchian.
Configuración:
El parámetro "Longitud Dinámica Inicial de MAS Price" controla la suavidad y la sensibilidad del indicador. Un valor alto suaviza el Promedio Móvil Simple (SMA), lo que hace que el indicador sea menos sensible a las fluctuaciones de precio a corto plazo. Por otro lado, un valor bajo hace que el indicador sea más sensible a las fluctuaciones de precio a corto plazo, generando señales más rápidas y volátiles.
Este parámetro, "Porcentaje de Amplitud de MAS," determina la amplitud como un porcentaje. Aumentar el valor de "Longitud Dinámica Inicial de MAS Price" dará como resultado una amplitud más grande en relación con el precio, lo que conducirá a rangos más amplios para el indicador. Disminuir este valor tendrá el efecto contrario, reduciendo la amplitud en relación con el precio. Aumentar "Porcentaje de A_mas" puede hacer que las señales sean más extremas y menos frecuentes, mientras que disminuirlo hará que las señales sean más suaves y más frecuentes.
Este parámetro, "Frecuencia Angular de MAS," afecta la frecuencia de las oscilaciones en el cálculo del "Precio Móvil Simple Inicial." Un valor más alto de "w" hará que las oscilaciones sean más rápidas y frecuentes, lo que significa que el indicador será más receptivo a cambios abruptos en el precio. Por otro lado, un valor más bajo hará que las oscilaciones sean más lentas y suaves, haciendo que el indicador sea menos sensible a cambios rápidos en el precio. Modificar "Frecuencia Angular de MAS" afecta directamente la frecuencia de las oscilaciones en el indicador.
Anchored Chandelier ExitThe Chandelier Exit is a popular tool among traders used to help determine appropriate stop loss levels. Originally developed by Chuck LeBeau, the Chandelier Exit takes into account market volatility and adjusts the stop loss level dynamically. This indicator builds upon the original Chandelier Exit by allowing the trader to select an anchor date or starting point for the indicator to begin calculating from.
The Original Chandelier Exit
Before we get into the details of the Anchored Chandelier Exit, let's review the original. Essentially a dynamic ATR stop loss, the Chandelier Exit provides a trailing stop that moves higher or lower based on volatility.
The Chandelier Exit is calculated based on the following criteria:
🔶ATR - The ATR is used to measure the volatility of a security over a lookback period. The ATR length determines the number of bars to consider when calculating the average true range. The shorter the length, the more responsive the level will be.
🔶ATR Multiplier - The default multiplier is set to 3. This is used to determine the sensitivity of the Chandelier Exit. The higher the ATR multiplier the wider the stop levels will be. A lower multiplier will tighten stop levels.
🔶Highest / Lowest Points - Determine the highest high (bullish trade) or lowest low (bearish trade) during the lookback period. The default length is 22 bars.
Calculating the Chandelier Exit
Bullish trades - Highest High - ATR * Multiplier
Bearish trades - Lowest Low + ATR * Multiplier
The Anchored Chandelier Exit
The Anchored Chandelier Exit is a new twist on the original, allowing traders to adapt their stop loss levels based on specific market events, levels or bars.
Similar to the original, traders can select the ATR length and multiplier, however, the high or low from which the ATR is subtracted or added is first determined at the anchor bar.
As new bars form, the indicator checks for the previous high/low to be breached. If the high or low is exceeded, the highest/lowest point is updated and the Chandelier Exit is recalculated.
When the indicator is first loaded to your chart, it will ask you to select an anchor bar and choose the bias for the trade.
A bullish (long) bias trade will plot the Chandelier Exit below price action, while a bearish (short) bias trade will plot the Chandelier Exit above price action.
Indicator Features
🔶Custom Start Date
🔶Bullish or Bearish Bias
🔶Selectable ATR Length & Multiplier
🔶Custom Colors
🔶Exit With Close or Wicks
🔶Exit Alerts
With careful parameter optimization, the Anchored Chandelier Exit can be a useful tool for helping traders manage risk based on market volatility.
Fibonacci Enhanced Bollinger BandsDiscover the synergistic power of Fibonacci ratios with traditional Bollinger Bands in the 'Fibonacci Enhanced Bollinger Bands' indicator. Ideal for traders seeking dynamic price levels for strategic entries and exits, this tool adds a unique Fibonacci twist to your technical analysis toolkit.
Introduction to Fibonacci Enhanced Bollinger Bands
'Fibonacci Enhanced Bollinger Bands' is a trading indicator that combines the classic Bollinger Bands approach with the powerful insights of Fibonacci ratios. By integrating these two concepts, this indicator offers traders a unique perspective on market volatility and potential support/resistance levels.
How It Works
Core Concept : The indicator calculates Bollinger Bands using a selected Fibonacci ratio. This ratio is applied to the standard deviation of the price series, providing a dynamic range around a Simple Moving Average (SMA).
Trading Strategies
Long Opportunities : The area below the lower band can be considered a potential zone for long positions. Prices in this zone may indicate an oversold market condition, suggesting a possible reversal or pullback.
Short Opportunities : Conversely, the area above the upper band might signal short-selling opportunities. Prices in this region could imply an overbought scenario, potentially leading to a price decline.
Versatility : Whether you're a day trader, swing trader, or long-term investor, this indicator adapts to various timeframes and assets, making it a versatile tool in your trading arsenal.
Conclusion
The 'Fibonacci Enhanced Bollinger Bands' indicator is designed for traders who wish to leverage the power of Fibonacci ratios in conjunction with the volatility insights provided by Bollinger Bands. It's an excellent tool for identifying potential reversal zones and refining entry and exit points. Try it out to enhance your market analysis and support your trading decisions with the combined wisdom of Fibonacci and Bollinger Bands.
Dynamic Volume-Volatility Adjusted MomentumThis Indicator in a refinement of my earlier script PC*VC Moving average Old with easier to follow color codes, overbought and oversold zones. This script has converted the previous script into a standardized measure by converting it into Z-scores and also incorporated a volatility based dynamic length option. Below is a detailed Explanation.
The "Dynamic Volume-Volatility Adjusted Momentum" or "Nasan Momentum Oscillator" is designed to capture market momentum while accounting for volume and volatility fluctuations. It leverages the Typical Price (TP), calculated as the average of high, low, and close prices, and introduces the Price Coefficient (PC) based on deviations from the simple moving average (SMA) across various time frames. Additionally, the Volume Coefficient (VC) compares current volume to SMA, and calculates Intraday Volatility (IDV) which gauges the daily price range relative to the close. Then intraday volatility ratio is calculated ( IDV Ratio) as the ratio of current Intraday Volatility (IDV) to the average of IDV for three different length periods, which provides a relative measure of current intraday volatility compared to its recent historical average. An inter-day ATR based Relative Volatility (RV) is calculated to adjusts for changing market volatility based on which the dynamic length adjustment adapts the moving average (standard length is 14). The PC *VC/IDV Ratio integrates price, volume, and volatility information which provides a volume and volatility adjusted momentum. This volume and volatility adjusted momentum is converted into a standardized Z-Score. The Z-Score measures deviations from the mean. Color-coded plots visually represent momentum, and thresholds aid in identifying overbought or oversold conditions.
The indicator incorporates a nuanced approach to emphasize the joint impact of price and volume while considering the stabilizing effect of lower intraday volatility. Placing the volume ratio (VC) in the numerator means that higher volume positively contributes to the overall ratio, aligning with the observation that increased volumes often accompany robust price movements. Simultaneously, the decision to include the inverse of intraday volatility (1/IDV) in the denominator acts as a dampener, reducing the impact of extreme intraday volatility on the momentum indicator. This design choice aims to filter out noise, giving more weight to significant price changes supported by substantial trading activity. In essence, the indicator's design seeks to provide a more robust momentum measure that balances the influence of price, volume, and volatility in the analysis of market dynamics.
Unbound RSIUnbound RSI
Description
The Unbound RSI or de-oscillated RSI indicator is a novel technical analysis indicator that combines the concepts of the Relative Strength Index (RSI) and moving averages, applied directly over the price chart. This indicator is unique in its approach by transforming the oscillatory nature of the RSI into a format that aligns with the price action, thereby offering a distinctive view of market momentum and trends.
Key Features
Multi-Length RSI Analysis: Incorporates three different lengths of RSI (short, medium, and long), providing insights into the momentum and trend strength at various timeframes.
Deoscillation of RSI: The RSI for each length is 'deoscillated' by adjusting its scale to align with the actual price movements. This is achieved by shifting and scaling the RSI values, effectively merging them with the price line.
Average True Range (ATR) Scaling: The deoscillation process includes scaling by the Average True Range (ATR), making the indicator responsive to the asset’s volatility.
Optional Smoothing: Provides an option to apply a simple moving average (SMA) smoothing to each deoscillated RSI line, reducing noise and highlighting more significant trends.
Dynamic Moving Average (MA) Baseline: Features a moving average calculated from the medium length (default value) de-oscillated RSI, serving as a dynamic baseline to identify overarching trends.
How It’s Different
Unlike standard RSI indicators that oscillate in a fixed range, this indicator transforms the RSI to move in tandem with the price, offering a unique perspective on momentum and trend changes. The use of multiple timeframes for RSI and the inclusion of a dynamic MA baseline provide a multifaceted view of market conditions.
Potential Usage
Trend Identification: The position of the price in relation to the different deoscillated RSI lines and the MA baseline can indicate the prevailing market trend.
Momentum Shifts: Crossovers of the price with the deoscillated RSI lines or the MA baseline can signal potential shifts in momentum, offering entry or exit points.
Volatility Awareness: The ATR-based scaling of the deoscillated RSI lines means the indicator adjusts to changes in volatility, potentially offering more reliable signals in different market conditions.
Comparative Analysis: By comparing the short, medium, and long deoscillated RSI lines, traders can gauge the strength of trends and the convergence or divergence of momentum across timeframes.
Best Practices
Backtesting: Given its novel nature, it’s crucial to backtest the indicator across different assets and market conditions.
Complementary Tools: Combine with other technical analysis tools (like support/resistance levels, other oscillators, volume analysis) for more robust trading signals.
Risk Management: Always use sound risk management strategies, as no single indicator provides foolproof signals.
Gap SMAGap SMA Indicator - Analyzing Price Gaps with Moving Averages
Description:
The Gap SMA (Simple Moving Average) indicator is a powerful tool designed to analyze price gaps, a phenomenon occurring when the market opens significantly higher or lower than the previous session's close. These gaps often signify abrupt shifts in market sentiment, driven by fundamental news, earnings reports, or overnight geopolitical events.
This indicator calculates and visualizes the average gap-up and gap-down based on historical data, aiding traders in identifying potential support or resistance levels driven by gap behavior.
What is a Gap?
In financial markets, a gap occurs when there is a notable difference (upward or downward) between the previous session's close and the current session's open. Gaps can be categorized as gap-ups (when the current open is higher than the previous close) or gap-downs (when the current open is lower than the previous close).
Key Features:
User-Defined Parameters: Adjust the number of gaps considered and a multiplier factor for precise customization.
Average Gap Visualization: Plotting lines representing the moving average of gap-ups and gap-downs.
Alert System: Alerts notify traders when the close price crosses above/below the average gap lines, offering potential entry or exit signals.
This tool is particularly useful for swing traders and investors interested in understanding historical gap patterns and integrating this information into their decision-making process. It can assist in determining potential stop-loss levels, defining entry or exit points, and gauging market sentiment based on gap behavior.
Feel free to experiment with various settings and timeframes to suit your trading strategy and risk tolerance. Your feedback and suggestions for further enhancements are highly appreciated!
Variable Keltner Channel For DCAHello Everyone,
Sharing the indicator that I'm using for Dollar Cost Averaging into the stocks & ETFs in my portfolio.
Instead of entering regularly each month, entry only happens when the share price is below the indicator.
This indicator is based on Exponential Moving Average & Keltner Channel.
When 21 EMA is above 34 EMA, the line is 1 ATR below the 21 EMA. (green color)
When 21 EMA is below 34 EMA, the line is 2 ATR below the 21 EMA. (red color)
Exploring ways to refine this further, especially during sideways or transition to downtrend, do comment if you have any idea.
This strategy itself was based on SMA 50 strategy for DCA.
Red Candle ATRThis ATR - Average True Range - Measures only red candles, giving the average true range of market declines.
SuperTrend ToolkitThe SuperTrend Toolkit (Super Kit) introduces a versatile approach to trend analysis by extending the application of the SuperTrend indicator to a wide array of @TradingView's built-in or Community Scripts . This tool facilitates the integration of the SuperTrend algorithm with various indicators, including oscillators, moving averages, overlays, and channels.
Methodology:
The SuperTrend, at its core, calculates a trend-following indicator based on the Average-True-Range (ATR) and price action. It creates dynamic support and resistance levels, adjusting to changing market conditions, and aiding in trend identification.
pine_st(simple float factor = 3., simple int length = 10) =>
float atr = ta.atr(length)
float up = hl2 + factor * atr
up := up < nz(up ) or close > nz(up ) ? up : nz(up )
float lo = hl2 - 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
@TradingView's native SuperTrend lacks the flexibility to incorporate different price sources into its calculation.
Community scripts, addressed the limitation by implementing the option to input different price sources, for example, one of the most popular publications, @KivancOzbilgic's SuperTrend script.
In May 2023, @TradingView introduced an update allowing the passing of another indicator's plot as a source value via the input.source() function. However, the built-in ta.atr function still relied on the chart's price data, limiting the formerly mentioned scripts to the chart's price data alone.
Unique Approach -
This script addresses the aforementioned limitations by processing the data differently.
Firstly we create a User-Defined-Type (UDT) replicating a bar's open, high, low, close (OHLC) values.
type bar
float o = open
float h = high
float l = low
float c = close
We then use this type to store the external input data.
src = input.source(close, "External Source")
bar b = bar.new(
nz(src ) , open 𝘷𝘢𝘭𝘶𝘦
math.max(nz(src ), src), high 𝘷𝘢𝘭𝘶𝘦
math.min(nz(src ), src), low 𝘷𝘢𝘭𝘶𝘦
src ) close 𝘷𝘢𝘭𝘶𝘦
Finally, we pass the data into our custom built SuperTrend with ATR functions to derive the external source's version of the SuperTrend indicator.
supertrend st = b.st(mlt, len)
- Setup Guide -
Utility and Use Cases:
Universal Compatibility - Apply SuperTrend to any built-in indicator or script, expanding its use beyond traditional price data.
- A simple example on one of my own public scripts -
Trend Analysis - Gain additional trend insights into otherwise mainly mean reverting or volume indicators.
- Alerts Setup Guide -
The Super Kit empowers traders and analysts with a tool that adapts the robust SuperTrend algorithm to a myriad of indicators, allowing comprehensive trend analysis and strategy development.
Market SessionsMarket Sessions Indicator Overview:
The "Market Sessions" indicator is a powerful tool designed to enhance traders' insights by providing comprehensive information about key market sessions, daily high/low values, and important exponential moving averages (EMAs) directly on the trading chart.
Key Features:
Market Sessions Display:
Visually represents Sydney/Tokyo, London, and New York sessions using distinct color-coded shapes.
Enhances visibility by dynamically changing the background color during specific trading sessions.
Daily High/Low:
Plots and labels the high and low values of the previous trading day on the chart.
Customizable colors for daily high and low markers.
Exponential Moving Averages (EMAs):
Includes 20, 50, and 200-period EMAs for comprehensive trend analysis.
Users have the flexibility to customize the visibility and color of each EMA.
Dashboard Information:
Real-time information about the current and upcoming market sessions.
Displays the time remaining for the upcoming session, aiding in timely decision-making.
Stock Session Information:
Clearly marks open and close times for Asia, Euro, and USA stock sessions.
Customizable visibility options for stock open/close lines, allowing for a tailored chart display.
Usage Guidelines:
Market Session Identification: Easily identify distinct market sessions using color-coded shapes and background color changes.
Daily Analysis: Quickly reference labeled lines for the high and low values of the previous trading day.
Trend Analysis: Observe the plotted EMAs on the chart for insights into the prevailing trends.
Real-time Monitoring: Utilize the dashboard for real-time information on current and upcoming sessions.
Stock Session Details: Identify specific open and close times for stock sessions, aiding in strategic planning.
Customization Options:
User-Friendly Parameters: Customize visibility, color, and positioning based on individual preferences.
Dashboard Configuration: Adjust dashboard position, text placement, and EMA parameters to tailor the indicator to specific needs.
Backtesting Feature:
The indicator includes a backtest feature, allowing users to visualize past sessions for testing and refining trading strategies.
This Market Sessions Indicator provides traders with a holistic view of market dynamics, facilitating informed decision-making and enhancing overall trading experiences.
Linear Regression MTF + Bands
Multiple Time Frames (MTFs): The indicator allows you to view linear regression trends over three different time frames (TF1, TF2, TF3) simultaneously. This means a trader can observe short, medium, and long-term trends on a single chart, which is valuable for understanding overall market direction and making cross-timeframe comparisons.
Linear Regression Bands: For each time frame, the indicator calculates linear regression bands. These bands represent the expected price range based on past prices. The middle line is the linear regression line, and the upper and lower lines are set at a specified deviation from this line. Traders can use these bands to spot potential overbought or oversold conditions, or to anticipate future price movements.
History Bands: Looking at linear regression channels can be deceiving if the user does not understand the calculation. In order to see where the channel was at in history the user can display the history bands to see where price actual was in a non-repainting fashion.
Customization Options: Traders can customize various aspects of the indicator, such as whether to display each time frame, the length of the linear regression (how many past data points it considers), and the deviation for the bands. This flexibility allows traders to adapt the indicator to their specific trading style and the asset they are analyzing.
Alerts: The script includes functionality to set alerts based on the price crossing the upper or lower bands of any time frame. This feature helps traders to be notified of potential trading opportunities or risks without constantly monitoring the chart.
Examples
The 15minute linear regression is overlayed onto a 5 minute chart. We are able to see higher timeframe average and extremes. The average is the middle of the channel and the extremes are the outer edges of the bands. The bands are non-repainting meaning that is the actual value of the channel at that place in time.
Here multiple channels are shown at once. We have a linear regression for the 5, 15, and 60 minute charts. If your strategy uses those timeframes you can see the average and overbought/oversold areas without having to flip through charts.
In this example we show just the history bands. The bands could be thought of as a "don't diddle in the middle" area if your strategy is looking for reversals
You can extend the channel into the future via the various input settings.
Logarithmic Bollinger Bands [MisterMoTA]The script plot the normal top and bottom Bollinger Bands and from them and SMA 20 it finds fibonacci logarithmic levels where price can find temporary support/resistance.
To get the best results need to change the standard deviation to your simbol value, like current for BTC the Standards Deviation is 2.61, current Standard Deviation for ETH is 2.55.. etc.. find the right current standard deviation of your simbol with a search online.
The lines ploted by indicators are:
Main line is a 20 SMA
2 retracement Logarithmic Fibonacci 0.382 levels above and bellow 20 sma
2 retracement Logarithmic Fibonacci 0.618 levels above and bellow 20 sma
Top and Bottom Bollindger bands (ticker than the rest of the lines)
2 expansion Logarithmic Fibonacci 0.382 levels above Top BB and bellow Bottom BB
2 expansion Logarithmic Fibonacci 0.618 levels above Top BB and bellow Bottom BB
2 expansion Logarithmic Fibonacci level 1 above Top BB and bellow Bottom BB
2 expansion Logarithmic Fibonacci 1.618 levels above Top BB and bellow Bottom BB
Let me know If you find the indicator useful or PM if you need any custom changes to it.
RMI Trend Sync - Strategy [presentTrading]█ Introduction and How It Is Different
The "RMI Trend Sync - Strategy " combines the strength of the Relative Momentum Index (RMI) with the dynamic nature of the Supertrend indicator. This strategy diverges from traditional methodologies by incorporating a dual analytical framework, leveraging both momentum and trend indicators to offer a more holistic market perspective. The integration of the RMI provides an enhanced understanding of market momentum, while the Super Trend indicator offers clear insights into the end of market trends, making this strategy particularly effective in diverse market conditions.
BTC 4h long/short performance
█ Strategy: How It Works - Detailed Explanation
- Understanding the Relative Momentum Index (RMI)
The Relative Momentum Index (RMI) is an adaptation of the traditional Relative Strength Index (RSI), designed to measure the momentum of price movements over a specified period. While RSI focuses on the speed and change of price movements, RMI incorporates the direction and magnitude of those movements, offering a more nuanced view of market momentum.
- Principle of RMI
Calculation Method: RMI is calculated by first determining the average gain and average loss over a given period (Length). It differs from RSI in that it uses the price change (close-to-close) rather than absolute gains or losses. The average gain is divided by the average loss, and this ratio is then normalized to fit within a 0-100 scale.
- Momentum Analysis in the Strategy
Thresholds for Decision Making: The strategy uses predetermined thresholds (pmom for positive momentum and nmom for negative momentum) to trigger trading decisions. When RMI crosses above the positive threshold and other conditions align (e.g., a bullish trend), it signals a potential long entry. Similarly, crossing below the negative threshold in a bearish trend may trigger a short entry.
- Super Trend and Trend Analysis
The Super Trend indicator is calculated based on a higher time frame, providing a broader view of the market trend. This indicator uses the Average True Range (ATR) to adapt to market volatility, making it an effective tool for identifying trend reversals.
The strategy employs a Volume Weighted Moving Average (VWMA) alongside the Super Trend, enhancing its capability to identify significant trend shifts.
ETH 4hr long/short performance
█ Trade Direction
The strategy offers flexibility in selecting the trading direction: long, short, or both. This versatility allows traders to adapt to their market outlook and risk tolerance, whether looking to capitalize on bullish trends, bearish trends, or a combination of both.
█ Usage
To effectively use the "RMI Trend Sync" strategy, traders should first set their preferred trading direction and adjust the RMI and Super Trend parameters according to their risk appetite and trading goals.
The strategy is designed to adapt to various market conditions, making it suitable for different asset classes and time frames.
█ Default Settings
RMI Settings: Length: 21, Positive Momentum Threshold: 70, Negative Momentum Threshold: 30
Super Trend Settings: Length: 10, Higher Time Frame: 480 minutes, Super Trend Factor: 3.5, MA Source: WMA
Visual Settings: Display Range MA: True, Bullish Color: #00bcd4, Bearish Color: #ff5252
Additional Settings: Band Length: 30, RWMA Length: 20
Optimal Length BackTester [YinYangAlgorithms]This Indicator allows for a ‘Optimal Length’ to be inputted within the Settings as a Source. Unlike most Indicators and/or Strategies that rely on either Static Lengths or Internal calculations for the length, this Indicator relies on the Length being derived from an external Indicator in the form of a Source Input.
This may not sound like much, but this application may allows limitless implementations of such an idea. By allowing the input of a Length within a Source Setting you may have an ‘Optimal Length’ that adjusts automatically without the need for manual intervention. This may allow for Traditional and Non-Traditional Indicators and/or Strategies to allow modifications within their settings as well to accommodate the idea of this ‘Optimal Length’ model to create an Indicator and/or Strategy that adjusts its length based on the top performing Length within the current Market Conditions.
This specific Indicator aims to allow backtesting with an ‘Optimal Length’ inputted as a ‘Source’ within the Settings.
This ‘Optimal Length’ may be used to display and potentially optimize multiple different Traditional Indicators within this BackTester. The following Traditional Indicators are included and available to be backtested with an ‘Optimal Length’ inputted as a Source in the Settings:
Moving Average; expressed as either a: Simple Moving Average, Exponential Moving Average or Volume Weighted Moving Average
Bollinger Bands; expressed based on the Moving Average Type
Donchian Channels; expressed based on the Moving Average Type
Envelopes; expressed based on the Moving Average Type
Envelopes Adjusted; expressed based on the Moving Average Type
All of these Traditional Indicators likewise may be displayed with multiple ‘Optimal Lengths’. They have the ability for multiple different ‘Optimal Lengths’ to be inputted and displayed, such as:
Fast Optimal Length
Slow Optimal Length
Neutral Optimal Length
By allowing for the input of multiple different ‘Optimal Lengths’ we may express the ‘Optimal Movement’ of such an expressed Indicator based on different Time Frames and potentially also movement based on Fast, Slow and Neutral (Inclusive) Lengths.
This in general is a simple Indicator that simply allows for the input of multiple different varieties of ‘Optimal Lengths’ to be displayed in different ways using Tradition Indicators. However, the idea and model of accepting a Length as a Source is unique and may be adopted in many different forms and endless ideas.
Tutorial:
You may add an ‘Optimal Length’ within the Settings as a ‘Source’ as followed in the example above. This Indicator allows for the input of a:
Neutral ‘Optimal Length’
Fast ‘Optimal Length’
Slow ‘Optimal Length’
It is important to account for all three as they generally encompass different min/max length values and therefore result in varying ‘Optimal Length’s’.
For instance, say you’re calculating the ‘Optimal Length’ and you use:
Min: 1
Max: 400
This would therefore be scanning for 400 (inclusive) lengths.
As a general way of calculating you may assume the following for which lengths are being used within an ‘Optimal Length’ calculation:
Fast: 1 - 199
Slow: 200 - 400
Neutral: 1 - 400
This allows for the calculation of a Fast and Slow length within the predetermined lengths allotted. However, it likewise allows for a Neutral length which is inclusive to all lengths alloted and may be deemed the ‘Most Accurate’ for these reasons. However, just because the Neutral is inclusive to all lengths, doesn’t mean the Fast and Slow lengths are irrelevant. The Fast and Slow length inputs may be useful for seeing how specifically zoned lengths may fair, and likewise when they cross over and/or under the Neutral ‘Optimal Length’.
This Indicator features the ability to display multiple different types of Traditional Indicators within the ‘Display Type’.
We will go over all of the different ‘Display Types’ with examples on how using a Fast, Slow and Neutral length would impact it:
Simple Moving Average:
In this example above have the Fast, Slow and Neutral Optimal Length formatted as a Slow Moving Average. The first example is on the 15 minute Time Frame and the second is on the 1 Day Time Frame, demonstrating how the length changes based on the Time Frame and the effects it may have.
Here we can see that by inputting ‘Optimal Lengths’ as a Simple Moving Average we may see moving averages that change over time with their ‘Optimal Lengths’. These lengths may help identify Support and/or Resistance locations. By using an 'Optimal Length' rather than a static length, we may create a Moving Average which may be more accurate as it attempts to be adaptive to current Market Conditions.
Bollinger Bands:
Bollinger Bands are a way to see a Simple Moving Average (SMA) that then uses Standard Deviation to identify how much deviation has occurred. This Deviation is then Added and Subtracted from the SMA to create the Bollinger Bands which help Identify possible movement zones that are ‘within range’. This may mean that the price may face Support / Resistance when it reaches the Outer / Inner bounds of the Bollinger Bands. Likewise, it may mean the Price is ‘Overbought’ when outside and above or ‘Underbought’ when outside and below the Bollinger Bands.
By applying All 3 different types of Optimal Lengths towards a Traditional Bollinger Band calculation we may hope to see different ranges of Bollinger Bands and how different lookback lengths may imply possible movement ranges on both a Short Term, Long Term and Neutral perspective. By seeing these possible ranges you may have the ability to identify more levels of Support and Resistance over different lengths and Trading Styles.
Donchian Channels:
Above you’ll see two examples of Machine Learning: Optimal Length applied to Donchian Channels. These are displayed with both the 15 Minute Time Frame and the 1 Day Time Frame.
Donchian Channels are a way of seeing potential Support and Resistance within a given lookback length. They are a way of withholding the High’s and Low’s of a specific lookback length and looking for deviation within this length. By applying a Fast, Slow and Neutral Machine Learning: Optimal Length to these Donchian Channels way may hope to achieve a viable range of High’s and Low’s that one may use to Identify Support and Resistance locations for different ranges of Optimal Lengths and likewise potentially different Trading Strategies.
Envelopes / Envelopes Adjusted:
Envelopes are an interesting one in the sense that they both may be perceived as useful; however we deem that with the use of an ‘Optimal Length’ that the ‘Envelopes Adjusted’ may work best. We will start with examples of the Traditional Envelope then showcase the Adjusted version.
Envelopes:
As you may see, a Traditional form of Envelopes even produced with a Machine Learning: Optimal Length may not produce optimal results. Unfortunately this may occur with some Traditional Indicators and they may need some adjustments as you’ll notice with the ‘Envelopes Adjusted’ version. However, even without the adjustments, these Envelopes may be useful for seeing ‘Overbought’ and ‘Oversold’ locations within a Machine Learning: Optimal Length standpoint.
Envelopes Adjusted:
By adding an adjustment to these Envelopes, we may hope to better reflect our Optimal Length within it. This is caused by adding a ratio reflection towards the current length of the Optimal Length and the max Length used. This allows for the Fast and Neutral (and potentially Slow if Neutral is greater) to achieve a potentially more accurate result.
Envelopes, much like Bollinger Bands are a way of seeing potential movement zones along with potential Support and Resistance. However, unlike Bollinger Bands which are based on Standard Deviation, Envelopes are based on percentages +/- from the Simple Moving Average.
We will conclude our Tutorial here. Hopefully this has given you some insight into how useful adding a ‘Optimal Length’ within an external (secondary) Indicator as a Source within the Settings may be. Likewise, how useful it may be for automation sake in the sense that when the ‘Optimal Length’ changes, it doesn’t rely on an alert where you need to manually update it yourself; instead it will update Automatically and you may reap the benefits of such with little manual input needed (aside from the initial setup).
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING!
MAC Spikes(Adam H Grimes)From Adam H Grimes: "Introducing a New Tool: The MAC Spike"
Mean Absolute Change Spikes (“MAC Spikes”).
Here are the steps to calculate it:
-Convert each day’s closing price to a change (difference) by subtracting it from the previous day’s closing price.
-Take the absolute value of that change.
-Average the past 20 days absolute values to create the baseline.
-Divide today’s change by yesterday’s baseline. (Still offsetting by one day.)
MAC Spikes- Indicator:
-Indicator Setup: The script defines an indicator with the name "MAC Spikes", not overlaying the main chart, and allows up to 99 lines to be plotted.
-User Inputs: It provides several user-configurable inputs, such as:
Length for standard deviation calculation (len).
-Type of spike to monitor (spikeType), with options for close, range, or open spikes.
-Option to filter spikes based on a threshold (filtered).
-The threshold value for spike significance (spike_thresh).
-Whether to display the spike histogram (disp_Spike).
-Line width for plotting (lw).
unconscious lineThis indicator was created with the idea that if everyone trades, it will move in that direction, i.e., it will repeatedly converge on an unaware area. The unaware area is defined by calculating the difference between the high and high of the current bar and the previous bar, and the low and low of the current bar, and then plotting the maximum and minimum values of the unaware area. If the price converges to this line, the time when it does not go to this line can be taken as the bias of the theoretical price, so it is not plotted, but the time when it does not touch the right edge of the indicator title is plotted.
Parameters
Arybuf -Specifies the range of values to be determined from the current time. The smaller the value, the more recent the value will be used.
Style
1. Display the smallest value in the judgment range
2. Display the largest value in the judgment range.
3. Display line 1 to draw the range with the largest difference.
Displays line 2 that draws the range with the largest difference.
The area with the largest difference, i.e., the unaware area, is the range of values from Style 3 to 4.
Period of noncoucentration.
This value is the number of bars that have not touched the least concentrated area.
Indicator Usage.
Set the value of the parameter.
Draw a long enough moving average.
Use the moving average to recognize the environment and make an entry at a push.
Note that this indicator draws a convergence point and does not predict the future. While this allows you to find a push, the value itself has no driving force.
When used in a contrarian manner, it should be used with the expectation that it will be caught at a buying or selling climax at some point in the future.
