GocchiMulti-Indicator: RSI & Moving Averages
This versatile TradingView indicator combines two essential tools for technical analysis—Relative Strength Index (RSI) and Moving Averages (MAs)—into one comprehensive solution. It is designed for traders seeking flexibility, customization, and efficiency in their charting experience.
Features:
Relative Strength Index (RSI):
Customizable RSI length.
Adjustable overbought and oversold levels.
Selectable source input (e.g., close, open, high, low).
Visual levels for overbought and oversold zones, aiding in quick trend and momentum identification.
Three Moving Averages:
Three independently customizable moving averages.
Options for Simple Moving Average (SMA) or Exponential Moving Average (EMA) for each line.
Adjustable lengths for short-, medium-, and long-term trend tracking.
Visual Enhancements:
Clear, color-coded plots for RSI and each moving average.
Overbought and oversold zones are highlighted with horizontal dotted lines.
Alerts:
Get notified when RSI crosses above the overbought level or below the oversold level.
Alerts help traders stay on top of potential market reversals or breakout opportunities.
Use Cases:
RSI Analysis: Spot overbought or oversold conditions to identify potential reversals.
Trend Following: Use moving averages to confirm trends or identify crossovers for potential entry and exit points.
Custom Strategies: Tailor the settings to fit specific trading styles, such as scalping, swing trading, or long-term investing.
This all-in-one indicator streamlines your analysis by reducing the need for multiple overlays, making your charts cleaner and more actionable. Whether you're a novice or an experienced trader, this tool provides the flexibility and insights you need to succeed in any market condition.
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Normalized Volume Rate of ChangeThis indicator is designed to help traders gauge changes in volume dynamics and identify potential shifts in buying or selling pressure. By normalizing the volume rate of change and comparing it to moving averages of itself, it offers valuable insights into market trends and can assist in making informed trading decisions.
Calculation:
The indicator calculates the Volume Rate of Change (VROC) by measuring the percentage change in volume over a specified length. This calculation provides a relative measure of how quickly the volume is increasing or decreasing. It then normalizes the VROC to a range of -1 to +1 by scaling it based on the highest and lowest values observed within the specified length. This normalization allows for easy comparison of the current VROC value with historical levels, enabling traders to assess the intensity of volume fluctuations.
Interpretation:
The main plot of the indicator displays the normalized VROC values as columns. The color of each column provides valuable information about the relationship between the VROC and the moving averages. Lime-colored columns indicate that the VROC is above both moving averages, suggesting increased buying pressure and potential bullish sentiment. Conversely, fuchsia-colored columns indicate that the VROC is below both moving averages, suggesting increased selling pressure and potential bearish sentiment. Yellow-colored columns indicate that the VROC is between the two moving averages, reflecting a period of consolidation or indecision in the market.
To further enhance interpretation, the indicator includes two moving averages. The Aqua line represents the faster moving average (MA1), and the Orange line represents the slower moving average (MA2). These moving averages provide additional context by smoothing out the VROC values and highlighting the overall trend. Traders can observe the interaction between the moving averages and the VROC to identify potential crossovers and assess the strength of trend reversals or continuations.
Colors:
-- Lime : The lime color is used to represent high volume rate of change above both moving averages. This color indicates a potentially bullish market sentiment, suggesting that buyers are dominant.
-- Fuchsia : The fuchsia color is used to represent low volume rate of change below both moving averages. This color indicates a potentially bearish market sentiment, suggesting that sellers are dominant.
-- Yellow : The yellow color is used to represent the volume rate of change between the two moving averages. This color reflects a transitional phase where neither buyers nor sellers have a clear advantage, signaling a period of consolidation or indecision in the market.
To provide additional visual cues for potential trade signals, the indicator includes lime-colored arrows below the price chart when there is a crossover upwards (MA1 crossing above MA2). This lime arrow indicates a potential bullish signal, suggesting a favorable time to consider long positions. Similarly, fuchsia-colored arrows are displayed above the price chart when there is a crossover downwards (MA1 crossing below MA2), signaling a potential bearish signal and suggesting a favorable time to consider short positions.
Applications:
This indicator offers various applications in trading strategies, including:
-- Trend Identification : By observing the relationship between the normalized VROC and the moving averages, traders can identify potential shifts in market trends. Lime-colored columns above both moving averages indicate a strong bullish trend, suggesting an opportunity to capitalize on upward price movements. Conversely, fuchsia-colored columns below both moving averages indicate a strong bearish trend, suggesting an opportunity to profit from downward price movements. Yellow-colored columns between the moving averages indicate a period of consolidation or uncertainty, signaling a potential trend reversal or continuation.
-- Confirmation of Price Moves : The indicator's ability to reflect volume dynamics in relation to the moving averages can help traders validate price moves. When significant price movements are accompanied by lime-colored columns (indicating high volume rate of change above both moving averages), it adds confirmation to the bullish sentiment. Similarly, fuchsia-colored columns accompanying downward price movements validate the bearish sentiment. This confirmation can enhance traders' confidence in the reliability of price moves.
-- Trade Timing : The indicator's moving average crossovers and the presence of arrows provide timing signals for trade entries and exits. Lime arrows appearing below the price chart signal potential long entry opportunities, indicating a bullish market sentiment. Conversely, fuchsia arrows appearing above the price chart suggest potential short entry opportunities, indicating a bearish market sentiment. These signals can be used in conjunction with other technical analysis tools to improve trade timing and increase the probability of successful trades.
Parameter Adjustments:
Traders can adjust the length of the VROC and the moving averages according to their trading preferences and timeframes. Longer VROC lengths provide a broader view of volume dynamics over an extended period, making it suitable for assessing long-term trends. Shorter VROC lengths offer a more sensitive measure of recent volume changes, making it suitable for shorter-term analysis. Similarly, adjusting the lengths of the moving averages can help adapt the indicator to different market conditions and trading styles.
Limitations:
While the indicator provides valuable insights, it has some limitations that traders should be aware of:
-- False Signals : Like any technical indicator, false signals can occur. During periods of low liquidity or in choppy markets, the indicator may generate misleading signals. It is essential to consider other indicators, price action, and fundamental analysis to confirm the signals before taking any trading actions.
-- Lagging Nature : Moving averages inherently lag behind the price action and volume changes. As a result, there may be a delay in the generation of signals and capturing trend reversals. Traders should exercise patience and avoid solely relying on this indicator for immediate trade decisions. Combining it with other indicators and tools can provide a more comprehensive picture of market conditions.
In conclusion, this indicator offers valuable insights into volume dynamics and trend analysis. By comparing the normalized VROC with moving averages, traders can identify shifts in buying or selling pressure, validate price moves, and improve trade timing. However, it is important to consider its limitations and use it in conjunction with other technical analysis tools to form a well-rounded trading strategy. Additionally, thorough testing, experimentation, and customization of the indicator's parameters are recommended to align it with individual trading preferences and market conditions.
Pivot Breakouts with MA FilterPivot Breakouts with MA Filter
This script identifies pivot breakouts (both bullish and bearish) using support and resistance levels and overlays breakout labels, arrows, and customizable Moving Averages. It allows traders to fine-tune their analysis with multiple options to customize the display and behavior of the breakout signals.
Key Features:
Pivot Support and Resistance:
Support is defined by the lowest low in a given range (using the lookback period).
Resistance is defined by the highest high in a given range (using the lookback period).
The script draws support and resistance boxes on the chart when these levels change, providing clear visual markers for potential breakout areas.
Breakout Detection:
Bullish Breakout: A breakout above resistance and the price is above the selected moving average (MA).
Bearish Breakout: A breakdown below support and the price is below the selected MA.
Breakout events trigger labels indicating "Resistance Breakout" (for bullish) and "Support Breakout" (for bearish).
The option to show Breakout Labels (with customizable colors) is available in the settings.
Moving Average Filter:
You can select the type of moving average (SMA or EMA) to use for filtering breakout signals.
MA Filter Length: This input allows you to set the period of the moving average to act as a filter for breakout conditions. This helps ensure the breakout aligns with the broader trend.
Multiple Moving Averages (Optional):
You can add up to four different moving averages (SMA or EMA), each with its own length and color.
You have the option to toggle each moving average on or off and adjust their appearance settings (color and length).
The script supports dynamic plots for each moving average, helping to visualize multiple trends at once.
Breakout Arrows:
The script can display arrows (or other shapes) below the bar for bullish breakouts and above the bar for bearish breakouts.
Arrows are optional and can be turned on/off in the settings.
You can customize the shape of the arrows (e.g., arrow, circle, square, or even a large or small triangle).
Customizable Colors and Labels:
The color of the breakout labels and arrows can be customized in the settings to make them fit your chart's style and personal preferences.
Alerts:
Alerts can be set for new support and resistance levels, as well as when breakouts occur (either bullish or bearish).
The alert system helps to notify traders when significant price action takes place without needing to constantly monitor the chart.
Settings:
Select Moving Average Type (SMA or EMA)
MA Filter Length: Length of the moving average used for filtering breakout conditions.
Lookback Range: Determines the range over which the pivot points (support and resistance) are calculated.
Breakout Labels: Option to turn on/off breakout labels, and customize label colors.
Show Breakout Arrows: Enable or disable breakout arrows with shape options (arrow, circle, square, large triangle, small triangle).
Multiple Moving Averages: Option to show up to 4 MAs with customizable colors and lengths.
buy/sell signals with Support/Resistance (InvestYourAsset) 📣The present indicator is a MACD based buy/sell signals indicator with support and resistance, that can be used to identify potential buy and sell signals in a security's price.
📣It is based on the MACD (Moving Average Convergence Divergence) indicator, which is a momentum indicator that shows the relationship between two moving averages of a security's price.
📣 The indicator also plots support and resistance levels, which can be used to confirm buy and sell signals. The support and resistance can also be used as a stoploss for existing position.
👉 To use the indicator, simply add it to your trading chart. The indicator will plot three sections:
📈 Price and Signals: This section plots the security's price and the MACD buy and sell signals.
📈 MACD Oscillator: This section plots the MACD oscillator, which is a histogram that shows the difference between the two moving averages.
📈 Moving Averages: This section plots the two moving averages that the MACD oscillator is based on.
📈 Support and Resistance: This section plots support and resistance levels, which are calculated based on the security's recent price action.
👉 To identify buy and sell signals, you can look for the following:
📈 Buy signal: When shorter Moving Average crosses over longer Moving Average.
📈 Sell signal: When shorter moving average crosses under longer moving average.
📈 You can also look for divergences between the MACD oscillator and the security's price. A divergence occurs when the MACD oscillator is moving in one direction, but the security's price is moving in the opposite direction. Divergences can be a sign of a potential trend reversal.
👉 To confirm buy and sell signals, you can look for support and resistance levels take a look at below snapshot. If a buy signal occurs at a support level, it is a stronger signal than if it occurs at a random price level. Similarly, if a sell signal occurs at a resistance level, it is a stronger signal than if it occurs at a random price level.
⚡ Here is a example of how to use the indicator to identify buy signal:
☑ Add the indicator to your trading chart.
☑Look for a buy signal when short MA crosses over Long MA.
☑Look for the buy signal to occur at a support level.
☑Enter a long position at the next candle.
☑Place a stop loss order below the support level.
☑Take profit when the MACD line crosses below the signal line, or when the security reaches a resistance level.
⚡ Here is an example of how to use the indicator to identify a sell signal:
☑Add the indicator to your trading chart.
☑Look for a sell signal, when shorter moving average crosses under longer moving average.
☑Look for the sell signal to occur at a resistance level.
☑Enter a short position at the next candle.
☑Place a stop loss order above the resistance level.
☑Take profit when the MACD line crosses above the signal line, or when the security reaches a support level.
✅Things to consider while using the indicator:
📈Look for buy signals in an uptrend and sell signals in a downtrend. This will increase the likelihood of your trades being successful.
📈Place your stop losses below the previous swing low or support for buy signals and above the previous swing high or resistance for sell signals. This will help to limit your losses if the trade goes against you.
📈Consider taking profits at key resistance and support levels. This will help you to lock in your profits and avoid giving them back to the market.
Follow us for timely updates regarding indicators that we may publish in future and give it a like if you appreciate the indicator.
Moving Average Multitool CrossoverAs per request, this is a moving average crossover version of my original moving average multitool script .
It allows you to easily access and switch between different types of moving averages, without having to continuously add and remove different moving averages from your chart. This should make backtesting moving average crossovers much, much more easier. It also has the option to show buy and sell signals for the crossovers of the chosen moving averages.
It contains the following moving averages:
Exponential Moving Average (EMA)
Simple Moving Average (SMA)
Weighted Moving Average (WMA)
Double Exponential Moving Average (DEMA)
Triple Exponential Moving Average (TEMA)
Triangular Moving Average (TMA)
Volume-Weighted Moving Average (VWMA)
Smoothed Moving Average (SMMA)
Hull Moving Average (HMA)
Least Squares Moving Average (LSMA)
Kijun-Sen line from the Ichimoku Kinko-Hyo system (Kijun)
McGinley Dynamic (MD)
Rolling Moving Average (RMA)
Jurik Moving Average (JMA)
Arnaud Legoux Moving Average (ALMA)
Vector Autoregression Moving Average (VAR)
Welles Wilder Moving Average (WWMA)
Sine Weighted Moving Average (SWMA)
Leo Moving Average (LMA)
Variable Index Dynamic Average (VIDYA)
Fractal Adaptive Moving Average (FRAMA)
Variable Moving Average (VAR)
Geometric Mean Moving Average (GMMA)
Corrective Moving Average (CMA)
Moving Median (MM)
Quick Moving Average (QMA)
Kaufman's Adaptive Moving Average (KAMA)
Volatility-Adjusted Moving Average (VAMA)
Modular Filter (MF)
Square Root Moving AverageAbstract
This script computes moving averages which the weighting of the recent quarter takes up about a half weight.
This script also provides their upper bands and lower bands.
You can apply moving average or band strategies with this script.
Introduction
Moving average is a popular indicator which can eliminate market noise and observe trend.
There are several moving average related strategies used by many traders.
The first one is trade when the price is far from moving average.
To measure if the price is far from moving average, traders may need a lower band and an upper band.
Bollinger bands use standard derivation and Keltner channels use average true range.
In up trend, moving average and lower band can be support.
In ranging market, lower band can be support and upper band can be resistance.
In down trend, moving average and upper band can be resistance.
An another group of moving average strategy is comparing short term moving average and long term moving average.
Moving average cross, Awesome oscillators and MACD belong to this group.
The period and weightings of moving averages are also topics.
Period, as known as length, means how many days are computed by moving averages.
Weighting means how much weight the price of a day takes up in moving averages.
For simple moving averages, the weightings of each day are equal.
For most of non-simple moving averages, the weightings of more recent days are higher than the weightings of less recent days.
Many trading courses say the concept of trading strategies is more important than the settings of moving averages.
However, we can observe some characteristics of price movement to design the weightings of moving averages and make them more meaningful.
In this research, we use the observation that when there are no significant events, when the time frame becomes 4 times, the average true range becomes about 2 times.
For example, the average true range in 4-hour chart is about 2 times of the average true range in 1-hour chart; the average true range in 1-hour chart is about 2 times of the average true range in 15-minute chart.
Therefore, the goal of design is making the weighting of the most recent quarter is close to the weighting of the rest recent three quarters.
For example, for the 24-day moving average, the weighting of the most recent 6 days is close to the weighting of the rest 18 days.
Computing the weighting
The formula of moving average is
sum ( price of day n * weighting of day n ) / sum ( weighting of day n )
Day 1 is the most recent day and day k+1 is the day before day k.
For more convenient explanation, we don't expect sum ( weighting of day n ) is equal to 1.
To make the weighting of the most recent quarter is close to the weighting of the rest recent three quarters, we have
sum ( weighting of day 4n ) = 2 * sum ( weighting of day n )
If when weighting of day 1 is 1, we have
sum ( weighting of day n ) = sqrt ( n )
weighting of day n = sqrt ( n ) - sqrt ( n-1 )
weighting of day 2 ≒ 1.414 - 1.000 = 0.414
weighting of day 3 ≒ 1.732 - 1.414 = 0.318
weighting of day 4 ≒ 2.000 - 1.732 = 0.268
If we follow this formula, the weighting of day 1 is too strong and the moving average may be not stable.
To reduce the weighting of day 1 and keep the spirit of the formula, we can add a parameter (we call it as x_1w2b).
The formula becomes
weighting of day n = sqrt ( n+x_1w2b ) - sqrt ( n-1+x_1w2b )
if x_1w2b is 0.25, then we have
weighting of day 1 = sqrt(1.25) - sqrt(0.25) ≒ 1.1 - 0.5 = 0.6
weighting of day 2 = sqrt(2.25) - sqrt(1.25) ≒ 1.5 - 1.1 = 0.4
weighting of day 3 = sqrt(3.25) - sqrt(2.25) ≒ 1.8 - 1.5 = 0.3
weighting of day 4 = sqrt(4.25) - sqrt(3.25) ≒ 2.06 - 1.8 = 0.26
weighting of day 5 = sqrt(5.25) - sqrt(4.25) ≒ 2.3 - 2.06 = 0.24
weighting of day 6 = sqrt(6.25) - sqrt(5.25) ≒ 2.5 - 2.3 = 0.2
weighting of day 7 = sqrt(7.25) - sqrt(6.25) ≒ 2.7 - 2.5 = 0.2
What you see and can adjust in this script
This script plots three moving averages described above.
The short term one is default magenta, 6 days and 1 atr.
The middle term one is default yellow, 24 days and 2 atr.
The long term one is default green, 96 days and 4 atr.
I arrange the short term 6 days to make it close to sma(5).
The other twos are arranged according to 4x length and 2x atr.
There are 9 curves plotted by this script. I made the lower bands and the upper bands less clear than moving averages so it is less possible misrecognizing lower or upper bands as moving averages.
x_src : how to compute the reference price of a day, using 1 to 4 of open, high, low and close.
len : how many days are computed by moving averages
atr : how many days are computed by average true range
multi : the distance from the moving average to the lower band and the distance from the moving average to the lower band are equal to multi * average true range.
x_1w2b : adjust this number to avoid the weighting of day 1 from being too strong.
Conclusion
There are moving averages which the weighting of the most recent quarter is close to the weighting of the rest recent three quarters.
We can apply strategies based on moving averages. Like most of indicators, oversold does not always means it is an opportunity to buy.
If the short term lower band is close to the middle term moving average or the middle term lower band is close to the long term moving average, it may be potential support value.
References
Computing FIR Filters Using Arrays
How to trade with moving averages : the eight trading signals concluded by Granville
How to trade with Bollinger bands
How to trade with double Bollinger bands
IBD Style Candles [tradeviZion]IBD Style Candles - Visualize Price Bars Like the Pros
Transform your chart with institutional-grade IBD-style bars and customizable moving averages for both daily and weekly timeframes. This indicator helps you visualize price action the way professionals at Investors Business Daily do.
What This Indicator Offers:
IBD-style bar visualization (clean, professional appearance)
Customizable coloring based on price movement or previous close
Automatic timeframe detection for appropriate moving averages
Four customizable moving averages for daily timeframes (10, 21, 50, 200)
Four customizable moving averages for weekly timeframes (10, 20, 30, 40)
Options to use SMAs or EMAs with adjustable colors and line widths
"The IBD-style bars provide a cleaner view of price action, allowing you to focus on market structure without the visual noise of traditional candles."
How to Apply the IBD-Style Bars:
On your TradingView chart, select "Bars" as the chart type from the main chart type selection menu (next to the time interval options).
Right-click on the chart and select "Settings".
Go to the "Symbol" tab.
Uncheck the "Thin Bars" option to display thicker bars.
Set the "Up Color" and "Down Color" opacity to 0 for a clean IBD-style appearance.
Enable "IBD-style Candles" from the script's settings.
To revert to the original chart style, repeat the above steps and restore the default settings.
Moving Average Configuration:
The indicator automatically detects your timeframe and displays the appropriate moving averages:
Daily Timeframe Moving Averages:
10-day moving average (SMA/EMA)
21-day moving average (SMA/EMA)
50-day moving average (SMA/EMA)
200-day moving average (SMA/EMA)
Weekly Timeframe Moving Averages:
10-week moving average (SMA/EMA)
20-week moving average (SMA/EMA)
30-week moving average (SMA/EMA)
40-week moving average (SMA/EMA)
Usage Tips:
Enable "Color bars based on previous close" to identify momentum shifts based on prior candle closes
Customize colors to match your chart theme or preference
Enable only the moving averages relevant to your trading strategy
For cleaner charts, reduce the number of visible moving averages
For stock trading, the 10/21/50/200 daily and 10/40 weekly MAs are most commonly used by institutions
// Example configuration for different timeframes
if timeframe.isweekly
// Weekly configuration
showSMA1_Weekly = true // 10-week MA
showSMA4_Weekly = true // 40-week MA
else
// Daily configuration
showMA2_Daily = true // 21-day MA
showMA3_Daily = true // 50-day MA
showMA4_Daily = true // 200-day MA
While the IBD style provides clarity, remember that no visualization method guarantees trading success. Always combine with proper analysis and risk management.
If you found this indicator helpful, please consider leaving a comment or suggestion for future improvements. Happy trading!
Combined EMA, SMMA, and 60-Day Cycle Indicator V2What This Script Does:
This script is designed to help traders visualize market trends and generate trading signals based on a combination of moving averages and price action. Here's a breakdown of its components and functionality:
Moving Averages:
EMAs (Exponential Moving Averages): These are indicators that smooth out price data to help identify trends. The script uses several EMAs:
200 EMA: A long-term trend indicator.
400 EMA: An even longer-term trend indicator.
55 EMA: A medium-term trend indicator.
89 EMA: Another medium-term trend indicator.
SMMA (Smoothed Moving Average): Similar to EMAs but with different smoothing. The script calculates:
21 SMMA: Short-term smoothed average.
9 SMMA: Very short-term smoothed average.
Cycle High and Low:
60-Day Cycle: The script looks back over the past 60 days to find the highest price (cycle high) and the lowest price (cycle low). These are plotted as horizontal lines on the chart.
Color-Coded Clouds:
Clouds: The script fills the area between certain EMAs with color-coded clouds to visually indicate trend conditions:
200 EMA vs. 400 EMA Cloud: Green when the 200 EMA is above the 400 EMA (bullish trend) and red when it’s below (bearish trend).
21 SMMA vs. 9 SMMA Cloud: Orange when the 21 SMMA is above the 9 SMMA and green when it’s below.
55 EMA vs. 89 EMA Cloud: Light green when the 55 EMA is above the 89 EMA and red when it’s below.
Trading Signals:
Buy Signal: This is shown when:
The price crosses above the 60-day low and
The EMAs indicate a bullish trend (e.g., the 200 EMA is above the 400 EMA and the 55 EMA is above the 89 EMA).
Sell Signal: This is shown when:
The price crosses below the 60-day high and
The EMAs indicate a bearish trend (e.g., the 200 EMA is below the 400 EMA and the 55 EMA is below the 89 EMA).
How It Helps Traders:
Trend Visualization: The colored clouds and EMA lines help you quickly see whether the market is in a bullish or bearish phase.
Trading Signals: The script provides clear visual signals (buy and sell labels) based on specific market conditions, helping you make more informed trading decisions.
In summary, this script combines several tools to help identify market trends and provide buy and sell signals based on price action relative to a 60-day high/low and the positioning of moving averages. It’s a useful tool for traders looking to visualize trends and automate some aspects of their trading strategy.
MA15, MA50 with Support/Resistance, CHoCH, Trend, and Entry/Exita comprehensive indicator that includes moving averages (MA), support and resistance levels, Change of Character (CHoCH) detection, trend identification, and entry/exit signals. Here's a breakdown of its components:
Input Parameters:
ma15_length and ma50_length: Lengths for the moving averages.
lookback: Period for detecting support and resistance levels.
Moving Averages:
ma15 and ma50 are simple moving averages with lengths defined by the user.
Support and Resistance Levels:
The script identifies swing highs and lows to update support and resistance levels.
These levels are plotted using extended lines for visualization.
Change of Character (CHoCH):
CHoCH up is detected when ma15 crosses above ma50.
CHoCH down is detected when ma15 crosses below ma50.
Corresponding signals are plotted on the chart.
Trend Identification:
An uptrend is confirmed when ma15 crosses above ma50 and the close price is above ma50.
A downtrend is confirmed when ma15 crosses below ma50 and the close price is below ma50.
Background colors are used to highlight uptrend (green) and downtrend (red).
Entry and Exit Signals:
Buy signals are generated when CHoCH up occurs, and the price pulls back to support during an uptrend.
Sell signals are generated when CHoCH down occurs, and the price pulls back to resistance during a downtrend.
These signals are plotted on the chart.
Alerts:
Alerts are set up to notify the user when a buy or sell signal is detected.
OBVious MA Indicator [1000X] On Balance Volume (OBV) is a gift to traders. OBV often provides a leading signal at the outset of a trend, when compression in the markets produces a surge in OBV prior to increased volatility.
This indicator demonstrates one method of utilizing OBV to your advantage. I call it the "OBVious MA Indicator ” only because it is simple in its mechanics. The primary utility of the OBVious MA indicator is as a volume confirmation filter that complements other components of a strategy.
Indicator Features:
• The Indicator revolves around the On Balance Volume indicator. OBV is a straightforward indicator: it registers a value by adding total volume traded on up candles, and subtracts total volume on down candles, generating a line by connecting those values. OBV was described in 1963 by Joe Granville in his book "Granville's New Key to Stock Market Profits” in which the author argues that OBV is the most vital key to success as a trader, with volume changes are a major predictor of price changes.
• Dual Moving Averages: here we use separate moving averages for entries and exits. This allows for more granular trade management; for example, one can either extend the length of the exit MA to hold positions longer, or shorten the MA for swifter exits, independently of the entry signals.
Execution: long trades are signalled when the OBV line crosses above the Long Entry Moving Average of the OBV. Long exits signals occur when the OBV line crosses under the Long Exit MA of the OBV. Shorts signal occur on a cross below the Short Entry MA, and exit signals come on a cross above the Short Exit MA.
Application:
While this indicator outlines entry and exit conditions based on OBV crossovers with designated moving averages, is is, as stated, best used in conjunction with a supporting cast of confirmatory indicators (feel free to drop me a note and tell me how you've used it). It can be used to confirm entries, or you might try using it as a sole exit indicator in a strategy.
Visualization:
The indicator includes conditional plotting of the OBV MAs, which plot based on the selected trading direction. This visualization aids in understanding how OBV interacts with the set moving averages.
Further Discussion:
We all know the importance of volume; this indicator demonstrates one simple yet effective method of incorporating the OBV for volume analysis. The OBV indicator can be used in many ways - for example, we can monitor OBV trend line breaks, look for divergences, or as we do here, watch for breaks of the moving average.
Despite its simplicity, I'm unaware of any previously published cases of this method. But the concept of applying MAs or EMAs to volume-based indicators like OBV is not uncommon in technical analysisIf, so I expect work like this has been done before. If you know of other similar indicators or strategies, please mention in the comments.
One comparable method uses EMAs of the OBV is QuantNomad’s "On Balance Volume Oscillator Strategy ”. That strategy uses a pair of EMAs on a normalized-range OBV-based oscillator. In that strategy, however, entry and exit signals occur on one EMA crossing the other, which places trades at distinctly different times than crossings of the OBV itself. Both are valid approaches with strength in simplicity.
Note: This is the indicator version of the Strategy found here .
OBVious MA Strategy [1000X Trader]Exploring OBV: The OBVious MA Strategy
Are you using On Balance Volume (OBV) effectively? OBV is a gift to traders. OBV often provides a leading signal at the outset of a trend, when compression in the markets produces a surge in OBV prior to increased volatility.
This strategy demonstrates one method of utilizing OBV to your advantage. I call it the "OBVious MA Strategy ” only because it is so simple in its mechanics. This is meant to be a demonstration, not a strategy to utilize in live trading, as the primary utility of the OBVious MA indicator is as a volume confirmation filter that complements other components of a strategy. That said, I felt useful to present this indicator in isolation in this strategy to demonstrate the power it holds.
Strategy Features:
• OBV is the core signal: this strategy revolves around the On Balance Volume indicator. OBV is a straightforward indicator: it registers a value by adding total volume traded on up candles, and subtracts total volume on down candles, generating a line by connecting those values. OBV was described in 1963 by Joe Granville in his book "Granville's New Key to Stock Market Profits” in which the author argues that OBV is the most vital key to success as a trader, as volume changes are a major predictor of price changes.
• Dual Moving Averages: here we use separate moving averages for entries and exits. This allows for more granular trade management; for example, one can either extend the length of the exit MA to hold positions longer, or shorten the MA for swifter exits, independently of the entry signals.
Execution: long trades are taken when the OBV line crosses above the Long Entry Moving Average of the OBV. Long exits occur when the OBV line crosses under the Long Exit MA of the OBV. Shorts enter on a cross below the Short Entry MA, and exit on a cross above the Short Exit MA.
• Directional Trading: a direction filter can be set to "long" or "short," but not “both”, given that there is no trend filter in this strategy. When used in a bi-directional strategy with a trend filter, we add “both” to the script as a third option.
Application:
While this strategy outlines entry and exit conditions based on OBV crossovers with designated moving averages, is is, as stated, best used in conjunction with a supporting cast of confirmatory indicators (feel free to drop me a note and tell me how you've used it). It can be used to confirm entries, or you might try using it as a sole exit indicator in a strategy.
Visualization:
The strategy includes conditional plotting of the OBV MAs, which plot based on the selected trading direction. This visualization aids in understanding how OBV interacts with the set moving averages.
Further Discussion:
We all know the importance of volume; this strategy demonstrates one simple yet effective method of incorporating the OBV for volume analysis. The OBV indicator can be used in many ways - for example, we can monitor OBV trend line breaks, look for divergences, or as we do here, watch for breaks of the moving average.
Despite its simplicity, I'm unaware of any previously published cases of this method. The concept of applying MAs or EMAs to volume-based indicators like OBV is not uncommon in technical analysis, so I expect that work like this has been done before. If you know of other similar indicators or strategies, please mention in the comments.
One comparable strategy that uses EMAs of the OBV is QuantNomad’s "On Balance Volume Oscillator Strategy ", which uses a pair of EMAs on a normalized-range OBV-based oscillator. In that strategy, however, entries and exits occur on one EMA crossing the other, which places trades at distinctly different times than crossings of the OBV itself. Both are valid approaches with strength in simplicity.
Herrick Payoff Index @shrilssThis indicator combines elements of price action, volume, and open interest to provide insights into market strength and potential trend reversals. This script calculates the Herrick Payoff Index (HPI) based on a modified formula that incorporates volume and open interest adjustments.
The HPI is derived from comparing the current day's mean price to the previous day's mean price, factoring in volume and open interest changes. By analyzing these factors, the indicator aims to gauge the effectiveness of market participants' positions.
Key Features:
- HPI Calculation: The HPI value is calculated using the formula: ((M - My) * C * V) * (1 + |OI - OI | / min(OI, OI )), where M represents the mean price for the current day, My represents the mean price for the previous day, C is a constant (set to 1), V is the volume, and OI is the open interest. This adjusted calculation accounts for changes in volume and open interest, providing a more nuanced view of market dynamics.
- Moving Averages: The script also includes two Exponential Moving Averages (EMAs) of the HPI values, allowing traders to identify trends and potential reversal points. Users can customize the length of these moving averages to suit their trading strategies.
- Visual Signals: The indicator visually represents the HPI values and their relationship to the moving averages. When the HPI value is above the shorter-term EMA, it suggests bullish momentum, while values below indicate bearish sentiment.
5 MAs w. alerts [LucF]Is this gazillionth MA indicator worth an addition to the already crowded field of contenders? I say yes! This one shows up to 5 MAs and 6 different marker conditions that can be used to create alerts, among many other goodies.
Features
MAs can be darkened when they are falling.
MAs from another time frame can be displayed, with the option of smoothing them.
Markers can be filtered to Longs or Shorts only.
EMAs can be selected for either all or the two shortest MAs.
The background can be colored using any of the marker states except no. 3.
Markers are:
1. On crosses between any two user-defined MAs,
2. When price is above or below an MA,
3. On Quick Flips (a specific setup involving a cross, multiple MA states and increasing volume, when available),
4. When the difference between two MAs is within a % of its high/low historic values,
5. When an MA has been rising/falling for n bars,
6. When the difference between two MAs is greater than a multiple of ATR.
Some markers use similar visual cues, so distinguishing them will be a challenge if they are used concurrently.
Alerts
Alerts can be created on any combination of alerts. Only non-consecutive instances of markers 5 and 6 will trigger the alert condition. Make sure you are on the interval you want the alert to run at. Using the “Once Per Bar Close” trigger condition is usually the best option.
When an alert is created in TradingView, a snapshot of the indicator’s settings is saved with the alert, which then takes on a life of its own. That is why even though there is only one alert to choose from when you bring up the alert creation dialog box and choose “5 MAs”, that alert can be triggered from any number of conditions. You select those conditions by activating the markers you want the alert to trigger on before creating the alert. If you have selected multiple conditions, then it can be a good idea to record a reminder in the alert’s message field. When the alert triggers, you will need the indicator on the chart to figure out which one of your conditions triggered the alert, as there is currently no way to dynamically change the alert’s message field from within the script.
Background settings will not trigger alerts; only marker configurations.
Notes
MAs are just… averages. Trader lure would have them act as support and resistance levels. I’m not sure about that, and not the only one thinking along these lines. Adam Grimes has studied moving averages in quite a bit of detail. His numbers point to no evidence indicating they act as support/resistance, and to specific MA lengths not being more meaningful than others. His point of view is debated by some—not by me. Mean reversion does not entail that price stops when it reaches its MA; rather, it makes sense to me that price would often more or less oscillate around its MA, which entails the MA does not act as support/resistance. Aren’t the best mean reversion opportunities when price is furthest away from its MA? If so, it should be more profitable to identify these areas, which some of this indicator’s markers try to do.
I think MAs can be much more powerful when thought of as instruments we can use to situate price events in contexts of various resolutions, from the instantaneous to the big picture. Accordingly, I use the relative positions and slopes of MAs in both discretionary and automated trading; but never their purported ability to support/resist.
Regardless of how you use MAs, I hope you will find this indicator useful.
Biased References
The Art and Science of Technical Analysis: Market Structure, Price Action, and Trading Strategies, Adam Grimes, 2012.
Does the 200 day moving average “work”?
Moving averages: digging deeper
KST Strategy [Skyrexio]Overview
KST Strategy leverages Know Sure Thing (KST) indicator in conjunction with the Williams Alligator and Moving average to obtain the high probability setups. KST is used for for having the high probability to enter in the direction of a current trend when momentum is rising, Alligator is used as a short term trend filter, while Moving average approximates the long term trend and allows trades only in its direction. Also strategy has the additional optional filter on Choppiness Index which does not allow trades if market is choppy, above the user-specified threshold. Strategy has the user specified take profit and stop-loss numbers, but multiplied by Average True Range (ATR) value on the moment when trade is open. The strategy opens only long trades.
Unique Features
ATR based stop-loss and take profit. Instead of fixed take profit and stop-loss percentage strategy utilizes user chosen numbers multiplied by ATR for its calculation.
Configurable Trading Periods. Users can tailor the strategy to specific market windows, adapting to different market conditions.
Optional Choppiness Index filter. Strategy allows to choose if it will use the filter trades with Choppiness Index and set up its threshold.
Methodology
The strategy opens long trade when the following price met the conditions:
Close price is above the Alligator's jaw line
Close price is above the filtering Moving average
KST line of Know Sure Thing indicator shall cross over its signal line (details in justification of methodology)
If the Choppiness Index filter is enabled its value shall be less than user defined threshold
When the long trade is executed algorithm defines the stop-loss level as the low minus user defined number, multiplied by ATR at the trade open candle. Also it defines take profit with close price plus user defined number, multiplied by ATR at the trade open candle. While trade is in progress, if high price on any candle above the calculated take profit level or low price is below the calculated stop loss level, trade is closed.
Strategy settings
In the inputs window user can setup the following strategy settings:
ATR Stop Loss (by default = 1.5, number of ATRs to calculate stop-loss level)
ATR Take Profit (by default = 3.5, number of ATRs to calculate take profit level)
Filter MA Type (by default = Least Squares MA, type of moving average which is used for filter MA)
Filter MA Length (by default = 200, length for filter MA calculation)
Enable Choppiness Index Filter (by default = true, setting to choose the optional filtering using Choppiness index)
Choppiness Index Threshold (by default = 50, Choppiness Index threshold, its value shall be below it to allow trades execution)
Choppiness Index Length (by default = 14, length used in Choppiness index calculation)
KST ROC Length #1 (by default = 10, value used in KST indicator calculation, more information in Justification of Methodology)
KST ROC Length #2 (by default = 15, value used in KST indicator calculation, more information in Justification of Methodology)
KST ROC Length #3 (by default = 20, value used in KST indicator calculation, more information in Justification of Methodology)
KST ROC Length #4 (by default = 30, value used in KST indicator calculation, more information in Justification of Methodology)
KST SMA Length #1 (by default = 10, value used in KST indicator calculation, more information in Justification of Methodology)
KST SMA Length #2 (by default = 10, value used in KST indicator calculation, more information in Justification of Methodology)
KST SMA Length #3 (by default = 10, value used in KST indicator calculation, more information in Justification of Methodology)
KST SMA Length #4 (by default = 15, value used in KST indicator calculation, more information in Justification of Methodology)
KST Signal Line Length (by default = 10, value used in KST indicator calculation, more information in Justification of Methodology)
User can choose the optimal parameters during backtesting on certain price chart.
Justification of Methodology
Before understanding why this particular combination of indicator has been chosen let's briefly explain what is KST, Williams Alligator, Moving Average, ATR and Choppiness Index.
The KST (Know Sure Thing) is a momentum oscillator developed by Martin Pring. It combines multiple Rate of Change (ROC) values, smoothed over different timeframes, to identify trend direction and momentum strength. First of all, what is ROC? ROC (Rate of Change) is a momentum indicator that measures the percentage change in price between the current price and the price a set number of periods ago.
ROC = 100 * (Current Price - Price N Periods Ago) / Price N Periods Ago
In our case N is the KST ROC Length inputs from settings, here we will calculate 4 different ROCs to obtain KST value:
KST = ROC1_smooth × 1 + ROC2_smooth × 2 + ROC3_smooth × 3 + ROC4_smooth × 4
ROC1 = ROC(close, KST ROC Length #1), smoothed by KST SMA Length #1,
ROC2 = ROC(close, KST ROC Length #2), smoothed by KST SMA Length #2,
ROC3 = ROC(close, KST ROC Length #3), smoothed by KST SMA Length #3,
ROC4 = ROC(close, KST ROC Length #4), smoothed by KST SMA Length #4
Also for this indicator the signal line is calculated:
Signal = SMA(KST, KST Signal Line Length)
When the KST line rises, it indicates increasing momentum and suggests that an upward trend may be developing. Conversely, when the KST line declines, it reflects weakening momentum and a potential downward trend. A crossover of the KST line above its signal line is considered a buy signal, while a crossover below the signal line is viewed as a sell signal. If the KST stays above zero, it indicates overall bullish momentum; if it remains below zero, it points to bearish momentum. The KST indicator smooths momentum across multiple timeframes, helping to reduce noise and provide clearer signals for medium- to long-term trends.
Next, let’s discuss the short-term trend filter, which combines the Williams Alligator and Williams Fractals. Williams Alligator
Developed by Bill Williams, the Alligator is a technical indicator that identifies trends and potential market reversals. It consists of three smoothed moving averages:
Jaw (Blue Line): The slowest of the three, based on a 13-period smoothed moving average shifted 8 bars ahead.
Teeth (Red Line): The medium-speed line, derived from an 8-period smoothed moving average shifted 5 bars forward.
Lips (Green Line): The fastest line, calculated using a 5-period smoothed moving average shifted 3 bars forward.
When the lines diverge and align in order, the "Alligator" is "awake," signaling a strong trend. When the lines overlap or intertwine, the "Alligator" is "asleep," indicating a range-bound or sideways market. This indicator helps traders determine when to enter or avoid trades.
The next indicator is Moving Average. It has a lot of different types which can be chosen to filter trades and the Least Squares MA is used by default settings. Let's briefly explain what is it.
The Least Squares Moving Average (LSMA) — also known as Linear Regression Moving Average — is a trend-following indicator that uses the least squares method to fit a straight line to the price data over a given period, then plots the value of that line at the most recent point. It draws the best-fitting straight line through the past N prices (using linear regression), and then takes the endpoint of that line as the value of the moving average for that bar. The LSMA aims to reduce lag and highlight the current trend more accurately than traditional moving averages like SMA or EMA.
Key Features:
It reacts faster to price changes than most moving averages.
It is smoother and less noisy than short-term EMAs.
It can be used to identify trend direction, momentum, and potential reversal points.
ATR (Average True Range) is a volatility indicator that measures how much an asset typically moves during a given period. It was introduced by J. Welles Wilder and is widely used to assess market volatility, not direction.
To calculate it first of all we need to get True Range (TR), this is the greatest value among:
High - Low
abs(High - Previous Close)
abs(Low - Previous Close)
ATR = MA(TR, n) , where n is number of periods for moving average, in our case equals 14.
ATR shows how much an asset moves on average per candle/bar. A higher ATR means more volatility; a lower ATR means a calmer market.
The Choppiness Index is a technical indicator that quantifies whether the market is trending or choppy (sideways). It doesn't indicate trend direction — only the strength or weakness of a trend. Higher Choppiness Index usually approximates the sideways market, while its low value tells us that there is a high probability of a trend.
Choppiness Index = 100 × log10(ΣATR(n) / (MaxHigh(n) - MinLow(n))) / log10(n)
where:
ΣATR(n) = sum of the Average True Range over n periods
MaxHigh(n) = highest high over n periods
MinLow(n) = lowest low over n periods
log10 = base-10 logarithm
Now let's understand how these indicators work in conjunction and why they were chosen for this strategy. KST indicator approximates current momentum, when it is rising and KST line crosses over the signal line there is high probability that short term trend is reversing to the upside and strategy allows to take part in this potential move. Alligator's jaw (blue) line is used as an approximation of a short term trend, taking trades only above it we want to avoid trading against trend to increase probability that long trade is going to be winning.
Almost the same for Moving Average, but it approximates the long term trend, this is just the additional filter. If we trade in the direction of the long term trend we increase probability that higher risk to reward trade will hit the take profit. Choppiness index is the optional filter, but if it turned on it is used for approximating if now market is in sideways or in trend. On the range bounded market the potential moves are restricted. We want to decrease probability opening trades in such condition avoiding trades if this index is above threshold value.
When trade is open script sets the stop loss and take profit targets. ATR approximates the current volatility, so we can make a decision when to exit a trade based on current market condition, it can increase the probability that strategy will avoid the excessive stop loss hits, but anyway user can setup how many ATRs to use as a stop loss and take profit target. As was said in the Methodology stop loss level is obtained by subtracting number of ATRs from trade opening candle low, while take profit by adding to this candle's close.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2025.05.01. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 60%
Maximum Single Position Loss: -5.53%
Maximum Single Profit: +8.35%
Net Profit: +5175.20 USDT (+51.75%)
Total Trades: 120 (56.67% win rate)
Profit Factor: 1.747
Maximum Accumulated Loss: 1039.89 USDT (-9.1%)
Average Profit per Trade: 43.13 USDT (+0.6%)
Average Trade Duration: 27 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 1h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrexio commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation.
Uptrick: Time Based ReversionIntroduction
The Uptrick: Time Based Reversion indicator is designed to provide a comprehensive view of market momentum and potential trend shifts by combining multiple moving averages, a streak-based trend analysis system, and adaptive color visualization. It helps traders identify strong trends, spot potential reversals, and make more informed trading decisions.
Purpose
The primary goal of this indicator is to assist traders in distinguishing between sustained market movements and short-lived fluctuations. By evaluating how price behaves relative to its moving averages, and by measuring consecutive streaks above or below these averages, the indicator highlights areas where trends are likely to continue or lose momentum.
Overview
Uptrick: Time Based Reversion calculates one or more moving averages of price data and then tracks the number of consecutive bars (streaks) above or below these averages. This streak-based detection provides insight into whether a trend is gaining strength or nearing a potential reversal point. The indicator offers:
• Multiple moving average types (SMA, EMA, WMA)
• Optional second and third moving average layers for additional smoothing of first moving average
• A streak detection system to quantify trend intensity
• A dynamic color scheme that changes with streak strength
• Optional buy and sell signals for potential trade entries and exits
• A ribbon mode that applies moving averages to Open, High, Low, and Close prices for a more detailed visualization of overall trend alignment
Originality and Uniqueness
Unlike traditional moving average indicators, Uptrick: Time Based Reversion incorporates a streak measurement system to detect trend strength. This approach helps clarify whether a price movement is merely a quick fluctuation or part of a longer-lasting trend. Additionally, the optional ribbon mode extends this logic to Open, High, Low, and Close prices, creating a layered and intuitive visualization that shows complete trend alignment.
Inputs and Features
1. Enable Ribbon Mode
This input lets you activate or deactivate the ribbon display of multiple moving averages. When enabled, the script plots moving averages for the Open, High, Low, and Close prices and uses color fills to show whether these four data points are collectively above or below their respective moving averages.
2. Color Scheme Selection
Users can choose from several predefined color schemes, such as Default, Emerald, Crimson, Sapphire, Gold, Purple, Teal, Orange, Gray, Lime, or Aqua. Each scheme assigns distinct bullish, bearish and neutral colors..
3. Show Buy/Sell Signals
The indicator can display buy or sell signals based on its streak analysis logic. These signals appear as markers on the chart, indicating a “Safe Uptrend” (buy) or “Safe Downtrend” (sell).
4. Moving Average Types and Lengths
• First MA Type and Length: Choose SMA, EMA, or WMA along with a customizable period.
• Second and Third MA Types and Lengths: You can optionally stack additional moving averages for further smoothing, each with its own customizable type and period.
5. Streak Threshold Multiplier
This numeric input determines how strong a streak must be before the script considers it a “safe” trend. A higher multiplier requires a longer or more intense streak for a buy or sell signal.
6. Dynamic Transparency Calculation
The color intensity adapts to the streak’s strength. Longer streaks increase the transparency of the opposing color, making the current dominant color stand out. This feature ensures that a vigorous uptrend or downtrend is visually distinct from short-lived or weaker moves.
7. Ribbon Moving Averages
In ribbon mode, the script calculates moving averages for the Open, High, Low, and Close prices. Each of these is optionally smoothed again if the second and/or third moving average layers are active. The final result is a ribbon of moving averages that helps confirm whether the market is uniformly aligned above or below these key reference points.
Calculation Methodology
1. Initial Moving Average
The script calculates the first moving average (SMA, EMA, or WMA) of the closing price over a user-defined period.
2. Optional Secondary and Tertiary Averages
If selected, the script then applies a second and/or third smoothing step. Each of these steps can be a different type of moving average (SMA, EMA, or WMA) with its own period length.
3. Streak Detection
The indicator counts consecutive bars above or below the smoothed moving average. A running total (streakUp or streakDown) increments with every bar that remains above or below that average.
4. Reversion Intensity
The script compares the current streak value to its own average (calculated over the final chosen period). This ratio determines whether the streak is nearing a likely reversion or is strong enough to continue.
5. Color Assignment and Signals
The indicator calculates color transparency based on streak intensity. Buy and sell signals appear when the streak meets or exceeds the threshold multiplier, indicating a safe uptrend or downtrend.
Color Schemes and Visualization
This indicator offers multiple predefined color sets. Each scheme specifies a unique bullish color, bearish color and neutral color. The script automatically varies transparency to highlight strong trends and fade weaker ones, making it visually clear when a trend is intensifying or losing momentum.
Smoothing Techniques
By allowing up to three layers of moving average smoothing, the indicator accommodates different trading styles. A single layer provides faster reactions to market changes, while more layers reduce noise at the cost of slower responsiveness. Traders can choose the right balance between responsiveness and stability for their strategy, whether it is short-term scalping or long-term trend following.
Why It Combines Specific Smoothing Techniques
The Uptrick: Time Based Reversion indicator strategically combines specific smoothing techniques—SMA, EMA, and WMA—to leverage their complementary strengths. The SMA provides stable and consistent trend identification by equally weighting all data points, while the EMA emphasizes recent price movements, allowing quicker responses to market changes. WMA enhances sensitivity to recent price shifts, which helps in detecting subtle momentum changes early. By integrating these methods in layers, the indicator effectively balances responsiveness with stability, helping traders clearly identify genuine trend changes while filtering out short-term noise and false signals.
Ribbon Mode
If Open, High, Low, and Close prices remain above or below their respective moving averages consistently, the script colors the bars fully bullish or bearish. When the data points are mixed, a neutral color is applied. This mode provides a thorough perspective on whether the entire price range is aligned in one direction or showing conflicting signals.
Summary
Uptrick: Time Based Reversion combines multiple moving averages, streak detection, and dynamic color adjustments to help traders identify significant trends and potential reversal areas. Its flexibility allows it to be used either in a simpler form, with one moving average and streak analysis, or in a more advanced configuration with ribbon mode that charts multiple smoothed averages for a deeper understanding of price alignment. By adapting color intensities based on streak strength and providing optional buy/sell signals, this indicator delivers a clear and flexible tool suited to various trading strategies.
Disclaimer
This indicator is designed as an analysis aid and does not guarantee profitable trades. Past performance does not indicate future success, and market conditions can change unexpectedly. Users are advised to employ proper risk management and thoroughly evaluate trades before taking positions. Use this indicator as part of a broader strategy, not as a sole decision-making tool.
[KVA]Volume ImpulseThe Volume Impulse indicator is designed to provide insights into market momentum by analyzing volume dynamics. It helps traders identify periods of strong buying and selling pressure, which can be crucial for making informed trading decisions.
What does the indicator do?
The Volume Impulse indicator calculates positive and negative volume percentages based on the price range within each bar. It allows traders to visualize the distribution of volume and detect potential shifts in market sentiment.
How does it work?
The indicator uses a customizable lookback period to analyze volume data, smoothing the results with user-defined moving averages. By comparing the positive and negative volume percentages, the indicator highlights overbought and oversold conditions, aiding in trend detection and potential reversal points.
How to use it?
Identify Momentum: Use the positive and negative volume percentages to gauge market momentum within the specified lookback period.
Detect Overbought/Oversold Conditions: Look for the indicator crossing above the overbought level or below the oversold level to identify potential reversal points.
Smooth Trends: Adjust the moving average type and lengths to smooth out the volume data and identify trends more clearly.
Key Features
Volume Analysis: Calculates the positive and negative volume based on the price range within each bar.
Lookback Period: Allows you to define a lookback period over which the indicator calculations are performed, providing flexibility in analyzing different timeframes.
Customizable Moving Averages: Choose from various types of moving averages (EMA, SMA, WMA, Hull) to smooth the volume data.
Overbought/Oversold Levels: Visual markers for overbought, middle, and oversold conditions to help identify potential reversal points.
Color-Coded Areas: Highlights overbought and oversold regions with customizable colors for easy visual interpretation.
Plotting Options: Displays the positive volume and its smoothed version using the selected moving average type and length.
Inputs:
Lookback Period: Define the period over which the volume analysis is performed.
Moving Average Type: Select the type of moving average (EMA, SMA, WMA, Hull) to be applied.
Moving Average Length: Set the length for the primary moving average.
Smooth Length: Define the length for the smoothed moving average.
Overbought Level: Set the threshold for overbought conditions.
Middle Level: Set the threshold for middle conditions.
Oversold Level: Set the threshold for oversold conditions.
Color Settings: Customize the colors for overbought and oversold areas and their respective fill colors.
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.
Half Causal EstimatorOverview
The Half Causal Estimator is a specialized filtering method that provides responsive averages of market variables (volume, true range, or price change) with significantly reduced time delay compared to traditional moving averages. It employs a hybrid approach that leverages both historical data and time-of-day patterns to create a timely representation of market activity while maintaining smooth output.
Core Concept
Traditional moving averages suffer from time lag, which can delay signals and reduce their effectiveness for real-time decision making. The Half Causal Estimator addresses this limitation by using a non-causal filtering method that incorporates recent historical data (the causal component) alongside expected future behavior based on time-of-day patterns (the non-causal component).
This dual approach allows the filter to respond more quickly to changing market conditions while maintaining smoothness. The name "Half Causal" refers to this hybrid methodology—half of the data window comes from actual historical observations, while the other half is derived from time-of-day patterns observed over multiple days. By incorporating these "future" values from past patterns, the estimator can reduce the inherent lag present in traditional moving averages.
How It Works
The indicator operates through several coordinated steps. First, it stores and organizes market data by specific times of day (minutes/hours). Then it builds a profile of typical behavior for each time period. For calculations, it creates a filtering window where half consists of recent actual data and half consists of expected future values based on historical time-of-day patterns. Finally, it applies a kernel-based smoothing function to weight the values in this composite window.
This approach is particularly effective because market variables like volume, true range, and price changes tend to follow recognizable intraday patterns (they are positive values without DC components). By leveraging these patterns, the indicator doesn't try to predict future values in the traditional sense, but rather incorporates the average historical behavior at those future times into the current estimate.
The benefit of using this "average future data" approach is that it counteracts the lag inherent in traditional moving averages. In a standard moving average, recent price action is underweighted because older data points hold equal influence. By incorporating time-of-day averages for future periods, the Half Causal Estimator essentially shifts the center of the filter window closer to the current bar, resulting in more timely outputs while maintaining smoothing benefits.
Understanding Kernel Smoothing
At the heart of the Half Causal Estimator is kernel smoothing, a statistical technique that creates weighted averages where points closer to the center receive higher weights. This approach offers several advantages over simple moving averages. Unlike simple moving averages that weight all points equally, kernel smoothing applies a mathematically defined weight distribution. The weighting function helps minimize the impact of outliers and random fluctuations. Additionally, by adjusting the kernel width parameter, users can fine-tune the balance between responsiveness and smoothness.
The indicator supports three kernel types. The Gaussian kernel uses a bell-shaped distribution that weights central points heavily while still considering distant points. The Epanechnikov kernel employs a parabolic function that provides efficient noise reduction with a finite support range. The Triangular kernel applies a linear weighting that decreases uniformly from center to edges. These kernel functions provide the mathematical foundation for how the filter processes the combined window of past and "future" data points.
Applicable Data Sources
The indicator can be applied to three different data sources: volume (the trading volume of the security), true range (expressed as a percentage, measuring volatility), and change (the absolute percentage change from one closing price to the next).
Each of these variables shares the characteristic of being consistently positive and exhibiting cyclical intraday patterns, making them ideal candidates for this filtering approach.
Practical Applications
The Half Causal Estimator excels in scenarios where timely information is crucial. It helps in identifying volume climaxes or diminishing volume trends earlier than conventional indicators. It can detect changes in volatility patterns with reduced lag. The indicator is also useful for recognizing shifts in price momentum before they become obvious in price action, and providing smoother data for algorithmic trading systems that require reduced noise without sacrificing timeliness.
When volatility or volume spikes occur, conventional moving averages typically lag behind, potentially causing missed opportunities or delayed responses. The Half Causal Estimator produces signals that align more closely with actual market turns.
Technical Implementation
The implementation of the Half Causal Estimator involves several technical components working together. Data collection and organization is the first step—the indicator maintains a data structure that organizes market data by specific times of day. This creates a historical record of how volume, true range, or price change typically behaves at each minute/hour of the trading day.
For each calculation, the indicator constructs a composite window consisting of recent actual data points from the current session (the causal half) and historical averages for upcoming time periods from previous sessions (the non-causal half). The selected kernel function is then applied to this composite window, creating a weighted average where points closer to the center receive higher weights according to the mathematical properties of the chosen kernel. Finally, the kernel weights are normalized to ensure the output maintains proper scaling regardless of the kernel type or width parameter.
This framework enables the indicator to leverage the predictable time-of-day components in market data without trying to predict specific future values. Instead, it uses average historical patterns to reduce lag while maintaining the statistical benefits of smoothing techniques.
Configuration Options
The indicator provides several customization options. The data period setting determines the number of days of observations to store (0 uses all available data). Filter length controls the number of historical data points for the filter (total window size is length × 2 - 1). Filter width adjusts the width of the kernel function. Users can also select between Gaussian, Epanechnikov, and Triangular kernel functions, and customize visual settings such as colors and line width.
These parameters allow for fine-tuning the balance between responsiveness and smoothness based on individual trading preferences and the specific characteristics of the traded instrument.
Limitations
The indicator requires minute-based intraday timeframes, securities with volume data (when using volume as the source), and sufficient historical data to establish time-of-day patterns.
Conclusion
The Half Causal Estimator represents an innovative approach to technical analysis that addresses one of the fundamental limitations of traditional indicators: time lag. By incorporating time-of-day patterns into its calculations, it provides a more timely representation of market variables while maintaining the noise-reduction benefits of smoothing. This makes it a valuable tool for traders who need to make decisions based on real-time information about volume, volatility, or price changes.
Moving Average Filters Add-on w/ Expanded Source Types [Loxx]Moving Average Filters Add-on w/ Expanded Source Types is a conglomeration of specialized and traditional moving averages that will be used in most of indicators that I publish moving forward. There are 39 moving averages included in this indicator as well as expanded source types including traditional Heiken Ashi and Better Heiken Ashi candles. You can read about the expanded source types clicking here . About half of these moving averages are closed source on other trading platforms. This indicator serves as a reference point for future public/private, open/closed source indicators that I publish to TradingView. Information about these moving averages was gleaned from various forex and trading forums and platforms as well as TASC publications and other assorted research publications.
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Included moving averages
ADXvma - Average Directional Volatility Moving Average
Linnsoft's ADXvma formula is a volatility-based moving average, with the volatility being determined by the value of the ADX indicator.
The ADXvma has the SMA in Chande's CMO replaced with an EMA, it then uses a few more layers of EMA smoothing before the "Volatility Index" is calculated.
A side effect is, those additional layers slow down the ADXvma when you compare it to Chande's Variable Index Dynamic Average VIDYA.
The ADXVMA provides support during uptrends and resistance during downtrends and will stay flat for longer, but will create some of the most accurate market signals when it decides to move.
Ahrens Moving Average
Richard D. Ahrens's Moving Average promises "Smoother Data" that isn't influenced by the occasional price spike. It works by using the Open and the Close in his formula so that the only time the Ahrens Moving Average will change is when the candlestick is either making new highs or new lows.
Alexander Moving Average - ALXMA
This Moving Average uses an elaborate smoothing formula and utilizes a 7 period Moving Average. It corresponds to fitting a second-order polynomial to seven consecutive observations. This moving average is rarely used in trading but is interesting as this Moving Average has been applied to diffusion indexes that tend to be very volatile.
Double Exponential Moving Average - DEMA
The Double Exponential Moving Average (DEMA) combines a smoothed EMA and a single EMA to provide a low-lag indicator. It's primary purpose is to reduce the amount of "lagging entry" opportunities, and like all Moving Averages, the DEMA confirms uptrends whenever price crosses on top of it and closes above it, and confirms downtrends when the price crosses under it and closes below it - but with significantly less lag.
Double Smoothed Exponential Moving Average - DSEMA
The Double Smoothed Exponential Moving Average is a lot less laggy compared to a traditional EMA. It's also considered a leading indicator compared to the EMA, and is best utilized whenever smoothness and speed of reaction to market changes are required.
Exponential Moving Average - EMA
The EMA places more significance on recent data points and moves closer to price than the SMA (Simple Moving Average). It reacts faster to volatility due to its emphasis on recent data and is known for its ability to give greater weight to recent and more relevant data. The EMA is therefore seen as an enhancement over the SMA.
Fast Exponential Moving Average - FEMA
An Exponential Moving Average with a short look-back period.
Fractal Adaptive Moving Average - FRAMA
The Fractal Adaptive Moving Average by John Ehlers is an intelligent adaptive Moving Average which takes the importance of price changes into account and follows price closely enough to display significant moves whilst remaining flat if price ranges. The FRAMA does this by dynamically adjusting the look-back period based on the market's fractal geometry.
Hull Moving Average - HMA
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points.
IE/2 - Early T3 by Tim Tilson
The IE/2 is a Moving Average that uses Linear Regression slope in its calculation to help with smoothing. It's a worthy Moving Average on it's own, even though it is the precursor and very early version of the famous "T3 Indicator".
Integral of Linear Regression Slope - ILRS
A Moving Average where the slope of a linear regression line is simply integrated as it is fitted in a moving window of length N (natural numbers in maths) across the data. The derivative of ILRS is the linear regression slope. ILRS is not the same as a SMA (Simple Moving Average) of length N, which is actually the midpoint of the linear regression line as it moves across the data.
Instantaneous Trendline
The Instantaneous Trendline is created by removing the dominant cycle component from the price information which makes this Moving Average suitable for medium to long-term trading.
Laguerre Filter
The Laguerre Filter is a smoothing filter which is based on Laguerre polynomials. The filter requires the current price, three prior prices, a user defined factor called Alpha to fill its calculation.
Adjusting the Alpha coefficient is used to increase or decrease its lag and it's smoothness.
Leader Exponential Moving Average
The Leader EMA was created by Giorgos E. Siligardos who created a Moving Average which was able to eliminate lag altogether whilst maintaining some smoothness. It was first described during his research paper "MACD Leader" where he applied this to the MACD to improve its signals and remove its lagging issue. This filter uses his leading MACD's "modified EMA" and can be used as a zero lag filter.
Linear Regression Value - LSMA (Least Squares Moving Average)
LSMA as a Moving Average is based on plotting the end point of the linear regression line. It compares the current value to the prior value and a determination is made of a possible trend, eg. the linear regression line is pointing up or down.
Linear Weighted Moving Average - LWMA
LWMA reacts to price quicker than the SMA and EMA. Although it's similar to the Simple Moving Average, the difference is that a weight coefficient is multiplied to the price which means the most recent price has the highest weighting, and each prior price has progressively less weight. The weights drop in a linear fashion.
McGinley Dynamic
John McGinley created this Moving Average to track price better than traditional Moving Averages. It does this by incorporating an automatic adjustment factor into its formula, which speeds (or slows) the indicator in trending, or ranging, markets.
McNicholl EMA
Dennis McNicholl developed this Moving Average to use as his center line for his "Better Bollinger Bands" indicator and was successful because it responded better to volatility changes over the standard SMA and managed to avoid common whipsaws.
Non lag moving average
The Non Lag Moving average follows price closely and gives very quick signals as well as early signals of price change. As a standalone Moving Average, it should not be used on its own, but as an additional confluence tool for early signals.
Parabolic Weighted Moving Average
The Parabolic Weighted Moving Average is a variation of the Linear Weighted Moving Average. The Linear Weighted Moving Average calculates the average by assigning different weight to each element in its calculation. The Parabolic Weighted Moving Average is a variation that allows weights to be changed to form a parabolic curve. It is done simply by using the Power parameter of this indicator.
Recursive Moving Trendline
Dennis Meyers's Recursive Moving Trendline uses a recursive (repeated application of a rule) polynomial fit, a technique that uses a small number of past values estimations of price and today's price to predict tomorrows price.
Simple Moving Average - SMA
The SMA calculates the average of a range of prices by adding recent prices and then dividing that figure by the number of time periods in the calculation average. It is the most basic Moving Average which is seen as a reliable tool for starting off with Moving Average studies. As reliable as it may be, the basic moving average will work better when it's enhanced into an EMA.
Sine Weighted Moving Average
The Sine Weighted Moving Average assigns the most weight at the middle of the data set. It does this by weighting from the first half of a Sine Wave Cycle and the most weighting is given to the data in the middle of that data set. The Sine WMA closely resembles the TMA (Triangular Moving Average).
Smoothed Moving Average - SMMA
The Smoothed Moving Average is similar to the Simple Moving Average (SMA), but aims to reduce noise rather than reduce lag. SMMA takes all prices into account and uses a long lookback period. Due to this, it's seen a an accurate yet laggy Moving Average.
Smoother
The Smoother filter is a faster-reacting smoothing technique which generates considerably less lag than the SMMA (Smoothed Moving Average). It gives earlier signals but can also create false signals due to its earlier reactions. This filter is sometimes wrongly mistaken for the superior Jurik Smoothing algorithm.
Super Smoother
The Super Smoother filter uses John Ehlers’s “Super Smoother” which consists of a a Two pole Butterworth filter combined with a 2-bar SMA (Simple Moving Average) that suppresses the 22050 Hz Nyquist frequency: A characteristic of a sampler, which converts a continuous function or signal into a discrete sequence.
Three pole Ehlers Butterworth
The 3 pole Ehlers Butterworth (as well as the Two pole Butterworth) are both superior alternatives to the EMA and SMA. They aim at producing less lag whilst maintaining accuracy. The 2 pole filter will give you a better approximation for price, whereas the 3 pole filter has superior smoothing.
Three pole Ehlers smoother
The 3 pole Ehlers smoother works almost as close to price as the above mentioned 3 Pole Ehlers Butterworth. It acts as a strong baseline for signals but removes some noise. Side by side, it hardly differs from the Three Pole Ehlers Butterworth but when examined closely, it has better overshoot reduction compared to the 3 pole Ehlers Butterworth.
Triangular Moving Average - TMA
The TMA is similar to the EMA but uses a different weighting scheme. Exponential and weighted Moving Averages will assign weight to the most recent price data. Simple moving averages will assign the weight equally across all the price data. With a TMA (Triangular Moving Average), it is double smoother (averaged twice) so the majority of the weight is assigned to the middle portion of the data.
The TMA and Sine Weighted Moving Average Filter are almost identical at times.
Triple Exponential Moving Average - TEMA
The TEMA uses multiple EMA calculations as well as subtracting lag to create a tool which can be used for scalping pullbacks. As it follows price closely, it's signals are considered very noisy and should only be used in extremely fast-paced trading conditions.
Two pole Ehlers Butterworth
The 2 pole Ehlers Butterworth (as well as the three pole Butterworth mentioned above) is another filter that cuts out the noise and follows the price closely. The 2 pole is seen as a faster, leading filter over the 3 pole and follows price a bit more closely. Analysts will utilize both a 2 pole and a 3 pole Butterworth on the same chart using the same period, but having both on chart allows its crosses to be traded.
Two pole Ehlers smoother
A smoother version of the Two pole Ehlers Butterworth. This filter is the faster version out of the 3 pole Ehlers Butterworth. It does a decent job at cutting out market noise whilst emphasizing a closer following to price over the 3 pole Ehlers.
Volume Weighted EMA - VEMA
Utilizing tick volume in MT4 (or real volume in MT5), this EMA will use the Volume reading in its decision to plot its moves. The more Volume it detects on a move, the more authority (confirmation) it has. And this EMA uses those Volume readings to plot its movements.
Studies show that tick volume and real volume have a very strong correlation, so using this filter in MT4 or MT5 produces very similar results and readings.
Zero Lag DEMA - Zero Lag Double Exponential Moving Average
John Ehlers's Zero Lag DEMA's aim is to eliminate the inherent lag associated with all trend following indicators which average a price over time. Because this is a Double Exponential Moving Average with Zero Lag, it has a tendency to overshoot and create a lot of false signals for swing trading. It can however be used for quick scalping or as a secondary indicator for confluence.
Zero Lag Moving Average
The Zero Lag Moving Average is described by its creator, John Ehlers, as a Moving Average with absolutely no delay. And it's for this reason that this filter will cause a lot of abrupt signals which will not be ideal for medium to long-term traders. This filter is designed to follow price as close as possible whilst de-lagging data instead of basing it on regular data. The way this is done is by attempting to remove the cumulative effect of the Moving Average.
Zero Lag TEMA - Zero Lag Triple Exponential Moving Average
Just like the Zero Lag DEMA, this filter will give you the fastest signals out of all the Zero Lag Moving Averages. This is useful for scalping but dangerous for medium to long-term traders, especially during market Volatility and news events. Having no lag, this filter also has no smoothing in its signals and can cause some very bizarre behavior when applied to certain indicators.
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What are Heiken Ashi "better" candles?
The "better formula" was proposed in an article/memo by BNP-Paribas (In Warrants & Zertifikate, No. 8, August 2004 (a monthly German magazine published by BNP Paribas, Frankfurt), there is an article by Sebastian Schmidt about further development (smoothing) of Heikin-Ashi chart.)
They proposed to use the following:
(Open+Close)/2+(((Close-Open)/( High-Low ))*ABS((Close-Open)/2))
instead of using :
haClose = (O+H+L+C)/4
According to that document the HA representation using their proposed formula is better than the traditional formula.
What are traditional Heiken-Ashi candles?
The Heikin-Ashi technique averages price data to create a Japanese candlestick chart that filters out market noise.
Heikin-Ashi charts, developed by Munehisa Homma in the 1700s, share some characteristics with standard candlestick charts but differ based on the values used to create each candle. Instead of using the open, high, low, and close like standard candlestick charts, the Heikin-Ashi technique uses a modified formula based on two-period averages. This gives the chart a smoother appearance, making it easier to spots trends and reversals, but also obscures gaps and some price data.
Expanded generic source types:
Close = close
Open = open
High = high
Low = low
Median = hl2
Typical = hlc3
Weighted = hlcc4
Average = ohlc4
Average Median Body = (open+close)/2
Trend Biased = (see code, too complex to explain here)
Trend Biased (extreme) = (see code, too complex to explain here)
Included:
-Toggle bar color on/off
-Toggle signal line on/off
T3 Super GuppyA Tillson T3 moving average implemented variation of the CM Super Guppy indicator by @FritzMurphy
The T3 moving average was developed by Tom Tilson which combines multiple EMAs into a single moving average. it is smoother and more responsive compared to traditional moving averages. The disadvantage is that it can overshoot price.
█ Description
T3 Super Guppy consists of 20 T3 moving averages:
• 7 fast T3 MAs
• 13 slow T3 MAs
Visuals:
• Compact view available for chart minimalists
• In compact view only 10 of the fastest T3 moving averages will be displayed
• Compact view will not affect how the colour scales with trend movement
• Ribbon transparency will automatically scale based on the display mode chosen
Colour Gradient
• The more T3 MAs that cross above or below their slower counterparts will result in how deep the chosen upTrend(Blue) or downTrend(Red) colour is displayed
• Helps to spot weakening trends or reversal signals when indicator colour starts converging into the opposite colour
• Single colour mode is available if you find the colour gradient distracting
█ Credits
@ChrisMoody original guppy idea:
@FritzMurphy super guppy format:
█ Examples
compact view:
full view:
Crypto Divergence from BTCThis script is used to indicate when price action of a crypto coin is diverging significantly from that of BTC.
Explanation of the Script:
Inputs:
roc_length: The period used for calculating the Rate of Change.
ma_length: The period used for the moving average of the ROC.
threshold: The percentage difference that indicates a divergence.
Price Data:
The script retrieves the current asset's price and Bitcoin's price.
ROC Calculation:
The ROC for both the current asset and BTC is calculated based on the defined roc_length.
Moving Averages:
Simple moving averages (SMA) of the ROC values are calculated to smooth out the data.
Divergence Detection:
The indicator checks if the current asset's ROC MA is significantly higher or lower than Bitcoin's ROC MA based on the specified threshold.
Plotting:
The script plots the ROC values and their moving averages.
It also highlights the background in green when a bullish divergence is detected (when the asset is moving up while BTC is lagging) and in red for a bearish divergence.
MENTFX AVERAGES MULTI TIMEFRAMEThe MENTFX AVERAGES MULTIME TIMEFRAME indicator is designed to provide traders with the ability to visualize multiple moving averages (MAs) from higher timeframes on their current chart, regardless of the chart's timeframe. It combines the power of exponential moving averages (EMAs) to help traders identify trends, spot potential reversal points, and make more informed trading decisions.
Key Features:
Multi-Timeframe Moving Averages: This indicator plots moving averages from daily timeframes directly on your chart, helping you keep track of higher timeframe trends while trading in any timeframe.
Customizable Moving Averages: You can adjust the length and visibility of up to three EMAs (default settings are 5, 10, and 20-period EMAs) to suit your trading style.
Overlay on Price: The indicator is designed to be overlaid on your price chart, seamlessly integrating with your existing analysis.
Simple but Effective: By offering a clear visual guide to where price is trading relative to important higher timeframe levels, this indicator helps traders avoid trading against major trends.
Why It’s Unique:
Validation Timeframe Flexibility: Unlike traditional moving average indicators that only work within the same chart's timeframe, the MENTFX AVERAGES M indicator allows you to pull moving averages from higher timeframes (default: Daily) and overlay them on any chart you're currently viewing, whether it's intraday (minutes) or even weekly. This cross-timeframe visibility is critical in determining the true market trend, adding context to your trades.
Customizability: Although the default settings focus on daily EMAs (5, 10, and 20 periods), traders can modify the parameters, including the type of moving average (Simple, Weighted, etc.), making it adaptable for any strategy. Whether you want shorter-term or longer-term averages, this indicator covers your needs.
Trend Confirmation Tool: The use of multiple EMAs helps traders confirm trend direction and potential price breakouts or reversals. For example, when the shorter-term 5 EMA crosses above the 20 EMA, it can signal a potential bullish trend, while the opposite could indicate bearish pressure.
How This Indicator Helps:
Identify Key Support and Resistance Levels: Higher timeframe moving averages often act as dynamic support and resistance. This indicator helps you stay aware of those critical levels, even when trading lower timeframes.
Trend Identification: Knowing where the market is relative to the 5, 10, and 20 EMAs from a higher timeframe gives you a clearer picture of whether you're trading with or against the prevailing trend.
Improved Decision Making: By aligning your trades with the direction of higher timeframe trends, you can increase your confidence in trade entries and exits, avoiding low-probability setups.
Multi-Market Use: This indicator works well across various asset classes—stocks, forex, crypto, and commodities—making it versatile for any trader.
How to Use:
Intraday Trading: Use the daily EMAs as a guide to see if intraday price movements align with longer-term trends.
Swing Trading: Plot daily EMAs to track the strength of a larger trend, using pullbacks to the moving averages as potential entry points.
Trend Trading: Monitor crossovers between the moving averages to signal potential changes in trend direction.
Default Settings:
5 EMA (Daily) – Blue Line
10 EMA (Daily) – Black Line
20 EMA (Daily) – Red Line
These lines will plot on your chart with a subtle opacity (33%) to ensure they don’t obstruct price action, while still providing crucial visual guidance on market trends.
This indicator is perfect for traders who want to blend technical analysis with multi-timeframe insights, helping you stay in sync with broader market movements while executing trades on any timeframe.
Jobinsabu014This Pine Script code is for an advanced trading indicator that displays enhanced moving averages with buy and sell labels, trend probability, and support/resistance levels. Here’s a detailed description of its components and functionality:
### Description:
1. **Indicator Initialization**:
- The indicator is named "Enhanced Moving Averages with Buy/Sell Labels and Trend Probability" and is set to overlay on the chart.
2. **Input Parameters**:
- **Moving Averages**: Four different moving averages (short and long periods for default and enhanced) with customizable periods.
- **Probability Threshold**: Determines the threshold for trend probability.
- **Support/Resistance Lookback**: Number of bars to look back for calculating support and resistance levels.
- **Signals Valid From**: Timestamp from which the signals are considered valid.
3. **Moving Averages Calculation**:
- **Default Moving Averages**: Calculated using simple moving averages (SMA) for the specified periods.
- **Enhanced Moving Averages**: Calculated using SMAs for different specified periods.
4. **Plotting Moving Averages**:
- Plots the default and enhanced moving averages with different colors for distinction.
5. **Crossover Detection**:
- Detects when the short moving average crosses above or below the long moving average for default moving averages.
6. **Buy/Sell Signal Labels**:
- Adds "BUY" and "SELL" labels on the chart when crossovers are detected after the specified valid timestamp.
- Tracks entry prices for buy/sell signals and adds labels when the price moves +100 points.
7. **Trend Detection for Enhanced Indicator**:
- Detects uptrend or downtrend based on the enhanced moving averages.
- Calculates a simple probability of trend based on price movement and EMA.
- Determines buy and sell signals based on trend conditions and volume-based buy/sell pressure.
8. **Plot Buy/Sell Signals for Enhanced Indicator**:
- Plots buy/sell signals based on the enhanced conditions.
9. **Background Color for Trends**:
- Changes the background color to green for uptrend and red for downtrend.
10. **Trend Lines**:
- Draws imaginary trend lines for uptrend and downtrend based on enhanced moving averages.
11. **Support and Resistance Levels**:
- Calculates and plots support and resistance levels using the specified lookback period.
- Stores and plots previous support and resistance levels with dashed lines.
12. **Expected Trend Labels**:
- Adds labels indicating expected uptrend or downtrend based on buy/sell signals.
13. **Alerts**:
- Sets alert conditions for buy and sell signals, triggering alerts when these conditions are met.
14. **Demand and Supply Zones**:
- Draws and extends horizontal lines for demand (support) and supply (resistance) zones.
### Summary:
This script enhances traditional moving average crossovers by adding trend probability calculations, volume-based pressure, and support/resistance levels. It visualizes expected trends and provides comprehensive buy/sell signals with corresponding labels, background color changes, and alerts to help traders make informed decisions.
