Reverse Cutlers Relative Strength IndexIntroduction
The Reverse Cutlers Relative Strength Index (RCRSI) is an indicator which tells the user what price is required to give a particular Cutlers Relative Strength Index (RSI) value, or cross its Moving Average (MA) signal line.
Overview
Background & Credits:
The relative strength index (RSI) is a momentum indicator used in technical analysis that was originally developed by J. Welles Wilder Jr. and introduced in his seminal 1978 book, “New Concepts in Technical Trading Systems.”.
Cutler created a variation of the RSI known as “Cutlers RSI” using a different formulation to avoid an inherent accuracy problem which arises when using Wilders method of smoothing.
Further developments in the use, and more nuanced interpretations of the RSI have been developed by Cardwell, and also by well-known chartered market technician, Constance Brown C.M.T., in her acclaimed book "Technical Analysis for the Trading Professional” 1999 where she described the idea of bull and bear market ranges for RSI, and while she did not actually reveal the formulas, she introduced the concept of “reverse engineering” the RSI to give price level outputs.
Renowned financial software developer, co-author of academic books on finance, and scientific fellow to the Department of Finance and Insurance at the Technological Educational Institute of Crete, Giorgos Siligardos PHD. brought a new perspective to Wilder’s RSI when he published his excellent and well-received articles "Reverse Engineering RSI " and "Reverse Engineering RSI II " in the June 2003, and August 2003 issues of Stocks & Commodities magazine, where he described his methods of reverse engineering Wilders RSI.
Several excellent Implementations of the Reverse Wilders Relative Strength Index have been published here on Tradingview and elsewhere.
My utmost respect, and all due credits to authors of related prior works.
Introduction
It is worth noting that while the general RSI formula, and the logic dictating the UpMove and DownMove data series as described above has remained the same as the Wilders original formulation, it has been interpreted in a different way by using a different method of averaging the upward, and downward moves.
Cutler recognized the issue of data length dependency when using wilders smoothing method of calculating RSI which means that wilders standard RSI will have a potential initialization error which reduces with every new data point calculated meaning early results should be regarded as unreliable until enough calculation iterations have occurred for convergence.
Hence Cutler proposed using Simple Moving Averaging for gain and loss data which this Indicator is based on.
Having "Reverse engineered" prices for any oscillator makes the planning, and execution of strategies around that oscillator far simpler, more timely and effective.
Introducing the Reverse Cutlers RSI which consists of plotted lines on a scale of 0 to 100, and an optional infobox.
The RSI scale is divided into zones:
• Scale high (100)
• Bull critical zone (80 - 100)
• Bull control zone (62 - 80)
• Scale midline (50)
• Bear critical zone (20 - 38)
• Bear control zone (0 - 20)
• Scale low (0)
The RSI plots are:
• Cutlers RSI
• RSI MA signal line
• Test price RSI
• Alert level high
• Alert level low
The info box displays output closing price levels where Cutlers RSI value will crossover:
• Its previous value. (RSI )
• Bull critical zone.
• Bull control zone.
• Mid-Line.
• Bear control zone.
• Bear critical zone.
• RSI MA signal line
• Alert level High
• Alert level low
And also displays the resultant RSI for a user defined closing price:
• Test price RSI
The infobox outputs can be shown for the current bar close, or the next bar close.
The user can easily select which information they want in the infobox from the setttings
Importantly:
All info box price levels for the current bar are calculated immediately upon the current bar closing and a new bar opening, they will not change until the current bar closes.
All info box price levels for the next bar are projections which are continually recalculated as the current price changes, and therefore fluctuate as the current price changes.
Understanding the Relative Strength Index
At its simplest the RSI is a measure of how quickly traders are bidding the price of an asset up or down.
It does this by calculating the difference in magnitude of price gains and losses over a specific lookback period to evaluate market conditions.
The RSI is displayed as an oscillator (a line graph that can move between two extremes) and outputs a value limited between 0 and 100.
It is typically accompanied by a moving average signal line.
Traditional interpretations
Overbought and oversold:
An RSI value of 70 or above indicates that an asset is becoming overbought (overvalued condition), and may be may be ready for a trend reversal or corrective pullback in price.
An RSI value of 30 or below indicates that an asset is becoming oversold (undervalued condition), and may be may be primed for a trend reversal or corrective pullback in price.
Midline Crossovers:
When the RSI crosses above its midline (RSI > 50%) a bullish bias signal is generated. (only take long trades)
When the RSI crosses below its midline (RSI < 50%) a bearish bias signal is generated. (only take short trades)
Bullish and bearish moving average signal Line crossovers:
When the RSI line crosses above its signal line, a bullish buy signal is generated
When the RSI line crosses below its signal line, a bearish sell signal is generated.
Swing Failures and classic rejection patterns:
If the RSI makes a lower high, and then follows with a downside move below the previous low, a Top Swing Failure has occurred.
If the RSI makes a higher low, and then follows with an upside move above the previous high, a Bottom Swing Failure has occurred.
Examples of classic swing rejection patterns
Bullish swing rejection pattern:
The RSI moves into oversold zone (below 30%).
The RSI rejects back out of the oversold zone (above 30%)
The RSI forms another dip without crossing back into oversold zone.
The RSI then continues the bounce to break up above the previous high.
Bearish swing rejection pattern:
The RSI moves into overbought zone (above 70%).
The RSI rejects back out of the overbought zone (below 70%)
The RSI forms another peak without crossing back into overbought zone.
The RSI then continues to break down below the previous low.
Divergences:
A regular bullish RSI divergence is when the price makes lower lows in a downtrend and the RSI indicator makes higher lows.
A regular bearish RSI divergence is when the price makes higher highs in an uptrend and the RSI indicator makes lower highs.
A hidden bullish RSI divergence is when the price makes higher lows in an uptrend and the RSI indicator makes lower lows.
A hidden bearish RSI divergence is when the price makes lower highs in a downtrend and the RSI indicator makes higher highs.
Regular divergences can signal a reversal of the trending direction.
Hidden divergences can signal a continuation in the direction of the trend.
Chart Patterns:
RSI regularly forms classic chart patterns that may not show on the underlying price chart, such as ascending and descending triangles & wedges, double tops, bottoms and trend lines etc.
Support and Resistance:
It is very often easier to define support or resistance levels on the RSI itself rather than the price chart.
Modern interpretations in trending markets:
Modern interpretations of the RSI stress the context of the greater trend when using RSI signals such as crossovers, overbought/oversold conditions, divergences and patterns.
Constance Brown, CMT, was one of the first who promoted the idea that an oversold reading on the RSI in an uptrend is likely much higher than 30%, and that an overbought reading on the RSI during a downtrend is much lower than the 70% level.
In an uptrend or bull market, the RSI tends to remain in the 40 to 90 range, with the 40-50 zone acting as support.
During a downtrend or bear market, the RSI tends to stay between the 10 to 60 range, with the 50-60 zone acting as resistance.
For ease of executing more modern and nuanced interpretations of RSI it is very useful to break the RSI scale into bull and bear control and critical zones.
These ranges will vary depending on the RSI settings and the strength of the specific market’s underlying trend.
Limitations of the RSI
Like most technical indicators, its signals are most reliable when they conform to the long-term trend.
True trend reversal signals are rare, and can be difficult to separate from false signals.
False signals or “fake-outs”, e.g. a bullish crossover, followed by a sudden decline in price, are common.
Since the indicator displays momentum, it can stay overbought or oversold for a long time when an asset has significant sustained momentum in either direction.
Data Length Dependency when using wilders smoothing method of calculating RSI means that wilders standard RSI will have a potential initialization error which reduces with every new data point calculated meaning early results should be regarded as unreliable until calculation iterations have occurred for convergence.
"Divergence" için komut dosyalarını ara
Volume Weighted RSI (VW RSI)The Volume Weighted RSI (VW RSI) is a momentum oscillator designed for TradingView, implemented in Pine Script v6, that enhances the traditional Relative Strength Index (RSI) by incorporating trading volume into its calculation. Unlike the standard RSI, which measures the speed and change of price movements based solely on price data, the VW RSI weights its analysis by volume, emphasizing price movements backed by significant trading activity. This makes the VW RSI particularly effective for identifying bullish or bearish momentum, overbought/oversold conditions, and potential trend reversals in markets where volume plays a critical role, such as stocks, forex, and cryptocurrencies.
Key Features
Volume-Weighted Momentum Calculation:
The VW RSI calculates momentum by comparing the volume associated with upward price movements (up-volume) to the volume associated with downward price movements (down-volume).
Up-volume is the volume on bars where the closing price is higher than the previous close, while down-volume is the volume on bars where the closing price is lower than the previous close.
These volumes are smoothed over a user-defined period (default: 14 bars) using a Running Moving Average (RMA), and the VW RSI is computed using the formula:
\text{VW RSI} = 100 - \frac{100}{1 + \text{VoRS}}
where
\text{VoRS} = \frac{\text{Average Up-Volume}}{\text{Average Down-Volume}}
.
Oscillator Range and Interpretation:
The VW RSI oscillates between 0 and 100, with a centerline at 50.
Above 50: Indicates bullish volume momentum, suggesting that volume on up bars dominates, which may signal buying pressure and a potential uptrend.
Below 50: Indicates bearish volume momentum, suggesting that volume on down bars dominates, which may signal selling pressure and a potential downtrend.
Overbought/Oversold Levels: User-defined thresholds (default: 70 for overbought, 30 for oversold) help identify potential reversal points:
VW RSI > 70: Overbought, indicating a possible pullback or reversal.
VW RSI < 30: Oversold, indicating a possible bounce or reversal.
Visual Elements:
VW RSI Line: Plotted in a separate pane below the price chart, colored dynamically based on its value:
Green when above 50 (bullish momentum).
Red when below 50 (bearish momentum).
Gray when at 50 (neutral).
Centerline: A dashed line at 50, optionally displayed, serving as the neutral threshold between bullish and bearish momentum.
Overbought/Oversold Lines: Dashed lines at the user-defined overbought (default: 70) and oversold (default: 30) levels, optionally displayed, to highlight extreme conditions.
Background Coloring: The background of the VW RSI pane is shaded red when the indicator is in overbought territory and green when in oversold territory, providing a quick visual cue of potential reversal zones.
Alerts:
Built-in alerts for key events:
Bullish Momentum: Triggered when the VW RSI crosses above 50, indicating a shift to bullish volume momentum.
Bearish Momentum: Triggered when the VW RSI crosses below 50, indicating a shift to bearish volume momentum.
Overbought Condition: Triggered when the VW RSI crosses above the overbought threshold (default: 70), signaling a potential pullback.
Oversold Condition: Triggered when the VW RSI crosses below the oversold threshold (default: 30), signaling a potential bounce.
Input Parameters
VW RSI Length (default: 14): The period over which the up-volume and down-volume are smoothed to calculate the VW RSI. A longer period results in smoother signals, while a shorter period increases sensitivity.
Overbought Level (default: 70): The threshold above which the VW RSI is considered overbought, indicating a potential reversal or pullback.
Oversold Level (default: 30): The threshold below which the VW RSI is considered oversold, indicating a potential reversal or bounce.
Show Centerline (default: true): Toggles the display of the 50 centerline, which separates bullish and bearish momentum zones.
Show Overbought/Oversold Lines (default: true): Toggles the display of the overbought and oversold threshold lines.
How It Works
Volume Classification:
For each bar, the indicator determines whether the price movement is upward or downward:
If the current close is higher than the previous close, the bar’s volume is classified as up-volume.
If the current close is lower than the previous close, the bar’s volume is classified as down-volume.
If the close is unchanged, both up-volume and down-volume are set to 0 for that bar.
Smoothing:
The up-volume and down-volume are smoothed using a Running Moving Average (RMA) over the specified period (default: 14 bars) to reduce noise and provide a more stable measure of volume momentum.
VW RSI Calculation:
The Volume Relative Strength (VoRS) is calculated as the ratio of smoothed up-volume to smoothed down-volume.
The VW RSI is then computed using the standard RSI formula, but with volume data instead of price changes, resulting in a value between 0 and 100.
Visualization and Alerts:
The VW RSI is plotted with dynamic coloring to reflect its momentum direction, and optional lines are drawn for the centerline and overbought/oversold levels.
Background coloring highlights overbought and oversold conditions, and alerts notify the trader of significant crossings.
Usage
Timeframe: The VW RSI can be used on any timeframe, but it is particularly effective on intraday charts (e.g., 1-hour, 4-hour) or daily charts where volume data is reliable. Shorter timeframes may require a shorter length for increased sensitivity, while longer timeframes may benefit from a longer length for smoother signals.
Markets: Best suited for markets with significant and reliable volume data, such as stocks, forex, and cryptocurrencies. It may be less effective in markets with low or inconsistent volume, such as certain futures contracts.
Trading Strategies:
Trend Confirmation:
Use the VW RSI to confirm the direction of a trend. For example, in an uptrend, look for the VW RSI to remain above 50, indicating sustained bullish volume momentum, and consider buying on pullbacks when the VW RSI dips but stays above 50.
In a downtrend, look for the VW RSI to remain below 50, indicating sustained bearish volume momentum, and consider selling on rallies when the VW RSI rises but stays below 50.
Overbought/Oversold Conditions:
When the VW RSI crosses above 70, the market may be overbought, suggesting a potential pullback or reversal. Consider taking profits on long positions or preparing for a short entry, but confirm with price action or other indicators.
When the VW RSI crosses below 30, the market may be oversold, suggesting a potential bounce or reversal. Consider entering long positions or covering shorts, but confirm with additional signals.
Divergences:
Look for divergences between the VW RSI and price to spot potential reversals. For example, if the price makes a higher high but the VW RSI makes a lower high, this bearish divergence may signal an impending downtrend.
Conversely, if the price makes a lower low but the VW RSI makes a higher low, this bullish divergence may signal an impending uptrend.
Momentum Shifts:
A crossover above 50 can signal the start of bullish momentum, making it a potential entry point for long trades.
A crossunder below 50 can signal the start of bearish momentum, making it a potential entry point for short trades or an exit for long positions.
Example
On a 4-hour SOLUSDT chart:
During an uptrend, the VW RSI might rise above 50 and stay there, confirming bullish volume momentum. If it approaches 70, it may indicate overbought conditions, as seen near a price peak of 145.08, suggesting a potential pullback.
During a downtrend, the VW RSI might fall below 50, confirming bearish volume momentum. If it drops below 30 near a price low of 141.82, it may indicate oversold conditions, suggesting a potential bounce, as seen in a slight recovery afterward.
A bullish divergence might occur if the price makes a lower low during the downtrend, but the VW RSI makes a higher low, signaling a potential reversal.
Limitations
Lagging Nature: Like the traditional RSI, the VW RSI is a lagging indicator because it relies on smoothed data (RMA). It may not react quickly to sudden price reversals, potentially missing the start of new trends.
False Signals in Ranging Markets: In choppy or ranging markets, the VW RSI may oscillate around 50, generating frequent crossovers that lead to false signals. Combining it with a trend filter (e.g., ADX) can help mitigate this.
Volume Data Dependency: The VW RSI relies on accurate volume data, which may be inconsistent or unavailable in some markets (e.g., certain forex pairs or futures contracts). In such cases, the indicator’s effectiveness may be reduced.
Overbought/Oversold in Strong Trends: During strong trends, the VW RSI can remain in overbought or oversold territory for extended periods, leading to premature exit signals. Use additional confirmation to avoid exiting too early.
Potential Improvements
Smoothing Options: Add options to use different smoothing methods (e.g., EMA, SMA) instead of RMA for the up/down volume calculations, allowing users to adjust the indicator’s responsiveness.
Divergence Detection: Include logic to detect and plot bullish/bearish divergences between the VW RSI and price, providing visual cues for potential reversals.
Customizable Colors: Allow users to customize the colors of the VW RSI line, centerline, overbought/oversold lines, and background shading.
Trend Filter: Integrate a trend strength filter (e.g., ADX > 25) to ensure signals are generated only during strong trends, reducing false signals in ranging markets.
The Volume Weighted RSI (VW RSI) is a powerful tool for traders seeking to incorporate volume into their momentum analysis, offering a unique perspective on market dynamics by emphasizing price movements backed by significant trading activity. It is best used in conjunction with other indicators and price action analysis to confirm signals and improve trading decisions.
MVRV Z-Score [AlgoAlpha]Introducing the ∑ MVRV Z-Score by AlgoAlpha, a dynamic and sophisticated tool designed for traders seeking to gain an edge in INDEX:BTCUSD analysis. This script employs advanced statistical techniques on Bitcoin On-Chain data to offer a deeper understanding of market conditions, focusing on valuation extremes and momentum trends. Let's explore the features and functionalities that make this tool a valuable addition to your trading arsenal.
Key Features:
🔶 Adjustable Parameters: Customize the Z score lookback length, moving average lookback length, and choose from six moving average types, tailoring the analysis to your trading style.
🔶 Heiken Ashi Compatibility: Incorporate Heiken Ashi plots to visualize market trends, adding a layer of clarity to your technical analysis.
🔶 Divergence Alerts: Detect significant bullish and bearish divergences, allowing for timely identification of potential market reversals.
🔶 Configurable Alerts: Set alerts for overbought, oversold, and divergence conditions, ensuring you never miss an opportunity.
How to Use:
1. ➡️ Parameter Selection: Start by configuring the Z-Score and moving average settings according to your analysis needs. This includes selecting the lookback period and the type of moving average.
2. ➡️ Visualization Options: Choose to enable Heiken Ashi plots for an alternative view of the Z-Score, which can help in identifying trend directions more clearly.
3. ➡️ Monitor for Signals: Keep an eye out for divergence signals and overbought/oversold conditions as potential indicators for entering or exiting trades.
4. ➡️ Alert Setup: Configure alerts based on your selected parameters to receive notifications for important market movements and conditions.
How It Works:
The core of this tool is the Z-Score calculation, which assesses the standard deviation of the current market value from its mean, highlighting overvalued or undervalued market conditions. Here's a brief overview of the script's operational mechanics:
1. 📊 Calculating the Z-Score: The script first calculates the mean over a user-defined lookback period of the MVRV ratio, then it computes the Z-Score to identify deviations from the average.
meanValue = ta.sma(marketValue, zScoreLookback)
zScoreValue = (marketValue - meanValue) / ta.stdev(marketValue, zScoreLookback)
2. 📈 Applying a Moving Average: To smooth the Z-Score data and make trends more discernible, a moving average is applied. Users can choose from several types, such as SMA, EMA, or HMA, based on their preference.
3. 🔄 Heiken Ashi Visualization: For those opting for a more intuitive trend analysis, Heiken Ashi plots can be enabled, transforming the Z-Score data into candlestick charts that simplify trend identification.
4. 🔍 Identifying Divergences: The script is equipped to spot divergences between the market price action and the Z-Score, signaling potential bullish or bearish market reversals.
oscHigherLow = haClose > ta.valuewhen(findPivotLow, haClose , 1) and isInRange(findPivotLow )
priceLowerLow = low < ta.valuewhen(findPivotLow, low , 1)
bullishCondition = enablePlotBullish and priceLowerLow and oscHigherLow and findPivotLow
5. 🚨 Configurable Alerts: Lastly, the script allows for the setting of customizable alerts based on the Z-Score, moving averages, and identified divergences, enabling traders to react promptly to market changes.
The ∑ MVRV Z-Score by AlgoAlpha is an essential tool for traders looking to analyze and interpret market dynamics through a quantitatively rigorous lens. Whether you're focused on identifying market extremes or tracking trend momentum, this script offers the insights needed to support informed trading decisions. 🌟📊💡
Hayden's Advanced Relative Strength Index (RSI)Preface: I'm just the bartender serving today's freshly blended concoction; I'd like to send a massive THANK YOU to @iFuSiiOnzZ, @Koalafied_3, @LonesomeTheBlue, @LazyBear, @dgtrd and the rest of the PineWizards for the locally-sourced ingredients. I am simply a code editor, not a code author. The book that inspired this indicator is a free download, plus all of the pieces I used were free code from the PineWizards; my hope is that any additional useful development of The Complete RSI trading system also is offered open-source to the community for collaboration.
Features: Fixed & Custom price targeting. Triple trend state detection. Advanced data ticker. Candles, bars, or line RSI . Stochastic of over 20 indicators for adjustable entry/exit signals. Customizable trader watermark. Trend lines for spotting wedges , triangles, pennants , etc. Divergences for spotting potential reversals and Momentum Discrepancy Reversal Point opportunities. RSI percent change and price pivot labels. Gradient bar coloring on-chart.
‼ IMPORTANT: Hover over labels for additional information. Google & read John Hayden's "The Complete RSI" pdf book for comprehensive instructions before attempting to trade with this indicator. Always keep an eye on higher/stronger timeframes.
⚠ DISCLAIMER: DYOR. Not financial advice. Not a trading system. I am not affiliated with TradingView or John Hayden; this is my own personally PineScripted presentation of a suitable RSI to use when trading according to Hayden's rules.
About the Editor: I am a former-FINRA Registered Representative, inventor/patent-holder, and self-taught PineScripter. I mostly code on a v3 Pinescript level so expect heavy scripts that could use some shortening with modern conventions.
Hayden's RSI Rules:
📈 An Uptrend is indicated when:
1. RSI is in the 80 to 40 range
2. The chart shows simple bearish divergence
3. The chart shows Hidden bullish divergence
4. The chart shows Momentum Discrepancy Reversal Up
5. Upside targets being hit
6. 9-bar simple MA is greater than the 45-bar EMA on RSI
7. Counter-trend declines do not exceed 50% of the previous rally
🔮 An Uptrend is in danger when:
1. Longer timeframe fading rally
2. a) Multiple long-term bearish divergences. b) Upside targets not being hit.
3. 9-bar simple MA is less than the 45-bar EMA on RSI
4. Hidden bearish divergence, or simple bullish divergence
5. Deep counter-trend retracements greater than 50%
📉 A Downtrend is indicated when:
1. RSI is in the 60 to 20 range
2. The chart shows simple bullish divergences.
3. The chart shows Hidden bearish divergence
4. The chart shows Momentum Discrepancy Reversal Down
5. Downside targets being hit
6. 9-bar simple MA is less than the 45-bar EMA on RSI
7. Counter-trend rallies do not exceed 50% of the previous decline
🔮 A Downtrend is in danger when:
1. Longer timeframe fading decline
2. a) Multiple long-term bullish divergences. b) Downside targets not being hit.
3. 9-bar simple MA is greater than the 45-bar EMA on RSI
4. Hidden bullish divergence , or simple bearish divergence
5. Steep counter-trend retracements greater than 50%
Delta Volume Columns [LucF]Displays delta volume columns using intrabar volume information. Each volume column is divided into three sections: buying, selling and neutral volume. Volume for each section is determined from the volume and price movement of each intrabar at a user-selected lower resolution.
Features include:
- Choice of color themes for either dark or light chart backgrounds
- Delta volume columns
- Volume Balance displayed as the difference between the MAs of buying and selling volume
- Display of divergences between a bar’s volume balance and the bar’s price movement (example: buying volume > selling volume but close < open). Divergences can be shown in 2 different color schemes (including green/red showing a tentative direction), on volume columns and/or on chart bars
- Display of bar by bar volume balance with highlighting of above average volume
- Display of the usual total volume MA
- Choice of the lower resolution used to retrieve intrabar information
- Alerts configurable on any combination of the markers, with control over long/short direction
- Choice of 3 different markers:
1. Double bumps: two consecutive bars where buying or selling volume is in the same direction and where volume > volume MA
2. Divergence confirmations: direction of the price bar following a price/volume balance divergence
3. Volume balance shifts: zero level crossings of the volume balance MA delta
The chart shows the two main modes of display:
- Top pane : shows the stacked volume columns with divergences in orange and the flattened volume balance MAs delta at the bottom of the volume columns. This volume balance is the same shown in the bottom pane. The top pane also shows the instant volume balance strip above the volume columns. The strip’s colors show which of the buying or selling volume was greater, and colors are brighter if the total volume was above the total volume MA.
- Bottom pane : shows the volume balance MAs delta with markers 1 and 2. Given that this graphic has no price momentum component, I find quite eerie how it often looks like a momentum-based signal.
The default 5 minute intrabar resolution is used in combination with the weekly chart, which is excessive.
This script uses a special characteristic of the security() function’s behavior when it is sent to a resolution lower than the chart’s resolution. Details are given in the script’s comments. This method has the advantage of working under more circumstances than some of the other loop-based methods, but it also has its limits.
IMPORTANT
This is what you need to know:
- The method used does not work on the realtime bar—only on historical bars. Consequently, the volume column shown on the realtime bar is a normal volume column plotted in green or red, following price movement. The column will only show delta volume information after it closes and becomes a historical bar.
- The indicator only works on some chart resolutions: 5, 10, 15 and 30 minutes, 1, 2, 4, 6, and 12 hours, 1 day, 1 week and 1 month. The script’s code can be modified to run on other resolutions, but chart resolutions must be divisible by the lower resolution used for intrabars.
- Intrabar resolutions can be selected from 1, 5, 15, 30, 45 minutes, 1, 2, 3, 4 hours, 1 day, 1 week and 1 month. The intrabar resolution must of course be smaller than the chart’s resolution.
- Contrary to my other indicators where alerts must be configured to trigger “Once Per Bar Close” in order to avoid false triggers (or repainting), all this indicator’s alerts are designed to trigger using previous bar information since the indicator’s calculations in the realtime bar are not exact. Markers are not plotted with a negative offset; they appear at the beginning of the realtime bar following confirmation of the marker’s condition on the previous bar. Alerts for this indicator should thus be configured to trigger “Once Per Bar” so they trigger at the beginning of the realtime bar. Note that the penalty is not that great, as it is simply the instant between the close of the previous realtime bar and the opening of the next. The advantage of using this technique is that the indicator does not repaint; a marker that appears at the beginning of the realtime bar will never disappear.
- The script only plots information that is reliable in the realtime bar, i.e., total volume and markers. All other plots are set to n/a to prevent misleading traders.
- When the difference between the chart’s resolution and the lower resolution is too important, volume columns will not calculate for all bars in the dataset.
On Delta Volume
Buying or selling volume are misnomers, as every unit of volume transacted is both bought and sold by 2 different traders. There is no such thing as “buy only” or “sell only” volume, but trader lingo is riddled with original fabulations.
Without access to order book information, traders work with the assumption that when price moves up during a bar, there was more buying pressure than selling pressure. The built-in volume indicator available on TradingView uses this logic to color the volume columns green or red. While this script’s numbers are more precise because it analyses a number of intrabars to calculate its information, it uses the exact same imperfect logic to calculate its buying/selling/neutral sections.
Until Pine scripts can have access to how much volume was transacted at the bid/ask prices, our so-called buying/selling volume information will always be a mere proxy.
Divergences
You may wonder how there can be divergences between buying/selling volume information and price movement. This will sometimes be due to the methodology’s shortcomings we have just discussed, but divergences may also occur in instances where because of order book structure, it takes less volume to increase the price of an asset than it takes to decrease it.
As usual, divergences are points of interest because they reveal imbalances, which may or may not become turning points. I do not share the overwhelming enthusiasm traders have for divergences. To your pattern-hungry brain, the orange bars this indicator shows on chart will—as divergences on other indicators do–appear to often indicate turnarounds. My opinion is that reality is generally quite sobering, as many who have tried building automated rules based on divergences will tell you. I do not have hard numbers on the lack of performance of divergences—only many failed attempts to make them perform, which a few experienced strategy modelers I know share with me. Please don’t try to read too much into them. While they look great on past data, I find they are often difficult to use in realtime to make bets with good odds.
Thanks to:
- A guy called Kuan who commented on a Backtest Rookies presentation of an intrabar delta volume indicator using a for loop. The heart of “my” indicator is code borrowed from Kuan; I just built a hopefully useful wrapper around it.
- @theheirophant, my partner in the exploration of the sometimes weird abysses of security() ’s behavior at lower resolutions.
Small Business Economic Conditions - Statistical Analysis ModelThe Small Business Economic Conditions Statistical Analysis Model (SBO-SAM) represents an econometric approach to measuring and analyzing the economic health of small business enterprises through multi-dimensional factor analysis and statistical methodologies. This indicator synthesizes eight fundamental economic components into a composite index that provides real-time assessment of small business operating conditions with statistical rigor. The model employs Z-score standardization, variance-weighted aggregation, higher-order moment analysis, and regime-switching detection to deliver comprehensive insights into small business economic conditions with statistical confidence intervals and multi-language accessibility.
1. Introduction and Theoretical Foundation
The development of quantitative models for assessing small business economic conditions has gained significant importance in contemporary financial analysis, particularly given the critical role small enterprises play in economic development and employment generation. Small businesses, typically defined as enterprises with fewer than 500 employees according to the U.S. Small Business Administration, constitute approximately 99.9% of all businesses in the United States and employ nearly half of the private workforce (U.S. Small Business Administration, 2024).
The theoretical framework underlying the SBO-SAM model draws extensively from established academic research in small business economics and quantitative finance. The foundational understanding of key drivers affecting small business performance builds upon the seminal work of Dunkelberg and Wade (2023) in their analysis of small business economic trends through the National Federation of Independent Business (NFIB) Small Business Economic Trends survey. Their research established the critical importance of optimism, hiring plans, capital expenditure intentions, and credit availability as primary determinants of small business performance.
The model incorporates insights from Federal Reserve Board research, particularly the Senior Loan Officer Opinion Survey (Federal Reserve Board, 2024), which demonstrates the critical importance of credit market conditions in small business operations. This research consistently shows that small businesses face disproportionate challenges during periods of credit tightening, as they typically lack access to capital markets and rely heavily on bank financing.
The statistical methodology employed in this model follows the econometric principles established by Hamilton (1989) in his work on regime-switching models and time series analysis. Hamilton's framework provides the theoretical foundation for identifying different economic regimes and understanding how economic relationships may vary across different market conditions. The variance-weighted aggregation technique draws from modern portfolio theory as developed by Markowitz (1952) and later refined by Sharpe (1964), applying these concepts to economic indicator construction rather than traditional asset allocation.
Additional theoretical support comes from the work of Engle and Granger (1987) on cointegration analysis, which provides the statistical framework for combining multiple time series while maintaining long-term equilibrium relationships. The model also incorporates insights from behavioral economics research by Kahneman and Tversky (1979) on prospect theory, recognizing that small business decision-making may exhibit systematic biases that affect economic outcomes.
2. Model Architecture and Component Structure
The SBO-SAM model employs eight orthogonalized economic factors that collectively capture the multifaceted nature of small business operating conditions. Each component is normalized using Z-score standardization with a rolling 252-day window, representing approximately one business year of trading data. This approach ensures statistical consistency across different market regimes and economic cycles, following the methodology established by Tsay (2010) in his treatment of financial time series analysis.
2.1 Small Cap Relative Performance Component
The first component measures the performance of the Russell 2000 index relative to the S&P 500, capturing the market-based assessment of small business equity valuations. This component reflects investor sentiment toward smaller enterprises and provides a forward-looking perspective on small business prospects. The theoretical justification for this component stems from the efficient market hypothesis as formulated by Fama (1970), which suggests that stock prices incorporate all available information about future prospects.
The calculation employs a 20-day rate of change with exponential smoothing to reduce noise while preserving signal integrity. The mathematical formulation is:
Small_Cap_Performance = (Russell_2000_t / S&P_500_t) / (Russell_2000_{t-20} / S&P_500_{t-20}) - 1
This relative performance measure eliminates market-wide effects and isolates the specific performance differential between small and large capitalization stocks, providing a pure measure of small business market sentiment.
2.2 Credit Market Conditions Component
Credit Market Conditions constitute the second component, incorporating commercial lending volumes and credit spread dynamics. This factor recognizes that small businesses are particularly sensitive to credit availability and borrowing costs, as established in numerous Federal Reserve studies (Bernanke and Gertler, 1995). Small businesses typically face higher borrowing costs and more stringent lending standards compared to larger enterprises, making credit conditions a critical determinant of their operating environment.
The model calculates credit spreads using high-yield bond ETFs relative to Treasury securities, providing a market-based measure of credit risk premiums that directly affect small business borrowing costs. The component also incorporates commercial and industrial loan growth data from the Federal Reserve's H.8 statistical release, which provides direct evidence of lending activity to businesses.
The mathematical specification combines these elements as:
Credit_Conditions = α₁ × (HYG_t / TLT_t) + α₂ × C&I_Loan_Growth_t
where HYG represents high-yield corporate bond ETF prices, TLT represents long-term Treasury ETF prices, and C&I_Loan_Growth represents the rate of change in commercial and industrial loans outstanding.
2.3 Labor Market Dynamics Component
The Labor Market Dynamics component captures employment cost pressures and labor availability metrics through the relationship between job openings and unemployment claims. This factor acknowledges that labor market tightness significantly impacts small business operations, as these enterprises typically have less flexibility in wage negotiations and face greater challenges in attracting and retaining talent during periods of low unemployment.
The theoretical foundation for this component draws from search and matching theory as developed by Mortensen and Pissarides (1994), which explains how labor market frictions affect employment dynamics. Small businesses often face higher search costs and longer hiring processes, making them particularly sensitive to labor market conditions.
The component is calculated as:
Labor_Tightness = Job_Openings_t / (Unemployment_Claims_t × 52)
This ratio provides a measure of labor market tightness, with higher values indicating greater difficulty in finding workers and potential wage pressures.
2.4 Consumer Demand Strength Component
Consumer Demand Strength represents the fourth component, combining consumer sentiment data with retail sales growth rates. Small businesses are disproportionately affected by consumer spending patterns, making this component crucial for assessing their operating environment. The theoretical justification comes from the permanent income hypothesis developed by Friedman (1957), which explains how consumer spending responds to both current conditions and future expectations.
The model weights consumer confidence and actual spending data to provide both forward-looking sentiment and contemporaneous demand indicators. The specification is:
Demand_Strength = β₁ × Consumer_Sentiment_t + β₂ × Retail_Sales_Growth_t
where β₁ and β₂ are determined through principal component analysis to maximize the explanatory power of the combined measure.
2.5 Input Cost Pressures Component
Input Cost Pressures form the fifth component, utilizing producer price index data to capture inflationary pressures on small business operations. This component is inversely weighted, recognizing that rising input costs negatively impact small business profitability and operating conditions. Small businesses typically have limited pricing power and face challenges in passing through cost increases to customers, making them particularly vulnerable to input cost inflation.
The theoretical foundation draws from cost-push inflation theory as described by Gordon (1988), which explains how supply-side price pressures affect business operations. The model employs a 90-day rate of change to capture medium-term cost trends while filtering out short-term volatility:
Cost_Pressure = -1 × (PPI_t / PPI_{t-90} - 1)
The negative weighting reflects the inverse relationship between input costs and business conditions.
2.6 Monetary Policy Impact Component
Monetary Policy Impact represents the sixth component, incorporating federal funds rates and yield curve dynamics. Small businesses are particularly sensitive to interest rate changes due to their higher reliance on variable-rate financing and limited access to capital markets. The theoretical foundation comes from monetary transmission mechanism theory as developed by Bernanke and Blinder (1992), which explains how monetary policy affects different segments of the economy.
The model calculates the absolute deviation of federal funds rates from a neutral 2% level, recognizing that both extremely low and high rates can create operational challenges for small enterprises. The yield curve component captures the shape of the term structure, which affects both borrowing costs and economic expectations:
Monetary_Impact = γ₁ × |Fed_Funds_Rate_t - 2.0| + γ₂ × (10Y_Yield_t - 2Y_Yield_t)
2.7 Currency Valuation Effects Component
Currency Valuation Effects constitute the seventh component, measuring the impact of US Dollar strength on small business competitiveness. A stronger dollar can benefit businesses with significant import components while disadvantaging exporters. The model employs Dollar Index volatility as a proxy for currency-related uncertainty that affects small business planning and operations.
The theoretical foundation draws from international trade theory and the work of Krugman (1987) on exchange rate effects on different business segments. Small businesses often lack hedging capabilities, making them more vulnerable to currency fluctuations:
Currency_Impact = -1 × DXY_Volatility_t
2.8 Regional Banking Health Component
The eighth and final component, Regional Banking Health, assesses the relative performance of regional banks compared to large financial institutions. Regional banks traditionally serve as primary lenders to small businesses, making their health a critical factor in small business credit availability and overall operating conditions.
This component draws from the literature on relationship banking as developed by Boot (2000), which demonstrates the importance of bank-borrower relationships, particularly for small enterprises. The calculation compares regional bank performance to large financial institutions:
Banking_Health = (Regional_Banks_Index_t / Large_Banks_Index_t) - 1
3. Statistical Methodology and Advanced Analytics
The model employs statistical techniques to ensure robustness and reliability. Z-score normalization is applied to each component using rolling 252-day windows, providing standardized measures that remain consistent across different time periods and market conditions. This approach follows the methodology established by Engle and Granger (1987) in their cointegration analysis framework.
3.1 Variance-Weighted Aggregation
The composite index calculation utilizes variance-weighted aggregation, where component weights are determined by the inverse of their historical variance. This approach, derived from modern portfolio theory, ensures that more stable components receive higher weights while reducing the impact of highly volatile factors. The mathematical formulation follows the principle that optimal weights are inversely proportional to variance, maximizing the signal-to-noise ratio of the composite indicator.
The weight for component i is calculated as:
w_i = (1/σᵢ²) / Σⱼ(1/σⱼ²)
where σᵢ² represents the variance of component i over the lookback period.
3.2 Higher-Order Moment Analysis
Higher-order moment analysis extends beyond traditional mean and variance calculations to include skewness and kurtosis measurements. Skewness provides insight into the asymmetry of the sentiment distribution, while kurtosis measures the tail behavior and potential for extreme events. These metrics offer valuable information about the underlying distribution characteristics and potential regime changes.
Skewness is calculated as:
Skewness = E / σ³
Kurtosis is calculated as:
Kurtosis = E / σ⁴ - 3
where μ represents the mean and σ represents the standard deviation of the distribution.
3.3 Regime-Switching Detection
The model incorporates regime-switching detection capabilities based on the Hamilton (1989) framework. This allows for identification of different economic regimes characterized by distinct statistical properties. The regime classification employs percentile-based thresholds:
- Regime 3 (Very High): Percentile rank > 80
- Regime 2 (High): Percentile rank 60-80
- Regime 1 (Moderate High): Percentile rank 50-60
- Regime 0 (Neutral): Percentile rank 40-50
- Regime -1 (Moderate Low): Percentile rank 30-40
- Regime -2 (Low): Percentile rank 20-30
- Regime -3 (Very Low): Percentile rank < 20
3.4 Information Theory Applications
The model incorporates information theory concepts, specifically Shannon entropy measurement, to assess the information content of the sentiment distribution. Shannon entropy, as developed by Shannon (1948), provides a measure of the uncertainty or information content in a probability distribution:
H(X) = -Σᵢ p(xᵢ) log₂ p(xᵢ)
Higher entropy values indicate greater unpredictability and information content in the sentiment series.
3.5 Long-Term Memory Analysis
The Hurst exponent calculation provides insight into the long-term memory characteristics of the sentiment series. Originally developed by Hurst (1951) for analyzing Nile River flow patterns, this measure has found extensive application in financial time series analysis. The Hurst exponent H is calculated using the rescaled range statistic:
H = log(R/S) / log(T)
where R/S represents the rescaled range and T represents the time period. Values of H > 0.5 indicate long-term positive autocorrelation (persistence), while H < 0.5 indicates mean-reverting behavior.
3.6 Structural Break Detection
The model employs Chow test approximation for structural break detection, based on the methodology developed by Chow (1960). This technique identifies potential structural changes in the underlying relationships by comparing the stability of regression parameters across different time periods:
Chow_Statistic = (RSS_restricted - RSS_unrestricted) / RSS_unrestricted × (n-2k)/k
where RSS represents residual sum of squares, n represents sample size, and k represents the number of parameters.
4. Implementation Parameters and Configuration
4.1 Language Selection Parameters
The model provides comprehensive multi-language support across five languages: English, German (Deutsch), Spanish (Español), French (Français), and Japanese (日本語). This feature enhances accessibility for international users and ensures cultural appropriateness in terminology usage. The language selection affects all internal displays, statistical classifications, and alert messages while maintaining consistency in underlying calculations.
4.2 Model Configuration Parameters
Calculation Method: Users can select from four aggregation methodologies:
- Equal-Weighted: All components receive identical weights
- Variance-Weighted: Components weighted inversely to their historical variance
- Principal Component: Weights determined through principal component analysis
- Dynamic: Adaptive weighting based on recent performance
Sector Specification: The model allows for sector-specific calibration:
- General: Broad-based small business assessment
- Retail: Emphasis on consumer demand and seasonal factors
- Manufacturing: Enhanced weighting of input costs and currency effects
- Services: Focus on labor market dynamics and consumer demand
- Construction: Emphasis on credit conditions and monetary policy
Lookback Period: Statistical analysis window ranging from 126 to 504 trading days, with 252 days (one business year) as the optimal default based on academic research.
Smoothing Period: Exponential moving average period from 1 to 21 days, with 5 days providing optimal noise reduction while preserving signal integrity.
4.3 Statistical Threshold Parameters
Upper Statistical Boundary: Configurable threshold between 60-80 (default 70) representing the upper significance level for regime classification.
Lower Statistical Boundary: Configurable threshold between 20-40 (default 30) representing the lower significance level for regime classification.
Statistical Significance Level (α): Alpha level for statistical tests, configurable between 0.01-0.10 with 0.05 as the standard academic default.
4.4 Display and Visualization Parameters
Color Theme Selection: Eight professional color schemes optimized for different user preferences and accessibility requirements:
- Gold: Traditional financial industry colors
- EdgeTools: Professional blue-gray scheme
- Behavioral: Psychology-based color mapping
- Quant: Value-based quantitative color scheme
- Ocean: Blue-green maritime theme
- Fire: Warm red-orange theme
- Matrix: Green-black technology theme
- Arctic: Cool blue-white theme
Dark Mode Optimization: Automatic color adjustment for dark chart backgrounds, ensuring optimal readability across different viewing conditions.
Line Width Configuration: Main index line thickness adjustable from 1-5 pixels for optimal visibility.
Background Intensity: Transparency control for statistical regime backgrounds, adjustable from 90-99% for subtle visual enhancement without distraction.
4.5 Alert System Configuration
Alert Frequency Options: Three frequency settings to match different trading styles:
- Once Per Bar: Single alert per bar formation
- Once Per Bar Close: Alert only on confirmed bar close
- All: Continuous alerts for real-time monitoring
Statistical Extreme Alerts: Notifications when the index reaches 99% confidence levels (Z-score > 2.576 or < -2.576).
Regime Transition Alerts: Notifications when statistical boundaries are crossed, indicating potential regime changes.
5. Practical Application and Interpretation Guidelines
5.1 Index Interpretation Framework
The SBO-SAM index operates on a 0-100 scale with statistical normalization ensuring consistent interpretation across different time periods and market conditions. Values above 70 indicate statistically elevated small business conditions, suggesting favorable operating environment with potential for expansion and growth. Values below 30 indicate statistically reduced conditions, suggesting challenging operating environment with potential constraints on business activity.
The median reference line at 50 represents the long-term equilibrium level, with deviations providing insight into cyclical conditions relative to historical norms. The statistical confidence bands at 95% levels (approximately ±2 standard deviations) help identify when conditions reach statistically significant extremes.
5.2 Regime Classification System
The model employs a seven-level regime classification system based on percentile rankings:
Very High Regime (P80+): Exceptional small business conditions, typically associated with strong economic growth, easy credit availability, and favorable regulatory environment. Historical analysis suggests these periods often precede economic peaks and may warrant caution regarding sustainability.
High Regime (P60-80): Above-average conditions supporting business expansion and investment. These periods typically feature moderate growth, stable credit conditions, and positive consumer sentiment.
Moderate High Regime (P50-60): Slightly above-normal conditions with mixed signals. Careful monitoring of individual components helps identify emerging trends.
Neutral Regime (P40-50): Balanced conditions near long-term equilibrium. These periods often represent transition phases between different economic cycles.
Moderate Low Regime (P30-40): Slightly below-normal conditions with emerging headwinds. Early warning signals may appear in credit conditions or consumer demand.
Low Regime (P20-30): Below-average conditions suggesting challenging operating environment. Businesses may face constraints on growth and expansion.
Very Low Regime (P0-20): Severely constrained conditions, typically associated with economic recessions or financial crises. These periods often present opportunities for contrarian positioning.
5.3 Component Analysis and Diagnostics
Individual component analysis provides valuable diagnostic information about the underlying drivers of overall conditions. Divergences between components can signal emerging trends or structural changes in the economy.
Credit-Labor Divergence: When credit conditions improve while labor markets tighten, this may indicate early-stage economic acceleration with potential wage pressures.
Demand-Cost Divergence: Strong consumer demand coupled with rising input costs suggests inflationary pressures that may constrain small business margins.
Market-Fundamental Divergence: Disconnection between small-cap equity performance and fundamental conditions may indicate market inefficiencies or changing investor sentiment.
5.4 Temporal Analysis and Trend Identification
The model provides multiple temporal perspectives through momentum analysis, rate of change calculations, and trend decomposition. The 20-day momentum indicator helps identify short-term directional changes, while the Hodrick-Prescott filter approximation separates cyclical components from long-term trends.
Acceleration analysis through second-order momentum calculations provides early warning signals for potential trend reversals. Positive acceleration during declining conditions may indicate approaching inflection points, while negative acceleration during improving conditions may suggest momentum loss.
5.5 Statistical Confidence and Uncertainty Quantification
The model provides comprehensive uncertainty quantification through confidence intervals, volatility measures, and regime stability analysis. The 95% confidence bands help users understand the statistical significance of current readings and identify when conditions reach historically extreme levels.
Volatility analysis provides insight into the stability of current conditions, with higher volatility indicating greater uncertainty and potential for rapid changes. The regime stability measure, calculated as the inverse of volatility, helps assess the sustainability of current conditions.
6. Risk Management and Limitations
6.1 Model Limitations and Assumptions
The SBO-SAM model operates under several important assumptions that users must understand for proper interpretation. The model assumes that historical relationships between economic variables remain stable over time, though the regime-switching framework helps accommodate some structural changes. The 252-day lookback period provides reasonable statistical power while maintaining sensitivity to changing conditions, but may not capture longer-term structural shifts.
The model's reliance on publicly available economic data introduces inherent lags in some components, particularly those based on government statistics. Users should consider these timing differences when interpreting real-time conditions. Additionally, the model's focus on quantitative factors may not fully capture qualitative factors such as regulatory changes, geopolitical events, or technological disruptions that could significantly impact small business conditions.
The model's timeframe restrictions ensure statistical validity by preventing application to intraday periods where the underlying economic relationships may be distorted by market microstructure effects, trading noise, and temporal misalignment with the fundamental data sources. Users must utilize daily or longer timeframes to ensure the model's statistical foundations remain valid and interpretable.
6.2 Data Quality and Reliability Considerations
The model's accuracy depends heavily on the quality and availability of underlying economic data. Market-based components such as equity indices and bond prices provide real-time information but may be subject to short-term volatility unrelated to fundamental conditions. Economic statistics provide more stable fundamental information but may be subject to revisions and reporting delays.
Users should be aware that extreme market conditions may temporarily distort some components, particularly those based on financial market data. The model's statistical normalization helps mitigate these effects, but users should exercise additional caution during periods of market stress or unusual volatility.
6.3 Interpretation Caveats and Best Practices
The SBO-SAM model provides statistical analysis and should not be interpreted as investment advice or predictive forecasting. The model's output represents an assessment of current conditions based on historical relationships and may not accurately predict future outcomes. Users should combine the model's insights with other analytical tools and fundamental analysis for comprehensive decision-making.
The model's regime classifications are based on historical percentile rankings and may not fully capture the unique characteristics of current economic conditions. Users should consider the broader economic context and potential structural changes when interpreting regime classifications.
7. Academic References and Bibliography
Bernanke, B. S., & Blinder, A. S. (1992). The Federal Funds Rate and the Channels of Monetary Transmission. American Economic Review, 82(4), 901-921.
Bernanke, B. S., & Gertler, M. (1995). Inside the Black Box: The Credit Channel of Monetary Policy Transmission. Journal of Economic Perspectives, 9(4), 27-48.
Boot, A. W. A. (2000). Relationship Banking: What Do We Know? Journal of Financial Intermediation, 9(1), 7-25.
Chow, G. C. (1960). Tests of Equality Between Sets of Coefficients in Two Linear Regressions. Econometrica, 28(3), 591-605.
Dunkelberg, W. C., & Wade, H. (2023). NFIB Small Business Economic Trends. National Federation of Independent Business Research Foundation, Washington, D.C.
Engle, R. F., & Granger, C. W. J. (1987). Co-integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251-276.
Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. Journal of Finance, 25(2), 383-417.
Federal Reserve Board. (2024). Senior Loan Officer Opinion Survey on Bank Lending Practices. Board of Governors of the Federal Reserve System, Washington, D.C.
Friedman, M. (1957). A Theory of the Consumption Function. Princeton University Press, Princeton, NJ.
Gordon, R. J. (1988). The Role of Wages in the Inflation Process. American Economic Review, 78(2), 276-283.
Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384.
Hurst, H. E. (1951). Long-term Storage Capacity of Reservoirs. Transactions of the American Society of Civil Engineers, 116(1), 770-799.
Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-291.
Krugman, P. (1987). Pricing to Market When the Exchange Rate Changes. In S. W. Arndt & J. D. Richardson (Eds.), Real-Financial Linkages among Open Economies (pp. 49-70). MIT Press, Cambridge, MA.
Markowitz, H. (1952). Portfolio Selection. Journal of Finance, 7(1), 77-91.
Mortensen, D. T., & Pissarides, C. A. (1994). Job Creation and Job Destruction in the Theory of Unemployment. Review of Economic Studies, 61(3), 397-415.
Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27(3), 379-423.
Sharpe, W. F. (1964). Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk. Journal of Finance, 19(3), 425-442.
Tsay, R. S. (2010). Analysis of Financial Time Series (3rd ed.). John Wiley & Sons, Hoboken, NJ.
U.S. Small Business Administration. (2024). Small Business Profile. Office of Advocacy, Washington, D.C.
8. Technical Implementation Notes
The SBO-SAM model is implemented in Pine Script version 6 for the TradingView platform, ensuring compatibility with modern charting and analysis tools. The implementation follows best practices for financial indicator development, including proper error handling, data validation, and performance optimization.
The model includes comprehensive timeframe validation to ensure statistical accuracy and reliability. The indicator operates exclusively on daily (1D) timeframes or higher, including weekly (1W), monthly (1M), and longer periods. This restriction ensures that the statistical analysis maintains appropriate temporal resolution for the underlying economic data sources, which are primarily reported on daily or longer intervals.
When users attempt to apply the model to intraday timeframes (such as 1-minute, 5-minute, 15-minute, 30-minute, 1-hour, 2-hour, 4-hour, 6-hour, 8-hour, or 12-hour charts), the system displays a comprehensive error message in the user's selected language and prevents execution. This safeguard protects users from potentially misleading results that could occur when applying daily-based economic analysis to shorter timeframes where the underlying data relationships may not hold.
The model's statistical calculations are performed using vectorized operations where possible to ensure computational efficiency. The multi-language support system employs Unicode character encoding to ensure proper display of international characters across different platforms and devices.
The alert system utilizes TradingView's native alert functionality, providing users with flexible notification options including email, SMS, and webhook integrations. The alert messages include comprehensive statistical information to support informed decision-making.
The model's visualization system employs professional color schemes designed for optimal readability across different chart backgrounds and display devices. The system includes dynamic color transitions based on momentum and volatility, professional glow effects for enhanced line visibility, and transparency controls that allow users to customize the visual intensity to match their preferences and analytical requirements. The clean confidence band implementation provides clear statistical boundaries without visual distractions, maintaining focus on the analytical content.
SMT SwiftEdge PowerhouseSMT SwiftEdge Powerhouse: Precision Trading with Divergence, Liquidity Grabs, and OTE Zones
The SMT SwiftEdge Powerhouse is a powerful trading tool designed to help traders identify high-probability entry points during the most active market sessions—London and New York. By combining Smart Money Technique (SMT) Divergence, Liquidity Grabs, and Optimal Trade Entry (OTE) Zones, this script provides a unique and cohesive strategy for capturing market reversals with precision. Whether you're a scalper or a swing trader, this indicator offers clear visual signals to enhance your trading decisions on any timeframe.
What Does This Script Do?
This script integrates three key concepts to identify potential trading opportunities:
SMT Divergence:
SMT Divergence compares the price action of two correlated assets (e.g., Nasdaq and S&P 500 futures) to detect hidden market reversals. When one asset makes a higher high while the other makes a lower high (bearish divergence), or one makes a lower low while the other makes a higher low (bullish divergence), it signals a potential reversal. This technique leverages institutional "smart money" behavior to anticipate market shifts.
Liquidity Grabs:
Liquidity Grabs occur when price breaks above recent highs or below recent lows on higher timeframes (5m and 15m), often triggering stop-loss orders from retail traders. These breakouts are identified using pivot points and confirm institutional activity, setting the stage for a reversal. The script focuses on liquidity grabs during the London and New York sessions for maximum market activity.
Optimal Trade Entry (OTE) Zones:
OTE Zones are Fibonacci-based retracement areas (e.g., 61.8%) calculated after a liquidity grab. These zones highlight where price is likely to retrace before continuing in the direction of the reversal, offering a high-probability entry point. The script adjusts the width of these zones using the Average True Range (ATR) to adapt to market volatility.
By combining these components, the script identifies when institutional activity (liquidity grabs) aligns with market reversals (SMT divergence) and pinpoints precise entry points (OTE zones) during high-liquidity sessions.
Why Combine These Components?
The integration of SMT Divergence, Liquidity Grabs, and OTE Zones creates a robust trading system for several reasons:
Synergy of Institutional Signals: SMT Divergence and Liquidity Grabs both reflect "smart money" behavior—divergence shows hidden reversals, while liquidity grabs confirm institutional intent to trap retail traders. Together, they provide a strong foundation for identifying high-probability setups.
Session-Based Precision: Focusing on the London and New York sessions ensures signals occur during periods of high volatility and liquidity, increasing their reliability.
Precision Entries with OTE: After confirming a setup with divergence and liquidity grabs, OTE zones provide a clear entry area, reducing guesswork and improving trade accuracy.
Adaptability: The script works on any timeframe, with adjustable settings for signal sensitivity, session times, and Fibonacci levels, making it versatile for different trading styles.
This combination makes the script unique by aligning institutional insights with actionable entry points, tailored to the most active market hours.
How to Use the Script
Setup:
Add the script to your chart (works on any timeframe, e.g., 1m, 5m, 15m).
Configure the settings in the indicator's inputs:
Session Settings: Adjust the start/end times for London and New York sessions (default: London 8-11 UTC, New York 13-16 UTC). You can disable session restrictions if desired.
Asset Settings: Set the primary and secondary assets for SMT Divergence (default: NQ1! and ES1!). Ensure the assets are correlated.
Signal Settings: Adjust the lookback period, ATR period, and signal sensitivity (Low/Medium/High) to control the frequency of signals.
OTE Settings: Choose the Fibonacci level for OTE zones (default: 61.8%).
Visual Settings: Enable/disable OTE zones, SMT labels, and debug labels for troubleshooting.
Interpreting Signals:
Blue Circles: Indicate a liquidity grab (price breaking a 5m or 15m pivot high/low), marking the start of a potential setup.
Blue OTE Zones: Appear after a liquidity grab, showing the retracement area (e.g., 61.8% Fibonacci level) where price is likely to enter for a reversal trade. The label "OTE Trigger 5m/15m" confirms the direction (Short/Long) and session.
Green/Red Entry Boxes: Mark precise entry points when price enters the OTE zone and confirms the SMT Divergence. Green boxes indicate a long entry, red boxes a short entry.
Trading Example:
On a 1m chart, a blue circle appears when price breaks a 5m pivot high during the London session.
A blue OTE zone forms, showing a retracement area (e.g., 61.8% Fibonacci level) with the label "OTE Trigger 5m/15m (Short, London)".
Price retraces into the OTE zone, and a red "Short Entry" box appears, confirming a bearish SMT Divergence.
Enter a short trade at the red box, with a stop-loss above the OTE zone and a take-profit at the next support level.
Originality and Utility
The SMT SwiftEdge Powerhouse stands out by merging SMT Divergence, Liquidity Grabs, and OTE Zones into a single, session-focused indicator. Unlike traditional indicators that focus on one aspect of price action, this script combines institutional reversal signals with precise entry zones, tailored to the most active market hours. Its adaptability across timeframes, customizable settings, and clear visual cues make it a versatile tool for traders seeking to capitalize on smart money movements with confidence.
Tips for Best Results
Use on correlated assets like NQ1! (Nasdaq futures) and ES1! (S&P 500 futures) for accurate SMT Divergence.
Test on lower timeframes (1m, 5m) for scalping or higher timeframes (15m, 1H) for swing trading.
Adjust the "Signal Sensitivity" to "High" for more signals or "Low" for fewer, high-quality setups.
Enable "Show Debug Labels" if signals are not appearing as expected, to troubleshoot pivot points and liquidity grabs.
Enhanced CCI with Trend FiltersThis indicator combines the power of the Commodity Channel Index (CCI) with adaptive trend filters and divergence detection to identify high-probability trading opportunities. It's designed to reduce false signals by incorporating trend direction and divergence confirmation.
🔍 How It Works:
1. Trend Detection:
- Uses an adaptive Simple Moving Average (SMA) that automatically adjusts between daily and weekly timeframes
- Daily mode: Default 200 SMA for longer-term trend
- Weekly mode: Default 40 SMA for broader market perspective
- Trend direction is determined by comparing current SMA value to its previous value
2. CCI Component:
- Traditional CCI calculation with customizable length (default: 20)
- Adjustable overbought (default: +100) and oversold (default: -100) levels
- Color-coded CCI line for quick visual reference:
* Green: Overbought and rising
* Red: Oversold and falling
* Gray: Neutral zone
3. Signal Generation:
- Buy Signal (Green Background):
* CCI crosses above oversold level AND
* Main trend is confirmed bullish (rising SMA)
- Sell Signal (Red Background):
* CCI crosses below overbought level AND
* Main trend is confirmed bearish (falling SMA)
4. Divergence Detection:
- Automatically identifies bullish and bearish divergences
- Bullish Divergence: Price makes lower low while CCI makes higher low
- Bearish Divergence: Price makes higher high while CCI makes lower high
- Visualized with distinct markers on the CCI line
- Can be used to set up alerts for divergence confirmations
⚙️ Customization Options:
- CCI calculation length and price source
- Overbought/Oversold levels
- SMA lengths for daily and weekly modes
- Divergence lookback period
- Signal colors and visualization settings
🔔 Alert Capabilities:
- Set alerts for CCI crossovers of overbought/oversold levels
- Create divergence detection alerts
- Configure trend change notifications
- Combine multiple conditions for complex alert strategies
💡 Trading Tips:
- Combine divergence signals with trend direction for better accuracy
- Wait for signal confirmation before entering trades
- Use additional indicators or price action for exit decisions
This indicator helps traders identify potential trend reversals while keeping them aligned with the main market trend. Perfect for both trend following and counter-trend strategies when used appropriately.
Tags: CCI, trend following, divergence, momentum, signals, SMA, multi-timeframe, technical analysis, trend filter, oversold, overbought
Note: Past performance does not guarantee future results. Always combine with proper risk management.
MACD All In One Screener [ChartPrime]INTRODUCTION
MACD All In One Screener (ChartPrime) is a multi instrument, multi timeframe indicator designed to provide traders with a comprehensive solution to monitoring the market. This indicator is designed to be easy to use and visually appealing while also being highly flexible and feature rich. Users can pick up to 10 symbols not including the chart's symbol and set up alerts for many different signals that the MACD produces. One standout feature of this indicator is its ability to display not only each symbol individually as a MACD but you can also view its chart from within this indicator. This removes the need to flip between symbols to see the price action for your basket.
On top of that we have designed this indicator to be friendly with "indicator on indicator" by providing outputs for all of the standards of price that users may want. Included is an overview section that shows all of the symbols signals symbolically over time. Additionally we have included a table for easy monitoring. This table includes the symbol, its timeframe, the current alert, and its histogram state. To make things as user friendly as possible we have also included rich error handling that tells you exactly what is wrong with your configuration.
HOW TO USE
To use this indicator, simply add it to your chart and navigate to the settings. From there select the symbols you want to monitor and the timeframes you want to use. Next you want to navigate down to the alerts section to select the what alerts you want to receive, and what symbols you want to get alerts for. Finally, you wan to create your alert using "Any alert() function call". Now your screener is all set up!
OVERVIEW OF INPUTS
View allows you to select what the indicator currently displays. You can pick from any one of the selected symbols, an overview of all of the symbols, or simply nothing. If you want to only use the table, "None" is provided so you can move the indicator into the chart panel.
View Toggle lets you pick from displaying the MACD for the selected symbol or the Price Action as a candle chart. To see your "indicator on indicator" you will have to select a symbol from the view list. There is a bug where if you select "Overview" while you are using "indicator on indicator" your added indicator will see the last symbol you viewed. To fix this, simply change the setting of your overlaid indicator and it will correct its self.
History Length is the number of historical bars to calculate over. This feature is here to prevent the indicator from breaking due to uneven historical data between the symbols.
Show Price Line toggles a dotted line that follows the current symbols closing price when "Price" is selected under the "View Toggle" dropdown.
Show Symbol Label toggles a label that displays the current symbols name and timeframe. This only impacts the single symbol view.
Overview Label Color adjusts the color of the symbol labels for both overview and single symbol view.
MA Type lets you pick what kind of moving average you want to use for the oscillator or signal. You can pick from the standard SMA or EMA.
Fast Length is a standard input for MACD. This lets you pick the period of the fast MA.
Slow Length , just like Fast Lenght, is a standard input for MACD. This lets you pick the period of the slow MA.
Signal Length is another standard input for MACD. This lets you configure the period of the signal MA.
MACD Cross Overlay Icon is a toggle to display MACD crosses when viewing a single symbol's MACD. When the MACD has a bullish cross it will plot a bullish dot, and when it has a bearish cross it will plot a bearish dot. This is purely visual.
Regular Bullish and Bearish toggles the visual display of the divergences on the single symbol view. This does not effect the indicators ability do send alerts.
Divergence Look Right adjusts the number of bars into the future to look for confirmation of a signal. This directly impacts lag but enhances stability.
Divergence Look Left adjusts the number of bars into the past to check for a signal. A longer period will filter out smaller moves
Maximum Lookback adjusts the maximum size of a divergence.
Minimum Lookback adjusts the minimum size of a divergence.
Divergence Drawings picks how you want to visualize the divergence. You can pick from displaying it as a line, a label, or both.
Enable Table toggles the overview table. When enabled it will show you the enabled symbols and their current state. From left to right: symbol name, timeframe, current alert, and histogram state.
Position picks where on the chart you want the table to be.
Text Color adjusts the text color of the table.
BG Color adjusts the background color of the table.
Frame Color adjust the frame color of the table.
Current Symbol Time Frame adjusts the timeframe of the chart's symbol.
Symbol 1 - 10 pick "Symbol's" symbol and timeframe. To use higher timeframes, the symbol's have to be the same type. You can't have a crypto and a stock using HTF at the same time as they don't have the same sessions and will result in an error. You can use unsafe mode (as described below) to potentially get around this.
Enable Symbol when enabled it will give you alerts for the symbol. This also enables the symbol in the overview. If this is disabled it won't send alerts, and it will not show up in overview, or the table.
Wait for Close enables waiting for the bar to close before printing an alert.
Alert Symbol Size picks what size you want the overview symbols to be.
Enable Cross Over 0 Alert: MACD crosses over the 0 line.
Enable Cross Under 0 Alert: MACD crosses under the 0 line.
Enable MACD Cross Bullish Alert: Bullish MACD cross.
Enable MACD Cross Bearish Alert: Bearish MACD cross.
Enable Histogram Bullish Turn Alert: MACD begins to turn bullish but hasn't crossed.
Enable Histogram Bearish Turn Alert: MACD begins to turn bearish but hasn't crossed.
Enable Histogram Bullish Continuation Alert: MACD is in a bullish cross state and it was declining but began rising again.
Enable Histogram Bearish Continuation Alert: MACD is in a bearish cross state and it was rising but began falling again.
Enable Bullish/Bearish Divergence Alert enables divergence alerts. Divergences are lagging, especially on a higher timeframe. These alerts will also tell you the time in the past when the divergence occurred.
Color Section is provided to allow for personalization of the indicator. Everything can be adjusted here.
Disable Error Checking: Only enable this if you want to bypass the built in error checking. This will enable 'Safe Requesting'. Safe Requesting will only request enabled symbols and you will not be able to view symbols that are not enabled in this mode. Only use this if you want to mix symbol types and you know it will work. (An example would be viewing stocks and SPY at the same time.)
CONCLUSION
The MACD All In One Screener (ChartPrime) is a versatile indicator designed to monitor multiple symbols across various timeframes. The flexibility in customization, from MACD settings to visual alerts and table presentations, allows users to tailor the screener to their needs and preferences. We hope you find this as useful and interesting as we do and wish you good luck in the market!
Enjoy
RSI Supreme Multi-Method [MyTradingCoder]Introducing the "RSI Supreme Multi-Method" indicator, a powerful tool that combines the Relative Strength Index (RSI) with selectable manipulation methods to identify overbought and oversold conditions in the market, along with the ability to detect divergences for enhanced trading insights.
The indicator features four distinct manipulation methods for the RSI, each providing valuable insights into market conditions:
1. Standard RSI Method: The indicator uses the traditional RSI calculation to identify overbought and oversold areas.
2. Volatility Weighted RSI Method: This method applies a volatility formula to the RSI calculation, allowing for a more responsive indication of market conditions during periods of heightened volatility. Users can adjust the length of the volatility formula to fine-tune this method.
3. Smoothed RSI Method: The smoothed RSI method utilizes a smoothing algorithm to reduce noise in the RSI values, presenting a clearer representation of overbought and oversold conditions. The length of the smoothing can be adjusted to match your trading preferences.
4. Session Weighted RSI Method: With this innovative method, users can specify multipliers for different time sessions throughout the day to manipulate the base RSI. Each session can be customized with start and end times, enabling or disabling specific sessions, and specifying the multiplier for each session. This feature allows traders to adapt the RSI to different market sessions dynamically.
Additionally, the "RSI Supreme Multi-Method" indicator draws divergences on the oscillator, providing an extra layer of analysis for traders. Divergences occur when the direction of the RSI differs from the direction of the price movement, potentially signaling trend reversals.
Key Settings:
RSI Length: Adjust the length of the base RSI before applying any manipulation.
RSI Source: Determine the data source for the base RSI calculation.
Overbought Value: Set the RSI value at which overbought conditions are indicated.
Oversold Value: Set the RSI value at which oversold conditions are indicated.
RSI Type: Choose from four options: Standard, Smoothed, Volatility Manipulated, or Session Manipulated.
Volatility Manipulated Settings: Adjust the length of the volatility formula (applicable to Volatility Manipulated method).
Smoothed Settings: Adjust the length of the smoothing (applicable to Smoothed method).
Session Manipulated Settings: Customize six different time sessions with start and end times, enable or disable specific sessions, and specify multipliers for each session.
Divergence Color: Adjust the color of the drawn divergences to suit your chart's aesthetics.
Divergence Tuning: Fine-tune the sensitivity of the divergence detection for more accurate signals.
The "RSI Supreme Multi-Method" indicator is a versatile and comprehensive tool that can be used to identify overbought and oversold areas, as well as to spot potential trend reversals through divergences. However, like all technical analysis tools, it should be used in conjunction with other indicators and analysis methods to make well-informed trading decisions.
Enhance your trading insights with the "RSI Supreme Multi-Method" indicator and gain an edge in identifying critical market conditions and divergences with precision.
taLibrary "ta"
This library is a Pine Script™ programmer’s tool containing calcs for my oscillators and some helper functions.
buoyancy(src, targetPeriod, maxLookback)
Calculates buoyancy using a target of `src` summed over `targetPeriod` bars, not searching back farther than `maxLookback` bars. See:
Parameters:
src : (series float) The source value that is summed to constitute the target.
targetPeriod : (series int) The qty of bars to sum `src` for in order to calculate the target.
maxLookback : (simple int) The maximum number of bars back the function will search.
Returns: (series float) Buoyancy: the gap between the avg distance of past up and dn bars added to reach the target, divided by the max distance reached. Returns zero when an error condition occurs.
efficientWork(length)
Calculates Efficient Work on `length` bars. See:
Parameters:
length : (simple int) The length of the ALMA used to calculate the result.
Returns: (series float) A -1 to +1 value representing the efficiency of price travel, bar to bar.
ma(type, src, length)
Returns the `type` MA of the `src` over the `length`.
Parameters:
type : (simple string) The type of MA required (uses constants that must be defined earlier in the script).
src : (series float) The source value used to calculate the MA.
length : (simple int) The length value used to calculate the MA.
Returns: (series float) The MA value.
divergenceChannel(divergence, hiSrc, loSrc, breachHiSrc, breachLoSrc)
Calculates the levels and states of divergence channels, which are created when divergences occur.
Parameters:
divergence : (series bool) `true` on divergences, which can be defined any way. On breached channels it creates a new channel, otherwise, channel levels are expanded.
hiSrc : (series float) The price source used to set the channel's hi level when a divergence occurs.
loSrc : (series float) The price source used to set the channel's lo level when a divergence occurs.
breachHiSrc : (series float) The price source that must breach over the channel's `channelHi` level for a breach to occur.
breachLoSrc : (series float) The price source that must breach under the channel's `channelLo` level for a breach to occur.
Returns: A tuple containing the following values:
sourceStrToFloat(srcString)
Converts the name of a source in the `srcString` to its numerical equivalent.
Parameters:
srcString : (series string) The string representing the name of the source value to be returned.
Returns: (series float) The source's value.
Volume CloudsI know it isn't much, but it is my first time coding an indicator!
You might need to modify distance to fit the volatility and time frame of the chart you are looking at (if candles stay on top of cloud in an ugly mess, increase distance until signals can be seen). Sometimes heikin ashi candles can really help clear the picture up as well.
How to use:
The lines are the Volume Weighted Moving Average (green) and Simple Moving Average (red).
Cloud is green when VWMA is above SMA ( bullish ) and red when SMA is above VWMA ( bearish ).
The thicker the cloud, the stronger the signal. Also, the farther price strays from the cloud, the more pressure there is for it to return.
Look for divergences:
When price passes below a green cloud you have a bullish divergence (or if cloud turns green at crossing point)
Price pass above red cloud = bearish divergence.
Or confirmations:
Price passes below red cloud = bearish confirmation.
Price pass above green cloud = bullish confirmation.
I hope this helps you at least a little! Twitter: GarrettZ
Cypto Oscillator with Sortino-like VolatilityEnhanced Inverted Ultimate Oscillator with Sortino-like Volatility
This indicator combines the power of the Ultimate Oscillator with a unique Sortino-like volatility calculation to provide a comprehensive view of market dynamics. It's designed to help traders identify potential turning points and assess the risk associated with price movements.
**Core Components:**
* **Ultimate Oscillator (UO):** The UO is a momentum indicator that incorporates short, medium, and long-term price action to identify overbought and oversold conditions. This indicator inverts and normalizes the UO to a 0-10 scale, providing a clear view of momentum shifts.
* **Sortino-like Volatility:** Instead of a standard deviation, this indicator uses a downside deviation calculation. This focuses specifically on *negative* price movements, offering a more relevant measure of risk for most traders. By not penalizing upside volatility, it avoids giving false signals during strong bull runs. The downside deviation is scaled as a percentage of the closing price for cross-asset comparability.
* **Volatility Signal:** The inverted UO is multiplied by the downside deviation to create a combined volatility signal. This signal reflects both momentum and downside risk, providing a more nuanced market perspective.
**Key Features and Uses:**
* **Identifying Potential Turning Points:** Divergences between the UO and price action can signal potential trend reversals. Look for the UO to make higher lows while price makes lower lows (bullish divergence) or the UO to make lower highs while price makes higher highs (bearish divergence).
* **Assessing Downside Risk:** The Sortino-like volatility component helps traders gauge the potential for downside price swings. Higher volatility suggests greater risk.
* **Dynamic Volatility Thresholds:** The indicator includes adjustable upper and lower volatility thresholds, based on a moving average of the volatility signal. These thresholds can be used to identify periods of unusually high or low volatility.
* **Customizable Lookback Periods:** Traders can adjust the lookback periods for the UO and the standard deviation calculation to fine-tune the indicator to their specific trading style and market conditions.
* **Visualizations:** The indicator provides several visual aids, including:
* A histogram of the volatility signal, colored dynamically based on its relationship to the moving average of volatility. Red indicates volatility above the upper bound, orange between the bounds and green below the lower bound.
* A line plot of the volatility signal.
* An optional moving average of the volatility signal.
* Optional upper and lower volatility threshold lines with a filled range for visual clarity.
* **Alerts:** The indicator includes alert conditions for when the volatility signal crosses above the upper threshold (high volatility) or below the lower threshold (low volatility).
**How to Use:**
1. **Inputs:** Adjust the input parameters to optimize the indicator for your chosen asset and timeframe.
2. **Divergences:** Look for divergences between the UO and price to identify potential trend reversals.
3. **Volatility:** Use the volatility signal and thresholds to assess downside risk.
4. **Alerts:** Enable alerts to be notified of high or low volatility events.
**Disclaimer:** This indicator is for informational purposes only and should not be considered financial advice. Always conduct your own thorough analysis before making any trading decisions.
Key improvements in this description:
Clear and concise language: Easy for traders to understand.
Focus on benefits: Highlights how the indicator can help traders.
Detailed explanation of features: Covers all the important aspects.
How-to-use section: Provides practical guidance.
Disclaimer: Includes a necessary disclaimer.
Emphasis on the Sortino-like approach: This is a unique selling point of your indicator.
Well-structured and formatted: Easy to read and digest.
This description should be a great starting point for sharing your indicator with the TradingView community. You can further customize it by adding screenshots of the indicator in action or linking to a chart where it's being used. Remember to respond to comments and questions from other users to build engagement and improve your indicator over time.
GOLDEN RSI by @thejamiulGOLDEN RSI thejamiul is a versatile Relative Strength Index (RSI)-based tool designed to provide enhanced visualization and additional insights into market trends and potential reversal points. This indicator improves upon the traditional RSI by integrating gradient fills for overbought/oversold zones and divergence detection features, making it an excellent choice for traders who seek precise and actionable signals.
Source of this indicator : This indicator is based on @TradingView original RSI indicator with a little bit of customisation to enhance overbought and oversold identification.
Key Features
1. Customizable RSI Settings:
RSI Length: Adjust the RSI calculation period to suit your trading style (default: 14).
Source Selection: Choose the price source (e.g., close, open, high, low) for RSI calculation.
2. Gradient-Filled RSI Zones:
Overbought Zone (80-100): Gradient fill with shades of green to indicate strong bullish conditions.
Oversold Zone (0-20): Gradient fill with shades of red to highlight strong bearish conditions.
3. Support and Resistance Levels:
Upper Band: 80
Middle Bands: 60 (bullish) and 40 (bearish)
Lower Band: 20
These levels help identify overbought, oversold, and neutral zones.
4. Divergence Detection:
Bullish Divergence: Detects lower lows in price with corresponding higher lows in RSI, signaling potential upward reversals.
Bearish Divergence: Detects higher highs in price with corresponding lower highs in RSI, indicating potential downward reversals.
Visual Indicators:
Bullish divergence is marked with green labels and line plots.
Bearish divergence is marked with red labels and line plots.
5. Alert Functionality:
Custom Alerts: Set up alerts for bullish or bearish divergences to stay notified of potential trading opportunities without constant chart monitoring.
6. Enhanced Chart Visualization:
RSI Plot: A smooth and visually appealing RSI curve.
Color Coding: Gradient and fills for better distinction of trading zones.
Pivot Labels: Clear identification of divergence points on the RSI plot.
DMI Delta by 0xjcfOverview
This indicator integrates the Directional Movement Index (DMI), Average Directional Index (ADX), and volume analysis into an Oscillator designed to help traders identify divergence-based trading signals. Unlike typical volume or momentum indicators, this combination provides insight into directional momentum and volume intensity, allowing traders to make well-informed decisions based on multiple facets of market behavior.
Purpose and How Components Work Together
By combining DMI and ADX with volume analysis, this indicator helps traders detect when momentum diverges from price action—a common precursor to potential reversals or significant moves. The ADX filter enhances this by distinguishing trending from range-bound conditions, while volume analysis highlights moments of extreme sentiment, such as solid buying or selling. Together, these elements provide traders with a comprehensive view of market strength, directional bias, and volume surges, which help filter out weaker signals.
Key Features
DMI Delta and Oscillator: The DMI indicator measures directional movement by comparing DI+ and DI- values. This difference (DMI Delta) is calculated and displayed as a histogram, visualizing changes in directional bias. When combined with ADX filtering, this histogram helps traders gauge the strength of momentum and spot directional shifts early. For instance, a rising histogram in a bearish price trend might signal a potential bullish reversal.
Volume Analysis with Extremes: Volume is monitored to reveal when market participation is unusually high, using a customizable multiplier to highlight significant volume spikes. These extreme levels are color-coded directly on the histogram, providing visual cues on whether buying or selling interest is particularly strong. Volume analysis adds depth to the directional insights from DMI, allowing traders to differentiate between regular and powerful moves.
ADX Trending Filter: The ADX component filters trends by measuring the overall strength of a price move, with a default threshold of 25. When ADX is above this level, it suggests that the market is trending strongly, making the DMI Delta readings more reliable. Below this threshold, the market is likely range-bound, cautioning traders that signals might not have as much follow-through.
Using the Indicator in Divergence Strategies
This indicator excels in divergence strategies by highlighting moments when price action diverges from directional momentum. Here’s how it aids in decision-making:
Bullish Divergence: If the price is falling to new lows while the DMI Delta histogram rises, it can indicate weakening bearish momentum and signal a potential price reversal to the upside.
Bearish Divergence: Conversely, if prices are climbing but the DMI Delta histogram falls, it may point to waning bullish momentum, suggesting a bearish reversal.
Visual Cues and Customization
The color-coded output enhances usability:
Bright Green/Red: Extreme volume with strong bullish or bearish signals, often at points of high potential for trend continuation or reversal.
Green/Red Shades: These shades reflect trending conditions (bullish or bearish) based on ADX, factoring in volume. Green signals a bullish trend, and red is a bearish trend.
Blue/Orange Shades: Indicates non-trending or weaker conditions, suggesting a more cautious approach in range-bound markets.
Customizable for Diverse Trading Styles
This indicator allows users to adjust settings like the ADX threshold and volume multiplier to optimize performance for various timeframes and strategies. Whether a trader prefers swing trading or intraday scalping, these parameters enable fine-tuning to enhance signal reliability across different market contexts.
Practical Usage Tips
Entry and Exit Signals: Use this indicator in conjunction with price action. Divergences between the price and DMI Delta histogram can reinforce entry or exit decisions.
Adjust Thresholds: Based on backtesting, customize the ADX Trending Threshold and Volume Multiplier to ensure optimal performance on different timeframes or trading styles.
In summary, this indicator is tailored for traders seeking a multi-dimensional approach to market analysis. It blends momentum, trend strength, and volume insights to support divergence-based strategies, helping traders confidently make informed decisions. Remember to validate signals through backtesting and use it alongside price action for the best results.
Stochastic Momentum Index (SMI) of Money Flow Index (MFI)"He who does not know how to make predictions and makes light of his opponents, underestimating his ability, will certainly be defeated by them."
(Sun Tzu - The Art of War)
▮ Introduction
The Stochastic Momentum Index (SMI) is a technical analysis indicator that uses the difference between the current closing price and the high or low price over a specific time period to measure price momentum.
On the other hand, the Money Flow Index (MFI) is an indicator that uses volume and price to measure buying and selling pressure.
When these two indicators are combined, they can provide a more comprehensive view of price direction and market strength.
▮ Improvements
By combining SMI with MFI, we can gain even more insights into the market. One way to do this is to use the MFI as an input to the SMI, rather than just using price.
This means we are measuring momentum based on buying and selling pressure rather than just price.
Another way to improve this indicator is to adjust the periods to suit your specific trading needs.
▮ What to look
When using the SMI MFI indicator, there are a few things to look out for.
First, look at the SMI signal line.
When the line crosses above -40, it is considered a buy signal, while the crossing below +40 is considered a sell signal.
Also, pay attention to divergences between the SMI MFI and the price.
If price is rising but the SMI MFI is showing negative divergence, it could indicate that momentum is waning and a reversal could be in the offing.
Likewise, if price is falling but the SMI MFI is showing positive divergence, this could indicate that momentum is building and a reversal could also be in the offing.
In the examples below, I show the use in conjunction with the price SMI, in which the MFI SMI helps to anticipate divergences:
In summary, the SMI MFI is a useful indicator that can provide valuable insights into market direction and price strength.
By adjusting the timeframes and paying attention to divergences and signal line crossovers, traders can use it as part of a broader trading strategy.
However, remember that no indicator is a magic bullet and should always be used in conjunction with other analytics and indicators to make informed trading decisions.
[blackcat] L1 New TRIX ScalperNOTE: Because the originally released script failed to comply with the House Rule in the description, it was banned. After revising and reviewing the description, it is republished again. Please forgive the inconvenience caused.
Level: 1
Background
The Triple Exponential Moving Average (TRIX) indicator is a strong technical analysis tool. It can help investors determine the price momentum and identify oversold and overbought signals in a financial asset. Jack Hutson is the creator of the TRIX indicator . He created it in the early 1980s to show the rate of change in a triple exponentially smoothed moving average.
When used as an oscillator, it shows a potential peak and trough price zones. A positive value tells traders that there is an overbought market while a negative value means an oversold market. When traders use TRIX as a momentum indicator, it filters spikes in the price that are vital to the general dominant trend.
A positive value means momentum is rising while a negative value means that momentum is reducing. A lot of analysts believe that when the TRIX crosses above the zero line it produces a buy signal, and when it closes below the zero line, it produces a sell signal.The indicator has three major components:
Zero line
TRIX line (or histograms)
Percentage Scale
Function
The TRIX indicator determines overbought and oversold markets, and it can also be a momentum indicator. Just as it is with most oscillators, TRIX oscillates around a zero line. Additionally, divergences between price and TRIX can mean great turning points in the market. TRIX calculates a triple exponential moving average of the log of the price input. It calculates this based on the time specified by the length input for the current bar.
Trading TRIX indicator signals
Zero line cross
TRIX can help determine the impulse of the market. With the 0 value acting as a centerline, if it crosses from below, it will be mean that the impulse is growing in the market.Traders can, therefore, look for opportunities to place buy orders in the market. Similarly, a cross of the centerline from above will mean a shrinking impulse in the market. Traders can, therefore, look for opportunities to sell in the market.
Signal line cross
To select the best entry points, investors add a signal line on the TRIX indicator. The signal line is a moving average of the TRIX indicator, and due to this, it will lag behind the TRIX.A signal to place a buy order will occur when the TRIX crosses the signal line from below. In the same way, a signal to place a sell order will come up when the TRIX crosses the signal line from above. This is applicable in both trending and ranging markets.In trending markets, a signal line cross will indicate an end of the price retracement, and the main trend will resume. In ranging markets, a signal line confirms that resistance and support zones have been upheld in the market.
Divergences
Traders can use the Triple Exponential Average can to identify when important turning points can happen in the market. They can achieve this by looking at divergences. Divergences happen when the price is moving in the opposite direction as the TRIX indicator.When price makes higher highs but the TRIX makes lower highs, it means that the up-trend is weakening, and a bearish reversal is about to form. When the price makes lower lows, but the TRIX makes higher lows, it means that a bullish reversal is about to happen. Bullish and bearish divergences happen when the security and the indicator do not confirm themselves. A bullish divergence can happen when the security makes a lower low, but the indicator forms a higher low. This higher low means less downside momentum that may foreshadow a bullish reversal. A bearish divergence happens when the commodity makes a higher low, but the indicator forms a lower high. This lower high indicates weak upside momentum that can foreshadow a bearish reversal sometimes. Bearish divergences do not work well in strong uptrends. Even though momentum appears to be weakening due to the indicator is making lower highs, momentum still has a bullish bias as long as it is above its centerline.When bullish and bearish divergences work, they work very well. The secret is to separate the bad signals from the good signals.
Key Signal
RXval --> new TRIX indicator.
AvgTRX --> linear regression average of new TRIX indicator.
Remarks
This is a Level 1 free and open source indicator.
Feedbacks are appreciated.
MACD Trend CandlesThe script combines 2 indicators (MACD and Stoch-RSI) and puts them visually directly on the candles - can be used with normal OHLC candles or Heiken Ashi candles. Furthermore, you can derive divergences exremely easy directly visually from the candles as well. Lastly, a SMA 20 high and a SMA 20 low line build a trend channel.
Script is best used in trending markets to trade with the trend.
1) SMA trend channel:
* uptrend: close above
* downtrend: close below
* aggressive entry (uptrend) closing inside channel from below
* conservative entry (uptrend) closing above channel from inside
* hold (uptrend) until close below channel
* can be used accordingly for the downtrend
2) MACD candles
* visualization of the MACD histogram directly on the candles
* dark blue: histogram > 0 and histogram > histogram of previous candle
* light blue: histogram > 0 and histogram < histogram of previous candle
* orange: histogram < 0 and histogram < histogram of previous candle
* light blue: histogram < 0 and histogram > histogram of previous candle
* hold uptrend (dark/light blue candles) - combined with trend channel (above channel)
* hold downtrend (orange /yellow candles) - combined with trend channel (below channel)
* Color divergence: light blue candle > dark blue candle (price and MACD show divergence (bearish)
* Color divergence: yellow candle < orange candle (price and MACD show divergence (bullish)
* Trend change (0 line cross to upside) yellow or orange to dark blue
* Trend change (0 line cross to downside) dark or light blue to orange
3) Stoch RSI diamonds
* visualization of the STOCH-RSI as diamonds above or below the candle
* k, d line > 80: diamond above the candle
* k, d line < 20: diamond below the candle
* divergence caldle without diamond above > candle with diamond above (bearish divergence)
* divergence caldle without diamond below < candle with diamond below (bullish divergence)
Feel free to test each part individually and combine it with other indicators, e.g. BBands and Ichimoku Cloud - you will see it is a powerful visualization script
HAVE FUN
Advanced Trend Momentum [Alpha Extract]The Advanced Trend Momentum indicator provides traders with deep insights into market dynamics by combining exponential moving average analysis with RSI momentum assessment and dynamic support/resistance detection. This sophisticated multi-dimensional tool helps identify trend changes, momentum divergences, and key structural levels, offering actionable buy and sell signals based on trend strength and momentum convergence.
🔶 CALCULATION
The indicator processes market data through multiple analytical methods:
Dual EMA Analysis: Calculates fast and slow exponential moving averages with dynamic trend direction assessment and ATR-normalized strength measurement.
RSI Momentum Engine: Implements RSI-based momentum analysis with enhanced overbought/oversold detection and momentum velocity calculations.
Pivot-Based Structure: Identifies and tracks dynamic support and resistance levels using pivot point analysis with configurable level management.
Signal Integration: Combines trend direction, momentum characteristics, and structural proximity to generate high-probability trading signals.
Formula:
Fast EMA = EMA(Close, Fast Length)
Slow EMA = EMA(Close, Slow Length)
Trend Direction = Fast EMA > Slow EMA ? 1 : -1
Trend Strength = |Fast EMA - Slow EMA| / ATR(Period) × 100
RSI Momentum = RSI(Close, RSI Length)
Momentum Value = Change(Close, 5) / ATR(10) × 100
Pivot Support/Resistance = Dynamic pivot arrays with configurable lookback periods
Bullish Signal = Trend Change + Momentum Confirmation + Strength > 1%
Bearish Signal = Trend Change + Momentum Confirmation + Strength > 1%
🔶 DETAILS
Visual Features:
Trend EMAs: Fast and slow exponential moving averages with dynamic color coding (bullish/bearish)
Enhanced RSI: RSI oscillator with color-coded zones, gradient fills, and reference bands at overbought/oversold levels
Trend Fill: Dynamic gradient between EMAs indicating trend strength and direction
Support/Resistance Lines: Horizontal levels extending from pivot-based calculations with configurable maximum levels
Momentum Candles: Color-coded candlestick overlay reflecting combined trend and momentum conditions
Divergence Markers: Diamond-shaped signals highlighting bullish and bearish momentum divergences
Analysis Table: Real-time summary of trend direction, strength percentage, RSI value, and momentum reading
Interpretation:
Trend Direction: Bullish when Fast EMA crosses above Slow EMA with strength confirmation
Trend Strength > 1%: Strong trending conditions with institutional participation
RSI > 70: Overbought conditions, potential selling opportunity
RSI < 30: Oversold conditions, potential buying opportunity
Momentum Divergence: Price and momentum moving opposite directions signal potential reversals
Support/Resistance Proximity: Dynamic levels provide optimal entry/exit zones
Combined Signals: Trend changes with momentum confirmation generate high-probability opportunities
🔶 EXAMPLES
Trend Confirmation: Fast EMA crossing above Slow EMA with trend strength exceeding 1% and positive momentum confirms strong bullish conditions.
Example: During institutional accumulation phases, EMA crossovers with momentum confirmation have historically preceded significant upward moves, providing optimal long entry points.
15min
4H
Momentum Divergence Detection: RSI reaching overbought levels while momentum decreases despite rising prices signals potential trend exhaustion.
Example: Bearish divergence signals appearing at resistance levels have marked major market tops, allowing traders to secure profits before corrections.
Support/Resistance Integration: Dynamic pivot-based levels combined with trend and momentum signals create high-probability trading zones.
Example: Bullish trend changes occurring near established support levels offer optimal risk-reward entries with clearly defined stop-loss levels.
Multi-Dimensional Confirmation: The indicator's combination of trend, momentum, and structural analysis provides comprehensive market validation.
Example: When trend direction aligns with momentum characteristics near key structural levels, the confluence creates institutional-grade trading opportunities with enhanced probability of success.
🔶 SETTINGS
Customization Options:
Trend Analysis: Fast EMA Length (default: 12), Slow EMA Length (default: 26), Trend Strength Period (default: 14)
Support & Resistance: Pivot Length for level detection (default: 10), Maximum S/R Levels displayed (default: 3), Toggle S/R visibility
Momentum Settings: RSI Length (default: 14), Oversold Level (default: 30), Overbought Level (default: 70)
Visual Configuration: Color schemes for bullish/bearish/neutral conditions, transparency settings for fills, momentum candle overlay toggle
Display Options: Analysis table visibility, divergence marker size, alert system configuration
The Advanced Trend Momentum indicator provides traders with comprehensive insights into market dynamics through its sophisticated integration of trend analysis, momentum assessment, and structural level detection. By combining multiple analytical dimensions into a unified framework, this tool helps identify high-probability opportunities while filtering out market noise through its multi-confirmation approach, enabling traders to make informed decisions across various market cycles and timeframes.
Ratio-Adjusted McClellan Summation Index RASI NASIRatio-Adjusted McClellan Summation Index (RASI NASI)
In Book "The Complete Guide to Market Breadth Indicators" Author Gregory L. Morris states
"It is the author’s opinion that the McClellan indicators, and in particular, the McClellan Summation Index, is the single best breadth indicator available. If you had to pick just one, this would be it."
What It Does: The Ratio-Adjusted McClellan Summation Index (RASI) is a market breadth indicator that tracks the cumulative strength of advancing versus declining issues for a user-selected exchange (NASDAQ, NYSE, or AMEX). Derived from the McClellan Oscillator, it calculates ratio-adjusted net advances, applies 19-day and 39-day EMAs, and sums the oscillator values to produce the RASI. This indicator helps traders assess market health, identify bullish or bearish trends, and detect potential reversals through divergences.
Key features:
Exchange Selection : Choose NASDAQ (USI:ADVN.NQ, USI:DECL.NQ), NYSE (USI:ADVN.NY, USI:DECL.NY), or AMEX (USI:ADVN.AM, USI:DECL.AM) data.
Trend-Based Coloring : RASI line displays user-defined colors (default: black for uptrend, red for downtrend) based on its direction.
Customizable Moving Average: Add a moving average (SMA, EMA, WMA, VWMA, or RMA) with user-defined length and color (default: EMA, 21, green).
Neutral Line at Zero: Marks the neutral level for trend interpretation.
Alerts: Six custom alert conditions for trend changes, MA crosses, and zero-line crosses.
How to Use
Add to Chart: Apply the indicator to any TradingView chart. Ensure access to advancing and declining issues data for the selected exchange.
Select Exchange: Choose NASDAQ, NYSE, or AMEX in the input settings.
Customize Settings: Adjust EMA lengths, RASI colors, MA type, length, and color to match your trading style.
Interpret the Indicator :
RASI Line: Black (default) indicates an uptrend (RASI rising); red indicates a downtrend (RASI falling).
Above Zero: Suggests bullish market breadth (more advancing issues).
Below Zero : Indicates bearish breadth (more declining issues).
MA Crosses: RASI crossing above its MA signals bullish momentum; crossing below signals bearish momentum.
Divergences: Compare RASI with the market index (e.g., NASDAQ Composite) to identify potential reversals.
Large Moves : A +3,600-point move from a low (e.g., -1,550 to +1,950) may signal a significant bull run.
Set Alerts:
Add the indicator to your chart, open the TradingView alert panel, and select from six conditions (see Alerts section).
Configure notifications (e.g., email, webhook, or popup) for each condition.
Settings
Market Selection:
Exchange: Select NASDAQ, NYSE, or AMEX for advancing/declining issues data.
EMA Settings:
19-day EMA Length: Period for the shorter EMA (default: 19).
39-day EMA Length: Period for the longer EMA (default: 39).
RASI Settings:
RASI Uptrend Color: Color for rising RASI (default: black).
RASI Downtrend Color: Color for falling RASI (default: red).
RASI MA Settings:
MA Type: Choose SMA, EMA, WMA, VWMA, or RMA (default: EMA).
MA Length: Set the MA period (default: 21).
MA Color: Color for the MA line (default: green).
Alerts
The indicator uses alertcondition() to create custom alerts. Available conditions:
RASI Trend Up: RASI starts rising (based on RASI > previous RASI, shown as black line).
RASI Trend Down: RASI starts falling (based on RASI ≤ previous RASI, shown as red line).
RASI Above MA: RASI crosses above its moving average.
RASI Below MA: RASI crosses below its moving average.
RASI Bullish: RASI crosses above zero (bullish market breadth).
RASI Bearish: RASI crosses below zero (bearish market breadth).
To set alerts, add the indicator to your chart, open the TradingView alert panel, and select the desired condition.
Notes
Data Requirements: Requires access to advancing/declining issues data (e.g., USI:ADVN.NQ, USI:DECL.NQ for NASDAQ). Some symbols may require a TradingView premium subscription.
Limitations: RASI is a medium- to long-term indicator and may lag in volatile or range-bound markets. Use alongside other technical tools for confirmation.
Data Reliability : Verify the selected exchange’s data accuracy, as inconsistencies can affect results.
Debugging: If no data appears, check symbol validity (e.g., try $ADVN/Q, $DECN/Q for NASDAQ) or contact TradingView support.
Credits
Based on the Ratio-Adjusted McClellan Summation Index methodology by McClellan Financial Publications. No external code was used; the implementation is original, inspired by standard market breadth concepts.
Disclaimer
This indicator is for informational purposes only and does not constitute financial advice. Past performance is not indicative of future results. Conduct your own research and combine with other tools for informed trading decisions.
IDX - 5UPThe UDX-5UP is a custom indicator designed to assist traders in identifying trends, entry and exit signals, and market reversal moments with greater accuracy. It combines price analysis, volume, and momentum (RSI) to provide clear buy ("Buy") and sell ("Sell") signals across any asset and timeframe, whether you're a scalper on the 5M chart or a swing trader on the 4H chart. Inspired by robust technical analysis strategies, the UDX-5UP is ideal for traders seeking a reliable tool to operate in volatile markets such as cryptocurrencies, forex, stocks, and futures.
Components of the UDX-5UP
The UDX-5UP consists of three main panels that work together to provide a comprehensive view of the market:
Main Panel (Price):
Pivot Supertrend: A dynamic line that changes color to indicate the trend. Green for an uptrend (look for buys), red for a downtrend (look for sells).
SMAs (Simple Moving Averages): Two SMAs (8 and 21 periods) to confirm the trend direction. When the SMA 8 crosses above the SMA 21, it’s a bullish signal; when it crosses below, it’s a bearish signal.
Entry/Exit Signals: "Buy" (green) and "Sell" (red) labels are plotted on the chart when entry or exit conditions are met.
Volume Panel:
Colored Volume Bars: Green bars indicate dominant buying volume, while red bars indicate dominant selling volume.
Volume Moving Average (MA 20): A blue line that helps identify whether the current volume is above or below the average, confirming the strength of the movement.
RSI Panel:
RSI (Relative Strength Index): Calculated with a period of 14, with overbought (70) and oversold (30) lines to identify momentum extremes.
Divergences: The indicator detects divergences between the RSI and price, plotting signals for potential reversals.
How the UDX-5UP Works
The UDX-5UP uses a combination of rules to generate buy and sell signals:
Buy Signal ("Buy"):
The Pivot Supertrend changes from red to green.
The SMA 8 crosses above the SMA 21.
The volume is above the MA 20, with green bars (indicating buying pressure).
The RSI is rising and, ideally, below 70 (not overbought).
Example: On the 4H chart, the price of Tether (USDT) is at 0.05515. The Pivot Supertrend turns green, the SMA 8 crosses above the SMA 21, the volume shows green bars above the MA 20, and the RSI is at 46. The UDX-5UP plots a "Buy".
Sell Signal ("Sell"):
The Pivot Supertrend changes from green to red.
The SMA 8 crosses below the SMA 21.
The volume is above the MA 20, with red bars (indicating selling pressure).
The RSI is falling and, ideally, above 70 (overbought).
Example: On the 4H chart, the price of Tether rises to 0.05817. The Pivot Supertrend turns red, the SMA 8 crosses below the SMA 21, the volume shows red bars, and the RSI is above 70. The UDX-5UP plots a "Sell".
RSI Divergences:
The indicator identifies bullish divergences (price makes a lower low, but RSI makes a higher low) and bearish divergences (price makes a higher high, but RSI makes a lower high), plotting alerts for potential reversals.
Adjustable Settings
The UDX-5UP is highly customizable to suit your trading style:
Pivot Supertrend Period: Default is 2. Increase to 3 or 4 for more conservative signals (fewer false positives, but more lag).
SMA Periods: Default is 8 and 21. Adjust to 5 and 13 for smaller timeframes (e.g., 5M) or 13 and 34 for larger timeframes (e.g., 1D).
RSI Period: Default is 14. Reduce to 10 for greater sensitivity or increase to 20 for smoother signals.
Overbought/Oversold Levels: Default is 70/30. Adjust to 80/20 in volatile markets.
Display Panels: You can enable/disable the volume and RSI panels to simplify the chart.
How to Use the UDX-5UP
Identify the Trend:
Use the Pivot Supertrend and SMAs to determine the market direction. Uptrend: look for buys. Downtrend: look for sells.
Confirm with Volume and RSI:
For buys: Volume above the MA 20 with green bars, RSI rising and below 70.
For sells: Volume above the MA 20 with red bars, RSI falling and above 70.
Enter the Trade:
Enter a buy when the UDX-5UP plots a "Buy" and all conditions are aligned.
Enter a sell when the UDX-5UP plots a "Sell" and all conditions are aligned.
Plan the Exit:
Use Fibonacci levels or support/resistance on the price chart to set targets.
Exit the trade when the UDX-5UP plots an opposite signal ("Sell" after a buy, "Buy" after a sell).
Tips for Beginners
Start with Larger Timeframes: Use the 4H or 1D chart for more reliable signals and less noise.
Combine with Other Indicators: Use the UDX-5UP with tools like Fibonacci or the Candles RSI (another powerful indicator) to confirm signals.
Practice in Demo Mode: Test the indicator in a demo account before using real money.
Manage Risk: Always use a stop-loss and don’t risk more than 1-2% of your capital per trade.
Why Use the UDX-5UP?
Simplicity: Clear "Buy" and "Sell" signals make trading accessible even for beginners.
Versatility: Works on any asset (crypto, forex, stocks) and timeframe.
Multiple Confirmations: Combines price, volume, and momentum to reduce false signals.
Customizable: Adjust the settings to match your trading style.
Author’s Notes
The UDX-5UP was developed based on years of trading and technical analysis experience. It is an evolution of tested strategies, designed to help traders navigate volatile markets with confidence. However, no indicator is infallible. Always combine the UDX-5UP with proper risk management and fundamental analysis, especially in unpredictable markets. Feedback is welcome – leave a comment or reach out with suggestions for improvements!
lib_divergenceLibrary "lib_divergence"
offers a commonly usable function to detect divergences. This will take the default RSI or other symbols / indicators / oscillators as source data.
divergence(osc, pivot_left_bars, pivot_right_bars, div_min_range, div_max_range, ref_low, ref_high, min_divergence_offset_fraction, min_divergence_offset_dev_len, min_divergence_offset_atr_mul)
Detects Divergences between Price and Oscillator action. For bullish divergences, look at trend lines between lows. For bearish divergences, look at trend lines between highs. (strong) oscillator trending, price opposing it | (medium) oscillator trending, price trend flat | (weak) price opposite trending, oscillator trend flat | (hidden) price trending, oscillator opposing it. Pivot detection is only properly done in oscillator data, reference price data is only compared at the oscillator pivot (speed optimization)
Parameters:
osc (float) : (series float) oscillator data (can be anything, even another instrument price)
pivot_left_bars (simple int) : (simple int) optional number of bars left of a confirmed pivot point, confirming it is the highest/lowest in the range before and up to the pivot (default: 5)
pivot_right_bars (simple int) : (simple int) optional number of bars right of a confirmed pivot point, confirming it is the highest/lowest in the range from and after the pivot (default: 5)
div_min_range (simple int) : (simple int) optional minimum distance to the pivot point creating a divergence (default: 5)
div_max_range (simple int) : (simple int) optional maximum amount of bars in a divergence (default: 50)
ref_low (float) : (series float) optional reference range to compare the oscillator pivot points to. (default: low)
ref_high (float) : (series float) optional reference range to compare the oscillator pivot points to. (default: high)
min_divergence_offset_fraction (simple float) : (simple float) optional scaling factor for the offset zone (xDeviation) around the last oscillator H/L detecting following equal H/Ls (default: 0.01)
min_divergence_offset_dev_len (simple int) : (simple int) optional lookback distance for the deviation detection for the offset zone around the last oscillator H/L detecting following equal H/Ls. Used as well for the ATR that does the equal H/L detection for the reference price. (default: 14)
min_divergence_offset_atr_mul (simple float) : (simple float) optional scaling factor for the offset zone (xATR) around the last price H/L detecting following equal H/Ls (default: 1)
@return A tuple of deviation flags.
On Balance Volume Oscillator of Trading Volume TrendOn Balance Volume Oscillator of Trading Volume Trend
Introduction
This indicator, the "On Balance Volume Oscillator of Trading Volume Trend," is a technical analysis tool designed to provide insights into market momentum and potential trend reversals by combining the On Balance Volume (OBV) and Relative Strength Index (RSI) indicators.
Calculation and Methodology
* OBV Calculation: The indicator first calculates the On Balance Volume, which is a cumulative total of the volume of up days minus the volume of down days. This provides a running tally of buying and selling pressure.
* RSI of OBV: The RSI is then applied to the OBV values to smooth the data and identify overbought or oversold conditions.
* Exponential Moving Averages (EMAs): Two EMAs are calculated on the RSI of OBV. A shorter-term EMA (9-period in this case) and a longer-term EMA (100-period) are used to generate signals.
Interpretation and Usage
* EMA Crossovers: When the shorter-term EMA crosses above the longer-term EMA, it suggests increasing bullish momentum. Conversely, a downward crossover indicates weakening bullish momentum or increasing bearish pressure.
* RSI Divergences: Divergences between the price and the indicator can signal potential trend reversals. For example, if the price is making new highs but the indicator is failing to do so, it could be a bearish divergence.
* Overbought/Oversold Conditions: When the RSI of OBV is above 70, it suggests the market may be overbought and a potential correction could be imminent. Conversely, when it is below 30, it suggests the market may be oversold.
Visual Representation
The indicator is plotted on a chart with multiple lines and filled areas:
* Two EMAs: The shorter-term EMA and longer-term EMA are plotted to show the trend of the OBV.
* Filled Areas: The area between the two EMAs is filled with a color to indicate the strength of the trend. The color changes based on whether the shorter-term EMA is above or below the longer-term EMA.
* RSI Bands: Horizontal lines at 30 and 70 mark the overbought and oversold levels for the RSI of OBV.
Summary
The On Balance Volume Oscillator of Trading Volume Trend provides a comprehensive view of market momentum and can be a valuable tool for traders. By combining the OBV and RSI, this indicator helps identify potential trend reversals, overbought and oversold conditions, and the strength of the current trend.
Note: This indicator should be used in conjunction with other technical analysis tools and fundamental analysis to make informed trading decisions.
