Double Candle Trend Counter [theEccentricTrader]█ OVERVIEW
This indicator counts the number of confirmed double candle trend scenarios on any given candlestick chart and displays the statistics in a table, which can be repositioned and resized at the user's discretion.
█ CONCEPTS
Green and Red Candles
• A green candle is one that closes with a close price equal to or above the price it opened.
• A red candle is one that closes with a close price that is lower than the price it opened.
Upper Candle Trends
• A higher high candle is one that closes with a higher high price than the high price of the preceding candle.
• A lower high candle is one that closes with a lower high price than the high price of the preceding candle.
• A double-top candle is one that closes with a high price that is equal to the high price of the preceding candle.
Lower Candle Trends
• A higher low candle is one that closes with a higher low price than the low price of the preceding candle.
• A lower low candle is one that closes with a lower low price than the low price of the preceding candle.
• A double-bottom candle is one that closes with a low price that is equal to the low price of the preceding candle.
Muti-Part Upper and Lower Candle Trends
• A multi-part higher high trend begins with the formation of a new higher high and continues until a new lower high ends the trend.
• A multi-part lower high trend begins with the formation of a new lower high and continues until a new higher high ends the trend.
• A multi-part higher low trend begins with the formation of a new higher low and continues until a new lower low ends the trend.
• A multi-part lower low trend begins with the formation of a new lower low and continues until a new higher low ends the trend.
Double Candle Trends
• A double uptrend candle trend is formed when a candle closes with both a higher high and a higher low.
• A double downtrend candle trend is formed when a candle closes with both a lower high and a lower low.
Multi-Part Double Candle Trends
• A multi-part double uptrend candle trend begins with the formation of a new double uptrend candle trend and continues until a new lower high or lower low ends the trend.
• A multi-part double downtrend candle trend begins with the formation of a new double downtrend candle trend and continues until a new higher high or higher low ends the trend.
█ FEATURES
Inputs
• Start Date
• End Date
• Position
• Text Size
• Show Plots
Table
The table is colour coded, consists of seven columns and, as many as, thirty-two rows. Blue cells denote the multi-part trend scenarios, green cells denote the corresponding double uptrend candle trend scenarios and red cells denote the corresponding double downtrend candle trend scenarios.
The multi-part double candle trend scenarios are listed in the first column with their corresponding total counts to the right, in the second and fifth columns. The last row in column one, displays the sample period which can be adjusted or hidden via indicator settings.
The third and sixth columns display the double candle trend scenarios as percentages of total 1-part double candle trends. And columns four and seven display the total double candle trend scenarios as percentages of the last, or preceding double candle trend part. For example 4-part double uptrend candle trends as percentages of 3-part double uptrend candle trends.
Plots
I have added plots as a visual aid to the double candle trend scenarios. Green up-arrows, with the number of the trend part, denote double uptrend candle trends. Red down-arrows, with the number of the trend part, denote double downtrend candle trends.
█ HOW TO USE
This indicator is intended for research purposes, strategy development and strategy optimisation. I hope it will be useful in helping to gain a better understanding of the underlying dynamics at play on any given market and timeframe.
It can, for example, give you an idea of whether the current double candle trend will continue or fail, based on the current trend scenario and what has happened in the past under similar circumstances. Such information can be useful when conducting top down analysis across multiple timeframes and making strategic decisions.
What you do with these statistics and how far you decide to take your research is entirely up to you, the possibilities are endless.
█ LIMITATIONS
Some higher timeframe candles on tickers with larger lookbacks such as the DXY , do not actually contain all the open, high, low and close (OHLC) data at the beginning of the chart. Instead, they use the close price for open, high and low prices. So, while we can determine whether the close price is higher or lower than the preceding close price, there is no way of knowing what actually happened intra-bar for these candles. And by default candles that close at the same price as the open price, will be counted as green. You can avoid this problem by utilising the sample period filter.
It is also worth noting that the sample size will be limited to your Trading View subscription plan. Premium users get 20,000 candles worth of data, pro+ and pro users get 10,000, and basic users get 5,000. If upgrading is currently not an option, you can always keep a rolling tally of the statistics in an excel spreadsheet or something of the like.
Search in scripts for "high low"
Upper and Lower Candle Trend Counter [theEccentricTrader]█ OVERVIEW
This indicator counts the number of confirmed upper and lower candle trend scenarios on any given candlestick chart and displays the statistics in a table, which can be repositioned and resized at the user's discretion.
█ CONCEPTS
Green and Red Candles
• A green candle is one that closes with a close price equal to or above the price it opened.
• A red candle is one that closes with a close price that is lower than the price it opened.
Upper Candle Trends
• A higher high candle is one that closes with a higher high price than the high price of the preceding candle.
• A lower high candle is one that closes with a lower high price than the high price of the preceding candle.
• A double-top candle is one that closes with a high price that is equal to the high price of the preceding candle.
Lower Candle Trends
• A higher low candle is one that closes with a higher low price than the low price of the preceding candle.
• A lower low candle is one that closes with a lower low price than the low price of the preceding candle.
• A double-bottom candle is one that closes with a low price that is equal to the low price of the preceding candle.
Muti-Part Upper and Lower Candle Trends
• A multi-part higher high trend begins with the formation of a new higher high and continues until a new lower high ends the trend.
• A multi-part lower high trend begins with the formation of a new lower high and continues until a new higher high ends the trend.
• A multi-part higher low trend begins with the formation of a new higher low and continues until a new lower low ends the trend.
• A multi-part lower low trend begins with the formation of a new lower low and continues until a new higher low ends the trend.
█ FEATURES
Inputs
• Start Date
• End Date
• Position
• Text Size
Table
The table is colour coded, consists of seven columns and, as many as, sixty-two rows. Blue cells denote the multi-part trend scenarios, green cells denote the corresponding upper candle trend scenarios and red cells denote the corresponding lower candle trend scenarios.
The multi-part candle trend scenarios are listed in the first column with their corresponding total counts to the right, in the second and fifth columns. The last row in column one, displays the sample period which can be adjusted or hidden via indicator settings.
The third and sixth columns display the candle trend scenarios as percentages of total 1-part candle trends. And columns four and seven display the total candle trend scenarios as percentages of the last, or preceding candle trend part. For example 4-part higher high trends as a percentages of 3-part higher high trends. This offers more insight into what might happen next at any given point in time.
Plots
For a visual aid to this indicator please use in conjunction with my Upper Candle Trends and Lower Candle Trends indicators which can both be found on my profile page under scripts, or in community scripts under the same names.
Green up-arrows, with the number of the trend part, denote higher high trends when above bar and higher low trends when below bar. Red down-arrows, with the number of the trend part, denote lower high trends when above bar and lower low trends when below bar.
█ HOW TO USE
This is intended for research purposes, strategy development and strategy optimisation. I hope it will be useful in helping to gain a better understanding of the underlying dynamics at play on any given market and timeframe.
It can, for example, give you an idea of whether the current upper or lower candle trend will continue or fail, based on the current trend scenario and what has happened in the past under similar circumstances. Such information can be useful when conducting top down analysis across multiple timeframes and making strategic decisions.
What you do with these statistics and how far you decide to take your research is entirely up to you, the possibilities are endless.
█ LIMITATIONS
Some higher timeframe candles on tickers with larger lookbacks such as the DXY , do not actually contain all the open, high, low and close (OHLC) data at the beginning of the chart. Instead, they use the close price for open, high and low prices. So, while we can determine whether the close price is higher or lower than the preceding close price, there is no way of knowing what actually happened intra-bar for these candles. And by default candles that close at the same price as the open price, will be counted as green. You can avoid this problem by utilising the sample period filter.
It is also worth noting that the sample size will be limited to your Trading View subscription plan. Premium users get 20,000 candles worth of data, pro+ and pro users get 10,000, and basic users get 5,000. If upgrading is currently not an option, you can always keep a rolling tally of the statistics in an excel spreadsheet or something of the like.
Parallel Projections [theEccentricTrader]█ OVERVIEW
This indicator automatically projects parallel trendlines or channels, from a single point of origin. In the example above I have applied the indicator twice to the 1D SPXUSD. The five upper lines (green) are projected at an angle of -5 from the 1-month swing high anchor point with a projection ratio of -72. And the seven lower lines (blue) are projected at an angle of 10 with a projection ratio of 36 from the 1-week swing low anchor point.
█ CONCEPTS
Green and Red Candles
• A green candle is one that closes with a high price equal to or above the price it opened.
• A red candle is one that closes with a low price that is lower than the price it opened.
Swing Highs and Swing Lows
• A swing high is a green candle or series of consecutive green candles followed by a single red candle to complete the swing and form the peak.
• A swing low is a red candle or series of consecutive red candles followed by a single green candle to complete the swing and form the trough.
Peak and Trough Prices (Basic)
• The peak price of a complete swing high is the high price of either the red candle that completes the swing high or the high price of the preceding green candle, depending on which is higher.
• The trough price of a complete swing low is the low price of either the green candle that completes the swing low or the low price of the preceding red candle, depending on which is lower.
Historic Peaks and Troughs
The current, or most recent, peak and trough occurrences are referred to as occurrence zero. Previous peak and trough occurrences are referred to as historic and ordered numerically from right to left, with the most recent historic peak and trough occurrences being occurrence one.
Support and Resistance
• Support refers to a price level where the demand for an asset is strong enough to prevent the price from falling further.
• Resistance refers to a price level where the supply of an asset is strong enough to prevent the price from rising further.
Support and resistance levels are important because they can help traders identify where the price of an asset might pause or reverse its direction, offering potential entry and exit points. For example, a trader might look to buy an asset when it approaches a support level , with the expectation that the price will bounce back up. Alternatively, a trader might look to sell an asset when it approaches a resistance level , with the expectation that the price will drop back down.
It's important to note that support and resistance levels are not always relevant, and the price of an asset can also break through these levels and continue moving in the same direction.
Trendlines
Trendlines are straight lines that are drawn between two or more points on a price chart. These lines are used as dynamic support and resistance levels for making strategic decisions and predictions about future price movements. For example traders will look for price movements along, and reactions to, trendlines in the form of rejections or breakouts/downs.
█ FEATURES
Inputs
• Anchor Point Type
• Swing High/Low Occurrence
• HTF Resolution
• Highest High/Lowest Low Lookback
• Angle Degree
• Projection Ratio
• Number Lines
• Line Color
Anchor Point Types
• Swing High
• Swing Low
• Swing High (HTF)
• Swing Low (HTF)
• Highest High
• Lowest Low
• Intraday Highest High (intraday charts only)
• Intraday Lowest Low (intraday charts only)
Swing High/Swing Low Occurrence
This input is used to determine which historic peak or trough to reference for swing high or swing low anchor point types.
HTF Resolution
This input is used to determine which higher timeframe to reference for swing high (HTF) or swing low (HTF) anchor point types.
Highest High/Lowest Low Lookback
This input is used to determine the lookback length for highest high or lowest low anchor point types.
Intraday Highest High/Lowest Low Lookback
When using intraday highest high or lowest low anchor point types, the lookback length is calculated automatically based on number of bars since the daily candle opened.
Angle Degree
This input is used to determine the angle of the trendlines. The output is expressed in terms of point or pips, depending on the symbol type, which is then passed through the built in math.todegrees() function. Positive numbers will project the lines upwards while negative numbers will project the lines downwards. Depending on the market and timeframe, the impact input values will have on the visible gaps between the lines will vary greatly. For example, an input of 10 will have a far greater impact on the gaps between the lines when viewed from the 1-minute timeframe than it would on the 1-day timeframe. The input is a float and as such the value passed through can go into as many decimal places as the user requires.
It is also worth mentioning that as more lines are added the gaps between the lines, that are closest to the anchor point, will get tighter as they make their way up the y-axis. Although the gaps between the lines will stay constant at the x2 plot, i.e. a distance of 10 points between them, they will gradually get tighter and tighter at the point of origin as the slope of the lines get steeper.
Projection Ratio
This input is used to determine the distance between the parallels, expressed in terms of point or pips. Positive numbers will project the lines upwards while negative numbers will project the lines downwards. Depending on the market and timeframe, the impact input values will have on the visible gaps between the lines will vary greatly. For example, an input of 10 will have a far greater impact on the gaps between the lines when viewed from the 1-minute timeframe than it would on the 1-day timeframe. The input is a float and as such the value passed through can go into as many decimal places as the user requires.
Number Lines
This input is used to determine the number of lines to be drawn on the chart, maximum is 500.
█ LIMITATIONS
All green and red candle calculations are based on differences between open and close prices, as such I have made no attempt to account for green candles that gap lower and close below the close price of the preceding candle, or red candles that gap higher and close above the close price of the preceding candle. This may cause some unexpected behaviour on some markets and timeframes. I can only recommend using 24-hour markets, if and where possible, as there are far fewer gaps and, generally, more data to work with.
If the lines do not draw or you see a study error saying that the script references too many candles in history, this is most likely because the higher timeframe anchor point is not present on the current timeframe. This problem usually occurs when referencing a higher timeframe, such as the 1-month, from a much lower timeframe, such as the 1-minute. How far you can lookback for higher timeframe anchor points on the current timeframe will also be limited by your Trading View subscription plan. Premium users get 20,000 candles worth of data, pro+ and pro users get 10,000, and basic users get 5,000.
█ RAMBLINGS
It is my current thesis that the indicator will work best when used in conjunction with my Wavemeter indicator, which can be used to set the angle and projection ratio. For example, the average wave height or amplitude could be used as the value for the angle and projection ratio inputs. Or some factor or multiple of such an average. I think this makes sense as it allows for objectivity when applying the indicator across different markets and timeframes with different energies and vibrations.
“If you want to find the secrets of the universe, think in terms of energy, frequency and vibration.”
― Nikola Tesla
Fan Projections [theEccentricTrader]█ OVERVIEW
This indicator automatically projects trendlines in the shape of a fan, from a single point of origin. In the example above I have applied the indicator twice to the 1D SPXUSD. The seven upper lines (green) are projected at an angle of -5 from the 1-month swing high anchor point. And the five lower lines (blue) are projected at an angle of 10 from the 1-week swing low anchor point.
█ CONCEPTS
Green and Red Candles
• A green candle is one that closes with a high price equal to or above the price it opened.
• A red candle is one that closes with a low price that is lower than the price it opened.
Swing Highs and Swing Lows
• A swing high is a green candle or series of consecutive green candles followed by a single red candle to complete the swing and form the peak.
• A swing low is a red candle or series of consecutive red candles followed by a single green candle to complete the swing and form the trough.
Peak and Trough Prices (Basic)
• The peak price of a complete swing high is the high price of either the red candle that completes the swing high or the high price of the preceding green candle, depending on which is higher.
• The trough price of a complete swing low is the low price of either the green candle that completes the swing low or the low price of the preceding red candle, depending on which is lower.
Historic Peaks and Troughs
The current, or most recent, peak and trough occurrences are referred to as occurrence zero. Previous peak and trough occurrences are referred to as historic and ordered numerically from right to left, with the most recent historic peak and trough occurrences being occurrence one.
Support and Resistance
• Support refers to a price level where the demand for an asset is strong enough to prevent the price from falling further.
• Resistance refers to a price level where the supply of an asset is strong enough to prevent the price from rising further.
Support and resistance levels are important because they can help traders identify where the price of an asset might pause or reverse its direction, offering potential entry and exit points. For example, a trader might look to buy an asset when it approaches a support level , with the expectation that the price will bounce back up. Alternatively, a trader might look to sell an asset when it approaches a resistance level , with the expectation that the price will drop back down.
It's important to note that support and resistance levels are not always relevant, and the price of an asset can also break through these levels and continue moving in the same direction.
Trendlines
Trendlines are straight lines that are drawn between two or more points on a price chart. These lines are used as dynamic support and resistance levels for making strategic decisions and predictions about future price movements. For example traders will look for price movements along, and reactions to, trendlines in the form of rejections or breakouts/downs.
█ FEATURES
Inputs
• Anchor Point Type
• Swing High/Low Occurrence
• HTF Resolution
• Highest High/Lowest Low Lookback
• Angle Degree
• Number Lines
• Line Color
Anchor Point Types
• Swing High
• Swing Low
• Swing High (HTF)
• Swing Low (HTF)
• Highest High
• Lowest Low
• Intraday Highest High (intraday charts only)
• Intraday Lowest Low (intraday charts only)
Swing High/Swing Low Occurrence
This input is used to determine which historic peak or trough to reference for swing high or swing low anchor point types.
HTF Resolution
This input is used to determine which higher timeframe to reference for swing high (HTF) or swing low (HTF) anchor point types.
Highest High/Lowest Low Lookback
This input is used to determine the lookback length for highest high or lowest low anchor point types.
Intraday Highest High/Lowest Low Lookback
When using intraday highest high or lowest low anchor point types, the lookback length is calculated automatically based on number of bars since the daily candle opened.
Angle Degree
This input is used to determine the angle of the trendlines. The output is expressed in terms of point or pips, depending on the symbol type, which is then passed through the built in math.todegrees() function. Positive numbers will project the lines upwards while negative numbers will project the lines downwards. Depending on the market and timeframe, the impact input values will have on the visible gaps between the lines will vary greatly. For example, an input of 10 will have a far greater impact on the gaps between the lines when viewed from the 1-minute timeframe than it would on the 1-day timeframe. The input is a float and as such the value passed through can go into as many decimal places as the user requires.
It is also worth mentioning that as more lines are added the gaps between the lines, that are closest to the anchor point, will get tighter as they make their way up the y-axis. Although the gaps between the lines will stay constant at the x2 plot, i.e. a distance of 10 points between them, they will gradually get tighter and tighter at the point of origin as the slope of the lines get steeper.
Number Lines
This input is used to determine the number of lines to be drawn on the chart, maximum is 500.
█ LIMITATIONS
All green and red candle calculations are based on differences between open and close prices, as such I have made no attempt to account for green candles that gap lower and close below the close price of the preceding candle, or red candles that gap higher and close above the close price of the preceding candle. This may cause some unexpected behaviour on some markets and timeframes. I can only recommend using 24-hour markets, if and where possible, as there are far fewer gaps and, generally, more data to work with.
If the lines do not draw or you see a study error saying that the script references too many candles in history, this is most likely because the higher timeframe anchor point is not present on the current timeframe. This problem usually occurs when referencing a higher timeframe, such as the 1-month, from a much lower timeframe, such as the 1-minute. How far you can lookback for higher timeframe anchor points on the current timeframe will also be limited by your Trading View subscription plan. Premium users get 20,000 candles worth of data, pro+ and pro users get 10,000, and basic users get 5,000.
█ RAMBLINGS
It is my current thesis that the indicator will work best when used in conjunction with my Wavemeter indicator, which can be used to set the angle. For example, the average wave height or amplitude could be used as the value for the angle input. Or some factor or multiple of such an average. I think this makes sense as it allows for objectivity when applying the indicator across different markets and timeframes with different energies and vibrations.
“If you want to find the secrets of the universe, think in terms of energy, frequency and vibration.”
― Nikola Tesla
Swing Counter [theEccentricTrader]█ OVERVIEW
This indicator counts the number of confirmed swing high and swing low scenarios on any given candlestick chart and displays the statistics in a table, which can be repositioned and resized at the user's discretion.
█ CONCEPTS
Green and Red Candles
• A green candle is one that closes with a high price equal to or above the price it opened.
• A red candle is one that closes with a low price that is lower than the price it opened.
Swing Highs and Swing Lows
• A swing high is a green candle or series of consecutive green candles followed by a single red candle to complete the swing and form the peak.
• A swing low is a red candle or series of consecutive red candles followed by a single green candle to complete the swing and form the trough.
Peak and Trough Prices (Basic)
• The peak price of a complete swing high is the high price of either the red candle that completes the swing high or the high price of the preceding green candle, depending on which is higher.
• The trough price of a complete swing low is the low price of either the green candle that completes the swing low or the low price of the preceding red candle, depending on which is lower.
Peak and Trough Prices (Advanced)
• The advanced peak price of a complete swing high is the high price of either the red candle that completes the swing high or the high price of the highest preceding green candle high price, depending on which is higher.
• The advanced trough price of a complete swing low is the low price of either the green candle that completes the swing low or the low price of the lowest preceding red candle low price, depending on which is lower.
Green and Red Peaks and Troughs
• A green peak is one that derives its price from the green candle/s that constitute the swing high.
• A red peak is one that derives its price from the red candle that completes the swing high.
• A green trough is one that derives its price from the green candle that completes the swing low.
• A red trough is one that derives its price from the red candle/s that constitute the swing low.
Historic Peaks and Troughs
The current, or most recent, peak and trough occurrences are referred to as occurrence zero. Previous peak and trough occurrences are referred to as historic and ordered numerically from right to left, with the most recent historic peak and trough occurrences being occurrence one.
Upper Trends
• A return line uptrend is formed when the current peak price is higher than the preceding peak price.
• A downtrend is formed when the current peak price is lower than the preceding peak price.
• A double-top is formed when the current peak price is equal to the preceding peak price.
Lower Trends
• An uptrend is formed when the current trough price is higher than the preceding trough price.
• A return line downtrend is formed when the current trough price is lower than the preceding trough price.
• A double-bottom is formed when the current trough price is equal to the preceding trough price.
█ FEATURES
Inputs
• Start Date
• End Date
• Position
• Text Size
• Show Sample Period
• Show Plots
• Show Lines
Table
The table is colour coded, consists of three columns and nine rows. Blue cells denote neutral scenarios, green cells denote return line uptrend and uptrend scenarios, and red cells denote downtrend and return line downtrend scenarios.
The swing scenarios are listed in the first column with their corresponding total counts to the right, in the second column. The last row in column one, row nine, displays the sample period which can be adjusted or hidden via indicator settings.
Rows three and four in the third column of the table display the total higher peaks and higher troughs as percentages of total peaks and troughs, respectively. Rows five and six in the third column display the total lower peaks and lower troughs as percentages of total peaks and troughs, respectively. And rows seven and eight display the total double-top peaks and double-bottom troughs as percentages of total peaks and troughs, respectively.
Plots
I have added plots as a visual aid to the swing scenarios listed in the table. Green up-arrows with ‘HP’ denote higher peaks, while green up-arrows with ‘HT’ denote higher troughs. Red down-arrows with ‘LP’ denote higher peaks, while red down-arrows with ‘LT’ denote lower troughs. Similarly, blue diamonds with ‘DT’ denote double-top peaks and blue diamonds with ‘DB’ denote double-bottom troughs. These plots can be hidden via indicator settings.
Lines
I have also added green and red trendlines as a further visual aid to the swing scenarios listed in the table. Green lines denote return line uptrends (higher peaks) and uptrends (higher troughs), while red lines denote downtrends (lower peaks) and return line downtrends (lower troughs). These lines can be hidden via indicator settings.
█ HOW TO USE
This indicator is intended for research purposes and strategy development. I hope it will be useful in helping to gain a better understanding of the underlying dynamics at play on any given market and timeframe. It can, for example, give you an idea of any inherent biases such as a greater proportion of higher peaks to lower peaks. Or a greater proportion of higher troughs to lower troughs. Such information can be very useful when conducting top down analysis across multiple timeframes, or considering entry and exit methods.
What I find most fascinating about this logic, is that the number of swing highs and swing lows will always find equilibrium on each new complete wave cycle. If for example the chart begins with a swing high and ends with a swing low there will be an equal number of swing highs to swing lows. If the chart starts with a swing high and ends with a swing high there will be a difference of one between the two total values until another swing low is formed to complete the wave cycle sequence that began at start of the chart. Almost as if it was a fundamental truth of price action, although quite common sensical in many respects. As they say, what goes up must come down.
The objective logic for swing highs and swing lows I hope will form somewhat of a foundational building block for traders, researchers and developers alike. Not only does it facilitate the objective study of swing highs and swing lows it also facilitates that of ranges, trends, double trends, multi-part trends and patterns. The logic can also be used for objective anchor points. Concepts I will introduce and develop further in future publications.
█ LIMITATIONS
Some higher timeframe candles on tickers with larger lookbacks such as the DXY , do not actually contain all the open, high, low and close (OHLC) data at the beginning of the chart. Instead, they use the close price for open, high and low prices. So, while we can determine whether the close price is higher or lower than the preceding close price, there is no way of knowing what actually happened intra-bar for these candles. And by default candles that close at the same price as the open price, will be counted as green. You can avoid this problem by utilising the sample period filter.
The green and red candle calculations are based solely on differences between open and close prices, as such I have made no attempt to account for green candles that gap lower and close below the close price of the preceding candle, or red candles that gap higher and close above the close price of the preceding candle. I can only recommend using 24-hour markets, if and where possible, as there are far fewer gaps and, generally, more data to work with. Alternatively, you can replace the scenarios with your own logic to account for the gap anomalies, if you are feeling up to the challenge.
The sample size will be limited to your Trading View subscription plan. Premium users get 20,000 candles worth of data, pro+ and pro users get 10,000, and basic users get 5,000. If upgrading is currently not an option, you can always keep a rolling tally of the statistics in an excel spreadsheet or something of the like.
█ NOTES
I feel it important to address the mention of advanced peak and trough price logic. While I have introduced the concept, I have not included the logic in my script for a number of reasons. The most pertinent of which being the amount of extra work I would have to do to include it in a public release versus the actual difference it would make to the statistics. Based on my experience, there are actually only a small number of cases where the advanced peak and trough prices are different from the basic peak and trough prices. And with adequate multi-timeframe analysis any high or low prices that are not captured using basic peak and trough price logic on any given time frame, will no doubt be captured on a higher timeframe. See the example below on the 1H FOREXCOM:USDJPY chart (Figure 1), where the basic peak price logic denoted by the indicator plot does not capture what would be the advanced peak price, but on the 2H FOREXCOM:USDJPY chart (Figure 2), the basic peak logic does capture the advanced peak price from the 1H timeframe.
Figure 1.
Figure 2.
█ RAMBLINGS
“Never was there an age that placed economic interests higher than does our own. Never was the need of a scientific foundation for economic affairs felt more generally or more acutely. And never was the ability of practical men to utilize the achievements of science, in all fields of human activity, greater than in our day. If practical men, therefore, rely wholly on their own experience, and disregard our science in its present state of development, it cannot be due to a lack of serious interest or ability on their part. Nor can their disregard be the result of a haughty rejection of the deeper insight a true science would give into the circumstances and relationships determining the outcome of their activity. The cause of such remarkable indifference must not be sought elsewhere than in the present state of our science itself, in the sterility of all past endeavours to find its empirical foundations.” (Menger, 1871, p.45).
█ BIBLIOGRAPHY
Menger, C. (1871) Principles of Economics. Reprint, Auburn, Alabama: Ludwig Von Mises Institute: 2007.
Price Displacement - Candlestick (OHLC) CalculationsA Magical little helper friend for Candle Math.
When composing scripts, it is often necessary to manipulate the math around the OHLC. At times, you want a scalar (absolute) value others you want a vector (+/-). Sometimes you want the open - close and sometimes you want just the positive number of the body size. You might want it in ticks or you might want it in points or you might want in percentages. And every time you try to put it together you waste precious time and brain power trying to think about how to properly structure what you're looking for. Not to mention it's normally not that aesthetically pleasing to look at in the code.
So, this fixes all of that.
Using this library. A function like 'pd.pt(_exp)' can call any kind of candlestick math you need. The function returns the candlestick math you define using particular expressions.
Candle Math Functions Include:
Points:
pt(_exp) Absolute Point Displacement. Point quantity of given size parameters according to _exp.
vpt(_exp) Vector Point Displacement. Point quantity of given size parameters according to _exp.
Ticks:
tick(_exp) Absolute Tick Displacement. Tick quantity of given size parameters according to _exp.
vtick(_exp) Vector Tick Displacement. Tick quantity of given size parameters according to _exp.
Percentages:
pct(_exp, _prec) Absolute Percent Displacement. (w/rounding overload). Percent quantity of bar range of given size parameters according to _exp.
vpct(_exp, _prec) Vector Percent Displacement (w/rounding overload). Percent quantity of bar range of given size parameters according to _exp.
Expressions You Can Use with Formulas:
The expressions are simple (simple strings that is) and I did my best to make them sensible, generally using just the ohlc abreviations. I also included uw, lw, bd, and rg for when you're just trying to pull a candle component out. That way you don't have to think about which of the ohlc you're trying to get just use pd.tick("uw") and now the variable is assigned the length of the upper wick, absolute value, in ticks. If you wanted the vector in pts its pd.vpt("uw"). It also makes changing things easy too as I write it out.
Expression List:
Combinations
"oh" = open - high
"ol" = open - low
"oc" = open - close
"ho" = high - open
"hl" = high - low
"hc" = high - close
"lo" = low - open
"lh" = low - high
"lc" = low - close
"co" = close - open
"ch" = close - high
"cl" = close - low
Candle Components
"uw" = Upper Wick
"bd" = Body
"lw" = Lower Wick
"rg" = Range
Pct() Only
"scp" = Scalar Close Position
"sop" = Scalar Open Position
"vcp" = Vector Close Position
"vop" = Vector Open Position
The attributes are going to be available in the pop up dialogue when you mouse over the function, so you don't really have to remember them. I tried to make that look as efficient as possible. You'll notice it follows the OHLC pattern. Thus, "oh" precedes "ho" (heyo) because "O" would be first in the OHLC. Its a way to help find the expression you're looking for quickly. Like looking through an alphabetized list for traders.
There is a copy/paste console friendly helper list in the script itself.
Additional Notes on the Pct() Only functions:
This is the original reason I started writing this. These concepts place a rating/value on the bar based on candle attributes in one number. These formulas put a open or close value in a percentile of the bar relative to another aspect of the bar.
Scalar - Non-directional. Absolute Value.
Scalar Position: The position of the price attribute relative to the scale of the bar range (high - low)
Example: high = 100. low = 0. close = 25.
(A) Measure price distance C-L. How high above the low did the candle close (e.g. close - low = 25)
(B) Divide by bar range (high - low). 25 / (100 - 0) = .25
Explaination: The candle closed at the 25th percentile of the bar range given the bar range low = 0 and bar range high = 100.
Formula: scp = (close - low) / (high - low)
Vector = Directional.
Vector Position: The position of the price attribute relative to the scale of the bar midpoint (Vector Position at hl2 = 0)
Example: high = 100. low = 0. close = 25.
(A) Measure Price distance C-L: How high above the low did the candle close (e.g. close - low = 25)
(B) Measure Price distance H-C: How far below the high did the candle close (e.g. high - close = 75)
(C) Take Difference: A - B = C = -50
(D) Divide by bar range (high - low). -50 / (100 - 0) = -0.50
Explaination: Candle close at the midpoint between hl2 and the low.
Formula: vcp = { / (high - low) }
Thank you for checking this out. I hope no one else has already done this (because it took half the day) and I hope you find value in it. Be well. Trade well.
Library "PD"
Price Displacement
pt(_exp) Absolute Point Displacement. Point quantity of given size parameters according to _exp.
Parameters:
_exp : (string) Price Parameter
Returns: Point size of given expression as an absolute value.
vpt(_exp) Vector Point Displacement. Point quantity of given size parameters according to _exp.
Parameters:
_exp : (string) Price Parameter
Returns: Point size of given expression as a vector.
tick(_exp) Absolute Tick Displacement. Tick quantity of given size parameters according to _exp.
Parameters:
_exp : (string) Price Parameter
Returns: Tick size of given expression as an absolute value.
vtick(_exp) Vector Tick Displacement. Tick quantity of given size parameters according to _exp.
Parameters:
_exp : (string) Price Parameter
Returns: Tick size of given expression as a vector.
pct(_exp, _prec) Absolute Percent Displacement (w/rounding overload). Percent quantity of bar range of given size parameters according to _exp.
Parameters:
_exp : (string) Expression
_prec : (int) Overload - Place value precision definition
Returns: Percent size of given expression as decimal.
vpct(_exp, _prec) Vector Percent Displacement (w/rounding overload). Percent quantity of bar range of given size parameters according to _exp.
Parameters:
_exp : (string) Expression
_prec : (int) Overload - Place value precision definition
Returns: Percent size of given expression as decimal.
Larry Williams Strategies IndicatorThis indicator is a trend following indicator. It plots some of the trend following strategies described by Larry Williams in his book 'Long Term Secrets to Short Term Trading'. Below are types of trend following strategies you can trade using this indicator. These are notes taken directly from Larry Williams' book.
Short Term Low Strategy
Short Term Low - Any daily low with higher lows on each side of it.
Intermediate Term Low – Any short term low with higher short term lows on each side of it.
Long Term Low – Any intermediate term low with higher intermediate term lows on each side of it.
Conceptual pattern for best buying opportunity is when forming an intermediate term low higher than the last intermediate term low.
This setup can be used on all time frames. However since Larry Williams usually trades the daily chart, the daily chart is probably the best timeframe to trade using this strategy.
Entry point – High of the day that has a higher high on the right side of it.
(My interpretation: price crossing above the high of the previous day is the buy signal)
Target – Markets have a strong tendency to rally above the last intermediate term high by the same amount it moved from the last intermediate term high to the lowest point prior to advancing to new highs.
Trailing Stop – Set stop to most recent short term low, move up as new short term lows are formed. Can also use formation of next intermediate term high as an exit point.
A 'run' to the upside is over when price fails to move higher the next day and falls below the prior day's low.
Short Term High Strategy
Short Term High - Any daily high with lower highs on each side of it.
Intermediate Term High – Any short term high with lower short term highs on each side of it.
Long Term High – Any intermediate term high with lower intermediate term highs on each side of it.
Conceptual pattern for best selling opportunity is when forming an intermediate term high lower than the last intermediate term high.
This setup can be used on all time frames. However since Larry Williams usually trades the daily chart, the daily chart is probably the best timeframe to trade using this strategy.
Entry point – Low of the day that has a lower low on the right side of it.
(My interpretation: price crossing below the low of the previous day is the sell short signal)
Target – Markets have a strong tendency to fall below the last intermediate term low by the same amount it moved from the last intermediate term low to the highest point prior to declining to new lows.
Trailing Stop – Set stop to most recent short term high, move down as new short term highs are formed. Can also use formation of next intermediate term low as an exit point.
A 'run' to the downside is over when price fails to move lower the next day and rises above the prior day's high.
Trend Reversals
A trend change from down to up occurs when a short term high is exceeded on the upside, a trend change from up to down is identified by price going below the most recent low.
Can take these signals to make trades, but it is best to filter them with a confirmation or edge such as Trading Day of the Week, Trading Day of the Month, trendlines, etc. to cut down on false signals.
Three Bar High/Low System
Calculate a three bar moving average of the highs and a three bar moving average of the lows.
Strategy is to buy at the at the price of the three bar moving average of the lows - if the trend is positive according to the swing point trend identification technique - and take profits at the three bar moving average of the highs.
Selling is just the opposite. Sell short at the three bar moving average of the highs and take profits at the three bar moving average of the lows, using the trend identification technique above for confirmation.
This strategy can work on any timeframe, but was described as a daytrading system by Larry Williams.
Kawabunga Swing Failure Points Candles (SFP) by RRBKawabunga Swing Failure Points Candles (SFP) by RagingRocketBull 2019
Version 1.0
This indicator shows Swing Failure Points (SFP) and Swing Confirmation Points (SCP) as candles on a chart.
SFP/SCP candles are used by traders as signals for trend confirmation/possible reversal.
The signal is stronger on a higher volume/larger candle size.
A Swing Failure Point (SFP) candle is used to spot a reversal:
- up trend SFP is a failure to close above prev high after making a new higher high => implies reversal down
- down trend SFP is a failure to close below prev low after making a new lower low => implies reversal up
A Swing Confirmation Point (SCP) candle is just the opposite and is used to confirm the current trend:
- up trend SCP is a successful close above prev high after making a new higher high => confirms the trend and implies continuation up
- down trend SCP is a successful close below prev low after making a new lower low => confirms the trend and implies continuation down
Features:
- uses fractal pivots with optional filter
- show/hide SFP/SCP candles, pivots, zigzag, last min/max pivot bands
- dim lag zones/hide false signals introduced by lagging fractals or
- use unconfirmed pivots to eliminate fractal lag/false signals. 2 modes: fractals 1,1 and highest/lowest
- filter only SFP/SCP candles confirmed with volume/candle size
- SFP/SCP candles color highlighting, dim non-important bars
Usage:
- adjust fractal settings to get pivots that best match your data (lower values => more frequent pivots. 0,0 - each candle is a pivot)
- use one of the unconfirmed pivot modes to eliminate false signals or just ignore all signals in the gray lag zones
- optionally filter only SFP/SCP candles with large volume/candle size (volume % change relative to prev bar, abs candle body size value)
- up/down trend SCP (lime/fuchsia) => continuation up/down; up/down trend SFP (orange/aqua) => possible reversal down/up. lime/aqua => up; fuchsia/orange => down.
- when in doubt use show/hide pivots/unconfirmed pivots, min/max pivot bands to see which prev pivot and min/max value were used in comparisons to generate a signal on the following candle.
- disable offset to check on which bar the signal was generated
Notes:
Fractal Pivots:
- SFP/SCP candles depend on fractal pivots, you will get different signals with different pivot settings. Usually 4,4 or 2,2 settings are used to produce fractal pivots, but you can try custom values that fit your data best.
- fractal pivots are a mixed series of highs and lows in no particular order. Pivots must be filtered to produce a proper zigzag where ideally a high is followed by a low and another high in orderly fashion.
Fractal Lag/False Signals:
- only past fractal pivots can be processed on the current bar introducing a lag, therefore, pivots and min/max pivot bands are shown with offset=-rightBars to match their target bars. For unconfirmed pivots an offset=-1 is used with a lag of just 1 bar.
- new pivot is not a confirmed fractal and "does not exist yet" while the distance between it and the current bar is < rightBars => prev old fractal pivot in the same dir is used for comparisons => gives a false signal for that dir
- to show false signals enable lag zones. SFP/SCP candles in lag zones are false. New pivots will be eventually confirmed, but meanwhile you get a false signal because prev pivot in the same dir was used instead.
- to solve this problem you can either temporary hide false signals or completely eliminate them by using unconfirmed pivots of a smaller degree/lag.
- hiding false signals only works for history and should be used only temporary (left disabled). In realtime/replay mode it disables all signals altogether due to TradingView's bug (barcolor doesn't support negative offsets)
Unconfirmed Pivots:
- you have 2 methods to check for unconfirmed pivots: highest/lowest(rightBars) or fractals(1,1) with a min possible step. The first is essentially fractals(0,0) where each candle is a pivot. Both produce more frequent pivots (weaker signals).
- an unconfirmed pivot is used in comparisons to generate a valid signal only when it is a higher high (> max high) or a lower low (< min low) in the dir of a trend. Confirmed pivots of a higher degree are not affected. Zigzag is not affected.
- you can also manually disable the offset to check on which bar the pivot was confirmed. If the pivot just before an SCP/SFP suddenly jumps ahead of it - prev pivot was used, generating a false signal.
- last max high/min low bands can be used to check which value was used in candle comparison to generate a signal: min(pivot min_low, upivot min_low) and max(pivot max_high, upivot max_high) are used
- in the unconfirmed pivots mode the max high/min low pivot bands partially break because you can't have a variable offset to match the random pos of an unconfirmed pivot (anywhere in 0..rightBars from the current bar) to its target bar.
- in the unconfirmed pivots mode h (green) and l (red) pivots become H and L, and h (lime) and l (fuchsia) are used to show unconfirmed pivots of a smaller degree. Some of them will be confirmed later as H and L pivots of a higher degree.
Pivot Filter:
- pivot filter is used to produce a better looking zigzag. Essentially it keeps only higher highs/lower lows in the trend direction until it changes, skipping:
- after a new high: all subsequent lower highs until a new low
- after a new low: all subsequent higher lows until a new high
- you can't filter out all prev highs/lows to keep just the last min/max pivots of the current swing because they were already confirmed as pivots and you can't delete/change history
- alternatively you could just pick the first high following a low and the first low following a high in a sequence and ignore the rest of the pivots in the same dir, producing a crude looking zigzag where obvious max high/min lows are ignored.
- pivot filter affects SCP/SFP signals because it skips some pivots
- pivot filter is not applied to/not affected by the unconfirmed pivots
- zigzag is affected by pivot filter, but not by the unconfirmed pivots. You can't have both high/low on the same bar in a zigzag. High has priority over Low.
- keep same bar pivots option lets you choose which pivots to keep when there are both high/low pivots on the same bar (both kept by default)
SCP/SFP Filters:
- you can confirm/filter only SCP/SFP signals with volume % change/candle size larger than delta. Higher volume/larger candle means stronger signal.
- technically SCP/SFP is always the first matching candle, but it can be invalidated by the following signal in the opposite dir which in turn can be negated by the next signal.
- show first matching SCP/SFP = true - shows only the first signal candle (and any invalidations that follow) and hides further duplicate signals in the same dir, does not highlight the trend.
- show first matching SCP/SFP = false - produces a sequence of candles with duplicate signals, highlights the whole trend until its dir changes (new pivot).
Good Luck! Feel free to learn from/reuse the code to build your own indicators!
Volume Profile DeltaMap [MHA Finverse]Volume Profile DeltaMap with Session Analysis
SHORT DESCRIPTION (for listing)
Advanced Volume Profile indicator with Delta Analysis, Value Area, Volume Nodes, Imbalance Zones, and Multi-Session Profiles. Professional tool for institutional-style volume analysis and market structure understanding.
---
DETAILED DESCRIPTION
📊 OVERVIEW
The Volume Profile DeltaMap is a comprehensive institutional-grade indicator that visualizes volume distribution across price levels, revealing where the most significant trading activity occurred. Unlike traditional indicators that plot data over time, Volume Profile analyzes price levels to identify key support/resistance zones, equilibrium areas, and buyer/seller dominance.
This indicator combines multiple advanced features:
- Volume Profile Analysis with customizable bins
- Delta Heat Map showing buyer vs seller pressure
- Value Area (VAH/VAL) calculations
- High/Low Volume Node Detection
- Imbalance Zone Identification
- Multi-Session Profile Separation (Tokyo, London, NY, Sydney)
- Point of Control (POC) highlighting
---
🎯 KEY FEATURES
1. Volume Profile Core
- Divides price range into customizable bins (10-100 levels)
- Accumulates volume at each price level over a lookback period
- Displays volume distribution horizontally on the chart
- Configurable lookback period (default: 200 bars)
2. Delta Analysis & Heat Map
- Delta (Δ) : Measures the difference between buying and selling pressure
- Color-coded visualization :
- Green/Teal = Buyer dominance
- Red/Pink = Seller dominance
- Heat map intensity : Shows volume concentration with gradient colors
- Percentage labels : Displays exact buyer/seller ratios at each level
3. Point of Control (POC)
- Automatically identifies the price level with maximum volume
- Marked with cyan border and volume label
- Acts as a strong magnetic level where price tends to return
- Often serves as major support/resistance
4. Value Area (VAH/VAL)
- Value Area : Price range containing 70% of total volume (configurable 50-90%)
- VAH (Value Area High) : Upper boundary - resistance level
- VAL (Value Area Low) : Lower boundary - support level
- Displayed with dashed lines and labels
- Represents fair value zone where institutional traders are most active
5. Volume Nodes
- HVN (High Volume Nodes) : Areas with ≥80% of maximum volume
- Highlighted in yellow/amber
- Strong support/resistance zones
- Price tends to consolidate here
- LVN (Low Volume Nodes) : Areas with ≤30% of maximum volume
- Highlighted in orange
- Low liquidity gaps
- Price moves quickly through these zones
- Potential breakout areas
6. Imbalance Zones
- Identifies areas with extreme directional bias (≥70% threshold)
- Buy Imbalance : Green overlay - exhaustion of buying pressure
- Sell Imbalance : Red overlay - exhaustion of selling pressure
- Indicates potential reversal or continuation zones
7. Session-Based Analysis
- Session Background Overlay : Color-codes current trading session
- Separate Session Profiles : Creates individual volume profiles for:
- 🇯🇵 Tokyo Session (00:00-09:00)
- 🇬🇧 London Session (07:00-16:00)
- 🇺🇸 New York Session (13:00-22:00)
- 🇦🇺 Sydney Session (21:00-06:00)
- Compare volume patterns across different market sessions
- Identify session-specific support/resistance levels
---
⚙️ CONFIGURATION SETTINGS
Basic Settings
- LookBack : Number of bars to analyze (50-500 recommended)
- Bins : Number of price levels (10-100, default: 30)
- Horizontal Offset : Adjust profile position on chart
#### Features Toggle
- Delta Heat Map
- Delta Labels
- Volume Bars (Buy/Sell split)
- POC Line
- Custom colors for positive/negative volume
Advanced Features
- Value Area calculation with adjustable percentage
- Volume Nodes (HVN/LVN) with custom thresholds
- Imbalance Zones with adjustable sensitivity
- Session backgrounds and separate profiles
- Profile spacing for multi-session view
---
📈 HOW TO USE THIS INDICATOR
Installation & Setup
1. Add to Chart :
- Search for "Volume Profile DeltaMap"
- Click "Add to favorites" ⭐
- Apply to your chart
2. Recommended Timeframes :
- Scalping : 1-5 minute charts
- Day Trading : 5-15 minute charts
- Swing Trading : 1-4 hour charts
- Position Trading : Daily charts
3. Initial Settings :
- Start with default settings
- For intraday: Set LookBack to 200-400 bars
- For higher timeframes: Use 100-200 bars
4. Enable Session Profiles (Optional):
- Go to Settings → Advanced Features
- Enable "Separate Profiles Per Session"
- Adjust "Profile Spacing" for better visibility
---
🔍 READING THE INDICATOR
Understanding the Display
Main Profile Elements:
- Horizontal bars : Length represents volume at that price
- Color gradient : Shows delta (buyer vs seller dominance)
- Bright cyan line : Point of Control (POC) - highest volume
- Green dashed line : Value Area High (VAH)
- Red dashed line : Value Area Low (VAL)
- Yellow highlights : High Volume Nodes (HVN)
- Orange highlights : Low Volume Nodes (LVN)
Volume Bars (if enabled):
- Top half (Red) : Selling volume percentage
- Bottom half (Teal) : Buying volume percentage
Delta Labels:
- Shows Δ percentage
- Positive = More buyers
- Negative = More sellers
---
📊 MARKET ANALYSIS & TRADING STRATEGIES
1. Support & Resistance Trading
POC as Key Level:
- Price tends to return to POC (magnetic effect)
- Strategy :
- When price is above POC → Look for pullbacks to POC for long entries
- When price is below POC → Look for rallies to POC for short entries
- POC acts as dynamic support/resistance
Value Area Trading:
- Inside Value Area (between VAH & VAL):
- Market is in balance/equilibrium
- Range-bound trading strategies
- Look for mean reversion
- Outside Value Area :
- Price accepted above VAH = Bullish breakout
- Price accepted below VAL = Bearish breakdown
- Trend-following strategies
Example Setup:
Price above VAH + Strong buying delta = Bullish trend
→ Wait for pullback to VAH
→ Enter long with stop below VAH
→ Target: Next HVN or previous session high
2. Volume Node Trading
High Volume Nodes (HVN):
- Characteristics : Strong support/resistance, consolidation zones
- Trading Strategy :
- Price approaching HVN from above → Potential support
- Price approaching HVN from below → Potential resistance
- Breakout from HVN → Strong momentum move
- Setup : Place limit orders at HVN boundaries
Low Volume Nodes (LVN):
- Characteristics : Low liquidity, fast price movement
- Trading Strategy :
- Price in LVN = Don't chase, wait for next HVN
- LVN breakout = Rapid moves, use wider stops
- Price rejection from LVN = Quick return to HVN
- Setup : Avoid placing stops in LVN zones
Example:
Price consolidating at HVN (yellow) near $50,000
→ Breakout above with volume
→ Fast move through LVN (orange) gap
→ Next target: Upper HVN at $51,500
3. Delta Analysis for Entry Timing
Strong Buying Delta (Green zones):
- Δ > +20% = Buyers in control
- Bullish Signal : Accumulation zone
- Strategy : Look for long entries on pullbacks
- Confirmation : Rising price + positive delta
Strong Selling Delta (Red zones):
- Δ < -20% = Sellers in control
- Bearish Signal : Distribution zone
- Strategy : Look for short entries on rallies
- Confirmation : Falling price + negative delta
Delta Divergence (Advanced):
- Bullish Divergence : Price making lower lows, but delta improving (less negative)
- Indicates selling pressure weakening
- Potential reversal signal
- Bearish Divergence : Price making higher highs, but delta weakening (less positive)
- Indicates buying pressure exhausting
- Potential reversal signal
4. Imbalance Zone Trading
Buy Imbalance (Bright Green):
- 70%+ buying pressure
- Interpretation :
- Potential exhaustion of buyers
- Smart money distribution
- Strategy :
- Look for reversal signals (bearish candles, resistance)
- Take profits on long positions
- Consider short entries with confirmation
Sell Imbalance (Bright Red):
- 70%+ selling pressure
- Interpretation :
- Potential exhaustion of sellers
- Smart money accumulation
- Strategy :
- Look for reversal signals (bullish candles, support)
- Take profits on short positions
- Consider long entries with confirmation
Example:
```
Price at VAH with 80% sell imbalance
→ Selling exhaustion likely
→ Wait for bullish reversal candle
→ Enter long with stop below VAL
```
5. Multi-Session Analysis
When "Separate Profiles Per Session" is enabled:
Session-Specific Levels:
- Each session creates its own POC and value area
- Compare sessions to identify:
- Where institutions accumulated/distributed
- Which levels each session respected
- Unfinished business from previous sessions
Trading Strategies:
A. Session POC Confluence
London POC: $49,500
NY POC: $49,550
→ Strong support zone at $49,500-$49,550
→ High probability long setup on pullback
B. Value Area Overlap
London VAH: $50,000
NY VAL: $49,800
→ Overlap creates strong consolidation zone
→ Breakout strategy: Enter on break above $50,000
C. Unfinished Business
London session rejected $51,000 (sell imbalance)
NY session hasn't tested this level yet
→ Watch for NY session to revisit $51,000
→ Potential reversal zone
D. Session Handoff
Tokyo session: Sideways, low volume
London session: Strong buying delta, break above VAH
NY session: Continuation or reversal?
→ Monitor NY open for direction confirmation
6. Market Profile Analysis
Profile Shape Interpretation:
A. P-Shape (Peak at Top)
- High volume at top of range
- Interpretation : Distribution, potential reversal down
- Strategy : Look for shorts at resistance
B. b-Shape (Peak at Bottom)
- High volume at bottom of range
- Interpretation : Accumulation, potential reversal up
- Strategy : Look for longs at support
C. D-Shape (Peak in Middle)
- Balanced profile, POC in center
- Interpretation : Equilibrium, neutral market
- Strategy : Range trading between VAH/VAL
D. Thin Profile (LVN Gap)
- Low volume throughout
- Interpretation : Trending market, little acceptance
- Strategy : Trend following, avoid counter-trend trades
---
🎯 COMPLETE TRADING WORKFLOW
Step 1: Market Structure Analysis
1. Identify overall profile shape
2. Locate POC, VAH, VAL
3. Note HVN and LVN zones
4. Check current price position relative to value area
Step 2: Delta & Imbalance Check
1. Review delta distribution (where are buyers/sellers?)
2. Identify imbalance zones
3. Look for delta divergences
4. Note any exhaustion signals
Step 3: Session Analysis (if enabled)
1. Compare current session vs previous sessions
2. Identify key levels each session created
3. Look for level confluences or gaps
4. Note unfinished business
Step 4: Trade Setup
1. Define your bias (long/short/neutral)
2. Identify entry zone (HVN, VAH/VAL, POC)
3. Set stop loss (below/above key level or opposite LVN)
4. Set target (next HVN, VAH/VAL, or session high/low)
Step 5: Execution & Management
1. Wait for price to reach entry zone
2. Confirm with price action (candlestick patterns)
3. Enter trade with defined risk
4. Move stop to breakeven at first target
5. Trail stop or take profits at resistance/support
---
📋 EXAMPLE TRADE SCENARIOS
Scenario 1: Long Setup at VAL
Setup:
- Price pulled back to VAL ($49,200)
- VAL coincides with HVN (yellow zone)
- Delta showing +15% buying (green)
- London session POC also at $49,200
Entry:
- Buy at $49,200 (VAL/HVN confluence)
- Stop loss: $49,000 (below VAL, in LVN)
- Target 1: $49,800 (POC)
- Target 2: $50,200 (VAH)
Management:
- Move stop to breakeven when Target 1 reached
- Trail stop below recent swing lows
- Exit 50% at VAH, let remainder run
Risk:Reward : 200 points risk / 1000 points potential = 1:5 R:R
---
Scenario 2: Short Setup at Sell Imbalance
Setup:
- Price at VAH ($50,500)
- Sell imbalance zone (85% sellers, bright red)
- Bearish divergence (higher high, weaker delta)
- Previous session rejected this level
Entry:
- Short at $50,500 after bearish engulfing candle
- Stop loss: $50,750 (above VAH + imbalance zone)
- Target 1: $50,000 (POC)
- Target 2: $49,600 (VAL)
Management:
- Take 50% profit at POC
- Trail stop above recent swing highs
- Exit remainder at VAL or if delta turns positive
Risk:Reward : 250 points risk / 900 points potential = 1:3.6 R:R
---
Scenario 3: Range Trading Inside Value Area
Setup:
- Market consolidating between VAH ($50,200) and VAL ($49,600)
- POC at $49,900
- Multiple HVNs creating range boundaries
- Delta oscillating between +/-10%
Long Trade:
- Entry: $49,650 (near VAL)
- Stop: $49,500 (below VAL)
- Target: $50,150 (near VAH)
- Risk:Reward: 150/500 = 1:3.3
Short Trade:
- Entry: $50,150 (near VAH)
- Stop: $50,300 (above VAH)
- Target: $49,700 (near VAL)
- Risk:Reward: 150/450 = 1:3
Management:
- Reduce position size in range trading
- Take profits at opposite boundary
- Exit if breakout occurs (stop hunt possible)
---
Scenario 4: Session Breakout Trade
Setup:
- London session: Range-bound $49,500-$50,000
- London VAH at $50,000 (resistance)
- NY session opens: Strong buying delta (+35%)
- Price breaks above $50,000 with momentum
Entry:
- Buy on breakout above $50,000
- Or buy on retest of $50,000 (old resistance = new support)
- Stop loss: $49,700 (below breakout level + buffer)
- Target 1: $50,500 (next HVN from previous day)
- Target 2: $51,000 (measured move)
Management:
- Enter 50% position on breakout
- Add remaining 50% on successful retest
- Move stop to breakeven when price +$300
- Trail stop below 20 EMA or recent higher lows
Risk:Reward : 300 points risk / 1000 points potential = 1:3.3 R:R
---
⚠️ BEST PRACTICES & RISK MANAGEMENT
Do's:
✅ Use on liquid markets (major crypto, forex, indices)
✅ Combine with price action and candlestick patterns
✅ Wait for confirmation before entering trades
✅ Always use stop losses based on volume structure
✅ Take partial profits at key levels (HVN, VAH/VAL)
✅ Adjust lookback period based on timeframe
✅ Use higher timeframe profiles for context
✅ Compare current profile with previous day/session
✅ Consider volume trends (increasing/decreasing)
✅ Backtest strategies on your specific market
Don'ts:
❌ Don't trade solely based on this indicator
❌ Don't ignore price action and market context
❌ Don't place stops in LVN zones (prone to spikes)
❌ Don't chase price in low volume areas
❌ Don't overtrade - wait for quality setups
❌ Don't use on extremely low volume/illiquid assets
❌ Don't forget to adjust for different market conditions
❌ Don't ignore fundamental news events
❌ Don't use excessive leverage even with good setups
❌ Don't force trades - patience is key
Risk Management Rules:
1. Risk per trade : Never risk more than 1-2% of capital
2. Position sizing : Based on stop loss distance
3. Stop placement : Always below/above key volume levels
4. Profit taking : Scale out at multiple targets
5. Drawdown limits : Stop trading after 3 consecutive losses
6. Win rate expectation : 50-60% is realistic
7. Risk:Reward minimum : Aim for 1:2 or better
8. Correlation : Don't take correlated positions
---
🔧 TROUBLESHOOTING & OPTIMIZATION
If profiles look too compressed:
- Increase "Bins" to 40-50
- Reduce "LookBack" period
- Adjust "Horizontal Offset"
If too cluttered:
- Disable "Delta Labels"
- Disable "Volume Bars"
- Keep only POC and Value Area
- Use "Session Background Overlay" instead of separate profiles
For scalping (1-5 min):
- LookBack: 300-500 bars
- Bins: 20-30
- Enable separate session profiles
- Focus on imbalance zones
For swing trading (1H-4H):
- LookBack: 100-200 bars
- Bins: 25-35
- Focus on VAH/VAL and HVN
- Disable session features
For position trading (Daily):
- LookBack: 50-100 bars
- Bins: 30-40
- Focus on weekly/monthly POC
- Compare with previous week profiles
---
📚 ADVANCED CONCEPTS
1. Composite Profiles
- Build profiles across multiple days
- Increase LookBack to 500+ bars on 15-min chart
- Identifies major support/resistance from weeks of data
- Use for swing trading key levels
2. Profile Migration
- Track how POC moves day over day
- Uptrend : POC migrating higher
- Downtrend : POC migrating lower
- Range : POC oscillating in same area
3. Failed Auctions
- Price briefly leaves value area but quickly returns
- Failed auction high : Bearish signal
- Failed auction low : Bullish signal
- Indicates rejection of new price levels
4. Overnight Inventory
- Compare previous day's close to value area
- Close above VAH : Bullish bias for next day
- Close below VAL : Bearish bias for next day
- Close in value area : Neutral, range expected
5. Volume Delta Momentum
- Track cumulative delta across time
- Rising cumulative delta + rising price : Strong trend
- Falling cumulative delta + rising price : Weak/topping
- Rising cumulative delta + falling price : Potential reversal
---
📊 INTEGRATION WITH OTHER INDICATORS
Complementary Indicators:
1. Moving Averages (20/50/200 EMA)
- Use with POC and VAH/VAL
- Confluence with EMAs = stronger levels
2. RSI/Stochastic
- Overbought at resistance (VAH/HVN) = strong short
- Oversold at support (VAL/HVN) = strong long
3. VWAP
- POC often aligns with VWAP
- Deviation from VWAP + Volume Profile = trade setup
4. Order Flow/Footprint Charts
- Confirm delta analysis
- Detailed buyer/seller pressure
5. Market Profile (TPO)
- Similar concept, different visualization
- Use together for complete picture
Example Multi-Indicator Setup:
Price at VAL ✓
+ 200 EMA support ✓
+ RSI oversold (30) ✓
+ Positive delta zone ✓
+ Bullish engulfing candle ✓
= High probability long entry
---
🎓 LEARNING CURVE & PRACTICE
Week 1-2: Understanding
- Study each feature individually
- Identify POC, VAH, VAL on historical charts
- Note HVN and LVN patterns
- Observe how price reacts to these levels
Week 3-4: Pattern Recognition
- Track different profile shapes
- Identify session-specific patterns
- Note delta distribution patterns
- Document imbalance zone outcomes
Week 5-6: Paper Trading
- Take simulated trades based on setups
- Record entry/exit reasoning
- Track win rate and R:R
- Refine strategy based on results
Week 7-8: Live Trading (Small Size)
- Start with minimal position sizes
- Focus on execution and discipline
- Build confidence with real money
- Gradually increase size as proficiency grows
Ongoing:
- Review trades weekly
- Keep trading journal
- Adapt to changing market conditions
- Continuously refine strategy
---
💡 KEY TAKEAWAYS
1. Volume Profile shows WHERE the market is most active (POC, HVN)
2. Delta shows WHO is in control (buyers vs sellers)
3. Value Area shows FAIR VALUE (equilibrium zone)
4. Volume Nodes show STRUCTURE (support/resistance)
5. Imbalances show EXHAUSTION (potential reversals)
6. Sessions show PARTICIPATION (institutional activity)
The indicator is a MAP, not a SIGNAL:
- It shows you the battlefield terrain
- You still need to decide when/how to engage
- Combine with price action for best results
- Risk management is always paramount
---
⚖️ DISCLAIMER
This indicator is for educational and informational purposes only.
- Not financial advice
- Past performance does not guarantee future results
- Trading involves substantial risk of loss
- Only trade with capital you can afford to lose
- Always do your own research and due diligence
- Test strategies thoroughly before risking real money
- Consider consulting a licensed financial advisor
The creator is not responsible for any trading losses incurred while using this indicator.
---
Happy Trading! 📈🚀
Historical Volatility EstimatorsHistorical volatility is a statistical measure of the dispersion of returns for a given security or market index over a given period. This indicator provides different historical volatility model estimators with percentile gradient coloring and volatility stats panel.
█ OVERVIEW There are multiple ways to estimate historical volatility. Other than the traditional close-to-close estimator. This indicator provides different range-based volatility estimators that take high low open into account for volatility calculation and volatility estimators that use other statistics measurements instead of standard deviation. The gradient coloring and stats panel provides an overview of how high or low the current volatility is compared to its historical values.
█ CONCEPTS We have mentioned the concepts of historical volatility in our previous indicators, Historical Volatility, Historical Volatility Rank, and Historical Volatility Percentile. You can check the definition of these scripts. The basic calculation is just the sample standard deviation of log return scaled with the square root of time. The main focus of this script is the difference between volatility models.
Close-to-Close HV Estimator: Close-to-Close is the traditional historical volatility calculation. It uses sample standard deviation. Note: the TradingView build in historical volatility value is a bit off because it uses population standard deviation instead of sample deviation. N – 1 should be used here to get rid of the sampling bias.
Pros:
• Close-to-Close HV estimators are the most commonly used estimators in finance. The calculation is straightforward and easy to understand. When people reference historical volatility, most of the time they are talking about the close to close estimator.
Cons:
• The Close-to-close estimator only calculates volatility based on the closing price. It does not take account into intraday volatility drift such as high, low. It also does not take account into the jump when open and close prices are not the same.
• Close-to-Close weights past volatility equally during the lookback period, while there are other ways to weight the historical data.
• Close-to-Close is calculated based on standard deviation so it is vulnerable to returns that are not normally distributed and have fat tails. Mean and Median absolute deviation makes the historical volatility more stable with extreme values.
Parkinson Hv Estimator:
• Parkinson was one of the first to come up with improvements to historical volatility calculation. • Parkinson suggests using the High and Low of each bar can represent volatility better as it takes into account intraday volatility. So Parkinson HV is also known as Parkinson High Low HV. • It is about 5.2 times more efficient than Close-to-Close estimator. But it does not take account into jumps and drift. Therefore, it underestimates volatility. Note: By Dividing the Parkinson Volatility by Close-to-Close volatility you can get a similar result to Variance Ratio Test. It is called the Parkinson number. It can be used to test if the market follows a random walk. (It is mentioned in Nassim Taleb's Dynamic Hedging book but it seems like he made a mistake and wrote the ratio wrongly.)
Garman-Klass Estimator:
• Garman Klass expanded on Parkinson’s Estimator. Instead of Parkinson’s estimator using high and low, Garman Klass’s method uses open, close, high, and low to find the minimum variance method.
• The estimator is about 7.4 more efficient than the traditional estimator. But like Parkinson HV, it ignores jumps and drifts. Therefore, it underestimates volatility.
Rogers-Satchell Estimator:
• Rogers and Satchell found some drawbacks in Garman-Klass’s estimator. The Garman-Klass assumes price as Brownian motion with zero drift.
• The Rogers Satchell Estimator calculates based on open, close, high, and low. And it can also handle drift in the financial series.
• Rogers-Satchell HV is more efficient than Garman-Klass HV when there’s drift in the data. However, it is a little bit less efficient when drift is zero. The estimator doesn’t handle jumps, therefore it still underestimates volatility.
Garman-Klass Yang-Zhang extension:
• Yang Zhang expanded Garman Klass HV so that it can handle jumps. However, unlike the Rogers-Satchell estimator, this estimator cannot handle drift. It is about 8 times more efficient than the traditional estimator.
• The Garman-Klass Yang-Zhang extension HV has the same value as Garman-Klass when there’s no gap in the data such as in cryptocurrencies.
Yang-Zhang Estimator:
• The Yang Zhang Estimator combines Garman-Klass and Rogers-Satchell Estimator so that it is based on Open, close, high, and low and it can also handle non-zero drift. It also expands the calculation so that the estimator can also handle overnight jumps in the data.
• This estimator is the most powerful estimator among the range-based estimators. It has the minimum variance error among them, and it is 14 times more efficient than the close-to-close estimator. When the overnight and daily volatility are correlated, it might underestimate volatility a little.
• 1.34 is the optimal value for alpha according to their paper. The alpha constant in the calculation can be adjusted in the settings. Note: There are already some volatility estimators coded on TradingView. Some of them are right, some of them are wrong. But for Yang Zhang Estimator I have not seen a correct version on TV.
EWMA Estimator:
• EWMA stands for Exponentially Weighted Moving Average. The Close-to-Close and all other estimators here are all equally weighted.
• EWMA weighs more recent volatility more and older volatility less. The benefit of this is that volatility is usually autocorrelated. The autocorrelation has close to exponential decay as you can see using an Autocorrelation Function indicator on absolute or squared returns. The autocorrelation causes volatility clustering which values the recent volatility more. Therefore, exponentially weighted volatility can suit the property of volatility well.
• RiskMetrics uses 0.94 for lambda which equals 30 lookback period. In this indicator Lambda is coded to adjust with the lookback. It's also easy for EWMA to forecast one period volatility ahead.
• However, EWMA volatility is not often used because there are better options to weight volatility such as ARCH and GARCH.
Adjusted Mean Absolute Deviation Estimator:
• This estimator does not use standard deviation to calculate volatility. It uses the distance log return is from its moving average as volatility.
• It’s a simple way to calculate volatility and it’s effective. The difference is the estimator does not have to square the log returns to get the volatility. The paper suggests this estimator has more predictive power.
• The mean absolute deviation here is adjusted to get rid of the bias. It scales the value so that it can be comparable to the other historical volatility estimators.
• In Nassim Taleb’s paper, he mentions people sometimes confuse MAD with standard deviation for volatility measurements. And he suggests people use mean absolute deviation instead of standard deviation when we talk about volatility.
Adjusted Median Absolute Deviation Estimator:
• This is another estimator that does not use standard deviation to measure volatility.
• Using the median gives a more robust estimator when there are extreme values in the returns. It works better in fat-tailed distribution.
• The median absolute deviation is adjusted by maximum likelihood estimation so that its value is scaled to be comparable to other volatility estimators.
█ FEATURES
• You can select the volatility estimator models in the Volatility Model input
• Historical Volatility is annualized. You can type in the numbers of trading days in a year in the Annual input based on the asset you are trading.
• Alpha is used to adjust the Yang Zhang volatility estimator value.
• Percentile Length is used to Adjust Percentile coloring lookbacks.
• The gradient coloring will be based on the percentile value (0- 100). The higher the percentile value, the warmer the color will be, which indicates high volatility. The lower the percentile value, the colder the color will be, which indicates low volatility.
• When percentile coloring is off, it won’t show the gradient color.
• You can also use invert color to make the high volatility a cold color and a low volatility high color. Volatility has some mean reversion properties. Therefore when volatility is very low, and color is close to aqua, you would expect it to expand soon. When volatility is very high, and close to red, you would it expect it to contract and cool down.
• When the background signal is on, it gives a signal when HVP is very low. Warning there might be a volatility expansion soon.
• You can choose the plot style, such as lines, columns, areas in the plotstyle input.
• When the show information panel is on, a small panel will display on the right.
• The information panel displays the historical volatility model name, the 50th percentile of HV, and HV percentile. 50 the percentile of HV also means the median of HV. You can compare the value with the current HV value to see how much it is above or below so that you can get an idea of how high or low HV is. HV Percentile value is from 0 to 100. It tells us the percentage of periods over the entire lookback that historical volatility traded below the current level. Higher HVP, higher HV compared to its historical data. The gradient color is also based on this value.
█ HOW TO USE If you haven’t used the hvp indicator, we suggest you use the HVP indicator first. This indicator is more like historical volatility with HVP coloring. So it displays HVP values in the color and panel, but it’s not range bound like the HVP and it displays HV values. The user can have a quick understanding of how high or low the current volatility is compared to its historical value based on the gradient color. They can also time the market better based on volatility mean reversion. High volatility means volatility contracts soon (Move about to End, Market will cooldown), low volatility means volatility expansion soon (Market About to Move).
█ FINAL THOUGHTS HV vs ATR The above volatility estimator concepts are a display of history in the quantitative finance realm of the research of historical volatility estimations. It's a timeline of range based from the Parkinson Volatility to Yang Zhang volatility. We hope these descriptions make more people know that even though ATR is the most popular volatility indicator in technical analysis, it's not the best estimator. Almost no one in quant finance uses ATR to measure volatility (otherwise these papers will be based on how to improve ATR measurements instead of HV). As you can see, there are much more advanced volatility estimators that also take account into open, close, high, and low. HV values are based on log returns with some calculation adjustment. It can also be scaled in terms of price just like ATR. And for profit-taking ranges, ATR is not based on probabilities. Historical volatility can be used in a probability distribution function to calculated the probability of the ranges such as the Expected Move indicator. Other Estimators There are also other more advanced historical volatility estimators. There are high frequency sampled HV that uses intraday data to calculate volatility. We will publish the high frequency volatility estimator in the future. There's also ARCH and GARCH models that takes volatility clustering into account. GARCH models require maximum likelihood estimation which needs a solver to find the best weights for each component. This is currently not possible on TV due to large computational power requirements. All the other indicators claims to be GARCH are all wrong.
Momentum Day Trading ToolkitMomentum Day Trading Toolkit
Complete User Guide
Table of Contents
Overview
Quick Start
The Dashboard
Module 1: 5 Pillars Screener
Module 2: Gap & Go
Module 3: Bull Flag / Flat Top
Module 4: Float Rotation
Module 5: R2G / G2R
Module 6: Micro Pullback
Signal Reference
Quality Score
Settings Guide
Alerts Setup
Trading Workflows
Troubleshooting
Overview
The Momentum Day Trading Toolkit combines 6 powerful indicators into one unified system for day trading momentum stocks.
ModulePurpose① 5 PillarsConfirms stock is "in play"② Gap & GoPre-market levels & gap analysis③ Bull Flag / Flat TopClassic breakout patterns④ Float RotationMeasures true interest level⑤ R2G / G2RTracks prior close crosses⑥ Micro PullbackPrecision continuation entries
All modules work together - the dashboard shows you everything at a glance, and you can enable/disable any module you don't need.
Quick Start
Step 1: Add to Chart
Add the indicator to any stock chart
Recommended timeframes: 1-minute, 5-minute, or 15-minute
Step 2: Check the Dashboard (Top Right)
Look for:
Status = Current state (Scanning, Entry Signal, etc.)
Quality Score = Setup rating out of 10
Green checkmarks (✓) = Criteria passing
Step 3: Watch for Entry Signals
Triangles, circles, diamonds below bars = Entry signals
Arrows = R2G/G2R crosses
Step 4: Set Alerts
Right-click chart → Add Alert
Select "Momentum Day Trading Toolkit"
Choose your alert condition
The Dashboard
The dashboard in the top-right corner gives you instant analysis:
┌─────────────────────────────┐
│ MOMENTUM TOOLKIT │
├─────────────────────────────┤
│ Status │ 🎯 ENTRY SIGNAL │
│ Day │ 🟢 GREEN │
│ Gap │ +8.5% 🔥 │
│ RVol │ 3.2x ✓ │
│ Rotation │ 1.45x 🔥 │
│ Float │ 5.2M 🔥 │
│ Change │ +12.3% ✓ │
│ Pattern │ BULL FLAG! │
│ EMA 9/20 │ Above Both ✓ │
│ VWAP │ Above ✓ │
│ Prior Cl │ 5.91 │
│ PM High │ 9.11 ✓ │
│ Price │ 9.46 ✓ │
└─────────────────────────────┘
Dashboard Row Reference
RowWhat It ShowsGood ValuesStatusCurrent state🎯 ENTRY SIGNALDayGreen/Red vs prior close🟢 GREENGapGap % from prior close🔥 (5%+) or 🔥🔥 (10%+)RVolRelative volume✓ (2x+) or ✓✓ (5x+)RotationFloat rotation🔥 (1x) or 🔥🔥 (2x+)FloatFloat in millions🔥 (<5M) or Low (<10M)ChangeDaily % change✓ (meets minimum)PatternPattern statusBREAKOUT!EMA 9/20Trend positionAbove Both ✓VWAPVWAP positionAbove ✓Prior CloseKey R2G levelReference pricePM HighPre-market high✓ = Above itPriceCurrent price✓ = In range
Status Messages
StatusMeaningActionScanning...Looking for setupsWait✅ ALL PILLARSStock qualifiesWatch for pattern⏳ PATTERN FORMINGSetup developingGet ready🎯 ENTRY SIGNALSignal triggeredExecute trade
Module 1: 5 Pillars Screener
What It Does
Confirms the stock meets basic criteria to be worth trading.
The 5 Pillars
PillarDefaultWhy It MattersRelative Volume2x+ (5x for "strong")Confirms unusual interestDaily Change5%+Stock is movingPrice Range$1-$20Sweet spot for momentumFloat Size<20M sharesLower float = bigger moves
Visual Indicator
Green background appears when ALL pillars pass
Dashboard Shows
Individual pillar status with ✓ checkmarks
Quality score includes pillar factors
Settings
SettingDefaultDescriptionMin RVol2.0xMinimum relative volumeStrong RVol5.0xVolume for full qualificationMin Change5%Minimum daily moveMin Price$1Minimum stock priceMax Price$20Maximum stock priceMax Float20MMaximum float size
Module 2: Gap & Go
What It Does
Analyzes pre-market gaps and displays key price levels.
Key Levels Displayed
LevelColorDescriptionPrior CloseOrangeYesterday's close - THE key levelPM HighGreenPre-market high - breakout levelPM LowRedPre-market low - support
Gap Classification
Gap SizeRatingMeaning5-9.9%🔥 QualifyingWorth watching10%+🔥🔥 StrongHigh priority
Entry Signal
Small green triangle = PM High Breakout
How to Trade
Stock gaps up in pre-market
Wait for market open
Look for break above PM High
Enter on breakout with stop below PM Low
Settings
SettingDefaultDescriptionMin Gap %5%Qualifying gap thresholdStrong Gap %10%Strong gap thresholdShow PM LevelsONDisplay PM high/low lines
Module 3: Bull Flag / Flat Top
What It Does
Detects classic continuation patterns and signals breakouts.
Bull Flag Pattern
▲ BREAKOUT (Entry Signal)
│
┌────┴────┐
│ Pullback │ ← 2-5 red candles
│ (flag) │ Max 50% retrace
└─────────┘
│
┌────┴────┐
│ Pole │ ← 3+ green candles
│ (move) │ Strong momentum
└─────────┘
Flat Top Pattern
═══════════════ Resistance (2+ touches)
│
▲ BREAKOUT above resistance
Entry Signals
SignalShapeColorPatternBull Flag Breakout▲ TriangleLimeFlag breaks upFlat Top Breakout◆ DiamondAquaResistance breaks
How to Trade Bull Flag
See 3+ green candles (the pole)
Price pulls back 2-5 red candles
Pullback stays above 50% of move
Enter on break above pullback high
Stop below pullback low
Settings
SettingDefaultDescriptionMin Pole Candles3Green candles neededMax Pullback5Max red candles allowedMax Retrace50%Max pullback depthFT Touches2Resistance touches neededFT Lookback10Bars to check for resistance
Module 4: Float Rotation
What It Does
Tracks how many times the entire float has traded hands today.
The Formula
Rotation = Cumulative Day Volume ÷ Float
Rotation Levels
RotationEmojiMeaning0.5x—Half float traded1.0x🔥FULL rotation - significant!2.0x🔥🔥Double rotation - extreme3.0x+🔥🔥🔥Triple rotation - rare event
Why It Matters
High rotation = Extreme interest
Everyone who owns shares has likely traded
Often precedes explosive moves
Shows "real" demand beyond just volume
Dashboard Shows
Current rotation level
Fire emojis for milestones
Settings
SettingDefaultDescriptionFloat SourceAutoAuto-detect or manualManual Float10MIf auto fails, use thisAlert Level1.0xAlert when rotation hits this
Module 5: R2G / G2R
What It Does
Tracks when price crosses the prior day's close - a key psychological level.
Red to Green (R2G) 🟢
Prior Close ─────────────────
↗ CROSS TO GREEN
↗
(opened red)
Stock opened below prior close (red)
Crosses above prior close (green)
BULLISH signal
Green to Red (G2R) 🔴
(opened green)
↘
↘ CROSS TO RED
Prior Close ─────────────────
Stock opened above prior close (green)
Crosses below prior close (red)
BEARISH signal
Entry Signals
SignalShapeColorMeaningR2G↑ ArrowLimeCrossed to greenG2R↓ ArrowRedCrossed to red
Why R2G Matters
Bears who shorted get squeezed
Creates FOMO buying
Prior close becomes support
Momentum often continues
Dashboard Shows
Current day status (🟢 GREEN / 🔴 RED)
Whether R2G or G2R occurred (R2G ✓ or G2R ✓)
Settings
SettingDefaultDescriptionRequire Opposite OpenONR2G needs red openShow Prior CloseONDisplay the line
Module 6: Micro Pullback
What It Does
Finds precision entries on brief 1-3 candle pullbacks after strong moves.
The Pattern
▲ ENTRY (break pullback high)
│
┌──┴───┐
│ 1-3 │ ← Micro pullback (brief!)
│ red │ Stop = low of this
└──────┘
│
┌──┴───┐
│ 3+ │ ← Strong move
│green │ Momentum building
└──────┘
Why Micro Pullbacks Work
Tight stop = Pullback low is close
Momentum intact = Only paused briefly
Early entry = Catch continuation early
Clear trigger = Break of pullback high
Entry Signal
SignalShapeColorMicro Pullback Entry● CircleYellow
How to Trade
See 3+ green candles (strong move)
1-3 red candles (brief pause)
Pullback stays above 50% retrace
Enter when green candle breaks pullback high
Stop at pullback low
Settings
SettingDefaultDescriptionMin Green Candles3Candles before pullbackMax Pullback3Max red candlesMax Retrace50%Max pullback depth
Signal Reference
All Entry Signals (Below Bar)
ShapeColorSignalModule▲ Large TriangleLimeBull Flag BreakoutPatterns◆ DiamondAquaFlat Top BreakoutPatterns● CircleYellowMicro Pullback EntryMicro PB▲ Small TriangleGreenPM High BreakoutGap & Go↑ ArrowLimeRed to GreenR2G/G2R
Warning Signals (Above Bar)
ShapeColorSignalModule↓ ArrowRedGreen to RedR2G/G2R
Optional Forming Signals (Disabled by Default)
ShapeColorSignal🚩 FlagFaded LimeBull Flag Forming● CircleFaded YellowMicro PB Forming
Enable "Show 'Forming' Markers" in settings to see these
Quality Score
The quality score (0-10) rates the overall setup strength.
Scoring Breakdown
FactorPointsRVol 5x++2RVol 2x++1Daily change 5%++1Low float (<20M)+1Strong gap (10%+)+2Qualifying gap (5%+)+1Rotation 1x++2Rotation 0.5x++1Above EMA 20+1
Score Interpretation
ScoreGradeAction8-10A+Best setups - full position6-7AGood setups - standard size4-5BAverage - reduced size0-3CWeak - skip or paper trade
Settings Guide
Module Toggles
Turn each module ON/OFF:
SettingDefaultDescription① 5 Pillars ScreenerONStock qualification② Gap & Go AnalysisONGap & level analysis③ Bull Flag / Flat TopONPattern detection④ Float RotationONRotation tracking⑤ R2G / G2R TrackerONPrior close crosses⑥ Micro PullbackONPullback entries
Visual Settings
SettingDefaultDescriptionShow DashboardONDisplay info tableTable SizeNormalSmall/Normal/LargeShow Entry SignalsONDisplay entry shapesShow 'Forming' MarkersOFFShow pattern formingShow Key LevelsONPrior close, PM levelsShow EMA 9/20ONTrend EMAsShow VWAPONVWAP line
Recommended Presets
Minimal (Clean Chart)
Show Dashboard: ON
Show Entry Signals: ON
Show 'Forming' Markers: OFF
Show Key Levels: OFF
Show EMA: OFF
Show VWAP: OFF
Standard (Balanced)
All defaults
Full Analysis
All settings ON
Alerts Setup
Available Alerts
AlertTriggerAny Bullish EntryAny entry signal firesBull Flag BreakoutBull flag breaks outFlat Top BreakoutFlat top breaks outMicro Pullback EntryMicro PB triggersPM High BreakoutBreaks above PM highRed to GreenR2G crossGreen to RedG2R crossFloat RotationHits rotation level5 Pillars PassAll pillars qualifyPattern FormingPattern starts formingHigh Quality EntryEntry with score 7+/10
How to Set Alerts
Right-click on chart
Select "Add Alert"
Condition: "Momentum Day Trading Toolkit"
Select alert type from dropdown
Set expiration and notifications
Click "Create"
Recommended Alerts
For Active Trading:
Any Bullish Entry
High Quality Entry
For Watchlist Monitoring:
5 Pillars Pass
Float Rotation
Trading Workflows
Workflow 1: Full Qualification
Step 1: 5 PILLARS
└─→ Wait for "✅ ALL PILLARS" status
Step 2: CHECK SETUP
└─→ Quality score 6+?
└─→ Above EMA and VWAP?
Step 3: WAIT FOR ENTRY
└─→ Bull Flag, Flat Top, or Micro PB signal
Step 4: EXECUTE
└─→ Enter on signal
└─→ Stop below pattern low
└─→ Target 2:1 minimum
Workflow 2: Gap & Go
Step 1: PRE-MARKET
└─→ Stock gaps 5%+ (shows in Gap row)
Step 2: MARKET OPEN
└─→ Note PM High level (green line)
Step 3: WAIT FOR BREAK
└─→ PM High Breakout signal (small triangle)
Step 4: CONFIRM
└─→ R2G if opened red (double confirmation)
└─→ RVol 2x+
Step 5: EXECUTE
└─→ Enter on PM High break
└─→ Stop below PM Low
Workflow 3: Micro Pullback Scalp
Step 1: FIND MOMENTUM
└─→ Stock moving, 3+ green candles
Step 2: WAIT FOR PAUSE
└─→ 1-3 red candles (brief pullback)
Step 3: ENTRY
└─→ Yellow circle signal appears
Step 4: QUICK TRADE
└─→ Enter at signal
└─→ Tight stop at pullback low
└─→ Quick target (1:1 to 2:1)
Troubleshooting
Q: Lines are moving/jumping on real-time chart?
A: This was fixed in latest version. Make sure you have the newest code. Lines now lock in place at market open.
Q: Too many signals, chart is cluttered?
A:
Turn off "Show 'Forming' Markers"
Disable modules you don't need
Use "Minimal" visual preset
Q: No signals appearing?
A:
Check if "Show Entry Signals" is ON
Make sure relevant module is enabled
Stock may not meet pattern criteria
Q: Dashboard shows wrong float?
A:
TradingView float data isn't available for all stocks
Switch Float Source to "Manual"
Enter correct float in millions
Q: PM High/Low not showing?
A:
Only appears during market hours
Needs pre-market data to calculate
Check if "Show Key Levels" is ON
Q: Quality score seems wrong?
A:
Score updates in real-time
Check individual factors in dashboard
RVol and rotation change throughout day
Q: Alert not triggering?
A:
Make sure alert is set on correct symbol
Check alert hasn't expired
Verify condition is set correctly
Quick Reference Card
Entry Signals
▲ Lime Triangle = Bull Flag Breakout
◆ Aqua Diamond = Flat Top Breakout
● Yellow Circle = Micro Pullback
▲ Green Triangle = PM High Break
↑ Lime Arrow = R2G (bullish)
↓ Red Arrow = G2R (bearish)
Dashboard Quick Read
🎯 = Entry signal active
✅ = All pillars pass
🟢 = Day is green
🔥 = Strong (gap/rotation)
✓ = Criteria met
✗ = Criteria failed
Quality Score
8-10 = A+ (Best)
6-7 = A (Good)
4-5 = B (Average)
0-3 = C (Weak)
Key Levels
Orange Line = Prior Close (R2G level)
Green Line = PM High (breakout level)
Red Line = PM Low (support)
Purple Line = VWAP
Yellow/Orange = EMA 9/20
Happy Trading! 🎯📈
For questions or issues, use TradingView's comment section on the indicator page.
Liquidity & inducementsHi all!
This indicator will show liquidity and inducements.
I will continue to try to add different types of liquidity and inducements, at this moment it contains 6 kinds of liquidity/inducement, they are:
• Grabs
• Big grabs
• Sweeps
• Turtle soups
• Equal highs/lows (liquidity and inducement)
• BSL & SSL
And 1 type of inducement:
• Retracement
This description will contain indicator examples of each individual liquidity and inducement. They will all be with the default settings.
Settings
First you will find settings for the market structure (BOS/CHoCH/CHoCH+). Select left and right pivot lengths and if the pivots should have a label or not.
This is the base foundation of this indicator and is possible with my library 'PriceAction' ().
You will see solid lines for break of structures (BOS), change of characters (CHoCH) and change of character plus (CHoCH+).
The pivots found will be the core of this indicator and will show you when the closing price breaks it. When that happens a break of structure (BOS) or a change of character (CHoCH or CHoCH+) will be created. The latest 5 pivots found within the current trend will be kept to take action on.
A break of structure is removed if an earlier pivot within the same trend is broken and the pivot's high price for a bullish trend or low price for a bearish trend is more extreme than the BOS pivot's price.
You are able to show the pivots that are used. "HH" (higher high), "HL" (higher low), "LH" (lower high), "LL" (lower low) and "H"/"L" (for pivots (high/low) when the trend has changed) are the labels used.
In the next section ('Liquidity ($$$)') you can select which types of liquidity you want to see. Note that 'Equal highs/lows' can also show inducement (more on that later).
In the section afterwards ('Inducement (IDM)') you can select if you want retracement inducements to be visible or not. More information on what they are later on.
The section for each individual liquidity and/or inducement can first contain a line named 'Pivot', where you can set the pivot lengths (first left, then right). Then you can set the 'Lookback', which means that the 'Lookback' number of past pivots is to take action on. After that you set the 'Timeframe' for the pivots used. That means that all available liquidity/inducements will be from your desired timeframe. Lastly you set the color of the liquidity/inducement (either a single color or bullish followed by bearish colors).
Lastly in the settings you can select the font sizes for the market structure and liquidity/inducements and what style liquidity/inducements lines will have. The sizes defaults to 7 and has a dotted line look.
Grabs
Liquidity grabs and liquidity sweeps are very similar. It all depends on if the current bar closed above/below the liquidity pivot and on if its a continuation or reversal. In a liquidity grab the bar that's above or below the liquidity pivot was not closed above or below it. Like this:
Or
The visual feedback will be a dotted line between the liquidity pivot and liquidity grab bar and a linefill between the high of the liquidity grab bar and the liquidity pivot.
Indicator example:
Big grabs
This is another 'grabs' option. You can show an additional grab if you want to. I suggest having this grab from a higher timeframe or with larger pivot lengths than the other grab.
The default is with the chart timeframe and 10/10 as pivot lengths.
Indicator example:
Sweeps
A liquidity sweep is like a liquidity grab but with the difference that price closes above/below and has a continuation instead of a reversal. If the liquidity pivot was at the same bar as a BOS/CHoCH/CHoCH+ it will not be a liquidity grab but a structural break instead.
They can look like this:
Indicator example;
Turtle soups
If only one candle is beyond the pivot it could be a liquidity grab. It's a grab if price didn't close beyond the liquidity pivot, if so it's invaliditet. Turtle soups are basically false breakouts that takes liquidity (is a false breakout from a pivot with the lengths and timeframe from the settings).
The turtle soup can have a confirmation in the terms of a change of character (CHoCH). You can enable this in the settings section for 'Turtle soups' through the 'Confirmation' checkbox (enabled by default). The turtle soup strategy usually comes with some sort of confirmation, in this case a CHoCH, but it can also be a market structure shift (MSS) or a change in state of delivery (CISD).
The addition of turtle soups is possible through my script 'Turtle soup' ().
The drawing will be a dotted line between the liquidity pivot and the last bar of the false breakout and a box from the start of the false breakout to the end of it.
Indicator example:
Equal highs/lows
Equal highs/lows will always show liquidity, but might also show inducement. Inducement will be shown on equal lows if the trend is bullish and on equal highs if it's bearish, like this:
Or
Equal highs can only be created if the second pivot is lower than the first one. Equal lows can only be created if the second pivot is higher than the first one. If that is not the case it could be a liquidity grab.
When equal highs or equal lows are find that produces inducement (equal lows in a bullish trend and equal highs in a bearish trend), the indicator will first display inducement and will show liquidity once traders are induced to enter the security. Stop loss placement, for liquidity, is 0.1 * the average true range (ATR, of length 14). They will look like this:
Only inducement:
Inducement and liquidity:
Indicator example:
Equal highs/lows inducements can not be triggered after a BOS/CHoCH/CHoCH+. They are cleared upon a structural break.
BSL & SSL
Buyside liquidity (BSL) and sellside liquidity (SSL) will be shown. A pivot that's been mitigated (touched by price) can never be BSL or SSL. The BSL/SSL available will be dynamic while price moves (work in Replay and lower timeframes that moves fast) and pick the latest pivot/s (with left and right lengths from the 'Market structure' section). You can define how many BSL/SSL you want to see with a default value of 1, meaning only 1 BSL and 1 SSL can be shown. If there is no unmitigated high (BSL) or low (SSL), no BSL/SSL will be available to show. If there are BSL/SSL available they're very useful to use as targets for entering a trade.
The will look like this when available;
And without BSL available:
Or
And without SSL available:
Note that the examples without BSL/SSL available could have liquidity available from previous price legs.
This can be an example of a BSL/SSL sequence:
First both buyside and sellside liquidity is available:
Then a new low appears and new sellside liquidity is available:
Then buyside liquidity is mitigated, so only sellside liquidity is available:
A new high pivot appears and buyside liquidity is available again:
Lastly a bearish CHoCH happens and sellside liquidity is mitigated, only buyside liquidity is available:
Retracement
The first retracement after a BOS/CHoCH/CHoCH+ is considered an inducement with the mission to get traders into a trade prematurely to get stopped out. This level is shown and look like this:
Or
A retracement inducement is removed when a new BOS/CHoCH/CHoCH+ appears and it's not triggered.
---------------------------
As of now there aren't any alerts available. You cannot use the Pine Screener from Tradingview either to see new liquidity/inducement events. I have this planned for future updates though.
I hope that this long description makes sense, let me know otherwise! Also let me know if you experience any bugs or have a feature request or just want to share good settings to use.
Best of trading luck!
Smart Money Flow Index (SMFI) - Advanced SMC [PhenLabs]📊Smart Money Flow Index (SMFI)
Version: PineScript™v6
📌Description
The Smart Money Flow Index (SMFI) is an advanced Smart Money Concepts implementation that tracks institutional trading behavior through multi-dimensional analysis. This comprehensive indicator combines volume-validated Order Block detection, Fair Value Gap identification with auto-mitigation tracking, dynamic Liquidity Zone mapping, and Break of Structure/Change of Character detection into a unified system.
Unlike basic SMC indicators, SMFI employs a proprietary scoring algorithm that weighs five critical factors: Order Block strength (validated by volume), Fair Value Gap size and recency, proximity to Liquidity Zones, market structure alignment (BOS/CHoCH), and multi-timeframe confluence. This produces a Smart Money Score (0-100) where readings above 70 represent optimal institutional setup conditions.
🚀Points of Innovation
Volume-Validated Order Block Detection – Only displays Order Blocks when formation candle exceeds customizable volume multiplier (default 1.5x average), filtering weak zones and highlighting true institutional accumulation/distribution
Auto-Mitigation Tracking System – Fair Value Gaps and Order Blocks automatically update status when price mitigates them, with visual distinction between active and filled zones preventing trades on dead levels
Proprietary Smart Money Score Algorithm – Combines weighted factors (OB strength 25%, FVG proximity 20%, Liquidity 20%, Structure 20%, MTF 15%) into single 0-100 confidence rating updating in real-time
ATR-Based Adaptive Calculations – All distance measurements use 14-period Average True Range ensuring consistent function across any instrument, timeframe, or volatility regime without manual recalibration
Dynamic Age Filtering – Automatically removes liquidity levels and FVGs older than configurable thresholds preventing chart clutter while maintaining relevant levels
Multi-Timeframe Confluence Integration – Analyzes higher timeframe bias with customizable multipliers (2-10x) and incorporates HTF trend direction into Smart Money Score for institutional alignment
🔧Core Components
Order Block Engine – Detects institutional supply/demand zones using characteristic patterns (down-move-then-strong-up for bullish, up-move-then-strong-down for bearish) with minimum volume threshold validation, tracks mitigation when price closes through zones
Fair Value Gap Scanner – Identifies price imbalances where current candle's low/high leaves gap with two-candle-prior high/low, filters by minimum size percentage, monitors 50% fill for mitigation status
Liquidity Zone Mapper – Uses pivot high/low detection with configurable lookback to mark swing points where stop losses cluster, extends horizontal lines to visualize sweep targets, manages lifecycle through age-based removal
Market Structure Analyzer – Tracks pivot progression to identify trend through higher-highs/higher-lows (bullish) or lower-highs/lower-lows (bearish), detects Break of Structure and Change of Character for trend/reversal confirmation
Scoring Calculation Engine – Evaluates proximity to nearest Order Blocks using ATR-normalized distance, assesses FVG recency and distance, calculates liquidity proximity with age weighting, combines structure bias and MTF trend into smoothed final score
🔥Key Features
Customizable Display Limits – Control maximum Order Blocks (1-10), Liquidity Zones (1-10), and FVG age (10-200 bars) to maintain clean charts focused on most relevant institutional levels
Gradient Strength Visualization – All zones render with transparency-adjustable coloring where stronger/newer zones appear more solid and weaker/older zones fade progressively providing instant visual hierarchy
Educational Label System – Optional labels identify each zone type (Bullish OB, Bearish OB, Bullish FVG, Bearish FVG, BOS) with color-coded text helping traders learn SMC concepts through practical application
Real-Time Smart Money Score Dashboard – Top-right table displays current score (0-100) with color coding (green >70, yellow 30-70, red <30) plus trend arrow for at-a-glance confidence assessment
Comprehensive Alert Suite – Configurable notifications for Order Block formation, Fair Value Gap detection, Break of Structure events, Change of Character signals, and high Smart Money Score readings (>70)
Buy/Sell Signal Integration – Automatically plots triangle markers when Smart Money Score exceeds 70 with aligned market structure and fresh Order Block detection providing clear entry signals
🎨Visualization
Order Block Boxes – Shaded rectangles extend from formation bar spanning high-to-low of institutional candle, bullish zones in green, bearish in red, with customizable transparency (80-98%)
Fair Value Gap Zones – Rectangular areas marking imbalances, active FVGs display in bright colors with adjustable transparency, mitigated FVGs switch to gray preventing trades on filled zones
Liquidity Level Lines – Dashed horizontal lines extend from pivot creation points, swing highs in bearish color (short targets above), swing lows in bullish color (long targets below), opacity decreases with age
Structure Labels – "BOS" labels appear above/below price when Break of Structure confirmed, colored by direction (green bullish, red bearish), positioned at 1% beyond highs/lows for visibility
Educational Info Panel – Bottom-right table explains key terminology (OB, FVG, BOS, CHoCH) and score interpretation (>70 high probability) with semi-transparent background for readability
📖Usage Guidelines
General Settings
Show Order Blocks – Default: On, toggles visibility of institutional supply/demand zones, disable when focusing solely on FVGs or Liquidity
Show Fair Value Gaps – Default: On, controls FVG zone display including active and mitigated imbalances
Show Liquidity Zones – Default: On, manages liquidity line visibility, disable on lower timeframes to reduce clutter
Show Market Structure – Default: On, toggles BOS/CHoCH label display
Show Smart Money Score – Default: On, controls score dashboard visibility
Order Block Settings
OB Lookback Period – Default: 20, Range: 5-100, controls bars scanned for Order Block patterns, lower values detect recent activity, higher values find older blocks
Min Volume Multiplier – Default: 1.5, Range: 1.0-5.0, sets minimum volume threshold as multiple of 20-period average, higher values (2.0+) filter for strongest institutional candles
Max Order Blocks to Display – Default: 3, Range: 1-10, limits simultaneous Order Blocks shown, lower settings (1-3) maintain focus on most recent zones
Fair Value Gap Settings
Min FVG Size (%) – Default: 0.3, Range: 0.1-2.0, defines minimum gap size as percentage of close price, lower values detect micro-imbalances, higher values focus on significant gaps
Max FVG Age (bars) – Default: 50, Range: 10-200, removes FVGs older than specified bars, lower settings (10-30) for scalping, higher (100-200) for swing trading
Show FVG Mitigation – Default: On, displays filled FVGs in gray providing visual history, disable to show only active untouched imbalances
Liquidity Zone Settings
Liquidity Lookback – Default: 50, Range: 20-200, sets pivot detection period for swing highs/lows, lower values (20-50) mark shorter-term liquidity, higher (100-200) identify major swings
Max Liquidity Age (bars) – Default: 100, Range: 20-500, removes liquidity lines older than specified bars, adjust based on timeframe
Liquidity Sensitivity – Default: 0.5, Range: 0.1-1.0, controls pivot detection sensitivity, lower values mark only major swings, higher values identify minor swings
Max Liquidity Zones to Display – Default: 3, Range: 1-10, limits total liquidity levels shown maintaining chart clarity
Market Structure Settings
Pivot Length – Default: 5, Range: 3-15, defines bars to left/right for pivot validation, lower values (3-5) create sensitive structure breaks, higher (10-15) filter for major shifts
Min Structure Move (%) – Default: 1.0, Range: 0.1-5.0, sets minimum percentage move required between pivots to confirm structure change
Multi-Timeframe Settings
Enable MTF Analysis – Default: On, activates higher timeframe trend analysis incorporation into Smart Money Score
Higher Timeframe Multiplier – Default: 4, Range: 2-10, multiplies current timeframe to determine analysis timeframe (4x on 15min = 1hour)
Visual Settings
Bullish Color – Default: Green (#089981), sets color for bullish Order Blocks, FVGs, and structure elements
Bearish Color – Default: Red (#f23645), defines color for bearish elements
Neutral Color – Default: Gray (#787b86), controls color of mitigated zones and neutral elements
Show Educational Labels – Default: On, displays text labels on zones identifying type (OB, FVG, BOS), disable once familiar with patterns
Order Block Transparency – Default: 92, Range: 80-98, controls Order Block box transparency
FVG Transparency – Default: 92, Range: 80-98, sets Fair Value Gap zone transparency independently from Order Blocks
Alert Settings
Alert on Order Block Formation – Default: On, triggers notification when new volume-validated Order Block detected
Alert on FVG Formation – Default: On, sends alert when Fair Value Gap appears enabling quick response to imbalances
Alert on Break of Structure – Default: On, notifies when BOS or CHoCH confirmed
Alert on High Smart Money Score – Default: On, alerts when Smart Money Score crosses above 70 threshold indicating high-probability setup
✅Best Use Cases
Order Block Retest Entries – After Break of Structure, wait for price retrace into fresh bullish Order Block with Smart Money Score >70, enter long on zone reaction targeting next liquidity level
Fair Value Gap Retracement Trading – When price creates FVG during strong move then retraces, enter as price approaches unfilled gap expecting institutional orders to continue trend
Liquidity Sweep Reversals – Monitor price approaching swing high/low liquidity zones against prevailing Smart Money Score trend, after stop hunt sweep watch for rejection into premium Order Block/FVG
Multi-Timeframe Confluence Setups – Identify alignment when current timeframe Order Block coincides with higher timeframe FVG plus MTF analysis showing matching trend bias
Break of Structure Continuations – After BOS confirms trend direction, trade pullbacks to nearest Order Block or FVG in direction of structure break using Smart Money Score >70 as entry filter
Change of Character Reversal Plays – When CHoCH detected indicating potential reversal, look for Smart Money Score pivot with opposing Order Block formation then enter on structure confirmation
⚠️Limitations
Lagging Pivot Calculations – Pivot-based features (Liquidity Zones, Market Structure) require bars to right of pivot for confirmation, meaning these elements identify levels retrospectively with delay equal to lookback period
Whipsaw in Ranging Markets – During choppy conditions, Order Blocks fail frequently and structure breaks produce false signals as Smart Money Score fluctuates without clear institutional bias, best used in trending markets
Volume Data Dependency – Order Block volume validation requires accurate volume data which may be incomplete on Forex pairs or limited in crypto exchange feeds
Subjectivity in Scoring Weights – Proprietary 25-20-20-20-15 weighting reflects general institutional behavior but may not optimize for specific instruments or market regimes, user cannot adjust factor weights
Visual Complexity on Lower Timeframes – Sub-hour timeframes generate excessive zones creating cluttered charts, requires aggressive display limit reduction and higher minimum thresholds
No Fundamental Integration – Indicator analyzes purely technical price action and volume without incorporating economic events, news catalysts, or fundamental shifts that override technical levels
💡What Makes This Unique
Unified SMC Ecosystem – Unlike indicators displaying Order Blocks OR FVGs OR Liquidity separately, SMFI combines all three institutional concepts plus market structure into single cohesive system
Proprietary Confidence Scoring – Rather than manual setup assessment, automated Smart Money Score quantifies probability by weighting five institutional dimensions into actionable 0-100 rating
Volume-Filtered Quality – Eliminates weak Order Blocks forming without institutional volume confirmation, ensuring displayed zones represent genuine accumulation/distribution
Adaptive Lifecycle Management – Automatically updates mitigation status and removes aged zones preventing trades on dead levels through continuous validity and age monitoring
Educational Integration – Built-in tooltips, labeled zones, and reference panel make indicator functional for both learning Smart Money Concepts and executing strategies
🔬How It Works
Order Block Detection – Scans for patterns where strong directional move follows counter-move creating last down-candle before rally (bullish OB) or last up-candle before sell-off (bearish OB), validates formations only when candle exhibits volume exceeding configurable multiple (default 1.5x) of 20-bar average volume
Fair Value Gap Identification – Compares current candle’s high/low against two-candles-prior low/high to detect price imbalances, calculates gap size as percentage of close and filters micro-gaps below minimum threshold (default 0.3%), monitors whether subsequent price fills 50% triggering mitigation status
Liquidity Zone Mapping – Employs pivot detection using configurable lookback (default 50 bars) to identify swing highs/lows where retail stops cluster, extends horizontal reference lines from pivot creation and applies age-based filtering to remove stale zones
Market Structure Analysis – Tracks pivot progression using structure-specific lookback (default 5 bars) to determine trend, confirms uptrend when new pivot high exceeds previous by minimum move percentage, detects Break of Structure when price breaks recent pivot level, flags Change of Character for potential reversals
Multi-Timeframe Confluence – When enabled, requests security data from higher timeframe (current TF × HTF multiplier, default 4x), compares HTF close against HTF 20-period MA to determine bias, contributes ±50 points to score ensuring alignment with institutional positioning on superior timeframe
Smart Money Score Calculation – Evaluates Order Block component via ATR-normalized distance producing max 100-point contribution weighted at 25%, assesses FVG factor through age penalty and distance at 20% weight, calculates Liquidity proximity at 20%, incorporates structure bias (±50-100 points) at 20%, adds MTF component at 15%, applies 3-period smoothing to reduce volatility
Visual Rendering and Lifecycle – Draws Order Block boxes, Fair Value Gap rectangles with color coding (green/red active, gray mitigated), extends liquidity dashed lines with fade-by-age opacity, plots BOS labels, displays Smart Money Score dashboard, continuously updates checking mitigation conditions and removing elements exceeding age/display limits
💡Note:
The Smart Money Flow Index combines multiple Smart Money Concepts into unified institutional order flow analysis. For optimal results, use the Smart Money Score as confluence filter rather than standalone entry signal – scores above 70 indicate high-probability setups but should be combined with risk management, higher timeframe bias, and market regime understanding.
Stochastic Enhanced [DCAUT]█ Stochastic Enhanced
📊 ORIGINALITY & INNOVATION
The Stochastic Enhanced indicator builds upon George Lane's classic momentum oscillator (developed in the late 1950s) by providing comprehensive smoothing algorithm flexibility. While traditional implementations limit users to Simple Moving Average (SMA) smoothing, this enhanced version offers 21 advanced smoothing algorithms, allowing traders to optimize the indicator's characteristics for different market conditions and trading styles.
Key Improvements:
Extended from single SMA smoothing to 21 professional-grade algorithms including adaptive filters (KAMA, FRAMA), zero-lag methods (ZLEMA, T3), and advanced digital filters (Kalman, Laguerre)
Maintains backward compatibility with traditional Stochastic calculations through SMA default setting
Unified smoothing algorithm applies to both %K and %D lines for consistent signal processing characteristics
Enhanced visual feedback with clear color distinction and background fill highlighting for intuitive signal recognition
Comprehensive alert system covering crossovers and zone entries for systematic trade management
Differentiation from Traditional Stochastic:
Traditional Stochastic indicators use fixed SMA smoothing, which introduces consistent lag regardless of market volatility. This enhanced version addresses the limitation by offering adaptive algorithms that adjust to market conditions (KAMA, FRAMA), reduce lag without sacrificing smoothness (ZLEMA, T3, HMA), or provide superior noise filtering (Kalman Filter, Laguerre filters). The flexibility helps traders balance responsiveness and stability according to their specific needs.
📐 MATHEMATICAL FOUNDATION
Core Stochastic Calculation:
The Stochastic Oscillator measures the position of the current close relative to the high-low range over a specified period:
Step 1: Raw %K Calculation
%K_raw = 100 × (Close - Lowest Low) / (Highest High - Lowest Low)
Where:
Close = Current closing price
Lowest Low = Lowest low over the %K Length period
Highest High = Highest high over the %K Length period
Result ranges from 0 (close at period low) to 100 (close at period high)
Step 2: Smoothed %K Calculation
%K = MA(%K_raw, K Smoothing Period, MA Type)
Where:
MA = Selected moving average algorithm (SMA, EMA, etc.)
K Smoothing = 1 for Fast Stochastic, 3+ for Slow Stochastic
Traditional Fast Stochastic uses %K_raw directly without smoothing
Step 3: Signal Line %D Calculation
%D = MA(%K, D Smoothing Period, MA Type)
Where:
%D acts as a signal line and moving average of %K
D Smoothing typically set to 3 periods in traditional implementations
Both %K and %D use the same MA algorithm for consistent behavior
Available Smoothing Algorithms (21 Options):
Standard Moving Averages:
SMA (Simple): Equal-weighted average, traditional default, consistent lag characteristics
EMA (Exponential): Recent price emphasis, faster response to changes, exponential decay weighting
RMA (Rolling/Wilder's): Smoothed average used in RSI, less reactive than EMA
WMA (Weighted): Linear weighting favoring recent data, moderate responsiveness
VWMA (Volume-Weighted): Incorporates volume data, reflects market participation intensity
Advanced Moving Averages:
HMA (Hull): Reduced lag with smoothness, uses weighted moving averages and square root period
ALMA (Arnaud Legoux): Gaussian distribution weighting, minimal lag with good noise reduction
LSMA (Least Squares): Linear regression based, fits trend line to data points
DEMA (Double Exponential): Reduced lag compared to EMA, uses double smoothing technique
TEMA (Triple Exponential): Further lag reduction, triple smoothing with lag compensation
ZLEMA (Zero-Lag Exponential): Lag elimination attempt using error correction, very responsive
TMA (Triangular): Double-smoothed SMA, very smooth but slower response
Adaptive & Intelligent Filters:
T3 (Tilson T3): Six-pass exponential smoothing with volume factor adjustment, excellent smoothness
FRAMA (Fractal Adaptive): Adapts to market fractal dimension, faster in trends, slower in ranges
KAMA (Kaufman Adaptive): Efficiency ratio based adaptation, responds to volatility changes
McGinley Dynamic: Self-adjusting mechanism following price more accurately, reduced whipsaws
Kalman Filter: Optimal estimation algorithm from aerospace engineering, dynamic noise filtering
Advanced Digital Filters:
Ultimate Smoother: Advanced digital filter design, superior noise rejection with minimal lag
Laguerre Filter: Time-domain filter with N-order implementation, adjustable lag characteristics
Laguerre Binomial Filter: 6-pole Laguerre filter, extremely smooth output for long-term analysis
Super Smoother: Butterworth filter implementation, removes high-frequency noise effectively
📊 COMPREHENSIVE SIGNAL ANALYSIS
Absolute Level Interpretation (%K Line):
%K Above 80: Overbought condition, price near period high, potential reversal or pullback zone, caution for new long entries
%K in 70-80 Range: Strong upward momentum, bullish trend confirmation, uptrend likely continuing
%K in 50-70 Range: Moderate bullish momentum, neutral to positive outlook, consolidation or mild uptrend
%K in 30-50 Range: Moderate bearish momentum, neutral to negative outlook, consolidation or mild downtrend
%K in 20-30 Range: Strong downward momentum, bearish trend confirmation, downtrend likely continuing
%K Below 20: Oversold condition, price near period low, potential bounce or reversal zone, caution for new short entries
Crossover Signal Analysis:
%K Crosses Above %D (Bullish Cross): Momentum shifting bullish, faster line overtakes slower signal, consider long entry especially in oversold zone, strongest when occurring below 20 level
%K Crosses Below %D (Bearish Cross): Momentum shifting bearish, faster line falls below slower signal, consider short entry especially in overbought zone, strongest when occurring above 80 level
Crossover in Midrange (40-60): Less reliable signals, often in choppy sideways markets, require additional confirmation from trend or volume analysis
Multiple Failed Crosses: Indicates ranging market or choppy conditions, reduce position sizes or avoid trading until clear directional move
Advanced Divergence Patterns (%K Line vs Price):
Bullish Divergence: Price makes lower low while %K makes higher low, indicates weakening bearish momentum, potential trend reversal upward, more reliable when %K in oversold zone
Bearish Divergence: Price makes higher high while %K makes lower high, indicates weakening bullish momentum, potential trend reversal downward, more reliable when %K in overbought zone
Hidden Bullish Divergence: Price makes higher low while %K makes lower low, indicates trend continuation in uptrend, bullish trend strength confirmation
Hidden Bearish Divergence: Price makes lower high while %K makes higher high, indicates trend continuation in downtrend, bearish trend strength confirmation
Momentum Strength Analysis (%K Line Slope):
Steep %K Slope: Rapid momentum change, strong directional conviction, potential for extended moves but also increased reversal risk
Gradual %K Slope: Steady momentum development, sustainable trends more likely, lower probability of sharp reversals
Flat or Horizontal %K: Momentum stalling, potential reversal or consolidation ahead, wait for directional break before committing
%K Oscillation Within Range: Indicates ranging market, sideways price action, better suited for range-trading strategies than trend following
🎯 STRATEGIC APPLICATIONS
Mean Reversion Strategy (Range-Bound Markets):
Identify ranging market conditions using price action or Bollinger Bands
Wait for Stochastic to reach extreme zones (above 80 for overbought, below 20 for oversold)
Enter counter-trend position when %K crosses %D in extreme zone (sell on bearish cross above 80, buy on bullish cross below 20)
Set profit targets near opposite extreme or midline (50 level)
Use tight stop-loss above recent swing high/low to protect against breakout scenarios
Exit when Stochastic reaches opposite extreme or %K crosses %D in opposite direction
Trend Following with Momentum Confirmation:
Identify primary trend direction using higher timeframe analysis or moving averages
Wait for Stochastic pullback to oversold zone (<20) in uptrend or overbought zone (>80) in downtrend
Enter in trend direction when %K crosses %D confirming momentum shift (bullish cross in uptrend, bearish cross in downtrend)
Use wider stops to accommodate normal trend volatility
Add to position on subsequent pullbacks showing similar Stochastic pattern
Exit when Stochastic shows opposite extreme with failed cross or bearish/bullish divergence
Divergence-Based Reversal Strategy:
Scan for divergence between price and Stochastic at swing highs/lows
Confirm divergence with at least two price pivots showing divergent Stochastic readings
Wait for %K to cross %D in direction of anticipated reversal as entry trigger
Enter position in divergence direction with stop beyond recent swing extreme
Target profit at key support/resistance levels or Fibonacci retracements
Scale out as Stochastic reaches opposite extreme zone
Multi-Timeframe Momentum Alignment:
Analyze Stochastic on higher timeframe (4H or Daily) for primary trend bias
Switch to lower timeframe (1H or 15M) for precise entry timing
Only take trades where lower timeframe Stochastic signal aligns with higher timeframe momentum direction
Higher timeframe Stochastic in bullish zone (>50) = only take long entries on lower timeframe
Higher timeframe Stochastic in bearish zone (<50) = only take short entries on lower timeframe
Exit when lower timeframe shows counter-signal or higher timeframe momentum reverses
Zone Transition Strategy:
Monitor Stochastic for transitions between zones (oversold to neutral, neutral to overbought, etc.)
Enter long when Stochastic crosses above 20 (exiting oversold), signaling momentum shift from bearish to neutral/bullish
Enter short when Stochastic crosses below 80 (exiting overbought), signaling momentum shift from bullish to neutral/bearish
Use zone midpoint (50) as dynamic support/resistance for position management
Trail stops as Stochastic advances through favorable zones
Exit when Stochastic fails to maintain momentum and reverses back into prior zone
📋 DETAILED PARAMETER CONFIGURATION
%K Length (Default: 14):
Lower Values (5-9): Highly sensitive to price changes, generates more frequent signals, increased false signals in choppy markets, suitable for very short-term trading and scalping
Standard Values (10-14): Balanced sensitivity and reliability, traditional default (14) widely used,适合 swing trading and intraday strategies
Higher Values (15-21): Reduced sensitivity, smoother oscillations, fewer but potentially more reliable signals, better for position trading and lower timeframe noise reduction
Very High Values (21+): Slow response, long-term momentum measurement, fewer trading signals, suitable for weekly or monthly analysis
%K Smoothing (Default: 3):
Value 1: Fast Stochastic, uses raw %K calculation without additional smoothing, most responsive to price changes, generates earliest signals with higher noise
Value 3: Slow Stochastic (default), traditional smoothing level, reduces false signals while maintaining good responsiveness, widely accepted standard
Values 5-7: Very slow response, extremely smooth oscillations, significantly reduced whipsaws but delayed entry/exit timing
Recommendation: Default value 3 suits most trading scenarios, active short-term traders may use 1, conservative long-term positions use 5+
%D Smoothing (Default: 3):
Lower Values (1-2): Signal line closely follows %K, frequent crossover signals, useful for active trading but requires strict filtering
Standard Value (3): Traditional setting providing balanced signal line behavior, optimal for most trading applications
Higher Values (4-7): Smoother signal line, fewer crossover signals, reduced whipsaws but slower confirmation, better for trend trading
Very High Values (8+): Signal line becomes slow-moving reference, crossovers rare and highly significant, suitable for long-term position changes only
Smoothing Type Algorithm Selection:
For Trending Markets:
ZLEMA, DEMA, TEMA: Reduced lag for faster trend entry, quick response to momentum shifts, suitable for strong directional moves
HMA, ALMA: Good balance of smoothness and responsiveness, effective for clean trend following without excessive noise
EMA: Classic choice for trending markets, faster than SMA while maintaining reasonable stability
For Ranging/Choppy Markets:
Kalman Filter, Super Smoother: Superior noise filtering, reduces false signals in sideways action, helps identify genuine reversal points
Laguerre Filters: Smooth oscillations with adjustable lag, excellent for mean reversion strategies in ranges
T3, TMA: Very smooth output, filters out market noise effectively, clearer extreme zone identification
For Adaptive Market Conditions:
KAMA: Automatically adjusts to market efficiency, fast in trends and slow in congestion, reduces whipsaws during transitions
FRAMA: Adapts to fractal market structure, responsive during directional moves, conservative during uncertainty
McGinley Dynamic: Self-adjusting smoothing, follows price naturally, minimizes lag in trending markets while filtering noise in ranges
For Conservative Long-Term Analysis:
SMA: Traditional choice, predictable behavior, widely understood characteristics
RMA (Wilder's): Smooth oscillations, reduced sensitivity to outliers, consistent behavior across market conditions
Laguerre Binomial Filter: Extremely smooth output, ideal for weekly/monthly timeframe analysis, eliminates short-term noise completely
Source Selection:
Close (Default): Standard choice using closing prices, most common and widely tested
HLC3 or OHLC4: Incorporates more price information, reduces impact of sudden spikes or gaps, smoother oscillator behavior
HL2: Midpoint of high-low range, emphasizes intrabar volatility, useful for markets with wide intraday ranges
Custom Source: Can use other indicators as input (e.g., Heikin Ashi close, smoothed price), creates derivative momentum indicators
📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES
Responsiveness Characteristics:
Traditional SMA-Based Stochastic:
Fixed lag regardless of market conditions, consistent delay of approximately (K Smoothing + D Smoothing) / 2 periods
Equal treatment of trending and ranging markets, no adaptation to volatility changes
Predictable behavior but suboptimal in varying market regimes
Enhanced Version with Adaptive Algorithms:
KAMA and FRAMA reduce lag by up to 40-60% in strong trends compared to SMA while maintaining similar smoothness in ranges
ZLEMA and T3 provide near-zero lag characteristics for early entry signals with acceptable noise levels
Kalman Filter and Super Smoother offer superior noise rejection, reducing false signals in choppy conditions by estimations of 30-50% compared to SMA
Performance improvements vary by algorithm selection and market conditions
Signal Quality Improvements:
Adaptive algorithms help reduce whipsaw trades in ranging markets by adjusting sensitivity dynamically
Advanced filters (Kalman, Laguerre, Super Smoother) provide clearer extreme zone readings for mean reversion strategies
Zero-lag methods (ZLEMA, DEMA, TEMA) generate earlier crossover signals in trending markets for improved entry timing
Smoother algorithms (T3, Laguerre Binomial) reduce false extreme zone touches for more reliable overbought/oversold signals
Comparison with Standard Implementations:
Versus Basic Stochastic: Enhanced version offers 21 smoothing options versus single SMA, allowing optimization for specific market characteristics and trading styles
Versus RSI: Stochastic provides range-bound measurement (0-100) with clear extreme zones, RSI measures momentum speed, Stochastic offers clearer visual overbought/oversold identification
Versus MACD: Stochastic bounded oscillator suitable for mean reversion, MACD unbounded indicator better for trend strength, Stochastic excels in range-bound and oscillating markets
Versus CCI: Stochastic has fixed bounds (0-100) for consistent interpretation, CCI unbounded with variable extremes, Stochastic provides more standardized extreme readings across different instruments
Flexibility Advantages:
Single indicator adaptable to multiple strategies through algorithm selection rather than requiring different indicator variants
Ability to optimize smoothing characteristics for specific instruments (e.g., smoother for crypto volatility, faster for forex trends)
Multi-timeframe analysis with consistent algorithm across timeframes for coherent momentum picture
Backtesting capability with algorithm as optimization parameter for strategy development
Limitations and Considerations:
Increased complexity from multiple algorithm choices may lead to over-optimization if parameters are curve-fitted to historical data
Adaptive algorithms (KAMA, FRAMA) have adjustment periods during market regime changes where signals may be less reliable
Zero-lag algorithms sacrifice some smoothness for responsiveness, potentially increasing noise sensitivity in very choppy conditions
Performance characteristics vary significantly across algorithms, requiring understanding and testing before live implementation
Like all oscillators, Stochastic can remain in extreme zones for extended periods during strong trends, generating premature reversal signals
USAGE NOTES
This indicator is designed for technical analysis and educational purposes to provide traders with enhanced flexibility in momentum analysis. The Stochastic Oscillator has limitations and should not be used as the sole basis for trading decisions.
Important Considerations:
Algorithm performance varies with market conditions - no single smoothing method is optimal for all scenarios
Extreme zone signals (overbought/oversold) indicate potential reversal areas but not guaranteed turning points, especially in strong trends
Crossover signals may generate false entries during sideways choppy markets regardless of smoothing algorithm
Divergence patterns require confirmation from price action or additional indicators before trading
Past indicator characteristics and backtested results do not guarantee future performance
Always combine Stochastic analysis with proper risk management, position sizing, and multi-indicator confirmation
Test selected algorithm on historical data of specific instrument and timeframe before live trading
Market regime changes may require algorithm adjustment for optimal performance
The enhanced smoothing options are intended to provide tools for optimizing the indicator's behavior to match individual trading styles and market characteristics, not to create a perfect predictive tool. Responsible usage includes understanding the mathematical properties of selected algorithms and their appropriate application contexts.
Extreme Pressure Zones Indicator (EPZ) [BullByte]Extreme Pressure Zones Indicator(EPZ)
The Extreme Pressure Zones (EPZ) Indicator is a proprietary market analysis tool designed to highlight potential overbought and oversold "pressure zones" in any financial chart. It does this by combining several unique measurements of price action and volume into a single, bounded oscillator (0–100). Unlike simple momentum or volatility indicators, EPZ captures multiple facets of market pressure: price rejection, trend momentum, supply/demand imbalance, and institutional (smart money) flow. This is not a random mashup of generic indicators; each component was chosen and weighted to reveal extreme market conditions that often precede reversals or strong continuations.
What it is?
EPZ estimates buying/selling pressure and highlights potential extreme zones with a single, bounded 0–100 oscillator built from four normalized components. Context-aware weighting adapts to volatility, trendiness, and relative volume. Visual tools include adaptive thresholds, confirmed-on-close extremes, divergence, an MTF dashboard, and optional gradient candles.
Purpose and originality (not a mashup)
Purpose: Identify when pressure is building or reaching potential extremes while filtering noise across regimes and symbols.
Originality: EPZ integrates price rejection, momentum cascade, pressure distribution, and smart money flow into one bounded scale with context-aware weighting. It is not a cosmetic mashup of public indicators.
Why a trader might use EPZ
EPZ provides a multi-dimensional gauge of market extremes that standalone indicators may miss. Traders might use it to:
Spot Reversals: When EPZ enters an "Extreme High" zone (high red), it implies selling pressure might soon dominate. This can hint at a topside reversal or at least a pause in rallies. Conversely, "Extreme Low" (green) can highlight bottom-fish opportunities. The indicator's divergence module (optional) also finds hidden bullish/bearish divergences between price and EPZ, a clue that price momentum is weakening.
Measure Momentum Shifts: Because EPZ blends momentum and volume, it reacts faster than many single metrics. A rising MPO indicates building bullish pressure, while a falling MPO shows increasing bearish pressure. Traders can use this like a refined RSI: above 50 means bullish bias, below 50 means bearish bias, but with context provided by the thresholds.
Filter Trades: In trend-following systems, one could require EPZ to be in the bullish (green) zone before taking longs, or avoid new trades when EPZ is extreme. In mean-reversion systems, one might specifically look to fade extremes flagged by EPZ.
Multi-Timeframe Confirmation: The dashboard can fetch a higher timeframe EPZ value. For example, you might trade a 15-minute chart only when the 60-minute EPZ agrees on pressure direction.
Components and how they're combined
Rejection (PRV) – Captures price rejection based on candle wicks and volume (see Price Rejection Volume).
Momentum Cascade (MCD) – Blends multiple momentum periods (3,5,8,13) into a normalized momentum score.
Pressure Distribution (PDI) – Measures net buy/sell pressure by comparing volume on up vs down candles.
Smart Money Flow (SMF) – An adaptation of money flow index that emphasizes unusual volume spikes.
Each of these components produces a 0–100 value (higher means more bullish pressure). They are then weighted and averaged into the final Market Pressure Oscillator (MPO), which is smoothed and scaled. By combining these four views, EPZ stands out as a comprehensive pressure gauge – the whole is greater than the sum of parts
Context-aware weighting:
Higher volatility → more PRV weight
Trendiness up (RSI of ATR > 25) → more MCD weight
Relative volume > 1.2x → more PDI weight
SMF holds a stable weight
The weighted average is smoothed and scaled into MPO ∈ with 50 as the neutral midline.
What makes EPZ stand out
Four orthogonal inputs (price action, momentum, pressure, flow) unified in a single bounded oscillator with consistent thresholds.
Adaptive thresholds (optional) plus robust extreme detection that also triggers on crossovers, so static thresholds work reliably too.
Confirm Extremes on Bar Close (default ON): dots/arrows/labels/alerts print on closed bars to avoid repaint confusion.
Clean dashboard, divergence tools, pre-alerts, and optional on-price gradients. Visual 3D layering uses offsets for depth only,no lookahead.
Recommended markets and timeframes
Best: liquid symbols (index futures, large-cap equities, major FX, BTC/ETH).
Timeframes: 5–15m (more signals; consider higher thresholds), 1H–4H (balanced), 1D (clear regimes).
Use caution on illiquid or very low TFs where wick/volume geometry is erratic.
Logic and thresholds
MPO ∈ ; 50 = neutral. Above 50 = bullish pressure; below 50 = bearish.
Static thresholds (defaults): thrHigh = 70, thrLow = 30; warning bands 5 pts inside extremes (65/35).
Adaptive thresholds (optional):
thrHigh = min(BaseHigh + 5, mean(MPO,100) + stdev(MPO,100) × ExtremeSensitivity)
thrLow = max(BaseLow − 5, mean(MPO,100) − stdev(MPO,100) × ExtremeSensitivity)
Extreme detection
High: MPO ≥ thrHigh with peak/slope or crossover filter.
Low: MPO ≤ thrLow with trough/slope or crossover filter.
Cooldown: 5 bars (default). A new extreme will not print until the cooldown elapses, even if MPO re-enters the zone.
Confirmation
"Confirm Extremes on Bar Close" (default ON) gates extreme markers, pre-alerts, and alerts to closed bars (non-repainting).
Divergences
Pivot-based bullish/bearish divergence; tags appear only after left/right bars elapse (lookbackPivot).
MTF
HTF MPO retrieved with lookahead_off; values can update intrabar and finalize at HTF close. This is disclosed and expected.
Inputs and defaults (key ones)
Core: Sensitivity=1.0; Analysis Period=14; Smoothing=3; Adaptive Thresholds=OFF.
Extremes: Base High=70, Base Low=30; Extreme Sensitivity=1.5; Confirm Extremes on Bar Close=ON; Cooldown=5; Dot size Small/Tiny.
Visuals: Heatmap ON; 3D depth optional; Strength bars ON; Pre-alerts OFF; Divergences ON with tags ON; Gradient candles OFF; Glow ON.
Dashboard: ON; Position=Top Right; Size=Normal; MTF ON; HTF=60m; compact overlay table on price chart.
Advanced caps: Max Oscillator Labels=80; Max Extreme Guide Lines=80; Divergence objects=60.
Dashboard: what each element means
Header: EPZ ANALYSIS.
Large readout: Current MPO; color reflects state (extreme, approaching, or neutral).
Status badge: "Extreme High/Low", "Approaching High/Low", "Bullish/Neutral/Bearish".
HTF cell (when MTF ON): Higher-timeframe MPO, color-coded vs extremes; updates intrabar, settles at HTF close.
Predicted (when MTF OFF): Simple MPO extrapolation using momentum/acceleration—illustrative only.
Thresholds: Current thrHigh/thrLow (static or adaptive).
Components: ASCII bars + values for PRV, MCD, PDI, SMF.
Market metrics: Volume Ratio (x) and ATR% of price.
Strength: Bar indicator of |MPO − 50| × 2.
Confidence: Heuristic gauge (100 in extremes, 70 in warnings, 50 with divergence, else |MPO − 50|). Convenience only, not probability.
How to read the oscillator
MPO Value (0–100): A reading of 50 is neutral. Values above ~55 are increasingly bullish (green), while below ~45 are increasingly bearish (red). Think of these as "market pressure".
Extreme Zones: When MPO climbs into the bright orange/red area (above the base-high line, default 70), the chart will display a dot and downward arrow marking that extreme. Traders often treat this as a sign to tighten stops or look for shorts. Similarly, a bright green dot/up-arrow appears when MPO falls below the base-low (30), hinting at a bullish setup.
Heatmap/Candles: If "Pressure Heatmap" is enabled, the background of the oscillator pane will fade green or red depending on MPO. Users can optionally color the price candles by MPO value (gradient candles) to see these extremes on the main chart.
Prediction Zone(optional): A dashed projection line extends the MPO forward by a small number of bars (prediction_bars) using current MPO momentum and acceleration. This is a heuristic extrapolation best used for short horizons (1–5 bars) to anticipate whether MPO may touch a warning or extreme zone. It is provisional and becomes less reliable with longer projection lengths — always confirm predicted moves with bar-close MPO and HTF context before acting.
Divergences: When price makes a higher high but EPZ makes a lower high (bearish divergence), the indicator can draw dotted lines and a "Bear Div" tag. The opposite (lower low price, higher EPZ) gives "Bull Div". These signals confirm waning momentum at extremes.
Zones: Warning bands near extremes; Extreme zones beyond thresholds.
Crossovers: MPO rising through 35 suggests easing downside pressure; falling through 65 suggests waning upside pressure.
Dots/arrows: Extreme markers appear on closed bars when confirmation is ON and respect the 5-bar cooldown.
Pre-alert dots (optional): Proximity cues in warning zones; also gated to bar close when confirmation is ON.
Histogram: Distance from neutral (50); highlights strengthening or weakening pressure.
Divergence tags: "Bear Div" = higher price high with lower MPO high; "Bull Div" = lower price low with higher MPO low.
Pressure Heatmap : Layered gradient background that visually highlights pressure strength across the MPO scale; adjustable intensity and optional zone overlays (warning / extreme) for quick visual scanning.
A typical reading: If the oscillator is rising from neutral towards the high zone (green→orange→red), the chart may see strong buying culminating in a stall. If it then turns down from the extreme, that peak EPZ dot signals sell pressure.
Alerts
EPZ: Extreme Context — fires on confirmed extremes (respects cooldown).
EPZ: Approaching Threshold — fires in warning zones if no extreme.
EPZ: Divergence — fires on confirmed pivot divergences.
Tip: Set alerts to "Once per bar close" to align with confirmation and avoid intrabar repaint.
Practical usage ideas
Trend continuation: In positive regimes (MPO > 50 and rising), pullbacks holding above 50 often precede continuation; mirror for bearish regimes.
Exhaustion caution: E High/E Low can mark exhaustion risk; many wait for MPO rollover or divergence to time fades or partial exits.
Adaptive thresholds: Useful on assets with shifting volatility regimes to maintain meaningful "extreme" levels.
MTF alignment: Prefer setups that agree with the HTF MPO to reduce countertrend noise.
Examples
Screenshots captured in TradingView Replay to freeze the bar at close so values don't fluctuate intrabar. These examples use default settings and are reproducible on the same bars; they are for illustration, not cherry-picking or performance claims.
Example 1 — BTCUSDT, 1h — E Low
MPO closed at 26.6 (below the 30 extreme), printing a confirmed E Low. HTF MPO is 26.6, so higher-timeframe pressure remains bearish. Components are subdued (Momentum/Pressure/Smart$ ≈ 29–37), with Vol Ratio ≈ 1.19x and ATR% ≈ 0.37%. A prior Bear Div flagged weakening impulse into the drop. With cooldown set to 5 bars, new extremes are rate-limited. Many traders wait for MPO to curl up and reclaim 35 or for a fresh Bull Div before considering countertrend ideas; if MPO cannot reclaim 35 and HTF stays weak, treat bounces cautiously. Educational illustration only.
Example 2 — ETHUSD, 30m — E High
A strong impulse pushed MPO into the extreme zone (≥ 70), printing a confirmed E High on close. Shortly after, MPO cooled to ~61.5 while a Bear Div appeared, showing momentum lag as price pushed a higher high. Volume and volatility were elevated (≈ 1.79x / 1.25%). With a 5-bar cooldown, additional extremes won't print immediately. Some treat E High as exhaustion risk—either waiting for MPO rollover under 65/50 to fade, or for a pullback that holds above 50 to re-join the trend if higher-timeframe pressure remains constructive. Educational illustration only.
Known limitations and caveats
The MPO line itself can change intrabar; extreme markers/alerts do not repaint when "Confirm Extremes on Bar Close" is ON.
HTF values settle at the close of the HTF bar.
Illiquid symbols or very low TFs can be noisy; consider higher thresholds or longer smoothing.
Prediction line (when enabled) is a visual extrapolation only.
For coders
Pine v6. MTF via request.security with lookahead_off.
Extremes include crossover triggers so static thresholds also yield E High/E Low.
Extreme markers and pre-alerts are gated by barstate.isconfirmed when confirmation is ON.
Arrays prune oldest objects to respect resource limits; defaults (80/80/60) are conservative for low TFs.
3D layering uses negative offsets purely for drawing depth (no lookahead).
Screenshot methodology:
To make labels legible and to demonstrate non-repainting behavior, the examples were captured in TradingView Replay with "Confirm Extremes on Bar Close" enabled. Replay is used only to freeze the bar at close so plots don't change intrabar. The examples use default settings, include both Extreme Low and Extreme High cases, and can be reproduced by scrolling to the same bars outside Replay. This is an educational illustration, not a performance claim.
Disclaimer
This script is for educational purposes only and does not constitute financial advice. Markets involve risk; past behavior does not guarantee future results. You are responsible for your own testing, risk management, and decisions.
ATR Future Movement Range Projection
The "ATR Future Movement Range Projection" is a custom TradingView Pine Script indicator designed to forecast potential price ranges for a stock (or any asset) over short-term (1-month) and medium-term (3-month) horizons. It leverages the Average True Range (ATR) as a measure of volatility to estimate how far the price might move, while incorporating recent momentum bias based on the proportion of bullish (green) vs. bearish (red) candles. This creates asymmetric projections: in bullish periods, the upside range is larger than the downside, and vice versa.
The indicator is overlaid on the chart, plotting horizontal lines for the projected high and low prices for both timeframes. Additionally, it displays a small table in the top-right corner summarizing the projected prices and the percentage change required from the current close to reach them. This makes it useful for traders assessing potential targets, risk-reward ratios, or option strategies, as it combines volatility forecasting with directional sentiment.
Key features:
- **Volatility Basis**: Uses weekly ATR to derive a stable daily volatility estimate, avoiding noise from shorter timeframes.
- **Momentum Adjustment**: Analyzes recent candle colors to tilt projections toward the prevailing trend (e.g., more upside if more green candles).
- **Time Horizons**: Fixed at 1 month (21 trading days) and 3 months (63 trading days), assuming ~21 trading days per month (excluding weekends/holidays).
- **User Adjustable**: The ATR length/lookback (default 50) can be tweaked via inputs.
- **Visuals**: Green/lime lines for highs, red/orange for lows; a semi-transparent table for quick reference.
- **Limitations**: This is a probabilistic projection based on historical volatility and momentum—it doesn't predict direction with certainty and assumes volatility persists. It ignores external factors like news, earnings, or market regimes. Best used on daily charts for stocks/ETFs.
The indicator doesn't generate buy/sell signals but helps visualize "expected" ranges, similar to how implied volatility informs option pricing.
### How It Works Step-by-Step
The script executes on each bar update (typically daily timeframe) and follows this logic:
1. **Input Configuration**:
- ATR Length (Lookback): Default 50 bars. This controls both the ATR calculation period and the candle count window. You can adjust it in the indicator settings.
2. **Calculate Weekly ATR**:
- Fetches the ATR from the weekly timeframe using `request.security` with a length of 50 weeks.
- ATR measures average price range (high-low, adjusted for gaps), representing volatility.
3. **Derive Daily ATR**:
- Divides the weekly ATR by 5 (approximating 5 trading days per week) to get an equivalent daily volatility estimate.
- Example: If weekly ATR is $5, daily ATR ≈ $1.
4. **Define Projection Periods**:
- 1 Month: 21 trading days.
- 3 Months: 63 trading days (21 × 3).
- These are hardcoded but based on standard trading calendar assumptions.
5. **Compute Base Projections**:
- Base projection = Daily ATR × Days in period.
- This gives the total expected movement (range) without direction: e.g., for 3 months, $1 daily ATR × 63 = $63 total range.
6. **Analyze Candle Momentum (Win Rate)**:
- Counts green candles (close > open) and red candles (close < open) over the last 50 bars (ignores dojis where close == open).
- Total colored candles = green + red.
- Win rate = green / total colored (as a fraction, e.g., 0.7 for 70%). Defaults to 0.5 if no colored candles.
- This acts as a simple momentum proxy: higher win rate implies bullish bias.
7. **Adjust Projections Asymmetrically**:
- Upside projection = Base projection × Win rate.
- Downside projection = Base projection × (1 - Win rate).
- This skews the range: e.g., 70% win rate means 70% of the total range allocated to upside, 30% to downside.
8. **Calculate Projected Prices**:
- High = Current close + Upside projection.
- Low = Current close - Downside projection.
- Done separately for 1M and 3M.
9. **Plot Lines**:
- 3M High: Solid green line.
- 3M Low: Solid red line.
- 1M High: Dashed lime line.
- 1M Low: Dashed orange line.
- Lines extend horizontally from the current bar onward.
10. **Display Table**:
- A 3-column table (Projection, Price, % Change) in the top-right.
- Rows for 1M High/Low and 3M High/Low, color-coded.
- % Change = ((Projected price - Close) / Close) × 100.
- Updates dynamically with new data.
The entire process repeats on each new bar, so projections evolve as volatility and momentum change.
### Examples
Here are two hypothetical examples using the indicator on a daily chart. Assume it's applied to a stock like AAPL, but with made-up data for illustration. (In TradingView, you'd add the script to see real outputs.)
#### Example 1: Bullish Scenario (High Win Rate)
- Current Close: $150.
- Weekly ATR (50 periods): $10 → Daily ATR: $10 / 5 = $2.
- Last 50 Candles: 35 green, 15 red → Total colored: 50 → Win Rate: 35/50 = 0.7 (70%).
- Base Projections:
- 1M: $2 × 21 = $42.
- 3M: $2 × 63 = $126.
- Adjusted Projections:
- 1M Upside: $42 × 0.7 = $29.4 → High: $150 + $29.4 = $179.4 (+19.6%).
- 1M Downside: $42 × 0.3 = $12.6 → Low: $150 - $12.6 = $137.4 (-8.4%).
- 3M Upside: $126 × 0.7 = $88.2 → High: $150 + $88.2 = $238.2 (+58.8%).
- 3M Downside: $126 × 0.3 = $37.8 → Low: $150 - $37.8 = $112.2 (-25.2%).
- On the Chart: Green/lime lines skewed higher; table shows bullish % changes (e.g., +58.8% for 3M high).
- Interpretation: Suggests stronger potential upside due to recent bullish momentum; useful for call options or long positions.
#### Example 2: Bearish Scenario (Low Win Rate)
- Current Close: $50.
- Weekly ATR (50 periods): $3 → Daily ATR: $3 / 5 = $0.6.
- Last 50 Candles: 20 green, 30 red → Total colored: 50 → Win Rate: 20/50 = 0.4 (40%).
- Base Projections:
- 1M: $0.6 × 21 = $12.6.
- 3M: $0.6 × 63 = $37.8.
- Adjusted Projections:
- 1M Upside: $12.6 × 0.4 = $5.04 → High: $50 + $5.04 = $55.04 (+10.1%).
- 1M Downside: $12.6 × 0.6 = $7.56 → Low: $50 - $7.56 = $42.44 (-15.1%).
- 3M Upside: $37.8 × 0.4 = $15.12 → High: $50 + $15.12 = $65.12 (+30.2%).
- 3M Downside: $37.8 × 0.6 = $22.68 → Low: $50 - $22.68 = $27.32 (-45.4%).
- On the Chart: Red/orange lines skewed lower; table highlights larger downside % (e.g., -45.4% for 3M low).
- Interpretation: Indicates bearish risk; might prompt protective puts or short strategies.
#### Example 3: Neutral Scenario (Balanced Win Rate)
- Current Close: $100.
- Weekly ATR: $5 → Daily ATR: $1.
- Last 50 Candles: 25 green, 25 red → Win Rate: 0.5 (50%).
- Projections become symmetric:
- 1M: Base $21 → Upside/Downside $10.5 each → High $110.5 (+10.5%), Low $89.5 (-10.5%).
- 3M: Base $63 → Upside/Downside $31.5 each → High $131.5 (+31.5%), Low $68.5 (-31.5%).
- Interpretation: Pure volatility-based range, no directional bias—ideal for straddle options or range trading.
In real use, test on historical data: e.g., if past projections captured actual moves ~68% of the time (1 standard deviation for ATR), it validates the volatility assumption. Adjust the lookback for different assets (shorter for volatile cryptos, longer for stable blue-chips).
PriceActionLibrary "PriceAction"
Hi all!
This library will help you to plot the market structure and liquidity. By now, the only part in the price action section is liquidity, but I plan to add more later on. The market structure will be split into two parts, 'Internal' and 'Swing' with separate pivot lengths. For these two trends it will show you:
• Break of structure (BOS)
• Change of character (CHoCH/CHoCH+) (mandatory)
• Equal high/low (EQH/EQL)
It's inspired by "Smart Money Concepts (SMC) " by LuxAlgo.
This library is now the same code as the code in my library 'MarketStructure', but it has evolved into a more price action oriented library than just a market structure library. This is more accurate and I will continue working on this library to keep it growing.
This code does not provide any examples, but you can look at my indicators 'Market structure' () and 'Order blocks' (), where I use the 'MarketStructure' library (which is the same code).
Market structure
Both of these market structures can be enabled/disabled by setting them to 'na'. The pivots lengths can be configured separately. The pivots found will be the 'base' of and will show you when price breaks it. When that happens a break of structure or a change of character will be created. The latest 5 pivots found within the current trends will be kept to take action on. They are cleared on a change of character, so nothing (break of structures or change of characters) can happen on pivots before a trend change. The internal market structure is shown with dashed lines and swing market structure is shown with solid lines.
Labels for a change of character can have either the text 'CHoCH' or 'CHoCH+'. A Change of Character plus is formed when price fails to form a higher high or a lower low before reversing. Note that a pivot that is created after the change of character might have a higher high or a lower low, thus not making the break a 'CHoCH+'. This is not changed after the pivot is found but is kept as is.
A break of structure is removed if an earlier pivot within the same trend is broken, i.e. another break of structure (with a longer distance) is created. Like in the images below, the first pivot (in the first image) is removed when an earlier pivot's higher price within the same trend is broken (the second image):
[image [https://www.tradingview.com/x/PRP6YtPA/
Equal high/lows have a configurable color setting and can be configured to be extended to the right. Equal high/lows are only possible if it's not been broken by price. A factor (percentage of width) of the Average True Length (of length 14) that the pivot must be within to to be considered an Equal high/low. Equal highs/lows can be of 2 pivots or more.
You are able to show the pivots that are used. "HH" (higher high), "HL" (higher low), "LH" (lower high), "LL" (lower low) and "H"/"L" (for pivots (high/low) when the trend has changed) are the labels used. There are also labels for break of structures ('BOS') and change of characters ('CHoCH' or 'CHoCH+'). The size of these texts is set in the 'FontSize' setting.
When programming I focused on simplicity and ease of read. I did not focus on performance, I will do so if it's a problem (haven't noticed it is one yet).
You can set alerts for when a change of character, break of structure or an equal high/low (new or an addition to a previously found) happens. The alerts that are fired are on 'once_per_bar_close' to avoid repainting. This has the drawback to alert you when the bar closes.
Price action
The indicator will create lines and zones for spotted liquidity. It will draw a line (with dotted style) at the price level that was liquidated, but it will also draw a zone from that level to the bar that broke the pivot high or low price. If that zone is large the liquidation is big and might be significant. This can be disabled in the settings. You can also change the confirmation candles (that does not close above or below the pivot level) needed after a liquidation and how many pivots back to look at.
The lines and boxes drawn will look like this if the color is orange:
Hope this is of help!
Will draw out the market structure for the disired pivot length.
Liqudity(liquidity)
Will draw liquidity.
Parameters:
liquidity (Liquidity) : The 'PriceAction.Liquidity' object.
Pivot(structure)
Sets the pivots in the structure.
Parameters:
structure (Structure)
PivotLabels(structure)
Draws labels for the pivots found.
Parameters:
structure (Structure)
EqualHighOrLow(structure)
Draws the boxes for equal highs/lows. Also creates labels for the pivots included.
Parameters:
structure (Structure)
BreakOfStructure(structure)
Will create lines when a break of strycture occures.
Parameters:
structure (Structure)
Returns: A boolean that represents if a break of structure was found or not.
ChangeOfCharacter(structure)
Will create lines when a change of character occures. This line will have a label with "CHoCH" or "CHoCH+".
Parameters:
structure (Structure)
Returns: A boolean that represents if a change of character was found or not.
VisualizeCurrent(structure)
Will create a box with a background for between the latest high and low pivots. This can be used as the current trading range (if the pivots broke strucure somehow).
Parameters:
structure (Structure)
StructureBreak
Holds drawings for a structure break.
Fields:
Line (series line) : The line object.
Label (series label) : The label object.
Pivot
Holds all the values for a found pivot.
Fields:
Price (series float) : The price of the pivot.
BarIndex (series int) : The bar_index where the pivot occured.
Type (series int) : The type of the pivot (-1 = low, 1 = high).
Time (series int) : The time where the pivot occured.
BreakOfStructureBroken (series bool) : Sets to true if a break of structure has happened.
LiquidityBroken (series bool) : Sets to true if a liquidity of the price level has happened.
ChangeOfCharacterBroken (series bool) : Sets to true if a change of character has happened.
Structure
Holds all the values for the market structure.
Fields:
LeftLength (series int) : Define the left length of the pivots used.
RightLength (series int) : Define the right length of the pivots used.
Type (series Type) : Set the type of the market structure. Two types can be used, 'internal' and 'swing' (0 = internal, 1 = swing).
Trend (series int) : This will be set internally and can be -1 = downtrend, 1 = uptrend.
EqualPivotsFactor (series float) : Set how the limits are for an equal pivot. This is a factor of the Average True Length (ATR) of length 14. If a low pivot is considered to be equal if it doesn't break the low pivot (is at a lower value) and is inside the previous low pivot + this limit.
ExtendEqualPivotsZones (series bool) : Set to true if you want the equal pivots zones to be extended.
ExtendEqualPivotsStyle (series string) : Set the style of equal pivot zones.
ExtendEqualPivotsColor (series color) : Set the color of equal pivot zones.
EqualHighs (array) : Holds the boxes for zones that contains equal highs.
EqualLows (array) : Holds the boxes for zones that contains equal lows.
BreakOfStructures (array) : Holds all the break of structures within the trend (before a change of character).
Pivots (array) : All the pivots in the current trend, added with the latest first, this is cleared when the trend changes.
FontSize (series int) : Holds the size of the font displayed.
AlertChangeOfCharacter (series bool) : Holds true or false if a change of character should be alerted or not.
AlertBreakOfStructure (series bool) : Holds true or false if a break of structure should be alerted or not.
AlerEqualPivots (series bool) : Holds true or false if equal highs/lows should be alerted or not.
Liquidity
Holds all the values for liquidity.
Fields:
LiquidityPivotsHigh (array) : All high pivots for liquidity.
LiquidityPivotsLow (array) : All low pivots for liquidity.
LiquidityConfirmationBars (series int) : The number of bars to confirm that a liquidity is valid.
LiquidityPivotsLookback (series int) : A number of pivots to look back for.
FontSize (series int) : Holds the size of the font displayed.
PriceAction
Holds all the values for the general price action and the market structures.
Fields:
Liquidity (Liquidity)
Swing (Structure) : Placeholder for all objects used for the swing market structure.
Internal (Structure) : Placeholder for all objects used for the internal market structure.
Markov Chain [3D] | FractalystWhat exactly is a Markov Chain?
This indicator uses a Markov Chain model to analyze, quantify, and visualize the transitions between market regimes (Bull, Bear, Neutral) on your chart. It dynamically detects these regimes in real-time, calculates transition probabilities, and displays them as animated 3D spheres and arrows, giving traders intuitive insight into current and future market conditions.
How does a Markov Chain work, and how should I read this spheres-and-arrows diagram?
Think of three weather modes: Sunny, Rainy, Cloudy.
Each sphere is one mode. The loop on a sphere means “stay the same next step” (e.g., Sunny again tomorrow).
The arrows leaving a sphere show where things usually go next if they change (e.g., Sunny moving to Cloudy).
Some paths matter more than others. A more prominent loop means the current mode tends to persist. A more prominent outgoing arrow means a change to that destination is the usual next step.
Direction isn’t symmetric: moving Sunny→Cloudy can behave differently than Cloudy→Sunny.
Now relabel the spheres to markets: Bull, Bear, Neutral.
Spheres: market regimes (uptrend, downtrend, range).
Self‑loop: tendency for the current regime to continue on the next bar.
Arrows: the most common next regime if a switch happens.
How to read: Start at the sphere that matches current bar state. If the loop stands out, expect continuation. If one outgoing path stands out, that switch is the typical next step. Opposite directions can differ (Bear→Neutral doesn’t have to match Neutral→Bear).
What states and transitions are shown?
The three market states visualized are:
Bullish (Bull): Upward or strong-market regime.
Bearish (Bear): Downward or weak-market regime.
Neutral: Sideways or range-bound regime.
Bidirectional animated arrows and probability labels show how likely the market is to move from one regime to another (e.g., Bull → Bear or Neutral → Bull).
How does the regime detection system work?
You can use either built-in price returns (based on adaptive Z-score normalization) or supply three custom indicators (such as volume, oscillators, etc.).
Values are statistically normalized (Z-scored) over a configurable lookback period.
The normalized outputs are classified into Bull, Bear, or Neutral zones.
If using three indicators, their regime signals are averaged and smoothed for robustness.
How are transition probabilities calculated?
On every confirmed bar, the algorithm tracks the sequence of detected market states, then builds a rolling window of transitions.
The code maintains a transition count matrix for all regime pairs (e.g., Bull → Bear).
Transition probabilities are extracted for each possible state change using Laplace smoothing for numerical stability, and frequently updated in real-time.
What is unique about the visualization?
3D animated spheres represent each regime and change visually when active.
Animated, bidirectional arrows reveal transition probabilities and allow you to see both dominant and less likely regime flows.
Particles (moving dots) animate along the arrows, enhancing the perception of regime flow direction and speed.
All elements dynamically update with each new price bar, providing a live market map in an intuitive, engaging format.
Can I use custom indicators for regime classification?
Yes! Enable the "Custom Indicators" switch and select any three chart series as inputs. These will be normalized and combined (each with equal weight), broadening the regime classification beyond just price-based movement.
What does the “Lookback Period” control?
Lookback Period (default: 100) sets how much historical data builds the probability matrix. Shorter periods adapt faster to regime changes but may be noisier. Longer periods are more stable but slower to adapt.
How is this different from a Hidden Markov Model (HMM)?
It sets the window for both regime detection and probability calculations. Lower values make the system more reactive, but potentially noisier. Higher values smooth estimates and make the system more robust.
How is this Markov Chain different from a Hidden Markov Model (HMM)?
Markov Chain (as here): All market regimes (Bull, Bear, Neutral) are directly observable on the chart. The transition matrix is built from actual detected regimes, keeping the model simple and interpretable.
Hidden Markov Model: The actual regimes are unobservable ("hidden") and must be inferred from market output or indicator "emissions" using statistical learning algorithms. HMMs are more complex, can capture more subtle structure, but are harder to visualize and require additional machine learning steps for training.
A standard Markov Chain models transitions between observable states using a simple transition matrix, while a Hidden Markov Model assumes the true states are hidden (latent) and must be inferred from observable “emissions” like price or volume data. In practical terms, a Markov Chain is transparent and easier to implement and interpret; an HMM is more expressive but requires statistical inference to estimate hidden states from data.
Markov Chain: states are observable; you directly count or estimate transition probabilities between visible states. This makes it simpler, faster, and easier to validate and tune.
HMM: states are hidden; you only observe emissions generated by those latent states. Learning involves machine learning/statistical algorithms (commonly Baum–Welch/EM for training and Viterbi for decoding) to infer both the transition dynamics and the most likely hidden state sequence from data.
How does the indicator avoid “repainting” or look-ahead bias?
All regime changes and matrix updates happen only on confirmed (closed) bars, so no future data is leaked, ensuring reliable real-time operation.
Are there practical tuning tips?
Tune the Lookback Period for your asset/timeframe: shorter for fast markets, longer for stability.
Use custom indicators if your asset has unique regime drivers.
Watch for rapid changes in transition probabilities as early warning of a possible regime shift.
Who is this indicator for?
Quants and quantitative researchers exploring probabilistic market modeling, especially those interested in regime-switching dynamics and Markov models.
Programmers and system developers who need a probabilistic regime filter for systematic and algorithmic backtesting:
The Markov Chain indicator is ideally suited for programmatic integration via its bias output (1 = Bull, 0 = Neutral, -1 = Bear).
Although the visualization is engaging, the core output is designed for automated, rules-based workflows—not for discretionary/manual trading decisions.
Developers can connect the indicator’s output directly to their Pine Script logic (using input.source()), allowing rapid and robust backtesting of regime-based strategies.
It acts as a plug-and-play regime filter: simply plug the bias output into your entry/exit logic, and you have a scientifically robust, probabilistically-derived signal for filtering, timing, position sizing, or risk regimes.
The MC's output is intentionally "trinary" (1/0/-1), focusing on clear regime states for unambiguous decision-making in code. If you require nuanced, multi-probability or soft-label state vectors, consider expanding the indicator or stacking it with a probability-weighted logic layer in your scripting.
Because it avoids subjectivity, this approach is optimal for systematic quants, algo developers building backtested, repeatable strategies based on probabilistic regime analysis.
What's the mathematical foundation behind this?
The mathematical foundation behind this Markov Chain indicator—and probabilistic regime detection in finance—draws from two principal models: the (standard) Markov Chain and the Hidden Markov Model (HMM).
How to use this indicator programmatically?
The Markov Chain indicator automatically exports a bias value (+1 for Bullish, -1 for Bearish, 0 for Neutral) as a plot visible in the Data Window. This allows you to integrate its regime signal into your own scripts and strategies for backtesting, automation, or live trading.
Step-by-Step Integration with Pine Script (input.source)
Add the Markov Chain indicator to your chart.
This must be done first, since your custom script will "pull" the bias signal from the indicator's plot.
In your strategy, create an input using input.source()
Example:
//@version=5
strategy("MC Bias Strategy Example")
mcBias = input.source(close, "MC Bias Source")
After saving, go to your script’s settings. For the “MC Bias Source” input, select the plot/output of the Markov Chain indicator (typically its bias plot).
Use the bias in your trading logic
Example (long only on Bull, flat otherwise):
if mcBias == 1
strategy.entry("Long", strategy.long)
else
strategy.close("Long")
For more advanced workflows, combine mcBias with additional filters or trailing stops.
How does this work behind-the-scenes?
TradingView’s input.source() lets you use any plot from another indicator as a real-time, “live” data feed in your own script (source).
The selected bias signal is available to your Pine code as a variable, enabling logical decisions based on regime (trend-following, mean-reversion, etc.).
This enables powerful strategy modularity : decouple regime detection from entry/exit logic, allowing fast experimentation without rewriting core signal code.
Integrating 45+ Indicators with Your Markov Chain — How & Why
The Enhanced Custom Indicators Export script exports a massive suite of over 45 technical indicators—ranging from classic momentum (RSI, MACD, Stochastic, etc.) to trend, volume, volatility, and oscillator tools—all pre-calculated, centered/scaled, and available as plots.
// Enhanced Custom Indicators Export - 45 Technical Indicators
// Comprehensive technical analysis suite for advanced market regime detection
//@version=6
indicator('Enhanced Custom Indicators Export | Fractalyst', shorttitle='Enhanced CI Export', overlay=false, scale=scale.right, max_labels_count=500, max_lines_count=500)
// |----- Input Parameters -----| //
momentum_group = "Momentum Indicators"
trend_group = "Trend Indicators"
volume_group = "Volume Indicators"
volatility_group = "Volatility Indicators"
oscillator_group = "Oscillator Indicators"
display_group = "Display Settings"
// Common lengths
length_14 = input.int(14, "Standard Length (14)", minval=1, maxval=100, group=momentum_group)
length_20 = input.int(20, "Medium Length (20)", minval=1, maxval=200, group=trend_group)
length_50 = input.int(50, "Long Length (50)", minval=1, maxval=200, group=trend_group)
// Display options
show_table = input.bool(true, "Show Values Table", group=display_group)
table_size = input.string("Small", "Table Size", options= , group=display_group)
// |----- MOMENTUM INDICATORS (15 indicators) -----| //
// 1. RSI (Relative Strength Index)
rsi_14 = ta.rsi(close, length_14)
rsi_centered = rsi_14 - 50
// 2. Stochastic Oscillator
stoch_k = ta.stoch(close, high, low, length_14)
stoch_d = ta.sma(stoch_k, 3)
stoch_centered = stoch_k - 50
// 3. Williams %R
williams_r = ta.stoch(close, high, low, length_14) - 100
// 4. MACD (Moving Average Convergence Divergence)
= ta.macd(close, 12, 26, 9)
// 5. Momentum (Rate of Change)
momentum = ta.mom(close, length_14)
momentum_pct = (momentum / close ) * 100
// 6. Rate of Change (ROC)
roc = ta.roc(close, length_14)
// 7. Commodity Channel Index (CCI)
cci = ta.cci(close, length_20)
// 8. Money Flow Index (MFI)
mfi = ta.mfi(close, length_14)
mfi_centered = mfi - 50
// 9. Awesome Oscillator (AO)
ao = ta.sma(hl2, 5) - ta.sma(hl2, 34)
// 10. Accelerator Oscillator (AC)
ac = ao - ta.sma(ao, 5)
// 11. Chande Momentum Oscillator (CMO)
cmo = ta.cmo(close, length_14)
// 12. Detrended Price Oscillator (DPO)
dpo = close - ta.sma(close, length_20)
// 13. Price Oscillator (PPO)
ppo = ta.sma(close, 12) - ta.sma(close, 26)
ppo_pct = (ppo / ta.sma(close, 26)) * 100
// 14. TRIX
trix_ema1 = ta.ema(close, length_14)
trix_ema2 = ta.ema(trix_ema1, length_14)
trix_ema3 = ta.ema(trix_ema2, length_14)
trix = ta.roc(trix_ema3, 1) * 10000
// 15. Klinger Oscillator
klinger = ta.ema(volume * (high + low + close) / 3, 34) - ta.ema(volume * (high + low + close) / 3, 55)
// 16. Fisher Transform
fisher_hl2 = 0.5 * (hl2 - ta.lowest(hl2, 10)) / (ta.highest(hl2, 10) - ta.lowest(hl2, 10)) - 0.25
fisher = 0.5 * math.log((1 + fisher_hl2) / (1 - fisher_hl2))
// 17. Stochastic RSI
stoch_rsi = ta.stoch(rsi_14, rsi_14, rsi_14, length_14)
stoch_rsi_centered = stoch_rsi - 50
// 18. Relative Vigor Index (RVI)
rvi_num = ta.swma(close - open)
rvi_den = ta.swma(high - low)
rvi = rvi_den != 0 ? rvi_num / rvi_den : 0
// 19. Balance of Power (BOP)
bop = (close - open) / (high - low)
// |----- TREND INDICATORS (10 indicators) -----| //
// 20. Simple Moving Average Momentum
sma_20 = ta.sma(close, length_20)
sma_momentum = ((close - sma_20) / sma_20) * 100
// 21. Exponential Moving Average Momentum
ema_20 = ta.ema(close, length_20)
ema_momentum = ((close - ema_20) / ema_20) * 100
// 22. Parabolic SAR
sar = ta.sar(0.02, 0.02, 0.2)
sar_trend = close > sar ? 1 : -1
// 23. Linear Regression Slope
lr_slope = ta.linreg(close, length_20, 0) - ta.linreg(close, length_20, 1)
// 24. Moving Average Convergence (MAC)
mac = ta.sma(close, 10) - ta.sma(close, 30)
// 25. Trend Intensity Index (TII)
tii_sum = 0.0
for i = 1 to length_20
tii_sum += close > close ? 1 : 0
tii = (tii_sum / length_20) * 100
// 26. Ichimoku Cloud Components
ichimoku_tenkan = (ta.highest(high, 9) + ta.lowest(low, 9)) / 2
ichimoku_kijun = (ta.highest(high, 26) + ta.lowest(low, 26)) / 2
ichimoku_signal = ichimoku_tenkan > ichimoku_kijun ? 1 : -1
// 27. MESA Adaptive Moving Average (MAMA)
mama_alpha = 2.0 / (length_20 + 1)
mama = ta.ema(close, length_20)
mama_momentum = ((close - mama) / mama) * 100
// 28. Zero Lag Exponential Moving Average (ZLEMA)
zlema_lag = math.round((length_20 - 1) / 2)
zlema_data = close + (close - close )
zlema = ta.ema(zlema_data, length_20)
zlema_momentum = ((close - zlema) / zlema) * 100
// |----- VOLUME INDICATORS (6 indicators) -----| //
// 29. On-Balance Volume (OBV)
obv = ta.obv
// 30. Volume Rate of Change (VROC)
vroc = ta.roc(volume, length_14)
// 31. Price Volume Trend (PVT)
pvt = ta.pvt
// 32. Negative Volume Index (NVI)
nvi = 0.0
nvi := volume < volume ? nvi + ((close - close ) / close ) * nvi : nvi
// 33. Positive Volume Index (PVI)
pvi = 0.0
pvi := volume > volume ? pvi + ((close - close ) / close ) * pvi : pvi
// 34. Volume Oscillator
vol_osc = ta.sma(volume, 5) - ta.sma(volume, 10)
// 35. Ease of Movement (EOM)
eom_distance = high - low
eom_box_height = volume / 1000000
eom = eom_box_height != 0 ? eom_distance / eom_box_height : 0
eom_sma = ta.sma(eom, length_14)
// 36. Force Index
force_index = volume * (close - close )
force_index_sma = ta.sma(force_index, length_14)
// |----- VOLATILITY INDICATORS (10 indicators) -----| //
// 37. Average True Range (ATR)
atr = ta.atr(length_14)
atr_pct = (atr / close) * 100
// 38. Bollinger Bands Position
bb_basis = ta.sma(close, length_20)
bb_dev = 2.0 * ta.stdev(close, length_20)
bb_upper = bb_basis + bb_dev
bb_lower = bb_basis - bb_dev
bb_position = bb_dev != 0 ? (close - bb_basis) / bb_dev : 0
bb_width = bb_dev != 0 ? (bb_upper - bb_lower) / bb_basis * 100 : 0
// 39. Keltner Channels Position
kc_basis = ta.ema(close, length_20)
kc_range = ta.ema(ta.tr, length_20)
kc_upper = kc_basis + (2.0 * kc_range)
kc_lower = kc_basis - (2.0 * kc_range)
kc_position = kc_range != 0 ? (close - kc_basis) / kc_range : 0
// 40. Donchian Channels Position
dc_upper = ta.highest(high, length_20)
dc_lower = ta.lowest(low, length_20)
dc_basis = (dc_upper + dc_lower) / 2
dc_position = (dc_upper - dc_lower) != 0 ? (close - dc_basis) / (dc_upper - dc_lower) : 0
// 41. Standard Deviation
std_dev = ta.stdev(close, length_20)
std_dev_pct = (std_dev / close) * 100
// 42. Relative Volatility Index (RVI)
rvi_up = ta.stdev(close > close ? close : 0, length_14)
rvi_down = ta.stdev(close < close ? close : 0, length_14)
rvi_total = rvi_up + rvi_down
rvi_volatility = rvi_total != 0 ? (rvi_up / rvi_total) * 100 : 50
// 43. Historical Volatility
hv_returns = math.log(close / close )
hv = ta.stdev(hv_returns, length_20) * math.sqrt(252) * 100
// 44. Garman-Klass Volatility
gk_vol = math.log(high/low) * math.log(high/low) - (2*math.log(2)-1) * math.log(close/open) * math.log(close/open)
gk_volatility = math.sqrt(ta.sma(gk_vol, length_20)) * 100
// 45. Parkinson Volatility
park_vol = math.log(high/low) * math.log(high/low)
parkinson = math.sqrt(ta.sma(park_vol, length_20) / (4 * math.log(2))) * 100
// 46. Rogers-Satchell Volatility
rs_vol = math.log(high/close) * math.log(high/open) + math.log(low/close) * math.log(low/open)
rogers_satchell = math.sqrt(ta.sma(rs_vol, length_20)) * 100
// |----- OSCILLATOR INDICATORS (5 indicators) -----| //
// 47. Elder Ray Index
elder_bull = high - ta.ema(close, 13)
elder_bear = low - ta.ema(close, 13)
elder_power = elder_bull + elder_bear
// 48. Schaff Trend Cycle (STC)
stc_macd = ta.ema(close, 23) - ta.ema(close, 50)
stc_k = ta.stoch(stc_macd, stc_macd, stc_macd, 10)
stc_d = ta.ema(stc_k, 3)
stc = ta.stoch(stc_d, stc_d, stc_d, 10)
// 49. Coppock Curve
coppock_roc1 = ta.roc(close, 14)
coppock_roc2 = ta.roc(close, 11)
coppock = ta.wma(coppock_roc1 + coppock_roc2, 10)
// 50. Know Sure Thing (KST)
kst_roc1 = ta.roc(close, 10)
kst_roc2 = ta.roc(close, 15)
kst_roc3 = ta.roc(close, 20)
kst_roc4 = ta.roc(close, 30)
kst = ta.sma(kst_roc1, 10) + 2*ta.sma(kst_roc2, 10) + 3*ta.sma(kst_roc3, 10) + 4*ta.sma(kst_roc4, 15)
// 51. Percentage Price Oscillator (PPO)
ppo_line = ((ta.ema(close, 12) - ta.ema(close, 26)) / ta.ema(close, 26)) * 100
ppo_signal = ta.ema(ppo_line, 9)
ppo_histogram = ppo_line - ppo_signal
// |----- PLOT MAIN INDICATORS -----| //
// Plot key momentum indicators
plot(rsi_centered, title="01_RSI_Centered", color=color.purple, linewidth=1)
plot(stoch_centered, title="02_Stoch_Centered", color=color.blue, linewidth=1)
plot(williams_r, title="03_Williams_R", color=color.red, linewidth=1)
plot(macd_histogram, title="04_MACD_Histogram", color=color.orange, linewidth=1)
plot(cci, title="05_CCI", color=color.green, linewidth=1)
// Plot trend indicators
plot(sma_momentum, title="06_SMA_Momentum", color=color.navy, linewidth=1)
plot(ema_momentum, title="07_EMA_Momentum", color=color.maroon, linewidth=1)
plot(sar_trend, title="08_SAR_Trend", color=color.teal, linewidth=1)
plot(lr_slope, title="09_LR_Slope", color=color.lime, linewidth=1)
plot(mac, title="10_MAC", color=color.fuchsia, linewidth=1)
// Plot volatility indicators
plot(atr_pct, title="11_ATR_Pct", color=color.yellow, linewidth=1)
plot(bb_position, title="12_BB_Position", color=color.aqua, linewidth=1)
plot(kc_position, title="13_KC_Position", color=color.olive, linewidth=1)
plot(std_dev_pct, title="14_StdDev_Pct", color=color.silver, linewidth=1)
plot(bb_width, title="15_BB_Width", color=color.gray, linewidth=1)
// Plot volume indicators
plot(vroc, title="16_VROC", color=color.blue, linewidth=1)
plot(eom_sma, title="17_EOM", color=color.red, linewidth=1)
plot(vol_osc, title="18_Vol_Osc", color=color.green, linewidth=1)
plot(force_index_sma, title="19_Force_Index", color=color.orange, linewidth=1)
plot(obv, title="20_OBV", color=color.purple, linewidth=1)
// Plot additional oscillators
plot(ao, title="21_Awesome_Osc", color=color.navy, linewidth=1)
plot(cmo, title="22_CMO", color=color.maroon, linewidth=1)
plot(dpo, title="23_DPO", color=color.teal, linewidth=1)
plot(trix, title="24_TRIX", color=color.lime, linewidth=1)
plot(fisher, title="25_Fisher", color=color.fuchsia, linewidth=1)
// Plot more momentum indicators
plot(mfi_centered, title="26_MFI_Centered", color=color.yellow, linewidth=1)
plot(ac, title="27_AC", color=color.aqua, linewidth=1)
plot(ppo_pct, title="28_PPO_Pct", color=color.olive, linewidth=1)
plot(stoch_rsi_centered, title="29_StochRSI_Centered", color=color.silver, linewidth=1)
plot(klinger, title="30_Klinger", color=color.gray, linewidth=1)
// Plot trend continuation
plot(tii, title="31_TII", color=color.blue, linewidth=1)
plot(ichimoku_signal, title="32_Ichimoku_Signal", color=color.red, linewidth=1)
plot(mama_momentum, title="33_MAMA_Momentum", color=color.green, linewidth=1)
plot(zlema_momentum, title="34_ZLEMA_Momentum", color=color.orange, linewidth=1)
plot(bop, title="35_BOP", color=color.purple, linewidth=1)
// Plot volume continuation
plot(nvi, title="36_NVI", color=color.navy, linewidth=1)
plot(pvi, title="37_PVI", color=color.maroon, linewidth=1)
plot(momentum_pct, title="38_Momentum_Pct", color=color.teal, linewidth=1)
plot(roc, title="39_ROC", color=color.lime, linewidth=1)
plot(rvi, title="40_RVI", color=color.fuchsia, linewidth=1)
// Plot volatility continuation
plot(dc_position, title="41_DC_Position", color=color.yellow, linewidth=1)
plot(rvi_volatility, title="42_RVI_Volatility", color=color.aqua, linewidth=1)
plot(hv, title="43_Historical_Vol", color=color.olive, linewidth=1)
plot(gk_volatility, title="44_GK_Volatility", color=color.silver, linewidth=1)
plot(parkinson, title="45_Parkinson_Vol", color=color.gray, linewidth=1)
// Plot final oscillators
plot(rogers_satchell, title="46_RS_Volatility", color=color.blue, linewidth=1)
plot(elder_power, title="47_Elder_Power", color=color.red, linewidth=1)
plot(stc, title="48_STC", color=color.green, linewidth=1)
plot(coppock, title="49_Coppock", color=color.orange, linewidth=1)
plot(kst, title="50_KST", color=color.purple, linewidth=1)
// Plot final indicators
plot(ppo_histogram, title="51_PPO_Histogram", color=color.navy, linewidth=1)
plot(pvt, title="52_PVT", color=color.maroon, linewidth=1)
// |----- Reference Lines -----| //
hline(0, "Zero Line", color=color.gray, linestyle=hline.style_dashed, linewidth=1)
hline(50, "Midline", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
hline(-50, "Lower Midline", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
hline(25, "Upper Threshold", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
hline(-25, "Lower Threshold", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
// |----- Enhanced Information Table -----| //
if show_table and barstate.islast
table_position = position.top_right
table_text_size = table_size == "Tiny" ? size.tiny : table_size == "Small" ? size.small : size.normal
var table info_table = table.new(table_position, 3, 18, bgcolor=color.new(color.white, 85), border_width=1, border_color=color.gray)
// Headers
table.cell(info_table, 0, 0, 'Category', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70))
table.cell(info_table, 1, 0, 'Indicator', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70))
table.cell(info_table, 2, 0, 'Value', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70))
// Key Momentum Indicators
table.cell(info_table, 0, 1, 'MOMENTUM', text_color=color.purple, text_size=table_text_size, bgcolor=color.new(color.purple, 90))
table.cell(info_table, 1, 1, 'RSI Centered', text_color=color.purple, text_size=table_text_size)
table.cell(info_table, 2, 1, str.tostring(rsi_centered, '0.00'), text_color=color.purple, text_size=table_text_size)
table.cell(info_table, 0, 2, '', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 1, 2, 'Stoch Centered', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 2, 2, str.tostring(stoch_centered, '0.00'), text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 0, 3, '', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 1, 3, 'Williams %R', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 2, 3, str.tostring(williams_r, '0.00'), text_color=color.red, text_size=table_text_size)
table.cell(info_table, 0, 4, '', text_color=color.orange, text_size=table_text_size)
table.cell(info_table, 1, 4, 'MACD Histogram', text_color=color.orange, text_size=table_text_size)
table.cell(info_table, 2, 4, str.tostring(macd_histogram, '0.000'), text_color=color.orange, text_size=table_text_size)
table.cell(info_table, 0, 5, '', text_color=color.green, text_size=table_text_size)
table.cell(info_table, 1, 5, 'CCI', text_color=color.green, text_size=table_text_size)
table.cell(info_table, 2, 5, str.tostring(cci, '0.00'), text_color=color.green, text_size=table_text_size)
// Key Trend Indicators
table.cell(info_table, 0, 6, 'TREND', text_color=color.navy, text_size=table_text_size, bgcolor=color.new(color.navy, 90))
table.cell(info_table, 1, 6, 'SMA Momentum %', text_color=color.navy, text_size=table_text_size)
table.cell(info_table, 2, 6, str.tostring(sma_momentum, '0.00'), text_color=color.navy, text_size=table_text_size)
table.cell(info_table, 0, 7, '', text_color=color.maroon, text_size=table_text_size)
table.cell(info_table, 1, 7, 'EMA Momentum %', text_color=color.maroon, text_size=table_text_size)
table.cell(info_table, 2, 7, str.tostring(ema_momentum, '0.00'), text_color=color.maroon, text_size=table_text_size)
table.cell(info_table, 0, 8, '', text_color=color.teal, text_size=table_text_size)
table.cell(info_table, 1, 8, 'SAR Trend', text_color=color.teal, text_size=table_text_size)
table.cell(info_table, 2, 8, str.tostring(sar_trend, '0'), text_color=color.teal, text_size=table_text_size)
table.cell(info_table, 0, 9, '', text_color=color.lime, text_size=table_text_size)
table.cell(info_table, 1, 9, 'Linear Regression', text_color=color.lime, text_size=table_text_size)
table.cell(info_table, 2, 9, str.tostring(lr_slope, '0.000'), text_color=color.lime, text_size=table_text_size)
// Key Volatility Indicators
table.cell(info_table, 0, 10, 'VOLATILITY', text_color=color.yellow, text_size=table_text_size, bgcolor=color.new(color.yellow, 90))
table.cell(info_table, 1, 10, 'ATR %', text_color=color.yellow, text_size=table_text_size)
table.cell(info_table, 2, 10, str.tostring(atr_pct, '0.00'), text_color=color.yellow, text_size=table_text_size)
table.cell(info_table, 0, 11, '', text_color=color.aqua, text_size=table_text_size)
table.cell(info_table, 1, 11, 'BB Position', text_color=color.aqua, text_size=table_text_size)
table.cell(info_table, 2, 11, str.tostring(bb_position, '0.00'), text_color=color.aqua, text_size=table_text_size)
table.cell(info_table, 0, 12, '', text_color=color.olive, text_size=table_text_size)
table.cell(info_table, 1, 12, 'KC Position', text_color=color.olive, text_size=table_text_size)
table.cell(info_table, 2, 12, str.tostring(kc_position, '0.00'), text_color=color.olive, text_size=table_text_size)
// Key Volume Indicators
table.cell(info_table, 0, 13, 'VOLUME', text_color=color.blue, text_size=table_text_size, bgcolor=color.new(color.blue, 90))
table.cell(info_table, 1, 13, 'Volume ROC', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 2, 13, str.tostring(vroc, '0.00'), text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 0, 14, '', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 1, 14, 'EOM', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 2, 14, str.tostring(eom_sma, '0.000'), text_color=color.red, text_size=table_text_size)
// Key Oscillators
table.cell(info_table, 0, 15, 'OSCILLATORS', text_color=color.purple, text_size=table_text_size, bgcolor=color.new(color.purple, 90))
table.cell(info_table, 1, 15, 'Awesome Osc', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 2, 15, str.tostring(ao, '0.000'), text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 0, 16, '', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 1, 16, 'Fisher Transform', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 2, 16, str.tostring(fisher, '0.000'), text_color=color.red, text_size=table_text_size)
// Summary Statistics
table.cell(info_table, 0, 17, 'SUMMARY', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.gray, 70))
table.cell(info_table, 1, 17, 'Total Indicators: 52', text_color=color.black, text_size=table_text_size)
regime_color = rsi_centered > 10 ? color.green : rsi_centered < -10 ? color.red : color.gray
regime_text = rsi_centered > 10 ? "BULLISH" : rsi_centered < -10 ? "BEARISH" : "NEUTRAL"
table.cell(info_table, 2, 17, regime_text, text_color=regime_color, text_size=table_text_size)
This makes it the perfect “indicator backbone” for quantitative and systematic traders who want to prototype, combine, and test new regime detection models—especially in combination with the Markov Chain indicator.
How to use this script with the Markov Chain for research and backtesting:
Add the Enhanced Indicator Export to your chart.
Every calculated indicator is available as an individual data stream.
Connect the indicator(s) you want as custom input(s) to the Markov Chain’s “Custom Indicators” option.
In the Markov Chain indicator’s settings, turn ON the custom indicator mode.
For each of the three custom indicator inputs, select the exported plot from the Enhanced Export script—the menu lists all 45+ signals by name.
This creates a powerful, modular regime-detection engine where you can mix-and-match momentum, trend, volume, or custom combinations for advanced filtering.
Backtest regime logic directly.
Once you’ve connected your chosen indicators, the Markov Chain script performs regime detection (Bull/Neutral/Bear) based on your selected features—not just price returns.
The regime detection is robust, automatically normalized (using Z-score), and outputs bias (1, -1, 0) for plug-and-play integration.
Export the regime bias for programmatic use.
As described above, use input.source() in your Pine Script strategy or system and link the bias output.
You can now filter signals, control trade direction/size, or design pairs-trading that respect true, indicator-driven market regimes.
With this framework, you’re not limited to static or simplistic regime filters. You can rigorously define, test, and refine what “market regime” means for your strategies—using the technical features that matter most to you.
Optimize your signal generation by backtesting across a universe of meaningful indicator blends.
Enhance risk management with objective, real-time regime boundaries.
Accelerate your research: iterate quickly, swap indicator components, and see results with minimal code changes.
Automate multi-asset or pairs-trading by integrating regime context directly into strategy logic.
Add both scripts to your chart, connect your preferred features, and start investigating your best regime-based trades—entirely within the TradingView ecosystem.
References & Further Reading
Ang, A., & Bekaert, G. (2002). “Regime Switches in Interest Rates.” Journal of Business & Economic Statistics, 20(2), 163–182.
Hamilton, J. D. (1989). “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle.” Econometrica, 57(2), 357–384.
Markov, A. A. (1906). "Extension of the Limit Theorems of Probability Theory to a Sum of Variables Connected in a Chain." The Notes of the Imperial Academy of Sciences of St. Petersburg.
Guidolin, M., & Timmermann, A. (2007). “Asset Allocation under Multivariate Regime Switching.” Journal of Economic Dynamics and Control, 31(11), 3503–3544.
Murphy, J. J. (1999). Technical Analysis of the Financial Markets. New York Institute of Finance.
Brock, W., Lakonishok, J., & LeBaron, B. (1992). “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns.” Journal of Finance, 47(5), 1731–1764.
Zucchini, W., MacDonald, I. L., & Langrock, R. (2017). Hidden Markov Models for Time Series: An Introduction Using R (2nd ed.). Chapman and Hall/CRC.
On Quantitative Finance and Markov Models:
Lo, A. W., & Hasanhodzic, J. (2009). The Heretics of Finance: Conversations with Leading Practitioners of Technical Analysis. Bloomberg Press.
Patterson, S. (2016). The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution. Penguin Press.
TradingView Pine Script Documentation: www.tradingview.com
TradingView Blog: “Use an Input From Another Indicator With Your Strategy” www.tradingview.com
GeeksforGeeks: “What is the Difference Between Markov Chains and Hidden Markov Models?” www.geeksforgeeks.org
What makes this indicator original and unique?
- On‑chart, real‑time Markov. The chain is drawn directly on your chart. You see the current regime, its tendency to stay (self‑loop), and the usual next step (arrows) as bars confirm.
- Source‑agnostic by design. The engine runs on any series you select via input.source() — price, your own oscillator, a composite score, anything you compute in the script.
- Automatic normalization + regime mapping. Different inputs live on different scales. The script standardizes your chosen source and maps it into clear regimes (e.g., Bull / Bear / Neutral) without you micromanaging thresholds each time.
- Rolling, bar‑by‑bar learning. Transition tendencies are computed from a rolling window of confirmed bars. What you see is exactly what the market did in that window.
- Fast experimentation. Switch the source, adjust the window, and the Markov view updates instantly. It’s a rapid way to test ideas and feel regime persistence/switch behavior.
Integrate your own signals (using input.source())
- In settings, choose the Source . This is powered by input.source() .
- Feed it price, an indicator you compute inside the script, or a custom composite series.
- The script will automatically normalize that series and process it through the Markov engine, mapping it to regimes and updating the on‑chart spheres/arrows in real time.
Credits:
Deep gratitude to @RicardoSantos for both the foundational Markov chain processing engine and inspiring open-source contributions, which made advanced probabilistic market modeling accessible to the TradingView community.
Special thanks to @Alien_Algorithms for the innovative and visually stunning 3D sphere logic that powers the indicator’s animated, regime-based visualization.
Disclaimer
This tool summarizes recent behavior. It is not financial advice and not a guarantee of future results.
EAOBS by MIGVersion 1
1. Strategy Overview Objective: Capitalize on breakout movements in Ethereum (ETH) price after the Asian open pre-market session (7:00 PM–7:59 PM EST) by identifying high and low prices during the session and trading breakouts above the high or below the low.
Timeframe: Any (script is timeframe-agnostic, but align with session timing).
Session: Pre-market session (7:00 PM–7:59 PM EST, adjustable for other time zones, e.g., 12:00 AM–12:59 AM GMT).
Risk-Reward Ratios (R:R): Targets range from 1.2:1 to 5.2:1, with a fixed stop loss.
Instrument: Ethereum (ETH/USD or ETH-based pairs).
2. Market Setup Session Monitoring: Monitor ETH price action during the pre-market session (7:00 PM–7:59 PM EST), which aligns with the Asian market open (e.g., 9:00 AM–9:59 AM JST).
The script tracks the highest high and lowest low during this session.
Breakout Triggers: Buy Signal: Price breaks above the session’s high after the session ends (7:59 PM EST).
Sell Signal: Price breaks below the session’s low after the session ends.
Visualization: The session is highlighted on the chart with a white background.
Horizontal lines are drawn at the session’s high and low, extended for 30 bars, along with take-profit (TP) and stop-loss (SL) levels.
3. Entry Rules Long (Buy) Entry: Enter a long position when the price breaks above the session’s high price after 7:59 PM EST.
Entry price: Just above the session high (e.g., add a small buffer, like 0.1–0.5%, to avoid false breakouts, depending on volatility).
Short (Sell) Entry: Enter a short position when the price breaks below the session’s low price after 7:59 PM EST.
Entry price: Just below the session low (e.g., subtract a small buffer, like 0.1–0.5%).
Confirmation: Use a candlestick close above/below the breakout level to confirm the entry.
Optionally, add volume confirmation or a momentum indicator (e.g., RSI or MACD) to filter out weak breakouts.
Position Size: Calculate position size based on risk tolerance (e.g., 1–2% of account per trade).
Risk is determined by the stop-loss distance (10 points, as defined in the script).
4. Exit Rules Take-Profit Levels (in points, based on script inputs):TP1: 12 points (1.2:1 R:R).
TP2: 22 points (2.2:1 R:R).
TP3: 32 points (3.2:1 R:R).
TP4: 42 points (4.2:1 R:R).
TP5: 52 points (5.2:1 R:R).
Example for Long: If session high is 3000, TP levels are 3012, 3022, 3032, 3042, 3052.
Example for Short: If session low is 2950, TP levels are 2938, 2928, 2918, 2908, 2898.
Strategy: Scale out of the position (e.g., close 20% at TP1, 20% at TP2, etc.) or take full profit at a preferred TP level based on market conditions.
Stop-Loss: Fixed at 10 points from the entry.
Long SL: Session high - 10 points (e.g., entry at 3000, SL at 2990).
Short SL: Session low + 10 points (e.g., entry at 2950, SL at 2960).
Trailing Stop (Optional):After reaching TP2 or TP3, consider trailing the stop to lock in profits (e.g., trail by 10–15 points below the current price).
5. Risk Management per Trade: Limit risk to 1–2% of your trading account per trade.
Calculate position size: Account Size × Risk % ÷ (Stop-Loss Distance × ETH Price per Point).
Example: $10,000 account, 1% risk = $100. If SL = 10 points and 1 point = $1, position size = $100 ÷ 10 = 0.1 ETH.
Daily Risk Limit: Cap daily losses at 3–5% of the account to avoid overtrading.
Maximum Exposure: Avoid taking both long and short positions simultaneously unless using separate accounts or strategies.
Volatility Consideration: Adjust position size during high-volatility periods (e.g., major news events like Ethereum upgrades or macroeconomic announcements).
6. Trade Management Monitoring :Watch for breakouts after 7:59 PM EST.
Monitor price action near TP and SL levels using alerts or manual checks.
Trade Duration: Breakout lines extend for 30 bars (script parameter). Close trades if no TP or SL is hit within this period, or reassess based on market conditions.
Adjustments: If the market shows strong momentum, consider holding beyond TP5 with a trailing stop.
If the breakout fails (e.g., price reverses before TP1), exit early to minimize losses.
7. Additional Considerations Market Conditions: The 7:00 PM–7:59 PM EST session aligns with the Asian market open (e.g., Tokyo Stock Exchange open at 9:00 AM JST), which may introduce higher volatility due to Asian trading activity.
Avoid trading during low-liquidity periods or extreme volatility (e.g., major crypto news).
Check for upcoming events (e.g., Ethereum network upgrades, ETF decisions) that could impact price.
Backtesting: Test the strategy on historical ETH data using the session high/low breakouts for the 7:00 PM–7:59 PM EST window to validate performance.
Adjust TP/SL levels based on backtest results if needed.
Broker and Fees: Use a low-fee crypto exchange (e.g., Binance, Kraken, Coinbase Pro) to maximize R:R.
Account for trading fees and slippage in your position sizing.
Time zone Adjustment: Adjust session time input for your time zone (e.g., "0000-0059" for GMT).
Ensure your trading platform’s clock aligns with the script’s time zone (default: America/New_York).
8. Example Trade Scenario: Session (7:00 PM–7:59 PM EST) records a high of 3050 and a low of 3000.
Long Trade: Entry: Price breaks above 3050 (e.g., enter at 3051).
TP Levels: 3063 (TP1), 3073 (TP2), 3083 (TP3), 3093 (TP4), 3103 (TP5).
SL: 3040 (3050 - 10).
Position Size: For a $10,000 account, 1% risk = $100. SL = 11 points ($11). Size = $100 ÷ 11 = ~0.09 ETH.
Short Trade: Entry: Price breaks below 3000 (e.g., enter at 2999).
TP Levels: 2987 (TP1), 2977 (TP2), 2967 (TP3), 2957 (TP4), 2947 (TP5).
SL: 3010 (3000 + 10).
Position Size: Same as above, ~0.09 ETH.
Execution: Set alerts for breakouts, enter with limit orders, and monitor TPs/SL.
9. Tools and Setup Platform: Use TradingView to implement the Pine Script and visualize breakout levels.
Alerts: Set price alerts for breakouts above the session high or below the session low after 7:59 PM EST.
Set alerts for TP and SL levels.
Chart Settings: Use a 1-minute or 5-minute chart for precise session tracking.
Overlay the script to see high/low lines, TP levels, and SL levels.
Optional Indicators: Add RSI (e.g., avoid overbought/oversold breakouts) or volume to confirm breakouts.
10. Risk Warnings Crypto Volatility: ETH is highly volatile; unexpected news can cause rapid price swings.
False Breakouts: Breakouts may fail, especially in low-volume sessions. Use confirmation signals.
Leverage: Avoid high leverage (e.g., >5x) to prevent liquidation during volatile moves.
Session Accuracy: Ensure correct session timing for your time zone to avoid misaligned entries.
11. Performance Tracking Journaling :Record each trade’s entry, exit, R:R, and outcome.
Note market conditions (e.g., trending, ranging, news-driven).
Review: Weekly: Assess win rate, average R:R, and adherence to the plan.
Monthly: Adjust TP/SL or session timing based on performance.
Trend Gauge [BullByte]Trend Gauge
Summary
A multi-factor trend detection indicator that aggregates EMA alignment, VWMA momentum scaling, volume spikes, ATR breakout strength, higher-timeframe confirmation, ADX-based regime filtering, and RSI pivot-divergence penalty into one normalized trend score. It also provides a confidence meter, a Δ Score momentum histogram, divergence highlights, and a compact, scalable dashboard for at-a-glance status.
________________________________________
## 1. Purpose of the Indicator
Why this was built
Traders often monitor several indicators in parallel - EMAs, volume signals, volatility breakouts, higher-timeframe trends, ADX readings, divergence alerts, etc., which can be cumbersome and sometimes contradictory. The “Trend Gauge” indicator was created to consolidate these complementary checks into a single, normalized score that reflects the prevailing market bias (bullish, bearish, or neutral) and its strength. By combining multiple inputs with an adaptive regime filter, scaling contributions by magnitude, and penalizing weakening signals (divergence), this tool aims to reduce noise, highlight genuine trend opportunities, and warn when momentum fades.
Key Design Goals
Signal Aggregation
Merged trend-following signals (EMA crossover, ATR breakout, higher-timeframe confirmation) and momentum signals (VWMA thrust, volume spikes) into a unified score that reflects directional bias more holistically.
Market Regime Awareness
Implemented an ADX-style filter to distinguish between trending and ranging markets, reducing the influence of trend signals during sideways phases to avoid false breakouts.
Magnitude-Based Scaling
Replaced binary contributions with scaled inputs: VWMA thrust and ATR breakout are weighted relative to recent averages, allowing for more nuanced score adjustments based on signal strength.
Momentum Divergence Penalty
Integrated pivot-based RSI divergence detection to slightly reduce the overall score when early signs of momentum weakening are detected, improving risk-awareness in entries.
Confidence Transparency
Added a live confidence metric that shows what percentage of enabled sub-indicators currently agree with the overall bias, making the scoring system more interpretable.
Momentum Acceleration Visualization
Plotted the change in score (Δ Score) as a histogram bar-to-bar, highlighting whether momentum is increasing, flattening, or reversing, aiding in more timely decision-making.
Compact Informational Dashboard
Presented a clean, scalable dashboard that displays each component’s status, the final score, confidence %, detected regime (Trending/Ranging), and a labeled strength gauge for quick visual assessment.
________________________________________
## 2. Why a Trader Should Use It
Main benefits and use cases
1. Unified View: Rather than juggling multiple windows or panels, this indicator delivers a single score synthesizing diverse signals.
2. Regime Filtering: In ranging markets, trend signals often generate false entries. The ADX-based regime filter automatically down-weights trend-following components, helping you avoid chasing false breakouts.
3. Nuanced Momentum & Volatility: VWMA and ATR breakout contributions are normalized by recent averages, so strong moves register strongly while smaller fluctuations are de-emphasized.
4. Early Warning of Weakening: Pivot-based RSI divergence is detected and used to slightly reduce the score when price/momentum diverges, giving a cautionary signal before a full reversal.
5. Confidence Meter: See at a glance how many sub-indicators align with the aggregated bias (e.g., “80% confidence” means 4 out of 5 components agree ). This transparency avoids black-box decisions.
6. Trend Acceleration/Deceleration View: The Δ Score histogram visualizes whether the aggregated score is rising (accelerating trend) or falling (momentum fading), supplementing the main oscillator.
7. Compact Dashboard: A corner table lists each check’s status (“Bull”, “Bear”, “Flat” or “Disabled”), plus overall Score, Confidence %, Regime, Trend Strength label, and a gauge bar. Users can scale text size (Normal, Small, Tiny) without removing elements, so the full picture remains visible even in compact layouts.
8. Customizable & Transparent: All components can be enabled/disabled and parameterized (lengths, thresholds, weights). The full Pine code is open and well-commented, letting users inspect or adapt the logic.
9. Alert-ready: Built-in alert conditions fire when the score crosses weak thresholds to bullish/bearish or returns to neutral, enabling timely notifications.
________________________________________
## 3. Component Rationale (“Why These Specific Indicators?”)
Each sub-component was chosen because it adds complementary information about trend or momentum:
1. EMA Cross
o Basic trend measure: compares a faster EMA vs. a slower EMA. Quickly reflects trend shifts but by itself can whipsaw in sideways markets.
2. VWMA Momentum
o Volume-weighted moving average change indicates momentum with volume context. By normalizing (dividing by a recent average absolute change), we capture the strength of momentum relative to recent history. This scaling prevents tiny moves from dominating and highlights genuinely strong momentum.
3. Volume Spikes
o Sudden jumps in volume combined with price movement often accompany stronger moves or reversals. A binary detection (+1 for bullish spike, -1 for bearish spike) flags high-conviction bars.
4. ATR Breakout
o Detects price breaking beyond recent highs/lows by a multiple of ATR. Measures breakout strength by how far beyond the threshold price moves relative to ATR, capped to avoid extreme outliers. This gives a volatility-contextual trend signal.
5. Higher-Timeframe EMA Alignment
o Confirms whether the shorter-term trend aligns with a higher timeframe trend. Uses request.security with lookahead_off to avoid future data. When multiple timeframes agree, confidence in direction increases.
6. ADX Regime Filter (Manual Calculation)
o Computes directional movement (+DM/–DM), smoothes via RMA, computes DI+ and DI–, then a DX and ADX-like value. If ADX ≥ threshold, market is “Trending” and trend components carry full weight; if ADX < threshold, “Ranging” mode applies a configurable weight multiplier (e.g., 0.5) to trend-based contributions, reducing false signals in sideways conditions. Volume spikes remain binary (optional behavior; can be adjusted if desired).
7. RSI Pivot-Divergence Penalty
o Uses ta.pivothigh / ta.pivotlow with a lookback to detect pivot highs/lows on price and corresponding RSI values. When price makes a higher high but RSI makes a lower high (bearish divergence), or price makes a lower low but RSI makes a higher low (bullish divergence), a divergence signal is set. Rather than flipping the trend outright, the indicator subtracts (or adds) a small penalty (configurable) from the aggregated score if it would weaken the current bias. This subtle adjustment warns of weakening momentum without overreacting to noise.
8. Confidence Meter
o Counts how many enabled components currently agree in direction with the aggregated score (i.e., component sign × score sign > 0). Displays this as a percentage. A high percentage indicates strong corroboration; a low percentage warns of mixed signals.
9. Δ Score Momentum View
o Plots the bar-to-bar change in the aggregated score (delta_score = score - score ) as a histogram. When positive, bars are drawn in green above zero; when negative, bars are drawn in red below zero. This reveals acceleration (rising Δ) or deceleration (falling Δ), supplementing the main oscillator.
10. Dashboard
• A table in the indicator pane’s top-right with 11 rows:
1. EMA Cross status
2. VWMA Momentum status
3. Volume Spike status
4. ATR Breakout status
5. Higher-Timeframe Trend status
6. Score (numeric)
7. Confidence %
8. Regime (“Trending” or “Ranging”)
9. Trend Strength label (e.g., “Weak Bullish Trend”, “Strong Bearish Trend”)
10. Gauge bar visually representing score magnitude
• All rows always present; size_opt (Normal, Small, Tiny) only changes text size via text_size, not which elements appear. This ensures full transparency.
________________________________________
## 4. What Makes This Indicator Stand Out
• Regime-Weighted Multi-Factor Score: Trend and momentum signals are adaptively weighted by market regime (trending vs. ranging) , reducing false signals.
• Magnitude Scaling: VWMA and ATR breakout contributions are normalized by recent average momentum or ATR, giving finer gradation compared to simple ±1.
• Integrated Divergence Penalty: Divergence directly adjusts the aggregated score rather than appearing as a separate subplot; this influences alerts and trend labeling in real time.
• Confidence Meter: Shows the percentage of sub-signals in agreement, providing transparency and preventing blind trust in a single metric.
• Δ Score Histogram Momentum View: A histogram highlights acceleration or deceleration of the aggregated trend score, helping detect shifts early.
• Flexible Dashboard: Always-visible component statuses and summary metrics in one place; text size scaling keeps the full picture available in cramped layouts.
• Lookahead-Safe HTF Confirmation: Uses lookahead_off so no future data is accessed from higher timeframes, avoiding repaint bias.
• Repaint Transparency: Divergence detection uses pivot functions that inherently confirm only after lookback bars; description documents this lag so users understand how and when divergence labels appear.
• Open-Source & Educational: Full, well-commented Pine v6 code is provided; users can learn from its structure: manual ADX computation, conditional plotting with series = show ? value : na, efficient use of table.new in barstate.islast, and grouped inputs with tooltips.
• Compliance-Conscious: All plots have descriptive titles; inputs use clear names; no unnamed generic “Plot” entries; manual ADX uses RMA; all request.security calls use lookahead_off. Code comments mention repaint behavior and limitations.
________________________________________
## 5. Recommended Timeframes & Tuning
• Any Timeframe: The indicator works on small (e.g., 1m) to large (daily, weekly) timeframes. However:
o On very low timeframes (<1m or tick charts), noise may produce frequent whipsaws. Consider increasing smoothing lengths, disabling certain components (e.g., volume spike if volume data noisy), or using a larger pivot lookback for divergence.
o On higher timeframes (daily, weekly), consider longer lookbacks for ATR breakout or divergence, and set Higher-Timeframe trend appropriately (e.g., 4H HTF when on 5 Min chart).
• Defaults & Experimentation: Default input values are chosen to be balanced for many liquid markets. Users should test with replay or historical analysis on their symbol/timeframe and adjust:
o ADX threshold (e.g., 20–30) based on instrument volatility.
o VWMA and ATR scaling lengths to match average volatility cycles.
o Pivot lookback for divergence: shorter for faster markets, longer for slower ones.
• Combining with Other Analysis: Use in conjunction with price action, support/resistance, candlestick patterns, order flow, or other tools as desired. The aggregated score and alerts can guide attention but should not be the sole decision-factor.
________________________________________
## 6. How Scoring and Logic Works (Step-by-Step)
1. Compute Sub-Scores
o EMA Cross: Evaluate fast EMA > slow EMA ? +1 : fast EMA < slow EMA ? -1 : 0.
o VWMA Momentum: Calculate vwma = ta.vwma(close, length), then vwma_mom = vwma - vwma . Normalize: divide by recent average absolute momentum (e.g., ta.sma(abs(vwma_mom), lookback)), clip to .
o Volume Spike: Compute vol_SMA = ta.sma(volume, len). If volume > vol_SMA * multiplier AND price moved up ≥ threshold%, assign +1; if moved down ≥ threshold%, assign -1; else 0.
o ATR Breakout: Determine recent high/low over lookback. If close > high + ATR*mult, compute distance = close - (high + ATR*mult), normalize by ATR, cap at a configured maximum. Assign positive contribution. Similarly for bearish breakout below low.
o Higher-Timeframe Trend: Use request.security(..., lookahead=barmerge.lookahead_off) to fetch HTF EMAs; assign +1 or -1 based on alignment.
2. ADX Regime Weighting
o Compute manual ADX: directional movements (+DM, –DM), smoothed via RMA, DI+ and DI–, then DX and ADX via RMA. If ADX ≥ threshold, market is considered “Trending”; otherwise “Ranging.”
o If trending, trend-based contributions (EMA, VWMA, ATR, HTF) use full weight = 1.0. If ranging, use weight = ranging_weight (e.g., 0.5) to down-weight them. Volume spike stays binary ±1 (optional to change if desired).
3. Aggregate Raw Score
o Sum weighted contributions of all enabled components. Count the number of enabled components; if zero, default count = 1 to avoid division by zero.
4. Divergence Penalty
o Detect pivot highs/lows on price and corresponding RSI values, using a lookback. When price and RSI diverge (bearish or bullish divergence), check if current raw score is in the opposing direction:
If bearish divergence (price higher high, RSI lower high) and raw score currently positive, subtract a penalty (e.g., 0.5).
If bullish divergence (price lower low, RSI higher low) and raw score currently negative, add a penalty.
o This reduces score magnitude to reflect weakening momentum, without flipping the trend outright.
5. Normalize and Smooth
o Normalized score = (raw_score / number_of_enabled_components) * 100. This yields a roughly range.
o Optional EMA smoothing of this normalized score to reduce noise.
6. Interpretation
o Sign: >0 = net bullish bias; <0 = net bearish bias; near zero = neutral.
o Magnitude Zones: Compare |score| to thresholds (Weak, Medium, Strong) to label trend strength (e.g., “Weak Bullish Trend”, “Medium Bearish Trend”, “Strong Bullish Trend”).
o Δ Score Histogram: The histogram bars from zero show change from previous bar’s score; positive bars indicate acceleration, negative bars indicate deceleration.
o Confidence: Percentage of sub-indicators aligned with the score’s sign.
o Regime: Indicates whether trend-based signals are fully weighted or down-weighted.
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## 7. Oscillator Plot & Visualization: How to Read It
Main Score Line & Area
The oscillator plots the aggregated score as a line, with colored fill: green above zero for bullish area, red below zero for bearish area. Horizontal reference lines at ±Weak, ±Medium, and ±Strong thresholds mark zones: crossing above +Weak suggests beginning of bullish bias, above +Medium for moderate strength, above +Strong for strong trend; similarly for bearish below negative thresholds.
Δ Score Histogram
If enabled, a histogram shows score - score . When positive, bars appear in green above zero, indicating accelerating bullish momentum; when negative, bars appear in red below zero, indicating decelerating or reversing momentum. The height of each bar reflects the magnitude of change in the aggregated score from the prior bar.
Divergence Highlight Fill
If enabled, when a pivot-based divergence is confirmed:
• Bullish Divergence : fill the area below zero down to –Weak threshold in green, signaling potential reversal from bearish to bullish.
• Bearish Divergence : fill the area above zero up to +Weak threshold in red, signaling potential reversal from bullish to bearish.
These fills appear with a lag equal to pivot lookback (the number of bars needed to confirm the pivot). They do not repaint after confirmation, but users must understand this lag.
Trend Direction Label
When score crosses above or below the Weak threshold, a small label appears near the score line reading “Bullish” or “Bearish.” If the score returns within ±Weak, the label “Neutral” appears. This helps quickly identify shifts at the moment they occur.
Dashboard Panel
In the indicator pane’s top-right, a table shows:
1. EMA Cross status: “Bull”, “Bear”, “Flat”, or “Disabled”
2. VWMA Momentum status: similarly
3. Volume Spike status: “Bull”, “Bear”, “No”, or “Disabled”
4. ATR Breakout status: “Bull”, “Bear”, “No”, or “Disabled”
5. Higher-Timeframe Trend status: “Bull”, “Bear”, “Flat”, or “Disabled”
6. Score: numeric value (rounded)
7. Confidence: e.g., “80%” (colored: green for high, amber for medium, red for low)
8. Regime: “Trending” or “Ranging” (colored accordingly)
9. Trend Strength: textual label based on magnitude (e.g., “Medium Bullish Trend”)
10. Gauge: a bar of blocks representing |score|/100
All rows remain visible at all times; changing Dashboard Size only scales text size (Normal, Small, Tiny).
________________________________________
## 8. Example Usage (Illustrative Scenario)
Example: BTCUSD 5 Min
1. Setup: Add “Trend Gauge ” to your BTCUSD 5 Min chart. Defaults: EMAs (8/21), VWMA 14 with lookback 3, volume spike settings, ATR breakout 14/5, HTF = 5m (or adjust to 4H if preferred), ADX threshold 25, ranging weight 0.5, divergence RSI length 14 pivot lookback 5, penalty 0.5, smoothing length 3, thresholds Weak=20, Medium=50, Strong=80. Dashboard Size = Small.
2. Trend Onset: At some point, price breaks above recent high by ATR multiple, volume spikes upward, faster EMA crosses above slower EMA, HTF EMA also bullish, and ADX (manual) ≥ threshold → aggregated score rises above +20 (Weak threshold) into +Medium zone. Dashboard shows “Bull” for EMA, VWMA, Vol Spike, ATR, HTF; Score ~+60–+70; Confidence ~100%; Regime “Trending”; Trend Strength “Medium Bullish Trend”; Gauge ~6–7 blocks. Δ Score histogram bars are green and rising, indicating accelerating bullish momentum. Trader notes the alignment.
3. Divergence Warning: Later, price makes a slightly higher high but RSI fails to confirm (lower RSI high). Pivot lookback completes; the indicator highlights a bearish divergence fill above zero and subtracts a small penalty from the score, causing score to stall or retrace slightly. Dashboard still bullish but score dips toward +Weak. This warns the trader to tighten stops or take partial profits.
4. Trend Weakens: Score eventually crosses below +Weak back into neutral; a “Neutral” label appears, and a “Neutral Trend” alert fires if enabled. Trader exits or avoids new long entries. If score subsequently crosses below –Weak, a “Bearish” label and alert occur.
5. Customization: If the trader finds VWMA noise too frequent on this instrument, they may disable VWMA or increase lookback. If ATR breakouts are too rare, adjust ATR length or multiplier. If ADX threshold seems off, tune threshold. All these adjustments are explained in Inputs section.
6. Visualization: The screenshot shows the main score oscillator with colored areas, reference lines at ±20/50/80, Δ Score histogram bars below/above zero, divergence fill highlighting potential reversal, and the dashboard table in the top-right.
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## 9. Inputs Explanation
A concise yet clear summary of inputs helps users understand and adjust:
1. General Settings
• Theme (Dark/Light): Choose background-appropriate colors for the indicator pane.
• Dashboard Size (Normal/Small/Tiny): Scales text size only; all dashboard elements remain visible.
2. Indicator Settings
• Enable EMA Cross: Toggle on/off basic EMA alignment check.
o Fast EMA Length and Slow EMA Length: Periods for EMAs.
• Enable VWMA Momentum: Toggle VWMA momentum check.
o VWMA Length: Period for VWMA.
o VWMA Momentum Lookback: Bars to compare VWMA to measure momentum.
• Enable Volume Spike: Toggle volume spike detection.
o Volume SMA Length: Period to compute average volume.
o Volume Spike Multiplier: How many times above average volume qualifies as spike.
o Min Price Move (%): Minimum percent change in price during spike to qualify as bullish or bearish.
• Enable ATR Breakout: Toggle ATR breakout detection.
o ATR Length: Period for ATR.
o Breakout Lookback: Bars to look back for recent highs/lows.
o ATR Multiplier: Multiplier for breakout threshold.
• Enable Higher Timeframe Trend: Toggle HTF EMA alignment.
o Higher Timeframe: E.g., “5” for 5-minute when on 1-minute chart, or “60” for 5 Min when on 15m, etc. Uses lookahead_off.
• Enable ADX Regime Filter: Toggles regime-based weighting.
o ADX Length: Period for manual ADX calculation.
o ADX Threshold: Value above which market considered trending.
o Ranging Weight Multiplier: Weight applied to trend components when ADX < threshold (e.g., 0.5).
• Scale VWMA Momentum: Toggle normalization of VWMA momentum magnitude.
o VWMA Mom Scale Lookback: Period for average absolute VWMA momentum.
• Scale ATR Breakout Strength: Toggle normalization of breakout distance by ATR.
o ATR Scale Cap: Maximum multiple of ATR used for breakout strength.
• Enable Price-RSI Divergence: Toggle divergence detection.
o RSI Length for Divergence: Period for RSI.
o Pivot Lookback for Divergence: Bars on each side to identify pivot high/low.
o Divergence Penalty: Amount to subtract/add to score when divergence detected (e.g., 0.5).
3. Score Settings
• Smooth Score: Toggle EMA smoothing of normalized score.
• Score Smoothing Length: Period for smoothing EMA.
• Weak Threshold: Absolute score value under which trend is considered weak or neutral.
• Medium Threshold: Score above Weak but below Medium is moderate.
• Strong Threshold: Score above this indicates strong trend.
4. Visualization Settings
• Show Δ Score Histogram: Toggle display of the bar-to-bar change in score as a histogram. Default true.
• Show Divergence Fill: Toggle background fill highlighting confirmed divergences. Default true.
Each input has a tooltip in the code.
________________________________________
## 10. Limitations, Repaint Notes, and Disclaimers
10.1. Repaint & Lag Considerations
• Pivot-Based Divergence Lag: The divergence detection uses ta.pivothigh / ta.pivotlow with a specified lookback. By design, a pivot is only confirmed after the lookback number of bars. As a result:
o Divergence labels or fills appear with a delay equal to the pivot lookback.
o Once the pivot is confirmed and the divergence is detected, the fill/label does not repaint thereafter, but you must understand and accept this lag.
o Users should not treat divergence highlights as predictive signals without additional confirmation, because they appear after the pivot has fully formed.
• Higher-Timeframe EMA Alignment: Uses request.security(..., lookahead=barmerge.lookahead_off), so no future data from the higher timeframe is used. This avoids lookahead bias and ensures signals are based only on completed higher-timeframe bars.
• No Future Data: All calculations are designed to avoid using future information. For example, manual ADX uses RMA on past data; security calls use lookahead_off.
10.2. Market & Noise Considerations
• In very choppy or low-liquidity markets, some components (e.g., volume spikes or VWMA momentum) may be noisy. Users can disable or adjust those components’ parameters.
• On extremely low timeframes, noise may dominate; consider smoothing lengths or disabling certain features.
• On very high timeframes, pivots and breakouts occur less frequently; adjust lookbacks accordingly to avoid sparse signals.
10.3. Not a Standalone Trading System
• This is an indicator, not a complete trading strategy. It provides signals and context but does not manage entries, exits, position sizing, or risk management.
• Users must combine it with their own analysis, money management, and confirmations (e.g., price patterns, support/resistance, fundamental context).
• No guarantees: past behavior does not guarantee future performance.
10.4. Disclaimers
• Educational Purposes Only: The script is provided as-is for educational and informational purposes. It does not constitute financial, investment, or trading advice.
• Use at Your Own Risk: Trading involves risk of loss. Users should thoroughly test and use proper risk management.
• No Guarantees: The author is not responsible for trading outcomes based on this indicator.
• License: Published under Mozilla Public License 2.0; code is open for viewing and modification under MPL terms.
________________________________________
## 11. Alerts
• The indicator defines three alert conditions:
1. Bullish Trend: when the aggregated score crosses above the Weak threshold.
2. Bearish Trend: when the score crosses below the negative Weak threshold.
3. Neutral Trend: when the score returns within ±Weak after being outside.
Good luck
– BullByte
Top-Down Trend and Key Levels with Swing Points//by antaryaami0
Overview
The “Top-Down Trend and Key Levels with Swing Points” indicator is a comprehensive tool designed to enhance your technical analysis by integrating multiple trading concepts into a single, easy-to-use script. It combines higher timeframe trend analysis, key price levels, swing point detection, and ranging market identification to provide a holistic view of market conditions. This indicator is particularly useful for traders who employ multi-timeframe analysis, support and resistance levels, and price action strategies.
Key Features
1. Higher Timeframe Trend Background Shading:
• Purpose: Identifies the prevailing trend on a higher timeframe to align lower timeframe trading decisions with the broader market direction.
• How it Works: The indicator compares the current higher timeframe close with the previous one to determine if the trend is up, down, or ranging.
• Customization:
• Trend Timeframe: Set your preferred higher timeframe (e.g., Daily, Weekly).
• Up Trend Color & Down Trend Color: Customize the background colors for uptrends and downtrends.
• Ranging Market Color: A separate color to indicate when the market is moving sideways.
2. Key Price Levels:
• Previous Day High (PDH) and Low (PDL):
• Purpose: Identifies key support and resistance levels from the previous trading day.
• Visualization: Plots horizontal lines at PDH and PDL with labels.
• Customization: Option to show or hide these levels and customize their colors.
• Pre-Market High (PMH) and Low (PML):
• Purpose: Highlights the price range during the pre-market session, which can indicate potential breakout levels.
• Visualization: Plots horizontal lines at PMH and PML with labels.
• Customization: Option to show or hide these levels and customize their colors.
3. First 5-Minute Marker (F5H/F5L):
• Purpose: Marks the high or low of the first 5 minutes after the market opens, which is significant for intraday momentum.
• How it Works:
• If the first 5-minute high is above the Pre-Market High (PMH), an “F5H” label is placed at the first 5-minute high.
• If the first 5-minute high is below the PMH, an “F5L” label is placed at the first 5-minute low.
• Visualization: Labels are placed at the 9:35 AM candle (closing of the first 5 minutes), colored in purple by default.
• Customization: Option to show or hide the marker and adjust the marker color.
4. Swing Points Detection:
• Purpose: Identifies significant pivot points in price action to help recognize trends and reversals.
• How it Works: Uses left and right bars to detect pivot highs and lows, then determines if they are Higher Highs (HH), Lower Highs (LH), Higher Lows (HL), or Lower Lows (LL).
• Visualization: Plots small markers (circles) with labels (HH, LH, HL, LL) at the corresponding swing points.
• Customization: Adjust the number of left and right bars for pivot detection and the size of the markers.
5. Ranging Market Detection:
• Purpose: Identifies periods when the market is consolidating (moving sideways) within a defined price range.
• How it Works: Calculates the highest high and lowest low over a specified period and determines if the price range is within a set percentage threshold.
• Visualization: Draws a gray box around the price action during the ranging period and labels the high and low prices at the end of the range.
• Customization: Adjust the range detection period and threshold, as well as the box color.
6. Trend Coloring on Chart:
• Purpose: Provides a visual cue for the short-term trend based on a moving average.
• How it Works: Colors the candles green if the price is above the moving average and red if below.
• Customization: Set the moving average length and customize the uptrend and downtrend colors.
How to Use the Indicator
1. Adding the Indicator to Your Chart:
• Copy the Pine Script code provided and paste it into the Pine Script Editor on TradingView.
• Click “Add to Chart” to apply the indicator.
2. Configuring Inputs and Settings:
• Access Inputs:
• Click on the gear icon next to the indicator’s name on your chart to open the settings.
• Customize Key Levels:
• Show Pre-Market High/Low: Toggle on/off.
• Show Previous Day High/Low: Toggle on/off.
• Show First 5-Minute Marker: Toggle on/off.
• Set Trend Parameters:
• Trend Timeframe for Background: Choose the higher timeframe for trend analysis.
• Moving Average Length for Bar Color: Set the period for the moving average used in bar coloring.
• Adjust Ranging Market Detection:
• Range Detection Period: Specify the number of bars to consider for range detection.
• Range Threshold (%): Set the maximum percentage range for the market to be considered ranging.
• Customize Visuals:
• Colors: Adjust colors for trends, levels, markers, and ranging market boxes.
• Label Font Size: Choose the size of labels displayed on the chart.
• Level Line Width: Set the thickness of the lines for key levels.
3. Interpreting the Indicator:
• Background Shading:
• Green Shade: Higher timeframe is in an uptrend.
• Red Shade: Higher timeframe is in a downtrend.
• Gray Box: Market is ranging (sideways movement).
• Key Levels and Markers:
• PDH and PDL Lines: Represent resistance and support from the previous day.
• PMH and PML Lines: Indicate potential breakout levels based on pre-market activity.
• F5H/F5L Labels: Early indication of intraday momentum after market open.
• Swing Point Markers:
• HH (Higher High): Suggests bullish momentum.
• LH (Lower High): May indicate a potential bearish reversal.
• HL (Higher Low): Supports bullish continuation.
• LL (Lower Low): Indicates bearish momentum.
• Ranging Market Box:
• Gray Box Around Price Action: Highlights consolidation periods where breakouts may occur.
• Range High and Low Labels: Provide the upper and lower bounds of the consolidation zone.
4. Applying the Indicator to Your Trading Strategy:
• Trend Alignment:
• Use the higher timeframe trend shading to align your trades with the broader market direction.
• Key Levels Trading:
• Watch for price reactions at PDH, PDL, PMH, and PML for potential entry and exit points.
• Swing Points Analysis:
• Identify trend continuations or reversals by observing the sequence of HH, HL, LH, and LL.
• Ranging Market Strategies:
• During ranging periods, consider range-bound trading strategies or prepare for breakout trades when the price exits the range.
• Intraday Momentum:
• Use the F5H/F5L marker to gauge early market sentiment and potential intraday trends.
Practical Tips
• Adjust Settings to Your Trading Style:
• Tailor the indicator’s inputs to match your preferred timeframes and trading instruments.
• Combine with Other Indicators:
• Use in conjunction with volume indicators, oscillators, or other technical tools for additional confirmation.
• Backtesting:
• Apply the indicator to historical data to observe how it performs and refine your settings accordingly.
• Stay Updated on Market Conditions:
• Be aware of news events or economic releases that may impact market behavior and the effectiveness of technical levels.
Customization Options
• Time Zone Adjustment:
• The script uses “America/New_York” time zone by default. Adjust the timezone variable in the script if your chart operates in a different time zone.
var timezone = "Your/Timezone"
• Session Times:
• Modify the Regular Trading Session and Pre-Market Session times in the indicator settings to align with the trading hours of different markets or exchanges.
• Visual Preferences:
• Colors: Personalize the indicator’s colors to suit your visual preferences or to enhance visibility.
• Label Sizes: Adjust label sizes if you find them too intrusive or not prominent enough.
• Marker Sizes: Further reduce or enlarge the swing point markers by modifying the swing_marker_size variable.
Understanding the Indicator’s Logic
1. Higher Timeframe Trend Analysis:
• The indicator retrieves the closing prices of a higher timeframe using the request.security() function.
• It compares the current higher timeframe close with the previous one to determine the trend direction.
2. Key Level Calculation:
• Previous Day High/Low: Calculated by tracking the highest and lowest prices of the previous trading day.
• Pre-Market High/Low: Calculated by monitoring price action during the pre-market session.
3. First 5-Minute Marker Logic:
• At 9:35 AM (end of the first 5 minutes after market open), the indicator evaluates whether the first 5-minute high is above or below the PMH.
• It then places the appropriate label (F5H or F5L) on the chart.
4. Swing Points Detection:
• The script uses ta.pivothigh() and ta.pivotlow() functions to detect pivot points.
• It then determines the type of swing point based on comparisons with previous swings.
5. Ranging Market Detection:
• The indicator looks back over a specified number of bars to find the highest high and lowest low.
• It calculates the percentage difference between these two points.
• If the difference is below the set threshold, the market is considered to be ranging, and a box is drawn around the price action.
Limitations and Considerations
• Indicator Limitations:
• Maximum Boxes and Labels: Due to Pine Script limitations, there is a maximum number of boxes and labels that can be displayed simultaneously.
• Performance Impact: Adding multiple visual elements (boxes, labels, markers) can affect the performance of the script on lower-end devices or with large amounts of data.
• Market Conditions:
• False Signals: Like any technical tool, the indicator may produce false signals, especially during volatile or erratic market conditions.
• Not a Standalone Solution: This indicator should be used as part of a comprehensive trading strategy, including risk management and other forms of analysis.
Conclusion
The “Top-Down Trend and Key Levels with Swing Points” indicator is a versatile tool that integrates essential aspects of technical analysis into one script. By providing insights into higher timeframe trends, highlighting key price levels, detecting swing points, and identifying ranging markets, it equips traders with valuable information to make more informed trading decisions. Whether you are a day trader looking for intraday opportunities or a swing trader aiming to align with the broader trend, this indicator can enhance your chart analysis and trading strategy.
Disclaimer
Trading involves significant risk, and it’s important to understand that past performance is not indicative of future results. This indicator is a tool to assist in analysis and should not be solely relied upon for making trading decisions. Always conduct thorough research and consider seeking advice from financial professionals before engaging in trading activities.
Weekly H/L DOTWThe Weekly High/Low Day Breakdown indicator provides a detailed statistical analysis of the days of the week (Monday to Sunday) on which weekly highs and lows occur for a given timeframe. It helps traders identify recurring patterns, correlations, and tendencies in price behavior across different days of the week. This can assist in planning trading strategies by leveraging day-specific patterns.
The indicator visually displays the statistical distribution of weekly highs and lows in an easy-to-read tabular format on your chart. Users can customize how the data is displayed, including whether the table is horizontal or vertical, the size of the text, and the position of the table on the chart.
Key Features:
Weekly Highs and Lows Identification:
Tracks the highest and lowest price of each trading week.
Records the day of the week on which these events occur.
Customizable Table Layout:
Option to display the table horizontally or vertically.
Text size can be adjusted (Small, Normal, or Large).
Table position is customizable (top-right, top-left, bottom-right, or bottom-left of the chart).
Flexible Value Representation:
Allows the display of values as percentages or as occurrences.
Default setting is occurrences, but users can toggle to percentages as needed.
Day-Specific Display:
Option to hide Saturday or Sunday if these days are not relevant to your trading strategy.
Visible Date Range:
Users can define a start and end date for the analysis, focusing the results on a specific period of interest.
User-Friendly Interface:
The table dynamically updates based on the selected timeframe and visibility of the chart, ensuring the displayed data is always relevant to the current context.
Adaptable to Custom Needs:
Includes all-day names from Monday to Sunday, but allows for specific days to be excluded based on the user’s preferences.
Indicator Logic:
Data Collection:
The indicator collects daily high, low, day of the week, and time data from the selected ticker using the request.security() function with a daily timeframe ('D').
Weekly Tracking:
Tracks the start and end times of each week.
During each week, it monitors the highest and lowest prices and the days they occurred.
Weekly Closure:
When a week ends (detected by Sunday’s daily candle), the indicator:
Updates the statistics for the respective days of the week where the weekly high and low occurred.
Resets tracking variables for the next week.
Visible Range Filter:
Only processes data for weeks that fall within the visible range of the chart, ensuring the table reflects only the visible portion of the chart.
Statistical Calculations:
Counts the number of weekly highs and lows for each day.
Calculates percentages relative to the total number of weeks in the visible range.
Dynamic Table Display:
Depending on user preferences, displays the data either horizontally or vertically.
Formats the table with proper alignment, colors, and text sizes for easy readability.
Custom Value Representation:
If set to "percentages," displays the percentage of weeks a high/low occurred on each day.
If set to "occurrences," displays the raw count of weekly highs/lows for each day.
Input Parameters:
High Text Color:
Color for the text in the "Weekly High" row or column.
Low Text Color:
Color for the text in the "Weekly Low" row or column.
High Background Color:
Background color for the "Weekly High" row or column.
Low Background Color:
Background color for the "Weekly Low" row or column.
Table Background Color:
General background color for the table.
Hide Saturday:
Option to exclude Saturday from the analysis and table.
Hide Sunday:
Option to exclude Sunday from the analysis and table.
Values Format:
Dropdown menu to select "percentages" or "occurrences."
Default value: "occurrences."
Table Position:
Dropdown menu to select the table position on the chart: "top_right," "top_left," "bottom_right," "bottom_left."
Default value: "top_right."
Text Size:
Dropdown menu to select text size: "Small," "Normal," "Large."
Default value: "Normal."
Vertical Table Format:
Checkbox to toggle the table layout:
Checked: Table displays days vertically, with Monday at the top.
Unchecked: Table displays days horizontally.
Start Date:
Allows users to specify the starting date for the analysis.
End Date:
Allows users to specify the ending date for the analysis.
Use Cases:
Day-Specific Pattern Recognition:
Identify if specific days, such as Monday or Friday, are more likely to form weekly highs or lows.
Seasonal Analysis:
Use the start and end date filters to analyze patterns during specific trading seasons.
Strategy Development:
Plan day-based entry and exit strategies by identifying recurring patterns in weekly highs/lows.
Historical Review:
Study historical data to understand how market behavior has changed over time.
TradingView TOS Compliance Notes:
Originality:
This script is uniquely designed to provide day-based statistics for weekly highs and lows, which is not a common feature in other publicly available indicators.
Usefulness:
Offers practical insights for traders interested in understanding day-specific price behavior.
Detailed Description:
Fully explains the purpose, features, logic, input settings, and use cases of the indicator.
Includes clear and concise details on how each input works.
Clear Input Descriptions:
All input parameters are clearly named and explained in the script and this description.
No Redundant Functionality:
Focused specifically on tracking weekly highs and lows, ensuring the indicator serves a distinct purpose without unnecessary features.
Exposure Oscillator (Cumulative 0 to ±100%)
Exposure Oscillator (Cumulative 0 to ±100%)
This Pine Script indicator plots an "Exposure Oscillator" on the chart, which tracks the cumulative market exposure from a range of technical buy and sell signals. The exposure is measured on a scale from -100% (maximum short exposure) to +100% (maximum long exposure), helping traders assess the strength of their position in the market. It provides an intuitive visual cue to aid decision-making for trend-following strategies.
Buy Signals (Increase Exposure Score by +10%)
Buy Signal 1 (Cross Above 21 EMA):
This signal is triggered when the price crosses above the 21-period Exponential Moving Average (EMA), where the current bar closes above the EMA21, and the previous bar closed below the EMA21. This indicates a potential upward price movement as the market shifts into a bullish trend.
buySignal1 = ta.crossover(close, ema21)
Buy Signal 2 (Trending Above 21 EMA):
This signal is triggered when the price closes above the 21-period EMA for each of the last 5 bars, indicating a sustained bullish trend. It confirms that the price is consistently above the EMA21 for a significant period.
buySignal2 = ta.barssince(close <= ema21) > 5
Buy Signal 3 (Living Above 21 EMA):
This signal is triggered when the price has closed above the 21-period EMA for each of the last 15 bars, demonstrating a strong, prolonged uptrend.
buySignal3 = ta.barssince(close <= ema21) > 15
Buy Signal 4 (Cross Above 50 SMA):
This signal is triggered when the price crosses above the 50-period Simple Moving Average (SMA), where the current bar closes above the 50 SMA, and the previous bar closed below it. It indicates a shift toward bullish momentum.
buySignal4 = ta.crossover(close, sma50)
Buy Signal 5 (Cross Above 200 SMA):
This signal is triggered when the price crosses above the 200-period Simple Moving Average (SMA), where the current bar closes above the 200 SMA, and the previous bar closed below it. This suggests a long-term bullish trend.
buySignal5 = ta.crossover(close, sma200)
Buy Signal 6 (Low Above 50 SMA):
This signal is true when the lowest price of the current bar is above the 50-period SMA, indicating strong bullish pressure as the price maintains itself above the moving average.
buySignal6 = low > sma50
Buy Signal 7 (Accumulation Day):
An accumulation day occurs when the closing price is in the upper half of the daily range (greater than 50%) and the volume is larger than the previous bar's volume, suggesting buying pressure and accumulation.
buySignal7 = (close - low) / (high - low) > 0.5 and volume > volume
Buy Signal 8 (Higher High):
This signal occurs when the current bar’s high exceeds the highest high of the previous 14 bars, indicating a breakout or strong upward momentum.
buySignal8 = high > ta.highest(high, 14)
Buy Signal 9 (Key Reversal Bar):
This signal is generated when the stock opens below the low of the previous bar but rallies to close above the previous bar’s high, signaling a potential reversal from bearish to bullish.
buySignal9 = open < low and close > high
Buy Signal 10 (Distribution Day Fall Off):
This signal is triggered when a distribution day (a day with high volume and a close near the low of the range) "falls off" the rolling 25-bar period, indicating the end of a bearish trend or selling pressure.
buySignal10 = ta.barssince(close < sma50 and close < sma50) > 25
Sell Signals (Decrease Exposure Score by -10%)
Sell Signal 1 (Cross Below 21 EMA):
This signal is triggered when the price crosses below the 21-period Exponential Moving Average (EMA), where the current bar closes below the EMA21, and the previous bar closed above it. It suggests that the market may be shifting from a bullish trend to a bearish trend.
sellSignal1 = ta.crossunder(close, ema21)
Sell Signal 2 (Trending Below 21 EMA):
This signal is triggered when the price closes below the 21-period EMA for each of the last 5 bars, indicating a sustained bearish trend.
sellSignal2 = ta.barssince(close >= ema21) > 5
Sell Signal 3 (Living Below 21 EMA):
This signal is triggered when the price has closed below the 21-period EMA for each of the last 15 bars, suggesting a strong downtrend.
sellSignal3 = ta.barssince(close >= ema21) > 15
Sell Signal 4 (Cross Below 50 SMA):
This signal is triggered when the price crosses below the 50-period Simple Moving Average (SMA), where the current bar closes below the 50 SMA, and the previous bar closed above it. It indicates the start of a bearish trend.
sellSignal4 = ta.crossunder(close, sma50)
Sell Signal 5 (Cross Below 200 SMA):
This signal is triggered when the price crosses below the 200-period Simple Moving Average (SMA), where the current bar closes below the 200 SMA, and the previous bar closed above it. It indicates a long-term bearish trend.
sellSignal5 = ta.crossunder(close, sma200)
Sell Signal 6 (High Below 50 SMA):
This signal is true when the highest price of the current bar is below the 50-period SMA, indicating weak bullishness or a potential bearish reversal.
sellSignal6 = high < sma50
Sell Signal 7 (Distribution Day):
A distribution day is identified when the closing range of a bar is less than 50% and the volume is larger than the previous bar's volume, suggesting that selling pressure is increasing.
sellSignal7 = (close - low) / (high - low) < 0.5 and volume > volume
Sell Signal 8 (Lower Low):
This signal occurs when the current bar's low is less than the lowest low of the previous 14 bars, indicating a breakdown or strong downward momentum.
sellSignal8 = low < ta.lowest(low, 14)
Sell Signal 9 (Downside Reversal Bar):
A downside reversal bar occurs when the stock opens above the previous bar's high but falls to close below the previous bar’s low, signaling a reversal from bullish to bearish.
sellSignal9 = open > high and close < low
Sell Signal 10 (Distribution Cluster):
This signal is triggered when a distribution day occurs three times in the rolling 7-bar period, indicating significant selling pressure.
sellSignal10 = ta.valuewhen((close < low) and volume > volume , 1, 7) >= 3
Theme Mode:
Users can select the theme mode (Auto, Dark, or Light) to match the chart's background or to manually choose a light or dark theme for the oscillator's appearance.
Exposure Score Calculation: The script calculates a cumulative exposure score based on a series of buy and sell signals.
Buy signals increase the exposure score, while sell signals decrease it. Each signal impacts the score by ±10%.
Signal Conditions: The buy and sell signals are derived from multiple conditions, including crossovers with moving averages (EMA21, SMA50, SMA200), trend behavior, and price/volume analysis.
Oscillator Visualization: The exposure score is visualized as a line on the chart, changing color based on whether the exposure is positive (long position) or negative (short position). It is limited to the range of -100% to +100%.
Position Type: The indicator also indicates the position type based on the exposure score, labeling it as "Long," "Short," or "Neutral."
Horizontal Lines: Reference lines at 0%, 100%, and -100% visually mark neutral, increasing long, and increasing short exposure levels.
Exposure Table: A table displays the current exposure level (in percentage) and position type ("Long," "Short," or "Neutral"), updated dynamically based on the oscillator’s value.
Inputs:
Theme Mode: Choose "Auto" to use the default chart theme, or manually select "Dark" or "Light."
Usage:
This oscillator is designed to help traders track market sentiment, gauge exposure levels, and manage risk. It can be used for long-term trend-following strategies or short-term trades based on moving average crossovers and volume analysis.
The oscillator operates in conjunction with the chart’s price action and provides a visual representation of the market’s current trend strength and exposure.
Important Considerations:
Risk Management: While the exposure score provides valuable insight, it should be combined with other risk management tools and analysis for optimal trading decisions.
Signal Sensitivity: The accuracy and effectiveness of the signals depend on market conditions and may require adjustments based on the user’s trading strategy or timeframe.
Disclaimer:
This script is for educational purposes only. Trading involves significant risk, and users should carefully evaluate all market conditions and apply appropriate risk management strategies before using this tool in live trading environments.
