Market Structure & Liquidity: CHoCHs+Nested Pivots+FVGs+Sweeps//Purpose:
This indicator combines several tools to help traders track and interpret price action/market structure; It can be divided into 4 parts;
1. CHoCHs, 2. Nested Pivot highs & lows, 3. Grade sweeps, 4. FVGs.
This gives the trader a toolkit for determining market structure and shifts in market structure to help determine a bull or bear bias, whether it be short-term, med-term or long-term.
This indicator also helps traders in determining liquidity targets: wether they be voids/gaps (FVGS) or old highs/lows+ typical sweep distances.
Finally, the incorporation of HTF CHoCH levels printing on your LTF chart helps keep the bigger picture in mind and tells traders at a glance if they're above of below Custom HTF CHoCH up or CHoCH down (these HTF CHoCHs can be anything from Hourly up to Monthly).
//Nomenclature:
CHoCH = Change of Character
STH/STL = short-term high or low
MTH/MTL = medium-term high or low
LTH/LTL = long-term high or low
FVG = Fair value gap
CE = consequent encroachement (the midline of a FVG)
~~~ The Four components of this indicator ~~~
1. CHoCHs:
•Best demonstrated in the below charts. This was a method taught to me by @Icecold_crypto. Once a 3 bar fractal pivot gets broken, we count backwards the consecutive higher lows or lower highs, then identify the CHoCH as the opposite end of the candle which ended the consecutive backwards count. This CHoCH (UP or DOWN) then becomes a level to watch, if price passes through it in earnest a trader would consider shifting their bias as market structure is deemed to have shifted.
•HTF CHoCHs: Option to print Higher time frame chochs (default on) of user input HTF. This prints only the last UP choch and only the last DOWN choch from the input HTF. Solid line by default so as to distinguish from local/chart-time CHoCHs. Can be any Higher timeframe you like.
•Show on table: toggle on show table(above/below) option to show in table cells (top right): is price above the latest HTF UP choch, or is price below HTF DOWN choch (or is it sat between the two, in a state of 'uncertainty').
•Most recent CHoCHs which have not been met by price will extend 10 bars into the future.
• USER INPUTS: overall setting: SHOW CHOCHS | Set bars lookback number to limit historical Chochs. Set Live CHoCHs number to control the number of active recent chochs unmet by price. Toggle shrink chochs once hit to declutter chart and minimize old chochs to their origin bars. Set Multi-timeframe color override : to make Color choices auto-set to your preference color for each of 1m, 5m, 15m, H, 4H, D, W, M (where up and down are same color, but 'up' icon for up chochs and down icon for down chochs remain printing as normal)
2. Nested Pivot Highs & Lows; aka 'Pivot Highs & Lows (ST/MT/LT)'
•Based on a seperate, longer lookback/lookforward pivot calculation. Identifies Pivot highs and lows with a 'spikeyness' filter (filtering out weak/rounded/unimpressive Pivot highs/lows)
•by 'nested' I mean that the pivot highs are graded based on whether a pivot high sits between two lower pivot highs or vice versa.
--for example: STH = normal pivot. MTH is pivot high with a lower STH on either side. LTH is a pivot high with a lower MTH on either side. Same applies to pivot lows (STL/MTL/LTL)
•This is a useful way to measure the significance of a high or low. Both in terms of how much it might be typically swept by (see later) and what it would imply for HTF bias were we to break through it in earnest (more than just a sweep).
• USER INPUTS: overall setting: show pivot highs & lows | Bars lookback (historical pivots to show) | Pivots: lookback/lookforward length (determines the scale of your pivot highs/lows) | toggle on/off Apply 'Spikeyness' filter (filters out smooth/unimpressive pivot highs/lows). Set Spikeyness index (determines the strength of this filter if turned on) | Individually toggle on each of STH, MTH, LTH, STL, MTL, LTL along with their label text type , and size . Toggle on/off line for each of these Pivot highs/lows. | Set label spacer (atr multiples above / below) | set line style and line width
3. Grade Sweeps:
•These are directly related to the nested pivots described above. Most assets will have a typical sweep distance. I've added some of my expected sweeps for various assets in the indicator tooltips.
--i.e. Eur/Usd 10-20-30 pips is a typical 'grade' sweep. S&P HKEX:5 - HKEX:10 is a typical grade sweep.
•Each of the ST/MT/LT pivot highs and lows have optional user defined grade sweep boxes which paint above until filled (or user option for historical filled boxes to remain).
•Numbers entered into sweep input boxes are auto converted into appropriate units (i.e. pips for FX, $ or 'handles' for indices, $ for Crypto. Very low $ units can be input for low unit value crypto altcoins.
• USER INPUTS: overall setting: Show sweep boxes | individually select colors of each of STH, MTH, LTH, STL, MTL, LTL sweep boxes. | Set Grade sweep ($/pips) number for each of ST, MT, LT. This auto converts between pips and $ (i.e. FX vs Indices/Crypto). Can be a float as small or large as you like ($0.000001 to HKEX:1000 ). | Set box text position (horizontal & vertical) and size , and color . | Set Box width (bars) (for non extended/ non-auto-terminating at price boxes). | toggle on/off Extend boxes/lines right . | Toggle on/off Shrink Grade sweeps on fill (they will disappear in realtime when filled/passed through)
4. FVGs:
•Fair Value gaps. Represent 'naked' candle bodies where the wicks to either side do not meet, forming a 'gap' of sorts which has a tendency to fill, or at least to fill to midline (CE).
•These are ICT concepts. 'UP' FVGS are known as BISIs (Buyside imbalance, sellside inefficiency); 'DOWN' FVGs are known as SIBIs (Sellside imbalance, buyside inefficiency).
• USER INPUTS: overall setting: show FVGs | Bars lookback (history). | Choose to display: 'UP' FVGs (BISI) and/or 'DOWN FVGs (SIBI) . Choose to display the midline: CE , the color and the line style . Choose threshold: use CE (as opposed to Full Fill) |toggle on/off Shrink FVG on fill (CE hit or Full fill) (declutter chart/see backtesting history)
////••Alerts (general notes & cautionary notes)::
•Alerts are optional for most of the levels printed by this indicator. Set them via the three dots on indicator status line.
•Due to dynamic repainting of levels, alerts should be used with caution. Best use these alerts either for Higher time frame levels, or when closely monitoring price.
--E.g. You may set an alert for down-fill of the latest FVG below; but price will keep marching up; form a newer/higher FVG, and the alert will trigger on THAT FVG being down-filled (not the original)
•Available Alerts:
-FVG(BISI) cross above threshold(CE or full-fill; user choice). Same with FVG(SIBI).
-HTF last CHoCH down, cross below | HTF last CHoCH up, cross above.
-last CHoCH down, cross below | last CHoCH up, cross above.
-LTH cross above, MTH cross above, STH cross above | LTL cross below, MTL cross below, STL cross below.
////••Formatting (general)::
•all table text color is set from the 'Pivot highs & Lows (ST, MT, LT)' section (for those of you who prefer black backgrounds).
•User choice of Line-style, line color, line width. Same with Boxes. Icon choice for chochs. Char or label text choices for ST/MT/LT pivot highs & lows.
////••User Inputs (general):
•Each of the 4 components of this indicator can be easily toggled on/off independently.
•Quite a lot of options and toggle boxes, as described in full above. Please take your time and read through all the tooltips (hover over '!' icon) to get an idea of formatting options.
•Several Lookback periods defined in bars to control how much history is shown for each of the 4 components of this indicator.
•'Shrink on fill' settings on FVGs and CHoCHs: Basically a way to declutter chart; toggle on/off depending on if you're backtesting or reading live price action.
•Table Display: applies to ST/MT/LT pivot highs and to HTF CHoCHs; Toggle table on or off (in part or in full)
////••Credits:
•Credit to ICT (Inner Circle Trader) for some of the concepts used in this indicator (FVGS & CEs; Grade sweeps).
•Credit to @Icecold_crypto for the specific and novel concept of identifying CHoCHs in a simple, objective and effective manner (as demonstrated in the 1st chart below).
CHoCH demo page 1: shifting tweak; arrow diagrams to demonstrate how CHoCHs are defined:
CHoCH demo page 2: Simplified view; short lookback history; few CHoCHs, demo of 'latest' choch being extended into the future by 10 bars:
USAGE: Bitcoin Hourly using HTF daily CHoCHs:
USAGE-2: Cotton Futures (CT1!) 2hr. Painting a rather bullish picture. Above HTF UP CHoCH, Local CHoCHs show bullish order flow, Nice targets above (MTH/LTH + grade sweeps):
Full Demo; 5min chart; CHoCHs, Short term pivot highs/lows, grade sweeps, FVGs:
Full Demo, Eur/Usd 15m: STH, MTH, LTH grade sweeps, CHoCHs, Usage for finding bias (part A):
Full Demo, Eur/Usd 15m: STH, MTH, LTH grade sweeps, CHoCHs, Usage for finding bias, 3hrs later (part B):
Realtime Vs Backtesting(A): btc/usd 15m; FVGs and CHoCHs: shrink on fill, once filled they repaint discreetly on their origin bar only. Realtime (Shrink on fill, declutter chart):
Realtime Vs Backtesting(B): btc/usd 15m; FVGs and CHoCHs: DON'T shrink on fill; they extend to the point where price crosses them, and fix/paint there. Backtesting (seeing historical behaviour):
Cari dalam skrip untuk "low"
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.
Failed Breakdown Detection'Failed Breakdowns' are a popular set up for long entries.
In short, the set up requires:
1) A significant low is made ('initial low')
2) Initial low is undercut with a new low
3) Price action then 'reclaims' the initial low by moving +8-10 points from the initial low
This script aims at detecting such set ups. It was coded with the ES Futures 15 minute chart in mind but may be useful on other instruments and time frames.
Business Logic:
1) Uses pivot lows to detect 'significant' initial lows
2) Uses amplitude threshold to detect a new low beneath the initial low; used /u/ben_zen script for this
3) Looks for a valid reclaim - a green candle that occurs within 10 bars of the new low
4) Price must reclaim at least 8 points for the set up to be valid
5) If a signal is detected, the initial low value (pivot low) is stored in array that prevents duplicate signals from being generated.
6) FBD Signal is plotted on the chart with "X"
7) Pivot low detection is plotted on the chart with "P" and a label
8) New lows are plotted on the chart with a blue triangle
Notes:
User input
- My preference is to use the defaults as is, but as always feel free to experiment
- Can modify pivot length but in my experience 10/10 work best for pivot lows
- New low detection - 55 bars and 0.05 amplitude work well based on visual checks of signals
- Can modify the number of points needed to reclaim a low, and the # of bars limit under which this must occur.
Alerts:
- Alerts are available for detection of new lows and detection of failed breakdowns
- Alerts are also available for these signals but only during 7:30PM-4PM EST - 'prime time' US trading hours
Limitations:
- Current version of the script only compares new lows to the most recent pivot low, does not look at anything prior to that
- Best used as a discretionary signal
Visit /u/ben_zen's Profile:
www.tradingview.com
Profile Link www.tradingview.com
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!
ICT PDA - Gold & BTC (QuickScalp Bias/FVG/OB/OTE + Alerts)What this script does
This indicator implements a complete ICT Price Delivery Algorithm (PDA) workflow tailored for XAUUSD and BTCUSD. It combines HTF bias, OTE zones, Fair Value Gaps, Order Blocks, micro-BOS confirmation, and liquidity references into a single, cohesive tool with early and final alerts. The script is not a mashup for cosmetic plotting; each component feeds the next decision step.
Why this is original/useful
Symbol-aware impulse filter: A dynamic displacement threshold kTune adapts to Gold/BTC volatility (body/ATR vs. per-symbol factor), reducing noise on fast markets without hiding signals.
Scalping preset: “Quick Clean” mode limits drawings to the most recent bars and keeps only the latest FVG/OB zones for a clear chart.
Three display modes: Full, Clean, and Signals-Only to match analysis vs. execution.
Actionable alerts: Early heads-up when price enters OTE in the HTF bias direction, and Final alerts once mitigation + micro-break confirm the setup.
How it works (high-level logic)
HTF Bias: Uses request.security() on a user-selected timeframe (e.g., 240m) and EMA filter. Bias = close above/below HTF EMA.
Dealing Range & OTE: Recent swing high/low (pivot length configurable) define the range; OTE (62–79%) boxes are drawn contextually for up/down ranges.
Displacement: A candle’s body/ATR must exceed kTune and break short-term structure (displacement up/down).
FVG: 3-bar imbalance (bull: low > high ; bear: high < low ). Latest gaps are tracked and extended.
Order Blocks: Last opposite candle prior to a qualifying displacement that breaks recent highs/lows; zones are drawn and extended.
Entry & Alerts:
Long: Bullish bias + price inside buy-OTE + mitigation of a bullish FVG or OB + micro BOS up → “PDA Long (Final)”.
Short: Bearish bias + price inside sell-OTE + mitigation of a bearish FVG or OB + micro BOS down → “PDA Short (Final)”.
Early Alerts: Trigger as soon as price enters OTE in the direction of the active bias.
Inputs & controls (key ones)
Bias (HTF): timeframe minutes, EMA length.
Structure: ATR length, Impulse Threshold (Body/ATR), swing pivot length, OB look-back.
OTE/FVG/OB/LP toggles: show/hide components.
Auto-Tune: per-symbol factors for Gold/BTC + manual tweak.
Display/Performance: View Mode, keep-N latest FVG/OB, limit drawings to last N bars.
Recommended usage (scalping)
Timeframes: Execute on M1–M5 with HTF bias from 120–240m.
Defaults (starting point): ATR=14, Impulse Threshold≈1.6; Gold factor≈1.05, BTC factor≈0.90; Keep FVG/OB=2; last 200–300 bars; View Mode=Clean.
Workflow: Wait for OTE in bias direction → see mitigation (FVG/OB) → confirm with micro BOS → manage risk to nearest liquidity (prev-day H/L or recent swing).
Alerts available
“PDA Early Long/Short”
“PDA Long (Final)” / “PDA Short (Final)”
Attach alerts on “Any alert() function call” or the listed conditions.
Chart & screenshots
Please include symbol and timeframe on screenshots. The on-chart HUD shows the script name and state to help reviewers understand context.
Limitations / notes
This is a discretionary framework. Signals can cluster during news or extreme volatility; use your own risk management. No guarantee of profitability.
Changelog (brief)
v1.2 QuickScalp: added Quick Clean preset, safer array handling, symbol-aware impulse tuning, display modes.
------------------------------
ملخص عربي:
المؤشر يطبق تسلسل PDA عملي للذهب والبتكوين: تحيز من فريم أعلى، مناطق OTE، فجوات FVG، بلوكات أوامر OB، وتأكيد micro-BOS، مع تنبيهات مبكرة ونهائية. تمت إضافة وضع “Quick Clean” لتقليل العناصر على الشارت وحساسية إزاحة تتكيّف مع الأصل. للاستخدام كسكالب: نفّذ على M1–M5 مع تحيز 120–240 دقيقة، وابدأ من الإعدادات المقترحة بالأعلى. هذا إطار سلوكي وليس توصية مالية.
Dynamic Equity Allocation Model"Cash is Trash"? Not Always. Here's Why Science Beats Guesswork.
Every retail trader knows the frustration: you draw support and resistance lines, you spot patterns, you follow market gurus on social media—and still, when the next bear market hits, your portfolio bleeds red. Meanwhile, institutional investors seem to navigate market turbulence with ease, preserving capital when markets crash and participating when they rally. What's their secret?
The answer isn't insider information or access to exotic derivatives. It's systematic, scientifically validated decision-making. While most retail traders rely on subjective chart analysis and emotional reactions, professional portfolio managers use quantitative models that remove emotion from the equation and process multiple streams of market information simultaneously.
This document presents exactly such a system—not a proprietary black box available only to hedge funds, but a fully transparent, academically grounded framework that any serious investor can understand and apply. The Dynamic Equity Allocation Model (DEAM) synthesizes decades of financial research from Nobel laureates and leading academics into a practical tool for tactical asset allocation.
Stop drawing colorful lines on your chart and start thinking like a quant. This isn't about predicting where the market goes next week—it's about systematically adjusting your risk exposure based on what the data actually tells you. When valuations scream danger, when volatility spikes, when credit markets freeze, when multiple warning signals align—that's when cash isn't trash. That's when cash saves your portfolio.
The irony of "cash is trash" rhetoric is that it ignores timing. Yes, being 100% cash for decades would be disastrous. But being 100% equities through every crisis is equally foolish. The sophisticated approach is dynamic: aggressive when conditions favor risk-taking, defensive when they don't. This model shows you how to make that decision systematically, not emotionally.
Whether you're managing your own retirement portfolio or seeking to understand how institutional allocation strategies work, this comprehensive analysis provides the theoretical foundation, mathematical implementation, and practical guidance to elevate your investment approach from amateur to professional.
The choice is yours: keep hoping your chart patterns work out, or start using the same quantitative methods that professionals rely on. The tools are here. The research is cited. The methodology is explained. All you need to do is read, understand, and apply.
The Dynamic Equity Allocation Model (DEAM) is a quantitative framework for systematic allocation between equities and cash, grounded in modern portfolio theory and empirical market research. The model integrates five scientifically validated dimensions of market analysis—market regime, risk metrics, valuation, sentiment, and macroeconomic conditions—to generate dynamic allocation recommendations ranging from 0% to 100% equity exposure. This work documents the theoretical foundations, mathematical implementation, and practical application of this multi-factor approach.
1. Introduction and Theoretical Background
1.1 The Limitations of Static Portfolio Allocation
Traditional portfolio theory, as formulated by Markowitz (1952) in his seminal work "Portfolio Selection," assumes an optimal static allocation where investors distribute their wealth across asset classes according to their risk aversion. This approach rests on the assumption that returns and risks remain constant over time. However, empirical research demonstrates that this assumption does not hold in reality. Fama and French (1989) showed that expected returns vary over time and correlate with macroeconomic variables such as the spread between long-term and short-term interest rates. Campbell and Shiller (1988) demonstrated that the price-earnings ratio possesses predictive power for future stock returns, providing a foundation for dynamic allocation strategies.
The academic literature on tactical asset allocation has evolved considerably over recent decades. Ilmanen (2011) argues in "Expected Returns" that investors can improve their risk-adjusted returns by considering valuation levels, business cycles, and market sentiment. The Dynamic Equity Allocation Model presented here builds on this research tradition and operationalizes these insights into a practically applicable allocation framework.
1.2 Multi-Factor Approaches in Asset Allocation
Modern financial research has shown that different factors capture distinct aspects of market dynamics and together provide a more robust picture of market conditions than individual indicators. Ross (1976) developed the Arbitrage Pricing Theory, a model that employs multiple factors to explain security returns. Following this multi-factor philosophy, DEAM integrates five complementary analytical dimensions, each tapping different information sources and collectively enabling comprehensive market understanding.
2. Data Foundation and Data Quality
2.1 Data Sources Used
The model draws its data exclusively from publicly available market data via the TradingView platform. This transparency and accessibility is a significant advantage over proprietary models that rely on non-public data. The data foundation encompasses several categories of market information, each capturing specific aspects of market dynamics.
First, price data for the S&P 500 Index is obtained through the SPDR S&P 500 ETF (ticker: SPY). The use of a highly liquid ETF instead of the index itself has practical reasons, as ETF data is available in real-time and reflects actual tradability. In addition to closing prices, high, low, and volume data are captured, which are required for calculating advanced volatility measures.
Fundamental corporate metrics are retrieved via TradingView's Financial Data API. These include earnings per share, price-to-earnings ratio, return on equity, debt-to-equity ratio, dividend yield, and share buyback yield. Cochrane (2011) emphasizes in "Presidential Address: Discount Rates" the central importance of valuation metrics for forecasting future returns, making these fundamental data a cornerstone of the model.
Volatility indicators are represented by the CBOE Volatility Index (VIX) and related metrics. The VIX, often referred to as the market's "fear gauge," measures the implied volatility of S&P 500 index options and serves as a proxy for market participants' risk perception. Whaley (2000) describes in "The Investor Fear Gauge" the construction and interpretation of the VIX and its use as a sentiment indicator.
Macroeconomic data includes yield curve information through US Treasury bonds of various maturities and credit risk premiums through the spread between high-yield bonds and risk-free government bonds. These variables capture the macroeconomic conditions and financing conditions relevant for equity valuation. Estrella and Hardouvelis (1991) showed that the shape of the yield curve has predictive power for future economic activity, justifying the inclusion of these data.
2.2 Handling Missing Data
A practical problem when working with financial data is dealing with missing or unavailable values. The model implements a fallback system where a plausible historical average value is stored for each fundamental metric. When current data is unavailable for a specific point in time, this fallback value is used. This approach ensures that the model remains functional even during temporary data outages and avoids systematic biases from missing data. The use of average values as fallback is conservative, as it generates neither overly optimistic nor pessimistic signals.
3. Component 1: Market Regime Detection
3.1 The Concept of Market Regimes
The idea that financial markets exist in different "regimes" or states that differ in their statistical properties has a long tradition in financial science. Hamilton (1989) developed regime-switching models that allow distinguishing between different market states with different return and volatility characteristics. The practical application of this theory consists of identifying the current market state and adjusting portfolio allocation accordingly.
DEAM classifies market regimes using a scoring system that considers three main dimensions: trend strength, volatility level, and drawdown depth. This multidimensional view is more robust than focusing on individual indicators, as it captures various facets of market dynamics. Classification occurs into six distinct regimes: Strong Bull, Bull Market, Neutral, Correction, Bear Market, and Crisis.
3.2 Trend Analysis Through Moving Averages
Moving averages are among the oldest and most widely used technical indicators and have also received attention in academic literature. Brock, Lakonishok, and LeBaron (1992) examined in "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns" the profitability of trading rules based on moving averages and found evidence for their predictive power, although later studies questioned the robustness of these results when considering transaction costs.
The model calculates three moving averages with different time windows: a 20-day average (approximately one trading month), a 50-day average (approximately one quarter), and a 200-day average (approximately one trading year). The relationship of the current price to these averages and the relationship of the averages to each other provide information about trend strength and direction. When the price trades above all three averages and the short-term average is above the long-term, this indicates an established uptrend. The model assigns points based on these constellations, with longer-term trends weighted more heavily as they are considered more persistent.
3.3 Volatility Regimes
Volatility, understood as the standard deviation of returns, is a central concept of financial theory and serves as the primary risk measure. However, research has shown that volatility is not constant but changes over time and occurs in clusters—a phenomenon first documented by Mandelbrot (1963) and later formalized through ARCH and GARCH models (Engle, 1982; Bollerslev, 1986).
DEAM calculates volatility not only through the classic method of return standard deviation but also uses more advanced estimators such as the Parkinson estimator and the Garman-Klass estimator. These methods utilize intraday information (high and low prices) and are more efficient than simple close-to-close volatility estimators. The Parkinson estimator (Parkinson, 1980) uses the range between high and low of a trading day and is based on the recognition that this information reveals more about true volatility than just the closing price difference. The Garman-Klass estimator (Garman and Klass, 1980) extends this approach by additionally considering opening and closing prices.
The calculated volatility is annualized by multiplying it by the square root of 252 (the average number of trading days per year), enabling standardized comparability. The model compares current volatility with the VIX, the implied volatility from option prices. A low VIX (below 15) signals market comfort and increases the regime score, while a high VIX (above 35) indicates market stress and reduces the score. This interpretation follows the empirical observation that elevated volatility is typically associated with falling markets (Schwert, 1989).
3.4 Drawdown Analysis
A drawdown refers to the percentage decline from the highest point (peak) to the lowest point (trough) during a specific period. This metric is psychologically significant for investors as it represents the maximum loss experienced. Calmar (1991) developed the Calmar Ratio, which relates return to maximum drawdown, underscoring the practical relevance of this metric.
The model calculates current drawdown as the percentage distance from the highest price of the last 252 trading days (one year). A drawdown below 3% is considered negligible and maximally increases the regime score. As drawdown increases, the score decreases progressively, with drawdowns above 20% classified as severe and indicating a crisis or bear market regime. These thresholds are empirically motivated by historical market cycles, in which corrections typically encompassed 5-10% drawdowns, bear markets 20-30%, and crises over 30%.
3.5 Regime Classification
Final regime classification occurs through aggregation of scores from trend (40% weight), volatility (30%), and drawdown (30%). The higher weighting of trend reflects the empirical observation that trend-following strategies have historically delivered robust results (Moskowitz, Ooi, and Pedersen, 2012). A total score above 80 signals a strong bull market with established uptrend, low volatility, and minimal losses. At a score below 10, a crisis situation exists requiring defensive positioning. The six regime categories enable a differentiated allocation strategy that not only distinguishes binarily between bullish and bearish but allows gradual gradations.
4. Component 2: Risk-Based Allocation
4.1 Volatility Targeting as Risk Management Approach
The concept of volatility targeting is based on the idea that investors should maximize not returns but risk-adjusted returns. Sharpe (1966, 1994) defined with the Sharpe Ratio the fundamental concept of return per unit of risk, measured as volatility. Volatility targeting goes a step further and adjusts portfolio allocation to achieve constant target volatility. This means that in times of low market volatility, equity allocation is increased, and in times of high volatility, it is reduced.
Moreira and Muir (2017) showed in "Volatility-Managed Portfolios" that strategies that adjust their exposure based on volatility forecasts achieve higher Sharpe Ratios than passive buy-and-hold strategies. DEAM implements this principle by defining a target portfolio volatility (default 12% annualized) and adjusting equity allocation to achieve it. The mathematical foundation is simple: if market volatility is 20% and target volatility is 12%, equity allocation should be 60% (12/20 = 0.6), with the remaining 40% held in cash with zero volatility.
4.2 Market Volatility Calculation
Estimating current market volatility is central to the risk-based allocation approach. The model uses several volatility estimators in parallel and selects the higher value between traditional close-to-close volatility and the Parkinson estimator. This conservative choice ensures the model does not underestimate true volatility, which could lead to excessive risk exposure.
Traditional volatility calculation uses logarithmic returns, as these have mathematically advantageous properties (additive linkage over multiple periods). The logarithmic return is calculated as ln(P_t / P_{t-1}), where P_t is the price at time t. The standard deviation of these returns over a rolling 20-trading-day window is then multiplied by √252 to obtain annualized volatility. This annualization is based on the assumption of independently identically distributed returns, which is an idealization but widely accepted in practice.
The Parkinson estimator uses additional information from the trading range (High minus Low) of each day. The formula is: σ_P = (1/√(4ln2)) × √(1/n × Σln²(H_i/L_i)) × √252, where H_i and L_i are high and low prices. Under ideal conditions, this estimator is approximately five times more efficient than the close-to-close estimator (Parkinson, 1980), as it uses more information per observation.
4.3 Drawdown-Based Position Size Adjustment
In addition to volatility targeting, the model implements drawdown-based risk control. The logic is that deep market declines often signal further losses and therefore justify exposure reduction. This behavior corresponds with the concept of path-dependent risk tolerance: investors who have already suffered losses are typically less willing to take additional risk (Kahneman and Tversky, 1979).
The model defines a maximum portfolio drawdown as a target parameter (default 15%). Since portfolio volatility and portfolio drawdown are proportional to equity allocation (assuming cash has neither volatility nor drawdown), allocation-based control is possible. For example, if the market exhibits a 25% drawdown and target portfolio drawdown is 15%, equity allocation should be at most 60% (15/25).
4.4 Dynamic Risk Adjustment
An advanced feature of DEAM is dynamic adjustment of risk-based allocation through a feedback mechanism. The model continuously estimates what actual portfolio volatility and portfolio drawdown would result at the current allocation. If risk utilization (ratio of actual to target risk) exceeds 1.0, allocation is reduced by an adjustment factor that grows exponentially with overutilization. This implements a form of dynamic feedback that avoids overexposure.
Mathematically, a risk adjustment factor r_adjust is calculated: if risk utilization u > 1, then r_adjust = exp(-0.5 × (u - 1)). This exponential function ensures that moderate overutilization is gently corrected, while strong overutilization triggers drastic reductions. The factor 0.5 in the exponent was empirically calibrated to achieve a balanced ratio between sensitivity and stability.
5. Component 3: Valuation Analysis
5.1 Theoretical Foundations of Fundamental Valuation
DEAM's valuation component is based on the fundamental premise that the intrinsic value of a security is determined by its future cash flows and that deviations between market price and intrinsic value are eventually corrected. Graham and Dodd (1934) established in "Security Analysis" the basic principles of fundamental analysis that remain relevant today. Translated into modern portfolio context, this means that markets with high valuation metrics (high price-earnings ratios) should have lower expected returns than cheaply valued markets.
Campbell and Shiller (1988) developed the Cyclically Adjusted P/E Ratio (CAPE), which smooths earnings over a full business cycle. Their empirical analysis showed that this ratio has significant predictive power for 10-year returns. Asness, Moskowitz, and Pedersen (2013) demonstrated in "Value and Momentum Everywhere" that value effects exist not only in individual stocks but also in asset classes and markets.
5.2 Equity Risk Premium as Central Valuation Metric
The Equity Risk Premium (ERP) is defined as the expected excess return of stocks over risk-free government bonds. It is the theoretical heart of valuation analysis, as it represents the compensation investors demand for bearing equity risk. Damodaran (2012) discusses in "Equity Risk Premiums: Determinants, Estimation and Implications" various methods for ERP estimation.
DEAM calculates ERP not through a single method but combines four complementary approaches with different weights. This multi-method strategy increases estimation robustness and avoids dependence on single, potentially erroneous inputs.
The first method (35% weight) uses earnings yield, calculated as 1/P/E or directly from operating earnings data, and subtracts the 10-year Treasury yield. This method follows Fed Model logic (Yardeni, 2003), although this model has theoretical weaknesses as it does not consistently treat inflation (Asness, 2003).
The second method (30% weight) extends earnings yield by share buyback yield. Share buybacks are a form of capital return to shareholders and increase value per share. Boudoukh et al. (2007) showed in "The Total Shareholder Yield" that the sum of dividend yield and buyback yield is a better predictor of future returns than dividend yield alone.
The third method (20% weight) implements the Gordon Growth Model (Gordon, 1962), which models stock value as the sum of discounted future dividends. Under constant growth g assumption: Expected Return = Dividend Yield + g. The model estimates sustainable growth as g = ROE × (1 - Payout Ratio), where ROE is return on equity and payout ratio is the ratio of dividends to earnings. This formula follows from equity theory: unretained earnings are reinvested at ROE and generate additional earnings growth.
The fourth method (15% weight) combines total shareholder yield (Dividend + Buybacks) with implied growth derived from revenue growth. This method considers that companies with strong revenue growth should generate higher future earnings, even if current valuations do not yet fully reflect this.
The final ERP is the weighted average of these four methods. A high ERP (above 4%) signals attractive valuations and increases the valuation score to 95 out of 100 possible points. A negative ERP, where stocks have lower expected returns than bonds, results in a minimal score of 10.
5.3 Quality Adjustments to Valuation
Valuation metrics alone can be misleading if not interpreted in the context of company quality. A company with a low P/E may be cheap or fundamentally problematic. The model therefore implements quality adjustments based on growth, profitability, and capital structure.
Revenue growth above 10% annually adds 10 points to the valuation score, moderate growth above 5% adds 5 points. This adjustment reflects that growth has independent value (Modigliani and Miller, 1961, extended by later growth theory). Net margin above 15% signals pricing power and operational efficiency and increases the score by 5 points, while low margins below 8% indicate competitive pressure and subtract 5 points.
Return on equity (ROE) above 20% characterizes outstanding capital efficiency and increases the score by 5 points. Piotroski (2000) showed in "Value Investing: The Use of Historical Financial Statement Information" that fundamental quality signals such as high ROE can improve the performance of value strategies.
Capital structure is evaluated through the debt-to-equity ratio. A conservative ratio below 1.0 multiplies the valuation score by 1.2, while high leverage above 2.0 applies a multiplier of 0.8. This adjustment reflects that high debt constrains financial flexibility and can become problematic in crisis times (Korteweg, 2010).
6. Component 4: Sentiment Analysis
6.1 The Role of Sentiment in Financial Markets
Investor sentiment, defined as the collective psychological attitude of market participants, influences asset prices independently of fundamental data. Baker and Wurgler (2006, 2007) developed a sentiment index and showed that periods of high sentiment are followed by overvaluations that later correct. This insight justifies integrating a sentiment component into allocation decisions.
Sentiment is difficult to measure directly but can be proxied through market indicators. The VIX is the most widely used sentiment indicator, as it aggregates implied volatility from option prices. High VIX values reflect elevated uncertainty and risk aversion, while low values signal market comfort. Whaley (2009) refers to the VIX as the "Investor Fear Gauge" and documents its role as a contrarian indicator: extremely high values typically occur at market bottoms, while low values occur at tops.
6.2 VIX-Based Sentiment Assessment
DEAM uses statistical normalization of the VIX by calculating the Z-score: z = (VIX_current - VIX_average) / VIX_standard_deviation. The Z-score indicates how many standard deviations the current VIX is from the historical average. This approach is more robust than absolute thresholds, as it adapts to the average volatility level, which can vary over longer periods.
A Z-score below -1.5 (VIX is 1.5 standard deviations below average) signals exceptionally low risk perception and adds 40 points to the sentiment score. This may seem counterintuitive—shouldn't low fear be bullish? However, the logic follows the contrarian principle: when no one is afraid, everyone is already invested, and there is limited further upside potential (Zweig, 1973). Conversely, a Z-score above 1.5 (extreme fear) adds -40 points, reflecting market panic but simultaneously suggesting potential buying opportunities.
6.3 VIX Term Structure as Sentiment Signal
The VIX term structure provides additional sentiment information. Normally, the VIX trades in contango, meaning longer-term VIX futures have higher prices than short-term. This reflects that short-term volatility is currently known, while long-term volatility is more uncertain and carries a risk premium. The model compares the VIX with VIX9D (9-day volatility) and identifies backwardation (VIX > 1.05 × VIX9D) and steep backwardation (VIX > 1.15 × VIX9D).
Backwardation occurs when short-term implied volatility is higher than longer-term, which typically happens during market stress. Investors anticipate immediate turbulence but expect calming. Psychologically, this reflects acute fear. The model subtracts 15 points for backwardation and 30 for steep backwardation, as these constellations signal elevated risk. Simon and Wiggins (2001) analyzed the VIX futures curve and showed that backwardation is associated with market declines.
6.4 Safe-Haven Flows
During crisis times, investors flee from risky assets into safe havens: gold, US dollar, and Japanese yen. This "flight to quality" is a sentiment signal. The model calculates the performance of these assets relative to stocks over the last 20 trading days. When gold or the dollar strongly rise while stocks fall, this indicates elevated risk aversion.
The safe-haven component is calculated as the difference between safe-haven performance and stock performance. Positive values (safe havens outperform) subtract up to 20 points from the sentiment score, negative values (stocks outperform) add up to 10 points. The asymmetric treatment (larger deduction for risk-off than bonus for risk-on) reflects that risk-off movements are typically sharper and more informative than risk-on phases.
Baur and Lucey (2010) examined safe-haven properties of gold and showed that gold indeed exhibits negative correlation with stocks during extreme market movements, confirming its role as crisis protection.
7. Component 5: Macroeconomic Analysis
7.1 The Yield Curve as Economic Indicator
The yield curve, represented as yields of government bonds of various maturities, contains aggregated expectations about future interest rates, inflation, and economic growth. The slope of the yield curve has remarkable predictive power for recessions. Estrella and Mishkin (1998) showed that an inverted yield curve (short-term rates higher than long-term) predicts recessions with high reliability. This is because inverted curves reflect restrictive monetary policy: the central bank raises short-term rates to combat inflation, dampening economic activity.
DEAM calculates two spread measures: the 2-year-minus-10-year spread and the 3-month-minus-10-year spread. A steep, positive curve (spreads above 1.5% and 2% respectively) signals healthy growth expectations and generates the maximum yield curve score of 40 points. A flat curve (spreads near zero) reduces the score to 20 points. An inverted curve (negative spreads) is particularly alarming and results in only 10 points.
The choice of two different spreads increases analysis robustness. The 2-10 spread is most established in academic literature, while the 3M-10Y spread is often considered more sensitive, as the 3-month rate directly reflects current monetary policy (Ang, Piazzesi, and Wei, 2006).
7.2 Credit Conditions and Spreads
Credit spreads—the yield difference between risky corporate bonds and safe government bonds—reflect risk perception in the credit market. Gilchrist and Zakrajšek (2012) constructed an "Excess Bond Premium" that measures the component of credit spreads not explained by fundamentals and showed this is a predictor of future economic activity and stock returns.
The model approximates credit spread by comparing the yield of high-yield bond ETFs (HYG) with investment-grade bond ETFs (LQD). A narrow spread below 200 basis points signals healthy credit conditions and risk appetite, contributing 30 points to the macro score. Very wide spreads above 1000 basis points (as during the 2008 financial crisis) signal credit crunch and generate zero points.
Additionally, the model evaluates whether "flight to quality" is occurring, identified through strong performance of Treasury bonds (TLT) with simultaneous weakness in high-yield bonds. This constellation indicates elevated risk aversion and reduces the credit conditions score.
7.3 Financial Stability at Corporate Level
While the yield curve and credit spreads reflect macroeconomic conditions, financial stability evaluates the health of companies themselves. The model uses the aggregated debt-to-equity ratio and return on equity of the S&P 500 as proxies for corporate health.
A low leverage level below 0.5 combined with high ROE above 15% signals robust corporate balance sheets and generates 20 points. This combination is particularly valuable as it represents both defensive strength (low debt means crisis resistance) and offensive strength (high ROE means earnings power). High leverage above 1.5 generates only 5 points, as it implies vulnerability to interest rate increases and recessions.
Korteweg (2010) showed in "The Net Benefits to Leverage" that optimal debt maximizes firm value, but excessive debt increases distress costs. At the aggregated market level, high debt indicates fragilities that can become problematic during stress phases.
8. Component 6: Crisis Detection
8.1 The Need for Systematic Crisis Detection
Financial crises are rare but extremely impactful events that suspend normal statistical relationships. During normal market volatility, diversified portfolios and traditional risk management approaches function, but during systemic crises, seemingly independent assets suddenly correlate strongly, and losses exceed historical expectations (Longin and Solnik, 2001). This justifies a separate crisis detection mechanism that operates independently of regular allocation components.
Reinhart and Rogoff (2009) documented in "This Time Is Different: Eight Centuries of Financial Folly" recurring patterns in financial crises: extreme volatility, massive drawdowns, credit market dysfunction, and asset price collapse. DEAM operationalizes these patterns into quantifiable crisis indicators.
8.2 Multi-Signal Crisis Identification
The model uses a counter-based approach where various stress signals are identified and aggregated. This methodology is more robust than relying on a single indicator, as true crises typically occur simultaneously across multiple dimensions. A single signal may be a false alarm, but the simultaneous presence of multiple signals increases confidence.
The first indicator is a VIX above the crisis threshold (default 40), adding one point. A VIX above 60 (as in 2008 and March 2020) adds two additional points, as such extreme values are historically very rare. This tiered approach captures the intensity of volatility.
The second indicator is market drawdown. A drawdown above 15% adds one point, as corrections of this magnitude can be potential harbingers of larger crises. A drawdown above 25% adds another point, as historical bear markets typically encompass 25-40% drawdowns.
The third indicator is credit market spreads above 500 basis points, adding one point. Such wide spreads occur only during significant credit market disruptions, as in 2008 during the Lehman crisis.
The fourth indicator identifies simultaneous losses in stocks and bonds. Normally, Treasury bonds act as a hedge against equity risk (negative correlation), but when both fall simultaneously, this indicates systemic liquidity problems or inflation/stagflation fears. The model checks whether both SPY and TLT have fallen more than 10% and 5% respectively over 5 trading days, adding two points.
The fifth indicator is a volume spike combined with negative returns. Extreme trading volumes (above twice the 20-day average) with falling prices signal panic selling. This adds one point.
A crisis situation is diagnosed when at least 3 indicators trigger, a severe crisis at 5 or more indicators. These thresholds were calibrated through historical backtesting to identify true crises (2008, 2020) without generating excessive false alarms.
8.3 Crisis-Based Allocation Override
When a crisis is detected, the system overrides the normal allocation recommendation and caps equity allocation at maximum 25%. In a severe crisis, the cap is set at 10%. This drastic defensive posture follows the empirical observation that crises typically require time to develop and that early reduction can avoid substantial losses (Faber, 2007).
This override logic implements a "safety first" principle: in situations of existential danger to the portfolio, capital preservation becomes the top priority. Roy (1952) formalized this approach in "Safety First and the Holding of Assets," arguing that investors should primarily minimize ruin probability.
9. Integration and Final Allocation Calculation
9.1 Component Weighting
The final allocation recommendation emerges through weighted aggregation of the five components. The standard weighting is: Market Regime 35%, Risk Management 25%, Valuation 20%, Sentiment 15%, Macro 5%. These weights reflect both theoretical considerations and empirical backtesting results.
The highest weighting of market regime is based on evidence that trend-following and momentum strategies have delivered robust results across various asset classes and time periods (Moskowitz, Ooi, and Pedersen, 2012). Current market momentum is highly informative for the near future, although it provides no information about long-term expectations.
The substantial weighting of risk management (25%) follows from the central importance of risk control. Wealth preservation is the foundation of long-term wealth creation, and systematic risk management is demonstrably value-creating (Moreira and Muir, 2017).
The valuation component receives 20% weight, based on the long-term mean reversion of valuation metrics. While valuation has limited short-term predictive power (bull and bear markets can begin at any valuation), the long-term relationship between valuation and returns is robustly documented (Campbell and Shiller, 1988).
Sentiment (15%) and Macro (5%) receive lower weights, as these factors are subtler and harder to measure. Sentiment is valuable as a contrarian indicator at extremes but less informative in normal ranges. Macro variables such as the yield curve have strong predictive power for recessions, but the transmission from recessions to stock market performance is complex and temporally variable.
9.2 Model Type Adjustments
DEAM allows users to choose between four model types: Conservative, Balanced, Aggressive, and Adaptive. This choice modifies the final allocation through additive adjustments.
Conservative mode subtracts 10 percentage points from allocation, resulting in consistently more cautious positioning. This is suitable for risk-averse investors or those with limited investment horizons. Aggressive mode adds 10 percentage points, suitable for risk-tolerant investors with long horizons.
Adaptive mode implements procyclical adjustment based on short-term momentum: if the market has risen more than 5% in the last 20 days, 5 percentage points are added; if it has declined more than 5%, 5 points are subtracted. This logic follows the observation that short-term momentum persists (Jegadeesh and Titman, 1993), but the moderate size of adjustment avoids excessive timing bets.
Balanced mode makes no adjustment and uses raw model output. This neutral setting is suitable for investors who wish to trust model recommendations unchanged.
9.3 Smoothing and Stability
The allocation resulting from aggregation undergoes final smoothing through a simple moving average over 3 periods. This smoothing is crucial for model practicality, as it reduces frequent trading and thus transaction costs. Without smoothing, the model could fluctuate between adjacent allocations with every small input change.
The choice of 3 periods as smoothing window is a compromise between responsiveness and stability. Longer smoothing would excessively delay signals and impede response to true regime changes. Shorter or no smoothing would allow too much noise. Empirical tests showed that 3-period smoothing offers an optimal ratio between these goals.
10. Visualization and Interpretation
10.1 Main Output: Equity Allocation
DEAM's primary output is a time series from 0 to 100 representing the recommended percentage allocation to equities. This representation is intuitive: 100% means full investment in stocks (specifically: an S&P 500 ETF), 0% means complete cash position, and intermediate values correspond to mixed portfolios. A value of 60% means, for example: invest 60% of wealth in SPY, hold 40% in money market instruments or cash.
The time series is color-coded to enable quick visual interpretation. Green shades represent high allocations (above 80%, bullish), red shades low allocations (below 20%, bearish), and neutral colors middle allocations. The chart background is dynamically colored based on the signal, enhancing readability in different market phases.
10.2 Dashboard Metrics
A tabular dashboard presents key metrics compactly. This includes current allocation, cash allocation (complement), an aggregated signal (BULLISH/NEUTRAL/BEARISH), current market regime, VIX level, market drawdown, and crisis status.
Additionally, fundamental metrics are displayed: P/E Ratio, Equity Risk Premium, Return on Equity, Debt-to-Equity Ratio, and Total Shareholder Yield. This transparency allows users to understand model decisions and form their own assessments.
Component scores (Regime, Risk, Valuation, Sentiment, Macro) are also displayed, each normalized on a 0-100 scale. This shows which factors primarily drive the current recommendation. If, for example, the Risk score is very low (20) while other scores are moderate (50-60), this indicates that risk management considerations are pulling allocation down.
10.3 Component Breakdown (Optional)
Advanced users can display individual components as separate lines in the chart. This enables analysis of component dynamics: do all components move synchronously, or are there divergences? Divergences can be particularly informative. If, for example, the market regime is bullish (high score) but the valuation component is very negative, this signals an overbought market not fundamentally supported—a classic "bubble warning."
This feature is disabled by default to keep the chart clean but can be activated for deeper analysis.
10.4 Confidence Bands
The model optionally displays uncertainty bands around the main allocation line. These are calculated as ±1 standard deviation of allocation over a rolling 20-period window. Wide bands indicate high volatility of model recommendations, suggesting uncertain market conditions. Narrow bands indicate stable recommendations.
This visualization implements a concept of epistemic uncertainty—uncertainty about the model estimate itself, not just market volatility. In phases where various indicators send conflicting signals, the allocation recommendation becomes more volatile, manifesting in wider bands. Users can understand this as a warning to act more cautiously or consult alternative information sources.
11. Alert System
11.1 Allocation Alerts
DEAM implements an alert system that notifies users of significant events. Allocation alerts trigger when smoothed allocation crosses certain thresholds. An alert is generated when allocation reaches 80% (from below), signaling strong bullish conditions. Another alert triggers when allocation falls to 20%, indicating defensive positioning.
These thresholds are not arbitrary but correspond with boundaries between model regimes. An allocation of 80% roughly corresponds to a clear bull market regime, while 20% corresponds to a bear market regime. Alerts at these points are therefore informative about fundamental regime shifts.
11.2 Crisis Alerts
Separate alerts trigger upon detection of crisis and severe crisis. These alerts have highest priority as they signal large risks. A crisis alert should prompt investors to review their portfolio and potentially take defensive measures beyond the automatic model recommendation (e.g., hedging through put options, rebalancing to more defensive sectors).
11.3 Regime Change Alerts
An alert triggers upon change of market regime (e.g., from Neutral to Correction, or from Bull Market to Strong Bull). Regime changes are highly informative events that typically entail substantial allocation changes. These alerts enable investors to proactively respond to changes in market dynamics.
11.4 Risk Breach Alerts
A specialized alert triggers when actual portfolio risk utilization exceeds target parameters by 20%. This is a warning signal that the risk management system is reaching its limits, possibly because market volatility is rising faster than allocation can be reduced. In such situations, investors should consider manual interventions.
12. Practical Application and Limitations
12.1 Portfolio Implementation
DEAM generates a recommendation for allocation between equities (S&P 500) and cash. Implementation by an investor can take various forms. The most direct method is using an S&P 500 ETF (e.g., SPY, VOO) for equity allocation and a money market fund or savings account for cash allocation.
A rebalancing strategy is required to synchronize actual allocation with model recommendation. Two approaches are possible: (1) rule-based rebalancing at every 10% deviation between actual and target, or (2) time-based monthly rebalancing. Both have trade-offs between responsiveness and transaction costs. Empirical evidence (Jaconetti, Kinniry, and Zilbering, 2010) suggests rebalancing frequency has moderate impact on performance, and investors should optimize based on their transaction costs.
12.2 Adaptation to Individual Preferences
The model offers numerous adjustment parameters. Component weights can be modified if investors place more or less belief in certain factors. A fundamentally-oriented investor might increase valuation weight, while a technical trader might increase regime weight.
Risk target parameters (target volatility, max drawdown) should be adapted to individual risk tolerance. Younger investors with long investment horizons can choose higher target volatility (15-18%), while retirees may prefer lower volatility (8-10%). This adjustment systematically shifts average equity allocation.
Crisis thresholds can be adjusted based on preference for sensitivity versus specificity of crisis detection. Lower thresholds (e.g., VIX > 35 instead of 40) increase sensitivity (more crises are detected) but reduce specificity (more false alarms). Higher thresholds have the reverse effect.
12.3 Limitations and Disclaimers
DEAM is based on historical relationships between indicators and market performance. There is no guarantee these relationships will persist in the future. Structural changes in markets (e.g., through regulation, technology, or central bank policy) can break established patterns. This is the fundamental problem of induction in financial science (Taleb, 2007).
The model is optimized for US equities (S&P 500). Application to other markets (international stocks, bonds, commodities) would require recalibration. The indicators and thresholds are specific to the statistical properties of the US equity market.
The model cannot eliminate losses. Even with perfect crisis prediction, an investor following the model would lose money in bear markets—just less than a buy-and-hold investor. The goal is risk-adjusted performance improvement, not risk elimination.
Transaction costs are not modeled. In practice, spreads, commissions, and taxes reduce net returns. Frequent trading can cause substantial costs. Model smoothing helps minimize this, but users should consider their specific cost situation.
The model reacts to information; it does not anticipate it. During sudden shocks (e.g., 9/11, COVID-19 lockdowns), the model can only react after price movements, not before. This limitation is inherent to all reactive systems.
12.4 Relationship to Other Strategies
DEAM is a tactical asset allocation approach and should be viewed as a complement, not replacement, for strategic asset allocation. Brinson, Hood, and Beebower (1986) showed in their influential study "Determinants of Portfolio Performance" that strategic asset allocation (long-term policy allocation) explains the majority of portfolio performance, but this leaves room for tactical adjustments based on market timing.
The model can be combined with value and momentum strategies at the individual stock level. While DEAM controls overall market exposure, within-equity decisions can be optimized through stock-picking models. This separation between strategic (market exposure) and tactical (stock selection) levels follows classical portfolio theory.
The model does not replace diversification across asset classes. A complete portfolio should also include bonds, international stocks, real estate, and alternative investments. DEAM addresses only the US equity allocation decision within a broader portfolio.
13. Scientific Foundation and Evaluation
13.1 Theoretical Consistency
DEAM's components are based on established financial theory and empirical evidence. The market regime component follows from regime-switching models (Hamilton, 1989) and trend-following literature. The risk management component implements volatility targeting (Moreira and Muir, 2017) and modern portfolio theory (Markowitz, 1952). The valuation component is based on discounted cash flow theory and empirical value research (Campbell and Shiller, 1988; Fama and French, 1992). The sentiment component integrates behavioral finance (Baker and Wurgler, 2006). The macro component uses established business cycle indicators (Estrella and Mishkin, 1998).
This theoretical grounding distinguishes DEAM from purely data-mining-based approaches that identify patterns without causal theory. Theory-guided models have greater probability of functioning out-of-sample, as they are based on fundamental mechanisms, not random correlations (Lo and MacKinlay, 1990).
13.2 Empirical Validation
While this document does not present detailed backtest analysis, it should be noted that rigorous validation of a tactical asset allocation model should include several elements:
In-sample testing establishes whether the model functions at all in the data on which it was calibrated. Out-of-sample testing is crucial: the model should be tested in time periods not used for development. Walk-forward analysis, where the model is successively trained on rolling windows and tested in the next window, approximates real implementation.
Performance metrics should be risk-adjusted. Pure return consideration is misleading, as higher returns often only compensate for higher risk. Sharpe Ratio, Sortino Ratio, Calmar Ratio, and Maximum Drawdown are relevant metrics. Comparison with benchmarks (Buy-and-Hold S&P 500, 60/40 Stock/Bond portfolio) contextualizes performance.
Robustness checks test sensitivity to parameter variation. If the model only functions at specific parameter settings, this indicates overfitting. Robust models show consistent performance over a range of plausible parameters.
13.3 Comparison with Existing Literature
DEAM fits into the broader literature on tactical asset allocation. Faber (2007) presented a simple momentum-based timing system that goes long when the market is above its 10-month average, otherwise cash. This simple system avoided large drawdowns in bear markets. DEAM can be understood as a sophistication of this approach that integrates multiple information sources.
Ilmanen (2011) discusses various timing factors in "Expected Returns" and argues for multi-factor approaches. DEAM operationalizes this philosophy. Asness, Moskowitz, and Pedersen (2013) showed that value and momentum effects work across asset classes, justifying cross-asset application of regime and valuation signals.
Ang (2014) emphasizes in "Asset Management: A Systematic Approach to Factor Investing" the importance of systematic, rule-based approaches over discretionary decisions. DEAM is fully systematic and eliminates emotional biases that plague individual investors (overconfidence, hindsight bias, loss aversion).
References
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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.
Trend Breakout Description:
This Pine Script indicator identifies pivot high and pivot low points based on user-defined left and right candle legs, detecting breakouts to signal potential trend changes. It plots horizontal lines at pivot highs (lime) and pivot lows (red), marking breakout signals with labels ("Br") when the price crosses above a pivot high or below a pivot low. The indicator also changes the background color to reflect the trend (green for uptrend, red for downtrend) with adjustable transparency. The indicator primarily focuses on recognizing specific pivot patterns to define trends and generate trading signals.
How It Works
• Pivot Detection: Identifies pivot highs and lows using configurable left (Left side Pivot Candle) and right (Right side Pivot Candle) periods.
• Pivot Highs (PH): A pivot high is identified when a candle's high is greater than a specified number of preceding candles (left leg) and succeeding candles (right leg).
• Pivot Lows (PL): Similarly, a pivot low is identified when a candle's low is less than a specified number of preceding and succeeding candles.
The script then tracks the last three pivot highs and pivot lows.
Trend Detection and Breakouts
1. High Line (Resistance): When a middle pivot high (out of the three tracked) is higher than both the previous and the next pivot high, a lime green line is drawn from that pivot high. This line acts as a dynamic resistance level.
2. Low Line (Support): Conversely, when a middle pivot low is lower than both the previous and the next pivot low, a red line is drawn from that pivot low. This line acts as a dynamic support level.
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Trading Signals : The indicator generates signals based on price crossing these dynamically drawn lines .
• Long Signal (Uptrend):
o A "Long" signal is triggered when the close price crosses above the current high line (resistance), and the indicator is not already in an uptrend.
o When a long signal occurs, the background turns green, and the high line becomes dotted and thinner. A "Br" (Breakout) label appears below the candle.
• Short Signal (Downtrend):
o A "Short" signal is triggered when the close price crosses below the current low line (support), and the indicator is not already in a downtrend.
o When a short signal occurs, the background turns red, and the low line becomes dotted and thinner. A "Br" (Breakout) label appears above the candle.
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Customizable Settings
The indicator provides three user-adjustable inputs:
• Right Side Pivot Candle (fpivotLeg): This setting (default 10) determines the number of candles to the right that must have lower highs/higher lows for a pivot to be confirmed.
• Left Side Pivot Candle (bpivotLeg): This setting (default 15) determines the number of candles to the left that must have lower highs/higher lows for a pivot to be confirmed.
• Adjust Color Visualization (Colortrnp): This setting (default 85) controls the transparency of the background color changes, allowing you to adjust how prominently the green (uptrend) and red (downtrend) backgrounds are displayed.
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How to Use It
This indicator can be used by traders to:
• Identify potential reversals: The formation of new pivot highs and lows can signal shifts in market direction.
• Spot breakout opportunities: Crossing above the high line or below the low line can indicate the start of a new trend or the continuation of an existing one.
• Confirm trend strength: The presence and extension of the high and low lines can provide visual cues about the prevailing trend.
• Ideal for swing traders or trend-following strategies.
• Use the breakout labels ("Br") and background color to confirm trend direction.
• Adjust pivot leg inputs to fine-tune sensitivity for different timeframes or assets.
• Customize transparency to suit chart readability.
Example:
On a breakout above a pivot high, a green "Br" label appears, the background turns green, and the pivot line becomes dotted. This signals a potential uptrend, helping traders identify entry points or trend confirmations.
Disclaimer: No indicator guarantees profits. Always use this indicator in conjunction with other analysis methods and proper risk management.
Divergence Strategy [Trendoscope®]🎲 Overview
The Divergence Strategy is a sophisticated TradingView strategy that enhances the Divergence Screener by adding automated trade signal generation, risk management, and trade visualization. It leverages the screener’s robust divergence detection to identify bullish, bearish, regular, and hidden divergences, then executes trades with precise entry, stop-loss, and take-profit levels. Designed for traders seeking automated trading solutions, this strategy offers customizable trade parameters and visual feedback to optimize performance across various markets and timeframes.
For core divergence detection features, including oscillator options, trend detection methods, zigzag pivot analysis, and visualization, refer to the Divergence Screener documentation. This description focuses on the strategy-specific enhancements for automated trading and risk management.
🎲 Strategy Features
🎯Automated Trade Signal Generation
Trade Direction Control : Restrict trades to long-only or short-only to align with market bias or strategy goals, preventing conflicting orders.
Divergence Type Selection : Choose to trade regular divergences (bullish/bearish), hidden divergences, or both, targeting reversals or trend continuations.
Entry Type Options :
Cautious : Enters conservatively at pivot points and exits quickly to minimize risk exposure.
Confident : Enters aggressively at the latest price and holds longer to capture larger moves.
Mixed : Combines conservative entries with delayed exits for a balanced approach.
Market vs. Stop Orders: Opt for market orders for instant execution or stop orders for precise price entry.
🎯 Enhanced Risk Management
Risk/Reward Ratio : Define a risk-reward ratio (default: 2.0) to set profit targets relative to stop-loss levels, ensuring consistent trade sizing.
Bracket Orders : Trades include entry, stop-loss, and take-profit levels calculated from divergence pivot points, tailored to the entry type and risk-reward settings.
Stop-Loss Placement : Stops are strategically set (e.g., at recent pivot or last price point) based on entry type, balancing risk and trade validity.
Order Cancellation : Optionally cancel pending orders when a divergence is broken (e.g., price moves past the pivot in the wrong direction), reducing invalid trades. This feature is toggleable for flexibility.
🎯 Trade Visualization
Target and Stop Boxes : Displays take-profit (lime) and stop-loss (orange) levels as boxes on the price chart, extending 10 bars forward for clear visibility.
Dynamic Trade Updates : Trade visualizations are added, updated, or removed as trades are executed, canceled, or invalidated, ensuring accurate feedback.
Overlay Integration : Trade levels overlay the price chart, complementing the screener’s oscillator-based divergence lines and labels.
🎯 Strategy Default Configuration
Capital and Sizing : Set initial capital (default: $1,000,000) and position size (default: 20% of equity) for realistic backtesting.
Pyramiding : Allows up to 4 concurrent trades, enabling multiple divergence-based entries in trending markets.
Commission and Margin : Accounts for commission (default: 0.01%) and margin (100% for long/short) to reflect trading costs.
Performance Optimization : Processes up to 5,000 bars dynamically, balancing historical analysis and real-time execution.
🎲 Inputs and Configuration
🎯Trade Settings
Direction : Select Long or Short (default: Long).
Divergence : Trade Regular, Hidden, or Both divergence types (default: Both).
Entry/Exit Type : Choose Cautious, Confident, or Mixed (default: Cautious).
Risk/Reward : Set the risk-reward ratio for profit targets (default: 2.0).
Use Market Order : Enable market orders for immediate entry (default: false, uses limit orders).
Cancel On Break : Cancel pending orders when divergence is broken (default: true).
🎯Inherited Settings
The strategy inherits all inputs from the Divergence Screener, including:
Oscillator Settings : Oscillator type (e.g., RSI, CCI), length, and external oscillator option.
Trend Settings : Trend detection method (Zigzag, MA Difference, External), MA type, and length.
Zigzag Settings : Zigzag length (fixed repaint = true).
🎲 Entry/Exit Types for Divergence Scenarios
The Divergence Strategy offers three Entry/Exit Type options—Cautious, Confident, and Mixed—which determine how trades are entered and exited based on divergence pivot points. This section explains how these settings apply to different divergence scenarios, with placeholders for screenshots to illustrate each case.
The divergence pattern forms after 3 pivots. The stop and entry levels are formed on one of these levels based on Entry/Exit types.
🎯Bullish Divergence (Reversal)
A bullish divergence occurs when price forms a lower low, but the oscillator forms a higher low, signaling a potential upward reversal.
💎 Cautious:
Entry : At the pivot high point for a conservative entry.
Exit : Stop-loss at the last pivot point (previous low that is higher than the current pivot low); take-profit at risk-reward ratio. Canceled if price breaks below the pivot (if Cancel On Break is enabled).
Behavior : Enters after confirmation and exits quickly to limit downside risk.
💎Confident:
Entry : At the last pivot low, (previous low which is higher than the current pivot low) for an aggressive entry.
Exit : Stop-loss at recent pivot low, which is the lowest point; take-profit at risk-reward ratio. Canceled if price breaks below the pivot. (lazy exit)
Behavior : Enters early to capture trend continuation, holding longer for gains.
💎Mixed:
Entry : At the pivot high point (conservative).
Exit : Stop-loss at the recent pivot point that has resulted in lower low (lazy exit). Canceled if price breaks below the pivot.
Behavior : Balances entry caution with extended holding for trend continuation.
🎯Bearish Divergence (Reversal)
A bearish divergence occurs when price forms a higher high, but the oscillator forms a lower high, indicating a potential downward reversal.
💎Cautious:
Entry : At the pivot low point (lower high) for a conservative short entry.
Exit : Stop-loss at the previous pivot high point (previous high); take-profit at risk-reward ratio. Canceled if price breaks above the pivot (if Cancel On Break is enabled).
Behavior : Enters conservatively and exits quickly to minimize risk.
💎Confident:
Entry : At the last price point (previous high) for an aggressive short entry.
Exit : Stop-loss at the pivot point; take-profit at risk-reward ratio. Canceled if price breaks above the pivot.
Behavior : Enters early to maximize trend continuation, holding longer.
💎Mixed:
Entry : At the previous piot high point (conservative).
Exit : Stop-loss at the last price point (delayed exit). Canceled if price breaks above the pivot.
Behavior : Combines conservative entry with extended holding for downtrend gains.
🎯Bullish Hidden Divergence (Continuation)
A bullish hidden divergence occurs when price forms a higher low, but the oscillator forms a lower low, suggesting uptrend continuation. In case of Hidden bullish divergence, b]Entry is always on the previous pivot high (unless it is a market order)
💎Cautious:
Exit : Stop-loss at the recent pivot low point (higher than previous pivot low); take-profit at risk-reward ratio. Canceled if price breaks below the pivot (if Cancel On Break is enabled).
Behavior : Enters after confirmation and exits quickly to limit downside risk.
💎Confident:
Exit : Stop-loss at previous pivot low, which is the lowest point; take-profit at risk-reward ratio. Canceled if price breaks below the pivot. (lazy exit)
Behavior : Enters early to capture trend continuation, holding longer for gains.
🎯Bearish Hidden Divergence (Continuation)
A bearish hidden divergence occurs when price forms a lower high, but the oscillator forms a higher high, suggesting downtrend continuation. In case of Hidden Bearish divergence, b]Entry is always on the previous pivot low (unless it is a market order)
💎Cautious:
Exit : Stop-loss at the latest pivot high point (which is a lower high); take-profit at risk-reward ratio. Canceled if price breaks above the pivot (if Cancel On Break is enabled).
Behavior : Enters conservatively and exits quickly to minimize risk.
💎Confident/Mixed:
Exit : Stop-loss at the previous pivot high point; take-profit at risk-reward ratio. Canceled if price breaks above the pivot.
Behavior : Uses the late exit point to hold longer.
🎲 Usage Instructions
🎯Add to Chart:
Add the Divergence Strategy to your TradingView chart.
The oscillator and divergence signals appear in a separate pane, with trade levels (target/stop boxes) overlaid on the price chart.
🎯Configure Settings:
Adjust trade settings (direction, divergence type, entry type, risk-reward, market orders, cancel on break).
Modify inherited Divergence Screener settings (oscillator, trend method, zigzag length) as needed.
Enable/disable alerts for divergence notifications.
🎯Interpret Signals:
Long Trades: Triggered on bullish or bullish hidden divergences (if allowed), shown with green/lime lines and labels.
Short Trades: Triggered on bearish or bearish hidden divergences (if allowed), shown with red/orange lines and labels.
Monitor lime (target) and orange (stop) boxes for trade levels.
Review strategy performance metrics (e.g., profit/loss, win rate) in the strategy tester.
🎯Backtest and Optimize:
Use TradingView’s strategy tester to evaluate performance on historical data.
Fine-tune risk-reward, entry type, position sizing, and cancellation settings to suit your market and timeframe.
For questions, suggestions, or support, contact Trendoscope via TradingView or official support channels. Stay tuned for updates and enhancements to the Divergence Strategy!
Wick SweepThe Wick Sweep indicator identifies potential trend reversal zones based on price action patterns and swing points. Specifically, it looks for "Wick Sweeps," a concept where the market temporarily breaks a swing low or high (creating a "wick"), only to reverse in the opposite direction. This pattern is often indicative of a market attempting to trap traders before making a larger move. The indicator marks these zones using dashed lines, helping traders spot key areas of potential price action.
Key Features:
* Swing Low and High Detection: The indicator identifies significant swing lows and highs within a user-defined period by employing Williams fractals.
* Wick Sweep Detection: Once a swing low or high is identified, the indicator looks for price movements that break through the low or high (creating a wick) and then reverses direction.
* Fractal Plotting: Optionally, the indicator plots fractal points (triangle shapes) on the chart when a swing low or high is detected. This can assist in visually identifying the potential wick sweep areas.
* Line Plotting: When a wick sweep is detected, a dashed line is drawn at the price level of the failed low or high, visually marking the potential reversal zone.
Inputs:
* Periods: The number of bars used to identify swing highs and lows. A higher value results in fewer, more significant swing points.
* Line Color: The color of the dashed lines drawn when a wick sweep is detected. Customize this to match your chart's theme or preferences.
* Show Fractals: A toggle that, when enabled, plots triangle shapes above and below bars indicating swing highs (up triangles) and swing lows (down triangles).
Functionality:
* Swing High and Low Calculation:
- The indicator calculates the swing low and swing high based on the periods input. A swing low is identified when the current low is the lowest within a range of (2 * periods + 1), with the lowest point being at the center of the period.
- Similarly, a swing high is identified when the current high is the highest within the same range.
* Wick Sweep Detection:
- Once a swing low or high is detected, the script looks for a potential wick. This happens when the price breaks the swing low or high and then reverses in the opposite direction.
- For a valid wick sweep, the price should briefly move beyond the identified swing point but then close in the opposite direction (i.e., a bullish reversal for a swing low and a bearish reversal for a swing high).
- A line is drawn at the price level of the failed low or high when a wick sweep is confirmed.
Confirmations for Reversal:
* The confirmation for a wick sweep requires that the price not only break the swing low/high but also close in the opposite direction (i.e., close above the low for a bullish reversal or close below the high for a bearish reversal).
* The confirmation is further refined by checking that the price movement is within a reasonable distance from the original swing point, which prevents the indicator from marking distant, unimportant price levels.
Additional Notes:
* The Wick Sweep indicator does not provide standalone trading signals; it is best used in conjunction with other technical analysis tools, such as trend analysis, oscillators, or volume indicators.
* The periods input can be adjusted based on the trader’s preferred level of sensitivity. A lower period value will result in more frequent swing points and potentially more signals, while a higher value will focus on more significant market swings.
* The indicator may work well in ranging markets where price tends to oscillate between key support and resistance levels.
Market Structure - HH, HL, LH, LL with Trendlines & AlertsMarket Structure Script – HH, HL, LH, LL with Trendlines & Alerts
This Pine Script is designed to help identify key market structure patterns such as Higher Highs (HH), Higher Lows (HL), Lower Highs (LH), and Lower Lows (LL) on price charts. It also draws trendlines connecting the respective swing points and provides alerts when these important price patterns occur.
Key Features:
Swing High and Low Detection:
The script uses the ta.pivothigh and ta.pivotlow functions to identify significant swing highs and swing lows based on the pivot length (pivotLen). These points mark local peaks and troughs in the price action.
Dynamic Pivot Length:
The script adjusts the pivotLen (which defines the number of bars used to calculate swing points) based on the current timeframe of the chart. For example, for a 15-minute chart, it uses a pivot length of 5 bars, while for a daily chart, it uses 10 bars. This dynamic adjustment ensures that the script works across different timeframes.
Higher Highs (HH), Higher Lows (HL), Lower Highs (LH), Lower Lows (LL):
Higher High (HH): Identifies a price peak that is higher than the previous swing high, indicating an uptrend.
Higher Low (HL): Identifies a price trough that is higher than the previous swing low, supporting the idea of an ongoing uptrend.
Lower High (LH): Identifies a price peak that is lower than the previous swing high, suggesting a potential reversal or downtrend.
Lower Low (LL): Identifies a price trough that is lower than the previous swing low, indicating a downtrend.
Trendlines:
For each identified Higher High, Higher Low, Lower High, or Lower Low, the script automatically draws a trendline connecting the corresponding swing points. These trendlines provide a visual representation of the market’s price structure, showing support and resistance levels.
Labels:
The script places labels on the chart next to the respective swing points. These labels mark whether the point is a Higher High (HH), Higher Low (HL), Lower High (LH), or Lower Low (LL). This helps traders easily visualize the price pattern at a glance.
Alerts:
Alert conditions are set for when a Higher High (HH), Higher Low (HL), Lower High (LH), or Lower Low (LL) is detected. Users can set up alerts to be notified whenever one of these key market structure patterns forms. Alerts are an essential feature for traders who want to act quickly when important trend changes are happening.
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.
PERFECT PIVOT RANGE DR ABIRAM SIVPRASAD (PPR)PERFECT PIVOT RANGE (PPR) by Dr. Abhiram Sivprasad
The Perfect Pivot Range (PPR) indicator is designed to provide traders with a comprehensive view of key support and resistance levels based on pivot points across different timeframes. This versatile tool allows users to visualize daily, weekly, and monthly pivots along with high and low levels from previous periods, helping traders identify potential areas of price reversals or breakouts.
Features:
Multi-Timeframe Pivots:
Daily, weekly, and monthly pivot levels (Pivot Point, Support 1 & 2, Resistance 1 & 2).
Helps traders understand price levels across various timeframes, from short-term (daily) to long-term (monthly).
Previous High-Low Levels:
Displays the previous week, month, and day high-low levels to highlight key zones of historical support and resistance.
Traders can easily see areas of price action from prior periods, giving context for future price movements.
Customizable Options:
Users can choose which pivot levels and high-lows to display, allowing for flexibility based on trading preferences.
Visual settings can be toggled on and off to suit different trading strategies and timeframes.
Real-Time Data:
All pivot points and levels are dynamically calculated based on real-time price data, ensuring accurate and up-to-date information for decision-making.
How to Use:
Pivot Points: Use daily, weekly, or monthly pivot points to find potential support or resistance levels. Prices above the pivot suggest bullish sentiment, while prices below indicate bearishness.
Previous High-Low: The high-low levels from previous days, weeks, or months can serve as critical zones where price may reverse or break through, indicating potential trade entries or exits.
Confluence: When pivot points or high-low levels overlap across multiple timeframes, they become even stronger levels of support or resistance.
This indicator is suitable for all types of traders (scalpers, swing traders, and long-term investors) looking to enhance their technical analysis and make more informed trading decisions.
Here are three detailed trading strategies for using the Perfect Pivot Range (PPR) indicator for options, stocks, and commodities:
1. Options Buying Strategy with PPR Indicator
Strategy: Buying Call and Put Options Based on Pivot Breakouts
Objective: To capitalize on sharp price movements when key pivot levels are breached, leading to high returns with limited risk in options trading.
Timeframe: 15-minute to 1-hour chart for intraday option trading.
Steps:
Identify the Key Levels:
Use weekly pivots for intraday trading, as they provide more significant levels for options.
Enable the "Previous Week High-Low" to gauge support and resistance from the previous week.
Call Option Setup (Bullish Breakout):
Condition: If the price breaks above the weekly pivot point (PP) with high momentum (indicated by a strong bullish candle), it signifies potential bullishness.
Action: Buy Call Options at the breakout of the weekly pivot.
Confirmation: Check if the price is sustaining above the pivot with a minimum of 1-2 candles (depending on timeframe) and the first resistance (R1) isn’t too far away.
Target: The first resistance (R1) or previous week’s high can be your target for exiting the trade.
Stop-Loss: Set a stop-loss just below the pivot point (PP) to limit risk.
Put Option Setup (Bearish Breakdown):
Condition: If the price breaks below the weekly pivot (PP) with strong bearish momentum, it’s a signal to expect a downward move.
Action: Buy Put Options on a breakdown below the weekly pivot.
Confirmation: Ensure that the price is closing below the pivot, and check for declining volumes or bearish candles.
Target: The first support (S1) or the previous week’s low.
Stop-Loss: Place the stop-loss just above the pivot point (PP).
Example:
Let’s say the weekly pivot point (PP) is at 1500, the price breaks above and sustains at 1510. You buy a Call Option with a strike price near 1500, and the target will be the first resistance (R1) at 1530.
2. Stock Trading Strategy with PPR Indicator
Strategy: Swing Trading Using Pivot Points and Previous High-Low Levels
Objective: To capture mid-term stock price movements using pivot points and historical high-low levels for better trade entries and exits.
Timeframe: 1-day or 4-hour chart for swing trading.
Steps:
Identify the Trend:
Start by determining the overall trend of the stock using the weekly pivots. If the price is consistently above the pivot point (PP), the trend is bullish; if below, the trend is bearish.
Buy Setup (Bullish Trend Reversal):
Condition: When the stock bounces off the weekly pivot point (PP) or previous week’s low, it signals a bullish reversal.
Action: Enter a long position near the pivot or previous week’s low.
Confirmation: Look for a bullish candle pattern or increasing volumes.
Target: Set your first target at the first resistance (R1) or the previous week’s high.
Stop-Loss: Place your stop-loss just below the previous week’s low or support (S1).
Sell Setup (Bearish Trend Reversal):
Condition: When the price hits the weekly resistance (R1) or previous week’s high and starts to reverse downwards, it’s an opportunity to short-sell the stock.
Action: Enter a short position near the resistance.
Confirmation: Watch for bearish candle patterns or decreasing volume at the resistance.
Target: Your first target would be the weekly pivot point (PP), with the second target as the previous week’s low.
Stop-Loss: Set a stop-loss just above the resistance (R1).
Use Previous High-Low Levels:
The previous week’s high and low are key levels where price reversals often occur, so use them as reference points for potential entry and exit.
Example:
Stock XYZ is trading at 200. The previous week’s low is 195, and it bounces off that level. You enter a long position with a target of 210 (previous week’s high) and place a stop-loss at 193.
3. Commodity Trading Strategy with PPR Indicator
Strategy: Trend Continuation and Reversal in Commodities
Objective: To capitalize on the strong trends in commodities by using pivot points as key support and resistance levels for trend continuation and reversal.
Timeframe: 1-hour to 4-hour charts for commodities like Gold, Crude Oil, Silver, etc.
Steps:
Identify the Trend:
Use monthly pivots for long-term commodities trading since commodities often follow macroeconomic trends.
The monthly pivot point (PP) will give an idea of the long-term trend direction.
Trend Continuation Setup (Bullish Commodity):
Condition: If the price is consistently trading above the monthly pivot and pulling back towards the pivot without breaking below it, it indicates a bullish continuation.
Action: Enter a long position when the price tests the monthly pivot (PP) and starts moving up again.
Confirmation: Look for a strong bullish candle or an increase in volume to confirm the continuation.
Target: The first resistance (R1) or previous month’s high.
Stop-Loss: Place the stop-loss below the monthly pivot (PP).
Trend Reversal Setup (Bearish Commodity):
Condition: When the price reverses from the monthly resistance (R1) or previous month’s high, it’s a signal for a bearish reversal.
Action: Enter a short position at the resistance level.
Confirmation: Watch for bearish candle patterns or decreasing volumes at the resistance.
Target: Set your first target as the monthly pivot (PP) or the first support (S1).
Stop-Loss: Stop-loss should be placed just above the resistance level.
Using Previous High-Low for Swing Trades:
The previous month’s high and low are important in commodities. They often act as barriers to price movement, so traders should look for breakouts or reversals near these levels.
Example:
Gold is trading at $1800, with a monthly pivot at $1780 and the previous month’s high at $1830. If the price pulls back to $1780 and starts moving up again, you enter a long trade with a target of $1830, placing your stop-loss below $1770.
Key Points Across All Strategies:
Multiple Timeframes: Always use a combination of timeframes for confirmation. For example, a daily chart may show a bullish setup, but the weekly pivot levels can provide a larger trend context.
Volume: Volume is key in confirming the strength of price movement. Always confirm breakouts or reversals with rising or declining volume.
Risk Management: Set tight stop-loss levels just below support or above resistance to minimize risk and lock in profits at pivot points.
Each of these strategies leverages the powerful pivot and high-low levels provided by the PPR indicator to give traders clear entry, exit, and risk management points across different markets
HTF TriangleHTF Triangle by ZeroHeroTrading aims at detecting ascending and descending triangles using higher time frame data, without repainting nor misalignment issues.
It addresses user requests for combining Ascending Triangle and Descending Triangle into one indicator.
Ascending triangles are defined by an horizontal upper trend line and a rising lower trend line. It is a chart pattern used in technical analysis to predict the continuation of an uptrend.
Descending triangles are defined by a falling upper trend line and an horizontal lower trend line. It is a chart pattern used in technical analysis to predict the continuation of a downtrend.
This indicator can be useful if you, like me, believe that higher time frames can offer a broader perspective and provide clearer signals, smoothing out market noise and showing longer-term trends.
You can change the indicator settings as you see fit to tighten or loosen the detection, and achieve the best results for your use case.
Features
It draws the detected ascending and descending triangles on the chart.
It supports alerting when a detection occurs.
It allows for selecting ascending and/or descending triangle detection.
It allows for setting the higher time frame to run the detection on.
It allows for setting the minimum number of consecutive valid higher time frame bars to fit the pattern criteria.
It allows for setting a high/low factor detection criteria to apply on higher time frame bars high/low as a proportion of the distance between the reference bar high/low and open/close.
It allows for turning on an adjustment of the triangle using highest/lowest values within valid higher time frame bars.
Settings
Ascending checkbox: Turns on/off ascending triangle detection. Default is on.
Descending checkbox: Turns on/off descending triangle detection. Default is on.
Higher Time Frame dropdown: Selects higher time frame to run the detection on. It must be higher than, and a multiple of, the chart's timeframe. Default is 5 minutes.
Valid Bars Minimum field: Sets minimum number of consecutive valid higher time frame bars to fit the pattern criteria. Default is 3. Minimum is 1.
High/Low Factor checkbox: Turns on/off high/low factor detection criteria. Default is on.
High/Low Factor field: Sets high/low factor to apply on higher time frame bars high/low as a proportion of the distance between the reference bar high/low and open/close. Default is 0. Minimum is 0. Maximum is 1.
Adjust Triangle checkbox: Turns on/off triangle adjustment using highest/lowest values within valid higher time frame bars. Default is on.
Detection Algorithm Notes
The detection algorithm recursively selects a higher time frame bar as reference. Then it looks at the consecutive higher time frame bars (as per the requested number of minimum valid bars) as follows:
Ascending Triangle
Low must be higher than previous bar.
Open/close max value must be lower than (or equal to) reference bar high.
When high/low factor criteria is turned on, high must be higher than (or equal to) reference bar open/close max value plus high/low factor proportion of the distance between reference bar high and open/close max value.
Descending Triangle
High must be lower than previous bar.
Open/close min value must be higher than (or equal to) reference bar low.
When high/low factor criteria is turned on, low must be lower than (or equal to) reference bar open/close min value minus high/low factor proportion of the distance between reference bar low and open/close min value.
Azmi Moving AveragesThis trading indicator, designed using Pine Script, incorporates two simple moving averages (SMAs) with the same length but different data sources. Here's a detailed description of the indicator:
### Indicator Overview
**Name:** Two Moving Averages
### Inputs
1. **Length (20):** The period over which the moving averages are calculated. Both moving averages use a length of 20 periods.
2. **Source:**
- **High:** The first moving average is calculated using the high prices of the candles.
- **Low:** The second moving average is calculated using the low prices of the candles.
### Calculations
1. **MA High (maHigh):** This is the simple moving average of the high prices over the specified length (20 periods). It smooths the high prices over time, showing the average high price trend.
2. **MA Low (maLow):** This is the simple moving average of the low prices over the same length (20 periods). It smooths the low prices over time, showing the average low price trend.
### Plotting
- **MA High (Blue Line):** This line represents the moving average of the high prices. It is plotted in blue with a line width of 2.
- **MA Low (Red Line):** This line represents the moving average of the low prices. It is plotted in red with a line width of 2.
### Interpretation
1. **Trend Identification:**
- **Bullish Trend:** When the MA High is above the MA Low, it generally indicates a bullish trend, as the average high prices are higher than the average low prices.
- **Bearish Trend:** When the MA High is below the MA Low, it suggests a bearish trend, as the average high prices are lower than the average low prices.
2. **Support and Resistance:**
- The MA High can act as a dynamic resistance level, where the price may face selling pressure.
- The MA Low can act as a dynamic support level, where the price may find buying interest.
3. **Price Channels:**
- The area between the MA High and MA Low creates a channel that can help traders visualize the range within which the price is fluctuating. This channel can be used to identify potential breakout or breakdown points.
### Example Usage
- **Buy Signal:** A potential buy signal may occur when the price crosses above both the MA High and MA Low, indicating a possible upward trend.
- **Sell Signal:** A potential sell signal may occur when the price crosses below both the MA High and MA Low, indicating a possible downward trend.
This indicator provides a visual representation of the average high and low prices, helping traders identify trends, potential support and resistance levels, and price channels for better trading decisions.
ZigZag Library [TradingFinder]🔵 Introduction
The "Zig Zag" indicator is an analytical tool that emerges from pricing changes. Essentially, it connects consecutive high and low points in an oscillatory manner. This method helps decipher price changes and can also be useful in identifying traditional patterns.
By sifting through partial price changes, "Zig Zag" can effectively pinpoint price fluctuations within defined time intervals.
🔵 Key Features
1. Drawing the Zig Zag based on Pivot points :
The algorithm is based on pivots that operate consecutively and alternately (switch between high and low swing). In this way, zigzag lines are connected from a swing high to a swing low and from a swing low to a swing high.
Also, with a very low probability, it is possible to have both low pivots and high pivots in one candle. In these cases, the algorithm tries to make the best decision to make the most suitable choice.
You can control what period these decisions are based on through the "PiPe" parameter.
2.Naming and labeling each pivot based on its position as "Higher High" (HH), "Lower Low" (LL), "Higher Low" (HL), and "Lower High" (LH).
Additionally, classic patterns such as HH, LH, LL, and HL can be recognized. All traders analyzing financial markets using classic patterns and Elliot Waves can benefit from the "zigzag" indicator to facilitate their analysis.
" HH ": When the price is higher than the previous peak (Higher High).
" HL ": When the price is higher than the previous low (Higher Low).
" LH ": When the price is lower than the previous peak (Lower High).
" LL ": When the price is lower than the previous low (Lower Low).
🔵 How to Use
First, you can add the library to your code as shown in the example below.
import TFlab/ZigZagLibrary_TradingFinder/1 as ZZ
Function "ZigZag" Parameters :
🟣 Logical Parameters
1. HIGH : You should place the "high" value here. High is a float variable.
2. LOW : You should place the "low" value here. Low is a float variable.
3. BAR_INDEX : You should place the "bar_index" value here. Bar_index is an integer variable.
4. PiPe : The desired pivot period for plotting Zig Zag is placed in this parameter. For example, if you intend to draw a Zig Zag with a Swing Period of 5, you should input 5.
PiPe is an integer variable.
Important :
Apart from the "PiPe" indicator, which is part of the customization capabilities of this indicator, you can create a multi-time frame mode for the indicator using 3 parameters "High", "Low" and "BAR_INDEX". In this way, instead of the data of the current time frame, use the data of other time frames.
Note that it is better to use the current time frame data, because using the multi-time frame mode is associated with challenges that may cause bugs in your code.
🟣 Setting Parameters
5. SHOW_LINE : It's a boolean variable. When true, the Zig Zag line is displayed, and when false, the Zig Zag line display is disabled.
6. STYLE_LINE : In this variable, you can determine the style of the Zig Zag line. You can input one of the 3 options: line.style_solid, line.style_dotted, line.style_dashed. STYLE_LINE is a constant string variable.
7. COLOR_LINE : This variable takes the input of the line color.
8. WIDTH_LINE : The input for this variable is a number from 1 to 3, which is used to adjust the thickness of the line that draws the Zig Zag. WIDTH_LINE is an integer variable.
9. SHOW_LABEL : It's a boolean variable. When true, labels are displayed, and when false, label display is disabled.
10. COLOR_LABEL : The color of the labels is set in this variable.
11. SIZE_LABEL : The size of the labels is set in this variable. You should input one of the following options: size.auto, size.tiny, size.small, size.normal, size.large, size.huge.
12. Show_Support : It's a boolean variable that, when true, plots the last support line, and when false, disables its plotting.
13. Show_Resistance : It's a boolean variable that, when true, plots the last resistance line, and when false, disables its plotting.
Suggestion :
You can use the following code snippet to import Zig Zag into your code for time efficiency.
//import Library
import TFlab/ZigZagLibrary_TradingFinder/1 as ZZ
// Input and Setting
// Zig Zag Line
ShZ = input.bool(true , 'Show Zig Zag Line', group = 'Zig Zag') //Show Zig Zag
PPZ = input.int(5 ,'Pivot Period Zig Zag Line' , group = 'Zig Zag') //Pivot Period Zig Zag
ZLS = input.string(line.style_dashed , 'Zig Zag Line Style' , options = , group = 'Zig Zag' )
//Zig Zag Line Style
ZLC = input.color(color.rgb(0, 0, 0) , 'Zig Zag Line Color' , group = 'Zig Zag') //Zig Zag Line Color
ZLW = input.int(1 , 'Zig Zag Line Width' , group = 'Zig Zag')//Zig Zag Line Width
// Label
ShL = input.bool(true , 'Label', group = 'Label') //Show Label
LC = input.color(color.rgb(0, 0, 0) , 'Label Color' , group = 'Label')//Label Color
LS = input.string(size.tiny , 'Label size' , options = , group = 'Label' )//Label size
Show_Support= input.bool(false, 'Show Last Support',
tooltip = 'Last Support' , group = 'Support and Resistance')
Show_Resistance = input.bool(false, 'Show Last Resistance',
tooltip = 'Last Resistance' , group = 'Support and Resistance')
//Call Function
ZZ.ZigZag(high ,low ,bar_index ,PPZ , ShZ ,ZLS , ZLC, ZLW ,ShL , LC , LS , Show_Support , Show_Resistance )
Key Levels | Flux Charts💎 GENERAL OVERVIEW
Introducing our new Key Levels indicator! This indicator allows you to see the key levels on the current chart such as previous day lows / highs, pre-market data, yesterday's close, today's open, pivot points, and much more! It's highly user-friendly with every line being individually customizable and having a wide range of text options.
Features of the new Key Levels indicator :
Today & Yesterday High, Low, Open & Close
Previous 3-10th Day Highs & Lows
Pre-Market Highs & Lows
Previous Month High & Low
High & Low Pivots
Combination Of Same Levels
Wide Customization Options
📌 HOW DOES IT WORK ?
Key levels are important areas in a chart where a significant reaction is expected. In this indicator, you can enable up to the previous 10 days highs and lows, yesterday's close / today's open, and the latest pivot points. Key levels generally act like support & resistance. Here are a few examples :
As shown, key levels play an important role determining the current trend and can be useful in identifying potential levels where the market will reverse or breakout.
🚩UNIQUENESS
1. More Key Levels
We believe that past key levels may be as important as current ones. Some of the key-levels indicators do not include them even though strong reactions can happen around them. Thus, our indicator let's you check up to 10 days backwards.
You can select the ones you think that are the most important and just enable them, making the indicator customizable to your liking.
2. Pre-Market Data
With assets that have pre-market data available, it's crucial to analyze it to have a better understanding of the market in regular trading hours. Our indicator will plot pre-market highs and lows, even if your chart is in the regular trading hours only mode. We believe this will be helpful with your analyzing process.
3. Combination
The indicator can dynamically combine same key levels, so you can have a clear look to the chart without lines & text colliding with each other. This would also help you determine stronger key levels as if a key level occured more than a time, it could be a sign that it's a stronger one. An example :
To summarize, using key levels is an essential skill while detecting zones where strong reactions are expected. This indicator provides up to 10 day's high and low levels, and all of them can be individually turned on / off. Traders that believe older key levels can be important and want to look at the whole picture may use this feature. Also for assets that have pre-market data available, the indicator provides pre-market levels as well. Besides all that, High & Low pivots will provide latest key levels so traders can use them in their decisions.
⚙️SETTINGS
1. General Configuration
You can enable / disable :
1. Today's High / Low / Open
2. Yesterday's High / Low / Close
3. 3th-10th Day High / Low
4. Pre-Market High / Low
5. Previous Month High / Low
You can also change the colors and switch their line styles between solid, dashed and dotted.
2. High & Low Pivots
Enabled -> Enable / Disable High & Low Pivots
Pivot Range -> The range used in the detection of pivot points. Larger values will result in less pivot points, while smaller values will provide more pivot points. This essentially determines how many bars to the right & left shouldn't exceed the pivot's high or low.
You can also change the text color and text size of the pivots from the settings.
3. Style settings
Text Offset -> How many bars of offset should the texts have to the right. Increase if text collides with bars while Align Labels option is set to "Right".
Extend Lines -> If enabled, lines will be extended infinitely to right & left. If disabled, all lines will be clamped in their timelines.
Show Line Values -> If enabled, line information text will contain their price.
Align Labels ->
Right = Align line labels to right.
Center = Line labels will always be at the center of the screen.
