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In den Scripts nach "high low" suchen

TR_HighLow_LibLibrary "TR_HighLow_Lib" TODO: add library description here ShowLabel(_Text, _X, _Y, _Style, _Size, _Yloc, _Color)   TODO: Function to display labels   Parameters:      _Text : TODO: text (series string) Label text.      _X : TODO: x (series int) Bar index.      _Y : TODO: y (series int/float) Price of the label position.      _Style : TODO: style (series string) Label style.      _Size : TODO: size (series string) Label size.      _Yloc : TODO: yloc (series string) Possible values are yloc.price, yloc.abovebar, yloc.belowbar.      _Color : TODO: color (series color) Color of the label border and arrow   Returns: TODO: No return values GetColor(_Index)   TODO: Function to take out 12 colors in order   Parameters:      _Index : TODO: color number.   Returns: TODO: color code Tbl_position(_Pos)   TODO: Table display position function   Parameters:      _Pos : TODO: position.   Returns: TODO: Table position DeleteLine()   TODO: Delete Line   Parameters:      : TODO: No parameter   Returns: TODO: No return value DeleteLabel()   TODO: Delete Label   Parameters:      : TODO: No parameter   Returns: TODO: No return value ZigZag(_a_PHiLo, _a_IHiLo, _a_FHiLo, _a_DHiLo, _Histories, _Provisional_PHiLo, _Provisional_IHiLo, _Color1, _Width1, _Color2, _Width2, _ShowLabel, _ShowHighLowBar, _HighLowBarWidth, _HighLow_LabelSize)   TODO: Draw a zig-zag line.   Parameters:      _a_PHiLo : TODO: High-Low price array      _a_IHiLo : TODO: High-Low INDEX array      _a_FHiLo : TODO: High-Low flag array sequence 1:High 2:Low      _a_DHiLo : TODO: High-Low Price Differential Array      _Histories : TODO: Array size (High-Low length)      _Provisional_PHiLo : TODO: Provisional High-Low Price      _Provisional_IHiLo : TODO: Provisional High-Low INDEX      _Color1 : TODO: Normal High-Low color      _Width1 : TODO: Normal High-Low width      _Color2 : TODO: Provisional High-Low color      _Width2 : TODO: Provisional High-Low width      _ShowLabel : TODO: Label display flag True: Displayed False: Not displayed      _ShowHighLowBar : TODO: High-Low bar display flag True:Show False:Hide      _HighLowBarWidth : TODO: High-Low bar width      _HighLow_LabelSize : TODO: Label Size   Returns: TODO: No return value TrendLine(_a_PHiLo, _a_IHiLo, _Histories, _MultiLine, _StartWidth, _EndWidth, _IncreWidth, _StartTrans, _EndTrans, _IncreTrans, _ColorMode, _Color1_1, _Color1_2, _Color2_1, _Color2_2, _Top_High, _Top_Low, _Bottom_High, _Bottom_Low)   TODO: Draw a Trend Line   Parameters:      _a_PHiLo : TODO: High-Low price array      _a_IHiLo : TODO: High-Low INDEX array      _Histories : TODO: Array size (High-Low length)      _MultiLine : TODO: Draw a multiple Line.      _StartWidth : TODO: Line width start value      _EndWidth : TODO: Line width end value      _IncreWidth : TODO: Line width increment value      _StartTrans : TODO: Transparent rate start value      _EndTrans : TODO: Transparent rate finally      _IncreTrans : TODO: Transparent rate increase value      _ColorMode : TODO: 0:Nomal 1:Gradation      _Color1_1 : TODO: Gradation Color 1_1      _Color1_2 : TODO: Gradation Color 1_2      _Color2_1 : TODO: Gradation Color 2_1      _Color2_2 : TODO: Gradation Color 2_2      _Top_High : TODO: _Top_High Value for Gradation      _Top_Low : TODO: _Top_Low Value for Gradation      _Bottom_High : TODO: _Bottom_High Value for Gradation      _Bottom_Low : TODO: _Bottom_Low Value for Gradation   Returns: TODO: No return value Fibonacci(_a_Fibonacci, _a_PHiLo, _Provisional_PHiLo, _Index, _FrontMargin, _BackMargin)   TODO: Draw a Fibonacci line   Parameters:      _a_Fibonacci : TODO: Fibonacci Percentage Array      _a_PHiLo : TODO: High-Low price array      _Provisional_PHiLo : TODO: Provisional High-Low price (when _Index is 0)      _Index : TODO: Where to draw the Fibonacci line      _FrontMargin : TODO: Fibonacci line front-margin      _BackMargin : TODO: Fibonacci line back-margin   Returns: TODO: No return value Fibonacci(_a_Fibonacci, _a_PHiLo, _Provisional_PHiLo, _Index1, _FrontMargin1, _BackMargin1, _Transparent1, _Index2, _FrontMargin2, _BackMargin2, _Transparent2)   TODO: Draw a Fibonacci line   Parameters:      _a_Fibonacci : TODO: Fibonacci Percentage Array      _a_PHiLo : TODO: High-Low price array      _Provisional_PHiLo : TODO: Provisional High-Low price (when _Index is 0)      _Index1 : TODO: Where to draw the Fibonacci line 1      _FrontMargin1 : TODO: Fibonacci line front-margin 1      _BackMargin1 : TODO: Fibonacci line back-margin 1      _Transparent1 : TODO: Transparent rate 1      _Index2 : TODO: Where to draw the Fibonacci line 2      _FrontMargin2 : TODO: Fibonacci line front-margin 2      _BackMargin2 : TODO: Fibonacci line back-margin 2      _Transparent2 : TODO: Transparent rate 2   Returns: TODO: No return value High_Low_Judgment(_Length, _Extension, _Difference)   TODO: Judges High-Low   Parameters:      _Length : TODO: High-Low Confirmation Length      _Extension : TODO: Length of extension when the difference did not open      _Difference : TODO: Difference size   Returns: TODO: _HiLo=High-Low flag 0:Neither high nor low、1:High、2:Low、3:High-Low _PHi=high price、_PLo=low price、_IHi=High Price Index、_ILo=Low Price Index、 _Cnt=count、_ECnt=Extension count、 _DiffHi=Difference from Start(High)、_DiffLo=Difference from Start(Low)、 _StartHi=Start value(High)、_StartLo=Start value(Low) High_Low_Data_AddedAndUpdated(_HiLo, _Histories, _PHi, _PLo, _IHi, _ILo, _DiffHi, _DiffLo, _a_PHiLo, _a_IHiLo, _a_FHiLo, _a_DHiLo)   TODO: Adds and updates High-Low related arrays from given parameters   Parameters:      _HiLo : TODO: High-Low flag      _Histories : TODO: Array size (High-Low length)      _PHi : TODO: Price Hi      _PLo : TODO: Price Lo      _IHi : TODO: Index Hi      _ILo : TODO: Index Lo      _DiffHi : TODO: Difference in High      _DiffLo : TODO: Difference in Low      _a_PHiLo : TODO: High-Low price array      _a_IHiLo : TODO: High-Low INDEX array      _a_FHiLo : TODO: High-Low flag array 1:High 2:Low      _a_DHiLo : TODO: High-Low Price Differential Array   Returns: TODO: _PHiLo price array、_IHiLo indexed array、_FHiLo flag array、_DHiLo price-matching array、 Provisional_PHiLo Provisional price、Provisional_IHiLo 暫定インデックス High_Low(_a_PHiLo, _a_IHiLo, _a_FHiLo, _a_DHiLo, _a_Fibonacci, _Length, _Extension, _Difference, _Histories, _ShowZigZag, _ZigZagColor1, _ZigZagWidth1, _ZigZagColor2, _ZigZagWidth2, _ShowZigZagLabel, _ShowHighLowBar, _ShowTrendLine, _TrendMultiLine, _TrendStartWidth, _TrendEndWidth, _TrendIncreWidth, _TrendStartTrans, _TrendEndTrans, _TrendIncreTrans, _TrendColorMode, _TrendColor1_1, _TrendColor1_2, _TrendColor2_1, _TrendColor2_2, _ShowFibonacci1, _FibIndex1, _FibFrontMargin1, _FibBackMargin1, _FibTransparent1, _ShowFibonacci2, _FibIndex2, _FibFrontMargin2, _FibBackMargin2, _FibTransparent2, _ShowInfoTable1, _TablePosition1, _ShowInfoTable2, _TablePosition2)   TODO: Draw the contents of the High-Low array.   Parameters:      _a_PHiLo : TODO: High-Low price array      _a_IHiLo : TODO: High-Low INDEX array      _a_FHiLo : TODO: High-Low flag sequence 1:High 2:Low      _a_DHiLo : TODO: High-Low Price Differential Array      _a_Fibonacci : TODO: Fibonacci Gnar Matching      _Length : TODO: Length of confirmation      _Extension : TODO: Extension Length of extension when the difference did not open      _Difference : TODO: Difference size      _Histories : TODO: High-Low Length      _ShowZigZag : TODO: ZigZag Display      _ZigZagColor1 : TODO: Colors of ZigZag1      _ZigZagWidth1 : TODO: Width of ZigZag1      _ZigZagColor2 : TODO: Colors of ZigZag2      _ZigZagWidth2 : TODO: Width of ZigZag2      _ShowZigZagLabel : TODO: ZigZagLabel Display      _ShowHighLowBar : TODO: High-Low Bar Display      _ShowTrendLine : TODO: Trend Line Display      _TrendMultiLine : TODO: Trend Multi Line Display      _TrendStartWidth : TODO: Line width start value      _TrendEndWidth : TODO: Line width end value      _TrendIncreWidth : TODO: Line width increment value      _TrendStartTrans : TODO: Starting transmittance value      _TrendEndTrans : TODO: Transmittance End Value      _TrendIncreTrans : TODO: Increased transmittance value      _TrendColorMode : TODO: color mode      _TrendColor1_1 : TODO: Trend Color 1_1      _TrendColor1_2 : TODO: Trend Color 1_2      _TrendColor2_1 : TODO: Trend Color 2_1      _TrendColor2_2 : TODO: Trend Color 2_2      _ShowFibonacci1 : TODO: Fibonacci1 Display      _FibIndex1 : TODO: Fibonacci1 Index No.      _FibFrontMargin1 : TODO: Fibonacci1 Front margin      _FibBackMargin1 : TODO: Fibonacci1 Back Margin      _FibTransparent1 : TODO: Fibonacci1 Transmittance      _ShowFibonacci2 : TODO: Fibonacci2 Display      _FibIndex2 : TODO: Fibonacci2 Index No.      _FibFrontMargin2 : TODO: Fibonacci2 Front margin      _FibBackMargin2 : TODO: Fibonacci2 Back Margin      _FibTransparent2 : TODO: Fibonacci2 Transmittance      _ShowInfoTable1 : TODO: InfoTable1 Display      _TablePosition1 : TODO: InfoTable1 position      _ShowInfoTable2 : TODO: InfoTable2 Display      _TablePosition2 : TODO: InfoTable2 position   Returns: TODO: 無し
Pine Script® Bibliothek
von Tommy_Rich
1
TR_HighLowLibrary "TR_HighLow" TODO: add library description here ShowLabel(_Text, _X, _Y, _Style, _Size, _Yloc, _Color)   TODO: Function to display labels   Parameters:      _Text : TODO: text (series string) Label text.      _X : TODO: x (series int) Bar index.      _Y : TODO: y (series int/float) Price of the label position.      _Style : TODO: style (series string) Label style.      _Size : TODO: size (series string) Label size.      _Yloc : TODO: yloc (series string) Possible values are yloc.price, yloc.abovebar, yloc.belowbar.      _Color : TODO: color (series color) Color of the label border and arrow   Returns: TODO: No return values GetColor(_Index)   TODO: Function to take out 12 colors in order   Parameters:      _Index : TODO: color number.   Returns: TODO: color code Tbl_position(_Pos)   TODO: Table display position function   Parameters:      _Pos : TODO: position.   Returns: TODO: Table position DeleteLine()   TODO: Delete Line   Parameters:      : TODO: No parameter   Returns: TODO: No return value DeleteLabel()   TODO: Delete Label   Parameters:      : TODO: No parameter   Returns: TODO: No return value ZigZag(_a_PHiLo, _a_IHiLo, _a_FHiLo, _a_DHiLo, _Histories, _Provisional_PHiLo, _Provisional_IHiLo, _Color1, _Width1, _Color2, _Width2, _ShowLabel, _ShowHighLowBar, _HighLowBarWidth, _HighLow_LabelSize)   TODO: Draw a zig-zag line.   Parameters:      _a_PHiLo : TODO: High-Low price array      _a_IHiLo : TODO: High-Low INDEX array      _a_FHiLo : TODO: High-Low flag array sequence 1:High 2:Low      _a_DHiLo : TODO: High-Low Price Differential Array      _Histories : TODO: Array size (High-Low length)      _Provisional_PHiLo : TODO: Provisional High-Low Price      _Provisional_IHiLo : TODO: Provisional High-Low INDEX      _Color1 : TODO: Normal High-Low color      _Width1 : TODO: Normal High-Low width      _Color2 : TODO: Provisional High-Low color      _Width2 : TODO: Provisional High-Low width      _ShowLabel : TODO: Label display flag True: Displayed False: Not displayed      _ShowHighLowBar : TODO: High-Low bar display flag True:Show False:Hide      _HighLowBarWidth : TODO: High-Low bar width      _HighLow_LabelSize : TODO: Label Size   Returns: TODO: No return value TrendLine(_a_PHiLo, _a_IHiLo, _Histories, _MultiLine, _StartWidth, _EndWidth, _IncreWidth, _StartTrans, _EndTrans, _IncreTrans, _ColorMode, _Color1_1, _Color1_2, _Color2_1, _Color2_2, _Top_High, _Top_Low, _Bottom_High, _Bottom_Low)   TODO: Draw a Trend Line   Parameters:      _a_PHiLo : TODO: High-Low price array      _a_IHiLo : TODO: High-Low INDEX array      _Histories : TODO: Array size (High-Low length)      _MultiLine : TODO: Draw a multiple Line.      _StartWidth : TODO: Line width start value      _EndWidth : TODO: Line width end value      _IncreWidth : TODO: Line width increment value      _StartTrans : TODO: Transparent rate start value      _EndTrans : TODO: Transparent rate finally      _IncreTrans : TODO: Transparent rate increase value      _ColorMode : TODO: 0:Nomal 1:Gradation      _Color1_1 : TODO: Gradation Color 1_1      _Color1_2 : TODO: Gradation Color 1_2      _Color2_1 : TODO: Gradation Color 2_1      _Color2_2 : TODO: Gradation Color 2_2      _Top_High : TODO: _Top_High Value for Gradation      _Top_Low : TODO: _Top_Low Value for Gradation      _Bottom_High : TODO: _Bottom_High Value for Gradation      _Bottom_Low : TODO: _Bottom_Low Value for Gradation   Returns: TODO: No return value Fibonacci(_a_Fibonacci, _a_PHiLo, _Provisional_PHiLo, _Index, _FrontMargin, _BackMargin)   TODO: Draw a Fibonacci line   Parameters:      _a_Fibonacci : TODO: Fibonacci Percentage Array      _a_PHiLo : TODO: High-Low price array      _Provisional_PHiLo : TODO: Provisional High-Low price (when _Index is 0)      _Index : TODO: Where to draw the Fibonacci line      _FrontMargin : TODO: Fibonacci line front-margin      _BackMargin : TODO: Fibonacci line back-margin   Returns: TODO: No return value Fibonacci(_a_Fibonacci, _a_PHiLo, _Provisional_PHiLo, _Index1, _FrontMargin1, _BackMargin1, _Transparent1, _Index2, _FrontMargin2, _BackMargin2, _Transparent2)   TODO: Draw a Fibonacci line   Parameters:      _a_Fibonacci : TODO: Fibonacci Percentage Array      _a_PHiLo : TODO: High-Low price array      _Provisional_PHiLo : TODO: Provisional High-Low price (when _Index is 0)      _Index1 : TODO: Where to draw the Fibonacci line 1      _FrontMargin1 : TODO: Fibonacci line front-margin 1      _BackMargin1 : TODO: Fibonacci line back-margin 1      _Transparent1 : TODO: Transparent rate 1      _Index2 : TODO: Where to draw the Fibonacci line 2      _FrontMargin2 : TODO: Fibonacci line front-margin 2      _BackMargin2 : TODO: Fibonacci line back-margin 2      _Transparent2 : TODO: Transparent rate 2   Returns: TODO: No return value High_Low_Judgment(_Length, _Extension, _Difference)   TODO: Judges High-Low   Parameters:      _Length : TODO: High-Low Confirmation Length      _Extension : TODO: Length of extension when the difference did not open      _Difference : TODO: Difference size   Returns: TODO: _HiLo=High-Low flag 0:Neither high nor low、1:High、2:Low、3:High-Low _PHi=high price、_PLo=low price、_IHi=High Price Index、_ILo=Low Price Index、 _Cnt=count、_ECnt=Extension count、 _DiffHi=Difference from Start(High)、_DiffLo=Difference from Start(Low)、 _StartHi=Start value(High)、_StartLo=Start value(Low) High_Low_Data_AddedAndUpdated(_HiLo, _Histories, _PHi, _PLo, _IHi, _ILo, _DiffHi, _DiffLo, _a_PHiLo, _a_IHiLo, _a_FHiLo, _a_DHiLo)   TODO: Adds and updates High-Low related arrays from given parameters   Parameters:      _HiLo : TODO: High-Low flag      _Histories : TODO: Array size (High-Low length)      _PHi : TODO: Price Hi      _PLo : TODO: Price Lo      _IHi : TODO: Index Hi      _ILo : TODO: Index Lo      _DiffHi : TODO: Difference in High      _DiffLo : TODO: Difference in Low      _a_PHiLo : TODO: High-Low price array      _a_IHiLo : TODO: High-Low INDEX array      _a_FHiLo : TODO: High-Low flag array 1:High 2:Low      _a_DHiLo : TODO: High-Low Price Differential Array   Returns: TODO: _PHiLo price array、_IHiLo indexed array、_FHiLo flag array、_DHiLo price-matching array、 Provisional_PHiLo Provisional price、Provisional_IHiLo 暫定インデックス High_Low(_a_PHiLo, _a_IHiLo, _a_FHiLo, _a_DHiLo, _a_Fibonacci, _Length, _Extension, _Difference, _Histories, _ShowZigZag, _ZigZagColor1, _ZigZagWidth1, _ZigZagColor2, _ZigZagWidth2, _ShowZigZagLabel, _ShowHighLowBar, _ShowTrendLine, _TrendMultiLine, _TrendStartWidth, _TrendEndWidth, _TrendIncreWidth, _TrendStartTrans, _TrendEndTrans, _TrendIncreTrans, _TrendColorMode, _TrendColor1_1, _TrendColor1_2, _TrendColor2_1, _TrendColor2_2, _ShowFibonacci1, _FibIndex1, _FibFrontMargin1, _FibBackMargin1, _FibTransparent1, _ShowFibonacci2, _FibIndex2, _FibFrontMargin2, _FibBackMargin2, _FibTransparent2, _ShowInfoTable1, _TablePosition1, _ShowInfoTable2, _TablePosition2)   TODO: Draw the contents of the High-Low array.   Parameters:      _a_PHiLo : TODO: High-Low price array      _a_IHiLo : TODO: High-Low INDEX array      _a_FHiLo : TODO: High-Low flag sequence 1:High 2:Low      _a_DHiLo : TODO: High-Low Price Differential Array      _a_Fibonacci : TODO: Fibonacci Gnar Matching      _Length : TODO: Length of confirmation      _Extension : TODO: Extension Length of extension when the difference did not open      _Difference : TODO: Difference size      _Histories : TODO: High-Low Length      _ShowZigZag : TODO: ZigZag Display      _ZigZagColor1 : TODO: Colors of ZigZag1      _ZigZagWidth1 : TODO: Width of ZigZag1      _ZigZagColor2 : TODO: Colors of ZigZag2      _ZigZagWidth2 : TODO: Width of ZigZag2      _ShowZigZagLabel : TODO: ZigZagLabel Display      _ShowHighLowBar : TODO: High-Low Bar Display      _ShowTrendLine : TODO: Trend Line Display      _TrendMultiLine : TODO: Trend Multi Line Display      _TrendStartWidth : TODO: Line width start value      _TrendEndWidth : TODO: Line width end value      _TrendIncreWidth : TODO: Line width increment value      _TrendStartTrans : TODO: Starting transmittance value      _TrendEndTrans : TODO: Transmittance End Value      _TrendIncreTrans : TODO: Increased transmittance value      _TrendColorMode : TODO: color mode      _TrendColor1_1 : TODO: Trend Color 1_1      _TrendColor1_2 : TODO: Trend Color 1_2      _TrendColor2_1 : TODO: Trend Color 2_1      _TrendColor2_2 : TODO: Trend Color 2_2      _ShowFibonacci1 : TODO: Fibonacci1 Display      _FibIndex1 : TODO: Fibonacci1 Index No.      _FibFrontMargin1 : TODO: Fibonacci1 Front margin      _FibBackMargin1 : TODO: Fibonacci1 Back Margin      _FibTransparent1 : TODO: Fibonacci1 Transmittance      _ShowFibonacci2 : TODO: Fibonacci2 Display      _FibIndex2 : TODO: Fibonacci2 Index No.      _FibFrontMargin2 : TODO: Fibonacci2 Front margin      _FibBackMargin2 : TODO: Fibonacci2 Back Margin      _FibTransparent2 : TODO: Fibonacci2 Transmittance      _ShowInfoTable1 : TODO: InfoTable1 Display      _TablePosition1 : TODO: InfoTable1 position      _ShowInfoTable2 : TODO: InfoTable2 Display      _TablePosition2 : TODO: InfoTable2 position   Returns: TODO: 無し
Pine Script® Bibliothek
von Tommy_Rich
ATAI Volume analysis with price action V 1.00ATAI Volume Analysis with Price Action 1. Introduction 1.1 Overview ATAI Volume Analysis with Price Action is a composite indicator designed for TradingView. It combines per‑side volume data —that is, how much buying and selling occurs during each bar—with standard price‑structure elements such as swings, trend lines and support/resistance. By blending these elements the script aims to help a trader understand which side is in control, whether a breakout is genuine, when markets are potentially exhausted and where liquidity providers might be active. The indicator is built around TradingView’s up/down volume feed accessed via the TradingView/ta/10 library. The following excerpt from the script illustrates how this feed is configured: import TradingView/ta/10 as tvta // Determine lower timeframe string based on user choice and chart resolution string lower_tf_breakout = use_custom_tf_input ? custom_tf_input : timeframe.isseconds ? "1S" : timeframe.isintraday ? "1" : timeframe.isdaily ? "5" : "60" // Request up/down volume (both positive) = tvta.requestUpAndDownVolume(lower_tf_breakout) Lower‑timeframe selection. If you do not specify a custom lower timeframe, the script chooses a default based on your chart resolution: 1 second for second charts, 1 minute for intraday charts, 5 minutes for daily charts and 60 minutes for anything longer. Smaller intervals provide a more precise view of buyer and seller flow but cover fewer bars. Larger intervals cover more history at the cost of granularity. Tick vs. time bars. Many trading platforms offer a tick / intrabar calculation mode that updates an indicator on every trade rather than only on bar close. Turning on one‑tick calculation will give the most accurate split between buy and sell volume on the current bar, but it typically reduces the amount of historical data available. For the highest fidelity in live trading you can enable this mode; for studying longer histories you might prefer to disable it. When volume data is completely unavailable (some instruments and crypto pairs), all modules that rely on it will remain silent and only the price‑structure backbone will operate. Figure caption, Each panel shows the indicator’s info table for a different volume sampling interval. In the left chart, the parentheses “(5)” beside the buy‑volume figure denote that the script is aggregating volume over five‑minute bars; the center chart uses “(1)” for one‑minute bars; and the right chart uses “(1T)” for a one‑tick interval. These notations tell you which lower timeframe is driving the volume calculations. Shorter intervals such as 1 minute or 1 tick provide finer detail on buyer and seller flow, but they cover fewer bars; longer intervals like five‑minute bars smooth the data and give more history. Figure caption, The values in parentheses inside the info table come directly from the Breakout — Settings. The first row shows the custom lower-timeframe used for volume calculations (e.g., “(1)”, “(5)”, or “(1T)”) 2. Price‑Structure Backbone Even without volume, the indicator draws structural features that underpin all other modules. These features are always on and serve as the reference levels for subsequent calculations. 2.1 What it draws • Pivots: Swing highs and lows are detected using the pivot_left_input and pivot_right_input settings. A pivot high is identified when the high recorded pivot_right_input bars ago exceeds the highs of the preceding pivot_left_input bars and is also higher than (or equal to) the highs of the subsequent pivot_right_input bars; pivot lows follow the inverse logic. The indicator retains only a fixed number of such pivot points per side, as defined by point_count_input, discarding the oldest ones when the limit is exceeded. • Trend lines: For each side, the indicator connects the earliest stored pivot and the most recent pivot (oldest high to newest high, and oldest low to newest low). When a new pivot is added or an old one drops out of the lookback window, the line’s endpoints—and therefore its slope—are recalculated accordingly. • Horizontal support/resistance: The highest high and lowest low within the lookback window defined by length_input are plotted as horizontal dashed lines. These serve as short‑term support and resistance levels. • Ranked labels: If showPivotLabels is enabled the indicator prints labels such as “HH1”, “HH2”, “LL1” and “LL2” near each pivot. The ranking is determined by comparing the price of each stored pivot: HH1 is the highest high, HH2 is the second highest, and so on; LL1 is the lowest low, LL2 is the second lowest. In the case of equal prices the newer pivot gets the better rank. Labels are offset from price using ½ × ATR × label_atr_multiplier, with the ATR length defined by label_atr_len_input. A dotted connector links each label to the candle’s wick. 2.2 Key settings • length_input: Window length for finding the highest and lowest values and for determining trend line endpoints. A larger value considers more history and will generate longer trend lines and S/R levels. • pivot_left_input, pivot_right_input: Strictness of swing confirmation. Higher values require more bars on either side to form a pivot; lower values create more pivots but may include minor swings. • point_count_input: How many pivots are kept in memory on each side. When new pivots exceed this number the oldest ones are discarded. • label_atr_len_input and label_atr_multiplier: Determine how far pivot labels are offset from the bar using ATR. Increasing the multiplier moves labels further away from price. • Styling inputs for trend lines, horizontal lines and labels (color, width and line style). Figure caption, The chart illustrates how the indicator’s price‑structure backbone operates. In this daily example, the script scans for bars where the high (or low) pivot_right_input bars back is higher (or lower) than the preceding pivot_left_input bars and higher or lower than the subsequent pivot_right_input bars; only those bars are marked as pivots. These pivot points are stored and ranked: the highest high is labelled “HH1”, the second‑highest “HH2”, and so on, while lows are marked “LL1”, “LL2”, etc. Each label is offset from the price by half of an ATR‑based distance to keep the chart clear, and a dotted connector links the label to the actual candle. The red diagonal line connects the earliest and latest stored high pivots, and the green line does the same for low pivots; when a new pivot is added or an old one drops out of the lookback window, the end‑points and slopes adjust accordingly. Dashed horizontal lines mark the highest high and lowest low within the current lookback window, providing visual support and resistance levels. Together, these elements form the structural backbone that other modules reference, even when volume data is unavailable. 3. Breakout Module 3.1 Concept This module confirms that a price break beyond a recent high or low is supported by a genuine shift in buying or selling pressure. It requires price to clear the highest high (“HH1”) or lowest low (“LL1”) and, simultaneously, that the winning side shows a significant volume spike, dominance and ranking. Only when all volume and price conditions pass is a breakout labelled. 3.2 Inputs • lookback_break_input : This controls the number of bars used to compute moving averages and percentiles for volume. A larger value smooths the averages and percentiles but makes the indicator respond more slowly. • vol_mult_input : The “spike” multiplier; the current buy or sell volume must be at least this multiple of its moving average over the lookback window to qualify as a breakout. • rank_threshold_input (0–100) : Defines a volume percentile cutoff: the current buyer/seller volume must be in the top (100−threshold)%(100−threshold)% of all volumes within the lookback window. For example, if set to 80, the current volume must be in the top 20 % of the lookback distribution. • ratio_threshold_input (0–1) : Specifies the minimum share of total volume that the buyer (for a bullish breakout) or seller (for bearish) must hold on the current bar; the code also requires that the cumulative buyer volume over the lookback window exceeds the seller volume (and vice versa for bearish cases). • use_custom_tf_input / custom_tf_input : When enabled, these inputs override the automatic choice of lower timeframe for up/down volume; otherwise the script selects a sensible default based on the chart’s timeframe. • Label appearance settings : Separate options control the ATR-based offset length, offset multiplier, label size and colors for bullish and bearish breakout labels, as well as the connector style and width. 3.3 Detection logic 1. Data preparation : Retrieve per‑side volume from the lower timeframe and take absolute values. Build rolling arrays of the last lookback_break_input values to compute simple moving averages (SMAs), cumulative sums and percentile ranks for buy and sell volume. 2. Volume spike: A spike is flagged when the current buy (or, in the bearish case, sell) volume is at least vol_mult_input times its SMA over the lookback window. 3. Dominance test: The buyer’s (or seller’s) share of total volume on the current bar must meet or exceed ratio_threshold_input. In addition, the cumulative sum of buyer volume over the window must exceed the cumulative sum of seller volume for a bullish breakout (and vice versa for bearish). A separate requirement checks the sign of delta: for bullish breakouts delta_breakout must be non‑negative; for bearish breakouts it must be non‑positive. 4. Percentile rank: The current volume must fall within the top (100 – rank_threshold_input) percent of the lookback distribution—ensuring that the spike is unusually large relative to recent history. 5. Price test: For a bullish signal, the closing price must close above the highest pivot (HH1); for a bearish signal, the close must be below the lowest pivot (LL1). 6. Labeling: When all conditions above are satisfied, the indicator prints “Breakout ↑” above the bar (bullish) or “Breakout ↓” below the bar (bearish). Labels are offset using half of an ATR‑based distance and linked to the candle with a dotted connector. Figure caption, (Breakout ↑ example) , On this daily chart, price pushes above the red trendline and the highest prior pivot (HH1). The indicator recognizes this as a valid breakout because the buyer‑side volume on the lower timeframe spikes above its recent moving average and buyers dominate the volume statistics over the lookback period; when combined with a close above HH1, this satisfies the breakout conditions. The “Breakout ↑” label appears above the candle, and the info table highlights that up‑volume is elevated relative to its 11‑bar average, buyer share exceeds the dominance threshold and money‑flow metrics support the move. Figure caption, In this daily example, price breaks below the lowest pivot (LL1) and the lower green trendline. The indicator identifies this as a bearish breakout because sell‑side volume is sharply elevated—about twice its 11‑bar average—and sellers dominate both the bar and the lookback window. With the close falling below LL1, the script triggers a Breakout ↓ label and marks the corresponding row in the info table, which shows strong down volume, negative delta and a seller share comfortably above the dominance threshold. 4. Market Phase Module (Volume Only) 4.1 Concept Not all markets trend; many cycle between periods of accumulation (buying pressure building up), distribution (selling pressure dominating) and neutral behavior. This module classifies the current bar into one of these phases without using ATR , relying solely on buyer and seller volume statistics. It looks at net flows, ratio changes and an OBV‑like cumulative line with dual‑reference (1‑ and 2‑bar) trends. The result is displayed both as on‑chart labels and in a dedicated row of the info table. 4.2 Inputs • phase_period_len: Number of bars over which to compute sums and ratios for phase detection. • phase_ratio_thresh : Minimum buyer share (for accumulation) or minimum seller share (for distribution, derived as 1 − phase_ratio_thresh) of the total volume. • strict_mode: When enabled, both the 1‑bar and 2‑bar changes in each statistic must agree on the direction (strict confirmation); when disabled, only one of the two references needs to agree (looser confirmation). • Color customisation for info table cells and label styling for accumulation and distribution phases, including ATR length, multiplier, label size, colors and connector styles. • show_phase_module: Toggles the entire phase detection subsystem. • show_phase_labels: Controls whether on‑chart labels are drawn when accumulation or distribution is detected. 4.3 Detection logic The module computes three families of statistics over the volume window defined by phase_period_len: 1. Net sum (buyers minus sellers): net_sum_phase = Σ(buy) − Σ(sell). A positive value indicates a predominance of buyers. The code also computes the differences between the current value and the values 1 and 2 bars ago (d_net_1, d_net_2) to derive up/down trends. 2. Buyer ratio: The instantaneous ratio TF_buy_breakout / TF_tot_breakout and the window ratio Σ(buy) / Σ(total). The current ratio must exceed phase_ratio_thresh for accumulation or fall below 1 − phase_ratio_thresh for distribution. The first and second differences of the window ratio (d_ratio_1, d_ratio_2) determine trend direction. 3. OBV‑like cumulative net flow: An on‑balance volume analogue obv_net_phase increments by TF_buy_breakout − TF_sell_breakout each bar. Its differences over the last 1 and 2 bars (d_obv_1, d_obv_2) provide trend clues. The algorithm then combines these signals: • For strict mode , accumulation requires: (a) current ratio ≥ threshold, (b) cumulative ratio ≥ threshold, (c) both ratio differences ≥ 0, (d) net sum differences ≥ 0, and (e) OBV differences ≥ 0. Distribution is the mirror case. • For loose mode , it relaxes the directional tests: either the 1‑ or the 2‑bar difference needs to agree in each category. If all conditions for accumulation are satisfied, the phase is labelled “Accumulation” ; if all conditions for distribution are satisfied, it’s labelled “Distribution” ; otherwise the phase is “Neutral” . 4.4 Outputs • Info table row : Row 8 displays “Market Phase (Vol)” on the left and the detected phase (Accumulation, Distribution or Neutral) on the right. The text colour of both cells matches a user‑selectable palette (typically green for accumulation, red for distribution and grey for neutral). • On‑chart labels : When show_phase_labels is enabled and a phase persists for at least one bar, the module prints a label above the bar ( “Accum” ) or below the bar ( “Dist” ) with a dashed or dotted connector. The label is offset using ATR based on phase_label_atr_len_input and phase_label_multiplier and is styled according to user preferences. Figure caption, The chart displays a red “Dist” label above a particular bar, indicating that the accumulation/distribution module identified a distribution phase at that point. The detection is based on seller dominance: during that bar, the net buyer-minus-seller flow and the OBV‑style cumulative flow were trending down, and the buyer ratio had dropped below the preset threshold. These conditions satisfy the distribution criteria in strict mode. The label is placed above the bar using an ATR‑based offset and a dashed connector. By the time of the current bar in the screenshot, the phase indicator shows “Neutral” in the info table—signaling that neither accumulation nor distribution conditions are currently met—yet the historical “Dist” label remains to mark where the prior distribution phase began. Figure caption, In this example the market phase module has signaled an Accumulation phase. Three bars before the current candle, the algorithm detected a shift toward buyers: up‑volume exceeded its moving average, down‑volume was below average, and the buyer share of total volume climbed above the threshold while the on‑balance net flow and cumulative ratios were trending upwards. The blue “Accum” label anchored below that bar marks the start of the phase; it remains on the chart because successive bars continue to satisfy the accumulation conditions. The info table confirms this: the “Market Phase (Vol)” row still reads Accumulation, and the ratio and sum rows show buyers dominating both on the current bar and across the lookback window. 5. OB/OS Spike Module 5.1 What overbought/oversold means here In many markets, a rapid extension up or down is often followed by a period of consolidation or reversal. The indicator interprets overbought (OB) conditions as abnormally strong selling risk at or after a price rally and oversold (OS) conditions as unusually strong buying risk after a decline. Importantly, these are not direct trade signals; rather they flag areas where caution or contrarian setups may be appropriate. 5.2 Inputs • minHits_obos (1–7): Minimum number of oscillators that must agree on an overbought or oversold condition for a label to print. • syncWin_obos: Length of a small sliding window over which oscillator votes are smoothed by taking the maximum count observed. This helps filter out choppy signals. • Volume spike criteria: kVolRatio_obos (ratio of current volume to its SMA) and zVolThr_obos (Z‑score threshold) across volLen_obos. Either threshold can trigger a spike. • Oscillator toggles and periods: Each of RSI, Stochastic (K and D), Williams %R, CCI, MFI, DeMarker and Stochastic RSI can be independently enabled; their periods are adjustable. • Label appearance: ATR‑based offset, size, colors for OB and OS labels, plus connector style and width. 5.3 Detection logic 1. Directional volume spikes: Volume spikes are computed separately for buyer and seller volumes. A sell volume spike (sellVolSpike) flags a potential OverBought bar, while a buy volume spike (buyVolSpike) flags a potential OverSold bar. A spike occurs when the respective volume exceeds kVolRatio_obos times its simple moving average over the window or when its Z‑score exceeds zVolThr_obos. 2. Oscillator votes: For each enabled oscillator, calculate its overbought and oversold state using standard thresholds (e.g., RSI ≥ 70 for OB and ≤ 30 for OS; Stochastic %K/%D ≥ 80 for OB and ≤ 20 for OS; etc.). Count how many oscillators vote for OB and how many vote for OS. 3. Minimum hits: Apply the smoothing window syncWin_obos to the vote counts using a maximum‑of‑last‑N approach. A candidate bar is only considered if the smoothed OB hit count ≥ minHits_obos (for OverBought) or the smoothed OS hit count ≥ minHits_obos (for OverSold). 4. Tie‑breaking: If both OverBought and OverSold spike conditions are present on the same bar, compare the smoothed hit counts: the side with the higher count is selected; ties default to OverBought. 5. Label printing: When conditions are met, the bar is labelled as “OverBought X/7” above the candle or “OverSold X/7” below it. “X” is the number of oscillators confirming, and the bracket lists the abbreviations of contributing oscillators. Labels are offset from price using half of an ATR‑scaled distance and can optionally include a dotted or dashed connector line. Figure caption, In this chart the overbought/oversold module has flagged an OverSold signal. A sell‑off from the prior highs brought price down to the lower trend‑line, where the bar marked “OverSold 3/7 DeM” appears. This label indicates that on that bar the module detected a buy‑side volume spike and that at least three of the seven enabled oscillators—in this case including the DeMarker—were in oversold territory. The label is printed below the candle with a dotted connector, signaling that the market may be temporarily exhausted on the downside. After this oversold print, price begins to rebound towards the upper red trend‑line and higher pivot levels. Figure caption, This example shows the overbought/oversold module in action. In the left‑hand panel you can see the OB/OS settings where each oscillator (RSI, Stochastic, Williams %R, CCI, MFI, DeMarker and Stochastic RSI) can be enabled or disabled, and the ATR length and label offset multiplier adjusted. On the chart itself, price has pushed up to the descending red trendline and triggered an “OverBought 3/7” label. That means the sell‑side volume spiked relative to its average and three out of the seven enabled oscillators were in overbought territory. The label is offset above the candle by half of an ATR and connected with a dashed line, signaling that upside momentum may be overextended and a pause or pullback could follow. 6. Buyer/Seller Trap Module 6.1 Concept A bull trap occurs when price appears to break above resistance, attracting buyers, but fails to sustain the move and quickly reverses, leaving a long upper wick and trapping late entrants. A bear trap is the opposite: price breaks below support, lures in sellers, then snaps back, leaving a long lower wick and trapping shorts. This module detects such traps by looking for price structure sweeps, order‑flow mismatches and dominance reversals. It uses a scoring system to differentiate risk from confirmed traps. 6.2 Inputs • trap_lookback_len: Window length used to rank extremes and detect sweeps. • trap_wick_threshold: Minimum proportion of a bar’s range that must be wick (upper for bull traps, lower for bear traps) to qualify as a sweep. • trap_score_risk: Minimum aggregated score required to flag a trap risk. (The code defines a trap_score_confirm input, but confirmation is actually based on price reversal rather than a separate score threshold.) • trap_confirm_bars: Maximum number of bars allowed for price to reverse and confirm the trap. If price does not reverse in this window, the risk label will expire or remain unconfirmed. • Label settings: ATR length and multiplier for offsetting, size, colours for risk and confirmed labels, and connector style and width. Separate settings exist for bull and bear traps. • Toggle inputs: show_trap_module and show_trap_labels enable the module and control whether labels are drawn on the chart. 6.3 Scoring logic The module assigns points to several conditions and sums them to determine whether a trap risk is present. For bull traps, the score is built from the following (bear traps mirror the logic with highs and lows swapped): 1. Sweep (2 points): Price trades above the high pivot (HH1) but fails to close above it and leaves a long upper wick at least trap_wick_threshold × range. For bear traps, price dips below the low pivot (LL1), fails to close below and leaves a long lower wick. 2. Close break (1 point): Price closes beyond HH1 or LL1 without leaving a long wick. 3. Candle/delta mismatch (2 points): The candle closes bullish yet the order flow delta is negative or the seller ratio exceeds 50%, indicating hidden supply. Conversely, a bearish close with positive delta or buyer dominance suggests hidden demand. 4. Dominance inversion (2 points): The current bar’s buyer volume has the highest rank in the lookback window while cumulative sums favor sellers, or vice versa. 5. Low‑volume break (1 point): Price crosses the pivot but total volume is below its moving average. The total score for each side is compared to trap_score_risk. If the score is high enough, a “Bull Trap Risk” or “Bear Trap Risk” label is drawn, offset from the candle by half of an ATR‑scaled distance using a dashed outline. If, within trap_confirm_bars, price reverses beyond the opposite level—drops back below the high pivot for bull traps or rises above the low pivot for bear traps—the label is upgraded to a solid “Bull Trap” or “Bear Trap” . In this version of the code, there is no separate score threshold for confirmation: the variable trap_score_confirm is unused; confirmation depends solely on a successful price reversal within the specified number of bars. Figure caption, In this example the trap module has flagged a Bear Trap Risk. Price initially breaks below the most recent low pivot (LL1), but the bar closes back above that level and leaves a long lower wick, suggesting a failed push lower. Combined with a mismatch between the candle direction and the order flow (buyers regain control) and a reversal in volume dominance, the aggregate score exceeds the risk threshold, so a dashed “Bear Trap Risk” label prints beneath the bar. The green and red trend lines mark the current low and high pivot trajectories, while the horizontal dashed lines show the highest and lowest values in the lookback window. If, within the next few bars, price closes decisively above the support, the risk label would upgrade to a solid “Bear Trap” label. Figure caption, In this example the trap module has identified both ends of a price range. Near the highs, price briefly pushes above the descending red trendline and the recent pivot high, but fails to close there and leaves a noticeable upper wick. That combination of a sweep above resistance and order‑flow mismatch generates a Bull Trap Risk label with a dashed outline, warning that the upside break may not hold. At the opposite extreme, price later dips below the green trendline and the labelled low pivot, then quickly snaps back and closes higher. The long lower wick and subsequent price reversal upgrade the previous bear‑trap risk into a confirmed Bear Trap (solid label), indicating that sellers were caught on a false breakdown. Horizontal dashed lines mark the highest high and lowest low of the lookback window, while the red and green diagonals connect the earliest and latest pivot highs and lows to visualize the range. 7. Sharp Move Module 7.1 Concept Markets sometimes display absorption or climax behavior—periods when one side steadily gains the upper hand before price breaks out with a sharp move. This module evaluates several order‑flow and volume conditions to anticipate such moves. Users can choose how many conditions must be met to flag a risk and how many (plus a price break) are required for confirmation. 7.2 Inputs • sharp Lookback: Number of bars in the window used to compute moving averages, sums, percentile ranks and reference levels. • sharpPercentile: Minimum percentile rank for the current side’s volume; the current buy (or sell) volume must be greater than or equal to this percentile of historical volumes over the lookback window. • sharpVolMult: Multiplier used in the volume climax check. The current side’s volume must exceed this multiple of its average to count as a climax. • sharpRatioThr: Minimum dominance ratio (current side’s volume relative to the opposite side) used in both the instant and cumulative dominance checks. • sharpChurnThr: Maximum ratio of a bar’s range to its ATR for absorption/churn detection; lower values indicate more absorption (large volume in a small range). • sharpScoreRisk: Minimum number of conditions that must be true to print a risk label. • sharpScoreConfirm: Minimum number of conditions plus a price break required for confirmation. • sharpCvdThr: Threshold for cumulative delta divergence versus price change (positive for bullish accumulation, negative for bearish distribution). • Label settings: ATR length (sharpATRlen) and multiplier (sharpLabelMult) for positioning labels, label size, colors and connector styles for bullish and bearish sharp moves. • Toggles: enableSharp activates the module; show_sharp_labels controls whether labels are drawn. 7.3 Conditions (six per side) For each side, the indicator computes six boolean conditions and sums them to form a score: 1. Dominance (instant and cumulative): – Instant dominance: current buy volume ≥ sharpRatioThr × current sell volume. – Cumulative dominance: sum of buy volumes over the window ≥ sharpRatioThr × sum of sell volumes (and vice versa for bearish checks). 2. Accumulation/Distribution divergence: Over the lookback window, cumulative delta rises by at least sharpCvdThr while price fails to rise (bullish), or cumulative delta falls by at least sharpCvdThr while price fails to fall (bearish). 3. Volume climax: The current side’s volume is ≥ sharpVolMult × its average and the product of volume and bar range is the highest in the lookback window. 4. Absorption/Churn: The current side’s volume divided by the bar’s range equals the highest value in the window and the bar’s range divided by ATR ≤ sharpChurnThr (indicating large volume within a small range). 5. Percentile rank: The current side’s volume percentile rank is ≥ sharp Percentile. 6. Mirror logic for sellers: The above checks are repeated with buyer and seller roles swapped and the price break levels reversed. Each condition that passes contributes one point to the corresponding side’s score (0 or 1). Risk and confirmation thresholds are then applied to these scores. 7.4 Scoring and labels • Risk: If scoreBull ≥ sharpScoreRisk, a “Sharp ↑ Risk” label is drawn above the bar. If scoreBear ≥ sharpScoreRisk, a “Sharp ↓ Risk” label is drawn below the bar. • Confirmation: A risk label is upgraded to “Sharp ↑” when scoreBull ≥ sharpScoreConfirm and the bar closes above the highest recent pivot (HH1); for bearish cases, confirmation requires scoreBear ≥ sharpScoreConfirm and a close below the lowest pivot (LL1). • Label positioning: Labels are offset from the candle by ATR × sharpLabelMult (full ATR times multiplier), not half, and may include a dashed or dotted connector line if enabled. Figure caption, In this chart both bullish and bearish sharp‑move setups have been flagged. Earlier in the range, a “Sharp ↓ Risk” label appears beneath a candle: the sell‑side score met the risk threshold, signaling that the combination of strong sell volume, dominance and absorption within a narrow range suggested a potential sharp decline. The price did not close below the lower pivot, so this label remains a “risk” and no confirmation occurred. Later, as the market recovered and volume shifted back to the buy side, a “Sharp ↑ Risk” label prints above a candle near the top of the channel. Here, buy‑side dominance, cumulative delta divergence and a volume climax aligned, but price has not yet closed above the upper pivot (HH1), so the alert is still a risk rather than a confirmed sharp‑up move. Figure caption, In this chart a Sharp ↑ label is displayed above a candle, indicating that the sharp move module has confirmed a bullish breakout. Prior bars satisfied the risk threshold — showing buy‑side dominance, positive cumulative delta divergence, a volume climax and strong absorption in a narrow range — and this candle closes above the highest recent pivot, upgrading the earlier “Sharp ↑ Risk” alert to a full Sharp ↑ signal. The green label is offset from the candle with a dashed connector, while the red and green trend lines trace the high and low pivot trajectories and the dashed horizontals mark the highest and lowest values of the lookback window. 8. Market‑Maker / Spread‑Capture Module 8.1 Concept Liquidity providers often “capture the spread” by buying and selling in almost equal amounts within a very narrow price range. These bars can signal temporary congestion before a move or reflect algorithmic activity. This module flags bars where both buyer and seller volumes are high, the price range is only a few ticks and the buy/sell split remains close to 50%. It helps traders spot potential liquidity pockets. 8.2 Inputs • scalpLookback: Window length used to compute volume averages. • scalpVolMult: Multiplier applied to each side’s average volume; both buy and sell volumes must exceed this multiple. • scalpTickCount: Maximum allowed number of ticks in a bar’s range (calculated as (high − low) / minTick). A value of 1 or 2 captures ultra‑small bars; increasing it relaxes the range requirement. • scalpDeltaRatio: Maximum deviation from a perfect 50/50 split. For example, 0.05 means the buyer share must be between 45% and 55%. • Label settings: ATR length, multiplier, size, colors, connector style and width. • Toggles : show_scalp_module and show_scalp_labels to enable the module and its labels. 8.3 Signal When, on the current bar, both TF_buy_breakout and TF_sell_breakout exceed scalpVolMult times their respective averages and (high − low)/minTick ≤ scalpTickCount and the buyer share is within scalpDeltaRatio of 50%, the module prints a “Spread ↔” label above the bar. The label uses the same ATR offset logic as other modules and draws a connector if enabled. Figure caption, In this chart the spread‑capture module has identified a potential liquidity pocket. Buyer and seller volumes both spiked above their recent averages, yet the candle’s range measured only a couple of ticks and the buy/sell split stayed close to 50 %. This combination met the module’s criteria, so it printed a grey “Spread ↔” label above the bar. The red and green trend lines link the earliest and latest high and low pivots, and the dashed horizontals mark the highest high and lowest low within the current lookback window. 9. Money Flow Module 9.1 Concept To translate volume into a monetary measure, this module multiplies each side’s volume by the closing price. It tracks buying and selling system money default currency on a per-bar basis and sums them over a chosen period. The difference between buy and sell currencies (Δ$) shows net inflow or outflow. 9.2 Inputs • mf_period_len_mf: Number of bars used for summing buy and sell dollars. • Label appearance settings: ATR length, multiplier, size, colors for up/down labels, and connector style and width. • Toggles: Use enableMoneyFlowLabel_mf and showMFLabels to control whether the module and its labels are displayed. 9.3 Calculations • Per-bar money: Buy $ = TF_buy_breakout × close; Sell $ = TF_sell_breakout × close. Their difference is Δ$ = Buy $ − Sell $. • Summations: Over mf_period_len_mf bars, compute Σ Buy $, Σ Sell $ and ΣΔ$ using math.sum(). • Info table entries: Rows 9–13 display these values as texts like “↑ USD 1234 (1M)” or “ΣΔ USD −5678 (14)”, with colors reflecting whether buyers or sellers dominate. • Money flow status: If Δ$ is positive the bar is marked “Money flow in” ; if negative, “Money flow out” ; if zero, “Neutral”. The cumulative status is similarly derived from ΣΔ.Labels print at the bar that changes the sign of ΣΔ, offset using ATR × label multiplier and styled per user preferences. Figure caption, The chart illustrates a steady rise toward the highest recent pivot (HH1) with price riding between a rising green trend‑line and a red trend‑line drawn through earlier pivot highs. A green Money flow in label appears above the bar near the top of the channel, signaling that net dollar flow turned positive on this bar: buy‑side dollar volume exceeded sell‑side dollar volume, pushing the cumulative sum ΣΔ$ above zero. In the info table, the “Money flow (bar)” and “Money flow Σ” rows both read In, confirming that the indicator’s money‑flow module has detected an inflow at both bar and aggregate levels, while other modules (pivots, trend lines and support/resistance) remain active to provide structural context. In this example the Money Flow module signals a net outflow. Price has been trending downward: successive high pivots form a falling red trend‑line and the low pivots form a descending green support line. When the latest bar broke below the previous low pivot (LL1), both the bar‑level and cumulative net dollar flow turned negative—selling volume at the close exceeded buying volume and pushed the cumulative Δ$ below zero. The module reacts by printing a red “Money flow out” label beneath the candle; the info table confirms that the “Money flow (bar)” and “Money flow Σ” rows both show Out, indicating sustained dominance of sellers in this period. 10. Info Table 10.1 Purpose When enabled, the Info Table appears in the lower right of your chart. It summarises key values computed by the indicator—such as buy and sell volume, delta, total volume, breakout status, market phase, and money flow—so you can see at a glance which side is dominant and which signals are active. 10.2 Symbols • ↑ / ↓ — Up (↑) denotes buy volume or money; down (↓) denotes sell volume or money. • MA — Moving average. In the table it shows the average value of a series over the lookback period. • Σ (Sigma) — Cumulative sum over the chosen lookback period. • Δ (Delta) — Difference between buy and sell values. • B / S — Buyer and seller share of total volume, expressed as percentages. • Ref. Price — Reference price for breakout calculations, based on the latest pivot. • Status — Indicates whether a breakout condition is currently active (True) or has failed. 10.3 Row definitions 1. Up volume / MA up volume – Displays current buy volume on the lower timeframe and its moving average over the lookback period. 2. Down volume / MA down volume – Shows current sell volume and its moving average; sell values are formatted in red for clarity. 3. Δ / ΣΔ – Lists the difference between buy and sell volume for the current bar and the cumulative delta volume over the lookback period. 4. Σ / MA Σ (Vol/MA) – Total volume (buy + sell) for the bar, with the ratio of this volume to its moving average; the right cell shows the average total volume. 5. B/S ratio – Buy and sell share of the total volume: current bar percentages and the average percentages across the lookback period. 6. Buyer Rank / Seller Rank – Ranks the bar’s buy and sell volumes among the last (n) bars; lower rank numbers indicate higher relative volume. 7. Σ Buy / Σ Sell – Sum of buy and sell volumes over the lookback window, indicating which side has traded more. 8. Breakout UP / DOWN – Shows the breakout thresholds (Ref. Price) and whether the breakout condition is active (True) or has failed. 9. Market Phase (Vol) – Reports the current volume‑only phase: Accumulation, Distribution or Neutral. 10. Money Flow – The final rows display dollar amounts and status: – ↑ USD / Σ↑ USD – Buy dollars for the current bar and the cumulative sum over the money‑flow period. – ↓ USD / Σ↓ USD – Sell dollars and their cumulative sum. – Δ USD / ΣΔ USD – Net dollar difference (buy minus sell) for the bar and cumulatively. – Money flow (bar) – Indicates whether the bar’s net dollar flow is positive (In), negative (Out) or neutral. – Money flow Σ – Shows whether the cumulative net dollar flow across the chosen period is positive, negative or neutral. The chart above shows a sequence of different signals from the indicator. A Bull Trap Risk appears after price briefly pushes above resistance but fails to hold, then a green Accum label identifies an accumulation phase. An upward breakout follows, confirmed by a Money flow in print. Later, a Sharp ↓ Risk warns of a possible sharp downturn; after price dips below support but quickly recovers, a Bear Trap label marks a false breakdown. The highlighted info table in the center summarizes key metrics at that moment, including current and average buy/sell volumes, net delta, total volume versus its moving average, breakout status (up and down), market phase (volume), and bar‑level and cumulative money flow (In/Out). 11. Conclusion & Final Remarks This indicator was developed as a holistic study of market structure and order flow. It brings together several well‑known concepts from technical analysis—breakouts, accumulation and distribution phases, overbought and oversold extremes, bull and bear traps, sharp directional moves, market‑maker spread bars and money flow—into a single Pine Script tool. Each module is based on widely recognized trading ideas and was implemented after consulting reference materials and example strategies, so you can see in real time how these concepts interact on your chart. A distinctive feature of this indicator is its reliance on per‑side volume: instead of tallying only total volume, it separately measures buy and sell transactions on a lower time frame. This approach gives a clearer view of who is in control—buyers or sellers—and helps filter breakouts, detect phases of accumulation or distribution, recognize potential traps, anticipate sharp moves and gauge whether liquidity providers are active. The money‑flow module extends this analysis by converting volume into currency values and tracking net inflow or outflow across a chosen window. Although comprehensive, this indicator is intended solely as a guide. It highlights conditions and statistics that many traders find useful, but it does not generate trading signals or guarantee results. Ultimately, you remain responsible for your positions. Use the information presented here to inform your analysis, combine it with other tools and risk‑management techniques, and always make your own decisions when trading.
Pine Script® Indikator
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SMC Structures and FVGสวัสดีครับ! ผมจะอธิบายอินดิเคเตอร์ "SMC Structures and FVG + MACD" ที่คุณให้มาอย่างละเอียดในแต่ละส่วน เพื่อให้คุณเข้าใจการทำงานของมันอย่างถ่องแท้ครับ อินดิเคเตอร์นี้เป็นการผสมผสานแนวคิดของ Smart Money Concept (SMC) ซึ่งเน้นการวิเคราะห์โครงสร้างตลาด (Market Structure) และ Fair Value Gap (FVG) เข้ากับอินดิเคเตอร์ MACD เพื่อใช้เป็นตัวกรองหรือตัวยืนยันสัญญาณ Choch/BoS (Change of Character / Break of Structure) 1. ภาพรวมอินดิเคเตอร์ (Overall Purpose) อินดิเคเตอร์นี้มีจุดประสงค์หลักคือ: ระบุโครงสร้างตลาด: ตีเส้นและป้ายกำกับ Choch (Change of Character) และ BoS (Break of Structure) บนกราฟโดยอัตโนมัติ ผสานการยืนยันด้วย MACD: สัญญาณ Choch/BoS จะถูกพิจารณาก็ต่อเมื่อ MACD Histogram เกิดการตัดขึ้นหรือลง (Zero Cross) ในทิศทางที่สอดคล้องกัน แสดง Fair Value Gap (FVG): หากเปิดใช้งาน จะมีการตีกล่อง FVG บนกราฟ แสดงระดับ Fibonacci: คำนวณและแสดงระดับ Fibonacci ที่สำคัญตามโครงสร้างตลาดปัจจุบัน ปรับตาม Timeframe: การคำนวณและการแสดงผลทั้งหมดจะปรับตาม Timeframe ที่คุณกำลังใช้งานอยู่โดยอัตโนมัติ 2. ส่วนประกอบหลักของโค้ด (Code Breakdown) โค้ดนี้สามารถแบ่งออกเป็นส่วนหลัก ๆ ได้ดังนี้: 2.1 Inputs (การตั้งค่า) ส่วนนี้คือตัวแปรที่คุณสามารถปรับแต่งได้ในหน้าต่างการตั้งค่าของอินดิเคเตอร์ (คลิกที่รูปฟันเฟืองข้างชื่ออินดิเคเตอร์บนกราฟ) MACD Settings (ตั้งค่า MACD): fast_len: ความยาวของ Fast EMA สำหรับ MACD (ค่าเริ่มต้น 12) slow_len: ความยาวของ Slow EMA สำหรับ MACD (ค่าเริ่มต้น 26) signal_len: ความยาวของ Signal Line สำหรับ MACD (ค่าเริ่มต้น 9) = ta.macd(close, fast_len, slow_len, signal_len): คำนวณค่า MACD Line, Signal Line และ Histogram โดยใช้ราคาปิด (close) และค่าความยาวที่กำหนด is_bullish_macd_cross: ตรวจสอบว่า MACD Histogram ตัดขึ้นเหนือเส้น 0 (จากค่าลบเป็นบวก) is_bearish_macd_cross: ตรวจสอบว่า MACD Histogram ตัดลงใต้เส้น 0 (จากค่าบวกเป็นลบ) Fear Value Gap (FVG) Settings: isFvgToShow: (Boolean) เปิด/ปิดการแสดง FVG บนกราฟ bullishFvgColor: สีสำหรับ Bullish FVG bearishFvgColor: สีสำหรับ Bearish FVG mitigatedFvgColor: สีสำหรับ FVG ที่ถูก Mitigate (ลดทอน) แล้ว fvgHistoryNbr: จำนวน FVG ย้อนหลังที่จะแสดง isMitigatedFvgToReduce: (Boolean) เปิด/ปิดการลดขนาด FVG เมื่อถูก Mitigate Structures (โครงสร้างตลาด) Settings: isStructBodyCandleBreak: (Boolean) หากเป็น true การ Break จะต้องเกิดขึ้นด้วย เนื้อเทียน ที่ปิดเหนือ/ใต้ Swing High/Low หากเป็น false แค่ไส้เทียนทะลุก็ถือว่า Break isCurrentStructToShow: (Boolean) เปิด/ปิดการแสดงเส้นโครงสร้างตลาดปัจจุบัน (เส้นสีน้ำเงินในภาพตัวอย่าง) pivot_len: ความยาวของแท่งเทียนที่ใช้ในการมองหาจุด Pivot (Swing High/Low) ยิ่งค่าน้อยยิ่งจับ Swing เล็กๆ ได้, ยิ่งค่ามากยิ่งจับ Swing ใหญ่ๆ ได้ bullishBosColor, bearishBosColor: สีสำหรับเส้นและป้าย BOS ขาขึ้น/ขาลง bosLineStyleOption, bosLineWidth: สไตล์ (Solid, Dotted, Dashed) และความหนาของเส้น BOS bullishChochColor, bearishChochColor: สีสำหรับเส้นและป้าย CHoCH ขาขึ้น/ขาลง chochLineStyleOption, chochLineWidth: สไตล์ (Solid, Dotted, Dashed) และความหนาของเส้น CHoCH currentStructColor, currentStructLineStyleOption, currentStructLineWidth: สี, สไตล์ และความหนาของเส้นโครงสร้างตลาดปัจจุบัน structHistoryNbr: จำนวนการ Break (Choch/BoS) ย้อนหลังที่จะแสดง Structure Fibonacci (จากโค้ดต้นฉบับ): เป็นชุด Input สำหรับเปิด/ปิด, กำหนดค่า, สี, สไตล์ และความหนาของเส้น Fibonacci Levels ต่างๆ (0.786, 0.705, 0.618, 0.5, 0.382) ที่จะถูกคำนวณจากโครงสร้างตลาดปัจจุบัน 2.2 Helper Functions (ฟังก์ชันช่วยทำงาน) getLineStyle(lineOption): ฟังก์ชันนี้ใช้แปลงค่า String ที่เลือกจาก Input (เช่น "─", "┈", "╌") ให้เป็นรูปแบบ line.style_ ที่ Pine Script เข้าใจ get_structure_highest_bar(lookback): ฟังก์ชันนี้พยายามหา Bar Index ของแท่งเทียนที่ทำ Swing High ภายในช่วง lookback ที่กำหนด get_structure_lowest_bar(lookback): ฟังก์ชันนี้พยายามหา Bar Index ของแท่งเทียนที่ทำ Swing Low ภายในช่วง lookback ที่กำหนด is_structure_high_broken(...): ฟังก์ชันนี้ตรวจสอบว่าราคาปัจจุบันได้ Break เหนือ _structureHigh (Swing High) หรือไม่ โดยพิจารณาจาก _highStructBreakPrice (ราคาปิดหรือราคา High ขึ้นอยู่กับการตั้งค่า isStructBodyCandleBreak) FVGDraw(...): ฟังก์ชันนี้รับ Arrays ของ FVG Boxes, Types, Mitigation Status และ Labels มาประมวลผล เพื่ออัปเดตสถานะของ FVG (เช่น ถูก Mitigate หรือไม่) และปรับขนาด/ตำแหน่งของ FVG Box และ Label บนกราฟ 2.3 Global Variables (ตัวแปรทั่วทั้งอินดิเคเตอร์) เป็นตัวแปรที่ประกาศด้วย var ซึ่งหมายความว่าค่าของมันจะถูกเก็บไว้และอัปเดตในแต่ละแท่งเทียน (persists across bars) structureLines, structureLabels: Arrays สำหรับเก็บอ็อบเจกต์ line และ label ของเส้น Choch/BoS ที่วาดบนกราฟ fvgBoxes, fvgTypes, fvgLabels, isFvgMitigated: Arrays สำหรับเก็บข้อมูลของ FVG Boxes และสถานะต่างๆ structureHigh, structureLow: เก็บราคาของ Swing High/Low ที่สำคัญของโครงสร้างตลาดปัจจุบัน structureHighStartIndex, structureLowStartIndex: เก็บ Bar Index ของจุดเริ่มต้นของ Swing High/Low ที่สำคัญ structureDirection: เก็บสถานะของทิศทางโครงสร้างตลาด (1 = Bullish, 2 = Bearish, 0 = Undefined) fiboXPrice, fiboXStartIndex, fiboXLine, fiboXLabel: ตัวแปรสำหรับเก็บข้อมูลและอ็อบเจกต์ของเส้น Fibonacci Levels isBOSAlert, isCHOCHAlert: (Boolean) ใช้สำหรับส่งสัญญาณ Alert (หากมีการตั้งค่า Alert ไว้) 2.4 FVG Processing (การประมวลผล FVG) ส่วนนี้จะตรวจสอบเงื่อนไขการเกิด FVG (Bullish FVG: high < low , Bearish FVG: low > high ) หากเกิด FVG และ isFvgToShow เป็น true จะมีการสร้าง box และ label ใหม่เพื่อแสดง FVG บนกราฟ มีการจัดการ fvgBoxes และ fvgLabels เพื่อจำกัดจำนวน FVG ที่แสดงตาม fvgHistoryNbr และลบ FVG เก่าออก ฟังก์ชัน FVGDraw จะถูกเรียกเพื่ออัปเดตสถานะของ FVG (เช่น การถูก Mitigate) และปรับการแสดงผล 2.5 Structures Processing (การประมวลผลโครงสร้างตลาด) Initialization: ที่ bar_index == 0 (แท่งเทียนแรกของกราฟ) จะมีการกำหนดค่าเริ่มต้นให้กับ structureHigh, structureLow, structureHighStartIndex, structureLowStartIndex Finding Current High/Low: highest, highestBar, lowest, lowestBar ถูกใช้เพื่อหา High/Low ที่สุดและ Bar Index ของมันใน 10 แท่งล่าสุด (หรือทั้งหมดหากกราฟสั้นกว่า 10 แท่ง) Calculating Structure Max/Min Bar: structureMaxBar และ structureMinBar ใช้ฟังก์ชัน get_structure_highest_bar และ get_structure_lowest_bar เพื่อหา Bar Index ของ Swing High/Low ที่แท้จริง (ไม่ใช่แค่ High/Low ที่สุดใน lookback แต่เป็นจุด Pivot ที่สมบูรณ์) Break Price: lowStructBreakPrice และ highStructBreakPrice จะเป็นราคาปิด (close) หรือราคา Low/High ขึ้นอยู่กับ isStructBodyCandleBreak isStuctureLowBroken / isStructureHighBroken: เงื่อนไขเหล่านี้ตรวจสอบว่าราคาได้ทำลาย structureLow หรือ structureHigh หรือไม่ โดยพิจารณาจากราคา Break, ราคาแท่งก่อนหน้า และ Bar Index ของจุดเริ่มต้นโครงสร้าง Choch/BoS Logic (ส่วนสำคัญที่ถูกผสานกับ MACD): if(isStuctureLowBroken and is_bearish_macd_cross): นี่คือจุดที่ MACD เข้ามามีบทบาท หากราคาทำลาย structureLow (สัญญาณขาลง) และ MACD Histogram เกิด Bearish Zero Cross (is_bearish_macd_cross เป็น true) อินดิเคเตอร์จะพิจารณาว่าเป็น Choch หรือ BoS หาก structureDirection == 1 (เดิมเป็นขาขึ้น) หรือ 0 (ยังไม่กำหนด) จะตีเป็น "CHoCH" (เปลี่ยนทิศทางโครงสร้างเป็นขาลง) หาก structureDirection == 2 (เดิมเป็นขาลง) จะตีเป็น "BOS" (ยืนยันโครงสร้างขาลง) มีการสร้าง line.new และ label.new เพื่อวาดเส้นและป้ายกำกับ structureDirection จะถูกอัปเดตเป็น 1 (Bullish) structureHighStartIndex, structureLowStartIndex, structureHigh, structureLow จะถูกอัปเดตเพื่อกำหนดโครงสร้างใหม่ else if(isStructureHighBroken and is_bullish_macd_cross): เช่นกันสำหรับขาขึ้น หากราคาทำลาย structureHigh (สัญญาณขาขึ้น) และ MACD Histogram เกิด Bullish Zero Cross (is_bullish_macd_cross เป็น true) อินดิเคเตอร์จะพิจารณาว่าเป็น Choch หรือ BoS หาก structureDirection == 2 (เดิมเป็นขาลง) หรือ 0 (ยังไม่กำหนด) จะตีเป็น "CHoCH" (เปลี่ยนทิศทางโครงสร้างเป็นขาขึ้น) หาก structureDirection == 1 (เดิมเป็นขาขึ้น) จะตีเป็น "BOS" (ยืนยันโครงสร้างขาขึ้น) มีการสร้าง line.new และ label.new เพื่อวาดเส้นและป้ายกำกับ structureDirection จะถูกอัปเดตเป็น 2 (Bearish) structureHighStartIndex, structureLowStartIndex, structureHigh, structureLow จะถูกอัปเดตเพื่อกำหนดโครงสร้างใหม่ การลบเส้นเก่า: d.delete_line (หากไลบรารีทำงาน) จะถูกเรียกเพื่อลบเส้นและป้ายกำกับเก่าออกเมื่อจำนวนเกิน structHistoryNbr Updating Structure High/Low (else block): หากไม่มีการ Break เกิดขึ้น แต่ราคาปัจจุบันสูงกว่า structureHigh หรือต่ำกว่า structureLow ในทิศทางที่สอดคล้องกัน (เช่น ยังคงเป็นขาขึ้นและทำ High ใหม่) structureHigh หรือ structureLow จะถูกอัปเดตเพื่อติดตาม High/Low ที่สุดของโครงสร้างปัจจุบัน Current Structure Display: หาก isCurrentStructToShow เป็น true อินดิเคเตอร์จะวาดเส้น structureHighLine และ structureLowLine เพื่อแสดงขอบเขตของโครงสร้างตลาดปัจจุบัน Fibonacci Display: หาก isFiboXToShow เป็น true อินดิเคเตอร์จะคำนวณและวาดเส้น Fibonacci Levels ต่างๆ (0.786, 0.705, 0.618, 0.5, 0.382) โดยอิงจาก structureHigh และ structureLow ของโครงสร้างตลาดปัจจุบัน Alerts: alertcondition: ใช้สำหรับตั้งค่า Alert ใน TradingView เมื่อเกิดสัญญาณ BOS หรือ CHOCH plot(na): plot(na) เป็นคำสั่งที่สำคัญในอินดิเคเตอร์ที่ไม่ได้ต้องการพล็อต Series ของข้อมูลบนกราฟ (เช่น ไม่ได้พล็อตเส้น EMA หรือ RSI) แต่ใช้วาดอ็อบเจกต์ (Line, Label, Box) โดยตรง การมี plot(na) ช่วยให้ Pine Script รู้ว่าอินดิเคเตอร์นี้มีเอาต์พุตที่แสดงผลบนกราฟ แม้ว่าจะไม่ได้เป็น Series ที่พล็อตตามปกติก็ตาม 3. วิธีใช้งาน คัดลอกโค้ดทั้งหมด ที่อยู่ในบล็อก immersive ด้านบน ไปที่ TradingView และเปิดกราฟที่คุณต้องการ คลิกที่เมนู "Pine Editor" ที่อยู่ด้านล่างของหน้าจอ ลบโค้ดเดิมที่มีอยู่ และ วางโค้ดที่คัดลอกมา ลงไปแทน คลิกที่ปุ่ม "Add to Chart" อินดิเคเตอร์จะถูกเพิ่มลงในกราฟของคุณโดยอัตโนมัติ คุณสามารถคลิกที่รูปฟันเฟืองข้างชื่ออินดิเคเตอร์บนกราฟเพื่อเข้าถึงหน้าต่างการตั้งค่าและปรับแต่งตามความต้องการของคุณได้ Hello! I will explain the "SMC Structures and FVG + MACD" indicator you provided in detail, section by section, so you can fully understand how it works.This indicator combines the concepts of Smart Money Concept (SMC), which focuses on analyzing Market Structure and Fair Value Gaps (FVG), with the MACD indicator to serve as a filter or confirmation for Choch (Change of Character) and BoS (Break of Structure) signals.1. Overall PurposeThe main purposes of this indicator are:Identify Market Structure: Automatically draw lines and label Choch (Change of Character) and BoS (Break of Structure) on the chart.Integrate MACD Confirmation: Choch/BoS signals will only be considered when the MACD Histogram performs a cross (Zero Cross) in the corresponding direction.Display Fair Value Gap (FVG): If enabled, FVG boxes will be drawn on the chart.Display Fibonacci Levels: Calculate and display important Fibonacci levels based on the current market structure.Adapt to Timeframe: All calculations and displays will automatically adjust to the timeframe you are currently using.2. Code BreakdownThis code can be divided into the following main sections:2.1 Inputs (Settings)This section contains variables that you can adjust in the indicator's settings window (click the gear icon next to the indicator's name on the chart).MACD Settings:fast_len: Length of the Fast EMA for MACD (default 12)slow_len: Length of the Slow EMA for MACD (default 26)signal_len: Length of the Signal Line for MACD (default 9) = ta.macd(close, fast_len, slow_len, signal_len): Calculates the MACD Line, Signal Line, and Histogram using the closing price (close) and the specified lengths.is_bullish_macd_cross: Checks if the MACD Histogram crosses above the 0 line (from negative to positive).is_bearish_macd_cross: Checks if the MACD Histogram crosses below the 0 line (from positive to negative).Fear Value Gap (FVG) Settings:isFvgToShow: (Boolean) Enables/disables the display of FVG on the chart.bullishFvgColor: Color for Bullish FVG.bearishFvgColor: Color for Bearish FVG.mitigatedFvgColor: Color for FVG that has been mitigated.fvgHistoryNbr: Number of historical FVG to display.isMitigatedFvgToReduce: (Boolean) Enables/disables reducing the size of FVG when mitigated.Structures (โครงสร้างตลาด) Settings:isStructBodyCandleBreak: (Boolean) If true, the break must occur with the candle body closing above/below the Swing High/Low. If false, a wick break is sufficient.isCurrentStructToShow: (Boolean) Enables/disables the display of the current market structure lines (blue lines in the example image).pivot_len: Lookback length for identifying Pivot points (Swing High/Low). A smaller value captures smaller, more frequent swings; a larger value captures larger, more significant swings.bullishBosColor, bearishBosColor: Colors for bullish/bearish BOS lines and labels.bosLineStyleOption, bosLineWidth: Style (Solid, Dotted, Dashed) and width of BOS lines.bullishChochColor, bearishChochColor: Colors for bullish/bearish CHoCH lines and labels.chochLineStyleOption, chochLineWidth: Style (Solid, Dotted, Dashed) and width of CHoCH lines.currentStructColor, currentStructLineStyleOption, currentStructLineWidth: Color, style, and width of the current market structure lines.structHistoryNbr: Number of historical breaks (Choch/BoS) to display.Structure Fibonacci (from original code):A set of inputs to enable/disable, define values, colors, styles, and widths for various Fibonacci Levels (0.786, 0.705, 0.618, 0.5, 0.382) that will be calculated from the current market structure.2.2 Helper FunctionsgetLineStyle(lineOption): This function converts the selected string input (e.g., "─", "┈", "╌") into a line.style_ format understood by Pine Script.get_structure_highest_bar(lookback): This function attempts to find the Bar Index of the Swing High within the specified lookback period.get_structure_lowest_bar(lookback): This function attempts to find the Bar Index of the Swing Low within the specified lookback period.is_structure_high_broken(...): This function checks if the current price has broken above _structureHigh (Swing High), considering _highStructBreakPrice (closing price or high price depending on isStructBodyCandleBreak setting).FVGDraw(...): This function takes arrays of FVG Boxes, Types, Mitigation Status, and Labels to process and update the status of FVG (e.g., whether it's mitigated) and adjust the size/position of FVG Boxes and Labels on the chart.2.3 Global VariablesThese are variables declared with var, meaning their values are stored and updated on each bar (persists across bars).structureLines, structureLabels: Arrays to store line and label objects for Choch/BoS lines drawn on the chart.fvgBoxes, fvgTypes, fvgLabels, isFvgMitigated: Arrays to store FVG box data and their respective statuses.structureHigh, structureLow: Stores the price of the significant Swing High/Low of the current market structure.structureHighStartIndex, structureLowStartIndex: Stores the Bar Index of the start point of the significant Swing High/Low.structureDirection: Stores the status of the market structure direction (1 = Bullish, 2 = Bearish, 0 = Undefined).fiboXPrice, fiboXStartIndex, fiboXLine, fiboXLabel: Variables to store data and objects for Fibonacci Levels.isBOSAlert, isCHOCHAlert: (Boolean) Used to trigger alerts in TradingView (if alerts are configured).2.4 FVG ProcessingThis section checks the conditions for FVG formation (Bullish FVG: high < low , Bearish FVG: low > high ).If FVG occurs and isFvgToShow is true, a new box and label are created to display the FVG on the chart.fvgBoxes and fvgLabels are managed to limit the number of FVG displayed according to fvgHistoryNbr and remove older FVG.The FVGDraw function is called to update the FVG status (e.g., whether it's mitigated) and adjust its display.2.5 Structures ProcessingInitialization: At bar_index == 0 (the first bar of the chart), structureHigh, structureLow, structureHighStartIndex, and structureLowStartIndex are initialized.Finding Current High/Low: highest, highestBar, lowest, lowestBar are used to find the highest/lowest price and its Bar Index of it in the last 10 bars (or all bars if the chart is shorter than 10 bars).Calculating Structure Max/Min Bar: structureMaxBar and structureMinBar use get_structure_highest_bar and get_structure_lowest_bar functions to find the Bar Index of the true Swing High/Low (not just the highest/lowest in the lookback but a complete Pivot point).Break Price: lowStructBreakPrice and highStructBreakPrice will be the closing price (close) or the Low/High price, depending on the isStructBodyCandleBreak setting.isStuctureLowBroken / isStructureHighBroken: These conditions check if the price has broken structureLow or structureHigh, considering the break price, previous bar prices, and the Bar Index of the structure's starting point.Choch/BoS Logic (Key Integration with MACD):if(isStuctureLowBroken and is_bearish_macd_cross): This is where MACD plays a role. If the price breaks structureLow (bearish signal) AND the MACD Histogram performs a Bearish Zero Cross (is_bearish_macd_cross is true), the indicator will consider it a Choch or BoS.If structureDirection == 1 (previously bullish) or 0 (undefined), it will be labeled "CHoCH" (changing structure direction to bearish).If structureDirection == 2 (already bearish), it will be labeled "BOS" (confirming bearish structure).line.new and label.new are used to draw the line and label.structureDirection will be updated to 1 (Bullish).structureHighStartIndex, structureLowStartIndex, structureHigh, structureLow will be updated to define the new structure.else if(isStructureHighBroken and is_bullish_macd_cross): Similarly for bullish breaks. If the price breaks structureHigh (bullish signal) AND the MACD Histogram performs a Bullish Zero Cross (is_bullish_macd_cross is true), the indicator will consider it a Choch or BoS.If structureDirection == 2 (previously bearish) or 0 (undefined), it will be labeled "CHoCH" (changing structure direction to bullish).If structureDirection == 1 (already bullish), it will be labeled "BOS" (confirming bullish structure).line.new and label.new are used to draw the line and label.structureDirection will be updated to 2 (Bearish).structureHighStartIndex, structureLowStartIndex, structureHigh, structureLow will be updated to define the new structure.Deleting Old Lines: d.delete_line (if the library works) will be called to delete old lines and labels when their number exceeds structHistoryNbr.Updating Structure High/Low (else block): If no break occurs, but the current price is higher than structureHigh or lower than structureLow in the corresponding direction (e.g., still bullish and making a new high), structureHigh or structureLow will be updated to track the highest/lowest point of the current structure.Current Structure Display:If isCurrentStructToShow is true, the indicator draws structureHighLine and structureLowLine to show the boundaries of the current market structure.Fibonacci Display:If isFiboXToShow is true, the indicator calculates and draws various Fibonacci Levels (0.786, 0.705, 0.618, 0.5, 0.382) based on the structureHigh and structureLow of the current market structure.Alerts:alertcondition: Used to set up alerts in TradingView when BOS or CHOCH signals occur.plot(na):plot(na) is an important statement in indicators that do not plot data series directly on the chart (e.g., not plotting EMA or RSI lines) but instead draw objects (Line, Label, Box).Having plot(na) helps Pine Script recognize that this indicator has an output displayed on the chart, even if it's not a regularly plotted series.3. How to UseCopy all the code in the immersive block above.Go to TradingView and open your desired chart.Click on the "Pine Editor" menu at the bottom of the screen.Delete any existing code and paste the copied code in its place.Click the "Add to Chart" button.The indicator will be added to your chart automatically. You can click the gear icon next to the indicator's name on the chart to access the settings window and customize it to your needs.I hope this explanation helps you understand this indicator in detail. If anything is unclear, or you need further adjustments, please let me know.
Pine Script® Indikator
von Mr-7TRILL
Market Zone Analyzer[BullByte]Understanding the Market Zone Analyzer --- 1. Purpose of the Indicator The Market Zone Analyzer is a Pine Script™ (version 6) indicator designed to streamline market analysis on TradingView. Rather than scanning multiple separate tools, it unifies four core dimensions—trend strength, momentum, price action, and market activity—into a single, consolidated view. By doing so, it helps traders: • Save time by avoiding manual cross-referencing of disparate signals. • Reduce decision-making errors that can arise from juggling multiple indicators. • Gain a clear, reliable read on whether the market is in a bullish, bearish, or sideways phase, so they can more confidently decide to enter, exit, or hold a position. --- 2. Why a Trader Should Use It • Unified View: Combines all essential market dimensions into one easy-to-read score and dashboard, eliminating the need to piece together signals manually. • Adaptability: Automatically adjusts its internal weighting for trend, momentum, and price action based on current volatility. Whether markets are choppy or calm, the indicator remains relevant. • Ease of Interpretation: Outputs a simple “BULLISH,” “BEARISH,” or “SIDEWAYS” label, supplemented by an intuitive on-chart dashboard and an oscillator plot that visually highlights market direction. • Reliability Features: Built-in smoothing of the net score and hysteresis logic (requiring consecutive confirmations before flips) minimize false signals during noisy or range-bound phases. --- 3. Why These Specific Indicators? This script relies on a curated set of well-established technical tools, each chosen for its particular strength in measuring one of the four core dimensions: 1. Trend Strength: • ADX/DMI (Average Directional Index / Directional Movement Index): Measures how strong a trend is, and whether the +DI line is above the –DI line (bullish) or vice versa (bearish). • Moving Average Slope (Fast MA vs. Slow MA): Compares a shorter-period SMA to a longer-period SMA; if the fast MA sits above the slow MA, it confirms an uptrend, and vice versa for a downtrend. • Ichimoku Cloud Differential (Senkou A vs. Senkou B): Provides a forward-looking view of trend direction; Senkou A above Senkou B signals bullishness, and the opposite signals bearishness. 2. Momentum: • Relative Strength Index (RSI): Identifies overbought (above its dynamically calculated upper bound) or oversold (below its lower bound) conditions; changes in RSI often precede price reversals. • Stochastic %K: Highlights shifts in short-term momentum by comparing closing price to the recent high/low range; values above its upper band signal bullish momentum, below its lower band signal bearish momentum. • MACD Histogram: Measures the difference between the MACD line and its signal line; a positive histogram indicates upward momentum, a negative histogram indicates downward momentum. 3. Price Action: • Highest High / Lowest Low (HH/LL) Range: Over a defined lookback period, this captures breakout or breakdown levels. A closing price near the recent highs (with a positive MA slope) yields a bullish score, and near the lows (with a negative MA slope) yields a bearish score. • Heikin-Ashi Doji Detection: Uses Heikin-Ashi candles to identify indecision or continuation patterns. A small Heikin-Ashi body (doji) relative to recent volatility is scored as neutral; a larger body in the direction of the MA slope is scored bullish or bearish. • Candle Range Measurement: Compares each candle’s high-low range against its own dynamic band (average range ± standard deviation). Large candles aligning with the prevailing trend score bullish or bearish accordingly; unusually small candles can indicate exhaustion or consolidation. 4. Market Activity: • Bollinger Bands Width (BBW): Measures the distance between BB upper and lower bands; wide bands indicate high volatility, narrow bands indicate low volatility. • Average True Range (ATR): Quantifies average price movement (volatility). A sudden spike in ATR suggests a volatile environment, while a contraction suggests calm. • Keltner Channels Width (KCW): Similar to BBW but uses ATR around an EMA. Provides a second layer of volatility context, confirming or contrasting BBW readings. • Volume (with Moving Average): Compares current volume to its moving average ± standard deviation. High volume validates strong moves; low volume signals potential lack of conviction. By combining these tools, the indicator captures trend direction, momentum strength, price-action nuances, and overall market energy, yielding a more balanced and comprehensive assessment than any single tool alone. --- 4. What Makes This Indicator Stand Out • Multi-Dimensional Analysis: Rather than relying on a lone oscillator or moving average crossover, it simultaneously evaluates trend, momentum, price action, and activity. • Dynamic Weighting: The relative importance of trend, momentum, and price action adjusts automatically based on real-time volatility (Market Activity State). For example, in highly volatile conditions, trend and momentum signals carry more weight; in calm markets, price action signals are prioritized. • Stability Mechanisms: • Smoothing: The net score is passed through a short moving average, filtering out noise, especially on lower timeframes. • Hysteresis: Both Market Activity State and the final bullish/bearish/sideways zone require two consecutive confirmations before flipping, reducing whipsaw. • Visual Interpretation: A fully customizable on-chart dashboard displays each sub-indicator’s value, regime, score, and comment, all color-coded. The oscillator plot changes color to reflect the current market zone (green for bullish, red for bearish, gray for sideways) and shows horizontal threshold lines at +2, 0, and –2. --- 5. Recommended Timeframes • Short-Term (5 min, 15 min): Day traders and scalpers can benefit from rapid signals, but should enable smoothing (and possibly disable hysteresis) to reduce false whipsaws. • Medium-Term (1 h, 4 h): Swing traders find a balance between responsiveness and reliability. Less smoothing is required here, and the default parameters (e.g., ADX length = 14, RSI length = 14) perform well. • Long-Term (Daily, Weekly): Position traders tracking major trends can disable smoothing for immediate raw readings, since higher-timeframe noise is minimal. Adjust lookback lengths (e.g., increase adxLength, rsiLength) if desired for slower signals. Tip: If you keep smoothing off, stick to timeframes of 1 h or higher to avoid excessive signal “chatter.” --- 6. How Scoring Works A. Individual Indicator Scores Each sub-indicator is assigned one of three discrete scores: • +1 if it indicates a bullish condition (e.g., RSI above its dynamically calculated upper bound). • 0 if it is neutral (e.g., RSI between upper and lower bounds). • –1 if it indicates a bearish condition (e.g., RSI below its dynamically calculated lower bound). Examples of individual score assignments: • ADX/DMI: • +1 if ADX ≥ adxThreshold and +DI > –DI (strong bullish trend) • –1 if ADX ≥ adxThreshold and –DI > +DI (strong bearish trend) • 0 if ADX < adxThreshold (trend strength below threshold) • RSI: • +1 if RSI > RSI_upperBound • –1 if RSI < RSI_lowerBound • 0 otherwise • ATR (as part of Market Activity): • +1 if ATR > (ATR_MA + stdev(ATR)) • –1 if ATR < (ATR_MA – stdev(ATR)) • 0 otherwise Each of the four main categories shares this same +1/0/–1 logic across their sub-components. B. Category Scores Once each sub-indicator reports +1, 0, or –1, these are summed within their categories as follows: • Trend Score = (ADX score) + (MA slope score) + (Ichimoku differential score) • Momentum Score = (RSI score) + (Stochastic %K score) + (MACD histogram score) • Price Action Score = (Highest-High/Lowest-Low score) + (Heikin-Ashi doji score) + (Candle range score) • Market Activity Raw Score = (BBW score) + (ATR score) + (KC width score) + (Volume score) Each category’s summed value can range between –3 and +3 (for Trend, Momentum, and Price Action), and between –4 and +4 for Market Activity raw. C. Market Activity State and Dynamic Weight Adjustments Rather than contributing directly to the netScore like the other three categories, Market Activity determines how much weight to assign to Trend, Momentum, and Price Action: 1. Compute Market Activity Raw Score by summing BBW, ATR, KCW, and Volume individual scores (each +1/0/–1). 2. Bucket into High, Medium, or Low Activity: • High if raw Score ≥ 2 (volatile market). • Low if raw Score ≤ –2 (calm market). • Medium otherwise. 3. Apply Hysteresis (if enabled): The state only flips after two consecutive bars register the same high/low/medium label. 4. Set Category Weights: • High Activity: Trend = 50 %, Momentum = 35 %, Price Action = 15 %. • Low Activity: Trend = 25 %, Momentum = 20 %, Price Action = 55 %. • Medium Activity: Use the trader’s base weight inputs (e.g., Trend = 40 %, Momentum = 30 %, Price Action = 30 % by default). D. Calculating the Net Score 5. Normalize Base Weights (so that the sum of Trend + Momentum + Price Action always equals 100 %). 6. Determine Current Weights based on the Market Activity State (High/Medium/Low). 7. Compute Each Category’s Contribution: Multiply (categoryScore) × (currentWeight). 8. Sum Contributions to get the raw netScore (a floating-point value that can exceed ±3 when scores are strong). 9. Smooth the netScore over two bars (if smoothing is enabled) to reduce noise. 10. Apply Hysteresis to the Final Zone: • If the smoothed netScore ≥ +2, the bar is classified as “Bullish.” • If the smoothed netScore ≤ –2, the bar is classified as “Bearish.” • Otherwise, it is “Sideways.” • To prevent rapid flips, the script requires two consecutive bars in the new zone before officially changing the displayed zone (if hysteresis is on). E. Thresholds for Zone Classification • BULLISH: netScore ≥ +2 • BEARISH: netScore ≤ –2 • SIDEWAYS: –2 < netScore < +2 --- 7. Role of Volatility (Market Activity State) in Scoring Volatility acts as a dynamic switch that shifts which category carries the most influence: 1. High Activity (Volatile): • Detected when at least two sub-scores out of BBW, ATR, KCW, and Volume equal +1. • The script sets Trend weight = 50 % and Momentum weight = 35 %. Price Action weight is minimized at 15 %. • Rationale: In volatile markets, strong trending moves and momentum surges dominate, so those signals are more reliable than nuanced candle patterns. 2. Low Activity (Calm): • Detected when at least two sub-scores out of BBW, ATR, KCW, and Volume equal –1. • The script sets Price Action weight = 55 %, Trend = 25 %, and Momentum = 20 %. • Rationale: In quiet, sideways markets, subtle price-action signals (breakouts, doji patterns, small-range candles) are often the best early indicators of a new move. 3. Medium Activity (Balanced): • Raw Score between –1 and +1 from the four volatility metrics. • Uses whatever base weights the trader has specified (e.g., Trend = 40 %, Momentum = 30 %, Price Action = 30 %). Because volatility can fluctuate rapidly, the script employs hysteresis on Market Activity State: a new High or Low state must occur on two consecutive bars before weights actually shift. This avoids constant back-and-forth weight changes and provides more stability. --- 8. Scoring Example (Hypothetical Scenario) • Symbol: Bitcoin on a 1-hour chart. • Market Activity: Raw volatility sub-scores show BBW (+1), ATR (+1), KCW (0), Volume (+1) → Total raw Score = +3 → High Activity. • Weights Selected: Trend = 50 %, Momentum = 35 %, Price Action = 15 %. • Trend Signals: • ADX strong and +DI > –DI → +1 • Fast MA above Slow MA → +1 • Ichimoku Senkou A > Senkou B → +1 → Trend Score = +3 • Momentum Signals: • RSI above upper bound → +1 • MACD histogram positive → +1 • Stochastic %K within neutral zone → 0 → Momentum Score = +2 • Price Action Signals: • Highest High/Lowest Low check yields 0 (close not near extremes) • Heikin-Ashi doji reading is neutral → 0 • Candle range slightly above upper bound but trend is strong, so → +1 → Price Action Score = +1 • Compute Net Score (before smoothing): • Trend contribution = 3 × 0.50 = 1.50 • Momentum contribution = 2 × 0.35 = 0.70 • Price Action contribution = 1 × 0.15 = 0.15 • Raw netScore = 1.50 + 0.70 + 0.15 = 2.35 • Since 2.35 ≥ +2 and hysteresis is met, the final zone is “Bullish.” Although the netScore lands at 2.35 (Bullish), smoothing might bring it slightly below 2.00 on the first bar (e.g., 1.90), in which case the script would wait for a second consecutive reading above +2 before officially classifying the zone as Bullish (if hysteresis is enabled). --- 9. Correlation Between Categories The four categories—Trend Strength, Momentum, Price Action, and Market Activity—often reinforce or offset one another. The script takes advantage of these natural correlations: • Bullish Alignment: If ADX is strong and pointed upward, fast MA is above slow MA, and Ichimoku is positive, that usually coincides with RSI climbing above its upper bound and the MACD histogram turning positive. In such cases, both Trend and Momentum categories generate +1 or +2. Because the Market Activity State is likely High (given the accompanying volatility), Trend and Momentum weights are at their peak, so the netScore quickly crosses into Bullish territory. • Sideways/Consolidation: During a low-volatility, sideways phase, ADX may fall below its threshold, MAs may flatten, and RSI might hover in the neutral band. However, subtle price-action signals (like a small breakout candle or a Heikin-Ashi candle with a slight bias) can still produce a +1 in the Price Action category. If Market Activity is Low, Price Action’s weight (55 %) can carry enough influence—even if Trend and Momentum are neutral—to push the netScore out of “Sideways” into a mild bullish or bearish bias. • Opposing Signals: When Trend is bullish but Momentum turns negative (for example, price continues up but RSI rolls over), the two scores can partially cancel. Market Activity may remain Medium, in which case the netScore lingers near zero (Sideways). The trader can then wait for either a clearer momentum shift or a fresh price-action breakout before committing. By dynamically recognizing these correlations and adjusting weights, the indicator ensures that: • When Trend and Momentum align (and volatility supports it), the netScore leaps strongly into Bullish or Bearish. • When Trend is neutral but Price Action shows an early move in a low-volatility environment, Price Action’s extra weight in the Low Activity State can still produce actionable signals. --- 10. Market Activity State & Its Role (Detailed) The Market Activity State is not a direct category score—it is an overarching context setter for how heavily to trust Trend, Momentum, or Price Action. Here’s how it is derived and applied: 1. Calculate Four Volatility Sub-Scores: • BBW: Compare the current band width to its own moving average ± standard deviation. If BBW > (BBW_MA + stdev), assign +1 (high volatility); if BBW < (BBW_MA × 0.5), assign –1 (low volatility); else 0. • ATR: Compare ATR to its moving average ± standard deviation. A spike above the upper threshold is +1; a contraction below the lower threshold is –1; otherwise 0. • KCW: Same logic as ATR but around the KCW mean. • Volume: Compare current volume to its volume MA ± standard deviation. Above the upper threshold is +1; below the lower threshold is –1; else 0. 2. Sum Sub-Scores → Raw Market Activity Score: Range between –4 and +4. 3. Assign Market Activity State: • High Activity: Raw Score ≥ +2 (at least two volatility metrics are strongly spiking). • Low Activity: Raw Score ≤ –2 (at least two metrics signal unusually low volatility or thin volume). • Medium Activity: Raw Score is between –1 and +1 inclusive. 4. Hysteresis for Stability: • If hysteresis is enabled, a new state only takes hold after two consecutive bars confirm the same High, Medium, or Low label. • This prevents the Market Activity State from bouncing around when volatility is on the fence. 5. Set Category Weights Based on Activity State: • High Activity: Trend = 50 %, Momentum = 35 %, Price Action = 15 %. • Low Activity: Trend = 25 %, Momentum = 20 %, Price Action = 55 %. • Medium Activity: Use trader’s base weights (e.g., Trend = 40 %, Momentum = 30 %, Price Action = 30 %). 6. Impact on netScore: Because category scores (–3 to +3) multiply by these weights, High Activity amplifies the effect of strong Trend and Momentum scores; Low Activity amplifies the effect of Price Action. 7. Market Context Tooltip: The dashboard includes a tooltip summarizing the current state—e.g., “High activity, trend and momentum prioritized,” “Low activity, price action prioritized,” or “Balanced market, all categories considered.” --- 11. Category Weights: Base vs. Dynamic Traders begin by specifying base weights for Trend Strength, Momentum, and Price Action that sum to 100 %. These apply only when volatility is in the Medium band. Once volatility shifts: • High Volatility Overrides: • Trend jumps from its base (e.g., 40 %) to 50 %. • Momentum jumps from its base (e.g., 30 %) to 35 %. • Price Action is reduced to 15 %. Example: If base weights were Trend = 40 %, Momentum = 30 %, Price Action = 30 %, then in High Activity they become 50/35/15. A Trend score of +3 now contributes 3 × 0.50 = +1.50 to netScore; a Momentum +2 contributes 2 × 0.35 = +0.70. In total, Trend + Momentum can easily push netScore above the +2 threshold on its own. • Low Volatility Overrides: • Price Action leaps from its base (30 %) to 55 %. • Trend falls to 25 %, Momentum falls to 20 %. Why? When markets are quiet, subtle candle breakouts, doji patterns, and small-range expansions tend to foreshadow the next swing more effectively than raw trend readings. A Price Action score of +3 in this state contributes 3 × 0.55 = +1.65, which can carry the netScore toward +2—even if Trend and Momentum are neutral or only mildly positive. Because these weight shifts happen only after two consecutive bars confirm a High or Low state (if hysteresis is on), the indicator avoids constantly flipping its emphasis during borderline volatility phases. --- 12. Dominant Category Explained Within the dashboard, a label such as “Trend Dominant,” “Momentum Dominant,” or “Price Action Dominant” appears when one category’s absolute weighted contribution to netScore is the largest. Concretely: • Compute each category’s weighted contribution = (raw category score) × (current weight). • Compare the absolute values of those three contributions. • The category with the highest absolute value is flagged as Dominant for that bar. Why It Matters: • Momentum Dominant: Indicates that the combined force of RSI, Stochastic, and MACD (after weighting) is pushing netScore farther than either Trend or Price Action. In practice, it means that short-term sentiment and speed of change are the primary drivers right now, so traders should watch for continued momentum signals before committing to a trade. • Trend Dominant: Means ADX, MA slope, and Ichimoku (once weighted) outweigh the other categories. This suggests a strong directional move is in place; trend-following entries or confirming pullbacks are likely to succeed. • Price Action Dominant: Occurs when breakout/breakdown patterns, Heikin-Ashi candle readings, and range expansions (after weighting) are the most influential. This often happens in calmer markets, where subtle shifts in candle structure can foreshadow bigger moves. By explicitly calling out which category is carrying the most weight at any moment, the dashboard gives traders immediate insight into why the netScore is tilting toward bullish, bearish, or sideways. --- 13. Oscillator Plot: How to Read It The “Net Score” oscillator sits below the dashboard and visually displays the smoothed netScore as a line graph. Key features: 1. Value Range: In normal conditions it oscillates roughly between –3 and +3, but extreme confluences can push it outside that range. 2. Horizontal Threshold Lines: • +2 Line (Bullish threshold) • 0 Line (Neutral midline) • –2 Line (Bearish threshold) 3. Zone Coloring: • Green Background (Bullish Zone): When netScore ≥ +2. • Red Background (Bearish Zone): When netScore ≤ –2. • Gray Background (Sideways Zone): When –2 < netScore < +2. 4. Dynamic Line Color: • The plotted netScore line itself is colored green in a Bullish Zone, red in a Bearish Zone, or gray in a Sideways Zone, creating an immediate visual cue. Interpretation Tips: • Crossing Above +2: Signals a strong enough combined trend/momentum/price-action reading to classify as Bullish. Many traders wait for a clear crossing plus a confirmation candle before entering a long position. • Crossing Below –2: Indicates a strong Bearish signal. Traders may consider short or exit strategies. • Rising Slope, Even Below +2: If netScore climbs steadily from neutral toward +2, it demonstrates building bullish momentum. • Divergence: If price makes a higher high but the oscillator fails to reach a new high, it can warn of weakening momentum and a potential reversal. --- 14. Comments and Their Necessity Every sub-indicator (ADX, MA slope, Ichimoku, RSI, Stochastic, MACD, HH/LL, Heikin-Ashi, Candle Range, BBW, ATR, KCW, Volume) generates a short comment that appears in the detailed dashboard. Examples: • “Strong bullish trend” or “Strong bearish trend” for ADX/DMI • “Fast MA above slow MA” or “Fast MA below slow MA” for MA slope • “RSI above dynamic threshold” or “RSI below dynamic threshold” for RSI • “MACD histogram positive” or “MACD histogram negative” for MACD Hist • “Price near highs” or “Price near lows” for HH/LL checks • “Bullish Heikin Ashi” or “Bearish Heikin Ashi” for HA Doji scoring • “Large range, trend confirmed” or “Small range, trend contradicted” for Candle Range Additionally, the top-row comment for each category is: • Trend: “Highly Bullish,” “Highly Bearish,” or “Neutral Trend.” • Momentum: “Strong Momentum,” “Weak Momentum,” or “Neutral Momentum.” • Price Action: “Bullish Action,” “Bearish Action,” or “Neutral Action.” • Market Activity: “Volatile Market,” “Calm Market,” or “Stable Market.” Reasons for These Comments: • Transparency: Shows exactly how each sub-indicator contributed to its category score. • Education: Helps traders learn why a category is labeled bullish, bearish, or neutral, building intuition over time. • Customization: If, for example, the RSI comment says “RSI neutral” despite an impending trend shift, a trader might choose to adjust RSI length or thresholds. In the detailed dashboard, hovering over each comment cell also reveals a tooltip with additional context (e.g., “Fast MA above slow MA” or “Senkou A above Senkou B”), helping traders understand the precise rule behind that +1, 0, or –1 assignment. --- 15. Real-Life Example (Consolidated) • Instrument & Timeframe: Bitcoin (BTCUSD), 1-hour chart. • Current Market Activity: BBW and ATR both spike (+1 each), KCW is moderately high (+1), but volume is only neutral (0) → Raw Market Activity Score = +2 → State = High Activity (after two bars, if hysteresis is on). • Category Weights Applied: Trend = 50 %, Momentum = 35 %, Price Action = 15 %. • Trend Sub-Scores: 1. ADX = 25 (above threshold 20) with +DI > –DI → +1. 2. Fast MA (20-period) sits above Slow MA (50-period) → +1. 3. Ichimoku: Senkou A > Senkou B → +1. → Trend Score = +3. • Momentum Sub-Scores: 4. RSI = 75 (above its moving average +1 stdev) → +1. 5. MACD histogram = +0.15 → +1. 6. Stochastic %K = 50 (mid-range) → 0. → Momentum Score = +2. • Price Action Sub-Scores: 7. Price is not within 1 % of the 20-period high/low and slope = positive → 0. 8. Heikin-Ashi body is slightly larger than stdev over last 5 bars with haClose > haOpen → +1. 9. Candle range is just above its dynamic upper bound but trend is already captured, so → +1. → Price Action Score = +2. • Calculate netScore (before smoothing): • Trend contribution = 3 × 0.50 = 1.50 • Momentum contribution = 2 × 0.35 = 0.70 • Price Action contribution = 2 × 0.15 = 0.30 • Raw netScore = 1.50 + 0.70 + 0.30 = 2.50 → Immediately classified as Bullish. • Oscillator & Dashboard Output: • The oscillator line crosses above +2 and turns green. • Dashboard displays: • Trend Regime “BULLISH,” Trend Score = 3, Comment = “Highly Bullish.” • Momentum Regime “BULLISH,” Momentum Score = 2, Comment = “Strong Momentum.” • Price Action Regime “BULLISH,” Price Action Score = 2, Comment = “Bullish Action.” • Market Activity State “High,” Comment = “Volatile Market.” • Weights: Trend 50 %, Momentum 35 %, Price Action 15 %. • Dominant Category: Trend (because 1.50 > 0.70 > 0.30). • Overall Score: 2.50, posCount = (three +1s in Trend) + (two +1s in Momentum) + (two +1s in Price Action) = 7 bullish signals, negCount = 0. • Final Zone = “BULLISH.” • The trader sees that both Trend and Momentum are reinforcing each other under high volatility. They might wait one more candle for confirmation but already have strong evidence to consider a long. --- • . --- Disclaimer This indicator is strictly a technical analysis tool and does not constitute financial advice. All trading involves risk, including potential loss of capital. Past performance is not indicative of future results. Traders should: • Always backtest the “Market Zone Analyzer ” on their chosen symbols and timeframes before committing real capital. • Combine this tool with sound risk management, position sizing, and, if possible, fundamental analysis. • Understand that no indicator is foolproof; always be prepared for unexpected market moves. Goodluck -BullByte! ---
Pine Script® Indikator
von BullByte
2
Long and Short Term Highs and LowsLong and Short Term Highs and Lows Overview: This indicator is designed to help traders identify significant price points by marking new highs and lows over two distinct timeframes—a long-term and a short-term period. It achieves this by drawing optional channel lines that outline the highest highs and lowest lows over the chosen time periods and by plotting visual markers (triangles) on the chart when a new high or low is detected. Key Features: Dual Timeframe Analysis: Long Term: Uses a user-defined “Time Period” (default 52) and “Time Unit” (default: Weekly) to determine long-term high and low levels. Short Term: Uses a separate “Time Period” (default 50) and “Time Unit” (default: Daily) to compute short-term high and low levels. Optional Channel Display: For both long and short term periods, you have the option to display a channel by plotting the highest and lowest values as lines. This visual channel helps to delineate the range within which the price has traded over the selected period. New High/Low Markers: The indicator identifies moments when the highest high or lowest low is updated relative to the previous bar. When a new high is established, an up triangle is plotted above the bar. Conversely, when a new low occurs, a down triangle is plotted below the bar. Separate input toggles allow you to enable or disable these markers independently for the long-term and short-term setups. Inputs and Settings: Long Term High/Low Period Settings: Show New High/Low? (STW): Toggle to enable or disable the plotting of new high/low markers for the long-term period. Time Period: The number of bars used to calculate the highest high and lowest low (default is 52). Time Unit: The timeframe on which the long-term calculation is based (default is Weekly). Show Channel? (SCW): Toggle to display the channel lines that connect the long-term high and low levels. Short Term High/Low Period Settings: Show New High/Low?: Toggle to enable or disable the plotting of new high/low markers for the short-term period. Time Period: The number of bars used for calculating the short-term extremes (default is 50). Time Unit: The timeframe on which the short-term calculations are based (default is Daily). Show Channel?: Toggle to display the channel lines for the short-term highs and lows. Indicator Logic: Channel Calculation: The script uses the request.security function to pull data from the specified timeframes. For each timeframe: It calculates the lowest low over the defined period using ta.lowest. It calculates the highest high over the defined period using ta.highest. These values can be optionally plotted as channel lines when the “Show Channel?” option is enabled. New High/Low Detection: For each timeframe, the indicator compares the current high (or low) with its immediate previous value: New High: When the current high exceeds the previous bar’s high, an up triangle is drawn above the bar. New Low: When the current low falls below the previous bar’s low, a down triangle is drawn below the bar. Usage and Interpretation: Trend Identification: When new highs (or lows) occur, they can signal the start of a strong upward (or downward) movement. The indicator helps you visually track these critical turning points over both longer and shorter periods. Channel Breakouts: The optional channel display offers additional context. Price movement beyond these channels may indicate a breakout or a significant shift in trend. Customizable Timeframes: You can adjust both the time period and time unit to fit your trading style—whether you’re focusing on longer-term trends or short-term price action. Conclusion: This indicator provides a dual-layer analysis by combining long-term and short-term perspectives, making it a versatile tool for identifying key highs and lows. Whether you are looking to confirm trend strength or spot potential breakouts, the “Long and Short Term Highs and Lows” indicator adds a valuable visual element to your TradingView charts.
Pine Script® Indikator
von AltcoinDreaming
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 Ang, A. (2014) *Asset Management: A Systematic Approach to Factor Investing*. Oxford: Oxford University Press. Ang, A., Piazzesi, M. and Wei, M. (2006) 'What does the yield curve tell us about GDP growth?', *Journal of Econometrics*, 131(1-2), pp. 359-403. Asness, C.S. (2003) 'Fight the Fed Model', *The Journal of Portfolio Management*, 30(1), pp. 11-24. Asness, C.S., Moskowitz, T.J. and Pedersen, L.H. (2013) 'Value and Momentum Everywhere', *The Journal of Finance*, 68(3), pp. 929-985. Baker, M. and Wurgler, J. (2006) 'Investor Sentiment and the Cross-Section of Stock Returns', *The Journal of Finance*, 61(4), pp. 1645-1680. Baker, M. and Wurgler, J. (2007) 'Investor Sentiment in the Stock Market', *Journal of Economic Perspectives*, 21(2), pp. 129-152. Baur, D.G. and Lucey, B.M. (2010) 'Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold', *Financial Review*, 45(2), pp. 217-229. Bollerslev, T. (1986) 'Generalized Autoregressive Conditional Heteroskedasticity', *Journal of Econometrics*, 31(3), pp. 307-327. Boudoukh, J., Michaely, R., Richardson, M. and Roberts, M.R. (2007) 'On the Importance of Measuring Payout Yield: Implications for Empirical Asset Pricing', *The Journal of Finance*, 62(2), pp. 877-915. Brinson, G.P., Hood, L.R. and Beebower, G.L. (1986) 'Determinants of Portfolio Performance', *Financial Analysts Journal*, 42(4), pp. 39-44. Brock, W., Lakonishok, J. and LeBaron, B. (1992) 'Simple Technical Trading Rules and the Stochastic Properties of Stock Returns', *The Journal of Finance*, 47(5), pp. 1731-1764. Calmar, T.W. (1991) 'The Calmar Ratio', *Futures*, October issue. Campbell, J.Y. and Shiller, R.J. (1988) 'The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors', *Review of Financial Studies*, 1(3), pp. 195-228. Cochrane, J.H. (2011) 'Presidential Address: Discount Rates', *The Journal of Finance*, 66(4), pp. 1047-1108. Damodaran, A. (2012) *Equity Risk Premiums: Determinants, Estimation and Implications*. Working Paper, Stern School of Business. Engle, R.F. (1982) 'Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation', *Econometrica*, 50(4), pp. 987-1007. Estrella, A. and Hardouvelis, G.A. (1991) 'The Term Structure as a Predictor of Real Economic Activity', *The Journal of Finance*, 46(2), pp. 555-576. Estrella, A. and Mishkin, F.S. (1998) 'Predicting U.S. Recessions: Financial Variables as Leading Indicators', *Review of Economics and Statistics*, 80(1), pp. 45-61. Faber, M.T. (2007) 'A Quantitative Approach to Tactical Asset Allocation', *The Journal of Wealth Management*, 9(4), pp. 69-79. Fama, E.F. and French, K.R. (1989) 'Business Conditions and Expected Returns on Stocks and Bonds', *Journal of Financial Economics*, 25(1), pp. 23-49. Fama, E.F. and French, K.R. (1992) 'The Cross-Section of Expected Stock Returns', *The Journal of Finance*, 47(2), pp. 427-465. Garman, M.B. and Klass, M.J. (1980) 'On the Estimation of Security Price Volatilities from Historical Data', *Journal of Business*, 53(1), pp. 67-78. Gilchrist, S. and Zakrajšek, E. (2012) 'Credit Spreads and Business Cycle Fluctuations', *American Economic Review*, 102(4), pp. 1692-1720. Gordon, M.J. (1962) *The Investment, Financing, and Valuation of the Corporation*. Homewood: Irwin. Graham, B. and Dodd, D.L. (1934) *Security Analysis*. New York: McGraw-Hill. Hamilton, J.D. (1989) 'A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle', *Econometrica*, 57(2), pp. 357-384. Ilmanen, A. (2011) *Expected Returns: An Investor's Guide to Harvesting Market Rewards*. Chichester: Wiley. Jaconetti, C.M., Kinniry, F.M. and Zilbering, Y. (2010) 'Best Practices for Portfolio Rebalancing', *Vanguard Research Paper*. Jegadeesh, N. and Titman, S. (1993) 'Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency', *The Journal of Finance*, 48(1), pp. 65-91. Kahneman, D. and Tversky, A. (1979) 'Prospect Theory: An Analysis of Decision under Risk', *Econometrica*, 47(2), pp. 263-292. Korteweg, A. (2010) 'The Net Benefits to Leverage', *The Journal of Finance*, 65(6), pp. 2137-2170. Lo, A.W. and MacKinlay, A.C. (1990) 'Data-Snooping Biases in Tests of Financial Asset Pricing Models', *Review of Financial Studies*, 3(3), pp. 431-467. Longin, F. and Solnik, B. (2001) 'Extreme Correlation of International Equity Markets', *The Journal of Finance*, 56(2), pp. 649-676. Mandelbrot, B. (1963) 'The Variation of Certain Speculative Prices', *The Journal of Business*, 36(4), pp. 394-419. Markowitz, H. (1952) 'Portfolio Selection', *The Journal of Finance*, 7(1), pp. 77-91. Modigliani, F. and Miller, M.H. (1961) 'Dividend Policy, Growth, and the Valuation of Shares', *The Journal of Business*, 34(4), pp. 411-433. Moreira, A. and Muir, T. (2017) 'Volatility-Managed Portfolios', *The Journal of Finance*, 72(4), pp. 1611-1644. Moskowitz, T.J., Ooi, Y.H. and Pedersen, L.H. (2012) 'Time Series Momentum', *Journal of Financial Economics*, 104(2), pp. 228-250. Parkinson, M. (1980) 'The Extreme Value Method for Estimating the Variance of the Rate of Return', *Journal of Business*, 53(1), pp. 61-65. Piotroski, J.D. (2000) 'Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers', *Journal of Accounting Research*, 38, pp. 1-41. Reinhart, C.M. and Rogoff, K.S. (2009) *This Time Is Different: Eight Centuries of Financial Folly*. Princeton: Princeton University Press. Ross, S.A. (1976) 'The Arbitrage Theory of Capital Asset Pricing', *Journal of Economic Theory*, 13(3), pp. 341-360. Roy, A.D. (1952) 'Safety First and the Holding of Assets', *Econometrica*, 20(3), pp. 431-449. Schwert, G.W. (1989) 'Why Does Stock Market Volatility Change Over Time?', *The Journal of Finance*, 44(5), pp. 1115-1153. Sharpe, W.F. (1966) 'Mutual Fund Performance', *The Journal of Business*, 39(1), pp. 119-138. Sharpe, W.F. (1994) 'The Sharpe Ratio', *The Journal of Portfolio Management*, 21(1), pp. 49-58. Simon, D.P. and Wiggins, R.A. (2001) 'S&P Futures Returns and Contrary Sentiment Indicators', *Journal of Futures Markets*, 21(5), pp. 447-462. Taleb, N.N. (2007) *The Black Swan: The Impact of the Highly Improbable*. New York: Random House. Whaley, R.E. (2000) 'The Investor Fear Gauge', *The Journal of Portfolio Management*, 26(3), pp. 12-17. Whaley, R.E. (2009) 'Understanding the VIX', *The Journal of Portfolio Management*, 35(3), pp. 98-105. Yardeni, E. (2003) 'Stock Valuation Models', *Topical Study*, 51, Yardeni Research. Zweig, M.E. (1973) 'An Investor Expectations Stock Price Predictive Model Using Closed-End Fund Premiums', *The Journal of Finance*, 28(1), pp. 67-78.
Pine Script® Indikator
von EdgeTools
4
Contrarian Period High & LowContrarian Period High & Low This indicator pairs nicely with the Contrarian 100 MA and can be located here: Overview The "Contrarian Period High & Low" indicator is a powerful technical analysis tool designed for traders seeking to identify key support and resistance levels and capitalize on contrarian trading opportunities. By tracking the highest highs and lowest lows over user-defined periods (Daily, Weekly, or Monthly), this indicator plots historical levels and generates buy and sell signals when price breaks these levels in a contrarian manner. A unique blue dot counter and action table enhance decision-making, making it ideal for swing traders, trend followers, and those trading forex, stocks, or cryptocurrencies. Optimized for daily charts, it can be adapted to other timeframes with proper testing. How It Works The indicator identifies the highest high and lowest low within a specified period (e.g., daily, weekly, or monthly) and draws horizontal lines for the previous period’s extremes on the chart. These levels act as dynamic support and resistance zones. Contrarian signals are generated when the price crosses below the previous period’s low (buy signal) or above the previous period’s high (sell signal), indicating potential reversals. A blue dot counter tracks consecutive buy signals, and a table displays the count and recommended action, helping traders decide whether to hold or flip positions. Key Components Period High/Low Levels: Tracks the highest high and lowest low for each period, plotting red lines for highs and green lines for lows from the bar where they occurred, extending for a user-defined length (default: 200 bars). Contrarian Signals: Generates buy signals (blue circles) when price crosses below the previous period’s low and sell signals (white circles) when price crosses above the previous period’s high, designed to capture potential reversals. Blue Dot Tracker: Counts consecutive buy signals (“blue dots”). If three or more occur, it suggests a stronger trend, with the table recommending whether to “Hold Investment” or “Flip Investment.” Action Table: A 2x2 table in the bottom-right corner displays the blue dot count and action (“Hold Investment” if count ≥ 4, else “Flip Investment”) for quick reference. Mathematical Concepts Period Detection: Uses an approximate bar count to define periods (1 bar for Daily, 5 bars for Weekly, 20 bars for Monthly on a daily chart). When a new period starts, the previous period’s high/low is finalized and plotted. High/Low Tracking: Highest high (periodHigh) and lowest low (periodLow) are updated within the period. Lines are drawn at these levels when the period ends, starting from the bar where the extreme occurred (periodHighBar, periodLowBar). Signal Logic: Buy signal: ta.crossunder(close , prevPeriodLow) and not lowBroken and barstate.isconfirmed Sell signal: ta.crossover(close , prevPeriodHigh) and not highBroken and barstate.isconfirmed Flags (highBroken, lowBroken) prevent multiple signals for the same level within a period. Blue Dot Counter: Increments on each buy signal, resets on a sell signal or if price exceeds the entry price after three or more buy signals. Entry and Exit Rules Buy Signal (Blue Circle): Triggered when the price crosses below the previous period’s low, suggesting a potential oversold condition and buying opportunity. The signal appears as a blue circle below the price bar. Sell Signal (White Circle): Triggered when the price crosses above the previous period’s high, indicating a potential overbought condition and selling opportunity. The signal appears as a white circle above the price bar. Blue Dot Tracker: Increments blueDotCount on each buy signal and sets an entryPrice on the first buy. Resets on a sell signal or if price exceeds entryPrice after three or more buy signals. If blueDotCount >= 3, the table suggests holding; if >= 4, it reinforces “Hold Investment.” Exit Rules: Exit a buy position on a sell signal or when price exceeds the entry price after three or more buy signals. Combine with other tools (e.g., trendlines, support/resistance) for additional confirmation. Always apply proper risk management. Recommended Usage The "Contrarian Period High & Low" indicator is optimized for daily charts but can be adapted to other timeframes (e.g., 1H, 4H) with adjustments to the period bar count. It excels in markets with clear support/resistance levels and potential reversal zones. Traders should: Backtest the indicator on their chosen asset and timeframe to validate signal reliability. Combine with other technical tools (e.g., moving averages, Fibonacci levels) for stronger trade confirmation. Adjust barsPerPeriod (e.g., ~120 bars for Weekly on hourly charts) based on the chart timeframe and market volatility. Monitor the action table to guide position management based on blue dot counts. Customization Options Period Type: Choose between Daily, Weekly, or Monthly periods (default: Monthly). Line Length: Set the length of high/low lines in bars (default: 200). Show Highs/Lows: Toggle visibility of period high (red) and low (green) lines. Max Lines to Keep: Limit the number of historical lines displayed (default: 10). Hide Signals: Toggle buy/sell signal visibility for a cleaner chart. Table Display: A fixed table in the bottom-right corner shows the blue dot count and action, with yellow (Hold) or green (Flip) backgrounds based on the count. Why Use This Indicator? The "Contrarian Period High & Low" indicator offers a unique blend of support/resistance visualization and contrarian signal generation, making it a versatile tool for identifying potential reversals. Its clear visual cues (lines and signals), blue dot tracker, and actionable table provide traders with an intuitive way to monitor market structure and manage trades. Whether you’re a beginner or an experienced trader, this indicator enhances your ability to spot key levels and time entries/exits effectively. Tips for Users Test the indicator thoroughly on your chosen market and timeframe to optimize settings (e.g., adjust barsPerPeriod for non-daily charts). Use in conjunction with price action or other indicators for stronger trade setups. Monitor the action table to decide whether to hold or flip positions based on blue dot counts. Ensure your chart timeframe aligns with the selected period type (e.g., daily chart for Monthly periods). Apply strict risk management to protect against false breakouts. Happy trading with the Contrarian Period High & Low indicator! Share your feedback and strategies in the TradingView community!
Pine Script® Indikator
von chuckaschultz
Fibonacci Retracement levels Automatically D/W/MIndicator Description: Fibonacci Retracement levels Automatically Fibonacci retracement levels based on the day, week, month High Low range and Fibonacci retracement levels draws automatically .This Pine Script indicator is designed to plot Fibonacci retracement levels based on the high and low prices of a user-selected timeframe (Daily, Weekly, or Monthly). It identifies bullish or bearish candles in the chosen timeframe, draws key price levels, and overlays Fibonacci retracement lines and semi-transparent colored boxes to highlight potential support and resistance zones. The indicator dynamically updates with each new period and extends lines, labels, and boxes to the current bar for real-time visualization. Key Features 1. Timeframe Selection: Users can choose the timeframe for analysis: Daily, Weekly, or Monthly via an input dropdown. The indicator retrieves the open, high, low, and close prices for the selected timeframe using `request.security`. 2. High and Low Tracking : Tracks the highest high and lowest low within the selected timeframe. Stores these values and their corresponding bar indices in arrays (`whigh`, `wlow`, `whighIdx`,`wlowIdx`). Limits the array size to the most recent period to optimize performance. 3. Bullish and Bearish Candle Detection : Identifies whether the previous period’s candle is bullish (`close > open`) or bearish (`close < open`). Uses this to determine the direction for Fibonacci retracement calculations. Bullish candle: Fibonacci levels are drawn from low to high Bearish candle: Fibonacci levels are drawn from high to low 4. Fibonacci Retracement Levels : Plots Fibonacci levels at 0.236, 0.382, 0.5, 0.618, and 0.786 between the high and low of the period. For bullish candles, levels are calculated from the low (support) to the high (resistance). For bearish candles, levels are calculated from the high (resistance) to the low (support). Each Fibonacci level is drawn as a horizontal line with a unique color: - 0.236: Blue - 0.382: Purple - 0.5: Yellow - 0.618: Teal - 0.786: Fuchsia 5. Visual Elements: - High/Low Lines and Labels: Draws a red line and label for the previous period’s high. Draws a green line and label for the previous period’s low. Fibonacci Lines and Labels: Each Fibonacci level has a horizontal line and a label displaying the ratio. Colored Boxes: Semi-transparent boxes are drawn between consecutive Fibonacci levels (including high and low) to highlight zones. 6. Dynamic Updates: - At the start of a new period (e.g., new week for Weekly timeframe), the indicator: - Clears previous Fibonacci lines, labels, and boxes. - Recalculates the high and low for the new period. - Redraws lines, labels, and boxes based on the new data. - Extends all lines, labels, and boxes to the current bar index for real-time tracking. 7. Performance Optimization: - Deletes old lines, labels, and boxes to prevent clutter. - Limits the storage of highs and lows to the most recent period. How It Works 1. Initialization: Defines variables for tracking bullish/bearish candles, lines, labels, and arrays for Fibonacci levels and boxes. Sets up color arrays for Fibonacci lines and boxes with distinct, semi-transparent colors. 2. Data Collection: Fetches the previous period’s OHLC (open, high, low, close) using `request.security`. Detects new periods (e.g., new week or month) using `ta.change(time(tf))`. 3. Fibonacci Calculation: On a new period, stores the high and low prices and their bar indices. - Identifies the maximum high and minimum low from the stored data. - Calculates Fibonacci levels based on the range (`maxHigh - minLow`) and the direction (bullish or bearish). 4. Drawing: - Draws high/low lines and labels at the identified price levels. Plots Fibonacci retracement lines and labels for each ratio. Creates semi-transparent boxes between Fibonacci levels to visually distinguish zones. 5. Updates: - Extends all lines, labels, and boxes to the current bar index when a new period is detected. Clears old Fibonacci elements to avoid overlap and ensure clarity. Usage - Purpose: This indicator is useful for traders who use Fibonacci retracement levels to identify potential support and resistance zones in financial markets. - Application: - Select the desired timeframe (Daily, Weekly, Monthly) via the input settings. - The indicator automatically plots the previous period’s high/low and Fibonacci levels on the chart. - Use the labeled Fibonacci levels and colored boxes to identify key price zones for trading decisions. - Customization: - Modify the `timeframe` input to switch between Daily, Weekly, or Monthly analysis. - Adjust the `fibLineColors` and `fibFillColors` arrays to change the visual appearance of lines and boxes. - The indicator is designed for use on TradingView with Pine Script. - The maximum array size for highs/lows is limited to 1 period in this version (can be adjusted by modifying the `array.shift` logic). - The indicator dynamically updates with each new period, ensuring real-time relevance. This indicator make educational purpose use only
Pine Script® Indikator
von Trend_MyFrend
5
GEEKSDOBYTE IFVG w/ Buy/Sell Signals1. Inputs & Configuration Swing Lookback (swingLen) Controls how many bars on each side are checked to mark a swing high or swing low (default = 5). Booleans to Toggle Plotting showSwings – Show small triangle markers at swing highs/lows showFVG – Show Fair Value Gap zones showSignals – Show “BUY”/“SELL” labels when price inverts an FVG showDDLine – Show a yellow “DD” line at the close of the inversion bar showCE – Show an orange dashed “CE” line at the midpoint of the gap area 2. Swing High / Low Detection isSwingHigh = ta.pivothigh(high, swingLen, swingLen) Marks a bar as a swing high if its high is higher than the highs of the previous swingLen bars and the next swingLen bars. isSwingLow = ta.pivotlow(low, swingLen, swingLen) Marks a bar as a swing low if its low is lower than the lows of the previous and next swingLen bars. Plotting If showSwings is true, small red downward triangles appear above swing highs, and green upward triangles below swing lows. 3. Fair Value Gap (3‐Bar) Identification A Fair Value Gap (FVG) is defined here using a simple three‐bar logic (sometimes called an “inefficiency” in price): Bullish FVG (bullFVG) Checks if, two bars ago, the low of that bar (low ) is strictly greater than the current bar’s high (high). In other words: bullFVG = low > high Bearish FVG (bearFVG) Checks if, two bars ago, the high of that bar (high ) is strictly less than the current bar’s low (low). In other words: bearFVG = high < low When either condition is true, it identifies a three‐bar “gap” or unfilled imbalance in the market. 4. Drawing FVG Zones If showFVG is enabled, each time a bullish or bearish FVG is detected: Bullish FVG Zone Draws a semi‐transparent green box from the bar two bars ago (where the gap began) at low up to the current bar’s high. Bearish FVG Zone Draws a semi‐transparent red box from the bar two bars ago at high down to the current bar’s low. These colored boxes visually highlight the “fair value imbalance” area on the chart. 5. Inversion (Fill) Detection & Entry Signals An inversion is defined as the price “closing through” that previously drawn FVG: Bullish Inversion (bullInversion) Occurs when a bullish FVG was identified on bar-2 (bullFVG), and on the current bar the close is greater than that old bar-2 low: bullInversion = bullFVG and close > low Bearish Inversion (bearInversion) Occurs when a bearish FVG was identified on bar-2 (bearFVG), and on the current bar the close is lower than that old bar-2 high: bearInversion = bearFVG and close < high When an inversion is true, the indicator optionally draws two lines and a label (depending on input toggles): Draw “DD” Line (yellow, solid) Plots a horizontal yellow line from the current bar’s close price extending five bars forward (bar_index + 5). This is often referred to as a “Demand/Daily Demand” line, marking where price inverted the gap. Draw “CE” Line (orange, dashed) Calculates the midpoint (ce) of the original FVG zone. For a bullish inversion: ce = (low + high) / 2 For a bearish inversion: ce = (high + low) / 2 Plots a horizontal dashed orange line at that midpoint for five bars forward. Plot Label (“BUY” / “SELL”) If showSignals is true, a green “BUY” label is placed at the low of the current bar when a bullish inversion occurs. Likewise, a red “SELL” label at the high of the current bar when a bearish inversion happens. 6. Putting It All Together Swing Markers (Optional): Visually confirm recent swing highs and swing lows with small triangles. FVG Zones (Optional): Highlight areas where price left a 3-bar gap (bullish in green, bearish in red). Inversion Confirmation: Wait for price to close beyond the old FVG boundary. Once that happens, draw the yellow “DD” line at the close, the orange dashed “CE” line at the zone’s midpoint, and place a “BUY” or “SELL” label exactly on that bar. User Controls: All of the above elements can be individually toggled on/off (showSwings, showFVG, showSignals, showDDLine, showCE). In Practice A bullish FVG forms whenever a strong drop leaves a gap in liquidity (three bars ago low > current high). When price later “fills” that gap by closing above the old low, the script signals a potential long entry (BUY), draws a demand line at the closing price, and marks the midpoint of that gap. Conversely, a bearish FVG marks a potential short zone (three bars ago high < current low). When price closes below that gap’s high, it signals a SELL, with similar lines drawn. By combining these elements, the indicator helps users visually identify inefficiencies (FVGs), confirm when price inverts/fills them, and place straightforward buy/sell labels alongside reference lines for trade management.
Pine Script® Indikator
von RayenGeeksdoByte
FvgCalculations█ OVERVIEW This library provides the core calculation engine for identifying Fair Value Gaps (FVGs) across different timeframes and for processing their interaction with price. It includes functions to detect FVGs on both the current chart and higher timeframes, as well as to check for their full or partial mitigation. █ CONCEPTS The library's primary functions revolve around the concept of Fair Value Gaps and their lifecycle. Fair Value Gap (FVG) Identification An FVG, or imbalance, represents a price range where buying or selling pressure was significant enough to cause a rapid price movement, leaving an "inefficiency" in the market. This library identifies FVGs based on three-bar patterns: Bullish FVG: Forms when the low of the current bar (bar 3) is higher than the high of the bar two periods prior (bar 1). The FVG is the space between the high of bar 1 and the low of bar 3. Bearish FVG: Forms when the high of the current bar (bar 3) is lower than the low of the bar two periods prior (bar 1). The FVG is the space between the low of bar 1 and the high of bar 3. The library provides distinct functions for detecting FVGs on the current (Low Timeframe - LTF) and specified higher timeframes (Medium Timeframe - MTF / High Timeframe - HTF). FVG Mitigation Mitigation refers to price revisiting an FVG. Full Mitigation: An FVG is considered fully mitigated when price completely closes the gap. For a bullish FVG, this occurs if the current low price moves below or touches the FVG's bottom. For a bearish FVG, it occurs if the current high price moves above or touches the FVG's top. Partial Mitigation (Entry/Fill): An FVG is partially mitigated when price enters the FVG's range but does not fully close it. The library tracks the extent of this fill. For a bullish FVG, if the current low price enters the FVG from above, that low becomes the new effective top of the remaining FVG. For a bearish FVG, if the current high price enters the FVG from below, that high becomes the new effective bottom of the remaining FVG. FVG Interaction This refers to any instance where the current bar's price range (high to low) touches or crosses into the currently unfilled portion of an active (visible and not fully mitigated) FVG. Multi-Timeframe Data Acquisition To detect FVGs on higher timeframes, specific historical bar data (high, low, and time of bars at indices and relative to the higher timeframe's last completed bar) is required. The requestMultiTFBarData function is designed to fetch this data efficiently. █ CALCULATIONS AND USE The functions in this library are typically used in a sequence to manage FVGs: 1. Data Retrieval (for MTF/HTF FVGs): Call requestMultiTFBarData() with the desired higher timeframe string (e.g., "60", "D"). This returns a tuple of htfHigh1, htfLow1, htfTime1, htfHigh3, htfLow3, htfTime3. 2. FVG Detection: For LTF FVGs: Call detectFvg() on each confirmed bar. It uses high , low, low , and high along with barstate.isconfirmed. For MTF/HTF FVGs: Call detectMultiTFFvg() using the data obtained from requestMultiTFBarData(). Both detection functions return an fvgObject (defined in FvgTypes) if an FVG is found, otherwise na. They also can classify FVGs as "Large Volume" (LV) if classifyLV is true and the FVG size (top - bottom) relative to the tfAtr (Average True Range of the respective timeframe) meets the lvAtrMultiplier. 3. FVG State Updates (on each new bar for existing FVGs): First, check for overall price interaction using fvgInteractionCheck(). This function determines if the current bar's high/low has touched or entered the FVG's currentTop or currentBottom. If interaction occurs and the FVG is not already mitigated: Call checkMitigation() to determine if the FVG has been fully mitigated by the current bar's currentHigh and currentLow. If true, the FVG's isMitigated status is updated. If not fully mitigated, call checkPartialMitigation() to see if the price has further entered the FVG. This function returns the newLevel to which the FVG has been filled (e.g., currentLow for a bullish FVG, currentHigh for bearish). This newLevel is then used to update the FVG's currentTop or currentBottom. The calling script (e.g., fvgMain.c) is responsible for storing and managing the array of fvgObject instances and passing them to these update functions. █ NOTES Bar State for LTF Detection: The detectFvg() function relies on barstate.isconfirmed to ensure FVG detection is based on closed bars, preventing FVGs from being detected prematurely on the currently forming bar. Higher Timeframe Data (lookahead): The requestMultiTFBarData() function uses lookahead = barmerge.lookahead_on. This means it can access historical data from the higher timeframe that corresponds to the current bar on the chart, even if the higher timeframe bar has not officially closed. This is standard for multi-timeframe analysis aiming to plot historical HTF data accurately on a lower timeframe chart. Parameter Typing: Functions like detectMultiTFFvg and detectFvg infer the type for boolean (classifyLV) and numeric (lvAtrMultiplier) parameters passed from the main script, while explicitly typed series parameters (like htfHigh1, currentAtr) expect series data. fvgObject Dependency: The FVG detection functions return fvgObject instances, and fvgInteractionCheck takes an fvgObject as a parameter. This UDT is defined in the FvgTypes library, making it a dependency for using FvgCalculations. ATR for LV Classification: The tfAtr (for MTF/HTF) and currentAtr (for LTF) parameters are expected to be the Average True Range values for the respective timeframes. These are used, if classifyLV is enabled, to determine if an FVG's size qualifies it as a "Large Volume" FVG based on the lvAtrMultiplier. MTF/HTF FVG Appearance Timing: When displaying FVGs from a higher timeframe (MTF/HTF) on a lower timeframe (LTF) chart, users might observe that the most recent MTF/HTF FVG appears one LTF bar later compared to its appearance on a native MTF/HTF chart. This is an expected behavior due to the detection mechanism in `detectMultiTFFvg`. This function uses historical bar data from the MTF/HTF (specifically, data equivalent to `HTF_bar ` and `HTF_bar `) to identify an FVG. Therefore, all three bars forming the FVG on the MTF/HTF must be fully closed and have shifted into these historical index positions relative to the `request.security` call from the LTF chart before the FVG can be detected and displayed on the LTF. This ensures that the MTF/HTF FVG is identified based on confirmed, closed bars from the higher timeframe. █ EXPORTED FUNCTIONS requestMultiTFBarData(timeframe)   Requests historical bar data for specific previous bars from a specified higher timeframe.   It fetches H , L , T (for the bar before last) and H , L , T (for the bar three periods prior) from the requested timeframe.   This is typically used to identify FVG patterns on MTF/HTF.   Parameters:      timeframe (simple string) : The higher timeframe to request data from (e.g., "60" for 1-hour, "D" for Daily).   Returns: A tuple containing: .   - htfHigh1 (series float): High of the bar at index 1 (one bar before the last completed bar on timeframe).   - htfLow1 (series float): Low of the bar at index 1.   - htfTime1 (series int) : Time of the bar at index 1.   - htfHigh3 (series float): High of the bar at index 3 (three bars before the last completed bar on timeframe).   - htfLow3 (series float): Low of the bar at index 3.   - htfTime3 (series int) : Time of the bar at index 3. detectMultiTFFvg(htfHigh1, htfLow1, htfTime1, htfHigh3, htfLow3, htfTime3, tfAtr, classifyLV, lvAtrMultiplier, tfType)   Detects a Fair Value Gap (FVG) on a higher timeframe (MTF/HTF) using pre-fetched bar data.   Parameters:      htfHigh1 (float) : High of the first relevant bar (typically high ) from the higher timeframe.      htfLow1 (float) : Low of the first relevant bar (typically low ) from the higher timeframe.      htfTime1 (int) : Time of the first relevant bar (typically time ) from the higher timeframe.      htfHigh3 (float) : High of the third relevant bar (typically high ) from the higher timeframe.      htfLow3 (float) : Low of the third relevant bar (typically low ) from the higher timeframe.      htfTime3 (int) : Time of the third relevant bar (typically time ) from the higher timeframe.      tfAtr (float) : ATR value for the higher timeframe, used for Large Volume (LV) FVG classification.      classifyLV (bool) : If true, FVGs will be assessed to see if they qualify as Large Volume.      lvAtrMultiplier (float) : The ATR multiplier used to define if an FVG is Large Volume.      tfType (series tfType enum from no1x/FvgTypes/1) : The timeframe type (e.g., types.tfType.MTF, types.tfType.HTF) of the FVG being detected.   Returns: An fvgObject instance if an FVG is detected, otherwise na. detectFvg(classifyLV, lvAtrMultiplier, currentAtr)   Detects a Fair Value Gap (FVG) on the current (LTF - Low Timeframe) chart.   Parameters:      classifyLV (bool) : If true, FVGs will be assessed to see if they qualify as Large Volume.      lvAtrMultiplier (float) : The ATR multiplier used to define if an FVG is Large Volume.      currentAtr (float) : ATR value for the current timeframe, used for LV FVG classification.   Returns: An fvgObject instance if an FVG is detected, otherwise na. checkMitigation(isBullish, fvgTop, fvgBottom, currentHigh, currentLow)   Checks if an FVG has been fully mitigated by the current bar's price action.   Parameters:      isBullish (bool) : True if the FVG being checked is bullish, false if bearish.      fvgTop (float) : The top price level of the FVG.      fvgBottom (float) : The bottom price level of the FVG.      currentHigh (float) : The high price of the current bar.      currentLow (float) : The low price of the current bar.   Returns: True if the FVG is considered fully mitigated, false otherwise. checkPartialMitigation(isBullish, currentBoxTop, currentBoxBottom, currentHigh, currentLow)   Checks for partial mitigation of an FVG by the current bar's price action.   It determines if the price has entered the FVG and returns the new fill level.   Parameters:      isBullish (bool) : True if the FVG being checked is bullish, false if bearish.      currentBoxTop (float) : The current top of the FVG box (this might have been adjusted by previous partial fills).      currentBoxBottom (float) : The current bottom of the FVG box (similarly, might be adjusted).      currentHigh (float) : The high price of the current bar.      currentLow (float) : The low price of the current bar.   Returns: The new price level to which the FVG has been filled (e.g., currentLow for a bullish FVG).   Returns na if no new partial fill occurred on this bar. fvgInteractionCheck(fvg, highVal, lowVal)   Checks if the current bar's price interacts with the given FVG.   Interaction means the price touches or crosses into the FVG's current (possibly partially filled) range.   Parameters:      fvg (fvgObject type from no1x/FvgTypes/1) : The FVG object to check.     Its isMitigated, isVisible, isBullish, currentTop, and currentBottom fields are used.      highVal (float) : The high price of the current bar.      lowVal (float) : The low price of the current bar.   Returns: True if price interacts with the FVG, false otherwise.
Pine Script® Bibliothek
von no1x
ICT Master Suite [Trading IQ]Hello Traders! We’re excited to introduce the ICT Master Suite by TradingIQ, a new tool designed to bring together several ICT concepts and strategies in one place. The Purpose Behind the ICT Master Suite There are a few challenges traders often face when using ICT-related indicators: Many available indicators focus on one or two ICT methods, which can limit traders who apply a broader range of ICT related techniques on their charts. There aren't many indicators for ICT strategy models, and we couldn't find ICT indicators that allow for testing the strategy models and setting alerts. Many ICT related concepts exist in the public domain as indicators, not strategies! This makes it difficult to verify that the ICT concept has some utility in the market you're trading and if it's worth trading - it's difficult to know if it's working! Some users might not have enough chart space to apply numerous ICT related indicators, which can be restrictive for those wanting to use multiple ICT techniques simultaneously. The ICT Master Suite is designed to offer a comprehensive option for traders who want to apply a variety of ICT methods. By combining several ICT techniques and strategy models into one indicator, it helps users maximize their chart space while accessing multiple tools in a single slot. Additionally, the ICT Master Suite was developed as a strategy . This means users can backtest various ICT strategy models - including deep backtesting. A primary goal of this indicator is to let traders decide for themselves what markets to trade ICT concepts in and give them the capability to figure out if the strategy models are worth trading! What Makes the ICT Master Suite Different There are many ICT-related indicators available on TradingView, each offering valuable insights. What the ICT Master Suite aims to do is bring together a wider selection of these techniques into one tool. This includes both key ICT methods and strategy models, allowing traders to test and activate strategies all within one indicator. Features The ICT Master Suite offers: Multiple ICT strategy models, including the 2022 Strategy Model and Unicorn Model, which can be built, tested, and used for live trading. Calculation and display of key price areas like Breaker Blocks, Rejection Blocks, Order Blocks, Fair Value Gaps, Equal Levels, and more. The ability to set alerts based on these ICT strategies and key price areas. A comprehensive, yet practical, all-inclusive ICT indicator for traders. Customizable Timeframe - Calculate ICT concepts on off-chart timeframes Unicorn Strategy Model 2022 Strategy Model Liquidity Raid Strategy Model OTE (Optimal Trade Entry) Strategy Model Silver Bullet Strategy Model Order blocks Breaker blocks Rejection blocks FVG Strong highs and lows Displacements Liquidity sweeps Power of 3 ICT Macros HTF previous bar high and low Break of Structure indications Market Structure Shift indications Equal highs and lows Swings highs and swing lows Fibonacci TPs and SLs Swing level TPs and SLs Previous day high and low TPs and SLs And much more! An ongoing project! How To Use Many traders will already be familiar with the ICT related concepts listed above, and will find using the ICT Master Suite quite intuitive! Despite this, let's go over the features of the tool in-depth and how to use the tool! The image above shows the ICT Master Suite with almost all techniques activated. ICT 2022 Strategy Model The ICT Master suite provides the ability to test, set alerts for, and live trade the ICT 2022 Strategy Model. The image above shows an example of a long position being entered following a complete setup for the 2022 ICT model. A liquidity sweep occurs prior to an upside breakout. During the upside breakout the model looks for the FVG that is nearest 50% of the setup range. A limit order is placed at this FVG for entry. The target entry percentage for the range is customizable in the settings. For instance, you can select to enter at an FVG nearest 33% of the range, 20%, 66%, etc. The profit target for the model generally uses the highest high of the range (100%) for longs and the lowest low of the range (100%) for shorts. Stop losses are generally set at 0% of the range. The image above shows the short model in action! Whether you decide to follow the 2022 model diligently or not, you can still set alerts when the entry condition is met. ICT Unicorn Model The image above shows an example of a long position being entered following a complete setup for the ICT Unicorn model. A lower swing low followed by a higher swing high precedes the overlap of an FVG and breaker block formed during the sequence. During the upside breakout the model looks for an FVG and breaker block that formed during the sequence and overlap each other. A limit order is placed at the nearest overlap point to current price. The profit target for this example trade is set at the swing high and the stop loss at the swing low. However, both the profit target and stop loss for this model are configurable in the settings. For Longs, the selectable profit targets are: Swing High Fib -0.5 Fib -1 Fib -2 For Longs, the selectable stop losses are: Swing Low Bottom of FVG or breaker block The image above shows the short version of the Unicorn Model in action! For Shorts, the selectable profit targets are: Swing Low Fib -0.5 Fib -1 Fib -2 For Shorts, the selectable stop losses are: Swing High Top of FVG or breaker block The image above shows the profit target and stop loss options in the settings for the Unicorn Model. Optimal Trade Entry (OTE) Model The image above shows an example of a long position being entered following a complete setup for the OTE model. Price retraces either 0.62, 0.705, or 0.79 of an upside move and a trade is entered. The profit target for this example trade is set at the -0.5 fib level. This is also adjustable in the settings. For Longs, the selectable profit targets are: Swing High Fib -0.5 Fib -1 Fib -2 The image above shows the short version of the OTE Model in action! For Shorts, the selectable profit targets are: Swing Low Fib -0.5 Fib -1 Fib -2 Liquidity Raid Model The image above shows an example of a long position being entered following a complete setup for the Liquidity Raid Modell. The user must define the session in the settings (for this example it is 13:30-16:00 NY time). During the session, the indicator will calculate the session high and session low. Following a “raid” of either the session high or session low (after the session has completed) the script will look for an entry at a recently formed breaker block. If the session high is raided the script will look for short entries at a bearish breaker block. If the session low is raided the script will look for long entries at a bullish breaker block. For Longs, the profit target options are: Swing high User inputted Lib level For Longs, the stop loss options are: Swing low User inputted Lib level Breaker block bottom The image above shows the short version of the Liquidity Raid Model in action! For Shorts, the profit target options are: Swing Low User inputted Lib level For Shorts, the stop loss options are: Swing High User inputted Lib level Breaker block top Silver Bullet Model The image above shows an example of a long position being entered following a complete setup for the Silver Bullet Modell. During the session, the indicator will determine the higher timeframe bias. If the higher timeframe bias is bullish the strategy will look to enter long at an FVG that forms during the session. If the higher timeframe bias is bearish the indicator will look to enter short at an FVG that forms during the session. For Longs, the profit target options are: Nearest Swing High Above Entry Previous Day High For Longs, the stop loss options are: Nearest Swing Low Previous Day Low The image above shows the short version of the Silver Bullet Model in action! For Shorts, the profit target options are: Nearest Swing Low Below Entry Previous Day Low For Shorts, the stop loss options are: Nearest Swing High Previous Day High Order blocks The image above shows indicator identifying and labeling order blocks. The color of the order blocks, and how many should be shown, are configurable in the settings! Breaker Blocks The image above shows indicator identifying and labeling order blocks. The color of the breaker blocks, and how many should be shown, are configurable in the settings! Rejection Blocks The image above shows indicator identifying and labeling rejection blocks. The color of the rejection blocks, and how many should be shown, are configurable in the settings! Fair Value Gaps The image above shows indicator identifying and labeling fair value gaps. The color of the fair value gaps, and how many should be shown, are configurable in the settings! Additionally, you can select to only show fair values gaps that form after a liquidity sweep. Doing so reduces "noisy" FVGs and focuses on identifying FVGs that form after a significant trading event. The image above shows the feature enabled. A fair value gap that occurred after a liquidity sweep is shown. Market Structure The image above shows the ICT Master Suite calculating market structure shots and break of structures! The color of MSS and BoS, and whether they should be displayed, are configurable in the settings. Displacements The images above show indicator identifying and labeling displacements. The color of the displacements, and how many should be shown, are configurable in the settings! Equal Price Points The image above shows the indicator identifying and labeling equal highs and equal lows. The color of the equal levels, and how many should be shown, are configurable in the settings! Previous Custom TF High/Low The image above shows the ICT Master Suite calculating the high and low price for a user-defined timeframe. In this case the previous day’s high and low are calculated. To illustrate the customizable timeframe function, the image above shows the indicator calculating the previous 4 hour high and low. Liquidity Sweeps The image above shows the indicator identifying a liquidity sweep prior to an upside breakout. The image above shows the indicator identifying a liquidity sweep prior to a downside breakout. The color and aggressiveness of liquidity sweep identification are adjustable in the settings! Power Of Three The image above shows the indicator calculating Po3 for two user-defined higher timeframes! Macros The image above shows the ICT Master Suite identifying the ICT macros! ICT Macros are only displayable on the 5 minute timeframe or less. Strategy Performance Table In addition to a full-fledged TradingView backtest for any of the ICT strategy models the indicator offers, a quick-and-easy strategy table exists for the indicator! The image above shows the strategy performance table in action. Keep in mind that, because the ICT Master Suite is a strategy script, you can perform fully automatic backtests, deep backtests, easily add commission and portfolio balance and look at pertinent metrics for the ICT strategies you are testing! Lite Mode Traders who want the cleanest chart possible can toggle on “Lite Mode”! In Lite Mode, any neon or “glow” like effects are removed and key levels are marked as strict border boxes. You can also select to remove box borders if that’s what you prefer! Settings Used For Backtest For the displayed backtest, a starting balance of $1000 USD was used. A commission of 0.02%, slippage of 2 ticks, a verify price for limit orders of 2 ticks, and 5% of capital investment per order. A commission of 0.02% was used due to the backtested asset being a perpetual future contract for a crypto currency. The highest commission (lowest-tier VIP) for maker orders on many exchanges is 0.02%. All entered positions take place as maker orders and so do profit target exits. Stop orders exist as stop-market orders. A slippage of 2 ticks was used to simulate more realistic stop-market orders. A verify limit order settings of 2 ticks was also used. Even though BTCUSDT.P on Binance is liquid, we just want the backtest to be on the safe side. Additionally, the backtest traded 100+ trades over the period. The higher the sample size the better; however, this example test can serve as a starting point for traders interested in ICT concepts. Community Assistance And Feedback Given the complexity and idiosyncratic applications of ICT concepts amongst its proponents, the ICT Master Suite’s built-in strategies and level identification methods might not align with everyone's interpretation. That said, the best we can do is precisely define ICT strategy rules and concepts to a repeatable process, test, and apply them! Whether or not an ICT strategy is trading precisely how you would trade it, seeing the model in action, taking trades, and with performance statistics is immensely helpful in assessing predictive utility. If you think we missed something, you notice a bug, have an idea for strategy model improvement, please let us know! The ICT Master Suite is an ongoing project that will, ideally, be shaped by the community. A big thank you to the @PineCoders for their Time Library! Thank you!
Pine Script® Strategie
von Trading-IQ
34
Alligator + Fractals + Divergent & Squat Bars + Signal AlertsThe indicator includes Williams Alligator, Williams Fractals, Divergent Bars, Market Facilitation Index, Highest and Lowest Bars, maximum and minimum peak of Awesome Oscillator, and signal alerts based on Bill Williams' Profitunity strategy. MFI and Awesome Oscillator According to the Market Facilitation Index Oscillator, the Squat bar is colored blue, all other bars are colored according to the Awesome Oscillator color, except for the Fake bars, colored with a lighter AO color. In the indicator settings, you can enable the display of "Green" bars (in the "Green Bars > Show" field). In the indicator style settings, you can disable changing the color of bars in accordance with the AO color (in the "AO bars" field), including changing the color for Fake bars (in the "Fake AO bars" field). MFI is calculated using the formula: (high - low) / volume. A Squat bar means that, compared to the previous bar, its MFI has decreased and at the same time its volume has increased, i.e. MFI < previous bar and volume > previous bar. A sign of a possible price reversal, so this is a particularly important signal. A Fake bar is the opposite of a Squat bar and means that, compared to the previous bar, its MFI has increased and at the same time its volume has decreased, i.e. MFI > previous bar and volume < previous bar. A "Green" bar means that, compared to the previous bar, its MFI has increased and at the same time its volume has increased, i.e. MFI > previous bar and volume > previous bar. A sign of trend continuation. But a more significant trend confirmation or warning of a possible reversal is the Awesome Oscillator, which measures market momentum by calculating the difference between the 5 Period and 34 Period Simple Moving Averages (SMA 5 - SMA 34) based on the midpoints of the bars (hl2). Therefore, by default, the "Green" bars and their opposite "Fade" bars are colored according to the color of the Awesome Oscillator. According to Bill Williams' Profitunity strategy, using the Awesome Oscillator, the third Elliott wave is determined by the maximum peak of AO in the range from 100 to 140 bars. The presence of divergence between the maximum AO peak and the subsequent lower AO peak in this interval also warns of a possible correction, especially if the AO crosses the zero line between these AO peaks. Therefore, the chart additionally displays the prices of the highest and lowest bars, as well as the maximum or minimum peak of AO in the interval of 140 bars from the last bar. In the indicator settings, you can hide labels, lines, change the number of bars and any parameters for the AO indicator - method (SMA, Smoothed SMA, EMA and others), length, source (open, high, low, close, hl2 and others). Bullish Divergent bar 🟢 A buy signal (Long) is a Bullish Divergent bar with a green circle displayed above it if such a bar simultaneously meets all of the following conditions: The high of the bar is below all lines of the Alligator indicator. The closing price of the bar is above its middle, i.e. close > (high + low) / 2. The low of the bar is below the low of 2 previous bars or below the low of one previous bar, and the low of the second previous bar is a lower fractal (▼). By default, Divergent bars are not displayed, the low of which is lower than the low of only one previous bar and the low of the 2nd previous bar is not a lower fractal (▼), but you can enable the display of any Divergent bars in the indicator settings (by setting the value "no" in the " field Divergent Bars > Filtration"). The following conditions strengthen the Bullish Divergent bar signal: The opening price of the bar, as well as the closing price, is higher than its middle, i.e. Open > (high + low) / 2. The high of the bar is below all lines of the open Alligator indicator, i.e. the green line (Lips) is below the red line (Teeth) and the red line is below the blue line (Jaw). In this case, the color of the circle above the Bullish Divergent bar is dark green. Squat Divergent bar. The bar following the Bullish Divergent bar corresponds to the green color of the Awesome Oscillator. Divergence on Awesome Oscillator. Formation of the lower fractal (▼), in which the low of the Divergent bar is the peak of the fractal. Bearish Divergent bar 🔴 A signal to sell (Short) is a Bearish Divergent bar under which a red circle is displayed if such a bar simultaneously meets all the following conditions: The low of the bar is above all lines of the Alligator indicator. The closing price of the bar is below its middle, i.e. close < (high + low) / 2. The high of the bar is higher than the high of 2 previous bars or higher than the high of one previous bar, and the high of the second previous bar is an upper fractal (▲). By default, Divergent bars are not displayed, the high of which is higher than the high of only one previous bar and the high of the 2nd previous bar is not an upper fractal (▲), but you can enable the display of any Divergent bars in the indicator settings (by setting the value "no" in the " field Divergent Bars > Filtration"). The following conditions strengthen the Bearish Divergent bar signal: The opening price of the bar, as well as the closing price, is below its middle, i.e. open < (high + low) / 2. The low of the bar is above all lines of the open Alligator indicator, i.e. the green line (Lips) is above the red line (Teeth) and the red line is above the blue line (Jaw). In this case, the color of the circle under the Bearish Divergent bar is dark red. Squat Divergent bar. The bar following the Bearish Divergent bar corresponds to the red color of the Awesome Oscillator. Divergence on Awesome Oscillator. Formation of the upper fractal (▲), in which the high of the Divergent bar is the peak of the fractal. Alligator lines crossing Bars crossing the green line (Lips) of the open Alligator indicator is the first warning of a possible correction (price rollback) if one of the following conditions is met: If the bar closed below the Lips line, which is above the Teeth line, and the Teeth line is above the Jaw line, while the closing price of the previous bar is above the Lips line. If the bar closed above the Lips line, which is below the Teeth line, and the Teeth line is below the Jaw line, while the closing price of the previous bar is below the Lips line. The intersection of all open Alligator lines by bars is a sign of a deep correction and a warning of a possible trend change. Frequent intersection of Alligator lines with each other is a sign of a sideways trend (flat). Signal Alerts To receive notifications about signals when creating an alert, you must select the condition "Any alert() function is call", in which case notifications will arrive in the following format: D — timeframe, for example: D, 4H, 15m. 🟢 BDB⎾ - a signal for a Bullish Divergent bar to buy (Long), triggers once after the bar closes and includes additional signals: /// — if Alligator is open. ⏉ — if the opening price of the bar, as well as the closing price, is above its middle. + Squat 🔷 - Squat bar or + Green ↑ - "Green" bar or + Fake ↓ - Fake bar. + AO 🟩 - if after the Divergent bar closes, the oscillator color change for the next bar corresponds the green color of the Awesome Oscillator. ┴/┬ — AO above/below the zero line. ∇ — if there is divergence on AO in the interval of 140 bars from the last bar. 🔴 BDB⎿ - a signal for a Bearish Divergent bar to sell (Short), triggers once after the bar closes and includes additional signals: /// — if Alligator is open. ⏊ — if the opening price of the bar, as well as the closing price, is below its middle. + Squat 🔷 - Squat bar or + Green ↑ - "Green" bar or + Fake ↓ - Fake bar. + AO 🟥 - if after the Divergent bar closes, the oscillator color change for the next bar corresponds to the red color of the Awesome Oscillator. ┴/┬ — AO above/below the zero line. ∇ — if there is divergence on AO in the interval of 140 bars from the last bar. Alert for bars crossing the green line (Lips) of the open Alligator indicator (can be disabled in the indicator settings in the "Alligator > Enable crossing lips alerts" field): 🔴 Crossing Lips ↓ - if the bar closed below the Lips line, which is above than the other lines, while the closing price of the previous bar is above the Lips line. 🟢 Crossing Lips ↑ - if the bar closed above the Lips line, which is below the other lines, while the closing price of the previous bar is below the Lips line. The fractal signal is triggered after the second bar closes, completing the formation of the fractal, if alerts about fractals are enabled in the indicator settings (the "Fractals > Enable alerts" field): 🟢 Fractal ▲ - upper (Bearish) fractal. 🔴 Fractal ▼ — lower (Bullish) fractal. ⚪️ Fractal ▲/▼ - both upper and lower fractal. ↳ (H=high - L=low) = difference. If you redirect notifications to a webhook URL, for example, to a Telegram bot, then you need to set the notification template for the webhook in the indicator settings in the "Webhook > Message" field (contains a tooltip with an example), in which you just need to specify the text {{message}}, which will be automatically replaced with the alert text with a ticker and a link to TradingView. ‼️ A signal is not a call to action, but only a reason to analyze the chart to make a decision based on the rules of your strategy. *** Индикатор включает в себя Williams Alligator, Williams Fractals, Дивергентные бары, Market Facilitation Index, самый высокий и самый низкий бары, максимальный и минимальный пик Awesome Oscillator, а также оповещения о сигналах на основе стратегии Profitunity Билла Вильямса. MFI и Awesome Oscillator В соответствии с осциллятором Market Facilitation Index Приседающий бар окрашен в синий цвет, все остальные бары окрашены в соответствии с цветом Awesome Oscillator, кроме Фальшивых баров, которые окрашены более светлым цветом AO. В настройках индикатора вы можете включить отображение "Зеленых" баров (в поле "Green Bars > Show"). В настройках стиля индикатора вы можете выключить изменение цвета баров в соответствии с цветом AO (в поле "AO bars"), в том числе изменить цвет для Фальшивых баров (в поле "Fake AO bars"). MFI рассчитывается по формуле: (high - low) / volume. Приседающий бар означает, что по сравнению с предыдущим баром его MFI снизился и в тоже время вырос его объем, т.е. MFI < предыдущего бара и объем > предыдущего бара. Признак возможного разворота цены, поэтому это особенно важный сигнал. Фальшивый бар является противоположностью Приседающему бару и означает, что по сравнению с предыдущим баром его MFI увеличился и в тоже время снизился его объем, т.е. MFI > предыдущего бара и объем < предыдущего бара. "Зеленый" бар означает, что по сравнению с предыдущим баром его MFI увеличился и в тоже время вырос его объем, т.е. MFI > предыдущего бара и объем > предыдущего бара. Признак продолжения тренда. Но более значимым подтверждением тренда или предупреждением о возможном развороте является Awesome Oscillator, который измеряет движущую силу рынка путем вычисления разницы между 5 Периодной и 34 Периодной Простыми Скользящими Средними (SMA 5 - SMA 34) по средним точкам баров (hl2). Поэтому по умолчанию "Зеленые" бары и противоположные им "Увядающие" бары окрашены в соответствии с цветом Awesome Oscillator. По стратегии Profitunity Билла Вильямса с помощью осциллятора Awesome Oscillator определяется третья волна Эллиота по максимальному пику AO в интервале от 100 до 140 баров. Наличие дивергенции между максимальным пиком AO и следующим за ним более низким пиком AO в этом интервале также предупреждает о возможной коррекции, особенно если AO переходит через нулевую линию между этими пиками AO. Поэтому на графике дополнительно отображаются цены самого высокого и самого низкого баров, а также максимальный или минимальный пик АО в интервале 140 баров от последнего бара. В настройках индикатора вы можете скрыть метки, линии, изменить количество баров и любые параметры для индикатора AO – метод (SMA, Smoothed SMA, EMA и другие), длину, источник (open, high, low, close, hl2 и другие). Бычий Дивергентный бар 🟢 Сигналом на покупку (Long) является Бычий Дивергентный бар над которым отображается зеленый круг, если такой бар соответствует одновременно всем следующим условиям: Максимум бара ниже всех линий индикатора Alligator. Цена закрытия бара выше его середины, т.е. close > (high + low) / 2. Минимум бара ниже минимума 2-х предыдущих баров или ниже минимума одного предыдущего бара, а минимум второго предыдущего бара является нижним фракталом (▼). По умолчанию не отображаются Дивергентные бары, минимум которых ниже минимума только одного предыдущего бара и минимум 2-го предыдущего бара не является нижним фракталом (▼), но вы можете включить отображение любых Дивергентных баров в настройках индикатора (установив значение "no" в поле "Divergent Bars > Filtration"). Усилением сигнала Бычьего Дивергентного бара являются следующие условия: Цена открытия бара, как и цена закрытия, выше его середины, т.е. Open > (high + low) / 2. Максимум бара ниже всех линий открытого индикатора Alligator, т.е. зеленая линия (Lips) ниже красной линии (Teeth) и красная линия ниже синей линии (Jaw). В этом случае цвет круга над Бычьим Дивергентным баром окрашен в темно-зеленый цвет. Приседающий Дивергентный бар. Бар, следующий за Бычьим Дивергентным баром, соответствует зеленому цвету Awesome Oscillator. Дивергенция на Awesome Oscillator. Образование нижнего фрактала (▼), у которого минимум Дивергентного бара является пиком фрактала. Медвежий Дивергентный бар 🔴 Сигналом на продажу (Short) является Медвежий Дивергентный бар под которым отображается красный круг, если такой бар соответствует одновременно всем следующим условиям: Минимум бара выше всех линий индикатора Alligator. Цена закрытия бара ниже его середины, т.е. close < (high + low) / 2. Максимум бара выше маскимума 2-х предыдущих баров или выше максимума одного предыдущего бара, а максимум второго предыдущего бара является верхним фракталом (▲). По умолчанию не отображаются Дивергентные бары, максимум которых выше максимума только одного предыдущего бара и максимум 2-го предыдущего бара не является верхним фракталом (▲), но вы можете включить отображение любых Дивергентных баров в настройках индикатора (установив значение "no" в поле "Divergent Bars > Filtration"). Усилением сигнала Медвежьего Дивергентного бара являются следующие условия: Цена открытия бара, как и цена закрытия, ниже его середины, т.е. open < (high + low) / 2. Минимум бара выше всех линий открытого индикатора Alligator, т.е. зеленая линия (Lips) выше красной линии (Teeth) и красная линия выше синей линии (Jaw). В этом случае цвет круга под Медвежьим Дивергентным Баром окрашен в темно-красный цвет. Приседающий Дивергентный бар. Бар, следующий за Медвежьим Дивергентным баром, соответствует красному цвету Awesome Oscillator. Дивергенция на Awesome Oscillator. Образование верхнего фрактала (▲), у которого максимум Дивергентного бара является пиком фрактала. Пересечение линий Alligator Пересечение барами зеленой линии (Lips) открытого индикатора Alligator является первым предупреждением о возможной коррекции (откате цены) при выполнении одного из следующих условий: Если бар закрылся ниже линии Lips, которая выше линии Teeth, а линия Teeth выше линии Jaw, при этом цена закрытия предыдущего бара находится выше линии Lips. Если бар закрылся выше линии Lips, которая ниже линии Teeth, а линия Teeth ниже линии Jaw, при этом цена закрытия предыдущего бара находится ниже линии Lips. Пересечение барами всех линий открытого Alligator является признаком глубокой коррекции и предупреждением о возможной смене тренда. Частое пересечение линий Alligator между собой является признаком бокового тренда (флэт). Оповещения о сигналах Для получения уведомлений о сигналах при создании оповещения необходимо выбрать условие "При любом вызове функции alert()", в таком случае уведомления будут приходить в следующем формате: D — таймфрейм, например: D, 4H, 15m. 🟢 BDB⎾ — сигнал Бычьего Дивергентного бара на покупку (Long), срабатывает один раз после закрытия бара и включает дополнительные сигналы: /// — если Alligator открыт. ⏉ — если цена открытия бара, как и цена закрытия, выше его середины. + Squat 🔷 — Приседающий бар или + Green ↑ — "Зеленый" бар или + Fake ↓ — Фальшивый бар. + AO 🟩 — если после закрытия Дивергентного бара, изменение цвета осциллятора для следующего бара соответствует зеленому цвету Awesome Oscillator. ┴/┬ — AO выше/ниже нулевой линии. ∇ — если есть дивергенция на AO в интервале 140 баров от последнего бара. 🔴 BDB⎿ — сигнал Медвежьего Дивергентного бара на продажу (Short), срабатывает один раз после закрытия бара и включает дополнительные сигналы: /// — если Alligator открыт. ⏊ — если цена открытия бара, как и цена закрытия, ниже его середины. + Squat 🔷 — Приседающий бар или + Green ↑ — "Зеленый" бар или + Fake ↓ — Фальшивый бар. + AO 🟥 — если после закрытия Дивергентного бара, изменение цвета осциллятора для следующего бара соответствует красному цвету Awesome Oscillator. ┴/┬ — AO выше/ниже нулевой линии. ∇ — если есть дивергенция на AO в интервале 140 баров от последнего бара. Сигнал пересечения барами зеленой линии (Lips) открытого индикатора Alligator (можно отключить в настройках индикатора в поле "Alligator > Enable crossing lips alerts"): 🔴 Crossing Lips ↓ — если бар закрылся ниже линии Lips, которая выше остальных линий, при этом цена закрытия предыдущего бара находится выше линии Lips. 🟢 Crossing Lips ↑ — если бар закрылся выше линии Lips, которая ниже остальных линий, при этом цена закрытия предыдущего бара находится ниже линии Lips. Сигнал фрактала срабатывает после закрытия второго бара, завершающего формирование фрактала, если оповещения о фракталах включены в настройках индикатора (поле "Fractals > Enable alerts"): 🟢 Fractal ▲ — верхний (Медвежий) фрактал. 🔴 Fractal ▼ — нижний (Бычий) фрактал. ⚪️ Fractal ▲/▼ — одновременно верхний и нижний фрактал. ↳ (H=high - L=low) = разница. Если вы перенаправляете оповещения на URL вебхука, например, в бота Telegram, то вам необходимо установить шаблон оповещения для вебхука в настройках индикатора в поле "Webhook > Message" (содержит подсказку с примером), в котором в качестве текста сообщения достаточно указать текст {{message}}, который будет автоматически заменен на текст оповещения с тикером и ссылкой на TradingView. ‼️ Сигнал — это не призыв к действию, а лишь повод проанализировать график для принятия решения на основе правил вашей стратегии.
Pine Script® Indikator
von grigorykov
45
YD_Divergence_RSI+CMFThe ‘YD_Divergence_RSI+CMF’ indicator can find divergence using RSI (Relative Strength Index) and CMF (Chaikin Money Flow) indicators. 📌 Key functions 1. Search pivot high and pivot low points in a certain length of price. 2. Connect pivot high to pivot high , pivot low to pivot low , forming two standards for divergence in result. The marker then plots only the higher high, lower low lines. (higher low and lower high in prices are referred to hidden divergence, which are not considered in this indicator) 3. Compare the two standards with RSI and CMF indicators, send an alert if there is a divergence. As a result, the indicator will find four combination of divergence. A. Higher high price / Lower RSI (Bearish RSI Divergence) B. Lower low price / Higher RSI (Bullish RSI Divergence) C. Higher high price / Lower CMF (Bearish CMF Divergence) D. Lower low price / Higher CMF (Bullish CMF Divergence) 📌 Details Developing the indicators, we put a lot of effort in making a customizable and user-friendly interface. #1. Pivot Setting Users can set the length to find the pivot high / pivot low in ‘Pivot Settings – Pivot Length.’ Increased pivot Length takes more candles to interpret the chart but reduce false signals since the it uses only the most certain pivot high / pivot low values. Obviously, decreased pivot length will act the opposite. Users can choose whether to use ‘High/Low’ or ‘Close’ in ‘Pivot Reference’ to set the swing point of prices. Users can also choose whether to display the pivot high / pivot low marker on the chart. #2 RSI & CMF Settings Users can adjust the length of RSI & CMF separately. (The default values are set to 14 and 20 each.) #3 Label Setting Users can adjust the text displayed on the chart label. (The default values is set to ‘Bullish / Bearish’, ‘RSI/CMF’, ‘Divergence’.) Users can reduce the length of text label or simply turn the label off. Just click the ‘Bull/Bear’ or ‘None’ button. ‘Divergence’ works the same. Users can decide whether to display the ‘Divergence Line and Label’, set custom settings for the label and line. (color, thickness, style, etc) 📌 Alert Alert are provided as a combination of the chart's symbol and the set label text. For example, ‘BINANCE:BTCUSDT.P, Bullish RSI Divergence’ ==================================================== "YD_Divergence_RSI+CMF" 지표 는 RSI와 CMF 지표를 이용해서 Divergence 를 찾아낼 수 있습니다. 📌 주요 기능 1. 정해진 가격 움직임 안에서 pivot high와 pivot low 포인트 를 찾아냅니다. 2. Pivot high로만 이어진 라인과, Pivot low로만 이어진 두 라인을 작도한 뒤 divergence의 기준으로 삼습니다. 이 지표에서는 normal divergence만 사용하기 때문에 차트에 higher high와 lower low만 표기 합니다. (higher low와 lower high는 hidden divergence로 정의되며, 이 지표에서는 다루지 않습니다. 3. 두 기준선과 RSI, CMF 지표를 각각 비교하고, 결과적으로 4개의 조합을 구할 수 있습니다. A. Higher high price / Lower RSI (Bearish RSI Divergence) B. Lower low price / Higher RSI (Bullish RSI Divergence) C. Higher high price / Lower CMF (Bearish CMF Divergence) D. Lower low price / Higher CMF (Bullish CMF Divergence) 📌 세부 사항 지표를 개발하며 사용자들이 원하는 방향으로 지표를 설정할 수 있게 작업에 많은 공을 들였습니다. 굉장히 다양한 옵션을 선택할 수 있으며, 원하는 방식으로 지표를 사용할 수 있습니다. #1 Pivot Setting Pivot setting에서는 Pivot Length를 변경할 수 있습니다. Pivot Length를 늘릴 경우, 보다 확실한 Swing High와 Swing Low만을 사용하게 되므로, False signal이 줄어들 수 있습니다. 하지만 Swing High/ Low를 판정하는 데에 더 긴 시간이 걸리게 되므로, Signal이 다소 늦게 발생하는 단점이 생기게 됩니다. Pivot Length를 줄일 경우, 반대로 Swing High/Low의 판정이 더 빨리 일어나기 때문에, Signal을 거래에 이용하기는 좋을 수 있습니다. 다만, Swing High와 Low가 훨씬 더 잦은 빈도로 발생하기 때문에 False Signal을 줄 가능성이 높아집니다. Pivot Reference에서는 가격의 Swing Point를 설정함에 있어, High/Low(고가/저가)를 이용할 지 Close (종가)를 이용할 지 선택할 수 있습니다. Pivot High/Low Marker를 선택할 경우 Pivot High/ Low에 Marker가 찍히게 됩니다. #2 RSI와 CMF Setting RSI와 CMF Setting에서는 RSI와 CMF의 길이를 각각 설정할 수 있습니다. 기본값은 14와 20으로 설정되어 있습니다. #3 Label Setting Label Setting에서는 Label에 표시되는 글자를 선택할 수 있습니다. 기본값은 "Bullish / Bearish", "RSI/CMF", "Divergence"로 선택되어 있으며, 너무 길다고 느껴질 경우 "Bull/Bear" 혹은 "None"을 클릭하여 길이를 줄일 수 있습니다. 마찬가지로 Divergence의 경우도 생략이 가능합니다. 하단에서는 Divergence Line과 Label을 켜고 끌 수 있으며, 선의 색깔, 굵기, 종류, 그리고 Label의 색깔, 크기, 종류를 선택할 수 있습니다. Label의 Text 색 역시 변경이 가능합니다. 📌 얼러트 얼러트는 자신이 설정한 차트의 심볼과 Label의 문구의 조합으로 제공되며 예를 들면 다음과 같습니다. "BINANCE:BTCUSDT.P, Bullish RSI Divergence"
Pine Script® Indikator
von Yonsei_dent
4
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):
Pine Script® Indikator
von twingall
18
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
Pine Script® Indikator
von theEccentricTrader
8
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
Pine Script® Indikator
von theEccentricTrader
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.
Pine Script® Indikator
von theEccentricTrader
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.
Pine Script® Indikator
von aberger96
9
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!
Pine Script® Indikator
von RagingRocketBull
21
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.
Pine Script® Bibliothek
von mickes
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.
Pine Script® Indikator
von Fractalyst
6
Enigma Sniper 369The "Enigma Sniper 369" is a custom-built Pine Script indicator designed for TradingView, tailored specifically for forex traders seeking high-probability entries during high-volatility market sessions. Unlike generic trend-following or scalping tools, this indicator uniquely combines session-based "kill zones" (London and US sessions), momentum-based candle analysis, and an optional EMA trend filter to pinpoint liquidity grabs and reversal opportunities. Its originality lies in its focus on liquidity hunting—identifying levels where stop losses are likely clustered (around swing highs/lows and wick midpoints)—and providing visual entry zones that are dynamically removed once price breaches them, reducing clutter and focusing on actionable signals. The name "369" reflects the structured approach of three key components (session timing, candle logic, and trend filter) working in harmony to snipe precise entries. What It Does "Enigma Sniper 369" identifies potential buy and sell opportunities by drawing two types of horizontal lines on the chart during user-defined London and US session kill zones: Solid Lines: Mark the swing low (for buys) or swing high (for sells) of a trigger candle, indicating a potential entry point where stop losses might be clustered. Dotted Lines: Mark the 50% level of the candle’s wick (lower wick for buys, upper wick for sells), serving as a secondary confirmation zone for entries or tighter stop-loss placement. These lines are plotted only when specific candle conditions are met within the kill zones, and they are automatically deleted once the price crosses them, signaling that the liquidity at that level has likely been grabbed. The indicator also includes an optional EMA filter to ensure trades align with the broader trend, reducing false signals in choppy markets. How It Works The indicator’s logic is built on a multi-layered approach: Kill Zone Timing: Trades are only considered during user-defined London and US session hours (e.g., London from 02:00 to 12:00 UTC, as seen in the screenshots). These sessions are known for high volatility and liquidity, making them ideal for capturing institutional moves. Candle-Based Momentum Logic: Buy Signal: A candle must close above its midpoint (indicating bullish momentum) and have a lower low than the previous candle (suggesting a potential liquidity grab below the previous swing low). This is expressed as close > (high + low) / 2 and low < low . Sell Signal: A candle must close below its midpoint (bearish momentum) and have a higher high than the previous candle (indicating a potential liquidity grab above the previous swing high), expressed as close < (high + low) / 2 and high > high . These conditions ensure the indicator targets candles that break recent structure to hunt stop losses while showing directional momentum. Optional EMA Filter: A 50-period EMA (customizable) can be enabled to filter signals based on trend direction. Buy signals are only generated if the EMA is trending upward (ema_value > ema_value ), and sell signals require a downward EMA trend (ema_value < ema_value ). This reduces noise by aligning entries with the broader market trend. Liquidity Levels and Deletion Logic: For a buy signal, a solid green line is drawn at the candle’s low, and a dotted green line at the 50% level of the lower wick (from the candle body’s bottom to the low). For a sell signal, a solid red line is drawn at the candle’s high, and a dotted red line at the 50% level of the upper wick (from the body’s top to the high). These lines extend to the right until the price crosses them, at which point they are deleted, indicating the liquidity at that level has been taken (e.g., stop losses triggered). Alerts: The indicator includes alert conditions for buy and sell signals, notifying traders when a new setup is identified. Underlying Concepts The indicator is grounded in the concept of liquidity hunting, a strategy often employed by institutional traders. Markets frequently move to levels where stop losses are clustered—typically just beyond swing highs or lows—before reversing in the opposite direction. The "Enigma Sniper 369" targets these moves by identifying candles that break structure (e.g., a lower low or higher high) during high-volatility sessions, suggesting a potential sweep of stop losses. The 50% wick level acts as a secondary confirmation, as this midpoint often represents a zone where tighter stop losses are placed by retail traders. The optional EMA filter adds a trend-following element, ensuring entries are taken in the direction of the broader market momentum, which is particularly useful on lower timeframes like the 15-minute chart shown in the screenshots. How to Use It Here’s a step-by-step guide based on the provided usage example on the GBP/USD 15-minute chart: Setup the Indicator: Add "Enigma Sniper 369" to your TradingView chart. Adjust the London and US session hours to match your timezone (e.g., London from 02:00 to 12:00 UTC, US from 13:00 to 22:00 UTC). Customize the EMA period (default 50) and line styles/colors if desired. Identify Kill Zones: The indicator highlights the London session in light green and the US session in light purple, as seen in the screenshots. Focus on these periods for signals, as they are the most volatile and likely to produce liquidity grabs. Wait for a Signal: Look for solid and dotted lines to appear during the kill zones: Buy Setup: A solid green line at the swing low and a dotted green line at the 50% lower wick level indicate a potential buy. This suggests the market may have grabbed liquidity below the swing low and is now poised to move higher. Sell Setup: A solid red line at the swing high and a dotted red line at the 50% upper wick level indicate a potential sell, suggesting liquidity was taken above the swing high. Place Your Trade: For a buy, set a buy limit order at the dotted green line (50% wick level), as this is a more conservative entry point. Place your stop loss just below the solid green line (swing low) to cover the full swing. For example, in the screenshots, the market retraces to the dotted line at 1.32980 after a liquidity grab below the swing low, triggering a buy limit order. For a sell, set a sell limit order at the dotted red line, with a stop loss just above the solid red line. Monitor Price Action: Once the price crosses a line, it is deleted, indicating the liquidity at that level has been taken. In the screenshots, after the buy limit is triggered, the market moves higher, confirming the setup. The caption notes, “The market returns and tags us in long with a buy limit,” highlighting this retracement strategy. Additional Context: Use the indicator to identify liquidity levels that may be targeted later. For example, the screenshot notes, “If a new session is about to open I will wait for the grab liquidity to go long,” showing how the indicator can be used to anticipate future moves at session opens (e.g., London open at 1.32980). Risk Management: Always set a stop loss below the swing low (for buys) or above the swing high (for sells) to protect against adverse moves. The 50% wick level helps tighten entries, improving the risk-reward ratio. Practical Example On the GBP/USD 15-minute chart, during the London session (02:00 UTC), the indicator identifies a buy setup with a solid green line at 1.32901 (swing low) and a dotted green line at 1.32980 (50% wick level). The market initially dips below the swing low, grabbing liquidity, then retraces to the dotted line, triggering a buy limit order. The price subsequently rises to 1.33404, yielding a profitable trade. The user notes, “The logic is in the last candle it provides new level to go long,” emphasizing the indicator’s ability to identify fresh levels after a liquidity sweep. Customization Tips Adjust the EMA period to suit your timeframe (e.g., a shorter period like 20 for faster signals on lower timeframes). Modify the session hours to align with your broker’s timezone or specific market conditions. Use the alert feature to get notified of new setups without constantly monitoring the chart. Why It’s Useful for Traders The "Enigma Sniper 369" stands out by combining session timing, momentum-based candle analysis, and liquidity hunting into a single tool. It provides clear, actionable levels for entries and stop losses, removes invalid signals dynamically, and aligns trades with high-probability market conditions. Whether you’re a scalper looking for quick moves during London open or a swing trader targeting session-based reversals, this indicator offers a structured, data-driven approach to trading.
Pine Script® Indikator
von Niko-picasso369
8