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.
ค้นหาในสคริปต์สำหรับ "ha溢价率"
TZtraderTZtrader
This is a TrendZones version with features to set stoploss and targets in short and long positions meant for use in intraday charts. It aims to provide signals for opening and closing long and short positions. In the comments under the TrendZones publication several people expressed a need for features for a short position similar to those for a long position as implemented in TrendZones, some want to use it for scalping, some asked for alerts. When I proposed to create a version for day trading with target lines based on ATR, several people liked the idea.
Full disclosure: I don’t do day trading, because, after I lost a lot of money, I had to promise my wife to stay away from it. I restrict myself to long term investing in stocks which are in uptrend. However I understand what a day trader needs. I gather from my experience that day trading or scalping is an attempt to earn something by opening a position in the morning and close, reopen and close it again during the day with a profit. It is usually done with leveraged instruments like CFD’s, futures, options, and what have you. Opening and closing positions is done within minutes, so the trader needs a quick and efficient way to set proper stoploss and target. TZtrader supports this by showing only three or four numbers on the price bar: The price of the instrument, The logical stop level (gray or green or maroon dots), and the target level (navy). All other numbers are suppressed to prevent mistakes. Also a clear feedback for current settings at the top-center of the pane and an alert feedback at bottom that flashes alerts during the development of the current bar and gives suppression status.
The script
First I made a bare bones version of TrendZones to which I added code for long and short trading setups and a bare setup for no position. The code for the logical stops in long setup had to be reviewed, after which this became the basis for stops in short setup.
Then I added code for 10 alert messages, which was a hassle, because this is the first time I coded alerts and the first time I used an array as a stack to avoid a complicated if-then construction. During testing the array caused a runtime error which I solved by adding ‘array.clear’ to the code, also I discovered that in TradingView separate alerts have to be created for all three setups - short, long and bare. Flipping setups is done in the inputs with a dropdown menu because Pine Script has no function for a clickable button.
One visual with three setups.
The visual has the TrendZones structure: Three near parallel very smooth curves, which border the moderate uptrend (green) and downtrend (orange) zone over and under the curve in the middle, the COG (Center Of Gravity). Where the price breaks out of these curves, strong trend zones show up over and under the curves, respectively strong uptrend (blue) and strong downtrend (red).
Three setups were made clearly different to avoid confusion and to provide oversight in case of multiple trades going on simultaneously which I imagine are monitored in one screen. You have to see which one is long, which short and which have no position. The long setup should not trigger short signals, nor should the short trigger long signals nor the bare setup exclusive long or short signals.
The Long setup is default, shown on the example chart. In this setup the Stoploss suggestions (green, gray and maroon dots) are under the price bars and the target line (navy) at a set distance above the High Border. A zone with a width of 1 ATR is drawn under the Low Border. In this setup 5 specific alerts are provided
The Short setup has the Stoploss suggestions over the price bars, the target line at a set distance under the Low Border. A zone with a width of 1 ATR is drawn above the High Border. This setup also has 5 specific alerts.
The Bare setup has no Stoploss suggestions, no target line and supports 4 alerts, 2 in common with the Long setup and 2 with Short.
The table below gives a summary of scripted alerts:
Setup = Where = When = Purpose
Long, Bare = Green Zone = Bars come from lower zones = Uptrend starts
Long, Bare = Green Zone = Sideways ends in uptrend = Uptrend resumes
Long = COG = First crossing = Uptrend might end warning
Long = Orange Zone = Bars come from higher zones = Uptrend ended take care
Long = Red Zone = Bars come from higher zones = Strong downtrend->close Long
Short, Bare = Orange Zone = Bars come from higher zones = Downtrend starts
Short, Bare = Orange Zone = Sideways ends in downtrend = Downtrend resumes
Short = COG = First crossing = Downtrend might end warning
Short = Green Zone = Bars come from lower zones = Downtrend ended take care
Short = Blue Zone = Bars come from lower zones = Strong uptrend -> close short
You can use script alerts in TradingView by clicking the clock in the sidebar, then ‘create alert’ or plus, as condition you choose ‘Tztrader’ in the dialog box, then the “Any alert() function call” option (the first item in the list). The script lets the valid alert trigger by TradingView after the bar is completed, this can differ from the flashed messages during its formation.
When you create alerts in Tradingview, I advice to do that for each setup, then to make only the alert active which matches the current setup, pause the other ones.
Suppressing false and annoying signals
The script has two ways to suppress such signals, which have to do with the numbers in the alert feedback. The numbers left and right of the message with a colored background, depict the zones in which the previous (left) and current (right) bar move. 1 is the strong downtrend zone (red), 2 the moderate downtrend zone (orange), 3 the sideways zones (gray), 4 the COG (gray), 5 the moderate uptrend zone (green), 6 the strong uptrend zone (blue), 7 something went wrong with assigning a zone (black). In extensive testing the number 7 never occurs, because I catch that error in the code. The idea is that an alert is only triggered if the previous bar was in a different zone. When the bars are in the same zone, no alert is possible. This way all annoying signals are suppressed and long, short and bare get the appropriate alerts.
The third number is a counter. It counts how often the COG is crossed without touching the outer curves. The counter will reset to zero when the upper or lower curve is touched. When the count is 1 you have zone situation 4 and appropriate alerts are flashed. When the count is 2 or higher, a sideways situation (3) is called and while the recrossings are going on, no alerts can be flashed. This suppresses false signals. The ATR zone and curves are brownish-gray where sideways happens(ed). When the channel is narrowed down to just the three curves, some false signals still might occur.
Inputs
“Setup”, default is long, drop down menu provides long, short and bare.
“Target ATR”, default is 2, sets the amount of ATR for the target line. In 1 minute charts 4 seems an appropriate setting, you have to learn by experience which setting works.
“show feedback …” default is on, This creates two feedback labels, a Setup feedback on top of the pane, which shows charted instrument, Setup type, Trend and timeframe of the chart. Background color of Trend feedback is green when it matches the setup, red when mismatches and gray when no match. The alert feedback at the bottom of the pane shows a number, a message and two numbers. The numbers will be explained in the chapter about false and annoying signals below. During formation of the bar, valid alerts are flashed with a blue background, otherwise the message ‘alerts for current bar suppressed’.
Logical Stops
The curves are the logical place to put stops, because, as these are averages of the high and low border of a Donchian channel, they signify the ‘natural’ current highest, lowest and main level in the lookback period that fit the monitored trend setup. A downtrend turns into an uptrend when a breakout of the upper curve occurs. If you are short, that is where you want to close position, so the logical place for the stoploss is the upper curve. Vice versa, when you are long, the logical stop is on the lower curve. The stops show up as green or gray dots on the curves, the green dots signify a nice entry level, the gray stops are there to suggest levels where unrealized profits might be secured, the maroon dots indicate that the trend mismatches the setup.
COG versus other lines
Any line used to identify a trend, be it some MA or some other line, is interpreted the same way: When the bars move above the line there is an uptrend and when below, a downtrend. COG is not different in that sense. If you put such a line in the same chart as TZtrader, you can see situations in which the other line shows uptrend or downtrend earlier than COG, also some other lines, e.g. Hull MA, are very good at showing tops and bottoms, while COG ignores these. On the other hand the other lines are usually a little nervous and let you shake out of position too soon. Just like the other lines, COG gives false signals when it is near horizontal. The advantage of the placement COG is the tolerance for pull backs. This way TZtrader keeps you longer in the trend. Such pull backs are often ‘flags’ which are interpreted in TA as confirming the trend. Tztrader aims to get you in position reasonably soon when a trend begins and out of position as soon as the trend turns against you. The placement of COG is done with a fundamentally different algorithm than other lines as it is not an average of prices, but the middle of two averages of borders of a Donchian channel. This gives the two zones between the curves the same quality as the two zones above and below the middle line of a standard Donchian Channel.
A multi timeframe application.
In this scenario you put a 5 minutes and 1 minute chart with Tztrader side by side. If the 5 minutes shows uptrend, set the 1 minute on long trading and open positions when the trend matches uptrend en close when it mismatches. Don’t open short positions. Once the 5 minute changes to downtrend, set Tztrader in the 1 minute to short trading and open positions when the trend matches downtrend and close when it mismatches.
The idea is that in a long ‘context’, provided by the 5 minutes, the uptrends in the 1 minute will last longer and go further, vice versa for the short ‘context’. This way you do swing trading in the 5 minute in a smart way, maximizing profits.
You can do this with any timeframe pairs with a proportion of around 5:1, 4:1, 6:1, like e.g. 60 minutes and 15 minutes or weeks and days (5 trading days in a week).
Dear day-traders, may this tool be helpful and may your days be blessed.
Take care
Advanced Range Theory - ART📊 Advanced Range Theory (ART): The Institutional Blueprint
Stop drawing lines. Start reading the blueprint of the market. Advanced Range Theory (ART) is not another support and resistance indicator; it is a military-grade market structure engine designed to decode the language of institutional capital. It operates on a single, powerful premise: markets move in phases of consolidation and expansion, and the key to anticipation lies in understanding the complete lifecycle of these phases.
ART provides a living, breathing map of the battlefield, identifying institutional accumulation zones and tracking them with unparalleled precision from their inception as "Pending" ranges to their ultimate classification after a breakout. This is your X-ray into the market's skeletal structure.
🔬 THEORETICAL FRAMEWORK: THE ARCHITECTURE OF PRICE ACTION
ART is built on a multi-layered system of logic that moves beyond static levels. It treats ranges as dynamic entities with a narrative—a beginning, a middle, and an end. The core of the system is the dynamic classification engine, which analyzes not just the range, but the character of the price action that resolves it.
1. The Range Lifecycle: From Accumulation to Classification
This is the revolutionary heart of ART. A range's true identity is only revealed by how it is broken.
Phase 1: PENDING (Yellow): A new range is identified based on a period of price consolidation (a "parent" candle followed by a minimum number of "inside" candles). At this stage, it is a neutral zone of potential energy—an area where institutions are likely building positions. It is a question the market has not yet answered.
Phase 2: MITIGATION & CLASSIFICATION: When price breaks out and reaches a calculated extension level, the range is considered "mitigated." At this exact moment, ART analyzes the breakout's DNA to classify the range's true intent:
TYPE 1 - BREAKOUT (Blue): Characterized by a strong, impulsive move with confirming volume. This is a high-conviction breakout, signaling aggressive institutional participation and the likely start of a new trend. It is a statement of intent.
TYPE 2 - REVERSAL (Orange): Occurs when price attempts to break one way but is aggressively rejected, reversing and breaking out the other side. This signals absorption and a "failed auction," often marking significant market turning points.
TYPE 3 - PIVOT (Green): A more balanced breakout, lacking the explosive momentum of a Type 1. This often represents a resolution after a period of indecision or a pivot within a larger trading range.
2. The Hierarchical Map: Source & S/R Levels
ART doesn't just draw boxes; it builds a genealogical map of market structure.
SOURCE LEVEL (Thick Gold Line): This is the "genesis" point—the most recently mitigated range. It acts as the primary point of origin for the current market swing and serves as a critical level for determining overall bias. Price action above the Source is generally bullish; below is bearish.
S/R LEVELS (Cyan Lines): When a range is mitigated, the price level where it broke becomes a key Support/Resistance zone for the future. ART tracks the two most recent S/R levels, as these often act as powerful magnets or rejection points for price.
3. The Multi-Factor Validation Engine
To eliminate noise and focus only on institutionally significant ranges, every potential range must pass a rigorous quality control check:
Time-Based Consolidation: Requires a minimum number of consecutive inside candles (minInsideCandles), ensuring a true period of balance.
Volatility-Based Significance: The range's size must be greater than a multiple of the Average True Range (minRangeSize), filtering out insignificant micro-consolidations.
Participation Confirmation: The parent candle of the range is checked against average volume to ensure there was meaningful activity during its formation.
⚙️ THE COMMAND CONSOLE: CONFIGURING YOUR ART ENGINE
Every input is designed to give you granular control over the detection engine, allowing you to tune ART to any market or timeframe with precision. Each tooltip in the script provides a deep dive, but here is a summary of the core controls.
🎯 ART Detection Engine
Minimum Inside Candles: The soul of the detection algorithm. It defines the minimum number of bars that must be contained within a single "parent" candle to qualify as a range. Higher values (3-4) find major, significant consolidation zones. Lower values (1-2) are more sensitive and will identify shorter-term accumulation patterns.
Extension Multiplier & Fibonacci Extension: These control the profit target projections. The Extension Multiplier uses a simple measured move (e.g., 1.0 = a 1:1 projection of the range's height). The Fibonacci Extension uses the golden ratio (1.618) for harmonically-derived targets.
Mitigation Method (Cross vs. Close): Determines how a breakout is confirmed. Cross is more responsive, triggering as soon as price touches the extension. Close is more conservative, requiring a full candle to close beyond the level, which helps filter out fake-outs from wicks.
Min Range Size (ATR): A crucial noise filter. It ensures that ART ignores tiny, insignificant ranges by requiring a range's height to be a certain multiple of the current market volatility (ATR).
📊 Display & Visual Configuration
These settings give you full control over the visual interface. You can toggle every single element—from the Webb Scanner to the S/R Levels—to create a clean or a comprehensive view. Choose a color theme that suits your charting environment or define a fully custom palette.
🕸️ Webb Analysis Scanner
This is a unique real-time flow analysis tool. It draws dynamic, animated lines from the current price to recent historical points. This visualization helps reveal hidden "tendrils" of momentum and short-term support/resistance that are not immediately obvious, acting as a "sonar" for immediate price flow.
📊 THE ANALYTICS HUB: YOUR DASHBOARD DECODED
The dashboard provides a real-time, at-a-glance intelligence briefing on the current state of market structure as seen by the ART engine.
RANGE METRICS: This section is a "census" of the market's structure. It tells you the total number of ranges identified, how many are still Pending (awaiting a breakout), how many are Unmitigated (active but not yet broken), and how many have been Mitigated (classified and complete).
TYPE BREAKDOWN: This is a powerful gauge of market character. A high count of Type 1 (Breakout) ranges suggests a strong, trending environment. A rising number of Type 2 (Reversal) ranges can signal market exhaustion and potential trend changes. A dominant Type 3 (Pivot) count indicates a balanced, rotational market.
KEY GUIDE: The Large dashboard includes a full legend, so you never have to guess what a line or color represents. It's your built-in user manual.
🎨 DECODING THE BLUEPRINT: A VISUAL INTERPRETATION GUIDE
Every line and color in ART is designed for instant, intuitive understanding.
The Range Lines:
Yellow Lines: A Pending range. This is an active zone of accumulation. Pay close attention.
Colored Lines (Blue/Orange/Green): An unmitigated, classified range. The color tells you its breakout character.
Dotted Lines: A Mitigated range. Its story has been told. These historical levels can still act as support or resistance.
The Identification Zones: These colored boxes appear at a range's origin point after it has been classified. They are the "birth certificate" of the range, permanently marking its type (Breakout, Reversal, or Pivot) and providing an immediate visual history of market behavior.
The Hierarchical Lines:
Thick Gold Line (Source): The most important line on your chart. It is the anchor for your bias.
Cyan Lines (S/R): High-probability decision points. Expect reactions here.
Purple Dotted Lines (Extensions): Logical, calculated profit targets for breaking ranges.
🔧 THE ARCHITECT'S VISION: THE DEVELOPMENT JOURNEY
ART was born from a deep frustration with the static and subjective nature of traditional market structure analysis. Drawing lines by hand is inconsistent, and most indicators are reactive, only confirming what has already happened. The goal was to create a proactive, objective, and dynamic framework that could think about the market in terms of phases and lifecycles.
The breakthrough came from a simple shift in perspective: a range's true character isn't defined when it forms, but by how it resolves. This led to the development of the "post-breakout classification engine," which waits for the market to show its hand before assigning a definitive type. The Webb Scanner was inspired by the desire to visualize the unseen, to create a tool that could feel the immediate "pull" and "push" of price flow. The result is not just an indicator; it is a new language for interpreting price action, built on a foundation of logic, clarity, and precision.
⚠️ RISK DISCLAIMER & BEST PRACTICES
Advanced Range Theory is a professional-grade analytical tool designed to enhance a trader's decision-making process. It does not provide direct buy or sell signals. The levels and classifications it generates are based on historical price action and mathematical probabilities. All trading involves substantial risk, and past performance is not indicative of future results. Always use this tool in conjunction with a robust risk management plan.
"I fear not the man who has practiced 10,000 kicks once, but I fear the man who has practiced one kick 10,000 times."
— Dskyz, Trade with insight. Trade with anticipation.
— Bruce Lee
RSI PotentialRSI Potential
This indicator does more than just track RSI; it measures the "energy" or "fuel" left in a trend. It answers a critical question: how much further can the price move before momentum is exhausted?
The key insight is that high momentum often means low potential, and vice versa. This inverse relationship is what allows the indicator to provide powerful, forward-looking signals about trend health and potential reversals.
Think of it like a race car:
Momentum is the car's current speed.
Potential is the amount of fuel left in the tank.
A car at top speed (high momentum) is burning fuel rapidly (potential is decreasing). A car just starting (low momentum) has a full tank of fuel (high potential). This indicator helps you see the fuel gauge, not just the speedometer.
This indicator plots three distinct components in a separate pane below your chart:
1. Upside Potential (Green Line)
What it shows: The percentage price increase required to hit the Overbought RSI Level. In other words, how much "fuel" is left for the upward trend.
How to interpret it:
Low Value (Approaching Zero): This is a warning sign. It means the price is already in high gear, and there is very little room left to run before hitting overbought exhaustion. Even if the price is rocketing up (high momentum), low potential signals the rally is likely on its last legs.
High Value: This indicates the market has a full tank of fuel for a rally. Even if the price is moving sideways or slowly (low momentum), the high potential suggests that if a new uptrend starts, it has the energy to be sustainable and significant.
2. Downside Potential (Red Line)
What it shows: The percentage price decrease required to hit the Oversold RSI Level—the "fuel" for a downtrend.
How to interpret it:
Low Value (Approaching Zero): A warning for bears. The price may be dropping fast (high momentum), but it's running out of energy to fall further. This signals seller exhaustion and increases the probability of a bounce or reversal.
High Value: The market has significant room to fall before becoming oversold. This can confirm the health of a new downtrend or suggest that a current downtrend has more to go.
3. Net Potential (Columns / Histogram)
What it shows: The net balance of energy: Upside Potential - Downside Potential. It answers, "Which side has more fuel in the tank?"
FVG Premium [no1x]█ OVERVIEW
This indicator provides a comprehensive toolkit for identifying, visualizing, and tracking Fair Value Gaps (FVGs) across three distinct timeframes (current chart, a user-defined Medium Timeframe - MTF, and a user-defined High Timeframe - HTF). It is designed to offer traders enhanced insight into FVG dynamics through detailed state monitoring (formation, partial fill, full mitigation, midline touch), extensive visual customization for FVG representation, and a rich alert system for timely notifications on FVG-related events.
█ CONCEPTS
This indicator is built upon the core concept of Fair Value Gaps (FVGs) and their significance in price action analysis, offering a multi-layered approach to their detection and interpretation across different timeframes.
Fair Value Gaps (FVGs)
A Fair Value Gap (FVG), also known as an imbalance, represents a range in price delivery where one side of the market (buying or selling) was more aggressive, leaving an inefficiency or an "imbalance" in the price action. This concept is prominently featured within Smart Money Concepts (SMC) and Inner Circle Trader (ICT) methodologies, where such gaps are often interpreted as footprints left by "smart money" due to rapid, forceful price movements. These methodologies suggest that price may later revisit these FVG zones to rebalance a prior inefficiency or to seek liquidity before continuing its path. These gaps are typically identified by a three-bar pattern:
Bullish FVG : This is a three-candle formation where the second candle shows a strong upward move. The FVG is the space created between the high of the first candle (bottom of FVG) and the low of the third candle (top of FVG). This indicates a strong upward impulsive move.
Bearish FVG : This is a three-candle formation where the second candle shows a strong downward move. The FVG is the space created between the low of the first candle (top of FVG) and the high of the third candle (bottom of FVG). This indicates a strong downward impulsive move.
FVGs are often watched by traders as potential areas where price might return to "rebalance" or find support/resistance.
Multi-Timeframe (MTF) Analysis
The indicator extends FVG detection beyond the current chart's timeframe (Low Timeframe - LTF) to two higher user-defined timeframes: Medium Timeframe (MTF) and High Timeframe (HTF). This allows traders to:
Identify FVGs that might be significant on a broader market structure.
Observe how FVGs from different timeframes align or interact.
Gain a more comprehensive perspective on potential support and resistance zones.
FVG State and Lifecycle Management
The indicator actively tracks the lifecycle of each detected FVG:
Formation : The initial identification of an FVG.
Partial Fill (Entry) : When price enters but does not completely pass through the FVG. The indicator updates the "current" top/bottom of the FVG to reflect the filled portion.
Midline (Equilibrium) Touch : When price touches the 50% level of the FVG.
Full Mitigation : When price completely trades through the FVG, effectively "filling" or "rebalancing" the gap. The indicator records the mitigation time.
This state tracking is crucial for understanding how price interacts with these zones.
FVG Classification (Large FVG)
FVGs can be optionally classified as "Large FVGs" (LV) if their size (top to bottom range) exceeds a user-defined multiple of the Average True Range (ATR) for that FVG's timeframe. This helps distinguish FVGs that are significantly larger relative to recent volatility.
Visual Customization and Information Delivery
A key concept is providing extensive control over how FVGs are displayed. This control is achieved through a centralized set of visual parameters within the indicator, allowing users to configure numerous aspects (colors, line styles, visibility of boxes, midlines, mitigation lines, labels, etc.) for each timeframe. Additionally, an on-chart information panel summarizes the nearest unmitigated bullish and bearish FVG levels for each active timeframe, providing a quick glance at key price points.
█ FEATURES
This indicator offers a rich set of features designed to provide a highly customizable and comprehensive Fair Value Gap (FVG) analysis experience. Users can tailor the FVG detection, visual representation, and alerting mechanisms across three distinct timeframes: the current chart (Low Timeframe - LTF), a user-defined Medium Timeframe (MTF), and a user-defined High Timeframe (HTF).
Multi-Timeframe FVG Detection and Display
The core strength of this indicator lies in its ability to identify and display FVGs from not only the current chart's timeframe (LTF) but also from two higher, user-selectable timeframes (MTF and HTF).
Timeframe Selection: Users can specify the exact MTF (e.g., "60", "240") and HTF (e.g., "D", "W") through dedicated inputs in the "MTF (Medium Timeframe)" and "HTF (High Timeframe)" settings groups. The visibility of FVGs from these higher timeframes can be toggled independently using the "Show MTF FVGs" and "Show HTF FVGs" checkboxes.
Consistent Detection Logic: The FVG detection logic, based on the classic three-bar imbalance pattern detailed in the 'Concepts' section, is applied consistently across all selected timeframes (LTF, MTF, HTF)
Timeframe-Specific Visuals: Each timeframe's FVGs (LTF, MTF, HTF) can be customized with unique colors for bullish/bearish states and their mitigated counterparts. This allows for easy visual differentiation of FVGs originating from different market perspectives.
Comprehensive FVG Visualization Options
The indicator provides extensive control over how FVGs are visually represented on the chart for each timeframe (LTF, MTF, HTF).
FVG Boxes:
Visibility: Main FVG boxes can be shown or hidden per timeframe using the "Show FVG Boxes" (for LTF), "Show Boxes" (for MTF/HTF) inputs.
Color Customization: Colors for bullish, bearish, active, and mitigated FVG boxes (including Large FVGs, if classified) are fully customizable for each timeframe.
Box Extension & Length: FVG boxes can either be extended to the right indefinitely ("Extend Boxes Right") or set to a fixed length in bars ("Short Box Length" or "Box Length" equivalent inputs).
Box Labels: Optional labels can display the FVG's timeframe and fill percentage on the box. These labels are configurable for all timeframes (LTF, MTF, and HTF). Please note: If FVGs are positioned very close to each other on the chart, their respective labels may overlap. This can potentially lead to visual clutter, and it is a known behavior in the current version of the indicator.
Box Borders: Visibility, width, style (solid, dashed, dotted), and color of FVG box borders are customizable per timeframe.
Midlines (Equilibrium/EQ):
Visibility: The 50% level (midline or EQ) of FVGs can be shown or hidden for each timeframe.
Style Customization: Width, style, and color of the midline are customizable per timeframe. The indicator tracks if this midline has been touched by price.
Mitigation Lines:
Visibility: Mitigation lines (representing the FVG's opening level that needs to be breached for full mitigation) can be shown or hidden for each timeframe. If shown, these lines are always extended to the right.
Style Customization: Width, style, and color of the mitigation line are customizable per timeframe.
Mitigation Line Labels: Optional price labels can be displayed on mitigation lines, with a customizable horizontal bar offset for positioning. For optimal label placement, the following horizontal bar offsets are recommended: 4 for LTF, 8 for MTF, and 12 for HTF.
Persistence After Mitigation: Users can choose to keep mitigation lines visible even after an FVG is fully mitigated, with a distinct color for such lines. Importantly, this option is only effective if the general setting 'Hide Fully Mitigated FVGs' is disabled, as otherwise, the entire FVG and its lines will be removed upon mitigation.
FVG State Management and Behavior
The indicator tracks and visually responds to changes in FVG states.
Hide Fully Mitigated FVGs: This option, typically found in the indicator's general settings, allows users to automatically remove all visual elements of an FVG from the chart once price has fully mitigated it. This helps maintain chart clarity by focusing on active FVGs.
Partial Fill Visualization: When price enters an FVG, the indicator offers a dynamic visual representation: the portion of the FVG that has been filled is shown as a "mitigated box" (typically with a distinct color), while the original FVG box shrinks to clearly highlight the remaining, unfilled portion. This two-part display provides an immediate visual cue about how much of the FVG's imbalance has been addressed and what potential remains within the gap.
Visual Filtering by ATR Proximity: To help users focus on the most relevant price action, FVGs can be dynamically hidden if they are located further from the current price than a user-defined multiple of the Average True Range (ATR). This behavior is controlled by the "Filter Band Width (ATR Multiple)" input; setting this to zero disables the filter entirely, ensuring all detected FVGs remain visible regardless of their proximity to price.
Alternative Usage Example: Mitigation Lines as Key Support/Resistance Levels
For traders preferring a minimalist chart focused on key Fair Value Gap (FVG) levels, the indicator's visualization settings can be customized to display only FVG mitigation lines. This approach leverages these lines as potential support and resistance zones, reflecting areas where price might revisit to address imbalances.
To configure this view:
Disable FVG Boxes: Turn off "Show FVG Boxes" (for LTF) or "Show Boxes" (for MTF/HTF) for the desired timeframes.
Hide Midlines: Disable the visibility of the 50% FVG Midlines (Equilibrium/EQ).
Ensure Mitigation Lines are Visible: Keep "Mitigation Lines" enabled.
Retain All Mitigation Lines:
Disable the "Hide Fully Mitigated FVGs" option in the general settings.
Enable the feature to "keep mitigation lines visible even after an FVG is fully mitigated". This ensures lines from all FVGs (active or fully mitigated) remain on the chart, which is only effective if "Hide Fully Mitigated FVGs" is disabled.
This setup offers:
A Decluttered Chart: Focuses solely on the FVG opening levels.
Precise S/R Zones: Treats mitigation lines as specific points for potential price reactions.
Historical Level Analysis: Includes lines from past, fully mitigated FVGs for a comprehensive view of significant price levels.
For enhanced usability with this focused view, consider these optional additions:
The on-chart Information Panel can be activated to display a quick summary of the nearest unmitigated FVG levels.
Mitigation Line Labels can also be activated for clear price level identification. A customizable horizontal bar offset is available for positioning these labels; for example, offsets of 4 for LTF, 8 for MTF, and 12 for HTF can be effective.
FVG Classification (Large FVG)
This feature allows for distinguishing FVGs based on their size relative to market volatility.
Enable Classification: Users can enable "Classify FVG (Large FVG)" to identify FVGs that are significantly larger than average.
ATR-Based Threshold: An FVG is classified as "Large" if its height (price range) is greater than or equal to the Average True Range (ATR) of its timeframe multiplied by a user-defined "Large FVG Threshold (ATR Multiple)". The ATR period for this calculation is also configurable.
Dedicated Colors: Large FVGs (both bullish/bearish and active/mitigated) can be assigned unique colors, making them easily distinguishable on the chart.
Panel Icon: Large FVGs are marked with a special icon in the Info Panel.
Information Panel
An on-chart panel provides a quick summary of the nearest unmitigated FVG levels.
Visibility and Position: The panel can be shown/hidden and positioned in any of the nine standard locations on the chart (e.g., Top Right, Middle Center).
Content: It displays the price levels of the nearest unmitigated bullish and bearish FVGs for LTF, MTF (if active), and HTF (if active). It also indicates if these nearest FVGs are Large FVGs (if classification is enabled) using a selectable icon.
Styling: Text size, border color, header background/text colors, default text color, and "N/A" cell background color are customizable.
Highlighting: Background and text colors for the cells displaying the overall nearest bullish and bearish FVG levels (across all active timeframes) can be customized to draw attention to the most proximate FVG.
Comprehensive Alert System
The indicator offers a granular alert system for various FVG-related events, configurable for each timeframe (LTF, MTF, HTF) independently. Users can enable alerts for:
New FVG Formation: Separate alerts for new bullish and new bearish FVG formations.
FVG Entry/Partial Fill: Separate alerts for price entering a bullish FVG or a bearish FVG.
FVG Full Mitigation: Separate alerts for full mitigation of bullish and bearish FVGs.
FVG Midline (EQ) Touch: Separate alerts for price touching the midline of a bullish or bearish FVG.
Alert messages are detailed, providing information such as the timeframe, FVG type (bull/bear, Large FVG), relevant price levels, and timestamps.
█ NOTES
This section provides additional information regarding the indicator's usage, performance considerations, and potential interactions with the TradingView platform. Understanding these points can help users optimize their experience and troubleshoot effectively.
Performance and Resource Management
Maximum FVGs to Track : The "Max FVGs to Track" input (defaulting to 25) limits the number of FVG objects processed for each category (e.g., LTF Bullish, MTF Bearish). Increasing this value significantly can impact performance due to more objects being iterated over and potentially drawn, especially when multiple timeframes are active.
Drawing Object Limits : To manage performance, this script sets its own internal limits on the number of drawing objects it displays. While it allows for up to approximately 500 lines (max_lines_count=500) and 500 labels (max_labels_count=500), the number of FVG boxes is deliberately restricted to a maximum of 150 (max_boxes_count=150). This specific limit for boxes is a key performance consideration: displaying too many boxes can significantly slow down the indicator, and a very high number is often not essential for analysis. Enabling all visual elements for many FVGs across all three timeframes can cause the indicator to reach these internal limits, especially the stricter box limit
Optimization Strategies : To help you manage performance, reduce visual clutter, and avoid exceeding drawing limits when using this indicator, I recommend the following strategies:
Maintain or Lower FVG Tracking Count: The "Max FVGs to Track" input defaults to 25. I find this value generally sufficient for effective analysis and balanced performance. You can keep this default or consider reducing it further if you experience performance issues or prefer a less dense FVG display.
Utilize Proximity Filtering: I suggest activating the "Filter Band Width (ATR Multiple)" option (found under "General Settings") to display only those FVGs closer to the current price. From my experience, a value of 5 for the ATR multiple often provides a good starting point for balanced performance, but you should feel free to adjust this based on market volatility and your specific trading needs.
Hide Fully Mitigated FVGs: I strongly recommend enabling the "Hide Fully Mitigated FVGs" option. This setting automatically removes all visual elements of an FVG from the chart once it has been fully mitigated by price. Doing so significantly reduces the number of active drawing objects, lessens computational load, and helps maintain chart clarity by focusing only on active, relevant FVGs.
Disable FVG Display for Unused Timeframes: If you are not actively monitoring certain higher timeframes (MTF or HTF) for FVG analysis, I advise disabling their display by unchecking "Show MTF FVGs" or "Show HTF FVGs" respectively. This can provide a significant performance boost.
Simplify Visual Elements: For active FVGs, consider hiding less critical visual elements if they are not essential for your specific analysis. This could include box labels, borders, or even entire FVG boxes if, for example, only the mitigation lines are of interest for a particular timeframe.
Settings Changes and Platform Limits : This indicator is comprehensive and involves numerous calculations and drawings. When multiple settings are changed rapidly in quick succession, it is possible, on occasion, for TradingView to issue a "Runtime error: modify_study_limit_exceeding" or similar. This can cause the indicator to temporarily stop updating or display errors.
Recommended Approach : When adjusting settings, it is advisable to wait a brief moment (a few seconds) after each significant change. This allows the indicator to reprocess and update on the chart before another change is made
Error Recovery : Should such a runtime error occur, making a minor, different adjustment in the settings (e.g., toggling a checkbox off and then on again) and waiting briefly will typically allow the indicator to recover and resume correct operation. This behavior is related to platform limitations when handling complex scripts with many inputs and drawing objects.
Multi-Timeframe (MTF/HTF) Data and Behavior
HTF FVG Confirmation is Essential: : For an FVG from a higher timeframe (MTF or HTF) to be identified and displayed on your current chart (LTF), the three-bar pattern forming the FVG on that higher timeframe must consist of fully closed bars. The indicator does not draw speculative FVGs based on incomplete/forming bars from higher timeframes.
Data Retrieval and LTF Processing: The indicator may use techniques like lookahead = barmerge.lookahead_on for timely data retrieval from higher timeframes. However, the actual detection of an FVG occurs after all its constituent bars on the HTF have closed.
Appearance Timing on LTF (1 LTF Candle Delay): As a natural consequence of this, an FVG that is confirmed on an HTF (i.e., its third bar closes) will typically become visible on your LTF chart one LTF bar after its confirmation on the HTF.
Example: Assume an FVG forms on a 30-minute chart at 15:30 (i.e., with the close of the 30-minute bar that covers the 15:00-15:30 period). If you are monitoring this FVG on a 15-minute chart, the indicator will detect this newly formed 30-minute FVG while processing the data for the 15-minute bar that starts at 15:30 and closes at 15:45. Therefore, the 30-minute FVG will become visible on your 15-minute chart at the earliest by 15:45 (i.e., with the close of that relevant 15-minute LTF candle). This means the HTF FVG is reflected on the LTF chart with a delay equivalent to one LTF candle.
FVG Detection and Display Logic
Fair Value Gaps (FVGs) on the current chart timeframe (LTF) are detected based on barstate.isconfirmed. This means the three-bar pattern must be complete with closed bars before an FVG is identified. This confirmation method prevents FVGs from being prematurely identified on the forming bar.
Alerts
Alert Setup : To receive alerts from this indicator, you must first ensure you have enabled the specific alert conditions you are interested in within the indicator's own settings (see 'Comprehensive Alert System' under the 'FEATURES' section). Once configured, open TradingView's 'Create Alert' dialog. In the 'Condition' tab, select this indicator's name, and crucially, choose the 'Any alert() function call' option from the dropdown list. This setup allows the indicator to trigger alerts based on the precise event conditions you have activated in its settings
Alert Frequency : Alerts are designed to trigger once per bar close (alert.freq_once_per_bar_close) for the specific event.
User Interface (UI) Tips
Settings Group Icons: In the indicator settings menu, timeframe-specific groups are marked with star icons for easier navigation: 🌟 for LTF (Current Chart Timeframe), 🌟🌟 for MTF (Medium Timeframe), and 🌟🌟🌟 for HTF (High Timeframe).
Dependent Inputs: Some input settings are dependent on others being enabled. These dependencies are visually indicated in the settings menu using symbols like "↳" (dependent setting on the next line), "⟷" (mutually exclusive inline options), or "➜" (directly dependent inline option).
Settings Layout Overview: The indicator settings are organized into logical groups for ease of use. Key global display controls – such as toggles for MTF FVGs, HTF FVGs (along with their respective timeframe selectors), and the Information Panel – are conveniently located at the very top within the '⚙️ General Settings' group. This placement allows for quick access to frequently adjusted settings. Other sections provide detailed customization options for each timeframe (LTF, MTF, HTF), specific FVG components, and alert configurations.
█ FOR Pine Script® CODERS
This section provides a high-level overview of the FVG Premium indicator's internal architecture, data flow, and the interaction between its various library components. It is intended for Pine Script™ programmers who wish to understand the indicator's design, potentially extend its functionality, or learn from its structure.
System Architecture and Modular Design
The indicator is architected moduarly, leveraging several custom libraries to separate concerns and enhance code organization and reusability. Each library has a distinct responsibility:
FvgTypes: Serves as the foundational data definition layer. It defines core User-Defined Types (UDTs) like fvgObject (for storing all attributes of an FVG) and drawSettings (for visual configurations), along with enumerations like tfType.
CommonUtils: Provides utility functions for common tasks like mapping user string inputs (e.g., "Dashed" for line style) to their corresponding Pine Script™ constants (e.g., line.style_dashed) and formatting timeframe strings for display.
FvgCalculations: Contains the core logic for FVG detection (both LTF and MTF/HTF via requestMultiTFBarData), FVG classification (Large FVGs based on ATR), and checking FVG interactions with price (mitigation, partial fill).
FvgObject: Implements an object-oriented approach by attaching methods to the fvgObject UDT. These methods manage the entire visual lifecycle of an FVG on the chart, including drawing, updating based on state changes (e.g., mitigation), and deleting drawing objects. It's responsible for applying the visual configurations defined in drawSettings.
FvgPanel: Manages the creation and dynamic updates of the on-chart information panel, which displays key FVG levels.
The main indicator script acts as the orchestrator, initializing these libraries, managing user inputs, processing data flow between libraries, and handling the main event loop (bar updates) for FVG state management and alerts.
Core Data Flow and FVG Lifecycle Management
The general data flow and FVG lifecycle can be summarized as follows:
Input Processing: User inputs from the "Settings" dialog are read by the main indicator script. Visual style inputs (colors, line styles, etc.) are consolidated into a types.drawSettings object (defined in FvgTypes). Other inputs (timeframes, filter settings, alert toggles) control the behavior of different modules. CommonUtils assists in mapping some string inputs to Pine constants.
FVG Detection:
For the current chart timeframe (LTF), FvgCalculations.detectFvg() identifies potential FVGs based on bar patterns.
For MTF/HTF, the main indicator script calls FvgCalculations.requestMultiTFBarData() to fetch necessary bar data from higher timeframes, then FvgCalculations.detectMultiTFFvg() identifies FVGs.
Newly detected FVGs are instantiated as types.fvgObject and stored in arrays within the main script. These objects also undergo classification (e.g., Large FVG) by FvgCalculations.
State Update & Interaction: On each bar, the main indicator script iterates through active FVG objects to manage their state based on price interaction:
Initially, the main script calls FvgCalculations.fvgInteractionCheck() to efficiently determine if the current bar's price might be interacting with a given FVG.
If a potential interaction is flagged, the main script then invokes methods directly on the fvgObject instance (e.g., updateMitigation(), updatePartialFill(), checkMidlineTouch(), which are part of FvgObject).
These fvgObject methods are responsible for the detailed condition checking and the actual modification of the FVG's state. For instance, the updateMitigation() and updatePartialFill() methods internally utilize specific helper functions from FvgCalculations (like checkMitigation() and checkPartialMitigation()) to confirm the precise nature of the interaction before updating the fvgObject’s state fields (such as isMitigated, currentTop, currentBottom, or isMidlineTouched).
Visual Rendering:
The FvgObject.updateDrawings() method is called for each fvgObject. This method is central to drawing management; it creates, updates, or deletes chart drawings (boxes, lines, labels) based on the FVG's current state, its prev_* (previous bar state) fields for optimization, and the visual settings passed via the drawSettings object.
Information Panel Update: The main indicator script determines the nearest FVG levels, populates a panelData object (defined in FvgPanelLib), and calls FvgPanel.updatePanel() to refresh the on-chart display.
Alert Generation: Based on the updated FVG states and user-enabled alert settings, the main indicator script constructs and triggers alerts using Pine Script's alert() function."
Key Design Considerations
UDT-Centric Design: The fvgObject UDT is pivotal, acting as a stateful container for all information related to a single FVG. Most operations revolve around creating, updating, or querying these objects.
State Management: To optimize drawing updates and manage FVG lifecycles, fvgObject instances store their previous bar's state (e.g., prevIsVisible, prevCurrentTop). The FvgObject.updateDrawings() method uses this to determine if a redraw is necessary, minimizing redundant drawing calls.
Settings Object: A drawSettings object is populated once (or when inputs change) and passed to drawing functions. This avoids repeatedly reading numerous input() values on every bar or within loops, improving performance.
Dynamic Arrays for FVG Storage: Arrays are used to store collections of fvgObject instances, allowing for dynamic management (adding new FVGs, iterating for updates).
FvgObject█ OVERVIEW
This library provides a suite of methods designed to manage the visual representation and lifecycle of Fair Value Gap (FVG) objects on a Pine Script™ chart. It extends the `fvgObject` User-Defined Type (UDT) by attaching object-oriented functionalities for drawing, updating, and deleting FVG-related graphical elements. The primary goal is to encapsulate complex drawing logic, making the main indicator script cleaner and more focused on FVG detection and state management.
█ CONCEPTS
This library is built around the idea of treating each Fair Value Gap as an "object" with its own visual lifecycle on the chart. This is achieved by defining methods that operate directly on instances of the `fvgObject` UDT.
Object-Oriented Approach for FVGs
Pine Script™ v6 introduced the ability to define methods for User-Defined Types (UDTs). This library leverages this feature by attaching specific drawing and state management functions (methods) directly to the `fvgObject` type. This means that instead of calling global functions with an FVG object as a parameter, you call methods *on* the FVG object itself (e.g., `myFvg.updateDrawings(...)`). This approach promotes better code organization and a more intuitive way to interact with FVG data.
FVG Visual Lifecycle Management
The core purpose of this library is to manage the complete visual journey of an FVG on the chart. This lifecycle includes:
Initial Drawing: Creating the first visual representation of a newly detected FVG, including its main box and optionally its midline and labels.
State Updates & Partial Fills: Modifying the FVG's appearance as it gets partially filled by price. This involves drawing a "mitigated" portion of the box and adjusting the `currentTop` or `currentBottom` of the remaining FVG.
Full Mitigation & Tested State: Handling how an FVG is displayed once fully mitigated. Depending on user settings, it might be hidden, or its box might change color/style to indicate it has been "tested." Mitigation lines can also be managed (kept or deleted).
Midline Interaction: Visually tracking if the price has touched the FVG's 50% equilibrium level (midline).
Visibility Control: Dynamically showing or hiding FVG drawings based on various criteria, such as user settings (e.g., hide mitigated FVGs, timeframe-specific visibility) or external filters (e.g., proximity to current price).
Deletion: Cleaning up all drawing objects associated with an FVG when it's no longer needed or when settings dictate its removal.
Centralized Drawing Logic
By encapsulating all drawing-related operations within the methods of this library, the main indicator script is significantly simplified. The main script can focus on detecting FVGs and managing their state (e.g., in arrays), while delegating the complex task of rendering and updating them on the chart to the methods herein.
Interaction with `fvgObject` and `drawSettings` UDTs
All methods within this library operate on an instance of the `fvgObject` UDT. This `fvgObject` holds not only the FVG's price/time data and state (like `isMitigated`, `currentTop`) but also the IDs of its associated drawing elements (e.g., `boxId`, `midLineId`).
The appearance of these drawings (colors, styles, visibility, etc.) is dictated by a `drawSettings` UDT instance, which is passed as a parameter to most drawing-related methods. This `drawSettings` object is typically populated from user inputs in the main script, allowing for extensive customization.
Stateful Drawing Object Management
The library's methods manage Pine Script™ drawing objects (boxes, lines, labels) by storing their IDs within the `fvgObject` itself (e.g., `fvgObject.boxId`, `fvgObject.mitigatedBoxId`, etc.). Methods like `draw()` create these objects and store their IDs, while methods like `updateDrawings()` modify them, and `deleteDrawings()` removes them using these stored IDs.
Drawing Optimization
The `updateDrawings()` method, which is the most comprehensive drawing management function, incorporates optimization logic. It uses `prev_*` fields within the `fvgObject` (e.g., `prevIsMitigated`, `prevCurrentTop`) to store the FVG's state from the previous bar. By comparing the current state with the previous state, and also considering changes in visibility or relevant drawing settings, it can avoid redundant and performance-intensive drawing operations if nothing visually significant has changed for that FVG.
█ METHOD USAGE AND WORKFLOW
The methods in this library are designed to be called in a logical sequence as an FVG progresses through its lifecycle. A crucial prerequisite for all visual methods in this library is a properly populated `drawSettings` UDT instance, which dictates every aspect of an FVG's appearance, from colors and styles to visibility and labels. This `settings` object must be carefully prepared in the main indicator script, typically based on user inputs, before being passed to these methods.
Here’s a typical workflow within a main indicator script:
1. FVG Instance Creation (External to this library)
An `fvgObject` instance is typically created by functions in another library (e.g., `FvgCalculations`) when a new FVG pattern is identified. This object will have its core properties (top, bottom, startTime, isBullish, tfType) initialized.
2. Initial Drawing (`draw` method)
Once a new `fvgObject` is created and its initial visibility is determined:
Call the `myFvg.draw(settings)` method on the new FVG object.
`settings` is an instance of the `drawSettings` UDT, containing all relevant visual configurations.
This method draws the primary FVG box, its midline (if enabled in `settings`), and any initial labels. It also initializes the `currentTop` and `currentBottom` fields of the `fvgObject` if they are `na`, and stores the IDs of the created drawing objects within the `fvgObject`.
3. Per-Bar State Updates & Interaction Checks
On each subsequent bar, for every active `fvgObject`:
Interaction Check (External Logic): It's common to first use logic (e.g., from `FvgCalculations`' `fvgInteractionCheck` function) to determine if the current bar's price interacts with the FVG.
State Field Updates (External Logic): Before calling the `FvgObjectLib` methods below, ensure that your `fvgObject`'s state fields (such as `isMitigated`, `currentTop`, `currentBottom`, `isMidlineTouched`) are updated using the current bar's price data and relevant functions from other libraries (e.g., `FvgCalculations`' `checkMitigation`, `checkPartialMitigation`, etc.). This library's methods render the FVG based on these pre-updated state fields.
If interaction occurs and the FVG is not yet fully mitigated:
Full Mitigation Update (`updateMitigation` method): Call `myFvg.updateMitigation(high, low)`. This method updates `myFvg.isMitigated` and `myFvg.mitigationTime` if full mitigation occurs, based on the interaction determined by external logic.
Partial Fill Update (`updatePartialFill` method): If not fully mitigated, call `myFvg.updatePartialFill(high, low, settings)`. This method updates `myFvg.currentTop` or `myFvg.currentBottom` and adjusts drawings to show the filled portion, again based on prior interaction checks and fill level calculations.
Midline Touch Check (`checkMidlineTouch` method): Call `myFvg.checkMidlineTouch(high, low)`. This method updates `myFvg.isMidlineTouched` if the price touches the FVG's 50% level.
4. Comprehensive Visual Update (`updateDrawings` method)
After the FVG's state fields have been potentially updated by external logic and the methods in step 3:
Call `myFvg.updateDrawings(isVisibleNow, settings)` on each FVG object.
`isVisibleNow` is a boolean indicating if the FVG should currently be visible.
`settings` is the `drawSettings` UDT instance.
This method synchronizes the FVG's visual appearance with its current state and settings, managing all drawing elements (boxes, lines, labels), their styles, and visibility. It efficiently skips redundant drawing operations if the FVG's state or visibility has not changed, thanks to its internal optimization using `prev_*` fields, which are also updated by this method.
5. Deleting Drawings (`deleteDrawings` method)
When an FVG object is no longer tracked:
Call `myFvg.deleteDrawings(deleteTestedToo)`.
This method removes all drawing objects associated with that `fvgObject`.
This workflow ensures that FVG visuals are accurately maintained throughout their existence on the chart.
█ NOTES
Dependencies: This library relies on `FvgTypes` for `fvgObject` and `drawSettings` definitions, and its methods (`updateMitigation`, `updatePartialFill`) internally call functions from `FvgCalculations`.
Drawing Object Management: Be mindful of TradingView's limits on drawing objects per script. The main script should manage the number of active FVG objects.
Performance and `updateDrawings()`: The `updateDrawings()` method is comprehensive. Its internal optimization (checking `hasStateChanged` based on `prev_*` fields) is crucial for performance. Call it judiciously.
Role of `settings.currentTime`: The `currentTime` field in `drawSettings` is key for positioning time-dependent elements like labels and the right edge of non-extended drawings.
Mutability of `fvgObject` Instances: Methods in this library directly modify the `fvgObject` instance they are called upon (e.g., its state fields and drawing IDs).
Drawing ID Checks: Methods generally check if drawing IDs are `na` before acting on them, preventing runtime errors.
█ EXPORTED FUNCTIONS
method draw(this, settings)
Draws the initial visual representation of the FVG object on the chart. This includes the main FVG box, its midline (if enabled), and a label
(if enabled for the specific timeframe). This method is typically invoked
immediately after an FVG is first detected and its initial properties are set. It uses drawing settings to customize the appearance based on the FVG's timeframe type.
Namespace types: types.fvgObject
Parameters:
this (fvgObject type from no1x/FvgTypes/1) : The FVG object instance to be drawn. Core properties (top, bottom,
startTime, isBullish, tfType) should be pre-initialized. This method will
initialize boxId, midLineId, boxLabelId (if applicable), and
currentTop/currentBottom (if currently na) on this object.
settings (drawSettings type from no1x/FvgTypes/1) : A drawSettings object providing all visual parameters. Reads display settings (colors, styles, visibility for boxes, midlines, labels,
box extension) relevant to this.tfType. settings.currentTime is used for
positioning labels and the right boundary of non-extended boxes.
method updateMitigation(this, highVal, lowVal)
Checks if the FVG has been fully mitigated by the current bar's price action.
Namespace types: types.fvgObject
Parameters:
this (fvgObject type from no1x/FvgTypes/1) : The FVG object instance. Reads this.isMitigated, this.isVisible,
this.isBullish, this.top, this.bottom. Updates this.isMitigated and
this.mitigationTime if full mitigation occurs.
highVal (float) : The high price of the current bar, used for mitigation check.
lowVal (float) : The low price of the current bar, used for mitigation check.
method updatePartialFill(this, highVal, lowVal, settings)
Checks for and processes partial fills of the FVG.
Namespace types: types.fvgObject
Parameters:
this (fvgObject type from no1x/FvgTypes/1) : The FVG object instance. Reads this.isMitigated, this.isVisible,
this.isBullish, this.currentTop, this.currentBottom, original this.top/this.bottom,
this.startTime, this.tfType, this.isLV. Updates this.currentTop or
this.currentBottom, creates/updates this.mitigatedBoxId, and may update this.boxId's
top/bottom to reflect the filled portion.
highVal (float) : The high price of the current bar, used for partial fill check.
lowVal (float) : The low price of the current bar, used for partial fill check.
settings (drawSettings type from no1x/FvgTypes/1) : The drawing settings. Reads timeframe-specific colors for mitigated
boxes (e.g., settings.mitigatedBullBoxColor, settings.mitigatedLvBullColor),
box extension settings (settings.shouldExtendBoxes, settings.shouldExtendMtfBoxes, etc.),
and settings.currentTime to style and position the mitigatedBoxId and potentially adjust the main boxId.
method checkMidlineTouch(this, highVal, lowVal)
Checks if the FVG's midline (50% level or Equilibrium) has been touched.
Namespace types: types.fvgObject
Parameters:
this (fvgObject type from no1x/FvgTypes/1) : The FVG object instance. Reads this.midLineId, this.isMidlineTouched,
this.top, this.bottom. Updates this.isMidlineTouched if a touch occurs.
highVal (float) : The high price of the current bar, used for midline touch check.
lowVal (float) : The low price of the current bar, used for midline touch check.
method deleteDrawings(this, deleteTestedToo)
Deletes all visual drawing objects associated with this FVG object.
Namespace types: types.fvgObject
Parameters:
this (fvgObject type from no1x/FvgTypes/1) : The FVG object instance. Deletes drawings referenced by boxId,
mitigatedBoxId, midLineId, mitLineId, boxLabelId, mitLineLabelId,
and potentially testedBoxId, keptMitLineId. Sets these ID fields to na.
deleteTestedToo (simple bool) : If true, also deletes drawings for "tested" FVGs
(i.e., testedBoxId and keptMitLineId).
method updateDrawings(this, isVisibleNow, settings)
Manages the comprehensive update of all visual elements of an FVG object
based on its current state (e.g., active, mitigated, partially filled) and visibility. It handles the drawing, updating, or deletion of FVG boxes (main and mitigated part),
midlines, mitigation lines, and their associated labels. Visibility is determined by the isVisibleNow parameter and relevant settings
(like settings.shouldHideMitigated or timeframe-specific show flags). This method is central to the FVG's visual lifecycle and includes optimization
to avoid redundant drawing operations if the FVG's relevant state or appearance
settings have not changed since the last bar. It also updates the FVG object's internal prev_* state fields for future optimization checks.
Namespace types: types.fvgObject
Parameters:
this (fvgObject type from no1x/FvgTypes/1) : The FVG object instance to update. Reads most state fields (e.g.,
isMitigated, currentTop, tfType, etc.) and updates all drawing ID fields
(boxId, midLineId, etc.), this.isVisible, and all this.prev_* state fields.
isVisibleNow (bool) : A flag indicating whether the FVG should be currently visible. Typically determined by external logic (e.g., visual range filter). Affects
whether active FVG drawings are created/updated or deleted by this method.
settings (drawSettings type from no1x/FvgTypes/1) : A fully populated drawSettings object. This method extensively
reads its fields (colors, styles, visibility toggles, timeframe strings, etc.)
to render FVG components according to this.tfType and current state. settings.currentTime is critical for positioning elements like labels and extending drawings.
Sector Relative StrengthDescription
This script compares sector performance relative to the S&P 500. Sector price levels or charts alone can mislead, because they tend to move with the broader market. An increase in a sector’s price does not necessarily indicate strength, as it may simply be following the index.
For more a more reliable picture, the script calculates a ratio between each sector ETF and SPY. If the ratio has increased, the sector has outperformed the index. In case it has declined, the sector has underperformed. If the value is near zero, the sector has moved in line with the index. The sectors are presented in a table and sorted on relative performance.
Calculation Method
The performance is expressed as a percentage change in the ratio over a user-defined lookback period. The default lookback is set to 21 bars, which corresponds to one month on a daily chart. This value can be adopted in the settings to match preferred time period.
Z-Score
In addition to the percentage change, the script calculates a Z-score of the ratio, which measures how far the current value deviates from its recent mean. A high positive Z-score indicates that the ratio is significantly above its average, while a negative value indicates it is below. This normalization allows for comparison between sectors with different price levels or volatility profiles.
Table Columns
- Relative %: The sector's performance relative to SPY over the selected lookback period
- Z-Score: Standardized measure of current performance ratio is relative to its average
- Trend Arrow: Indicates the direction of relative performance up down or flat
Example Interpretation
For example, if XLK shows a 3.7% change, it has outperformed SPY over the selected period. Another sector might show a -2.1% change, which indicates underperformance. While both values shows relative strength or weakness, the Z-score is optional and can provide additional context based on how unusual that performance is compared to the sector's own recent behavior.
Use Case
This approach helps evaluate overall market conditions and supports a top-down method. By starting with sector performance, it becomes easier to identify where the market is showing leadership or weakness. This allows the stock selection process to be more deliberate and can help refine or customize screeners based on certain sectors.
UTSStrategyHelperLibrary "UTSStrategyHelper"
TODO: add library description here
stopLossPrice(sig, atr, factor, isLong)
Calculates the stop loss price using a distance determined by ATR multiplied by a factor. Example for Long trade SL: PRICE - (ATR * factor).
Parameters:
sig (float)
atr (float) : (float): The value of the atr.
factor (float)
isLong (bool) : (bool): The current trade direction.
Returns: (bool): A boolean value.
takeProfitPrice(sig, atr, factor, isLong)
Calculates the take profit price using a distance determined by ATR multiplied by a factor. Example for Long trade TP: PRICE + (ATR * factor). When take profit price is reached usually 50 % of the position is closed and the other 50 % get a trailing stop assigned.
Parameters:
sig (float)
atr (float) : (float): The value of the atr.
factor (float)
isLong (bool) : (bool): The current trade direction.
Returns: (bool): A boolean value.
trailingStopPrice(initialStopPrice, atr, factor, priceSource, isLong)
Calculates a trailing stop price using a distance determined by ATR multiplied by a factor. It takes an initial price and follows the price closely if it changes in a favourable way.
Parameters:
initialStopPrice (float) : (float): The initial stop price which, for consistency also should be ATR * factor behind price: e.g. Long trade: PRICE - (ATR * factor)
atr (float) : (float): The value of the atr. Ideally the ATR value at trade open is taken and used for subsequent calculations.
factor (float)
priceSource (float) : (float): The current price.
isLong (bool) : (bool): The current trade direction.
Returns: (bool): A boolean value.
hasGreaterPositionSize(positionSize)
Determines if the strategy's position size has grown since the last bar.
Parameters:
positionSize (float) : (float): The size of the position.
Returns: (bool): A boolean value.
hasSmallerPositionSize(positionSize)
Determines if the strategy's position size has decreased since the last bar.
Parameters:
positionSize (float) : (float): The size of the position.
Returns: (bool): A boolean value.
hasUnchangedPositionSize(positionSize)
Determines if the strategy's position size has changed since the last bar.
Parameters:
positionSize (float) : (float): The size of the position.
Returns: (bool): A boolean value.
exporthasLongPosition(positionSize)
Determines if the strategy has an open long position.
Parameters:
positionSize (float) : (float): The size of the position.
Returns: (bool): A boolean value.
hasShortPosition(positionSize)
Determines if the strategy has an open short position.
Parameters:
positionSize (float) : (float): The size of the position.
Returns: (bool): A boolean value.
hasAnyPosition(positionSize)
Determines if the strategy has any open position, regardless of short or long.
Parameters:
positionSize (float) : (float): The size of the position.
Returns: (bool): A boolean value.
hasSignal(value)
Determines if the given argument contains a valid value (means not 'na').
Parameters:
value (float) : (float): The actual value.
Returns: (bool): A boolean value.
Adaptive ATR Limits█ OVERVIEW
This indicator plots adaptive ATR limits for intraday trading. A key feature of this indicator, which makes it different from other ATR limit indicators, is that the top and bottom ATR limit lines are always exactly one ATR apart from each other (in "auto" mode; there is also a "basic" mode, which plots the limits in the more traditional way—i.e., one ATR above the low and one ATR below the high at all times—and this can be used for comparison).
█ FEATURES
Provides an algorithm to plot the most reasonable intraday ATR top/bottom limits based on currently available information
Dynamically adapts limits as the price evolves during the day
Works correctly and consistently on both RTH and ETH charts
Has a user-selected ADR mode to base the limits on ADR instead of ATR
Option to include the current pre-market and previous day's post-market range in the calculation
Configurable ATR/ADR averaging length
Provides a visual smoothing option
Provides an information box showing the current numerical ATR/ADR values
Reasonable defaults that work well if the user changes nothing
Well-documented, high-quality, open-source code for those interested
█ HOW TO USE
At a minimum, there is nothing that needs to be set. The defaults work well. The ATR top line (red, configurable) gives you the most reasonable move given the currently available information. The line will move away from the price as the price approaches it; that is normal—it is reacting to new information. This happens until the ATR bottom limit hits the lower of the daily low and the previous day's close (in ATR mode). The ATR bottom line (green, configurable) works the same way, with reversed logic.
There is an option to use ADR instead of ATR. The ATR includes the previous day's RTH close in the range, whereas ADR does not. Another option allows the user to add the current day's pre-market range or the previous day's post-market into the current day's range, which has an effect if either of those went outside of today's RTH range, plus yesterday's RTH close (in the default ATR mode). Pre-market and post-market range is not typically included in the daily true range, so only change it if you really know you want it.
█ CONCEPTS
Most traditional ATR limit indicators plot the top ATR limit one ATR above the current daily low, and the bottom ATR limit one ATR below the current daily high. This indicator can also do that (in "basic" mode), but its value lies in its default "auto" mode, which uses an algorithm to dynamically adapt the ATR limits throughout the day, keeping them one ATR apart at all times. It tries to plot the most sensible ATR limits based on the current daily ATR, in order to provide a reasonable visual intraday target, given the available information at that point in time.
"Auto" mode is actually a weighted average of two methods: midpoint and relative (both of which can also be explicitly selected). The midpoint method places the midpoint of the ATR limit equal to the midpoint of the currently established daily range. The relative method measures the currently established daily range and calculates the position of the current price within it (as a ratio between 0 and 1). It then uses that value as a weight in a weighted average of extreme locations for the ATR limits, which are: the ATR top anchored to one ATR above the daily low, and the ATR bottom anchored to one ATR below the daily high.
The relative method is more advanced and better for most of the day; however, it can cause wild swings in the early market or pre-market before a reasonable range (as a percentage of ATR) has been established. "Auto" mode therefore takes another weighted average between the two methods, with the weight determined by the percentage of the ATR currently established within the day, more strongly weighting the calmer midpoint method before a good range is established. Once the full ATR has been achieved, the algorithm in "auto" mode will have fully switched to the relative method and will remain with that method for the rest of the day.
To explain the effect further, as an example, imagine that the price is approaching the full ATR range on the high side. At this point, the indicator will have almost fully transitioned to the second (relative) method. The lower ATR limit will now be anchored to the daily low as the price hits the upper ATR limit. If the price goes beyond the upper ATR, the lower ATR limit will stay anchored to the daily low, and the upper limit will stay anchored to one ATR above the lower limit. This allows you to see how far the price is going beyond the upper ATR limit. If the price then returns and backs off the upper ATR limit, the lower ATR limit will un-anchor from the daily low (it will actually rise, since the daily ATR range has been exceeded, so the lower ATR limit needs to come up because the actual daily range can’t fit into the ATR range anymore). The overall effect is to give you the best visual indication of where the price is in relation to a possible upper ATR-based target. Reverse this example for when the price low approaches the ATR range on the low side.
Care was taken so that the code uses no hard-coded time zones, exchanges, or session times. For this reason, it can in principle work globally. However, it very much depends on the information provided by the exchange, which is reflected in built-in Pine Script variables (see Limitations below).
█ LIMITATIONS
The indicator was developed for US/European equities and is tested on them only. It is also known to work on US futures; in this case, the whole 23-hour session is used, and the "Sessions to include in range" setting has no effect. It may or may not work as intended on security types and equities/futures for other countries.
[Stop!Loss] ADR Signal ADR Signal - a technical indicator located in a separate window, which displays by default the 80%-level , as well as the 100%-level of the average daily range (ADR) for the last 10 days and compares it with the current intraday range. The indicator helps not only with the use of a mathematical-statistical method to identify a potential reversal at the moment during intraday trading, but can also serves as an effective assistant in risk management.
👉 Basic mechanics of the indicator
Firstly, this indicator tracks the performance of the standard ATR indicator on the daily chart, in other words, ADR (Average Daily Range).
Important ❗️The ATR (Average True Range) indicator was created by J. Welles Wilder Jr. He first introduced ATR in his book "New Concepts in Technical Trading Systems", published in 1978. Wilder developed this indicator to measure market volatility to help traders estimate the range of price movements. This indicator is built into TradingView, more details can be found by link: www.tradingview.com
Like ATR , ADR calculates the average true range for a specified period. In this case, the distance in points from the maximum of each day to its minimum is calculated, after which the arithmetic mean is calculated - this is ADR .
👉 Visualization
ADR Signal is located in a separate window on the chart and has 3 levels:
1) "ADR level" (green line) - the same parameter, the calculations of which are briefly described above. There is 100%-level of ATR on the daily chart (ADR).
2) "Current level" (red line) - this is the current price passage within the day, calculated in points. At the start of a new day, this parameter is reset. Therefore, in the indicator window, this line has sharp drops at the start of a new trading day: "A new trading day - the instrument's power reserve is renewed again".
3) "Signal level" (blue line) - this is an individually customized value that demonstrates a certain part of the ADR parameter.
👉 Inputs
1) - is responsible for the ATR indicator period, the value of which will always be calculated on the daily chart. The default value is "10", that is, ATR is calculated for the last 10 days (not including the current one).
2) - signal level (in %). The default value is "0.8", that is, 80%-level of the ADR parameter (set earlier) is calculated.
👉 Style
1) - by default, this level is colored "blue".
2) - by default, this level is colored "red".
3) - by default, this level is colored "green".
👉 How to use this indicator
Important❗️ The two methods of the use of the ADR Signal indicator described below will be most effective when trading intraday (which is highlighted quite well below), so it is more logical to use the indicator information on time periods H1 and below.
1) Identifying potential reversals during intraday trading:
The ADR Signal indicator can be used as a potential individual reversal strategy.
Important ❗️It should be noted that using it in it without additional confirming analysis tools will be a rather aggressive trading approach. Therefore, it is best to support the entry point in particular with other methods.
In this case, the crossing of the red line (the number of points passed within the current day, that is, from the minimum of the current day to its maximum) and the blue line (color of the Signal level based on the default settings), indicates that the trading instrument has passed 80% (based on the default settings for the "Signal level") of its average distance from the maximum to the minimum over the past 10 days (based on the default settings for the "ADR Length"). Such a situation in the context of the mathematical-statistical approach indicates a probable reversal, since the "power reserve" of this instrument is mostly exhausted, so one can expect with a higher probability, at least, a price stop and possibly a reversal. In case of crossing of the red line and the green one (ADR level), it says again that based on the mathematical-statistical approach, this trading instrument has completely exhausted its intraday "power reserve". In this situation, a stop or reversal of the price will be even more likely.
Of course, using the "Signal level" parameter, one can filter out even more reliable situations for potential price reversals within a day, namely, by specifying, for example, 1.5 in the field of this parameter. Under such conditions, in the case of crossing the red and blue lines (based on the default style settings), to say that the trading instrument has passed 150% of its average distance over the last 10 days (based on the default style settings "ADR length"). In this case, the probability of a stop or reversal of the price increases even more.
2) Use in risk management:
In terms of risk management, this indicator is more applicable to open trades. For example, if one had an open Buy-position (especially if it is an intraday trade) and the price has raised significantly during the day, then the crossing of the red line with the blue line , and especially the red line with the green line , may indicate that the price will most likely stop growing, since the "power reserve" is almost or completely exhausted for this instrument within the current day. In this case, one can, at a minimum, move the trade to breakeven or even partially fix the profit.
We will continue to discuss the methods of using this indicator and strategies based on it here. And we are always waiting for your reactions and feedback on this topic 💬.
Thank you for your support 🚀
Trend Trader-Remastered StrategyOfficial Strategy for Trend Trader - Remastered
Indicator: Trend Trader-Remastered (TTR)
Overview:
The Trend Trader-Remastered is a refined and highly sophisticated implementation of the Parabolic SAR designed to create strategic buy and sell entry signals, alongside precision take profit and re-entry signals based on marked Bill Williams (BW) fractals. Built with a deep emphasis on clarity and accuracy, this indicator ensures that only relevant and meaningful signals are generated, eliminating any unnecessary entries or exits.
Please check the indicator details and updates via the link above.
Important Disclosure:
My primary objective is to provide realistic strategies and a code base for the TradingView Community. Therefore, the default settings of the strategy version of the indicator have been set to reflect realistic world trading scenarios and best practices.
Key Features:
Strategy execution date&time range.
Take Profit Reduction Rate: The percentage of progressive reduction on active position size for take profit signals.
Example:
TP Reduce: 10%
Entry Position Size: 100
TP1: 100 - 10 = 90
TP2: 90 - 9 = 81
Re-Entry When Rate: The percentage of position size on initial entry of the signal to determine re-entry.
Example:
RE When: 50%
Entry Position Size: 100
Re-Entry Condition: Active Position Size < 50
Re-Entry Fill Rate: The percentage of position size on initial entry of the signal to be completed.
Example:
RE Fill: 75%
Entry Position Size: 100
Active Position Size: 50
Re-Entry Order Size: 25
Final Active Position Size:75
Important: Even RE When condition is met, the active position size required to drop below RE Fill rate to trigger re-entry order.
Key Points:
'Process Orders on Close' is enabled as Take Profit and Re-Entry signals must be executed on candle close.
'Calculate on Every Tick' is enabled as entry signals are required to be executed within candle time.
'Initial Capital' has been set to 10,000 USD.
'Default Quantity Type' has been set to 'Percent of Equity'.
'Default Quantity' has been set to 10% as the best practice of investing 10% of the assets.
'Currency' has been set to USD.
'Commission Type' has been set to 'Commission Percent'
'Commission Value' has been set to 0.05% to reflect the most realistic results with a common taker fee value.
Fractal Breakout Trend Following StrategyOverview
The Fractal Breakout Trend Following Strategy is a trend-following system which utilizes the Willams Fractals and Alligator to execute the long trades on the fractal's breakouts which have a high probability to be the new uptrend phase beginning. This system also uses the normalized Average True Range indicator to filter trades after a large moves, because it's more likely to see the trend continuation after a consolidation period. Strategy can execute only long trades.
Unique Features
Trend and volatility filtering system: Strategy uses Williams Alligator to filter the counter-trend fractals breakouts and normalized Average True Range to avoid the trades after large moves, when volatility is high
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Flexible Risk Management: Users can choose the stop-loss percent (by default = 3%) for trades, but strategy also has the dynamic stop-loss level using down fractals.
Methodology
The strategy places stop order at the last valid fractal breakout level. Validity of this fractal is defined by the Williams Alligator indicator. If at the moment of time when price breaking the last fractal price is higher than Alligator's teeth line (8 period SMA shifted 5 bars in the future) this is a valid breakout. Moreover strategy has the additional volatility filtering system using normalized ATR. It calculates the average normalized ATR for last user-defined number of bars and if this value lower than the user-defined threshold value the long trade is executed.
When trade is opened, script places the stop loss at the price higher of two levels: user defined stop-loss from the position entry price or down fractal validation level. The down fractal is valid with the rule, opposite as the up fractal validation. Price shall break to the downside the last down fractal below the Willians Alligator's teeth line.
Strategy has no fixed take profit. Exit level changes with the down fractal validation level. If price is in strong uptrend trade is going to be active until last down fractal is not valid. Strategy closes trade when price hits the down fractal validation level.
Risk Management
The strategy employs a combined approach to risk management:
It allows positions to ride the trend as long as the price continues to move favorably, aiming to capture significant price movements. It features a user-defined stop-loss parameter to mitigate risks based on individual risk tolerance. By default, this stop-loss is set to a 3% drop from the entry point, but it can be adjusted according to the trader's preferences.
Justification of Methodology
This strategy leverages Williams Fractals to open long trade when price has broken the key resistance level to the upside. This resistance level is the last up fractal and is shall be broken above the Williams Alligator's teeth line to be qualified as the valid breakout according to this strategy. The Alligator filtering increases the probability to avoid the false breakouts against the current trend.
Moreover strategy has an additional filter using Average True Range(ATR) indicator. If average value of ATR for the last user-defined number of bars is lower than user-defined threshold strategy can open the long trade according to open trade condition above. The logic here is following: we want to open trades after period of price consolidation inside the range because before and after a big move price is more likely to be in sideways, but we need a trend move to have a profit.
Another one important feature is how the exit condition is defined. On the one hand, strategy has the user-defined stop-loss (3% below the entry price by default). It's made to give users the opportunity to restrict their losses according to their risk-tolerance. On the other hand, strategy utilizes the dynamic exit level which is defined by down fractal activation. If we assume the breaking up fractal is the beginning of the uptrend, breaking down fractal can be the start of downtrend phase. We don't want to be in long trade if there is a high probability of reversal to the downside. This approach helps to not keep open trade if trend is not developing and hold it if price continues going up.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.05.01. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 30%
Maximum Single Position Loss: -3.19%
Maximum Single Profit: +24.97%
Net Profit: +3036.90 USDT (+30.37%)
Total Trades: 83 (28.92% win rate)
Profit Factor: 1.953
Maximum Accumulated Loss: 963.98 USDT (-8.29%)
Average Profit per Trade: 36.59 USDT (+1.12%)
Average Trade Duration: 72 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 4h and higher time frames and the BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
time_libraryLibrary "time_library"
This library provides utilities for working with time intervals in milliseconds, seconds, minutes, hours, days, and weeks. It includes functions to handle conditions based on time rather than bars.
ms(TIME)
ms - Converts a time period in string format to milliseconds.
Parameters:
TIME (string) : (series ) - The time period ("ms", "s", "m", "h", "d", "w").
Returns: (int) - The corresponding time period in milliseconds.
true_in(timestamp, period, multiplier)
true_in - Checks if the current time has reached a specific time after the given timestamp.
Parameters:
timestamp (int) : (series ) - The starting timestamp.
period (string) : (series |series ) - The period in string format ("ms", "s", "m", "h", "d", "w"), or as an integer in milliseconds.
multiplier (float) : (series ) - Multiplier to extend the period.
Returns: (bool) - True if current time is equal or past the end date calculated from timestamp and period.
true_in(timestamp, period, multiplier)
true_in - Checks if the current time has reached a specific time after the given timestamp.
Parameters:
timestamp (int) : (series ) - The starting timestamp.
period (int) : (series |series ) - The period in string format ("ms", "s", "m", "h", "d", "w"), or as an integer in milliseconds.
multiplier (float) : (series ) - Multiplier to extend the period.
Returns: (bool) - True if current time is equal or past the end date calculated from timestamp and period.
true_after(trigger, period, multiplier)
true_after - Returns true after a specified period multiplied by a multiplier has passed since a trigger was last true.
Parameters:
trigger (bool) : (series ) - The condition that triggers the timer.
period (string) : (series |series ) - The period in string format ("ms", "s", "m", "h", "d", "w"), or as an integer in milliseconds.
multiplier (float) : (series ) - Multiplier to extend the period.
Returns: (bool) - True if the specified time has passed since the last trigger.
true_after(trigger, ms, multiplier)
true_after - Returns true after a specified period multiplied by a multiplier has passed since a trigger was last true.
Parameters:
trigger (bool) : (series ) - The condition that triggers the timer.
ms (int)
multiplier (float) : (series ) - Multiplier to extend the period.
Returns: (bool) - True if the specified time has passed since the last trigger.
MS
MS - Holds common time intervals in milliseconds.
Fields:
ms (series int) : (int) - Milliseconds.
s (series int) : (int) - Seconds converted to milliseconds.
m (series int) : (int) - Minutes converted to milliseconds.
h (series int) : (int) - Hours converted to milliseconds.
d (series int) : (int) - Days converted to milliseconds.
w (series int) : (int) - Weeks converted to milliseconds.
RiskMetrics█ OVERVIEW
This library is a tool for Pine programmers that provides functions for calculating risk-adjusted performance metrics on periodic price returns. The calculations used by this library's functions closely mirror those the Broker Emulator uses to calculate strategy performance metrics (e.g., Sharpe and Sortino ratios) without depending on strategy-specific functionality.
█ CONCEPTS
Returns, risk, and volatility
The return on an investment is the relative gain or loss over a period, often expressed as a percentage. Investment returns can originate from several sources, including capital gains, dividends, and interest income. Many investors seek the highest returns possible in the quest for profit. However, prudent investing and trading entails evaluating such returns against the associated risks (i.e., the uncertainty of returns and the potential for financial losses) for a clearer perspective on overall performance and sustainability.
One way investors and analysts assess the risk of an investment is by analyzing its volatility , i.e., the statistical dispersion of historical returns. Investors often use volatility in risk estimation because it provides a quantifiable way to gauge the expected extent of fluctuation in returns. Elevated volatility implies heightened uncertainty in the market, which suggests higher expected risk. Conversely, low volatility implies relatively stable returns with relatively minimal fluctuations, thus suggesting lower expected risk. Several risk-adjusted performance metrics utilize volatility in their calculations for this reason.
Risk-free rate
The risk-free rate represents the rate of return on a hypothetical investment carrying no risk of financial loss. This theoretical rate provides a benchmark for comparing the returns on a risky investment and evaluating whether its excess returns justify the risks. If an investment's returns are at or below the theoretical risk-free rate or the risk premium is below a desired amount, it may suggest that the returns do not compensate for the extra risk, which might be a call to reassess the investment.
Since the risk-free rate is a theoretical concept, investors often utilize proxies for the rate in practice, such as Treasury bills and other government bonds. Conventionally, analysts consider such instruments "risk-free" for a domestic holder, as they are a form of government obligation with a low perceived likelihood of default.
The average yield on short-term Treasury bills, influenced by economic conditions, monetary policies, and inflation expectations, has historically hovered around 2-3% over the long term. This range also aligns with central banks' inflation targets. As such, one may interpret a value within this range as a minimum proxy for the risk-free rate, as it may correspond to the minimum rate required to maintain purchasing power over time.
The built-in Sharpe and Sortino ratios that strategies calculate and display in the Performance Summary tab use a default risk-free rate of 2%, and the metrics in this library's example code use the same default rate. Users can adjust this value to fit their analysis needs.
Risk-adjusted performance
Risk-adjusted performance metrics gauge the effectiveness of an investment by considering its returns relative to the perceived risk. They aim to provide a more well-rounded picture of performance by factoring in the level of risk taken to achieve returns. Investors can utilize such metrics to help determine whether the returns from an investment justify the risks and make informed decisions.
The two most commonly used risk-adjusted performance metrics are the Sharpe ratio and the Sortino ratio.
1. Sharpe ratio
The Sharpe ratio , developed by Nobel laureate William F. Sharpe, measures the performance of an investment compared to a theoretically risk-free asset, adjusted for the investment risk. The ratio uses the following formula:
Sharpe Ratio = (𝑅𝑎 − 𝑅𝑓) / 𝜎𝑎
Where:
• 𝑅𝑎 = Average return of the investment
• 𝑅𝑓 = Theoretical risk-free rate of return
• 𝜎𝑎 = Standard deviation of the investment's returns (volatility)
A higher Sharpe ratio indicates a more favorable risk-adjusted return, as it signifies that the investment produced higher excess returns per unit of increase in total perceived risk.
2. Sortino ratio
The Sortino ratio is a modified form of the Sharpe ratio that only considers downside volatility , i.e., the volatility of returns below the theoretical risk-free benchmark. Although it shares close similarities with the Sharpe ratio, it can produce very different values, especially when the returns do not have a symmetrical distribution, since it does not penalize upside and downside volatility equally. The ratio uses the following formula:
Sortino Ratio = (𝑅𝑎 − 𝑅𝑓) / 𝜎𝑑
Where:
• 𝑅𝑎 = Average return of the investment
• 𝑅𝑓 = Theoretical risk-free rate of return
• 𝜎𝑑 = Downside deviation (standard deviation of negative excess returns, or downside volatility)
The Sortino ratio offers an alternative perspective on an investment's return-generating efficiency since it does not consider upside volatility in its calculation. A higher Sortino ratio signifies that the investment produced higher excess returns per unit of increase in perceived downside risk.
█ CALCULATIONS
Return period detection
Calculating risk-adjusted performance metrics requires collecting returns across several periods of a given size. Analysts may use different period sizes based on the context and their preferences. However, two widely used standards are monthly or daily periods, depending on the available data and the investment's duration. The built-in ratios displayed in the Strategy Tester utilize returns from either monthly or daily periods in their calculations based on the following logic:
• Use monthly returns if the history of closed trades spans at least two months.
• Use daily returns if the trades span at least two days but less than two months.
• Do not calculate the ratios if the trade data spans fewer than two days.
This library's `detectPeriod()` function applies related logic to available chart data rather than trade data to determine which period is appropriate:
• It returns true if the chart's data spans at least two months, indicating that it's sufficient to use monthly periods.
• It returns false if the chart's data spans at least two days but not two months, suggesting the use of daily periods.
• It returns na if the length of the chart's data covers less than two days, signifying that the data is insufficient for meaningful ratio calculations.
It's important to note that programmers should only call `detectPeriod()` from a script's global scope or within the outermost scope of a function called from the global scope, as it requires the time value from the first bar to accurately measure the amount of time covered by the chart's data.
Collecting periodic returns
This library's `getPeriodicReturns()` function tracks price return data within monthly or daily periods and stores the periodic values in an array . It uses a `detectPeriod()` call as the condition to determine whether each element in the array represents the return over a monthly or daily period.
The `getPeriodicReturns()` function has two overloads. The first overload requires two arguments and outputs an array of monthly or daily returns for use in the `sharpe()` and `sortino()` methods. To calculate these returns:
1. The `percentChange` argument should be a series that represents percentage gains or losses. The values can be bar-to-bar return percentages on the chart timeframe or percentages requested from a higher timeframe.
2. The function compounds all non-na `percentChange` values within each monthly or daily period to calculate the period's total return percentage. When the `percentChange` represents returns from a higher timeframe, ensure the requested data includes gaps to avoid compounding redundant values.
3. After a period ends, the function queues the compounded return into the array , removing the oldest element from the array when its size exceeds the `maxPeriods` argument.
The resulting array represents the sequence of closed returns over up to `maxPeriods` months or days, depending on the available data.
The second overload of the function includes an additional `benchmark` parameter. Unlike the first overload, this version tracks and collects differences between the `percentChange` and the specified `benchmark` values. The resulting array represents the sequence of excess returns over up to `maxPeriods` months or days. Passing this array to the `sharpe()` and `sortino()` methods calculates generalized Information ratios , which represent the risk-adjustment performance of a sequence of returns compared to a risky benchmark instead of a risk-free rate. For consistency, ensure the non-na times of the `benchmark` values align with the times of the `percentChange` values.
Ratio methods
This library's `sharpe()` and `sortino()` methods respectively calculate the Sharpe and Sortino ratios based on an array of returns compared to a specified annual benchmark. Both methods adjust the annual benchmark based on the number of periods per year to suit the frequency of the returns:
• If the method call does not include a `periodsPerYear` argument, it uses `detectPeriod()` to determine whether the returns represent monthly or daily values based on the chart's history. If monthly, the method divides the `annualBenchmark` value by 12. If daily, it divides the value by 365.
• If the method call does specify a `periodsPerYear` argument, the argument's value supersedes the automatic calculation, facilitating custom benchmark adjustments, such as dividing by 252 when analyzing collected daily stock returns.
When the array passed to these methods represents a sequence of excess returns , such as the result from the second overload of `getPeriodicReturns()`, use an `annualBenchmark` value of 0 to avoid comparing those excess returns to a separate rate.
By default, these methods only calculate the ratios on the last available bar to minimize their resource usage. Users can override this behavior with the `forceCalc` parameter. When the value is true , the method calculates the ratio on each call if sufficient data is available, regardless of the bar index.
Look first. Then leap.
█ FUNCTIONS & METHODS
This library contains the following functions:
detectPeriod()
Determines whether the chart data has sufficient coverage to use monthly or daily returns
for risk metric calculations.
Returns: (bool) `true` if the period spans more than two months, `false` if it otherwise spans more
than two days, and `na` if the data is insufficient.
getPeriodicReturns(percentChange, maxPeriods)
(Overload 1 of 2) Tracks periodic return percentages and queues them into an array for ratio
calculations. The span of the chart's historical data determines whether the function uses
daily or monthly periods in its calculations. If the chart spans more than two months,
it uses "1M" periods. Otherwise, if the chart spans more than two days, it uses "1D"
periods. If the chart covers less than two days, it does not store changes.
Parameters:
percentChange (float) : (series float) The change percentage. The function compounds non-na values from each
chart bar within monthly or daily periods to calculate the periodic changes.
maxPeriods (simple int) : (simple int) The maximum number of periodic returns to store in the returned array.
Returns: (array) An array containing the overall percentage changes for each period, limited
to the maximum specified by `maxPeriods`.
getPeriodicReturns(percentChange, benchmark, maxPeriods)
(Overload 2 of 2) Tracks periodic excess return percentages and queues the values into an
array. The span of the chart's historical data determines whether the function uses
daily or monthly periods in its calculations. If the chart spans more than two months,
it uses "1M" periods. Otherwise, if the chart spans more than two days, it uses "1D"
periods. If the chart covers less than two days, it does not store changes.
Parameters:
percentChange (float) : (series float) The change percentage. The function compounds non-na values from each
chart bar within monthly or daily periods to calculate the periodic changes.
benchmark (float) : (series float) The benchmark percentage to compare against `percentChange` values.
The function compounds non-na values from each bar within monthly or
daily periods and subtracts the results from the compounded `percentChange` values to
calculate the excess returns. For consistency, ensure this series has a similar history
length to the `percentChange` with aligned non-na value times.
maxPeriods (simple int) : (simple int) The maximum number of periodic excess returns to store in the returned array.
Returns: (array) An array containing monthly or daily excess returns, limited
to the maximum specified by `maxPeriods`.
method sharpeRatio(returnsArray, annualBenchmark, forceCalc, periodsPerYear)
Calculates the Sharpe ratio for an array of periodic returns.
Callable as a method or a function.
Namespace types: array
Parameters:
returnsArray (array) : (array) An array of periodic return percentages, e.g., returns over monthly or
daily periods.
annualBenchmark (float) : (series float) The annual rate of return to compare against `returnsArray` values. When
`periodsPerYear` is `na`, the function divides this value by 12 to calculate a
monthly benchmark if the chart's data spans at least two months or 365 for a daily
benchmark if the data otherwise spans at least two days. If `periodsPerYear`
has a specified value, the function divides the rate by that value instead.
forceCalc (bool) : (series bool) If `true`, calculates the ratio on every call. Otherwise, ratio calculation
only occurs on the last available bar. Optional. The default is `false`.
periodsPerYear (simple int) : (simple int) If specified, divides the annual rate by this value instead of the value
determined by the time span of the chart's data.
Returns: (float) The Sharpe ratio, which estimates the excess return per unit of total volatility.
method sortinoRatio(returnsArray, annualBenchmark, forceCalc, periodsPerYear)
Calculates the Sortino ratio for an array of periodic returns.
Callable as a method or a function.
Namespace types: array
Parameters:
returnsArray (array) : (array) An array of periodic return percentages, e.g., returns over monthly or
daily periods.
annualBenchmark (float) : (series float) The annual rate of return to compare against `returnsArray` values. When
`periodsPerYear` is `na`, the function divides this value by 12 to calculate a
monthly benchmark if the chart's data spans at least two months or 365 for a daily
benchmark if the data otherwise spans at least two days. If `periodsPerYear`
has a specified value, the function divides the rate by that value instead.
forceCalc (bool) : (series bool) If `true`, calculates the ratio on every call. Otherwise, ratio calculation
only occurs on the last available bar. Optional. The default is `false`.
periodsPerYear (simple int) : (simple int) If specified, divides the annual rate by this value instead of the value
determined by the time span of the chart's data.
Returns: (float) The Sortino ratio, which estimates the excess return per unit of downside
volatility.
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.
TouchedLibrary "Touched"
Breakout(zone, lookback)
Tells if the zone has been broken on the current bar.
Parameters:
zone (Zone) : The definitiin of the zone.
lookback (int) : How many bars to look back.
Returns: a Touch object that tells if the zone has been broken up or down.
FalseBreakout(zone, lookback)
Tells if the zone has a false breakout on the current bar.
Parameters:
zone (Zone) : The definitiin of the zone.
lookback (int) : How many bars to look back.
Returns: a Touch object that tells if the zone has had a false breakout up or down.
Retest(zone, lookback)
Tells if the zone has been retested on the current bar.
Parameters:
zone (Zone) : The definitiin of the zone.
lookback (int) : How many bars to look back.
Returns: a Touch object that tells if the zone has been retested up or down.
Zone
Fields:
High (series__float)
Low (series__float)
BaseTime (series__integer)
Touch
Fields:
Up (series__bool)
Down (series__bool)
A_Traders_Edge__LibraryLibrary "A_Traders_Edge__Library"
- A Trader's Edge (ATE)_Library was created to assist in constructing Market Overview Scanners (MOS)
LabelLocation(_firstLocation)
This function is used when there's a desire to print an assets ALERT LABELS at a set location on the scale that will
NOT change throughout the progression of the script. This is created so that if a lot of alerts are triggered, they
will stay relatively visible and not overlap each other. Ex. If you set your '_firstLocation' parameter as 1, since
there are a max of 40 assets that can be scanned, the 1st asset's location is assigned the value in the '_firstLocation' parameter,
the 2nd asset's location is the (1st asset's location+1)...and so on. If your first location is set to 81 then
the 1st asset is 81 and 2nd is 82 and so on until the 40th location = 120(in this particular example).
Parameters:
_firstLocation (simple int) : (simple int)
Optional(starts at 1 if no parameter added).
Location that you want the first asset to print its label if is triggered to do so.
ie. loc2=loc1+1, loc3=loc2+1, etc.
Returns: Returns 40 output variables each being a different location to print the labels so that an asset is asssigned to
a particular location on the scale. Regardless of if you have the maximum amount of assets being screened (40 max), this
function will output 40 locations… So there needs to be 40 variables assigned in the tuple in this function. What I
mean by that is you need to have 40 output location variables within your tuple (ie. between the ' ') regarless of
if your scanning 40 assets or not. If you only have 20 assets in your scripts input settings, then only the first 20
variables within the ' ' Will be assigned to a value location and the other 20 will be assigned 'NA', but their
variables still need to be present in the tuple.
SeparateTickerids(_string)
You must form this single tickerID input string exactly as described in the scripts info panel (little gray 'i' that
is circled at the end of the settings in the settings/input panel that you can hover your cursor over this 'i' to read the
details of that particular input). IF the string is formed correctly then it will break up this single string parameter into
a total of 40 separate strings which will be all of the tickerIDs that the script is using in your MO scanner.
Parameters:
_string (simple string) : (string)
A maximum of 40 Tickers (ALL joined as 1 string for the input parameter) that is formulated EXACTLY as described
within the tooltips of the TickerID inputs in my MOS Scanner scripts:
assets = input.text_area(tIDset1, title="TickerID (MUST READ TOOLTIP)", tooltip="Accepts 40 TICKERID's for each
copy of the script on the chart. TEXT FORMATTING RULES FOR TICKERID'S:
(1) To exclude the EXCHANGE NAME in the Labels, de-select the next input option.
(2) MUST have a space (' ') AFTER each TickerID.
(3) Capitalization in the Labels will match cap of these TickerID's.
(4) If your asset has a BaseCurrency & QuoteCurrency (ie. ADAUSDT ) BUT you ONLY want Labels
to show BaseCurrency(ie.'ADA'), include a FORWARD SLASH ('/') between the Base & Quote (ie.'ADA/USDT')", display=display.none)
Returns: Returns 40 output variables of the different strings of TickerID's (ie. you need to output 40 variables within the
tuple ' ' regardless of if you were scanning using all possible (40) assets or not.
If your scanning for less than 40 assets, then once the variables are assigned to all of the tickerIDs, the rest
of the 40 variables in the tuple will be assigned "NA".
TickeridForLabelsAndSecurity(_includeExchange, _ticker)
This function accepts the TickerID Name as its parameter and produces a single string that will be used in all of your labels.
Parameters:
_includeExchange (simple bool) : (bool)
Optional(if parameter not included in function it defaults to false ).
Used to determine if the Exchange name will be included in all labels/triggers/alerts.
_ticker (simple string) : (string)
For this parameter, input the varible named '_coin' from your 'f_main()' function for this parameter. It is the raw
Ticker ID name that will be processed.
Returns: ( )
Returns 2 output variables:
1st ('_securityTickerid') is to be used in the 'request.security()' function as this string will contain everything
TV needs to pull the correct assets data.
2nd ('lblTicker') is to be used in all of the labels in your MOS as it will only contain what you want your labels
to show as determined by how the tickerID is formulated in the MOS's input.
InvalidTID(_tablePosition, _stackVertical, _close, _securityTickerid, _invalidArray)
This is to add a table in the middle right of your chart that prints all the TickerID's that were either not formulated
correctly in the '_source' input or that is not a valid symbol and should be changed.
Parameters:
_tablePosition (simple string) : (string)
Optional(if parameter not included, it defaults to position.middle_right). Location on the chart you want the table printed.
Possible strings include: position.top_center, position.top_left, position.top_right, position.middle_center,
position.middle_left, position.middle_right, position.bottom_center, position.bottom_left, position.bottom_right.
_stackVertical (simple bool) : (bool)
Optional(if parameter not included, it defaults to true). All of the assets that are counted as INVALID will be
created in a list. If you want this list to be prited as a column then input 'true' here.
_close (float) : (float)
If you want them printed as a single row then input 'false' here.
This should be the closing value of each of the assets being tested to determine in the TickerID is valid or not.
_securityTickerid (string) : (string)
Throughout the entire charts updates, if a '_close' value is never regestered then the logic counts the asset as INVALID.
This will be the 1st TickerID varible (named _securityTickerid) outputted from the tuple of the TickeridForLabels()
function above this one.
_invalidArray (string ) : (array string)
Input the array from the original script that houses all of the invalidArray strings.
Returns: (na)
Returns a table with the screened assets Invalid TickerID's. Table draws automatically if any are Invalid, thus,
no output variable to deal with.
LabelSizes(_barCnt, _lblSzRfrnce)
This function sizes your Alert Trigger Labels according to the amount of Printed Bars the chart has printed within
a set time period, while also keeping in mind the smallest relative reference size you input in the 'lblSzRfrnceInput'
parameter of this function. A HIGHER % of Printed Bars(aka...more trades occurring for that asset on the exchange),
the LARGER the Name Label will print, potentially showing you the better opportunities on the exchange to avoid
exchange manipulation liquidations.
*** SHOULD NOT be used as size of labels that are your asset Name Labels next to each asset's Line Plot...
if your MOS includes these as you want these to be the same size for every asset so the larger ones dont cover the
smaller ones if the plots are all close to each other ***
Parameters:
_barCnt (float) : (float)
Get the 1st variable('barCnt') from the 'PrintedBarCount' function's tuple and input it as this functions 1st input
parameter which will directly affect the size of the 2nd output variable ('alertTrigLabel') outputted by this function.
_lblSzRfrnce (string) : (string)
Optional(if parameter not included, it defaults to size.small). This will be the size of the 1st variable outputted
by this function ('assetNameLabel') BUT also affects the 2nd variable outputted by this function.
Returns: ( )
Returns 2 variables:
1st output variable ('AssetNameLabel') is assigned to the size of the 'lblSzRfrnceInput' parameter.
2nd output variable('alertTrigLabel') can be of variying sizes depending on the 'barCnt' parameter...BUT the smallest
size possible for the 2nd output variable ('alertTrigLabel') will be the size set in the 'lblSzRfrnceInput' parameter.
AssetColor()
This function is used to assign 40 different colors to 40 variables to be used for the different labels/plots.
Returns: Returns 40 output variables each with a different color assigned to them to be used in your plots & labels.
Regardless of if you have the maximum amount of assets your scanning(40 max) or less,
this function will assign 40 colors to 40 variables that you have between the ' '.
PrintedBarCount(_time, _barCntLength, _barCntPercentMin)
The Printed BarCount Filter looks back a User Defined amount of minutes and calculates the % of bars that have printed
out of the TOTAL amount of bars that COULD HAVE been printed within the same amount of time.
Parameters:
_time (int) : (int)
The time associated with the chart of the particular asset that is being screened at that point.
_barCntLength (int) : (int)
The amount of time (IN MINUTES) that you want the logic to look back at to calculate the % of bars that have actually
printed in the span of time you input into this parameter.
_barCntPercentMin (int) : (int)
The minimum % of Printed Bars of the asset being screened has to be GREATER than the value set in this parameter
for the output variable 'bc_gtg' to be true.
Returns: ( )
Returns 2 outputs:
1st is the % of Printed Bars that have printed within the within the span of time you input in the '_barCntLength' parameter.
2nd is true/false according to if the Printed BarCount % is above the threshold that you input into the '_barCntPercentMin' parameter.
RCI(_rciLength, _source, _interval)
You will see me using this a lot. DEFINITELY my favorite oscillator to utilize for SO many different things from
timing entries/exits to determining trends.Calculation of this indicator based on Spearmans Correlation.
Parameters:
_rciLength (int) : (int)
Amount of bars back to use in RCI calculations.
_source (float) : (float)
Source to use in RCI calculations (can use ANY source series. Ie, open,close,high,low,etc).
_interval (int) : (int)
Optional(if parameter not included, it defaults to 3). RCI calculation groups bars by this amount and then will.
rank these groups of bars.
Returns: (float)
Returns a single RCI value that will oscillates between -100 and +100.
RCIAVG(firstLength, _amtBtLengths, _rciSMAlen, _source, _interval)
20 RCI's are averaged together to get this RCI Avg (Rank Correlation Index Average). Each RCI (of the 20 total RCI)
has a progressively LARGER Lookback Length. Though the RCI Lengths are not individually adjustable,
there are 2 factors that ARE:
(1) the Lookback Length of the 1st RCI and
(2) the amount of values between one RCI's Lookback Length and the next.
*** If you set 'firstLength' to it's default of 200 and '_amtBtLengths' to it's default of 120 (aka AMOUNT BETWEEN LENGTHS=120)...
then RCI_2 Length=320, RCI_3 Length=440, RCI_4 Length=560, and so on.
Parameters:
firstLength (int) : (int)
Optional(if parameter is not included when the function is called, then it defaults to 200).
This parameter is the Lookback Length for the 1st RCI used in the RCI Avg.
_amtBtLengths (int) : (int)
Optional(if parameter not included when the function is called, then it defaults to 120).
This parameter is the value amount between each of the progressively larger lengths used for the 20 RCI's that
are averaged in the RCI Avg.
***** BEWARE ***** Too large of a value here will cause the calc to look back too far, causing an error(thus the value must be lowered)
_rciSMAlen (int) : (int)
Unlike the Single RCI Function, this function smooths out the end result using an SMA with a length value that is this parameter.
_source (float) : (float)
Source to use in RCI calculations (can use ANY source series. Ie, open,close,high,low,etc).
_interval (int) : (int)
Optional(if parameter not included, it defaults to 3). Within the RCI calculation, bars next to each other are grouped together
and then these groups are Ranked against each other. This parameter is the number of adjacent bars that are grouped together.
Returns: (float)
Returns a single RCI value that is the Avg of many RCI values that will oscillate between -100 and +100.
PercentChange(_startingValue, _endingValue)
This is a quick function to calculate how much % change has occurred between the '_startingValue' and the '_endingValue'
that you input into the function.
Parameters:
_startingValue (float) : (float)
The source value to START the % change calculation from.
_endingValue (float) : (float)
The source value to END the % change caluclation from.
Returns: Returns a single output being the % value between 0-100 (with trailing numbers behind a decimal). If you want only
a certain amount of numbers behind the decimal, this function needs to be put within a formatting function to do so.
Rescale(_source, _oldMin, _oldMax, _newMin, _newMax)
Rescales series with a known '_oldMin' & '_oldMax'. Use this when the scale of the '_source' to
rescale is known (bounded).
Parameters:
_source (float) : (float)
Source to be normalized.
_oldMin (int) : (float)
The known minimum of the '_source'.
_oldMax (int) : (float)
The known maximum of the '_source'.
_newMin (int) : (float)
What you want the NEW minimum of the '_source' to be.
_newMax (int) : (float)
What you want the NEW maximum of the '_source' to be.
Returns: Outputs your previously bounded '_source', but now the value will only move between the '_newMin' and '_newMax'
values you set in the variables.
Normalize_Historical(_source, _minimumLvl, _maximumLvl)
Normalizes '_source' that has a previously unknown min/max(unbounded) determining the max & min of the '_source'
FROM THE ENTIRE CHARTS HISTORY. ]
Parameters:
_source (float) : (float)
Source to be normalized.
_minimumLvl (int) : (float)
The Lower Boundary Level.
_maximumLvl (int) : (float)
The Upper Boundary Level.
Returns: Returns your same '_source', but now the value will MOSTLY stay between the minimum and maximum values you set in the
'_minimumLvl' and '_maximumLvl' variables (ie. if the source you input is an RSI...the output is the same RSI value but
instead of moving between 0-100 it will move between the maxand min you set).
Normailize_Local(_source, _length, _minimumLvl, _maximumLvl)
Normalizes series with previously unknown min/max(unbounded). Much like the Normalize_Historical function above this one,
but rather than using the Highest/Lowest Values within the ENTIRE charts history, this on looks for the Highest/Lowest
values of '_source' within the last ___ bars (set by user as/in the '_length' parameter. ]
Parameters:
_source (float) : (float)
Source to be normalized.
_length (int) : (float)
The amount of bars to look back to determine the highest/lowest '_source' value.
_minimumLvl (int) : (float)
The Lower Boundary Level.
_maximumLvl (int) : (float)
The Upper Boundary Level.
Returns: Returns a single output variable being the previously unbounded '_source' that is now normalized and bound between
the values used for '_minimumLvl'/'_maximumLvl' of the '_source' within the user defined lookback period.
[Spinn] Average True RangeThe "Average True Range" indicator is a popular tool that measures price volatility. In this modified indicator, I present two methods of calculating ATR: the outdated classical one based on RMA (EMA, SMA, WMA), and the modernized one using the Super Smoother filter.
Why has exponential smoothing become outdated?
Exponential smoothing (EMA) has drawbacks, especially when it comes to identifying cyclical components in the data (and RMA is a variant of EMA). EMA creates phase shifts and distortions, making it less predictable and accurate in tracking real price movements. Modern filters, such as Super Smoother, offer a higher degree of adaptability and precision while ensuring significantly less lag, better smoothness, and superior cycle detection.
Why use more contemporary filters like Super Smoother?
The Super Smoother filter combines exponential smoothing and trigonometric functions for more accurate and smooth tracking of price movements. This filter enhances cycle tracking and reduces the lag often found when using EMA. As a result, signals based on Super Smoother are often more precise and representative of real price movements.
Drawbacks of other smoothing filters commonly used with ATR:
SMA. The lag is (N-1)/2, where N = period. This is terrible.
WMA. According to John F. Ehlers, "It appears that the WMA was invented by a trader who did not have a firm grasp of filter theory in hopes of reducing lag". It has been proven that WMA has worse suppression than the equivalent SMA, and WMA has more delay in the passband than the equivalent EMA. In short, WMA has drawbacks but no advantages compared to other popular moving averages.
It is also a good idea to use the median to average the results.
Test, experiment, use!
Support & Resistance AI (K means/median) [ThinkLogicAI]█ OVERVIEW
K-means is a clustering algorithm commonly used in machine learning to group data points into distinct clusters based on their similarities. While K-means is not typically used directly for identifying support and resistance levels in financial markets, it can serve as a tool in a broader analysis approach.
Support and resistance levels are price levels in financial markets where the price tends to react or reverse. Support is a level where the price tends to stop falling and might start to rise, while resistance is a level where the price tends to stop rising and might start to fall. Traders and analysts often look for these levels as they can provide insights into potential price movements and trading opportunities.
█ BACKGROUND
The K-means algorithm has been around since the late 1950s, making it more than six decades old. The algorithm was introduced by Stuart Lloyd in his 1957 research paper "Least squares quantization in PCM" for telecommunications applications. However, it wasn't widely known or recognized until James MacQueen's 1967 paper "Some Methods for Classification and Analysis of Multivariate Observations," where he formalized the algorithm and referred to it as the "K-means" clustering method.
So, while K-means has been around for a considerable amount of time, it continues to be a widely used and influential algorithm in the fields of machine learning, data analysis, and pattern recognition due to its simplicity and effectiveness in clustering tasks.
█ COMPARE AND CONTRAST SUPPORT AND RESISTANCE METHODS
1) K-means Approach:
Cluster Formation: After applying the K-means algorithm to historical price change data and visualizing the resulting clusters, traders can identify distinct regions on the price chart where clusters are formed. Each cluster represents a group of similar price change patterns.
Cluster Analysis: Analyze the clusters to identify areas where clusters tend to form. These areas might correspond to regions of price behavior that repeat over time and could be indicative of support and resistance levels.
Potential Support and Resistance Levels: Based on the identified areas of cluster formation, traders can consider these regions as potential support and resistance levels. A cluster forming at a specific price level could suggest that this level has been historically significant, causing similar price behavior in the past.
Cluster Standard Deviation: In addition to looking at the means (centroids) of the clusters, traders can also calculate the standard deviation of price changes within each cluster. Standard deviation is a measure of the dispersion or volatility of data points around the mean. A higher standard deviation indicates greater price volatility within a cluster.
Low Standard Deviation: If a cluster has a low standard deviation, it suggests that prices within that cluster are relatively stable and less likely to exhibit sudden and large price movements. Traders might consider placing tighter stop-loss orders for trades within these clusters.
High Standard Deviation: Conversely, if a cluster has a high standard deviation, it indicates greater price volatility within that cluster. Traders might opt for wider stop-loss orders to allow for potential price fluctuations without getting stopped out prematurely.
Cluster Density: Each data point is assigned to a cluster so a cluster that is more dense will act more like gravity and
2) Traditional Approach:
Trendlines: Draw trendlines connecting significant highs or lows on a price chart to identify potential support and resistance levels.
Chart Patterns: Identify chart patterns like double tops, double bottoms, head and shoulders, and triangles that often indicate potential reversal points.
Moving Averages: Use moving averages to identify levels where the price might find support or resistance based on the average price over a specific period.
Psychological Levels: Identify round numbers or levels that traders often pay attention to, which can act as support and resistance.
Previous Highs and Lows: Identify significant previous price highs and lows that might act as support or resistance.
The key difference lies in the approach and the foundation of these methods. Traditional methods are based on well-established principles of technical analysis and market psychology, while the K-means approach involves clustering price behavior without necessarily incorporating market sentiment or specific price patterns.
It's important to note that while the K-means approach might provide an interesting way to analyze price data, it should be used cautiously and in conjunction with other traditional methods. Financial markets are influenced by a wide range of factors beyond just price behavior, and the effectiveness of any method for identifying support and resistance levels should be thoroughly tested and validated. Additionally, developments in trading strategies and analysis techniques could have occurred since my last update.
█ K MEANS ALGORITHM
The algorithm for K means is as follows:
Initialize cluster centers
assign data to clusters based on minimum distance
calculate cluster center by taking the average or median of the clusters
repeat steps 1-3 until cluster centers stop moving
█ LIMITATIONS OF K MEANS
There are 3 main limitations of this algorithm:
Sensitive to Initializations: K-means is sensitive to the initial placement of centroids. Different initializations can lead to different cluster assignments and final results.
Assumption of Equal Sizes and Variances: K-means assumes that clusters have roughly equal sizes and spherical shapes. This may not hold true for all types of data. It can struggle with identifying clusters with uneven densities, sizes, or shapes.
Impact of Outliers: K-means is sensitive to outliers, as a single outlier can significantly affect the position of cluster centroids. Outliers can lead to the creation of spurious clusters or distortion of the true cluster structure.
█ LIMITATIONS IN APPLICATION OF K MEANS IN TRADING
Trading data often exhibits characteristics that can pose challenges when applying indicators and analysis techniques. Here's how the limitations of outliers, varying scales, and unequal variance can impact the use of indicators in trading:
Outliers are data points that significantly deviate from the rest of the dataset. In trading, outliers can represent extreme price movements caused by rare events, news, or market anomalies. Outliers can have a significant impact on trading indicators and analyses:
Indicator Distortion: Outliers can skew the calculations of indicators, leading to misleading signals. For instance, a single extreme price spike could cause indicators like moving averages or RSI (Relative Strength Index) to give false signals.
Risk Management: Outliers can lead to overly aggressive trading decisions if not properly accounted for. Ignoring outliers might result in unexpected losses or missed opportunities to adjust trading strategies.
Different Scales: Trading data often includes multiple indicators with varying units and scales. For example, prices are typically in dollars, volume in units traded, and oscillators have their own scale. Mixing indicators with different scales can complicate analysis:
Normalization: Indicators on different scales need to be normalized or standardized to ensure they contribute equally to the analysis. Failure to do so can lead to one indicator dominating the analysis due to its larger magnitude.
Comparability: Without normalization, it's challenging to directly compare the significance of indicators. Some indicators might have a larger numerical range and could overshadow others.
Unequal Variance: Unequal variance in trading data refers to the fact that some indicators might exhibit higher volatility than others. This can impact the interpretation of signals and the performance of trading strategies:
Volatility Adjustment: When combining indicators with varying volatility, it's essential to adjust for their relative volatilities. Failure to do so might lead to overemphasizing or underestimating the importance of certain indicators in the trading strategy.
Risk Assessment: Unequal variance can impact risk assessment. Indicators with higher volatility might lead to riskier trading decisions if not properly taken into account.
█ APPLICATION OF THIS INDICATOR
This indicator can be used in 2 ways:
1) Make a directional trade:
If a trader thinks price will go higher or lower and price is within a cluster zone, The trader can take a position and place a stop on the 1 sd band around the cluster. As one can see below, the trader can go long the green arrow and place a stop on the one standard deviation mark for that cluster below it at the red arrow. using this we can calculate a risk to reward ratio.
Calculating risk to reward: targeting a risk reward ratio of 2:1, the trader could clearly make that given that the next resistance area above that in the orange cluster exceeds this risk reward ratio.
2) Take a reversal Trade:
We can use cluster centers (support and resistance levels) to go in the opposite direction that price is currently moving in hopes of price forming a pivot and reversing off this level.
Similar to the directional trade, we can use the standard deviation of the cluster to place a stop just in case we are wrong.
In this example below we can see that shorting on the red arrow and placing a stop at the one standard deviation above this cluster would give us a profitable trade with minimal risk.
Using the cluster density table in the upper right informs the trader just how dense the cluster is. Higher density clusters will give a higher likelihood of a pivot forming at these levels and price being rejected and switching direction with a larger move.
█ FEATURES & SETTINGS
General Settings:
Number of clusters: The user can select from 3 to five clusters. A good rule of thumb is that if you are trading intraday, less is more (Think 3 rather than 5). For daily 4 to 5 clusters is good.
Cluster Method: To get around the outlier limitation of k means clustering, The median was added. This gives the user the ability to choose either k means or k median clustering. K means is the preferred method if the user things there are no large outliers, and if there appears to be large outliers or it is assumed there are then K medians is preferred.
Bars back To train on: This will be the amount of bars to include in the clustering. This number is important so that the user includes bars that are recent but not so far back that they are out of the scope of where price can be. For example the last 2 years we have been in a range on the sp500 so 505 days in this setting would be more relevant than say looking back 5 years ago because price would have to move far to get there.
Show SD Bands: Select this to show the 1 standard deviation bands around the support and resistance level or unselect this to just show the support and resistance level by itself.
Features:
Besides the support and resistance levels and standard deviation bands, this indicator gives a table in the upper right hand corner to show the density of each cluster (support and resistance level) and is color coded to the cluster line on the chart. Higher density clusters mean price has been there previously more than lower density clusters and could mean a higher likelihood of a reversal when price reaches these areas.
█ WORKS CITED
Victor Sim, "Using K-means Clustering to Create Support and Resistance", 2020, towardsdatascience.com
Chris Piech, "K means", stanford.edu
█ ACKNOLWEDGMENTS
@jdehorty- Thanks for the publish template. It made organizing my thoughts and work alot easier.
Filtered Volume Profile [ChartPrime]The "Filtered Volume Profile" is a powerful tool that offers insights into market activity. It's a technical analysis tool used to understand the behavior of financial markets. It uses a fixed range volume profile to provide a histogram representing how much volume occurred at distinct price levels.
Profile in action with various significant levels displayed
How to Use
The script is designed to analyze cumulative trading volumes in different price bins over a certain period, also known as `'lookback'`. This lookback period can be defined by the user and it represents the number of bars to look back for calculating levels of support and resistance.
The `'Smoothing'` input determines the degree to which the output is smoothed. Higher values lead to smoother results but may impede the responsiveness of the indicator to rapid changes in volatility.
The `'Peak Sensitivity'` input is used to adjust the sensitivity of the script's peak detection algorithm. Setting this to a lower value makes the algorithm more sensitive to local changes in trading volume and may result in "noisier" outputs.
The `'Peak Threshold'` input specifies the number of bins that the peak detection mechanism should account for. Larger numbers imply that more volume bins are taken into account, and the resultant peaks are based on wider intervals.
The `'Mean Score Length'` input is used for scaling the mean score range. This is particularly important in defining the length of lookback bars that will be used to calculate the average close price.
Sinc Filter
The application of the sinc-filter to the Filtered Volume Profile reduces the risk of viewing artefacts that may misrepresent the underlying market behavior. Sinc filtering is a high-quality and sharp filter that doesn't manifest any ringing effects, making it an optimal choice for such volume profiling.
Histogram
On the histogram, the volume profile is colored based on the balance of bullish to bearish volume. If a particular bar is more intense in color, it represents a larger than usual volume during a single price bar. This is a clear signal of a strong buying or selling pressure at a particular price level.
Threshold for Peaks
The `peak_thresh` input determines the number of bins the algorithm takes in account for the peak detection feature. The 'peak' represents the level where a significant amount of volume trading has occurred, and usually is of interest as an indicative of support or resistance level.
By increasing the `peak_thresh`, you're raising the bar for what the algorithm perceives as a peak. This could result in fewer, but more significant peaks being identified.
History of Volume Profiles and Evolution into Sinc Filtering
Volume profiling has a rich history in market analysis, dating back to the 1950s when Richard D. Wyckoff, a legendary trader, introduced the concept of volume studies. He understood the critical significance of volume and its relationship with market price movement. The core of Wyckoff's technical analysis suite was the relationship between prices and volume, often termed as "Effort vs Results".
Moving forward, in the early 1800s, the esteemed mathematician J. R. Carson made key improvements to the sinc function, which formed the basis for sinc filtering application in time series data. Following these contributions, trading studies continued to create and integrate more advanced statistical measures into market analysis.
This culminated in the 1980s with J. Peter Steidlmayer’s introduction of Market Profile. He suggested that markets were a function of continuous two-way auction processes thus introducing the concept of viewing markets in price/time continuum and price distribution forms. Steidlmayer's Market Profile was the first wide-scale operation of organized volume and price data.
However, despite the introduction of such features, challenges in the analysis persisted, especially due to noise that could misinform trading decisions. This gap has given rise to the need for smoothing functions to help eliminate the noise and better interpret the data. Among such techniques, the sinc filter has become widely recognized within the trading community.
The sinc filter, because of its properties of constructing a smooth passing through all data points precisely and its ability to eliminate high-frequency noise, has been considered a natural transition in the evolution of volume profile strategies. The superior ability of the sinc filter to reduce noise and shield against over-fitting makes it an ideal choice for smoothing purposes in trading scripts, particularly where volume profiling forms the crux of the market analysis strategy, such as in Filtered Volume Profile.
Moving ahead, the use of volume-based studies seems likely to remain a core part of technical analysis. As long as markets operate based on supply and demand principles, understanding volume will remain key to discerning the intent behind price movements. And with the incorporation of advanced methods like sinc filtering, the accuracy and insight provided by these methodologies will only improve.
Mean Score
The mean score in the Filtered Volume Profile script plays an important role in probabilistic inferences regarding future price direction. This score essentially characterizes the statistical likelihood of price trends based on historical data.
The mean score is calculated over a configurable `'Mean Score Length'`. This variable sets the window or the timeframe for calculation of the mean score of the closing prices.
Statistically, this score takes advantage of the concept of z-scores and probabilities associated with the t-distribution (a type of probability distribution that is symmetric and bell-shaped, just like the standard normal distribution, but has heavier tails).
The z-score represents how many standard deviations an element is from the mean. In this case, the "element" is the price level (Point of Control).
The mean score section of the script calculates standard errors for the root mean squared error (RMSE) and addresses the uncertainty in the prediction of the future value of a random variable.
The RMSE of a model prediction concerning observed values is used to measure the differences between values predicted by a model and the values observed.
The lower the RMSE, the better the model is able to predict. A zero RMSE means a perfect fit to the data. In essence, it's a measure of how concentrated the data is around the line of best fit.
Through the mean score, the script effectively predicts the likelihood of the future close price being above or below our identified price level.
Summary
Filtered Volume Profile is a comprehensive trading view indicator which utilizes volume profiling, peak detection, mean score computations, and sinc-filter smoothing, altogether providing the finer details of market behavior.
It offers a customizable look back period, smoothing options, and peak sensitivity setting along with a uniquely set peak threshold. The application of the Sinc Filter ensures a high level of accuracy and noise reduction in volume profiling, making this script a reliable tool for gaining market insights.
Furthermore, the use of mean score calculations provides probabilistic insights into price movements, thus providing traders with a statistically sound foundation for their trading decisions. As trading markets advance, the use of such methodologies plays a pivotal role in formulating effective trading strategies and the Filtered Volume Profile is a successful embodiment of such advancements in the field of market analysis.
Any Screener (Multiple)I suppose it's time to publish something relatively useful :). Here's the first try, Any Screener.
This script is an advanced version of the Alphatrend - Screener that I've coded as a humble "thank you" to Kıvanç Özbilgiç (KivancOzbilgic), who always inspired me.
INTRODUCTION
I developed this version with a unique method because I couldn't find an example with the following features:
It presents the valid signal status of multiple indicators for 15 different symbols in the form of a report.
It indicates how many bars have passed after the signal has occurred.
It indicates the signal direction with dynamic colors and chars.
It can also be used for data (just indicator value) that is only intended to be displayed as text. (Default color is grey).
Long and short signals can optionally be ploted on the chart.
It includes advanced configuration settings.
USAGE OF PANEL
The screener panel is simple to use. On the far left, assets are listed. The names of the indicators appear at the top. In the column with the name of each indicator, the signals of that indicator appear as green or red. The green ones represent the long signals (uptrend) and the red ones represent the short signals (down trend). The numbers in square brackets indicate how many bars have passed after the last signal has occurred. (For example: According to the indicator at the top, when the green bullish triangle and 21 appeared on allign of BTCUSDT, Bitcoin switched to buy signal 21 bars ago. A tip : If the signal distance is 0, the signal occurred at the current bar. It is recommended to wait for the bar to close before entering the trade). Signal distance is an essential output for both manual and algorithmic trading. Users often require mentioned data the most during real time trading.
THE SCRIPT
There are two sections in the script; indicators and screener.
SECTION 1 : "INDICATORS"
In the indicator section, you'll find efficient details about switch methods, normalization, avoid pyramyding (in momentum oscillators) etc. On the other hand, I intended to present a "how to example" of a multiple screener, so it has to include more than one indicator.
OTT : Optimized Trend Tracker is developed by dear Anıl Özekşi, known as the "Old Fisherman" :). In my opinion, it is a pretty cool trend-following indicator that offers a mathematical elegance. This indicator aim to detect the current market trend direction, the indicator detect an up-trending market when the support line is superior to the OTT, and a down trending market when the support line is inferior to the OTT. It has three parameters; moving average type, length and percentage. In this version when the percentage parameter is set to 0.0, OTT turns into the selected moving average. And the signals are generated by the crossing of the closing price. It means, this screener is able to compile and present status of moving averages as well. Also VAR (VIDYA) and EVWMA has been re-designed, both moving averages no longer start at zero at the beginning of the chart (That was a big problem for backtests).
PSAR : J. Welles Wilder's Parabolic Stop And Reversal is an important trend following indicator. PSAR detects an up-trending market when below the market price and a down-trend when above. It can work in harmony with OTT according to the parameter combinations.
OSCILLATORS : Also optional three momentum oscillators have been added. MFI (Money Flow Index), RSI (Relative Strength Index) and STOCH (Stochastic %k). All three oscillators are widely used in markets and quite successful in explaining price movements by using different sources. Oscillators generate long and short signals based on oversold and overbought parameters.
VOLATILITY & TREND : There are three optional indicators. ADX (Average Directional Index), BBW-N (Normalized Bollinger Bandwidth) and REG-N (Normalized value of standard error of linear regression). These three indicators don't generate any long or short signals. Instead, they are used to measure the strength of trends and volatility. Therefore, only the numerical results (0-100) are displayed in screener panel and it is grey. (Note : The second length parameter of ADX has the same value with the first one. Bollinger Bandwith's multiplier is 2.0. REG-N is a variable that developed by Paul Kirshenbaum for Kirshenbaum Bands.)
SECTION 2 : "SCREENER"
The second section processes the main idea. This Screener model is based on generating an integer direction variable from boolean signals. The direction value serves multiple purposes: calculating the distance of signal, determining the color based on the direction, and creating "clean" data for the security function. The final step is to present the obtained data as text to the user.
HOW CAN I "SCREEN" MY CONDITIONS?
That's piece a cake, delete the Section 1 in the script :). If you change totally 11 variables according to your own strategy, you can create your new screener! The method is explained at lines 169-171.
SINCERELY THANKS
To allanster for patiently answering my primitive questions,
And to KivancOzbilgic for mind blowing suggestions (especially while we're drinking Raki) :)...
DISCLEIMER
This is just an indicator, nothing more. The script is for informational and educational purposes only. The use of the script does not constitute professional and/or financial advice. The responsibility for risks associated with the use of the script is solely owned by the user. Do not forget to manage your risk. And trade as safely as possible. Good luck!
Quinn-Fernandes Fourier Transform of Filtered Price [Loxx]Down the Rabbit Hole We Go: A Deep Dive into the Mysteries of Quinn-Fernandes Fast Fourier Transform and Hodrick-Prescott Filtering
In the ever-evolving landscape of financial markets, the ability to accurately identify and exploit underlying market patterns is of paramount importance. As market participants continuously search for innovative tools to gain an edge in their trading and investment strategies, advanced mathematical techniques, such as the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter, have emerged as powerful analytical tools. This comprehensive analysis aims to delve into the rich history and theoretical foundations of these techniques, exploring their applications in financial time series analysis, particularly in the context of a sophisticated trading indicator. Furthermore, we will critically assess the limitations and challenges associated with these transformative tools, while offering practical insights and recommendations for overcoming these hurdles to maximize their potential in the financial domain.
Our investigation will begin with a comprehensive examination of the origins and development of both the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter. We will trace their roots from classical Fourier analysis and time series smoothing to their modern-day adaptive iterations. We will elucidate the key concepts and mathematical underpinnings of these techniques and demonstrate how they are synergistically used in the context of the trading indicator under study.
As we progress, we will carefully consider the potential drawbacks and challenges associated with using the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter as integral components of a trading indicator. By providing a critical evaluation of their computational complexity, sensitivity to input parameters, assumptions about data stationarity, performance in noisy environments, and their nature as lagging indicators, we aim to offer a balanced and comprehensive understanding of these powerful analytical tools.
In conclusion, this in-depth analysis of the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter aims to provide a solid foundation for financial market participants seeking to harness the potential of these advanced techniques in their trading and investment strategies. By shedding light on their history, applications, and limitations, we hope to equip traders and investors with the knowledge and insights necessary to make informed decisions and, ultimately, achieve greater success in the highly competitive world of finance.
█ Fourier Transform and Hodrick-Prescott Filter in Financial Time Series Analysis
Financial time series analysis plays a crucial role in making informed decisions about investments and trading strategies. Among the various methods used in this domain, the Fourier Transform and the Hodrick-Prescott (HP) Filter have emerged as powerful techniques for processing and analyzing financial data. This section aims to provide a comprehensive understanding of these two methodologies, their significance in financial time series analysis, and their combined application to enhance trading strategies.
█ The Quinn-Fernandes Fourier Transform: History, Applications, and Use in Financial Time Series Analysis
The Quinn-Fernandes Fourier Transform is an advanced spectral estimation technique developed by John J. Quinn and Mauricio A. Fernandes in the early 1990s. It builds upon the classical Fourier Transform by introducing an adaptive approach that improves the identification of dominant frequencies in noisy signals. This section will explore the history of the Quinn-Fernandes Fourier Transform, its applications in various domains, and its specific use in financial time series analysis.
History of the Quinn-Fernandes Fourier Transform
The Quinn-Fernandes Fourier Transform was introduced in a 1993 paper titled "The Application of Adaptive Estimation to the Interpolation of Missing Values in Noisy Signals." In this paper, Quinn and Fernandes developed an adaptive spectral estimation algorithm to address the limitations of the classical Fourier Transform when analyzing noisy signals.
The classical Fourier Transform is a powerful mathematical tool that decomposes a function or a time series into a sum of sinusoids, making it easier to identify underlying patterns and trends. However, its performance can be negatively impacted by noise and missing data points, leading to inaccurate frequency identification.
Quinn and Fernandes sought to address these issues by developing an adaptive algorithm that could more accurately identify the dominant frequencies in a noisy signal, even when data points were missing. This adaptive algorithm, now known as the Quinn-Fernandes Fourier Transform, employs an iterative approach to refine the frequency estimates, ultimately resulting in improved spectral estimation.
Applications of the Quinn-Fernandes Fourier Transform
The Quinn-Fernandes Fourier Transform has found applications in various fields, including signal processing, telecommunications, geophysics, and biomedical engineering. Its ability to accurately identify dominant frequencies in noisy signals makes it a valuable tool for analyzing and interpreting data in these domains.
For example, in telecommunications, the Quinn-Fernandes Fourier Transform can be used to analyze the performance of communication systems and identify interference patterns. In geophysics, it can help detect and analyze seismic signals and vibrations, leading to improved understanding of geological processes. In biomedical engineering, the technique can be employed to analyze physiological signals, such as electrocardiograms, leading to more accurate diagnoses and better patient care.
Use of the Quinn-Fernandes Fourier Transform in Financial Time Series Analysis
In financial time series analysis, the Quinn-Fernandes Fourier Transform can be a powerful tool for isolating the dominant cycles and frequencies in asset price data. By more accurately identifying these critical cycles, traders can better understand the underlying dynamics of financial markets and develop more effective trading strategies.
The Quinn-Fernandes Fourier Transform is used in conjunction with the Hodrick-Prescott Filter, a technique that separates the underlying trend from the cyclical component in a time series. By first applying the Hodrick-Prescott Filter to the financial data, short-term fluctuations and noise are removed, resulting in a smoothed representation of the underlying trend. This smoothed data is then subjected to the Quinn-Fernandes Fourier Transform, allowing for more accurate identification of the dominant cycles and frequencies in the asset price data.
By employing the Quinn-Fernandes Fourier Transform in this manner, traders can gain a deeper understanding of the underlying dynamics of financial time series and develop more effective trading strategies. The enhanced knowledge of market cycles and frequencies can lead to improved risk management and ultimately, better investment performance.
The Quinn-Fernandes Fourier Transform is an advanced spectral estimation technique that has proven valuable in various domains, including financial time series analysis. Its adaptive approach to frequency identification addresses the limitations of the classical Fourier Transform when analyzing noisy signals, leading to more accurate and reliable analysis. By employing the Quinn-Fernandes Fourier Transform in financial time series analysis, traders can gain a deeper understanding of the underlying financial instrument.
Drawbacks to the Quinn-Fernandes algorithm
While the Quinn-Fernandes Fourier Transform is an effective tool for identifying dominant cycles and frequencies in financial time series, it is not without its drawbacks. Some of the limitations and challenges associated with this indicator include:
1. Computational complexity: The adaptive nature of the Quinn-Fernandes Fourier Transform requires iterative calculations, which can lead to increased computational complexity. This can be particularly challenging when analyzing large datasets or when the indicator is used in real-time trading environments.
2. Sensitivity to input parameters: The performance of the Quinn-Fernandes Fourier Transform is dependent on the choice of input parameters, such as the number of harmonic periods, frequency tolerance, and Hodrick-Prescott filter settings. Choosing inappropriate parameter values can lead to inaccurate frequency identification or reduced performance. Finding the optimal parameter settings can be challenging, and may require trial and error or a more sophisticated optimization process.
3. Assumption of stationary data: The Quinn-Fernandes Fourier Transform assumes that the underlying data is stationary, meaning that its statistical properties do not change over time. However, financial time series data is often non-stationary, with changing trends and volatility. This can limit the effectiveness of the indicator and may require additional preprocessing steps, such as detrending or differencing, to ensure the data meets the assumptions of the algorithm.
4. Limitations in noisy environments: Although the Quinn-Fernandes Fourier Transform is designed to handle noisy signals, its performance may still be negatively impacted by significant noise levels. In such cases, the identification of dominant frequencies may become less reliable, leading to suboptimal trading signals or strategies.
5. Lagging indicator: As with many technical analysis tools, the Quinn-Fernandes Fourier Transform is a lagging indicator, meaning that it is based on past data. While it can provide valuable insights into historical market dynamics, its ability to predict future price movements may be limited. This can result in false signals or late entries and exits, potentially reducing the effectiveness of trading strategies based on this indicator.
Despite these drawbacks, the Quinn-Fernandes Fourier Transform remains a valuable tool for financial time series analysis when used appropriately. By being aware of its limitations and adjusting input parameters or preprocessing steps as needed, traders can still benefit from its ability to identify dominant cycles and frequencies in financial data, and use this information to inform their trading strategies.
█ Deep-dive into the Hodrick-Prescott Fitler
The Hodrick-Prescott (HP) filter is a statistical tool used in economics and finance to separate a time series into two components: a trend component and a cyclical component. It is a powerful tool for identifying long-term trends in economic and financial data and is widely used by economists, central banks, and financial institutions around the world.
The HP filter was first introduced in the 1990s by economists Robert Hodrick and Edward Prescott. It is a simple, two-parameter filter that separates a time series into a trend component and a cyclical component. The trend component represents the long-term behavior of the data, while the cyclical component captures the shorter-term fluctuations around the trend.
The HP filter works by minimizing the following objective function:
Minimize: (Sum of Squared Deviations) + λ (Sum of Squared Second Differences)
Where:
1. The first term represents the deviation of the data from the trend.
2. The second term represents the smoothness of the trend.
3. λ is a smoothing parameter that determines the degree of smoothness of the trend.
The smoothing parameter λ is typically set to a value between 100 and 1600, depending on the frequency of the data. Higher values of λ lead to a smoother trend, while lower values lead to a more volatile trend.
The HP filter has several advantages over other smoothing techniques. It is a non-parametric method, meaning that it does not make any assumptions about the underlying distribution of the data. It also allows for easy comparison of trends across different time series and can be used with data of any frequency.
Another significant advantage of the HP Filter is its ability to adapt to changes in the underlying trend. This feature makes it particularly well-suited for analyzing financial time series, which often exhibit non-stationary behavior. By employing the HP Filter to smooth financial data, traders can more accurately identify and analyze the long-term trends that drive asset prices, ultimately leading to better-informed investment decisions.
However, the HP filter also has some limitations. It assumes that the trend is a smooth function, which may not be the case in some situations. It can also be sensitive to changes in the smoothing parameter λ, which may result in different trends for the same data. Additionally, the filter may produce unrealistic trends for very short time series.
Despite these limitations, the HP filter remains a valuable tool for analyzing economic and financial data. It is widely used by central banks and financial institutions to monitor long-term trends in the economy, and it can be used to identify turning points in the business cycle. The filter can also be used to analyze asset prices, exchange rates, and other financial variables.
The Hodrick-Prescott filter is a powerful tool for analyzing economic and financial data. It separates a time series into a trend component and a cyclical component, allowing for easy identification of long-term trends and turning points in the business cycle. While it has some limitations, it remains a valuable tool for economists, central banks, and financial institutions around the world.
█ Combined Application of Fourier Transform and Hodrick-Prescott Filter
The integration of the Fourier Transform and the Hodrick-Prescott Filter in financial time series analysis can offer several benefits. By first applying the HP Filter to the financial data, traders can remove short-term fluctuations and noise, effectively isolating the underlying trend. This smoothed data can then be subjected to the Fourier Transform, allowing for the identification of dominant cycles and frequencies with greater precision.
By combining these two powerful techniques, traders can gain a more comprehensive understanding of the underlying dynamics of financial time series. This enhanced knowledge can lead to the development of more effective trading strategies, better risk management, and ultimately, improved investment performance.
The Fourier Transform and the Hodrick-Prescott Filter are powerful tools for financial time series analysis. Each technique offers unique benefits, with the Fourier Transform being adept at identifying dominant cycles and frequencies, and the HP Filter excelling at isolating long-term trends from short-term noise. By combining these methodologies, traders can develop a deeper understanding of the underlying dynamics of financial time series, leading to more informed investment decisions and improved trading strategies. As the financial markets continue to evolve, the combined application of these techniques will undoubtedly remain an essential aspect of modern financial analysis.
█ Features
Endpointed and Non-repainting
This is an endpointed and non-repainting indicator. These are crucial factors that contribute to its usefulness and reliability in trading and investment strategies. Let us break down these concepts and discuss why they matter in the context of a financial indicator.
1. Endpoint nature: An endpoint indicator uses the most recent data points to calculate its values, ensuring that the output is timely and reflective of the current market conditions. This is in contrast to non-endpoint indicators, which may use earlier data points in their calculations, potentially leading to less timely or less relevant results. By utilizing the most recent data available, the endpoint nature of this indicator ensures that it remains up-to-date and relevant, providing traders and investors with valuable and actionable insights into the market dynamics.
2. Non-repainting characteristic: A non-repainting indicator is one that does not change its values or signals after they have been generated. This means that once a signal or a value has been plotted on the chart, it will remain there, and future data will not affect it. This is crucial for traders and investors, as it offers a sense of consistency and certainty when making decisions based on the indicator's output.
Repainting indicators, on the other hand, can change their values or signals as new data comes in, effectively "repainting" the past. This can be problematic for several reasons:
a. Misleading results: Repainting indicators can create the illusion of a highly accurate or successful trading system when backtesting, as the indicator may adapt its past signals to fit the historical price data. This can lead to overly optimistic performance results that may not hold up in real-time trading.
b. Decision-making uncertainty: When an indicator repaints, it becomes challenging for traders and investors to trust its signals, as the signal that prompted a trade may change or disappear after the fact. This can create confusion and indecision, making it difficult to execute a consistent trading strategy.
The endpoint and non-repainting characteristics of this indicator contribute to its overall reliability and effectiveness as a tool for trading and investment decision-making. By providing timely and consistent information, this indicator helps traders and investors make well-informed decisions that are less likely to be influenced by misleading or shifting data.
Inputs
Source: This input determines the source of the price data to be used for the calculations. Users can select from options like closing price, opening price, high, low, etc., based on their preferences. Changing the source of the price data (e.g., from closing price to opening price) will alter the base data used for calculations, which may lead to different patterns and cycles being identified.
Calculation Bars: This input represents the number of past bars used for the calculation. A higher value will use more historical data for the analysis, while a lower value will focus on more recent price data. Increasing the number of past bars used for calculation will incorporate more historical data into the analysis. This may lead to a more comprehensive understanding of long-term trends but could also result in a slower response to recent price changes. Decreasing this value will focus more on recent data, potentially making the indicator more responsive to short-term fluctuations.
Harmonic Period: This input represents the harmonic period, which is the number of harmonics used in the Fourier Transform. A higher value will result in more harmonics being used, potentially capturing more complex cycles in the price data. Increasing the harmonic period will include more harmonics in the Fourier Transform, potentially capturing more complex cycles in the price data. However, this may also introduce more noise and make it harder to identify clear patterns. Decreasing this value will focus on simpler cycles and may make the analysis clearer, but it might miss out on more complex patterns.
Frequency Tolerance: This input represents the frequency tolerance, which determines how close the frequencies of the harmonics must be to be considered part of the same cycle. A higher value will allow for more variation between harmonics, while a lower value will require the frequencies to be more similar. Increasing the frequency tolerance will allow for more variation between harmonics, potentially capturing a broader range of cycles. However, this may also introduce noise and make it more difficult to identify clear patterns. Decreasing this value will require the frequencies to be more similar, potentially making the analysis clearer, but it might miss out on some cycles.
Number of Bars to Render: This input determines the number of bars to render on the chart. A higher value will result in more historical data being displayed, but it may also slow down the computation due to the increased amount of data being processed. Increasing the number of bars to render on the chart will display more historical data, providing a broader context for the analysis. However, this may also slow down the computation due to the increased amount of data being processed. Decreasing this value will speed up the computation, but it will provide less historical context for the analysis.
Smoothing Mode: This input allows the user to choose between two smoothing modes for the source price data: no smoothing or Hodrick-Prescott (HP) smoothing. The choice depends on the user's preference for how the price data should be processed before the Fourier Transform is applied. Choosing between no smoothing and Hodrick-Prescott (HP) smoothing will affect the preprocessing of the price data. Using HP smoothing will remove some of the short-term fluctuations from the data, potentially making the analysis clearer and more focused on longer-term trends. Not using smoothing will retain the original price fluctuations, which may provide more detail but also introduce noise into the analysis.
Hodrick-Prescott Filter Period: This input represents the Hodrick-Prescott filter period, which is used if the user chooses to apply HP smoothing to the price data. A higher value will result in a smoother curve, while a lower value will retain more of the original price fluctuations. Increasing the Hodrick-Prescott filter period will result in a smoother curve for the price data, emphasizing longer-term trends and minimizing short-term fluctuations. Decreasing this value will retain more of the original price fluctuations, potentially providing more detail but also introducing noise into the analysis.
Alets and signals
This indicator featues alerts, signals and bar coloring. You have to option to turn these on/off in the settings menu.
Maximum Bars Restriction
This indicator requires a large amount of processing power to render on the chart. To reduce overhead, the setting "Number of Bars to Render" is set to 500 bars. You can adjust this to you liking.
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Advanced VWAP_Pullback Strategy_Trend-Template QualifierGeneral Description and Unique Features of this Script
Introducing the Advanced VWAP Momentum-Pullback Strategy (long-only) that offers several unique features:
1. Our script/strategy utilizes Mark Minervini's Trend-Template as a qualifier for identifying stocks and other financial securities in confirmed uptrends. Mark Minervini, a 2x US Investment Champion, developed the Trend-Template, which covers eight different and independent characteristics that can be adjusted and optimized in this trend-following strategy to ensure the best results. The strategy will only trigger buy-signals in case the optimized qualifiers are being met.
2. Our strategy is based on the supply/demand balance in the market, making it timeless and effective across all timeframes. Whether you are day trading using 1- or 5-min charts or swing-trading using daily charts, this strategy can be applied and works very well.
3. We have also integrated technical indicators such as the RSI and the MA / VWAP crossover into this strategy to identify low-risk pullback entries in the context of confirmed uptrends. By doing so, the risk profile of this strategy and drawdowns are being reduced to an absolute minimum.
Minervini’s Trend-Template and the ‘Stage-Analysis’ of the Markets
This strategy is a so-called 'long-only' strategy. This means that we only take long positions, short positions are not considered.
The best market environment for such strategies are periods of stable upward trends in the so-called stage 2 - uptrend.
In stable upward trends, we increase our market exposure and risk.
In sideways markets and downward trends or bear markets, we reduce our exposure very quickly or go 100% to cash and wait for the markets to recover and improve. This allows us to avoid major losses and drawdowns.
This simple rule gives us a significant advantage over most undisciplined traders and amateurs!
'The Trend is your Friend'. This is a very old but true quote.
What's behind it???
• 98% of stocks made their biggest gains in a Phase 2 upward trend.
• If a stock is in a stable uptrend, this is evidence that larger institutions are buying the stock sustainably.
• By focusing on stocks that are in a stable uptrend, the chances of profit are significantly increased.
• In a stable uptrend, investors know exactly what to expect from further price developments. This makes it possible to locate low-risk entry points.
The goal is not to buy at the lowest price – the goal is to buy at the right price!
Each stock goes through the same maturity cycle – it starts at stage 1 and ends at stage 4
Stage 1 – Neglect Phase – Consolidation
Stage 2 – Progressive Phase – Accumulation
Stage 3 – Topping Phase – Distribution
Stage 4 – Downtrend – Capitulation
This strategy focuses on identifying stocks in confirmed stage 2 uptrends. This in itself gives us an advantage over long-term investors and less professional traders.
By focusing on stocks in a stage 2 uptrend, we avoid losses in downtrends (stage 4) or less profitable consolidation phases (stages 1 and 3). We are fully invested and put our money to work for us, and we are fully invested when stocks are in their stage 2 uptrends.
But how can we use technical chart analysis to find stocks that are in a stable stage 2 uptrend?
Mark Minervini has developed the so-called 'trend template' for this purpose. This is an essential part of our JS-TechTrading pullback strategy. For our watchlists, only those individual values that meet the tough requirements of Minervini's trend template are eligible.
The Trend Template
• 200d MA increasing over a period of at least 1 month, better 4-5 months or longer
• 150d MA above 200d MA
• 50d MA above 150d MA and 200d MA
• Course above 50d MA, 150d MA and 200d MA
• Ideally, the 50d MA is increasing over at least 1 month
• Price at least 25% above the 52w low
• Price within 25% of 52w high
• High relative strength according to IBD.
NOTE: In this basic version of the script, the Trend-Template has to be used as a separate indicator on TradingView (Public Trend-Template indicators are available in TradingView – community scripts). It is recommended to only execute buy signals in case the stock or financial security is in a stage 2 uptrend, which means that the criteria of the trend-template are fulfilled.
This strategy can be applied to all timeframes from 5 min to daily.
The VWAP Momentum-Pullback Strategy
For the JS-TechTrading VWAP Momentum-Pullback Strategy, only stocks and other financial instruments that meet the selected criteria of Mark Minervini's trend template are recommended for algorithmic trading with this startegy.
A further prerequisite for generating a buy signals is that the individual value is in a short-term oversold state (RSI).
When the selling pressure is over and the continuation of the uptrend can be confirmed by the MA / VWAP crossover after reaching a price low, a buy signal is issued by this strategy.
Stop-loss limits and profit targets can be set variably. You also have the option to make use of the trailing stop exit strategy.
Relative Strength Index (RSI)
The Relative Strength Index (RSI) is a technical indicator developed by Welles Wilder in 1978. The RSI is used to perform a market value analysis and identify the strength of a trend as well as overbought and oversold conditions. The indicator is calculated on a scale from 0 to 100 and shows how much an asset has risen or fallen relative to its own price in recent periods.
The RSI is calculated as the ratio of average profits to average losses over a certain period of time. A high value of the RSI indicates an overbought situation, while a low value indicates an oversold situation. Typically, a value > 70 is considered an overbought threshold and a value < 30 is considered an oversold threshold. A value above 70 signals that a single value may be overvalued and a decrease in price is likely , while a value below 30 signals that a single value may be undervalued and an increase in price is likely.
For example, let's say you're watching a stock XYZ. After a prolonged falling movement, the RSI value of this stock has fallen to 26. This means that the stock is oversold and that it is time for a potential recovery. Therefore, a trader might decide to buy this stock in the hope that it will rise again soon.
The MA / VWAP Crossover Trading Strategy
This strategy combines two popular technical indicators: the Moving Average (MA) and the Volume Weighted Average Price (VWAP). The MA VWAP crossover strategy is used to identify potential trend reversals and entry/exit points in the market.
The VWAP is calculated by taking the average price of an asset for a given period, weighted by the volume traded at each price level. The MA, on the other hand, is calculated by taking the average price of an asset over a specified number of periods. When the MA crosses above the VWAP, it suggests that buying pressure is increasing, and it may be a good time to enter a long position. When the MA crosses below the VWAP, it suggests that selling pressure is increasing, and it may be a good time to exit a long position or enter a short position.
Traders typically use the MA VWAP crossover strategy in conjunction with other technical indicators and fundamental analysis to make more informed trading decisions. As with any trading strategy, it is important to carefully consider the risks and potential rewards before making any trades.
This strategy is applicable to all timeframes and the relevant parameters for the underlying indicators (RSI and MA/VWAP) can be adjusted and optimized as needed.
Backtesting
Backtesting gives outstanding results on all timeframes and drawdowns can be reduced to a minimum level. In this example, the hourly chart for MCFT has been used.
Settings for backtesting are:
- Period from Jan 2020 until March 2023
- Starting capital 100k USD
- Position size = 25% of equity
- 0.01% commission = USD 2.50.- per Trade
- Slippage = 2 ticks
Other comments
- This strategy has been designed to identify the most promising, highest probability entries and trades for each stock or other financial security.
- The combination of the Trend-Template and the RSI qualifiers results in a highly selective strategy which only considers the most promising swing-trading entries. As a result, you will normally only find a low number of trades for each stock or other financial security per year in case you apply this strategy for the daily charts. Shorter timeframes will result in a higher number of trades / year.
- Consequently, traders need to apply this strategy for a full watchlist rather than just one financial security.
