Point and Figure (PnF) ChartThis is live and non-repainting Point and Figure Charting tool. The tool has it’s own P&F engine and not using integrated function of Trading View.
Point and Figure method is over 150 years old. It consist of columns that represent filtered price movements. Time is not a factor on P&F chart but as you can see with this script P&F chart created on time chart.
P&F chart provide several advantages, some of them are filtering insignificant price movements and noise, focusing on important price movements and making support/resistance levels much easier to identify.
If you are new to Point & Figure Chart then you better get some information about it before using this tool. There are very good web sites and books. Please PM me if you need help about resources.
Options in the Script
Box size is one of the most important part of Point and Figure Charting. Chart price movement sensitivity is determined by the Point and Figure scale. Large box sizes see little movement across a specific price region, small box sizes see greater price movement on P&F chart. There are four different box scaling with this tool: Traditional, Percentage, Dynamic (ATR), or User-Defined
4 different methods for Box size can be used in this tool.
User Defined: The box size is set by user. A larger box size will result in more filtered price movements and fewer reversals. A smaller box size will result in less filtered price movements and more reversals.
ATR: Box size is dynamically calculated by using ATR, default period is 20.
Percentage: uses box sizes that are a fixed percentage of the stock's price. If percentage is 1 and stock’s price is $100 then box size will be $1
Traditional: uses a predefined table of price ranges to determine what the box size should be.
Price Range Box Size
Under 0.25 0.0625
0.25 to 1.00 0.125
1.00 to 5.00 0.25
5.00 to 20.00 0.50
20.00 to 100 1.0
100 to 200 2.0
200 to 500 4.0
500 to 1000 5.0
1000 to 25000 50.0
25000 and up 500.0
Default value is “ATR”, you may use one of these scaling method that suits your trading strategy.
If ATR or Percentage is chosen then there is rounding algorithm according to mintick value of the security. For example if mintick value is 0.001 and box size (ATR/Percentage) is 0.00124 then box size becomes 0.001.
And also while using dynamic box size (ATR or Percentage), box size changes only when closing price changed.
Reversal : It is the number of boxes required to change from a column of Xs to a column of Os or from a column of Os to a column of Xs. Default value is 3 (most used). For example if you choose reversal = 2 then you get the chart similar to Renko chart.
Source: Closing price or High-Low prices can be chosen as data source for P&F charting.
Chart Style: There are 3 options for chart style: “Candle”, “Area” or “Don’t show”.
As Area:
As Candle:
X/O Column Style: it can show all columns from opening price or only last Xs/Os.
Color Theme: different themes exist => Green/Red, Yellow/Blue, White/Yellow, Orange/Blue, Lime/Red, Blue/Red
Show Breakouts is the option to show Breakouts
This tool detects & shows following Breakouts:
Triple Top/Bottom,
Triple Top Ascending,
Triple Bottom Descending,
Simple Buy/Sell (Double Top/Bottom),
Simple Buy With Rising Bottom,
Simple Sell With Declining Top
Catapult bullish/bearish
Show Horizontal Count Targets: Finds the congestion or consolidation pattern and if there is breakout then it calculates the Target by using Horizontal Count method (based on the width of congestion pattern). It shows how many column exist on congestion area. There is no guarantee that prices will reach the target.
Show Vertical Count Targets: When Triple Top/Bottom Breakouts occured the script calculates the target by using Vertical Count Method (based on the length of the column). There is no guarantee that prices will reach the target.
For both methods there is auto target cancellation if price goes below congestion bottom or above congestion top.
trend is calculated by EMA of closing price of the P&F
Whipsaw protection:
Last options are “Show info panel” and Labeling Offset. Script shows current box size, reversal, and recommanded minimum and maximum box size. And also it shows the price level to reverse the column (Xs <-> Os) and the price level to add at least 1 more box to column. This is the option to put these labels 10, 20, 30, 50 or 100 bars away from the last bar. Labeling content and color change according to X/O column.
do not hesitate to comment.
Tìm kiếm tập lệnh với "马斯克+100万"
Technical Analysis - Panel Info//A. Oscillators & B. Moving Averages base on TradingView's Technical Analysis by ThiagoSchmitz
//C.Pivot base on Ultimate Pivot Points Alerts by elbartt
//D. Summary & Panel info by anhnguyen14
Panel Info base on these indicators:
A. Oscillators
1. Rsi (14)
2. Stochastic (14,3,3)
3. CCI (20)
4. ADX (14)
5. AO
6. Momentum (10)
7. MACD (12,26)
8. Stoch RSI (3,3,14,14)
9. %R (14)
10. Bull bear
11. UO (7,14,28)
B. Moving Averages
1. SMA & EMA: 5-10-20-30-50-100-200
2. Ichimoku Cloud - Baseline (26)
3. Hull MA (9)
C. Pivot
1. Traditional
2. Fibonacci
3. Woodie
4. Camarilla
D. Summary
Sum_red=A_red+B_red+C_red
Sum_blue=A_blue+B_blue+C_blue
sell_point=(Sum_red/32)*100
buy_point=(Sum_blue/32)*100
sell =
Sum_red>Sum_blue
and sell_point>50
Strong_sell =
A_red>A_blue
and B_red>B_blue
and C_red>C_blue
and sell_point>50
and not crossunder(sell_point,75)
buy =
Sum_red>Sum_blue
and buy_point>50
Strong_buy =
A_red50
and not crossunder(buy_point,75)
neutral = not sell and not Strong_sell and not buy and not Strong_buy
CCI RiderThis is my thank you to the TradingView community, for the people who are sharing their scripts, which allowed me to learn Pine Script.
So here is my first creation, feel free to experiment, modify and use it as you wish.
It is a CCI(default value is 100, can be changed), combined with an EMA of that CCI(default 21,changeable) that then colors the background according to the strength of the signal(if selected to do so).
To generate strong signals, it also uses Bollinger Bands to prevent whipsaws in high volatility situations.
The best signals are generated when the CCI crosses the limits set by the user (default is 100/-100), and is above/belov its EMA.
Exit signals are indicated, when the CCI crosses its EMA.
Unfortunately in strong trends, this exit signal is sometimes premature, using a 3x resolution of the indicator will improve this, maybe I will implement this in a later version.
I use it mostly in 15min charts and higher, I found in shorter timeframes still a lot of whipsaws, maybe experimenting with different lengths and levels will improve this.
As the Indicator allows the user to experiment with different lenghts and levels, and the colors will change according the setting, I find it a nice tool to search for the best mixture for different securities and timeframes.
See below an example of a nice signal.
I do suggest to use it in combination with other indicators.
Yield Curve Version 2.55.2Welcome to Yield Curve Version 2.55.2
US10Y-US02Y
* Please read description to help understand the information displayed.
* NOTE - This script requires 1 real time update before accurate information is displayed, therefore WILL NOT display the correct information if the Bond Market is Closed over the Weekend.
* NOTE - When values are changed Via Input setting they do take a bit to display based off all the information that is required to display this script.
**FEATURES**
* Input Features let you view the information the way YOU like via Input Settings
* Displays Current Version Title - Toggleable On/Off via Input Settings - Default On
* Plots the Yield Curve of the Bonds listed (Middle Green and Red Line)
* Displays the Spread for each Bond (Top Green and Red Labels) - Toggleable On/Off via Input Settings - Change Size via Input Settings - Default On
* Displays the current Yield for each Bond (Bottom Green and Red Labels) - Toggleable On/Off via Input Settings - Change Size via Input Settings - Default On - Large Size
* Plots the Average of the Entire Yield Curve (BLUE Line within the Yield Curve) - Toggleable On/Off via Input Settings - Default On
* Displays messages based off Yield Inversions (Orange Text) - Toggleable On/Off via Input Settings - Default On if Applicable
* Displays 2 10 Inversion Warning Message (Orange Text) - Toggleable On/Off via Input Settings - Default On if Applicable
* Plots Column Data at the Bottom that tries to help determine the Stability of the Yield Curve (More information Below about Stability) - Toggleable On/Off via Input Settings - Default On
* Plots the 7,20 and 100 SMA of the STABILITY MAX OVERLOAD (More information Below about Stability Max Overload) - Toggleable On/Off via Input Settings - Default On for 100 SMA , 20 SMA and 7 SMA
* Ability to Display Indicator Name and Value via Input Settings - Default On - Displays Stability Max Overload SMA Labels. Toggleable to Non SMA Values. See Below.
**Bottom Columns are all about STABILITY**
* I have tried to come up with an algorithm that helps understand the Stability of the Yield Curve. There are 3 Sections to the Bottom Columns.
* Section 1 - STABILITY (Displayed as the lightest Green or Red Column) Values range from 0 to 1 where 1 equals the MOST UNSTABLE Curve and 0 equals the MOST STABLE Curve
* Section 2 - STABILITY OVERLOAD (Displayed just above the Stability Column a shade darker Green or Red Column)
* Section 3 - STABILITY MAX OVERLOAD (Displayed just above the Stability Overload Column a shade darker Green or Red Column)
What this section tries to do is help understand the Stability of the Curve based on the inversions data. Lower values represent a MORE STABLE curve. If the Yield Curve currently has 0 Inversions all Stability factors should equal 0 and therefore not plot any lower columns. As the Yield Curve becomes more inverted each section represents a value based off that data. GREEN columns represent a MORE Stable Curve from the resolution prior and vise versa.
(S SO SMO)
STABILITY - tests the current Stability of the Curve itself again ranging from 0 to 1 where 0 equals the MOST Stable Curve and 1 equals the MOST Unstable Curve.
STABILIY OVERLOAD - adds a value to STABLITY based off STABILITY itself.
STABILITY MAX OVERLOAD - adds the Entire value to STABILITY derived again from STABILITY.
This section also allows us to see the 7,20 and 100 SMA of the STABILITY MAX OVERLOAD which should always be the GREATEST of ALL STABILTY VALUES.
*Indicator Labels How to use*
Indicator Labels by default are turned On and will display Name and Value Labels for Stability Max Overload SMA values. To switch to (S SO SMO) Labels, toggle "Indicator Labels / SMO SMA Labels", via Input Settings. This button allows you to switch between the two Indicator Label Display options. You must have "Indicators" turned On to view the Labels and therefore is turned On by Default. To turn all of the Indicator Labels Off, simply disable "Indicators" via Input Settings.
Remember - All information displayed can be tuned On or Off besides the Curve itself. There are also other Features Accessible Via the Input Settings.
I will continue to update this script as there is more information I would like to gather and display!
I hope you enjoy,
OpptionsOnly
Ultimate Moving Average Package (17 MA's)Included is the:
VWAP
Current time frame 10 EMA
Current time frame 20 EMA
Current time frame 50 EMA
Current time frame 10 SMA
Current time frame 20 SMA
Current time frame 50 SMA
Daily 10 EMA
Daily 20 EMA
Daily 50 EMA
Daily 50 SMA
Daily 100 SMA
Daily 200 SMA
Weekly 100 SMA
Weekly 200 SMA
Monthly 100 SMA
Monthly 200 SMA
All Daily/Weekly/Monthly MA's can be seen on intraday charts. Current time frame MA's change depending on your time frame. Obviously you dont need all 17 on your chart but you can pick the ones you like and disable the rest.
Bilateral Stochastic Oscillator - For The Sake Of EfficiencyIntroduction
The stochastic oscillator is a feature scaling method commonly used in technical analysis, this method is the same as the running min-max normalization method except that the stochastic oscillator is in a range of (0,100) while min-max normalization is in a range of (0,1). The stochastic oscillator in itself is efficient since it tell's us when the price reached its highest/lowest or crossed this average, however there could be ways to further develop the stochastic oscillator, this is why i propose this new indicator that aim to show all the information a classical stochastic oscillator would give with some additional features.
Min-Max Derivation
The min-max normalization of the price is calculated as follow : (price - min)/(max - min) , this calculation is efficient but there is alternates forms such as :
price - (max - min) - min/(max - min)
This alternate form is the one i chosen to make the indicator except that both range (max - min) are smoothed with a simple moving average, there are also additional modifications that you can see on the code.
The Indicator
The indicator return two main lines, in blue the bull line who show the buying force and in red the bear line who show the selling force.
An orange line show the signal line who represent the moving average of the max(bull,bear), this line aim to show possible exit/reversals points for the current trend.
Length control the highest/lowest period as well as the smoothing amount, signal length control the moving average period of the signal line, the pre-filtering setting indicate which smoothing method will be used to smooth the input source before applying normalization.
The default pre-filtering method is the sma.
The ema method is slightly faster as you can see above.
The triangular moving average is the moving average of another moving average, the impulse response of this filter is a triangular function hence its name. This moving average is really smooth.
The lsma or least squares moving average is the fastest moving average used in this indicator, this filter try to best fit a linear function to the data in a certain window by using the least squares method.
No filtering will use the source price without prior smoothing for the indicator calculation.
Relationship With The Stochastic Oscillator
The crosses between the bull and bear line mean that the stochastic oscillator crossed the 50 level. When the Bull line is equal to 0 this mean that the stochastic oscillator is equal to 0 while a bear line equal to 0 mean a stochastic oscillator equal to 100.
The indicator and below a stochastic oscillator of both period 100
Using Levels
Unlike a stochastic oscillator who would clip at the 0 and 100 level the proposed indicator is not heavily constrained in a range like the stochastic oscillator, this mean that you can apply levels to trigger signals
Possible levels could be 1,2,3... even if the indicator rarely go over 3.
Its then possible to create strategies using such levels as support or resistance one.
Conclusion
I've showed a modified stochastic oscillator who aim to show additional information to the user while keeping all the information a classical stochastic oscillator would give. The proposed indicator is no longer constrained in an hard range and posses more liberty to exploit its scale which in return allow to create strategies based on levels.
For pinescript users what you can learn from this is that alternates forms of specific formulas can be extremely interesting to modify, changes can be really surprising so if you are feeling stuck, modifying alternates forms of know indicators can give great results, use tools such as sympy gamma to get alternates forms of formulas.
Thanks for reading !
If you are looking for something or just want to say thanks try to pm me :)
High/Low bandsGives good idea about trend.
In last 100 days the lowest price was this.
In last 100 days the highest price was this.
Price makes new 100 days high! (uptrend)
Chaikin MF% (CMFP) w. Alerts, Bells & Whistles [LucF]This is Chaikin’s Money Flow indicator on a 0-100 scale with buy/sell signals, alerts and other bells & whistles.
It includes:
- a fast EMA (16 periods by default),
- a slow MA (64 periods by default),
- histograms,
- 3 different sorts of crosses,
- big swings identification,
- buy/sell signals on CMFP crossing back from outside user-defined levels,
- buy/sell signals on the slow MA pivots above/below user-defined levels,
- alerts on big swings and buy/sells.
This indicator started with @LazyBear code (VAPI) at:
@cI8DH then changed the scale to 0-100, which I find very useful:
I then added the rest.
The chart above shows both clean and busy versions of the indicator.
Note that the default length is 10 rather than the commonly used 20. I use CMFP in conjunction with VFI and like the fact that it is faster than VFI. The default inputs show the way I normally use this indicator, with the slow MA shown in histogram mode. I find it gives good context to the signal line. Crosses between the two are often useful.
The buy/sell signals aren’t the main attraction of this indicator, and nothing to write home about. Like the big swing markers, I think it’s more realistic to view them as pointers to potentially interesting areas on charts. Their nature makes them more suited to identifying reversals. They certainly aren’t reliable enough to turn this study into a strategy and I normally don’t use them. The levels pre-defined for the buy/sell signals on CMFP are most useful on short intervals. The buy/sell signals on the slow MA pivots work on a more complete range of intervals. Optimization for your specific instruments and intervals will improve their reliability.
As usual when defining alerts, be sure you already have defined proper inputs and that you are on the intended interval, as they will be used when triggering alerts.
3 of SlowStochastics
스토캐스틱 3개를 한번에 볼수 있습니다. 천장과 바닥은 각 100의 위치마다 존재합니다
You can see three slow stochastics at once. The ceiling and floor are located at each 100 (0 - 100 - 200- 300)
Percentage Price Oscillator (PPO)The Percentage Price Oscillator (PPO) is a momentum oscillator that measures the difference between two moving averages as a percentage of the larger moving average. As with its cousin, MACD, the Percentage Price Oscillator is shown with a signal line, a histogram and a centerline. Signals are generated with signal line crossovers, centerline crossovers, and divergences. First, PPO readings are not subject to the price level of the security. Second, PPO readings for different securities can be compared, even when there are large differences in the price.
Calculations
PPO: {(12-day EMA - 26-day EMA)/26-day EMA} x 100
Signal Line: 9-day EMA of PPO
PPO Histogram: PPO - Signal Line
While MACD measures the absolute difference between two moving averages, PPO makes this a relative value by dividing the difference by the slower moving average (26-day EMA). PPO is simply the MACD value divided by the longer moving average. The result is multiplied by 100 to move the decimal place two spots.
Interpretation
As with MACD, the PPO reflects the convergence and divergence of two moving averages. PPO is positive when the shorter moving average is above the longer moving average. The indicator moves further into positive territory as the shorter moving average distances itself from the longer moving average. This reflects strong upside momentum. The PPO is negative when the shorter moving average is below the longer moving average. Negative readings grow when the shorter moving average distances itself from the longer moving average (goes further negative). This reflects strong downside momentum. The histogram represents the difference between PPO and its 9-day EMA, the signal line. The histogram is positive when PPO is above its 9-day EMA and negative when PPO is below its 9-day EMA. The PPO-Histogram can be used to anticipate signal line crossovers in the PPO.
MACD, PPO and Price
MACD levels are affected by the price of a security. A high-priced security will have higher or lower MACD values than a low-priced security, even if volatility is basically equal. This is because MACD is based on the absolute difference in the two moving averages. Because MACD is based on absolute levels, large price changes can affect MACD levels over an extended period of time. If a stock advances from 20 to 100, its MACD levels will be considerably smaller around 20 than around 100. The PPO solves this problem by showing MACD values in percentage terms.
Conclusions
The Percentage Price Oscillator (PPO) generates the same signals as the MACD, but provides an added dimension as a percentage version of MACD. The PPO levels of the Dow Industrials (price > 20K) can be compared against the PPO levels of IBM (price < 200) because the PPO “levels” the playing field. In addition, PPO levels in one security can be compared over extended periods of time, even if the price has doubled or tripled. This is not the case for the MACD.
Limitations
Despite its advantages, the PPO is still not the best oscillator to identify overbought or oversold conditions because movements are unlimited (in theory). Levels for RSI and the Stochastic Oscillator are limited and this makes them better suited to identify overbought and oversold levels.
Source: Stockcharts
Multiple Moving AveragesThis is really simple. But useful for me as I don't have a paid account. No-pro users can only use 3 indicators at once and because I rely heavily on simple moving averages it can be a real pain.
This one indicator features:
20 MA
50 MA
100 MA
200 MA
which I find are the most useful overall. The 20 and 50 over all time frame but in particular < 1 day, the 100 and 200 at > 4 hr time frames. In general I don't use the 100 MA that much. The daily 200 MA is a critical support for many assets like stocks and cryptos. I'm by no means a pro and if you are learning I recommend becoming familiar with moving averages right at the beginning.
If you want to deactivate some of the lines, you can do it via the indicator's settings icon.
Exponential Moving Average (Set of 3) [Krypt] + 13/34 EMAsI took Krypt's script and essentially added on to it.
the 20/50/100/200 EMAs should be used together as support and resistance as normal.
Wait for price to break 200 EMA
Wait for 50 EMA to cross 200 EMA
Wait for pullback to 50 EMA to open position
20 and 100 EMAs are for extra information about moving support and resistance
and 13/34 EMAs should be used in conjunction
When 13 EMA crosses 34 EMA, open position
When price gets far from 13/34, close position (because price will attempt to revert back to mean)
This is better for scalping and swing trades than the 20/50/100/200 setup.
Twitter: @AzorAhai06
Ichimoku Cloud Score v1.0This script calculates a simple Ichimoku Score based on the signals documented here , with a few additions. Each of the score components can be individually weighted via the script inputs . The output is a plot of the normalized Ichimoku score, in the range of -100 to 100.
This script has been heavily modified from 'Ichimoku Cloud Signal Score v2.0.0 '. Credit to user 'dashed' for the initial implementation.
This has been modified with several refinements:
Clean/Organized Code
Simplified Inputs
Improved Style
Scores normalized to a range (-100, 100)
Bugfixes and Improvements
Script Inputs: i.imgur.com
Volume RatioDefinition:
Volume ratio can be obtained in a similar way to RSI.
Volume Ratio (%) = 100 - 100/(1+vr)
The parameter "vr" is defined as
vr=(A+U/2)/(D+U/2)
A=Total volume of the periods when the price advanced
D=Total volume of the periods when the price declined
U=Total volume of the periods when the price unchanged
After substitution, following expression can be derived and the denominator represents total volume of all periods.
Volume Ratio (%) = 100 x (A+U/2)/(A+D+U)
Notes:
A similar method to interpret RSI can be employed.
1) Overbought level over 70% and oversold level under 30%. These levels need to be adjusted according to the periods, time frames and issues.
2) Bullish picture over 50% line and bearish picture under 50% line.
3) Crossing oversold level to the upside can be taken as a confirmation of bullish reversal. - and vice versa for a bearish reversal.
4) After a long-term bearish market, the increase of volume can happen in the early stage of a bullish market.
5) Buying opportunity can be suggested when the volume ratio is declining and the price is either advancing or leveling off.
CCI with Volume Weighted EMA Here is an attempt to improve on the CCI using a volume weighted ema which is then plugged into the CCI formula.
Use:
The CCI with VW EMA is an oscillator that gives readings between -100 and +100. The usual use is to 'go long' with values over +100 and short on values less than -100.
Another use of this oscillator is a countertrend indicator where one sells at crosses under +100 and buys on crosses over -100.
Multi-Functional Fisher Transform MTF with MACDL TRIGGERWhat this indicator gives you is a true signal when price is exhausted and ready for a fast turnaround. Fisher Transform is set for multi-time frame and also allows the user to change the length. This way a user can compare two or more time spans and lengths to look for these MACDL divergent triggers after a Fisher exhaustion. With so many indicators, it's probably best to merge these indicators and change the Fisher and Trigger colors so you can still have a look at price action (remember to scale right after merger). I've noticed from time to time when you have Fisher 34 100 and 300 up and running on two different time frames such as 5 and 15 min charts, with MACDL triggers on the 100/300 or 34/100 you get a high probability trade trigger. However, there are rare exceptions such as when price moves in a parabolic state up or down for a long period where this indication does not work. Ideally this indicator works best in a sideways market or slow rising/descending moving market.
This indicator was worked on by Glaz, nmike and myself
LazyBear also introduced the MACDL indicator
CCI Crossover AlertThis very simple indicator will give you a blue background where the CCI crossed from below -100 to above -100, and a red background where it crossed from above 100 to below 100.
NQ-VIX Expected Move LevelsNQ -VIX Daily Price Bands
This indicator plots dynamic intraday price bands for NQ futures based on real-time volatility levels measured by the VIX (CBOE Volatility Index). The bands evolve throughout the trading day, providing volatility-adjusted price targets.
Formulas:
Upper Band = Daily Open + (NQ Price × VIX ÷ √252 ÷ 100)
Lower Band = Daily Open - (NQ Price × VIX ÷ √252 ÷ 100)
The calculation uses the square root of 252 (trading days per year) to convert annualized VIX volatility into an expected daily move, then scales it as a percentage adjustment from the current day's open.
Features:
Real-time band calculation that updates throughout the trading session
Upper band (green) extends from the current day's open
Lower band (red) contracts from the current day's open
Inner upper band (green) at 50% of expected move
Inner lower band (red) at 50% of expected move
Middle Inner upper band (green) at 80% of expected move
Middle Inner lower band (red) at 80% of expected move
Information table displaying:
Current NQ price and VIX level
Daily Open
Expected move
NQ-VIX Expected Move LTF LevelsNQ -VIX LTF Price Bands
This indicator plots dynamic intraday price bands for NQ futures based on real-time volatility levels measured by the VIX (CBOE Volatility Index). The bands evolve throughout the trading day, providing volatility-adjusted price targets.
Formulas:
Upper Band = (Input TF Open) + (NQ Price × VIX x √(Input TF ÷ (23h in min) ) ÷ 100
Lower Band = Daily Open - (NQ Price × VIX x √(Input TF ÷ (23h in min) ) ÷ 100
The calculation uses the square root of Input TF ÷ (23h in min) to convert annualized VIX volatility into an expected TF move, then scales it as a percentage adjustment from the current TF input's open.
Features:
Real-time band calculation that updates throughout the trading session
Upper band (green) extends from the current TF's open
Lower band (red) contracts from the current TF's open
Inner upper band (green) at 50% of expected move
Inner lower band (red) at 50% of expected move
Middle Inner upper band (green) at 80% of expected move
Middle Inner lower band (red) at 80% of expected move
Information table displaying:
Current input TF
Current NQ price and VIX level
Current input TF Open
Expected move
Viprasol Elite Advanced Pattern Scanner# 🚀 Viprasol Elite Advanced Pattern Scanner
## Overview
The **Viprasol Elite Advanced Pattern Scanner** is a sophisticated technical analysis tool designed to identify high-probability double bottom (DISCOUNT) and double top (PREMIUM) patterns with unprecedented accuracy. Unlike basic pattern detectors, this elite scanner employs an AI-powered quality scoring system to filter out false signals and highlight only the most reliable trading opportunities.
## 🎯 Key Features
### Advanced Pattern Detection
- **DISCOUNT Patterns** (Double Bottoms): Identifies bullish reversal zones where price may bounce
- **PREMIUM Patterns** (Double Tops): Detects bearish reversal zones where price may decline
- Multi-point validation system (5-point structure)
- Symmetry analysis with customizable tolerance
### 🤖 AI Quality Scoring System
Each pattern receives a quality score (0-100) based on:
- **Symmetry Analysis** (32% weight): How closely the two bottoms/tops match
- **Trend Context** (22% weight): Strength of the preceding trend using ADX
- **Volume Profile** (22% weight): Volume confirmation at key points
- **Pattern Depth** (16% weight): Significance of the pattern's price range
- **Structure Quality** (16% weight): Overall pattern formation quality
Quality Grades:
- ⭐ **ELITE** (88-100): Highest probability setups
- ✨ **VERY STRONG** (77-87): Strong trade opportunities
- ✓ **STRONG** (67-76): Valid patterns with good potential
- ○ **VALID** (65-66): Acceptable patterns meeting minimum criteria
### 🎯 Intelligent Target System
Three target modes per pattern direction:
- **Conservative**: 0.618 Fibonacci extension (safer, closer targets)
- **Balanced**: 1.0 extension (moderate risk/reward)
- **Aggressive**: 1.618 extension (higher risk/reward)
Targets automatically adjust based on pattern quality score.
### 🔧 Advanced Filtering Options
- **Volatility Filter (ATR)**: Excludes patterns during extreme volatility
- **Momentum Filter (ADX)**: Ensures sufficient trend strength
- **Liquidity Filter (Volume)**: Confirms adequate trading volume
### 📊 Pattern Lifecycle Management
- Real-time neckline tracking with extension multiplier
- Pattern invalidation after extended wait period
- Breakout/breakdown confirmation
- Reversal detection (pattern failure scenarios)
- Target achievement tracking
### 🌈 Premium Visual System
- Color-coded quality levels
- Cyber-themed color scheme (Neon Green/Hot Pink/Purple/Cyan)
- Transparent fills for pattern zones
- Dynamic labels with pattern information
- Elite dashboard showing live pattern stats
## 📈 How To Use
### Basic Setup
1. Add indicator to your chart
2. Enable desired patterns (DISCOUNT and/or PREMIUM)
3. Adjust quality threshold (default: 65) - higher = fewer but better signals
4. Set your preferred target mode
### Trading DISCOUNT Patterns (Bullish)
1. Wait for pattern detection (labeled points 1-4)
2. Check quality score on dashboard
3. Entry on breakout above neckline (point 5)
4. Stop loss below the lowest bottom
5. Target shown automatically based on your mode
6. ⚠️ Watch for pattern failure (break below bottoms = SHORT signal)
### Trading PREMIUM Patterns (Bearish)
1. Wait for pattern detection (labeled points 1-4)
2. Check quality score on dashboard
3. Entry on breakdown below neckline (point 5)
4. Stop loss above the highest top
5. Target shown automatically based on your mode
6. ⚠️ Watch for pattern failure (break above tops = LONG signal)
## ⚙️ Input Settings Guide
### 🔍 Detection Engine
- **Left/Right Pivots**: Higher = fewer but cleaner patterns (default: 6/4)
- **Min Pattern Width**: Minimum bars between bottoms/tops (default: 12)
- **Symmetry Tolerance**: Max % difference allowed between levels (default: 1.8%)
- **Extension Multiplier**: How long to wait for breakout (default: 2.2x pattern width)
### ⭐ Quality AI
- **Min Quality Score**: Only show patterns above this score (default: 65)
- **Weight Distribution**: Customize what matters most (symmetry/trend/volume/depth/structure)
### 🔧 Filters
- **Volatility Filter**: Avoid choppy markets (recommended: ON)
- **Momentum Filter**: Ensure trend strength (recommended: ON)
- **Liquidity Filter**: Volume confirmation (recommended: ON)
### 💎 Target System
- Choose target aggression for each pattern type and direction
- Higher quality patterns get adjusted targets automatically
## 🎨 Visual Customization
- Adjust colors for DISCOUNT/PREMIUM patterns
- Set quality-based color coding
- Customize label sizes
- Toggle dashboard visibility and position
- Show/hide historical patterns
## 🚨 Alert System
Set up TradingView alerts for:
- 🚀 **LONG Signals**: DISCOUNT breakout, PREMIUM failure
- 📉 **SHORT Signals**: PREMIUM breakdown, DISCOUNT failure
- ✅ **Target Achievement**: When price hits your target
## 💡 Pro Tips
1. **Higher Timeframes = Better Signals**: Patterns on 4H, Daily, Weekly are more reliable
2. **Quality Over Quantity**: Focus on ELITE and VERY STRONG grades
3. **Combine with Trend**: DISCOUNT in uptrend, PREMIUM in downtrend = best results
4. **Watch Pattern Failures**: Failed patterns often provide strong counter-trend signals
5. **Adjust for Your Style**: Intraday traders use Conservative, swing traders use Aggressive
## 🔒 Pattern Invalidation
Patterns become invalid if:
- No breakout/breakdown within extension period
- Support/resistance levels are broken prematurely
- Pattern shown in faded colors = no longer active
## ⚠️ Risk Disclaimer
This indicator is a tool for technical analysis and does not guarantee profitable trades. Always:
- Use proper risk management
- Combine with other analysis methods
- Never risk more than you can afford to lose
- Past performance does not indicate future results
ES-VIX Expected Move LTF LevelsES-VIX LTF Price Bands
This indicator plots dynamic intraday price bands for ES futures based on real-time volatility levels measured by the VIX (CBOE Volatility Index). The bands evolve throughout the trading day, providing volatility-adjusted price targets.
Formulas:
Upper Band = (Input TF Open) + (ES Price × VIX x √(Input TF ÷ (23h in min) ) ÷ 100
Lower Band = Daily Open - (ES Price × VIX x √(Input TF ÷ (23h in min) ) ÷ 100
The calculation uses the square root of Input TF ÷ (23h in min) to convert annualized VIX volatility into an expected TF move, then scales it as a percentage adjustment from the current TF input's open.
Features:
Real-time band calculation that updates throughout the trading session
Upper band (green) extends from the current TF's open
Lower band (red) contracts from the current TF's open
Inner upper band (green) at 50% of expected move
Inner lower band (red) at 50% of expected move
Middle Inner upper band (green) at 80% of expected move
Middle Inner lower band (red) at 80% of expected move
Information table displaying:
Current input TF
Current ES price and VIX level
Current input TF Open
Expected move
GainzAlgo V2 [Proficient]// © GainzAlgo
//@version=5
indicator('GainzAlgo V2 ', overlay=true, max_labels_count=500)
show_tp_sl = input.bool(true, 'Display TP & SL', group='Techical', tooltip='Display the exact TP & SL price levels for BUY & SELL signals.')
rrr = input.string('1:2', 'Risk to Reward Ratio', group='Techical', options= , tooltip='Set a risk to reward ratio (RRR).')
tp_sl_multi = input.float(1, 'TP & SL Multiplier', 1, group='Techical', tooltip='Multiplies both TP and SL by a chosen index. Higher - higher risk.')
tp_sl_prec = input.int(2, 'TP & SL Precision', 0, group='Techical')
candle_stability_index_param = 0.7
rsi_index_param = 60
candle_delta_length_param = 4
disable_repeating_signals_param = input.bool(true, 'Disable Repeating Signals', group='Techical', tooltip='Removes repeating signals. Useful for removing clusters of signals and general clarity.')
GREEN = color.rgb(29, 255, 40)
RED = color.rgb(255, 0, 0)
TRANSPARENT = color.rgb(0, 0, 0, 100)
label_size = input.string('huge', 'Label Size', options= , group='Cosmetic')
label_style = input.string('text bubble', 'Label Style', , group='Cosmetic')
buy_label_color = input(GREEN, 'BUY Label Color', inline='Highlight', group='Cosmetic')
sell_label_color = input(RED, 'SELL Label Color', inline='Highlight', group='Cosmetic')
label_text_color = input(color.white, 'Label Text Color', inline='Highlight', group='Cosmetic')
stable_candle = math.abs(close - open) / ta.tr > candle_stability_index_param
rsi = ta.rsi(close, 14)
atr = ta.atr(14)
bullish_engulfing = close < open and close > open and close > open
rsi_below = rsi < rsi_index_param
decrease_over = close < close
var last_signal = ''
var tp = 0.
var sl = 0.
bull_state = bullish_engulfing and stable_candle and rsi_below and decrease_over and barstate.isconfirmed
bull = bull_state and (disable_repeating_signals_param ? (last_signal != 'buy' ? true : na) : true)
bearish_engulfing = close > open and close < open and close < open
rsi_above = rsi > 100 - rsi_index_param
increase_over = close > close
bear_state = bearish_engulfing and stable_candle and rsi_above and increase_over and barstate.isconfirmed
bear = bear_state and (disable_repeating_signals_param ? (last_signal != 'sell' ? true : na) : true)
round_up(number, decimals) =>
factor = math.pow(10, decimals)
math.ceil(number * factor) / factor
if bull
last_signal := 'buy'
dist = atr * tp_sl_multi
tp_dist = rrr == '2:3' ? dist / 2 * 3 : rrr == '1:2' ? dist * 2 : rrr == '1:4' ? dist * 4 : dist
tp := round_up(close + tp_dist, tp_sl_prec)
sl := round_up(close - dist, tp_sl_prec)
if label_style == 'text bubble'
label.new(bar_index, low, 'BUY', color=buy_label_color, style=label.style_label_up, textcolor=label_text_color, size=label_size)
else if label_style == 'triangle'
label.new(bar_index, low, 'BUY', yloc=yloc.belowbar, color=buy_label_color, style=label.style_triangleup, textcolor=TRANSPARENT, size=label_size)
else if label_style == 'arrow'
label.new(bar_index, low, 'BUY', yloc=yloc.belowbar, color=buy_label_color, style=label.style_arrowup, textcolor=TRANSPARENT, size=label_size)
label.new(show_tp_sl ? bar_index : na, low, 'TP: ' + str.tostring(tp) + '\nSL: ' + str.tostring(sl), yloc=yloc.price, color=color.gray, style=label.style_label_down, textcolor=label_text_color)
if bear
last_signal := 'sell'
dist = atr * tp_sl_multi
tp_dist = rrr == '2:3' ? dist / 2 * 3 : rrr == '1:2' ? dist * 2 : rrr == '1:4' ? dist * 4 : dist
tp := round_up(close - tp_dist, tp_sl_prec)
sl := round_up(close + dist, tp_sl_prec)
if label_style == 'text bubble'
label.new(bear ? bar_index : na, high, 'SELL', color=sell_label_color, style=label.style_label_down, textcolor=label_text_color, size=label_size)
else if label_style == 'triangle'
label.new(bear ? bar_index : na, high, 'SELL', yloc=yloc.abovebar, color=sell_label_color, style=label.style_triangledown, textcolor=TRANSPARENT, size=label_size)
else if label_style == 'arrow'
label.new(bear ? bar_index : na, high, 'SELL', yloc=yloc.abovebar, color=sell_label_color, style=label.style_arrowdown, textcolor=TRANSPARENT, size=label_size)
label.new(show_tp_sl ? bar_index : na, low, 'TP: ' + str.tostring(tp) + '\nSL: ' + str.tostring(sl), yloc=yloc.price, color=color.gray, style=label.style_label_up, textcolor=label_text_color)
alertcondition(bull or bear, 'BUY & SELL Signals', 'New signal!')
alertcondition(bull, 'BUY Signals (Only)', 'New signal: BUY')
alertcondition(bear, 'SELL Signals (Only)', 'New signal: SELL')
Hurst Exponent - Detrended Fluctuation AnalysisIn stochastic processes, chaos theory and time series analysis, detrended fluctuation analysis (DFA) is a method for determining the statistical self-affinity of a signal. It is useful for analyzing time series that appear to be long-memory processes and noise.
█ OVERVIEW
We have introduced the concept of Hurst Exponent in our previous open indicator Hurst Exponent (Simple). It is an indicator that measures market state from autocorrelation. However, we apply a more advanced and accurate way to calculate Hurst Exponent rather than simple approximation. Therefore, we recommend using this version of Hurst Exponent over our previous publication going forward. The method we used here is called detrended fluctuation analysis. (For folks that are not interested in the math behind the calculation, feel free to skip to "features" and "how to use" section. However, it is recommended that you read it all to gain a better understanding of the mathematical reasoning).
█ Detrend Fluctuation Analysis
Detrended Fluctuation Analysis was first introduced by by Peng, C.K. (Original Paper) in order to measure the long-range power-law correlations in DNA sequences . DFA measures the scaling-behavior of the second moment-fluctuations, the scaling exponent is a generalization of Hurst exponent.
The traditional way of measuring Hurst exponent is the rescaled range method. However DFA provides the following benefits over the traditional rescaled range method (RS) method:
• Can be applied to non-stationary time series. While asset returns are generally stationary, DFA can measure Hurst more accurately in the instances where they are non-stationary.
• According the the asymptotic distribution value of DFA and RS, the latter usually overestimates Hurst exponent (even after Anis- Llyod correction) resulting in the expected value of RS Hurst being close to 0.54, instead of the 0.5 that it should be. Therefore it's harder to determine the autocorrelation based on the expected value. The expected value is significantly closer to 0.5 making that threshold much more useful, using the DFA method on the Hurst Exponent (HE).
• Lastly, DFA requires lower sample size relative to the RS method. While the RS method generally requires thousands of observations to reduce the variance of HE, DFA only needs a sample size greater than a hundred to accomplish the above mentioned.
█ Calculation
DFA is a modified root-mean-squares (RMS) analysis of a random walk. In short, DFA computes the RMS error of linear fits over progressively larger bins (non-overlapped “boxes” of similar size) of an integrated time series.
Our signal time series is the log returns. First we subtract the mean from the log return to calculate the demeaned returns. Then, we calculate the cumulative sum of demeaned returns resulting in the cumulative sum being mean centered and we can use the DFA method on this. The subtraction of the mean eliminates the “global trend” of the signal. The advantage of applying scaling analysis to the signal profile instead of the signal, allows the original signal to be non-stationary when needed. (For example, this process converts an i.i.d. white noise process into a random walk.)
We slice the cumulative sum into windows of equal space and run linear regression on each window to measure the linear trend. After we conduct each linear regression. We detrend the series by deducting the linear regression line from the cumulative sum in each windows. The fluctuation is the difference between cumulative sum and regression.
We use different windows sizes on the same cumulative sum series. The window sizes scales are log spaced. Eg: powers of 2, 2,4,8,16... This is where the scale free measurements come in, how we measure the fractal nature and self similarity of the time series, as well as how the well smaller scale represent the larger scale.
As the window size decreases, we uses more regression lines to measure the trend. Therefore, the fitness of regression should be better with smaller fluctuation. It allows one to zoom into the “picture” to see the details. The linear regression is like rulers. If you use more rulers to measure the smaller scale details you will get a more precise measurement.
The exponent we are measuring here is to determine the relationship between the window size and fitness of regression (the rate of change). The more complex the time series are the more it will depend on decreasing window sizes (using more linear regression lines to measure). The less complex or the more trend in the time series, it will depend less. The fitness is calculated by the average of root mean square errors (RMS) of regression from each window.
Root mean Square error is calculated by square root of the sum of the difference between cumulative sum and regression. The following chart displays average RMS of different window sizes. As the chart shows, values for smaller window sizes shows more details due to higher complexity of measurements.
The last step is to measure the exponent. In order to measure the power law exponent. We measure the slope on the log-log plot chart. The x axis is the log of the size of windows, the y axis is the log of the average RMS. We run a linear regression through the plotted points. The slope of regression is the exponent. It's easy to see the relationship between RMS and window size on the chart. Larger RMS equals less fitness of the regression. We know the RMS will increase (fitness will decrease) as we increases window size (use less regressions to measure), we focus on the rate of RMS increasing (how fast) as window size increases.
If the slope is < 0.5, It means the rate of of increase in RMS is small when window size increases. Therefore the fit is much better when it's measured by a large number of linear regression lines. So the series is more complex. (Mean reversion, negative autocorrelation).
If the slope is > 0.5, It means the rate of increase in RMS is larger when window sizes increases. Therefore even when window size is large, the larger trend can be measured well by a small number of regression lines. Therefore the series has a trend with positive autocorrelation.
If the slope = 0.5, It means the series follows a random walk.
█ FEATURES
• Sample Size is the lookback period for calculation. Even though DFA requires a lower sample size than RS, a sample size larger > 50 is recommended for accurate measurement.
• When a larger sample size is used (for example = 1000 lookback length), the loading speed may be slower due to a longer calculation. Date Range is used to limit numbers of historical calculation bars. When loading speed is too slow, change the data range "all" into numbers of weeks/days/hours to reduce loading time. (Credit to allanster)
• “show filter” option applies a smoothing moving average to smooth the exponent.
• Log scale is my work around for dynamic log space scaling. Traditionally the smallest log space for bars is power of 2. It requires at least 10 points for an accurate regression, resulting in the minimum lookback to be 1024. I made some changes to round the fractional log space into integer bars requiring the said log space to be less than 2.
• For a more accurate calculation a larger "Base Scale" and "Max Scale" should be selected. However, when the sample size is small, a larger value would cause issues. Therefore, a general rule to be followed is: A larger "Base Scale" and "Max Scale" should be selected for a larger the sample size. It is recommended for the user to try and choose a larger scale if increasing the value doesn't cause issues.
The following chart shows the change in value using various scales. As shown, sometimes increasing the value makes the value itself messy and overshoot.
When using the lowest scale (4,2), the value seems stable. When we increase the scale to (8,2), the value is still alright. However, when we increase it to (8,4), it begins to look messy. And when we increase it to (16,4), it starts overshooting. Therefore, (8,2) seems to be optimal for our use.
█ How to Use
Similar to Hurst Exponent (Simple). 0.5 is a level for determine long term memory.
• In the efficient market hypothesis, market follows a random walk and Hurst exponent should be 0.5. When Hurst Exponent is significantly different from 0.5, the market is inefficient.
• When Hurst Exponent is > 0.5. Positive Autocorrelation. Market is Trending. Positive returns tend to be followed by positive returns and vice versa.
• Hurst Exponent is < 0.5. Negative Autocorrelation. Market is Mean reverting. Positive returns trends to follow by negative return and vice versa.
However, we can't really tell if the Hurst exponent value is generated by random chance by only looking at the 0.5 level. Even if we measure a pure random walk, the Hurst Exponent will never be exactly 0.5, it will be close like 0.506 but not equal to 0.5. That's why we need a level to tell us if Hurst Exponent is significant.
So we also computed the 95% confidence interval according to Monte Carlo simulation. The confidence level adjusts itself by sample size. When Hurst Exponent is above the top or below the bottom confidence level, the value of Hurst exponent has statistical significance. The efficient market hypothesis is rejected and market has significant inefficiency.
The state of market is painted in different color as the following chart shows. The users can also tell the state from the table displayed on the right.
An important point is that Hurst Value only represents the market state according to the past value measurement. Which means it only tells you the market state now and in the past. If Hurst Exponent on sample size 100 shows significant trend, it means according to the past 100 bars, the market is trending significantly. It doesn't mean the market will continue to trend. It's not forecasting market state in the future.
However, this is also another way to use it. The market is not always random and it is not always inefficient, the state switches around from time to time. But there's one pattern, when the market stays inefficient for too long, the market participants see this and will try to take advantage of it. Therefore, the inefficiency will be traded away. That's why Hurst exponent won't stay in significant trend or mean reversion too long. When it's significant the market participants see that as well and the market adjusts itself back to normal.
The Hurst Exponent can be used as a mean reverting oscillator itself. In a liquid market, the value tends to return back inside the confidence interval after significant moves(In smaller markets, it could stay inefficient for a long time). So when Hurst Exponent shows significant values, the market has just entered significant trend or mean reversion state. However, when it stays outside of confidence interval for too long, it would suggest the market might be closer to the end of trend or mean reversion instead.
Larger sample size makes the Hurst Exponent Statistics more reliable. Therefore, if the user want to know if long term memory exist in general on the selected ticker, they can use a large sample size and maximize the log scale. Eg: 1024 sample size, scale (16,4).
Following Chart is Bitcoin on Daily timeframe with 1024 lookback. It suggests the market for bitcoin tends to have long term memory in general. It generally has significant trend and is more inefficient at it's early stage.
