Grid Bot AutoThis script is an auto-adjusting grid bot simulator. This is an improved version of the original Grid Bot Simulator. The grid bot is best used for ranging/choppy markets. Prices are divided into grids, or trade zones, that will trigger signals each time a new zone is entered. During ranging markets, each transaction is followed by a “take profit.” As the market starts to trend, transactions are stacked (compare to DCA ), until the market consolidates. No signals are triggered above the Upper Limit or Below the Lower Limit. Unlike the previous version, the upper and lower limits are calculated automatically. Grid levels are determined by four factors: Smoothing, Laziness, Elasticity, and Grid Intervals.
Smoothing:
A moving average (or linear regression) is applied to each close price as a basis. Options for smoothing are Linear Regression, Simple Moving Average, Exponential Moving Average, Volume-Weighted Moving Average, Triple-Exponential Moving Average.
Laziness:
Laziness is the percentage change required to reach the next level. If laziness is 1.5, the price must move up or down by 1.5% before the grid will change. This concept is based on Alex Grover’s Efficient Trend Step. This allows the grids to be based on even price levels, as opposed to jagged moving averages.
Elasticity:
Elasticity is the degree of “stickiness” to the current price trend. If the smoothing line remains above (or below) the current grid center without reverting but still not enough to reach the next grid level, the grid line will start to curve toward the next grid level. Elasticity is added to (or subtracted from) the gridline by a factor of minimum system ticks for the current pair. Elasticity of zero will keep the gridlines horizontal. If elasticity is too high, the grid will distort.
Grid Intervals:
Grid intervals are the percentage of space between each grid.
Laziness = 4%, Elasticity = 0. Price must move at least 4% before reaching the next level. With zero elasticity, gridlines are straight.
Laziness = 5%, Elasticity = 100. For each bar at a new grid level, the grid will start “curve” toward the next price level (up if price is greater than the middle grid, down if less than middle grid). Elasticity is calculated by the user-inputted “Elasticity” multiplied by the minimum tick for the current pair (ELSTX = syminfo.mintick * iELSTX)
Try experimenting with different combinations of the Smoothing Length, Smoothing Type, Laziness, Elasticity, and Grid Intervals to find the optimum settings for each chart. Lower-priced pairs (e.g. XRP/ADA/DODGE) will require lower Elasticity. Also note that different exchanges may have different minimum tick values. For example, minimum tick for BITMEX:XBTUSD and BYBIT:BTCUSD is .5, but BINANCE:BTCUSDT and COINBASE:BTCUSD is .01.
s3.tradingview.com
DODGEUSDT, 5min. Laziness: 4%, Elasticity 2.5
Number of Grids: 2. Laziness: 3.75%. Elasticity: 150. Grid Interval 2%.
Settings Overview
Smoothing Length : Smoothing period
Smoothing Type : Linear Regression, Simple Moving Average, Exponential Moving Average, Volume-Weighted Moving Average, Triple-Exponential Moving Average
Laziness : Percentage required for price to move until it reaches the next level. If price does not reach the next level (up or down), the grid will remain the same as previous grid (because it’s lazy).
Elasticity : Amount of curvature toward the next grid, based on the current price trend. As elasticity increases, gridlines will curve up or down by a factor of the number of ticks since the last grid change.
Grid Interval : Percent between grid levels.
Number of Grids : Number of grids to show.
Cooldown : Number of bars to wait to prevent consecutive signals.
Grid Line Transparency : Lower transparencies brighten the gridlines; higher transparencies dim the gridlines. To hide the gridlines completely, enter 100.
Fill Transparency: Lower transparencies brighten the fill box; higher transparencies dim the fill box. To hide the fill box completely, enter 100.
Signal Size : Make signal triangles large or small.
Reset Buy/Sell Index When Grids Change : When a new grid is formed, resetting the index may prevent false signals (experimental)
Use Highs/Lows for Signals : If enabled, signals are triggered as soon as the price touches the next zone. If disabled, signals are triggered after bar closes. Enable this for “Once Per Bar alerts. Disable for “Once Per Bar Close” alerts.
Show Min Tick : If checked, syminfo.mintick is displayed in upper-righthand corner. Useful for estimating Laziness.
Reverse Fill Colors : Default fill for fill boxes is green after buy and red after sell. Check this box to reverse.
Note: The Grid Bot Simulator scripts are experimental and works in progress. Please feel free to comment or contact me if you have suggestions/complaints.
在脚本中搜索"curve"
Raff Regression Channel by DGTRᴀꜰꜰ Rᴇɢʀᴇꜱꜱɪᴏɴ Cʜᴀɴɴᴇʟ (RRC)
This study aims to automate Raff Regression Channel drawing either based on ZigZag Indicator or optionally User Preference
The Raff Regression Channel , developed by Gilbert Raff, is based on a linear regression, which is the least-squares line-of-best-fit for a price series, with evenly spaced trend lines above and below . The width of the channel is set by determining the high or low that is the furthest from the linear regression.
Because the channel distance is based off the largest pullback or highest peak within a trend, for effectively drawing and using a Raff Regression Channel it is recommend/required that a Raff Regression Channel is applied to “mature” trends. Knowing this requirement, for better automated drawing results this study benefits from the Zig Zag Indicator, where the Zig Zag indicator is used to help identify price trends and changes in price trends. Option to manually adjust lengths for drawing a Raff Regression Channel is also made available.
Using a Raff Regression Channel
Once The Raff Regression Channel is drawn, covering an existing trend, Exᴛᴇɴꜱɪᴏɴ Lɪɴᴇꜱ are drawn to identify ᴛʜᴇ ꜱᴜᴘᴘᴏʀᴛ﹐ʀᴇꜱɪꜱᴛᴀɴᴄᴇ ᴏʀ ʀᴇᴠᴇʀꜱᴀʟ ᴘᴏɪɴᴛꜱ
The trend is up as long as prices rise within this channel. An uptrend may be reversing (not always, but likely) when price breaks below the channel extension . The trend is down as long as prices decline within the channel. Similarly, a downtrend may be reversing (not always, but likely) when price breaks above the channel extension . Moves outside the channel extensions can be indication of a reversal or can denote overbought or oversold conditions
For further details please refer to education post Raff Regression Channel
█ FEATURES
- AUTO or MANUALLY adjusted Raff Regression Channel and Channel Extentions drawing
- ALERTs, for Linear Regression Line, Raff Regression Upper and Lower Channel Extentions
- LSMA , Least Squares Moving Average, in other words Linear Regression Curve
█ SETTINGS
Setting Loopback and Number of Bars are the most important part for The Raff Regression Channel, where ;
- Lookback, defines where the Raff Regression Channel is starting, it is recommended to set to a trend begining
- Number of Bars, defines how many bars to be assumed for calculation, or simply stated the end of the Raff Regression Channel drawing (not extentions but the main channel, extentions by default will be drawn till the last bar)
Setting of Loopback and Number of Bars is performed eigher automatically based on Zig Zag indicator or users may prefer to set them manually. If selected automatically then
- Deviation and Depth values of Zig Zag indicator are used for calculations (enabling visually plotting of ZigZag Lines will help to identify better visually the points), where ;
Deviation, is a multiplier that affects how much the price should deviate from the previous pivot in order for the bar to become a new pivot.
Depth, affects the minimum number of bars that will be taken into account when building
Short-term traders may wish to apply the channel to small waves of a trend so they can reduce the value of the Deviation and Depth
█ OTHER CHANNEL CONSEPTS
Linear Regression Channels, , what linear regression channels are? and linear regression channel/curve/slope study
Fibonacci Channels, how to apply fibonacci channels and automated fibonacci channels study
Andrews’ Pitchfork, how to apply pitchfork and automated pitchfork study
Special Thanks to @Kiss66000 for his kind suggestion, je vous remercie beaucoup @Kiss66000
Disclaimer :
Trading success is all about following your trading strategy and the indicators should fit within your trading strategy, and not to be traded upon solely
The script is for informational and educational purposes only. Use of the script does not constitute professional and/or financial advice. You alone have the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd TradingView user liable for any possible claim for damages arising from any decision you make based on use of the script
Excellent ADXThe Average Directional movement indeX (ADX) is an indicator that helps you determine the trend direction, pivot points, and much more else! But it looks not so easy as other famous indicators. It seems strange or even terrible, but don't be afraid. Let's understand how it works and get its power into your analysis tactics.
In the beginning, imagine a drunk man goes through a ladder: step by step. Up, up, down, up, down, down, up...
How can we understand which direction he goes? Exactly! We can count the number of steps in each direction. In the above example, in the upward – 4, in the downward – 3. So, it looks like he goes in an upward direction.
The ADX indicator counts the same steps, but for price. The size of each step equals 1 ATR for "DI Length" candles. On the indicator chart, we have the green and red lines. The green line represents a number of steps upward. The red line shows one downward. When the red line upper green, then the price goes below, then the trend is directed down. Later the green line comes above the red one, and then the trend changes the direction to upward. Wow? After that, you can easy detect the trend direction on the market!
But it is still not the end. On the chart, we also have the fat blue line. This is the ADX line, and it represents the power of the trend. It is calculated from a distance between the green and red curves. The ADX line value grows if the distance is increased. If the movement is really powerful, then a number of steps into a direction much more prominent than one in an opposed direction. Then the blue line grows faster. But if the growth has stopped and the blue line turns back or already had changed self-direction, then it is a signal that the trend has ended too. It's an excellent sign to close the position (but not always). Easy? Not quite. Thresholds help you there. The indicator has two additional parameters: upper and lower thresholds to evaluate the trend-over signal strength. An u-turn of the ADX line above the upper threshold sends a strong signal. If one occurs between both thresholds, it is a bit weak signal. But if the blue line goes below the lower threshold, it looks like there is no trend, and the price goes side. We can also say that the price goes side when the ADX value gradually falls down.
The Excellent ADX indicator helps you catch pivot/pullback signals based on green, red, and blue lines. Each such signal is highlighted as a green (buy) or red (sell) dot on the plot. The size of the dot represents the strength of the signal. You can also check the position of green and red lines from each other to determine the trend direction and the place where it has been changed. The Excellent ADX indicator helps you there too. It highlights the trend direction by the background-color, so you'll never miss it! The Excellent ADX good compliance with the Price Channel indicator built for the same length. You can use them together to be on a trend wave always!
MACD ProMoving average convergence divergence pro.
Original MACD with new features, Including...
1. Three different modes.
Basic, Logarithmic, Percent (calculates difference of oscillator MAs in percent)
2. Additional moving averages for oscillator, signal and even histogram.
EMA, WMA (linearly weighted), LMA (logarithmically weighted), SMA
Volume Weighted RMA (I've been suggested to make a MACD with the VWEMA that I published recently but that was too fast, this almost 2 times slower because of using RMA instead of EMA)
VWRMA(s) (an alternative for VWRMA which uses candle formation to simulate the volume, can be useful when volume is not provided for the symbol or it is not proper)
And DEMA (Double Exponential MA)
3. Signal Displacement.
If you want to add some delay to signal, could help for extra confirmation of center crosses and removal of some falss ones.
4. Histogram Smoother.
For those who like the smooth curves. Can deliver a cleaner histogram even in volatile markets.
5. Bar color for more fun.
Basic BIASBasic BIAS
Deviation rate (bias), also known as deviation rate, or y-value for short, is an indicator to reflect the deviation degree between the price and MA in a certain period of time by calculating the percentage difference between the market index or closing price and a moving average, so as to obtain the possibility that the price will reverse or rebound due to deviation from moving average trend in case of severe fluctuation, and that the price will move within the normal fluctuation range Form the credibility of continuing the original potential.
The deviation rate is a percentage of the deviation degree (gap rate) between the price and ma.
The departure rate curve (bias) is a curve that connects the values of each bias into a line and obtains a wave extension curve with the value of 0 as the horizontal axis.
Quadratic Least Squares Moving Average - Smoothing + Forecast Introduction
Technical analysis make often uses of classical statistical procedures, one of them being regression analysis, and since fitting polynomial functions that minimize the sum of squares can be achieved with the use of the mean, variance, covariance...etc, technical analyst only needed to replace the mean in all those calculations with a moving average, we then end up with a low lag filter called least squares moving average (lsma) .
The least squares moving average could be classified as a rolling linear regression, altho this sound really bad it is useful to understand the relationship of both methods, both have the same form, that is ax + b , where a and b are coefficients of the model. However in a simple linear regression a and b are constant, while the lsma use variables instead.
In a simple lsma we model the relationship of the closing price (dependent variable) with a linear sequence (independent variable), therefore x = 1,2,3,4..etc. However we can use polynomial of higher degrees to model such relationship, this is required if we want more reactivity. Therefore we can use a quadratic form, that is ax^2 + bx + c , where a,b and c are variables.
This is the quadratic least squares moving average (qlsma), a not so official term, but we'll stick with it because it still represent the aim of the filter quite well. In this indicator i make the calculations of the qlsma less troublesome, therefore one might understand how it would work, note that in general the coefficients of a polynomial regression model are found using matrix calculus.
The Indicator
A qlsma, unlike the classic lsma, will fit better to the price and will be more reactive, this is the advantage of using an higher degrees for its calculation, we can model more complex relationship.
lsma in green, qlsma in red, with both length = 200
However the over/under shoots are greater, i'll explain why in the next sections, but this is one of the drawbacks of using higher degrees.
The indicator allow to forecast future values, the ahead period of the forecast is determined by the forecast setting. The value for this setting should be lower than length, else the forecasts can easily over/under shoot which heavily damage the forecast. In order to get a view on how well the forecast is performing you can check the option "Show past predicted values".
Of course understanding the logic behind the forecast is important, in short regressions models best fit a certain curve to the data, this curve can be a line (linear regression), a parabola (quadratic regression) and so on, the type of curve is determined by the degree of the polynomial used, here 2, which is a parabola. Lets use a linear regression model as example :
ax + b where x is a linear sequence 1,2,3...and a/b are constants. Our goal is to find the values for a and b that minimize the sum of squares of the line with the dependent variable y, here the closing price, so our hypothesis is that :
closing price = ax + b + ε
where ε is white noise, a component that the model couldn't forecast. The forecast of the closing price 14 step ahead would be equal to :
closing price 14 step aheads = a(x+14) + b
Since x is a linear sequence we only need to sum it with the forecasting horizon period, the same is done here with :
a*(n+forecast)^2 + b*(n + forecast) + c
Note that the forecast proposed in the indicator is more for teaching purpose that anything else, this indicator can't possibly forecast future values, even on a meh rate.
Low lag filters have been used to provide noise free crosses with slow moving average, a bad practice in my opinion due to the ability low lag filters have to overshoot/undershoot, more interesting use cases might be to use the qlsma as input for other indicators.
On The Code
Some of you might know that i posted a "quadratic regression" indicator long ago, the original calculations was coming from a forum, but because the calculation was ugly as hell as well as extra inefficient (dogfood level) i had to do something about it, the name was also terribly misleading.
We can see in the code that we make heavy use of the variance and covariance, both estimated with :
VAR(x) = SMA(x^2) - SMA(x)^2
COV(x,y) = SMA(xy) - SMA(x)SMA(y)
Those elements are then combined, we can easily recognize the intercept element c , who don't change much from the classical lsma.
As Digital Filter
The frequency response of the qlsma is similar to the one of the lsma, those filters amplify certain frequencies in the passband, and have ripples in the stop band. There is something interesting about those filters, first using higher degrees allow to greater boost of the frequencies in the passband, which result in greater over/under shoots. Another funny thing is that the peak/valley of the ripples is equal the peak or valley in the ripples of another lsma of different degree.
The transient response of those filters, that is impulse response, step response...etc is related to the degree of the polynomial used, therefore lets denote a lsma of degree p : lsma(p) , the impulse response of lsma(p) is a polynomial of degree p, and the step response is simple a polynomial of order p+1.
This is why it was more interesting to estimate the qlsma using convolution, however we can no longer forecast future values.
Conclusion
I proposed a more usable quadratic least squares moving average, with more options, as well as a cleaner and more efficient code. The process of shrinking the original code is made easier when you know about the estimations of both variance and covariance.
I hope the proposed indicator/calculation is useful.
Thx for reading !
MAX TRENDS Spark 0.3.1.1This is a solid modification of Waves with extra volatility curves.
Very sophisticated for the day trading and forex swing.
XBT Contango Calculator v1.1
This indicator measures value of basis (or spread) of current Futures contracts compared to spot. The default settings are specifically for Bitmex XBTU19 and XBTZ19 futures contracts. These will need to be updated after expiration. Also, it seems that Tradingview does not keep charts of expired contracts. If anyone knows how to import data from previous expired contracts, please let me know. This historical data could be valuable for evaluating previous XBT futures curves.
Also, VERY important to understand is this indicator only works with Spot Bitcoin charts (XBTUSD, BTCUSD, etc). If you add this to any other asset chart, it would not be useful (unless you changed settings to evaluate a different Futures product).
Contango and Backwardation are important fundamental indicators to keep track of while trading Futures markets. For a better explanation, Ugly Old Goat had done several medium articles on this. Please check out link below for his latest article on the subject...
uglyoldgoat.com
Notes on chart above should explain most of what you need to know on to use this indicator. The zero line is the spot price on the chart, so a positive value means Futures are trading at a premium (or in Contango). You can set a value of extreme Contango which will give an alert as red background (default setting is +$500). Green background will appear when Futures are trading at a discount to spot (Backwardation).
Hope some people get some use out of this. This is my first attempt at coding anything, so any feedback would be greatly appreciated!
BTC Donations: 3CypEdvBcvVHbqzHUt1FDiUG53U7pYWviV
Moving AverageDisplay of simple moving average and exponential mobile average depending on period.
Simple moving average are for D, W, and M period.
Minutes and Hours periods display exponential curves.
Multi SMA EMA WMA HMA BB (4x3 MAs Bollinger Bands) Pro MTF - RRBMulti SMA EMA WMA HMA 4x3 Moving Averages with Bollinger Bands Pro MTF by RagingRocketBull 2018
Version 1.0
This indicator shows multiple MAs of any type SMA EMA WMA HMA etc with BB and MTF support, can show MAs as dynamically moving levels.
There are 4 MA groups + 1 BB group. You can assign any type/timeframe combo to a group, for example:
- EMAs 50,100,200 x H1, H4, D1, W1 (4 TFs x 3 MAs x 1 type)
- EMAs 8,13,21,55,100,200 x M15, H1 (2 TFs x 6 MAs x 1 type)
- D1 EMAs and SMAs 12,26,50,100,200,400 (1 TF x 6 MAs x 2 types)
- H1 WMAs 7,77,231; H4 HMAs 50,100,200; D1 EMAs 144,169,233; W1 SMAs 50,100,200 (4 TFs x 3 MAs x 4 types)
- +1 extra MA type/timeframe for BB
compile time: 25-30 sec
full redraw time after parameter change in UI: 3 sec
There are several versions: Simple, MTF, Pro MTF, Advanced MTF and Ultimate MTF. This is the Pro MTF version. The Differences are listed below. All versions have BB
- Simple: you have 2 groups of MAs that can be assigned any type (5+5)
- MTF: +2 custom Timeframes for each group (2x5 MTF)
- Pro MTF: +4 custom Timeframes for each group (4x3 MTF), MA levels and show max bars back options
- Advanced MTF: +2 extra MAs/group (4x5 MTF), custom Ticker/Symbol, backreferences for type, TF and MA lengths in UI
- Ultimate MTF: +individual settings for each MA, custom Ticker/Symbols
Features:
- 4x3 = 12 MAs of any type including Hull Moving Average (HMA)
- 4x MTF groups with step line smoothing
- BB +1 extra TF/type for BB MAs
- 12 MA levels with adjustable group offsets, indents and shift
- show max bars back
- you can show/hide both groups of MAs/levels and individual MAs
Notes:
1. based on 3EmaBB, uses plot*, barssince and security functions
2. you can't set certain constants from input due to Pinescript limitations - change the code as needed, recompile and use as a private version
3. Levels = trackprice implementation
4. Show Max Bars Back = show_last implementation
5. uses timeframe textbox instead of input resolution to allow for 120 240 and other custom TFs. Also supports TFs in hours: 2H or H2
6. swma has a fixed length = 4, alma and linreg have additional offset and smoothing params
7. Smoothing is applied by default for visual aesthetics on MTF. To use exact ma mtf values (lines with stair stepping) - disable it
MTF Notes:
- uses simple timeframe textbox instead of input resolution dropdown to allow for 120, 240 and other custom TFs, also supports timeframes in H: 2H, H2
- Groups that are not assigned a Custom TF will use Current Timeframe (0).
- MTF will work for any MA type assigned to the group
- MTF works both ways: you can display a higher TF MA/BB on a lower TF or a lower TF MA/BB on a higher TF.
- MTF MA values are normally aligned at the boundary of their native timeframe. This produces stair stepping when a higher TF MA is viewed on a lower TF.
Therefore X Y Point Density/Smoothing is applied by default on MA MTF for visual aesthetics. Set both to 0 to disable and see exact ma mtf values (lines with stair stepping and original mtf alignment).
- Smoothing is disabled for BB MTF bands because fill doesn't work with smoothed MAs after duplicate values are replaced with na.
- MTF MA Value fluctuation is possible on the current bar due to default security lookahead
Smoothing:
- X,Y == 0 - X,Y smoothing disabled (stair stepping on high TFs)
- X == 0, Y > 0 - X,Y smoothing applied to all TFs
- Y == 0, X > 0 - X smoothing applied to all TFs < deltaX_max_tf, Y smoothing disabled
- X > 0, Y > 0 - Y smoothing applied to all TFs, then X smoothing applied to all TFs < deltaX_max_tf
X Smoothing with Y == 0 - shows only every deltaX-th point starting from the first bar.
X Smoothing with Y > 0 - shows only every deltaX-th point starting from the last shown Y point, essentially filling huge gaps remaining after Y Smoothing with points and preserving the curve's general shape
X Smoothing on high TFs with already scarce points produces weird curve shapes, it works best only on high density lower TFs
Y Smoothing reduces points on all TFs, removes adjacent points with prices within deltaY, while preserving the smaller curve details.
A combination of X,Y produces the most accurate smoothing. Higher delta value - larger range, more points removed.
Show Max Bars Back:
- can't set plot show_last from input -> implemented using a timenow based range check
- you can't delete/modify history once plotted, so essentially it just sets a start point for plotting (from num_bars bars back) that works only in realtime mode (not in replay)
Levels:
You can plot current MA value using plot trackprice=true or by checking Show Price Line in Style. Problem is:
- you can only change color (not the dashed line style, width), have both ma + price line (not just the line), and it's full screen wide
- you can't set plot trackprice from input => implemented using plotshape/plotchar with fixed text labels serving as levels
- there's no other way of creating a dynamic level: hline, plot, offset - nothing else works.
- you can't plot a text var - all text strings must be constants, so you can't change the style, width and text labels without recompiling.
- from input you can only adjust offset, indent and shift for each level group, and change color
- the dot below each level line is the exact MA value. If you want just the line swap plotshape with plotchar, recompile and save as your private version, adjust Y shift.
To speed up redraw times: reduce last_bars to ~2000, recompile and use as your own private version
Pinescript is a rudimentary language (should be called Painscript instead) that can basically only plot data. You can't do much else. Please see the code for tips and hints.
Certain things just can't be done or require shady workarounds and weeks of testing trying to resolve weird node.js compiler errors.
Feel free to learn from/reuse/change the code as needed and use as your own private version. See comments in code. Good Luck!
Tunable SWMADissected the standard SWMA function and added options for user to change just about every part of it. Weights ,Lookback ,Source can all be changed in the settings.
Green is the standard SWMA, Using the Input value selected.(MAs/LRC/VWAP)
Red is the tuned SWMA, with the option of applying a final Output filter (MAs/LRC/VWAP). Uses 8 datapoints instead of 4 for the default.
Customization can really help expand upon the standard SWMA I find. Enjoy tuning to your hearts content
Market Regime# MARKET REGIME IDENTIFICATION & TRADING SYSTEM
## Complete User Guide
---
## 📋 TABLE OF CONTENTS
1. (#overview)
2. (#regimes)
3. (#indicator-usage)
4. (#entry-signals)
5. (#exit-signals)
6. (#regime-strategies)
7. (#confluence)
8. (#backtesting)
9. (#optimization)
10. (#examples)
---
## OVERVIEW
### What This System Does
This is a **complete market regime identification and trading system** that:
1. **Identifies 6 distinct market regimes** automatically
2. **Adapts trading tactics** to each regime
3. **Provides high-probability entry signals** with confluence scoring
4. **Shows optimal exit points** for each trade
5. **Can be backtested** to validate performance
### Two Components Provided
1. **Indicator** (`market_regime_indicator.pine`)
- Visual regime identification
- Entry/exit signals on chart
- Dynamic support/resistance
- Info tables with live data
- Use for manual trading
2. **Strategy** (`market_regime_strategy.pine`)
- Fully automated backtestable version
- Same logic as indicator
- Position sizing and risk management
- Performance metrics
- Use for backtesting and automation
---
## THE 6 MARKET REGIMES
### 1. 🟢 BULL TRENDING
**Characteristics:**
- Strong uptrend
- Price above SMA50 and SMA200
- ADX > 25 (strong trend)
- Higher highs and higher lows
- DI+ > DI- (bullish momentum)
**What It Means:**
- Market has clear upward direction
- Buyers in control
- Pullbacks are buying opportunities
- Strongest regime for long positions
**How to Trade:**
- ✅ **BUY dips to EMA20 or SMA20**
- ✅ Enter when RSI < 60 on pullback
- ✅ Hold through minor corrections
- ❌ Don't short against the trend
- ❌ Don't sell too early
**Expected Behavior:**
- Pullbacks are shallow (5-10%)
- Bounces are strong
- Support at moving averages holds
- Volume increases on rallies
---
### 2. 🔴 BEAR TRENDING
**Characteristics:**
- Strong downtrend
- Price below SMA50 and SMA200
- ADX > 25 (strong trend)
- Lower highs and lower lows
- DI- > DI+ (bearish momentum)
**What It Means:**
- Market has clear downward direction
- Sellers in control
- Rallies are selling opportunities
- Strongest regime for short positions
**How to Trade:**
- ✅ **SELL rallies to EMA20 or SMA20**
- ✅ Enter when RSI > 40 on bounce
- ✅ Hold through minor bounces
- ❌ Don't buy against the trend
- ❌ Don't cover shorts too early
**Expected Behavior:**
- Rallies are weak (5-10%)
- Selloffs are strong
- Resistance at moving averages holds
- Volume increases on declines
---
### 3. 🔵 BULL RANGING
**Characteristics:**
- Bullish bias but consolidating
- Price near or above SMA50
- ADX < 20 (weak trend)
- Trading in range
- Choppy price action
**What It Means:**
- Uptrend is pausing
- Accumulation phase
- Support and resistance zones clear
- Lower volatility
**How to Trade:**
- ✅ **BUY at support zone**
- ✅ Enter when RSI < 40
- ✅ Take profits at resistance
- ⚠️ Smaller position sizes
- ⚠️ Tighter stops
**Expected Behavior:**
- Range-bound oscillations
- Support bounces repeatedly
- Resistance rejections common
- Eventually breaks higher (usually)
---
### 4. 🟠 BEAR RANGING
**Characteristics:**
- Bearish bias but consolidating
- Price near or below SMA50
- ADX < 20 (weak trend)
- Trading in range
- Choppy price action
**What It Means:**
- Downtrend is pausing
- Distribution phase
- Support and resistance zones clear
- Lower volatility
**How to Trade:**
- ✅ **SELL at resistance zone**
- ✅ Enter when RSI > 60
- ✅ Take profits at support
- ⚠️ Smaller position sizes
- ⚠️ Tighter stops
**Expected Behavior:**
- Range-bound oscillations
- Resistance holds repeatedly
- Support bounces are weak
- Eventually breaks lower (usually)
---
### 5. ⚪ CONSOLIDATION
**Characteristics:**
- No clear direction
- Range compression
- Very low ADX (< 15 often)
- Price inside tight range
- Neutral sentiment
**What It Means:**
- Market is coiling
- Building energy for next move
- Indecision between buyers/sellers
- Calm before the storm
**How to Trade:**
- ✅ **WAIT for breakout direction**
- ✅ Enter on high-volume breakout
- ✅ Direction becomes clear
- ❌ Don't trade inside the range
- ❌ Avoid choppy scalping
**Expected Behavior:**
- Narrow range
- Low volume
- False breakouts possible
- Explosive move when it breaks
---
### 6. 🟣 CHAOS (High Volatility)
**Characteristics:**
- Extreme volatility
- No clear direction
- Erratic price swings
- ATR > 2x average
- Unpredictable
**What It Means:**
- Market panic or euphoria
- News-driven moves
- Emotion dominates logic
- Highest risk environment
**How to Trade:**
- ❌ **STAY OUT!**
- ❌ No positions
- ❌ Wait for stability
- ✅ Protect existing positions
- ✅ Reduce risk
**Expected Behavior:**
- Large intraday swings
- Gaps up/down
- Stop hunts
- Whipsaws
- Eventually calms down
---
## INDICATOR USAGE
### Visual Elements
#### 1. Background Colors
- **Light Green** = Bull Trending (go long)
- **Light Red** = Bear Trending (go short)
- **Light Teal** = Bull Ranging (buy dips)
- **Light Orange** = Bear Ranging (sell rallies)
- **Light Gray** = Consolidation (wait)
- **Purple** = Chaos (stay out!)
#### 2. Regime Labels
- Appear when regime changes
- Show new regime name
- Positioned at highs (bullish) or lows (bearish)
#### 3. Entry Signals
- **Green "LONG"** labels = Buy here
- **Red "SHORT"** labels = Sell here
- Number shows confluence score (X/5 signals)
- Hover for details (stop, target, RSI, etc.)
#### 4. Exit Signals
- **Orange "EXIT LONG"** = Close long position
- **Orange "EXIT SHORT"** = Close short position
- Shows exit reason in tooltip
#### 5. Support/Resistance Lines
- **Green line** = Dynamic support (buy zone)
- **Red line** = Dynamic resistance (sell zone)
- Adapts to regime automatically
#### 6. Moving Averages
- **Blue** = SMA 20 (short-term trend)
- **Orange** = SMA 50 (medium-term trend)
- **Purple** = SMA 200 (long-term trend)
### Information Tables
#### Top Right Table (Main Info)
Shows real-time market conditions:
- **Current Regime** - What regime we're in
- **Bias** - Long, Short, Breakout, or Stay Out
- **ADX** - Trend strength (>25 = strong)
- **Trend** - Strong, Moderate, or Weak
- **Volatility** - High or Normal
- **Vol Ratio** - Current vs average volatility
- **RSI** - Momentum (>70 overbought, <30 oversold)
- **vs SMA50/200** - Price position relative to MAs
- **Support/Resistance** - Exact price levels
- **Long/Short Signals** - Confluence scores (X/5)
#### Bottom Right Table (Regime Guide)
Quick reference for each regime:
- What action to take
- What strategy to use
- Color-coded for quick identification
---
## ENTRY SIGNALS EXPLAINED
### Confluence Scoring System (5 Factors)
Each entry signal is scored 0-5 based on how many factors align:
#### For LONG Entries:
1. ✅ **Regime Alignment** - In Bull Trending or Bull Ranging
2. ✅ **RSI Pullback** - RSI between 35-50 (not overbought)
3. ✅ **Near Support** - Price within 2% of dynamic support
4. ✅ **MACD Turning Up** - Momentum shifting bullish
5. ✅ **Volume Confirmation** - Above average volume
#### For SHORT Entries:
1. ✅ **Regime Alignment** - In Bear Trending or Bear Ranging
2. ✅ **RSI Rejection** - RSI between 50-65 (not oversold)
3. ✅ **Near Resistance** - Price within 2% of dynamic resistance
4. ✅ **MACD Turning Down** - Momentum shifting bearish
5. ✅ **Volume Confirmation** - Above average volume
### Confluence Requirements
**Minimum Confluence** (default = 2):
- 2/5 = Entry signal triggered
- 3/5 = Good signal
- 4/5 = Strong signal
- 5/5 = Excellent signal (rare)
**Higher confluence = Higher probability = Better trades**
### Specific Entry Patterns
#### 1. Bull Trending Entry
```
Requirements:
- Regime = Bull Trending
- Price pulls back to EMA20
- Close above EMA20 (bounce)
- Up candle (close > open)
- RSI < 60
- Confluence ≥ 2
```
#### 2. Bear Trending Entry
```
Requirements:
- Regime = Bear Trending
- Price rallies to EMA20
- Close below EMA20 (rejection)
- Down candle (close < open)
- RSI > 40
- Confluence ≥ 2
```
#### 3. Bull Ranging Entry
```
Requirements:
- Regime = Bull Ranging
- RSI < 40 (oversold)
- Price at or below support
- Up candle (reversal)
- Confluence ≥ 1 (more lenient)
```
#### 4. Bear Ranging Entry
```
Requirements:
- Regime = Bear Ranging
- RSI > 60 (overbought)
- Price at or above resistance
- Down candle (rejection)
- Confluence ≥ 1 (more lenient)
```
#### 5. Consolidation Breakout
```
Requirements:
- Regime = Consolidation
- Price breaks above/below range
- Volume > 1.5x average (explosive)
- Strong directional candle
```
---
## EXIT SIGNALS EXPLAINED
### Three Types of Exits
#### 1. Regime Change Exits (Automatic)
- **Long Exit**: Regime changes to Bear Trending or Chaos
- **Short Exit**: Regime changes to Bull Trending or Chaos
- **Reason**: Market character changed, strategy no longer valid
#### 2. Support/Resistance Break Exits
- **Long Exit**: Price breaks below support by 2%
- **Short Exit**: Price breaks above resistance by 2%
- **Reason**: Key level violated, trend may be reversing
#### 3. Momentum Exits
- **Long Exit**: RSI > 70 (overbought) AND down candle
- **Short Exit**: RSI < 30 (oversold) AND up candle
- **Reason**: Overextension, take profits
### Stop Loss & Take Profit
**Stop Loss** (Automatic in strategy):
- Placed at Entry - (ATR × 2)
- Adapts to volatility
- Protected from whipsaws
- Typically 2-4% for stocks, 5-10% for crypto
**Take Profit** (Automatic in strategy):
- Placed at Entry + (Stop Distance × R:R Ratio)
- Default 2.5:1 reward:risk
- Example: $2 risk = $5 reward target
- Allows winners to run
---
## TRADING EACH REGIME
### BULL TRENDING - Most Profitable Long Environment
**Strategy: Buy Every Dip**
**Entry Rules:**
1. Wait for pullback to EMA20 or SMA20
2. Look for RSI < 60
3. Enter when candle closes above MA
4. Confluence should be 2+
**Stop Loss:**
- Below the recent swing low
- Or 2 × ATR below entry
**Take Profit:**
- At previous high
- Or 2.5:1 R:R minimum
**Position Size:**
- Can use full size (2% risk)
- High win rate regime
**Example Trade:**
```
Price: $100, pulls back to $98 (EMA20)
Entry: $98.50 (close above EMA)
Stop: $96.50 (2 ATR)
Target: $103.50 (2.5:1)
Risk: $2, Reward: $5
```
---
### BEAR TRENDING - Most Profitable Short Environment
**Strategy: Sell Every Rally**
**Entry Rules:**
1. Wait for bounce to EMA20 or SMA20
2. Look for RSI > 40
3. Enter when candle closes below MA
4. Confluence should be 2+
**Stop Loss:**
- Above the recent swing high
- Or 2 × ATR above entry
**Take Profit:**
- At previous low
- Or 2.5:1 R:R minimum
**Position Size:**
- Can use full size (2% risk)
- High win rate regime
**Example Trade:**
```
Price: $100, rallies to $102 (EMA20)
Entry: $101.50 (close below EMA)
Stop: $103.50 (2 ATR)
Target: $96.50 (2.5:1)
Risk: $2, Reward: $5
```
---
### BULL RANGING - Buy Low, Sell High
**Strategy: Range Trading (Long Bias)**
**Entry Rules:**
1. Wait for price at support zone
2. Look for RSI < 40
3. Enter on reversal candle
4. Confluence should be 1-2+
**Stop Loss:**
- Below support zone
- Tighter than trending (1.5 ATR)
**Take Profit:**
- At resistance zone
- Don't hold through resistance
**Position Size:**
- Reduce to 1-1.5% risk
- Lower win rate than trending
**Example Trade:**
```
Range: $95-$105
Entry: $96 (at support, RSI 35)
Stop: $94 (below support)
Target: $104 (at resistance)
Risk: $2, Reward: $8 (4:1)
```
---
### BEAR RANGING - Sell High, Buy Low
**Strategy: Range Trading (Short Bias)**
**Entry Rules:**
1. Wait for price at resistance zone
2. Look for RSI > 60
3. Enter on rejection candle
4. Confluence should be 1-2+
**Stop Loss:**
- Above resistance zone
- Tighter than trending (1.5 ATR)
**Take Profit:**
- At support zone
- Don't hold through support
**Position Size:**
- Reduce to 1-1.5% risk
- Lower win rate than trending
**Example Trade:**
```
Range: $95-$105
Entry: $104 (at resistance, RSI 65)
Stop: $106 (above resistance)
Target: $96 (at support)
Risk: $2, Reward: $8 (4:1)
```
---
### CONSOLIDATION - Wait for Breakout
**Strategy: Breakout Trading**
**Entry Rules:**
1. Identify consolidation range
2. Wait for VOLUME SURGE (1.5x+ avg)
3. Enter on close outside range
4. Direction must be clear
**Stop Loss:**
- Opposite side of range
- Or 2 ATR
**Take Profit:**
- Measure range height, project it
- Example: $10 range = $10 move expected
**Position Size:**
- Reduce to 1% risk
- 50% false breakout rate
**Example Trade:**
```
Consolidation: $98-$102 (4-point range)
Breakout: $102.50 (high volume)
Entry: $103
Stop: $100 (back in range)
Target: $107 (4-point range projected)
Risk: $3, Reward: $4
```
---
### CHAOS - STAY OUT!
**Strategy: Preservation**
**What to Do:**
- ❌ NO new positions
- ✅ Close existing positions if near entry
- ✅ Tighten stops on profitable trades
- ✅ Reduce position sizes dramatically
- ✅ Wait for regime to stabilize
**Why It's Dangerous:**
- Stop hunts are common
- Whipsaws everywhere
- News-driven volatility
- No technical reliability
- Even "perfect" setups fail
**When Does It End:**
- Volatility ratio drops < 1.5
- ADX starts rising (direction appears)
- Price respects support/resistance again
- Usually 1-5 days
---
## CONFLUENCE SYSTEM
### How It Works
The system scores each potential entry on 5 factors. More factors aligning = higher probability.
### Confluence Requirements by Regime
**Trending Regimes** (strictest):
- Minimum 2/5 required
- 3/5 = Good
- 4-5/5 = Excellent
**Ranging Regimes** (moderate):
- Minimum 1-2/5 required
- 2/5 = Good
- 3+/5 = Excellent
**Consolidation** (breakout only):
- Volume is most critical
- Direction confirmation
- Less confluence needed
### Adjusting Minimum Confluence
**If too few signals:**
- Lower from 2 to 1
- More trades, lower quality
**If too many false signals:**
- Raise from 2 to 3
- Fewer trades, higher quality
**Recommendation:**
- Start at 2
- Adjust based on win rate
- Aim for 55-65% win rate
---
## STRATEGY BACKTESTING
### Loading the Strategy
1. Copy `market_regime_strategy.pine`
2. Open Pine Editor in TradingView
3. Paste and "Add to Chart"
4. Strategy Tester tab opens at bottom
### Initial Settings
```
Risk Per Trade: 2%
ATR Stop Multiplier: 2.0
Reward:Risk Ratio: 2.5
Trade Longs: ✓
Trade Shorts: ✓
Trade Trending Only: ✗ (test both)
Avoid Chaos: ✓
Minimum Confluence: 2
```
### What to Look For
**Good Results:**
- Win Rate: 50-60%
- Profit Factor: 1.8-2.5
- Net Profit: Positive
- Max Drawdown: <20%
- Consistent equity curve
**Warning Signs:**
- Win Rate: <45% (too many losses)
- Profit Factor: <1.5 (barely profitable)
- Max Drawdown: >30% (too risky)
- Erratic equity curve (unstable)
### Testing Different Regimes
**Test 1: Trending Only**
```
Trade Trending Only: ✓
Result: Higher win rate, fewer trades
```
**Test 2: All Regimes**
```
Trade Trending Only: ✗
Result: More trades, potentially lower win rate
```
**Test 3: Long Only**
```
Trade Longs: ✓
Trade Shorts: ✗
Result: Works in bull markets
```
**Test 4: Short Only**
```
Trade Longs: ✗
Trade Shorts: ✓
Result: Works in bear markets
```
---
## SETTINGS OPTIMIZATION
### Key Parameters to Adjust
#### 1. Risk Per Trade (Most Important)
- **0.5%** = Very conservative
- **1.0%** = Conservative (recommended for beginners)
- **2.0%** = Moderate (recommended)
- **3.0%** = Aggressive
- **5.0%** = Very aggressive (not recommended)
**Impact:** Higher risk = higher returns BUT bigger drawdowns
#### 2. Reward:Risk Ratio
- **2:1** = More wins needed, hit target faster
- **2.5:1** = Balanced (recommended)
- **3:1** = Fewer wins needed, hold longer
- **4:1** = Very patient, best in trending
**Impact:** Higher R:R = can have lower win rate
#### 3. Minimum Confluence
- **1** = More signals, lower quality
- **2** = Balanced (recommended)
- **3** = Fewer signals, higher quality
- **4** = Very selective
- **5** = Almost never triggers
**Impact:** Higher = fewer but better trades
#### 4. ADX Thresholds
- **Trending: 20-30** (default 25)
- Lower = detect trends earlier
- Higher = only strong trends
- **Ranging: 15-25** (default 20)
- Lower = identify ranging earlier
- Higher = only weak trends
#### 5. Trend Period (SMA)
- **20-50** = Short-term trends
- **50** = Medium-term (default, recommended)
- **100-200** = Long-term trends
**Impact:** Longer period = slower regime changes, more stable
### Optimization Workflow
**Step 1: Baseline**
- Use all default settings
- Test on 3+ years
- Record: Win Rate, PF, Drawdown
**Step 2: Risk Optimization**
- Test 1%, 1.5%, 2%, 2.5%
- Find best risk-adjusted return
- Balance profit vs drawdown
**Step 3: R:R Optimization**
- Test 2:1, 2.5:1, 3:1
- Check which maximizes profit factor
- Consider holding time
**Step 4: Confluence Optimization**
- Test 1, 2, 3
- Find sweet spot for win rate
- Aim for 55-65% win rate
**Step 5: Regime Filter**
- Test with/without trend filter
- Test with/without chaos filter
- Find what works for your asset
---
## REAL TRADING EXAMPLES
### Example 1: Bull Trending - SPY
**Setup:**
- Regime: BULL TRENDING
- Price pulls back from $450 to $445
- EMA20 at $444
- RSI drops to 45
- Confluence: 4/5
**Entry:**
- Price closes at $445.50 (above EMA20)
- LONG signal appears
- Enter at $445.50
**Risk Management:**
- Stop: $443 (2 ATR = $2.50)
- Target: $451.75 (2.5:1 = $6.25)
- Risk: $2.50 per share
- Position: 80 shares (2% of $10k = $200 risk)
**Outcome:**
- Price rallies to $452 in 3 days
- Target hit
- Profit: $6.50 × 80 = $520
- Return: 2.6 × risk (excellent)
---
### Example 2: Bear Ranging - AAPL
**Setup:**
- Regime: BEAR RANGING
- Range: $165-$175
- Price rallies to $174
- Resistance at $175
- RSI at 68
- Confluence: 3/5
**Entry:**
- Rejection candle at $174
- SHORT signal appears
- Enter at $173.50
**Risk Management:**
- Stop: $176 (above resistance)
- Target: $166 (support)
- Risk: $2.50
- Position: 80 shares
**Outcome:**
- Price drops to $167 in 2 days
- Target hit
- Profit: $6.50 × 80 = $520
- Return: 2.6 × risk
---
### Example 3: Consolidation Breakout - BTC
**Setup:**
- Regime: CONSOLIDATION
- Range: $28,000 - $30,000
- Compressed for 2 weeks
- Volume declining
**Breakout:**
- Price breaks $30,000
- Volume surges 200%
- Close at $30,500
- LONG signal
**Entry:**
- Enter at $30,500
**Risk Management:**
- Stop: $29,500 (back in range)
- Target: $32,000 (range height = $2k)
- Risk: $1,000
- Position: 0.2 BTC ($200 risk on $10k)
**Outcome:**
- Price runs to $33,000
- Target exceeded
- Profit: $2,500 × 0.2 = $500
- Return: 2.5 × risk
---
### Example 4: Avoiding Chaos - Tesla
**Setup:**
- Regime: BULL TRENDING
- LONG position from $240
- Elon tweets something crazy
- Regime changes to CHAOS
**Action:**
- EXIT signal appears
- Close position immediately
- Current price: $242 (small profit)
**Outcome:**
- Next 3 days: wild swings
- High $255, Low $230
- By staying out, avoided:
- Potential stop out
- Whipsaw losses
- Stress
**Result:**
- Small profit preserved
- Capital protected
- Re-enter when regime stabilizes
---
## ALERTS SETUP
### Available Alerts
1. **Bull Trending Regime** - Market goes bullish
2. **Bear Trending Regime** - Market goes bearish
3. **Chaos Regime** - High volatility, stay out
4. **Long Entry Signal** - Buy opportunity
5. **Short Entry Signal** - Sell opportunity
6. **Long Exit Signal** - Close long
7. **Short Exit Signal** - Close short
### How to Set Up
1. Click **⏰ (Alert)** icon in TradingView
2. Select **Condition**: Choose indicator + alert type
3. **Options**: Popup, Email, Webhook, etc.
4. **Message**: Customize notification
5. Click **Create**
### Recommended Alert Strategy
**For Active Traders:**
- Long Entry Signal
- Short Entry Signal
- Long Exit Signal
- Short Exit Signal
**For Position Traders:**
- Bull Trending Regime (enter longs)
- Bear Trending Regime (enter shorts)
- Chaos Regime (exit all)
**For Conservative:**
- Only regime change alerts
- Manually review entries
- More selective
---
## TIPS FOR SUCCESS
### 1. Start Small
- Paper trade first
- Then 0.5% risk
- Build to 1-2% over time
### 2. Follow the Regime
- Don't fight it
- Adapt your style
- Different tactics for each
### 3. Trust the Confluence
- 4-5/5 = Best trades
- 2-3/5 = Good trades
- 1/5 = Skip unless desperate
### 4. Respect Exits
- Don't hope and hold
- Cut losses quickly
- Take profits at targets
### 5. Avoid Chaos
- Seriously, just stay out
- Protect your capital
- Wait for clarity
### 6. Keep a Journal
- Record every trade
- Note regime and confluence
- Review weekly
- Learn patterns
### 7. Backtest Thoroughly
- 3+ years minimum
- Multiple market conditions
- Different assets
- Walk-forward test
### 8. Be Patient
- Best setups are rare
- 1-3 trades per week is normal
- Quality over quantity
- Compound over time
---
## COMMON QUESTIONS
**Q: How many trades per month should I expect?**
A: Depends on timeframe and settings. Daily chart: 5-15 trades/month. 4H chart: 15-30 trades/month.
**Q: What's a good win rate?**
A: 55-65% is excellent. 50-55% is good. Below 50% needs adjustment.
**Q: Should I trade all regimes?**
A: Beginners: Only trending. Intermediate: Trending + ranging. Advanced: All except chaos.
**Q: Can I use this on any timeframe?**
A: Best on Daily and 4H. Works on 1H with more noise. Not recommended <1H.
**Q: What if I'm in a trade and regime changes?**
A: Exit immediately (if using indicator) or let strategy handle it automatically.
**Q: How do I know if I'm over-optimizing?**
A: If results are perfect on one period but fail on another. Use walk-forward testing.
**Q: Should I always take 5/5 confluence trades?**
A: Yes, but they're rare (1-2/month). Don't wait only for these.
**Q: Can I combine this with other indicators?**
A: Yes, but keep it simple. RSI, MACD already included. Maybe add volume profile.
**Q: What assets work best?**
A: Liquid stocks, major crypto, futures. Avoid forex spot (use futures), penny stocks.
**Q: How long to hold positions?**
A: Trending: Days to weeks. Ranging: Hours to days. Breakout: Days. Let the regime guide you.
---
## FINAL THOUGHTS
This system gives you:
- ✅ Clear market context (regime)
- ✅ High-probability entries (confluence)
- ✅ Defined exits (automatic signals)
- ✅ Adaptable tactics (regime-specific)
- ✅ Backtestable results (strategy version)
**Success requires:**
- 📚 Understanding each regime
- 🎯 Following the signals
- 💪 Discipline to wait
- 🧠 Emotional control
- 📊 Proper risk management
**Start your journey:**
1. Load the indicator
2. Watch for 1 week (no trading)
3. Identify regime patterns
4. Paper trade for 1 month
5. Go live with small size
6. Scale up as you gain confidence
**Remember:** The market will always be here. There's no rush. Master one regime at a time, and you'll be profitable in all conditions!
Good luck! 🚀
Dynamic Pivot Point [MarkitTick]Title: Dynamic Pivot Point MarkitTick
Concept
Unlike traditional Pivot Points, which plot static horizontal levels based on the previous period's High, Low, and Close, this script introduces a dynamic element by applying an Exponential Moving Average (EMA) to the calculated pivot levels. This approach allows the Support and Resistance zones to adapt more fluidly to recent price action, reducing the jagged steps often seen in standard multi-timeframe pivot indicators.
How It Works
The script operates in two distinct phases of calculation:
1. Data Extraction and Core Math:
The indicator first requests the High, Low, and Close data from a user-defined timeframe (e.g., Daily, Weekly). Using this data, it calculates the standard Pivot Point (P) alongside three levels of Support (S1, S2, S3) and three levels of Resistance (R1, R2, R3) using standard geometric formulas:
Pivot = (High + Low + Close) / 3
R1 = 2 * Pivot - Low
S1 = 2 * Pivot - High
(Subsequent levels follow standard Floor Pivot logic).
2. Dynamic Smoothing:
Instead of plotting these raw values directly, the script processes each calculated level (P, S1-S3, R1-R3) through an Exponential Moving Average (EMA). The length of this EMA is controlled by the Pivot Length input. This smoothing process filters out minor volatility and creates curved, dynamic trajectories for the pivot levels rather than static straight lines.
How to Use
Traders can use this tool to identify dynamic areas of interest where price may react.
The White Line represents the Central Pivot. Price action relative to this line helps determine the immediate bias (above for bullish, below for bearish).
Green Lines (Support 1, 2, 3) indicate potential demand zones where price may bounce during a downtrend.
Red Lines (Resistance 1, 2, 3) indicate potential supply zones where price may reject during an uptrend.
Because the levels are smoothed, they can also act as dynamic trend followers, similar to moving averages, but derived from pivot geometry.
Settings
Show Pivot Points: Toggles the visibility of the plot lines on the chart.
Pivot Length: Defines the lookback period for the EMA smoothing applied to the pivot levels. A higher number results in smoother, slower-reacting lines.
Timeframe: Determines the timeframe used for the underlying High/Low/Close data (e.g., selecting "D" calculates pivots based on Daily data while viewing a lower timeframe chart).
Disclaimer This tool is for educational and technical analysis purposes only. Breakouts can fail (fake-outs), and past geometric patterns do not guarantee future price action. Always manage risk and use this tool in conjunction with other forms of analysis.
Jurik Angle Flow [Kodexius]Jurik Angle Flow is a Jurik based momentum and trend strength oscillator that converts Jurik Moving Average behavior into an intuitive angle based flow gauge. Instead of showing a simple moving average line, this tool measures the angular slope of a smoothed Jurik curve, normalizes it and presents it as a bounded oscillator between plus ninety and minus ninety degrees.
The script uses two Jurik engines with different responsiveness, then blends their information into a single power score that drives both the oscillator display and the on chart gauge. This makes it suitable for identifying trend direction, trend strength, exhaustion conditions and early shifts in market structure. Built in divergence detection between price and the Jurik angle slope helps highlight potential reversal zones while bar coloring and a configurable no trade zone assist with visual filtering of choppy conditions.
🔹 Features
🔸 Dual Jurik slope engine
The indicator internally runs two Jurik Moving Average calculations on the selected source price. A slower Jurik stream models the primary trend while a faster Jurik stream reacts more quickly to recent changes. Their slopes are measured as angles in degrees, scaled by Average True Range so that the slope is comparable across different instruments and timeframes.
🔸 Angle based oscillator output
Both Jurik streams are converted into angle values by comparing the current value to a lookback value and normalizing by ATR. The result is passed through the arctangent function and expressed in degrees. This creates a smooth oscillator that directly represents steepness and direction of the Jurik curve instead of raw price distance.
🔸 Normalized power score
The angle values are transformed into a normalized score between zero and one hundred based on their absolute magnitude, then the sign of the angle is reapplied. This yields a symmetric score where extreme positive values represent strong bullish pressure and extreme negative values represent strong bearish pressure. The final power score is a weighted blend of the slow and fast Jurik scores.
🔸 Adaptive color gradients
The main oscillator area and the fast slope line use gradient colors that react to the angle strength and direction. Rising green tones reflect bullish angular momentum while red tones reflect bearish pressure. Neutral or shallow slopes remain visually softer to indicate indecision or consolidation.
🔸 Trend flip markers
Whenever the primary Jurik slope crosses through zero from negative to positive, an up marker is printed at the bottom of the oscillator panel. Whenever it crosses from positive to negative, a down marker is drawn at the top. These flips act as clean visual signals of potential trend initiation or termination.
🔸 Divergence detection on Jurik slope
The script optionally scans the fast Jurik slope for pivot highs and lows. It then compares those oscillator pivots against corresponding price pivots.
Regular bullish divergence is detected when the oscillator prints a higher low while price prints a lower low.
Regular bearish divergence is detected when the oscillator prints a lower high while price prints a higher high.
When detected, the tool draws matching divergence lines both on the oscillator and on the chart itself, making divergence zones easy to notice at a glance.
🔸 Bar coloring and no trade filter
Bars can be colored according to the primary Jurik slope gradient so that price bars reflect the same directional information as the oscillator. Additionally a configurable no trade threshold can visually mute bars when the absolute angle is small. This highlights trending sequences and visually suppresses noisy sideways stretches.
🔸 On chart power gauge
A creative on chart gauge displays the composite power score beside the current price action. It shows a vertical range from plus ninety to minus ninety with a filled block that grows proportionally to the normalized score. Color and label updates occur in real time and provide a quick visual summary of current Jurik flow strength without needing to read exact oscillator levels.
🔹 Calculations
Below are the main calculation blocks that drive the core logic of Jurik Angle Flow.
Jurik core update
method update(JMA self, float _src) =>
self.src := _src
float phaseRatio = self.phase < -100 ? 0.5 : self.phase > 100 ? 2.5 : self.phase / 100.0 + 1.5
float beta = 0.45 * (self.length - 1) / (0.45 * (self.length - 1) + 2)
float alpha = math.pow(beta, self.power)
if na(self.e0)
self.e0 := _src
self.e1 := 0.0
self.e2 := 0.0
self.jma := 0.0
self.e0 := (1 - alpha) * _src + alpha * self.e0
self.e1 := (_src - self.e0) * (1 - beta) + beta * self.e1
float prevJma = self.jma
self.e2 := (self.e0 + phaseRatio * self.e1 - prevJma) * math.pow(1 - alpha, 2) + math.pow(alpha, 2) * self.e2
self.jma := self.e2 + prevJma
self.jma
This method implements the Jurik Moving Average engine with internal state and phase control, producing a smooth adaptive value stored in self.jma.
Angle calculation in degrees
method getAngle(float src, int lookback=1) =>
float rad2degree = 180 / math.pi
float slope = (src - src ) / ta.atr(14)
float ang = rad2degree * math.atan(slope)
ang
The slope between the current value and a lookback value is divided by ATR, then converted from radians to degrees through the arctangent. This creates a volatility normalized angle oscillator.
Normalized score from angle
method normScore(float ang) =>
float s = math.abs(ang)
float p = s / 60.0 * 100.0
if p > 100
p := 100
p
The absolute angle is scaled so that sixty degrees corresponds to a score of one hundred. Values above that are capped, which keeps the final score within a fixed range. The sign is later reapplied to restore direction.
Slow and fast Jurik streams and power score
var JMA jmaSlow = JMA.new(jmaLen, jmaPhase, jmaPower, na, na, na, na, na)
var JMA jmaFast = JMA.new(jmaLen, jmaPhase, 2.0, na, na, na, na, na)
float jmaValue = jmaSlow.update(src)
float jmaFastValue = jmaFast.update(src)
float jmaSlope = jmaValue.getAngle()
float jmaFastSlope = jmaFastValue.getAngle()
float scoreJma = normScore(jmaSlope) * math.sign(jmaSlope)
float scoreJmaFast = normScore(jmaFastSlope) * math.sign(jmaFastSlope)
float totalScore = (scoreJma * 0.6 + scoreJmaFast * 0.4)
A slower Jurik and a faster Jurik are updated on each bar, each converted to an angle and then to a signed normalized score. The final composite power score is a weighted blend of the slow and fast scores, where the slow score has slightly more influence. This composite drives the on chart gauge and summarizes the overall Jurik flow.
UM VIX30-rolling/VIX Ratio oscillatorSUMMARY
A forward-looking volatility tool that often signals VIX spikes and market reversals before they happen. MA direction flips spotlight the moment volatility pressure shifts.
DESCRIPTION
This indicator compares spot VIX to a synthetic 30-day constant-maturity volatility estimate (“VIX30”) built from VX1 and VX2 futures. The VIX30/VIX Ratio reveals short-term volatility pressure and regime shifts that traditional VX1/VX2 roll-yield alone often misses.
VIX30 is constructed using true calendar-day interpolation between VX1 and VX2, with VX1% and VX2% showing the real-time weights behind the 30-day volatility anchor. The table displays the volatility regime, the VX1/VX2 weights, spot-term roll yield (VIX30/VIX), and futures-term roll yield (VX2/VX1), giving a complete, front-of-the-curve perspective on volatility dynamics.
Use this to spot early vol expansions, collapsing contango, and regime transitions that influence VXX, UVXY, SVIX, VX options, and VIX futures.
⸻
HOW IT WORKS
The script calculates the exact calendar days to expiration for the front two VIX futures. It then applies linear interpolation to blend VX1 and VX2 into a 30-day constant-maturity synthetic volatility measure (“VIX30”). Comparing VIX30 to spot VIX produces the VIX30/VIX Ratio, which highlights short-term volatility pressure and regime direction. A full term-structure table summarizes regime, VX1%/VX2% weights, and both spot-term and futures-term roll yields.
⸻
DEFAULT SETTINGS
VX1! and VX2! are used by default for front-month and second-month futures. These may be manually overridden if TradingView rolls contracts early. The default timeframe is 30 minutes, and the VIX30/VIX Ratio uses a 21-period EMA for regime smoothing. The historical threshold is set to 1.08, reflecting the long-run average relationship between VIX30 and VIX. All settings are user-configurable.
⸻
SUGGESTED USES
• Identify early volatility expansions before they appear in VX1/VX2 roll yield.
• Confirm contango/backwardation shifts with front-of-curve context.
• Time long/short volatility trades in VXX, UVXY, SVIX, and VX options.
• Monitor regime transitions (Low → Cautionary → High) to anticipate trend inflections.
• Combine with price action, NW trends, or MA color-flip systems for higher-confidence entries.
• MA red → green flips may signal opportunities to short volatility or increase equity exposure.
• MA green → red flips may signal opportunities to go long volatility, reduce equity exposure, or even take short-equity positions.
⸻
ALERTS
Alerts trigger when the ratio crosses above or below the historical threshold or when the moving-average slope flips direction. A green flip signals rising volatility pressure; a red flip signals fading or collapsing volatility. These can be used to automate long/short volatility bias shifts or trade-entry notifications.
⸻
FURTHER HINTS
• Increasing orange/red in the table suggests an emerging higher-volatility environment.
• SVIX (inverse volatility ETF) can trend strongly when volatility decays; on a 6h chart, MA green flips often align with attractive short-volatility opportunities.
• For long-volatility trades, consider shrinking to a 30-minute chart and watching for MA green → red flips as early entry cues.
• Experiment with different timeframes and smoothing lengths to match your trading style.
• Higher VIX30/VIX and VX2/VX1 roll yields generally imply faster decay in VXX, UVXY, and UVIX — or stronger upside momentum in SVIX.
Physics of PricePhysics of Price is a non-repainting kinematic reversal and volatility overlay. It models price as a physical object with position, velocity, and acceleration, then builds adaptive bands and a short-term predictive “ghost cone” to highlight where reversals are statistically more likely.
CONCEPT
Instead of using only moving averages, the core engine tracks a smoothed price (position), trend speed (velocity), and change in trend speed (acceleration). Standard deviation of the model error defines probabilistic bands around this kinematic centerline. When price stretches too far away and snaps back, the move is treated as a potential exhaustion event.
CORE COMPONENTS
– Kinematic centerline (Alpha–Beta–Gamma style filter) that bends with trend instead of lagging like a simple MA.
– Inner and outer bands based on the standard deviation of residuals between price and the kinematic model.
– Regime filter using R² and band width to avoid signals in chaotic or ultra-wide regimes.
– Optional RSI “hook” filter that waits for momentum to actually turn instead of buying into a falling RSI.
– Optional divergence add-on using kinematic velocity, so a marginal new price extreme with weaker velocity is recognized as a possible exhaustion pattern.
REVERSAL EVENTS AND SCORING
Raw events are detected when price wicks through the outer band and closes back inside (band hit with snap). These are plotted as diamonds and treated as candidates, not automatic trades.
Each event is then scored from 0 to 100 using several factors:
– How far price overshot the outer band.
– How strongly it snapped back inside.
– Whether an RSI hook is present (if enabled).
– Regime quality from the kinematic model.
– Basic kinematic safety to avoid the most aggressive “knife-catch” situations.
– Optional divergence bonus when price makes a new extreme but velocity does not.
Only events with a score above the chosen threshold become confirmed signals (triangles labeled PHYSICS REV).
GHOST CONE (PREDICTIVE BAND)
On the latest bar, the script projects a short-horizon “ghost cone” into the future using position, velocity, and a damped acceleration term. This creates a curved predictive band that visualizes a plausible short-term path and range, rather than a simple straight line. The cone is meant as context for trade management and risk, not as a hard target.
FILTERS AND OPTIONS
– Regime filter (R² and band width) can be tightened or relaxed depending on how selective you want the engine to be.
– RSI and volume filters can be toggled on for extra confirmation or off to see the raw kinematic behavior.
– An optional trend baseline (EMA) can be enabled to bias or restrict reversals relative to a higher-timeframe trend.
– Dynamic cooldown scales with volatility so the script does not spam signals in fast environments.
HOW TO USE
Physics of Price is primarily a mean-reversion and exhaustion tool. It works best in markets that respect ranges, swings, and two-sided order flow. Confirmed PHYSICS REV signals near the outer bands, with decent model health and a clean RSI hook, are the core use case. The bands and ghost cone can also be used as a context overlay alongside your own entries, exits, and risk framework.
This is an indicator, not a complete trading system. It does not use lookahead or higher-timeframe security calls and is designed for “once per bar close” alerts. Always combine it with your own risk management and confluence.
RSI-Adaptive T3 & SAR Strategy [PrimeAutomation]⯁ OVERVIEW
The RSI-Adaptive T3 and SAR Confluence Strategy combines adaptive smoothing with dynamic trend confirmation to identify precise trend reversals and continuation opportunities. It fuses the power of an RSI-based adaptive T3 moving average with the Parabolic SAR filter , aiming to trade in harmony with dominant momentum shifts while maintaining tight control through automatic stop-loss placement.
The RSI-Adaptive T3 is a precision trend-following tool built around the legendary T3 smoothing algorithm developed by Tim Tillson, designed to enhance responsiveness while reducing lag compared to traditional moving averages. Current implementation takes it a step further by dynamically adapting the smoothing length based on real-time RSI conditions — allowing the T3 to “breathe” with market volatility. This dynamic length makes the curve faster in trending moves and smoother during consolidations.
To help traders visualize volatility and directional momentum, adaptive volatility bands are plotted around the T3 line, with visual crossover markers and a dynamic info panel on the chart. It’s ideal for identifying trend shifts, spotting momentum surges, and adapting strategy execution to the pace of the market.
⯁ LOGIC
The T3 moving average length dynamically adjusts based on RSI values — when RSI is high, the smoothing period shortens to react faster; when RSI is low, the period increases for stability in slow markets.
A Parabolic SAR filter confirms directional bias, ensuring trades only occur in alignment with the broader market trend.
Long Entries: Trigger when the T3 curve crosses upward while the current price remains above the SAR — signaling bullish momentum alignment.
Short Entries: Trigger when the T3 crosses downward while the price remains below the SAR — confirming bearish trend alignment.
Stops: Dynamic stops are placed using the highest or lowest price over a set lookback period, adapting automatically to market volatility.
⯁ FEATURES
RSI-Adaptive T3 Filter: Adjusts smoothing in real time to market conditions, blending responsiveness with noise reduction.
SAR Confluence Check: Prevents counter-trend entries by confirming momentum direction via the Parabolic SAR.
Automatic Stop Placement: Uses recent highs or lows as stop-loss anchors, minimizing risk exposure.
Color-coded Visualization: The T3 line dynamically changes color based on slope direction, making momentum shifts visually intuitive.
Smoothed Trend Structure: Reduces market noise, allowing cleaner, more reliable trend recognition across different assets.
⯁ CONCLUSION
The RSI-Adaptive T3 and SAR Confluence Strategy delivers an advanced fusion of adaptive smoothing and structural confirmation. By combining RSI-driven reactivity with Parabolic SAR trend validation, this strategy offers a balanced approach to identifying sustainable momentum reversals while maintaining strong risk management through automatic stop levels. Ideal for traders who seek precision entries aligned with adaptive trend dynamics.
Coin Jin Multi SMA+ BB+ SMA forecast Ver2.02This script provides a complete trend-analysis system based on the
5 / 20 / 60 / 112 / 224 / 448 / 896 SMAs.
It precisely detects bullish/bearish alignment and automatically identifies
12 advanced trend-shift signals (Start, End, and Reversal).
Key Features:
● 9 SMA lines (including custom X1 & X2)
Each SMA supports custom color, width, and style (Line/Step/Circles).
● Bollinger Bands with customizable options
Fully adjustable length, source, width, style, fill transparency, and more.
● SMA Forecast (curved projection)
– Slope computed via linear regression
– Predicts up to 30 future bars
– Forced dotted style ensures visibility at all zoom levels
● 12 Advanced Trend Signals (alertcondition)
Automatically detects:
Start of full alignment (with/without SMA 896)
End of alignment
Bull ↔ Bear transitions
Perfect for momentum trading, trend-following, reversal detection, or automated alert systems.
● Labeling last value of each SMA
Each SMA prints a label such as "5", “20”, “60”, “896”, or custom lengths at the latest bar.
이 스크립트는 5 / 20 / 60 / 112 / 224 / 448 / 896 이동평균선을 기반으로
정배열·역배열 상태를 정밀하게 분석하고,
총 12가지 고급 추세 신호(시작·종료·전환) 를 자동으로 감지하는 통합 추세 분석 도구입니다.
주요 기능:
● 9개의 SMA 표시 (커스텀 X1, X2 포함)
각 SMA는 색상·굵기·형태(Line/Step/Circle)를 개별 설정할 수 있습니다.
● 볼린저밴드 표시 및 채우기 옵션
BB 길이, 소스, 타입, 두께, 투명도 등을 자유롭게 조절 가능.
● SMA Forecast (미래 방향 곡선 예측)
– 기울기 기반 선형회귀 슬로프 계산
– 곡선 형태로 미래 30봉까지 예측
– 점선(Dotted) 강제 적용으로 어떤 배율에서도 선명하게 표시
● 12가지 고급 추세 신호(alertcondition)
정배열·역배열의
Start (처음 완성될 때)
End (깨질 때)
Switch (전환)
을 모두 자동 탐지하여 트레이딩뷰 알림으로 받을 수 있음.
● SMA 마지막 가격 라벨 표시
각 SMA 끝 지점에 “5 / 20 / 60 / ... / 896” 식으로 라벨 표시.
Volatility-Targeted Momentum Portfolio [BackQuant]Volatility-Targeted Momentum Portfolio
A complete momentum portfolio engine that ranks assets, targets a user-defined volatility, builds long, short, or delta-neutral books, and reports performance with metrics, attribution, Monte Carlo scenarios, allocation pie, and efficiency scatter plots. This description explains the theory and the mechanics so you can configure, validate, and deploy it with intent.
Table of contents
What the script does at a glance
Momentum, what it is, how to know if it is present
Volatility targeting, why and how it is done here
Portfolio construction modes: Long Only, Short Only, Delta Neutral
Regime filter and when the strategy goes to cash
Transaction cost modelling in this script
Backtest metrics and definitions
Performance attribution chart
Monte Carlo simulation
Scatter plot analysis modes
Asset allocation pie chart
Inputs, presets, and deployment checklist
Suggested workflow
1) What the script does at a glance
Pulls a list of up to 15 tickers, computes a simple momentum score on each over a configurable lookback, then volatility-scales their bar-to-bar return stream to a target annualized volatility.
Ranks assets by raw momentum, selects the top 3 and bottom 3, builds positions according to the chosen mode, and gates exposure with a fast regime filter.
Accumulates a portfolio equity curve with risk and performance metrics, optional benchmark buy-and-hold for comparison, and a full alert suite.
Adds visual diagnostics: performance attribution bars, Monte Carlo forward paths, an allocation pie, and scatter plots for risk-return and factor views.
2) Momentum: definition, detection, and validation
Momentum is the tendency of assets that have performed well to continue to perform well, and of underperformers to continue underperforming, over a specific horizon. You operationalize it by selecting a horizon, defining a signal, ranking assets, and trading the leaders versus laggards subject to risk constraints.
Signal choices . Common signals include cumulative return over a lookback window, regression slope on log-price, or normalized rate-of-change. This script uses cumulative return over lookback bars for ranking (variable cr = price/price - 1). It keeps the ranking simple and lets volatility targeting handle risk normalization.
How to know momentum is present .
Leaders and laggards persist across adjacent windows rather than flipping every bar.
Spread between average momentum of leaders and laggards is materially positive in sample.
Cross-sectional dispersion is non-trivial. If everything is flat or highly correlated with no separation, momentum selection will be weak.
Your validation should include a diagnostic that measures whether returns are explained by a momentum regression on the timeseries.
Recommended diagnostic tool . Before running any momentum portfolio, verify that a timeseries exhibits stable directional drift. Use this indicator as a pre-check: It fits a regression to price, exposes slope and goodness-of-fit style context, and helps confirm if there is usable momentum before you force a ranking into a flat regime.
3) Volatility targeting: purpose and implementation here
Purpose . Volatility targeting seeks a more stable risk footprint. High-vol assets get sized down, low-vol assets get sized up, so each contributes more evenly to total risk.
Computation in this script (per asset, rolling):
Return series ret = log(price/price ).
Annualized volatility estimate vol = stdev(ret, lookback) * sqrt(tradingdays).
Leverage multiplier volMult = clamp(targetVol / vol, 0.1, 5.0).
This caps sizing so extremely low-vol assets don’t explode weight and extremely high-vol assets don’t go to zero.
Scaled return stream sr = ret * volMult. This is the per-bar, risk-adjusted building block used in the portfolio combinations.
Interpretation . You are not levering your account on the exchange, you are rescaling the contribution each asset’s daily move has on the modeled equity. In live trading you would reflect this with position sizing or notional exposure.
4) Portfolio construction modes
Cross-sectional ranking . Assets are sorted by cr over the chosen lookback. Top and bottom indices are extracted without ties.
Long Only . Averages the volatility-scaled returns of the top 3 assets: avgRet = mean(sr_top1, sr_top2, sr_top3). Position table shows per-asset leverages and weights proportional to their current volMult.
Short Only . Averages the negative of the volatility-scaled returns of the bottom 3: avgRet = mean(-sr_bot1, -sr_bot2, -sr_bot3). Position table shows short legs.
Delta Neutral . Long the top 3 and short the bottom 3 in equal book sizes. Each side is sized to 50 percent notional internally, with weights within each side proportional to volMult. The return stream mixes the two sides: avgRet = mean(sr_top1,sr_top2,sr_top3, -sr_bot1,-sr_bot2,-sr_bot3).
Notes .
The selection metric is raw momentum, the execution stream is volatility-scaled returns. This separation is deliberate. It avoids letting volatility dominate ranking while still enforcing risk parity at the return contribution stage.
If everything rallies together and dispersion collapses, Long Only may behave like a single beta. Delta Neutral is designed to extract cross-sectional momentum with low net beta.
5) Regime filter
A fast EMA(12) vs EMA(21) filter gates exposure.
Long Only active when EMA12 > EMA21. Otherwise the book is set to cash.
Short Only active when EMA12 < EMA21. Otherwise cash.
Delta Neutral is always active.
This prevents taking long momentum entries during obvious local downtrends and vice versa for shorts. When the filter is false, equity is held flat for that bar.
6) Transaction cost modelling
There are two cost touchpoints in the script.
Per-bar drag . When the regime filter is active, the per-bar return is reduced by fee_rate * avgRet inside netRet = avgRet - (fee_rate * avgRet). This models proportional friction relative to traded impact on that bar.
Turnover-linked fee . The script tracks changes in membership of the top and bottom baskets (top1..top3, bot1..bot3). The intent is to charge fees when composition changes. The template counts changes and scales a fee by change count divided by 6 for the six slots.
Use case: increase fee_rate to reflect taker fees and slippage if you rebalance every bar or trade illiquid assets. Reduce it if you rebalance less often or use maker orders.
Practical advice .
If you rebalance daily, start with 5–20 bps round-trip per switch on liquid futures and adjust per venue.
For crypto perp microcaps, stress higher cost assumptions and add slippage buffers.
If you only rotate on lookback boundaries or at signals, use alert-driven rebalances and lower per-bar drag.
7) Backtest metrics and definitions
The script computes a standard set of portfolio statistics once the start date is reached.
Net Profit percent over the full test.
Max Drawdown percent, tracked from running peaks.
Annualized Mean and Stdev using the chosen trading day count.
Variance is the square of annualized stdev.
Sharpe uses daily mean adjusted by risk-free rate and annualized.
Sortino uses downside stdev only.
Omega ratio of sum of gains to sum of losses.
Gain-to-Pain total gains divided by total losses absolute.
CAGR compounded annual growth from start date to now.
Alpha, Beta versus a user-selected benchmark. Beta from covariance of daily returns, Alpha from CAPM.
Skewness of daily returns.
VaR 95 linear-interpolated 5th percentile of daily returns.
CVaR average of the worst 5 percent of daily returns.
Benchmark Buy-and-Hold equity path for comparison.
8) Performance attribution
Cumulative contribution per asset, adjusted for whether it was held long or short and for its volatility multiplier, aggregated across the backtest. You can filter to winners only or show both sides. The panel is sorted by contribution and includes percent labels.
9) Monte Carlo simulation
The panel draws forward equity paths from either a Normal model parameterized by recent mean and stdev, or non-parametric bootstrap of recent daily returns. You control the sample length, number of simulations, forecast horizon, visibility of individual paths, confidence bands, and a reproducible seed.
Normal uses Box-Muller with your seed. Good for quick, smooth envelopes.
Bootstrap resamples realized returns, preserving fat tails and volatility clustering better than a Gaussian assumption.
Bands show 10th, 25th, 75th, 90th percentiles and the path mean.
10) Scatter plot analysis
Four point-cloud modes, each plotting all assets and a star for the current portfolio position, with quadrant guides and labels.
Risk-Return Efficiency . X is risk proxy from leverage, Y is expected return from annualized momentum. The star shows the current book’s composite.
Momentum vs Volatility . Visualizes whether leaders are also high vol, a cue for turnover and cost expectations.
Beta vs Alpha . X is a beta proxy, Y is risk-adjusted excess return proxy. Useful to see if leaders are just beta.
Leverage vs Momentum . X is volMult, Y is momentum. Shows how volatility targeting is redistributing risk.
11) Asset allocation pie chart
Builds a wheel of current allocations.
Long Only, weights are proportional to each long asset’s current volMult and sum to 100 percent.
Short Only, weights show the short book as positive slices that sum to 100 percent.
Delta Neutral, 50 percent long and 50 percent short books, each side leverage-proportional.
Labels can show asset, percent, and current leverage.
12) Inputs and quick presets
Core
Portfolio Strategy . Long Only, Short Only, Delta Neutral.
Initial Capital . For equity scaling in the panel.
Trading Days/Year . 252 for stocks, 365 for crypto.
Target Volatility . Annualized, drives volMult.
Transaction Fees . Per-bar drag and composition change penalty, see the modelling notes above.
Momentum Lookback . Ranking horizon. Shorter is more reactive, longer is steadier.
Start Date . Ensure every symbol has data back to this date to avoid bias.
Benchmark . Used for alpha, beta, and B&H line.
Diagnostics
Metrics, Equity, B&H, Curve labels, Daily return line, Rolling drawdown fill.
Attribution panel. Toggle winners only to focus on what matters.
Monte Carlo mode with Normal or Bootstrap and confidence bands.
Scatter plot type and styling, labels, and portfolio star.
Pie chart and labels for current allocation.
Presets
Crypto Daily, Long Only . Lookback 25, Target Vol 50 percent, Fees 10 bps, Regime filter on, Metrics and Drawdown on. Monte Carlo Bootstrap with Recent 200 bars for bands.
Crypto Daily, Delta Neutral . Lookback 25, Target Vol 50 percent, Fees 15–25 bps, Regime filter always active for this mode. Use Scatter Risk-Return to monitor efficiency and keep the star near upper left quadrants without drifting rightward.
Equities Daily, Long Only . Lookback 60–120, Target Vol 15–20 percent, Fees 5–10 bps, Regime filter on. Use Benchmark SPX and watch Alpha and Beta to keep the book from becoming index beta.
13) Suggested workflow
Universe sanity check . Pick liquid tickers with stable data. Thin assets distort vol estimates and fees.
Check momentum existence . Run on your timeframe. If slope and fit are weak, widen lookback or avoid that asset or timeframe.
Set risk budget . Choose a target volatility that matches your drawdown tolerance. Higher target increases turnover and cost sensitivity.
Pick mode . Long Only for bull regimes, Short Only for sustained downtrends, Delta Neutral for cross-sectional harvesting when index direction is unclear.
Tune lookback . If leaders rotate too often, lengthen it. If entries lag, shorten it.
Validate cost assumptions . Increase fee_rate and stress Monte Carlo. If the edge vanishes with modest friction, refine selection or lengthen rebalance cadence.
Run attribution . Confirm the strategy’s winners align with intuition and not one unstable outlier.
Use alerts . Enable position change, drawdown, volatility breach, regime, momentum shift, and crash alerts to supervise live runs.
Important implementation details mapped to code
Momentum measure . cr = price / price - 1 per symbol for ranking. Simplicity helps avoid overfitting.
Volatility targeting . vol = stdev(log returns, lookback) * sqrt(tradingdays), volMult = clamp(targetVol / vol, 0.1, 5), sr = ret * volMult.
Selection . Extract indices for top1..top3 and bot1..bot3. The arrays rets, scRets, lev_vals, and ticks_arr track momentum, scaled returns, leverage multipliers, and display tickers respectively.
Regime filter . EMA12 vs EMA21 switch determines if the strategy takes risk for Long or Short modes. Delta Neutral ignores the gate.
Equity update . Equity multiplies by 1 + netRet only when the regime was active in the prior bar. Buy-and-hold benchmark is computed separately for comparison.
Tables . Position tables show current top or bottom assets with leverage and weights. Metric table prints all risk and performance figures.
Visualization panels . Attribution, Monte Carlo, scatter, and pie use the last bars to draw overlays that update as the backtest proceeds.
Final notes
Momentum is a portfolio effect. The edge comes from cross-sectional dispersion, adequate risk normalization, and disciplined turnover control, not from a single best asset call.
Volatility targeting stabilizes path but does not fix selection. Use the momentum regression link above to confirm structure exists before you size into it.
Always test higher lag costs and slippage, then recheck metrics, attribution, and Monte Carlo envelopes. If the edge persists under stress, you have something robust.
VWAP Trend
**Overview**
The VWAP Trend indicator is a volume-weighted price analysis tool that visualizes the relationship between price and the anchored Volume Weighted Average Price (VWAP) over different timeframes. This script is designed to reveal when the market is trending above or below its volume-weighted equilibrium point, providing a clear framework for identifying directional bias, trend strength, and potential reversals.
By combining an anchored VWAP with exponential smoothing and a secondary trend EMA, the indicator helps traders distinguish between short-term price fluctuations and genuine volume-supported directional moves.
**Core Concept**
VWAP (Volume Weighted Average Price) represents the average price of an asset weighted by traded volume. It reflects where the majority of trading activity has taken place within a chosen period, serving as a critical reference level for institutions and professional traders.
This indicator extends the traditional VWAP concept by:
1. Allowing users to **anchor VWAP to different timeframes** (Daily, Weekly, or Monthly).
2. Applying **smoothing** to create a stable reference curve less prone to noise.
3. Overlaying a **trend EMA** to identify whether current price momentum aligns with or diverges from VWAP equilibrium.
The combination of these elements produces a visual representation of price’s relationship to its fair value across time, helping to identify accumulation and distribution phases.
**Calculation Methodology**
1. **Anchored VWAP Calculation:**
The script resets cumulative volume and cumulative volume–price data at the start of each new VWAP session (based on the selected anchor timeframe). It continuously accumulates the product of price and volume, dividing this by total volume to compute the current VWAP value.
2. **Smoothing Process:**
The raw VWAP line is smoothed using an Exponential Moving Average (EMA) of user-defined length, producing a cleaner, more stable trend curve that minimizes intraperiod noise.
3. **Trend Determination:**
An additional EMA is calculated on the closing price. By comparing the position of this EMA to the smoothed VWAP, the indicator determines the prevailing market bias:
* When the trend EMA is above the smoothed VWAP, the market is considered to be in an **uptrend**.
* When the trend EMA is below the smoothed VWAP, the market is classified as a **downtrend**.
**Visual Structure**
The indicator uses color dynamics and chart overlays to make interpretation intuitive:
* **Smoothed VWAP Line:** The main trend reference, colored blue during bullish conditions and orange during bearish conditions.
* **Price Fill Region:** The area between the smoothed VWAP and price is filled with a translucent color matching the current trend, visually representing whether price is trading above or below equilibrium.
* **Trend EMA (implicit):** Although not separately plotted, it drives the color state of the VWAP, ensuring seamless visual transitions between bullish and bearish conditions.
**Inputs and Parameters**
* **VWAP Timeframe:** Choose between Daily, Weekly, or Monthly anchoring. This determines the reset frequency for cumulative volume and price data.
* **VWAP Smoothing Length:** Defines how many periods are used to smooth the VWAP line. Shorter values produce a more reactive line; longer values create smoother, steadier signals.
* **Trend EMA Length:** Sets the period for the trend detection EMA applied to price. Adjust this to calibrate how quickly the indicator reacts to directional changes.
**Interpretation and Use Cases**
* **Trend Confirmation:** When price and the trend EMA both remain above the smoothed VWAP, the market is showing strong bullish control. Conversely, consistent price action below the VWAP suggests sustained bearish sentiment.
* **Fair Value Assessment:** VWAP serves as a dynamic equilibrium level. Price repeatedly reverting to this line indicates consolidation or fair value zones, while strong directional moves away from VWAP highlight momentum phases.
* **Institutional Benchmarking:** Because large market participants often benchmark entries and exits relative to VWAP, this indicator helps align retail analysis with institutional logic.
* **Reversal Detection:** Sudden crossovers of the trend EMA relative to the VWAP can signal potential reversals or shifts in momentum strength.
**Trading Applications**
* **Trend Following:** Use VWAP’s direction and color state to determine trade bias. Long entries are favored when the VWAP turns blue, while short entries align with orange phases.
* **Mean Reversion:** In ranging conditions, traders may look for price deviations far above or below VWAP as potential reversion opportunities.
* **Multi-Timeframe Confluence:** Combine the Daily VWAP Trend with higher anchor periods (e.g., Weekly or Monthly) to confirm larger trend structure.
* **Support and Resistance Mapping:** VWAP often acts as a strong intraday or session-level support/resistance zone. The smoothed version refines this behavior into a cleaner, more reliable reference.
**Originality and Innovation**
The VWAP Trend indicator stands apart from conventional VWAP scripts through several original features:
1. **Anchor Flexibility:** Most VWAP indicators fix the anchor to a specific session (like daily). This version allows switching between Daily, Weekly, and Monthly anchors dynamically, adapting to various trading styles and time horizons.
2. **Volume-Weighted Smoothing:** The use of an EMA smoothing layer over the raw VWAP provides enhanced stability without compromising responsiveness, delivering a more analytically consistent signal.
3. **EMA-Based Trend Comparison:** By introducing a second trend EMA, the indicator creates a comparative framework that merges volume-weighted price analysis with classical momentum tracking — a rare and powerful combination.
4. **Adaptive Visual System:** The color-shifting and shaded fill between VWAP and price are integrated into a single, lightweight structure, giving traders immediate insight into market bias without the clutter of multiple overlapping indicators.
**Advantages**
* Adaptable to any market, timeframe, or trading style.
* Provides both equilibrium (VWAP) and momentum (EMA) perspectives.
* Smooths out noise while retaining the integrity of volume-based price dynamics.
* Enhances situational awareness through intuitive color-coded visualization.
* Ideal for professional, swing, and intraday traders seeking context-driven market direction.
**Summary**
The VWAP Trend indicator is a modern enhancement of the classical VWAP methodology. By merging anchored volume-weighted analysis with smoothed trend detection and visual state feedback, it provides a comprehensive perspective on market equilibrium and directional strength. It is built for traders who seek more than static price references — offering an adaptive, volume-aware framework for identifying market trends, reversals, and fair-value zones with precision and clarity.
Central Limit Theorem Reversion IndicatorDear TV community, let me introduce you to the first-ever Central Limit Theorem indicator on TradingView.
The Central Limit Theorem is used in statistics and it can be quite useful in quant trading and understanding market behaviors.
In short, the CLT states: "When you take repeated samples from any population and calculate their averages, those averages will form a normal (bell curve) distribution—no matter what the original data looks like."
In this CLT indicator, I use statistical theory to identify high-probability mean reversion opportunities in the markets. It calculates statistical confidence bands and z-scores to identify when price movements deviate significantly from their expected distribution, signaling potential reversion opportunities with quantifiable probability levels.
Mathematical Foundation
The Central Limit Theorem (CLT) says that when you average many data points together, those averages will form a predictable bell-curve pattern, even if the original data is completely random and unpredictable (which often is in the markets). This works no matter what you're measuring, and it gets more reliable as you use more data points.
Why using it for trading?
Individual price movements seem random and chaotic, but when we look at the average of many price movements, we can actually predict how they should behave statistically. This lets us spot when prices have moved "too far" from what's normal—and those extreme moves tend to snap back (mean reversion).
Key Formula:
Z = (X̄ - μ) / (σ / √n)
Where:
- X̄ = Sample mean (average return over n periods)
- μ = Population mean (long-term expected return)
- σ = Population standard deviation (volatility)
- n = Sample size
- σ/√n = Standard error of the mean
How I Apply CLT
Step 1: Calculate Returns
Measures how much price changed from one bar to the next (using logarithms for better statistical properties)
Step 2: Average Recent Returns
Takes the average of the last n returns (e.g., last 100 bars). This is your "sample mean."
Step 3: Find What's "Normal"
Looks at historical data to determine: a) What the typical average return should be (the long-term mean) and b) How volatile the market usually is (standard deviation)
Step 4: Calculate Standard Error
Determines how much sample averages naturally vary. Larger samples = smaller expected variation.
Step 5: Calculate Z-Score
Measures how unusual the current situation is.
Step 6: Draw Confidence Bands
Converts these statistical boundaries into actual price levels on your chart, showing where price is statistically expected to stay 95% and 99% of the time.
Interpretation & Usage
The Z-Score:
The z-score tells you how statistically unusual the current price deviation is:
|Z| < 1.0 → Normal behavior, no action
|Z| = 1.0 to 1.96 → Moderate deviation, watch closely
|Z| = 1.96 to 2.58 → Significant deviation (95%+), consider entry
|Z| > 2.58 → Extreme deviation (99%+), high probability setup
The Confidence Bands
- Upper Red Bands: 95% and 99% overbought zones → Expect mean reversion downward as the price is not likely to cross these lines.
- Center Gray Line: Statistical expectation (fair value)
- Lower Blue Bands: 95% and 99% oversold zones → Expect mean reversion upward
Trading Logic:
- When price exceeds the upper 95% band (z-score > +1.96), there's only a 5% probability this is random noise → Strong sell/short signal
- When price falls below the lower 95% band (z-score < -1.96), there's a 95% statistical expectation of upward reversion → Strong buy/long signal
Background Gradient
The background color provides real-time visual feedback:
- Blue shades: Oversold conditions, expect upward reversion
- Red shades: Overbought conditions, expect downward reversion
- Intensity: Darker colors indicate stronger statistical significance
Trading Strategy Examples
Hypothetically, this is how the indicator could be used:
- Long: Z-score < -1.96 (below 95% confidence band)
- Short: Z-score > +1.96 (above 95% confidence band)
- Take profit when price returns to center line (Z ≈ 0)
Input Parameters
Sample Size (n) - Default: 100
Lookback Period (m) - Default: 100
You can also create alerts based on the indicator.
Final notes:
- The indicator uses logarithmic returns for better statistical properties
- Converts statistical bands back to price space for practical use
- Adaptive volatility: Bands automatically widen in high volatility, narrow in low volatility
- No repainting: yay! All calculations use historical data only
Feedback is more than welcome!
Henri
Dynamic ~ CVDDynamic - CVD is a smart, time-adaptive version of the classic Cumulative Volume Delta (CVD) indicator, designed to help traders visualize market buying and selling pressure across all timeframes with minimal manual tweaking.
Overview
Cumulative Volume Delta tracks the difference between buying and selling volume during each bar. It reveals whether aggressive buyers or sellers dominate the market, offering deep insight into real-time market sentiment and underlying momentum.
This version of CVD automatically adjusts its EMA smoothing length based on your selected timeframe, ensuring optimal sensitivity and consistency across intraday, daily, weekly, and even monthly charts.
Features
Dynamic EMA Length — Automatically adapts smoothing parameters based on the chart timeframe:
1–59 min → 50
1–23 h → 21
Daily & Weekly → 100
Monthly → 10
CVD Visualization — Displays cumulative delta to show the ongoing buying/selling imbalance.
CVD‑EMA Curve — Offers a clear trend signal by comparing the CVD line with its EMA.
Adaptive Color Logic — EMA curve changes color dynamically:
Green when CVD > EMA (bullish pressure)
Gray when CVD < EMA (bearish pressure)
How to Use
Use Dynamic - CVD to gauge whether the market is accumulating (net buying) or distributing (net selling).
When CVD rises above its EMA, it often signals consistent buying pressure and potential bullish continuation.
When CVD stays below its EMA, it highlights sustained selling pressure and possible weakness.
The dynamic EMA makes it suitable for scalping, swing trading, and longer-term trend analysis—no need to manually adjust settings.
Best For
Traders looking to measure real buying/selling flow rather than price movement alone.
Market participants who want a plug‑and‑play CVD that stays accurate across all timeframes.
Anyone interested in volume‑based momentum confirmation tools.
Disclaimer
This script is provided for educational and analytical purposes only. It does not constitute financial advice or a recommendation to buy or sell any asset. Past performance is not indicative of future results. Always perform your own analysis and consult a licensed financial advisor before making investment decisions. The author is not responsible for any financial losses or trading outcomes arising from the use of this indicator.
3D Candles (Zeiierman)█ Overview
3D Candles (Zeiierman) is a unique 3D take on classic candlesticks, offering a fresh, high-clarity way to visualize price action directly on your chart. Visualizing price in alternative ways can help traders interpret the same data differently and potentially gain a new perspective.
█ How It Works
⚪ 3D Body Construction
For each bar, the script computes the candle body (open/close bounds), then projects a top face offset by a depth amount. The depth is proportional to that candle’s high–low range, so it looks consistent across symbols with different prices/precisions.
rng = math.max(1e-10, high - low ) // candle range
depthMag = rng * depthPct * factorMag // % of range, shaped by tilt amount
depth = depthMag * factorSign // direction from dev (up/down)
depthPct → how “thick” the 3D effect is, as a % of each candle’s own range.
factorMag → scales the effect based on your tilt input (dev), with a smooth curve so small tilts still show.
factorSign → applies the direction of the tilt (up or down).
⚪ Tilt & Perspective
Tilt is controlled by dev and translated into a gentle perspective factor:
slope = (4.0 * math.abs(dev)) / width
factorMag = math.pow(math.min(1.0, slope), 0.5) // sqrt softens response
factorSign = dev == 0 ? 0.0 : math.sign(dev) // direction (up/down)
Larger dev → stronger 3D presence (up to a cap).
The square-root curve makes small dev values noticeable without overdoing it.
█ How to Use
Traders can use 3D Candles just like regular candlesticks. The difference is the 3D visualization, which can broaden your view and help you notice price behavior from a fresh perspective.
⚪ Quick setup (dual-view):
Split your TradingView layout into two synchronized charts.
Right pane: keep your standard candlestick or bar chart for live execution.
Left pane: add 3D Candles (Zeiierman) to compare the same symbol/timeframe.
Observe differences: the 3D rendering can make expansion/contraction and body emphasis easier to spot at a glance.
█ Go Full 3D
Take the experience further by pairing 3D Candles (Zeiierman) with Volume Profile 3D (Zeiierman) , a perfect complement that shows where activity is concentrated, while your 3D candles show how the price unfolded.
█ Settings
Candles — How many 3D candles to draw. Higher values draw more shapes and may impact performance on slower machines.
Block Width (bars) — Visual thickness of each 3D candle along the x-axis. Larger values look chunkier but can overlap more.
Up/Down — Controls the tilt and strength of the 3D top face.
3D depth (% of range) — Thickness of the 3D effect as a percentage of each candle’s own high–low range. Larger values exaggerate the depth.
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Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
