Multi HMA Lines by NB(ENG)
The Hull Moving Average (HMA) line responds quickly to volatile markets,
sometimes it provides more accurate information than the Exponancital Moving Average (EMA).
In particular, the 200 HMA line is easy to decide the overall trend of the market,
and it serves the basis entry position.
So I made indicator that provides these HMA lines into various periods so that they can be checked in one.
In addition, a custom TimeFrame HMA line function has been added so that you can check
not only the TimeFrame that meets your trading standards, but also the HMA of the other TimeFrame that you custome sets.
For example, if you want to see the 200 HMA of the 60-minute bar, you can select and set the different TimeFrame in the Multi TF section below.
For reference, 200 HMA at the 15-minute bar is the same value as 50 HMA at the 1-hour bar, so as shown in the following chart,
I use 4 HMA lines at the 15-minute bar : 20 HMA, 50 HMA, 200 HMA, and 200 HMA from 60-minute TimeFrame.
We hope it will help you in your trading. :)
(KOR)
HMA(Hull Moving Average) 라인은 변동성이 심한 시장에 빠르게 반응하며,
때때로 EMA(Exponancital Moving Average)보다 더 정확한 정보를 제공하곤 합니다.
특히 200HMA 라인은 시장의 전반적인 추세를 판단하기에 용이하며,
큰 틀에서의 포지션 진입 근거의 기반이 됩니다.
이러한 HMA 라인을 다양한 기간으로 나누어 하나의 지표에서 확인 할 수 있도록 만들어 보았습니다.
아울러, 자신의 매매 기준에 맞는 타임 프레임은 물론, 다른 타임 프레임의 HMA도 확인 할 수 있도록
커스텀 타임 프레임 HMA 라인 기능을 추가로 넣었습니다.
예를 들어, 15분 타임 프레임이 본인 매매 기준표이지만, 60분 봉의 200 HMA도 보고 싶다면
밑의 Multi TF 항목에서 해당 타임 프레임을 선택 후 설정하시면 됩니다.
참고로 15분 봉에서의 200 HMA은 1시간 봉에서의 50 HMA과 동일한 값이므로 저는 다음 차트 그림과 같이
15분 봉에서 20 HMA, 50 HMA, 200 HMA, 그리고 1시간 봉에서 200 HMA 이렇게 4개의 라인을 참고 하고 있습니다.
여러분 거래에 도움이 되기를 바랍니다. :)
在腳本中搜尋"股价站上60月线"
RexDog Hour Close LinesThe RexDog Hour Close Lines plots the last 4 previous hour (60 minute) closes. Extremely helpful indicator for traders who trade on lower timeframes below the 60.
The plotted lines are also offset to represent that hours close location on the chart-- but keep the below in mind.
The offset is set for a default resolution of 5 minutes. In that chart timeframe, the offset is correct as to the close location. Changing the timeframe to 3m for instance the offset is not accurate to that particular bar. I am sure there is a simple way to do this but maybe I'm just not smart enough to figure it out. Either way, the offset in any timeframe is easy to distinguish the oldest hour close to the newest.
This indicator has the following options:
You can enable or disable any previous 4 hour close line
You can change all line sizes
You can change all line colors. I do apologize if it's inconvenient that I've defaulted the lines to different colors.
I've limited the visibility to only periods below 60 minutes-- but and maybe there is a better way to do this (if so please share). The limit is based on the most common periods below 60: 1, 2, 3, 5, 10, 12, 15, and 30.
Will most likely release the 240 and 30-minute version of this I have on a few charts.
A simple double moving average system# This simple code is describing the double moving average system, thanks for the contribution of Lei and jchang264
# The moving average system is including the SMA(20,60,120) and EMA(20,60,120), which use the different colours and style
# The bar is using the different colours to describe the different state, for example the black one mean the season of the trendy didn't form, the blue mean to reach the first phase of the trendy, the state of gray bar just between the black and blue, The gold one mean the season of the trend has already forming (SMA20 > SMA60 > SMA120), which one I think it is important.
# Price mark mean the deduction price of 20, 60 and 120
Forex system 10This is old method I used in the past for forex
can be apply to any time frame . can be apply to any asset
all you have to do is to follow the colors (red =sell, green=buy)
the system is my modification to fibs system in order to make it more accurate
here is example of setting for 5 min chart
the color determine by cross of the median of high and low
the script try to give us more accurate levels of resistance and support levels especially when we do it in lower TF
the Min is the control need to be same or higher then chart
60 min chart for example
gold 60 min
btc
another forex example 15 min
here 60 min TF on 5 min chart on crypto link
VPoC per barThis study prints the current bar VPoC as an horizontal line.
It's aimed originally at BTCUSDT pair and 15m timeframe.
HOW IT WORKS
Zoom In mode: This is the default mode.
The study zooms in into the latest 15 1-minute bar candles in order to calculate the 15 minute candle VPoC.
Zoom Out mode: The VPoC from the last n bars from the current timeframe that match desired timeframe is shown on each bar.
In either case you are recommended to click on the '...' button associated to this study
and select 'Visual Order. Bring to Front.' so that it's properly shown in your chart.
HOW IT WORKS - Zoom In mode
Make sure that '(VP) Zoom into the VP timeframe' setting is set to true.
Choose the zoomed in timeframe where to calculate VPoC from thanks to the '(VP) Zoomed timeframe {1 minute}' setting.
Change '(VP) Zoomed in timeframe bars per current timeframe bar {15}' to its appropiated value. You just need to divide the current timeframe minutes per the zoomed in timeframe minutes per bar. E.g. If you are in 60 minute timeframe and you want to zoom in into 5 minute timeframe: 60 / 5 = 12 . You will write 12 here.
HOW IT WORKS - Zoom Out mode
Make sure that '(VP) Zoom into the VP timeframe' setting is set to false.
If you are using the Zoom out mode you might want to set '(VP) Print VPoC price as discrete lines {True}' to false.
Either choose the zoommed out timeframe where to calculate VPoC from thanks to the '(VP) Zoomed timeframe {1 minute}' setting or turn on the '(VP) Use number of bars (not VP timeframe)' setting in order to use '(VP) Number of bars {100}' as a custom number of bars.
WARNING - Zoom In mode last bar
The way that PineScript handles security function in last bar might result on the last bar not being accurate enough.
SETTINGS
__ SETTINGS - Volume Profile
(VP) Zoomed timeframe {1 minute}: Timeframe in which to zoom in or zoom out to calculate an accurate VPoC for the current timeframe.
(VP) Zoomed in timeframe bars per current timeframe bar {15}: Check 'HOW IT WORKS - Zoom In mode' above. Note : It is only used in 'Zoom in' mode.
(VP) Number of bars {100}: If 'Use number of bars (not VP timeframe)' is turned on this setting is used to calculate session VPoC. Note : It is only used in 'Zoom out' mode.
(VP) Price levels {24}: Price levels for calculating VPoC.
__ SETTINGS - MAIN TURN ON/OFF OPTIONS
(VP) Print VPoC price {True}: Show VPoC price
(VP) Zoom into the VP timeframe: When set to true the VPoC is calculated by zooming into the lower timeframe. When set to false a higher timeframe (or number of bars) is used.
(VP) Realtime Zoom in (Beta): Enable real time zoom for the last bar. It's beta because it would only work with zoomed in timeframe under 60 minutes. And when ratio between zoomout and zoomin is less than 60. Note : It is only used in 'Zoom in' mode.
(VP) Use number of bars (not VP timeframe): Uses 'Number of bars {100}' setting instead of 'Volume Profile timeframe' setting for calculating session VPoC. Note : It is only used in 'Zoom out' mode.
(VP) Print VPoC price as discrete lines {True}: When set to true the VPoC is shown as an small line in the center of each bar. When set to the false the VPoC line is printed as a normal line.
__ SETTINGS - EXTRA
(VP) VPoC color: Change the VPoC color
(VP) VPoC line width {1}: Change VPoC line width (in pixels).
(VP) Use number of bars (not VP timeframe): Uses 'Number of bars {100}' setting instead of 'Volume Profile timeframe' setting for calculating session VPoC. Note : It is only used in 'Zoom out' mode.
(VP) Print VPoC price as discrete lines {True}: When set to true the VPoC is shown as an small line in the center of each bar. When set to the false the VPoC line is printed as a normal line.
CREDITS
I have reused and adapted some code from
"Poor man's volume profile" study
which it's from TradingView IldarAkhmetgaleev user.
Traders Dynamic Index(RSI) w/ Bull&Bear Control ZonesMomentum (RSI) is one of the most commonly used indicators for trading, but the vast majority of traders who use it, simply apply it as an oscillator to measure overbought and oversold conditions. However, momentum is much more complex than that and using a basic RSI fails to highlight these complexities.
What this highlights are some of the areas/zones that many people may not even know about or are unaware what the RSI can actually reveal about a particular trend.
What this indicator is showing:
Fast moving RSI (Green) - 1 period
Slow moving RSI (Red) - 9 period
Bollinger Bands
Relative Strength: 1 - 100
Bearish Control Zone: 30(Below) - 45
Bullish Control Zone: 60 - 70 (Above)
How this identifies trends:
Bear Market(Bearish Control Zone):
-Support: 20(Below) - 30
-Resistance: 55 - 65
-Momentum will test resistance but will fail to hold support at 50
Bull Market(Bullish Control Zone):
-Support: 45 - 50
-Resistance: 80 - 90(Above)
-Momentum will test support but will not continue past the 45 support
How this identifies reversals:
If a market is bullish, but loses support at 45 and tests 30, it has begun reversal. If a market is bearish, but breaks 60 and tests 70, it has begun reversal.
-A bull market reversal is confirmed if it finds resistance at 60 after testing bearish support
-A bear market reversal is confirmed if it finds support at 50 after testing bullish resistance
Slow & Fast RSI w/ Boll Bands:
-The Slow and Fast RSI crossovers will act as Intermediate trends within the Macro trend - Fast crosses slow, bullish. Slow cross fast, bearish.
-Use in confluence with the Macro trend.
-While under Bearish Control, the Slow RSI will act as resistance for the Fast RSI.
-While under Bullish Control, the Slow RSI will act as support for the Fast RSI.
-The two will have an impulsive crossover when the Macro trend reverses.
-The Bollinger Bands will act as a volatility gauge for potential approaching tests of Support & Resistances. (Expansions & Contractions)
This is an analog of TDIGM (GoldMinds)
-Added Bullish/Bearish Control Zones.
-Changed Fast RSI to Green and Slow RSI to Red.
Simple Harmonic Oscillator (SHO)The indicator is based on Akram El Sherbini's article "Time Cycle Oscillators" published in IFTA journal 2018 (pages 78-80) (www.ftaa.org.hk)
The SHO is a bounded oscillator for the simple harmonic index that calculates the period of the market’s cycle. The oscillator is used for short and intermediate terms and moves within a range of -100 to 100 percent. The SHO has overbought and oversold levels at +40 and -40, respectively. At extreme periods, the oscillator may reach the levels of +60 and -60. The zero level demonstrates an equilibrium between the periods of bulls and bears. The SHO oscillates between +40 and -40. The crossover at those levels creates buy and sell signals. In an uptrend, the SHO fluctuates between 0 and +40 where the bulls are controlling the market. On the contrary, the SHO fluctuates between 0 and -40 during downtrends where the bears control the market. Reaching the extreme level -60 in an uptrend is a sign of weakness. Mostly, the oscillator will retrace from its centerline rather than the upper boundary +40. On the other hand, reaching +60 in a downtrend is a sign of strength and the oscillator will not be able to reach its lower boundary -40.
Centerline Crossover Tactic
This tactic is tested during uptrends. The buy signals are generated when the WPO/SHI cross their centerlines to the upside. The sell signals are generated when the WPO/SHI cross down their centerlines. To define the uptrend in the system, stocks closing above their 50-day EMA are considered while the ADX is above 18.
Uptrend Tactic
During uptrends, the bulls control the markets, and the oscillators will move above their centerline with an increase in the period of cycles. The lower boundaries and equilibrium line crossovers generate buy signals, while crossing the upper boundaries will generate sell signals. The “Re-entry” and “Exit at weakness” tactics are combined with the uptrend tactic. Consequently, we will have three buy signals and two sell signals.
Sideways Tactic
During sideways, the oscillators fluctuate between their upper and lower boundaries. Crossing the lower boundary to the upside will generate a buy signal. On the other hand, crossing the upper boundary to the downside will generate a sell signal. When the bears take control, the oscillators will cross down the lower boundaries, triggering exit signals. Therefore, this tactic will consist of one buy signal and two sell signals. The sideway tactic is defined when stocks close above their 50-day EMA and the ADX is below 18
Volume Profile [Makit0]VOLUME PROFILE INDICATOR v0.5 beta
Volume Profile is suitable for day and swing trading on stock and futures markets, is a volume based indicator that gives you 6 key values for each session: POC, VAH, VAL, profile HIGH, LOW and MID levels. This project was born on the idea of plotting the RTH sessions Value Areas for /ES in an automated way, but you can select between 3 different sessions: RTH, GLOBEX and FULL sessions.
Some basic concepts:
- Volume Profile calculates the total volume for the session at each price level and give us market generated information about what price and range of prices are the most traded (where the value is)
- Value Area (VA): range of prices where 70% of the session volume is traded
- Value Area High (VAH): highest price within VA
- Value Area Low (VAL): lowest price within VA
- Point of Control (POC): the most traded price of the session (with the most volume)
- Session HIGH, LOW and MID levels are also important
There are a huge amount of things to know of Market Profile and Auction Theory like types of days, types of openings, relationships between value areas and openings... for those interested Jim Dalton's work is the way to come
I'm in my 2nd trading year and my goal for this year is learning to daytrade the futures markets thru the lens of Market Profile
For info on Volume Profile: TV Volume Profile wiki page at www.tradingview.com
For info on Market Profile and Market Auction Theory: Jim Dalton's book Mind over markets (this is a MUST)
BE AWARE: this indicator is based on the current chart's time interval and it only plots on 1, 2, 3, 5, 10, 15 and 30 minutes charts.
This is the correlation table TV uses in the Volume Profile Session Volume indicator (from the wiki above)
Chart Indicator
1 - 5 1
6 - 15 5
16 - 30 10
31 - 60 15
61 - 120 30
121 - 1D 60
This indicator doesn't follow that correlation, it doesn't get the volume data from a lower timeframe, it gets the data from the current chart resolution.
FEATURES
- 6 key values for each session: POC (solid yellow), VAH (solid red), VAL (solid green), profile HIGH (dashed silver), LOW (dashed silver) and MID (dotted silver) levels
- 3 sessions to choose for: RTH, GLOBEX and FULL
- select the numbers of sessions to plot by adding 12 hours periods back in time
- show/hide POC
- show/hide VAH & VAL
- show/hide session HIGH, LOW & MID levels
- highlight the periods of time out of the session (silver)
- extend the plotted lines all the way to the right, be careful this can turn the chart unreadable if there are a lot of sessions and lines plotted
SETTINGS
- Session: select between RTH (8:30 to 15:15 CT), GLOBEX (17:00 to 8:30 CT) and FULL (17:00 to 15:15 CT) sessions. RTH by default
- Last 12 hour periods to show: select the deph of the study by adding periods, for example, 60 periods are 30 natural days and around 22 trading days. 1 period by default
- Show POC (Point of Control): show/hide POC line. true by default
- Show VA (Value Area High & Low): show/hide VAH & VAL lines. true by default
- Show Range (Session High, Low & Mid): show/hide session HIGH, LOW & MID lines. true by default
- Highlight out of session: show/hide a silver shadow over the non session periods. true by default
- Extension: Extend all the plotted lines to the right. false by default
HOW TO SETUP
BE AWARE THIS INDICATOR PLOTS ONLY IN THE FOLLOWING CHART RESOLUTIONS: 1, 2, 3, 5, 10, 15 AND 30 MINUTES CHARTS. YOU MUST SELECT ONE OF THIS RESOLUTIONS TO THE INDICATOR BE ABLE TO PLOT
- By default this indicator plots all the levels for the last RTH session within the last 12 hours, if there is no plot try to adjust the 12 hours periods until the seesion and the periods match
- For Globex/Full sessions just select what you want from the dropdown menu and adjust the periods to plot the values
- Show or hide the levels you want with the 3 groups: POC line, VA lines and Session Range lines
- The highlight and extension options are for a better visibility of the levels as POC or VAH/VAL
THANKS TO
@watsonexchange for all the help, ideas and insights on this and the last two indicators (Market Delta & Market Internals) I'm working on my way to a 'clean chart' but for me it's not an easy path
@PineCoders for all the amazing stuff they do and all the help and tools they provide, in special the Script-Stopwatch at that was key in lowering this indicator's execution time
All the TV and Pine community, open source and shared knowledge are indeed the best way to help each other
IF YOU REALLY LIKE THIS WORK, please send me a comment or a private message and TELL ME WHAT you trade, HOW you trade it and your FAVOURITE SETUP for pulling out money from the market in a consistent basis, I'm learning to trade (this is my 2nd year) and I need all the help I can get
GOOD LUCK AND HAPPY TRADING
lsi (study about length and MTF) Here in this example I took lazy bear famous momentum squeeze indicator . the problem that there is lagging in the indicator so the buy and sell will be late . So instead the KC length that the original script had we put
int1=input(30)
int2=input(60)
lengthKC=isintraday and interval >= int1 ? int2/interval * 7 : isintraday and interval < 60 ? 60/interval * 24 * 7 : 7
this allow us to create a time and length related function to indicator and result in better output with no lagging
The second and most important thing is the ability to create indicator with time function as MTF without the security function that create repaint
all you need to do is to change int2 (to the time min of your choice ) and you can create an indicator with MTF function without the security function .And by this hopefully avoid the repainting issue
when you use this indicator change the setting of int1 and int 2 according to time frame that you use
lets say 15 min graph
make the int1 <15 min and the int2 at 15 min. if you want to see it as MTF just increase the int2 to the time set of your choice and play little with int1 to best setting
RSI with Visual Buy/Sell Setup | Corrective/Impulsive IndicatorRSI with Visual Buy/Sell Setup | 40-60 Support/Resistance | Corrective/Impulsive Indicator v2.15
|| RSI - The Complete Guide PDF ||
Modified Zones with Colors for easy recognition of Price Action.
Resistance @ downtrend = 60
Support @ uptrend = 40
Over 70 = Strong Bullish Impulse
Under 30 = Strong Bearish Impulse
Uptrend : 40-80
Downtrend: 60-20
--------------------
Higher Highs in price, Lower Highs in RSI = Bearish Divergence
Lower Lows in price, Higher Lows in RSI = Bullish Divergence
--------------------
Trendlines from Higher/Lower Peaks, breakout + retest for buy/sell setups.
###################
There are multiple ways for using RSI, not only divergences, but it confirms the trend, possible bounce for continuation and signals for possible trend reversal.
There's more advanced use of RSI inside the book RSI: The Complete Guide
Go with the force, and follow the trend.
"The Force is more your friend than the trend"
Build A Bot Hull TriggerThis is the automated trading system we built during the 60-Minute Build-A-Bot webinar on September 12, 2018. We had a lot of fun, and implemented a TON of indicators LIVE during this webinar! And the best part is that as a group we researched, designed, and built a profitable robot in exactly 60 minutes!
We started by voting on the type of trading system, and this is a trend following system because it got the most votes. Then, the attendees in the webinar sent in their suggestions for indicators and settings during the live webinar (still counting toward the 60 minutes). Once we had the indicators on the chart, and we discussed various settings we could use, we got to work building the robot, and ran the first strategy test...and it was profitable!
This version uses the Hull Moving Average as a trigger for initiating the trade, and everything else is the same for the filters. The other version uses the CCI as a trigger for the trade, and many other indicators as filters.
Indicators: Volume Zone Indicator & Price Zone IndicatorVolume Zone Indicator (VZO) and Price Zone Indicator (PZO) are by Waleed Aly Khalil.
Volume Zone Indicator (VZO)
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VZO is a leading volume oscillator that evaluates volume in relation to the direction of the net price change on each bar.
A value of 40 or above shows bullish accumulation. Low values (< 40) are bearish. Near zero or between +/- 20, the market is either in consolidation or near a break out. When VZO is near +/- 60, an end to the bull/bear run should be expected soon. If that run has been opposite to the long term price trend direction, then a reversal often will occur.
Traditional way of looking at this also works:
* +/- 40 levels are overbought / oversold
* +/- 60 levels are extreme overbought / oversold
More info:
drive.google.com
Price Zone Indicator (PZO)
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PZO is interpreted the same way as VZO (same formula with "close" substituted for "volume").
Chart Markings
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In the chart above,
* The red circles indicate a run-end (or reversal) zones (VZO +/- 60).
* Blue rectangle shows the consolidation zone (VZO betwen +/- 20)
I have been trying out VZO only for a week now, but I think this has lot of potential. Give it a try, let me know what you think.
Info TableOverview
The Info Table V1 is a versatile TradingView indicator tailored for intraday futures traders, particularly those focusing on MESM2 (Micro E-mini S&P 500 futures) on 1-minute charts. It presents essential market insights through two customizable tables: the Main Table for predictive and macro metrics, and the New Metrics Table for momentum and volatility indicators. Designed for high-activity sessions like 9:30 AM–11:00 AM CDT, this tool helps traders assess price alignment, sentiment, and risk in real-time. Metrics update dynamically (except weekly COT data), with optional alerts for key conditions like volatility spikes or momentum shifts.
This indicator builds on foundational concepts like linear regression for predictions and adapts open-source elements for enhanced functionality. Gradient code is adapted from TradingView's Color Library. QQE logic is adapted from LuxAlgo's QQE Weighted Oscillator, licensed under CC BY-NC-SA 4.0. The script is released under the Mozilla Public License 2.0.
Key Features
Two Customizable Tables: Positioned independently (e.g., top-right for Main, bottom-right for New Metrics) with toggle options to show/hide for a clutter-free chart.
Gradient Coloring: User-defined high/low colors (default green/red) for quick visual interpretation of extremes, such as overbought/oversold or high volatility.
Arrows for Directional Bias: In the New Metrics Table, up (↑) or down (↓) arrows appear in value cells based on metric thresholds (top/bottom 25% of range), indicating bullish/high or bearish/low conditions.
Consensus Highlighting: The New Metrics Table's title cells ("Metric" and "Value") turn green if all arrows are ↑ (strong bullish consensus), red if all are ↓ (strong bearish consensus), or gray otherwise.
Predicted Price Plot: Optional line (default blue) overlaying the ML-predicted price for visual comparison with actual price action.
Alerts: Notifications for high/low Frahm Volatility (≥8 or ≤3) and QQE Bias crosses (bullish/bearish momentum shifts).
Main Table Metrics
This table focuses on predictive, positional, and macro insights:
ML-Predicted Price: A linear regression forecast using normalized price, volume, and RSI over a customizable lookback (default 500 bars). Gradient scales from low (red) to high (green) relative to the current price ± threshold (default 100 points).
Deviation %: Percentage difference between current price and predicted price. Gradient highlights extremes (±0.5% default threshold), signaling potential overextensions.
VWAP Deviation %: Percentage difference from Volume Weighted Average Price (VWAP). Gradient indicates if price is above (green) or below (red) fair value (±0.5% default).
FRED UNRATE % Change: Percentage change in U.S. unemployment rate (via FRED data). Cell turns red for increases (economic weakness), green for decreases (strength), gray if zero or disabled.
Open Interest: Total open MESM2 futures contracts. Gradient scales from low (red) to high (green) up to a hardcoded 300,000 threshold, reflecting market participation.
COT Commercial Long/Short: Weekly Commitment of Traders data for commercial positions. Long cell green if longs > shorts (bullish institutional sentiment); Short cell red if shorts > longs (bearish); gray otherwise.
New Metrics Table Metrics
This table emphasizes technical momentum and volatility, with arrows for quick bias assessment:
QQE Bias: Smoothed RSI vs. trailing stop (default length 14, factor 4.236, smooth 5). Green for bullish (RSI > stop, ↑ arrow), red for bearish (RSI < stop, ↓ arrow), gray for neutral.
RSI: Relative Strength Index (default period 14). Gradient from oversold (red, <30 + threshold offset, ↓ arrow if ≤40) to overbought (green, >70 - offset, ↑ arrow if ≥60).
ATR Volatility: Score (1–20) based on Average True Range (default period 14, lookback 50). High scores (green, ↑ if ≥15) signal swings; low (red, ↓ if ≤5) indicate calm.
ADX Trend: Average Directional Index (default period 14). Gradient from weak (red, ↓ if ≤0.25×25 threshold) to strong trends (green, ↑ if ≥0.75×25).
Volume Momentum: Score (1–20) comparing current to historical volume (lookback 50). High (green, ↑ if ≥15) suggests pressure; low (red, ↓ if ≤5) implies weakness.
Frahm Volatility: Score (1–20) from true range over a window (default 24 hours, multiplier 9). Dynamic gradient (green/red/yellow); ↑ if ≥7.5, ↓ if ≤2.5.
Frahm Avg Candle (Ticks): Average candle size in ticks over the window. Blue gradient (or dynamic green/red/yellow); ↑ if ≥0.75 percentile, ↓ if ≤0.25.
Arrows trigger on metric-specific logic (e.g., RSI ≥60 for ↑), providing directional cues without strict color ties.
Customization Options
Adapt the indicator to your strategy:
ML Inputs: Lookback (10–5000 bars) and RSI period (2+) for prediction sensitivity—shorter for volatility, longer for trends.
Timeframes: Individual per metric (e.g., 1H for QQE Bias to match higher frames; blank for chart timeframe).
Thresholds: Adjust gradients and arrows (e.g., Deviation 0.1–5%, ADX 0–100, RSI overbought/oversold).
QQE Settings: Length, factor, and smooth for fine-tuned momentum.
Data Toggles: Enable/disable FRED, Open Interest, COT for focus (e.g., disable macro for pure intraday).
Frahm Options: Window hours (1+), scale multiplier (1–10), dynamic colors for avg candle.
Plot/Table: Line color, positions, gradients, and visibility.
Ideal Use Case
Perfect for MESM2 scalpers and trend traders. Use the Main Table for entry confirmation via predicted deviations and institutional positioning. Leverage the New Metrics Table arrows for short-term signals—enter bullish on green consensus (all ↑), avoid chop on low volatility. Set alerts to catch shifts without constant monitoring.
Why It's Valuable
Info Table V1 consolidates diverse metrics into actionable visuals, answering critical questions: Is price mispriced? Is momentum aligning? Is volatility manageable? With real-time updates, consensus highlights, and extensive customization, it enhances precision in fast markets, reducing guesswork for confident trades.
Note: Optimized for futures; some metrics (OI, COT) unavailable on non-futures symbols. Test on demo accounts. No financial advice—use at your own risk.
The provided script reuses open-source elements from TradingView's Color Library and LuxAlgo's QQE Weighted Oscillator, as noted in the script comments and description. Credits are appropriately given in both the description and code comments, satisfying the requirement for attribution.
Regarding significant improvements and proportion:
The QQE logic comprises approximately 15 lines of code in a script exceeding 400 lines, representing a small proportion (<5%).
Adaptations include integration with multi-timeframe support via request.security, user-customizable inputs for length, factor, and smooth, and application within a broader table-based indicator for momentum bias display (with color gradients, arrows, and alerts). This extends the original QQE beyond standalone oscillator use, incorporating it as one of seven metrics in the New Metrics Table for confluence analysis (e.g., consensus highlighting when all metrics align). These are functional enhancements, not mere stylistic or variable changes.
The Color Library usage is via official import (import TradingView/Color/1 as Color), leveraging built-in gradient functions without copying code, and applied to enhance visual interpretation across multiple metrics.
The script complies with the rules: reused code is minimal, significantly improved through integration and expansion, and properly credited. It qualifies for open-source publication under the Mozilla Public License 2.0, as stated.
Entry Signal Paint (RSI + DMI + Stoch + MACD)RSI above 60
Stoch - cross overslod
DMI - Ungli
// === RSI Condition ===
rsi = ta.rsi(rsiSource, rsiPeriod)
rsiCondition = rsi > 60
// === ADX and DI Condition ===
adx = ta.adx(adxPeriod)
plusDI = ta.plus_di(adxPeriod)
minusDI = ta.minus_di(adxPeriod)
adxCondition = adx > 15 and plusDI > minusDI
// === Stochastic Condition ===
k = ta.stoch(close, high, low, stochK)
d = ta.sma(k, stochD)
stochOversoldCross = ta.crossover(k, d) and k < 20
GOLD Auto-Alert Strategy [Enhanced+Signals+UT Bot]New Version The Indicator
✅ BUY Signal Triggers Only If:
UT Buy signal flips (from down to up)
ADX > Threshold → market has strength
RSI is outside the 40–60 range
Volume is spiking above 20-bar average × 1.2
EMA Fast > EMA Slow → uptrend confirmed
Close > EMA Fast → price above short-term trend
Bullish Engulfing candle
🔻 SELL Signal Triggers Only If:
UT Sell signal flips (from up to down)
ADX > Threshold → market has strength
RSI is outside the 40–60 range
Volume is spiking above average
EMA Fast < EMA Slow → downtrend confirmed
Close < EMA Fast → bearish structure
Bearish Engulfing candle
Alt Szn Oracle - Institutional GradeThe Alt Szn Oracle is a macro-level indicator built to help traders front-run altseason by tracking liquidity, dominance rotation, sentiment, and capital flows—all in one signal. It’s designed for those who don’t just chase pumps, but want to understand when the tide is turning and why. This tool doesn't predict specific coin breakouts—it tells you when the market as a whole is gearing up to rotate into higher beta assets like altcoins, including memes and microcaps.
The index consolidates ten macro inputs into a normalized, smoothed score from 0–100. These include Bitcoin and Ethereum dominance, ETH/BTC, altcoin market cap (Total3), relative volume flows, and stablecoin supply (USDT, USDC, DAI)—which act as proxies for risk-on appetite and dry powder entering the system. It also incorporates manually updated sentiment metrics from Google Trends and the Fear & Greed Index, giving it a behavioral edge that most indicators lack.
The logic is simple but powerful: when BTC dominance is falling, ETH/BTC is rising, altcoin volume increases relative to BTC/ETH, and stablecoins start moving—you're likely in the early innings of rotation. The index is also filtered through a volatility threshold and smoothed with an EMA to eliminate chop and fakeouts.
Use this indicator on macro charts like TOTAL3, TOTAL2, or ETHBTC to gauge market health, or overlay it on specific coins like PEPE, DOGE, or SOL to confirm if the tide is in your favor. Interpreting the score is straightforward: readings above 80 suggest euphoria and signal it’s time to de-risk, 60–80 indicates expansion and confirms altseason is underway, 40–60 is neutral, and 20–40 is a capitulation zone where smart money accumulates.
What sets this apart is that it doesn’t just track price—it reflects the flow of capital, the positioning of liquidity, and the sentiment of the crowd. Most altseason indicators are lagging, overfitted, or too simplistic. This one is modular, forward-looking, and grounded in real capital rotation theory.
If you're a trader who wants to time the cycle, not guess it, this is your tool. Refine it, fork it, or expand it to your niche—DeFi, NFTs, meme coins, or L1s. It’s a framework for reading the macro winds, not a signal service. Use it with discipline, and you’ll catch the wave while others drown in noise.
Non-Lagging Longevity Zones [BigBeluga]🔵 OVERVIEW
A clean, non-lagging system for identifying price zones that persist over time—ranking them visually based on how long they survive without being invalidated.
Non-Lagging Longevity Zones uses non-lagging pivots to automatically build upper and lower zones that reflect key resistance and support. These zones are kept alive as long as price respects them and are instantly removed when invalidated. The indicator assigns a unique lifespan label to each zone in Days (D), Months (M), or Years (Y), providing instant context for historical relevance.
🔵 CONCEPTS
Non-Lag Pivot Detection: Detects upper and lower pivots using non-lagging swing identification (highest/lowest over length period).
h = ta.highest(len)
l = ta.lowest(len)
high_pivot = high == h and high < h
low_pivot = low == l and low > l
Longevity Ranking: Zones are preserved as long as price doesn't breach them. Levels that remain intact grow in visual intensity.
Time-Based Weighting: Each zone is labeled with its lifespan in days , emphasizing how long it has survived.
duration = last_bar_index - start
days_ = int(duration*(timeframe.in_seconds("")/60/60/24))
days = days_ >= 365 ? int(days_ / 365) : days_ >= 30 ? int(days_ / 30) : days_
marker = days_ >= 365 ? " Y" : days_ >= 30 ? " M" : " D"
Dynamic Coloring: Older zones are drawn with stronger fill, while newer ones appear fainter—making it easy to assess significance.
Self-Cleaning Logic: If price invalidates a zone, it’s instantly removed, keeping the chart clean and focused.
🔵 FEATURES
Upper and Lower Zones: Auto-detects valid high/low pivots and plots horizontal zones with ATR-based thickness.
Real-Time Validation: Zones are extended only if price stays outside them—giving precise control zones.
Gradient Fill Intensity: The longer a level survives, the more opaque the fill becomes.
Duration-Based Labeling: Time alive is shown at the root of each zone:
• D – short-term zones
• M – medium-term structure
• Y – long-term legacy levels
Smart Zone Clearing: Zones are deleted automatically once invalidated by price, keeping the display accurate.
Efficient Memory Handling: Keeps only the 10 most recent valid levels per side for optimal performance.
🔵 HOW TO USE
Track durable S/R zones that survived price tests without being breached.
Use longer-lived zones as high-confidence confluence areas for entries or targets.
Observe fill intensity to judge structural importance at a glance .
Layer with volume or momentum tools to confirm bounce or breakout probability.
Ideal for swing traders, structure-based traders, or macro analysis.
🔵 CONCLUSION
Non-Lagging Longevity Zones lets the market speak for itself—by spotlighting levels with proven survival over time. Whether you're trading trend continuation, mean reversion, or structure-based reversals, this tool equips you with an immediate read on what price zones truly matter—and how long they've stood the test of time.
Candle Emotion Oscillator [CEO]Candle Emotion Oscillator (CEO) - Revolutionary User Guide
🧠 World's First Market Psychology Oscillator
The Candle Emotion Oscillator (CEO) is a groundbreaking indicator that measures market emotions through pure candle price action analysis. This is the first oscillator ever created that translates candle patterns into psychological states, giving you unprecedented insight into market sentiment.
🚀 Revolutionary Concept
What Makes CEO Unique
100% Pure Price Action: No volume, no external data - just candle analysis
Market Psychology: Measures actual emotions: Fear, Greed, Panic, Euphoria
Never Been Done Before: First oscillator to analyze market emotions
Exhaustion Prediction: Detects emotional fatigue before reversals
Fast Response: Perfect for your 2-5 minute scalping setup
The Four Core Emotions
🟢 GREED (Positive Values)
What it measures: Market conviction and decisiveness
Candle Pattern: Large bodies, small wicks
Psychology: Traders are confident and decisive
Oscillator: Positive values (0 to +100)
Trading Implication: Trend continuation likely
🔴 FEAR (Negative Values)
What it measures: Market uncertainty and indecision
Candle Pattern: Small bodies, large wicks
Psychology: Traders are uncertain and hesitant
Oscillator: Negative values (0 to -100)
Trading Implication: Consolidation or reversal likely
🚀 EUPHORIA (Extreme Positive)
What it measures: Excessive optimism and buying pressure
Candle Pattern: Large green bodies with upper wicks
Psychology: Extreme bullish sentiment
Oscillator: Values above +60
Trading Implication: Overbought, reversal warning
💥 PANIC (Extreme Negative)
What it measures: Capitulation and selling pressure
Candle Pattern: Large red bodies with lower wicks
Psychology: Extreme bearish sentiment
Oscillator: Values below -60
Trading Implication: Oversold, reversal opportunity
📊 Visual Elements Explained
Main Components
Thick Colored Line: Primary emotion oscillator
Green: Greed (positive emotions)
Red: Fear (negative emotions)
Bright Green: Euphoria (extreme positive)
Dark Red: Panic (extreme negative)
Thin Blue Line: Emotion trend (longer-term context)
Background Gradient: Emotional intensity
Darker = stronger emotions
Lighter = weaker emotions
Diamond Signals: 🔶 Emotional exhaustion detected
Rocket Signals: 🚀 Extreme euphoria warning
Explosion Signals: 💥 Extreme panic warning
Information Table (Top Right)
The Sequences of FibonacciThe Sequences of Fibonacci - Advanced Multi-Timeframe Confluence Analysis System
THEORETICAL FOUNDATION & MATHEMATICAL INNOVATION
The Sequences of Fibonacci represents a revolutionary approach to market analysis that synthesizes classical Fibonacci mathematics with modern adaptive signal processing. This indicator transcends traditional Fibonacci retracement tools by implementing a sophisticated multi-dimensional confluence detection system that reveals hidden market structure through mathematical precision.
Core Mathematical Framework
Dynamic Fibonacci Grid System:
Unlike static Fibonacci tools, this system calculates highest highs and lowest lows across true Fibonacci sequence periods (8, 13, 21, 34, 55 bars) creating a dynamic grid of mathematical support and resistance levels that adapt to market structure in real-time.
Multi-Dimensional Confluence Detection:
The engine employs advanced mathematical clustering algorithms to identify areas where multiple derived Fibonacci retracement levels (0.382, 0.500, 0.618) from different timeframe perspectives converge. These "Confluence Zones" are mathematically classified by strength:
- CRITICAL Zones: 8+ converging Fibonacci levels
- HIGH Zones: 6-7 converging levels
- MEDIUM Zones: 4-5 converging levels
- LOW Zones: 3+ converging levels
Adaptive Signal Processing Architecture:
The system implements adaptive Stochastic RSI calculations with dynamic overbought/oversold levels that adjust to recent market volatility rather than using fixed thresholds. This prevents false signals during changing market conditions.
COMPREHENSIVE FEATURE ARCHITECTURE
Quantum Field Visualization System
Dynamic Price Field Mathematics:
The Quantum Field creates adaptive price channels based on EMA center points and ATR-based amplitude calculations, influenced by the Unified Field metric. This visualization system helps traders understand:
- Expected price volatility ranges
- Potential overextension zones
- Mathematical pressure points in market structure
- Dynamic support/resistance boundaries
Field Amplitude Calculation:
Field Amplitude = ATR × (1 + |Unified Field| / 10)
The system generates three quantum levels:
- Q⁰ Level: 0.618 × Field Amplitude (Primary channel)
- Q¹ Level: 1.0 × Field Amplitude (Secondary boundary)
- Q² Level: 1.618 × Field Amplitude (Extreme extension)
Advanced Market Analysis Dashboard
Unified Field Analysis:
A composite metric combining:
- Price momentum (40% weighting)
- Volume momentum (30% weighting)
- Trend strength (30% weighting)
Market Resonance Calculation:
Measures price-volume correlation over 14 periods to identify harmony between price action and volume participation.
Signal Quality Assessment:
Synthesizes Unified Field, Market Resonance, and RSI positioning to provide real-time evaluation of setup potential.
Tiered Signal Generation Logic
Tier 1 Signals (Highest Conviction):
Require ALL conditions:
- Adaptive StochRSI setup (exiting dynamic OB/OS levels)
- Classic StochRSI divergence confirmation
- Strong reversal bar pattern (adaptive ATR-based sizing)
- Level rejection from Confluence Zone or Fibonacci level
- Supportive Unified Field context
Tier 2 Signals (Enhanced Opportunity Detection):
Generated when Tier 1 conditions aren't met but exceptional circumstances exist:
- Divergence candidate patterns (relaxed divergence requirements)
- Exceptionally strong reversal bars at critical levels
- Enhanced level rejection criteria
- Maintained context filtering
Intelligent Visualization Features
Fractal Matrix Grid:
Multi-layer visualization system displaying:
- Shadow Layer: Foundational support (width 5)
- Glow Layer: Core identification (width 3, white)
- Quantum Layer: Mathematical overlay (width 1, dotted)
Smart Labeling System:
Prevents overlap using ATR-based minimum spacing while providing:
- Fibonacci period identification
- Topological complexity classification (0, I, II, III)
- Exact price levels
- Strength indicators (○ ◐ ● ⚡)
Wick Pressure Analysis:
Dynamic visualization showing momentum direction through:
- Multi-beam projection lines
- Particle density effects
- Progressive transparency for natural flow
- Strength-based sizing adaptation
PRACTICAL TRADING IMPLEMENTATION
Signal Interpretation Framework
Entry Protocol:
1. Confluence Zone Approach: Monitor price approaching High/Critical confluence zones
2. Adaptive Setup Confirmation: Wait for StochRSI to exit adaptive OB/OS levels
3. Divergence Verification: Confirm classic or candidate divergence patterns
4. Reversal Bar Assessment: Validate strong rejection using adaptive ATR criteria
5. Context Evaluation: Ensure Unified Field provides supportive environment
Risk Management Integration:
- Stop Placement: Beyond rejected confluence zone or Fibonacci level
- Position Sizing: Based on signal tier and confluence strength
- Profit Targets: Next significant confluence zone or quantum field boundary
Adaptive Parameter System
Dynamic StochRSI Levels:
Unlike fixed 80/20 levels, the system calculates adaptive OB/OS based on recent StochRSI range:
- Adaptive OB: Recent minimum + (range × OB percentile)
- Adaptive OS: Recent minimum + (range × OS percentile)
- Lookback Period: Configurable 20-100 bars for range calculation
Intelligent ATR Adaptation:
Bar size requirements adjust to market volatility:
- High Volatility: Reduced multiplier (bars naturally larger)
- Low Volatility: Increased multiplier (ensuring significance)
- Base Multiplier: 0.6× ATR with adaptive scaling
Optimization Guidelines
Timeframe-Specific Settings:
Scalping (1-5 minutes):
- Fibonacci Rejection Sensitivity: 0.3-0.8
- Confluence Threshold: 2-3 levels
- StochRSI Lookback: 20-30 bars
Day Trading (15min-1H):
- Fibonacci Rejection Sensitivity: 0.5-1.2
- Confluence Threshold: 3-4 levels
- StochRSI Lookback: 40-60 bars
Swing Trading (4H-1D):
- Fibonacci Rejection Sensitivity: 1.0-2.0
- Confluence Threshold: 4-5 levels
- StochRSI Lookback: 60-80 bars
Asset-Specific Optimization:
Cryptocurrency:
- Higher rejection sensitivity (1.0-2.5) for volatile conditions
- Enable Tier 2 signals for increased opportunity detection
- Shorter adaptive lookbacks for rapid market changes
Forex Major Pairs:
- Moderate sensitivity (0.8-1.5) for stable trending
- Focus on Higher/Critical confluence zones
- Longer lookbacks for institutional flow detection
Stock Indices:
- Conservative sensitivity (0.5-1.0) for institutional participation
- Standard confluence thresholds
- Balanced adaptive parameters
IMPORTANT USAGE CONSIDERATIONS
Realistic Performance Expectations
This indicator provides probabilistic advantages based on mathematical confluence analysis, not guaranteed outcomes. Signal quality varies with market conditions, and proper risk management remains essential regardless of signal tier.
Understanding Adaptive Features:
- Adaptive parameters react to historical data, not future market conditions
- Dynamic levels adjust to past volatility patterns
- Signal quality reflects mathematical alignment probability, not certainty
Market Context Awareness:
- Strong trending markets may produce fewer reversal signals
- Range-bound conditions typically generate more confluence opportunities
- News events and fundamental factors can override technical analysis
Educational Value
Mathematical Concepts Introduced:
- Multi-dimensional confluence analysis
- Adaptive signal processing techniques
- Dynamic parameter optimization
- Mathematical field theory applications in trading
- Advanced Fibonacci sequence applications
Skill Development Benefits:
- Understanding market structure through mathematical lens
- Recognition of multi-timeframe confluence principles
- Appreciation for adaptive vs. static analysis methods
- Integration of classical Fibonacci with modern signal processing
UNIQUE INNOVATIONS
First-Ever Implementations
1. True Fibonacci Sequence Periods: First indicator using authentic Fibonacci numbers (8,13,21,34,55) for timeframe analysis
2. Mathematical Confluence Clustering: Advanced algorithm identifying true Fibonacci level convergence
3. Adaptive StochRSI Boundaries: Dynamic OB/OS levels replacing fixed thresholds
4. Tiered Signal Architecture: Democratic signal weighting with quality classification
5. Quantum Field Price Visualization: Mathematical field representation of price dynamics
Visualization Breakthroughs
- Multi-Layer Fibonacci Grid: Three-layer rendering with intelligent spacing
- Dynamic Confluence Zones: Strength-based color coding and sizing
- Adaptive Parameter Display: Real-time visualization of dynamic calculations
- Mathematical Field Effects: Quantum-inspired price channel visualization
- Progressive Transparency Systems: Natural visual flow without chart clutter
COMPREHENSIVE DASHBOARD SYSTEM
Multi-Size Display Options
Small Dashboard: Core metrics for mobile/limited screen space
Normal Dashboard: Balanced information density for standard desktop use
Large Dashboard: Complete analysis suite including adaptive parameter values
Real-Time Metrics Tracking
Market Analysis Section:
- Unified Field strength with visual meter
- Market Resonance percentage
- Signal Quality assessment with emoji indicators
- Market Bias classification (Bullish/Bearish/Neutral)
Confluence Intelligence:
- Total active zones count
- High/Critical zone identification
- Nearest zone distance and strength
- Price-to-zone ATR measurement
Adaptive Parameters (Large Dashboard):
- Current StochRSI OB/OS levels
- Active ATR multiplier for bar sizing
- Volatility ratio for adaptive scaling
- Real-time StochRSI positioning
TECHNICAL SPECIFICATIONS
Pine Script Version: v5 (Latest)
Calculation Method: Real-time with confirmed bar processing
Maximum Objects: 500 boxes, 500 lines, 500 labels
Dashboard Positions: 4 corner options with size selection
Visual Themes: Quantum, Holographic, Crystalline, Plasma
Alert Integration: Complete alert system for all signal types
Performance Optimizations:
- Efficient confluence zone calculation using advanced clustering
- Smart label spacing prevents overlap
- Progressive transparency for visual clarity
- Memory-optimized array management
EDUCATIONAL FRAMEWORK
Learning Progression
Beginner Level:
- Understanding Fibonacci sequence applications
- Recognition of confluence zone concepts
- Basic signal interpretation
- Dashboard metric comprehension
Intermediate Level:
- Adaptive parameter optimization
- Multi-timeframe confluence analysis
- Signal quality assessment techniques
- Risk management integration
Advanced Level:
- Mathematical field theory applications
- Custom parameter optimization strategies
- Market regime adaptation techniques
- Professional trading system integration
DEVELOPMENT ACKNOWLEDGMENT
Special acknowledgment to @AlgoTrader90 - the foundational concepts of this system came from him and we developed it through a collaborative discussions about multi-timeframe Fibonacci analysis. While the original framework came from AlgoTrader90's innovative approach, this implementation represents a complete evolution of the logic with enhanced mathematical precision, adaptive parameters, and sophisticated signal filtering to deliver meaningful, actionable trading signals.
CONCLUSION
The Sequences of Fibonacci represents a quantum leap in technical analysis, successfully merging classical Fibonacci mathematics with cutting-edge adaptive signal processing. Through sophisticated confluence detection, intelligent parameter adaptation, and comprehensive market analysis, this system provides traders with unprecedented insight into market structure and potential reversal points.
The mathematical foundation ensures lasting relevance while the adaptive features maintain effectiveness across changing market conditions. From the dynamic Fibonacci grid to the quantum field visualization, every component reflects a commitment to mathematical precision, visual elegance, and practical utility.
Whether you're a beginner seeking to understand market confluence or an advanced trader requiring sophisticated analytical tools, this system provides the mathematical framework for informed decision-making based on time-tested Fibonacci principles enhanced with modern computational techniques.
Trade with mathematical precision. Trade with the power of confluence. Trade with The Sequences of Fibonacci.
"Mathematics is the language with which God has written the universe. In markets, Fibonacci sequences reveal the hidden harmonies that govern price movement, and those who understand these mathematical relationships hold the key to anticipating market behavior."
* Galileo Galilei (adapted for modern markets)
— Dskyz, Trade with insight. Trade with anticipation.
Intraday & Annual CAPM AlphaIntraday & Annual CAPM Alpha
This TradingView™ Pine v6 indicator computes and plots a stock’s CAPM α (alpha) on both intraday and daily/annualized timeframes, allowing you to monitor relative performance against a chosen benchmark (e.g. SPX, NDX).
⸻
Key Outputs
1. Intraday α per Bar (blue line)
• Calculates α from a rolling-window linear regression of the last N bars’ returns (default 60).
• Expressed as “extra return per bar” vs. the benchmark.
2. Intraday α Daily-Equivalent (stepped blue line)
• Scales the per-bar α to a full trading day (390 minutes), showing “if this pace held all day, outperformance (%)”.
3. Annualized α (yellow line)
• Performs the same CAPM regression on daily returns over a D-day lookback (default 252), then annualizes α by multiplying by 252.
• Indicates longer-term relative strength/weakness vs. the benchmark.
⸻
Inputs
• Benchmark Symbol: Choose any index or ETF (e.g. “SPX”, “NDX”).
• Intraday Lookback Bars: Number of bars for intraday α regression (default 60).
• Daily Lookback Days: Number of trading days for daily CAPM regression (default 252).
• Use Log Returns?: Toggle between arithmetic vs. log returns.
⸻
How to Use
• Short-Term Signals:
• Watch the blue α/bar line on 1–15 min charts. A cross from negative to positive suggests intraday outperformance; a reversal warns of weakening momentum.
• The blue daily-equivalent α gives a smoother view—e.g. > +1% signals strong intraday bias, < –1% signals underperformance.
• Long-Term Trends:
• On daily charts, focus on the yellow annualized α. A sustained positive α implies this stock has historically beaten the benchmark; sustained negative α implies the opposite.
• Combining Timeframes:
• Use intraday α for timing entries/exits within the session, and annualized α to confirm whether you want a bullish or bearish bias over days to weeks.
⸻
Install & Configure
1. Copy the Pine v6 script into the TradingView Pine Editor.
2. Set your favorite benchmark, lookback periods, and returns type.
3. Add to your chart to start visualizing real-time CAPM α signals!
Feel free to adjust the lookback windows and threshold levels to suit your trading style.
Tensor Market Analysis Engine (TMAE)# Tensor Market Analysis Engine (TMAE)
## Advanced Multi-Dimensional Mathematical Analysis System
*Where Quantum Mathematics Meets Market Structure*
---
## 🎓 THEORETICAL FOUNDATION
The Tensor Market Analysis Engine represents a revolutionary synthesis of three cutting-edge mathematical frameworks that have never before been combined for comprehensive market analysis. This indicator transcends traditional technical analysis by implementing advanced mathematical concepts from quantum mechanics, information theory, and fractal geometry.
### 🌊 Multi-Dimensional Volatility with Jump Detection
**Hawkes Process Implementation:**
The TMAE employs a sophisticated Hawkes process approximation for detecting self-exciting market jumps. Unlike traditional volatility measures that treat price movements as independent events, the Hawkes process recognizes that market shocks cluster and exhibit memory effects.
**Mathematical Foundation:**
```
Intensity λ(t) = μ + Σ α(t - Tᵢ)
```
Where market jumps at times Tᵢ increase the probability of future jumps through the decay function α, controlled by the Hawkes Decay parameter (0.5-0.99).
**Mahalanobis Distance Calculation:**
The engine calculates volatility jumps using multi-dimensional Mahalanobis distance across up to 5 volatility dimensions:
- **Dimension 1:** Price volatility (standard deviation of returns)
- **Dimension 2:** Volume volatility (normalized volume fluctuations)
- **Dimension 3:** Range volatility (high-low spread variations)
- **Dimension 4:** Correlation volatility (price-volume relationship changes)
- **Dimension 5:** Microstructure volatility (intrabar positioning analysis)
This creates a volatility state vector that captures market behavior impossible to detect with traditional single-dimensional approaches.
### 📐 Hurst Exponent Regime Detection
**Fractal Market Hypothesis Integration:**
The TMAE implements advanced Rescaled Range (R/S) analysis to calculate the Hurst exponent in real-time, providing dynamic regime classification:
- **H > 0.6:** Trending (persistent) markets - momentum strategies optimal
- **H < 0.4:** Mean-reverting (anti-persistent) markets - contrarian strategies optimal
- **H ≈ 0.5:** Random walk markets - breakout strategies preferred
**Adaptive R/S Analysis:**
Unlike static implementations, the TMAE uses adaptive windowing that adjusts to market conditions:
```
H = log(R/S) / log(n)
```
Where R is the range of cumulative deviations and S is the standard deviation over period n.
**Dynamic Regime Classification:**
The system employs hysteresis to prevent regime flipping, requiring sustained Hurst values before regime changes are confirmed. This prevents false signals during transitional periods.
### 🔄 Transfer Entropy Analysis
**Information Flow Quantification:**
Transfer entropy measures the directional flow of information between price and volume, revealing lead-lag relationships that indicate future price movements:
```
TE(X→Y) = Σ p(yₜ₊₁, yₜ, xₜ) log
```
**Causality Detection:**
- **Volume → Price:** Indicates accumulation/distribution phases
- **Price → Volume:** Suggests retail participation or momentum chasing
- **Balanced Flow:** Market equilibrium or transition periods
The system analyzes multiple lag periods (2-20 bars) to capture both immediate and structural information flows.
---
## 🔧 COMPREHENSIVE INPUT SYSTEM
### Core Parameters Group
**Primary Analysis Window (10-100, Default: 50)**
The fundamental lookback period affecting all calculations. Optimization by timeframe:
- **1-5 minute charts:** 20-30 (rapid adaptation to micro-movements)
- **15 minute-1 hour:** 30-50 (balanced responsiveness and stability)
- **4 hour-daily:** 50-100 (smooth signals, reduced noise)
- **Asset-specific:** Cryptocurrency 20-35, Stocks 35-50, Forex 40-60
**Signal Sensitivity (0.1-2.0, Default: 0.7)**
Master control affecting all threshold calculations:
- **Conservative (0.3-0.6):** High-quality signals only, fewer false positives
- **Balanced (0.7-1.0):** Optimal risk-reward ratio for most trading styles
- **Aggressive (1.1-2.0):** Maximum signal frequency, requires careful filtering
**Signal Generation Mode:**
- **Aggressive:** Any component signals (highest frequency)
- **Confluence:** 2+ components agree (balanced approach)
- **Conservative:** All 3 components align (highest quality)
### Volatility Jump Detection Group
**Volatility Dimensions (2-5, Default: 3)**
Determines the mathematical space complexity:
- **2D:** Price + Volume volatility (suitable for clean markets)
- **3D:** + Range volatility (optimal for most conditions)
- **4D:** + Correlation volatility (advanced multi-asset analysis)
- **5D:** + Microstructure volatility (maximum sensitivity)
**Jump Detection Threshold (1.5-4.0σ, Default: 3.0σ)**
Standard deviations required for volatility jump classification:
- **Cryptocurrency:** 2.0-2.5σ (naturally volatile)
- **Stock Indices:** 2.5-3.0σ (moderate volatility)
- **Forex Major Pairs:** 3.0-3.5σ (typically stable)
- **Commodities:** 2.0-3.0σ (varies by commodity)
**Jump Clustering Decay (0.5-0.99, Default: 0.85)**
Hawkes process memory parameter:
- **0.5-0.7:** Fast decay (jumps treated as independent)
- **0.8-0.9:** Moderate clustering (realistic market behavior)
- **0.95-0.99:** Strong clustering (crisis/event-driven markets)
### Hurst Exponent Analysis Group
**Calculation Method Options:**
- **Classic R/S:** Original Rescaled Range (fast, simple)
- **Adaptive R/S:** Dynamic windowing (recommended for trading)
- **DFA:** Detrended Fluctuation Analysis (best for noisy data)
**Trending Threshold (0.55-0.8, Default: 0.60)**
Hurst value defining persistent market behavior:
- **0.55-0.60:** Weak trend persistence
- **0.65-0.70:** Clear trending behavior
- **0.75-0.80:** Strong momentum regimes
**Mean Reversion Threshold (0.2-0.45, Default: 0.40)**
Hurst value defining anti-persistent behavior:
- **0.35-0.45:** Weak mean reversion
- **0.25-0.35:** Clear ranging behavior
- **0.15-0.25:** Strong reversion tendency
### Transfer Entropy Parameters Group
**Information Flow Analysis:**
- **Price-Volume:** Classic flow analysis for accumulation/distribution
- **Price-Volatility:** Risk flow analysis for sentiment shifts
- **Multi-Timeframe:** Cross-timeframe causality detection
**Maximum Lag (2-20, Default: 5)**
Causality detection window:
- **2-5 bars:** Immediate causality (scalping)
- **5-10 bars:** Short-term flow (day trading)
- **10-20 bars:** Structural flow (swing trading)
**Significance Threshold (0.05-0.3, Default: 0.15)**
Minimum entropy for signal generation:
- **0.05-0.10:** Detect subtle information flows
- **0.10-0.20:** Clear causality only
- **0.20-0.30:** Very strong flows only
---
## 🎨 ADVANCED VISUAL SYSTEM
### Tensor Volatility Field Visualization
**Five-Layer Resonance Bands:**
The tensor field creates dynamic support/resistance zones that expand and contract based on mathematical field strength:
- **Core Layer (Purple):** Primary tensor field with highest intensity
- **Layer 2 (Neutral):** Secondary mathematical resonance
- **Layer 3 (Info Blue):** Tertiary harmonic frequencies
- **Layer 4 (Warning Gold):** Outer field boundaries
- **Layer 5 (Success Green):** Maximum field extension
**Field Strength Calculation:**
```
Field Strength = min(3.0, Mahalanobis Distance × Tensor Intensity)
```
The field amplitude adjusts to ATR and mathematical distance, creating dynamic zones that respond to market volatility.
**Radiation Line Network:**
During active tensor states, the system projects directional radiation lines showing field energy distribution:
- **8 Directional Rays:** Complete angular coverage
- **Tapering Segments:** Progressive transparency for natural visual flow
- **Pulse Effects:** Enhanced visualization during volatility jumps
### Dimensional Portal System
**Portal Mathematics:**
Dimensional portals visualize regime transitions using category theory principles:
- **Green Portals (◉):** Trending regime detection (appear below price for support)
- **Red Portals (◎):** Mean-reverting regime (appear above price for resistance)
- **Yellow Portals (○):** Random walk regime (neutral positioning)
**Tensor Trail Effects:**
Each portal generates 8 trailing particles showing mathematical momentum:
- **Large Particles (●):** Strong mathematical signal
- **Medium Particles (◦):** Moderate signal strength
- **Small Particles (·):** Weak signal continuation
- **Micro Particles (˙):** Signal dissipation
### Information Flow Streams
**Particle Stream Visualization:**
Transfer entropy creates flowing particle streams indicating information direction:
- **Upward Streams:** Volume leading price (accumulation phases)
- **Downward Streams:** Price leading volume (distribution phases)
- **Stream Density:** Proportional to information flow strength
**15-Particle Evolution:**
Each stream contains 15 particles with progressive sizing and transparency, creating natural flow visualization that makes information transfer immediately apparent.
### Fractal Matrix Grid System
**Multi-Timeframe Fractal Levels:**
The system calculates and displays fractal highs/lows across five Fibonacci periods:
- **8-Period:** Short-term fractal structure
- **13-Period:** Intermediate-term patterns
- **21-Period:** Primary swing levels
- **34-Period:** Major structural levels
- **55-Period:** Long-term fractal boundaries
**Triple-Layer Visualization:**
Each fractal level uses three-layer rendering:
- **Shadow Layer:** Widest, darkest foundation (width 5)
- **Glow Layer:** Medium white core line (width 3)
- **Tensor Layer:** Dotted mathematical overlay (width 1)
**Intelligent Labeling System:**
Smart spacing prevents label overlap using ATR-based minimum distances. Labels include:
- **Fractal Period:** Time-based identification
- **Topological Class:** Mathematical complexity rating (0, I, II, III)
- **Price Level:** Exact fractal price
- **Mahalanobis Distance:** Current mathematical field strength
- **Hurst Exponent:** Current regime classification
- **Anomaly Indicators:** Visual strength representations (○ ◐ ● ⚡)
### Wick Pressure Analysis
**Rejection Level Mathematics:**
The system analyzes candle wick patterns to project future pressure zones:
- **Upper Wick Analysis:** Identifies selling pressure and resistance zones
- **Lower Wick Analysis:** Identifies buying pressure and support zones
- **Pressure Projection:** Extends lines forward based on mathematical probability
**Multi-Layer Glow Effects:**
Wick pressure lines use progressive transparency (1-8 layers) creating natural glow effects that make pressure zones immediately visible without cluttering the chart.
### Enhanced Regime Background
**Dynamic Intensity Mapping:**
Background colors reflect mathematical regime strength:
- **Deep Transparency (98% alpha):** Subtle regime indication
- **Pulse Intensity:** Based on regime strength calculation
- **Color Coding:** Green (trending), Red (mean-reverting), Neutral (random)
**Smoothing Integration:**
Regime changes incorporate 10-bar smoothing to prevent background flicker while maintaining responsiveness to genuine regime shifts.
### Color Scheme System
**Six Professional Themes:**
- **Dark (Default):** Professional trading environment optimization
- **Light:** High ambient light conditions
- **Classic:** Traditional technical analysis appearance
- **Neon:** High-contrast visibility for active trading
- **Neutral:** Minimal distraction focus
- **Bright:** Maximum visibility for complex setups
Each theme maintains mathematical accuracy while optimizing visual clarity for different trading environments and personal preferences.
---
## 📊 INSTITUTIONAL-GRADE DASHBOARD
### Tensor Field Status Section
**Field Strength Display:**
Real-time Mahalanobis distance calculation with dynamic emoji indicators:
- **⚡ (Lightning):** Extreme field strength (>1.5× threshold)
- **● (Solid Circle):** Strong field activity (>1.0× threshold)
- **○ (Open Circle):** Normal field state
**Signal Quality Rating:**
Democratic algorithm assessment:
- **ELITE:** All 3 components aligned (highest probability)
- **STRONG:** 2 components aligned (good probability)
- **GOOD:** 1 component active (moderate probability)
- **WEAK:** No clear component signals
**Threshold and Anomaly Monitoring:**
- **Threshold Display:** Current mathematical threshold setting
- **Anomaly Level (0-100%):** Combined volatility and volume spike measurement
- **>70%:** High anomaly (red warning)
- **30-70%:** Moderate anomaly (orange caution)
- **<30%:** Normal conditions (green confirmation)
### Tensor State Analysis Section
**Mathematical State Classification:**
- **↑ BULL (Tensor State +1):** Trending regime with bullish bias
- **↓ BEAR (Tensor State -1):** Mean-reverting regime with bearish bias
- **◈ SUPER (Tensor State 0):** Random walk regime (neutral)
**Visual State Gauge:**
Five-circle progression showing tensor field polarity:
- **🟢🟢🟢⚪⚪:** Strong bullish mathematical alignment
- **⚪⚪🟡⚪⚪:** Neutral/transitional state
- **⚪⚪🔴🔴🔴:** Strong bearish mathematical alignment
**Trend Direction and Phase Analysis:**
- **📈 BULL / 📉 BEAR / ➡️ NEUTRAL:** Primary trend classification
- **🌪️ CHAOS:** Extreme information flow (>2.0 flow strength)
- **⚡ ACTIVE:** Strong information flow (1.0-2.0 flow strength)
- **😴 CALM:** Low information flow (<1.0 flow strength)
### Trading Signals Section
**Real-Time Signal Status:**
- **🟢 ACTIVE / ⚪ INACTIVE:** Long signal availability
- **🔴 ACTIVE / ⚪ INACTIVE:** Short signal availability
- **Components (X/3):** Active algorithmic components
- **Mode Display:** Current signal generation mode
**Signal Strength Visualization:**
Color-coded component count:
- **Green:** 3/3 components (maximum confidence)
- **Aqua:** 2/3 components (good confidence)
- **Orange:** 1/3 components (moderate confidence)
- **Gray:** 0/3 components (no signals)
### Performance Metrics Section
**Win Rate Monitoring:**
Estimated win rates based on signal quality with emoji indicators:
- **🔥 (Fire):** ≥60% estimated win rate
- **👍 (Thumbs Up):** 45-59% estimated win rate
- **⚠️ (Warning):** <45% estimated win rate
**Mathematical Metrics:**
- **Hurst Exponent:** Real-time fractal dimension (0.000-1.000)
- **Information Flow:** Volume/price leading indicators
- **📊 VOL:** Volume leading price (accumulation/distribution)
- **💰 PRICE:** Price leading volume (momentum/speculation)
- **➖ NONE:** Balanced information flow
- **Volatility Classification:**
- **🔥 HIGH:** Above 1.5× jump threshold
- **📊 NORM:** Normal volatility range
- **😴 LOW:** Below 0.5× jump threshold
### Market Structure Section (Large Dashboard)
**Regime Classification:**
- **📈 TREND:** Hurst >0.6, momentum strategies optimal
- **🔄 REVERT:** Hurst <0.4, contrarian strategies optimal
- **🎲 RANDOM:** Hurst ≈0.5, breakout strategies preferred
**Mathematical Field Analysis:**
- **Dimensions:** Current volatility space complexity (2D-5D)
- **Hawkes λ (Lambda):** Self-exciting jump intensity (0.00-1.00)
- **Jump Status:** 🚨 JUMP (active) / ✅ NORM (normal)
### Settings Summary Section (Large Dashboard)
**Active Configuration Display:**
- **Sensitivity:** Current master sensitivity setting
- **Lookback:** Primary analysis window
- **Theme:** Active color scheme
- **Method:** Hurst calculation method (Classic R/S, Adaptive R/S, DFA)
**Dashboard Sizing Options:**
- **Small:** Essential metrics only (mobile/small screens)
- **Normal:** Balanced information density (standard desktop)
- **Large:** Maximum detail (multi-monitor setups)
**Position Options:**
- **Top Right:** Standard placement (avoids price action)
- **Top Left:** Wide chart optimization
- **Bottom Right:** Recent price focus (scalping)
- **Bottom Left:** Maximum price visibility (swing trading)
---
## 🎯 SIGNAL GENERATION LOGIC
### Multi-Component Convergence System
**Component Signal Architecture:**
The TMAE generates signals through sophisticated component analysis rather than simple threshold crossing:
**Volatility Component:**
- **Jump Detection:** Mahalanobis distance threshold breach
- **Hawkes Intensity:** Self-exciting process activation (>0.2)
- **Multi-dimensional:** Considers all volatility dimensions simultaneously
**Hurst Regime Component:**
- **Trending Markets:** Price above SMA-20 with positive momentum
- **Mean-Reverting Markets:** Price at Bollinger Band extremes
- **Random Markets:** Bollinger squeeze breakouts with directional confirmation
**Transfer Entropy Component:**
- **Volume Leadership:** Information flow from volume to price
- **Volume Spike:** Volume 110%+ above 20-period average
- **Flow Significance:** Above entropy threshold with directional bias
### Democratic Signal Weighting
**Signal Mode Implementation:**
- **Aggressive Mode:** Any single component triggers signal
- **Confluence Mode:** Minimum 2 components must agree
- **Conservative Mode:** All 3 components must align
**Momentum Confirmation:**
All signals require momentum confirmation:
- **Long Signals:** RSI >50 AND price >EMA-9
- **Short Signals:** RSI <50 AND price 0.6):**
- **Increase Sensitivity:** Catch momentum continuation
- **Lower Mean Reversion Threshold:** Avoid counter-trend signals
- **Emphasize Volume Leadership:** Institutional accumulation/distribution
- **Tensor Field Focus:** Use expansion for trend continuation
- **Signal Mode:** Aggressive or Confluence for trend following
**Range-Bound Markets (Hurst <0.4):**
- **Decrease Sensitivity:** Avoid false breakouts
- **Lower Trending Threshold:** Quick regime recognition
- **Focus on Price Leadership:** Retail sentiment extremes
- **Fractal Grid Emphasis:** Support/resistance trading
- **Signal Mode:** Conservative for high-probability reversals
**Volatile Markets (High Jump Frequency):**
- **Increase Hawkes Decay:** Recognize event clustering
- **Higher Jump Threshold:** Avoid noise signals
- **Maximum Dimensions:** Capture full volatility complexity
- **Reduce Position Sizing:** Risk management adaptation
- **Enhanced Visuals:** Maximum information for rapid decisions
**Low Volatility Markets (Low Jump Frequency):**
- **Decrease Jump Threshold:** Capture subtle movements
- **Lower Hawkes Decay:** Treat moves as independent
- **Reduce Dimensions:** Simplify analysis
- **Increase Position Sizing:** Capitalize on compressed volatility
- **Minimal Visuals:** Reduce distraction in quiet markets
---
## 🚀 ADVANCED TRADING STRATEGIES
### The Mathematical Convergence Method
**Entry Protocol:**
1. **Fractal Grid Approach:** Monitor price approaching significant fractal levels
2. **Tensor Field Confirmation:** Verify field expansion supporting direction
3. **Portal Signal:** Wait for dimensional portal appearance
4. **ELITE/STRONG Quality:** Only trade highest quality mathematical signals
5. **Component Consensus:** Confirm 2+ components agree in Confluence mode
**Example Implementation:**
- Price approaching 21-period fractal high
- Tensor field expanding upward (bullish mathematical alignment)
- Green portal appears below price (trending regime confirmation)
- ELITE quality signal with 3/3 components active
- Enter long position with stop below fractal level
**Risk Management:**
- **Stop Placement:** Below/above fractal level that generated signal
- **Position Sizing:** Based on Mahalanobis distance (higher distance = smaller size)
- **Profit Targets:** Next fractal level or tensor field resistance
### The Regime Transition Strategy
**Regime Change Detection:**
1. **Monitor Hurst Exponent:** Watch for persistent moves above/below thresholds
2. **Portal Color Change:** Regime transitions show different portal colors
3. **Background Intensity:** Increasing regime background intensity
4. **Mathematical Confirmation:** Wait for regime confirmation (hysteresis)
**Trading Implementation:**
- **Trending Transitions:** Trade momentum breakouts, follow trend
- **Mean Reversion Transitions:** Trade range boundaries, fade extremes
- **Random Transitions:** Trade breakouts with tight stops
**Advanced Techniques:**
- **Multi-Timeframe:** Confirm regime on higher timeframe
- **Early Entry:** Enter on regime transition rather than confirmation
- **Regime Strength:** Larger positions during strong regime signals
### The Information Flow Momentum Strategy
**Flow Detection Protocol:**
1. **Monitor Transfer Entropy:** Watch for significant information flow shifts
2. **Volume Leadership:** Strong edge when volume leads price
3. **Flow Acceleration:** Increasing flow strength indicates momentum
4. **Directional Confirmation:** Ensure flow aligns with intended trade direction
**Entry Signals:**
- **Volume → Price Flow:** Enter during accumulation/distribution phases
- **Price → Volume Flow:** Enter on momentum confirmation breaks
- **Flow Reversal:** Counter-trend entries when flow reverses
**Optimization:**
- **Scalping:** Use immediate flow detection (2-5 bar lag)
- **Swing Trading:** Use structural flow (10-20 bar lag)
- **Multi-Asset:** Compare flow between correlated assets
### The Tensor Field Expansion Strategy
**Field Mathematics:**
The tensor field expansion indicates mathematical pressure building in market structure:
**Expansion Phases:**
1. **Compression:** Field contracts, volatility decreases
2. **Tension Building:** Mathematical pressure accumulates
3. **Expansion:** Field expands rapidly with directional movement
4. **Resolution:** Field stabilizes at new equilibrium
**Trading Applications:**
- **Compression Trading:** Prepare for breakout during field contraction
- **Expansion Following:** Trade direction of field expansion
- **Reversion Trading:** Fade extreme field expansion
- **Multi-Dimensional:** Consider all field layers for confirmation
### The Hawkes Process Event Strategy
**Self-Exciting Jump Trading:**
Understanding that market shocks cluster and create follow-on opportunities:
**Jump Sequence Analysis:**
1. **Initial Jump:** First volatility jump detected
2. **Clustering Phase:** Hawkes intensity remains elevated
3. **Follow-On Opportunities:** Additional jumps more likely
4. **Decay Period:** Intensity gradually decreases
**Implementation:**
- **Jump Confirmation:** Wait for mathematical jump confirmation
- **Direction Assessment:** Use other components for direction
- **Clustering Trades:** Trade subsequent moves during high intensity
- **Decay Exit:** Exit positions as Hawkes intensity decays
### The Fractal Confluence System
**Multi-Timeframe Fractal Analysis:**
Combining fractal levels across different periods for high-probability zones:
**Confluence Zones:**
- **Double Confluence:** 2 fractal levels align
- **Triple Confluence:** 3+ fractal levels cluster
- **Mathematical Confirmation:** Tensor field supports the level
- **Information Flow:** Transfer entropy confirms direction
**Trading Protocol:**
1. **Identify Confluence:** Find 2+ fractal levels within 1 ATR
2. **Mathematical Support:** Verify tensor field alignment
3. **Signal Quality:** Wait for STRONG or ELITE signal
4. **Risk Definition:** Use fractal level for stop placement
5. **Profit Targeting:** Next major fractal confluence zone
---
## ⚠️ COMPREHENSIVE RISK MANAGEMENT
### Mathematical Position Sizing
**Mahalanobis Distance Integration:**
Position size should inversely correlate with mathematical field strength:
```
Position Size = Base Size × (Threshold / Mahalanobis Distance)
```
**Risk Scaling Matrix:**
- **Low Field Strength (<2.0):** Standard position sizing
- **Moderate Field Strength (2.0-3.0):** 75% position sizing
- **High Field Strength (3.0-4.0):** 50% position sizing
- **Extreme Field Strength (>4.0):** 25% position sizing or no trade
### Signal Quality Risk Adjustment
**Quality-Based Position Sizing:**
- **ELITE Signals:** 100% of planned position size
- **STRONG Signals:** 75% of planned position size
- **GOOD Signals:** 50% of planned position size
- **WEAK Signals:** No position or paper trading only
**Component Agreement Scaling:**
- **3/3 Components:** Full position size
- **2/3 Components:** 75% position size
- **1/3 Components:** 50% position size or skip trade
### Regime-Adaptive Risk Management
**Trending Market Risk:**
- **Wider Stops:** Allow for trend continuation
- **Trend Following:** Trade with regime direction
- **Higher Position Size:** Trend probability advantage
- **Momentum Stops:** Trail stops based on momentum indicators
**Mean-Reverting Market Risk:**
- **Tighter Stops:** Quick exits on trend continuation
- **Contrarian Positioning:** Trade against extremes
- **Smaller Position Size:** Higher reversal failure rate
- **Level-Based Stops:** Use fractal levels for stops
**Random Market Risk:**
- **Breakout Focus:** Trade only clear breakouts
- **Tight Initial Stops:** Quick exit if breakout fails
- **Reduced Frequency:** Skip marginal setups
- **Range-Based Targets:** Profit targets at range boundaries
### Volatility-Adaptive Risk Controls
**High Volatility Periods:**
- **Reduced Position Size:** Account for wider price swings
- **Wider Stops:** Avoid noise-based exits
- **Lower Frequency:** Skip marginal setups
- **Faster Exits:** Take profits more quickly
**Low Volatility Periods:**
- **Standard Position Size:** Normal risk parameters
- **Tighter Stops:** Take advantage of compressed ranges
- **Higher Frequency:** Trade more setups
- **Extended Targets:** Allow for compressed volatility expansion
### Multi-Timeframe Risk Alignment
**Higher Timeframe Trend:**
- **With Trend:** Standard or increased position size
- **Against Trend:** Reduced position size or skip
- **Neutral Trend:** Standard position size with tight management
**Risk Hierarchy:**
1. **Primary:** Current timeframe signal quality
2. **Secondary:** Higher timeframe trend alignment
3. **Tertiary:** Mathematical field strength
4. **Quaternary:** Market regime classification
---
## 📚 EDUCATIONAL VALUE AND MATHEMATICAL CONCEPTS
### Advanced Mathematical Concepts
**Tensor Analysis in Markets:**
The TMAE introduces traders to tensor analysis, a branch of mathematics typically reserved for physics and advanced engineering. Tensors provide a framework for understanding multi-dimensional market relationships that scalar and vector analysis cannot capture.
**Information Theory Applications:**
Transfer entropy implementation teaches traders about information flow in markets, a concept from information theory that quantifies directional causality between variables. This provides intuition about market microstructure and participant behavior.
**Fractal Geometry in Trading:**
The Hurst exponent calculation exposes traders to fractal geometry concepts, helping understand that markets exhibit self-similar patterns across multiple timeframes. This mathematical insight transforms how traders view market structure.
**Stochastic Process Theory:**
The Hawkes process implementation introduces concepts from stochastic process theory, specifically self-exciting point processes. This provides mathematical framework for understanding why market events cluster and exhibit memory effects.
### Learning Progressive Complexity
**Beginner Mathematical Concepts:**
- **Volatility Dimensions:** Understanding multi-dimensional analysis
- **Regime Classification:** Learning market personality types
- **Signal Democracy:** Algorithmic consensus building
- **Visual Mathematics:** Interpreting mathematical concepts visually
**Intermediate Mathematical Applications:**
- **Mahalanobis Distance:** Statistical distance in multi-dimensional space
- **Rescaled Range Analysis:** Fractal dimension measurement
- **Information Entropy:** Quantifying uncertainty and causality
- **Field Theory:** Understanding mathematical fields in market context
**Advanced Mathematical Integration:**
- **Tensor Field Dynamics:** Multi-dimensional market force analysis
- **Stochastic Self-Excitation:** Event clustering and memory effects
- **Categorical Composition:** Mathematical signal combination theory
- **Topological Market Analysis:** Understanding market shape and connectivity
### Practical Mathematical Intuition
**Developing Market Mathematics Intuition:**
The TMAE serves as a bridge between abstract mathematical concepts and practical trading applications. Traders develop intuitive understanding of:
- **How markets exhibit mathematical structure beneath apparent randomness**
- **Why multi-dimensional analysis reveals patterns invisible to single-variable approaches**
- **How information flows through markets in measurable, predictable ways**
- **Why mathematical models provide probabilistic edges rather than certainties**
---
## 🔬 IMPLEMENTATION AND OPTIMIZATION
### Getting Started Protocol
**Phase 1: Observation (Week 1)**
1. **Apply with defaults:** Use standard settings on your primary trading timeframe
2. **Study visual elements:** Learn to interpret tensor fields, portals, and streams
3. **Monitor dashboard:** Observe how metrics change with market conditions
4. **No trading:** Focus entirely on pattern recognition and understanding
**Phase 2: Pattern Recognition (Week 2-3)**
1. **Identify signal patterns:** Note what market conditions produce different signal qualities
2. **Regime correlation:** Observe how Hurst regimes affect signal performance
3. **Visual confirmation:** Learn to read tensor field expansion and portal signals
4. **Component analysis:** Understand which components drive signals in different markets
**Phase 3: Parameter Optimization (Week 4-5)**
1. **Asset-specific tuning:** Adjust parameters for your specific trading instrument
2. **Timeframe optimization:** Fine-tune for your preferred trading timeframe
3. **Sensitivity adjustment:** Balance signal frequency with quality
4. **Visual customization:** Optimize colors and intensity for your trading environment
**Phase 4: Live Implementation (Week 6+)**
1. **Paper trading:** Test signals with hypothetical trades
2. **Small position sizing:** Begin with minimal risk during learning phase
3. **Performance tracking:** Monitor actual vs. expected signal performance
4. **Continuous optimization:** Refine settings based on real performance data
### Performance Monitoring System
**Signal Quality Tracking:**
- **ELITE Signal Win Rate:** Track highest quality signals separately
- **Component Performance:** Monitor which components provide best signals
- **Regime Performance:** Analyze performance across different market regimes
- **Timeframe Analysis:** Compare performance across different session times
**Mathematical Metric Correlation:**
- **Field Strength vs. Performance:** Higher field strength should correlate with better performance
- **Component Agreement vs. Win Rate:** More component agreement should improve win rates
- **Regime Alignment vs. Success:** Trading with mathematical regime should outperform
### Continuous Optimization Process
**Monthly Review Protocol:**
1. **Performance Analysis:** Review win rates, profit factors, and maximum drawdown
2. **Parameter Assessment:** Evaluate if current settings remain optimal
3. **Market Adaptation:** Adjust for changes in market character or volatility
4. **Component Weighting:** Consider if certain components should receive more/less emphasis
**Quarterly Deep Analysis:**
1. **Mathematical Model Validation:** Verify that mathematical relationships remain valid
2. **Regime Distribution:** Analyze time spent in different market regimes
3. **Signal Evolution:** Track how signal characteristics change over time
4. **Correlation Analysis:** Monitor correlations between different mathematical components
---
## 🌟 UNIQUE INNOVATIONS AND CONTRIBUTIONS
### Revolutionary Mathematical Integration
**First-Ever Implementations:**
1. **Multi-Dimensional Volatility Tensor:** First indicator to implement true tensor analysis for market volatility
2. **Real-Time Hawkes Process:** First trading implementation of self-exciting point processes
3. **Transfer Entropy Trading Signals:** First practical application of information theory for trade generation
4. **Democratic Component Voting:** First algorithmic consensus system for signal generation
5. **Fractal-Projected Signal Quality:** First system to predict signal quality at future price levels
### Advanced Visualization Innovations
**Mathematical Visualization Breakthroughs:**
- **Tensor Field Radiation:** Visual representation of mathematical field energy
- **Dimensional Portal System:** Category theory visualization for regime transitions
- **Information Flow Streams:** Real-time visual display of market information transfer
- **Multi-Layer Fractal Grid:** Intelligent spacing and projection system
- **Regime Intensity Mapping:** Dynamic background showing mathematical regime strength
### Practical Trading Innovations
**Trading System Advances:**
- **Quality-Weighted Signal Generation:** Signals rated by mathematical confidence
- **Regime-Adaptive Strategy Selection:** Automatic strategy optimization based on market personality
- **Anti-Spam Signal Protection:** Mathematical prevention of signal clustering
- **Component Performance Tracking:** Real-time monitoring of algorithmic component success
- **Field-Strength Position Sizing:** Mathematical volatility integration for risk management
---
## ⚖️ RESPONSIBLE USAGE AND LIMITATIONS
### Mathematical Model Limitations
**Understanding Model Boundaries:**
While the TMAE implements sophisticated mathematical concepts, traders must understand fundamental limitations:
- **Markets Are Not Purely Mathematical:** Human psychology, news events, and fundamental factors create unpredictable elements
- **Past Performance Limitations:** Mathematical relationships that worked historically may not persist indefinitely
- **Model Risk:** Complex models can fail during unprecedented market conditions
- **Overfitting Potential:** Highly optimized parameters may not generalize to future market conditions
### Proper Implementation Guidelines
**Risk Management Requirements:**
- **Never Risk More Than 2% Per Trade:** Regardless of signal quality
- **Diversification Mandatory:** Don't rely solely on mathematical signals
- **Position Sizing Discipline:** Use mathematical field strength for sizing, not confidence
- **Stop Loss Non-Negotiable:** Every trade must have predefined risk parameters
**Realistic Expectations:**
- **Mathematical Edge, Not Certainty:** The indicator provides probabilistic advantages, not guaranteed outcomes
- **Learning Curve Required:** Complex mathematical concepts require time to master
- **Market Adaptation Necessary:** Parameters must evolve with changing market conditions
- **Continuous Education Important:** Understanding underlying mathematics improves application
### Ethical Trading Considerations
**Market Impact Awareness:**
- **Information Asymmetry:** Advanced mathematical analysis may provide advantages over other market participants
- **Position Size Responsibility:** Large positions based on mathematical signals can impact market structure
- **Sharing Knowledge:** Consider educational contributions to trading community
- **Fair Market Participation:** Use mathematical advantages responsibly within market framework
### Professional Development Path
**Skill Development Sequence:**
1. **Basic Mathematical Literacy:** Understand fundamental concepts before advanced application
2. **Risk Management Mastery:** Develop disciplined risk control before relying on complex signals
3. **Market Psychology Understanding:** Combine mathematical analysis with behavioral market insights
4. **Continuous Learning:** Stay updated on mathematical finance developments and market evolution
---
## 🔮 CONCLUSION
The Tensor Market Analysis Engine represents a quantum leap forward in technical analysis, successfully bridging the gap between advanced pure mathematics and practical trading applications. By integrating multi-dimensional volatility analysis, fractal market theory, and information flow dynamics, the TMAE reveals market structure invisible to conventional analysis while maintaining visual clarity and practical usability.
### Mathematical Innovation Legacy
This indicator establishes new paradigms in technical analysis:
- **Tensor analysis for market volatility understanding**
- **Stochastic self-excitation for event clustering prediction**
- **Information theory for causality-based trade generation**
- **Democratic algorithmic consensus for signal quality enhancement**
- **Mathematical field visualization for intuitive market understanding**
### Practical Trading Revolution
Beyond mathematical innovation, the TMAE transforms practical trading:
- **Quality-rated signals replace binary buy/sell decisions**
- **Regime-adaptive strategies automatically optimize for market personality**
- **Multi-dimensional risk management integrates mathematical volatility measures**
- **Visual mathematical concepts make complex analysis immediately interpretable**
- **Educational value creates lasting improvement in trading understanding**
### Future-Proof Design
The mathematical foundations ensure lasting relevance:
- **Universal mathematical principles transcend market evolution**
- **Multi-dimensional analysis adapts to new market structures**
- **Regime detection automatically adjusts to changing market personalities**
- **Component democracy allows for future algorithmic additions**
- **Mathematical visualization scales with increasing market complexity**
### Commitment to Excellence
The TMAE represents more than an indicator—it embodies a philosophy of bringing rigorous mathematical analysis to trading while maintaining practical utility and visual elegance. Every component, from the multi-dimensional tensor fields to the democratic signal generation, reflects a commitment to mathematical accuracy, trading practicality, and educational value.
### Trading with Mathematical Precision
In an era where markets grow increasingly complex and computational, the TMAE provides traders with mathematical tools previously available only to institutional quantitative research teams. Yet unlike academic mathematical models, the TMAE translates complex concepts into intuitive visual representations and practical trading signals.
By combining the mathematical rigor of tensor analysis, the statistical power of multi-dimensional volatility modeling, and the information-theoretic insights of transfer entropy, traders gain unprecedented insight into market structure and dynamics.
### Final Perspective
Markets, like nature, exhibit profound mathematical beauty beneath apparent chaos. The Tensor Market Analysis Engine serves as a mathematical lens that reveals this hidden order, transforming how traders perceive and interact with market structure.
Through mathematical precision, visual elegance, and practical utility, the TMAE empowers traders to see beyond the noise and trade with the confidence that comes from understanding the mathematical principles governing market behavior.
Trade with mathematical insight. Trade with the power of tensors. Trade with the TMAE.
*"In mathematics, you don't understand things. You just get used to them." - John von Neumann*
*With the TMAE, mathematical market understanding becomes not just possible, but intuitive.*
— Dskyz, Trade with insight. Trade with anticipation.
Trend Gauge [BullByte]Trend Gauge
Summary
A multi-factor trend detection indicator that aggregates EMA alignment, VWMA momentum scaling, volume spikes, ATR breakout strength, higher-timeframe confirmation, ADX-based regime filtering, and RSI pivot-divergence penalty into one normalized trend score. It also provides a confidence meter, a Δ Score momentum histogram, divergence highlights, and a compact, scalable dashboard for at-a-glance status.
________________________________________
## 1. Purpose of the Indicator
Why this was built
Traders often monitor several indicators in parallel - EMAs, volume signals, volatility breakouts, higher-timeframe trends, ADX readings, divergence alerts, etc., which can be cumbersome and sometimes contradictory. The “Trend Gauge” indicator was created to consolidate these complementary checks into a single, normalized score that reflects the prevailing market bias (bullish, bearish, or neutral) and its strength. By combining multiple inputs with an adaptive regime filter, scaling contributions by magnitude, and penalizing weakening signals (divergence), this tool aims to reduce noise, highlight genuine trend opportunities, and warn when momentum fades.
Key Design Goals
Signal Aggregation
Merged trend-following signals (EMA crossover, ATR breakout, higher-timeframe confirmation) and momentum signals (VWMA thrust, volume spikes) into a unified score that reflects directional bias more holistically.
Market Regime Awareness
Implemented an ADX-style filter to distinguish between trending and ranging markets, reducing the influence of trend signals during sideways phases to avoid false breakouts.
Magnitude-Based Scaling
Replaced binary contributions with scaled inputs: VWMA thrust and ATR breakout are weighted relative to recent averages, allowing for more nuanced score adjustments based on signal strength.
Momentum Divergence Penalty
Integrated pivot-based RSI divergence detection to slightly reduce the overall score when early signs of momentum weakening are detected, improving risk-awareness in entries.
Confidence Transparency
Added a live confidence metric that shows what percentage of enabled sub-indicators currently agree with the overall bias, making the scoring system more interpretable.
Momentum Acceleration Visualization
Plotted the change in score (Δ Score) as a histogram bar-to-bar, highlighting whether momentum is increasing, flattening, or reversing, aiding in more timely decision-making.
Compact Informational Dashboard
Presented a clean, scalable dashboard that displays each component’s status, the final score, confidence %, detected regime (Trending/Ranging), and a labeled strength gauge for quick visual assessment.
________________________________________
## 2. Why a Trader Should Use It
Main benefits and use cases
1. Unified View: Rather than juggling multiple windows or panels, this indicator delivers a single score synthesizing diverse signals.
2. Regime Filtering: In ranging markets, trend signals often generate false entries. The ADX-based regime filter automatically down-weights trend-following components, helping you avoid chasing false breakouts.
3. Nuanced Momentum & Volatility: VWMA and ATR breakout contributions are normalized by recent averages, so strong moves register strongly while smaller fluctuations are de-emphasized.
4. Early Warning of Weakening: Pivot-based RSI divergence is detected and used to slightly reduce the score when price/momentum diverges, giving a cautionary signal before a full reversal.
5. Confidence Meter: See at a glance how many sub-indicators align with the aggregated bias (e.g., “80% confidence” means 4 out of 5 components agree ). This transparency avoids black-box decisions.
6. Trend Acceleration/Deceleration View: The Δ Score histogram visualizes whether the aggregated score is rising (accelerating trend) or falling (momentum fading), supplementing the main oscillator.
7. Compact Dashboard: A corner table lists each check’s status (“Bull”, “Bear”, “Flat” or “Disabled”), plus overall Score, Confidence %, Regime, Trend Strength label, and a gauge bar. Users can scale text size (Normal, Small, Tiny) without removing elements, so the full picture remains visible even in compact layouts.
8. Customizable & Transparent: All components can be enabled/disabled and parameterized (lengths, thresholds, weights). The full Pine code is open and well-commented, letting users inspect or adapt the logic.
9. Alert-ready: Built-in alert conditions fire when the score crosses weak thresholds to bullish/bearish or returns to neutral, enabling timely notifications.
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## 3. Component Rationale (“Why These Specific Indicators?”)
Each sub-component was chosen because it adds complementary information about trend or momentum:
1. EMA Cross
o Basic trend measure: compares a faster EMA vs. a slower EMA. Quickly reflects trend shifts but by itself can whipsaw in sideways markets.
2. VWMA Momentum
o Volume-weighted moving average change indicates momentum with volume context. By normalizing (dividing by a recent average absolute change), we capture the strength of momentum relative to recent history. This scaling prevents tiny moves from dominating and highlights genuinely strong momentum.
3. Volume Spikes
o Sudden jumps in volume combined with price movement often accompany stronger moves or reversals. A binary detection (+1 for bullish spike, -1 for bearish spike) flags high-conviction bars.
4. ATR Breakout
o Detects price breaking beyond recent highs/lows by a multiple of ATR. Measures breakout strength by how far beyond the threshold price moves relative to ATR, capped to avoid extreme outliers. This gives a volatility-contextual trend signal.
5. Higher-Timeframe EMA Alignment
o Confirms whether the shorter-term trend aligns with a higher timeframe trend. Uses request.security with lookahead_off to avoid future data. When multiple timeframes agree, confidence in direction increases.
6. ADX Regime Filter (Manual Calculation)
o Computes directional movement (+DM/–DM), smoothes via RMA, computes DI+ and DI–, then a DX and ADX-like value. If ADX ≥ threshold, market is “Trending” and trend components carry full weight; if ADX < threshold, “Ranging” mode applies a configurable weight multiplier (e.g., 0.5) to trend-based contributions, reducing false signals in sideways conditions. Volume spikes remain binary (optional behavior; can be adjusted if desired).
7. RSI Pivot-Divergence Penalty
o Uses ta.pivothigh / ta.pivotlow with a lookback to detect pivot highs/lows on price and corresponding RSI values. When price makes a higher high but RSI makes a lower high (bearish divergence), or price makes a lower low but RSI makes a higher low (bullish divergence), a divergence signal is set. Rather than flipping the trend outright, the indicator subtracts (or adds) a small penalty (configurable) from the aggregated score if it would weaken the current bias. This subtle adjustment warns of weakening momentum without overreacting to noise.
8. Confidence Meter
o Counts how many enabled components currently agree in direction with the aggregated score (i.e., component sign × score sign > 0). Displays this as a percentage. A high percentage indicates strong corroboration; a low percentage warns of mixed signals.
9. Δ Score Momentum View
o Plots the bar-to-bar change in the aggregated score (delta_score = score - score ) as a histogram. When positive, bars are drawn in green above zero; when negative, bars are drawn in red below zero. This reveals acceleration (rising Δ) or deceleration (falling Δ), supplementing the main oscillator.
10. Dashboard
• A table in the indicator pane’s top-right with 11 rows:
1. EMA Cross status
2. VWMA Momentum status
3. Volume Spike status
4. ATR Breakout status
5. Higher-Timeframe Trend status
6. Score (numeric)
7. Confidence %
8. Regime (“Trending” or “Ranging”)
9. Trend Strength label (e.g., “Weak Bullish Trend”, “Strong Bearish Trend”)
10. Gauge bar visually representing score magnitude
• All rows always present; size_opt (Normal, Small, Tiny) only changes text size via text_size, not which elements appear. This ensures full transparency.
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## 4. What Makes This Indicator Stand Out
• Regime-Weighted Multi-Factor Score: Trend and momentum signals are adaptively weighted by market regime (trending vs. ranging) , reducing false signals.
• Magnitude Scaling: VWMA and ATR breakout contributions are normalized by recent average momentum or ATR, giving finer gradation compared to simple ±1.
• Integrated Divergence Penalty: Divergence directly adjusts the aggregated score rather than appearing as a separate subplot; this influences alerts and trend labeling in real time.
• Confidence Meter: Shows the percentage of sub-signals in agreement, providing transparency and preventing blind trust in a single metric.
• Δ Score Histogram Momentum View: A histogram highlights acceleration or deceleration of the aggregated trend score, helping detect shifts early.
• Flexible Dashboard: Always-visible component statuses and summary metrics in one place; text size scaling keeps the full picture available in cramped layouts.
• Lookahead-Safe HTF Confirmation: Uses lookahead_off so no future data is accessed from higher timeframes, avoiding repaint bias.
• Repaint Transparency: Divergence detection uses pivot functions that inherently confirm only after lookback bars; description documents this lag so users understand how and when divergence labels appear.
• Open-Source & Educational: Full, well-commented Pine v6 code is provided; users can learn from its structure: manual ADX computation, conditional plotting with series = show ? value : na, efficient use of table.new in barstate.islast, and grouped inputs with tooltips.
• Compliance-Conscious: All plots have descriptive titles; inputs use clear names; no unnamed generic “Plot” entries; manual ADX uses RMA; all request.security calls use lookahead_off. Code comments mention repaint behavior and limitations.
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## 5. Recommended Timeframes & Tuning
• Any Timeframe: The indicator works on small (e.g., 1m) to large (daily, weekly) timeframes. However:
o On very low timeframes (<1m or tick charts), noise may produce frequent whipsaws. Consider increasing smoothing lengths, disabling certain components (e.g., volume spike if volume data noisy), or using a larger pivot lookback for divergence.
o On higher timeframes (daily, weekly), consider longer lookbacks for ATR breakout or divergence, and set Higher-Timeframe trend appropriately (e.g., 4H HTF when on 5 Min chart).
• Defaults & Experimentation: Default input values are chosen to be balanced for many liquid markets. Users should test with replay or historical analysis on their symbol/timeframe and adjust:
o ADX threshold (e.g., 20–30) based on instrument volatility.
o VWMA and ATR scaling lengths to match average volatility cycles.
o Pivot lookback for divergence: shorter for faster markets, longer for slower ones.
• Combining with Other Analysis: Use in conjunction with price action, support/resistance, candlestick patterns, order flow, or other tools as desired. The aggregated score and alerts can guide attention but should not be the sole decision-factor.
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## 6. How Scoring and Logic Works (Step-by-Step)
1. Compute Sub-Scores
o EMA Cross: Evaluate fast EMA > slow EMA ? +1 : fast EMA < slow EMA ? -1 : 0.
o VWMA Momentum: Calculate vwma = ta.vwma(close, length), then vwma_mom = vwma - vwma . Normalize: divide by recent average absolute momentum (e.g., ta.sma(abs(vwma_mom), lookback)), clip to .
o Volume Spike: Compute vol_SMA = ta.sma(volume, len). If volume > vol_SMA * multiplier AND price moved up ≥ threshold%, assign +1; if moved down ≥ threshold%, assign -1; else 0.
o ATR Breakout: Determine recent high/low over lookback. If close > high + ATR*mult, compute distance = close - (high + ATR*mult), normalize by ATR, cap at a configured maximum. Assign positive contribution. Similarly for bearish breakout below low.
o Higher-Timeframe Trend: Use request.security(..., lookahead=barmerge.lookahead_off) to fetch HTF EMAs; assign +1 or -1 based on alignment.
2. ADX Regime Weighting
o Compute manual ADX: directional movements (+DM, –DM), smoothed via RMA, DI+ and DI–, then DX and ADX via RMA. If ADX ≥ threshold, market is considered “Trending”; otherwise “Ranging.”
o If trending, trend-based contributions (EMA, VWMA, ATR, HTF) use full weight = 1.0. If ranging, use weight = ranging_weight (e.g., 0.5) to down-weight them. Volume spike stays binary ±1 (optional to change if desired).
3. Aggregate Raw Score
o Sum weighted contributions of all enabled components. Count the number of enabled components; if zero, default count = 1 to avoid division by zero.
4. Divergence Penalty
o Detect pivot highs/lows on price and corresponding RSI values, using a lookback. When price and RSI diverge (bearish or bullish divergence), check if current raw score is in the opposing direction:
If bearish divergence (price higher high, RSI lower high) and raw score currently positive, subtract a penalty (e.g., 0.5).
If bullish divergence (price lower low, RSI higher low) and raw score currently negative, add a penalty.
o This reduces score magnitude to reflect weakening momentum, without flipping the trend outright.
5. Normalize and Smooth
o Normalized score = (raw_score / number_of_enabled_components) * 100. This yields a roughly range.
o Optional EMA smoothing of this normalized score to reduce noise.
6. Interpretation
o Sign: >0 = net bullish bias; <0 = net bearish bias; near zero = neutral.
o Magnitude Zones: Compare |score| to thresholds (Weak, Medium, Strong) to label trend strength (e.g., “Weak Bullish Trend”, “Medium Bearish Trend”, “Strong Bullish Trend”).
o Δ Score Histogram: The histogram bars from zero show change from previous bar’s score; positive bars indicate acceleration, negative bars indicate deceleration.
o Confidence: Percentage of sub-indicators aligned with the score’s sign.
o Regime: Indicates whether trend-based signals are fully weighted or down-weighted.
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## 7. Oscillator Plot & Visualization: How to Read It
Main Score Line & Area
The oscillator plots the aggregated score as a line, with colored fill: green above zero for bullish area, red below zero for bearish area. Horizontal reference lines at ±Weak, ±Medium, and ±Strong thresholds mark zones: crossing above +Weak suggests beginning of bullish bias, above +Medium for moderate strength, above +Strong for strong trend; similarly for bearish below negative thresholds.
Δ Score Histogram
If enabled, a histogram shows score - score . When positive, bars appear in green above zero, indicating accelerating bullish momentum; when negative, bars appear in red below zero, indicating decelerating or reversing momentum. The height of each bar reflects the magnitude of change in the aggregated score from the prior bar.
Divergence Highlight Fill
If enabled, when a pivot-based divergence is confirmed:
• Bullish Divergence : fill the area below zero down to –Weak threshold in green, signaling potential reversal from bearish to bullish.
• Bearish Divergence : fill the area above zero up to +Weak threshold in red, signaling potential reversal from bullish to bearish.
These fills appear with a lag equal to pivot lookback (the number of bars needed to confirm the pivot). They do not repaint after confirmation, but users must understand this lag.
Trend Direction Label
When score crosses above or below the Weak threshold, a small label appears near the score line reading “Bullish” or “Bearish.” If the score returns within ±Weak, the label “Neutral” appears. This helps quickly identify shifts at the moment they occur.
Dashboard Panel
In the indicator pane’s top-right, a table shows:
1. EMA Cross status: “Bull”, “Bear”, “Flat”, or “Disabled”
2. VWMA Momentum status: similarly
3. Volume Spike status: “Bull”, “Bear”, “No”, or “Disabled”
4. ATR Breakout status: “Bull”, “Bear”, “No”, or “Disabled”
5. Higher-Timeframe Trend status: “Bull”, “Bear”, “Flat”, or “Disabled”
6. Score: numeric value (rounded)
7. Confidence: e.g., “80%” (colored: green for high, amber for medium, red for low)
8. Regime: “Trending” or “Ranging” (colored accordingly)
9. Trend Strength: textual label based on magnitude (e.g., “Medium Bullish Trend”)
10. Gauge: a bar of blocks representing |score|/100
All rows remain visible at all times; changing Dashboard Size only scales text size (Normal, Small, Tiny).
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## 8. Example Usage (Illustrative Scenario)
Example: BTCUSD 5 Min
1. Setup: Add “Trend Gauge ” to your BTCUSD 5 Min chart. Defaults: EMAs (8/21), VWMA 14 with lookback 3, volume spike settings, ATR breakout 14/5, HTF = 5m (or adjust to 4H if preferred), ADX threshold 25, ranging weight 0.5, divergence RSI length 14 pivot lookback 5, penalty 0.5, smoothing length 3, thresholds Weak=20, Medium=50, Strong=80. Dashboard Size = Small.
2. Trend Onset: At some point, price breaks above recent high by ATR multiple, volume spikes upward, faster EMA crosses above slower EMA, HTF EMA also bullish, and ADX (manual) ≥ threshold → aggregated score rises above +20 (Weak threshold) into +Medium zone. Dashboard shows “Bull” for EMA, VWMA, Vol Spike, ATR, HTF; Score ~+60–+70; Confidence ~100%; Regime “Trending”; Trend Strength “Medium Bullish Trend”; Gauge ~6–7 blocks. Δ Score histogram bars are green and rising, indicating accelerating bullish momentum. Trader notes the alignment.
3. Divergence Warning: Later, price makes a slightly higher high but RSI fails to confirm (lower RSI high). Pivot lookback completes; the indicator highlights a bearish divergence fill above zero and subtracts a small penalty from the score, causing score to stall or retrace slightly. Dashboard still bullish but score dips toward +Weak. This warns the trader to tighten stops or take partial profits.
4. Trend Weakens: Score eventually crosses below +Weak back into neutral; a “Neutral” label appears, and a “Neutral Trend” alert fires if enabled. Trader exits or avoids new long entries. If score subsequently crosses below –Weak, a “Bearish” label and alert occur.
5. Customization: If the trader finds VWMA noise too frequent on this instrument, they may disable VWMA or increase lookback. If ATR breakouts are too rare, adjust ATR length or multiplier. If ADX threshold seems off, tune threshold. All these adjustments are explained in Inputs section.
6. Visualization: The screenshot shows the main score oscillator with colored areas, reference lines at ±20/50/80, Δ Score histogram bars below/above zero, divergence fill highlighting potential reversal, and the dashboard table in the top-right.
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## 9. Inputs Explanation
A concise yet clear summary of inputs helps users understand and adjust:
1. General Settings
• Theme (Dark/Light): Choose background-appropriate colors for the indicator pane.
• Dashboard Size (Normal/Small/Tiny): Scales text size only; all dashboard elements remain visible.
2. Indicator Settings
• Enable EMA Cross: Toggle on/off basic EMA alignment check.
o Fast EMA Length and Slow EMA Length: Periods for EMAs.
• Enable VWMA Momentum: Toggle VWMA momentum check.
o VWMA Length: Period for VWMA.
o VWMA Momentum Lookback: Bars to compare VWMA to measure momentum.
• Enable Volume Spike: Toggle volume spike detection.
o Volume SMA Length: Period to compute average volume.
o Volume Spike Multiplier: How many times above average volume qualifies as spike.
o Min Price Move (%): Minimum percent change in price during spike to qualify as bullish or bearish.
• Enable ATR Breakout: Toggle ATR breakout detection.
o ATR Length: Period for ATR.
o Breakout Lookback: Bars to look back for recent highs/lows.
o ATR Multiplier: Multiplier for breakout threshold.
• Enable Higher Timeframe Trend: Toggle HTF EMA alignment.
o Higher Timeframe: E.g., “5” for 5-minute when on 1-minute chart, or “60” for 5 Min when on 15m, etc. Uses lookahead_off.
• Enable ADX Regime Filter: Toggles regime-based weighting.
o ADX Length: Period for manual ADX calculation.
o ADX Threshold: Value above which market considered trending.
o Ranging Weight Multiplier: Weight applied to trend components when ADX < threshold (e.g., 0.5).
• Scale VWMA Momentum: Toggle normalization of VWMA momentum magnitude.
o VWMA Mom Scale Lookback: Period for average absolute VWMA momentum.
• Scale ATR Breakout Strength: Toggle normalization of breakout distance by ATR.
o ATR Scale Cap: Maximum multiple of ATR used for breakout strength.
• Enable Price-RSI Divergence: Toggle divergence detection.
o RSI Length for Divergence: Period for RSI.
o Pivot Lookback for Divergence: Bars on each side to identify pivot high/low.
o Divergence Penalty: Amount to subtract/add to score when divergence detected (e.g., 0.5).
3. Score Settings
• Smooth Score: Toggle EMA smoothing of normalized score.
• Score Smoothing Length: Period for smoothing EMA.
• Weak Threshold: Absolute score value under which trend is considered weak or neutral.
• Medium Threshold: Score above Weak but below Medium is moderate.
• Strong Threshold: Score above this indicates strong trend.
4. Visualization Settings
• Show Δ Score Histogram: Toggle display of the bar-to-bar change in score as a histogram. Default true.
• Show Divergence Fill: Toggle background fill highlighting confirmed divergences. Default true.
Each input has a tooltip in the code.
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## 10. Limitations, Repaint Notes, and Disclaimers
10.1. Repaint & Lag Considerations
• Pivot-Based Divergence Lag: The divergence detection uses ta.pivothigh / ta.pivotlow with a specified lookback. By design, a pivot is only confirmed after the lookback number of bars. As a result:
o Divergence labels or fills appear with a delay equal to the pivot lookback.
o Once the pivot is confirmed and the divergence is detected, the fill/label does not repaint thereafter, but you must understand and accept this lag.
o Users should not treat divergence highlights as predictive signals without additional confirmation, because they appear after the pivot has fully formed.
• Higher-Timeframe EMA Alignment: Uses request.security(..., lookahead=barmerge.lookahead_off), so no future data from the higher timeframe is used. This avoids lookahead bias and ensures signals are based only on completed higher-timeframe bars.
• No Future Data: All calculations are designed to avoid using future information. For example, manual ADX uses RMA on past data; security calls use lookahead_off.
10.2. Market & Noise Considerations
• In very choppy or low-liquidity markets, some components (e.g., volume spikes or VWMA momentum) may be noisy. Users can disable or adjust those components’ parameters.
• On extremely low timeframes, noise may dominate; consider smoothing lengths or disabling certain features.
• On very high timeframes, pivots and breakouts occur less frequently; adjust lookbacks accordingly to avoid sparse signals.
10.3. Not a Standalone Trading System
• This is an indicator, not a complete trading strategy. It provides signals and context but does not manage entries, exits, position sizing, or risk management.
• Users must combine it with their own analysis, money management, and confirmations (e.g., price patterns, support/resistance, fundamental context).
• No guarantees: past behavior does not guarantee future performance.
10.4. Disclaimers
• Educational Purposes Only: The script is provided as-is for educational and informational purposes. It does not constitute financial, investment, or trading advice.
• Use at Your Own Risk: Trading involves risk of loss. Users should thoroughly test and use proper risk management.
• No Guarantees: The author is not responsible for trading outcomes based on this indicator.
• License: Published under Mozilla Public License 2.0; code is open for viewing and modification under MPL terms.
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## 11. Alerts
• The indicator defines three alert conditions:
1. Bullish Trend: when the aggregated score crosses above the Weak threshold.
2. Bearish Trend: when the score crosses below the negative Weak threshold.
3. Neutral Trend: when the score returns within ±Weak after being outside.
Good luck
– BullByte
MTF RSI MA System + Adaptive BandsMTF RSI MA System + Adaptive Bands
Overview
MTF RSI MA System + Adaptive Bands is a highly customizable Pine Script indicator for traders seeking a versatile tool for multi-timeframe (MTF) analysis. Unlike traditional RSI, it focuses on the Moving Average of RSI (RSI MA), delivering smoother and more flexible trading signals. The main screenshot displays the indicator in two panels to showcase its diverse capabilities.
Important: Timeframes do not adjust automatically – users must manually set them to match the chart’s timeframe.
Features
Core Component: Built around RSI MA, not raw RSI, for smoother trend signals.
Multi-Timeframe: Analyze RSI MA across three customizable timeframes (default: 4H, 8H, 12H).
Adaptive Bands: Three band calculation methods (Fixed, Percent, StdDev) for dynamic signals.
Flexible Signals: Generated via RSI MA crossovers, band interactions, or directional alignment across timeframes.
Background Coloring: Highlights when RSI MAs across timeframes move in the same direction, aiding trend confirmation.
Screenshot Panels Configuration
Upper Panel: Shows RSI, RSI MA, and fixed bands for reversal strategies (RSI crossing bands).
Lower Panel: Displays three RSI MAs (Alligator-style) for trend-following, with background coloring for directional alignment.
Band Calculation Methods
The indicator offers three ways to calculate bands around RSI MA, each with unique characteristics:
Fixed Bands
Set at a fixed point value (default: 10) above and below RSI MA.
Example: If RSI MA = 50, band value = 10 → upper band = 60, lower = 40.
Use Case: Best for stable markets or fixed-range preferences.
Tip: Adjust the band value to widen or narrow the range based on asset volatility.
Percent Bands
Calculated as a percentage of RSI MA (default: 10%).
Example: If RSI MA = 50, band value = 10% → upper band = 55, lower = 45.
Use Case: Ideal for assets with varying volatility, as bands scale with RSI MA.
Tip: Experiment with percentage values to match typical price swings.
Standard Deviation Bands (StdDev)
Based on RSI’s standard deviation over the MA period, multiplied by a user-defined factor (default: 10).
Example: If RSI MA = 50, standard deviation = 5, factor = 2 → upper band = 60, lower = 40.
Important: The default value (10) may produce wide bands. Reduce to 1–2 for tighter, practical bands.
Use Case: Best for dynamic markets with fluctuating volatility.
Configuration Options
RSI Length: Set RSI calculation period (default: 20).
MA Length: Set RSI MA period (default: 20).
MA Type: Choose SMA or EMA for RSI MA (default: EMA).
Timeframes: Configure three timeframes (default: 4H, 8H, 12H) for MTF analysis.
Overbought/Oversold Levels: Optionally display fixed levels (default: 70/30).
Background Coloring: Enable/disable for each timeframe to highlight directional alignment.
How to Use
Add Indicator: Load it onto your TradingView chart.
Setup:
Reversals: Configure like the upper panel (RSI, RSI MA, bands) and watch for RSI crossing bands.
Trends: Configure like the lower panel (three RSI MAs) and look for fastest MA crossovers and background coloring.
Adjust Timeframes: Manually set tf1, tf2, tf3 (e.g., 1H, 2H, 4H on a 1H chart) to suit your strategy.
Adjust Bands: Choose band type (Fixed, Percent, StdDev) and value. For StdDev, reduce to 1–2 for tighter bands.
Experiment: Test settings to match your trading style, whether scalping, swing trading, or long-term.
Notes
Timeframes: Always match tf1, tf2, tf3 to your chart’s needs, as they don’t auto-adjust.
StdDev Bands: Lower the default value (10) to avoid overly wide bands.
Versatility: Works across markets (stocks, forex, crypto).
