AI Momentum [YinYang]Overview:
AI Momentum is a kernel function based momentum Indicator. It uses Rational Quadratics to help smooth out the Moving Averages, this may give them a more accurate result. This Indicator has 2 main uses, first it displays ‘Zones’ that help you visualize the potential movement areas and when the price is out of bounds (Overvalued or Undervalued). Secondly it creates signals that display the momentum of the current trend.
The Zones are composed of the Highest Highs and Lowest lows turned into a Rational Quadratic over varying lengths. These create our Rational High and Low zones. There is however a second zone. The second zone is composed of the avg of the Inner High and Inner Low zones (yellow line) and the Rational Quadratic of the current Close. This helps to create a second zone that is within the High and Low bounds that may represent momentum changes within these zones. When the Rationalized Close crosses above the High and Low Zone Average it may signify a bullish momentum change and vice versa when it crosses below.
There are 3 different signals created to display momentum:
Bullish and Bearish Momentum. These signals display when there is current bullish or bearish momentum happening within the trend. When the momentum changes there will likely be a lull where there are neither Bullish or Bearish momentum signals. These signals may be useful to help visualize when the momentum has started and stopped for both the bulls and the bears. Bullish Momentum is calculated by checking if the Rational Quadratic Close > Rational Quadratic of the Highest OHLC4 smoothed over a VWMA. The Bearish Momentum is calculated by checking the opposite.
Overly Bullish and Bearish Momentum. These signals occur when the bar has Bullish or Bearish Momentum and also has an Rationalized RSI greater or less than a certain level. Bullish is >= 57 and Bearish is <= 43. There is also the option to ‘Factor Volume’ into these signals. This means, the Overly Bullish and Bearish Signals will only occur when the Rationalized Volume > VWMA Rationalized Volume as well as the previously mentioned factors above. This can be useful for removing ‘clutter’ as volume may dictate when these momentum changes will occur, but it can also remove some of the useful signals and you may miss the swing too if the volume just was low. Overly Bullish and Bearish Momentum may dictate when a momentum change will occur. Remember, they are OVERLY Bullish and Bearish, meaning there is a chance a correction may occur around these signals.
Bull and Bear Crosses. These signals occur when the Rationalized Close crosses the Gaussian Close that is 2 bars back. These signals may show when there is a strong change in momentum, but be careful as more often than not they’re predicting that the momentum may change in the opposite direction.
Tutorial:
As we can see in the example above, generally what happens is we get the regular Bullish or Bearish momentum, followed by the Rationalized Close crossing the Zone average and finally the Overly Bullish or Bearish signals. This is normally the order of operations but isn’t always how it happens as sometimes momentum changes don’t make it that far; also the Rationalized Close and Zone Average don’t follow any of the same math as the Signals which can result in differing appearances. The Bull and Bear Crosses are also quite sporadic in appearance and don’t generally follow any sort of order of operations. However, they may occur as a Predictor between Bullish and Bearish momentum, signifying the beginning of the momentum change.
The Bull and Bear crosses may be a Predictor of momentum change. They generally happen when there is no Bullish or Bearish momentum happening; and this helps to add strength to their prediction. When they occur during momentum (orange circle) there is a less likely chance that it will happen, and may instead signify the exact opposite; it may help predict a large spike in momentum in the direction of the Bullish or Bearish momentum. In the case of the orange circle, there is currently Bearish Momentum and therefore the Bull Cross may help predict a large momentum movement is about to occur in favor of the Bears.
We have disabled signals here to properly display and talk about the zones. As you can see, Rationalizing the Highest Highs and Lowest Lows over 2 different lengths creates inner and outer bounds that help to predict where parabolic movement and momentum may move to. Our Inner and Outer zones are great for seeing potential Support and Resistance locations.
The secondary zone, which can cross over and change from Green to Red is also a very important zone. Let's zoom in and talk about it specifically.
The Middle Zone Crosses may help deduce where parabolic movement and strong momentum changes may occur. Generally what may happen is when the cross occurs, you will see parabolic movement to the High / Low zones. This may be the Inner zone but can sometimes be the outer zone too. The hard part is sometimes it can be a Fakeout, like displayed with the Blue Circle. The Cross doesn’t mean it may move to the opposing side, sometimes it may just be predicting Parabolic movement in a general sense.
When we turn the Momentum Signals back on, we can see where the Fakeout occurred that it not only almost hit the Inner Low Zone but it also exhibited 2 Overly Bearish Signals. Remember, Overly bearish signals mean a momentum change in favor of the Bulls may occur soon and overly Bullish signals mean a momentum change in favor of the Bears may occur soon.
You may be wondering, well what does “may occur soon” mean and how do we tell?
The purpose of the momentum signals is not only to let you know when Momentum has occurred and when it is still prevalent. It also matters A LOT when it has STOPPED!
In this example above, we look at when the Overly Bullish and Bearish Momentum has STOPPED. As you can see, when the Overly Bullish or Bearish Momentum stopped may be a strong predictor of potential momentum change in the opposing direction.
We will conclude our Tutorial here, hopefully this Indicator has been helpful for showing you where momentum is occurring and help predict how far it may move. We have been dabbling with and are planning on releasing a Strategy based on this Indicator shortly.
Settings:
1. Momentum:
Show Signals: Sometimes it can be difficult to visualize the zones with signals enabled.
Factor Volume: Factor Volume only applies to Overly Bullish and Bearish Signals. It's when the Volume is > VWMA Volume over the Smoothing Length.
Zone Inside Length: The Zone Inside is the Inner zone of the High and Low. This is the length used to create it.
Zone Outside Length: The Zone Outside is the Outer zone of the High and Low. This is the length used to create it.
Smoothing length: Smoothing length is the length used to smooth out our Bullish and Bearish signals, along with our Overly Bullish and Overly Bearish Signals.
2. Kernel Settings:
Lookback Window: The number of bars used for the estimation. This is a sliding value that represents the most recent historical bars. Recommended range: 3-50.
Relative Weighting: Relative weighting of time frames. As this value approaches zero, the longer time frames will exert more influence on the estimation. As this value approaches infinity, the behavior of the Rational Quadratic Kernel will become identical to the Gaussian kernel. Recommended range: 0.25-25.
Start Regression at Bar: Bar index on which to start regression. The first bars of a chart are often highly volatile, and omission of these initial bars often leads to a better overall fit. Recommended range: 5-25.
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING!
Cerca negli script per "bear"
Dee_MeterHere's how you can effectively use the Dee Meter indicator:
1. **Understanding the Basics**:
- Dee Meter evaluates the market sentiment across various sectors.
- It calculates the overall market trend and presents it in percentage form through a line graph.
2. **Indicator Results**:
- When you add the Dee Meter indicator to your chart, you'll notice two key results: Bull and Bear percentages, along with a line graph.
- The Bull percentage reflects the strength of bullish (positive) sentiment, while the Bear percentage indicates bearish (negative) sentiment.
- For example, if the Bull percentage is 55% and the Bear percentage is 45%, it signifies that the bulls currently have a stronger influence in the market.
3. **Interpreting Percentages**:
- Utilize the Bull and Bear percentages to craft your analysis strategy.
- A high Bull percentage in a bullish market suggests strong bullish momentum.
- In the case of a bullish trend showing signs of weakening (e.g., a double top pattern), monitor the Bull and Bear percentages for early reversal indications.
- A decrease in the Bull percentage and an increase in the Bear percentage could hint at a potential market reversal.
4. **Line Graph Analysis**:
- The line graph visually depicts the strength of bulls (green line) and bears (red line) over time.
- During a bullish trend, the green line rises while the red line remains lower, indicating bullish strength.
- Conversely, during a bearish trend, the red line climbs higher, indicating bearish dominance.
5. **Cross Over and Cross Under**:
- Cross-over and cross-under scenarios occur when the market abruptly reverses direction.
- For instance, in a bullish market that suddenly turns bearish, the red line could cross above the green line, indicating increased bearish power.
- In a bearish market that experiences a sudden influx of buying activity, the green line might cross above the red line, signifying strong buying interest.
6. **Applying the Indicator**:
- Use the Dee Meter to build your own trading strategies and make informed decisions.
- Keep an eye on changes in Bull and Bear percentages to identify shifts in market sentiment.
- Monitor line graph movements to assess the relative strength of bulls and bears.
In summary, the Dee Meter indicator is a valuable tool for assessing market sentiment and confirming trends in the Indian market. By understanding and utilizing the Bull and Bear percentages, line graph analysis, and cross-over/cross-under scenarios, you can develop effective trading strategies and trade with greater confidence.
RedK EVEREX - Effort Versus Results ExplorerRedK EVEREX is an experimental indicator that explores "Volume Price Analysis" basic concepts and Wyckoff law "Effort versus Result" - by inspecting the relative volume (effort) and the associated (relative) price action (result) for each bar - showing the analysis as an easy to read "stacked bands" visual. From that analysis, we calculate a "Relative Rate of Flow" - an easy to use +100/-100 oscilator that can be used to trigger a signal when a bullish or bearish mode is detected for a certain user-selected length of bars.
Basic Concepts of VPA
-------------------------------
(The topics of VPA & Wyckoff Effort vs Results law are too comprehensive to cover here - So here's just a very basic summary - please review these topics in detail in various sources available here in TradingView or on the web)
* Volume Price Analysis (VPA) is the examination of the number of shares or contracts of a security that have been traded in a given period, and the associated price movement. By analyzing trends in volume in conjunction with price movements, traders can determine the significance of changes in price and what may unfold in the near future.
* Oftentimes, high volumes of trading can infer a lot about investors’ outlook on a market or security. A significant price increase along with a significant volume increase, for example, could be a credible sign of a continued bullish trend or a bullish reversal. Adversely, a significant price decrease with a significant volume increase can point to a continued bearish trend or a bearish trend reversal.
* Incorporating volume into a trading decision can help an investor to have a more balanced view of all the broad market factors that could be influencing a security’s price, which helps an investor to make a more informed decision.
* Wyckoff's law "Effort versus results" dictates that large effort is expected to be accompanied with big results - which means that we should expect to see a big price move (result) associated with a large relative volume (effort) for a certain trading period (bar).
* The way traders use this concept in chart analysis is to mainly look for imbalances or invalidation. for example, when we observe a large relative volume that is associated with very limited price change - that should trigger an early flag/warning sign that the current price trend is facing challenges and may be an early sign of "reversal" - this applies in both bearish and bullish conditions. on the other hand, when price starts to trend in a certain direction and that's associated with increasing volume, that can act as kind of validation, or a confirmation that the market supports that move.
How does EVEREX work
---------------------------------
* EVEREX inspects each bar and calculates a relative value for volume (effort) and "strength of price movement" (result) compared to a specified lookback period. The results are then visualized as stacked bands - the lower band represents the relative volume, the upper band represents the relative price strength - with clear color coding for easier analysis.
* The scale of the band is initially set to 100 (each band can occupy up to 50) - and that can be changed in the settings to 200 or 400 - mainly to allow a "zoom in" on the bands.
* Reading the resulting stacked bands makes it easier to see "balanced" volume/price action (where both bands are either equally strong, or equally weak), or when there's imbalance between volume and price (for example, a compression bar will show with high volume band and very small/tiny price action band) - another favorite pattern in VPA is the "Ease of Move", which will show as a relatively small volume band associated with a large "price action band" (either bullish or bearish) .. and so on.
* a bit of a techie piece: why the use of a custom "Normalize()" function to calculate "relative" values in EVEREX?
When we evaluate a certain value against an average (for example, volume) we need a mechanism to deal with "super high" values that largely exceed that average - I also needed a mechanism that mimics how a trader looks at a volume bar and decides that this volume value is super low, low, average, above average, high or super high -- the issue with using a stoch() function, which is the usual technique for comparing a data point against a lookback average, is that this function will produce a "zero" for low values, and cause a large distortion of the next few "ratios" when super large values occur in the data series - i researched multiple techniques here and decided to use the custom Normalize() function - and what i found is, as long as we're applying the same formula consistently to the data series, since it's all relative to itself, we can confidently use the result. Please feel free to play around with this part further if you like - the code is commented for those who would like to research this further.
* Overall, the hope is to make the bar-by-bar analysis easier and faster for traders who apply VPA concepts in their trading
What is RROF?
--------------------------
* Once we have the values of relative volume and relative price strength, it's easy from there to combine these values into a moving index that can be used to track overall strength and detect reversals in market direction - if you think about it this a very similar concept to a volume-weighted RSI. I call that index the "Relative Rate of Flow" - or RROF (cause we're not using the direct volume and price values in the calculation, but rather relative values that we calculated with the proprietary "Normalize" function in the script.
* You can show RROF as a single or double-period - and you can customize it in terms of smoothing, and signal line - and also utilize the basic alerts to get notified when a change in strength from one side to the other (bullish vs bearish) is detected
* In the chart above, you can see how the RROF was able to detect change in market condition from Bearsh to Bullish - then from Bullish to Bearish for TSLA with good accuracy.
Other Usage Options in EVEREX
------------------------------------
* I wrote EVEREX with a lot of flexibility and utilization in mind, while focusing on a clean and easy to use visual - EVEREX should work with any time frame and any instrument - in instruments with no volume data, only price data will be used.
* You can completely hide the "EVEREX bands" and use EVEREX as a single or dual period strength indicator (by exposing the Bias/Sentiment plot which is hidden by default) -
here's how this setup would look like - in this mode, you will basically be using EVEREX the same way you're using a volume-weighted RSI
* or you can hide the bias/sentiment, and expose the Bulls & Bears plots (using the indicator's "Style" tab), and trade it like a Bull/Bear Pressure Index like this
* you can choose Moving Average type for most plot elements in EVEREX, including how to deal with the Lookback averaging
* you can set EVEREX to a different time frame than the chart
* did i mention basic alerts in this v1.0 ?? There's room to add more VPA-specific alerts in future version (for example, when Ease-of-Move or Compression bars are detected...etc) - let me know if the comments what you want to see
Final Thoughts
--------------------
* EVEREX can be used for bar-by-bar VPA analysis - There are so much literature out there about VPA and it's highly recommended that traders read more about what VPA is and how it works - as it adds an interesting (and critical) dimension to technical analysis and will improve decision making
* RROF is a "strength indicator" - it does not track price values (levels) or momentum - as you will see when you use it, the price can be moving up, while the RROF signal line starts moving down, reflecting decreasing strength (or otherwise, increasing bear strength) - So if you incorporate EVEREX in your trading you will need to use it alongside other momentum and price value indicators (like MACD, MA's, Trend Channels, Support & Resistance Lines, Fib / Donchian..etc) - to use for trade confirmation
Bar metrics / quantifytools— Overview
Rather than eyeball evaluating bullishness/bearishness in any given bar, bar metrics allow a quantified approach using three basic fundamental data points: relative close, relative volatility and relative volume. These data points are visualized in a discreet data dashboard form, next to all real-time bars. Each value also has a dot in front, representing color coded extremes in the values.
Relative close represents position of bar's close relative to high and low, high of bar being 100% and low of bar being 0%. Relative close indicates strength of bulls/bears in a given bar, the higher the better for bulls, the lower the better for bears. Relative volatility (bar range, high - low) and relative volume are presented in a form of a multiplier, relative to their respective moving averages (SMA 20). A value of 1x indicates volume/volatility being on par with moving average, 2x indicates volume/volatility being twice as much as moving average and so on. Relative volume and volatility can be used for measuring general market participant interest, the "weight of the bar" as it were.
— Features
Users can gauge past bar metrics using lookback via input menu. Past bars, especially recent ones, are helpful for giving context for current bar metrics. Lookback bars are highlighted on the chart using a yellow box and metrics presented on the data dashboard with lookback symbols:
To inspect bar metric data and its implications, users can highlight bars with specified bracket values for each metric:
When bar highlighter is toggled on and desired bar metric values set, alert for the specified combination can be toggled on via alert menu. Note that bar highlighter must be enabled in order for alerts to function.
— Visuals
Bar metric dots are gradient colored the following way:
Relative volatility & volume
0x -> 1x / Neutral (white) -> Light (yellow)
1x -> 1.7x / Light (yellow) -> Medium (orange)
1.7x -> 2.4x / Medium (orange) -> Heavy (red)
Relative close
0% -> 25% / Heavy bearish (red) -> Light bearish (dark red)
25% -> 45% / Light bearish (dark red) -> Neutral (white)
45% - 55% / Neutral (white)
55% -> 75% / Neutral (white) -> Light bullish (dark green)
75% -> 100% / Light bullish (dark green) -> Heavy bullish (green)
All colors can be adjusted via input menu. Label size, label distance from bar (offset) and text format (regular/stealth) can be adjusted via input menu as well:
— Practical guide
As interpretation of bar metrics is highly contextual, it is especially important to use other means in conjunction with the metrics. Levels, oscillators, moving averages, whatever you have found useful for your process. In short, relative close indicates directional bias and relative volume/volatility indicates "weight" of directional bias.
General interpretation
High relative close, low relative volume/volatility = mildly bullish, bias up/consolidation
High relative close, medium relative volume/volatility = bullish, bias up
High relative close, high relative volume/volatility = exuberantly bullish, bias up/down depending on context
Medium relative close, low relative volume/volatility = noise, no bias
Medium relative close, medium to high relative volume/volatility = indecision, further evidence needed to evaluate bias
Low relative close, low relative volume/volatility = mildly bearish, bias down/consolidation
Low relative close, medium relative volume/volatility = bearish, bias down
Low relative close, high relative volume/volatility = exuberantly bearish, bias down/up depending on context
Nuances & considerations
As to relative close, it's important to note that each bar is a trading range when viewed on a lower timeframe, ES 1W vs. ES 4H:
When relative close is high, bulls were able to push price to range high by the time of close. When relative close is low, bears were able to push price to range low by the time of close. In other words, bulls/bears were able to gain the upper hand over a given trading range, hinting strength for the side that made the final push. When relative close is around middle range (40-60%), it can be said neither side is clearly dominating the range, hinting neutral/indecision bias from a relative close perspective.
As to relative volume/volatility, low values (less than ~0.7x) imply bar has low market participant interest and therefore is likely insignificant, as it is "lacking weight". Values close to or above 1x imply meaningful market participant interest, whereas values well above 1x (greater than ~1.3x) imply exuberance. This exuberance can manifest as initiation (beginning of a trend) or as exhaustion (end of a trend):
Tactical Holding [SwissAlgo]Tactical Holding
A visual framework for managing long-term positions across market cycles
--------------------------------------------------------------
Purpose
Instead of holding a fixed position through all market conditions , you can use this framework to adjust your exposure tactically . By reducing positions during distribution phases and accumulating during favorable accumulation zones, you may end up holding more units of the asset over complete market cycles - even if you temporarily exit or reduce exposure during unfavorable periods. This approach aims to help you compound your holdings by taking advantage of market volatility rather than simply enduring it.
--------------------------------------------------------------
Recommended Settings
Timeframe : Weekly (1W) chart
Chart Type : Standard candlesticks (select 'Bar' type Candles)
This indicator is designed for higher timeframe analysis. While it can be applied to other timeframes, the logic and signal generation are optimized for weekly charts to filter out short-term noise and focus on major market cycles.
--------------------------------------------------------------
Key Features
♦ Market State Classification
The indicator aims to categorize potential market conditions into five color-coded states based on technical confluences:
* Bull (bright green): Multiple bullish indicators align
* Bull Retrace (teal): Bullish structure with temporary weakness
* Bull ⇆ Bear Reversal (yellow): Transitional phase between trends
* Bear (bright red): Multiple bearish indicators align
* Bear Retrace (Pale Red/Maroon): Bearish structure with temporary strength
♦ Visual Elements
* Candles change color based on the current market state
* A 50-period EMA tracks with the same color coding, providing visual trend context
* Small arrow markers appear when specific pattern conditions are met (zones for potential distribution or accumulation)
* A legend table (toggle on/off) explains the color system
* A label shows the current state name on the chart
♦ Pattern Recognition
The system monitors for two types of potential entry/exit zones:
1. State transition patterns after periods of market regime consistency
2. RSI divergence patterns (when price and momentum move in opposite directions)
♦ Customization
* Toggle the legend table visibility through settings
* All calculations are transparent and use standard technical analysis methods
--------------------------------------------------------------
How It Works
Think of this indicator as a traffic light system for your portfolio:
♦ Green zones suggest the asset might be in an environment where long-term holders historically have remained invested
Bright green (Bull) : Multiple technical indicators align in a potentially strong bullish phase
Pale green (Bull Retrace) : Bullish structure remains intact, but momentum shows temporary weakness - often a pullback within an uptrend
♦ Red zones suggest conditions where long-term holders might consider reducing exposure or waiting for better entry points
Dark red (Bear) : Multiple technical indicators align in a potentially strong bearish phase
Pale red (Bear Retrace) : Bearish structure remains intact but shows temporary strength - often a bounce within a downtrend
♦ Yellow zones indicate the market is in transition between bull and bear regimes - a time for increased attention as the trend direction becomes uncertain
The system doesn't predict future prices. Instead, it helps you understand the current technical environment by doing the heavy lifting of analyzing multiple indicators at once and presenting them in a simple visual format.
Example: During the 2022 crypto bear market, the indicator would have displayed extended red periods, signaling defensive conditions for holders. When accumulation arrows appeared in late 2022-early 2023, it highlighted potential re-entry zones as the technical regime transitioned back toward green, before the 2024 recovery.
--------------------------------------------------------------
Who This Is For
♦ Long-term investors who want to hold assets through cycles but prefer a systematic approach to position sizing and timing rather than buying and never selling .
♦ Portfolio managers looking for a visual tool to help determine when to increase or decrease exposure to specific assets based on technical regime changes.
♦ Swing traders on higher timeframes who want to align their positions with the broader market structure rather than fighting the trend.
This is not designed for:
* Day traders or scalpers
* Those seeking exact entry/exit prices
* Automated trading systems (this is a visual decision-support tool)
--------------------------------------------------------------
Understanding the Visuals
When you apply Tactical Holding to a chart, you'll see:
1. Colored candles - Instantly see what market regime the asset is in
2. Colored EMA line (thick line) - Provides a dynamic support/resistance reference that changes color with market conditions
3. Small arrows (↑ ↓) - Mark bars where specific technical patterns complete
4. State label - Shows current market classification
5. Legend table (top right) - Quick reference guide for the color system
6. Warning banner (top center) - Reminds you to use weekly charts
The visual design prioritizes clarity over complexity. You should be able to glance at a chart and immediately understand the current technical environment.
--------------------------------------------------------------
Important Limitations
This indicator cannot:
* Predict future price movements
* Guarantee profitable trades
* Work equally well on all assets or timeframes
* Replace your own research and risk management
Technical considerations:
* Divergence detection has a 3-bar confirmation lag (by design, to avoid false signals)
* State transitions require multiple technical confirmations, which may cause delayed reactions to rapid market changes
* The system is reactive, not predictive - it responds to price action after it occurs
* Performance varies significantly between trending assets (like Solana) and stable assets (like Apple)
--------------------------------------------------------------
Practical Application
Consider using this indicator as one component of a broader investment framework:
♦ Understanding Position Context:
The color-coded states can help frame your thinking about current holdings:
Bull: Technical conditions that have historically been associated with sustained uptrends
Bull Retrace: Pullbacks within an overall bullish structure- these periods may offer opportunities to evaluate entry points or reassess existing positions
Reversal (Yellow): Transitional phases where the trend direction is unclear - periods that may warrant closer monitoring
Bear Retrace: Temporary strength within an overall bearish structure - rallies that historically have often faded
Bear: Technical conditions that have historically been associated with sustained downtrends
♦ Interpreting Signal Arrows:
Arrow markers indicate when specific technical pattern conditions have been met. These are observation points, not instructions:
A signal appearing doesn't mean immediate action is required
Treat arrows as prompts for further analysis rather than automatic triggers
Consider the broader context: fundamentals, your investment timeline, risk tolerance, and overall market conditions
Signals show when historical technical patterns have formed - not whether those patterns will lead to the same outcomes as in the past
The framework is designed to organize information visually, not to tell you what to do. Your investment decisions should incorporate this technical perspective alongside other factors relevant to your situation.
--------------------------------------------------------------
Technical Methodology
For transparency, the indicator uses:
* RSI (14) with a 14-period SMA to assess momentum direction
* MACD (12,26,9) to confirm trend strength and histogram momentum
* Stochastic RSI with K and D line crossovers for additional confirmation
* 50-period EMA as the primary trend filter
* Linear regression-based slope analysis to detect flat/transitional periods
* Pivot-based divergence detection following standard technical analysis principles
All calculations use publicly available technical analysis formulas. Nothing is hidden or proprietary beyond the specific combination and weighting of these standard tools.
--------------------------------------------------------------
Disclaimer
This indicator is an educational and analytical tool only. It is not financial advice.
* Trading and investing involve substantial risk of loss
* Past performance of any technical system does not indicate future results
* No indicator can predict market movements with certainty
* Always conduct your own research and consult with qualified financial professionals
* Never invest more than you can afford to lose
* The creators of this indicator are not responsible for any trading losses
* This tool is not affiliated with, endorsed by, or connected to TradingView, 3Commas, or any other trading platform
* Use of this indicator is at your own risk
Risk Management: Regardless of what any indicator shows, always use proper position sizing, stop losses, and risk management appropriate to your personal financial situation.
This indicator provides a framework for analysis. Your decisions, research, and risk management determine your results.
MPO4 Lines – Modal Engine█ OVERVIEW
MPO4 Lines – Modal Engine is an advanced multi-line modal oscillator for TradingView, designed to detect momentum shifts, trend strength, and reversal points through candle-based pressure analysis with multiple fast lines and a reference slow line. It features divergence detection on Fast Line A, overbought/oversold return signals, dynamic coloring modes, and layered gradient visualizations for enhanced clarity and decision-making.
█ CONCEPT
The indicator is built upon the Market Pressure Oscillator (MPO) and serves as its expanded evolution, aimed at enabling broader market analysis through multiple lines with varying parameters. It calculates modal pressure using candle body size and direction, weighted against average body size over a lookback period, then normalized and smoothed via EMA. It generates four distinct oscillator lines: a heavily smoothed Slow Line (trend reference), two Fast Lines (A & B) for momentum and support/resistance, and an optional Line 4 for additional confirmation. Divergence is calculated solely on Fast Line A, with visual gradients between lines and bands for intuitive interpretation.
█ WHY USE IT?
- Multi-Layer Momentum: Combines slow trend reference with dual fast lines for precise entry/exit timing.
- Divergence Precision: Bullish/bearish divergences on Fast Line A with labeled confirmation.
- OB/OS Return Signals: Clear buy/sell markers when Fast Line A exits oversold/overbought zones.
- Dynamic Visuals: Gradient fills, line-to-line shading, and band gradients for instant market state recognition.
- Flexible Coloring: Slow Line color by direction or zero-position; fast lines by sign.
- Full Customization: Independent lengths, smoothing, visibility, and transparency — by adjusting the lengths of different lines, you can tailor results for various strategies; for example, enabling Line 4 and tuning its length allows trading based on crossovers between different lines.
█ HOW IT WORKS?
- Candle Pressure Calculation: Body = math.abs(close - open); avgBody = ta.sma(body, len). Direction = +1 (bull), –1 (bear), 0 (neutral). Weight = body / avgBody. Contribution = direction × weight.
- Rolling Sum & Normalization: Sums contributions over lookback, normalizes to ±100 scale (÷ (len × 2) × 100).
Smoothing: Applies primary EMA (smoothLen), with extra EMA on Slow Line for stability.
Line Structure:
- Slow Line = calcCPO(len1=20, smoothLen1=5) → extra EMA (5)
- Fast Line A = calcCPO(len2=6, smoothLen2=7)
- Fast Line B = calcCPO(len3=6, smoothLen3=10)
- Line 4 = calcCPO(len4=14, smoothLen4=1)
Divergence Detection: Uses ta.pivothigh/low on price and Fast Line A (pivotLength left/right). Bullish: lower price low + higher osc low. Bearish: higher price high + lower osc high. Valid within 5–60 bar window.
Signals:
- Buy: Fast Line A crosses above oversold (–30)
- Sell: Fast Line A crosses below overbought (+30)
- Slow Line color flip (direction or zero-cross)
- Divergence labels ("Bull" / "Bear")
- Band Coloring as Momentum Signal:
When Fast Line A ≤ Fast Line B → Overbought band turns red (bearish pressure building)
When Fast Line A > Fast Line B → Oversold band turns green (bullish pressure building) This dynamic coloring serves as visual confirmation of momentum shift following fast line crossovers
Visualization:
- Gradients: Fast B → Zero (multi-layer fade), Fast A ↔ B fill, OB/OS bands
- Dynamic colors: Green/red based on sign or trend
- Zero line + dashed OB/OS thresholds
Alerts: Trigger on OB/OS returns, Slow Line changes, and divergences.
█ SETTINGS AND CUSTOMIZATION
- Line Visibility: Toggle Slow, Fast A, Fast B, Line 4 independently.
Line Lengths:
- Slow Line: Base (20), Primary EMA (5), Extra EMA (5)
- Fast A: Lookback (6), EMA (7)
- Fast B: Lookback (6), EMA (10)
- Line 4: Lookback (14), EMA (1)
- Slow Line Coloring Mode: “Direction” (trend-based) or “Position vs Zero”.
- Bands & Thresholds: Overbought (+30), Oversold (–30), step 0.1.
- Signals: Enable Fast A OB/OS return markers (default: on).
- Divergence: Enable/disable, Pivot Length (default: 2, min 1).
- Colors & Appearance: Full control over bullish/bearish hues for all lines, zero, bands, divergence, and text.
Gradients & Transparency:
- Fast B → Zero: 75 (default)
- Fast A ↔ B fill: 50
- Band gradients: 40
- Toggle each gradient independently
█ USAGE EXAMPLES
The indicator allows users to configure various strategies manually, though no built-in alerts exist for them. Entry signals can include color of fast lines, crossovers between different lines, alignment of colors across lines, or consistency in direction.
- Trend Confirmation: Slow Line above zero + green = bullish bias; below + red = bearish.
- Entry Timing: Buy on Fast A crossing above –30 (circle marker), especially if Slow Line is rising or near zero.
- Reversal Setup: Bullish divergence (“Bull” label) + Fast A in oversold + green gradient band = high-probability long.
- Scalping: Fast A vs Fast B crossover in direction of Slow Line trend.
- Noise Reduction: Increase extraSmoothLen on Slow Line
█ USER NOTES
- Best combined with volume, support/resistance, or trend channels.
- Adjust lookback and smoothing to asset volatility.
- Divergence delay = pivotLength; plan entries accordingly.
GTI BGTI: RSI Suite (Standard • Stochastic • Smoothed)
A three-layer momentum and trend toolkit that combines Standard RSI, Stochastic RSI, and a Smoothed/“Macro” RSI to help you read intraday swings, trend transitions, and high-probability reversal/continuation spots.
All in one pane with intuitive coloring and optional divergence markers and alerts.
Why this works
* Stochastic RSI (K/D) visualizes fast momentum swings and timing.
* Standard RSI moves more gradually, helping confirm trend transitions that may span several Stochastic cycles.
* Smoothed RSI (Average → Macro) adds a second-pass filter and slope persistence to reveal the macro direction while suppressing noise.
Used together, Stochastic guides entries/exits around local highs/lows, while the RSI layers improve confidence when a small swing is likely part of a larger turn.
What you’ll see
* Standard RSI (yellow; pink above Bull line, aqua below Bear line).
* Stochastic RSI (K/D) with contextual colors:
* Greens when RSI is weak/oversold (bearish conditions → watch for bullish reversals/continuations).
* Reds when RSI is strong/overbought (bullish conditions → watch for bearish reversals/continuations).
* Smoothed (Macro) RSI with trend color:
* Red when macro is ascending (bullish),
* Aqua when macro is descending (bearish).
* Divergences (optional markers):
* Bearish: RSI Lower High + Price Higher High (red ⬇).
* Bullish: RSI Higher Low + Price Lower Low (green ⬆).
* No repaint: pivots confirm after the chosen right-bars window.
How to use it
* Bullish Reversal
* Macro RSI is reversing at a higher low after price has been in a overall downtrend
* Stochastic RSI is switching from green to red in an overall downtrend
* Bullish Oversold
* Macro RSI is reversing from a significantly low level after price has a short but strong dip during an overall uptrend
* Stochastic RSI is switching from green to red in an overall uptrend
* Bullish Continuation
* Macro RSI is ascending with a strong slope or forming a higher low above the 50 line
* Stochastic RSI is reaching a bottom but still painted red
* Bearish Reversal
* Macro RSI is reversing at a lower high after price has been in a overall uptrend
* Stochastic RSI is switching from red to green in an overall uptrend
* Bearish Overbought
* Macro RSI is reversing from a significantly high level after price has a short but strong jump during an overall downtrend
* Stochastic RSI is switching from red to green in an overall downtrend
* Bearish Continuation
* Macro RSI is descending with a strong slope or forming a lower high below the 50 line
* Stochastic RSI is reaching a top but still painted green
* Divergences: Use as signals of exhaustion—best when aligned with Macro RSI color/slope and key levels (e.g., Bull/Bear lines, 50 midline).
*** IMPORTANT ***
* Stack confluence, don’t single-signal trade. Look for:
* 1) Macro RSI color & slope (red = ascending/bullish, aqua = descending/bearish)
* 2) Standard RSI location (above/below Bull/Bear lines or 50)
* 3) Stoch flip + direction
* 4) Price structure (HH/HL vs LH/LL)
* 5) Divergence type (regular vs hidden) at meaningful levels
* Trade with the macro
* Prioritize longs when Macro RSI is red or just flipped up
* Prioritize shorts when Macro RSI is aqua or just flipped down
* Counter-trend setups = smaller size and faster management.
* Location > signal
* The same crossover/divergence is higher quality near Bull (~60)/Bear(~40) or extremes than in the mid-range chop around 50.
* Early vs confirmed
* Use the early pivot heads-up for anticipation, but scale in only after the confirmed pivot (right-bars complete). If early signal fails to confirm, stand down.
* Define invalidation upfront
* For divergence entries, place stops beyond the pivot extreme (LL/HH). If Macro RSI flips against your trade or RSI breaks back through 50 with slope, exit or tighten.
* Multi-timeframe alignment
* Best results come when entry timeframe (e.g., 1H) aligns with higher-TF macro (e.g., 4H/D). If they disagree, treat it as mean-reversion only.
* Avoid common traps
* Skip: isolated Stochastic flips without RSI support, divergences without price HH/LL confirmation, and serial divergences when Macro RSI slope is strong against the idea.
* Parameter guidance
* Start with defaults; then tune: confirmBars 3–7, minSlope 0.05–0.15 RSI pts/bar, pivot left/right tighter for faster but noisier signals, wider for cleaner but fewer.
* Alerts = workflow, not auto-trades
* Use Macro Flip + Divergence alerts as a checklist trigger; enter only when your confluence rules are met and risk is defined.
Key inputs (tweak to your market/timeframe)
* RSI / Stochastic lengths and K/D smoothing.
* Bull / Bear Lines (default 61.1 / 43.6).
* Average RSI Method/Length (SMA/EMA/RMA/WMA) + Macro Smooth Length.
* Trend confirmation: bars of persistence and minimum slope to reduce flip noise.
* Pivot look-back (left/right) for divergence confirmation strictness.
Alerts included
* Macro Flip Up / Down (Smoothed RSI regime change).
* RSI Bullish/Bearish Divergence (confirmed at pivot).
* Stochastic RSI continuation/divergence (optional).
Tips
* Level + Slope matter. High/low RSI level flags conditions; slope confirms impulse/continuation.
* Let Stochastic time the swing; let Macro RSI filter the trend.
* Tighten or loosen pivot windows to trade fewer/cleaner vs. more/faster signals.
COT IndexTHE HIDDEN INTELLIGENCE IN FUTURES MARKETS
What if you could see what the smartest players in the futures markets are doing before the crowd catches on? While retail traders chase momentum indicators and moving averages, obsess over Japanese candlestick patterns, and debate whether the RSI should be set to fourteen or twenty-one periods, institutional players leave footprints in the sand through their mandatory reporting to the Commodity Futures Trading Commission. These footprints, published weekly in the Commitment of Traders reports, have been hiding in plain sight for decades, available to anyone with an internet connection, yet remarkably few traders understand how to interpret them correctly. The COT Index indicator transforms this raw institutional positioning data into actionable trading signals, bringing Wall Street intelligence to your trading screen without requiring expensive Bloomberg terminals or insider connections.
The uncomfortable truth is this: Most retail traders operate in a binary world. Long or short. Buy or sell. They apply technical analysis to individual positions, constrained by limited capital that forces them to concentrate risk in single directional bets. Meanwhile, institutional traders operate in an entirely different dimension. They manage portfolios dynamically weighted across multiple markets, adjusting exposure based on evolving market conditions, correlation shifts, and risk assessments that retail traders never see. A hedge fund might be simultaneously long gold, short oil, neutral on copper, and overweight agricultural commodities, with position sizes calibrated to volatility and portfolio Greeks. When they increase gold exposure from five percent to eight percent of portfolio allocation, this rebalancing decision reflects sophisticated analysis of opportunity cost, risk parity, and cross-market dynamics that no individual chart pattern can capture.
This portfolio reweighting activity, multiplied across hundreds of institutional participants, manifests in the aggregate positioning data published weekly by the CFTC. The Commitment of Traders report does not show individual trades or strategies. It shows the collective footprint of how actual commercial hedgers and large speculators have allocated their capital across different markets. When mining companies collectively increase forward gold sales to hedge thirty percent more production than last quarter, they are not reacting to a moving average crossover. They are making strategic allocation decisions based on production forecasts, cost structures, and price expectations derived from operational realities invisible to outside observers. This is portfolio management in action, revealed through positioning data rather than price charts.
If you want to understand how institutional capital actually flows, how sophisticated traders genuinely position themselves across market cycles, the COT report provides a rare window into that hidden world. But understand what you are getting into. This is not a tool for scalpers seeking confirmation of the next five-minute move. This is not an oscillator that flashes oversold at market bottoms with convenient precision. COT analysis operates on a timescale measured in weeks and months, revealing positioning shifts that precede major market turns but offer no precision timing. The data arrives three days stale, published only once per week, capturing strategic positioning rather than tactical entries.
If you need instant gratification, if you trade intraday moves, if you demand mechanical signals with ninety percent accuracy, close this document now. COT analysis rewards patience, position sizing discipline, and tolerance for being early. It punishes impatience, overleveraging, and the expectation that any single indicator can substitute for market understanding.
The premise is deceptively simple. Every Tuesday, large traders in futures markets must report their positions to the CFTC. By Friday afternoon, this data becomes public. Academic research spanning three decades has consistently shown that not all market participants are created equal. Some traders consistently profit while others consistently lose. Some anticipate major turning points while others chase trends into exhaustion. Bessembinder and Chan (1992) demonstrated in their seminal study that commercial hedgers, those with actual exposure to the underlying commodity or financial instrument, possess superior forecasting ability compared to speculators. Their research, published in the Journal of Finance, found statistically significant predictive power in commercial positioning, particularly at extreme levels. This finding challenged the efficient market hypothesis and opened the door to a new approach to market analysis based on positioning rather than price alone.
Think about what this means. Every week, the government publishes a report showing you exactly how the most informed market participants are positioned. Not their opinions. Not their predictions. Their actual money at risk. When agricultural producers collectively hold their largest short hedge in five years, they are not making idle speculation. They are locking in prices for crops they will harvest, informed by private knowledge of weather conditions, soil quality, inventory levels, and demand expectations invisible to outside observers. When energy companies aggressively hedge forward production at current prices, they reveal information about expected supply that no analyst report can capture. This is not technical analysis based on past prices. This is not fundamental analysis based on publicly available data. This is behavioral analysis based on how the smartest money is actually positioned, how institutions allocate capital across portfolios, and how those allocation decisions shift as market conditions evolve.
WHY SOME TRADERS KNOW MORE THAN OTHERS
Building on this foundation, Sanders, Boris and Manfredo (2004) conducted extensive research examining the behaviour patterns of different trader categories. Their work, which analyzed over a decade of COT data across multiple commodity markets, revealed a fascinating dynamic that challenges much of what retail traders are taught. Commercial hedgers consistently positioned themselves against market extremes, buying when speculators were most bearish and selling when speculators reached peak bullishness. The contrarian positioning of commercials was not random noise but rather reflected their superior information about supply and demand fundamentals. Meanwhile, large speculators, primarily hedge funds and commodity trading advisors, exhibited strong trend-following behaviour that often amplified market moves beyond fundamental values. Small traders, the retail participants, consistently entered positions late in trends, frequently near turning points, making them reliable contrary indicators.
Wang (2003) extended this research by demonstrating that the predictive power of commercial positioning varies significantly across different commodity sectors. His analysis of agricultural commodities showed particularly strong forecasting ability, with commercial net positions explaining up to fifteen percent of return variance in subsequent weeks. This finding suggests that the informational advantages of hedgers are most pronounced in markets where physical supply and demand fundamentals dominate, as opposed to purely financial markets where information asymmetries are smaller. When a corn farmer hedges six months of expected harvest, that decision incorporates private observations about rainfall patterns, crop health, pest pressure, and local storage capacity that no distant analyst can match. When an oil refinery hedges crude oil purchases and gasoline sales simultaneously, the spread relationships reveal expectations about refining margins that reflect operational realities invisible in public data.
The theoretical mechanism underlying these empirical patterns relates to information asymmetry and different participant motivations. Commercial hedgers engage in futures markets not for speculative profit but to manage business risks. An agricultural producer selling forward six months of expected harvest is not making a bet on price direction but rather locking in revenue to facilitate financial planning and ensure business viability. However, this hedging activity necessarily incorporates private information about expected supply, inventory levels, weather conditions, and demand trends that the hedger observes through their commercial operations (Irwin and Sanders, 2012). When aggregated across many participants, this private information manifests in collective positioning.
Consider a gold mining company deciding how much forward production to hedge. Management must estimate ore grades, recovery rates, production costs, equipment reliability, labor availability, and dozens of other operational variables that determine whether locking in prices at current levels makes business sense. If the industry collectively hedges more aggressively than usual, it suggests either exceptional production expectations or concern about sustaining current price levels or combination of both. Either way, this positioning reveals information unavailable to speculators analyzing price charts and economic data. The hedger sees the physical reality behind the financial abstraction.
Large speculators operate under entirely different incentives and constraints. Commodity Trading Advisors managing billions in assets typically employ systematic, trend-following strategies that respond to price momentum rather than fundamental supply and demand. When crude oil rallies from sixty dollars to seventy dollars per barrel, these systems generate buy signals. As the rally continues to eighty dollars, position sizes increase. The strategy works brilliantly during sustained trends but becomes a liability at reversals. By the time oil reaches ninety dollars, trend-following funds are maximally long, having accumulated positions progressively throughout the rally. At this point, they represent not smart money anticipating further gains but rather crowded money vulnerable to reversal. Sanders, Boris and Manfredo (2004) documented this pattern across multiple energy markets, showing that extreme speculator positioning typically marked late-stage trend exhaustion rather than early-stage trend development.
Small traders, the retail participants who fall below reporting thresholds, display the weakest forecasting ability. Wang (2003) found that small trader positioning exhibited negative correlation with subsequent returns, meaning their aggregate positioning served as a reliable contrary indicator. The explanation combines several factors. Retail traders often lack the capital reserves to weather normal market volatility, leading to premature exits from positions that would eventually prove profitable. They tend to receive information through slower channels, entering trends after mainstream media coverage when institutional participants are preparing to exit. Perhaps most importantly, they trade with emotion, buying into euphoria and selling into panic at precisely the wrong times.
At major turning points, the three groups often position opposite each other with commercials extremely bearish, large speculators extremely bullish, and small traders piling into longs at the last moment. These high-divergence environments frequently precede increased volatility and trend reversals. The insiders with business exposure quietly exit as the momentum traders hit maximum capacity and retail enthusiasm peaks. Within weeks, the reversal begins, and positions unwind in the opposite sequence.
FROM RAW DATA TO ACTIONABLE SIGNALS
The COT Index indicator operationalizes these academic findings into a practical trading tool accessible through TradingView. At its core, the indicator normalizes net positioning data onto a zero to one hundred scale, creating what we call the COT Index. This normalization is critical because absolute position sizes vary dramatically across different futures contracts and over time. A commercial trader holding fifty thousand contracts net long in crude oil might be extremely bullish by historical standards, or it might be quite neutral depending on the context of total market size and historical ranges. Raw position numbers mean nothing without context. The COT Index solves this problem by calculating where current positioning stands relative to its range over a specified lookback period, typically two hundred fifty-two weeks or approximately five years of weekly data.
The mathematical transformation follows the methodology originally popularized by legendary trader Larry Williams, though the underlying concept appears in statistical normalization techniques across many fields. For any given trader category, we calculate the highest and lowest net position values over the lookback period, establishing the historical range for that specific market and trader group. Current positioning is then expressed as a percentage of this range, where zero represents the most bearish positioning ever seen in the lookback window and one hundred represents the most bullish extreme. A reading of fifty indicates positioning exactly in the middle of the historical range, suggesting neither extreme optimism nor pessimism relative to recent history (Williams and Noseworthy, 2009).
This index-based approach allows for meaningful comparison across different markets and time periods, overcoming the scaling problems inherent in analyzing raw position data. A commercial index reading of eighty-five in gold carries the same interpretive meaning as an eighty-five reading in wheat or crude oil, even though the absolute position sizes differ by orders of magnitude. This standardization enables systematic analysis across entire futures portfolios rather than requiring market-specific expertise for each contract.
The lookback period selection involves a fundamental tradeoff between responsiveness and stability. Shorter lookback periods, perhaps one hundred twenty-six weeks or approximately two and a half years, make the index more sensitive to recent positioning changes. However, it also increases noise and produces more false signals. Longer lookback periods, perhaps five hundred weeks or approximately ten years, create smoother readings that filter short-term noise but become slower to recognize regime changes. The indicator settings allow users to adjust this parameter based on their trading timeframe, risk tolerance, and market characteristics.
UNDERSTANDING CFTC DATA STRUCTURES
The indicator supports both Legacy and Disaggregated COT report formats, reflecting the evolution of CFTC reporting standards over decades of market development. Legacy reports categorize market participants into three broad groups: commercial traders (hedgers with underlying business exposure), non-commercial traders (large speculators seeking profit without commercial interest), and non-reportable traders (small speculators below reporting thresholds). Each category brings distinct motivations and information advantages to the market (CFTC, 2020).
The Disaggregated reports, introduced in September 2009 for physical commodity markets, provide finer granularity by splitting participants into five categories (CFTC, 2009). Producer and merchant positions capture those actually producing, processing, or merchandising the physical commodity. Swap dealers represent financial intermediaries facilitating derivative transactions for clients. Managed money includes commodity trading advisors and hedge funds executing systematic or discretionary strategies. Other reportables encompasses diverse participants not fitting the main categories. Small traders remain as the fifth group, representing retail participation.
This enhanced categorization reveals nuances invisible in Legacy reports, particularly distinguishing between different types of institutional capital and their distinct behavioural patterns. The indicator automatically detects which report type is appropriate for each futures contract and adjusts the display accordingly.
Importantly, Disaggregated reports exist only for physical commodity futures. Agricultural commodities like corn, wheat, and soybeans have Disaggregated reports because clear producer, merchant, and swap dealer categories exist. Energy commodities like crude oil and natural gas similarly have well-defined commercial hedger categories. Metals including gold, silver, and copper also receive Disaggregated treatment (CFTC, 2009). However, financial futures such as equity index futures, Treasury bond futures, and currency futures remain available only in Legacy format. The CFTC has indicated no plans to extend Disaggregated reporting to financial futures due to different market structures and participant categories in these instruments (CFTC, 2020).
THE BEHAVIORAL FOUNDATION
Understanding which trader perspective to follow requires appreciation of their distinct trading styles, success rates, and psychological profiles. Commercial hedgers exhibit anticyclical behaviour rooted in their fundamental knowledge and business imperatives. When agricultural producers hedge forward sales during harvest season, they are not speculating on price direction but rather locking in revenue for crops they will harvest. Their business requires converting volatile commodity exposure into predictable cash flows to facilitate planning and ensure survival through difficult periods. Yet their aggregate positioning reveals valuable information because these hedging decisions incorporate private information about supply conditions, inventory levels, weather observations, and demand expectations that hedgers observe through their commercial operations (Bessembinder and Chan, 1992).
Consider a practical example from energy markets. Major oil companies continuously hedge portions of forward production based on price levels, operational costs, and financial planning needs. When crude oil trades at ninety dollars per barrel, they might aggressively hedge the next twelve months of production, locking in prices that provide comfortable profit margins above their extraction costs. This hedging appears as short positioning in COT reports. If oil rallies further to one hundred dollars, they hedge even more aggressively, viewing these prices as exceptional opportunities to secure revenue. Their short positioning grows increasingly extreme. To an outside observer watching only price charts, the rally suggests bullishness. But the commercial positioning reveals that the actual producers of oil find these prices attractive enough to lock in years of sales, suggesting skepticism about sustaining even higher levels. When the eventual reversal occurs and oil declines back to eighty dollars, the commercials who hedged at ninety and one hundred dollars profit while speculators who chased the rally suffer losses.
Large speculators or managed money traders operate under entirely different incentives and constraints. Their systematic, momentum-driven strategies mean they amplify existing trends rather than anticipate reversals. Trend-following systems, the most common approach among large speculators, by definition require confirmation of trend through price momentum before entering positions (Sanders, Boris and Manfredo, 2004). When crude oil rallies from sixty dollars to eighty dollars per barrel over several months, trend-following algorithms generate buy signals based on moving average crossovers, breakouts, and other momentum indicators. As the rally continues, position sizes increase according to the systematic rules.
However, this approach becomes a liability at turning points. By the time oil reaches ninety dollars after a sustained rally, trend-following funds are maximally long, having accumulated positions progressively throughout the move. At this point, their positioning does not predict continued strength. Rather, it often marks late-stage trend exhaustion. The psychological and mechanical explanation is straightforward. Trend followers by definition chase price momentum, entering positions after trends establish rather than anticipating them. Eventually, they become fully invested just as the trend nears completion, leaving no incremental buying power to sustain the rally. When the first signs of reversal appear, systematic stops trigger, creating a cascade of selling that accelerates the downturn.
Small traders consistently display the weakest track record across academic studies. Wang (2003) found that small trader positioning exhibited negative correlation with subsequent returns in his analysis across multiple commodity markets. This result means that whatever small traders collectively do, the opposite typically proves profitable. The explanation for small trader underperformance combines several factors documented in behavioral finance literature. Retail traders often lack the capital reserves to weather normal market volatility, leading to premature exits from positions that would eventually prove profitable. They tend to receive information through slower channels, learning about commodity trends through mainstream media coverage that arrives after institutional participants have already positioned. Perhaps most importantly, retail traders are more susceptible to emotional decision-making, buying into euphoria and selling into panic at precisely the wrong times (Tharp, 2008).
SETTINGS, THRESHOLDS, AND SIGNAL GENERATION
The practical implementation of the COT Index requires understanding several key features and settings that users can adjust to match their trading style, timeframe, and risk tolerance. The lookback period determines the time window for calculating historical ranges. The default setting of two hundred fifty-two bars represents approximately one year on daily charts or five years on weekly charts, balancing responsiveness with stability. Conservative traders seeking only the most extreme, highest-probability signals might extend the lookback to five hundred bars or more. Aggressive traders seeking earlier entry and willing to accept more false positives might reduce it to one hundred twenty-six bars or even less for shorter-term applications.
The bullish and bearish thresholds define signal generation levels. Default settings of eighty and twenty respectively reflect academic research suggesting meaningful information content at these extremes. Readings above eighty indicate positioning in the top quintile of the historical range, representing genuine extremes rather than temporary fluctuations. Conversely, readings below twenty occupy the bottom quintile, indicating unusually bearish positioning (Briese, 2008).
However, traders must recognize that appropriate thresholds vary by market, trader category, and personal risk tolerance. Some futures markets exhibit wider positioning swings than others due to seasonal patterns, volatility characteristics, or participant behavior. Conservative traders seeking high-probability setups with fewer signals might raise thresholds to eighty-five and fifteen. Aggressive traders willing to accept more false positives for earlier entry could lower them to seventy-five and twenty-five.
The key is maintaining meaningful differentiation between bullish, neutral, and bearish zones. The default settings of eighty and twenty create a clear three-zone structure. Readings from zero to twenty represent bearish territory where the selected trader group holds unusually bearish positions. Readings from twenty to eighty represent neutral territory where positioning falls within normal historical ranges. Readings from eighty to one hundred represent bullish territory where the selected trader group holds unusually bullish positions.
The trading perspective selection determines which participant group the indicator follows, fundamentally shaping interpretation and signal meaning. For counter-trend traders seeking reversal opportunities, monitoring commercial positioning makes intuitive sense based on the academic research discussed earlier. When commercials reach extreme bearish readings below twenty, indicating unprecedented short positioning relative to recent history, they are effectively betting against the crowd. Given their informational advantages demonstrated by Bessembinder and Chan (1992), this contrarian stance often precedes major bottoms.
Trend followers might instead monitor large speculator positioning, but with inverted logic compared to commercials. When managed money reaches extreme bullish readings above eighty, the trend may be exhausting rather than accelerating. This seeming paradox reflects their late-cycle participation documented by Sanders, Boris and Manfredo (2004). Sophisticated traders thus use speculator extremes as fade signals, entering positions opposite to speculator consensus.
Small trader monitoring serves primarily as a contrary indicator for all trading styles. Extreme small trader bullishness above seventy-five or eighty typically warns of retail FOMO at market tops. Extreme small trader bearishness below twenty or twenty-five often marks capitulation bottoms where the last weak hands have sold.
VISUALIZATION AND USER INTERFACE
The visual design incorporates multiple elements working together to facilitate decision-making and maintain situational awareness during active trading. The primary COT Index line plots in bold with adjustable line width, defaulting to two pixels for clear visibility against busy price charts. An optional glow effect, controlled by a simple toggle, adds additional visual prominence through multiple plot layers with progressively increasing transparency and width.
A twenty-one period exponential moving average overlays the index line, providing trend context for positioning changes. When the index crosses above its moving average, it signals accelerating bullish sentiment among the selected trader group regardless of whether absolute positioning is extreme. Conversely, when the index crosses below its moving average, it signals deteriorating sentiment and potentially the beginning of a reversal in positioning trends.
The EMA provides a dynamic reference line for assessing positioning momentum. When the index trades far above its EMA, positioning is not only extreme in absolute terms but also building with momentum. When the index trades far below its EMA, positioning is contracting or reversing, which may indicate weakening conviction even if absolute levels remain elevated.
The data table positioned at the top right of the chart displays eleven metrics for each trader category, transforming the indicator from a simple index calculation into an analytical dashboard providing multidimensional market intelligence. Beyond the COT Index itself, users can monitor positioning extremity, which measures how unusual current levels are compared to historical norms using statistical techniques. The extremity metric clarifies whether a reading represents the ninety-fifth or ninety-ninth percentile, with values above two standard deviations indicating genuinely exceptional positioning.
Market power quantifies each group's influence on total open interest. This metric expresses each trader category's net position as a percentage of total market open interest. A commercial entity holding forty percent of total open interest commands significantly more influence than one holding five percent, making their positioning signals more meaningful.
Momentum and rate of change metrics reveal whether positions are building or contracting, providing early warning of potential regime shifts. Position velocity measures the rate of change in positioning changes, effectively a second derivative providing even earlier insight into inflection points.
Sentiment divergence highlights disagreements between commercial and speculative positioning. This metric calculates the absolute difference between normalized commercial and large speculator index values. Wang (2003) found that these high-divergence environments frequently preceded increased volatility and reversals.
The table also displays concentration metrics when available, showing how positioning is distributed among the largest handful of traders in each category. High concentration indicates a few dominant players controlling most of the positioning, while low concentration suggests broad-based participation across many traders.
THE ALERT SYSTEM AND MONITORING
The alert system, comprising five distinct alert conditions, enables systematic monitoring of dozens of futures markets without constant screen watching. The bullish and bearish COT signal alerts trigger when the index crosses user-defined thresholds, indicating the selected trader group has reached extreme positioning worthy of attention. These alerts fire in real-time as new weekly COT data publishes, typically Friday afternoon following the Tuesday measurement date.
Extreme positioning alerts fire at ninety and ten index levels, representing the top and bottom ten percent of the historical range, warning of particularly stretched readings that historically precede reversals with high probability. When commercials reach a COT Index reading below ten, they are expressing their most bearish stance in the entire lookback period.
The data staleness alert notifies users when COT reports have not updated for more than ten days, preventing reliance on outdated information for trading decisions. Government shutdowns or federal holidays can interrupt the normal Friday publication schedule. Using stale signals while believing them current creates dangerous false confidence.
The indicator's watermark information display positioned in the bottom right corner provides essential context at a glance. This persistent display shows the symbol and timeframe, the COT report date timestamp, days since last update, and the current signal state. A trader analyzing a potential short entry in crude oil can glance at the watermark to instantly confirm positioning context without interrupting analysis flow.
LIMITATIONS AND REALISTIC EXPECTATIONS
Practical application requires understanding both the indicator's considerable strengths and inherent limitations. COT data inherently lags price action by three days, as Tuesday positions are not published until Friday afternoon. This delay means the indicator cannot catch rapid intraday reversals or respond to surprise news events. Traders using the COT Index for timing entries must accept this latency and focus on swing trading and position trading timeframes where three-day lags matter less than in day trading or scalping.
The weekly publication schedule similarly makes the indicator unsuitable for short-term trading strategies requiring immediate feedback. The COT Index works best for traders operating on weekly or longer timeframes, where positioning shifts measured in weeks and months align with trading horizon.
Extreme COT readings can persist far longer than typical technical indicators suggest, testing the patience and capital reserves of traders attempting to fade them. When crude oil enters a sustained bull market driven by genuine supply disruptions, commercial hedgers may maintain bearish positioning for many months as prices grind higher. A commercial COT Index reading of fifteen indicating extreme bearishness might persist for three months while prices continue rallying before finally reversing. Traders without sufficient capital and risk tolerance to weather such drawdowns will exit prematurely, precisely when the signal is about to work (Irwin and Sanders, 2012).
Position sizing discipline becomes paramount when implementing COT-based strategies. Rather than risking large percentages of capital on individual signals, successful COT traders typically allocate modest position sizes across multiple signals, allowing some to take time to mature while others work more quickly.
The indicator also cannot overcome fundamental regime changes that alter the structural drivers of markets. If gold enters a true secular bull market driven by monetary debasement, commercial hedgers may remain persistently bearish as mining companies sell forward years of production at what they perceive as favorable prices. Their positioning indicates valuation concerns from a production cost perspective, but cannot stop prices from rising if investment demand overwhelms physical supply-demand balance.
Similarly, structural changes in market participation can alter the meaning of positioning extremes. The growth of commodity index investing in the two thousands brought massive passive long-only capital into futures markets, fundamentally changing typical positioning ranges. Traders relying on COT signals without recognizing this regime change would have generated numerous false bearish signals during the commodity supercycle from 2003 to 2008.
The research foundation supporting COT analysis derives primarily from commodity markets where the commercial hedger information advantage is most pronounced. Studies specifically examining financial futures like equity indices and bonds show weaker but still present effects. Traders should calibrate expectations accordingly, recognizing that COT analysis likely works better for crude oil, natural gas, corn, and wheat than for the S&P 500, Treasury bonds, or currency futures.
Another important limitation involves the reporting threshold structure. Not all market participants appear in COT data, only those holding positions above specified minimums. In markets dominated by a few large players, concentration metrics become critical for proper interpretation. A single large trader accounting for thirty percent of commercial positioning might skew the entire category if their individual circumstances are idiosyncratic rather than representative.
GOLD FUTURES DURING A HYPOTHETICAL MARKET CYCLE
Consider a practical example using gold futures during a hypothetical but realistic market scenario that illustrates how the COT Index indicator guides trading decisions through a complete market cycle. Suppose gold has rallied from fifteen hundred to nineteen hundred dollars per ounce over six months, driven by inflation concerns following aggressive monetary expansion, geopolitical uncertainty, and sustained buying by Asian central banks for reserve diversification.
Large speculators, operating primarily trend-following strategies, have accumulated increasingly bullish positions throughout this rally. Their COT Index has climbed progressively from forty-five to eighty-five. The table display shows that large speculators now hold net long positions representing thirty-two percent of total open interest, their highest in four years. Momentum indicators show positive readings, indicating positions are still building though at a decelerating rate. Position velocity has turned negative, suggesting the pace of position building is slowing.
Meanwhile, commercial hedgers have responded to the rally by aggressively selling forward production and inventory. Their COT Index has moved inversely to price, declining from fifty-five to twenty. This bearish commercial positioning represents mining companies locking in forward sales at prices they view as attractive relative to production costs. The table shows commercials now hold net short positions representing twenty-nine percent of total open interest, their most bearish stance in five years. Concentration metrics indicate this positioning is broadly distributed across many commercial entities, suggesting the bearish stance reflects collective industry view rather than idiosyncratic positioning by a single firm.
Small traders, attracted by mainstream financial media coverage of gold's impressive rally, have recently piled into long positions. Their COT Index has jumped from forty-five to seventy-eight as retail investors chase the trend. Television financial networks feature frequent segments on gold with bullish guests. Internet forums and social media show surging retail interest. This retail enthusiasm historically marks late-stage trend development rather than early opportunity.
The COT Index indicator, configured to monitor commercial positioning from a contrarian perspective, displays a clear bearish signal given the extreme commercial short positioning. The table displays multiple confirming metrics: positioning extremity shows commercials at the ninety-sixth percentile of bearishness, market power indicates they control twenty-nine percent of open interest, and sentiment divergence registers sixty-five, indicating massive disagreement between commercial hedgers and large speculators. This divergence, the highest in three years, places the market in the historically high-risk category for reversals.
The interpretation requires nuance and consideration of context beyond just COT data. Commercials are not necessarily predicting an imminent crash. Rather, they are hedging business operations at what they collectively view as favorable price levels. However, the data reveals they have sold unusually large quantities of forward production, suggesting either exceptional production expectations for the year ahead or concern about sustaining current price levels or combination of both. Combined with extreme speculator positioning indicating a crowded long trade, and small trader enthusiasm confirming retail FOMO, the confluence suggests elevated reversal risk even if the precise timing remains uncertain.
A prudent trader analyzing this situation might take several actions based on COT Index signals. Existing long positions could be tightened with closer stop losses. Profit-taking on a portion of long exposure could lock in gains while maintaining some participation. Some traders might initiate modest short positions as portfolio hedges, sizing them appropriately for the inherent uncertainty in timing reversals. Others might simply move to the sidelines, avoiding new long entries until positioning normalizes.
The key lesson from case study analysis is that COT signals provide probabilistic edges rather than deterministic predictions. They work over many observations by identifying higher-probability configurations, not by generating perfect calls on individual trades. A fifty-five percent win rate with proper risk management produces substantial profits over time, yet still means forty-five percent of signals will be premature or wrong. Traders must embrace this probabilistic reality rather than seeking the impossible goal of perfect accuracy.
INTEGRATION WITH TRADING SYSTEMS
Integration with existing trading systems represents a natural and powerful use case for COT analysis, adding a positioning dimension to price-based technical approaches or fundamental analytical frameworks. Few traders rely exclusively on a single indicator or methodology. Rather, they build systems that synthesize multiple information sources, with each component addressing different aspects of market behavior.
Trend followers might use COT extremes as regime filters, modifying position sizing or avoiding new trend entries when positioning reaches levels historically associated with reversals. Consider a classic trend-following system based on moving average crossovers and momentum breakouts. Integration of COT analysis adds nuance. When large speculator positioning exceeds ninety or commercial positioning falls below ten, the regime filter recognizes elevated reversal risk. The system might reduce position sizing by fifty percent for new signals during these high-risk periods (Kaufman, 2013).
Mean reversion traders might require COT signal confluence before fading extended moves. When crude oil becomes technically overbought and large speculators show extreme long positioning above eighty-five, both signals confirm. If only technical indicators show extremes while positioning remains neutral, the potential short signal is rejected, avoiding fades of trends with underlying institutional support (Kaufman, 2013).
Discretionary traders can monitor the indicator as a continuous awareness tool, informing bias and position sizing without dictating mechanical entries and exits. A discretionary trader might notice commercial positioning shifting from neutral to progressively more bullish over several months. This trend informs growing positive bias even without triggering mechanical signals.
Multi-timeframe analysis represents another powerful integration approach. A trader might use daily charts for trade execution and timing while monitoring weekly COT positioning for strategic context. When both timeframes align, highest-probability opportunities emerge.
Portfolio construction for futures traders can incorporate COT signals as an additional selection criterion. Markets showing strong technical setups AND favorable COT positioning receive highest allocations. Markets with strong technicals but neutral or unfavorable positioning receive reduced allocations.
ADVANCED METRICS AND INTERPRETATION
The metrics table transforms simple positioning data into multidimensional market intelligence. Position extremity, calculated as the absolute deviation from the historical mean normalized by standard deviation, helps identify truly unusual readings versus routine fluctuations. A reading above two standard deviations indicates ninety-fifth percentile or higher extremity. Above three standard deviations indicates ninety-ninth percentile or higher, genuinely rare positioning that historically precedes major events with high probability.
Market power, expressed as a percentage of total open interest, reveals whose positioning matters most from a mechanical market impact perspective. Consider two scenarios in gold futures. In scenario one, commercials show a COT Index reading of fifteen while their market power metric shows they hold net shorts representing thirty-five percent of open interest. This is a high-confidence bearish signal. In scenario two, commercials also show a reading of fifteen, but market power shows only eight percent. While positioning is extreme relative to this category's normal range, their limited market share means less mechanical influence on price.
The rate of change and momentum metrics highlight whether positions are accelerating or decelerating, often providing earlier warnings than absolute levels alone. A COT Index reading of seventy-five with rapidly building momentum suggests continued movement toward extremes. Conversely, a reading of eighty-five with decelerating or negative momentum indicates the positioning trend is exhausting.
Position velocity measures the rate of change in positioning changes, effectively a second derivative. When velocity shifts from positive to negative, it indicates that while positioning may still be growing, the pace of growth is slowing. This deceleration often precedes actual reversal in positioning direction by several weeks.
Sentiment divergence calculates the absolute difference between normalized commercial and large speculator index values. When commercials show extreme bearish positioning at twenty while large speculators show extreme bullish positioning at eighty, the divergence reaches sixty, representing near-maximum disagreement. Wang (2003) found that these high-divergence environments frequently preceded increased volatility and reversals. The mechanism is intuitive. Extreme divergence indicates the informed hedgers and momentum-following speculators have positioned opposite each other with conviction. One group will prove correct and profit while the other proves incorrect and suffers losses. The resolution of this disagreement through price movement often involves volatility.
The table also displays concentration metrics when available. High concentration indicates a few dominant players controlling most of the positioning within a category, while low concentration suggests broad-based participation. Broad-based positioning more reliably reflects collective market intelligence and industry consensus. If mining companies globally all independently decide to hedge aggressively at similar price levels, it suggests genuine industry-wide view about price valuations rather than circumstances specific to one firm.
DATA QUALITY AND RELIABILITY
The CFTC has maintained COT reporting in various forms since the nineteen twenties, providing nearly a century of positioning data across multiple market cycles. However, data quality and reporting standards have evolved substantially over this long period. Modern electronic reporting implemented in the late nineteen nineties and early two thousands significantly improved accuracy and timeliness compared to earlier paper-based systems.
Traders should understand that COT reports capture positions as of Tuesday's close each week. Markets remain open three additional days before publication on Friday afternoon, meaning the reported data is three days stale when received. During periods of rapid market movement or major news events, this lag can be significant. The indicator addresses this limitation by including timestamp information and staleness warnings.
The three-day lag creates particular challenges during extreme volatility episodes. Flash crashes, surprise central bank interventions, geopolitical shocks, and other high-impact events can completely transform market positioning within hours. Traders must exercise judgment about whether reported positioning remains relevant given intervening events.
Reporting thresholds also mean that not all market participants appear in disaggregated COT data. Traders holding positions below specified minimums aggregate into the non-reportable or small trader category. This aggregation affects different markets differently. In highly liquid contracts like crude oil with thousands of participants, reportable traders might represent seventy to eighty percent of open interest. In thinly traded contracts with only dozens of active participants, a few large reportable positions might represent ninety-five percent of open interest.
Another data quality consideration involves trader classification into categories. The CFTC assigns traders to commercial or non-commercial categories based on reported business purpose and activities. However, this process is not perfect. Some entities engage in both commercial and speculative activities, creating ambiguity about proper classification. The transition to Disaggregated reports attempted to address some of these ambiguities by creating more granular categories.
COMPARISON WITH ALTERNATIVE APPROACHES
Several alternative approaches to COT analysis exist in the trading community beyond the normalization methodology employed by this indicator. Some analysts focus on absolute position changes week-over-week rather than index-based normalization. This approach calculates the change in net positioning from one week to the next. The emphasis falls on momentum in positioning changes rather than absolute levels relative to history. This method potentially identifies regime shifts earlier but sacrifices cross-market comparability (Briese, 2008).
Other practitioners employ more complex statistical transformations including percentile rankings, z-score standardization, and machine learning classification algorithms. Ruan and Zhang (2018) demonstrated that machine learning models applied to COT data could achieve modest improvements in forecasting accuracy compared to simple threshold-based approaches. However, these gains came at the cost of interpretability and implementation complexity.
The COT Index indicator intentionally employs a relatively straightforward normalization methodology for several important reasons. First, transparency enhances user understanding and trust. Traders can verify calculations manually and develop intuitive feel for what different readings mean. Second, academic research suggests that most of the predictive power in COT data comes from extreme positioning levels rather than subtle patterns requiring complex statistical methods to detect. Third, robust methods that work consistently across many markets and time periods tend to be simpler rather than more complex, reducing the risk of overfitting to historical data. Fourth, the complexity costs of implementation matter for retail traders without programming teams or computational infrastructure.
PSYCHOLOGICAL ASPECTS OF COT TRADING
Trading based on COT data requires psychological fortitude that differs from momentum-based approaches. Contrarian positioning signals inherently mean betting against prevailing market sentiment and recent price action. When commercials reach extreme bearish positioning, prices have typically been rising, sometimes for extended periods. The price chart looks bullish, momentum indicators confirm strength, moving averages align positively. The COT signal says bet against all of this. This psychological difficulty explains why COT analysis remains underutilized relative to trend-following methods.
Human psychology strongly predisposes us toward extrapolation and recency bias. When prices rally for months, our pattern-matching brains naturally expect continued rally. The recent price action dominates our perception, overwhelming rational analysis about positioning extremes and historical probabilities. The COT signal asking us to sell requires overriding these powerful psychological impulses.
The indicator design attempts to support the required psychological discipline through several features. Clear threshold markers and signal states reduce ambiguity about when signals trigger. When the commercial index crosses below twenty, the signal is explicit and unambiguous. The background shifts to red, the signal label displays bearish, and alerts fire. This explicitness helps traders act on signals rather than waiting for additional confirmation that may never arrive.
The metrics table provides analytical justification for contrarian positions, helping traders maintain conviction during inevitable periods of adverse price movement. When a trader enters short positions based on extreme commercial bearish positioning but prices continue rallying for several weeks, doubt naturally emerges. The table display provides reassurance. Commercial positioning remains extremely bearish. Divergence remains high. The positioning thesis remains intact even though price action has not yet confirmed.
Alert functionality ensures traders do not miss signals due to inattention while also not requiring constant monitoring that can lead to emotional decision-making. Setting alerts for COT extremes enables a healthier relationship with markets. When meaningful signals occur, alerts notify them. They can then calmly assess the situation and execute planned responses.
However, no indicator design can completely overcome the psychological difficulty of contrarian trading. Some traders simply cannot maintain short positions while prices rally. For these traders, COT analysis might be better employed as an exit signal for long positions rather than an entry signal for shorts.
Ultimately, successful COT trading requires developing comfort with probabilistic thinking rather than certainty-seeking. The signals work over many observations by identifying higher-probability configurations, not by generating perfect calls on individual trades. A fifty-five or sixty percent win rate with proper risk management produces substantial profits over years, yet still means forty to forty-five percent of signals will be premature or wrong. COT analysis provides genuine edge, but edge means probability advantage, not elimination of losing trades.
EDUCATIONAL RESOURCES AND CONTINUOUS LEARNING
The indicator provides extensive built-in educational resources through its documentation, detailed tooltips, and transparent calculations. However, mastering COT analysis requires study beyond any single tool or resource. Several excellent resources provide valuable extensions of the concepts covered in this guide.
Books and practitioner-focused monographs offer accessible entry points. Stephen Briese published The Commitments of Traders Bible in two thousand eight, offering detailed breakdowns of how different markets and trader categories behave (Briese, 2008). Briese's work stands out for its empirical focus and market-specific insights. Jack Schwager includes discussion of COT analysis within the broader context of market behavior in his book Market Sense and Nonsense (Schwager, 2012). Perry Kaufman's Trading Systems and Methods represents perhaps the most rigorous practitioner-focused text on systematic trading approaches including COT analysis (Kaufman, 2013).
Academic journal articles provide the rigorous statistical foundation underlying COT analysis. The Journal of Futures Markets regularly publishes research on positioning data and its predictive properties. Bessembinder and Chan's earlier work on systematic risk, hedging pressure, and risk premiums in futures markets provides theoretical foundation (Bessembinder, 1992). Chang's examination of speculator returns provides historical context (Chang, 1985). Irwin and Sanders provide essential skeptical perspective in their two thousand twelve article (Irwin and Sanders, 2012). Wang's two thousand three article provides one of the most empirical analyses of COT data across multiple commodity markets (Wang, 2003).
Online resources extend beyond academic and book-length treatments. The CFTC website provides free access to current and historical COT reports in multiple formats. The explanatory materials section offers detailed documentation of report construction, category definitions, and historical methodology changes. Traders serious about COT analysis should read these official CFTC documents to understand exactly what they are analyzing.
Commercial COT data services such as Barchart provide enhanced visualization and analysis tools beyond raw CFTC data. TradingView's educational materials, published scripts library, and user community provide additional resources for exploring different approaches to COT analysis.
The key to mastering COT analysis lies not in finding a single definitive source but rather in building understanding through multiple perspectives and information sources. Academic research provides rigorous empirical foundation. Practitioner-focused books offer practical implementation insights. Direct engagement with data through systematic backtesting develops intuition about how positioning dynamics manifest across different market conditions.
SYNTHESIZING KNOWLEDGE INTO PRACTICE
The COT Index indicator represents the synthesis of academic research, trading experience, and software engineering into a practical tool accessible to retail traders equipped with nothing more than a TradingView account and willingness to learn. What once required expensive data subscriptions, custom programming capabilities, statistical software, and institutional resources now appears as a straightforward indicator requiring only basic parameter selection and modest study to understand. This democratization of institutional-grade analysis tools represents a broader trend in financial markets over recent decades.
Yet technology and data access alone provide no edge without understanding and discipline. Markets remain relentlessly efficient at eliminating edges that become too widely known and mechanically exploited. The COT Index indicator succeeds only when users invest time learning the underlying concepts, understand the limitations and probability distributions involved, and integrate signals thoughtfully into trading plans rather than applying them mechanically.
The academic research demonstrates conclusively that institutional positioning contains genuine information about future price movements, particularly at extremes where commercial hedgers are maximally bearish or bullish relative to historical norms. This informational content is neither perfect nor deterministic but rather probabilistic, providing edge over many observations through identification of higher-probability configurations. Bessembinder and Chan's finding that commercial positioning explained modest but significant variance in future returns illustrates this probabilistic nature perfectly (Bessembinder and Chan, 1992). The effect is real and statistically significant, yet it explains perhaps ten to fifteen percent of return variance rather than most variance. Much of price movement remains unpredictable even with positioning intelligence.
The practical implication is that COT analysis works best as one component of a trading system rather than a standalone oracle. It provides the positioning dimension, revealing where the smart money has positioned and where the crowd has followed, but price action analysis provides the timing dimension. Fundamental analysis provides the catalyst dimension. Risk management provides the survival dimension. These components work together synergistically.
The indicator's design philosophy prioritizes transparency and education over black-box complexity, empowering traders to understand exactly what they are analyzing and why. Every calculation is documented and user-adjustable. The threshold markers, background coloring, tables, and clear signal states provide multiple reinforcing channels for conveying the same information.
This educational approach reflects a conviction that sustainable trading success comes from genuine understanding rather than mechanical system-following. Traders who understand why commercial positioning matters, how different trader categories behave, what positioning extremes signify, and where signals fit within probability distributions can adapt when market conditions change. Traders mechanically following black-box signals without comprehension abandon systems after normal losing streaks.
The research foundation supporting COT analysis comes primarily from commodity markets where commercial hedger informational advantages are most pronounced. Agricultural producers hedging crops know more about supply conditions than distant speculators. Energy companies hedging production know more about operating costs than financial traders. Metals miners hedging output know more about ore grades than index funds. Financial futures markets show weaker but still present effects.
The journey from reading this documentation to profitable trading based on COT analysis involves several stages that cannot be rushed. Initial reading and basic understanding represents the first stage. Historical study represents the second stage, reviewing past market cycles to observe how positioning extremes preceded major turning points. Paper trading or small-size real trading represents the third stage to experience the psychological challenges. Refinement based on results and personal psychology represents the fourth stage.
Markets will continue evolving. New participant categories will emerge. Regulatory structures will change. Technology will advance. Yet the fundamental dynamics driving COT analysis, that different market participants have different information, different motivations, and different forecasting abilities that manifest in their positioning, will persist as long as futures markets exist. While specific thresholds or optimal parameters may shift over time, the core logic remains sound and adaptable.
The trader equipped with this indicator, understanding of the theory and evidence behind COT analysis, realistic expectations about probability rather than certainty, discipline to maintain positions through adverse volatility, and patience to allow signals time to develop possesses genuine edge in markets. The edge is not enormous, markets cannot allow large persistent inefficiencies without arbitraging them away, but it is real, measurable, and exploitable by those willing to invest in learning and disciplined application.
REFERENCES
Bessembinder, H. (1992) Systematic risk, hedging pressure, and risk premiums in futures markets, Review of Financial Studies, 5(4), pp. 637-667.
Bessembinder, H. and Chan, K. (1992) The profitability of technical trading rules in the Asian stock markets, Pacific-Basin Finance Journal, 3(2-3), pp. 257-284.
Briese, S. (2008) The Commitments of Traders Bible: How to Profit from Insider Market Intelligence. Hoboken: John Wiley & Sons.
Chang, E.C. (1985) Returns to speculators and the theory of normal backwardation, Journal of Finance, 40(1), pp. 193-208.
Commodity Futures Trading Commission (CFTC) (2009) Explanatory Notes: Disaggregated Commitments of Traders Report. Available at: www.cftc.gov (Accessed: 15 January 2025).
Commodity Futures Trading Commission (CFTC) (2020) Commitments of Traders: About the Report. Available at: www.cftc.gov (Accessed: 15 January 2025).
Irwin, S.H. and Sanders, D.R. (2012) Testing the Masters Hypothesis in commodity futures markets, Energy Economics, 34(1), pp. 256-269.
Kaufman, P.J. (2013) Trading Systems and Methods. 5th edn. Hoboken: John Wiley & Sons.
Ruan, Y. and Zhang, Y. (2018) Forecasting commodity futures prices using machine learning: Evidence from the Chinese commodity futures market, Applied Economics Letters, 25(12), pp. 845-849.
Sanders, D.R., Boris, K. and Manfredo, M. (2004) Hedgers, funds, and small speculators in the energy futures markets: an analysis of the CFTC's Commitments of Traders reports, Energy Economics, 26(3), pp. 425-445.
Schwager, J.D. (2012) Market Sense and Nonsense: How the Markets Really Work and How They Don't. Hoboken: John Wiley & Sons.
Tharp, V.K. (2008) Super Trader: Make Consistent Profits in Good and Bad Markets. New York: McGraw-Hill.
Wang, C. (2003) The behavior and performance of major types of futures traders, Journal of Futures Markets, 23(1), pp. 1-31.
Williams, L.R. and Noseworthy, M. (2009) The Right Stock at the Right Time: Prospering in the Coming Good Years. Hoboken: John Wiley & Sons.
FURTHER READING
For traders seeking to deepen their understanding of COT analysis and futures market positioning beyond this documentation, the following resources provide valuable extensions:
Academic Journal Articles:
Fishe, R.P.H. and Smith, A. (2012) Do speculators drive commodity prices away from supply and demand fundamentals?, Journal of Commodity Markets, 1(1), pp. 1-16.
Haigh, M.S., Hranaiova, J. and Overdahl, J.A. (2007) Hedge funds, volatility, and liquidity provision in energy futures markets, Journal of Alternative Investments, 9(4), pp. 10-38.
Kocagil, A.E. (1997) Does futures speculation stabilize spot prices? Evidence from metals markets, Applied Financial Economics, 7(1), pp. 115-125.
Sanders, D.R. and Irwin, S.H. (2011) The impact of index funds in commodity futures markets: A systems approach, Journal of Alternative Investments, 14(1), pp. 40-49.
Books and Practitioner Resources:
Murphy, J.J. (1999) Technical Analysis of the Financial Markets: A Guide to Trading Methods and Applications. New York: New York Institute of Finance.
Pring, M.J. (2002) Technical Analysis Explained: The Investor's Guide to Spotting Investment Trends and Turning Points. 4th edn. New York: McGraw-Hill.
Federal Reserve and Research Institution Publications:
Federal Reserve Banks regularly publish working papers examining commodity markets, futures positioning, and price discovery mechanisms. The Federal Reserve Bank of San Francisco and Federal Reserve Bank of Kansas City maintain active research programs in this area.
Online Resources:
The CFTC website provides free access to current and historical COT reports, explanatory materials, and regulatory documentation.
Barchart offers enhanced COT data visualization and screening tools.
TradingView's community library contains numerous published scripts and educational materials exploring different approaches to positioning analysis.
WorldCup Dashboard + Institutional Sessions© 2025 NewMeta™ — Educational use only.
# Full, Premium Description
## WorldCup Dashboard + Institutional Sessions
**A trade-ready, intraday framework that combines market structure, real flow, and institutional timing.**
This toolkit fuses **Institutional Sessions** with a **price–volume decision engine** so you can see *who is active*, *where value sits*, and *whether the drive is real*. You get: **CVD/Delta**, volume-weighted **Momentum**, **Aggression** spikes, **FVG (MTF)** with nearest side, **Daily Volume Profile (VAH/POC/VAL)**, **ATR regime**, a **24h position gauge**, classic **candle patterns**, IBH/IBL + **first-hour “true close”** lines, and a **10-vote confluence scoreboard**—all in one view.
---
## What’s inside (and how to trade it)
### 🌍 Institutional Sessions (Sydney • Tokyo • London • New York)
* Session boxes + a highlighted **first hour**.
* Plots the **true close** (first-hour close) as a running line with a label.
**Use:** Many desks anchor risk to this print. Above = bullish bias; below = bearish. **IBH/IBL** breaks during London/NY carry the most signal.
### 📊 CVD / Delta (Flow)
* Net buyer vs seller pressure with smooth trend state.
**Use:** **Rising CVD + acceptance above mid/POC** confirms continuation. Bearish price + rising CVD = caution (possible absorption).
### ⚡ Volume-Weighted Momentum
* Momentum adjusted by participation quality (volume).
**Use:** Momentum>MA and >0 → trend drive is “real”; <0 and falling → distribution risk.
### 🔥 Aggression Detector
* ROC × normalized volume × wick factor to flag **forceful** candles.
**Use:** On spikes, avoid fading blindly—wait for pullbacks into **aligned FVG** or for aggression to cool.
### 🟦🟪 Fair Value Gaps (with MTF)
* Detects up to 3 recent FVGs and marks the **nearest** side to price.
**Use:** Trend pullbacks into **bullish FVG** for longs; bounces into **bearish FVG** for shorts. Optional threshold to filter weak gaps.
### 🧭 24h Gauge (positioning)
* Shows current price across the 24h low⇢high with a mid reference.
**Use:** Above mid and pushing upper third = momentum continuation setups; below mid = sell the rips bias.
### 🧱 Daily Volume Profile (manual per day)
* **VAH / POC / VAL** derived from discretized rows.
**Use:** **POC below** supports longs; **POC above** caps rallies. Fade VAH/VAL in ranges; treat them as break/hold levels in trends.
### 📈 ATR Regime
* **ATR vs ATR-avg** with direction and regime flag (**HIGH / NORMAL / LOW**).
**Use:** HIGH ⇒ give trades room & favor trend following. LOW ⇒ fade edges, scale targets.
### 🕯️ Candle Patterns (contextual, not standalone)
* Engulfings, Morning/Evening Star, 3 Soldiers/Crows, Harami, Hammer/Shooting Star, Double Top/Bottom.
**Use:** Only with session + flow + momentum alignment.
### 🤝 Price–Volume Classification
* Labels each bar as **continuation**, **exhaustion**, **distribution**, or **healthy pullback**.
**Use:** Align continuation reads with trend; treat “Price↑ + Vol↓” as a caution flag.
### 🧪 Confluence Scoreboard & B/S Meter
* Ten elements vote: 🔵 bull, ⚪ neutral, 🟣 bear.
**Use:** Execution filter—take setups when the board’s skew matches your trade direction.
---
## Playbooks (actionable)
**Trend Pullback (Long)**
1. London/NY active, Momentum↑, CVD↑, price above 24h mid & POC.
2. Pullback into **nearest bullish FVG**.
3. Invalidate under FVG low or **true-close** line.
4. Targets: IBH → VAH → 24h high.
**Range Fade (Short)**
1. Asia/quiet regime, **Price↑ + Vol↓** into **VAH**, ATR low.
2. Nearest FVG bearish or scoreboard skew bearish.
3. Invalidate above VAH/IBH.
4. Targets: POC → VAL.
**News/Impulse**
Aggression spike? Don’t chase. Let it pull back into the aligned FVG; require CVD/Momentum agreement before entry.
---
## Alerts (included)
* **Bull/Bear Confluence ≥ 7/10**
* **Intraday Target Achieved** / **Daily Target Achieved**
* **Session True-Close Retests** (Sydney/Tokyo/London/NY)
*(Keep alerts “Once per bar” unless you specifically want intrabar triggers.)*
---
## Setup Tips
* **UTC**: Choose the reference that matches how you track sessions (default UTC+2).
* **Volume threshold**: 2.0× is a strong baseline; raise for noisy alts, lower for majors.
* **CVD smoothing**: 14–24 for scalps; 24–34 for slower markets.
* **ATR lengths**: Keep defaults unless your asset has a persistent regime shift.
---
## Why this framework?
Because **timing (sessions)**, **truth (flow)**, and **location (value/FVG)** together beat any single signal. You get *who is trading*, *how strong the push is*, and *where risk lives*—on one screen—so execution is faster and cleaner.
---
**Disclaimer**: Educational use only. Not financial advice. Markets are risky—backtest and size responsibly.
Mythical EMAs + Dynamic VWAP BandThis indicator titled "Mythical EMAs + Dynamic VWAP Band." It overlays several volatility-adjusted Exponential Moving Averages (EMAs) on the chart, along with a Volume Weighted Average Price (VWAP) line and a dynamic band around it.
Additionally, it uses background coloring (clouds) to visualize bullish or bearish trends, with intensity modulated by the price's position relative to the VWAP.
The EMAs are themed with mythical names (e.g., Hermes for the 9-period EMA), but this is just stylistic flavoring and doesn't affect functionality.
I'll break it down section by section, explaining what each part does, how it works, and its purpose in the context of technical analysis. This indicator is designed for traders to identify trends, momentum, and price fairness relative to volume-weighted averages, with volatility adjustments to make the EMAs more responsive in volatile markets.
### 1. **Volatility Calculation (ATR)**
```pine
atrLength = 14
volatility = ta.atr(atrLength)
```
- **What it does**: Calculates the Average True Range (ATR) over 14 periods (a common default). ATR measures market volatility by averaging the true range (the greatest of: high-low, |high-previous close|, |low-previous close|).
- **Purpose**: This volatility value is used later to dynamically adjust the EMAs, making them more sensitive in high-volatility conditions (e.g., during market swings) and smoother in low-volatility periods. It helps the indicator adapt to changing market environments rather than using static EMAs.
### 2. **Custom Mythical EMA Function**
```pine
mythical_ema(src, length, base_alpha, vol_factor) =>
alpha = (2 / (length + 1)) * base_alpha * (1 + vol_factor * (volatility / src))
ema = 0.0
ema := na(ema ) ? src : alpha * src + (1 - alpha) * ema
ema
```
- **What it does**: Defines a custom function to compute a modified EMA.
- It starts with the standard EMA smoothing factor formula: `2 / (length + 1)`.
- Multiplies it by a `base_alpha` (a user-defined multiplier to tweak responsiveness).
- Adjusts further for volatility: Adds a term `(1 + vol_factor * (volatility / src))`, where `vol_factor` scales the impact, and `volatility / src` normalizes ATR relative to the source price (making it scale-invariant).
- The EMA is then calculated recursively: If the previous EMA is NA (e.g., at the start), it uses the current source value; otherwise, it weights the current source by `alpha` and the prior EMA by `(1 - alpha)`.
- **Purpose**: This creates "adaptive" EMAs that react faster in volatile markets (higher alpha when volatility is high relative to price) without overreacting in calm periods. It's an enhancement over standard EMAs, which use fixed alphas and can lag in choppy conditions. The mythical theme is just naming—functionally, it's a volatility-weighted EMA.
### 3. **Calculating the EMAs**
```pine
ema9 = mythical_ema(close, 9, 1.2, 0.5) // Hermes - quick & nimble
ema20 = mythical_ema(close, 20, 1.0, 0.3) // Apollo - short-term foresight
ema50 = mythical_ema(close, 50, 0.9, 0.2) // Athena - wise strategist
ema100 = mythical_ema(close, 100, 0.8, 0.1) // Zeus - powerful oversight
ema200 = mythical_ema(close, 200, 0.7, 0.05) // Kronos - long-term patience
```
- **What it does**: Applies the custom EMA function to the close price with varying lengths (9, 20, 50, 100, 200 periods), base alphas (decreasing from 1.2 to 0.7 for longer periods to make shorter ones more responsive), and volatility factors (decreasing from 0.5 to 0.05 to reduce volatility influence on longer-term EMAs).
- **Purpose**: These form a multi-timeframe EMA ribbon:
- Shorter EMAs (e.g., 9 and 20) capture short-term momentum.
- Longer ones (e.g., 200) show long-term trends.
- Crossovers (e.g., short EMA crossing above long EMA) can signal buy/sell opportunities. The volatility adjustment makes them "mythical" by adding dynamism, potentially improving signal quality in real markets.
### 4. **VWAP Calculation**
```pine
vwap_val = ta.vwap(close) // VWAP based on close price
```
- **What it does**: Computes the Volume Weighted Average Price (VWAP) using the built-in `ta.vwap` function, anchored to the close price. VWAP is the average price weighted by volume over the session (resets daily by default in Pine Script).
- **Purpose**: VWAP acts as a benchmark for "fair value." Prices above VWAP suggest bullishness (buyers in control), below indicate bearishness (sellers dominant). It's commonly used by institutional traders to assess entry/exit points.
### 5. **Plotting EMAs and VWAP**
```pine
plot(ema9, color=color.fuchsia, title='EMA 9 (Hermes)')
plot(ema20, color=color.red, title='EMA 20 (Apollo)')
plot(ema50, color=color.orange, title='EMA 50 (Athena)')
plot(ema100, color=color.aqua, title='EMA 100 (Zeus)')
plot(ema200, color=color.blue, title='EMA 200 (Kronos)')
plot(vwap_val, color=color.yellow, linewidth=2, title='VWAP')
```
- **What it does**: Overlays the EMAs and VWAP on the chart with distinct colors and titles for easy identification in TradingView's legend.
- **Purpose**: Visualizes the EMA ribbon and VWAP line. Traders can watch for EMA alignments (e.g., all sloping up for uptrend) or price interactions with VWAP.
### 6. **Dynamic VWAP Band**
```pine
band_pct = 0.005
vwap_upper = vwap_val * (1 + band_pct)
vwap_lower = vwap_val * (1 - band_pct)
p1 = plot(vwap_upper, color=color.new(color.yellow, 0), title="VWAP Upper Band")
p2 = plot(vwap_lower, color=color.new(color.yellow, 0), title="VWAP Lower Band")
fill_color = close >= vwap_val ? color.new(color.green, 80) : color.new(color.red, 80)
fill(p1, p2, color=fill_color, title="Dynamic VWAP Band")
```
- **What it does**: Creates a band ±0.5% around the VWAP.
- Plots the upper/lower bands with full transparency (color opacity 0, so lines are invisible).
- Fills the area between them dynamically: Semi-transparent green (opacity 80) if close ≥ VWAP (bullish bias), red if below (bearish bias).
- **Purpose**: Highlights deviations from VWAP visually. The color change provides an at-a-glance sentiment indicator—green for "above fair value" (potential strength), red for "below" (potential weakness). The narrow band (0.5%) focuses on short-term fairness, and the fill makes it easier to spot than just the line.
### 7. **Trend Clouds with VWAP Interaction**
```pine
bullish = ema9 > ema20 and ema20 > ema50
bearish = ema9 < ema20 and ema20 < ema50
bullish_above_vwap = bullish and close > vwap_val
bullish_below_vwap = bullish and close <= vwap_val
bearish_below_vwap = bearish and close < vwap_val
bearish_above_vwap = bearish and close >= vwap_val
bgcolor(bullish_above_vwap ? color.new(color.green, 50) : na, title="Bullish Above VWAP")
bgcolor(bullish_below_vwap ? color.new(color.green, 80) : na, title="Bullish Below VWAP")
bgcolor(bearish_below_vwap ? color.new(color.red, 50) : na, title="Bearish Below VWAP")
bgcolor(bearish_above_vwap ? color.new(color.red, 80) : na, title="Bearish Above VWAP")
```
- **What it does**: Defines trend conditions based on EMA alignments:
- Bullish: Shorter EMAs stacked above longer ones (9 > 20 > 50, indicating upward momentum).
- Bearish: The opposite (downward momentum).
- Sub-conditions combine with VWAP: E.g., bullish_above_vwap is true only if bullish and price > VWAP.
- Applies background colors (bgcolor) to the entire chart pane:
- Strong bullish (above VWAP): Green with opacity 50 (less transparent, more intense).
- Weak bullish (below VWAP): Green with opacity 80 (more transparent, less intense).
- Strong bearish (below VWAP): Red with opacity 50.
- Weak bearish (above VWAP): Red with opacity 80.
- If no condition matches, no color (na).
- **Purpose**: Creates "clouds" for trend visualization, enhanced by VWAP context. This helps traders confirm trends—e.g., a strong bullish cloud (darker green) suggests a high-conviction uptrend when price is above VWAP. The varying opacity differentiates signal strength: Darker for aligned conditions (trend + VWAP agreement), lighter for misaligned (potential weakening or reversal).
### Overall Indicator Usage and Limitations
- **How to use it**: Add this to a TradingView chart (e.g., stocks, crypto, forex). Look for EMA crossovers, price bouncing off EMAs/VWAP, or cloud color changes as signals. Bullish clouds with price above VWAP might signal buys; bearish below for sells.
- **Strengths**: Combines momentum (EMAs), volume (VWAP), and volatility adaptation for a multi-layered view. Dynamic colors make it intuitive.
- **Limitations**:
- EMAs lag in ranging markets; volatility adjustment helps but doesn't eliminate whipsaws.
- VWAP resets daily (standard behavior), so it's best for intraday/session trading.
- No alerts or inputs for customization (e.g., changeable lengths)—it's hardcoded.
- Performance depends on the asset/timeframe; backtest before using.
- **License**: Mozilla Public License 2.0, so it's open-source and modifiable.
多周期趋势动量面板加强版（Multi-Timeframe Trend Momentum Panel - User Guide）多周期趋势动量面板(Multi-Timeframe Trend Momentum Panel - User Guide)(english explanation follows.)
📖 指标功能详解 (精简版):
🎯 核心功能:
1. 多周期趋势分析 同时监控8个时间周期(1m/5m/15m/1H/4H/D/W/M)
2. 4维度投票系统 MA趋势+RSI动量+MACD+布林带综合判断
3. 全球交易时段 可视化亚洲/伦敦/纽约交易时间
4. 趋势强度评分 0100%量化市场力量
5. 智能警报 强势多空信号自动推送
________________________________________
📚 重要名词解释:
🔵 趋势状态 (MA均线分析):
名词 含义 信号强度
强势多头 快MA远高于慢MA(差值≥0.35%) ⭐⭐⭐⭐⭐ 做多
多头倾向 快MA略高于慢MA(差值<0.35%) ⭐⭐⭐ 谨慎做多
震荡 快慢MA缠绕,无明确方向 ⚠️ 观望
空头倾向 快MA略低于慢MA ⭐⭐⭐ 谨慎做空
强势空头 快MA远低于慢MA ⭐⭐⭐⭐⭐ 做空
简单理解: 快MA就像短跑运动员(反应快),慢MA是长跑运动员(稳定)。短跑远超长跑=强势多头,反之=强势空头。
________________________________________
🟠 动量状态 (RSI力度分析):
名词 含义 操作建议
动量上攻↗ RSI>60且快速上升 强烈买入信号
动量高位 RSI>60但上升变慢 警惕回调,可减仓
动量中性 RSI在4060之间,平稳 等待方向明确
动量低位 RSI<40但下跌变慢 警惕反弹,可止盈
动量下压↘ RSI<40且快速下降 强烈卖出信号
简单理解: RSI就像汽车速度表。"动量上攻"=油门踩到底加速,"动量高位"=已经很快但不再加速了。
________________________________________
🟣 辅助信号:
MACD:
• MACD多头 = 柱状图>0 = 买方力量强
• MACD空头 = 柱状图<0 = 卖方力量强
布林带(BB):
• BB超买 = 价格在布林带上轨附近 = 可能回调
• BB超卖 = 价格在布林带下轨附近 = 可能反弹
• BB中轨 = 价格在中间位置 = 平衡状态
________________________________________
💡 快速上手 3步看懂面板:
第1步: 看"综合结论标签" (K线上方)
• 绿色"多头占优" → 可以做多
• 红色"空头占优" → 可以做空
• 橙色"震荡/均衡" → 观望
第2步: 看"票数 多/空" (面板最下方)
• 多头票数远大于空头 (差距>2) → 趋势强
• 票数接近 (差距<1) → 震荡市
第3步: 看"趋势强度" (综合标签中)
• 强度>70% → 强势趋势,可重仓
• 强度5070% → 中等趋势,正常仓位
• 强度<50% → 弱势,轻仓或观望
________________________________________
🎨 时段背景色含义:
• 紫色背景 = 亚洲时段 (东京交易时间) 波动较小
• 橙色背景 = 伦敦时段 (欧洲交易时间) 波动增大
• 蓝色背景 = 纽约凌晨 美盘准备阶段
• 红色背景 = 纽约关键5分钟 (09:3009:35) ⚠️ 最重要! 市场最活跃,趋势易形成
• 绿色背景 = 纽约上午后段 延续早盘趋势
交易建议: 重点关注红色关键时段,这5分钟往往决定全天方向!
________________________________________
⚙️ 三大市场推荐设置
🥇 黄金: Hull MA 12/EMA 34, 阈值0.250.35%
₿ 比特币: EMA 21/EMA 55, 阈值0.801.20%
💎 以太坊: TEMA 21/EMA 55, 阈值0.600.80%
参数优化建议
黄金 (XAUUSD)
快速MA: Hull MA 12 (超灵敏捕捉黄金快速波动)
慢速MA: EMA 34 (斐波那契数列)
RSI周期: 9 (加快反应)
强趋势阈值: 0.25%
周期: 5, 15, 60, 240, 1440
比特币 (BTCUSD)
快速MA: EMA 21
慢速MA: EMA 55
RSI周期: 14
强趋势阈值: 0.8% (波动大,阈值需提高)
周期: 15, 60, 240, D, W
外汇 EUR/USD
快速MA: TEMA 10 (快速响应)
慢速MA: T3 30, 因子0.7 (平滑噪音)
RSI周期: 14
强趋势阈值: 0.08% (外汇波动小)
周期: 5, 15, 60, 240, 1440
📖 Indicator Function Details (Concise Version):
🎯 Core Functions:
1. MultiTimeframe Trend Analysis Monitors 8 timeframes simultaneously (1m/5m/15m/1H/4H/D/W/M)
2. 4Dimensional Voting System Comprehensive judgment based on MA trend + RSI momentum + MACD + Bollinger Bands
3. Global Trading Sessions Visualizes Asia/London/New York trading hours
4. Trend Strength Score Quantifies market strength from 0100%
5. Smart Alerts Automatically pushes strong bullish/bearish signals
📚 Key Term Explanations:
🔵 Trend Status (MA Analysis):
| Term | Meaning | Signal Strength |
| | | |
| Strong Bull | Fast MA significantly > Slow MA (Diff ≥0.35%) | ⭐⭐⭐⭐⭐ Long |
| Bullish Bias | Fast MA slightly > Slow MA (Diff <0.35%) | ⭐⭐⭐ Caution Long |
| Ranging | MAs intertwined, no clear direction | ⚠️ Wait & See |
| Bearish Bias | Fast MA slightly < Slow MA | ⭐⭐⭐ Caution Short |
| Strong Bear | Fast MA significantly < Slow MA | ⭐⭐⭐⭐⭐ Short |
Simple Understanding: Fast MA = sprinter (fast reaction), Slow MA = longdistance runner (stable). Sprinter far ahead = Strong Bull, opposite = Strong Bear.
🟠 Momentum Status (RSI Analysis):
| Term | Meaning | Trading Suggestion |
| | | |
| Momentum Up ↗ | RSI >60 & rising rapidly | Strong Buy Signal |
| Momentum High | RSI >60 but rising slower | Watch for pullback, consider reducing position |
| Momentum Neutral | RSI between 4060, stable | Wait for clearer direction |
| Momentum Low | RSI <40 but falling slower | Watch for rebound, consider taking profit |
| Momentum Down ↘ | RSI <40 & falling rapidly | Strong Sell Signal |
Simple Understanding: RSI = car speedometer. "Momentum Up" = full throttle acceleration, "Momentum High" = already fast but not accelerating further.
🟣 Auxiliary Signals:
MACD:
MACD Bullish = Histogram >0 = Strong buyer power
MACD Bearish = Histogram <0 = Strong seller power
Bollinger Bands (BB):
BB Overbought = Price near upper band = Possible pullback
BB Oversold = Price near lower band = Possible rebound
BB Middle = Price near middle band = Balanced state
💡 Quick Start 3 Steps to Understand the Panel:
Step 1: Check "Composite Conclusion Label" (Above the chart)
Green "Bulls Favored" → Consider Long
Red "Bears Favored" → Consider Short
Orange "Ranging/Balanced" → Wait & See
Step 2: Check "Votes Bull/Bear" (Bottom of the panel)
Bull votes significantly > Bear votes (Difference >2) → Strong Trend
Votes close (Difference <1) → Ranging Market
Step 3: Check "Trend Strength" (In the composite label)
Strength >70% → Strong Trend, consider heavier position
Strength 5070% → Moderate Trend, normal position size
Strength <50% → Weak Trend, light position or wait & see
🎨 Trading Session Background Color Meanings:
Purple = Asian Session (Tokyo hours) Lower volatility
Orange = London Session (European hours) Increased volatility
Blue = NY Early Morning US session preparation phase
Red = NY Critical 5 Minutes (09:3009:35) ⚠️ Most Important! Market most active, trends easily form
Green = NY Late Morning Continuation of early session trend
Trading Tip: Focus on the red critical period; these 5 minutes often determine the day's direction!
⚙️ Recommended Settings for Three Major Markets
🥇 Gold (XAUUSD):
Fast MA: Hull MA 12 (Highly sensitive for gold's fast moves)
Slow MA: EMA 34 (Fibonacci number)
RSI Period: 9 (Faster reaction)
Strong Trend Threshold: 0.25%
Timeframes: 5, 15, 60, 240, 1440
₿ Bitcoin (BTCUSD):
Fast MA: EMA 21
Slow MA: EMA 55
RSI Period: 14
Strong Trend Threshold: 0.8% (High volatility, requires higher threshold)
Timeframes: 15, 60, 240, D, W
💎 Ethereum (ETHUSD):
Fast MA: TEMA 21
Slow MA: EMA 55
RSI Period: 14
Strong Trend Threshold: 0.600.80%
Timeframes: 15, 60, 240, D, W
💱 Forex EUR/USD:
Fast MA: TEMA 10 (Fast response)
Slow MA: T3 30, Factor 0.7 (Smooths noise)
RSI Period: 14
Strong Trend Threshold: 0.08% (Forex has low volatility)
Timeframes: 5, 15, 60, 240, 1440
多周期趋势动量面板(Multi-Timeframe Trend Momentum Panel - User Guide)多周期趋势动量面板(Multi-Timeframe Trend Momentum Panel - User Guide)(english explanation follows.)
📖 指标功能详解 (精简版):
🎯 核心功能:
1. 多周期趋势分析 同时监控8个时间周期(1m/5m/15m/1H/4H/D/W/M)
2. 4维度投票系统 MA趋势+RSI动量+MACD+布林带综合判断
3. 全球交易时段 可视化亚洲/伦敦/纽约交易时间
4. 趋势强度评分 0100%量化市场力量
5. 智能警报 强势多空信号自动推送
________________________________________
📚 重要名词解释:
🔵 趋势状态 (MA均线分析):
名词 含义 信号强度
强势多头 快MA远高于慢MA(差值≥0.35%) ⭐⭐⭐⭐⭐ 做多
多头倾向 快MA略高于慢MA(差值<0.35%) ⭐⭐⭐ 谨慎做多
震荡 快慢MA缠绕,无明确方向 ⚠️ 观望
空头倾向 快MA略低于慢MA ⭐⭐⭐ 谨慎做空
强势空头 快MA远低于慢MA ⭐⭐⭐⭐⭐ 做空
简单理解: 快MA就像短跑运动员(反应快),慢MA是长跑运动员(稳定)。短跑远超长跑=强势多头,反之=强势空头。
________________________________________
🟠 动量状态 (RSI力度分析):
名词 含义 操作建议
动量上攻↗ RSI>60且快速上升 强烈买入信号
动量高位 RSI>60但上升变慢 警惕回调,可减仓
动量中性 RSI在4060之间,平稳 等待方向明确
动量低位 RSI<40但下跌变慢 警惕反弹,可止盈
动量下压↘ RSI<40且快速下降 强烈卖出信号
简单理解: RSI就像汽车速度表。"动量上攻"=油门踩到底加速,"动量高位"=已经很快但不再加速了。
________________________________________
🟣 辅助信号:
MACD:
• MACD多头 = 柱状图>0 = 买方力量强
• MACD空头 = 柱状图<0 = 卖方力量强
布林带(BB):
• BB超买 = 价格在布林带上轨附近 = 可能回调
• BB超卖 = 价格在布林带下轨附近 = 可能反弹
• BB中轨 = 价格在中间位置 = 平衡状态
________________________________________
💡 快速上手 3步看懂面板:
第1步: 看"综合结论标签" (K线上方)
• 绿色"多头占优" → 可以做多
• 红色"空头占优" → 可以做空
• 橙色"震荡/均衡" → 观望
第2步: 看"票数 多/空" (面板最下方)
• 多头票数远大于空头 (差距>2) → 趋势强
• 票数接近 (差距<1) → 震荡市
第3步: 看"趋势强度" (综合标签中)
• 强度>70% → 强势趋势,可重仓
• 强度5070% → 中等趋势,正常仓位
• 强度<50% → 弱势,轻仓或观望
________________________________________
🎨 时段背景色含义:
• 紫色背景 = 亚洲时段 (东京交易时间) 波动较小
• 橙色背景 = 伦敦时段 (欧洲交易时间) 波动增大
• 蓝色背景 = 纽约凌晨 美盘准备阶段
• 红色背景 = 纽约关键5分钟 (09:3009:35) ⚠️ 最重要! 市场最活跃,趋势易形成
• 绿色背景 = 纽约上午后段 延续早盘趋势
交易建议: 重点关注红色关键时段,这5分钟往往决定全天方向!
________________________________________
⚙️ 三大市场推荐设置
🥇 黄金: Hull MA 12/EMA 34, 阈值0.250.35%
₿ 比特币: EMA 21/EMA 55, 阈值0.801.20%
💎 以太坊: TEMA 21/EMA 55, 阈值0.600.80%
参数优化建议
黄金 (XAUUSD)
快速MA: Hull MA 12 (超灵敏捕捉黄金快速波动)
慢速MA: EMA 34 (斐波那契数列)
RSI周期: 9 (加快反应)
强趋势阈值: 0.25%
周期: 5, 15, 60, 240, 1440
比特币 (BTCUSD)
快速MA: EMA 21
慢速MA: EMA 55
RSI周期: 14
强趋势阈值: 0.8% (波动大,阈值需提高)
周期: 15, 60, 240, D, W
外汇 EUR/USD
快速MA: TEMA 10 (快速响应)
慢速MA: T3 30, 因子0.7 (平滑噪音)
RSI周期: 14
强趋势阈值: 0.08% (外汇波动小)
周期: 5, 15, 60, 240, 1440
📖 Indicator Function Details (Concise Version):
🎯 Core Functions:
1. MultiTimeframe Trend Analysis Monitors 8 timeframes simultaneously (1m/5m/15m/1H/4H/D/W/M)
2. 4Dimensional Voting System Comprehensive judgment based on MA trend + RSI momentum + MACD + Bollinger Bands
3. Global Trading Sessions Visualizes Asia/London/New York trading hours
4. Trend Strength Score Quantifies market strength from 0100%
5. Smart Alerts Automatically pushes strong bullish/bearish signals
📚 Key Term Explanations:
🔵 Trend Status (MA Analysis):
| Term | Meaning | Signal Strength |
| | | |
| Strong Bull | Fast MA significantly > Slow MA (Diff ≥0.35%) | ⭐⭐⭐⭐⭐ Long |
| Bullish Bias | Fast MA slightly > Slow MA (Diff <0.35%) | ⭐⭐⭐ Caution Long |
| Ranging | MAs intertwined, no clear direction | ⚠️ Wait & See |
| Bearish Bias | Fast MA slightly < Slow MA | ⭐⭐⭐ Caution Short |
| Strong Bear | Fast MA significantly < Slow MA | ⭐⭐⭐⭐⭐ Short |
Simple Understanding: Fast MA = sprinter (fast reaction), Slow MA = longdistance runner (stable). Sprinter far ahead = Strong Bull, opposite = Strong Bear.
🟠 Momentum Status (RSI Analysis):
| Term | Meaning | Trading Suggestion |
| | | |
| Momentum Up ↗ | RSI >60 & rising rapidly | Strong Buy Signal |
| Momentum High | RSI >60 but rising slower | Watch for pullback, consider reducing position |
| Momentum Neutral | RSI between 4060, stable | Wait for clearer direction |
| Momentum Low | RSI <40 but falling slower | Watch for rebound, consider taking profit |
| Momentum Down ↘ | RSI <40 & falling rapidly | Strong Sell Signal |
Simple Understanding: RSI = car speedometer. "Momentum Up" = full throttle acceleration, "Momentum High" = already fast but not accelerating further.
🟣 Auxiliary Signals:
MACD:
MACD Bullish = Histogram >0 = Strong buyer power
MACD Bearish = Histogram <0 = Strong seller power
Bollinger Bands (BB):
BB Overbought = Price near upper band = Possible pullback
BB Oversold = Price near lower band = Possible rebound
BB Middle = Price near middle band = Balanced state
💡 Quick Start 3 Steps to Understand the Panel:
Step 1: Check "Composite Conclusion Label" (Above the chart)
Green "Bulls Favored" → Consider Long
Red "Bears Favored" → Consider Short
Orange "Ranging/Balanced" → Wait & See
Step 2: Check "Votes Bull/Bear" (Bottom of the panel)
Bull votes significantly > Bear votes (Difference >2) → Strong Trend
Votes close (Difference <1) → Ranging Market
Step 3: Check "Trend Strength" (In the composite label)
Strength >70% → Strong Trend, consider heavier position
Strength 5070% → Moderate Trend, normal position size
Strength <50% → Weak Trend, light position or wait & see
🎨 Trading Session Background Color Meanings:
Purple = Asian Session (Tokyo hours) Lower volatility
Orange = London Session (European hours) Increased volatility
Blue = NY Early Morning US session preparation phase
Red = NY Critical 5 Minutes (09:3009:35) ⚠️ Most Important! Market most active, trends easily form
Green = NY Late Morning Continuation of early session trend
Trading Tip: Focus on the red critical period; these 5 minutes often determine the day's direction!
⚙️ Recommended Settings for Three Major Markets
🥇 Gold (XAUUSD):
Fast MA: Hull MA 12 (Highly sensitive for gold's fast moves)
Slow MA: EMA 34 (Fibonacci number)
RSI Period: 9 (Faster reaction)
Strong Trend Threshold: 0.25%
Timeframes: 5, 15, 60, 240, 1440
₿ Bitcoin (BTCUSD):
Fast MA: EMA 21
Slow MA: EMA 55
RSI Period: 14
Strong Trend Threshold: 0.8% (High volatility, requires higher threshold)
Timeframes: 15, 60, 240, D, W
💎 Ethereum (ETHUSD):
Fast MA: TEMA 21
Slow MA: EMA 55
RSI Period: 14
Strong Trend Threshold: 0.600.80%
Timeframes: 15, 60, 240, D, W
💱 Forex EUR/USD:
Fast MA: TEMA 10 (Fast response)
Slow MA: T3 30, Factor 0.7 (Smooths noise)
RSI Period: 14
Strong Trend Threshold: 0.08% (Forex has low volatility)
Timeframes: 5, 15, 60, 240, 1440
HTF Cross Breakout [CHE] HTF Cross Breakout — Detects higher timeframe close crossovers for breakout signals, anchors VWAP for trend validation, and flags continuations or traps with visual extensions for delta percent and stop levels.
Summary
This indicator spots moments when the current chart's close price crosses a higher timeframe close, marking potential breakouts only when the current bar shows directional strength. It anchors a volume-weighted average price line from the breakout point to track trend health, updating labels to show if the move continues or reverses into a trap. Extensions add a dotted line linking the breakout level to the current close with percent change display, plus a stop-loss marker at the VWAP end. Signals gain robustness from higher timeframe confirmation and anti-repainting options, reducing noise in live bars compared to simple crossover tools.
Motivation: Why this design?
Traders often face false breakouts from intrabar wiggles on lower timeframes, especially without higher timeframe alignment, leading to whipsaws in volatile sessions. This design uses higher timeframe close as a stable reference for crossover detection, combined with anchored volume weighting to gauge sustained momentum. It addresses these by enforcing bar confirmation and directional filters, providing clearer entry validation and risk points without overcomplicating the chart.
What’s different vs. standard approaches?
Reference baseline
Standard crossover indicators like moving average crosses operate solely on the chart timeframe, ignoring higher timeframe context and lacking volume anchoring.
Architecture differences
- Higher timeframe data pulls via security calls with optional repainting control for stability.
- Anchored VWAP resets at each signal, accumulating from the breakout bar only.
- Label dynamics update in real-time for continuation checks, with extensions for visual delta and stop computation.
- Event-driven line finalization prunes old elements after a set bar extension.
Practical effect
Charts show persistent lines and labels that extend live but finalize cleanly on new events, avoiding clutter. This matters for spotting trap reversals early via label color shifts, and extensions provide quick risk visuals without manual calculations, improving decision speed in trend trades.
How it works (technical)
The indicator first determines a higher timeframe based on user selection, pulling its close price securely. It checks for crossovers or crossunders of the current close against this higher close, but only triggers on confirmed bars with matching directional opens and closes. On a valid event, a horizontal line and label mark the higher close level, while a dashed VWAP line starts accumulating typical price times volume from that bar onward. During the active phase, the breakout line extends to the current bar, the label repositions and updates text based on whether the current close holds above or below the level for bulls or bears. A background tint warns if the close deviates adversely from the current VWAP. Extensions draw a vertical dotted line at the last bar between the breakout level and close, placing a midpoint label with percent difference; separately, a label at the VWAP end shows a computed stop price. Persistent variables track the active state and accumulators, resetting on new events after briefly extending old elements. Repaint risk from security calls is mitigated by confirmed bar gating or user opt-in.
Parameter Guide
Plateau Length (reserved for future, currently unused): Sets a length for potential plateau detection in extensions; default 3, minimum 1. Higher values would increase stability but are not active yet—leave at default to avoid tuning.
Line Width: Controls thickness of breakout, VWAP, and extension lines; default 2, range 1 to 5. Thicker lines improve visibility on busy charts but may obscure price action—use 1 for clean views, 3 or more for emphasis.
+Bars after next HTF event (finalize old, then delete): Extends old lines and labels by this many bars before deletion on new signals; default 20, minimum 0. Shorter extensions keep charts tidy but risk cutting visuals prematurely; longer aids review but builds clutter over time.
Evaluate label only on HTF close (prevents gray traps intrabar): When true, label updates wait for higher timeframe confirmation; default true. Enabling reduces intrabar flips for stabler signals, though it may delay feedback—disable for faster live trading at repaint cost.
Allow Repainting: Permits real-time security data without confirmation offset; default false. False ensures historical accuracy but lags live bars; true speeds updates but can repaint on HTF closes.
Timeframe Type: Chooses HTF method—Auto Timeframe (dynamic steps up), Multiplier (chart multiple), or Manual (fixed string); default Auto Timeframe. Auto adapts to chart scale for convenience; Multiplier suits custom scaling like 5 times current; Manual for precise like 1D on any chart.
Multiplier for Alternate Resolution: Scales chart timeframe when Multiplier type selected; default 5, minimum 1. Values near 1 mimic current resolution for subtle shifts; higher like 10 jumps to broader context, increasing signal rarity.
Manual Resolution: Direct timeframe string like 60 for 1H when Manual type; default 60. Match to trading horizon—shorter for swing, longer for positional—to balance frequency and reliability.
Show Extension 1: Toggles dotted line and delta percent label between breakout level and current close; default true. Disable to simplify for basic use, enable for precise momentum tracking.
Dotted Line Width: Thickness for Extension 1 line; default 2, range 1 to 5. Align with main Line Width for consistency.
Text Size: Size for delta percent label; options tiny, small, normal, large; default normal. Smaller reduces overlap on dense charts; larger aids glance reads.
Decimals for Δ%: Precision in percent change display; default 2, range 0 to 6. Fewer decimals speed reading; more suit low-volatility assets.
Positive Δ Color: Hue for upward percent changes; default lime. Choose contrasting for visibility.
Negative Δ Color: Hue for downward percent changes; default red. Pair with positive for quick polarity scan.
Dotted Line Color: Color for Extension 1 line; default gray. Neutral tones blend well; brighter for emphasis.
Background Transparency (0..100): Opacity for delta label background; default 90. Higher values fade for subtlety; lower solidifies for readability.
Show Extension 2: Toggles stop-loss label at VWAP end; default true. Turn off for entry focus only.
Stop Method: Percent from VWAP end or fixed ticks; options Percent, Ticks; default Percent. Percent scales with price levels; Ticks suits tick-based instruments.
Stop %: Distance as fraction of VWAP for Percent method; default 1.0, step 0.05, minimum 0.0. Tighter like 0.5 reduces risk but increases stops; wider like 2.0 allows breathing room.
Stop Ticks: Tick count offset for Ticks method; default 20, minimum 0. Adjust per asset volatility—fewer for tight control.
Price Decimals: Rounding for stop price text; default 4, range 0 to 10. Match syminfo.precision for clean display.
Text Size: Size for stop label; options tiny, small, normal, large; default normal. Scale to chart zoom.
Text Color: Foreground for stop text; default white. Ensure contrast with background.
Inherit VWAP Color (BG tint): Bases stop label background on VWAP hue; default true. True maintains theme; false allows custom black base.
BG Transparency (0..100): Opacity for stop label background; default 0. Zero for no tint; up to 100 for full fade.
Reading & Interpretation
Breakout lines appear green for bullish crosses or red for bearish, extending live until a new event finalizes them briefly then deletes. Labels start blank, updating to Bull Cont. or Bear Cont. in matching colors if holding the level, or gray Bull Trap/Bear Trap on reversal. VWAP dashes yellow for bulls, orange for bears, sloping with accumulated volume weight—deviations trigger faint red background warnings. Extension 1's dotted vertical shows at the last bar, with midpoint label green/red for positive/negative percent from breakout to close. Extension 2 places a left-aligned label at VWAP end with stop price and method note, tinted to VWAP for context.
Practical Workflows & Combinations
For trend following, enter long on green Bull Cont. labels above VWAP with higher highs confirmation, filtering via rising structure; short on red Bear Cont. below. Pair with volume surges or RSI above 50 for bulls to avoid traps. For exits, trail stops using the Extension 2 level, tightening on warnings or gray labels—aggressive on continuations, conservative post-trap. In multi-timeframe setups, use default Auto on 15m charts for 1H signals, scaling multiplier to 4 for daily context on hourly; test on forex/stocks where volume is reliable, avoiding low-liquidity assets.
Behavior, Constraints & Performance
Signals confirm on bar close with HTF gating when strict mode active, but live bars may update if repainting enabled—opt false for backtest fidelity, true for intraday speed. Security calls risk minor repaints on HTF closes, mitigated by confirmation offsets. Resources cap at 1000 bars back, 50 lines/labels total, with event prunes to stay under budgets—no loops, minimal arrays. Limits include VWAP lag in low-volume periods and dependency on accurate HTF data; gaps or holidays may skew anchors.
Sensible Defaults & Quick Tuning
Defaults suit 5m-1H charts on liquid assets: Auto HTF, no repaint, 1% stops. For choppy markets with excess signals, enable strict eval and bump multiplier to 10 for rarer triggers. If sluggish in trends, shorten extend bars to 10 and allow repainting for quicker visuals. On high-vol like crypto, widen stop % to 2.0 and use Ticks method; for stables like indices, tighten to 0.5% and keep Percent.
What this indicator is—and isn’t
This is a signal visualization layer for breakout confirmation and basic risk marking, best as a filter in discretionary setups. It isn’t a standalone system or predictive oracle—combine with price structure, news awareness, and sizing rules for real edges.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
ORDER BLCOK custom strategy# OB Matrix Strategy - Documentation
**Version:** 1.0
**Author:** HPotter
**Date:** 31/07/2017
The **OB Matrix Strategy** is based on the identification of **bullish and bearish Order Blocks** and the management of conditional orders with multiple Take Profit (TP) and Stop Loss (SL) levels. It uses trend filters, ATR, and percentage-based risk management.
---
## 1. Main Parameters
### Strategy
- `initial_capital`: 50
- `default_qty_type`: percentage of capital
- `default_qty_value`: 10
### Money Management
- `rr_threshold`: minimum Risk/Reward threshold to open a trade
- `risk_percent`: percentage of capital to risk per trade (default 2%)
- `maxPendingBars`: maximum number of bars for a pending order
- `maxBarsOpen`: maximum number of bars for an open position
- `qty_tp1`, `qty_tp2`, `qty_tp3`: quantity percentages for multiple TPs
---
## 2. Order Block Identification
### Order Block Parameters
- `obLookback`: number of bars to identify an Order Block
- `obmode`: method to calculate the block (`Full` or `Breadth`)
- `obmiti`: method to determine block mitigation (`Close`, `Wick`, `Avg`)
- `obMaxBlocks`: maximum number of Order Blocks displayed
### Main Variables
- `bullBlocks`: array of bullish blocks
- `bearBlocks`: array of bearish blocks
- `last_bull_volume`, `last_bear_volume`: volume of the last block
- `dom_block`: dominant block type (Bullish/Bearish/None)
- `block_strength`: block strength (normalized volume)
- `price_distance`: distance between current price and nearest block
---
## 3. Visual Parameters
- `Width`: line thickness for swing high/low
- `amountOfBoxes`: block grid segments
- `showBorder`: show block borders
- `borderWidth`: width of block borders
- `showVolume`: display volume inside blocks
- `volumePosition`: vertical position of volume text
Customizable colors:
- `obHighVolumeColor`, `obLowVolumeColor`, `obBearHighVolumeColor`, `obBearLowVolumeColor`
- `obBullBorderColor`, `obBearBorderColor`
- `obBullFillColor`, `obBearFillColor`
- `volumeTextColor`
---
## 4. Screener Table
- `showScreener`: display the screener table
- `tablePosition`: table position (`Top Left`, `Top Right`, `Bottom Left`, `Bottom Right`)
- `tableSize`: table size (`Small`, `Normal`, `Large`)
The table shows:
- Symbol, Timeframe
- Type and status of Order Block
- Number of retests
- Bullish and bearish volumes
---
## 5. Trend Filters
- EMA as a trend filter (`emaPeriod`, default 223)
- `bullishTrend` if close > EMA
- `bearishTrend` if close < EMA
---
## 6. ATR and Swing Points
- ATR calculated with a customizable period (`atrLength`)
- Swing High/Low for SL/TP calculation
- `f_getSwingTargets` function to calculate SL and TP based on direction
---
## 7. Trade Logic
### Buy Limit on Bullish OB
- Conditions:
- New bullish block
- Uptrend
- RR > threshold (`rr_threshold`)
- SL: `bullishOBPrice * (1 - atr * atrMultiplier)`
- Multiple TPs: TP1 (50%), TP2 (80%), TP3 (100% max)
- Quantity calculation based on percentage risk
### Sell Limit on Bearish OB
- Conditions:
- New bearish block
- Downtrend
- RR > threshold (`rr_threshold`)
- SL: `bearishOBPrice * (1 + atr * atrMultiplier)`
- Multiple TPs: TP1 (50%), TP2 (80%), TP3 (100% max)
- Quantity calculation based on percentage risk
---
## 8. Order Management and Timeout
- Close pending orders after `maxPendingBars` bars
- Close open positions after `maxBarsOpen` bars
- Label management for open orders
---
## 9. Alert Conditions
- `bull_touch`: price inside maximum bullish volume zone
- `bear_touch`: price inside maximum bearish volume zone
- `bull_reject`: confirmation of bullish zone rejection
- `bear_reject`: confirmation of bearish zone rejection
- `new_bull`: new bullish block
- `new_bear`: new bearish block
---
## 10. Level Calculation
- Swing levels based on selected timeframe (`SelectPeriod`)
- `xHigh` and `xLow` for S1 and R1 calculation
- Levels plotted on chart
---
## 11. Take Profit / Stop Loss
- Extended horizontal lines (`extendBars`) to visualize TP and SL
- Customizable colors (`tpColor`, `slColor`)
---
## 12. Notes
- Complete script based on Pine Script v5
- Advanced graphical management with boxes, lines, labels
- Dynamically displays volumes and Order Blocks
- Integrated internal screener
---
### End of Documentation
Reversal Probability Meter PRO [optimized for Xau/Usd m5]🎯 Reversal Probability Meter PRO
A powerful multi-factor reversal probability detector that calculates the likelihood of bullish or bearish reversals using RSI, EMA bias, ATR spikes, candle patterns, volume spikes, and higher timeframe (HTF) trend alignment.
🧩 MAIN FEATURES
1. Reversal Probability (Bullish & Bearish)
Displays two key metrics:
Bull % — probability of bullish reversal
Bear % — probability of bearish reversal
These are computed using RSI, EMAs, ATR, demand/supply zones, candle confirmations, and volume spikes.
📊 Interpretation:
Bull % > 70% → Buying pressure building up
Bull % > 85% → Strong bullish reversal confirmed
Bear % > 70% → Selling pressure building up
Bear % > 85% → Strong bearish reversal confirmed
2. Alert Probability Threshold
Adjustable via alertThreshold (default = 85%).
Alerts trigger only when probability ≥ threshold, and confirmed by zone + volume spike + candle pattern.
🔔 Alerts Available:
✅ Bullish Smart Reversal
🔻 Bearish Smart Reversal
To activate: Right-click chart → “Add alert” → choose the alert condition from the indicator.
3. Demand / Supply Zone Detection
The script determines the price position within the last zoneLook (default 30) bars:
🟢 DEMAND → Lower 35% of range (potential bounce zone)
🔴 SUPPLY → Upper 35% of range (potential rejection zone)
⚪ MID → Neutral area
📘 Purpose: Validates reversals based on context:
Bullish only valid in Demand zones
Bearish only valid in Supply zones
4. Higher Timeframe (HTF) Trend Alignment
Reads EMA bias from a higher timeframe (default = 15m) for trend confirmation.
Reversals against HTF trend are automatically weighted down prevents false countertrend signals.
📈 Example:
M5 chart under M15 downtrend → Bullish probability is reduced.
5. Candle Confirmation Patterns
Two key price action confirmations:
Bullish: Engulfing or Pin Bar
Bearish: Engulfing or Pin Bar
A valid reversal requires both a candle confirmation and a volume spike.
6. Volume & ATR Spike Filters
Volume Spike: volume > SMA(20) × 1.3
ATR Spike: ATR > SMA(ATR, 50) × volMult
🎯 Ensures that only strong market moves with real energy are considered valid reversals.
7. Reversal Momentum Histogram
A color-gradient oscillator showing the momentum difference:
Green = bullish dominance
Red = bearish dominance
Flat near 0 = neutral
Controlled by showOscillator toggle.
8. Smart Info Panel
A compact dashboard displayed on the top-right with 4 rows:
Row Info Description
1 Bull % Bullish reversal probability
2 Bear % Bearish reversal probability
3 Zone Market context (DEMAND / SUPPLY / MID)
4 Signal Strength Current signal intensity (probability %)
Dynamic Colors:
90% → Bright (strong signal)
75–90% → Yellow/Orange (medium)
<75% → Gray (weak)
9. Sensitivity Mode
Fine-tunes indicator reactivity:
🟥 Aggressive: Detects reversals early (more signals, less accurate)
🟨 Normal: Balanced, default mode
🟩 Conservative: Filters only strongest reversals (fewer but more reliable)
10. Custom Color Options
Customize bullish and bearish colors via bullBaseColor and bearBaseColor inputs for your preferred chart theme.
⚙️ HOW TO USE
Add to Chart
→ Paste the script into Pine Editor → “Add to chart”.
Select Timeframe
→ Best for M5–M30 (scalping/intraday).
→ H1–H4 for swing trading.
Monitor the Info Panel:
Bull % ≥ 85% + Zone = Demand → Strong bullish reversal signal
Bear % ≥ 85% + Zone = Supply → Strong bearish reversal signal
Watch the Histogram:
Rising green bars = bullish momentum gaining
Deep red bars = bearish momentum gaining
Enable Alerts:
Right-click chart → “Add alert”
Choose Bullish Smart Reversal or Bearish Smart Reversal
🧠 TRADING TIPS
Use Conservative mode for noisy lower timeframes (M5–M15).
Use Aggressive mode for higher timeframes (H1–H4).
Combine with manual support/resistance or zone boxes for precision entries. Personally i use Order Block.
Best reversal setups occur when all align:
Bull % > 85%
Zone = DEMAND
Volume spike present
Candle = Bullish engulfing
HTF trend supportive
FVG Scanner ProFVG Scanner Pro — Smart Fair Value Gap Detector (with HTF context & proximity alerts)
What it does
FVG Scanner Pro automatically finds Fair Value Gaps (FVGs) on your current chart and (optionally) on a higher timeframe (HTF), draws them as color-coded zones, and notifies you when price comes close to a gap boundary using an ADR-based proximity trigger and (optional) volume confirmation. It’s designed for ICT-style gap trading, confluence building, and clean visual execution.
How it works:
FVG definition
* Bullish FVG (gap up): low > high (the current candle’s low is above the high 2 bars ago).
* Bearish FVG (gap down): high < low (the current candle’s high is below the low 2 bars ago).
* Gaps smaller than your Min FVG Size (%) are ignored. (Gap size = (top-bottom)/bottom * 100.)
Higher-timeframe logic (auto-selected)
The script auto picks a sensible HTF:
1–5m → 15m, 15m → 1H, 1H → 4H, 4H → 1D, 1D → 1W, 1W → 1M, small 1M → 3M, big ≥3M → 12M.
You can display HTF FVGs and even filter so current-TF FVGs only show when they overlap an HTF gap.
Proximity alerts (ADR-based)
The script computes ADR on the current chart timeframe over a user-set lookback (default 20 bars).
An alert fires when price moves toward the closest actionable boundary and comes within ADR × Multiplier:
Bullish: price moving down, within distance of the bottom of a bullish FVG.
Bearish: price moving up, within distance of the top of a bearish FVG.
Yellow ▲/▼ markers show where a proximity alert triggered.
Volume filter (optional)
Require volume to be greater than SMA(20) × multiplier to accept a newly formed FVG.
Lifecycle
Each gap remains active for Extend FVG Box (Bars) bars.
You can delete the box after fill, or keep filled gaps visible as gray zones, or hide them.
Color legend
Current-TF Bullish: Pink/Magenta box
Current-TF Bearish: Cyan/Turquoise box
HTF Bullish: Gold box
HTF Bearish: Orange box
Filled (if shown): Gray box
Alert markers: Yellow ▲ (bullish), Yellow ▼ (bearish)
Inputs (what to tweak)
Show FVGs: Bullish / Bearish / Both
Max Bars Back to Find FVG: collection window & cleanup guard
Extend FVG Box (Bars): how long a zone stays tradable/active
Min FVG Size (%): ignore micro gaps
Delete Box After Fill & Show Filled FVGs: choose how you want completed gaps handled
Show Alert Markers: show/hide the yellow proximity arrows
Show Higher Timeframe FVG: overlay HTF gaps (auto TF)
HTF Filter: only display current-TF gaps that overlap an HTF gap
ADR Lookback & Proximity Multiplier: tune alert sensitivity to your market & timeframe
Volume Filter & Volume > MA Multiple: require above-average volume for new gaps
Built-in alerts (ready to use)
Create alerts in TradingView (⚠️ “Once per bar” or “Once per bar close”, your choice) and select from:
🟢 Bullish FVG Proximity — price approaching a bullish gap bottom
🔴 Bearish FVG Proximity — price approaching a bearish gap top
✅ New Bullish FVG Formed
⚠️ New Bearish FVG Formed
The alert messages include the symbol and price; proximity markers are also plotted on chart.
Tips & best practices
Use FVGs with market structure (break of structure, swing points), order blocks, or liquidity pools for confluence.
On very low timeframes, raise Min FVG Size and/or lower Max Bars Back to reduce noise and keep things fast.
Extend FVG Box controls how long a zone is considered valid; align it with your holding horizon (scalp vs swing).
Information panel (top-right)
Shows your mode, current HTF, number of gaps in memory, active bull/bear counts, and current-TF ADR.
RSI Divergence + Hidden RSI Divergence + Hidden (TV-like pairing, final)
What it does
This indicator plots RSI and automatically detects both regular and hidden divergences by pairing RSI pivots with price pivots. It supports a TradingView-like loose pairing (within a user-defined bar tolerance) and a strict same-bar pairing. Detected signals are drawn with lines and optional labels on the RSI pane for quick visual verification.
Divergence logic
Regular Bullish (label: Bull)
Price makes a lower low while RSI makes a higher low → potential upward reversal.
Regular Bearish (label: Bear)
Price makes a higher high while RSI makes a lower high → potential downward reversal.
Hidden Bullish (label: H_Bull)
Price makes a higher low while RSI makes a lower low → trend-continuation bias upward.
Hidden Bearish (label: H_Bear)
Price makes a lower high while RSI makes a higher high → trend-continuation bias downward.
All conditions use pivot-to-pivot comparisons with optional equality tolerance for price and RSI to reduce false “equal” mismatches.
Pairing modes
TV-like
Pairs the latest price and RSI pivots if their pivot bars occur within ±tolBars.
A lightweight “pending” buffer allows pairing a newly detected pivot with a recent opposite pivot that arrived a few bars earlier/later (within tolerance).
Same Bar
Price and RSI pivots must occur on the exact same bar to form a pair.
Key inputs
RSI Source & Length: srcRsi, rsiLen (default 14). RSI line and reference levels (70/50/30) can be shown/hidden.
Pivot Window: leftBars, rightBars for both price and RSI pivots.
Pairing: pairMode = TV-like or Same Bar; tolBars for bar tolerance (TV-like only).
Price Pivot Basis: priceMode = High/Low (default) or Close.
Equality Tolerance:
allowEqual (use >=/<=),
priceEpsTks (ticks) for price equality slack,
rsiEps (points) for RSI equality slack.
Visibility: showRSI, showRegular, showHidden, showLabels.
Visuals
Lines (on RSI):
Regular Bearish: red
Regular Bullish: lime
Hidden Bearish: orange
Hidden Bullish: teal
Labels (optional): "Bear", "Bull", "H_Bear", "H_Bull" placed on the RSI series at the second pivot.
Alerts
Four alert conditions are provided and fire when the corresponding divergence is confirmed:
Bear (Regular)
Bull (Regular)
H_Bear (Hidden)
H_Bull (Hidden)
Notes & tips
Divergences are evaluated only when both price and RSI pivots exist and can be paired under the selected mode.
Pivot sensitivity: smaller leftBars/rightBars → earlier but noisier signals; larger values → fewer, more stable pivots.
Tolerance: If you miss valid setups because pivots land a few bars apart, use TV-like with a small tolBars (e.g., 1–2). If you prefer stricter confirmation, use Same Bar.
Equality slack: Use priceEpsTks and rsiEps to avoid rejecting near-equal highs/lows due to tiny differences.
Works on any symbol/timeframe; as with all divergence tools, treat signals as context—combine with trend, structure, and risk management.
Dashboard Trends📊 Dashboard Trends + Anchored Daily VWAP
This indicator provides a real-time multi-timeframe trend dashboard alongside a daily anchored VWAP system, helping you assess both macro and intraday market sentiment at a glance.
🔍 Key Features
✅ Multi-Timeframe Trend Analysis
Tracks whether EMA(22) is above EMA(200) across:
1m, 10m, 30m, 4h, 1D timeframes
Color-coded "Bullish"/"Bearish" status for each
Aggregated trend summary using 6 signals (including VWAP)
✅ Anchored Daily VWAP
Uses ta.vwap to provide a session-resetting daily VWAP
VWAP resets at the beginning of each trading day
Live update throughout the day
Supports pre-market and after-hours if chart includes ETH
✅ VWAP Bands (±1σ to ±3σ)
Optional bands show price deviation from VWAP
Fully customizable:
Enable/disable each band
Set deviation multiplier
Adjust color and visibility
✅ Visual Dashboard
Table display in the bottom-right corner
Shows trend status per timeframe + VWAP + Summary
Easy-to-read green/yellow/red color codes
⚙️ Customization
Toggle VWAP line on/off
Enable or disable any band level
Adjust standard deviation multiplier
Choose your VWAP and band colors
🧠 Summary Logic
Strong Bull: 6 bullish signals
Bull: 5 signals
Mixed: 3–4 signals
Bear: 2 signals
Strong Bear: 0–1 signals
This tool is perfect for traders looking to combine trend-following and intraday mean-reversion awareness, with all the critical data visualized in one compact dashboard.
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.
________________________________________
## 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.
________________________________________
## 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.
________________________________________
## 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.
________________________________________
## 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.
________________________________________
## 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).
________________________________________
## 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.
________________________________________
## 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.
________________________________________
## 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.
________________________________________
## 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
Dskyz (DAFE) Adaptive Regime - Quant Machine ProDskyz (DAFE) Adaptive Regime - Quant Machine Pro:
Buckle up for the Dskyz (DAFE) Adaptive Regime - Quant Machine Pro, is a strategy that’s your ultimate edge for conquering futures markets like ES, MES, NQ, and MNQ. This isn’t just another script—it’s a quant-grade powerhouse, crafted with precision to adapt to market regimes, deliver multi-factor signals, and protect your capital with futures-tuned risk management. With its shimmering DAFE visuals, dual dashboards, and glowing watermark, it turns your charts into a cyberpunk command center, making trading as thrilling as it is profitable.
Unlike generic scripts clogging up the space, the Adaptive Regime is a DAFE original, built from the ground up to tackle the chaos of futures trading. It identifies market regimes (Trending, Range, Volatile, Quiet) using ADX, Bollinger Bands, and HTF indicators, then fires trades based on a weighted scoring system that blends candlestick patterns, RSI, MACD, and more. Add in dynamic stops, trailing exits, and a 5% drawdown circuit breaker, and you’ve got a system that’s as safe as it is aggressive. Whether you’re a newbie or a prop desk pro, this strat’s your ticket to outsmarting the markets. Let’s break down every detail and see why it’s a must-have.
Why Traders Need This Strategy
Futures markets are a gauntlet—fast moves, volatility spikes (like the April 28, 2025 NQ 1k-point drop), and institutional traps that punish the unprepared. Meanwhile, platforms are flooded with low-effort scripts that recycle old ideas with zero innovation. The Adaptive Regime stands tall, offering:
Adaptive Intelligence: Detects market regimes (Trending, Range, Volatile, Quiet) to optimize signals, unlike one-size-fits-all scripts.
Multi-Factor Precision: Combines candlestick patterns, MA trends, RSI, MACD, volume, and HTF confirmation for high-probability trades.
Futures-Optimized Risk: Calculates position sizes based on $ risk (default: $300), with ATR or fixed stops/TPs tailored for ES/MES.
Bulletproof Safety: 5% daily drawdown circuit breaker and trailing stops keep your account intact, even in chaos.
DAFE Visual Mastery: Pulsing Bollinger Band fills, dynamic SL/TP lines, and dual dashboards (metrics + position) make signals crystal-clear and charts a work of art.
Original Craftsmanship: A DAFE creation, built with community passion, not a rehashed clone of generic code.
Traders need this because it’s a complete, adaptive system that blends quant smarts, user-friendly design, and DAFE flair. It’s your edge to trade with confidence, cut through market noise, and leave the copycats in the dust.
Strategy Components
1. Market Regime Detection
The strategy’s brain is its ability to classify market conditions into five regimes, ensuring signals match the environment.
How It Works:
Trending (Regime 1): ADX > 20, fast/slow EMA spread > 0.3x ATR, HTF RSI > 50 or MACD bullish (htf_trend_bull/bear).
Range (Regime 2): ADX < 25, price range < 3% of close, no HTF trend.
Volatile (Regime 3): BB width > 1.5x avg, ATR > 1.2x avg, HTF RSI overbought/oversold.
Quiet (Regime 4): BB width < 0.8x avg, ATR < 0.9x avg.
Other (Regime 5): Default for unclear conditions.
Indicators: ADX (14), BB width (20), ATR (14, 50-bar SMA), HTF RSI (14, daily default), HTF MACD (12,26,9).
Why It’s Brilliant:
Regime detection adapts signals to market context, boosting win rates in trending or volatile conditions.
HTF RSI/MACD add a big-picture filter, rare in basic scripts.
Visualized via gradient background (green for Trending, orange for Range, red for Volatile, gray for Quiet, navy for Other).
2. Multi-Factor Signal Scoring
Entries are driven by a weighted scoring system that combines candlestick patterns, trend, momentum, and volume for robust signals.
Candlestick Patterns:
Bullish: Engulfing (0.5), hammer (0.4 in Range, 0.2 else), morning star (0.2), piercing (0.2), double bottom (0.3 in Volatile, 0.15 else). Must be near support (low ≤ 1.01x 20-bar low) with volume spike (>1.5x 20-bar avg).
Bearish: Engulfing (0.5), shooting star (0.4 in Range, 0.2 else), evening star (0.2), dark cloud (0.2), double top (0.3 in Volatile, 0.15 else). Must be near resistance (high ≥ 0.99x 20-bar high) with volume spike.
Logic: Patterns are weighted higher in specific regimes (e.g., hammer in Range, double bottom in Volatile).
Additional Factors:
Trend: Fast EMA (20) > slow EMA (50) + 0.5x ATR (trend_bull, +0.2); opposite for trend_bear.
RSI: RSI (14) < 30 (rsi_bull, +0.15); > 70 (rsi_bear, +0.15).
MACD: MACD line > signal (12,26,9, macd_bull, +0.15); opposite for macd_bear.
Volume: ATR > 1.2x 50-bar avg (vol_expansion, +0.1).
HTF Confirmation: HTF RSI < 70 and MACD bullish (htf_bull_confirm, +0.2); RSI > 30 and MACD bearish (htf_bear_confirm, +0.2).
Scoring:
bull_score = sum of bullish factors; bear_score = sum of bearish. Entry requires score ≥ 1.0.
Example: Bullish engulfing (0.5) + trend_bull (0.2) + rsi_bull (0.15) + htf_bull_confirm (0.2) = 1.05, triggers long.
Why It’s Brilliant:
Multi-factor scoring ensures signals are confirmed by multiple market dynamics, reducing false positives.
Regime-specific weights make patterns more relevant (e.g., hammers shine in Range markets).
HTF confirmation aligns with the big picture, a quant edge over simplistic scripts.
3. Futures-Tuned Risk Management
The risk system is built for futures, calculating position sizes based on $ risk and offering flexible stops/TPs.
Position Sizing:
Logic: Risk per trade (default: $300) ÷ (stop distance in points * point value) = contracts, capped at max_contracts (default: 5). Point value = tick value (e.g., $12.5 for ES) * ticks per point (4) * contract multiplier (1 for ES, 0.1 for MES).
Example: $300 risk, 8-point stop, ES ($50/point) → 0.75 contracts, rounded to 1.
Impact: Precise sizing prevents over-leverage, critical for micro contracts like MES.
Stops and Take-Profits:
Fixed: Default stop = 8 points, TP = 16 points (2:1 reward/risk).
ATR-Based: Stop = 1.5x ATR (default), TP = 3x ATR, enabled via use_atr_for_stops.
Logic: Stops set at swing low/high ± stop distance; TPs at 2x stop distance from entry.
Impact: ATR stops adapt to volatility, while fixed stops suit stable markets.
Trailing Stops:
Logic: Activates at 50% of TP distance. Trails at close ± 1.5x ATR (atr_multiplier). Longs: max(trail_stop_long, close - ATR * 1.5); shorts: min(trail_stop_short, close + ATR * 1.5).
Impact: Locks in profits during trends, a game-changer in volatile sessions.
Circuit Breaker:
Logic: Pauses trading if daily drawdown > 5% (daily_drawdown = (max_equity - equity) / max_equity).
Impact: Protects capital during black swan events (e.g., April 27, 2025 ES slippage).
Why It’s Brilliant:
Futures-specific inputs (tick value, multiplier) make it plug-and-play for ES/MES.
Trailing stops and circuit breaker add pro-level safety, rare in off-the-shelf scripts.
Flexible stops (ATR or fixed) suit different trading styles.
4. Trade Entry and Exit Logic
Entries and exits are precise, driven by bull_score/bear_score and protected by drawdown checks.
Entry Conditions:
Long: bull_score ≥ 1.0, no position (position_size <= 0), drawdown < 5% (not pause_trading). Calculates contracts, sets stop at swing low - stop points, TP at 2x stop distance.
Short: bear_score ≥ 1.0, position_size >= 0, drawdown < 5%. Stop at swing high + stop points, TP at 2x stop distance.
Logic: Tracks entry_regime for PNL arrays. Closes opposite positions before entering.
Exit Conditions:
Stop-Loss/Take-Profit: Hits stop or TP (strategy.exit).
Trailing Stop: Activates at 50% TP, trails by ATR * 1.5.
Emergency Exit: Closes if price breaches stop (close < long_stop_price or close > short_stop_price).
Reset: Clears stop/TP prices when flat (position_size = 0).
Why It’s Brilliant:
Score-based entries ensure multi-factor confirmation, filtering out weak signals.
Trailing stops maximize profits in trends, unlike static exits in basic scripts.
Emergency exits add an extra safety layer, critical for futures volatility.
5. DAFE Visuals
The visuals are pure DAFE magic, blending function with cyberpunk flair to make signals intuitive and charts stunning.
Shimmering Bollinger Band Fill:
Display: BB basis (20, white), upper/lower (green/red, 45% transparent). Fill pulses (30–50 alpha) by regime, with glow (60–95 alpha) near bands (close ≥ 0.995x upper or ≤ 1.005x lower).
Purpose: Highlights volatility and key levels with a futuristic glow.
Visuals make complex regimes and signals instantly clear, even for newbies.
Pulsing effects and regime-specific colors add a DAFE signature, setting it apart from generic scripts.
BB glow emphasizes tradeable levels, enhancing decision-making.
Chart Background (Regime Heatmap):
Green — Trending Market: Strong, sustained price movement in one direction. The market is in a trend phase—momentum follows through.
Orange — Range-Bound: Market is consolidating or moving sideways, with no clear up/down trend. Great for mean reversion setups.
Red — Volatile Regime: High volatility, heightened risk, and larger/faster price swings—trade with caution.
Gray — Quiet/Low Volatility: Market is calm and inactive, with small moves—often poor conditions for most strategies.
Navy — Other/Neutral: Regime is uncertain or mixed; signals may be less reliable.
Bollinger Bands Glow (Dynamic Fill):
Neon Red Glow — Warning!: Price is near or breaking above the upper band; momentum is overstretched, watch for overbought conditions or reversals.
Bright Green Glow — Opportunity!: Price is near or breaking below the lower band; market could be oversold, prime for bounce or reversal.
Trend Green Fill — Trending Regime: Fills between bands with green when the market is trending, showing clear momentum.
Gold/Yellow Fill — Range Regime: Fills with gold/aqua in range conditions, showing the market is sideways/oscillating.
Magenta/Red Fill — Volatility Spike: Fills with vivid magenta/red during highly volatile regimes.
Blue Fill — Neutral/Quiet: A soft blue glow for other or uncertain market states.
Moving Averages:
Display: Blue fast EMA (20), red slow EMA (50), 2px.
Purpose: Shows trend direction, with trend_dir requiring ATR-scaled spread.
Dynamic SL/TP Lines:
Display: Pulsing colors (red SL, green TP for Trending; yellow/orange for Range, etc.), 3px, with pulse_alpha for shimmer.
Purpose: Tracks stops/TPs in real-time, color-coded by regime.
6. Dual Dashboards
Two dashboards deliver real-time insights, making the strat a quant command center.
Bottom-Left Metrics Dashboard (2x13):
Metrics: Mode (Active/Paused), trend (Bullish/Bearish/Neutral), ATR, ATR avg, volume spike (YES/NO), RSI (value + Oversold/Overbought/Neutral), HTF RSI, HTF trend, last signal (Buy/Sell/None), regime, bull score.
Display: Black (29% transparent), purple title, color-coded (green for bullish, red for bearish).
Purpose: Consolidates market context and signal strength.
Top-Right Position Dashboard (2x7):
Metrics: Regime, position side (Long/Short/None), position PNL ($), SL, TP, daily PNL ($).
Display: Black (29% transparent), purple title, color-coded (lime for Long, red for Short).
Purpose: Tracks live trades and profitability.
Why It’s Brilliant:
Dual dashboards cover market context and trade status, a rare feature.
Color-coding and concise metrics guide beginners (e.g., green “Buy” = go).
Real-time PNL and SL/TP visibility empower disciplined trading.
7. Performance Tracking
Logic: Arrays (regime_pnl_long/short, regime_win/loss_long/short) track PNL and win/loss by regime (1–5). Updated on trade close (barstate.isconfirmed).
Purpose: Prepares for future adaptive thresholds (e.g., adjust bull_score min based on regime performance).
Why It’s Brilliant: Lays the groundwork for self-optimizing logic, a quant edge over static scripts.
Key Features
Regime-Adaptive: Optimizes signals for Trending, Range, Volatile, Quiet markets.
Futures-Optimized: Precise sizing for ES/MES with tick-based risk inputs.
Multi-Factor Signals: Candlestick patterns, RSI, MACD, and HTF confirmation for robust entries.
Dynamic Exits: ATR/fixed stops, 2:1 TPs, and trailing stops maximize profits.
Safe and Smart: 5% drawdown breaker and emergency exits protect capital.
DAFE Visuals: Shimmering BB fill, pulsing SL/TP, and dual dashboards.
Backtest-Ready: Fixed qty and tick calc for accurate historical testing.
How to Use
Add to Chart: Load on a 5min ES/MES chart in TradingView.
Configure Inputs: Set instrument (ES/MES), tick value ($12.5/$1.25), multiplier (1/0.1), risk ($300 default). Enable ATR stops for volatility.
Monitor Dashboards: Bottom-left for regime/signals, top-right for position/PNL.
Backtest: Run in strategy tester to compare regimes.
Live Trade: Connect to Tradovate or similar. Watch for slippage (e.g., April 27, 2025 ES issues).
Replay Test: Try April 28, 2025 NQ drop to see regime shifts and stops.
Disclaimer
Trading futures involves significant risk of loss and is not suitable for all investors. Past performance does not guarantee future results. Backtest results may differ from live trading due to slippage, fees, or market conditions. Use this strategy at your own risk, and consult a financial advisor before trading. Dskyz (DAFE) Trading Systems is not responsible for any losses incurred.
Backtesting:
Frame: 2023-09-20 - 2025-04-29
Slippage: 3
Fee Typical Range (per side, per contract)
CME Exchange $1.14 – $1.20
Clearing $0.10 – $0.30
NFA Regulatory $0.02
Firm/Broker Commis. $0.25 – $0.80 (retail prop)
TOTAL $1.60 – $2.30 per side
Round Turn: (enter+exit) = $3.20 – $4.60 per contract
Final Notes
The Dskyz (DAFE) Adaptive Regime - Quant Machine Pro is more than a strategy—it’s a revolution. Crafted with DAFE’s signature precision, it rises above generic scripts with adaptive regimes, quant-grade signals, and visuals that make trading a thrill. Whether you’re scalping MES or swinging ES, this system empowers you to navigate markets with confidence and style. Join the DAFE crew, light up your charts, and let’s dominate the futures game!
(This publishing will most likely be taken down do to some miscellaneous rule about properly displaying charting symbols, or whatever. Once I've identified what part of the publishing they want to pick on, I'll adjust and repost.)
Use it with discipline. Use it with clarity. Trade smarter.
**I will continue to release incredible strategies and indicators until I turn this into a brand or until someone offers me a contract.
Created by Dskyz, powered by DAFE Trading Systems. Trade smart, trade bold.
Combined EMA Technical AnalysisThis script is written in Pine Script (version 5) for TradingView and creates a comprehensive technical analysis indicator called "Combined EMA Technical Analysis." It overlays multiple technical indicators on a price chart, including Exponential Moving Averages (EMAs), VWAP, MACD, PSAR, RSI, Bollinger Bands, ADX, and external data from the S&P 500 (SPX) and VIX indices. The script also provides visual cues through colors, shapes, and a customizable table to help traders interpret market conditions.
Here’s a breakdown of the script:
---
### **1. Purpose**
- The script combines several popular technical indicators to analyze price trends, momentum, volatility, and market sentiment.
- It uses color coding (green for bullish, red for bearish, gray/white for neutral) and a table to display key information.
---
### **2. Custom Colors**
- Defines custom RGB colors for bullish (`customGreen`), bearish (`customRed`), and neutral (`neutralGray`) signals to enhance visual clarity.
---
### **3. User Inputs**
- **EMA Colors**: Users can customize the colors of five EMAs (8, 20, 9, 21, 50 periods).
- **MACD Settings**: Adjustable short length (12), long length (26), and signal length (9).
- **RSI Settings**: Adjustable length (14).
- **Bollinger Bands Settings**: Length (20), multiplier (2), and proximity threshold (0.1% of band width).
- **ADX Settings**: Adjustable length (14).
- **Table Settings**: Position (e.g., "Bottom Right") and text size (e.g., "Small").
---
### **4. Indicator Calculations**
#### **Exponential Moving Averages (EMAs)**
- Calculates five EMAs: 8, 20, 9, 21, and 50 periods based on the closing price.
- Used to identify short-term and long-term trends.
#### **Volume Weighted Average Price (VWAP)**
- Resets daily and calculates the average price weighted by volume.
- Color-coded: green if price > VWAP (bullish), red if price < VWAP (bearish), white if neutral.
#### **MACD (Moving Average Convergence Divergence)**
- Uses short (12) and long (26) EMAs to compute the MACD line, with a 9-period signal line.
- Displays "Bullish" (green) if MACD > signal, "Bearish" (red) if MACD < signal.
#### **Parabolic SAR (PSAR)**
- Calculated with acceleration factors (start: 0.02, increment: 0.02, max: 0.2).
- Indicates trend direction: green if price > PSAR (bullish), red if price < PSAR (bearish).
#### **Relative Strength Index (RSI)**
- Measures momentum over 14 periods.
- Highlighted in green if > 70 (overbought), red if < 30 (oversold), white otherwise.
#### **Bollinger Bands (BB)**
- Uses a 20-period SMA with a 2-standard-deviation multiplier.
- Color-coded based on price position:
- Green: Above upper band or close to it.
- Red: Below lower band or close to it.
- Gray: Neutral (within bands).
#### **Average Directional Index (ADX)**
- Manually calculates ADX to measure trend strength:
- Strong trend: ADX > 25.
- Very strong trend: ADX > 50.
- Direction: Bullish if +DI > -DI, bearish if -DI > +DI.
#### **EMA Crosses**
- Detects bullish (crossover) and bearish (crossunder) events for:
- EMA 9 vs. EMA 21.
- EMA 8 vs. EMA 20.
- Visualized with green (bullish) or red (bearish) circles.
#### **SPX and VIX Data**
- Fetches daily closing prices for the S&P 500 (SPX) and VIX (volatility index).
- SPX trend: Bullish if EMA 9 > EMA 21, bearish if EMA 9 < EMA 21.
- VIX levels: High (> 25, fear), Low (< 15, stability).
- VIX color: Green if SPX bullish and VIX low, red if SPX bearish and VIX high, white otherwise.
---
### **5. Visual Outputs**
#### **Plots**
- EMAs, VWAP, and PSAR are plotted on the chart with their respective colors.
- EMA crosses are marked with circles (green for bullish, red for bearish).
#### **Table**
- Displays a summary of indicators in a customizable position and size.
- Indicators shown (if enabled):
- EMA 8/20, 9/21, 50: Green dot if bullish, red if bearish.
- VWAP: Green if price > VWAP, red if price < VWAP.
- MACD: Green if bullish, red if bearish.
- MACD Zero: Green if MACD > 0, red if MACD < 0.
- PSAR: Green if price > PSAR, red if price < PSAR.
- ADX: Arrows for very strong trends (↑/↓), dots for weaker trends, colored by direction.
- Bollinger Bands: Arrows (↑/↓) or dots based on price position.
- RSI: Numeric value, colored by overbought/oversold levels.
- VIX: Numeric value, colored based on SPX trend and VIX level.
---
### **6. Alerts**
- Triggers alerts for EMA 8/20 crosses:
- Bullish: "EMA 8/20 Bullish Cross on Candle Close!"
- Bearish: "EMA 8/20 Bearish Cross on Candle Close!"
---
### **7. Key Features**
- **Flexibility**: Users can toggle indicators on/off in the table and adjust parameters.
- **Visual Clarity**: Consistent use of green (bullish), red (bearish), and neutral colors.
- **Comprehensive**: Combines trend, momentum, volatility, and market sentiment indicators.
---
### **How to Use**
1. Add the script to TradingView.
2. Customize inputs (colors, lengths, table position) as needed.
3. Interpret the chart and table:
- Green signals suggest bullish conditions.
- Red signals suggest bearish conditions.
- Neutral signals indicate indecision or consolidation.
4. Set up alerts for EMA crosses to catch trend changes.
This script is ideal for traders who want a multi-indicator dashboard to monitor price action and market conditions efficiently.
Volume Standard Deviation Alert GusPurpose
The script detects and alerts traders when the volume of a trading asset significantly exceeds a calculated threshold based on the standard deviation of volume over a specified lookback period. It optionally filters these alerts based on whether the price action is bullish or bearish.
Key Components
Inputs
lookback (default: 20)
The number of bars to consider when calculating the moving average and standard deviation of volume.
stdDevFactor (default: 2.0)
The multiplier for the standard deviation to determine the threshold for a volume spike.
alertOnClose (default: true)
Determines whether alerts should only be triggered after the bar has closed.
checkBullBear (default: false)
Enables filtering of alerts based on the bullishness or bearishness of the bar.
Calculations
volSMA
The simple moving average (SMA) of the volume over the lookback period.
volStd
The standard deviation of the volume over the lookback period.
threshold
The alert threshold is calculated as:
Threshold
=
volSMA
+
(
stdDevFactor
×
volStd
)
Threshold=volSMA+(stdDevFactor×volStd)
isBullish & isBearish
Determines whether the current bar is bullish (close > open) or bearish (close < open).
volumeSpikeCondition
A condition that triggers when the current volume exceeds the calculated threshold.
bullishCondition & bearishCondition
Refines the spike condition by requiring the bar to be bullish or bearish when checkBullBear is enabled.
finalCondition
The ultimate alert condition based on the user’s preference for bullish/bearish filtering.
finalTrigger
Ensures the alert only triggers at bar close if alertOnClose is set to true.
Visualization
Plots the SMA of the volume (volSMA) and the threshold line (threshold), helping traders visually understand the conditions.
Histograms the current volume and colors the bars:
Red: Volume exceeds the threshold.
Blue: Volume is below the threshold.
Alerts
The script generates an alert message when the finalTrigger condition is met:
"Bullish Volume Spike!" if the bar is bullish.
"Bearish Volume Spike!" if the bar is bearish.
"High Volume Spike!" if no bull/bear filter is applied.
Alerts are sent using alert() with the message and set to trigger once per bar close.
Usage
Traders can use this script to identify unusual volume activity, which often precedes significant price movements.
Customizability allows traders to tune the lookback period, standard deviation multiplier, and whether to filter for bullish/bearish spikes.
Visual and audible cues help in identifying important market events in real time.
This indicator is particularly useful for spotting market breakouts or breakdowns driven by high trading activity.
Custom RSI & MACD Momentum Entry SignalsIndicator Explanation: Custom RSI & MACD Momentum Entry Signals
Introduction
The "Custom RSI & MACD Momentum Entry Signals" indicator combines the Relative Strength Index (RSI) and the Moving Average Convergence Divergence (MACD) to generate precise long and short entry signals. This indicator offers a powerful combination of overbought/oversold zones, momentum analysis, and RSI-EMA crossovers to assist traders in making better decisions.
How the Indicator Works
1. RSI Calculation and EMA
The RSI is calculated based on the closing price with an adjustable period (default: 14).
An Exponential Moving Average (EMA) of the RSI (default: 9) is plotted to identify RSI trend changes.
When the RSI crosses its EMA upwards, it signals a bullish impulse. Conversely, a downward cross indicates a bearish impulse.
2. MACD Calculation and Momentum Shifts
The MACD line is derived from the difference between a fast EMA (default: 12) and a slow EMA (default: 26).
The Signal line is the EMA of the MACD line (default: 9).
The MACD histogram represents the difference between the MACD line and the Signal line.
Momentum shifts are detected as follows:
Weakening Bearish: Histogram is negative but increasing (less bearish pressure).
Strengthening Bullish: Histogram is positive and rising.
Weakening Bullish: Histogram is positive but decreasing.
Strengthening Bearish: Histogram is negative and falling.
Signal Generation
Long Signals
A Long signal is triggered when all of the following conditions are met:
The RSI was previously below 30 (oversold condition).
MACD momentum shifts from "strengthening bearish" to "weakening bearish" or turns bullish.
The RSI crosses its EMA upwards.
A green upward arrow is displayed below the bar, and the background is lightly shaded green for additional visualization.
Short Signals
A Short signal is triggered when all of the following conditions are met:
The RSI was previously above 70 (overbought condition).
MACD momentum shifts from "strengthening bullish" to "weakening bullish" or turns bearish.
The RSI crosses its EMA downwards.
A red downward arrow is displayed above the bar, and the background is lightly shaded red for additional visualization.
Visual Elements
RSI and EMA:
The RSI is shown in purple.
The RSI EMA is shown in blue.
Horizontal lines at 30 (oversold) and 70 (overbought) provide additional context.
MACD:
The MACD line is displayed in blue.
The Signal line is displayed in orange.
The zero line is added for easier interpretation.
Signals:
Green arrows: Long signals.
Red arrows: Short signals.
Background color: Light green for long conditions, light red for short conditions.
Use Cases
This indicator is ideal for:
Trend Followers: Combining RSI and MACD allows traders to identify entry points during impulsive trend shifts.
Swing Traders: Long and short signals can be used at reversal points to capture short-term price movements.
Momentum Traders: By considering MACD momentum, the indicator provides additional confidence in signal generation.
Customizable Settings
The indicator provides flexible input options:
RSI Period (default: 14)
RSI EMA Period (default: 9)
MACD Parameters: Fast, slow, and signal EMAs can be adjusted.
Conclusion
The Custom RSI & MACD Momentum Entry Signals indicator is a powerful tool for traders looking to combine RSI and MACD to identify high-probability entry signals. With clear visualization and precise signal generation, traders can make decisions more efficiently and capitalize on market movements.
