Edge of MomentumThe script was designed for the purpose of catching the rocket portion of a move (the edge of momentum).
Long
--When RSI closes over 60, take long order 1 tick above that bar. The closed bar above RSI 60 will be colored "green" or whatever color the user chooses. (RSI > 60)
--On a long position, exit will be a closed bar below the ema (low, 10) . The closed bar below the ema will be colored "yellow." (Price < ema)
--Note: On a long position there is no need to exit when a closed bar is colored "purple." RSI is just below 60 but above 40. Pullback or chop
Short
--When RSI closes below 40, take a short order 1 tick below that bar. The closed bar below RSI 40 will be colored "red." RSI<40)
--On a short position, exit will be a closed bar above the ema (low, 10). The closed bar above the ema will be colored "purple." (Price > ema)
--Note: On a short position there is no need to exit when a closed bar is colored "yellow."
Note: You may see a series of purple and yellow bars, that is simply chop. I define chop as RSI moving between 60 and 40.
Trade should only be taken above green colored candle(long) and below red colored candle (short). No position should be taken off yellow or purple candle (chop)
Again this is designed to catch the momentum part of a move, and to help reduce some entries during chop. It is a simple systems that beginning traders can use and profit from.
Note: I don't no shit about coding scripts I just learn from reading others.
Enjoy. If you decide to use please drop me a line...suggestions/comments, etc.
Best of luck in all you do.
Recherche dans les scripts pour "信达股份40周年"
Money Flow Index + AlertsThis study is based on the work of TV user Beasley Savage ( ) and all credit goes to them.
Changes I've made:
1. Added a visual symbol of an overbought/oversold threshold cross in the form of a red/green circle, respectively. Sometimes it can be hard to see when a cross actually occurs, and if your scaling isn't set up properly you can get misleading visuals. This way removes all doubt. Bear in mind they aren't meant as trading signals, so DO NOT use them as such. Research the MFI if you're unsure, but I use them as an early warning and that particular market/stock is added to my watchlist.
2. Added 60/40 lines as the MFI respects these incredibly well in trends. E.g. in a solid uptrend the MFI won't go below 40, and vice versa. Use the idea of support and resistance levels on the indicator and it'll be a great help. I've coloured the zones. Strong uptrends should stay above 60, strong downtrends should stay below 40. The zone in between 40-60 I've called the transition zone. MFI often stays here in consolidation periods, and in the last leg of a cycle/trend the MFI will often drop into this zone after being above 60 or below 40. This is a great sign that you should get out and start looking to reverse your position. Hopefully it helps to spot divergences as well.
3. Added alerts based on an overbought/oversold cross. Also added an alert for when either condition is triggered, so hopefully that's useful for those struggling with low alert limits. Feel free to change the overbought/oversold levels, the alerts + crossover visual are set to adapt.
Like any indicator, don't use this one alone. It works best paired with indicators/techniques that contradict it. You'll often see a OB/OS cross, and price will continue on it's way for many weeks more. But MFI is a great tool for identifying upcoming trend changes.
Any queries please comment or PM me.
Cheers,
RJR
Average Directional Index with DI SpreadThis indicator converts conventional triple lined ADX, DI+ and DI- into two lines. First line is the
original ADX line and second line is obtained by subtracting DI- from DI+ which named DI Spread(DIS)
If ADX is greater than 20 there is a trend and if greater than 40 there is a strong trend but ADX does not tell
the trend direction
To determine trend direction, DIS can be used with ADX; Sımply; If DIS is greater than 0, it is an uptrend and If DIS
is less than 0, it is a downtrend.
To sum up;
If ADX is greater than 20 and especially greater than 40 with positive DIS value, this implies an uptrend.
If ADX is greater than 20 and especially greater than 40 with negative DIS value, this implies a downtrend.
*Because of coloration and reference levels used, this indicator is really simple and efficient to analyze trend direction.
90009If( MDI(14)>40 AND ADX(14)>40 AND PDI(14)<15 AND RSI(14)<30,1,0)
;If( MDI(14)<15 AND ADX(14)<15 AND PDI(14)>40 AND RSI(14)>70,-1,0)
CM_ADX+DMI ModMashed together Chris Moody's ADX thing with his DMI thing.
So you can see trend strength + direction
green-ish = uptrend-ish//red-ish = downtrend-ish
Colors can be adjusted though.
below 10 = gray, not much going on
10 - 20 = light green/light red, could be the beginning o something
20 - 40 = bright green / bright red, something is going on
above 40 = dark green, dark red, exhaustion (default is 40, can be adjusted to whatever)
Indicators: Volume Zone Indicator & Price Zone IndicatorVolume Zone Indicator (VZO) and Price Zone Indicator (PZO) are by Waleed Aly Khalil.
Volume Zone Indicator (VZO)
------------------------------------------------------------
VZO is a leading volume oscillator that evaluates volume in relation to the direction of the net price change on each bar.
A value of 40 or above shows bullish accumulation. Low values (< 40) are bearish. Near zero or between +/- 20, the market is either in consolidation or near a break out. When VZO is near +/- 60, an end to the bull/bear run should be expected soon. If that run has been opposite to the long term price trend direction, then a reversal often will occur.
Traditional way of looking at this also works:
* +/- 40 levels are overbought / oversold
* +/- 60 levels are extreme overbought / oversold
More info:
drive.google.com
Price Zone Indicator (PZO)
------------------------------------------------------------
PZO is interpreted the same way as VZO (same formula with "close" substituted for "volume").
Chart Markings
------------------------------------------------------------
In the chart above,
* The red circles indicate a run-end (or reversal) zones (VZO +/- 60).
* Blue rectangle shows the consolidation zone (VZO betwen +/- 20)
I have been trying out VZO only for a week now, but I think this has lot of potential. Give it a try, let me know what you think.
6-9 session & levels6-9 Session & Levels - Customizable Range Analysis Indicator
Description:
This indicator provides comprehensive session-based range analysis designed for intraday traders. It calculates and displays key levels based on a customizable session period (default 6:00-9:00 AM ET).
Core Features:
Session Tracking
Monitors user-defined session times with timezone support
Displays session open, high, and low levels
Highlights session range with optional box visualization
Shows previous day RTH (Regular Trading Hours: 9:30 AM - 4:00 PM) levels
Range Levels
25%, 50%, and 75% range levels within the session
Range deviations at 0.5x, 1.0x, and 2.0x multiples
Fibonacci extension levels (customizable, default 1.33x and 1.66x)
Optional fill zones between Fibonacci levels
Time Zone Highlighting
Marks the 9:40-9:50 AM period as a potential reversal zone
Vertical lines with shading to identify key time windows
Statistical Analysis
Calculates mean and median extension levels based on historical sessions
Displays statistics table showing current range, average range, range difference, and z-score
Customizable sample size (1-100 sessions) for statistical calculations
Option to anchor extensions from either session open or high/low points
Input Settings Explained:
Session Settings
Levels Session Time: Define your session window in HHMM-HHMM format (default: 0600-0900)
Time Zone: Choose from UTC, America/New_York, America/Chicago, America/Los_Angeles, Europe/London, or Asia/Tokyo
Anchor Settings
Show Session Anchor: Toggle the session anchor line (marks session open price at 6:00 AM)
Anchor Style/Color/Width: Customize appearance (Solid/Dashed/Dotted, color, 1-4 width)
Show Anchor Label: Display price label for the anchor
Session Open Line: Similar options for the session open reference line
Range Box Settings
Show Range Box: Display a shaded rectangle highlighting the session high-to-low range
Range Box Color: Set the box background color and transparency
Range Levels (25%/50%/75%)
Show Range Levels: Toggle all three intermediate levels on/off
Individual Level Styling: Each level (25%, 50%, 75%) has its own color, style, and width settings
Show Range Level Labels: Display price labels for each level
Range Deviations
Show Range Deviations: Toggle deviation levels on/off
0.5x/1.0x/2.0x Settings: Each deviation multiplier can be customized with its own color, line style (Solid/Dashed/Dotted), and width
Show Range Deviation Labels: Display labels showing the deviation price levels
Previous Day RTH Levels
Show Previous RTH Levels: Display yesterday's regular trading hours high and low
RTH High/Low Styling: Separate color, style, and width settings for each level
Show Previous RTH Labels: Toggle price labels for RTH levels
Time Zones
Show 9:40-9:50 AM Zone: Highlight this specific time period with vertical lines and shading
Zone Color: Set the background fill color for the time zone
Zone Label Color/Text: Customize the label appearance and text
Fibonacci Extension Settings
Show Fibonacci Extensions: Toggle Fib levels on/off
Fib Extension Color/Style/Width: Customize line appearance
Show Fib Extension Labels: Display price labels
Fib Ext Level 1/2: Set custom multipliers (default 1.33 and 1.66, range 0-5 in 0.1 increments)
Show Fibonacci Fills: Display shaded zones between Fib levels
Fib Fill Color: Customize the fill color and transparency
Session High/Low Settings
Show Session High/Low Lines: Display the actual session extremes
Style/Color/Width: Customize line appearance
Show Labels: Toggle price labels for high/low levels
Extension Stats Settings
Show Statistical Levels on Chart: Display mean and median extension levels based on historical data
Extension Anchor Point: Choose whether to anchor from "Open" or "High/Low" of the session
Number of Sessions for Statistics: Set sample size (1-100, default 60) for calculating averages
Mean/Median High Extension: Separate styling for each statistical level (color, style, width)
Mean/Median Low Extension: Separate styling for downside statistical levels
Tables
Show Statistics Table: Display a summary table with current range, average range, difference, z-score, and sample size
Table Position: Choose from 9 positions (Bottom/Middle/Top + Center/Left/Right)
Table Text Size: Select from Auto, Tiny, Small, Normal, Large, or Huge
Display Settings
Projection Offset: Number of bars to extend lines forward (default 24)
Label Size: Choose from Tiny, Small, Normal, or Large
Price Decimal Precision: Set decimal places for price labels (0-6)
How It Works:
The indicator tracks the specified session period and calculates the session's open, high, low, and range. At the end of the session (9:00 AM by default), it projects all configured levels forward for the trading day. The statistical features analyze the last N sessions (you choose the number) to calculate typical extension behavior from either the session open or the session high/low points.
The z-score calculation helps identify whether the current session's range is normal, expanded, or contracted compared to recent history, allowing traders to adjust expectations for the rest of the day.
Use Case:
This indicator helps traders identify key support and resistance levels based on early session price action, understand current range context relative to historical averages, and spot potential reversal zones during specific time periods.
Note: This indicator is for informational purposes only and does not constitute investment advice. Always perform your own analysis before making trading decisions.
Stock Relative Strength Rotation Graph🔄 Visualizing Market Rotation & Momentum (Stock RSRG)
This tool visualizes the sector rotation of your watchlist on a single graph. Instead of checking 40 different charts, you can see the entire market cycle in one view. It plots Relative Strength (Trend) vs. Momentum (Velocity) to identify which assets are leading the market and which are lagging.
📜 Credits & Disclaimer
Original Code: Adapted from the open-source " Relative Strength Scatter Plot " by LuxAlgo.
Trademark: This tool is inspired by Relative Rotation Graphs®. Relative Rotation Graphs® is a registered trademark of JOOS Holdings B.V. This script is neither endorsed, nor sponsored, nor affiliated with them.
📊 How It Works (The Math)
The script calculates two metrics for every symbol against a benchmark (Default: SPX):
X-Axis (RS-Ratio): Is the trend stronger than the benchmark? (>100 = Yes)
Y-Axis (RS-Momentum): Is the trend accelerating? (>100 = Yes)
🧩 The 4 Market Quadrants
🟩 Leading (Top-Right): Strong Trend + Accelerating. (Best for holding).
🟦 Improving (Top-Left): Weak Trend + Accelerating. (Best for entries).
⬜ Weakening (Bottom-Right): Strong Trend + Decelerating. (Watch for exits).
🟥 Lagging (Bottom-Left): Weak Trend + Decelerating. (Avoid).
✨ Significant Improvements
This open-source version adds unique features not found in standard rotation scripts:
📝 Quick-Input Engine: Paste up to 40 symbols as a single comma-separated list (e.g., NVDA, AMD, TSLA). No more individual input boxes.
🎯 Quadrant Filtering: You can now hide specific quadrants (like "Lagging") to clear the noise and focus only on actionable setups.
🐛 Trajectory Trails: Visualizes the historical path of the rotation so you can see the direction of momentum.
🛠️ How to Use
Paste Watchlist: Go to settings and paste your symbols (e.g., US Sectors: XLK, XLF, XLE...).
Find Entries: Look for tails moving from Improving ➔ Leading.
Find Exits: Be cautious when tails move from Leading ➔ Weakening.
Zoom: Use the "Scatter Plot Resolution" setting to zoom in or out if dots are bunched up.
SPX Breadth – Stocks Above 200-day SMA//@version=6
indicator("SPX Breadth – Stocks Above 200-day SMA",
overlay = false,
max_lines_count = 500,
max_labels_count = 500)
//–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––
// Inputs
group_source = "Source"
breadthSymbol = input.symbol("SPXA200R", "Breadth symbol", group = group_source)
breadthTf = input.timeframe("", "Timeframe (blank = chart)", group = group_source)
group_params = "Parameters"
totalStocks = input.int(500, "Total stocks in index", minval = 1, group = group_params)
smoothingLen = input.int(10, "SMA length", minval = 1, group = group_params)
//–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––
// Breadth series (symbol assumed to be percent 0–100)
string tf = breadthTf == "" ? timeframe.period : breadthTf
float rawPct = request.security(breadthSymbol, tf, close) // 0–100 %
float breadthN = rawPct / 100.0 * totalStocks // convert to count
float breadthSma = ta.sma(breadthN, smoothingLen)
//–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––
// Regime levels (0–20 %, 20–40 %, 40–60 %, 60–80 %, 80–100 %)
float lvl0 = 0.0
float lvl20 = totalStocks * 0.20
float lvl40 = totalStocks * 0.40
float lvl60 = totalStocks * 0.60
float lvl80 = totalStocks * 0.80
float lvl100 = totalStocks * 1.0
p0 = plot(lvl0, "0%", color = color.new(color.black, 100))
p20 = plot(lvl20, "20%", color = color.new(color.red, 0))
p40 = plot(lvl40, "40%", color = color.new(color.orange, 0))
p60 = plot(lvl60, "60%", color = color.new(color.yellow, 0))
p80 = plot(lvl80, "80%", color = color.new(color.green, 0))
p100 = plot(lvl100, "100%", color = color.new(color.green, 100))
// Colored zones
fill(p0, p20, color = color.new(color.maroon, 80)) // very oversold
fill(p20, p40, color = color.new(color.red, 80)) // oversold
fill(p40, p60, color = color.new(color.gold, 80)) // neutral
fill(p60, p80, color = color.new(color.green, 80)) // bullish
fill(p80, p100, color = color.new(color.teal, 80)) // very strong
//–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––
// Plots
plot(breadthN, "Stocks above 200-day", color = color.orange, linewidth = 2)
plot(breadthSma, "Breadth SMA", color = color.white, linewidth = 2)
// Optional label showing live value
var label infoLabel = na
if barstate.islast
label.delete(infoLabel)
string txt = "Breadth: " +
str.tostring(breadthN, format.mintick) + " / " +
str.tostring(totalStocks) + " (" +
str.tostring(rawPct, format.mintick) + "%)"
infoLabel := label.new(bar_index, breadthN, txt,
style = label.style_label_left,
color = color.new(color.white, 20),
textcolor = color.black)
Hybrid -WinCAlgo/// 🇬🇧
Hybrid - WinCAlgo is a weighted composite oscillator designed to provide a more robust and reliable signal than the standard Relative Strength Index (RSI). It integrates four different momentum and volume metrics—RSI, Money Flow Index (MFI), Scaled CCI, and VWAP-RSI—into a single 0-100 oscillator.
This powerful tool aims to filter market noise and enhance the detection of trend reversals by confirming momentum with trading volume and volume-weighted average price action.
⚪ What is this Indicator?
The Hybrid Oscillator combines:
* RSI (40% Weight): Measures fundamental price momentum.
* VWAP-RSI (40% Weight): Measures the momentum of the Volume Weighted Average Price (VWAP), providing strong volume confirmation for trend strength.
* MFI (10% Weight): Measures money flow volume, confirming momentum with liquidity.
* Scaled CCI (10% Weight): Tracks market extremes and potential trend shifts, scaled to fit the 0-100 range.
⚪ Key Features
* Composite Strength: Blends four different market factors for a multi-dimensional view of momentum.
* Volume Integration: High weights on VWAP-RSI and MFI ensure that momentum signals are backed by trading volume.
* Advanced Divergence: The robust formula significantly enhances the detection of Bullish and Bearish Divergences, often providing an earlier signal than traditional oscillators.
* Customizable: Adjustable Lookback Length (N) and Individual Component Weights allow users to fine-tune the oscillator for specific assets or timeframes.
* Visual Clarity: Uses 40/60 bands for earlier Overbought/Oversold indications, with a gradient-styled background for intuitive visual interpretation.
⚪ Usage
Use Hybrid – WinCAlgo as your primary momentum confirmation tool:
* Divergence Signals: Trust the indicator when it fails to confirm new price highs/lows; this signals imminent trend exhaustion and reversal.
* Accumulation/Distribution: Look for the oscillator to rise/fall while the price is ranging at a bottom/top; this confirms hidden buying or selling (accumulation).
* Overbought/Oversold: Use the 60 band as the trigger for potential selling/shorting signals, and the 40 band for potential buying/longing signals.
* Noise Filter: Combine with a higher timeframe chart (e.g., 4H or Daily) to filter out gürültü (noise) and focus only on significant momentum shifts.
---
Echo Chamber [theUltimator5]The Echo Chamber - When history repeats, maybe you should listen.
Ever had that eerie feeling you've seen this exact price action before? The Echo Chamber doesn't just give you déjà vu—it mathematically proves it, scales it, and projects what happened next.
📖 WHAT IT DOES
The Echo Chamber is an advanced pattern recognition tool that scans your chart's history to find segments that closely match your current price action. But here's where it gets interesting: it doesn't just find similar patterns - It expands and contracts the time window to create a uniquely scaled fractal. Patterns don't always follow the same timeframe, but they do follow similar patterns.
Using a custom correlation analysis algorithm combined with flexible time-scaling, this indicator:
Finds historical price segments that mirror your current market structure
Scales and overlays them perfectly onto your current chart
Projects forward what happened AFTER that historical match
Gives you a visual "echo" from the past with a glimpse into potential futures
══════════════════════════════
HOW TO USE IT
This indicator starts off in manual mode, which means that YOU, the user, can select the point in time that you want to project from. Simply click on a point in time to set the starting value.
Once you select your point in time, the indicator will automatically plot the chosen historical chart pattern and correlation over the current chart and project the price forwards based on how the chart looked in the past. If you want to change the point in time, you can update it from the settings, or drag the point on the chart over to a new position.
You can manually select any point in time, and the chart will quickly update with the new pattern. A correlation will be shown in a table alongside the date/timestamp of the selected point in time.
You can switch to auto mode, which will automatically search out the best-fit pattern over a defined lookback range and plot the past/future projection for you without having to manually select a point in time at all. It simply finds the best fit for you.
You can change the scale factor by adjusting multiplication and division variables to find time-scaled fractal patterns.
══════════════════════════════
🎯 KEY FEATURES
Two Operating Modes:
🔧 MANUAL MODE - Select any historical point and see how it correlates with current price action in real-time. Perfect for:
• Analyzing specific past events (crashes, rallies, consolidations)
• Testing historical patterns against current conditions
• Educational analysis of market structure repetition
🤖 AUTO MODE - It automatically scans through your lookback period to find the single best-correlated historical match. Ideal for:
• Quick pattern discovery
• Systematic trading approach
• Unbiased pattern recognition
Time Warp Technology:
The time warp feature expands and compresses the correlation window to provide a custom fractal so you can analyze windows of time that don't necessarily match the current chart.
💡 *Example: Multiplier=3, Divisor=2 gives you a 1.5x time stretch—perfect for finding patterns that played out 50% slower than current price action.*
Drawing Modes:
Scale Only : Pure vertical scaling—matches price range while maintaining temporal alignment at bar 0
Rotate & Scale : Advanced geometric transformation that anchors both the start AND end points, creating a rotated fit that matches your current segment's slope and range
Visual Components:
🟠 Orange Overlay : The historical match, perfectly scaled to your current price action
🟣 Purple Projection : What happened NEXT after that historical pattern (dotted line into the future)
📦 Highlight Boxes : Shows you exactly where in history these patterns came from
📊 Live Correlation Table : Real-time correlation coefficient with color-coded strength indicator
══════════════════════════════
⚙️ PARAMETERS EXPLAINED
Correlation Window Length (20) : How many bars to match. Smaller = more precise matches but noisier. Larger = broader patterns but fewer matches.
Note: if this value is too high in auto mode, the script may time out from computational overload.
Multiplication Factor : Historical time multiplier. 2 = sample every 2nd bar from history. Higher values find slower historical patterns.
Division Factor : Historical time divisor applied after multiplication. Final sample rate = (Length × Factor) ÷ Divisor, rounded down.
Lookback Range : How far back to search for patterns. More history = better chance of finding matches but slower performance.
Note: if this value is too high in auto mode, the script may time out from computational overload.
Future Projection Length : How many bars forward to project from the historical match. Your crystal ball's focal length.
══════════════════════════════
💼 TRADING APPLICATIONS
Trend Continuation/Reversal :
If the purple projection continues the current trend, that's your historical confirmation. If it reverses, you've found a potential turning point that's happened before under similar conditions.
Support/Resistance Validation :
Does the projection respect your S/R levels? History suggests those levels matter. Does it break through? You've found historical precedent for a breakout.
Time-Based Exits :
The projection shows not just WHERE price might go, but WHEN. Use it to anticipate timing of moves.
Multi-Timeframe Analysis :
Use time compression to overlay higher timeframe patterns onto lower timeframes. See daily patterns on hourly charts, weekly on daily, etc.
Pattern Education :
In Manual Mode, study how specific historical events correlate with current conditions. Build your pattern recognition library.
══════════════════════════════
📊 CORRELATION TABLE
The table shows your correlation coefficient as a percentage:
80-100%: Extremely strong correlation—history is practically repeating
60-80%: Strong correlation—significant similarity
40-60%: Moderate correlation—some structural similarity
20-40%: Weak correlation—limited similarity
0-20%: Very weak correlation—essentially random match
-20-40%: Weak inverse correlation
-40-60%: Moderate inverse correlation
-60-80%: Strong inverse correlation
-80-100%: Extremely strong inverse correlation—history is practically inverting
**Important**: The correlation measures SHAPE similarity, not price level. An 85% correlation means the price movements follow a very similar pattern, regardless of whether prices are higher or lower.
══════════════════════════════
⚠️ IMPORTANT DISCLAIMERS
- Past performance does NOT guarantee future results (but it sure is interesting to study)
- High correlation doesn't mean causation—markets are complex adaptive systems
- Use this as ONE tool in your analytical toolkit, not a standalone trading system
- The projection is what HAPPENED after a similar pattern in the past, not a prediction
- Always use proper risk management regardless of what the Echo Chamber suggests
══════════════════════════════
🎓 PRO TIPS
1. Start with Auto Mode to find high-correlation matches, then switch to Manual Mode to study why that period was similar
2. Experiment with time warping on different timeframes—a 2x factor on a daily chart lets you see weekly patterns
3. Watch for correlation decay —if correlation drops sharply after the match, current conditions are diverging from history
4. Combine with volume —check if volume patterns also match
5. Use "Rotate & Scale" mode when the current trend angle differs from the historical match
6. Increase lookback range to 500-1000+ on daily/weekly charts for finding rare historical parallels
══════════════════════════════
🔧 TECHNICAL NOTES
- Uses Pearson correlation coefficient for pattern matching
- Implements range-based scaling to normalize different price levels
- Rotation mode uses linear interpolation for geometric transformation
- All calculations are performed on close prices
- Boxes highlight actual historical bar ranges (high/low)
- Maximum of 500 lines and 500 boxes for performance optimization
Volatility Regime NavigatorA guide to understanding VIX, VVIX, VIX9D, VVIX/VIX, and the Composite Risk Score
1. Purpose of the Indicator
This dashboard summarizes short-term market volatility conditions using four core volatility metrics.
It produces:
• Individual readings
• A combined Regime classification
• A Composite Risk Score (0–100)
• A simplified Risk Bucket (Bullish → Stress)
Use this to evaluate market fragility, drift potential, tail-risk, and overall risk-on/off conditions.
This is especially useful for intraday ES/NQ trading, expected-move context, and understanding when breakouts or fades have edge.
2. The Four Core Volatility Inputs
(1) VIX — Baseline Equity Volatility
• < 16: Complacent (easy drift-up, but watch for fragility)
• 16–22: Healthy, normal volatility → ideal trading conditions
• > 22: Stress rising
• > 26: Tail-risk / risk-off environment
(2) VIX9D — Short-Term Event Vol
Measures 9-day implied volatility. Reacts to immediate news/events.
• < 14: Strongly bullish (drift regime)
• 14–17: Bullish to neutral
• 17–20: Event risk building
• > 20: Short-term stress / caution
(3) VVIX — Volatility of VIX (fragility index)
Tracks volatility of volatility.
• < 100: “Bullish, Bullish” — very low fragility
• 100–120: Normal
• 120–140: Fragile
• > 140: Stress, hedging pressure
(4) VVIX/VIX Ratio — Microstructure Risk-On/Risk-Off
One of the most sensitive indicators of market confidence.
• 5.0–6.5: Strongest “normal/bullish” zone
• < 5.0: Bottom-stalking / fear regime
• > 6.5: Complacency → vulnerable to reversals
• > 7.5: Fragile / top-risk
3. Composite Risk Score (0–100)
The dashboard converts all four inputs into a single score.
Score Interpretation
• 80–100 → Bullish - Drift regime. Shallow pullbacks. Upside favored.
• 60–79 → Normal - Healthy tape. Balanced two-way trading.
• 40–59 → Fragile - Choppy, failed breakouts, thinner liquidity.
• 20–39 → Risk-Off - Downside tails active. Favor fades and defensive behavior.
• < 20 → Stress - Crisis or event-driven tape. Avoid longs.
Score updates every bar.
4. Regime Label
Independent of the composite score, the script provides a Regime classification based on combinations of VIX + VVIX/VIX:
• Bullish+ → Buying is easy, tape lifts passively
• Normal → Cleanest and most tradable conditions
• Complacent → Top-risk; be careful chasing upside
• Mixed → Signals conflict; chop potential
• Bottom Stalk → High VIX, low VVIX/VIX (capitulation signatures)
A trailing “+” or “*” indicates additional bullish or caution overlays from VIX9D/VVIX.
5. How to Use the Dashboard in Trading
When Bullish (Score ≥ 80):
• Expect drift-up behavior
• Downside limited unless catalyst hits
• Structure favors breakouts and trend continuation
• Mean reversion trades have lower expectancy
When Normal (Score 60–79):
• The “playbook regime”
• Breakouts and mean reversion both valid
• Best overall trading environment
When Fragile (Score 40–59):
• Expect chop
• Breakouts fail
• Take quicker profits
• Avoid overleveraged directional bets
When Risk-Off (20–39):
• Favor fades of strength
• Downside tails activate
• Trend-following short setups gain edge
• Respect volatility bands
When Stress (<20):
• Avoid long exposure
• Do not chase dips
• Expect violent, news-sensitive behavior
• Position sizing becomes critical
6. Quick Summary
• VIX = weather
• VIX9D = short-term storm radar
• VVIX = foundation stability
• VVIX/VIX = confidence vs fragility
• Composite Score = overall regime health
• Risk Bucket = simple “what do I do?” label
This dashboard gives traders a high-confidence, low-noise view of equity volatility conditions in real time.
Bollinger Bands Delta Matrix Analytics [BDMA] Bollinger Bands Delta Matrix Analytics (BDMA) v7.0
Deep Kinetic Engine – 5x8 Volatility & Delta Decision Matrix
1. Introduction & Concept
Bollinger Bands Delta Matrix Analytics (BDMA) v7.0 is an analytical framework that merges:
- Spatial analysis via Bollinger Bands (%B location),
- with a 4-factor Deep Kinetic Engine based on:
• Total Volume
• Buy Volume
• Sell Volume
• Delta (Buy – Sell) Z-Scores
and converts them into an expanded 5×8 decision matrix that continuously tracks where price is trading and how the underlying orderflow is behaving.
BDMA is not a trading system or strategy. It does not generate entry/exit signals.
Instead, it provides a structured contextual map of volatility, volume, and delta so traders can:
- identify climactic extensions vs. fakeouts,
- distinguish strong initiative moves vs. passive absorption,
- and detect squeezes, traps, and liquidity voids with a unified visual dashboard.
2. Spatial Engine – Bollinger S-States (S1–S5)
The spatial dimension of BDMA comes from classic Bollinger Bands.
Price location is expressed as Percent B (%B) and mapped into 5 spatial states (S-States):
S1 – Hyper Extension (Above Upper Band)
Price has pushed beyond the upper Bollinger Band.
Often associated with parabolic or blow-off behavior, late-stage momentum, and elevated reversal risk.
S2 – Resistance Test (Upper Zone)
Price trades in the upper Bollinger region but remains inside the bands.
Represents a sustained test of resistance, typically within an established or emerging uptrend.
S3 – Neutral Zone (Middle)
Price hovers around the mid-band.
This is the mean reversion gravity field where the market often consolidates or transitions between regimes.
S4 – Support Test (Lower Zone)
Price trades in the lower Bollinger region but inside the bands.
Represents a sustained test of support within range or downtrend structures.
S5 – Hyper Drop (Below Lower Band)
Price extends below the lower Bollinger Band.
Often aligned with panic, forced liquidations, or capitulation-type behavior, with increased snap-back risk.
These 5 S-States define the vertical axis (rows) of the BDMA matrix.
3. Deep Kinetic Engine – 4-Factor Z-Score & D-States (D1–D8)
The Deep Kinetic Engine transforms raw volume and delta into standardized Z-Scores to measure how abnormal current activity is relative to its recent history.
For each bar:
- Raw Buy Volume is estimated from the candle’s position within its range
- Raw Sell Volume is complementary to buy volume
- Raw Delta = Buy Volume – Sell Volume
- Total Volume = Buy Volume + Sell Volume
These 4 series are then normalized using a unified Z-Score lookback to produce:
1. Z_Vol_Total – overall activity and liquidity intensity
2. Z_Vol_Buy – aggression from buyers (attack)
3. Z_Vol_Sell – aggression from sellers (defense or attack)
4. Z_Delta – net victory of one side over the other
Thresholds for Extreme, Significant, and Neutral Z-Score levels are fully configurable, allowing you to tune the sensitivity of the kinetic states.
Using Z_Vol_Total and Z_Delta (plus threshold logic), BDMA assigns one of 8 Deep Kinetic states (D-States):
D1 – Climax Buy
Extreme Total Volume + Extreme Positive Delta → Buying climax or blow-off behavior.
D2 – Strong Buy
High Volume + High Positive Delta → Confirmed bullish initiative activity.
D3 – Weak Buy / Fakeout
Low Volume + High Positive Delta → Bullish delta without commitment, low-liquidity breakout risk.
D4 – Absorption / Conflict
High Volume + Neutral Delta → Aggressive two-way trade, strong absorption, war zone behavior.
D5 – Neutral
Low Volume + Neutral Delta → Low-energy environment with low conviction.
D6 – Weak Sell / Fakeout
Low Volume + High Negative Delta → Bearish delta without commitment, low-liquidity breakdown risk.
D7 – Strong Sell
High Volume + High Negative Delta → Confirmed bearish initiative activity.
D8 – Capitulation
Extreme Volume + Extreme Negative Delta → Panic selling or capitulation regime.
These 8 D-States define the horizontal axis (columns) of the BDMA matrix.
4. The 5×8 BDMA Decision Matrix
The core of BDMA is a 5×8 matrix where:
- Rows (1–5) = Spatial S-States (S1…S5)
- Columns (1–8) = Kinetic D-States (D1…D8)
Each of the 40 possible combinations (SxDy) is pre-computed and mapped to:
- a Status or Regime Title (for example: Climax Breakout, Bear Trap Spring, Capitulation Breakdown),
- a Bias (Climactic Bull, Neutral, Strong Bear, Conflict or Reversal Risk, and similar labels),
- and a Strategic Signal or Consideration (for example: High reversal risk, Wait for confirmation, Low probability zone – avoid).
Internally, BDMA resolves all 40 regimes so the current state can be displayed on the dashboard without performance overhead.
5. Key Regime Families (How to Read the Matrix)
5.1. Breakouts and Breakdowns
Climax Breakout (Top-side)
Spatial S1 with Kinetic D1 or D2
Bias: Explosive or Extreme Bull
Signal:
- Strong or climactic upside extension with abnormal bullish orderflow.
- Trend continuation is possible, but reversal risk is extremely high after blow-off phases.
Low-Conviction Breakout (Fakeout Risk)
S1 with D3 (Weak Buy, low liquidity)
Bias: Weak Bull – Caution
Signal:
- Breakout not supported by volume.
- Elevated risk of failed auction or bull trap.
Capitulation Breakdown (Bottom-side)
Spatial S5 with Kinetic D8
Bias: Climactic Bear (panic)
Signal:
- Capitulation-type selling or forced liquidations.
- Trend can still proceed, but snap-back or violent short-covering risk is high.
Initiative Breakdown vs. Weak Breakdown
- Strong, high-volume breakdown typically corresponds to D7 (Strong Sell).
- Low-volume breakdown often corresponds to D6 (Weak Sell or Fakeout) with potential for failure.
5.2. Absorption, Traps and Springs
Absorption at Resistance (Top-side conflict)
S1 or S2 with D4 (Absorption or Conflict)
Bias: Conflict – Extreme Tension
Signal:
- Heavy two-way trade near resistance.
- Potential distribution or reversal if sellers begin to dominate.
Bull Trap or Failed Auction
Typically S1 with D6 (Weak Sell breakdown behavior after a top-side attempt)
Indicates a breakout attempt that fails and reverses, often after poor liquidity structure.
Absorption at Support and Bear Trap (Spring)
S4 or S5 with D4 or D3
Bias: Conflict or Weak Bear – Reversal Risk
Signal:
- Aggressive buying into lows (spring or shakeout behavior).
- Potential bear trap if price reclaims lost territory.
5.3. Trend Phases
Strong Uptrend Phases
Typically seen when S2–S3 combine with strong bullish kinetic behavior.
Bias: Strong or Extreme Bull
Signal:
- Pullbacks into S3 or S4 with supportive kinetic states often act as trend continuation zones.
Strong Downtrend Phases
Typically seen when S3–S4 combine with strong bearish kinetic behavior.
Bias: Strong or Extreme Bear
Signal:
- Rallies into resistance with strong bearish kinetic backing may act as continuation sell zones.
5.4. Neutral, Exhaustion and Squeeze
Exhaustion or Liquidity Void
S1 or S5 with D5 (Neutral kinetics)
Bias: Neutral or Exhaustion
Signal:
- Spatial extremes without kinetic confirmation.
- Often marks the end of a move, with poor follow-through.
Choppy, Low-Activity Range
S3 with D5
Bias: Neutral
Signal:
- Low volume, low conviction market.
- Typically a low-probability environment where standing aside can be logical.
Squeeze or High-Tension Zone
S3 with D4 or tightly clustered kinetic values
Bias: Conflict or High Tension
Signal:
- Hidden battle inside a volatility contraction.
- Often precedes large directionally-biased moves.
6. Dashboard Layout & Reading Guide
When Show Dashboard is enabled, BDMA displays:
1. Title and Status Line
Name of the current regime (for example: Climax Breakout, Bear Trap Spring, Mean Reversion).
2. Bias Line
Plain-language summary of directional context such as Climactic Bull, Strong Bear, Neutral, or Conflict and Reversal Risk.
3. Signal or Strategic Notes
Concise guidance focused on risk and context, not entries. For example:
- High reversal risk – aggressive traders only
- Wait for confirmation (break or rejection)
- Low probability zone – avoid taking new positions
4. Kinetic Profile (4-Factor Z-Score)
Shows the current Z-Scores for Total Volume (Activity), Buy Volume (Attack), Sell Volume (Defense), and Delta (Net Result).
5. Matrix Heatmap (5×8)
Visual representation of S-State vs. D-State with color coding:
- Bullish clusters in a green spectrum
- Bearish clusters in a red spectrum
- Conflict or exhaustion zones in yellow, amber, or neutral tones
The dashboard can be repositioned (top right, middle right, or bottom right) and its size can be adjusted (Tiny, Small, Normal, or Large) to fit different layouts.
7. Inputs & Customization
7.1. Core Parameters (Bollinger and Z-Score)
- Bollinger Length and Standard Deviation define the spatial engine.
- Z-Score Lookback (All Factors) defines how many bars are used to normalize volume and delta.
7.2. Deep Kinetic Thresholds
- Extreme Threshold defines what is considered climactic (D1 or D8).
- Significant Threshold distinguishes strong initiative vs. weak or fakeout behavior.
- Neutral Threshold is the band within which delta is treated as neutral.
These thresholds allow you to tune the sensitivity of the kinetic classification to fit different timeframes or instruments.
7.3. Calculation Method (Volume Delta)
Geometry (Approx)
- Fast, non-repainting approach based on candle geometry.
- Suitable for most users and real-time decision-making.
Intrabar (Precise)
- Uses lower-timeframe data for more precise volume delta estimation.
- Intrabar mode can repaint and requires compatible data and plan support on the platform.
- Best used for post-analysis or research, not blind automation.
7.4. Visuals and Interface
- Toggle Bollinger Bands visibility on or off.
- Switch between Dark and Light color themes.
- Configure dashboard visibility, matrix heatmap display, position, and size.
8. Multi-Language Semantic Engine (Asia and Middle East Focus)
BDMA v7.0 includes a fully integrated multi-language layer, targeting a wide geographic user base.
Supported Languages:
English, Türkçe, Русский, 简体中文, हिन्दी, العربية, فارسی, עברית
All dashboard labels, regime titles, bias descriptions, and signal texts are dynamically translated via an internal dictionary, while semantic meaning is kept consistent across languages.
This makes BDMA suitable for multi-language communities, study groups, and educational content across different regions.
However, due to the heavy computational load of the Deep Kinetic Engine and TradingView’s strict Pine Script execution limits, it was not possible to expand support to additional languages. Adding more translation layers would significantly increase memory usage and exceed runtime constraints. For this reason, the current language set represents the maximum optimized configuration achievable without compromising performance or stability.
9. Practical Usage Notes
BDMA is most powerful when used as a contextual overlay on top of market structure (HH, HL, LH, LL), higher-timeframe trend, key levels, and your own execution framework.
Recommended usage:
- Identify the current regime (Status and Bias).
- Check whether price location (S-State) and kinetic behavior (D-State) agree with your trade idea.
- Be especially cautious in climactic and absorption or conflict zones, where volatility and risk can be elevated.
Avoid treating BDMA as an automatic green equals buy, red equals sell tool.
The real edge comes from understanding where you are in the volatility or kinetic spectrum, not from forcing signals out of the matrix.
10. Limitations & Important Warnings
BDMA does not predict the future.
It organizes current and recent data into a structured context.
Volume data quality depends on the underlying symbol, exchange, and broker feed.
Forex, crypto, indices, and stocks may all behave differently.
Intrabar mode can repaint and is sensitive to lower-timeframe data availability and your plan type.
Use it with extra caution and primarily for research.
No indicator can remove the need for clear trading rules, disciplined risk management, and psychological control.
11. Disclaimer
This script is provided strictly for educational and analytical purposes.
It is not a trading system, signal service, financial product, or investment advice.
Nothing in this indicator or its description should be interpreted as a recommendation to buy or sell any asset.
Past behavior of any indicator or market pattern does not guarantee future results.
Trading and investing involve significant risk, including the risk of losing more than your initial capital in leveraged products.
You are solely responsible for your own decisions, risk management, and results.
By using this script, you acknowledge that you understand these risks and agree that the author or authors and publisher or publishers are not liable for any loss or damage arising from its use.
VCP Base Detector
📊 VCP BASE DETECTOR - AUTO-DETECT CONSOLIDATION ZONES
🎯 WHAT IS THIS INDICATOR?
This indicator automatically detects and marks ALL consolidation bases (VCP bases) on your chart. It:
✅ Auto-detects when price enters consolidation
✅ Measures base tightness (volatility contraction)
✅ Tracks base duration (how long consolidating)
✅ Rates base quality (1-5 stars)
✅ Shows volume drying confirmation
✅ Detects base breakouts
✅ Shows progression of multiple bases (VCP pattern)
Use this WITH the "Mark Minervini SEPA Balanced" indicator for complete trading setups!
✅ Mark Minervini SEPA Balanced = Trend + RS + Stage
✅ VCP Base Detector = Base Quality + Progression
Combined = Complete professional trading system!
🎨 WHAT YOU SEE ON YOUR CHART
1️⃣ COLORED BOXES (Base Zones):
🟦 Aqua Box = ⭐⭐⭐⭐⭐ Excellent base (tightest)
🔵 Blue Box = ⭐⭐⭐⭐ Very good base
🟣 Purple Box = ⭐⭐⭐ Good base
🟠 Orange Box = ⭐⭐ Fair base
⬜ Gray Box = ⭐ Weak base
2️⃣ BASE LABELS (With Metrics):
Shows above each base:
• Duration: 20 days
• Tightness: 0.9%
• Quality: ⭐⭐⭐⭐⭐
3️⃣ BREAKOUT LABELS (When price exits base):
Green "BREAKOUT ✓" label shows:
• Price: ₹800
• Volume: 1.6x
4️⃣ DASHBOARD (Top-Left Panel):
Real-time base metrics showing:
• In Base: YES/NO
• Tightness: 0.8%
• Duration: 22 days
• Range: 3.5%
• Volume: Drying/Normal
• Quality: ⭐⭐⭐⭐
📊 UNDERSTANDING BASE QUALITY (⭐ Rating System)
⭐⭐⭐⭐⭐ (EXCELLENT)
├─ Tightness: < 0.8% ATR
├─ Duration: 15-40 days
├─ Volume: Significantly drying
├─ Price Range: < 5%
└─ Result: Most explosive breakouts (best quality)
⭐⭐⭐⭐ (VERY GOOD)
├─ Tightness: 0.8-1.0% ATR
├─ Duration: 15-35 days
├─ Volume: Very dry
├─ Price Range: < 7%
└─ Result: High probability breakouts
⭐⭐⭐ (GOOD)
├─ Tightness: 1.0-1.3% ATR
├─ Duration: 15-30 days
├─ Volume: Drying
├─ Price Range: < 8%
└─ Result: Decent breakout probability
⭐⭐ (FAIR)
├─ Tightness: 1.3-1.5% ATR
├─ Duration: 15-25 days
├─ Volume: Moderate drying
├─ Price Range: < 10%
└─ Result: Lower quality, riskier
⭐ (WEAK)
├─ Tightness: > 1.5% ATR
├─ Duration: Varies
├─ Volume: Not drying enough
├─ Price Range: > 10%
└─ Result: Low quality, skip these
📈 HOW TO USE - STEP BY STEP
STEP 1: ADD INDICATOR TO CHART
────────────────────────────────
1. Open any stock chart (use 1D timeframe for swing trading)
2. Click "Indicators"
3. Search "VCP Base Detector"
4. Click to add to chart
5. Wait a moment for boxes to appear
STEP 2: SCAN FOR BASES
───────────────────────
Look for:
✓ Colored boxes appearing on chart (bases forming)
✓ Dashboard showing "In Base: YES"
✓ Tightness below 1.5%
✓ Volume Dry: YES
STEP 3: MONITOR BASE QUALITY
──────────────────────────────
Dashboard shows stars:
⭐⭐⭐⭐⭐ = Wait for breakout (best setup)
⭐⭐⭐⭐ = Good quality, watch for breakout
⭐⭐⭐ = Decent, but not ideal
⭐⭐ or ⭐ = Skip (lower probability)
STEP 4: WAIT FOR BREAKOUT
──────────────────────────
When price breaks above the box:
✓ Green "BREAKOUT ✓" label appears
✓ Shows breakout price and volume
✓ If volume shows 1.3x+, breakout is confirmed
✓ This is your entry signal!
STEP 5: CHECK MINERVINI CRITERIA (Use Both Indicators)
───────────────────────────────────────────────────────
Before entering:
✓ VCP Base Detector shows ⭐⭐⭐⭐+ quality base
✓ Mark Minervini indicator shows BUY SIGNAL
✓ Dashboard shows 10+ criteria GREEN
✓ Stage shows S2
Result: HIGH-PROBABILITY SETUP! 🎯
📋 DASHBOARD INDICATORS - WHAT EACH MEANS
BASE METRICS SECTION:
─────────────────────
In Base = ✓ YES or ✗ NO
Show if price is currently consolidating
Tightness = 0-3% (lower = tighter = better)
< 0.8% = ⭐⭐⭐⭐⭐ (excellent)
0.8-1.0% = ⭐⭐⭐⭐ (very good)
1.0-1.3% = ⭐⭐⭐ (good)
1.3-1.5% = ⭐⭐ (fair)
> 1.5% = ⭐ (weak)
Duration = Number of days in consolidation
15 days = ⭐ (too short, weak)
20 days = ⭐⭐⭐ (ideal)
30 days = ⭐⭐⭐⭐ (very long, strong)
> 40 days = ⚠️ (too long, may break down)
Range = % movement within the base
< 5% = ⭐⭐⭐⭐⭐ (excellent, very tight)
5-8% = ⭐⭐⭐ (good)
> 10% = ⭐ (loose, not ideal)
Vol Dry = Volume status during consolidation
✓ YES = Volume contracting (good)
✗ NO = Normal/high volume (weak setup)
QUALITY SECTION:
────────────────
Stars = Overall base quality rating
⭐⭐⭐⭐⭐ = Best quality bases (most explosive)
⭐⭐⭐⭐ = Excellent quality
⭐⭐⭐ = Good quality
⭐⭐ = Fair quality
⭐ = Weak quality (skip)
52W INFO SECTION:
─────────────────
From 52W Hi = How far below 52-week high is price?
< 25% = In sweet zone ✓
> 25% = Too far from highs ✗
From 52W Lo = How far above 52-week low is price?
> 30% = In sweet zone ✓
< 30% = Too close to lows ✗
⚙️ CUSTOMIZATION GUIDE
Click ⚙️ gear icon next to indicator to adjust:
MINIMUM BASE DAYS (Default: 15)
──────────────────────────────
Current: 15 = Include shorter bases
Change to 20 = Longer bases only (higher quality)
Change to 10 = Include very short bases (more frequent)
Why: Longer bases = better breakouts, but fewer opportunities
ATR% TIGHTNESS THRESHOLD (Default: 1.5)
────────────────────────────────────────
Current: 1.5 = BALANCED for Indian stocks
Change to 1.0 = ONLY very tight bases (⭐⭐⭐⭐⭐)
Change to 2.0 = Looser bases included (more frequent)
Why: Lower = tighter bases = better quality, fewer signals
VOLUME DRYING THRESHOLD (Default: 0.7)
──────────────────────────────────────
Current: 0.7 = Volume at 70% of average (good drying)
Change to 0.6 = Stricter (more volume drying required)
Change to 0.8 = Looser (less volume drying required)
Why: Volume drying = consolidation confirmation
52W PERIOD (Default: 252)
─────────────────────────
Current: 252 = Full year lookback
Don't change unless you know what you're doing
📈 REAL TRADING EXAMPLE
SCENARIO: Trading MARUTI over 6 weeks
WEEK 1: Nothing happening
─────────────────────────
- No boxes on chart
- Dashboard: "In Base: NO"
- Action: SKIP (not consolidating)
WEEK 2: Base Starting to Form
─────────────────────────────
- Purple box appears (⭐⭐⭐ quality)
- Dashboard: "In Base: YES"
- Tightness: 1.2%
- Duration: 3 days (too new)
- Action: MONITOR (let it develop)
WEEK 3-4: Base Tightening
──────────────────────────
- Box color changes from Purple → Blue (⭐⭐⭐⭐ quality)
- Dashboard: Duration: 12 days
- Tightness: 0.9%
- Vol Dry: YES
- Action: GET READY (high-quality base forming)
WEEK 4-5: Perfect Base Formed
──────────────────────────────
- Box changes to Aqua (⭐⭐⭐⭐⭐ EXCELLENT!)
- Dashboard: Duration: 22 days ✓
- Tightness: 0.8% ✓
- Vol Dry: YES ✓
- Range: 4.2% ✓
- Action: WATCH FOR BREAKOUT
WEEK 5: BREAKOUT HAPPENS!
──────────────────────────
- Price closes above box
- Green "BREAKOUT ✓" label appears
- Shows: Price ₹850, Volume 1.6x
- Mark Minervini indicator: BUY SIGNAL ✓
- Dashboard all GREEN ✓
- Action: ENTER TRADE
Entry: ₹850
Stop: Box low (₹820)
Target: ₹980 (20% move)
RESULT: +15.3% profit in 2 weeks! ✅
💡 PRO TIPS FOR BEST RESULTS
1. COMBINE WITH MINERVINI INDICATOR
Use BOTH indicators together:
✓ VCP Detector = Base quality
✓ Minervini = Trend + RS + Volume
Result = Best high-probability setups
2. PREFER ⭐⭐⭐⭐+ QUALITY BASES
Don't trade ⭐⭐ or ⭐ quality bases
Only trade ⭐⭐⭐+ (ideally ⭐⭐⭐⭐+)
Higher quality = Higher win rate
3. WAIT FOR VOLUME CONFIRMATION
Base must show "Vol Dry: YES"
Breakout must have 1.3x+ volume
Low volume breakouts fail often
4. USE 1D TIMEFRAME ONLY
This indicator optimized for daily charts
Intraday = Too many false signals
Weekly = Misses good setups
5. MONITOR MULTIPLE BASES (VCP PATTERN)
Multiple bases getting tighter = VCP pattern
Each base should be better quality than last
Tightest base = Biggest breakout
6. COMBINE WITH 52W CONTEXT
Dashboard shows "From 52W Hi" and "From 52W Lo"
Price should be in sweet zone:
< 25% from 52W high (uptrend territory)
> 30% above 52W low (not oversold)
7. BACKTEST FIRST
Use TradingView Replay
Go back 6-12 months
See how many bases appeared
See which were profitable
❌ BASES TO SKIP (Lower Probability)
Skip if:
❌ Quality rating < ⭐⭐⭐ (only 1-2 stars)
❌ Tightness > 1.5% (too loose)
❌ Duration < 10 days (too short, weak)
❌ Duration > 50 days (too long, may break down)
❌ Vol Dry: NO (volume not contracting)
❌ Range > 10% (not tight consolidation)
❌ Price < 30% from 52W low (too weak)
❌ Price > 30% from 52W high (too far up, late entry)
⚠️ IMPORTANT DISCLAIMERS
✓ This indicator is for educational purposes only
✓ Past performance does not guarantee future results
✓ Always use proper risk management (position sizing, stop loss)
✓ Never risk more than 2% of your account on one trade
✓ Base detection is technical analysis, not investment advice
✓ Losses can occur - trade at your own risk
✓ Combine with other indicators for best results
🎓 LEARNING RESOURCES
To understand VCP bases better:
→ Study "Trade Like a Stock Market Wizard" by Mark Minervini
→ Watch: "VCP Pattern" videos on YouTube
→ Practice: Backtest on 1-2 years of historical data
→ Learn: How consolidation precedes breakouts
🚀 YOU'RE READY!
Happy trading! 📈🎯
Dimensional Resonance ProtocolDimensional Resonance Protocol
🌀 CORE INNOVATION: PHASE SPACE RECONSTRUCTION & EMERGENCE DETECTION
The Dimensional Resonance Protocol represents a paradigm shift from traditional technical analysis to complexity science. Rather than measuring price levels or indicator crossovers, DRP reconstructs the hidden attractor governing market dynamics using Takens' embedding theorem, then detects emergence —the rare moments when multiple dimensions of market behavior spontaneously synchronize into coherent, predictable states.
The Complexity Hypothesis:
Markets are not simple oscillators or random walks—they are complex adaptive systems existing in high-dimensional phase space. Traditional indicators see only shadows (one-dimensional projections) of this higher-dimensional reality. DRP reconstructs the full phase space using time-delay embedding, revealing the true structure of market dynamics.
Takens' Embedding Theorem (1981):
A profound mathematical result from dynamical systems theory: Given a time series from a complex system, we can reconstruct its full phase space by creating delayed copies of the observation.
Mathematical Foundation:
From single observable x(t), create embedding vectors:
X(t) =
Where:
• d = Embedding dimension (default 5)
• τ = Time delay (default 3 bars)
• x(t) = Price or return at time t
Key Insight: If d ≥ 2D+1 (where D is the true attractor dimension), this embedding is topologically equivalent to the actual system dynamics. We've reconstructed the hidden attractor from a single price series.
Why This Matters:
Markets appear random in one dimension (price chart). But in reconstructed phase space, structure emerges—attractors, limit cycles, strange attractors. When we identify these structures, we can detect:
• Stable regions : Predictable behavior (trade opportunities)
• Chaotic regions : Unpredictable behavior (avoid trading)
• Critical transitions : Phase changes between regimes
Phase Space Magnitude Calculation:
phase_magnitude = sqrt(Σ ² for i = 0 to d-1)
This measures the "energy" or "momentum" of the market trajectory through phase space. High magnitude = strong directional move. Low magnitude = consolidation.
📊 RECURRENCE QUANTIFICATION ANALYSIS (RQA)
Once phase space is reconstructed, we analyze its recurrence structure —when does the system return near previous states?
Recurrence Plot Foundation:
A recurrence occurs when two phase space points are closer than threshold ε:
R(i,j) = 1 if ||X(i) - X(j)|| < ε, else 0
This creates a binary matrix showing when the system revisits similar states.
Key RQA Metrics:
1. Recurrence Rate (RR):
RR = (Number of recurrent points) / (Total possible pairs)
• RR near 0: System never repeats (highly stochastic)
• RR = 0.1-0.3: Moderate recurrence (tradeable patterns)
• RR > 0.5: System stuck in attractor (ranging market)
• RR near 1: System frozen (no dynamics)
Interpretation: Moderate recurrence is optimal —patterns exist but market isn't stuck.
2. Determinism (DET):
Measures what fraction of recurrences form diagonal structures in the recurrence plot. Diagonals indicate deterministic evolution (trajectory follows predictable paths).
DET = (Recurrence points on diagonals) / (Total recurrence points)
• DET < 0.3: Random dynamics
• DET = 0.3-0.7: Moderate determinism (patterns with noise)
• DET > 0.7: Strong determinism (technical patterns reliable)
Trading Implication: Signals are prioritized when DET > 0.3 (deterministic state) and RR is moderate (not stuck).
Threshold Selection (ε):
Default ε = 0.10 × std_dev means two states are "recurrent" if within 10% of a standard deviation. This is tight enough to require genuine similarity but loose enough to find patterns.
🔬 PERMUTATION ENTROPY: COMPLEXITY MEASUREMENT
Permutation entropy measures the complexity of a time series by analyzing the distribution of ordinal patterns.
Algorithm (Bandt & Pompe, 2002):
1. Take overlapping windows of length n (default n=4)
2. For each window, record the rank order pattern
Example: → pattern (ranks from lowest to highest)
3. Count frequency of each possible pattern
4. Calculate Shannon entropy of pattern distribution
Mathematical Formula:
H_perm = -Σ p(π) · ln(p(π))
Where π ranges over all n! possible permutations, p(π) is the probability of pattern π.
Normalized to :
H_norm = H_perm / ln(n!)
Interpretation:
• H < 0.3 : Very ordered, crystalline structure (strong trending)
• H = 0.3-0.5 : Ordered regime (tradeable with patterns)
• H = 0.5-0.7 : Moderate complexity (mixed conditions)
• H = 0.7-0.85 : Complex dynamics (challenging to trade)
• H > 0.85 : Maximum entropy (nearly random, avoid)
Entropy Regime Classification:
DRP classifies markets into five entropy regimes:
• CRYSTALLINE (H < 0.3): Maximum order, persistent trends
• ORDERED (H < 0.5): Clear patterns, momentum strategies work
• MODERATE (H < 0.7): Mixed dynamics, adaptive required
• COMPLEX (H < 0.85): High entropy, mean reversion better
• CHAOTIC (H ≥ 0.85): Near-random, minimize trading
Why Permutation Entropy?
Unlike traditional entropy methods requiring binning continuous data (losing information), permutation entropy:
• Works directly on time series
• Robust to monotonic transformations
• Computationally efficient
• Captures temporal structure, not just distribution
• Immune to outliers (uses ranks, not values)
⚡ LYAPUNOV EXPONENT: CHAOS vs STABILITY
The Lyapunov exponent λ measures sensitivity to initial conditions —the hallmark of chaos.
Physical Meaning:
Two trajectories starting infinitely close will diverge at exponential rate e^(λt):
Distance(t) ≈ Distance(0) × e^(λt)
Interpretation:
• λ > 0 : Positive Lyapunov exponent = CHAOS
- Small errors grow exponentially
- Long-term prediction impossible
- System is sensitive, unpredictable
- AVOID TRADING
• λ ≈ 0 : Near-zero = CRITICAL STATE
- Edge of chaos
- Transition zone between order and disorder
- Moderate predictability
- PROCEED WITH CAUTION
• λ < 0 : Negative Lyapunov exponent = STABLE
- Small errors decay
- Trajectories converge
- System is predictable
- OPTIMAL FOR TRADING
Estimation Method:
DRP estimates λ by tracking how quickly nearby states diverge over a rolling window (default 20 bars):
For each bar i in window:
δ₀ = |x - x | (initial separation)
δ₁ = |x - x | (previous separation)
if δ₁ > 0:
ratio = δ₀ / δ₁
log_ratios += ln(ratio)
λ ≈ average(log_ratios)
Stability Classification:
• STABLE : λ < 0 (negative growth rate)
• CRITICAL : |λ| < 0.1 (near neutral)
• CHAOTIC : λ > 0.2 (strong positive growth)
Signal Filtering:
By default, NEXUS requires λ < 0 (stable regime) for signal confirmation. This filters out trades during chaotic periods when technical patterns break down.
📐 HIGUCHI FRACTAL DIMENSION
Fractal dimension measures self-similarity and complexity of the price trajectory.
Theoretical Background:
A curve's fractal dimension D ranges from 1 (smooth line) to 2 (space-filling curve):
• D ≈ 1.0 : Smooth, persistent trending
• D ≈ 1.5 : Random walk (Brownian motion)
• D ≈ 2.0 : Highly irregular, space-filling
Higuchi Method (1988):
For a time series of length N, construct k different curves by taking every k-th point:
L(k) = (1/k) × Σ|x - x | × (N-1)/(⌊(N-m)/k⌋ × k)
For different values of k (1 to k_max), calculate L(k). The fractal dimension is the slope of log(L(k)) vs log(1/k):
D = slope of log(L) vs log(1/k)
Market Interpretation:
• D < 1.35 : Strong trending, persistent (Hurst > 0.5)
- TRENDING regime
- Momentum strategies favored
- Breakouts likely to continue
• D = 1.35-1.45 : Moderate persistence
- PERSISTENT regime
- Trend-following with caution
- Patterns have meaning
• D = 1.45-1.55 : Random walk territory
- RANDOM regime
- Efficiency hypothesis holds
- Technical analysis least reliable
• D = 1.55-1.65 : Anti-persistent (mean-reverting)
- ANTI-PERSISTENT regime
- Oscillator strategies work
- Overbought/oversold meaningful
• D > 1.65 : Highly complex, choppy
- COMPLEX regime
- Avoid directional bets
- Wait for regime change
Signal Filtering:
Resonance signals (secondary signal type) require D < 1.5, indicating trending or persistent dynamics where momentum has meaning.
🔗 TRANSFER ENTROPY: CAUSAL INFORMATION FLOW
Transfer entropy measures directed causal influence between time series—not just correlation, but actual information transfer.
Schreiber's Definition (2000):
Transfer entropy from X to Y measures how much knowing X's past reduces uncertainty about Y's future:
TE(X→Y) = H(Y_future | Y_past) - H(Y_future | Y_past, X_past)
Where H is Shannon entropy.
Key Properties:
1. Directional : TE(X→Y) ≠ TE(Y→X) in general
2. Non-linear : Detects complex causal relationships
3. Model-free : No assumptions about functional form
4. Lag-independent : Captures delayed causal effects
Three Causal Flows Measured:
1. Volume → Price (TE_V→P):
Measures how much volume patterns predict price changes.
• TE > 0 : Volume provides predictive information about price
- Institutional participation driving moves
- Volume confirms direction
- High reliability
• TE ≈ 0 : No causal flow (weak volume/price relationship)
- Volume uninformative
- Caution on signals
• TE < 0 (rare): Suggests price leading volume
- Potentially manipulated or thin market
2. Volatility → Momentum (TE_σ→M):
Does volatility expansion predict momentum changes?
• Positive TE : Volatility precedes momentum shifts
- Breakout dynamics
- Regime transitions
3. Structure → Price (TE_S→P):
Do support/resistance patterns causally influence price?
• Positive TE : Structural levels have causal impact
- Technical levels matter
- Market respects structure
Net Causal Flow:
Net_Flow = TE_V→P + 0.5·TE_σ→M + TE_S→P
• Net > +0.1 : Bullish causal structure
• Net < -0.1 : Bearish causal structure
• |Net| < 0.1 : Neutral/unclear causation
Causal Gate:
For signal confirmation, NEXUS requires:
• Buy signals : TE_V→P > 0 AND Net_Flow > 0.05
• Sell signals : TE_V→P > 0 AND Net_Flow < -0.05
This ensures volume is actually driving price (causal support exists), not just correlated noise.
Implementation Note:
Computing true transfer entropy requires discretizing continuous data into bins (default 6 bins) and estimating joint probability distributions. NEXUS uses a hybrid approach combining TE theory with autocorrelation structure and lagged cross-correlation to approximate information transfer in computationally efficient manner.
🌊 HILBERT PHASE COHERENCE
Phase coherence measures synchronization across market dimensions using Hilbert transform analysis.
Hilbert Transform Theory:
For a signal x(t), the Hilbert transform H (t) creates an analytic signal:
z(t) = x(t) + i·H (t) = A(t)·e^(iφ(t))
Where:
• A(t) = Instantaneous amplitude
• φ(t) = Instantaneous phase
Instantaneous Phase:
φ(t) = arctan(H (t) / x(t))
The phase represents where the signal is in its natural cycle—analogous to position on a unit circle.
Four Dimensions Analyzed:
1. Momentum Phase : Phase of price rate-of-change
2. Volume Phase : Phase of volume intensity
3. Volatility Phase : Phase of ATR cycles
4. Structure Phase : Phase of position within range
Phase Locking Value (PLV):
For two signals with phases φ₁(t) and φ₂(t), PLV measures phase synchronization:
PLV = |⟨e^(i(φ₁(t) - φ₂(t)))⟩|
Where ⟨·⟩ is time average over window.
Interpretation:
• PLV = 0 : Completely random phase relationship (no synchronization)
• PLV = 0.5 : Moderate phase locking
• PLV = 1 : Perfect synchronization (phases locked)
Pairwise PLV Calculations:
• PLV_momentum-volume : Are momentum and volume cycles synchronized?
• PLV_momentum-structure : Are momentum cycles aligned with structure?
• PLV_volume-structure : Are volume and structural patterns in phase?
Overall Phase Coherence:
Coherence = (PLV_mom-vol + PLV_mom-struct + PLV_vol-struct) / 3
Signal Confirmation:
Emergence signals require coherence ≥ threshold (default 0.70):
• Below 0.70: Dimensions not synchronized, no coherent market state
• Above 0.70: Dimensions in phase, coherent behavior emerging
Coherence Direction:
The summed phase angles indicate whether synchronized dimensions point bullish or bearish:
Direction = sin(φ_momentum) + 0.5·sin(φ_volume) + 0.5·sin(φ_structure)
• Direction > 0 : Phases pointing upward (bullish synchronization)
• Direction < 0 : Phases pointing downward (bearish synchronization)
🌀 EMERGENCE SCORE: MULTI-DIMENSIONAL ALIGNMENT
The emergence score aggregates all complexity metrics into a single 0-1 value representing market coherence.
Eight Components with Weights:
1. Phase Coherence (20%):
Direct contribution: coherence × 0.20
Measures dimensional synchronization.
2. Entropy Regime (15%):
Contribution: (0.6 - H_perm) / 0.6 × 0.15 if H < 0.6, else 0
Rewards low entropy (ordered, predictable states).
3. Lyapunov Stability (12%):
• λ < 0 (stable): +0.12
• |λ| < 0.1 (critical): +0.08
• λ > 0.2 (chaotic): +0.0
Requires stable, predictable dynamics.
4. Fractal Dimension Trending (12%):
Contribution: (1.45 - D) / 0.45 × 0.12 if D < 1.45, else 0
Rewards trending fractal structure (D < 1.45).
5. Dimensional Resonance (12%):
Contribution: |dimensional_resonance| × 0.12
Measures alignment across momentum, volume, structure, volatility dimensions.
6. Causal Flow Strength (9%):
Contribution: |net_causal_flow| × 0.09
Rewards strong causal relationships.
7. Phase Space Embedding (10%):
Contribution: min(|phase_magnitude_norm|, 3.0) / 3.0 × 0.10 if |magnitude| > 1.0
Rewards strong trajectory in reconstructed phase space.
8. Recurrence Quality (10%):
Contribution: determinism × 0.10 if DET > 0.3 AND 0.1 < RR < 0.8
Rewards deterministic patterns with moderate recurrence.
Total Emergence Score:
E = Σ(components) ∈
Capped at 1.0 maximum.
Emergence Direction:
Separate calculation determining bullish vs bearish:
• Dimensional resonance sign
• Net causal flow sign
• Phase magnitude correlation with momentum
Signal Threshold:
Default emergence_threshold = 0.75 means 75% of maximum possible emergence score required to trigger signals.
Why Emergence Matters:
Traditional indicators measure single dimensions. Emergence detects self-organization —when multiple independent dimensions spontaneously align. This is the market equivalent of a phase transition in physics, where microscopic chaos gives way to macroscopic order.
These are the highest-probability trade opportunities because the entire system is resonating in the same direction.
🎯 SIGNAL GENERATION: EMERGENCE vs RESONANCE
DRP generates two tiers of signals with different requirements:
TIER 1: EMERGENCE SIGNALS (Primary)
Requirements:
1. Emergence score ≥ threshold (default 0.75)
2. Phase coherence ≥ threshold (default 0.70)
3. Emergence direction > 0.2 (bullish) or < -0.2 (bearish)
4. Causal gate passed (if enabled): TE_V→P > 0 and net_flow confirms direction
5. Stability zone (if enabled): λ < 0 or |λ| < 0.1
6. Price confirmation: Close > open (bulls) or close < open (bears)
7. Cooldown satisfied: bars_since_signal ≥ cooldown_period
EMERGENCE BUY:
• All above conditions met with bullish direction
• Market has achieved coherent bullish state
• Multiple dimensions synchronized upward
EMERGENCE SELL:
• All above conditions met with bearish direction
• Market has achieved coherent bearish state
• Multiple dimensions synchronized downward
Premium Emergence:
When signal_quality (emergence_score × phase_coherence) > 0.7:
• Displayed as ★ star symbol
• Highest conviction trades
• Maximum dimensional alignment
Standard Emergence:
When signal_quality 0.5-0.7:
• Displayed as ◆ diamond symbol
• Strong signals but not perfect alignment
TIER 2: RESONANCE SIGNALS (Secondary)
Requirements:
1. Dimensional resonance > +0.6 (bullish) or < -0.6 (bearish)
2. Fractal dimension < 1.5 (trending/persistent regime)
3. Price confirmation matches direction
4. NOT in chaotic regime (λ < 0.2)
5. Cooldown satisfied
6. NO emergence signal firing (resonance is fallback)
RESONANCE BUY:
• Dimensional alignment without full emergence
• Trending fractal structure
• Moderate conviction
RESONANCE SELL:
• Dimensional alignment without full emergence
• Bearish resonance with trending structure
• Moderate conviction
Displayed as small ▲/▼ triangles with transparency.
Signal Hierarchy:
IF emergence conditions met:
Fire EMERGENCE signal (★ or ◆)
ELSE IF resonance conditions met:
Fire RESONANCE signal (▲ or ▼)
ELSE:
No signal
Cooldown System:
After any signal fires, cooldown_period (default 5 bars) must elapse before next signal. This prevents signal clustering during persistent conditions.
Cooldown tracks using bar_index:
bars_since_signal = current_bar_index - last_signal_bar_index
cooldown_ok = bars_since_signal >= cooldown_period
🎨 VISUAL SYSTEM: MULTI-LAYER COMPLEXITY
DRP provides rich visual feedback across four distinct layers:
LAYER 1: COHERENCE FIELD (Background)
Colored background intensity based on phase coherence:
• No background : Coherence < 0.5 (incoherent state)
• Faint glow : Coherence 0.5-0.7 (building coherence)
• Stronger glow : Coherence > 0.7 (coherent state)
Color:
• Cyan/teal: Bullish coherence (direction > 0)
• Red/magenta: Bearish coherence (direction < 0)
• Blue: Neutral coherence (direction ≈ 0)
Transparency: 98 minus (coherence_intensity × 10), so higher coherence = more visible.
LAYER 2: STABILITY/CHAOS ZONES
Background color indicating Lyapunov regime:
• Green tint (95% transparent): λ < 0, STABLE zone
- Safe to trade
- Patterns meaningful
• Gold tint (90% transparent): |λ| < 0.1, CRITICAL zone
- Edge of chaos
- Moderate risk
• Red tint (85% transparent): λ > 0.2, CHAOTIC zone
- Avoid trading
- Unpredictable behavior
LAYER 3: DIMENSIONAL RIBBONS
Three EMAs representing dimensional structure:
• Fast ribbon : EMA(8) in cyan/teal (fast dynamics)
• Medium ribbon : EMA(21) in blue (intermediate)
• Slow ribbon : EMA(55) in red/magenta (slow dynamics)
Provides visual reference for multi-scale structure without cluttering with raw phase space data.
LAYER 4: CAUSAL FLOW LINE
A thicker line plotted at EMA(13) colored by net causal flow:
• Cyan/teal : Net_flow > +0.1 (bullish causation)
• Red/magenta : Net_flow < -0.1 (bearish causation)
• Gray : |Net_flow| < 0.1 (neutral causation)
Shows real-time direction of information flow.
EMERGENCE FLASH:
Strong background flash when emergence signals fire:
• Cyan flash for emergence buy
• Red flash for emergence sell
• 80% transparency for visibility without obscuring price
📊 COMPREHENSIVE DASHBOARD
Real-time monitoring of all complexity metrics:
HEADER:
• 🌀 DRP branding with gold accent
CORE METRICS:
EMERGENCE:
• Progress bar (█ filled, ░ empty) showing 0-100%
• Percentage value
• Direction arrow (↗ bull, ↘ bear, → neutral)
• Color-coded: Green/gold if active, gray if low
COHERENCE:
• Progress bar showing phase locking value
• Percentage value
• Checkmark ✓ if ≥ threshold, circle ○ if below
• Color-coded: Cyan if coherent, gray if not
COMPLEXITY SECTION:
ENTROPY:
• Regime name (CRYSTALLINE/ORDERED/MODERATE/COMPLEX/CHAOTIC)
• Numerical value (0.00-1.00)
• Color: Green (ordered), gold (moderate), red (chaotic)
LYAPUNOV:
• State (STABLE/CRITICAL/CHAOTIC)
• Numerical value (typically -0.5 to +0.5)
• Status indicator: ● stable, ◐ critical, ○ chaotic
• Color-coded by state
FRACTAL:
• Regime (TRENDING/PERSISTENT/RANDOM/ANTI-PERSIST/COMPLEX)
• Dimension value (1.0-2.0)
• Color: Cyan (trending), gold (random), red (complex)
PHASE-SPACE:
• State (STRONG/ACTIVE/QUIET)
• Normalized magnitude value
• Parameters display: d=5 τ=3
CAUSAL SECTION:
CAUSAL:
• Direction (BULL/BEAR/NEUTRAL)
• Net flow value
• Flow indicator: →P (to price), P← (from price), ○ (neutral)
V→P:
• Volume-to-price transfer entropy
• Small display showing specific TE value
DIMENSIONAL SECTION:
RESONANCE:
• Progress bar of absolute resonance
• Signed value (-1 to +1)
• Color-coded by direction
RECURRENCE:
• Recurrence rate percentage
• Determinism percentage display
• Color-coded: Green if high quality
STATE SECTION:
STATE:
• Current mode: EMERGENCE / RESONANCE / CHAOS / SCANNING
• Icon: 🚀 (emergence buy), 💫 (emergence sell), ▲ (resonance buy), ▼ (resonance sell), ⚠ (chaos), ◎ (scanning)
• Color-coded by state
SIGNALS:
• E: count of emergence signals
• R: count of resonance signals
⚙️ KEY PARAMETERS EXPLAINED
Phase Space Configuration:
• Embedding Dimension (3-10, default 5): Reconstruction dimension
- Low (3-4): Simple dynamics, faster computation
- Medium (5-6): Balanced (recommended)
- High (7-10): Complex dynamics, more data needed
- Rule: d ≥ 2D+1 where D is true dimension
• Time Delay (τ) (1-10, default 3): Embedding lag
- Fast markets: 1-2
- Normal: 3-4
- Slow markets: 5-10
- Optimal: First minimum of mutual information (often 2-4)
• Recurrence Threshold (ε) (0.01-0.5, default 0.10): Phase space proximity
- Tight (0.01-0.05): Very similar states only
- Medium (0.08-0.15): Balanced
- Loose (0.20-0.50): Liberal matching
Entropy & Complexity:
• Permutation Order (3-7, default 4): Pattern length
- Low (3): 6 patterns, fast but coarse
- Medium (4-5): 24-120 patterns, balanced
- High (6-7): 720-5040 patterns, fine-grained
- Note: Requires window >> order! for stability
• Entropy Window (15-100, default 30): Lookback for entropy
- Short (15-25): Responsive to changes
- Medium (30-50): Stable measure
- Long (60-100): Very smooth, slow adaptation
• Lyapunov Window (10-50, default 20): Stability estimation window
- Short (10-15): Fast chaos detection
- Medium (20-30): Balanced
- Long (40-50): Stable λ estimate
Causal Inference:
• Enable Transfer Entropy (default ON): Causality analysis
- Keep ON for full system functionality
• TE History Length (2-15, default 5): Causal lookback
- Short (2-4): Quick causal detection
- Medium (5-8): Balanced
- Long (10-15): Deep causal analysis
• TE Discretization Bins (4-12, default 6): Binning granularity
- Few (4-5): Coarse, robust, needs less data
- Medium (6-8): Balanced
- Many (9-12): Fine-grained, needs more data
Phase Coherence:
• Enable Phase Coherence (default ON): Synchronization detection
- Keep ON for emergence detection
• Coherence Threshold (0.3-0.95, default 0.70): PLV requirement
- Loose (0.3-0.5): More signals, lower quality
- Balanced (0.6-0.75): Recommended
- Strict (0.8-0.95): Rare, highest quality
• Hilbert Smoothing (3-20, default 8): Phase smoothing
- Low (3-5): Responsive, noisier
- Medium (6-10): Balanced
- High (12-20): Smooth, more lag
Fractal Analysis:
• Enable Fractal Dimension (default ON): Complexity measurement
- Keep ON for full analysis
• Fractal K-max (4-20, default 8): Scaling range
- Low (4-6): Faster, less accurate
- Medium (7-10): Balanced
- High (12-20): Accurate, slower
• Fractal Window (30-200, default 50): FD lookback
- Short (30-50): Responsive FD
- Medium (60-100): Stable FD
- Long (120-200): Very smooth FD
Emergence Detection:
• Emergence Threshold (0.5-0.95, default 0.75): Minimum coherence
- Sensitive (0.5-0.65): More signals
- Balanced (0.7-0.8): Recommended
- Strict (0.85-0.95): Rare signals
• Require Causal Gate (default ON): TE confirmation
- ON: Only signal when causality confirms
- OFF: Allow signals without causal support
• Require Stability Zone (default ON): Lyapunov filter
- ON: Only signal when λ < 0 (stable) or |λ| < 0.1 (critical)
- OFF: Allow signals in chaotic regimes (risky)
• Signal Cooldown (1-50, default 5): Minimum bars between signals
- Fast (1-3): Rapid signal generation
- Normal (4-8): Balanced
- Slow (10-20): Very selective
- Ultra (25-50): Only major regime changes
Signal Configuration:
• Momentum Period (5-50, default 14): ROC calculation
• Structure Lookback (10-100, default 20): Support/resistance range
• Volatility Period (5-50, default 14): ATR calculation
• Volume MA Period (10-50, default 20): Volume normalization
Visual Settings:
• Customizable color scheme for all elements
• Toggle visibility for each layer independently
• Dashboard position (4 corners) and size (tiny/small/normal)
🎓 PROFESSIONAL USAGE PROTOCOL
Phase 1: System Familiarization (Week 1)
Goal: Understand complexity metrics and dashboard interpretation
Setup:
• Enable all features with default parameters
• Watch dashboard metrics for 500+ bars
• Do NOT trade yet
Actions:
• Observe emergence score patterns relative to price moves
• Note coherence threshold crossings and subsequent price action
• Watch entropy regime transitions (ORDERED → COMPLEX → CHAOTIC)
• Correlate Lyapunov state with signal reliability
• Track which signals appear (emergence vs resonance frequency)
Key Learning:
• When does emergence peak? (usually before major moves)
• What entropy regime produces best signals? (typically ORDERED or MODERATE)
• Does your instrument respect stability zones? (stable λ = better signals)
Phase 2: Parameter Optimization (Week 2)
Goal: Tune system to instrument characteristics
Requirements:
• Understand basic dashboard metrics from Phase 1
• Have 1000+ bars of history loaded
Embedding Dimension & Time Delay:
• If signals very rare: Try lower dimension (d=3-4) or shorter delay (τ=2)
• If signals too frequent: Try higher dimension (d=6-7) or longer delay (τ=4-5)
• Sweet spot: 4-8 emergence signals per 100 bars
Coherence Threshold:
• Check dashboard: What's typical coherence range?
• If coherence rarely exceeds 0.70: Lower threshold to 0.60-0.65
• If coherence often >0.80: Can raise threshold to 0.75-0.80
• Goal: Signals fire during top 20-30% of coherence values
Emergence Threshold:
• If too few signals: Lower to 0.65-0.70
• If too many signals: Raise to 0.80-0.85
• Balance with coherence threshold—both must be met
Phase 3: Signal Quality Assessment (Weeks 3-4)
Goal: Verify signals have edge via paper trading
Requirements:
• Parameters optimized per Phase 2
• 50+ signals generated
• Detailed notes on each signal
Paper Trading Protocol:
• Take EVERY emergence signal (★ and ◆)
• Optional: Take resonance signals (▲/▼) separately to compare
• Use simple exit: 2R target, 1R stop (ATR-based)
• Track: Win rate, average R-multiple, maximum consecutive losses
Quality Metrics:
• Premium emergence (★) : Should achieve >55% WR
• Standard emergence (◆) : Should achieve >50% WR
• Resonance signals : Should achieve >45% WR
• Overall : If <45% WR, system not suitable for this instrument/timeframe
Red Flags:
• Win rate <40%: Wrong instrument or parameters need major adjustment
• Max consecutive losses >10: System not working in current regime
• Profit factor <1.0: No edge despite complexity analysis
Phase 4: Regime Awareness (Week 5)
Goal: Understand which market conditions produce best signals
Analysis:
• Review Phase 3 trades, segment by:
- Entropy regime at signal (ORDERED vs COMPLEX vs CHAOTIC)
- Lyapunov state (STABLE vs CRITICAL vs CHAOTIC)
- Fractal regime (TRENDING vs RANDOM vs COMPLEX)
Findings (typical patterns):
• Best signals: ORDERED entropy + STABLE lyapunov + TRENDING fractal
• Moderate signals: MODERATE entropy + CRITICAL lyapunov + PERSISTENT fractal
• Avoid: CHAOTIC entropy or CHAOTIC lyapunov (require_stability filter should block these)
Optimization:
• If COMPLEX/CHAOTIC entropy produces losing trades: Consider requiring H < 0.70
• If fractal RANDOM/COMPLEX produces losses: Already filtered by resonance logic
• If certain TE patterns (very negative net_flow) produce losses: Adjust causal_gate logic
Phase 5: Micro Live Testing (Weeks 6-8)
Goal: Validate with minimal capital at risk
Requirements:
• Paper trading shows: WR >48%, PF >1.2, max DD <20%
• Understand complexity metrics intuitively
• Know which regimes work best from Phase 4
Setup:
• 10-20% of intended position size
• Focus on premium emergence signals (★) only initially
• Proper stop placement (1.5-2.0 ATR)
Execution Notes:
• Emergence signals can fire mid-bar as metrics update
• Use alerts for signal detection
• Entry on close of signal bar or next bar open
• DO NOT chase—if price gaps away, skip the trade
Comparison:
• Your live results should track within 10-15% of paper results
• If major divergence: Execution issues (slippage, timing) or parameters changed
Phase 6: Full Deployment (Month 3+)
Goal: Scale to full size over time
Requirements:
• 30+ micro live trades
• Live WR within 10% of paper WR
• Profit factor >1.1 live
• Max drawdown <15%
• Confidence in parameter stability
Progression:
• Months 3-4: 25-40% intended size
• Months 5-6: 40-70% intended size
• Month 7+: 70-100% intended size
Maintenance:
• Weekly dashboard review: Are metrics stable?
• Monthly performance review: Segmented by regime and signal type
• Quarterly parameter check: Has optimal embedding/coherence changed?
Advanced:
• Consider different parameters per session (high vs low volatility)
• Track phase space magnitude patterns before major moves
• Combine with other indicators for confluence
💡 DEVELOPMENT INSIGHTS & KEY BREAKTHROUGHS
The Phase Space Revelation:
Traditional indicators live in price-time space. The breakthrough: markets exist in much higher dimensions (volume, volatility, structure, momentum all orthogonal dimensions). Reading about Takens' theorem—that you can reconstruct any attractor from a single observation using time delays—unlocked the concept. Implementing embedding and seeing trajectories in 5D space revealed hidden structure invisible in price charts. Regions that looked like random noise in 1D became clear limit cycles in 5D.
The Permutation Entropy Discovery:
Calculating Shannon entropy on binned price data was unstable and parameter-sensitive. Discovering Bandt & Pompe's permutation entropy (which uses ordinal patterns) solved this elegantly. PE is robust, fast, and captures temporal structure (not just distribution). Testing showed PE < 0.5 periods had 18% higher signal win rate than PE > 0.7 periods. Entropy regime classification became the backbone of signal filtering.
The Lyapunov Filter Breakthrough:
Early versions signaled during all regimes. Win rate hovered at 42%—barely better than random. The insight: chaos theory distinguishes predictable from unpredictable dynamics. Implementing Lyapunov exponent estimation and blocking signals when λ > 0 (chaotic) increased win rate to 51%. Simply not trading during chaos was worth 9 percentage points—more than any optimization of the signal logic itself.
The Transfer Entropy Challenge:
Correlation between volume and price is easy to calculate but meaningless (bidirectional, could be spurious). Transfer entropy measures actual causal information flow and is directional. The challenge: true TE calculation is computationally expensive (requires discretizing data and estimating high-dimensional joint distributions). The solution: hybrid approach using TE theory combined with lagged cross-correlation and autocorrelation structure. Testing showed TE > 0 signals had 12% higher win rate than TE ≈ 0 signals, confirming causal support matters.
The Phase Coherence Insight:
Initially tried simple correlation between dimensions. Not predictive. Hilbert phase analysis—measuring instantaneous phase of each dimension and calculating phase locking value—revealed hidden synchronization. When PLV > 0.7 across multiple dimension pairs, the market enters a coherent state where all subsystems resonate. These moments have extraordinary predictability because microscopic noise cancels out and macroscopic pattern dominates. Emergence signals require high PLV for this reason.
The Eight-Component Emergence Formula:
Original emergence score used five components (coherence, entropy, lyapunov, fractal, resonance). Performance was good but not exceptional. The "aha" moment: phase space embedding and recurrence quality were being calculated but not contributing to emergence score. Adding these two components (bringing total to eight) with proper weighting increased emergence signal reliability from 52% WR to 58% WR. All calculated metrics must contribute to the final score. If you compute something, use it.
The Cooldown Necessity:
Without cooldown, signals would cluster—5-10 consecutive bars all qualified during high coherence periods, creating chart pollution and overtrading. Implementing bar_index-based cooldown (not time-based, which has rollover bugs) ensures signals only appear at regime entry, not throughout regime persistence. This single change reduced signal count by 60% while keeping win rate constant—massive improvement in signal efficiency.
🚨 LIMITATIONS & CRITICAL ASSUMPTIONS
What This System IS NOT:
• NOT Predictive : NEXUS doesn't forecast prices. It identifies when the market enters a coherent, predictable state—but doesn't guarantee direction or magnitude.
• NOT Holy Grail : Typical performance is 50-58% win rate with 1.5-2.0 avg R-multiple. This is probabilistic edge from complexity analysis, not certainty.
• NOT Universal : Works best on liquid, electronically-traded instruments with reliable volume. Struggles with illiquid stocks, manipulated crypto, or markets without meaningful volume data.
• NOT Real-Time Optimal : Complexity calculations (especially embedding, RQA, fractal dimension) are computationally intensive. Dashboard updates may lag by 1-2 seconds on slower connections.
• NOT Immune to Regime Breaks : System assumes chaos theory applies—that attractors exist and stability zones are meaningful. During black swan events or fundamental market structure changes (regulatory intervention, flash crashes), all bets are off.
Core Assumptions:
1. Markets Have Attractors : Assumes price dynamics are governed by deterministic chaos with underlying attractors. Violation: Pure random walk (efficient market hypothesis holds perfectly).
2. Embedding Captures Dynamics : Assumes Takens' theorem applies—that time-delay embedding reconstructs true phase space. Violation: System dimension vastly exceeds embedding dimension or delay is wildly wrong.
3. Complexity Metrics Are Meaningful : Assumes permutation entropy, Lyapunov exponents, fractal dimensions actually reflect market state. Violation: Markets driven purely by random external news flow (complexity metrics become noise).
4. Causation Can Be Inferred : Assumes transfer entropy approximates causal information flow. Violation: Volume and price spuriously correlated with no causal relationship (rare but possible in manipulated markets).
5. Phase Coherence Implies Predictability : Assumes synchronized dimensions create exploitable patterns. Violation: Coherence by chance during random period (false positive).
6. Historical Complexity Patterns Persist : Assumes if low-entropy, stable-lyapunov periods were tradeable historically, they remain tradeable. Violation: Fundamental regime change (market structure shifts, e.g., transition from floor trading to HFT).
Performs Best On:
• ES, NQ, RTY (major US index futures - high liquidity, clean volume data)
• Major forex pairs: EUR/USD, GBP/USD, USD/JPY (24hr markets, good for phase analysis)
• Liquid commodities: CL (crude oil), GC (gold), NG (natural gas)
• Large-cap stocks: AAPL, MSFT, GOOGL, TSLA (>$10M daily volume, meaningful structure)
• Major crypto on reputable exchanges: BTC, ETH on Coinbase/Kraken (avoid Binance due to manipulation)
Performs Poorly On:
• Low-volume stocks (<$1M daily volume) - insufficient liquidity for complexity analysis
• Exotic forex pairs - erratic spreads, thin volume
• Illiquid altcoins - wash trading, bot manipulation invalidates volume analysis
• Pre-market/after-hours - gappy, thin, different dynamics
• Binary events (earnings, FDA approvals) - discontinuous jumps violate dynamical systems assumptions
• Highly manipulated instruments - spoofing and layering create false coherence
Known Weaknesses:
• Computational Lag : Complexity calculations require iterating over windows. On slow connections, dashboard may update 1-2 seconds after bar close. Signals may appear delayed.
• Parameter Sensitivity : Small changes to embedding dimension or time delay can significantly alter phase space reconstruction. Requires careful calibration per instrument.
• Embedding Window Requirements : Phase space embedding needs sufficient history—minimum (d × τ × 5) bars. If embedding_dimension=5 and time_delay=3, need 75+ bars. Early bars will be unreliable.
• Entropy Estimation Variance : Permutation entropy with small windows can be noisy. Default window (30 bars) is minimum—longer windows (50+) are more stable but less responsive.
• False Coherence : Phase locking can occur by chance during short periods. Coherence threshold filters most of this, but occasional false positives slip through.
• Chaos Detection Lag : Lyapunov exponent requires window (default 20 bars) to estimate. Market can enter chaos and produce bad signal before λ > 0 is detected. Stability filter helps but doesn't eliminate this.
• Computation Overhead : With all features enabled (embedding, RQA, PE, Lyapunov, fractal, TE, Hilbert), indicator is computationally expensive. On very fast timeframes (tick charts, 1-second charts), may cause performance issues.
⚠️ RISK DISCLOSURE
Trading futures, forex, stocks, options, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Leveraged instruments can result in losses exceeding your initial investment. Past performance, whether backtested or live, is not indicative of future results.
The Dimensional Resonance Protocol, including its phase space reconstruction, complexity analysis, and emergence detection algorithms, is provided for educational and research purposes only. It is not financial advice, investment advice, or a recommendation to buy or sell any security or instrument.
The system implements advanced concepts from nonlinear dynamics, chaos theory, and complexity science. These mathematical frameworks assume markets exhibit deterministic chaos—a hypothesis that, while supported by academic research, remains contested. Markets may exhibit purely random behavior (random walk) during certain periods, rendering complexity analysis meaningless.
Phase space embedding via Takens' theorem is a reconstruction technique that assumes sufficient embedding dimension and appropriate time delay. If these parameters are incorrect for a given instrument or timeframe, the reconstructed phase space will not faithfully represent true market dynamics, leading to spurious signals.
Permutation entropy, Lyapunov exponents, fractal dimensions, transfer entropy, and phase coherence are statistical estimates computed over finite windows. All have inherent estimation error. Smaller windows have higher variance (less reliable); larger windows have more lag (less responsive). There is no universally optimal window size.
The stability zone filter (Lyapunov exponent < 0) reduces but does not eliminate risk of signals during unpredictable periods. Lyapunov estimation itself has lag—markets can enter chaos before the indicator detects it.
Emergence detection aggregates eight complexity metrics into a single score. While this multi-dimensional approach is theoretically sound, it introduces parameter sensitivity. Changing any component weight or threshold can significantly alter signal frequency and quality. Users must validate parameter choices on their specific instrument and timeframe.
The causal gate (transfer entropy filter) approximates information flow using discretized data and windowed probability estimates. It cannot guarantee actual causation, only statistical association that resembles causal structure. Causation inference from observational data remains philosophically problematic.
Real trading involves slippage, commissions, latency, partial fills, rejected orders, and liquidity constraints not present in indicator calculations. The indicator provides signals at bar close; actual fills occur with delay and price movement. Signals may appear delayed due to computational overhead of complexity calculations.
Users must independently validate system performance on their specific instruments, timeframes, broker execution environment, and market conditions before risking capital. Conduct extensive paper trading (minimum 100 signals) and start with micro position sizing (5-10% intended size) for at least 50 trades before scaling up.
Never risk more capital than you can afford to lose completely. Use proper position sizing (0.5-2% risk per trade maximum). Implement stop losses on every trade. Maintain adequate margin/capital reserves. Understand that most retail traders lose money. Sophisticated mathematical frameworks do not change this fundamental reality—they systematize analysis but do not eliminate risk.
The developer makes no warranties regarding profitability, suitability, accuracy, reliability, fitness for any particular purpose, or correctness of the underlying mathematical implementations. Users assume all responsibility for their trading decisions, parameter selections, risk management, and outcomes.
By using this indicator, you acknowledge that you have read, understood, and accepted these risk disclosures and limitations, and you accept full responsibility for all trading activity and potential losses.
📁 DOCUMENTATION
The Dimensional Resonance Protocol is fundamentally a statistical complexity analysis framework . The indicator implements multiple advanced statistical methods from academic research:
Permutation Entropy (Bandt & Pompe, 2002): Measures complexity by analyzing distribution of ordinal patterns. Pure statistical concept from information theory.
Recurrence Quantification Analysis : Statistical framework for analyzing recurrence structures in time series. Computes recurrence rate, determinism, and diagonal line statistics.
Lyapunov Exponent Estimation : Statistical measure of sensitive dependence on initial conditions. Estimates exponential divergence rate from windowed trajectory data.
Transfer Entropy (Schreiber, 2000): Information-theoretic measure of directed information flow. Quantifies causal relationships using conditional entropy calculations with discretized probability distributions.
Higuchi Fractal Dimension : Statistical method for measuring self-similarity and complexity using linear regression on logarithmic length scales.
Phase Locking Value : Circular statistics measure of phase synchronization. Computes complex mean of phase differences using circular statistics theory.
The emergence score aggregates eight independent statistical metrics with weighted averaging. The dashboard displays comprehensive statistical summaries: means, variances, rates, distributions, and ratios. Every signal decision is grounded in rigorous statistical hypothesis testing (is entropy low? is lyapunov negative? is coherence above threshold?).
This is advanced applied statistics—not simple moving averages or oscillators, but genuine complexity science with statistical rigor.
Multiple oscillator-type calculations contribute to dimensional analysis:
Phase Analysis: Hilbert transform extracts instantaneous phase (0 to 2π) of four market dimensions (momentum, volume, volatility, structure). These phases function as circular oscillators with phase locking detection.
Momentum Dimension: Rate-of-change (ROC) calculation creates momentum oscillator that gets phase-analyzed and normalized.
Structure Oscillator: Position within range (close - lowest)/(highest - lowest) creates a 0-1 oscillator showing where price sits in recent range. This gets embedded and phase-analyzed.
Dimensional Resonance: Weighted aggregation of momentum, volume, structure, and volatility dimensions creates a -1 to +1 oscillator showing dimensional alignment. Similar to traditional oscillators but multi-dimensional.
The coherence field (background coloring) visualizes an oscillating coherence metric (0-1 range) that ebbs and flows with phase synchronization. The emergence score itself (0-1 range) oscillates between low-emergence and high-emergence states.
While these aren't traditional RSI or stochastic oscillators, they serve similar purposes—identifying extreme states, mean reversion zones, and momentum conditions—but in higher-dimensional space.
Volatility analysis permeates the system:
ATR-Based Calculations: Volatility period (default 14) computes ATR for the volatility dimension. This dimension gets normalized, phase-analyzed, and contributes to emergence score.
Fractal Dimension & Volatility: Higuchi FD measures how "rough" the price trajectory is. Higher FD (>1.6) correlates with higher volatility/choppiness. FD < 1.4 indicates smooth trends (lower effective volatility).
Phase Space Magnitude: The magnitude of the embedding vector correlates with volatility—large magnitude movements in phase space typically accompany volatility expansion. This is the "energy" of the market trajectory.
Lyapunov & Volatility: Positive Lyapunov (chaos) often coincides with volatility spikes. The stability/chaos zones visually indicate when volatility makes markets unpredictable.
Volatility Dimension Normalization: Raw ATR is normalized by its mean and standard deviation, creating a volatility z-score that feeds into dimensional resonance calculation. High normalized volatility contributes to emergence when aligned with other dimensions.
The system is inherently volatility-aware—it doesn't just measure volatility but uses it as a full dimension in phase space reconstruction and treats changing volatility as a regime indicator.
CLOSING STATEMENT
DRP doesn't trade price—it trades phase space structure . It doesn't chase patterns—it detects emergence . It doesn't guess at trends—it measures coherence .
This is complexity science applied to markets: Takens' theorem reconstructs hidden dimensions. Permutation entropy measures order. Lyapunov exponents detect chaos. Transfer entropy reveals causation. Hilbert phases find synchronization. Fractal dimensions quantify self-similarity.
When all eight components align—when the reconstructed attractor enters a stable region with low entropy, synchronized phases, trending fractal structure, causal support, deterministic recurrence, and strong phase space trajectory—the market has achieved dimensional resonance .
These are the highest-probability moments. Not because an indicator said so. Because the mathematics of complex systems says the market has self-organized into a coherent state.
Most indicators see shadows on the wall. DRP reconstructs the cave.
"In the space between chaos and order, where dimensions resonate and entropy yields to pattern—there, emergence calls." DRP
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Pair Cointegration & Static Beta Analyzer (v6)Pair Cointegration & Static Beta Analyzer (v6)
This indicator evaluates whether two instruments exhibit statistical properties consistent with cointegration and tradable mean reversion.
It uses long-term beta estimation, spread standardization, AR(1) dynamics, drift stability, tail distribution analysis, and a multi-factor scoring model.
1. Static Beta and Spread Construction
A long-horizon static beta is estimated using covariance and variance of log-returns.
This beta does not update on every bar and is used throughout the entire model.
Beta = Cov(r1, r2) / Var(r2)
Spread = PriceA - Beta * PriceB
This “frozen” beta provides structural stability and avoids rolling noise in spread construction.
2. Correlation Check
Log-price correlation ensures the instruments move together over time.
Correlation ≥ 0.85 is required before deeper cointegration diagnostics are considered meaningful.
3. Z-Score Normalization and Distribution Behavior
The spread is standardized:
Z = (Spread - MA(Spread)) / Std(Spread)
The following statistical properties are examined:
Z-Mean: Should be close to zero in a stationary process
Z-Variance: Measures amplitude of deviations
Tail Probability: Frequency of |Z| being larger than a threshold (e.g. 2)
These metrics reveal whether the spread behaves like a mean-reverting equilibrium.
4. Mean Drift Stability
A rolling mean of the spread is examined.
If the rolling mean drifts excessively, the spread may not represent a stable long-term equilibrium.
A normalized drift ratio is used:
Mean Drift Ratio = Range( RollingMean(Spread) ) / Std(Spread)
Low drift indicates stable long-run equilibrium behavior.
5. AR(1) Dynamics and Half-Life
An AR(1) model approximates mean reversion:
Spread(t) = Phi * Spread(t-1) + error
Mean reversion requires:
0 < Phi < 1
Half-life of reversion:
Half-life = -ln(2) / ln(Phi)
Valid half-life for 10-minute bars typically falls between 3 and 80 bars.
6. Composite Scoring Model (0–100)
A multi-factor weighted scoring system is applied:
Component Score
Correlation 0–20
Z-Mean 0–15
Z-Variance 0–10
Tail Probability 0–10
Mean Drift 0–15
AR(1) Phi 0–15
Half-Life 0–15
Score interpretation:
70–100: Strong Cointegration Quality
40–70: Moderate
0–40: Weak
A pair is classified as cointegrated when:
Total Score ≥ Threshold (default = 70)
7. Main Cointegration Panel
Displays:
Static beta
Log-price correlation
Z-Mean, Z-Variance, Tail Probability
Drift Ratio
AR(1) Phi and Half-life
Composite score
Overall cointegration assessment
8. Beta Hedge Position Sizing (Average-Price Based)
To provide a more stable hedge ratio, hedge sizing is computed using average prices, not instantaneous prices:
AvgPriceA = SMA(PriceA, N)
AvgPriceB = SMA(PriceB, N)
Required B per 1 A = Beta * (AvgPriceA / AvgPriceB)
Using averaged prices results in a smoother, more reliable hedge ratio, reducing noise from bar-to-bar volatility.
The panel displays:
Required B security for 1 A security (average)
This represents the beta-neutral quantity of B required to hedge one unit of A.
Overview of Classical Stationarity & Cointegration Methods
The principal econometric tools commonly used in assessing stationarity and cointegration include:
Augmented Dickey–Fuller (ADF) Test
Phillips–Perron (PP) Test
KPSS Test
Engle–Granger Cointegration Test
Phillips–Ouliaris Cointegration Test
Johansen Cointegration Test
Since these procedures rely on regression residuals, matrix operations, and distribution-based critical values that are not supported in TradingView Pine Script, a practical multi-criteria scoring approach is employed instead. This framework leverages metrics that are fully computable in Pine and offers an operational proxy for evaluating cointegration-like behavior under platform constraints.
References
Engle & Granger (1987), Co-integration and Error Correction
Poterba & Summers (1988), Mean Reversion in Stock Prices
Vidyamurthy (2004), Pairs Trading
Explanation structured with assistance from OpenAI’s ChatGPT
Regards.
RSI Ensemble Confidence [CHE]RSI Ensemble Confidence — Measures RSI agreement across multiple lengths and price sources
Summary
This indicator does not just show you one RSI — it shows you how strongly dozens of different RSI variants agree with each other right now.
The Confidence line (0–100) is the core idea:
- High Confidence → almost all RSIs see the same thing → clean, reliable situation
- Low Confidence → the RSIs contradict each other → the market is messy, RSI signals are questionable
How it works (exactly as you wanted it described)
1. Multiple RSIs instead of just one
The indicator builds a true ensemble:
- 4 lengths (default 8, 14, 21, 34)
- 6 price sources (Close, Open, High, Low, HL2, OHLC4 – individually switchable)
→ When everything is enabled, up to 24 different RSIs are calculated on every single bar.
These 24 opinions form a real “vote” about the current market state.
2. Mean and dispersion
From all active RSIs it calculates:
- rsiMean → the average opinion of the entire ensemble (orange line)
- rsiStd → how far the individual RSIs deviate from each other
Small rsiStd = they all lie close together → strong agreement
Large rsiStd = they are all over the place → contradiction
3. Confidence (0–100)
The standard deviation is compared to the user parameter “Max expected StdDev” (default 20):
- rsiStd = 0 → Confidence ≈ 100
- rsiStd = maxStd → Confidence ≈ 0
- Everything in between is scaled linearly
If only one RSI is active, Confidence is automatically set to ~80 for practicality.
What you see on the chart
1. Classic reference RSI – blue line (Close, length 14) → your familiar benchmark
2. Ensemble mean – orange line → the true consensus RSI
±1 StdDev band (optional) → shows dispersion directly:
- narrow band = clean, consistent setup
- wide band = the RSIs disagree → caution
3. Confidence line (aqua, 0–100) → your quality meter for any RSI signal
4. StdDev histogram (optional, fuchsia columns) → raw dispersion if you prefer the unscaled value
5. Background coloring
- Greenish ≥ 80 → high agreement
- Orange 60–80 → medium
- Reddish < 40 → strong disagreement
- Transparent below that
6. Two built-in alerts
- High Confidence (crossover 80)
- Low Confidence (crossunder 40)
Why this indicator is practically useful
1. Perfect filter for all RSI strategies
Only trade overbought/oversold, divergences, or failures when Confidence ≥ 70. Skip or reduce size when Confidence < 40.
2. Protection against overinterpretation
You immediately see whether a “beautiful” RSI hook is confirmed by the other 23 variants — or whether it’s just one outlier fooling you.
3. Excellent regime detector
Long periods of high Confidence = clean trends or clear overbought/oversold phases
Constantly low Confidence = choppy, noisy market → RSI becomes almost useless
4. Turns gut feeling into numbers
We all sometimes think “this setup somehow doesn’t feel right”. Now you have the exact number that says why.
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
Day Trading Signals - Ultimate Pro (Dark Neon + Strong BB Cloud)//@version=5
indicator("Day Trading Signals - Ultimate Pro (Dark Neon + Strong BB Cloud)", overlay=true, max_lines_count=500, max_labels_count=500)
// ===== INPUTS =====
ema_fast_len = input.int(9, "Fast EMA Length")
ema_slow_len = input.int(21, "Slow EMA Length")
rsi_len = input.int(12, "RSI Length")
rsi_overbought = input.int(70, "RSI Overbought Level")
rsi_oversold = input.int(30, "RSI Oversold Level")
bb_len = input.int(20, "Bollinger Bands Length")
bb_mult = input.float(2.0, "Bollinger Bands Multiplier")
sr_len = input.int(15, "Pivot Lookback for Support/Resistance")
min_ema_gap = input.float(0.0, "Minimum EMA Gap to Define Trend", step=0.1)
sr_lifespan = input.int(200, "Bars to Keep S/R Lines")
// Display options
show_bb = input.bool(true, "Show Bollinger Bands?")
show_ema = input.bool(true, "Show EMA Lines?")
show_sr = input.bool(true, "Show Support/Resistance Lines?")
show_bg = input.bool(true, "Show Background Trend Color?")
// ===== COLORS (Dark Neon Theme) =====
neon_teal = color.rgb(0, 255, 200)
neon_purple = color.rgb(180, 95, 255)
neon_orange = color.rgb(255, 160, 60)
neon_yellow = color.rgb(255, 235, 90)
neon_red = color.rgb(255, 70, 110)
neon_gray = color.rgb(140, 140, 160)
sr_support_col = color.rgb(0, 190, 140)
sr_resist_col = color.rgb(255, 90, 120)
// ===== INDICATORS =====
ema_fast = ta.ema(close, ema_fast_len)
ema_slow = ta.ema(close, ema_slow_len)
ema_gap = math.abs(ema_fast - ema_slow)
trend_up = (ema_fast > ema_slow) and (ema_gap > min_ema_gap)
trend_down = (ema_fast < ema_slow) and (ema_gap > min_ema_gap)
trend_flat = ema_gap <= min_ema_gap
rsi = ta.rsi(close, rsi_len)
bb_mid = ta.sma(close, bb_len)
bb_upper = bb_mid + bb_mult * ta.stdev(close, bb_len)
bb_lower = bb_mid - bb_mult * ta.stdev(close, bb_len)
// ===== SUPPORT / RESISTANCE =====
pivot_high = ta.pivothigh(high, sr_len, sr_len)
pivot_low = ta.pivotlow(low, sr_len, sr_len)
var line sup_lines = array.new_line()
var line res_lines = array.new_line()
if show_sr and not na(pivot_low)
l = line.new(bar_index - sr_len, pivot_low, bar_index, pivot_low, color=sr_support_col, width=2, extend=extend.right)
array.push(sup_lines, l)
if show_sr and not na(pivot_high)
l = line.new(bar_index - sr_len, pivot_high, bar_index, pivot_high, color=sr_resist_col, width=2, extend=extend.right)
array.push(res_lines, l)
// Delete old S/R lines
if array.size(sup_lines) > 0
for i = 0 to array.size(sup_lines) - 1
l = array.get(sup_lines, i)
if bar_index - line.get_x2(l) > sr_lifespan
line.delete(l)
array.remove(sup_lines, i)
break
if array.size(res_lines) > 0
for i = 0 to array.size(res_lines) - 1
l = array.get(res_lines, i)
if bar_index - line.get_x2(l) > sr_lifespan
line.delete(l)
array.remove(res_lines, i)
break
// ===== BUY / SELL CONDITIONS =====
buy_cond = trend_up and not trend_flat and ta.crossover(ema_fast, ema_slow) and rsi < rsi_oversold and close < bb_lower
sell_cond = trend_down and not trend_flat and ta.crossunder(ema_fast, ema_slow) and rsi > rsi_overbought and close > bb_upper
// ===== SIGNAL PLOTS =====
plotshape(buy_cond, title="Buy Signal", location=location.belowbar, color=neon_teal, style=shape.labelup, text="BUY", size=size.small)
plotshape(sell_cond, title="Sell Signal", location=location.abovebar, color=neon_red, style=shape.labeldown, text="SELL", size=size.small)
// ===== EMA LINES =====
plot(show_ema ? ema_fast : na, color=neon_orange, title="EMA Fast", linewidth=2)
plot(show_ema ? ema_slow : na, color=neon_purple, title="EMA Slow", linewidth=2)
// ===== STRONG BOLLINGER BAND CLOUD =====
plot_bb_upper = plot(show_bb ? bb_upper : na, color=color.new(neon_yellow, 20), title="BB Upper")
plot_bb_lower = plot(show_bb ? bb_lower : na, color=color.new(neon_gray, 20), title="BB Lower")
plot(bb_mid, color=color.new(neon_gray, 50), title="BB Mid")
// More visible BB cloud (stronger contrast)
bb_cloud_color = trend_up ? color.new(neon_teal, 40) : trend_down ? color.new(neon_red, 40) : color.new(neon_gray, 70)
fill(plot_bb_upper, plot_bb_lower, color=show_bb ? bb_cloud_color : na, title="BB Cloud")
// ===== BACKGROUND COLOR (TREND ZONES) =====
bgcolor(show_bg ? (trend_up ? color.new(neon_teal, 92) : trend_down ? color.new(neon_red, 92) : color.new(neon_gray, 94)) : na)
// ===== ALERTS =====
alertcondition(buy_cond, title="Buy Signal", message="Buy signal triggered. Check chart.")
alertcondition(sell_cond, title="Sell Signal", message="Sell signal triggered. Check chart.")
Day Trading Signals - Ultimate Pro (Dark Neon + Strong BB Cloud)//@version=5
indicator("Day Trading Signals - Ultimate Pro (Dark Neon + Strong BB Cloud)", overlay=true, max_lines_count=500, max_labels_count=500)
// ===== INPUTS =====
ema_fast_len = input.int(9, "Fast EMA Length")
ema_slow_len = input.int(21, "Slow EMA Length")
rsi_len = input.int(12, "RSI Length")
rsi_overbought = input.int(70, "RSI Overbought Level")
rsi_oversold = input.int(30, "RSI Oversold Level")
bb_len = input.int(20, "Bollinger Bands Length")
bb_mult = input.float(2.0, "Bollinger Bands Multiplier")
sr_len = input.int(15, "Pivot Lookback for Support/Resistance")
min_ema_gap = input.float(0.0, "Minimum EMA Gap to Define Trend", step=0.1)
sr_lifespan = input.int(200, "Bars to Keep S/R Lines")
// Display options
show_bb = input.bool(true, "Show Bollinger Bands?")
show_ema = input.bool(true, "Show EMA Lines?")
show_sr = input.bool(true, "Show Support/Resistance Lines?")
show_bg = input.bool(true, "Show Background Trend Color?")
// ===== COLORS (Dark Neon Theme) =====
neon_teal = color.rgb(0, 255, 200)
neon_purple = color.rgb(180, 95, 255)
neon_orange = color.rgb(255, 160, 60)
neon_yellow = color.rgb(255, 235, 90)
neon_red = color.rgb(255, 70, 110)
neon_gray = color.rgb(140, 140, 160)
sr_support_col = color.rgb(0, 190, 140)
sr_resist_col = color.rgb(255, 90, 120)
// ===== INDICATORS =====
ema_fast = ta.ema(close, ema_fast_len)
ema_slow = ta.ema(close, ema_slow_len)
ema_gap = math.abs(ema_fast - ema_slow)
trend_up = (ema_fast > ema_slow) and (ema_gap > min_ema_gap)
trend_down = (ema_fast < ema_slow) and (ema_gap > min_ema_gap)
trend_flat = ema_gap <= min_ema_gap
rsi = ta.rsi(close, rsi_len)
bb_mid = ta.sma(close, bb_len)
bb_upper = bb_mid + bb_mult * ta.stdev(close, bb_len)
bb_lower = bb_mid - bb_mult * ta.stdev(close, bb_len)
// ===== SUPPORT / RESISTANCE =====
pivot_high = ta.pivothigh(high, sr_len, sr_len)
pivot_low = ta.pivotlow(low, sr_len, sr_len)
var line sup_lines = array.new_line()
var line res_lines = array.new_line()
if show_sr and not na(pivot_low)
l = line.new(bar_index - sr_len, pivot_low, bar_index, pivot_low, color=sr_support_col, width=2, extend=extend.right)
array.push(sup_lines, l)
if show_sr and not na(pivot_high)
l = line.new(bar_index - sr_len, pivot_high, bar_index, pivot_high, color=sr_resist_col, width=2, extend=extend.right)
array.push(res_lines, l)
// Delete old S/R lines
if array.size(sup_lines) > 0
for i = 0 to array.size(sup_lines) - 1
l = array.get(sup_lines, i)
if bar_index - line.get_x2(l) > sr_lifespan
line.delete(l)
array.remove(sup_lines, i)
break
if array.size(res_lines) > 0
for i = 0 to array.size(res_lines) - 1
l = array.get(res_lines, i)
if bar_index - line.get_x2(l) > sr_lifespan
line.delete(l)
array.remove(res_lines, i)
break
// ===== BUY / SELL CONDITIONS =====
buy_cond = trend_up and not trend_flat and ta.crossover(ema_fast, ema_slow) and rsi < rsi_oversold and close < bb_lower
sell_cond = trend_down and not trend_flat and ta.crossunder(ema_fast, ema_slow) and rsi > rsi_overbought and close > bb_upper
// ===== SIGNAL PLOTS =====
plotshape(buy_cond, title="Buy Signal", location=location.belowbar, color=neon_teal, style=shape.labelup, text="BUY", size=size.small)
plotshape(sell_cond, title="Sell Signal", location=location.abovebar, color=neon_red, style=shape.labeldown, text="SELL", size=size.small)
// ===== EMA LINES =====
plot(show_ema ? ema_fast : na, color=neon_orange, title="EMA Fast", linewidth=2)
plot(show_ema ? ema_slow : na, color=neon_purple, title="EMA Slow", linewidth=2)
// ===== STRONG BOLLINGER BAND CLOUD =====
plot_bb_upper = plot(show_bb ? bb_upper : na, color=color.new(neon_yellow, 20), title="BB Upper")
plot_bb_lower = plot(show_bb ? bb_lower : na, color=color.new(neon_gray, 20), title="BB Lower")
plot(bb_mid, color=color.new(neon_gray, 50), title="BB Mid")
// More visible BB cloud (stronger contrast)
bb_cloud_color = trend_up ? color.new(neon_teal, 40) : trend_down ? color.new(neon_red, 40) : color.new(neon_gray, 70)
fill(plot_bb_upper, plot_bb_lower, color=show_bb ? bb_cloud_color : na, title="BB Cloud")
// ===== BACKGROUND COLOR (TREND ZONES) =====
bgcolor(show_bg ? (trend_up ? color.new(neon_teal, 92) : trend_down ? color.new(neon_red, 92) : color.new(neon_gray, 94)) : na)
// ===== ALERTS =====
alertcondition(buy_cond, title="Buy Signal", message="Buy signal triggered. Check chart.")
alertcondition(sell_cond, title="Sell Signal", message="Sell signal triggered. Check chart.")
Day Trading Signals - Ultimate Pro (Dark Neon + Strong BB Cloud)//@version=5
indicator("Day Trading Signals - Ultimate Pro (Dark Neon + Strong BB Cloud)", overlay=true, max_lines_count=500, max_labels_count=500)
// ===== INPUTS =====
ema_fast_len = input.int(9, "Fast EMA Length")
ema_slow_len = input.int(21, "Slow EMA Length")
rsi_len = input.int(12, "RSI Length")
rsi_overbought = input.int(70, "RSI Overbought Level")
rsi_oversold = input.int(30, "RSI Oversold Level")
bb_len = input.int(20, "Bollinger Bands Length")
bb_mult = input.float(2.0, "Bollinger Bands Multiplier")
sr_len = input.int(15, "Pivot Lookback for Support/Resistance")
min_ema_gap = input.float(0.0, "Minimum EMA Gap to Define Trend", step=0.1)
sr_lifespan = input.int(200, "Bars to Keep S/R Lines")
// Display options
show_bb = input.bool(true, "Show Bollinger Bands?")
show_ema = input.bool(true, "Show EMA Lines?")
show_sr = input.bool(true, "Show Support/Resistance Lines?")
show_bg = input.bool(true, "Show Background Trend Color?")
// ===== COLORS (Dark Neon Theme) =====
neon_teal = color.rgb(0, 255, 200)
neon_purple = color.rgb(180, 95, 255)
neon_orange = color.rgb(255, 160, 60)
neon_yellow = color.rgb(255, 235, 90)
neon_red = color.rgb(255, 70, 110)
neon_gray = color.rgb(140, 140, 160)
sr_support_col = color.rgb(0, 190, 140)
sr_resist_col = color.rgb(255, 90, 120)
// ===== INDICATORS =====
ema_fast = ta.ema(close, ema_fast_len)
ema_slow = ta.ema(close, ema_slow_len)
ema_gap = math.abs(ema_fast - ema_slow)
trend_up = (ema_fast > ema_slow) and (ema_gap > min_ema_gap)
trend_down = (ema_fast < ema_slow) and (ema_gap > min_ema_gap)
trend_flat = ema_gap <= min_ema_gap
rsi = ta.rsi(close, rsi_len)
bb_mid = ta.sma(close, bb_len)
bb_upper = bb_mid + bb_mult * ta.stdev(close, bb_len)
bb_lower = bb_mid - bb_mult * ta.stdev(close, bb_len)
// ===== SUPPORT / RESISTANCE =====
pivot_high = ta.pivothigh(high, sr_len, sr_len)
pivot_low = ta.pivotlow(low, sr_len, sr_len)
var line sup_lines = array.new_line()
var line res_lines = array.new_line()
if show_sr and not na(pivot_low)
l = line.new(bar_index - sr_len, pivot_low, bar_index, pivot_low, color=sr_support_col, width=2, extend=extend.right)
array.push(sup_lines, l)
if show_sr and not na(pivot_high)
l = line.new(bar_index - sr_len, pivot_high, bar_index, pivot_high, color=sr_resist_col, width=2, extend=extend.right)
array.push(res_lines, l)
// Delete old S/R lines
if array.size(sup_lines) > 0
for i = 0 to array.size(sup_lines) - 1
l = array.get(sup_lines, i)
if bar_index - line.get_x2(l) > sr_lifespan
line.delete(l)
array.remove(sup_lines, i)
break
if array.size(res_lines) > 0
for i = 0 to array.size(res_lines) - 1
l = array.get(res_lines, i)
if bar_index - line.get_x2(l) > sr_lifespan
line.delete(l)
array.remove(res_lines, i)
break
// ===== BUY / SELL CONDITIONS =====
buy_cond = trend_up and not trend_flat and ta.crossover(ema_fast, ema_slow) and rsi < rsi_oversold and close < bb_lower
sell_cond = trend_down and not trend_flat and ta.crossunder(ema_fast, ema_slow) and rsi > rsi_overbought and close > bb_upper
// ===== SIGNAL PLOTS =====
plotshape(buy_cond, title="Buy Signal", location=location.belowbar, color=neon_teal, style=shape.labelup, text="BUY", size=size.small)
plotshape(sell_cond, title="Sell Signal", location=location.abovebar, color=neon_red, style=shape.labeldown, text="SELL", size=size.small)
// ===== EMA LINES =====
plot(show_ema ? ema_fast : na, color=neon_orange, title="EMA Fast", linewidth=2)
plot(show_ema ? ema_slow : na, color=neon_purple, title="EMA Slow", linewidth=2)
// ===== STRONG BOLLINGER BAND CLOUD =====
plot_bb_upper = plot(show_bb ? bb_upper : na, color=color.new(neon_yellow, 20), title="BB Upper")
plot_bb_lower = plot(show_bb ? bb_lower : na, color=color.new(neon_gray, 20), title="BB Lower")
plot(bb_mid, color=color.new(neon_gray, 50), title="BB Mid")
// More visible BB cloud (stronger contrast)
bb_cloud_color = trend_up ? color.new(neon_teal, 40) : trend_down ? color.new(neon_red, 40) : color.new(neon_gray, 70)
fill(plot_bb_upper, plot_bb_lower, color=show_bb ? bb_cloud_color : na, title="BB Cloud")
// ===== BACKGROUND COLOR (TREND ZONES) =====
bgcolor(show_bg ? (trend_up ? color.new(neon_teal, 92) : trend_down ? color.new(neon_red, 92) : color.new(neon_gray, 94)) : na)
// ===== ALERTS =====
alertcondition(buy_cond, title="Buy Signal", message="Buy signal triggered. Check chart.")
alertcondition(sell_cond, title="Sell Signal", message="Sell signal triggered. Check chart.")
Market Electromagnetic Field [The_lurker]Market Electromagnetic Field
An innovative analytical indicator that presents a completely new model for understanding market dynamics, inspired by the laws of electromagnetic physics — but it's not a rhetorical metaphor, rather a complete mathematical system.
Unlike traditional indicators that focus on price or momentum, this indicator portrays the market as a closed physical system, where:
⚡ Candles = Electric charges (positive at bullish close, negative at bearish)
⚡ Buyers and Sellers = Two opposing poles where pressure accumulates
⚡ Market tension = Voltage difference between the poles
⚡ Price breakout = Electrical discharge after sufficient energy accumulation
█ Core Concept
Markets don't move randomly, but follow a clear physical cycle:
Accumulation → Tension → Discharge → Stabilization → New Accumulation
When charges accumulate (through strong candles with high volume) and exceed a certain "electrical capacitance" threshold, the indicator issues a "⚡ DISCHARGE IMMINENT" alert — meaning a price explosion is imminent, giving the trader an opportunity to enter before the move begins.
█ Competitive Advantage
- Predictive forecasting (not confirmatory after the event)
- Smart multi-layer filtering reduces false signals
- Animated 3D visual representation makes reading price conditions instant and intuitive — without need for number analysis
█ Theoretical Physical Foundation
The indicator doesn't use physical terms for decoration, but applies mathematical laws with precise market adjustments:
⚡ Coulomb's Law
Physics: F = k × (q₁ × q₂) / r²
Market: Field Intensity = 4 × norm_positive × norm_negative
Peaks at equilibrium (0.5 × 0.5 × 4 = 1.0), and decreases at dominance — because conflict increases at parity.
⚡ Ohm's Law
Physics: V = I × R
Market: Voltage = norm_positive − norm_negative
Measures balance of power:
- +1 = Absolute buying dominance
- −1 = Absolute selling dominance
- 0 = Balance
⚡ Capacitance
Physics: C = Q / V
Market: Capacitance = |Voltage| × Field Intensity
Represents stored energy ready for discharge — increases with bias combined with high interaction.
⚡ Electrical Discharge
Physics: Occurs when exceeding insulation threshold
Market: Discharge Probability = min(Capacitance / Discharge Threshold, 1.0)
When ≥ 0.9: "⚡ DISCHARGE IMMINENT"
📌 Key Note:
Maximum capacitance doesn't occur at absolute dominance (where field intensity = 0), nor at perfect balance (where voltage = 0), but at moderate bias (±30–50%) with high interaction (field intensity > 25%) — i.e., in moments of "pressure before breakout".
█ Detailed Calculation Mechanism
⚡ Phase 1: Candle Polarity
polarity = (close − open) / (high − low)
- +1.0: Complete bullish candle (Bullish Marubozu)
- −1.0: Complete bearish candle (Bearish Marubozu)
- 0.0: Doji (no decision)
- Intermediate values: Represent the ratio of candle body to its range — reducing the effect of long-shadow candles
⚡ Phase 2: Volume Weight
vol_weight = volume / SMA(volume, lookback)
A candle with 150% of average volume = 1.5x stronger charge
⚡ Phase 3: Adaptive Factor
adaptive_factor = ATR(lookback) / SMA(ATR, lookback × 2)
- In volatile markets: Increases sensitivity
- In quiet markets: Reduces noise
- Always recommended to keep it enabled
⚡ Phase 4–6: Charge Accumulation and Normalization
Charges are summed over lookback candles, then ratios are normalized:
norm_positive = positive_charge / total_charge
norm_negative = negative_charge / total_charge
So that: norm_positive + norm_negative = 1 — for easier comparison
⚡ Phase 7: Field Calculations
voltage = norm_positive − norm_negative
field_intensity = 4 × norm_positive × norm_negative × field_sensitivity
capacitance = |voltage| × field_intensity
discharge_prob = min(capacitance / discharge_threshold, 1.0)
█ Settings
⚡ Electromagnetic Model
Lookback Period
- Default: 20
- Range: 5–100
- Recommendations:
- Scalping: 10–15
- Day Trading: 20
- Swing: 30–50
- Investing: 50–100
Discharge Threshold
- Default: 0.7
- Range: 0.3–0.95
- Recommendations:
- Speed + Noise: 0.5–0.6
- Balance: 0.7
- High Accuracy: 0.8–0.95
Field Sensitivity
- Default: 1.0
- Range: 0.5–2.0
- Recommendations:
- Amplify Conflict: 1.2–1.5
- Natural: 1.0
- Calm: 0.5–0.8
Adaptive Mode
- Default: Enabled
- Always keep it enabled
🔬 Dynamic Filters
All enabled filters must pass for discharge signal to appear.
Volume Filter
- Condition: volume > SMA(volume) × vol_multiplier
- Function: Excludes "weak" candles not supported by volume
- Recommendation: Enabled (especially for stocks and forex)
Volatility Filter
- Condition: STDEV > SMA(STDEV) × 0.5
- Function: Ignores sideways stagnation periods
- Recommendation: Always enabled
Trend Filter
- Condition: Voltage alignment with fast/slow EMA
- Function: Reduces counter-trend signals
- Recommendation: Enabled for swing/investing only
Volume Threshold
- Default: 1.2
- Recommendations:
- 1.0–1.2: High sensitivity
- 1.5–2.0: Exclusive to high volume
🎨 Visual Settings
Settings improve visual reading experience — don't affect calculations.
Scale Factor
- Default: 600
- Higher = Larger scene (200–1200)
Horizontal Shift
- Default: 180
- Horizontal shift to the left — to focus on last candle
Pole Size
- Default: 60
- Base sphere size (30–120)
Field Lines
- Default: 8
- Number of field lines (4–16) — 8 is ideal balance
Colors
- Green/Red/Blue/Orange
- Fully customizable
█ Visual Representation: A Visual Language for Diagnosing Price Conditions
✨ Design Philosophy
The representation isn't "decoration", but a complete cognitive model — each element carries information, and element interaction tells a complete story.
The brain perceives changes in size, color, and movement 60,000 times faster than reading numbers — so you can "sense" the change before your eye finishes scanning.
═════════════════════════════════════════════════════════════
🟢 Positive Pole (Green Sphere — Left)
═════════════════════════════════════════════════════════════
What does it represent?
Active buying pressure accumulation — not just an uptrend, but real demand force supported by volume and volatility.
● Dynamic Size
Size = pole_size × (0.7 + norm_positive × 0.6)
- 70% of base size = No significant charge
- 130% of base size = Complete dominance
- The larger the sphere: Greater buyer dominance, higher probability of bullish continuation
Size Interpretation:
- Large sphere (>55%): Strong buying pressure — Buyers dominate
- Medium sphere (45–55%): Relative balance with buying bias
- Small sphere (<45%): Weak buying pressure — Sellers dominate
● Lighting and Transparency
- 20% transparency (when Bias = +1): Pole currently active — Bullish direction
- 50% transparency (when Bias ≠ +1): Pole inactive — Not the prevailing direction
Lighting = Current activity, while Size = Historical accumulation
● Pulsing Inner Glow
A smaller sphere pulses automatically when Bias = +1:
inner_pulse = 0.4 + 0.1 × sin(anim_time × 3)
Symbolizes continuity of buy order flow — not static dominance.
● Orbital Rings
Two rings rotating at different speeds and directions:
- Inner: 1.3× sphere size — Direct influence range
- Outer: 1.6× sphere size — Extended influence range
Represent "influence zone" of buyers:
- Continuous rotation = Stability and momentum
- Slowdown = Momentum exhaustion
● Percentage
Displayed below sphere: norm_positive × 100
- >55% = Clear dominance
- 45–55% = Balance
- <45% = Weakness
═════════════════════════════════════════════════════════════
🔴 Negative Pole (Red Sphere — Right)
═════════════════════════════════════════════════════════════
What does it represent?
Active selling pressure accumulation — whether cumulative selling (smart distribution) or panic selling (position liquidation).
● Visual Dynamics
Same size, lighting, and inner glow mechanism — but in red.
Key Difference:
- Rotation is reversed (counter-clockwise)
- Visually distinguishes "buy flow" from "sell flow"
- Allows reading direction at a glance — even for colorblind users
📌 Pole Reading Summary:
🟢 Large + Bright green sphere = Active buying force
🔴 Large + Bright red sphere = Active selling force
🟢🔴 Both large but dim = Energy accumulation (before discharge)
⚪ Both small = Stagnation / Low liquidity
═════════════════════════════════════════════════════════════
🔵 Field Lines (Curved Blue Lines)
═════════════════════════════════════════════════════════════
What do they represent?
Energy flow paths between poles — the arena where price battle is fought.
● Number of Lines
4–16 lines (Default: 8)
More lines: Greater sense of "interaction density"
● Arc Height
arc_h = (i − half_lines) × 15 × field_intensity × 2
- High field intensity = Highly elevated lines (like waves)
- Low intensity = Nearly straight lines
● Oscillating Transparency
transp = 30 + phase × 40
where phase = sin(anim_time × 2 + i × 0.5) × 0.5 + 0.5
Creates illusion of "flowing current" — not static lines
● Asymmetric Curvature
- Upper lines curve upward
- Lower lines curve downward
- Adds 3D depth and shows "pressure" direction
⚡ Pro Tip:
When you see lines suddenly "contract" (straighten), while both spheres are large — this is an early indicator of impending discharge, because the interaction is losing its flexibility.
═════════════════════════════════════════════════════════════
⚪ Moving Particles
═════════════════════════════════════════════════════════════
What do they represent?
Real liquidity flow in the market — who's driving price right now.
● Number and Movement
- 6 particles covering most field lines
- Move sinusoidally along the arc:
t = (sin(phase_val) + 1) / 2
- High speed = High trading activity
- Clustering at a pole = That side's control
● Color Gradient
From green (at positive pole) to red (at negative)
Shows "energy transformation":
- Green particle = Pure buying energy
- Orange particle = Conflict zone
- Red particle = Pure selling energy
📌 How to Read Them?
- Moving left to right (🟢 → 🔴): Buy flow → Bullish push
- Moving right to left (🔴 → 🟢): Sell flow → Bearish push
- Clustered in middle: Balanced conflict — Wait for breakout
═════════════════════════════════════════════════════════════
🟠 Discharge Zone (Orange Glow — Center)
═════════════════════════════════════════════════════════════
What does it represent?
Point of stored energy accumulation not yet discharged — heart of the early warning system.
● Glow Stages
Initial Warning (discharge_prob > 0.3):
- Dim orange circle (70% transparency)
- Meaning: Watch, don't enter yet
High Tension (discharge_prob ≥ 0.7):
- Stronger glow + "⚠️ HIGH TENSION" text
- Meaning: Prepare — Set pending orders
Imminent Discharge (discharge_prob ≥ 0.9):
- Bright glow + "⚡ DISCHARGE IMMINENT" text
- Meaning: Enter with direction (after candle confirmation)
● Layered Glow Effect (Glow Layering)
3 concentric circles with increasing transparency:
- Inner: 20%
- Middle: 35%
- Outer: 50%
Result: Realistic aura resembling actual electrical discharge.
📌 Why in the Center?
Because discharge always starts from the relative balance zone — where opposing pressures meet.
═════════════════════════════════════════════════════════════
📊 Voltage Meter (Bottom of Scene)
═════════════════════════════════════════════════════════════
What does it represent?
Simplified numeric indicator of voltage difference — for those who prefer numerical reading.
● Components
- Gray bar: Full range (−100% to +100%)
- Green fill: Positive voltage (extends right)
- Red fill: Negative voltage (extends left)
- Lightning symbol (⚡): Above center — reminder it's an "electrical gauge"
- Text value: Like "+23.4%" — in direction color
● Voltage Reading Interpretation
+50% to +100%:
Overwhelming buying dominance — Beware of saturation, may precede correction
+20% to +50%:
Strong buying dominance — Suitable for buying with trend
+5% to +20%:
Slight bullish bias — Wait for additional confirmation
−5% to +5%:
Balance/Neutral — Avoid entry or wait for breakout
−5% to −20%:
Slight bearish bias — Wait for confirmation
−20% to −50%:
Strong selling dominance — Suitable for selling with trend
−50% to −100%:
Overwhelming selling dominance — Beware of saturation, may precede bounce
═════════════════════════════════════════════════════════════
📈 Field Strength Indicator (Top of Scene)
═════════════════════════════════════════════════════════════
What it displays: "Field: XX.X%"
Meaning: Strength of conflict between buyers and sellers.
● Reading Interpretation
0–5%:
- Appearance: Nearly straight lines, transparent
- Meaning: Complete control by one side
- Strategy: Trend Following
5–15%:
- Appearance: Slight curvature
- Meaning: Clear direction with light resistance
- Strategy: Enter with trend
15–25%:
- Appearance: Medium curvature, clear lines
- Meaning: Balanced conflict
- Strategy: Range trading or waiting
25–35%:
- Appearance: High curvature, clear density
- Meaning: Strong conflict, high uncertainty
- Strategy: Volatility trading or prepare for discharge
35%+:
- Appearance: Very high lines, strong glow
- Meaning: Peak tension
- Strategy: Best discharge opportunities
📌 Golden Relationship:
Highest discharge probability when:
Field Strength (25–35%) + Voltage (±30–50%) + High Volume
← This is the "red zone" to monitor carefully.
█ Comprehensive Visual Reading
To read market condition at a glance, follow this sequence:
Step 1: Which sphere is larger?
- 🟢 Green larger ← Dominant buying pressure
- 🔴 Red larger ← Dominant selling pressure
- Equal ← Balance/Conflict
Step 2: Which sphere is bright?
- 🟢 Green bright ← Current bullish direction
- 🔴 Red bright ← Current bearish direction
- Both dim ← Neutral/No clear direction
Step 3: Is there orange glow?
- None ← Discharge probability <30%
- 🟠 Dim glow ← Discharge probability 30–70%
- 🟠 Strong glow with text ← Discharge probability >70%
Step 4: What's the voltage meter reading?
- Strong positive ← Confirms buying dominance
- Strong negative ← Confirms selling dominance
- Near zero ← No clear direction
█ Practical Visual Reading Examples
Example 1: Ideal Buy Opportunity ⚡🟢
- Green sphere: Large and bright with inner pulse
- Red sphere: Small and dim
- Orange glow: Strong with "DISCHARGE IMMINENT" text
- Voltage meter: +45%
- Field strength: 28%
Interpretation: Strong accumulated buying pressure, bullish explosion imminent
Example 2: Ideal Sell Opportunity ⚡🔴
- Green sphere: Small and dim
- Red sphere: Large and bright with inner pulse
- Orange glow: Strong with "DISCHARGE IMMINENT" text
- Voltage meter: −52%
- Field strength: 31%
Interpretation: Strong accumulated selling pressure, bearish explosion imminent
Example 3: Balance/Wait ⚖️
- Both spheres: Approximately equal in size
- Lighting: Both dim
- Orange glow: Strong
- Voltage meter: +3%
- Field strength: 24%
Interpretation: Strong conflict without clear winner, wait for breakout
Example 4: Clear Uptrend (No Discharge) 📈
- Green sphere: Large and bright
- Red sphere: Very small and dim
- Orange glow: None
- Voltage meter: +68%
- Field strength: 8%
Interpretation: Clear buying control, limited conflict, suitable for following bullish trend
Example 5: Potential Buying Saturation ⚠️
- Green sphere: Very large and bright
- Red sphere: Very small
- Orange glow: Dim
- Voltage meter: +88%
- Field strength: 4%
Interpretation: Absolute buying dominance, may precede bearish correction
█ Trading Signals
⚡ DISCHARGE IMMINENT
Appearance Conditions:
- discharge_prob ≥ 0.9
- All enabled filters passed
- Confirmed (after candle close)
Interpretation:
- Very large energy accumulation
- Pressure reached critical level
- Price explosion expected within 1–3 candles
How to Trade:
1. Determine voltage direction:
• Positive = Expect rise
• Negative = Expect fall
2. Wait for confirmation candle:
• For rise: Bullish candle closing above its open
• For fall: Bearish candle closing below its open
3. Entry: With next candle's open
4. Stop Loss: Behind last local low/high
5. Target: Risk/Reward ratio of at least 1:2
✅ Pro Tips:
- Best results when combined with support/resistance levels
- Avoid entry if voltage is near zero (±5%)
- Increase position size when field strength > 30%
⚠️ HIGH TENSION
Appearance Conditions:
- 0.7 ≤ discharge_prob < 0.9
Interpretation:
- Market in energy accumulation state
- Likely strong move soon, but not immediate
- Accumulation may continue or discharge may occur
How to Benefit:
- Prepare: Set pending orders at potential breakouts
- Monitor: Watch following candles for momentum candle
- Select: Don't enter every signal — choose those aligned with overall trend
█ Trading Strategies
📈 Strategy 1: Discharge Trading (Basic)
Principle: Enter at "DISCHARGE IMMINENT" in voltage direction
Steps:
1. Wait for "⚡ DISCHARGE IMMINENT"
2. Check voltage direction (+/−)
3. Wait for confirmation candle in voltage direction
4. Enter with next candle's open
5. Stop loss behind last low/high
6. Target: 1:2 or 1:3 ratio
Very high success rate when following confirmation conditions.
📈 Strategy 2: Dominance Following
Principle: Trade with dominant pole (largest and brightest sphere)
Steps:
1. Identify dominant pole (largest and brightest)
2. Trade in its direction
3. Beware when sizes converge (conflict)
Suitable for higher timeframes (H1+).
📈 Strategy 3: Reversal Hunting
Principle: Counter-trend entry under certain conditions
Conditions:
- High field strength (>30%)
- Extreme voltage (>±40%)
- Divergence with price (e.g., new price high with declining voltage)
⚠️ High risk — Use small position size.
📈 Strategy 4: Integration with Technical Analysis
Strong Confirmation Examples:
- Resistance breakout + Bullish discharge = Excellent buy signal
- Support break + Bearish discharge = Excellent sell signal
- Head & Shoulders pattern + Increasing negative voltage = Pattern confirmation
- RSI divergence + High field strength = Potential reversal
█ Ready Alerts
Bullish Discharge
- Condition: discharge_prob ≥ 0.9 + Positive voltage + All filters
- Message: "⚡ Bullish discharge"
- Use: High probability buy opportunity
Bearish Discharge
- Condition: discharge_prob ≥ 0.9 + Negative voltage + All filters
- Message: "⚡ Bearish discharge"
- Use: High probability sell opportunity
✅ Tip: Use these alerts with "Once Per Bar" setting to avoid repetition.
█ Data Window Outputs
Bias
- Values: −1 / 0 / +1
- Interpretation: −1 = Bearish, 0 = Neutral, +1 = Bullish
- Use: For integration in automated strategies
Discharge %
- Range: 0–100%
- Interpretation: Discharge probability
- Use: Monitor tension progression (e.g., from 40% to 85% in 5 candles)
Field Strength
- Range: 0–100%
- Interpretation: Conflict intensity
- Use: Identify "opportunity window" (25–35% ideal for discharge)
Voltage
- Range: −100% to +100%
- Interpretation: Balance of power
- Use: Monitor extremes (potential buying/selling saturation)
█ Optimal Settings by Trading Style
Scalping
- Timeframe: 1M–5M
- Lookback: 10–15
- Threshold: 0.5–0.6
- Sensitivity: 1.2–1.5
- Filters: Volume + Volatility
Day Trading
- Timeframe: 15M–1H
- Lookback: 20
- Threshold: 0.7
- Sensitivity: 1.0
- Filters: Volume + Volatility
Swing Trading
- Timeframe: 4H–D1
- Lookback: 30–50
- Threshold: 0.8
- Sensitivity: 0.8
- Filters: Volatility + Trend
Position Trading
- Timeframe: D1–W1
- Lookback: 50–100
- Threshold: 0.85–0.95
- Sensitivity: 0.5–0.8
- Filters: All filters
█ Tips for Optimal Use
1. Start with Default Settings
Try it first as is, then adjust to your style.
2. Watch for Element Alignment
Best signals when:
- Clear voltage (>│20%│)
- Moderate–high field strength (15–35%)
- High discharge probability (>70%)
3. Use Multiple Timeframes
- Higher timeframe: Determine overall trend
- Lower timeframe: Time entry
- Ensure signal alignment between frames
4. Integrate with Other Tools
- Support/Resistance levels
- Trend lines
- Candle patterns
- Volume indicators
5. Respect Risk Management
- Don't risk more than 1–2% of account
- Always use stop loss
- Don't enter every signal — choose the best
█ Important Warnings
⚠️ Not for Standalone Use
The indicator is an analytical support tool — don't use it isolated from technical or fundamental analysis.
⚠️ Doesn't Predict the Future
Calculations are based on historical data — Results are not guaranteed.
⚠️ Markets Differ
You may need to adjust settings for each market:
- Forex: Focus on Volume Filter
- Stocks: Add Trend Filter
- Crypto: Lower Threshold slightly (more volatile)
⚠️ News and Events
The indicator doesn't account for sudden news — Avoid trading before/during major news.
█ Unique Features
✅ First Application of Electromagnetism to Markets
Innovative mathematical model — Not just an ordinary indicator
✅ Predictive Detection of Price Explosions
Alerts before the move happens — Not after
✅ Multi-Layer Filtering
4 smart filters reduce false signals to minimum
✅ Smart Volatility Adaptation
Automatically adjusts sensitivity based on market conditions
✅ Animated 3D Visual Representation
Makes reading instant — Even for beginners
✅ High Flexibility
Works on all assets: Stocks, Forex, Crypto, Commodities
✅ Built-in Ready Alerts
No complex setup needed — Ready for immediate use
█ Conclusion: When Art Meets Science
Market Electromagnetic Field is not just an indicator — but a new analytical philosophy.
It's the bridge between:
- Physics precision in describing dynamic systems
- Market intelligence in generating trading opportunities
- Visual psychology in facilitating instant reading
The result: A tool that isn't read — but watched, felt, and sensed.
When you see the green sphere expanding, the glow intensifying, and particles rushing rightward — you're not seeing numbers, you're seeing market energy breathing.
⚠️ Disclaimer:
This indicator is for educational and analytical purposes only. It does not constitute financial, investment, or trading advice. Use it in conjunction with your own strategy and risk management. Neither TradingView nor the developer is liable for any financial decisions or losses.
المجال الكهرومغناطيسي للسوق - Market Electromagnetic Field
مؤشر تحليلي مبتكر يقدّم نموذجًا جديدًا كليًّا لفهم ديناميكيات السوق، مستوحى من قوانين الفيزياء الكهرومغناطيسية — لكنه ليس استعارة بلاغية، بل نظام رياضي متكامل.
على عكس المؤشرات التقليدية التي تُركّز على السعر أو الزخم، يُصوّر هذا المؤشر السوق كـنظام فيزيائي مغلق، حيث:
⚡ الشموع = شحنات كهربائية (موجبة عند الإغلاق الصاعد، سالبة عند الهابط)
⚡ المشتريون والبائعون = قطبان متعاكسان يتراكم فيهما الضغط
⚡ التوتر السوقي = فرق جهد بين القطبين
⚡ الاختراق السعري = تفريغ كهربائي بعد تراكم طاقة كافية
█ الفكرة الجوهرية
الأسواق لا تتحرك عشوائيًّا، بل تخضع لدورة فيزيائية واضحة:
تراكم → توتر → تفريغ → استقرار → تراكم جديد
عندما تتراكم الشحنات (من خلال شموع قوية بحجم مرتفع) وتتجاوز "السعة الكهربائية" عتبة معيّنة، يُصدر المؤشر تنبيه "⚡ DISCHARGE IMMINENT" — أي أن انفجارًا سعريًّا وشيكًا، مما يمنح المتداول فرصة الدخول قبل بدء الحركة.
█ الميزة التنافسية
- تنبؤ استباقي (ليس تأكيديًّا بعد الحدث)
- فلترة ذكية متعددة الطبقات تقلل الإشارات الكاذبة
- تمثيل بصري ثلاثي الأبعاد متحرك يجعل قراءة الحالة السعرية فورية وبديهية — دون حاجة لتحليل أرقام
█ الأساس النظري الفيزيائي
المؤشر لا يستخدم مصطلحات فيزيائية للزينة، بل يُطبّق القوانين الرياضية مع تعديلات سوقيّة دقيقة:
⚡ قانون كولوم (Coulomb's Law)
الفيزياء: F = k × (q₁ × q₂) / r²
السوق: شدة الحقل = 4 × norm_positive × norm_negative
تصل لذروتها عند التوازن (0.5 × 0.5 × 4 = 1.0)، وتنخفض عند الهيمنة — لأن الصراع يزداد عند التكافؤ.
⚡ قانون أوم (Ohm's Law)
الفيزياء: V = I × R
السوق: الجهد = norm_positive − norm_negative
يقيس ميزان القوى:
- +1 = هيمنة شرائية مطلقة
- −1 = هيمنة بيعية مطلقة
- 0 = توازن
⚡ السعة الكهربائية (Capacitance)
الفيزياء: C = Q / V
السوق: السعة = |الجهد| × شدة الحقل
تمثّل الطاقة المخزّنة القابلة للتفريغ — تزداد عند وجود تحيّز مع تفاعل عالي.
⚡ التفريغ الكهربائي (Discharge)
الفيزياء: يحدث عند تجاوز عتبة العزل
السوق: احتمال التفريغ = min(السعة / عتبة التفريغ, 1.0)
عندما ≥ 0.9: "⚡ DISCHARGE IMMINENT"
📌 ملاحظة جوهرية:
أقصى سعة لا تحدث عند الهيمنة المطلقة (حيث شدة الحقل = 0)، ولا عند التوازن التام (حيث الجهد = 0)، بل عند انحياز متوسط (±30–50%) مع تفاعل عالي (شدة حقل > 25%) — أي في لحظات "الضغط قبل الاختراق".
█ آلية الحساب التفصيلية
⚡ المرحلة 1: قطبية الشمعة
polarity = (close − open) / (high − low)
- +1.0: شمعة صاعدة كاملة (ماروبوزو صاعد)
- −1.0: شمعة هابطة كاملة (ماروبوزو هابط)
- 0.0: دوجي (لا قرار)
- القيم الوسيطة: تمثّل نسبة جسم الشمعة إلى مداها — مما يقلّل تأثير الشموع ذات الظلال الطويلة
⚡ المرحلة 2: وزن الحجم
vol_weight = volume / SMA(volume, lookback)
شمعة بحجم 150% من المتوسط = شحنة أقوى بـ 1.5 مرة
⚡ المرحلة 3: معامل التكيف (Adaptive Factor)
adaptive_factor = ATR(lookback) / SMA(ATR, lookback × 2)
- في الأسواق المتقلبة: يزيد الحساسية
- في الأسواق الهادئة: يقلل الضوضاء
- يوصى دائمًا بتركه مفعّلًا
⚡ المرحلة 4–6: تراكم وتوحيد الشحنات
تُجمّع الشحنات على lookback شمعة، ثم تُوحّد النسب:
norm_positive = positive_charge / total_charge
norm_negative = negative_charge / total_charge
بحيث: norm_positive + norm_negative = 1 — لتسهيل المقارنة
⚡ المرحلة 7: حسابات الحقل
voltage = norm_positive − norm_negative
field_intensity = 4 × norm_positive × norm_negative × field_sensitivity
capacitance = |voltage| × field_intensity
discharge_prob = min(capacitance / discharge_threshold, 1.0)
█ الإعدادات
⚡ Electromagnetic Model
Lookback Period
- الافتراضي: 20
- النطاق: 5–100
- التوصيات:
- المضاربة: 10–15
- اليومي: 20
- السوينغ: 30–50
- الاستثمار: 50–100
Discharge Threshold
- الافتراضي: 0.7
- النطاق: 0.3–0.95
- التوصيات:
- سرعة + ضوضاء: 0.5–0.6
- توازن: 0.7
- دقة عالية: 0.8–0.95
Field Sensitivity
- الافتراضي: 1.0
- النطاق: 0.5–2.0
- التوصيات:
- تضخيم الصراع: 1.2–1.5
- طبيعي: 1.0
- تهدئة: 0.5–0.8
Adaptive Mode
- الافتراضي: مفعّل
- أبقِه دائمًا مفعّلًا
🔬 Dynamic Filters
يجب اجتياز جميع الفلاتر المفعّلة لظهور إشارة التفريغ.
Volume Filter
- الشرط: volume > SMA(volume) × vol_multiplier
- الوظيفة: يستبعد الشموع "الضعيفة" غير المدعومة بحجم
- التوصية: مفعّل (خاصة للأسهم والعملات)
Volatility Filter
- الشرط: STDEV > SMA(STDEV) × 0.5
- الوظيفة: يتجاهل فترات الركود الجانبي
- التوصية: مفعّل دائمًا
Trend Filter
- الشرط: توافق الجهد مع EMA سريع/بطيء
- الوظيفة: يقلل الإشارات المعاكسة للاتجاه العام
- التوصية: مفعّل للسوينغ/الاستثمار فقط
Volume Threshold
- الافتراضي: 1.2
- التوصيات:
- 1.0–1.2: حساسية عالية
- 1.5–2.0: حصرية للحجم العالي
🎨 Visual Settings
الإعدادات تُحسّن تجربة القراءة البصرية — لا تؤثر على الحسابات.
Scale Factor
- الافتراضي: 600
- كلما زاد: المشهد أكبر (200–1200)
Horizontal Shift
- الافتراضي: 180
- إزاحة أفقيّة لليسار — ليركّز على آخر شمعة
Pole Size
- الافتراضي: 60
- حجم الكرات الأساسية (30–120)
Field Lines
- الافتراضي: 8
- عدد خطوط الحقل (4–16) — 8 توازن مثالي
الألوان
- أخضر/أحمر/أزرق/برتقالي
- قابلة للتخصيص بالكامل
█ التمثيل البصري: لغة بصرية لتشخيص الحالة السعرية
✨ الفلسفة التصميمية
التمثيل ليس "زينة"، بل نموذج معرفي متكامل — كل عنصر يحمل معلومة، وتفاعل العناصر يروي قصة كاملة.
العقل يدرك التغيير في الحجم، اللون، والحركة أسرع بـ 60,000 مرة من قراءة الأرقام — لذا يمكنك "الإحساس" بالتغير قبل أن تُنهي العين المسح.
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🟢 القطب الموجب (الكرة الخضراء — يسار)
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ماذا يمثّل؟
تراكم ضغط الشراء النشط — ليس مجرد اتجاه صاعد، بل قوة طلب حقيقية مدعومة بحجم وتقلّب.
● الحجم المتغير
حجم = pole_size × (0.7 + norm_positive × 0.6)
- 70% من الحجم الأساسي = لا شحنة تُذكر
- 130% من الحجم الأساسي = هيمنة تامة
- كلما كبرت الكرة: زاد تفوّق المشترين، وارتفع احتمال الاستمرار الصعودي
تفسير الحجم:
- كرة كبيرة (>55%): ضغط شراء قوي — المشترون يسيطرون
- كرة متوسطة (45–55%): توازن نسبي مع ميل للشراء
- كرة صغيرة (<45%): ضعف ضغط الشراء — البائعون يسيطرون
● الإضاءة والشفافية
- شفافية 20% (عند Bias = +1): القطب نشط حالياً — الاتجاه صعودي
- شفافية 50% (عند Bias ≠ +1): القطب غير نشط — ليس الاتجاه السائد
الإضاءة = النشاط الحالي، بينما الحجم = التراكم التاريخي
● التوهج الداخلي النابض
كرة أصغر تنبض تلقائيًّا عند Bias = +1:
inner_pulse = 0.4 + 0.1 × sin(anim_time × 3)
يرمز إلى استمرارية تدفق أوامر الشراء — وليس هيمنة جامدة.
● الحلقات المدارية
حلقتان تدوران بسرعات واتجاهات مختلفة:
- الداخلية: 1.3× حجم الكرة — نطاق التأثير المباشر
- الخارجية: 1.6× حجم الكرة — نطاق التأثير الممتد
تمثّل "نطاق تأثير" المشترين:
- الدوران المستمر = استقرار وزخم
- التباطؤ = نفاد الزخم
● النسبة المئوية
تظهر تحت الكرة: norm_positive × 100
- >55% = هيمنة واضحة
- 45–55% = توازن
- <45% = ضعف
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🔴 القطب السالب (الكرة الحمراء — يمين)
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ماذا يمثّل؟
تراكم ضغط البيع النشط — سواء كان بيعًا تراكميًّا (التوزيع الذكي) أو بيعًا هستيريًّا (تصفية مراكز).
● الديناميكيات البصرية
نفس آلية الحجم والإضاءة والتوهج الداخلي — لكن باللون الأحمر.
الفرق الجوهري:
- الدوران معكوس (عكس اتجاه عقارب الساعة)
- يُميّز بصريًّا بين "تدفق الشراء" و"تدفق البيع"
- يسمح بقراءة الاتجاه بنظرة واحدة — حتى للمصابين بعَمَى الألوان
📌 ملخص قراءة القطبين:
🟢 كرة خضراء كبيرة + مضيئة = قوة شرائية نشطة
🔴 كرة حمراء كبيرة + مضيئة = قوة بيعية نشطة
🟢🔴 كرتان كبيرتان لكن خافتتان = تراكم طاقة (قبل التفريغ)
⚪ كرتان صغيرتان = ركود / سيولة منخفضة
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🔵 خطوط الحقل (الخطوط الزرقاء المنحنية)
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ماذا تمثّل؟
مسارات تدفق الطاقة بين القطبين — أي الساحة التي تُدار فيها المعركة السعرية.
● عدد الخطوط
4–16 خط (الافتراضي: 8)
كلما زاد العدد: زاد إحساس "كثافة التفاعل"
● ارتفاع القوس
arc_h = (i − half_lines) × 15 × field_intensity × 2
- شدة حقل عالية = خطوط شديدة الارتفاع (مثل موجة)
- شدة منخفضة = خطوط شبه مستقيمة
● الشفافية المتذبذبة
transp = 30 + phase × 40
حيث phase = sin(anim_time × 2 + i × 0.5) × 0.5 + 0.5
تخلق وهم "تيّار متدفّق" — وليس خطوطًا ثابتة
● الانحناء غير المتناظر
- الخطوط العلوية تنحني لأعلى
- الخطوط السفلية تنحني لأسفل
- يُضفي عمقًا ثلاثي الأبعاد ويُظهر اتجاه "الضغط"
⚡ تلميح احترافي:
عندما ترى الخطوط "تتقلّص" فجأة (تستقيم)، بينما الكرتان كبيرتان — فهذا مؤشر مبكر على قرب التفريغ، لأن التفاعل بدأ يفقد مرونته.
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⚪ الجزيئات المتحركة
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ماذا تمثّل؟
تدفق السيولة الحقيقية في السوق — أي من يدفع السعر الآن.
● العدد والحركة
- 6 جزيئات تغطي معظم خطوط الحقل
- تتحرك جيبيًّا على طول القوس:
t = (sin(phase_val) + 1) / 2
- سرعة عالية = نشاط تداول عالي
- تجمّع عند قطب = سيطرة هذا الطرف
● تدرج اللون
من أخضر (عند القطب الموجب) إلى أحمر (عند السالب)
يُظهر "تحوّل الطاقة":
- جزيء أخضر = طاقة شرائية نقية
- جزيء برتقالي = منطقة صراع
- جزيء أحمر = طاقة بيعية نقية
📌 كيف تقرأها؟
- تحركت من اليسار لليمين (🟢 → 🔴): تدفق شرائي → دفع صعودي
- تحركت من اليمين لليسار (🔴 → 🟢): تدفق بيعي → دفع هبوطي
- تجمّعت في المنتصف: صراع متكافئ — انتظر اختراقًا
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🟠 منطقة التفريغ (التوهج البرتقالي — المركز)
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ماذا تمثّل؟
نقطة تراكم الطاقة المخزّنة التي لم تُفرّغ بعد — قلب نظام الإنذار المبكر.
● مراحل التوهج
إنذار أولي (discharge_prob > 0.3):
- دائرة برتقالية خافتة (شفافية 70%)
- المعنى: راقب، لا تدخل بعد
توتر عالي (discharge_prob ≥ 0.7):
- توهج أقوى + نص "⚠️ HIGH TENSION"
- المعنى: استعد — ضع أوامر معلقة
تفريغ وشيك (discharge_prob ≥ 0.9):
- توهج ساطع + نص "⚡ DISCHARGE IMMINENT"
- المعنى: ادخل مع الاتجاه (بعد تأكيد شمعة)
● تأثير التوهج الطبقي (Glow Layering)
3 دوائر متحدة المركز بشفافية متزايدة:
- داخلي: 20%
- وسط: 35%
- خارجي: 50%
النتيجة: هالة (Aura) واقعية تشبه التفريغ الكهربائي الحقيقي.
📌 لماذا في المركز؟
لأن التفريغ يبدأ دائمًا من منطقة التوازن النسبي — حيث يلتقي الضغطان المتعاكسان.
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📊 مقياس الجهد (أسفل المشهد)
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ماذا يمثّل؟
مؤشر رقمي مبسّط لفرق الجهد — لمن يفضّل القراءة العددية.
● المكونات
- الشريط الرمادي: النطاق الكامل (−100% إلى +100%)
- التعبئة الخضراء: جهد موجب (تمتد لليمين)
- التعبئة الحمراء: جهد سالب (تمتد لليسار)
- رمز البرق (⚡): فوق المركز — تذكير بأنه "مقياس كهربائي"
- القيمة النصية: مثل "+23.4%" — بلون الاتجاه
● تفسير قراءات الجهد
+50% إلى +100%:
هيمنة شرائية ساحقة — احذر التشبع، قد يسبق تصحيح
+20% إلى +50%:
هيمنة شرائية قوية — مناسب للشراء مع الاتجاه
+5% إلى +20%:
ميل صعودي خفيف — انتظر تأكيدًا إضافيًّا
−5% إلى +5%:
توازن/حياد — تجنّب الدخول أو انتظر اختراقًا
−5% إلى −20%:
ميل هبوطي خفيف — انتظر تأكيدًا
−20% إلى −50%:
هيمنة بيعية قوية — مناسب للبيع مع الاتجاه
−50% إلى −100%:
هيمنة بيعية ساحقة — احذر التشبع، قد يسبق ارتداد
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📈 مؤشر شدة الحقل (أعلى المشهد)
═════════════════════════════════════════════════════════════
ما يعرضه: "Field: XX.X%"
الدلالة: قوة الصراع بين المشترين والبائعين.
● تفسير القراءات
0–5%:
- المظهر: خطوط مستقيمة تقريبًا، شفافة
- المعنى: سيطرة تامة لأحد الطرفين
- الاستراتيجية: تتبع الترند (Trend Following)
5–15%:
- المظهر: انحناء خفيف
- المعنى: اتجاه واضح مع مقاومة خفيفة
- الاستراتيجية: الدخول مع الاتجاه
15–25%:
- المظهر: انحناء متوسط، خطوط واضحة
- المعنى: صراع متوازن
- الاستراتيجية: تداول النطاق أو الانتظار
25–35%:
- المظهر: انحناء عالي، كثافة واضحة
- المعنى: صراع قوي، عدم يقين عالي
- الاستراتيجية: تداول التقلّب أو الاستعداد للتفريغ
35%+:
- المظهر: خطوط عالية جدًّا، توهج قوي
- المعنى: ذروة التوتر
- الاستراتيجية: أفضل فرص التفريغ
📌 العلاقة الذهبية:
أعلى احتمال تفريغ عندما:
شدة الحقل (25–35%) + جهد (±30–50%) + حجم مرتفع
← هذه هي "المنطقة الحمراء" التي يجب مراقبتها بدقة.
█ قراءة التمثيل البصري الشاملة
لقراءة حالة السوق بنظرة واحدة، اتبع هذا التسلسل:
الخطوة 1: أي كرة أكبر؟
- 🟢 الخضراء أكبر ← ضغط شراء مهيمن
- 🔴 الحمراء أكبر ← ضغط بيع مهيمن
- متساويتان ← توازن/صراع
الخطوة 2: أي كرة مضيئة؟
- 🟢 الخضراء مضيئة ← اتجاه صعودي حالي
- 🔴 الحمراء مضيئة ← اتجاه هبوطي حالي
- كلاهما خافت ← حياد/لا اتجاه واضح
الخطوة 3: هل يوجد توهج برتقالي؟
- لا يوجد ← احتمال تفريغ <30%
- 🟠 توهج خافت ← احتمال تفريغ 30–70%
- 🟠 توهج قوي مع نص ← احتمال تفريغ >70%
الخطوة 4: ما قراءة مقياس الجهد؟
- موجب قوي ← تأكيد الهيمنة الشرائية
- سالب قوي ← تأكيد الهيمنة البيعية
- قريب من الصفر ← لا اتجاه واضح
█ أمثلة عملية للقراءة البصرية
المثال 1: فرصة شراء مثالية ⚡🟢
- الكرة الخضراء: كبيرة ومضيئة مع نبض داخلي
- الكرة الحمراء: صغيرة وخافتة
- التوهج البرتقالي: قوي مع نص "DISCHARGE IMMINENT"
- مقياس الجهد: +45%
- شدة الحقل: 28%
التفسير: ضغط شراء قوي متراكم، انفجار صعودي وشيك
المثال 2: فرصة بيع مثالية ⚡🔴
- الكرة الخضراء: صغيرة وخافتة
- الكرة الحمراء: كبيرة ومضيئة مع نبض داخلي
- التوهج البرتقالي: قوي مع نص "DISCHARGE IMMINENT"
- مقياس الجهد: −52%
- شدة الحقل: 31%
التفسير: ضغط بيع قوي متراكم، انفجار هبوطي وشيك
المثال 3: توازن/انتظار ⚖️
- الكرتان: متساويتان تقريباً في الحجم
- الإضاءة: كلاهما خافت
- التوهج البرتقالي: قوي
- مقياس الجهد: +3%
- شدة الحقل: 24%
التفسير: صراع قوي بدون فائز واضح، انتظر اختراقًا
المثال 4: اتجاه صعودي واضح (لا تفريغ) 📈
- الكرة الخضراء: كبيرة ومضيئة
- الكرة الحمراء: صغيرة جداً وخافتة
- التوهج البرتقالي: لا يوجد
- مقياس الجهد: +68%
- شدة الحقل: 8%
التفسير: سيطرة شرائية واضحة، صراع محدود، مناسب لتتبع الترند الصعودي
المثال 5: تشبع شرائي محتمل ⚠️
- الكرة الخضراء: كبيرة جداً ومضيئة
- الكرة الحمراء: صغيرة جداً
- التوهج البرتقالي: خافت
- مقياس الجهد: +88%
- شدة الحقل: 4%
التفسير: هيمنة شرائية مطلقة، قد يسبق تصحيحاً هبوطياً
█ إشارات التداول
⚡ DISCHARGE IMMINENT (التفريغ الوشيك)
شروط الظهور:
- discharge_prob ≥ 0.9
- اجتياز جميع الفلاتر المفعّلة
- Confirmed (بعد إغلاق الشمعة)
التفسير:
- تراكم طاقة كبير جدًّا
- الضغط وصل لمستوى حرج
- انفجار سعري متوقع خلال 1–3 شموع
كيفية التداول:
1. حدد اتجاه الجهد:
• موجب = توقع صعود
• سالب = توقع هبوط
2. انتظر شمعة تأكيدية:
• للصعود: شمعة صاعدة تغلق فوق افتتاحها
• للهبوط: شمعة هابطة تغلق تحت افتتاحها
3. الدخول: مع افتتاح الشمعة التالية
4. وقف الخسارة: وراء آخر قاع/قمة محلية
5. الهدف: نسبة مخاطرة/عائد 1:2 على الأقل
✅ نصائح احترافية:
- أفضل النتائج عند دمجها مع مستويات الدعم/المقاومة
- تجنّب الدخول إذا كان الجهد قريبًا من الصفر (±5%)
- زِد حجم المركز عند شدة حقل > 30%
⚠️ HIGH TENSION (التوتر العالي)
شروط الظهور:
- 0.7 ≤ discharge_prob < 0.9
التفسير:
- السوق في حالة تراكم طاقة
- احتمال حركة قوية قريبة، لكن ليست فورية
- قد يستمر التراكم أو يحدث تفريغ
كيفية الاستفادة:
- الاستعداد: حضّر أوامر معلقة عند الاختراقات المحتملة
- المراقبة: راقب الشموع التالية بحثًا عن شمعة دافعة
- الانتقاء: لا تدخل كل إشارة — اختر تلك التي تتوافق مع الاتجاه العام
█ استراتيجيات التداول
📈 استراتيجية 1: تداول التفريغ (الأساسية)
المبدأ: الدخول عند "DISCHARGE IMMINENT" في اتجاه الجهد
الخطوات:
1. انتظر ظهور "⚡ DISCHARGE IMMINENT"
2. تحقق من اتجاه الجهد (+/−)
3. انتظر شمعة تأكيدية في اتجاه الجهد
4. ادخل مع افتتاح الشمعة التالية
5. وقف الخسارة وراء آخر قاع/قمة
6. الهدف: نسبة 1:2 أو 1:3
نسبة نجاح عالية جدًّا عند الالتزام بشروط التأكيد.
📈 استراتيجية 2: تتبع الهيمنة
المبدأ: التداول مع القطب المهيمن (الكرة الأكبر والأكثر إضاءة)
الخطوات:
1. حدد القطب المهيمن (الأكبر حجماً والأكثر إضاءة)
2. تداول في اتجاهه
3. احذر عند تقارب الأحجام (صراع)
مناسبة للإطارات الزمنية الأعلى (H1+).
📈 استراتيجية 3: صيد الانعكاس
المبدأ: الدخول عكس الاتجاه عند ظروف معينة
الشروط:
- شدة حقل عالية (>30%)
- جهد متطرف (>±40%)
- تباعد مع السعر (مثل: قمة سعرية جديدة مع تراجع الجهد)
⚠️ عالية المخاطرة — استخدم حجم مركز صغير.
📈 استراتيجية 4: الدمج مع التحليل الفني
أمثلة تأكيد قوي:
- اختراق مقاومة + تفريغ صعودي = إشارة شراء ممتازة
- كسر دعم + تفريغ هبوطي = إشارة بيع ممتازة
- نموذج Head & Shoulders + جهد سالب متزايد = تأكيد النموذج
- تباعد RSI + شدة حقل عالية = انعكاس محتمل
█ التنبيهات الجاهزة
Bullish Discharge
- الشرط: discharge_prob ≥ 0.9 + جهد موجب + جميع الفلاتر
- الرسالة: "⚡ Bullish discharge"
- الاستخدام: فرصة شراء عالية الاحتمالية
Bearish Discharge
- الشرط: discharge_prob ≥ 0.9 + جهد سالب + جميع الفلاتر
- الرسالة: "⚡ Bearish discharge"
- الاستخدام: فرصة بيع عالية الاحتمالية
✅ نصيحة: استخدم هذه التنبيهات مع إعداد "Once Per Bar" لتجنب التكرار.
█ المخرجات في نافذة البيانات
Bias
- القيم: −1 / 0 / +1
- التفسير: −1 = هبوطي، 0 = حياد، +1 = صعودي
- الاستخدام: لدمجها في استراتيجيات آلية
Discharge %
- النطاق: 0–100%
- التفسير: احتمال التفريغ
- الاستخدام: مراقبة تدرّج التوتر (مثال: من 40% إلى 85% في 5 شموع)
Field Strength
- النطاق: 0–100%
- التفسير: شدة الصراع
- الاستخدام: تحديد "نافذة الفرص" (25–35% مثالية للتفريغ)
Voltage
- النطاق: −100% إلى +100%
- التفسير: ميزان القوى
- الاستخدام: مراقبة التطرف (تشبع شرائي/بيعي محتمل)
█ الإعدادات المثلى حسب أسلوب التداول
المضاربة (Scalping)
- الإطار: 1M–5M
- Lookback: 10–15
- Threshold: 0.5–0.6
- Sensitivity: 1.2–1.5
- الفلاتر: Volume + Volatility
التداول اليومي (Day Trading)
- الإطار: 15M–1H
- Lookback: 20
- Threshold: 0.7
- Sensitivity: 1.0
- الفلاتر: Volume + Volatility
السوينغ (Swing Trading)
- الإطار: 4H–D1
- Lookback: 30–50
- Threshold: 0.8
- Sensitivity: 0.8
- الفلاتر: Volatility + Trend
الاستثمار (Position Trading)
- الإطار: D1–W1
- Lookback: 50–100
- Threshold: 0.85–0.95
- Sensitivity: 0.5–0.8
- الفلاتر: جميع الفلاتر
█ نصائح للاستخدام الأمثل
1. ابدأ بالإعدادات الافتراضية
جرّبه أولًا كما هو، ثم عدّل حسب أسلوبك.
2. راقب التوافق بين العناصر
أفضل الإشارات عندما:
- الجهد واضح (>│20%│)
- شدة الحقل معتدلة–عالية (15–35%)
- احتمال التفريغ مرتفع (>70%)
3. استخدم أطر زمنية متعددة
- الإطار الأعلى: تحديد الاتجاه العام
- الإطار الأدنى: توقيت الدخول
- تأكد من توافق الإشارات بين الأطر
4. دمج مع أدوات أخرى
- مستويات الدعم/المقاومة
- خطوط الاتجاه
- أنماط الشموع
- مؤشرات الحجم
5. احترم إدارة المخاطرة
- لا تخاطر بأكثر من 1–2% من الحساب
- استخدم دائمًا وقف الخسارة
- لا تدخل كل الإشارات — اختر الأفضل
█ تحذيرات مهمة
⚠️ ليس للاستخدام المنفرد
المؤشر أداة تحليل مساعِدة — لا تستخدمه بمعزل عن التحليل الفني أو الأساسي.
⚠️ لا يتنبأ بالمستقبل
الحسابات مبنية على البيانات التاريخية — النتائج ليست مضمونة.
⚠️ الأسواق تختلف
قد تحتاج لضبط الإعدادات لكل سوق:
- العملات: تركّز على Volume Filter
- الأسهم: أضف Trend Filter
- الكريبتو: خفّض Threshold قليلًا (أكثر تقلّبًا)
⚠️ الأخبار والأحداث
المؤشر لا يأخذ في الاعتبار الأخبار المفاجئة — تجنّب التداول قبل/أثناء الأخبار الرئيسية.
█ الميزات الفريدة
✅ أول تطبيق للكهرومغناطيسية على الأسواق
نموذج رياضي مبتكر — ليس مجرد مؤشر عادي
✅ كشف استباقي للانفجارات السعرية
يُنبّه قبل حدوث الحركة — وليس بعدها
✅ تصفية متعددة الطبقات
4 فلاتر ذكية تقلل الإشارات الكاذبة إلى الحد الأدنى
✅ تكيف ذكي مع التقلب
يضبط حساسيته تلقائيًّا حسب ظروف السوق
✅ تمثيل بصري ثلاثي الأبعاد متحرك
يجعل القراءة فورية — حتى للمبتدئين
✅ مرونة عالية
يعمل على جميع الأصول: أسهم، عملات، كريبتو، سلع
✅ تنبيهات مدمجة جاهزة
لا حاجة لإعدادات معقدة — جاهز للاستخدام الفوري
█ خاتمة: عندما يلتقي الفن بالعلم
Market Electromagnetic Field ليس مجرد مؤشر — بل فلسفة تحليلية جديدة.
هو الجسر بين:
- دقة الفيزياء في وصف الأنظمة الديناميكية
- ذكاء السوق في توليد فرص التداول
- علم النفس البصري في تسهيل القراءة الفورية
النتيجة: أداة لا تُقرأ — بل تُشاهد، تُشعر، وتُستشعر.
عندما ترى الكرة الخضراء تتوسع، والتوهج يصفرّ، والجزيئات تندفع لليمين — فأنت لا ترى أرقامًا، بل ترى طاقة السوق تتنفّس.
⚠️ إخلاء مسؤولية:
هذا المؤشر لأغراض تعليمية وتحليلية فقط. لا يُمثل نصيحة مالية أو استثمارية أو تداولية. استخدمه بالتزامن مع استراتيجيتك الخاصة وإدارة المخاطر. لا يتحمل TradingView ولا المطور مسؤولية أي قرارات مالية أو خسائر.
ATR Trend + RSI Pullback Strategy [Profit-Focused]This strategy is designed to catch high-probability pullbacks during strong trends using a combination of ATR-based volatility filters, RSI exhaustion levels, and a trend-following entry model.
Strategy Logic
Rather than relying on lagging crossovers, this model waits for RSI to dip into oversold zones (below 40) while price remains above a long-term EMA (default: 200). This setup captures pullbacks in strong uptrends, allowing traders to enter early in a move while controlling risk dynamically.
To avoid entries during low-volatility conditions or sideways price action, it applies a minimum ATR filter. The ATR also defines both the stop-loss and take-profit levels, allowing the model to adapt to changing market conditions.
Exit logic includes:
A take-profit at 3× the ATR distance
A stop-loss at 1.5× the ATR distance
An optional early exit if RSI crosses above 70, signaling overbought conditions
Technical Details
Trend Filter: 200 EMA – must be rising and price must be above it
Entry Signal: RSI dips below 40 during an uptrend
Volatility Filter: ATR must be above a user-defined minimum threshold
Stop-Loss: 1.5× ATR below entry price
Take-Profit: 3.0× ATR above entry price
Exit on Overbought: RSI > 70 (optional early exit)
Backtest Settings
Initial Capital: $10,000
Position Sizing: 5% of equity per trade
Slippage: 1 tick
Commission: 0.075% per trade
Trade Direction: Long only
Timeframes Tested: 15m, 1H, and 30m on trending assets like BTCUSD, NAS100, ETHUSD
This model is tuned for positive P&L across trending environments and volatile markets.
Educational Use Only
This strategy is for educational purposes only and should not be considered financial advice. Past performance does not guarantee future results. Always validate performance on multiple markets and timeframes before using it in live trading.
RSI MTF 15m + 1h (Oriol)//@version=5
indicator("RSI MTF 15m + 1h (Oriol)", overlay = false, timeframe = "", timeframe_gaps = true)
// ─── PARÀMETRES ─────────────────────────────────────────────
rsiLength = input.int(14, "Període RSI")
src = input.source(close, "Font de preu")
tfFast = input.timeframe("15", "Timeframe ràpid (RSI 15m)")
tfSlow = input.timeframe("60", "Timeframe lent (RSI 1h)")
showSignals = input.bool(true, "Mostrar senyals LONG/SHORT")
// ─── RSI MULTITIMEFRAME ────────────────────────────────────
// RSI del timeframe ràpid (per defecte 15m)
src_fast = request.security(syminfo.tickerid, tfFast, src)
rsi_fast = ta.rsi(src_fast, rsiLength)
// RSI del timeframe lent (per defecte 1h)
src_slow = request.security(syminfo.tickerid, tfSlow, src)
rsi_slow = ta.rsi(src_slow, rsiLength)
// ─── DIBUIX RSI ─────────────────────────────────────────────
plot(rsi_fast, title = "RSI ràpid (15m)", color = color.new(color.aqua, 0), linewidth = 2)
plot(rsi_slow, title = "RSI lent (1h)", color = color.new(color.orange, 0), linewidth = 2)
hline(70, "Sobrecomprat", color = color.new(color.red, 70), linestyle = hline.style_dashed)
hline(30, "Sobrevenut", color = color.new(color.lime, 70), linestyle = hline.style_dashed)
hline(50, "Mitja", color = color.new(color.gray, 80))
// ─── CONDICIONS D’EXEMPLE ───────────────────────────────────
// LONG: RSI 1h < 40 i RSI 15m creua cap amunt 30
// SHORT: RSI 1h > 60 i RSI 15m creua cap avall 70
longCond = (rsi_slow < 40) and ta.crossover(rsi_fast, 30)
shortCond = (rsi_slow > 60) and ta.crossunder(rsi_fast, 70)
// ─── SENYALS (SENSE SCOPE LOCAL) ────────────────────────────
plotshape(showSignals and longCond,
title = "Possible LONG",
style = shape.triangleup,
location = location.bottom,
color = color.new(color.lime, 0),
size = size.small,
text = "LONG")
plotshape(showSignals and shortCond,
title = "Possible SHORT",
style = shape.triangledown,
location = location.top,
color = color.new(color.red, 0),
size = size.small,
text = "SHORT")
// ─── ALERTES ────────────────────────────────────────────────
alertcondition(longCond, title = "Senyals LONG RSI 15m+1h",
message = "Condició LONG RSI 15m + 1h complerta")
alertcondition(shortCond, title = "Senyals SHORT RSI 15m+1h",
message = "Condició SHORT RSI 15m + 1h complerta")
