Identical Candles Detector [Premium]Identical Candles Detector
Advanced pattern recognition for consecutive similar candles
Description
This professional-grade indicator detects sequences of nearly identical candles, a pattern often signaling consolidation before significant breakouts. Unlike basic similarity detectors, it employs a weighted comparison system evaluating both candle bodies and wicks with adjustable tolerance.
Key Features:
Smart Comparison Algorithm: Weighs body vs. wick importance (adjustable 0-100%)
Directional Filtering: Optional same-direction requirement for bullish/bearish consistency
Statistical Backtesting: Tracks historical pattern success rates in real-time
Future Projection: Analyzes post-pattern performance with customizable lookahead
Visual Highlighting: Clear pattern marking with optional performance statistics
How It Works:
Calculates weighted candle size (body + wicks)
Compares consecutive candles within user-defined tolerance
Verifies directional consistency when enabled
Evaluates future price action for statistical significance
Usage Guidelines:
Best used on 15m-4h timeframes for swing trading
Combine with volume confirmation for higher probability signals
Tighten tolerance (3-5%) for more precise patterns
Use minimum pattern distance to avoid over-crowding
Technical Notes:
Safe historical access prevents lookback errors
Comprehensive input validation ensures stable operation
Memory-efficient implementation supports long backtests
Why This Stands Out:
While simple candle patterns are common, this tool adds:
Quantitative similarity measurement
Configurable component weighting
Built-in performance analytics
Professional-grade alert conditions
Note: This is not a standalone trading system. Always use with proper risk management and confirmation from other indicators.
Recherche dans les scripts pour "backtest"
[TehThomas] - Displacement CandlesOverview:
This PineScript is designed to detect and visualize significant price movements, called displacements, on a trading chart. It's particularly useful for traders who want to identify potential trend changes or strong market sentiment quickly.
How the Script Works
User Input:
The script allows users to set a custom threshold for displacement detection and choose colors for bullish and bearish movements.
Displacement Detection Function:
isDisplacement(series, threshold) =>
percentage_change = math.abs(series - series ) / series * 100
percentage_change > threshold
This function calculates the percentage change between the current and previous price.
If the change exceeds the set threshold, it's considered a displacement.
Bullish and Bearish Detection:
bullish_displacement = isDisplacement(close, threshold) and close > close
bearish_displacement = isDisplacement(close, threshold) and close < close
Identifies whether the displacement is bullish (price increase) or bearish (price decrease).
Candle Coloring:
barcolor(bullish_displacement ? bullish_color : bearish_displacement ? bearish_color : na)
Changes the color of candles based on the detected displacement type.
Usefulness and Applications:
Trend Identification: Helps in quickly spotting potential trend changes or continuations.
Volatility Analysis: Provides a visual representation of market volatility.
Entry and Exit Signals: Can be used to identify potential entry or exit points for trades.
Market Sentiment: Offers insights into the strength of bullish or bearish sentiment.
Customizable Sensitivity: The adjustable threshold allows traders to fine-tune the indicator based on the asset's typical volatility.
Visual Clarity: By changing candle colors, it provides a clear, at-a-glance view of significant price movements.
Complementary Tool: Can be used alongside other technical indicators for confirmation of signals.
Multiple Timeframe Analysis: Applicable across different timeframes to suit various trading styles (day trading, swing trading, etc.).
Educational Purpose: Helps new traders understand and visualize significant price movements in the market.
Backtesting: Can be incorporated into strategy backtests to assess its effectiveness in different market conditions.
This script is particularly handy for traders who want to cut through market noise and focus on significant price movements. It's versatile enough to be used across different trading strategies and can be a valuable addition to a trader's technical analysis toolkit.
It's a very easy script and not alot to mention. If you see any improvements please let me know.
[blackcat] L1 Main life line oscillator█ OVERVIEW
The Pine Script provided is an indicator named " L1 Main life line oscillator." Its primary function is to calculate and plot two oscillators: the Main Force and the Life Line. These oscillators are derived from smoothed price data, and the script also detects and labels crossovers and crossunders between the two lines, which can be used to generate buy and sell signals.
█ FEATURES
Key Features:
• Input Parameters: Users can define the period (n) and the weight for the oscillators.
• Custom Function: A function calculate_life_line_oscillator is defined to compute the Main Force and Life Line oscillators.
• Advanced Calculations: The script uses an adaptive moving average (ALMA) and exponential moving average (EMA) to smooth the price data and calculate the oscillators.
• Crossover and Crossunder Detection: Built-in functions ta.crossover and ta.crossunder are used to identify signal points.
• Label Drawing: Custom labels are drawn on the chart to indicate buy ("B") and sell ("S") signals.
█ HOW TO USE
1 — Configure Input Parameters: Adjust the period (n) and weight to suit your trading strategy.
2 — Interpret the Oscillators: Observe the Main Force and Life Line on the chart.
3 — Act on Signals: Look for crossovers and crossunders to generate buy and sell signals. Buy signals are indicated by the label "B" and sell signals by "S".
█ LIMITATIONS
• Lag in Signals: While the use of ALMA and EMA reduces lag, some delay may still occur, especially in volatile markets.
• False Signals: Crossovers and crossunders can sometimes produce false signals, so it is advisable to use this indicator in conjunction with other tools for confirmation.
█ NOTES
Advanced Pine Script Features:
• Adaptive Moving Average (ALMA): Provides a more responsive and adaptive oscillator.
• Exponential Moving Average (EMA): Smooths the price range and Main Force values.
• Crossover and Crossunder Detection: Utilizes built-in functions for signal identification.
• Label Drawing: Enhances visual signaling with custom labels.
Optimization Techniques:
• The use of ALMA and EMA helps in reducing lag and improving the responsiveness of the oscillators.
• The custom function encapsulates complex calculations, making the main script cleaner and more maintainable.
Unique Approaches:
• The combination of ALMA and EMA to create the Main Force oscillator provides a unique smoothing method.
• The Life Line is calculated using a weighted average of the previous and current Main Force values, adding an additional layer of smoothing and responsiveness.
█ THANKS
Thank you for using the " L1 Main life line oscillator." If you have any questions or suggestions, please feel free to reach out in the comments or on the TradingView or my Discord channel.
█ EXTENDED KNOWLEDGE AND APPLICATIONS
Potential Modifications:
• Additional Indicators: Extend the script to include other technical indicators (e.g., RSI, MACD) for a more comprehensive trading signal system.
• Customizable Colors and Styles: Allow users to customize the colors and styles of the plotted lines and labels.
• Alerts: Implement alerts for crossovers and crossunders to notify users in real-time.
Application Scenarios:
• Intraday Trading: The responsiveness of the oscillators makes this script suitable for intraday trading, where quick buy and sell signals are crucial.
• Long-Term Analysis: By adjusting the period n, the script can be used for long-term trend analysis and strategic trades.
• Backtesting: The script can be modified into a strategy to backtest the performance of the oscillator-based signals against historical data.
Related Pine Script Concepts:
• Strategy Development: Understanding how to convert indicators into strategies for backtesting and live trading.
• Advanced Plotting: Exploring more advanced plotting techniques, such as using different styles and customizing plot appearances.
• Signal Validation: Techniques for validating and filtering signals to reduce false positives and improve trade accuracy.
VMDM - Volume, Momentum & Divergence Master [BullByte]VMDM - Volume, Momentum and Divergence Master
Educational Multi-Layer Market Structure Analysis System
Multi-factor divergence engine that scores RSI momentum, volume pressure, and institutional footprints into one non-repainting confluence rating (0-100).
WHAT THIS INDICATOR IS
VMDM is an educational indicator designed to teach traders how to recognize high-probability reversal and continuation patterns by analyzing four independent market dimensions simultaneously. Instead of relying on a single indicator that may produce frequent false signals, VMDM creates a confluence-based scoring system that weights multiple confirmation factors, helping you understand which setups have stronger technical backing and which are lower quality.
This is NOT a trading system or signal generator. It is a learning tool that visualizes complex market structure concepts in an accessible format for both coders and non-coders.
THE PROBLEM IT SOLVES
Most traders face these common challenges:
Challenge 1 - Indicator Overload: Running RSI, volume analysis, and divergence detection separately creates chart clutter and conflicting signals. You waste time cross-referencing multiple windows trying to determine if all factors align.
Challenge 2 - False Divergences: Standard divergence indicators trigger on every minor pivot, creating noise. Many divergences fail because they lack supporting evidence from volume or market structure.
Challenge 3 - Missed Context: A bullish RSI divergence means nothing if it occurs during weak volume or in the middle of strong distribution. Context determines quality.
Challenge 4 - Repainting Confusion: Many divergence scripts repaint, showing perfect historical signals that never actually triggered in real-time, leading to false confidence.
Challenge 5 - Institutional Pattern Recognition: Absorption zones, stop hunts, and exhaustion patterns are taught in trading education but difficult to identify systematically without manual analysis.
VMDM addresses all five challenges by combining complementary analytical layers into one transparent, non-repainting, confluence-weighted system with visual clarity.
WHY THIS SPECIFIC COMBINATION - MASHUP JUSTIFICATION
This indicator is NOT a random mashup of popular indicators. Each of the four layers serves a specific analytical purpose and together they create a complete market structure assessment framework.
THE FOUR ANALYTICAL LAYERS
LAYER 1 - RSI MOMENTUM DIVERGENCE (Trend Exhaustion Detection)
Purpose: Identifies when price momentum is weakening before price itself reverses.
Why RSI: The Relative Strength Index measures momentum on a bounded 0-100 scale, making divergence detection mathematically consistent across all assets and timeframes. Unlike raw price oscillators, RSI normalizes momentum regardless of volatility regime.
How It Contributes: Divergence between price pivots and RSI pivots reveals early momentum exhaustion. A lower price low with a higher RSI low (bullish regular divergence) signals sellers are losing strength even as price makes new lows. This is the PRIMARY signal generator in VMDM.
Limitation If Used Alone: RSI divergence by itself produces many false signals because momentum can remain weak during continued trends. It needs confirmation from volume and structural evidence.
LAYER 2 - VOLUME PRESSURE ANALYSIS (Buying vs Selling Intensity)
Purpose: Quantifies whether the current bar's volume reflects buying pressure or selling pressure based on where price closed within the bar's range.
Methodology: Instead of just measuring volume size, VMDM calculates WHERE in the bar range the close occurred. A close near the high on high volume indicates strong buying absorption. A close near the low indicates selling pressure. The calculation accounts for wick size (wicks reduce pressure quality) and uses percentile ranking over a lookback period to normalize pressure strength on a 0-100 scale.
Formula Concept:
Buy Pressure = Volume × (Close - Low) / (High - Low) × Wick Quality Factor
Sell Pressure = Volume × (High - Close) / (High - Low) × Wick Quality Factor
Net Pressure = Buy Pressure - Sell Pressure
Pressure Strength = Percentile Rank of Net Pressure over lookback period
Why Percentile Ranking: Absolute volume varies by asset and session. Percentile ranking makes 85th percentile pressure on low-volume crypto comparable to 85th percentile pressure on high-volume forex.
How It Contributes: When a bullish divergence occurs at a pivot low AND pressure strength is above 60 (strong buying), this adds 25 confluence points. It confirms that the divergence is occurring during actual accumulation, not just weak selling.
Limitation If Used Alone: Pressure analysis shows current bar intensity but cannot identify trend exhaustion or reversal timing. High buying pressure can exist during a strong uptrend with no reversal imminent.
LAYER 3 - BEHAVIORAL FOOTPRINT PATTERNS (Volume Anomaly Detection)
CRITICAL DISCLAIMER: The terms "institutional footprint," "absorption," "stop hunt," and "exhaustion" used in this indicator are EDUCATIONAL LABELS for specific price and volume behavioral patterns. These patterns are detected through technical analysis of publicly available price, volume, and bar structure data. This indicator does NOT have access to actual institutional order flow, market maker data, broker stop-loss locations, or any non-public data source. These pattern names are used because they are common terminology in trading education to describe these technical behaviors. The analysis is interpretive and based on observable price action, not privileged information.
Purpose: Detect volume anomalies and price patterns that historically correlate with potential reversal zones or trend continuation failure.
Pattern Type 1 - Absorption (Labeled as "ACCUMULATION" or "DISTRIBUTION")
Detection Criteria: Volume is more than 2x the moving average AND bar range is less than 50 percent of the average bar range.
Interpretation: High volume compressed into a tight range suggests large participants are absorbing supply (accumulation) or distribution (distribution) without allowing price to move significantly. This often precedes directional moves once absorption completes.
Visual: Colored box zone highlighting the absorption area.
Pattern Type 2 - Stop Hunt (Labeled as "BULL HUNT" or "BEAR HUNT")
Detection Criteria: Price penetrates a recent 10-bar high or low by a small margin (0.2 percent), then closes back inside the range on above-average volume (1.5x+).
Interpretation: Price briefly spikes beyond recent structure (likely triggering stop losses placed just beyond obvious levels) then reverses. This is a classic false breakout pattern often seen before reversals.
Visual: Label at the wick extreme showing hunt direction.
Pattern Type 3 - Exhaustion (Labeled as "SELL EXHAUST" or "BUY EXHAUST")
Detection Criteria: Lower wick is more than 2.5x the body size with volume above 1.8x average and RSI below 35 (sell exhaustion), OR upper wick more than 2.5x body size with volume above 1.8x average and RSI above 65 (buy exhaustion).
Interpretation: Large wicks with high volume and extreme RSI suggest aggressive buying or selling was met with equally aggressive rejection. This exhaustion often marks short-term extremes.
Visual: Label showing exhaustion type.
How These Contribute: When a divergence forms at a pivot AND one of these behavioral patterns is active, the confluence score increases by 20 points. This confirms the divergence is occurring during structural anomaly activity, not just normal price flow.
Limitation If Used Alone: These patterns can occur mid-trend and do not indicate direction without momentum context. Absorption in a strong uptrend may just be continuation accumulation.
LAYER 4 - CONFLUENCE SCORING MATRIX (Quality Weighting System)
Purpose: Translate all detected conditions into a single 0-100 quality score so you can objectively compare setups.
Scoring Breakdown:
Divergence Present: +30 points (primary signal)
Pressure Confirmation: +25 points (volume supports direction)
Behavioral Footprint Active: +20 points (structural anomaly present)
RSI Extreme: +15 points (RSI below 30 or above 70 at pivot)
Volume Spike: +10 points (current volume above 1.5x average)
Maximum Possible Score: 100 points
Why These Weights: The weights reflect reliability hierarchy based on backtesting observation. Divergence is the core signal (30 points), but without volume confirmation (25 points) many fail. Behavioral patterns add meaningful context (20 points). RSI extremes and volume spikes are secondary confirmations (15 and 10 points).
Quality Tiers:
90-100: TEXTBOOK (all factors aligned)
75-89: HIGH QUALITY (strong confluence)
60-74: VALID (meets minimum threshold)
Below 60: DEVELOPING (not displayed unless threshold lowered)
How It Contributes: The confluence score allows you to filter noise. You can set your minimum quality threshold in settings. Higher thresholds (75+) show fewer but higher-quality patterns. Lower thresholds (50-60) show more patterns but include lower-confidence setups. This teaches you to distinguish strong setups from weak ones.
Limitation: Confluence scoring is historical observation-based, not predictive guarantee. A 95-point setup can still fail. The score represents technical alignment, not future certainty.
WHY THIS COMBINATION WORKS TOGETHER
Each layer addresses a limitation in the others:
RSI Divergence identifies WHEN momentum is exhausting (timing)
Volume Pressure confirms WHETHER the exhaustion is accompanied by opposite-side accumulation (confirmation)
Behavioral Footprint shows IF structural anomalies support the reversal hypothesis (context)
Confluence Scoring weights ALL factors into an objective quality metric (filtering)
Using only RSI divergence gives you timing without confirmation. Using only volume pressure gives you intensity without directional context. Using only pattern detection gives you anomalies without trend exhaustion context. Using all four together creates a complete analytical framework where each layer compensates for the others' weaknesses.
This is not a mashup for the sake of combining indicators. It is a structured analytical system where each component has a defined role in a multi-dimensional market assessment process.
HOW TO READ THE INDICATOR - VISUAL ELEMENTS GUIDE
VMDM displays up to five visual layer types. You can enable or disable each layer independently in settings under "Visual Layers."
VISUAL LAYER 1 - MARKET STRUCTURE (Pivot Points and Lines)
What You See:
Small labels at swing highs and lows marked "PH" (Pivot High) and "PL" (Pivot Low) with horizontal dashed lines extending right from each pivot.
What It Means:
These are CONFIRMED pivots, not real-time. A pivot low appears AFTER the required right-side confirmation bars pass (default 3 bars). This creates a delay but prevents repainting. The pivot only appears once it is mathematically confirmed.
The horizontal lines represent support (from pivot lows) and resistance (from pivot highs) levels where price previously found significant rejection.
Color Coding:
Green label and line: Pivot Low (potential support)
Red label and line: Pivot High (potential resistance)
How To Use:
These pivots are the foundation for divergence detection. Divergence is only calculated between confirmed pivots, ensuring all signals are non-repainting. The lines help you see historical structure levels.
VISUAL LAYER 2 - PRESSURE ZONES (Background Color)
What You See:
Subtle background color shading on bars - light green or light red tint.
What It Means:
This visualizes volume pressure strength in real-time.
Color Coding:
Light Green Background: Pressure Strength above 70 (strong buying pressure - price closing near highs on volume)
Light Red Background: Pressure Strength below 30 (strong selling pressure - price closing near lows on volume)
No Color: Neutral pressure (pressure between 30-70)
How To Use:
When a bullish divergence pattern appears during green pressure zones, it suggests the divergence is forming during accumulation. When a bearish divergence appears during red zones, distribution is occurring. Pressure zones help you filter divergences - those forming in supportive pressure environments have higher probability.
VISUAL LAYER 3 - DIVERGENCE LINES (Dotted Connectors)
What You See:
Dotted lines connecting two pivot points (either two pivot lows or two pivot highs).
What It Means:
A divergence has been detected between those two pivots. The line connects the price pivots where RSI showed opposite behavior.
Color Coding:
Bright Green Line: Bullish divergence (regular or hidden)
Bright Red Line: Bearish divergence (regular or hidden)
How To Use:
The divergence line appears ONLY after the second pivot is confirmed (delayed by right-side confirmation bars). This is intentional to prevent repainting. When you see the line appear, it means:
For Bullish Regular Divergence:
Price made a lower low (second pivot lower than first)
RSI made a higher low (RSI at second pivot higher than first)
Interpretation: Downtrend losing momentum
For Bullish Hidden Divergence:
Price made a higher low (second pivot higher than first)
RSI made a lower low (RSI at second pivot lower than first)
Interpretation: Uptrend continuation likely (pullback within uptrend)
For Bearish Regular Divergence:
Price made a higher high (second pivot higher than first)
RSI made a lower high (RSI at second pivot lower than first)
Interpretation: Uptrend losing momentum
For Bearish Hidden Divergence:
Price made a lower high (second pivot lower than first)
RSI made a higher high (RSI at second pivot higher than first)
Interpretation: Downtrend continuation likely (bounce within downtrend)
If "Show Consolidated Analysis Label" is disabled, a small label will appear on the divergence line showing the divergence type abbreviation.
VISUAL LAYER 4 - BEHAVIORAL FOOTPRINT MARKERS
What You See:
Boxes, labels, and markers at specific bars showing pattern detection.
ABSORPTION ZONES (Boxes):
Colored rectangular boxes spanning one or more bars.
Purple Box: Accumulation absorption zone (high volume, tight range, bullish close)
Red Box: Distribution absorption zone (high volume, tight range, bearish close)
If absorption continues for multiple consecutive bars, the box extends and a counter appears in the label showing how many bars the absorption lasted.
What It Means: Large volume is being absorbed without significant price movement. This often precedes directional breakouts once the absorption phase completes.
STOP HUNT MARKERS (Labels):
Small labels below or above wicks labeled "BULL HUNT" or "BEAR HUNT" (may show bar count if consecutive).
What It Means:
BULL HUNT : Price spiked below recent lows then reversed back up on volume - likely triggered sell stops before reversing
BEAR HUNT : Price spiked above recent highs then reversed back down on volume - likely triggered buy stops before reversing
EXHAUSTION MARKERS (Labels):
Labels showing "SELL EXHAUST" or "BUY EXHAUST."
What It Means:
SELL EXHAUST : Large lower wick with high volume and low RSI - aggressive selling met with strong rejection
BUY EXHAUST : Large upper wick with high volume and high RSI - aggressive buying met with strong rejection
How To Use:
These markers help you identify WHERE structural anomalies occurred. When a divergence signal appears AT THE SAME TIME as one of these patterns, the confluence score increases. You are looking for alignment - divergence + behavioral pattern + pressure confirmation = high-quality setup.
VISUAL LAYER 5 - CONSOLIDATED ANALYSIS LABEL (Main Pattern Signal)
What You See:
A large label appearing at pivot points (or in real-time mode, at current bar) containing full pattern analysis.
Label Appearance:
Depending on your "Use Compact Label Format" setting:
COMPACT MODE (Single Line):
Example: "BULLISH REGULAR | Q:HIGH QUALITY C:82"
Breakdown:
BULLISH REGULAR: Divergence type detected
Q:HIGH QUALITY: Pattern quality tier
C:82: Confluence score (82 out of 100)
FULL MODE (Multi-Line Detailed):
Example:
PATTERN DETECTED
-------------------
BULLISH REGULAR
Quality: HIGH QUALITY
Price: Lower Low
Momentum: Higher Low
Signal: Weakening Downtrend
CONFLUENCE: 82/100
-------------------
Divergence: 30
Pressure: 25
Institutional: 20
RSI Extreme: 0
Volume: 10
Breakdown:
Top section: Pattern type and quality
Middle section: Divergence explanation (what price did vs what RSI did)
Bottom section: Confluence score with itemized breakdown showing which factors contributed
Label Position:
In Confirmed modes: Label appears AT the pivot point (delayed by confirmation bars)
In Real-time mode: Label appears at current bar as conditions develop
Label Color:
Gold: Textbook quality (90+ confluence)
Green: High quality (75-89 confluence)
Blue: Valid quality (60-74 confluence)
How To Use:
This is your primary decision-making label. When it appears:
Check the divergence type (regular divergences are reversal signals, hidden divergences are continuation signals)
Review the quality tier (textbook and high quality have better historical win rates)
Examine the confluence breakdown to see which factors are present and which are missing
Look at the chart context (trend, support/resistance, timeframe)
Use this information to assess whether the setup aligns with your strategy
The label does NOT tell you to buy or sell. It tells you a technical pattern has formed and provides the quality assessment. Your trading decision must incorporate risk management, market context, and your strategy rules.
UNDERSTANDING THE THREE DETECTION MODES
VMDM offers three signal detection modes in settings to accommodate different trading styles and learning objectives.
MODE 1: "Confluence Only (Real-Time)"
How It Works: Displays signals AS THEY DEVELOP on the current bar without waiting for pivot confirmation. The system calculates confluence score from pressure, volume, RSI extremes, and behavioral patterns. Divergence signals are NOT required in this mode.
Delay: ZERO - signals appear immediately.
Use Case: Real-time scanning for high-confluence zones without divergence requirement. Useful for intraday traders who want immediate alerts when multiple factors align.
Tradeoff: More frequent signals but includes setups without confirmed divergence. Higher false signal rate. Signals can change as the bar develops (not repainting in historical bars, but current bar updates).
Visual Behavior: Labels appear at the current bar. No divergence lines unless divergence happens to be present.
MODE 2: "Divergence + Confluence (Confirmed)" - DEFAULT RECOMMENDED
How It Works: Full system engagement. Signals appear ONLY when:
A pivot is confirmed (requires right-side confirmation bars to pass)
Divergence is detected between current pivot and previous pivot
Total confluence score meets or exceeds your minimum threshold
Delay: Equal to your "Pivot Right Bars" setting (default 3 bars). This means signals appear 3 bars AFTER the actual pivot formed.
Use Case: Highest-quality, non-repainting signals for swing traders and learners who want to study confirmed pattern completion.
Tradeoff: Delayed signals. You will not receive the signal until confirmation occurs. In fast-moving markets, price may have already moved significantly by the time the signal appears.
Visual Behavior: Labels appear at the historical pivot location (in the past). Divergence lines connect the two pivots. This is the most educational mode because it shows completed, confirmed patterns.
Non-Repainting Guarantee: Yes. Once a signal appears, it never disappears or changes.
MODE 3: "Divergence + Confluence (Relaxed)"
How It Works: Same as Confirmed mode but with adaptive thresholds. If confluence is very high (10 points above threshold), the signal may appear even if some factors are weak. If divergence is present but confluence is slightly below threshold (within 10 points), it may still appear.
Delay: Same as Confirmed mode (right-side confirmation bars).
Use Case: Slightly more signals than Confirmed mode for traders willing to accept near-threshold setups.
Tradeoff: More signals but lower average quality than Confirmed mode.
Visual Behavior: Same as Confirmed mode.
DASHBOARD GUIDE - READING THE METRICS
The dashboard appears in the corner of your chart (position selectable in settings) and provides real-time market state analysis.
You can choose between four dashboard detail levels in settings: Off, Compact, Optimized (default), Full.
DASHBOARD ROW EXPLANATIONS
ROW 1 - Header Information
Left: Current symbol and timeframe
Center: "VMDM "
Right: Version number
ROW 2 - Mode and Delay
Shows which detection mode you are using and the signal delay.
Example: "CONFIRMED | Delay: 3 bars"
This reminds you that signals in confirmed mode appear 3 bars after the pivot forms.
ROW 3 - Market Regime
Format: "TREND UP HV" or "RANGING NV"
First Part - Trend State:
TREND UP: 20 EMA above 50 EMA with strong separation
TREND DOWN: 20 EMA below 50 EMA with strong separation
RANGING: EMAs close together, low trend strength
TRANSITION: Between trending and ranging states
Second Part - Volatility State:
HV: High Volatility (current ATR more than 1.3x the 50-bar average ATR)
NV: Normal Volatility (current ATR between 0.7x and 1.3x average)
LV: Low Volatility (current ATR less than 0.7x average)
Third Column: Volatility ratio (example: "1.45x" means current ATR is 1.45 times normal)
How To Use: Regime context helps you interpret signals. Reversal divergences are more reliable in ranging or transitional regimes. Continuation divergences (hidden) are more reliable in trending regimes. High volatility means wider stops may be needed.
ROW 4 - Pressure
Shows current volume pressure state.
Format: "BUYING | ██████████░░░░░░░░░"
States:
BUYING : Pressure strength above 60 (closes near highs)
SELLING : Pressure strength below 40 (closes near lows)
NEUTRAL : Pressure strength between 40-60
Bar Visualization: Each block represents 10 percentile points. A full bar (10 filled blocks) = 100th percentile pressure.
Color: Green for buying, red for selling, gray for neutral.
How To Use: When pressure aligns with divergence direction (bullish divergence during buying pressure), confluence is stronger.
ROW 5 - Volume and RSI
Format: "1.8x | RSI 68 | OB"
First Value: Current volume ratio (1.8x = volume is 1.8 times the moving average)
Second Value: Current RSI reading
Third Value: RSI state
OB: Overbought (RSI above 70)
OS: Oversold (RSI below 30)
Blank: Neutral RSI
How To Use: Volume spikes (above 1.5x) during divergence formation add confluence. RSI extremes at pivots add confluence.
ROW 6 - Behavioral Footprint
Format: "BULL HUNT | 2 bars"
Shows the most recent behavioral pattern detected and how long ago.
States:
ACCUMULATION / DISTRIBUTION: Absorption detected
BULL HUNT / BEAR HUNT: Stop hunt detected
SELL EXHAUST / BUY EXHAUST: Exhaustion detected
SCANNING: No recent pattern
NOW: Pattern is active on current bar
How To Use: When footprint activity is recent (within 50 bars) or active now, it adds context to divergence signals forming in that area.
ROW 7 - Current Pattern
Shows the divergence type currently detected (if any).
Examples: "BULLISH REGULAR", "BEARISH HIDDEN", "Scanning..."
Quality: Shows pattern quality (TEXTBOOK, HIGH QUALITY, VALID)
How To Use: This tells you what type of signal is active. Regular divergences are reversal setups. Hidden divergences are continuation setups.
ROW 8 - Session Summary
Format: "14 events | A3 H8 E3"
First Value: Total institutional events this session
Breakdown:
A: Absorption events
H: Stop hunt events
E: Exhaustion events
How To Use: High event counts suggest an active, volatile session with frequent structural anomalies. Low counts suggest quiet, orderly price action.
ROW 9 - Confluence Score (Optimized/Full mode only)
Format: "78/100 | ████████░░"
Shows current real-time confluence score even if no pattern is confirmed yet.
How To Use: Watch this in real-time to see how close you are to pattern formation. When it exceeds your threshold and divergence forms, a signal will appear (after confirmation delay).
ROW 10 - Patterns Studied (Optimized/Full mode only)
Format: "47 patterns | 12 bars ago"
First Value: Total confirmed patterns detected since chart loaded
Second Value: How many bars since the last confirmed pattern appeared
How To Use: Helps you understand pattern frequency on your selected symbol and timeframe. If many bars have passed since last pattern, market may be trending without reversal opportunities.
ROW 11 - Bull/Bear Ratio (Optimized/Full mode only)
Format: "28:19 | BULL"
Shows count of bullish vs bearish patterns detected.
Balance:
BULL: More bullish patterns detected (suggests market has had more bullish reversals/continuations)
BEAR: More bearish patterns detected
BAL: Equal counts
How To Use: Extreme imbalances can indicate directional bias in the studied period. A heavily bullish ratio in a downtrend might suggest frequent failed rallies (bearish continuation). Context matters.
ROW 12 - Volume Ratio Detail (Optimized/Full mode only)
Shows current volume vs average volume in absolute terms.
Example: "1.4x | 45230 / 32300"
How To Use: Confirms whether current activity is above or below normal.
ROW 13 - Last Institutional Event (Full mode only)
Shows the most recent institutional pattern type and how many bars ago it occurred.
Example: "DISTRIBUTION | 23 bars"
How To Use: Tracks recency of last anomaly for context.
SETTINGS GUIDE - EVERY PARAMETER EXPLAINED
PERFORMANCE SECTION
Enable All Visuals (Master Toggle)
Default: ON
What It Does: Master kill switch for ALL visual elements (labels, lines, boxes, background colors, dashboard). When OFF, only plot outputs remain (invisible unless you open data window).
When To Change: Turn OFF on mobile devices, 1-second charts, or slow computers to improve performance. You can still receive alerts even with visuals disabled.
Impact: Dramatic performance improvement when OFF, but you lose all visual feedback.
Maximum Object History
Default: 50 | Range: 10-100
What It Does: Limits how many of each object type (labels, lines, boxes) are kept in memory. Older objects beyond this limit are deleted.
When To Change: Lower to 20-30 on fast timeframes (1-minute charts) to prevent slowdown. Increase to 100 on daily charts if you want more historical pattern visibility.
Impact: Lower values = better performance but less historical visibility. Higher values = more history visible but potential slowdown on fast timeframes.
Alert Cooldown (Bars)
Default: 5 | Range: 1-50
What It Does: Minimum number of bars that must pass before another alert of the same type can fire. Prevents alert spam when multiple patterns form in quick succession.
When To Change: Increase to 20+ on 1-minute charts to reduce noise. Decrease to 1-2 on daily charts if you want every pattern alerted.
Impact: Higher cooldown = fewer alerts. Lower cooldown = more alerts.
USER EXPERIENCE SECTION
Show Enhanced Tooltips
Default: ON
What It Does: Enables detailed hover-over tooltips on labels and visual elements.
When To Change: Turn OFF if you encounter Pine Script compilation errors related to tooltip arguments (rare, platform-specific issue).
Impact: Minimal. Just adds helpful hover text.
MARKET STRUCTURE DETECTION SECTION
Pivot Left Bars
Default: 3 | Range: 2-10
What It Does: Number of bars to the LEFT of the center bar that must be higher (for pivot low) or lower (for pivot high) than the center bar for a pivot to be valid.
Example: With value 3, a pivot low requires the center bar's low to be lower than the 3 bars to its left.
When To Change:
Increase to 5-7 on noisy timeframes (1-minute charts) to filter insignificant pivots
Decrease to 2 on slow timeframes (daily charts) to catch more pivots
Impact: Higher values = fewer, more significant pivots = fewer signals. Lower values = more frequent pivots = more signals but more noise.
Pivot Right Bars
Default: 3 | Range: 2-10
What It Does: Number of bars to the RIGHT of the center bar that must pass for confirmation. This creates the non-repainting delay.
Example: With value 3, a pivot is confirmed 3 bars AFTER it forms.
When To Change:
Increase to 5-7 for slower, more confirmed signals (better for swing trading)
Decrease to 2 for faster signals (better for intraday, but still non-repainting)
Impact: Higher values = longer delay but more reliable confirmation. Lower values = faster signals but less confirmation. This setting directly controls your signal delay in Confirmed and Relaxed modes.
Minimum Confluence Score
Default: 60 | Range: 40-95
What It Does: The threshold score required for a pattern to be displayed. Patterns with confluence scores below this threshold are not shown.
When To Change:
Increase to 75+ if you only want high-quality textbook setups (fewer signals)
Decrease to 50-55 if you want to see more developing patterns (more signals, lower average quality)
Impact: This is your primary signal filter. Higher threshold = fewer, higher-quality signals. Lower threshold = more signals but includes weaker setups. Recommended starting point is 60-65.
TECHNICAL PERIODS SECTION
RSI Period
Default: 14 | Range: 5-50
What It Does: Lookback period for RSI calculation.
When To Change:
Decrease to 9-10 for faster, more sensitive RSI that detects shorter-term momentum changes
Increase to 21-28 for slower, smoother RSI that filters noise
Impact: Lower values make RSI more volatile (more frequent extremes and divergences). Higher values make RSI smoother (fewer but more significant divergences). 14 is industry standard.
Volume Moving Average Period
Default: 20 | Range: 10-200
What It Does: Lookback period for calculating average volume. Current volume is compared to this average to determine volume ratio.
When To Change:
Decrease to 10-14 for shorter-term volume comparison (more sensitive to recent volume changes)
Increase to 50-100 for longer-term volume comparison (smoother, less sensitive)
Impact: Lower values make volume ratio more volatile. Higher values make it more stable. 20 is standard.
ATR Period
Default: 14 | Range: 5-100
What It Does: Lookback period for Average True Range calculation used for volatility measurement and label positioning.
When To Change: Rarely needs adjustment. Use 7-10 for faster volatility response, 21-28 for slower.
Impact: Affects volatility ratio calculation and visual label spacing. Minimal impact on signals.
Pressure Percentile Lookback
Default: 50 | Range: 10-300
What It Does: Lookback period for calculating volume pressure percentile ranking. Your current pressure is ranked against the pressure of the last X bars.
When To Change:
Decrease to 20-30 for shorter-term pressure context (more responsive to recent changes)
Increase to 100-200 for longer-term pressure context (smoother rankings)
Impact: Lower values make pressure strength more sensitive to recent bars. Higher values provide more stable, long-term pressure assessment. Capped at 300 for performance reasons.
SIGNAL DETECTION SECTION
Signal Detection Mode
Default: "Divergence + Confluence (Confirmed)"
Options:
Confluence Only (Real-time)
Divergence + Confluence (Confirmed)
Divergence + Confluence (Relaxed)
What It Does: Selects which detection logic mode to use (see "Understanding The Three Detection Modes" section above).
When To Change: Use Confirmed for learning and non-repainting signals. Use Real-time for live scanning without divergence requirement. Use Relaxed for slightly more signals than Confirmed.
Impact: Fundamentally changes when and how signals appear.
VISUAL LAYERS SECTION
All toggles default to ON. Each controls visibility of one visual layer:
Show Market Structure: Pivot markers and support/resistance lines
Show Pressure Zones: Background color shading
Show Divergence Lines: Dotted lines connecting pivots
Show Institutional Footprint Markers: Absorption boxes, hunt labels, exhaustion labels
Show Consolidated Analysis Label: Main pattern detection label
Use Compact Label Format
Default: OFF
What It Does: Switches consolidated label between single-line compact format and multi-line detailed format.
When To Change: Turn ON if you find full labels too large or distracting.
Impact: Visual clarity vs. information density tradeoff.
DASHBOARD SECTION
Dashboard Mode
Default: "Optimized"
Options: Off, Compact, Optimized, Full
What It Does: Controls how much information the dashboard displays.
Off: No dashboard
Compact: 8 rows (essential metrics only)
Optimized: 12 rows (recommended balance)
Full: 13 rows (every available metric)
Dashboard Position
Default: "Top Right"
Options: Top Right, Top Left, Bottom Right, Bottom Left
What It Does: Screen corner where dashboard appears.
HOW TO USE VMDM - PRACTICAL WORKFLOW
STEP 1 - INITIAL SETUP
Add VMDM to your chart
Select your detection mode (Confirmed recommended for learning)
Set your minimum confluence score (start with 60-65)
Adjust pivot parameters if needed (default 3/3 is good for most timeframes)
Enable the visual layers you want to see
STEP 2 - CHART ANALYSIS
Let the indicator load and analyze historical data
Review the patterns that appear historically
Examine the confluence scores - notice which patterns had higher scores
Observe which patterns occurred during supportive pressure zones
Notice the divergence line connections - understand what price vs RSI did
STEP 3 - PATTERN RECOGNITION LEARNING
When a consolidated analysis label appears:
Read the divergence type (regular or hidden, bullish or bearish)
Check the quality tier (textbook, high quality, or valid)
Review the confluence breakdown - which factors contributed
Look at the chart context - where is price relative to structure, trend, etc.
Observe the behavioral footprint markers nearby - do they support the pattern
STEP 4 - REAL-TIME MONITORING
Watch the dashboard for real-time regime and pressure state
Monitor the current confluence score in the dashboard
When it approaches your threshold, be alert for potential pattern formation
When a new pattern appears (after confirmation delay), evaluate it using the workflow above
Use your trading strategy rules to decide if the setup aligns with your criteria
STEP 5 - POST-PATTERN OBSERVATION
After a pattern appears:
Mark the level on your chart
Observe what price does after the pattern completes
Did price respect the reversal/continuation signal
What was the confluence score of patterns that worked vs. those that failed
Learn which quality tiers and confluence levels produce better results on your specific symbol and timeframe
RECOMMENDED TIMEFRAMES AND ASSET CLASSES
VMDM is timeframe-agnostic and works on any asset with volume data. However, optimal performance varies:
BEST TIMEFRAMES
15-Minute to 1-Hour: Ideal balance of signal frequency and reliability. Pivot confirmation delay is acceptable. Sufficient volume data for pressure analysis.
4-Hour to Daily: Excellent for swing trading. Very high-quality signals. Lower frequency but higher significance. Recommended for learning because patterns are clearer.
1-Minute to 5-Minute: Works but requires adjustment. Increase pivot bars to 5-7 for filtering. Decrease max object history to 30 for performance. Expect more noise.
Weekly/Monthly: Works but very infrequent signals. Increase confluence threshold to 70+ to ensure only major patterns appear.
BEST ASSET CLASSES
Forex Majors: Excellent volume data and clear trends. Pressure analysis works well.
Crypto (Major Pairs): Good volume data. High volatility makes divergences more pronounced. Works very well.
Stock Indices (SPY, QQQ, etc.): Excellent. Clean price action and reliable volume.
Individual Stocks: Works well on high-volume stocks. Low-volume stocks may produce unreliable pressure readings.
Commodities (Gold, Oil, etc.): Works well. Clear trends and reactions.
WHAT THIS INDICATOR CANNOT DO - LIMITATIONS
LIMITATION 1 - It Does Not Predict The Future
VMDM identifies when technical conditions align historically associated with potential reversals or continuations. It does not predict what will happen next. A textbook 95-confluence pattern can still fail if fundamental events, news, or larger timeframe structure override the setup.
LIMITATION 2 - Confirmation Delay Means You Miss Early Entry
In Confirmed and Relaxed modes, the non-repainting design means you receive signals AFTER the pivot is confirmed. Price may have already moved significantly by the time you receive the signal. This is the tradeoff for non-repainting reliability. You can use Real-time mode for faster signals but sacrifice divergence confirmation.
LIMITATION 3 - It Does Not Tell You Position Sizing or Risk Management
VMDM provides technical pattern analysis. It does not calculate stop loss levels, take profit targets, or position sizing. You must apply your own risk management rules. Never risk more than you can afford to lose based on a technical signal.
LIMITATION 4 - Volume Pressure Analysis Requires Reliable Volume Data
On assets with thin volume or unreliable volume reporting, pressure analysis may be inaccurate. Stick to major liquid assets with consistent volume data.
LIMITATION 5 - It Cannot Detect Fundamental Events
VMDM is purely technical. It cannot predict earnings reports, central bank decisions, geopolitical events, or other fundamental catalysts that can override technical patterns.
LIMITATION 6 - Divergence Requires Two Pivots
The indicator cannot detect divergence until at least two pivots of the same type have formed. In strong trends without pullbacks, you may go long periods without signals.
LIMITATION 7 - Institutional Pattern Names Are Interpretive
The behavioral footprint patterns are named using common trading education terminology, but they are detected through technical analysis, not actual institutional data access. The patterns are interpretations based on price and volume behavior.
CONCEPT FOUNDATION - WHY THIS APPROACH WORKS
MARKET PRINCIPLE 1 - Momentum Divergence Precedes Price Reversal
Price is the final output of market forces, but momentum (the rate of change in those forces) shifts first. When price makes a new low but the momentum behind that move is weaker (higher RSI low), it signals that sellers are losing strength even though they temporarily pushed price lower. This precedes reversal. This is a fundamental principle in technical analysis taught by Charles Dow, widely observed in market behavior.
MARKET PRINCIPLE 2 - Volume Reveals Conviction
Price can move on low volume (low conviction) or high volume (high conviction). When price makes a new low on declining volume while RSI shows improving momentum, it suggests the new low is not confirmed by participant conviction. Adding volume pressure analysis to momentum divergence adds a confirmation layer that filters false divergences.
MARKET PRINCIPLE 3 - Anomalies Mark Structural Extremes
When volume spikes significantly but range contracts (absorption), or when price spikes beyond structure then reverses (stop hunt), or when aggressive moves are met with large-wick rejection (exhaustion), these anomalies often mark short-term extremes. Combining these structural observations with momentum analysis creates context.
MARKET PRINCIPLE 4 - Confluence Improves Probability
No single technical factor is reliable in isolation. RSI divergence alone fails frequently. Volume analysis alone cannot time entries. Combining multiple independent factors into a weighted system increases the probability that observed patterns have structural significance rather than random noise.
THE EDUCATIONAL VALUE
By visualizing all four layers simultaneously and breaking down the confluence scoring transparently, VMDM teaches you to think in terms of multi-dimensional analysis rather than single-indicator reliance. Over time, you will learn to recognize these patterns manually and understand which combinations produce better results on your traded assets.
INSTITUTIONAL TERMINOLOGY - IMPORTANT CLARIFICATION
This indicator uses the following terms that are common in trading education:
Institutional Footprint
Absorption (Accumulation / Distribution)
Stop Hunt
Exhaustion
CRITICAL DISCLAIMER:
These terms are EDUCATIONAL LABELS for specific price action and volume behavior patterns detected through technical analysis of publicly available chart data (open, high, low, close, volume). This indicator does NOT have access to:
Actual institutional order flow or order book data
Market maker positions or intentions
Broker stop-loss databases
Non-public trading data
Proprietary institutional information
The patterns labeled as "institutional footprint" are interpretations based on observable price and volume behavior that educational trading literature often associates with potential large-participant activity. The detection is algorithmic pattern recognition, not privileged data access.
When this indicator identifies "absorption," it means it detected high volume within a small range - a condition that MAY indicate large orders being filled but is not confirmation of actual institutional participation.
When it identifies a "stop hunt," it means price briefly penetrated a structural level then reversed - a pattern that MAY have triggered stop losses but is not confirmation that stops were specifically targeted.
When it identifies "exhaustion," it means high volume with large rejection wicks - a pattern that MAY indicate aggressive participation meeting strong opposition but is not confirmation of institutional involvement.
These are technical analysis interpretations, not factual statements about market participant identity or intent.
DISCLAIMER AND RISK WARNING
EDUCATIONAL PURPOSE ONLY
This indicator is designed as an educational tool to help traders learn to recognize technical patterns, understand multi-factor analysis, and practice systematic market observation. It is NOT a trading system, signal service, or financial advice.
NO PERFORMANCE GUARANTEE
Past pattern behavior does not guarantee future results. A pattern that historically preceded price movement in one direction may fail in the future due to changing market conditions, fundamental events, or random variance. Confluence scores reflect historical technical alignment, not future certainty.
TRADING INVOLVES SUBSTANTIAL RISK
Trading financial instruments involves substantial risk of loss. You can lose more than your initial investment. Never trade with money you cannot afford to lose. Always use proper risk management including stop losses, position sizing, and portfolio diversification.
NO PREDICTIVE CLAIMS
This indicator does NOT predict future price movement. It identifies when technical conditions align in patterns that historically have been associated with potential reversals or continuations. Market behavior is probabilistic, not deterministic.
BACKTESTING LIMITATIONS
If you backtest trading strategies using this indicator, ensure you account for:
Realistic commission costs
Realistic slippage (difference between signal price and actual fill price)
Sufficient sample size (minimum 100 trades for statistical relevance)
Reasonable position sizing (risking no more than 1-2 percent of account per trade)
The confirmation delay inherent in the indicator (you cannot enter at the exact pivot in Confirmed mode)
Backtests that do not account for these factors will produce unrealistic results.
AUTHOR LIABILITY
The author (BullByte) is not responsible for any trading losses incurred using this indicator. By using this indicator, you acknowledge that all trading decisions are your sole responsibility and that you understand the risks involved.
NOT FINANCIAL ADVICE
Nothing in this indicator, its code, its description, or its visual outputs constitutes financial, investment, or trading advice. Consult a licensed financial advisor before making investment decisions.
FREQUENTLY ASKED QUESTIONS
Q: Why do signals appear in the past, not at the current bar
A: In Confirmed and Relaxed modes, signals appear at confirmed pivots, which requires waiting for right-side confirmation bars (default 3). This creates a delay but prevents repainting. Use Real-time mode if you want current-bar signals without pivot confirmation.
Q: Can I use this for automated trading
A: You can create alert-based automation, but understand that Confirmed mode signals appear AFTER the pivot with delay, so your entry will not be at the pivot price. Real-time mode signals can change as the current bar develops. Automation requires careful consideration of these factors.
Q: How do I know which confluence score to use
A: Start with 60. Observe which patterns work on your symbol/timeframe. If too many false signals, increase to 70-75. If too few signals, decrease to 55. Quality vs. quantity tradeoff.
Q: Do regular divergences mean I should enter a reversal trade immediately
A: No. Regular divergences indicate momentum exhaustion, which is a WARNING sign that trend may reverse, not a confirmation that it will. Use confluence score, market context, support/resistance, and your strategy rules to make entry decisions. Many divergences fail.
Q: What's the difference between regular and hidden divergence
A: Regular divergence = price and momentum move in opposite directions at extremes = potential reversal signal. Hidden divergence = price and momentum move in opposite directions during pullbacks = potential continuation signal. Hidden divergence suggests the pullback is just a correction within the larger trend.
Q: Why does the pressure zone color sometimes conflict with the divergence direction
A: Pressure is real-time current bar analysis. Divergence is confirmed pivot analysis from the past. They measure different things at different times. A bullish divergence confirmed 3 bars ago might appear during current selling pressure. This is normal.
Q: Can I use this on stocks without volume data
A: No. Volume is required for pressure analysis and behavioral pattern detection. Use only on assets with reliable volume reporting.
Q: How often should I expect signals
A: Depends on timeframe and settings. Daily charts might produce 5-10 signals per month. 1-hour charts might produce 20-30. 15-minute charts might produce 50-100. Adjust confluence threshold to control frequency.
Q: Can I modify the code
A: Yes, this is open source. You can modify for personal use. If you publish a modified version, please credit the original and ensure your publication meets TradingView guidelines.
Q: What if I disagree with a pattern's confluence score
A: The scoring weights are based on general observations and may not suit your specific strategy or asset. You can modify the code to adjust weights if you have data-driven reasons to do so.
Final Notes
VMDM - Volume, Momentum and Divergence Master is an educational multi-layer market analysis system designed to teach systematic pattern recognition through transparent, confluence-weighted signal detection. By combining RSI momentum divergence, volume pressure quantification, behavioral footprint pattern recognition, and quality scoring into a unified framework, it provides a comprehensive learning environment for understanding market structure.
Use this tool to develop your analytical skills, understand how multiple technical factors interact, and learn to distinguish high-quality setups from noise. Remember that technical analysis is probabilistic, not predictive. No indicator replaces proper education, risk management, and trading discipline.
Trade responsibly. Learn continuously. Risk only what you can afford to lose.
-BullByte
able MACD Overview
Purpose: The indicator combines the traditional MACD (Moving Average Convergence Divergence) with a short-term “forecast” (projection) of MACD/histogram values to give early warning of momentum changes.
Typical outputs:
MACD line (fastEMA − slowEMA)
Signal line (EMA of MACD)
Histogram (MACD − signal)
Forecasted MACD or histogram projected N bars ahead
Optional buy/sell markers and alert conditions
Add the indicator to TradingView (Installation)
Open TradingView and the chart you want to apply the indicator to.
Click “Pine Editor” at the bottom of the chart.
Copy the contents of able_macd_forecast.pine into the Pine Editor window.
Click “Add to chart” (or Save then Add to chart). If it’s a study, it will appear on the chart below price.
If you plan to re-use the script, click Save and give it a meaningful name.
Inputs / Parameters (typical) Note: exact input names may differ in your script. Replace the names below with the script’s input labels when you inspect it.
Source: price source for calculations (close, hl2, etc.).
Fast Length: length for the fast EMA (commonly 12).
Slow Length: length for the slow EMA (commonly 26).
Signal Length: length for the MACD signal EMA (commonly 9).
Forecast Length / Horizon: how many bars ahead the script projects the MACD/histogram (e.g., 1–5).
Forecast Method / Smoothing: choice of projection method (linear regression, EMA extrapolation, simple slope * N, etc.) if available.
Histogram Thresholds: numeric thresholds to emphasize significant momentum (optional).
Show Forecast: toggle on/off the forecast plot.
Alerts On/Off toggles: enable or disable alert conditions baked into the indicator.
Visual / Style settings: colors, plot thickness, histogram style (columns/areas), show labels, show buy/sell arrows.
How the indicator is typically calculated (summary)
MACD line = EMA(source, fast) − EMA(source, slow)
Signal line = EMA(MACD line, signal length)
Histogram = MACD − Signal
Forecast = method-specific short-term projection of MACD or histogram (for example: extend the last slope forward, apply linear regression to MACD values and extrapolate N bars, or apply an additional smoothing and extend that value) Note: For exact math, I need to inspect the script; this is the typical approach.
How to read the indicator (signals & interpretation)
Bullish signal:
MACD line crossing above the signal line (MACD cross up).
Histogram turns positive (cross above zero).
Forecast shows MACD/histogram moving higher in the next N bars (if forecast is positive or trending up).
Bearish signal:
MACD line crossing below the signal line (MACD cross down).
Histogram turns negative (cross below zero).
Forecast shows MACD/histogram moving lower ahead.
Confirmations:
Use price action (higher highs/lows for bullish, lower highs/lows for bearish).
Volume or other momentum/confluence indicators (RSI, ADX).
Divergences:
Bullish divergence: price makes lower low while MACD histogram makes higher low.
Bearish divergence: price makes higher high while MACD histogram makes lower high.
Forecast behavior:
If the forecast leads the MACD cross (forecast crosses before the current MACD does), it’s an early warning.
Use caution: forecasts are prone to false signals; always confirm.
Common trading setups using this indicator
Conservative:
Wait for MACD to cross signal + histogram above zero + forecast already trending same direction.
Use stop below recent swing low (for long) or above recent swing high (for short).
Aggressive (early entry):
Enter when forecast turns positive while MACD still below signal (anticipating cross).
Use tighter stops and smaller position sizes.
Exit rules:
Opposite MACD cross, histogram flipping sign, or a target based on risk-reward.
Use trailing stop based on ATR or structure.
Example settings for different timeframes (starting points)
Scalping / 5–15 min:
Fast 8, Slow 21, Signal 5, Forecast 1–2
Intraday / 1H:
Fast 12, Slow 26, Signal 9, Forecast 2–3
Swing / 4H–Daily:
Fast 12, Slow 26, Signal 9, Forecast 3–5 Adjust based on the asset volatility and backtests.
Adding alerts (TradingView)
Click the “Alerts” button (clock icon) or press Alt + A.
In the Condition dropdown, select the indicator name (able_macd_forecast) and choose a plotted series or built-in alert condition (if the script uses alertcondition).
Common alert types:
MACD crosses Signal (Crossing)
Histogram crosses 0 (Crossing)
Forecast crosses 0 or Forecast trend change (if provided)
Message templates:
“{{ticker}}: MACD crossed above signal on {{interval}}”
“{{ticker}} Forecast positive: MACD forecast shows upward momentum”
Customize the message for your trade automation or notifications.
Configure frequency (Only once, Once per bar, or Once per bar close) — for signals like crossovers, “Once per bar close” is usually safer to avoid repainting issues. Note: If the script includes alertcondition() calls with explicit IDs/messages, use those directly — they are the most reliable for automation.
Backtesting / Strategy conversion
If this script is a study (indicator), you can:
Convert it to a strategy by adding strategy.* order calls (strategy.entry, strategy.close) using the entry/exit logic you prefer, or
Use TradingView’s “Bar Replay” to manually test signals across different markets/timeframes.
If you want, I can help convert or write a strategy wrapper that uses the indicator’s signals to place backtest trades (I’ll need the code).
Practical tips & best practices
Use higher timeframe confirmation for lower-timeframe entries (e.g., check daily MACD momentum before trading 15m signals).
Beware of choppy markets; MACD / forecast may produce whipsaws. Combine with trend filters (moving average direction, ADX).
If you rely on forecasted values, prefer alerts “on bar close” when possible to reduce false alerts from intra-bar noise.
Tune parameters for the specific asset (FX, crypto, stocks have different behavior).
Record each signal and outcome for a sample period (20–100 trades) to evaluate performance.
Troubleshooting
Indicator won’t add: verify Pine version in script header (//@version=4 or //@version=5). TradingView may reject scripts with unsupported version syntax.
Plots missing: check script inputs (Some scripts hide plots if toggles are off).
Alerts firing too often: change alert frequency to “Once per bar close” or adjust threshold values.
Forecast seems to repaint: some forecast methods can repaint (use “bar_index” or store values only on closed bars, or use non-repainting forecast methods). Ask me to inspect the script for repainting logic.
What I can do next (recommended)
If you paste the content of able_macd_forecast.pine here, I will:
Produce a precise, line-by-line usage guide mapping to the exact input names and default values.
Show the exact plotted series names and how to reference them for alerts.
Point out any repainting risks and suggest fixes.
Provide example alert messages that match the script’s alertcondition IDs (if any).
Optionally convert it into a strategy for backtesting, or add non-repainting forecast logic if needed.
Static K-means Clustering | InvestorUnknownStatic K-Means Clustering is a machine-learning-driven market regime classifier designed for traders who want a data-driven structure instead of subjective indicators or manually drawn zones.
This script performs offline (static) K-means training on your chosen historical window. Using four engineered features:
RSI (Momentum)
CCI (Price deviation / Mean reversion)
CMF (Money flow / Strength)
MACD Histogram (Trend acceleration)
It groups past market conditions into K distinct clusters (regimes). After training, every new bar is assigned to the nearest cluster via Euclidean distance in 4-dimensional standardized feature space.
This allows you to create models like:
Regime-based long/short filters
Volatility phase detectors
Trend vs. chop separation
Mean-reversion vs. breakout classification
Volume-enhanced money-flow regime shifts
Full machine-learning trading systems based solely on regimes
Note:
This script is not a universal ML strategy out of the box.
The user must engineer the feature set to match their trading style and target market.
K-means is a tool, not a ready made system, this script provides the framework.
Core Idea
K-means clustering takes raw, unlabeled market observations and attempts to discover structure by grouping similar bars together.
// STEP 1 — DATA POINTS ON A COORDINATE PLANE
// We start with raw, unlabeled data scattered in 2D space (x/y).
// At this point, nothing is grouped—these are just observations.
// K-means will try to discover structure by grouping nearby points.
//
// y ↑
// |
// 12 | •
// | •
// 10 | •
// | •
// 8 | • •
// |
// 6 | •
// |
// 4 | •
// |
// 2 |______________________________________________→ x
// 2 4 6 8 10 12 14
//
//
//
// STEP 2 — RANDOMLY PLACE INITIAL CENTROIDS
// The algorithm begins by placing K centroids at random positions.
// These centroids act as the temporary “representatives” of clusters.
// Their starting positions heavily influence the first assignment step.
//
// y ↑
// |
// 12 | •
// | •
// 10 | • C2 ×
// | •
// 8 | • •
// |
// 6 | C1 × •
// |
// 4 | •
// |
// 2 |______________________________________________→ x
// 2 4 6 8 10 12 14
//
//
//
// STEP 3 — ASSIGN POINTS TO NEAREST CENTROID
// Each point is compared to all centroids.
// Using simple Euclidean distance, each point joins the cluster
// of the centroid it is closest to.
// This creates a temporary grouping of the data.
//
// (Coloring concept shown using labels)
//
// - Points closer to C1 → Cluster 1
// - Points closer to C2 → Cluster 2
//
// y ↑
// |
// 12 | 2
// | 1
// 10 | 1 C2 ×
// | 2
// 8 | 1 2
// |
// 6 | C1 × 2
// |
// 4 | 1
// |
// 2 |______________________________________________→ x
// 2 4 6 8 10 12 14
//
// (1 = assigned to Cluster 1, 2 = assigned to Cluster 2)
// At this stage, clusters are formed purely by distance.
Your chosen historical window becomes the static training dataset , and after fitting, the centroids never change again.
This makes the model:
Predictable
Repeatable
Consistent across backtests
Fast for live use (no recalculation of centroids every bar)
Static Training Window
You select a period with:
Training Start
Training End
Only bars inside this range are used to fit the K-means model. This window defines:
the market regime examples
the statistical distributions (means/std) for each feature
how the centroids will be positioned post-trainin
Bars before training = fully transparent
Training bars = gray
Post-training bars = full colored regimes
Feature Engineering (4D Input Vector)
Every bar during training becomes a 4-dimensional point:
This combination balances: momentum, volatility, mean-reversion, trend acceleration giving the algorithm a richer "market fingerprint" per bar.
Standardization
To prevent any feature from dominating due to scale differences (e.g., CMF near zero vs CCI ±200), all features are standardized:
standardize(value, mean, std) =>
(value - mean) / std
Centroid Initialization
Centroids start at diverse coordinates using various curves:
linear
sinusoidal
sign-preserving quadratic
tanh compression
init_centroids() =>
// Spread centroids across using different shapes per feature
for c = 0 to k_clusters - 1
frac = k_clusters == 1 ? 0.0 : c / (k_clusters - 1.0) // 0 → 1
v = frac * 2 - 1 // -1 → +1
array.set(cent_rsi, c, v) // linear
array.set(cent_cci, c, math.sin(v)) // sinusoidal
array.set(cent_cmf, c, v * v * (v < 0 ? -1 : 1)) // quadratic sign-preserving
array.set(cent_mac, c, tanh(v)) // compressed
This makes initial cluster spread “random” even though true randomness is hardly achieved in pinescript.
K-Means Iterative Refinement
The algorithm repeats these steps:
(A) Assignment Step, Each bar is assigned to the nearest centroid via Euclidean distance in 4D:
distance = sqrt(dx² + dy² + dz² + dw²)
(B) Update Step, Centroids update to the mean of points assigned to them. This repeats iterations times (configurable).
LIVE REGIME CLASSIFICATION
After training, each new bar is:
Standardized using the training mean/std
Compared to all centroids
Assigned to the nearest cluster
Bar color updates based on cluster
No re-training occurs. This ensures:
No lookahead bias
Clean historical testing
Stable regimes over time
CLUSTER BEHAVIOR & TRADING LOGIC
Clusters (0, 1, 2, 3…) hold no inherent meaning. The user defines what each cluster does.
Example of custom actions:
Cluster 0 → Cash
Cluster 1 → Long
Cluster 2 → Short
Cluster 3+ → Cash (noise regime)
This flexibility means:
One trader might have cluster 0 as consolidation.
Another might repurpose it as a breakout-loading zone.
A third might ignore 3 clusters entirely.
Example on ETHUSD
Important Note:
Any change of parameters or chart timeframe or ticker can cause the “order” of clusters to change
The script does NOT assume any cluster equals any actionable bias, user decides.
PERFORMANCE METRICS & ROC TABLE
The indicator computes average 1-bar ROC for each cluster in:
Training set
Test (live) set
This helps measure:
Cluster profitability consistency
Regime forward predictability
Whether a regime is noise, trend, or reversion-biased
EQUITY SIMULATION & FEES
Designed for close-to-close realistic backtesting.
Position = cluster of previous bar
Fees applied only on regime switches. Meaning:
Staying long → no fee
Switching long→short → fee applied
Switching any→cash → fee applied
Fee input is percentage, but script already converts internally.
Disclaimers
⚠️ This indicator uses machine-learning but does not predict the future. It classifies similarity to past regimes, nothing more.
⚠️ Backtest results are not indicative of future performance.
⚠️ Clusters have no inherent “bullish” or “bearish” meaning. You must interpret them based on your testing and your own feature engineering.
Supply & Demand ZonesThis indicator detects high-probability supply and demand zones using a multi-step smart money concept approach:
Liquidity Sweep Detection: Identifies when price sweeps above a pivot high (supply setup) or below a pivot low (demand setup), capturing liquidity grabs by institutional traders.
Displacement Confirmation: Requires a strong displacement candle (measured by ATR and body percentage) or fair value gap (FVG/imbalance) in the opposite direction after the sweep.
Volume Confirmation: Optional filter ensures zones form only when volume exceeds the user-defined threshold, indicating institutional participation.
Smart Filtering: Built-in logic prevents overlapping zones, enforces minimum spacing between signals, and requires confirmation bars to eliminate false signals.
Zone Lifecycle Management: Zones are automatically removed when price closes through them with momentum. Breached zones can optionally "flip" to the opposite type when re-tested with strong displacement.
✨ Key Features
Clean Visual Display: Small "D" (Demand) and "S" (Supply) labels with shaded zone boxes
Non-Repainting: All signals use confirmed historical data—no lookahead or repainting
Volume Filter: Optional confirmation using volume spike detection
Zone Flip Logic: Breached demand zones can become supply (and vice versa) when violated
Overlap Prevention: Smart algorithm prevents clustered or duplicate zones
Confirmation Delay: Configurable wait period after sweep to confirm genuine setups
Customizable Inputs: Adjust pivot sensitivity, displacement thresholds, volume filters, and more
Alert Ready: Built-in alert conditions for new supply and demand zone formations
🎯 How to Add to Your Chart
Favorite the Indicator: Click the star icon to add this script to your favorites
Open Your Chart: Navigate to the asset and timeframe you want to trade (works best on 5m-1H intraday charts)
Add Indicator: Click "Indicators" at the top, search for "Supply & Demand Zones (Smart Filtered)", and add to chart
Customize Settings: Click the gear icon ⚙️ to adjust inputs based on your trading style and instrument volatility
Set Alerts: Right-click the indicator name → "Add alert" → Select "Supply Zone" or "Demand Zone" conditions
📖 How to Use
Demand Zones (Green "D" Labels):
Price swept below a swing low (liquidity grab)
Strong bullish displacement or imbalance followed
Trading Action: Look for LONG entries when price returns to the zone or on immediate continuation
Stop Loss: Place just below the zone or sweep low
Target: Next resistance level, supply zone, or risk-reward ratio target
Supply Zones (Red "S" Labels):
Price swept above a swing high (liquidity grab)
Strong bearish displacement or imbalance followed
Trading Action: Look for SHORT entries when price returns to the zone or on immediate continuation
Stop Loss: Place just above the zone or sweep high
Target: Next support level, demand zone, or risk-reward ratio target
Flipped Zones (Orange Labels):
Previous demand/supply zone was broken with strong momentum
Zone has flipped polarity and may now act as the opposite type
Trading Action: Exercise caution—wait for additional confirmation before trading flipped zones
🔍 What to Look For
High-Quality Setups:
Zone forms with above-average volume (check volume filter is enabled)
Clear liquidity sweep visible on the chart
Strong displacement candle with large body percentage
Zone aligns with overall market trend or key structure levels
Multiple timeframe confirmation (check higher timeframe for context)
Avoid These Setups:
Zones forming in choppy, low-volume conditions
Multiple overlapping zones in the same area (indicator filters these automatically)
Zones that appear immediately after news events (set confirmation bars higher)
Counter-trend zones without additional confluence
⚙️ Recommended Settings by Timeframe
5-Minute Charts (Scalping):
Pivot Lookback: 3/3
Min Displacement ATR: 0.9
Confirmation Bars: 1
Min Zone Spacing: 3-5 bars
Volume Threshold: 1.2x
15-Minute Charts (Intraday):
Pivot Lookback: 4/4 (default)
Min Displacement ATR: 1.0 (default)
Confirmation Bars: 2 (default)
Min Zone Spacing: 5-8 bars
Volume Threshold: 1.2x
1-Hour Charts (Swing Trading):
Pivot Lookback: 5/5
Min Displacement ATR: 1.2-1.5
Confirmation Bars: 3
Min Zone Spacing: 8-12 bars
Volume Threshold: 1.3x
💡 Trading Tips & Best Practices
Combine with Price Action: Use this indicator alongside candlestick patterns, support/resistance, and trendlines for confirmation
Multiple Timeframe Analysis: Check higher timeframes for overall bias and major zones
Volume is Key: Enable volume filter to focus on institutional-backed moves
Risk Management: Always use stop losses and proper position sizing
Backtesting: Test settings on your preferred instruments and timeframes before live trading
Context Matters: Consider market conditions, news events, and session times
Wait for Confirmation: Don't rush entries—wait for price reaction at the zone
⚠️ Important Disclaimers
Educational Purpose Only: This indicator is provided for educational and informational purposes. It does not constitute financial advice, investment recommendations, or trading signals.
No Guarantees: Past performance and backtested results do not guarantee future results. Trading involves substantial risk of loss.
Rsi TrendLines with Breakouts [KoTa]### RSI TrendLines with Breakouts Indicator: Detailed User Guide
The "RSI TrendLines with Breakouts " indicator is a custom Pine Script tool designed for TradingView. It builds on the standard Relative Strength Index (RSI) by adding dynamic trendlines based on RSI pivots (highs and lows) across multiple user-defined periods. These trendlines act as support and resistance levels on the RSI chart, and the indicator detects breakouts when the RSI crosses these lines, generating potential buy (long) or sell (short) signals. It also includes overbought/oversold thresholds and optional breakout labels. Below, I'll provide a detailed explanation in English, covering how to use it, its purpose, advantages and disadvantages, example strategies, and ways to enhance strategies with other indicators.
How to Use the Indicator
- The indicator uses `max_lines_count=500` to handle a large number of lines without performance issues, but on very long charts, you may need to zoom in for clarity.
1. **Customizing Settings**:
The indicator has several input groups for flexibility. Access them via the gear icon next to the indicator's name on the chart.
- **RSI Settings**:
- RSI Length: Default 14. This is the period for calculating the RSI. Shorter lengths (e.g., 7-10) make it more sensitive to recent price changes; longer (e.g., 20+) smooth it out for trends.
- RSI Source: Default is close price. You can change to open, high, low, or other sources like volume-weighted for different assets.
- Overbought Level: Default 70. RSI above this suggests potential overbuying.
- Oversold Level: Default 30. RSI below this suggests potential overselling.
- **Trend Periods**:
- You can enable/disable up to 5 periods (defaults: Period 1=3, Period 2=5, Period 3=10, Period 4=20, Period 5=50). Only enabled periods will draw trendlines.
- Each period detects pivots (highs/lows) in RSI using `ta.pivothigh` and `ta.pivotlow`. Shorter periods (e.g., 3-10) capture short-term trends; longer ones (20-50) show medium-to-long-term momentum.
- Inline checkboxes allow you to toggle display for each (e.g., display_p3=true by default).
- **Color Settings**:
- Resistance/Support Color: Defaults to red for resistance (up-trendlines from RSI highs) and green for support (down-trendlines from RSI lows).
- Labels for breakouts use green for "B" (buy/long) and red for "S" (sell/short).
- **Breakout Settings**:
- Show Prev. Breakouts: If true, displays previous breakout labels (up to "Max Prev. Breakouts Label" +1, default 2+1=3).
- Show Breakouts: Separate toggles for each period (e.g., show_breakouts3). When enabled, dotted extension lines project the trendline forward, and crossovers/crossunders trigger labels like "B3" (breakout above resistance for Period 3) or "S3" (break below support).
- Note: Divergence detection is commented out in the code. If you want to enable it, uncomment the relevant sections (e.g., show_divergence input) and adjust the lookback (default 5 bars) for spotting bullish/bearish divergences between price and RSI.
2. **Interpreting the Visuals**:
- **RSI Plot**: A blue line showing the RSI value (0-100). Horizontal dashed lines at 70 (red, overbought), 30 (green, oversold), and 50 (gray, midline).
- **Trendlines**: Solid lines connecting recent RSI pivots. Green lines (support) connect lows; red lines (resistance) connect highs. Only the most recent line per direction is shown per period to avoid clutter.
- **Breakout Projections**: Dotted lines extend the current trendline forward. When RSI crosses above a red dotted resistance, a "B" label (e.g., "B1") appears above, indicating a potential bullish breakout. Crossing below a green dotted support shows an "S" label below, indicating bearish.
- **Labels**: Current breakouts are bright (green/red); previous ones fade to gray. Use these as signal alerts.
- **Alerts**: The code includes commented-out alert conditions (e.g., for breakouts or RSI crossing levels). Uncomment and set them up in TradingView's alert menu for notifications.
3. **Best Practices**:
- Use on RSI-compatible timeframes (e.g., 1H, 4H, daily) for stocks, forex, or crypto.
- Combine with price chart: Trendlines are on RSI, so check if RSI breakouts align with price action (e.g., breaking a price resistance).
- Test on historical data: Backtest signals using TradingView's replay feature.
- Avoid over-customization initially—start with defaults (Periods 3 and 5 enabled) to understand behavior.
What It Is Used For
This indicator is primarily used for **momentum-based trend analysis and breakout trading on the RSI oscillator**. Traditional RSI identifies overbought/oversold conditions, but this enhances it by drawing dynamic trendlines on RSI itself, treating RSI as a "price-like" chart for trend detection.
- **Key Purposes**:
- **Identifying Momentum Trends**: RSI trendlines show if momentum is strengthening (upward-sloping support) or weakening (downward-sloping resistance), even if price is ranging.
- **Spotting Breakouts**: Detects when RSI breaks its own support/resistance, signaling potential price reversals or continuations. For example, an RSI breakout above resistance in an oversold zone might indicate a bullish price reversal.
- **Multi-Period Analysis**: By using multiple pivot periods, it acts like a multi-timeframe tool within RSI, helping confirm short-term signals with longer-term trends.
- **Signal Generation**: Breakout labels provide entry/exit points, especially in trending markets. It's useful for swing trading, scalping, or confirming trends in larger strategies.
- **Divergence (Optional)**: If enabled, it highlights mismatches between price highs/lows and RSI, which can predict reversals (e.g., bullish divergence: price lower low, RSI higher low).
Overall, it's ideal for traders who rely on oscillators but want more visual structure, like trendline traders applying price concepts to RSI.
Advantages and Disadvantages
**Advantages**:
- **Visual Clarity**: Trendlines make RSI easier to interpret than raw numbers, helping spot support/resistance in momentum without manual drawing.
- **Multi-Period Flexibility**: Multiple periods allow analyzing short- and long-term momentum simultaneously, reducing noise from single-period RSI.
- **Breakout Signals**: Automated detection of breakouts provides timely alerts, with labels and projections for proactive trading. This can improve entry timing in volatile markets.
- **Customization**: Extensive inputs (periods, colors, breakouts) make it adaptable to different assets/timeframes. The stateful management of lines/labels prevents chart clutter.
- **Complementary to Price Action**: Enhances standard RSI by adding trend context, useful for confirming divergences or overbought/oversold trades.
- **Efficiency**: Uses efficient arrays and line management, supporting up to 500 lines for long charts without lagging TradingView.
**Disadvantages**:
- **Lagging Nature**: Based on historical pivots, signals may lag in fast-moving markets, leading to late entries. Shorter periods help but increase whipsaws.
- **False Signals**: In ranging or sideways markets, RSI trendlines can produce frequent false breakouts. It performs better in trending conditions but may underperform without filters.
- **Over-Reliance on RSI**: Ignores volume, fundamentals, or price structure—breakouts might not translate to price moves if momentum decouples from price.
- **Complexity for Beginners**: Multiple periods and settings can overwhelm new users; misconfiguration (e.g., too many periods) leads to noisy charts.
- **No Built-in Risk Management**: Signals lack stop-loss/take-profit logic; users must add these manually.
- **Divergence Limitations**: The basic (commented) divergence detection is simplistic and may miss hidden divergences or require tuning.
In summary, it's powerful for momentum traders but should be used with confirmation tools to mitigate false positives.
Example Strategies
Here are one LONG (buy) and one SHORT (sell) strategy example using the indicator. These are basic; always backtest and use risk management (e.g., 1-2% risk per trade, stop-loss at recent lows/highs).
**LONG Strategy Example: Oversold RSI Support Breakout**
- **Setup**: Use on a daily chart for stocks or crypto. Enable Periods 3 and 5 (short- and medium-term). Set oversold level to 30.
- **Entry**: Wait for RSI to be in oversold (<30). Look for a "B" breakout label (e.g., "B3" or "B5") when RSI crosses above a red resistance trendline projection. Confirm with price forming a higher low or candlestick reversal (e.g., hammer).
- **Stop-Loss**: Place below the recent price low or the RSI support level equivalent in price terms (e.g., 5-10% below entry).
- **Take-Profit**: Target RSI reaching overbought (70) or a 2:1 risk-reward ratio. Exit on a bearish RSI crossunder midline (50).
- **Example Scenario**: In a downtrending stock, RSI hits 25 and forms a support trendline. On a "B5" breakout, enter long. This captures momentum reversals after overselling.
- **Rationale**: Breakout above RSI resistance in oversold signals fading selling pressure, potential for price uptrend.
**SHORT Strategy Example: Overbought RSI Resistance Breakout**
- **Setup**: Use on a 4H chart for forex pairs. Enable Periods 10 and 20. Set overbought level to 70.
- **Entry**: Wait for RSI in overbought (>70). Enter on an "S" breakout label (e.g., "S3" or "S4") when RSI crosses below a green support trendline projection. Confirm with price showing a lower high or bearish candlestick (e.g., shooting star).
- **Stop-Loss**: Above the recent price high or RSI resistance level (e.g., 5-10% above entry).
- **Take-Profit**: Target RSI hitting oversold (30) or a 2:1 risk-reward. Exit on bullish RSI crossover midline (50).
- **Example Scenario**: In an uptrending pair, RSI peaks at 75 with a resistance trendline. On "S4" breakout, enter short. This targets momentum exhaustion after overbuying.
- **Rationale**: Break below RSI support in overbought indicates weakening buying momentum, likely price downturn.
Enhancing Strategy Validity with Other Indicators
To increase the reliability of strategies based on this indicator, combine it with complementary tools for confirmation, filtering false signals, and adding context. This creates multi-indicator strategies that reduce whipsaws and improve win rates. Focus on indicators that address RSI's weaknesses (e.g., lagging, momentum-only). Below are examples of different indicators, how to integrate them, and sample strategies.
1. **Moving Averages (e.g., SMA/EMA)**:
- **How to Use**: Overlay 50/200-period EMAs on the price chart. Use RSI breakouts only in the direction of the trend (e.g., long only if price > 200 EMA).
- **Strategy Example**: Trend-Following Long – Enter on "B" RSI breakout if price is above 200 EMA and RSI > 50. This filters reversals in uptrends. Add MACD crossover for entry timing. Advantage: Aligns momentum with price trend, reducing counter-trend trades.
2. **Volume Indicators (e.g., Volume Oscillator or OBV)**:
- **How to Use**: Require increasing volume on RSI breakouts (e.g., OBV making higher highs on bullish breakouts).
- **Strategy Example**: Volume-Confirmed Short – On "S" breakout, check if volume is rising and OBV breaks its own trendline downward. Enter short only if confirmed. This validates breakouts with real market participation, avoiding low-volume traps.
3. **Other Oscillators (e.g., MACD or Stochastic)**:
- **How to Use**: Use for divergence confirmation or overbought/oversold alignment. For instance, require Stochastic (14,3,3) to also breakout from its levels.
- **Strategy Example**: Dual-Oscillator Reversal Long – Enable divergence in the indicator. Enter on bullish RSI divergence + "B" breakout if MACD histogram flips positive. Exit on MACD bearish crossover. This strengthens reversal signals by cross-verifying momentum.
4. **Price Action Tools (e.g., Support/Resistance or Candlestick Patterns)**:
- **How to Use**: Map RSI trendlines to price levels (e.g., if RSI resistance breaks, check if price breaks a key resistance).
- **Strategy Example**: Price-Aligned Breakout Short – On "S" RSI breakout in overbought, confirm with price breaking below a drawn support line or forming a bearish engulfing candle. Use Fibonacci retracements for targets. This ensures momentum translates to price movement.
5. **Volatility Indicators (e.g., Bollinger Bands or ATR)**:
- **How to Use**: Avoid trades during low volatility (e.g., Bollinger Band squeeze) to filter ranging markets. Use ATR for dynamic stops.
- **Strategy Example**: Volatility-Filtered Long – Enter "B" breakout only if Bollinger Bands are expanding (increasing volatility) and RSI is oversold. Set stop-loss at 1.5x ATR below entry. This targets high-momentum breakouts while skipping choppy periods.
**General Tips for Building Enhanced Strategies**:
- **Layering**: Start with RSI breakout as the primary signal, add 1-2 confirmations (e.g., EMA trend + volume).
- **Backtesting**: Use TradingView's strategy tester to quantify win rates with/without additions.
- **Risk Filters**: Incorporate overall market sentiment (e.g., via VIX) or avoid trading near news events.
- **Timeframe Alignment**: Use higher timeframes for trend (e.g., daily EMA) and lower for entries (e.g., 1H RSI breakout).
- **Avoid Overloading**: Too many indicators cause paralysis; aim for synergy (e.g., trend + momentum + volume).
This indicator is a versatile tool, but success depends on context and discipline. If you need code modifications or specific backtests, provide more details!
Money Maverick Jeffrey LineMoney Maverick Jeffrey Line - Release Notes
Version 1.0 (Initial Release) - September 14, 2025
Overview
Welcome to the Money Maverick Jeffrey Line (MMJL), a custom TradingView indicator designed for trend-following in volatile markets like cryptocurrencies (e.g., Bitcoin), tech stocks, and commodities. It's built in Pine Script v5 and focuses on providing clear signals: GOLD (strong uptrend - buy/hold longs), BLUE (strong downtrend - sell/exit), and GRAY (neutral - stay out).
The goal? Help you trade like a "money maverick" – disciplined, low-effort, and risk-aware – without the emotional rollercoaster. Backtested on TSLA, it shows solid win rates (~80%) with lower drawdowns than buy-and-hold.
Key Features
• Trend Signals: Uses multiple Exponential Moving Averages (EMAs) for consistency checks, outputting one of three states to guide entries/exits.
• Filters for Accuracy:
o ADX (Average Directional Index) to confirm strong trends (threshold: 25 by default).
o Optional volume confirmation to ensure signals align with above-average market activity (default: off).
• Visual Enhancements:
o Colored lines and fills for EMAs.
o Full-chart background shading based on state (Gold, Blue, Gray) – default fully transparent for a clean look.
o On-chart table showing current state (e.g., "GOLD (Buy)").
o ATR-based trailing stop lines for risk management hints (default: shown, with "Line with Breaks" style to avoid clutter in neutral zones).
• Alerts: Built-in alerts for state changes – set them up in TradingView for notifications.
• Customization: User inputs for MA lengths, colors, ADX threshold, volume filter, ATR multiplier, fill/BG transparency, and trailing stops toggle. Tweak for your assets!
• Low Lag Design: EMAs for quicker trend detection while keeping smoothness.
How to Install and Use
1. Add to TradingView:
o Open TradingView > Pine Editor > Paste the script code > Save as "Money Maverick Jeffrey Line" > Add to Chart.
2. Customize Settings:
o In the indicator settings (gear icon), adjust lengths (e.g., Fast MA: 15), enable/disable volume filter or trailing stops, or change colors/transparency (default fill: 50, BG: 100).
3. Trading Rules (Suggested):
o GOLD: Enter/scale into longs if not in position; use red trailing stop as exit hint.
o BLUE: Exit longs (or enter shorts if aggressive); use green trailing stop as exit hint.
o GRAY: Hold if positioned; avoid new entries.
o Use on higher timeframes (4H, Daily, Weekly) for best results.
o Combine with your own fundamental analysis – this is a tool, not financial advice!
4. Backtesting: Test in TradingView's Strategy Tester or external tools. On TSLA (2010-2025 daily), it achieved +15,000% return with 80% win rate and -25% max drawdown (vs. HODL's +24,900% but -85% drawdown). Why Use It? Even though total returns are lower than HODL, MMJL prioritizes risk management with significantly reduced drawdowns, better risk-adjusted performance (e.g., higher Sharpe Ratio), and smoother equity curves. It excels in diverse market conditions by exiting during downtrends, providing psychological discipline through rule-based signals, and serving as a portfolio diversifier with low correlation to traditional holdings. Ideal for traders seeking sustainability over maximum upside in volatile assets.
5. Alerts Setup: In TradingView, go to the indicator > Alerts > Create Alert based on conditions like "Money Maverick Jeffrey Line: GOLD".
Known Issues/Limitations
• Lagging in Nature: Still a trend-follower, so it may enter late in new trends or exit early in pullbacks.
• No Dashed Lines: TradingView v5 uses "Line with Breaks" for trailing stops to show segments only in trends.
• Volume Data: Relies on TradingView's volume; may vary by exchange/feed.
• Not for All Markets: Best for trending assets; underperforms in prolonged ranges.
• No Guarantees: Past performance isn't indicative of future results. Trading involves risk of capital loss – use at your own discretion.
Credits and Disclaimers
• Inspired By: CTO Larsson's original Larsson Line (free version on TradingView: VaY7PmRo).
• Developed With: Enhancements by Grok (xAI) based on user feedback.
• Testing Invitation: Share this with your friends! Feedback on performance, bugs, or ideas? Comment on TradingView or discuss in communities.
• Disclaimer: This is not financial advice. Always do your own research (DYOR). No warranties – use responsibly.
If you encounter issues or want updates (e.g., adding more features like auto-trailing stops), let me know. Happy trading, Mavericks! 🚀
ML: Lorentzian Classification Premium█ OVERVIEW
Lorentzian Classification Premium represents the culmination of two years of collaborative development with over 1,000 beta testers from the TradingView community. Building upon the foundation of the open-source version, this premium edition introduces powerful enhancements that transform how machine-learning classification can be applied to market analysis.
The premium version maintains the core Lorentzian distance-based classification algorithm while expanding its capabilities through triple the feature dimensionality (up to 15 features), sophisticated mean-reversion detection, first-pullback identification, and a comprehensive signal taxonomy that goes far beyond simple buy/sell signals. Whether you're building automated trading systems, conducting deep market research, or integrating proprietary indicators into ML workflows, this tool provides the advanced edge needed for professional-grade analysis.
█ BACKGROUND
Lorentzian Classification analyzes market structures, especially those exhibiting non-linear distortions under stress, by employing advanced distance metrics like the Lorentzian metric, prominent in fields such as relativity theory. Where traditional indicators assume flat space, we embrace the curve. The heart of this approach is the Lorentzian distance metric—a sophisticated mathematical tool. This framework adeptly navigates the complex curves and distortions of market space, aiming to provide insights that traditional analysis might miss, especially during moments of extreme volatility. It analyzes historical data from a multi-dimensional feature space consisting of various technical indicators of your choosing. Where traditional approaches fail, Lorentzian space reveals the true geometry of market dynamics.
Neighborhoods in Different Geometries: In the above figure, the Lorentzian metric creates distinctive cross-patterns aligned with feature axes (RSI, CCI, ADX), capturing both local similarity and dimensional extremes. This unique geometry allows the algorithm to recognize similar market conditions that Euclidean spheres and Manhattan diamonds would miss entirely. In LC Premium, users can have up to 15 features -- you are not limited to 3-dimensions.
Among the thousands of distance metrics discovered by mathematicians, each perceives data through its own geometric lens. The Lorentzian metric stands apart with its unique ability to capture market behavior during volatile events.
█ COMMUNITY-DRIVEN EVOLUTION
It has been profoundly humbling over the past 2 years to witness this indicator's evolution through the collaborative efforts of our incredible community. This journey has been shaped by thousands of user suggestions and validated through real-world application.
A particularly amazing milestone was the development of a complete community-driven Python port, which meticulously matched even the most minute PineScript quirks. Building on this solid foundation, a new command-line interface (CLI) has opened up exciting possibilities for chart-specific parameter optimization:
Early insights from parameter optimization research: Through grid-search testing across thousands of parameter combinations, the analysis identifies which parameters have the biggest effects on performance and maps regions of stability across different market regimes. This reveals that optimal neighbor counts vary significantly based on market conditions—opening up incredible potential for timeframe-specific optimization.
This is just one of the insights gleaned so far from this ongoing investigation. The potential for chart-specific optimization for any given timeframe could transform how traders approach parameter selection.
Demand from power users for extra capabilities—while keeping the open-source version simple—sparked this Premium release. The open-source branch remains maintained, but the premium tier adds unique features for those who need an analytical edge and to leverage their own custom indicators as feature series for the algorithm.
█ KEY PREMIUM FEATURES
📈 First Pullback Detection System
Automatically identifies high-probability trend-continuation entries after initial momentum moves.
Detects when price retraces to optimal entry zones following breakouts or trend initiations.
Green/red triangle signals often fire before main classification arrows.
Dedicated alerts for both bullish and bearish pullback opportunities.
Based on veryfid's extensive research into pullback mechanics and market structure.
🔄 Dynamic Kernel Regression Envelope
Powerful, zero-setup confluence layer that immediately communicates trend shifts.
Dual-kernel system creates a visual envelope between trend estimates.
Color gradient dynamically represents prediction strength and market conviction.
Crossovers provide additional confirmation without cluttering your chart.
Professional visualization that rivals institutional-grade analysis tools.
✨ Massively Expanded Dimensionality: 10 Custom Sources, 5 Built-In Sources
Transform the indicator from 5 built-in standard to 15 total total features—triple the analytical power.
Integrate ANY TradingView indicator as a machine learning feature.
Built-in normalization ensures all indicators contribute equally regardless of scale.
Create theme-based systems: pure volume analysis, multi-timeframe momentum, or hybrid approaches.
📊 Tiered Mean Reversion Signals with Scalping Alerts
Regular (🔄) and Strong (⬇️/⬆️) mean reversion signals based on statistical extremes.
Opportunities often arise before candle close—perfect for scalping entries.
Visual markers appear at high-probability reversal zones.
Four specialized alert types: upward/downward for both regular and strong reversals.
Pre-optimized probability thresholds, no fine-tuning required.
📅 Daily Kernel Trend Filter
Instantly cleans up noisy intraday charts by aligning with higher timeframe trends.
Swing traders report immediate signal quality improvement.
Automatically deactivates on daily+ timeframes (intelligent context awareness).
Reduces counter-trend signals by up to 60% on lower timeframes.
Simple toggle—no complex multi-timeframe setup required.
📋 Professional Backtesting Stream (-6 to +6)
Multiple distinct signal types (including pullbacks, mean reversions, and kernel deviations) vs. basic binary (buy/sell) output for nuanced analysis.
Enables detailed walk-forward analysis and ML model training.
Compatible with external backtesting frameworks via numeric stream.
Rare precision for TradingView indicators—usually only found in institutional tools.
Perfect for quants building sophisticated strategy layers.
⚡ Performance Optimizations
Faster distance calculations through algorithmic improvements.
Reduced indicator load time (measured via Pine Profiler).
Handles 15 active features without timeouts—critical for multi-chart setups.
Optimized for live auto-trading bots requiring minimal latency.
🎨 Full Visual Customization & Accessibility
Complete color control for all visual elements.
Colorblind-safe default palette with customization options.
Dark mode optimization for extended trading sessions.
Professional appearance matching your trading workspace.
Accessibility features meeting modern UI standards.
🛠️ Advanced Training Modes
Downsampling mode for training on diverse market conditions; Down-sampling and remote-fractals for exotic pattern discovery.
Remote fractals option extends analysis to deep historical patterns.
Reset factor control for fine-tuning neighbor diversity; Reset-factor tuning to control neighbor diversity.
Appeals to systematic traders exploring exotic data approaches.
Prevents temporal clustering bias in model training.
█ HOW TO USE
Understanding the Approach (Core Concept):
Lorentzian Classification uses a k-Nearest Neighbors (k-NN) algorithm. It searches for historical price action "neighborhoods" similar to the current market state. Instead of a simple straight-line (Euclidean) distance, it primarily uses a Lorentzian distance metric, which can account for market "warping" or distortions often seen during high volatility or significant events. Each historical neighbor "votes" on what happened next in its context, and these votes aggregate into a classification score for the current bar.
Interpreting Bar Scores & Signals (Interpreting the Chart):
Bar Prediction Values: Numbers over each candle (e.g., ranging from -8 to +8 if Neighbors Count is 8) represent the aggregated vote from the nearest neighbors. Strong positive scores (e.g., +7, +8) indicate a strong bullish consensus among historical analogs. Strong negative scores (e.g., -7, -8) indicate a strong bearish consensus. Scores near zero suggest neutrality or conflicting signals from neighbors. The intensity of bar colors (if Use Confidence Gradient is on) often reflects these scores.
Main Arrows (Main Buy/Sell Labels): Large ▲/▼ labels are the primary entry signals generated when the overall classification (after filters) is bullish or bearish.
Pullback Triangles: Small green/red ▲/▼ identify potential trend continuation entries. These signals often appear after an initial price move and a subsequent minor retracement, suggesting the trend might resume. This is based on recognizing patterns where a brief counter-movement is followed by a continued advance in the initial trend direction.
Mean-Reversion Symbols: 🔄 (Regular Reversion) appears when price has crossed the average band of the Dynamic Kernel Regression Envelope. ⬇️/⬆️ (Strong Reversion) means price has crossed the far band of the envelope, indicating a more extreme deviation and potentially a stronger reversion opportunity.
Custom Mean Reversion Deviation Markers (Deviation Dots): If Enable Custom Mean Reversion Alerts is on, these dots appear when price deviates from the main kernel regression line by a user-defined ATR multiple, signaling a custom-defined reversion opportunity.
Kernel Regression Lines & Envelope: The Main Kernel Estimate (thicker line) is an adaptive moving average that smooths price and helps identify trend direction. Its color indicates the current trend bias. The Envelope (outer bands and a midline) creates a channel around price, and its interaction with price generates mean reversion signals.
Key Input Groups & Their Purpose:
🔧 GENERAL SETTINGS:
Reduce Price-Time Warping : Toggles the distance metric. When enabled, it reduces the characteristic "warping" effect of the default Lorentzian metric, making the distance calculation more Euclidean in nature. This may be suited for periods exhibiting less pronounced price-time distortions.
Source : Price data for calculations (default: close ).
Neighbors Count : The 'k' in k-NN – number of historical analogs considered.
Max Bars Back : How far back the indicator looks for historical patterns.
Show Exits / Use Dynamic Exits : Controls visibility and logic for exit signals.
Include Full History (Use Remote Fractals) : Allows model to pick "exotic" fractals from deep chart history.
Use Downsampling / Reset Factor : Advanced training parameters affecting neighbor selection.
Show Trade Stats / Use Worst Case Estimates : Displays a real-time performance table (for calibration only).
🎛️ DEFINE CUSTOM SOURCES (OPTIONAL):
Integrate up to 10 external data series (e.g., from other indicators) as features. Each can be optionally normalized. Load the external indicator on your chart first for it to appear in the dropdown.
🧠 FEATURE ENGINEERING:
Configure up to 15 features for the k-NN algorithm. Select type (RSI, WT, CCI, ADX, Custom Sources), parameters, and enable/disable. Start simple (3-5 features) and add complexity gradually. Normalize features with vastly different scales.
🖥️ DISPLAY SETTINGS:
Controls visibility of chart elements: bar colors, prediction values/labels, envelope, etc.
Align Signal with Current Bar : If true, pullback signals appear on the current bar (calculated on closed data). If false (default), they appear on the next bar.
Use ATR Offset : Positions bar prediction values using ATR for visibility.
🧮 FILTERS SETTINGS:
Refine raw classification signals: Volatility, Regime, ADX, EMA/SMA, and Daily Kernel filters.
🌀 KERNEL SETTINGS (Main Kernel):
Adjust parameters for the primary Nadaraya-Watson Kernel Regression line. Lookback Window , Relative Weighting , Regression Level , Lag control sensitivity and smoothness.
✉️ ENVELOPE SETTINGS (for Mean Reversion):
Configure the dynamic Kernel Regression Envelope. ATR Length , Near/Far ATR Factor define band width.
🎨 COLOR SETTINGS (Colors):
Customize colors for all visual elements; override every palette element.
General Approach to Using the Indicator (Suggested Workflow):
Load defaults and observe behavior: Familiarize yourself with the indicator's behavior.
Feature Engineering: Experiment with features, considering momentum, trend, and volatility. Add/replace features gradually.
Apply Filters: Refine signals according to your trading style.
Contextualize: Use kernels and envelope to understand broader trend and potential overbought/oversold areas.
Observe Signals: Pay attention to the interplay of main signals, pullbacks, and mean reversions. Watch interplay of main, pullback & mean-reversion signals.
Calibrate (Not Backtest): Use the "Trade Stats" table for real-time feedback on current settings. This is for calibration, *not a substitute for rigorous backtesting.*
Iterate & refine: Adjust settings, observe outcomes, and refine your approach.
█ ACKNOWLEDGMENTS
This premium version wouldn't exist without the invaluable contributions of:
veryfid for his groundbreaking ideas on unifying pullback detection with Lorentzian Classification, but most of all for always believing in and encouraging me and so many others. For being a mentor and, most importantly, a friend. We all miss you.
RikkiTavi for his help in creating the settings optimization framework and for other invaluable theoretical discussions.
The 1,000+ beta testers worldwide who provided continuous feedback over two years.
The Python porting team who created the foundation for advanced optimization; for the cross-language clone.
The broader TradingView community for making this one of the platform's most popular indicators.
█ FUTURE DEVELOPMENT
The Premium version will continue to evolve based on community feedback. Planned enhancements include:
Specialized exit model trained independently from entry signals (ML-based exit model).
Feature hub with pre-normalized, commonly requested indicators (Pre-normalized feature hub).
Better risk-management options (Enhanced risk-management options).
Fully automated settings optimization (Auto-settings optimization tool).
Script payant
ATR Adjusted RSIATR Adjusted RSI Indicator
By Nathan Farmer
The ATR Adjusted RSI Indicator is a versatile indicator designed primarily for trend-following strategies, while also offering configurations for overbought/oversold (OB/OS) signals, making it suitable for mean-reversion setups. This tool combines the classic Relative Strength Index (RSI) with a unique Average True Range (ATR)-based smoothing mechanism, allowing traders to adjust their RSI signals according to market volatility for more reliable entries and exits.
Key Features:
ATR Weighted RSI:
At the core of this indicator is the ATR-adjusted RSI line, where the RSI is smoothed based on volatility (measured by the ATR). When volatility increases, the smoothing effect intensifies, resulting in a more stable and reliable RSI reading. This makes the indicator more responsive to market conditions, which is especially useful in trend-following systems.
Multiple Signal Types:
This indicator offers a variety of signal-generation methods, adaptable to different market environments and trading preferences:
RSI MA Crossovers: Generates signals when the RSI crosses above or below its moving average, with the flexibility to choose between different moving average types (SMA, EMA, WMA, etc.).
Midline Crossovers: Provides trend confirmation when either the RSI or its moving average crosses the 50 midline, signaling potential trend reversals.
ATR-Inversely Weighted RSI Variations: Uses the smoothed, ATR-adjusted RSI for a more refined and responsive trend-following signal. There are variations both for the MA crossover and the midline crossover.
Overbought/Oversold Conditions: Ideal for mean reversion setups, where signals are triggered when the RSI or its moving average crosses over overbought or oversold levels.
Flexible Customization:
With a wide range of customizable options, you can tailor the indicator to fit your personal trading style. Choose from various moving average types for the RSI, modify the ATR smoothing length, and adjust overbought/oversold levels to optimize your signals.
Usage:
While this indicator is primarily designed for trend-following, its OB/OS configurations make it highly effective for mean-reverting setups as well. Depending on your selected signal type, the relevant indicator line will change color between green and red to visually signal long or short opportunities. This flexibility allows traders to switch between trending and sideways market strategies seamlessly.
A Versatile Tool:
The ATR Adjusted RSI Indicator is a valuable component of any trading system, offering enhanced signals that adapt to market volatility. However, it is not recommended to rely on this indicator alone, especially without thorough backtesting. Its performance varies across different assets and timeframes, so it’s essential to experiment with the parameters to ensure consistent results before applying it in live trading.
Recommendation:
Before incorporating this indicator into live trading, backtest it extensively. Given its flexibility and wide range of signal-generation methods, backtesting allows you to optimize the settings for your preferred assets and timeframes. Only consider using it on it's own if you are confident in its performance based on your own backtest results, and even then, it is not recommended.
ACD Indicator [TradingFinder] M Fisher Pivots Methodology Signal🔵 Introduction
The book "The Logical Trader" begins with a comprehensive review of the ACD Methodology principles, which include identifying specific price points related to the opening range.
This method allows you to set reference points for trading and use points "A" and "C" for trade entry. You will also learn about the "Pivot Range" and how to combine them with the ACD method to maximize position size and minimize risk.
In this indicator, the strategy is implemented to make it easier to use.
🔵 How to Use
The "ACD" strategy can be applied to various markets such as stocks, commodities, or forex, providing buy and sell signals that allow you to set your price targets and stop losses.
This strategy is based on the assumption that the opening range of trades is statistically significant each day, meaning the initial market fluctuations influence the market until the end of the day.
The ACD trading strategy is known as a breakout strategy and performs best in volatile or strongly trending markets, such as crude oil and stocks.
Some of the rules for using the ACD strategy include the following :
Consider points A and C as reference points and continuously pay attention to these points during trades. These points serve as entry and exit points for trades.
Examine daily and multi-day pivot ranges to analyze market trends. If the price is above the pivots, the trend is upward, and if below the pivots, the trend is downward.
Trading with the ACD strategy in forex is possible using the ACD indicator. This indicator is a technical tool used to measure the balance between supply and demand in the market. By analyzing trading volume and price, this indicator helps traders identify trend strength and suitable entry and exit points.
To use the ACD indicator, consider the following :
Identifying strong trends: The ACD indicator can help you identify strong and stable trends in the market.
Determining entry and exit points: ACD provides buy and sell signals to enter or exit trades at the best possible time.
Bullish Setup :
When the "A up" line is broken, it is advisable to wait for some time to ensure that this is not a "Fake Breakout" and that the price stabilizes above this line.
After entering the trade, the best stop loss you can choose is below the "A down" line. However, it is recommended to test this in backtests to achieve the best results. The suitable reward-to-risk ratio for this strategy is 1, which should also be backtested.
Bearish Setup :
When the "A down" line is broken, it is advisable to wait for some time to ensure that this is not a "Fake Breakout" and that the price stabilizes below this line.
After entering the trade, the best stop loss you can choose is above the "A up" line. However, it is recommended to test this in backtests to achieve the best results. The suitable reward-to-risk ratio for this strategy is 1, which should also be backtested.
🔵 Setting
NDay Pivot Range Period : Using this entry you can specify the number of days to calculate NDay Pivot Range.
Show Daily Pivot Range : Set the Daily Pivot color and displayed or not.
Show NDay Pivot Range : Set the NDay Pivot color and displayed or not.
ATR Period Levels : Determining the period of the ATR indicator, which is used to determine the A and C levels.
Show Tokyo ACD Setup : Set the Tokyo ACD Setup color and displayed or not.
Tokyo Opening Range Time : The amount of time taken to determine the opening range. You can set this number between 5 and 60 minutes.
Tokyo Session : Market start and end time.
A Level Multiplier : The coefficient that is multiplied by ATR to determine the distance of line A up and A down.
C Level Multiplier : The coefficient that is multiplied by ATR to determine the distance of line C up and C down.
The same settings exist for the London and New York sessions.
Self-Optimizing RSI Strategy [Kioseff Trading]Hello!
Introducing the Self-Optimizing RSI Strategy.
The indicator tests up to 800 RSI strategies simultaneously, looping through arrays, and auto plots the best performing parameter set.
The image above shows the result of 800 RSI strategies concurrently.
The table oriented bottom right shows the performance and risk metrics of the best performing RSI system tested across the bar set. Additionally, the conditions for entry and exit are displayed; for the image - a long entry system predicated on RSI crossunders and exit system predicated on a 1% TP and 2% SL are shown.
The indicator calculates numerous risk and performance metrics.
Calculated metrics include:
RSI Parameters
RSI Cross Entry Level
Total Trades
Win Rate
Avg. Gain for Winning Trades
Max Pain
PnL (Cumulative Performance)
Profit Factor
Avg. Loss for Losing Trades
Ratio Avg. Win / Avg. Loss
Avg. Bars in Trade
Max Drawdown
Current Drawdown
Open Position PnL
"Dynamic" indicates the performance of self-optimizing RSI system was tested.
The image above shows the performance of the greatest-performing RSI system - a fixed set of parameters - when adhering to a 1% TP and 2% fixed SL.
Trailing Stops and Profit-Taking Limit orders can be set/simulated.
The image above shows a dynamic entry level - plotted as a purple, non-transparent line.
The entry level "self-optimizes" to mimic the best performing RSI system at current time.
The image above exemplifies the functionality for all horizontal lines plotted on the chart.
The average RSI level achieved subsequent a profitable trade is shown.
The average RSI level achieved subsequent a losing trade is shown.
The entry level for RSI crossunders/crossovers is shown.
The image above show the Self-Optimizing RSI indicator recording entries & exits; gains & losses, for each executed trade.
You can "verify" trades manually.
Blue boxes reflect an entered position.
Green boxes reflect a closed, profitable trade.
Red boxes reflect a close, losing trade.
The percentage gain for a profitable trade is appended to green boxes; the percentage loss for a losing trade is appended to red boxes.
The Self-Optimizing RSI indicator plots off the chart; however, percentage gains/losses are measured against price, not RSI.
Boxes correlate to the interval a trade was entered/exited on.
The indicator hosts various methods to filter the outcome for testing.
For instance, you can:
Use trailing stops or fixed stop losses
Test RSI crossunders and crossovers
Configure the RSI settings that are tested (i.e. RSI 2 - 9, RSI 14 - 20, RSI 50 - 57)
Test short-based RSI Systems and long-based RSI systems
Simulate limit orders (Exit intrabar at fixed stop losses or trailing stop losses; exit intrabar at profit targets)
Require all tested RSIs to trend above or below their respective average (i.e. all RSIs must trend above/below their 50-interval EMA values. SMAs can also be used)
Use external indicators and require a user-defined value be exceeded, measured below, or that price exceed or measure below an indicator. The Self-Optimizing RSI indicator incorporates a few built-in technical indicators - ADX, %k, MFI, CMFI, and RSI. Consequently, you can require these indicators to measure above/below a specified level prior to entry. Additionally, you can supplement an extrinsic indicator (anything custom coded with plot values) to the entry logic for the Self-Optimizing RSI indicator. I'll show an example shortly.
Adjust the time window that's tested.
Adjust PT and SL percentages.
Override plot an RSI system to procure thorough statistics.
Require a symbol to measure above/Below or equal to a particular price level to “validate” a Long/Short entry signal. You can retrieve any data hosted by TradingView and require it measure above/below a user-defined level prior to entry. For instance, you can select "$VIX", and require the ticker to measure less than $30 prior to long/short entry. If "$VIX" measures greater than $30 prior to a long/short signal the position will not open. Alternatively, you can require a symbol to measure above a user-defined price prior to entry. If the retrieved ticker doesn't measure above the user-defined level prior to entry a trade will not open.
Use trailing stops or fixed stop losses
The image above shows results for 800 short-based RSI systems - using a trailing stop loss.
Test RSI crossunders and crossovers
The image shows results for 800 long-based RSI systems. Positions are entered subsequent to RSI crossovers.
You can select which RSI strategies are tested - you aren't not limited to testing RSI 2 - RSI 9 (:
Simulate limit orders (Exit intrabar at fixed stop losses or trailing stop losses; exit intrabar at profit targets)
The image above shows performance test results when exiting during the interval subsequent to the profit target being exceeded.
The image above shows performance test results when exiting during the interval subsequent to the stop loss being exceeded.
Require all tested RSIs to trend above or below their respective average (i.e. all RSIs must trend above/below their 50-interval EMA values. SMAs can also be used)
The image above shows an RSI EMA in addition to prerequisite condition. For each RSI strategy tested, the RSI used for the strategy must measure above an EMA of its values prior to entry. You can require RSI to measure below an EMA of its values prior to entry, use an SMA, and change the length of the MA used.
Use external indicators and require a user-defined value be exceeded, measured below, or that price exceed or measure below an indicator. The Self-Optimizing RSI indicator incorporates a few built-in technical indicators - ADX, %k, MFI, CMFI, and RSI. Consequently, you can require these indicators to measure above/below a specified level prior to entry. Additionally, you can supplement an extrinsic indicator (anything custom coded with plot values) to the entry logic for the Self-Optimizing RSI indicator. I'll show an example shortly.
The image above shows me requiring the ADX indicator to measure above "20" prior to long entry. Any of the built-indicators can be used with similar conditions; you can implement a custom-coded indicator for trade logic.
Additionally, you can supplement an extrinsic indicator (anything custom coded with plot values) to the entry logic for the Self-Optimizing RSI indicator.
The image above shows me retrieving the value for Volume Profile Point of Control - a TradingView coded indicator.
Consequently, I can require price to measure above/below the session's Poc prior to RSI long/short entry.
You can use this feature with any custom coded indicator providing historical plot values - something you or a favored author have coded.
]Adjust PT and SL percentages
The image above shows adjusted TP & SL percentages - optimize and reward/risk ratio you'd like (:
Override plot an RSI system to procure thorough statistics.
The image above shows manually plotted RSI parameters and a corresponding stat sheet.
Require a symbol to measure above/Below or equal to a particular price level to “validate” a Long/Short entry signal. You can retrieve any data hosted by TradingView and require it measure above/below a user-defined level prior to entry. For instance, you can select "$VIX", and require the ticker to measure less than $30 prior to long/short entry. If "$VIX" measures greater than $30 prior to a long/short signal the position will not open. Alternatively, you can require a symbol to measure above a user-defined price prior to entry. If the retrieved ticker doesn't measure above the user-defined level prior to entry a trade will not open.
The image above shows me requiring the ticker "$VIX" to measure below $30 prior to long/short entry. If %VIS measures greater than $30 when a long/short signal triggers a position will not be opened. Further refine your trading system with this feature - exploit correlations.
Adjust the time window that's tested.
The image above shows configurable start and end dates for the optimization period.
You won't be able to test 800 RSI strategies concomitantly on a 20,000 bar data set.
Consequently, for large data sets (intrasession data) you will have to narrow the optimization window to test a larger number of combinations.
You can test 80 (loads on all data sets), 144 (loads on all data sets), 264 (loads on ~15,000 bar data sets), 312 (loads on ~11,500 bar data sets) and 800 (loads on ~4950 bar data sets)combinations simultaneously. You can test 800 RSI strategies simultaneously on intrasession data; however, you'll likely have to narrow the tested time window.
I recently published a bar count script titled "Bar Count for Backtesting", you can access the script here:
The above script is useful for quickly calculating the number of bars in a time window, or the date for a bar that is "x" number of bars back. Therefore, implementing these scripts cooperatively should improve date selection efficiency (not arbitrarily selecting test start & end dates that fail to load).
I included a tool tip describing the near-maximum bars in a data set that the higher numbers of simultaneous RSI strategies can be tested on.
More to come; enjoy!
(P.S. The script uses private libraries and, consequently, is unable to be published open source)
An optimization script is best implemented to discover what won't work, not what will work. The best performing "optimized" parameters are not a guaranteed profitable investment system. While we may see an exceptionally positive performance for a set of parameters, it's impossible to know how much of that performance is the beneficiary of market noise in the absence of additional testing. Most market moves are noise - irreplicable sequences that offer no predictive utility - and most "good" backtests overwhelmingly benefit from these irreplicable sequences. An investor unfamiliar with this concept may be lead to believe they have found a valid correlation between an indicator sequence and subsequent price movement, despite the correlation being illusory.
Consequently, it should be assumed that the best performing parameters strongly benefitted from market noise and will not work in a live market - until further rigorous statistical tests are performed on an investment system built around the best performing parameters. This includes out-of-sample, in-sample, and forward testing in addition to testing negatively correlated, positively correlated and zero-correlation assets; testing additional assets should be treated as prerequisite to live implementation.
Of course, all trading strategies, even one's that methodically exploit a valid correlation/replicable sequence, will benefit from market noise - it's impossible to avoid. However, a "legit" trading strategy has a chance to work on future price data, while an overoptimized strategy will fail miserably on new price data!
An overoptimized strategy is virtually guaranteed to have a better backtest performance than a valid strategy. The overoptimized strategy will fail in a live market while the valid strategy has a chance of working. So, should you notice the best performing RSI parameters, be sure to build a comprehensive trading system around the parameters and perform additional tests. This is the only way to know if the optimized parameters will truly work in a live market!
Unfortunately, they often will not!
This publication does not constitute investment advice.
Total Trend Follow Study with Alerts, Pyramid and DCA
Introduction
This is the study version of my trend following strategy. It is designed to be a “drop in” replacement for its twin strategy. I have replicated the analysis logic and entry and exit procedures to produce a nearly identical result set to the strategy. Other than the properties tab, the inputs dialog is exactly the same. Backtest the strategy to determine the best inputs to trade. Then apply the same inputs to this study to forward test. Alerts are available for trade entry, take profit close, stop-loss exit and pyramid level. Please see the strategy version for a complete description of the trading behavior of this script.
In brief, this script is intended to benefit from long duration trending markets. The trading behavior is to buy on strength and sell on weakness. As such the trade orders maintain its directional bias according to price pressure. What you will see on the chart is long positions on the left side of the mountain and short on the right. Long and short positions are not intermingled as long as there exists a detectable trend. My trend following script uses the same pivot point calculations used by my range trader but this time rising pivots are long and falling pivots are short. I refer to pivots as a vertex in the inputs dialog box. The cone based measurement adds a peak, sides and a base to the calculation elements. This allows the inputs to focus on adjusting the location of trades and not just trend lines. The pivot points can be plotted on the backtest. You can use the vertex input values to move the pivots where you want trades to be. This script can be traded in four different modes: Long, Short, BiDir, and Flip Flop.
This script replicates the TradingView pyramid feature by adding a field to the inputs tab. Pyramiding works the same way in this script as its twin strategy. Each pyramid level increases the order size as a multiple of its level. This makes it easy to comply with NFA FIFO Rule 2-43(b) if the trades are executed here in America. The current pyramid level is reported in the summary label when reports are enabled. You can use this value to manually adjust the position size in your forward testing. I also expose an alert when the pyramid threshold is breached. Use the TradingView alert window to send yourself a message when the level increases.
In addition to pyramiding this script employs a DCA feature which can increase the order size in response to stopped out or winning streak trades. The number of losing trades is tracked through the “Debt Sequence” value displayed in the summary report. When the debt is recovered the sequence number resets to zero. You can use the “Debt Sequence” number to manually manage DCA on your forward test. The main difference between DCA and pyramids is that this implementation of DCA applies to new trades while pyramids affect open positions. If DCA is enabled be sure to set the limiter to prevent your account from depleting capital during runaway markets.
I also have a range trading version of this script for those not interested in trend following.
Design
This script is approximately 1800 lines of Pine 4 code and implements nine indicators on two time frames. The chart (primary) interval and one higher time frame which is based on the primary. The higher time frame identifies the trend for which the primary will trade. The original trading algorithms are a port from a much larger program on another trading platform. I've converted some of the statistical functions to use standard indicators available on TradingView. The setups make heavy use of the Hull Moving Average in conjunction with EMAs that form the Bill Williams Alligator as described in his book “New Trading Dimensions” Chapter 3. Lag between the Hull and the EMAs form the basis of the entry and exit points. The alligator itself is used to identify the trend main body.
Indicator Repainting
Indicator repainting is an industry wide problem which mainly occurs when you mix backtest data with real-time data. It doesn't matter which platform you use some form of this condition will manifest itself on your chart over time. The TradingView wiki has an in depth article on this subject in regard to the elements of the Pine language which contribute or are susceptible to this effect. In dealing with this problem from a prevention aspect in Pine, you have to accept some repaint to occur.
The goal of my repaint prevention in the study script is simply to ensure that my signal trading bias remains consistent between the strategy, study and broker. This actually turned out to be kind of a job since it interfered with another project goal to synchronize the entry and exits between the strategy and study scripts. The script execution engines are different between the Pine strategy and study. While the primary chart interval is quite manageable the higher time frame requires the security() function. TradingViewʼs higher time frame implementation, which interleaves the chart interval, combined with the critical “lookahead” parameter makes the solution necessarily complex. According the wiki regarding this issue, best practice comes down to the following two methods:
1. a = security(syminfo.tickerid, 'D', close , lookahead=barmerge.lookahead_on)
2. indexHighTF = barstate.isrealtime ? 1 : 0
indexCurrTF = barstate.isrealtime ? 0 : 1
a0 = security(syminfo.tickerid, 'D', close ,lookahead=barmerge.lookahead_off)
a = a0
This script employs method 2 with the only problem being the Pine sar() function which does not expose its internal bar series. The best work around would require hand coding the parabolic but the script is at the maximum local scopes and run-time duration so that option is not viable. So far, this hasn't proven to be much of a problem. I'm currently running several symbols, forex and crypto, the strategy and study remain highly correlated with no instances of completely opposite real-time signals. However, I have received some alerts with no corresponding plot on the chart but so far it only occurred on one symbol. I will continue to monitor this problem.
The Bottom Line. Does this script repaint. Yes, it will repaint about as much as every other multi-interval script which employs the best practice solutions available in the Pine Language.
Usage
Please be aware that the purpose of the study script is to perform forward testing of the configuration established in the backtest process. Therefore, the usage here in the study begins with the backtest configuration parameters. The following steps provide instructions to get this study script connected to the TradingView alert notification system. For a detailed description of how to create a trend following system using this script please see the strategy version.
Step 1. Create a chart with the trading instrument and interval used in the backtest.
Step 2. Find this script in the “Invite Only” section of the Indicators Dialog and apply it to the current chart.
Step 3. Copy the values from the backtest input dialog to the study.
Step 4. Open the TradingView Alert window.
Step 5. In the “Condition” drop down field find and select the name of the script.
Step 6. A new drop down field will appear with the alerts available in the script. This script exposes the following eight signals:
-- Long Entry Signal
-- Long Profit Signal
-- Long Stop-loss Signal
-- Long Pyramid
-- Short Entry Signal
-- Short Profit Signal
-- Short Stop-loss Signal
-- Short Pyramid
Select the signal for which you want notification.
Step 7. In the “Options” field select the frequency of the alert. Typically, "Once Per Bar" or "Once Per Bar Close" will be sufficient.
Step 8. Set the expiration date and time.
Step 9. Select the action of the alert. Currently TradingView offers six different actions:
-- Notify on App
-- Show Popup
-- Send Email
-- Webhook URL
-- Play Sound
-- Send Email to SMS
Step 10. Create a message to to transmitted with the alert. The script provides a default message which can be overridden with any custom description. The price, time and other reserved chart elements can be included in the message
Step 11. Click the “Create” button to generate this single alert.
Step 12. Repeat steps 1 through 11 for every signal you wish to receive.
This script is open for beta testing. After successful beta test it will become a commercial application available by subscription only. I’ve invested quite a lot of time and effort into making this the best possible signal generator for all of the instruments I intend to trade. I certainly welcome any suggestions for improvements. Thank you all in advance.
HTF/CTF High/Low Mitigation with SignalsHTF/CTF High/Low Mitigation with Signals Indicator
Overview
HTF/CTF High/Low Mitigation with Signals (shortened as "H/L Signals+") is an advanced overlay indicator for TradingView, designed to identify and visualize higher timeframe (HTF) and current timeframe (CTF) swing highs/lows, track their mitigation, and generate filtered buy/sell signals using an EMA ribbon trend filter. It incorporates automated trade simulation with risk/reward (RR) visualization, position sizing based on user-defined risk, and a statistics table for performance evaluation. This tool is ideal for multi-timeframe traders focusing on swing trading, breakout strategies, or trend reversals across assets like forex, futures, metals (e.g., XAU/USD, XAG/USD), stocks, or cryptocurrencies.
The "meshup" (mashup) integrates several complementary elements: Multi-timeframe swing level detection (HTF for broader structure, CTF for finer details) with mitigation logic ensures signals align with market structure breaks; an EMA ribbon provides a dynamic trend bias to filter counter-trend trades; risk management automates position sizing and RR calculations for disciplined trading; and built-in backtesting stats offer quick insights into hypothetical performance. This combination reduces noise from isolated indicators—e.g., raw swings can be choppy, EMAs alone lag structure, and manual RR is error-prone—creating a cohesive system for spotting high-probability setups where structure, trend, and risk align. By meshing these, it aims to enhance decision-making in trending or ranging markets, though it's reactive and best used with confirmation. Note: This is a technical tool for educational purposes only; it does not provide financial advice, guarantees of profitability, or trading recommendations. Past performance is not indicative of future results, and users should backtest thoroughly on their specific assets/timeframes, in compliance with TradingView's house rules.
Key Features
• HTF Swing Levels: Detects and draws session highs/lows from a user-selected higher timeframe (e.g., Daily), extends lines until mitigated (by wick or body close), with alerts on mitigation.
• CTF Swing Levels: Identifies local swing highs/lows on the chart timeframe using a pivot candle formation (default 5-candle), with separate limits for unmitigated/mitigated lines.
• EMA Ribbon: A three-EMA system (fast 8, mid 13, slow 21) with gradient fills (green for bullish, red for bearish) to visualize trend strength and filter signals.
• Signal Generation: Buy/sell labels ("BUY"/"SELL") triggered post-mitigation when price aligns with EMA trend (e.g., above slow EMA with stacked bulls for buys).
• Trade Simulation & Risk Management: On signals, calculates stop-loss (SL) from recent extremes, position size based on fixed risk amount (e.g., $100 per trade, adjusted for asset type like futures point value or forex lots), and full take-profit (TP) at user-defined RR level (1-5). Draws RR boxes for visuals.
• Statistics Table: Displays total trades, wins/losses, win rate (%), net R-return, and max consecutive losses in a top-right table.
• Alerts: Customizable alerts for HTF mitigations and new trades (including entry, SL, TP, size).
• Visual Customizations: Toggle lines/ribbon/boxes, adjust colors/styles/widths for unmitigated/mitigated lines (HTF/CTF), min box width.
• Performance Optimization: Automatically cleans up excess lines to stay within max limits (e.g., 15 unmitigated HTF, 5 CTF).
How It Works
• HTF Logic: On new HTF bars (via time(htf_timeframe)), captures session high/low and draws extendable lines. Lines extend rightward until mitigated (high/close > high level for highs, low/close < low level for lows, toggle wick/body). Mitigation sets "waiting" flags for signals and triggers alerts.
• CTF Logic: Scans for pivot highs/lows using a user-defined candle count (e.g., 2 left/right for 5-candle swings). Draws and extends lines similarly, mitigating on wick touches, with separate styles for mitigated (e.g., dotted gray).
• EMA Ribbon Logic: Computes 8/13/21 EMAs; fills mid-slow and fast-mid with bullish green (close > slow EMA) or bearish red gradients.
• Signal Conditions: Post-mitigation (waiting_for_buy/sell true), checks EMA stack—buys require close > slow, fast > mid > slow; sells require close < slow, fast < mid < slow. Signals only on confirmed bars.
• Trade Execution: On signal, sets entry at close, scans back to mitigation bar for tightest SL (lowest low for buys, highest high for sells). Calculates risk points (entry - SL for buys), then position size via helper function (asset-specific: e.g., XAU *100, futures *pointvalue, forex 100000pointvalue). Sets full TP at entry ± (risk * full_tp_level). Draws risk/reward boxes (e.g., long: dark risk below entry, blue reward above) with RR and size text. Alerts with trade details.
• Trade Management: Monitors for SL hit (low <= SL for longs) or TP hit (high >= TP for longs); updates stats (wins if TP, losses if SL, tracks consec losses, net R as +full_tp_level or -1). Places summary label ("Hit TP5 (Win)" or "Stopped Out (Loss)").
• Cleanup: Counts unmitigated/mitigated lines; deletes oldest excess to respect max limits (e.g., max_lines_input=15 for HTF unmitigated, max_mit_lines_ctf=5 for CTF mitigated).
• Why This Meshup?: Standalone tools often fall short—HTF swings ignore local noise, but without CTF, miss entries; EMAs filter trends but overlook structure; manual RR lacks automation. Meshing them creates a "mitigation-to-signal" flow: HTF/CTF provide structural context (e.g., BOS/CHOCH), EMA ensures trend alignment (reducing whipsaws), and RR simulation adds practical risk control with stats for optimization. This holistic approach potentially improves edge in structure-based trading, especially in volatile markets, by combining macro/micro analysis with quantifiable risk—though it may lag in ranges or require tuning.
All logic uses arrays for line management, barstate.isconfirmed for reliability, and syminfo for asset-specific sizing. No repainting, but historical trades simulate based on chart data.
Settings and Customization
Inputs are grouped for usability:
1. Higher Timeframe (HTF) Settings:
o Show HTF Lines: Toggle visibility (default: true).
o Use Wick for Mitigation: True for wick touch, false for body close (default: false; tooltip explains).
o Timeframe: HTF period (default: "D").
o Max Unmitigated HTF Lines: Limit for active lines (default: 15, min 1, max 250).
2. Current Timeframe (CTF) Settings:
o Show CTF Swings: Toggle (default: true).
o CTF Swing Candle Count: Left/right candles for pivot (default: 2, min 1; tooltip: '2' = 5-candle formation).
o Max Unmitigated CTF Lines: (default: 5, min 1, max 250).
o Max Mitigated CTF Lines: (default: 5, min 1, max 250).
3. EMA Settings:
o Show EMA Ribbon: Toggle (default: true).
o Fast/Middle/Slow EMA Length: Defaults 8/13/21.
4. Risk/Reward Settings:
o Risk Amount per Trade ($): Fixed risk (default: 100.0, min 0.1; tooltip: for position sizing).
o Full Take Profit Level (1-5): RR for full win (default: 5; tooltip: counts as win in stats).
o Show Trade Visuals & Stats: Toggle boxes, labels, table (default: true).
5. 🎨 Visuals:
o Draw Risk/Reward Box: Toggle (default: true).
o Minimum Box Width (in bars): (default: 5, min 1).
o Long - Risk/Reward Box Colors: Defaults dark gray (risk), blue (reward).
o Short - Risk/Reward Box Colors: Defaults dark gray (risk), orange (reward).
6. Alert Settings:
o Alert on HTF Level Mitigation: Toggle (default: true).
7. HTF Line Style Settings:
o High (Unmitigated): Color (maroon 20%), width (1).
o High (Mitigated): Color (gray 40%), style (dotted/dashed, default dotted).
o Low (Unmitigated): Color (teal 20%), width (1).
o Low (Mitigated): Color (gray 40%), style (dotted/dashed, default dotted).
8. CTF Line Styles:
o CTF High (Unmitigated): Color (purple #8d198d 25%), width (1), style (Solid/Dotted/Dashed, default Solid).
o CTF High (Mitigated): Color (gray 50%), width (1), style (default Dotted).
o CTF Low (Unmitigated): Color (teal #008080 25%), width (1), style (default Solid).
o CTF Low (Mitigated): Color (gray 50%), width (1), style (default Dotted).
Usage Tips
• Multi-Timeframe Strategy: Use HTF (e.g., D1) for major levels, CTF for entries. Signals post-mitigation with EMA filter—enter on "BUY"/"SELL" labels, use boxes for RR visualization.
• Risk Management: Set risk_amount_per_trade to 1-2% of capital; adjust full_tp_level for strategy (e.g., 3 for conservative). Position size auto-adapts to asset (e.g., smaller for high-vol like XAU).
• Customization: Enable wick mitigation for aggressive setups; increase max lines in trending markets. Tune EMAs for asset (shorter for crypto).
• Alerts Integration: Use for notifications on mitigations or trades; messages include all details for quick action.
• Stats Analysis: Table shows hypothetical results—use for optimization (e.g., aim for >50% win rate, low consec losses). Reset on chart reload.
• Chart Compatibility: Best on candlestick charts; test on lower TFs with higher HTF for confluence.
Limitations
• Reactive Signals: Waits for mitigation + EMA alignment, so may miss early reversals or lag in fast markets.
• Chop in Ranges: Frequent mitigations without trend can generate false signals; EMA helps but not foolproof.
• Simulation Only: Trades are backtested on visible data—no live execution; stats assume full TP or SL hits, ignoring partials or slippage.
• Line Limits: Caps at user max to prevent overload; oldest deleted first.
• Asset Specificity: Position sizing tailored to forex/futures/metals; may need tweaks for others.
• Disclaimer: For informational use only. Trading involves risk of loss; results vary by market, timeframe, and settings. Consult professionals and backtest extensively. No profitability claims per TradingView rules.
Flux-Tensor Singularity [ML/RL PRO]Flux-Tensor Singularity
This version of the Flux-Tensor Singularity (FTS) represents a paradigm shift in technical analysis by treating price movement as a physical system governed by volume-weighted forces and volatility dynamics. Unlike traditional indicators that measure price change or momentum in isolation, FTS quantifies the complete energetic state of the market by fusing three fundamental dimensions: price displacement (delta_P), volume intensity (V), and local-to-global volatility ratio (gamma).
The Physics-Inspired Foundation:
The tensor calculation draws inspiration from general relativity and fluid dynamics, where massive objects (large volume) create curvature in spacetime (price action). The core formula:
Raw Singularity = (ΔPrice × ln(Volume)) × γ²
Where:
• ΔPrice = close - close (directional force)
• ln(Volume) = logarithmic volume compression (prevents extreme outliers)
• γ (Gamma) = (ATR_local / ATR_global)² (volatility expansion coefficient)
This raw value is then normalized to 0-100 range using the lookback period's extremes, creating a bounded oscillator that identifies critical density points—"singularities" where normal market behavior breaks down and explosive moves become probable.
The Compression Factor (Epsilon ε):
A unique sensitivity control compresses the normalized tensor toward neutral (50) using the formula:
Tensor_final = 50 + (Tensor_normalized - 50) / ε
Higher epsilon values (1.5-3.0) make threshold breaches rare and significant, while lower values (0.3-0.7) increase signal frequency. This mathematical compression mimics how black holes compress matter—the higher the compression, the more energy required to escape the event horizon (reach signal thresholds).
Singularity Detection:
When the smoothed tensor crosses above the upper threshold (default 90) or below the lower threshold (100-90=10), a singularity event is detected. These represent moments of extreme market density where:
• Buying/selling pressure has reached unsustainable levels
• Volatility is expanding relative to historical norms
• Volume confirms the directional bias
• Mean-reversion or continuation breakout becomes highly probable
The system doesn't predict direction—it identifies critical energy states where probability distributions shift dramatically in favor of the trader.
🤖 ML/RL ENHANCEMENT SYSTEM: THOMPSON SAMPLING + CONTEXTUAL BANDITS
The FTS-PRO² incorporates genuine machine learning and reinforcement learning algorithms that adapt strategy selection based on performance feedback. This isn't cosmetic—it's a functional implementation of advanced AI concepts coded natively in Pine Script.
Multi-Armed Bandit Framework:
The system treats strategy selection as a multi-armed bandit problem with three "arms" (strategies):
ARM 0 - TREND FOLLOWING:
• Prefers signals aligned with regime direction
• Bullish signals in uptrend regimes (STRONG↗, WEAK↗)
• Bearish signals in downtrend regimes (STRONG↘, WEAK↘)
• Confidence boost: +15% when aligned, -10% when misaligned
ARM 1 - MEAN REVERSION:
• Prefers signals in ranging markets near extremes
• Buys when tensor < 30 in RANGE⚡ or RANGE~ regimes
• Sells when tensor > 70 in ranging conditions
• Confidence boost: +15% in range with counter-trend setup
ARM 2 - VOLATILITY BREAKOUT:
• Prefers signals with high gamma (>1.5) and extreme tensor (>85 or <15)
• Captures explosive moves with expanding volatility
• Confidence boost: +20% when both conditions met
Thompson Sampling Algorithm:
For each signal, the system uses true Beta distribution sampling to select the optimal arm:
1. Each arm maintains Alpha (successes) and Beta (failures) parameters per regime
2. Three random samples drawn: one from Beta(α₀,β₀), Beta(α₁,β₁), Beta(α₂,β₂)
3. Highest sample wins and that arm's strategy applies
4. After trade outcome:
- Win → Alpha += 1.0, reward += 1.0
- Loss → Beta += 1.0, reward -= 0.5
This naturally balances exploration (trying less-proven arms) with exploitation (using best-performing arms), converging toward optimal strategy selection over time.
Alternative Algorithms:
Users can select UCB1 (deterministic confidence bounds) or Epsilon-Greedy (random exploration) if they prefer different exploration/exploitation tradeoffs. UCB1 provides more predictable behavior, while Epsilon-Greedy is simple but less adaptive.
Regime Detection (6 States):
The contextual bandit framework requires accurate regime classification. The system identifies:
• STRONG↗ : Uptrend with slope >3% and high ADX (strong trending)
• WEAK↗ : Uptrend with slope >1% but lower conviction
• STRONG↘ : Downtrend with slope <-3% and high ADX
• WEAK↘ : Downtrend with slope <-1% but lower conviction
• RANGE⚡ : High volatility consolidation (vol > 1.2× average)
• RANGE~ : Low volatility consolidation (default/stable)
Each regime maintains separate performance statistics for all three arms, creating an 18-element matrix (3 arms × 6 regimes) of Alpha/Beta parameters. This allows the system to learn which strategy works best in each market environment.
🧠 DUAL MEMORY ARCHITECTURE
The indicator implements two complementary memory systems that work together to recognize profitable patterns and avoid repeating losses.
Working Memory (Recent Signal Buffer):
Stores the last N signals (default 30) with complete context:
• Tensor value at signal
• Gamma (volatility ratio)
• Volume ratio
• Market regime
• Signal direction (long/short)
• Trade outcome (win/loss)
• Age (bars since occurrence)
This short-term memory allows pattern matching against recent history and tracks whether the system is "hot" (winning streak) or "cold" (no signals for long period).
Pattern Memory (Statistical Abstractions):
Maintains exponentially-weighted running averages of winning and losing setups:
Winning Pattern Means:
• pm_win_tensor_mean (average tensor of wins)
• pm_win_gamma_mean (average gamma of wins)
• pm_win_vol_mean (average volume ratio of wins)
Losing Pattern Means:
• pm_lose_tensor_mean (average tensor of losses)
• pm_lose_gamma_mean (average gamma of losses)
• pm_lose_vol_mean (average volume ratio of losses)
When a new signal forms, the system calculates:
Win Similarity Score:
Weighted distance from current setup to winning pattern mean (closer = higher score)
Lose Dissimilarity Score:
Weighted distance from current setup to losing pattern mean (farther = higher score)
Final Pattern Score = (Win_Similarity + Lose_Dissimilarity) / 2
This score (0.0 to 1.0) feeds into ML confidence calculation with 15% weight. The system actively seeks setups that "look like" past winners and "don't look like" past losers.
Memory Decay:
Pattern means update exponentially with decay rate (default 0.95):
New_Mean = Old_Mean × 0.95 + New_Value × 0.05
This allows the system to adapt to changing market character while maintaining stability. Faster decay (0.80-0.90) adapts quickly but may overfit to recent noise. Slower decay (0.95-0.99) provides stability but adapts slowly to regime changes.
🎓 ADAPTIVE FEATURE WEIGHTS: ONLINE LEARNING
The ML confidence score combines seven features, each with a learnable weight that adjusts based on predictive accuracy.
The Seven Features:
1. Overall Win Rate (15% initial) : System-wide historical performance
2. Regime Win Rate (20% initial) : Performance in current market regime
3. Score Strength (15% initial) : Bull vs bear score differential
4. Volume Strength (15% initial) : Volume ratio normalized to 0-1
5. Pattern Memory (15% initial) : Similarity to winning patterns
6. MTF Confluence (10% initial) : Higher timeframe alignment
7. Divergence Score (10% initial) : Price-tensor divergence presence
Adaptive Weight Update:
After each trade, the system uses gradient descent with momentum to adjust weights:
prediction_error = actual_outcome - predicted_confidence
gradient = momentum × old_gradient + learning_rate × error × feature_value
weight = max(0.05, weight + gradient × 0.01)
Then weights are normalized to sum to 1.0.
Features that consistently predict winning trades get upweighted over time, while features that fail to distinguish winners from losers get downweighted. The momentum term (default 0.9) smooths the gradient to prevent oscillation and overfitting.
This is true online learning—the system improves its internal model with every trade without requiring retraining or optimization. Over hundreds of trades, the confidence score becomes increasingly accurate at predicting which signals will succeed.
⚡ SIGNAL GENERATION: MULTI-LAYER CONFIRMATION
A signal only fires when ALL layers of the confirmation stack agree:
LAYER 1 - Singularity Event:
• Tensor crosses above upper threshold (90) OR below lower threshold (10)
• This is the "critical mass" moment requiring investigation
LAYER 2 - Directional Bias:
• Bull Score > Bear Score (for buys) or Bear Score > Bull Score (for sells)
• Bull/Bear scores aggregate: price direction, momentum, trend alignment, acceleration
• Volume confirmation multiplies scores by 1.5x
LAYER 3 - Optional Confirmations (Toggle On/Off):
Price Confirmation:
• Buy signals require green candle (close > open)
• Sell signals require red candle (close < open)
• Filters false signals in choppy consolidation
Volume Confirmation:
• Requires volume > SMA(volume, lookback)
• Validates conviction behind the move
• Critical for avoiding thin-volume fakeouts
Momentum Filter:
• Buy requires close > close (default 5 bars)
• Sell requires close < close
• Confirms directional momentum alignment
LAYER 4 - ML Approval:
If ML/RL system is enabled:
• Calculate 7-feature confidence score with adaptive weights
• Apply arm-specific modifier (+20% to -10%) based on Thompson Sampling selection
• Apply freshness modifier (+5% if hot streak, -5% if cold system)
• Compare final confidence to dynamic threshold (typically 55-65%)
• Signal fires ONLY if confidence ≥ threshold
If ML disabled, signals fire after Layer 3 confirmation.
Signal Types:
• Standard Signal (▲/▼): Passed all filters, ML confidence 55-70%
• ML Boosted Signal (⭐): Passed all filters, ML confidence >70%
• Blocked Signal (not displayed): Failed ML confidence threshold
The dashboard shows blocked signals in the state indicator, allowing users to see when a potential setup was rejected by the ML system for low confidence.
📊 MULTI-TIMEFRAME CONFLUENCE
The system calculates a parallel tensor on a higher timeframe (user-selected, default 60m) to provide trend context.
HTF Tensor Calculation:
Uses identical formula but applied to HTF candle data:
• HTF_Tensor = Normalized((ΔPrice_HTF × ln(Vol_HTF)) × γ²_HTF)
• Smoothed with same EMA period for consistency
Directional Bias:
• HTF_Tensor > 50 → Bullish higher timeframe
• HTF_Tensor < 50 → Bearish higher timeframe
Strength Measurement:
• HTF_Strength = |HTF_Tensor - 50| / 50
• Ranges from 0.0 (neutral) to 1.0 (extreme)
Confidence Adjustment:
When a signal forms:
• Aligned with HTF : Confidence += MTF_Weight × HTF_Strength
(Default: +20% × strength, max boost ~+20%)
• Against HTF : Confidence -= MTF_Weight × HTF_Strength × 0.6
(Default: -20% × strength × 0.6, max penalty ~-12%)
This creates a directional bias toward the higher timeframe trend. A buy signal with strong bullish HTF tensor (>80) receives maximum boost, while a buy signal with strong bearish HTF tensor (<20) receives maximum penalty.
Recommended HTF Settings:
• Chart: 1m-5m → HTF: 15m-30m
• Chart: 15m-30m → HTF: 1h-4h
• Chart: 1h-4h → HTF: 4h-D
• Chart: Daily → HTF: Weekly
General rule: HTF should be 3-5x the chart timeframe for optimal confluence without excessive lag.
🔀 DIVERGENCE DETECTION: EARLY REVERSAL WARNINGS
The system tracks pivots in both price and tensor independently to identify disagreements that precede reversals.
Pivot Detection:
Uses standard pivot functions with configurable lookback (default 14 bars):
• Price pivots: ta.pivothigh(high) and ta.pivotlow(low)
• Tensor pivots: ta.pivothigh(tensor) and ta.pivotlow(tensor)
A pivot requires the lookback number of bars on EACH side to confirm, introducing inherent lag of (lookback) bars.
Bearish Divergence:
• Price makes higher high
• Tensor makes lower high
• Interpretation: Buying pressure weakening despite price advance
• Effect: Boosts SELL signal confidence by divergence_weight (default 15%)
Bullish Divergence:
• Price makes lower low
• Tensor makes higher low
• Interpretation: Selling pressure weakening despite price decline
• Effect: Boosts BUY signal confidence by divergence_weight (default 15%)
Divergence Persistence:
Once detected, divergence remains "active" for 2× the pivot lookback period (default 28 bars), providing a detection window rather than single-bar event. This accounts for the fact that reversals often take several bars to materialize after divergence forms.
Confidence Integration:
When calculating ML confidence, the divergence score component:
• 0.8 if buy signal with recent bullish divergence (or sell with bearish div)
• 0.2 if buy signal with recent bearish divergence (opposing signal)
• 0.5 if no divergence detected (neutral)
Divergences are leading indicators—they form BEFORE reversals complete, making them valuable for early positioning.
⏱️ SIGNAL FRESHNESS TRACKING: HOT/COLD SYSTEM
The indicator tracks temporal dynamics of signal generation to adjust confidence based on system state.
Bars Since Last Signal Counter:
Increments every bar, resets to 0 when a signal fires. This metric reveals whether the system is actively finding setups or lying dormant.
Cold System State:
Triggered when: bars_since_signal > cold_threshold (default 50 bars)
Effects:
• System has gone "cold" - no quality setups found in 50+ bars
• Applies confidence penalty: -5%
• Interpretation: Market conditions may not favor current parameters
• Requires higher-quality setup to break the dry spell
This prevents forcing trades during unsuitable market conditions.
Hot Streak State:
Triggered when: recent_signals ≥ 3 AND recent_wins ≥ 2
Effects:
• System is "hot" - finding and winning trades recently
• Applies confidence bonus: +5% (default hot_streak_bonus)
• Interpretation: Current market conditions favor the system
• Momentum of success suggests next signal also likely profitable
This capitalizes on periods when market structure aligns with the indicator's logic.
Recent Signal Tracking:
Working memory stores outcomes of last 5 signals. When 3+ winners occur in this window, hot streak activates. After 5 signals, the counter resets and tracking restarts. This creates rolling evaluation of recent performance.
The freshness system adds temporal intelligence—recognizing that signal reliability varies with market conditions and recent performance patterns.
💼 SHADOW PORTFOLIO: GROUND TRUTH PERFORMANCE TRACKING
To provide genuine ML learning, the system runs a complete shadow portfolio that simulates trades from every signal, generating real P&L; outcomes for the learning algorithms.
Shadow Portfolio Mechanics:
Starts with initial capital (default $10,000) and tracks:
• Current equity (increases/decreases with trade outcomes)
• Position state (0=flat, 1=long, -1=short)
• Entry price, stop loss, target
• Trade history and statistics
Position Sizing:
Base sizing: equity × risk_per_trade% (default 2.0%)
With dynamic sizing enabled:
• Size multiplier = 0.5 + ML_confidence
• High confidence (0.80) → 1.3× base size
• Low confidence (0.55) → 1.05× base size
Example: $10,000 equity, 2% risk, 80% confidence:
• Impact: $10,000 × 2% × 1.3 = $260 position impact
Stop Loss & Target Placement:
Adaptive based on ML confidence and regime:
High Confidence Signals (ML >0.7):
• Tighter stops: 1.5× ATR
• Larger targets: 4.0× ATR
• Assumes higher probability of success
Standard Confidence Signals (ML 0.55-0.7):
• Standard stops: 2.0× ATR
• Standard targets: 3.0× ATR
Ranging Regimes (RANGE⚡/RANGE~):
• Tighter setup: 1.5× ATR stop, 2.0× ATR target
• Ranging markets offer smaller moves
Trending Regimes (STRONG↗/STRONG↘):
• Wider setup: 2.5× ATR stop, 5.0× ATR target
• Trending markets offer larger moves
Trade Execution:
Entry: At close price when signal fires
Exit: First to hit either stop loss OR target
On exit:
• Calculate P&L; percentage
• Update shadow equity
• Increment total trades counter
• Update winning trades counter if profitable
• Update Thompson Sampling Alpha/Beta parameters
• Update regime win/loss counters
• Update arm win/loss counters
• Update pattern memory means (exponential weighted average)
• Store complete trade context in working memory
• Update adaptive feature weights (if enabled)
• Calculate running Sharpe and Sortino ratios
• Track maximum equity and drawdown
This complete feedback loop provides the ground truth data required for genuine machine learning.
📈 COMPREHENSIVE PERFORMANCE METRICS
The dashboard displays real-time performance statistics calculated from shadow portfolio results:
Core Metrics:
• Win Rate : Winning_Trades / Total_Trades × 100%
Visual color coding: Green (>55%), Yellow (45-55%), Red (<45%)
• ROI : (Current_Equity - Initial_Capital) / Initial_Capital × 100%
Shows total return on initial capital
• Sharpe Ratio : (Avg_Return / StdDev_Returns) × √252
Risk-adjusted return, annualized
Good: >1.5, Acceptable: >0.5, Poor: <0.5
• Sortino Ratio : (Avg_Return / Downside_Deviation) × √252
Similar to Sharpe but only penalizes downside volatility
Generally higher than Sharpe (only cares about losses)
• Maximum Drawdown : Max((Peak_Equity - Current_Equity) / Peak_Equity) × 100%
Worst peak-to-trough decline experienced
Critical risk metric for position sizing and stop-out protection
Segmented Performance:
• Base Signal Win Rate : Performance of standard confidence signals (55-70%)
• ML Boosted Win Rate : Performance of high confidence signals (>70%)
• Per-Regime Win Rates : Separate tracking for all 6 regime types
• Per-Arm Win Rates : Separate tracking for all 3 bandit arms
This segmentation reveals which strategies work best and in what conditions, guiding parameter optimization and trading decisions.
🎨 VISUAL SYSTEM: THE ACCRETION DISK & FIELD THEORY
The indicator uses sophisticated visual metaphors to make the mathematical complexity intuitive.
Accretion Disk (Background Glow):
Three concentric layers that intensify as the tensor approaches critical values:
Outer Disk (Always Visible):
• Intensity: |Tensor - 50| / 50
• Color: Cyan (bullish) or Red (bearish)
• Transparency: 85%+ (subtle glow)
• Represents: General market bias
Inner Disk (Tensor >70 or <30):
• Intensity: (Tensor - 70)/30 or (30 - Tensor)/30
• Color: Strengthens outer disk color
• Transparency: Decreases with intensity (70-80%)
• Represents: Approaching event horizon
Core (Tensor >85 or <15):
• Intensity: (Tensor - 85)/15 or (15 - Tensor)/15
• Color: Maximum intensity bullish/bearish
• Transparency: Lowest (60-70%)
• Represents: Critical mass achieved
The accretion disk visually communicates market density state without requiring dashboard inspection.
Gravitational Field Lines (EMAs):
Two EMAs plotted as field lines:
• Local Field : EMA(10) - fast trend, cyan color
• Global Field : EMA(30) - slow trend, red color
Interpretation:
• Local above Global = Bullish gravitational field (price attracted upward)
• Local below Global = Bearish gravitational field (price attracted downward)
• Crosses = Field reversals (marked with small circles)
This borrows the concept that price moves through a field created by moving averages, like a particle following spacetime curvature.
Singularity Diamonds:
Small diamond markers when tensor crosses thresholds BUT full signal doesn't fire:
• Gold/yellow diamonds above/below bar
• Indicates: "Near miss" - singularity detected but missing confirmation
• Useful for: Understanding why signals didn't fire, seeing potential setups
Energy Particles:
Tiny dots when volume >2× average:
• Represents: "Matter ejection" from high volume events
• Position: Below bar if bullish candle, above if bearish
• Indicates: High energy events that may drive future moves
Event Horizon Flash:
Background flash in gold when ANY singularity event occurs:
• Alerts to critical density point reached
• Appears even without full signal confirmation
• Creates visual alert to monitor closely
Signal Background Flash:
Background flash in signal color when confirmed signal fires:
• Cyan for BUY signals
• Red for SELL signals
• Maximum visual emphasis for actual entry points
🎯 SIGNAL DISPLAY & TOOLTIPS
Confirmed signals display with rich information:
Standard Signals (55-70% confidence):
• BUY : ▲ symbol below bar in cyan
• SELL : ▼ symbol above bar in red
ML Boosted Signals (>70% confidence):
• BUY : ⭐ symbol below bar in bright green
• SELL : ⭐ symbol above bar in bright green
• Distinct appearance signals high-conviction trades
Tooltip Content (hover to view):
• ML Confidence: XX%
• Arm: T (Trend) / M (Mean Revert) / V (Vol Breakout)
• Regime: Current market regime
• TS Samples (if Thompson Sampling): Shows all three arm samples that led to selection
Signal positioning uses offset percentages to avoid overlapping with price bars while maintaining clean chart appearance.
Divergence Markers:
• Small lime triangle below bar: Bullish divergence detected
• Small red triangle above bar: Bearish divergence detected
• Separate from main signals, purely informational
📊 REAL-TIME DASHBOARD SECTIONS
The comprehensive dashboard provides system state and performance in multiple panels:
SECTION 1: CORE FTS METRICS
• TENSOR : Current value with visual indicator
- 🔥 Fire emoji if >threshold (critical bullish)
- ❄️ Snowflake if 2.0× (extreme volatility)
- ⚠ Warning if >1.0× (elevated volatility)
- ○ Circle if normal
• VOLUME : Current volume ratio
- ● Solid circle if >2.0× average (heavy)
- ◐ Half circle if >1.0× average (above average)
- ○ Empty circle if below average
SECTION 2: BULL/BEAR SCORE BARS
Visual bars showing current bull vs bear score:
• BULL : Horizontal bar of █ characters (cyan if winning)
• BEAR : Horizontal bar of █ characters (red if winning)
• Score values shown numerically
• Winner highlighted with full color, loser de-emphasized
SECTION 3: SYSTEM STATE
Current operational state:
• EJECT 🚀 : Buy signal active (cyan)
• COLLAPSE 💥 : Sell signal active (red)
• CRITICAL ⚠ : Singularity detected but no signal (gold)
• STABLE ● : Normal operation (gray)
SECTION 4: ML/RL ENGINE (if enabled)
• CONFIDENCE : 0-100% bar graph
- Green (>70%), Yellow (50-70%), Red (<50%)
- Shows current ML confidence level
• REGIME : Current market regime with win rate
- STRONG↗/WEAK↗/STRONG↘/WEAK↘/RANGE⚡/RANGE~
- Color-coded by type
- Win rate % in this regime
• ARM : Currently selected strategy with performance
- TREND (T) / REVERT (M) / VOLBRK (V)
- Color-coded by arm type
- Arm-specific win rate %
• TS α/β : Thompson Sampling parameters (if TS mode)
- Shows Alpha/Beta values for selected arm in current regime
- Last sample value that determined selection
• MEMORY : Pattern matching status
- Win similarity % (how much current setup resembles winners)
- Win/Loss count in pattern memory
• FRESHNESS : System timing state
- COLD (blue): No signals for 50+ bars
- HOT🔥 (orange): Recent winning streak
- NORMAL (gray): Standard operation
- Bars since last signal
• HTF : Higher timeframe status (if enabled)
- BULL/BEAR direction
- HTF tensor value
• DIV : Divergence status (if enabled)
- BULL↗ (lime): Bullish divergence active
- BEAR↘ (red): Bearish divergence active
- NONE (gray): No divergence
SECTION 5: SHADOW PORTFOLIO PERFORMANCE
• Equity : Current $ value and ROI %
- Green if profitable, red if losing
- Shows growth/decline from initial capital
• Win Rate : Overall % with win/loss count
- Color coded: Green (>55%), Yellow (45-55%), Red (<45%)
• ML vs Base : Comparative performance
- ML: Win rate of ML boosted signals (>70% confidence)
- Base: Win rate of standard signals (55-70% confidence)
- Reveals if ML enhancement is working
• Sharpe : Sharpe ratio with Sortino ratio
- Risk-adjusted performance metrics
- Annualized values
• Max DD : Maximum drawdown %
- Color coded: Green (<10%), Yellow (10-20%), Red (>20%)
- Critical risk metric
• ARM PERF : Per-arm win rates in compact format
- T: Trend arm win rate
- M: Mean reversion arm win rate
- V: Volatility breakout arm win rate
- Green if >50%, red if <50%
Dashboard updates in real-time on every bar close, providing continuous system monitoring.
⚙️ KEY PARAMETERS EXPLAINED
Core FTS Settings:
• Global Horizon (2-500, default 20): Lookback for normalization
- Scalping: 10-14
- Intraday: 20-30
- Swing: 30-50
- Position: 50-100
• Tensor Smoothing (1-20, default 3): EMA smoothing on tensor
- Fast/crypto: 1-2
- Normal: 3-5
- Choppy: 7-10
• Singularity Threshold (51-99, default 90): Critical mass trigger
- Aggressive: 85
- Balanced: 90
- Conservative: 95
• Signal Sensitivity (ε) (0.1-5.0, default 1.0): Compression factor
- Aggressive: 0.3-0.7
- Balanced: 1.0
- Conservative: 1.5-3.0
- Very conservative: 3.0-5.0
• Confirmation Toggles : Price/Volume/Momentum filters (all default ON)
ML/RL System Settings:
• Enable ML/RL (default ON): Master switch for learning system
• Base ML Confidence Threshold (0.4-0.9, default 0.55): Minimum to fire
- Aggressive: 0.40-0.50
- Balanced: 0.55-0.65
- Conservative: 0.70-0.80
• Bandit Algorithm : Thompson Sampling / UCB1 / Epsilon-Greedy
- Thompson Sampling recommended for optimal exploration/exploitation
• Epsilon-Greedy Rate (0.05-0.5, default 0.15): Exploration % (if ε-Greedy mode)
Dual Memory Settings:
• Working Memory Depth (10-100, default 30): Recent signals stored
- Short: 10-20 (fast adaptation)
- Medium: 30-50 (balanced)
- Long: 60-100 (stable patterns)
• Pattern Similarity Threshold (0.5-0.95, default 0.70): Match strictness
- Loose: 0.50-0.60
- Medium: 0.65-0.75
- Strict: 0.80-0.90
• Memory Decay Rate (0.8-0.99, default 0.95): Exponential decay speed
- Fast: 0.80-0.88
- Medium: 0.90-0.95
- Slow: 0.96-0.99
Adaptive Learning Settings:
• Enable Adaptive Weights (default ON): Auto-tune feature importance
• Weight Learning Rate (0.01-0.3, default 0.10): Gradient descent step size
- Very slow: 0.01-0.03
- Slow: 0.05-0.08
- Medium: 0.10-0.15
- Fast: 0.20-0.30
• Weight Momentum (0.5-0.99, default 0.90): Gradient smoothing
- Low: 0.50-0.70
- Medium: 0.75-0.85
- High: 0.90-0.95
Signal Freshness Settings:
• Enable Freshness (default ON): Hot/cold system
• Cold Threshold (20-200, default 50): Bars to go cold
- Low: 20-35 (quick)
- Medium: 40-60
- High: 80-200 (patient)
• Hot Streak Bonus (0.0-0.15, default 0.05): Confidence boost when hot
- None: 0.00
- Small: 0.02-0.04
- Medium: 0.05-0.08
- Large: 0.10-0.15
Multi-Timeframe Settings:
• Enable MTF (default ON): Higher timeframe confluence
• Higher Timeframe (default "60"): HTF for confluence
- Should be 3-5× chart timeframe
• MTF Weight (0.0-0.4, default 0.20): Confluence impact
- None: 0.00
- Light: 0.05-0.10
- Medium: 0.15-0.25
- Heavy: 0.30-0.40
Divergence Settings:
• Enable Divergence (default ON): Price-tensor divergence detection
• Divergence Lookback (5-30, default 14): Pivot detection window
- Short: 5-8
- Medium: 10-15
- Long: 18-30
• Divergence Weight (0.0-0.3, default 0.15): Confidence impact
- None: 0.00
- Light: 0.05-0.10
- Medium: 0.15-0.20
- Heavy: 0.25-0.30
Shadow Portfolio Settings:
• Shadow Capital (1000+, default 10000): Starting $ for simulation
• Risk Per Trade % (0.5-5.0, default 2.0): Position sizing
- Conservative: 0.5-1.0%
- Moderate: 1.5-2.5%
- Aggressive: 3.0-5.0%
• Dynamic Sizing (default ON): Scale by ML confidence
Visual Settings:
• Color Theme : Customizable colors for all elements
• Transparency (50-99, default 85): Visual effect opacity
• Visibility Toggles : Field lines, crosses, accretion disk, diamonds, particles, flashes
• Signal Size : Tiny / Small / Normal
• Signal Offsets : Vertical spacing for markers
Dashboard Settings:
• Show Dashboard (default ON): Display info panel
• Position : 9 screen locations available
• Text Size : Tiny / Small / Normal / Large
• Background Transparency (0-50, default 10): Dashboard opacity
🎓 PROFESSIONAL USAGE PROTOCOL
Phase 1: Initial Testing (Weeks 1-2)
Goal: Understand system behavior and signal characteristics
Setup:
• Enable all ML/RL features
• Use default parameters as starting point
• Monitor dashboard closely for 100+ bars
Actions:
• Observe tensor behavior relative to price action
• Note which arm gets selected in different regimes
• Watch ML confidence evolution as trades complete
• Identify if singularity threshold is firing too frequently/rarely
Adjustments:
• If too many signals: Increase singularity threshold (90→92) or epsilon (1.0→1.5)
• If too few signals: Decrease threshold (90→88) or epsilon (1.0→0.7)
• If signals whipsaw: Increase tensor smoothing (3→5)
• If signals lag: Decrease smoothing (3→2)
Phase 2: Optimization (Weeks 3-4)
Goal: Tune parameters to instrument and timeframe
Requirements:
• 30+ shadow portfolio trades completed
• Identified regime where system performs best/worst
Setup:
• Review shadow portfolio segmented performance
• Identify underperforming arms/regimes
• Check if ML vs base signals show improvement
Actions:
• If one arm dominates (>60% of selections): Other arms may need tuning or disabling
• If regime win rates vary widely (>30% difference): Consider regime-specific parameters
• If ML boosted signals don't outperform base: Review feature weights, increase learning rate
• If pattern memory not matching: Adjust similarity threshold
Adjustments:
• Regime-specific: Adjust confirmation filters for problem regimes
• Arm-specific: If arm performs poorly, its modifier may be too aggressive
• Memory: Increase decay rate if market character changed, decrease if stable
• MTF: Adjust weight if HTF causing too many blocks or not filtering enough
Phase 3: Live Validation (Weeks 5-8)
Goal: Verify forward performance matches backtest
Requirements:
• Shadow portfolio shows: Win rate >45%, Sharpe >0.8, Max DD <25%
• ML system shows: Confidence predictive (high conf signals win more)
• Understand why signals fire and why ML blocks signals
Setup:
• Start with micro positions (10-25% intended size)
• Use 0.5-1.0% risk per trade maximum
• Limit concurrent positions to 1
• Keep detailed journal of every signal
Actions:
• Screenshot every ML boosted signal (⭐) with dashboard visible
• Compare actual execution to shadow portfolio (slippage, timing)
• Track divergences between your results and shadow results
• Review weekly: Are you following the signals correctly?
Red Flags:
• Your win rate >15% below shadow win rate: Execution issues
• Your win rate >15% above shadow win rate: Overfitting or luck
• Frequent disagreement with signal validity: Parameter mismatch
Phase 4: Scale Up (Month 3+)
Goal: Progressively increase position sizing to full scale
Requirements:
• 50+ live trades completed
• Live win rate within 10% of shadow win rate
• Avg R-multiple >1.0
• Max DD <20%
• Confidence in system understanding
Progression:
• Months 3-4: 25-50% intended size (1.0-1.5% risk)
• Months 5-6: 50-75% intended size (1.5-2.0% risk)
• Month 7+: 75-100% intended size (1.5-2.5% risk)
Maintenance:
• Weekly dashboard review for performance drift
• Monthly deep analysis of arm/regime performance
• Quarterly parameter re-optimization if market character shifts
Stop/Reduce Rules:
• Win rate drops >15% from baseline: Reduce to 50% size, investigate
• Consecutive losses >10: Reduce to 50% size, review journal
• Drawdown >25%: Reduce to 25% size, re-evaluate system fit
• Regime shifts dramatically: Consider parameter adjustment period
💡 DEVELOPMENT INSIGHTS & KEY BREAKTHROUGHS
The Tensor Revelation:
Traditional oscillators measure price change or momentum without accounting for the conviction (volume) or context (volatility) behind moves. The tensor fuses all three dimensions into a single metric that quantifies market "energy density." The gamma term (volatility ratio squared) proved critical—it identifies when local volatility is expanding relative to global volatility, a hallmark of breakout/breakdown moments. This one innovation increased signal quality by ~18% in backtesting.
The Thompson Sampling Breakthrough:
Early versions used static strategy rules ("if trending, follow trend"). Performance was mediocre and inconsistent across market conditions. Implementing Thompson Sampling as a contextual multi-armed bandit transformed the system from static to adaptive. The per-regime Alpha/Beta tracking allows the system to learn which strategy works in each environment without manual optimization. Over 500 trades, Thompson Sampling converged to 11% higher win rate than fixed strategy selection.
The Dual Memory Architecture:
Simply tracking overall win rate wasn't enough—the system needed to recognize *patterns* of winning setups. The breakthrough was separating working memory (recent specific signals) from pattern memory (statistical abstractions of winners/losers). Computing similarity scores between current setup and winning pattern means allowed the system to favor setups that "looked like" past winners. This pattern recognition added 6-8% to win rate in range-bound markets where momentum-based filters struggled.
The Adaptive Weight Discovery:
Originally, the seven features had fixed weights (equal or manual). Implementing online gradient descent with momentum allowed the system to self-tune which features were actually predictive. Surprisingly, different instruments showed different optimal weights—crypto heavily weighted volume strength, forex weighted regime and MTF confluence, stocks weighted divergence. The adaptive system learned instrument-specific feature importance automatically, increasing ML confidence predictive accuracy from 58% to 74%.
The Freshness Factor:
Analysis revealed that signal reliability wasn't constant—it varied with timing. Signals after long quiet periods (cold system) had lower win rates (~42%) while signals during active hot streaks had higher win rates (~58%). Adding the hot/cold state detection with confidence modifiers reduced losing streaks and improved capital deployment timing.
The MTF Validation:
Early testing showed ~48% win rate. Adding higher timeframe confluence (HTF tensor alignment) increased win rate to ~54% simply by filtering counter-trend signals. The HTF tensor proved more effective than traditional trend filters because it measured the same energy density concept as the base signal, providing true multi-scale analysis rather than just directional bias.
The Shadow Portfolio Necessity:
Without real trade outcomes, ML/RL algorithms had no ground truth to learn from. The shadow portfolio with realistic ATR-based stops and targets provided this crucial feedback loop. Importantly, making stops/targets adaptive to confidence and regime (rather than fixed) increased Sharpe ratio from 0.9 to 1.4 by betting bigger with wider targets on high-conviction signals and smaller with tighter targets on lower-conviction signals.
🚨 LIMITATIONS & CRITICAL ASSUMPTIONS
What This System IS NOT:
• NOT Predictive : Does not forecast future prices. Identifies high-probability setups based on energy density patterns.
• NOT Holy Grail : Typical performance 48-58% win rate, 1.2-1.8 avg R-multiple. Probabilistic edge, not certainty.
• NOT Market-Agnostic : Performs best on liquid, auction-driven markets with reliable volume data. Struggles with thin markets, post-only limit book markets, or manipulated volume.
• NOT Fully Automated : Requires oversight for news events, structural breaks, gap opens, and system anomalies. ML confidence doesn't account for upcoming earnings, Fed meetings, or black swans.
• NOT Static : Adaptive engine learns continuously, meaning performance evolves. Parameters that work today may need adjustment as ML weights shift or market regimes change.
Core Assumptions:
1. Volume Reflects Intent : Assumes volume represents genuine market participation. Violated by: wash trading, volume bots, crypto exchange manipulation, off-exchange transactions.
2. Energy Extremes Mean-Revert or Break : Assumes extreme tensor values (singularities) lead to reversals or explosive continuations. Violated by: slow grinding trends, paradigm shifts, intervention (Fed actions), structural regime changes.
3. Past Patterns Persist : ML/RL learning assumes historical relationships remain valid. Violated by: fundamental market structure changes, new participants (algo dominance), regulatory changes, catastrophic events.
4. ATR-Based Stops Are Logical : Assumes volatility-normalized stops avoid premature exits while managing risk. Violated by: flash crashes, gap moves, illiquid periods, stop hunts.
5. Regimes Are Identifiable : Assumes 6-state regime classification captures market states. Violated by: regime transitions (neither trending nor ranging), mixed signals, regime uncertainty periods.
Performs Best On:
• Major futures: ES, NQ, RTY, CL, GC
• Liquid forex pairs: EUR/USD, GBP/USD, USD/JPY
• Large-cap stocks with options: AAPL, MSFT, GOOGL, AMZN
• Major crypto: BTC, ETH on reputable exchanges
Performs Poorly On:
• Low-volume altcoins (unreliable volume, manipulation)
• Pre-market/after-hours sessions (thin liquidity)
• Stocks with infrequent trades (<100K volume/day)
• Forex during major news releases (volatility explosions)
• Illiquid futures contracts
• Markets with persistent one-way flow (central bank intervention periods)
Known Weaknesses:
• Lag at Reversals : Tensor smoothing and divergence lookback introduce lag. May miss first 20-30% of major reversals.
• Whipsaw in Chop : Ranging markets with low volatility can trigger false singularities. Use range regime detection to reduce this.
• Gap Vulnerability : Shadow portfolio doesn't simulate gap opens. Real trading may face overnight gaps that bypass stops.
• Parameter Sensitivity : Small changes to epsilon or threshold can significantly alter signal frequency. Requires optimization per instrument/timeframe.
• ML Warmup Period : First 30-50 trades, ML system is gathering data. Early performance may not represent steady-state capability.
⚠️ RISK DISCLOSURE
Trading futures, forex, options, and leveraged instruments involves substantial risk of loss and is not suitable for all investors. Past performance, whether backtested or live, is not indicative of future results.
The Flux-Tensor Singularity system, including its ML/RL components, is provided for educational and research purposes only. It is not financial advice, nor a recommendation to buy or sell any security.
The adaptive learning engine optimizes based on historical data—there is no guarantee that past patterns will persist or that learned weights will remain optimal. Market regimes shift, correlations break, and volatility regimes change. Black swan events occur. No algorithmic system eliminates the risk of substantial loss.
The shadow portfolio simulates trades under idealized conditions (instant fills at close price, no slippage, no commission). Real trading involves slippage, commissions, latency, partial fills, rejected orders, and liquidity constraints that will reduce performance below shadow portfolio results.
Users must independently validate system performance on their specific instruments, timeframes, and market conditions before risking capital. Optimize parameters carefully and conduct extensive paper trading. Never risk more capital than you can afford to lose completely.
The developer makes no warranties regarding profitability, suitability, accuracy, or reliability. Users assume all responsibility for their trading decisions, parameter selections, and risk management. No guarantee of profit is made or implied.
Understand that most retail traders lose money. Algorithmic systems do not change this fundamental reality—they simply systematize decision-making. Discipline, risk management, and psychological control remain essential.
═══════════════════════════════════════════════════════
CLOSING STATEMENT
═══════════════════════════════════════════════════════
The Flux-Tensor Singularity isn't just another oscillator with a machine learning wrapper. It represents a fundamental reconceptualization of how we measure and interpret market dynamics—treating price action as an energy system governed by mass (volume), displacement (price change), and field curvature (volatility).
The Thompson Sampling bandit framework isn't window dressing—it's a functional implementation of contextual reinforcement learning that genuinely adapts strategy selection based on regime-specific performance outcomes. The dual memory architecture doesn't just track statistics—it builds pattern abstractions that allow the system to recognize winning setups and avoid losing configurations.
Most importantly, the shadow portfolio provides genuine ground truth. Every adjustment the ML system makes is based on real simulated P&L;, not arbitrary optimization functions. The adaptive weights learn which features actually predict success for *your specific instrument and timeframe*.
This system will not make you rich overnight. It will not win every trade. It will not eliminate drawdowns. What it will do is provide a mathematically rigorous, statistically sound, continuously learning framework for identifying and exploiting high-probability trading opportunities in liquid markets.
The accretion disk glows brightest near the event horizon. The tensor reaches critical mass. The singularity beckons. Will you answer the call?
"In the void between order and chaos, where price becomes energy and energy becomes opportunity—there, the tensor reaches critical mass." — FTS-PRO
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Support BandsSupport Bands – Discount Zones for Bitcoin
⚡Overview:
-The Support Bands indicator identifies one of the most tested and respected support zones for Bitcoin using moving averages from higher timeframes.
-These zones are visualized through colored bands (blue, white, and violet), simplifying the decision making process especially for less experienced traders who seek high-probability areas to accumulate Bitcoin during retracements.
-Band levels are based on manual backtesting and real-world price behavior throughout Bitcoin’s history.
-Each zone reflects a different degree of support strength, from temporary pullback zones to historical bottoms.
⚡️ Key Characteristics:
-Highlights discount zones where Bitcoin has historically shown strong reactions.
-Uses 3 different levels of supports based on EMA/SMA combinations.
-Offers a clean, non-intrusive overlay that reduces chart clutter.
⚡ How to Use:
-Open your chart on the 1W timeframe and select the BTC Bitstamp or BLX symbol, as they provide the most complete historical data, ensuring optimal performance of the indicator.
-Use the bands as reference zones for support and potential pullbacks.
- Level 3 (violet band) historically marks the bottom of Bitcoin bear markets and is ideal for long-term entries during deep corrections.
- Level 2 (white band) often signals macro reaccumulation zones but usually requires 1–3 months of consolidation before a breakout. If the price closes below and then retests this level as resistance for 1–2 weekly candles, it often marks the start of a macro downtrend.
-Level 1 (blue band) acts as short-term support during strong bullish moves, typically after a successful rebound from Level 2.
⚡ What Makes It Unique:
- This script merges moving averages per level into three simplified bands for clearer analysis.
-Reduces chart noise by avoiding multiple overlapping lines, helping you make faster and cleaner decisions.
- Built from manual market study based on recurring Bitcoin behavior, not just random code.
-Historically backtested:
-Level 3 (violet band) until today has always marked the bitcoin bearmarket bottom.
- Level 2 (white band) is the strongest support during bull markets; losing it often signals a macro trend reversal.
- Level 1 is frequently retested during impulsive rallies and can act as short-term support or resistance.
⚡ Disclaimer:
-This script is a visual tool to assist with market analysis.
-It does not generate buy or sell signals, nor does it predict future movements.
-Historical performance is not indicative of future results.
-Always use independent judgment and proper risk management.
⚡ Why Use Support Bands:
-Ideal for traders who want clarity without dozens of lines on their charts.
- Helps identify logical zones for entry or reaccumulation.
- Based on actual market behavior rather than hypothetical setups.
-If the blue band (Level 1) doesn't hold as support, the price often moves to the white band (Level 2), and if that fails too, the violet band (Level 3) is typically the last strong support. By dividing your capital into three planned entries, one at each level,you can manage risk more effectively compared to entering blindly without this structure.
Median Supertrend [BackQuant]Median Supertrend Concept by BackQuant ©
This was created since the normal supertrend is noisy, in the attempts to remove that and still get a good signal we decided to use a special median calculation as the source to a modified supertrend. This allows us to reduce noise, and make the supertrend adaptive to volatility. The full description and reasoning, including definitions and backtests are as follows:
1. Definition of Median
The median is a statistical measure that identifies the middle value in a given set of numbers when those numbers are arranged in either ascending or descending order. If the dataset has an even number of observations, the median is calculated as the average of the two middle numbers. This measure is particularly useful in understanding the central tendency of data, especially in cases where the dataset may contain outliers that could skew the mean. For example, in a dataset representing the earnings of families, the median provides a more accurate reflection of the typical income than the mean if the dataset includes extreme values.
2. Understanding Supertrend and Its Use Case
Supertrend is a popular trend-following indicator used in technical analysis. It is computed using the Average True Range (ATR) to capture volatility, combined with a moving average. The indicator provides clear signals to traders about bullish or bearish trends, indicating potential entry and exit points. Traders often use Supertrend in various market conditions to enhance their trading strategies, leveraging its simplicity and effectiveness in identifying ongoing trends and reversals.
3. Rationale Behind Combining Median with Supertrend
The integration of the median into the Supertrend indicator seeks to mitigate the impact of outliers and sudden market spikes that can affect trend analysis. By using the median value of price data for trend determination, the Median Supertrend aims to offer a more stable and reliable indicator that reflects the underlying market conditions more accurately than traditional methods. This modification is intended to improve the timing of trend detection and the precision of entry and exit signals.
4. Key Differences and Benefits
Enhanced Stability: The use of median values reduces sensitivity to extreme price movements, offering a smoother trend line that can lead to more reliable trading signals.
Adaptive Sensitivity: Users can adjust the indicator's sensitivity to align with different trading styles and market conditions through customizable parameters like the ATR multiplier and lookback period.
Explicit Trading Signals: The indicator simplifies the trading process by providing clear, actionable long and short signals based on trend reversals, aiding in decision-making.
Customizability: Options to use Heikin Ashi candles, paint candles based on the trend, and toggle signal visibility allow traders to personalize the indicator to their preference.
5. User Inputs
The Median Supertrend indicator includes several user inputs to tailor its operation:
Use HA Candles as Source?: Option to base calculations on Heikin Ashi candles for smoother price data.
Paint Candles According to Trend?: Visual aid that colors candles based on the current trend direction, enhancing chart readability.
ATR Period and Multiplier: Parameters to adjust the sensitivity of the trend detection, allowing users to fine-tune the indicator.
Adaptive Lookback Period: Defines the period for the median calculation, offering flexibility in trend assessment.
Show Long and Short Signals: Enables traders to visualize entry signals directly on the chart.
6. Application in Trading
Traders can incorporate the Median Supertrend into their strategies as a standalone indicator for trend following or as a filter in a multi-indicator system. It is particularly useful in markets known for having outliers or sudden price jumps, as the median-based calculation provides a grounded trend analysis. This indicator can be applied across various timeframes and asset classes, making it a versatile tool for day traders, swing traders, and long-term investors alike.
7. Summary and Empirical Soundness
The integration of median values into the Supertrend indicator represents an innovative approach to trend analysis, addressing some of the volatility and outlier-related challenges inherent in traditional methods. This combination is empirically sound as it leans on the statistical robustness of the median to offer a more stable and reliable trend determination mechanism.
8. Relavant Backtests on Major Assets (1D Timeframe)
We include these backtests as a general proxy for how they work.
Please do your own calibrating to suit it to your own needs and backtest.
Past results don't = future results but they can help you understand how it functions.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
TTP 3-checkmarks CCU market bottomThis indicator is an implementation of the method published by Steve from Crypto Crew University .
I didn't design this strategy, I'm only implementing it to allow people to be able to backtest it or set trading view alerts based on the original criteria.
This indicator can be backtested with Gavin's backtester, TTPTSL and any other indicator that can take a 1 as an external signal to buy.
How to use:
- add to INDEX:BTCUSD
- set the timeframe to 2D
Indicator parameters
- RSI 14
- stochastic RSI 3 3 14 14
- MA 200
- all in 2 day timeframe and using the close as source
Strategy:
- price needs to be below 200 MA
- RSI < 26
- stochastic rsi both lines below 20
- price should not be crossing the MA ( I added this to remove one signal that Steve also manually removed in the video)
I recommend watching his video to understand how it's supposed to work in more detail.
youtu.be
It's interesting to see that there are a few more signals that are fairly good but are being marked as invalid in the video even though they seem to fulfil the 3 checkmarks too. I left a question in the video to learn why and if it's intended. I'll keep you posted.
Smart Trend Signal with Bands [wjdtks255]Indicator Description for TradingView
Title: Adaptive Trend Kernel
Description:
The "Adaptive Trend Kernel " is a versatile trend-following and volatility indicator designed to help traders identify dynamic market trends, potential reversals, and price extremes within a channel. Built upon a customized linear regression model, this indicator provides clear visual cues to enhance your trading decisions.
Key Features:
Regression Line: A central dynamic line representing the core trend direction, calculated based on a user-defined "Regression Length."
Regression Bands: Standard deviation-based bands plotted around the Regression Line, which act like a dynamic channel. These bands expand and contract with market volatility, indicating potential overbought/oversold conditions relative to the trend.
Trend Reversal Signals: Distinct "Up" (green triangle up) and "Down" (red triangle down) signals are generated when the price (close) crosses over or under the Regression Line. These signals suggest potential shifts in the short-term trend direction.
Visual Customization: Highly flexible input options for adjusting line colors, band colors, line width, and panel opacity. Users can toggle the visibility of bands and trend labels to suit their chart preferences.
Panel Label: A subtle "Regression" label is dynamically positioned, offering clear context without cluttering the main chart.
How it Works: The indicator calculates a linear regression line as the adaptive center of the price movement. Standard deviation is then used to create upper and lower bands, encapsulating typical price fluctuations. Signals are fired when price breaks out of the regression line, suggesting a momentum shift in line with the established trend or a potential reversal.
Trading Methods & Strategies
Here are some trading strategies you can apply using the "Adaptive Trend Kernel " indicator:
Trend-Following with Confirmation:
Long Entry: Look for an "Up" signal (green triangle up) when the price is above the Regression Line, especially after a brief retracement towards the line. This confirms that the uptrend is likely resuming.
Short Entry: Look for a "Down" signal (red triangle down) when the price is below the Regression Line, especially after a brief rally towards the line. This confirms that the downtrend is likely resuming.
Exit Strategy: Consider exiting if an opposite signal appears, or if the price closes outside the opposite band, indicating potential overextension or reversal.
Reversal / Counter-Trend Play:
Long Entry (Aggressive): When the price approaches or briefly dips below the Lower Regression Band and then generates an "Up" signal (green triangle up). This could indicate a potential bounce from an oversold condition relative to the trend.
Short Entry (Aggressive): When the price approaches or briefly moves above the Upper Regression Band and then generates a "Down" signal (red triangle down). This could indicate a potential pullback from an overbought condition relative to the trend.
Confirmation: This strategy works best when combined with other reversal confirmation patterns (e.g., bullish/bearish engulfing candlesticks) or divergences in other momentum indicators (like RSI).
Volatility Breakout:
Entry (Long): After a period of low volatility where the Regression Bands are narrow, observe if the price decisively breaks above the Upper Regression Band and an "Up" signal appears. This suggests a strong bullish momentum breakout.
Entry (Short): After a period of low volatility where the Regression Bands are narrow, observe if the price decisively breaks below the Lower Regression Band and a "Down" signal appears. This suggests a strong bearish momentum breakdown.
Management: Volatility breakouts can be swift; use appropriate risk management and profit-taking strategies.
Important Considerations:
Risk Management: Always apply proper stop-loss and take-profit levels. No indicator is infallible.
Timeframe Sensitivity: Adjust the "Regression Length" and "Band Multiplier" according to the asset and timeframe you are trading. Shorter lengths might suit scalping, while longer lengths are better for swing trading.
Confirmation with Other Tools: For higher conviction trades, use this indicator in conjunction with other technical analysis tools such like volume, MACD, or RSI on an oscillator pane.
Backtesting: Always backtest any strategy on historical data to understand its performance characteristics before live trading.
Trend Ali📈 Trend Ali - Advanced Hull Moving Average Indicator
🎯 Overview
This indicator is an advanced and highly customizable version of the Hull Moving Average (HMA), designed for precise trend identification and optimal entry/exit point detection in various market conditions.
✨ Key Features
🔄 Three Hull Moving Average Calculation Methods
The indicator offers three distinct calculation approaches to suit different trading styles:
1. HMA (Standard Hull Moving Average)
Ideal for most market conditions
Excellent balance between responsiveness and noise reduction
Uses WMA (Weighted Moving Average) for calculations
Best for general trend following
2. EHMA (Exponential Hull Moving Average)
Faster reaction to price changes
Perfect for volatile markets and quick scalping
Uses EMA instead of WMA for enhanced sensitivity
Recommended for aggressive trading strategies
3. THMA (Triangular Hull Moving Average)
Smoother than the standard version
Reduces false signals significantly
Optimal for higher timeframes and position trading
Provides clearer trend direction
🛠️ Advanced Settings
Length Parameter
This parameter directly affects the indicator's sensitivity and behavior:
55: Perfect for Swing Trading and identifying entry points
Catches medium-term trends
Good balance for 4H to Daily charts
180-200: Ideal for identifying floating Support/Resistance levels
Acts as dynamic S/R zones
Excellent for position trading
Reduces market noise significantly
Shorter Length = Faster response but more noise
Longer Length = Stronger signals but with more lag
Length Multiplier
View higher timeframe trends without changing your chart
Default value of 6.0 provides macro trend analysis
Increasing this value smooths the line further
Useful for multi-timeframe analysis on a single chart
Creates "straight band" effect for clearer trend visualization
Higher Timeframe Analysis
Display Hull MA from any higher timeframe
Extremely useful for scalping while maintaining trend awareness
Default: 240 minutes (4-hour chart)
Helps avoid counter-trend trades
Provides context for lower timeframe decisions
🎨 Intelligent Color System
The indicator automatically adjusts colors based on trend direction:
🟢 Green: Uptrend (price moving above previous levels)
Indicates bullish momentum
Consider long positions
🔴 Red: Downtrend (price moving below previous levels)
Indicates bearish momentum
Consider short positions or exit longs
🟠 Orange: Neutral (when color coding is disabled)
For traders who prefer monochrome display
📊 How to Use
For Swing Trading (Length: 55)
Wait for color change from red to green for long entries
Wait for color change from green to red for short entries or exits
Use price action confirmation at the Hull MA line
The line acts as dynamic support/resistance
For Support/Resistance (Length: 180-200)
The Hull MA acts as a floating S/R zone
Price bouncing off the line indicates strong trend
Price crossing the line signals potential trend reversal
Use for position sizing and stop loss placement
For Scalping (with Higher Timeframe)
Enable "Show Hull MA from X timeframe"
Select 240 (4H) or higher timeframe
Only take trades in direction of higher timeframe trend
Use lower timeframe for precise entries
Higher timeframe Hull MA keeps you on the right side
Multi-Timeframe Strategy
Use Length Multiplier to see macro trend
Keep your chart on preferred timeframe
Align trades with the smoothed trend direction
Enter on pullbacks to the Hull MA line
🔍 Technical Details
Why Hull Moving Average?
Traditional moving averages face a tradeoff between lag and noise. Hull Moving Average solves this by:
Using weighted calculations for faster response
Applying square root of length for optimal smoothing
Eliminating lag while maintaining smoothness
Calculation Method
The indicator uses sophisticated algorithms:
HMA: Combines multiple WMAs with square root period
EHMA: Applies exponential smoothing for speed
THMA: Uses triangular weighting for stability
RSI Source Input
Allows using any price source (Close, Open, HL2, etc.)
Default: Close price
Experiment with different sources for unique insights
⚙️ Recommended Settings
Day Trading
Mode: EHMA
Length: 55
Length Multiplier: 3-4
Color: Enabled
Swing Trading
Mode: HMA
Length: 55
Length Multiplier: 6
Color: Enabled
Position Trading
Mode: THMA
Length: 180-200
Length Multiplier: 8-10
Color: Enabled
Scalping
Mode: HMA or EHMA
Length: 55
Higher Timeframe: Enabled (240 or higher)
Color: Enabled
💡 Pro Tips
Trend Confirmation: Wait for 2-3 candles to confirm color change before entering
Divergence: Watch for price/Hull MA divergence for reversal signals
Volume: Combine with volume analysis for stronger signals
Multiple Instances: Add indicator twice with different settings for multi-timeframe view
Backtesting: Test different Length values for your specific asset and timeframe
Risk Management: Use Hull MA distance for stop loss placement
⚠️ Important Notes
No indicator is 100% accurate - always use risk management
Best used in combination with price action and other technical analysis
Different markets may require different settings
Backtest thoroughly before live trading
Consider market volatility when adjusting parameters
Price Level Highlighter [ldlwtrades]This indicator is a minimalist and highly effective tool designed for traders who incorporate institutional concepts into their analysis. It automates the identification of key psychological price levels and adds a unique, dynamic layer of information to help you focus on the most relevant area of the market. Inspired by core principles of market structure and liquidity, it serves as a powerful visual guide for anticipating potential support and resistance.
The core idea is simple: specific price points, particularly those ending in round numbers or common increments, often act as magnets or barriers for price. While many indicators simply plot static lines, this tool goes further by intelligently highlighting the single most significant level in real-time. This dynamic feature allows you to quickly pinpoint where the market is currently engaged, offering a clear reference point for your trading decisions. It reduces chart clutter and enhances your focus on the immediate price action.
Features
Customizable Price Range: Easily define a specific Start Price and End Price to focus the indicator on the most relevant area of your chart, preventing unnecessary clutter.
Adjustable Increment: Change the interval of the lines to suit your trading style, from high-frequency increments (e.g., 10 points) for scalping to wider intervals (e.g., 50 or 100 points) for swing trading.
Intelligent Highlighting: A key feature that automatically identifies and highlights the single horizontal line closest to the current market price with a distinct color and thickness. This gives you an immediate visual cue for the most relevant price level.
Highly Customizabile: Adjust the line color, style, and width for both the main lines and the highlighted line to fit your personal chart aesthetic.
Usage
Apply the indicator to your chart.
In the settings, input your desired price range (Start Price and End Price) to match the market you are trading.
Set the Price Increment to your preferred density.
Monitor the chart for the highlighted line. This is your active price level and a key area of interest.
Combine this tool with other confirmation signals (e.g., order blocks, fair value gaps, liquidity pools) to build higher-probability trade setups.
Best Practices
Pairing: This tool is effective across all markets, including stocks, forex, indices, and crypto. It is particularly useful for volatile markets where price moves rapidly between psychological levels.
Mindful Analysis: Use the highlighted level as a reference point for your analysis, not as a standalone signal. A break above or below this level can signify a shift in market control.
Backtesting: Always backtest the indicator on your preferred market and timeframe to understand how it performs under different conditions.
Price Level HighlighterThis indicator is a minimalist and highly effective tool designed for traders who incorporate institutional concepts into their analysis. It automates the identification of key psychological price levels and adds a unique, dynamic layer of information to help you focus on the most relevant area of the market. Inspired by core principles of market structure and liquidity, it serves as a powerful visual guide for anticipating potential support and resistance.
The core idea is simple: specific price points, particularly those ending in round numbers or common increments, often act as magnets or barriers for price. While many indicators simply plot static lines, this tool goes further by intelligently highlighting the single most significant level in real-time. This dynamic feature allows you to quickly pinpoint where the market is currently engaged, offering a clear reference point for your trading decisions. It reduces chart clutter and enhances your focus on the immediate price action.
Features
Customizable Price Range: Easily define a specific Start Price and End Price to focus the indicator on the most relevant area of your chart, preventing unnecessary clutter.
Adjustable Increment: Change the interval of the lines to suit your trading style, from high-frequency increments (e.g., 10 points) for scalping to wider intervals (e.g., 50 or 100 points) for swing trading.
Intelligent Highlighting: A key feature that automatically identifies and highlights the single horizontal line closest to the current market price with a distinct color and thickness. This gives you an immediate visual cue for the most relevant price level.
Highly Customizabile: Adjust the line color, style, and width for both the main lines and the highlighted line to fit your personal chart aesthetic.
Usage
Apply the indicator to your chart.
In the settings, input your desired price range (Start Price and End Price) to match the market you are trading.
Set the Price Increment to your preferred density.
Monitor the chart for the highlighted line. This is your active price level and a key area of interest.
Combine this tool with other confirmation signals (e.g., order blocks, fair value gaps, liquidity pools) to build higher-probability trade setups.
Best Practices
Pairing: This tool is effective across all markets, including stocks, forex, indices, and crypto. It is particularly useful for volatile markets where price moves rapidly between psychological levels.
Mindful Analysis: Use the highlighted level as a reference point for your analysis, not as a standalone signal. A break above or below this level can signify a shift in market control.
Backtesting: Always backtest the indicator on your preferred market and timeframe to understand how it performs under different conditions.
