Support Resistance ImportanceThe Support Resistance Importance indicator is designed to highlight key price levels based on the relationship between fractal occurrences and volume distribution within a given price range. By dividing the range into bins, the indicator calculates the total volume traded at each fractal level and normalizes the values for easy visualization. The normalized values represent an "importance score" for each price range, helping traders identify critical support and resistance levels where price action might react.
Key Features:
Fractal Detection:
The indicator detects Williams Fractals, which are specific price patterns representing potential market reversals. It identifies both upward fractals (potential resistance) and downward fractals (potential support).
Price Range Binning:
The price range is divided into a user-defined number of bins (default is 20). Each bin represents a segment of the total price range, allowing the indicator to bucket price action and track fractal volumes in each bin.
Volume-Based Importance Calculation:
For each bin, the indicator sums up the volume traded at the time a fractal occurred. The volumes are then normalized to reflect their relative importance.
The importance score is calculated as the relative volume in each bin, representing the potential influence of that price range. Higher scores indicate stronger support or resistance levels.
Normalization:
The volume data is normalized to allow for better comparison across bins. This normalization ensures that the highest and lowest volumes are scaled between 0 and 1 for visualization purposes. The smallest volume value is used to scale the rest, ensuring meaningful comparisons.
Visualization:
The indicator provides a table-based visualization showing the price range and the corresponding importance score for each bin.
Each bin is color-coded based on the normalized importance score, with blue or greenish shades indicating higher importance levels. The current price range is highlighted to help traders quickly identify relevant areas of interest.
Trading Utility:
Traders can use the importance scores to identify price levels where significant volume has accumulated at fractals. A higher importance score suggests a stronger likelihood of the price reacting to that level.
If a price moves towards a bin with a high score and the bins above it have much smaller values, it suggests that the price may "pump" up to the next high-scored range, similar to how price drops can occur.
Example Use Case:
Suppose the price approaches a bin with an importance score of 25, and the bins above have much smaller values. This suggests that price may break higher towards the next significant level of resistance, offering traders an opportunity to capitalize on the move by entering long positions or adjusting their stop losses.
This indicator is particularly useful for support and resistance trading, where understanding key levels of price action and volume can improve decision-making in anticipating market reactions.
Komut dosyalarını "Fractal" için ara
Grothendieck-Teichmüller Geometric SynthesisDskyz's Grothendieck-Teichmüller Geometric Synthesis (GTGS)
THEORETICAL FOUNDATION: A SYMPHONY OF GEOMETRIES
The 🎓 GTGS is built upon a revolutionary premise: that market dynamics can be modeled as geometric and topological structures. While not a literal academic implementation—such a task would demand computational power far beyond current trading platforms—it leverages core ideas from advanced mathematical theories as powerful analogies and frameworks for its algorithms. Each component translates an abstract concept into a practical market calculation, distinguishing GTGS by identifying deeper structural patterns rather than relying on standard statistical measures.
1. Grothendieck-Teichmüller Theory: Deforming Market Structure
The Theory : Studies symmetries and deformations of geometric objects, focusing on the "absolute" structure of mathematical spaces.
Indicator Analogy : The calculate_grothendieck_field function models price action as a "deformation" from its immediate state. Using the nth root of price ratios (math.pow(price_ratio, 1.0/prime)), it measures market "shape" stretching or compression, revealing underlying tensions and potential shifts.
2. Topos Theory & Sheaf Cohomology: From Local to Global Patterns
The Theory : A framework for assembling local properties into a global picture, with cohomology measuring "obstructions" to consistency.
Indicator Analogy : The calculate_topos_coherence function uses sine waves (math.sin) to represent local price "sections." Summing these yields a "cohomology" value, quantifying price action consistency. High values indicate coherent trends; low values signal conflict and uncertainty.
3. Tropical Geometry: Simplifying Complexity
The Theory : Transforms complex multiplicative problems into simpler, additive, piecewise-linear ones using min(a, b) for addition and a + b for multiplication.
Indicator Analogy : The calculate_tropical_metric function applies tropical_add(a, b) => math.min(a, b) to identify the "lowest energy" state among recent price points, pinpointing critical support levels non-linearly.
4. Motivic Cohomology & Non-Commutative Geometry
The Theory : Studies deep arithmetic and quantum-like properties of geometric spaces.
Indicator Analogy : The motivic_rank and spectral_triple functions compute weighted sums of historical prices to capture market "arithmetic complexity" and "spectral signature." Higher values reflect structured, harmonic price movements.
5. Perfectoid Spaces & Homotopy Type Theory
The Theory : Abstract fields dealing with p-adic numbers and logical foundations of mathematics.
Indicator Analogy : The perfectoid_conv and type_coherence functions analyze price convergence and path identity, assessing the "fractal dust" of price differences and price path cohesion, adding fractal and logical analysis.
The Combination is Key : No single theory dominates. GTGS ’s Unified Field synthesizes all seven perspectives into a comprehensive score, ensuring signals reflect deep structural alignment across mathematical domains.
🎛️ INPUTS: CONFIGURING THE GEOMETRIC ENGINE
The GTGS offers a suite of customizable inputs, allowing traders to tailor its behavior to specific timeframes, market sectors, and trading styles. Below is a detailed breakdown of key input groups, their functionality, and optimization strategies, leveraging provided tooltips for precision.
Grothendieck-Teichmüller Theory Inputs
🧬 Deformation Depth (Absolute Galois) :
What It Is : Controls the depth of Galois group deformations analyzed in market structure.
How It Works : Measures price action deformations under automorphisms of the absolute Galois group, capturing market symmetries.
Optimization :
Higher Values (15-20) : Captures deeper symmetries, ideal for major trends in swing trading (4H-1D).
Lower Values (3-8) : Responsive to local deformations, suited for scalping (1-5min).
Timeframes :
Scalping (1-5min) : 3-6 for quick local shifts.
Day Trading (15min-1H) : 8-12 for balanced analysis.
Swing Trading (4H-1D) : 12-20 for deep structural trends.
Sectors :
Stocks : Use 8-12 for stable trends.
Crypto : 3-8 for volatile, short-term moves.
Forex : 12-15 for smooth, cyclical patterns.
Pro Tip : Increase in trending markets to filter noise; decrease in choppy markets for sensitivity.
🗼 Teichmüller Tower Height :
What It Is : Determines the height of the Teichmüller modular tower for hierarchical pattern detection.
How It Works : Builds modular levels to identify nested market patterns.
Optimization :
Higher Values (6-8) : Detects complex fractals, ideal for swing trading.
Lower Values (2-4) : Focuses on primary patterns, faster for scalping.
Timeframes :
Scalping : 2-3 for speed.
Day Trading : 4-5 for balanced patterns.
Swing Trading : 5-8 for deep fractals.
Sectors :
Indices : 5-8 for robust, long-term patterns.
Crypto : 2-4 for rapid shifts.
Commodities : 4-6 for cyclical trends.
Pro Tip : Higher towers reveal hidden fractals but may slow computation; adjust based on hardware.
🔢 Galois Prime Base :
What It Is : Sets the prime base for Galois field computations.
How It Works : Defines the field extension characteristic for market analysis.
Optimization :
Prime Characteristics :
2 : Binary markets (up/down).
3 : Ternary states (bull/bear/neutral).
5 : Pentagonal symmetry (Elliott waves).
7 : Heptagonal cycles (weekly patterns).
11,13,17,19 : Higher-order patterns.
Timeframes :
Scalping/Day Trading : 2 or 3 for simplicity.
Swing Trading : 5 or 7 for wave or cycle detection.
Sectors :
Forex : 5 for Elliott wave alignment.
Stocks : 7 for weekly cycle consistency.
Crypto : 3 for volatile state shifts.
Pro Tip : Use 7 for most markets; 5 for Elliott wave traders.
Topos Theory & Sheaf Cohomology Inputs
🏛️ Temporal Site Size :
What It Is : Defines the number of time points in the topological site.
How It Works : Sets the local neighborhood for sheaf computations, affecting cohomology smoothness.
Optimization :
Higher Values (30-50) : Smoother cohomology, better for trends in swing trading.
Lower Values (5-15) : Responsive, ideal for reversals in scalping.
Timeframes :
Scalping : 5-10 for quick responses.
Day Trading : 15-25 for balanced analysis.
Swing Trading : 25-50 for smooth trends.
Sectors :
Stocks : 25-35 for stable trends.
Crypto : 5-15 for volatility.
Forex : 20-30 for smooth cycles.
Pro Tip : Match site size to your average holding period in bars for optimal coherence.
📐 Sheaf Cohomology Degree :
What It Is : Sets the maximum degree of cohomology groups computed.
How It Works : Higher degrees capture complex topological obstructions.
Optimization :
Degree Meanings :
1 : Simple obstructions (basic support/resistance).
2 : Cohomological pairs (double tops/bottoms).
3 : Triple intersections (complex patterns).
4-5 : Higher-order structures (rare events).
Timeframes :
Scalping/Day Trading : 1-2 for simplicity.
Swing Trading : 3 for complex patterns.
Sectors :
Indices : 2-3 for robust patterns.
Crypto : 1-2 for rapid shifts.
Commodities : 3-4 for cyclical events.
Pro Tip : Degree 3 is optimal for most trading; higher degrees for research or rare event detection.
🌐 Grothendieck Topology :
What It Is : Chooses the Grothendieck topology for the site.
How It Works : Affects how local data integrates into global patterns.
Optimization :
Topology Characteristics :
Étale : Finest topology, captures local-global principles.
Nisnevich : A1-invariant, good for trends.
Zariski : Coarse but robust, filters noise.
Fpqc : Faithfully flat, highly sensitive.
Sectors :
Stocks : Zariski for stability.
Crypto : Étale for sensitivity.
Forex : Nisnevich for smooth trends.
Indices : Zariski for robustness.
Timeframes :
Scalping : Étale for precision.
Swing Trading : Nisnevich or Zariski for reliability.
Pro Tip : Start with Étale for precision; switch to Zariski in noisy markets.
Unified Field Configuration Inputs
⚛️ Field Coupling Constant :
What It Is : Sets the interaction strength between geometric components.
How It Works : Controls signal amplification in the unified field equation.
Optimization :
Higher Values (0.5-1.0) : Strong coupling, amplified signals for ranging markets.
Lower Values (0.001-0.1) : Subtle signals for trending markets.
Timeframes :
Scalping : 0.5-0.8 for quick, strong signals.
Swing Trading : 0.1-0.3 for trend confirmation.
Sectors :
Crypto : 0.5-1.0 for volatility.
Stocks : 0.1-0.3 for stability.
Forex : 0.3-0.5 for balance.
Pro Tip : Default 0.137 (fine structure constant) is a balanced starting point; adjust up in choppy markets.
📐 Geometric Weighting Scheme :
What It Is : Determines the framework for combining geometric components.
How It Works : Adjusts emphasis on different mathematical structures.
Optimization :
Scheme Characteristics :
Canonical : Equal weighting, balanced.
Derived : Emphasizes higher-order structures.
Motivic : Prioritizes arithmetic properties.
Spectral : Focuses on frequency domain.
Sectors :
Stocks : Canonical for balance.
Crypto : Spectral for volatility.
Forex : Derived for structured moves.
Indices : Motivic for arithmetic cycles.
Timeframes :
Day Trading : Canonical or Derived for flexibility.
Swing Trading : Motivic for long-term cycles.
Pro Tip : Start with Canonical; experiment with Spectral in volatile markets.
Dashboard and Visual Configuration Inputs
📋 Show Enhanced Dashboard, 📏 Size, 📍 Position :
What They Are : Control dashboard visibility, size, and placement.
How They Work : Display key metrics like Unified Field , Resonance , and Signal Quality .
Optimization :
Scalping : Small size, Bottom Right for minimal chart obstruction.
Swing Trading : Large size, Top Right for detailed analysis.
Sectors : Universal across markets; adjust size based on screen setup.
Pro Tip : Use Large for analysis, Small for live trading.
📐 Show Motivic Cohomology Bands, 🌊 Morphism Flow, 🔮 Future Projection, 🔷 Holographic Mesh, ⚛️ Spectral Flow :
What They Are : Toggle visual elements representing mathematical calculations.
How They Work : Provide intuitive representations of market dynamics.
Optimization :
Timeframes :
Scalping : Enable Morphism Flow and Spectral Flow for momentum.
Swing Trading : Enable all for comprehensive analysis.
Sectors :
Crypto : Emphasize Morphism Flow and Future Projection for volatility.
Stocks : Focus on Cohomology Bands for stable trends.
Pro Tip : Disable non-essential visuals in fast markets to reduce clutter.
🌫️ Field Transparency, 🔄 Web Recursion Depth, 🎨 Mesh Color Scheme :
What They Are : Adjust visual clarity, complexity, and color.
How They Work : Enhance interpretability of visual elements.
Optimization :
Transparency : 30-50 for balanced visibility; lower for analysis.
Recursion Depth : 6-8 for balanced detail; lower for older hardware.
Color Scheme :
Purple/Blue : Analytical focus.
Green/Orange : Trading momentum.
Pro Tip : Use Neon Purple for deep analysis; Neon Green for active trading.
⏱️ Minimum Bars Between Signals :
What It Is : Minimum number of bars required between consecutive signals.
How It Works : Prevents signal clustering by enforcing a cooldown period.
Optimization :
Higher Values (10-20) : Fewer signals, avoids whipsaws, suited for swing trading.
Lower Values (0-5) : More responsive, allows quick reversals, ideal for scalping.
Timeframes :
Scalping : 0-2 bars for rapid signals.
Day Trading : 3-5 bars for balance.
Swing Trading : 5-10 bars for stability.
Sectors :
Crypto : 0-3 for volatility.
Stocks : 5-10 for trend clarity.
Forex : 3-7 for cyclical moves.
Pro Tip : Increase in choppy markets to filter noise.
Hardcoded Parameters
Tropical, Motivic, Spectral, Perfectoid, Homotopy Inputs : Fixed to optimize performance but influence calculations (e.g., tropical_degree=4 for support levels, perfectoid_prime=5 for convergence).
Optimization : Experiment with codebase modifications if advanced customization is needed, but defaults are robust across markets.
🎨 ADVANCED VISUAL SYSTEM: TRADING IN A GEOMETRIC UNIVERSE
The GTTMTSF ’s visuals are direct representations of its mathematics, designed for intuitive and precise trading decisions.
Motivic Cohomology Bands :
What They Are : Dynamic bands ( H⁰ , H¹ , H² ) representing cohomological support/resistance.
Color & Meaning : Colors reflect energy levels ( H⁰ tightest, H² widest). Breaks into H¹ signal momentum; H² touches suggest reversals.
How to Trade : Use for stop-loss/profit-taking. Band bounces with Dashboard confirmation are high-probability setups.
Morphism Flow (Webbing) :
What It Is : White particle streams visualizing market momentum.
Interpretation : Dense flows indicate strong trends; sparse flows signal consolidation.
How to Trade : Follow dominant flow direction; new flows post-consolidation signal trend starts.
Future Projection Web (Fractal Grid) :
What It Is : Fibonacci-period fractal projections of support/resistance.
Color & Meaning : Three-layer lines (white shadow, glow, colored quantum) with labels showing price, topological class, anomaly strength (φ), resonance (ρ), and obstruction ( H¹ ). ⚡ marks extreme anomalies.
How to Trade : Target ⚡/● levels for entries/exits. High-anomaly levels with weakening Unified Field are reversal setups.
Holographic Mesh & Spectral Flow :
What They Are : Visuals of harmonic interference and spectral energy.
How to Trade : Bright mesh nodes or strong Spectral Flow warn of building pressure before price movement.
📊 THE GEOMETRIC DASHBOARD: YOUR MISSION CONTROL
The Dashboard translates complex mathematics into actionable intelligence.
Unified Field & Signals :
FIELD : Master value (-10 to +10), synthesizing all geometric components. Extreme readings (>5 or <-5) signal structural limits, often preceding reversals or continuations.
RESONANCE : Measures harmony between geometric field and price-volume momentum. Positive amplifies bullish moves; negative amplifies bearish moves.
SIGNAL QUALITY : Confidence meter rating alignment. Trade only STRONG or EXCEPTIONAL signals for high-probability setups.
Geometric Components :
What They Are : Breakdown of seven mathematical engines.
How to Use : Watch for convergence. A strong Unified Field is reliable when components (e.g., Grothendieck , Topos , Motivic ) align. Divergence warns of trend weakening.
Signal Performance :
What It Is : Tracks indicator signal performance.
How to Use : Assesses real-time performance to build confidence and understand system behavior.
🚀 DEVELOPMENT & UNIQUENESS: BEYOND CONVENTIONAL ANALYSIS
The GTTMTSF was developed to analyze markets as evolving geometric objects, not statistical time-series.
Why This Is Unlike Anything Else :
Theoretical Depth : Uses geometry and topology, identifying patterns invisible to statistical tools.
Holistic Synthesis : Integrates seven deep mathematical frameworks into a cohesive Unified Field .
Creative Implementation : Translates PhD-level mathematics into functional Pine Script , blending theory and practice.
Immersive Visualization : Transforms charts into dynamic geometric landscapes for intuitive market understanding.
The GTTMTSF is more than an indicator; it’s a new lens for viewing markets, for traders seeking deeper insight into hidden order within chaos.
" Where there is matter, there is geometry. " - Johannes Kepler
— Dskyz , Trade with insight. Trade with anticipation.
rsi wf breakoutRSI Breakout Asif
RSI Breakout Asif Indicator
Overview:
The RSI Breakout Asif indicator is a custom script designed to analyze and highlight potential
breakout points using the Relative Strength Index (RSI) combined with Williams Fractals. This
indicator is specifically developed for traders who want to identify key momentum shifts in the
market.
Features:
1. RSI Analysis:
- The RSI is calculated using a user-defined length and price source.
- Horizontal lines are plotted at levels 70 (overbought), 50 (neutral), and 30 (oversold) to visually
aid decision-making.
2. Williams Fractals on RSI:
- Detects fractal highs and lows based on RSI values.
- Highlights these fractal points with dynamic, symmetrical lines for better visibility.
3. Customization:
- Users can adjust the RSI length and price source for personalized analysis.
- Fractal settings (left and right bar length) are also adjustable, making the indicator versatile for
different trading styles.
4. Visual Enhancements:
- Fractal highs are marked in red, while fractal lows are marked in green.
Asif - Page 1
RSI Breakout Asif
- Precise line placement ensures clarity and reduces chart clutter.
5. Practical Utility:
- Use the fractal breakout signals in conjunction with other technical indicators for enhanced
decision-making.
Usage:
- Add the RSI Breakout Asif indicator to your TradingView chart.
- Adjust the settings according to your trading strategy.
- Observe the RSI values and fractal points to identify potential breakout zones.
Disclaimer:
This indicator is a technical analysis tool and should be used in combination with other analysis
methods. It does not guarantee profitable trades.
Watermarked by Asif.
Asif - Page 2
taLibrary "ta"
█ OVERVIEW
This library holds technical analysis functions calculating values for which no Pine built-in exists.
Look first. Then leap.
█ FUNCTIONS
cagr(entryTime, entryPrice, exitTime, exitPrice)
It calculates the "Compound Annual Growth Rate" between two points in time. The CAGR is a notional, annualized growth rate that assumes all profits are reinvested. It only takes into account the prices of the two end points — not drawdowns, so it does not calculate risk. It can be used as a yardstick to compare the performance of two instruments. Because it annualizes values, the function requires a minimum of one day between the two end points (annualizing returns over smaller periods of times doesn't produce very meaningful figures).
Parameters:
entryTime : The starting timestamp.
entryPrice : The starting point's price.
exitTime : The ending timestamp.
exitPrice : The ending point's price.
Returns: CAGR in % (50 is 50%). Returns `na` if there is not >=1D between `entryTime` and `exitTime`, or until the two time points have not been reached by the script.
█ v2, Mar. 8, 2022
Added functions `allTimeHigh()` and `allTimeLow()` to find the highest or lowest value of a source from the first historical bar to the current bar. These functions will not look ahead; they will only return new highs/lows on the bar where they occur.
allTimeHigh(src)
Tracks the highest value of `src` from the first historical bar to the current bar.
Parameters:
src : (series int/float) Series to track. Optional. The default is `high`.
Returns: (float) The highest value tracked.
allTimeLow(src)
Tracks the lowest value of `src` from the first historical bar to the current bar.
Parameters:
src : (series int/float) Series to track. Optional. The default is `low`.
Returns: (float) The lowest value tracked.
█ v3, Sept. 27, 2022
This version includes the following new functions:
aroon(length)
Calculates the values of the Aroon indicator.
Parameters:
length (simple int) : (simple int) Number of bars (length).
Returns: ( [float, float ]) A tuple of the Aroon-Up and Aroon-Down values.
coppock(source, longLength, shortLength, smoothLength)
Calculates the value of the Coppock Curve indicator.
Parameters:
source (float) : (series int/float) Series of values to process.
longLength (simple int) : (simple int) Number of bars for the fast ROC value (length).
shortLength (simple int) : (simple int) Number of bars for the slow ROC value (length).
smoothLength (simple int) : (simple int) Number of bars for the weigted moving average value (length).
Returns: (float) The oscillator value.
dema(source, length)
Calculates the value of the Double Exponential Moving Average (DEMA).
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Length for the smoothing parameter calculation.
Returns: (float) The double exponentially weighted moving average of the `source`.
dema2(src, length)
An alternate Double Exponential Moving Average (Dema) function to `dema()`, which allows a "series float" length argument.
Parameters:
src : (series int/float) Series of values to process.
length : (series int/float) Length for the smoothing parameter calculation.
Returns: (float) The double exponentially weighted moving average of the `src`.
dm(length)
Calculates the value of the "Demarker" indicator.
Parameters:
length (simple int) : (simple int) Number of bars (length).
Returns: (float) The oscillator value.
donchian(length)
Calculates the values of a Donchian Channel using `high` and `low` over a given `length`.
Parameters:
length (int) : (series int) Number of bars (length).
Returns: ( [float, float, float ]) A tuple containing the channel high, low, and median, respectively.
ema2(src, length)
An alternate ema function to the `ta.ema()` built-in, which allows a "series float" length argument.
Parameters:
src : (series int/float) Series of values to process.
length : (series int/float) Number of bars (length).
Returns: (float) The exponentially weighted moving average of the `src`.
eom(length, div)
Calculates the value of the Ease of Movement indicator.
Parameters:
length (simple int) : (simple int) Number of bars (length).
div (simple int) : (simple int) Divisor used for normalzing values. Optional. The default is 10000.
Returns: (float) The oscillator value.
frama(source, length)
The Fractal Adaptive Moving Average (FRAMA), developed by John Ehlers, is an adaptive moving average that dynamically adjusts its lookback period based on fractal geometry.
Parameters:
source (float) : (series int/float) Series of values to process.
length (int) : (series int) Number of bars (length).
Returns: (float) The fractal adaptive moving average of the `source`.
ft(source, length)
Calculates the value of the Fisher Transform indicator.
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Number of bars (length).
Returns: (float) The oscillator value.
ht(source)
Calculates the value of the Hilbert Transform indicator.
Parameters:
source (float) : (series int/float) Series of values to process.
Returns: (float) The oscillator value.
ichimoku(conLength, baseLength, senkouLength)
Calculates values of the Ichimoku Cloud indicator, including tenkan, kijun, senkouSpan1, senkouSpan2, and chikou. NOTE: offsets forward or backward can be done using the `offset` argument in `plot()`.
Parameters:
conLength (int) : (series int) Length for the Conversion Line (Tenkan). The default is 9 periods, which returns the mid-point of the 9 period Donchian Channel.
baseLength (int) : (series int) Length for the Base Line (Kijun-sen). The default is 26 periods, which returns the mid-point of the 26 period Donchian Channel.
senkouLength (int) : (series int) Length for the Senkou Span 2 (Leading Span B). The default is 52 periods, which returns the mid-point of the 52 period Donchian Channel.
Returns: ( [float, float, float, float, float ]) A tuple of the Tenkan, Kijun, Senkou Span 1, Senkou Span 2, and Chikou Span values. NOTE: by default, the senkouSpan1 and senkouSpan2 should be plotted 26 periods in the future, and the Chikou Span plotted 26 days in the past.
ift(source)
Calculates the value of the Inverse Fisher Transform indicator.
Parameters:
source (float) : (series int/float) Series of values to process.
Returns: (float) The oscillator value.
kvo(fastLen, slowLen, trigLen)
Calculates the values of the Klinger Volume Oscillator.
Parameters:
fastLen (simple int) : (simple int) Length for the fast moving average smoothing parameter calculation.
slowLen (simple int) : (simple int) Length for the slow moving average smoothing parameter calculation.
trigLen (simple int) : (simple int) Length for the trigger moving average smoothing parameter calculation.
Returns: ( [float, float ]) A tuple of the KVO value, and the trigger value.
pzo(length)
Calculates the value of the Price Zone Oscillator.
Parameters:
length (simple int) : (simple int) Length for the smoothing parameter calculation.
Returns: (float) The oscillator value.
rms(source, length)
Calculates the Root Mean Square of the `source` over the `length`.
Parameters:
source (float) : (series int/float) Series of values to process.
length (int) : (series int) Number of bars (length).
Returns: (float) The RMS value.
rwi(length)
Calculates the values of the Random Walk Index.
Parameters:
length (simple int) : (simple int) Lookback and ATR smoothing parameter length.
Returns: ( [float, float ]) A tuple of the `rwiHigh` and `rwiLow` values.
stc(source, fast, slow, cycle, d1, d2)
Calculates the value of the Schaff Trend Cycle indicator.
Parameters:
source (float) : (series int/float) Series of values to process.
fast (simple int) : (simple int) Length for the MACD fast smoothing parameter calculation.
slow (simple int) : (simple int) Length for the MACD slow smoothing parameter calculation.
cycle (simple int) : (simple int) Number of bars for the Stochastic values (length).
d1 (simple int) : (simple int) Length for the initial %D smoothing parameter calculation.
d2 (simple int) : (simple int) Length for the final %D smoothing parameter calculation.
Returns: (float) The oscillator value.
stochFull(periodK, smoothK, periodD)
Calculates the %K and %D values of the Full Stochastic indicator.
Parameters:
periodK (simple int) : (simple int) Number of bars for Stochastic calculation. (length).
smoothK (simple int) : (simple int) Number of bars for smoothing of the %K value (length).
periodD (simple int) : (simple int) Number of bars for smoothing of the %D value (length).
Returns: ( [float, float ]) A tuple of the slow %K and the %D moving average values.
stochRsi(lengthRsi, periodK, smoothK, periodD, source)
Calculates the %K and %D values of the Stochastic RSI indicator.
Parameters:
lengthRsi (simple int) : (simple int) Length for the RSI smoothing parameter calculation.
periodK (simple int) : (simple int) Number of bars for Stochastic calculation. (length).
smoothK (simple int) : (simple int) Number of bars for smoothing of the %K value (length).
periodD (simple int) : (simple int) Number of bars for smoothing of the %D value (length).
source (float) : (series int/float) Series of values to process. Optional. The default is `close`.
Returns: ( [float, float ]) A tuple of the slow %K and the %D moving average values.
supertrend(factor, atrLength, wicks)
Calculates the values of the SuperTrend indicator with the ability to take candle wicks into account, rather than only the closing price.
Parameters:
factor (float) : (series int/float) Multiplier for the ATR value.
atrLength (simple int) : (simple int) Length for the ATR smoothing parameter calculation.
wicks (simple bool) : (simple bool) Condition to determine whether to take candle wicks into account when reversing trend, or to use the close price. Optional. Default is false.
Returns: ( [float, int ]) A tuple of the superTrend value and trend direction.
szo(source, length)
Calculates the value of the Sentiment Zone Oscillator.
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Length for the smoothing parameter calculation.
Returns: (float) The oscillator value.
t3(source, length, vf)
Calculates the value of the Tilson Moving Average (T3).
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Length for the smoothing parameter calculation.
vf (simple float) : (simple float) Volume factor. Affects the responsiveness.
Returns: (float) The Tilson moving average of the `source`.
t3Alt(source, length, vf)
An alternate Tilson Moving Average (T3) function to `t3()`, which allows a "series float" `length` argument.
Parameters:
source (float) : (series int/float) Series of values to process.
length (float) : (series int/float) Length for the smoothing parameter calculation.
vf (simple float) : (simple float) Volume factor. Affects the responsiveness.
Returns: (float) The Tilson moving average of the `source`.
tema(source, length)
Calculates the value of the Triple Exponential Moving Average (TEMA).
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Length for the smoothing parameter calculation.
Returns: (float) The triple exponentially weighted moving average of the `source`.
tema2(source, length)
An alternate Triple Exponential Moving Average (TEMA) function to `tema()`, which allows a "series float" `length` argument.
Parameters:
source (float) : (series int/float) Series of values to process.
length (float) : (series int/float) Length for the smoothing parameter calculation.
Returns: (float) The triple exponentially weighted moving average of the `source`.
trima(source, length)
Calculates the value of the Triangular Moving Average (TRIMA).
Parameters:
source (float) : (series int/float) Series of values to process.
length (int) : (series int) Number of bars (length).
Returns: (float) The triangular moving average of the `source`.
trima2(src, length)
An alternate Triangular Moving Average (TRIMA) function to `trima()`, which allows a "series int" length argument.
Parameters:
src : (series int/float) Series of values to process.
length : (series int) Number of bars (length).
Returns: (float) The triangular moving average of the `src`.
trix(source, length, signalLength, exponential)
Calculates the values of the TRIX indicator.
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Length for the smoothing parameter calculation.
signalLength (simple int) : (simple int) Length for smoothing the signal line.
exponential (simple bool) : (simple bool) Condition to determine whether exponential or simple smoothing is used. Optional. The default is `true` (exponential smoothing).
Returns: ( [float, float, float ]) A tuple of the TRIX value, the signal value, and the histogram.
uo(fastLen, midLen, slowLen)
Calculates the value of the Ultimate Oscillator.
Parameters:
fastLen (simple int) : (series int) Number of bars for the fast smoothing average (length).
midLen (simple int) : (series int) Number of bars for the middle smoothing average (length).
slowLen (simple int) : (series int) Number of bars for the slow smoothing average (length).
Returns: (float) The oscillator value.
vhf(source, length)
Calculates the value of the Vertical Horizontal Filter.
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Number of bars (length).
Returns: (float) The oscillator value.
vi(length)
Calculates the values of the Vortex Indicator.
Parameters:
length (simple int) : (simple int) Number of bars (length).
Returns: ( [float, float ]) A tuple of the viPlus and viMinus values.
vzo(length)
Calculates the value of the Volume Zone Oscillator.
Parameters:
length (simple int) : (simple int) Length for the smoothing parameter calculation.
Returns: (float) The oscillator value.
williamsFractal(period)
Detects Williams Fractals.
Parameters:
period (int) : (series int) Number of bars (length).
Returns: ( [bool, bool ]) A tuple of an up fractal and down fractal. Variables are true when detected.
wpo(length)
Calculates the value of the Wave Period Oscillator.
Parameters:
length (simple int) : (simple int) Length for the smoothing parameter calculation.
Returns: (float) The oscillator value.
█ v7, Nov. 2, 2023
This version includes the following new and updated functions:
atr2(length)
An alternate ATR function to the `ta.atr()` built-in, which allows a "series float" `length` argument.
Parameters:
length (float) : (series int/float) Length for the smoothing parameter calculation.
Returns: (float) The ATR value.
changePercent(newValue, oldValue)
Calculates the percentage difference between two distinct values.
Parameters:
newValue (float) : (series int/float) The current value.
oldValue (float) : (series int/float) The previous value.
Returns: (float) The percentage change from the `oldValue` to the `newValue`.
donchian(length)
Calculates the values of a Donchian Channel using `high` and `low` over a given `length`.
Parameters:
length (int) : (series int) Number of bars (length).
Returns: ( [float, float, float ]) A tuple containing the channel high, low, and median, respectively.
highestSince(cond, source)
Tracks the highest value of a series since the last occurrence of a condition.
Parameters:
cond (bool) : (series bool) A condition which, when `true`, resets the tracking of the highest `source`.
source (float) : (series int/float) Series of values to process. Optional. The default is `high`.
Returns: (float) The highest `source` value since the last time the `cond` was `true`.
lowestSince(cond, source)
Tracks the lowest value of a series since the last occurrence of a condition.
Parameters:
cond (bool) : (series bool) A condition which, when `true`, resets the tracking of the lowest `source`.
source (float) : (series int/float) Series of values to process. Optional. The default is `low`.
Returns: (float) The lowest `source` value since the last time the `cond` was `true`.
relativeVolume(length, anchorTimeframe, isCumulative, adjustRealtime)
Calculates the volume since the last change in the time value from the `anchorTimeframe`, the historical average volume using bars from past periods that have the same relative time offset as the current bar from the start of its period, and the ratio of these volumes. The volume values are cumulative by default, but can be adjusted to non-accumulated with the `isCumulative` parameter.
Parameters:
length (simple int) : (simple int) The number of periods to use for the historical average calculation.
anchorTimeframe (simple string) : (simple string) The anchor timeframe used in the calculation. Optional. Default is "D".
isCumulative (simple bool) : (simple bool) If `true`, the volume values will be accumulated since the start of the last `anchorTimeframe`. If `false`, values will be used without accumulation. Optional. The default is `true`.
adjustRealtime (simple bool) : (simple bool) If `true`, estimates the cumulative value on unclosed bars based on the data since the last `anchor` condition. Optional. The default is `false`.
Returns: ( [float, float, float ]) A tuple of three float values. The first element is the current volume. The second is the average of volumes at equivalent time offsets from past anchors over the specified number of periods. The third is the ratio of the current volume to the historical average volume.
rma2(source, length)
An alternate RMA function to the `ta.rma()` built-in, which allows a "series float" `length` argument.
Parameters:
source (float) : (series int/float) Series of values to process.
length (float) : (series int/float) Length for the smoothing parameter calculation.
Returns: (float) The rolling moving average of the `source`.
supertrend2(factor, atrLength, wicks)
An alternate SuperTrend function to `supertrend()`, which allows a "series float" `atrLength` argument.
Parameters:
factor (float) : (series int/float) Multiplier for the ATR value.
atrLength (float) : (series int/float) Length for the ATR smoothing parameter calculation.
wicks (simple bool) : (simple bool) Condition to determine whether to take candle wicks into account when reversing trend, or to use the close price. Optional. Default is `false`.
Returns: ( [float, int ]) A tuple of the superTrend value and trend direction.
vStop(source, atrLength, atrFactor)
Calculates an ATR-based stop value that trails behind the `source`. Can serve as a possible stop-loss guide and trend identifier.
Parameters:
source (float) : (series int/float) Series of values that the stop trails behind.
atrLength (simple int) : (simple int) Length for the ATR smoothing parameter calculation.
atrFactor (float) : (series int/float) The multiplier of the ATR value. Affects the maximum distance between the stop and the `source` value. A value of 1 means the maximum distance is 100% of the ATR value. Optional. The default is 1.
Returns: ( [float, bool ]) A tuple of the volatility stop value and the trend direction as a "bool".
vStop2(source, atrLength, atrFactor)
An alternate Volatility Stop function to `vStop()`, which allows a "series float" `atrLength` argument.
Parameters:
source (float) : (series int/float) Series of values that the stop trails behind.
atrLength (float) : (series int/float) Length for the ATR smoothing parameter calculation.
atrFactor (float) : (series int/float) The multiplier of the ATR value. Affects the maximum distance between the stop and the `source` value. A value of 1 means the maximum distance is 100% of the ATR value. Optional. The default is 1.
Returns: ( [float, bool ]) A tuple of the volatility stop value and the trend direction as a "bool".
Removed Functions:
allTimeHigh(src)
Tracks the highest value of `src` from the first historical bar to the current bar.
allTimeLow(src)
Tracks the lowest value of `src` from the first historical bar to the current bar.
trima2(src, length)
An alternate Triangular Moving Average (TRIMA) function to `trima()`, which allows a
"series int" length argument.
Advanced Physics Financial Indicator Each component represents a scientific theory and is applied to the price data in a way that reflects key principles from that theory.
Detailed Explanation
1. Fractal Geometry - High/Low Signal
Concept: Fractal geometry studies self-similar patterns that repeat at different scales. In markets, fractals can be used to detect recurring patterns or turning points.
Implementation: The script detects pivot highs and lows using ta.pivothigh and ta.pivotlow, representing local turning points in price. The fractalSignal is set to 1 for a pivot high, -1 for a pivot low, and 0 if there is no signal. This logic reflects the cyclical, self-similar nature of price movements.
Practical Use: This signal is useful for identifying local tops and bottoms, allowing traders to spot potential reversals or consolidation points where fractal patterns emerge.
2. Quantum Mechanics - Probabilistic Monte Carlo Simulation
Concept: Quantum mechanics introduces uncertainty and probability into systems, much like how future price movements are inherently uncertain. Monte Carlo simulations are used to model a range of possible outcomes based on random inputs.
Implementation: In this script, we simulate 100 random outcomes by generating a random number between -1 and 1 for each iteration. These random values are stored in an array, and the average of these values is calculated to represent the Quantum Signal.
Practical Use: This probabilistic signal provides a sense of randomness and uncertainty in the market, reflecting the possibility of price movement in either direction. It simulates the market’s chaotic nature by considering multiple possible outcomes and their average.
3. Thermodynamics - Efficiency Ratio Signal
Concept: Thermodynamics deals with energy efficiency and entropy in systems. The efficiency ratio in financial terms can be used to measure how efficiently the price is moving relative to volatility.
Implementation: The Efficiency Ratio is calculated as the absolute price change over n periods divided by the sum of absolute changes for each period within n. This ratio shows how much of the price movement is directional versus random, mimicking the concept of efficiency in thermodynamic systems.
Practical Use: A high efficiency ratio suggests that the market is trending smoothly (high efficiency), while a low ratio indicates choppy, non-directional movement (low efficiency, or high entropy).
4. Chaos Theory - ATR Signal
Concept: Chaos theory studies how complex systems are highly sensitive to initial conditions, leading to unpredictable behavior. In markets, chaotic price movements can often be captured through volatility indicators.
Implementation: The script uses a very long ATR period (1000) to reflect slow-moving chaos over time. The Chaos Signal is computed by measuring the deviation of the current price from its long-term average (SMA), normalized by ATR. This captures price deviations over time, hinting at chaotic market behavior.
Practical Use: The signal measures how far the price deviates from its long-term average, which can signal the degree of chaos or extreme behavior in the market. High deviations indicate chaotic or volatile conditions, while low deviations suggest stability.
5. Network Theory - Correlation with BTC
Concept: Network theory studies how different components within a system are interconnected. In markets, assets are often correlated, meaning that price movements in one asset can influence or be influenced by another.
Implementation: This indicator calculates the correlation between the asset’s price and the price of Bitcoin (BTC) over 30 periods. The Network Signal shows how connected the asset is to BTC, reflecting broader market dynamics.
Practical Use: In a highly correlated market, BTC can act as a leading indicator for other assets. A strong correlation with BTC might suggest that the asset is likely to move in line with Bitcoin, while a weak or negative correlation might indicate that the asset is moving independently.
6. String Theory - RSI & MACD Interaction
Concept: String theory attempts to unify the fundamental forces of nature into a single framework. In trading, we can view the RSI and MACD as interacting forces that provide insights into momentum and trend.
Implementation: The script calculates the RSI and MACD and combines them into a single signal. The formula for String Signal is (RSI - 50) / 100 + (MACD Line - Signal Line) / 100, normalizing both indicators to a scale where their contributions are additive. The RSI represents momentum, and MACD shows trend direction and strength.
Practical Use: This signal helps in detecting moments where momentum (RSI) and trend strength (MACD) align, giving a clearer picture of the asset's direction and overbought/oversold conditions. It unifies these two indicators to create a more holistic view of market behavior.
7. Fluid Dynamics - On-Balance Volume (OBV) Signal
Concept: Fluid dynamics studies how fluids move and flow. In markets, volume can be seen as a "flow" that drives price movement, much like how fluid dynamics describe the flow of liquids.
Implementation: The script uses the OBV (On-Balance Volume) indicator to track the cumulative flow of volume based on price changes. The signal is further normalized by its moving average to smooth out fluctuations and make it more reflective of price pressure over time.
Practical Use: The Fluid Signal shows how the flow of volume is driving price action. If the OBV rises significantly, it suggests that there is strong buying pressure, while a falling OBV indicates selling pressure. It’s analogous to how pressure builds in a fluid system.
8. Final Signal - Combining All Physics-Based Indicators
Implementation: Each of the seven physics-inspired signals is combined into a single Final Signal by averaging their values. This approach blends different market insights from various scientific domains, creating a comprehensive view of the market’s condition.
Practical Use: The final signal gives you a holistic, multi-dimensional view of the market by merging different perspectives (fractal behavior, quantum probability, efficiency, chaos, correlation, momentum/trend, and volume flow). This approach helps traders understand the market's dynamics from multiple angles, offering deeper insights than any single indicator.
9. Color Coding Based on Signal Extremes
Concept: The color of the final signal plot dynamically reflects whether the market is in an extreme state.
Implementation: The signal color is determined using percentiles. If the Final Signal is in the top 55th percentile of its range, the signal is green (bullish). If it is between the 45th and 55th percentiles, it is orange (neutral). If it falls below the 45th percentile, it is red (bearish).
Practical Use: This visual representation helps traders quickly identify the strength of the signal. Bullish conditions (green), neutral conditions (orange), and bearish conditions (red) are clearly distinguished, simplifying decision-making.
HMA Buy Sell Signals - Profit ManagerNote : Settings should be adjusted according to the selected time frame. Try to find the best setting according to the profitability rate
Overall Functionality
This script combines several trading tools to create a comprehensive system for trend analysis, trade execution, and performance tracking. Users can identify market trends using specific moving averages and RSI indicators while managing profit and loss levels automatically.
Trend Detection and Trade Signals
Hull Moving Averages (HMA):
Two HMAs (a faster one and a slower one) are used to determine the market trend.
A buy signal is generated when the faster HMA crosses above the slower HMA.
Conversely, a sell signal is triggered when the faster HMA crosses below the slower one.
Visual Feedback:
Trend lines on the chart change color to reflect the trend direction (e.g., green for upward trends and red for downward trends).
Trade Levels and Management
Entry, Take-Profit, and Stop-Loss Levels:
When the trend shifts upwards, the script calculates entry, take-profit, and stop-loss levels based on the opening price.
Similarly, for downward trends, these levels are determined for short trades.
Commission Tracking:
Each trade includes a commission cost, which is factored into net profit and loss calculations.
Dynamic Labels:
Entry, take-profit, and stop-loss levels are visually marked on the chart for easier tracking.
Performance Tracking
Profit and Loss Tracking:
The script keeps a running total of profits, losses, and commissions for both long and short trades.
It also calculates the net profit after all costs are considered.
Performance Table:
A table is displayed on the chart summarizing:
The number of trades.
Total profit and loss for long and short positions.
Commission costs.
Net profit.
Fractal Support and Resistance
Dynamic Lines:
The script identifies the most recent significant highs and lows using fractals.
It draws support and resistance lines that automatically update as new fractals form.
Simplified Visuals:
The chart always shows the last two support and resistance lines, keeping the visualization clean and focused.
RSI-Based Signals
Overbought and Oversold Levels:
RSI is used to identify overbought (above 80) and oversold (below 20) conditions.
The script generates buy signals at oversold levels and sell signals at overbought levels.
Chart Indicators:
Arrows and labels appear on the chart to highlight these RSI-based opportunities.
Customization
The script allows users to customize key parameters such as:
Moving average lengths for trend detection.
Take-profit and stop-loss percentages.
Timeframes for backtesting.
Starting capital and commission rates.
Conclusion
This script is a versatile tool for traders, combining trend detection, automated trade management, and visual feedback. It simplifies decision-making by providing clear signals and tracking performance metrics, making it suitable for both beginners and experienced traders.
* The most recently drawn fractals represent potential support and resistance levels. If the price aligns with these levels at the time of entering a trade, it may indicate a likelihood of reversal. In such cases, it’s advisable to either avoid entering the trade altogether or proceed with increased caution.
ICT Donchian Smart Money Structure (Expo)█ Concept Overview
The Inner Circle Trader (ICT) methodology is focused on understanding the actions and implications of the so-called "smart money" - large institutions and professional traders who often influence market movements. Key to this is the concept of market structure and how it can provide insights into potential price moves.
Over time, however, there has been a notable shift in how some traders interpret and apply this methodology. Initially, it was designed with a focus on the fractal nature of markets. Fractals are recurring patterns in price action that are self-similar across different time scales, providing a nuanced and dynamic understanding of market structure.
However, as the ICT methodology has grown in popularity, there has been a drift away from this fractal-based perspective. Instead, many traders have started to focus more on pivot points as their primary tool for understanding market structure.
Pivot points provide static levels of potential support and resistance. While they can be useful in some contexts, relying heavily on them could provide a skewed perspective of market structure. They offer a static, backward-looking view that may not accurately reflect real-time changes in market sentiment or the dynamic nature of markets.
This shift from a fractal-based perspective to a pivot point perspective has significant implications. It can lead traders to misinterpret market structure and potentially make incorrect trading decisions.
To highlight this issue, you've developed a Donchian Structure indicator that mirrors the use of pivot points. The Donchian Channels are formed by the highest high and the lowest low over a certain period, providing another representation of potential market extremes. The fact that the Donchian Structure indicator produces the same results as pivot points underscores the inherent limitations of relying too heavily on these tools.
While the Donchian Structure indicator or pivot points can be useful tools, they should not replace the original, fractal-based perspective of the ICT methodology. These tools can provide a broad overview of market structure but may not capture the intricate dynamics and real-time changes that a fractal-based approach can offer.
It's essential for traders to understand these differences and to apply these tools correctly within the broader context of the ICT methodology and the Smart Money Concept Structure. A well-rounded approach that incorporates fractals, along with other tools and forms of analysis, is likely to provide a more accurate and comprehensive understanding of market structure.
█ Smart Money Concept - Misunderstandings
The Smart Money Concept is a popular concept among traders, and it's based on the idea that the "smart money" - typically large institutional investors, market makers, and professional traders - have superior knowledge or information, and their actions can provide valuable insight for other traders.
One of the biggest misunderstandings with this concept is the belief that tracking smart money activity can guarantee profitable trading.
█ Here are a few common misconceptions:
Following Smart Money Equals Guaranteed Success: Many traders believe that if they can follow the smart money, they will be successful. However, tracking the activity of large institutional investors and other professionals isn't easy, as they use complex strategies, have access to information not available to the public, and often intentionally hide their moves to prevent others from detecting their strategies.
Instantaneous Reaction and Results: Another misconception is that market movements will reflect smart money actions immediately. However, large institutions often slowly accumulate or distribute positions over time to avoid moving the market drastically. As a result, their actions might not produce an immediate noticeable effect on the market.
Smart Money Always Wins: It's not accurate to assume that smart money always makes the right decisions. Even the most experienced institutional investors and professional traders make mistakes, misjudge market conditions, or are affected by unpredictable events.
Smart Money Activity is Transparent: Understanding what constitutes smart money activity can be quite challenging. There are many indicators and metrics that traders use to try and track smart money, such as the COT (Commitments of Traders) reports, Level II market data, block trades, etc. However, these can be difficult to interpret correctly and are often misleading.
Assuming Uniformity Among Smart Money: 'Smart Money' is not a monolithic entity. Different institutional investors and professional traders have different strategies, risk tolerances, and investment horizons. What might be a good trade for a long-term institutional investor might not be a good trade for a short-term professional trader, and vice versa.
█ Market Structure
The Smart Money Concept Structure deals with the interpretation of price action that forms the market structure, focusing on understanding key shifts or changes in the market that may indicate where 'smart money' (large institutional investors and professional traders) might be moving in the market.
█ Three common concepts in this regard are Change of Character (CHoCH), and Shift in Market Structure (SMS), Break of Structure (BMS/BoS).
Change of Character (CHoCH): This refers to a noticeable change in the behavior of price movement, which could suggest that a shift in the market might be about to occur. This might be signaled by a sudden increase in volatility, a break of a trendline, or a change in volume, among other things.
Shift in Market Structure (SMS): This is when the overall structure of the market changes, suggesting a potential new trend. It usually involves a sequence of lower highs and lower lows for a downtrend, or higher highs and higher lows for an uptrend.
Break of Structure (BMS/BoS): This is when a previously defined trend or pattern in the price structure is broken, which may suggest a trend continuation.
A key component of this approach is the use of fractals, which are repeating patterns in price action that can give insights into potential market reversals. They appear at all scales of a price chart, reflecting the self-similar nature of markets.
█ Market Structure - Misunderstandings
One of the biggest misunderstandings about the ICT approach is the over-reliance or incorrect application of pivot points. Pivot points are a popular tool among traders due to their simplicity and easy-to-understand nature. However, when it comes to the Smart Money Concept and trying to follow the steps of professional traders or large institutions, relying heavily on pivot points can create misconceptions and lead to confusion. Here's why:
Delayed and Static Information: Pivot points are inherently backward-looking because they're calculated based on the previous period's data. As such, they may not reflect real-time market dynamics or sudden changes in market sentiment. Furthermore, they present a static view of market structure, delineating pre-defined levels of support and resistance. This static nature can be misleading because markets are fundamentally dynamic and constantly changing due to countless variables.
Inadequate Representation of Market Complexity: Markets are influenced by a myriad of factors, including economic indicators, geopolitical events, institutional actions, and market sentiment, among others. Relying on pivot points alone for reading market structure oversimplifies this complexity and can lead to a myopic understanding of market dynamics.
False Signals and Misinterpretations: Pivot points can often give false signals, especially in volatile markets. Prices might react to these levels temporarily but then continue in the original direction, leading to potential misinterpretation of market structure and sentiment. Also, a trader might wrongly perceive a break of a pivot point as a significant market event, when in fact, it could be due to random price fluctuations or temporary volatility.
Over-simplification: Viewing market structure only through the lens of pivot points simplifies the market to static levels of support and resistance, which can lead to misinterpretation of market dynamics. For instance, a trader might view a break of a pivot point as a definite sign of a trend, when it could just be a temporary price spike.
Ignoring the Fractal Nature of Markets: In the context of the Smart Money Concept Structure, understanding the fractal nature of markets is crucial. Fractals are self-similar patterns that repeat at all scales and provide a more dynamic and nuanced understanding of market structure. They can help traders identify shifts in market sentiment or direction in real-time, providing more relevant and timely information compared to pivot points.
The key takeaway here is not that pivot points should be entirely avoided or that they're useless. They can provide valuable insights and serve as a useful tool in a trader's toolbox when used correctly. However, they should not be the sole or primary method for understanding the market structure, especially in the context of the Smart Money Concept Structure.
█ Fractals
Instead, traders should aim for a comprehensive understanding of markets that incorporates a range of tools and concepts, including but not limited to fractals, order flow, volume analysis, fundamental analysis, and, yes, even pivot points. Fractals offer a more dynamic and nuanced view of the market. They reflect the recursive nature of markets and can provide valuable insights into potential market reversals. Because they appear at all scales of a price chart, they can provide a more holistic and real-time understanding of market structure.
In contrast, the Smart Money Concept Structure, focusing on fractals and comprehensive market analysis, aims to capture a more holistic and real-time view of the market. Fractals, being self-similar patterns that repeat at different scales, offer a dynamic understanding of market structure. As a result, they can help to identify shifts in market sentiment or direction as they happen, providing a more detailed and timely perspective.
Furthermore, a comprehensive market analysis would consider a broader set of factors, including order flow, volume analysis, and fundamental analysis, which could provide additional insights into 'smart money' actions.
█ Donchian Structure
Donchian Channels are a type of indicator used in technical analysis to identify potential price breakouts and trends, and they may also serve as a tool for understanding market structure. The channels are formed by taking the highest high and the lowest low over a certain number of periods, creating an envelope of price action.
Donchian Channels (or pivot points) can be useful tools for providing a general view of market structure, and they may not capture the intricate dynamics associated with the Smart Money Concept Structure. A more nuanced approach, centered on real-time fractals and a comprehensive analysis of various market factors, offers a more accurate understanding of 'smart money' actions and market structure.
█ Here is why Donchian Structure may be misleading:
Lack of Nuance: Donchian Channels, like pivot points, provide a simplified view of market structure. They don't take into account the nuanced behaviors of price action or the complex dynamics between buyers and sellers that can be critical in the Smart Money Concept Structure.
Limited Insights into 'Smart Money' Actions: While Donchian Channels can highlight potential breakout points and trends, they don't necessarily provide insights into the actions of 'smart money'. These large institutional traders often use sophisticated strategies that can't be easily inferred from price action alone.
█ Indicator Overview
We have built this Donchian Structure indicator to show that it returns the same results as using pivot points. The Donchian Structure indicator can be a useful tool for market analysis. However, it should not be seen as a direct replacement or equivalent to the original Smart Money concept, nor should any indicator based on pivot points. The indicator highlights the importance of understanding what kind of trading tools we use and how they can affect our decisions.
The Donchian Structure Indicator displays CHoCH, SMS, BoS/BMS, as well as premium and discount areas. This indicator plots everything in real-time and allows for easy backtesting on any market and timeframe. A unique candle coloring has been added to make it more engaging and visually appealing when identifying new trading setups and strategies. This candle coloring is "leading," meaning it can signal a structural change before it actually happens, giving traders ample time to plan their next trade accordingly.
█ How to use
The indicator is great for traders who want to simplify their view on the market structure and easily backtest Smart Money Concept Strategies. The added candle coloring function serves as a heads-up for structure change or can be used as trend confirmation. This new candle coloring feature can generate many new Smart Money Concepts strategies.
█ Features
Market Structure
The market structure is based on the Donchian channel, to which we have added what we call 'Structure Response'. This addition makes the indicator more useful, especially in trending markets. The core concept involves traders buying at a discount and selling or shorting at a premium, depending on the order flow. Structure response enables traders to determine the order flow more clearly. Consequently, more trading opportunities will appear in trending markets.
Structure Candles
Structure Candles highlight the current order flow and are significantly more responsive to structural changes. They can provide traders with a heads-up before a break in structure occurs
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
[VDB]TrendScalp-FractalBox-3EMAThere are many indicators with William’s Fractal and Alligator. As many use EMA’s it may be useful to define a 3-EMA ribbon and combining Fractal Levels/Box (filling background between top and bottom fractals) for trend scalping. I searched for this kind of indicator in community – some show fractals, some just levels, some with alligator etc. but couldn't find the one needed. Hence thought of this indicator which may be of interest to other users too.
Key Points:
EMA ribbon is created using 3 EMA’s 35/70/105. Users can change these as per their preference. This is used for trend identification – 1. Bullish bias if Price > EMA1 > EMA2 > EMA3. 2. Bearish bias if Price < EMA1 < EMA2 < EMA3.
Background is marked during crossing of EMA1 and EMA2 to alert possible trend change.
5-bar fractals are used to mark the Fractal levels and background between top and bottom fractals are filled to create a Fractal Box.
Fractal levels are marked only when the fractal formation is complete. Given offset is used this is lagging.
How to Use:
Sloping EMA ribbon is used for identifying the trend.
Fractal box break-out/ break-downs are used to trigger the trade with fractal high/low for entry/SL. Waiting for price contraction towards EMA ribbon resulting in smaller boxes is key to initiate trade. Avoid bigger boxes as SL’s will be big and price may move within. To draw the vertical lines of FractalBox change fractal level0 style to step-line.
This indicator combined with the cycle high/low (overbought/oversold) indicators such as CCI/Stochastic/RSI etc. can make it a good trend scalping setup while trading in the direction of momentum in higher timeframe.
This setup could be used for any timeframes. Do your back-testing before using it in live market.
This indicator was achieved by combing some fractal ideas from “Fractal and Alligator Alerts by JustUncleL”
DISCLAIMER : This indicator has been created for educational reference only and do not constitute investment advice. This indicator should not be relied upon as a substitute for extensive independent market research before making your actual trading decisions. Market data or any other content is subject to change at any time without notice. Liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from use of this indicator is accountability of user using it.
Kapua Whenua LANDZZ1Kapua Whenua means Earth's Clouds in Maori language, this indicator was created to show impulses and trends of the asset's price movement both up and down.
The Indicator was made based on key numbers of the golden ratio:
Conversion Line: Kw 17 (Purple Color)
Kw 34 - Short Period Fractal (Light Blue Color)
Kw 72 - Short/Medium Period Fractal (Orange)
Kw 144 - Medium Period Fractal (Yellow Color)
Kw 305 - Medium/Long Period Fractal (Dark Blue Color)
Kw 610 - Long Period Fractal (Grey)
Kw 1292 - Long Period Fractal+ (Black Color)
The baseline or also called the conversion line is identified by the Color Purple of value 17.
How to read the indicator:
Every time the conversion line (Purple Color 17 periods) crosses a Kw value (Kapua Whenua) it will always look for the next KW line above or below the value as support or resistance.
For example:
If we are in a bull market, and the price crosses below the KW17 conversion line it will go towards KW 34 as support, if the price breaks KW 34 it will go towards the next line below KW 34 as support.
Every time the conversion line or the price crosses a higher value of Kapua Whenua (KW) this trend will be stronger, it means, if the conversion line (KW 17) crosses above KW 305 it will indicate more strength than if it had crossed above the KW 72 or 144 for example. So to get better results trading with the trend, always observe if the conversion line and the price are below or above some Medium/Long Period KW.
Note also that, in an uptrend, it could be that all KW are below each other. In a downtrend, it could be that all KW are on top of each other. This indicates that the farther the price is from the fractals the stronger the trend is, also, when there is a narrowing of the fractals means that the price will start to go sideways. If the price is between 2 or more Fractals, it will indicate consolidation.
A really good trend is considered when the price or the Short/Medium Period Fractals are all above or below at KW 610, which is a long period fractal, meaning a strong uptrend or downtrend.
A larger KW can be at the same point as a smaller KW, however, the stronger color will be shown above the weaker one.
***Larger chart timeframes are better to see longer KW fractals that are above or below the price, if your chart timeframe doesn't show a bigger support or resistance fractal, change the chart time to another longer period**
Tip - Get used to looking at line colors as your indicators, just like moving averages. You can also take or place any fractal at any time in the configuration menu.
Kawabunga Swing Failure Points Candles (SFP) by RRBKawabunga Swing Failure Points Candles (SFP) by RagingRocketBull 2019
Version 1.0
This indicator shows Swing Failure Points (SFP) and Swing Confirmation Points (SCP) as candles on a chart.
SFP/SCP candles are used by traders as signals for trend confirmation/possible reversal.
The signal is stronger on a higher volume/larger candle size.
A Swing Failure Point (SFP) candle is used to spot a reversal:
- up trend SFP is a failure to close above prev high after making a new higher high => implies reversal down
- down trend SFP is a failure to close below prev low after making a new lower low => implies reversal up
A Swing Confirmation Point (SCP) candle is just the opposite and is used to confirm the current trend:
- up trend SCP is a successful close above prev high after making a new higher high => confirms the trend and implies continuation up
- down trend SCP is a successful close below prev low after making a new lower low => confirms the trend and implies continuation down
Features:
- uses fractal pivots with optional filter
- show/hide SFP/SCP candles, pivots, zigzag, last min/max pivot bands
- dim lag zones/hide false signals introduced by lagging fractals or
- use unconfirmed pivots to eliminate fractal lag/false signals. 2 modes: fractals 1,1 and highest/lowest
- filter only SFP/SCP candles confirmed with volume/candle size
- SFP/SCP candles color highlighting, dim non-important bars
Usage:
- adjust fractal settings to get pivots that best match your data (lower values => more frequent pivots. 0,0 - each candle is a pivot)
- use one of the unconfirmed pivot modes to eliminate false signals or just ignore all signals in the gray lag zones
- optionally filter only SFP/SCP candles with large volume/candle size (volume % change relative to prev bar, abs candle body size value)
- up/down trend SCP (lime/fuchsia) => continuation up/down; up/down trend SFP (orange/aqua) => possible reversal down/up. lime/aqua => up; fuchsia/orange => down.
- when in doubt use show/hide pivots/unconfirmed pivots, min/max pivot bands to see which prev pivot and min/max value were used in comparisons to generate a signal on the following candle.
- disable offset to check on which bar the signal was generated
Notes:
Fractal Pivots:
- SFP/SCP candles depend on fractal pivots, you will get different signals with different pivot settings. Usually 4,4 or 2,2 settings are used to produce fractal pivots, but you can try custom values that fit your data best.
- fractal pivots are a mixed series of highs and lows in no particular order. Pivots must be filtered to produce a proper zigzag where ideally a high is followed by a low and another high in orderly fashion.
Fractal Lag/False Signals:
- only past fractal pivots can be processed on the current bar introducing a lag, therefore, pivots and min/max pivot bands are shown with offset=-rightBars to match their target bars. For unconfirmed pivots an offset=-1 is used with a lag of just 1 bar.
- new pivot is not a confirmed fractal and "does not exist yet" while the distance between it and the current bar is < rightBars => prev old fractal pivot in the same dir is used for comparisons => gives a false signal for that dir
- to show false signals enable lag zones. SFP/SCP candles in lag zones are false. New pivots will be eventually confirmed, but meanwhile you get a false signal because prev pivot in the same dir was used instead.
- to solve this problem you can either temporary hide false signals or completely eliminate them by using unconfirmed pivots of a smaller degree/lag.
- hiding false signals only works for history and should be used only temporary (left disabled). In realtime/replay mode it disables all signals altogether due to TradingView's bug (barcolor doesn't support negative offsets)
Unconfirmed Pivots:
- you have 2 methods to check for unconfirmed pivots: highest/lowest(rightBars) or fractals(1,1) with a min possible step. The first is essentially fractals(0,0) where each candle is a pivot. Both produce more frequent pivots (weaker signals).
- an unconfirmed pivot is used in comparisons to generate a valid signal only when it is a higher high (> max high) or a lower low (< min low) in the dir of a trend. Confirmed pivots of a higher degree are not affected. Zigzag is not affected.
- you can also manually disable the offset to check on which bar the pivot was confirmed. If the pivot just before an SCP/SFP suddenly jumps ahead of it - prev pivot was used, generating a false signal.
- last max high/min low bands can be used to check which value was used in candle comparison to generate a signal: min(pivot min_low, upivot min_low) and max(pivot max_high, upivot max_high) are used
- in the unconfirmed pivots mode the max high/min low pivot bands partially break because you can't have a variable offset to match the random pos of an unconfirmed pivot (anywhere in 0..rightBars from the current bar) to its target bar.
- in the unconfirmed pivots mode h (green) and l (red) pivots become H and L, and h (lime) and l (fuchsia) are used to show unconfirmed pivots of a smaller degree. Some of them will be confirmed later as H and L pivots of a higher degree.
Pivot Filter:
- pivot filter is used to produce a better looking zigzag. Essentially it keeps only higher highs/lower lows in the trend direction until it changes, skipping:
- after a new high: all subsequent lower highs until a new low
- after a new low: all subsequent higher lows until a new high
- you can't filter out all prev highs/lows to keep just the last min/max pivots of the current swing because they were already confirmed as pivots and you can't delete/change history
- alternatively you could just pick the first high following a low and the first low following a high in a sequence and ignore the rest of the pivots in the same dir, producing a crude looking zigzag where obvious max high/min lows are ignored.
- pivot filter affects SCP/SFP signals because it skips some pivots
- pivot filter is not applied to/not affected by the unconfirmed pivots
- zigzag is affected by pivot filter, but not by the unconfirmed pivots. You can't have both high/low on the same bar in a zigzag. High has priority over Low.
- keep same bar pivots option lets you choose which pivots to keep when there are both high/low pivots on the same bar (both kept by default)
SCP/SFP Filters:
- you can confirm/filter only SCP/SFP signals with volume % change/candle size larger than delta. Higher volume/larger candle means stronger signal.
- technically SCP/SFP is always the first matching candle, but it can be invalidated by the following signal in the opposite dir which in turn can be negated by the next signal.
- show first matching SCP/SFP = true - shows only the first signal candle (and any invalidations that follow) and hides further duplicate signals in the same dir, does not highlight the trend.
- show first matching SCP/SFP = false - produces a sequence of candles with duplicate signals, highlights the whole trend until its dir changes (new pivot).
Good Luck! Feel free to learn from/reuse the code to build your own indicators!
AuriumFlowAURIUM (GOLD-Weighted Average with Fractal Dynamics)
Aurium is a cutting-edge indicator that blends volume-weighted moving averages (VWMA), fractal geometry, and Fibonacci-inspired calculations to deliver a precise and holistic view of market trends. By dynamically adjusting to price and volume, Aurium uncovers key levels of confluence for trend reversals and continuations, making it a powerful tool for traders.
Key Features:
Dynamic Trendline (GOLD):
The central trendline is a weighted moving average based on price and volume, tuned using Fibonacci-based fast (34) and slow (144) exponential moving average lengths. This ensures the trendline adapts seamlessly to the flow of market dynamics.
Formula:
GOLD = VWMA(34) * Volume Factor + VWMA(144) * (1 - Volume Factor)
Fractal Highs and Lows:
Detects pivotal market points using a fractal lookback period (default 5, odd-numbered). Fractals identify local highs and lows over a defined window, capturing the structure of market cycles.
Trend Background Highlighting:
Bullish Zone: Price above the GOLD line with a green background.
Bearish Zone: Price below the GOLD line with a red background.
Buy and Sell Alerts:
Generates actionable signals when fractals align with GOLD. Bullish fractals confirm continuation or reversal in an uptrend, while bearish fractals validate a downtrend.
The Math Behind Aurium:
Volume-Weighted Adjustments:
By integrating volume into the calculation, Aurium dynamically emphasizes price levels with greater participation, giving traders insight into zones of institutional interest.
Formula:
VWMA = EMA(Close * Volume) / EMA(Volume)
Fractal Calculations:
Fractals are identified as local maxima (highs) or minima (lows) based on the surrounding bars, leveraging the natural symmetry in price behavior.
Fibonacci Relationships:
The 34 and 144 EMA lengths are Fibonacci numbers, offering a natural alignment with price cycles and market rhythms.
Ideal For:
Traders seeking a precise and intuitive indicator for aligning with trends and detecting reversals.
Strategies inspired by Bill Williams, with added volume and fractal-based insights.
Short-term scalpers and long-term trend-followers alike.
Unlock deeper market insights and trade with precision using Aurium!
Musashi_Fractal_Dimension === Musashi-Fractal-Dimension ===
This tool is part of my research on the fractal nature of the markets and understanding the relation between fractal dimension and chaos theory.
To take full advantage of this indicator, you need to incorporate some principles and concepts:
- Traditional Technical Analysis is linear and Euclidean, which makes very difficult its modeling.
- Linear techniques cannot quantify non-linear behavior
- Is it possible to measure accurately a wave or the surface of a mountain with a simple ruler?
- Fractals quantify what Euclidean Geometry can’t, they measure chaos, as they identify order in apparent randomness.
- Remember: Chaos is order disguised as randomness.
- Chaos is the study of unstable aperiodic behavior in deterministic non-linear dynamic systems
- Order and randomness can coexist, allowing predictability.
- There is a reason why Fractal Dimension was invented, we had no way of measuring fractal-based structures.
- Benoit Mandelbrot used to explain it by asking: How do we measure the coast of Great Britain?
- An easy way of getting the need of a dimension in between is looking at the Koch snowflake.
- Market prices tend to seek natural levels of ranges of balance. These levels can be described as attractors and are determinant.
Fractal Dimension Index ('FDI')
Determines the persistence or anti-persistence of a market.
- A persistent market follows a market trend. An anti-persistent market results in substantial volatility around the trend (with a low r2), and is more vulnerable to price reversals
- An easy way to see this is to think that fractal dimension measures what is in between mainstream dimensions. These are:
- One dimension: a line
- Two dimensions: a square
- Three dimensions: a cube.
--> This will hint you that at certain moment, if the market has a Fractal Dimension of 1.25 (which is low), the market is behaving more “line-like”, while if the market has a high Fractal Dimension, it could be interpreted as “square-like”.
- 'FDI' is trend agnostic, which means that doesn't consider trend. This makes it super useful as gives you clean information about the market without trying to include trend stuff.
Question: If we have a game where you must choose between two options.
1. a horizontal line
2. a vertical line.
Each iteration a Horizontal Line or a Square will appear as continuation of a figure. If it that iteration shows a square and you bet vertical you win, same as if it is horizontal and it is a line.
- Wouldn’t be useful to know that Fractal dimension is 1.8? This will hint square. In the markets you can use 'FD' to filter mean-reversal signals like Bollinger bands, stochastics, Regular RSI divergences, etc.
- Wouldn’t be useful to know that Fractal dimension is 1.2? This will hint Line. In the markets you can use 'FD' to confirm trend following strategies like Moving averages, MACD, Hidden RSI divergences.
Calculation method:
Fractal dimension is obtained from the ‘hurst exponent’.
'FDI' = 2 - 'Hurst Exponent'
Musashi version of the Classic 'OG' Fractal Dimension Index ('FDI')
- By default, you get 3 fast 'FDI's (11,12,13) + 1 Slow 'FDI' (21), their interaction gives useful information.
- Fast 'FDI' cross will give you gray or red dots while Slow 'FDI' cross with the slowest of the fast 'FDI's will give white and orange dots. This are great to early spot trend beginnings or trend ends.
- A baseline (purple) is also provided, this is calculated using a 21 period Bollinger bands with 1.618 'SD', once calculated, you just take midpoint, this is the 'TDI's (Traders Dynamic Index) way. The indicator will print purple dots when Slow 'FDI' and baseline crosses, I see them as Short-Term cycle changes.
- Negative slope 'FDI' means trending asset.
- Positive most of the times hints correction, but if it got overextended it might hint a rocket-shot.
TDI Ranges:
- 'FDI' between 1.0≤ 'FDI' ≤1.4 will confirm trend following continuation signals.
- 'FDI' between 1.6≥ 'FDI' ≥2.0 will confirm reversal signals.
- 'FDI' == 1.5 hints a random unpredictable market.
Fractal Attractors
- As you must know, fractals tend orbit certain spots, this are named Attractors, this happens with any fractal behavior. The market of course also shows them, in form of Support & Resistance, Supply Demand, etc. It’s obvious they are there, but now we understand that they’re not linear, as the market is fractal, so simple trendline might not be the best tool to model this.
- I’ve noticed that when the Musashi version of the 'FDI' indicator start making a cluster of multicolor dots, this end up being an attractor, I tend to draw a rectangle as that area as price tend to come back (I still researching here).
Extra useful stuff
- Momentum / speed: Included by checking RSI Study in the indicator properties. This will add two RSI’s (9 and a 7 periods) plus a baseline calculated same way as explained for 'FDI'. This gives accurate short-term trends. It also includes RSI divergences (regular and hidden), deactivate with a simple check in the RSI section of the properties.
- BBWP (Bollinger Bands with Percentile): Efficient way of visualizing volatility as the percentile of Bollinger bands expansion. This line varies color from Iced blue when low volatility and magma red when high. By default, comes with the High vols deactivated for better view of 'FDI' and RSI while all studies are included. DDWP is trend agnostic, just like 'FDI', which make it very clean at providing information.
- Ultra Slow 'FDI': I noticed that while using BBWP and RSI, the indicator gets overcrowded, so there is the possibility of adding only one 'FDI' + its baseline.
Final Note: I’ve shown you few ways of using this indicator, please backtest before using in real trading. As you know trading is more about risk and trade management than the strategy used. This still a work in progress, I really hope you find value out of it. I use it combination with a tool named “Musashi_Katana” (also found in TradingView).
Best!
Musashi
[JL] How Many Signals last N barsGot this idea after I found Multiple Indicators Screener from QuantNomad.
This script learnt some codes from QuantNomad's great script. Thanks to him.
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This table show how many signals happened during the last N bars.
I only take care Forex, so this table only has 28 symbols. Feel free to change it.
Calculate the following signals:
RSI cross over/under 50
Short Moving average cross over/under long moving average
Stochastic k cross over/under d
MACD hist cross over/under 0
Williams Fractals: Up and Down fractals happened.
The concept is simple: Range period will always happen more cross signals than the trend period.
When the counter is less than median of all symbols, will be set green color. So more green mean more chance to be trend.
[blackcat] L2 Ehlers FRAMALevel: 2
Background
John F. Ehlers introuced Fractal Adaptive Moving Average (FRAMA) in 2004.
Function
The objective of using filters is to separate the desired signals from the undesired signals (or noise). The practical application of moving averages often involves a tradeoff between the amount of smoothness required and the amount of lag that can be tolerated. Moving averages have this problem because the price data is not stationary, and may have different bandwidths over different time intervals. Various momentum-adaptive filtering techniques have been developed to take advantage of the nonstationary structure of prices. Adaptive filters have also been developed based on price statistics and the cyclic content of the price data . Dr. Ehlers described a different class of filters that monitor a different measure of temporal nonstationarity and alters their bandwidth in response to this measure.
There is no argument that market prices are fractal. Fractal shapes are self-similar because they tend to have the same roughness and sparseness regardless of the magnification used to view them. If you remove the labels from a 5 minute chart, a daily chart, and a weekly chart you would have difficulty telling them apart. This is the characteristic that makes them fractal. The self-similarity can be defined by the fractal dimension that describes the sparseness at all magnification levels.
To determine the fractal dimension of a generalized pattern, Dr. Ehlers cover the pattern with a number “N” of small objects of several various sizes “s”. As with any moving average, we are forced to compromise between responsiveness and smoothness. FRAMA can be a valuable weapon in your arsenal of technical indicators. It rapidly follows significant changes in price but becomes very flat in congestion zones so that bad whipsaw trades can be eliminated.
Key Signal
Filt --> FRAMA fast line
Trigger --> FRAMA slow line
Pros and Cons
100% John F. Ehlers definition translation, even variable names are the same. This help readers who would like to use pine to read his book.
Remarks
The 63th script for Blackcat1402 John F. Ehlers Week publication.
Readme
In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Tradingview is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Tradingview community. Welcome everyone to interact with me to discuss these interesting pine scripts.
The scripts posted are categorized into 5 levels according to my efforts or manhours put into these works.
Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element.
Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level.
Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated.
Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop.
Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many of them to achieve private trading strategy.
Order Blocks v2Order Blocks v2 – Smart OB Detection with Time & FVG Filters
Order Blocks v2 is an advanced tool designed to identify potential institutional footprints in the market by dynamically plotting bullish and bearish order blocks.
This indicator refines classic OB logic by combining:
Fractal-based break conditions
Time-level filtering (Power of 3)
Optional Fair Value Gap (FVG) confirmation
Real-time plotting and auto-invalidation
Perfect for traders using ICT, Smart Money, or algorithmic timing models like Hopplipka.
🧠 What the indicator does
Detects order blocks after break of bullish/bearish fractals
Supports 3-bar or 5-bar fractal structures
Allows OB detection based on close breaks or high/low breaks
Optionally confirms OBs only if followed by a Fair Value Gap within N candles
Filters OBs based on specific time levels (3, 7, 11, 14) — core anchors in many algorithmic models
Automatically deletes invalidated OBs once price closes through the zone
⚙️ How it works
The indicator:
Tracks local fractal highs/lows
Once a fractal is broken by price, it backtracks to identify the best OB candle (highest bullish or lowest bearish)
Validates the level by checking:
OB type logic (close or HL break)
Time stamp match with algorithmic time anchors (e.g. 3, 7, 11, 14 – known from the Power of 3 concept)
Optional FVG confirmation after OB
Plots OB zones as lines (body or wick-based) and removes them if invalidated by a candle close
This ensures traders see only valid, active levels — removing noise from broken or out-of-context zones.
🔧 Customization
Choose 3-bar or 5-bar fractals
OB detection type: close break or HL break
Enable/disable OBs only on times 3, 7, 11, 14 (Hopplipka style)
Optional: require nearby FVG for validation
Line style: solid, dashed, or dotted
Adjust OB length, width, color, and use body or wick for OB height
🚀 How to use it
Add the script to your chart
Choose your preferred OB detection mode and filters
Use plotted OB zones to:
Anticipate price rejections and reversals
Validate Smart Money or ICT-based entry zones
Align setups with algorithmic time sequences (3, 7, 11, 14)
Filter out invalid OBs automatically, keeping your chart clean
The tool is useful on any timeframe but performs best when combined with a liquidity-based or time-anchored trading model.
💡 What makes it original
Combines fractal logic with OB confirmation and time anchors
Implements time-based filtering inspired by Hopplipka’s interpretation of the "Power of 3"
Allows OB validation via optional FVG follow-up — rarely available in public indicators
Auto-cleans invalidated OBs to reduce clutter
Designed to reflect market structure logic used by institutions and algorithms
💬 Why it’s worth using
Order Blocks v2 simplifies one of the most nuanced parts of SMC: identifying clean and high-probability OBs.
It removes subjectivity, adds clear timing logic, and integrates optional confluence tools — like FVG.
For traders serious about algorithmic-level structure and clean setups, this tool delivers both logic and clarity.
⚠️ Important
This indicator:
Is not a signal generator or financial advice tool
Is intended for experienced traders using OB/SMC/time-based logic
Does not predict market direction — it provides visual structural levels only
price action reversion bands - [SigmaStreet]█ OVERVIEW
The "Price Action Reversion Bands" is designed to help traders identify potential reversal zones through the integration of polynomial regression, fractal analysis, and pinbar detection. This tool overlays directly onto the price chart, providing dynamic visual cues and signals for market reversals. Its unique synthesis of these methodologies offers traders a powerful, multifaceted approach to market analysis.
█ CONCEPTS
Polynomial Regression Bands:
What It Does:
Models the main trend using a polynomial equation to create a middle trend line with dynamic support and resistance bands.
How It Works:
Calculates polynomial coefficients to plot a regression line and adjusts the bands according to market volatility and conditions.
Fibonacci Retracement Levels:
What It Does:
Provides additional lines inside the regression bands at key Fibonacci ratios to identify potential support and resistance areas.
How It Works:
Calculates retracement levels by identifying high and low points over the same period used to calculate the regression bands, applying Fibonacci ratios to these points.
Fractal Analysis:
What It Does: Identifies natural resistance and support levels, indicating potential reversal zones.
How It Works: Detects fractals based on a specific pattern of price action, using Williams Fractal methodology.
Pinbar Detection:
What It Does: Signals potential price reversals through pinbar candlestick patterns.
How It Works: Analyzes
candlesticks to identify pinbars which show a rejection of prices, suggesting possible reversals.
█ ORIGINALITY AND USEFULNESS
The price action reversion bands distinguishes itself through its innovative integration of several advanced analytical methods, providing traders with a holistic view of potential market reversals:
Unique Combination:
While many tools use these techniques in isolation, this indicator synergistically combines polynomial regression, Fibonacci retracement levels, fractal analysis, and pinbar detection. This multi-faceted approach allows traders to assess strength, potential reversal zones, and price rejection more effectively than using traditional single-method indicators.
Advanced Polynomial Regression Application:
Unlike standard regression tools that offer static insights, this indicator dynamically adjusts its regression bands based on real-time market volatility, providing a more accurate reflection of market conditions.
Enhanced Signal Reliability:
By using fractals and pinbars in conjunction to validate each other, the indicator significantly increases the reliability of its reversal signals. This dual-validation method filters out less probable signals, focusing on high-probability trading opportunities.
Customization and Flexibility:
It offers unprecedented customization options, allowing traders to fine-tune the tool according to their trading style and market conditions. Traders can adjust the polynomial degree, the sensitivity of the Fibonacci retracements, and even the definition of what constitutes a significant pinbar, making it highly adaptable to various trading scenarios.
Educational Value:
The indicator not only aids in trading but also serves as an educational tool that helps traders understand the interaction between different types of market analysis techniques. This contributes to a deeper knowledge base and better trading decisions over time.
These distinctive features make the "Price Action Reversion Bands - " not just another indicator but a comprehensive trading tool that enhances decision-making through a well-rounded analysis of market dynamics.
█ HOW TO USE
Installation and Setup:
Apply the indicator to your TradingView chart from the "Indicators" menu.
Select either polynomial regression or Fibonacci retracement as the basis for the bands through the indicator settings.
Reading the Indicator:
Monitor the approach of price to the upper and lower bands which indicate potential reversal zones.
Look for fractal and pinbar formations near these bands for additional signal confirmation.
Customization:
Adjust settings such as the polynomial degree, data window length, and engagement zones to tailor the bands to your trading style.
Modify visual aspects like color and line type for better clarity and personal preference.
█ FEATURES
Dynamic Adjustment:
Bands adjust in real-time based on incoming price data and selected settings.
Multiple Analysis Techniques: Combines several analytical techniques to provide a comprehensive view of potential market movements. The integration of polynomial regression with Fibonacci levels, supplemented by fractal and pinbar analysis, marks this tool as particularly innovative, offering a level of synthesis that enhances predictive accuracy and usability.
User-Friendly Customization: Allows for extensive customization to suit individual trading strategies and preferences.
█ LIMITATIONS
Market Dependency:
Performance may vary significantly across different markets and conditions.
Parameter Sensitivity: Requires fine-tuning of parameters to ensure optimal performance, which might demand a steep learning curve for new users.
█ NOTES
For best results, combine this tool with other forms of analysis, such as fundamental analysis and other technical indicators, to confirm signals and enhance decision-making.
█ THANKS
Special thanks to the PineCoders community the Pine Coders themselves for their foundational contributions to the concepts used in this script. Their pioneering work in the fields of technical analysis and Pine Script development has been invaluable. This script is a testament to the collaborative spirit of the TradingView developer community, integrating analytical techniques with innovative approaches to offer a tool that is both modern and cutting-edge.
Support & Resistance ZonesTitle: A Comprehensive Guide to the Support & Resistance Zones Indicator
Introduction
In the world of technical analysis, the Support & Resistance Zones indicator plays a crucial role in identifying potential trading opportunities. These zones are essential for traders looking to capitalize on bounces or break and retests. In this article, we will delve into the specifics of the Support & Resistance Zones indicator, outlining how it works, how it finds and marks zones, and the various options available for traders.
What the indicator is about
The Support & Resistance Zones indicator, developed by @HarryCTC, is a powerful tool for detecting areas of potential price reversal or consolidation in a financial market. These zones are significant as they can act as a guide for traders to make informed decisions on entering or exiting positions. Specifically, the indicator helps identify:
1. Support Zones: Areas where the price has a tendency to bounce back up after falling, indicating a potential buying opportunity.
2. Resistance Zones: Areas where the price has a tendency to reverse after rising, indicating a potential selling opportunity.
How the indicator finds its zones
The Support & Resistance Zones indicator utilizes pivot points to identify potential support and resistance levels. By analyzing the fractal structure of the price chart, the indicator identifies key turning points, known as bull and bear fractals. The bull fractal is a high pivot point, while the bear fractal is a low pivot point.
The fractal structure is determined by the 'Switch Zone Period' input, which can be adjusted to suit the trader's preferences. A higher value will result in fewer zones being identified, while a lower value will result in more zones.
How it marks zones and why it marks zones
The indicator marks the support and resistance zones by creating rectangular boxes around the identified fractal points. The zones are extended horizontally from the fractal point, allowing traders to visualize the potential areas of price reversal.
The zones are marked for the following reasons:
1. To provide a clear visual representation of potential support and resistance levels.
2. To help traders identify potential entry and exit points based on the price's reaction to these zones.
3. To serve as a reference for stop-loss and take-profit levels when planning trades.
The indicator's for traders trading bounces or break and retests
Traders who focus on trading bounces or break and retests can benefit immensely from the Support & Resistance Zones indicator. By providing a visual representation of key support and resistance levels, the indicator enables traders to:
1. Identify potential buying opportunities at support zones where the price is likely to bounce back up.
2. Identify potential selling opportunities at resistance zones where the price is likely to reverse after rising.
3. Make informed decisions on stop-loss and take-profit levels based on the price's proximity to support and resistance zones.
4. Monitor the market for potential breakouts or breakdowns when the price breaches these zones.
Indicator options
The Support & Resistance Zones indicator offers several customizable options to suit the trader's preferences. These options include:
1. Switch Zone Period: Adjusts the number of periods used to calculate the fractal structure, influencing the number of identified zones.
2. No. of Displayed Zones: Determines the maximum number of zones displayed on the chart, ranging from 1 to 8.
3. Zone Extension: Adjusts the horizontal extension of the support and resistance zones.
4. Resistance Zone Color: Customizes the color of the resistance zone boxes.
5. Support Zone Color: Customizes the color of the support zone boxes.
6. Zone Border Color: Customizes the color of the zone box borders.
Conclusion
The Support & Resistance Zones indicator is a valuable tool for traders looking to identify potential trading opportunities based on the price's interaction with support and resistance levels. By providing a clear visual representation of these zones, the
indicator allows traders to make informed decisions on entry and exit points, stop-loss, and take-profit levels. With customizable options, the indicator can be tailored to suit individual trading preferences and strategies.
BEST ABCD Pattern StrategyHello traders
This is the strategy version of this script
I - Concept
I present to you, ladies and gentlemen, the first screener for harmonic patterns.
Starting with an ACBD pattern screener this time!!
I used the calculations from Ricardo Santo's script
In short, he's using fractals (regular or Bill Williams ) for the pattern calculations. A masterpiece !!!
II - Definitions
The ABCD pattern ( AB=CD ) is one of the classic chart patterns which is repeated over and over again.
The ABCD pattern shows perfect harmony between price and time.
The Williams Fractal is an indicator, developed by Bill Williams, that aims to detect reversal points (highs and lows) and marks them with arrows.
Up fractals and down fractals have specific shapes. The Williams Fractal indicator helps users determine in which direction price will develop
💎Strategy filters💎
I included some cool backtest filters:
- flexible take profit in USD value (plotted in blue)
- flexible stop loss in USD value (plotted in red)
The take profit and stop loss should work with Forex/FX pairs as well
All the BEST
Dave
BEST ABCD Pattern ScreenerHello ladies and gentlemen traders
Continuing deeper and stronger with the screeners' educational series one more time.
This one is heavy crazy mega cool (pardon my french).
I - Concept
I present to you, ladies and gentlemen, the first screener for harmonic patterns.
Starting with an ACBD pattern screener this time!!
I used the calculations from Ricardo Santo's script
In short, he's using fractals (regular or Bill Williams ) for the pattern calculations. A masterpiece !!!
The screener will show in 1 consolidated chart the ABCD patterns for 5 selected assets. Could be stocks, forex, crypto, whatever you feel like making money with
II - Definitions
The ABCD pattern ( AB=CD ) is one of the classic chart patterns which is repeated over and over again.
The ABCD pattern shows perfect harmony between price and time.
More info here
The Williams Fractal is an indicator, developed by Bill Williams, that aims to detect reversal points (highs and lows) and marks them with arrows.
Up fractals and down fractals have specific shapes. The Williams Fractal indicator helps users determine in which direction price will develop
Source: www.tradingview.com
III - How did I set the screener
The visual signals are as follow:
- square: whenever there is an ABCD pattern detected
Then the colors are:
- green when a bullish pattern is detected
- red when a bearish pattern is detected
🔸 The script screenshot shows two red squares matching two ABCD bearish pattern from the above panel. As the chart is showing APPL, I highlighted where the screener mentioned the fruit stock ( APPLE, pun, bad joke, Dave out...)
🔸 I tried to make it as clear as I could with red arrows
Once again, we pushed together the limits of pine script beyond of what we thought was possible, beyond the realm of this world, reaching finally the dreamt pine script heaven (am I going too far? feel free to tell me)
Best regards,
Dave
Volume-based Support & Resistance Zones-V1 By Trade Mastership™ The all-new Support & Resistance Zones indicator, which has been upgraded to offer traders more powerful features and functionality. This innovative indicator identifies high-volume fractal lows or highs to create zones based on the size of the wick for that timeframe's candle. This makes it easy for traders to visualize which price levels are the most significant for either a trend continuation or a reversal when zones are broken and retested.
The original script for this indicator was created by Trade Mastership, with additional modifications by L N Behera. Credit goes to both of them for the majority of the logic behind this script. Since then, the script has been improved with several changes, including:
Changing the default S/R lines from plots to lines, and giving users the option to change between solid, dashed, or dotted lines for both S/R lines
Adding additional timeframes and more options for TF1, beyond the current TF. Now, users have four timeframes to plot S/R zones from
Giving users the option to easily change the line thickness for all S/R lines
Making it easier to change the colors of S/R lines and zones by consolidating the options under settings (rather than under style)
Adding extensions to active SR Zones to extend all the way right
Adding the option to extend or not extend the previous S/R zones up to the next S/R zone
Adding optional timeframe labels to active S/R zones, with left and right options, as well as the option to adjust how far to the right the label is set
Fixing an issue where the higher timeframe S/R zone was not properly starting from the high/low of fractal. Now, any higher timeframe S/R will begin exactly at the High/Low points. Note that this may not work perfectly on stocks, and if a fractal high/low is too many bars in the past, it will revert to a default max bars back to avoid script errors.
Adding a function to prevent S/R zones from lower timeframes displaying while on a higher timeframe. This helps clean up the chart quite a bit.
Creating arrays for each timeframe's boxes and lines so that the number of S/R zones can be controlled for each timeframe and limit memory consumption.
Adding new alert options and customized alert messages
Here's how this indicator works: it looks for fractal highs or fractal lows with volume that pierces above the volume's Moving Average. This moving average value can be modified in the settings for each timeframe. The fractal highs will be confirmed with three successive higher highs followed by two successive lower highs and vice versa for the fractal lows. The zone is created from the fractal high/low and the close of the candle for whatever timeframe you selected. The bigger the zone, the more significant that zone is.
Traders can disable any zone, change the zones to show lines only, and modify all the colors, transparencies, and thickness of lines for all the zones. To create alerts, traders can enable the types of alerts they want for each timeframe in the indicator's settings. After applying changes, right-click on one of the zones on the chart, and click "Add Alert on Vol S/R Zones." You do not need to add a title, as the correct alert messages are already built-in.
The latest update has migrated the script to Pine Script Version 5 and added a higher number of total boxes/lines to show on the chart. It has also increased the max bars count to the maximum Pine Script allows, enabling traders to utilize as many bars as possible when drawing the left side of SR zones that are very far back on the chart. Additionally, the update fixed issues where the indicator would not load on 1 minute and 3-minute charts unless higher timeframe SR zones
Volume-based Support & Resistance ZonesThe new and improved Support & Resistance Zones indicator is here. This indicator is based on high volume at fractal lows or fractal highs with the zones based on the size of the wick for that timeframe’s candle.
This helps traders visualize which price levels are of the most significance for either reversals or continuation of the trend when zones are broken and then re-tested.
Original script is thanks to synapticex and additional modifications is thanks to Lij_MC. Credit to both of them for most of the logic behind this script.
Since then I have made many changes to this script as noted below:
Changed default S/R lines from plots to lines, and gave option to user to change between solid line, dashed line, or dotted line for both S/R lines.
Added additional time frame and gave more TF options for TF1 other than current TF. Now you will have 4 time frames to plot S/R zones from.
Gave user option to easily change line thickness for all S/R lines.
Made it easier to change colors of S/R lines and zones by consolidating the options under settings (rather than under style).
Added extensions to active SR Zones to extend all the way right.
Added option to extend or not extend the previous S/R zones up to next S/R zone.
Added optional time frame labels to active S/R zones, with left and right options as well as option to adjust how far to the right label is set.
Fixed issue where the higher time frame S/R zone was not properly starting from the high/low of fractal. Now any higher time frame S/R will begin exactly at the High/Low points. Note that this may not work perfectly on stocks and if a fractal high/low is too many bars in the past, it will revert to a default max bars back to avoid script errors.
Added to script a function that will prevent S/R zones from lower time frames displaying while on a higher time frame. This helps clean up the chart quite a bit.
Created arrays for each time frame's boxes and lines so that the number of S/R zones can be controlled for each time frame and limit memory consumption.
New alert options added and customized alert messages.
- The way this indicator works is it looks for fractal highs or fractal lows with volume that pierces above the volume's Moving Average. This moving average value can be modified in the settings for each time frame.
- The fractal highs will be confirmed with 3 successive higher highs followed by 2 successive lower highs and vice versa for the fractal lows.
- The zone is created from the fractal high/low and the close of the candle for whatever time frame you selected. The bigger the zone, the more significant that zone is.
- You can disable any zone, change the zones to show lines only, and modify all the colors, transparencies, and thickness of lines for all the zones.
- To create alerts, you first want to enable the types of alerts you want for each time frame in the indicator's settings. Then after you apply changes, right click on one of the zones on the chart, and click "Add Alert on Vol S/R Zones". You do not need to add a title as the correct alert messages are already built-in.
- More changes will be coming in the future!
I hope you find this indicator useful, if so please give it a thumbs up!
If you have any suggestions or features you would like to see, just let me know in the comment section. Thanks and enjoy!
[Daveatt] BEST ABCD Pattern Strategy (Trailing SL + TP)Hello traders
This is the strategy version of my ABCD pattern. I added the Trailing Stop and Trailing Profit upon a follower request
I - Concept
I present to you, ladies and gentlemen, the first screener for harmonic patterns .
Starting with an ACBD pattern screener this time!!
I used the calculations from Ricardo Santo's script
In short, he's using fractals (regular or Bill Williams ) for the pattern calculations. A masterpiece !!!
II - Definitions
The ABCD pattern ( AB=CD ) is one of the classic chart patterns which is repeated over and over again.
The ABCD pattern shows perfect harmony between price and time.
The Williams Fractal is an indicator, developed by Bill Williams , that aims to detect reversal points (highs and lows) and marks them with arrows.
Up fractals and down fractals have specific shapes. The Williams Fractal indicator helps users determine in which direction price will develop
💎Strategy filters💎
I included some cool backtest filters:
- Trailing stop with trigger concept coming from this Trailing Stop Strategy
- Trailing profit with trigger coming from this Trailing Profit Strategy
Last words
It works for all asset classes (Forex, crypto, indices, etc...) and does not repaint
All the BEST
Dave
Storm Trading System This script is inspired by the following :
Fractal Dow RSI Support and Resistance ;
Moving Average Clouds ;
Let's start.
This command is based on a fun description of where we are.
Technical analysis methods are likened to a storm.
Clouds as moving average,risk factor as lightning,
fractals were taken as green and red rain.
In this system:
4 Exponential Moving Averages, ( EMA15, EMA30 , EMA45 , EMA60 ),
interpretation of my own work, Dow Factor RSI, as Fractal Support and Resistance,
interpretation of my own work , DVOG Risk Factor : with changeable background and bar color.
Fractal support resistance level codes do not belong to me.
So I'm not putting a license.
But the other codes are my labor.
Consider the risk factor not as a stop, but as a region of high attention.
It is a warning before hard movements.
And watch out for turbulence in the clouds :)
The regions above and below the clouds are major trend zones, which may take a long time.
Guide the fractals in these areas.
It allows you to comment on this and tons of similar things.
And you see where you are in the big trade from a different perspective.
Repaint issue :
Firstly our source is close . Repaint will only cause the following issue and solution:
There may be a time difference between countries as the dow factor depends on the indexes.
Do not use a low graph time frame in stocks.
