Many Moving AveragesThis script allows you to add two moving averages to a chart, where the type of moving average can be chosen from a collection of 15 different moving average algorithms. Each moving average can also have different lengths and crossovers/unders can be displayed and alerted on.
The supported moving average types are:
Simple Moving Average ( SMA )
Exponential Moving Average ( EMA )
Double Exponential Moving Average ( DEMA )
Triple Exponential Moving Average ( TEMA )
Weighted Moving Average ( WMA )
Volume Weighted Moving Average ( VWMA )
Smoothed Moving Average ( SMMA )
Hull Moving Average ( HMA )
Least Square Moving Average/Linear Regression ( LSMA )
Arnaud Legoux Moving Average ( ALMA )
Jurik Moving Average ( JMA )
Volatility Adjusted Moving Average ( VAMA )
Fractal Adaptive Moving Average ( FRAMA )
Zero-Lag Exponential Moving Average ( ZLEMA )
Kauman Adaptive Moving Average ( KAMA )
Many of the moving average algorithms were taken from other peoples' scripts. I'd like to thank the authors for making their code available.
JayRogers
Alex Orekhov (everget)
Alex Orekhov (everget)
Joris Duyck (JD)
nemozny
Shizaru
KobySK
Jurik Research and Consulting for inventing the JMA.
Cari dalam skrip untuk "algo"
BitradertrackerEste Indicador ya no consiste en líneas móviles que se cruzan para dar señales de entrada o salida, si no que va más allá e interpreta gráficamente lo que está sucediendo con el valor.
Es un algoritmo potente, que incluye 4 indicadores de tendencia y 2 indicadores de volumen.
Con este indicador podemos movernos con las "manos fuertes" del mercado, rastrear sus intenciones y tomar decisiones de compra y venta.
Diseñado para operar en criptomonedas.
En cuanto a qué temporalidad usar, cuanto más grande mejor, ya que al final lo que estamos haciendo es el análisis de datos y, por lo tanto, cuanto más datos, mejor. Personalmente recomiendo usarlo en velas de 30 minutos, 1 hora y 4 horas.
Recuerde, ningún indicador es 100% efectivo.
Este indicador nos muestra en las áreas de color púrpura (manos fuertes) y en las áreas de color verde (manos débiles) y al mostrármelo gráficamente ya el indicador vale la pena.
El mercado está impulsado por dos tipos de inversores, que se denominan manos fuertes o ballenas (agencias, fondos, empresas, bancos, etc.) y manos débiles o peces pequeños (es decir, nosotros).
No tenemos la capacidad de manipular un valor, ya que nuestra cartera es limitada, pero podemos ingresar y salir de los valores fácilmente ya que no tenemos mucho dinero.
Las ballenas pueden manipular un valor ya que tienen muchos bitcoins y / o dinero, sin embargo, no pueden moverse fácilmente.
Entonces, ¿como pueden comprar o vender sus monedas las ballenas? Bueno, ellos hacen su juego: Tratan de hacernos creer que la moneda esta barata cuando nos quieren vender sus monedas o hacernos creer que la moneda es cara cuando quieren comprar nuestras monedas. Esta manipulación se realiza de muchas maneras, la mayoría por noticias.
Nosotros, los pequeños peces, no podemos competir contra las ballenas, pero podemos descubrir qué están haciendo (recuerde, son lentas, mueven sus monstruosas cantidades de dinero) debemos movernos con ellas e imitarlas. Mejor estar bajo la ballena que delante de ella.
Con este indicador puedes ver cuando las ballenas están operando y reaccionar ; porque el enfoque matemático que los sustenta ha demostrado ser bastante exitoso.
Cuando las manos fuertes están por debajo de cero, se dice que están comprando. Lo mismo ocurre con las manos débiles. Generalmente, si las manos fuertes están comprando o vendiendo, el precio está lateralizado. El movimiento del precio está asociado con las compras y ventas realizadas por la mano débil.
Espero que les sea de mucha utilidad.
Bitrader4.0
This indicator no longer consists of mobile lines that intersect to give input or output signals, but it goes further and graphically interprets what is happening with the value.
It is a powerful algorithm, which includes 4 trend indicators and 2 volume indicators.
With this indicator we can move with the "strong hands" of the market, track their intentions and make buying and selling decisions.
Designed to operate in cryptocurrencies.
As for what temporality to use, the bigger the better, since in the end what we are doing is the analysis of data and, therefore, the more data, the better. Personally I recommend using it in candles of 30 minutes, 1 hour and 4 hours.
Remember, no indicator is 100% effective.
This indicator shows us in the areas of color purple (strong hands) and in the areas of color green (weak hands) and by showing it graphically and the indicator is worth it.
The market is driven by two types of investors, which are called strong hands or whales (agencies, funds, companies, banks, etc.) and weak hands or small fish (that is, us).
We do not have the ability to manipulate a value, since our portfolio is limited, but we can enter and exit the securities easily since we do not have much money.
Whales can manipulate a value since they have many bitcoins and / or money, however, they can not move easily.
So, how can whales buy or sell their coins? Well, they make their game: They try to make us believe that the currency is cheap when they want to sell their coins or make us believe that the currency is expensive when they want to buy our coins. This manipulation is done in many ways, most by news.
We, small fish, can not compete against whales, but we can find out what they are doing (remember, they are slow, move their monstrous amounts of money) we must move with them and imitate them. Better to be under the whale than in front of her.
With this indicator you can see when the whales are operating and reacting; because the mathematical approach that sustains them has proven to be quite successful.
When strong hands are below zero, they say they are buying. The same goes for weak hands. Generally, if strong hands are buying or selling, the price is lateralized. The movement of the price is associated with the purchases and sales made by the weak hand.
I hope you find it very useful.
Bitrader4.0
META: STDEV Study (Scripting Exercise)While trying to figure out how to make the STDEV function use an exponential moving average instead of simple moving average , I discovered the builtin function doesn't really use either.
Check it out, it's amazing how different the two-pass algorithm is from the builtin!
Eventually I reverse-engineered and discovered that STDEV uses the Naiive algorithm and doesn't apply "Bessel's Correction". K can be 0, it doesn't seem to change the data although having it included should make it a little more precise.
en.wikipedia.org
Acc/DistAMA with FRACTAL DEVIATION BANDS by @XeL_ArjonaACCUMULATION/DISTRIBUTION ADAPTIVE MOVING AVERAGE with FRACTAL DEVIATION BANDS
Ver. 2.5 @ 16.09.2015
By Ricardo M Arjona @XeL_Arjona
DISCLAIMER:
The Following indicator/code IS NOT intended to be a formal investment advice or recommendation by the
author, nor should be construed as such. Users will be fully responsible by their use regarding their own trading vehicles/assets.
The embedded code and ideas within this work are FREELY AND PUBLICLY available on the Web for NON LUCRATIVE ACTIVITIES and must remain as is.
Pine Script code MOD's and adaptations by @XeL_Arjona with special mention in regard of:
Buy (Bull) and Sell (Bear) "Power Balance Algorithm" by:
Stocks & Commodities V. 21:10 (68-72): "Bull And Bear Balance Indicator by Vadim Gimelfarb"
Fractal Deviation Bands by @XeL_Arjona.
Color Cloud Fill by @ChrisMoody
CHANGE LOG:
Following a "Fractal Approach" now the lookback window is hardcode correlated with a given timeframe. (Default @ 126 days as Half a Year / 252 bars)
Clean and speed up of Adaptive Moving Average Algo.
Fractal Deviation Band Cloud coloring smoothed.
>
ALL NEW IDEAS OR MODIFICATIONS to these indicator(s) are Welcome in favor to deploy a better and more accurate readings. I will be very glad to be notified at Twitter or TradingVew accounts at: @XeL_Arjona
Any important addition to this work MUST REMAIN PUBLIC by means of CreativeCommons CC & TradingView. Copyright 2015
Volume Pressure Composite Average with Bands by @XeL_ArjonaVOLUME PRESSURE COMPOSITE AVERAGE WITH BANDS
Ver. 1.0.beta.10.08.2015
By Ricardo M Arjona @XeL_Arjona
DISCLAIMER:
The Following indicator/code IS NOT intended to be a formal investment advice or recommendation by the author, nor should be construed as such. Users will be fully responsible by their use regarding their own trading vehicles/assets.
The embedded code and ideas within this work are FREELY AND PUBLICLY available on the Web for NON LUCRATIVE ACTIVITIES and must remain as is.
Pine Script code MOD's and adaptations by @XeL_Arjona with special mention in regard of:
Buy (Bull) and Sell (Bear) "Power Balance Algorithm" by :
Stocks & Commodities V. 21:10 (68-72):
"Bull And Bear Balance Indicator by Vadim Gimelfarb"
Adjusted Exponential Adaptation from original Volume Weighted Moving Average (VEMA) by @XeL_Arjona with help given at the @pinescript chat room with special mention to @RicardoSantos
Color Cloud Fill Condition algorithm by @ChrisMoody
WHAT IS THIS?
The following indicators try to acknowledge in a K-I-S-S approach to the eye (Keep-It-Simple-Stupid), the two most important aspects of nearly every trading vehicle: -- PRICE ACTION IN RELATION BY IT'S VOLUME --
A) My approach is to make this indicator both as a "Trend Follower" as well as a Volatility expressed in the Bands which are the weighting basis of the trend given their "Cross Signal" given by the Buy & Sell Volume Pressures algorithm. >
B) Please experiment with lookback periods against different timeframes. Given the nature of the Volume Mathematical Monster this kind of study is and in concordance with Price Action; at first glance I've noted that both in short as in long term periods, the indicator tends to adapt quite well to general price action conditions. BE ADVICED THIS IS EXPERIMENTAL!
C) ALL NEW IDEAS OR MODIFICATIONS to these indicator(s) are Welcome in favor to deploy a better and more accurate readings. I will be very glad to be notified at Twitter or TradingVew accounts at: @XeL_Arjona
Any important addition to this work MUST REMAIN PUBLIC by means of CreativeCommons CC & TradingView. --- All Authorship Rights RESERVED 2015 ---
EMA 12-26-100 Momentum Strategy# Triple EMA Multi-Signal Momentum Strategy
## 📊 Overview
**Triple EMA Multi-Signal** is a comprehensive trend-following momentum strategy designed specifically for cryptocurrency markets. It combines multiple technical indicators and signal types to identify high-probability trading opportunities while maintaining strict risk management protocols.
The strategy excels in trending markets and uses adaptive position sizing with trailing stops to maximize profits during strong trends while protecting capital during choppy conditions.
## 🎯 Core Algorithm
### Triple EMA System
The strategy employs a three-layer EMA system to identify trend direction and strength:
- **Fast EMA (12)**: Quick response to price changes
- **Slow EMA (26)**: Confirmation of trend direction
- **Trend EMA (100)**: Overall market bias filter
Trades are only taken when all three EMAs align in the same direction, ensuring we trade with the dominant trend.
### Multi-Signal Confirmation (8 Signal Types)
The strategy requires at least 1-2 confirmed signals from multiple independent sources before entering a position:
1. **EMA Crossover** - Fast EMA crossing Slow EMA (primary signal)
2. **MACD Cross** - MACD line crossing signal line (momentum confirmation)
3. **RSI Reversal** - RSI bouncing from oversold/overbought zones
4. **Price Action** - Strong bullish/bearish candles (>60% of range)
5. **Volume Spike** - Above-average volume confirmation
6. **Breakout** - Price breaking 20-period high/low with volume
7. **Pullback to EMA** - Trend continuation after healthy retracement
8. **Bollinger Bounce** - Price bouncing from BB bands
This multi-signal approach significantly reduces false signals and improves win rate.
## 💰 Risk Management
### Position Sizing
- Default: 20-25% of equity per trade
- Adjustable based on risk tolerance
- Smaller positions recommended for leveraged trading
### Stop Loss & Take Profit
- **Stop Loss**: 2.0% (tight control of risk)
- **Take Profit**: 5.5% (2.75:1 reward-to-risk ratio)
- Both levels are fixed at entry to avoid emotional decisions
### Trailing Stop System
- Activates after 1.8% profit
- Trails at 1.3% below current price
- Locks in profits during extended trends
- Automatically adjusts as price moves in your favor
### Maximum Hold Time
- 36-48 hours maximum (configurable)
- Designed to minimize funding rate costs on futures
- Forces position closure to avoid excessive exposure
- Helps maintain capital velocity
## 📈 Key Features
### Trend Filters
- **ADX Filter**: Ensures sufficient trend strength (threshold: 20)
- **EMA Alignment**: All three EMAs must confirm trend direction
- **RSI Boundaries**: Avoids extreme overbought/oversold entries
### Volume Analysis
- Volume must exceed 20-period moving average
- Configurable multiplier (default: 1.0x)
- Helps identify institutional participation
### Automatic Exit Conditions
1. Take Profit target reached
2. Stop Loss triggered
3. Trailing stop activated
4. Trend reversal (EMA cross in opposite direction)
5. Maximum hold time exceeded
## 🎮 Recommended Settings
### For Spot Trading (Conservative)
```
Position Size: 15-20%
Stop Loss: 2.5%
Take Profit: 6.0%
Max Hold: 72 hours
Leverage: 1x
```
### For Futures 3-5x Leverage (Balanced)
```
Position Size: 12-15%
Stop Loss: 2.0%
Take Profit: 5.5%
Max Hold: 36 hours
Trailing: Active
```
### For Aggressive Trading 5-10x (High Risk)
```
Position Size: 8-12%
Stop Loss: 1.5%
Take Profit: 4.5%
Max Hold: 24 hours
ADX Filter: Disabled
```
## 📊 Performance Metrics
### Backtested Results (BTC/USDT 1H, 2 years)
- **Total Return**: ~19% (spot) / ~75% (5x leverage)*
- **Total Trades**: 240-300
- **Win Rate**: 49-52%
- **Profit Factor**: 1.25-1.50
- **Max Drawdown**: ~18-22%
- **Average Trade**: 0.5-3 days
*Leverage results exclude funding rates and real-world slippage
### Optimal Timeframes
- **1 Hour**: Best for active trading (recommended)
- **4 Hour**: More stable, fewer signals
- **15 Min**: High frequency (requires monitoring)
### Best Performing Assets
- BTC/USDT (most tested)
- ETH/USDT
- Major altcoins with good liquidity
- Not recommended for low-cap or illiquid pairs
## ⚙️ How to Use
1. **Add to Chart**: Apply strategy to 1H BTC/USDT chart
2. **Adjust Settings**: Configure risk parameters based on your preference
3. **Review Signals**: Green = Long, Red = Short, labels show signal count
4. **Monitor Performance**: Check strategy tester for detailed statistics
5. **Optimize**: Use strategy optimization to find best parameters for your market
## 🎨 Visual Indicators
The strategy provides clear visual feedback:
- **EMA Lines**: Blue (Fast), Red (Slow), Orange (Trend)
- **BUY/SELL Labels**: Show entry points with signal count
- **Stop/Target Lines**: Red (SL), Green (TP) displayed during active trades
- **Background Color**: Light green (long), light red (short) when in position
- **Info Panel**: Shows current trend, RSI, ADX, and volume status
## ⚠️ Important Notes
### Risk Disclaimer
- This strategy is for educational purposes only
- Past performance does not guarantee future results
- Cryptocurrency trading involves substantial risk
- Only trade with capital you can afford to lose
- Always use proper position sizing and risk management
### Limitations
- Performs poorly in sideways/choppy markets
- Requires sufficient liquidity for best execution
- Backtests do not include:
- Real-world slippage (especially during volatility)
- Funding rates (for perpetual futures)
- Exchange downtime or connection issues
- Emotional trading decisions
### For Futures Trading
If using this strategy on futures with leverage:
- Reduce position size proportionally to leverage
- Account for funding rates (~0.01% per 8h)
- Set max hold time to minimize funding costs
- Use lower leverage (3-5x max recommended)
- Monitor liquidation price carefully
## 🔧 Customization
All parameters are fully customizable:
- EMA periods (fast/slow/trend)
- MACD settings (12/26/9)
- RSI levels (30/70)
- Stop Loss / Take Profit percentages
- Trailing stop activation and offset
- Volume multiplier
- ADX threshold
- Maximum hold time
## 📚 Strategy Logic
The strategy follows this decision tree:
```
1. Check Trend Direction (EMA alignment)
↓
2. Scan for Entry Signals (8 types)
↓
3. Confirm with Filters (ADX, Volume, RSI)
↓
4. Enter Position with Fixed SL/TP
↓
5. Monitor for Exit Conditions:
- TP Hit → Close with profit
- SL Hit → Close with loss
- Trailing Active → Follow price
- Trend Reversal → Close position
- Max Time → Force close
```
## 🎓 Best Practices
1. **Start Conservative**: Use smaller position sizes initially
2. **Track Performance**: Monitor actual vs backtested results
3. **Optimize Regularly**: Market conditions change, adapt parameters
4. **Combine with Analysis**: Don't rely solely on automated signals
5. **Manage Emotions**: Stick to the system, avoid manual overrides
6. **Paper Trade First**: Test on demo before risking real capital
## 📞 Support & Updates
This strategy is actively maintained and updated based on:
- Market condition changes
- User feedback and suggestions
- Performance optimization
- Bug fixes and improvements
## 🏆 Conclusion
Triple EMA Multi-Signal Strategy offers a robust, systematic approach to cryptocurrency trading by combining trend following, momentum indicators, and strict risk management. Its multi-signal confirmation system helps filter false signals while the trailing stop mechanism captures extended trends.
The strategy is suitable for both manual traders looking for high-probability setups and algorithmic traders seeking a proven systematic approach.
**Remember**: No strategy wins 100% of the time. Success comes from consistent application, proper risk management, and continuous adaptation to changing market conditions.
---
*Version: 1.0*
*Last Updated: November 2025*
*Tested on: BTC/USDT, ETH/USDT (1H, 4H timeframes)*
*Recommended Capital: $5,000+ for optimal position sizing*
Dimensional Resonance ProtocolDimensional Resonance Protocol
🌀 CORE INNOVATION: PHASE SPACE RECONSTRUCTION & EMERGENCE DETECTION
The Dimensional Resonance Protocol represents a paradigm shift from traditional technical analysis to complexity science. Rather than measuring price levels or indicator crossovers, DRP reconstructs the hidden attractor governing market dynamics using Takens' embedding theorem, then detects emergence —the rare moments when multiple dimensions of market behavior spontaneously synchronize into coherent, predictable states.
The Complexity Hypothesis:
Markets are not simple oscillators or random walks—they are complex adaptive systems existing in high-dimensional phase space. Traditional indicators see only shadows (one-dimensional projections) of this higher-dimensional reality. DRP reconstructs the full phase space using time-delay embedding, revealing the true structure of market dynamics.
Takens' Embedding Theorem (1981):
A profound mathematical result from dynamical systems theory: Given a time series from a complex system, we can reconstruct its full phase space by creating delayed copies of the observation.
Mathematical Foundation:
From single observable x(t), create embedding vectors:
X(t) =
Where:
• d = Embedding dimension (default 5)
• τ = Time delay (default 3 bars)
• x(t) = Price or return at time t
Key Insight: If d ≥ 2D+1 (where D is the true attractor dimension), this embedding is topologically equivalent to the actual system dynamics. We've reconstructed the hidden attractor from a single price series.
Why This Matters:
Markets appear random in one dimension (price chart). But in reconstructed phase space, structure emerges—attractors, limit cycles, strange attractors. When we identify these structures, we can detect:
• Stable regions : Predictable behavior (trade opportunities)
• Chaotic regions : Unpredictable behavior (avoid trading)
• Critical transitions : Phase changes between regimes
Phase Space Magnitude Calculation:
phase_magnitude = sqrt(Σ ² for i = 0 to d-1)
This measures the "energy" or "momentum" of the market trajectory through phase space. High magnitude = strong directional move. Low magnitude = consolidation.
📊 RECURRENCE QUANTIFICATION ANALYSIS (RQA)
Once phase space is reconstructed, we analyze its recurrence structure —when does the system return near previous states?
Recurrence Plot Foundation:
A recurrence occurs when two phase space points are closer than threshold ε:
R(i,j) = 1 if ||X(i) - X(j)|| < ε, else 0
This creates a binary matrix showing when the system revisits similar states.
Key RQA Metrics:
1. Recurrence Rate (RR):
RR = (Number of recurrent points) / (Total possible pairs)
• RR near 0: System never repeats (highly stochastic)
• RR = 0.1-0.3: Moderate recurrence (tradeable patterns)
• RR > 0.5: System stuck in attractor (ranging market)
• RR near 1: System frozen (no dynamics)
Interpretation: Moderate recurrence is optimal —patterns exist but market isn't stuck.
2. Determinism (DET):
Measures what fraction of recurrences form diagonal structures in the recurrence plot. Diagonals indicate deterministic evolution (trajectory follows predictable paths).
DET = (Recurrence points on diagonals) / (Total recurrence points)
• DET < 0.3: Random dynamics
• DET = 0.3-0.7: Moderate determinism (patterns with noise)
• DET > 0.7: Strong determinism (technical patterns reliable)
Trading Implication: Signals are prioritized when DET > 0.3 (deterministic state) and RR is moderate (not stuck).
Threshold Selection (ε):
Default ε = 0.10 × std_dev means two states are "recurrent" if within 10% of a standard deviation. This is tight enough to require genuine similarity but loose enough to find patterns.
🔬 PERMUTATION ENTROPY: COMPLEXITY MEASUREMENT
Permutation entropy measures the complexity of a time series by analyzing the distribution of ordinal patterns.
Algorithm (Bandt & Pompe, 2002):
1. Take overlapping windows of length n (default n=4)
2. For each window, record the rank order pattern
Example: → pattern (ranks from lowest to highest)
3. Count frequency of each possible pattern
4. Calculate Shannon entropy of pattern distribution
Mathematical Formula:
H_perm = -Σ p(π) · ln(p(π))
Where π ranges over all n! possible permutations, p(π) is the probability of pattern π.
Normalized to :
H_norm = H_perm / ln(n!)
Interpretation:
• H < 0.3 : Very ordered, crystalline structure (strong trending)
• H = 0.3-0.5 : Ordered regime (tradeable with patterns)
• H = 0.5-0.7 : Moderate complexity (mixed conditions)
• H = 0.7-0.85 : Complex dynamics (challenging to trade)
• H > 0.85 : Maximum entropy (nearly random, avoid)
Entropy Regime Classification:
DRP classifies markets into five entropy regimes:
• CRYSTALLINE (H < 0.3): Maximum order, persistent trends
• ORDERED (H < 0.5): Clear patterns, momentum strategies work
• MODERATE (H < 0.7): Mixed dynamics, adaptive required
• COMPLEX (H < 0.85): High entropy, mean reversion better
• CHAOTIC (H ≥ 0.85): Near-random, minimize trading
Why Permutation Entropy?
Unlike traditional entropy methods requiring binning continuous data (losing information), permutation entropy:
• Works directly on time series
• Robust to monotonic transformations
• Computationally efficient
• Captures temporal structure, not just distribution
• Immune to outliers (uses ranks, not values)
⚡ LYAPUNOV EXPONENT: CHAOS vs STABILITY
The Lyapunov exponent λ measures sensitivity to initial conditions —the hallmark of chaos.
Physical Meaning:
Two trajectories starting infinitely close will diverge at exponential rate e^(λt):
Distance(t) ≈ Distance(0) × e^(λt)
Interpretation:
• λ > 0 : Positive Lyapunov exponent = CHAOS
- Small errors grow exponentially
- Long-term prediction impossible
- System is sensitive, unpredictable
- AVOID TRADING
• λ ≈ 0 : Near-zero = CRITICAL STATE
- Edge of chaos
- Transition zone between order and disorder
- Moderate predictability
- PROCEED WITH CAUTION
• λ < 0 : Negative Lyapunov exponent = STABLE
- Small errors decay
- Trajectories converge
- System is predictable
- OPTIMAL FOR TRADING
Estimation Method:
DRP estimates λ by tracking how quickly nearby states diverge over a rolling window (default 20 bars):
For each bar i in window:
δ₀ = |x - x | (initial separation)
δ₁ = |x - x | (previous separation)
if δ₁ > 0:
ratio = δ₀ / δ₁
log_ratios += ln(ratio)
λ ≈ average(log_ratios)
Stability Classification:
• STABLE : λ < 0 (negative growth rate)
• CRITICAL : |λ| < 0.1 (near neutral)
• CHAOTIC : λ > 0.2 (strong positive growth)
Signal Filtering:
By default, NEXUS requires λ < 0 (stable regime) for signal confirmation. This filters out trades during chaotic periods when technical patterns break down.
📐 HIGUCHI FRACTAL DIMENSION
Fractal dimension measures self-similarity and complexity of the price trajectory.
Theoretical Background:
A curve's fractal dimension D ranges from 1 (smooth line) to 2 (space-filling curve):
• D ≈ 1.0 : Smooth, persistent trending
• D ≈ 1.5 : Random walk (Brownian motion)
• D ≈ 2.0 : Highly irregular, space-filling
Higuchi Method (1988):
For a time series of length N, construct k different curves by taking every k-th point:
L(k) = (1/k) × Σ|x - x | × (N-1)/(⌊(N-m)/k⌋ × k)
For different values of k (1 to k_max), calculate L(k). The fractal dimension is the slope of log(L(k)) vs log(1/k):
D = slope of log(L) vs log(1/k)
Market Interpretation:
• D < 1.35 : Strong trending, persistent (Hurst > 0.5)
- TRENDING regime
- Momentum strategies favored
- Breakouts likely to continue
• D = 1.35-1.45 : Moderate persistence
- PERSISTENT regime
- Trend-following with caution
- Patterns have meaning
• D = 1.45-1.55 : Random walk territory
- RANDOM regime
- Efficiency hypothesis holds
- Technical analysis least reliable
• D = 1.55-1.65 : Anti-persistent (mean-reverting)
- ANTI-PERSISTENT regime
- Oscillator strategies work
- Overbought/oversold meaningful
• D > 1.65 : Highly complex, choppy
- COMPLEX regime
- Avoid directional bets
- Wait for regime change
Signal Filtering:
Resonance signals (secondary signal type) require D < 1.5, indicating trending or persistent dynamics where momentum has meaning.
🔗 TRANSFER ENTROPY: CAUSAL INFORMATION FLOW
Transfer entropy measures directed causal influence between time series—not just correlation, but actual information transfer.
Schreiber's Definition (2000):
Transfer entropy from X to Y measures how much knowing X's past reduces uncertainty about Y's future:
TE(X→Y) = H(Y_future | Y_past) - H(Y_future | Y_past, X_past)
Where H is Shannon entropy.
Key Properties:
1. Directional : TE(X→Y) ≠ TE(Y→X) in general
2. Non-linear : Detects complex causal relationships
3. Model-free : No assumptions about functional form
4. Lag-independent : Captures delayed causal effects
Three Causal Flows Measured:
1. Volume → Price (TE_V→P):
Measures how much volume patterns predict price changes.
• TE > 0 : Volume provides predictive information about price
- Institutional participation driving moves
- Volume confirms direction
- High reliability
• TE ≈ 0 : No causal flow (weak volume/price relationship)
- Volume uninformative
- Caution on signals
• TE < 0 (rare): Suggests price leading volume
- Potentially manipulated or thin market
2. Volatility → Momentum (TE_σ→M):
Does volatility expansion predict momentum changes?
• Positive TE : Volatility precedes momentum shifts
- Breakout dynamics
- Regime transitions
3. Structure → Price (TE_S→P):
Do support/resistance patterns causally influence price?
• Positive TE : Structural levels have causal impact
- Technical levels matter
- Market respects structure
Net Causal Flow:
Net_Flow = TE_V→P + 0.5·TE_σ→M + TE_S→P
• Net > +0.1 : Bullish causal structure
• Net < -0.1 : Bearish causal structure
• |Net| < 0.1 : Neutral/unclear causation
Causal Gate:
For signal confirmation, NEXUS requires:
• Buy signals : TE_V→P > 0 AND Net_Flow > 0.05
• Sell signals : TE_V→P > 0 AND Net_Flow < -0.05
This ensures volume is actually driving price (causal support exists), not just correlated noise.
Implementation Note:
Computing true transfer entropy requires discretizing continuous data into bins (default 6 bins) and estimating joint probability distributions. NEXUS uses a hybrid approach combining TE theory with autocorrelation structure and lagged cross-correlation to approximate information transfer in computationally efficient manner.
🌊 HILBERT PHASE COHERENCE
Phase coherence measures synchronization across market dimensions using Hilbert transform analysis.
Hilbert Transform Theory:
For a signal x(t), the Hilbert transform H (t) creates an analytic signal:
z(t) = x(t) + i·H (t) = A(t)·e^(iφ(t))
Where:
• A(t) = Instantaneous amplitude
• φ(t) = Instantaneous phase
Instantaneous Phase:
φ(t) = arctan(H (t) / x(t))
The phase represents where the signal is in its natural cycle—analogous to position on a unit circle.
Four Dimensions Analyzed:
1. Momentum Phase : Phase of price rate-of-change
2. Volume Phase : Phase of volume intensity
3. Volatility Phase : Phase of ATR cycles
4. Structure Phase : Phase of position within range
Phase Locking Value (PLV):
For two signals with phases φ₁(t) and φ₂(t), PLV measures phase synchronization:
PLV = |⟨e^(i(φ₁(t) - φ₂(t)))⟩|
Where ⟨·⟩ is time average over window.
Interpretation:
• PLV = 0 : Completely random phase relationship (no synchronization)
• PLV = 0.5 : Moderate phase locking
• PLV = 1 : Perfect synchronization (phases locked)
Pairwise PLV Calculations:
• PLV_momentum-volume : Are momentum and volume cycles synchronized?
• PLV_momentum-structure : Are momentum cycles aligned with structure?
• PLV_volume-structure : Are volume and structural patterns in phase?
Overall Phase Coherence:
Coherence = (PLV_mom-vol + PLV_mom-struct + PLV_vol-struct) / 3
Signal Confirmation:
Emergence signals require coherence ≥ threshold (default 0.70):
• Below 0.70: Dimensions not synchronized, no coherent market state
• Above 0.70: Dimensions in phase, coherent behavior emerging
Coherence Direction:
The summed phase angles indicate whether synchronized dimensions point bullish or bearish:
Direction = sin(φ_momentum) + 0.5·sin(φ_volume) + 0.5·sin(φ_structure)
• Direction > 0 : Phases pointing upward (bullish synchronization)
• Direction < 0 : Phases pointing downward (bearish synchronization)
🌀 EMERGENCE SCORE: MULTI-DIMENSIONAL ALIGNMENT
The emergence score aggregates all complexity metrics into a single 0-1 value representing market coherence.
Eight Components with Weights:
1. Phase Coherence (20%):
Direct contribution: coherence × 0.20
Measures dimensional synchronization.
2. Entropy Regime (15%):
Contribution: (0.6 - H_perm) / 0.6 × 0.15 if H < 0.6, else 0
Rewards low entropy (ordered, predictable states).
3. Lyapunov Stability (12%):
• λ < 0 (stable): +0.12
• |λ| < 0.1 (critical): +0.08
• λ > 0.2 (chaotic): +0.0
Requires stable, predictable dynamics.
4. Fractal Dimension Trending (12%):
Contribution: (1.45 - D) / 0.45 × 0.12 if D < 1.45, else 0
Rewards trending fractal structure (D < 1.45).
5. Dimensional Resonance (12%):
Contribution: |dimensional_resonance| × 0.12
Measures alignment across momentum, volume, structure, volatility dimensions.
6. Causal Flow Strength (9%):
Contribution: |net_causal_flow| × 0.09
Rewards strong causal relationships.
7. Phase Space Embedding (10%):
Contribution: min(|phase_magnitude_norm|, 3.0) / 3.0 × 0.10 if |magnitude| > 1.0
Rewards strong trajectory in reconstructed phase space.
8. Recurrence Quality (10%):
Contribution: determinism × 0.10 if DET > 0.3 AND 0.1 < RR < 0.8
Rewards deterministic patterns with moderate recurrence.
Total Emergence Score:
E = Σ(components) ∈
Capped at 1.0 maximum.
Emergence Direction:
Separate calculation determining bullish vs bearish:
• Dimensional resonance sign
• Net causal flow sign
• Phase magnitude correlation with momentum
Signal Threshold:
Default emergence_threshold = 0.75 means 75% of maximum possible emergence score required to trigger signals.
Why Emergence Matters:
Traditional indicators measure single dimensions. Emergence detects self-organization —when multiple independent dimensions spontaneously align. This is the market equivalent of a phase transition in physics, where microscopic chaos gives way to macroscopic order.
These are the highest-probability trade opportunities because the entire system is resonating in the same direction.
🎯 SIGNAL GENERATION: EMERGENCE vs RESONANCE
DRP generates two tiers of signals with different requirements:
TIER 1: EMERGENCE SIGNALS (Primary)
Requirements:
1. Emergence score ≥ threshold (default 0.75)
2. Phase coherence ≥ threshold (default 0.70)
3. Emergence direction > 0.2 (bullish) or < -0.2 (bearish)
4. Causal gate passed (if enabled): TE_V→P > 0 and net_flow confirms direction
5. Stability zone (if enabled): λ < 0 or |λ| < 0.1
6. Price confirmation: Close > open (bulls) or close < open (bears)
7. Cooldown satisfied: bars_since_signal ≥ cooldown_period
EMERGENCE BUY:
• All above conditions met with bullish direction
• Market has achieved coherent bullish state
• Multiple dimensions synchronized upward
EMERGENCE SELL:
• All above conditions met with bearish direction
• Market has achieved coherent bearish state
• Multiple dimensions synchronized downward
Premium Emergence:
When signal_quality (emergence_score × phase_coherence) > 0.7:
• Displayed as ★ star symbol
• Highest conviction trades
• Maximum dimensional alignment
Standard Emergence:
When signal_quality 0.5-0.7:
• Displayed as ◆ diamond symbol
• Strong signals but not perfect alignment
TIER 2: RESONANCE SIGNALS (Secondary)
Requirements:
1. Dimensional resonance > +0.6 (bullish) or < -0.6 (bearish)
2. Fractal dimension < 1.5 (trending/persistent regime)
3. Price confirmation matches direction
4. NOT in chaotic regime (λ < 0.2)
5. Cooldown satisfied
6. NO emergence signal firing (resonance is fallback)
RESONANCE BUY:
• Dimensional alignment without full emergence
• Trending fractal structure
• Moderate conviction
RESONANCE SELL:
• Dimensional alignment without full emergence
• Bearish resonance with trending structure
• Moderate conviction
Displayed as small ▲/▼ triangles with transparency.
Signal Hierarchy:
IF emergence conditions met:
Fire EMERGENCE signal (★ or ◆)
ELSE IF resonance conditions met:
Fire RESONANCE signal (▲ or ▼)
ELSE:
No signal
Cooldown System:
After any signal fires, cooldown_period (default 5 bars) must elapse before next signal. This prevents signal clustering during persistent conditions.
Cooldown tracks using bar_index:
bars_since_signal = current_bar_index - last_signal_bar_index
cooldown_ok = bars_since_signal >= cooldown_period
🎨 VISUAL SYSTEM: MULTI-LAYER COMPLEXITY
DRP provides rich visual feedback across four distinct layers:
LAYER 1: COHERENCE FIELD (Background)
Colored background intensity based on phase coherence:
• No background : Coherence < 0.5 (incoherent state)
• Faint glow : Coherence 0.5-0.7 (building coherence)
• Stronger glow : Coherence > 0.7 (coherent state)
Color:
• Cyan/teal: Bullish coherence (direction > 0)
• Red/magenta: Bearish coherence (direction < 0)
• Blue: Neutral coherence (direction ≈ 0)
Transparency: 98 minus (coherence_intensity × 10), so higher coherence = more visible.
LAYER 2: STABILITY/CHAOS ZONES
Background color indicating Lyapunov regime:
• Green tint (95% transparent): λ < 0, STABLE zone
- Safe to trade
- Patterns meaningful
• Gold tint (90% transparent): |λ| < 0.1, CRITICAL zone
- Edge of chaos
- Moderate risk
• Red tint (85% transparent): λ > 0.2, CHAOTIC zone
- Avoid trading
- Unpredictable behavior
LAYER 3: DIMENSIONAL RIBBONS
Three EMAs representing dimensional structure:
• Fast ribbon : EMA(8) in cyan/teal (fast dynamics)
• Medium ribbon : EMA(21) in blue (intermediate)
• Slow ribbon : EMA(55) in red/magenta (slow dynamics)
Provides visual reference for multi-scale structure without cluttering with raw phase space data.
LAYER 4: CAUSAL FLOW LINE
A thicker line plotted at EMA(13) colored by net causal flow:
• Cyan/teal : Net_flow > +0.1 (bullish causation)
• Red/magenta : Net_flow < -0.1 (bearish causation)
• Gray : |Net_flow| < 0.1 (neutral causation)
Shows real-time direction of information flow.
EMERGENCE FLASH:
Strong background flash when emergence signals fire:
• Cyan flash for emergence buy
• Red flash for emergence sell
• 80% transparency for visibility without obscuring price
📊 COMPREHENSIVE DASHBOARD
Real-time monitoring of all complexity metrics:
HEADER:
• 🌀 DRP branding with gold accent
CORE METRICS:
EMERGENCE:
• Progress bar (█ filled, ░ empty) showing 0-100%
• Percentage value
• Direction arrow (↗ bull, ↘ bear, → neutral)
• Color-coded: Green/gold if active, gray if low
COHERENCE:
• Progress bar showing phase locking value
• Percentage value
• Checkmark ✓ if ≥ threshold, circle ○ if below
• Color-coded: Cyan if coherent, gray if not
COMPLEXITY SECTION:
ENTROPY:
• Regime name (CRYSTALLINE/ORDERED/MODERATE/COMPLEX/CHAOTIC)
• Numerical value (0.00-1.00)
• Color: Green (ordered), gold (moderate), red (chaotic)
LYAPUNOV:
• State (STABLE/CRITICAL/CHAOTIC)
• Numerical value (typically -0.5 to +0.5)
• Status indicator: ● stable, ◐ critical, ○ chaotic
• Color-coded by state
FRACTAL:
• Regime (TRENDING/PERSISTENT/RANDOM/ANTI-PERSIST/COMPLEX)
• Dimension value (1.0-2.0)
• Color: Cyan (trending), gold (random), red (complex)
PHASE-SPACE:
• State (STRONG/ACTIVE/QUIET)
• Normalized magnitude value
• Parameters display: d=5 τ=3
CAUSAL SECTION:
CAUSAL:
• Direction (BULL/BEAR/NEUTRAL)
• Net flow value
• Flow indicator: →P (to price), P← (from price), ○ (neutral)
V→P:
• Volume-to-price transfer entropy
• Small display showing specific TE value
DIMENSIONAL SECTION:
RESONANCE:
• Progress bar of absolute resonance
• Signed value (-1 to +1)
• Color-coded by direction
RECURRENCE:
• Recurrence rate percentage
• Determinism percentage display
• Color-coded: Green if high quality
STATE SECTION:
STATE:
• Current mode: EMERGENCE / RESONANCE / CHAOS / SCANNING
• Icon: 🚀 (emergence buy), 💫 (emergence sell), ▲ (resonance buy), ▼ (resonance sell), ⚠ (chaos), ◎ (scanning)
• Color-coded by state
SIGNALS:
• E: count of emergence signals
• R: count of resonance signals
⚙️ KEY PARAMETERS EXPLAINED
Phase Space Configuration:
• Embedding Dimension (3-10, default 5): Reconstruction dimension
- Low (3-4): Simple dynamics, faster computation
- Medium (5-6): Balanced (recommended)
- High (7-10): Complex dynamics, more data needed
- Rule: d ≥ 2D+1 where D is true dimension
• Time Delay (τ) (1-10, default 3): Embedding lag
- Fast markets: 1-2
- Normal: 3-4
- Slow markets: 5-10
- Optimal: First minimum of mutual information (often 2-4)
• Recurrence Threshold (ε) (0.01-0.5, default 0.10): Phase space proximity
- Tight (0.01-0.05): Very similar states only
- Medium (0.08-0.15): Balanced
- Loose (0.20-0.50): Liberal matching
Entropy & Complexity:
• Permutation Order (3-7, default 4): Pattern length
- Low (3): 6 patterns, fast but coarse
- Medium (4-5): 24-120 patterns, balanced
- High (6-7): 720-5040 patterns, fine-grained
- Note: Requires window >> order! for stability
• Entropy Window (15-100, default 30): Lookback for entropy
- Short (15-25): Responsive to changes
- Medium (30-50): Stable measure
- Long (60-100): Very smooth, slow adaptation
• Lyapunov Window (10-50, default 20): Stability estimation window
- Short (10-15): Fast chaos detection
- Medium (20-30): Balanced
- Long (40-50): Stable λ estimate
Causal Inference:
• Enable Transfer Entropy (default ON): Causality analysis
- Keep ON for full system functionality
• TE History Length (2-15, default 5): Causal lookback
- Short (2-4): Quick causal detection
- Medium (5-8): Balanced
- Long (10-15): Deep causal analysis
• TE Discretization Bins (4-12, default 6): Binning granularity
- Few (4-5): Coarse, robust, needs less data
- Medium (6-8): Balanced
- Many (9-12): Fine-grained, needs more data
Phase Coherence:
• Enable Phase Coherence (default ON): Synchronization detection
- Keep ON for emergence detection
• Coherence Threshold (0.3-0.95, default 0.70): PLV requirement
- Loose (0.3-0.5): More signals, lower quality
- Balanced (0.6-0.75): Recommended
- Strict (0.8-0.95): Rare, highest quality
• Hilbert Smoothing (3-20, default 8): Phase smoothing
- Low (3-5): Responsive, noisier
- Medium (6-10): Balanced
- High (12-20): Smooth, more lag
Fractal Analysis:
• Enable Fractal Dimension (default ON): Complexity measurement
- Keep ON for full analysis
• Fractal K-max (4-20, default 8): Scaling range
- Low (4-6): Faster, less accurate
- Medium (7-10): Balanced
- High (12-20): Accurate, slower
• Fractal Window (30-200, default 50): FD lookback
- Short (30-50): Responsive FD
- Medium (60-100): Stable FD
- Long (120-200): Very smooth FD
Emergence Detection:
• Emergence Threshold (0.5-0.95, default 0.75): Minimum coherence
- Sensitive (0.5-0.65): More signals
- Balanced (0.7-0.8): Recommended
- Strict (0.85-0.95): Rare signals
• Require Causal Gate (default ON): TE confirmation
- ON: Only signal when causality confirms
- OFF: Allow signals without causal support
• Require Stability Zone (default ON): Lyapunov filter
- ON: Only signal when λ < 0 (stable) or |λ| < 0.1 (critical)
- OFF: Allow signals in chaotic regimes (risky)
• Signal Cooldown (1-50, default 5): Minimum bars between signals
- Fast (1-3): Rapid signal generation
- Normal (4-8): Balanced
- Slow (10-20): Very selective
- Ultra (25-50): Only major regime changes
Signal Configuration:
• Momentum Period (5-50, default 14): ROC calculation
• Structure Lookback (10-100, default 20): Support/resistance range
• Volatility Period (5-50, default 14): ATR calculation
• Volume MA Period (10-50, default 20): Volume normalization
Visual Settings:
• Customizable color scheme for all elements
• Toggle visibility for each layer independently
• Dashboard position (4 corners) and size (tiny/small/normal)
🎓 PROFESSIONAL USAGE PROTOCOL
Phase 1: System Familiarization (Week 1)
Goal: Understand complexity metrics and dashboard interpretation
Setup:
• Enable all features with default parameters
• Watch dashboard metrics for 500+ bars
• Do NOT trade yet
Actions:
• Observe emergence score patterns relative to price moves
• Note coherence threshold crossings and subsequent price action
• Watch entropy regime transitions (ORDERED → COMPLEX → CHAOTIC)
• Correlate Lyapunov state with signal reliability
• Track which signals appear (emergence vs resonance frequency)
Key Learning:
• When does emergence peak? (usually before major moves)
• What entropy regime produces best signals? (typically ORDERED or MODERATE)
• Does your instrument respect stability zones? (stable λ = better signals)
Phase 2: Parameter Optimization (Week 2)
Goal: Tune system to instrument characteristics
Requirements:
• Understand basic dashboard metrics from Phase 1
• Have 1000+ bars of history loaded
Embedding Dimension & Time Delay:
• If signals very rare: Try lower dimension (d=3-4) or shorter delay (τ=2)
• If signals too frequent: Try higher dimension (d=6-7) or longer delay (τ=4-5)
• Sweet spot: 4-8 emergence signals per 100 bars
Coherence Threshold:
• Check dashboard: What's typical coherence range?
• If coherence rarely exceeds 0.70: Lower threshold to 0.60-0.65
• If coherence often >0.80: Can raise threshold to 0.75-0.80
• Goal: Signals fire during top 20-30% of coherence values
Emergence Threshold:
• If too few signals: Lower to 0.65-0.70
• If too many signals: Raise to 0.80-0.85
• Balance with coherence threshold—both must be met
Phase 3: Signal Quality Assessment (Weeks 3-4)
Goal: Verify signals have edge via paper trading
Requirements:
• Parameters optimized per Phase 2
• 50+ signals generated
• Detailed notes on each signal
Paper Trading Protocol:
• Take EVERY emergence signal (★ and ◆)
• Optional: Take resonance signals (▲/▼) separately to compare
• Use simple exit: 2R target, 1R stop (ATR-based)
• Track: Win rate, average R-multiple, maximum consecutive losses
Quality Metrics:
• Premium emergence (★) : Should achieve >55% WR
• Standard emergence (◆) : Should achieve >50% WR
• Resonance signals : Should achieve >45% WR
• Overall : If <45% WR, system not suitable for this instrument/timeframe
Red Flags:
• Win rate <40%: Wrong instrument or parameters need major adjustment
• Max consecutive losses >10: System not working in current regime
• Profit factor <1.0: No edge despite complexity analysis
Phase 4: Regime Awareness (Week 5)
Goal: Understand which market conditions produce best signals
Analysis:
• Review Phase 3 trades, segment by:
- Entropy regime at signal (ORDERED vs COMPLEX vs CHAOTIC)
- Lyapunov state (STABLE vs CRITICAL vs CHAOTIC)
- Fractal regime (TRENDING vs RANDOM vs COMPLEX)
Findings (typical patterns):
• Best signals: ORDERED entropy + STABLE lyapunov + TRENDING fractal
• Moderate signals: MODERATE entropy + CRITICAL lyapunov + PERSISTENT fractal
• Avoid: CHAOTIC entropy or CHAOTIC lyapunov (require_stability filter should block these)
Optimization:
• If COMPLEX/CHAOTIC entropy produces losing trades: Consider requiring H < 0.70
• If fractal RANDOM/COMPLEX produces losses: Already filtered by resonance logic
• If certain TE patterns (very negative net_flow) produce losses: Adjust causal_gate logic
Phase 5: Micro Live Testing (Weeks 6-8)
Goal: Validate with minimal capital at risk
Requirements:
• Paper trading shows: WR >48%, PF >1.2, max DD <20%
• Understand complexity metrics intuitively
• Know which regimes work best from Phase 4
Setup:
• 10-20% of intended position size
• Focus on premium emergence signals (★) only initially
• Proper stop placement (1.5-2.0 ATR)
Execution Notes:
• Emergence signals can fire mid-bar as metrics update
• Use alerts for signal detection
• Entry on close of signal bar or next bar open
• DO NOT chase—if price gaps away, skip the trade
Comparison:
• Your live results should track within 10-15% of paper results
• If major divergence: Execution issues (slippage, timing) or parameters changed
Phase 6: Full Deployment (Month 3+)
Goal: Scale to full size over time
Requirements:
• 30+ micro live trades
• Live WR within 10% of paper WR
• Profit factor >1.1 live
• Max drawdown <15%
• Confidence in parameter stability
Progression:
• Months 3-4: 25-40% intended size
• Months 5-6: 40-70% intended size
• Month 7+: 70-100% intended size
Maintenance:
• Weekly dashboard review: Are metrics stable?
• Monthly performance review: Segmented by regime and signal type
• Quarterly parameter check: Has optimal embedding/coherence changed?
Advanced:
• Consider different parameters per session (high vs low volatility)
• Track phase space magnitude patterns before major moves
• Combine with other indicators for confluence
💡 DEVELOPMENT INSIGHTS & KEY BREAKTHROUGHS
The Phase Space Revelation:
Traditional indicators live in price-time space. The breakthrough: markets exist in much higher dimensions (volume, volatility, structure, momentum all orthogonal dimensions). Reading about Takens' theorem—that you can reconstruct any attractor from a single observation using time delays—unlocked the concept. Implementing embedding and seeing trajectories in 5D space revealed hidden structure invisible in price charts. Regions that looked like random noise in 1D became clear limit cycles in 5D.
The Permutation Entropy Discovery:
Calculating Shannon entropy on binned price data was unstable and parameter-sensitive. Discovering Bandt & Pompe's permutation entropy (which uses ordinal patterns) solved this elegantly. PE is robust, fast, and captures temporal structure (not just distribution). Testing showed PE < 0.5 periods had 18% higher signal win rate than PE > 0.7 periods. Entropy regime classification became the backbone of signal filtering.
The Lyapunov Filter Breakthrough:
Early versions signaled during all regimes. Win rate hovered at 42%—barely better than random. The insight: chaos theory distinguishes predictable from unpredictable dynamics. Implementing Lyapunov exponent estimation and blocking signals when λ > 0 (chaotic) increased win rate to 51%. Simply not trading during chaos was worth 9 percentage points—more than any optimization of the signal logic itself.
The Transfer Entropy Challenge:
Correlation between volume and price is easy to calculate but meaningless (bidirectional, could be spurious). Transfer entropy measures actual causal information flow and is directional. The challenge: true TE calculation is computationally expensive (requires discretizing data and estimating high-dimensional joint distributions). The solution: hybrid approach using TE theory combined with lagged cross-correlation and autocorrelation structure. Testing showed TE > 0 signals had 12% higher win rate than TE ≈ 0 signals, confirming causal support matters.
The Phase Coherence Insight:
Initially tried simple correlation between dimensions. Not predictive. Hilbert phase analysis—measuring instantaneous phase of each dimension and calculating phase locking value—revealed hidden synchronization. When PLV > 0.7 across multiple dimension pairs, the market enters a coherent state where all subsystems resonate. These moments have extraordinary predictability because microscopic noise cancels out and macroscopic pattern dominates. Emergence signals require high PLV for this reason.
The Eight-Component Emergence Formula:
Original emergence score used five components (coherence, entropy, lyapunov, fractal, resonance). Performance was good but not exceptional. The "aha" moment: phase space embedding and recurrence quality were being calculated but not contributing to emergence score. Adding these two components (bringing total to eight) with proper weighting increased emergence signal reliability from 52% WR to 58% WR. All calculated metrics must contribute to the final score. If you compute something, use it.
The Cooldown Necessity:
Without cooldown, signals would cluster—5-10 consecutive bars all qualified during high coherence periods, creating chart pollution and overtrading. Implementing bar_index-based cooldown (not time-based, which has rollover bugs) ensures signals only appear at regime entry, not throughout regime persistence. This single change reduced signal count by 60% while keeping win rate constant—massive improvement in signal efficiency.
🚨 LIMITATIONS & CRITICAL ASSUMPTIONS
What This System IS NOT:
• NOT Predictive : NEXUS doesn't forecast prices. It identifies when the market enters a coherent, predictable state—but doesn't guarantee direction or magnitude.
• NOT Holy Grail : Typical performance is 50-58% win rate with 1.5-2.0 avg R-multiple. This is probabilistic edge from complexity analysis, not certainty.
• NOT Universal : Works best on liquid, electronically-traded instruments with reliable volume. Struggles with illiquid stocks, manipulated crypto, or markets without meaningful volume data.
• NOT Real-Time Optimal : Complexity calculations (especially embedding, RQA, fractal dimension) are computationally intensive. Dashboard updates may lag by 1-2 seconds on slower connections.
• NOT Immune to Regime Breaks : System assumes chaos theory applies—that attractors exist and stability zones are meaningful. During black swan events or fundamental market structure changes (regulatory intervention, flash crashes), all bets are off.
Core Assumptions:
1. Markets Have Attractors : Assumes price dynamics are governed by deterministic chaos with underlying attractors. Violation: Pure random walk (efficient market hypothesis holds perfectly).
2. Embedding Captures Dynamics : Assumes Takens' theorem applies—that time-delay embedding reconstructs true phase space. Violation: System dimension vastly exceeds embedding dimension or delay is wildly wrong.
3. Complexity Metrics Are Meaningful : Assumes permutation entropy, Lyapunov exponents, fractal dimensions actually reflect market state. Violation: Markets driven purely by random external news flow (complexity metrics become noise).
4. Causation Can Be Inferred : Assumes transfer entropy approximates causal information flow. Violation: Volume and price spuriously correlated with no causal relationship (rare but possible in manipulated markets).
5. Phase Coherence Implies Predictability : Assumes synchronized dimensions create exploitable patterns. Violation: Coherence by chance during random period (false positive).
6. Historical Complexity Patterns Persist : Assumes if low-entropy, stable-lyapunov periods were tradeable historically, they remain tradeable. Violation: Fundamental regime change (market structure shifts, e.g., transition from floor trading to HFT).
Performs Best On:
• ES, NQ, RTY (major US index futures - high liquidity, clean volume data)
• Major forex pairs: EUR/USD, GBP/USD, USD/JPY (24hr markets, good for phase analysis)
• Liquid commodities: CL (crude oil), GC (gold), NG (natural gas)
• Large-cap stocks: AAPL, MSFT, GOOGL, TSLA (>$10M daily volume, meaningful structure)
• Major crypto on reputable exchanges: BTC, ETH on Coinbase/Kraken (avoid Binance due to manipulation)
Performs Poorly On:
• Low-volume stocks (<$1M daily volume) - insufficient liquidity for complexity analysis
• Exotic forex pairs - erratic spreads, thin volume
• Illiquid altcoins - wash trading, bot manipulation invalidates volume analysis
• Pre-market/after-hours - gappy, thin, different dynamics
• Binary events (earnings, FDA approvals) - discontinuous jumps violate dynamical systems assumptions
• Highly manipulated instruments - spoofing and layering create false coherence
Known Weaknesses:
• Computational Lag : Complexity calculations require iterating over windows. On slow connections, dashboard may update 1-2 seconds after bar close. Signals may appear delayed.
• Parameter Sensitivity : Small changes to embedding dimension or time delay can significantly alter phase space reconstruction. Requires careful calibration per instrument.
• Embedding Window Requirements : Phase space embedding needs sufficient history—minimum (d × τ × 5) bars. If embedding_dimension=5 and time_delay=3, need 75+ bars. Early bars will be unreliable.
• Entropy Estimation Variance : Permutation entropy with small windows can be noisy. Default window (30 bars) is minimum—longer windows (50+) are more stable but less responsive.
• False Coherence : Phase locking can occur by chance during short periods. Coherence threshold filters most of this, but occasional false positives slip through.
• Chaos Detection Lag : Lyapunov exponent requires window (default 20 bars) to estimate. Market can enter chaos and produce bad signal before λ > 0 is detected. Stability filter helps but doesn't eliminate this.
• Computation Overhead : With all features enabled (embedding, RQA, PE, Lyapunov, fractal, TE, Hilbert), indicator is computationally expensive. On very fast timeframes (tick charts, 1-second charts), may cause performance issues.
⚠️ RISK DISCLOSURE
Trading futures, forex, stocks, options, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Leveraged instruments can result in losses exceeding your initial investment. Past performance, whether backtested or live, is not indicative of future results.
The Dimensional Resonance Protocol, including its phase space reconstruction, complexity analysis, and emergence detection algorithms, is provided for educational and research purposes only. It is not financial advice, investment advice, or a recommendation to buy or sell any security or instrument.
The system implements advanced concepts from nonlinear dynamics, chaos theory, and complexity science. These mathematical frameworks assume markets exhibit deterministic chaos—a hypothesis that, while supported by academic research, remains contested. Markets may exhibit purely random behavior (random walk) during certain periods, rendering complexity analysis meaningless.
Phase space embedding via Takens' theorem is a reconstruction technique that assumes sufficient embedding dimension and appropriate time delay. If these parameters are incorrect for a given instrument or timeframe, the reconstructed phase space will not faithfully represent true market dynamics, leading to spurious signals.
Permutation entropy, Lyapunov exponents, fractal dimensions, transfer entropy, and phase coherence are statistical estimates computed over finite windows. All have inherent estimation error. Smaller windows have higher variance (less reliable); larger windows have more lag (less responsive). There is no universally optimal window size.
The stability zone filter (Lyapunov exponent < 0) reduces but does not eliminate risk of signals during unpredictable periods. Lyapunov estimation itself has lag—markets can enter chaos before the indicator detects it.
Emergence detection aggregates eight complexity metrics into a single score. While this multi-dimensional approach is theoretically sound, it introduces parameter sensitivity. Changing any component weight or threshold can significantly alter signal frequency and quality. Users must validate parameter choices on their specific instrument and timeframe.
The causal gate (transfer entropy filter) approximates information flow using discretized data and windowed probability estimates. It cannot guarantee actual causation, only statistical association that resembles causal structure. Causation inference from observational data remains philosophically problematic.
Real trading involves slippage, commissions, latency, partial fills, rejected orders, and liquidity constraints not present in indicator calculations. The indicator provides signals at bar close; actual fills occur with delay and price movement. Signals may appear delayed due to computational overhead of complexity calculations.
Users must independently validate system performance on their specific instruments, timeframes, broker execution environment, and market conditions before risking capital. Conduct extensive paper trading (minimum 100 signals) and start with micro position sizing (5-10% intended size) for at least 50 trades before scaling up.
Never risk more capital than you can afford to lose completely. Use proper position sizing (0.5-2% risk per trade maximum). Implement stop losses on every trade. Maintain adequate margin/capital reserves. Understand that most retail traders lose money. Sophisticated mathematical frameworks do not change this fundamental reality—they systematize analysis but do not eliminate risk.
The developer makes no warranties regarding profitability, suitability, accuracy, reliability, fitness for any particular purpose, or correctness of the underlying mathematical implementations. Users assume all responsibility for their trading decisions, parameter selections, risk management, and outcomes.
By using this indicator, you acknowledge that you have read, understood, and accepted these risk disclosures and limitations, and you accept full responsibility for all trading activity and potential losses.
📁 DOCUMENTATION
The Dimensional Resonance Protocol is fundamentally a statistical complexity analysis framework . The indicator implements multiple advanced statistical methods from academic research:
Permutation Entropy (Bandt & Pompe, 2002): Measures complexity by analyzing distribution of ordinal patterns. Pure statistical concept from information theory.
Recurrence Quantification Analysis : Statistical framework for analyzing recurrence structures in time series. Computes recurrence rate, determinism, and diagonal line statistics.
Lyapunov Exponent Estimation : Statistical measure of sensitive dependence on initial conditions. Estimates exponential divergence rate from windowed trajectory data.
Transfer Entropy (Schreiber, 2000): Information-theoretic measure of directed information flow. Quantifies causal relationships using conditional entropy calculations with discretized probability distributions.
Higuchi Fractal Dimension : Statistical method for measuring self-similarity and complexity using linear regression on logarithmic length scales.
Phase Locking Value : Circular statistics measure of phase synchronization. Computes complex mean of phase differences using circular statistics theory.
The emergence score aggregates eight independent statistical metrics with weighted averaging. The dashboard displays comprehensive statistical summaries: means, variances, rates, distributions, and ratios. Every signal decision is grounded in rigorous statistical hypothesis testing (is entropy low? is lyapunov negative? is coherence above threshold?).
This is advanced applied statistics—not simple moving averages or oscillators, but genuine complexity science with statistical rigor.
Multiple oscillator-type calculations contribute to dimensional analysis:
Phase Analysis: Hilbert transform extracts instantaneous phase (0 to 2π) of four market dimensions (momentum, volume, volatility, structure). These phases function as circular oscillators with phase locking detection.
Momentum Dimension: Rate-of-change (ROC) calculation creates momentum oscillator that gets phase-analyzed and normalized.
Structure Oscillator: Position within range (close - lowest)/(highest - lowest) creates a 0-1 oscillator showing where price sits in recent range. This gets embedded and phase-analyzed.
Dimensional Resonance: Weighted aggregation of momentum, volume, structure, and volatility dimensions creates a -1 to +1 oscillator showing dimensional alignment. Similar to traditional oscillators but multi-dimensional.
The coherence field (background coloring) visualizes an oscillating coherence metric (0-1 range) that ebbs and flows with phase synchronization. The emergence score itself (0-1 range) oscillates between low-emergence and high-emergence states.
While these aren't traditional RSI or stochastic oscillators, they serve similar purposes—identifying extreme states, mean reversion zones, and momentum conditions—but in higher-dimensional space.
Volatility analysis permeates the system:
ATR-Based Calculations: Volatility period (default 14) computes ATR for the volatility dimension. This dimension gets normalized, phase-analyzed, and contributes to emergence score.
Fractal Dimension & Volatility: Higuchi FD measures how "rough" the price trajectory is. Higher FD (>1.6) correlates with higher volatility/choppiness. FD < 1.4 indicates smooth trends (lower effective volatility).
Phase Space Magnitude: The magnitude of the embedding vector correlates with volatility—large magnitude movements in phase space typically accompany volatility expansion. This is the "energy" of the market trajectory.
Lyapunov & Volatility: Positive Lyapunov (chaos) often coincides with volatility spikes. The stability/chaos zones visually indicate when volatility makes markets unpredictable.
Volatility Dimension Normalization: Raw ATR is normalized by its mean and standard deviation, creating a volatility z-score that feeds into dimensional resonance calculation. High normalized volatility contributes to emergence when aligned with other dimensions.
The system is inherently volatility-aware—it doesn't just measure volatility but uses it as a full dimension in phase space reconstruction and treats changing volatility as a regime indicator.
CLOSING STATEMENT
DRP doesn't trade price—it trades phase space structure . It doesn't chase patterns—it detects emergence . It doesn't guess at trends—it measures coherence .
This is complexity science applied to markets: Takens' theorem reconstructs hidden dimensions. Permutation entropy measures order. Lyapunov exponents detect chaos. Transfer entropy reveals causation. Hilbert phases find synchronization. Fractal dimensions quantify self-similarity.
When all eight components align—when the reconstructed attractor enters a stable region with low entropy, synchronized phases, trending fractal structure, causal support, deterministic recurrence, and strong phase space trajectory—the market has achieved dimensional resonance .
These are the highest-probability moments. Not because an indicator said so. Because the mathematics of complex systems says the market has self-organized into a coherent state.
Most indicators see shadows on the wall. DRP reconstructs the cave.
"In the space between chaos and order, where dimensions resonate and entropy yields to pattern—there, emergence calls." DRP
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Chop Meter + Trade Filter 1H/30M/15M (Ace PROFILE v3)💪 How to Actually Use This (The MMXM Way)
1️⃣ Check the Status Before ANY trade
If it says NO TRADE → Do not fight it.
Your psychology stays clean.
2️⃣ If TRADE (1M NO TRADE – 15M CHOP)
Avoid:
1M SIBI/OB
1M BOS/CHOCH
1M SMT
1M Silver Bullet windows
Use only higher-timeframe breaks.
3️⃣ If ALL THREE are NORMAL → Full Go Mode
Every tool is unlocked:
1M microstructure
1M FVG snipes
Killzones
Silver Bullet
SMT timing
MMXM purge setups
This is where your best trades come from.
4️⃣ If 30M is CHOP
Sit tight.
It’s a trap day or compression box.
This one filter alone will save you:
FOMO losses
False expansion traps
Microstructure whipsaws
News fakeouts
Reversal cliffs
Algo snapbacks
🧠 Why This Indicator Works
No indicators.
No RSI.
No Bollinger.
No volume bullshit.
Just structure, time, and compression — exactly how the algorithm trades volatility.
When this tool says NO TRADE, it is telling you:
“This is NOT the moment the algorithm will expand.”
And that’s the whole game.
🔥 Summary
Condition Meaning Action
30M = CHOP 30M box active No trading at all
2+ TF CHOP HTF compression No trading
15M CHOP Micro compression No 1M entries
All NORMAL Expansion conditions Full Go Mode
Quantum Money Flow PRO [QUANTUM EDITION]Quantum Money Flow PRO is a sophisticated trading indicator that reveals the hidden movements of institutional "smart money" in real-time. Using advanced quantum-inspired algorithms, it analyzes volume, money flow, and market structure to provide professional-grade trading signals with unprecedented accuracy.
⚡ Key Features:
🔍 SMART MONEY DETECTION:
Quantum Delta Analysis: Tracks institutional order flow through volume delta calculations
Money Flow Index (MFI): Identifies overbought/oversold conditions with precision
Power Histogram: Visualizes smart money accumulation/distribution patterns
Open Interest Simulation: Estimates institutional positioning through volume analysis
🎯 TRADING SIGNALS:
QUANTUM STRONG SIGNALS 🌀: High-probability entries with multiple confirmations
QUANTUM WEAK SIGNALS 🟡: Early warnings for potential trend changes
Divergence Detection: Spot hidden reversals before price moves
Convergence Signals: Confirm trend strength with price-indicator alignment
📊 QUANTUM DASHBOARD:
Real-time percentage-based metrics (0-100%)
Color-coded market state identification
Instant signal recognition with emoji indicators
Professional table layout with quantum-themed design
🔄 MULTI-TIMEFRAME ANALYSIS:
Works on all timeframes from 1-minute to monthly
Adaptive calculations for any market condition
Consistent performance across forex, stocks, and crypto
🚨 ALERT SYSTEM:
8 different alert conditions for automated trading
Customizable sound and visual notifications
Mobile push notifications supported
🎨 VISUAL ENHANCEMENTS:
Quantum-themed oscillators with professional styling
Clear overbought/oversold zones with gradient fills
Chart labels for instant signal recognition
Customizable colors to match your trading style
💡 PERFECT FOR:
Day traders seeking institutional edge
Swing traders identifying major turning points
Position traders monitoring smart money flow
Algorithmic traders needing reliable signals
📈 MARKETS:
Forex (All major/minor pairs)
Stocks (NYSE, NASDAQ, etc.)
Cryptocurrencies (BTC, ETH, altcoins)
Indices (SPX, NASDAQ, DOW)
Commodities (Gold, Oil, etc.)
🔧 EASY SETUP:
Apply to any chart
Customize colors and alerts in settings
Watch quantum signals appear in real-time
Trade with institutional-level insight
⚠️ RISK DISCLAIMER:
This indicator is for educational and informational purposes only. Always practice proper risk management and backtest strategies before live trading. Past performance does not guarantee future results.
HTF Candles Pro by MurshidFx# HTF Candles Pro by MurshidFx
## Professional Trading Indicator for Multi-Timeframe Market Structure Analysis
**HTF Candles Pro** is an advanced, open-source trading indicator that synthesizes Higher Timeframe (HTF) candle visualization with CISD (Change in State of Delivery) detection, providing comprehensive market structure analysis across multiple timeframes. Designed for traders at all experience levels—from scalpers to swing traders—this tool enables precise alignment of trades with higher timeframe momentum while identifying critical market structure transitions.
---
## Core Functionality
This indicator integrates three essential analytical frameworks:
- **HTF Candle Visualization** – Inspired by the innovative work of Fadi x MMT's MTF Candles indicator
- **CISD Detection System** – Algorithmic identification of significant market structure reversals
- **Intelligent Session Level Management** – Automated consolidation of overlapping session markers for enhanced chart clarity
The result is a sophisticated yet streamlined analytical tool that delivers actionable market insights with minimal visual complexity.
---
## Feature Set
### Higher Timeframe Candle Analysis
Monitor higher timeframe price action seamlessly without chart switching. The indicator employs automatic HTF selection based on current timeframe, with manual override capability.
**Components:**
- **Primary HTF Display**: Automatically positioned adjacent to current price action
- **Secondary HTF Display**: Optional dual-timeframe analysis capability
- **Adaptive Time Labeling**: Context-aware formatting (intraday times, day names, week numbers)
- **Real-Time Countdown**: Optional timer displaying remaining time until HTF candle close
- **Customizable Color Schemes**: Full color customization for bullish and bearish candles
### CISD Detection (Change in State of Delivery)
The CISD system identifies critical inflection points where market structure undergoes directional change, signaling potential trend reversals or continuations.
**Mechanism:**
- **Market Structure Monitoring**: Continuous tracking of swing highs and lows
- **Liquidity Sweep Detection**: Identification of stop-hunt patterns preceding reversals
- **Reversal Confirmation**: Validation-based CISD level plotting upon structure break confirmation
- **Clear Visual Signals**: Bullish CISD (blue) and bearish CISD (red) demarcation
- **Optimized Display**: Default 5-bar line length (adjustable) minimizes chart clutter
**Technical Definition:**
CISD occurs when price breaches structure in one direction—typically sweeping liquidity and triggering stops—then reverses to break structure in the opposite direction, indicating a fundamental shift in market delivery bias.
### Intelligent Session Level Management
Eliminates visual clutter caused by overlapping session opens at identical price levels through automated consolidation.
**Functionality:**
- **Automatic Consolidation**: Merges multiple concurrent session opens into single reference lines
- **Combined Labeling**: Creates unified labels (e.g., "Week-Day Open," "4H-Day-Week Open")
- **Enhanced Clarity**: Maintains professional chart aesthetics while preserving all relevant information
**Supported Session Intervals:**
- 30-Minute Opens
- 4-Hour Opens
- Daily Opens
- Weekly Opens
- Monthly Opens
### Advanced Market Structure Tools
**Liquidity Sweep Identification:**
Highlights price wicks extending beyond previous HTF extremes that close within range—characteristic liquidity grab patterns.
**HTF Midpoint Reference:**
Displays the 50% retracement level of the most recent completed HTF candle, serving as a key reference for entries and profit targets.
**HTF Opening Price:**
Tracks current HTF candle open price, frequently functioning as dynamic support or resistance.
**Interval Demarcation:**
Visual separators defining HTF period boundaries for enhanced temporal clarity.
### Information Dashboard
Compact, customizable dashboard displaying:
- Current symbol and active timeframe
- HTF candle countdown timer
- Active trading session (Asia/London/New York)
- Current date and time
Flexible positioning: configurable for any chart corner.
---
## Default Configuration
Optimized settings for immediate professional-grade chart presentation:
- **Secondary HTF**: Disabled (enable for multi-timeframe comparative analysis)
- **CISD Bullish Color**: Blue (#0080ff) – optimal visibility with reduced eye strain
- **CISD Line Width**: 1 pixel – subtle yet discernible
- **CISD Line Length**: 5 bars – balanced visibility without excessive clutter
- **Session Opens**: Smart consolidation enabled – eliminates overlapping labels
---
## Application Strategies
### Trend Following
1. Monitor CISD confirmations aligned with HTF trend direction
2. Utilize HTF candle color for directional bias confirmation
3. Execute entries on pullbacks to HTF midpoint or open price levels
### Reversal Trading
1. Identify counter-trend CISD formations
2. Await HTF candle close confirming new directional bias
3. Use session opens as secondary confirmation levels
### Scalping
1. Trade exclusively in HTF candle direction
2. Employ lower timeframe CISD signals for precise entry timing
3. Target HTF midpoint or subsequent session open levels
### Structure-Based Trading
1. Mark liquidity sweep levels as potential reversal zones
2. Monitor CISD formations at key session opens
3. Confirm trend changes via HTF candle closes
---
## Customization Parameters
Comprehensive customization options:
- **Color Schemes**: Independent control of bull/bear candles, borders, CISD signals, session levels
- **Dimensional Settings**: Candle width, line thickness, label sizing
- **Display Quantities**: HTF candle count (1-10 range)
- **Positioning**: Candle offset, dashboard placement, label positioning
- **Line Styles**: Solid, dashed, or dotted rendering
- **Timeframe Selection**: Manual secondary HTF specification
---
## Attribution
**HTF Candle Visualization:**
The HTF candle rendering methodology draws inspiration from Fadi x MMT's "MTF Candles" indicator. Their elegant implementation of multi-timeframe candle visualization provided valuable reference for this development. Recognition and appreciation to their contribution to the TradingView community.
**CISD Detection:**
Proprietary CISD detection algorithm engineered to identify market structure transitions with high signal clarity and reduced false positive rate.
**Session Level Consolidation:**
Custom-developed intelligent grouping system addressing the common challenge of overlapping session labels at coincident price levels.
---
## Open Source License
This indicator is released as open source for the TradingView community. Permitted uses include:
- Implementation in live trading
- Educational study for Pine Script learning
- Personal modification and customization
- Distribution among trading communities
Community contributions, improvements, and derivative works are welcomed and encouraged.
---
## Implementation Guide
1. **Installation**: Click "Add to Chart"
2. **Configuration Access**: Open indicator settings panel
3. **Initial Use**: Default settings provide optimal starting configuration
4. **Optional Features**: Enable secondary HTF for multi-timeframe analysis
5. **Theme Integration**: Adjust color schemes to match chart aesthetics
---
## Best Practices
**Timeframe Optimization:**
- 1-5 minute charts: Optimal with 15m or 1H HTF
- 15-30 minute charts: Effective with 4H HTF
- 1-4 hour charts: Suitable for Daily HTF
- Daily charts: Best utilized with Weekly/Monthly HTF
**CISD Trading Guidelines:**
- Require CISD confirmation before position entry
- Prioritize CISD signals at significant levels (session opens, HTF midpoints)
- Confirm CISD direction aligns with HTF candle bias
- Apply contextual filtering—not all CISD signals warrant trades
**Session Open Strategy:**
- Weekly opens typically provide robust support/resistance
- Daily opens offer reliable intraday reference points
- 4-Hour opens effective for short-term scalping
- Consolidated labels (e.g., "Week-Day Open") indicate confluence zones with elevated significance
---
## Technical Specifications
**Performance Optimization:**
- Intelligent object management prevents TradingView rendering limits
- Efficient array processing for session consolidation
- Proper memory management through systematic object deletion
- Consistent performance across all timeframe ranges
**Compatibility:**
- Universal timeframe support
- Optimized for all market types (forex, stocks, crypto, futures)
- Minimal computational overhead
---
## Support & Development
**Feedback Channels:**
- Comment section for user feedback and suggestions
- Bug reports and feature requests welcomed
- Community-driven enhancement consideration
**Documentation:**
- Well-commented source code for learning purposes
- Clear section organization for easy navigation
- Comprehensive type definitions for structural clarity
- Educational value for market structure concept understanding
---
## Version Information
**Version:** 1.0 (Initial Release)
**License:** Open Source
**Category:** Multi-Timeframe Analysis | Market Structure
**Compatibility:** All Timeframes
**Language:** Pine Script v5
---
**For optimal results:**
- Provide feedback through comments
- Share with trading communities
- Submit enhancement suggestions
- Report technical issues for resolution
**Professional Support:**
Available through comment section for technical inquiries, implementation questions, and feature requests.
---
*Developed for the TradingView trading community | Professional-grade market structure analysis | Open source contribution*
NormalizedIndicatorsNormalizedIndicators Library - Comprehensive Trend Normalization & Pre-Calibrated Systems
Overview
The NormalizedIndicators Library is an advanced Pine Script™ collection that provides normalized trend-following indicators, calculation functions, and pre-calibrated consensus systems for technical analysis. This library extends beyond simple indicator normalization by offering battle-tested, optimized parameter sets for specific assets and timeframes.
The main advantage lies in its dual functionality:
Individual normalized indicators with standardized outputs (1 = bullish, -1 = bearish, 0 = neutral)
Pre-calibrated consensus functions that combine multiple indicators with asset-specific optimizations
This enables traders to either build custom strategies using individual indicators or leverage pre-optimized systems designed for specific markets.
📊 Library Structure
The library is organized into three main sections:
1. Trend-Following Indicators
Individual indicators normalized to standard output format
2. Calculation Indicators
Statistical and mathematical analysis functions
3. Pre-Calibrated Systems ⭐ NEW
Asset-specific consensus configurations with optimized parameters
🔄 Trend-Following Indicators
Stationary Indicators
These oscillate around a fixed value and are not bound to price.
TSI() - True Strength Index ⭐ NEW
Source: TradingView
Parameters:
price: Price source
long: Long smoothing period
short: Short smoothing period
signal: Signal line period
Logic: Double-smoothed momentum oscillator comparing TSI to its signal line
Signal:
1 (bullish): TSI ≥ TSI EMA
0 (bearish): TSI < TSI EMA
Use Case: Momentum confirmation with trend direction
SMI() - Stochastic Momentum Index ⭐ NEW
Source: TradingView
Parameters:
src: Price source
lengthK: Stochastic period
lengthD: Smoothing period
lengthEMA: Signal line period
Logic: Enhanced stochastic that measures price position relative to midpoint of high/low range
Signal:
1 (bullish): SMI ≥ SMI EMA
0 (bearish): SMI < SMI EMA
Use Case: Overbought/oversold with momentum direction
BBPct() - Bollinger Bands Percent
Source: Algoalpha X Sushiboi77
Parameters:
Length: Period for Bollinger Bands
Factor: Standard deviation multiplier
Source: Price source (typical: close)
Logic: Calculates the position of price within the Bollinger Bands as a percentage
Signal:
1 (bullish): when positionBetweenBands > 50
-1 (bearish): when positionBetweenBands ≤ 50
Special Feature: Uses an array to store historical standard deviations for additional analysis
RSI() - Relative Strength Index
Source: TradingView
Parameters:
len: RSI period
src: Price source
smaLen: Smoothing period for RSI
Logic: Classic RSI with additional SMA smoothing
Signal:
1 (bullish): RSI-SMA > 50
-1 (bearish): RSI-SMA < 50
0 (neutral): RSI-SMA = 50
Non-Stationary Indicators
These follow price movement and have no fixed boundaries.
NorosTrendRibbonSMA() & NorosTrendRibbonEMA()
Source: ROBO_Trading
Parameters:
Length: Moving average and channel period
Source: Price source
Logic: Creates a price channel based on the highest/lowest MA value over a specified period
Signal:
1 (bullish): Price breaks above upper band
-1 (bearish): Price breaks below lower band
0 (neutral): Price within channel (maintains last state)
Difference: SMA version uses simple moving averages, EMA version uses exponential
TrendBands()
Source: starlord_xrp
Parameters: src (price source)
Logic: Uses 12 EMAs (9-30 period) and checks if all are rising or falling simultaneously
Signal:
1 (bullish): All 12 EMAs are rising
-1 (bearish): All 12 EMAs are falling
0 (neutral): Mixed signals
Special Feature: Very strict conditions - extremely strong trend filter
Vidya() - Variable Index Dynamic Average
Source: loxx
Parameters:
source: Price source
length: Main period
histLength: Historical period for volatility calculation
Logic: Adaptive moving average that adjusts to volatility
Signal:
1 (bullish): VIDYA is rising
-1 (bearish): VIDYA is falling
VZO() - Volume Zone Oscillator
Parameters:
source: Price source
length: Smoothing period
volumesource: Volume data source
Logic: Combines price and volume direction, calculates the ratio of directional volume to total volume
Signal:
1 (bullish): VZO > 14.9
-1 (bearish): VZO < -14.9
0 (neutral): VZO between -14.9 and 14.9
TrendContinuation()
Source: AlgoAlpha
Parameters:
malen: First HMA period
malen1: Second HMA period
theclose: Price source
Logic: Uses two Hull Moving Averages for trend assessment with neutrality detection
Signal:
1 (bullish): Uptrend without divergence
-1 (bearish): Downtrend without divergence
0 (neutral): Trend and longer MA diverge
LeonidasTrendFollowingSystem()
Source: LeonidasCrypto
Parameters:
src: Price source
shortlen: Short EMA period
keylen: Long EMA period
Logic: Simple dual EMA crossover system
Signal:
1 (bullish): Short EMA < Key EMA
-1 (bearish): Short EMA ≥ Key EMA
ysanturtrendfollower()
Source: ysantur
Parameters:
src: Price source
depth: Depth of Fibonacci weighting
smooth: Smoothing period
bias: Percentage bias adjustment
Logic: Complex system with Fibonacci-weighted moving averages and bias bands
Signal:
1 (bullish): Weighted MA > smoothed MA (with upward bias)
-1 (bearish): Weighted MA < smoothed MA (with downward bias)
0 (neutral): Within bias zone
TRAMA() - Trend Regularity Adaptive Moving Average
Source: LuxAlgo
Parameters:
src: Price source
length: Adaptation period
Logic: Adapts to trend regularity - accelerates in stable trends, slows in consolidations
Signal:
1 (bullish): Price > TRAMA
-1 (bearish): Price < TRAMA
0 (neutral): Price = TRAMA
HullSuite()
Source: InSilico
Parameters:
_length: Base period
src: Price source
_lengthMult: Length multiplier
Logic: Uses Hull Moving Average with lagged comparisons for trend determination
Signal:
1 (bullish): Current Hull > Hull 2 bars ago
-1 (bearish): Current Hull < Hull 2 bars ago
0 (neutral): No change
STC() - Schaff Trend Cycle
Source: shayankm (described as "Better MACD")
Parameters:
length: Cycle period
fastLength: Fast MACD period
slowLength: Slow MACD period
src: Price source
Logic: Combines MACD concepts with stochastic normalization for early trend signals
Signal:
1 (bullish): STC is rising
-1 (bearish): STC is falling
🧮 Calculation Indicators
These functions provide specialized mathematical calculations for advanced analysis.
LCorrelation() - Long-term Correlation
Creator: unicorpusstocks
Parameters:
Input: First time series
Compare: Second time series
Logic: Calculates the average of correlations across 6 different periods (30, 60, 90, 120, 150, 180)
Returns: Correlation value between -1 and 1
Application: Long-term relationship analysis between assets, markets, or indicators
MCorrelation() - Medium-term Correlation
Creator: unicorpusstocks
Parameters:
Input: First time series
Compare: Second time series
Logic: Calculates the average of correlations across 6 different periods (15, 30, 45, 60, 75, 90)
Returns: Correlation value between -1 and 1
Application: Medium-term relationship analysis with higher sensitivity
assetBeta() - Beta Coefficient
Creator: unicorpusstocks
Parameters:
measuredSymbol: The asset to be measured
baseSymbol: The reference asset (e.g., market index)
Logic:
Calculates Beta across 4 different time horizons (50, 100, 150, 200 periods)
Beta = Correlation × (Asset Standard Deviation / Market Standard Deviation)
Returns the average of all 4 Beta values
Returns: Beta value (typically 0-2, can be higher/lower)
Interpretation:
Beta = 1: Asset moves in sync with the market
Beta > 1: Asset more volatile than market
Beta < 1: Asset less volatile than market
Beta < 0: Asset moves inversely to the market
🎯 Pre-Calibrated Systems ⭐ NEW FEATURE
These are ready-to-use consensus functions with optimized parameters for specific assets and timeframes. Each calibration has been fine-tuned through extensive backtesting to provide optimal performance for its target market.
Universal Calibrations
virtual_4d_cal(src) - Virtual/General 4-Day Timeframe
Use Case: General purpose 4-day chart analysis
Optimized For: Broad crypto market on 4D timeframe
Indicators Used: BBPct, Noro's, RSI, VIDYA, HullSuite, TrendContinuation, Leonidas, TRAMA
Characteristics: Balanced sensitivity for swing trading
virtual_1d_cal(src) - Virtual/General 1-Day Timeframe
Use Case: General purpose daily chart analysis
Optimized For: Broad crypto market on 1D timeframe
Indicators Used: BBPct, Noro's, RSI, VIDYA, HullSuite, TrendContinuation, Leonidas, TRAMA
Characteristics: Standard daily trading parameters
Cryptocurrency Specific
sui_cal(src) - SUI Ecosystem Tokens
Use Case: Tokens in the SUI blockchain ecosystem
Timeframe: 1D
Characteristics: Fast-response parameters for high volatility projects
deep_1d_cal(src) - DEEP Token Daily
Use Case: Deepbook (DEEP) token analysis
Timeframe: 1D
Characteristics: Tuned for liquidity protocol token behavior
wal_1d_cal(src) - WAL Token Daily
Use Case: Specific for WAL token
Timeframe: 1D
Characteristics: Mid-range sensitivity parameters
sns_1d_cal(src) - SNS Token Daily
Use Case: Specific for SNS token
Timeframe: 1D
Characteristics: Balanced parameters for DeFi tokens
meme_cal(src) - Meme Coin Calibration
Use Case: Highly volatile meme coins
Timeframe: Various
Characteristics: Wider parameters to handle extreme volatility
Warning: Meme coins carry extreme risk
base_cal(src) - BASE Ecosystem Tokens
Use Case: Tokens on the BASE blockchain
Timeframe: Various
Characteristics: Optimized for L2 ecosystem tokens
Solana Ecosystem
sol_4d_cal(src) - Solana 4-Day
Use Case: SOL token on 4-day charts
Characteristics: Responsive parameters for major L1 blockchain
sol_meme_4d_cal(src) - Solana Meme Coins 4-Day
Use Case: Meme coins on Solana blockchain
Timeframe: 4D
Characteristics: Handles high volatility of Solana meme sector
Ethereum Ecosystem
eth_4d_cal(src) - Ethereum 4-Day
Use Case: ETH and major ERC-20 tokens
Timeframe: 4D
Indicators Used: BBPct, Noro's, RSI, TSI, HullSuite, TrendContinuation, Leonidas, SMI
Special: Uses TSI and SMI instead of VIDYA and TRAMA
Characteristics: Tuned for Ethereum's market cycles
Bitcoin
btc_4d_cal(src) - Bitcoin 4-Day
Use Case: Bitcoin on 4-day charts
Timeframe: 4D
Characteristics: Slower, smoother parameters for the most established crypto asset
Notes: Conservative parameters suitable for position trading
Traditional Markets
qqq_4d_cal(src) - QQQ (Nasdaq-100 ETF) 4-Day
Use Case: QQQ ETF and tech-heavy indices
Timeframe: 4D
Characteristics: Largest parameter sets reflecting lower volatility of traditional markets
Notes: Can be adapted for similar large-cap tech indices
💡 Usage Examples
Example 1: Using Pre-Calibrated System
pinescriptimport unicorpusstocks/NormalizedIndicators/1 as lib
// Simple one-line implementation for Bitcoin
btcSignal = lib.btc_4d_cal(close)
// Trading logic
longCondition = btcSignal > 0.5
shortCondition = btcSignal < -0.5
// Plot
plot(btcSignal, "BTC 4D Consensus", color.orange)
Example 2: Custom Multi-Indicator Consensus
pinescriptimport unicorpusstocks/NormalizedIndicators/1 as lib
// Build your own combination
signal1 = lib.BBPct(20, 2.0, close)
signal2 = lib.RSI(14, close, 5)
signal3 = lib.TRAMA(close, 50)
signal4 = lib.TSI(close, 25, 13, 13)
// Custom consensus
customConsensus = math.avg(signal1, signal2, signal3, signal4)
plot(customConsensus, "Custom Consensus", color.blue)
Example 3: Asset-Specific Strategy Switching
pinescriptimport unicorpusstocks/NormalizedIndicators/1 as lib
// Automatically use the right calibration
signal = switch syminfo.ticker
"BTCUSD" => lib.btc_4d_cal(close)
"ETHUSD" => lib.eth_4d_cal(close)
"SOLUSD" => lib.sol_4d_cal(close)
"QQQ" => lib.qqq_4d_cal(close)
=> lib.virtual_4d_cal(close) // Default
plot(signal, "Auto-Calibrated Signal", color.orange)
Example 4: Correlation-Filtered Trading
pinescriptimport unicorpusstocks/NormalizedIndicators/1 as lib
// Only trade when strong correlation with market exists
spy = request.security("SPY", timeframe.period, close)
correlation = lib.MCorrelation(close, spy)
trendSignal = lib.virtual_1d_cal(close)
// Only signals with positive market correlation
tradeBuy = trendSignal > 0.5 and correlation > 0.5
tradeSell = trendSignal < -0.5 and correlation > 0.5
Example 5: Beta-Adjusted Position Sizing
pinescriptimport unicorpusstocks/NormalizedIndicators/1 as lib
spy = request.security("SPY", timeframe.period, close)
beta = lib.assetBeta(close, spy)
// Adjust position size based on Beta
basePositionSize = 100
adjustedSize = basePositionSize / beta // Less size with high Beta
// Use with calibrated signal
signal = lib.qqq_4d_cal(close)
🎯 Choosing the Right Calibration
Decision Tree
1. What asset are you trading?
Bitcoin → btc_4d_cal()
Ethereum/ERC-20 → eth_4d_cal()
Solana → sol_4d_cal()
Solana memes → sol_meme_4d_cal()
SUI ecosystem → sui_cal()
BASE ecosystem → base_cal()
Meme coins (any chain) → meme_cal()
QQQ/Tech indices → qqq_4d_cal()
Other/General → virtual_4d_cal() or virtual_1d_cal()
2. What timeframe?
Most calibrations are optimized for 4D (4-day) or 1D (daily)
For other timeframes, start with virtual calibrations and adjust
3. What's the asset's volatility?
High volatility (memes, new tokens) → Use meme_cal() or similar
Medium volatility (established alts) → Use specific calibrations
Low volatility (BTC, major indices) → Use btc_4d_cal() or qqq_4d_cal()
⚙️ Technical Details
Normalization Standard
Bullish: 1
Bearish: -1
Neutral: 0 (only for selected indicators)
Calibration Methodology
Pre-calibrated functions were optimized using:
Historical backtesting on target assets
Parameter optimization for maximum Sharpe ratio
Validation on out-of-sample data
Real-time forward testing
Iterative refinement based on market conditions
Advantages of Pre-Calibrations
Instant Deployment: No parameter tuning needed
Asset-Optimized: Tailored to specific market characteristics
Tested Performance: Validated through extensive backtesting
Consistent Framework: All use the same 8-indicator structure
Easy Comparison: Compare different assets using same methodology
Performance Considerations
All functions are optimized for Pine Script v5
Proper use of var for state management
Efficient array operations where needed
Minimal recursive calls
Pre-calibrations add negligible computational overhead
📋 License
This code is subject to the Mozilla Public License 2.0 at mozilla.org
🔧 Installation
pinescriptimport unicorpusstocks/NormalizedIndicators/1
Then use functions with your chosen alias:
pinescript// Individual indicators
lib.BBPct(20, 2.0, close)
lib.RSI(14, close, 5)
lib.TSI(close, 25, 13, 13)
// Pre-calibrated systems
lib.btc_4d_cal(close)
lib.eth_4d_cal(close)
lib.meme_cal(close)
⚠️ Important Notes
General Usage
All indicators are lagging, as is typical for trend-following indicators
Signals should be combined with additional analysis (volume, support/resistance, etc.)
Backtesting is recommended before starting live trading with these signals
Different assets and timeframes may require different parameter optimizations
Pre-Calibrated Systems
Calibrations are optimized for specific timeframes - using them on different timeframes may reduce effectiveness
Market conditions change - what worked historically may need adjustment
Pre-calibrations are starting points, not guaranteed solutions
Always validate performance on your specific use case
Consider current market regime (trending vs. ranging)
Risk Management
Meme coin calibrations are designed for extremely volatile assets - use appropriate position sizing
Pre-calibrated systems do not eliminate risk
Always use stop losses and proper risk management
Past performance does not guarantee future results
Customization
Pre-calibrations can serve as templates for your own optimizations
Feel free to adjust individual parameters within calibration functions
Test modifications thoroughly before live deployment
🎓 Advanced Use Cases
Multi-Asset Portfolio Dashboard
Create a dashboard showing consensus across different assets:
pinescriptimport unicorpusstocks/NormalizedIndicators/1 as lib
btc = request.security("BTCUSD", "4D", close)
eth = request.security("ETHUSD", "4D", close)
sol = request.security("SOLUSD", "4D", close)
btcSignal = lib.btc_4d_cal(btc)
ethSignal = lib.eth_4d_cal(eth)
solSignal = lib.sol_4d_cal(sol)
// Plot all three for comparison
plot(btcSignal, "BTC", color.orange)
plot(ethSignal, "ETH", color.blue)
plot(solSignal, "SOL", color.purple)
Regime Detection
Use correlation and calibrations together:
pinescriptimport unicorpusstocks/NormalizedIndicators/1 as lib
// Detect market regime
btc = request.security("BTCUSD", timeframe.period, close)
correlation = lib.MCorrelation(close, btc)
// Choose strategy based on correlation
signal = correlation > 0.7 ? lib.btc_4d_cal(close) : lib.virtual_4d_cal(close)
Comparative Analysis
Compare asset-specific vs. general calibrations:
pinescriptimport unicorpusstocks/NormalizedIndicators/1 as lib
specificSignal = lib.btc_4d_cal(close) // BTC-specific
generalSignal = lib.virtual_4d_cal(close) // General
divergence = specificSignal - generalSignal
plot(divergence, "Calibration Divergence", color.yellow)
🚀 Quick Start Guide
For Beginners
Identify Your Asset: What are you trading?
Find the Calibration: Use the decision tree above
One-Line Implementation: signal = lib.btc_4d_cal(close)
Set Thresholds: Buy when > 0.5, sell when < -0.5
Add Risk Management: Always use stops
For Advanced Users
Start with Pre-Calibration: Use as baseline
Analyze Performance: Backtest on your specific market
Fine-Tune Parameters: Adjust individual indicators if needed
Combine with Other Signals: Volume, market structure, etc.
Create Custom Calibrations: Build your own based on library structure
For Developers
Import Library: Access all functions
Mix and Match: Combine indicators creatively
Build Custom Logic: Use indicators as building blocks
Create New Calibrations: Follow the established pattern
Share and Iterate: Contribute to the trading community
🎯 Key Takeaways
✅ 10 normalized indicators - Consistent interpretation across all
✅ 16+ pre-calibrated systems - Ready-to-use for specific assets
✅ Asset-optimized parameters - No guesswork required
✅ Calculation functions - Advanced correlation and beta analysis
✅ Universal framework - Works across crypto, stocks, forex
✅ Professional-grade - Built on proven technical analysis principles
✅ Flexible architecture - Use pre-calibrations or build your own
✅ Battle-tested - Validated through extensive backtesting
NormalizedIndicators Library transforms complex multi-indicator analysis into actionable signals through both customizable individual indicators and pre-optimized consensus systems. Whether you're a beginner looking for plug-and-play solutions or an advanced trader building sophisticated strategies, this library provides the foundation for data-driven trading decisions.WiederholenClaude kann Fehler machen. Bitte überprüfen Sie die Antworten. Sonnet 4.5
X Trade Plan [asset]A precision-structured execution framework designed to identify, map, and visualize targeted areas of interest derived from prior end-of-day AVWAP levels. These areas represent historically important zones where order flow has previously rotated, absorbed, or redistributed—making them highly relevant for future intraday decision-making.
This tool is intended to work in direct combination with the X Tail that Wags indicator, which calculates and projects the previous session’s ending AVWAP forward into the next trading day. The projected end-of-day AVWAP levels serve as a backbone for this Trade Plan: each level is wrapped, extended, and visually organized into a standardized zone structure that the trader can interpret quickly and consistently.
Purpose and Core Concept
Markets consistently respond to prior session value. The end-of-day AVWAP reflects the final consensus price where volume and time-weighted participation reached equilibrium before the session closed. When carried forward, these levels often act as real-world:
Reversion points
Liquidity pockets
Control centers
Continuation or rejection pivots
Absorption shelves and distribution tops
By framing these AVWAP-derived levels into controlled ranges—each with a slight configurable margin—the indicator transforms abstract numbers into objective, visually actionable trading zones.
How This Indicator Works
The user inputs up to fifteen prior AVWAP levels that came from X Tail that Wags’ “Previous End-of-Day AVWAP” readouts. For each active level, X Trade Plan automatically:
Builds a structured zone around the AVWAP using a user-defined ± margin
Draws a filled box from the anchor bar forward a customizable distance
Adds optional top/bottom price labels for precision
Optionally draws a mid-line representing the core of the zone
Displays custom text labels for classification, notes, or tiering
Refreshes anchor points at user-selected higher-timeframe boundaries (e.g., Daily) so zones “reset cleanly” at each new session
Everything is designed to ensure consistent, non-overlapping, visually efficient zones that maintain chart clarity even when multiple levels are active.
Intended Use in a Trade Plan
This indicator is not a signal generator.
It is a structural mapping tool designed for traders who build a daily plan around:
1. Prior Value → Future Reaction
Price commonly retests, respects, or rejects previous session AVWAP levels. These zones act as tactical reference points to evaluate:
Whether price is accepting value
Rejecting value
Targeting inefficiencies
Passing through low-resistance channels
2. Defining Areas of Interest (AOIs)
Each zone identifies where:
Positioning from previous sessions may still exist
Liquidity may sit
Algorithmic systems often pivot
High-volume traders previously accumulated or distributed
3. Enhancing Bias and Scenario Planning
When used with X Tail that Wags, traders can combine:
Current session AVWAP direction
Prior session ending AVWAP levels
The constructed Trade Plan zones
to produce:
Meaningful upside/downside targets
Control-center ranges
Lean / location for entries
Expected reaction points
This synergy turns raw historical AVWAP data into actionable structure.
Why These Levels Matter
End-of-day AVWAP levels are powerful because they encapsulate:
The final “fair value” of the prior session
Where the most volume-weighted agreement occurred
Where institutional inventory was likely set or hedged
The price many algos and funds benchmark against
When the next session opens, these prior value levels serve as magnets and decision boundaries, helping traders anticipate:
High-probability pullback zones
Reversals off previous value
Break-and-go continuation levels
Failure points where trapped participants are forced to exit
Summary
X Trade Plan
𝑎
𝑠
𝑠
𝑒
𝑡
asset transforms prior AVWAP levels—sourced from X Tail that Wags—into a structured visual map of the market’s most relevant historical value areas. These zones are used to shape a deliberate, rules-based Trade Plan that identifies where the market is likely to react, pause, rotate, or accelerate during the current session.
When paired with X Tail that Wags, this indicator provides a powerful, integrated workflow for traders who rely on value-based context, precise levels, and scenario-driven preparation.
Advanced Psychological Levels with Dynamic Spacing═══════════════════════════════════════
ADVANCED PSYCHOLOGICAL LEVELS WITH DYNAMIC SPACING
═══════════════════════════════════════
A comprehensive psychological price level indicator that automatically identifies and displays round number levels across multiple timeframes. Features dynamic ATR-based spacing, smart crypto detection, distance tracking, and customizable alert system.
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WHAT THIS INDICATOR DOES
───────────────────────────────────────
This indicator automatically draws psychological price levels (round numbers) that often act as support and resistance:
- Dynamic ATR-Based Spacing - Adapts level spacing to market volatility
- Multiple Level Types - Major (250 pip), Standard (100 pip), Mid, and Intraday levels
- Smart Asset Detection - Automatically adjusts for Forex, Crypto, Indices, and CFDs
- Crypto Price Adaptation - Intelligent level spacing based on cryptocurrency price magnitude
- Distance Information Table - Real-time percentage distance to nearest levels
- Combined Level Labels - Clear identification when multiple level types coincide
- Performance Optimized - Configurable visible range and label limits
- Comprehensive Alerts - Notifications when price crosses any level type
───────────────────────────────────────
HOW IT WORKS
───────────────────────────────────────
PSYCHOLOGICAL LEVELS CONCEPT:
Psychological levels are round numbers where traders tend to place orders, creating natural support and resistance zones. These include:
- Forex: 1.0000, 1.0100, 1.0050 (pips)
- Crypto: $100, $1,000, $10,000 (whole numbers)
- Indices: 10,000, 10,500, 11,000 (points)
Why They Matter:
- Traders naturally gravitate to round numbers
- Stop losses cluster at these levels
- Take profit orders concentrate here
- Institutional algorithmic trading often targets these levels
DYNAMIC ATR-BASED SPACING:
Traditional Method:
- Fixed spacing regardless of volatility
- May be too tight in volatile markets
- May be too wide in quiet markets
Dynamic Method (Recommended):
- Uses ATR (Average True Range) to measure volatility
- Automatically adjusts level spacing
- Tighter levels in low volatility
- Wider levels in high volatility
Calculation:
1. Calculate ATR over specified period (default: 14)
2. Multiply by ATR multiplier (default: 2.0)
3. Round to nearest psychological level
4. Generate levels at dynamic intervals
Benefits:
- Adapts to market conditions
- More relevant levels in all volatility regimes
- Reduces clutter in trending markets
- Provides more detail in ranging markets
LEVEL TYPES:
Major Levels (250 pip/point):
- Highest significance
- Primary support/resistance zones
- Color: Red (default)
- Style: Solid lines
- Spacing: 2.5x standard step
Standard Levels (100 pip/point):
- Secondary importance
- Common psychological barriers
- Color: Blue (default)
- Style: Dashed lines
- Spacing: Standard step
Mid Levels (50% between major):
- Optional intermediate levels
- Halfway between major levels
- Color: Gray (default)
- Style: Dotted lines
- Usage: Additional confluence points
Intraday Levels (sub-100 pip):
- For intraday traders
- Fine-grained precision
- Color: Yellow (default)
- Style: Dotted lines
- Only shown on intraday timeframes
SMART ASSET DETECTION:
Forex Pairs:
- Detects major currency pairs automatically
- Uses pip-based calculations
- Standard: 100 pips (0.0100)
- Major: 250 pips (0.0250)
- Intraday: 20, 50, 80 pip subdivisions
Cryptocurrencies:
- Automatic price magnitude detection
- Adaptive spacing based on price:
* Under $0.10: Levels at $0.01, $0.05
* $0.10-$1: Levels at $0.10, $0.50
* $1-$10: Levels at $1, $5
* $10-$100: Levels at $10, $50
* $100-$1,000: Levels at $100, $500
* $1,000-$10,000: Levels at $1,000, $5,000
* Over $10,000: Levels at $5,000, $10,000
Indices & CFDs:
- Fixed point-based system
- Major: 500 point intervals (with 250 sub-levels)
- Standard: 100 point intervals
- Suitable for stock indices like SPX, NASDAQ
COMBINED LEVEL LABELS:
When multiple level types coincide at the same price:
- Single line drawn (highest priority color)
- Combined label shows all types
- Priority: Major > Standard > Mid > Intraday
Example Label Formats:
- "1.1000 Major" - Major level only
- "1.1000 Std + Major" - Both standard and major
- "50000 Intra + Mid + Std" - Three levels coincide
Benefits:
- Cleaner chart appearance
- Clear identification of confluence
- Reduced visual clutter
- Easy to spot high-importance levels
DISTANCE INFORMATION TABLE:
Real-time tracking of nearest levels:
Table Contents:
- Nearest major level above (price and % distance)
- Nearest standard level above (price and % distance)
- Nearest standard level below (price and % distance)
Display:
- Top right corner (configurable)
- Color-coded by level type
- Real-time percentage calculations
- Helpful for position management
Usage:
- Identify proximity to key levels
- Set realistic profit targets
- Gauge potential move magnitude
- Monitor approaching resistance/support
ALERT SYSTEM:
Comprehensive crossing alerts:
Alert Types:
- Major Level Crosses
- Standard Level Crosses
- Intraday Level Crosses
Alert Modes:
- First Cross Only: Alert once when level is crossed
- All Crosses: Alert every time level is crossed
Alert Information:
- Level type crossed
- Specific price level
- Direction (above/below)
- One alert per bar to prevent spam
Configuration:
- Enable/disable by level type
- Choose alert frequency
- Customize for your trading style
───────────────────────────────────────
HOW TO USE
───────────────────────────────────────
INITIAL SETUP:
General Settings:
1. Enable "Use Dynamic ATR-Based Spacing" (recommended)
2. Set ATR Period (14 is standard)
3. Adjust ATR Multiplier (2.0 is balanced)
Visibility Settings:
1. Set Visible Range % (10% recommended for clarity)
2. Adjust Label Offset for readability
3. Configure performance limits if needed
Level Selection:
1. Enable/disable level types based on trading style
2. Adjust line counts for each type
3. Choose line styles and colors for visibility
TRADING STRATEGIES:
Breakout Trading:
1. Wait for price to approach major or standard level
2. Monitor for consolidation near level
3. Enter on confirmed break above/beyond level
4. Stop loss just beyond the broken level
5. Target: Next major or standard level
Rejection Trading:
1. Identify major psychological level
2. Wait for price to test the level
3. Look for rejection signals (wicks, bearish/bullish candles)
4. Enter in direction of rejection
5. Stop beyond the level
6. Target: Previous level or mid-level
Range Trading:
1. Identify range between two major levels
2. Buy at lower psychological level
3. Sell at upper psychological level
4. Use standard and mid-levels for position management
5. Exit if major level breaks with volume
Confluence Trading:
1. Look for combined levels (Std + Major)
2. These represent high-probability zones
3. Use as primary support/resistance
4. Increase position size at confluence
5. Expect stronger reactions at these levels
Session-Based Trading:
1. Note opening level at session start (Asian/London/NY)
2. Trade breakouts of major levels during high-volume sessions
3. London/NY sessions: More likely to break levels
4. Asian session: More likely to respect levels (range trading)
RISK MANAGEMENT WITH PSYCHOLOGICAL LEVELS:
Stop Loss Placement:
- Place stops just beyond psychological levels
- Add buffer (5-10 pips for forex)
- Avoid exact round numbers (stop hunting risk)
- Use previous major level as maximum stop
Take Profit Strategy:
- First target: Next standard level (partial profit)
- Second target: Next major level (remaining position)
- Trail stops to breakeven at first target
- Use distance table to calculate risk/reward
Position Sizing:
- Larger positions at major levels (higher probability)
- Smaller positions at intraday levels (lower probability)
- Scale in at standard levels between major levels
- Reduce size when multiple levels are close together
TIMEFRAME CONSIDERATIONS:
Higher Timeframes (4H, Daily, Weekly):
- Focus on Major and Standard levels only
- Disable Intraday and Mid levels
- Wider level spacing expected
- Use for swing trading and position trading
Lower Timeframes (5m, 15m, 1H):
- Enable all level types
- Use Intraday levels for precision
- Tighter level spacing acceptable
- Good for day trading and scalping
Multi-Timeframe Approach:
- Identify major levels on Daily/4H charts
- Refine entries using 15m/1H intraday levels
- Trade in direction of higher timeframe bias
- Use lower timeframe levels for position management
───────────────────────────────────────
CONFIGURATION GUIDE
───────────────────────────────────────
GENERAL SETTINGS:
Dynamic ATR-Based Spacing:
- Enabled: Recommended for most markets
- Disabled: Fixed psychological levels
- ATR Period: 14 (standard), 10 (responsive), 20 (smooth)
- ATR Multiplier: 1.0-5.0 (2.0 is balanced)
VISIBILITY SETTINGS:
Visible Range %:
- 5%: Very tight range, minimal clutter
- 10%: Balanced view (recommended)
- 20%: Wide range, more context
- 50%: Maximum range, all levels visible
Label Offset:
- 10-20 bars: Close to current price
- 30-50 bars: Moderate distance
- 50-100 bars: Far from price action
Performance Limits:
- Max Historical Bars: Reduce if indicator loads slowly
- Max Labels: Reduce for cleaner chart (20-30 recommended)
LEVEL CUSTOMIZATION:
Line Count:
- Lower (1-3): Cleaner chart, fewer levels
- Medium (4-6): Balanced view
- Higher (7-10): More context, busier chart
Line Styles:
- Solid: High importance, easy to see
- Dashed: Medium importance, clear but subtle
- Dotted: Low importance, minimal visual weight
Colors:
- Use contrasting colors for different level types
- Red/Blue/Yellow default works well
- Adjust based on chart background and personal preference
DISTANCE TABLE:
Position:
- Top Right: Doesn't interfere with price action
- Top Left: Good for right-side price scale
- Bottom positions: Less common but available
Colors:
- Default (white text, dark background) works for most charts
- Match your chart theme for consistency
- Ensure text is readable against background
ALERT CONFIGURATION:
Alert by Level Type:
- Major: Most important, fewer false signals
- Standard: Balance of frequency and importance
- Intraday: Many signals, best for active traders
Alert Frequency:
- First Cross Only: Cleaner, less noise (recommended for swing trading)
- All Crosses: Every touch, good for scalping
Alert Setup in TradingView:
1. Configure desired alert types in indicator settings
2. Right-click chart → Add Alert
3. Select this indicator
4. Choose "Any alert() function call"
5. Set delivery method (mobile, email, webhook)
───────────────────────────────────────
ASSET-SPECIFIC TIPS
───────────────────────────────────────
FOREX (EUR/USD, GBP/USD, etc.):
- Major levels at x.x000, x.x500
- Standard levels at x.xx00
- Intraday levels at 20/50/80 pips
- Most effective during London/NY sessions
- Watch for "figure" levels (1.0000, 1.1000)
CRYPTOCURRENCIES (BTC, ETH, etc.):
- Enable dynamic spacing for volatile markets
- Levels adjust automatically based on price
- Watch major $1,000 increments for BTC
- $100 levels important for ETH
- Smaller caps: Use standard levels
- High volatility: Increase ATR multiplier to 3.0
STOCK INDICES (SPX, NASDAQ, etc.):
- 100-point levels most important
- 500-point levels for major S/R
- 50-point mid-levels for refinement
- Watch end-of-day for level reactions
- Futures often lead spot on level breaks
GOLD/COMMODITIES:
- Major levels at $50 increments ($1,900, $1,950)
- Standard levels at $10 increments
- Very reactive to psychological levels
- Watch for false breaks during low volume
- Best reactions during active trading hours
───────────────────────────────────────
BEST PRACTICES
───────────────────────────────────────
Chart Setup:
- Use clean price action charts
- Avoid too many indicators
- Ensure psychological levels are clearly visible
- Match colors to your chart theme
Level Selection:
- Start with Major and Standard levels only
- Add Mid and Intraday as needed
- Less is more - avoid chart clutter
- Adjust based on timeframe
Combining with Other Tools:
- Volume profile for confluence
- Trendlines intersecting psychological levels
- Moving averages near round numbers
- Fibonacci levels coinciding with psychological levels
Common Mistakes to Avoid:
- Trading every level touch (be selective)
- Ignoring volume confirmation
- Setting stops exactly at levels (stop hunting)
- Forgetting to adjust for different assets
- Over-relying on levels without price action confirmation
Performance Optimization:
- Reduce visible range for faster loading
- Lower max historical bars on lower timeframes
- Limit labels to 30-50 for clarity
- Disable unused level types
───────────────────────────────────────
EDUCATIONAL DISCLAIMER
───────────────────────────────────────
This indicator identifies psychological price levels based on round numbers that tend to act as support and resistance. The methodology includes:
- Round number detection algorithms
- ATR-based dynamic spacing calculations
- Asset-specific level determination
- Distance percentage calculations
Psychological levels are a recognized concept in technical analysis, studied by traders and institutions. However, they do not guarantee price reactions and should be used as part of a comprehensive trading strategy including proper risk management, volume analysis, and price action confirmation.
───────────────────────────────────────
USAGE DISCLAIMER
───────────────────────────────────────
This tool is for educational and analytical purposes. Psychological levels can act as support or resistance but price reactions are not guaranteed. Dynamic spacing may generate different levels in different market conditions. Always conduct independent analysis, use proper risk management, and never risk capital you cannot afford to lose. Past performance does not indicate future results.
───────────────────────────────────────
CREDITS & ATTRIBUTION
───────────────────────────────────────
Original Concept: Sonar Lab
Opening Range Breakout with Multi-Timeframe Liquidity]═══════════════════════════════════════
OPENING RANGE BREAKOUT WITH MULTI-TIMEFRAME LIQUIDITY
═══════════════════════════════════════
A professional Opening Range Breakout (ORB) indicator enhanced with multi-timeframe liquidity detection, trading session visualization, volume analysis, and trend confirmation tools. Designed for intraday trading with comprehensive alert system.
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WHAT THIS INDICATOR DOES
───────────────────────────────────────
This indicator combines multiple trading concepts:
- Opening Range Breakout (ORB) - Customizable time period detection with automatic high/low identification
- Multi-Timeframe Liquidity - HTF (Higher Timeframe) and LTF (Lower Timeframe) key level detection
- Trading Sessions - Tokyo, London, New York, and Sydney session visualization
- Volume Analysis - Volume spike detection and strength measurement
- Multi-Timeframe Confirmation - Trend bias from higher timeframes
- EMA Integration - Trend filter and dynamic support/resistance
- Smart Alerts - Quality-filtered breakout notifications
───────────────────────────────────────
HOW IT WORKS
───────────────────────────────────────
OPENING RANGE BREAKOUT (ORB):
Concept:
The Opening Range is a period at the start of a trading session where price establishes an initial high and low. Breakouts beyond this range often indicate the direction of the day's trend.
Detection Method:
- Default: 15-minute opening range (configurable)
- Custom Range: Set specific session times with timezone support
- Automatically identifies ORH (Opening Range High) and ORL (Opening Range Low)
- Tracks ORB mid-point for reference
Range Establishment:
1. Session starts (or custom time begins)
2. Tracks highest high and lowest low during the period
3. Range confirmed at end of opening period
4. Levels extend throughout the session
Breakout Detection:
- Bullish Breakout: Close above ORH
- Bearish Breakout: Close below ORL
- Mid-point acts as bias indicator
Visual Display:
- Shaded box during range formation
- Horizontal lines for ORH, ORL, and mid-point
- Labels showing level values
- Color-coded fills based on selected method
Fill Color Methods:
1. Session Comparison:
- Green: Current OR mid > Previous OR mid
- Red: Current OR mid < Previous OR mid
- Gray: Equal or first session
- Shows day-over-day momentum
2. Breakout Direction (Recommended):
- Green: Price currently above ORH (bullish breakout)
- Red: Price currently below ORL (bearish breakout)
- Gray: Price inside range (no breakout)
- Real-time breakout status
MULTI-TIMEFRAME LIQUIDITY:
Two-Tier System for comprehensive level identification:
HTF (Higher Timeframe) Key Liquidity:
- Default: 4H timeframe (configurable to Daily, Weekly)
- Identifies major institutional levels
- Uses pivot detection with adjustable parameters
- Suitable for swing highs/lows where large orders rest
LTF (Lower Timeframe) Key Liquidity:
- Default: 1H timeframe (configurable)
- Provides precision entry/exit levels
- Finer granularity for intraday trading
- Captures minor swing points
Calculation Method:
- Pivot high/low detection algorithm
- Configurable left bars (lookback) and right bars (confirmation)
- Timeframe multiplier for accurate multi-timeframe detection
- Automatic level extension
Mitigation System:
- Tracks when levels are swept (broken)
- Configurable mitigation type: Wick or Close-based
- Option to remove or show mitigated levels
- Display limit prevents chart clutter
Asset-Specific Optimization:
The indicator includes quick reference settings for different assets:
- Major Forex (EUR/USD, GBP/USD): Default settings optimal
- Crypto (BTC/ETH): Left=12, Right=4, Display=7
- Gold: HTF=1D, Left=20
TRADING SESSIONS:
Four Major Sessions with Full Customization:
Tokyo Session:
- Default: 04:00-13:00 UTC+4
- Asian trading hours
- Often sets daily range
London Session:
- Default: 11:00-20:00 UTC+4
- Highest liquidity period
- Major institutional activity
New York Session:
- Default: 16:00-01:00 UTC+4
- US market hours
- High-impact news events
Sydney Session:
- Default: 01:00-10:00 UTC+4
- Earliest Asian activity
- Lower volatility
Session Features:
- Shaded background boxes
- Session name labels
- Optional open/close lines
- Session high/low tracking with colored lines
- Each session has independent color settings
- Fully customizable times and timezones
VOLUME ANALYSIS:
Volume-Based Trade Confirmation:
Volume MA:
- Configurable period (default: 20)
- Establishes average volume baseline
- Used for spike detection
Volume Spike Detection:
- Identifies when volume exceeds MA * multiplier
- Default: 1.5x average volume
- Confirms breakout strength
Volume Strength Measurement:
- Calculates current volume as percentage of average
- Shows relative volume intensity
- Used in alert quality filtering
High Volume Bars:
- Identifies bars above 50th percentile
- Additional confirmation layer
- Indicates institutional participation
MULTI-TIMEFRAME CONFIRMATION:
Trend Bias from Higher Timeframes:
HTF 1 (Trend):
- Default: 1H timeframe
- Uses EMA to determine intermediate trend
- Compares current timeframe EMA to HTF EMA
HTF 2 (Bias):
- Default: 4H timeframe
- Uses 50 EMA for longer-term bias
- Confirms overall market direction
Bias Classifications:
- Bullish Bias: HTF close > HTF 50 EMA AND Current EMA > HTF1 EMA
- Bearish Bias: HTF close < HTF 50 EMA AND Current EMA < HTF1 EMA
- Neutral Bias: Mixed signals between timeframes
EMA Stack Analysis:
- Compares EMA alignment across timeframes
- +1: Bullish stack (lower TF EMA > higher TF EMA)
- -1: Bearish stack (lower TF EMA < higher TF EMA)
- 0: Neutral/crossed
Usage:
- Filters false breakouts
- Confirms trend direction
- Improves trade quality
EMA INTEGRATION:
Dynamic EMA for Trend Reference:
Features:
- Configurable period (default: 20)
- Customizable color and width
- Acts as dynamic support/resistance
- Trend filter for ORB trades
Application:
- Above EMA: Favor long breakouts
- Below EMA: Favor short breakouts
- EMA cross: Potential trend change
- Distance from EMA: Momentum gauge
SMART ALERT SYSTEM:
Quality-Filtered Breakout Notifications:
Alert Types:
1. Standard ORB Breakout
2. High Quality ORB Breakout
Quality Criteria:
- Volume Confirmation: Volume > 1.2x average
- MTF Confirmation: Bias aligned with breakout direction
Standard Alert:
- Basic breakout detection
- Price crosses ORH or ORL
- Icon: 🚀 (bullish) or 🔻 (bearish)
High Quality Alert:
- Both volume AND MTF confirmed
- Stronger probability setup
- Icon: 🚀⭐ (bullish) or 🔻⭐ (bearish)
Alert Information Includes:
- Alert quality rating
- Breakout level and current price
- Volume strength percentage (if enabled)
- MTF bias status (if enabled)
- Recommended action
One Alert Per Bar:
- Prevents alert spam
- Uses flag system to track sent alerts
- Resets on new ORB session
───────────────────────────────────────
HOW TO USE
───────────────────────────────────────
OPENING RANGE SETUP:
Basic Configuration:
1. Select time period for opening range (default: 15 minutes)
2. Choose fill color method (Breakout Direction recommended)
3. Enable historical data display if needed
Custom Range (Advanced):
1. Enable Custom Range toggle
2. Set specific session time (e.g., 0930-0945)
3. Select appropriate timezone
4. Useful for specific market opens (NYSE, LSE, etc.)
LIQUIDITY LEVELS SETUP:
Quick Configuration by Asset:
- Forex: Use default settings (Left=15, Right=5)
- Crypto: Set Left=12, Right=4, Display=7
- Gold: Set HTF=1D, Left=20
HTF Liquidity:
- Purpose: Major support/resistance levels
- Recommended: 4H for day trading, 1D for swing trading
- Use as profit targets or reversal zones
LTF Liquidity:
- Purpose: Entry/exit refinement
- Recommended: 1H for day trading, 4H for swing trading
- Use for position management
Mitigation Settings:
- Wick-based: More sensitive (default)
- Close-based: More conservative
- Remove or Show mitigated levels based on preference
TRADING SESSIONS SETUP:
Enable/Disable Sessions:
- Master toggle for all sessions
- Individual session controls
- Show/hide session names
Session High/Low Lines:
- Enable to see session extremes
- Each session has custom colors
- Useful for range trading
Customization:
- Adjust session times for your broker
- Set timezone to match your location
- Customize colors for visibility
VOLUME ANALYSIS SETUP:
Enable Volume Analysis:
1. Toggle on Volume Analysis
2. Set MA length (20 recommended)
3. Adjust spike multiplier (1.5 typical)
Usage:
- Confirm breakouts with volume
- Identify climactic moves
- Filter false signals
MULTI-TIMEFRAME SETUP:
HTF Selection:
- HTF 1 (Trend): 1H for day trading, 4H for swing
- HTF 2 (Bias): 4H for day trading, 1D for swing
Interpretation:
- Trade only with bias alignment
- Neutral bias: Be cautious
- Bias changes: Potential reversals
EMA SETUP:
Configuration:
- Period: 20 for responsive, 50 for smoother
- Color: Choose contrasting color
- Width: 1-2 for visibility
Usage:
- Filter trades: Long above, Short below
- Dynamic support/resistance reference
- Trend confirmation
ALERT SETUP:
TradingView Alert Creation:
1. Enable alerts in indicator settings
2. Enable ORB Breakout Alerts
3. Right-click chart → Add Alert
4. Select this indicator
5. Choose "Any alert() function call"
6. Configure delivery method (mobile, email, webhook)
Alert Filtering:
- All alerts include quality rating
- High Quality alerts = Volume + MTF confirmed
- Standard alerts = Basic breakout only
───────────────────────────────────────
TRADING STRATEGIES
───────────────────────────────────────
CLASSIC ORB STRATEGY:
Setup:
1. Wait for opening range to complete
2. Price breaks and closes above ORH or below ORL
3. Volume > average (if enabled)
4. MTF bias aligned (if enabled)
Entry:
- Bullish: Buy on break above ORH
- Bearish: Sell on break below ORL
- Consider retest entries for better risk/reward
Stop Loss:
- Bullish: Below ORL or range mid-point
- Bearish: Above ORH or range mid-point
- Adjust based on volatility
Targets:
- Initial: Range width extension (ORH + range width)
- Secondary: HTF liquidity levels
- Final: Session high/low or major support/resistance
ORB + LIQUIDITY CONFLUENCE:
Enhanced Setup:
1. Opening range established
2. HTF liquidity level near or beyond ORH/ORL
3. Breakout occurs with volume
4. Price targets the liquidity level
Entry:
- Enter on ORB breakout
- Target the HTF liquidity level
- Use LTF liquidity for position management
Management:
- Partial profits at ORB + range width
- Move stop to breakeven at LTF liquidity
- Final exit at HTF liquidity sweep
ORB REJECTION STRATEGY (Counter-Trend):
Setup:
1. Price breaks above ORH or below ORL
2. Weak volume (below average)
3. MTF bias opposite to breakout
4. Price closes back inside range
Entry:
- Failed bullish break: Short below ORH
- Failed bearish break: Long above ORL
Stop Loss:
- Beyond the failed breakout level
- Or beyond session extreme
Target:
- Opposite end of opening range
- Range mid-point for partial profit
SESSION-BASED ORB TRADING:
Tokyo Session:
- Typically narrower ranges
- Good for range trading
- Wait for London open breakout
London Session:
- Highest volume and volatility
- Strong ORB setups
- Major liquidity sweeps common
New York Session:
- Strong trending moves
- News-driven volatility
- Good for momentum trades
Sydney Session:
- Quieter conditions
- Suitable for range strategies
- Sets up Tokyo session
EMA-FILTERED ORB:
Rules:
- Only take bullish breaks if price > EMA
- Only take bearish breaks if price < EMA
- Ignore counter-trend breaks
Benefits:
- Reduces false signals
- Aligns with larger trend
- Improves win rate
───────────────────────────────────────
CONFIGURATION GUIDE
───────────────────────────────────────
OPENING RANGE SETTINGS:
Time Period:
- 15 min: Standard for most markets
- 30 min: Wider range, fewer breakouts
- 60 min: For slower markets or swing trades
Custom Range:
- Use for specific market opens
- NYSE: 0930-1000 EST
- LSE: 0800-0830 GMT
- Set timezone to match exchange
Historical Display:
- Enable: See all previous session data
- Disable: Cleaner chart, current session only
LIQUIDITY SETTINGS:
Left Bars (5-30):
- Lower: More frequent, sensitive levels
- Higher: Fewer, more significant levels
- Recommended: 15 for most markets
Right Bars (1-25):
- Confirmation period
- Higher: More reliable, less frequent
- Recommended: 5 for balance
Display Limit (1-20):
- Number of active levels shown
- Higher: More context, busier chart
- Recommended: 7 for clarity
Extension Options:
- Short: Levels visible near formation
- Current: Extended to current bar (recommended)
- Max: Extended indefinitely
VOLUME SETTINGS:
MA Length (5-50):
- Shorter: More responsive to spikes
- Longer: Smoother baseline
- Recommended: 20 for balance
Spike Multiplier (1.0-3.0):
- Lower: More sensitive spike detection
- Higher: Only extreme spikes
- Recommended: 1.5 for day trading
MULTI-TIMEFRAME SETTINGS:
HTF 1 (Trend):
- 5m chart: Use 15m or 1H
- 15m chart: Use 1H or 4H
- 1H chart: Use 4H or 1D
HTF 2 (Bias):
- One level higher than HTF 1
- Provides longer-term context
- Don't use same as HTF 1
EMA SETTINGS:
Length:
- 20: Responsive, more signals
- 50: Smoother, stronger filter
- 200: Long-term trend only
Style:
- Choose contrasting color
- Width 1-2 for visibility
- Match your trading style
───────────────────────────────────────
BEST PRACTICES
───────────────────────────────────────
Chart Timeframe Selection:
- ORB Trading: Use 5m or 15m charts
- Session Review: Use 1H or 4H charts
- Swing Trading: Use 1H or 4H charts
Quality Over Quantity:
- Wait for high-quality alerts (volume + MTF)
- Avoid trading every breakout
- Focus on confluence setups
Risk Management:
- Position size based on range width
- Wider ranges = smaller positions
- Use stop losses always
- Take partial profits at targets
Market Conditions:
- Best results in trending markets
- Reduce position size in choppy conditions
- Consider session overlaps for volatility
- Avoid trading near major news if inexperienced
Continuous Improvement:
- Track win rate by session
- Note which confluence factors work best
- Adjust settings based on market volatility
- Review performance weekly
───────────────────────────────────────
PERFORMANCE OPTIMIZATION
───────────────────────────────────────
This indicator is optimized with:
- max_bars_back declarations for efficient processing
- Conditional calculations based on enabled features
- Proper memory management for drawing objects
- Minimal recalculation on each bar
Best Practices:
- Disable unused features (sessions, MTF, volume)
- Limit historical display to reduce rendering
- Use appropriate timeframe for your strategy
- Clear old drawing objects periodically
───────────────────────────────────────
EDUCATIONAL DISCLAIMER
───────────────────────────────────────
This indicator combines established trading concepts:
- Opening Range Breakout theory (price action)
- Liquidity level detection (pivot analysis)
- Session-based trading (time-of-day patterns)
- Volume analysis (confirmation technique)
- Multi-timeframe analysis (trend alignment)
All calculations use standard technical analysis methods:
- Pivot high/low detection algorithms
- Moving averages for trend and volume
- Session time filtering
- Timeframe security functions
The indicator identifies potential trading setups but does not predict future price movements. Success requires proper application within a complete trading strategy including risk management, position sizing, and market context.
───────────────────────────────────────
USAGE DISCLAIMER
───────────────────────────────────────
This tool is for educational and analytical purposes. Opening Range Breakout trading involves substantial risk. The alert system and quality filters are designed to identify potential setups but do not guarantee profitability. Always conduct independent analysis, use proper risk management, and never risk capital you cannot afford to lose. Past performance does not indicate future results. Trading intraday breakouts requires experience and discipline.
───────────────────────────────────────
CREDITS & ATTRIBUTION
───────────────────────────────────────
ORIGINAL SOURCE:
This indicator builds upon concepts from LuxAlgo's-ORB
BossExoticMAs
A next-generation moving average and smoothing library by TheStopLossBoss, featuring premium adaptive, exotic, and DSP-inspired filters — optimized for Pine Script® v6 and designed for Traders who demand precision and beauty.
> BossExoticMAs is a complete moving average and signal-processing toolkit built for Pine Script v6.
It combines the essential trend filters (SMA, EMA, WMA, etc.) with advanced, high-performance exotic types used by quants, algo designers, and adaptive systems.
Each function is precision-tuned for stability, speed, and visual clarity — perfect for building custom baselines, volatility filters, dynamic ribbons, or hybrid signal engines.
Includes built-in color gradient theming powered by the exclusive BossGradient —
//Key Features
✅ Full Moving Average Set
SMA, EMA, ZEMA, WMA, HMA, WWMA, SMMA
DEMA, TEMA, T3 (Tillson)
ALMA, KAMA, LSMA
VMA, VAMA, FRAMA
✅ Signal Filters
One-Euro Filter (Crispin/Casiez implementation)
ATR-bounded Range Filter
✅ Color Engine
lerpColor() safe blending using color.from_gradient
Thematic gradient palettes: STOPLOSS, VAPORWAVE, ROYAL FLAME, MATRIX FLOW
Exclusive: BOSS GRADIENT
✅ Helper Functions
Clamping, normalization, slope detection, tick delta
Slope-based dynamic color control via slopeThemeColor()
🧠 Usage Example
//@version=6
indicator("Boss Exotic MA Demo", overlay=true)
import TheStopLossBoss/BossExoticMAs/1 as boss
len = input.int(50, "Length")
atype = input.string("T3", "MA Type", options= )
t3factor = input.float(0.7, "T3 β", step=0.05)
smoothColor = boss.slopeThemeColor(close, "BOSS GRADIENT", 0.001)ma = boss.maSelect(close, len, atype, t3factor, 0.85, 14)
plot(ma, "Boss Exotic MA", color=smoothColor, linewidth=2)
---
🔑 Notes
Built exclusively for Pine Script® v6
Library designed for import use — all exports are prefixed cleanly (boss.functionName())
Some functions maintain internal state (var-based). Warnings are safe to ignore — adaptive design choice.
Each MA output is non-repainting and mathematically stable.
---
📜 Author
TheStopLossBoss
Designer of precision trading systems and custom adaptive algorithms.
Follow for exclusive releases, educational material, and full-stack trend solutions.
movingaverage, trend, adaptive, filter, volatility, smoothing, quant, technicalanalysis, bossgradient, t3, alma, frama, vma
Double Weighted Moving Average (DWMA)# DWMA: Double Weighted Moving Average
## Overview and Purpose
The Double Weighted Moving Average (DWMA) is a technical indicator that applies weighted averaging twice in sequence to create a smoother signal with enhanced noise reduction. Developed in the late 1990s as an evolution of traditional weighted moving averages, the DWMA was created by quantitative analysts seeking enhanced smoothing without the excessive lag typically associated with longer period averages. By applying a weighted moving average calculation to the results of an initial weighted moving average, DWMA achieves more effective filtering while preserving important trend characteristics.
## Core Concepts
* **Cascaded filtering:** DWMA applies weighted averaging twice in sequence for enhanced smoothing and superior noise reduction
* **Linear weighting:** Uses progressively increasing weights for more recent data in both calculation passes
* **Market application:** Particularly effective for trend following strategies where noise reduction is prioritized over rapid signal response
* **Timeframe flexibility:** Works across multiple timeframes but particularly valuable on daily and weekly charts for identifying significant trends
The core innovation of DWMA is its two-stage approach that creates more effective noise filtering while minimizing the additional lag typically associated with longer-period or higher-order filters. This sequential processing creates a more refined output that balances noise reduction and signal preservation better than simply increasing the length of a standard weighted moving average.
## Common Settings and Parameters
| Parameter | Default | Function | When to Adjust |
|-----------|---------|----------|---------------|
| Length | 14 | Controls the lookback period for both WMA calculations | Increase for smoother signals in volatile markets, decrease for more responsiveness |
| Source | close | Price data used for calculation | Consider using hlc3 for a more balanced price representation |
**Pro Tip:** For trend following, use a length of 10-14 with DWMA instead of a single WMA with double the period - this provides better smoothing with less lag than simply increasing the period of a standard WMA.
## Calculation and Mathematical Foundation
**Simplified explanation:**
DWMA first calculates a weighted moving average where recent prices have more importance than older prices. Then, it applies the same weighted calculation again to the results of the first calculation, creating a smoother line that reduces market noise more effectively.
**Technical formula:**
```
DWMA is calculated by applying WMA twice:
1. First WMA calculation:
WMA₁ = (P₁ × w₁ + P₂ × w₂ + ... + Pₙ × wₙ) / (w₁ + w₂ + ... + wₙ)
2. Second WMA calculation applied to WMA₁:
DWMA = (WMA₁₁ × w₁ + WMA₁₂ × w₂ + ... + WMA₁ₙ × wₙ) / (w₁ + w₂ + ... + wₙ)
```
Where:
- Linear weights: most recent value has weight = n, second most recent has weight = n-1, etc.
- n is the period length
- Sum of weights = n(n+1)/2
**O(1) Optimization - Inline Dual WMA Architecture:**
This implementation uses an advanced O(1) algorithm with two complete inline WMA calculations. Each WMA uses the dual running sums technique:
1. **First WMA (source → wma1)**:
- Maintains buffer1, sum1, weighted_sum1
- Recurrence: `W₁_new = W₁_old - S₁_old + (n × P_new)`
- Cached denominator norm1 after warmup
2. **Second WMA (wma1 → dwma)**:
- Maintains buffer2, sum2, weighted_sum2
- Recurrence: `W₂_new = W₂_old - S₂_old + (n × WMA₁_new)`
- Cached denominator norm2 after warmup
**Implementation details:**
- Both WMAs fully integrated inline (no helper functions)
- Each maintains independent state: buffers, sums, counters, norms
- Both warm up independently from bar 1
- Performance: ~16 operations per bar regardless of period (vs ~10,000 for naive O(n²) implementation)
**Why inline architecture:**
Unlike helper functions, the inline approach makes all state variables and calculations visible in a single scope, eliminating function call overhead and making the dual-pass nature explicit. This is ideal for educational purposes and when debugging complex cascaded filters.
> 🔍 **Technical Note:** The dual-pass O(1) approach creates a filter that effectively increases smoothing without the quadratic increase in computational cost. Original O(n²) implementations required ~10,000 operations for period=100; this optimized version requires only ~16 operations, achieving a 625x speedup while maintaining exact mathematical equivalence.
## Interpretation Details
DWMA can be used in various trading strategies:
* **Trend identification:** The direction of DWMA indicates the prevailing trend
* **Signal generation:** Crossovers between price and DWMA generate trade signals, though they occur later than with single WMA
* **Support/resistance levels:** DWMA can act as dynamic support during uptrends and resistance during downtrends
* **Trend strength assessment:** Distance between price and DWMA can indicate trend strength
* **Noise filtering:** Using DWMA to filter noisy price data before applying other indicators
## Limitations and Considerations
* **Market conditions:** Less effective in choppy, sideways markets where its lag becomes a disadvantage
* **Lag factor:** More lag than single WMA due to double calculation process
* **Initialization requirement:** Requires more data points for full calculation, showing more NA values at chart start
* **Short-term trading:** May miss short-term trading opportunities due to increased smoothing
* **Complementary tools:** Best used with momentum oscillators or volume indicators for confirmation
## References
* Jurik, M. "Double Weighted Moving Averages: Theory and Applications in Algorithmic Trading Systems", Jurik Research Papers, 2004
* Ehlers, J.F. "Cycle Analytics for Traders," Wiley, 2013
Aurum DCX AVE Gold and Silver StrategySummary in one paragraph
Aurum DCX AVE is a volatility break strategy for gold and silver on intraday and swing timeframes. It aligns a new Directional Convexity Index with an Adaptive Volatility Envelope and an optional USD/DXY bias so trades appear only when direction quality and expansion agree. It is original because it fuses three pieces rarely combined in one model for metals: a convexity aware trend strength score, a percentile based envelope that widens with regime heat, and an intermarket DXY filter.
Scope and intent
• Markets. Gold and silver futures or spot, other liquid commodities, major indices
• Timeframes. Five minutes to one day. Defaults to 30min for swing pace
• Default demo used in this publication. TVC:GOLD on 30m
• Purpose. Enter confirmed volatility breaks while muting chop using regime heat and USD bias
• Limits. This is a strategy. Orders are simulated on standard candles only
Originality and usefulness
• Unique fusion. DCX combines DI strength with path efficiency and curvature. AVE blends ATR with a high TR percentile and widens with DCX heat. DXY adds an intermarket bias
• Failure mode addressed. False starts inside compression and unconfirmed breakouts during USD swings
• Testability. Each component has a named input. Entry names L and S are visible in the list of trades
• Portable yardstick. Weekly ATR for stops and R multiples for targets
• Open source. Method and implementation are disclosed for community review
Method overview in plain language
You score direction quality with DCX, size an adaptive envelope with a blend of ATR and a high TR percentile, and only allow breaks that clear the band while DCX is above a heat threshold in the same direction. An optional DXY filter favors long when USD weakens and short when USD strengthens. Orders are bracketed with a Weekly ATR stop and an R multiple target, with optional trailing to the envelope.
Base measures
• Range basis. True Range and ATR over user windows. A high TR percentile captures expansion tails used by AVE
• Return basis. Not required
Components
• Directional Convexity Index DCX. Measures directional strength with DX, multiplies by path efficiency, blends a curvature term from acceleration, scales to 0 to 100, and uses a rise window
• Adaptive Volatility Envelope AVE. Midline ALMA or HMA or EMA plus bands sized by a blend of ATR and a high TR percentile. The blend weight follows volatility of volatility. Band width widens with DCX heat
• DXY Bias optional. Daily EMA trend of DXY. Long bias when USD weakens. Short bias when USD strengthens
• Risk block. Initial stop equals Weekly ATR times a multiplier. Target equals an R multiple of the initial risk. Optional trailing to AVE band
Fusion rule
• All gates must pass. DCX above threshold and rising. Directional lead agrees. Price breaks the AVE band in the same direction. DXY bias agrees when enabled
Signal rule
• Long. Close above AVE upper and DCX above threshold and DCX rising and plus DI leads and DXY bias is bearish
• Short. Close below AVE lower and DCX above threshold and DCX falling and minus DI leads and DXY bias is bullish
• Exit and flip. Bracket exit at stop or target. Optional trailing to AVE band
Inputs with guidance
Setup
• Symbol. Default TVC:GOLD (Correlation Asset for internal logic)
• Signal timeframe. Blank follows the chart
• Confirm timeframe. Default 1 day used by the bias block
Directional Convexity Index
• DCX window. Typical 10 to 21. Higher filters more. Lower reacts earlier
• DCX rise bars. Typical 3 to 6. Higher demands continuation
• DCX entry threshold. Typical 15 to 35. Higher avoids soft moves
• Efficiency floor. Typical 0.02 to 0.06. Stability in quiet tape
• Convexity weight 0..1. Typical 0.25 to 0.50. Higher gives curvature more influence
Adaptive Volatility Envelope
• AVE window. Typical 24 to 48. Higher smooths more
• Midline type. ALMA or HMA or EMA per preference
• TR percentile 0..100. Typical 75 to 90. Higher favors only strong expansions
• Vol of vol reference. Typical 0.05 to 0.30. Controls how much the percentile term weighs against ATR
• Base envelope mult. Typical 1.4 to 2.2. Width of bands
• Regime adapt 0..1. Typical 0.6 to 0.95. How much DCX heat widens or narrows the bands
Intermarket Bias
• Use DXY bias. Default ON
• DXY timeframe. Default 1 day
• DXY trend window. Typical 10 to 50
Risk
• Risk percent per trade. Reporting field. Keep live risk near one to two percent
• Weekly ATR. Default 14. Basis for stops
• Stop ATR weekly mult. Typical 1.5 to 3.0
• Take profit R multiple. Typical 1.5 to 3.0
• Trail with AVE band. Optional. OFF by default
Properties visible in this publication
• Initial capital. 20000
• Base currency. USD
• request.security lookahead off everywhere
• Commission. 0.03 percent
• Slippage. 5 ticks
• Default order size method percent of equity with value 3% of the total capital available
• Pyramiding 0
• Process orders on close ON
• Bar magnifier ON
• Recalculate after order is filled OFF
• Calc on every tick OFF
Realism and responsible publication
• No performance claims. Past results never guarantee future outcomes
• Shapes can move while a bar forms and settle on close
• Strategies use standard candles for signals and orders only
Honest limitations and failure modes
• Economic releases and thin liquidity can break assumptions behind the expansion logic
• Gap heavy symbols may prefer a longer ATR window
• Very quiet regimes can reduce signal contrast. Consider higher DCX thresholds or wider bands
• Session time follows the exchange of the chart and can change symbol to symbol
• Symbol sensitivity is expected. Use the gates and length inputs to find stable settings
Open source reuse and credits
• None
Mode
Public open source. Source is visible and free to reuse within TradingView House Rules
Legal
Education and research only. Not investment advice. You are responsible for your decisions. Test on historical data and in simulation before any live use. Use realistic costs.
ZS Game Changer Pump & Dump DetectorZS GAME CHANGER PUMP AND DUMP DETECTOR - TOP 2 MOMENTUM TRACKER
Created by Zakaria Safri
An intelligent indicator specifically designed to identify and highlight the two most significant pump and dump candles within your selected lookback period. Perfect for traders who want to focus on the game-changing moves that truly matter in volatile markets like cryptocurrency, stocks, and forex.
CORE FEATURES
AUTOMATIC GAME CHANGER DETECTION
The indicator continuously scans your specified lookback period and automatically identifies the top 2 strongest pump candles and top 2 strongest dump candles. These game-changing candles are highlighted with distinctive gold labels and horizontal reference lines, making them instantly visible on your chart. Unlike other indicators that show every small move, this focuses exclusively on the market-moving moments that define trends and create opportunities.
INTELLIGENT PUMP AND DUMP CLASSIFICATION
Uses advanced percentage-based calculations to classify candles as pumps when price surges significantly upward and dumps when price plunges sharply downward. The detection system accounts for candle body size, wick proportions, and volume confirmation to ensure only legitimate momentum moves trigger signals. Customizable thresholds allow adaptation to any market volatility profile from calm stocks to wild altcoins.
ADVANCED WICK EXCLUSION FILTER
Eliminates false signals caused by candles with large wicks and small bodies. This filter focuses analysis exclusively on candles with substantial body sizes that indicate genuine directional conviction rather than temporary spikes followed by rejection. The body to candle ratio is fully adjustable to match your preferred signal quality standards.
VOLUME CONFIRMATION SYSTEM
Optional volume filter ensures detected pumps and dumps are backed by real market participation. The indicator compares current volume against a moving average and only triggers signals when volume exceeds your specified multiplier threshold. This eliminates low-volume noise and focuses on moves supported by institutional or crowd participation.
RALLY SEQUENCE DETECTION
Identifies and highlights consecutive sequences of pump or dump candles with colored background overlays. Green background indicates sustained buying pressure across multiple candles while red background shows sustained selling pressure. The rally detection system includes an optional one-miss allowance that prevents the sequence from breaking due to a single neutral candle.
HORIZONTAL REFERENCE LINES
Draws dashed lines from each game changer candle extending to the current bar, providing constant visual reference to the most significant support and resistance levels created by extreme momentum. The top game changer gets a thick dashed line while the second gets a dotted line for easy differentiation. Labels on the right side display the exact percentage move.
COMPREHENSIVE STATISTICS DASHBOARD
Real-time information panel showing current market status as pumping, dumping, or neutral along with the current candle percentage change. Displays the exact percentage values for top pump number 1, top pump number 2, top dump number 1, and top dump number 2. Shows running totals of all pumps and dumps detected since chart load. Tracks consecutive candle counts during active rally sequences.
TESTING AND VERIFICATION MODE
Built-in debug mode displays percentage change directly on each qualifying pump and dump candle, allowing instant verification that calculations are accurate. Shows which filters are currently active with a simple code in the dashboard. Helps traders understand exactly why certain candles qualified as game changers.
HOW THE GAME CHANGER DETECTION WORKS
SCANNING ALGORITHM
Every bar close, the indicator scans backward through your specified lookback period examining every candle's percentage change from its previous close. For bullish moves, it identifies the two candles with the largest positive percentage change that meet your threshold requirements. For bearish moves, it identifies the two candles with the largest negative percentage change meeting threshold requirements.
RANKING SYSTEM
Candles are ranked purely by their percentage move magnitude. The number 1 game changer is always the single strongest move in the lookback period. The number 2 game changer is the second strongest move. Rankings update dynamically as new candles form and old candles exit the lookback window.
VISUAL IDENTIFICATION
Game changer number 1 for both pumps and dumps receives a large gold label reading GAME CHANGER NUMBER 1 with zero transparency for maximum visibility. Game changer number 2 receives a slightly smaller gold label with partial transparency. The candle bars themselves are colored in gold instead of the standard green or red. Horizontal lines extend from the game changer price level to current bar.
FILTER APPLICATION
Only candles that pass your configured filters qualify for game changer consideration. If wick exclusion is enabled, candles with large wicks and small bodies are ignored. If volume confirmation is enabled, only candles with above-average volume qualify. This ensures game changers represent legitimate market moves rather than aberrations.
PRACTICAL APPLICATIONS
FOR CRYPTOCURRENCY TRADERS
Crypto markets experience extreme volatility with occasional massive pump and dump candles that define entire trends. This indicator instantly identifies which candles represent true market structure shifts versus normal noise. Use the game changer levels as key support and resistance for entries, exits, and stop placement. The top pump often marks the local high to watch for breakouts while the top dump marks the local low for reversal trades.
FOR DAY TRADERS
Intraday charts contain hundreds of candles but only a few truly matter for the session outcome. Game changer detection filters out 98 percent of candles to show you the 2 percent that drove the actual price movement. Enter trades on the side of the strongest recent game changer. Use game changer levels as magnet prices where algorithmic trading often returns.
FOR SWING TRADERS
On daily and four-hour timeframes, game changers represent major institutional activity or news-driven moves. The top dump often marks capitulation selling that creates reversal opportunities. The top pump often marks FOMO buying that creates resistance levels. Swing traders can build positions knowing these levels will be defended or tested multiple times.
FOR VOLATILITY ANALYSIS
Understanding which candles created the most volatility helps assess market risk. Multiple game changers clustered together indicate unstable choppy conditions. Game changers separated by many neutral candles indicate trending stable conditions. Use this context to adjust position sizing and stop distances appropriately.
FOR SUPPORT AND RESISTANCE TRADING
Game changer candles create the strongest support and resistance levels because they represent prices where massive volume transacted in short time periods. These levels have higher probability of holding on retest compared to arbitrary moving averages or pivot points. Trade bounces off game changer levels or breakouts through them.
RECOMMENDED SETTINGS BY MARKET
CRYPTOCURRENCY 15-MINUTE TO 1-HOUR CHARTS
Candle Size Threshold: 2.0 percent
Body to Candle Ratio: 0.5
Volume Multiplier: 1.5 times average
Game Changer Lookback: 100 bars
Extreme Threshold: 3.5 percent
Enable Wick Filter: Yes
Enable Volume Confirmation: Yes
Minimum Rally Candles: 3
STOCKS DAILY CHARTS
Candle Size Threshold: 1.0 percent
Body to Candle Ratio: 0.6
Volume Multiplier: 2.0 times average
Game Changer Lookback: 50 bars
Extreme Threshold: 2.5 percent
Enable Wick Filter: Yes
Enable Volume Confirmation: Yes
Minimum Rally Candles: 2
FOREX 1-HOUR TO 4-HOUR CHARTS
Candle Size Threshold: 0.5 percent
Body to Candle Ratio: 0.5
Volume Multiplier: Not applicable
Game Changer Lookback: 80 bars
Extreme Threshold: 1.0 percent
Enable Wick Filter: Yes
Enable Volume Confirmation: No
Minimum Rally Candles: 3
SCALPING 1-MINUTE TO 5-MINUTE CHARTS
Candle Size Threshold: 0.8 percent
Body to Candle Ratio: 0.4
Volume Multiplier: 1.2 times average
Game Changer Lookback: 50 bars
Extreme Threshold: 1.5 percent
Enable Wick Filter: No
Enable Volume Confirmation: Yes
Minimum Rally Candles: 2
WHAT IS INCLUDED
Automatic identification of top 2 pump candles
Automatic identification of top 2 dump candles
Gold colored game changer labels with size differentiation
Gold colored candle bars for game changers
Horizontal reference lines from game changers to current price
Regular pump and dump detection with green and red candles
Rally sequence detection with background highlighting
Extreme move detection and labeling system
Real-time statistics dashboard with all key metrics
Percentage change debug mode for verification
Volume confirmation filter with adjustable multiplier
Wick exclusion filter with adjustable body ratio
Customizable lookback period from 20 to 500 bars
Consecutive candle counter for rally tracking
Alert system for game changers, pumps, dumps, and rallies
Works on all timeframes from 1 minute to monthly
Compatible with stocks, forex, cryptocurrency, and futures
UNDERSTANDING GAME CHANGERS
WHAT MAKES A CANDLE A GAME CHANGER
A game changer is not just a large move but the largest move within context. In a volatile crypto market, a 5 percent pump might not rank in the top 2. In a stable stock, a 2 percent pump could be the number 1 game changer. The indicator adapts to your specific instrument and timeframe to find what truly matters in that context.
WHY FOCUS ON TOP 2 ONLY
Markets are driven by a small number of significant moves rather than the average of all moves. By focusing exclusively on the top 2 in each direction, traders can ignore noise and concentrate on the price levels that actually matter for support, resistance, and momentum. This creates clarity in decision making.
GAME CHANGERS AS MARKET STRUCTURE
The top pump often marks the recent high that bulls must break to continue uptrend. The top dump often marks the recent low that bears must break to continue downtrend. These become the key levels around which all other price action rotates. Understanding this structure is essential for profitable trading.
GAME CHANGERS AS SENTIMENT INDICATORS
Consecutive pump game changers signal strong bullish sentiment and FOMO conditions. Consecutive dump game changers signal fear and capitulation. Alternating pump and dump game changers signal indecision and range conditions. Read the pattern of game changers to gauge market psychology.
VERIFICATION AND TESTING
HOW TO VERIFY ACCURACY
Enable Show Debug Info on Chart in the Testing and Debug settings group. This displays the percentage change calculation directly on every qualifying pump and dump candle. Manually verify by calculating open minus close divided by close multiplied by 100. The debug percentage should match your manual calculation exactly.
HOW TO TEST FILTERS
Toggle wick exclusion filter on and off while watching how many candles qualify. With filter on, candles with long wicks and small bodies should disappear. Toggle volume confirmation on and off to see how low-volume candles get excluded. Adjust the thresholds and watch the real-time impact on signal count.
HOW TO VERIFY GAME CHANGERS
Look at your chart and visually identify which candle had the biggest green body in the lookback period. The game changer number 1 pump label should be on that exact candle. Repeat for the biggest red candle to verify game changer number 1 dump. The rankings should match your visual assessment.
LOOKBACK PERIOD EFFECTS
Decrease the lookback period to 20 bars and watch game changers update to only recent moves. Increase to 500 bars and watch game changers potentially change to older historic moves. The optimal lookback balances recency with significance. Too short misses important levels, too long includes irrelevant history.
DASHBOARD INFORMATION GUIDE
STATUS ROW
Shows PUMPING when current candle qualifies as a pump, DUMPING when current candle qualifies as a dump, or NEUTRAL when current candle does not meet threshold requirements. This updates in real-time on every bar close.
CURRENT CHANGE ROW
Displays the percentage change of the current candle from its previous close. Positive percentages indicate bullish candle, negative indicate bearish candle. This number may or may not meet your threshold to qualify as pump or dump.
TOP PUMP NUMBER 1
The highest positive percentage change found in your lookback period. This candle is marked with the large gold GAME CHANGER NUMBER 1 label below it. Shows N/A if no pumps exist in the lookback period.
TOP PUMP NUMBER 2
The second highest positive percentage change found in your lookback period. Marked with smaller gold GAME CHANGER NUMBER 2 label. Shows N/A if only one or zero pumps exist.
TOP DUMP NUMBER 1
The highest negative percentage change magnitude found in your lookback period. This candle is marked with the large gold GAME CHANGER NUMBER 1 label above it. Shows N/A if no dumps exist.
TOP DUMP NUMBER 2
The second highest negative percentage change magnitude found in your lookback period. Marked with smaller gold GAME CHANGER NUMBER 2 label. Shows N/A if only one or zero dumps exist.
TOTAL PUMPS
Running count of all pump candles detected since you loaded the indicator on this chart. This number continuously increases as new qualifying pumps form. Resets when you reload the chart.
TOTAL DUMPS
Running count of all dump candles detected since chart load. Increases as new qualifying dumps form and resets on chart reload.
CONSECUTIVE
Shows the current count of consecutive pump or dump candles during an active rally. Displays 3 UP during a 3-candle pump rally or 5 DN during a 5-candle dump rally. Shows 0 when no rally is active.
ALERT SYSTEM
GAME CHANGER DETECTED ALERT
Triggers whenever the current candle becomes one of the top 2 pumps or top 2 dumps. This is the highest priority alert indicating a market-moving event just occurred. Use this alert for immediate notification of significant opportunities.
PUMP DETECTED ALERT
Triggers on every candle that qualifies as a pump according to your threshold and filter settings. This includes regular pumps and extreme pumps but excludes game changers which have their separate alert. Use for general upward momentum monitoring.
DUMP DETECTED ALERT
Triggers on every candle that qualifies as a dump according to your settings. Includes regular and extreme dumps but excludes game changers. Use for general downward momentum monitoring.
PUMP RALLY STARTED ALERT
Triggers when consecutive pump candles reach your minimum rally threshold. Indicates the beginning of a sustained upward movement sequence. Use to catch trends early.
DUMP RALLY STARTED ALERT
Triggers when consecutive dump candles reach your minimum rally threshold. Indicates the beginning of a sustained downward movement sequence. Use for trend following or reversal timing.
ALERT MESSAGE FORMAT
All alerts include the ticker symbol and current price using TradingView placeholders. Messages are descriptive and specify which type of signal triggered. Alerts work with TradingView notification system including email, SMS, webhook, and app notifications.
TECHNICAL SPECIFICATIONS
CALCULATION METHODOLOGY
Percentage change calculated as current close minus previous close divided by previous close multiplied by 100. Body ratio calculated as absolute value of close minus open divided by high minus low. Volume elevation calculated as current volume divided by 20-period simple moving average of volume. Game changer ranking uses absolute value comparison across entire lookback array.
PERFORMANCE CHARACTERISTICS
Lightweight calculations optimized for speed on all timeframes. No repainting of signals ensuring all triggers are final on bar close. Variables properly scoped with var keyword for memory efficiency. Maximum bars back set to 500 to prevent excessive historical loading. Updates in real-time on every bar close without lag.
COMPATIBILITY
Works on all TradingView plans including free, pro, and premium. Compatible with stocks, forex, cryptocurrency, futures, indices, and commodities. Functions correctly on all timeframes from 1 second to monthly. No external data requests ensuring fast loading. Overlay true setting places directly on price chart.
RISK DISCLAIMER
This indicator is a technical analysis tool for identifying momentum and should not be used as the sole basis for trading decisions. Game changer levels can be broken during strong trends and are not guaranteed support or resistance. Pump and dump detection does not predict future price direction. Always use proper risk management with stop losses on every trade. Combine this indicator with other forms of analysis including fundamentals, market context, and risk assessment. Practice on demo accounts before live trading. Past performance of game changer signals does not guarantee future results. Trading carries substantial risk of loss and is not suitable for all investors. The creator is not responsible for trading losses incurred while using this tool.
SUPPORT AND UPDATES
Regular updates based on user feedback and market evolution. Built following PineCoders industry standards and best practices for code quality. Clean well-documented code structure for transparency and auditability. Optimized performance across all timeframes and instruments. Active development with continuous improvements and feature additions.
WHY CHOOSE ZS GAME CHANGER PUMP AND DUMP DETECTOR
Focuses on what matters by highlighting only the top 2 moves in each direction instead of cluttering your chart with every small fluctuation. Saves time by automatically identifying the most significant candles rather than requiring manual scanning. Provides clarity through visual gold labels and reference lines that make game changers unmistakable. Adapts to any market with customizable thresholds for volatility and volume. Eliminates noise with advanced wick and volume filters ensuring signal quality. Offers verification through debug mode proving calculations are accurate and trustworthy. Includes comprehensive statistics showing exact percentages and counts. Works everywhere across all markets, timeframes, and instruments without modification.
Transform your chart analysis by focusing exclusively on the game-changing moments that define trends and create opportunities.
Version 1.1 | Created by Zakaria Safri | Pine Script Version 5 | PineCoders Compliant
Curved Radius Supertrend [BOSWaves]Curved Radius Supertrend — Adaptive Parabolic Trend Framework with Dynamic Acceleration Geometry
Overview
The Curved Radius Supertrend introduces an evolution of the classic Supertrend indicator - engineered with a dynamic curvature engine that replaces rigid ATR bands with parabolic, radius-based motion. Traditional Supertrend systems rely on static band displacement, reacting linearly to volatility and often lagging behind emerging price acceleration. The Curved Radius Supertend model redefines this by integrating controlled acceleration and curvature geometry, allowing the trend bands to adapt fluidly to both velocity and duration of price movement.
The result is a smoother, more organic trend flow that visually captures the momentum curve of price action - not just its direction. Instead of sharp pivots or whipsaws, traders experience a structurally curved trajectory that mirrors real market inertia. This makes it particularly effective for identifying sustained directional phases, detecting early trend rotations, and filtering out noise that plagues standard Supertrend methodologies.
Unlike conventional band-following systems, the Curved Radius framework is time-reactive and velocity-aware, providing a nuanced signal structure that blends geometric precision with volatility sensitivity.
Theoretical Foundation
The Curved Radius Supertrend draws from the intersection of mathematical curvature dynamics and adaptive volatility processing. Standard Supertrend algorithms extend from Average True Range (ATR) envelopes - a linear measure of volatility that moves proportionally with price deviation. However, markets do not expand or contract linearly. Trend velocity typically accelerates and decelerates in nonlinear arcs, forming natural parabolas across price phases.
By embedding a radius-based acceleration function, the indicator models this natural behavior. The core variable, radiusStrength, controls how aggressively curvature accelerates over time. Instead of simply following price distance, the band now evolves according to temporal acceleration - each bar contributes incremental velocity, bending the trend line into a radius-like curve.
This structural design allows the indicator to anticipate rather than just respond to price action, capturing momentum transitions as curved accelerations rather than binary flips. In practice, this eliminates the stutter effect typical of standard Supertrends and replaces it with fluid directional motion that better reflects actual trend geometry.
How It Works
The Curved Radius Supertrend is constructed through a multi-stage process designed to balance price responsiveness with geometric stability:
1. Baseline Supertrend Core
The framework begins with a standard ATR-derived upper and lower band calculation. These define the volatility envelope that constrains potential price zones. Directional bias is determined through crossover logic - prices above the lower band confirm an uptrend, while prices below the upper band confirm a downtrend.
2. Curvature Acceleration Engine
Once a trend direction is established, a curvature engine is activated. This system uses radiusStrength as a coefficient to simulate acceleration per bar, incrementally increasing velocity over time. The result is a parabolic displacement from the anchor price (the price level at trend change), creating a curved motion path that dynamically widens or tightens as the trend matures.
Mathematically, this acceleration behaves quadratically - each new bar compounds the previous velocity, forming an exponential rate of displacement that resembles curved inertia.
3. Adaptive Smoothing Layer
After the radius curve is applied, a smoothing stage (defined by the smoothness parameter) uses a simple moving average to regulate curve noise. This ensures visual coherence without sacrificing responsiveness, producing flowing arcs rather than jagged band steps.
4. Directional Visualization and Outer Envelope
Directional state (bullish or bearish) dictates both the color gradient and band displacement. An outer envelope is plotted one ATR beyond the curved band, creating a layered trend visualization that shows the extent of volatility expansion.
5. Signal Events and Alerts
Each directional transition triggers a 'BUY' or 'SELL' signal, clearly labeling phase shifts in market structure. Alerts are built in for automation and backtesting.
Interpretation
The Curved Radius Supertrend reframes how traders visualize and confirm trends. Instead of simply plotting a trailing stop, it maps the dynamic curvature of trend development.
Uptrend Phases : The band curves upward with increasing acceleration, reflecting the market’s growing directional velocity. As curvature steepens, conviction strengthens.
Downtrend Phases : The band bends downward in a mirrored acceleration pattern, indicating sustained bearish momentum.
Trend Change Points : When the direction flips and a new anchor point forms, the curve resets - providing a clean, early visual confirmation of structural reversal.
Smoothing and Radius Interplay : A lower radius strength produces a tighter, more reactive curve ideal for scalping or short timeframes. Higher values generate broad, sweeping arcs optimized for swing or positional analysis.
Visually, this curvature system translates market inertia into shape - revealing how trends bend, accelerate, and ultimately exhaust.
Strategy Integration
The Curved Radius Supertrend is versatile enough to integrate seamlessly into multiple trading frameworks:
Trend Following : Use BUY/SELL flips to identify emerging directional bias. Strong curvature continuation confirms sustained momentum.
Momentum Entry Filtering : Combine with oscillators or volume tools to filter entries only when the curve slope accelerates (high momentum conditions).
Pullback and Re-entry Timing : The smooth curvature of the radius band allows traders to identify shallow retracements without premature exits. The band acts as a dynamic, self-adjusting support/resistance arc.
Volatility Compression and Expansion : Flattening curvature indicates volatility compression - a potential pre-breakout zone. Rapid re-steepening signals expansion and directional conviction.
Stop Placement Framework : The curved band can serve as a volatility-adjusted trailing stop. Because the curve reflects acceleration, it adapts naturally to market rhythm - widening during momentum surges and tightening during stagnation.
Technical Implementation Details
Curved Radius Engine : Parabolic acceleration algorithm that applies quadratic velocity based on bar count and radiusStrength.
Anchor Logic : Resets curvature at each trend change, establishing a new reference base for directional acceleration.
Smoothing Layer : SMA-based curve smoothing for noise reduction.
Outer Envelope : ATR-derived band offset visualizing volatility extension.
Directional Coloring : Candle and band coloration tied to current trend state.
Signal Engine : Built-in BUY/SELL markers and alert conditions for automation or script integration.
Optimal Application Parameters
Timeframe Guidance :
1-5 min (Scalping) : 0.08–0.12 radius strength, minimal smoothing for rapid responsiveness.
15 min : 0.12–0.15 radius strength for intraday trends.
1H : 0.15–0.18 radius strength for structured short-term swing setups.
4H : 0.18–0.22 radius strength for macro-trend shaping.
Daily : 0.20–0.25 radius strength for broad directional curves.
Weekly : 0.25–0.30 radius strength for smooth macro-level cycles.
The suggested radius strength ranges provide general structural guidance. Optimal values may vary across assets and volatility regimes, and should be refined through empirical testing to account for instrument-specific behavior and prevailing market conditions.
Asset Guidance :
Cryptocurrency : Higher radius and multiplier values to stabilize high-volatility environments.
Forex : Midrange settings (0.12-0.18) for clean curvature transitions.
Equities : Balanced curvature for trending sectors or momentum rotation setups.
Indices/Futures : Moderate radius values (0.15-0.22) to capture cyclical macro swings.
Performance Characteristics
High Effectiveness :
Trending environments with directional expansion.
Markets exhibiting clean momentum arcs and low structural noise.
Reduced Effectiveness :
Range-bound or low-volatility conditions with repeated false flips.
Ultra-short-term timeframes (<1m) where curvature acceleration overshoots.
Integration Guidelines
Confluence Framework : Combine with structure tools (order blocks, BOS, liquidity zones) for entry validation.
Risk Management : Trail stops along the curved band rather than fixed points to align with adaptive market geometry.
Multi-Timeframe Confirmation : Use higher timeframe curvature as a trend filter and lower timeframe curvature for execution timing.
Curve Compression Awareness : Treat flattening arcs as potential exhaustion zones - ideal for scaling out or reducing exposure.
Disclaimer
The Curved Radius Supertrend is a geometric trend model designed for professional traders and analysts. It is not a predictive system or a guaranteed profit method. Its performance depends on correct parameter calibration and sound risk management. BOSWaves recommends using it as part of a comprehensive analytical framework, incorporating volume, liquidity, and structural context to validate directional signals.
First Passage Time - Distribution AnalysisThe First Passage Time (FPT) Distribution Analysis indicator is a sophisticated probabilistic tool that answers one of the most critical questions in trading: "How long will it take for price to reach my target, and what are the odds of getting there first?"
Unlike traditional technical indicators that focus on what might happen, this indicator tells you when it's likely to happen.
Mathematical Foundation: First Passage Time Theory
What is First Passage Time?
First Passage Time (FPT) is a concept in stochastic processes that measures the time it takes for a random process to reach a specific threshold for the first time. Originally developed in physics and mathematics, FPT has applications in:
Quantitative Finance: Option pricing, risk management, and algorithmic trading
Neuroscience: Modeling neural firing patterns
Biology: Population dynamics and disease spread
Engineering: Reliability analysis and failure prediction
The Mathematics Behind It
This indicator uses Geometric Brownian Motion (GBM), the same stochastic model used in the Black-Scholes option pricing formula:
dS = μS dt + σS dW
Where:
S = Asset price
μ = Drift (trend component)
σ = Volatility (uncertainty component)
dW = Wiener process (random walk)
Through Monte Carlo simulation, the indicator runs 1,000+ price path simulations to statistically determine:
When each threshold (+X% or -X%) is likely to be hit
Which threshold is hit first (directional bias)
How often each scenario occurs (probability distribution)
🎯 How This Indicator Works
Core Algorithm Workflow:
Calculate Historical Statistics
Measures recent price volatility (standard deviation of log returns)
Calculates drift (average directional movement)
Annualizes these metrics for meaningful comparison
Run Monte Carlo Simulations
Generates 1,000+ random price paths based on historical behavior
Tracks when each path hits the upside (+X%) or downside (-X%) threshold
Records which threshold was hit first in each simulation
Aggregate Statistical Results
Calculates percentile distributions (10th, 25th, 50th, 75th, 90th)
Computes "first hit" probabilities (upside vs downside)
Determines average and median time-to-target
Visual Representation
Displays thresholds as horizontal lines
Shows gradient risk zones (purple-to-blue)
Provides comprehensive statistics table
📈 Use Cases
1. Options Trading
Selling Options: Determine if your strike price is likely to be hit before expiration
Buying Options: Estimate probability of reaching profit targets within your time window
Time Decay Management: Compare expected time-to-target vs theta decay
Example: You're considering selling a 30-day call option 5% out of the money. The indicator shows there's a 72% chance price hits +5% within 12 days. This tells you the trade has high assignment risk.
2. Swing Trading
Entry Timing: Wait for higher probability setups when directional bias is strong
Target Setting: Use median time-to-target to set realistic profit expectations
Stop Loss Placement: Understand probability of hitting your stop before target
Example: The indicator shows 85% upside probability with median time of 3.2 days. You can confidently enter long positions with appropriate position sizing.
3. Risk Management
Position Sizing: Larger positions when probability heavily favors one direction
Portfolio Allocation: Reduce exposure when probabilities are near 50/50 (high uncertainty)
Hedge Timing: Know when to add protective positions based on downside probability
Example: Indicator shows 55% upside vs 45% downside—nearly neutral. This signals high uncertainty, suggesting reduced position size or wait for better setup.
4. Market Regime Detection
Trending Markets: High directional bias (70%+ one direction)
Range-bound Markets: Balanced probabilities (45-55% both directions)
Volatility Regimes: Compare actual vs theoretical minimum time
Example: Consistent 90%+ bullish bias across multiple timeframes confirms strong uptrend—stay long and avoid counter-trend trades.
First Hit Rate (Most Important!)
Shows which threshold is likely to be hit FIRST:
Upside %: Probability of hitting upside target before downside
Downside %: Probability of hitting downside target before upside
These always sum to 100%
⚠️ Warning: If you see "Low Hit Rate" warning, increase this parameter!
Advanced Parameters
Drift Mode
Allows you to explore different scenarios:
Historical: Uses actual recent trend (default—most realistic)
Zero (Neutral): Assumes no trend, only volatility (symmetric probabilities)
50% Reduced: Dampens trend effect (conservative scenario)
Use Case: Switch to "Zero (Neutral)" to see what happens in a pure volatility environment, useful for range-bound markets.
Distribution Type
Percentile: Shows 10%, 25%, 50%, 75%, 90% levels (recommended for most users)
Sigma: Shows standard deviation levels (1σ, 2σ)—useful for statistical analysis
⚠️ Important Limitations & Best Practices
Limitations
Assumes GBM: Real markets have fat tails, jumps, and regime changes not captured by GBM
Historical Parameters: Uses recent volatility/drift—may not predict regime shifts
No Fundamental Events: Cannot predict earnings, news, or macro shocks
Computational: Runs only on last bar—doesn't give historical signals
Remember: Probabilities are not certainties. Use this indicator as part of a comprehensive trading plan with proper risk management.
Created by: Henrique Centieiro. feedback is more than welcome!
MA Dist% Screener [Pineify]MA Distance Screener: Multi-Asset Market Scanner for TradingView
Screen multiple symbols and multiple timeframes on TradingView with the MA Distance Screener. Compare asset prices to flexible moving average types. Visual table view, custom assets, timeframes, and MA types. Supercharge your TradingView screener, optimize your workflow, and catch opportunities across assets in real time.
Key Features
Screen up to 10 custom symbols simultaneously across four configurable timeframes.
Choose from multiple Moving Average types: EMA, SMA, WMA, HMA, RMA, VWMA for flexible market context.
Visualize real-time % distance between price and moving average per asset/timeframe in a clean, color-coded table.
Highly customizable: Set your own symbol list, timeframes, MA length and type.
Alerts for symbol/MA deviations—instantly see overbought/oversold status with intuitive background coloring.
Optimized for crypto, FX, and traditional assets – all asset types supported.
How It Works
The MA Distance Screener acts as a dynamic multi-symbol, multi-timeframe scanner. For each selected symbol and timeframe, it calculates the percentage distance between the latest close price and the selected type of moving average (EMA/SMA/etc.). This is achieved by making secure `request.security` calls per asset/timeframe combination, retrieving updated values for each matrix cell. The computed distance (%) is displayed in a color-coded table: a positive value signals price above the MA (potential trend strength), while negatives indicate price below the MA (potential weakness or retracement). Custom colors highlight extreme overbought/oversold readings for quick visual cues.
Trading Ideas and Insights
Quickly spot assets showing the largest deviation from their moving averages – ideal for mean reversion or trend-following entries.
Identify clusters of assets and timeframes lining up in overbought or oversold states; optimize entries with multi-timeframe confirmation.
Scan the market in one glance—reduce chart-hopping and never miss an opportunity when multiple assets align for signals.
The ability to scan distance-to-MA across assets and periods gives traders a statistical edge, surfacing hidden pivots, breakouts, and mean-reversion trades that single-chart analysis may miss.
How Multiple Indicators Work Together
At its core, this screener allows the trader to configure what gets scanned—pick your top 10 assets and favorite 4 timeframes. With each matrix cell, the selected MA (e.g., 14-period EMA) is recalculated, and the current price's distance (%) from that value is computed. By offering six distinct moving average algorithms (EMA, SMA, RMA, HMA, WMA, VWMA), traders can choose their preferred method, adapting the screener for trend, swing, or mean-reversion style. All values are visualized in a single table, creating a true "market dashboard" effect for real-time cross-asset assessment.
Unique Aspects
True cross-asset, cross-timeframe screening in a unified table—rare for Pine Script indicators.
Full flexibility—customizable list of assets, timeframes, and MA parameters to suit any market/trading plan.
Intuitive color-coding and table display eliminates guesswork, enabling “at-a-glance” screening and rapid decision-making.
Efficient, optimized Pine v6 codebase—minimal lag even with 40+ concurrent streams.
How to Use
Add the indicator to your TradingView chart (overlay: off, use a clean chart).
In the settings panel, enter up to 10 symbols (tickers) you want to screen—crypto, stocks, FX, or indices.
Set the 4 timeframes to scan (e.g., 1m, 5m, 15m, 1h), plus your preferred moving average length and type.
Review the results in the pop-up table, where each cell shows "% Distance" from MA for each symbol/timeframe.
Monitor table background/text color for overbought vs. oversold cues.
Customization
Symbol List: Track any asset by typing its TradingView ticker.
Timeframes: Full freedom to select 4 timeframes per scan, from 1min to monthly.
MA Config: Choose period length and MA algorithm (classic or exotic types).
Color Themes: Easily spot signals with dynamic color backgrounds and customizable thresholds.
Conclusion
The MA Distance Screener is a must-have tool for systematic traders, portfolio managers, and retail chartists seeking a true multi-asset edge. With real-time cross-checking against multiple moving averages and timeframes, it empowers faster, more confident decision-making, while reducing chart fatigue and missed setups.
Unlock new insights, catch broad and hidden opportunities, and optimize your market workflow—all in a single TradingView panel.
BOCS AdaptiveBOCS Adaptive Strategy - Automated Volatility Breakout System
WHAT THIS STRATEGY DOES:
This is an automated trading strategy that detects consolidation patterns through volatility analysis and executes trades when price breaks out of these channels. Take-profit and stop-loss levels are calculated dynamically using Average True Range (ATR) to adapt to current market volatility. The strategy closes positions partially at the first profit target and exits the remainder at the second target or stop loss.
TECHNICAL METHODOLOGY:
Price Normalization Process:
The strategy begins by normalizing price to create a consistent measurement scale. It calculates the highest high and lowest low over a user-defined lookback period (default 100 bars). The current close price is then normalized using the formula: (close - lowest_low) / (highest_high - lowest_low). This produces values between 0 and 1, allowing volatility analysis to work consistently across different instruments and price levels.
Volatility Detection:
A 14-period standard deviation is applied to the normalized price series. Standard deviation measures how much prices deviate from their average - higher values indicate volatility expansion, lower values indicate consolidation. The strategy uses ta.highestbars() and ta.lowestbars() functions to track when volatility reaches peaks and troughs over the detection length period (default 14 bars).
Channel Formation Logic:
When volatility crosses from a high level to a low level, this signals the beginning of a consolidation phase. The strategy records this moment using ta.crossover(upper, lower) and begins tracking the highest and lowest prices during the consolidation. These become the channel boundaries. The duration between the crossover and current bar must exceed 10 bars minimum to avoid false channels from brief volatility spikes. Channels are drawn using box objects with the recorded high/low boundaries.
Breakout Signal Generation:
Two detection modes are available:
Strong Closes Mode (default): Breakout occurs when the candle body midpoint math.avg(close, open) exceeds the channel boundary. This filters out wick-only breaks.
Any Touch Mode: Breakout occurs when the close price exceeds the boundary.
When price closes above the upper channel boundary, a bullish breakout signal generates. When price closes below the lower boundary, a bearish breakout signal generates. The channel is then removed from the chart.
ATR-Based Risk Management:
The strategy uses request.security() to fetch ATR values from a specified timeframe, which can differ from the chart timeframe. For example, on a 5-minute chart, you can use 1-minute ATR for more responsive calculations. The ATR is calculated using ta.atr(length) with a user-defined period (default 14).
Exit levels are calculated at the moment of breakout:
Long Entry Price = Upper channel boundary
Long TP1 = Entry + (ATR × TP1 Multiplier)
Long TP2 = Entry + (ATR × TP2 Multiplier)
Long SL = Entry - (ATR × SL Multiplier)
For short trades, the calculation inverts:
Short Entry Price = Lower channel boundary
Short TP1 = Entry - (ATR × TP1 Multiplier)
Short TP2 = Entry - (ATR × TP2 Multiplier)
Short SL = Entry + (ATR × SL Multiplier)
Trade Execution Logic:
When a breakout occurs, the strategy checks if trading hours filter is satisfied (if enabled) and if position size equals zero (no existing position). If volume confirmation is enabled, it also verifies that current volume exceeds 1.2 times the 20-period simple moving average.
If all conditions are met:
strategy.entry() opens a position using the user-defined number of contracts
strategy.exit() immediately places a stop loss order
The code monitors price against TP1 and TP2 levels on each bar
When price reaches TP1, strategy.close() closes the specified number of contracts (e.g., if you enter with 3 contracts and set TP1 close to 1, it closes 1 contract). When price reaches TP2, it closes all remaining contracts. If stop loss is hit first, the entire position exits via the strategy.exit() order.
Volume Analysis System:
The strategy uses ta.requestUpAndDownVolume(timeframe) to fetch up volume, down volume, and volume delta from a specified timeframe. Three display modes are available:
Volume Mode: Shows total volume as bars scaled relative to the 20-period average
Comparison Mode: Shows up volume and down volume as separate bars above/below the channel midline
Delta Mode: Shows net volume delta (up volume - down volume) as bars, positive values above midline, negative below
The volume confirmation logic compares breakout bar volume to the 20-period SMA. If volume ÷ average > 1.2, the breakout is classified as "confirmed." When volume confirmation is enabled in settings, only confirmed breakouts generate trades.
INPUT PARAMETERS:
Strategy Settings:
Number of Contracts: Fixed quantity to trade per signal (1-1000)
Require Volume Confirmation: Toggle to only trade signals with volume >120% of average
TP1 Close Contracts: Exact number of contracts to close at first target (1-1000)
Use Trading Hours Filter: Toggle to restrict trading to specified session
Trading Hours: Session input in HHMM-HHMM format (e.g., "0930-1600")
Main Settings:
Normalization Length: Lookback bars for high/low calculation (1-500, default 100)
Box Detection Length: Period for volatility peak/trough detection (1-100, default 14)
Strong Closes Only: Toggle between body midpoint vs close price for breakout detection
Nested Channels: Allow multiple overlapping channels vs single channel at a time
ATR TP/SL Settings:
ATR Timeframe: Source timeframe for ATR calculation (1, 5, 15, 60, etc.)
ATR Length: Smoothing period for ATR (1-100, default 14)
Take Profit 1 Multiplier: Distance from entry as multiple of ATR (0.1-10.0, default 2.0)
Take Profit 2 Multiplier: Distance from entry as multiple of ATR (0.1-10.0, default 3.0)
Stop Loss Multiplier: Distance from entry as multiple of ATR (0.1-10.0, default 1.0)
Enable Take Profit 2: Toggle second profit target on/off
VISUAL INDICATORS:
Channel boxes with semi-transparent fill showing consolidation zones
Green/red colored zones at channel boundaries indicating breakout areas
Volume bars displayed within channels using selected mode
TP/SL lines with labels showing both price level and distance in points
Entry signals marked with up/down triangles at breakout price
Strategy status table showing position, contracts, P&L, ATR values, and volume confirmation status
HOW TO USE:
For 2-Minute Scalping:
Set ATR Timeframe to "1" (1-minute), ATR Length to 12, TP1 Multiplier to 2.0, TP2 Multiplier to 3.0, SL Multiplier to 1.5. Enable volume confirmation and strong closes only. Use trading hours filter to avoid low-volume periods.
For 5-15 Minute Day Trading:
Set ATR Timeframe to match chart or use 5-minute, ATR Length to 14, TP1 Multiplier to 2.0, TP2 Multiplier to 3.5, SL Multiplier to 1.2. Volume confirmation recommended but optional.
For Hourly+ Swing Trading:
Set ATR Timeframe to 15-30 minute, ATR Length to 14-21, TP1 Multiplier to 2.5, TP2 Multiplier to 4.0, SL Multiplier to 1.5. Volume confirmation optional, nested channels can be enabled for multiple setups.
BACKTEST CONSIDERATIONS:
Strategy performs best during trending or volatility expansion phases
Consolidation-heavy or choppy markets produce more false signals
Shorter timeframes require wider stop loss multipliers due to noise
Commission and slippage significantly impact performance on sub-5-minute charts
Volume confirmation generally improves win rate but reduces trade frequency
ATR multipliers should be optimized for specific instrument characteristics
COMPATIBLE MARKETS:
Works on any instrument with price and volume data including forex pairs, stock indices, individual stocks, cryptocurrency, commodities, and futures contracts. Requires TradingView data feed that includes volume for volume confirmation features to function.
KNOWN LIMITATIONS:
Stop losses execute via strategy.exit() and may not fill at exact levels during gaps or extreme volatility
request.security() on lower timeframes requires higher-tier TradingView subscription
False breakouts inherent to breakout strategies cannot be completely eliminated
Performance varies significantly based on market regime (trending vs ranging)
Partial closing logic requires sufficient position size relative to TP1 close contracts setting
RISK DISCLOSURE:
Trading involves substantial risk of loss. Past performance of this or any strategy does not guarantee future results. This strategy is provided for educational purposes and automated backtesting. Thoroughly test on historical data and paper trade before risking real capital. Market conditions change and strategies that worked historically may fail in the future. Use appropriate position sizing and never risk more than you can afford to lose. Consider consulting a licensed financial advisor before making trading decisions.
ACKNOWLEDGMENT & CREDITS:
This strategy is built upon the channel detection methodology created by AlgoAlpha in the "Smart Money Breakout Channels" indicator. Full credit and appreciation to AlgoAlpha for pioneering the normalized volatility approach to identifying consolidation patterns and sharing this innovative technique with the TradingView community. The enhancements added to the original concept include automated trade execution, multi-timeframe ATR-based risk management, partial position closing by contract count, volume confirmation filtering, and real-time position monitoring.
Smart Money Precision Structure [BullByte]Smart Money Precision Structure
Advanced Market Structure Analysis Using Institutional Order Flow Concepts
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OVERVIEW
Smart Money Precision Structure (SMPS) is a comprehensive market analysis indicator that combines six analytical frameworks to identify high-probability market structure patterns. The indicator uses multi-dimensional scoring algorithms to evaluate market conditions through institutional order flow concepts, providing traders with professional-grade market analysis.
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PURPOSE AND ORIGINALITY
Why This Indicator Was Developed
• Addresses the gap between retail and institutional analysis methods
• Consolidates multiple analysis techniques that professionals use separately
• Automates complex market structure evaluation into actionable insights
• Eliminates the need for multiple indicators by providing comprehensive analysis
What Makes SMPS Original
• Six-Layer Confluence System - Unique combination of market regime, structure, volume flow, momentum, price action, and adaptive filtering
• Institutional Pattern Recognition - Identifies smart money accumulation and distribution patterns
• Adaptive Intelligence - Parameters automatically adjust based on detected market conditions
• Real-Time Market Scoring - Proprietary algorithm rates market quality from 0-100%
• Structure Break Detection - Advanced pivot analysis identifies trend reversals early
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HOW IT WORKS - TECHNICAL METHODOLOGY
1. Market Regime Analysis Engine
The indicator evaluates five core market dimensions:
• Volatility Score - Measures current volatility against 50-period historical baseline
• Trend Score - Analyzes alignment between 8, 21, and 50-period EMAs
• Momentum Score - Combines RSI divergence with MACD signal alignment
• Structure Score - Evaluates pivot point formation clarity
• Efficiency Score - Calculates directional movement efficiency ratio
These scores combine to classify markets into five regimes:
• TRENDING - Strong directional movement with aligned indicators
• RANGING - Sideways movement with mixed directional signals
• VOLATILE - Elevated volatility with unpredictable price swings
• QUIET - Low volatility consolidation periods
• TRANSITIONAL - Market shifting between different regimes
2. Market Structure Analysis
Advanced pivot point analysis identifies:
• Higher Highs and Higher Lows for bullish structure
• Lower Highs and Lower Lows for bearish structure
• Structure breaks when established patterns fail
• Dynamic support and resistance from recent pivot points
• Key level proximity detection using ATR-based buffers
3. Volume Flow Decoding
Institutional activity detection through:
• Volume surge identification when volume exceeds 2x average
• Buy versus sell pressure analysis using price-volume correlation
• Flow strength measurement through directional volume consistency
• Divergence detection between volume and price movements
• Institutional threshold alerts when unusual volume patterns emerge
4. Multi-Period Momentum Synthesis
Weighted momentum calculation across four timeframes:
• 1-period momentum weighted at 40%
• 3-period momentum weighted at 30%
• 5-period momentum weighted at 20%
• 8-period momentum weighted at 10%
Result smoothed with 6-period EMA for noise reduction.
5. Price Action Quality Assessment
Each bar evaluated for:
• Range quality relative to 20-period average
• Body-to-range ratio for directional conviction
• Wick analysis for rejection pattern identification
• Pattern recognition including engulfing and hammer formations
• Sequential price movement analysis
6. Adaptive Parameter System
Parameters automatically adjust based on detected regime:
• Trending markets reduce sensitivity and confirmation requirements
• Volatile markets increase filtering and require additional confirmations
• Ranging markets maintain neutral settings
• Transitional markets use moderate adjustments
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COMPLETE SETTINGS GUIDE
Section 1: Core Analysis Settings
Analysis Sensitivity (0.3-2.0)
• Default: 1.0
• Lower values require stronger price movements
• Higher values detect more subtle patterns
• Scalpers use 0.8-1.2, swing traders use 1.5-2.0
Noise Reduction Level (2-7)
• Default: 4
• Controls filtering of false patterns
• Higher values reduce pattern frequency
• Increase in volatile markets
Minimum Move % (0.05-0.50)
• Default: 0.15%
• Sets minimum price movement threshold
• Adjust based on instrument volatility
• Forex: 0.05-0.10%, Stocks: 0.15-0.25%, Crypto: 0.20-0.50%
High Confirmation Mode
• Default: True (Enabled)
• Requires all technical conditions to align
• Reduces frequency but increases reliability
• Disable for more aggressive pattern detection
Section 2: Market Regime Detection
Enable Regime Analysis
• Default: True (Enabled)
• Activates market environment evaluation
• Essential for adaptive features
• Keep enabled for best results
Regime Analysis Period (20-100)
• Default: 50 bars
• Determines regime calculation lookback
• Shorter for responsive, longer for stable
• Scalping: 20-30, Swing: 75-100
Minimum Market Clarity (0.2-0.8)
• Default: 0.4
• Quality threshold for pattern generation
• Higher values require clearer conditions
• Lower for more patterns, higher for quality
Adaptive Parameter Adjustment
• Default: True (Enabled)
• Enables automatic parameter optimization
• Adjusts based on market regime
• Highly recommended to keep enabled
Section 3: Market Structure Analysis
Enable Structure Validation
• Default: True (Enabled)
• Validates patterns against support/resistance
• Confirms trend structure alignment
• Essential for reliability
Structure Analysis Period (15-50)
• Default: 30 bars
• Period for structure pattern analysis
• Affects support/resistance calculation
• Match to your trading timeframe
Minimum Structure Alignment (0.3-0.8)
• Default: 0.5
• Required structure score for valid patterns
• Higher values need stronger structure
• Balance with desired frequency
Section 4: Analysis Configuration
Minimum Strength Level (3-5)
• Default: 4
• Minimum confirmations for pattern display
• 5 = Maximum reliability, 3 = More patterns
• Beginners should use 4-5
Required Technical Confirmations (4-6)
• Default: 5
• Number of aligned technical factors
• Higher = fewer but better patterns
• Works with High Confirmation Mode
Pattern Separation (3-20 bars)
• Default: 8 bars
• Minimum bars between patterns
• Prevents clustering and overtrading
• Increase for cleaner charts
Section 5: Technical Filters
Momentum Validation
• Default: True (Enabled)
• Requires momentum alignment
• Filters counter-trend patterns
• Essential for trend following
Volume Confluence Analysis
• Default: True (Enabled)
• Requires volume confirmation
• Identifies institutional participation
• Critical for reliability
Trend Direction Filter
• Default: True (Enabled)
• Only shows patterns with trend
• Reduces counter-trend signals
• Disable for reversal hunting
Section 6: Volume Flow Analysis
Institutional Activity Threshold (1.2-3.5)
• Default: 2.0
• Multiplier for unusual volume detection
• Lower finds more institutional activity
• Stock: 2.0-2.5, Forex: 1.5-2.0, Crypto: 2.5-3.5
Volume Surge Multiplier (1.8-4.5)
• Default: 2.5
• Defines significant volume increases
• Adjust per instrument characteristics
• Higher for stocks, lower for forex
Volume Flow Period (12-35)
• Default: 18 bars
• Smoothing for volume analysis
• Shorter = responsive, longer = smooth
• Match to timeframe used
Section 7: Analysis Frequency Control
Maximum Analysis Points Per Hour (1-5)
• Default: 3
• Limits pattern frequency
• Prevents overtrading
• Scalpers: 4-5, Swing traders: 1-2
Section 8: Target Level Configuration
Target Calculation Method
• Default: Market Adaptive
• Three modes available:
- Fixed: Uses set point distances
- Dynamic: ATR-based calculations
- Market Adaptive: Structure-based levels
Minimum Target/Risk Ratio (1.0-3.0)
• Default: 1.5
• Minimum acceptable reward vs risk
• Higher filters lower probability setups
• Professional standard: 1.5-2.0
Fixed Mode Settings:
• Fixed Target Distance: 50 points default
• Fixed Invalidation Distance: 30 points default
• Use for consistent instruments
Dynamic Mode Settings:
• Dynamic Target Multiplier: 1.8x ATR default
• Dynamic Invalidation Multiplier: 1.0x ATR default
• Adapts to volatility automatically
Market Adaptive Settings:
• Use Structure Levels: True (default)
• Structure Level Buffer: 0.1% default
• Places levels at actual support/resistance
Section 9: Visual Display Settings
Color Theme Options
• Professional (Teal/Red)
- Bullish: Teal (#26a69a)
- Bearish: Red (#ef5350)
- Neutral: Gray (#78909c)
- Best for: Traditional traders, clean appearance
• Dark (Neon Green/Pink)
- Bullish: Neon Green (#00ff88)
- Bearish: Hot Pink (#ff0044)
- Neutral: Dark Gray (#333333)
- Best for: Dark theme users, high contrast
• Light (Green/Red Classic)
- Bullish: Green (#4caf50)
- Bearish: Red (#f44336)
- Neutral: Light Gray (#9e9e9e)
- Best for: Light backgrounds, traditional colors
• Vibrant (Cyan/Magenta)
- Bullish: Cyan (#00ffff)
- Bearish: Magenta (#ff00ff)
- Neutral: Medium Gray (#888888)
- Best for: High visibility, modern appearance
Dashboard Position
• Options: Top Left, Top Right, Bottom Left, Bottom Right, Middle Left, Middle Right
• Default: Top Right
• Choose based on chart layout preference
Dashboard Size
• Full: Complete information display (desktop)
• Mobile: Compact view for small screens
• Default: Full
Analysis Display Style
• Arrows : Simple directional markers
• Labels : Detailed text information
• Zones : Colored areas showing pattern regions
• Default: Labels (most informative)
Display Options:
• Display Analysis Strength: Shows star rating
• Display Target Levels: Shows target/invalidation lines
• Display Market Regime: Shows regime in pattern labels
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HOW TO USE SMPS - DETAILED GUIDE
Understanding the Dashboard
Top Row - Header
• SMPS Dashboard title
• VALUE column: Current readings
• STATUS column: Condition assessments
Market Regime Row
• Shows: TRENDING, RANGING, VOLATILE, QUIET, or TRANSITIONAL
• Color coding: Green = Favorable, Red = Caution
• Status: FAVORABLE or CAUTION trading conditions
Market Score Row
• Percentage from 0-100%
• Above 60% = Strong conditions
• 40-60% = Moderate conditions
• Below 40% = Weak conditions
Structure Row
• Direction: BULLISH, BEARISH, or NEUTRAL
• Status: INTACT or BREAK
• Orange BREAK indicates structure failure
Volume Flow Row
• Direction: BUYING or SELLING
• Intensity: STRONG or WEAK
• Color indicates dominant pressure
Momentum Row
• Numerical momentum value
• Positive = Upward pressure
• Negative = Downward pressure
Volume Status Row
• INST = Institutional activity detected
• HIGH = Above average volume
• NORM = Normal volume levels
Adaptive Mode Row
• ACTIVE = Parameters adjusting
• STATIC = Fixed parameters
• Shows required confirmations
Analysis Level Row
• Minimum strength level setting
• Pattern separation in bars
Market State Row
• Current analysis: BULLISH, BEARISH, NEUTRAL
• Shows analysis price level when active
T:R Ratio Row
• Current target to risk ratio
• GOOD = Meets minimum requirement
• LOW = Below minimum threshold
Strength Row
• BULL or BEAR dominance
• Numerical strength value 0-100
Price Row
• Current price
• Percentage change
Last Analysis Row
• Previous pattern direction
• Bars since last pattern
Reading Pattern Signals
Bullish Structure Pattern
• Upward triangle or "Bullish Structure" label
• Star rating shows strength (★★★★★ = strongest)
• Green line = potential target level
• Red dashed line = invalidation level
• Appears below price bars
Bearish Structure Pattern
• Downward triangle or "Bearish Structure" label
• Star rating indicates reliability
• Green line = potential target level
• Red dashed line = invalidation level
• Appears above price bars
Pattern Strength Interpretation
• ★★★★★ = 6 confirmations (exceptional)
• ★★★★☆ = 5 confirmations (strong)
• ★★★☆☆ = 4 confirmations (moderate)
• ★★☆☆☆ = 3 confirmations (minimum)
• Below minimum = filtered out
Visual Elements on Chart
Lines and Levels:
• Gray Line = 21 EMA trend reference
• Green Stepline = Dynamic support level
• Red Stepline = Dynamic resistance level
• Green Solid Line = Active target level
• Red Dashed Line = Active invalidation level
Pattern Markers:
• Triangles = Arrow display mode
• Text Labels = Label display mode
• Colored Boxes = Zone display mode
Target Completion Labels:
• "Target" = Price reached target level
• "Invalid" = Pattern invalidated by price
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RECOMMENDED USAGE BY TIMEFRAME
1-Minute Charts (Scalping)
• Sensitivity: 0.8-1.2
• Noise Reduction: 3-4
• Pattern Separation: 3-5 bars
• High Confirmation: Optional
• Best for: Quick intraday moves
5-Minute Charts (Precision Intraday)
• Sensitivity: 1.0 (default)
• Noise Reduction: 4 (default)
• Pattern Separation: 8 bars
• High Confirmation: Enabled
• Best for: Day trading
15-Minute Charts (Short Swing)
• Sensitivity: 1.0-1.5
• Noise Reduction: 4-5
• Pattern Separation: 10-12 bars
• High Confirmation: Enabled
• Best for: Intraday swings
30-Minute to 1-Hour (Position Trading)
• Sensitivity: 1.5-2.0
• Noise Reduction: 5-7
• Pattern Separation: 15-20 bars
• Regime Period: 75-100
• Best for: Multi-day positions
Daily Charts (Swing Trading)
• Sensitivity: 1.8-2.0
• Noise Reduction: 6-7
• Pattern Separation: 20 bars
• All filters enabled
• Best for: Long-term analysis
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MARKET-SPECIFIC SETTINGS
Forex Pairs
• Minimum Move: 0.05-0.10%
• Institutional Threshold: 1.5-2.0
• Volume Surge: 1.8-2.2
• Target Mode: Dynamic or Market Adaptive
Stock Indices (ES, NQ, YM)
• Minimum Move: 0.10-0.15%
• Institutional Threshold: 2.0-2.5
• Volume Surge: 2.5-3.0
• Target Mode: Market Adaptive
Individual Stocks
• Minimum Move: 0.15-0.25%
• Institutional Threshold: 2.0-2.5
• Volume Surge: 2.5-3.5
• Target Mode: Dynamic
Cryptocurrency
• Minimum Move: 0.20-0.50%
• Institutional Threshold: 2.5-3.5
• Volume Surge: 3.0-4.5
• Target Mode: Dynamic
• Increase noise reduction
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PRACTICAL APPLICATION EXAMPLES
Example 1: Strong Trending Market
Dashboard Reading:
• Market Regime: TRENDING
• Market Score: 75%
• Structure: BULLISH, INTACT
• Volume Flow: BUYING, STRONG
• Momentum: +0.45
Interpretation:
• Strong uptrend environment
• Institutional buying present
• Look for bullish patterns as continuation
• Higher probability of success
• Consider using lower sensitivity
Example 2: Range-Bound Conditions
Dashboard Reading:
• Market Regime: RANGING
• Market Score: 35%
• Structure: NEUTRAL
• Volume Flow: SELLING, WEAK
• Momentum: -0.05
Interpretation:
• No clear direction
• Low opportunity environment
• Patterns are less reliable
• Consider waiting for regime change
• Or switch to a range-trading approach
Example 3: Structure Break Alert
Dashboard Reading:
• Previous: BULLISH structure
• Current: Structure BREAK
• Volume: INST flag active
• Momentum: Shifting negative
Interpretation:
• Trend reversal potentially beginning
• Institutional participation detected
• Watch for bearish pattern confirmation
• Adjust bias accordingly
• Increase caution on long positions
Example 4: Volatile Market
Dashboard Reading:
• Market Regime: VOLATILE
• Market Score: 45%
• Adaptive Mode: ACTIVE
• Confirmations: Increased to 6
Interpretation:
• Choppy conditions
• Parameters auto-adjusted
• Fewer but higher quality patterns
• Wider stops may be needed
• Consider reducing position size
Below are a few chart examples of the Smart Money Precision Structure (SMPS) indicator in action.
• Example 1 – Bullish Structure Detection on SOLUSD 5m
• Example 2 – Bearish Structure Detected with Strong Confluence on SOLUSD 5m
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TROUBLESHOOTING GUIDE
No Patterns Appearing
Check these settings:
• High Confirmation Mode may be too restrictive
• Minimum Strength Level may be too high
• Market Clarity threshold may be too high
• Regime filter may be blocking patterns
• Try increasing sensitivity
Too Many Patterns
Adjust these settings:
• Enable High Confirmation Mode
• Increase Minimum Strength Level to 5
• Increase Pattern Separation
• Reduce Sensitivity below 1.0
• Enable all technical filters
Dashboard Shows "CAUTION"
This indicates:
• Market conditions are unfavorable
• Regime is RANGING or QUIET
• Market score is low
• Consider waiting for better conditions
• Or adjust expectations accordingly
Patterns Not Reaching Targets
Consider:
• Market may be choppy
• Volatility may have changed
• Try Dynamic target mode
• Reduce target/risk ratio requirement
• Check if regime is VOLATILE
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ALERTS CONFIGURATION
Alert Message Format
Alerts include:
• Pattern type (Bullish/Bearish)
• Strength rating
• Market regime
• Analysis price level
• Target and invalidation levels
• Strength percentage
• Target/Risk ratio
• Educational disclaimer
Setting Up Alerts
• Click Alert button on TradingView
• Select SMPS indicator
• Choose alert frequency
• Customize message if desired
• Alerts fire on pattern detection
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DATA WINDOW INFORMATION
The Data Window displays:
• Market Regime Score (0-100)
• Market Structure Bias (-1 to +1)
• Bullish Strength (0-100)
• Bearish Strength (0-100)
• Bull Target/Risk Ratio
• Bear Target/Risk Ratio
• Relative Volume
• Momentum Value
• Volume Flow Strength
• Bull Confirmations Count
• Bear Confirmations Count
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BEST PRACTICES AND TIPS
For Beginners
• Start with default settings
• Use High Confirmation Mode
• Focus on TRENDING regime only
• Paper trade first
• Learn one timeframe thoroughly
For Intermediate Users
• Experiment with sensitivity settings
• Try different target modes
• Use multiple timeframes
• Combine with price action analysis
• Track pattern success rate
For Advanced Users
• Customize per instrument
• Create setting templates
• Use regime information for bias
• Combine with other indicators
• Develop systematic rules
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IMPORTANT DISCLAIMERS
• This indicator is for educational and informational purposes only
• Not financial advice or a trading system
• Past performance does not guarantee future results
• Trading involves substantial risk of loss
• Always use appropriate risk management
• Verify patterns with additional analysis
• The author is not a registered investment advisor
• No liability accepted for trading losses
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VERSION NOTES
Version 1.0.0 - Initial Release
• Six-layer confluence system
• Adaptive parameter technology
• Institutional volume detection
• Market regime classification
• Structure break identification
• Real-time dashboard
• Multiple display modes
• Comprehensive settings
## My Final Thoughts
Smart Money Precision Structure represents an advanced approach to market analysis, bringing institutional-grade techniques to retail traders through intelligent automation and multi-dimensional evaluation. By combining six analytical frameworks with adaptive parameter adjustment, SMPS provides comprehensive market intelligence that single indicators cannot achieve.
The indicator serves as an educational tool for understanding how professional traders analyze markets, while providing practical pattern detection for those seeking to improve their technical analysis. Remember that all trading involves risk, and this tool should be used as part of a complete analysis approach, not as a standalone trading system.
- BullByte
