Hierarchical Hidden Markov ModelHierarchical Hidden Markov Models (HHMMs) are an advanced version of standard Hidden Markov Models (HMMs). While HMMs model systems with a single layer of hidden states, each transitioning to other states based on fixed probabilities, HHMMs introduce multiple layers of hidden states. This hierarchical structure allows for more complex and nuanced modeling of systems, making HHMMs particularly useful in representing systems with nested states or regimes. In HHMMs, the hidden states are organized into levels, where each state at a higher level is defined by a set of states at a lower level. This nesting of states enables the model to capture longer-term dependencies in the time series, as each state at a higher level can represent a broader regime, and the states within it can represent finer sub-regimes. For example, in financial markets, a high-level state might represent a general market condition like high volatility, while the nested lower-level states could represent more specific conditions such as trending or oscillating within the high volatility regime.
The hierarchical nature of HHMMs is facilitated through the concept of termination probabilities. A termination probability is the probability that a given state will stop emitting observations and transition control back to its parent state. This mechanism allows the model to dynamically switch between different levels of the hierarchy, thereby modeling the nested structure effectively. Beside the transition, emission and initial probabilities that generally define a HMM, termination probabilities distinguish HHMMs from HMMs because they define when the process in a sub-state concludes, allowing the model to transition back to the higher-level state and potentially move to a different branch of the hierarchy.
In financial markets, HHMMs can be applied similiarly to HMMs to model latent market regimes such as high volatility, low volatility, or neutral, along with their respective sub-regimes. By identifying the most likely market regime and sub-regime, traders and analysts can make informed decisions based on a more granular probabilistic assessment of market conditions. For instance, during a high volatility regime, the model might detect sub-regimes that indicate different types of price movements, helping traders to adapt their strategies accordingly.
MODEL FIT:
By default, the indicator displays the posterior probabilities, which represent the likelihood that the market is in a specific hidden state at any given time, based on the observed data and the model fit. These posterior probabilities strictly represent the model fit, reflecting how well the model explains the historical data it was trained on. This model fit is inherently different from out-of-sample predictions, which are generated using data that was not included in the training process. The posterior probabilities from the model fit provide a probabilistic assessment of the state the market was in at a particular time based on the data that came before and after it in the training sequence. Out-of-sample predictions, on the other hand, offer a forward-looking evaluation to test the model's predictive capability.
MODEL TESTING:
When the "Test Out of Sample" option is enabled, the indicator plots the selected display settings based on models' out-of-sample predictions. The display settings for out-of-sample testing include several options:
State Probability option displays the probability of each state at a given time for segments of data points not included in the training process. This is particularly useful for real-time identification of market regimes, ensuring that the model's predictive capability is tested on unseen data. These probabilities are calculated using the forward algorithm, which efficiently computes the likelihood of the observed sequence given the model parameters. Higher probabilities for a particular state suggest that the market is currently in that state. Traders can use this information to adjust their strategies according to the identified market regime and their statistical features.
Confidence Interval Bands option plots the upper, lower, and median confidence interval bands for predicted values. These bands provide a range within which future values are expected to lie with a certain confidence level. The width of the interval is determined by the current probability of different states in the model and the distribution of data within these states. The confidence level can be specified in the Confidence Interval setting.
Omega Ratio option displays a risk-adjusted performance measure that offers a more comprehensive view of potential returns compared to traditional metrics like the Sharpe ratio. It takes into account all moments of the returns distribution, providing a nuanced perspective on the risk-return tradeoff in the context of the HHMM's identified market regimes. The minimum acceptable return (MAR) used for the calculation of the omega can be specified in the settings of the indicator. The plot displays both the current Omega ratio and a forecasted "N day Omega" ratio. A higher Omega ratio suggests better risk-adjusted performance, essentially comparing the probability of gains versus the probability of losses relative to the minimum acceptable return. The Omega ratio plot is color-coded, green indicates that the long-term forecasted Omega is higher than the current Omega (suggesting improving risk-adjusted returns over time), while red indicates the opposite. Traders can use omega ratio to assess the risk-adjusted forecast of the model, under current market conditions with a specific target return requirement (MAR). By leveraging the HHMM's ability to identify different market states, the Omega ratio provides a forward-looking risk assessment tool, helping traders make more informed decisions about position sizing, risk management, and strategy selection.
Model Complexity option shows the complexity of the model, as well as complexity of individual states if the “complexity components” option is enabled. Model complexity is measured in terms of the entropy expressed through transition probabilities. The used complexity metric can be related to the models entropy rate and is calculated as the sum of the p*log(p) for every transition probability of a given state. Complexity in this context informs us on how complex the models transitions are. A model that might transition between states more often would be characterised by higher complexity, while a model that tends to transition less often would have lower complexity. High complexity can also suggest the model captures noise rather than the underlying market structure also known as overfitting, whereas lower complexity might indicate underfitting, where the model is too simplistic to capture important market dynamics. It is useful to assess the stability of the model complexity as well as understand where changes come from when a shift happens. A model with irregular complexity values can be strong sign of overfitting, as it suggests that the process that the model is capturing changes siginficantly over time.
Akaike/Bayesian Information Criterion option plots the AIC or BIC values for the model on both the training and out-of-sample data. These criteria are used for model selection, helping to balance model fit and complexity, as they take into account both the goodness of fit (likelihood) and the number of parameters in the model. The metric therefore provides a value we can use to compare different models with different number of parameters. Lower values generally indicate a better model. AIC is considered more liberal while BIC is considered a more conservative criterion which penalizes the likelihood more. Beside comparing different models, we can also assess how much the AIC and BIC differ between the training sets and test sets. A test set metric, which is consistently significantly higher than the training set metric can point to a drift in the models parameters, a strong drift of model parameters might again indicate overfitting or underfitting the sampled data.
Indicator settings:
- Source : Data source which is used to fit the model
- Training Period : Adjust based on the amount of historical data available. Longer periods can capture more trends but might be computationally intensive.
- EM Iterations : Balance between computational efficiency and model fit. More iterations can improve the model but at the cost of speed.
- Test Out of Sample : turn on predict the test data out of sample, based on the model that is retrained every N bars
- Out of Sample Display: A selection of metrics to evaluate out of sample. Pick among State probability, confidence interval, model complexity and AIC/BIC.
- Test Model on N Bars : set the number of bars we perform out of sample testing on.
- Retrain Model on N Bars: Set based on how often you want to retrain the model when testing out of sample segments
- Confidence Interval : When confidence interval is selected in the out of sample display you can adjust the percentage to reflect the desired confidence level for predictions.
- Omega forecast: Specifies the number of days ahead the omega ratio will be forecasted to get a long run measure.
- Minimum Acceptable Return : Specifies the target minimum acceptable return for the omega ratio calculation
- Complexity Components : When model complexity is selected in the out of sample display, this option will display the complexity of each individual state.
-Bayesian Information Criterion : When AIC/BIC is selected, turning this on this will ensure BIC is calculated instead of AIC.
Statistics
Hidden Markov ModelHidden Markov Models (HMMs) are a class of statistical models used to represent systems that follow a Markov process with hidden states. In such models, the system being modeled transitions between a finite number of states, with the probability of each transition dependent only on the current state. The hidden states are not directly observable; instead, we observe a sequence of emissions or outputs generated by these states. HMMs are widely used in various fields, including speech recognition, bioinformatics, and financial market analysis. In the context of financial markets, HMMs can be utilized to model the latent market regimes (e.g., bullish, bearish, or neutral) that influence the observed market data such as asset prices or returns. By estimating the posterior probabilities of these hidden states, traders and analysts can identify the most likely market regime and make informed decisions based on the probabilistic assessment of market conditions.
The Hidden Markov Model (HMM) comprises several states that work together to model the hidden market dynamics. The states represent the unobservable market regimes such as bullish, bearish or neutral. The states are 'hidden' in nature because we need to infer them from the data and cannot directly observe them.
Model components:
Initial Probabilities: These denote the likelihood of starting in each hidden state. They can be related to long-run probabilities, reflecting the overall likelihood of each state across extended periods. In equilibrium, these initial probabilities may converge to the stationary distribution of the Markov chain.
Transition Probabilities: These capture the likelihood of moving between states, including the probability of remaining in the current state. They model how market regimes evolve over time, allowing the HMM to adapt to changing conditions.
Emission Probabilities: Also known as observation likelihoods, these represent the probability of observing specific market data (like returns) given each hidden state. Emission probabilities can be often represented by continuous probability distributions. In our case we are using a laplace distribution with its location parameter reflecting the central tendency of returns in each state and the scale reflecting the dispersion or the magnitude of the returns.
The power of HMMs in financial modeling lies in their ability to capture complex market dynamics probabilistically. By analyzing patterns in market, the model can estimate the likelihood of being in each state at any given time. This can reveal insights into market behavior and dynamics that might not be apparent from data alone.
MODEL FIT:
By default, the indicator displays the posterior probabilities, which represent the likelihood that the market is in a specific hidden state at any given time, based on the observed data and the model fit. It is crucial to understand that these posterior probabilities strictly represent the model fit, reflecting how well the model explains the historical data it was trained on. This model fit is inherently different from out-of-sample predictions, which are generated using data that was not included in the training process. The posterior probabilities from the model fit provide a probabilistic assessment of the state the market was in at a particular time based on the data that came before and after it in the training sequeence. Out-of-sample predictions on the other hand offer a forward-looking evaluation to test the model's predictive capability.
MODEL TEST:
When the "Test Out of Sample” option is enabled, the indicator plots the selected display settings based on models out-of-sample predictions. The display settings for out-of-sample testing include several options:
State Probability option displays the probability of each state at a given time for segments of datapoints that were not included in the traning process. This is particularly useful for real-time identification of market regimes, ensuring that the model's predictive capability is rigorously tested on unseen data. These probabilities are calculated using the forward algorithm, which efficiently computes the likelihood of the observed sequence given the model parameters. Higher probability for a particular state indicate a higher likelihood that the market is currently in that state. Traders can use this information to adjust their strategies according to the identified market regime and their statistical features.
Confidence Interval Bands option plots the upper, lower, and median confidence interval bands for predicted values. These bands provide a range within which future values are expected to lie with a certain confidence level. The width of the interval is determined by the current probability of different states in the model and the distribution of data within these states. The confidence level can be specified in the Confidence Interval setting.
Omega Ratio option displays a risk-adjusted performance measure that offers a more comprehensive view of potential returns compared to traditional metrics like the Sharpe ratio. It takes into account all moments of the returns distribution, providing a nuanced perspective on the risk-return tradeoff in the context of the HHMM's identified market regimes. The minimum acceptable return (MAR) used for the calculation of the omega can be specified in the settings of the indicator. The plot displays both the current Omega ratio and a forecasted "N day Omega" ratio. A higher Omega ratio suggests better risk-adjusted performance, essentially comparing the probability of gains versus the probability of losses relative to the minimum acceptable return. The Omega ratio plot is color-coded, green indicates that the long-term forecasted Omega is higher than the current Omega (suggesting improving risk-adjusted returns over time), while red indicates the opposite. Traders can use omega ratio to assess the risk-adjusted forecast of the model, under current market conditions with a specific target return requirement (MAR). By leveraging the HHMM's ability to identify different market states, the Omega ratio provides a forward-looking risk assessment tool, helping traders make more informed decisions about position sizing, risk management, and strategy selection.
Model Complexity option shows the complexity of the model, as well as complexity of individual states if the “complexity components” option is enabled. Model complexity is measured in terms of the entropy expressed through transition probabilities. The used complexity metric can be related to the models entropy rate and is calculated as the sum of the p*log(p) for every transition probability of a given state. Complexity in this context informs us on how complex the models transitions are. A model that might transition between states more often would be characterised by higher complexity, while a model that tends to transition less often would have lower complexity. High complexity can also suggest the model captures noise rather than the underlying market structure also known as overfitting, whereas too low complexity might indicate underfitting, where the model is too simplistic to capture important market dynamics. It is also useful to assess the stability of the model complexity. A model with irregular complexity values can be sign of overfitting, as it suggests that the process that the model is capturing changes significantly over time.
Akaike/Bayesian Information Criterion option plots the AIC or BIC values for the model on both the training and out-of-sample data. These criteria are used for model selection, helping to balance model fit and complexity, as they take into account both the goodness of fit (likelihood) and the number of parameters in the model. The metric therefore provides a value we can use to compare different models with different number of parameters. Lower values generally indicate a better model. AIC is considered more liberal while BIC is considered a more conservative criterion which penalizes the likelihood more. Beside comparing different models, we can also assess how much the AIC and BIC differ between the training sets and test sets. A test set metric, which is consistently significantly higher than the training set metric can point to a drift in the models parameters, a strong drift of model parameters might again indicate overfitting or underfitting the sampled data.
Indicator settings:
- Source : Data source which is used to fit the model
- Training Period : Adjust based on the amount of historical data available. Longer periods can capture more trends but might be computationally intensive.
- EM Iterations : Balance between computational efficiency and model fit. More iterations can improve the model but at the cost of speed.
- Test Out of Sample : turn on predict the test data out of sample, based on the model that is retrained every N bars
- Out of Sample Display: A selection of metrics to evaluate out of sample. Pick among State probability, confidence interval, model complexity and AIC/BIC.
- Test Model on N Bars : set the number of bars we perform out of sample testing on.
- Retrain Model on N Bars: Set based on how often you want to retrain the model when testing out of sample segments
- Confidence Interval : When confidence interval is selected in the out of sample display you can adjust the percentage to reflect the desired confidence level for predictions.
- Omega forecast: Specifies the number of days ahead the omega ratio will be forecasted to get a long run measure.
- Minimum Acceptable Return : Specifies the target minimum acceptable return for the omega ratio calculation
- Complexity Components : When model complexity is selected in the out of sample display, this option will display the complexity of each individual state.
-Bayesian Information Criterion : When AIC/BIC is selected, turning this on this will ensure BIC is calculated instead of AIC.
Real Relative Strength Indicator### What is RRS (Real Relative Strength)?
RRS is a volatility-normalized relative strength indicator that shows you – in real time – whether your stock, crypto, or any asset is genuinely beating or lagging the broader market after adjusting for risk and volatility. Unlike the classic “price ÷ SPY” line that gets completely fooled by volatility regimes, RRS answers the only question that actually matters to professional traders:
“Is this ticker moving better (or worse) than the market on a risk-adjusted basis right now?”
It does this by measuring the excess momentum of your ticker versus a benchmark (SPY, QQQ, BTC, etc.) and then dividing that excess by the average volatility (ATR) of both instruments. The result is a clean, centered-around-zero oscillator that works the same way in calm markets, crash markets, or parabolic bull runs.
### How to Use the RRS Indicator (Aqua/Purple Area Version) in Practice
The indicator is deliberately simple to read once you know the rules:
Positive area (aqua) means genuine outperformance.
Negative area (purple) means genuine underperformance.
The farther from zero, the stronger the leadership or weakness.
#### Core Signals and How to Trade Them
- RRS crossing above zero → one of the highest-probability long signals in existence. The asset has just started outperforming the market on a risk-adjusted basis. Enter or add aggressively if price structure agrees.
- RRS crossing below zero → leadership is ending. Tighten stops, take partial or full profits, or flip short if you trade both sides.
- RRS above +2 (bright aqua area) → clear leadership. This is where the real money is made in bull markets. Trail stops, add on pullbacks, let winners run.
- RRS below –2 (bright purple area) → clear distribution or capitulation. Avoid new longs, consider short entries or protective puts.
- Extreme readings above +4 or below –4 (background tint appears) → rare, very high-conviction moves. Treat these like once-a-month opportunities.
- Divergence (not plotted here, but easy to spot visually): price making new highs while the aqua area is shrinking → distribution. Price making new lows while the purple area is shrinking → hidden buying and coming reversal.
#### Best Settings by Style and Asset Class
For stocks and ETFs: keep benchmark as SPY (or QQQ for tech-heavy names) and length 14–20 on daily/4H charts.
For crypto: change the benchmark to BTCUSD (or ETHUSD) immediately — otherwise the reading is meaningless. Length 10–14 works best on 1H–4H crypto charts because volatility is higher.
For day trading: drop length to 10–12 and use 15-minute or 5-minute charts. Signals are faster and still extremely clean.
#### Highest-Edge Setups (What Actually Prints Money)
- RRS crosses above zero while price is still below a major moving average (50 EMA, 200 SMA, etc.) → early leadership, often catches the exact bottom of a new leg up.
- RRS already deep aqua (+3 or higher) and price pulls back to support without RRS dropping below +1 → textbook add-on or re-entry zone.
- RRS deep purple and suddenly turns flat or starts curling up while price is still falling → hidden accumulation, usually the exact low tick.
That’s it. Master these few rules and the RRS becomes one of the most powerful edge tools you will ever use for rotation trading...
MACD Forecast Colorful [DiFlip]MACD Forecast Colorful
The Future of Predictive MACD — is one of the most advanced and customizable MACD indicators ever published on TradingView. Built on the classic MACD foundation, this upgraded version integrates statistical forecasting through linear regression to anticipate future movements — not just react to the past.
With a total of 22 fully configurable long and short entry conditions, visual enhancements, and full automation support, this indicator is designed for serious traders seeking an analytical edge.
⯁ Real-Time MACD Forecasting
For the first time, a public MACD script combines the classic structure of MACD with predictive analytics powered by linear regression. Instead of simply responding to current values, this tool projects the MACD line, signal line, and histogram n bars into the future, allowing you to trade with foresight rather than hindsight.
⯁ Fully Customizable
This indicator is built for flexibility. It includes 22 entry conditions, all of which are fully configurable. Each condition can be turned on/off, chained using AND/OR logic, and adapted to your trading model.
Whether you're building a rules-based quant system, automating alerts, or refining discretionary signals, MACD Forecast Colorful gives you full control over how signals are generated, displayed, and triggered.
⯁ With MACD Forecast Colorful, you can:
• Detect MACD crossovers before they happen.
• Anticipate trend reversals with greater precision.
• React earlier than traditional indicators.
• Gain a powerful edge in both discretionary and automated strategies.
• This isn’t just smarter MACD — it’s predictive momentum intelligence.
⯁ Scientifically Powered by Linear Regression
MACD Forecast Colorful is the first public MACD indicator to apply least-squares predictive modeling to MACD behavior — effectively introducing machine learning logic into a time-tested tool.
It uses statistical regression to analyze historical behavior of the MACD and project future trajectories. The result is a forward-shifted MACD forecast that can detect upcoming crossovers and divergences before they appear on the chart.
⯁ Linear Regression: Technical Foundation
Linear regression is a statistical method that models the relationship between a dependent variable (y) and one or more independent variables (x). The basic formula for simple linear regression is:
y = β₀ + β₁x + ε
Where:
y = predicted variable (e.g., future MACD value)
x = independent variable (e.g., bar index)
β₀ = intercept
β₁ = slope
ε = random error (residual)
The regression model calculates β₀ and β₁ using the least squares method, minimizing the sum of squared prediction errors to produce the best-fit line through historical values. This line is then extended forward, generating a forecast based on recent price momentum.
⯁ Least Squares Estimation
The regression coefficients are computed with the following formulas:
β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²)
β₀ = ȳ - β₁x̄
Where:
Σ denotes summation; x̄ and ȳ are the means of x and y; and i ranges from 1 to n (number of observations). These equations produce the best linear unbiased estimator under the Gauss–Markov assumptions — constant variance (homoscedasticity) and a linear relationship between variables.
⯁ Regression in Machine Learning
Linear regression is a foundational model in supervised learning. Its ability to provide precise, explainable, and fast forecasts makes it critical in AI systems and quantitative analysis.
Applying linear regression to MACD forecasting is the equivalent of injecting artificial intelligence into one of the most widely used momentum tools in trading.
⯁ Visual Interpretation
Picture the MACD values over time like this:
Time →
MACD →
A regression line is fitted to recent MACD values, then projected forward n periods. The result is a predictive trajectory that can cross over the real MACD or signal line — offering an early-warning system for trend shifts and momentum changes.
The indicator plots both current MACD and forecasted MACD, allowing you to visually compare short-term future behavior against historical movement.
⯁ Scientific Concepts Used
Linear Regression: models the relationship between variables using a straight line.
Least Squares Method: minimizes squared prediction errors for best-fit.
Time-Series Forecasting: projects future data based on past patterns.
Supervised Learning: predictive modeling using labeled inputs.
Statistical Smoothing: filters noise to highlight trends.
⯁ Why This Indicator Is Revolutionary
First open-source MACD with real-time predictive modeling.
Scientifically grounded with linear regression logic.
Automatable through TradingView alerts and bots.
Smart signal generation using forecasted crossovers.
Highly customizable with 22 buy/sell conditions.
Enhanced visuals with background (bgcolor) and area fill (fill) support.
This isn’t just an update — it’s the next evolution of MACD forecasting.
⯁ Example of simple linear regression with one independent variable
This example demonstrates how a basic linear regression works when there is only one independent variable influencing the dependent variable. This type of model is used to identify a direct relationship between two variables.
⯁ In linear regression, observations (red) are considered the result of random deviations (green) from an underlying relationship (blue) between a dependent variable (y) and an independent variable (x)
This concept illustrates that sampled data points rarely align perfectly with the true trend line. Instead, each observed point represents the combination of the true underlying relationship and a random error component.
⯁ Visualizing heteroscedasticity in a scatterplot with 100 random fitted values using Matlab
Heteroscedasticity occurs when the variance of the errors is not constant across the range of fitted values. This visualization highlights how the spread of data can change unpredictably, which is an important factor in evaluating the validity of regression models.
⯁ The datasets in Anscombe’s quartet were designed to have nearly the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but look very different when plotted
This classic example shows that summary statistics alone can be misleading. Even with identical numerical metrics, the datasets display completely different patterns, emphasizing the importance of visual inspection when interpreting a model.
⯁ Result of fitting a set of data points with a quadratic function
This example illustrates how a second-degree polynomial model can better fit certain datasets that do not follow a linear trend. The resulting curve reflects the true shape of the data more accurately than a straight line.
⯁ What is the MACD?
The Moving Average Convergence Divergence (MACD) is a technical analysis indicator developed by Gerald Appel. It measures the relationship between two moving averages of a security’s price to identify changes in momentum, direction, and strength of a trend. The MACD is composed of three components: the MACD line, the signal line, and the histogram.
⯁ How to use the MACD?
The MACD is calculated by subtracting the 26-period Exponential Moving Average (EMA) from the 12-period EMA. A 9-period EMA of the MACD line, called the signal line, is then plotted on top of the MACD line. The MACD histogram represents the difference between the MACD line and the signal line.
Here are the primary signals generated by the MACD:
• Bullish Crossover: When the MACD line crosses above the signal line, indicating a potential buy signal.
• Bearish Crossover: When the MACD line crosses below the signal line, indicating a potential sell signal.
• Divergence: When the price of the security diverges from the MACD, suggesting a potential reversal.
• Overbought/Oversold Conditions: Indicated by the MACD line moving far away from the signal line, though this is less common than in oscillators like the RSI.
⯁ How to use MACD forecast?
The MACD Forecast is built on the same foundation as the classic MACD, but with predictive capabilities.
Step 1 — Spot Predicted Crossovers:
Watch for forecasted bullish or bearish crossovers. These signals anticipate when the MACD line will cross the signal line in the future, letting you prepare trades before the move.
Step 2 — Confirm with Histogram Projection:
Use the projected histogram to validate momentum direction. A rising histogram signals strengthening bullish momentum, while a falling projection points to weakening or bearish conditions.
Step 3 — Combine with Multi-Timeframe Analysis:
Use forecasts across multiple timeframes to confirm signal strength (e.g., a 1h forecast aligned with a 4h forecast).
Step 4 — Set Entry Conditions & Automation:
Customize your buy/sell rules with the 20 forecast-based conditions and enable automation for bots or alerts.
Step 5 — Trade Ahead of the Market:
By preparing for future momentum shifts instead of reacting to the past, you’ll always stay one step ahead of lagging traders.
📈 BUY
🍟 Signal Validity: The signal will remain valid for X bars.
🍟 Signal Sequence: Configurable as AND or OR.
🍟 MACD > Signal Smoothing
🍟 MACD < Signal Smoothing
🍟 Histogram > 0
🍟 Histogram < 0
🍟 Histogram Positive
🍟 Histogram Negative
🍟 MACD > 0
🍟 MACD < 0
🍟 Signal > 0
🍟 Signal < 0
🍟 MACD > Histogram
🍟 MACD < Histogram
🍟 Signal > Histogram
🍟 Signal < Histogram
🍟 MACD (Crossover) Signal
🍟 MACD (Crossunder) Signal
🍟 MACD (Crossover) 0
🍟 MACD (Crossunder) 0
🍟 Signal (Crossover) 0
🍟 Signal (Crossunder) 0
🔮 MACD (Crossover) Signal Forecast
🔮 MACD (Crossunder) Signal Forecast
📉 SELL
🍟 Signal Validity: The signal will remain valid for X bars.
🍟 Signal Sequence: Configurable as AND or OR.
🍟 MACD > Signal Smoothing
🍟 MACD < Signal Smoothing
🍟 Histogram > 0
🍟 Histogram < 0
🍟 Histogram Positive
🍟 Histogram Negative
🍟 MACD > 0
🍟 MACD < 0
🍟 Signal > 0
🍟 Signal < 0
🍟 MACD > Histogram
🍟 MACD < Histogram
🍟 Signal > Histogram
🍟 Signal < Histogram
🍟 MACD (Crossover) Signal
🍟 MACD (Crossunder) Signal
🍟 MACD (Crossover) 0
🍟 MACD (Crossunder) 0
🍟 Signal (Crossover) 0
🍟 Signal (Crossunder) 0
🔮 MACD (Crossover) Signal Forecast
🔮 MACD (Crossunder) Signal Forecast
🤖 Automation
All BUY and SELL conditions can be automated using TradingView alerts. Every configurable condition can trigger alerts suitable for fully automated or semi-automated strategies.
⯁ Unique Features
Linear Regression: (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Table of Conditions: BUY/SELL
Conditions Label: BUY/SELL
Plot Labels in the graph above: BUY/SELL
Automate & Monitor Signals/Alerts: BUY/SELL
Background Colors: "bgcolor"
Background Colors: "fill"
Linear Regression (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Table of Conditions: BUY/SELL
Conditions Label: BUY/SELL
Plot Labels in the graph above: BUY/SELL
Automate & Monitor Signals/Alerts: BUY/SELL
Background Colors: "bgcolor"
Background Colors: "fill"
Goal Setting Strategies Viprasol# 🎯 Goal Setting Strategies Viprasol
A powerful goal tracking tool designed for disciplined traders who want to monitor their trading objectives, milestones, and progress directly on their charts.
## ✨ KEY FEATURES
### 📊 Flexible Goal Management
- Track anywhere from 1 to 20 trading goals simultaneously
- Adjustable goal count via simple input slider
- Each goal has its own unique emoji identifier
- Real-time progress counter
### ✅ Visual Tracking System
- Interactive checkbox system for goal completion
- Clear visual indicators (✅ completed, ⬜️ pending)
- Customizable goal names and descriptions
- Dynamic progress display
### 🎨 Full Customization
- **4 Position Options**: Top Left, Top Right, Bottom Left, Bottom Right
- **5 Font Sizes**: Tiny, Small, Normal, Large, Huge (optimized for all screen sizes)
- **Custom Colors**: Header, labels, background, achievement text
- **Premium Styling**: Modern cyber-themed design with professional appearance
### 💡 Perfect For:
- Daily/Weekly trading goal tracking
- Risk management milestones
- Profit target monitoring
- Trading plan compliance
- Personal development objectives
- Learning milestones
## 🔧 HOW TO USE
1. **Set Your Primary Goal**: Enter your main objective in "Primary Goal" field
2. **Choose Goal Count**: Select how many goals you want (1-20)
3. **Name Your Goals**: Customize each goal name in the "Goal Definitions" section
4. **Track Progress**: Check off goals as you complete them
5. **Customize Display**: Adjust colors, sizes, and position to match your chart setup
## 📐 INPUT GROUPS
### 🎯 Viprasol Goal Configuration
- Primary Goal Name
- Number of Goals (1-20)
### 📋 Goal Definitions
- All 20 goals with individual names and checkboxes
- Only enabled goals (based on count) will display
### 🌈 Premium Styling
- Goal Header Color
- Label Color
- Panel Background Color
- Achievement Color
- Header Font Size
- Milestone Font Size (Tiny/Small optimized for space)
### 📍 Elite Display
- Dashboard Position selector
## 💎 UNIQUE FEATURES
- **Space Efficient**: Tiny and Small font options for compact displays
- **Scalable**: Grow from 1 goal to 20 as your needs evolve
- **Non-Intrusive**: Overlay indicator that doesn't interfere with price action
- **Professional Design**: Clean, modern interface with cyber aesthetic
## 🎓 USE CASES
**Day Traders**: Track daily profit targets, trade count limits, max loss thresholds
**Swing Traders**: Monitor weekly/monthly goals, position management rules
**New Traders**: Learning milestones, strategy development checkpoints
**Experienced Traders**: Advanced risk management, portfolio objectives
## ⚙️ TECHNICAL DETAILS
- Version: Pine Script v5
- Type: Overlay Indicator
- Max Labels: 500
- Table-based display system
- No repainting
- Lightweight performance
## 🚀 GETTING STARTED
1. Add indicator to your chart
2. Set "Number of Goals" to your desired count (start small, scale up)
3. Customize goal names
4. Check boxes as you achieve goals
5. Watch your progress build!
## 📊 DISPLAY OPTIMIZATION
- Use "Tiny" or "Small" for maximum goals on small screens
- Use "Normal" or "Large" for standard monitors
- Use "Huge" for presentation or large displays
- Adjust position to avoid chart overlap
## 🎯 TRADING DISCIPLINE
This tool helps reinforce:
- Goal-oriented trading mindset
- Progress tracking accountability
- Milestone celebration
- Structured approach to trading development
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**© viprasol**
*Designed for traders who take their goals seriously.*
NYC Session Candle AnalysisCandle Analysis is a volatility-measurement tool that calculates average candle movements starting from a selected reference point, such as a fixed number of candles, a specific time and timezone, or a major trading session like New York, London, or Tokyo. It measures multiple candle ranges including High–Low, Open–Low, Open–Close, and others, then displays the averages in both points and ticks. The indicator helps evaluate market behavior during session opens, analyze open-range volatility, and understand typical candle movement patterns across different markets and timeframes.
⏰Forex Market Clock Table (DST Auto)⏰ Forex Market Clock Table (DST Auto)
Keep track of key forex session times with this clean, real-time table showing local time, market status (open/closed), and automatic Daylight Saving Time (DST) adjustments for Sydney, Tokyo, London, and New York. Displays countdowns to session open/close and highlights weekends. Fully customizable position, colors, and text size—perfect for multi-session traders.
Fibonacci Projection with Volume & Delta Profile (Zeiierman)█ Overview
Fibonacci Projection with Volume & Delta Profile (Zeiierman) blends classic Fibonacci swing analysis with modern volume-flow reading to create a unified, projection-based market framework. The indicator automatically detects the latest swing high and swing low, builds a complete Fibonacci structure, and then projects future extension targets with clear visual pathways.
What makes this tool unique is the integration of two volume-based systems directly into the Fibonacci structure. A Fib-aligned Volume Profile shows how bullish and bearish volume accumulated inside the swing range, while a separate Delta Profile reveals the imbalance of buy–sell pressure inside each Fibonacci interval. Together, these elements transform the standard Fibonacci tool into a multi-dimensional structural and volume-flow map.
█ How It Works
The indicator first detects the most recent swing high and swing low using the Period setting. That swing defines the Fibonacci range, from which the script draws retracement levels (0.236–0.786) and builds a forward projection path using the chosen Projection Level and a 1.272 extension.
Along this path, it draws projection lines, target boxes, and percentage labels that show how far each projected leg extends relative to the previous one.
Inside the same swing range, the script builds a Fib-based Volume Profile by splitting price into rows and assigning each bar’s volume as bullish (close > open) or bearish (close ≤ open). On top of that, it calculates a Volume Delta Profile between each pair of fib levels, showing whether buyers or sellers dominated that band and how strong that imbalance was.
█ How to Use
This tool helps traders quickly understand market structure and where the price may be heading next. The projection engine shows the most likely future targets, highlights strong or weak legs in the move, and updates automatically whenever a new swing forms. This ensures you always see the most relevant and up-to-date projection path.
The Fib Volume Profile shows where volume supported the move and where it did not. Thick bullish buckets reveal zones where buyers stepped in aggressively, often becoming retestable support. Thick bearish buckets highlight zones of resistance or rejection, particularly useful if projected levels align with prior liquidity.
The Delta Profile adds a second dimension to volume reading by showing where buy–sell pressure was truly imbalanced. A projected Fibonacci target that aligns with a strong bullish delta, for example, may suggest continuation. A projection into a band dominated by bearish delta may warn of reversal or hesitation.
█ Settings
Period – bars used to determine swing high/low
Projection Level – chosen Fib ratio for projection path
-----------------
Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
JINN: A Multi-Paradigm Quantitative Trading and Execution EngineI. Core Philosophy: A Substitute for Static Analysis
JINN (Joint Investment Neural and Network) represents a paradigm shift from static indicators to a living, adaptive analytical ecosystem. Traditional tools provide a fixed snapshot of the market. JINN operates on a fundamentally different premise: it treats the market as a dynamic, regime-driven system. It processes market data through a hierarchical suite of advanced, interacting models, arbitrates their outputs through a rules-based engine, and adapts its own logic in real-time.
It is designed as a complete framework for traders who think in terms of statistical edge, market regimes, probabilistic outcomes, and adaptive risk management.
II. The JINN Branded Architecture: Your Command and Control Centre
JINN’s power emerges from the synergy of its proprietary, branded architectural components. You do not simply "use" JINN; you command its engines.
1. JINN Signal Arbitration (JSA) Engine
The heart of JINN. The JSA is your configurable arbitration desk for weighing evidence from all internal models. As the Head Strategist, you define the entire arbitration philosophy:
• Priority and Weighting : Define a "chain of command". Specify which model's opinion must be considered first and assign custom weights to their outputs, directly controlling the hierarchy of your analytical flow.
• Arbitration Modes :
First Wins: For high-conviction, rapid signal deployment based on your most trusted leading model.
Highest Score: A "best evidence" approach that runs a full analysis and selects the signal with the highest weighted probabilistic backing.
Consensus: An ultra-conservative, "all-clear" mode that requires a unanimous pass from all active models, ensuring maximum confluence.
2. JINN Threshold Fusion (JTF) Engine
Static entry thresholds can be limiting in a dynamic market. The JTF engine replaces them with a robust, adaptive "breathing" channel.
• Kalman Filter Core : A noise-reducing, parametric filter that provides a smooth, responsive centre for the entry bands.
• Exponentially Weighted Quantile (EWQ) : A non-parametric, robust measure of the signal's recent distribution, resistant to outliers.
• Dynamic Fusion : The JTF engine intelligently fuses these two methodologies. In stable conditions, it can blend them; in volatile conditions, it can be configured to use the "Minimum Width" of the two, ensuring your entry criteria are always the most statistically relevant.
3. JINN Pattern Veto (JPV) with Dynamic Time Warping
The definitive filter for behavioural edge and pattern recognition. The JPV moves beyond value-based analysis to analyse the shape of market dynamics.
• Dynamic Time Warping (DTW) : A powerful algorithm from computer science that compares the similarity of time series.
• Pattern Veto : Define a "toxic" price action template—a pattern that has historically preceded failed signals. If the JPV detects this pattern, it will veto an otherwise valid trade, providing a sophisticated layer of qualitative, shape-based filtering.
4. JINN Flow VWAP
This is not a standard VWAP. The JINN Flow VWAP is an institutionally-aware variant that analyses volume dynamics to create a "liquidity pressure" band. It helps visualise and gate trades based on the probable activity of larger market participants, offering a nuanced view of where significant flow is occurring.
III. The Advanced Model Suite: Your Pre-Built Quantitative Toolkit
JINN provides you with a turnkey suite of institutional-grade models, saving you thousands of hours of research and development.
1. Auto-Tuning Hyperparameters Engine (Online Meta-Learning)
Markets evolve. A static strategy is an incomplete strategy. JINN’s Auto-Tuning engine is a meta-learning layer inspired by the Hedge (EWA) algorithm, designed to combat alpha decay.
• Portfolio of Experts : It treats a curated set of internal strategic presets as a portfolio of "experts".
• Adaptive Weighting : It runs an online learning algorithm that continuously measures the risk-adjusted performance of each expert (using a sophisticated reward function blending Expected Value and Brier Score).
• Dynamic Adaptation : The engine dynamically allocates more influence to the expert strategy that is performing best in the current market regime, allowing JINN’s core logic to adapt without manual intervention.
2. Lorentzian Classification and PCA-Lite EigenTrend
• Lorentzian Engine : A powerful probabilistic classifier that generates a continuous probability (0-1) of market state. Its adaptive, volatility-scaled distribution is specifically designed to handle the "fat tails" and non-Gaussian nature of financial returns.
• PCA-Lite EigenTrend : A Principal Component Analysis engine. It reduces the complex, multi-dimensional data from the Technical and Order-Flow ensembles into a single, maximally descriptive "EigenTrend". This factor represents the dominant, underlying character of the market, providing a pure, decorrelated input for the Lorentzian engine and other modules.
3. Adaptive Markov Chain Model
A forward-looking, state-based model that calculates the probability of the market transitioning between Uptrend, Downtrend, and Sideways states. Our implementation is academically robust, using an EMA-based adaptive transition matrix and Laplace Smoothing to ensure stability and prevent model failure in sparse data environments.
IV. The Execution Layer: JINN Execution Latch Options
A good signal is worthless without intelligent execution. The JINN Execution Latch is a suite of micro-rules and safety mechanisms that govern the "last mile" of a trade, ensuring signals are executed only under optimal, low-risk conditions. This is your final pre-flight check.
• Execution Latch and Dynamic Cool-Down : A core safety feature that enforces a dynamic cool-down period after each trade to prevent over-trading in choppy, whipsaw markets. The latch duration intelligently adapts, using shorter periods in low-volatility and longer periods in high-volatility environments.
• Volatility-Scaled Real-Time Threshold : A sophisticated gate for real-time entries. It dynamically raises the entry threshold during sudden spikes in volatility, effectively filtering out noise and preventing entries based on erratic, unsustainable price jerks.
• Noise Debounce : In market conditions identified as "noisy" by the Shannon Entropy module, this feature requires a real-time signal to persist for an extra tick before it is considered valid. This is a simple but powerful heuristic to filter out fleeting, insignificant price flickers.
• Liquidity Pressure Confirmation : An institutional-grade check. This gate requires a minimum threshold of "Liquidity Pressure" (a measure of volume-driven momentum) to be present before validating a real-time signal, ensuring you are entering with market participation on your side.
• Time-of-Day (ToD) Weighting : A practical filter that recognises not all hours of the trading day are equal. It can be configured to automatically raise entry thresholds during historically low-volume, low-liquidity sessions (e.g., lunch hours), reducing the risk of entering trades on "fake" moves.
• Adaptive Expectancy Gate : A self-regulating feedback mechanism. This gate monitors the strategy's recent, realised performance (its Expected Value). If the rolling expectancy drops below a user-defined threshold, the system automatically tightens its entry criteria, becoming more selective until performance recovers.
• Bar-Close Quantile Confirmation : A final layer of confirmation for bar-close signals. It requires the signal's final score to be in the top percentile (e.g., 85th percentile) of all signal scores over a lookback period, ensuring only the highest conviction signals are taken.
V. The Contextual and Ensemble Frameworks
1. Multi-Factor Ensembles and Bayesian Fusion
JINN is built on the principle of diversification. Its signals are derived from two comprehensive, fully customizable ensembles:
• Technical Ensemble : A weighted combination of over a dozen technical features, from cyclical analysis (MAMA, Hilbert Transforms) and momentum (Fisher Transform) to trend efficiency (KAMA, Fractal Efficiency Ratio).
• Order-Flow Ensemble : A deep dive into market microstructure, incorporating Volume Delta, Absorption, Imbalance, and Delta Divergence to decode institutional footprints.
• Bayesian Fusion : Move beyond simple AND/OR logic. JINN’s Bayesian engine allows you to probabilistically combine evidence from trend and order-flow filters, weighing each according to its perceived reliability to derive a final posterior probability.
2. Context-Aware Framework and Entropy Engine
JINN understands that a successful strategy requires not just a good entry, but an intelligent exit and a dynamic approach to risk.
• Shannon Entropy Filter : A direct application of information theory. JINN quantifies market randomness and allows you to set a precise entropy ceiling to automatically halt trading in unpredictable, high-entropy conditions.
• Adaptive Exits and Regime Awareness : The script uses its entropy-derived regime awareness to dynamically scale your Take Profit and Trailing Stop parameters . It can be configured to automatically take smaller profits in choppy markets and let winners run in strong trends, hard-coding adaptive risk management into your system.
VI. The Dashboard: Your Mission Control
JINN features a dynamic, dual-mode dashboard that provides a comprehensive, real-time overview of the entire system's state.
Mode 1: Signal Gate Metrics Dashboard
This dashboard is your pre-flight checklist. It displays the real-time Pass/Fail/Off status of every single gating and filtering component within JINN, including:
• Core Ensembles : Technical and Order-Flow Ensemble status.
• Trend Filters : VWAP, VWMA, ADX, ATR Slope, and Linear Regression Angle gates.
• Advanced Models : Dual-Lorentzian Consensus, Markov Probability, and JPV Veto status.
• Regime and Safety : Shannon Entropy, Execution Latch, and Expectancy Gate status.
• Final Confirmation : A master "All Hard Filters" status, giving you an at-a-glance confirmation of system readiness.
Mode 2: Quantitative Metrics Dashboard
This dashboard provides a high-level, institutional-style data readout of the current market state, as seen through JINN's analytical lens. It includes over 60 key metrics for both Signal Gate and Quantitative Metrics, such as:
• Ensemble and Confidence Scores : The raw numerical output of the Technical, Order-Flow, and Lorentzian models.
• Volatility and Volume Analysis : Realised Volatility (%), Relative Volume, Volume Sigma Score, and ATR Z-Score.
• Momentum and Market Position : ADX, RSI Z-Score, VWAP Distance (%), and Distance from 252-Bar High/Low.
• Regime Metrics : The numerical value of the Shannon Entropy score and the Model Confidence score.
VII. The User as the Head Strategist
With over 178 meticulously designed user inputs, JINN is the ultimate "glass box" engine. The internal code is proprietary, but the control surface is transparent and grants you architectural-level command.
• Prototype Sophisticated Strategies : Test complex, multi-model theses at your own pace that would otherwise take weeks of coding. Want to test a strategy that uses a Lorentzian classifier driven by the EigenTrend, arbitrated by JSA in "highest score" mode, and filtered by a strict Markov trend gate? These can be configured and unified.
• Tune the Engine to Any Market : The inputs provide the control surface to optimise JINN's behaviour for specific assets and timeframes, from crypto scalping to swing trading indices.
• Build Trust Through Configuration : The granular controls allow you to align the script's behaviour precisely with your own market view, building trust in your own deployment of the tool.
JINN is a commitment. It is a tool for the serious analyst who seeks to move from discretionary trading to a systematic, quantitative, and adaptive approach. If this aligns with your philosophy, we invite you to apply for access.
Disclaimer
This script is for informational and educational purposes only. It does not constitute financial, investment, or trading advice, nor is it a recommendation to buy or sell any asset.
All trading and investment decisions are the sole responsibility of the user. It is strongly recommended to thoroughly test any strategy on a paper trading account for at least one week before risking real capital.
Trading financial markets involves a high risk of loss, and you may lose more than your initial investment. Past performance is not indicative of future results. The developer is not responsible for any losses incurred from the use of this script.
SCOTTGO - Day Trade Stock Quote V4This Pine Script indicator, titled "SCOTTGO - Day Trade Stock Quote V4," is a comprehensive, customizable dashboard designed for active traders. It acts as a single, centralized reference point, displaying essential financial and technical data directly on your chart in a compact table overlay.
📊 Key Information Provided
The indicator is split into sections, aggregating various critical data points to provide a holistic picture of the stock's current state and momentum:
1. Ownership & Short Flow
This section provides fundamental context and short-interest data:
Market Cap, Shares Float, and Shares Outstanding: Key figures on the company's size and publicly tradable shares.
Short Volume %: Indicates the percentage of trading activity driven by short sellers.
Daily Change %: Shows the day's price movement relative to the previous close.
2. Price & Volatility
This tracks historical and immediate price levels:
Previous Close, Day High/Low: Key daily reference prices.
52-Week High/Low: Important long-term boundaries.
Earnings Date: A crucial fundamental date (currently displayed as a placeholder).
3. Momentum & Volume
These metrics are essential for understanding intraday buying and selling pressure:
Volume & Average Volume: The current trade volume compared to its historical average.
Relative Volume (RVOL): Measures how much volume is currently trading compared to the average rate for that time period (shown for both Daily and 5-Minute rates).
Volume Buzz (%): A percentage representation of how much current volume exceeds or falls below the average.
ADR % & ATR %: Measures of volatility.
RSI, U/D Ratio, and P/E Ratio: Momentum and valuation indicators.
4. Context
This provides background information on the security:
Includes the Symbol, Exchange, Industry, and Sector (note: some fields use placeholder data as this information is not always available via Pine Script).
⚙️ Customization
The dashboard is highly customizable via the indicator settings:
You can control the visibility of every single metric using the Section toggles.
You can change the position (Top Left, Top Right, etc.), size, and colors of the entire table.
In summary, this script is a powerful tool for day traders who need to monitor a large number of fundamental, technical, and volatility metrics simultaneously without cluttering the main chart area.
NYSE CME Market Session Clock This indicator can only work on short-term timeframes, since the time before the opening and before the closing of the session is updated only with the appearance of a new candle.
FRAN CRASH PLAY RULESPurpose
It creates a fixed information panel in the top right corner of your chart that shows the "FRAN CRASH PLAY RULES" - a checklist of criteria for identifying potential crash play setups.
Key Features
Display Panel:
Shows 5 trading rules as bullet points
Permanently visible in the top right corner
Stays fixed while you scroll or zoom the chart
Current Rules Displayed:
DYNAMIC 3 TO 5 LEG RUN
NEAR VERTICAL ACCELERATION
FINAL BAR OF THE RUN UP MUST BE THE BIGGEST
3 FINGER SPREAD / DUAL SPACE
ATLEAST 2 OF 5 CRITERIA NEEDS TO HIT
Customization Options:
Editable Text - Change any of the 5 rules through the settings
Text Color - Adjust the color of the text
Text Size - Choose from tiny, small, normal, large, or huge
Background Color - Customize the panel background and transparency
Frame Color - Change the border color
Show/Hide Frame - Toggle the border on or off
Use Case
This indicator serves as a constant visual reminder of your trading strategy criteria, helping you stay disciplined and only take trades that meet your specific crash play requirements. It's essentially a "cheat sheet" that lives on your chart so you don't have to memorize or look elsewhere for your trading rules.
VB Finviz-style MTF Screener📊 VB Multi-Timeframe Stock Screener (Daily + 4H + 1H)
A structured, high-signal stock screener that blends Daily fundamentals, 4H trend confirmation, and 1H entry timing to surface strong trading opportunities with institutional discipline.
🟦 1. Daily Screener — Core Stock Selection
All fundamental and structural filters run strictly on Daily data for maximum stability and signal quality.
Daily filters include:
📈 Average Volume & Relative Volume
💲 Minimum Price Threshold
📊 Beta vs SPY
🏢 Market Cap (Billions)
🔥 ATR Liquidity Filter
🧱 Float Requirements
📘 Price Above Daily SMA50
🚀 Minimum Gap-Up Condition
This layer acts like a Finviz-style engine, identifying stocks worth trading before momentum or timing is considered.
🟩 2. 4H Trend Confirmation — Momentum Check
Once a stock passes the Daily screen, the 4-hour timeframe validates trend strength:
🔼 Price above 4H MA
📈 MA pointing upward
This removes structurally good stocks that are not in a healthy trend.
🟧 3. 1H Entry Alignment — Timing Layer
The Hourly timeframe refines near-term timing:
🔼 Price above 1H MA
📉 Short-term upward movement detected
This step ensures the stock isn’t just good on paper—it’s moving now.
🧪 MTF Debug Table (Your Transparency Engine)
A live diagnostic table shows:
All Daily values
All 4H checks
All 1H checks
Exact PASS/FAIL per condition
Perfect for tuning thresholds or understanding why a ticker qualifies or fails.
🎯 Who This Screener Is For
Swing traders
Momentum/trend traders
Systematic and rules-based traders
Traders who want clean, multi-timeframe alignment
By combining Daily fundamentals, 4H trend structure, and 1H momentum, this screener filters the market down to the stocks that are strong, aligned, and ready.
ZynIQ Volatility Master Pro v2 - (Pro Plus Pack)Overview
ZynIQ Volatility Master Pro v2 analyses expansion and contraction in price behaviour using adaptive volatility logic. It highlights periods of compression, breakout potential and increased directional movement, helping traders understand when the market is shifting between quiet and active phases.
Key Features
• Multi-layer volatility modelling
• Adaptive compression and expansion detection
• Optional trend-aware volatility colouring
• Configurable sensitivity for different assets and timeframes
• Clean visual presentation designed for intraday and swing analysis
• Complements breakout, trend, structure and volume indicators
Use Cases
• Identifying contraction phases before expansion
• Filtering trades during low-volatility conditions
• Spotting volatility increases that accompany breakouts
• Combining volatility context with your other tools for confluence
Notes
This tool provides volatility context and regime awareness. It is not a trading system on its own. Use it with your preferred confirmation and risk management.
ZynIQ Order Block Master Pro v2 - (Pro Plus Pack)Overview
ZynIQ Order Block Master Pro v2 identifies areas where price showed strong displacement and left behind significant zones of interest. It highlights potential reaction areas, continuation blocks and mitigation zones based on structural behaviour and directional flow.
Key Features
• Automatic detection of bullish and bearish order block zones
• Optional refinement filters for higher-quality zones
• Displacement-aware logic to reduce weak signals
• Optional mitigation markers when price revisits a zone
• Configurable sensitivity for different markets and timeframes
• Clean labels and minimal chart clutter
• Complements structure, liquidity and FVG tools
Use Cases
• Highlighting key reaction areas based on previous strong moves
• Tracking potential continuation or reversal zones
• Combining order blocks with BOS/CHOCH and liquidity mapping
• Building confluence with breakout or volume tools
Notes
This tool provides contextual price zones based on displacement and structural movement. It is not a standalone trading system. Use with your own confirmation and risk management.
ZynIQ Market Regime Master Pro v2 - (Pro Plus Pack)Overview
ZynIQ Market Regime Master Pro v2 identifies shifts in market conditions by analysing volatility, directional flow and structural behaviour. It highlights when the market transitions between trending, ranging, expansion and contraction phases, giving traders clearer context for decision making.
Key Features
• Multi-factor regime detection (trend, range, expansion, contraction)
• Adaptive volatility and momentum analysis
• Direction-aware colour transitions
• Optional HTF regime overlay
• Configurable sensitivity to match different markets
• Clean visuals suitable for intraday or swing trading
• Complements trend, breakout, liquidity and volume tools
Use Cases
• Determining whether the market is trending or ranging
• Identifying expansion phases vs contraction phases
• Filtering signals during unfavourable regimes
• Combining regime context with structure or breakout tools
Notes
This tool provides regime classification and contextual analysis. It is not a trading system by itself. Use with your own confirmation and risk management.
ZynIQ Core Pro Suite v2 - (Pro Plus Pack)Overview
ZynIQ Breakout Core Pro Suite v2 is an advanced breakout engine designed to analyse compression, expansion and directional bias with high precision. It incorporates multi-factor filtering, adaptive volatility logic and refined breakout mapping to highlight moments where the market transitions from contraction to expansion.
Key Features
• Adaptive breakout zones with refined volatility filters
• Direction-aware breakout confirmation
• Optional multi-stage filtering for higher-quality expansions
• Pullback and continuation gating to reduce noise
• Integrated structure awareness for more reliable triggers
• Clean labels and minimal chart clutter
• Optimised for intraday, swing and high-volatility markets
Use Cases
• Identifying structurally significant breakout points
• Avoiding false expansions during low-volatility phases
• Combining breakout logic with trend, structure or volume tools
• Mapping expansion phases after compression builds
Notes
This tool provides structural and volatility-aware breakout context. It is not a complete trading system. Use with your own confirmation tools and risk management.
ZynIQ FVG Master Pro v2 - (Pro Pack)Overview
ZynIQ FVG Master v2 (Pro) identifies fair value gaps and highlights key imbalance zones within price action. It includes detection for standard and extended FVGs, optional mitigation logic and context filters to help traders understand where inefficiencies may be filled.
Key Features
• Detection of regular and extended FVGs
• Optional mitigation and fill markers
• Configurable minimum gap size and sensitivity
• Direction-aware colour coding
• Optional smart filtering to reduce low-quality gaps
• Clean visuals designed for intraday and swing analysis
• Can be used alongside structure and liquidity tools for confluence
Use Cases
• Identifying imbalance zones likely to be revisited
• Spotting high-probability mitigation areas
• Combining FVGs with BOS/CHOCH or liquidity sweeps
• Mapping context for continuation and reversal setups
Notes
This tool provides FVG and imbalance context. It is not a standalone trading system. Use with your preferred confirmation and risk management.
ZynIQ Liquidity Master Pro v2 - (Pro Pack)Overview
ZynIQ Liquidity Master v2 (Pro) identifies key liquidity pools and sweep zones using automated swing logic, equal-high/low detection and multi-level liquidity mapping. It provides a clear view of where liquidity may be resting above or below price, helping traders understand potential sweep or mitigation behaviour.
Key Features
• Automatic detection of EQH/EQL (equal highs/lows)
• Mapping of major swing liquidity zones
• Optional PDH/PDL (previous day high/low) and weekly levels
• Detection of potential liquidity sweep areas
• Clean labels for swing points and liquidity clusters
• Configurable sensitivity for different markets or timeframes
• Lightweight visuals with minimal clutter
Use Cases
• Identifying major liquidity pools above or below price
• Spotting potential sweep conditions before reversals
• Anchoring market structure or FVG tools with liquidity context
• Understanding where price may target during expansion moves
Notes
This tool identifies areas of resting liquidity based on swing and equal-high/low logic. It is not a standalone trading system. Use with your preferred confirmation and risk management.
ZynIQ Market Structure Master v2 - (Pro Pack)Overview
ZynIQ Market Structure Master v2 (Pro) maps structural shifts in price action using automated BOS/CHOCH detection, swing analysis and directional flow. It provides a clear view of when the market transitions between expansion, pullback and reversal phases.
Key Features
• Automated BOS (Break of Structure) and CHOCH detection
• Swing high/low mapping with optional filtering
• Directional flow logic for identifying trend vs reversal phases
• Optional EQ levels and mitigation markers
• Configurable structure sensitivity for different timeframes
• Clean labels and minimal clutter for fast interpretation
• Suitable for intraday and swing structure analysis
Use Cases
• Identifying key structural shifts in trend
• Spotting early reversal signals via CHOCH
• Assessing trend continuation vs distribution/accumulation
• Combining structure with liquidity, FVG or breakout tools
Notes
This tool provides structural context using break-of-structure and swing logic. It is not a trading system by itself. Use alongside your own confirmation and risk management.
ZynIQ Breakout Pro v2 - (Pro Pack)Overview
ZynIQ Breakout Pro v2 is an advanced breakout framework designed to identify high-quality expansion points from compression zones. It includes adaptive volatility filters, directional detection, optional confirmation logic and an integrated risk-mapping system for structured trade planning.
Key Features
• Adaptive breakout range detection with smart volatility filters
• Direction-aware breakout triggers
• Optional ADX or volatility conditions for confirmation
• Pullback gating to reduce low-quality continuation attempts
• Integrated Risk Helper for SL/TP structure
• Clean labels and minimal chart clutter
• Suitable for intraday and swing trading
Use Cases
• Identifying breakout moments with stronger confirmation
• Avoiding noise and clustering during choppy phases
• Structuring entries around expansion from compression
• Combining breakout signals with trend, momentum or volume tools
Notes
Breakout Pro v2 provides structural and volatility-aware breakout context. It is not a standalone trading system. Use with your own confirmation tools and risk management.
ZynIQ Trend Master V2 - (Pro Pack)Overview
ZynIQ Trend Master v2 (Pro) provides a structured, multi-layered approach to trend analysis. It combines volatility-aware trend detection, adaptive cloud colouring, and pullback signalling to help traders see trend strength, continuation phases and potential shift points with clarity.
Key Features
• Multi-profile trend modes (Scalping / Intraday / Swing)
• Adaptive trend cloud with colour transitions based on strength
• Volatility-aware pullback detection
• Optional HTF trend alignment
• Clean labels marking key transitions
• Configurable filters for smoothing and responsiveness
• Lightweight visuals for fast intraday charting
Use Cases
• Identifying conditions where trend strength is increasing or weakening
• Timing entries during pullbacks within a trend
• Aligning intraday and HTF directional bias
• Combining with breakout, volume or market structure tools for confirmation
Notes
This tool provides structured trend context and momentum flow. It is not a trading system on its own. Use with your preferred confirmation and risk management.
ZynIQ Session Master v2 - (Lite Pack)Overview
ZynIQ Session Master v2 (Lite) highlights key market sessions and their associated ranges, helping traders understand when volatility tends to shift between Asian, London and New York sessions. It provides clean visual context for intraday trading without overwhelming the chart.
Key Features
• Automatic detection and shading of major trading sessions
• Configurable session highlighting
• Optional range markers for Asia, London and New York
• Lightweight visuals suitable for fast intraday charting
• Simple session-based structure for context around volatility shifts
• Optional labels marking session transitions
Use Cases
• Seeing where session volatility typically increases
• Identifying when price is leaving a session range
• Timing trades around session opens
• Combining session structure with breakout, trend or momentum tools
Notes
This script provides session structure and volatility context. It is not a standalone trading system. Use alongside your preferred confirmation and risk management.
