Event HorizonEvent Horizon is a historical analog projection fan designed to answer one practical question:
What has price tended to do after market conditions similar to the current one?
Instead of using a fixed crossover, oscillator threshold, or trend flip, this indicator builds a market fingerprint from the current chart, searches historical bars for similar conditions, and projects how those past analogs moved forward. The result is a visual forward fan showing possible path behavior, consensus direction, dispersion, confidence, and the closest historical analog path.
The goal is not to predict the future with certainty. The goal is to give traders a structured way to compare the current setup against similar historical environments and quickly see whether the analogs are aligned, scattered, bullish, bearish, or not useful.
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What makes this script different
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Most projection tools draw a channel, regression, moving average extension, or volatility cone from a fixed formula.
Event Horizon uses a historical analog engine. Each bar is converted into a multi-factor feature profile, then compared against prior market states using weighted Euclidean similarity. The closest historical analogs are used to create a forward projection fan.
The script combines:
• Historical analog matching
• Weighted Euclidean distance
• Regime-aware scoring
• Volatility and trend-state filtering
• Consensus projection logic
• Closest historical path overlay
• Agreement and confidence scoring
• Directional historical event dots
• A visual fan that shows uncertainty instead of one hard prediction
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How it works
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1. Market fingerprint
The script measures the current market using multiple dimensions, including:
• Recent price movement
• Trend slope
• ATR expansion and compression
• Candle range and body behavior
• Wick imbalance
• Position relative to recent structure
• Breakout distance
• Volume ratio and volume trend
• ATR percentile
• ADX / trend strength
• Historical shape samples
This creates a multi-dimensional profile of the current setup.
2. Historical analog search
The current profile is compared to historical profiles on the same chart. Similarity is calculated with weighted Euclidean distance, so higher-value features such as trend, volatility regime, and price-shape behavior can matter more than smaller candle details.
Closer historical examples receive stronger match scores.
3. Regime awareness
The script also classifies the current environment into regimes such as trend, compression, volatility expansion, volume shock, or range/chop. Historical examples from incompatible regimes are penalized, helping reduce weak comparisons.
4. Forward projection
Once the best analogs are selected, the script looks at what actually happened after those historical setups. Those forward moves are normalized and projected from the current anchor point.
5. Consensus and confidence
The indicator summarizes the analog group with:
• Directional bias
• Agreement percentage
• Dispersion
• Confidence score
• Edge state: TRADEABLE, CAUTION, or NO EDGE
• Historical self-test statistics
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How to read the fan
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The colored fan shows the projected analog field.
The colored median shows the consensus path of the analog set.
The white line shows the closest visible historical analog path. It is not a guaranteed target. It is the path taken by the most similar past setup selected by the engine.
The wider the fan, the more disagreement there is between analogs.
The tighter the fan, the more historically aligned the analogs are.
The confidence and edge label are important. A bullish-looking fan with low confidence or high dispersion should be treated differently than a bullish fan with strong agreement and cleaner regime structure.
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Historical dots
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Historical event dots help visually review where prior projection events occurred.
• Bullish projection dots appear below price
• Bearish projection dots appear above price
• Mixed or neutral readings are visually separated
This makes it easier to inspect whether the indicator has been identifying useful directional conditions on the current symbol and timeframe.
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How to use it
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For newer traders:
1. Start on the Daily or 4H chart.
2. Look at the colored median.
3. Check whether the white analog path agrees with the median.
4. Check confidence and agreement.
5. Avoid forcing trades when the label says NO EDGE or when dispersion is high.
A stronger bullish read usually has:
• Median path rising
• White analog path also rising
• Agreement above roughly 65%
• Confidence above roughly 70
• Low or medium dispersion
• Edge state showing TRADEABLE or CAUTION, not NO EDGE
A weaker or avoidable read usually has:
• Median and white path disagreeing
• Agreement near 50%
• High dispersion
• Low confidence
• Range/chop regime
• NO EDGE label
For experienced traders:
Use the fan as an analog-based context layer. It is most useful when combined with your own structure, liquidity, trend, support/resistance, volume, or macro view. The script is designed to show whether historical analog behavior supports or conflicts with the trade idea you already see on the chart.
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Suggested settings
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Balanced stocks / ETFs:
• Mode: Current Bar Projection
• Visual Mode: Cinematic
• Fan Style: Hybrid Swarm + Contours
• Horizon: 30
• Memory Lookback: 1800
• Max Analogs: 30
• Minimum Analogs: 6
• Pre-Event Window: 20
• Shape Samples: 6
• Path Scale: 1.0
Crypto:
• Horizon: 24
• Memory Lookback: 2000 to 2500
• Path Scale: 0.75 to 0.90
• Flexible direction matching
Intraday:
• Horizon: 20 to 24
• Minimum Analogs: 8
• Path Scale: 0.75 to 1.0
• Use liquid symbols only
Trend continuation:
• Direction Matching: Strict
• Mirror Opposite Direction: Off
• Path Scale: 1.0
Reversal / exhaustion:
• Direction Matching: Flexible
• Mirror Opposite Direction: On
• Path Scale: 0.75
• Shorter horizon preferred
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Best use cases
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Event Horizon is best suited for:
• Liquid stocks
• Major ETFs
• Index products
• Major crypto pairs
• Trend continuation setups
• Post-compression expansion
• Structure breaks
• Swing-trade context
• Daily and 4H analysis
It is less suitable for:
• Illiquid symbols
• Very new tickers with limited history
• Low-volume penny stocks
• Earnings gaps
• Binary news events
• Extremely short scalping timeframes
• Markets with sudden one-off catalysts
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Important notes
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This is an analog projection tool, not a standalone buy/sell system.
Historical similarity does not guarantee future behavior. Market structure, liquidity, volatility, news, and macro conditions can change quickly. The fan should be used as a decision-support layer, not as a guaranteed forecast.
The strongest readings occur when the median, white analog path, agreement, confidence, and regime state all point in the same direction.
The weakest readings occur when analogs are scattered, confidence is low, dispersion is high, or the script identifies a no-edge environment.
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Summary
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Event Horizon turns historical market behavior into a forward analog projection fan.
It helps traders see:
• What similar past setups did next
• Whether those analogs agree or disagree
• Whether the current regime supports the projection
• Whether the projected path is tight or scattered
• Whether the setup has enough confidence to matter
Use it to add historical context, probability awareness, and regime-based discipline to your chart analysis.
Indikator
Polynomial/Linear Regression Volume Profile [BigBeluga]Polynomial/Linear Regression Volume Profile is a state-of-the-art charting framework that blends advanced statistical modeling with localized volume distribution analysis. By evolving past traditional, static horizontal volume profiles, this indicator dynamically curves the volume profile matrix around mathematical trend baselines, giving you a hyper-localized view of value zones, support, and resistance across the trend’s lifecycle.
Equipped with a switchable Ordinary Least Squares (OLS) calculation engine, traders can analyze price distribution relative to a straight path (Linear) or an adaptive structural arc (Polynomial).
🔵 RECURSIVE REGRESSION BASELINES
Adaptive Curve Fitting Engine: Choose between a straight-line trend tracking framework (Linear) or an advanced second-degree curved path (Polynomial). This non-linear baseline curves dynamically to track real institutional momentum shifts, avoiding the lag or rigid delays typical of standard moving averages.
Symmetric Grid Segmentation: The indicator slices the regression space into dynamic parallel layers above and below the center line. These tracking cells act as a structural map of the trend, automatically expanding or contracting based on the mathematical bounds of the lookback period.
Standard Deviation Wave Bands: Plots dedicated tracking envelopes at 1, 2, and 3 Standard Deviations. This maps statistical extremes instantly, highlighting key valuation zones directly on the chart.
🔵 CURVED ORDER FLOW PROFILE
Dynamic Trend-Anchored Volume Profile: Traditional volume profiles are anchored strictly to vertical price grids. This framework bends the profile horizontally along the path of the regression curve. This ensures volume is localized directly relative to the trend's value matrix rather than arbitrary static prices.
Dynamic Point of Control Matrix (POC): The tool calculates cumulative transaction weights across each regression row. The absolute highest volume cluster is highlighted across the entire lookback window as a vivid Point of Control (POC) baseline, serving as a primary target magnet for price discovery.
Gradient Density Mapping: Volume bins are colored with a responsive heat-map gradient. Low-volume zones fade into deep baseline tones, while high-volume institutional interest areas light up dynamically, reflecting heavy positional accumulation.
🔵 DATA INTERFACE & CONTROLS
Regression Matrix Dashboard (Top-Right): A neat information center providing live metrics, including current trend direction (Bullish/Bearish), the numerical value of the POC level, the exact transactional volume resting at that key node, and structural $\pm3\text{ SD}$ channel limits.
Precision Profile Scaling: Adjust the profile width parameters to limit or extend how far back profile bins stretch across your chart space to prevent layout clutter.
Complete Style Personalization: Individualized visual controls allow you to switch line architectures (Solid, Dashed, Dotted) across baselines, boundaries, and POC paths.
🔵 STRATEGIC APPLICATION
Trading the Trend Value Nodes: Treat the dynamic POC line as a trend anchor. In a strong bullish trend, pullback entries occurring at a highly concentrated, heat-mapped POC node represent low-risk, high-probability entry criteria.
Mean Reversion at Statistical Boundaries: When price extends completely out to the dynamic outer channel limit and volume density in that outer bin thins out, look for a swift mean-reversion snapback toward the baseline.
Volume Profile Breakouts: Low-volume zones (gaps in the curved profile) indicate price levels that the market skipped quickly due to high momentum. If price breaks past a thick volume node into a low-volume zone, it is likely to sprint quickly toward the next major heat-mapped node.
Structural Regime Tracking: Use the upper-right dashboard to instantly evaluate macro status. If the matrix shifts between Bullish and Bearish while price hovers consistently near a high-volume POC, it implies heavy institutional distribution is occurring before the next major expansion.
Polynomial/Linear Regression Volume Profile redefines volume structure. By wrapping the laws of order flow directly around mathematical curves, it gives trend traders an elite perspective to trade with precision, statistical logic, and institutional order flow visibility. Indikator
regressionsLibrary "regressions"
This library computes least square regression models for polynomials of any form for a given data set of x and y values.
fit(X, y, reg_type, degrees)
Takes a list of X and y values and the degrees of the polynomial and returns a least square regression for the given polynomial on the dataset.
Parameters:
X (array) : (float ) X inputs for regression fit.
y (array) : (float ) y outputs for regression fit.
reg_type (string) : (string) The type of regression. If passing value for degrees use reg.type_custom
degrees (array) : (int ) The degrees of the polynomial which will be fit to the data. ex: passing array.from(0, 3) would be a polynomial of form c1x^0 + c2x^3 where c2 and c1 will be coefficients of the best fitting polynomial.
Returns: (regression) returns a regression with the best fitting coefficients for the selecected polynomial
regress(reg, x)
Regress one x input.
Parameters:
reg (regression) : (regression) The fitted regression which the y_pred will be calulated with.
x (float) : (float) The input value cooresponding to the y_pred.
Returns: (float) The best fit y value for the given x input and regression.
predict(reg, X)
Predict a new set of X values with a fitted regression. -1 is one bar ahead of the realtime
Parameters:
reg (regression) : (regression) The fitted regression which the y_pred will be calulated with.
X (array)
Returns: (float ) The best fit y values for the given x input and regression.
generate_points(reg, x, y, left_index, right_index)
Takes a regression object and creates chart points which can be used for plotting visuals like lines and labels.
Parameters:
reg (regression) : (regression) Regression which has been fitted to a data set.
x (array) : (float ) x values which coorispond to passed y values
y (array) : (float ) y values which coorispond to passed x values
left_index (int) : (int) The offset of the bar farthest to the realtime bar should be larger than left_index value.
right_index (int) : (int) The offset of the bar closest to the realtime bar should be less than right_index value.
Returns: (chart.point ) Returns an array of chart points
plot_reg(reg, x, y, left_index, right_index, curved, close, line_color, line_width)
Simple plotting function for regression for more custom plotting use generate_points() to create points then create your own plotting function.
Parameters:
reg (regression) : (regression) Regression which has been fitted to a data set.
x (array)
y (array)
left_index (int) : (int) The offset of the bar farthest to the realtime bar should be larger than left_index value.
right_index (int) : (int) The offset of the bar closest to the realtime bar should be less than right_index value.
curved (bool) : (bool) If the polyline is curved or not.
close (bool) : (bool) If true the polyline will be closed.
line_color (color) : (color) The color of the line.
line_width (int) : (int) The width of the line.
Returns: (polyline) The polyline for the regression.
series_to_list(src, left_index, right_index)
Convert a series to a list. Creates a list of all the cooresponding source values
from left_index to right_index. This should be called at the highest scope for consistency.
Parameters:
src (float) : (float ) The source the list will be comprised of.
left_index (int) : (float ) The left most bar (farthest back historical bar) which the cooresponding source value will be taken for.
right_index (int) : (float ) The right most bar closest to the realtime bar which the cooresponding source value will be taken for.
Returns: (float ) An array of size left_index-right_index
range_list(start, stop, step)
Creates an from the start value to the stop value.
Parameters:
start (int) : (float ) The true y values.
stop (int) : (float ) The predicted y values.
step (int) : (int) Positive integer. The spacing between the values. ex: start=1, stop=6, step=2:
Returns: (float ) An array of size stop-start
regression
Fields:
coeffs (array__float)
degrees (array__float)
type_linear (series__string)
type_quadratic (series__string)
type_cubic (series__string)
type_custom (series__string)
_squared_error (series__float)
X (array__float) Bibliothek
Indikator
Indikator
PA-Adaptive Polynomial Regression Fitted Moving Average [Loxx]PA-Adaptive Polynomial Regression Fitted Moving Average is a moving average that is calculated using Polynomial Regression Analysis. The purpose of this indicator is to introduce polynomial fitting that is to be used in future indicators. This indicator also has Phase Accumulation adaptive period inputs. Even though this first indicator is for demonstration purposes only, its still one of the only viable implementations of Polynomial Regression Analysis on TradingView is suitable for trading, and while this same method can be used to project prices forward, I won't be doing that since forecasting is generally worthless and causes unavoidable repainting. This indicator only repaints on the current bar. Once the bar closes, any signal on that bar won't change.
For other similar Polynomial Regression Fitted methodologies, see here
Poly Cycle
What is the Phase Accumulation Cycle?
The phase accumulation method of computing the dominant cycle is perhaps the easiest to comprehend. In this technique, we measure the phase at each sample by taking the arctangent of the ratio of the quadrature component to the in-phase component. A delta phase is generated by taking the difference of the phase between successive samples. At each sample we can then look backwards, adding up the delta phases.When the sum of the delta phases reaches 360 degrees, we must have passed through one full cycle, on average.The process is repeated for each new sample.
The phase accumulation method of cycle measurement always uses one full cycle’s worth of historical data.This is both an advantage and a disadvantage.The advantage is the lag in obtaining the answer scales directly with the cycle period.That is, the measurement of a short cycle period has less lag than the measurement of a longer cycle period. However, the number of samples used in making the measurement means the averaging period is variable with cycle period. longer averaging reduces the noise level compared to the signal.Therefore, shorter cycle periods necessarily have a higher out- put signal-to-noise ratio.
What is Polynomial Regression?
In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modelled as an nth degree polynomial in x. Polynomial regression fits a nonlinear relationship between the value of x and the corresponding conditional mean of y, denoted E(y |x). Although polynomial regression fits a nonlinear model to the data, as a statistical estimation problem it is linear, in the sense that the regression function E(y | x) is linear in the unknown parameters that are estimated from the data. For this reason, polynomial regression is considered to be a special case of multiple linear regression.
Things to know
You can select from 33 source types
The source is smoothed before being injected into the Polynomial fitting algorithm, there are 35+ moving averages to choose from for smoothing
The output of the Polynomial fitting algorithm is then smoothed to create the signal, there are 35+ moving averages to choose from for smoothing
Included
Alerts
Signals
Bar coloring
Indikator
