Volume Analytics
Real Nodes. Real Structure.
Zero Settings
Zero Tuning
Visible Range
Log Scale
Statistics Driven
Gaussian Weighted
Overview
Volume profiles are supposed to reveal structure. Most tools reduce them to a picture, and traders end up treating a single POC like an answer.
When volume is just summed into bars, the profile flattens. Nodes smear. The “analysis” becomes interpretation work you do manually.
Volume Analytics builds structure, not a drawing. It adapts to your visible range, extracts real nodes and cores, and stays objective without tuning. You get levels you can validate at a glance.
Core Features
01
Built for any market, any scale. Precise structure, zero tuning.
Resolution Control
Precisely control data density with a high-resolution engine supporting up to 200 rows and log-scale algorithms. Analyze everything from micro-fluctuations to macro-trends without distortion. Optimized architecture ensures fluid performance even at the highest resolutions.
Visible Range Scaling
Automatically recalculate visible data in real-time as you navigate or zoom, ensuring a structure optimized for your current view. By overcoming the limitations of fixed periods, you can perform precise volume analysis at any point in history, adapted perfectly to your scale.
Auto Node Segmentation
Automatically identify and structurally separate independent energy nodes within volume profile data. By highlighting the specific node where price resides in real-time, our system enables you to intuitively identify the dominant energy zone within complex market structures.
Core + NPOC
Beyond simple volume listings, we precisely extract Core areas with the highest trade concentration and Naked Point of Control (NPOC) levels. This allows you to instantly identify high-reliability support and resistance zones, providing clear, data-driven benchmarks for price reversals and breakouts.
Technical Identity
02
System architecture built for stable real-time rendering under heavy computation.
Object Reuse Layer
Boxes, lines, and labels are reused instead of repeatedly deleted and recreated. This reduces graphical overhead and keeps rendering stable during heavy updates.
Visible Range Sync
Computation is scoped to the current visible range. When you zoom or pan, the system refreshes only what the view requires, keeping updates responsive and aligned with what you are actually looking at.
Deterministic Refresh Pipeline
Updates run through a single refresh path, minimizing redundant work. This prevents inconsistent states and keeps the output stable as the chart context changes.
Resource Bound Design
The architecture is built around Pine’s graphical limits. It prioritizes predictable object counts and controlled updates to avoid resource exhaustion in real world use.
Low Noise UI Rules
Text and detail are automatically constrained when density is high. The interface stays readable without manual toggles or constant tweaking.
Algorithmic Logic
03
Mathematical Integrity & The Foundation of Probabilistic Analysis.
Density Modeling (Gaussian, Log Space)
Volume is allocated across log-spaced rows using exp-based Gaussian weighting and per-bar normalization to preserve volume integrity.
Node Segmentation (Peak Valley Terrain)
Nodes are split from the smoothed density landscape by detecting local peaks and cutting at scored valleys, avoiding fixed thresholds.
Per-Node POC (NPOC)
Each node computes its own POC by locating the peak volume row inside the node range, not relying on a single global POC.
Core Extraction
The core zone expands outward from the node peak until the target fraction of node volume is reached, highlighting the highest concentration region.
Under the Hood
04
Fragments of the logic behind the nodes.
Log Domain Row Mapping
logMin = log(Pmin)
logStep = (log(Pmax) - logMin) / rows
idx(x) = clamp(int((log(x) - logMin) / logStep), 0, rows - 1)
Normalized Kernel Allocation
z = (mid(row) - mu) / sigma
w = exp(-0.5 * z * z)sumW += w
profile[row] += vol * (w / max(sumW, eps)) * Bias(row)
Terrain Split With Quantile Gate
depth = 1 - clamp(minV / refPeak, 0, 1)
sep = clamp((pR - pL) / rows, 0, 1)
score = depth * sep * areaScorekeep = score >= Gate(scores)
Core Expansion From Peak
target = nodeMass * T
while cum < target
takeLeft = PickSide(vL, vR)
expand(takeLeft)
