[Saga Trading] OBV Pro

Description

[Saga Trading] OBV Pro is a statistically-normalized On-Balance Volume framework designed to transform traditional OBV into a structured, regime-aware participation model. This script is not a simple OBV with added labels.

It is built around three core principles:
- Adaptive OBV computation
- Statistical normalization & regime measurement
- Structured divergence qualification


What makes this different from classic OBV scripts?

Traditional OBV:
- Is cumulative and unbounded.
- Cannot be compared easily across assets or timeframes.
- Often produces noisy divergences without contextual filtering.


OBV Pro addresses these limitations through:

1) Multi-mode OBV engine

Instead of a single formula, OBV Pro includes four calculation modes:
- Classic OBV
- Weighted % Change (volume weighted by percentage displacement)
- Weighted Candle Body (volume weighted by directional body strength)
- CVD Proxy (Buy–Sell) (directional volume proxy based on candle structure)

This allows participation measurement to adapt to volatility structure rather than relying on a fixed cumulative model.

2) Statistical normalization framework

The core innovation is the transformation of OBV into a measurable regime variable:
- OBV is compared to its own moving average.
- The distance is expressed in standard deviations (σ).
- A dynamic visual zone reflects intensity based on σ distance.

Additionally, three display domains are available:
- Raw mode → structural participation bias.
- Z-Score mode → standardized OBV (mean-normalized).
- ROC mode → participation acceleration.
- Z-Score mode enables objective statistical reference levels (±1 / ±2 / ±3σ).

This makes OBV comparable across:
- Crypto
- Index
- Forex
- Commodities
- Stocks

3) Pivot-based divergence model (not candle-to-candle)

Divergences are calculated using:
- Confirmed price pivots.
- OBV sampled at the pivot bar itself.
- Optional RSI / Bollinger condition filters.

A composite Divergence Score (0–100) based on:
- σ displacement at pivot
- OBV slope impulse
- Regime alignment bonus

This scoring system is designed to reduce random divergence noise and prioritize structurally meaningful participation shifts.

4) Multi-timeframe regime alignment

Optional higher timeframe OBV alignment can be required before signals are validated.
This prevents lower timeframe divergences from triggering against higher timeframe participation structure.

5) Asset & timeframe adaptive presets

The script includes internal adaptive parameters based on:
- Asset category
- Timeframe structure

Users may override these manually, but the default system adapts smoothing and divergence lookback automatically.


Why this script is invite-only / closed-source ?

This script integrates:
- Adaptive OBV modeling
- Statistical σ-based regime detection
- Divergence scoring logic
- MTF regime gating
- Asset/timeframe adaptive presets

The value lies in the internal integration of these components into a coherent participation model. This is not a mashup of public scripts but a unified framework built around participation normalization and structured divergence qualification. This script is provided for analytical purposes only and does not constitute financial advice.


It Include :
- Core Engine
- Multi-mode OBV calculation
- Session reset options
- Auto MA type & length by asset/timeframe
- Manual override controls
- Regime Framework
- OBV vs OBV MA dynamic zone
- σ-based distance measurement
- Z-Score normalization
- ROC acceleration view
- Optional gradient visualization
- Divergence Model
- Pivot-confirmed divergences
- Hidden divergences
- RSI / Bollinger filters
- Divergence Score (0–100)
- Score-threshold alert gating
- Context Tools
- HTF OBV overlay
- Optional MTF alignment requirement
- OBV oscillator
- OBV momentum
- RSI of OBV
- OBV/Price correlation
- OBV rolling profile range
- Alerts
- OBV regime crossover
- Pivot divergences
- Z-Score extremes
- ROC thresholds
- Scored divergence alerts


How to use ?

A) Identify participation regime :
Use Raw mode + Dynamic Zone
OBV above MA → bullish participation bias
OBV below MA → bearish participation bias
Large σ distance → strong participation pressure

B) Detect statistical extremes :
Use Z-Score mode
±2σ → extended participation
±3σ → statistically extreme condition
Combine with price structure. Extremes do not automatically imply reversal.

C) Evaluate acceleration :
Use ROC mode
Helps identify: Participation expansion / Participation exhaustion

D) Trade divergences selectively :
Enable:
Pivot divergences
Filters (RSI / Bollinger)
Divergence Score
Higher score = stronger structural imbalance.
Optional: enable MTF alignment for stricter confirmation.