How to Use Distribution Zone

How to Use Distribution Zone Data

This page explains how to read and interpret the Distribution Zone table. This filter is designed to identify stocks where selling pressure is emerging through delivery-based activity, often after a prior up move or extended consolidation.

Each row represents one stock for one trading day, evaluated using price behavior, delivery trends, and volume comparison to detect distribution rather than accumulation.


Understanding Each Column

Symbol

The stock symbol as listed on the exchange (e.g., NSE). Each symbol uniquely identifies a listed company.

Date

The trading date on which the distribution signal was detected. All values in the row correspond to this specific trading session.

Close

The closing price of the stock on the given date.

  • Represents the final market consensus for the day
  • Used to assess whether selling is occurring with price weakness or stability

Volume

The total number of shares traded during the trading day.

  • Reflects overall participation
  • Evaluated relative to recent historical averages

Delivery Volume

The number of shares that resulted in actual delivery rather than intraday trades.

  • Represents positional selling or transfer of ownership
  • Elevated delivery during price weakness can signal distribution

Delivery %

The percentage of total traded volume that resulted in delivery.

  • High values indicate conviction-based activity
  • In a distribution context, this reflects sustained selling rather than speculation

Price Change % (5d)

The percentage change in the stock’s price over the last 5 trading days.

  • Flat or negative values suggest loss of upward momentum
  • Declining price alongside high delivery supports a distribution view

Recent Delivery % Avg (5d)

The average delivery percentage over the most recent 5 trading days.

  • Shows short-term delivery behavior
  • Rising values indicate increasing conviction in selling activity

Previous Delivery % Avg (15d)

The average delivery percentage over the preceding 15 trading days.

  • Serves as a baseline for comparison
  • A higher 5-day average versus 15-day average signals recent acceleration

Recent Avg Volume (5d)

The average daily trading volume over the last 5 trading days.

  • Used to detect short-term increase in participation
  • Rising volume supports the validity of distribution

Previous Avg Volume (15d)

The average daily trading volume over the previous 15 trading days.

  • Represents normal trading activity
  • Comparison highlights abnormal volume expansion during selling

How to Read This Data Holistically

The Distribution Zone filter should be interpreted by combining price behavior with delivery and volume trends.

  • High delivery % with flat or falling price suggests distribution
  • 5-day delivery average above 15-day average confirms recent selling strength
  • Rising short-term volume reinforces the distribution signal

What This Filter Indicates

  • Supply entering the market after prior accumulation or uptrend
  • Transfer of shares from strong hands to weaker hands
  • Potential trend exhaustion or reversal zone

What This Filter Does Not Indicate

  • It is not a short-selling or exit signal by itself
  • It does not guarantee immediate price decline
  • Confirmation must come from price structure and trend breakdown

Important Notes

  • This data reflects observed market behavior, not predictions
  • Distribution can occur over multiple sessions, not a single day
  • Always validate with support-resistance and broader market context

Disclaimer: This information is provided for educational and analytical purposes only and should not be considered trading or investment advice.


Data Update Frequency

Distribution Zone data is updated after market hours for each trading day. Comparative averages are calculated dynamically using rolling windows.


Summary

The Distribution Zone filter helps identify stocks where selling pressure is increasing despite sustained delivery-based activity. It is best used to spot potential exhaustion or transition phases when combined with price action and market structure analysis.