The Accumulation/Distribution Line — Marc Chaikin's volume-weighted indicator.
A/D = Σ(CLV × Volume)
CLV (Close Location Value) ranges from −1 to +1. Close near the high → CLV near +1 (accumulation). Close near the low → CLV near −1 (distribution).
The close location value says where the close sits in the day’s range: +1 at the high, −1 at the low. Day 15: high 105, low 101, close 104, so CLV = ((104 − 101) − (105 − 104)) / 4 = 0.5, and 0.5 × 71,000 shares = +35,500. A/D ends at 23,833, against OBV’s 1,000: it credits the closes near the highs of days 14 and 15 more than the falls before them.
The example data: 15 days, used in every indicator here
| Day | High | Low | Close | Volume |
|---|---|---|---|---|
| 1 | 101 | 99 | 100 | 50,000 |
| 2 | 103 | 100 | 102 | 62,000 |
| 3 | 103 | 100 | 101 | 48,000 |
| 4 | 105 | 101 | 104 | 70,000 |
| 5 | 108 | 104 | 107 | 85,000 |
| 6 | 108 | 105 | 106 | 60,000 |
| 7 | 110 | 106 | 109 | 90,000 |
| 8 | 110 | 107 | 108 | 72,000 |
| 9 | 108 | 104 | 105 | 80,000 |
| 10 | 105 | 102 | 103 | 66,000 |
| 11 | 105 | 102 | 104 | 55,000 |
| 12 | 104 | 101 | 102 | 75,000 |
| 13 | 102 | 98 | 99 | 95,000 |
| 14 | 102 | 99 | 101 | 64,000 |
| 15 | 105 | 101 | 104 | 71,000 |
- It ignores gaps: a stock that gaps down 5% and closes at the day’s high counts as accumulation.
- Like OBV, its level depends on the start date; only the direction means anything.
- On a day when the high equals the low, CLV is 0 / 0 and has to be set to zero by convention.
clv = ((df['close'] - df['low']) - (df['high'] - df['close'])) / (df['high'] - df['low']) df['ad'] = (clv * df['volume']).cumsum()
Assumes import numpy as np, import pandas as pd, and a DataFrame df with columns high, low, close and volume — the 15 days above.
- Technical Analysis from A to Z (2nd ed.), S. B. Achelis, McGraw-Hill, 2000 — Chaikin’s accumulation/distribution line
- L. Blume, D. Easley & M. O’Hara, “Market Statistics and Technical Analysis: The Role of Volume”, Journal of Finance 49(1), 1994 — why volume can carry information
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