mirror of
https://github.com/mihakralj/QuanTAlib.git
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Miha Kralj
6f0a339c9b
- Sar.Quantower.Tests.cs: add missing opening quote on string literal (line 48) - Exports.cs: rename Correlation.Batch → Correl.Batch (CS0103) - Ad.Validation.Tests.cs: fix Ooples OutputValues key "Ad" → "Adl"
100 lines
4.1 KiB
Markdown
100 lines
4.1 KiB
Markdown
# AD: Accumulation/Distribution Line
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> *Volume precedes price.*
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| Property | Value |
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| ---------------- | -------------------------------- |
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| **Category** | Volume |
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| **Inputs** | OHLCV bar (TBar) |
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| **Parameters** | None |
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| **Outputs** | Single series (AD) |
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| **Output range** | Unbounded |
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| **Warmup** | 1 bar |
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| **PineScript** | [ad.pine](ad.pine) |
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- The Accumulation/Distribution Line (AD) is the bedrock of volume analysis.
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- No configurable parameters; computation is stateless per bar.
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- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
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The Accumulation/Distribution Line (AD) is the bedrock of volume analysis. It attempts to answer a single, vital question: "Are the big players buying or selling?"
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Unlike On-Balance Volume (OBV), which treats every up-day as 100% buying, AD is nuanced. It looks at *where* the price closed within the day's range. A close near the high on massive volume screams "Accumulation." A close near the low on massive volume screams "Distribution."
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## Historical Context
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Developed by Marc Chaikin, the AD was originally designed to spot divergences. Chaikin noticed that if a stock made a new high but the AD failed to make a new high, a crash was imminent. He essentially quantified the "smart money" flow.
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## Architecture & Physics
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AD is a cumulative indicator, meaning it has infinite memory. Today's value depends on the sum of all yesterdays.
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The core mechanic is the **Money Flow Multiplier (MFM)**, also known as the Close Location Value (CLV). This value ranges from -1 to +1:
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* **+1**: Close = High (Maximum Accumulation)
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* **-1**: Close = Low (Maximum Distribution)
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* **0**: Close is exactly in the middle
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This multiplier is then applied to the volume to determine the "Money Flow Volume" for the period.
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## Mathematical Foundation
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### 1. Money Flow Multiplier (MFM)
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$$
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MFM = \frac{(Close - Low) - (High - Close)}{High - Low}
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$$
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### 2. Money Flow Volume (MFV)
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$$
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MFV = MFM \times Volume
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$$
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### 3. Accumulation/Distribution Line (AD)
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$$
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AD_t = AD_{t-1} + MFV_t
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$$
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## Performance Profile
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### Operation Count (Streaming Mode)
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AD computes Money Flow Multiplier (MFM) from bar data, multiplies by volume, and accumulates cumulatively — O(1).
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| Operation | Count | Cost (cycles) | Subtotal |
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| :--- | :---: | :---: | :---: |
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| MFM = ((C-L)-(H-C)) / (H-L) | 1 | 5 cy | ~5 cy |
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| MFV = MFM * Volume | 1 | 3 cy | ~3 cy |
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| AD += MFV (cumulative sum) | 1 | 1 cy | ~1 cy |
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| Zero guard on H-L | 1 | 2 cy | ~2 cy |
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| NaN guard + state update | 1 | 2 cy | ~2 cy |
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| **Total** | **O(1)** | — | **~13 cy** |
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O(1) cumulative indicator — no window, no buffer. Throughput ~4 ns/bar. Division is the critical path (H-L guard prevents divide-by-zero on doji bars).
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| Metric | Score | Notes |
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| :--- | :--- | :--- |
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| **Throughput** | 10 | High; O(1) calculation with simple arithmetic. |
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| **Allocations** | 0 | Zero-allocation in hot paths. |
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| **Complexity** | O(1) | Constant time per update. |
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| **Accuracy** | 10 | Matches all standard libraries exactly. |
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| **Timeliness** | 10 | No lag; updates immediately with each bar. |
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| **Overshoot** | N/A | Cumulative indicator; concept doesn't apply. |
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| **Smoothness** | 2 | Jagged; reflects raw volume and price location. |
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## Validation
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| Library | Status | Notes |
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| :--- | :--- | :--- |
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| **QuanTAlib** | ✅ | Validated. |
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| **TA-Lib** | ✅ | Matches `TA_AD` exactly. |
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| **Skender** | ✅ | Matches `GetAd` exactly. |
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| **Tulip** | ✅ | Matches `ad` exactly. |
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| **Ooples** | ✅ | Matches `CalculateAccumulationDistributionLine`. |
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### Common Pitfalls
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* **Gaps**: AD ignores gaps. If a stock gaps up but closes near its low, AD will register distribution, even if the price is higher than yesterday.
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* **Scale**: The absolute value of AD is meaningless; it depends on the start date of the data. Only the *trend* and *divergence* matter.
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* **Volume Spikes**: A single bad data point with erroneous volume can permanently skew the AD. Sanitize your data. |