kingchenc
d2f99efd78
The original taxonomy was four classical families plus a statistics group, with the F1-F12 expansion slotted in as sub-categories. This regroups the whole 71-indicator catalogue into eight top-level families, each with at least five members: Moving Averages (12), Momentum Oscillators (13), Trend & Directional (9), Price Oscillators (5), Volatility & Bands (12), Trailing Stops (5), Volume (9), Price Statistics (7). - Wiki: docs/wiki/indicators/ reorganised into eight family folders; all 71 indicator pages moved with `git mv`. Every internal cross-link is normalised to `../<family>/Indicator-X.md`, each page's `Family` field is set to its new family, and two pre-existing `../Indicator-Chaining.md` links (should have been `../../`) are corrected. A link check confirms every relative wiki link resolves. - Indicators-Overview.md fully rewritten around the eight families; Home.md indicator reference and the README family table follow suit. - Warmup-Periods.md gains the eight F13 indicators; CHANGELOG records the 46-indicator expansion (25 -> 71) and the eight-family taxonomy. - Tests: Node indicators.test.js and Python test_new_indicators.py cover all eight new indicators (Node 91/91, Python 117/117 green). cargo fmt + clippy (core/wickra/data/wasm/node) clean; 508 core tests, 25 data tests and 74 doctests green.
144 lines
4.4 KiB
Markdown
144 lines
4.4 KiB
Markdown
# NATR
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> Normalized Average True Range — ATR expressed as a percentage of price, so
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> volatility is comparable across instruments.
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## Quick reference
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| Field | Value |
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|-------|-------|
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| Family | Volatility & Bands |
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| Input type | `Candle` (uses `high`, `low`, `close`) |
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| Output type | `f64` |
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| Output range | `[0, ∞)` (percent) |
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| Default parameters | `period = 14` (Python) |
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| Warmup period | `period` |
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| Interpretation | Average true range as a percent of the close. |
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## Formula
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```
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NATR = 100 · ATR(period) / close
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```
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[`Atr`](../volatility-bands/Indicator-Atr.md) measures volatility in raw price units — a `2.0`
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ATR is large on a $10 stock and tiny on a $5000 index. Dividing by the
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current close converts it to a percentage, so a NATR of `2.0` always
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means "the average true range is 2 % of price". That makes NATR readings
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comparable across a portfolio, and stop or position-size rules expressed
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as a NATR multiple behave consistently regardless of price level.
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## Parameters
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| Name | Type | Default | Valid range | Description |
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|----------|---------|---------------|-------------|-------------|
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| `period` | `usize` | `14` (Python) | `>= 1` | Wilder smoothing period of the underlying ATR. `0` errors with `Error::PeriodZero`. |
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The Python binding defaults `period` to `14`.
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## Inputs / Outputs
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From `crates/wickra-core/src/indicators/natr.rs`:
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```rust
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impl Indicator for Natr {
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type Input = Candle;
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type Output = f64;
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// update(&mut self, input: Candle) -> Option<f64>
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}
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```
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`NATR` is a **candle-input** indicator: it reads `high`, `low` and
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`close`. In Python the streaming `update` accepts a 6-tuple or a dict; the
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batch helper takes `high`, `low`, `close` numpy arrays. Node and WASM
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expose `update(high, low, close)` and `batch(high, low, close)`.
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## Warmup
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`Natr::new(period).warmup_period() == period` — identical to the
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underlying `Atr`, which is Wilder-seeded over `period` true ranges.
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## Edge cases
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- **Flat market.** A market with no range has `ATR = 0`, so `NATR = 0`
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(`flat_market_yields_zero` pins this).
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- **Zero close.** NATR is undefined against a `0.0` close; the indicator
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reports `0.0` for that bar.
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- **Identity.** NATR equals `100 · ATR / close` bar for bar
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(`natr_is_atr_over_close_as_percent` pins this).
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- **Reset.** `natr.reset()` clears the underlying ATR.
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## Examples
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### Rust
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```rust
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use wickra::{BatchExt, Candle, Indicator, Natr};
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fn main() -> Result<(), Box<dyn std::error::Error>> {
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let mut natr = Natr::new(14)?;
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let candles: Vec<Candle> = (0..40)
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.map(|i| {
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let p = 100.0 + f64::from(i);
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Candle::new(p, p + 2.0, p - 2.0, p, 10.0, i64::from(i)).unwrap()
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})
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.collect();
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let out = natr.batch(&candles);
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println!("warmup_period = {}", natr.warmup_period());
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println!("last = {:?}", out.last().unwrap());
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Ok(())
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}
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```
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### Python
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```python
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import numpy as np
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import wickra as ta
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natr = ta.NATR(14)
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high = np.arange(102.0, 142.0)
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low = high - 4.0
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close = high - 2.0
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print(natr.batch(high, low, close)[-1])
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```
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### Node
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```javascript
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const ta = require('wickra');
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const natr = new ta.NATR(14);
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const high = Array.from({ length: 40 }, (_, i) => 102 + i);
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const low = high.map((h) => h - 4);
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const close = high.map((h) => h - 2);
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console.log(natr.batch(high, low, close).at(-1));
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```
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## Interpretation
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`Natr` is the tool of choice whenever an ATR-based rule must work across
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instruments or across long stretches of time where the price level
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drifts. A volatility filter like "skip entries when NATR > 5" or a stop
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at "entry − 3 × NATR %" stays meaningful on any symbol. Use raw
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[`Atr`](../volatility-bands/Indicator-Atr.md) only when you specifically want the answer in
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price units (e.g. to place a stop a fixed number of points away).
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## Common pitfalls
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- **Feeding it scalar prices.** It needs `high`/`low`/`close`.
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- **Confusing it with ATR.** NATR is a percentage; an ATR-multiple stop
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and a NATR-multiple stop are different distances.
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## References
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NATR is the percentage-normalised ATR as implemented by TA-Lib (`NATR`);
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the underlying ATR is Wilder's from *New Concepts in Technical Trading
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Systems* (1978).
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## See also
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- [Indicator-Atr.md](../volatility-bands/Indicator-Atr.md) — the price-unit original.
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- [Indicator-HistoricalVolatility.md](../volatility-bands/Indicator-HistoricalVolatility.md) —
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return-based annualised volatility.
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- [Indicators-Overview.md](../../Indicators-Overview.md) — the full taxonomy.
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