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.
147 lines
3.9 KiB
Markdown
147 lines
3.9 KiB
Markdown
# ChoppinessIndex
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> Choppiness Index — is the market trending or just chopping sideways?
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## Quick reference
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| Field | Value |
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|-------|-------|
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| Family | Trend & Directional |
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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, 100]` (typical) |
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| Default parameters | `period = 14` (Python) |
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| Warmup period | `period` |
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| Interpretation | High = choppy/ranging, low = trending; `61.8` / `38.2` thresholds. |
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## Formula
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```
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CI = 100 · log10( Σ(TR, n) / (highest_high(n) − lowest_low(n)) ) / log10(n)
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```
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The ratio compares the distance price *actually travelled* (the summed true
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range) with the *net ground it covered* (the high-low span of the window). A
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clean trend travels almost exactly its span, so the ratio is near `1` and `CI`
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near `0`; a choppy market criss-crosses far more than its span, so the ratio
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is large and `CI` climbs toward `100`. The conventional reading is `CI > 61.8`
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ranging, `CI < 38.2` trending.
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## Parameters
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`period` — the lookback window. Must be at least `2` (the `log10(period)`
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denominator is zero for `period == 1`). The Python binding defaults it to `14`.
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## Inputs / Outputs
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From `crates/wickra-core/src/indicators/choppiness_index.rs`:
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```rust
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impl Indicator for ChoppinessIndex {
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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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`ChoppinessIndex` is a **candle-input** indicator that reads `high`, `low` and
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`close` (the close drives the true range across bar gaps). Python's streaming
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`update` accepts a 6-tuple or a dict; the batch helper takes `high`, `low`,
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`close` numpy arrays. Node and WASM expose `update(high, low, close)` and the
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matching `batch`.
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## Warmup
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`ChoppinessIndex::new(14).warmup_period() == 14`. The first value lands once
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the window holds a full `period` bars.
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## Edge cases
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- **Flat window.** A window with `high == low` everywhere has a zero span;
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`CI` is defined as `100` (maximal choppiness).
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- **Steady trend.** A one-directional march reads well below `50`.
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- **`period < 2`.** Rejected at construction.
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- **Reset.** `ci.reset()` clears the true-range and high/low windows.
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## Examples
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### Rust
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```rust
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use wickra::{BatchExt, Candle, Indicator, ChoppinessIndex};
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fn main() -> Result<(), Box<dyn std::error::Error>> {
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let mut ci = ChoppinessIndex::new(2)?;
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// Two H=11 L=9 C=10 bars: ΣTR = 4, span = 2 -> CI = 100·log10(2)/log10(2).
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let out = ci.batch(&[
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Candle::new(10.0, 11.0, 9.0, 10.0, 1.0, 0)?,
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Candle::new(10.0, 11.0, 9.0, 10.0, 1.0, 1)?,
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]);
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println!("{:?}", out);
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Ok(())
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}
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```
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Output:
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```
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[None, Some(100.0)]
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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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ci = ta.ChoppinessIndex(2)
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high = np.array([11.0, 11.0])
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low = np.array([9.0, 9.0])
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close = np.array([10.0, 10.0])
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print(ci.batch(high, low, close))
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```
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Output:
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```
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[ nan 100.]
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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 ci = new ta.ChoppinessIndex(2);
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console.log(ci.batch([11, 11], [9, 9], [10, 10]));
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```
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Output:
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```
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[ NaN, 100 ]
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```
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## Interpretation
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The Choppiness Index is not directional — it does not say *which way* price is
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going, only *whether* it is going anywhere. Use it as a regime filter: above
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`61.8` favour mean-reversion / range tactics; below `38.2` favour
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trend-following. It pairs naturally with a directional indicator that picks
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the side once a trend is confirmed.
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## Common pitfalls
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- **Expecting a direction.** It has none — combine it with a trend indicator.
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- **Tiny periods.** `period = 2` is allowed but noisy; `14` is conventional.
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## References
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E. W. Dreiss' Choppiness Index; the summed-true-range formulation here is the
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standard one.
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## See also
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- [Indicator-VerticalHorizontalFilter.md](../trend-directional/Indicator-VerticalHorizontalFilter.md)
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— the same trending-vs-ranging question on an inverted scale.
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- [Indicators-Overview.md](../../Indicators-Overview.md) — the full taxonomy.
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