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.
201 lines
6.7 KiB
Markdown
201 lines
6.7 KiB
Markdown
# EMA
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> Exponential Moving Average with smoothing factor `α = 2 / (period + 1)`,
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> seeded from the SMA of the first `period` inputs (the TA-Lib convention).
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## Quick reference
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| Field | Value |
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|-------|-------|
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| Family | Moving Averages |
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| Input type | `f64` (single close) |
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| Output type | `f64` |
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| Output range | unbounded; tracks the input price scale |
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| Default parameters | `period` is required; or `Ema::with_alpha(α)` for a custom smoothing factor |
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| Warmup period | `period` |
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| Interpretation | Smoother, less laggy than `Sma` of the same length. |
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## Formula
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For `t >= period` (after warmup):
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```
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α = 2 / (period + 1)
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seed = (1 / period) * Σ_{i=0}^{period-1} price_i // SMA of first `period` inputs
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EMA_t = α * price_t + (1 - α) * EMA_{t-1} // recursive update
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```
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The first emitted value (at input `period`) is the seed itself, identical
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to `Sma::new(period)` on the same prefix. From input `period + 1` onward
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the recursive formula takes over. (`Ema::with_alpha(α)` skips the seed and
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uses the very first input as the initial state, so `warmup_period() == 1`
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in that mode — see the `with_alpha` method for details.)
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Wilder's smoothing (used by `Rsi`/`Atr`/`Adx`) uses `α = 1/period`
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instead; that is a different smoothing constant and a different
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indicator family.
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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` | none | `>= 1` | Window length used to derive `α`. `period = 0` errors with `Error::PeriodZero`. |
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| `α` (alternative constructor `Ema::with_alpha`) | `f64` | none | `(0.0, 1.0]` and finite | Custom smoothing factor; bypasses the period-derived α. Reported `period` is 1, `warmup_period() == 1`. Invalid `α` errors with `Error::InvalidPeriod`. |
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(The Python class `wickra.EMA(period)` does not set a `#[pyo3(signature)]`
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default; the period must be passed explicitly. `with_alpha` is Rust-only.)
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## Inputs / Outputs
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From `crates/wickra-core/src/indicators/ema.rs`:
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```rust
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impl Indicator for Ema {
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type Input = f64;
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type Output = f64;
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// update(&mut self, input: f64) -> Option<f64>
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}
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```
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Python streams as `float | None`, batches as a 1-D `numpy.ndarray`
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(`NaN` for warmup). Node streams as `number | null`, batches as
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`Array<number>` with `NaN` placeholders.
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## Warmup
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`Ema::new(period).warmup_period() == period`. The first non-empty value
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is the SMA of the first `period` inputs (the "seed"); from there each
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new input contributes `α * input + (1 − α) * previous`. The unit test
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`warmup_returns_none_until_seed` and the test
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`first_value_equals_sma_seed` pin this contract.
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This is the same warmup count as `Sma::new(period)` because the seed
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itself is an SMA — `Ema` is "no slower to start emitting than `Sma`, just
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more reactive afterwards".
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## Edge cases
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- **Constant series.** Feeding `[42.0; n]` returns `Some(42.0)` from input
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`period` onward; the seed is `42.0`, and `α · 42 + (1 − α) · 42 = 42`.
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The unit test `constant_series_converges_to_constant` pins this.
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- **NaN / infinity inputs.** The first line of `update` is
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`if !input.is_finite() { return self.state; }`. Non-finite inputs are
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silently dropped: they do not advance warmup, do not corrupt the
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state, and the previously emitted value (if any) is returned. The unit
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test `ignores_non_finite_input` pins this.
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- **Reset.** `ema.reset()` clears both the smoothed state and the warmup
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buffer; the next `update` starts a new warmup countdown.
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## Examples
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### Rust
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```rust
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use wickra::{BatchExt, Ema, Indicator};
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fn main() -> Result<(), Box<dyn std::error::Error>> {
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let mut ema = Ema::new(3)?;
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let out: Vec<Option<f64>> = ema.batch(&[1.0, 2.0, 3.0, 10.0]);
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println!("{:?}", out);
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println!("alpha = {}", ema.alpha());
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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, None, Some(2.0), Some(6.0)]
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alpha = 0.5
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```
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`period = 3` gives `α = 2 / 4 = 0.5`. The seed at input 3 is the SMA of
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`[1, 2, 3] = 2.0`; the next step is `0.5 · 10 + 0.5 · 2 = 6.0`. This
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matches the `step_after_seed_uses_alpha_formula` unit test.
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### Python
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```python
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import wickra as ta
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ema = ta.EMA(3)
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for x in [1.0, 2.0, 3.0, 10.0]:
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print(x, '->', ema.update(x))
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print('alpha:', ema.alpha)
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print('warmup_period:', ema.warmup_period())
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```
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Output:
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```
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1.0 -> None
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2.0 -> None
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3.0 -> 2.0
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10.0 -> 6.0
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alpha: 0.5
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warmup_period: 3
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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 ema = new ta.EMA(3);
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for (const x of [1, 2, 3, 10]) {
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console.log(x, '->', ema.update(x));
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}
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```
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Output:
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```
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1 -> null
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2 -> null
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3 -> 2
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10 -> 6
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```
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## Interpretation
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`Ema` is the "default" smoothed trend filter for most practitioners. The
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two main signals are price-vs-EMA and EMA-fast-vs-EMA-slow crossovers
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(the latter is the basis of `MacdIndicator`). Compared with `Sma` at the
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same period, `Ema` reacts faster to direction changes at the cost of
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slightly noisier output — useful when you care about the inflection
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point, not the long-run level.
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Prefer `Ema` over `Sma` when you want a single-line trend filter with
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moderate lag. Prefer `Dema` / `Tema` when the EMA lag is too much for
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your timeframe. Prefer `Hma` when you want lag reduction *and* a built-in
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noise filter (a triple WMA chain rather than a triple EMA chain).
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## Common pitfalls
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- **Confusing `α = 2/(n+1)` with Wilder's `α = 1/n`.** Wickra's `Ema`
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uses the TA-Lib convention `α = 2/(n+1)`. The same numerical period
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passed to `Rsi(14)` or `Atr(14)` uses `α = 1/14 ≈ 0.0714`, not
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`α = 2/15 ≈ 0.1333`. They are different smoothing schemes; comparing
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an EMA(14) line directly to the RSI/ATR's internal smoothing will not
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match. If you want a Wilder-style EMA, build it on top of `Ema` with
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the custom factor: `Ema::with_alpha(1.0 / 14.0)`.
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- **Assuming the first emitted EMA is "the EMA".** The first value is
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the SMA seed, not a recursively-smoothed EMA. The series only starts
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behaving like an EMA from input `period + 1` onward. For short series,
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this means the first emission tracks `Sma::new(period)` exactly — that
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is the intended behaviour, not a bug.
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## References
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The TA-Lib seeding convention used here ("EMA is seeded with an SMA")
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is documented in the TA-Lib source and replicated by virtually every
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commercial charting platform. The recursive form
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`EMA_t = α · price + (1 − α) · EMA_{t-1}` is the standard exponential
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smoothing identity attributed to Robert Brown (1956).
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## See also
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- [Indicator-Sma.md](../moving-averages/Indicator-Sma.md) — equal weights, identical seed.
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- [Indicator-Dema.md](../moving-averages/Indicator-Dema.md) — `2·EMA − EMA(EMA)`.
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- [Indicator-Tema.md](../moving-averages/Indicator-Tema.md) — `3·EMA − 3·EMA(EMA) + EMA(EMA(EMA))`.
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- [Indicators-Overview.md](../../Indicators-Overview.md) — the full taxonomy.
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