nautilus_indicators/average/
wma.rsuse std::fmt::Display;
use nautilus_core::correctness::{check_predicate_true, FAILED};
use nautilus_model::{
data::{Bar, QuoteTick, TradeTick},
enums::PriceType,
};
use crate::indicator::{Indicator, MovingAverage};
#[repr(C)]
#[derive(Debug)]
#[cfg_attr(
feature = "python",
pyo3::pyclass(module = "nautilus_trader.core.nautilus_pyo3.indicators")
)]
pub struct WeightedMovingAverage {
pub period: usize,
pub weights: Vec<f64>,
pub price_type: PriceType,
pub value: f64,
pub initialized: bool,
pub inputs: Vec<f64>,
has_inputs: bool,
}
impl Display for WeightedMovingAverage {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
write!(f, "{}({},{:?})", self.name(), self.period, self.weights)
}
}
impl WeightedMovingAverage {
#[must_use]
pub fn new(period: usize, weights: Vec<f64>, price_type: Option<PriceType>) -> Self {
Self::new_checked(period, weights, price_type).expect(FAILED)
}
pub fn new_checked(
period: usize,
weights: Vec<f64>,
price_type: Option<PriceType>,
) -> anyhow::Result<Self> {
check_predicate_true(
period == weights.len(),
"`period` must be equal to `weights` length",
)?;
Ok(Self {
period,
weights,
price_type: price_type.unwrap_or(PriceType::Last),
value: 0.0,
inputs: Vec::with_capacity(period),
initialized: false,
has_inputs: false,
})
}
fn weighted_average(&self) -> f64 {
let mut sum = 0.0;
let mut weight_sum = 0.0;
let reverse_weights: Vec<f64> = self.weights.iter().copied().rev().collect();
for (index, input) in self.inputs.iter().rev().enumerate() {
let weight = reverse_weights.get(index).unwrap();
sum += input * weight;
weight_sum += weight;
}
sum / weight_sum
}
}
impl Indicator for WeightedMovingAverage {
fn name(&self) -> String {
stringify!(WeightedMovingAverage).to_string()
}
fn has_inputs(&self) -> bool {
self.has_inputs
}
fn initialized(&self) -> bool {
self.initialized
}
fn handle_quote(&mut self, quote: &QuoteTick) {
self.update_raw(quote.extract_price(self.price_type).into());
}
fn handle_trade(&mut self, trade: &TradeTick) {
self.update_raw((&trade.price).into());
}
fn handle_bar(&mut self, bar: &Bar) {
self.update_raw((&bar.close).into());
}
fn reset(&mut self) {
self.value = 0.0;
self.has_inputs = false;
self.initialized = false;
self.inputs.clear();
}
}
impl MovingAverage for WeightedMovingAverage {
fn value(&self) -> f64 {
self.value
}
fn count(&self) -> usize {
self.inputs.len()
}
fn update_raw(&mut self, value: f64) {
if !self.has_inputs {
self.has_inputs = true;
self.inputs.push(value);
self.value = value;
return;
}
if self.inputs.len() == self.period {
self.inputs.remove(0);
}
self.inputs.push(value);
self.value = self.weighted_average();
if !self.initialized && self.count() >= self.period {
self.initialized = true;
}
}
}
#[cfg(test)]
mod tests {
use rstest::rstest;
use crate::{
average::wma::WeightedMovingAverage,
indicator::{Indicator, MovingAverage},
stubs::*,
};
#[rstest]
fn test_wma_initialized(indicator_wma_10: WeightedMovingAverage) {
let display_str = format!("{indicator_wma_10}");
assert_eq!(
display_str,
"WeightedMovingAverage(10,[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0])"
);
assert_eq!(indicator_wma_10.name(), "WeightedMovingAverage");
assert!(!indicator_wma_10.has_inputs());
assert!(!indicator_wma_10.initialized());
}
#[rstest]
#[should_panic]
fn test_different_weights_len_and_period_error() {
let _ = WeightedMovingAverage::new(10, vec![0.5, 0.5, 0.5], None);
}
#[rstest]
fn test_value_with_one_input(mut indicator_wma_10: WeightedMovingAverage) {
indicator_wma_10.update_raw(1.0);
assert_eq!(indicator_wma_10.value, 1.0);
}
#[rstest]
fn test_value_with_two_inputs_equal_weights() {
let mut wma = WeightedMovingAverage::new(2, vec![0.5, 0.5], None);
wma.update_raw(1.0);
wma.update_raw(2.0);
assert_eq!(wma.value, 1.5);
}
#[rstest]
fn test_value_with_four_inputs_equal_weights() {
let mut wma = WeightedMovingAverage::new(4, vec![0.25, 0.25, 0.25, 0.25], None);
wma.update_raw(1.0);
wma.update_raw(2.0);
wma.update_raw(3.0);
wma.update_raw(4.0);
assert_eq!(wma.value, 2.5);
}
#[rstest]
fn test_value_with_two_inputs(mut indicator_wma_10: WeightedMovingAverage) {
indicator_wma_10.update_raw(1.0);
indicator_wma_10.update_raw(2.0);
let result = 2.0f64.mul_add(1.0, 1.0 * 0.9) / 1.9;
assert_eq!(indicator_wma_10.value, result);
}
#[rstest]
fn test_value_with_three_inputs(mut indicator_wma_10: WeightedMovingAverage) {
indicator_wma_10.update_raw(1.0);
indicator_wma_10.update_raw(2.0);
indicator_wma_10.update_raw(3.0);
let result = 1.0f64.mul_add(0.8, 3.0f64.mul_add(1.0, 2.0 * 0.9)) / (1.0 + 0.9 + 0.8);
assert_eq!(indicator_wma_10.value, result);
}
#[rstest]
fn test_value_expected_with_exact_period(mut indicator_wma_10: WeightedMovingAverage) {
for i in 1..11 {
indicator_wma_10.update_raw(f64::from(i));
}
assert_eq!(indicator_wma_10.value, 7.0);
}
#[rstest]
fn test_value_expected_with_more_inputs(mut indicator_wma_10: WeightedMovingAverage) {
for i in 1..=11 {
indicator_wma_10.update_raw(f64::from(i));
}
assert_eq!(indicator_wma_10.value(), 8.000_000_000_000_002);
}
#[rstest]
fn test_reset(mut indicator_wma_10: WeightedMovingAverage) {
indicator_wma_10.update_raw(1.0);
indicator_wma_10.update_raw(2.0);
indicator_wma_10.reset();
assert_eq!(indicator_wma_10.value, 0.0);
assert_eq!(indicator_wma_10.count(), 0);
assert!(!indicator_wma_10.has_inputs);
assert!(!indicator_wma_10.initialized);
}
}