Pratik Bhadane
82 lines
2.8 KiB
Rust
82 lines
2.8 KiB
Rust
use super::common::rolling_linreg_apply;
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use crate::validation;
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use numpy::{IntoPyArray, PyArray1, PyReadonlyArray1};
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use pyo3::prelude::*;
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use std::f64::consts::PI;
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/// Linear regression fitted value at the last point of the window.
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#[pyfunction]
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#[pyo3(signature = (close, timeperiod = 14))]
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pub fn linearreg<'py>(
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py: Python<'py>,
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close: PyReadonlyArray1<'py, f64>,
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timeperiod: usize,
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) -> PyResult<Bound<'py, PyArray1<f64>>> {
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validation::validate_timeperiod(timeperiod, "timeperiod", 1)?;
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let prices = close.as_slice()?;
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let last_x = (timeperiod - 1) as f64;
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let result = rolling_linreg_apply(prices, timeperiod, |slope, intercept| {
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intercept + slope * last_x
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});
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Ok(result.into_pyarray(py))
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}
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/// Slope of the rolling linear regression line.
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#[pyfunction]
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#[pyo3(signature = (close, timeperiod = 14))]
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pub fn linearreg_slope<'py>(
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py: Python<'py>,
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close: PyReadonlyArray1<'py, f64>,
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timeperiod: usize,
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) -> PyResult<Bound<'py, PyArray1<f64>>> {
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validation::validate_timeperiod(timeperiod, "timeperiod", 1)?;
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let prices = close.as_slice()?;
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let result = rolling_linreg_apply(prices, timeperiod, |slope, _| slope);
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Ok(result.into_pyarray(py))
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}
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/// Intercept of the rolling linear regression line.
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#[pyfunction]
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#[pyo3(signature = (close, timeperiod = 14))]
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pub fn linearreg_intercept<'py>(
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py: Python<'py>,
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close: PyReadonlyArray1<'py, f64>,
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timeperiod: usize,
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) -> PyResult<Bound<'py, PyArray1<f64>>> {
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validation::validate_timeperiod(timeperiod, "timeperiod", 1)?;
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let prices = close.as_slice()?;
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let result = rolling_linreg_apply(prices, timeperiod, |_, intercept| intercept);
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Ok(result.into_pyarray(py))
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}
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/// Angle of the regression line in degrees (atan(slope) * 180/π).
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#[pyfunction]
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#[pyo3(signature = (close, timeperiod = 14))]
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pub fn linearreg_angle<'py>(
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py: Python<'py>,
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close: PyReadonlyArray1<'py, f64>,
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timeperiod: usize,
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) -> PyResult<Bound<'py, PyArray1<f64>>> {
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validation::validate_timeperiod(timeperiod, "timeperiod", 1)?;
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let prices = close.as_slice()?;
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let result = rolling_linreg_apply(prices, timeperiod, |slope, _| slope.atan() * 180.0 / PI);
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Ok(result.into_pyarray(py))
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}
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/// Time series forecast: linear regression extrapolated one period ahead.
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#[pyfunction]
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#[pyo3(signature = (close, timeperiod = 14))]
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pub fn tsf<'py>(
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py: Python<'py>,
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close: PyReadonlyArray1<'py, f64>,
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timeperiod: usize,
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) -> PyResult<Bound<'py, PyArray1<f64>>> {
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validation::validate_timeperiod(timeperiod, "timeperiod", 1)?;
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let prices = close.as_slice()?;
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let forecast_x = timeperiod as f64;
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let result = rolling_linreg_apply(prices, timeperiod, |slope, intercept| {
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intercept + slope * forecast_x
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});
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Ok(result.into_pyarray(py))
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}
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