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160 lines (144 loc) · 4.27 KB
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use nanobook::optimize;
use pyo3::prelude::*;
use pyo3::types::PyDict;
fn to_weights_dict<'py>(
py: Python<'py>,
symbols: &[String],
weights: Vec<f64>,
) -> PyResult<Bound<'py, PyDict>> {
let out = PyDict::new(py);
if symbols.len() != weights.len() {
return Ok(out);
}
for (s, w) in symbols.iter().zip(weights.iter()) {
out.set_item(s, *w)?;
}
Ok(out)
}
fn sanitize_symbols(symbols: Vec<String>) -> Vec<String> {
// Preserve order but reject empty names.
symbols
.into_iter()
.filter(|s| !s.trim().is_empty())
.collect()
}
#[pyfunction]
pub fn optimize_min_variance(
py: Python<'_>,
returns_matrix: Vec<Vec<f64>>,
symbols: Vec<String>,
) -> PyResult<Py<PyAny>> {
let symbols = sanitize_symbols(symbols);
let w = py.detach(|| optimize::optimize_min_variance(&returns_matrix));
Ok(to_weights_dict(py, &symbols, w)?.into_any().unbind())
}
#[pyfunction]
pub fn py_optimize_min_variance(
py: Python<'_>,
returns_matrix: Vec<Vec<f64>>,
symbols: Vec<String>,
) -> PyResult<Py<PyAny>> {
optimize_min_variance(py, returns_matrix, symbols)
}
#[pyfunction]
#[pyo3(signature = (returns_matrix, symbols, risk_free=0.0))]
pub fn optimize_max_sharpe(
py: Python<'_>,
returns_matrix: Vec<Vec<f64>>,
symbols: Vec<String>,
risk_free: f64,
) -> PyResult<Py<PyAny>> {
let symbols = sanitize_symbols(symbols);
let w = py.detach(|| optimize::optimize_max_sharpe(&returns_matrix, risk_free));
Ok(to_weights_dict(py, &symbols, w)?.into_any().unbind())
}
#[pyfunction]
#[pyo3(signature = (returns_matrix, symbols, risk_free=0.0))]
pub fn py_optimize_max_sharpe(
py: Python<'_>,
returns_matrix: Vec<Vec<f64>>,
symbols: Vec<String>,
risk_free: f64,
) -> PyResult<Py<PyAny>> {
optimize_max_sharpe(py, returns_matrix, symbols, risk_free)
}
#[pyfunction]
pub fn optimize_risk_parity(
py: Python<'_>,
returns_matrix: Vec<Vec<f64>>,
symbols: Vec<String>,
) -> PyResult<Py<PyAny>> {
let symbols = sanitize_symbols(symbols);
let w = py.detach(|| optimize::optimize_risk_parity(&returns_matrix));
Ok(to_weights_dict(py, &symbols, w)?.into_any().unbind())
}
#[pyfunction]
pub fn py_optimize_risk_parity(
py: Python<'_>,
returns_matrix: Vec<Vec<f64>>,
symbols: Vec<String>,
) -> PyResult<Py<PyAny>> {
optimize_risk_parity(py, returns_matrix, symbols)
}
#[pyfunction]
#[pyo3(signature = (returns_matrix, symbols, alpha=0.95))]
pub fn inverse_cvar_weights(
py: Python<'_>,
returns_matrix: Vec<Vec<f64>>,
symbols: Vec<String>,
alpha: f64,
) -> PyResult<Py<PyAny>> {
let symbols = sanitize_symbols(symbols);
let w = py.detach(|| optimize::inverse_cvar_weights(&returns_matrix, alpha));
Ok(to_weights_dict(py, &symbols, w)?.into_any().unbind())
}
#[pyfunction]
#[pyo3(signature = (returns_matrix, symbols, alpha=0.95))]
pub fn py_inverse_cvar_weights(
py: Python<'_>,
returns_matrix: Vec<Vec<f64>>,
symbols: Vec<String>,
alpha: f64,
) -> PyResult<Py<PyAny>> {
inverse_cvar_weights(py, returns_matrix, symbols, alpha)
}
#[pyfunction]
#[pyo3(signature = (returns_matrix, symbols, alpha=0.95))]
pub fn inverse_cdar_weights(
py: Python<'_>,
returns_matrix: Vec<Vec<f64>>,
symbols: Vec<String>,
alpha: f64,
) -> PyResult<Py<PyAny>> {
let symbols = sanitize_symbols(symbols);
let w = py.detach(|| optimize::inverse_cdar_weights(&returns_matrix, alpha));
Ok(to_weights_dict(py, &symbols, w)?.into_any().unbind())
}
#[pyfunction]
#[pyo3(signature = (returns_matrix, symbols, alpha=0.95))]
pub fn py_inverse_cdar_weights(
py: Python<'_>,
returns_matrix: Vec<Vec<f64>>,
symbols: Vec<String>,
alpha: f64,
) -> PyResult<Py<PyAny>> {
inverse_cdar_weights(py, returns_matrix, symbols, alpha)
}
#[pyfunction]
pub fn optimize_hrp(
py: Python<'_>,
returns_matrix: Vec<Vec<f64>>,
symbols: Vec<String>,
) -> PyResult<Py<PyAny>> {
let symbols = sanitize_symbols(symbols);
let w = py.detach(|| optimize::optimize_hrp(&returns_matrix));
Ok(to_weights_dict(py, &symbols, w)?.into_any().unbind())
}
#[pyfunction]
pub fn py_optimize_hrp(
py: Python<'_>,
returns_matrix: Vec<Vec<f64>>,
symbols: Vec<String>,
) -> PyResult<Py<PyAny>> {
optimize_hrp(py, returns_matrix, symbols)
}