stochastic-rs-quant 2.3.0

Quantitative finance: pricing, calibration, vol surfaces, instruments.
Documentation 
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use pyo3::exceptions::PyValueError;
use pyo3::prelude::*;

/// Value-at-Risk with Gaussian / historical / Monte-Carlo methods.
#[pyclass(name = "VaR", unsendable)]
pub struct PyVaR {
  value: f64,
  method: String,
}

#[pymethods]
impl PyVaR {
  /// `method`: one of "gaussian" / "historical" / "monte_carlo".
  /// `orientation`: "pnl" (default — losses are `-x`) or "loss".
  #[new]
  #[pyo3(signature = (samples, confidence=0.99, method="historical", orientation="pnl"))]
  fn new<'py>(
    samples: numpy::PyReadonlyArray1<'py, f64>,
    confidence: f64,
    method: &str,
    orientation: &str,
  ) -> PyResult<Self> {
    use crate::risk::var::PnlOrLoss;
    use crate::risk::var::VarMethod;
    let m = match method.to_ascii_lowercase().as_str() {
      "gaussian" => VarMethod::Gaussian,
      "historical" => VarMethod::Historical,
      "monte_carlo" | "mc" => VarMethod::MonteCarlo,
      o => {
        return Err(PyValueError::new_err(format!(
          "method must be one of gaussian/historical/monte_carlo, got '{o}'"
        )));
      }
    };
    let pol = match orientation.to_ascii_lowercase().as_str() {
      "pnl" => PnlOrLoss::Pnl,
      "loss" => PnlOrLoss::Loss,
      o => {
        return Err(PyValueError::new_err(format!(
          "orientation must be 'pnl' or 'loss', got '{o}'"
        )));
      }
    };
    let value = crate::risk::var::value_at_risk(samples.as_array(), confidence, pol, m);
    Ok(Self {
      value,
      method: format!("{m:?}"),
    })
  }

  #[getter]
  fn value(&self) -> f64 {
    self.value
  }
  #[getter]
  fn method(&self) -> String {
    self.method.clone()
  }
}

#[pyclass(name = "ExpectedShortfall", unsendable)]
pub struct PyExpectedShortfall {
  value: f64,
}

#[pymethods]
impl PyExpectedShortfall {
  #[new]
  #[pyo3(signature = (samples, confidence=0.99, method="historical", orientation="pnl"))]
  fn new<'py>(
    samples: numpy::PyReadonlyArray1<'py, f64>,
    confidence: f64,
    method: &str,
    orientation: &str,
  ) -> PyResult<Self> {
    use crate::risk::var::PnlOrLoss;
    use crate::risk::var::VarMethod;
    let m = match method.to_ascii_lowercase().as_str() {
      "gaussian" => VarMethod::Gaussian,
      "historical" => VarMethod::Historical,
      "monte_carlo" | "mc" => VarMethod::MonteCarlo,
      o => {
        return Err(PyValueError::new_err(format!(
          "method must be one of gaussian/historical/monte_carlo, got '{o}'"
        )));
      }
    };
    let pol = match orientation.to_ascii_lowercase().as_str() {
      "pnl" => PnlOrLoss::Pnl,
      "loss" => PnlOrLoss::Loss,
      o => {
        return Err(PyValueError::new_err(format!(
          "orientation must be 'pnl' or 'loss', got '{o}'"
        )));
      }
    };
    let value =
      crate::risk::expected_shortfall::expected_shortfall(samples.as_array(), confidence, pol, m);
    Ok(Self { value })
  }

  #[getter]
  fn value(&self) -> f64 {
    self.value
  }
}

#[pyclass(name = "DrawdownStats", unsendable)]
pub struct PyDrawdownStats {
  inner: crate::risk::drawdown::DrawdownStats<f64>,
}

#[pymethods]
impl PyDrawdownStats {
  #[new]
  fn new<'py>(equity: numpy::PyReadonlyArray1<'py, f64>) -> Self {
    Self {
      inner: crate::risk::drawdown::DrawdownStats::from_equity(equity.as_array()),
    }
  }

  #[getter]
  fn max(&self) -> f64 {
    self.inner.max
  }
  #[getter]
  fn max_index(&self) -> usize {
    self.inner.max_index
  }
  #[getter]
  fn longest_duration(&self) -> usize {
    self.inner.longest_duration
  }
  #[getter]
  fn average(&self) -> f64 {
    self.inner.average
  }

  fn series<'py>(&self, py: Python<'py>) -> pyo3::Bound<'py, numpy::PyArray1<f64>> {
    use numpy::IntoPyArray;
    self.inner.series.clone().into_pyarray(py)
  }
}