2026-03-30 12:45:52 +05:30
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//! Performance attribution (thin PyO3 wrapper over ferro_ta_core::attribution).
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2026-03-23 23:34:28 +05:30
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use numpy::{IntoPyArray, PyArray1, PyReadonlyArray1};
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use pyo3::exceptions::PyValueError;
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use pyo3::prelude::*;
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2026-03-30 12:45:52 +05:30
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use crate::validation;
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2026-03-23 23:34:28 +05:30
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/// Compute trade-level statistics from trade PnL and hold durations.
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#[pyfunction]
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pub fn trade_stats(
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pnl: PyReadonlyArray1<'_, f64>,
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hold_bars: PyReadonlyArray1<'_, f64>,
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) -> PyResult<(f64, f64, f64, f64, f64)> {
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let p = pnl.as_slice()?;
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let h = hold_bars.as_slice()?;
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let n = p.len();
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if n == 0 {
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return Err(PyValueError::new_err("pnl must be non-empty"));
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}
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2026-03-30 12:45:52 +05:30
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validation::validate_equal_length(&[(n, "pnl"), (h.len(), "hold_bars")])?;
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Ok(ferro_ta_core::attribution::trade_stats(p, h))
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2026-03-23 23:34:28 +05:30
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}
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/// Group per-bar returns by month index and sum each month's contribution.
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#[pyfunction]
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#[allow(clippy::type_complexity)]
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pub fn monthly_contribution<'py>(
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py: Python<'py>,
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bar_returns: PyReadonlyArray1<'py, f64>,
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month_index: PyReadonlyArray1<'py, i64>,
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) -> PyResult<(Bound<'py, PyArray1<i64>>, Bound<'py, PyArray1<f64>>)> {
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let ret = bar_returns.as_slice()?;
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let mi = month_index.as_slice()?;
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let n = ret.len();
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2026-03-30 12:45:52 +05:30
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validation::validate_equal_length(&[(n, "bar_returns"), (mi.len(), "month_index")])?;
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let (months, contributions) = ferro_ta_core::attribution::monthly_contribution(ret, mi);
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2026-03-23 23:34:28 +05:30
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Ok((months.into_pyarray(py), contributions.into_pyarray(py)))
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}
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/// Attribute per-bar returns to each signal label.
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#[pyfunction]
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#[allow(clippy::type_complexity)]
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pub fn signal_attribution<'py>(
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py: Python<'py>,
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bar_returns: PyReadonlyArray1<'py, f64>,
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signal_labels: PyReadonlyArray1<'py, i64>,
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) -> PyResult<(Bound<'py, PyArray1<i64>>, Bound<'py, PyArray1<f64>>)> {
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let ret = bar_returns.as_slice()?;
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let lbl = signal_labels.as_slice()?;
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let n = ret.len();
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2026-03-30 12:45:52 +05:30
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validation::validate_equal_length(&[(n, "bar_returns"), (lbl.len(), "signal_labels")])?;
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let (labels, contributions) = ferro_ta_core::attribution::signal_attribution(ret, lbl);
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2026-03-23 23:34:28 +05:30
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Ok((labels.into_pyarray(py), contributions.into_pyarray(py)))
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}
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2026-03-24 14:28:51 +05:30
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/// Extract trade-level pnl and hold durations from positions and strategy returns.
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#[pyfunction]
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#[allow(clippy::type_complexity)]
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pub fn extract_trades<'py>(
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py: Python<'py>,
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positions: PyReadonlyArray1<'py, f64>,
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strategy_returns: PyReadonlyArray1<'py, f64>,
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) -> PyResult<(Bound<'py, PyArray1<f64>>, Bound<'py, PyArray1<f64>>)> {
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let pos = positions.as_slice()?;
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let ret = strategy_returns.as_slice()?;
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let n = pos.len();
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2026-03-30 12:45:52 +05:30
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validation::validate_equal_length(&[(n, "positions"), (ret.len(), "strategy_returns")])?;
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let (pnl, hold) = ferro_ta_core::attribution::extract_trades(pos, ret);
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2026-03-24 14:28:51 +05:30
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Ok((pnl.into_pyarray(py), hold.into_pyarray(py)))
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}
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2026-03-23 23:34:28 +05:30
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pub fn register(m: &Bound<'_, PyModule>) -> PyResult<()> {
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m.add_function(wrap_pyfunction!(trade_stats, m)?)?;
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m.add_function(wrap_pyfunction!(monthly_contribution, m)?)?;
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m.add_function(wrap_pyfunction!(signal_attribution, m)?)?;
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2026-03-24 14:28:51 +05:30
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m.add_function(wrap_pyfunction!(extract_trades, m)?)?;
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2026-03-23 23:34:28 +05:30
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Ok(())
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}
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