Pratik Bhadane
436954138f
Update version numbers across Rust, Python, and documentation files to 1.1.0. Enhance the .gitignore to include macOS dSYM files and plans directory. Introduce new dependencies in the Rust core library and update the README to reflect recent performance benchmarks and backtesting engine capabilities. Add new artifacts to the benchmarks manifest and improve documentation for the backtesting engine API.
80 lines
3.0 KiB
Rust
80 lines
3.0 KiB
Rust
//! Performance attribution (thin PyO3 wrapper over ferro_ta_core::attribution).
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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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use crate::validation;
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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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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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}
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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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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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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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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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Ok((labels.into_pyarray(py), contributions.into_pyarray(py)))
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}
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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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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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Ok((pnl.into_pyarray(py), hold.into_pyarray(py)))
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}
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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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m.add_function(wrap_pyfunction!(extract_trades, m)?)?;
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Ok(())
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}
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