use std::fmt::Display;
use nautilus_model::position::Position;
use crate::{Returns, statistic::PortfolioStatistic};
#[repr(C)]
#[derive(Debug, Clone)]
#[cfg_attr(
feature = "python",
pyo3::pyclass(module = "nautilus_trader.core.nautilus_pyo3.analysis", from_py_object)
)]
#[cfg_attr(
feature = "python",
pyo3_stub_gen::derive::gen_stub_pyclass(module = "nautilus_trader.analysis")
)]
pub struct MinWinner {}
impl Display for MinWinner {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
write!(f, "Min Winner")
}
}
impl PortfolioStatistic for MinWinner {
type Item = f64;
fn name(&self) -> String {
self.to_string()
}
fn calculate_from_realized_pnls(&self, realized_pnls: &[f64]) -> Option<Self::Item> {
if realized_pnls.is_empty() {
return Some(f64::NAN);
}
let winners: Vec<f64> = realized_pnls
.iter()
.filter(|&&pnl| pnl > 0.0)
.copied()
.collect();
if winners.is_empty() {
return Some(f64::NAN);
}
winners
.iter()
.min_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
.copied()
}
fn calculate_from_returns(&self, _returns: &Returns) -> Option<Self::Item> {
None
}
fn calculate_from_positions(&self, _positions: &[Position]) -> Option<Self::Item> {
None
}
}
#[cfg(test)]
mod tests {
use nautilus_core::approx_eq;
use rstest::rstest;
use super::*;
#[rstest]
fn test_empty_pnls() {
let min_winner = MinWinner {};
let result = min_winner.calculate_from_realized_pnls(&[]);
assert!(result.is_some());
assert!(result.unwrap().is_nan());
}
#[rstest]
fn test_no_winning_trades() {
let min_winner = MinWinner {};
let realized_pnls = vec![-100.0, -50.0, -200.0];
let result = min_winner.calculate_from_realized_pnls(&realized_pnls);
assert!(result.is_some());
assert!(result.unwrap().is_nan());
}
#[rstest]
fn test_all_winning_trades() {
let min_winner = MinWinner {};
let realized_pnls = vec![100.0, 50.0, 200.0];
let result = min_winner.calculate_from_realized_pnls(&realized_pnls);
assert!(result.is_some());
assert!(approx_eq!(f64, result.unwrap(), 50.0, epsilon = 1e-9)); }
#[rstest]
fn test_mixed_trades() {
let min_winner = MinWinner {};
let realized_pnls = vec![100.0, -50.0, 200.0, -100.0];
let result = min_winner.calculate_from_realized_pnls(&realized_pnls);
assert!(result.is_some());
assert!(approx_eq!(f64, result.unwrap(), 100.0, epsilon = 1e-9)); }
#[rstest]
fn test_single_winning_trade() {
let min_winner = MinWinner {};
let realized_pnls = vec![50.0];
let result = min_winner.calculate_from_realized_pnls(&realized_pnls);
assert!(result.is_some());
assert!(approx_eq!(f64, result.unwrap(), 50.0, epsilon = 1e-9));
}
#[rstest]
fn test_name() {
let min_winner = MinWinner {};
assert_eq!(min_winner.name(), "Min Winner");
}
}