nautilus-analysis 0.55.0

// -------------------------------------------------------------------------------------------------
//  Copyright (C) 2015-2026 Nautech Systems Pty Ltd. All rights reserved.
//  https://nautechsystems.io
//
//  Licensed under the GNU Lesser General Public License Version 3.0 (the "License");
//  You may not use this file except in compliance with the License.
//  You may obtain a copy of the License at https://www.gnu.org/licenses/lgpl-3.0.en.html
//
//  Unless required by applicable law or agreed to in writing, software
//  distributed under the License is distributed on an "AS IS" BASIS,
//  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
//  See the License for the specific language governing permissions and
//  limitations under the License.
// -------------------------------------------------------------------------------------------------

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 MinLoser {}

impl Display for MinLoser {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        write!(f, "Min Loser")
    }
}

impl PortfolioStatistic for MinLoser {
    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 losers: Vec<f64> = realized_pnls
            .iter()
            .filter(|&&pnl| pnl < 0.0)
            .copied()
            .collect();

        if losers.is_empty() {
            return Some(f64::NAN);
        }

        losers
            .iter()
            .max_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_loser = MinLoser {};
        let result = min_loser.calculate_from_realized_pnls(&[]);
        assert!(result.is_some());
        assert!(result.unwrap().is_nan());
    }

    #[rstest]
    fn test_all_positive() {
        let min_loser = MinLoser {};
        let pnls = vec![10.0, 20.0, 30.0];
        let result = min_loser.calculate_from_realized_pnls(&pnls);
        assert!(result.is_some());
        assert!(result.unwrap().is_nan());
    }

    #[rstest]
    fn test_all_negative() {
        let min_loser = MinLoser {};
        let pnls = vec![-10.0, -20.0, -30.0];
        let result = min_loser.calculate_from_realized_pnls(&pnls);
        assert!(result.is_some());
        assert!(approx_eq!(f64, result.unwrap(), -10.0, epsilon = 1e-9));
    }

    #[rstest]
    fn test_mixed_pnls() {
        let min_loser = MinLoser {};
        let pnls = vec![10.0, -20.0, 30.0, -40.0];
        let result = min_loser.calculate_from_realized_pnls(&pnls);
        assert!(result.is_some());
        assert!(approx_eq!(f64, result.unwrap(), -20.0, epsilon = 1e-9));
    }

    #[rstest]
    fn test_with_zero() {
        let min_loser = MinLoser {};
        let pnls = vec![10.0, 0.0, -20.0, -30.0];
        let result = min_loser.calculate_from_realized_pnls(&pnls);
        assert!(result.is_some());
        // Zero is excluded, so min loser is -20.0 (least negative loss)
        assert!(approx_eq!(f64, result.unwrap(), -20.0, epsilon = 1e-9));
    }

    #[rstest]
    fn test_single_negative() {
        let min_loser = MinLoser {};
        let pnls = vec![-10.0];
        let result = min_loser.calculate_from_realized_pnls(&pnls);
        assert!(result.is_some());
        assert!(approx_eq!(f64, result.unwrap(), -10.0, epsilon = 1e-9));
    }

    #[rstest]
    fn test_name() {
        let min_loser = MinLoser {};
        assert_eq!(min_loser.name(), "Min Loser");
    }
}