yuzu-core 0.2.0

//! Standard performance metrics (CAGR, Sharpe, Sortino, max drawdown, etc.). All
//! functions operate on a daily equity curve (`&[f64]`, base 1.0) or a daily
//! returns slice. Conventions: annualization 252, rf = 0, std ddof = 1.

use chrono::NaiveDate;

pub fn to_returns(equity: &[f64]) -> Vec<f64> {
    let mut out = vec![f64::NAN; equity.len()];
    for i in 1..equity.len() {
        out[i] = equity[i] / equity[i - 1] - 1.0;
    }
    out
}

pub fn total_return(equity: &[f64]) -> f64 {
    if equity.is_empty() {
        return f64::NAN;
    }
    equity[equity.len() - 1] / equity[0] - 1.0
}

pub fn drawdown_series(equity: &[f64]) -> Vec<f64> {
    let mut out = vec![0.0; equity.len()];
    let mut peak = f64::NEG_INFINITY;
    for (i, &e) in equity.iter().enumerate() {
        if e > peak {
            peak = e;
        }
        out[i] = e / peak - 1.0;
    }
    out
}

pub fn max_drawdown(equity: &[f64]) -> f64 {
    if equity.is_empty() {
        return f64::NAN;
    }
    drawdown_series(equity).into_iter().fold(0.0, f64::min)
}

fn to_naive(yyyymmdd: i32) -> NaiveDate {
    let y = yyyymmdd / 10000;
    let m = (yyyymmdd / 100 % 100) as u32;
    let d = (yyyymmdd % 100) as u32;
    NaiveDate::from_ymd_opt(y, m, d).unwrap()
}

pub fn year_frac(start: i32, end: i32) -> f64 {
    let secs = (to_naive(end) - to_naive(start)).num_seconds() as f64;
    secs / 31_557_600.0
}

/// sample mean + std (ddof=1) over the non-NaN entries.
fn mean_std(xs: &[f64]) -> (f64, f64) {
    let v: Vec<f64> = xs.iter().copied().filter(|x| !x.is_nan()).collect();
    let n = v.len() as f64;
    if n == 0.0 {
        return (f64::NAN, f64::NAN);
    }
    let mean = v.iter().sum::<f64>() / n;
    if n < 2.0 {
        return (mean, f64::NAN);
    }
    let var = v.iter().map(|x| (x - mean).powi(2)).sum::<f64>() / (n - 1.0);
    (mean, var.sqrt())
}

pub fn cagr(equity: &[f64], dates: &[i32]) -> f64 {
    if equity.len() < 2 || dates.len() < 2 {
        return f64::NAN;
    }
    let yf = year_frac(dates[0], dates[dates.len() - 1]);
    (equity[equity.len() - 1] / equity[0]).powf(1.0 / yf) - 1.0
}

pub fn ann_volatility(equity: &[f64]) -> f64 {
    let (_, std) = mean_std(&to_returns(equity));
    std * 252.0_f64.sqrt()
}

pub fn sharpe(equity: &[f64]) -> f64 {
    let r = to_returns(equity);
    let (mean, std) = mean_std(&r);
    (mean / std.max(1e-6)) * 252.0_f64.sqrt()
}

pub fn sortino(equity: &[f64]) -> f64 {
    let r = to_returns(equity);
    // ffn: er = returns (rf=0); negative_returns = min(er[1:], 0).
    let (mean, _) = mean_std(&r);
    let downside: Vec<f64> = r
        .iter()
        .skip(1)
        .map(|&x| if x < 0.0 { x } else { 0.0 })
        .collect();
    let (_, dstd) = mean_std(&downside);
    if dstd <= 0.0 || dstd.is_nan() {
        return f64::NAN;
    }
    (mean / dstd) * 252.0_f64.sqrt()
}

pub fn calmar(equity: &[f64], dates: &[i32]) -> f64 {
    let c = cagr(equity, dates);
    // Short/empty input: cagr already returns NaN ("not enough data"); keep that,
    // don't let the zero-drawdown guard below reinterpret it as "no drawdown".
    if c.is_nan() {
        return f64::NAN;
    }
    let mdd = max_drawdown(equity).abs();
    if mdd == 0.0 {
        return f64::INFINITY;
    }
    c / mdd
}

pub fn recovery_factor(equity: &[f64]) -> f64 {
    let mdd = max_drawdown(equity).abs();
    if mdd == 0.0 {
        return f64::INFINITY;
    }
    total_return(equity) / mdd
}

/// Longest run of consecutive rows strictly below the running peak (drawdown < 0),
/// counted in trading-day rows.
pub fn max_drawdown_duration(equity: &[f64]) -> f64 {
    let (mut max, mut cur) = (0u32, 0u32);
    for d in drawdown_series(equity) {
        if d < 0.0 {
            cur += 1;
            max = max.max(cur);
        } else {
            cur = 0;
        }
    }
    max as f64
}

use crate::backtest::Trade;

fn closed(trades: &[Trade]) -> Vec<&Trade> {
    trades.iter().filter(|t| t.exit_date.is_some()).collect()
}

pub fn win_rate(trades: &[Trade]) -> f64 {
    let c = closed(trades);
    if c.is_empty() {
        return f64::NAN;
    }
    c.iter().filter(|t| t.ret > 0.0).count() as f64 / c.len() as f64
}

pub fn profit_factor(trades: &[Trade]) -> f64 {
    let c = closed(trades);
    if c.is_empty() {
        return f64::NAN;
    }
    let gains: f64 = c.iter().filter(|t| t.ret > 0.0).map(|t| t.ret).sum();
    let losses: f64 = c.iter().filter(|t| t.ret < 0.0).map(|t| t.ret).sum();
    if losses == 0.0 {
        return f64::INFINITY;
    }
    gains / losses.abs()
}

pub fn expectancy(trades: &[Trade]) -> f64 {
    let c = closed(trades);
    if c.is_empty() {
        return f64::NAN;
    }
    c.iter().map(|t| t.ret).sum::<f64>() / c.len() as f64
}

pub fn avg_holding_period(trades: &[Trade]) -> f64 {
    let c = closed(trades);
    if c.is_empty() {
        return f64::NAN;
    }
    c.iter().map(|t| t.period as f64).sum::<f64>() / c.len() as f64
}

pub fn num_trades(trades: &[Trade]) -> f64 {
    closed(trades).len() as f64
}

pub fn avg_win(trades: &[Trade]) -> f64 {
    let w: Vec<f64> = closed(trades)
        .iter()
        .map(|t| t.ret)
        .filter(|&r| r > 0.0)
        .collect();
    if w.is_empty() {
        return f64::NAN;
    }
    w.iter().sum::<f64>() / w.len() as f64
}

pub fn avg_loss(trades: &[Trade]) -> f64 {
    let l: Vec<f64> = closed(trades)
        .iter()
        .map(|t| t.ret)
        .filter(|&r| r < 0.0)
        .collect();
    if l.is_empty() {
        return f64::NAN;
    }
    l.iter().sum::<f64>() / l.len() as f64
}

pub fn payoff_ratio(trades: &[Trade]) -> f64 {
    let (aw, al) = (avg_win(trades), avg_loss(trades));
    if aw.is_nan() || al.is_nan() {
        return f64::NAN;
    }
    aw / al.abs()
}

pub fn best_trade(trades: &[Trade]) -> f64 {
    let c = closed(trades);
    if c.is_empty() {
        return f64::NAN;
    }
    c.iter().map(|t| t.ret).fold(f64::NEG_INFINITY, f64::max)
}

pub fn worst_trade(trades: &[Trade]) -> f64 {
    let c = closed(trades);
    if c.is_empty() {
        return f64::NAN;
    }
    c.iter().map(|t| t.ret).fold(f64::INFINITY, f64::min)
}

pub fn max_consecutive_losses(trades: &[Trade]) -> f64 {
    let mut c = closed(trades);
    c.sort_by_key(|t| t.exit_date); // closed -> Some; chronological
    let (mut max, mut cur) = (0u32, 0u32);
    for t in c {
        if t.ret < 0.0 {
            cur += 1;
            max = max.max(cur);
        } else {
            cur = 0;
        }
    }
    max as f64
}

pub fn time_in_market(exposure: &[f64]) -> f64 {
    if exposure.is_empty() {
        return f64::NAN;
    }
    exposure.iter().filter(|&&e| e > 0.0).count() as f64 / exposure.len() as f64
}

pub fn avg_exposure(exposure: &[f64]) -> f64 {
    if exposure.is_empty() {
        return f64::NAN;
    }
    exposure.iter().sum::<f64>() / exposure.len() as f64
}

// ---- calendar-period and rolling metrics -----------------------------------

/// One calendar bucket's return: `period` is `"2024-01"` (monthly) or `"2024"`
/// (yearly); `ret` is the equity return over the bucket, chained off the
/// previous bucket's closing equity (the first bucket chains off `equity[0]`).
#[derive(Debug, Clone, serde::Serialize)]
pub struct PeriodReturn {
    pub period: String,
    pub ret: f64,
}

fn period_returns(dates: &[i32], equity: &[f64], monthly: bool) -> Vec<PeriodReturn> {
    let key = |d: i32| if monthly { d / 100 } else { d / 10000 };
    let label = |k: i32| {
        if monthly {
            format!("{}-{:02}", k / 100, k % 100)
        } else {
            k.to_string()
        }
    };
    let mut out = Vec::new();
    if dates.is_empty() || equity.len() != dates.len() {
        return out;
    }
    let mut baseline = equity[0];
    let mut cur = key(dates[0]);
    for i in 0..dates.len() {
        let k = key(dates[i]);
        if k != cur {
            // row i-1 closed the previous bucket
            out.push(PeriodReturn {
                period: label(cur),
                ret: equity[i - 1] / baseline - 1.0,
            });
            baseline = equity[i - 1];
            cur = k;
        }
    }
    out.push(PeriodReturn {
        period: label(cur),
        ret: equity[equity.len() - 1] / baseline - 1.0,
    });
    out
}

pub fn monthly_returns(dates: &[i32], equity: &[f64]) -> Vec<PeriodReturn> {
    period_returns(dates, equity, true)
}

pub fn yearly_returns(dates: &[i32], equity: &[f64]) -> Vec<PeriodReturn> {
    period_returns(dates, equity, false)
}

/// Rolling annualized volatility over a `window` of daily returns; NaN until
/// `window` returns are available (row `window` onward, since row 0 has none).
pub fn rolling_volatility(equity: &[f64], window: usize) -> Vec<f64> {
    let r = to_returns(equity);
    let mut out = vec![f64::NAN; r.len()];
    for i in window..r.len() {
        let (_, std) = mean_std(&r[i + 1 - window..=i]);
        out[i] = std * 252.0_f64.sqrt();
    }
    out
}

/// Rolling annualized Sharpe (rf = 0) over a `window` of daily returns; NaN
/// until `window` returns are available.
pub fn rolling_sharpe(equity: &[f64], window: usize) -> Vec<f64> {
    let r = to_returns(equity);
    let mut out = vec![f64::NAN; r.len()];
    for i in window..r.len() {
        let (mean, std) = mean_std(&r[i + 1 - window..=i]);
        out[i] = (mean / std.max(1e-6)) * 252.0_f64.sqrt();
    }
    out
}

// ---- benchmark-relative metrics ------------------------------------------
// All take the strategy and benchmark equity curves (same length, aligned by
// row); daily-return pairs where either side is NaN are dropped.

/// Paired daily returns (strategy, benchmark), NaN pairs removed.
fn paired_returns(equity: &[f64], bench: &[f64]) -> (Vec<f64>, Vec<f64>) {
    let r = to_returns(equity);
    let b = to_returns(bench);
    let mut rs = Vec::new();
    let mut bs = Vec::new();
    for i in 0..r.len().min(b.len()) {
        if !r[i].is_nan() && !b[i].is_nan() {
            rs.push(r[i]);
            bs.push(b[i]);
        }
    }
    (rs, bs)
}

/// CAPM beta: cov(r, b) / var(b) over paired daily returns (ddof = 1).
pub fn beta(equity: &[f64], bench: &[f64]) -> f64 {
    let (rs, bs) = paired_returns(equity, bench);
    let n = rs.len() as f64;
    if n < 2.0 {
        return f64::NAN;
    }
    let (rm, _) = mean_std(&rs);
    let (bm, bstd) = mean_std(&bs);
    let cov = rs
        .iter()
        .zip(&bs)
        .map(|(r, b)| (r - rm) * (b - bm))
        .sum::<f64>()
        / (n - 1.0);
    let var = bstd * bstd;
    if var == 0.0 {
        return f64::NAN;
    }
    cov / var
}

/// Annualized CAPM alpha (rf = 0): `(mean(r) - beta * mean(b)) * 252`.
pub fn alpha(equity: &[f64], bench: &[f64]) -> f64 {
    let (rs, bs) = paired_returns(equity, bench);
    let beta = beta(equity, bench);
    if beta.is_nan() {
        return f64::NAN;
    }
    let (rm, _) = mean_std(&rs);
    let (bm, _) = mean_std(&bs);
    (rm - beta * bm) * 252.0
}

/// Annualized tracking error: `std(r - b, ddof = 1) * sqrt(252)`.
pub fn tracking_error(equity: &[f64], bench: &[f64]) -> f64 {
    let (rs, bs) = paired_returns(equity, bench);
    let diff: Vec<f64> = rs.iter().zip(&bs).map(|(r, b)| r - b).collect();
    let (_, std) = mean_std(&diff);
    std * 252.0_f64.sqrt()
}

/// Information ratio: `mean(r - b) / std(r - b, ddof = 1) * sqrt(252)`.
pub fn information_ratio(equity: &[f64], bench: &[f64]) -> f64 {
    let (rs, bs) = paired_returns(equity, bench);
    let diff: Vec<f64> = rs.iter().zip(&bs).map(|(r, b)| r - b).collect();
    let (mean, std) = mean_std(&diff);
    (mean / std.max(1e-6)) * 252.0_f64.sqrt()
}

/// Benchmark total return over its first/last non-NaN observations.
pub fn benchmark_return(bench: &[f64]) -> f64 {
    let first = bench.iter().copied().find(|x| !x.is_nan());
    let last = bench.iter().rev().copied().find(|x| !x.is_nan());
    match (first, last) {
        (Some(f), Some(l)) if f != 0.0 => l / f - 1.0,
        _ => f64::NAN,
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn period_returns_bucket_by_month_and_year() {
        // Dec 2023 (2 rows) -> Jan 2024 (2 rows) -> Feb 2024 (1 row).
        let dates = [20231228, 20231229, 20240102, 20240131, 20240201];
        let eq = [1.0, 1.1, 1.1, 1.32, 1.32];
        let m = monthly_returns(&dates, &eq);
        assert_eq!(m.len(), 3);
        assert_eq!(m[0].period, "2023-12");
        assert!((m[0].ret - 0.1).abs() < 1e-12);
        assert_eq!(m[1].period, "2024-01");
        assert!((m[1].ret - 0.2).abs() < 1e-12); // 1.32/1.1 - 1
        assert_eq!(m[2].period, "2024-02");
        assert!(m[2].ret.abs() < 1e-12);
        let y = yearly_returns(&dates, &eq);
        assert_eq!(y.len(), 2);
        assert_eq!(y[0].period, "2023");
        assert!((y[0].ret - 0.1).abs() < 1e-12);
        assert_eq!(y[1].period, "2024");
        assert!((y[1].ret - 0.2).abs() < 1e-12);
    }

    #[test]
    fn rolling_metrics_warm_up_then_fill() {
        // Constant +1% daily: vol 0, sharpe huge; window 3 -> NaN rows 0..=2.
        let mut eq = vec![1.0];
        for _ in 0..5 {
            let prev = *eq.last().unwrap();
            eq.push(prev * 1.01);
        }
        let vol = rolling_volatility(&eq, 3);
        let sh = rolling_sharpe(&eq, 3);
        assert!(vol[2].is_nan() && sh[2].is_nan());
        assert!(vol[3].abs() < 1e-9, "constant returns -> zero vol");
        assert!(sh[3] > 0.0);
        assert_eq!(vol.len(), eq.len());
    }

    #[test]
    fn benchmark_relative_metrics() {
        // Strategy daily returns are exactly 2x the benchmark's.
        let bench = [1.0, 1.01, 1.0201, 1.01, 1.0201];
        let mut eq = vec![1.0];
        for i in 1..bench.len() {
            let b = bench[i] / bench[i - 1] - 1.0;
            let prev = *eq.last().unwrap();
            eq.push(prev * (1.0 + 2.0 * b));
        }
        assert!((beta(&eq, &bench) - 2.0).abs() < 1e-9, "beta");
        assert!(alpha(&eq, &bench).abs() < 1e-9, "alpha ~ 0");

        // Identical curves: beta 1, zero tracking error and IR.
        assert!((beta(&bench, &bench) - 1.0).abs() < 1e-12);
        assert!(tracking_error(&bench, &bench).abs() < 1e-12);
        assert!(information_ratio(&bench, &bench).abs() < 1e-9);

        // Flat benchmark: no variance -> beta NaN.
        let flat = [1.0, 1.0, 1.0];
        assert!(beta(&bench[..3], &flat).is_nan());

        // Benchmark total return ignores leading/trailing NaN.
        let with_nan = [f64::NAN, 1.0, 1.1, f64::NAN];
        assert!((benchmark_return(&with_nan) - 0.1).abs() < 1e-12);
        assert!(benchmark_return(&[f64::NAN]).is_nan());
    }

    #[test]
    fn returns_drawdown_and_totals() {
        let eq = [1.0, 1.02, 1.01, 1.05];
        let r = to_returns(&eq);
        assert!(r[0].is_nan());
        assert!((r[1] - 0.02).abs() < 1e-12);
        assert!((total_return(&eq) - 0.05).abs() < 1e-12);
        // drawdown: peak 1.02 then 1.01 -> -0.009803...
        let dd = drawdown_series(&eq);
        assert_eq!(dd[0], 0.0);
        assert!((dd[2] - (1.01 / 1.02 - 1.0)).abs() < 1e-12);
        assert!((max_drawdown(&eq) - (1.01 / 1.02 - 1.0)).abs() < 1e-12);
    }

    #[test]
    fn empty_and_single_inputs() {
        assert!(max_drawdown(&[]).is_nan());
        assert!(total_return(&[]).is_nan());
        let one = to_returns(&[1.0]);
        assert_eq!(one.len(), 1);
        assert!(one[0].is_nan());
        assert_eq!(drawdown_series(&[1.0]), vec![0.0]);
    }

    #[test]
    fn cagr_and_calmar_guard_short_input() {
        assert!(cagr(&[1.0], &[20240102]).is_nan());
        assert!(calmar(&[1.0], &[20240102]).is_nan());
    }

    #[test]
    fn calmar_guards_zero_drawdown() {
        // flat curve: cagr=0 and max_drawdown=0 -> 0/0 = NaN without a guard.
        // mirror recovery_factor and return +inf for the no-drawdown case.
        assert!(calmar(&[1.0, 1.0, 1.0], &[20240102, 20240103, 20240104]).is_infinite());
    }

    #[test]
    fn trade_level_metrics() {
        use crate::backtest::Trade;
        let t = |ret: f64, period: u32| Trade {
            symbol: "X".into(),
            entry_date: 20240102,
            exit_date: Some(20240105),
            ret,
            period,
            mae: None,
            mfe: None,
        };
        let trades = vec![t(0.10, 3), t(-0.05, 2), t(0.20, 5)];
        assert!((win_rate(&trades) - 2.0 / 3.0).abs() < 1e-12);
        assert!((profit_factor(&trades) - (0.30 / 0.05)).abs() < 1e-12);
        assert!((expectancy(&trades) - (0.25 / 3.0)).abs() < 1e-12);
        assert!((avg_holding_period(&trades) - (10.0 / 3.0)).abs() < 1e-12);
    }

    #[test]
    fn extended_trade_level_metrics() {
        use crate::backtest::Trade;
        let t = |ret: f64, exit: i32| Trade {
            symbol: "X".into(),
            entry_date: 20240102,
            exit_date: Some(exit),
            ret,
            period: 1,
            mae: None,
            mfe: None,
        };
        // chronological by exit_date: +0.10, -0.05, -0.20, +0.30, -0.10
        let trades = vec![
            t(0.10, 20240105),
            t(-0.05, 20240106),
            t(-0.20, 20240107),
            t(0.30, 20240108),
            t(-0.10, 20240109),
        ];
        assert_eq!(num_trades(&trades), 5.0);
        assert!((avg_win(&trades) - (0.40 / 2.0)).abs() < 1e-12); // (0.10+0.30)/2
        assert!((avg_loss(&trades) - (-0.35 / 3.0)).abs() < 1e-12); // (-0.05-0.20-0.10)/3
        assert!((payoff_ratio(&trades) - (0.20 / (0.35 / 3.0))).abs() < 1e-12);
        assert!((best_trade(&trades) - 0.30).abs() < 1e-12);
        assert!((worst_trade(&trades) + 0.20).abs() < 1e-12);
        // losses at exits 106,107 are consecutive (run 2); 109 is a lone run -> max 2.
        assert_eq!(max_consecutive_losses(&trades), 2.0);
    }

    #[test]
    fn trade_metrics_handle_empty_and_one_sided() {
        use crate::backtest::Trade;
        let win = vec![Trade {
            symbol: "X".into(),
            entry_date: 20240102,
            exit_date: Some(20240103),
            ret: 0.1,
            period: 1,
            mae: None,
            mfe: None,
        }];
        assert_eq!(num_trades(&[]), 0.0);
        assert!(avg_win(&[]).is_nan());
        assert!(avg_loss(&win).is_nan()); // no losers
        assert!(payoff_ratio(&win).is_nan()); // loss side empty
        assert!(best_trade(&[]).is_nan());
        assert_eq!(max_consecutive_losses(&win), 0.0);
    }

    #[test]
    fn max_consecutive_losses_sorts_by_exit_date() {
        use crate::backtest::Trade;
        // Array order is NOT chronological: exit dates 105, 103, 104.
        // Chronological by exit_date: 103 (win), 104 (loss), 105 (loss) -> streak 2.
        // Without the exit_date sort, array order gives loss, win(reset), loss -> 1.
        // Asserting 2 therefore fails if the sort is ever dropped.
        let t = |ret: f64, exit: i32| Trade {
            symbol: "X".into(),
            entry_date: 20240102,
            exit_date: Some(exit),
            ret,
            period: 1,
            mae: None,
            mfe: None,
        };
        let trades = vec![t(-0.1, 20240105), t(0.2, 20240103), t(-0.1, 20240104)];
        assert_eq!(max_consecutive_losses(&trades), 2.0);
    }

    #[test]
    fn equity_and_exposure_metrics() {
        // peak 1.0 then underwater rows 1,2,3 (0.9,0.8,0.9), recover at row4.
        let eq = [1.0, 0.9, 0.8, 0.9, 1.0];
        // total_return = 0.0; max_drawdown = 0.8/1.0 - 1 = -0.2 -> recovery 0/0.2 = 0.
        assert!((recovery_factor(&eq) - 0.0).abs() < 1e-12);
        assert_eq!(max_drawdown_duration(&eq), 3.0); // 3 consecutive rows below peak

        // recovery_factor returns +inf when there is no drawdown.
        assert!(recovery_factor(&[1.0, 1.1, 1.2]).is_infinite());

        let exposure = [1.0, 0.0, 0.5, 0.5];
        assert!((time_in_market(&exposure) - 0.75).abs() < 1e-12); // 3 of 4 rows > 0
        assert!((avg_exposure(&exposure) - 0.5).abs() < 1e-12); // (1+0+0.5+0.5)/4
        assert!(time_in_market(&[]).is_nan());
        assert!(avg_exposure(&[]).is_nan());
    }
}