// Rolling / expanding window statistics (CPU FP64 reference).
// Implementation detail: running sums use Neumaier compensation AND a periodic exact re-summation of the window
// (every `resum_every` steps) so that drift cannot accumulate over 390 000-bar series.
#pragma once
#include <span>
#include <vector>
#include <limits>
#include "raid/core/types.hpp"

namespace raid::stats {

constexpr f64 kNaN = std::numeric_limits<f64>::quiet_NaN();

struct RollingOptions {
  usize window{22};
  usize min_periods{0};        // 0 = window (full windows only); otherwise partial windows allowed at the start
  usize resum_every{1024};     // exact re-summation cadence
};

std::vector<f64> rolling_sum(std::span<const f64> x, const RollingOptions& o);
std::vector<f64> rolling_mean(std::span<const f64> x, const RollingOptions& o);
std::vector<f64> rolling_variance(std::span<const f64> x, const RollingOptions& o, int ddof = 1);
std::vector<f64> rolling_covariance(std::span<const f64> x, std::span<const f64> y, const RollingOptions& o, int ddof = 1);
std::vector<f64> rolling_correlation(std::span<const f64> x, std::span<const f64> y, const RollingOptions& o);
std::vector<f64> expanding_mean(std::span<const f64> x, usize min_periods = 1);
std::vector<f64> expanding_variance(std::span<const f64> x, usize min_periods = 2, int ddof = 1);
/// Causal lag (positive k) or lead (negative k); NaN where undefined.
std::vector<f64> shift(std::span<const f64> x, long k);

}  // namespace raid::stats