"""Local Pearson comparison of daily PnL changes; no platform eligibility decisions.

The four-year window and signed 0.7 threshold follow the legacy research tool.
Thirty paired changes is a local minimum, not a claim about BRAIN's own checks.
"""

import math
from datetime import datetime, timezone
from statistics import StatisticsError, correlation

THRESHOLD = 0.7
MIN_SAMPLES = 30
WINDOW_YEARS = 4


def daily_changes(points):
    """Return dated changes and the final date, rejecting ambiguous daily records.

    Parameters are normalized PnL points. Missing values break a change interval;
    each change retains its start date so differently spaced samples never pair.
    Raises ValueError for malformed dates, duplicate days or non-finite values.
    """
    daily = {}
    for point in points:
        try:
            timestamp = datetime.fromisoformat(point["date"].replace("Z", "+00:00"))
            day = (
                (timestamp.replace(tzinfo=timezone.utc) if timestamp.tzinfo is None else timestamp)
                .astimezone(timezone.utc)
                .date()
            )
        except (ValueError, TypeError, KeyError, AttributeError):
            raise ValueError("PnL 日期无法识别") from None
        if day in daily:
            raise ValueError("PnL 同一天存在多条记录")
        value = point.get("value")
        if value is not None and (
            isinstance(value, bool) or not isinstance(value, (int, float)) or not math.isfinite(value)
        ):
            raise ValueError("PnL 包含无效数值")
        daily[day] = value
    changes, previous_day, previous = {}, None, None
    for day, value in sorted(daily.items()):
        if value is not None and previous is not None:
            delta = value - previous
            if not math.isfinite(delta):
                raise ValueError("PnL 变化超出有效数值范围")
            changes[day] = (previous_day, delta)
        previous_day, previous = day, value
    return changes, max(daily) if daily else None


def calculate_correlation(target_points, references):
    """Compare a target with supplied reference caches and return a JSON-safe report.

    Each reference has alpha_id, points, fetched_at and optionally error. Callers
    select the same-region submitted set and exclude the target. Incomplete
    coverage can report high correlation, but can never report an all-clear.
    """
    result = {
        "status": "insufficient_data",
        "threshold": THRESHOLD,
        "min_samples": MIN_SAMPLES,
        "window_years": WINDOW_YEARS,
        "window_from": None,
        "window_to": None,
        "max_correlation": None,
        "most_correlated_alpha_id": None,
        "candidate_count": len(references),
        "compared_count": 0,
        "skipped_count": 0,
        "matches": [],
        "skipped": [],
        "reason": None,
    }
    try:
        target, latest = daily_changes(target_points)
    except ValueError as exc:
        result["reason"] = str(exc)
        return result
    if latest is None or not target:
        result["reason"] = "目标 Alpha 没有可用的 PnL 日变化"
        return result
    try:
        cutoff = latest.replace(year=latest.year - WINDOW_YEARS)
    except ValueError:
        cutoff = latest.replace(year=latest.year - WINDOW_YEARS, day=28)
    result["window_from"], result["window_to"] = cutoff.isoformat(), latest.isoformat()
    matches, skipped = [], []
    for reference in references:
        alpha_id, reason = reference["alpha_id"], reference.get("error")
        if not reason:
            try:
                changes, _ = daily_changes(reference["points"])
                days = sorted(
                    day
                    for day in target.keys() & changes.keys()
                    if cutoff < day <= latest and target[day][0] == changes[day][0]
                )
                if len(days) < MIN_SAMPLES:
                    reason = f"共同有效样本不足 {MIN_SAMPLES} 个（实际 {len(days)}）"
                else:
                    coefficient = correlation([target[d][1] for d in days], [changes[d][1] for d in days])
                    if not math.isfinite(coefficient):
                        reason = "无法计算有效相关系数"
                    else:
                        matches.append(
                            {
                                "alpha_id": alpha_id,
                                "correlation": coefficient,
                                "sample_count": len(days),
                                "date_from": days[0].isoformat(),
                                "date_to": days[-1].isoformat(),
                                "pnl_fetched_at": reference.get("fetched_at"),
                            }
                        )
            except StatisticsError:
                reason = "目标或基准 PnL 日变化为常量"
            except (ValueError, OverflowError) as exc:
                reason = str(exc)
        if reason:
            skipped.append({"alpha_id": alpha_id, "reason": reason})
    matches.sort(key=lambda row: (-row["correlation"], row["alpha_id"]))
    result.update(
        compared_count=len(matches), skipped_count=len(skipped), matches=matches[:10], skipped=skipped[:100]
    )
    if matches:
        maximum = matches[0]["correlation"]
        result.update(
            max_correlation=maximum,
            most_correlated_alpha_id=matches[0]["alpha_id"],
            status="high" if maximum >= THRESHOLD else "partial" if skipped else "low",
        )
    else:
        result["reason"] = (
            "没有同地区已提交 Alpha 可供比较" if not references else "所有基准均缺少足够的有效样本"
        )
    return result