# Copyright (c) 2025-2026 马建军. All rights reserved.
# 专有软件 — 未经授权禁止复制、传播、转售。
# 严禁用于：带单/代客理财、向他人推荐期货品种或买卖建议、融资配资等业务。
# 详见 LICENSE.zh-CN.txt 与 docs/软件购买与使用协议.md

"""交易统计计算与缓存结构。"""
from __future__ import annotations

import json
import threading
from datetime import datetime
from typing import Any, Optional

from zoneinfo import ZoneInfo

from db_conn import commit_retry, execute_retry

_stats_refresh_lock = threading.Lock()

TZ = ZoneInfo("Asia/Shanghai")

STATS_VIEWS = [
    {"key": "by_time", "label": "按时间统计"},
    {"key": "by_week", "label": "周统计"},
    {"key": "by_month", "label": "月统计"},
    {"key": "by_symbol", "label": "按品种统计"},
    {"key": "by_fee", "label": "按手续费统计"},
    {"key": "by_direction", "label": "按方向统计"},
    {"key": "by_trade_type", "label": "按交易类型统计"},
    {"key": "by_emotion", "label": "情绪单统计"},
]

BREAKDOWN_COLUMNS = [
    {"key": "label", "label": "维度"},
    {"key": "count", "label": "交易次数"},
    {"key": "wins", "label": "盈利笔数"},
    {"key": "losses", "label": "亏损笔数"},
    {"key": "win_rate", "label": "胜率(%)"},
    {"key": "avg_profit", "label": "平均盈利"},
    {"key": "avg_loss", "label": "平均亏损"},
    {"key": "profit_loss_ratio", "label": "盈亏比"},
    {"key": "total_fee", "label": "累计手续费"},
    {"key": "total_net", "label": "净盈亏合计"},
    {"key": "max_loss", "label": "最大亏损"},
    {"key": "max_profit", "label": "最大盈利"},
]


def _parse_dt(value: str) -> Optional[datetime]:
    if not value:
        return None
    text = value.strip().replace(" ", "T")
    try:
        return datetime.fromisoformat(text)
    except ValueError:
        return None


def _row_dict(row) -> dict:
    return dict(row) if row is not None else {}


def _net_pnl(row: dict) -> float:
    if row.get("pnl_net") is not None:
        return float(row["pnl_net"])
    pnl = float(row.get("pnl") or 0)
    fee = float(row.get("fee") or 0)
    return round(pnl - fee, 2)


def _fee(row: dict) -> float:
    return float(row.get("fee") or 0)


def _margin_pct(pnl_net: float, margin: Optional[float]) -> Optional[float]:
    if margin and margin > 0:
        return round(pnl_net / margin * 100, 2)
    return None


def _agg_group(rows: list[dict], key_fn) -> list[dict]:
    groups: dict[str, list[dict]] = {}
    for row in rows:
        key = key_fn(row) or "未知"
        groups.setdefault(key, []).append(row)
    result = []
    for label, items in sorted(groups.items(), key=lambda x: x[0]):
        result.append(_agg_metrics(label, items))
    return result


def _agg_metrics(label: str, items: list[dict]) -> dict:
    nets = [_net_pnl(r) for r in items]
    wins = [n for n in nets if n > 0]
    losses = [n for n in nets if n < 0]
    count = len(items)
    win_cnt = len(wins)
    loss_cnt = len(losses)
    avg_profit = round(sum(wins) / len(wins), 2) if wins else 0.0
    avg_loss = round(sum(losses) / len(losses), 2) if losses else 0.0
    pl_ratio = round(avg_profit / abs(avg_loss), 2) if wins and losses and avg_loss != 0 else 0.0
    total_fee = round(sum(_fee(r) for r in items), 2)
    total_net = round(sum(nets), 2)
    max_loss = round(min(nets), 2) if nets else 0.0
    max_profit = round(max(nets), 2) if nets else 0.0
    win_rate = round(win_cnt / count * 100, 2) if count else 0.0
    return {
        "label": label,
        "count": count,
        "wins": win_cnt,
        "losses": loss_cnt,
        "win_rate": win_rate,
        "avg_profit": avg_profit,
        "avg_loss": avg_loss,
        "profit_loss_ratio": pl_ratio,
        "total_fee": total_fee,
        "total_net": total_net,
        "max_loss": max_loss,
        "max_profit": max_profit,
    }


def _max_consecutive_losses(nets: list[float]) -> int:
    streak = 0
    best = 0
    for n in nets:
        if n < 0:
            streak += 1
            best = max(best, streak)
        else:
            streak = 0
    return best


def _max_drawdown(nets: list[float], initial_capital: float) -> tuple[float, float]:
    equity = initial_capital
    peak = initial_capital
    max_dd = 0.0
    max_dd_pct = 0.0
    for n in nets:
        equity += n
        if equity > peak:
            peak = equity
        dd = peak - equity
        if dd > max_dd:
            max_dd = dd
        if peak > 0:
            pct = dd / peak * 100
            if pct > max_dd_pct:
                max_dd_pct = pct
    return round(max_dd, 2), round(max_dd_pct, 2)


def fetch_trade_rows(conn) -> list[dict]:
    rows = conn.execute(
        "SELECT * FROM trade_logs ORDER BY close_time ASC, id ASC"
    ).fetchall()
    return [_row_dict(r) for r in rows]


def fetch_review_rows(conn) -> list[dict]:
    rows = conn.execute(
        "SELECT * FROM review_records ORDER BY close_time ASC, id ASC"
    ).fetchall()
    return [_row_dict(r) for r in rows]


def compute_summary(trades: list[dict], reviews: list[dict], live_capital: float) -> dict:
    nets = [_net_pnl(t) for t in trades]
    count = len(trades)
    wins = [n for n in nets if n > 0]
    losses = [n for n in nets if n < 0]
    win_cnt = len(wins)
    loss_cnt = len(losses)
    avg_profit = round(sum(wins) / len(wins), 2) if wins else 0.0
    avg_loss = round(sum(losses) / len(losses), 2) if losses else 0.0
    pl_ratio = round(avg_profit / abs(avg_loss), 2) if wins and losses and avg_loss != 0 else 0.0
    total_fee = round(sum(_fee(t) for t in trades) + sum(_fee(r) for r in reviews), 2)
    max_loss_amt = round(min(nets), 2) if nets else 0.0
    max_profit_amt = round(max(nets), 2) if nets else 0.0

    margins_loss = [
        _margin_pct(_net_pnl(t), t.get("margin"))
        for t in trades
        if _net_pnl(t) < 0 and t.get("margin")
    ]
    margins_profit = [
        _margin_pct(_net_pnl(t), t.get("margin"))
        for t in trades
        if _net_pnl(t) > 0 and t.get("margin")
    ]
    max_loss_pct = round(min(margins_loss), 2) if margins_loss else 0.0
    max_profit_pct = round(max(margins_profit), 2) if margins_profit else 0.0

    consec_loss = _max_consecutive_losses(nets)
    max_dd, max_dd_pct = _max_drawdown(nets, live_capital)

    emotion_cnt = sum(1 for r in reviews if r.get("is_emotion"))
    review_cnt = len(reviews)
    denom = count if count else review_cnt
    emotion_ratio = round(emotion_cnt / denom * 100, 2) if denom else 0.0

    return {
        "total_trades": count,
        "win_rate": round(win_cnt / count * 100, 2) if count else 0.0,
        "avg_profit": avg_profit,
        "avg_loss": avg_loss,
        "profit_loss_ratio": pl_ratio,
        "consecutive_losses": consec_loss,
        "max_drawdown": max_dd,
        "max_drawdown_pct": max_dd_pct,
        "max_loss_amount": max_loss_amt,
        "max_loss_pct": max_loss_pct,
        "max_profit_amount": max_profit_amt,
        "max_profit_pct": max_profit_pct,
        "total_fee": total_fee,
        "emotion_count": emotion_cnt,
        "emotion_ratio": emotion_ratio,
        "review_count": review_cnt,
        "win_count": win_cnt,
        "loss_count": loss_cnt,
    }


def compute_breakdowns(trades: list[dict], reviews: list[dict]) -> dict[str, dict]:
    def day_key(row: dict) -> str:
        dt = _parse_dt(row.get("close_time") or row.get("created_at") or "")
        return dt.date().isoformat() if dt else "未知"

    def week_key(row: dict) -> str:
        dt = _parse_dt(row.get("close_time") or row.get("created_at") or "")
        if not dt:
            return "未知"
        iso = dt.isocalendar()
        return f"{iso.year}-W{iso.week:02d}"

    def month_key(row: dict) -> str:
        dt = _parse_dt(row.get("close_time") or row.get("created_at") or "")
        return dt.strftime("%Y-%m") if dt else "未知"

    def symbol_key(row: dict) -> str:
        return row.get("symbol_name") or row.get("symbol") or "未知"

    def direction_key(row: dict) -> str:
        d = row.get("direction") or ""
        return "做多" if d == "long" else ("做空" if d == "short" else d or "未知")

    def type_key(row: dict) -> str:
        return row.get("monitor_type") or "未知"

    by_fee_rows = []
    fee_groups = {}
    for t in trades:
        key = symbol_key(t)
        fee_groups.setdefault(key, []).append(t)
    for label, items in sorted(fee_groups.items()):
        row = _agg_metrics(label, items)
        row["avg_fee"] = round(row["total_fee"] / row["count"], 2) if row["count"] else 0.0
        by_fee_rows.append(row)

    emotion_trades = [r for r in reviews if r.get("is_emotion")]
    non_emotion = [r for r in reviews if not r.get("is_emotion")]
    emotion_rows = [
        _agg_metrics("情绪单", emotion_trades),
        _agg_metrics("非情绪单", non_emotion),
    ]

    fee_columns = BREAKDOWN_COLUMNS + [{"key": "avg_fee", "label": "平均手续费"}]

    return {
        "by_time": {"columns": BREAKDOWN_COLUMNS, "rows": _agg_group(trades, day_key)},
        "by_week": {"columns": BREAKDOWN_COLUMNS, "rows": _agg_group(trades, week_key)},
        "by_month": {"columns": BREAKDOWN_COLUMNS, "rows": _agg_group(trades, month_key)},
        "by_symbol": {"columns": BREAKDOWN_COLUMNS, "rows": _agg_group(trades, symbol_key)},
        "by_fee": {"columns": fee_columns, "rows": by_fee_rows},
        "by_direction": {"columns": BREAKDOWN_COLUMNS, "rows": _agg_group(trades, direction_key)},
        "by_trade_type": {"columns": BREAKDOWN_COLUMNS, "rows": _agg_group(trades, type_key)},
        "by_emotion": {"columns": BREAKDOWN_COLUMNS, "rows": emotion_rows},
    }


def build_all_stats(conn, live_capital: float = 0.0) -> dict:
    trades = fetch_trade_rows(conn)
    reviews = fetch_review_rows(conn)
    summary = compute_summary(trades, reviews, live_capital)
    breakdowns = compute_breakdowns(trades, reviews)
    return {
        "updated_at": datetime.now(TZ).isoformat(timespec="seconds"),
        "summary": summary,
        "views": STATS_VIEWS,
        "breakdowns": breakdowns,
    }


def save_stats_cache(conn, data: dict) -> None:
    execute_retry(
        conn,
        """INSERT INTO stats_cache (key, data_json, updated_at)
           VALUES ('all', ?, ?)
           ON CONFLICT(key) DO UPDATE SET data_json=excluded.data_json, updated_at=excluded.updated_at""",
        (json.dumps(data, ensure_ascii=False), data["updated_at"]),
    )
    commit_retry(conn)


def load_stats_cache(conn) -> Optional[dict]:
    row = conn.execute(
        "SELECT data_json FROM stats_cache WHERE key='all'"
    ).fetchone()
    if not row:
        return None
    try:
        return json.loads(row["data_json"])
    except json.JSONDecodeError:
        return None


def refresh_stats_cache(conn, live_capital: float = 0.0) -> dict:
    with _stats_refresh_lock:
        data = build_all_stats(conn, live_capital)
        save_stats_cache(conn, data)
        return data