"""
完整流水线：遍历日期范围，执行五阶段处理。

本地测试：
  python src/scripts/pipeline.py --start 20240311 --end 20240315 --output ./outputs

Sandbox 全量：
  python src/scripts/pipeline.py --start 20230101 --end 20260331 --output ./outputs --full
"""

from __future__ import annotations

import argparse
import json
import os
import sys
from collections import defaultdict
from pathlib import Path
from typing import Dict, List

import numpy as np
import pandas as pd
from tqdm import tqdm

# 项目根加入 path
sys.path.insert(0, str(Path(__file__).resolve().parent.parent.parent))

from src.data.loader import load_l2_day, BLACKLIST_DATES
from src.features.passive_orders import (
    compute_vwap,
    extract_passive_orders,
    prepare_features,
    select_candidates,
)
from src.clustering.daily_cluster import cluster_candidates
from src.matching.cross_day_match import match_multi_window
from src.tracking.entity_tracker import EntityTracker


def _make_output_dirs(base: str):
    for d in ["passive_orders", "clusters", "matches", "signals", "reports"]:
        os.makedirs(os.path.join(base, d), exist_ok=True)


def process_single_day(
    date: int,
    output_base: str,
    save_intermediate: bool = False,
) -> tuple:
    """
    处理单个交易日。

    Returns:
        (candidates_df, clusters_dict, centroids_dict) 或空
    """
    try:
        data = load_l2_day(date)
    except Exception as e:
        print(f"  [{date}] 加载失败: {e}")
        return None, {}

    trades = data["trades"]

    # 过滤撤单
    if "is_cancellation" in trades.columns:
        trades = trades[~trades["is_cancellation"]]

    if trades.empty:
        return None, {}

    vwap = compute_vwap(trades)

    # 阶段1：被动单提取
    passive = extract_passive_orders(trades, vwap)
    if passive.empty:
        return None, {}

    candidates = select_candidates(passive, top_n=150)
    if candidates.empty:
        return None, {}

    if save_intermediate:
        candidates.to_parquet(
            os.path.join(output_base, "passive_orders", f"{date}.parquet"),
            index=False,
        )

    # 阶段2：聚类
    feats = prepare_features(candidates)
    if feats.shape[0] < 5:
        candidates["cluster_id"] = -1
        return candidates, {}

    labeled, centroids = cluster_candidates(candidates, feats)

    if save_intermediate:
        labeled.to_parquet(
            os.path.join(output_base, "clusters", f"{date}.parquet"),
            index=False,
        )

    return labeled, centroids


def run_pipeline(
    dates: List[int],
    output_base: str,
    save_intermediate: bool = False,
):
    """执行完整流水线。"""
    _make_output_dirs(output_base)

    tracker = EntityTracker(inactive_threshold=5)

    # 缓存最近 3 天的聚类结果供匹配
    recent_history: Dict[int, Dict[int, dict]] = {}
    # 日期 → 标记了簇的 candidates
    daily_labeled: Dict[int, pd.DataFrame] = {}

    all_signals = []
    all_matches = []

    print(f"处理 {len(dates)} 个交易日 ({dates[0]} ~ {dates[-1]})")

    for i, date in enumerate(tqdm(dates, desc="Processing days")):
        if date in BLACKLIST_DATES:
            continue

        labeled, centroids = process_single_day(
            date, output_base, save_intermediate
        )

        if labeled is None or not centroids:
            recent_history[date] = {}
            daily_labeled[date] = labeled if labeled is not None else pd.DataFrame()
            # 清理旧缓存
            for old_date in list(recent_history.keys()):
                if date - old_date > 3:
                    del recent_history[old_date]
            continue

        daily_labeled[date] = labeled
        recent_history[date] = centroids

        # 阶段3：多窗口跨日匹配
        # 需要往前看 T-1, T-2
        prev_dates = [d for d in sorted(recent_history.keys()) if d < date]
        prev_dates = prev_dates[-2:]  # 最近 2 个有数据的交易日

        prev_centroids_for_match = {
            d: recent_history[d] for d in prev_dates if recent_history.get(d)
        }

        matches = match_multi_window(
            date, centroids, prev_centroids_for_match, max_cost=3.5
        )

        # 记录匹配
        for prev_d, prev_cid, curr_cid, cost in matches:
            all_matches.append({
                "date": date,
                "prev_date": prev_d,
                "prev_cid": prev_cid,
                "curr_cid": curr_cid,
                "cost": cost,
            })

        # 阶段4：实体追踪
        cid_to_entity = tracker.process_day(date, centroids, matches)

        # 阶段5：仓位推断
        signal = tracker.compute_position_signal(date)
        signal["date"] = date
        all_signals.append(signal)

        # 清理旧缓存（保留最近 3 天）
        for old_date in list(recent_history.keys()):
            if date - old_date > 3:
                del recent_history[old_date]

    # ---- 导出 ----

    # 实体时间线
    entity_df = tracker.get_entity_timeline()
    entity_df.to_parquet(os.path.join(output_base, "entity_timeline.parquet"))

    # 簇注册表
    cluster_reg_df = tracker.get_cluster_registry_table()
    cluster_reg_df.to_parquet(os.path.join(output_base, "cluster_registry.parquet"))

    # 每日信号
    signals_df = tracker.get_daily_signals()
    signals_df.to_parquet(os.path.join(output_base, "signals", "position_signal_daily.parquet"))

    # 匹配记录
    if all_matches:
        pd.DataFrame(all_matches).to_parquet(
            os.path.join(output_base, "matches", "match_pairs.parquet")
        )

    # 完整追踪器状态（可恢复继续处理新日）
    tracker.save_state(os.path.join(output_base, "tracker_state.pkl"))

    # 汇总报告
    report = {
        "total_dates_processed": len(dates),
        "total_entities_discovered": len(tracker.entities),
        "active_entities": int(entity_df["status"].value_counts().get("active", 0)),
        "inactive_entities": int(entity_df["status"].value_counts().get("inactive", 0)),
        "avg_entities_per_day": float(
            np.mean([len(v) for v in tracker.daily_active.values()])
        ) if tracker.daily_active else 0,
        "bid_dominant_entities": int((entity_df["dominant_side"] == "bid").sum()),
        "ask_dominant_entities": int((entity_df["dominant_side"] == "ask").sum()),
        "total_matches": len(all_matches),
        "signal_summary": {
            "mean_score": float(signals_df["score"].mean()) if len(signals_df) > 0 else 0,
            "std_score": float(signals_df["score"].std()) if len(signals_df) > 0 else 0,
        },
    }

    with open(os.path.join(output_base, "reports", "summary.json"), "w") as f:
        json.dump(report, f, indent=2, ensure_ascii=False)

    print(f"\n===== 汇总 =====")
    print(f"交易日: {len(dates)}")
    print(f"发现实体总数: {report['total_entities_discovered']}")
    print(f"活跃实体: {report['active_entities']}, 已退出: {report['inactive_entities']}")
    print(f"买方实体: {report['bid_dominant_entities']}, 卖方实体: {report['ask_dominant_entities']}")
    print(f"跨日匹配对数: {report['total_matches']}")
    print(f"输出目录: {output_base}")


def main():
    parser = argparse.ArgumentParser(description="跨日主力行为指纹追踪")
    parser.add_argument("--start", type=int, required=True, help="起始日期 YYYYMMDD")
    parser.add_argument("--end", type=int, required=True, help="结束日期 YYYYMMDD")
    parser.add_argument("--output", type=str, default="./outputs", help="输出目录")
    parser.add_argument("--full", action="store_true", help="全量模式（无交互）")
    parser.add_argument("--save-intermediate", action="store_true", help="保存中间产物")
    parser.add_argument("--dates", type=str, nargs="*", help="指定日期列表（覆盖 start/end）")
    args = parser.parse_args()

    if args.dates:
        dates = sorted(int(d) for d in args.dates)
    else:
        # 生成日期范围（仅限交易日的近似，实际会跳过数据缺失的日期）
        from datetime import datetime, timedelta

        start_dt = datetime.strptime(str(args.start), "%Y%m%d")
        end_dt = datetime.strptime(str(args.end), "%Y%m%d")
        dates = []
        curr = start_dt
        while curr <= end_dt:
            d = int(curr.strftime("%Y%m%d"))
            if d not in BLACKLIST_DATES and curr.weekday() < 5:
                dates.append(d)
            curr += timedelta(days=1)

    run_pipeline(dates, args.output, save_intermediate=args.save_intermediate)


if __name__ == "__main__":
    main()