"""应用配置管理"""

import os
from datetime import datetime
from pydantic_settings import BaseSettings


class Settings(BaseSettings):
    # Tushare Pro
    tushare_token: str = ""

    # 服务配置
    host: str = "0.0.0.0"
    port: int = 8000
    debug: bool = False

    # 数据库
    database_url: str = "sqlite:///./astock.db"

    # 缓存 TTL（秒）
    cache_ttl_realtime: int = 30       # 实时数据 30 秒
    cache_ttl_daily: int = 300         # 日级数据 5 分钟
    cache_ttl_sector: int = 300        # 板块数据 5 分钟
    cache_ttl_static: int = 86400      # 静态数据 24 小时

    # Tushare 限流
    tushare_request_delay: float = 0.3  # 请求间隔（秒）
    tushare_max_retry: int = 3          # 最大重试次数

    # 筛选参数
    top_sector_count: int = 5           # 关注板块数量
    top_stock_count: int = 6            # 最终推荐输出上限
    candidate_pool_limit: int = 90      # 多路召回后的候选池上限
    actionable_limit: int = 3           # 最多可操作标的
    watch_limit: int = 5                # 最多重点关注标的
    min_turnover_rate: float = 2.0      # 最小换手率 %
    max_turnover_rate: float = 30.0     # 最大换手率 %
    min_circ_mv: float = 50.0           # 最小流通市值（亿）
    max_circ_mv: float = 500.0          # 最大流通市值（亿）
    min_list_days: int = 60             # 最小上市天数

    # 买入信号阈值
    buy_score_threshold: int = 60       # 买入最低分
    buy_min_signals: int = 3            # 最少满足信号数

    # 风控
    stop_loss_pct: float = 5.0          # 止损比例 %

    # 趋势突破策略参数
    breakout_min_volume_ratio: float = 1.2    # 突破型最小量比
    pullback_max_shrink_ratio: float = 0.85   # 回踩型最大缩量比
    consolidation_max_range_pct: float = 8.0  # 启动型最大整理振幅 %

    # 新闻/政策催化采集
    news_collection_enabled: bool = True
    news_tushare_enabled: bool = False
    news_tushare_sources: str = ""
    news_tushare_sources_per_run: int = 1
    news_tushare_daily_quota: int = 0
    news_tushare_alert_errors: bool = False
    news_rss_sources: str = ""          # name|url,name|url
    news_web_sources: str = "eastmoney_roll|https://roll.eastmoney.com/"
    news_akshare_enabled: bool = True
    news_akshare_stock_limit: int = 20
    news_akshare_news_per_stock: int = 3
    news_fetch_lookback_hours: int = 24
    news_fetch_limit_per_source: int = 30
    news_analyze_limit_per_run: int = 50
    news_min_title_length: int = 8

    # LLM (DeepSeek)
    deepseek_api_key: str = ""
    deepseek_base_url: str = "https://api.deepseek.com/v1"
    deepseek_model: str = "deepseek-chat"
    llm_max_tokens: int = 2000
    llm_temperature: float = 0.3

    # 告警（Feishu / Lark Incoming Webhook）
    alert_enabled: bool = False
    feishu_webhook_url: str = ""
    alert_dedup_ttl_seconds: int = 300
    alert_max_detail_chars: int = 1200
    alert_app_name: str = "AStock Agent"
    alert_environment: str = "local"

    # 飞书推荐推送
    recommendation_push_enabled: bool = False
    recommendation_push_webhook_url: str = ""
    recommendation_push_max_items: int = 8
    recommendation_push_dedup_ttl_seconds: int = 600

    # 研究层风险 Agent
    research_risk_enabled: bool = True
    research_risk_stock_limit: int = 12
    risk_unlock_lookahead_days: int = 45
    risk_holder_trade_lookback_days: int = 120
    risk_forecast_lookback_days: int = 210
    risk_announcement_lookback_days: int = 180
    risk_financial_lookback_days: int = 540
    risk_unlock_reject_ratio: float = 15.0
    risk_pledge_reject_ratio: float = 55.0
    risk_goodwill_assets_warning_ratio: float = 20.0
    risk_goodwill_assets_reject_ratio: float = 35.0
    risk_debt_assets_warning_ratio: float = 70.0
    risk_debt_assets_reject_ratio: float = 85.0
    research_stock_llm_enabled: bool = True
    research_stock_llm_limit: int = 8
    research_stock_news_limit: int = 6

    # 前端
    frontend_url: str = "http://localhost:3002"

    # JWT 认证
    jwt_secret: str = "change-me-in-production"
    jwt_expiry_hours: int = 24
    jwt_algorithm: str = "HS256"

    # 默认管理员（首次启动自动创建）
    admin_username: str = "75981230@qq.com"
    admin_email: str = "75981230@qq.com"
    admin_password: str = "880803"

    # 认证体系
    auth_min_password_length: int = 6
    invite_code_required: bool = True
    email_code_expiry_minutes: int = 10
    email_code_cooldown_seconds: int = 60

    # SMTP
    smtp_host: str = ""
    smtp_port: int = 465
    smtp_username: str = ""
    smtp_password: str = ""
    smtp_sender: str = ""
    smtp_use_ssl: bool = True

    model_config = {"env_file": ".env", "env_prefix": "ASTOCK_"}


settings = Settings()


def is_trading_hours() -> bool:
    """判断当前是否在 A 股交易时段（9:30-11:30, 13:00-15:00）"""
    from zoneinfo import ZoneInfo
    now = datetime.now(ZoneInfo("Asia/Shanghai"))
    weekday = now.weekday()  # 0=Mon, 6=Sun
    if weekday >= 5:
        return False
    t = now.hour * 100 + now.minute
    return (930 <= t <= 1130) or (1300 <= t <= 1500)


def is_market_session() -> bool:
    """判断当前是否在 A 股交易日内（含午休 11:30-13:00）

    午休期间腾讯实时行情仍返回上午收盘价，可用于展示。
    与 is_trading_hours() 的区别：午休时返回 True。
    """
    from zoneinfo import ZoneInfo
    now = datetime.now(ZoneInfo("Asia/Shanghai"))
    weekday = now.weekday()
    if weekday >= 5:
        return False
    t = now.hour * 100 + now.minute
    return 930 <= t <= 1500


def is_pre_close() -> bool:
    """判断是否在收盘后、数据更新前（15:00-15:30 数据尚未完全更新）"""
    from zoneinfo import ZoneInfo
    now = datetime.now(ZoneInfo("Asia/Shanghai"))
    t = now.hour * 100 + now.minute
    return 1500 <= t <= 1530


def today_trade_date() -> str:
    """返回上海时区下的今天日期（YYYYMMDD）。"""
    from zoneinfo import ZoneInfo
    return datetime.now(ZoneInfo("Asia/Shanghai")).strftime("%Y%m%d")


def should_prefer_realtime_today(latest_trade_date: str | None = None) -> bool:
    """是否应该优先使用“今天”的实时数据。

    规则：
    1. 非工作日直接 False
    2. 交易日 09:15 后都优先实时源，包括收盘后；腾讯/东方财富会保留当日收盘快照
    3. 盘前不使用今日实时源，避免拿到昨日收盘价却标成今日
    """
    from zoneinfo import ZoneInfo

    now = datetime.now(ZoneInfo("Asia/Shanghai"))
    if now.weekday() >= 5:
        return False

    t = now.hour * 100 + now.minute
    if t < 915:
        return False

    today = today_trade_date()
    if latest_trade_date and latest_trade_date.replace("-", "") > today:
        return False

    return True