Agent/examples/auto_put_ad_mini/config.py

"""
广告智能决策引擎配置 — auto_put_ad_mini

运营可直接修改此文件调整决策参数。
当前模式：智能判断
  - 基于 动态 ROI (7日均值) 的精细化决策
  - AI 推理结合领域知识
  - 三级分类：零消耗待关停（规则）+ 待优化评估（智能）+ 正常运行（规则）
"""
import os
import logging
from pathlib import Path
from agent.core.runner import RunConfig, KnowledgeConfig

# 初始化 logger（必须在使用前定义）
logger = logging.getLogger(__name__)

# 加载 .env 文件（如果存在）
try:
    from dotenv import load_dotenv
    load_dotenv(Path(__file__).parent / ".env")
except ImportError:
    pass

# ═══════════════════════════════════════════
# Agent 运行配置
# ═══════════════════════════════════════════

MAIN_CONFIG = RunConfig(
    model="anthropic/claude-sonnet-4.5",
    temperature=0.3,
    max_iterations=50,
    name="广告智能调控助手",
    tools=[
        "fetch_creative_data",
        "merge_creative_data",
        "calculate_roi_metrics",
        "calculate_creative_roi",      # 创意级动态 ROI(广告级 pause 候选的二次细化)
        "calculate_portfolio_summary",
        "get_ads_for_review",
        "apply_decisions",
        "query_ad_detail",      # Mode 2: 查询广告详情
        "modify_decisions",     # Mode 3: 修改已有决策
        "validate_decisions",
        "generate_report",
        # 执行引擎 + IM 审批（已集成阻塞式审批流）：
        "execute_decisions",
        "check_execution_feedback",
        "send_approval_request",
        "check_approval_status",
        "send_feishu_text_message",  # 执行后向您同步 diff / 确认 / 质疑回应
        # 飞书文档（报告导入 & 分享）：
        "import_to_feishu",
        # 注：曾考虑用内置 "agent" 工具按 tier 并行委托子 Agent，
        # 但框架的 agent 工具只返回文本 summary，主 Agent 拿不回结构化决策，
        # 会陷入"无法 apply"的死循环。直接在主 Agent 单次输出完成全部 decisions 更可靠。
    ],
    skills=[
        "ad-domain",           # 业务模型：裂变模型、R值、ROI公式、字段定义
        "platform-rules",      # 平台硬约束：oCPM学习期、调价上限、数据口径
        "decision-strategy",   # 决策策略：角色 + 基准 + 候选标记 + 年龄策略 + 7种action + 输出规范
        "posterior-wisdom",    # 后验经验：学习中断/降价恢复/创意冷启动/置信度分级
    ],
    extra_llm_params={"max_tokens": 32000},
    knowledge=KnowledgeConfig(
        enable_extraction=False,              # 从决策过程中提取后验经验（投放后开启）
        enable_completion_extraction=False,    # 完成后总结本轮经验（投放后开启）
        enable_injection=False,               # 决策时自动注入相关历史经验（投放后开启）
        owner="ad_mini_team",
    ),
)

SKILLS_DIR = str(Path(__file__).parent / "skills")
TRACE_STORE_PATH = ".trace"
LOG_LEVEL = "INFO"
LOG_FILE = None

# ═══════════════════════════════════════════
# 时区配置（海外部署）
# ═══════════════════════════════════════════
TIMEZONE = os.getenv("TZ", "UTC")
logger.info(f"运行时区：{TIMEZONE}")

# ═══════════════════════════════════════════
# V3 数据窗口配置
# ═══════════════════════════════════════════
DATA_WINDOW_DAYS = 14  # 数据采集窗口：14 天历史数据
ROI_CALCULATION_DAYS = 7  # 动态 ROI (7日均值) 计算窗口（保持 7 天）

# ═══════════════════════════════════════════
# V3 决策阈值（默认值，可被 SKILL 覆盖）
# ═══════════════════════════════════════════
MIN_DAILY_COST = 100  # 日消耗 >= 100元才参与 ROI 计算
MIN_AD_AGE_DAYS = 3  # 广告创建 >= 3天才参与决策（与 min_periods 对齐）
ROI_LOW_FACTOR = 0.75  # 动态 ROI (7日均值) < 全体均值 × 0.75 → 关停
NO_SPEND_THRESHOLD = 10  # 7日消耗均值 < 10元 → 关停
STABLE_SPEND_THRESHOLD = 100  # 稳定消耗定义：>100元/天

# ═══════════════════════════════════════════
# 出价调整配置
# ═══════════════════════════════════════════
BID_ADJUSTMENT_ENABLED = True
BID_DOWN_ROI_FACTOR = 0.90      # ROI < 均值×0.90 → 考虑降价（低于渠道均值10%）
BID_UP_ROI_FACTOR = 1.05        # ROI > 均值×1.05 → 考虑提价（高于渠道均值5%）
BID_UP_MAX_SPEND = 1000          # 提价消耗上限：均值消耗<1000才提价（投手经验原文）
BID_CHANGE_MIN_PCT = 0.03       # 最小调幅 3%（兼容旧代码）
BID_CHANGE_MAX_PCT = 0.10       # 最大单次调幅 10%（兼容旧代码）
BID_UP_MIN_PCT = 0.05           # 提价最小幅度 5%
BID_UP_MAX_PCT = 0.10           # 提价最大幅度 10%
BID_DOWN_MIN_PCT = 0.03         # 降价最小幅度 3%
BID_DOWN_MAX_PCT = 0.05         # 降价最大幅度 5%
BID_DOWN_MIN_SPEND = 500        # 降价消耗门槛：7日日均消耗≥500元
BID_FLOOR_YUAN = 0.05           # 出价下限（元）
BID_CEILING_YUAN = 1.00         # 出价上限（元）

# 广告年龄分段（基于决策树图片）
COLD_START_DAYS = 3             # 冷启动期（≤3天）：极度保护，几乎不干预
EARLY_GROWTH_DAYS = 7           # 早期成长期（4-7天）：可提价放量（满足ROI+消耗条件）
AD_AGE_MATURE = 7               # 成熟期（>7天）：全面调控

# 兼容性（已废弃）
AD_AGE_NEWBORN = COLD_START_DAYS   # 兼容旧代码
CAUTIOUS_DAYS = EARLY_GROWTH_DAYS  # 兼容旧代码

# 高燃烧预警配置
HIGH_BURN_AGE_THRESHOLD = 3     # 广告年龄>3天才检查
HIGH_BURN_COST_THRESHOLD = 300  # 昨日消耗>300元触发预警
ROI_LOW_MIN_YESTERDAY_COST = 300  # 关停消耗门槛：昨日消耗≥300才检查关停（投手经验2.4）

# ═══════════════════════════════════════════
# 创意级 pause 细化配置
# ═══════════════════════════════════════════
# 当广告级判 pause 时,先做创意级二次分析:全员低于阈值才真关广告,部分拖累只关差创意
CREATIVE_PAUSE_ENABLED = True       # 总开关:False 时全部走广告级 pause(降级路径)
CREATIVE_MIN_COST_SHARE = 0.15      # 创意 7 日消耗占比 < 此值视为数据稀疏,不纳入"判死刑"
CREATIVE_MIN_AGE_DAYS = 7           # 创意年龄 < 此值视为冷启动,不纳入"判死刑"(对齐广告级 EARLY_GROWTH_DAYS)
CREATIVE_MIN_VALID_DAYS = 3         # 创意有效 ROI 数据天数 < 此值视为不充分,不纳入"判死刑"
CREATIVE_MIN_REMAINING = 2          # 关停后剩余 eligible 创意数 < 此值,升级为广告级 pause
CREATIVE_MAX_PAUSE_COST_SHARE = 0.80 # pause_targets 总占消耗 > 此值,本质是关广告,升级为广告级 pause
CREATIVE_RATELIMIT_DAYS = 7         # 同一创意 7 天内不允许重复 pause

# ═══════════════════════════════════════════
# 安全护栏配置
# ═══════════════════════════════════════════
GUARDRAILS_ENABLED = True
DRY_RUN_MODE = False                       # 关闭干运行，让护栏正常放行（实际执行由 EXECUTION_ENABLED 控制）
MAX_ADJUSTMENTS_PER_AD_PER_DAY = 2
MIN_ADJUSTMENT_INTERVAL_HOURS = 6
MAX_DAILY_CUMULATIVE_CHANGE_PCT = 0.20     # 日累计调幅上限 20%
MAX_DAILY_OPS = 10000                       # 单日最多操作广告数（实际不限制）
DATA_FRESHNESS_MAX_HOURS = 96               # 数据超过 96 小时视为过期（已从48小时放宽至96小时）

# ═══════════════════════════════════════════
# 执行引擎配置
# ═══════════════════════════════════════════

# 执行开关（优先级：数据库 > 环境变量 > 默认值False）
EXECUTION_ENABLED = False
try:
    from db import get_system_config
    _db_execution_enabled = get_system_config("execution_enabled", default=None)
    if _db_execution_enabled is not None:
        EXECUTION_ENABLED = _db_execution_enabled
        logger.info(f"✅ 从数据库读取执行开关：{EXECUTION_ENABLED}")
    else:
        # 降级到环境变量
        _env_execution_enabled = os.getenv("EXECUTION_ENABLED", "").strip().lower()
        if _env_execution_enabled:
            EXECUTION_ENABLED = _env_execution_enabled in ("true", "1", "yes")
            logger.info(f"从环境变量读取执行开关：{EXECUTION_ENABLED}")
except Exception as e:
    logger.warning(f"⚠️ 数据库读取执行开关失败（{e}），使用默认值：{EXECUTION_ENABLED}")

API_QPS_LIMIT = 8                  # 保守QPS（平台上限10）
API_MAX_RETRIES = 3
TIER1_MAX_CHANGE_PCT = 0.00       # Tier1自动执行已禁用（改为0%，所有操作都需审批）
TIER3_MIN_DAILY_SPEND = 1500      # 高价值广告门槛（元/天）
FEEDBACK_CHECK_HOURS = 6

# ═══════════════════════════════════════════
# IM 审批配置（飞书直连）
# ═══════════════════════════════════════════
IM_ENABLED = True                  # IM 主开关（True 时审批消息发飞书）
IM_APPROVAL_TIMEOUT_MINUTES = 120  # 审批超时（分钟）— 2小时
IM_APPROVAL_POLL_INTERVAL_SECONDS = 30  # 审批轮询间隔（秒）

# 飞书应用凭据（"增长投放"机器人）— 优先从环境变量读取
FEISHU_APP_ID = os.getenv("FEISHU_APP_ID", "cli_a955e97067f85cb3")
FEISHU_APP_SECRET = os.getenv("FEISHU_APP_SECRET", "NQaG4ci1plXRDTgwCqrLJgMLLoA2tdF8")

# 运营审批人飞书信息
FEISHU_OPERATOR_OPEN_ID = os.getenv("FEISHU_OPERATOR_OPEN_ID", "ou_498988d823b61ab89c9afe4310f85bb4")
FEISHU_OPERATOR_CHAT_ID = os.getenv("FEISHU_OPERATOR_CHAT_ID", "oc_88e0a1970a7de02eb5ac225a8b0cedea")

# 投放项目群聊 — 用于接收决策结果通知和审批回复
# 置空则不发送到群，仅发送到个人
FEISHU_AD_PROJECT_CHAT_ID = os.getenv("FEISHU_AD_PROJECT_CHAT_ID", "oc_7940ec97cde40b245cff9cb606ff1ac7")

# 腾讯广告默认账户（测试账户）
TENCENT_AD_ACCOUNT_ID = int(os.getenv("TENCENT_AD_ACCOUNT_ID", "80769799"))

# ═══════════════════════════════════════════
# 账户白名单配置
# ═══════════════════════════════════════════

# 白名单模式开关（优先级：数据库 > 环境变量）
WHITELIST_ENABLED = None
WHITELIST_ACCOUNTS = []

# 尝试从数据库读取配置
try:
    from db import get_whitelist_accounts, get_system_config

    # 读取白名单开关
    WHITELIST_ENABLED = get_system_config("whitelist_enabled", default=None)

    # 读取白名单账户列表
    WHITELIST_ACCOUNTS = get_whitelist_accounts()

    logger.info(f"✅ 从数据库读取白名单配置：{len(WHITELIST_ACCOUNTS)} 个账户")
except Exception as db_error:
    logger.warning(f"⚠️ 数据库读取失败（{db_error}），降级到环境变量配置")

    # 降级方案1：从环境变量读取
    _whitelist_str = os.getenv("WHITELIST_ACCOUNTS", "")
    if _whitelist_str:
        # 格式：逗号分隔，如 "80769799,71305011"
        WHITELIST_ACCOUNTS = [int(x.strip()) for x in _whitelist_str.split(",") if x.strip()]
        logger.info(f"从环境变量读取白名单：{len(WHITELIST_ACCOUNTS)} 个账户")
    else:
        # 降级方案2：从文件读取（可选）
        _whitelist_file = Path(__file__).parent / "whitelist.json"
        if _whitelist_file.exists():
            import json
            with open(_whitelist_file) as f:
                whitelist_data = json.load(f)
                WHITELIST_ACCOUNTS = whitelist_data.get("accounts", [])
                logger.info(f"从 whitelist.json 读取白名单：{len(WHITELIST_ACCOUNTS)} 个账户")

# 白名单开关降级处理
if WHITELIST_ENABLED is None:
    WHITELIST_ENABLED = os.getenv("WHITELIST_ENABLED", "true").lower() == "true"

# 向后兼容：单账户模式
if not WHITELIST_ACCOUNTS:
    WHITELIST_ACCOUNTS = [TENCENT_AD_ACCOUNT_ID]
    logger.info(f"白名单为空，使用单账户模式：{TENCENT_AD_ACCOUNT_ID}")

logger.info(
    f"白名单配置：{'启用' if WHITELIST_ENABLED else '禁用'}，"
    f"账户数={len(WHITELIST_ACCOUNTS)}，列表={WHITELIST_ACCOUNTS[:5]}..."
)

# ═══════════════════════════════════════════
# 实验范围 Scope(MVP)
# ═══════════════════════════════════════════
# 2026-06-08:DB 白名单已直接收窄到 2 个测试账户,不再 override
# 单一真相源 = DB account_whitelist 表 enabled=1 的行
# 若要临时收窄,改这里为 {account_id...}
EXPERIMENTAL_SCOPE_ACCOUNTS = None
EXPERIMENTAL_SCOPE_REASON = "已迁 DB,config 不再 override"

if EXPERIMENTAL_SCOPE_ACCOUNTS is not None:
    _scope_before = len(WHITELIST_ACCOUNTS)
    WHITELIST_ACCOUNTS = [a for a in WHITELIST_ACCOUNTS if a in EXPERIMENTAL_SCOPE_ACCOUNTS]
    # 兜底:如 scope 中账户不在 DB 白名单(或 DB 读取失败),直接采用 scope 列表
    if not WHITELIST_ACCOUNTS:
        WHITELIST_ACCOUNTS = list(EXPERIMENTAL_SCOPE_ACCOUNTS)
        logger.warning(
            f"⚠️ EXPERIMENTAL_SCOPE {EXPERIMENTAL_SCOPE_ACCOUNTS} 与现有白名单交集为空，"
            f"直接使用 scope 列表"
        )
    logger.info(
        f"🧪 EXPERIMENTAL_SCOPE 启用：WHITELIST_ACCOUNTS 由 {_scope_before} → "
        f"{len(WHITELIST_ACCOUNTS)} 个 ({WHITELIST_ACCOUNTS})。原因：{EXPERIMENTAL_SCOPE_REASON}"
    )

# ═══════════════════════════════════════════
# 一账一包映射（账户级 audience pack · 2026-06-05 业务确认）
# ═══════════════════════════════════════════
# 用户明确约束:一个账户只用一个人群包,即使有多个广告也不变
# audience_pack_id 由运营提供;LLM 不参与选择
# 字段格式:account_id -> (audience_pack_id, audience_tier_label)
# - audience_pack_id 为 None 表示该账户不使用人群包(仅靠地域/年龄/性别自然定向)
ACCOUNT_AUDIENCE_PACK_MAPPING = {
    83846793: (None, "no_audience_pack"),       # 不传 custom_audience,按"泛人群"出价
    83846804: (44722088, "R330+"),              # Q-R_330+_WX_UNIONID_2026-06-02 (865 万人)
}

# ═══════════════════════════════════════════════════════════════════
# [CREATION SOP] 广告搭建 SOP 固定参数(2026-06-05 业务确认)
# ═══════════════════════════════════════════════════════════════════
# 投放 SOP:广告搭建 = 固定定向 & 人群 & 出价
# 小程序产品:票圈 | 3亿人喜欢的视频平台
# 几乎所有维度都是固定的,LLM 不参与"营销内容/定向/出价类型"决策
# 唯一可变维度:site_set 组合(3 种) + 出价数值(从区间内取)

# --- 营销内容(全固定)---
MARKETING_GOAL = "MARKETING_GOAL_USER_GROWTH"
MARKETING_SUB_GOAL = "MARKETING_SUB_GOAL_UNKNOWN"
# 修正(2026-06-05 真实样本反推):marketing_carrier_type 是 JUMP_PAGE,不是 MINI_PROGRAM_WECHAT
# 小程序信息通过 marketing_asset_outer_spec 嵌套传递
MARKETING_CARRIER_TYPE = "MARKETING_CARRIER_TYPE_JUMP_PAGE"
MARKETING_CARRIER_NAME = "票圈 | 3亿人喜欢的视频平台"
MARKETING_CARRIER_GH_ID = "gh_ecd1ea0b84cf"          # 小程序 GH ID
MARKETING_CARRIER_WX_APP_ID = "wx89e7eb06478361d7"   # 小程序 WX AppID(可能 add 时不传,待 dry run)

# marketing_asset_outer_spec 嵌套结构(add 时传)
MARKETING_TARGET_TYPE = "MARKETING_TARGET_TYPE_MINI_PROGRAM_WECHAT"
MARKETING_ASSET_OUTER_SPEC = {
    "marketing_target_type": MARKETING_TARGET_TYPE,
    "marketing_asset_outer_id": MARKETING_CARRIER_GH_ID,
}
# conversion_id 不传(可选 + 不支持朋友圈版位)
# 改用 optimization_goal 直接走

# ═══════════════════════════════════════════════════════════════════
# 账户级 feedback_id 映射(2026-06-05 业务确认)
# ═══════════════════════════════════════════════════════════════════
# feedback_id = 监测链接 ID,是账户级配置,广告 add 时必填
# 短期:本字典占位;长期:迁到 DB(扩展 account_whitelist 表加列)
# 设计接口:get_account_feedback_id(account_id) → 先查 DB,fallback 本字典
ACCOUNT_FEEDBACK_ID_MAPPING = {
    # account_id : feedback_id
    83846793: 6700703,            # 用户 2026-06-05 提供(非人群包账户)
    83846804: 6700002,            # 用户 2026-06-05 提供(R330+ 人群包账户)
}


def get_account_feedback_id(account_id: int):
    """获取账户的 feedback_id(监测链接 ID)。

    优先级:DB(将来) > config.py 字典 > None
    返回 None 时调用方应反问或报错,不能猜测。
    """
    # TODO: 后续接入 DB,从 account_whitelist 表读
    return ACCOUNT_FEEDBACK_ID_MAPPING.get(account_id)

OPTIMIZATION_GOAL = "OPTIMIZATIONGOAL_PROMOTION_VIEW_KEY_PAGE"   # 关键页面访问次数(USER_GROWTH 配套)
# 注:2026-06-05 业务确认 — 两个测试账户均走 USER_GROWTH + PAGE_KEY 路线
# 即使 83846804 带人群包,仍用此优化目标,不切换到 BRAND_PROMOTION + CLICK

# --- 定向(全固定 SOP)---
FIXED_TARGETING_AGE = [{"min": 45, "max": 66}]        # 45-66 岁(自定义)
FIXED_TARGETING_GENDER = "ALL"                        # 不限制(不传 gender)
FIXED_TARGETING_LOCATION_TYPES = ["LIVE_IN"]          # 常住地

# 地域 region_id 列表 — 从 JSON 读(运营可改 JSON 调整生效地域)
# 来源:ad 95205841163 (account 81214386) 实际投放地域反推
# 实际排除:港澳台 + 东三省 + 河南(共 7 个一级行政区)
FIXED_TARGETING_REGIONS_JSON_PATH = Path(__file__).parent / "data" / "tencent_constants" / "regions_sop_current.json"
try:
    import json as _json
    with open(FIXED_TARGETING_REGIONS_JSON_PATH, encoding="utf-8") as _f:
        _regions_data = _json.load(_f)
    FIXED_TARGETING_REGION_IDS = [r["id"] for r in _regions_data["list"]]
    logger.info(
        f"✅ 从 {FIXED_TARGETING_REGIONS_JSON_PATH.name} 加载 {len(FIXED_TARGETING_REGION_IDS)} 个地域 region_id"
    )
except FileNotFoundError:
    FIXED_TARGETING_REGION_IDS = []
    logger.warning(
        f"⚠️ 地域 JSON 未找到:{FIXED_TARGETING_REGIONS_JSON_PATH},新建广告时 targeting.geo_location 为空"
    )

# 实际排除的一级行政区(给审批表 / 报告人类可读用)
EXCLUDED_PROVINCES_SEMANTIC = ["香港", "澳门", "台湾", "辽宁", "吉林", "黑龙江", "河南"]

# --- 出价 / 计费 ---
BID_MODE = "BID_MODE_OCPM"
SMART_BID_TYPE = "SMART_BID_TYPE_CUSTOM"
BID_STRATEGY = "BID_STRATEGY_AVERAGE_COST"
# 一键起量(auto_acquisition)默认关闭(用户 2026-06-05 确认,与多数样本一致)
# 注:腾讯硬约束 — 若启用,budget 必须 >= 20000(200 元)
AUTO_ACQUISITION_ENABLED = False
AUTO_ACQUISITION_BUDGET_FEN = 20000                   # 占位最小值,enabled=False 时不生效
AUTO_DERIVED_CREATIVE_ENABLED = False
AIM_SMART_TARGETING_ENABLED = False
AIM_SMART_SITE_ENABLED = False
DEEP_CONVERSION_SPEC: dict = {}

# --- 转化(conversion_id)---
# 用户 2026-06-05 指示:两个测试账户都用 1007(与样本 92067863445 一致)
# 长期:迁到 account_whitelist 表加列 conversion_id
DEFAULT_CONVERSION_ID = 1007

# --- 搜索场景扩量 · 定向拓展开关(用户 2026-06-05 反推确认)---
# 3 条线上样本均为 CLOSE,我们若不传腾讯默认 OPEN → 与 SOP 不一致
# 用 ad_api 反推得知字段名:search_expand_targeting_switch
SEARCH_EXPAND_TARGETING_SWITCH = "SEARCH_EXPAND_TARGETING_SWITCH_CLOSE"

# --- 版位(用户 2026-06-05 调整:与样本 92067863445 一致)---
AVAILABLE_SITE_SETS = [
    "SITE_SET_WECHAT",                 # 微信公众号
    "SITE_SET_WECHAT_PLUGIN",          # 微信插件
    "SITE_SET_SEARCH_SCENE",           # 搜索场景
]
# MVP 阶段:单一固定版位组合(差异化先不靠 site_set)
SITE_SET_COMBINATIONS = [
    ["SITE_SET_WECHAT", "SITE_SET_WECHAT_PLUGIN", "SITE_SET_SEARCH_SCENE"],
]

# --- 时段 / 日期(真实样本 95205841163 反推:6:00-22:30 投放)---
# 一天 48 段 × 7 天 = 336 位字符串
TIME_SERIES_ONE_DAY = "000000000000" + "1" * 34 + "00"   # 0:00-6:00 关 / 6:00-23:00 投 / 23:00-24:00 关
TIME_SERIES_DEFAULT = TIME_SERIES_ONE_DAY * 7
# 长度自验
assert len(TIME_SERIES_DEFAULT) == 336, f"time_series 长度错误:{len(TIME_SERIES_DEFAULT)}"

DEFAULT_BEGIN_DATE_OFFSET_DAYS = 0                    # 创建当日开始
DEFAULT_END_DATE = "0"                                # 真实样本是字符串 "0",不是 None(腾讯特殊表示长期)

# --- 预算(用户 2026-06-05 确认:200 元/广告)---
# 真实样本是 0(不限),但 MVP 阶段我们设硬上限保护
DEFAULT_DAILY_BUDGET_YUAN = 200                       # 单广告日预算
DEFAULT_DAILY_BUDGET_FEN = DEFAULT_DAILY_BUDGET_YUAN * 100   # 元 → 分

# --- 出价区间表(按 audience tier label 索引)---
# 来源:用户提供的投放 SOP 出价区间表
AUDIENCE_BID_RANGES = {
    # tier_label                : (min_yuan, max_yuan)
    "R50_泛惊奇_奇观技艺":         (0.35, 0.45),
    "R50_泛知识_生活科普":         (0.35, 0.45),
    "R50_泛知识_时政历史":         (0.35, 0.45),
    "R50_泛祝福":                 (0.35, 0.45),
    "R50_全品类":                 (0.25, 0.38),
    "R50_同感个体_个人情感":       (0.35, 0.45),
    "R50_同感个体_退休榜样":       (0.35, 0.45),
    "R500_全品类":                (0.38, 0.48),
    "回流100-180":                (0.22, 0.28),
    "回流180-330":                (0.30, 0.40),
    "回流330+":                   (0.35, 0.40),
    "R330+":                     (0.35, 0.40),         # 别名,83846804(Q-R_330+)用
    "回流50-100":                 (0.19, 0.22),
    "泛人群":                     (0.19, 0.25),
    "no_audience_pack":          (0.19, 0.25),         # 别名,83846793(无人群包)用
}

# --- 出价取值策略 ---
BID_PICK_STRATEGY = "midpoint"                        # midpoint / max / min / random
COLD_START_BID_PICK_STRATEGY = "midpoint"             # 冷启动期取中位(可改 max 抢量)

# --- 朋友圈版位专属设置(运营标准模板,待提供)---
FEED_AD_SETTING_HEAD_IMAGE_URL = None                 # TODO
FEED_AD_SETTING_NICK_NAME = None                      # TODO
FEED_AD_SETTING_CONVERSION_BUTTON_TEXT = "查看详情"   # 默认

# --- 单账户起步广告条数(Cold Start)---
COLD_START_PER_ACCOUNT_AD_COUNT = 3                   # 对应 3 种 site_set 组合
COLD_START_TOTAL_ADS = COLD_START_PER_ACCOUNT_AD_COUNT * len(WHITELIST_ACCOUNTS)  # 跟随 DB

# ═══════════════════════════════════════════
# 输出路径配置
# ═══════════════════════════════════════════
OUTPUTS_DIR = Path(__file__).parent / "outputs"
RAW_DATA_DIR = OUTPUTS_DIR / "raw"  # 创意级原始 CSV
AD_STATUS_DIR = OUTPUTS_DIR / "ad_status"  # 广告状态 CSV
REPORTS_DIR = OUTPUTS_DIR / "reports"  # 决策报告
EXECUTION_LOG_DIR = OUTPUTS_DIR / "execution_log"  # 执行审计日志
DATA_DIR = OUTPUTS_DIR / "data"  # 运行时数据（如调整历史）
ADJUSTMENT_HISTORY_PATH = DATA_DIR / "adjustment_history.json"

# ═══════════════════════════════════════════
# 人群包系数（保留，用于展示）
# ═══════════════════════════════════════════
AUDIENCE_COEFFICIENTS = {
    "R500":    3.0,
    "R330+":   2.5,
    "R330":    2.0,
    "R180":    1.5,
    "R100":    1.2,
    "R50":     1.0,
    "R10":     1.0,
    "R2":      1.0,
    "default": 1.0,
}

# 从广告名称提取 R 值的匹配顺序
AUDIENCE_TIER_PATTERNS = [
    ("R500",   ["R500", "R_500", "r500"]),
    ("R330+",  ["回流330+", "回流330+-", "回流q330", "330+全品类", "R330+", "R_330+"]),
    ("R330",   ["回流330", "R330", "R_330", "定向330", "r330", "r300"]),
    ("R180",   ["回流180", "R180", "R_180", "定向180", "r180",
                "r180-330", "r180-300", "R100-180", "R_100-180", "r100-180"]),
    ("R100",   ["回流100", "R100", "R_100", "定向100", "r100", "R50-100"]),
    ("R50",    ["回流50", "R50", "R_50", "r50"]),
    ("R10",    ["R_10", "R10", "r10"]),
    ("R2",     ["R_2", "R2", "r2"]),
]