from typing import Literal

from pydantic import BaseModel, Field


Precision = Literal["realtime_exact", "close_final", "historical_exact", "unavailable"]
MarketState = Literal["pre_open", "trading_am", "midday_break", "trading_pm", "finalizing", "closed"]
PushStatus = Literal["pending", "sent", "failed", "skipped"]


class HealthResponse(BaseModel):
    status: str


class ValueWithStatus(BaseModel):
    amount_hkd_billion: float | None = None
    precision: Precision
    label: str


class OverviewSnapshot(BaseModel):
    trade_date: str
    snapshot_time: str | None = None
    market_state: MarketState
    total_net_inflow: ValueWithStatus
    cumulative_net_inflow: ValueWithStatus
    shanghai_net_inflow: ValueWithStatus
    shenzhen_net_inflow: ValueWithStatus
    buy_amount: ValueWithStatus
    sell_amount: ValueWithStatus
    net_buy_amount: ValueWithStatus
    one_min_change: ValueWithStatus
    five_min_change: ValueWithStatus
    threshold_progress: float = Field(ge=0, le=1)
    next_threshold_hkd_billion: float
    source_name: str
    source_url: str | None = None
    updated_at: str | None = None
    unavailable_reason: str | None = None


class TimelinePoint(BaseModel):
    timestamp: str
    amount_hkd_billion: float | None = None
    precision: Precision


class BenchmarkTimelinePoint(BaseModel):
    timestamp: str
    value: float | None = None


class BenchmarkTimelineSeries(BaseModel):
    key: str
    label: str
    unit: str
    detail_url: str | None = None
    points: list[BenchmarkTimelinePoint]


class PushRecord(BaseModel):
    id: str
    triggered_at: str
    push_type: str
    rule_code: str
    trigger_value_hkd_billion: float | None = None
    description: str
    email_subject: str
    email_summary: str
    status: PushStatus
    error_message: str | None = None


class PushRecordsResponse(BaseModel):
    records: list[PushRecord]


class OverviewResponse(BaseModel):
    snapshot: OverviewSnapshot
    minute_timeline: list[TimelinePoint]
    benchmark_series: list[BenchmarkTimelineSeries]
    recent_push_records: list[PushRecord]


class StatPoint(BaseModel):
    period: str
    amount_hkd_billion: float


class RecentTradeDay(BaseModel):
    trade_date: str
    total_net_inflow_hkd_billion: float
    precision: Precision


class HistorySummary(BaseModel):
    cumulative_net_inflow_hkd_billion: float
    trading_day_count: int
    max_single_day_inflow_hkd_billion: float
    max_single_day_outflow_hkd_billion: float
    longest_inflow_streak: int
    longest_outflow_streak: int


class HistoryResponse(BaseModel):
    start_date: str
    daily: list[StatPoint]
    weekly: list[StatPoint]
    monthly: list[StatPoint]
    cumulative: list[StatPoint]
    benchmark_history: dict[str, list[StatPoint]]
    recent_trade_days: list[RecentTradeDay]
    summary: HistorySummary


class RuleItem(BaseModel):
    key: str
    label: str
    value: str
    description: str


class RulesResponse(BaseModel):
    items: list[RuleItem]


class SourceDiagnosticsResponse(BaseModel):
    source_name: str
    realtime_available: bool
    historical_available: bool
    last_success_at: str | None = None
    last_failure_at: str | None = None
    last_error_reason: str | None = None
    last_success_url: str | None = None
    last_persisted_at: str | None = None


class MetaResponse(BaseModel):
    product_name: str
    version: str
    timezone: str
    market_state: MarketState
    current_trade_date: str
    source_name: str
    source_strategy: str
    note: str


class AShareFlowRecord(BaseModel):
    trade_date: str
    code: str
    name: str
    detail_url: str | None = None
    latest_price: float | None = None
    change_amount: float | None = None
    change_percent: float | None = None
    main_net_inflow: float | None = None
    main_net_inflow_ratio: float | None = None
    super_large_net_inflow: float | None = None
    super_large_net_inflow_ratio: float | None = None
    large_net_inflow: float | None = None
    large_net_inflow_ratio: float | None = None
    medium_net_inflow: float | None = None
    medium_net_inflow_ratio: float | None = None
    small_net_inflow: float | None = None
    small_net_inflow_ratio: float | None = None
    rolling_net_inflow_5d: float | None = None
    rolling_net_inflow_10d: float | None = None
    rolling_net_inflow_30d: float | None = None
    rolling_net_inflow_60d: float | None = None
    rolling_net_inflow_90d: float | None = None
    updated_at: str | None = None
    source_name: str
    source_url: str | None = None
    precision: Precision
    snapshot_time: str | None = None
    sector_type: str | None = None
    sector_type_label: str | None = None


class AShareSectorGroup(BaseModel):
    label: str
    records: list[AShareFlowRecord]


class AShareIndexFlowResponse(BaseModel):
    trade_date: str
    updated_at: str | None = None
    source_name: str
    source_url: str | None = None
    precision: Precision
    records: list[AShareFlowRecord]


class AShareSectorFlowResponse(BaseModel):
    trade_date: str
    updated_at: str | None = None
    source_name: str
    source_url: str | None = None
    precision: Precision
    sector_types: dict[str, AShareSectorGroup]