from pydantic import BaseModel, Field
from typing import Optional, List
from datetime import datetime
from enum import Enum


class TradeAction(str, Enum):
    BUY = "BUY"
    SELL = "SELL"


class Recommendation(str, Enum):
    BUY = "BUY"
    SELL = "SELL"
    HOLD = "HOLD"


class RiskLevel(str, Enum):
    LOW = "LOW"
    MEDIUM = "MEDIUM"
    HIGH = "HIGH"


class PriceData(BaseModel):
    time: int
    open: float
    high: float
    low: float
    close: float
    volume: Optional[float] = None


class TradeCreate(BaseModel):
    action: TradeAction
    quantity: float
    price: float


class TradeResponse(BaseModel):
    id: int
    simulation_id: int
    action: TradeAction
    quantity: float
    price: float
    total: float
    pnl: Optional[float] = None
    timestamp: datetime

    class Config:
        from_attributes = True


class PositionResponse(BaseModel):
    symbol: str
    quantity: float
    avg_price: float
    current_price: float
    unrealized_pnl: float
    unrealized_pnl_percent: float

    class Config:
        from_attributes = True


class PortfolioResponse(BaseModel):
    cash: float
    initial_capital: float
    total_value: float
    total_pnl: float
    total_pnl_percent: float
    position: Optional[PositionResponse] = None
    trades: List[TradeResponse] = []


class MarketDataResponse(BaseModel):
    symbol: str = "XAU/USD"
    price: float
    change: float
    change_percent: float
    high_24h: float
    low_24h: float
    volume: float


class SupportResistance(BaseModel):
    support: List[float] = []
    resistance: List[float] = []


class AIAnalysisRequest(BaseModel):
    price_data: List[PriceData]
    indicators: List[dict]
    current_price: float


class AIAnalysisResponse(BaseModel):
    recommendation: Recommendation
    confidence: float = Field(..., ge=0, le=100)
    reasoning: str
    support_resistance: SupportResistance
    risk_level: RiskLevel


class IndicatorData(BaseModel):
    time: int
    value: float


# News and Sentiment Schemas
class Sentiment(str, Enum):
    POSITIVE = "POSITIVE"
    NEGATIVE = "NEGATIVE"
    NEUTRAL = "NEUTRAL"


class NewsArticle(BaseModel):
    id: str
    source: str
    title: str
    description: Optional[str] = None
    url: str
    published_at: datetime
    sentiment: Sentiment
    sentiment_score: float = Field(..., ge=-1, le=1)
    impact_on_gold: str  # HIGH, MEDIUM, LOW
    relevance_score: float = Field(..., ge=0, le=1)
    category: str  # MONETARY_POLICY, GEOPOLITICS, ECONOMIC_DATA, etc.


class NewsFeedResponse(BaseModel):
    articles: List[NewsArticle]
    total_count: int
    bullish_count: int
    bearish_count: int
    neutral_count: int
    overall_sentiment: Sentiment
    avg_sentiment_score: float


class EconomicEvent(BaseModel):
    id: str
    title: str
    country: str
    currency: str
    event_date: datetime
    importance: str  # HIGH, MEDIUM, LOW
    forecast: Optional[str] = None
    previous: Optional[str] = None
    actual: Optional[str] = None
    impact_on_gold: str


class EconomicCalendarResponse(BaseModel):
    events: List[EconomicEvent]
    upcoming_high_impact: int


# Alert Schemas
class AlertType(str, Enum):
    PRICE_SPIKE = "PRICE_SPIKE"
    PRICE_DROP = "PRICE_DROP"
    NEWS_BREAKING = "NEWS_BREAKING"
    SUPPORT_BREACH = "SUPPORT_BREACH"
    RESISTANCE_BREACH = "RESISTANCE_BREACH"
    HIGH_VOLATILITY = "HIGH_VOLATILITY"
    ECONOMIC_EVENT = "ECONOMIC_EVENT"


class AlertSeverity(str, Enum):
    CRITICAL = "CRITICAL"
    HIGH = "HIGH"
    MEDIUM = "MEDIUM"
    LOW = "LOW"


class Alert(BaseModel):
    id: str
    type: AlertType
    severity: AlertSeverity
    title: str
    message: str
    price: Optional[float] = None
    change_percent: Optional[float] = None
    timestamp: datetime
    related_news: Optional[List[str]] = []  # URLs to related news
    action_required: bool = False


class AlertsResponse(BaseModel):
    alerts: List[Alert]
    critical_count: int
    unread_count: int


# News-Price Correlation
class NewsPriceCorrelation(BaseModel):
    news_id: str
    news_title: str
    news_time: datetime
    price_before: float
    price_after: float
    price_change: float
    price_change_percent: float
    time_delta_minutes: int
    correlation_strength: str  # STRONG, MODERATE, WEAK


class CorrelationAnalysisResponse(BaseModel):
    correlations: List[NewsPriceCorrelation]
    significant_events: int
    avg_price_impact: float