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


# Phase 1: Daily Helper Schemas

class UserProfileCreate(BaseModel):
    email: Optional[str] = None
    username: Optional[str] = None
    timezone: str = "UTC"
    preferred_trading_start: str = "09:00"
    preferred_trading_end: str = "17:00"
    risk_tolerance: str = "moderate"
    trading_style: str = "day_trader"
    daily_target: Optional[float] = None
    max_loss: Optional[float] = None


class UserProfileUpdate(BaseModel):
    timezone: Optional[str] = None
    preferred_trading_start: Optional[str] = None
    preferred_trading_end: Optional[str] = None
    risk_tolerance: Optional[str] = None
    trading_style: Optional[str] = None
    daily_target: Optional[float] = None
    max_loss: Optional[float] = None
    notifications_enabled: Optional[bool] = None
    email_reports: Optional[bool] = None
    sms_enabled: Optional[bool] = None
    push_notifications: Optional[bool] = None
    phone_number: Optional[str] = None


class UserProfileResponse(BaseModel):
    id: int
    email: Optional[str]
    username: Optional[str]
    timezone: str
    preferred_trading_start: str
    preferred_trading_end: str
    risk_tolerance: str
    trading_style: str
    daily_target: Optional[float]
    max_loss: Optional[float]
    notifications_enabled: bool
    email_reports: bool
    sms_enabled: bool
    push_notifications: bool
    created_at: datetime
    updated_at: datetime

    class Config:
        from_attributes = True


class DailyRoutineCreate(BaseModel):
    routine_type: str  # morning, active_trading, evening
    scheduled_time: str  # HH:MM
    tasks: List[str] = []
    enabled: bool = True


class DailyRoutineUpdate(BaseModel):
    scheduled_time: Optional[str] = None
    tasks: Optional[List[str]] = None
    enabled: Optional[bool] = None


class DailyRoutineResponse(BaseModel):
    id: int
    routine_type: str
    scheduled_time: str
    tasks: List[str]
    enabled: bool
    created_at: datetime
    updated_at: datetime

    class Config:
        from_attributes = True


class RoutineExecutionResponse(BaseModel):
    id: int
    routine_id: int
    executed_at: datetime
    completion_status: str
    tasks_completed: List[str]
    execution_notes: Optional[str]

    class Config:
        from_attributes = True


class NotificationCreate(BaseModel):
    notification_type: str
    title: str
    message: str
    priority: str = "normal"
    delivery_method: str = "push"
    data: Optional[dict] = None


class NotificationResponse(BaseModel):
    id: int
    notification_type: str
    title: str
    message: str
    priority: str
    delivery_method: str
    read: bool
    created_at: datetime
    read_at: Optional[datetime]

    class Config:
        from_attributes = True


class NotificationListResponse(BaseModel):
    notifications: List[NotificationResponse]
    unread_count: int
    total_count: int


class ChecklistItem(BaseModel):
    id: str
    title: str
    completed: bool = False
    completed_at: Optional[datetime] = None


class DailyChecklistCreate(BaseModel):
    checklist_type: str  # morning, active_trading, evening, all
    items: Optional[List[ChecklistItem]] = None
    notes: Optional[str] = None


class DailyChecklistUpdate(BaseModel):
    items: Optional[List[ChecklistItem]] = None
    notes: Optional[str] = None


class DailyChecklistResponse(BaseModel):
    id: int
    checklist_date: str  # ISO date string
    checklist_type: str
    items: List[ChecklistItem]
    completion_percentage: float
    notes: Optional[str]
    created_at: datetime
    updated_at: datetime

    class Config:
        from_attributes = True


class HabitTrackerCreate(BaseModel):
    habit_name: str
    frequency: str = "daily"  # daily, weekly


class HabitTrackerResponse(BaseModel):
    id: int
    habit_name: str
    frequency: str
    current_streak: int
    longest_streak: int
    total_completions: int
    created_at: datetime
    updated_at: datetime

    class Config:
        from_attributes = True


class HabitCompletionRequest(BaseModel):
    habit_id: int
    completion_date: Optional[str] = None  # ISO date string, defaults to today


# Phase 3: Advanced Analytics Schemas

class PerformanceSnapshotCreate(BaseModel):
    snapshot_date: Optional[str] = None  # ISO date, defaults to today
    daily_pnl: float
    daily_pnl_percent: float
    total_trades: int
    winning_trades: int
    losing_trades: int
    win_rate: float
    best_trade: Optional[float] = None
    worst_trade: Optional[float] = None
    avg_win: Optional[float] = None
    avg_loss: Optional[float] = None
    sharpe_ratio: Optional[float] = None
    profit_factor: Optional[float] = None
    max_drawdown: Optional[float] = None
    cumulative_pnl: float
    portfolio_value: Optional[float] = None


class PerformanceSnapshotResponse(BaseModel):
    id: int
    snapshot_date: str
    daily_pnl: float
    daily_pnl_percent: float
    total_trades: int
    winning_trades: int
    losing_trades: int
    win_rate: float
    best_trade: Optional[float]
    worst_trade: Optional[float]
    avg_win: Optional[float]
    avg_loss: Optional[float]
    sharpe_ratio: Optional[float]
    profit_factor: Optional[float]
    max_drawdown: Optional[float]
    cumulative_pnl: float
    portfolio_value: Optional[float]
    created_at: datetime

    class Config:
        from_attributes = True


class TradePatternCreate(BaseModel):
    pattern_name: str
    description: Optional[str] = None
    win_rate: float
    avg_win: float
    avg_loss: float
    sample_count: int
    best_timeframe: Optional[str] = None
    best_time_of_day: Optional[str] = None
    confidence_score: float
    indicators_used: List[str] = []
    market_conditions: Optional[str] = None


class TradePatternResponse(BaseModel):
    id: int
    pattern_name: str
    description: Optional[str]
    win_rate: float
    avg_win: float
    avg_loss: float
    sample_count: int
    best_timeframe: Optional[str]
    best_time_of_day: Optional[str]
    confidence_score: float
    indicators_used: List[str]
    market_conditions: Optional[str]
    total_profit: float
    created_at: datetime
    updated_at: datetime

    class Config:
        from_attributes = True


class LessonLearnedCreate(BaseModel):
    category: str  # entry, exit, risk, psychology, market
    lesson_text: str
    related_trades: List[int] = []
    impact: str = "neutral"  # positive, negative, neutral
    tags: List[str] = []
    importance: str = "helpful"  # critical, important, helpful


class LessonLearnedResponse(BaseModel):
    id: int
    date_learned: datetime
    category: str
    lesson_text: str
    related_trades: List[int]
    impact: str
    tags: List[str]
    importance: str
    status: str
    created_at: datetime

    class Config:
        from_attributes = True


class MonthlyReviewCreate(BaseModel):
    year: int
    month: int
    total_trades: int
    total_pnl: float
    total_pnl_percent: float
    best_day: Optional[str] = None  # ISO date
    worst_day: Optional[str] = None
    best_trade: Optional[float] = None
    worst_trade: Optional[float] = None
    win_rate: float
    avg_daily_pnl: Optional[float] = None
    sharpe_ratio: Optional[float] = None
    max_drawdown: Optional[float] = None
    trading_days: int
    best_pattern: Optional[str] = None
    summary: Optional[str] = None
    improvements: List[str] = []
    goals_met: List[str] = []
    goals_missed: List[str] = []


class MonthlyReviewResponse(BaseModel):
    id: int
    year: int
    month: int
    total_trades: int
    total_pnl: float
    total_pnl_percent: float
    best_day: Optional[str]
    worst_day: Optional[str]
    best_trade: Optional[float]
    worst_trade: Optional[float]
    win_rate: float
    avg_daily_pnl: Optional[float]
    sharpe_ratio: Optional[float]
    max_drawdown: Optional[float]
    trading_days: int
    best_pattern: Optional[str]
    summary: Optional[str]
    improvements: List[str]
    goals_met: List[str]
    goals_missed: List[str]
    created_at: datetime

    class Config:
        from_attributes = True