"""
Pydantic schemas for structured LLM outputs.

These schemas ensure type-safe, validated responses from LLM calls,
eliminating the need for manual parsing and reducing errors.
"""

from typing import List, Literal, Optional

from pydantic import BaseModel, ConfigDict, Field


class TradeDecision(BaseModel):
    """Structured output for trading decisions."""

    decision: Literal["BUY", "SELL", "HOLD"] = Field(description="The final trading decision")
    rationale: str = Field(description="Detailed explanation of the decision")
    confidence: Literal["high", "medium", "low"] = Field(
        description="Confidence level in the decision"
    )
    key_factors: List[str] = Field(description="List of key factors influencing the decision")


class TickerList(BaseModel):
    """Structured output for ticker symbol lists."""

    tickers: List[str] = Field(
        description="List of valid stock ticker symbols (1-5 uppercase letters)"
    )


class TickerWithContext(BaseModel):
    """Individual ticker with context description."""

    ticker: str = Field(description="Stock ticker symbol (1-5 uppercase letters)")
    context: str = Field(
        description="Brief description of why this ticker is relevant (key metrics, catalyst, etc.)"
    )


class TickerContextList(BaseModel):
    """Structured output for tickers with context."""

    candidates: List[TickerWithContext] = Field(
        description="List of stock tickers with context explaining their relevance"
    )


class RedditTicker(BaseModel):
    """Individual ticker extracted from Reddit with source classification."""

    ticker: str = Field(
        description="Stock ticker symbol (1-5 uppercase letters only, e.g., AAPL, NVDA, TSLA)"
    )
    source: Literal["trending", "dd"] = Field(
        description="Source type: 'trending' for social mentions, 'dd' for due diligence research"
    )
    context: str = Field(
        description="Brief description of the sentiment, thesis, or why the ticker was mentioned"
    )
    confidence: Literal["high", "medium", "low"] = Field(
        default="medium",
        description="Confidence that this is a valid stock ticker (not crypto, index, or gibberish)",
    )


class RedditTickerList(BaseModel):
    """Structured output for Reddit ticker extraction."""

    model_config = ConfigDict(extra="forbid")  # Strict validation

    tickers: List[RedditTicker] = Field(
        description="List of stock tickers extracted from Reddit posts"
    )


class ThemeList(BaseModel):
    """Structured output for market themes."""

    themes: List[str] = Field(description="List of trending market themes or sectors")


class MarketMover(BaseModel):
    """Individual market mover entry."""

    ticker: str = Field(description="Stock ticker symbol")
    type: Literal["gainer", "loser"] = Field(description="Whether this is a top gainer or loser")
    change_percent: Optional[float] = Field(default=None, description="Percent change for the move")
    reason: Optional[str] = Field(default=None, description="Brief reason for the movement")


class MarketMovers(BaseModel):
    """Structured output for market movers."""

    movers: List[MarketMover] = Field(description="List of market movers (gainers and losers)")


class NewsItem(BaseModel):
    """Individual news item entry."""

    model_config = ConfigDict(extra="forbid")

    title: str = Field(description="Headline title of the news item")
    summary: str = Field(description="2-3 sentence summary of the key points")
    published_at: Optional[str] = Field(
        default=None, description="ISO-8601 timestamp of publication if available"
    )
    companies_mentioned: List[str] = Field(
        description="List of company names or ticker symbols mentioned"
    )
    themes: List[str] = Field(description="List of key themes or categories")
    sentiment: Literal["positive", "negative", "neutral"] = Field(
        default="neutral", description="Expected price impact direction"
    )
    importance: int = Field(ge=1, le=10, description="Importance score from 1-10")


class NewsList(BaseModel):
    """Structured output for news items."""

    model_config = ConfigDict(extra="forbid")

    news: List[NewsItem] = Field(description="List of news items")


class FilingItem(BaseModel):
    """Individual SEC filing entry."""

    model_config = ConfigDict(extra="forbid")

    title: str = Field(description="Company name and filing type")
    summary: str = Field(description="Summary of the material event")
    published_at: Optional[str] = Field(
        default=None, description="ISO-8601 timestamp of publication if available"
    )
    companies_mentioned: List[str] = Field(description="Company names or tickers mentioned")
    themes: List[str] = Field(
        description="Type of event (e.g., acquisition, guidance, executive change)"
    )
    sentiment: Literal["positive", "negative", "neutral"] = Field(
        default="neutral", description="Expected price impact direction"
    )
    importance: int = Field(ge=1, le=10, description="Importance score from 1-10")


class FilingsList(BaseModel):
    """Structured output for SEC filings."""

    model_config = ConfigDict(extra="forbid")

    filings: List[FilingItem] = Field(description="List of important SEC filings")


class DiscoveryRankingItem(BaseModel):
    """Individual discovery ranking entry."""

    ticker: str = Field(description="Stock ticker symbol")
    rank: int = Field(ge=1, description="Rank order (1 is highest)")
    strategy_match: str = Field(
        description="Primary strategy match (e.g., Momentum, Contrarian, Insider)"
    )
    base_score: float = Field(ge=0, le=10, description="Base strategy score before modifiers")
    modifiers: str = Field(description="Score modifiers with brief rationale")
    final_score: float = Field(description="Final score after modifiers")
    confidence: int = Field(ge=1, le=10, description="Confidence score from 1-10")
    reason: str = Field(description="Specific rationale with actionable insight")


class DiscoveryRankingList(BaseModel):
    """Structured output for discovery rankings."""

    rankings: List[DiscoveryRankingItem] = Field(
        description="Ranked list of top discovery opportunities"
    )


class InvestmentOpportunity(BaseModel):
    """Individual investment opportunity."""

    ticker: str = Field(description="Stock ticker symbol")
    score: int = Field(ge=1, le=10, description="Investment score from 1-10")
    rationale: str = Field(description="Why this is a good opportunity")
    risk_level: Literal["low", "medium", "high"] = Field(description="Risk level assessment")


class RankedOpportunities(BaseModel):
    """Structured output for ranked investment opportunities."""

    opportunities: List[InvestmentOpportunity] = Field(
        description="List of investment opportunities ranked by score"
    )
    market_context: str = Field(description="Brief overview of current market conditions")


class DebateDecision(BaseModel):
    """Structured output for debate/research manager decisions."""

    decision: Literal["BUY", "SELL", "HOLD"] = Field(description="Investment recommendation")
    summary: str = Field(description="Summary of the debate and key arguments")
    bull_points: List[str] = Field(description="Key bullish arguments")
    bear_points: List[str] = Field(description="Key bearish arguments")
    investment_plan: str = Field(description="Detailed investment plan for the trader")


class RiskAssessment(BaseModel):
    """Structured output for risk management decisions."""

    final_decision: Literal["BUY", "SELL", "HOLD"] = Field(
        description="Final trading decision after risk assessment"
    )
    risk_level: Literal["low", "medium", "high", "very_high"] = Field(
        description="Overall risk level"
    )
    adjusted_plan: str = Field(description="Risk-adjusted investment plan")
    risk_factors: List[str] = Field(description="Key risk factors identified")
    mitigation_strategies: List[str] = Field(description="Strategies to mitigate identified risks")