trading/shared/schemas/trading.py

"""Trading-related Pydantic schemas for Redis Streams messages and API payloads."""

from datetime import UTC, datetime
from decimal import Decimal
from enum import Enum
from typing import Any, Literal
from uuid import UUID, uuid4

from pydantic import BaseModel, Field, model_validator


class OrderType(str, Enum):
    MARKET = "market"
    LIMIT = "limit"
    STOP = "stop"


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


class OrderStatus(str, Enum):
    PENDING = "PENDING"
    FILLED = "FILLED"
    CANCELLED = "CANCELLED"
    REJECTED = "REJECTED"


class SignalDirection(str, Enum):
    LONG = "LONG"
    SHORT = "SHORT"
    NEUTRAL = "NEUTRAL"
    EXIT = "EXIT"


# ---------------------------------------------------------------------------
# API request / response schemas
# ---------------------------------------------------------------------------


class OrderRequest(BaseModel):
    """Submitted by the trade executor or the API to place an order."""

    ticker: str
    side: OrderSide
    qty: float = Field(gt=0)
    order_type: OrderType = OrderType.MARKET
    limit_price: float | None = None
    stop_price: float | None = None

    order_class: Literal["simple", "bracket"] = "simple"
    take_profit_price: float | None = None
    stop_loss_price: float | None = None

    model_config = {"from_attributes": True}

    @model_validator(mode="after")
    def _bracket_requires_legs(self) -> "OrderRequest":
        if self.order_class == "bracket" and (
            self.take_profit_price is None or self.stop_loss_price is None
        ):
            raise ValueError(
                "bracket orders require take_profit_price + stop_loss_price"
            )
        return self


class OrderResult(BaseModel):
    """Returned after order submission or status query."""

    order_id: str
    ticker: str
    side: OrderSide
    qty: float
    filled_price: float | None = None
    status: OrderStatus
    timestamp: datetime

    model_config = {"from_attributes": True}


class PositionInfo(BaseModel):
    """Current position state — used in API responses and portfolio views."""

    ticker: str
    qty: float
    avg_entry: float
    current_price: float
    unrealized_pnl: float
    market_value: float

    model_config = {"from_attributes": True}


class AccountInfo(BaseModel):
    """Account-level summary from the brokerage."""

    equity: float
    cash: float
    buying_power: float
    portfolio_value: float

    model_config = {"from_attributes": True}


# ---------------------------------------------------------------------------
# Redis Stream message schemas
# ---------------------------------------------------------------------------


class TradeSignal(BaseModel):
    """Published to ``signals:generated`` by the signal generator."""

    signal_id: UUID = Field(default_factory=uuid4)
    ticker: str
    direction: SignalDirection
    strength: float = Field(ge=0.0, le=1.0)
    strategy_sources: list[str]
    sentiment_context: dict[str, Any] | None = None
    timestamp: datetime = Field(default_factory=lambda: datetime.now(UTC))

    # --- Kevin v2 extensions (optional) ---
    strategy_id: UUID | None = None
    target_dollars: Decimal | None = None
    stop_loss_pct: Decimal | None = None
    take_profit_pct: Decimal | None = None
    current_price: Decimal | None = None

    model_config = {"from_attributes": True}


class TradeExecution(BaseModel):
    """Published to ``trades:executed`` by the trade executor."""

    trade_id: UUID
    ticker: str
    side: OrderSide
    qty: float
    price: float
    status: OrderStatus
    signal_id: UUID | None = None
    strategy_id: UUID | None = None
    strategy_sources: list[str] = Field(default_factory=list)
    timestamp: datetime

    model_config = {"from_attributes": True}


class OHLCVBar(BaseModel):
    """Single OHLCV bar."""

    timestamp: datetime
    open: float
    high: float
    low: float
    close: float
    volume: float


class FundamentalsSnapshot(BaseModel):
    """Fundamental financial data for a single ticker — cached daily."""

    ticker: str
    eps_ttm: float | None = None
    pe_ratio: float | None = None
    peg_ratio: float | None = None
    revenue_growth_yoy: float | None = None
    profit_margin: float | None = None
    debt_to_equity: float | None = None
    market_cap: float | None = None
    fetched_at: datetime

    model_config = {"from_attributes": True}


class MarketSnapshot(BaseModel):
    """Snapshot of market data for a single ticker — used by strategies."""

    ticker: str
    current_price: float
    open: float
    high: float
    low: float
    close: float
    volume: float
    sma_20: float | None = None
    sma_50: float | None = None
    rsi: float | None = None
    # Technical indicators — computed by MarketDataManager
    ema_9: float | None = None
    ema_21: float | None = None
    sma_200: float | None = None
    macd: float | None = None
    macd_signal: float | None = None
    macd_histogram: float | None = None
    bollinger_upper: float | None = None
    bollinger_mid: float | None = None
    bollinger_lower: float | None = None
    vwap: float | None = None
    atr: float | None = None
    bars: list[dict[str, Any]] = Field(default_factory=list)
    fundamentals: FundamentalsSnapshot | None = None

    model_config = {"from_attributes": True}


class SentimentContext(BaseModel):
    """Aggregated sentiment for a ticker — passed to strategies."""

    ticker: str
    avg_score: float = Field(ge=-1.0, le=1.0)
    article_count: int = Field(ge=0)
    recent_scores: list[float] = Field(default_factory=list)
    avg_confidence: float = Field(ge=0.0, le=1.0)

    model_config = {"from_attributes": True}