trading/shared/schemas/trading.py

193 lines
4.7 KiB
Python

"""Trading-related Pydantic schemas for Redis Streams messages and API payloads."""
from datetime import datetime
from enum import Enum
from typing import Any
from uuid import UUID, uuid4
from pydantic import BaseModel, Field
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"
# ---------------------------------------------------------------------------
# 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
model_config = {"from_attributes": True}
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
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
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}