e2a3bd456d
Wire the trading bot to real Alpaca market data and persist pipeline state to the database so the dashboard displays live information. - Add market-data service fetching OHLCV bars from Alpaca, publishing to market:bars Redis Stream; signal generator consumes bars and injects current_price into signals for position sizing - Sentiment analyzer now persists Article + ArticleSentiment rows to DB after scoring, with duplicate and error handling - API gateway runs a background portfolio sync task that snapshots Alpaca account state into PortfolioSnapshot/Position DB tables during market hours - TradeSignal carries a signal_id UUID; signal generator and trade executor both persist their records to DB with cross-references - 303 unit tests pass (57 new tests added)
164 lines
3.8 KiB
Python
164 lines
3.8 KiB
Python
"""Trading-related Pydantic schemas for Redis Streams messages and API payloads."""
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from datetime import datetime
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from enum import Enum
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from typing import Any
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from uuid import UUID, uuid4
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from pydantic import BaseModel, Field
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class OrderType(str, Enum):
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MARKET = "market"
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LIMIT = "limit"
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STOP = "stop"
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class OrderSide(str, Enum):
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BUY = "BUY"
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SELL = "SELL"
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class OrderStatus(str, Enum):
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PENDING = "PENDING"
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FILLED = "FILLED"
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CANCELLED = "CANCELLED"
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REJECTED = "REJECTED"
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class SignalDirection(str, Enum):
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LONG = "LONG"
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SHORT = "SHORT"
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NEUTRAL = "NEUTRAL"
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# ---------------------------------------------------------------------------
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# API request / response schemas
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# ---------------------------------------------------------------------------
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class OrderRequest(BaseModel):
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"""Submitted by the trade executor or the API to place an order."""
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ticker: str
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side: OrderSide
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qty: float = Field(gt=0)
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order_type: OrderType = OrderType.MARKET
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limit_price: float | None = None
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stop_price: float | None = None
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model_config = {"from_attributes": True}
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class OrderResult(BaseModel):
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"""Returned after order submission or status query."""
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order_id: str
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ticker: str
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side: OrderSide
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qty: float
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filled_price: float | None = None
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status: OrderStatus
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timestamp: datetime
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model_config = {"from_attributes": True}
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class PositionInfo(BaseModel):
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"""Current position state — used in API responses and portfolio views."""
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ticker: str
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qty: float
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avg_entry: float
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current_price: float
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unrealized_pnl: float
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market_value: float
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model_config = {"from_attributes": True}
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class AccountInfo(BaseModel):
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"""Account-level summary from the brokerage."""
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equity: float
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cash: float
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buying_power: float
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portfolio_value: float
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model_config = {"from_attributes": True}
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# ---------------------------------------------------------------------------
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# Redis Stream message schemas
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# ---------------------------------------------------------------------------
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class TradeSignal(BaseModel):
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"""Published to ``signals:generated`` by the signal generator."""
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signal_id: UUID = Field(default_factory=uuid4)
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ticker: str
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direction: SignalDirection
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strength: float = Field(ge=0.0, le=1.0)
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strategy_sources: list[str]
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sentiment_context: dict[str, Any] | None = None
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timestamp: datetime
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model_config = {"from_attributes": True}
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class TradeExecution(BaseModel):
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"""Published to ``trades:executed`` by the trade executor."""
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trade_id: UUID
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ticker: str
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side: OrderSide
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qty: float
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price: float
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status: OrderStatus
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signal_id: UUID | None = None
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strategy_id: UUID | None = None
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timestamp: datetime
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model_config = {"from_attributes": True}
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class OHLCVBar(BaseModel):
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"""Single OHLCV bar."""
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timestamp: datetime
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open: float
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high: float
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low: float
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close: float
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volume: float
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class MarketSnapshot(BaseModel):
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"""Snapshot of market data for a single ticker — used by strategies."""
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ticker: str
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current_price: float
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open: float
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high: float
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low: float
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close: float
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volume: float
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sma_20: float | None = None
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sma_50: float | None = None
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rsi: float | None = None
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bars: list[dict[str, Any]] = Field(default_factory=list)
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model_config = {"from_attributes": True}
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class SentimentContext(BaseModel):
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"""Aggregated sentiment for a ticker — passed to strategies."""
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ticker: str
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avg_score: float = Field(ge=-1.0, le=1.0)
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article_count: int = Field(ge=0)
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recent_scores: list[float] = Field(default_factory=list)
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avg_confidence: float = Field(ge=0.0, le=1.0)
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model_config = {"from_attributes": True}
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