zephyrdark
eb06dfc48b
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Phase 1 (Critical): - Add bulk rebalance apply API + UI with confirmation modal - Add strategy results to portfolio targets flow (shared component) Phase 2 (Important): - Show current holdings in signal execute modal with auto-fill - Add DC pension risk asset ratio warning (70% limit) - Add KOSPI benchmark comparison to portfolio returns - Track signal execution details (price, quantity, timestamp) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
63 lines
1.4 KiB
Python
63 lines
1.4 KiB
Python
"""
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Signal related Pydantic schemas.
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"""
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from datetime import date, datetime
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from decimal import Decimal
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from typing import Optional, List
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from enum import Enum
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from pydantic import BaseModel, Field
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from app.schemas.portfolio import FloatDecimal
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class SignalExecuteRequest(BaseModel):
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portfolio_id: int
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quantity: int = Field(..., gt=0)
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price: FloatDecimal = Field(..., gt=0)
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class SignalType(str, Enum):
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BUY = "buy"
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SELL = "sell"
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PARTIAL_SELL = "partial_sell"
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class SignalStatus(str, Enum):
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ACTIVE = "active"
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EXECUTED = "executed"
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EXPIRED = "expired"
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class SignalResponse(BaseModel):
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id: int
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date: date
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ticker: str
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name: Optional[str] = None
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signal_type: str
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entry_price: Optional[FloatDecimal] = None
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target_price: Optional[FloatDecimal] = None
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stop_loss_price: Optional[FloatDecimal] = None
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reason: Optional[str] = None
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status: str
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created_at: datetime
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executed_price: Optional[FloatDecimal] = None
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executed_quantity: Optional[int] = None
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executed_at: Optional[datetime] = None
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class Config:
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from_attributes = True
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class ActivePosition(BaseModel):
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ticker: str
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name: Optional[str] = None
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entry_date: date
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entry_price: FloatDecimal
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current_price: FloatDecimal
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shares: int
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stop_loss_price: FloatDecimal
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target_price: FloatDecimal
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pnl_percent: FloatDecimal
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pnl_amount: FloatDecimal
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