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>
45 lines
1.2 KiB
Python
45 lines
1.2 KiB
Python
"""
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Trading signal models.
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"""
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import enum
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from datetime import datetime
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from sqlalchemy import (
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Column, Integer, String, Numeric, DateTime, Date,
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Text, Enum as SQLEnum,
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)
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from app.core.database import Base
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class SignalType(str, enum.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.Enum):
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ACTIVE = "active"
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EXECUTED = "executed"
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EXPIRED = "expired"
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class Signal(Base):
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__tablename__ = "signals"
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id = Column(Integer, primary_key=True, index=True)
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date = Column(Date, nullable=False, index=True)
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ticker = Column(String(20), nullable=False, index=True)
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name = Column(String(100))
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signal_type = Column(SQLEnum(SignalType), nullable=False)
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entry_price = Column(Numeric(12, 2))
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target_price = Column(Numeric(12, 2))
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stop_loss_price = Column(Numeric(12, 2))
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reason = Column(String(200))
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status = Column(SQLEnum(SignalStatus), default=SignalStatus.ACTIVE)
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created_at = Column(DateTime, default=datetime.utcnow)
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# Execution tracking fields
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executed_price = Column(Numeric(12, 2), nullable=True)
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executed_quantity = Column(Integer, nullable=True)
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executed_at = Column(DateTime, nullable=True)
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