12d235a1f1
Phase 1: - Real-time signal alerts (Discord/Telegram webhook) - Trading journal with entry/exit tracking - Position sizing calculator (Fixed/Kelly/ATR) Phase 2: - Pension asset allocation (DC/IRP 70% risk limit) - Drawdown monitoring with SVG gauge - Benchmark dashboard (portfolio vs KOSPI vs deposit) Phase 3: - Tax benefit simulation (Korean pension tax rules) - Correlation matrix heatmap - Parameter optimizer with grid search + overfit detection
109 lines
2.5 KiB
Python
109 lines
2.5 KiB
Python
"""
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Pension account Pydantic schemas.
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"""
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from datetime import datetime
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from enum import Enum
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from typing import Optional, List
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from pydantic import BaseModel, Field
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from app.schemas.portfolio import FloatDecimal
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class AccountType(str, Enum):
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DC = "dc"
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IRP = "irp"
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PERSONAL = "personal"
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class AssetRiskType(str, Enum):
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SAFE = "safe"
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RISKY = "risky"
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# --- Account schemas ---
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class PensionAccountCreate(BaseModel):
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account_type: AccountType
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account_name: str = Field(..., min_length=1, max_length=100)
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total_amount: FloatDecimal = Field(..., ge=0)
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birth_year: int = Field(..., ge=1940, le=2010)
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target_retirement_age: int = Field(60, ge=50, le=70)
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class PensionAccountUpdate(BaseModel):
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account_name: Optional[str] = Field(None, min_length=1, max_length=100)
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total_amount: Optional[FloatDecimal] = Field(None, ge=0)
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target_retirement_age: Optional[int] = Field(None, ge=50, le=70)
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class PensionHoldingResponse(BaseModel):
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id: int
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account_id: int
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asset_name: str
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asset_type: str
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amount: FloatDecimal
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ratio: FloatDecimal
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class Config:
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from_attributes = True
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class PensionAccountResponse(BaseModel):
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id: int
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user_id: int
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account_type: str
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account_name: str
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total_amount: FloatDecimal
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birth_year: int
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target_retirement_age: int
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created_at: datetime
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updated_at: datetime
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holdings: List[PensionHoldingResponse] = []
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class Config:
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from_attributes = True
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# --- Allocation schemas ---
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class AllocationItem(BaseModel):
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asset_name: str
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asset_type: str # safe / risky
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amount: FloatDecimal
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ratio: FloatDecimal
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class AllocationResult(BaseModel):
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account_id: int
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account_type: str
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total_amount: FloatDecimal
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risky_limit_pct: FloatDecimal
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safe_min_pct: FloatDecimal
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glide_path_equity_pct: FloatDecimal
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glide_path_bond_pct: FloatDecimal
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current_age: int
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years_to_retirement: int
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allocations: List[AllocationItem]
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# --- Recommendation schemas ---
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class RecommendationItem(BaseModel):
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asset_name: str
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asset_type: str
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category: str # tdf, bond_etf, equity_etf, deposit
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ratio: FloatDecimal
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reason: str
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class RecommendationResult(BaseModel):
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account_id: int
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birth_year: int
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current_age: int
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target_retirement_age: int
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years_to_retirement: int
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glide_path_equity_pct: FloatDecimal
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glide_path_bond_pct: FloatDecimal
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recommendations: List[RecommendationItem]
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