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
35 lines
1.1 KiB
Python
35 lines
1.1 KiB
Python
"""
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Correlation analysis schemas.
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"""
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from typing import List, Optional
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from pydantic import BaseModel, Field
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class CorrelationMatrixRequest(BaseModel):
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stock_codes: List[str] = Field(..., description="종목 코드 리스트")
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period_days: int = Field(60, description="분석 기간 (일)", ge=7, le=365)
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class HighCorrelationPair(BaseModel):
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stock_a: str
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stock_b: str
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correlation: float = Field(..., description="상관계수 (-1 ~ 1)")
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class CorrelationMatrixResponse(BaseModel):
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stock_codes: List[str]
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matrix: List[List[Optional[float]]] = Field(..., description="상관 행렬 (NxN)")
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high_correlation_pairs: List[HighCorrelationPair] = Field(
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default_factory=list, description="높은 상관관계 종목 쌍 (|r| > 0.7)"
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)
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class DiversificationResponse(BaseModel):
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portfolio_id: int
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diversification_score: float = Field(
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..., description="분산 효과 점수 (0=집중, 1=완벽 분산)", ge=0, le=1
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)
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stock_count: int
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high_correlation_pairs: List[HighCorrelationPair] = Field(default_factory=list)
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