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
108 lines
3.2 KiB
Python
108 lines
3.2 KiB
Python
"""
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Position sizing module.
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Supports three methods:
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- fixed_ratio: Equal allocation (quant.md default, 30% cash reserve)
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- kelly_criterion: Kelly criterion (conservative 1/4 Kelly)
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- atr_based: ATR-based volatility sizing
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"""
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def fixed_ratio(
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capital: float,
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num_positions: int,
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cash_ratio: float = 0.3,
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) -> dict:
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"""Equal allocation across positions with cash reserve.
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Per quant.md: 5-10 positions, 30% cash, max loss per position -3%.
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"""
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if capital <= 0:
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raise ValueError("capital must be positive")
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if num_positions <= 0:
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raise ValueError("num_positions must be positive")
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if cash_ratio < 0 or cash_ratio >= 1.0:
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raise ValueError("cash_ratio must be in [0, 1)")
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investable = capital * (1 - cash_ratio)
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position_size = investable / num_positions
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risk_amount = position_size * 0.03 # -3% max loss per position (quant.md)
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return {
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"method": "fixed",
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"position_size": position_size,
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"shares": 0, # needs price to calculate
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"risk_amount": risk_amount,
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"notes": (
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f"균등 분배: 투자금 {investable:,.0f}원을 {num_positions}개 종목에 배분. "
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f"종목당 최대 손실 -3% = {risk_amount:,.0f}원."
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),
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}
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def kelly_criterion(
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win_rate: float,
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avg_win: float,
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avg_loss: float,
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fraction: float = 0.25,
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) -> dict:
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"""Kelly criterion position sizing (default: conservative 1/4 Kelly).
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Kelly% = W - (1-W)/R where W=win_rate, R=avg_win/avg_loss
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"""
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if win_rate <= 0 or win_rate > 1.0:
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raise ValueError("win_rate must be in (0, 1]")
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if avg_win <= 0:
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raise ValueError("avg_win must be positive")
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if avg_loss <= 0:
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raise ValueError("avg_loss must be positive")
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if fraction <= 0 or fraction > 1.0:
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raise ValueError("fraction must be in (0, 1]")
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win_loss_ratio = avg_win / avg_loss
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kelly_pct = win_rate - (1 - win_rate) / win_loss_ratio
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position_size = max(kelly_pct * fraction, 0.0)
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return {
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"method": "kelly",
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"position_size": position_size,
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"shares": 0,
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"risk_amount": position_size * avg_loss,
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"notes": (
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f"켈리 기준: Full Kelly = {kelly_pct:.2%}, "
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f"{fraction:.0%} Kelly = {position_size:.2%}. "
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f"승률 {win_rate:.0%}, 평균 수익 {avg_win:.1%}, 평균 손실 {avg_loss:.1%}."
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),
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}
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def atr_based(
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capital: float,
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atr: float,
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risk_pct: float = 0.02,
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) -> dict:
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"""ATR-based volatility position sizing.
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Shares = (Capital * Risk%) / ATR
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"""
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if capital <= 0:
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raise ValueError("capital must be positive")
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if atr <= 0:
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raise ValueError("atr must be positive")
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if risk_pct <= 0 or risk_pct > 1.0:
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raise ValueError("risk_pct must be in (0, 1]")
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risk_amount = capital * risk_pct
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shares = int(risk_amount / atr)
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return {
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"method": "atr",
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"position_size": risk_amount,
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"shares": shares,
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"risk_amount": risk_amount,
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"notes": (
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f"ATR 사이징: 자본금의 {risk_pct:.1%} = {risk_amount:,.0f}원 위험 허용. "
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f"ATR {atr:,.0f} 기준 {shares}주."
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),
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}
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