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CLAUDE.md §5.2 기준으로 계산해 매수 추천 금액 아래에 함께 표시. · 손절 = ATR(2.5×)과 -7% 중 타이트한 쪽(가격 높은 쪽), 적응형 모드면 그 값 사용 · 목표 = 진입가 + 2R (R = 진입가 − 손절가) — +2R 50% 분할 매도 지점 ATR 은 stock_analysis.atr_14 우선, 없으면 DB 일봉으로 계산(KIS API 미사용). 테스트 4건 추가. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
105 lines
4.4 KiB
Python
105 lines
4.4 KiB
Python
"""AI 투자 분석 — 매수 추천 금액 산출 검증 (자동매매 사이징과 동일 기준)."""
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from unittest.mock import patch
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import pytest
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from app.api.endpoints.market_data import _suggest_buy_amount
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from app.api.endpoints.portfolio import portfolio_cache
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def _summary(total_assets=10_000_000, cash=5_000_000, stock_value=3_000_000):
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return {"total_assets": total_assets, "available_cash": cash, "total_value": stock_value}
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@pytest.fixture(autouse=True)
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def _cache():
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portfolio_cache.summary = _summary()
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yield
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portfolio_cache.summary = None
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class _Adp:
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fallback_used = False
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position_size = 0.06
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def test_buy_amount_uses_position_cap_and_confidence():
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with patch("app.services.adaptive_strategy.get_current_adaptive_params", return_value=_Adp()):
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out = _suggest_buy_amount(current_price=70_000, recommendation="BUY",
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confidence=0.7, stock_code="005930")
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# 1,000만 × 6% = 60만, 신뢰도 0.7 → 보정 1.0 → 70,000원 × 8주 = 560,000
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assert out["suggested_quantity"] == 8
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assert out["suggested_amount"] == 560_000
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assert out["position_ratio"] == pytest.approx(5.6, abs=0.01)
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assert "6%" in out["sizing_note"]
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def test_high_confidence_scales_up_capped_at_120pct():
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with patch("app.services.adaptive_strategy.get_current_adaptive_params", return_value=_Adp()):
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out = _suggest_buy_amount(70_000, "BUY", 1.0, "005930")
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# 60만 × min(1.2, 1.0/0.7=1.43) = 72만 → 10주
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assert out["suggested_quantity"] == 10
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def test_non_buy_recommendation_returns_zero():
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for rec in ("HOLD", "SELL"):
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out = _suggest_buy_amount(70_000, rec, 0.8, "005930")
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assert out["suggested_amount"] == 0 and rec in out["sizing_note"]
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def test_limited_by_available_cash_and_reserve():
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portfolio_cache.summary = _summary(cash=600_000) # 예수금 60만, 최소보유 5% = 50만
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with patch("app.services.adaptive_strategy.get_current_adaptive_params", return_value=_Adp()):
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out = _suggest_buy_amount(70_000, "BUY", 0.7, "005930")
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assert out["suggested_quantity"] == 1 # 사용 가능 10만 → 1주
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def test_limited_by_swing_capital_cap():
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# 스윙 상한 50% = 500만, 이미 490만 보유 → 잔여 10만
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portfolio_cache.summary = _summary(stock_value=4_900_000)
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with patch("app.services.adaptive_strategy.get_current_adaptive_params", return_value=_Adp()):
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out = _suggest_buy_amount(70_000, "BUY", 0.7, "005930")
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assert out["suggested_quantity"] == 1
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def test_no_balance_info_reports_reason():
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portfolio_cache.summary = None
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out = _suggest_buy_amount(70_000, "BUY", 0.8, "005930")
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assert out["suggested_amount"] == 0 and "잔고" in out["sizing_note"]
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# ── 목표가 · 손절가 ─────────────────────────────────────────────────────────
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from app.api.endpoints.market_data import _suggest_targets # noqa: E402
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def test_targets_use_tighter_stop_and_2r():
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"""ATR 손절(2.5×)과 -7% 중 타이트한 쪽(가격 높은 쪽), 목표는 +2R."""
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with patch("app.services.adaptive_strategy.get_current_adaptive_params", side_effect=Exception):
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# ATR 1,000 → ATR손절 100,000-2,500 = 97,500 / -7% = 93,000 → 97,500 채택
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out = _suggest_targets(100_000, atr=1_000.0)
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assert out["stop_loss_price"] == 97_500
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assert out["target_price"] == 105_000 # 100,000 + 2×2,500
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assert out["stop_loss_pct"] == pytest.approx(-2.5, abs=0.01)
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assert out["target_pct"] == pytest.approx(5.0, abs=0.01)
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assert out["risk_reward"] == 2.0
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assert "ATR" in out["target_note"]
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def test_percent_stop_wins_when_atr_is_wide():
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with patch("app.services.adaptive_strategy.get_current_adaptive_params", side_effect=Exception):
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# ATR 5,000 → ATR손절 87,500 < -7% 93,000 → 93,000(타이트) 채택
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out = _suggest_targets(100_000, atr=5_000.0)
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assert out["stop_loss_price"] == 93_000
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assert out["target_price"] == 114_000
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assert "-7%" in out["target_note"]
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def test_targets_without_atr_fall_back_to_percent():
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with patch("app.services.adaptive_strategy.get_current_adaptive_params", side_effect=Exception):
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out = _suggest_targets(100_000, atr=None)
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assert out["stop_loss_price"] == 93_000 and "ATR 없음" in out["target_note"]
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def test_targets_zero_price():
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assert _suggest_targets(0, atr=1000.0)["target_price"] == 0
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