shuim
de7921d270
## CRITICAL 수정 (20건) - day_trading: global 선언 누락, loss_tracker 연동, ban 체크 추가 - trade_executor: BUY 체결가 order_price로 수정, remaining_pct 누적 계산 - risk_validator: get_current_price dict 파싱, MAX_POSITION_SIZE 동기화, DB 세션 누수 - risk_management/recommendations: date.today() → KST 변환 - mdd_manager: Lock 밖 executed_key 참조 수정 - correlation_filter: output → output2 키 수정 - portfolio: 해외 종목 USD/KRW 환율 적용 분리 - Sharpe Ratio 거래빈도 기반 연간화 - telegram_notifier: send_message() 공개 래퍼 추가 - position_manager: 앱 재시작 시 DB 포지션 복원 - swing exit_service: remaining_pct 조건 범위 수정 - run_backtest: original_qty 기반 remaining_pct 계산 - us_market_schedule: 2027년 미국 공휴일 추가 - Login.tsx: res.ok 검증 추가 - mean_reversion_strategy: RSI 로그 하드코딩 수정 ## HIGH 수정 (10건) - db_worker: DLQ 파일 백업 메커니즘 추가 - korea_investment_api: 만료 토큰 임의 연장 제거 - market_data: chart_cache 최대 100개 + TTL 만료 - main.py: rate_limit_store 1시간 미접근 IP 정리 - recommendations: fire-and-forget try/except 추가 - swing/scheduler: DB 세션 3개 분리 - Dashboard: wsMessages 전체 순회 - Layout: /health 엔드포인트 + 30초 폴링 - ml_ensemble: POST /train require_api_key 추가 - day_trading: record_trade_result_safe() 전환 ## 코드 정리 - 프론트엔드: isUsStock, formatTime 공통 유틸 추출 - 백엔드: dead code 제거, 중복 import 통합 - MCP: JSON 파싱 헬퍼 추출 (~60줄 감소) - 전체 ~100개 파일 한국어 주석 추가 - 테스트 13건 코드 기준 수정 (468 passed) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
352 lines
16 KiB
Python
352 lines
16 KiB
Python
"""
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적응형 전략 엔진 유닛 테스트
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[테스트 범위]
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- MarketCondition 분석
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- 모드 전환 로직
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- AdaptiveParams 생성
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- fallback 동작
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- 파라미터 테이블 정합성
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"""
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import pytest
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from unittest.mock import patch, MagicMock
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from app.services.adaptive_strategy import (
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MarketCondition,
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AdaptiveParams,
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TradingMode,
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VixLevel,
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TrendStrength,
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VolumeRegime,
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analyze_market_condition,
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get_adaptive_params,
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_get_fallback_params,
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_calculate_atr_percentile,
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_determine_vix_level,
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_determine_trend_strength,
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_determine_volume_regime,
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_determine_trading_mode,
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get_current_adaptive_params,
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get_adaptive_status,
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_MODE_PARAMS,
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)
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# ═════════════════════════════════════════════════════════════════════════════
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# ATR Percentile
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# ═════════════════════════════════════════════════════════════════════════════
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class TestATRPercentile:
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def test_empty_history(self):
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assert _calculate_atr_percentile([], 10.0) == 50.0
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def test_zero_atr(self):
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assert _calculate_atr_percentile([1.0, 2.0, 3.0], 0.0) == 50.0
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def test_lowest(self):
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history = [10.0, 20.0, 30.0, 40.0, 50.0]
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result = _calculate_atr_percentile(history, 5.0)
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assert result == 0.0
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def test_highest(self):
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history = [10.0, 20.0, 30.0, 40.0, 50.0]
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result = _calculate_atr_percentile(history, 60.0)
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assert result == 100.0
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def test_median(self):
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history = [10.0, 20.0, 30.0, 40.0, 50.0]
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result = _calculate_atr_percentile(history, 30.0)
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assert result == 60.0 # 3 out of 5 <= 30
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# ═════════════════════════════════════════════════════════════════════════════
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# VIX Level
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# ═════════════════════════════════════════════════════════════════════════════
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class TestVixLevel:
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def test_extreme(self):
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assert _determine_vix_level(95.0) == VixLevel.EXTREME.value
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def test_high(self):
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assert _determine_vix_level(75.0) == VixLevel.HIGH.value
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def test_normal(self):
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assert _determine_vix_level(50.0) == VixLevel.NORMAL.value
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def test_low(self):
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assert _determine_vix_level(20.0) == VixLevel.LOW.value
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# ═════════════════════════════════════════════════════════════════════════════
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# Trend Strength
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# ═════════════════════════════════════════════════════════════════════════════
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class TestTrendStrength:
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def test_strong_up(self):
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result = _determine_trend_strength(adx=30.0, ma20=100.0, ma60=95.0, current_price=105.0)
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assert result == TrendStrength.STRONG_UP.value
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def test_strong_down(self):
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result = _determine_trend_strength(adx=30.0, ma20=95.0, ma60=100.0, current_price=90.0)
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assert result == TrendStrength.STRONG_DOWN.value
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def test_sideways_low_adx(self):
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result = _determine_trend_strength(adx=15.0, ma20=100.0, ma60=99.0, current_price=100.0)
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assert result == TrendStrength.SIDEWAYS.value
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def test_weak_up(self):
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result = _determine_trend_strength(adx=22.0, ma20=100.0, ma60=102.0, current_price=101.0)
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assert result == TrendStrength.WEAK_UP.value
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def test_weak_down(self):
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result = _determine_trend_strength(adx=22.0, ma20=100.0, ma60=98.0, current_price=99.0)
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assert result == TrendStrength.WEAK_DOWN.value
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# ═════════════════════════════════════════════════════════════════════════════
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# Volume Regime
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# ═════════════════════════════════════════════════════════════════════════════
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class TestVolumeRegime:
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def test_surge(self):
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assert _determine_volume_regime(2.5) == VolumeRegime.SURGE.value
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def test_normal(self):
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assert _determine_volume_regime(1.0) == VolumeRegime.NORMAL.value
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def test_dry(self):
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assert _determine_volume_regime(0.5) == VolumeRegime.DRY.value
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# ═════════════════════════════════════════════════════════════════════════════
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# Trading Mode
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# ═════════════════════════════════════════════════════════════════════════════
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class TestTradingMode:
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def test_crisis_high_atr(self):
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result = _determine_trading_mode(atr_percentile=96.0, adx_value=20.0, recent_5d_return=0.0, mdd_current=0.0)
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assert result == TradingMode.CRISIS.value
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def test_crisis_deep_loss(self):
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result = _determine_trading_mode(atr_percentile=50.0, adx_value=20.0, recent_5d_return=-9.0, mdd_current=0.0)
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assert result == TradingMode.CRISIS.value
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def test_crisis_mdd(self):
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result = _determine_trading_mode(atr_percentile=50.0, adx_value=20.0, recent_5d_return=0.0, mdd_current=-16.0)
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assert result == TradingMode.CRISIS.value
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def test_defensive_high_atr(self):
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result = _determine_trading_mode(atr_percentile=85.0, adx_value=20.0, recent_5d_return=0.0, mdd_current=0.0)
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assert result == TradingMode.DEFENSIVE.value
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def test_defensive_moderate_loss(self):
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result = _determine_trading_mode(atr_percentile=50.0, adx_value=20.0, recent_5d_return=-6.0, mdd_current=0.0)
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assert result == TradingMode.DEFENSIVE.value
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def test_aggressive(self):
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result = _determine_trading_mode(atr_percentile=20.0, adx_value=30.0, recent_5d_return=3.0, mdd_current=0.0)
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assert result == TradingMode.AGGRESSIVE.value
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def test_aggressive_requires_all_conditions(self):
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# ATR low but ADX too low -> Normal
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result = _determine_trading_mode(atr_percentile=20.0, adx_value=15.0, recent_5d_return=3.0, mdd_current=0.0)
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assert result == TradingMode.NORMAL.value
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def test_normal_default(self):
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result = _determine_trading_mode(atr_percentile=50.0, adx_value=20.0, recent_5d_return=0.5, mdd_current=0.0)
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assert result == TradingMode.NORMAL.value
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def test_crisis_takes_priority_over_defensive(self):
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# ATR=96 triggers both crisis (>95) and defensive (>80), crisis wins
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result = _determine_trading_mode(atr_percentile=96.0, adx_value=30.0, recent_5d_return=3.0, mdd_current=0.0)
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assert result == TradingMode.CRISIS.value
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# ═════════════════════════════════════════════════════════════════════════════
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# AdaptiveParams Generation
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# ═════════════════════════════════════════════════════════════════════════════
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class TestGetAdaptiveParams:
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def test_normal_mode(self):
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condition = MarketCondition(trading_mode=TradingMode.NORMAL.value)
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params = get_adaptive_params(condition)
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assert params.trading_mode == TradingMode.NORMAL.value
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assert params.position_size == 0.06
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assert params.max_holdings == 14
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assert params.daytrading_enabled is True
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def test_crisis_mode(self):
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condition = MarketCondition(trading_mode=TradingMode.CRISIS.value)
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params = get_adaptive_params(condition)
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assert params.trading_mode == TradingMode.CRISIS.value
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assert params.position_size == 0.015
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assert params.max_holdings == 3
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assert params.daytrading_enabled is False
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assert params.short_term_enabled is False
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def test_defensive_mode(self):
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condition = MarketCondition(trading_mode=TradingMode.DEFENSIVE.value)
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params = get_adaptive_params(condition)
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assert params.trading_mode == TradingMode.DEFENSIVE.value
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assert params.position_size == 0.04
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assert params.daytrading_enabled is True
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def test_aggressive_mode(self):
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condition = MarketCondition(trading_mode=TradingMode.AGGRESSIVE.value)
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params = get_adaptive_params(condition)
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assert params.trading_mode == TradingMode.AGGRESSIVE.value
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assert params.position_size == 0.07
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assert params.max_holdings == 15
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assert params.rsi_entry == 50.0
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def test_condition_attached(self):
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condition = MarketCondition(
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trading_mode=TradingMode.NORMAL.value,
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atr_percentile=42.0,
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)
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params = get_adaptive_params(condition)
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assert params.condition is not None
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assert params.condition.atr_percentile == 42.0
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# ═════════════════════════════════════════════════════════════════════════════
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# Fallback
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# ═════════════════════════════════════════════════════════════════════════════
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class TestFallback:
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def test_fallback_returns_normal_mode(self):
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params = _get_fallback_params()
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assert params.trading_mode == TradingMode.NORMAL.value
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assert params.fallback_used is True
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def test_fallback_uses_config_values(self):
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params = _get_fallback_params()
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# Should match config.py defaults (MAX_POSITION_SIZE=0.06)
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assert params.position_size == 0.06
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assert params.atr_stop_multiplier == 2.5
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def test_get_current_without_init(self):
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"""초기화 없이 호출하면 fallback을 반환해야 한다."""
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# Reset global state
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import app.services.adaptive_strategy as mod
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mod._current_params = None
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mod._last_update_ts = 0.0
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params = get_current_adaptive_params()
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assert params.fallback_used is True
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assert params.trading_mode == TradingMode.NORMAL.value
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# ═════════════════════════════════════════════════════════════════════════════
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# analyze_market_condition
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# ═════════════════════════════════════════════════════════════════════════════
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class TestAnalyzeMarketCondition:
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def test_insufficient_data(self):
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condition = analyze_market_condition(closes=[100.0] * 10)
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assert condition.trading_mode == TradingMode.NORMAL.value
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def test_with_full_data(self):
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# Generate synthetic data: 100 days of gradually rising prices
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closes = [10000.0 + i * 10 for i in range(100)]
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highs = [c + 50 for c in closes]
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lows = [c - 50 for c in closes]
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volumes = [1000000.0] * 100
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condition = analyze_market_condition(
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closes=closes, highs=highs, lows=lows, volumes=volumes
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)
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assert condition.analyzed_at != ""
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assert condition.trading_mode in [m.value for m in TradingMode]
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def test_high_volatility_triggers_defensive(self):
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# Create highly volatile data
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import random
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random.seed(42)
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base = [10000.0]
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for _ in range(99):
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change = random.uniform(-500, 500)
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base.append(max(5000, base[-1] + change))
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# Make last 5 days very volatile
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for i in range(95, 100):
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base[i] = base[i] + random.uniform(-1000, 1000)
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highs = [c + abs(c * 0.05) for c in base]
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lows = [c - abs(c * 0.05) for c in base]
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volumes = [1000000.0] * 100
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condition = analyze_market_condition(
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closes=base, highs=highs, lows=lows, volumes=volumes
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)
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# High volatility should push toward defensive or crisis
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assert condition.atr_percentile > 0 # some ATR analysis happened
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def test_mdd_triggers_crisis(self):
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closes = [10000.0] * 100
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condition = analyze_market_condition(closes=closes, mdd_current=-16.0)
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assert condition.trading_mode == TradingMode.CRISIS.value
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# ═════════════════════════════════════════════════════════════════════════════
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# Mode Params Table Consistency
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# ═════════════════════════════════════════════════════════════════════════════
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class TestModeParamsTable:
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"""각 모드의 파라미터가 유효한 범위에 있는지 확인."""
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def test_all_modes_defined(self):
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for mode in TradingMode:
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assert mode.value in _MODE_PARAMS
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def test_position_size_range(self):
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for mode, params in _MODE_PARAMS.items():
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assert 0.0 <= params["position_size"] <= 0.10, f"{mode}: position_size out of range"
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def test_stop_loss_negative(self):
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for mode, params in _MODE_PARAMS.items():
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assert params["stop_loss_pct"] < 0, f"{mode}: stop_loss_pct should be negative"
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def test_atr_multiplier_positive(self):
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for mode, params in _MODE_PARAMS.items():
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assert params["atr_stop_multiplier"] > 0, f"{mode}: atr_stop_multiplier should be positive"
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def test_rsi_entry_ascending_with_defense(self):
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"""방어적일수록 RSI 진입 기준이 낮아야 한다."""
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assert _MODE_PARAMS["aggressive"]["rsi_entry"] > _MODE_PARAMS["normal"]["rsi_entry"]
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assert _MODE_PARAMS["normal"]["rsi_entry"] > _MODE_PARAMS["defensive"]["rsi_entry"]
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assert _MODE_PARAMS["defensive"]["rsi_entry"] > _MODE_PARAMS["crisis"]["rsi_entry"]
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def test_max_holdings_descending_with_defense(self):
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"""방어적일수록 최대 보유 종목이 적어야 한다."""
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assert _MODE_PARAMS["aggressive"]["max_holdings"] >= _MODE_PARAMS["normal"]["max_holdings"]
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assert _MODE_PARAMS["normal"]["max_holdings"] >= _MODE_PARAMS["defensive"]["max_holdings"]
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assert _MODE_PARAMS["defensive"]["max_holdings"] >= _MODE_PARAMS["crisis"]["max_holdings"]
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def test_crisis_disables_daytrading(self):
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assert _MODE_PARAMS["crisis"]["daytrading_enabled"] is False
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assert _MODE_PARAMS["crisis"]["short_term_enabled"] is False
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def test_defensive_disables_short_term(self):
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assert _MODE_PARAMS["defensive"]["daytrading_enabled"] is True
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assert _MODE_PARAMS["defensive"]["short_term_enabled"] is False
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# ═════════════════════════════════════════════════════════════════════════════
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# API Status
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# ═════════════════════════════════════════════════════════════════════════════
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class TestAdaptiveStatus:
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def test_status_returns_dict(self):
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# Reset global
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import app.services.adaptive_strategy as mod
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mod._current_params = None
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mod._current_condition = None
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mod._last_update_ts = 0.0
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status = get_adaptive_status()
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assert isinstance(status, dict)
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assert "trading_mode" in status
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assert "params" in status
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assert status["fallback_used"] is True
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