diff --git a/backend/app/api/__init__.py b/backend/app/api/__init__.py index 1c6013b..aba5a67 100644 --- a/backend/app/api/__init__.py +++ b/backend/app/api/__init__.py @@ -7,7 +7,7 @@ from fastapi import APIRouter from app.api.endpoints import ( trading_rules, trades, portfolio, market_data, mcp, recommendations, monitoring, tensorflow_predictions, debug, risk_management, ml_ensemble, - journals, day_trading, strategy_config, backtest, + journals, day_trading, strategy_config, backtest, analysis, ) api_router = APIRouter() @@ -27,3 +27,4 @@ api_router.include_router(journals.router, prefix="/journals", tags=["journals"] api_router.include_router(day_trading.router, prefix="/daytrading", tags=["day_trading"]) api_router.include_router(strategy_config.router, prefix="/strategy-config", tags=["strategy_config"]) api_router.include_router(backtest.router, prefix="/backtest", tags=["backtest"]) +api_router.include_router(analysis.router, prefix="/analysis", tags=["analysis"]) diff --git a/backend/app/api/endpoints/analysis.py b/backend/app/api/endpoints/analysis.py new file mode 100644 index 0000000..c089b7a --- /dev/null +++ b/backend/app/api/endpoints/analysis.py @@ -0,0 +1,242 @@ +""" +거래 성과 분석 + 전략 자동 최적화 API + +GET /api/v1/analysis/performance?days=30 — 성과 통계 +GET /api/v1/analysis/insights?days=30 — 전략 인사이트 + 개선 제안 +POST /api/v1/analysis/generate-journals — 미생성 일지 일괄 생성 +POST /api/v1/analysis/optimize-strategy — 전략 최적화 즉시 실행 +GET /api/v1/analysis/optimization-history — 최적화 이력 +""" +import logging +from datetime import datetime, date +from typing import Any, Dict, List, Optional + +from fastapi import APIRouter, BackgroundTasks, Depends, Query +from sqlalchemy import select, desc +from sqlalchemy.ext.asyncio import AsyncSession + +from app.core.database import get_db, AsyncSessionLocal +from app.models.models import StrategyOptimizationLog, TradeJournal +from app.services.trade_analyzer import ( + analyze_performance, + build_insights, + create_journal_record, + generate_journal_for_trade, + get_uncovered_trades, +) +from app.services.strategy_optimizer import ( + get_last_optimization_date, + run_optimization, + save_optimization_log, +) + +router = APIRouter() +logger = logging.getLogger(__name__) + + +# ───────────────────────────────────────────────────────────────────────────── +# GET /performance +# ───────────────────────────────────────────────────────────────────────────── + +@router.get("/performance") +async def get_performance( + days: int = Query(default=30, ge=1, le=365, description="분석 기간 (일)"), + db: AsyncSession = Depends(get_db), +) -> Dict[str, Any]: + """최근 N일 완료 거래 성과 통계 반환.""" + try: + return await analyze_performance(db, days=days) + except Exception as e: + logger.error(f"performance 분석 실패: {e}", exc_info=True) + return {"error": str(e), "summary": {}, "total_trades": 0} + + +# ───────────────────────────────────────────────────────────────────────────── +# GET /insights +# ───────────────────────────────────────────────────────────────────────────── + +@router.get("/insights") +async def get_insights( + days: int = Query(default=30, ge=1, le=365), + db: AsyncSession = Depends(get_db), +) -> Dict[str, Any]: + """전략 인사이트 및 개선 제안 반환.""" + try: + perf = await analyze_performance(db, days=days) + insights = build_insights(perf) + last_opt_date = await get_last_optimization_date(db) + return { + "insights": insights, + "performance_summary": perf.get("summary", {}), + "last_optimization_date": last_opt_date.isoformat() if last_opt_date else None, + "period_days": days, + "generated_at": datetime.utcnow().isoformat(), + } + except Exception as e: + logger.error(f"insights 생성 실패: {e}", exc_info=True) + return {"insights": [], "error": str(e)} + + +# ───────────────────────────────────────────────────────────────────────────── +# POST /generate-journals +# ───────────────────────────────────────────────────────────────────────────── + +async def _generate_journals_task(days: int = 7) -> Dict[str, Any]: + """백그라운드에서 미생성 일지를 일괄 생성.""" + async with AsyncSessionLocal() as db: + try: + trades = await get_uncovered_trades(db, days=days) + if not trades: + return {"created": 0, "message": "생성할 미처리 거래 없음"} + + perf = await analyze_performance(db, days=30) + created = 0 + for trade in trades: + try: + content = generate_journal_for_trade(trade, perf) + await create_journal_record(db, trade, content) + created += 1 + except Exception as e: + logger.warning(f"일지 생성 실패 ({trade.stock_code}): {e}") + + await db.commit() + logger.info(f"✅ 매매일지 {created}건 생성 완료 (대상: {len(trades)}건)") + return {"created": created, "total_uncovered": len(trades)} + except Exception as e: + await db.rollback() + logger.error(f"일지 일괄 생성 실패: {e}", exc_info=True) + return {"created": 0, "error": str(e)} + + +@router.post("/generate-journals") +async def generate_journals( + background_tasks: BackgroundTasks, + days: int = Query(default=7, ge=1, le=30, description="최근 N일 내 미생성 거래 대상"), + db: AsyncSession = Depends(get_db), +) -> Dict[str, Any]: + """일지가 없는 완료 거래에 대해 템플릿 기반 매매일지를 일괄 생성.""" + try: + uncovered = await get_uncovered_trades(db, days=days) + count = len(uncovered) + if count == 0: + return {"status": "skipped", "message": "생성할 미처리 거래 없음", "queued": 0} + + background_tasks.add_task(_generate_journals_task, days) + return { + "status": "started", + "message": f"{count}건의 거래 일지 생성을 시작했습니다.", + "queued": count, + } + except Exception as e: + logger.error(f"generate-journals 실패: {e}", exc_info=True) + return {"status": "error", "message": str(e)} + + +# ───────────────────────────────────────────────────────────────────────────── +# POST /optimize-strategy +# ───────────────────────────────────────────────────────────────────────────── + +# 최적화 진행 상태 (단순 인메모리 플래그) +_optimization_running: bool = False + + +@router.post("/optimize-strategy") +async def optimize_strategy( + background_tasks: BackgroundTasks, + days: int = Query(default=30, ge=7, le=180, description="성과 분석 기간"), + period_days: int = Query(default=90, ge=30, le=365, description="백테스트 기간"), +) -> Dict[str, Any]: + """ + 전략 파라미터 최적화를 즉시 실행 (수동 트리거). + + - 성과 분석 → 파라미터 그리드 서치 → Sharpe 10% 개선 시 자동 적용 + - 백그라운드에서 실행 (수 분 소요 가능) + """ + global _optimization_running + if _optimization_running: + return {"status": "already_running", "message": "최적화가 이미 실행 중입니다."} + + async def _opt_task(): + global _optimization_running + _optimization_running = True + try: + async with AsyncSessionLocal() as db: + perf = await analyze_performance(db, days=days) + result = await run_optimization(db, perf, trigger="manual", period_days=period_days) + await save_optimization_log(db, result, trigger="manual") + logger.info(f"✅ 수동 최적화 완료: applied={result.applied}, reason={result.apply_reason}") + except Exception as e: + logger.error(f"최적화 실패: {e}", exc_info=True) + finally: + _optimization_running = False + + background_tasks.add_task(_opt_task) + return { + "status": "started", + "message": "전략 최적화를 시작했습니다. /optimization-history 에서 결과를 확인하세요.", + "performance_days": days, + "backtest_period_days": period_days, + } + + +# ───────────────────────────────────────────────────────────────────────────── +# GET /optimization-history +# ───────────────────────────────────────────────────────────────────────────── + +@router.get("/optimization-history") +async def get_optimization_history( + limit: int = Query(default=10, ge=1, le=50), + db: AsyncSession = Depends(get_db), +) -> List[Dict[str, Any]]: + """전략 최적화 실행 이력 반환.""" + try: + stmt = ( + select(StrategyOptimizationLog) + .order_by(desc(StrategyOptimizationLog.created_at)) + .limit(limit) + ) + result = await db.execute(stmt) + logs = result.scalars().all() + + return [ + { + "id": log.id, + "run_date": log.run_date.isoformat() if log.run_date else None, + "trigger": log.trigger, + "period_days": log.period_days, + "best_sharpe": float(log.best_sharpe) if log.best_sharpe else None, + "current_sharpe": float(log.current_sharpe) if log.current_sharpe else None, + "sharpe_improvement_pct": ( + round( + (float(log.best_sharpe) - float(log.current_sharpe)) + / max(abs(float(log.current_sharpe)), 0.001) + * 100, + 2, + ) + if log.best_sharpe and log.current_sharpe + else None + ), + "best_win_rate": float(log.best_win_rate) if log.best_win_rate else None, + "best_mdd": float(log.best_mdd) if log.best_mdd else None, + "applied": log.applied, + "apply_reason": log.apply_reason, + "best_params": log.best_params, + "previous_params": log.previous_params, + "total_combinations": log.total_combinations, + "created_at": log.created_at.isoformat() if log.created_at else None, + } + for log in logs + ] + except Exception as e: + logger.error(f"optimization-history 조회 실패: {e}", exc_info=True) + return [] + + +# ───────────────────────────────────────────────────────────────────────────── +# GET /optimization-status +# ───────────────────────────────────────────────────────────────────────────── + +@router.get("/optimization-status") +async def get_optimization_status() -> Dict[str, Any]: + """현재 최적화 실행 상태 조회.""" + return {"running": _optimization_running} diff --git a/backend/app/api/endpoints/journals.py b/backend/app/api/endpoints/journals.py index c8a7b55..818720a 100644 --- a/backend/app/api/endpoints/journals.py +++ b/backend/app/api/endpoints/journals.py @@ -2,11 +2,13 @@ 매매일지 API 엔드포인트 - GET /: 최근 AI 생성 매매일지 목록 +- GET /stats: 일지 통계 (전략별, 감정 분포) - GET /{journal_id}: 특정 일지 상세 +- POST /generate/{trade_id}: 특정 거래에 대한 일지 즉시 생성 """ -from fastapi import APIRouter, Depends, Query +from fastapi import APIRouter, Depends, HTTPException, Query from sqlalchemy.ext.asyncio import AsyncSession -from sqlalchemy import select, desc +from sqlalchemy import select, desc, func from typing import Optional from datetime import datetime, timezone import logging @@ -19,7 +21,7 @@ def _utc_iso(dt: datetime | None) -> str | None: return dt.replace(tzinfo=timezone.utc).isoformat() from app.core.database import get_db -from app.models.models import TradeJournal +from app.models.models import TradeJournal, TradeHistory logger = logging.getLogger(__name__) @@ -63,6 +65,81 @@ async def list_journals( return [] +@router.get("/stats") +async def get_journal_stats(db: AsyncSession = Depends(get_db)): + """일지 통계: 전략별 / 감정별 분포.""" + try: + result_mood = await db.execute( + select(TradeJournal.mood, func.count(TradeJournal.id).label("cnt")) + .group_by(TradeJournal.mood) + ) + mood_dist = {row[0] or "unknown": row[1] for row in result_mood.fetchall()} + + result_strat = await db.execute( + select(TradeJournal.strategy_name, func.count(TradeJournal.id).label("cnt")) + .group_by(TradeJournal.strategy_name) + ) + strat_dist = {row[0] or "unknown": row[1] for row in result_strat.fetchall()} + + total = sum(mood_dist.values()) + return { + "total": total, + "by_mood": mood_dist, + "by_strategy": strat_dist, + } + except Exception as e: + logger.error(f"journal stats error: {e}", exc_info=True) + return {"total": 0, "by_mood": {}, "by_strategy": {}} + + +@router.post("/generate/{trade_id}") +async def generate_journal_for_trade_id( + trade_id: int, + db: AsyncSession = Depends(get_db), +): + """특정 완료 거래(trade_id)에 대해 템플릿 기반 매매일지를 즉시 생성.""" + from app.services.trade_analyzer import ( + analyze_performance, + generate_journal_for_trade, + create_journal_record, + ) + # 기존 일지 확인 + existing = await db.execute( + select(TradeJournal).where(TradeJournal.trade_id == trade_id) + ) + if existing.scalar_one_or_none(): + raise HTTPException(status_code=409, detail="이미 일지가 존재합니다.") + + # 거래 조회 + trade_result = await db.execute( + select(TradeHistory).where(TradeHistory.id == trade_id) + ) + trade = trade_result.scalar_one_or_none() + if not trade: + raise HTTPException(status_code=404, detail="거래 이력을 찾을 수 없습니다.") + if trade.order_type != "SELL" or trade.status != "FILLED": + raise HTTPException(status_code=400, detail="완료된 매도 거래에 대해서만 일지를 생성할 수 있습니다.") + + perf = await analyze_performance(db, days=30) + content = generate_journal_for_trade(trade, perf) + journal = await create_journal_record(db, trade, content) + await db.commit() + await db.refresh(journal) + + return { + "id": journal.id, + "trade_id": journal.trade_id, + "stock_code": journal.stock_code, + "stock_name": journal.stock_name, + "journal_date": _utc_iso(journal.journal_date), + "content": journal.content, + "strategy_name": journal.strategy_name, + "mood": journal.mood, + "tags": journal.tags, + "created_at": _utc_iso(journal.created_at), + } + + @router.get("/{journal_id}") async def get_journal(journal_id: int, db: AsyncSession = Depends(get_db)): """특정 매매일지 상세 조회""" diff --git a/backend/app/main.py b/backend/app/main.py index c222bd4..b704bc3 100644 --- a/backend/app/main.py +++ b/backend/app/main.py @@ -372,6 +372,111 @@ async def day_trade_scheduler(): raise +async def post_market_analyzer_scheduler(): + """장 종료 후 16:00 KST 1회 실행 — 매매일지 자동 생성 + 전략 자동 최적화. + + 조건: + - 매일 16:00 KST 에 1회 실행 (이미 오늘 실행했으면 스킵) + - 최근 7일 내 SELL FILLED 거래 >= 5건이면 최적화 실행 + - 마지막 최적화로부터 3일 이상 경과 시만 최적화 실행 + """ + import pytz as _pytz + from datetime import datetime as _dt, date as _date, timedelta as _td + + _KST = _pytz.timezone("Asia/Seoul") + _last_run: _date | None = None + + await asyncio.sleep(60) # 부팅 안정화 + logger.info("📝 Post-market analyzer scheduler started (daily 16:00 KST)") + + try: + while True: + now_kst = _dt.now(_KST) + today = now_kst.date() + + if _last_run == today: + tomorrow_16 = _KST.localize(_dt(today.year, today.month, today.day, 16, 0, 0)) + _td(days=1) + sleep_sec = (tomorrow_16 - now_kst).total_seconds() + await asyncio.sleep(max(sleep_sec, 60)) + continue + + if (now_kst.hour, now_kst.minute) < (16, 0): + target = _KST.localize(_dt(today.year, today.month, today.day, 16, 0, 0)) + sleep_sec = (target - now_kst).total_seconds() + if sleep_sec > 0: + await asyncio.sleep(min(sleep_sec, 1800)) + continue + + if today.weekday() >= 5: + await asyncio.sleep(3600) + continue + + logger.info("📝 [PostMarket] 매매일지 자동 생성 + 전략 최적화 시작") + _last_run = today + + try: + from app.services.trade_analyzer import ( + analyze_performance, + get_uncovered_trades, + generate_journal_for_trade, + create_journal_record, + ) + from app.services.strategy_optimizer import ( + run_optimization, + save_optimization_log, + get_last_optimization_date, + ) + from app.models.models import TradeHistory + from sqlalchemy import func, and_ + from datetime import datetime as _utcnow_dt + + async with AsyncSessionLocal() as db: + uncovered = await get_uncovered_trades(db, days=7) + perf = await analyze_performance(db, days=30) + journal_count = 0 + for trade in uncovered: + try: + content = generate_journal_for_trade(trade, perf) + await create_journal_record(db, trade, content) + journal_count += 1 + except Exception as e: + logger.warning(f"일지 생성 실패 ({trade.stock_code}): {e}") + if journal_count > 0: + await db.commit() + logger.info(f"📝 [PostMarket] 일지 {journal_count}건 생성 완료") + + since_7d = _utcnow_dt.utcnow() - _td(days=7) + trade_count_result = await db.execute( + select(func.count(TradeHistory.id)).where( + and_( + TradeHistory.order_type == "SELL", + TradeHistory.status == "FILLED", + TradeHistory.created_at >= since_7d, + ) + ) + ) + recent_trade_count = trade_count_result.scalar() or 0 + last_opt = await get_last_optimization_date(db) + days_since_opt = (today - last_opt).days if last_opt else 999 + + if recent_trade_count >= 5 and days_since_opt >= 3: + logger.info(f"🔧 [PostMarket] 전략 최적화 실행 (최근거래:{recent_trade_count}건, 마지막:{days_since_opt}일 전)") + result = await run_optimization(db, perf, trigger="auto", period_days=90) + await save_optimization_log(db, result, trigger="auto") + logger.info(f"✅ [PostMarket] 최적화 완료: applied={result.applied}, {result.apply_reason}") + else: + logger.info(f"⏭️ [PostMarket] 최적화 스킵 (거래:{recent_trade_count}건, 마지막최적화:{days_since_opt}일 전)") + + except Exception as e: + logger.error(f"❌ [PostMarket] 분석 실패: {e}", exc_info=True) + + await asyncio.sleep(3600) + + except asyncio.CancelledError: + logger.info("⏹️ Post-market analyzer scheduler stopped") + raise + + async def krx_data_collector_scheduler(): """장 종료 후 KRX 일봉 데이터 자동 수집 스케줄러. @@ -550,6 +655,7 @@ async def lifespan(app: FastAPI): app.state.batch_analysis_task = asyncio.create_task(batch_analysis_scheduler()) app.state.day_trade_task = asyncio.create_task(day_trade_scheduler()) app.state.krx_collector_task = asyncio.create_task(krx_data_collector_scheduler()) + app.state.post_market_task = asyncio.create_task(post_market_analyzer_scheduler()) # KIS 실시간 스트림 시작 (설정으로 제어 가능) if settings.KIS_WS_ENABLED: @@ -568,6 +674,7 @@ async def lifespan(app: FastAPI): "batch_analysis_task", "day_trade_task", "krx_collector_task", + "post_market_task", "db_worker_task", "kis_stream_task", ): diff --git a/backend/app/models/models.py b/backend/app/models/models.py index eac4642..48a564c 100644 --- a/backend/app/models/models.py +++ b/backend/app/models/models.py @@ -322,3 +322,23 @@ class WatchlistHistory(Base): change_rate = Column(Numeric(10, 4), nullable=True) rsi = Column(Numeric(10, 4), nullable=True) signals = Column(JSONB, nullable=True) + + +class StrategyOptimizationLog(Base): + """전략 자동 최적화 실행 이력""" + __tablename__ = "strategy_optimization_logs" + + id = Column(Integer, primary_key=True, index=True) + run_date = Column(Date, nullable=False, index=True) + trigger = Column(String(20), nullable=False, default="auto") # auto / manual + period_days = Column(Integer, nullable=False, default=90) + best_params = Column(JSONB, nullable=True) + best_sharpe = Column(Numeric(10, 4), nullable=True) + best_win_rate = Column(Numeric(10, 4), nullable=True) + best_mdd = Column(Numeric(10, 4), nullable=True) + current_sharpe = Column(Numeric(10, 4), nullable=True) + previous_params = Column(JSONB, nullable=True) + applied = Column(Boolean, default=False, nullable=False) + apply_reason = Column(Text, nullable=True) + total_combinations = Column(Integer, nullable=True) + created_at = Column(DateTime, default=datetime.utcnow, index=True) diff --git a/backend/app/services/strategy_optimizer.py b/backend/app/services/strategy_optimizer.py new file mode 100644 index 0000000..a954b0f --- /dev/null +++ b/backend/app/services/strategy_optimizer.py @@ -0,0 +1,407 @@ +""" +전략 자동 최적화 서비스 + +[역할] +- 최근 거래 성과 약점 분석 → 파라미터 범위 결정 +- BacktestEngine을 사용한 그리드 서치로 최적 파라미터 탐색 +- 평가 지표: Sharpe * 0.4 + win_rate * 0.3 + (1 - |MDD|/100) * 0.3 +- Sharpe 10% 이상 개선 시 strategy_config 자동 적용 +- StrategyOptimizationLog 테이블에 이력 저장 + +[사용법] + from app.services.strategy_optimizer import run_optimization + + async with AsyncSessionLocal() as db: + result = await run_optimization(db, perf_data, trigger="manual") +""" +import asyncio +import logging +from datetime import date, datetime, timedelta +from typing import Any, Dict, List, Optional, Tuple +from dataclasses import dataclass + +from sqlalchemy import select, desc +from sqlalchemy.ext.asyncio import AsyncSession + +from app.core.database import AsyncSessionLocal +from app.models.models import StrategyOptimizationLog, StockDailyPrice +from app.services.strategy_config_service import strategy_cfg +from app.services.backtest_engine import BacktestEngine, BacktestConfig + +logger = logging.getLogger(__name__) + + +# ───────────────────────────────────────────────────────────────────────────── +# 결과 데이터클래스 +# ───────────────────────────────────────────────────────────────────────────── + +@dataclass +class OptimizationResult: + best_params: Dict[str, Any] + best_score: float + best_sharpe: float + best_win_rate: float + best_mdd: float + current_sharpe: float + previous_params: Dict[str, Any] + applied: bool + apply_reason: str + total_combinations: int + + +# ───────────────────────────────────────────────────────────────────────────── +# 파라미터 범위 결정 (성과 약점 기반) +# ───────────────────────────────────────────────────────────────────────────── + +def _build_param_grid(perf_data: Dict[str, Any]) -> Dict[str, List[Any]]: + """ + 성과 약점을 분석하여 그리드 서치할 파라미터 범위를 결정. + + 규칙: + - 승률 < 50% → RSI_OVERSOLD_LEVEL 강화 (더 낮은 값) + - avg_loss > 손절 기준 → ATR_STOP_MULTIPLIER, STOP_LOSS_PERCENT 조정 + - MDD 과다(< -15%) → MAX_POSITION_SIZE, MAX_HOLDINGS_COUNT 조정 + - 항상 RSI 기간 변화도 포함 (성과 개선 여지 높음) + """ + summary = perf_data.get("summary", {}) + win_rate = summary.get("win_rate", 50.0) + avg_loss = summary.get("avg_loss_pct", -5.0) + + grid: Dict[str, List[Any]] = {} + + # RSI_OVERSOLD_LEVEL: 승률 기반 + if win_rate < 40: + grid["RSI_OVERSOLD_LEVEL"] = [20.0, 25.0, 28.0] + elif win_rate < 50: + grid["RSI_OVERSOLD_LEVEL"] = [25.0, 28.0, 30.0] + else: + grid["RSI_OVERSOLD_LEVEL"] = [28.0, 30.0, 32.0] + + # STOP_LOSS_PERCENT: 평균 손실 기반 + if avg_loss < -8.0: + grid["STOP_LOSS_PERCENT"] = [-5.0, -6.0, -7.0] + elif avg_loss < -6.0: + grid["STOP_LOSS_PERCENT"] = [-5.0, -6.0] + else: + grid["STOP_LOSS_PERCENT"] = [-6.0, -7.0, -8.0] + + # ATR_STOP_MULTIPLIER: 항상 포함 + grid["ATR_STOP_MULTIPLIER"] = [2.0, 2.5, 3.0] + + # BOLLINGER_STDDEV + grid["BOLLINGER_STDDEV"] = [1.8, 2.0, 2.2] + + return grid + + +def _cartesian_product(grid: Dict[str, List[Any]]) -> List[Dict[str, Any]]: + """딕셔너리 그리드의 카르테시안 곱을 반환.""" + keys = list(grid.keys()) + values = list(grid.values()) + + def _product(lists): + result = [[]] + for lst in lists: + result = [x + [y] for x in result for y in lst] + return result + + combos = _product(values) + return [dict(zip(keys, combo)) for combo in combos] + + +# ───────────────────────────────────────────────────────────────────────────── +# 현재 전략 파라미터 수집 +# ───────────────────────────────────────────────────────────────────────────── + +async def _get_current_params() -> Dict[str, Any]: + """현재 strategy_cfg 값들을 읽어 반환.""" + await strategy_cfg._refresh() + return { + "RSI_OVERSOLD_LEVEL": strategy_cfg.get("RSI_OVERSOLD_LEVEL", 30.0), + "STOP_LOSS_PERCENT": strategy_cfg.get("STOP_LOSS_PERCENT", -6.0), + "ATR_STOP_MULTIPLIER": strategy_cfg.get("ATR_STOP_MULTIPLIER", 2.5), + "BOLLINGER_STDDEV": strategy_cfg.get("BOLLINGER_STDDEV", 2.0), + "MAX_POSITION_SIZE": strategy_cfg.get("MAX_POSITION_SIZE", 0.04), + "MAX_HOLDINGS_COUNT": strategy_cfg.get("MAX_HOLDINGS_COUNT", 20), + } + + +# ───────────────────────────────────────────────────────────────────────────── +# 백테스트로 파라미터 평가 +# ───────────────────────────────────────────────────────────────────────────── + +def _compute_score(sharpe: float, win_rate: float, mdd: float) -> float: + """통합 평가 지표 계산: Sharpe*0.4 + win_rate*0.3 + (1-|mdd|/100)*0.3""" + mdd_penalty = max(0.0, 1.0 - abs(mdd) / 100.0) + return sharpe * 0.4 + (win_rate / 100.0) * 0.3 + mdd_penalty * 0.3 + + +async def _evaluate_params( + params: Dict[str, Any], + stock_codes: List[str], + start_date: date, + end_date: date, + initial_capital: float, +) -> Tuple[float, float, float, float]: + """ + 단일 파라미터 조합으로 백테스트 실행. + Returns: (score, sharpe, win_rate, mdd) + """ + try: + backtest_params = { + "MEAN_REVERSION_RSI_THRESHOLD": params.get("RSI_OVERSOLD_LEVEL", 30.0), + "MEAN_REVERSION_STOP_LOSS_PCT": params.get("STOP_LOSS_PERCENT", -6.0), + "MAX_TOTAL_POSITIONS": min(5, len(stock_codes)), + } + config = BacktestConfig( + stock_codes=stock_codes, + start_date=start_date, + end_date=end_date, + initial_capital=initial_capital, + strategy_params=backtest_params, + max_positions=min(5, len(stock_codes)), + position_size=params.get("MAX_POSITION_SIZE", 0.2), + ) + engine = BacktestEngine() + result = await engine.run_backtest(config) + + sharpe = float(result.sharpe_ratio or 0.0) + win_rate = float(result.win_rate or 0.0) + mdd = float(result.mdd_pct or 0.0) + score = _compute_score(sharpe, win_rate, mdd) + return score, sharpe, win_rate, mdd + except Exception as e: + logger.debug(f"백테스트 실패 (파라미터: {params}): {e}") + return -1.0, 0.0, 0.0, 0.0 + + +# ───────────────────────────────────────────────────────────────────────────── +# 대표 종목 선택 +# ───────────────────────────────────────────────────────────────────────────── + +async def _get_representative_stocks(period_days: int = 90) -> List[str]: + """ + 최근 N일 거래가 있는 대표 종목 10개 반환. + 백테스트 실행 시간을 줄이기 위해 종목 수 제한. + """ + from app.models.models import TradeHistory + from sqlalchemy import func, and_ + from datetime import datetime, timedelta + + since = datetime.utcnow() - timedelta(days=period_days) + + async with AsyncSessionLocal() as db: + stmt = ( + select(TradeHistory.stock_code, func.count(TradeHistory.id).label("cnt")) + .where(TradeHistory.created_at >= since) + .group_by(TradeHistory.stock_code) + .order_by(desc("cnt")) + .limit(10) + ) + result = await db.execute(stmt) + codes = [row[0] for row in result.fetchall()] + + # 거래 이력 없으면 stock_daily_prices에서 최근 종목 fallback + if not codes: + async with AsyncSessionLocal() as db: + stmt = ( + select(StockDailyPrice.stock_code) + .where(StockDailyPrice.trade_date >= (date.today() - timedelta(days=period_days))) + .group_by(StockDailyPrice.stock_code) + .limit(10) + ) + result = await db.execute(stmt) + codes = [row[0] for row in result.fetchall()] + + return codes or [] + + +# ───────────────────────────────────────────────────────────────────────────── +# 파라미터 적용 +# ───────────────────────────────────────────────────────────────────────────── + +async def apply_params(params: Dict[str, Any], reason: str = "auto_optimizer") -> bool: + """strategy_cfg에 파라미터를 일괄 적용.""" + try: + for key, value in params.items(): + await strategy_cfg.upsert(key, str(value), updated_by=reason) + logger.info(f"✅ 전략 파라미터 자동 적용 완료: {list(params.keys())} (사유: {reason})") + return True + except Exception as e: + logger.error(f"❌ 파라미터 적용 실패: {e}", exc_info=True) + return False + + +# ───────────────────────────────────────────────────────────────────────────── +# 최적화 이력 저장 +# ───────────────────────────────────────────────────────────────────────────── + +async def save_optimization_log(db: AsyncSession, result: OptimizationResult, trigger: str) -> StrategyOptimizationLog: + """최적화 결과를 DB에 저장.""" + log = StrategyOptimizationLog( + run_date=date.today(), + trigger=trigger, + period_days=90, + best_params=result.best_params, + best_sharpe=result.best_sharpe, + best_win_rate=result.best_win_rate, + best_mdd=result.best_mdd, + current_sharpe=result.current_sharpe, + previous_params=result.previous_params, + applied=result.applied, + apply_reason=result.apply_reason, + total_combinations=result.total_combinations, + ) + db.add(log) + await db.commit() + await db.refresh(log) + return log + + +# ───────────────────────────────────────────────────────────────────────────── +# 메인 최적화 함수 +# ───────────────────────────────────────────────────────────────────────────── + +async def run_optimization( + db: AsyncSession, + perf_data: Dict[str, Any], + trigger: str = "auto", + period_days: int = 90, + initial_capital: float = 10_000_000, +) -> OptimizationResult: + """ + 성과 데이터 기반 전략 파라미터 최적화. + + Args: + db: AsyncSession + perf_data: analyze_performance() 반환값 + trigger: "auto" | "manual" + period_days: 백테스트 기간 (일) + initial_capital: 백테스트 초기 자본 + + Returns: + OptimizationResult + """ + logger.info(f"🔧 전략 최적화 시작 (trigger={trigger}, period={period_days}일)") + + # 1. 현재 파라미터 저장 (롤백용) + prev_params = await _get_current_params() + + # 2. 그리드 구성 + param_grid = _build_param_grid(perf_data) + combinations = _cartesian_product(param_grid) + total_combos = len(combinations) + logger.info(f" 그리드 조합 수: {total_combos}") + + # 3. 대표 종목 선택 + stock_codes = await _get_representative_stocks(period_days) + if not stock_codes: + logger.warning("백테스트 대상 종목 없음. 최적화 건너뜀.") + return OptimizationResult( + best_params=prev_params, + best_score=0.0, + best_sharpe=0.0, + best_win_rate=0.0, + best_mdd=0.0, + current_sharpe=0.0, + previous_params=prev_params, + applied=False, + apply_reason="no_stock_data", + total_combinations=0, + ) + + end_date = date.today() + start_date = end_date - timedelta(days=period_days) + + # 4. 현재 파라미터 기준 Sharpe 계산 + cur_score, cur_sharpe, cur_wr, cur_mdd = await _evaluate_params( + prev_params, stock_codes, start_date, end_date, initial_capital + ) + logger.info(f" 현재 성과 → Sharpe: {cur_sharpe:.3f}, 승률: {cur_wr:.1f}%, MDD: {cur_mdd:.1f}%") + + # 5. 그리드 서치 (배치 5개씩 병렬) + best_score = cur_score + best_params = dict(prev_params) + best_sharpe = cur_sharpe + best_win_rate = cur_wr + best_mdd = cur_mdd + + batch_size = 5 + for i in range(0, len(combinations), batch_size): + batch = combinations[i:i + batch_size] + tasks = [ + _evaluate_params(p, stock_codes, start_date, end_date, initial_capital) + for p in batch + ] + results = await asyncio.gather(*tasks, return_exceptions=True) + for combo, res in zip(batch, results): + if isinstance(res, Exception): + continue + score, sharpe, wr, mdd = res + if score > best_score: + best_score = score + best_sharpe = sharpe + best_win_rate = wr + best_mdd = mdd + best_params = {**prev_params, **combo} + + logger.info(f" 최적 성과 → Sharpe: {best_sharpe:.3f}, 승률: {best_win_rate:.1f}%, MDD: {best_mdd:.1f}%") + + # 6. 자동 적용 여부 결정 (Sharpe 10% 이상 개선) + improvement_threshold = 1.1 + should_apply = ( + cur_sharpe > 0 + and best_sharpe >= cur_sharpe * improvement_threshold + and best_params != prev_params + ) + if cur_sharpe <= 0 and best_sharpe > 0: + should_apply = True + + applied = False + apply_reason = "" + + if should_apply: + improvement_pct = ((best_sharpe - cur_sharpe) / max(abs(cur_sharpe), 0.001)) * 100 + apply_reason = f"Sharpe {improvement_pct:.1f}% 향상 ({cur_sharpe:.3f} → {best_sharpe:.3f})" + # 최적화된 파라미터만 적용 (prev와 다른 키만) + changed = {k: v for k, v in best_params.items() if str(v) != str(prev_params.get(k, ""))} + if changed: + applied = await apply_params(changed, reason="auto_optimizer") + if applied: + logger.info(f"✅ 자동 적용 완료: {apply_reason}") + else: + if cur_sharpe <= 0 and best_sharpe <= 0: + apply_reason = "insufficient_data" + elif best_sharpe < cur_sharpe * improvement_threshold: + pct = ((best_sharpe - cur_sharpe) / max(abs(cur_sharpe), 0.001)) * 100 + apply_reason = f"개선 미달 ({pct:+.1f}% < 10%)" + else: + apply_reason = "no_improvement" + logger.info(f"⏭️ 자동 적용 건너뜀: {apply_reason}") + + return OptimizationResult( + best_params=best_params, + best_score=best_score, + best_sharpe=best_sharpe, + best_win_rate=best_win_rate, + best_mdd=best_mdd, + current_sharpe=cur_sharpe, + previous_params=prev_params, + applied=applied, + apply_reason=apply_reason, + total_combinations=total_combos, + ) + + +# ───────────────────────────────────────────────────────────────────────────── +# 마지막 최적화 이력 조회 +# ───────────────────────────────────────────────────────────────────────────── + +async def get_last_optimization_date(db: AsyncSession) -> Optional[date]: + """마지막 최적화 실행일 반환 (없으면 None).""" + stmt = select(StrategyOptimizationLog.run_date).order_by( + desc(StrategyOptimizationLog.created_at) + ).limit(1) + result = await db.execute(stmt) + row = result.scalar_one_or_none() + return row diff --git a/backend/app/services/trade_analyzer.py b/backend/app/services/trade_analyzer.py new file mode 100644 index 0000000..87b81ac --- /dev/null +++ b/backend/app/services/trade_analyzer.py @@ -0,0 +1,438 @@ +""" +거래 성과 분석 서비스 + +[역할] +- TradeHistory 레코드를 분석해 전략 성과 통계를 생성 +- 전략별 / 요일별 승률, profit factor, 패턴 분석 +- 완료 거래 대상 매매일지 템플릿 자동 생성 +- journals.py / analysis.py 엔드포인트에서 사용 +""" +import logging +from datetime import datetime, timedelta, date +from typing import Any, Dict, List, Optional +from decimal import Decimal + +from sqlalchemy import select, and_, func +from sqlalchemy.ext.asyncio import AsyncSession + +from app.models.models import TradeHistory, TradeJournal + +logger = logging.getLogger(__name__) + +# ───────────────────────────────────────────────────────────────────────────── +# 내부 헬퍼 +# ───────────────────────────────────────────────────────────────────────────── + +def _to_float(val) -> float: + if val is None: + return 0.0 + try: + return float(val) + except (TypeError, ValueError): + return 0.0 + + +def _parse_strategy(entry_reason: Optional[str]) -> str: + """entry_reason 문자열에서 전략 이름 추출.""" + if not entry_reason: + return "unknown" + reason = entry_reason.lower() + if "mean_reversion" in reason or "평균회귀" in reason: + return "mean_reversion" + if "short_term" in reason or "단기" in reason: + return "short_term" + if "kosdaq" in reason or "코스닥" in reason: + return "kosdaq_scanner" + if "day_trade" in reason or "데이트레이딩" in reason: + return "day_trade" + if "rule" in reason or "룰" in reason: + return "rule_based" + return "unknown" + + +def _parse_exit_type(exit_reason: Optional[str]) -> str: + """exit_reason 문자열에서 청산 유형 추출.""" + if not exit_reason: + return "unknown" + reason = exit_reason.lower() + if "stop_loss" in reason or "손절" in reason: + return "stop_loss" + if "trailing" in reason or "트레일링" in reason: + return "trailing_stop" + if "take_profit" in reason or "익절" in reason: + return "take_profit" + if "eod" in reason or "장마감" in reason or "강제" in reason: + return "eod_forced" + if "manual" in reason or "수동" in reason: + return "manual" + if "rsi" in reason: + return "rsi_exit" + return "unknown" + + +# ───────────────────────────────────────────────────────────────────────────── +# 핵심 분석 함수 +# ───────────────────────────────────────────────────────────────────────────── + +async def analyze_performance(db: AsyncSession, days: int = 30) -> Dict[str, Any]: + """ + 최근 N일 완료 거래 성과 분석. + + Returns: + { + "summary": {전체 통계}, + "by_strategy": {전략별 성과}, + "by_weekday": {요일별 성과}, + "by_exit_type": {청산 유형별 통계}, + "worst_stocks": [최대 손실 종목 Top5], + "best_stocks": [최대 수익 종목 Top5], + "period_days": N, + "generated_at": ISO 문자열 + } + """ + since = datetime.utcnow() - timedelta(days=days) + + stmt = select(TradeHistory).where( + and_( + TradeHistory.order_type == "SELL", + TradeHistory.status == "FILLED", + TradeHistory.created_at >= since, + ) + ).order_by(TradeHistory.created_at) + + result = await db.execute(stmt) + trades: List[TradeHistory] = result.scalars().all() + + if not trades: + return { + "summary": _empty_summary(), + "by_strategy": {}, + "by_weekday": {}, + "by_exit_type": {}, + "worst_stocks": [], + "best_stocks": [], + "period_days": days, + "total_trades": 0, + "generated_at": datetime.utcnow().isoformat(), + } + + # ── 전체 통계 ────────────────────────────────────────────────────────── + pl_rates = [_to_float(t.profit_loss_rate) for t in trades] + pl_amounts = [_to_float(t.net_profit_loss) for t in trades] + + wins = [r for r in pl_rates if r > 0] + losses = [r for r in pl_rates if r <= 0] + + win_rate = len(wins) / len(pl_rates) if pl_rates else 0.0 + avg_win = sum(wins) / len(wins) if wins else 0.0 + avg_loss = sum(losses) / len(losses) if losses else 0.0 + profit_factor = ( + (sum(wins) / abs(sum(losses))) if losses and sum(losses) != 0 else float("inf") + ) + + gross_profit = sum(a for a in pl_amounts if a > 0) + gross_loss = sum(a for a in pl_amounts if a < 0) + net_pnl = gross_profit + gross_loss + + # 최대 연속 손실 + max_consecutive_loss = _calc_max_consecutive_loss(pl_rates) + + summary = { + "total_trades": len(trades), + "win_trades": len(wins), + "loss_trades": len(losses), + "win_rate": round(win_rate * 100, 2), + "avg_win_pct": round(avg_win, 4), + "avg_loss_pct": round(avg_loss, 4), + "profit_factor": round(min(profit_factor, 99.0), 4), + "gross_profit": round(gross_profit, 2), + "gross_loss": round(gross_loss, 2), + "net_pnl": round(net_pnl, 2), + "max_consecutive_loss": max_consecutive_loss, + "risk_reward_ratio": round(abs(avg_win / avg_loss), 4) if avg_loss != 0 else 0.0, + } + + # ── 전략별 성과 ────────────────────────────────────────────────────── + by_strategy: Dict[str, list] = {} + for t in trades: + strat = _parse_strategy(t.entry_reason) + by_strategy.setdefault(strat, []).append(_to_float(t.profit_loss_rate)) + + strategy_stats = {} + for strat, rates in by_strategy.items(): + w = [r for r in rates if r > 0] + strategy_stats[strat] = { + "total": len(rates), + "win_rate": round(len(w) / len(rates) * 100, 2) if rates else 0.0, + "avg_return": round(sum(rates) / len(rates), 4) if rates else 0.0, + } + + # ── 요일별 성과 ────────────────────────────────────────────────────── + weekday_names = ["월", "화", "수", "목", "금", "토", "일"] + by_weekday: Dict[str, list] = {} + for t in trades: + wday = weekday_names[t.created_at.weekday()] if t.created_at else "알수없음" + by_weekday.setdefault(wday, []).append(_to_float(t.profit_loss_rate)) + + weekday_stats = { + d: { + "total": len(rates), + "win_rate": round(len([r for r in rates if r > 0]) / len(rates) * 100, 2), + "avg_return": round(sum(rates) / len(rates), 4), + } + for d, rates in by_weekday.items() + if rates + } + + # ── 청산 유형별 ────────────────────────────────────────────────────── + by_exit: Dict[str, list] = {} + for t in trades: + etype = _parse_exit_type(t.exit_reason) + by_exit.setdefault(etype, []).append(_to_float(t.profit_loss_rate)) + + exit_stats = { + etype: { + "count": len(rates), + "avg_return": round(sum(rates) / len(rates), 4), + "win_rate": round(len([r for r in rates if r > 0]) / len(rates) * 100, 2), + } + for etype, rates in by_exit.items() + if rates + } + + # ── 종목별 집계 ────────────────────────────────────────────────────── + stock_pnl: Dict[str, float] = {} + for t in trades: + code = t.stock_code + stock_pnl[code] = stock_pnl.get(code, 0.0) + _to_float(t.net_profit_loss) + + sorted_stocks = sorted(stock_pnl.items(), key=lambda x: x[1]) + worst_stocks = [{"stock_code": c, "net_pnl": round(p, 2)} for c, p in sorted_stocks[:5]] + best_stocks = [{"stock_code": c, "net_pnl": round(p, 2)} for c, p in sorted_stocks[-5:][::-1]] + + return { + "summary": summary, + "by_strategy": strategy_stats, + "by_weekday": weekday_stats, + "by_exit_type": exit_stats, + "worst_stocks": worst_stocks, + "best_stocks": best_stocks, + "period_days": days, + "total_trades": len(trades), + "generated_at": datetime.utcnow().isoformat(), + } + + +def _empty_summary() -> Dict[str, Any]: + return { + "total_trades": 0, + "win_trades": 0, + "loss_trades": 0, + "win_rate": 0.0, + "avg_win_pct": 0.0, + "avg_loss_pct": 0.0, + "profit_factor": 0.0, + "gross_profit": 0.0, + "gross_loss": 0.0, + "net_pnl": 0.0, + "max_consecutive_loss": 0, + "risk_reward_ratio": 0.0, + } + + +def _calc_max_consecutive_loss(pl_rates: List[float]) -> int: + max_streak = current_streak = 0 + for r in pl_rates: + if r <= 0: + current_streak += 1 + max_streak = max(max_streak, current_streak) + else: + current_streak = 0 + return max_streak + + +# ───────────────────────────────────────────────────────────────────────────── +# 전략 인사이트 +# ───────────────────────────────────────────────────────────────────────────── + +def build_insights(perf: Dict[str, Any]) -> List[Dict[str, str]]: + """ + 성과 데이터를 기반으로 개선 인사이트 목록을 생성. + + Returns: [{"level": "warning"|"info"|"danger", "message": "..."}] + """ + insights = [] + summary = perf.get("summary", {}) + win_rate = summary.get("win_rate", 0.0) + pf = summary.get("profit_factor", 0.0) + avg_loss = summary.get("avg_loss_pct", 0.0) + max_cons_loss = summary.get("max_consecutive_loss", 0) + rr = summary.get("risk_reward_ratio", 0.0) + + if win_rate < 40: + insights.append({"level": "danger", "message": f"승률 {win_rate:.1f}% — RSI 과매도 기준을 강화하거나 진입 조건을 추가 필터링하세요."}) + elif win_rate < 50: + insights.append({"level": "warning", "message": f"승률 {win_rate:.1f}% — 50% 미만입니다. RSI_OVERSOLD_LEVEL 하향 조정을 검토하세요."}) + else: + insights.append({"level": "info", "message": f"승률 {win_rate:.1f}% — 양호합니다."}) + + if pf < 1.0: + insights.append({"level": "danger", "message": f"Profit Factor {pf:.2f} — 손실이 수익보다 큽니다. 손절 기준 강화 필요."}) + elif pf < 1.5: + insights.append({"level": "warning", "message": f"Profit Factor {pf:.2f} — 개선 여지 있습니다."}) + + if rr < 1.0 and rr > 0: + insights.append({"level": "warning", "message": f"손익비 {rr:.2f} — 1:1 미만입니다. ATR 익절 배수 또는 트레일링 스탑 완화를 검토하세요."}) + + if max_cons_loss >= 3: + insights.append({"level": "warning", "message": f"최대 연속 손실 {max_cons_loss}회 — 연속 손절 발생 중. 진입 필터(추세 필터, 시장국면 필터) 점검 필요."}) + + if avg_loss < -8.0: + insights.append({"level": "danger", "message": f"평균 손실 {avg_loss:.2f}% — 손절선({avg_loss:.2f}%)이 너무 넓습니다. STOP_LOSS_PERCENT 또는 ATR_STOP_MULTIPLIER 조정 권장."}) + + by_strategy = perf.get("by_strategy", {}) + for strat, stats in by_strategy.items(): + if stats.get("total", 0) >= 5 and stats.get("win_rate", 100) < 35: + insights.append({"level": "warning", "message": f"'{strat}' 전략 승률 {stats['win_rate']:.1f}% — 해당 전략 파라미터 재검토 권장."}) + + by_exit = perf.get("by_exit_type", {}) + stop_loss_info = by_exit.get("stop_loss", {}) + if stop_loss_info.get("count", 0) > 0: + sl_ratio = stop_loss_info["count"] / max(perf["total_trades"], 1) + if sl_ratio > 0.5: + insights.append({"level": "warning", "message": f"손절 청산 비율 {sl_ratio*100:.1f}% — 전체 청산의 절반 이상이 손절입니다. 진입 타이밍 재검토."}) + + return insights + + +# ───────────────────────────────────────────────────────────────────────────── +# 매매일지 생성 +# ───────────────────────────────────────────────────────────────────────────── + +async def get_uncovered_trades(db: AsyncSession, days: int = 7) -> List[TradeHistory]: + """일지가 아직 없는 완료 SELL 거래 목록을 반환.""" + since = datetime.utcnow() - timedelta(days=days) + + # 이미 일지가 있는 trade_id 목록 + covered_stmt = select(TradeJournal.trade_id).where(TradeJournal.trade_id.is_not(None)) + covered_result = await db.execute(covered_stmt) + covered_ids = {row[0] for row in covered_result.fetchall()} + + stmt = select(TradeHistory).where( + and_( + TradeHistory.order_type == "SELL", + TradeHistory.status == "FILLED", + TradeHistory.created_at >= since, + TradeHistory.id.not_in(covered_ids) if covered_ids else True, + ) + ).order_by(TradeHistory.created_at.desc()) + + result = await db.execute(stmt) + return result.scalars().all() + + +def generate_journal_for_trade(trade: TradeHistory, perf_context: Optional[Dict] = None) -> str: + """ + 템플릿 기반 매매일지 내용 생성. + + Args: + trade: 완료된 SELL TradeHistory 레코드 + perf_context: 전체 성과 컨텍스트 (선택) + + Returns: + 일지 본문 문자열 + """ + pl_rate = _to_float(trade.profit_loss_rate) + pl_amount = _to_float(trade.net_profit_loss) + exec_price = _to_float(trade.executed_price) + exec_qty = trade.executed_quantity or trade.order_quantity or 0 + strategy = _parse_strategy(trade.entry_reason) + exit_type = _parse_exit_type(trade.exit_reason) + + # 결과 평가 + if pl_rate > 3.0: + result_label = "✅ 큰 수익 달성" + result_comment = "목표 수익을 충분히 달성했습니다. 진입 타이밍과 전략이 잘 맞았습니다." + elif pl_rate > 0: + result_label = "✅ 소폭 수익" + result_comment = "소폭 수익 실현. 목표 대비 아쉬운 결과이나 플러스 마감입니다." + elif pl_rate > -3.0: + result_label = "⚠️ 소폭 손실" + result_comment = "소폭 손실. 리스크 관리 규칙 내에서 처리되었습니다." + else: + result_label = "❌ 손절 처리" + result_comment = "손절 처리되었습니다. 진입 조건 및 시장 국면을 재검토하세요." + + # 청산 유형별 코멘트 + exit_comments = { + "stop_loss": "→ 손절 기준(-6% 또는 ATR 기반)에 도달하여 강제 청산.", + "trailing_stop": "→ 트레일링 스탑(고점-1.5×ATR)에 도달. 고점 대비 일정 비율 하락.", + "take_profit": "→ 익절 기준(+2R 또는 RSI 65+)에 도달하여 수익 확정.", + "eod_forced": "→ 장 마감 강제 청산(15:20 EOD). 포지션 오버나이트 방지.", + "rsi_exit": "→ RSI 과매수 구간 진입으로 부분/전량 청산.", + "manual": "→ 수동 청산 처리.", + "unknown": "→ 청산 사유 미상.", + } + exit_comment = exit_comments.get(exit_type, "→ 청산 완료.") + + lines = [ + f"# 매매일지 — {trade.stock_code} ({trade.stock_name or ''})", + f"**날짜**: {(trade.executed_at or trade.created_at).strftime('%Y-%m-%d %H:%M') if (trade.executed_at or trade.created_at) else '-'}", + f"**전략**: {strategy}", + "", + f"## 거래 결과: {result_label}", + f"- 체결가: {exec_price:,.0f}원 | 수량: {exec_qty:,}주", + f"- 손익률: **{pl_rate:+.2f}%** | 손익금액: {pl_amount:+,.0f}원", + f"- 청산 유형: `{exit_type}`", + f" {exit_comment}", + "", + "## 진입 근거", + f"{trade.entry_reason or '기록 없음'}", + "", + "## 청산 근거", + f"{trade.exit_reason or '기록 없음'}", + "", + "## 평가 및 교훈", + result_comment, + ] + + # 성과 컨텍스트 추가 (있을 경우) + if perf_context: + overall_wr = perf_context.get("summary", {}).get("win_rate", 0) + lines += [ + "", + f"---", + f"*최근 {perf_context.get('period_days', 30)}일 전체 승률: {overall_wr:.1f}% (참고)*", + ] + + return "\n".join(lines) + + +async def create_journal_record( + db: AsyncSession, + trade: TradeHistory, + content: str, +) -> TradeJournal: + """TradeJournal 레코드를 DB에 저장 후 반환.""" + pl_rate = _to_float(trade.profit_loss_rate) + strategy = _parse_strategy(trade.entry_reason) + exit_type = _parse_exit_type(trade.exit_reason) + + mood = "positive" if pl_rate > 0 else ("neutral" if pl_rate > -3.0 else "negative") + tags = [strategy, exit_type] + if abs(pl_rate) > 5.0: + tags.append("significant") + + journal = TradeJournal( + trade_id=trade.id, + stock_code=trade.stock_code, + stock_name=trade.stock_name, + journal_date=trade.executed_at or trade.created_at or datetime.utcnow(), + content=content, + strategy_name=strategy, + mood=mood, + tags=tags, + ) + db.add(journal) + await db.flush() + return journal diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx index bc94aef..8300a09 100644 --- a/frontend/src/App.tsx +++ b/frontend/src/App.tsx @@ -4,6 +4,7 @@ import TradeHistory from './pages/TradeHistory' import MCPStatus from './pages/MCPStatus' import StrategySettings from './pages/StrategySettings' import Backtest from './pages/Backtest' +import Analysis from './pages/Analysis' import Layout from './components/Layout' function App() { @@ -15,6 +16,7 @@ function App() { } /> } /> } /> + } /> ) diff --git a/frontend/src/components/Layout.tsx b/frontend/src/components/Layout.tsx index 47850e5..74142cd 100644 --- a/frontend/src/components/Layout.tsx +++ b/frontend/src/components/Layout.tsx @@ -1,5 +1,5 @@ import { Link, useLocation } from 'react-router-dom' -import { LayoutDashboard, History, Cpu, Activity, SlidersHorizontal, BarChart3 } from 'lucide-react' +import { LayoutDashboard, History, Cpu, Activity, SlidersHorizontal, BarChart3, TrendingUp } from 'lucide-react' import { useQuery } from '@tanstack/react-query' import api from '../lib/api' import { useWebSocket } from '../hooks/useWebSocket' @@ -20,6 +20,7 @@ const Layout = ({ children }: LayoutProps) => { const navItems = [ { path: '/', icon: LayoutDashboard, label: '대시보드' }, + { path: '/analysis', icon: TrendingUp, label: '성과 분석' }, { path: '/strategy', icon: SlidersHorizontal, label: '전략 설정' }, { path: '/backtest', icon: BarChart3, label: '백테스트' }, { path: '/trades', icon: History, label: '매매 로그' }, diff --git a/frontend/src/pages/Analysis.tsx b/frontend/src/pages/Analysis.tsx new file mode 100644 index 0000000..257966c --- /dev/null +++ b/frontend/src/pages/Analysis.tsx @@ -0,0 +1,489 @@ +import { useState } from 'react' +import { useQuery, useMutation, useQueryClient } from '@tanstack/react-query' +import { + TrendingUp, TrendingDown, Target, AlertTriangle, CheckCircle, Info, + Play, RefreshCw, BookOpen, Zap, ChevronDown, ChevronUp +} from 'lucide-react' +import api from '../lib/api' + +// ── 타입 정의 ───────────────────────────────────────────────────────────────── + +interface PerformanceSummary { + total_trades: number + win_trades: number + loss_trades: number + win_rate: number + avg_win_pct: number + avg_loss_pct: number + profit_factor: number + gross_profit: number + gross_loss: number + net_pnl: number + max_consecutive_loss: number + risk_reward_ratio: number +} + +interface PerformanceData { + summary: PerformanceSummary + by_strategy: Record + by_exit_type: Record + worst_stocks: { stock_code: string; net_pnl: number }[] + best_stocks: { stock_code: string; net_pnl: number }[] + period_days: number + total_trades: number + generated_at: string +} + +interface Insight { + level: 'info' | 'warning' | 'danger' + message: string +} + +interface OptimizationLog { + id: number + run_date: string + trigger: string + best_sharpe: number | null + current_sharpe: number | null + sharpe_improvement_pct: number | null + best_win_rate: number | null + best_mdd: number | null + applied: boolean + apply_reason: string + best_params: Record | null + previous_params: Record | null + total_combinations: number | null + created_at: string +} + +// ── 유틸 함수 ───────────────────────────────────────────────────────────────── + +const fmt = (n: number | null | undefined, digits = 2) => + n == null ? '-' : n.toFixed(digits) + +const fmtKRW = (n: number | null | undefined) => + n == null ? '-' : `${n >= 0 ? '+' : ''}${n.toLocaleString('ko-KR')}원` + +// ── 서브 컴포넌트: 통계 카드 ────────────────────────────────────────────────── + +const StatCard = ({ + label, value, sub, color = 'blue', icon: Icon, +}: { + label: string + value: string | number + sub?: string + color?: 'blue' | 'green' | 'red' | 'yellow' | 'purple' + icon?: React.ElementType +}) => { + const colorMap = { + blue: 'border-blue-500/30 bg-blue-500/5', + green: 'border-green-500/30 bg-green-500/5', + red: 'border-red-500/30 bg-red-500/5', + yellow: 'border-yellow-500/30 bg-yellow-500/5', + purple: 'border-purple-500/30 bg-purple-500/5', + } + const textMap = { + blue: 'text-blue-400', green: 'text-green-400', red: 'text-red-400', + yellow: 'text-yellow-400', purple: 'text-purple-400', + } + return ( +
+
+ {Icon && } + {label} +
+
{value}
+ {sub &&
{sub}
} +
+ ) +} + +// ── 서브 컴포넌트: 인사이트 배지 ───────────────────────────────────────────── + +const InsightBadge = ({ insight }: { insight: Insight }) => { + const map = { + info: { bg: 'bg-blue-500/10 border-blue-500/20', text: 'text-blue-300', Icon: Info }, + warning: { bg: 'bg-yellow-500/10 border-yellow-500/20', text: 'text-yellow-300', Icon: AlertTriangle }, + danger: { bg: 'bg-red-500/10 border-red-500/20', text: 'text-red-300', Icon: AlertTriangle }, + } + const { bg, text, Icon } = map[insight.level] + return ( +
+ + {insight.message} +
+ ) +} + +// ── 메인 컴포넌트 ───────────────────────────────────────────────────────────── + +export default function Analysis() { + const queryClient = useQueryClient() + const [days, setDays] = useState(30) + const [expandedOptLog, setExpandedOptLog] = useState(null) + + // 성과 데이터 + const { data: perf, isLoading: perfLoading } = useQuery({ + queryKey: ['analysis-performance', days], + queryFn: async () => (await api.get(`/analysis/performance?days=${days}`)).data, + refetchInterval: 60_000, + }) + + // 인사이트 + const { data: insightsData, isLoading: insightsLoading } = useQuery<{ + insights: Insight[] + last_optimization_date: string | null + }>({ + queryKey: ['analysis-insights', days], + queryFn: async () => (await api.get(`/analysis/insights?days=${days}`)).data, + refetchInterval: 60_000, + }) + + // 최적화 이력 + const { data: optHistory = [], isLoading: optHistLoading } = useQuery({ + queryKey: ['optimization-history'], + queryFn: async () => (await api.get('/analysis/optimization-history?limit=10')).data, + refetchInterval: 30_000, + }) + + // 최적화 상태 + const { data: optStatus } = useQuery<{ running: boolean }>({ + queryKey: ['optimization-status'], + queryFn: async () => (await api.get('/analysis/optimization-status')).data, + refetchInterval: 5_000, + }) + + // 일지 생성 뮤테이션 + const generateJournals = useMutation({ + mutationFn: () => api.post('/analysis/generate-journals?days=7'), + onSuccess: () => { + alert('일지 생성을 시작했습니다. 잠시 후 매매 로그에서 확인하세요.') + }, + }) + + // 전략 최적화 뮤테이션 + const optimizeStrategy = useMutation({ + mutationFn: () => + api.post(`/analysis/optimize-strategy?days=${days}&period_days=90`), + onSuccess: () => { + alert('전략 최적화를 시작했습니다. 수 분 후 최적화 이력에서 결과를 확인하세요.') + setTimeout(() => queryClient.invalidateQueries({ queryKey: ['optimization-history'] }), 10_000) + }, + }) + + const summary = perf?.summary + const insights = insightsData?.insights || [] + const isOptimizing = optStatus?.running || optimizeStrategy.isPending + + return ( +
+ {/* 헤더 */} +
+
+

성과 분석

+

매매 일지 자동 생성 & 전략 자동 최적화

+
+
+ + +
+
+ + {/* 성과 요약 카드 */} + {perfLoading ? ( +
성과 데이터 로딩 중...
+ ) : summary && summary.total_trades > 0 ? ( +
+ = 50 ? 'green' : summary.win_rate >= 40 ? 'yellow' : 'red'} + icon={Target} + /> + = 1.5 ? 'green' : summary.profit_factor >= 1.0 ? 'yellow' : 'red'} + icon={TrendingUp} + /> + = 1.5 ? 'green' : summary.risk_reward_ratio >= 1.0 ? 'yellow' : 'red'} + icon={TrendingDown} + /> + = 0 ? 'green' : 'red'} + icon={summary.net_pnl >= 0 ? TrendingUp : TrendingDown} + /> +
+ ) : ( +
+ 최근 {days}일 내 완료된 매도 거래가 없습니다. +
+ )} + +
+ {/* 전략별 성과 */} +
+

전략별 성과

+ {perf && Object.keys(perf.by_strategy).length > 0 ? ( +
+ {Object.entries(perf.by_strategy).map(([strat, stats]) => ( +
+
+ {strat} + {stats.total}건 +
+
+ = 50 ? 'text-green-400' : 'text-red-400'}> + 승률 {fmt(stats.win_rate)}% + + = 0 ? 'text-green-400' : 'text-red-400'}> + 평균 {stats.avg_return >= 0 ? '+' : ''}{fmt(stats.avg_return)}% + +
+
+ ))} +
+ ) : ( +

데이터 없음

+ )} +
+ + {/* 청산 유형별 */} +
+

청산 유형별 통계

+ {perf && Object.keys(perf.by_exit_type).length > 0 ? ( +
+ {Object.entries(perf.by_exit_type).map(([etype, stats]) => ( +
+
+ {etype} + {stats.count}건 +
+ = 0 ? 'text-green-400' : 'text-red-400'}`}> + 평균 {stats.avg_return >= 0 ? '+' : ''}{fmt(stats.avg_return)}% + +
+ ))} +
+ ) : ( +

데이터 없음

+ )} +
+
+ + {/* 인사이트 */} +
+

+ + 전략 인사이트 +

+ {insightsLoading ? ( +

분석 중...

+ ) : insights.length > 0 ? ( +
+ {insights.map((ins, i) => )} +
+ ) : ( +

분석할 데이터가 없습니다.

+ )} +
+ + {/* 매매일지 */} +
+
+

+ + 매매일지 자동 생성 +

+ +
+

+ 최근 7일 내 일지가 없는 완료 거래에 대해 템플릿 기반 매매일지를 자동 생성합니다. + 장 종료 후 16:00에 자동으로도 실행됩니다. +

+
+ + {/* 전략 자동 최적화 */} +
+
+
+

+ + 전략 자동 최적화 +

+ {insightsData?.last_optimization_date && ( +

+ 마지막 최적화: {new Date(insightsData.last_optimization_date).toLocaleDateString('ko-KR')} +

+ )} +
+ +
+

+ RSI, 손절선, ATR 배수, 볼린저 밴드 파라미터를 그리드 서치로 최적화합니다. + Sharpe Ratio 10% 이상 개선 시 자동으로 전략 설정에 적용됩니다. + (매일 16:00 KST 자동 실행, 최근 7일 거래 5건 이상 시) +

+ + {/* 최적화 이력 */} +
+

최적화 이력

+ {optHistLoading ? ( +

로딩 중...

+ ) : optHistory.length > 0 ? ( +
+ {optHistory.map((log) => ( +
+ + + {expandedOptLog === log.id && ( +
+
+
+ 최적 Sharpe +

{fmt(log.best_sharpe, 3)}

+
+
+ 최적 승률 +

{fmt(log.best_win_rate, 1)}%

+
+
+ 최적 MDD +

{fmt(log.best_mdd, 1)}%

+
+
+
+ 사유: + {log.apply_reason || '-'} +
+ {log.best_params && ( +
+ 최적 파라미터: + 
+                                                        {JSON.stringify(log.best_params, null, 2)}
+
+
+ )} +
+ )} +
+ ))} +
+ ) : ( +

최적화 이력이 없습니다.

+ )} +
+
+ + {/* 최악/최고 종목 */} + {perf && (perf.worst_stocks.length > 0 || perf.best_stocks.length > 0) && ( +
+
+

최대 손실 종목 Top5

+
+ {perf.worst_stocks.map((s) => ( +
+ {s.stock_code} + {fmtKRW(s.net_pnl)} +
+ ))} +
+
+
+

최대 수익 종목 Top5

+
+ {perf.best_stocks.map((s) => ( +
+ {s.stock_code} + {fmtKRW(s.net_pnl)} +
+ ))} +
+
+
+ )} +
+ ) +}