From 7168d8f3e9d23f249b6e6d07dfe1cd4ae2cfc9f9 Mon Sep 17 00:00:00 2001 From: Lxy Date: Mon, 6 Jul 2026 22:20:11 +0800 Subject: [PATCH] fix: restore trade reflection system code that was accidentally deleted --- app/analysis_db.py | 98 +- app/analysis_models.py | 152 +++ app/api/trade_review.py | 547 ++++++++++ app/services/reflection_ai_analysis.py | 335 ++++++ app/services/trade_pairing_engine.py | 336 ++++++ app/static/futures_analysis.html | 610 +++++++++-- app/static/futures_analysis.js | 980 +++++++++++++++++- docs/futures_analysis_module.md | 322 ++++++ ...26-07-05-trade-reflection-system-design.md | 575 ++++++++++ .../trade-reflection-system/.comet.yaml | 15 + .../.comet/handoff/brainstorm-summary.md | 38 + .../.comet/handoff/design-context.md | 33 + .../trade-reflection-system/.openspec.yaml | 2 + .../trade-reflection-system/proposal.md | 33 + .../specs/ai-reanalysis/spec.md | 35 + .../specs/daily-reflection/spec.md | 21 + .../specs/experience-library/spec.md | 32 + .../specs/trade-pairing/spec.md | 34 + .../specs/trade-reflection/spec.md | 26 + .../specs/trade-tagging/spec.md | 37 + .../archive/trade-reflection-system/tasks.md | 120 +++ tests/test_trade_reflection.py | 287 +++++ 22 files changed, 4585 insertions(+), 83 deletions(-) create mode 100644 app/services/reflection_ai_analysis.py create mode 100644 app/services/trade_pairing_engine.py create mode 100644 docs/futures_analysis_module.md create mode 100644 docs/superpowers/specs/2026-07-05-trade-reflection-system-design.md create mode 100644 openspec/archive/trade-reflection-system/.comet.yaml create mode 100644 openspec/archive/trade-reflection-system/.comet/handoff/brainstorm-summary.md create mode 100644 openspec/archive/trade-reflection-system/.comet/handoff/design-context.md create mode 100644 openspec/archive/trade-reflection-system/.openspec.yaml create mode 100644 openspec/archive/trade-reflection-system/proposal.md create mode 100644 openspec/archive/trade-reflection-system/specs/ai-reanalysis/spec.md create mode 100644 openspec/archive/trade-reflection-system/specs/daily-reflection/spec.md create mode 100644 openspec/archive/trade-reflection-system/specs/experience-library/spec.md create mode 100644 openspec/archive/trade-reflection-system/specs/trade-pairing/spec.md create mode 100644 openspec/archive/trade-reflection-system/specs/trade-reflection/spec.md create mode 100644 openspec/archive/trade-reflection-system/specs/trade-tagging/spec.md create mode 100644 openspec/archive/trade-reflection-system/tasks.md create mode 100644 tests/test_trade_reflection.py diff --git a/app/analysis_db.py b/app/analysis_db.py index c8a04fa..0f17499 100644 --- a/app/analysis_db.py +++ b/app/analysis_db.py @@ -7,6 +7,9 @@ from sqlalchemy import create_engine from sqlalchemy.orm import sessionmaker, declarative_base from datetime import datetime +from app.mysql_database import MySQLSessionLocal +from app.storage_manager import get_storage_manager + logger = logging.getLogger(__name__) # 数据目录 @@ -28,14 +31,38 @@ AnalysisBase = declarative_base() def get_analysis_db(): """获取期货智析数据库会话。 - 分析数据量较小（复盘计划、评分等），始终使用 SQLite 以保证稳定性。 - AnalysisBase 绑定的是 analysis_engine (SQLite)，ORM 模型均基于此定义。 + 根据 MySQL 可用性动态选择后端： + - MySQL 可用时返回 MySQL Session + - MySQL 不可用时返回 SQLite Session（兜底） """ - db = AnalysisSessionLocal() - try: - yield db - finally: - db.close() + storage = get_storage_manager() + + if MySQLSessionLocal is not None and storage.check_mysql(): + db = None + try: + db = MySQLSessionLocal() + yield db + except Exception as e: + logger.warning(f"创建 MySQL analysis session 失败，降级到 SQLite: {e}") + if db is not None: + try: + db.close() + except Exception: + pass + db = AnalysisSessionLocal() + yield db + finally: + if db is not None: + try: + db.close() + except Exception: + pass + else: + db = AnalysisSessionLocal() + try: + yield db + finally: + db.close() def init_analysis_db(): @@ -48,12 +75,17 @@ def init_analysis_db(): AIAnalysisCache, ReviewDate, SymbolRanking, TradingPlan, SymbolScoreV2, TradingPlanV2, SectorHeat, ReviewPlanV2, TradeRecord, TradeImportBatch, + TradePair, TradeReflection, DailyReflection, TradeTag, TradeRecordTag, TradeExperience, + TradeAIAnalysis, ) AnalysisBase.metadata.create_all(bind=analysis_engine) # 迁移：为已有表添加新增列（SQLite 不支持 IF NOT EXISTS，用异常处理） _migrate_add_columns() + # 初始化预设标签 + _init_preset_tags() + def init_analysis_mysql(mysql_engine): """初始化期货智析数据库表（MySQL）。""" @@ -63,6 +95,8 @@ def init_analysis_mysql(mysql_engine): AIAnalysisCache, ReviewDate, SymbolRanking, TradingPlan, SymbolScoreV2, TradingPlanV2, SectorHeat, ReviewPlanV2, TradeRecord, TradeImportBatch, + TradePair, TradeReflection, DailyReflection, TradeTag, TradeRecordTag, TradeExperience, + TradeAIAnalysis, ) AnalysisBase.metadata.create_all(bind=mysql_engine) logger.info("MySQL analysis 表结构初始化完成") @@ -88,3 +122,53 @@ def _migrate_add_columns(): conn.commit() conn.close() + + +def _init_preset_tags(): + """初始化预设标签""" + from app.analysis_models import TradeTag + from sqlalchemy import inspect + + session = AnalysisSessionLocal() + try: + # 检查表是否存在 + inspector = inspect(session.get_bind()) + if 'trade_tags' not in inspector.get_table_names(): + return + + # 检查是否已有预设标签 + existing = session.query(TradeTag).filter_by(is_preset=True).first() + if existing: + return + + preset_tags = [ + # 操作类 + TradeTag(name="追涨杀跌", category="operation", is_preset=True), + TradeTag(name="逆市操作", category="operation", is_preset=True), + TradeTag(name="提前入场", category="operation", is_preset=True), + TradeTag(name="延迟出场", category="operation", is_preset=True), + TradeTag(name="加仓过早", category="operation", is_preset=True), + TradeTag(name="减仓过晚", category="operation", is_preset=True), + # 心态类 + TradeTag(name="情绪化交易", category="mindset", is_preset=True), + TradeTag(name="恐惧平仓", category="mindset", is_preset=True), + TradeTag(name="贪婪持仓", category="mindset", is_preset=True), + TradeTag(name="侥幸心理", category="mindset", is_preset=True), + # 纪律类 + TradeTag(name="严格执行计划", category="discipline", is_preset=True), + TradeTag(name="违反止损", category="discipline", is_preset=True), + TradeTag(name="超仓交易", category="discipline", is_preset=True), + TradeTag(name="频繁交易", category="discipline", is_preset=True), + # 技术类 + TradeTag(name="突破交易", category="technical", is_preset=True), + TradeTag(name="回调交易", category="technical", is_preset=True), + TradeTag(name="趋势跟踪", category="technical", is_preset=True), + TradeTag(name="震荡交易", category="technical", is_preset=True), + TradeTag(name="反转交易", category="technical", is_preset=True), + ] + session.add_all(preset_tags) + session.commit() + except Exception: + session.rollback() + finally: + session.close() diff --git a/app/analysis_models.py b/app/analysis_models.py index ca5f155..b1f931e 100644 --- a/app/analysis_models.py +++ b/app/analysis_models.py @@ -323,3 +323,155 @@ class TradeImportBatch(AnalysisBase): def __repr__(self): return f"" + + +# ==================== 交易反思系统模型 ==================== + +class TradePair(AnalysisBase): + """交易配对表 - 将开仓和平仓配对为一个完整交易""" + __tablename__ = "trade_pairs" + + id = Column(Integer, primary_key=True, autoincrement=True) + variety = Column(String(16), nullable=False, index=True, comment="品种代码 AG") + direction = Column(String(8), nullable=False, comment="方向: long/short") + open_record_ids = Column(JSON, nullable=False, comment="开仓记录ID列表 [id1, id2, ...]") + close_record_ids = Column(JSON, nullable=False, comment="平仓记录ID列表 [id1, id2, ...]") + open_price = Column(Float, nullable=True, comment="平均开仓价") + close_price = Column(Float, nullable=True, comment="平均平仓价") + total_volume = Column(Float, nullable=True, comment="总手数") + close_pnl = Column(Float, nullable=True, default=0.0, comment="平仓盈亏") + total_commission = Column(Float, nullable=True, default=0.0, comment="总手续费") + net_pnl = Column(Float, nullable=True, default=0.0, comment="净盈亏 = 平仓盈亏 - 手续费") + open_date = Column(String(16), nullable=True, index=True, comment="开仓日期") + close_date = Column(String(16), nullable=True, index=True, comment="平仓日期") + created_at = Column(DateTime, nullable=False, default=datetime.now) + + __table_args__ = ( + Index('ix_trade_pairs_variety_date', 'variety', 'open_date'), + ) + + def __repr__(self): + return f"" + + +class TradeReflection(AnalysisBase): + """交易反思表 - 每笔交易的反思内容""" + __tablename__ = "trade_reflections" + + id = Column(Integer, primary_key=True, autoincrement=True) + trade_pair_id = Column(Integer, nullable=False, index=True, comment="关联交易配对ID") + trade_date = Column(String(16), nullable=False, index=True, comment="交易日期") + # 模板字段 + entry_reason = Column(Text, nullable=True, comment="入场理由") + entry_timing = Column(String(8), nullable=True, comment="入场时机评价: good/fair/poor") + position_management = Column(Text, nullable=True, comment="仓位管理反思") + exit_reason = Column(Text, nullable=True, comment="出场理由") + exit_timing = Column(String(8), nullable=True, comment="出场时机评价: good/fair/poor") + discipline_score = Column(Integer, nullable=True, comment="纪律评分 1-5") + # 自由反思 + free_reflection = Column(Text, nullable=True, comment="自由反思内容") + # 状态 + ai_analyzed = Column(Boolean, nullable=False, default=False, comment="是否已AI分析") + ai_version = Column(Integer, nullable=True, comment="AI分析版本号") + created_at = Column(DateTime, nullable=False, default=datetime.now) + updated_at = Column(DateTime, nullable=False, default=datetime.now, onupdate=datetime.now) + + def __repr__(self): + return f"" + + +class DailyReflection(AnalysisBase): + """当日反思表 - 每天的整体反思""" + __tablename__ = "daily_reflections" + + id = Column(Integer, primary_key=True, autoincrement=True) + reflection_date = Column(String(16), nullable=False, unique=True, index=True, comment="反思日期 YYYY-MM-DD") + emotion_state = Column(String(16), nullable=True, comment="情绪状态: 平静/兴奋/焦虑/恐惧/贪婪") + market_judgment = Column(Text, nullable=True, comment="市场判断") + discipline_score = Column(Integer, nullable=True, comment="执行纪律评分 1-5") + overall_rating = Column(Integer, nullable=True, comment="总体评价 1-5") + summary = Column(Text, nullable=True, comment="总结") + improvements = Column(Text, nullable=True, comment="改进方向") + created_at = Column(DateTime, nullable=False, default=datetime.now) + updated_at = Column(DateTime, nullable=False, default=datetime.now, onupdate=datetime.now) + + def __repr__(self): + return f"" + + +class TradeTag(AnalysisBase): + """标签定义表""" + __tablename__ = "trade_tags" + + id = Column(Integer, primary_key=True, autoincrement=True) + name = Column(String(32), nullable=False, unique=True, index=True, comment="标签名称") + category = Column(String(16), nullable=False, comment="分类: operation/mindset/discipline/technical/custom") + is_preset = Column(Boolean, nullable=False, default=False, comment="是否预设标签") + color = Column(String(16), nullable=True, comment="标签颜色（前端用）") + created_at = Column(DateTime, nullable=False, default=datetime.now) + + def __repr__(self): + return f"" + + +class TradeRecordTag(AnalysisBase): + """交易-标签关联表""" + __tablename__ = "trade_record_tags" + + id = Column(Integer, primary_key=True, autoincrement=True) + trade_pair_id = Column(Integer, nullable=False, index=True, comment="交易配对ID") + tag_id = Column(Integer, nullable=False, index=True, comment="标签ID") + created_at = Column(DateTime, nullable=False, default=datetime.now) + + __table_args__ = ( + UniqueConstraint("trade_pair_id", "tag_id", name="uq_pair_tag"), + ) + + def __repr__(self): + return f"" + + +class TradeExperience(AnalysisBase): + """经验教训表""" + __tablename__ = "trade_experiences" + + id = Column(Integer, primary_key=True, autoincrement=True) + title = Column(String(128), nullable=False, comment="经验标题") + content = Column(Text, nullable=False, comment="经验内容") + exp_type = Column(String(16), nullable=False, comment="类型: lesson/tip/warning") + source_pair_id = Column(Integer, nullable=True, index=True, comment="来源交易配对ID") + source_date = Column(String(16), nullable=True, index=True, comment="来源交易日期") + tags = Column(JSON, nullable=True, comment="关联标签名称列表") + created_at = Column(DateTime, nullable=False, default=datetime.now) + updated_at = Column(DateTime, nullable=False, default=datetime.now, onupdate=datetime.now) + + def __repr__(self): + return f"" + + +class TradeAIAnalysis(AnalysisBase): + """交易 AI 分析结果表（多版本）""" + __tablename__ = "trade_ai_analysis" + + id = Column(Integer, primary_key=True, autoincrement=True) + trade_pair_id = Column(Integer, nullable=False, index=True, comment="交易配对ID") + version = Column(Integer, nullable=False, comment="分析版本号") + # AI 输出结构化字段 + overall_evaluation = Column(Text, nullable=True, comment="综合评价") + strengths = Column(JSON, nullable=True, comment="优点列表") + weaknesses = Column(JSON, nullable=True, comment="不足列表") + experience_suggestion = Column(Text, nullable=True, comment="经验提炼建议") + suggestion_type = Column(String(16), nullable=True, comment="建议类型: lesson/tip/warning") + raw_response = Column(Text, nullable=True, comment="AI 原始响应") + prompt_snapshot = Column(Text, nullable=True, comment="提示词快照") + # 是否已保存到经验库 + saved_to_experience = Column(Boolean, nullable=False, default=False, comment="是否已保存到经验库") + experience_id = Column(Integer, nullable=True, comment="关联经验ID") + created_at = Column(DateTime, nullable=False, default=datetime.now) + + __table_args__ = ( + Index('ix_trade_ai_analysis_pair_version', 'trade_pair_id', 'version', unique=True), + ) + + def __repr__(self): + return f"" diff --git a/app/api/trade_review.py b/app/api/trade_review.py index a5a9917..0a2408d 100644 --- a/app/api/trade_review.py +++ b/app/api/trade_review.py @@ -18,6 +18,18 @@ from app.services.trade_parser import ( calc_overall_statistics, get_trade_pairs, ) +from app.services.trade_pairing_engine import ( + auto_pair_trades, + get_daily_trades_with_pairs, + create_manual_pair, + delete_pair, +) +from app.services.reflection_ai_analysis import ( + analyze_with_reflection, + get_analysis_history, + get_latest_analysis, + save_suggestion_as_experience, +) logger = logging.getLogger(__name__) router = APIRouter(prefix="/trade-review", tags=["交易复盘"]) @@ -588,3 +600,538 @@ async def analyze_trade( except Exception as e: logger.exception("AI分析交易失败") return {"success": False, "message": f"AI分析失败: {str(e)}"} + + +# ==================== 交易反思系统 API ==================== + +from app.analysis_models import ( + TradePair, TradeReflection, DailyReflection, + TradeTag, TradeRecordTag, TradeExperience, +) +from pydantic import BaseModel + + +# --- 按天交易视图 --- + +@router.get("/daily-trades/{trade_date}") +def api_get_daily_trades( + trade_date: str, + db: Session = Depends(get_analysis_db), +): + """获取指定日期的交易+配对+反思状态""" + # 先执行自动配对 + auto_pair_trades(db, trade_date) + db.commit() + + data = get_daily_trades_with_pairs(db, trade_date) + return {"success": True, "data": data} + + +# --- 手动配对 --- + +class ManualPairRequest(BaseModel): + open_record_ids: list[int] + close_record_ids: list[int] + + +@router.post("/trade-pairs") +def api_create_manual_pair( + req: ManualPairRequest, + db: Session = Depends(get_analysis_db), +): + """手动创建交易配对""" + try: + pair = create_manual_pair(db, req.open_record_ids, req.close_record_ids) + db.commit() + return {"success": True, "data": {"id": pair.id}} + except ValueError as e: + raise HTTPException(status_code=400, detail=str(e)) + except Exception as e: + db.rollback() + logger.exception("创建配对失败") + raise HTTPException(status_code=500, detail=f"创建配对失败: {str(e)}") + + +@router.delete("/trade-pairs/{pair_id}") +def api_delete_pair( + pair_id: int, + db: Session = Depends(get_analysis_db), +): + """删除交易配对""" + success = delete_pair(db, pair_id) + if not success: + raise HTTPException(status_code=404, detail="配对不存在") + db.commit() + return {"success": True, "message": "配对已删除"} + + +# --- 反思 CRUD --- + +class ReflectionRequest(BaseModel): + trade_pair_id: int + trade_date: str + entry_reason: Optional[str] = None + entry_timing: Optional[str] = None + position_management: Optional[str] = None + exit_reason: Optional[str] = None + exit_timing: Optional[str] = None + discipline_score: Optional[int] = None + free_reflection: Optional[str] = None + + +@router.post("/reflections") +def api_create_reflection( + req: ReflectionRequest, + db: Session = Depends(get_analysis_db), +): + """创建交易反思""" + # 检查是否已存在 + existing = db.query(TradeReflection).filter( + TradeReflection.trade_pair_id == req.trade_pair_id + ).first() + if existing: + raise HTTPException(status_code=400, detail="该交易已有反思，请使用更新接口") + + reflection = TradeReflection( + trade_pair_id=req.trade_pair_id, + trade_date=req.trade_date, + entry_reason=req.entry_reason, + entry_timing=req.entry_timing, + position_management=req.position_management, + exit_reason=req.exit_reason, + exit_timing=req.exit_timing, + discipline_score=req.discipline_score, + free_reflection=req.free_reflection, + ) + db.add(reflection) + db.commit() + return {"success": True, "data": {"id": reflection.id}} + + +@router.put("/reflections/{reflection_id}") +def api_update_reflection( + reflection_id: int, + req: ReflectionRequest, + db: Session = Depends(get_analysis_db), +): + """更新交易反思""" + reflection = db.query(TradeReflection).filter(TradeReflection.id == reflection_id).first() + if not reflection: + raise HTTPException(status_code=404, detail="反思不存在") + + # 更新字段 + for field in ['entry_reason', 'entry_timing', 'position_management', + 'exit_reason', 'exit_timing', 'discipline_score', 'free_reflection']: + val = getattr(req, field) + if val is not None: + setattr(reflection, field, val) + + # 重新保存时重置 AI 分析状态（因为内容变了） + reflection.ai_analyzed = False + reflection.ai_version = None + + db.commit() + return {"success": True, "message": "反思已更新"} + + +@router.get("/reflections") +def api_get_reflections( + trade_pair_id: Optional[int] = Query(None), + trade_date: Optional[str] = Query(None), + db: Session = Depends(get_analysis_db), +): + """查询反思列表""" + query = db.query(TradeReflection) + if trade_pair_id: + query = query.filter(TradeReflection.trade_pair_id == trade_pair_id) + if trade_date: + query = query.filter(TradeReflection.trade_date == trade_date) + + reflections = query.order_by(TradeReflection.created_at.desc()).all() + return {"success": True, "data": [{ + "id": r.id, + "trade_pair_id": r.trade_pair_id, + "trade_date": r.trade_date, + "entry_reason": r.entry_reason, + "entry_timing": r.entry_timing, + "position_management": r.position_management, + "exit_reason": r.exit_reason, + "exit_timing": r.exit_timing, + "discipline_score": r.discipline_score, + "free_reflection": r.free_reflection, + "ai_analyzed": r.ai_analyzed, + "ai_version": r.ai_version, + "created_at": r.created_at.strftime('%Y-%m-%d %H:%M:%S') if r.created_at else None, + "updated_at": r.updated_at.strftime('%Y-%m-%d %H:%M:%S') if r.updated_at else None, + } for r in reflections]} + + +# --- 当日反思 CRUD --- + +class DailyReflectionRequest(BaseModel): + reflection_date: str + emotion_state: Optional[str] = None + market_judgment: Optional[str] = None + discipline_score: Optional[int] = None + overall_rating: Optional[int] = None + summary: Optional[str] = None + improvements: Optional[str] = None + + +@router.post("/daily-reflections") +def api_create_daily_reflection( + req: DailyReflectionRequest, + db: Session = Depends(get_analysis_db), +): + """创建或更新当日反思""" + existing = db.query(DailyReflection).filter( + DailyReflection.reflection_date == req.reflection_date + ).first() + + if existing: + # 更新 + for field in ['emotion_state', 'market_judgment', 'discipline_score', + 'overall_rating', 'summary', 'improvements']: + val = getattr(req, field) + if val is not None: + setattr(existing, field, val) + db.commit() + return {"success": True, "data": {"id": existing.id, "action": "updated"}} + else: + # 创建 + dr = DailyReflection( + reflection_date=req.reflection_date, + emotion_state=req.emotion_state, + market_judgment=req.market_judgment, + discipline_score=req.discipline_score, + overall_rating=req.overall_rating, + summary=req.summary, + improvements=req.improvements, + ) + db.add(dr) + db.commit() + return {"success": True, "data": {"id": dr.id, "action": "created"}} + + +@router.get("/daily-reflections/{reflection_date}") +def api_get_daily_reflection( + reflection_date: str, + db: Session = Depends(get_analysis_db), +): + """获取当日反思""" + dr = db.query(DailyReflection).filter( + DailyReflection.reflection_date == reflection_date + ).first() + + if not dr: + return {"success": True, "data": None} + + return {"success": True, "data": { + "id": dr.id, + "reflection_date": dr.reflection_date, + "emotion_state": dr.emotion_state, + "market_judgment": dr.market_judgment, + "discipline_score": dr.discipline_score, + "overall_rating": dr.overall_rating, + "summary": dr.summary, + "improvements": dr.improvements, + }} + + +# --- 标签系统 --- + +class TagRequest(BaseModel): + name: str + category: str = "custom" + + +@router.get("/tags") +def api_get_tags( + category: Optional[str] = Query(None), + db: Session = Depends(get_analysis_db), +): + """获取所有标签""" + query = db.query(TradeTag) + if category: + query = query.filter(TradeTag.category == category) + + tags = query.order_by(TradeTag.is_preset.desc(), TradeTag.name).all() + return {"success": True, "data": [{ + "id": t.id, + "name": t.name, + "category": t.category, + "is_preset": t.is_preset, + } for t in tags]} + + +@router.post("/tags") +def api_create_tag( + req: TagRequest, + db: Session = Depends(get_analysis_db), +): + """创建自定义标签（含去重）""" + existing = db.query(TradeTag).filter(TradeTag.name == req.name).first() + if existing: + return {"success": True, "data": {"id": existing.id, "action": "exists"}} + + tag = TradeTag(name=req.name, category=req.category, is_preset=False) + db.add(tag) + db.commit() + return {"success": True, "data": {"id": tag.id, "action": "created"}} + + +@router.post("/trade-pairs/{pair_id}/tags") +def api_add_tag_to_trade( + pair_id: int, + tag_id: int, + db: Session = Depends(get_analysis_db), +): + """给交易打标签""" + # 检查是否已存在 + existing = db.query(TradeRecordTag).filter( + TradeRecordTag.trade_pair_id == pair_id, + TradeRecordTag.tag_id == tag_id, + ).first() + if existing: + return {"success": True, "message": "标签已存在"} + + relation = TradeRecordTag(trade_pair_id=pair_id, tag_id=tag_id) + db.add(relation) + db.commit() + return {"success": True, "message": "标签已添加"} + + +@router.delete("/trade-pairs/{pair_id}/tags/{tag_id}") +def api_remove_tag_from_trade( + pair_id: int, + tag_id: int, + db: Session = Depends(get_analysis_db), +): + """移除交易标签""" + db.query(TradeRecordTag).filter( + TradeRecordTag.trade_pair_id == pair_id, + TradeRecordTag.tag_id == tag_id, + ).delete() + db.commit() + return {"success": True, "message": "标签已移除"} + + +@router.get("/trade-pairs/{pair_id}/tags") +def api_get_trade_tags( + pair_id: int, + db: Session = Depends(get_analysis_db), +): + """获取交易的标签列表""" + relations = db.query(TradeRecordTag).filter( + TradeRecordTag.trade_pair_id == pair_id + ).all() + + tag_ids = [r.tag_id for r in relations] + tags = db.query(TradeTag).filter(TradeTag.id.in_(tag_ids)).all() + + return {"success": True, "data": [{ + "id": t.id, + "name": t.name, + "category": t.category, + } for t in tags]} + + +# --- 经验库 --- + +class ExperienceRequest(BaseModel): + title: str + content: str + exp_type: str # lesson/tip/warning + source_pair_id: Optional[int] = None + source_date: Optional[str] = None + tags: Optional[list[str]] = None + + +@router.get("/experiences") +def api_get_experiences( + exp_type: Optional[str] = Query(None), + tag: Optional[str] = Query(None), + keyword: Optional[str] = Query(None), + page: int = Query(1, ge=1), + page_size: int = Query(20, ge=1, le=100), + db: Session = Depends(get_analysis_db), +): + """获取经验列表（支持筛选和分页）""" + query = db.query(TradeExperience) + if exp_type: + query = query.filter(TradeExperience.exp_type == exp_type) + if tag: + query = query.filter(TradeExperience.tags.contains([tag])) + if keyword: + query = query.filter( + (TradeExperience.title.contains(keyword)) | + (TradeExperience.content.contains(keyword)) + ) + + total = query.count() + experiences = query.order_by( + TradeExperience.created_at.desc() + ).offset((page - 1) * page_size).limit(page_size).all() + + return {"success": True, "data": { + "total": total, + "page": page, + "page_size": page_size, + "items": [{ + "id": e.id, + "title": e.title, + "content": e.content, + "exp_type": e.exp_type, + "source_pair_id": e.source_pair_id, + "source_date": e.source_date, + "tags": e.tags or [], + "created_at": e.created_at.strftime('%Y-%m-%d %H:%M:%S') if e.created_at else None, + } for e in experiences], + }} + + +@router.post("/experiences") +def api_create_experience( + req: ExperienceRequest, + db: Session = Depends(get_analysis_db), +): + """创建经验""" + exp = TradeExperience( + title=req.title, + content=req.content, + exp_type=req.exp_type, + source_pair_id=req.source_pair_id, + source_date=req.source_date, + tags=req.tags or [], + ) + db.add(exp) + db.commit() + return {"success": True, "data": {"id": exp.id}} + + +@router.put("/experiences/{exp_id}") +def api_update_experience( + exp_id: int, + req: ExperienceRequest, + db: Session = Depends(get_analysis_db), +): + """更新经验""" + exp = db.query(TradeExperience).filter(TradeExperience.id == exp_id).first() + if not exp: + raise HTTPException(status_code=404, detail="经验不存在") + + for field in ['title', 'content', 'exp_type', 'source_pair_id', 'source_date', 'tags']: + val = getattr(req, field) + if val is not None: + setattr(exp, field, val) + + db.commit() + return {"success": True, "message": "经验已更新"} + + +@router.delete("/experiences/{exp_id}") +def api_delete_experience( + exp_id: int, + db: Session = Depends(get_analysis_db), +): + """删除经验""" + exp = db.query(TradeExperience).filter(TradeExperience.id == exp_id).first() + if not exp: + raise HTTPException(status_code=404, detail="经验不存在") + + db.delete(exp) + db.commit() + return {"success": True, "message": "经验已删除"} + + +@router.get("/experiences/tag-stats") +def api_get_experience_tag_stats( + db: Session = Depends(get_analysis_db), +): + """按标签统计经验数量""" + from collections import Counter + + experiences = db.query(TradeExperience).all() + tag_counter = Counter() + for exp in experiences: + for tag in (exp.tags or []): + tag_counter[tag] += 1 + + # 同时返回所有标签的定义信息 + tags = db.query(TradeTag).all() + tag_map = {t.name: {"id": t.id, "category": t.category, "is_preset": t.is_preset} for t in tags} + + data = [] + for name, count in tag_counter.most_common(): + info = tag_map.get(name, {}) + data.append({ + "name": name, + "count": count, + "tag_id": info.get("id"), + "category": info.get("category", "custom"), + "is_preset": info.get("is_preset", False), + }) + + return {"success": True, "data": data} + + +# ==================== AI 重新分析 ==================== + +class ReanalyzeRequest(BaseModel): + trade_pair_id: int + + +@router.post("/reanalyze") +async def api_reanalyze_trade( + req: ReanalyzeRequest, + db: Session = Depends(get_analysis_db), +): + """基于反思内容重新 AI 分析交易""" + result = analyze_with_reflection(db, req.trade_pair_id) + if not result.get("success"): + raise HTTPException(status_code=400, detail=result.get("message", "分析失败")) + return result + + +@router.get("/reanalyze/{trade_pair_id}/history") +def api_get_analysis_history( + trade_pair_id: int, + db: Session = Depends(get_analysis_db), +): + """获取交易的 AI 分析历史版本""" + history = get_analysis_history(db, trade_pair_id) + return {"success": True, "data": history} + + +@router.get("/reanalyze/{trade_pair_id}/latest") +def api_get_latest_analysis( + trade_pair_id: int, + db: Session = Depends(get_analysis_db), +): + """获取交易最新 AI 分析结果""" + analysis = get_latest_analysis(db, trade_pair_id) + return {"success": True, "data": analysis} + + +class SaveSuggestionRequest(BaseModel): + title: Optional[str] = None + content: Optional[str] = None + tags: Optional[list[str]] = None + + +@router.post("/ai-analysis/{analysis_id}/save-experience") +def api_save_suggestion_as_experience( + analysis_id: int, + req: SaveSuggestionRequest, + db: Session = Depends(get_analysis_db), +): + """将 AI 分析建议保存到经验库""" + result = save_suggestion_as_experience( + db, analysis_id, + title=req.title, + content=req.content, + tags=req.tags, + ) + if not result.get("success"): + raise HTTPException(status_code=400, detail=result.get("message", "保存失败")) + return result diff --git a/app/services/reflection_ai_analysis.py b/app/services/reflection_ai_analysis.py new file mode 100644 index 0000000..c1363d0 --- /dev/null +++ b/app/services/reflection_ai_analysis.py @@ -0,0 +1,335 @@ +""" +交易反思 AI 分析服务 - 基于反思内容重新分析交易并提炼经验 +""" +import json +import logging +import re +from typing import Dict, List, Optional +from sqlalchemy.orm import Session + +from app.analysis_models import TradePair, TradeReflection, TradeAIAnalysis, TradeExperience +from app.services.ai_analysis import AIFuturesAnalyzer + +logger = logging.getLogger(__name__) + + +REFLECTION_ANALYSIS_PROMPT = """你是一位资深的交易心理与策略复盘教练。请基于以下交易数据和交易者的反思内容，进行深度分析。 + +=== 交易基础信息 === +品种：{variety} +方向：{direction_text} +开仓日期时间：{open_date} {open_time} +平仓日期时间：{close_date} {close_time} +开仓均价：{open_price} +平仓均价：{close_price} +交易手数：{volume} +平仓盈亏：{close_pnl} +手续费：{commission} +净盈亏：{net_pnl} + +=== 交易者反思内容 === +入场理由：{entry_reason} +入场时机评价：{entry_timing} +仓位管理反思：{position_management} +出场理由：{exit_reason} +出场时机评价：{exit_timing} +纪律评分（1-5）：{discipline_score} +自由反思：{free_reflection} + +=== 当日整体反思 === +情绪状态：{emotion_state} +市场判断：{market_judgment} +当日总结：{daily_summary} +改进方向：{daily_improvements} + +=== 任务要求 === +请严格按以下 JSON 格式输出分析结果（不要输出任何其他内容）： + +{{ + "overall_evaluation": "综合评价：这笔交易的执行质量、决策逻辑、与市场的匹配度等", + "strengths": ["优点1", "优点2", "优点3"], + "weaknesses": ["不足1", "不足2", "不足3"], + "experience_suggestion": "提炼出的核心经验教训或改进建议，语言简洁 actionable", + "suggestion_type": "lesson", + "actionable_plan": ["下次可以做的具体改进1", "下次可以做的具体改进2"] +}} + +注意： +1. suggestion_type 只能是 lesson（经验）、tip（技巧）、warning（警告）三者之一 +2. 必须结合反思内容进行分析，不要脱离交易者自己的总结 +3. 如果交易亏损，重点分析亏损原因和避免方案 +4. 如果交易盈利，重点分析盈利是否来自计划还是运气 +""" + + +def _timing_text(timing: Optional[str]) -> str: + """时机评价转中文""" + mapping = {"good": "良好", "fair": "一般", "poor": "较差"} + return mapping.get(timing, timing or "未评价") + + +def _direction_text(direction: Optional[str]) -> str: + """方向转中文""" + mapping = {"long": "多头", "short": "空头"} + return mapping.get(direction, direction or "未知") + + +def _get_records_text(db: Session, pair: TradePair) -> str: + """获取配对关联的原始记录信息""" + from app.analysis_models import TradeRecord + record_ids = (pair.open_record_ids or []) + (pair.close_record_ids or []) + if not record_ids: + return "" + records = db.query(TradeRecord).filter(TradeRecord.id.in_(record_ids)).all() + lines = [] + for r in sorted(records, key=lambda x: f"{x.trade_date} {x.trade_time or ''}"): + lines.append(f"{r.trade_date} {r.trade_time or ''} {r.symbol} {r.direction}{r.offset} {r.price}×{r.volume}") + return "\n".join(lines) + + +def build_reflection_prompt(db: Session, pair: TradePair, reflection: TradeReflection, + daily_reflection: Optional[Dict] = None) -> str: + """构建反思增强分析提示词""" + daily = daily_reflection or {} + + # 获取第一条开仓和第一条平仓记录的时间 + from app.analysis_models import TradeRecord + open_time = "" + close_time = "" + open_record_ids = pair.open_record_ids or [] + close_record_ids = pair.close_record_ids or [] + if open_record_ids: + open_rec = db.query(TradeRecord).filter(TradeRecord.id == open_record_ids[0]).first() + if open_rec: + open_time = open_rec.trade_time or "" + if close_record_ids: + close_rec = db.query(TradeRecord).filter(TradeRecord.id == close_record_ids[0]).first() + if close_rec: + close_time = close_rec.trade_time or "" + + return REFLECTION_ANALYSIS_PROMPT.format( + variety=pair.variety, + direction_text=_direction_text(pair.direction), + open_date=pair.open_date or "", + open_time=open_time, + close_date=pair.close_date or "", + close_time=close_time, + open_price=pair.open_price, + close_price=pair.close_price, + volume=pair.total_volume, + close_pnl=pair.close_pnl, + commission=pair.total_commission, + net_pnl=pair.net_pnl, + entry_reason=reflection.entry_reason or "未填写", + entry_timing=_timing_text(reflection.entry_timing), + position_management=reflection.position_management or "未填写", + exit_reason=reflection.exit_reason or "未填写", + exit_timing=_timing_text(reflection.exit_timing), + discipline_score=reflection.discipline_score or "未评分", + free_reflection=reflection.free_reflection or "未填写", + emotion_state=daily.get("emotion_state", "未填写"), + market_judgment=daily.get("market_judgment", "未填写"), + daily_summary=daily.get("summary", "未填写"), + daily_improvements=daily.get("improvements", "未填写"), + ) + + +def _parse_ai_response(response: str) -> Dict: + """解析 AI 返回的 JSON""" + try: + # 尝试直接解析 + data = json.loads(response) + return data + except json.JSONDecodeError: + pass + + # 尝试从 Markdown 代码块中提取 + code_block_pattern = re.compile(r'```(?:json)?\s*([\s\S]*?)```', re.MULTILINE) + matches = code_block_pattern.findall(response) + for match in matches: + try: + return json.loads(match.strip()) + except json.JSONDecodeError: + continue + + # 尝试从文本中提取第一个 JSON 对象 + json_pattern = re.compile(r'\{[\s\S]*\}') + matches = json_pattern.findall(response) + for match in matches: + try: + return json.loads(match) + except json.JSONDecodeError: + continue + + # 解析失败，返回原始内容作为 overall_evaluation + return { + "overall_evaluation": response, + "strengths": [], + "weaknesses": [], + "experience_suggestion": "", + "suggestion_type": "lesson", + "actionable_plan": [], + } + + +def _get_next_version(db: Session, trade_pair_id: int) -> int: + """获取下一个版本号""" + latest = db.query(TradeAIAnalysis).filter( + TradeAIAnalysis.trade_pair_id == trade_pair_id + ).order_by(TradeAIAnalysis.version.desc()).first() + return (latest.version + 1) if latest else 1 + + +def analyze_with_reflection(db: Session, trade_pair_id: int) -> Dict: + """ + 基于反思内容重新分析交易 + 返回：{success, data/version, message} + """ + pair = db.query(TradePair).filter(TradePair.id == trade_pair_id).first() + if not pair: + return {"success": False, "message": "交易配对不存在"} + + reflection = db.query(TradeReflection).filter( + TradeReflection.trade_pair_id == trade_pair_id + ).first() + if not reflection: + return {"success": False, "message": "该交易尚未填写反思，无法进行分析"} + + # 获取当日反思 + daily_reflection = None + from app.analysis_models import DailyReflection + dr = db.query(DailyReflection).filter( + DailyReflection.reflection_date == pair.close_date + ).first() + if dr: + daily_reflection = { + "emotion_state": dr.emotion_state, + "market_judgment": dr.market_judgment, + "summary": dr.summary, + "improvements": dr.improvements, + } + + # 构建提示词 + prompt = build_reflection_prompt(db, pair, reflection, daily_reflection) + + # 调用 AI + analyzer = AIFuturesAnalyzer(db) + model = analyzer.get_active_model() + if not model: + return {"success": False, "message": "未配置AI模型或模型未激活"} + + response = analyzer.call_ai_model(prompt, model) + if not response: + return {"success": False, "message": "AI模型返回空响应，请稍后重试"} + + # 解析结果 + parsed = _parse_ai_response(response) + + # 计算版本号 + version = _get_next_version(db, trade_pair_id) + + # 保存分析结果 + analysis = TradeAIAnalysis( + trade_pair_id=trade_pair_id, + version=version, + overall_evaluation=parsed.get("overall_evaluation", ""), + strengths=parsed.get("strengths", []), + weaknesses=parsed.get("weaknesses", []), + experience_suggestion=parsed.get("experience_suggestion", ""), + suggestion_type=parsed.get("suggestion_type", "lesson"), + raw_response=response, + prompt_snapshot=prompt, + ) + db.add(analysis) + + # 更新反思的 AI 分析状态 + reflection.ai_analyzed = True + reflection.ai_version = version + + db.commit() + + return { + "success": True, + "data": { + "version": version, + "overall_evaluation": analysis.overall_evaluation, + "strengths": analysis.strengths, + "weaknesses": analysis.weaknesses, + "experience_suggestion": analysis.experience_suggestion, + "suggestion_type": analysis.suggestion_type, + "actionable_plan": parsed.get("actionable_plan", []), + }, + } + + +def get_analysis_history(db: Session, trade_pair_id: int) -> List[Dict]: + """获取交易的所有 AI 分析版本""" + analyses = db.query(TradeAIAnalysis).filter( + TradeAIAnalysis.trade_pair_id == trade_pair_id + ).order_by(TradeAIAnalysis.version.desc()).all() + + return [{ + "id": a.id, + "version": a.version, + "overall_evaluation": a.overall_evaluation, + "strengths": a.strengths, + "weaknesses": a.weaknesses, + "experience_suggestion": a.experience_suggestion, + "suggestion_type": a.suggestion_type, + "saved_to_experience": a.saved_to_experience, + "experience_id": a.experience_id, + "created_at": a.created_at.strftime('%Y-%m-%d %H:%M:%S') if a.created_at else None, + } for a in analyses] + + +def get_latest_analysis(db: Session, trade_pair_id: int) -> Optional[Dict]: + """获取最新 AI 分析结果""" + analysis = db.query(TradeAIAnalysis).filter( + TradeAIAnalysis.trade_pair_id == trade_pair_id + ).order_by(TradeAIAnalysis.version.desc()).first() + + if not analysis: + return None + + return { + "id": analysis.id, + "version": analysis.version, + "overall_evaluation": analysis.overall_evaluation, + "strengths": analysis.strengths, + "weaknesses": analysis.weaknesses, + "experience_suggestion": analysis.experience_suggestion, + "suggestion_type": analysis.suggestion_type, + "saved_to_experience": analysis.saved_to_experience, + "experience_id": analysis.experience_id, + "created_at": analysis.created_at.strftime('%Y-%m-%d %H:%M:%S') if analysis.created_at else None, + } + + +def save_suggestion_as_experience(db: Session, analysis_id: int, title: Optional[str] = None, + content: Optional[str] = None, tags: Optional[List[str]] = None) -> Dict: + """将 AI 分析建议保存为经验""" + analysis = db.query(TradeAIAnalysis).filter(TradeAIAnalysis.id == analysis_id).first() + if not analysis: + return {"success": False, "message": "分析记录不存在"} + + pair = db.query(TradePair).filter(TradePair.id == analysis.trade_pair_id).first() + + exp_title = title or analysis.experience_suggestion[:50] if analysis.experience_suggestion else "AI提炼经验" + exp_content = content or analysis.experience_suggestion or "" + + exp = TradeExperience( + title=exp_title, + content=exp_content, + exp_type=analysis.suggestion_type or "lesson", + source_pair_id=analysis.trade_pair_id, + source_date=pair.close_date if pair else None, + tags=tags or [], + ) + db.add(exp) + db.flush() + + analysis.saved_to_experience = True + analysis.experience_id = exp.id + db.commit() + + return {"success": True, "data": {"experience_id": exp.id}} diff --git a/app/services/trade_pairing_engine.py b/app/services/trade_pairing_engine.py new file mode 100644 index 0000000..900dc63 --- /dev/null +++ b/app/services/trade_pairing_engine.py @@ -0,0 +1,336 @@ +""" +交易配对引擎 - 将开仓和平仓配对为完整交易，持久化到 TradePair 表 +""" +import logging +from typing import Optional + +from sqlalchemy.orm import Session +from collections import defaultdict + +from app.analysis_models import TradeRecord, TradePair, TradeReflection, DailyReflection + +logger = logging.getLogger(__name__) + + +def auto_pair_trades(db: Session, trade_date: str) -> dict: + """ + 自动配对指定日期及之前未平仓的交易记录 + 按品种+方向分组，FIFO 规则配对 + 返回：{created: 新建配对数, updated: 更新配对数, unpaired: 未配对记录数} + """ + # 查询指定日期的记录 + current_records = db.query(TradeRecord).filter( + TradeRecord.trade_date == trade_date + ).order_by( + TradeRecord.variety, TradeRecord.symbol, TradeRecord.trade_date, TradeRecord.trade_time + ).all() + + if not current_records: + return {"created": 0, "updated": 0, "unpaired": 0} + + # 查询之前未配对的记录（跨日持仓） + # 找出所有已经配对的记录 ID + all_pairs = db.query(TradePair).all() + paired_ids = set() + for p in all_pairs: + paired_ids.update(p.open_record_ids or []) + paired_ids.update(p.close_record_ids or []) + + # 查询 trade_date 之前的未配对记录 + previous_unpaired = db.query(TradeRecord).filter( + TradeRecord.trade_date < trade_date, + TradeRecord.id.notin_(paired_ids) if paired_ids else True, + ).order_by( + TradeRecord.variety, TradeRecord.symbol, TradeRecord.trade_date, TradeRecord.trade_time + ).all() + + # 合并当前记录和之前未配对记录 + all_records = previous_unpaired + current_records + + # 按品种分组 + by_variety = defaultdict(list) + for r in all_records: + by_variety[r.variety].append(r) + + result = {"created": 0, "updated": 0, "unpaired": 0} + + for variety, recs in by_variety.items(): + pair_result = _pair_single_variety(db, variety, recs) + result["created"] += pair_result["created"] + result["updated"] += pair_result["updated"] + result["unpaired"] += pair_result["unpaired"] + + return result + + +def _pair_single_variety(db: Session, variety: str, records: list) -> dict: + """单个品种的配对逻辑""" + # 分离开仓和平仓 + opens = [r for r in records if r.offset == '开'] + closes = [r for r in records if r.offset == '平'] + + result = {"created": 0, "updated": 0, "unpaired": 0} + + # 按方向分组 + long_opens = [r for r in opens if r.direction == '买'] + short_opens = [r for r in opens if r.direction == '卖'] + long_closes = [r for r in closes if r.direction == '卖'] # 卖平 = 多头平仓 + short_closes = [r for r in closes if r.direction == '买'] # 买平 = 空头平仓 + + # 多头配对 + result["created"] += _pair_direction(db, variety, long_opens, long_closes, "long") + # 空头配对 + result["created"] += _pair_direction(db, variety, short_opens, short_closes, "short") + + # 统计未配对 + all_paired_open_ids = set() + all_paired_close_ids = set() + existing_pairs = db.query(TradePair).filter( + TradePair.variety == variety, + ).all() + for p in existing_pairs: + all_paired_open_ids.update(p.open_record_ids or []) + all_paired_close_ids.update(p.close_record_ids or []) + + unpaired_count = 0 + for r in records: + if r.id not in all_paired_open_ids and r.id not in all_paired_close_ids: + unpaired_count += 1 + result["unpaired"] = unpaired_count + + return result + + +def _pair_direction(db: Session, variety: str, opens: list, closes: list, direction: str) -> int: + """单方向配对（FIFO，支持多开合并），返回新建配对数 + + 每个平仓记录会尽可能与前面的开仓记录配对，形成一个完整配对。 + 如果多个开仓合并才能匹配一个平仓，则这些开仓记录会归入同一个配对。 + """ + created = 0 + # 开仓队列，每个元素是 (record, remaining_volume) + open_queue = [(r, r.volume or 0) for r in opens] + close_queue = [(r, r.volume or 0) for r in closes] + + while open_queue and close_queue: + close_rec, close_remaining = close_queue[0] + + # 从开仓队列中取足够手数来匹配这个平仓 + needed = close_remaining + used_opens = [] # (record, take_volume) + while needed > 0 and open_queue: + open_rec, open_remaining = open_queue[0] + take = min(open_remaining, needed) + used_opens.append((open_rec, take)) + needed -= take + if take >= open_remaining: + open_queue.pop(0) + else: + open_queue[0] = (open_rec, open_remaining - take) + + if not used_opens: + break + + # 实际匹配的手数 + matched_volume = close_remaining - needed + + # 更新平仓队列 + if matched_volume >= close_remaining: + close_queue.pop(0) + else: + close_queue[0] = (close_rec, close_remaining - matched_volume) + + # 计算均价（按手数加权） + open_volume_sum = sum(take for _, take in used_opens) + open_price = sum((r.price or 0) * take for r, take in used_opens) / open_volume_sum if open_volume_sum > 0 else 0 + close_price = close_rec.price or 0 + + # 计算盈亏和手续费 + # 平仓盈亏按比例分配到该平仓记录 + close_pnl = (close_rec.close_pnl or 0) * (matched_volume / close_rec.volume) if close_rec.volume else 0 + total_commission = sum((r.commission or 0) * (take / r.volume) for r, take in used_opens if r.volume) + \ + (close_rec.commission or 0) * (matched_volume / close_rec.volume) if close_rec.volume else 0 + net_pnl = close_pnl - total_commission + + # 最早开仓日期和最早平仓日期 + open_date = min(r.trade_date for r, _ in used_opens) + close_date = close_rec.trade_date + + pair = TradePair( + variety=variety, + direction=direction, + open_record_ids=[r.id for r, _ in used_opens], + close_record_ids=[close_rec.id], + open_price=round(open_price, 2), + close_price=round(close_price, 2), + total_volume=matched_volume, + close_pnl=round(close_pnl, 2), + total_commission=round(total_commission, 2), + net_pnl=round(net_pnl, 2), + open_date=open_date, + close_date=close_date, + ) + db.add(pair) + created += 1 + + db.flush() + return created + + +def get_daily_trades_with_pairs(db: Session, trade_date: str) -> dict: + """ + 获取指定日期的交易数据，包含配对信息和反思状态 + 返回：{date, stats, pairs, unpaired_records, daily_reflection} + """ + # 获取所有配对 + pairs = db.query(TradePair).filter( + (TradePair.open_date == trade_date) | (TradePair.close_date == trade_date) + ).order_by(TradePair.open_date, TradePair.close_date).all() + + # 获取所有交易记录 + records = db.query(TradeRecord).filter( + TradeRecord.trade_date == trade_date + ).order_by(TradeRecord.trade_date, TradeRecord.trade_time).all() + + # 计算统计 + total_pnl = sum(p.net_pnl or 0 for p in pairs) + total_trades = len(pairs) + len(records) # 配对 + 未配对 + winning_trades = sum(1 for p in pairs if (p.net_pnl or 0) > 0) + win_rate = round(winning_trades / total_trades * 100, 1) if total_trades > 0 else 0 + + # 获取反思状态 + pair_ids = [p.id for p in pairs] + reflections = db.query(TradeReflection).filter( + TradeReflection.trade_pair_id.in_(pair_ids) + ).all() + reflection_map = {r.trade_pair_id: r for r in reflections} + + # 获取当日反思 + daily_reflection = db.query(DailyReflection).filter( + DailyReflection.reflection_date == trade_date + ).first() + + # 构建返回数据 + pairs_data = [] + for p in pairs: + reflection = reflection_map.get(p.id) + pairs_data.append({ + "id": p.id, + "variety": p.variety, + "direction": p.direction, + "open_price": p.open_price, + "close_price": p.close_price, + "total_volume": p.total_volume, + "close_pnl": p.close_pnl, + "total_commission": p.total_commission, + "net_pnl": p.net_pnl, + "open_date": p.open_date, + "close_date": p.close_date, + "open_record_ids": p.open_record_ids, + "close_record_ids": p.close_record_ids, + "reflection_status": "done" if reflection else ("pending" if _has_unpaired_records(db, p) else "none"), + "ai_analyzed": reflection.ai_analyzed if reflection else False, + "ai_version": reflection.ai_version if reflection else None, + }) + + # 未配对记录 + paired_record_ids = set() + for p in pairs: + paired_record_ids.update(p.open_record_ids or []) + paired_record_ids.update(p.close_record_ids or []) + + unpaired = [r for r in records if r.id not in paired_record_ids] + unpaired_data = [{ + "id": r.id, + "symbol": r.symbol, + "variety": r.variety, + "direction": r.direction, + "offset": r.offset, + "price": r.price, + "volume": r.volume, + "trade_date": r.trade_date, + "trade_time": r.trade_time, + } for r in unpaired] + + return { + "date": trade_date, + "stats": { + "total_pnl": round(total_pnl, 2), + "total_trades": total_trades, + "winning_trades": winning_trades, + "win_rate": win_rate, + "paired_count": len(pairs), + "unpaired_count": len(unpaired), + "reflected_count": sum(1 for r in reflection_map.values()), + }, + "pairs": pairs_data, + "unpaired_records": unpaired_data, + "daily_reflection": { + "id": daily_reflection.id if daily_reflection else None, + "emotion_state": daily_reflection.emotion_state if daily_reflection else None, + "market_judgment": daily_reflection.market_judgment if daily_reflection else None, + "discipline_score": daily_reflection.discipline_score if daily_reflection else None, + "overall_rating": daily_reflection.overall_rating if daily_reflection else None, + "summary": daily_reflection.summary if daily_reflection else None, + "improvements": daily_reflection.improvements if daily_reflection else None, + } if daily_reflection else None, + } + + +def _has_unpaired_records(db: Session, pair: TradePair) -> bool: + """检查配对是否有未配对的开仓或平仓记录""" + # 简化逻辑：如果开仓日期和平仓日期不同，可能存在跨日未配对 + return pair.open_date != pair.close_date + + +def create_manual_pair(db: Session, open_record_ids: list[int], close_record_ids: list[int]) -> TradePair: + """手动创建配对""" + open_records = db.query(TradeRecord).filter(TradeRecord.id.in_(open_record_ids)).all() + close_records = db.query(TradeRecord).filter(TradeRecord.id.in_(close_record_ids)).all() + + if not open_records or not close_records: + raise ValueError("开仓或平仓记录不存在") + + variety = open_records[0].variety + direction = "long" if open_records[0].direction == '买' else "short" + + # 计算统计 + total_volume = sum(r.volume or 0 for r in open_records) + total_close_pnl = sum(r.close_pnl or 0 for r in close_records) + total_commission = sum((r.commission or 0) for r in open_records) + sum((r.commission or 0) for r in close_records) + net_pnl = total_close_pnl - total_commission + + avg_open_price = sum(r.price or 0 for r in open_records) / len(open_records) if open_records else 0 + avg_close_price = sum(r.price or 0 for r in close_records) / len(close_records) if close_records else 0 + + pair = TradePair( + variety=variety, + direction=direction, + open_record_ids=open_record_ids, + close_record_ids=close_record_ids, + open_price=round(avg_open_price, 2), + close_price=round(avg_close_price, 2), + total_volume=total_volume, + close_pnl=total_close_pnl, + total_commission=total_commission, + net_pnl=round(net_pnl, 2), + open_date=open_records[0].trade_date, + close_date=close_records[-1].trade_date, + ) + db.add(pair) + db.flush() + return pair + + +def delete_pair(db: Session, pair_id: int) -> bool: + """删除配对（不删除原始交易记录）""" + pair = db.query(TradePair).filter(TradePair.id == pair_id).first() + if not pair: + return False + + # 删除关联的反思 + db.query(TradeReflection).filter(TradeReflection.trade_pair_id == pair_id).delete() + # 删除配对 + db.delete(pair) + db.flush() + return True diff --git a/app/static/futures_analysis.html b/app/static/futures_analysis.html index b36717c..e36e665 100644 --- a/app/static/futures_analysis.html +++ b/app/static/futures_analysis.html @@ -495,6 +495,7 @@ .tr-toolbar { display: flex; justify-content: space-between; align-items: center; margin-bottom: 20px; flex-wrap: wrap; gap: 12px; } .tr-toolbar-left, .tr-toolbar-right { display: flex; align-items: center; gap: 10px; flex-wrap: wrap; } .tr-btn { padding: 10px 20px; border-radius: 12px; font-size: 14px; font-weight: 600; cursor: pointer; border: none; display: flex; align-items: center; gap: 8px; transition: all 0.2s; font-family: inherit; } + .tr-btn:disabled { opacity: 0.5; cursor: not-allowed; } .tr-btn-primary { background: var(--color-brand); color: #fff; } .tr-btn-primary:hover { background: #0066d6; box-shadow: 0 4px 10px rgba(0,122,255,0.3); } .tr-btn-secondary { background: #fff; color: var(--text-secondary); box-shadow: var(--shadow-sm); border: 1px solid rgba(0,0,0,0.05); } @@ -582,12 +583,146 @@ .tr-empty-icon { font-size: 56px; opacity: 0.3; } .tr-empty-text { font-size: 15px; color: var(--text-tertiary); } + /* ============================================ + 交易反思子系统样式 + ============================================ */ + .tr-subtabs { display: flex; gap: 8px; margin-bottom: 24px; background: rgba(255,255,255,0.6); padding: 4px; border-radius: 14px; backdrop-filter: blur(8px); width: fit-content; } + .tr-subtab { padding: 8px 20px; border-radius: 10px; font-size: 14px; font-weight: 500; cursor: pointer; border: none; background: transparent; color: var(--text-secondary); transition: all 0.2s; font-family: inherit; } + .tr-subtab:hover { background: rgba(0,0,0,0.03); color: var(--text-primary); } + .tr-subtab.active { background: #fff; color: var(--color-brand); box-shadow: var(--shadow-sm); font-weight: 600; } + + .tr-tab-panel { display: none; } + .tr-tab-panel.active { display: block; } + + /* 交易反思 - 日期导航 */ + .tr-refl-datebar { display: flex; align-items: center; justify-content: center; gap: 16px; margin-bottom: 24px; } + .tr-refl-datebtn { width: 36px; height: 36px; border-radius: 50%; border: none; background: #fff; color: var(--text-secondary); cursor: pointer; box-shadow: var(--shadow-sm); transition: all 0.2s; font-size: 16px; } + .tr-refl-datebtn:hover { background: var(--color-brand); color: #fff; } + .tr-refl-datetext { font-size: 20px; font-weight: 700; color: var(--text-primary); min-width: 120px; text-align: center; } + .tr-refl-actions { display: flex; gap: 10px; margin-left: auto; } + + /* 反思统计卡片 */ + .tr-refl-stats { display: grid; grid-template-columns: repeat(4, 1fr); gap: 16px; margin-bottom: 24px; } + .tr-refl-statcard { background: var(--bg-card); border-radius: var(--radius-card); padding: 20px; text-align: center; box-shadow: var(--shadow-sm); transition: transform 0.2s; } + .tr-refl-statcard:hover { transform: translateY(-2px); box-shadow: var(--shadow-md); } + .tr-refl-statlabel { font-size: 12px; color: var(--text-tertiary); margin-bottom: 8px; font-weight: 600; } + .tr-refl-statvalue { font-size: 26px; font-weight: 700; } + .tr-refl-statvalue.profit { color: var(--color-down); } + .tr-refl-statvalue.loss { color: var(--color-up); } + .tr-refl-reflected { display: inline-flex; align-items: center; gap: 6px; padding: 4px 12px; border-radius: 9999px; font-size: 12px; font-weight: 600; } + .tr-refl-reflected.yes { background: rgba(52,199,89,0.12); color: var(--color-down); } + .tr-refl-reflected.no { background: rgba(255,59,48,0.12); color: var(--color-up); } + + /* 当日反思区域 */ + .tr-refl-daily { background: var(--bg-card); border-radius: var(--radius-card); padding: 24px; margin-bottom: 24px; box-shadow: var(--shadow-sm); } + .tr-refl-daily-header { display: flex; justify-content: space-between; align-items: center; margin-bottom: 16px; } + .tr-refl-daily-title { font-size: 17px; font-weight: 700; color: var(--text-primary); } + .tr-refl-daily-grid { display: grid; grid-template-columns: repeat(4, 1fr); gap: 16px; } + .tr-refl-daily-item { background: #F5F5F7; border-radius: 12px; padding: 14px; } + .tr-refl-daily-label { font-size: 11px; color: var(--text-tertiary); margin-bottom: 6px; font-weight: 600; text-transform: uppercase; } + .tr-refl-daily-value { font-size: 14px; color: var(--text-primary); font-weight: 500; line-height: 1.4; } + .tr-refl-daily-value.empty { color: var(--text-tertiary); font-style: italic; } + + /* 交易配对卡片 */ + .tr-refl-section-title { font-size: 17px; font-weight: 700; margin-bottom: 16px; color: var(--text-primary); } + .tr-refl-pairs { display: grid; grid-template-columns: repeat(auto-fill, minmax(340px, 1fr)); gap: 16px; margin-bottom: 24px; } + .tr-refl-card { background: var(--bg-card); border-radius: var(--radius-card); padding: 20px; box-shadow: var(--shadow-sm); transition: transform 0.2s, box-shadow 0.2s; position: relative; overflow: hidden; } + .tr-refl-card:hover { transform: translateY(-2px); box-shadow: var(--shadow-md); } + .tr-refl-card::before { content: ""; position: absolute; top: 0; left: 0; right: 0; height: 3px; background: var(--color-brand); } + .tr-refl-card.profit::before { background: var(--color-down); } + .tr-refl-card.loss::before { background: var(--color-up); } + .tr-refl-card-header { display: flex; justify-content: space-between; align-items: flex-start; margin-bottom: 12px; } + .tr-refl-card-symbol { font-size: 16px; font-weight: 700; } + .tr-refl-card-dir { font-size: 12px; padding: 3px 10px; border-radius: 6px; font-weight: 600; } + .tr-refl-card-dir.long { background: rgba(52,199,89,0.12); color: var(--color-down); } + .tr-refl-card-dir.short { background: rgba(255,59,48,0.12); color: var(--color-up); } + .tr-refl-card-pnl { font-size: 20px; font-weight: 700; text-align: right; } + .tr-refl-card-pnl.profit { color: var(--color-down); } + .tr-refl-card-pnl.loss { color: var(--color-up); } + .tr-refl-card-nodes { font-size: 13px; color: var(--text-secondary); margin-bottom: 14px; line-height: 1.6; } + .tr-refl-card-tags { display: flex; flex-wrap: wrap; gap: 6px; margin-bottom: 14px; } + .tr-refl-tag { font-size: 11px; padding: 4px 10px; border-radius: 9999px; background: #F5F5F7; color: var(--text-secondary); font-weight: 500; } + .tr-refl-tag.active { background: rgba(0,122,255,0.12); color: var(--color-brand); } + .tr-refl-card-actions { display: flex; gap: 8px; flex-wrap: wrap; } + .tr-refl-card-actions .tr-btn { padding: 6px 12px; font-size: 12px; } + + /* 未配对交易 */ + .tr-refl-unpaired { background: var(--bg-card); border-radius: var(--radius-card); padding: 20px; box-shadow: var(--shadow-sm); } + .tr-refl-unpaired-header { display: flex; justify-content: space-between; align-items: center; margin-bottom: 16px; } + .tr-refl-unpaired-header .tr-refl-section-title { margin-bottom: 0; } + .tr-refl-unpaired-table { width: 100%; border-collapse: collapse; font-size: 13px; } + .tr-refl-unpaired-table th { background: #F5F5F7; padding: 10px 8px; text-align: left; font-weight: 600; color: var(--text-tertiary); font-size: 12px; } + .tr-refl-unpaired-table td { padding: 10px 8px; border-bottom: 1px solid rgba(0,0,0,0.04); color: var(--text-primary); } + .tr-refl-unpaired-table input[type="checkbox"] { width: 16px; height: 16px; accent-color: var(--color-brand); cursor: pointer; } + + /* 经验库 */ + .tr-exp-toolbar { display: flex; gap: 12px; margin-bottom: 20px; flex-wrap: wrap; align-items: center; } + .tr-exp-search { background: #fff; border: 1px solid rgba(0,0,0,0.05); border-radius: 12px; padding: 0 14px; height: 40px; font-size: 14px; width: 260px; outline: none; } + .tr-exp-search:focus { border-color: var(--color-brand); box-shadow: 0 0 0 3px rgba(0,122,255,0.15); } + .tr-exp-filter { background: #fff; border: 1px solid rgba(0,0,0,0.05); border-radius: 10px; padding: 0 12px; height: 36px; font-size: 13px; color: var(--text-primary); outline: none; } + .tr-exp-viewtabs { display: flex; gap: 6px; margin-left: auto; } + .tr-exp-viewtab { padding: 6px 14px; border-radius: 10px; font-size: 13px; cursor: pointer; border: none; background: #fff; color: var(--text-secondary); transition: all 0.2s; } + .tr-exp-viewtab:hover { background: #F5F5F7; } + .tr-exp-viewtab.active { background: var(--color-brand); color: #fff; } + .tr-exp-grid { display: grid; grid-template-columns: repeat(auto-fill, minmax(320px, 1fr)); gap: 16px; } + .tr-exp-card { background: var(--bg-card); border-radius: var(--radius-card); padding: 20px; box-shadow: var(--shadow-sm); transition: all 0.2s; } + .tr-exp-card:hover { transform: translateY(-2px); box-shadow: var(--shadow-md); } + .tr-exp-card-title { font-size: 15px; font-weight: 700; margin-bottom: 10px; color: var(--text-primary); } + .tr-exp-card-meta { font-size: 12px; color: var(--text-tertiary); margin-bottom: 12px; } + .tr-exp-card-content { font-size: 13px; color: var(--text-secondary); line-height: 1.7; margin-bottom: 14px; } + .tr-exp-card-tags { display: flex; flex-wrap: wrap; gap: 6px; } + .tr-exp-pagination { display: flex; align-items: center; justify-content: center; gap: 16px; margin-top: 24px; } + .tr-exp-pageinfo { font-size: 13px; color: var(--text-secondary); font-weight: 500; } + .tr-exp-detail-meta { font-size: 12px; color: var(--text-tertiary); margin-bottom: 16px; } + .tr-exp-detail-content { font-size: 14px; color: var(--text-primary); line-height: 1.8; background: #F5F5F7; border-radius: 12px; padding: 16px; margin-bottom: 16px; white-space: pre-wrap; } + .tr-exp-detail-tags { display: flex; flex-wrap: wrap; gap: 6px; margin-bottom: 16px; } + .tr-exp-detail-source { font-size: 13px; color: var(--text-secondary); } + .tr-exp-detail-source .link { cursor: pointer; } + + /* Modal 表单 */ + .tr-form-group { margin-bottom: 18px; } + .tr-form-label { display: block; font-size: 13px; font-weight: 600; color: var(--text-secondary); margin-bottom: 8px; } + .tr-form-input, .tr-form-select, .tr-form-textarea { width: 100%; background: #F5F5F7; border: 1px solid transparent; border-radius: 10px; padding: 10px 12px; font-size: 14px; color: var(--text-primary); font-family: inherit; outline: none; transition: all 0.2s; } + .tr-form-input:focus, .tr-form-select:focus, .tr-form-textarea:focus { border-color: var(--color-brand); background: #fff; box-shadow: 0 0 0 3px rgba(0,122,255,0.1); } + .tr-form-textarea { min-height: 90px; resize: vertical; } + .tr-form-row { display: grid; grid-template-columns: 1fr 1fr; gap: 16px; } + .tr-form-rating { display: flex; gap: 6px; } + .tr-form-star { font-size: 22px; color: #E5E5E7; cursor: pointer; transition: color 0.15s; } + .tr-form-star.active { color: #FF9F0A; } + .tr-form-actions { display: flex; justify-content: flex-end; gap: 10px; margin-top: 24px; } + + /* 标签管理 */ + .tr-tag-section { margin-bottom: 18px; } + .tr-tag-section-title { font-size: 13px; font-weight: 600; color: var(--text-secondary); margin-bottom: 10px; } + .tr-tag-pool { display: flex; flex-wrap: wrap; gap: 8px; margin-bottom: 8px; } + .tr-tag-chip { padding: 6px 12px; border-radius: 9999px; font-size: 12px; cursor: pointer; border: 1px solid rgba(0,0,0,0.06); background: #fff; color: var(--text-secondary); transition: all 0.15s; } + .tr-tag-chip:hover { border-color: var(--color-brand); color: var(--color-brand); } + .tr-tag-chip.selected { background: rgba(0,122,255,0.12); border-color: var(--color-brand); color: var(--color-brand); font-weight: 500; } + .tr-tag-custom { display: flex; gap: 8px; margin-top: 12px; } + .tr-tag-custom .tr-form-input { flex: 1; } + + /* AI 分析结果 */ + .tr-ai-version-row { display: flex; align-items: center; gap: 12px; margin-bottom: 18px; padding: 12px 16px; background: #F5F5F7; border-radius: 12px; } + .tr-ai-version-label { font-size: 13px; font-weight: 600; color: var(--text-tertiary); } + .tr-ai-version-row .tr-form-select { flex: 1; min-width: 200px; margin: 0; } + .tr-ai-result-section { margin-bottom: 18px; } + .tr-ai-result-title { font-size: 14px; font-weight: 700; color: var(--color-brand); margin-bottom: 8px; } + .tr-ai-result-content { font-size: 14px; color: var(--text-primary); line-height: 1.8; background: #F5F5F7; border-radius: 12px; padding: 14px; } + .tr-ai-list { padding-left: 18px; margin: 0; } + .tr-ai-list li { margin-bottom: 6px; line-height: 1.6; } + @media (max-width: 1200px) { .tr-stats-grid { grid-template-columns: repeat(3, 1fr); } + .tr-refl-stats { grid-template-columns: repeat(2, 1fr); } + .tr-refl-daily-grid { grid-template-columns: repeat(2, 1fr); } } @media (max-width: 768px) { .tr-stats-grid { grid-template-columns: repeat(2, 1fr); } .tr-overview-grid { grid-template-columns: repeat(2, 1fr); } + .tr-refl-stats { grid-template-columns: 1fr; } + .tr-refl-daily-grid { grid-template-columns: 1fr; } + .tr-form-row { grid-template-columns: 1fr; } + .tr-refl-pairs { grid-template-columns: 1fr; } } @media (max-width: 768px) { @@ -974,82 +1109,429 @@
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