Phase 3: Advanced Analytics Foundation - Models, Schemas, and API

backend/app/models/models.py

@@ -169,3 +169,100 @@ class HabitTracker(Base):
     total_completions = Column(Integer, default=0)
     created_at = Column(DateTime(timezone=True), server_default=func.now())
     updated_at = Column(DateTime(timezone=True), onupdate=func.now())
+
+
+# Phase 3: Advanced Analytics
+
+class PerformanceSnapshot(Base):
+    """Daily performance snapshot for historical tracking"""
+    __tablename__ = "performance_snapshots"
+
+    id = Column(Integer, primary_key=True, index=True)
+    user_id = Column(String, nullable=True)
+    snapshot_date = Column(Date, default=func.current_date())
+    daily_pnl = Column(Float, default=0.0)
+    daily_pnl_percent = Column(Float, default=0.0)
+    total_trades = Column(Integer, default=0)
+    winning_trades = Column(Integer, default=0)
+    losing_trades = Column(Integer, default=0)
+    win_rate = Column(Float, default=0.0)
+    best_trade = Column(Float, nullable=True)
+    worst_trade = Column(Float, nullable=True)
+    avg_win = Column(Float, nullable=True)
+    avg_loss = Column(Float, nullable=True)
+    sharpe_ratio = Column(Float, nullable=True)
+    profit_factor = Column(Float, nullable=True)
+    max_drawdown = Column(Float, nullable=True)
+    cumulative_pnl = Column(Float, default=0.0)
+    portfolio_value = Column(Float, nullable=True)
+    equity_curve = Column(JSON, default=[])  # Time series
+    streak_type = Column(String, nullable=True)  # win_streak, loss_streak
+    streak_count = Column(Integer, default=0)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+
+
+class TradePattern(Base):
+    """Identified profitable trade patterns"""
+    __tablename__ = "trade_patterns"
+
+    id = Column(Integer, primary_key=True, index=True)
+    user_id = Column(String, nullable=True)
+    pattern_name = Column(String)  # e.g., "Morning breakout", "Reversal near support"
+    description = Column(Text, nullable=True)
+    win_rate = Column(Float)  # Percentage
+    avg_win = Column(Float)
+    avg_loss = Column(Float)
+    sample_count = Column(Integer)  # Number of matching trades
+    best_timeframe = Column(String, nullable=True)  # 1m, 5m, 15m, 1h, 1d
+    best_time_of_day = Column(String, nullable=True)  # e.g., "09:30-10:30"
+    confidence_score = Column(Float)  # 0-100
+    indicators_used = Column(JSON, default=[])  # List of indicators
+    market_conditions = Column(String, nullable=True)  # bullish, bearish, neutral
+    total_profit = Column(Float, default=0.0)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), onupdate=func.now())
+
+
+class LessonLearned(Base):
+    """Track lessons and insights from trading"""
+    __tablename__ = "lessons_learned"
+
+    id = Column(Integer, primary_key=True, index=True)
+    user_id = Column(String, nullable=True)
+    date_learned = Column(DateTime(timezone=True), server_default=func.now())
+    category = Column(String)  # entry, exit, risk, psychology, market
+    lesson_text = Column(Text)
+    related_trades = Column(JSON, default=[])  # Trade IDs
+    impact = Column(String)  # positive, negative, neutral
+    tags = Column(JSON, default=[])  # Searchable tags
+    importance = Column(String)  # critical, important, helpful
+    status = Column(String, default="active")  # active, archived
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+
+
+class MonthlyReview(Base):
+    """Monthly trading performance review"""
+    __tablename__ = "monthly_reviews"
+
+    id = Column(Integer, primary_key=True, index=True)
+    user_id = Column(String, nullable=True)
+    year = Column(Integer)
+    month = Column(Integer)
+    total_trades = Column(Integer, default=0)
+    total_pnl = Column(Float, default=0.0)
+    total_pnl_percent = Column(Float, default=0.0)
+    best_day = Column(Date, nullable=True)
+    worst_day = Column(Date, nullable=True)
+    best_trade = Column(Float, nullable=True)
+    worst_trade = Column(Float, nullable=True)
+    win_rate = Column(Float, default=0.0)
+    avg_daily_pnl = Column(Float, nullable=True)
+    sharpe_ratio = Column(Float, nullable=True)
+    max_drawdown = Column(Float, nullable=True)
+    trading_days = Column(Integer, default=0)
+    best_pattern = Column(String, nullable=True)
+    summary = Column(Text, nullable=True)
+    improvements = Column(JSON, default=[])
+    goals_met = Column(JSON, default=[])
+    goals_missed = Column(JSON, default=[])
+    created_at = Column(DateTime(timezone=True), server_default=func.now())