112 lines
4.0 KiB
Python
112 lines
4.0 KiB
Python
"""数据库表定义"""
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from sqlalchemy import (
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MetaData, Table, Column, Integer, Float, Text, Boolean, DateTime,
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func
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)
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metadata = MetaData()
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recommendations_table = Table(
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"recommendations", metadata,
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Column("id", Integer, primary_key=True, autoincrement=True),
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Column("ts_code", Text, nullable=False),
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Column("name", Text, nullable=False),
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Column("sector", Text),
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Column("score", Float),
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Column("market_temp_score", Float),
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Column("sector_score", Float),
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Column("capital_score", Float),
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Column("technical_score", Float),
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Column("supply_demand_score", Float, default=0),
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Column("price_action_score", Float, default=0),
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Column("position_score", Float),
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Column("valuation_score", Float),
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Column("signal", Text),
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Column("entry_price", Float),
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Column("target_price", Float),
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Column("stop_loss", Float),
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Column("reasons", Text),
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Column("llm_analysis", Text, default=""),
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Column("strategy", Text, default="trend_breakout"),
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Column("entry_signal_type", Text, default="none"),
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Column("llm_score", Float, default=None),
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Column("scan_session", Text),
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Column("created_at", DateTime, server_default=func.now()),
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)
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sector_heat_table = Table(
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"sector_heat", metadata,
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Column("id", Integer, primary_key=True, autoincrement=True),
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Column("sector_code", Text, nullable=False),
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Column("sector_name", Text, nullable=False),
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Column("pct_change", Float),
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Column("capital_inflow", Float),
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Column("limit_up_count", Integer),
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Column("heat_score", Float),
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Column("stage", Text),
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Column("days_continuous", Integer),
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Column("member_count", Integer),
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Column("leading_stocks", Text), # JSON string
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Column("pct_trend", Text), # JSON string
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Column("turnover_avg", Float),
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Column("main_force_ratio", Float),
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Column("trade_date", Text, nullable=False),
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Column("created_at", DateTime, server_default=func.now()),
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)
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market_temperature_table = Table(
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"market_temperature", metadata,
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Column("id", Integer, primary_key=True, autoincrement=True),
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Column("trade_date", Text, nullable=False, unique=True),
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Column("up_count", Integer),
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Column("down_count", Integer),
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Column("limit_up_count", Integer),
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Column("limit_down_count", Integer),
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Column("max_streak", Integer),
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Column("broken_rate", Float),
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Column("temperature", Float),
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Column("created_at", DateTime, server_default=func.now()),
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)
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recommendation_tracking_table = Table(
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"recommendation_tracking", metadata,
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Column("id", Integer, primary_key=True, autoincrement=True),
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Column("recommendation_id", Integer),
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Column("track_date", Text, nullable=False),
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Column("current_price", Float),
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Column("pct_from_entry", Float),
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Column("hit_target", Boolean, default=False),
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Column("hit_stop_loss", Boolean, default=False),
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Column("status", Text, default="active"),
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Column("created_at", DateTime, server_default=func.now()),
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)
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users_table = Table(
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"users", metadata,
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Column("id", Integer, primary_key=True, autoincrement=True),
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Column("username", Text, nullable=False, unique=True),
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Column("password_hash", Text, nullable=False),
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Column("role", Text, default="user"),
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Column("is_active", Boolean, default=True),
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Column("created_at", DateTime, server_default=func.now()),
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Column("updated_at", DateTime, server_default=func.now()),
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)
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daily_reviews_table = Table(
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"daily_reviews", metadata,
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Column("id", Integer, primary_key=True, autoincrement=True),
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Column("trade_date", Text, nullable=False, unique=True),
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Column("content", Text, default=""),
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Column("created_at", DateTime, server_default=func.now()),
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)
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stock_diagnoses_table = Table(
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"stock_diagnoses", metadata,
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Column("id", Integer, primary_key=True, autoincrement=True),
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Column("ts_code", Text, nullable=False),
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Column("name", Text, nullable=False),
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Column("diagnosis", Text, nullable=False),
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Column("created_at", DateTime, server_default=func.now()),
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)
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