179 lines
6.6 KiB
Python
179 lines
6.6 KiB
Python
"""Pydantic 数据模型"""
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from pydantic import BaseModel
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from datetime import datetime
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class StockQuote(BaseModel):
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ts_code: str
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name: str
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price: float
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pct_chg: float
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volume: float # 成交量（手）
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amount: float # 成交额（万元）
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turnover_rate: float # 换手率 %
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pe: float | None = None
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pb: float | None = None
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circ_mv: float | None = None # 流通市值（亿）
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total_mv: float | None = None # 总市值（亿）
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volume_ratio: float | None = None # 量比
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high: float | None = None
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low: float | None = None
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open: float | None = None
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pre_close: float | None = None
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limit_up: float | None = None
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limit_down: float | None = None
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amplitude: float | None = None # 振幅 %
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class CapitalFlow(BaseModel):
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ts_code: str
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trade_date: str
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buy_elg_amount: float = 0 # 特大单买入（万）
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sell_elg_amount: float = 0
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buy_lg_amount: float = 0 # 大单买入（万）
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sell_lg_amount: float = 0
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buy_md_amount: float = 0 # 中单买入（万）
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sell_md_amount: float = 0
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buy_sm_amount: float = 0 # 小单买入（万）
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sell_sm_amount: float = 0
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net_mf_amount: float = 0 # 主力净流入（万）
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@property
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def main_net_inflow(self) -> float:
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"""主力净流入 = 特大单净买 + 大单净买"""
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return (self.buy_elg_amount - self.sell_elg_amount +
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self.buy_lg_amount - self.sell_lg_amount)
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@property
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def total_amount(self) -> float:
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return (self.buy_elg_amount + self.sell_elg_amount +
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self.buy_lg_amount + self.sell_lg_amount +
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self.buy_md_amount + self.sell_md_amount +
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self.buy_sm_amount + self.sell_sm_amount)
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class SectorInfo(BaseModel):
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sector_code: str
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sector_name: str
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pct_change: float = 0 # 涨跌幅 %
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capital_inflow: float = 0 # 主力净流入（万元，原始数据来自Tushare亿元×10000）
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limit_up_count: int = 0 # 涨停数
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days_continuous: int = 0 # 连续资金流入天数
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heat_score: float = 0 # 热度综合评分
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stage: str = "mid" # 板块阶段: early/mid/late/end
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# ── 板块分析增强字段 ──
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member_count: int = 0 # 成分股数量
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leading_stocks: list[dict] = [] # 领涨股 [{ts_code, name, pct_chg, amount}]
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capital_trend: list[float] = [] # 近5日资金净流入趋势（万）
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pct_trend: list[float] = [] # 近5日涨跌幅趋势
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turnover_avg: float = 0 # 板块平均换手率
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main_force_ratio: float = 0 # 主力资金占比（主力净流入/总成交额）
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class MarketTemperature(BaseModel):
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trade_date: str
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up_count: int = 0 # 上涨家数
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down_count: int = 0 # 下跌家数
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limit_up_count: int = 0 # 涨停数（自然涨停）
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limit_down_count: int = 0 # 跌停数
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max_streak: int = 0 # 最高连板
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broken_rate: float = 0 # 炸板率 %
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index_above_ma20: bool = False # 上证在 MA20 上方
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temperature: float = 0 # 综合温度 0-100
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class TechnicalSignal(BaseModel):
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ts_code: str
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name: str = ""
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ma_bullish: bool = False # 均线多头排列
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volume_breakout: bool = False # 放量突破
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macd_golden: bool = False # MACD金叉
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rsi_healthy: bool = False # RSI健康区间
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pullback_support: bool = False # 缩量回踩支撑
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big_yang: bool = False # 底部放量长阳
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boll_support: bool = False # 布林带下轨支撑
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score: float = 0 # 信号触发计数分
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trend_score: float = 0 # 趋势评分（推荐体系用的技术面分数）
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signal_count: int = 0 # 满足的信号数量
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# 位置安全评估（防追高）
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rally_pct_5d: float = 0 # 近5日累计涨幅
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rally_pct_10d: float = 0 # 近10日累计涨幅
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distance_from_high: float = 0 # 距离60日高点 %（负=已回调）
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position_score: float = 50 # 位置安全得分 0-100
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# 价格参考
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support_price: float | None = None # 支撑位
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resist_price: float | None = None # 压力位
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stop_loss_price: float | None = None # 止损价
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class Recommendation(BaseModel):
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ts_code: str
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name: str
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sector: str
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score: float # 综合评分
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market_temp_score: float
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sector_score: float
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capital_score: float
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technical_score: float
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supply_demand_score: float = 0 # 供需评分（主评分50%权重）
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price_action_score: float = 0 # 价格行为评分（主评分40%权重）
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position_score: float = 50 # 位置安全得分
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valuation_score: float = 50 # 估值安全得分
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signal: str # BUY / SELL / HOLD
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entry_price: float | None = None
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target_price: float | None = None
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stop_loss: float | None = None
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reasons: list[str] = []
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risk_note: str = ""
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level: str = "" # 强烈推荐/推荐/观望/回避
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strategy: str = "trend_breakout" # trend_breakout / momentum(旧) / potential(旧)
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entry_signal_type: str = "none" # breakout / pullback / launch / none
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entry_timing: str = "" # 进场时机建议（盘中适用）
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action_plan: str = "观察" # 可操作 / 重点关注 / 观察
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trigger_condition: str = "" # 触发条件
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invalidation_condition: str = "" # 失效条件
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suggested_position_pct: float = 0 # 建议仓位 %
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review_after_days: int = 3 # 建议复盘天数
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lifecycle_status: str = "candidate" # candidate / actionable / tracking / closed
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data_freshness: str = "" # 数据新鲜度说明
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llm_analysis: str = "" # LLM 深度分析
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llm_score: float | None = None # AI 评分 1-10
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scan_session: str = ""
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created_at: datetime | None = None
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class StrategyFocus(BaseModel):
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label: str
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description: str
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class StrategySectorFocus(BaseModel):
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sector_name: str
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stage: str = "mid"
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heat_score: float = 0
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pct_change: float = 0
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limit_up_count: int = 0
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view: str = ""
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class StrategyBoard(BaseModel):
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trade_date: str
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market_regime: str
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risk_level: str
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action_bias: str
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position_suggestion: str
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summary: str
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recommended_mode: str
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strategy_focus: list[StrategyFocus] = []
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watch_sectors: list[StrategySectorFocus] = []
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avoid_rules: list[str] = []
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iteration_notes: list[str] = []
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iteration_report: dict = {}
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metrics: dict = {}
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ai_review: str = ""
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generated_by: str = "rules"
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