167 lines
7.0 KiB
Python
167 lines
7.0 KiB
Python
"""动态策略选择器
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根据市场温度和板块状态，纯规则选择当日策略 profile。
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不再使用 LLM 或数据库配置覆盖。
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"""
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import logging
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from pydantic import BaseModel
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from app.config import settings
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from app.data.models import MarketTemperature, SectorInfo
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logger = logging.getLogger(__name__)
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class StrategyProfile(BaseModel):
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strategy_id: str
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name: str
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description: str
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entry_signal_priority: list[str]
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score_weights: dict[str, float]
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min_score: float
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buy_threshold: float
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max_position_pct: float
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allow_trading: bool = True
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actionable_limit: int = 2
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watch_limit: int = 4
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target_focus_sectors: int = 2
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market_stance: str = ""
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decision_note: str = ""
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notes: list[str] = []
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feedback_applied: bool = False
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feedback_notes: list[str] = []
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generated_by: str = "rules"
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def get_strategy_profile_by_id(strategy_id: str) -> StrategyProfile:
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normalized = strategy_id or "defensive_watch"
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if normalized == "trend_breakout":
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normalized = "breakout_attack"
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actionable_cap = max(0, settings.actionable_limit)
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watch_cap = max(0, settings.watch_limit)
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profiles = {
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"pre_market_ambush": StrategyProfile(
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strategy_id="pre_market_ambush",
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name="盘前埋伏",
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description="盘前选出缩量整理到位、回踩支撑、尚未启动的埋伏标的。",
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entry_signal_priority=["launch", "pullback", "breakout_confirm", "reversal", "breakout"],
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score_weights={"catalyst": 0.25, "theme_money": 0.22, "stock_money": 0.18, "emotion_role": 0.10, "timing": 0.25},
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min_score=55,
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buy_threshold=58,
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max_position_pct=25,
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allow_trading=True,
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actionable_limit=min(3, actionable_cap),
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watch_limit=min(5, watch_cap),
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target_focus_sectors=3,
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market_stance="埋伏待发",
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decision_note="盘前选票重点看整理充分度和催化预期，不追已启动标的。",
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notes=["优先缩量整理到位+催化预期", "回踩支撑位附近的主线成分优先"],
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),
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"breakout_attack": StrategyProfile(
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strategy_id="breakout_attack",
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name="主线突破",
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description="市场偏强，优先寻找主线板块内的突破和突破确认。",
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entry_signal_priority=["breakout", "breakout_confirm", "launch", "pullback", "reversal"],
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score_weights={"catalyst": 0.30, "theme_money": 0.25, "stock_money": 0.20, "emotion_role": 0.15, "timing": 0.10},
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min_score=62,
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buy_threshold=66,
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max_position_pct=30,
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allow_trading=True,
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actionable_limit=min(3, actionable_cap),
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watch_limit=min(4, watch_cap),
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target_focus_sectors=2,
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market_stance="主线进攻",
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decision_note="只处理最强主线前排，不扩散到跟风和后排。",
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notes=["优先做主线早中期板块", "放量突破优先于回踩低吸"],
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),
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"pullback_rotation": StrategyProfile(
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strategy_id="pullback_rotation",
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name="回踩轮动",
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description="市场震荡分化，优先做回踩支撑和板块轮动中的低吸确认。",
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entry_signal_priority=["pullback", "breakout_confirm", "launch", "breakout", "reversal"],
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score_weights={"catalyst": 0.25, "theme_money": 0.28, "stock_money": 0.20, "emotion_role": 0.12, "timing": 0.15},
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min_score=60,
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buy_threshold=63,
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max_position_pct=20,
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allow_trading=True,
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actionable_limit=min(2, actionable_cap),
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watch_limit=min(5, watch_cap),
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target_focus_sectors=2,
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market_stance="轮动低吸",
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decision_note="先等回踩承接和板块回流，再决定是否出手。",
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notes=["降低追高仓位", "更看重位置安全和回踩承接"],
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),
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"launch_probe": StrategyProfile(
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strategy_id="launch_probe",
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name="启动试错",
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description="市场偏弱，适合少量观察启动型和反转型机会，不做强追涨。",
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entry_signal_priority=["launch", "reversal", "pullback", "breakout_confirm", "breakout"],
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score_weights={"catalyst": 0.28, "theme_money": 0.22, "stock_money": 0.20, "emotion_role": 0.12, "timing": 0.18},
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min_score=58,
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buy_threshold=61,
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max_position_pct=10,
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allow_trading=True,
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actionable_limit=min(1, actionable_cap),
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watch_limit=min(4, watch_cap),
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target_focus_sectors=1,
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market_stance="轻仓试错",
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decision_note="只有极少数启动确认标的值得小仓试错。",
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notes=["仅做小仓位试错", "突破型需要更强板块一致性才可介入"],
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),
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"defensive_watch": StrategyProfile(
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strategy_id="defensive_watch",
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name="防守观察",
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description="市场退潮，系统以观察池为主，不主动扩大出手。",
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entry_signal_priority=["pullback", "launch", "reversal", "breakout_confirm", "breakout"],
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score_weights={"catalyst": 0.22, "theme_money": 0.25, "stock_money": 0.18, "emotion_role": 0.15, "timing": 0.20},
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min_score=56,
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buy_threshold=64,
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max_position_pct=5,
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allow_trading=False,
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actionable_limit=0,
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watch_limit=min(3, watch_cap),
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target_focus_sectors=1,
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market_stance="防守观察",
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decision_note="今天不主动出手，只保留少量观察名单。",
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notes=["原则上只保留观察池", "等待市场温度修复后再转入主动进攻"],
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),
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}
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return profiles.get(normalized, profiles["defensive_watch"]).model_copy(deep=True)
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async def select_strategy_profile(
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market_temp: MarketTemperature | None,
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hot_sectors: list[SectorInfo],
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intraday: bool,
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scan_session: str = "",
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) -> StrategyProfile:
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"""纯规则策略选择。盘前埋伏 session 走独立 profile。"""
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if scan_session == "pre_market_ambush" or scan_session == "pre_market":
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return get_strategy_profile_by_id("pre_market_ambush")
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return _select_rule_profile(market_temp, hot_sectors, intraday)
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def _select_rule_profile(
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market_temp: MarketTemperature | None,
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hot_sectors: list[SectorInfo],
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intraday: bool,
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) -> StrategyProfile:
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temp = market_temp.temperature if market_temp else 0
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early_count = sum(1 for s in hot_sectors[:5] if s.stage == "early")
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late_count = sum(1 for s in hot_sectors[:5] if s.stage in ("late", "end"))
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if temp >= 65 and early_count >= 1:
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return get_strategy_profile_by_id("breakout_attack")
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if temp >= 45 and late_count < 2:
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return get_strategy_profile_by_id("pullback_rotation")
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if temp >= 30:
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return get_strategy_profile_by_id("launch_probe")
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return get_strategy_profile_by_id("defensive_watch")
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