260 lines
10 KiB
Python
260 lines
10 KiB
Python
"""动态策略选择器
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在固定筛选引擎前增加一层“先选打法，再选股票”的策略决策。
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规则负责稳定分类，LLM 负责补充语义判断和操作建议。
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"""
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import json
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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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"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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) -> StrategyProfile:
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from app.llm.strategy_config import load_active_strategy_profile
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profile = _select_rule_profile(market_temp, hot_sectors, intraday)
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profile = await load_active_strategy_profile(profile)
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if settings.deepseek_api_key:
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llm_profile = await _select_llm_profile(market_temp, hot_sectors, intraday, profile)
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if llm_profile:
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profile = await load_active_strategy_profile(llm_profile)
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return profile
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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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async def _apply_strategy_feedback(profile: StrategyProfile) -> StrategyProfile:
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from app.llm.strategy_iteration import build_strategy_feedback_controls
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try:
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controls = await build_strategy_feedback_controls(limit=50)
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except Exception as e:
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logger.debug(f"策略反馈控制生成失败: {e}")
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return profile
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if not controls.get("enabled"):
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return profile
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updated = profile.model_copy(deep=True)
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updated.feedback_applied = True
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if controls.get("force_defensive"):
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updated.allow_trading = False
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updated.actionable_limit = 0
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updated.watch_limit = min(updated.watch_limit, 3)
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updated.max_position_pct = min(updated.max_position_pct, 10)
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updated.market_stance = "防守观察"
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updated.buy_threshold = max(updated.min_score, min(updated.buy_threshold + int(controls.get("buy_threshold_delta") or 0), 80))
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updated.max_position_pct = max(0, min(updated.max_position_pct + int(controls.get("max_position_pct_delta") or 0), 40))
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updated.actionable_limit = max(0, min(updated.actionable_limit + int(controls.get("actionable_limit_delta") or 0), settings.actionable_limit))
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updated.watch_limit = max(1, min(updated.watch_limit + int(controls.get("watch_limit_delta") or 0), settings.watch_limit))
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notes = controls.get("notes") or []
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if notes:
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updated.feedback_notes = notes[:3]
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updated.notes.extend(notes[:2])
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updated.decision_note = notes[0]
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updated.generated_by = f"{updated.generated_by}+feedback"
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return updated
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async def _select_llm_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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fallback: StrategyProfile,
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) -> StrategyProfile | None:
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from app.llm.client import chat_completion
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sector_text = "\n".join(
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f"- {s.sector_name}: 涨幅{s.pct_change}%, 热度{s.heat_score}, 阶段{s.stage}, 涨停{s.limit_up_count}"
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for s in hot_sectors[:5]
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) or "暂无板块数据"
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user_msg = f"""你需要为今日A股环境选择一个短线策略模板。
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市场温度: {market_temp.temperature if market_temp else 0}
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上涨家数: {market_temp.up_count if market_temp else 0}
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下跌家数: {market_temp.down_count if market_temp else 0}
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涨停数: {market_temp.limit_up_count if market_temp else 0}
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炸板率: {market_temp.broken_rate if market_temp else 0}
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盘中模式: {'是' if intraday else '否'}
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热门板块:
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{sector_text}
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规则候选策略:
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- breakout_attack: 主线突破
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- pullback_rotation: 回踩轮动
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- launch_probe: 启动试错
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- defensive_watch: 防守观察
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请输出 JSON，格式:
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{{
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"strategy_id": "上面四选一",
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"notes": ["两条以内理由"],
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"buy_threshold_delta": -3到3之间的整数
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}}
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"""
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resp = await chat_completion([
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{"role": "system", "content": "你是一位A股短线策略研究员，只能在给定策略模板中选择，不要发明新策略。回复必须是 JSON。"},
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{"role": "user", "content": user_msg},
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])
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if not resp or not resp.content:
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return None
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try:
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data = json.loads(resp.content)
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strategy_id = data.get("strategy_id")
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if strategy_id not in {"breakout_attack", "pullback_rotation", "launch_probe", "defensive_watch"}:
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return None
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selected = _select_rule_profile(market_temp, hot_sectors, intraday)
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if selected.strategy_id != strategy_id:
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selected = get_strategy_profile_by_id(strategy_id)
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delta = int(data.get("buy_threshold_delta", 0))
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delta = max(-3, min(3, delta))
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selected.buy_threshold += delta
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selected.notes.extend(data.get("notes", [])[:2])
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selected.generated_by = "rules+llm"
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return selected
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except Exception as e:
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logger.debug(f"LLM 策略选择解析失败: {e}")
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return fallback
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