"""动态策略选择器 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。 生产筛选只使用规则和策略配置，保证同一份行情输入得到稳定输出。 LLM 只能用于离线复盘、配置建议或解释，不参与盘中策略换挡。 """ import json import logging from pydantic import BaseModel from app.config import settings from app.data.models import MarketTemperature, SectorInfo logger = logging.getLogger(__name__) class StrategyProfile(BaseModel): strategy_id: str name: str description: str entry_signal_priority: list[str] score_weights: dict[str, float] min_score: float buy_threshold: float max_position_pct: float allow_trading: bool = True actionable_limit: int = 2 watch_limit: int = 4 target_focus_sectors: int = 2 market_stance: str = "" decision_note: str = "" notes: list[str] = [] feedback_applied: bool = False feedback_notes: list[str] = [] generated_by: str = "rules" def get_strategy_profile_by_id(strategy_id: str) -> StrategyProfile: normalized = strategy_id or "defensive_watch" if normalized == "trend_breakout": normalized = "breakout_attack" actionable_cap = max(0, settings.actionable_limit) watch_cap = max(0, settings.watch_limit) profiles = { "breakout_attack": StrategyProfile( strategy_id="breakout_attack", name="主线突破", description="市场偏强，优先寻找主线板块内的突破和突破确认。", entry_signal_priority=["breakout", "breakout_confirm", "launch", "pullback", "reversal"], score_weights={"catalyst": 0.30, "theme_money": 0.25, "stock_money": 0.20, "emotion_role": 0.15, "timing": 0.10}, min_score=62, buy_threshold=66, max_position_pct=30, allow_trading=True, actionable_limit=min(3, actionable_cap), watch_limit=min(4, watch_cap), target_focus_sectors=2, market_stance="主线进攻", decision_note="只处理最强主线前排，不扩散到跟风和后排。", notes=["优先做主线早中期板块", "放量突破优先于回踩低吸"], ), "pullback_rotation": StrategyProfile( strategy_id="pullback_rotation", name="回踩轮动", 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。", entry_signal_priority=["pullback", "breakout_confirm", "launch", "breakout", "reversal"], score_weights={"catalyst": 0.25, "theme_money": 0.28, "stock_money": 0.20, "emotion_role": 0.12, "timing": 0.15}, min_score=60, buy_threshold=63, max_position_pct=20, allow_trading=True, actionable_limit=min(2, actionable_cap), watch_limit=min(5, watch_cap), target_focus_sectors=2, market_stance="轮动低吸", decision_note="先等回踩承接和板块回流，再决定是否出手。", notes=["降低追高仓位", "更看重位置安全和回踩承接"], ), "launch_probe": StrategyProfile( strategy_id="launch_probe", name="启动试错", 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。", entry_signal_priority=["launch", "reversal", "pullback", "breakout_confirm", "breakout"], score_weights={"catalyst": 0.28, "theme_money": 0.22, "stock_money": 0.20, "emotion_role": 0.12, "timing": 0.18}, min_score=58, buy_threshold=61, max_position_pct=10, allow_trading=True, actionable_limit=min(1, actionable_cap), watch_limit=min(4, watch_cap), target_focus_sectors=1, market_stance="轻仓试错", decision_note="只有极少数启动确认标的值得小仓试错。", notes=["仅做小仓位试错", "突破型需要更强板块一致性才可介入"], ), "defensive_watch": StrategyProfile( strategy_id="defensive_watch", name="防守观察", description="市场退潮，系统以观察池为主，不主动扩大出手。", entry_signal_priority=["pullback", "launch", "reversal", "breakout_confirm", "breakout"], score_weights={"catalyst": 0.22, "theme_money": 0.25, "stock_money": 0.18, "emotion_role": 0.15, "timing": 0.20}, min_score=56, buy_threshold=64, max_position_pct=5, allow_trading=False, actionable_limit=0, watch_limit=min(3, watch_cap), target_focus_sectors=1, market_stance="防守观察", decision_note="今天不主动出手，只保留少量观察名单。", 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"], ), } return profiles.get(normalized, profiles["defensive_watch"]).model_copy(deep=True) async def select_strategy_profile( market_temp: MarketTemperature | None, hot_sectors: list[SectorInfo], intraday: bool, ) -> StrategyProfile: from app.llm.strategy_config import load_active_strategy_profile profile = _select_rule_profile(market_temp, hot_sectors, intraday) return await load_active_strategy_profile(profile) def _select_rule_profile( market_temp: MarketTemperature | None, hot_sectors: list[SectorInfo], intraday: bool, ) -> StrategyProfile: temp = market_temp.temperature if market_temp else 0 early_count = sum(1 for s in hot_sectors[:5] if s.stage == "early") late_count = sum(1 for s in hot_sectors[:5] if s.stage in ("late", "end")) if temp >= 65 and early_count >= 1: return get_strategy_profile_by_id("breakout_attack") if temp >= 45 and late_count < 2: return get_strategy_profile_by_id("pullback_rotation") if temp >= 30: return get_strategy_profile_by_id("launch_probe") return get_strategy_profile_by_id("defensive_watch") async def _apply_strategy_feedback(profile: StrategyProfile) -> StrategyProfile: from app.llm.strategy_iteration import build_strategy_feedback_controls try: controls = await build_strategy_feedback_controls(limit=50) except Exception as e: logger.debug(f"策略反馈控制生成失败: {e}") return profile if not controls.get("enabled"): return profile updated = profile.model_copy(deep=True) updated.feedback_applied = True if controls.get("force_defensive"): updated.allow_trading = False updated.actionable_limit = 0 updated.watch_limit = min(updated.watch_limit, 3) updated.max_position_pct = min(updated.max_position_pct, 10) updated.market_stance = "防守观察" updated.buy_threshold = max(updated.min_score, min(updated.buy_threshold + int(controls.get("buy_threshold_delta") or 0), 80)) updated.max_position_pct = max(0, min(updated.max_position_pct + int(controls.get("max_position_pct_delta") or 0), 40)) updated.actionable_limit = max(0, min(updated.actionable_limit + int(controls.get("actionable_limit_delta") or 0), settings.actionable_limit)) updated.watch_limit = max(1, min(updated.watch_limit + int(controls.get("watch_limit_delta") or 0), settings.watch_limit)) notes = controls.get("notes") or [] if notes: updated.feedback_notes = notes[:3] updated.notes.extend(notes[:2]) updated.decision_note = notes[0] updated.generated_by = f"{updated.generated_by}+feedback" return updated async def _select_llm_profile( market_temp: MarketTemperature | None, hot_sectors: list[SectorInfo], intraday: bool, fallback: StrategyProfile, ) -> StrategyProfile | None: from app.llm.client import chat_completion sector_text = "\n".join( f"- {s.sector_name}: 涨幅{s.pct_change}%, 热度{s.heat_score}, 阶段{s.stage}, 涨停{s.limit_up_count}" for s in hot_sectors[:5] ) or "暂无板块数据" user_msg = f"""你需要为今日A股环境选择一个短线策略模板。 市场温度: {market_temp.temperature if market_temp else 0} 上涨家数: {market_temp.up_count if market_temp else 0} 下跌家数: {market_temp.down_count if market_temp else 0} 涨停数: {market_temp.limit_up_count if market_temp else 0} 炸板率: {market_temp.broken_rate if market_temp else 0} 盘中模式: {'是' if intraday else '否'} 热门板块: {sector_text} 规则候选策略: - breakout_attack: 主线突破 - pullback_rotation: 回踩轮动 - launch_probe: 启动试错 - defensive_watch: 防守观察 请输出 JSON，格式: {{ "strategy_id": "上面四选一", "notes": ["两条以内理由"], "buy_threshold_delta": -3到3之间的整数 }} """ resp = await chat_completion([ {"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。"}, {"role": "user", "content": user_msg}, ]) if not resp or not resp.content: return None try: data = json.loads(resp.content) strategy_id = data.get("strategy_id") if strategy_id not in {"breakout_attack", "pullback_rotation", "launch_probe", "defensive_watch"}: return None selected = _select_rule_profile(market_temp, hot_sectors, intraday) if selected.strategy_id != strategy_id: selected = get_strategy_profile_by_id(strategy_id) delta = int(data.get("buy_threshold_delta", 0)) delta = max(-3, min(3, delta)) selected.buy_threshold += delta selected.notes.extend(data.get("notes", [])[:2]) selected.generated_by = "rules+llm" return selected except Exception as e: logger.debug(f"LLM 策略选择解析失败: {e}") return fallback