360 lines
14 KiB
Python
360 lines
14 KiB
Python
"""市场作战面板
|
||
|
||
把市场温度、板块、推荐和历史跟踪结果汇总成每天可执行的策略视图。
|
||
规则层保证稳定输出，LLM 层负责补充解释和迭代建议。
|
||
"""
|
||
|
||
import logging
|
||
|
||
from app.analysis.sector_realtime import enrich_sectors_with_realtime
|
||
from app.analysis.sector_realtime import get_today_realtime_sector_board
|
||
from app.config import settings, should_prefer_realtime_today, today_trade_date
|
||
from app.data.models import (
|
||
MarketTemperature,
|
||
Recommendation,
|
||
SectorInfo,
|
||
StrategyBoard,
|
||
StrategyFocus,
|
||
StrategySectorFocus,
|
||
)
|
||
|
||
logger = logging.getLogger(__name__)
|
||
|
||
|
||
def _sector_pct(sector: SectorInfo) -> float:
|
||
return float(sector.realtime_pct_change if sector.realtime_pct_change is not None else sector.pct_change)
|
||
|
||
|
||
def _sector_turnover(sector: SectorInfo) -> float:
|
||
return float(sector.realtime_turnover_rate if sector.realtime_turnover_rate is not None else sector.turnover_avg)
|
||
|
||
|
||
def _sector_up_count(sector: SectorInfo) -> int:
|
||
return int(sector.realtime_up_count if sector.realtime_up_count is not None else 0)
|
||
|
||
|
||
def _sector_down_count(sector: SectorInfo) -> int:
|
||
return int(sector.realtime_down_count if sector.realtime_down_count is not None else 0)
|
||
|
||
|
||
def _sector_breadth(sector: SectorInfo) -> int:
|
||
return _sector_up_count(sector) - _sector_down_count(sector)
|
||
|
||
|
||
def _sector_strength_score(sector: SectorInfo) -> float:
|
||
strength = _sector_pct(sector) * 12
|
||
strength += min(max(_sector_breadth(sector), -30), 30) * 0.6
|
||
strength += min(_sector_turnover(sector), 12) * 1.5
|
||
strength += min(sector.limit_up_count, 8) * 2.5
|
||
strength += min(sector.days_continuous, 5) * 1.5
|
||
return round(strength, 1)
|
||
|
||
|
||
async def build_strategy_board(include_llm: bool = False) -> dict:
|
||
"""生成今日市场作战面板。"""
|
||
from app.engine.recommender import (
|
||
get_latest_recommendations,
|
||
get_latest_sectors,
|
||
get_performance_stats,
|
||
)
|
||
|
||
latest = await get_latest_recommendations()
|
||
market_temp = latest.get("market_temp")
|
||
recommendations = latest.get("recommendations", [])
|
||
sectors = await get_latest_sectors()
|
||
snapshot_trade_date = sectors[0].trade_date if sectors else ""
|
||
if should_prefer_realtime_today(snapshot_trade_date) or snapshot_trade_date != today_trade_date():
|
||
realtime_sectors = await get_today_realtime_sector_board(limit=20)
|
||
sectors = realtime_sectors or await enrich_sectors_with_realtime(sectors)
|
||
else:
|
||
sectors = await enrich_sectors_with_realtime(sectors)
|
||
performance = await get_performance_stats()
|
||
from app.llm.strategy_iteration import build_strategy_iteration_report
|
||
iteration_report = await build_strategy_iteration_report(limit=50, include_llm=include_llm)
|
||
|
||
board = _build_rule_board(market_temp, sectors, recommendations, performance)
|
||
board.iteration_report = iteration_report
|
||
if iteration_report.get("adjustment_suggestions"):
|
||
board.iteration_notes = [
|
||
s.get("reason", "")
|
||
for s in iteration_report["adjustment_suggestions"][:3]
|
||
if s.get("reason")
|
||
] or board.iteration_notes
|
||
|
||
if include_llm and settings.deepseek_api_key:
|
||
board.ai_review = await _generate_ai_review(board, recommendations, performance)
|
||
if board.ai_review:
|
||
board.generated_by = "rules+llm"
|
||
|
||
return board.model_dump()
|
||
|
||
|
||
def _build_rule_board(
|
||
market_temp: MarketTemperature | None,
|
||
sectors: list[SectorInfo],
|
||
recommendations: list[Recommendation],
|
||
performance: dict,
|
||
) -> StrategyBoard:
|
||
temp = market_temp.temperature if market_temp else 0
|
||
raw_trade_date = market_temp.trade_date if market_temp else ""
|
||
prefer_realtime = should_prefer_realtime_today(raw_trade_date)
|
||
trade_date = today_trade_date() if prefer_realtime else raw_trade_date
|
||
data_mode = "realtime_today" if prefer_realtime else "daily_snapshot"
|
||
market_regime, risk_level, action_bias, position_suggestion = _classify_market(temp, market_temp)
|
||
|
||
actionable = [r for r in recommendations if r.action_plan == "可操作"]
|
||
watch = [r for r in recommendations if r.action_plan == "重点关注"]
|
||
strong_sectors = [
|
||
s for s in sectors[:5]
|
||
if _sector_pct(s) >= 1.5 and (_sector_breadth(s) > 0 or s.limit_up_count >= 2)
|
||
]
|
||
avg_score = (
|
||
round(sum(r.score for r in recommendations) / len(recommendations), 1)
|
||
if recommendations else 0
|
||
)
|
||
|
||
recommended_mode = _choose_strategy_mode(temp, sectors, recommendations)
|
||
strategy_focus = _build_strategy_focus(temp, sectors, recommendations)
|
||
watch_sectors = [_sector_focus(s) for s in sectors[:5]]
|
||
avoid_rules = _build_avoid_rules(temp, sectors, recommendations)
|
||
iteration_notes = _build_iteration_notes(performance, recommendations)
|
||
mode_prefix = "今日实时视角：" if prefer_realtime else ""
|
||
|
||
summary = (
|
||
f"{mode_prefix}{market_regime}，风险等级{risk_level}。"
|
||
f"当前 {len(recommendations)} 只入选，其中 {len(actionable)} 只可操作、"
|
||
f"{len(watch)} 只重点关注，平均分 {avg_score}。"
|
||
f"{'主线活跃板块 ' + ' / '.join(s.sector_name for s in strong_sectors[:3]) + '。' if strong_sectors else '板块尚未形成强共振，优先等确认。'}"
|
||
)
|
||
|
||
metrics = {
|
||
"temperature": temp,
|
||
"recommendation_count": len(recommendations),
|
||
"actionable_count": len(actionable),
|
||
"watch_count": len(watch),
|
||
"avg_score": avg_score,
|
||
"win_rate": performance.get("win_rate", 0),
|
||
"avg_return": performance.get("avg_return", 0),
|
||
"tracked": performance.get("tracked", 0),
|
||
}
|
||
|
||
return StrategyBoard(
|
||
trade_date=trade_date,
|
||
data_mode=data_mode,
|
||
market_regime=market_regime,
|
||
risk_level=risk_level,
|
||
action_bias=action_bias,
|
||
position_suggestion=position_suggestion,
|
||
summary=summary,
|
||
recommended_mode=recommended_mode,
|
||
strategy_focus=strategy_focus,
|
||
watch_sectors=watch_sectors,
|
||
avoid_rules=avoid_rules,
|
||
iteration_notes=iteration_notes,
|
||
metrics=metrics,
|
||
)
|
||
|
||
|
||
def _classify_market(
|
||
temp: float, market_temp: MarketTemperature | None
|
||
) -> tuple[str, str, str, str]:
|
||
if temp >= 75:
|
||
return ("强势进攻", "低", "可积极关注主线龙头和突破确认", "单票 20%-30%，总仓 50%-70%")
|
||
if temp >= 60:
|
||
return ("修复偏强", "中低", "优先做早中期板块的突破/回踩确认", "单票 15%-25%，总仓 40%-60%")
|
||
if temp >= 45:
|
||
return ("震荡分化", "中", "只做板块一致性强的低吸或确认机会", "单票 10%-20%，总仓 25%-40%")
|
||
if temp >= 30:
|
||
return ("弱势防守", "中高", "以观察池为主，减少追高，只等强确认", "单票 0%-10%，总仓 0%-25%")
|
||
return ("退潮冰点", "高", "暂停主动出手，等待市场修复和主线重新出现", "空仓或极低仓观察")
|
||
|
||
|
||
def _choose_strategy_mode(
|
||
temp: float, sectors: list[SectorInfo], recommendations: list[Recommendation]
|
||
) -> str:
|
||
top = sectors[:5]
|
||
strong = [s for s in top if _sector_pct(s) >= 2 and _sector_breadth(s) > 0]
|
||
active = [s for s in top if _sector_pct(s) >= 1 and (_sector_turnover(s) >= 2 or s.limit_up_count >= 2)]
|
||
end_stage = [s for s in top if s.stage == "end" and _sector_pct(s) > 0]
|
||
|
||
if temp >= 65 and len(strong) >= 2:
|
||
return "顺主线做强势确认"
|
||
if temp >= 55 and active:
|
||
return "围绕强板块做回踩确认"
|
||
if temp < 45 or end_stage:
|
||
return "缩仓观察，回避后排追高"
|
||
if recommendations:
|
||
return "观察池跟踪，等待触发"
|
||
return "防守观察"
|
||
|
||
|
||
def _build_strategy_focus(
|
||
temp: float, sectors: list[SectorInfo], recommendations: list[Recommendation]
|
||
) -> list[StrategyFocus]:
|
||
focus: list[StrategyFocus] = []
|
||
signal_counts: dict[str, int] = {}
|
||
for rec in recommendations:
|
||
signal_counts[rec.entry_signal_type] = signal_counts.get(rec.entry_signal_type, 0) + 1
|
||
|
||
top_signal = max(signal_counts, key=signal_counts.get) if signal_counts else ""
|
||
signal_label = {
|
||
"breakout": "突破型",
|
||
"breakout_confirm": "突破确认型",
|
||
"pullback": "回踩型",
|
||
"launch": "启动型",
|
||
"reversal": "反转型",
|
||
}.get(top_signal, "观察型")
|
||
|
||
focus.append(StrategyFocus(
|
||
label=signal_label,
|
||
description=f"当前推荐中该类型占比较高，适合作为今日主要观察模板。",
|
||
))
|
||
|
||
if sectors:
|
||
main = sectors[0]
|
||
sector_pct = _sector_pct(main)
|
||
breadth = _sector_breadth(main)
|
||
turnover = _sector_turnover(main)
|
||
focus.append(StrategyFocus(
|
||
label=f"{main.sector_name} 主线跟踪",
|
||
description=(
|
||
f"当前涨幅 {sector_pct:+.2f}% ，广度 {breadth:+d}，换手 {turnover:.1f}% ，"
|
||
f"阶段 {main.stage}，优先确认资金是否延续。"
|
||
),
|
||
))
|
||
|
||
if len(sectors) > 1:
|
||
runner_up = sectors[1]
|
||
focus.append(StrategyFocus(
|
||
label=f"{runner_up.sector_name} 轮动监控",
|
||
description=(
|
||
f"强度分 {_sector_strength_score(runner_up)}，"
|
||
f"若涨幅继续扩大且广度转强，可切入今日第二梯队。"
|
||
),
|
||
))
|
||
|
||
if temp < 45:
|
||
focus.append(StrategyFocus(
|
||
label="防守优先",
|
||
description="市场温度不足，推荐只作为观察池，不宜扩大仓位。",
|
||
))
|
||
elif sectors and _sector_pct(sectors[0]) < 1:
|
||
focus.append(StrategyFocus(
|
||
label="等一致性强化",
|
||
description="板块领涨幅度仍有限，优先等主线扩散和个股触发后再出手。",
|
||
))
|
||
|
||
return focus
|
||
|
||
|
||
def _sector_focus(sector: SectorInfo) -> StrategySectorFocus:
|
||
stage_view = {
|
||
"early": "早期，重点观察资金是否连续流入",
|
||
"mid": "中期，适合寻找回踩或突破确认",
|
||
"late": "后期，防止加速后分歧",
|
||
"end": "末期，谨慎追高",
|
||
}.get(sector.stage, "阶段不明，等待确认")
|
||
|
||
return StrategySectorFocus(
|
||
sector_name=sector.sector_name,
|
||
stage=sector.stage,
|
||
heat_score=sector.heat_score,
|
||
pct_change=_sector_pct(sector),
|
||
limit_up_count=sector.limit_up_count,
|
||
turnover_rate=_sector_turnover(sector),
|
||
up_count=_sector_up_count(sector),
|
||
down_count=_sector_down_count(sector),
|
||
data_mode=sector.data_mode,
|
||
view=(
|
||
f"{stage_view}；当前涨幅 {_sector_pct(sector):+.2f}%"
|
||
+ (
|
||
f"，上涨/下跌 {_sector_up_count(sector)}/{_sector_down_count(sector)}"
|
||
if sector.is_realtime else ""
|
||
)
|
||
),
|
||
)
|
||
|
||
|
||
def _build_avoid_rules(
|
||
temp: float, sectors: list[SectorInfo], recommendations: list[Recommendation]
|
||
) -> list[str]:
|
||
rules = []
|
||
top = sectors[:5]
|
||
if temp < 45:
|
||
rules.append("市场温度低于45时，不追突破首日，只等次日确认或回踩。")
|
||
if any(s.stage == "end" for s in top):
|
||
rules.append("板块进入末期时，降低同板块追高标的权重。")
|
||
if top and _sector_pct(top[0]) < 1:
|
||
rules.append("主线板块涨幅不足1%时，不把局部异动当成全面进攻信号。")
|
||
if any(_sector_breadth(s) < 0 for s in top[:3] if s.is_realtime):
|
||
rules.append("板块涨幅与广度背离时，优先回避后排补涨和冲高追单。")
|
||
if any(_sector_turnover(s) > 8 and s.stage in ("late", "end") for s in top):
|
||
rules.append("高换手叠加板块后期阶段时，防止情绪过热后的快速分歧。")
|
||
if any(r.position_score < 35 for r in recommendations):
|
||
rules.append("位置安全分低于35的标的，只观察不主动追入。")
|
||
if not rules:
|
||
rules.append("推荐失效条件触发后不补仓，等待下一次扫描重新确认。")
|
||
return rules
|
||
|
||
|
||
def _build_iteration_notes(performance: dict, recommendations: list[Recommendation]) -> list[str]:
|
||
notes = []
|
||
tracked = performance.get("tracked", 0) or 0
|
||
win_rate = performance.get("win_rate", 0) or 0
|
||
avg_return = performance.get("avg_return", 0) or 0
|
||
hit_stop = performance.get("hit_stop_count", 0) or 0
|
||
hit_target = performance.get("hit_target_count", 0) or 0
|
||
|
||
if tracked < 10:
|
||
notes.append("跟踪样本不足，暂不自动调整策略权重，优先积累推荐生命周期数据。")
|
||
else:
|
||
if win_rate < 45:
|
||
notes.append("近期胜率偏低，下轮应提高入场确认门槛，减少弱势环境下的突破型推荐。")
|
||
if avg_return < 0:
|
||
notes.append("平均收益为负，建议收紧止损触发和推荐失效条件。")
|
||
if hit_stop > hit_target:
|
||
notes.append("止损次数多于命中目标，优先复查追高和板块末期惩罚是否不足。")
|
||
|
||
actionable_count = sum(1 for r in recommendations if r.action_plan == "可操作")
|
||
if actionable_count > 5:
|
||
notes.append("可操作标的偏多，前端应按板块集中度和评分排序控制关注数量。")
|
||
|
||
return notes
|
||
|
||
|
||
async def _generate_ai_review(
|
||
board: StrategyBoard,
|
||
recommendations: list[Recommendation],
|
||
performance: dict,
|
||
) -> str:
|
||
"""用 LLM 生成简短的策略解释，不参与硬性交易决策。"""
|
||
from app.llm.client import chat_completion
|
||
|
||
rec_lines = "\n".join(
|
||
f"- {r.name}({r.ts_code}) {r.action_plan} {r.entry_signal_type} "
|
||
f"评分{r.score} 仓位{r.suggested_position_pct}% 触发: {r.trigger_condition}"
|
||
for r in recommendations[:8]
|
||
) or "暂无推荐"
|
||
|
||
user_msg = f"""请基于以下系统数据，生成一段今日A股策略作战说明，要求：
|
||
1. 明确区分市场事实、策略推断和风险约束；
|
||
2. 不要承诺收益，不要给绝对化买卖结论；
|
||
3. 最多220字，中文。
|
||
|
||
市场状态: {board.market_regime}
|
||
风险等级: {board.risk_level}
|
||
操作倾向: {board.action_bias}
|
||
仓位建议: {board.position_suggestion}
|
||
推荐策略: {board.recommended_mode}
|
||
历史跟踪: 胜率{performance.get('win_rate', 0)}%, 平均收益{performance.get('avg_return', 0)}%
|
||
|
||
推荐摘要:
|
||
{rec_lines}
|
||
"""
|
||
|
||
resp = await chat_completion([
|
||
{"role": "system", "content": "你是一位谨慎的A股交易研究助手，擅长把量化结果转成可执行但有风险边界的策略说明。"},
|
||
{"role": "user", "content": user_msg},
|
||
])
|
||
return resp.content.strip() if resp and resp.content else ""
|