352 lines
12 KiB
Python
352 lines
12 KiB
Python
"""趋势突破统一筛选器
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三阶段管道：全市场批量预筛 → 资金流过滤 → 逐股深度分析
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评分公式：趋势&时机30% + 资金流25% + 供需20% + 板块共振15% + 市场温度10%
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自动检测是否在交易时段：
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- 盘中模式：用前一日 Tushare 数据 + 腾讯实时行情混合筛选
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- 盘后模式：用当日 Tushare 完整数据筛选
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"""
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import logging
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from app.analysis.market_temp import calculate_market_temperature
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from app.analysis.sector_scanner import scan_hot_sectors
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from app.analysis.trend_scanner import scan_trend_breakout
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from app.analysis.signals import generate_signals
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from app.analysis.intraday import intraday_market_temperature, intraday_filter_stocks, intraday_sector_scan
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from app.data.models import MarketTemperature, SectorInfo, TechnicalSignal, Recommendation
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from app.config import settings, is_trading_hours, is_market_session
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logger = logging.getLogger(__name__)
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async def run_screening(trade_date: str = None) -> dict:
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"""执行趋势突破筛选流程
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返回: {
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"market_temp": MarketTemperature,
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"hot_sectors": [SectorInfo],
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"recommendations": [Recommendation],
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"scan_mode": "intraday" | "post_market",
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}
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"""
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intraday = is_market_session()
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scan_mode = "intraday" if intraday else "post_market"
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logger.info(f"=== 筛选模式: {'盘中实时' if intraday else '盘后'} ===")
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# ── 市场温度 ──
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logger.info("=== 市场温度计 ===")
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market_temp = calculate_market_temperature(trade_date)
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if intraday:
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market_temp = await intraday_market_temperature(market_temp)
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logger.info(f"盘中市场温度(实时调整): {market_temp.temperature}")
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else:
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logger.info(f"市场温度: {market_temp.temperature}")
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market_temp_score = market_temp.temperature
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# ── 板块热度（用于板块共振评分） ──
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logger.info("=== 板块热度扫描 ===")
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all_sectors = scan_hot_sectors(trade_date)
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hot_sectors = all_sectors[:settings.top_sector_count]
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# 盘中用实时行情更新板块涨幅和涨停数
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if intraday:
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hot_sectors = await intraday_sector_scan(hot_sectors)
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# ── 趋势突破三阶段管道 ──
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logger.info("=== 趋势突破扫描 ===")
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candidates = await scan_trend_breakout(
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trade_date=trade_date,
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market_temp=market_temp,
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hot_sectors=hot_sectors,
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intraday=intraday,
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)
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if not candidates:
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logger.info("=== 筛选完成: 0 只股票 ===")
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return {
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"market_temp": market_temp,
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"hot_sectors": hot_sectors,
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"recommendations": [],
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"scan_mode": scan_mode,
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}
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# ── 构建推荐列表 ──
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recommendations = _build_trend_recommendations(
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candidates, market_temp, hot_sectors, market_temp_score, intraday,
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)
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# 过滤低质量推荐
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recommendations = [r for r in recommendations if r.score >= 40]
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logger.info(f"=== 筛选完成: {len(recommendations)} 只股票 ({scan_mode}) ===")
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for r in recommendations[:5]:
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signal_map = {"breakout": "突破型", "pullback": "回踩型", "launch": "启动型"}
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signal_label = signal_map.get(r.entry_signal_type, r.entry_signal_type)
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logger.info(f" [{signal_label}] {r.name}({r.ts_code}) {r.level} 评分={r.score} 信号={r.signal}")
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return {
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"market_temp": market_temp,
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"hot_sectors": hot_sectors,
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"recommendations": recommendations,
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"scan_mode": scan_mode,
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}
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def _build_trend_recommendations(
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candidates: list[dict],
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market_temp: MarketTemperature,
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hot_sectors: list[SectorInfo],
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market_temp_score: float = 0,
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intraday: bool = False,
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) -> list[Recommendation]:
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"""从趋势突破扫描结果构建推荐列表
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评分公式：趋势&时机30% + 资金流25% + 供需20% + 板块共振15% + 市场温度10%
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"""
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recommendations = []
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for stock in candidates:
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ts_code = stock["ts_code"]
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name = stock["name"]
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sector = stock["sector"]
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entry_signal_type = stock.get("entry_signal_type", "none")
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entry_signal_score = stock.get("entry_signal_score", 0)
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tech_signal = stock.get("tech_signal")
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# 各维度得分
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trend_timing_score = stock.get("trend_timing_score", 50)
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supply_demand_score = stock.get("supply_demand_score", 50)
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capital_score = stock.get("capital_score", 50)
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position_score = stock.get("position_score", 50)
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valuation_score = stock.get("valuation_score", 50)
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# 板块共振评分
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sector_score = _score_sector_resonance(sector, hot_sectors)
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sector_stage = _get_sector_stage(sector, hot_sectors)
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# 综合评分（新权重）
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final_score = (
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trend_timing_score * 0.30 +
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capital_score * 0.25 +
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supply_demand_score * 0.20 +
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sector_score * 0.15 +
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market_temp_score * 0.10
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)
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# 风险乘数
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if tech_signal:
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if tech_signal.rally_pct_5d > 20:
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final_score *= 0.65
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elif tech_signal.rally_pct_5d > 15:
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final_score *= 0.80
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if sector_stage == "end":
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final_score *= 0.70
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elif sector_stage == "late":
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final_score *= 0.88
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if market_temp_score < 30:
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final_score *= 0.75
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# 入场信号高置信度奖励
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if entry_signal_score >= 80:
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final_score *= 1.10
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# 确定信号和等级
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level = _score_to_level(final_score)
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signal = "HOLD"
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if entry_signal_type != "none" and entry_signal_score >= 50 and position_score >= 30 and final_score >= 60:
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signal = "BUY"
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# 价格参考
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entry_price = None
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target_price = None
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stop_loss = None
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if tech_signal:
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entry_price = tech_signal.support_price
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target_price = tech_signal.resist_price
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stop_loss = tech_signal.stop_loss_price
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# 根据入场信号类型调整参考价
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details = stock.get("entry_signal_details", {})
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if entry_signal_type == "breakout" and details.get("resist_level"):
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entry_price = details["resist_level"]
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target_price = round(entry_price * 1.05, 2)
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elif entry_signal_type == "pullback" and details.get("support_price"):
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entry_price = details["support_price"]
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target_price = round(entry_price * 1.05, 2)
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elif entry_signal_type == "launch" and details.get("resist_level"):
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entry_price = round(details["resist_level"] * 1.01, 2)
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target_price = round(details["resist_level"] * 1.08, 2)
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# 生成推荐理由
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reasons = _generate_reasons(stock, tech_signal, market_temp, intraday)
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# 风险提示
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risk_note = _generate_risk_note(market_temp, tech_signal, stock)
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rec = Recommendation(
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ts_code=ts_code,
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name=name,
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sector=sector,
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score=round(final_score, 1),
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market_temp_score=round(market_temp_score, 1),
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sector_score=round(sector_score, 1),
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capital_score=round(capital_score, 1),
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technical_score=round(stock.get("technical_score", 50), 1),
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position_score=round(position_score, 1),
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valuation_score=round(valuation_score, 1),
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signal=signal,
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entry_price=entry_price,
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target_price=target_price,
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stop_loss=stop_loss,
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reasons=reasons,
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risk_note=risk_note,
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level=level,
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strategy="trend_breakout",
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entry_signal_type=entry_signal_type,
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)
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recommendations.append(rec)
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return recommendations
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def _score_sector_resonance(sector_name: str, hot_sectors: list[SectorInfo]) -> float:
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"""板块共振评分 (0-100)"""
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for s in hot_sectors:
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if s.sector_name == sector_name:
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score = 40 # 在热门板块列表中
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score += s.heat_score * 0.3 # 板块热度贡献
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if s.stage == "early":
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score += 30
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elif s.stage == "mid":
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score += 20
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elif s.stage == "late":
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score += 5
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return min(score, 100)
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return 10.0 # 不在热门板块
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def _get_sector_score(sector_name: str, hot_sectors: list[SectorInfo]) -> float:
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"""获取板块在热门板块中的得分"""
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for s in hot_sectors:
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if s.sector_name == sector_name:
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return s.heat_score
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return 30.0
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def _get_sector_stage(sector_name: str, hot_sectors: list[SectorInfo]) -> str:
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"""获取板块所处阶段"""
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for s in hot_sectors:
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if s.sector_name == sector_name:
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return s.stage
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return "mid"
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def _score_to_level(score: float) -> str:
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if score >= 80:
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return "强烈推荐"
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elif score >= 60:
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return "推荐"
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elif score >= 40:
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return "观望"
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else:
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return "回避"
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def _generate_reasons(
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stock: dict, tech: TechnicalSignal | None,
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market: MarketTemperature, intraday: bool = False,
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) -> list[str]:
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"""生成推荐理由"""
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reasons = []
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entry_type = stock.get("entry_signal_type", "none")
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signal_map = {"breakout": "突破型", "pullback": "回踩型", "launch": "启动型"}
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entry_label = signal_map.get(entry_type, "")
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# 入场信号
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if entry_label:
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details = stock.get("entry_signal_details", {})
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if entry_type == "breakout":
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breakout_pct = details.get("breakout_pct", 0)
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vol_ratio = details.get("volume_ratio", 0)
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reasons.append(f"放量突破20日阻力位（涨幅{breakout_pct:.1f}%，量比{vol_ratio:.1f}倍）")
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elif entry_type == "pullback":
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support = details.get("support_ma", "")
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shrink = details.get("volume_shrink_ratio", 0)
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reasons.append(f"缩量回踩{support}支撑（量能收缩至{shrink:.0%}）")
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elif entry_type == "launch":
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range_pct = details.get("price_range_pct", 0)
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shrink = details.get("volume_shrink_ratio", 0)
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reasons.append(f"高位缩量整理{range_pct:.1f}%后即将变盘（量缩至{shrink:.0%}）")
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# 供需分析
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vol_trend = stock.get("volume_trend", "")
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ds_ratio = stock.get("demand_supply_ratio", 1)
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if ds_ratio > 1.5:
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reasons.append(f"需求主导（上涨均量/下跌均量={ds_ratio:.1f}）")
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elif vol_trend == "expanding":
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reasons.append("量能逐步放大，资金持续介入")
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# 资金流
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main_net = stock.get("main_net_inflow", 0)
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if main_net > 5000:
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reasons.append(f"主力资金大幅流入{main_net:.0f}万元")
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elif main_net > 1000:
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reasons.append(f"主力资金持续流入{main_net:.0f}万元")
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# 板块
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sector = stock.get("sector", "")
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if sector:
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reasons.append(f"所属板块【{sector}】")
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# 技术面
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if tech:
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tech_reasons = []
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if tech.ma_bullish:
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tech_reasons.append("均线多头排列")
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if tech.macd_golden:
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tech_reasons.append("MACD金叉")
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if tech.pullback_support:
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tech_reasons.append("缩量回踩支撑")
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if tech_reasons:
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reasons.append("技术面: " + "、".join(tech_reasons))
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return reasons[:3]
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def _generate_risk_note(
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market: MarketTemperature,
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tech: TechnicalSignal | None,
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stock: dict,
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) -> str:
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"""生成风险提示"""
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notes = []
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entry_type = stock.get("entry_signal_type", "")
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if entry_type == "breakout":
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notes.append("突破型需警惕假突破，关注量能是否持续")
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elif entry_type == "pullback":
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notes.append("回踩型可能继续下探支撑，注意止损纪律")
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elif entry_type == "launch":
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notes.append("启动型整理可能延长，注意时间成本")
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if market.temperature < 30:
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notes.append("市场情绪偏冷，系统性风险较高")
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elif market.temperature < 50:
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notes.append("市场情绪一般，注意仓位控制")
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if tech:
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if tech.position_score < 30:
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notes.append(f"近期涨幅较大(5日{tech.rally_pct_5d}%)，追高风险")
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if tech.rally_pct_10d > 20:
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notes.append(f"10日累涨{tech.rally_pct_10d}%，警惕回调")
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if not notes:
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return "注意设好止损，控制仓位"
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return "；".join(notes)
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