311 lines
9.5 KiB
Python
311 lines
9.5 KiB
Python
"""买卖信号生成
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基于量价与趋势判断辅助信号。RSI 等滞后指标只做风险备注，不主导推荐。
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"""
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import logging
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import pandas as pd
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import numpy as np
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from app.data.tushare_client import tushare_client
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from app.analysis.technical import add_all_indicators
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from app.data.models import TechnicalSignal
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from app.config import settings
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logger = logging.getLogger(__name__)
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def _check_ma_bullish(df: pd.DataFrame) -> bool:
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"""均线多头排列: MA5>MA10>MA20>MA60 且 MA20 向上"""
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if len(df) < 60:
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return False
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last = df.iloc[-1]
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prev = df.iloc[-3] if len(df) >= 3 else last
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return (
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last["ma5"] > last["ma10"] > last["ma20"] > last["ma60"]
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and last["ma20"] > prev["ma20"] # MA20 向上
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)
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def _check_volume_breakout(df: pd.DataFrame) -> bool:
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"""放量突破: 当日量 > MA5量的1.5倍 且 价格突破近20日高点"""
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if len(df) < 20:
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return False
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last = df.iloc[-1]
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recent_high = df["high"].iloc[-20:-1].max()
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return (
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last["vol"] > last["vol_ma5"] * 1.5
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and last["close"] > recent_high
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)
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def _check_macd_golden(df: pd.DataFrame) -> bool:
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"""MACD 金叉: DIF 上穿 DEA，且在零轴附近或上方"""
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if len(df) < 3:
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return False
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last = df.iloc[-1]
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prev = df.iloc[-2]
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return (
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prev["dif"] <= prev["dea"]
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and last["dif"] > last["dea"]
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and last["dif"] > -0.5 # 零轴附近或上方
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)
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def _check_rsi_healthy(df: pd.DataFrame) -> bool:
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"""RSI 节奏区间: RSI(14) 在 40-70，仅作为辅助节奏提示"""
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if df.empty:
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return False
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rsi = df.iloc[-1].get("rsi14", 50)
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return 40 <= rsi <= 70
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def _check_pullback_support(df: pd.DataFrame) -> bool:
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"""缩量回踩支撑: 回调至 MA10/MA20 附近，回调量 < 上涨量的60%"""
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if len(df) < 20:
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return False
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last = df.iloc[-1]
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close = last["close"]
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ma10 = last["ma10"]
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ma20 = last["ma20"]
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# 价格在 MA10 或 MA20 附近（±3%）
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near_ma = (
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abs(close - ma10) / ma10 < 0.03
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or abs(close - ma20) / ma20 < 0.03
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)
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if not near_ma:
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return False
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# 近3日为回调（跌或小涨），量缩
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recent_3 = df.tail(3)
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prev_5 = df.iloc[-8:-3] if len(df) >= 8 else df.head(5)
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avg_recent_vol = recent_3["vol"].mean()
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avg_prev_vol = prev_5["vol"].mean() if len(prev_5) > 0 else avg_recent_vol
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return avg_recent_vol < avg_prev_vol * 0.6
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def _check_big_yang(df: pd.DataFrame) -> bool:
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"""底部放量长阳: 近10日内出现涨幅>5%的放量阳线"""
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if len(df) < 10:
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return False
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recent = df.tail(10)
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for _, row in recent.iterrows():
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pct = row.get("pct_chg", 0) or 0
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vol = row["vol"]
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vol_ma = row["vol_ma5"]
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if pct > 5 and vol > vol_ma * 1.3:
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return True
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return False
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def _check_boll_support(df: pd.DataFrame) -> bool:
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"""布林带下轨支撑: 价格触及下轨后反弹 且 带宽收窄后扩张"""
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if len(df) < 5:
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return False
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# 近5日内有触及下轨
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recent = df.tail(5)
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touched = False
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for _, row in recent.iterrows():
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if row["low"] <= row["boll_lower"] * 1.01:
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touched = True
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break
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if not touched:
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return False
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# 当前价格在下轨上方（反弹）
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last = df.iloc[-1]
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if last["close"] <= last["boll_lower"]:
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return False
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# 带宽从收窄变扩张
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bw = df["boll_bw"].tail(10)
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if len(bw) < 5:
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return False
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min_bw_idx = bw.idxmin()
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last_idx = bw.index[-1]
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return min_bw_idx != last_idx and bw.iloc[-1] > bw.iloc[-3]
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def _calc_position_score(df: pd.DataFrame) -> tuple[float, float, float, float]:
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"""计算位置安全得分，防追高
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返回: (rally_pct_5d, rally_pct_10d, distance_from_high, position_score)
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position_score: 0-100，越高越安全（底部/低位），越低越危险（高位/连涨）
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"""
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if len(df) < 10:
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return 0, 0, 0, 50
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last_close = float(df.iloc[-1]["close"])
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# 近5日累计涨幅
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close_5d_ago = float(df.iloc[-6]["close"]) if len(df) >= 6 else float(df.iloc[0]["close"])
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rally_pct_5d = (last_close - close_5d_ago) / close_5d_ago * 100
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# 近10日累计涨幅
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close_10d_ago = float(df.iloc[-11]["close"]) if len(df) >= 11 else float(df.iloc[0]["close"])
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rally_pct_10d = (last_close - close_10d_ago) / close_10d_ago * 100
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# 距离60日高点
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lookback = min(60, len(df))
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high_60d = float(df["high"].tail(lookback).max())
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distance_from_high = (last_close - high_60d) / high_60d * 100
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# 位置安全评分
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score = 100.0
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# 1) 近5日涨幅惩罚 (权重40%)
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if rally_pct_5d > 20:
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score -= 40 # 5日涨超20%，极端追高
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elif rally_pct_5d > 15:
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score -= 32
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elif rally_pct_5d > 10:
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score -= 24
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elif rally_pct_5d > 5:
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score -= 12
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elif rally_pct_5d > 0:
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score -= 4 # 温和上涨，小扣分
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else:
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score -= 0 # 回调中，不扣分
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# 2) 近10日涨幅惩罚 (权重30%)
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if rally_pct_10d > 30:
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score -= 30
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elif rally_pct_10d > 20:
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score -= 22
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elif rally_pct_10d > 15:
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score -= 16
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elif rally_pct_10d > 8:
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score -= 8
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elif rally_pct_10d > 0:
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score -= 2
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# 3) 距离60日高点的位置 (权重30%)
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# distance_from_high <= 0 表示低于高点（已回调），越负越安全
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# distance_from_high == 0 表示就在高点，最危险
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if distance_from_high >= 0:
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score -= 25 # 创新高或刚好在高点
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elif distance_from_high > -3:
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score -= 18 # 接近高点
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elif distance_from_high > -8:
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score -= 10 # 轻微回调
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elif distance_from_high > -15:
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score -= 3 # 适度回调，较安全
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else:
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score -= 0 # 大幅回调，位置低
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# 补充奖励：缩量回踩 MA20 附近 (最多+10)
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if len(df) >= 20:
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ma20 = float(df.iloc[-1].get("ma20", last_close))
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if ma20 > 0 and abs(last_close - ma20) / ma20 < 0.03:
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# 在 MA20 附近
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recent_vol = float(df["vol"].tail(3).mean())
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prev_vol = float(df["vol"].iloc[-8:-3].mean()) if len(df) >= 8 else recent_vol
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if recent_vol < prev_vol * 0.7:
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score += 10 # 缩量回踩 MA20，好位置
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return (
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round(rally_pct_5d, 2),
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round(rally_pct_10d, 2),
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round(distance_from_high, 2),
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round(max(0, min(100, score)), 1),
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)
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def _calc_support_resist(df: pd.DataFrame) -> tuple[float | None, float | None]:
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"""计算支撑位和压力位"""
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if len(df) < 20:
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return None, None
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last = df.iloc[-1]
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# 支撑位：MA20 和近20日最低价取较高者
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ma20 = last["ma20"]
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recent_low = df["low"].tail(20).min()
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support = max(ma20, recent_low)
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# 压力位：近20日最高价
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resist = df["high"].tail(20).max()
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return round(support, 2), round(resist, 2)
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def generate_signals(ts_code: str, name: str = "") -> TechnicalSignal:
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"""对单只股票生成技术面买卖信号"""
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df = tushare_client.get_stock_daily(ts_code, days=120)
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if df.empty or len(df) < 20:
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return TechnicalSignal(ts_code=ts_code, name=name)
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# 按日期升序
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df = df.sort_values("trade_date").reset_index(drop=True)
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df = add_all_indicators(df)
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# 检查各项信号
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ma_bullish = _check_ma_bullish(df)
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volume_breakout = _check_volume_breakout(df)
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macd_golden = _check_macd_golden(df)
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rsi_healthy = _check_rsi_healthy(df)
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pullback_support = _check_pullback_support(df)
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big_yang = _check_big_yang(df)
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boll_support = _check_boll_support(df)
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# 计算分数
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score = 0
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signal_count = 0
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signal_map = [
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(ma_bullish, 15),
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(volume_breakout, 20),
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(macd_golden, 15),
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(rsi_healthy, 3),
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(pullback_support, 15),
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(big_yang, 15),
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(boll_support, 10),
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]
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for is_true, points in signal_map:
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if is_true:
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score += points
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signal_count += 1
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# 趋势评分（与推荐体系一致）
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from app.engine.screener import _score_trend
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trend_score = round(_score_trend(df), 1)
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# 支撑压力位
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support, resist = _calc_support_resist(df)
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last_close = float(df.iloc[-1]["close"])
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stop_loss = round(last_close * (1 - settings.stop_loss_pct / 100), 2)
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# 位置安全评估
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rally_5d, rally_10d, dist_high, pos_score = _calc_position_score(df)
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result = TechnicalSignal(
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ts_code=ts_code,
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name=name,
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ma_bullish=ma_bullish,
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volume_breakout=volume_breakout,
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macd_golden=macd_golden,
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rsi_healthy=rsi_healthy,
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pullback_support=pullback_support,
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big_yang=big_yang,
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boll_support=boll_support,
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score=score,
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trend_score=trend_score,
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signal_count=signal_count,
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rally_pct_5d=rally_5d,
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rally_pct_10d=rally_10d,
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distance_from_high=dist_high,
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position_score=pos_score,
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support_price=support,
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resist_price=resist,
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stop_loss_price=stop_loss,
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)
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logger.debug(f"技术信号 {name}({ts_code}): score={score} signals={signal_count} "
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f"MA={ma_bullish} VOL={volume_breakout} MACD={macd_golden} "
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f"RSI={rsi_healthy} PB={pullback_support} YANG={big_yang} BOLL={boll_support} "
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f"位置安全={pos_score} 5d涨幅={rally_5d}% 10d涨幅={rally_10d}%")
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return result
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