649 lines
19 KiB
Python
649 lines
19 KiB
Python
"""趋势突破入场信号分类 + 供需分析 + 趋势评分
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三种入场信号类型：
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- 突破型 (breakout): 放量突破 20 日阻力位
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- 回踩型 (pullback): 上升趋势中缩量回踩均线支撑
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- 启动型 (launch): 高位缩量整理后即将变盘
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"""
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import logging
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from enum import Enum
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import numpy as np
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import pandas as pd
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from app.config import settings
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logger = logging.getLogger(__name__)
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class EntrySignal(str, Enum):
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BREAKOUT = "breakout"
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PULLBACK = "pullback"
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LAUNCH = "launch"
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NONE = "none"
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# ── 入场信号检测 ──
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def classify_entry_signal(df: pd.DataFrame) -> dict:
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"""分类入场信号类型
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优先级: breakout > pullback > launch
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Returns:
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{
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"signal_type": EntrySignal,
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"signal_score": float (0-100),
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"details": dict, # 信号相关详情
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}
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"""
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if df is None or len(df) < 30:
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return {"signal_type": EntrySignal.NONE, "signal_score": 0, "details": {}}
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# 优先检测突破型（最符合 1-5 天操作窗口）
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result = detect_breakout(df)
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if result:
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result["signal_score"] = _score_breakout(df, result)
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return result
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# 回踩型（风险收益比最好）
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result = detect_pullback(df)
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if result:
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result["signal_score"] = _score_pullback(df, result)
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return result
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# 启动型（需要耐心等待）
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result = detect_launch(df)
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if result:
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result["signal_score"] = _score_launch(df, result)
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return result
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return {"signal_type": EntrySignal.NONE, "signal_score": 0, "details": {}}
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def detect_breakout(df: pd.DataFrame) -> dict | None:
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"""突破型检测
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条件（全部满足）：
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1. 价格突破 20 日阻力位
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2. 量能放大 (vol > vol_ma5 * 1.5)
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3. 短期均线多头 (MA5 > MA10 > MA20)
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4. 收盘偏强 (close 在当日区间上半部分)
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"""
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last = df.iloc[-1]
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# 必需指标列
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required = ["close", "high", "low", "vol", "ma5", "ma10", "ma20", "vol_ma5"]
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if any(c not in df.columns for c in required):
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return None
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if pd.isna(last["vol_ma5"]) or last["vol_ma5"] == 0:
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return None
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# 1. 突破 20 日阻力（允许距阻力位 1% 以内，贴近即视为突破前兆）
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if len(df) < 22:
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return None
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resist_20d = df["high"].iloc[-21:-1].max()
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if last["close"] < resist_20d * 0.99:
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return None
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# 2. 量能放大
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min_vol_ratio = getattr(settings, "breakout_min_volume_ratio", 1.2)
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if last["vol"] <= last["vol_ma5"] * min_vol_ratio:
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return None
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# 3. 短期均线偏多（MA5 > MA20 即可，不要求三线完美排列）
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if not (last["ma5"] > last["ma20"]):
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return None
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# 4. 收盘偏强（不低于当日中点）
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day_range = last["high"] - last["low"]
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if day_range > 0 and (last["close"] - last["low"]) / day_range < 0.5:
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return None
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breakout_pct = (last["close"] - resist_20d) / resist_20d * 100
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return {
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"signal_type": EntrySignal.BREAKOUT,
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"details": {
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"resist_level": round(resist_20d, 2),
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"breakout_pct": round(breakout_pct, 2),
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"volume_ratio": round(last["vol"] / last["vol_ma5"], 2),
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},
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}
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def detect_pullback(df: pd.DataFrame) -> dict | None:
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"""回踩型检测
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条件（全部满足）：
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1. MA20 > MA60 — 中长期上升趋势
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2. 近 5 日内有 MA5 > MA20 — 近期处于上升趋势
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3. 价格靠近 MA10 或 MA20 (<2%)
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4. 缩量回调 (近 3 日均量 < 前 5 日均量 * 0.7)
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5. RSI 40-65
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"""
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last = df.iloc[-1]
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required = ["close", "vol", "ma5", "ma10", "ma20", "ma60", "rsi14"]
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if any(c not in df.columns for c in required):
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return None
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if pd.isna(last["ma60"]) or pd.isna(last["rsi14"]):
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return None
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# 1. 中长期上升趋势
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if not (last["ma20"] > last["ma60"]):
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return None
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# 2. 近 5 日内有 MA5 > MA20
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recent_5 = df.tail(5)
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has_uptrend = any(
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row["ma5"] > row["ma20"]
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for _, row in recent_5.iterrows()
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if not pd.isna(row["ma5"]) and not pd.isna(row["ma20"])
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)
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if not has_uptrend:
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return None
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# 3. 价格靠近 MA10 或 MA20（放宽至 4%）
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near_ma10 = (
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not pd.isna(last["ma10"])
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and last["ma10"] > 0
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and abs(last["close"] - last["ma10"]) / last["ma10"] < 0.04
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)
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near_ma20 = (
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not pd.isna(last["ma20"])
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and last["ma20"] > 0
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and abs(last["close"] - last["ma20"]) / last["ma20"] < 0.04
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)
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if not (near_ma10 or near_ma20):
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return None
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# 4. 缩量回调（放宽至 0.85）
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if len(df) < 8:
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return None
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recent_3_vol = df["vol"].tail(3).mean()
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prev_5_vol = df["vol"].iloc[-8:-3].mean()
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if prev_5_vol == 0:
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return None
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shrink_ratio = recent_3_vol / prev_5_vol
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max_shrink = getattr(settings, "pullback_max_shrink_ratio", 0.85)
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if shrink_ratio >= max_shrink:
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return None
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# 5. RSI 范围（放宽至 35-70）
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if not (35 <= last["rsi14"] <= 70):
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return None
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support_ma = "MA10" if near_ma10 else "MA20"
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support_price = last["ma10"] if near_ma10 else last["ma20"]
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return {
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"signal_type": EntrySignal.PULLBACK,
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"details": {
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"support_ma": support_ma,
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"support_price": round(support_price, 2),
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"volume_shrink_ratio": round(shrink_ratio, 2),
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},
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}
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def detect_launch(df: pd.DataFrame) -> dict | None:
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"""启动型检测（高位缩量整理后即将变盘）
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条件（全部满足）：
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1. close > MA60 且距 20 日高点 < 5%
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2. 近 10 日振幅 < 5%，每日涨跌幅 < 3%
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3. 近 5 日均量 < 前 5 日均量 * 0.6
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4. MA5 > MA20（多头格局未破）
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5. 布林带宽收窄
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6. DIF > 0
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"""
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last = df.iloc[-1]
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required = ["close", "high", "vol", "pct_chg", "ma5", "ma20", "ma60",
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"dif", "boll_bw"]
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if any(c not in df.columns for c in required):
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return None
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if pd.isna(last["ma60"]) or pd.isna(last["dif"]) or pd.isna(last["boll_bw"]):
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return None
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# 1. 高位
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if last["close"] < last["ma60"]:
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return None
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high_20d = df["high"].tail(20).max()
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if (high_20d - last["close"]) / high_20d > 0.05:
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return None
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# 2. 窄幅整理（放宽振幅至 8%，每日涨跌幅至 4%）
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if len(df) < 10:
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return None
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recent = df.tail(10)
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close_range = (recent["close"].max() - recent["close"].min()) / recent["close"].min()
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max_range = getattr(settings, "consolidation_max_range_pct", 8.0) / 100
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if close_range > max_range:
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return None
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if any(abs(row["pct_chg"]) > 4 for _, row in recent.iterrows() if not pd.isna(row.get("pct_chg"))):
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return None
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# 3. 缩量（放宽至 0.75）
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if len(df) < 15:
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return None
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recent_5_vol = df["vol"].tail(5).mean()
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earlier_5_vol = df["vol"].iloc[-15:-10].mean()
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if earlier_5_vol == 0:
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return None
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vol_ratio = recent_5_vol / earlier_5_vol
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if vol_ratio >= 0.75:
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return None
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# 4. 多头格局未破
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if last["ma5"] <= last["ma20"]:
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return None
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# 5-6. 布林收窄 + MACD 零轴上方（软条件，不满足不淘汰）
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boll_narrowing = False
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dif_positive = False
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if len(df) >= 20 and not pd.isna(last["boll_bw"]):
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bw_10d_ago = df.iloc[-10]["boll_bw"]
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if not pd.isna(bw_10d_ago) and last["boll_bw"] < bw_10d_ago:
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boll_narrowing = True
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if not pd.isna(last["dif"]) and last["dif"] > 0:
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dif_positive = True
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return {
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"signal_type": EntrySignal.LAUNCH,
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"details": {
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"consolidation_days": 10,
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"volume_shrink_ratio": round(vol_ratio, 2),
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"price_range_pct": round(close_range * 100, 2),
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"resist_level": round(high_20d, 2),
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"boll_narrowing": boll_narrowing,
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"dif_positive": dif_positive,
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},
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}
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# ── 信号质量评分 ──
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def _score_breakout(df: pd.DataFrame, signal: dict) -> float:
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"""突破型信号质量评分 (0-100)"""
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last = df.iloc[-1]
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score = 0
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# 突破力度 (25)
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breakout_pct = signal["details"].get("breakout_pct", 0)
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if breakout_pct > 3:
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score += 25
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elif breakout_pct > 1.5:
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score += 18
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elif breakout_pct > 0.5:
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score += 10
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# 量能放大程度 (25)
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vol_ratio = signal["details"].get("volume_ratio", 1)
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if vol_ratio > 2.5:
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score += 25
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elif vol_ratio > 2.0:
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score += 20
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elif vol_ratio > 1.5:
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score += 12
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# 均线完整性 (25)
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if all(c in df.columns for c in ["ma60"]):
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if not pd.isna(last["ma60"]) and last["ma5"] > last["ma10"] > last["ma20"] > last["ma60"]:
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score += 25
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elif last["ma5"] > last["ma10"] > last["ma20"]:
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score += 15
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else:
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score += 10
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# MACD 状态 (15)
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if all(c in df.columns for c in ["dif", "dea"]):
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if not pd.isna(last["dif"]) and last["dif"] > last["dea"] and last["dif"] > 0:
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score += 15
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elif not pd.isna(last["dif"]) and last["dif"] > last["dea"]:
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score += 8
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# RSI 区间 (10)
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if "rsi14" in df.columns and not pd.isna(last["rsi14"]):
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if 50 <= last["rsi14"] <= 75:
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score += 10
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elif 45 <= last["rsi14"] <= 80:
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score += 5
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return min(score, 100)
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def _score_pullback(df: pd.DataFrame, signal: dict) -> float:
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"""回踩型信号质量评分 (0-100)"""
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last = df.iloc[-1]
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score = 0
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# 缩量程度 (30)
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shrink = signal["details"].get("volume_shrink_ratio", 1)
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if shrink < 0.4:
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score += 30
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elif shrink < 0.5:
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score += 22
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elif shrink < 0.6:
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score += 15
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# 支撑有效性 (25)
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support_ma = signal["details"].get("support_ma", "")
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if support_ma == "MA20":
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score += 25 # MA20 支撑更强
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elif support_ma == "MA10":
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score += 15
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# 均线多头 (25)
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if all(c in df.columns for c in ["ma60"]):
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if not pd.isna(last["ma60"]) and last["ma5"] > last["ma10"] > last["ma20"] > last["ma60"]:
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score += 25
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elif last["ma5"] > last["ma10"] > last["ma20"]:
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score += 15
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# MACD 状态 (10)
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if all(c in df.columns for c in ["dif", "dea"]):
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if not pd.isna(last["dif"]) and last["dif"] > 0:
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score += 10
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elif not pd.isna(last["dif"]) and last["dif"] > last["dea"]:
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score += 5
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# RSI (10)
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if "rsi14" in df.columns and not pd.isna(last["rsi14"]):
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if 45 <= last["rsi14"] <= 60:
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score += 10
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elif 40 <= last["rsi14"] <= 65:
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score += 5
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return min(score, 100)
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def _score_launch(df: pd.DataFrame, signal: dict) -> float:
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"""启动型信号质量评分 (0-100)"""
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last = df.iloc[-1]
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score = 0
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details = signal.get("details", {})
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# 整理充分度 (25) — 振幅越小越好
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range_pct = details.get("price_range_pct", 10)
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if range_pct < 2:
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score += 25
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elif range_pct < 4:
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score += 18
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elif range_pct < 8:
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score += 10
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# 缩量程度 (25)
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shrink = details.get("volume_shrink_ratio", 1)
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if shrink < 0.3:
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score += 25
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elif shrink < 0.5:
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score += 18
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elif shrink < 0.75:
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score += 10
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# 均线保持 (20)
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if all(c in df.columns for c in ["ma60"]):
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if not pd.isna(last["ma60"]) and last["ma5"] > last["ma20"] > last["ma60"]:
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score += 20
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elif last["ma5"] > last["ma20"]:
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score += 10
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# 布林收窄 (15) — 软条件，满足加分
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if details.get("boll_narrowing"):
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if "boll_bw" in df.columns and len(df) >= 20:
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bw_now = last["boll_bw"]
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bw_10d = df.iloc[-10]["boll_bw"]
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if not pd.isna(bw_10d) and bw_now > 0:
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narrowing = (bw_10d - bw_now) / bw_10d * 100
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if narrowing > 30:
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score += 15
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elif narrowing > 15:
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score += 10
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else:
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score += 5
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# MACD (15) — 软条件，满足加分
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if details.get("dif_positive"):
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score += 15
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elif all(c in df.columns for c in ["dif"]) and not pd.isna(last.get("dif")):
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score += 5
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return min(score, 100)
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# ── 趋势 & 时机评分 ──
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def score_trend_timing(df: pd.DataFrame, entry_signal: dict) -> float:
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"""趋势&时机评分 (0-100)
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维度：
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- MA 排列 (25)
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- MA20 方向 (10)
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- 更高高点形态 (15)
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- 入场信号质量 (50)
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"""
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last = df.iloc[-1]
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score = 0
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# MA 排列 (25)
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ma_cols = [c for c in ["ma5", "ma10", "ma20", "ma60"] if c in df.columns]
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if len(ma_cols) >= 4 and not any(pd.isna(last[c]) for c in ma_cols):
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if last["ma5"] > last["ma10"] > last["ma20"] > last["ma60"]:
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score += 25
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elif last["ma5"] > last["ma10"] > last["ma20"]:
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score += 18
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elif last["ma5"] > last["ma20"]:
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score += 10
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# MA20 方向 (10)
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if "ma20" in df.columns and len(df) >= 3:
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if not pd.isna(last["ma20"]) and not pd.isna(df.iloc[-3]["ma20"]):
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if last["ma20"] > df.iloc[-3]["ma20"]:
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score += 10
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# 更高高点形态 (15)
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if _check_higher_highs(df):
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score += 15
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# 入场信号质量 (50)
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signal_score = entry_signal.get("signal_score", 0)
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score += signal_score * 0.5
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return min(score, 100)
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# ── 供需分析评分 ──
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def score_supply_demand(df: pd.DataFrame) -> float:
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"""供需评分 (0-100)
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维度：
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- 上涨日 vs 下跌日量比 (50): 需求主导度
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- 量能收缩/扩张 (30): 供需变化趋势
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- 量价配合 (20): 上涨放量+下跌缩量
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"""
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score = 0
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if len(df) < 10:
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return 50.0
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recent = df.tail(10)
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last = df.iloc[-1]
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# 上涨日 vs 下跌日量比 (50)
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up_days = recent[recent["pct_chg"] > 0]
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down_days = recent[recent["pct_chg"] <= 0]
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if len(up_days) > 0 and len(down_days) > 0:
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avg_up_vol = up_days["vol"].mean()
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avg_down_vol = down_days["vol"].mean()
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if avg_down_vol > 0:
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ratio = avg_up_vol / avg_down_vol
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if ratio > 2.0:
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score += 50
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elif ratio > 1.5:
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score += 38
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elif ratio > 1.2:
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score += 25
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elif ratio > 1.0:
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score += 12
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elif len(up_days) > 0:
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score += 40 # 全部上涨日，需求强劲
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||
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# 量能收缩/扩张 (30)
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vol_ma_col = "vol_ma10" if "vol_ma10" in df.columns else "vol_ma5"
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if vol_ma_col in df.columns and not pd.isna(last[vol_ma_col]) and last[vol_ma_col] > 0:
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recent_3_vol = recent["vol"].tail(3).mean()
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shrink_ratio = recent_3_vol / last[vol_ma_col]
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||
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if shrink_ratio < 0.4:
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score += 30 # 极度缩量，供应枯竭
|
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elif shrink_ratio < 0.6:
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||
score += 22
|
||
elif shrink_ratio < 0.8:
|
||
score += 12
|
||
elif shrink_ratio > 1.5:
|
||
score += 25 # 放量，需求进入
|
||
elif shrink_ratio > 1.2:
|
||
score += 15
|
||
|
||
# 量价配合 (20)
|
||
if _check_bullish_volume_divergence(df):
|
||
score += 20
|
||
|
||
return min(score, 100)
|
||
|
||
|
||
def analyze_volume_pattern(df: pd.DataFrame) -> dict:
|
||
"""量价模式分析
|
||
|
||
Returns:
|
||
{
|
||
"demand_supply_ratio": float, # 上涨日均量/下跌日均量
|
||
"volume_trend": str, # expanding/contracting/stable
|
||
"accumulation_score": float, # 0-100
|
||
}
|
||
"""
|
||
default = {"demand_supply_ratio": 1.0, "volume_trend": "stable", "accumulation_score": 0}
|
||
|
||
if len(df) < 10:
|
||
return default
|
||
|
||
recent = df.tail(10)
|
||
|
||
# 需求/供给比
|
||
up_days = recent[recent["pct_chg"] > 0]
|
||
down_days = recent[recent["pct_chg"] <= 0]
|
||
avg_up_vol = up_days["vol"].mean() if len(up_days) > 0 else 0
|
||
avg_down_vol = down_days["vol"].mean() if len(down_days) > 0 else 1
|
||
ratio = round(avg_up_vol / max(avg_down_vol, 1), 2)
|
||
|
||
# 量能趋势
|
||
if len(df) >= 10:
|
||
vol_5d_ago = df["vol"].iloc[-10:-5].mean()
|
||
vol_recent = df["vol"].tail(5).mean()
|
||
if vol_5d_ago > 0:
|
||
trend_ratio = vol_recent / vol_5d_ago
|
||
trend = "expanding" if trend_ratio > 1.2 else ("contracting" if trend_ratio < 0.8 else "stable")
|
||
else:
|
||
trend = "stable"
|
||
else:
|
||
trend = "stable"
|
||
|
||
# 累积评分
|
||
accumulation = 0
|
||
if ratio > 1.5:
|
||
accumulation += 40
|
||
if trend == "expanding" and df.iloc[-1]["pct_chg"] > 0:
|
||
accumulation += 30
|
||
if _check_volume_dry_up_before_rally(df):
|
||
accumulation += 30
|
||
|
||
return {
|
||
"demand_supply_ratio": ratio,
|
||
"volume_trend": trend,
|
||
"accumulation_score": accumulation,
|
||
}
|
||
|
||
|
||
# ── 辅助函数 ──
|
||
|
||
|
||
def _check_higher_highs(df: pd.DataFrame) -> bool:
|
||
"""检查最近是否形成更高高点形态
|
||
|
||
取近 20 日的局部高点，检查是否有上升趋势。
|
||
"""
|
||
if len(df) < 20:
|
||
return False
|
||
|
||
recent = df.tail(20)
|
||
highs = recent["high"].values
|
||
|
||
# 简单检查：最近的高点是否比之前的高点更高
|
||
# 将 20 日分为两段，各取最高点
|
||
first_half_max = highs[:10].max()
|
||
second_half_max = highs[10:].max()
|
||
|
||
return second_half_max >= first_half_max
|
||
|
||
|
||
def _check_bullish_volume_divergence(df: pd.DataFrame) -> bool:
|
||
"""检查量价配合是否健康（上涨放量，下跌缩量）"""
|
||
if len(df) < 6:
|
||
return False
|
||
|
||
recent = df.tail(5)
|
||
up_days = recent[recent["pct_chg"] > 0]
|
||
down_days = recent[recent["pct_chg"] <= 0]
|
||
|
||
if len(up_days) == 0 or len(down_days) == 0:
|
||
return len(up_days) > len(down_days) # 主要是上涨日也算
|
||
|
||
avg_up_vol = up_days["vol"].mean()
|
||
avg_down_vol = down_days["vol"].mean()
|
||
|
||
return avg_up_vol > avg_down_vol
|
||
|
||
|
||
def _check_volume_dry_up_before_rally(df: pd.DataFrame) -> bool:
|
||
"""检查是否有缩量后放量的突破前奏
|
||
|
||
经典突破模式：量能萎缩至极低，然后在突破日放量。
|
||
"""
|
||
if len(df) < 15:
|
||
return False
|
||
|
||
last = df.iloc[-1]
|
||
|
||
# 检查近 5-10 日内是否有极度缩量日
|
||
vol_ma_col = "vol_ma10" if "vol_ma10" in df.columns else "vol_ma5"
|
||
if vol_ma_col not in df.columns:
|
||
return False
|
||
|
||
recent_10 = df.tail(10)
|
||
had_dry_up = any(
|
||
row["vol"] < row[vol_ma_col] * 0.5
|
||
for _, row in recent_10.iterrows()
|
||
if not pd.isna(row[vol_ma_col]) and row[vol_ma_col] > 0
|
||
)
|
||
|
||
# 且当日量能放大
|
||
vol_ma_col_5 = "vol_ma5" if "vol_ma5" in df.columns else vol_ma_col
|
||
vol_expanding = (
|
||
not pd.isna(last[vol_ma_col_5])
|
||
and last[vol_ma_col_5] > 0
|
||
and last["vol"] > last[vol_ma_col_5] * 1.2
|
||
)
|
||
|
||
return had_dry_up and vol_expanding
|