打分算法全面改进：修复方向性bug，引入自适应权重与分数动量
This commit is contained in:
fish
@@ -1,4 +1,5 @@
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tushare>=1.4.0
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numpy>=2.0.0
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pandas>=2.2.0
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fastapi>=0.115.0
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uvicorn[standard]>=0.34.0
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@@ -37,6 +37,9 @@ class RunResponse(BaseModel):
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long_term: float
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composite: float
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signal: str
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vol_penalty: float = 1.0
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composite_delta: Optional[float] = None
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composite_delta_5d: Optional[float] = None
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@app.on_event("startup")
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@@ -74,6 +77,9 @@ def run_pipeline(req: RunRequest):
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long_term=result.long_term,
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composite=result.composite,
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signal=result.signal,
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vol_penalty=result.vol_penalty,
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composite_delta=result.composite_delta,
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composite_delta_5d=result.composite_delta_5d,
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)
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@@ -74,8 +74,24 @@ def run(ts_code: str, trade_date: Optional[str] = None) -> int:
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print(f"\n[波动率调整]")
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print(f" 日波动率(30d std): {vd['daily_vol_pct']*100:.2f}%")
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print(f" ATR%: {vd['atr_pct']*100:.2f}%")
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vol_risk = vd.get("vol_risk", vd["daily_vol_pct"])
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print(f" 综合波动风险: {vol_risk*100:.2f}%")
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print(f" 惩罚系数: {vd['vol_penalty']:.3f}")
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aw = result.detail.adaptive_weights
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if aw:
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print(f"\n[自适应权重]")
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print(f" 趋势强度: {aw['trend_strength']:.2f}")
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print(f" 短期权重: {aw['w_short']:.2%} (基准 40%)")
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print(f" 中期权重: {aw['w_medium']:.2%} (基准 35%)")
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print(f" 长期权重: {aw['w_long']:.2%} (基准 25%)")
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if result.composite_delta is not None:
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print(f"\n[分数动量]")
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print(f" 日变化 (Δ1d): {result.composite_delta:+.1f}")
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if result.composite_delta_5d is not None:
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print(f" 周变化 (Δ5d): {result.composite_delta_5d:+.1f}")
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print(f"\n[OK] 数据已持久化到 PostgreSQL")
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return 0
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@@ -118,10 +134,12 @@ def run_range(ts_code: str, start_date: str, end_date: str) -> int:
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"accumulation": "增仓上涨", "distribution": "增仓下跌",
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"covering": "减仓上涨", "liquidation": "减仓下跌", "flat": "持仓持平",
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}
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print(f"\n{'日期':<12} {'收盘':>10} {'综合':>8} {'信号':<20}")
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print("-" * 55)
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print(f"\n{'日期':<12} {'收盘':>10} {'综合':>8} {'Δ1d':>6} {'Δ5d':>6} {'信号':<20}")
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print("-" * 70)
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for r in results:
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print(f"{r.trade_date:<12} {r.close:>10.2f} {r.composite:>8.1f} {r.signal:<20}")
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d1 = f"{r.composite_delta:+.1f}" if r.composite_delta is not None else " -"
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d5 = f"{r.composite_delta_5d:+.1f}" if r.composite_delta_5d is not None else " -"
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print(f"{r.trade_date:<12} {r.close:>10.2f} {r.composite:>8.1f} {d1:>6} {d5:>6} {r.signal:<20}")
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print(f"\n[OK] {len(results)} 条打分已持久化到 PostgreSQL")
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return 0
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@@ -23,6 +23,7 @@ class ScoreDetail:
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medium_detail: dict = field(default_factory=dict)
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long_detail: dict = field(default_factory=dict)
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volatility: dict = field(default_factory=dict)
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adaptive_weights: dict = field(default_factory=dict)
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@dataclass
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@@ -38,3 +39,6 @@ class ScoreResult:
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composite: float
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signal: str
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detail: ScoreDetail
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vol_penalty: float = 1.0
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composite_delta: Optional[float] = None # 与前一日综合分差值
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composite_delta_5d: Optional[float] = None # 与5日前综合分差值
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@@ -1,12 +1,18 @@
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import math
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from typing import Optional
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import numpy as np
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import pandas as pd
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from .models import ScoreDetail, ScoreResult
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# ---------------------------------------------------------------------------
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# 短期动力 — 单日打分
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# ---------------------------------------------------------------------------
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def _daily_short_score(row: pd.Series, avg_vol_7d: float) -> dict:
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"""单日短期动力打分（连续值 + 幅度因子 + 量能确认）。"""
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"""单日短期动力打分（连续值 + 方向性幅度加成 + 量能确认）。"""
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oi = float(row["oi"])
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oi_chg = float(row["oi_chg"])
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close = float(row["close"])
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@@ -18,37 +24,42 @@ def _daily_short_score(row: pd.Series, avg_vol_7d: float) -> dict:
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price_up = close >= pre_close
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oi_increasing = oi_chg > 0
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# 象限基础分
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# ── 象限基础分 ──
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if abs(oi_chg_pct) < 0.01:
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base = 60.0 if price_up else 40.0
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quadrant = "flat"
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elif oi_increasing and price_up:
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base = 75.0
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quadrant = "accumulation"
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quadrant = "accumulation" # 增仓上涨
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elif oi_increasing and not price_up:
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base = 25.0
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quadrant = "distribution"
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quadrant = "distribution" # 增仓下跌
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elif not oi_increasing and price_up:
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base = 65.0
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quadrant = "covering"
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quadrant = "covering" # 减仓上涨
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else:
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base = 20.0
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quadrant = "liquidation"
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quadrant = "liquidation" # 减仓下跌
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# 幅度加成：OI 变化率封顶 5%，价格涨跌幅封顶 3%
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# ── 幅度加成（方向性）──
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# OI 变化率封顶 5%，价格涨跌幅封顶 3%
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oi_mag = min(1.0, abs(oi_chg_pct) / 0.05)
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price_mag = min(1.0, abs(price_chg_pct) / 0.03)
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if quadrant in ("accumulation", "liquidation"):
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boost = (oi_mag + price_mag) / 2.0 * 20.0
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if quadrant == "accumulation":
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boost = (oi_mag + price_mag) / 2.0 * 20.0 # 看多，加成推高分数
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elif quadrant == "liquidation":
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boost = -(oi_mag + price_mag) / 2.0 * 20.0 # 看空，扣分强化信号
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elif quadrant == "flat":
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boost = price_mag * 10.0
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else:
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boost = 0.0
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boost = price_mag * 10.0 if price_up else -(price_mag * 10.0)
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elif quadrant == "distribution":
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boost = -price_mag * 10.0 # 增仓下跌：价格幅度扣分
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else: # covering
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boost = price_mag * 10.0 # 减仓上涨：价格幅度加分
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# 量能确认
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# ── 量能确认（ratio=1.0 时为中性因子 1.0）──
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vol_ratio = vol / avg_vol_7d if avg_vol_7d > 0 else 1.0
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vol_factor = 0.9 + 0.2 * min(vol_ratio, 1.5)
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vol_factor = 0.8 + 0.2 * min(vol_ratio, 2.0) # 范围 [0.8, 1.2]，中性 = 1.0
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score = max(0.0, min(100.0, (base + boost) * vol_factor))
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@@ -67,7 +78,12 @@ def _daily_short_score(row: pd.Series, avg_vol_7d: float) -> dict:
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}
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# ---------------------------------------------------------------------------
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# 短期动力 — 7 日窗口，指数加权平均
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# ---------------------------------------------------------------------------
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def calc_short_term(df: pd.DataFrame, window: int = 7) -> tuple[float, list]:
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"""近 window 日逐日打分，指数加权平均（越近权重越高）。"""
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recent = df.iloc[-window:].copy()
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avg_vol_7d = float(recent["vol"].mean()) if "vol" in recent.columns else 0.0
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@@ -78,8 +94,17 @@ def calc_short_term(df: pd.DataFrame, window: int = 7) -> tuple[float, list]:
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scores.append(detail["score"])
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details.append(detail)
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return sum(scores) / len(scores), details
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# 指数加权：权重从 exp(0)=1 递增到 exp(1)≈2.718
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weights = np.exp(np.linspace(0, 1, window))
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weights = weights / weights.sum()
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weighted_avg = float(np.average(scores, weights=weights))
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return weighted_avg, details
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# ---------------------------------------------------------------------------
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# 中期趋势 — 15 日窗口，四象限资金意愿
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# ---------------------------------------------------------------------------
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def calc_medium_term(df: pd.DataFrame, window: int = 15) -> tuple[float, dict]:
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if len(df) < window + 1:
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@@ -89,46 +114,65 @@ def calc_medium_term(df: pd.DataFrame, window: int = 15) -> tuple[float, dict]:
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close_now = float(df.iloc[-1]["close"])
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close_before = float(df.iloc[-window - 1]["close"])
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# ── 价格信号 ──
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price_return = (close_now - close_before) / close_before if close_before != 0 else 0
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price_score = max(0.0, min(100.0, 50.0 + price_return * 500))
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long_up = 0
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long_down = 0
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for _, row in recent.iterrows():
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if row["oi_chg"] > 0:
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if row["close"] >= row["pre_close"]:
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long_up += 1
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else:
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long_down += 1
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# ── 资金意愿（四象限全计入）──
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accumulation = 0 # 增仓上涨
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distribution = 0 # 增仓下跌
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covering = 0 # 减仓上涨
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liquidation = 0 # 减仓下跌
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for _, row in recent.iterrows():
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oi_inc = row["oi_chg"] > 0
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price_up = row["close"] >= row["pre_close"]
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if oi_inc and price_up:
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accumulation += 1
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elif oi_inc and not price_up:
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distribution += 1
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elif not oi_inc and price_up:
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covering += 1
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else:
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liquidation += 1
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# 增仓驱动权重 1.0，减仓驱动权重 0.5（持续性较差）
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fund_score = 50.0 + (
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accumulation * 1.0 - distribution * 1.0
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+ covering * 0.5 - liquidation * 0.5
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) / window * 50.0
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fund_score = max(0.0, min(100.0, fund_score))
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fund_score = 50.0 + (long_up - long_down) / window * 50.0
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score = price_score * 0.6 + fund_score * 0.4
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detail = {
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"price_return_pct": round(price_return * 100, 2),
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"price_signal": round(price_score, 1),
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"long_up_days": long_up,
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"long_down_days": long_down,
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"accumulation_days": accumulation,
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"distribution_days": distribution,
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"covering_days": covering,
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"liquidation_days": liquidation,
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"fund_signal": round(fund_score, 1),
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"window": window,
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}
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return score, detail
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# ---------------------------------------------------------------------------
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# 长期结构 — 30 日窗口，端点 OI 比较
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# ---------------------------------------------------------------------------
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def calc_long_term(df: pd.DataFrame, window: int = 30) -> tuple[float, dict]:
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if len(df) < window + 1:
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raise ValueError(f"数据不足,需要至少 {window + 1} 行")
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recent_oi = df.iloc[-window:]["oi"]
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avg_oi = float(recent_oi.mean())
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# ── OI 趋势分（端点比较，消除均值滞后）──
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oi_now = float(df.iloc[-1]["oi"])
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oi_before = float(df.iloc[-window - 1]["oi"])
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oi_change_pct = (avg_oi - oi_before) / oi_before if oi_before != 0 else 0.0
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# OI 趋势分 (60%)
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oi_change_pct = (oi_now - oi_before) / oi_before if oi_before != 0 else 0.0
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oi_score = max(0.0, min(100.0, 50.0 + oi_change_pct * 250))
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# 价格趋势分 (40%)
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# ── 价格趋势分 ──
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close_now = float(df.iloc[-1]["close"])
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price_before = float(df.iloc[-window - 1]["close"])
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price_return_30d = (close_now - price_before) / price_before if price_before != 0 else 0.0
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@@ -136,19 +180,27 @@ def calc_long_term(df: pd.DataFrame, window: int = 30) -> tuple[float, dict]:
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score = oi_score * 0.6 + price_score * 0.4
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# 额外统计近 30 日 OI 均值供参考
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recent_oi = df.iloc[-window:]["oi"]
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detail = {
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"avg_oi": round(avg_oi, 0),
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"oi_now": round(oi_now, 0),
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"oi_before": round(oi_before, 0),
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"change_pct": round(oi_change_pct * 100, 2),
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"oi_change_pct": round(oi_change_pct * 100, 2),
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"oi_score": round(oi_score, 1),
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"price_score": round(price_score, 1),
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"price_return_30d_pct": round(price_return_30d * 100, 2),
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"price_before_30d": round(price_before, 2),
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"avg_oi_30d": round(float(recent_oi.mean()), 0),
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"window": window,
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}
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return score, detail
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# ---------------------------------------------------------------------------
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# 信号解读
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# ---------------------------------------------------------------------------
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def _interpret(composite: float) -> str:
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if composite >= 80:
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return "强烈看多区域 — 价格与资金共振,趋势多头的温床"
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@@ -159,6 +211,10 @@ def _interpret(composite: float) -> str:
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return "强烈看空区域 — 资金主动且持续地打压价格"
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# ---------------------------------------------------------------------------
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# 主打分函数
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# ---------------------------------------------------------------------------
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def score_daily(df: pd.DataFrame, trade_date: Optional[str] = None) -> ScoreResult:
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"""对 DataFrame 中指定日期或最新一条记录打分。"""
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if len(df) < 31:
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@@ -176,11 +232,12 @@ def score_daily(df: pd.DataFrame, trade_date: Optional[str] = None) -> ScoreResu
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latest = df.iloc[-1]
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# ── 三层原始分 ──
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short, short_details = calc_short_term(df, 7)
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medium, medium_detail = calc_medium_term(df, 15)
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long_, long_detail = calc_long_term(df, 30)
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# 波动率调整
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# ── 波动率（日收益率标准差 + ATR%）──
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recent_30 = df.iloc[-30:].copy()
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recent_30["ret"] = recent_30["close"].pct_change()
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daily_vol = float(recent_30["ret"].std())
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@@ -197,13 +254,36 @@ def score_daily(df: pd.DataFrame, trade_date: Optional[str] = None) -> ScoreResu
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avg_close_30 = float(recent_30["close"].mean())
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atr_pct = (atr / avg_close_30) if avg_close_30 else 0.0
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# 综合波动率风险度量：日收益标准差 70% + ATR% 30%
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vol_risk = daily_vol * 0.7 + atr_pct * 0.3
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vol_ref = 0.015
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if daily_vol <= vol_ref:
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if vol_risk <= vol_ref:
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vol_penalty = 1.0
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else:
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vol_penalty = max(0.85, 1.0 - (daily_vol - vol_ref) * 10)
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vol_penalty = max(0.85, 1.0 - (vol_risk - vol_ref) * 10)
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composite_raw = short * 0.4 + medium * 0.35 + long_ * 0.25
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# ── 自适应权重（趋势越强，长期权重越高）──
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price_return_30d = (
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(float(df.iloc[-1]["close"]) - float(df.iloc[-31]["close"]))
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/ float(df.iloc[-31]["close"])
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if df.iloc[-31]["close"] != 0
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else 0.0
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)
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# 趋势效率 = |30日收益率| / 日波动率，比率高 = 方向明确
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trend_strength = abs(price_return_30d) / max(daily_vol, 0.005)
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trend_factor = min(trend_strength / 3.0, 1.0) # 归一化到 [0, 1]
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w_short_base = 0.40
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w_medium_base = 0.35
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w_long_base = 0.25
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shift = trend_factor * 0.10 # 最多转移 10% 权重
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w_short = w_short_base - shift
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w_medium = w_medium_base
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w_long = w_long_base + shift
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# ── 综合分数 ──
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composite_raw = short * w_short + medium * w_medium + long_ * w_long
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composite = round(composite_raw * vol_penalty, 1)
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signal = _interpret(composite)
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@@ -218,6 +298,7 @@ def score_daily(df: pd.DataFrame, trade_date: Optional[str] = None) -> ScoreResu
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long_term=round(long_, 1),
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composite=round(composite, 1),
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signal=signal,
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vol_penalty=round(float(vol_penalty), 4),
|
||||
detail=ScoreDetail(
|
||||
short_details=short_details,
|
||||
medium_detail=medium_detail,
|
||||
@@ -225,12 +306,24 @@ def score_daily(df: pd.DataFrame, trade_date: Optional[str] = None) -> ScoreResu
|
||||
volatility={
|
||||
"daily_vol_pct": round(float(daily_vol), 4),
|
||||
"atr_pct": round(float(atr_pct), 4),
|
||||
"vol_risk": round(float(vol_risk), 4),
|
||||
"vol_penalty": round(float(vol_penalty), 4),
|
||||
},
|
||||
adaptive_weights={
|
||||
"trend_strength": round(float(trend_strength), 2),
|
||||
"trend_factor": round(float(trend_factor), 2),
|
||||
"w_short": round(float(w_short), 4),
|
||||
"w_medium": round(float(w_medium), 4),
|
||||
"w_long": round(float(w_long), 4),
|
||||
},
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# 区间 / 全量打分
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def score_range(
|
||||
df: pd.DataFrame, start_date: str, end_date: str
|
||||
) -> tuple[list[ScoreResult], list[str]]:
|
||||
@@ -253,6 +346,7 @@ def score_range(
|
||||
except ValueError as e:
|
||||
warnings.append(str(e))
|
||||
|
||||
_fill_deltas(results)
|
||||
return results, warnings
|
||||
|
||||
|
||||
@@ -277,6 +371,21 @@ def score_all(df: pd.DataFrame) -> tuple[list[ScoreResult], list[str], int, int]
|
||||
except ValueError as e:
|
||||
warnings.append(f"{trade_date}: {e}")
|
||||
|
||||
_fill_deltas(results)
|
||||
|
||||
total_days = len(df) - 30
|
||||
scored_count = len(results)
|
||||
return results, warnings, total_days, scored_count
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# 辅助：填充分数动量
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def _fill_deltas(results: list[ScoreResult]):
|
||||
"""为结果列表中的每个 ScoreResult 填充 composite_delta 和 composite_delta_5d。"""
|
||||
for i, r in enumerate(results):
|
||||
if i >= 1 and results[i - 1].composite is not None:
|
||||
r.composite_delta = round(r.composite - results[i - 1].composite, 1)
|
||||
if i >= 5 and results[i - 5].composite is not None:
|
||||
r.composite_delta_5d = round(r.composite - results[i - 5].composite, 1)
|
||||
|
||||
@@ -103,6 +103,19 @@ def save_score(score: ScoreResult, db_url: str = DEFAULT_DB_URL):
|
||||
conn = _get_conn(db_url)
|
||||
try:
|
||||
with conn.cursor() as cur:
|
||||
detail_payload = {
|
||||
"short_details": score.detail.short_details,
|
||||
"medium_detail": score.detail.medium_detail,
|
||||
"long_detail": score.detail.long_detail,
|
||||
"volatility": score.detail.volatility,
|
||||
"adaptive_weights": score.detail.adaptive_weights,
|
||||
"vol_penalty": score.vol_penalty,
|
||||
}
|
||||
if score.composite_delta is not None:
|
||||
detail_payload["composite_delta"] = score.composite_delta
|
||||
if score.composite_delta_5d is not None:
|
||||
detail_payload["composite_delta_5d"] = score.composite_delta_5d
|
||||
|
||||
cur.execute(
|
||||
"""
|
||||
INSERT INTO scores
|
||||
@@ -132,11 +145,7 @@ def save_score(score: ScoreResult, db_url: str = DEFAULT_DB_URL):
|
||||
score.long_term,
|
||||
score.composite,
|
||||
score.signal,
|
||||
json.dumps({
|
||||
"short_details": score.detail.short_details,
|
||||
"medium_detail": score.detail.medium_detail,
|
||||
"long_detail": score.detail.long_detail,
|
||||
}, ensure_ascii=False, default=str),
|
||||
json.dumps(detail_payload, ensure_ascii=False, default=str),
|
||||
),
|
||||
)
|
||||
conn.commit()
|
||||
|
||||
Reference in New Issue
Block a user