From 76ddc495c19d7ed86899c5a83a0ac6e7b3847582 Mon Sep 17 00:00:00 2001 From: fish Date: Mon, 4 May 2026 22:57:45 +0800 Subject: [PATCH] =?UTF-8?q?=E4=BC=98=E5=8C=96=E4=B8=89=E5=B1=82=E6=89=93?= =?UTF-8?q?=E5=88=86=E6=A8=A1=E5=9E=8B=EF=BC=9A=E7=9F=AD=E6=9C=9F=E5=BC=95?= =?UTF-8?q?=E5=85=A5=E5=B9=85=E5=BA=A6=E5=9B=A0=E5=AD=90=E4=B8=8E=E9=87=8F?= =?UTF-8?q?=E8=83=BD=E7=A1=AE=E8=AE=A4=EF=BC=8C=E4=B8=AD=E6=9C=9F=E8=B5=84?= =?UTF-8?q?=E9=87=91=E4=BF=A1=E5=8F=B7=E8=BF=9E=E7=BB=AD=E5=8C=96=EF=BC=8C?= =?UTF-8?q?=E9=95=BF=E6=9C=9F=E5=8A=A0=E5=85=A5=E4=BB=B7=E6=A0=BC=E7=BB=B4?= =?UTF-8?q?=E5=BA=A6=EF=BC=8C=E6=96=B0=E5=A2=9E=E6=B3=A2=E5=8A=A8=E7=8E=87?= =?UTF-8?q?=E8=B0=83=E6=95=B4?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-Authored-By: Claude Opus 4.7 --- tushare/src/main.py | 31 +++- tushare/src/models.py | 1 + tushare/src/scorer.py | 152 +++++++++++++----- web/frontend/src/api/scores.ts | 18 +++ .../src/components/ScoreDetailDrawer.vue | 106 ++++++++++-- 5 files changed, 247 insertions(+), 61 deletions(-) diff --git a/tushare/src/main.py b/tushare/src/main.py index 8be32a3..b340c2f 100644 --- a/tushare/src/main.py +++ b/tushare/src/main.py @@ -1,5 +1,6 @@ import argparse import sys +from typing import Optional from . import contracts, fetcher, scorer, storage @@ -37,15 +38,19 @@ def run(ts_code: str, trade_date: Optional[str] = None) -> int: print(f"\n信号: {result.signal}") print("=" * 65) + quadrant_names = { + "accumulation": "增仓上涨", "distribution": "增仓下跌", + "covering": "减仓上涨", "liquidation": "减仓下跌", "flat": "持仓持平", + } print("\n[短期动力] 近7日逐日打分:") - print("-" * 65) + print("-" * 80) for d in result.detail.short_details: - tag = "增仓" if d["oi_chg"] > 0 else "减仓" - if abs(d["oi_chg"] / d["oi"]) < 0.01: - tag = "持平" - price_dir = "涨" if d["close"] >= d["pre_close"] else "跌" - print(f" {d['trade_date']} {tag:>4} + {price_dir} " - f"持仓{d['oi_chg']:>+8,.0f} 得分: {d['score']:>3}") + q = quadrant_names.get(d["quadrant"], d["quadrant"]) + print(f" {d['trade_date']} {q} " + f"涨跌{d['price_chg_pct']*100:>+.2f}% " + f"OI变化{d['oi_chg_pct']*100:>+.2f}% " + f"量比{d['vol_ratio']:.2f} " + f"得分: {d['score']:>5.1f}") md = result.detail.medium_detail print(f"\n[中期趋势] 明细:") @@ -53,14 +58,24 @@ def run(ts_code: str, trade_date: Optional[str] = None) -> int: print(f" 价格信号得分: {md['price_signal']:.1f}") print(f" 增仓上涨天数: {md['long_up_days']} 天") print(f" 增仓下跌天数: {md['long_down_days']} 天") - print(f" 资金意愿得分: {md['fund_signal']} 分") + print(f" 资金意愿得分: {md['fund_signal']:.1f} 分") ld = result.detail.long_detail print(f"\n[长期结构] 明细:") + print(f" OI趋势得分: {ld['oi_score']:.1f} (权重 60%)") + print(f" 价格趋势得分: {ld['price_score']:.1f} (权重 40%)") + print(f" 30日价格收益: {ld['price_return_30d_pct']:+.2f}%") + print(f" 30日前收盘价: {ld['price_before_30d']:.2f}") print(f" 近30日日均持仓: {ld['avg_oi']:,.0f}") print(f" 30日前持仓量: {ld['oi_before']:,.0f}") print(f" 持仓变化幅度: {ld['change_pct']:+.2f}%") + vd = result.detail.volatility + print(f"\n[波动率调整]") + print(f" 日波动率(30d std): {vd['daily_vol_pct']*100:.2f}%") + print(f" ATR%: {vd['atr_pct']*100:.2f}%") + print(f" 惩罚系数: {vd['vol_penalty']:.3f}") + print(f"\n[OK] 数据已持久化到 PostgreSQL") return 0 diff --git a/tushare/src/models.py b/tushare/src/models.py index 8434a6d..318037d 100644 --- a/tushare/src/models.py +++ b/tushare/src/models.py @@ -22,6 +22,7 @@ class ScoreDetail: short_details: list = field(default_factory=list) medium_detail: dict = field(default_factory=dict) long_detail: dict = field(default_factory=dict) + volatility: dict = field(default_factory=dict) @dataclass diff --git a/tushare/src/scorer.py b/tushare/src/scorer.py index a93a4ea..ad9a891 100644 --- a/tushare/src/scorer.py +++ b/tushare/src/scorer.py @@ -5,44 +5,79 @@ import pandas as pd from .models import ScoreDetail, ScoreResult -def _daily_short_score(row: pd.Series) -> int: - """单日短期动力打分。""" +def _daily_short_score(row: pd.Series, avg_vol_7d: float) -> dict: + """单日短期动力打分（连续值 + 幅度因子 + 量能确认）。""" oi = float(row["oi"]) oi_chg = float(row["oi_chg"]) close = float(row["close"]) pre_close = float(row["pre_close"]) + vol = float(row.get("vol", 0)) - oi_change_pct = abs(oi_chg / oi) if oi != 0 else 0 + oi_chg_pct = oi_chg / oi if oi != 0 else 0.0 + price_chg_pct = (close - pre_close) / pre_close if pre_close != 0 else 0.0 price_up = close >= pre_close - - if oi_change_pct < 0.01: - return 60 if price_up else 40 - oi_increasing = oi_chg > 0 - if oi_increasing and price_up: - return 100 - if oi_increasing and not price_up: - return 0 - if not oi_increasing and price_up: - return 70 - return 30 + + # 象限基础分 + if abs(oi_chg_pct) < 0.01: + base = 60.0 if price_up else 40.0 + quadrant = "flat" + elif oi_increasing and price_up: + base = 75.0 + quadrant = "accumulation" + elif oi_increasing and not price_up: + base = 25.0 + quadrant = "distribution" + elif not oi_increasing and price_up: + base = 65.0 + quadrant = "covering" + else: + base = 20.0 + quadrant = "liquidation" + + # 幅度加成：OI 变化率封顶 5%，价格涨跌幅封顶 3% + oi_mag = min(1.0, abs(oi_chg_pct) / 0.05) + price_mag = min(1.0, abs(price_chg_pct) / 0.03) + + if quadrant in ("accumulation", "liquidation"): + boost = (oi_mag + price_mag) / 2.0 * 20.0 + elif quadrant == "flat": + boost = price_mag * 10.0 + else: + boost = 0.0 + + # 量能确认 + vol_ratio = vol / avg_vol_7d if avg_vol_7d > 0 else 1.0 + vol_factor = 0.9 + 0.2 * min(vol_ratio, 1.5) + + score = max(0.0, min(100.0, (base + boost) * vol_factor)) + + return { + "trade_date": str(row["trade_date"]), + "close": close, + "pre_close": pre_close, + "oi": oi, + "oi_chg": oi_chg, + "oi_chg_pct": round(float(oi_chg_pct), 4), + "price_chg_pct": round(float(price_chg_pct), 4), + "vol": float(vol), + "vol_ratio": round(float(vol_ratio), 2), + "quadrant": quadrant, + "score": round(float(score), 1), + } def calc_short_term(df: pd.DataFrame, window: int = 7) -> tuple[float, list]: recent = df.iloc[-window:].copy() + avg_vol_7d = float(recent["vol"].mean()) if "vol" in recent.columns else 0.0 + scores = [] details = [] for _, row in recent.iterrows(): - score = _daily_short_score(row) - scores.append(score) - details.append({ - "trade_date": str(row["trade_date"]), - "close": float(row["close"]), - "pre_close": float(row["pre_close"]), - "oi": float(row["oi"]), - "oi_chg": float(row["oi_chg"]), - "score": score, - }) + detail = _daily_short_score(row, avg_vol_7d) + scores.append(detail["score"]) + details.append(detail) + return sum(scores) / len(scores), details @@ -66,7 +101,7 @@ def calc_medium_term(df: pd.DataFrame, window: int = 15) -> tuple[float, dict]: else: long_down += 1 - fund_score = 80 if long_up > long_down else (20 if long_up < long_down else 50) + fund_score = 50.0 + (long_up - long_down) / window * 50.0 score = price_score * 0.6 + fund_score * 0.4 detail = { @@ -74,7 +109,8 @@ def calc_medium_term(df: pd.DataFrame, window: int = 15) -> tuple[float, dict]: "price_signal": round(price_score, 1), "long_up_days": long_up, "long_down_days": long_down, - "fund_signal": fund_score, + "fund_signal": round(fund_score, 1), + "window": window, } return score, detail @@ -84,26 +120,31 @@ def calc_long_term(df: pd.DataFrame, window: int = 30) -> tuple[float, dict]: raise ValueError(f"数据不足,需要至少 {window + 1} 行") recent_oi = df.iloc[-window:]["oi"] - avg_oi = recent_oi.mean() + avg_oi = float(recent_oi.mean()) oi_before = float(df.iloc[-window - 1]["oi"]) - change_pct = (avg_oi - oi_before) / oi_before if oi_before != 0 else 0 + oi_change_pct = (avg_oi - oi_before) / oi_before if oi_before != 0 else 0.0 - if change_pct > 0.10: - score = 90 - elif change_pct > 0.05: - score = 70 - elif change_pct > -0.05: - score = 50 - elif change_pct > -0.10: - score = 30 - else: - score = 10 + # OI 趋势分 (60%) + oi_score = max(0.0, min(100.0, 50.0 + oi_change_pct * 250)) + + # 价格趋势分 (40%) + close_now = float(df.iloc[-1]["close"]) + price_before = float(df.iloc[-window - 1]["close"]) + price_return_30d = (close_now - price_before) / price_before if price_before != 0 else 0.0 + price_score = max(0.0, min(100.0, 50.0 + price_return_30d * 200)) + + score = oi_score * 0.6 + price_score * 0.4 detail = { - "avg_oi": round(float(avg_oi), 0), + "avg_oi": round(avg_oi, 0), "oi_before": round(oi_before, 0), - "change_pct": round(change_pct * 100, 2), + "change_pct": round(oi_change_pct * 100, 2), + "oi_score": round(oi_score, 1), + "price_score": round(price_score, 1), + "price_return_30d_pct": round(price_return_30d * 100, 2), + "price_before_30d": round(price_before, 2), + "window": window, } return score, detail @@ -139,7 +180,31 @@ def score_daily(df: pd.DataFrame, trade_date: Optional[str] = None) -> ScoreResu medium, medium_detail = calc_medium_term(df, 15) long_, long_detail = calc_long_term(df, 30) - composite = short * 0.4 + medium * 0.35 + long_ * 0.25 + # 波动率调整 + recent_30 = df.iloc[-30:].copy() + recent_30["ret"] = recent_30["close"].pct_change() + daily_vol = float(recent_30["ret"].std()) + + recent_30["tr"] = recent_30.apply( + lambda r: max( + r["high"] - r["low"], + abs(r["high"] - r["pre_close"]), + abs(r["low"] - r["pre_close"]), + ), + axis=1, + ) + atr = float(recent_30["tr"].mean()) + avg_close_30 = float(recent_30["close"].mean()) + atr_pct = (atr / avg_close_30) if avg_close_30 else 0.0 + + vol_ref = 0.015 + if daily_vol <= vol_ref: + vol_penalty = 1.0 + else: + vol_penalty = max(0.85, 1.0 - (daily_vol - vol_ref) * 10) + + composite_raw = short * 0.4 + medium * 0.35 + long_ * 0.25 + composite = round(composite_raw * vol_penalty, 1) signal = _interpret(composite) return ScoreResult( @@ -157,5 +222,10 @@ def score_daily(df: pd.DataFrame, trade_date: Optional[str] = None) -> ScoreResu short_details=short_details, medium_detail=medium_detail, long_detail=long_detail, + volatility={ + "daily_vol_pct": round(float(daily_vol), 4), + "atr_pct": round(float(atr_pct), 4), + "vol_penalty": round(float(vol_penalty), 4), + }, ), ) diff --git a/web/frontend/src/api/scores.ts b/web/frontend/src/api/scores.ts index 1e17992..5eb0c43 100644 --- a/web/frontend/src/api/scores.ts +++ b/web/frontend/src/api/scores.ts @@ -7,6 +7,11 @@ export interface ShortDetail { oi: number oi_chg: number score: number + oi_chg_pct: number + price_chg_pct: number + vol: number + vol_ratio: number + quadrant: string } export interface MediumDetail { @@ -15,18 +20,31 @@ export interface MediumDetail { long_up_days: number long_down_days: number fund_signal: number + window: number } export interface LongDetail { avg_oi: number oi_before: number change_pct: number + oi_score: number + price_score: number + price_return_30d_pct: number + price_before_30d: number + window: number +} + +export interface VolatilityDetail { + daily_vol_pct: number + atr_pct: number + vol_penalty: number } export interface ScoreDetail { short_details?: ShortDetail[] medium_detail?: MediumDetail long_detail?: LongDetail + volatility?: VolatilityDetail } export interface Score { diff --git a/web/frontend/src/components/ScoreDetailDrawer.vue b/web/frontend/src/components/ScoreDetailDrawer.vue index 72bb573..28756a2 100644 --- a/web/frontend/src/components/ScoreDetailDrawer.vue +++ b/web/frontend/src/components/ScoreDetailDrawer.vue @@ -34,10 +34,32 @@ watch( } }, ) + +const quadrantTag = (q: string) => { + const map: Record = { + accumulation: 'success', + distribution: 'warning', + covering: 'info', + liquidation: 'danger', + flat: '', + } + return map[q] ?? '' +} + +const quadrantLabel = (q: string) => { + const map: Record = { + accumulation: '增仓涨', + distribution: '增仓跌', + covering: '减仓涨', + liquidation: '减仓跌', + flat: '持平', + } + return map[q] ?? q +}