diff --git a/tushare/requirements.txt b/tushare/requirements.txt index 2ba4caf..108adde 100644 --- a/tushare/requirements.txt +++ b/tushare/requirements.txt @@ -1,4 +1,5 @@ tushare>=1.4.0 +numpy>=2.0.0 pandas>=2.2.0 fastapi>=0.115.0 uvicorn[standard]>=0.34.0 diff --git a/tushare/src/api.py b/tushare/src/api.py index 8158b8c..0596071 100644 --- a/tushare/src/api.py +++ b/tushare/src/api.py @@ -37,6 +37,9 @@ class RunResponse(BaseModel): long_term: float composite: float signal: str + vol_penalty: float = 1.0 + composite_delta: Optional[float] = None + composite_delta_5d: Optional[float] = None @app.on_event("startup") @@ -74,6 +77,9 @@ def run_pipeline(req: RunRequest): long_term=result.long_term, composite=result.composite, signal=result.signal, + vol_penalty=result.vol_penalty, + composite_delta=result.composite_delta, + composite_delta_5d=result.composite_delta_5d, ) diff --git a/tushare/src/main.py b/tushare/src/main.py index 8333169..2b2f260 100644 --- a/tushare/src/main.py +++ b/tushare/src/main.py @@ -74,8 +74,24 @@ def run(ts_code: str, trade_date: Optional[str] = None) -> int: 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}%") + vol_risk = vd.get("vol_risk", vd["daily_vol_pct"]) + print(f" 综合波动风险: {vol_risk*100:.2f}%") print(f" 惩罚系数: {vd['vol_penalty']:.3f}") + aw = result.detail.adaptive_weights + if aw: + print(f"\n[自适应权重]") + print(f" 趋势强度: {aw['trend_strength']:.2f}") + print(f" 短期权重: {aw['w_short']:.2%} (基准 40%)") + print(f" 中期权重: {aw['w_medium']:.2%} (基准 35%)") + print(f" 长期权重: {aw['w_long']:.2%} (基准 25%)") + + if result.composite_delta is not None: + print(f"\n[分数动量]") + print(f" 日变化 (Δ1d): {result.composite_delta:+.1f}") + if result.composite_delta_5d is not None: + print(f" 周变化 (Δ5d): {result.composite_delta_5d:+.1f}") + print(f"\n[OK] 数据已持久化到 PostgreSQL") return 0 @@ -118,10 +134,12 @@ def run_range(ts_code: str, start_date: str, end_date: str) -> int: "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(f"\n{'日期':<12} {'收盘':>10} {'综合':>8} {'信号':<20}") - print("-" * 55) + print(f"\n{'日期':<12} {'收盘':>10} {'综合':>8} {'Δ1d':>6} {'Δ5d':>6} {'信号':<20}") + print("-" * 70) for r in results: - print(f"{r.trade_date:<12} {r.close:>10.2f} {r.composite:>8.1f} {r.signal:<20}") + d1 = f"{r.composite_delta:+.1f}" if r.composite_delta is not None else " -" + d5 = f"{r.composite_delta_5d:+.1f}" if r.composite_delta_5d is not None else " -" + print(f"{r.trade_date:<12} {r.close:>10.2f} {r.composite:>8.1f} {d1:>6} {d5:>6} {r.signal:<20}") print(f"\n[OK] {len(results)} 条打分已持久化到 PostgreSQL") return 0 diff --git a/tushare/src/models.py b/tushare/src/models.py index 318037d..7b6b588 100644 --- a/tushare/src/models.py +++ b/tushare/src/models.py @@ -23,6 +23,7 @@ class ScoreDetail: medium_detail: dict = field(default_factory=dict) long_detail: dict = field(default_factory=dict) volatility: dict = field(default_factory=dict) + adaptive_weights: dict = field(default_factory=dict) @dataclass @@ -38,3 +39,6 @@ class ScoreResult: composite: float signal: str detail: ScoreDetail + vol_penalty: float = 1.0 + composite_delta: Optional[float] = None # 与前一日综合分差值 + composite_delta_5d: Optional[float] = None # 与5日前综合分差值 diff --git a/tushare/src/scorer.py b/tushare/src/scorer.py index c558794..1c54eda 100644 --- a/tushare/src/scorer.py +++ b/tushare/src/scorer.py @@ -1,12 +1,18 @@ +import math from typing import Optional +import numpy as np import pandas as pd from .models import ScoreDetail, ScoreResult +# --------------------------------------------------------------------------- +# 短期动力 — 单日打分 +# --------------------------------------------------------------------------- + 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）。""" + """单日短期动力打分（连续值 + 方向性幅度加成 + 量能确认）。""" oi = float(row["oi"]) oi_chg = float(row["oi_chg"]) close = float(row["close"]) @@ -18,37 +24,42 @@ def _daily_short_score(row: pd.Series, avg_vol_7d: float) -> dict: price_up = close >= pre_close oi_increasing = oi_chg > 0 - # 象限基础分 + # ── 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" + quadrant = "accumulation" # 增仓上涨 elif oi_increasing and not price_up: base = 25.0 - quadrant = "distribution" + quadrant = "distribution" # 增仓下跌 elif not oi_increasing and price_up: base = 65.0 - quadrant = "covering" + quadrant = "covering" # 减仓上涨 else: base = 20.0 - quadrant = "liquidation" + quadrant = "liquidation" # 减仓下跌 - # 幅度加成：OI 变化率封顶 5%，价格涨跌幅封顶 3% + # ── 幅度加成（方向性）── + # 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 + if quadrant == "accumulation": + boost = (oi_mag + price_mag) / 2.0 * 20.0 # 看多，加成推高分数 + elif quadrant == "liquidation": + boost = -(oi_mag + price_mag) / 2.0 * 20.0 # 看空，扣分强化信号 elif quadrant == "flat": - boost = price_mag * 10.0 - else: - boost = 0.0 + boost = price_mag * 10.0 if price_up else -(price_mag * 10.0) + elif quadrant == "distribution": + boost = -price_mag * 10.0 # 增仓下跌：价格幅度扣分 + else: # covering + boost = price_mag * 10.0 # 减仓上涨：价格幅度加分 - # 量能确认 + # ── 量能确认（ratio=1.0 时为中性因子 1.0）── 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) + vol_factor = 0.8 + 0.2 * min(vol_ratio, 2.0) # 范围 [0.8, 1.2]，中性 = 1.0 score = max(0.0, min(100.0, (base + boost) * vol_factor)) @@ -67,7 +78,12 @@ def _daily_short_score(row: pd.Series, avg_vol_7d: float) -> dict: } +# --------------------------------------------------------------------------- +# 短期动力 — 7 日窗口，指数加权平均 +# --------------------------------------------------------------------------- + def calc_short_term(df: pd.DataFrame, window: int = 7) -> tuple[float, list]: + """近 window 日逐日打分，指数加权平均（越近权重越高）。""" recent = df.iloc[-window:].copy() avg_vol_7d = float(recent["vol"].mean()) if "vol" in recent.columns else 0.0 @@ -78,8 +94,17 @@ def calc_short_term(df: pd.DataFrame, window: int = 7) -> tuple[float, list]: scores.append(detail["score"]) details.append(detail) - return sum(scores) / len(scores), details + # 指数加权：权重从 exp(0)=1 递增到 exp(1)≈2.718 + weights = np.exp(np.linspace(0, 1, window)) + weights = weights / weights.sum() + weighted_avg = float(np.average(scores, weights=weights)) + return weighted_avg, details + + +# --------------------------------------------------------------------------- +# 中期趋势 — 15 日窗口，四象限资金意愿 +# --------------------------------------------------------------------------- def calc_medium_term(df: pd.DataFrame, window: int = 15) -> tuple[float, dict]: if len(df) < window + 1: @@ -89,46 +114,65 @@ def calc_medium_term(df: pd.DataFrame, window: int = 15) -> tuple[float, dict]: close_now = float(df.iloc[-1]["close"]) close_before = float(df.iloc[-window - 1]["close"]) + # ── 价格信号 ── price_return = (close_now - close_before) / close_before if close_before != 0 else 0 price_score = max(0.0, min(100.0, 50.0 + price_return * 500)) - long_up = 0 - long_down = 0 - for _, row in recent.iterrows(): - if row["oi_chg"] > 0: - if row["close"] >= row["pre_close"]: - long_up += 1 - else: - long_down += 1 + # ── 资金意愿（四象限全计入）── + accumulation = 0 # 增仓上涨 + distribution = 0 # 增仓下跌 + covering = 0 # 减仓上涨 + liquidation = 0 # 减仓下跌 + + for _, row in recent.iterrows(): + oi_inc = row["oi_chg"] > 0 + price_up = row["close"] >= row["pre_close"] + if oi_inc and price_up: + accumulation += 1 + elif oi_inc and not price_up: + distribution += 1 + elif not oi_inc and price_up: + covering += 1 + else: + liquidation += 1 + + # 增仓驱动权重 1.0，减仓驱动权重 0.5（持续性较差） + fund_score = 50.0 + ( + accumulation * 1.0 - distribution * 1.0 + + covering * 0.5 - liquidation * 0.5 + ) / window * 50.0 + fund_score = max(0.0, min(100.0, fund_score)) - fund_score = 50.0 + (long_up - long_down) / window * 50.0 score = price_score * 0.6 + fund_score * 0.4 detail = { "price_return_pct": round(price_return * 100, 2), "price_signal": round(price_score, 1), - "long_up_days": long_up, - "long_down_days": long_down, + "accumulation_days": accumulation, + "distribution_days": distribution, + "covering_days": covering, + "liquidation_days": liquidation, "fund_signal": round(fund_score, 1), "window": window, } return score, detail +# --------------------------------------------------------------------------- +# 长期结构 — 30 日窗口，端点 OI 比较 +# --------------------------------------------------------------------------- + def calc_long_term(df: pd.DataFrame, window: int = 30) -> tuple[float, dict]: if len(df) < window + 1: raise ValueError(f"数据不足,需要至少 {window + 1} 行") - recent_oi = df.iloc[-window:]["oi"] - avg_oi = float(recent_oi.mean()) + # ── OI 趋势分（端点比较，消除均值滞后）── + oi_now = float(df.iloc[-1]["oi"]) oi_before = float(df.iloc[-window - 1]["oi"]) - - oi_change_pct = (avg_oi - oi_before) / oi_before if oi_before != 0 else 0.0 - - # OI 趋势分 (60%) + oi_change_pct = (oi_now - oi_before) / oi_before if oi_before != 0 else 0.0 oi_score = max(0.0, min(100.0, 50.0 + oi_change_pct * 250)) - # 价格趋势分 (40%) + # ── 价格趋势分 ── 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 @@ -136,19 +180,27 @@ def calc_long_term(df: pd.DataFrame, window: int = 30) -> tuple[float, dict]: score = oi_score * 0.6 + price_score * 0.4 + # 额外统计近 30 日 OI 均值供参考 + recent_oi = df.iloc[-window:]["oi"] + detail = { - "avg_oi": round(avg_oi, 0), + "oi_now": round(oi_now, 0), "oi_before": round(oi_before, 0), - "change_pct": round(oi_change_pct * 100, 2), + "oi_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), + "avg_oi_30d": round(float(recent_oi.mean()), 0), "window": window, } return score, detail +# --------------------------------------------------------------------------- +# 信号解读 +# --------------------------------------------------------------------------- + def _interpret(composite: float) -> str: if composite >= 80: return "强烈看多区域 — 价格与资金共振,趋势多头的温床" @@ -159,6 +211,10 @@ def _interpret(composite: float) -> str: return "强烈看空区域 — 资金主动且持续地打压价格" +# --------------------------------------------------------------------------- +# 主打分函数 +# --------------------------------------------------------------------------- + def score_daily(df: pd.DataFrame, trade_date: Optional[str] = None) -> ScoreResult: """对 DataFrame 中指定日期或最新一条记录打分。""" if len(df) < 31: @@ -176,11 +232,12 @@ def score_daily(df: pd.DataFrame, trade_date: Optional[str] = None) -> ScoreResu latest = df.iloc[-1] + # ── 三层原始分 ── short, short_details = calc_short_term(df, 7) medium, medium_detail = calc_medium_term(df, 15) long_, long_detail = calc_long_term(df, 30) - # 波动率调整 + # ── 波动率（日收益率标准差 + ATR%）── recent_30 = df.iloc[-30:].copy() recent_30["ret"] = recent_30["close"].pct_change() daily_vol = float(recent_30["ret"].std()) @@ -197,13 +254,36 @@ def score_daily(df: pd.DataFrame, trade_date: Optional[str] = None) -> ScoreResu avg_close_30 = float(recent_30["close"].mean()) atr_pct = (atr / avg_close_30) if avg_close_30 else 0.0 + # 综合波动率风险度量：日收益标准差 70% + ATR% 30% + vol_risk = daily_vol * 0.7 + atr_pct * 0.3 vol_ref = 0.015 - if daily_vol <= vol_ref: + if vol_risk <= vol_ref: vol_penalty = 1.0 else: - vol_penalty = max(0.85, 1.0 - (daily_vol - vol_ref) * 10) + vol_penalty = max(0.85, 1.0 - (vol_risk - vol_ref) * 10) - composite_raw = short * 0.4 + medium * 0.35 + long_ * 0.25 + # ── 自适应权重（趋势越强，长期权重越高）── + price_return_30d = ( + (float(df.iloc[-1]["close"]) - float(df.iloc[-31]["close"])) + / float(df.iloc[-31]["close"]) + if df.iloc[-31]["close"] != 0 + else 0.0 + ) + # 趋势效率 = |30日收益率| / 日波动率，比率高 = 方向明确 + trend_strength = abs(price_return_30d) / max(daily_vol, 0.005) + trend_factor = min(trend_strength / 3.0, 1.0) # 归一化到 [0, 1] + + w_short_base = 0.40 + w_medium_base = 0.35 + w_long_base = 0.25 + + shift = trend_factor * 0.10 # 最多转移 10% 权重 + w_short = w_short_base - shift + w_medium = w_medium_base + w_long = w_long_base + shift + + # ── 综合分数 ── + composite_raw = short * w_short + medium * w_medium + long_ * w_long composite = round(composite_raw * vol_penalty, 1) signal = _interpret(composite) @@ -218,6 +298,7 @@ def score_daily(df: pd.DataFrame, trade_date: Optional[str] = None) -> ScoreResu long_term=round(long_, 1), composite=round(composite, 1), signal=signal, + 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) diff --git a/tushare/src/storage.py b/tushare/src/storage.py index a348116..44792a9 100644 --- a/tushare/src/storage.py +++ b/tushare/src/storage.py @@ -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()