feat(rotation): 增加最新调仓信号展示功能

- 配置中取消固定end_date，改为默认使用当前日期 - 添加打印最新调仓信号的功能，显示持仓明细及调出品种 - 在报告生成流程中调用最新调仓信号打印函数 - 图表展示中新增最新调仓信号表格，支持颜色区分调入、调出和维持 - 优化报告图表布局，调整画布高度适应信号表内容 - 删除无用test.py测试脚本及相关冗余代码
2026-03-19 00:22:25 +08:00
parent 02ba6a5df2
commit 9b154a1a25
4 changed files with 262 additions and 106 deletions
--- a/config/strategies/rotation.yaml
+++ b/config/strategies/rotation.yaml
@@ -31,7 +31,7 @@ code_list:

 # ==================== 回测参数 ====================
 start_date: "2018-01-01"
-end_date: "2025-03-17"
+# end_date: "2025-03-17"

 # ==================== 因子参数 ====================
 # 动量/趋势窗口期（天数）
--- a/scripts/run_rotation.py
+++ b/scripts/run_rotation.py
@@ -53,6 +53,12 @@ def main():

    # 加载配置
    config = load_config(args.config)
+
+    # 如果未设置 end_date，默认使用最新日期
+    if not config.get('end_date'):
+        from datetime import datetime
+        config['end_date'] = datetime.now().strftime('%Y-%m-%d')
+
    print(f"\n配置文件: {args.config}")
    print(f"候选标的: {len(config['code_list'])} 只")
    print(f"回测区间: {config['start_date']} ~ {config['end_date']}")
--- a/strategies/rotation/report.py
+++ b/strategies/rotation/report.py
@@ -77,6 +77,9 @@ def generate_performance_report(
    print(f'  {"最大回撤区间":<22} {str(s_dd_start.date()):>10} ~ {str(s_dd_end.date())}')
    print("=" * 70)

+    # 打印最新调仓信号
+    _print_latest_signal(backtest_result, code_list, code_name_map, select_num)
+
    # 绘制图表
    _plot_report_chart(
        backtest_result, code_list, code_name_map,
@@ -99,6 +102,178 @@ def generate_performance_report(
    }


+def _print_latest_signal(backtest_result: pd.DataFrame, code_list: list, code_name_map: dict, select_num: int):
+    """打印最新调仓信号"""
+    latest = _extract_latest_positions(backtest_result, code_list, code_name_map, select_num)
+    signal_date_str = latest["signal_date"].strftime("%Y-%m-%d")
+
+    print("\n")
+    print("=" * 100)
+    print("                        最新调仓信号 (下一交易日执行)")
+    print("=" * 100)
+    print(f"  数据截止: {signal_date_str}")
+    print()
+
+    # 表头
+    print(f'  {"品种名称":<8} {"代码":>10} {"仓位":>6} {"得分":>8} {"进场日期":>12} {"进场价":>10} {"最新价":>10} {"操作":>6} {"持有天数":>8} {"盈亏":>10}')
+    print("  " + "-" * 115)
+
+    # 下期持仓（调入/维持）
+    for pos in latest["positions"]:
+        pnl_str = f'{pos["pnl"]:>+9.2%}' if pos["pnl"] is not None else '       —'
+        days_str = f'{pos["holding_days"]:>7}天' if pos["holding_days"] is not None else '      —'
+        entry_str = f'{pos["entry_price"]:>10.2f}' if pos["entry_price"] is not None else '        —'
+        entry_date_str = pos["entry_date"].strftime("%Y-%m-%d") if pos.get("entry_date") else '          —'
+        score_str = f'{pos["score"]:>8.2f}' if pos["score"] is not None else '        —'
+        flag = '▲' if pos["action"] == "调入" else ' '
+
+        print(f'  {pos["name"]:<8} {pos["code"]:>10} {pos["weight"]:>6.0%} {score_str} {entry_date_str:>12} {entry_str} {pos["current_price"]:>10.2f} {flag}{pos["action"]:>4} {days_str} {pnl_str}')
+
+    # 需调出的品种
+    if latest["exit_positions"]:
+        print()
+        print("  需调出:")
+        for pos in latest["exit_positions"]:
+            pnl_str = f'{pos["pnl"]:>+9.2%}' if pos["pnl"] is not None else '       —'
+            days_str = f'{pos["holding_days"]:>7}天' if pos["holding_days"] is not None else '      —'
+            entry_str = f'{pos["entry_price"]:>10.2f}' if pos["entry_price"] is not None else '        —'
+            entry_date_str = pos["entry_date"].strftime("%Y-%m-%d") if pos.get("entry_date") else '          —'
+            score_str = '        —'  # 调出品种无得分
+
+            print(f'    {pos["name"]:<8} {pos["code"]:>10} {pos["weight"]:>6.0%} {score_str} {entry_date_str:>12} {entry_str} {pos["current_price"]:>10.2f} ▼调出 {days_str} {pnl_str}')
+
+    print("=" * 120)
+
+
+def _extract_latest_positions(backtest_result: pd.DataFrame, code_list: list, code_name_map: dict, select_num: int) -> dict:
+    """提取最新持仓和下期调仓建议"""
+    last_date = backtest_result.index[-1]
+    last_row = backtest_result.iloc[-1]
+
+    # 当前持仓
+    current_signal = last_row["信号"]
+    if select_num == 1:
+        current_codes = [current_signal]
+    else:
+        current_codes = current_signal.split(",")
+
+    # 下期建议
+    score_cols = [f"得分_{code}" for code in code_list if f"得分_{code}" in backtest_result.columns]
+    scores = pd.to_numeric(last_row[score_cols], errors="coerce")
+    top_n = scores.nlargest(select_num)
+    next_codes = [c.replace("得分_", "") for c in top_n.index]
+
+    # 计算持仓信息
+    positions_info = []
+    weight = 1.0 / select_num
+
+    for code in next_codes:
+        name = code_name_map.get(code, code)
+        action = "维持" if code in current_codes else "调入"
+
+        # 获取当前价格和得分
+        current_price = last_row.get(code, 0)
+        score = scores.get(f"得分_{code}", None)
+
+        # 计算持仓信息（如果是维持的仓位）
+        entry_date = None
+        entry_price = None
+        holding_days = None
+        pnl = None
+
+        if action == "维持":
+            # 找到该标的最近一次连续持仓的起始日期
+            signal_series = backtest_result["信号"]
+            mask = signal_series == code if select_num == 1 else signal_series.str.contains(code, regex=False, na=False)
+
+            # 找到连续持仓段（从后往前找）
+            is_holding = mask.values
+            dates = backtest_result.index
+
+            # 从最后一天往前遍历，找到连续持仓的起始点
+            entry_date = None
+            for i in range(len(is_holding) - 1, -1, -1):
+                if is_holding[i]:
+                    entry_date = dates[i]
+                else:
+                    break
+
+            if entry_date is not None:
+                entry_price = backtest_result.loc[entry_date, code]
+                holding_days = (last_date - entry_date).days
+
+                if entry_price and entry_price != 0:
+                    pnl = current_price / entry_price - 1
+
+        positions_info.append({
+            "code": code,
+            "name": name,
+            "weight": weight,
+            "score": score,
+            "action": action,
+            "current_price": current_price,
+            "entry_date": entry_date,
+            "entry_price": entry_price,
+            "holding_days": holding_days,
+            "pnl": pnl,
+        })
+
+    # 需调出的品种信息
+    exit_positions = []
+    for code in current_codes:
+        if code not in next_codes:
+            name = code_name_map.get(code, code)
+            current_price = last_row.get(code, 0)
+
+            # 计算调出品种的持仓信息（最近一次连续持仓）
+            signal_series = backtest_result["信号"]
+            mask = signal_series == code if select_num == 1 else signal_series.str.contains(code, regex=False, na=False)
+
+            # 找到连续持仓段（从后往前找）
+            is_holding = mask.values
+            dates = backtest_result.index
+
+            entry_price = None
+            holding_days = None
+            pnl = None
+
+            # 从最后一天往前遍历，找到连续持仓的起始点
+            entry_date = None
+            for i in range(len(is_holding) - 1, -1, -1):
+                if is_holding[i]:
+                    entry_date = dates[i]
+                else:
+                    break
+
+            if entry_date is not None:
+                entry_price = backtest_result.loc[entry_date, code]
+                holding_days = (last_date - entry_date).days
+
+                if entry_price and entry_price != 0:
+                    pnl = current_price / entry_price - 1
+
+            exit_positions.append({
+                "code": code,
+                "name": name,
+                "weight": weight,
+                "score": None,  # 调出品种无得分
+                "action": "调出",
+                "current_price": current_price,
+                "entry_date": entry_date,
+                "entry_price": entry_price,
+                "holding_days": holding_days,
+                "pnl": pnl,
+            })
+
+    return {
+        "signal_date": last_date,
+        "current_codes": current_codes,
+        "next_codes": next_codes,
+        "positions": positions_info,
+        "exit_positions": exit_positions,
+    }
+
+
 def _plot_report_chart(
    backtest_result: pd.DataFrame,
    code_list: list,
@@ -114,10 +289,86 @@ def _plot_report_chart(
    strategy_nav = backtest_result["轮动策略净值"]
    benchmark_nav = backtest_result["基准净值"]

-    fig, axes = plt.subplots(3, 1, figsize=(14, 12))
+    # 提取最新调仓信息
+    latest = _extract_latest_positions(backtest_result, code_list, code_name_map, select_num)
+
+    # 计算表格行数
+    n_table_rows = len(latest["positions"]) + len(latest["exit_positions"])
+    table_height = max(1.5, 0.5 + n_table_rows * 0.28)
+
+    fig = plt.figure(figsize=(14, 10 + table_height + 8))
+    gs = fig.add_gridspec(4, 1, height_ratios=[table_height, 3, 1, 1.2], hspace=0.35)
+
+    # 面板0: 最新调仓信号表
+    ax0 = fig.add_subplot(gs[0])
+    ax0.axis("off")
+
+    signal_date_str = latest["signal_date"].strftime("%Y-%m-%d")
+    ax0.set_title(f"最新调仓信号  (数据截止: {signal_date_str}，下一交易日执行)", fontsize=14, fontweight="bold", loc="left", pad=15)
+
+    # 构建表格数据
+    table_data = []
+    col_labels = ["品种名称", "代码", "仓位", "得分", "进场日期", "进场价", "最新价", "操作", "持有天数", "盈亏"]
+
+    # 下期持仓（调入/维持）
+    for pos in latest["positions"]:
+        pnl_str = f'{pos["pnl"]:+.2%}' if pos["pnl"] is not None else "—"
+        days_str = f'{pos["holding_days"]}' if pos["holding_days"] is not None else "—"
+        entry_str = f'{pos["entry_price"]:.2f}' if pos["entry_price"] is not None else "—"
+        entry_date_str = pos["entry_date"].strftime("%m-%d") if pos.get("entry_date") else "—"
+        score_str = f'{pos["score"]:.2f}' if pos["score"] is not None else "—"
+
+        table_data.append([
+            pos["name"], pos["code"], f'{pos["weight"]:.0%}',
+            score_str, entry_date_str, entry_str, f'{pos["current_price"]:.2f}',
+            pos["action"], days_str, pnl_str
+        ])
+
+    # 需调出的品种
+    for pos in latest["exit_positions"]:
+        pnl_str = f'{pos["pnl"]:+.2%}' if pos["pnl"] is not None else "—"
+        days_str = f'{pos["holding_days"]}' if pos["holding_days"] is not None else "—"
+        entry_str = f'{pos["entry_price"]:.2f}' if pos["entry_price"] is not None else "—"
+        entry_date_str = pos["entry_date"].strftime("%m-%d") if pos.get("entry_date") else "—"
+        score_str = "—"  # 调出品种无得分
+
+        table_data.append([
+            pos["name"], pos["code"], f'{pos["weight"]:.0%}',
+            score_str, entry_date_str, entry_str, f'{pos["current_price"]:.2f}',
+            "调出", days_str, pnl_str
+        ])
+
+    if table_data:
+        table = ax0.table(
+            cellText=table_data,
+            colLabels=col_labels,
+            loc="upper center",
+            cellLoc="center",
+            colWidths=[0.08, 0.08, 0.05, 0.06, 0.06, 0.07, 0.07, 0.05, 0.06, 0.07],
+        )
+        table.auto_set_font_size(False)
+        table.set_fontsize(10)
+        table.scale(1, 2.0)
+
+        # 表头深色
+        for j in range(len(col_labels)):
+            table[0, j].set_facecolor("#2C3E50")
+            table[0, j].set_text_props(color="white", fontweight="bold")
+
+        # 数据行按操作着色
+        for i in range(len(table_data)):
+            action = table_data[i][3]
+            if action == "调入":
+                color = "#d4edda"  # 绿色
+            elif action == "调出":
+                color = "#f8d7da"  # 红色
+            else:
+                color = "#fff3cd"  # 黄色
+            for j in range(len(col_labels)):
+                table[i + 1, j].set_facecolor(color)

    # 面板1: 净值曲线
-    ax1 = axes[0]
+    ax1 = fig.add_subplot(gs[1])
    ax1.plot(strategy_nav.index, strategy_nav.values,
             label="轮动策略", linewidth=2, color="#E74C3C")
    ax1.plot(benchmark_nav.index, benchmark_nav.values,
@@ -139,7 +390,7 @@ def _plot_report_chart(
    ax1.set_yscale("log")

    # 面板2: 回撤曲线
-    ax2 = axes[1]
+    ax2 = fig.add_subplot(gs[2])
    cummax = strategy_nav.cummax()
    drawdown = (strategy_nav - cummax) / cummax
    ax2.fill_between(drawdown.index, drawdown.values, 0, alpha=0.5, color="#E74C3C")
@@ -148,7 +399,7 @@ def _plot_report_chart(
    ax2.grid(True, alpha=0.3)

    # 面板3: 持仓分布
-    ax3 = axes[2]
+    ax3 = fig.add_subplot(gs[3])
    signal_series = backtest_result["信号"]
    for i, code in enumerate(code_list):
        name = code_name_map.get(code, code)
@@ -168,7 +419,6 @@ def _plot_report_chart(
    ax3.set_yticklabels(ylabels, fontsize=7)
    ax3.grid(True, alpha=0.3)

-    plt.tight_layout()
    chart_path = f"{save_path}_chart.png"
    plt.savefig(chart_path, dpi=150, bbox_inches="tight")
    plt.close()
--- a/test.py
+++ b/test.py
@@ -1,100 +0,0 @@
-import pandas as pd  
-import numpy as np  
-import vectorbt as vbt  
-from numba import njit  
-import vectorbt as vbt
-import pandas as pd
-import numpy as np
-
-import pandas as pd
-from loguru import logger
-from chart import resample_data, QuantChart
-from db_config import DatabaseManager, DatabaseConfig
-from vectorbt.base.reshape_fns import to_2d_array  
-
-
-def get_kline(code: str) -> list:
-    """
-    获取所有指数代码
-    :return:
-    """
-    db_config = DatabaseConfig()
-    logger.info(f"数据库连接: {db_config.connection_string}")
-
-    db_manager = DatabaseManager(db_config)
-    sql = f"SELECT date as time, open, high, low, close, volume FROM public.index_kline where code='{code}' order by date;"
-    res = db_manager.execute_query(sql)
-    data_list = [dict(item) for item in res]
-    df = pd.DataFrame(data_list)
-    df["time"] = pd.to_datetime(df["time"])
-    num_cols = ["open", "high", "low", "close", "volume"]
-    for col in num_cols:
-        if col in df.columns:
-            df[col] = pd.to_numeric(df[col], errors="coerce").astype(float)
-    return df
-
-symbol = "399998"
-timeframe = "1D"
-
-df = get_kline(code=symbol)
-df = resample_data(df, timeframe)
-df.rename(columns={'time': 'date'}, inplace=True)
-print(df.head())
-if 'date' in df.columns:  
-    df = df.set_index('date')  
-price = df['close']  
-  
-# 2. 计算90天滚动波动率(年化)  
-returns = price.pct_change()  
-volatility_90d = returns.rolling(window=90, min_periods=90).std() * np.sqrt(365)  
-  
-# 3. 计算波动率倒数作为权重  
-inv_vol = 1 / volatility_90d  
-# 标准化权重(可选,使其更易解释)  
-inv_vol_normalized = inv_vol / inv_vol.rolling(window=252).mean()  
-  
-# 4. 创建每周重新平衡的信号  
-# 获取每周最后一个交易日  
-weekly_rebalance = pd.Series(False, index=price.index)  
-weekly_last_days = price.resample('W').last().index  
-for date in weekly_last_days:  
-    # 找到最接近的交易日  
-    idx = price.index.get_indexer([date], method='ffill')[0]  
-    if idx >= 0:  
-        weekly_rebalance.iloc[idx] = True  
-  
-# 5. 定义订单函数  
-@njit  
-def order_func_nb(c, inv_vol_arr, rebalance_arr):  
-    # 获取当前的波动率倒数权重  
-    inv_vol_now = vbt.nb.flex_select_auto_nb(inv_vol_arr, c.i, c.col, False)  
-    rebalance_now = vbt.nb.flex_select_auto_nb(rebalance_arr, c.i, c.col, False)  
-      
-    # 只在重新平衡日调整仓位  
-    if not rebalance_now or np.isnan(inv_vol_now):  
-        return vbt.nb.order_nothing_nb()  
-      
-    # 目标仓位 = 总价值 * 波动率倒数权重  
-    # 这里使用 TargetPercent 类型,权重越高仓位越大  
-    target_percent = min(inv_vol_now, 1.0)  # 限制最大100%仓位  
-      
-    return vbt.nb.order_nb(  
-        size=target_percent,  
-        size_type=vbt.SizeType.TargetPercent,  
-        direction=vbt.Direction.LongOnly  
-    )  
-  
-# 6. 运行回测  
-pf = vbt.Portfolio.from_order_func(  
-    price,  
-    order_func_nb,  
-    to_2d_array(inv_vol_normalized),  
-    to_2d_array(weekly_rebalance),  
-    init_cash=100,  
-    freq='1D'  
-)  
-  
-# 7. 查看结果  
-print(pf.stats())  
-print(f"\n波动率倒数策略 vs 买入持有:")  
-print(f"总收益率: {pf.total_return():.2%}")