fix(scheduler): 修复setup_schedule未传递no_detail/no_report参数的问题

setup_schedule() 在定时模式下未将 --no-detail 和 --no-report 参数传递给 daily_task，导致定时任务始终生成 detail JSON
chore(config): 启用钉钉机器人群2配置
2026-06-09 00:07:01 +08:00 · 2026-06-08 23:43:20 +08:00 · 2026-06-08 23:07:37 +08:00 · 2026-06-08 23:05:39 +08:00 · 2026-06-08 23:05:26 +08:00
5 changed files with 252 additions and 11 deletions
--- a/.env
+++ b/.env
@@ -7,9 +7,9 @@ TUSHARE_TOKEN=725296d48ec74da89422e8be76bd770895a4bf93b4998aca4b898db6
 DINGTALK_WEBHOOK_1=https://oapi.dingtalk.com/robot/send?access_token=fb70c1561d8beba94b4f11568f4bb15e3ae07ccbdc8ac19676434a9d1cd17546
 DINGTALK_SECRET_1=SEC1ae7cd2f1a6f9da3611af37da3e7d954c1e8533fc073c6c8cc5e5af3b6e5926b

-# 钉钉机器人配置 - 群2
-# DINGTALK_WEBHOOK_2=https://oapi.dingtalk.com/robot/send?access_token=87c7abfcdd69b699c32da4e4f5981cd2ca6b0445474fc6ffb36f2ed0f6262fbb
-# DINGTALK_SECRET_2=SECf3d6b43f2f8a87ab91feffd052e71ec314fbf57a1842e483fe07af3c0a0e5aa6
+钉钉机器人配置 - 群2
+DINGTALK_WEBHOOK_2=https://oapi.dingtalk.com/robot/send?access_token=87c7abfcdd69b699c32da4e4f5981cd2ca6b0445474fc6ffb36f2ed0f6262fbb
+DINGTALK_SECRET_2=SECf3d6b43f2f8a87ab91feffd052e71ec314fbf57a1842e483fe07af3c0a0e5aa6



--- a/docs/strategy_summaries/strategy_summary_20260607_kelly_weight.md
+++ b/docs/strategy_summaries/strategy_summary_20260607_kelly_weight.md
@@ -0,0 +1,213 @@
+# Kelly 仓位权重模式实现总结
+
+**日期**: 2026-06-07  
+**Commit**: `8b7bcf2`  
+**相关文件**: 
+- `rotation/simple_rotation.py`
+- `rotation/config_loader.py`
+- `rotation/config_simple.yaml`
+
+---
+
+## 1. 背景与动机
+
+### 1.1 问题提出
+原有仓位管理支持两种模式：
+- **equal**: 等权分配 (1/N)
+- **rank**: 按排名三角权重 (第1名50%, 第2名33%, 第3名17%)
+
+用户询问能否使用 Kelly 准则进行仓位分配。
+
+### 1.2 Kelly 准则简介
+经典 Kelly 公式: **f* = W - (1-W)/R**
+- W = 胜率（历史盈利交易占比）
+- R = 盈亏比（平均盈利/平均亏损）
+
+### 1.3 经典 Kelly 的挑战
+
+| 问题 | 说明 |
+|------|------|
+| 样本量不足 | 每个标的被持有的天数有限，统计胜率/盈亏比不稳定 |
+| 非平稳性 | 市场环境变化导致历史统计不代表未来 |
+| 极端值敏感 | 一次大亏会剧烈改变 Kelly 比例 |
+| 需要 expanding window | 回测中每天用截止当天的历史来估计，计算量大 |
+
+---
+
+## 2. 解决方案：Score-Proportional Kelly 近似
+
+### 2.1 核心思路
+利用当前动量分数 `weighted_momentum_score = annualized_return × R²` 作为 edge 代理，构造 Kelly 近似：
+
+```
+w_i = max(score_i, 0) / Σ max(score_j, 0)
+```
+
+### 2.2 设计优势
+- **无需额外历史统计**：每天从截面数据直接计算
+- **天然支持 expanding window**：每天用最新数据
+- **负分自动排除**：Kelly 原则 - 不下注负期望
+- **可插拔设计**：与现有 equal/rank 模式统一接口
+
+### 2.3 公式推导
+动量分数 `score = annualized_return × R²` 包含：
+- **annualized_return**: 趋势方向和强度
+- **R²**: 趋势质量（信噪比）
+
+正 score 意味着正期望，Kelly 建议按 edge 比例下注。归一化后得到仓位权重。
+
+---
+
+## 3. 代码实现
+
+### 3.1 枚举扩展 (config_loader.py)
+```python
+class WeightType(str, Enum):
+    """仓位加权模式"""
+    EQUAL = "equal"         # 等权
+    RANK = "rank"           # 按排名加权
+    KELLY = "kelly"         # Kelly准则近似
+```
+
+### 3.2 核心函数 (simple_rotation.py)
+```python
+def compute_position_weights(
+    ranked_holdings: List[str],
+    weight_type: str = 'equal',
+    scores: Dict[str, float] = None,  # 新增参数
+) -> Dict[str, float]:
+    """
+    Schemes:
+        equal:  each slot = 1/N, duplicates summed.
+        rank:   slot i (0-indexed) = (N-i) / triangular(N), duplicates summed.
+        kelly:  w_i = max(score_i, 0) / sum(max(score_j, 0)).
+                Score-proportional weighting as Kelly criterion proxy.
+                Negative scores excluded (Kelly: don't bet on negative edge).
+    """
+    N = len(ranked_holdings)
+    if N == 0:
+        return {}
+
+    weights: Dict[str, float] = {}
+
+    if weight_type == 'kelly':
+        if not scores:
+            raise ValueError("Kelly weighting requires 'scores' parameter")
+        # Kelly proxy: weight proportional to positive scores
+        positive_scores = {c: max(scores.get(c, 0.0), 0.0) for c in set(ranked_holdings)}
+        total = sum(positive_scores.values())
+        if total <= 0:
+            # Fallback to equal if all scores non-positive
+            w = 1.0 / len(positive_scores)
+            for code in positive_scores:
+                weights[code] = w
+        else:
+            for code in ranked_holdings:
+                w = positive_scores.get(code, 0.0) / total
+                weights[code] = weights.get(code, 0.0) + w
+
+    elif weight_type == 'rank':
+        triangular = N * (N + 1) / 2
+        for i, code in enumerate(ranked_holdings):
+            w = (N - i) / triangular
+            weights[code] = weights.get(code, 0.0) + w
+    else:
+        # equal (default)
+        w = 1.0 / N
+        for code in ranked_holdings:
+            weights[code] = weights.get(code, 0.0) + w
+
+    return weights
+```
+
+### 3.3 调用点修改
+```python
+# _generate_signals 中传递 scores
+self._pending_weights = compute_position_weights(
+    ranked_holdings, self.weight_type, scores=factors,
+)
+```
+
+### 3.4 配置使用 (config_simple.yaml)
+```yaml
+rotation:
+  diversified: true
+  select_num: 3
+  weight: kelly  # 可选: equal, rank, kelly
+```
+
+---
+
+## 4. 回测结果对比
+
+**回测区间**: 2020-01-10 ~ 2026-06-08 (1550 交易日)
+
+| 指标 | equal | rank | **kelly** |
+|------|-------|------|-----------|
+| 累计收益 | 204.97% | 255.45% | **405.23%** |
+| 年化收益 | 19.88% | 22.90% | **30.13%** |
+| 最大回撤 | **-14.65%** | -16.27% | -20.44% |
+| 夏普比率 | 1.13 | 1.12 | **1.15** |
+| Calmar比率 | 1.36 | 1.41 | **1.47** |
+| 日胜率 | 54.07% | 53.75% | **54.10%** |
+| 调仓次数 | 392 | 392 | 392 |
+
+### 4.1 结果分析
+
+**Kelly 模式特点**:
+- **收益最高**: 按动量分数比例分配权重，强势标的获得更大仓位
+- **夏普最高**: 风险调整后收益最优
+- **Calmar 最高**: 收益/回撤比最优
+- **回撤较大**: 集中度更高导致波动更大
+
+**三种模式定位**:
+- **equal**: 保守型，分散风险，适合风险厌恶
+- **rank**: 平衡型，按排名阶梯分配
+- **kelly**: 进攻型，按 edge 比例集中配置
+
+### 4.2 为什么日胜率会变化？
+
+虽然信号生成（调仓日期、持仓标的）完全相同，但仓位权重影响每日组合收益：
+
+```
+daily_return = Σ (weight_i × return_i)
+```
+
+当某天收益接近 0 时，权重分配的变化可能让它在正/负之间翻转。例如：
+- 排名第1的标的大跌
+- rank 模式给 50% 权重 → 组合收益可能变负
+- equal 模式只给 33% → 影响较小，可能仍为正
+
+---
+
+## 5. 设计原则
+
+### 5.1 可插拔架构
+- 统一函数签名，通过 `weight_type` 参数切换
+- 新增模式只需添加分支，不影响现有逻辑
+- `scores` 参数可选，仅 kelly 模式需要
+
+### 5.2 防御性设计
+- Kelly 模式校验 `scores` 参数
+- 全负分时自动 fallback 到等权
+- 与 bond fill 机制兼容（债券 score 通常为负）
+
+### 5.3 配置驱动
+- 通过 YAML 配置切换，无需修改代码
+- 支持环境变量覆盖
+- 与现有配置体系一致
+
+---
+
+## 6. 后续优化方向
+
+1. **Half-Kelly**: 使用 f*/2 降低波动
+2. **动态 Kelly**: 根据市场状态调整 Kelly 系数
+3. **风险预算**: 结合波动率进行风险平价分配
+4. **多因子 Kelly**: 综合多个因子 score 计算 edge
+
+---
+
+## 7. 结论
+
+Kelly 仓位模式通过 score-proportional 近似，在保持可插拔架构的同时，实现了最优风险调整后收益。对于追求收益最大化的场景，kelly 模式是首选；对于风险厌恶场景，equal 模式更稳健。
--- a/rotation/config_loader.py
+++ b/rotation/config_loader.py
@@ -53,6 +53,7 @@ class WeightType(str, Enum):
    """仓位加权模式"""
    EQUAL = "equal"         # 等权
    RANK = "rank"           # 按排名加权 (slot i gets (N-i)/triangular(N))
+    KELLY = "kelly"         # Kelly准则近似 (score-proportional weighting)


 class DataSourceType(str, Enum):
--- a/rotation/daily_scheduler.py
+++ b/rotation/daily_scheduler.py
@@ -256,7 +256,9 @@ def send_report_to_dingtalk(chart_path: str, summary_text: str = "", title: str
 def setup_schedule(target_time: str = "15:30",
                   config_path: str = "strategies/rotation/config.yaml",
                   strategy: str = "all",
-                   simple_config: str = None):
+                   simple_config: str = None,
+                   no_detail: bool = False,
+                   no_report: bool = False):
    """
    设置定时任务
    
@@ -265,8 +267,10 @@ def setup_schedule(target_time: str = "15:30",
        config_path: legacy策略配置文件路径
        strategy: 策略选择 - "simple" / "legacy" / "all"
        simple_config: simple_rotation 配置文件路径
+        no_detail: 跳过 detail JSON 导出
+        no_report: 跳过 report PNG 生成
    """
-    logger.info(f"设置定时任务: 每天 {target_time} 执行 (策略: {strategy})")
+    logger.info(f"设置定时任务: 每天 {target_time} 执行 (策略: {strategy}, no_detail={no_detail}, no_report={no_report})")
    
    # 清除已有任务
    schedule.clear()
@@ -276,7 +280,9 @@ def setup_schedule(target_time: str = "15:30",
        daily_task,
        config_path=config_path,
        strategy=strategy,
-        simple_config=simple_config
+        simple_config=simple_config,
+        no_detail=no_detail,
+        no_report=no_report
    )
    
    logger.info("定时任务设置完成，等待执行...")
@@ -407,14 +413,14 @@ def main():
        daily_task(args.config, args.strategy, args.simple_config, args.no_detail, args.no_report)
    elif args.no_daemon:
        # 非后台模式：执行一次后进入定时循环
-        setup_schedule(args.time, args.config, args.strategy, args.simple_config)
+        setup_schedule(args.time, args.config, args.strategy, args.simple_config, args.no_detail, args.no_report)
        logger.info("执行一次测试...")
        daily_task(args.config, args.strategy, args.simple_config, args.no_detail, args.no_report)
        logger.info("测试完成，启动定时任务循环（Ctrl+C 停止）...")
        run_scheduler_loop()
    else:
        # 默认：后台定时模式
-        setup_schedule(args.time, args.config, args.strategy, args.simple_config)
+        setup_schedule(args.time, args.config, args.strategy, args.simple_config, args.no_detail, args.no_report)
        run_scheduler_loop()


--- a/rotation/simple_rotation.py
+++ b/rotation/simple_rotation.py
@@ -171,13 +171,15 @@ def momentum_score(prices: np.ndarray) -> float:
 def compute_position_weights(
    ranked_holdings: List[str],
    weight_type: str = 'equal',
+    scores: Dict[str, float] = None,
 ) -> Dict[str, float]:
    """Compute position weights from ranked slot list.

    Args:
        ranked_holdings: Ordered list of signal codes, best first.
                         May contain duplicates (e.g. bond fills).
-        weight_type: 'equal' or 'rank'.
+        weight_type: 'equal', 'rank', or 'kelly'.
+        scores: Required for 'kelly'. Dict mapping code -> momentum score.

    Returns:
        Dict mapping each unique code to its total weight (sum of slots).
@@ -186,6 +188,9 @@ def compute_position_weights(
        equal:  each slot = 1/N, duplicates summed.
        rank:   slot i (0-indexed) = (N-i) / triangular(N), duplicates summed.
                For N=3: [3/6, 2/6, 1/6] = [50%, 33%, 17%].
+        kelly:  w_i = max(score_i, 0) / sum(max(score_j, 0)).
+                Score-proportional weighting as Kelly criterion proxy.
+                Negative scores excluded (Kelly: don't bet on negative edge).
    """
    N = len(ranked_holdings)
    if N == 0:
@@ -193,7 +198,23 @@ def compute_position_weights(

    weights: Dict[str, float] = {}

-    if weight_type == 'rank':
+    if weight_type == 'kelly':
+        if not scores:
+            raise ValueError("Kelly weighting requires 'scores' parameter")
+        # Kelly proxy: weight proportional to positive scores
+        positive_scores = {c: max(scores.get(c, 0.0), 0.0) for c in set(ranked_holdings)}
+        total = sum(positive_scores.values())
+        if total <= 0:
+            # Fallback to equal if all scores non-positive
+            w = 1.0 / len(positive_scores)
+            for code in positive_scores:
+                weights[code] = w
+        else:
+            for code in ranked_holdings:
+                w = positive_scores.get(code, 0.0) / total
+                weights[code] = weights.get(code, 0.0) + w
+
+    elif weight_type == 'rank':
        triangular = N * (N + 1) / 2
        for i, code in enumerate(ranked_holdings):
            w = (N - i) / triangular
@@ -587,7 +608,7 @@ class SimpleRotationStrategy:
        # These are *pending* weights; the caller (run) locks them in
        # only when an actual rebalance occurs.
        self._pending_weights = compute_position_weights(
-            ranked_holdings, self.weight_type,
+            ranked_holdings, self.weight_type, scores=factors,
        )

        return sorted(ranked_holdings), factors, bond_momentum
Author	SHA1	Message	Date
aszerW	5c4aeb75d2	fix(scheduler): 修复setup_schedule未传递no_detail/no_report参数的问题 setup_schedule() 在定时模式下未将 --no-detail 和 --no-report 参数传递给 daily_task，导致定时任务始终生成 detail JSON	2026-06-09 00:07:01 +08:00
aszerW	710f3d9d68	chore(config): 启用钉钉机器人群2配置	2026-06-08 23:43:20 +08:00
aszerW	0c19e45300	chore(config): 恢复 weight 为 rank 模式	2026-06-08 23:07:37 +08:00
aszerW	e4bb570e5f	docs: 更新 kelly 文档 commit hash	2026-06-08 23:05:39 +08:00
aszerW	8b7bcf206a	feat(weight): 实现 Kelly 仓位权重模式 - config_loader.py: WeightType 枚举新增 KELLY - simple_rotation.py: compute_position_weights 新增 kelly 分支 - 公式: w_i = max(score_i, 0) / sum(max(score_j, 0)) - 负分自动排除 (Kelly: 不下注负期望) - 全负分时 fallback 到等权 - _generate_signals 传递 scores 给 kelly 模式 - config_simple.yaml: weight 改为 kelly - 新增策略总结文档: kelly_weight.md 回测对比 (2020-2026): - equal: 年化 19.88%, 夏普 1.13, 回撤 -14.65% - rank: 年化 22.90%, 夏普 1.12, 回撤 -16.27% - kelly: 年化 30.13%, 夏普 1.15, 回撤 -20.44%	2026-06-08 23:05:26 +08:00