factor优化（暂存版）

.env

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+PYTHONPATH=${PYTHONPATH}:${workspaceFolder}

test_operator_optimization.py

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+"""
+测试operator框架优化后的功能
+验证因子列名设置和返回数据结构
+"""
+
+import polars as pl
+import numpy as np
+from main.factor.operator_framework import StockWiseOperator, DateWiseOperator, OperatorConfig
+
+
+class TestStockFactorOperator(StockWiseOperator):
+    """测试股票因子算子"""
+    
+    def __init__(self, window: int = 5):
+        config = OperatorConfig(
+            name=f"test_stock_factor_{window}",
+            description=f"测试股票因子{window}日",
+            required_columns=['close'],
+            output_columns=[f'test_stock_factor_{window}'],
+            parameters={'window': window}
+        )
+        super().__init__(config)
+        self.window = window
+    
+    def apply_stock(self, stock_df: pl.DataFrame, **kwargs) -> pl.DataFrame:
+        """计算测试因子 - 简单移动平均"""
+        # 计算移动平均线
+        ma = stock_df['close'].rolling_mean(window_size=self.window)
+        
+        # 返回原始df加上新列
+        return stock_df.with_columns(
+            ma.alias(f'test_stock_factor_{self.window}')
+        )
+
+
+class TestDateFactorOperator(DateWiseOperator):
+    """测试日期因子算子"""
+    
+    def __init__(self):
+        config = OperatorConfig(
+            name="test_date_factor",
+            description="测试日期因子",
+            required_columns=['close'],
+            output_columns=['test_date_factor'],
+            parameters={}
+        )
+        super().__init__(config)
+    
+    def apply_date(self, date_df: pl.DataFrame, **kwargs) -> pl.DataFrame:
+        """计算测试因子 - 当日涨跌幅排名"""
+        # 计算当日涨跌幅
+        pct_chg = date_df['close'].pct_change()
+        
+        # 返回原始df加上新列
+        return date_df.with_columns(
+            pct_chg.alias('test_date_factor')
+        )
+
+
+def create_test_data():
+    """创建测试数据"""
+    # 创建模拟股票数据
+    dates = pl.date_range(pl.date(2023, 1, 1), pl.date(2023, 12, 31), "1d", eager=True).to_list()
+    stocks = ['000001.SZ', '000002.SZ', '000003.SZ']
+    
+    data = []
+    for stock in stocks:
+        for date in dates:
+            # 模拟价格数据
+            base_price = 10 + np.random.randn() * 2
+            data.append({
+                'ts_code': stock,
+                'trade_date': date,
+                'close': base_price + np.random.randn() * 0.5,
+                'vol': np.random.randint(100000, 1000000)
+            })
+    
+    return pl.DataFrame(data)
+
+
+def test_stock_wise_operator():
+    """测试股票切面算子"""
+    print("=== 测试股票切面算子 ===")
+    
+    # 创建测试数据
+    df = create_test_data()
+    print(f"原始数据形状: {df.shape}")
+    print(f"原始数据列: {df.columns}")
+    
+    # 创建算子
+    operator = TestStockFactorOperator(window=10)
+    
+    # 应用算子
+    result_df = operator(df)
+    
+    print(f"结果数据形状: {result_df.shape}")
+    print(f"结果数据列: {result_df.columns}")
+    
+    # 检查新列是否存在
+    new_column = 'test_stock_factor_10'
+    if new_column in result_df.columns:
+        print(f"✓ 新列 '{new_column}' 成功添加")
+        
+        # 检查数据完整性
+        original_cols = ['ts_code', 'trade_date', 'close', 'vol']
+        for col in original_cols:
+            if col in result_df.columns:
+                print(f"✓ 原始列 '{col}' 保留")
+            else:
+                print(f"✗ 原始列 '{col}' 丢失")
+    else:
+        print(f"✗ 新列 '{new_column}' 未找到")
+    
+    # 检查数据排序
+    sample = result_df.filter(pl.col('ts_code') == '000001.SZ').select(['trade_date', 'close', new_column]).head(15)
+    print("\n样本数据:")
+    print(sample)
+
+
+def test_date_wise_operator():
+    """测试日期切面算子"""
+    print("\n=== 测试日期切面算子 ===")
+    
+    # 创建测试数据
+    df = create_test_data()
+    print(f"原始数据形状: {df.shape}")
+    print(f"原始数据列: {df.columns}")
+    
+    # 创建算子
+    operator = TestDateFactorOperator()
+    
+    # 应用算子
+    result_df = operator(df)
+    
+    print(f"结果数据形状: {result_df.shape}")
+    print(f"结果数据列: {result_df.columns}")
+    
+    # 检查新列是否存在
+    new_column = 'test_date_factor'
+    if new_column in result_df.columns:
+        print(f"✓ 新列 '{new_column}' 成功添加")
+    else:
+        print(f"✗ 新列 '{new_column}' 未找到")
+    
+    # 检查数据排序
+    sample = result_df.filter(pl.col('trade_date') == pl.date(2023, 1, 10)).select(['ts_code', 'close', new_column])
+    print("\n样本数据 (2023-01-10):")
+    print(sample)
+
+
+if __name__ == "__main__":
+    test_stock_wise_operator()
+    test_date_wise_operator()