feat(factors): 添加时间序列函数及智能路由

- 新增 8 个国泰君安 191 兼容的时间序列函数：ts_sma, ts_wma, ts_decay_linear, ts_argmax, ts_argmin, ts_count, ts_prod, ts_sumac
- max_/min_ 函数智能路由：正整数参数自动调用 ts_max/ts_min 实现滚动窗口逻辑

tests/test_new_ts_functions.py

@@ -0,0 +1,130 @@
+"""测试新增的时间序列函数和智能分发逻辑。"""
+
+import pytest
+import polars as pl
+import numpy as np
+from src.factors.dsl import Symbol, FunctionNode
+from src.factors.translator import PolarsTranslator
+
+
+def test_ts_sma_translate():
+    """测试 ts_sma 翻译正确。"""
+    close = Symbol("close")
+    expr = FunctionNode("ts_sma", close, 10, 5)
+    translator = PolarsTranslator()
+    result = translator.translate(expr)
+    assert isinstance(result, pl.Expr)
+
+
+def test_ts_wma_translate():
+    """测试 ts_wma 翻译正确。"""
+    close = Symbol("close")
+    expr = FunctionNode("ts_wma", close, 20)
+    translator = PolarsTranslator()
+    result = translator.translate(expr)
+    assert isinstance(result, pl.Expr)
+
+
+def test_ts_sumac_translate():
+    """测试 ts_sumac 翻译正确。"""
+    close = Symbol("close")
+    expr = FunctionNode("ts_sumac", close)
+    translator = PolarsTranslator()
+    result = translator.translate(expr)
+    assert isinstance(result, pl.Expr)
+
+
+def test_max_intelligent_dispatch():
+    """测试 max_ 智能分发: int -> ts_max，其他 -> element-wise max。"""
+    from src.factors.api import max_, close
+
+    # 正整数 -> ts_max
+    result = max_(close, 20)
+    assert result.func_name == "ts_max"
+
+    # 零或负数 -> element-wise max
+    result = max_(close, 0)
+    assert result.func_name == "max"
+
+    result = max_(close, -1)
+    assert result.func_name == "max"
+
+    # 浮点数 -> element-wise max
+    result = max_(close, 10.5)
+    assert result.func_name == "max"
+
+
+def test_min_intelligent_dispatch():
+    """测试 min_ 智能分发: int -> ts_min，其他 -> element-wise min。"""
+    from src.factors.api import min_, close
+
+    # 正整数 -> ts_min
+    result = min_(close, 20)
+    assert result.func_name == "ts_min"
+
+    # 零或负数 -> element-wise min
+    result = min_(close, 0)
+    assert result.func_name == "min"
+
+
+def create_test_data() -> pl.DataFrame:
+    """创建测试数据。"""
+    np.random.seed(42)
+    n = 100
+    return pl.DataFrame(
+        {
+            "ts_code": ["000001.SZ"] * n,
+            "trade_date": list(range(20240101, 20240101 + n)),
+            "close": np.random.randn(n).cumsum() + 100,
+        }
+    )
+
+
+def test_ts_sma_computation():
+    """测试 ts_sma 计算与原生 Polars 一致。"""
+    df = create_test_data()
+    translator = PolarsTranslator()
+
+    # 翻译因子
+    close = Symbol("close")
+    expr_node = FunctionNode("ts_sma", close, 10, 5)
+    expr = translator.translate(expr_node)
+
+    # 使用翻译后的表达式计算
+    result = df.select(["ts_code", "trade_date", "close", expr.alias("ts_sma_result")])
+
+    # 原生 Polars 计算
+    native = df.with_columns(
+        [pl.col("close").ewm_mean(alpha=5 / 10, adjust=False).alias("native_sma")]
+    )
+
+    # 对比结果
+    assert np.allclose(
+        result["ts_sma_result"].to_numpy()[9:],
+        native["native_sma"].to_numpy()[9:],
+        equal_nan=True,
+    )
+
+
+def test_ts_sumac_computation():
+    """测试 ts_sumac 计算与原生 Polars 一致。"""
+    df = create_test_data()
+    translator = PolarsTranslator()
+
+    close = Symbol("close")
+    expr_node = FunctionNode("ts_sumac", close)
+    expr = translator.translate(expr_node)
+
+    result = df.select(
+        ["ts_code", "trade_date", "close", expr.alias("ts_sumac_result")]
+    )
+
+    native = df.with_columns([pl.col("close").cum_sum().alias("native_sumac")])
+
+    assert np.allclose(
+        result["ts_sumac_result"].to_numpy(), native["native_sumac"].to_numpy()
+    )
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])