diff --git a/czsc/__init__.py b/czsc/__init__.py
index 89a234871..2c63d06fb 100644
--- a/czsc/__init__.py
+++ b/czsc/__init__.py
@@ -215,6 +215,9 @@
     cal_symbols_factor,
     weights_simple_ensemble,
     unify_weights,
+    sma_long_bear,
+    dif_long_bear,
+    tsf_type,
 )
 
 
diff --git a/czsc/eda.py b/czsc/eda.py
index 2c544113b..32264ccac 100644
--- a/czsc/eda.py
+++ b/czsc/eda.py
@@ -416,3 +416,92 @@ def unify_weights(dfw: pd.DataFrame, **kwargs):
     return dfw
 
 
+def sma_long_bear(df: pd.DataFrame, **kwargs):
+    """均线牛熊分界指标过滤持仓，close 在长期均线上方为牛市，下方为熊市
+
+    牛市只做多，熊市只做空。
+
+    :param df: DataFrame, 必须包含 dt, close, symbol, weight 列
+    :return: DataFrame
+    """
+    assert df["symbol"].nunique() == 1, "数据中包含多个品种，必须单品种"
+    assert df["dt"].is_monotonic_increasing, "数据未按日期排序，必须升序排列"
+    assert df["dt"].is_unique, "数据中存在重复dt，必须唯一"
+
+    window = kwargs.get("window", 200)
+
+    if kwargs.get("copy", True):
+        df = df.copy()
+
+    df['SMA_LB'] = df['close'].rolling(window).mean()
+    df['raw_weight'] = df['weight']
+    df['weight'] = np.where(np.sign(df['close'] - df['SMA_LB']) == np.sign(df['weight']), df['weight'], 0)
+    return df
+
+
+def dif_long_bear(df: pd.DataFrame, **kwargs):
+    """DIF牛熊分界指标过滤持仓，DIF 在0上方为牛市，下方为熊市
+
+    牛市只做多，熊市只做空。
+
+    :param df: DataFrame, 必须包含 dt, close, symbol, weight 列
+    :return: DataFrame
+    """
+    from czsc.utils.ta import MACD
+
+    assert df["symbol"].nunique() == 1, "数据中包含多个品种，必须单品种"
+    assert df["dt"].is_monotonic_increasing, "数据未按日期排序，必须升序排列"
+    assert df["dt"].is_unique, "数据中存在重复dt，必须唯一"
+
+    if kwargs.get("copy", True):
+        df = df.copy()
+
+    df['DIF_LB'], _, _ = MACD(df['close'])
+    df['raw_weight'] = df['weight']
+    df['weight'] = np.where(np.sign(df['DIF_LB']) == np.sign(df['weight']), df['weight'], 0)
+    return df
+
+
+def tsf_type(df: pd.DataFrame, factor, n=5, **kwargs):
+    """时序因子的类型定性分析
+
+    tsf 是 time series factor 的缩写，时序因子的类型定性分析，是指对某个时序因子进行分层，然后计算每个分层的平均收益，
+
+    :param df: pd.DataFrame, 必须包含 dt, symbol, factor, price 列，其中 dt 为日期，symbol 为标的代码，factor 为因子值
+    :param factor: str, 因子列名
+    :param n: int, 分层数量
+    :param kwargs:
+
+        - window: int, 窗口大小，默认为600
+        - min_periods: int, 最小样本数量，默认为300
+
+    :return: str, 返回分层收益排序（从大到小）结果，例如：第01层->第02层->第03层->第04层->第05层
+    """
+    window = kwargs.get("window", 600)
+    min_periods = kwargs.get("min_periods", 300)
+    target = kwargs.get("target", "n1b")
+    assert target in df.columns, f"数据中不存在 {target} 列"
+    assert factor in df.columns, f"数据中不存在 {factor} 列"
+
+    if target == 'n1b' and 'n1b' not in df.columns:
+        from czsc.utils.trade import update_nxb
+        df = update_nxb(df, nseq=(1,))
+
+    rows = []
+    for symbol, dfg in df.groupby("symbol"):
+        dfg = dfg.copy().reset_index(drop=True)
+        dfg = rolling_layers(dfg, factor, n=n, window=window, min_periods=min_periods)
+        rows.append(dfg)
+
+    df = pd.concat(rows, ignore_index=True)
+    layers = [x for x in df[f"{factor}分层"].unique() if x != "第00层" and str(x).endswith("层")]
+
+    # 计算每个分层的平均收益
+    layer_returns = {}
+    for layer in layers:
+        dfg = df[df[f"{factor}分层"] == layer].copy()
+        dfg = dfg.groupby("dt")[target].mean().reset_index()
+        layer_returns[layer] = dfg[target].sum()
+
+    sorted_layers = sorted(layer_returns.items(), key=lambda x: x[1], reverse=True)
+    return "->".join([f"{x[0]}" for x in sorted_layers])