diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml
index 92d00c571..2872d9dd9 100644
--- a/.github/workflows/pythonpackage.yml
+++ b/.github/workflows/pythonpackage.yml
@@ -5,7 +5,7 @@ name: Python package
 
 on:
   push:
-    branches: [ master, V0.9.41 ]
+    branches: [ master, V0.9.42 ]
   pull_request:
     branches: [ master ]
 
diff --git a/czsc/__init__.py b/czsc/__init__.py
index 204cd49aa..98f5dae6c 100644
--- a/czsc/__init__.py
+++ b/czsc/__init__.py
@@ -98,6 +98,7 @@
 # streamlit 量化分析组件
 from czsc.utils.st_components import (
     show_daily_return,
+    show_yearly_stats,
     show_splited_daily,
     show_monthly_return,
     show_correlation,
@@ -126,10 +127,14 @@
     find_most_similarity,
 )
 
-__version__ = "0.9.41"
+from czsc.features.utils import (
+    is_event_feature,
+)
+
+__version__ = "0.9.42"
 __author__ = "zengbin93"
 __email__ = "zeng_bin8888@163.com"
-__date__ = "20240114"
+__date__ = "20240121"
 
 
 def welcome():
diff --git a/czsc/connectors/cooperation.py b/czsc/connectors/cooperation.py
index 84e924715..0c1120cb8 100644
--- a/czsc/connectors/cooperation.py
+++ b/czsc/connectors/cooperation.py
@@ -11,6 +11,7 @@
 import czsc
 import pandas as pd
 from tqdm import tqdm
+from loguru import logger
 from datetime import datetime
 from czsc import RawBar, Freq
 
@@ -55,14 +56,27 @@ def get_symbols(name, **kwargs):
     :return:
     """
     if name == "股票":
-        data = dc.stock_basic(nobj=1, status=1)
-        return data['code'].tolist()
+        df = dc.stock_basic(nobj=1, status=1)
+        symbols = [f"{row['code']}#STOCK" for _, row in df.iterrows()]
+        return symbols
 
     if name == "ETF":
-        raise NotImplementedError
+        df = dc.etf_basic(v=2, fields='code,name')
+        dfk = dc.pro_bar(trade_date="2023-11-17", asset="e", v=2)
+        df = df[df['code'].isin(dfk['code'])].reset_index(drop=True)
+        symbols = [f"{row['code']}#ETF" for _, row in df.iterrows()]
+        return symbols
 
     if name == "A股指数":
-        raise NotImplementedError
+        # 指数 https://s0cqcxuy3p.feishu.cn/wiki/KuSAweAAhicvsGk9VPTc1ZWKnAd
+        df = dc.index_basic(v=2, market='SSE,SZSE')
+        symbols = [f"{row['code']}#INDEX" for _, row in df.iterrows()]
+        return symbols
+
+    if name == "南华指数":
+        df = dc.index_basic(v=2, market='NH')
+        symbols = [row['code'] for _, row in df.iterrows()]
+        return symbols
 
     if name == "期货主力":
         kline = dc.future_klines(trade_date="20231101")
@@ -71,6 +85,28 @@ def get_symbols(name, **kwargs):
     raise ValueError(f"{name} 分组无法识别，获取标的列表失败！")
 
 
+def get_min_future_klines(code, sdt, edt, freq='1m'):
+    """分段获取期货1分钟K线后合并"""
+    dates = pd.date_range(start=sdt, end=edt, freq='1M')
+    dates = [d.strftime('%Y%m%d') for d in dates] + [sdt, edt]
+    dates = sorted(list(set(dates)))
+
+    rows = []
+    for sdt_, edt_ in tqdm(zip(dates[:-1], dates[1:]), total=len(dates) - 1):
+        df = dc.future_klines(code=code, sdt=sdt_, edt=edt_, freq=freq)
+        if df.empty:
+            continue
+        logger.info(f"{code}获取K线范围：{df['dt'].min()} - {df['dt'].max()}")
+        rows.append(df)
+
+    df = pd.concat(rows, ignore_index=True)
+    df.rename(columns={'code': 'symbol'}, inplace=True)
+    df['dt'] = pd.to_datetime(df['dt'])
+
+    df = df.drop_duplicates(subset=['dt', 'symbol'], keep='last')
+    return df
+
+
 def get_raw_bars(symbol, freq, sdt, edt, fq='前复权', **kwargs):
     """获取 CZSC 库定义的标准 RawBar 对象列表
 
@@ -85,29 +121,44 @@ def get_raw_bars(symbol, freq, sdt, edt, fq='前复权', **kwargs):
     """
     freq = czsc.Freq(freq)
 
-    if symbol.endswith(".SH") or symbol.endswith(".SZ"):
+    if "SH" in symbol or "SZ" in symbol:
         fq_map = {"前复权": "qfq", "后复权": "hfq", "不复权": None}
         adj = fq_map.get(fq, None)
+
+        code, asset = symbol.split("#")
+
         if freq.value.endswith('分钟'):
-            df = dc.pro_bar(code=symbol, sdt=sdt, edt=edt, freq='min', adj=adj)
+            df = dc.pro_bar(code=code, sdt=sdt, edt=edt, freq='min', adj=adj, asset=asset[0].lower(), v=2)
             df = df[~df['dt'].str.endswith("09:30:00")].reset_index(drop=True)
         else:
-            df = dc.pro_bar(code=symbol, sdt=sdt, edt=edt, freq='day', adj=adj)
+            df = dc.pro_bar(code=code, sdt=sdt, edt=edt, freq='day', adj=adj, asset=asset[0].lower(), v=2)
+
         df.rename(columns={'code': 'symbol'}, inplace=True)
         df['dt'] = pd.to_datetime(df['dt'])
         return czsc.resample_bars(df, target_freq=freq)
 
     if symbol.endswith("9001"):
+        # https://s0cqcxuy3p.feishu.cn/wiki/WLGQwJLWQiWPCZkPV7Xc3L1engg
+        if fq == "前复权":
+            logger.warning("期货主力合约暂时不支持前复权，已自动切换为后复权")
+
+        freq_rd = '1m' if freq.value.endswith('分钟') else '1d'
         if freq.value.endswith('分钟'):
-            df = dc.future_klines(code=symbol, sdt=sdt, edt=edt, freq='1m')
+            df = get_min_future_klines(code=symbol, sdt=sdt, edt=edt, freq='1m')
         else:
-            df = dc.future_klines(code=symbol, sdt=sdt, edt=edt, freq='1d')
-        df.rename(columns={'code': 'symbol'}, inplace=True)
+            df = dc.future_klines(code=symbol, sdt=sdt, edt=edt, freq=freq_rd)
+            df.rename(columns={'code': 'symbol'}, inplace=True)
+
         df['amount'] = df['vol'] * df['close']
         df = df[['symbol', 'dt', 'open', 'close', 'high', 'low', 'vol', 'amount']].copy().reset_index(drop=True)
         df['dt'] = pd.to_datetime(df['dt'])
         return czsc.resample_bars(df, target_freq=freq)
 
+    if symbol.endswith(".NH"):
+        if freq != Freq.D:
+            raise ValueError("南华指数只支持日线数据")
+        df = dc.nh_daily(code=symbol, sdt=sdt, edt=edt)
+
     raise ValueError(f"symbol {symbol} 无法识别，获取数据失败！")
 
 
diff --git a/czsc/features/__init__.py b/czsc/features/__init__.py
new file mode 100644
index 000000000..08bc3518e
--- /dev/null
+++ b/czsc/features/__init__.py
@@ -0,0 +1,27 @@
+# -*- coding: utf-8 -*-
+"""
+author: zengbin93
+email: zeng_bin8888@163.com
+create_dt: 2024/02/14 17:48
+describe: 时序特征因子库
+
+因子函数编写规范：https://s0cqcxuy3p.feishu.cn/wiki/A9yawT6o1il9SrkUoBNchtXjnBK
+"""
+
+from .ret import (
+    RET001,
+    RET002,
+    RET003,
+    RET004,
+    RET005,
+    RET006,
+    RET007,
+    RET008,
+)
+
+from .vpf import (
+    VPF001,
+    VPF002,
+    VPF003,
+    VPF004,
+)
\ No newline at end of file
diff --git a/czsc/features/ret.py b/czsc/features/ret.py
new file mode 100644
index 000000000..64b3ee618
--- /dev/null
+++ b/czsc/features/ret.py
@@ -0,0 +1,214 @@
+"""
+用于计算未来收益相关的因子，含有未来信息，不可用于实际交易
+通常用作模型训练、因子评价的标准
+"""
+import numpy as np
+import pandas as pd
+
+
+def RET001(df, **kwargs):
+    """用 close 价格计算未来 N 根K线的收益率
+
+    参数空间：
+
+    :param df: 标准K线数据，DataFrame结构
+    :param kwargs: 其他参数
+
+        - tag: str, 因子字段标记
+
+    :return: None
+    """
+    tag = kwargs.get('tag', 'A')
+    n = kwargs.get('n', 5)
+
+    col = f'F#RET001#{tag}'
+    df[col] = df['close'].shift(-n) / df['close'] - 1
+    df[col] = df[col].fillna(0)
+
+
+def RET002(df, **kwargs):
+    """用 open 价格计算未来 N 根K线的收益率
+
+    参数空间：
+
+    :param df: 标准K线数据，DataFrame结构
+    :param kwargs: 其他参数
+
+        - tag: str, 因子字段标记
+
+    :return: None
+    """
+    tag = kwargs.get('tag', 'A')
+    n = kwargs.get('n', 5)
+
+    col = f'F#RET002#{tag}'
+    df[col] = df['open'].shift(-n - 1) / df['open'].shift(-1) - 1
+    df[col] = df[col].fillna(0)
+
+
+def RET003(df, **kwargs):
+    """未来 N 根K线的收益波动率
+
+    参数空间：
+
+    :param df: 标准K线数据，DataFrame结构
+    :param kwargs: 其他参数
+
+        - tag: str, 因子字段标记
+        - n: int, 计算未来 N 根K线的收益波动率
+
+    :return: None
+    """
+    tag = kwargs.get('tag', 'A')
+    n = kwargs.get('n', 5)
+
+    col = f'F#RET003#{tag}'
+    df['tmp'] = df['close'].pct_change()
+    df[col] = df['tmp'].rolling(n).std().shift(-n)
+    df[col] = df[col].fillna(0)
+    df.drop(columns=['tmp'], inplace=True)
+
+
+def RET004(df, **kwargs):
+    """未来 N 根K线的最大收益盈亏比
+
+    注意：
+    1. 约束盈亏比的范围是 [0, 10]
+    2. 当未来 N 根K线内收益最小值为0时，会导致计算结果为无穷大，此时将结果设置为10
+
+    :param df: 标准K线数据，DataFrame结构
+    :param kwargs: 其他参数
+
+        - tag: str, 因子字段标记
+        - n: int, 计算未来 N 根K线的收益盈亏比
+
+    :return: None
+    """
+    tag = kwargs.get('tag', 'A')
+    n = kwargs.get('n', 5)
+
+    col = f'F#RET004#{tag}'
+    df['max_ret'] = df['close'].rolling(n).apply(lambda x: x.max() / x[0] - 1, raw=True)
+    df['min_ret'] = df['close'].rolling(n).apply(lambda x: x.min() / x[0] - 1, raw=True)
+    df[col] = (df['max_ret'] / df['min_ret'].abs()).shift(-n)
+    df[col] = df[col].fillna(0)
+    df[col] = df[col].clip(0, 10)
+    df.drop(columns=['max_ret', 'min_ret'], inplace=True)
+
+
+def RET005(df, **kwargs):
+    """未来 N 根K线的逐K胜率
+
+    :param df: 标准K线数据，DataFrame结构
+    :param kwargs: 其他参数
+
+        - tag: str, 因子字段标记
+        - n: int, 滚动窗口大小
+
+    :return: None
+    """
+    tag = kwargs.get('tag', 'A')
+    n = kwargs.get('n', 5)
+
+    col = f'F#RET005#{tag}'
+    df['ret'] = df['close'].pct_change()
+    df[col] = df['ret'].rolling(n).apply(lambda x: np.sum(x > 0) / n).shift(-n)
+    df[col] = df[col].fillna(0)
+    df.drop(columns=['ret'], inplace=True)
+
+
+def RET006(df, **kwargs):
+    """未来 N 根K线的逐K盈亏比
+
+    注意：
+    1. 约束盈亏比的范围是 [0, 10]
+
+    :param df: 标准K线数据，DataFrame结构
+    :param kwargs: 其他参数
+
+        - tag: str, 因子字段标记
+        - n: int, 滚动窗口大小
+
+    :return: None
+    """
+    tag = kwargs.get('tag', 'A')
+    n = kwargs.get('n', 5)
+
+    col = f'F#RET006#{tag}'
+    df['ret'] = df['close'].pct_change()
+    df['mean_win'] = df['ret'].rolling(n).apply(lambda x: np.sum(x[x > 0]) / np.sum(x > 0))
+    df['mean_loss'] = df['ret'].rolling(n).apply(lambda x: np.sum(x[x < 0]) / np.sum(x < 0))
+    df[col] = (df['mean_win'] / df['mean_loss'].abs()).shift(-n)
+    df[col] = df[col].fillna(0)
+    df[col] = df[col].clip(0, 10)
+    df.drop(columns=['ret', 'mean_win', 'mean_loss'], inplace=True)
+
+
+def RET007(df, **kwargs):
+    """未来 N 根K线的最大跌幅
+
+    :param df: 标准K线数据，DataFrame结构
+    :param kwargs: 其他参数
+
+        - tag: str, 因子字段标记
+        - n: int, 滚动窗口大小
+
+    :return: None
+    """
+    tag = kwargs.get('tag', 'A')
+    n = kwargs.get('n', 5)
+
+    col = f'F#RET007#{tag}'
+    df[col] = df['close'].rolling(n).apply(lambda x: np.min(x) / x[0] - 1, raw=True).shift(-n)
+    df[col] = df[col].fillna(0)
+
+
+def RET008(df, **kwargs):
+    """未来 N 根K线的最大涨幅
+
+    :param df: 标准K线数据，DataFrame结构
+    :param kwargs: 其他参数
+
+        - tag: str, 因子字段标记
+        - n: int, 滚动窗口大小
+
+    :return: None
+    """
+    tag = kwargs.get('tag', 'A')
+    n = kwargs.get('n', 5)
+
+    col = f'F#RET008#{tag}'
+    df[col] = df['close'].rolling(n).apply(lambda x: np.max(x) / x[0] - 1, raw=True).shift(-n)
+    df[col] = df[col].fillna(0)
+
+
+def test_ret_functions():
+    from czsc.connectors import cooperation as coo
+
+    df = coo.dc.pro_bar(code="000001.SZ", freq="day", sdt="2020-01-01", edt="2021-01-31")
+    df['dt'] = pd.to_datetime(df['dt'])
+    df.rename(columns={'code': 'symbol'}, inplace=True)
+
+    RET001(df, tag='A')
+    assert 'F#RET001#A' in df.columns
+
+    RET002(df, tag='A')
+    assert 'F#RET002#A' in df.columns
+
+    RET003(df, tag='A')
+    assert 'F#RET003#A' in df.columns
+
+    RET004(df, tag='A')
+    assert 'F#RET004#A' in df.columns
+
+    RET005(df, tag='A')
+    assert 'F#RET005#A' in df.columns
+
+    RET006(df, tag='A')
+    assert 'F#RET006#A' in df.columns
+
+    RET007(df, tag='A')
+    assert 'F#RET007#A' in df.columns
+
+    RET008(df, tag='A')
+    assert 'F#RET008#A' in df.columns
diff --git a/czsc/features/utils.py b/czsc/features/utils.py
new file mode 100644
index 000000000..f1a3f60bb
--- /dev/null
+++ b/czsc/features/utils.py
@@ -0,0 +1,13 @@
+# 工具函数
+
+
+def is_event_feature(df, col, **kwargs):
+    """事件类因子的判断函数
+
+    事件因子的特征：多头事件发生时，因子值为1；空头事件发生时，因子值为-1；其他情况，因子值为0。
+
+    :param df: DataFrame
+    :param col: str, 因子字段名称
+    """
+    unique_values = df[col].unique()
+    return all([x in [0, 1, -1] for x in unique_values])
diff --git a/czsc/features/vpf.py b/czsc/features/vpf.py
new file mode 100644
index 000000000..4d8c31564
--- /dev/null
+++ b/czsc/features/vpf.py
@@ -0,0 +1,105 @@
+# 标准量价因子
+import inspect
+import numpy as np
+import pandas as pd
+
+
+def VPF001(df, **kwargs):
+    """比较开盘价、收盘价与当日最高价和最低价的中点的关系，来判断市场的强弱
+
+    :param df: 标准K线数据，DataFrame结构
+    :param kwargs: 其他参数
+        - tag: str, defaults to 'N2'  因子字段标记
+        - num: int, defaults to 2  参数值
+    """
+    num = kwargs.get('num', 2)
+    tag = kwargs.get('tag', f'N{num}')
+
+    factor_name = inspect.stack()[0].function
+    factor_col = f'F#{factor_name}#{tag}'
+
+    con = df['open'] >= 1 / num * (df['high'] + df['low'])
+    con &= df['close'] >= 1 / num * (df['high'] + df['low'])
+
+    red = df['open'] < 1 / num * (df['high'] + df['low'])
+    red &= df['close'] < 1 / num * (df['high'] + df['low'])
+
+    df[factor_col] = 0
+    df[factor_col] = np.where(con, -1, df[factor_col])
+    df[factor_col] = np.where(red, 1, df[factor_col])
+
+
+def VPF002(df, **kwargs):
+    """比较过去收益率的正负，以及当日最高价、最低价与开盘价或收盘价的关系
+
+    :param df: 标准K线数据，DataFrame结构
+    :param kwargs: 其他参数
+
+        - tag: str, defaults to 'N4'  因子字段标记
+        - num: int, defaults to 4  参数值
+
+    :return: None
+    """
+    num = kwargs.get('num', 4)
+    tag = kwargs.get('tag', f'N{num}')
+
+    factor_name = inspect.stack()[0].function
+    factor_col = f'F#{factor_name}#{tag}'
+
+    df['return'] = df['close'] / df['close'].shift(1) - 1
+    red1 = df['return'].rolling(window=num, min_periods=1).sum() >= 0
+    red2 = (df['high'] - df['close']) / (df['close'] - df['low']) >= 1
+
+    df[factor_col] = np.where(red1 | red2, 1, -1)
+    df.drop(columns=['return'], axis=1, inplace=True)
+
+
+def VPF003(df, **kwargs):
+    """比较过去N天最高价、最低价、开盘价和收盘价的比例，判断市场强弱
+
+    :param df: 标准K线数据，DataFrame结构
+    :param kwargs: 其他参数
+
+        - tag: str
+        - num: int, defaults to 60  参数值
+    """
+    num = kwargs.get('num', 2)
+    tag = kwargs.get('tag', f'N{num}')
+
+    factor_name = inspect.stack()[0].function
+    factor_col = f'F#{factor_name}#{tag}'
+
+    df['hol'] = (df['high'] - df['open']) / (df['high'] - df['low'])
+    df['clh'] = (df['close'] - df['low']) / (df['high'] - df['low'])
+
+    con = df['hol'].rolling(window=num, min_periods=1).mean() >= 0.5
+    con1 = (df['high'] + df['low'] - df['open'] - df['close']) >= 0
+    df[factor_col] = np.where(con | con1, 1, -1)
+    red = df['clh'].rolling(window=num, min_periods=1).mean() >= 0.5
+    df.loc[red, factor_col] = -1
+
+    df.drop(['hol', 'clh'], axis=1, inplace=True)
+
+
+def VPF004(df, **kwargs):
+    """EMA指标
+
+    :param df: 标准K线数据，DataFrame结构
+    :param kwargs: 其他参数
+
+        - tag: str, 因子字段标记
+        - n: int, EMA的周期参数
+
+    :return: None
+    """
+    n = kwargs.get('n', 7)
+    tag = kwargs.get('tag', f'N{n}')
+
+    factor_name = inspect.stack()[0].function
+    factor_col = f'F#{factor_name}#{tag}'
+
+    ema1 = df['close'].ewm(span=n, adjust=False).mean()
+    ema2 = ema1.ewm(span=n, adjust=False).mean()
+    ema3 = ema2.ewm(span=n, adjust=False).mean()
+    df[factor_col] = 3 * (ema1 - ema2) + ema3
+    df[factor_col] = df[factor_col].fillna(0)
diff --git a/czsc/signals/__init__.py b/czsc/signals/__init__.py
index 5f6084000..79d2290ce 100644
--- a/czsc/signals/__init__.py
+++ b/czsc/signals/__init__.py
@@ -3,7 +3,7 @@
 author: zengbin93
 email: zeng_bin8888@163.com
 create_dt: 2021/11/21 17:48
-describe: 信号系统，注意：这里仅仅只是提供一些写信号的例子，用来做策略是不太行的
+describe: 信号函数
 """
 # ======================================================================================================================
 # 以下是 0.9.1 开始的新标准下实现的信号函数，规范定义：
@@ -108,6 +108,7 @@
     bar_window_std_V230731,
     bar_window_ps_V230731,
     bar_window_ps_V230801,
+    bar_trend_V240209,
 )
 
 from czsc.signals.jcc import (
@@ -163,6 +164,7 @@
     tas_ma_round_V221206,
     tas_double_ma_V221203,
     tas_double_ma_V230511,
+    tas_double_ma_V240208,
     tas_ma_system_V230513,
 
     tas_boll_power_V221112,
diff --git a/czsc/signals/bar.py b/czsc/signals/bar.py
index d291903ee..0e9c259da 100644
--- a/czsc/signals/bar.py
+++ b/czsc/signals/bar.py
@@ -16,7 +16,7 @@
 from czsc.traders.base import CzscSignals
 from czsc.objects import RawBar
 from czsc.utils.sig import check_pressure_support
-from czsc.signals.tas import update_ma_cache
+from czsc.signals.tas import update_ma_cache, update_macd_cache
 from czsc.utils.bar_generator import freq_end_time
 from czsc.utils import single_linear, freq_end_time, get_sub_elements, create_single_signal
 
@@ -1648,3 +1648,67 @@ def bar_window_ps_V230801(c: CZSC, **kwargs) -> OrderedDict:
     pcts = [int(max((x.close - L_line) / (H_line - L_line), 0) * 10) for x in c.bars_raw[-w:]]
     v1, v2, v3 = f"最大N{max(pcts)}", f"最小N{min(pcts)}", f"当前N{pcts[-1]}"
     return create_single_signal(k1=k1, k2=k2, k3=k3, v1=v1, v2=v2, v3=v3)
+
+
+def bar_trend_V240209(c: CZSC, **kwargs) -> OrderedDict:
+    """趋势跟踪信号
+
+    参数模板："{freq}_D{di}N{N}趋势跟踪_BS辅助V240209"
+
+    **信号逻辑：**
+
+    以多头为例：
+    1. 低点出现在高点之后，且低点右侧的高点到当前K线之间的K线数量在5-30之间；
+    2. 低点右侧的K线的DIF值小于前N根K线的DIF值的标准差的一半；
+    3. 低点右侧的K线的最低价大于低点的最低价；
+    4. 低点右侧的K线的MACD值小于前N根K线的MACD值的标准差的一半。
+
+    **信号列表：**
+
+    - Signal('60分钟_D1N60趋势跟踪_BS辅助V240209_多头_任意_任意_0')
+    - Signal('60分钟_D1N60趋势跟踪_BS辅助V240209_空头_任意_任意_0')
+
+    :param c: CZSC对象
+    :param kwargs: 参数设置
+
+        - di: int, default 1, 倒数第几根K线
+        - N: int, default 20, 窗口大小
+
+    :return: 信号识别结果
+    """
+    di = int(kwargs.get('di', 1))
+    N = int(kwargs.get('N', 60))
+
+    freq = c.freq.value
+    k1, k2, k3 = f"{freq}_D{di}N{N}趋势跟踪_BS辅助V240209".split('_')
+    v1 = '其他'
+    cache_key = update_macd_cache(c)
+    bars = get_sub_elements(c.bars_raw, di=di, n=N)
+    max_bar = max(bars, key=lambda x: x.high)
+    min_bar = min(bars, key=lambda x: x.low)
+    dif_std = np.std([x.cache[cache_key]['dif'] for x in bars])
+    macd_std = np.std([x.cache[cache_key]['macd'] for x in bars])
+
+    if min_bar.dt < max_bar.dt:
+        right_bars = [x for x in c.bars_raw if x.dt >= max_bar.dt]
+        right_min_bar = min(right_bars, key=lambda x: x.low)
+        c1 = 30 > right_min_bar.id - max_bar.id > 5
+        c2 = abs(right_bars[-1].cache[cache_key]['dif']) < dif_std        # type: ignore
+        c3 = right_min_bar.low > min_bar.low
+        c4 = abs(right_bars[-1].cache[cache_key]['macd']) < macd_std     # type: ignore
+
+        if c1 and c2 and c3 and c4:
+            return create_single_signal(k1=k1, k2=k2, k3=k3, v1="多头")
+
+    if min_bar.dt > max_bar.dt:
+        right_bars = [x for x in c.bars_raw if x.dt >= min_bar.dt]
+        right_max_bar = max(right_bars, key=lambda x: x.high)
+        c1 = 30 > right_max_bar.id - min_bar.id > 5
+        c2 = abs(right_bars[-1].cache[cache_key]['dif']) < dif_std        # type: ignore
+        c3 = right_max_bar.high < max_bar.high
+        c4 = abs(right_bars[-1].cache[cache_key]['macd']) < macd_std      # type: ignore
+
+        if c1 and c2 and c3 and c4:
+            return create_single_signal(k1=k1, k2=k2, k3=k3, v1="空头")
+
+    return create_single_signal(k1=k1, k2=k2, k3=k3, v1=v1)
diff --git a/czsc/signals/tas.py b/czsc/signals/tas.py
index 310ad107a..108fcf99a 100644
--- a/czsc/signals/tas.py
+++ b/czsc/signals/tas.py
@@ -3416,3 +3416,56 @@ def tas_slope_V231019(c: CZSC, **kwargs) -> OrderedDict:
     elif q < 1 - th / 100:
         v1 = '看空'
     return create_single_signal(k1=k1, k2=k2, k3=k3, v1=v1)
+
+
+def tas_double_ma_V240208(c: CZSC, **kwargs) -> OrderedDict:
+    """双均线多空信号，辅助V240208
+
+    参数模板："{freq}_D{di}N{N}M{M}双均线_BS辅助V240208"
+
+    **信号逻辑：**
+
+    1. 找出最近3个均线交叉点，时间上由远到近，分别为 X1，X2，X3
+    2. 以多头为例：X3 和 X1 为金叉，且 X2 的价格最高
+
+    **信号列表：**
+
+    - Signal('60分钟_D1N5M21双均线_BS辅助V240208_多头_任意_任意_0')
+    - Signal('60分钟_D1N5M21双均线_BS辅助V240208_空头_任意_任意_0')
+
+    :param c: CZSC对象
+    :param kwargs: 参数设置
+
+        - di: int, default 1, 倒数第几根K线
+        - N: int, default 20, 快线周期
+        - M: int, default 60, 慢线周期
+
+    :return: 信号识别结果
+    """
+    di = int(kwargs.get('di', 1))
+    N = int(kwargs.get('N', 20))
+    M = int(kwargs.get('M', 60))
+    assert N < M, "N 必须小于 M"
+
+    freq = c.freq.value
+    k1, k2, k3 = f"{freq}_D{di}N{N}M{M}双均线_BS辅助V240208".split('_')
+    v1 = '其他'
+    fast_ma_key = update_ma_cache(c, ma_type='SMA', timeperiod=N)
+    slow_ma_key = update_ma_cache(c, ma_type='SMA', timeperiod=M)
+
+    bars = get_sub_elements(c.bars_raw, di=di, n=M * 30)
+    fast_ma = [x.cache[fast_ma_key] for x in bars]
+    slow_ma = [x.cache[slow_ma_key] for x in bars]
+    cross_info = fast_slow_cross(fast_ma, slow_ma)
+
+    if len(cross_info) < 3:
+        return create_single_signal(k1=k1, k2=k2, k3=k3, v1=v1)
+
+    x1, x2, x3 = cross_info[-3:]
+    if x3['类型'] == "金叉" and x2['快线'] > max(x1['快线'], x3['快线']):
+        return create_single_signal(k1=k1, k2=k2, k3=k3, v1='多头')
+
+    if x3['类型'] == "死叉" and x2['快线'] < min(x1['快线'], x3['快线']):
+        return create_single_signal(k1=k1, k2=k2, k3=k3, v1='空头')
+
+    return create_single_signal(k1=k1, k2=k2, k3=k3, v1=v1)
diff --git a/czsc/utils/cache.py b/czsc/utils/cache.py
index a2b6fe4a4..24a3ca095 100644
--- a/czsc/utils/cache.py
+++ b/czsc/utils/cache.py
@@ -8,9 +8,10 @@
 import os
 import time
 import dill
+import json
 import shutil
 import hashlib
-import json
+import inspect
 import pandas as pd
 from pathlib import Path
 from loguru import logger
@@ -94,6 +95,10 @@ def get(self, k: str, suffix: str = "pkl") -> Any:
             res = pd.read_csv(file, encoding='utf-8')
         elif suffix == "xlsx":
             res = pd.read_excel(file)
+        elif suffix == "feather":
+            res = pd.read_feather(file)
+        elif suffix == "parquet":
+            res = pd.read_parquet(file)
         else:
             raise ValueError(f"suffix {suffix} not supported")
         return res
@@ -132,6 +137,16 @@ def set(self, k: str, v: Any, suffix: str = "pkl"):
                 raise ValueError("suffix xlsx only support pd.DataFrame")
             v.to_excel(file, index=False)
 
+        elif suffix == "feather":
+            if not isinstance(v, pd.DataFrame):
+                raise ValueError("suffix feather only support pd.DataFrame")
+            v.to_feather(file)
+
+        elif suffix == "parquet":
+            if not isinstance(v, pd.DataFrame):
+                raise ValueError("suffix parquet only support pd.DataFrame")
+            v.to_parquet(file)
+
         else:
             raise ValueError(f"suffix {suffix} not supported")
 
@@ -150,15 +165,14 @@ def disk_cache(path: str, suffix: str = "pkl", ttl: int = -1):
     :param suffix: 缓存文件后缀，支持 pkl, json, txt, csv, xlsx
     :param ttl: 缓存文件有效期，单位：秒
     """
-    assert suffix in ["pkl", "json", "txt", "csv", "xlsx"], "suffix not supported"
-
     def decorator(func):
         nonlocal path
         _c = DiskCache(path=Path(path) / func.__name__)
 
         def cached_func(*args, **kwargs):
             hash_str = f"{func.__name__}{args}{kwargs}"
-            k = hashlib.md5(hash_str.encode('utf-8')).hexdigest().upper()[:8]
+            code_str = inspect.getsource(func)
+            k = hashlib.md5((code_str + hash_str).encode('utf-8')).hexdigest().upper()[:8]
             k = f"{k}_{func.__name__}"
 
             if _c.is_found(k, suffix=suffix, ttl=ttl):
diff --git a/czsc/utils/data_client.py b/czsc/utils/data_client.py
index 360bec487..778e42351 100644
--- a/czsc/utils/data_client.py
+++ b/czsc/utils/data_client.py
@@ -53,6 +53,8 @@ def __init__(self, token=None, url='http://api.tushare.pro', timeout=300, **kwar
             - cache_path: str, 缓存路径
 
         """
+        from czsc.utils.cache import get_dir_size
+
         self.__token = token or get_url_token(url)
         self.__http_url = url
         self.__timeout = timeout
@@ -60,7 +62,8 @@ def __init__(self, token=None, url='http://api.tushare.pro', timeout=300, **kwar
         assert self.__token, "请设置czsc_token凭证码，如果没有请联系管理员申请"
         self.cache_path = Path(kwargs.get("cache_path", os.path.expanduser("~/.quant_data_cache")))
         self.cache_path.mkdir(exist_ok=True, parents=True)
-        logger.info(f"数据URL: {url}  数据缓存路径：{self.cache_path}")
+
+        logger.info(f"数据URL: {url} 数据缓存路径：{self.cache_path} 占用磁盘空间：{get_dir_size(self.cache_path) / 1024 / 1024:.2f} MB")
         if kwargs.get("clear_cache", False):
             self.clear_cache()
 
diff --git a/czsc/utils/plotly_plot.py b/czsc/utils/plotly_plot.py
index c46bf93cc..39d33ee1d 100644
--- a/czsc/utils/plotly_plot.py
+++ b/czsc/utils/plotly_plot.py
@@ -182,7 +182,12 @@ def add_macd(self, kline: pd.DataFrame, row=3, **kwargs):
         slowperiod = kwargs.get('slowperiod', 26)
         signalperiod = kwargs.get('signalperiod', 9)
         line_width = kwargs.get('line_width', 0.6)
-        diff, dea, macd = MACD(df["close"], fastperiod=fastperiod, slowperiod=slowperiod, signalperiod=signalperiod)
+
+        if 'DIFF' in df.columns and 'DEA' in df.columns and 'MACD' in df.columns:
+            diff, dea, macd = df['DIFF'], df['DEA'], df['MACD']
+        else:
+            diff, dea, macd = MACD(df["close"], fastperiod=fastperiod, slowperiod=slowperiod, signalperiod=signalperiod)
+
         macd_colors = np.where(macd > 0, self.color_red, self.color_green)
         self.add_scatter_indicator(df['dt'], diff, name="DIFF", row=row,
                                    line_color='white', show_legend=False, line_width=line_width)
diff --git a/czsc/utils/sig.py b/czsc/utils/sig.py
index 516410606..e90e8a2b8 100644
--- a/czsc/utils/sig.py
+++ b/czsc/utils/sig.py
@@ -160,7 +160,7 @@ def check_gap_info(bars: List[RawBar]):
     return gap_info
 
 
-def fast_slow_cross(fast: [List, np.array], slow: [List, np.array]):
+def fast_slow_cross(fast, slow):
     """计算 fast 和 slow 的交叉信息
 
     :param fast: 快线
diff --git a/czsc/utils/st_components.py b/czsc/utils/st_components.py
index e260b0099..ac8712deb 100644
--- a/czsc/utils/st_components.py
+++ b/czsc/utils/st_components.py
@@ -44,6 +44,7 @@ def _stats(df_, type_='持有日'):
         # stats = stats.style.background_gradient(cmap='RdYlGn_r', axis=None, subset=fmt_cols).format('{:.4f}')
 
         stats = stats.style.background_gradient(cmap='RdYlGn_r', axis=None, subset=['年化'])
+        stats = stats.background_gradient(cmap='RdYlGn_r', axis=None, subset=['绝对收益'])
         stats = stats.background_gradient(cmap='RdYlGn_r', axis=None, subset=['夏普'])
         stats = stats.background_gradient(cmap='RdYlGn', axis=None, subset=['最大回撤'])
         stats = stats.background_gradient(cmap='RdYlGn_r', axis=None, subset=['卡玛'])
@@ -63,6 +64,7 @@ def _stats(df_, type_='持有日'):
                 '年化': '{:.2%}',
                 '夏普': '{:.2f}',
                 '非零覆盖': '{:.2%}',
+                '绝对收益': '{:.2%}',
                 '日胜率': '{:.2%}',
                 '新高间隔': '{:.2f}',
                 '新高占比': '{:.2%}',
@@ -301,7 +303,7 @@ def show_symbol_factor_layering(df, x_col, y_col='n1b', **kwargs):
     tabs = st.tabs(["分层收益率", "多空组合"])
 
     with tabs[0]:
-        show_daily_return(mrr)
+        show_daily_return(mrr, stat_hold_days=False)
 
     with tabs[1]:
         col1, col2 = st.columns(2)
@@ -360,7 +362,7 @@ def show_weight_backtest(dfw, **kwargs):
 
     dret = wb.results['品种等权日收益']
     dret.index = pd.to_datetime(dret.index)
-    show_daily_return(dret, legend_only_cols=dfw['symbol'].unique().tolist())
+    show_daily_return(dret, legend_only_cols=dfw['symbol'].unique().tolist(), **kwargs)
 
     if kwargs.get("show_backtest_detail", False):
         c1, c2 = st.columns([1, 1])
@@ -419,7 +421,7 @@ def show_splited_daily(df, ret_col, **kwargs):
         row['开始日期'] = sdt.strftime('%Y-%m-%d')
         row['结束日期'] = last_dt.strftime('%Y-%m-%d')
         row['收益名称'] = name
-        row['绝对收益'] = df1[ret_col].sum()
+        # row['绝对收益'] = df1[ret_col].sum()
         rows.append(row)
     dfv = pd.DataFrame(rows).set_index('收益名称')
     cols = ['开始日期', '结束日期', '绝对收益', '年化', '夏普', '最大回撤', '卡玛', '年化波动率', '非零覆盖', '日胜率', '盈亏平衡点']
@@ -450,6 +452,66 @@ def show_splited_daily(df, ret_col, **kwargs):
     st.dataframe(dfv, use_container_width=True)
 
 
+def show_yearly_stats(df, ret_col, **kwargs):
+    """按年计算日收益表现
+
+    :param df: pd.DataFrame，数据源
+    :param ret_col: str，收益列名
+    :param kwargs:
+
+        - sub_title: str, 子标题
+    """
+    if not df.index.dtype == 'datetime64[ns]':
+        df['dt'] = pd.to_datetime(df['dt'])
+        df.set_index('dt', inplace=True)
+
+    assert df.index.dtype == 'datetime64[ns]', "index必须是datetime64[ns]类型, 请先使用 pd.to_datetime 进行转换"
+    df = df.copy().fillna(0)
+    df.sort_index(inplace=True, ascending=True)
+
+    df['年份'] = df.index.year
+
+    _stats = []
+    for year, df_ in df.groupby('年份'):
+        _yst = czsc.daily_performance(df_[ret_col].to_list())
+        _yst['年份'] = year
+        _stats.append(_yst)
+
+    stats = pd.DataFrame(_stats).set_index('年份')
+
+    stats = stats.style.background_gradient(cmap='RdYlGn_r', axis=None, subset=['年化'])
+    stats = stats.background_gradient(cmap='RdYlGn_r', axis=None, subset=['夏普'])
+    stats = stats.background_gradient(cmap='RdYlGn_r', axis=None, subset=['绝对收益'])
+    stats = stats.background_gradient(cmap='RdYlGn', axis=None, subset=['最大回撤'])
+    stats = stats.background_gradient(cmap='RdYlGn_r', axis=None, subset=['卡玛'])
+    stats = stats.background_gradient(cmap='RdYlGn', axis=None, subset=['年化波动率'])
+    stats = stats.background_gradient(cmap='RdYlGn', axis=None, subset=['盈亏平衡点'])
+    stats = stats.background_gradient(cmap='RdYlGn_r', axis=None, subset=['日胜率'])
+    stats = stats.background_gradient(cmap='RdYlGn_r', axis=None, subset=['非零覆盖'])
+    stats = stats.background_gradient(cmap='RdYlGn', axis=None, subset=['新高间隔'])
+    stats = stats.background_gradient(cmap='RdYlGn_r', axis=None, subset=['新高占比'])
+
+    stats = stats.format(
+        {
+            '盈亏平衡点': '{:.2f}',
+            '年化波动率': '{:.2%}',
+            '最大回撤': '{:.2%}',
+            '卡玛': '{:.2f}',
+            '年化': '{:.2%}',
+            '夏普': '{:.2f}',
+            '非零覆盖': '{:.2%}',
+            '绝对收益': '{:.2%}',
+            '日胜率': '{:.2%}',
+            '新高间隔': '{:.2f}',
+            '新高占比': '{:.2%}',
+        }
+    )
+
+    if kwargs.get('sub_title'):
+        st.subheader(kwargs.get('sub_title'), divider="rainbow")
+    st.dataframe(stats, use_container_width=True)
+
+
 def show_ts_rolling_corr(df, col1, col2, **kwargs):
     """时序上按 rolling 的方式计算相关系数
 
diff --git a/czsc/utils/stats.py b/czsc/utils/stats.py
index 5a234838f..86434e48b 100644
--- a/czsc/utils/stats.py
+++ b/czsc/utils/stats.py
@@ -7,6 +7,7 @@
 """
 import numpy as np
 import pandas as pd
+from collections import Counter
 
 
 def cal_break_even_point(seq) -> float:
@@ -87,7 +88,7 @@ def daily_performance(daily_returns):
     daily_returns = np.array(daily_returns, dtype=np.float64)
 
     if len(daily_returns) == 0 or np.std(daily_returns) == 0 or all(x == 0 for x in daily_returns):
-        return {"年化": 0, "夏普": 0, "最大回撤": 0, "卡玛": 0, "日胜率": 0,
+        return {"绝对收益": 0, "年化": 0, "夏普": 0, "最大回撤": 0, "卡玛": 0, "日胜率": 0,
                 "年化波动率": 0, "非零覆盖": 0, "盈亏平衡点": 0, "新高间隔": 0, "新高占比": 0}
 
     annual_returns = np.sum(daily_returns) / len(daily_returns) * 252
@@ -100,14 +101,11 @@ def daily_performance(daily_returns):
     annual_volatility = np.std(daily_returns) * np.sqrt(252)
     none_zero_cover = len(daily_returns[daily_returns != 0]) / len(daily_returns)
 
-    # 计算最大新高时间
-    high_index = [i for i, x in enumerate(dd) if x == 0]
-    max_interval = 0
-    for i in range(len(high_index) - 1):
-        max_interval = max(max_interval, high_index[i + 1] - high_index[i])
+    # 计算最大新高间隔
+    max_interval = Counter(np.maximum.accumulate(cum_returns).tolist()).most_common(1)[0][1]
 
     # 计算新高时间占比
-    high_pct = len(high_index) / len(dd)
+    high_pct = len([i for i, x in enumerate(dd) if x == 0]) / len(dd)
 
     def __min_max(x, min_val, max_val, digits=4):
         if x < min_val:
@@ -119,6 +117,7 @@ def __min_max(x, min_val, max_val, digits=4):
         return round(x1, digits)
 
     sta = {
+        "绝对收益": round(np.sum(daily_returns), 4),
         "年化": round(annual_returns, 4),
         "夏普": __min_max(sharpe_ratio, -5, 5, 2),
         "最大回撤": round(max_drawdown, 4),
diff --git a/examples/signals_dev/dropit/bar_trend_V240209.py b/examples/signals_dev/dropit/bar_trend_V240209.py
new file mode 100644
index 000000000..0400aa411
--- /dev/null
+++ b/examples/signals_dev/dropit/bar_trend_V240209.py
@@ -0,0 +1,85 @@
+import numpy as np
+from collections import OrderedDict
+from czsc.analyze import CZSC
+from loguru import logger
+from czsc.signals.tas import update_ma_cache, update_macd_cache
+from czsc.utils import create_single_signal, get_sub_elements, fast_slow_cross
+
+
+def bar_trend_V240209(c: CZSC, **kwargs) -> OrderedDict:
+    """趋势跟踪信号
+
+    参数模板："{freq}_D{di}N{N}趋势跟踪_BS辅助V240209"
+
+    **信号逻辑：**
+
+    以多头为例：
+    1. 低点出现在高点之后，且低点右侧的高点到当前K线之间的K线数量在5-30之间；
+    2. 低点右侧的K线的DIF值小于前N根K线的DIF值的标准差的一半；
+    3. 低点右侧的K线的最低价大于低点的最低价；
+    4. 低点右侧的K线的MACD值小于前N根K线的MACD值的标准差的一半。
+
+    **信号列表：**
+
+    - Signal('60分钟_D1N60趋势跟踪_BS辅助V240209_多头_任意_任意_0')
+    - Signal('60分钟_D1N60趋势跟踪_BS辅助V240209_空头_任意_任意_0')
+
+    :param c: CZSC对象
+    :param kwargs: 参数设置
+
+        - di: int, default 1, 倒数第几根K线
+        - N: int, default 20, 窗口大小
+
+    :return: 信号识别结果
+    """
+    di = int(kwargs.get('di', 1))
+    N = int(kwargs.get('N', 60))
+
+    freq = c.freq.value
+    k1, k2, k3 = f"{freq}_D{di}N{N}趋势跟踪_BS辅助V240209".split('_')
+    v1 = '其他'
+    cache_key = update_macd_cache(c)
+    bars = get_sub_elements(c.bars_raw, di=di, n=N)
+    max_bar = max(bars, key=lambda x: x.high)
+    min_bar = min(bars, key=lambda x: x.low)
+    dif_std = np.std([x.cache[cache_key]['dif'] for x in bars])
+    macd_std = np.std([x.cache[cache_key]['macd'] for x in bars])
+
+    if min_bar.dt < max_bar.dt:
+        right_bars = [x for x in c.bars_raw if x.dt >= max_bar.dt]
+        right_min_bar = min(right_bars, key=lambda x: x.low)
+        c1 = 30 > right_min_bar.id - max_bar.id > 5
+        c2 = abs(right_bars[-1].cache[cache_key]['dif']) < dif_std        # type: ignore
+        c3 = right_min_bar.low > min_bar.low
+        c4 = abs(right_bars[-1].cache[cache_key]['macd']) < macd_std     # type: ignore
+
+        if c1 and c2 and c3 and c4:
+            return create_single_signal(k1=k1, k2=k2, k3=k3, v1="多头")
+
+    if min_bar.dt > max_bar.dt:
+        right_bars = [x for x in c.bars_raw if x.dt >= min_bar.dt]
+        right_max_bar = max(right_bars, key=lambda x: x.high)
+        c1 = 30 > right_max_bar.id - min_bar.id > 5
+        c2 = abs(right_bars[-1].cache[cache_key]['dif']) < dif_std        # type: ignore
+        c3 = right_max_bar.high < max_bar.high
+        c4 = abs(right_bars[-1].cache[cache_key]['macd']) < macd_std      # type: ignore
+
+        if c1 and c2 and c3 and c4:
+            return create_single_signal(k1=k1, k2=k2, k3=k3, v1="空头")
+
+    return create_single_signal(k1=k1, k2=k2, k3=k3, v1=v1)
+
+
+def check():
+    from czsc.connectors import research
+    from czsc.traders.base import check_signals_acc
+
+    symbols = research.get_symbols('A股主要指数')
+    bars = research.get_raw_bars(symbols[0], '15分钟', '20181101', '20210101', fq='前复权')
+
+    signals_config = [{'name': bar_trend_V240209, 'freq': "60分钟", 'N': 60}]
+    check_signals_acc(bars, signals_config=signals_config, height='780px', delta_days=1)  # type: ignore
+
+
+if __name__ == '__main__':
+    check()
diff --git a/examples/signals_dev/dropit/tas_double_ma_V240208.py b/examples/signals_dev/dropit/tas_double_ma_V240208.py
new file mode 100644
index 000000000..99e00c988
--- /dev/null
+++ b/examples/signals_dev/dropit/tas_double_ma_V240208.py
@@ -0,0 +1,72 @@
+from collections import OrderedDict
+from czsc.analyze import CZSC
+from czsc.signals.tas import update_ma_cache
+from czsc.utils import create_single_signal, get_sub_elements, fast_slow_cross
+
+
+def tas_double_ma_V240208(c: CZSC, **kwargs) -> OrderedDict:
+    """双均线多空信号，辅助V240208
+
+    参数模板："{freq}_D{di}N{N}M{M}双均线_BS辅助V240208"
+
+    **信号逻辑：**
+
+    1. 找出最近3个均线交叉点，时间上由远到近，分别为 X1，X2，X3
+    2. 以多头为例：X3 和 X1 为金叉，且 X2 的价格最高
+
+    **信号列表：**
+
+    - Signal('60分钟_D1N5M21双均线_BS辅助V240208_多头_任意_任意_0')
+    - Signal('60分钟_D1N5M21双均线_BS辅助V240208_空头_任意_任意_0')
+
+    :param c: CZSC对象
+    :param kwargs: 参数设置
+
+        - di: int, default 1, 倒数第几根K线
+        - N: int, default 20, 快线周期
+        - M: int, default 60, 慢线周期
+
+    :return: 信号识别结果
+    """
+    di = int(kwargs.get('di', 1))
+    N = int(kwargs.get('N', 20))
+    M = int(kwargs.get('M', 60))
+    assert N < M, "N < M"
+
+    freq = c.freq.value
+    k1, k2, k3 = f"{freq}_D{di}N{N}M{M}双均线_BS辅助V240208".split('_')
+    v1 = '其他'
+    fast_ma_key = update_ma_cache(c, ma_type='SMA', timeperiod=N)
+    slow_ma_key = update_ma_cache(c, ma_type='SMA', timeperiod=M)
+
+    bars = get_sub_elements(c.bars_raw, di=di, n=M * 30)
+    fast_ma = [x.cache[fast_ma_key] for x in bars]
+    slow_ma = [x.cache[slow_ma_key] for x in bars]
+    cross_info = fast_slow_cross(fast_ma, slow_ma)
+
+    if len(cross_info) < 3:
+        return create_single_signal(k1=k1, k2=k2, k3=k3, v1=v1)
+
+    x1, x2, x3 = cross_info[-3:]
+    if x3['类型'] == "金叉" and x2['快线'] > max(x1['快线'], x3['快线']):
+        return create_single_signal(k1=k1, k2=k2, k3=k3, v1='多头')
+
+    if x3['类型'] == "死叉" and x2['快线'] < min(x1['快线'], x3['快线']):
+        return create_single_signal(k1=k1, k2=k2, k3=k3, v1='空头')
+
+    return create_single_signal(k1=k1, k2=k2, k3=k3, v1=v1)
+
+
+def check():
+    from czsc.connectors import research
+    from czsc.traders.base import check_signals_acc
+
+    symbols = research.get_symbols('A股主要指数')
+    bars = research.get_raw_bars(symbols[0], '15分钟', '20181101', '20210101', fq='前复权')
+
+    signals_config = [{'name': tas_double_ma_V240208, 'freq': "60分钟", 'N': 5, 'M': 21}]
+    check_signals_acc(bars, signals_config=signals_config, height='780px', delta_days=5)  # type: ignore
+
+
+if __name__ == '__main__':
+    check()
diff --git a/examples/signals_dev/tas_macd_bc_V230803.py b/examples/signals_dev/dropit/tas_macd_bc_V230803.py
similarity index 100%
rename from examples/signals_dev/tas_macd_bc_V230803.py
rename to examples/signals_dev/dropit/tas_macd_bc_V230803.py
diff --git a/examples/signals_dev/tas_macd_bc_V230804.py b/examples/signals_dev/dropit/tas_macd_bc_V230804.py
similarity index 100%
rename from examples/signals_dev/tas_macd_bc_V230804.py
rename to examples/signals_dev/dropit/tas_macd_bc_V230804.py
diff --git a/examples/signals_dev/tas_macd_bc_ubi_V230804.py b/examples/signals_dev/dropit/tas_macd_bc_ubi_V230804.py
similarity index 100%
rename from examples/signals_dev/tas_macd_bc_ubi_V230804.py
rename to examples/signals_dev/dropit/tas_macd_bc_ubi_V230804.py
diff --git a/examples/test_offline/test_cooperation.py b/examples/test_offline/test_cooperation.py
new file mode 100644
index 000000000..a1b1b447e
--- /dev/null
+++ b/examples/test_offline/test_cooperation.py
@@ -0,0 +1,40 @@
+import sys
+sys.path.insert(0, r'D:\ZB\git_repo\waditu\czsc')
+import czsc
+from czsc.connectors.cooperation import *
+
+czsc.welcome()
+
+
+def test_cooperation():
+    # 获取股票列表
+    symbols = get_symbols(name="股票")
+    print(f"股票数量：{len(symbols)}")
+
+    # 获取日线数据
+    kline = get_raw_bars(symbol="000001.SZ#STOCK", freq="日线", sdt="20220101", edt="20230101", fq="前复权")
+    print(kline[-5:])
+
+    # 获取60分钟数据
+    kline = get_raw_bars(symbol="000001.SZ#STOCK", freq="60分钟", sdt="20220101", edt="20230101", fq="前复权")
+    print(kline[-10:])
+
+    # 获取ETF列表
+    symbols = get_symbols(name="ETF")
+    print(f"ETF数量：{len(symbols)}")
+
+    # 获取指数列表
+    symbols = get_symbols(name="A股指数")
+    print(f"指数数量：{len(symbols)}")
+
+    # 获取南华指数列表
+    symbols = get_symbols(name="南华指数")
+    print(f"南华指数数量：{len(symbols)}")
+
+    # 获取期货主力列表
+    symbols = get_symbols(name="期货主力")
+    print(f"期货主力数量：{len(symbols)}")
+
+    # 获取日线数据
+    kline = get_raw_bars(symbol="SFIC9001", freq="日线", sdt="20210101", edt="20230101", fq="前复权")
+    kline = get_raw_bars(symbol="SFIC9001", freq="30分钟", sdt="20220101", edt="20230101", fq="前复权")
diff --git a/test/test_features.py b/test/test_features.py
new file mode 100644
index 000000000..e5393e073
--- /dev/null
+++ b/test/test_features.py
@@ -0,0 +1,14 @@
+import pytest
+import pandas as pd
+
+
+def test_is_event_feature():
+    from czsc.features.utils import is_event_feature
+
+    # 测试事件类因子
+    df1 = pd.DataFrame({'factor': [0, 1, -1, 0, 1, -1]})
+    assert is_event_feature(df1, 'factor') is True
+
+    # 测试非事件类因子
+    df2 = pd.DataFrame({'factor': [0, 1, 2, 3, 4, 5]})
+    assert is_event_feature(df2, 'factor') is False
diff --git a/test/test_utils.py b/test/test_utils.py
index 880048378..f35064f7d 100644
--- a/test/test_utils.py
+++ b/test/test_utils.py
@@ -96,28 +96,28 @@ def test_daily_performance():
 
     # Test case 1: empty daily returns
     result = daily_performance([])
-    assert result == {'年化': 0, '夏普': 0, '最大回撤': 0, '卡玛': 0, '日胜率': 0, '年化波动率': 0,
+    assert result == {'绝对收益': 0, '年化': 0, '夏普': 0, '最大回撤': 0, '卡玛': 0, '日胜率': 0, '年化波动率': 0,
                       '非零覆盖': 0, '盈亏平衡点': 0, '新高间隔': 0, '新高占比': 0}
 
     # Test case 2: daily returns with zero standard deviation
     result = daily_performance([1, 1, 1, 1, 1])
-    assert result == {"年化": 0, "夏普": 0, "最大回撤": 0, "卡玛": 0, "日胜率": 0,
+    assert result == {'绝对收益': 0, "年化": 0, "夏普": 0, "最大回撤": 0, "卡玛": 0, "日胜率": 0,
                       "年化波动率": 0, "非零覆盖": 0, "盈亏平衡点": 0, '新高间隔': 0, '新高占比': 0}
 
     # Test case 3: daily returns with all zeros
     result = daily_performance([0, 0, 0, 0, 0])
-    assert result == {"年化": 0, "夏普": 0, "最大回撤": 0, "卡玛": 0, "日胜率": 0,
+    assert result == {'绝对收益': 0, "年化": 0, "夏普": 0, "最大回撤": 0, "卡玛": 0, "日胜率": 0,
                       "年化波动率": 0, "非零覆盖": 0, "盈亏平衡点": 0, '新高间隔': 0, '新高占比': 0}
 
     # Test case 4: normal daily returns
     daily_returns = np.array([0.01, 0.02, -0.01, 0.03, 0.02, -0.02, 0.01, -0.01, 0.02, 0.01])
     result = daily_performance(daily_returns)
-    assert result == {'年化': 2.016, '夏普': 5, '最大回撤': 0.02, '卡玛': 10, '日胜率': 0.7, '年化波动率': 0.2439,
-                       '非零覆盖': 1.0, '盈亏平衡点': 0.7, '新高间隔': 4, '新高占比': 0.6}
+    assert result == {'绝对收益': 0.08, '年化': 2.016, '夏普': 5, '最大回撤': 0.02, '卡玛': 10, '日胜率': 0.7, '年化波动率': 0.2439,
+                      '非零覆盖': 1.0, '盈亏平衡点': 0.7, '新高间隔': 5, '新高占比': 0.6}
 
     result = daily_performance([0.01, 0.02, -0.01, 0.03, 0.02, -0.02, 0.01, -0.01, 0.02, 0.01])
-    assert result == {'年化': 2.016, '夏普': 5, '最大回撤': 0.02, '卡玛': 10, '日胜率': 0.7, '年化波动率': 0.2439,
-                      '非零覆盖': 1.0, '盈亏平衡点': 0.7, '新高间隔': 4, '新高占比': 0.6}
+    assert result == {'绝对收益': 0.08, '年化': 2.016, '夏普': 5, '最大回撤': 0.02, '卡玛': 10, '日胜率': 0.7, '年化波动率': 0.2439,
+                      '非零覆盖': 1.0, '盈亏平衡点': 0.7, '新高间隔': 5, '新高占比': 0.6}
 
 
 def test_find_most_similarity():
diff --git a/test/test_utils_cache.py b/test/test_utils_cache.py
index 15be44bf9..4a01e86b3 100644
--- a/test/test_utils_cache.py
+++ b/test/test_utils_cache.py
@@ -12,12 +12,34 @@ def run_func_x(x):
     return x * 2
 
 
+@disk_cache(path=temp_path, suffix="txt", ttl=100)
+def run_func_text(x):
+    return f"hello {x}"
+
+
+@disk_cache(path=temp_path, suffix="json", ttl=100)
+def run_func_json(x):
+    return {"a": 1, "b": 2, "x": x}
+
+
 @disk_cache(path=temp_path, suffix="xlsx", ttl=100)
 def run_func_y(x):
     df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6], 'x': [x, x, x]})
     return df
 
 
+@disk_cache(path=temp_path, suffix="feather", ttl=100)
+def run_feather(x):
+    df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6], 'x': [x, x, x]})
+    return df
+
+
+@disk_cache(path=temp_path, suffix="parquet", ttl=100)
+def run_parquet(x):
+    df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6], 'x': [x, x, x]})
+    return df
+
+
 def test_disk_cache():
     # Call the function
     result = run_func_x(5)
@@ -31,6 +53,24 @@ def test_disk_cache():
     # Check if the output is still correct
     assert result == 10
 
+    # Call the function with a different argument
+    result = run_func_text(6)
+    result = run_func_text(6)
+    assert result == "hello 6"
+
+    # Call the function with a different argument
+    result = run_func_json(7)
+    result = run_func_json(7)
+    assert result == {"a": 1, "b": 2, "x": 7}
+
+    result = run_feather(8)
+    result = run_feather(8)
+    assert isinstance(result, pd.DataFrame)
+
+    result = run_parquet(9)
+    result = run_parquet(9)
+    assert isinstance(result, pd.DataFrame)
+
     # Check if the cache file exists
     files = os.listdir(os.path.join(temp_path, "run_func_x"))
     assert len(files) == 1