0.9.24 update

czsc/__init__.py

@@ -23,7 +23,7 @@
 from czsc.utils import CrossSectionalPerformance
 from czsc.sensors import holds_concepts_effect, CTAResearch, EventMatchSensor
 from czsc.utils.signal_analyzer import SignalAnalyzer, SignalPerformance
-from czsc.utils.stats import daily_performance, net_value_stats
+from czsc.utils.stats import daily_performance, net_value_stats, subtract_fee
 
 
 __version__ = "0.9.24"

czsc/connectors/qmt_connector.py

@@ -113,7 +113,7 @@ def get_kline(symbol, period, start_time, end_time, count=-1, dividend_type='fro
         return format_stock_kline(df, freq=freq_map[period])
 
 
-def get_raw_bars(symbol, freq, sdt, edt, fq='前复权', **kwargs):
+def get_raw_bars(symbol, freq, sdt, edt, fq='前复权', **kwargs) -> List[RawBar]:
     """获取 CZSC 库定义的标准 RawBar 对象列表
 
     :param symbol: 标的代码

czsc/sensors/event.py

@@ -6,6 +6,7 @@
 describe: Event 相关的传感器
 """
 import os
+import shutil   
 import pandas as pd
 from copy import deepcopy
 from loguru import logger
@@ -43,7 +44,9 @@ def __init__(self, events: List[Union[Dict[str, Any], Event]], symbols: List[str
         self.events_map = {event.name: event for event in self.events}
         self.events_name = [event.name for event in self.events]
         self.results_path = kwargs.pop("results_path")
-        if os.path.exists(self.results_path):
+        self.refresh = kwargs.pop("refresh", False)
+        if os.path.exists(self.results_path) and self.refresh:
+            shutil.rmtree(self.results_path)
             logger.warning(f"文件夹 {self.results_path} 已存在，程序将覆盖该文件夹下的所有文件")
         os.makedirs(self.results_path, exist_ok=True)
         save_json({e.name: e.dump() for e in self.events}, os.path.join(self.results_path, "events.json"))

czsc/utils/__init__.py

@@ -17,7 +17,7 @@
 from .plotly_plot import KlineChart
 from .trade import cal_trade_price, update_nbars, update_bbars, update_tbars
 from .cross import CrossSectionalPerformance
-from .stats import daily_performance, net_value_stats
+from .stats import daily_performance, net_value_stats, subtract_fee
 
 
 sorted_freqs = ['Tick', '1分钟', '5分钟', '15分钟', '30分钟', '60分钟', '日线', '周线', '月线', '季线', '年线']

czsc/utils/stats.py

@@ -9,6 +9,28 @@
 import pandas as pd
 
 
+def subtract_fee(df, fee=1):
+    """依据单品种持仓信号扣除手续费"""
+    assert 'dt' in df.columns, 'dt 列必须存在'
+    assert 'pos' in df.columns, 'pos 列必须存在'
+    assert all(x in [0, 1, -1] for x in df['pos'].unique()), "pos 列的值必须是 0, 1, -1 中的一个"
+
+    if 'n1b' not in df.columns:
+        assert 'price' in df.columns, '当n1b列不存在时，price 列必须存在'
+        df['n1b'] = (df['price'].shift(-1) / df['price'] - 1) * 10000
+
+    df['date'] = df['dt'].dt.date
+    df['edge_pre_fee'] = df['pos'] * df['n1b']
+    df['edge_post_fee'] = df['pos'] * df['n1b']
+
+    # 扣费规则, 开仓扣费在第一个持仓K线上，平仓扣费在最后一个持仓K线上
+    open_pos = (df['pos'].shift() != df['pos']) & (df['pos'] != 0)
+    exit_pos = (df['pos'].shift(-1) != df['pos']) & (df['pos'] != 0)
+    df.loc[open_pos, 'edge_post_fee'] = df.loc[open_pos, 'edge_post_fee'] - fee
+    df.loc[exit_pos, 'edge_post_fee'] = df.loc[exit_pos, 'edge_post_fee'] - fee
+    return df
+
+
 def daily_performance(daily_returns):
     """计算日收益数据的年化收益率、夏普比率、最大回撤、卡玛比率
 
@@ -27,15 +49,16 @@ def daily_performance(daily_returns):
     cum_returns = np.cumprod(1 + daily_returns)
     max_drawdown = np.max(np.maximum.accumulate(cum_returns) - cum_returns) / np.max(cum_returns)
     kama = annual_returns / max_drawdown if max_drawdown != 0 else 10
+    win_pct = len(daily_returns[daily_returns > 0]) / len(daily_returns)
     return {
         "年化": round(annual_returns, 4),
         "夏普": round(sharpe_ratio, 2),
-        "最大回撤": round(max_drawdown, 4),
-        "卡玛": round(kama, 2)
+        "回撤": round(max_drawdown, 4),
+        "卡玛": round(kama, 2),
+        "胜率": round(win_pct, 4),
     }
 
 
-
 def net_value_stats(nv: pd.DataFrame, exclude_zero: bool = False, sub_cost=True) -> dict:
     """统计净值曲线的年化收益、夏普等