0.9.29 update

czsc/cmd.py

@@ -8,7 +8,6 @@
 https://click.palletsprojects.com/en/8.0.x/quickstart/
 """
 import click
-from loguru import logger
 
 
 @click.group()

czsc/data/base.py

@@ -17,6 +17,7 @@
               "3600s": "60分钟", "1d": "日线"}
 freq_cn2gm = {v: k for k, v in freq_gm2cn.items()}
 
+
 def jq_symbol_to_gm(symbol: str) -> str:
     """聚宽代码转掘金代码"""
     code, exchange = symbol.split(".")
@@ -154,4 +155,3 @@ def save_symbols_to_ebk(symbols, file_ebk, source='ts'):
     tdx_symbols = [symbol_to_tdx(ts_code) for ts_code in symbols]
     with open(file_ebk, encoding='utf-8', mode='w') as f:
         f.write("\n".join(tdx_symbols))
-

czsc/eda.py

@@ -6,12 +6,11 @@
 describe: 用于探索性分析的函数
 """
 import numpy as np
-import pandas as pd
 
 
 def vwap(price: np.array, volume: np.array, **kwargs) -> float:
     """计算成交量加权平均价
-    
+
     :param price: 价格序列
     :param volume: 成交量序列
     :return: 平均价
@@ -26,7 +25,3 @@ def twap(price: np.array, **kwargs) -> float:
     :return: 平均价
     """
     return np.average(price)
-
-
-
-

czsc/enum.py

@@ -39,10 +39,18 @@ def __str__(self):
 class Freq(Enum):
     Tick = "Tick"
     F1 = "1分钟"
+    F2 = "2分钟"
+    F3 = "3分钟"
+    F4 = "4分钟"
     F5 = "5分钟"
+    F6 = "6分钟"
+    F10 = "10分钟"
+    F12 = "12分钟"
     F15 = "15分钟"
+    F20 = "20分钟"
     F30 = "30分钟"
     F60 = "60分钟"
+    F120 = "120分钟"
     D = "日线"
     W = "周线"
     M = "月线"

czsc/envs.py

@@ -50,5 +50,5 @@ def get_bi_change_th(v: float = None) -> float:
     """
     bi_change_th = v if v else os.environ.get('czsc_bi_change_th', '1')
     bi_change_th = float(bi_change_th)
-    assert 2 >= bi_change_th >= 0.5 or bi_change_th == -1, f"czsc_bi_change_th not in [0.5, 2]"
+    assert 2 >= bi_change_th >= 0.5 or bi_change_th == -1, "czsc_bi_change_th not in [0.5, 2]"
     return bi_change_th

czsc/utils/bar_generator.py

@@ -6,9 +6,109 @@
 describe: 从任意周期K线开始合成更高周期K线的工具类
 """
 import pandas as pd
-from datetime import datetime, timedelta
+from datetime import datetime, timedelta, date
 from typing import List, Union, AnyStr
 from czsc.objects import RawBar, Freq
+from pathlib import Path
+from loguru import logger
+from czsc.utils.calendar import next_trading_date
+
+
+mss = pd.read_feather(Path(__file__).parent / "minites_split.feather")
+freq_market_times, freq_edt_map = {}, {}
+for _m, dfg in mss.groupby('market'):
+    for _f in [x for x in mss.columns if x.endswith("分钟")]:
+        freq_market_times[f"{_f}_{_m}"] = list(dfg[_f].unique())
+        freq_edt_map[f"{_f}_{_m}"] = {k: v for k, v in dfg[["time", _f]].values}
+
+
+def check_freq_and_market(time_seq: List[AnyStr]):
+    """检查时间序列是否为同一周期，是否为同一市场
+
+    :param time_seq: 时间序列，如 ['11:00', '15:00', '23:00', '01:00', '02:30']
+    :return:
+        - freq      K线周期
+        - market    交易市场
+    """
+    assert len(time_seq) >= 2, "time_seq长度必须大于等于2"
+    res = {}
+    for key, tts in freq_market_times.items():
+        if set(tts) == set(time_seq):
+            freq, market = key.split("_")
+            return freq, market
+
+        if len(time_seq) < len(tts) * 0.9 or len(time_seq) > len(tts) * 1.1:
+            continue
+
+        res[key] = len(set(time_seq).intersection(set(tts))) / len(tts)
+    freq, market = sorted(res.items(), key=lambda x: x[1], reverse=True)[0][0].split("_")
+    return freq, market
+
+
+def freq_end_date(dt, freq: Union[Freq, AnyStr]):
+    """交易日结束时间计算"""
+    if not isinstance(dt, date):
+        dt = pd.to_datetime(dt).date()
+    if not isinstance(freq, Freq):
+        freq = Freq(freq)
+
+    dt = pd.to_datetime(dt)
+    if freq == Freq.D:
+        return dt
+
+    if freq == Freq.W:
+        return dt + timedelta(days=5 - dt.isoweekday())
+
+    if freq == Freq.Y:
+        return datetime(year=dt.year, month=12, day=31)
+
+    if freq == Freq.M:
+        if dt.month == 12:
+            edt = datetime(year=dt.year, month=12, day=31)
+        else:
+            edt = datetime(year=dt.year, month=dt.month + 1, day=1) - timedelta(days=1)
+        return edt
+
+    if freq == Freq.S:
+        dt_m = dt.month
+        if dt_m in [1, 2, 3]:
+            edt = datetime(year=dt.year, month=4, day=1) - timedelta(days=1)
+        elif dt_m in [4, 5, 6]:
+            edt = datetime(year=dt.year, month=7, day=1) - timedelta(days=1)
+        elif dt_m in [7, 8, 9]:
+            edt = datetime(year=dt.year, month=10, day=1) - timedelta(days=1)
+        else:
+            edt = datetime(year=dt.year, month=12, day=31)
+        return edt
+
+    logger.warning(f'error: {dt} - {freq}')
+    return dt
+
+
+def freq_end_time_V230921(dt: datetime, freq: Union[Freq, AnyStr], market="A股") -> datetime:
+    """A股与期货市场精确的获取 dt 对应的K线周期结束时间
+
+    :param dt: datetime
+    :param freq: Freq
+    :return: datetime
+    """
+    assert market in ['A股', '期货', '默认'], "market 参数必须为 A股 或 期货 或 默认"
+    if not isinstance(freq, Freq):
+        freq = Freq(freq)
+    if dt.second > 0 or dt.microsecond > 0:
+        dt = dt.replace(second=0, microsecond=0) + timedelta(minutes=1)
+
+    hm = dt.strftime("%H:%M")
+    key = f"{freq.value}_{market}"
+    if freq.value.endswith("分钟"):
+        h, m = freq_edt_map[key][hm].split(":")
+        edt = dt.replace(hour=int(h), minute=int(m))
+        return edt
+
+    if not ("15:00" > hm > "09:00") and market == "期货":
+        dt = next_trading_date(dt.strftime("%Y-%m-%d"), 1)
+
+    return freq_end_date(dt.date(), freq)
 
 
 def freq_end_time(dt: datetime, freq: Union[Freq, AnyStr]) -> datetime:
@@ -44,39 +144,7 @@ def freq_end_time(dt: datetime, freq: Union[Freq, AnyStr]) -> datetime:
                     return edt
 
     # 处理 日、周、月、季、年 的结束时间
-    dt = dt.replace(hour=0, minute=0)
-
-    if freq == Freq.D:
-        return dt
-
-    if freq == Freq.W:
-        sdt = dt + timedelta(days=5 - dt.isoweekday())
-        return sdt
-
-    if freq == Freq.M:
-        if dt.month == 12:
-            sdt = datetime(year=dt.year + 1, month=1, day=1) - timedelta(days=1)
-        else:
-            sdt = datetime(year=dt.year, month=dt.month + 1, day=1) - timedelta(days=1)
-        return sdt
-
-    if freq == Freq.S:
-        dt_m = dt.month
-        if dt_m in [1, 2, 3]:
-            sdt = datetime(year=dt.year, month=4, day=1) - timedelta(days=1)
-        elif dt_m in [4, 5, 6]:
-            sdt = datetime(year=dt.year, month=7, day=1) - timedelta(days=1)
-        elif dt_m in [7, 8, 9]:
-            sdt = datetime(year=dt.year, month=10, day=1) - timedelta(days=1)
-        else:
-            sdt = datetime(year=dt.year + 1, month=1, day=1) - timedelta(days=1)
-        return sdt
-
-    if freq == Freq.Y:
-        return datetime(year=dt.year, month=12, day=31)
-
-    print(f'freq_end_time error: {dt} - {freq}')
-    return dt
+    return freq_end_date(dt.date(), freq)
 
 
 def resample_bars(df: pd.DataFrame, target_freq: Union[Freq, AnyStr], raw_bars=True, **kwargs):
@@ -102,7 +170,8 @@ def resample_bars(df: pd.DataFrame, target_freq: Union[Freq, AnyStr], raw_bars=T
     if not isinstance(target_freq, Freq):
         target_freq = Freq(target_freq)
 
-    df['freq_edt'] = df['dt'].apply(lambda x: freq_end_time(x, target_freq))
+    market = kwargs.get("market", "默认")
+    df['freq_edt'] = df['dt'].apply(lambda x: freq_end_time_V230921(x, target_freq, market))
     dfk1 = df.groupby('freq_edt').agg(
         {'symbol': 'first', 'dt': 'last', 'open': 'first', 'close': 'last', 'high': 'max',
          'low': 'min', 'vol': 'sum', 'amount': 'sum', 'freq_edt': 'last'})

czsc/utils/st_components.py

@@ -0,0 +1,32 @@
+import czsc
+import pandas as pd
+import streamlit as st
+import plotly.express as px
+
+
+def show_daily_return(df, **kwargs):
+    """用streamlit展示日收益"""
+    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"
+    type_ = "持有日" if kwargs.get("none_zero", False) else "交易日"
+
+    df = df.copy().fillna(0)
+    stats = []
+    for col in df.columns:
+        col_stats = czsc.daily_performance([x for x in df[col] if x != 0]) if type_ == '持有日' else czsc.daily_performance(df[col])
+        col_stats['日收益名称'] = col
+        stats.append(col_stats)
+
+    stats = pd.DataFrame(stats).set_index('日收益名称')
+    fmt_cols = ['年化', '夏普', '最大回撤', '卡玛', '年化波动率', '非零覆盖']
+    stats = stats.style.background_gradient(cmap='RdYlGn_r', axis=None).format('{:.4f}', subset=fmt_cols)
+
+    df = df.cumsum()
+    fig = px.line(df, y=df.columns.to_list(), title="日收益累计曲线")
+    for col in kwargs.get("legend_only_cols", []):
+        fig.update_traces(visible="legendonly", selector=dict(name=col))
+
+    with st.container():
+        st.subheader(f'{kwargs.get("title", "日收益表现评价")}（{type_}）')
+        st.divider()
+        st.dataframe(stats, use_container_width=True)
+        st.plotly_chart(fig, use_container_width=True)