0.9.29 新增交易日历

czsc/__init__.py

@@ -26,7 +26,6 @@
     stock_holds_performance,
     DummyBacktest,
     SignalsParser,
-    get_signals_by_conf,
     get_signals_config,
     get_signals_freqs,
     WeightBacktest,

czsc/connectors/qmt_connector.py

@@ -88,10 +88,10 @@ def get_kline(symbol, period, start_time, end_time, count=-1, dividend_type='fro
     """
     start_time = pd.to_datetime(start_time).strftime('%Y%m%d%H%M%S')
     if '1d' == period:
-        end_time = pd.to_datetime(end_time).replace(hour=15,minute=0).strftime('%Y%m%d%H%M%S')
+        end_time = pd.to_datetime(end_time).replace(hour=15, minute=0).strftime('%Y%m%d%H%M%S')
     else:
         end_time = pd.to_datetime(end_time).strftime('%Y%m%d%H%M%S')
-    
+
     if kwargs.get("download_hist", True):
         xtdata.download_history_data(symbol, period=period, start_time=start_time, end_time=end_time)
 

czsc/connectors/research.py

@@ -56,5 +56,3 @@ def get_raw_bars(symbol, freq, sdt, edt, fq='前复权', **kwargs):
         return []
     _bars = czsc.resample_bars(kline, freq, raw_bars=True)
     return _bars
-
-

czsc/fsa/im.py

@@ -95,7 +95,7 @@ def send_text(self, text, receive_id, receive_id_type='open_id'):
         payload = {"receive_id": receive_id, "content": {"text": text}, "msg_type": "text"}
         return self.send(payload, receive_id_type)
 
-    def send_image(self,  image_path, receive_id, receive_id_type='open_id'):
+    def send_image(self, image_path, receive_id, receive_id_type='open_id'):
         """发送图片
 
         :param image_path: 图片路径

czsc/fsa/spreed_sheets.py

@@ -281,7 +281,7 @@ def append(self, token, sheet_id, df: pd.DataFrame, batch_size=2000, overwrite=T
         if df.empty:
             logger.warning("待写入的数据为空，不执行写入操作")
             return
-        
+
         if overwrite:
             self.delete_values(token, sheet_id)
             cols = df.columns.tolist()
@@ -298,7 +298,7 @@ def append(self, token, sheet_id, df: pd.DataFrame, batch_size=2000, overwrite=T
         assert df.shape[1] == col_count, f"df 列数 {df.shape[1]} 与表格列数 {col_count} 不一致"
 
         for i in range(0, len(df), batch_size):
-            dfi = df.iloc[i : i + batch_size]
+            dfi = df.iloc[i: i + batch_size]
             si = i + start_index + 1
             ei = si + batch_size
             vol_range = f"{sheet_id}!A{si}:{string.ascii_uppercase[col_count - 1]}{ei}"

czsc/sensors/feature.py

@@ -73,7 +73,7 @@ def _one_symbol_features(self, symbol):
         except Exception as e:
             logger.error(f"{symbol} error: {e}")
             return pd.DataFrame()
-    
+
     def get_features(self) -> pd.DataFrame:
         """
         此方法检索self.symbols中每个符号的K线数据，
@@ -96,7 +96,7 @@ def get_features(self) -> pd.DataFrame:
                     res.append(df)
 
         return pd.concat(res, ignore_index=True)
-    
+
     def layering(self, feature, min_q, max_q):
         """分层回测"""
         df = self.dfs.copy()
@@ -106,7 +106,7 @@ def layering(self, feature, min_q, max_q):
         dfm = df1.groupby('dt').agg({'n1b': 'mean'}).fillna(0)
         logger.info(f"分层累计收益 {feature} {min_q}-{max_q}：{dfm['n1b'].sum():.4f}; {daily_performance((dfm['n1b'] / 10000).to_list())}")
         return dfm
-    
+
     def report(self):
         """打印特征分析报告"""
         results_path = self.kwargs.get('results_path', None)
@@ -130,7 +130,7 @@ def report(self):
             _ = self.layering(feature, 0.9, 1)
             _ = self.layering(feature, 0, 0.1)
             _ = self.layering(feature, 0, 0.05)
-            
+
             df1['年月'] = df1['dt'].apply(lambda x: x.strftime("%Y年%m月"))
             dfm = df1.groupby('年月').agg({'ic': 'mean'})
             logger.info(f"特征 {feature} 与未来1日收益的相关系数月度描述：{dfm.describe().round(4).to_dict()}")
@@ -139,15 +139,15 @@ def report(self):
 
 class FixedNumberSelector:
     """选择固定数量（等权）的交易品种
-    
+
     可优化项：
     1. 传入 res_path, 将分析过程和分析结果保存下来
     2. 支持传入大盘择时信号，例如：大盘择时信号为空头时，多头只平不开
     """
-    
+
     def __init__(self, dfs, k, d, **kwargs):
         """
-        
+
         :param dfs: pd.DataFrame，所有交易品种的特征打分数据，必须包含以下列：dt, open, close, high, low, vol, amount, score；数据样例：
 
             ===================  =========  =======  =======  =======  =======  ========  =========  ========  =============
@@ -163,7 +163,7 @@ def __init__(self, dfs, k, d, **kwargs):
             2017-01-12 00:00:00  000001.SZ  956.441  958.536  960.631  956.441  42800677  391869402   10.9411    2.5563e-11
             2017-01-13 00:00:00  000001.SZ  957.488  959.583  962.726  955.393  43430137  397601906  -32.7865    2.51649e-11
             2017-01-16 00:00:00  000001.SZ  958.536  957.488  959.583  950.155  68316586  623025820   21.9292   -3.19607e-11
-            ===================  =========  =======  =======  =======  =======  ========  =========  ========  =============   
+            ===================  =========  =======  =======  =======  =======  ========  =========  ========  =============
 
         :param k: int，每期固定选择的数量
         :param d: int，每期允许变动的数量
@@ -193,7 +193,7 @@ def __preprocess(self):
         last_dt_map = {dt: dts[i-1] for i, dt in enumerate(dts)}
         self.dts, self.last_dt_map = dts, last_dt_map
         self.score_map = {dt: dfg[['symbol', 'dt', 'open', 'close', 'high', 'low', 'score', 'n1b']].copy() for dt, dfg in self.dfs.groupby('dt')}
-    
+
     def __deal_one_time(self, dt):
         """单次调整记录"""
         k, d, is_stocks = self.k, self.d, self.is_stocks
@@ -217,8 +217,8 @@ def __deal_one_time(self, dt):
 
             _df_operates = [{'symbol': row['symbol'], 'dt': dt, 'action': 'buy', 'price': row['close']} for _, row in _df.iterrows()]
             self.operates[dt] = pd.DataFrame(_df_operates)
-            return 
-        
+            return
+
         # 有持仓的情况
         score = self.score_map[dt]
         last_dt = self.last_dt_map[dt]
@@ -239,7 +239,7 @@ def __deal_one_time(self, dt):
         assert len(buy_symbols) == len(sell_symbols), "买入品种数量必须等于卖出品种数量"
         assert len(keep_symbols + buy_symbols) == k, "保持品种数量+买入品种数量必须等于k"
         _df = score[score.symbol.isin(keep_symbols + buy_symbols)].sort_values(by='score', ascending=False)
-    
+
         if len(_df) != k:
             logger.warning(f"选择的品种数量不等于{k}，当前只有{len(_df)}个品种")
 
@@ -250,9 +250,9 @@ def __deal_one_time(self, dt):
         last_holds['edge'] = last_holds.apply(lambda row: row['edge'] - self.operate_fee if row['symbol'] in sell_symbols else row['edge'], axis=1)
         self.holds[last_dt] = last_holds
 
-        _sell_operates = [{'symbol': row['symbol'], 'dt': dt, 'action': 'sell', 'price': row['close']} 
+        _sell_operates = [{'symbol': row['symbol'], 'dt': dt, 'action': 'sell', 'price': row['close']}
                           for _, row in score[score.symbol.isin(sell_symbols)].iterrows()]
         _buy_operates = [{'symbol': row['symbol'], 'dt': dt, 'action': 'buy', 'price': row['close']}
-                            for _, row in score[score.symbol.isin(buy_symbols)].iterrows()]
+                         for _, row in score[score.symbol.isin(buy_symbols)].iterrows()]
         _df_operates = pd.DataFrame(_sell_operates + _buy_operates)
         self.operates[dt] = _df_operates

czsc/sensors/plates.py

@@ -251,4 +251,3 @@ def create_holds(self, mean_col, mean_col_bins, sort_col, sort_col_bins, max_num
         from czsc.traders.performance import stock_holds_performance
         stock_holds_performance(self.dc, dfh, res_path=res_path)
         return dfh
-

czsc/sensors/utils.py

@@ -8,11 +8,9 @@
 import numpy as np
 from tqdm import tqdm
 from collections import Counter
-from typing import Callable, List, AnyStr
+from typing import List
 from sklearn.preprocessing import KBinsDiscretizer
-from deprecated import deprecated
-
-from ..data import TsDataCache, freq_cn2ts
+from ..data import TsDataCache
 
 
 def discretizer(df: pd.DataFrame, col: str, n_bins=20, encode='ordinal', strategy='quantile'):

czsc/traders/__init__.py

@@ -6,8 +6,7 @@
 describe: 交易员（traders）：使用 CZSC 分析工具进行择时策略的开发，交易等
 """
 from czsc.traders.base import (
-    CzscSignals, CzscTrader, generate_czsc_signals, check_signals_acc, get_unique_signals,
-    get_signals_by_conf
+    CzscSignals, CzscTrader, generate_czsc_signals, check_signals_acc, get_unique_signals
 )
 
 from czsc.traders.performance import (

czsc/traders/base.py

@@ -29,7 +29,7 @@
 class CzscSignals:
     """缠中说禅技术分析理论之多级别信号计算"""
 
-    def __init__(self, bg: BarGenerator = None,  **kwargs):
+    def __init__(self, bg: Optional[BarGenerator] = None, **kwargs):
         """
 
         :param bg: K线合成器
@@ -69,12 +69,14 @@ def get_signals_by_conf(self):
         """通过信号参数配置获取信号
 
         信号参数配置，格式如下：
+
             signals_config = [
                 {'name': 'czsc.signals.tas_ma_base_V221101', 'freq': '日线', 'di': 1, 'ma_type': 'SMA', 'timeperiod': 5},
                 {'name': 'czsc.signals.tas_ma_base_V221101', 'freq': '日线', 'di': 5, 'ma_type': 'SMA', 'timeperiod': 5},
                 {'name': 'czsc.signals.tas_double_ma_V221203', 'freq': '日线', 'di': 1, 'ma_seq': (5, 20), 'th': 100},
                 {'name': 'czsc.signals.tas_double_ma_V221203', 'freq': '日线', 'di': 5, 'ma_seq': (5, 20), 'th': 100},
             ]
+
         :return: 信号字典
         """
         s = OrderedDict()
@@ -152,32 +154,6 @@ def update_signals(self, bar: RawBar):
         self.s.update(last_bar.__dict__)
 
 
-@deprecated(version="0.9.16", reason="请使用 CzscSignals 类")
-def get_signals_by_conf(cat: CzscSignals, conf):
-    """通过信号参数配置获取信号
-
-    :param cat:
-    :param conf: 信号参数配置，格式如下：
-        conf = [
-            {'name': 'czsc.signals.tas_ma_base_V221101', 'freq': '日线', 'di': 1, 'ma_type': 'SMA', 'timeperiod': 5},
-            {'name': 'czsc.signals.tas_ma_base_V221101', 'freq': '日线', 'di': 5, 'ma_type': 'SMA', 'timeperiod': 5},
-            {'name': 'czsc.signals.tas_double_ma_V221203', 'freq': '日线', 'di': 1, 'ma_seq': (5, 20), 'th': 100},
-            {'name': 'czsc.signals.tas_double_ma_V221203', 'freq': '日线', 'di': 5, 'ma_seq': (5, 20), 'th': 100},
-        ]
-    :return: 信号字典
-    """
-    s = OrderedDict({"symbol": cat.symbol, "dt": cat.end_dt, "close": cat.latest_price})
-    for param in conf:
-        param = dict(param)
-        sig_func = import_by_name(param.pop('name'))
-        freq = param.pop('freq', None)
-        if freq in cat.kas:  # 如果指定了 freq，那么就使用 CZSC 对象作为输入
-            s.update(sig_func(cat.kas[freq], **param))   # type: ignore
-        else:                # 否则使用 CAT 作为输入
-            s.update(sig_func(cat, **param))
-    return s
-
-
 def generate_czsc_signals(bars: List[RawBar], signals_config: List[dict],
                           sdt: Union[AnyStr, datetime] = "20170101", init_n: int = 500, df=False, **kwargs):
     """使用 CzscSignals 生成信号
@@ -409,7 +385,7 @@ def get_ensemble_pos(self, method: Union[AnyStr, Callable] = None) -> float:
 
         return pos
 
-    def get_position(self, name: str) -> Position:
+    def get_position(self, name: str) -> Optional[Position]:
         """获取指定名称的仓位策略对象
 
         :param name: 仓位名称

czsc/traders/performance.py

@@ -69,7 +69,7 @@ def stock_holds_performance(dc: TsDataCache, dfh, res_path):
 
     # 绘制收益曲线
     plt.close()
-    fig = plt.figure(figsize=(13, 4*len(index_list)))
+    fig = plt.figure(figsize=(13, 4 * len(index_list)))
     axes = fig.subplots(len(index_list), 1, sharex=True)
     for i, _index in enumerate(index_list):
         ax = axes[i]
@@ -325,5 +325,3 @@ def combine_dates_and_pairs(dates: list, pairs: pd.DataFrame, results_path):
     tp_old.agg_to_excel(os.path.join(results_path, "原始交易评价.xlsx"))
     tp_new.agg_to_excel(os.path.join(results_path, "组合过滤评价.xlsx"))
     return tp_old, tp_new
-
-

czsc/traders/sig_parse.py

@@ -3,7 +3,7 @@
 author: zengbin93
 email: [email protected]
 create_dt: 2023/3/29 10:04
-describe: 
+describe:
 """
 import re
 from loguru import logger
@@ -61,7 +61,7 @@ def parse_params(self, name, signal):
             return None
 
         try:
-            params = parse(pats, key).named # type: ignore
+            params = parse(pats, key).named     # type: ignore
             if 'di' in params:
                 params['di'] = int(params['di'])
 
@@ -140,4 +140,3 @@ def get_signals_freqs(signals_seq: List) -> List[str]:
         if _freqs:
             freqs.extend(_freqs)
     return [x for x in sorted_freqs if x in freqs]
-

czsc/traders/weight_backtest.py

@@ -70,7 +70,7 @@ def __init__(self, dfw, digits=2, **kwargs) -> None:
 
         :param dfw: pd.DataFrame, columns = ['dt', 'symbol', 'weight', 'price'], 持仓权重数据，其中
 
-            dt      为K线结束时间，
+            dt      为K线结束时间，必须是连续的交易时间序列，不允许有时间断层
             symbol  为合约代码，
             weight  为K线结束时间对应的持仓权重，
             price   为结束时间对应的交易价格，可以是当前K线的收盘价，或者下一根K线的开盘价，或者未来N根K线的TWAP、VWAP等
@@ -109,6 +109,7 @@ def get_symbol_daily(self, symbol):
 
         :param symbol: str，合约代码
         :return: pd.DataFrame，品种每日收益率，
+
             columns = ['date', 'symbol', 'edge', 'return', 'cost']
             其中
                 date    为交易日，
@@ -143,7 +144,7 @@ def get_symbol_pairs(self, symbol):
         """获取某个合约的开平交易记录"""
         dfs = self.dfw[self.dfw['symbol'] == symbol].copy()
         dfs['volume'] = (dfs['weight'] * pow(10, self.digits)).astype(int)
-        dfs['bar_id'] = list(range(1, len(dfs)+1))
+        dfs['bar_id'] = list(range(1, len(dfs) + 1))
 
         # 根据权重变化生成开平仓记录
         operates = []

czsc/utils/calendar.py

@@ -0,0 +1,63 @@
+# -*- coding: utf-8 -*-
+"""
+author: zengbin93
+email: [email protected]
+create_dt: 2023/9/10 17:53
+describe: A股+期货的交易日历
+"""
+import pandas as pd
+from pathlib import Path
+
+
+def __prepare_cal():
+    """使用tushare获取交易日历，保存到本地"""
+    import pandas as pd
+    import tushare as ts
+
+    pro = ts.pro_api()
+    exchanges = ['SSE', 'SZSE', 'CFFEX', 'SHFE', 'CZCE', 'DCE', 'INE']
+    res = []
+    for exchange in exchanges:
+        df = pro.trade_cal(exchange=exchange, start_date='20100101', end_date='20251231')
+        res.append(df)
+    df = pd.concat(res, ignore_index=True)
+    df['is_open'] = df['is_open'].astype(int)
+    dfc = pd.pivot_table(df, index='cal_date', columns='exchange', values='is_open').fillna(0).reset_index()
+    dfc['cal_date'] = pd.to_datetime(dfc['cal_date'])
+    dfc.to_feather('calendar.feather')
+
+    # 验证：是不是所有交易所的交易日都一样
+    dfc = dfc[dfc['cal_date'] >= '2021-01-01']
+    dfc['sum'] = dfc[exchanges].sum(axis=1)
+    assert dfc['sum'].value_counts()
+
+    # 所有国内交易所的交易日历都是一样的
+    df = pro.trade_cal(exchange="SSE", start_date='20100101', end_date='20251231')
+    df['cal_date'] = pd.to_datetime(df['cal_date'])
+    df.sort_values('cal_date', inplace=True, ascending=True)
+    df = df.reset_index(drop=True)
+    df[['cal_date', 'is_open']].to_feather('china_calendar.feather')
+
+
+calendar = pd.read_feather(Path(__file__).parent / "china_calendar.feather")
+
+
+def is_trading_date(date):
+    """判断是否是交易日"""
+    date = pd.to_datetime(date)
+    is_open = calendar[calendar['cal_date'] == date].iloc[0]['is_open']
+    return is_open == 1
+
+
+def next_trading_date(date, n=1):
+    """获取未来第N个交易日"""
+    date = pd.to_datetime(date)
+    df = calendar[calendar['cal_date'] >= date]
+    return df[df['is_open'] == 1].iloc[n - 1]['cal_date']
+
+
+def prev_trading_date(date, n=1):
+    """获取过去第N个交易日"""
+    date = pd.to_datetime(date)
+    df = calendar[calendar['cal_date'] <= date]
+    return df[df['is_open'] == 1].iloc[-n]['cal_date']