modify - 20190718

README.md

@@ -22,4 +22,8 @@ pip install git+git://github.com/zengbin93/chan.git
 ```
 
 * vr 表示 量比
-* dt 表示 该周期的交易结束时间
\ No newline at end of file
+* dt 表示 该周期的交易结束时间
+
+
+### K线绘图
+

chan/__init__.py

 # coding: utf-8
-
+import os
 from . import a
 
 
@@ -8,3 +8,6 @@ version = "0.0.1"
 email = "zeng_bin8888@163.com"
 
 
+cache_path = os.path.join(os.path.expanduser('~'), ".chan")
+
+

chan/a/__init__.py

@@ -6,4 +6,4 @@
 """
 
 from .daily_classfier import daily_classifier
-from .utils import get_kline
+from .utils import get_kline, get_realtime_kline

chan/a/calendar.py

+# coding: utf-8
+
+import os
+import time
+import functools
+import tushare as ts
+import pandas as pd
+import warnings
+from datetime import datetime
+
+from chan import cache_path
+
+# A股交易日历
+# --------------------------------------------------------------------
+FILE_CALENDAR = os.path.join(cache_path, 'calendar.csv')
+if os.path.exists(FILE_CALENDAR) and \
+        time.time() - os.path.getmtime(FILE_CALENDAR) < 3600 * 24 * 300:
+    trade_calendar = pd.read_csv(FILE_CALENDAR, encoding='utf-8')
+else:
+    trade_calendar = ts.trade_cal()  # tushare提供的交易日历
+    trade_calendar.to_csv(FILE_CALENDAR, index=False, encoding='utf-8')
+
+
+def is_trade_day(date):
+    """判断date日期是不是交易日
+
+    :param date: str or datetime.date, 如 2018-03-15
+    :return: Bool
+    """
+    trade_day = trade_calendar[trade_calendar["isOpen"] == 1]
+    trade_day_list = list(trade_day['calendarDate'])
+    if isinstance(date, datetime):
+        date = str(date.date())
+    if date in trade_day_list:
+        return True
+    else:
+        return False
+
+
+def in_trade_time():
+    """判断当前是否是交易时间"""
+    today = str(datetime.now().date())
+    if not is_trade_day(today):  # 如果今天不是交易日，返回False
+        return False
+    t1 = today + " " + "09:30:00"
+    t1 = datetime.strptime(t1, "%Y-%m-%d %H:%M:%S")
+    t2 = today + " " + "11:30:00"
+    t2 = datetime.strptime(t2, "%Y-%m-%d %H:%M:%S")
+    t3 = today + " " + "13:00:00"
+    t3 = datetime.strptime(t3, "%Y-%m-%d %H:%M:%S")
+    t4 = today + " " + "15:00:00"
+    t4 = datetime.strptime(t4, "%Y-%m-%d %H:%M:%S")
+    if t1 <= datetime.now() <= t2 or t3 <= datetime.now() <= t4:
+        return True
+    else:
+        return False
+
+
+def recent_trade_days(date, n=10):
+    """返回任一日期date前后的n个交易日日期
+
+    :param n: int
+        返回交易日的数量n。
+        如果 n > 0，则返回date日期未来的n个交易日日期；
+        如果 n < 0，则返回date日期过去的n个交易日日期。
+    :param date: str  默认值 today
+    :return: list
+        n个交易日日期列表
+    """
+    cal_date = list(trade_calendar['calendarDate'])
+    date_index = cal_date.index(date)
+    if n >= 0:
+        recent_days = cal_date[date_index:date_index + n + 20]
+    else:
+        recent_days = cal_date[date_index + n - 20:date_index + 1]
+        recent_days = recent_days[::-1]
+
+    res = []
+    for d in recent_days:
+        if is_trade_day(d):
+            res.append(d)
+        if len(res) == abs(n):
+            break
+    return res
+
+
+def run_at_trade_time(func):
+    """控制函数func仅在交易时间段运行"""
+
+    @functools.wraps(func)
+    def wrapper(*args, **kw):
+        if in_trade_time():
+            res = func(*args, **kw)
+        else:
+            msg = "%s 不是交易时间，函数 %s 仅在交易时间执行" % (
+                datetime.now().__str__(), func.__name__
+            )
+            warnings.warn(msg)
+            res = None
+        return res
+
+    return wrapper
+
+

chan/a/daily_classfier.py

@@ -21,6 +21,7 @@ def daily_classifier(ts_code, trade_date, return_central=False):
     >>> kind, central = daily_classifier('600122.SH', "20190613", return_central=True)
     >>> print(kind, central)
     """
+
     start_date = datetime.strptime(trade_date, '%Y%m%d')
     end_date = start_date + timedelta(days=1)
     end_date = end_date.date().__str__().replace("-", "")
@@ -62,12 +63,15 @@ def daily_classifier(ts_code, trade_date, return_central=False):
     elif (first_central is None and last_central) or (first_central and last_central is None):
         max_p = max(data.iloc[:3, :]['close'])
         min_p = min(data.iloc[:3, :]['close'])
+
         # 1、在前三根30分钟K线出现当天高点
         if max(data['close']) == max_p:
             kind = "弱平衡市"
+
         # 2、在前三根30分钟K线出现当天低点
         elif min(data['close']) == min_p:
             kind = "强平衡市"
+
         # 3、在前三根30分钟K线不出现当天高低点
         else:
             kind = "转折平衡市"
@@ -80,6 +84,7 @@ def daily_classifier(ts_code, trade_date, return_central=False):
             kind = "向上两中枢走势"
         else:
             raise ValueError("两中枢的最低价不可以相等")
+
     else:
         raise ValueError('中枢计算错误')
 

chan/a/utils.py

@@ -15,7 +15,7 @@ def get_kline(ts_code, start_date, end_date, freq='30min'):
     :param end_date: str
         日期，如 20190610
     :return: pd.DataFrame
-        columns = ["symbol", "dt", "open", "close", "high", "low", "vr"]
+        columns = ["symbol", "dt", "open", "close", "high", "low", "vol"]
 
     example:
     >>> get_kline(ts_code='600122.SH', start_date='20190601', end_date='20190610', freq='30min')
@@ -33,10 +33,35 @@ def get_kline(ts_code, start_date, end_date, freq='30min'):
 
     df.sort_values('dt', inplace=True)
     df.reset_index(drop=True, inplace=True)
-    df['vr'] = 0
-    for i in range(5, len(df)):
-        df.loc[i, 'vr'] = round(df.loc[i, 'vol'] / df.loc[i-5:i-1, 'vol'].mean(), 4)
 
-    return df[['symbol', 'dt', 'open', 'close', 'high', 'low', 'vr']]
+    # 计算量比
+    # df['vr'] = 0
+    # for i in range(5, len(df)):
+    #     df.loc[i, 'vr'] = round(df.loc[i, 'vol'] / df.loc[i-5:i-1, 'vol'].mean(), 4)
+
+    return df[['symbol', 'dt', 'open', 'close', 'high', 'low', 'vol']]
+
+
+def get_realtime_kline(ts_code, freq="5min"):
+    """实时获取分钟K线
+
+    :param ts_code: str
+        tushare 股票代码，如 600122.SH
+    :param freq: str
+        K线周期，分钟级别，可选值 5min 15min 30min 60min
+
+    :return: pd.DataFrame
+        columns = ["symbol", "dt", "open", "close", "high", "low", "vol"]
+
+    """
+    code = ts_code[:6]
+
+    df = ts.get_k_data(code=code, ktype=freq.replace("min", ""))
+    df['symbol'] = ts_code
+    df.rename(columns={'date': 'dt', 'volume': 'vol'}, inplace=True)
+    df.sort_values('dt', inplace=True)
+    df.reset_index(drop=True, inplace=True)
+
+    return df[["symbol", "dt", "open", "close", "high", "low", "vol"]]
 
 

chan/analyze.py

@@ -80,9 +80,3 @@ def find_fx(kline):
     return kline
 
 
-def find_bi(kline):
-    kline1 = kline.dropna(axis=0, how='any',  subset=['fx'])
-    kline2 = kline.dropna(axis=0, how='any',  subset=['high_m', 'low_m'])
-    pass
-
-

requirements.txt

+tqdm
+pandas
 tushare