优化择时买入基础类，减少上层策略冗余编码

.gitignore

@@ -5,4 +5,5 @@ logging.log
 p.sh
 *.pyc
 tmp
-test.py
+test.py
+/abupy/RomDataBu/df_kl.h5

abupy/FactorBuyBu/ABuFactorBuyBase.py

@@ -222,6 +222,47 @@ def fit_day(self, today):
     #     pass
 
 
+class AbuFactorBuyTD(AbuFactorBuyBase):
+    """很多策略中在fit_day中不仅仅使用今天的数据，经常使用昨天，前天数据，方便获取昨天，前天的封装"""
+
+    def read_fit_day(self, today):
+        """
+        覆盖base函数完成:
+        1. 为fit_day中截取昨天self.yesterday
+        2. 为fit_day中截取前天self.bf_yesterday
+        :param today: 当前驱动的交易日金融时间序列数据
+        :return: 生成的交易订单AbuOrder对象
+        """
+        if self.skip_days > 0:
+            self.skip_days -= 1
+            return None
+
+        # 今天这个交易日在整个金融时间序列的序号
+        self.today_ind = int(today.key)
+        # 回测中默认忽略最后一个交易日
+        if self.today_ind >= self.kl_pd.shape[0] - 1:
+            return None
+
+        # 忽略不符合买入的天（统计周期内前2天，因为需要昨天和前天）
+        if self.today_ind < 2:
+            return None
+
+        # 为fit_day中截取昨天
+        self.yesterday = self.kl_pd.iloc[self.today_ind - 1]
+        # 为fit_day中截取前天
+        self.bf_yesterday = self.kl_pd.iloc[self.today_ind - 2]
+
+        return self.fit_day(today)
+
+    def _init_self(self, **kwargs):
+        """raise NotImplementedError"""
+        raise NotImplementedError('NotImplementedError _init_self')
+
+    def fit_day(self, today):
+        """raise NotImplementedError"""
+        raise NotImplementedError('NotImplementedError fit_day')
+
+
 class AbuFactorBuyXD(AbuFactorBuyBase):
     """以周期为重要参数的策略，xd代表参数'多少天'如已周期为参数可直接继承使用"""
 

abupy/FactorBuyBu/ABuFactorBuyDemo.py

@@ -10,7 +10,7 @@
 import numpy as np
 import pandas as pd
 
-from .ABuFactorBuyBase import AbuFactorBuyBase, BuyCallMixin
+from .ABuFactorBuyBase import AbuFactorBuyBase, AbuFactorBuyXD, AbuFactorBuyTD, BuyCallMixin
 from .ABuFactorBuyBreak import AbuFactorBuyBreak
 from ..TLineBu.ABuTL import AbuTLine
 from ..FactorBuyBu.ABuBuyFactorWrap import AbuLeastPolyWrap
@@ -20,19 +20,16 @@
 
 
 # noinspection PyAttributeOutsideInit
-class AbuSDBreak(AbuFactorBuyBase, BuyCallMixin):
+class AbuSDBreak(AbuFactorBuyXD, BuyCallMixin):
     """示例买入因子： 在AbuFactorBuyBreak基础上进行降低交易频率，提高系统的稳定性处理"""
 
     def _init_self(self, **kwargs):
+        super(AbuSDBreak, self)._init_self(**kwargs)
         # 外部可以设置poly阀值，self.poly在fit_month中和每一个月大盘计算的poly比较，若是大盘的poly大于poly认为走势震荡
         self.poly = kwargs.pop('poly', 2)
         # 是否封锁买入策略进行择时交易
         self.lock = False
 
-        # 下面的代码和AbuFactorBuyBase的实现一摸一样
-        self.xd = kwargs['xd']
-        self.factor_name = '{}:{}'.format(self.__class__.__name__, self.xd)
-
     def fit_month(self, today):
         # fit_month即在回测策略中每一个月执行一次的方法
         # 策略中拥有self.benchmark，即交易基准对象，AbuBenchmark实例对象，benchmark.kl_pd即对应的市场大盘走势
@@ -66,20 +63,13 @@ def fit_day(self, today):
             # 如果封锁策略进行交易的情况下，策略不进行择时
             return None
 
-        # 下面的代码和AbuFactorBuyBase的实现一摸一样
-        if self.today_ind < self.xd - 1:
-            return None
         # 今天的收盘价格达到xd天内最高价格则符合买入条件
-        if today.close == self.kl_pd.close[self.today_ind - self.xd + 1:self.today_ind + 1].max():
-            # 把突破新高参数赋值skip_days，这里也可以考虑make_buy_order确定是否买单成立，但是如果停盘太长时间等也不好
-            self.skip_days = self.xd
-            # 生成买入订单, 由于使用了今天的收盘价格做为策略信号判断，所以信号发出后，只能明天买
+        if today.close == self.xd_kl.close.max():
             return self.buy_tomorrow()
-        return None
 
 
 @AbuLeastPolyWrap()
-class AbuTwoDayBuy(AbuFactorBuyBase, BuyCallMixin):
+class AbuTwoDayBuy(AbuFactorBuyTD, BuyCallMixin):
     """示例AbuLeastPolyWrap，混入BuyCallMixin，即向上突破触发买入event"""
 
     def _init_self(self, **kwargs):
@@ -92,14 +82,10 @@ def fit_day(self, today):
         :param today: 当前驱动的交易日金融时间序列数据
         :return:
         """
-        # 忽略不符合买入的天（统计周期内前第1天, 因为要用到昨天的交易数据）
-        if self.today_ind == 0:
-            return None
-
         # 今天的涨幅
         td_change = today.p_change
         # 昨天的涨幅
-        yd_change = self.kl_pd.ix[self.today_ind - 1].p_change
+        yd_change = self.yesterday.p_change
 
         if td_change > 0 and 0 < yd_change < td_change:
             # 连续涨两天, 且今天的涨幅比昨天还高 －>买入, 用到了今天的涨幅，只能明天买
@@ -172,10 +158,7 @@ def _init_self(self, **kwargs):
             与AbuFactorBuyBreak基本相同，唯一区别是关键子参数中添加了通过AbuFactorBuyBreakUmpDemo记录训练好的决策器
             self.hit_ml = kwargs['hit_ml']
         """
-        # 突破参数 xd， 比如20，30，40天...突破, 不要使用kwargs.pop('xd', 20), 明确需要参数xq
-        self.xd = kwargs['xd']
-        # 在输出生成的orders_pd中显示的名字
-        self.factor_name = '{}:{}'.format(self.__class__.__name__, self.xd)
+        super(AbuFactorBuyBreakHitPredictDemo, self)._init_self(**kwargs)
         # 添加了通过AbuFactorBuyBreakUmpDemo记录训练好的决策器
         self.hit_ml = kwargs['hit_ml']
 

abupy/FactorBuyBu/ABuFactorBuyWD.py

@@ -0,0 +1,111 @@
+# -*- encoding:utf-8 -*-
+"""
+    买入择时示例因子：黄金分割线买入择时因子
+"""
+
+from __future__ import absolute_import
+from __future__ import print_function
+from __future__ import division
+
+from .ABuFactorBuyBase import AbuFactorBuyTD, BuyCallMixin
+
+__author__ = '阿布'
+__weixin__ = 'abu_quant'
+
+
+class AbuFactorBuyWD(AbuFactorBuyTD, BuyCallMixin):
+    """
+        示例短线：日胜率均值回复策略
+
+        1. 默认以40天为周期(8周)结合涨跌阀值计算周几适合买入
+        2. 回测运行中每一月重新计算一次上述的周几适合买入
+        3. 在策略日任务中买入信号为：昨天下跌，今天开盘也下跌，且明天是计算出来的上涨概率大的'周几'
+    """
+
+    def _init_self(self, **kwargs):
+        """
+            kwargs中可选参数：buy_dw:    代表周期胜率阀值，默认0.55即55%
+            kwargs中可选参数：buy_dwm:   代表涨幅比例阀值系数，默认0.618
+            kwargs中可选参数：dw_period: 代表分析dw，dwm所使用的交易周期，默认40天周期(8周)
+        """
+        self.buy_dw = kwargs.pop('buy_dw', 0.55)
+        self.buy_dwm = kwargs.pop('buy_dwm', 0.618)
+        self.dw_period = kwargs.pop('dw_period', 40)
+
+        # combine_kl_pd中包含择时金融时间数据与择时之前一年的金融时间数据, 先取出择时开始之前的周期数据
+        last_kl = self.combine_kl_pd.loc[:self.kl_pd.index[0]]
+        if last_kl.shape[0] > self.dw_period:
+            last_kl = last_kl[-self.dw_period:]
+        # 开始计算周几买，_make_buy_date把结果被放在self.buy_date_week序列中
+        self._make_buy_date(last_kl)
+
+    def fit_month(self, today):
+        """月任务，每一个重新取之前一年的金融时间序列数据，重新计算一遍'周几买'"""
+        end_ind = self.combine_kl_pd[self.combine_kl_pd.date == today.date].key.values[0]
+        start_ind = end_ind - self.dw_period if end_ind - self.dw_period > 0 else 0
+        # 根据当前的交易日，切片过去的一年金融时间序列
+        last_kl = self.combine_kl_pd.iloc[start_ind:end_ind]
+        # 重新计算一遍'周几买'
+        self._make_buy_date(last_kl)
+
+    def fit_day(self, today):
+        """日任务：昨天下跌，今天开盘也下跌，根据今天是周几，在不在序列self.buy_date_week中决定今天买不买"""
+        if self.yesterday.p_change < 0 and today.open < self.yesterday.close \
+                and int(today.date_week) in self.buy_date_week:
+            # 由于没有用到今天的收盘价格等，可以直接使用buy_today
+            return self.buy_today()
+        return None
+
+    # noinspection PyProtectedMember
+    def _make_buy_date(self, last_kl):
+        """
+        根据金融时间周期数据切片具体计算'周几'买上涨概率大
+
+        :param last_kl: 金融时间周期数据切片，pd.DataFrame
+        """
+        from ..UtilBu import ABuKLUtil
+
+        self.buy_date_week = []
+        # 计算周期内，周期的胜率
+        last_dw = ABuKLUtil.date_week_win(last_kl)
+        # 摘取大于阀值self.buy_dw的'周几'，buy_dw默认0.55
+        last_dw_vd = last_dw[last_dw.win >= self.buy_dw]
+        """
+            eg: last_dw_vd
+                       0  1   win
+            date_week
+            周四         3  5  0.62
+            周五         2  6  0.75
+        """
+        if len(last_dw_vd) > 0:
+            # 如果胜率有符合要求的，使用周几平均涨幅计算date_week_mean
+            last_dwm = ABuKLUtil.date_week_mean(last_kl)
+            # 摘取满足胜率的last_dw_vd
+            last_dwm_vd = last_dwm.loc[last_dw_vd.index]
+            """
+                eg: last_dwm_vd
+                           _p_change
+                date_week
+                周四              1.55
+                周五              1.12
+            """
+            # 阀值计算方式1
+            dwm1 = abs(last_dwm.sum()).values[0] / self.buy_dwm
+            # 阀值计算方式2
+            dwm2 = abs(last_dwm._p_change).mean() / self.buy_dwm
+            # 如果symbol多可以使用&的关系
+            dm_effect = (last_dwm_vd._p_change > dwm1) | (last_dwm_vd._p_change > dwm2)
+            buy_date_loc = last_dwm_vd[dm_effect].index
+            """
+                eg: buy_date_loc
+                Index(['周四', '周五'], dtype='object', name='date_week')
+            """
+            if len(buy_date_loc) > 0:
+                # 如果涨跌幅阀值也满足，tolist，eg：['周一', '周二', '周三', '周四', '周五']
+                dw_index = last_dw.index.tolist()
+                # 如果是一周5个交易日的就是4，如果是比特币等7天交易日的就是6
+                max_ind = len(dw_index) - 1
+                for bdl in buy_date_loc:
+                    sell_ind = dw_index.index(bdl)
+                    buy_ind = sell_ind - 1 if sell_ind > 0 else max_ind
+                    self.buy_date_week.append(buy_ind)

abupy/FactorBuyBu/__init__.py

@@ -1,16 +1,19 @@
 from __future__ import absolute_import
 
-from .ABuFactorBuyBase import AbuFactorBuyBase, AbuFactorBuyXD, BuyCallMixin, BuyPutMixin
+from .ABuFactorBuyBase import AbuFactorBuyBase, AbuFactorBuyXD, AbuFactorBuyTD, BuyCallMixin, BuyPutMixin
 from .ABuFactorBuyBreak import AbuFactorBuyBreak, AbuFactorBuyPutBreak
+from .ABuFactorBuyWD import AbuFactorBuyWD
 from .ABuFactorBuyDemo import AbuSDBreak, AbuTwoDayBuy, AbuFactorBuyBreakUmpDemo
 from .ABuFactorBuyDemo import AbuFactorBuyBreakReocrdHitDemo, AbuFactorBuyBreakHitPredictDemo
 
 __all__ = [
     'AbuFactorBuyBase',
     'AbuFactorBuyXD',
+    'AbuFactorBuyTD',
     'BuyCallMixin',
     'BuyPutMixin',
     'AbuFactorBuyBreak',
+    'AbuFactorBuyWD',
     'AbuFactorBuyPutBreak',
     'AbuFactorBuyBreakUmpDemo',
     'AbuFactorBuyBreakReocrdHitDemo',

abupy/FactorSellBu/ABuFactorSellBase.py

@@ -209,4 +209,4 @@ def support_direction(self):
 
     def fit_day(self, today, orders):
         """raise NotImplementedError"""
-        raise NotImplementedError('NotImplementedError support_direction')
+        raise NotImplementedError('NotImplementedError fit_day')

abupy/FactorSellBu/ABuFactorSellNDay.py

@@ -19,6 +19,7 @@ class AbuFactorSellNDay(AbuFactorSellBase):
     def _init_self(self, **kwargs):
         """kwargs中可以包含: 参数sell_n：代表买入后持有的天数，默认1天"""
         self.sell_n = kwargs.pop('sell_n', 1)
+        self.is_sell_today = kwargs.pop('is_sell_today', False)
         self.sell_type_extra = '{}:sell_n={}'.format(self.__class__.__name__, self.sell_n)
 
     def support_direction(self):
@@ -36,4 +37,4 @@ def fit_day(self, today, orders):
             order.keep_days += 1
             if order.keep_days >= self.sell_n:
                 # 只要超过self.sell_n即卖出
-                self.sell_tomorrow(order)
+                self.sell_today(order) if self.is_sell_today else self.sell_tomorrow(order)

abupy/MarketBu/ABuSymbolPd.py

@@ -338,8 +338,10 @@ def combine_pre_kl_pd(kl_pd, n_folds=1):
     pre_kl_pd = make_kl_df(kl_pd.name, data_mode=ABuEnv.EMarketDataSplitMode.E_DATA_SPLIT_SE, n_folds=n_folds,
                            end=end)
     # 再合并两段时间序列，pre_kl_pd[:-1]跳过重复的end
-    pre_kl_pd = kl_pd if pre_kl_pd is None else pre_kl_pd[:-1].append(kl_pd)
-    return pre_kl_pd
+    combine_kl = kl_pd if pre_kl_pd is None else pre_kl_pd[:-1].append(kl_pd)
+    # 根据combine_kl长度重新进行key计算
+    combine_kl['key'] = list(range(0, len(combine_kl)))
+    return combine_kl
 
 
 def calc_atr(kline_df):

abupy_lecture/16-UMP主裁交易决策(ABU量化使用文档).ipynb

@@ -1294,10 +1294,7 @@
     "            与AbuFactorBuyBreak基本相同，唯一区别是关键子参数中添加了通过AbuFactorBuyBreakUmpDemo记录训练好的决策器\n",
     "            self.hit_ml = kwargs['hit_ml']\n",
     "        \"\"\"\n",
-    "        # 突破参数 xd， 比如20，30，40天...突破, 不要使用kwargs.pop('xd', 20), 明确需要参数xq\n",
-    "        self.xd = kwargs['xd']\n",
-    "        # 在输出生成的orders_pd中显示的名字\n",
-    "        self.factor_name = '{}:{}'.format(self.__class__.__name__, self.xd)\n",
+    "        super(AbuFactorBuyBreakHitPredictDemo, self)._init_self(**kwargs)\n",
     "        # 添加了通过AbuFactorBuyBreakUmpDemo记录训练好的决策器\n",
     "        self.hit_ml = kwargs['hit_ml']\n",
     "\n",
@@ -1316,7 +1313,7 @@
     "        if result == -1:\n",
     "            return True\n",
     "        return False\n",
-    "\n",
+    "    \n",
     "# 通过import的方式导入AbuFactorBuyBreakHitPredictDemo\n",
     "# 因为在windows系统上，启动并行后，在ipython notebook中定义的类会在子进程中无法找到\n",
     "from abupy import AbuFactorBuyBreakHitPredictDemo"