def get_bool_average(self, average_data):
self.average_data = average_data
length = len(average_data)
ref_al = REF(average_data, 1).series[length - 500 - 1:]
ref_al_2 = REF(average_data, 2).series[length - 500 - 2:]
average_line = average_data.series[length - 500:]
if (len(average_line) != len(ref_al)):
return None, None
ref_bool_data_position_2 = np.where(ref_al < ref_al_2)
ref_bool_data_position = np.where(ref_al < average_line)
intersect1d_list = np.intersect1d(ref_bool_data_position_2,
ref_bool_data_position)
self.average_axis_buy = {}
temp1 = np.where(average_line < 0.5)[-1]
self.average_axis_buy["X"] = np.intersect1d(intersect1d_list,
temp1) # 买点 小于 0.5
self.average_axis_buy["Z"] = average_line[self.average_axis_buy["X"]]
self.average_axis_sell = {}
self.average_axis_sell["X"] = np.where(average_line < ref_al)[-1]
ref_data = average_line - ref_al
ref_bool_data = ref_data > 0 if True else False
average_map = {}
average_map["X"] = self.average_axis_buy["X"]
average_map["Y"] = self.average_axis_buy["Z"]
average_map["C"] = "#DC143C"
self.additional_average_map_list.append(average_map)
return ref_bool_data, average_line
def get_buy_bottomUp_flag_list(self):
intersect1d_list = self.average_axis_buy["X"]
ref_ma3_1 = REF(self.ma3, 1)
ref_ma3_2 = REF(self.ma3, 2)
ma3_up_flag = np.where(
self.ma3.series[-500:] > ref_ma3_1.series[-500:])
ma3_down_flag = np.where(
ref_ma3_2.series[-500:] > ref_ma3_1.series[-500:])
right_flag = np.intersect1d(ma3_up_flag, ma3_down_flag)
X = self.X
my_close_list = np.array(X[:, 4], dtype=np.float)
my_close_list = pd.Series(my_close_list)
close_data = np.array(my_close_list)
close_data = NumericSeries(close_data)
my_open_list = np.array(X[:, 1], dtype=np.float)
my_open_list = pd.Series(my_open_list)
open_data = np.array(my_open_list)
open_data = NumericSeries(open_data)
columns = np.abs(my_close_list / my_open_list - 1)
columns = columns[-500:]
wanted_columns1 = np.where(0.0005 < columns)[0]
wanted_columns2 = np.where(columns < 0.005)[0]
wanted_columns = np.intersect1d(wanted_columns1, wanted_columns2)
right_flag = np.intersect1d(right_flag, wanted_columns)
right_flag = np.intersect1d(right_flag, intersect1d_list)
#高价等差
hhv_20 = HHV(self.ma3, 50)
hhv_rate_list = hhv_20 / close_data
hhv_rate_list = hhv_rate_list.series[-500:]
hhv_rate_list_flag = np.where(hhv_rate_list > 1.05)
right_flag = np.intersect1d(right_flag, hhv_rate_list_flag)
self.zhibiao_axis_map = {}
self.zhibiao_axis_map["X"] = right_flag
my_close_list = my_close_list.values[-500:]
self.zhibiao_axis_map["Y"] = my_close_list[right_flag]
numbers_columns = {}
numbers_columns["X"] = np.where(columns > 0.01)[0]
numbers_columns["Y"] = my_close_list[numbers_columns["X"]]
numbers_columns["Z"] = columns.values[numbers_columns["X"]]
self.additional_map_list.append(numbers_columns)
self.get_top_flag_position_list()
if len(right_flag) > 0:
flag = right_flag[-1] == 499
return flag
return False
def get_top_flag_position_list(self):
abs_close_list = self.abs_close_list()
close_series = NumericSeries(abs_close_list)
ref_close_2 = REF(close_series, 2)
ref_close_1 = REF(close_series, 1)
list1 = np.where(ref_close_1.series[-500:] > ref_close_2.series[-500:])
list2 = np.where(
ref_close_1.series[-500:] > close_series.series[-500:])
intersect1d_list = np.intersect1d(list1, list2)
self.top_flag_position_list = intersect1d_list
def getMountainAverage(self, my_close_list):
temp = NumericSeries(self.average_axis_buy["Z"])
ref_temp = REF(temp, 1)
size = ref_temp.series.size
if (size < 1):
return False
temp = temp.series[-size:]
temp_average = self.average_data.series[-500:]
buy_axis = {}
buy_axis["X"] = []
buy_axis["Y"] = []
buy_axis["Z"] = []
#找出买点
point_save = 0
for i in range(size):
if i + 1 <= size and i >= point_save:
i1 = self.average_axis_buy["X"][i]
value_i1 = temp[i]
for j in range(i + 1, size):
j1 = self.average_axis_buy["X"][j]
value_j1 = temp[j]
gap_length = j1 - i1
if (gap_length < 100 and gap_length > 20):
gap_abs = np.abs(value_i1 - value_j1)
if (gap_abs < 0.08):
#找出两点之间最高值
temp_y = temp_average[i1:j1]
temp_area = sum(temp_y) - value_i1 * (len(temp_y) -
2)
if temp_area > 0:
buy_axis["X"].append(i1)
buy_axis["X"].append(j1)
buy_axis["Y"].append(value_i1)
buy_axis["Y"].append(value_j1)
buy_axis["Z"].append(temp_area)
buy_axis["Z"].append(temp_area)
point_save = j
break
if len(buy_axis["X"]) == 0:
return False
temp_map = {}
temp_map["X"] = np.array(buy_axis["X"])
temp_map["Y"] = temp_average[temp_map["X"]]
temp_map["Z"] = np.array(buy_axis["Z"])
temp_map["C"] = "#0A0A0A"
self.additional_average_map_list.append(temp_map)
temp_map2 = {}
temp_map2["X"] = temp_map["X"]
temp_map2["Y"] = my_close_list[temp_map2["X"]]
self.zhibiao_axis_map = temp_map2
if temp_map["X"].size > 0 and temp_map["X"][-1] == 499:
return True
return False
def divergence_average(self, average_line,my_close_list,bool_data):
self.average_line=average_line
bool_data = NumericSeries(bool_data)
ref_bool_data = REF(bool_data,1)
select_bool = bool_data >ref_bool_data
ret=np.where(select_bool.series==True)[0]
if select_bool==True:
first_one_position = ret[-1]
first_average = average_line[first_one_position]
first_close = my_close_list[first_one_position]
second_one_position = ret[-2]
second_average = average_line[second_one_position]
second_close = my_close_list[second_one_position]
#不是底分型，就去除
high_data=np.array(self.X[:,2],dtype=np.float)
bottom_up=my_close_list[-1]>high_data[-2]
#divergence_average 背驰
flag=first_average <1.5 and second_average<1.5 and first_average>second_average and first_close< second_close \
and bottom_up
if(flag):
return True
self.get_buy_flag_list(ret, average_line, my_close_list)
return False
def get_sell_flag(self, average_line):
ref_average = REF(self.average_data, 1)
temp_flag = self.average_data < ref_average
temp_flag = temp_flag.series[-500:]
positions = np.where(temp_flag == True)[0]
temp = {}
temp["X"] = positions
temp["Y"] = average_line[positions]
temp["C"] = "#7FFF00"
self.additional_average_map_list.append(temp)
if positions[-1] == 499:
return True
return False
def double_cross(self, average_line, average_data, bool_data):
bool_data = NumericSeries(bool_data)
ref_bool_data = REF(bool_data, 1)
select_bool = bool_data > ref_bool_data
select_bool = select_bool.series[99 - 20:]
ret = np.where(select_bool == True)
size = ret[0].size
if (size < 2):
return False
average_line = average_line[100 - 20:]
first_one_position = ret[0][-1]
first_one = average_line[first_one_position]
second_one_position = ret[0][-2]
second_one = average_line[second_one_position]
if (first_one > second_one and select_bool[-1] == True):
return True
return False
def getBreakthroughHighPoint(self, my_close_list):
temp = NumericSeries(self.average_axis_buy["Z"])
ref_temp = REF(temp, 1)
size = ref_temp.series.size
if (size < 1):
return False
temp = temp.series[-size:]
bool_temp = temp - ref_temp.series > 0.25
bool_temp = np.insert(bool_temp, 0, False)
position_list = np.where(bool_temp == True)[0]
temp_map = {}
temp_map["X"] = self.average_axis_buy["X"][position_list]
temp_map["Y"] = self.average_axis_buy["Z"][position_list]
temp_map["C"] = "#0A0A0A"
self.additional_average_map_list.append(temp_map)
temp_map2 = {}
temp_map2["X"] = self.average_axis_buy["X"][position_list]
temp_map2["Y"] = my_close_list[temp_map2["X"]]
self.zhibiao_axis_map = temp_map2
if temp_map["X"].size > 0 and temp_map["X"][-1] == 499:
return True
return False