def on_data_update(self, data):
if data.type == "Bar_1day":
#用日度交易数据来收集数据并确定SEP
#增加最新数据
self.history = np.append(self.history, data.close)
self.date = np.append(self.date, data.date)
if (len(self.history)-10>0):
for i in range(max(4,len(self.history)-10),len(self.history)-5):
if(self.history[i] > max(self.history[i-4:i]) and self.history[i] > max(self.history[i+1:i+5]) and (self.date[i+5] not in self.Trade_date)):
## 判断SEP条件
self.Trade_date.append(self.date[i+5])
if data.date in self.Trade_date:
cal = chrono.Calendar("ChinaStocks")
self.date0 = data.date
print "由SEP确定的交易日为：",cal.add(data.date, 1) ## SEP 之后第五个交易日被记录，其下一天为交易日期
# 集合竞价 9：25
self.t0 = self.add_alarm(chrono.str_to_time(str(cal.add(self.date0, 1))+"01:25:00.000000"))
self.t1 = self.add_alarm(chrono.str_to_time(str(cal.add(self.date0, 1))+"01:31:00.000000"))
self.t2 = self.add_alarm(chrono.str_to_time(str(cal.add(self.date0, 1))+"01:37:00.000000"))
self.t3 = self.add_alarm(chrono.str_to_time(str(cal.add(self.date0, 1))+"01:43:00.000000"))
self.t4 = self.add_alarm(chrono.str_to_time(str(cal.add(self.date0, 1))+"01:49:00.000000"))
self.t5 = self.add_alarm(chrono.str_to_time(str(cal.add(self.date0, 1))+"01:55:00.000000"))
cal = chrono.Calendar("ChinaStocks")
if cal.add(data.date,-1) in self.Trade_date:
print "五次下单分时价格为:",self.bar1,self.bar2,self.bar3,self.bar4,self.bar5
if data.type == "Bar_1min":
cal = chrono.Calendar("ChinaStocks")
if chrono.time_to_date(data.time) == cal.add(self.date0, 1):
if chrono.str_to_time(str(cal.add(self.date0, 1))+"01:31:00.000000") == data.time:
self.bar1 = data.close
if chrono.str_to_time(str(cal.add(self.date0, 1))+"01:37:00.000000") == data.time:
self.bar2 = data.close
if chrono.str_to_time(str(cal.add(self.date0, 1))+"01:43:00.000000") == data.time:
self.bar3 = data.close
if chrono.str_to_time(str(cal.add(self.date0, 1))+"01:49:00.000000") == data.time:
self.bar4 = data.close
if chrono.str_to_time(str(cal.add(self.date0, 1))+"01:55:00.000000") == data.time:
self.bar5 = data.close
def on_data_update(self, data):
if data.type == "Bar_1day":
#用日度交易数据来收集数据并确定SEP
#增加最新数据，去掉最旧数据
self.history = np.append(self.history, data.close)
self.date = np.append(self.date, data.date)
if (len(self.history) - 10 > 0):
for i in range(max(4,
len(self.history) - 10),
len(self.history) - 5):
if (self.history[i] > max(self.history[i - 4:i]) and
self.history[i] > max(self.history[i + 1:i + 5])
and (self.date[i + 5] not in self.Trade_date)):
## 判断SEP条件
self.Trade_date.append(self.date[i + 5])
if data.date in self.Trade_date:
cal = chrono.Calendar("ChinaStocks")
print "由SEP确定的交易日为：", cal.add(data.date, 1)
# 集合竞价 9：25
self.t0 = self.add_alarm(
chrono.str_to_time(
str(cal.add(data.date, 1)) + "01:25:00.000000"))
self.t1 = self.add_alarm(
chrono.str_to_time(
str(cal.add(data.date, 1)) + "01:30:00.000000"))
self.t2 = self.add_alarm(
chrono.str_to_time(
str(cal.add(data.date, 1)) + "01:36:00.000000"))
self.t3 = self.add_alarm(
chrono.str_to_time(
str(cal.add(data.date, 1)) + "01:42:00.000000"))
self.t4 = self.add_alarm(
chrono.str_to_time(
str(cal.add(data.date, 1)) + "01:48:00.000000"))
self.t5 = self.add_alarm(
chrono.str_to_time(
str(cal.add(data.date, 1)) + "01:54:00.000000"))
def on_data_update(self, data):
for instr in self.list['name']:
if str(data.instrument) == instr:
if data.type == 'Bar_1day':
self.report['report'][instr]['end_time'].append(
chrono.time_to_str(get_current_time()))
cal = chrono.Calendar("ChinaStocks")
self.df['history'][instr] = np.append(
self.df['history'][instr], data.close)
self.df['date'][instr] = np.append(self.df['date'][instr],
data.date)
print 'Today is:', data.date, 'Stock id: ', instr
self.report['report'][instr]['date'].append(
cal.add(data.date, 1))
self.report['report'][instr]['stock_id'].append(str(instr))
self.report['report'][instr]['history'].append(data.close)
print "日期记录:", self.report['report'][instr][
'date'], '\n', "交易价记录:", self.report['report'][instr][
'price'], '\n', "每日收盘价记录:", self.report['report'][instr][
'history'], '\n', 'value:', self.report['report'][
instr]['value'], 'size:', self.report['report'][
instr]['size'], 'order id: ', self.report[
'report'][instr][
'states'], '\n', "订单时间：", self.report[
'report'][instr][
'order_time'], '\n', 'data返回终止时间：', self.report[
'report'][instr][
'end_time'], '\n'
self.df['t0'][instr] = self.add_alarm(
chrono.str_to_time(
str(cal.add(data.date, 1)) + "01:55:00.000000"))
self.df['t1'][instr] = self.add_alarm(
chrono.str_to_time(
str(cal.add(data.date, 1)) + "06:55:00.000000"))
def on_data_update(self, data):
for instr in self.list['name']: ##遍历股票池中的股票
if str(data.instrument) == instr: ##对于某只特定的股票：
self.date[instr] = [] ## 字典中对应于该只股票的空列表，存档每只股票的交易日，因为不同股票可能不一样
self.Trade_date[instr] = [
] ##字典中对应于该只股票的空列表，搜集SEP后的第五个交易日期，每一个列表中的交易日，我们将在下一个交易日交易
self.history[instr] = [] ## 字典中对应于该只股票的空列表， 存档每日收盘价
## 这几个变量都得用字典形式存储，防止数据跑窜了地方
self.order0[instr] = 0 ##6个订单编号
self.order1[instr] = 0
self.order2[instr] = 0
self.order3[instr] = 0
self.order4[instr] = 0
self.order5[instr] = 0
self.bar0[instr] = 0 ##6个收集bartype收盘价的全局变量
self.bar1[instr] = 0
self.bar2[instr] = 0
self.bar3[instr] = 0
self.bar4[instr] = 0
self.bar5[instr] = 0
self.date0[instr] = self.default_date
if data.type == "Bar_1day":
#用日度交易数据来收集数据并确定SEP
#增加最新数据
self.history[instr] = np.append(self.history[instr],
data.close)
self.date[instr] = np.append(self.date[instr], data.date)
if (len(self.history[instr]) - 10 > 0):
for i in range(max(4,
len(self.history[instr]) - 10),
len(self.history[instr]) - 5):
if (self.history[instr][i] > max(
self.history[instr][i - 4:i])
and self.history[instr][i] > max(
self.history[instr][i + 1:i + 5])
and (self.date[instr][i + 5]
not in self.Trade_date[instr])):
## 判断SEP条件
self.Trade_date[instr].append(
self.date[instr][i + 5])
if data.date in self.Trade_date[instr]:
## 如果某天是 该只股票的某个SEP日期的后五日：
cal = chrono.Calendar("ChinaStocks")
self.date0[instr] = data.date
print "由SEP确定的交易日为：", cal.add(
data.date, 1) ## SEP 之后第五个交易日被记录，其下一天为交易日期
## 设定不同的闹钟进行买入交易
self.t0[instr] = self.add_alarm(
chrono.str_to_time(
str(cal.add(data.date, 1)) +
"01:25:00.000000"))
self.t1[instr] = self.add_alarm(
chrono.str_to_time(
str(cal.add(data.date, 1)) +
"01:31:00.000000"))
self.t2[instr] = self.add_alarm(
chrono.str_to_time(
str(cal.add(data.date, 1)) +
"01:37:00.000000"))
self.t3[instr] = self.add_alarm(
chrono.str_to_time(
str(cal.add(data.date, 1)) +
"01:43:00.000000"))
self.t4[instr] = self.add_alarm(
chrono.str_to_time(
str(cal.add(data.date, 1)) +
"01:49:00.000000"))
self.t5[instr] = self.add_alarm(
chrono.str_to_time(
str(cal.add(data.date, 1)) +
"01:55:00.000000"))
cal = chrono.Calendar("ChinaStocks")
if cal.add(data.date, -1) in self.Trade_date[instr]:
## 如果某一天的前一天是 instr该只股票SEP之后第五个交易日被记录（已被记录在self.Trade_date[instr]),那么他这一天将是买入的日期
## 在这里打印是为了对应 data.type=="Bar_1day",只需要每天打一次就行
print "五次下单分时价格为:", self.bar1[instr], self.bar2[
instr], self.bar3[instr], self.bar4[
instr], self.bar5[instr]
if data.type == "Bar_1min":
cal = chrono.Calendar("ChinaStocks")
if chrono.time_to_date(data.time) == cal.add(
self.date0[instr], 1):
## 对应某一只股票，改天1Min bar线对应的日期为交易日的话：
if chrono.str_to_time(
str(cal.add(self.date0[instr], 1)) +
"01:31:00.000000") == data.time:
self.bar1[instr] = data.close
if chrono.str_to_time(
str(cal.add(self.date0[instr], 1)) +
"01:37:00.000000") == data.time:
self.bar2[instr] = data.close
if chrono.str_to_time(
str(cal.add(self.date0[instr], 1)) +
"01:43:00.000000") == data.time:
self.bar3[instr] = data.close
if chrono.str_to_time(
str(cal.add(self.date0[instr], 1)) +
"01:49:00.000000") == data.time:
self.bar4[instr] = data.close
if chrono.str_to_time(
str(cal.add(self.date0[instr], 1)) +
"01:55:00.000000") == data.time:
self.bar5[instr] = data.close
def on_data_update(self, data):
for instr in self.list['name']:
if str(data.instrument) == instr:
if data.type == "Bar_1day":
self.df['history'][instr] = np.append(
self.df['history'][instr], data.close)
self.df['date'][instr] = np.append(self.df['date'][instr],
data.date)
if (len(self.df['history'][instr]) - 10 > 0):
for i in range(
max(4,
len(self.df['history'][instr]) - 10),
len(self.df['history'][instr]) - 5):
if (self.df['history'][instr][i] > max(
self.df['history'][instr][i - 4:i])
and self.df['history'][instr][i] > max(
self.df['history'][instr][i + 1:i + 5])
and (self.df['date'][instr][i + 5]
not in self.df['Trade_date'][instr])):
## 判断SEP条件
self.df['Trade_date'][instr].append(
self.df['date'][instr][i + 5])
if data.date in self.df['Trade_date'][
instr]: ##########################################################################################
## 如果某天是 该只股票的某个SEP日期的后五日： #
cal = chrono.Calendar("ChinaStocks") #
self.df['date0'][instr] = data.date #
print instr, "由SEP确定的交易日为：", cal.add(
self.df['date0'][instr], 1
) ## SEP 之后第五个交易日被记录，其下一天为交易日期 #
#
self.df['t0'][instr] = self.add_alarm(
chrono.str_to_time(
str(cal.add(self.df['date0'][instr], 1)) +
"01:25:00.000000")) #
self.df['t1'][instr] = self.add_alarm(
chrono.str_to_time(
str(cal.add(self.df['date0'][instr], 1)) +
"01:31:00.000000")) #
self.df['t2'][instr] = self.add_alarm(
chrono.str_to_time(
str(cal.add(self.df['date0'][instr], 1)) +
"01:37:00.000000")) #
self.df['t3'][instr] = self.add_alarm(
chrono.str_to_time(
str(cal.add(self.df['date0'][instr], 1)) +
"01:43:00.000000")) #
self.df['t4'][instr] = self.add_alarm(
chrono.str_to_time(
str(cal.add(self.df['date0'][instr], 1)) +
"01:49:00.000000")) #
self.df['t5'][instr] = self.add_alarm(
chrono.str_to_time(
str(cal.add(self.df['date0'][instr], 1)) +
"01:55:00.000000")) #
#
cal = chrono.Calendar("ChinaStocks") #
#
if cal.add(data.date, -1) in self.df['Trade_date'][
instr]: #############################################################################
## 如果某一天的前一天是 instr该只股票SEP之后第五个交易日被记录（已被记录在self.Trade_date[instr]),那么他这一天将是买入的日期
## 在这里打印是为了对应 data.type=="Bar_1day",只需要每天打一次就行
print instr, "五次下单分时价格为:", self.df['bar1'][
instr], self.df['bar2'][instr], self.df['bar3'][
instr], self.df['bar4'][instr], self.df[
'bar5'][instr]
if data.type == "Bar_1min":
cal = chrono.Calendar("ChinaStocks")
if chrono.time_to_date(data.time) == cal.add(
self.df['date0'][instr], 1):
## 对应某一只股票，该天1Min bar线对应的日期为交易日的话：
if chrono.str_to_time(
str(cal.add(self.df['date0'][instr], 1)) +
"01:31:00.000000") == data.time:
self.df['bar1'][instr] = data.close
if chrono.str_to_time(
str(cal.add(self.df['date0'][instr], 1)) +
"01:37:00.000000") == data.time:
self.df['bar2'][instr] = data.close
if chrono.str_to_time(
str(cal.add(self.df['date0'][instr], 1)) +
"01:43:00.000000") == data.time:
self.df['bar3'][instr] = data.close
if chrono.str_to_time(
str(cal.add(self.df['date0'][instr], 1)) +
"01:49:00.000000") == data.time:
self.df['bar4'][instr] = data.close
if chrono.str_to_time(
str(cal.add(self.df['date0'][instr], 1)) +
"01:55:00.000000") == data.time:
self.df['bar5'][instr] = data.close
def on_data_update(self, data):
for instr in self.list['name']: ##遍历股票池中的股票
if str(data.instrument) == instr: ##对于某只特定的股票：
if data.type == "Bar_1day":
#用日度交易数据来收集数据并确定SEP
#增加最新数据
self.df['history'][instr]= np.append(self.df['history'][instr], data.close)
self.df['date'][instr]= np.append(self.df['date'][instr], data.date)
if (len(self.df['history'][instr])-10>0):
for i in range(max(4,len(self.df['history'][instr])-10),len(self.df['history'][instr])-5):
if(self.df['history'][instr][i] > max(self.df['history'][instr][i-4:i]) and self.df['history'][instr][i] > max(self.df['history'][instr][i+1:i+5]) and (self.df['history'][instr][i+5] not in self.df['Trade_date'][instr])):
## 判断SEP条件
self.df['Trade_date'][instr].append(self.df['date'][instr][i+5])
if data.date in self.df['Trade_date'][instr]:
## 如果某天是 该只股票的某个SEP日期的后五日：
cal = chrono.Calendar("ChinaStocks")
self.df['date0'][instr] = data.date
print "由SEP确定的交易日为：",cal.add(data.date, 1) ## SEP 之后第五个交易日被记录，其下一天为交易日期
## 设定不同的闹钟进行买入交易
self.df['t0'][instr] = self.add_alarm(chrono.str_to_time(str(cal.add(data.date, 1))+"01:25:00.000000"))
self.df['t1'][instr] = self.add_alarm(chrono.str_to_time(str(cal.add(data.date, 1))+"01:31:00.000000"))
self.df['t2'][instr] = self.add_alarm(chrono.str_to_time(str(cal.add(data.date, 1))+"01:37:00.000000"))
self.df['t3'][instr] = self.add_alarm(chrono.str_to_time(str(cal.add(data.date, 1))+"01:43:00.000000"))
self.df['t4'][instr] = self.add_alarm(chrono.str_to_time(str(cal.add(data.date, 1))+"01:49:00.000000"))
self.df['t5'][instr] = self.add_alarm(chrono.str_to_time(str(cal.add(data.date, 1))+"01:55:00.000000"))
cal = chrono.Calendar("ChinaStocks")
if cal.add(data.date,-1) in self.df['Trade_date'][instr]:
## 如果某一天的前一天是 instr该只股票SEP之后第五个交易日被记录（已被记录在self.Trade_date[instr]),那么他这一天将是买入的日期
## 在这里打印是为了对应 data.type=="Bar_1day",只需要每天打一次就行
print "五次下单分时价格为:",self.df['bar1'][instr],self.df['bar2'][instr],self.df['bar3'][instr],self.df['bar4'][instr],self.df['bar5'][instr]
if data.type == "Bar_1min":
cal = chrono.Calendar("ChinaStocks")
if chrono.time_to_date(data.time) == cal.add(self.df['date0'][instr], 1):
## 对应某一只股票，改天1Min bar线对应的日期为交易日的话：
if chrono.str_to_time(str(cal.add(self.df['date0'][instr], 1))+"01:31:00.000000") == data.time:
self.df['bar1'][instr] = data.close
if chrono.str_to_time(str(cal.add(self.df['date0'][instr], 1))+"01:37:00.000000") == data.time:
self.df['bar2'][instr] = data.close
if chrono.str_to_time(str(cal.add(self.df['date0'][instr], 1))+"01:43:00.000000") == data.time:
self.df['bar3'][instr] = data.close
if chrono.str_to_time(str(cal.add(self.df['date0'][instr], 1))+"01:49:00.000000") == data.time:
self.df['bar4'][instr] = data.close
if chrono.str_to_time(str(cal.add(self.df['date0'][instr], 1))+"01:55:00.000000") == data.time:
self.df['bar5'][instr] = data.close
def on_alarm(self,id):
for instr in self.list['name']: ##遍历股票池中的股票
if id == self.df['t0'][instr] :
self.df['order0'][instr] = self.place_stock_order(instr, 5 , trade.MarketPrice())
self.df['total_volumn'][instr] = 0
self.df['total_size'][instr] = 0
## 每到一个新的指定交易日，清零之前的累加（用来求新的交易均价）
if id == self.df['t1'][instr]:
self.df['order1'][instr] = self.place_stock_order(instr, 1 , trade.MarketPrice())
if id == self.df['t2'][instr]:
self.df['order2'][instr] = self.place_stock_order(instr, 1 , trade.MarketPrice())
if id == self.df['t3'][instr]:
self.df['order3'][instr] = self.place_stock_order(instr, 1 , trade.MarketPrice())
if id == self.df['t4'][instr]:
self.df['order4'][instr] = self.place_stock_order(instr, 1, trade.MarketPrice())
if id == self.df['t5'][instr]:
self.df['order5'][instr] = self.place_stock_order(instr, 1, trade.MarketPrice())
def on_order_update(self, order):
for instr in self.list['name']: ##遍历股票池中的股票
if order.id == self.df['order0'][instr] and order.state == OrderState.FINAL:
## 集合竞价得到的平均购入价格
self.df['average_price_once'][instr] = order.filled_value / order.filled_size
if order.id != self.df['order0'][instr] and order.state == OrderState.FINAL:
## 累积加和分次下单的总价和下单总量
self.df['total_volumn'][instr] += order.filled_value
self.df['total_size'][instr] += order.filled_size
if order.id == self.df['order5'][instr] and order.state ==OrderState.FINAL:
## 最后一单交易完成之后，核算平均成交价格
self.df['average_price'][instr] = self.df['total_volumn'][instr] / self.df['total_size'][instr]
print "Stock_id:",instr,"分次下单总交易量:",self.df['total_volumn'][instr],"分次下单总股数:",self.df['total_size'][instr] ,"分次下单均价：",self.df['average_price'][instr],"集合竞价均价:",self.df['average_price_once'][instr]
def on_data_update(self, data):
for instr in self.list['name']:
if str(data.instrument) == instr:
if data.type == "Bar_1day":
cal = chrono.Calendar("ChinaStocks")
self.df['history'][instr] = np.append(
self.df['history'][instr], data.close)
self.df['date'][instr] = np.append(self.df['date'][instr],
data.date)
if (len(self.df['history'][instr]) - 10 > 0):
for i in range(
max(4,
len(self.df['history'][instr]) - 10),
len(self.df['history'][instr]) - 5):
if (self.df['history'][instr][i] > max(
self.df['history'][instr][i - 4:i])
and self.df['history'][instr][i] > max(
self.df['history'][instr][i + 1:i + 5])
and (self.df['date'][instr][i + 5]
not in self.df['Trade_date'][instr])):
## 判断SEP条件
self.df['Trade_date'][instr].append(
self.df['date'][instr][i + 5])
if data.date in self.df['Trade_date'][instr]:
self.df['date0'][instr] = data.date
self.df['t0'][instr] = self.add_alarm(
chrono.str_to_time(
str(cal.add(self.df['date0'][instr], 1)) +
"01:25:00.000000"))
self.df['t1'][instr] = self.add_alarm(
chrono.str_to_time(
str(cal.add(self.df['date0'][instr], 1)) +
"01:31:00.000000"))
self.df['t2'][instr] = self.add_alarm(
chrono.str_to_time(
str(cal.add(self.df['date0'][instr], 1)) +
"01:37:00.000000"))
self.df['t3'][instr] = self.add_alarm(
chrono.str_to_time(
str(cal.add(self.df['date0'][instr], 1)) +
"01:43:00.000000"))
self.df['t4'][instr] = self.add_alarm(
chrono.str_to_time(
str(cal.add(self.df['date0'][instr], 1)) +
"01:49:00.000000"))
self.df['t5'][instr] = self.add_alarm(
chrono.str_to_time(
str(cal.add(self.df['date0'][instr], 1)) +
"01:55:00.000000"))
if cal.add(data.date, -1) in self.df['Trade_date'][instr]:
##记录交易日#####
self.report['report'][instr]['date'].append(data.date)
self.report['report'][instr]['stock_id'].append(instr)
###$$$$$#######
print instr, "由SEP确定的交易日为：", data.date, "五次下单分时价格为:", self.df[
'bar1'][instr], self.df['bar2'][instr], self.df[
'bar3'][instr], self.df['bar4'][
instr], self.df['bar5'][instr]
print self.report['order_record'][instr]
if abs((self.df['average_price'][instr] -
self.df['average_price_once'][instr]) /
self.df['average_price_once'][instr]) >= 0.02:
self.report['report'][instr][
'possible_error0'].append('Error')
#记录可能出现的错误:集合竞价与分次下单平均价格差别太大。
else:
self.report['report'][instr][
'possible_error0'].append('Correct')
if abs(
(self.df['bar1'][instr] - self.df['price1'][instr])
/ self.df['bar1'][instr]) + abs(
(self.df['bar2'][instr] -
self.df['price2'][instr]) /
self.df['bar2'][instr]) + abs(
(self.df['bar3'][instr] -
self.df['price3'][instr]) /
self.df['bar3'][instr]) + abs(
(self.df['bar4'][instr] -
self.df['price4'][instr]) /
self.df['bar4'][instr]) + abs(
(self.df['bar5'][instr] -
self.df['price5'][instr]) /
self.df['bar5'][instr]) >= 0.1:
#记录可能出现的错误:bar线数据与实际成交数据差别太大。
self.report['report'][instr][
'possible_error1'].append('Error')
else:
self.report['report'][instr][
'possible_error1'].append('Correct')
if data.type == "Bar_1min":
cal = chrono.Calendar("ChinaStocks")
if chrono.time_to_date(data.time) == cal.add(
self.df['date0'][instr], 1):
## 对应某一只股票，该天1Min bar线对应的日期为交易日的话：
if chrono.str_to_time(
str(cal.add(self.df['date0'][instr], 1)) +
"01:31:00.000000") == data.time:
self.df['bar1'][instr] = data.close
if chrono.str_to_time(
str(cal.add(self.df['date0'][instr], 1)) +
"01:37:00.000000") == data.time:
self.df['bar2'][instr] = data.close
if chrono.str_to_time(
str(cal.add(self.df['date0'][instr], 1)) +
"01:43:00.000000") == data.time:
self.df['bar3'][instr] = data.close
if chrono.str_to_time(
str(cal.add(self.df['date0'][instr], 1)) +
"01:49:00.000000") == data.time:
self.df['bar4'][instr] = data.close
if chrono.str_to_time(
str(cal.add(self.df['date0'][instr], 1)) +
"01:55:00.000000") == data.time:
self.df['bar5'][instr] = data.close