def on_initialize(self):
self.myinstr = "XSHE_000513"
self.bartype1 = "Bar_1day" #type1:订阅1day k线
self.bartype2 = "Bar_1min" #type2:订阅1min k线
self.subscribe(self.myinstr, self.bartype1) #订阅1day K线 of 白云机场
self.subscribe(self.myinstr, self.bartype2)
self.date = []
self.order0 = 0 ##6个订单编号
self.order1 = 0
self.order2 = 0
self.order3 = 0
self.order4 = 0
self.order5 = 0
self.bar0 = 0 ##6个收集bartype收盘价的全局变量
self.bar1 = 0
self.bar2 = 0
self.bar3 = 0
self.bar4 = 0
self.bar5 = 0
self.default_date = chrono.Date(19700101)
self.date0 = self.default_date
self.total_volumn = 0 ##用于计算每次分次下单的总交易额，在下一个交易日分次下单之前清零。
self.total_size = 0 ## 用于计算每次分次下单的总股数，在下一个交易日分次下单之前清零。
self.average_price = 0 ## 分次下单的平均价格
self.average_price_once = 0 ## 集合竞价的平均撮合价格
self.Trade_date = [] ##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 = [] # 存档历史数据 这个应该是bar类型
def on_initialize(self):
self.bartype1 = "Bar_1day" #type1:订阅1day k线
self.bartype2 = "Bar_1min" #type2:订阅1min k线
self.default_date = chrono.Date(19700101)
self.list = DataFrame(pd.read_csv("/workspace/user-files/Sh_list.csv"))
self.df = DataFrame(columns=[
'date', 'history', 'order0', 'order1', 'order2', 'order3',
'order4', 'order5', 'bar0', 'bar1', 'bar2', 'bar3', 'bar4', 'bar5',
't0', 't1', 't2', 't3', 't4', 't5', 'Trade_date', 'date0',
'total_volumn', 'total_size', 'average_price', 'average_price_once'
],
index=self.list['name'])
## 可以将异常数据和统计量统一打到这张DataFrame里面，然后取出这些行列打表
#
#结果输出可以用pd.concat([])去写
self.report = DataFrame(columns=['report'], index=self.list['name'])
for instr in self.list['name']:
self.subscribe(instr, self.bartype1) #订阅1day K线
self.subscribe(instr, self.bartype2) #订阅1min K线
self.df['date'][instr] = [] ## 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.df['Trade_date'][instr] = [
] ## 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.df['history'][instr] = [] ## 对应于该只股票的空列表，存档每日收盘价
self.df['date0'][instr] = self.default_date
def on_initialize(self):
#load参数
self.myinstr = "XSHE_000513"
self.bartype = "Bar_5min" #订阅5分钟k线
self.buyunit = 100 # 1手
self.subscribe(self.myinstr,self.bartype)
self.best_day = chrono.Date(20160712)
self.a0 = self.add_alarm(chrono.str_to_time(str(self.best_day)+" 01:35:00.000000"))
def on_initialize(self):
self.myinstr = "XSHE_000513"
self.bartype1 = "Bar_1day" #type1:订阅1day k线
#self.bartype2 = "Bar_1min" #type2:订阅1min k线
self.buyunit = 100 # 1手
self.subscribe(self.myinstr, self.bartype1) #订阅1day K线 of 白云机场
#self.subscribe(self.myinstr,self.bartype2)
self.default_date = chrono.Date(19700101)
self.date = []
self.Trade_date = [] ##空列表，搜集SEP后的交易日期
self.history = [] # 存档300天历史数据 这个应该是bar类型
def on_initialize(self):
self.bartype1 = "Bar_1day" #type1:订阅1day k线
self.bartype2 = "Bar_1min" #type2:订阅1min k线
self.default_date = chrono.Date(19700101)
self.list = DataFrame(
pd.read_csv("/workspace/user-files/Sh_list2.csv"))
self.df = DataFrame(columns=[
'date', 'history', 'order0', 'order1', 'order2', 'order3',
'order4', 'order5', 'bar0', 'bar1', 'bar2', 'bar3', 'bar4', 'bar5',
't0', 't1', 't2', 't3', 't4', 't5', 'Trade_date', 'date0',
'total_volumn', 'total_size', 'average_price',
'average_price_once', 'price1', 'price2', 'price3', 'price4',
'price5'
],
index=self.list['name'])
self.report = DataFrame(columns=['report', 'order_record'],
index=self.list['name'])
## 可以将异常数据和统计量统一打到这张DataFrame里面，然后取出这些行列打表
############初始化################################################
for instr in self.list['name']:
self.subscribe(instr, self.bartype1) #订阅1day K线
self.subscribe(instr, self.bartype2) #订阅1min K线
self.df['date'][instr] = [] ## 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.df['Trade_date'][instr] = [
] ## 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.df['history'][instr] = [] ## 对应于该只股票的空列表，存档每日收盘价
self.df['date0'][instr] = self.default_date
## report 为记录输出结果的DataFrame，‘report'用来记录交易日的信息，’order_record'用来记录订单的详情。
self.report['report'][instr] = {}
self.report['report'][instr]['date'] = [] #记录交易日
self.report['report'][instr]['stock_id'] = [] #记录交易品种
self.report['report'][instr]['possible_error0'] = [
] #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。
self.report['report'][instr]['possible_error1'] = [
] #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['order_record'][instr] = {}
self.report['order_record'][instr]['datetime'] = [] #订单对应的交易日
self.report['report'][instr]['stock_id'] = [] #重复记录交易品种 其实可有可无
self.report['order_record'][instr]['time0'] = [] #每天order0结束成交的时间
self.report['order_record'][instr]['time1'] = [] #每天order1结束成交的时间
self.report['order_record'][instr]['time2'] = [] #每天order2结束成交的时间
self.report['order_record'][instr]['time3'] = [] #每天order3结束成交的时间
self.report['order_record'][instr]['time4'] = [] #每天order4结束成交的时间
self.report['order_record'][instr]['time5'] = [] #每天order5结束成交的时间
self.report['order_record'][instr]['value_single'] = [] #集合竞价的总价值
self.report['order_record'][instr]['size_single'] = [] #集合竞价的单子大小
self.report['order_record'][instr]['auction_price'] = [] #集合竞价价格
self.report['order_record'][instr]['value_all'] = [] #分次下单的总价值
self.report['order_record'][instr]['size_all'] = [] #分次下单的总单量
self.report['order_record'][instr]['average_price'] = [] #分次下单的均价
def on_initialize(self):
self.list = DataFrame(pd.read_csv("/workspace/user-files/Sh_list.csv"))
self.bartype1 = "Bar_1day" #type1:订阅1day k线
self.bartype2 = "Bar_1min" #type2:订阅1min k线
for instr in self.list['name']:
self.subscribe(instr, self.bartype1) #订阅1day K线
self.subscribe(instr, self.bartype2) #订阅1min K线
self.date = {}
self.order0 = {} ##6个订单编号
self.order1 = {}
self.order2 = {}
self.order3 = {}
self.order4 = {}
self.order5 = {}
self.bar0 = {} ##6个收集bartype收盘价的全局变量
self.bar1 = {}
self.bar2 = {}
self.bar3 = {}
self.bar4 = {}
self.bar5 = {}
self.Trade_date = {} ##空字典，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 = {} # 存档历史数据 这个应该是bar类型
self.t0 = {} ##存档对应于某一只股票的交易闹钟
self.t1 = {}
self.t2 = {}
self.t3 = {}
self.t4 = {}
self.t5 = {}
self.default_date = chrono.Date(19700101)
self.date0 = {} ##用来在timeline上更新 对于某一只股票来说，每一日是否是买入日
self.total_volumn = {} ##用于计算每次分次下单的总交易额，在下一个交易日分次下单之前清零。
self.total_size = {} ## 用于计算每次分次下单的总股数，在下一个交易日分次下单之前清零。
self.average_price = {} ## 分次下单的平均价格
self.average_price_once = {} ## 集合竞价价格
def on_data_update(self, data):
#增加最新数据，去掉最旧数据
self.history = np.append(self.history, data.close)
self.history = np.delete(self.history, 0)
self.date = np.append(self.date, data.date)
self.date = np.delete(self.date, 0)
record = []
SEP_date = []
for i in range(4,len(self.history)-4):
if(self.history[i] > max(self.history[i-4:i]) and self.history[i] > max(self.history[i+1:i+5])):
record.append(i)
SEP_date.append(self.date[i])
for j in record:
print j
print chrono.Date(SEP_date)
def on_initialize(self):
self.bartype1 = "Bar_1day" #type1:订阅1day k线
self.bartype2 = "Bar_1min" #type2:订阅1min k线
self.default_date = chrono.Date(19700101)
self.list = DataFrame(pd.read_csv("/workspace/user-files/Sh_list.csv"))
self.df = DataFrame(columns=[
'date', 'history', 'order0', 'order1', 'order2', 'order3',
'order4', 'order5', 'bar0', 'bar1', 'bar2', 'bar3', 'bar4', 'bar5',
't0', 't1', 't2', 't3', 't4', 't5', 'Trade_date', 'date0',
'total_volumn', 'total_size', 'average_price', 'average_price_once'
],
index=self.list['name'])
for instr in self.list['name']:
self.subscribe(instr, self.bartype1) #订阅1day K线
self.subscribe(instr, self.bartype2) #订阅1min K线
# self.df['date'][instr] = []
# self.df['history'][instr] = []
self.df['date0'][instr] = self.default_date
self.df['order0'][instr] = 0
self.df['order1'][instr] = 0
self.df['order2'][instr] = 0
self.df['order3'][instr] = 0
self.df['order4'][instr] = 0
self.df['order5'][instr] = 0
self.df['bar0'][instr] = 0
self.df['bar1'][instr] = 0
self.df['bar2'][instr] = 0
self.df['bar3'][instr] = 0
self.df['bar4'][instr] = 0
self.df['bar5'][instr] = 0
self.df['total_volumn'][instr] = 0
self.df['total_size'][instr] = 0
self.df['average_price'][instr] = 0
self.df['average_price_once'][instr] = 0
import numpy as np
import quant
from quant import *
from quant import chrono
from quant import instr
%matplotlib inline
import matplotlib.pyplot as plt
quant.mustang.load_config("../default.ini")
###########################################################################################################
#获取Instrument
instr1 = instr.Instrument('XSHG_600004')
#定义起始时间
start = chrono.date_to_time(chrono.Date(20150303))
#定义截止时间
stop = chrono.date_to_time(chrono.Date(20150630))
#定义数据类型
datatype = "Bar_1day"
#取得数据
data1 = data.MarketData(instr1, datatype, start, stop)
#取得从起始时间至截止时间每日收盘价的array data1.close : type 为np.narray
record = []
# an empty list
#
# ##########################################################################################################
for i in range(4,len(data1.close)):
if(data1.close[i] > max(data1.close[i-4:i]) and data1.close[i] > max(data1.close[i+1:i+5])):
record.append(i)
# 判断SEP的准则，并记录下每一个符合要求的日期的顺序
for j in record:
print data1.date[j]
## <type 'numpy.int32'>
