0.4.0-alpha 更新
1.17 增加了一个带参数的延时装饰器
2017/7/4
QUANTAXIS.QAUtil.QADate.QA_util_time_delay()
使用方式
from QUANTAXIS import QA_util_time_dalay
@QA_util_time_dalay(2)
#延时2秒
def pp():
print(1)
1.18 web 部分的改进
2017/7/10
web部分,修改了页面布局和回测的显示方式
去除了之前单个股票和行情的对比
改成了资金曲线和组合分析的方式
1.19 回测部分的最后一天停牌情况的处理
2017/7/10
_remains_day=0
while __message['header']['status']==500:
#停牌状态,这个时候按停牌的最后一天计算价值(假设平仓)
__last_bid['date'] = self.trade_list[self.end_real_id-_remains_day]
_remains_day+=1
__message = self.market.receive_bid(__last_bid, self.setting.client)
#直到市场不是为0状态位置,停止前推日期在原来的代码里,组合的最后一天需要被平仓,而如果恰巧这一天停牌:(600127,601001 在2017/6/30都是停牌状态)
那么就会一直陷入市场返回500错误,而回测框架一直在组装报价,来回轮询,陷入死循环
现在加入了对于市场的数据的检测,如果是500状态,则会将交易日向前递推一天,重新组装报价,直到停牌的最后一天为止
1.20 新增一个时间接口 QA_util_time_now()
2017/7/10
返回的就是datetime.datetime.now()的结果
1.21 新增一个QAWeb的接口
2017/7/10
可以在命令行直接输入 quantaxis_web 来启动这个服务器,端口在5050(带socket)
1.22 新增一个QACSV的接口
2017/7/13
现在可以便捷的吧数据存成csv格式:
import QUANTAXIS as QA
data=['1',2,'x','t']
QA.QA_util_save_csv(data,'test')1.23 新增一个交易时间的变量
2017/7/13
from QUANTAXIS import trade_date_sse
print(trade_date_sse)1.24 新增一个k线接口(通达信)
2017/7/13
import QUANTAXIS as QA
QA.QA_fetch_get_stock_day('tdx','601801','2017-01-01','2017-07-01')Out[3]:
open close high low vol amount year month day \
0 14.39 14.70 14.76 14.36 164426.0 239606672.0 2016 12 14
1 14.55 14.99 15.10 14.55 188986.0 281594560.0 2016 12 15
2 15.03 16.00 16.11 14.86 180712.0 281681280.0 2016 12 16
3 15.93 16.79 17.05 15.93 207338.0 345849056.0 2016 12 19
4 16.87 16.80 16.87 16.39 81613.0 135869952.0 2016 12 20
5 16.90 16.88 17.12 16.66 98858.0 166289104.0 2016 12 21
6 16.95 16.61 16.96 16.28 92686.0 153868304.0 2016 12 22
7 16.58 16.51 17.05 16.03 89121.0 147409888.0 2016 12 23
8 16.59 17.30 17.65 16.26 226506.0 387817440.0 2016 12 26
9 17.35 17.49 17.62 17.14 106783.0 186348288.0 2016 12 27
10 17.56 17.89 17.96 17.40 181507.0 321431424.0 2016 12 28
11 17.69 17.59 17.84 17.35 109785.0 193161984.0 2016 12 29
12 17.58 17.57 17.90 17.24 142565.0 251420576.0 2016 12 30
13 17.60 17.45 17.60 17.15 106347.0 185482976.0 2017 1 3
14 17.40 17.00 17.40 16.41 204781.0 344751936.0 2017 1 4
15 16.85 16.86 17.00 16.55 102506.0 171934160.0 2017 1 5
16 16.89 16.90 17.13 16.73 77188.0 130475344.0 2017 1 6
17 16.86 16.81 17.10 16.76 77502.0 131223856.0 2017 1 9
18 16.81 17.40 17.53 16.10 193194.0 325042304.0 2017 1 10
19 17.20 17.68 17.84 17.10 368129.0 651724672.0 2017 1 11
20 17.68 17.96 18.24 17.61 408000.0 735998016.0 2017 1 12
21 17.96 18.31 18.84 17.80 337911.0 619458496.0 2017 1 13
22 18.30 18.56 18.66 17.36 284054.0 518380576.0 2017 1 16
23 18.39 18.57 19.00 17.60 263141.0 491844896.0 2017 1 17
24 18.23 17.99 18.80 17.90 95064.0 173332688.0 2017 1 18
25 17.81 18.24 18.39 17.80 49353.0 89376280.0 2017 1 19
26 18.32 18.46 18.69 18.19 143792.0 265606432.0 2017 1 20
27 18.28 18.84 19.00 18.26 228183.0 427935936.0 2017 1 23
28 18.80 19.00 19.10 18.50 113616.0 214593088.0 2017 1 24
29 19.00 18.78 19.14 18.72 265691.0 504179328.0 2017 1 25
.. ... ... ... ... ... ... ... ... ...
89 12.73 12.98 13.16 12.68 176499.0 228946128.0 2017 5 17
90 12.78 13.26 13.27 12.71 145098.0 189804288.0 2017 5 18
91 13.20 13.09 13.33 13.08 78134.0 102854576.0 2017 5 19
92 13.15 12.63 13.18 12.50 96282.0 123843520.0 2017 5 22
93 12.64 12.76 13.16 12.53 111309.0 141901152.0 2017 5 23
94 12.74 13.17 13.19 12.62 104595.0 136030272.0 2017 5 25
95 13.12 13.09 13.25 13.08 69110.0 90972200.0 2017 5 26
96 13.25 13.42 13.44 13.18 110524.0 147250112.0 2017 5 31
97 13.43 13.26 13.46 13.07 88958.0 117311632.0 2017 6 1
98 13.09 12.91 13.09 12.70 91351.0 117710656.0 2017 6 2
99 12.98 12.83 12.98 12.74 110453.0 141482496.0 2017 6 5
100 12.77 12.83 12.85 12.68 62182.0 79344728.0 2017 6 6
101 12.80 13.26 13.30 12.74 144478.0 188284080.0 2017 6 7
102 13.19 13.13 13.33 13.09 101171.0 133169712.0 2017 6 8
103 13.12 13.04 13.15 12.96 87196.0 113572160.0 2017 6 9
104 13.00 12.76 13.00 12.74 86848.0 111470976.0 2017 6 12
105 12.62 12.96 13.02 12.60 67389.0 87066136.0 2017 6 13
106 13.00 12.75 13.00 12.72 62876.0 80648232.0 2017 6 14
107 12.74 12.85 12.95 12.70 71232.0 91385064.0 2017 6 15
108 12.87 12.71 12.87 12.67 59412.0 75701632.0 2017 6 16
109 12.72 12.90 12.92 12.69 77521.0 98993784.0 2017 6 19
110 12.86 13.21 13.24 12.86 180431.0 237042944.0 2017 6 20
111 13.28 13.21 13.32 13.00 117902.0 154974640.0 2017 6 21
112 13.20 13.37 13.58 13.15 191968.0 256520880.0 2017 6 22
113 13.28 13.21 13.29 12.99 114182.0 150111952.0 2017 6 23
114 13.22 13.71 13.73 13.16 186511.0 252084368.0 2017 6 26
115 13.74 13.78 13.81 13.54 135037.0 184420320.0 2017 6 27
116 13.73 13.98 14.05 13.56 242043.0 335440128.0 2017 6 28
117 13.94 13.96 14.01 13.84 178812.0 248913488.0 2017 6 29
118 13.89 13.82 13.94 13.71 83402.0 115219784.0 2017 6 30
hour minute datetime
0 15 0 2016-12-14 15:00
1 15 0 2016-12-15 15:00
2 15 0 2016-12-16 15:00
3 15 0 2016-12-19 15:00
4 15 0 2016-12-20 15:00
5 15 0 2016-12-21 15:00
6 15 0 2016-12-22 15:00
7 15 0 2016-12-23 15:00
8 15 0 2016-12-26 15:00
9 15 0 2016-12-27 15:00
10 15 0 2016-12-28 15:00
11 15 0 2016-12-29 15:00
12 15 0 2016-12-30 15:00
13 15 0 2017-01-03 15:00
14 15 0 2017-01-04 15:00
15 15 0 2017-01-05 15:00
16 15 0 2017-01-06 15:00
17 15 0 2017-01-09 15:00
18 15 0 2017-01-10 15:00
19 15 0 2017-01-11 15:00
20 15 0 2017-01-12 15:00
21 15 0 2017-01-13 15:00
22 15 0 2017-01-16 15:00
23 15 0 2017-01-17 15:00
24 15 0 2017-01-18 15:00
25 15 0 2017-01-19 15:00
26 15 0 2017-01-20 15:00
27 15 0 2017-01-23 15:00
28 15 0 2017-01-24 15:00
29 15 0 2017-01-25 15:00
.. ... ... ...
89 15 0 2017-05-17 15:00
90 15 0 2017-05-18 15:00
91 15 0 2017-05-19 15:00
92 15 0 2017-05-22 15:00
93 15 0 2017-05-23 15:00
94 15 0 2017-05-25 15:00
95 15 0 2017-05-26 15:00
96 15 0 2017-05-31 15:00
97 15 0 2017-06-01 15:00
98 15 0 2017-06-02 15:00
99 15 0 2017-06-05 15:00
100 15 0 2017-06-06 15:00
101 15 0 2017-06-07 15:00
102 15 0 2017-06-08 15:00
103 15 0 2017-06-09 15:00
104 15 0 2017-06-12 15:00
105 15 0 2017-06-13 15:00
106 15 0 2017-06-14 15:00
107 15 0 2017-06-15 15:00
108 15 0 2017-06-16 15:00
109 15 0 2017-06-19 15:00
110 15 0 2017-06-20 15:00
111 15 0 2017-06-21 15:00
112 15 0 2017-06-22 15:00
113 15 0 2017-06-23 15:00
114 15 0 2017-06-26 15:00
115 15 0 2017-06-27 15:00
116 15 0 2017-06-28 15:00
117 15 0 2017-06-29 15:00
118 15 0 2017-06-30 15:00
[119 rows x 12 columns]
1.25 在回测的时候,增加一个回测内全局变量
2017/7/14
现在的回测加载的函数句柄里面会增加一个叫info的句柄,这个句柄在引擎里面是交易日循环外部的变量,可以存贮一些连续周期信息,和一些点位信息
在quantaxis/test/new test的strategy里面可以看到改动
1.26 新增一个创建多维list的函数
2017/7/14
在QUANTAXIS.QAUtil中, 接口的名称是 QA_util_multi_demension_list(row_,col_)
如果需要创建一个[[],[]], 那就用 row_=2,col=0
其他时候,返回的都是[[None]]
import QUANTAXIS as QA
QA.QAUtil.QA_util_multi_demension_list(3,0)
QA.QAUtil.QA_util_multi_demension_list(3,3)[[], [], []]
[[None, None, None], [None, None, None], [None, None, None]]1.27 修改了一个QABacktest的传参, 现在在策略中,需要指定买卖状态
2017/7/19
现在 QUANTAXIS在接受策略的传参的时候,需要指定买卖状态
在开始的时候,quantaxis使用的是if_buy和账户状态来判断是否买卖的
- 账户持仓, if_buy=1 视为卖出
- 账户空仓, if_buy=1 视为买入
买卖行为和账户状态相关联对于买卖的行为有很大的限制,现在对于这个行为和账户持仓状态进行了解耦
同时,为了兼容性,如果策略并没有给出if_buy或者if_sell,则会将其初始化为0,及不操作
以下是改动后的源代码
def __QA_backtest_excute_bid(self, __result, __date, __hold, __code, __amount):
"""
这里是处理报价的逻辑部分
2017/7/19 修改
__result传进来的变量重新区分: 现在需要有 if_buy, if_sell
因为需要对于: 持仓状态下继续购买进行进一步的支持*简单的情形就是 浮盈加仓
if_buy, if_sell都需要传入
现在的 买卖状态 和 持仓状态 是解耦的
"""
# 为了兼容性考虑,我们会在开始的时候检查是否有这些变量
if 'if_buy' not in list(__result.keys()):
__result['if_buy'] = 0
if 'if_sell' not in list(__result.keys()):
__result['if_sell'] = 0
self.__QA_backtest_set_bid_model()
if self.bid.bid['bid_model'] == 'strategy':
__bid_price = __result['price']
else:
__bid_price = self.bid.bid['price']
__bid = self.bid.bid
__bid['order_id'] = str(random.random())
__bid['user'] = self.setting.QA_setting_user_name
__bid['strategy'] = self.strategy_name
__bid['code'] = __code
__bid['date'] = __date
__bid['price'] = __bid_price
__bid['amount'], __bid['amount_model'] = self.__QA_bid_amount(
__result['amount'], __amount)
if __result['if_buy'] == 1:
# 这是买入的情况
__bid['towards'] = 1
__message = self.market.receive_bid(
__bid, self.setting.client)
if float(self.account.message['body']['account']['cash'][-1]) > \
float(__message['body']['bid']['price']) * \
float(__message['body']['bid']['amount']):
# 这里是买入资金充足的情况
# 不去考虑
pass
else:
# 如果买入资金不充足,则按照可用资金去买入
__message['body']['bid']['amount'] = int(float(
self.account.message['body']['account']['cash'][-1]) / float(
float(str(__message['body']['bid']['price'])[0:5]) * 100)) * 100
if __message['body']['bid']['amount'] > 0:
# 这个判断是为了 如果买入资金不充足,所以买入报了一个0量单的情况
#如果买入量>0, 才判断为成功交易
self.account.QA_account_receive_deal(__message)
elif __result['if_buy'] == 0:
# 如果买入状态为0,则不进行任何买入操作
pass
# 下面是卖出操作,这里在卖出前需要考虑一个是否有仓位的问题:
# 因为在股票中是不允许卖空操作的,所以这里是股票的交易引擎和期货的交易引擎的不同所在
if __result['if_sell'] == 1 and __hold == 1:
__bid['towards'] = -1
__message = self.market.receive_bid(
__bid, self.setting.client)
self.account.QA_account_receive_deal(
__message)1.28 对于backtest的加载的外部函数的接口进行修改
2017/7/20
对于backtest的加载的外部策略,现在暂时有以下三个句柄
- on_start
- strategy
- on_end
其中,on_start/on_end这两个句柄可有可无, strategy句柄是必须要有的
1.29 修改了QA_Query 里返回的数组格式
2017/7/21
def QA_fetch_stock_day(code, startDate, endDate, collections=QA_Setting.client.quantaxis.stock_day, type_='numpy'):
# print(datetime.datetime.now())
startDate = str(startDate)[0:10]
endDate = str(endDate)[0:10]
if QA_util_date_valid(endDate) == True:
list_a = []
for item in collections.find({
'code': str(code)[0:6], "date_stamp": {
"$lte": QA_util_date_stamp(endDate),
"$gte": QA_util_date_stamp(startDate)}}):
list_a.append([str(item['code']), float(item['open']), float(item['high']), float(
item['low']), float(item['close']), float(item['volume']), item['date'], float(item['turnover'])])
## 多种数据格式
if type_ == 'numpy':
data = numpy.asarray(list_a)
elif type_ == 'list':
data = list_a
elif type_ == 'pandas':
data = DataFrame(list_a, columns=[
'code', 'open', 'high', 'low', 'close', 'volume', 'date', 'turnover'])
return data
else:
QA_util_log_info('something wrong with date')现在可以通过QA_fetch_stock_day里面选择是返回list,numpy还是dataframe. 为了兼容性考虑,默认依然是numpy
1.30 修改了QA_util_save_csv的模式
2017/07/21
def QA_util_save_csv(data: list, name: str, column=None, location=None):
# 重写了一下保存的模式
assert isinstance(data, list)
if location == None:
path = './' + str(name) + '.csv'
else:
path = location + str(name) + '.csv'
with open(path, 'w', newline='') as f:
csvwriter = csv.writer(f)
if column == 'None':
pass
else:
csvwriter.writerow(column)
for item in data:
csvwriter.writerow(item)示例:
import QUANTAXIS as QA
QA.QA_util_save_csv(QA.QA_fetch_stock_day('000001','2017-01-01','2017-07-01','list'),'000001')000001,9.11,9.18,9.09,9.16,459840.47,2017-01-03,0.31
000001,9.15,9.18,9.14,9.16,449329.53,2017-01-04,0.31
000001,9.17,9.18,9.15,9.17,344372.91,2017-01-05,0.24
000001,9.17,9.17,9.11,9.13,358154.19,2017-01-06,0.24
000001,9.13,9.17,9.11,9.15,361081.56,2017-01-09,0.21
000001,9.15,9.16,9.14,9.15,241053.95,2017-01-10,0.14
000001,9.14,9.17,9.13,9.14,303430.88,2017-01-11,0.18
000001,9.13,9.17,9.13,9.15,428006.75,2017-01-12,0.25
000001,9.14,9.19,9.12,9.16,434301.38,2017-01-13,0.26
000001,9.15,9.16,9.07,9.14,683165.81,2017-01-16,0.4
000001,9.12,9.16,9.1,9.15,545552.38,2017-01-17,0.32
000001,9.14,9.19,9.13,9.17,574269.38,2017-01-18,0.34
000001,9.15,9.24,9.15,9.18,437712.88,2017-01-19,0.26
000001,9.17,9.23,9.17,9.22,393328.56,2017-01-20,0.23
000001,9.22,9.26,9.2,9.22,420299.31,2017-01-23,0.25
000001,9.23,9.28,9.2,9.27,470244.09,2017-01-24,0.28
000001,9.27,9.28,9.25,9.26,304401.97,2017-01-25,0.18
000001,9.27,9.34,9.26,9.33,420712.56,2017-01-26,0.25
000001,9.45,10.26,9.33,10.26,19345.0,2017-02-02,0.01
000001,9.34,9.36,9.23,9.26,315472.25,2017-02-03,0.19
000001,9.26,9.32,9.26,9.31,516786.12,2017-02-06,0.31
000001,9.31,9.32,9.27,9.3,396884.97,2017-02-07,0.23
...........
1.31 通达信5分钟线解析保存
2017/7/23
首先要下载:
- 深圳5分钟线
http://www.tdx.com.cn/products/data/data/vipdoc/sz5fz.zip
- 上证5分钟线
http://www.tdx.com.cn/products/data/data/vipdoc/sh5fz.zip
然后解压到一个目录里
import QUANTAXIS as QA
QA.QA_SU_save_stock_min_5('你解压好的目录')
#例如 QA.QA_SU_save_stock_min_5('C:\\users\\yutiansut\\desktop\\sh5fz')In [2]: QA.QA_SU_save_stock_min_5('C:\\users\\yutiansut\\desktop\\sz5fz')
QUANTAXIS>> Now_saving 000001's 5 min tick
QUANTAXIS>> Now_saving 000002's 5 min tick
QUANTAXIS>> Now_saving 000004's 5 min tick
QUANTAXIS>> Now_saving 000005's 5 min tick
QUANTAXIS>> Now_saving 000006's 5 min tick
QUANTAXIS>> Now_saving 000007's 5 min tick
QUANTAXIS>> Now_saving 000008's 5 min tick
QUANTAXIS>> Now_saving 000009's 5 min tick
QUANTAXIS>> Now_saving 000010's 5 min tick
QUANTAXIS>> Now_saving 000011's 5 min tick
QUANTAXIS>> Now_saving 000012's 5 min tick
QUANTAXIS>> Now_saving 000014's 5 min tick
QUANTAXIS>> Now_saving 000016's 5 min tick
QUANTAXIS>> Now_saving 000017's 5 min tick
QUANTAXIS>> Now_saving 000018's 5 min tick
QUANTAXIS>> Now_saving 000019's 5 min tick
QUANTAXIS>> Now_saving 000020's 5 min tick
QUANTAXIS>> Now_saving 000021's 5 min tick
QUANTAXIS>> Now_saving 000022's 5 min tick
QUANTAXIS>> Now_saving 000023's 5 min tick
QUANTAXIS>> Now_saving 000024's 5 min tick
QUANTAXIS>> Now_saving 000025's 5 min tick
QUANTAXIS>> Now_saving 000026's 5 min tick
QUANTAXIS>> Now_saving 000027's 5 min tick
QUANTAXIS>> Now_saving 000028's 5 min tick1.32 获取指数k线的api更新
2017/7/24
QA_fetch_index_day(code, startDate, endDate,type_='numpy' ,collections=QA_Setting.client.quantaxis.stock_day)一般而言,直接使用QA_fetch_index_day(code, startDate, endDate)就可以了
1.33 QA_util更新 QA_util_time_stamp
2017/7/24
QA_util_time_stamp用于将时间转化成时间戳
import QUANTAXIS as QA
QA.QA_util_time_stamp('2017-01-01 10:25:08')