Ptrade获取L2逐笔数据，免费，券商提供

Ptrade • 李魔佛 发表了文章 • 0 个评论 • 135 次浏览 • 2025-05-24 00:22 • 来自相关话题

本月初国金的Ptrade做了一次大版本的升级，内置的python版本从老旧的python3.5一下子升级到了python3.11。

并且还发现居然可以免费使用L2逐笔数据，分时逐笔委托队列等高频毫秒级的数据

Ptrade接口文档已更新： https://ptradeapi.com


Python 3.5是2015 年发布，Python 3.11是2022 年发布。

性能上来说是有很大的提升，尤其对做回测和高频策略。 

如果本身策略很简单或者日线交易的，影响可以忽略不计。 

不过这个升级并不是个人可选的，只要你的Ptrade的券商升级了，就只能使用升级后的版本接口格式，旧代码需要做的修改来适配。

性能对比示例（Python 3.5 vs 3.11） By 豆包

以下是部分操作在 Python 3.5 和 3.11 中的性能对比（仅供参考）：







并且相应的第三方库版本亦做了升级。






接口变动影响比较大的是pandas在 Python3.11 中，Pandas已不再支持Panel格式。

pandas里的Pannel类型是用于存储三维或以上的矩阵数据。

而升级后就不支持之前的多维行情数据格式。但笔者觉得现在更加统一和简单。

以前不同入参，有时候返回的是dataframe，有时候返回的是panel，比较混乱。

现在统一返回的dataframe. (或者返回dict，可以自己设定)




影响的API有get_history，get_price ,get_individual_transaction ,get_fundamentals等。




比如通过get_history获取的历史日线数据
 
count = 10
code_list = ['000695.SZ','001367.SZ']
df = get_history(count, frequency='1d', field=['open','close'], security_list=code_list, fq=None, include=False, fill='nan',is_dict=False)
print(type(df))
print(df)
返回的dataframe数据格式如下
 
<class 'pandas.core.frame.DataFrame'>

code open close
2025-05-15 000695.SZ NaN NaN
2025-05-16 000695.SZ NaN NaN
2025-05-19 000695.SZ 10.34 10.34
2025-05-20 000695.SZ 11.37 11.37
2025-05-21 000695.SZ 12.51 12.51
2025-05-22 000695.SZ 13.76 13.76
2025-05-09 001367.SZ 29.89 29.58
2025-05-12 001367.SZ 29.70 29.60
2025-05-13 001367.SZ 30.11 29.96
2025-05-14 001367.SZ 29.96 29.83
2025-05-15 001367.SZ 29.80 30.18
2025-05-16 001367.SZ 30.13 30.39
2025-05-19 001367.SZ 30.46 30.01
2025-05-20 001367.SZ 20.25 20.08
2025-05-21 001367.SZ 20.05 20.70
2025-05-22 001367.SZ 20.98 22.77
 
如果要提取 000695.SZ的数据，只需要
 
df[df['code']=='000695.SZ']就可以了。
  code open close
2025-05-09 001367.SZ 29.89 29.58
2025-05-12 001367.SZ 29.70 29.60
2025-05-13 001367.SZ 30.11 29.96
2025-05-14 001367.SZ 29.96 29.83
2025-05-15 001367.SZ 29.80 30.18
2025-05-16 001367.SZ 30.13 30.39
2025-05-19 001367.SZ 30.46 30.01
2025-05-20 001367.SZ 20.25 20.08
2025-05-21 001367.SZ 20.05 20.70
2025-05-22 001367.SZ 20.98 22.77

然后dataframe提取收盘价或者开盘价
 
# 收盘价
df[df['code']=='001367.SZ']['close']
# 开盘价
df[df['code']=='001367.SZ']['open']

再拿收盘价来计算MACD：
 
# 计算 MACD 值
def MACD(series):
ema_short = series.ewm(span=12).mean()
ema_long = series.ewm(span=26).mean()
macd_series = ema_short - ema_long
return macd_series.iloc[-1]

close_price_series = df[df['code']=='001367.SZ']['close']
macd_val = MACD(close_price_series)


同理get_price，get_individual_transaction ,get_fundamentals与之类似。


并且在get_histroy的入参里面，多了一个is_dict的参数，默认是False，返回的是dataframe数据，如果设置为True，则返回的dict数据。

返回的有序字典(OrderDict)类型数据：(省略了部分类型)
 
{'001367.SZ', array([(20250509, 29.89, 29.58), (20250512, 29.7 , 29.6 ),
(20250513, 30.11, 29.96), (20250514, 29.96, 29.83),
(20250515, 29.8 , 30.18), (20250516, 30.13, 30.39),
(20250519, 30.46, 30.01), (20250520, 20.25, 20.08),
(20250521, 20.05, 20.7 ), (20250522, 20.98, 22.77)]
}
但感觉这种列表形式下，提取数据反而没有使用dataframe方便和直观，它获取数据时需要用下标提取。

但官网说，返回dict类型数据的速度比DataFrame,Panel类型数据有大幅提升，对速度有要求的，建议使用is_dict=True 读取上述数据格式。

获取L2逐笔数据
L2逐笔委托数据接口函数
get_individual_entrust(stocks=None, data_count=50, start_pos=0, search_direction=1, is_dict=False)
参数：

data_count: 数据条数，默认为50，最大为200(int)；

start_pos: 起始位置，默认为0(int)；


虽然一次只能获取200条，但配合start_pos游标可遍历获取全部数据。


示例代码：

获取股票001367.SZ封涨停时的最新的逐笔委托行情。
code_list = ['001367.SZ']
df = get_individual_entrust(code_list)
print(df[['business_time','hq_px','business_amount','order_no']])
 
 
输出数据如下，因数据列太多，为了美观只取了其中的4列：   

时间戳，委托价格，委托数量，委托编号


因为当前已经是收盘状态，是从14:59:57.020往前取50条数据
business_time(时间戳) hq_px(委托价格) business_amount(委托数量) order_no(委托编号)
20250523145702150 24.60 100 32493957
20250523145714840 24.95 100 32505581
20250523145718430 25.05 1000 32509041
20250523145719840 25.05 1000 32510350
20250523145721750 25.05 400 32512000
20250523145727610 25.05 200 32517007
20250523145730270 25.05 400 32519085
20250523145730330 25.04 1600 32519133
20250523145732330 25.05 2900 32520779
20250523145740780 25.05 500 32527204
20250523145743550 25.05 400 32529290
20250523145743810 25.05 200 32529474
20250523145749460 25.05 4300 32533295
20250523145749910 25.05 1000 32533630
20250523145749960 25.05 300 32533666
# 省略若干.....
20250523145926790 25.05 100 32589496
20250523145927480 25.05 1200 32589844
20250523145936090 25.05 200 32594134
20250523145946600 25.05 300 32601758
20250523145946800 25.05 49100 32601865
20250523145950800 25.05 36800 32607381
20250523145953780 25.05 55200 32611265
20250523145954240 25.05 500 32611713
20250523145956830 25.05 55300 32614479
20250523145957020 25.04 8500 32614683
可以看到上面的有些1秒内有多个的委托单，每单的委托量和委托编号。

逐笔成交

同理，L2的逐笔成交数据函数的用法一样，但返回字段不同，成交的时候有成交方向，可以通过程序计算资金流入或流出。
 
get_individual_transaction(stocks=None, data_count=50, start_pos=0, search_direction=1, is_dict=False)


同样用上面的股票例子获取到的数据如下： 
business_time hq_px business_amount business_direction
20250523145648440 0.00 2500 1
20250523145648970 0.00 3200 1
20250523145649300 0.00 100 1
20250523145649370 0.00 900 1
20250523145649880 0.00 1800 1
20250523145650150 0.00 800 1
20250523145650360 0.00 200 1
20250523145650400 0.00 500 1
20250523145650420 0.00 100 1
20250523145652050 0.00 800 1
# 省略若干.....
20250523145659220 0.00 24100 1
20250523145659550 0.00 700 1
20250523145659600 0.00 400 1
20250523150000000 25.05 100 0
20250523150000000 25.05 100 0
20250523150000000 25.05 1600 0
20250523150000000 25.05 800 0
20250523150000000 25.05 1730 0
20250523150000000 25.05 670 0
20250523150000000 25.05 2430 0
20250523150000000 25.05 1200 0
20250523150000000 25.05 9270 0
20250523150000000 25.05 148 0
20250523150000000 25.05 100 0
20250523150000000 25.05 200 0
20250523150000000 25.05 1000 0
20250523150000000 25.05 2000 0
 
  business_direction是 成交方向，0是卖出，1是买入，从而判断该笔成交的资金是流入还是流出。

获取分时成交
get_tick_direction(symbols=None, query_date=0, start_pos=0, search_direction=1, data_count=50, is_dict=False)
这个分时成交函数可获取到当前和历史某个毫秒下，该笔总成交量下有多少笔子订单。

比如用上面的涨停股001367.SZ做示例

最后一刻3点的时候，成交了21348股，里面共有14笔成交。  
time_stamp hq_px business_amount business_count
20250523143721000 25.05 100 1
20250523143724000 25.05 200 1
20250523143745000 25.05 444 1
20250523143809000 25.05 10000 2
20250523144024000 25.05 100 1
20250523144048000 25.05 1500 1
20250523144106000 25.05 300 1
20250523144112000 25.05 11692 2
20250523144124000 25.05 100 1
20250523144142000 25.05 100 1
20250523144157000 25.05 100 1
20250523144200000 25.05 2360 1
# 省略若干.....
20250523145415000 25.05 1381 1
20250523145421000 25.05 480 1
20250523145439000 25.05 100 1
20250523145451000 25.05 1753 1
20250523145521000 25.05 5384 3
20250523145539000 25.05 3600 2
20250523145557000 25.05 1000 1
20250523145609000 25.05 400 2
20250523145615000 25.05 100 1
20250523150000000 25.05 21348 14
get_gear_price 获取当前时刻的逐笔委托队列
这个函数的功能：获取委买/卖1 至 委买/卖10 10档行情的价格与委托量，但现在发现它可以获取到委托1档下逐笔委托队列。



比如当前的股票是涨停状态，你下了一笔订单有8888股去排板，那么这个函数可以获取当前委1档下的委托队列（前50名），比如下面返回的数据，可以看到你的8888手排在第9个委托队列，还有前后的单子的逐笔数量。
 
{'bid_grp': {1: [13.68, 15225900, 4905, {1: 33200, 2: 104800, 3: 1000, 4: 100, 5: 51800, 6: 6300, 7: 777800, 8: 184000, 9: 8888, 10: 100, 11: 600, 12: 141800, 13: 100, 14: 141800, 15: 2700,
16: 900, 17: 58600, 18: 2900, 19: 800, 20: 133800, 21: 2800, 22: 900, 23: 2900, 24: 800, 25: 400, 26: 400, 27: 48200, 28: 51800, 29: 99400, 30: 26600, 31: 81800, 32: 493000, 33: 427300, 34: 27700, 35: 2800, 36: 460500,
37: 170200, 38: 166500, 39: 24100, 40: 800, 41: 29300, 42: 500, 43: 500, 44: 500, 45: 2800, 46: 1300, 47: 61700, 48: 27000, 49: 500, 50: 78900}],
2: [13.67, 30200, 32],
3: [13.66, 11100, 16],
4: [13.65, 134400, 11],
5: [13.64, 8500, 7],
6: [13.63, 1000, 3],
7: [13.62, 200, 2],
8: [13.6, 7300, 17],
9: [13.59, 600, 2],
10: [13.58, 10700, 7]},

# 因为是涨停，所以委卖是空的，挂单就立马成交，委托队列是空的
'offer_grp': {1: [0.0, 0, 0, {}],
2: [0.0, 0, 0],
3: [0.0, 0, 0],
4: [0.0, 0, 0],
5: [0.0, 0, 0],
6: [0.0, 0, 0],
7: [0.0, 0, 0],
8: [0.0, 0, 0],
9: [0.0, 0, 0],
10: [0.0, 0, 0]}
}
 
所以这次ptrade的升级除了python版本的升级，还带来了更多粒度的数据，有利于高频策略的开发。


还有一些新功能的函数就等下次继续更新，记得点赞关注+收藏哦~

待续......
 
关注公众号： 查看全部
本月初国金的Ptrade做了一次大版本的升级，内置的python版本从老旧的python3.5一下子升级到了python3.11。

并且还发现居然可以免费使用L2逐笔数据，分时逐笔委托队列等高频毫秒级的数据

Ptrade接口文档已更新： https://ptradeapi.com


Python 3.5是2015 年发布，Python 3.11是2022 年发布。

性能上来说是有很大的提升，尤其对做回测和高频策略。 

如果本身策略很简单或者日线交易的，影响可以忽略不计。 

不过这个升级并不是个人可选的，只要你的Ptrade的券商升级了，就只能使用升级后的版本接口格式，旧代码需要做的修改来适配。

性能对比示例（Python 3.5 vs 3.11） By 豆包

以下是部分操作在 Python 3.5 和 3.11 中的性能对比（仅供参考）：


20250524001221.png


并且相应的第三方库版本亦做了升级。

20250523183828.png


接口变动影响比较大的是pandas在 Python3.11 中，Pandas已不再支持Panel格式。

pandas里的Pannel类型是用于存储三维或以上的矩阵数据。

而升级后就不支持之前的多维行情数据格式。但笔者觉得现在更加统一和简单。

以前不同入参，有时候返回的是dataframe，有时候返回的是panel，比较混乱。

现在统一返回的dataframe. (或者返回dict，可以自己设定)




影响的API有get_history，get_price ,get_individual_transaction ,get_fundamentals等。




比如通过get_history获取的历史日线数据
 
count = 10

code_list = ['000695.SZ','001367.SZ']

df = get_history(count, frequency='1d', field=['open','close'], security_list=code_list, fq=None, include=False, fill='nan',is_dict=False)

print(type(df))

print(df)

返回的dataframe数据格式如下
 
<class 'pandas.core.frame.DataFrame'>



			   code   open  close

2025-05-15  000695.SZ    NaN    NaN

2025-05-16  000695.SZ    NaN    NaN

2025-05-19  000695.SZ  10.34  10.34

2025-05-20  000695.SZ  11.37  11.37

2025-05-21  000695.SZ  12.51  12.51

2025-05-22  000695.SZ  13.76  13.76

2025-05-09  001367.SZ  29.89  29.58

2025-05-12  001367.SZ  29.70  29.60

2025-05-13  001367.SZ  30.11  29.96

2025-05-14  001367.SZ  29.96  29.83

2025-05-15  001367.SZ  29.80  30.18

2025-05-16  001367.SZ  30.13  30.39

2025-05-19  001367.SZ  30.46  30.01

2025-05-20  001367.SZ  20.25  20.08

2025-05-21  001367.SZ  20.05  20.70

2025-05-22  001367.SZ  20.98  22.77

 
如果要提取 000695.SZ的数据，只需要
 
df[df['code']=='000695.SZ']
就可以了。
 
           code        open  close

2025-05-09  001367.SZ  29.89  29.58

2025-05-12  001367.SZ  29.70  29.60

2025-05-13  001367.SZ  30.11  29.96

2025-05-14  001367.SZ  29.96  29.83

2025-05-15  001367.SZ  29.80  30.18

2025-05-16  001367.SZ  30.13  30.39

2025-05-19  001367.SZ  30.46  30.01

2025-05-20  001367.SZ  20.25  20.08

2025-05-21  001367.SZ  20.05  20.70

2025-05-22  001367.SZ  20.98  22.77


然后dataframe提取收盘价或者开盘价
 
# 收盘价

df[df['code']=='001367.SZ']['close']

# 开盘价

df[df['code']=='001367.SZ']['open']


再拿收盘价来计算MACD：
 
# 计算 MACD 值

def MACD(series):

    ema_short = series.ewm(span=12).mean()

    ema_long = series.ewm(span=26).mean()

    macd_series = ema_short - ema_long

    return macd_series.iloc[-1]



close_price_series = df[df['code']=='001367.SZ']['close']

macd_val = MACD(close_price_series)



同理get_price，get_individual_transaction ,get_fundamentals与之类似。


并且在get_histroy的入参里面，多了一个is_dict的参数，默认是False，返回的是dataframe数据，如果设置为True，则返回的dict数据。

返回的有序字典(OrderDict)类型数据：(省略了部分类型)
 
{'001367.SZ', array([(20250509, 29.89, 29.58), (20250512, 29.7 , 29.6 ),

       (20250513, 30.11, 29.96), (20250514, 29.96, 29.83),

       (20250515, 29.8 , 30.18), (20250516, 30.13, 30.39),

       (20250519, 30.46, 30.01), (20250520, 20.25, 20.08),

       (20250521, 20.05, 20.7 ), (20250522, 20.98, 22.77)]

}

但感觉这种列表形式下，提取数据反而没有使用dataframe方便和直观，它获取数据时需要用下标提取。

但官网说，返回dict类型数据的速度比DataFrame,Panel类型数据有大幅提升，对速度有要求的，建议使用is_dict=True 读取上述数据格式。

获取L2逐笔数据
L2逐笔委托数据接口函数
get_individual_entrust(stocks=None, data_count=50, start_pos=0, search_direction=1, is_dict=False)

参数：

data_count: 数据条数，默认为50，最大为200(int)；

start_pos: 起始位置，默认为0(int)；


虽然一次只能获取200条，但配合start_pos游标可遍历获取全部数据。


示例代码：

获取股票001367.SZ封涨停时的最新的逐笔委托行情。
code_list = ['001367.SZ']

df = get_individual_entrust(code_list)

print(df[['business_time','hq_px','business_amount','order_no']])

 
 
输出数据如下，因数据列太多，为了美观只取了其中的4列：   

时间戳，委托价格，委托数量，委托编号


因为当前已经是收盘状态，是从14:59:57.020往前取50条数据
    business_time(时间戳)  hq_px(委托价格)  business_amount(委托数量)  order_no(委托编号)

  20250523145702150  24.60              100  32493957

  20250523145714840  24.95              100  32505581

  20250523145718430  25.05             1000  32509041

  20250523145719840  25.05             1000  32510350

  20250523145721750  25.05              400  32512000

  20250523145727610  25.05              200  32517007

  20250523145730270  25.05              400  32519085

  20250523145730330  25.04             1600  32519133

  20250523145732330  25.05             2900  32520779

  20250523145740780  25.05              500  32527204

  20250523145743550  25.05              400  32529290

  20250523145743810  25.05              200  32529474

  20250523145749460  25.05             4300  32533295

  20250523145749910  25.05             1000  32533630

  20250523145749960  25.05              300  32533666

# 省略若干.....

  20250523145926790  25.05              100  32589496

  20250523145927480  25.05             1200  32589844

  20250523145936090  25.05              200  32594134

  20250523145946600  25.05              300  32601758

  20250523145946800  25.05            49100  32601865

  20250523145950800  25.05            36800  32607381

  20250523145953780  25.05            55200  32611265

  20250523145954240  25.05              500  32611713

  20250523145956830  25.05            55300  32614479

  20250523145957020  25.04             8500  32614683

可以看到上面的有些1秒内有多个的委托单，每单的委托量和委托编号。

逐笔成交

同理，L2的逐笔成交数据函数的用法一样，但返回字段不同，成交的时候有成交方向，可以通过程序计算资金流入或流出。
 
get_individual_transaction(stocks=None, data_count=50, start_pos=0, search_direction=1, is_dict=False)



同样用上面的股票例子获取到的数据如下： 
	business_time      hq_px         business_amount   business_direction

   20250523145648440   0.00             2500                   1

   20250523145648970   0.00             3200                   1

   20250523145649300   0.00              100                   1

   20250523145649370   0.00              900                   1

   20250523145649880   0.00             1800                   1

   20250523145650150   0.00              800                   1

   20250523145650360   0.00              200                   1

   20250523145650400   0.00              500                   1

   20250523145650420   0.00              100                   1

   20250523145652050   0.00              800                   1

# 省略若干.....

  20250523145659220   0.00            24100                   1

  20250523145659550   0.00              700                   1

  20250523145659600   0.00              400                   1

  20250523150000000  25.05              100                   0

  20250523150000000  25.05              100                   0

  20250523150000000  25.05             1600                   0

  20250523150000000  25.05              800                   0

  20250523150000000  25.05             1730                   0

  20250523150000000  25.05              670                   0

  20250523150000000  25.05             2430                   0

  20250523150000000  25.05             1200                   0

  20250523150000000  25.05             9270                   0

  20250523150000000  25.05              148                   0

  20250523150000000  25.05              100                   0

  20250523150000000  25.05              200                   0

  20250523150000000  25.05             1000                   0

  20250523150000000  25.05             2000                   0

 
  business_direction是 成交方向，0是卖出，1是买入，从而判断该笔成交的资金是流入还是流出。

获取分时成交
get_tick_direction(symbols=None, query_date=0, start_pos=0, search_direction=1, data_count=50, is_dict=False)

这个分时成交函数可获取到当前和历史某个毫秒下，该笔总成交量下有多少笔子订单。

比如用上面的涨停股001367.SZ做示例

最后一刻3点的时候，成交了21348股，里面共有14笔成交。  
	 time_stamp  		hq_px  		business_amount  business_count

  20250523143721000  25.05              100               1

  20250523143724000  25.05              200               1

  20250523143745000  25.05              444               1

  20250523143809000  25.05            10000               2

  20250523144024000  25.05              100               1

  20250523144048000  25.05             1500               1

  20250523144106000  25.05              300               1

  20250523144112000  25.05            11692               2

  20250523144124000  25.05              100               1

  20250523144142000  25.05              100               1

  20250523144157000  25.05              100               1

  20250523144200000  25.05             2360               1

# 省略若干.....

  20250523145415000  25.05             1381               1

  20250523145421000  25.05              480               1

  20250523145439000  25.05              100               1

  20250523145451000  25.05             1753               1

  20250523145521000  25.05             5384               3

  20250523145539000  25.05             3600               2

  20250523145557000  25.05             1000               1

  20250523145609000  25.05              400               2

  20250523145615000  25.05              100               1

  20250523150000000  25.05            21348              14

get_gear_price 获取当前时刻的逐笔委托队列
这个函数的功能：获取委买/卖1 至 委买/卖10 10档行情的价格与委托量，但现在发现它可以获取到委托1档下逐笔委托队列。



比如当前的股票是涨停状态，你下了一笔订单有8888股去排板，那么这个函数可以获取当前委1档下的委托队列（前50名），比如下面返回的数据，可以看到你的8888手排在第9个委托队列，还有前后的单子的逐笔数量。
 
{'bid_grp': {1: [13.68, 15225900, 4905, {1: 33200, 2: 104800, 3: 1000, 4: 100, 5: 51800, 6: 6300, 7: 777800, 8: 184000, 9: 8888, 10: 100, 11: 600, 12: 141800, 13: 100, 14: 141800, 15: 2700, 

16: 900, 17: 58600, 18: 2900, 19: 800, 20: 133800, 21: 2800, 22: 900, 23: 2900, 24: 800, 25: 400, 26: 400, 27: 48200, 28: 51800, 29: 99400, 30: 26600, 31: 81800, 32: 493000, 33: 427300, 34: 27700, 35: 2800, 36: 460500, 

37: 170200, 38: 166500, 39: 24100, 40: 800, 41: 29300, 42: 500, 43: 500, 44: 500, 45: 2800, 46: 1300, 47: 61700, 48: 27000, 49: 500, 50: 78900}], 

2: [13.67, 30200, 32], 

3: [13.66, 11100, 16], 

4: [13.65, 134400, 11], 

5: [13.64, 8500, 7], 

6: [13.63, 1000, 3], 

7: [13.62, 200, 2], 

8: [13.6, 7300, 17], 

9: [13.59, 600, 2], 

10: [13.58, 10700, 7]}, 



 # 因为是涨停，所以委卖是空的，挂单就立马成交，委托队列是空的

'offer_grp': {1: [0.0, 0, 0, {}], 

2: [0.0, 0, 0], 

3: [0.0, 0, 0], 

4: [0.0, 0, 0], 

5: [0.0, 0, 0], 

6: [0.0, 0, 0], 

7: [0.0, 0, 0], 

8: [0.0, 0, 0], 

9: [0.0, 0, 0], 

10: [0.0, 0, 0]}

}

 
所以这次ptrade的升级除了python版本的升级，还带来了更多粒度的数据，有利于高频策略的开发。


还有一些新功能的函数就等下次继续更新，记得点赞关注+收藏哦~

待续......
 
关注公众号：

ptrade的python版本升级到3.11, 部分数据返回格式也改变了，无语

Ptrade • 李魔佛 发表了文章 • 0 个评论 • 230 次浏览 • 2025-05-07 14:22 • 来自相关话题

比如：get_history - 获取历史行情
返回数据可以为dataframe，也可以为dict。
为python3.5会根据数据入参不同，返回dataframe，pannel




而新的函数返回，只有dataframe，当然这很好。只是以前的代码需要改动做兼容。
 
而修改的地方不止这一处。

  查看全部
比如：get_history - 获取历史行情
返回数据可以为dataframe，也可以为dict。
为python3.5会根据数据入参不同，返回dataframe，pannel
20250507142032.png

而新的函数返回，只有dataframe，当然这很好。只是以前的代码需要改动做兼容。
 
而修改的地方不止这一处。

 

Ptrade LOF跌停抄底T+0 程序

Ptrade • 李魔佛 发表了文章 • 0 个评论 • 283 次浏览 • 2025-04-29 09:02 • 来自相关话题

$原油LOF易方达(SZ161129)$ 
$黄金主题LOF(SZ161116)$ 
$港股小盘LOF(SZ161124)$

吃早餐的突然想到，快速写了个监控跌停，封单小于X就买入，冲高后止盈卖出，T+0 ptrade 自动交易。这样抄底就不用自己盯盘啦
 





  查看全部
$原油LOF易方达(SZ161129)$ 
$黄金主题LOF(SZ161116)$ 
$港股小盘LOF(SZ161124)$

吃早餐的突然想到，快速写了个监控跌停，封单小于X就买入，冲高后止盈卖出，T+0 ptrade 自动交易。这样抄底就不用自己盯盘啦
 

20250429085814.png

 

部署Ptrade deepseek编程AI助手

Ptrade • 李魔佛 发表了文章 • 0 个评论 • 963 次浏览 • 2025-03-22 19:19 • 来自相关话题

不少小伙伴使用Deepseek或者豆包等AI工具写ptrade，qmt代码，发现基本都是无法运行，并且改动起来也很吃力。





（第一行：根本没有ptrade_api这个库）

一个很大的原因，是互联网的残次不齐的语料污染了Deepseek的分析结果。很多不相关的广告或者垃圾内容进入到Deepseek的分析参考对象。三人成虎，Deepseek获取的3个网站都说那个图片里的猫是只老虎，那么它就真的认为它是只老虎。

其实我们可以把API文档作为知识库，提交给Deepseek，让它学习后再替我们编写Ptrade量化程序，代码的完成度会有很大的提升。甚至基本不用修改就可以直接运行。




本文手把手带大家，部署属于自己的量化交易AI编程助手。

当然，不需要高级硬件，也不用付费。0元购。

效果图：








本次依然使用腾讯云提供的Deepseek大模型来运行。

打开链接：https://lke.cloud.tencent.com/

点击大模型知识引擎，如果没有开通就点击开通。个人有免费的token额度可用，不收费。[图片]进去之后点击“新建应用", 名称可以任意填，





 
 
在模型这里选择 Deepseek-R1 或者 Deepseek-V3










 




 





 
它支持从本地文档导入和网页URL导入。

我们使用的API文档URL为：https://ptradeapi.com/ 
 
但笔者发现，它这个从URL获取数据的工程能力太弱了，获取到的数据只有标题，没有内容。

所以只好手工把网站的内容全选，然后复制保存到本地的word文档。





 
然后点击导入文档











 
这里需要几分钟的时间解析，内容审核。因为这个个人定制的知识库，是可以发布出去公开使用的，所以会对问答的内容做审核。





 
 
知识库里有对字符限制，免费的可以有个300w的字符可用。本次使用的ptrade api文档用了26w的字符，所以还可以上传更多的个人编写代码库。

知识库的状态变成"待发布"之后，可以点击"发布"按钮。





 
然后返回到应用配置，可以在提示词里添加一些限制。

比如目前的ptrade是基于python3.5的代码编写的。如果不限制，到时写出来的语法，比如用了诸如f-string的语法，ptrade会报错的。

f'{name}' -- wrong

'{}'.format(name) -- right





 
如果这里不限制，最后AI写出来的代码大概率在ptrade会报错。

设置完成之后，就完成了你个人专属的ptrade代码AI助手。

现在让它写一个ptrade网格交易程序
















 
至少里面的策略逻辑代码框架整体问题不大，至少不再出现 from ptrade_api import * 这种不存在的包导入的情况。

但细节里还是有些bug的，比如卖出的时候是查enable_amount，而不是amount，因为当日买入的数量不一定能卖出。

run_interval(execute_grid, seconds=3)，还有函数的入参不符合。

并没有get_price这个函数，要用get_snapshot

复制到ptrade里面跑下回测验证一下语法。





 
出现语法错误，依然需要小改一下。run_interval(context,execute_grid, seconds=3)  就好了。

当然上面的代码离实盘依然有一定的距离，依然依赖提问者技术水平，但作为代码辅助工具，对编写代码效率是很大的提升。

一些没写过复杂代码的自媒体经常尬吹，AI完成替代程序员的工作，其实大部分只是满足生成最基础的demo级别的通用任务，对于高度定制的业务，依然依赖提问者的技术广度和深度，对细节的掌控。

同样使用Deepseek，一个家庭主妇和张小龙，张小龙的确可能在完全不写代码的情况下使用Deepseek写出一个微信，而家庭主妇只能用Deepseek绘出一个类似的微信的UI，却无法运行起来。

但AI作为生产力工具，无疑大大减少了很多重复基础性工作，提升效率，让强者恒强。

需要开通Ptrade，QMT，掘金，Matic，BigQuant等量化工具的读者可以在公众号菜单获取联系方式。像BigQuan已经把AI编写实盘和回测的功能集成进去了。将公众号设为星标，第一时间收到笔者的最新文章啦。
  查看全部
不少小伙伴使用Deepseek或者豆包等AI工具写ptrade，qmt代码，发现基本都是无法运行，并且改动起来也很吃力。

微信图片_20250322122653.png

（第一行：根本没有ptrade_api这个库）

一个很大的原因，是互联网的残次不齐的语料污染了Deepseek的分析结果。很多不相关的广告或者垃圾内容进入到Deepseek的分析参考对象。三人成虎，Deepseek获取的3个网站都说那个图片里的猫是只老虎，那么它就真的认为它是只老虎。

其实我们可以把API文档作为知识库，提交给Deepseek，让它学习后再替我们编写Ptrade量化程序，代码的完成度会有很大的提升。甚至基本不用修改就可以直接运行。




本文手把手带大家，部署属于自己的量化交易AI编程助手。

当然，不需要高级硬件，也不用付费。0元购。

效果图：

20250322122220.png




本次依然使用腾讯云提供的Deepseek大模型来运行。

打开链接：https://lke.cloud.tencent.com/

点击大模型知识引擎，如果没有开通就点击开通。个人有免费的token额度可用，不收费。[图片]进去之后点击“新建应用", 名称可以任意填，

20250322104128.png

 
 
在模型这里选择 Deepseek-R1 或者 Deepseek-V3

20250322104619.png


20250322105233-1.png

 
20250322105248.png

 

20250322105301.png

 
它支持从本地文档导入和网页URL导入。

我们使用的API文档URL为：https://ptradeapi.com/ 
 
但笔者发现，它这个从URL获取数据的工程能力太弱了，获取到的数据只有标题，没有内容。

所以只好手工把网站的内容全选，然后复制保存到本地的word文档。

20250322105650.png

 
然后点击导入文档


20250322105955.png


20250322110024.png

 
这里需要几分钟的时间解析，内容审核。因为这个个人定制的知识库，是可以发布出去公开使用的，所以会对问答的内容做审核。

20250322110314.png

 
 
知识库里有对字符限制，免费的可以有个300w的字符可用。本次使用的ptrade api文档用了26w的字符，所以还可以上传更多的个人编写代码库。

知识库的状态变成"待发布"之后，可以点击"发布"按钮。

20250322111124.png

 
然后返回到应用配置，可以在提示词里添加一些限制。

比如目前的ptrade是基于python3.5的代码编写的。如果不限制，到时写出来的语法，比如用了诸如f-string的语法，ptrade会报错的。

f'{name}' -- wrong

'{}'.format(name) -- right

20250322113909.png

 
如果这里不限制，最后AI写出来的代码大概率在ptrade会报错。

设置完成之后，就完成了你个人专属的ptrade代码AI助手。

现在让它写一个ptrade网格交易程序

20250322111727.png


20250322111739.png



20250322114006.png

 
至少里面的策略逻辑代码框架整体问题不大，至少不再出现 from ptrade_api import * 这种不存在的包导入的情况。

但细节里还是有些bug的，比如卖出的时候是查enable_amount，而不是amount，因为当日买入的数量不一定能卖出。

run_interval(execute_grid, seconds=3)，还有函数的入参不符合。

并没有get_price这个函数，要用get_snapshot

复制到ptrade里面跑下回测验证一下语法。

20250322114548.png

 
出现语法错误，依然需要小改一下。run_interval(context,execute_grid, seconds=3)  就好了。

当然上面的代码离实盘依然有一定的距离，依然依赖提问者技术水平，但作为代码辅助工具，对编写代码效率是很大的提升。

一些没写过复杂代码的自媒体经常尬吹，AI完成替代程序员的工作，其实大部分只是满足生成最基础的demo级别的通用任务，对于高度定制的业务，依然依赖提问者的技术广度和深度，对细节的掌控。

同样使用Deepseek，一个家庭主妇和张小龙，张小龙的确可能在完全不写代码的情况下使用Deepseek写出一个微信，而家庭主妇只能用Deepseek绘出一个类似的微信的UI，却无法运行起来。

但AI作为生产力工具，无疑大大减少了很多重复基础性工作，提升效率，让强者恒强。

需要开通Ptrade，QMT，掘金，Matic，BigQuant等量化工具的读者可以在公众号菜单获取联系方式。像BigQuan已经把AI编写实盘和回测的功能集成进去了。将公众号设为星标，第一时间收到笔者的最新文章啦。
 

ptrade支持北交所股票交易吗？

Ptrade • 李魔佛 发表了文章 • 0 个评论 • 479 次浏览 • 2025-03-12 20:25 • 来自相关话题

目前为止不支持的。（2025-03-12）
 
可以运行下面的代码：
def initialize(context):
# 初始化策略
g.security = "600570.SS"
set_universe(g.security)
run_daily(context, func,'20:13')

def handle_data(context, data):
print('公众号：可转债量化分析')


def func(context):
info = get_Ashares(date=None)
print(info)
它会打印沪深A股的代码：
北交所有83开头的，或者找一个北交所的股票代码，在输出里面找一下：
[code]2025-03-12 20:13:00 - INFO - ['600000.SS', '600004.SS', '600006.SS', '600007.SS', '600008.SS', '600009.SS', '600010.SS', '600011.SS', '600012.SS', '600015.SS', '600016.SS', '600017.SS', '600018.SS', '600019.SS', '600020.SS', '600021.SS', '600022.SS', '600023.SS', '600025.SS', '600026.SS', '600027.SS', '600028.SS', '600029.SS', '600030.SS', '600031.SS', '600032.SS', '600033.SS', '600035.SS', '600036.SS', '600037.SS', '600038.SS', '600039.SS', '600048.SS', '600050.SS', '600051.SS', '600052.SS', '600053.SS', '600054.SS', '600055.SS', '600056.SS', '600057.SS', '600058.SS', '600059.SS', '600060.SS', '600061.SS', '600062.SS', '600063.SS', '600064.SS', '600066.SS', '600067.SS', '600070.SS', '600071.SS', '600072.SS', '600073.SS', '600075.SS', '600076.SS', '600078.SS', '600079.SS', '600080.SS', '600081.SS', '600082.SS', '600084.SS', '600085.SS', '600088.SS', '600089.SS', '600094.SS', '600095.SS', '600096.SS', '600097.SS', '600098.SS', '600099.SS', '600100.SS', '600101.SS', '600103.SS', '600104.SS', '600105.SS', '600106.SS', '600107.SS', '600108.SS', '600109.SS', '600110.SS', '600111.SS', '600113.SS', '600114.SS', '600115.SS', '600116.SS', '600117.SS', '600118.SS', '600119.SS', '600120.SS', '600121.SS', '600123.SS', '600125.SS', '600126.SS', '600127.SS', '600128.SS', '600129.SS', '600130.SS', '600131.SS', '600132.SS', '600133.SS', '600135.SS', '600136.SS', '600137.SS', '600138.SS', '600141.SS', '600143.SS', '600148.SS', '600149.SS', '600150.SS', '600151.SS', '600152.SS', '600153.SS', '600155.SS', '600156.SS', '600157.SS', '600158.SS', '600159.SS', '600160.SS', '600161.SS', '600162.SS', '600163.SS', '600165.SS', '600166.SS', '600167.SS', '600168.SS', '600169.SS', '600170.SS', '600171.SS', '600172.SS', '600173.SS', '600176.SS', '600177.SS', '600178.SS', '600179.SS', '600180.SS', '600182.SS', '600183.SS', '600184.SS', '600185.SS', '600186.SS', '600187.SS', '600188.SS', '600189.SS', '600190.SS', '600191.SS', '600192.SS', '600193.SS', '600195.SS', '600196.SS', '600197.SS', '600198.SS', '600199.SS', '600200.SS', '600201.SS', '600202.SS', '600203.SS', '600206.SS', '600207.SS', '600208.SS', '600210.SS', '600211.SS', '600212.SS', '600215.SS', '600216.SS', '600217.SS', '600218.SS', '600219.SS', '600221.SS', '600222.SS', '600223.SS', '600226.SS', '600227.SS', '600228.SS', '600229.SS', '600230.SS', '600231.SS', '600232.SS', '600233.SS', '600234.SS', '600235.SS', '600236.SS', '600237.SS', '600238.SS', '600239.SS', '600241.SS', '600243.SS', '600246.SS', '600248.SS', '600249.SS', '600250.SS', '600251.SS', '600252.SS', '600255.SS', '600256.SS', '600257.SS', '600258.SS', '600259.SS', '600261.SS', '600262.SS', '600265.SS', '600266.SS', '600267.SS', '600268.SS', '600269.SS', '600271.SS', '600272.SS', '600273.SS', '600276.SS', '600278.SS', '600279.SS', '600280.SS', '600281.SS', '600282.SS', '600283.SS', '600284.SS', '600285.SS', '600287.SS', '600288.SS', '600289.SS', '600292.SS', '600293.SS', '600295.SS', '600298.SS', '600299.SS', '600300.SS', '600301.SS', '600302.SS', 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目前为止不支持的。（2025-03-12）
 
可以运行下面的代码：
def initialize(context):

    # 初始化策略

    g.security = "600570.SS"

    set_universe(g.security)

    run_daily(context, func,'20:13')



def handle_data(context, data):

    print('公众号：可转债量化分析')

    

    

def func(context):

    info = get_Ashares(date=None)

    print(info)

它会打印沪深A股的代码：
北交所有83开头的，或者找一个北交所的股票代码，在输出里面找一下：
[code]2025-03-12 20:13:00 - INFO - ['600000.SS', '600004.SS', '600006.SS', '600007.SS', '600008.SS', '600009.SS', '600010.SS', '600011.SS', '600012.SS', '600015.SS', '600016.SS', '600017.SS', '600018.SS', '600019.SS', '600020.SS', '600021.SS', '600022.SS', '600023.SS', '600025.SS', '600026.SS', '600027.SS', '600028.SS', '600029.SS', '600030.SS', '600031.SS', '600032.SS', '600033.SS', '600035.SS', '600036.SS', '600037.SS', '600038.SS', '600039.SS', '600048.SS', '600050.SS', '600051.SS', '600052.SS', '600053.SS', '600054.SS', '600055.SS', '600056.SS', '600057.SS', '600058.SS', '600059.SS', '600060.SS', '600061.SS', '600062.SS', '600063.SS', '600064.SS', '600066.SS', '600067.SS', '600070.SS', '600071.SS', '600072.SS', '600073.SS', '600075.SS', '600076.SS', '600078.SS', '600079.SS', '600080.SS', '600081.SS', '600082.SS', '600084.SS', '600085.SS', '600088.SS', '600089.SS', '600094.SS', '600095.SS', '600096.SS', '600097.SS', '600098.SS', '600099.SS', '600100.SS', '600101.SS', '600103.SS', '600104.SS', '600105.SS', '600106.SS', '600107.SS', '600108.SS', '600109.SS', '600110.SS', '600111.SS', '600113.SS', '600114.SS', '600115.SS', '600116.SS', '600117.SS', '600118.SS', '600119.SS', '600120.SS', '600121.SS', '600123.SS', '600125.SS', '600126.SS', '600127.SS', '600128.SS', '600129.SS', '600130.SS', '600131.SS', '600132.SS', '600133.SS', '600135.SS', '600136.SS', '600137.SS', '600138.SS', '600141.SS', '600143.SS', '600148.SS', '600149.SS', '600150.SS', '600151.SS', '600152.SS', '600153.SS', '600155.SS', '600156.SS', '600157.SS', '600158.SS', '600159.SS', '600160.SS', '600161.SS', '600162.SS', '600163.SS', '600165.SS', '600166.SS', '600167.SS', '600168.SS', '600169.SS', '600170.SS', '600171.SS', '600172.SS', '600173.SS', '600176.SS', '600177.SS', '600178.SS', '600179.SS', '600180.SS', '600182.SS', '600183.SS', '600184.SS', '600185.SS', '600186.SS', '600187.SS', '600188.SS', '600189.SS', '600190.SS', '600191.SS', '600192.SS', '600193.SS', '600195.SS', '600196.SS', '600197.SS', '600198.SS', '600199.SS', '600200.SS', '600201.SS', '600202.SS', '600203.SS', '600206.SS', '600207.SS', '600208.SS', '600210.SS', '600211.SS', '600212.SS', '600215.SS', '600216.SS', '600217.SS', '600218.SS', '600219.SS', '600221.SS', '600222.SS', '600223.SS', '600226.SS', '600227.SS', '600228.SS', '600229.SS', '600230.SS', '600231.SS', '600232.SS', '600233.SS', '600234.SS', '600235.SS', '600236.SS', '600237.SS', '600238.SS', '600239.SS', '600241.SS', '600243.SS', '600246.SS', '600248.SS', '600249.SS', '600250.SS', '600251.SS', '600252.SS', '600255.SS', '600256.SS', '600257.SS', '600258.SS', '600259.SS', '600261.SS', '600262.SS', '600265.SS', '600266.SS', '600267.SS', '600268.SS', '600269.SS', '600271.SS', '600272.SS', '600273.SS', '600276.SS', '600278.SS', '600279.SS', '600280.SS', '600281.SS', '600282.SS', '600283.SS', '600284.SS', '600285.SS', '600287.SS', '600288.SS', '600289.SS', '600292.SS', '600293.SS', '600295.SS', '600298.SS', '600299.SS', '600300.SS', '600301.SS', '600302.SS', '600303.SS', '600305.SS', '600307.SS', '600308.SS', '600309.SS', '600310.SS', '600312.SS', '600313.SS', '600315.SS', '600316.SS', '600318.SS', '600319.SS', '600320.SS', '600322.SS', '600323.SS', '600325.SS', '600326.SS', '600327.SS', '600328.SS', '600329.SS', '600330.SS', '600331.SS', '600332.SS', '600333.SS', '600335.SS', '600336.SS', '600337.SS', '600338.SS', '600339.SS', '600340.SS', '600343.SS', '600345.SS', '600346.SS', '600348.SS', '600350.SS', '600351.SS', '600352.SS', '600353.SS', '600354.SS', '600355.SS', '600356.SS', '600358.SS', '600359.SS', '600360.SS', '600361.SS', '600362.SS', '600363.SS', '600365.SS', '600366.SS', '600367.SS', '600368.SS', '600369.SS', '600370.SS', '600371.SS', '600372.SS', '600373.SS', '600375.SS', '600376.SS', '600377.SS', '600378.SS', '600379.SS', '600380.SS', '600381.SS', '600382.SS', '600383.SS', '600386.SS', '600387.SS', '600388.SS', '600389.SS', '600390.SS', '600391.SS', '600392.SS', '600395.SS', '600396.SS', '600397.SS', '600398.SS', '600399.SS', '600400.SS', '600403.SS', '600405.SS', '600406.SS', '600408.SS', '600409.SS', '600410.SS', '600415.SS', '600416.SS', '600418.SS', '600419.SS', '600420.SS', '600421.SS', '600422.SS', '600423.SS', '600425.SS', '600426.SS', '600428.SS', '600429.SS', '600433.SS', '600435.SS', '600436.SS', '600438.SS', '600439.SS', '600444.SS', '600446.SS', '600448.SS', '600449.SS', '600452.SS', '600455.SS', '600456.SS', '600458.SS', '600459.SS', '600460.SS', '600461.SS', '600462.SS', '600463.SS', '600467.SS', '600468.SS', '600469.SS', '600470.SS', '600475.SS', '600476.SS', '600477.SS', '600478.SS', '600479.SS', '600480.SS', '600481.SS', '600482.SS', '600483.SS', '600486.SS', '600487.SS', '600488.SS', '600489.SS', '600490.SS', '600491.SS', '600493.SS', '600495.SS', '600496.SS', '600497.SS', '600498.SS', '600499.SS', '600500.SS', '600501.SS', '600502.SS', '600503.SS', '600505.SS', '600506.SS', '600507.SS', '600508.SS', '600509.SS', '600510.SS', '600511.SS', '600512.SS', '600513.SS', '600515.SS', '600516.SS', '600517.SS', '600518.SS', '600519.SS', '600520.SS', '600521.SS', '600522.SS', '600523.SS', '600525.SS', '600526.SS', '600527.SS', '600528.SS', '600529.SS', '600530.SS', '600531.SS', '600533.SS', '600535.SS', '600536.SS', '600537.SS', '600538.SS', '600539.SS', '600540.SS', '600543.SS', '600545.SS', '600546.SS', '600547.SS', '600548.SS', '600549.SS', '600550.SS', '600551.SS', '600552.SS', '600556.SS', '600557.SS', '600558.SS', '600559.SS', '600560.SS', '600561.SS', '600562.SS', '600563.SS', '600566.SS', '600567.SS', '600568.SS', '600569.SS', '600570.SS', '600571.SS', '600572.SS', '600573.SS', '600575.SS', '600576.SS', '600577.SS', '600578.SS', '600579.SS', '600580.SS', '600581.SS', '600582.SS', '600583.SS', '600584.SS', '600585.SS', '600586.SS', '600587.SS', '600588.SS', '600589.SS', '600590.SS', '600592.SS', '600593.SS', '600594.SS', '600595.SS', '600596.SS', '600597.SS', '600598.SS', '600599.SS', '600600.SS', '600601.SS', '600602.SS', '600603.SS', '600604.SS', '600605.SS', '600606.SS', '600608.SS', '600609.SS', '600610.SS', '600611.SS', '600612.SS', '600613.SS', '600615.SS', '600616.SS', '600617.SS', '600618.SS', '600619.SS', '600620.SS', '600621.SS', '600622.SS', '600623.SS', '600624.SS', '600626.SS', '600628.SS', '600629.SS', '600630.SS', '600633.SS', '600635.SS', '600636.SS', '600637.SS', '600638.SS', '600639.SS', '600640.SS', '600641.SS', '600642.SS', '600643.SS', '600644.SS', '600645.SS', '600648.SS', '600649.SS', '600650.SS', '600651.SS', '600653.SS', '600654.SS', '600655.SS', '600657.SS', '600658.SS', '600660.SS', '600661.SS', '600662.SS', '600663.SS', '600664.SS', '600665.SS', '600666.SS', '600667.SS', '600668.SS', '600671.SS', '600673.SS', '600674.SS', '600675.SS', '600676.SS', '600678.SS', '600679.SS', '600681.SS', '600682.SS', '600683.SS', '600684.SS', '600685.SS', '600686.SS', '600688.SS', '600689.SS', '600690.SS', '600691.SS', '600692.SS', '600693.SS', '600694.SS', '600696.SS', '600697.SS', '600698.SS', '600699.SS', '600702.SS', '600703.SS', '600704.SS', 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ptrade python最新版本支持3.11

Ptrade • 李魔佛 发表了文章 • 0 个评论 • 779 次浏览 • 2025-02-27 17:42 • 来自相关话题

为更好的给投资者提供更优质的交易服务。 

国金证券 PTRADE智能策略交易终端预期3月底4月中做实盘版本升级。
 
操作手册和开发手册请登录测试客户端后点击右上角帮助和量化菜单下帮助获取。
 
量化接口升级说明下载链接:https:/pan.baidu.com/s/1S5xXHpOLFVDYdJrOgYDBqA 提取码:gjz9。
 
本次主干版本升级涉及的问题修复、功能优化和需求实现较多，策略量化AP变动较大，python版本升级311。
 
如您有在运行的策略程序、工具功能等，请务必进行测试环境验证调整，确认策略程序在python311环境、工具功能等可正常运行。
 
实盘确认升级完成后，您需要将测试环境验证后策略迁移实盘后重新启用。
 
如您需新开通测试环境账户，可与我司服务老师联系获取。感谢各位长期以来对我公司的信任和支持，由此给各位带来的不便敬请谅解。谢谢!






 
需要开通的可以联系： 查看全部
为更好的给投资者提供更优质的交易服务。 

国金证券 PTRADE智能策略交易终端预期3月底4月中做实盘版本升级。
 
操作手册和开发手册请登录测试客户端后点击右上角帮助和量化菜单下帮助获取。
 
量化接口升级说明下载链接:https:/pan.baidu.com/s/1S5xXHpOLFVDYdJrOgYDBqA 提取码:gjz9。
 
本次主干版本升级涉及的问题修复、功能优化和需求实现较多，策略量化AP变动较大，python版本升级311。
 
如您有在运行的策略程序、工具功能等，请务必进行测试环境验证调整，确认策略程序在python311环境、工具功能等可正常运行。
 
实盘确认升级完成后，您需要将测试环境验证后策略迁移实盘后重新启用。
 
如您需新开通测试环境账户，可与我司服务老师联系获取。感谢各位长期以来对我公司的信任和支持，由此给各位带来的不便敬请谅解。谢谢!

20250227135305.png


 
需要开通的可以联系：

QMT获取北交所市场代码

QMT • 李魔佛 发表了文章 • 0 个评论 • 520 次浏览 • 2025-02-21 11:30 • 来自相关话题

现在qmt支持北交所交易，但前提也是需要你的账号已经开通了北交所的权限。
 
现在的券商只要你开了科创板，可以直接开通北交所，不再需要20交易日，50w的要求。
 
QMT获取北交所代码：





 
运行输出结果：





 
需要开通量化api权限的券商，可以扫码关注并咨询 查看全部
现在qmt支持北交所交易，但前提也是需要你的账号已经开通了北交所的权限。
 
现在的券商只要你开了科创板，可以直接开通北交所，不再需要20交易日，50w的要求。
 
QMT获取北交所代码：

微信图片_20250221112823.png

 
运行输出结果：

微信图片_20250221112829.png

 
需要开通量化api权限的券商，可以扫码关注并咨询

ptrade支持外网，mysql 远程数据库的券商

Ptrade • 李魔佛 发表了文章 • 0 个评论 • 650 次浏览 • 2025-02-17 14:16 • 来自相关话题

目前大部分券商不支持获取外部网络数据，全部数据都是通过内置的api函数获取。
 
比如如果你要获取可转债的一些强赎数据，内置数据是不支持的。
 
那么如果你想要获取集思录的数据，那么就会无能为力。
 
不过有一个券商可以支持访问外部数据的。因为ptrade内置了pymysq，redis，zeromq，wsocket等第三方库，
 
所以你可以在ptrade内部，直接范围mysql的数据，zeromq的数据，甚至直接和外部的api进行双向通信。
 
实例中直接用requests访问的百度
 
实盘，模拟盘中均可操作。
 

 
需要开通的朋友可以公众号联系：
费率也支持万0.854 免5哦~ 查看全部
目前大部分券商不支持获取外部网络数据，全部数据都是通过内置的api函数获取。
 
比如如果你要获取可转债的一些强赎数据，内置数据是不支持的。
 
那么如果你想要获取集思录的数据，那么就会无能为力。
 
不过有一个券商可以支持访问外部数据的。因为ptrade内置了pymysq，redis，zeromq，wsocket等第三方库，
 
所以你可以在ptrade内部，直接范围mysql的数据，zeromq的数据，甚至直接和外部的api进行双向通信。
 
实例中直接用requests访问的百度
 
实盘，模拟盘中均可操作。
 

 
需要开通的朋友可以公众号联系：
费率也支持万0.854 免5哦~

ptrade测试端get_price部分数据为空，天坑

回复

Ptrade • 李魔佛 发起了问题 • 1 人关注 • 0 个回复 • 705 次浏览 • 2025-02-13 18:57 • 来自相关话题

江海证券支持QMT，miniQMT，且低门槛开通

QMT • 李魔佛 发表了文章 • 0 个评论 • 1034 次浏览 • 2025-02-12 00:46 • 来自相关话题

最近新推出的支持QMT，miniQMT的券商，江海证券。

江海证券有限公司于2003年12月15日在哈尔滨市市场监督管理局登记成立。法定代表人孙名扬，公司经营范围包括证券经纪，证券承销与保荐，证券投资咨询，证券自营等。






 
这logo看起来有点像银河呢。
 
江海证券现有分支机构75家，其中17家分公司，58家营业部，遍布北京、上海、广州、深圳、厦门、青岛、大连、成都、武汉、长沙、合肥及黑龙江省内等主要城市，形成了“覆盖龙江、辐射沿海、布局全国”的业务发展格局。
 
不过上面的是2018年的数据，最新的可能会少一些。因为现在很多营业部都更加注重线上业务。也就是通过线上就可以开通账号。
 

 
目前江海证券能够开通量化QMT，和miniQMT的自动化交易权限。





 
 
下载地址：

江海证券QMT实盘_实盘_1.0.0.36190.exe
 
或者直接到官网：
https://www.jhzq.com.cn/main/home/software/index.shtml
选择QMT下载即可
 
江海证券也支持无限易量化平台。

需要的可以咨询公众号： 查看全部
最近新推出的支持QMT，miniQMT的券商，江海证券。


江海证券有限公司于2003年12月15日在哈尔滨市市场监督管理局登记成立。法定代表人孙名扬，公司经营范围包括证券经纪，证券承销与保荐，证券投资咨询，证券自营等。



20250212004427.jpg

 
这logo看起来有点像银河呢。
 
江海证券现有分支机构75家，其中17家分公司，58家营业部，遍布北京、上海、广州、深圳、厦门、青岛、大连、成都、武汉、长沙、合肥及黑龙江省内等主要城市，形成了“覆盖龙江、辐射沿海、布局全国”的业务发展格局。
 
不过上面的是2018年的数据，最新的可能会少一些。因为现在很多营业部都更加注重线上业务。也就是通过线上就可以开通账号。
 

 
目前江海证券能够开通量化QMT，和miniQMT的自动化交易权限。

20250212004842.png

 
 
下载地址：

江海证券QMT实盘_实盘_1.0.0.36190.exe
 
或者直接到官网：
https://www.jhzq.com.cn/main/home/software/index.shtml
选择QMT下载即可
 
江海证券也支持无限易量化平台。

需要的可以咨询公众号：

聚宽打板策略代码转为ptrade代码

Ptrade • 李魔佛 发表了文章 • 0 个评论 • 1094 次浏览 • 2025-02-02 17:42 • 来自相关话题

策略简述：
根据昨日涨停  或最近N天的股票出现连板的数量，然后选股。
高开X之后进入股票池。
然后加入均线，热度，板块等因子，盘中买入。





 
PS: 现在的ptrade回测速度是越来越慢的了。 估计是用户越来越多的缘故了。。。
比我刚开始用的那个时候，简直慢了有100倍.....
 

  查看全部
策略简述：
根据昨日涨停  或最近N天的股票出现连板的数量，然后选股。
高开X之后进入股票池。
然后加入均线，热度，板块等因子，盘中买入。

20250202174033.png

 
PS: 现在的ptrade回测速度是越来越慢的了。 估计是用户越来越多的缘故了。。。
比我刚开始用的那个时候，简直慢了有100倍.....
 

 

ptrade一个策略里可以同时执行多少个run_daily?

Ptrade • 李魔佛 发表了文章 • 0 个评论 • 878 次浏览 • 2025-01-17 10:28 • 来自相关话题

ptrade里的run_daily 是一个定时函数，可以在交易日里指定时间运行你的函数和任务。
 
run_daily 可以在0-24小时都能够执行，并没有限制要求交易时间09:30到15:00.
 
因此如果需要执行集合竞价的部分，那么就需要用run_daily 去操作了。
 
比如开盘打新，尾盘逆回购，






 
而ptrade里面也对run_daily做了限制，就是一个策略里面同时只能设置5个run_daily
 
对于一般人而已，应该够用的了。如果不够，那么就用handle_data处理，也是可以。handle_data，里面可以加一个时间判断的语句，那么你可以一天在指定时间执行多少个任务都没有问题，随意突破5个。
 
 
更多技术问题，可以关注公众号：可转债量化分析 查看全部
ptrade里的run_daily 是一个定时函数，可以在交易日里指定时间运行你的函数和任务。
 
run_daily 可以在0-24小时都能够执行，并没有限制要求交易时间09:30到15:00.
 
因此如果需要执行集合竞价的部分，那么就需要用run_daily 去操作了。
 
比如开盘打新，尾盘逆回购，

20250117103204.png


 
而ptrade里面也对run_daily做了限制，就是一个策略里面同时只能设置5个run_daily
 
对于一般人而已，应该够用的了。如果不够，那么就用handle_data处理，也是可以。handle_data，里面可以加一个时间判断的语句，那么你可以一天在指定时间执行多少个任务都没有问题，随意突破5个。
 
 
更多技术问题，可以关注公众号：可转债量化分析

不同券商的ptrade实盘客户端的回测时间

Ptrade • 李魔佛 发表了文章 • 0 个评论 • 1405 次浏览 • 2024-12-03 15:41 • 来自相关话题

券商的模拟客户端在任何时间都可以回测。
 
而券商的实盘客户端的回测时间会有做限制。
 
1. 国金：实盘客户端不允许回测。
 
2. 国盛：实盘客户端回测时间为收盘后15:30之后
 
3. 湘财：实盘客户端回测时间为收盘后15:30之后
 
需要开通量化账号的读者朋友，可以关注公众号：
 

  查看全部
券商的模拟客户端在任何时间都可以回测。
 
而券商的实盘客户端的回测时间会有做限制。
 
1. 国金：实盘客户端不允许回测。
 
2. 国盛：实盘客户端回测时间为收盘后15:30之后
 
3. 湘财：实盘客户端回测时间为收盘后15:30之后
 
需要开通量化账号的读者朋友，可以关注公众号：
 

 

ptrade上的get_cb_info函数无法使用

Ptrade • 李魔佛 发表了文章 • 0 个评论 • 1015 次浏览 • 2024-11-20 10:32 • 来自相关话题

比如示例代码：
def initialize(context):
pass

def handle_data(context, data):
df = get_cb_info()
log.info(df)
最后跑出来的结果如下：
2024-11-20 10:22:00 - ERROR - 用户策略执行异常
2024-11-20 10:22:00 - ERROR - Exception: Traceback (most recent call last):
File /home/fly/sim_backtest/result/29fa7074-a6e6-11ef-b05b-c40778d9af27/user_strategy.py, line 6 in handle_data
df = get_cb_info()
--> data = BarDict(600570.SS)
--> context = <StrategyContext {'recorded_vars': {}, 'commission': <Commission {'cost': 0.0003, 'min_trade_cost': 5.0, 'tax': 0.001}>, 'blotter': <Blotter {'current_dt': date ...
NameError: name 'get_cb_info' is not defined

说明这个函数就根本没有做进去了。
 
目前我试的券商，国盛ptrade是无法使用的。
 
所以如果需要获取可转债的数据，需要自己写一个接口获取。我之前的很多文章里面也有写过类似的api接口。可以参考参考。
 

  查看全部
比如示例代码：
def initialize(context):

    pass



def handle_data(context, data):

    df = get_cb_info()

    log.info(df)

最后跑出来的结果如下：
2024-11-20 10:22:00 - ERROR - 用户策略执行异常

2024-11-20 10:22:00 - ERROR - Exception: Traceback (most recent call last):

  File /home/fly/sim_backtest/result/29fa7074-a6e6-11ef-b05b-c40778d9af27/user_strategy.py, line 6 in handle_data

    df = get_cb_info()

    --> data = BarDict(600570.SS)

    --> context = <StrategyContext {'recorded_vars': {}, 'commission': <Commission {'cost': 0.0003, 'min_trade_cost': 5.0, 'tax': 0.001}>, 'blotter': <Blotter {'current_dt': date ...

NameError: name 'get_cb_info' is not defined

说明这个函数就根本没有做进去了。
 
目前我试的券商，国盛ptrade是无法使用的。
 
所以如果需要获取可转债的数据，需要自己写一个接口获取。我之前的很多文章里面也有写过类似的api接口。可以参考参考。
 

 

QMT获取市场可转债代码

可转债 • 李魔佛 发表了文章 • 0 个评论 • 1164 次浏览 • 2024-11-14 11:12 • 来自相关话题

QMT本身内置的数据包含了可转债的代码，不需要额外通过外部网络获取。
 
#encoding:gbk

def init(ContextInfo):
pass

def handlebar(ContextInfo):
index = ContextInfo.barpos
realtime = ContextInfo.get_bar_timetag(index)
print(ContextInfo.get_stock_list_in_sector('沪深转债', realtime))







欢迎关注公众号：可转债量化分析 查看全部
QMT本身内置的数据包含了可转债的代码，不需要额外通过外部网络获取。
 
#encoding:gbk



def init(ContextInfo):

    pass

    

def handlebar(ContextInfo):

    index = ContextInfo.barpos

    realtime = ContextInfo.get_bar_timetag(index)

    print(ContextInfo.get_stock_list_in_sector('沪深转债', realtime))


20241114111109.png


欢迎关注公众号：可转债量化分析

ptrade：登录请求失败，服务器状态400

Ptrade • 李魔佛 发表了文章 • 0 个评论 • 958 次浏览 • 2024-11-11 10:03 • 来自相关话题

登录之后打开交易菜单，显示：
ptrade：登录请求失败，服务器状态400





用户数多，占用了资源多了，服务器没有扩容，导致资源不够用了。
 
周一一大早就崩了。
 
券商ptrade的运营工作人员只能重启。 但不增加资源，不优化隔离资源分配，一旦内存不够，cpu负载占满，全部人一起陪葬，这样好么？
 
  查看全部
登录之后打开交易菜单，显示：
ptrade：登录请求失败，服务器状态400

20241111095840.png

用户数多，占用了资源多了，服务器没有扩容，导致资源不够用了。
 
周一一大早就崩了。
 
券商ptrade的运营工作人员只能重启。 但不增加资源，不优化隔离资源分配，一旦内存不够，cpu负载占满，全部人一起陪葬，这样好么？
 
 

特朗普和拜登在任期间，上证指数的涨幅是多少？

股票 • 李魔佛 发表了文章 • 0 个评论 • 994 次浏览 • 2024-11-06 12:31 • 来自相关话题

 特朗普上一次在任期间，上证指数的点位：

2017-1-20 - 3123

2021-1-20 - 3583

涨幅为14.7%


而拜登在任期间，截至到今天为止：

2021-1-20 - 3583

2024-11-06 - 3392

涨幅为 -5.3%

#美国大选#
 
欢迎关注公众号： 查看全部
微信图片_20241106122226.jpg

 特朗普上一次在任期间，上证指数的点位：

2017-1-20 - 3123

2021-1-20 - 3583

涨幅为14.7%


而拜登在任期间，截至到今天为止：

2021-1-20 - 3583

2024-11-06 - 3392

涨幅为 -5.3%

#美国大选#
 
欢迎关注公众号：

Ptrade获取历史涨停的股票|python代码

Ptrade • 李魔佛 发表了文章 • 0 个评论 • 1206 次浏览 • 2024-11-01 18:41 • 来自相关话题

用当天的收盘价对比最高价，如果是相等的就是涨停。
下面的程序用于监控可转债的正股，在过去10天里是否出现涨停。
 
下面的ptrade的代码片段。需要完整代码，可关注公众号私信获取。
 
def hit_limit_recent():
# 选出最近N天正股有涨停的可转债
N =10
scale = 5
latest_price = 160
bond_name_dict, bond_zg_dict = get_all_bond_info(scale=scale,latest_price=latest_price)
zg_list = list(bond_zg_dict.values())
panel_info = get_history(N, frequency='1d', field=['close','high_limit'], security_list=zg_list, fq='pre', include=False, fill='nan')
df = panel_info.swapaxes("minor_axis", "items")
target_list = []
for code in zg_list:
stock_df = df[code]

hit_high = stock_df[stock_df['close']==stock_df['high_limit']]
if len(hit_high) > 0:
# print(hit_high.index)
target_list.append(code)

zz_target_list = []
for code,zg_code in bond_zg_dict.items():
if zg_code in target_list:
print(code, bond_name_dict[code])
zz_target_list.append(code)

return zz_target_list当然，会有一个情形，就是实际最后是开板状态，但是收盘价格和涨停价格一样。
这种属于涨停开板状态，需要利用tick的委卖买来判断。
  查看全部
用当天的收盘价对比最高价，如果是相等的就是涨停。
下面的程序用于监控可转债的正股，在过去10天里是否出现涨停。
 
下面的ptrade的代码片段。需要完整代码，可关注公众号私信获取。
 
def hit_limit_recent():

    # 选出最近N天正股有涨停的可转债

    N =10

    scale = 5

    latest_price = 160

    bond_name_dict, bond_zg_dict = get_all_bond_info(scale=scale,latest_price=latest_price)

    zg_list = list(bond_zg_dict.values())

    panel_info = get_history(N, frequency='1d', field=['close','high_limit'], security_list=zg_list, fq='pre', include=False, fill='nan')

    df = panel_info.swapaxes("minor_axis", "items")

    target_list = []

    for code in zg_list:

        stock_df = df[code]



        hit_high = stock_df[stock_df['close']==stock_df['high_limit']]

        if len(hit_high) > 0:

            # print(hit_high.index)

            target_list.append(code)



    zz_target_list = []

    for code,zg_code in bond_zg_dict.items():

        if zg_code in target_list:

            print(code, bond_name_dict[code])

            zz_target_list.append(code)



    return zz_target_list
当然，会有一个情形，就是实际最后是开板状态，但是收盘价格和涨停价格一样。
这种属于涨停开板状态，需要利用tick的委卖买来判断。
 

可转债不下修名单 - 铁公鸡一览表

可转债 • 李魔佛 发表了文章 • 0 个评论 • 1010 次浏览 • 2024-11-01 08:15 • 来自相关话题

如果可转债公布了某个时间段内的不下修转股价，在溢价率很高的情况下，那么在该时间段内，其溢价率回归的办法只剩拉正股了，而在这上涨阶段中，站在概率面，持有转债的涨幅是会大幅低于正股的。

对于笔者而言，建仓标的会选择排除这些公告了不下修的转债，且对应的转股价值低于70-80的转债。

上市公司为何不下修转股价？

摘录网上比较官方的原因：

第一，下修转股价会稀释现有股东的股权，公司不希望现有股东权益被稀释，选择不下修转股价；

第二，公司可能有长期的战略规划，不希望因为短期的财务压力下修转股价而改变资本结构；

第三，下修转股价可能会被市场解读为公司财务状况不佳或未来盈利前景不明朗，公司为了避免这种负面反应而选择不下修转股价。

“上市公司选择不下修转股价，实际上也是在向市场传递公司对股价的信心。” 业内人士表示，公司不下修转股价，一方面说明公司现金流状况良好，有到期偿还负债的意愿；另一方面，公司可能认为当前的转股价已经反映了公司的合理价值，不需要通过下修来吸引投资者。

不下修转债数据更新至2024-10-30日晚。

文末附原始数据获取方法。






















省略若干....

后台回复关键字：不下修列表202410 获取原始excel表格数据

因为文章更新并不频繁，微信并不会把最新的公众号文章及时推送在读者最新的列表中，可以将***公众号设为*****星标，这样就能第一时间收到笔者的最新文章啦。**

获取数据不易，喜欢本文的记得点“**赞”和“在看”哦，欢迎转载本文。**
  查看全部
如果可转债公布了某个时间段内的不下修转股价，在溢价率很高的情况下，那么在该时间段内，其溢价率回归的办法只剩拉正股了，而在这上涨阶段中，站在概率面，持有转债的涨幅是会大幅低于正股的。

对于笔者而言，建仓标的会选择排除这些公告了不下修的转债，且对应的转股价值低于70-80的转债。

上市公司为何不下修转股价？

摘录网上比较官方的原因：

第一，下修转股价会稀释现有股东的股权，公司不希望现有股东权益被稀释，选择不下修转股价；

第二，公司可能有长期的战略规划，不希望因为短期的财务压力下修转股价而改变资本结构；

第三，下修转股价可能会被市场解读为公司财务状况不佳或未来盈利前景不明朗，公司为了避免这种负面反应而选择不下修转股价。

“上市公司选择不下修转股价，实际上也是在向市场传递公司对股价的信心。” 业内人士表示，公司不下修转股价，一方面说明公司现金流状况良好，有到期偿还负债的意愿；另一方面，公司可能认为当前的转股价已经反映了公司的合理价值，不需要通过下修来吸引投资者。

不下修转债数据更新至2024-10-30日晚。

文末附原始数据获取方法。

20241031152023.png


20241031152056.png


20241031152113.png


20241031152132.png



省略若干....

后台回复关键字：不下修列表202410 获取原始excel表格数据

因为文章更新并不频繁，微信并不会把最新的公众号文章及时推送在读者最新的列表中，可以将***公众号设为*****星标，这样就能第一时间收到笔者的最新文章啦。**

获取数据不易，喜欢本文的记得点“**赞”和“在看”哦，欢迎转载本文。**
 

哪些股票突破了10月8日的最高点？

股票 • 李魔佛 发表了文章 • 0 个评论 • 1582 次浏览 • 2024-10-25 10:28 • 来自相关话题

昨天的文章贴了可转债从10月8日高点到现在的涨跌情况。
 
本文继续贴一下股票从10月8日的最高，到目前的涨跌分布。

数据包含北交所数据。






当前价格相对10月8日高点，涨幅前面的基本是北交所，创业板的股票。天马新材涨幅高达284%，一路涨停板30%，45度角冲上来。






创业板的光智科技8连板，20%一个板，最终今天开板后又封住，录得相对8号高点到目前的涨幅为258%






这些不知名的股票，要么处于亏损状态，要么四五百的市盈率，日后大概率会遵循怎么上去就怎么下来的规律。

比如像下面跌幅榜排名前面的，从高点跌去80%的股票。






长联科技节前最后一天上市，上市当天就吸引了足够的关注，涨了足足17倍。打新中签者，一签浮盈17万。而节后第一天该股冲高回落，依然大涨收盘。

而该股后面就开启了暴跌模式。

跌到今天之后，相对高点跌幅达到81%。






10月8日高点下来的A股个股数据统计






平均跌幅为-8.73%，中位数跌幅为-11%。当天开盘前，散户幻觉认为牛市来了，股票高开后，肯定不能轻易被买到，纷纷挂高价，甚至涨停价。（当时氛围太火爆，当时我也有这种牛市冲冲冲的幻觉）

而截至昨日收盘价，依然有652只股票，突破了10月8日的最高点，相对10日8日的最高点获得了正涨幅，比例为12%，比例比转债的稍微大一些。

A股的股票看起来暴富机会比转债要大的多，但同样会伴随更大的概率，让你一贫如洗，盈亏同源。
  查看全部
昨天的文章贴了可转债从10月8日高点到现在的涨跌情况。
 
本文继续贴一下股票从10月8日的最高，到目前的涨跌分布。

数据包含北交所数据。

20241024000500.png


当前价格相对10月8日高点，涨幅前面的基本是北交所，创业板的股票。天马新材涨幅高达284%，一路涨停板30%，45度角冲上来。

20241024002212.png


创业板的光智科技8连板，20%一个板，最终今天开板后又封住，录得相对8号高点到目前的涨幅为258%

20241024002326.png


这些不知名的股票，要么处于亏损状态，要么四五百的市盈率，日后大概率会遵循怎么上去就怎么下来的规律。

比如像下面跌幅榜排名前面的，从高点跌去80%的股票。

20241024001333.png


长联科技节前最后一天上市，上市当天就吸引了足够的关注，涨了足足17倍。打新中签者，一签浮盈17万。而节后第一天该股冲高回落，依然大涨收盘。

而该股后面就开启了暴跌模式。

跌到今天之后，相对高点跌幅达到81%。

20241024004105.png


10月8日高点下来的A股个股数据统计

20241024004435.png


平均跌幅为-8.73%，中位数跌幅为-11%。当天开盘前，散户幻觉认为牛市来了，股票高开后，肯定不能轻易被买到，纷纷挂高价，甚至涨停价。（当时氛围太火爆，当时我也有这种牛市冲冲冲的幻觉）

而截至昨日收盘价，依然有652只股票，突破了10月8日的最高点，相对10日8日的最高点获得了正涨幅，比例为12%，比例比转债的稍微大一些。

A股的股票看起来暴富机会比转债要大的多，但同样会伴随更大的概率，让你一贫如洗，盈亏同源。
 

突破10月8日最高点的转债名单 | 高点回落幅度排行榜

可转债 • 李魔佛 发表了文章 • 0 个评论 • 1039 次浏览 • 2024-10-23 10:16 • 来自相关话题

倒后镜看10月8日之后的走势，开盘即高点的转债比比皆是，不少还是涨停价开盘。







开盘冲进去的散户被挂在光秃秃的大阴线的顶部。

但也有一些转债已经突破了10月8日高点。

本文简单罗列一下数据。需要原始数据的读者朋友可以公众号后台获取。






价格突破10月8日的可转债红相转债和城地转债相对8日的高点涨幅达到了50%以上，属于一波流涨停冲上去的。

不过整体只有22只突破了8日的高点，占比只有4%，大部分是低于8日的最高价。

涨幅倒序排序








跌幅榜跌幅榜排名下，比较惨的是证券类转债。

由于很多人因为国庆假期港股的券商板块暴涨，8日A股券商开盘出现涨停潮，正股，ETF涨停买不进，于是转向买入没有达到涨停的券商转债（因为转债人比较怂？）。结果截至22日的收盘价格，浙22转债高点下来跌了24%，财通转债高点跌了18%。

相对10月8日高点的涨跌分布统计如下：
 





 

统计分布最新转债价格相对8日的高点跌幅的中位数和平均值为 -6.x%，转债持有人的当前市值跟8日高点相比，平均少了6%+。

一天的冲高回落，并不影响整体趋势。9月30日的转债价格中位数为113.798， 而当前（10月22日收盘）转债价格中位数为115, 比节前还略有提升。

所以节前一直持有的转债人，而不是8日冲高进场的，相对于节前的市值，也是稳定提升的。


需要原始excel数据，可后台回复：10月8日回落
  查看全部
倒后镜看10月8日之后的走势，开盘即高点的转债比比皆是，不少还是涨停价开盘。

20241023020919.png



开盘冲进去的散户被挂在光秃秃的大阴线的顶部。

但也有一些转债已经突破了10月8日高点。

本文简单罗列一下数据。需要原始数据的读者朋友可以公众号后台获取。

20241023003904.png


价格突破10月8日的可转债红相转债和城地转债相对8日的高点涨幅达到了50%以上，属于一波流涨停冲上去的。

不过整体只有22只突破了8日的高点，占比只有4%，大部分是低于8日的最高价。

涨幅倒序排序


20241023005404.png



跌幅榜跌幅榜排名下，比较惨的是证券类转债。

由于很多人因为国庆假期港股的券商板块暴涨，8日A股券商开盘出现涨停潮，正股，ETF涨停买不进，于是转向买入没有达到涨停的券商转债（因为转债人比较怂？）。结果截至22日的收盘价格，浙22转债高点下来跌了24%，财通转债高点跌了18%。

相对10月8日高点的涨跌分布统计如下：
 

20241023005452.png

 

统计分布最新转债价格相对8日的高点跌幅的中位数和平均值为 -6.x%，转债持有人的当前市值跟8日高点相比，平均少了6%+。

一天的冲高回落，并不影响整体趋势。9月30日的转债价格中位数为113.798， 而当前（10月22日收盘）转债价格中位数为115, 比节前还略有提升。

所以节前一直持有的转债人，而不是8日冲高进场的，相对于节前的市值，也是稳定提升的。


需要原始excel数据，可后台回复：10月8日回落
 

可转债现金替代策略 | QMT | Ptrade

李魔佛 发表了文章 • 0 个评论 • 1290 次浏览 • 2024-10-15 11:05 • 来自相关话题

策略需求：
适合大资金，求稳。
 
挑选低价格的AAA可转债，比如 正股是 银行，高分红的国企股，比如 大秦铁路的转债，大秦转债等，且到期收益为正。作为标的池。
 
然后 先 在标的池里挑选出一个价格最低的转债，1/3 仓位 买入，其余仓位买入银华日利。
 
程序每分钟监控。或者不用那么频繁，可以设置每小时，每天都可以。
 
如果转债价格下跌了X，就卖出银华日利（1/10仓位），买入转债； （这里仓位随意举例）
 
如果转债价格上涨了Y，就卖出转债（1/5仓位），买入银华日利；（这里仓位随意举例）
 
一般AAA的大规模转债，其波动比较小，很少会遇到趋势上涨。 所以大部分的时间是做有波动的高抛低吸。
 
但，一旦遇到趋势上涨，或者突破，那么按照策略 会不断卖出转债；
 
一旦转债仓位为0，就可以在标的池买入另外一只标的（1/3仓位），从而继续下一轮的高抛低吸。
 
如果转债价格一直跌，但由于AAA的转债有保底，且有回售，转股，下修等各种手段，来兜底，
 
所以一般遇到跌幅行情，下跌不会超过10%，所以策略可以一直在卖出银华日利，买入转债；
 
如果中途，出现了其他好的标的，你需要手动交易，那么可以手动卖出银华日利或者可转债，腾出仓位，来操作。
 
也就是这个策略的可转债，纯粹当做现金来替代来使用。
 
接着就是使用QMT和Ptrade实现。
 
待续............
 

 
  查看全部
策略需求：
适合大资金，求稳。
 
挑选低价格的AAA可转债，比如 正股是 银行，高分红的国企股，比如 大秦铁路的转债，大秦转债等，且到期收益为正。作为标的池。
 
然后 先 在标的池里挑选出一个价格最低的转债，1/3 仓位 买入，其余仓位买入银华日利。
 
程序每分钟监控。或者不用那么频繁，可以设置每小时，每天都可以。
 
如果转债价格下跌了X，就卖出银华日利（1/10仓位），买入转债； （这里仓位随意举例）
 
如果转债价格上涨了Y，就卖出转债（1/5仓位），买入银华日利；（这里仓位随意举例）
 
一般AAA的大规模转债，其波动比较小，很少会遇到趋势上涨。 所以大部分的时间是做有波动的高抛低吸。
 
但，一旦遇到趋势上涨，或者突破，那么按照策略 会不断卖出转债；
 
一旦转债仓位为0，就可以在标的池买入另外一只标的（1/3仓位），从而继续下一轮的高抛低吸。
 
如果转债价格一直跌，但由于AAA的转债有保底，且有回售，转股，下修等各种手段，来兜底，
 
所以一般遇到跌幅行情，下跌不会超过10%，所以策略可以一直在卖出银华日利，买入转债；
 
如果中途，出现了其他好的标的，你需要手动交易，那么可以手动卖出银华日利或者可转债，腾出仓位，来操作。
 
也就是这个策略的可转债，纯粹当做现金来替代来使用。
 
接着就是使用QMT和Ptrade实现。
 
待续............
 

 
 

LOF基金出现大量套利机会，微信发送套利提示机会

李魔佛 发表了文章 • 0 个评论 • 1829 次浏览 • 2024-10-05 17:54 • 来自相关话题

节前A股行情火爆，节中港股继续开市，港股行情火爆。

对于踏空的投资来说，简直比满仓套牢还难受。

虽然行情火爆，但头脑依然要保持清醒，不要看到什么热门就一股脑冲进去。

错误的方式，让你赚到了大钱，实际会后患无穷。

“有勇敢的飞行员，有年老的飞行员，但鲜有勇敢的年老飞行员"

行情爆发，大部分人连指数都追不上，所以索性直接打板指数。以至于各种指数10CM，甚至20CM。





 
实际指数的成分股并没有出现全部涨停那么夸张。

所以这时出现不少溢价的LOF基金，基金价格高于其净值。于是就出现了套利的机会。


溢价率越高，其安全垫也就越高。对于当前空仓或者轻仓的投资者而言，是一个不错的介入机会。（具体套利操作就是，场内净值申购，T+2或者T+3到账后，场内现价卖出）


这里笔者根据之前的LOF监控程序，程序自动获取限购-溢价LOF基金套利，并推送到微信消息，获取了目前溢价率大于5%的LOF基金，然后按照其成交量从大到小排序，得到下表：
 




 
成交量越大的，说明该LOF越活跃，这样就不至于在套利大军进场的时候，承接不住，被砸到跌停出不去。

目前成交量最大的是中概互联LOF164906，成交量1.9亿。成交量最少的是鼎弘LOF167003，成交量只有4万块不到，即使有14%的溢价率的肉垫，但容量太少，随便一个大户砸下来就跌停了。

所以这里也建议要介入额投资者，不仅仅只看溢价率，同时也要关注成交量。


同时也要注意申购状态那一列，积极配置FOF和原油LOF易方达 目前处于暂停申购状态，也就是当前是无法进行套利的。

而恒生LOF 是限购状态，限购5000元，因为港股假期猛涨了一波，所以大概率这个基金周二开盘会直接顶住涨停的。该基金是深市基金，可以6+1拖拉机拉满。申购7户，35000元。




结合之前的程序，LOF溢价率监控+微信推送，可以做到收盘前推送数据：








当然你用飞书，钉钉也行。如果要用微信，早期开通的api还能继续用，新开的就需要备案的服务器了，手续就麻烦一点。
 
广告时间：需要低佣免5开户的可以公众号菜单联系，支持Ptrade，QMT，miniQMT，套利拖拉机。
  查看全部
节前A股行情火爆，节中港股继续开市，港股行情火爆。

对于踏空的投资来说，简直比满仓套牢还难受。

虽然行情火爆，但头脑依然要保持清醒，不要看到什么热门就一股脑冲进去。

错误的方式，让你赚到了大钱，实际会后患无穷。

“有勇敢的飞行员，有年老的飞行员，但鲜有勇敢的年老飞行员"

行情爆发，大部分人连指数都追不上，所以索性直接打板指数。以至于各种指数10CM，甚至20CM。

Screenshot_2024_1005_171022.png

 
实际指数的成分股并没有出现全部涨停那么夸张。

所以这时出现不少溢价的LOF基金，基金价格高于其净值。于是就出现了套利的机会。


溢价率越高，其安全垫也就越高。对于当前空仓或者轻仓的投资者而言，是一个不错的介入机会。（具体套利操作就是，场内净值申购，T+2或者T+3到账后，场内现价卖出）


这里笔者根据之前的LOF监控程序，程序自动获取限购-溢价LOF基金套利，并推送到微信消息，获取了目前溢价率大于5%的LOF基金，然后按照其成交量从大到小排序，得到下表：
 
20241005170056.png

 
成交量越大的，说明该LOF越活跃，这样就不至于在套利大军进场的时候，承接不住，被砸到跌停出不去。

目前成交量最大的是中概互联LOF164906，成交量1.9亿。成交量最少的是鼎弘LOF167003，成交量只有4万块不到，即使有14%的溢价率的肉垫，但容量太少，随便一个大户砸下来就跌停了。

所以这里也建议要介入额投资者，不仅仅只看溢价率，同时也要关注成交量。


同时也要注意申购状态那一列，积极配置FOF和原油LOF易方达 目前处于暂停申购状态，也就是当前是无法进行套利的。

而恒生LOF 是限购状态，限购5000元，因为港股假期猛涨了一波，所以大概率这个基金周二开盘会直接顶住涨停的。该基金是深市基金，可以6+1拖拉机拉满。申购7户，35000元。




结合之前的程序，LOF溢价率监控+微信推送，可以做到收盘前推送数据：


Screenshot_2024_1005_173638.jpg



当然你用飞书，钉钉也行。如果要用微信，早期开通的api还能继续用，新开的就需要备案的服务器了，手续就麻烦一点。
 
广告时间：需要低佣免5开户的可以公众号菜单联系，支持Ptrade，QMT，miniQMT，套利拖拉机。
 

QMT获取不到行情，一般是什么原因？或者获取行情有中断的情况

QMT • 李魔佛 发表了文章 • 0 个评论 • 1504 次浏览 • 2024-09-29 10:27 • 来自相关话题

8-9成原因是 因为你选择的行情服务器掉线了





 
或者你获取的5档tick行情，但是你的行情菜单那里，选择的是 最新价，这样你也是无法获取到 5档tick数据的。





 
然后还有一个问题，如果你用get_market_data 或者get_market_data_ex 这个函数，获取的股票数量很多（比如全市场的股票），那么第一次的启动数据会非常的慢，并且你的内存消耗非常大，整个QMT像死机了一样。
 
如何解决？ 下回分解
  查看全部
8-9成原因是 因为你选择的行情服务器掉线了

20231120204016.png

 
或者你获取的5档tick行情，但是你的行情菜单那里，选择的是 最新价，这样你也是无法获取到 5档tick数据的。

20240929102328.png

 
然后还有一个问题，如果你用get_market_data 或者get_market_data_ex 这个函数，获取的股票数量很多（比如全市场的股票），那么第一次的启动数据会非常的慢，并且你的内存消耗非常大，整个QMT像死机了一样。
 
如何解决？ 下回分解
 

Ptrade回测模式下获取实时的分钟数据：只能使用handle_data，数据不能用get_snapshot

Ptrade • 李魔佛 发表了文章 • 0 个评论 • 1599 次浏览 • 2024-09-21 17:32 • 来自相关话题

好久没有使用Ptrade做分钟级别的回测了。
发现有点蛋疼，记录一下。
 
因为回测模式下，不能使用run_interval 函数；
 
而run_daily模式，只能在固定时间运行，无法分钟级别。
 
所以只能使用 handle_data
而在handle_data 里面获取当前的分钟价格数据，也无法使用 get_snapshot ，get_gear_price,函数。
 
所以只能使用handle_data(Context,data) 里面的data里。
 
而handle_data 里面的data数据，使用方法如下：

 

import datetime

target_list = [
'600000.SS',
'000333.SZ'
]

def execution(context, data):
now = context.current_dt.strftime('%H:%M')
for code in target_list:
tick_info = data[code]
price = tick_info['price']
print('now: {} code : {} price:{}'.format(now,code ,price))




# 标准
def initialize(context):
log.info("公众号：可转债量化分析 ---- start ----")


def handle_data(context, data):
execution(context, data)

 

获取数据结果：2024-09-20 14:42:00 - INFO - now: 14:42 code : 000333.SZ price:66.22
2024-09-20 14:43:00 - INFO - now: 14:43 code : 000333.SZ price:66.26
2024-09-20 14:44:00 - INFO - now: 14:44 code : 000333.SZ price:66.19
2024-09-20 14:45:00 - INFO - now: 14:45 code : 000333.SZ price:66.2
2024-09-20 14:46:00 - INFO - now: 14:46 code : 000333.SZ price:66.19
2024-09-20 14:47:00 - INFO - now: 14:47 code : 000333.SZ price:66.18
2024-09-20 14:48:00 - INFO - now: 14:48 code : 000333.SZ price:66.14
2024-09-20 14:49:00 - INFO - now: 14:49 code : 000333.SZ price:66.25
2024-09-20 14:50:00 - INFO - now: 14:50 code : 000333.SZ price:66.19
2024-09-20 14:51:00 - INFO - now: 14:51 code : 000333.SZ price:66.18
2024-09-20 14:52:00 - INFO - now: 14:52 code : 000333.SZ price:66.19
2024-09-20 14:53:00 - INFO - now: 14:53 code : 000333.SZ price:66.2
2024-09-20 14:54:00 - INFO - now: 14:54 code : 000333.SZ price:66.19
2024-09-20 14:55:00 - INFO - now: 14:55 code : 000333.SZ price:66.25
2024-09-20 14:56:00 - INFO - now: 14:56 code : 000333.SZ price:66.27
2024-09-20 14:57:00 - INFO - now: 14:57 code : 000333.SZ price:66.28
2024-09-20 14:58:00 - INFO - now: 14:58 code : 000333.SZ price:66.28
2024-09-20 14:59:00 - INFO - now: 14:59 code : 000333.SZ price:66.28
2024-09-20 15:00:00 - INFO - now: 15:00 code : 000333.SZ price:66.06
 用同花顺，对了一下结果，是满足的了。
 

  查看全部
好久没有使用Ptrade做分钟级别的回测了。
发现有点蛋疼，记录一下。
 
因为回测模式下，不能使用run_interval 函数；
 
而run_daily模式，只能在固定时间运行，无法分钟级别。
 
所以只能使用 handle_data
而在handle_data 里面获取当前的分钟价格数据，也无法使用 get_snapshot ，get_gear_price,函数。
 
所以只能使用handle_data(Context,data) 里面的data里。
 
而handle_data 里面的data数据，使用方法如下：

 




import datetime



target_list = [

'600000.SS', 

'000333.SZ'

]



def execution(context, data):

    now = context.current_dt.strftime('%H:%M')

	for code in target_list:

        tick_info = data[code]

        price = tick_info['price']

        print('now: {} code : {} price:{}'.format(now,code ,price))

        







# 标准

def initialize(context):

    log.info("公众号：可转债量化分析 ---- start ----")





def handle_data(context, data):

    execution(context, data)
 

获取数据结果：
2024-09-20 14:42:00 - INFO - now: 14:42 code : 000333.SZ price:66.22

2024-09-20 14:43:00 - INFO - now: 14:43 code : 000333.SZ price:66.26

2024-09-20 14:44:00 - INFO - now: 14:44 code : 000333.SZ price:66.19

2024-09-20 14:45:00 - INFO - now: 14:45 code : 000333.SZ price:66.2

2024-09-20 14:46:00 - INFO - now: 14:46 code : 000333.SZ price:66.19

2024-09-20 14:47:00 - INFO - now: 14:47 code : 000333.SZ price:66.18

2024-09-20 14:48:00 - INFO - now: 14:48 code : 000333.SZ price:66.14

2024-09-20 14:49:00 - INFO - now: 14:49 code : 000333.SZ price:66.25

2024-09-20 14:50:00 - INFO - now: 14:50 code : 000333.SZ price:66.19

2024-09-20 14:51:00 - INFO - now: 14:51 code : 000333.SZ price:66.18

2024-09-20 14:52:00 - INFO - now: 14:52 code : 000333.SZ price:66.19

2024-09-20 14:53:00 - INFO - now: 14:53 code : 000333.SZ price:66.2

2024-09-20 14:54:00 - INFO - now: 14:54 code : 000333.SZ price:66.19

2024-09-20 14:55:00 - INFO - now: 14:55 code : 000333.SZ price:66.25

2024-09-20 14:56:00 - INFO - now: 14:56 code : 000333.SZ price:66.27

2024-09-20 14:57:00 - INFO - now: 14:57 code : 000333.SZ price:66.28

2024-09-20 14:58:00 - INFO - now: 14:58 code : 000333.SZ price:66.28

2024-09-20 14:59:00 - INFO - now: 14:59 code : 000333.SZ price:66.28

2024-09-20 15:00:00 - INFO - now: 15:00 code : 000333.SZ price:66.06

 用同花顺，对了一下结果，是满足的了。
 

 

QMT的handlebar设置成一分钟周期的时候，运行是3秒一次的

QMT • 李魔佛 发表了文章 • 0 个评论 • 1746 次浏览 • 2024-09-13 11:46 • 来自相关话题

平时很少用handlebar驱动。
 
刚好有个策略是一分钟周期运行的，所以就用了handle驱动。





 
 
结果发现，经常查询重复买入。
 
调试后发现，原来这个handlebar是3m触发一次的。
 
如果要一分钟运行一次，还是用
ContextInfo.run_time("execution", INTERVAL_STRING, running_time)
 
INTERVAL_STRING 用1分钟表示。
 
示例代码：
一分钟打印当前时间：
 # -*-coding:gbk-*-
# 作者公众号：可转债量化分析
import datetime
import json
import redis

####### 以下为固定配置，请勿随意修改 ##########
# 注意：程序需要在9：30前启动

START_TIME = '09:30' # 启动时间，可以修改为开盘任意时间9：30-14：57
STOCK_ACCOUNT = '' # 股票账户


INTERVAL_SECOND = 60 # 交易间隔
INTERVAL_STRING = "{}nSecond".format(INTERVAL_SECOND)

def today_date():
return datetime.datetime.now().strftime('%Y%m%d')


def init(ContextInfo):
now = datetime.datetime.now()
print('策略初始化 {}'.format(now))
now = datetime.datetime.now()
running_time = '{} {}'.format(today_date(), START_TIME)
print('策略初始化 {} , 程序启动时间 {}'.format(now, START_TIME))
ContextInfo.run_time("execution", INTERVAL_STRING, running_time)



def handlebar(ContextInfo):
pass

def execution(ContextInfo):
if not ContextInfo.is_last_bar():
return
now = datetime.datetime.now()
print(now)


公众号： 查看全部
平时很少用handlebar驱动。
 
刚好有个策略是一分钟周期运行的，所以就用了handle驱动。

20240913113955.png

 
 
结果发现，经常查询重复买入。
 
调试后发现，原来这个handlebar是3m触发一次的。
 
如果要一分钟运行一次，还是用
ContextInfo.run_time("execution", INTERVAL_STRING, running_time)
 
INTERVAL_STRING 用1分钟表示。
 
示例代码：
一分钟打印当前时间：
 
# -*-coding:gbk-*-

# 作者公众号：可转债量化分析

import datetime

import json

import redis



####### 以下为固定配置，请勿随意修改 ##########

# 注意：程序需要在9：30前启动



START_TIME = '09:30'  # 启动时间，可以修改为开盘任意时间9：30-14：57

STOCK_ACCOUNT = ''  # 股票账户





INTERVAL_SECOND = 60  # 交易间隔

INTERVAL_STRING = "{}nSecond".format(INTERVAL_SECOND)



def today_date():

    return datetime.datetime.now().strftime('%Y%m%d')





def init(ContextInfo):

    now = datetime.datetime.now()

    print('策略初始化 {}'.format(now))

    now = datetime.datetime.now()

    running_time = '{} {}'.format(today_date(), START_TIME)

    print('策略初始化 {} , 程序启动时间 {}'.format(now, START_TIME))

    ContextInfo.run_time("execution", INTERVAL_STRING, running_time)







def handlebar(ContextInfo):

    pass

    

def execution(ContextInfo):

    if not ContextInfo.is_last_bar():

        return

    now = datetime.datetime.now()

    print(now)

公众号：

QMT/iQuant居然禁止redis获取股票行情数据，如何破解

QMT • 李魔佛 发表了文章 • 0 个评论 • 1825 次浏览 • 2024-09-10 11:26 • 来自相关话题

QMT禁止redis获取股票行情数据，如何破解

因为QMT没有转债的规模，而国信iquant本身又把requests 库给封禁了，它无法通过requests 获取任何外部数据，比如访问东财，tushare都会提示访问超时。（后面发现它修改了requests的源码）

于是发现iquant的redis是内置库，不需要pip安装，所以第一时间就想到使用redis获取转债数据。

简单测试了一下，redis是可以获取数据的。比如随便丢一个字符到redis，然后QMT可以获取到这个字符数据

于是就把原来的策略里，使用requests 调用API的部分，改成redis。

可是运行后发现报错：







2024-09-10 10:05:19,851 [INFO] [0x00000e24] [msg service] msg: 0C:\iquant\python\新建策略文件.py_SH00030021

DataError:Sensitive Data Detected, Forbidden!

报错信息说的很明显，敏感信息检测到，禁止！

居然对redis的数据进行检验，很明显这部分是QMT对redis的源码进行了修改。

于是根据报错的提示，找到 目录：

D:\tool\gjzq_qmt_simulation\bin.x64\Lib\site-packages\redis，下面的client.py

找到对应的行：








的确有一个check_response 的函数用于检测 redis的内容，看正则表达式和变量命名（search_stock），类似股票代码的数据传输被阻止了，检测到之后直接raise Error，程序会停止的。

解决办法，也很简单，直接把这一段 代码删除就好了。

然后需要重启你的QMT，不然修改的代码不会生效。

然后重新运行的你的策略，发现一起正常了。
 
需要开通QMT和代写量化策略，可以关注公众号： 查看全部
QMT禁止redis获取股票行情数据，如何破解

因为QMT没有转债的规模，而国信iquant本身又把requests 库给封禁了，它无法通过requests 获取任何外部数据，比如访问东财，tushare都会提示访问超时。（后面发现它修改了requests的源码）

于是发现iquant的redis是内置库，不需要pip安装，所以第一时间就想到使用redis获取转债数据。

简单测试了一下，redis是可以获取数据的。比如随便丢一个字符到redis，然后QMT可以获取到这个字符数据

于是就把原来的策略里，使用requests 调用API的部分，改成redis。

可是运行后发现报错：


微信图片_20240910103921.jpg


2024-09-10 10:05:19,851 [INFO] [0x00000e24] [msg service] msg: 0C:\iquant\python\新建策略文件.py_SH00030021

DataError:Sensitive Data Detected, Forbidden!

报错信息说的很明显，敏感信息检测到，禁止！

居然对redis的数据进行检验，很明显这部分是QMT对redis的源码进行了修改。

于是根据报错的提示，找到 目录：

D:\tool\gjzq_qmt_simulation\bin.x64\Lib\site-packages\redis，下面的client.py

找到对应的行：


微信图片_20240910103929.png



的确有一个check_response 的函数用于检测 redis的内容，看正则表达式和变量命名（search_stock），类似股票代码的数据传输被阻止了，检测到之后直接raise Error，程序会停止的。

解决办法，也很简单，直接把这一段 代码删除就好了。

然后需要重启你的QMT，不然修改的代码不会生效。

然后重新运行的你的策略，发现一起正常了。
 
需要开通QMT和代写量化策略，可以关注公众号：

国信iquant requests 爬虫 获取数据 还有使用 tushare，akshare 无法连接，提示超时

QMT • 李魔佛 发表了文章 • 0 个评论 • 1801 次浏览 • 2024-08-28 14:29 • 来自相关话题

经过验证，比如把下面的代码放入到国信的iquant里运行
#encoding:gbk


import requests

def get_baidu():

url = 'https://www.baidu.com'
req = requests.get(url,headers={'User-Agent':'Mozilla/5.0'})
print(req.text)

def init(ContextInfo):
get_baidu()

def handlebar(ContextInfo):
pass
会报错，同时访问baidu.com 超时：





 
明显是国信的iquant内部设置了proxy，导致request出去的时候走了proxy。
 
所以连baidu都访问不了。
 
而tushare，akshare底层是周的爬虫 （requests）, 所以对于iquant，底层走http requests的库，或者获取数据的手段是失效了的。
 
不过也有其他的手段可以实现获取外部数据。
 
笔者亲测了redis可以访问外部的redis数据库，为啥用redis，因为iquant没有内置pymysql，需要自己安装，客户也不懂怎么安装，（到iquant的安装文件路径下的python 包路径下，运行 pip install pymysq）
 
而iquant内置了redis 的库，所以可以直接import redis
 
redis测试代码：
def base_usage():
print('start')
r = RedisCls()
data = {'name': 'zhangsan', 'age': 18}
key = 'test_key'
r.push(key, json.dumps(data))
print('push')
print(r.delete('key')) # reutrn 1 if delete success, else return 0
start_time = time.time()
ret_data = r.pop(key)
end_time = time.time()
print('cost time: ', end_time - start_time)
print(ret_data)



base_usage()
上面的代码是正常运行，不报错，就说明正常的了。
PS: 
r = RedisCls() 只是封装了
r = redis.Redis 连接的ip和端口，密码 而已。
 
按照网上的redis连接教程使用就好了。
 

  查看全部
经过验证，比如把下面的代码放入到国信的iquant里运行
#encoding:gbk





import requests



def get_baidu():



    url = 'https://www.baidu.com'

    req = requests.get(url,headers={'User-Agent':'Mozilla/5.0'})

    print(req.text)

    

def init(ContextInfo):

    get_baidu()

    

def handlebar(ContextInfo):

    pass

会报错，同时访问baidu.com 超时：

微信图片_20240828141957.png

 
明显是国信的iquant内部设置了proxy，导致request出去的时候走了proxy。
 
所以连baidu都访问不了。
 
而tushare，akshare底层是周的爬虫 （requests）, 所以对于iquant，底层走http requests的库，或者获取数据的手段是失效了的。
 
不过也有其他的手段可以实现获取外部数据。
 
笔者亲测了redis可以访问外部的redis数据库，为啥用redis，因为iquant没有内置pymysql，需要自己安装，客户也不懂怎么安装，（到iquant的安装文件路径下的python 包路径下，运行 pip install pymysq）
 
而iquant内置了redis 的库，所以可以直接import redis
 
redis测试代码：
def base_usage():

    print('start')

    r = RedisCls()

    data = {'name': 'zhangsan', 'age': 18}

    key = 'test_key'

    r.push(key, json.dumps(data))

    print('push')

    print(r.delete('key'))  # reutrn 1 if delete success, else return 0

    start_time = time.time()

    ret_data = r.pop(key)

    end_time = time.time()

    print('cost time: ', end_time - start_time)

    print(ret_data)







base_usage()

上面的代码是正常运行，不报错，就说明正常的了。
PS: 
r = RedisCls() 只是封装了
r = redis.Redis 连接的ip和端口，密码 而已。
 
按照网上的redis连接教程使用就好了。
 

 

QMT如何获取持仓成本 盈亏比例

QMT • 李魔佛 发表了文章 • 0 个评论 • 1784 次浏览 • 2024-08-25 07:50 • 来自相关话题

比如多次买入一个股票，每次买入不同的数量，如果中间又有分红除权，虽然可以通过自己写一个计算函数记录，但是会非常复杂。
 
那么QMT有没有内置的可以获取持仓成本的函数呢？
 
 position 持仓对象 里面有一个字段：
 
m_dOpenPrice: 持仓成本
 
可以用来获取当前的持仓成本：
 
具体代码如下：
 # encoding:gbk

ACCOUNT = 'xxxxxxx' # 填入你的QMT账户ID, 如果没有，可以联系我开通 QMT权限


def init(ContextInfo):
# hs300成分股中sh和sz市场各自流通市值最大的前3只股票
pass


def handlebar(ContextInfo):
# 计算当前主图的cci
position_info = get_trade_detail_data(ACCOUNT, 'stock', 'position')
for i in position_info:
print('股票', i.m_strInstrumentID, '持仓数',
i.m_nVolume, '持有成本', round(i.m_dOpenPrice, 2),
'持仓盈亏', round(i.m_dPositionProfit, 2),
'持仓盈亏比例', round(i.m_dProfitRate*100, 2)
)


 得到的输出结果：
 





 
扫码免费开通QMT：

  查看全部
比如多次买入一个股票，每次买入不同的数量，如果中间又有分红除权，虽然可以通过自己写一个计算函数记录，但是会非常复杂。
 
那么QMT有没有内置的可以获取持仓成本的函数呢？
 
 position 持仓对象 里面有一个字段：
 
m_dOpenPrice: 持仓成本
 
可以用来获取当前的持仓成本：
 
具体代码如下：
 
# encoding:gbk



ACCOUNT = 'xxxxxxx' # 填入你的QMT账户ID, 如果没有，可以联系我开通 QMT权限





def init(ContextInfo):

    # hs300成分股中sh和sz市场各自流通市值最大的前3只股票

    pass





def handlebar(ContextInfo):

    # 计算当前主图的cci

    position_info = get_trade_detail_data(ACCOUNT, 'stock', 'position')

    for i in position_info:

        print('股票', i.m_strInstrumentID, '持仓数',

              i.m_nVolume, '持有成本', round(i.m_dOpenPrice, 2),

              '持仓盈亏', round(i.m_dPositionProfit, 2),

              '持仓盈亏比例', round(i.m_dProfitRate*100, 2)

              )

 得到的输出结果：
 

20240825081954.png

 
扫码免费开通QMT：

 

python Ptrade获取热门板块，连板股票 python代码

Ptrade • 李魔佛 发表了文章 • 0 个评论 • 2366 次浏览 • 2024-08-23 16:57 • 来自相关话题

之前有人咨询，ptrade如何获取不同的概念板块个股。其实很容易，本身有获取板块信息的API函数：

Ptrade API文档：https://ptradeapi.com/#get_sort_msg


get_sort_msg – 获取板块、行业的涨幅排名
get_sort_msg(sort_type_grp=None, sort_field_name=None, sort_type=1, data_count=100)
 接口说明
 该接口用于获取板块、行业的涨幅排名。 ​
 参数 sort_type_grp: 板块或行业的代码(list[str]/str)；
(暂时只支持XBHS.DY地域、XBHS.GN概念、XBHS.ZJHHY证监会行业、XBHS.ZS指数、XBHS.HY行业等)
 
示例代码：按概念板块涨幅倒序排名
import datetime
START_TIME = (datetime.datetime.now() + datetime.timedelta(minutes=1)).strftime('%H:%M')


def execution(context):
#获取XBHS.GN的概念排名信息
sort_data = get_sort_msg(sort_type_grp='XBHS.GN', sort_field_name='px_change_rate', sort_type=1, data_count=100)

for data in sort_data:
log.info('板块： {} '.format(data['prod_name']))
for sub_stock in data['rise_first_grp']:
log.info('{} 涨幅 ：{}'.format(sub_stock['prod_name'],sub_stock['px_change_rate']))

log.info('\n')


def initialize(context):
# 初始化策略
run_daily(context, execution, time=START_TIME) # 扫描
log.info("公众号：可转债量化分析\n")

def handle_data(context, data):
pass
 
上面代码在ptrade启动后一分钟拿到结果。不限制要求开盘时间的。其实Ptrade可以在24小时任意时刻启动。








get_sort_msg 返回的数据结构体如下：








具体字段的含义：
prod_code: 行业代码(str:str)；
prod_name: 行业名称(str:str)；
hq_type_code: 行业板块代码(str:str)；
time_stamp: 时间戳毫秒级(str:int)；
trade_mins: 交易分钟数(str:int)；
trade_status: 交易状态(str:str)；
preclose_px: 昨日收盘价(str:float)；
open_px: 今日开盘价(str:float)；
last_px: 最新价(str:float)；
high_px: 最高价(str:float)；
low_px: 最低价(str:float)；
wavg_px: 加权平均价(str:float)；
business_amount: 总成交量(str:int)；
business_balance: 总成交额(str:int)；
px_change: 涨跌额(str:float)；
amplitude: 振幅(str:int)；
px_change_rate: 涨跌幅(str:float)；
circulation_amount: 流通股本(str:int)；
total_shares: 总股本(str:int)；
market_value: 市值(str:int)；
circulation_value: 流通市值(str:int)；
vol_ratio: 量比(str:float)；
shares_per_hand: 每手股数(str:int)；
rise_count: 上涨家数(str:int)；
fall_count: 下跌家数(str:int)；
member_count: 成员个数(str:int)；
rise_first_grp: 领涨股票(其包含以下五个字段)(str:list[dict{str:int,str:str,str:str,str:float,str:float},...])；
prod_code: 股票代码(str:str)；
prod_name: 证券名称(str:str)；
hq_type_code: 类型代码(str:str)；
last_px: 最新价(str:float)；
px_change_rate: 涨跌幅(str:float)；
fall_first_grp: 领跌股票(其包含以下五个字段)(str:list[dict{str:int,str:str,str:str,str:float,str:float},...])；
prod_code: 股票代码(str:str)；
prod_name: 证券名称(str:str)；
hq_type_code: 类型代码(str:str)；
last_px: 最新价(str:float)；
px_change_rate: 涨跌幅(str:float)；
 
这个返回数据是实时的，可以用来选股，选择热门股，热门板块，涨停板块，昨日涨停，昨日连板板块。

比如上面运行结果里就有 昨日连板的板块个股，有9个，在rise_first_grp 字段里面：








需要开通Ptrade的读者朋友可以后天联系哦，提供不同券商ptrade，低门槛，低费率，还有技术支持群！
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之前有人咨询，ptrade如何获取不同的概念板块个股。其实很容易，本身有获取板块信息的API函数：

Ptrade API文档：https://ptradeapi.com/#get_sort_msg


get_sort_msg – 获取板块、行业的涨幅排名
get_sort_msg(sort_type_grp=None, sort_field_name=None, sort_type=1, data_count=100)

 接口说明
 该接口用于获取板块、行业的涨幅排名。 ​
 参数 sort_type_grp: 板块或行业的代码(list[str]/str)；
(暂时只支持XBHS.DY地域、XBHS.GN概念、XBHS.ZJHHY证监会行业、XBHS.ZS指数、XBHS.HY行业等)
 
示例代码：按概念板块涨幅倒序排名
import datetime

START_TIME = (datetime.datetime.now() + datetime.timedelta(minutes=1)).strftime('%H:%M')





def execution(context):

    #获取XBHS.GN的概念排名信息

    sort_data = get_sort_msg(sort_type_grp='XBHS.GN', sort_field_name='px_change_rate', sort_type=1, data_count=100)



    for data in sort_data:

        log.info('板块： {} '.format(data['prod_name']))

        for sub_stock in data['rise_first_grp']:

            log.info('{} 涨幅 ：{}'.format(sub_stock['prod_name'],sub_stock['px_change_rate']))

        

        log.info('\n')





def initialize(context):

    # 初始化策略

    run_daily(context, execution, time=START_TIME)  # 扫描

    log.info("公众号：可转债量化分析\n")



def handle_data(context, data):

    pass

 
上面代码在ptrade启动后一分钟拿到结果。不限制要求开盘时间的。其实Ptrade可以在24小时任意时刻启动。


20240823123757.png



get_sort_msg 返回的数据结构体如下：


20240823123253.png



具体字段的含义：
prod_code: 行业代码(str:str)；

prod_name: 行业名称(str:str)；

hq_type_code: 行业板块代码(str:str)；

time_stamp: 时间戳毫秒级(str:int)；

trade_mins: 交易分钟数(str:int)；

trade_status: 交易状态(str:str)；

preclose_px: 昨日收盘价(str:float)；

open_px: 今日开盘价(str:float)；

last_px: 最新价(str:float)；

high_px: 最高价(str:float)；

low_px: 最低价(str:float)；

wavg_px: 加权平均价(str:float)；

business_amount: 总成交量(str:int)；

business_balance: 总成交额(str:int)；

px_change: 涨跌额(str:float)；

amplitude: 振幅(str:int)；

px_change_rate: 涨跌幅(str:float)；

circulation_amount: 流通股本(str:int)；

total_shares: 总股本(str:int)；

market_value: 市值(str:int)；

circulation_value: 流通市值(str:int)；

vol_ratio: 量比(str:float)；

shares_per_hand: 每手股数(str:int)；

rise_count: 上涨家数(str:int)；

fall_count: 下跌家数(str:int)；

member_count: 成员个数(str:int)；

rise_first_grp: 领涨股票(其包含以下五个字段)(str:list[dict{str:int,str:str,str:str,str:float,str:float},...])；

prod_code: 股票代码(str:str)；

prod_name: 证券名称(str:str)；

hq_type_code: 类型代码(str:str)；

last_px: 最新价(str:float)；

px_change_rate: 涨跌幅(str:float)；

fall_first_grp: 领跌股票(其包含以下五个字段)(str:list[dict{str:int,str:str,str:str,str:float,str:float},...])；

prod_code: 股票代码(str:str)；

prod_name: 证券名称(str:str)；

hq_type_code: 类型代码(str:str)；

last_px: 最新价(str:float)；

px_change_rate: 涨跌幅(str:float)；

 
这个返回数据是实时的，可以用来选股，选择热门股，热门板块，涨停板块，昨日涨停，昨日连板板块。

比如上面运行结果里就有 昨日连板的板块个股，有9个，在rise_first_grp 字段里面：


20240823124205.png



需要开通Ptrade的读者朋友可以后天联系哦，提供不同券商ptrade，低门槛，低费率，还有技术支持群！
 
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