Ptrade获取L2逐笔数据，免费，券商提供

李魔佛 发表了文章 • 0 个评论 • 147 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

李魔佛 发表了文章 • 0 个评论 • 232 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

李魔佛 发表了文章 • 0 个评论 • 292 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

李魔佛 发表了文章 • 0 个评论 • 973 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？

李魔佛 发表了文章 • 0 个评论 • 484 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

李魔佛 发表了文章 • 0 个评论 • 783 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：

ptrade支持外网，mysql 远程数据库的券商

李魔佛 发表了文章 • 0 个评论 • 656 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

李魔佛 发起了问题 • 1 人关注 • 0 个回复 • 708 次浏览 • 2025-02-13 18:57 • 来自相关话题

聚宽打板策略代码转为ptrade代码

李魔佛 发表了文章 • 0 个评论 • 1099 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?

李魔佛 发表了文章 • 0 个评论 • 883 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

李魔佛 发表了文章 • 0 个评论 • 1411 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

李魔佛 发表了文章 • 0 个评论 • 1020 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。
 

 

ptrade：登录请求失败，服务器状态400

李魔佛 发表了文章 • 0 个评论 • 961 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？
 
 

Ptrade获取历史涨停的股票|python代码

李魔佛 发表了文章 • 0 个评论 • 1208 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。
 

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

李魔佛 发表了文章 • 0 个评论 • 1600 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。
 

 

python Ptrade获取热门板块，连板股票 python代码

李魔佛 发表了文章 • 0 个评论 • 2372 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！
  查看全部
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！
 

【保姆教程】使用ptrade做一个持仓监控提醒软件 （二）

李魔佛 发表了文章 • 0 个评论 • 1920 次浏览 • 2024-08-08 14:01 • 来自相关话题

接下来完成代码实现部分：
 
主要框架如下：
 
盘前我们先去读取数据库的数据：
 
格式很简单，就记录了代码和名字：





 

df = pd.read_sql('select * from tb_holding_stock_list', con=engine)
 
 
def initialize(context):
# 初始化策略
engine = DBSelector().get_engine()
df = pd.read_sql('select * from tb_holding_stock_list', con=engine)
df['code']=df['code'].astype(str)
result = {}
for index, row in df.iterrows():
code = add_code_postfix(row['code'])
result[code] = {'name': row['name'], 'source': row['source']}
g.holding_stock_dict = result
g.holding_stock_list = list(result.keys())
g.__cache = Cache()

run_interval(context, execution, seconds=INTERVAL) # 扫描


def handle_data(context, data):
pass


def tick_data(context, data):
pass

def before_trading_start(context, data):
'''
盘前
'''
if DEBUG:
log.info('盘前运行开始', str(context.blotter.current_dt))


def after_trading_end(context, data):
'''
盘后
'''
if DEBUG:
log.info('盘后时间 ', str(context.blotter.current_dt))
然后主要部分在 execution 这个监控函数这里。
 
def execution(context):
tick_info = get_snapshot(g.holding_stock_list)
for code, tick in tick_info.items():
px_change_rate = tick['px_change_rate']
if px_change_rate > abs(HIT_TARGET):
if g.__cache.check(code):
# 通知
name = g.holding_stock_dict.get(code)['name']
source = g.holding_stock_dict.get(code)['source']
msg = '{}-{} 涨幅-{},{}'.format(code,
name, px_change_rate, source)
send_message_via_wechat(msg)

send_message_via_wechat 这个函数是发送微信消息的。
 
然后基本完成了整体的代码编写，里面一些自定义的函数为了判断 下一次通知要等待多久。
 
因为不能因为同一个股票满足条件了，然后每隔3秒发一次微信消息。你手机会一直滴滴滴地响的。
 
而且很容易把其他刚出现的提示给覆盖了。
 





 
【保姆教程】使用ptrade做一个持仓监控提醒软件 （一）
 
 
  查看全部
接下来完成代码实现部分：
 
主要框架如下：
 
盘前我们先去读取数据库的数据：
 
格式很简单，就记录了代码和名字：

20240808135700.png

 

df = pd.read_sql('select * from tb_holding_stock_list', con=engine)
 
 
def initialize(context):

    # 初始化策略

    engine = DBSelector().get_engine()

    df = pd.read_sql('select * from tb_holding_stock_list', con=engine)

    df['code']=df['code'].astype(str)

    result = {}

    for index, row in df.iterrows():

        code = add_code_postfix(row['code'])

        result[code] = {'name': row['name'], 'source': row['source']}

    g.holding_stock_dict = result

    g.holding_stock_list = list(result.keys())

    g.__cache = Cache()



    run_interval(context, execution, seconds=INTERVAL)  # 扫描





def handle_data(context, data):

    pass





def tick_data(context, data):

    pass



def before_trading_start(context, data):

    '''

    盘前

    '''

    if DEBUG:

        log.info('盘前运行开始', str(context.blotter.current_dt))





def after_trading_end(context, data):

    '''

    盘后

    '''

    if DEBUG:

        log.info('盘后时间 ', str(context.blotter.current_dt))

然后主要部分在 execution 这个监控函数这里。
 
def execution(context):

    tick_info = get_snapshot(g.holding_stock_list)

    for code, tick in tick_info.items():

        px_change_rate = tick['px_change_rate']

        if px_change_rate > abs(HIT_TARGET):

            if g.__cache.check(code):

                # 通知

                name = g.holding_stock_dict.get(code)['name']

                source = g.holding_stock_dict.get(code)['source']

                msg = '{}-{} 涨幅-{},{}'.format(code,

                                              name, px_change_rate, source)

                send_message_via_wechat(msg)


send_message_via_wechat 这个函数是发送微信消息的。
 
然后基本完成了整体的代码编写，里面一些自定义的函数为了判断 下一次通知要等待多久。
 
因为不能因为同一个股票满足条件了，然后每隔3秒发一次微信消息。你手机会一直滴滴滴地响的。
 
而且很容易把其他刚出现的提示给覆盖了。
 

Screenshot_2024_0808_132624.jpg

 
【保姆教程】使用ptrade做一个持仓监控提醒软件 （一）
 
 
 

【保姆教程】使用ptrade做一个持仓监控提醒软件 （一）

李魔佛 发表了文章 • 0 个评论 • 2174 次浏览 • 2024-08-07 10:23 • 来自相关话题

背景：
因为有多个券商，比如银河，华宝，国金，国盛等。 而且也有家人的账户，可能一个银河就有5-6个账户。
所以如果持仓比较多的话，没有时间管得过来。 设置条件单比较繁琐，也不一定能管得过来。
 
要求：
把所有的持仓股Excel导出，输入的数据库（这里选择mysql），然后Ptrade读取了股票池，每隔3s扫描一次行情，如果遇到大涨或者大跌的个股，转债，ETF，就发送微信消息提醒（涨幅/跌幅大于7%）
 
这个是某个客户的简单需求。
 
后面就按照上面的需求做一个客户端，除了可以录入上述资料，还能提供web服务，输入，删除持仓股，做到实时更新。
 
最后提醒效果如下：





国金QMT的字样，用来区分我这个标的是哪一个券商的持仓。比如 有可能是 家人1-银河，家人2-国盛，这样的哈
 
下一篇：
【保姆教程】使用ptrade做一个持仓监控提醒软件 （二）

欢迎关注公众号：可转债量化分析 查看全部
背景：
因为有多个券商，比如银河，华宝，国金，国盛等。 而且也有家人的账户，可能一个银河就有5-6个账户。
所以如果持仓比较多的话，没有时间管得过来。 设置条件单比较繁琐，也不一定能管得过来。
 
要求：
把所有的持仓股Excel导出，输入的数据库（这里选择mysql），然后Ptrade读取了股票池，每隔3s扫描一次行情，如果遇到大涨或者大跌的个股，转债，ETF，就发送微信消息提醒（涨幅/跌幅大于7%）
 
这个是某个客户的简单需求。
 
后面就按照上面的需求做一个客户端，除了可以录入上述资料，还能提供web服务，输入，删除持仓股，做到实时更新。
 
最后提醒效果如下：

Screenshot_2024_0808_132624.jpg

国金QMT的字样，用来区分我这个标的是哪一个券商的持仓。比如 有可能是 家人1-银河，家人2-国盛，这样的哈
 
下一篇：
【保姆教程】使用ptrade做一个持仓监控提醒软件 （二）

欢迎关注公众号：可转债量化分析

ptrade获取的历史数据最长到哪一年？ptrade如何获取上证指数

李魔佛 发表了文章 • 0 个评论 • 1597 次浏览 • 2024-06-30 13:48 • 来自相关话题

Ptrade获取日线，分钟线数据最长可以到2005年。
文档里面也有说明：
 

7、该接口只能获取2005年后的数据。






ptrade官网api接口文档：
https://ptradeapi.com/#
 
实测也是符合要求的：
 
ptrade如何获取上证指数， 代码是  000001.SS
test_data = data = get_price(security='000001.SS',start_date='20050201',end_date='20050630',frequency='1d')
ptrade获取上证指数2005年的数据：
 





 
需要低佣，低门槛开通ptrade的朋友，可以扫描关注关注号： 查看全部
Ptrade获取日线，分钟线数据最长可以到2005年。
文档里面也有说明：
 


7、该接口只能获取2005年后的数据。



20240630134315.png

ptrade官网api接口文档：
https://ptradeapi.com/#
 
实测也是符合要求的：
 
ptrade如何获取上证指数， 代码是  000001.SS
test_data = data = get_price(security='000001.SS',start_date='20050201',end_date='20050630',frequency='1d')

ptrade获取上证指数2005年的数据：
 

20240630134443.png

 
需要低佣，低门槛开通ptrade的朋友，可以扫描关注关注号：

Ptrade成交回调函数无法执行的原因？ | ptrade bug

李魔佛 发表了文章 • 0 个评论 • 1464 次浏览 • 2024-05-28 00:08 • 来自相关话题

目前的ptrade有一个隐藏很深的bug。
 
就是回调函数里面有一个字段entrust_no.
 
这个字段是什么意思呢？ 是营业部的下单编号。比如你挂了一个委托单，就会有一个entrust_no, 比如 100001
 
这个编号对于一天的数据来说，是唯一不重复的，也就是一天内再不会出现100001。
 
而ptrade的成交回调依赖的是这个entrust_no， 如果系统里面已经触发过了一个entrust_no 为 100001的成交委托，那么如果又有一个重复的订单entrust_no 100001成交，那么，此时的ptrade的 成交回调函数是不会触发的！
 
那么上面说的一天内这个entrust_no是不会重复的。
 
可是，这个entrust_no挂单编号，在同一个营业部单元里，第二天会重复的，比如你第二天挂单也是entrust_no 100001，并且你的ptrade策略没有重启，也就是一直运行的话，那么如果碰巧你的下单entrust_no上昨天或者之前某一天（ptrade策略没有重启开始算起），entrust_no重复了的情况下。 
 
此时的ptrade 成交回调函数 on_trade_repsonse 是不会执行的！！！
 
天坑！
  查看全部
目前的ptrade有一个隐藏很深的bug。
 
就是回调函数里面有一个字段entrust_no.
 
这个字段是什么意思呢？ 是营业部的下单编号。比如你挂了一个委托单，就会有一个entrust_no, 比如 100001
 
这个编号对于一天的数据来说，是唯一不重复的，也就是一天内再不会出现100001。
 
而ptrade的成交回调依赖的是这个entrust_no， 如果系统里面已经触发过了一个entrust_no 为 100001的成交委托，那么如果又有一个重复的订单entrust_no 100001成交，那么，此时的ptrade的 成交回调函数是不会触发的！
 
那么上面说的一天内这个entrust_no是不会重复的。
 
可是，这个entrust_no挂单编号，在同一个营业部单元里，第二天会重复的，比如你第二天挂单也是entrust_no 100001，并且你的ptrade策略没有重启，也就是一直运行的话，那么如果碰巧你的下单entrust_no上昨天或者之前某一天（ptrade策略没有重启开始算起），entrust_no重复了的情况下。 
 
此时的ptrade 成交回调函数 on_trade_repsonse 是不会执行的！！！
 
天坑！
 

国盛证券的Ptrade数据无论是回测还是实盘很有问题，前复权不正确，数据断崖

李魔佛 发表了文章 • 0 个评论 • 1734 次浏览 • 2024-04-18 00:19 • 来自相关话题

连基本的历史数据都无法保证数据正确。
 
举个例子，比如 煤炭ETF 515220，





 
在4月12日进行的除权，1股变2股，因此，所以4月12日之后的价格会是原来的1/2，如果做前复权，那么前面的价格也都是要根据当前的价格做复权处理。
 
结果国盛的ptrade的历史数据，取的是前复权数据，前复权数据，（重点强调），在4月12日的的时候就出现了断崖。也就是没有做复权的处理。





 
测试代码很简单：
def initialize(context):
run_daily(context, event, '09:38')


def handle_data(context, data):
pass


def event(context):
his60 = get_history(60, '1d', ['close'], ['515220.SS'], fq='pre', include=False)
print(his60)运行时间改成任意的就行。
 
获取历史数据用
get_history，取过去60天的前复权的数据。 然后就是断崖的数据。 已经确定是国盛的ptrade数据问题。因为我用上面的代码，在东莞证券，国金证券，湘财证券的ptrade上运行，均能得到正确的数据。
 
然后更为搞笑的，这么一个问题，反馈了，没有回应。无语。
 
 
  查看全部
连基本的历史数据都无法保证数据正确。
 
举个例子，比如 煤炭ETF 515220，

20240418001102.png

 
在4月12日进行的除权，1股变2股，因此，所以4月12日之后的价格会是原来的1/2，如果做前复权，那么前面的价格也都是要根据当前的价格做复权处理。
 
结果国盛的ptrade的历史数据，取的是前复权数据，前复权数据，（重点强调），在4月12日的的时候就出现了断崖。也就是没有做复权的处理。

微信图片_20240418001026.png

 
测试代码很简单：
def initialize(context):

    run_daily(context, event, '09:38')





def handle_data(context, data):

    pass





def event(context):

    his60 = get_history(60, '1d', ['close'],  ['515220.SS'], fq='pre', include=False)

    print(his60)
运行时间改成任意的就行。
 
获取历史数据用
get_history，取过去60天的前复权的数据。 然后就是断崖的数据。 已经确定是国盛的ptrade数据问题。因为我用上面的代码，在东莞证券，国金证券，湘财证券的ptrade上运行，均能得到正确的数据。
 
然后更为搞笑的，这么一个问题，反馈了，没有回应。无语。
 
 
 

ptrade调试经验分享（坑） 委托成交回调函数

李魔佛 发表了文章 • 0 个评论 • 2122 次浏览 • 2023-12-22 10:30 • 来自相关话题

特么的在里面的报错了是不会有任何显示！！！！！！
 





 
之前的代码里面，由于少了一个if，导致如果code不在
g.start_buy_sell_queue 
这个集合里面的话，就会报错。（但正常情况下都会有值，但问题就出现在一些特殊情况下）
 
不然你试试在 
on_trade_response
里面直接raise一个Exception出来，日志里也不会有任何显示。
 
切近！！ 
on_trade_response 里面做好安全防护！！ 最好是有些业务逻辑完成了在里面print一下，以确保是执行到后面的。 查看全部
特么的在里面的报错了是不会有任何显示！！！！！！
 

20231222102356.png

 
之前的代码里面，由于少了一个if，导致如果code不在
g.start_buy_sell_queue 
这个集合里面的话，就会报错。（但正常情况下都会有值，但问题就出现在一些特殊情况下）
 
不然你试试在 
on_trade_response
里面直接raise一个Exception出来，日志里也不会有任何显示。
 
切近！！ 
on_trade_response 里面做好安全防护！！ 最好是有些业务逻辑完成了在里面print一下，以确保是执行到后面的。

ptrade精确参与集合竞价交易 时间设置问题

李魔佛 发表了文章 • 0 个评论 • 2446 次浏览 • 2023-11-16 10:10 • 来自相关话题

最近几次的集合竞价都没有卖出成功。
 
查了下日志。run_daily 设置的9:25运行，下单委托的时间在9:25:01 这个时间就被推到9:30开盘去成交了。
所以实际没有参与到9:25的集合竞价。
 
所以要参与集合竞价，需要设定在9:24分开始，然后不断在一个循环里面，用更小的时间颗粒，比如100ms去监听。
 
等到9:24:59的时间，才去下单。
import datetime
import time

RUN_TIME = '09:24'

def execution(context):

while 1:
current_second = datetime.datetime.now().strftime('%S')
if current_second >= '58':
break
time.sleep(0.1)

do_something()

def initialize(context):
# 初始化策略
run_daily(context, execution, RUN_TIME)

def handle_data(context, data):
pass





 





 
具体代码可以参照我的知识星球

 
 
  查看全部
最近几次的集合竞价都没有卖出成功。
 
查了下日志。run_daily 设置的9:25运行，下单委托的时间在9:25:01 这个时间就被推到9:30开盘去成交了。
所以实际没有参与到9:25的集合竞价。
 
所以要参与集合竞价，需要设定在9:24分开始，然后不断在一个循环里面，用更小的时间颗粒，比如100ms去监听。
 
等到9:24:59的时间，才去下单。
import datetime 

import time



RUN_TIME = '09:24'



def execution(context):



    while 1:

        current_second = datetime.datetime.now().strftime('%S')

        if current_second >= '58':

            break

        time.sleep(0.1)



    do_something()



def initialize(context):

    # 初始化策略

    run_daily(context, execution, RUN_TIME)



def handle_data(context, data):

    pass


20231116100349-v1.png

 

20231116100758-v1.png

 
具体代码可以参照我的知识星球

 
 
 

ptrade 全局对象g持久化对象保存失败

李魔佛 发表了文章 • 0 个评论 • 2065 次浏览 • 2023-10-18 09:36 • 来自相关话题

 2023-10-18 09:25:12 - ERROR - 全局对象g持久化对象保存失败，对象名：TARGET_STOCK_CODE，错误原因：Traceback (most recent call last):
  File "./fly_docker/IQEngine/utils/global_variable.py", line 50, in save
_pickle.PicklingError: Can't pickle <class 'IQEngine.user_module.PositionManager'>: attribute lookup PositionManager on IQEngine.user_module failed

 
原因是全局变量g 不能被持久化, 需要前面加__, 比如g.Name 要改成 g.__Name
 
全局变量g中不能被序列化的变量将不会被保存。您可在initialize中初始化该变量时名字以'__'开头；
涉及到IO(打开的文件，实例化的类对象等)的对象是不能被序列化的；
全局变量g中以'__'开头的变量为私有变量，持久化时将不会被保存；
 






 
具体可以参加 API文档:
https://ptradeapi.com 查看全部


 2023-10-18 09:25:12 - ERROR - 全局对象g持久化对象保存失败，对象名：TARGET_STOCK_CODE，错误原因：Traceback (most recent call last):
  File "./fly_docker/IQEngine/utils/global_variable.py", line 50, in save
_pickle.PicklingError: Can't pickle <class 'IQEngine.user_module.PositionManager'>: attribute lookup PositionManager on IQEngine.user_module failed


 
原因是全局变量g 不能被持久化, 需要前面加__, 比如g.Name 要改成 g.__Name
 
全局变量g中不能被序列化的变量将不会被保存。您可在initialize中初始化该变量时名字以'__'开头；
涉及到IO(打开的文件，实例化的类对象等)的对象是不能被序列化的；
全局变量g中以'__'开头的变量为私有变量，持久化时将不会被保存；
 

20231018095102.png


 
具体可以参加 API文档:
https://ptradeapi.com

ptrade/qmt 判断股票是否涨停

李魔佛 发表了文章 • 0 个评论 • 2806 次浏览 • 2023-10-09 11:03 • 来自相关话题

 1. 可以直接用代码实现:
以ptrade为例:
 
先通过 get_snapshot - 取行情快照
 
其中里面有2个字段:
up_px:涨停价格(str:float)；
down_px:跌停价格(str:float)；用当前的最新价格和涨停跌停价格比较:
 
last_px:最新成交价(str:float)；
 
if last_px>=up_px 就是达到涨停价, 
 
还有判断此时的卖一上是否有挂单. 如果还有卖单, 说明此时的涨停板并没有封住, 被人砸开了.
 
跌停板的判断也是如此.
 
 
2. 使用现有的API函数, 更加简单方便, 这个方法只适用于ptrade, qmt没有类似的函数.
 
check_limit - 代码涨跌停状态判断
 
使用场景
该函数仅在交易模块可用。

接口说明
该接口用于标识当日股票的涨跌停情况。

注意事项：

无

参数
security：单只股票代码或者多只股票代码组成的列表，必填字段(list[str]/str)；

返回
正常返回一个dict类型数据，包含每只股票代码的涨停状态。多只股票代码查询时其中部分股票代码查询异常则该代码返回既不涨停也不跌停状态0。(dict[str:int])

涨跌停状态说明：

2：触板涨停（已经是涨停价格，但还有卖盘）；
1：涨停；
0：既不涨停也不跌停；
-1：跌停；
-2：触板跌停（已经是跌停价格，但还有买盘）；
示例代码:
def initialize(context):
g.security = '600570.SS'
set_universe(g.security)

def handle_data(context, data):
# 代码涨跌停状态
stock_flag = check_limit(g.security)
log.info(stock_flag)
公众号: 可转债量化分析

 
  查看全部
 1. 可以直接用代码实现:
以ptrade为例:
 
先通过 get_snapshot - 取行情快照
 
其中里面有2个字段:
up_px:涨停价格(str:float)；

down_px:跌停价格(str:float)；
用当前的最新价格和涨停跌停价格比较:
 
last_px:最新成交价(str:float)；
 
if last_px>=up_px 就是达到涨停价, 
 
还有判断此时的卖一上是否有挂单. 如果还有卖单, 说明此时的涨停板并没有封住, 被人砸开了.
 
跌停板的判断也是如此.
 
 
2. 使用现有的API函数, 更加简单方便, 这个方法只适用于ptrade, qmt没有类似的函数.
 
check_limit - 代码涨跌停状态判断
 
使用场景

该函数仅在交易模块可用。



接口说明

该接口用于标识当日股票的涨跌停情况。



注意事项：



无



参数

security：单只股票代码或者多只股票代码组成的列表，必填字段(list[str]/str)；



返回

正常返回一个dict类型数据，包含每只股票代码的涨停状态。多只股票代码查询时其中部分股票代码查询异常则该代码返回既不涨停也不跌停状态0。(dict[str:int])



涨跌停状态说明：



2：触板涨停（已经是涨停价格，但还有卖盘）；

1：涨停；

0：既不涨停也不跌停；

-1：跌停；

-2：触板跌停（已经是跌停价格，但还有买盘）；

示例代码:
def initialize(context):

    g.security = '600570.SS'

    set_universe(g.security)



def handle_data(context, data):

    # 代码涨跌停状态

    stock_flag = check_limit(g.security)

    log.info(stock_flag)

公众号: 可转债量化分析

 
 

小市值轮动-量化交易-程序化交易-Ptrade实盘

李魔佛 发表了文章 • 0 个评论 • 3140 次浏览 • 2023-10-07 14:14 • 来自相关话题

运行了一段时间的实盘策略。中途不断加条件，避免买入暴雷的品种；遇到一字板涨停的不要急于轮动卖出。等破板再卖出。
 
当前策略持有30只。




点击查看大图




点击查看大图

基于股票的策略不敢多买，属于试验阶段，后期仍然会不断根据市场调仓； 主仓依然在可转债。
 
公众号：可转债量化分析

如果需要策略代写，（ptrade、qmt，其他量化平台）
可以公众号后台回复：
策略代写

  查看全部
运行了一段时间的实盘策略。中途不断加条件，避免买入暴雷的品种；遇到一字板涨停的不要急于轮动卖出。等破板再卖出。
 
当前策略持有30只。

20231007002.jpg
点击查看大图

20231007003.jpg
点击查看大图

基于股票的策略不敢多买，属于试验阶段，后期仍然会不断根据市场调仓； 主仓依然在可转债。
 
公众号：可转债量化分析

如果需要策略代写，（ptrade、qmt，其他量化平台）
可以公众号后台回复：
策略代写

 

ptrade最多支持同时运行多少个策略?

李魔佛 发表了文章 • 0 个评论 • 2048 次浏览 • 2023-09-21 17:16 • 来自相关话题

Ptrade上支持写无限个策略。





 
但同时运行的策略只有5个。





 
如果不需要的策略，可以把它暂停了，记住，不要随意暂停。 因为暂停了，重启后你的日志就会随之被清空。
平时也应该做好日志备份的习惯。 部分券商可以连接mysql，可以把数据导出，也可以顺便把日志也导出。





 
需要开通Ptrade或者代写的朋友可以咨询:

  查看全部
Ptrade上支持写无限个策略。

20230921170144-v1.png

 
但同时运行的策略只有5个。

20230921170529-v2.png

 
如果不需要的策略，可以把它暂停了，记住，不要随意暂停。 因为暂停了，重启后你的日志就会随之被清空。
平时也应该做好日志备份的习惯。 部分券商可以连接mysql，可以把数据导出，也可以顺便把日志也导出。

20230921171333-v1.png

 
需要开通Ptrade或者代写的朋友可以咨询:

 

Ptrade跟踪雪球组合自动调仓

李魔佛 发表了文章 • 0 个评论 • 2076 次浏览 • 2023-09-19 20:25 • 来自相关话题

Task: 
根据指定的雪球组合, 自动跟踪组合的调仓与比例.





 
图随便截取的,具体跟踪的组合,客户自己可以直接配置.
 
目前是每10分钟刷新一次 组合数据,如果有更新就马上根据调仓.
 
盘前和收盘前2分钟, 会定期扫码, 以免到了收盘来不及成交,  留够足够的时间下单与撤单.
 

PS:图片与策略无关
 
耗时地方仍然是调试. 
  查看全部
Task: 
根据指定的雪球组合, 自动跟踪组合的调仓与比例.

20230919201907.png

 
图随便截取的,具体跟踪的组合,客户自己可以直接配置.
 
目前是每10分钟刷新一次 组合数据,如果有更新就马上根据调仓.
 
盘前和收盘前2分钟, 会定期扫码, 以免到了收盘来不及成交,  留够足够的时间下单与撤单.
 

PS:图片与策略无关
 
耗时地方仍然是调试. 
 

ptrade量化策略：低位首板启动板-首板+低吸+单阳不破

李魔佛 发表了文章 • 0 个评论 • 2284 次浏览 • 2023-09-05 22:44 • 来自相关话题

低位的首板通常是启动板，首板+低吸+单阳不破，胜率还过得去。毛估估大于55%
 
Ptrade实现实盘自动交易代码。
 
（图片截图非本策略，随意贴的）

 
里面细节比较多。
 
得慢慢调。 查看全部
低位的首板通常是启动板，首板+低吸+单阳不破，胜率还过得去。毛估估大于55%
 
Ptrade实现实盘自动交易代码。
 
（图片截图非本策略，随意贴的）

 
里面细节比较多。
 
得慢慢调。

国盛证券Ptrade测试版下载 Ptrade模拟客户端 模拟账号

李魔佛 发表了文章 • 0 个评论 • 2948 次浏览 • 2023-09-01 22:53 • 来自相关话题

国盛证券Ptrade有实盘正式版 和 测试版， 测试版提供的是模拟账户，里面的资金是模拟的，默认有500万，随意你操作，亏完拉倒重新设置就好了。
国盛证券Ptrade测试版下载 Ptrade模拟客户端 模拟账号
仿真客户端








国盛Ptrade测试版 模拟账户下载：
https://download.gszq.com/ptrade/PTrade1.0-Client-V201906-00-000.zip
仿真账户： ***** / ****** 量化回测：支持1分钟、日线回测。 量化交易：支持LEVEL1 tick股票交易。 量化研究：提供云Ipython Notebook研究环境、行情数据2005年至今、可使用全市场金融数据。





虽然ptrade有测试版本，但是个人还是非常不推荐使用测试版本。 以前在上面写过回测或者模拟盘，发现问题非常多，一个是数据缺了，数据错乱。以前被它坑过，后面基本都就不敢用了。 群里的兄弟大部分也被坑过，进群公告就是告诫他们，远离测试版。。。哈
 
实盘版本的需要开通才能申请，不同券商的门槛不一样。需要的朋友可以扫码咨询：
 

  查看全部
国盛证券Ptrade有实盘正式版 和 测试版， 测试版提供的是模拟账户，里面的资金是模拟的，默认有500万，随意你操作，亏完拉倒重新设置就好了。
国盛证券Ptrade测试版下载 Ptrade模拟客户端 模拟账号
仿真客户端
20230901004.jpg

20231022124203-v1.png

国盛Ptrade测试版 模拟账户下载：
https://download.gszq.com/ptrade/PTrade1.0-Client-V201906-00-000.zip
 

仿真账户： ***** / ******  量化回测：支持1分钟、日线回测。 量化交易：支持LEVEL1 tick股票交易。 量化研究：提供云Ipython Notebook研究环境、行情数据2005年至今、可使用全市场金融数据。

20230901006.jpg


虽然ptrade有测试版本，但是个人还是非常不推荐使用测试版本。 以前在上面写过回测或者模拟盘，发现问题非常多，一个是数据缺了，数据错乱。以前被它坑过，后面基本都就不敢用了。 群里的兄弟大部分也被坑过，进群公告就是告诫他们，远离测试版。。。哈
 
实盘版本的需要开通才能申请，不同券商的门槛不一样。需要的朋友可以扫码咨询：
 

 
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