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

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
 





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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。
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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', '600705.SS', '600706.SS', '600707.SS', '600708.SS', '600710.SS', '600711.SS', '600712.SS', '600713.SS', '600714.SS', '600715.SS', '600716.SS', '600717.SS', '600718.SS', '600719.SS', '600720.SS', '600721.SS', '600722.SS', '600724.SS', '600725.SS', '600726.SS', '600727.SS', '600728.SS', '600729.SS', '600730.SS', '600731.SS', '600732.SS', '600733.SS', '600734.SS', '600735.SS', '600736.SS', '600737.SS', '600738.SS', '600739.SS', '600740.SS', '600741.SS', '600742.SS', '600743.SS', '600744.SS', '600745.SS', '600746.SS', '600748.SS', '600749.SS', '600750.SS', '600751.SS', '600753.SS', '600754.SS', '600755.SS', '600756.SS', '600757.SS', '600758.SS', '600759.SS', '600760.SS', '600761.SS', '600763.SS', '600764.SS', '600765.SS', '600768.SS', '600769.SS', '600770.SS', '600771.SS', '600773.SS', '600774.SS', '600775.SS', '600776.SS', '600777.SS', '600778.SS', '600779.SS', '600780.SS', '600782.SS', '600783.SS', '600784.SS', '600785.SS', '600787.SS', '600789.SS', '600790.SS', '600791.SS', '600792.SS', '600793.SS', '600794.SS', '600795.SS', '600796.SS', '600797.SS', '600798.SS', '600800.SS', '600801.SS', '600802.SS', '600803.SS', '600804.SS', '600805.SS', '600807.SS', '600808.SS', '600809.SS', '600810.SS', '600811.SS', '600812.SS', '600814.SS', '600815.SS', '600816.SS', '600817.SS', '600818.SS', '600819.SS', '600820.SS', '600821.SS', '600822.SS', '600824.SS', '600825.SS', '600826.SS', '600827.SS', '600828.SS', '600829.SS', '600830.SS', '600831.SS', '600833.SS', '600834.SS', '600835.SS', '600838.SS', '600839.SS', '600841.SS', '600843.SS', '600844.SS', '600845.SS', '600846.SS', '600847.SS', '600848.SS', '600850.SS', '600851.SS', '600853.SS', '600854.SS', '600855.SS', '600857.SS', '600858.SS', '600859.SS', '600860.SS', '600861.SS', '600862.SS', '600863.SS', '600864.SS', '600865.SS', '600866.SS', '600867.SS', '600868.SS', '600869.SS', '600871.SS', '600872.SS', '600873.SS', '600874.SS', '600875.SS', '600876.SS', '600877.SS', '600879.SS', '600880.SS', '600881.SS', '600882.SS', '600883.SS', '600884.SS', '600885.SS', '600886.SS', '600887.SS', '600888.SS', '600889.SS', '600892.SS', '600893.SS', '600894.SS', '600895.SS', '600897.SS', '600900.SS', '600901.SS', '600903.SS', '600905.SS', '600906.SS', '600908.SS', '600909.SS', '600916.SS', '600917.SS', '600918.SS', '600919.SS', '600925.SS', '600926.SS', '600927.SS', '600928.SS', '600929.SS', '600933.SS', '600935.SS', '600936.SS', '600938.SS', '600939.SS', '600941.SS', '600955.SS', '600956.SS', '600958.SS', '600959.SS', '600960.SS', '600961.SS', '600962.SS', '600963.SS', '600965.SS', '600966.SS', '600967.SS', '600968.SS', '600969.SS', '600970.SS', '600971.SS', '600973.SS', '600975.SS', '600976.SS', '600977.SS', '600979.SS', '600980.SS', '600981.SS', '600982.SS', '600983.SS', '600984.SS', '600985.SS', '600986.SS', '600987.SS', '600988.SS', '600989.SS', '600990.SS', '600992.SS', '600993.SS', '600995.SS', '600996.SS', '600997.SS', '600998.SS', '600999.SS', '601000.SS', '601001.SS', '601002.SS', '601003.SS', '601005.SS', '601006.SS', 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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)

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

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

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

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

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

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

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

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

def initialize(context):

    pass



def handle_data(context, data):

    df = get_cb_info()

    log.info(df)

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

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

#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))

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

 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

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

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

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

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

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

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

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

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

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

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)

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

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

# -*-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)

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

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

#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

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()

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

# 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)

              )

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

get_sort_msg(sort_type_grp=None, sort_field_name=None, sort_type=1, data_count=100)

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

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)；

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

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))

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)

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）

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