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量化交易吧 /  量化平台 帖子：3366811 新帖：18

多股追涨策略修订

交易资深人士发表于：5 月 10 日 06：10回复(1)

本文主要是对于【新手入门教程】多股票追涨策略的修订，
主要是发现策略思想和策略代码中有较多的出入，错误太多，有很多代码根本就没有执行，因此对原策略进行了修订，
这个策略并没有什么亮点，也很垃圾，
我写她的主要目的也是便于新人学习，

学习内容：
?学会多股票追涨策略
?动态设置股票池，并去除ST、停牌
?通过attribute_history读取历史数据
?通过逻辑语言设置多重买入卖出条件并生成list股票列表
?list切片方法初体验以及最值、均值
?止损设置
1 确定策略内容
在之前的多股票策略中，我们学习了利用计算机强大的数据处理能力，同时监视市场上多只股票，如果满足条件就进行相应交易。简言之，对多个股票分别实行原本的单股票策略。但当时策略中我们使用的股票池手动添加的。
def initialize(context):
g.security = ['002043.XSHE','002582.XSHE'] # 将兔宝宝、好想你 两只股票代码存入list中
这种方法存在的一个明显缺陷是，操作对象始终是这两只股票，难以将范围扩大到全部股票市场中，同时也缺乏必要的止损设置。为了达到动态调整股票池的效果，这就要用到我们今天涉及的多股票的追涨策略。
基本思路：
?筛选出符合：
0<市盈率（pe_ratio）<30, 市净率（pb_ratio)<3, 净利润环比增长率(inc_net_profit_annual)>0.3的股票，按照销售毛利率进行降序排列，选取前150只
?买入：股票成交量突破20日内最高并且股票价格向上突破10日均线；
?卖出：成交量创10日内新低或者股价向下突破5日新低
?止损：当个股价格比买入价下跌超过10%时卖出止损
理清楚了基本思路，下一步就是将这个思路翻译成计算机能懂的语言：
1.运用get_fundalmentals函数查询相关财务数据并筛选出符合条件的股票
2.运用attribute_history查询单只股票确定时段内成交量和收盘价信息
3.利用if条件语句以及and、or逻辑语句进行买入卖出条件设置
4.设置个股止损条件

研究功能简介¶

感谢您使用JoinQuant的研究功能~~

JoinQuant 研究功能基于IPython Notebook，支持灵活的图表处理、数据处理，后续我们还会增加财务数据。

研究功能支持导入第三方库，您可以根据需要引入。

https://www.joinquant.com/api#third_lib

研究功能提供分钟级数据，采用Docker技术隔离，资源独立、安全性更高、性能更好，同步支持Python2、Python3。

操作说明¶

操作说明

1 使用帮助（只读）

新手指引，常用API示例。

2 新手指引

研究功能简介，操作说明，常用API示例。

3 常用操作

New：

Text File：新建txt文件；

Folder：新建文件夹；

Python 2：新建基于Python 2的notebook；

Python 3：新建基于Python 3的notebook。

Upload：

上传文件，文件上传后可以在研究和回测中使用。

Notebook常用操作¶

  • 使用“Shift+Enter”运行每个代码块
  • 使用“?”获取帮助
  • 使用Tab键补全代码
#使用“?”获取帮助示例，使用“Shift+Enter”运行；
get_price?

import pandas as pd
pd.DataFrame?

#使用Tab键补全函数名
get_

研究相关API¶

研究新增如下API

  • get_price()
  • get_all_securities()
  • get_index_stocks()
  • get_industry_stocks()

详见 https://www.joinquant.com/api#res_api

#导入需要的程序包
import pandas as pd
import seaborn as sns

#获取股票510300.XSHG2015年1月的日级交易数据
df = get_price('510300.XSHG', start_date='2014-01-01', end_date='2015-01-31', frequency='daily', fields=['open','close']) 
df
open close
2014-01-02 2.37 2.37
2014-01-03 2.36 2.34
2014-01-06 2.34 2.29
2014-01-07 2.27 2.29
2014-01-08 2.29 2.28
2014-01-09 2.28 2.27
2014-01-10 2.27 2.26
2014-01-13 2.26 2.24
2014-01-14 2.24 2.25
2014-01-15 2.25 2.25
2014-01-16 2.25 2.26
2014-01-17 2.25 2.22
2014-01-20 2.22 2.21
2014-01-21 2.17 2.18
2014-01-22 2.18 2.24
2014-01-23 2.24 2.23
2014-01-24 2.22 2.24
2014-01-27 2.23 2.21
2014-01-28 2.22 2.22
2014-01-29 2.22 2.22
2014-01-30 2.22 2.19
2014-02-07 2.17 2.20
2014-02-10 2.22 2.27
2014-02-11 2.26 2.28
2014-02-12 2.28 2.28
2014-02-13 2.28 2.28
2014-02-14 2.28 2.29
2014-02-17 2.31 2.30
2014-02-18 2.30 2.28
2014-02-19 2.27 2.30
... ... ...
2014-12-18 3.39 3.38
2014-12-19 3.39 3.43
2014-12-22 3.43 3.41
2014-12-23 3.38 3.34
2014-12-24 3.34 3.25
2014-12-25 3.27 3.37
2014-12-26 3.37 3.50
2014-12-29 3.57 3.48
2014-12-30 3.48 3.48
2014-12-31 3.49 3.57
2015-01-05 3.60 3.69
2015-01-06 3.66 3.66
2015-01-07 3.64 3.66
2015-01-08 3.67 3.58
2015-01-09 3.57 3.56
2015-01-12 3.56 3.54
2015-01-13 3.54 3.54
2015-01-14 3.54 3.53
2015-01-15 3.53 3.63
2015-01-16 3.64 3.66
2015-01-19 3.47 3.30
2015-01-20 3.31 3.38
2015-01-21 3.39 3.54
2015-01-22 3.54 3.54
2015-01-23 3.57 3.56
2015-01-26 3.58 3.59
2015-01-27 3.60 3.56
2015-01-28 3.53 3.50
2015-01-29 3.46 3.46
2015-01-30 3.48 3.42

265 rows × 2 columns

#获取平台支持的所有股票, ETF基金信息
get_all_securities()
display_name name start_date end_date
000001.XSHE 平安银行 PAYH 1991-04-03 9999-01-01
000002.XSHE 万 科Ａ WKA 1991-01-29 9999-01-01
000004.XSHE 国农科技 GNKJ 1990-12-01 9999-01-01
000005.XSHE 世纪星源 SJXY 1990-12-10 9999-01-01
000006.XSHE 深振业Ａ SZYA 1992-04-27 9999-01-01
000007.XSHE 零七股份 LQGF 1992-04-13 9999-01-01
000008.XSHE 神州高铁 SZGT 1992-05-07 9999-01-01
000009.XSHE 中国宝安 ZGBA 1991-06-25 9999-01-01
000010.XSHE 深华新 SHX 1995-10-27 9999-01-01
000011.XSHE 深物业A SWYA 1992-03-30 9999-01-01
000012.XSHE 南 玻Ａ NBA 1992-02-28 9999-01-01
000014.XSHE 沙河股份 SHGF 1992-06-02 9999-01-01
000016.XSHE 深康佳Ａ SKJA 1992-03-27 9999-01-01
000017.XSHE 深中华A SZHA 1992-03-31 9999-01-01
000018.XSHE 中冠A ZGA 1992-06-16 9999-01-01
000019.XSHE 深深宝Ａ SSBA 1992-10-12 9999-01-01
000020.XSHE 深华发Ａ SHFA 1992-04-28 9999-01-01
000021.XSHE 深科技 SKJ 1994-02-02 9999-01-01
000022.XSHE 深赤湾Ａ SCWA 1993-05-05 9999-01-01
000023.XSHE 深天地Ａ STDA 1993-04-29 9999-01-01
000024.XSHE 招商地产 ZSDC 1993-06-07 9999-01-01
000025.XSHE 特 力Ａ TLA 1993-06-21 9999-01-01
000026.XSHE 飞亚达Ａ FYDA 1993-06-03 9999-01-01
000027.XSHE 深圳能源 SZNY 1993-09-03 9999-01-01
000028.XSHE 国药一致 GYYZ 1993-08-09 9999-01-01
000029.XSHE 深深房Ａ SSFA 1993-09-15 9999-01-01
000030.XSHE 富奥股份 FAGF 1993-09-29 9999-01-01
000031.XSHE 中粮地产 ZLDC 1993-10-08 9999-01-01
000032.XSHE 深桑达Ａ SSDA 1993-10-28 9999-01-01
000033.XSHE *ST新都 STXD 1994-01-03 9999-01-01
... ... ... ... ...
IF1407 期货 None 2014-05-19 2014-07-18
IF1408 期货 None 2014-06-23 2014-08-15
IF1409 期货 None 2014-01-20 2014-09-19
IF1410 期货 None 2014-08-18 2014-10-17
IF1411 期货 None 2014-09-22 2014-11-21
IF1412 期货 None 2014-04-21 2014-12-19
IF1501 期货 None 2014-11-24 2015-01-16
IF1502 期货 None 2014-12-22 2015-02-25
IF1503 期货 None 2014-07-21 2015-03-20
IF1504 期货 None 2015-02-26 2015-04-17
IF1505 期货 None 2015-03-23 2015-05-15
IF1506 期货 None 2014-10-20 2015-06-19
IF1507 期货 None 2015-05-18 2015-07-17
IF1508 期货 None 2015-06-23 2015-08-21
IF1509 期货 None 2015-01-19 2015-09-18
IF1510 期货 None 2015-08-24 2015-10-16
IF1511 期货 None 2015-09-21 2015-11-20
IF1512 期货 None 2015-04-20 2015-12-18
IF1603 期货 None 2015-07-20 2016-03-18
IF1606 期货 None 2015-10-19 2016-06-17
IH1505 期货 None 2015-04-16 2015-05-15
IH1506 期货 None 2015-04-16 2015-06-19
IH1507 期货 None 2015-05-18 2015-07-17
IH1508 期货 None 2015-06-23 2015-08-21
IH1509 期货 None 2015-04-16 2015-09-18
IH1510 期货 None 2015-08-24 2015-10-16
IH1511 期货 None 2015-09-21 2015-11-20
IH1512 期货 None 2015-04-16 2015-12-18
IH1603 期货 None 2015-07-20 2016-03-18
IH1606 期货 None 2015-10-19 2016-06-17

2937 rows × 4 columns

#获取沪深300指数的所有股票
get_index_stocks('000300.XSHG')

[u'000630.XSHE',
 u'000750.XSHE',
 u'002344.XSHE',
 u'600546.XSHG',
 u'601216.XSHG',
 u'600648.XSHG',
 u'601857.XSHG',
 u'601238.XSHG',
 u'000061.XSHE',
 u'601299.XSHG',
 u'002400.XSHE',
 u'600111.XSHG',
 u'601118.XSHG',
 u'600340.XSHG',
 u'600741.XSHG',
 u'600100.XSHG',
 u'000876.XSHE',
 u'000729.XSHE',
 u'002353.XSHE',
 u'000024.XSHE',
 u'600252.XSHG',
 u'600718.XSHG',
 u'002450.XSHE',
 u'002065.XSHE',
 u'002500.XSHE',
 u'600011.XSHG',
 u'601866.XSHG',
 u'000783.XSHE',
 u'601666.XSHG',
 u'002202.XSHE',
 u'600066.XSHG',
 u'601088.XSHG',
 u'601991.XSHG',
 u'600900.XSHG',
 u'002008.XSHE',
 u'600498.XSHG',
 u'000826.XSHE',
 u'002146.XSHE',
 u'000425.XSHE',
 u'600316.XSHG',
 u'000069.XSHE',
 u'600489.XSHG',
 u'600332.XSHG',
 u'600597.XSHG',
 u'601166.XSHG',
 u'601699.XSHG',
 u'600048.XSHG',
 u'601992.XSHG',
 u'600516.XSHG',
 u'000027.XSHE',
 u'600406.XSHG',
 u'601555.XSHG',
 u'603000.XSHG',
 u'002269.XSHE',
 u'600277.XSHG',
 u'601179.XSHG',
 u'600660.XSHG',
 u'002001.XSHE',
 u'600010.XSHG',
 u'600637.XSHG',
 u'600395.XSHG',
 u'000338.XSHE',
 u'601018.XSHG',
 u'600123.XSHG',
 u'000100.XSHE',
 u'002422.XSHE',
 u'600518.XSHG',
 u'600166.XSHG',
 u'002673.XSHE',
 u'600259.XSHG',
 u'000963.XSHE',
 u'000960.XSHE',
 u'600157.XSHG',
 u'601111.XSHG',
 u'600690.XSHG',
 u'601009.XSHG',
 u'002081.XSHE',
 u'600079.XSHG',
 u'000878.XSHE',
 u'601225.XSHG',
 u'600535.XSHG',
 u'600019.XSHG',
 u'600062.XSHG',
 u'600809.XSHG',
 u'600256.XSHG',
 u'600372.XSHG',
 u'600267.XSHG',
 u'000718.XSHE',
 u'000012.XSHE',
 u'002475.XSHE',
 u'600893.XSHG',
 u'002416.XSHE',
 u'600315.XSHG',
 u'002007.XSHE',
 u'600663.XSHG',
 u'600519.XSHG',
 u'000413.XSHE',
 u'000002.XSHE',
 u'600880.XSHG',
 u'000869.XSHE',
 u'601288.XSHG',
 u'600887.XSHG',
 u'601818.XSHG',
 u'601006.XSHG',
 u'000402.XSHE',
 u'002385.XSHE',
 u'000686.XSHE',
 u'600018.XSHG',
 u'000538.XSHE',
 u'600089.XSHG',
 u'002470.XSHE',
 u'000800.XSHE',
 u'600118.XSHG',
 u'601929.XSHG',
 u'000157.XSHE',
 u'002038.XSHE',
 u'002310.XSHE',
 u'000917.XSHE',
 u'601258.XSHG',
 u'600104.XSHG',
 u'000598.XSHE',
 u'601098.XSHG',
 u'000001.XSHE',
 u'600352.XSHG',
 u'600376.XSHG',
 u'002653.XSHE',
 u'600705.XSHG',
 u'000656.XSHE',
 u'601336.XSHG',
 u'000651.XSHE',
 u'601600.XSHG',
 u'000709.XSHE',
 u'600999.XSHG',
 u'600497.XSHG',
 u'600547.XSHG',
 u'600549.XSHG',
 u'600783.XSHG',
 u'600588.XSHG',
 u'601117.XSHG',
 u'600873.XSHG',
 u'600837.XSHG',
 u'002294.XSHE',
 u'002415.XSHE',
 u'601988.XSHG',
 u'601633.XSHG',
 u'601231.XSHG',
 u'600096.XSHG',
 u'601398.XSHG',
 u'601998.XSHG',
 u'600150.XSHG',
 u'601333.XSHG',
 u'600998.XSHG',
 u'600309.XSHG',
 u'002399.XSHE',
 u'601607.XSHG',
 u'600177.XSHG',
 u'600827.XSHG',
 u'600036.XSHG',
 u'000401.XSHE',
 u'600583.XSHG',
 u'601888.XSHG',
 u'000581.XSHE',
 u'000400.XSHE',
 u'000568.XSHE',
 u'600000.XSHG',
 u'601899.XSHG',
 u'000839.XSHE',
 u'000961.XSHE',
 u'002456.XSHE',
 u'600895.XSHG',
 u'000895.XSHE',
 u'002129.XSHE',
 u'002230.XSHE',
 u'601390.XSHG',
 u'600276.XSHG',
 u'000629.XSHE',
 u'000983.XSHE',
 u'601668.XSHG',
 u'600863.XSHG',
 u'000776.XSHE',
 u'000793.XSHE',
 u'600403.XSHG',
 u'601158.XSHG',
 u'600188.XSHG',
 u'600023.XSHG',
 u'600600.XSHG',
 u'601618.XSHG',
 u'002051.XSHE',
 u'601168.XSHG',
 u'002292.XSHE',
 u'000536.XSHE',
 u'601139.XSHG',
 u'000423.XSHE',
 u'000623.XSHE',
 u'000937.XSHE',
 u'000858.XSHE',
 u'600642.XSHG',
 u'601688.XSHG',
 u'600436.XSHG',
 u'002252.XSHE',
 u'600008.XSHG',
 u'600867.XSHG',
 u'600028.XSHG',
 u'601377.XSHG',
 u'600170.XSHG',
 u'600664.XSHG',
 u'000039.XSHE',
 u'600362.XSHG',
 u'600108.XSHG',
 u'601628.XSHG',
 u'600633.XSHG',
 u'603699.XSHG',
 u'000999.XSHE',
 u'601928.XSHG',
 u'600058.XSHG',
 u'600196.XSHG',
 u'601169.XSHG',
 u'600348.XSHG',
 u'000562.XSHE',
 u'600208.XSHG',
 u'600804.XSHG',
 u'000625.XSHE',
 u'600886.XSHG',
 u'600221.XSHG',
 u'601318.XSHG',
 u'601898.XSHG',
 u'601918.XSHG',
 u'000758.XSHE',
 u'601901.XSHG',
 u'601328.XSHG',
 u'600050.XSHG',
 u'600031.XSHG',
 u'600029.XSHG',
 u'601601.XSHG',
 u'600027.XSHG',
 u'000831.XSHE',
 u'601808.XSHG',
 u'600109.XSHG',
 u'600009.XSHG',
 u'600655.XSHG',
 u'600085.XSHG',
 u'000768.XSHE',
 u'002375.XSHE',
 u'000728.XSHE',
 u'603993.XSHG',
 u'600016.XSHG',
 u'600703.XSHG',
 u'002429.XSHE',
 u'600271.XSHG',
 u'600674.XSHG',
 u'600068.XSHG',
 u'600369.XSHG',
 u'000060.XSHE',
 u'600115.XSHG',
 u'600585.XSHG',
 u'601939.XSHG',
 u'002142.XSHE',
 u'600839.XSHG',
 u'600143.XSHG',
 u'601800.XSHG',
 u'002594.XSHE',
 u'002410.XSHE',
 u'600649.XSHG',
 u'000725.XSHE',
 u'600153.XSHG',
 u'600415.XSHG',
 u'002570.XSHE',
 u'002304.XSHE',
 u'000503.XSHE',
 u'000883.XSHE',
 u'600216.XSHG',
 u'002024.XSHE',
 u'600015.XSHG',
 u'002236.XSHE',
 u'600219.XSHG',
 u'000333.XSHE',
 u'002603.XSHE',
 u'600875.XSHG',
 u'600688.XSHG',
 u'600060.XSHG',
 u'000063.XSHE',
 u'002465.XSHE',
 u'000156.XSHE',
 u'601933.XSHG',
 u'601989.XSHG',
 u'600383.XSHG',
 u'000009.XSHE',
 u'000970.XSHE',
 u'601958.XSHG',
 u'601766.XSHG',
 u'600795.XSHG',
 u'002241.XSHE',
 u'000778.XSHE',
 u'002106.XSHE',
 u'600030.XSHG',
 u'601669.XSHG',
 u'600832.XSHG',
 u'601186.XSHG',
 u'000792.XSHE',
 u'600739.XSHG']
#获取互联网和相关行业的所有股票
get_industry_stocks('I64')

[u'000503.XSHE',
 u'002095.XSHE',
 u'002174.XSHE',
 u'002315.XSHE',
 u'002354.XSHE',
 u'002439.XSHE',
 u'002467.XSHE',
 u'300059.XSHE',
 u'300104.XSHE',
 u'300113.XSHE',
 u'300431.XSHE',
 u'300467.XSHE',
 u'300226.XSHE',
 u'300295.XSHE',
 u'300383.XSHE',
 u'300392.XSHE',
 u'300418.XSHE',
 u'600804.XSHG',
 u'603000.XSHG']

pandas和seaborn库的使用说明见Tutorials and Documentation（使用帮助）文件夹¶

彩蛋¶

为了增加研究功能的体验，我们新增了如下两个彩蛋

回测和研究打通¶

为了打通回测和研究功能，我们新增了两个回测API，回测过程中需要保存的文件，您可以保存到研究模块，也可以直接调用研究模块的文件。

详见https://www.joinquant.com/api#write_file

回测和研究打通

分享到社区¶

为了便于交流，研究模块支持分享，您可以把notebook分享到社区，与大家交流。

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