谁都想买个公寓有30个房间出租,一个房间月租1000元,每月下来有3万。那么问题来了,如果购买有30个房间的公寓,每个房间平均80平米,就是2400平米,每平米需1万元投入也要2400万,如此庞大的资金,普通人是承受不起的。那怎么办呢,不用急,现在有这么个好办法,一个房间只需投3万,每月有1000元收入,30个房间需90万,月收入3万。比购买公寓2400万省20几倍的投资啊,那么有人会问,怎么操盘呢?针对这个问题,下面我给大家举个栗(例)子
今年有个叫马云的,(不是那个马云爸爸哦)在2018年10月29日以2.45元的价位买入1万股50ETF,马云愿意以高出10%也就是在2.55元的价格卖出50ETF,或者愿意在价格下跌10%也就是2.35元的价格加仓1万股 。 具体操作卖开仓:Call2.55@0.0600,表示行权价为2.55元的10月认购期权;Put2.35@0.0400表示行权价为2.35元的10月认沽期权。然后就等待11月27日到期收房租吧。
11月27日期权到期日会发生什么呢?可能出现三个方向:
方向A:如果到期50ETF大于2.55元,认购期权将被行权,于是马云以2.55元行权卖出手中的1万股50ETF,每股价差收入=2.55-2.45=0.1元,加上认购和认沽权利金收入0.1元,每股总盈亏=0.2元,乘以1万股,等于2000元,哇塞,月租金2000元啊,年化80%。实现了溢价卖股票相当于2.65元卖出,完成了高抛。耐心等待价格回落再开启策略(或用其他策略)。
方向B:如果到期50ETF介于2.35与2.55元之间,两份期权都不会被行权,于是马云还是持有着1万股,并将0.1元的权利金全部收入囊中,把原来2.45元的建仓成本降低到了2.35元。期权的权利金1000元就是房租钱,年化40%。根据当前价格再卖开一对期权,开启下个月收房租的历程。
方向C:如果到期50ETF小于2.35元,认沽期权将被行权,于是马云必须再准备出23500元(2.3510000)的现金买入1万股50ETF,考虑到两份期权权利金收入为1000元(0.110000),马云实际的再买入成本是2.25元。等于在2.25元支付22500元再购一间出租房。结合原来1万股的建仓成本为2.35元,于是平摊下来,马云相当于以2.3元建仓了2万股50ETF,实现了折价购2间出租房,为后续潜在的上涨完成了低吸,房子可在下一组期权卖认购位置变现,还有租金收,也是1000元房租,年化也是40%。循环往复的收租金,几年下来,马云就是马云爸爸了。
看完策略后,你在EXCEL表格进行模拟运算,会提出很多问题,持续下跌怎么办,暴涨不是赚少了吗?您别急,我一一回答你。暴涨时,只能按认购行权价卖出,这时要结合牛市策略组合。持续下跌或断崖下跌要有资金加仓和承受浮亏的心理,因为风险要加倍。但魏杰的观点:2018、2019、2020这三年国家要稳金融,稳增长,稳开放度过三年调整期,因此市场宏观面,适合本策略运行。为应对极端情况,风控采用亏损2000元止损离场,卧薪尝胆等下次机会,再者可组合熊市策略,这里泛泛而谈,不是本文的重点,但你一定要重视。本策略就是适合慢牛或慢熊,最心仪的行情就是盘整,用代码写的趋势策略在50ETF当前行情下运行会亏的很惨,但加上本策略会平滑你的资金曲线,甚至缓慢向上。中国股市行情,跌多涨少,熊途漫漫,怎么办?开启本策略后,背起你的行囊,当一个旅游达人的包租公,每月定时记得收租金就OVER。
此外在资管新规下,本策略是做市商策略,起到稳定市场,平抑股市、期货市场的作用,市场容量大,不用担心大资金无法进入,更不用担心监管的问题,无政策风险。目前市场只有50ETF和铜两个品种是欧式期权,但随着国内金融市场开放,市场容量可达万万亿,远大前程等着你。
感谢您使用研究功能~~
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。
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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。
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(可修改文件后缀名,修改为任意文件类型);
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:
#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_
研究新增如下API
get_industry_stocks()
get_price - 获取历史数据
#导入需要的库
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 | 2.2199 | 2.2190 |
2014-01-03 | 2.2096 | 2.1946 |
2014-01-06 | 2.1880 | 2.1421 |
2014-01-07 | 2.1271 | 2.1431 |
2014-01-08 | 2.1431 | 2.1412 |
2014-01-09 | 2.1365 | 2.1281 |
2014-01-10 | 2.1262 | 2.1131 |
2014-01-13 | 2.1131 | 2.0981 |
2014-01-14 | 2.0981 | 2.1121 |
2014-01-15 | 2.1112 | 2.1121 |
2014-01-16 | 2.1084 | 2.1131 |
2014-01-17 | 2.1065 | 2.0831 |
2014-01-20 | 2.0821 | 2.0728 |
2014-01-21 | 2.0785 | 2.0900 |
2014-01-22 | 2.0929 | 2.1475 |
2014-01-23 | 2.1446 | 2.1360 |
2014-01-24 | 2.1284 | 2.1475 |
2014-01-27 | 2.1322 | 2.1197 |
2014-01-28 | 2.1264 | 2.1217 |
2014-01-29 | 2.1274 | 2.1284 |
2014-01-30 | 2.1245 | 2.0987 |
2014-02-07 | 2.0814 | 2.1121 |
2014-02-10 | 2.1217 | 2.1734 |
2014-02-11 | 2.1695 | 2.1839 |
2014-02-12 | 2.1849 | 2.1877 |
2014-02-13 | 2.1830 | 2.1820 |
2014-02-14 | 2.1839 | 2.1897 |
2014-02-17 | 2.2107 | 2.2050 |
2014-02-18 | 2.2059 | 2.1791 |
2014-02-19 | 2.1772 | 2.2079 |
... | ... | ... |
2014-12-18 | 3.2471 | 3.2414 |
2014-12-19 | 3.2443 | 3.2893 |
2014-12-22 | 3.2893 | 3.2663 |
2014-12-23 | 3.2423 | 3.2002 |
2014-12-24 | 3.2002 | 3.1111 |
2014-12-25 | 3.1303 | 3.2241 |
2014-12-26 | 3.2261 | 3.3496 |
2014-12-29 | 3.4195 | 3.3333 |
2014-12-30 | 3.3362 | 3.3333 |
2014-12-31 | 3.3410 | 3.4234 |
2015-01-05 | 3.4521 | 3.5326 |
2015-01-06 | 3.5105 | 3.5086 |
2015-01-07 | 3.4914 | 3.5077 |
2015-01-08 | 3.5172 | 3.4310 |
2015-01-09 | 3.4215 | 3.4061 |
2015-01-12 | 3.4071 | 3.3956 |
2015-01-13 | 3.3927 | 3.3889 |
2015-01-14 | 3.3956 | 3.3812 |
2015-01-15 | 3.3812 | 3.4770 |
2015-01-16 | 3.4895 | 3.5077 |
2015-01-19 | 3.3228 | 3.1571 |
2015-01-20 | 3.2045 | 3.2742 |
2015-01-21 | 3.2858 | 3.4242 |
2015-01-22 | 3.4300 | 3.4300 |
2015-01-23 | 3.4542 | 3.4465 |
2015-01-26 | 3.4659 | 3.4794 |
2015-01-27 | 3.4871 | 3.4446 |
2015-01-28 | 3.4184 | 3.3894 |
2015-01-29 | 3.3448 | 3.3535 |
2015-01-30 | 3.3729 | 3.3119 |
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 | type | |
---|---|---|---|---|---|
000001.XSHE | 平安银行 | PAYH | 1991-04-03 | 2200-01-01 | stock |
000002.XSHE | 万 科A | WKA | 1991-01-29 | 2200-01-01 | stock |
000004.XSHE | 国农科技 | GNKJ | 1990-12-01 | 2200-01-01 | stock |
000005.XSHE | 世纪星源 | SJXY | 1990-12-10 | 2200-01-01 | stock |
000006.XSHE | 深振业A | SZYA | 1992-04-27 | 2200-01-01 | stock |
000007.XSHE | 全新好 | QXH | 1992-04-13 | 2200-01-01 | stock |
000008.XSHE | 神州高铁 | SZGT | 1992-05-07 | 2200-01-01 | stock |
000009.XSHE | 中国宝安 | ZGBA | 1991-06-25 | 2200-01-01 | stock |
000010.XSHE | 美丽生态 | SHX | 1995-10-27 | 2200-01-01 | stock |
000011.XSHE | 深物业A | SWYA | 1992-03-30 | 2200-01-01 | stock |
000012.XSHE | 南 玻A | NBA | 1992-02-28 | 2200-01-01 | stock |
000014.XSHE | 沙河股份 | SHGF | 1992-06-02 | 2200-01-01 | stock |
000016.XSHE | 深康佳A | SKJA | 1992-03-27 | 2200-01-01 | stock |
000017.XSHE | 深中华A | SZHA | 1992-03-31 | 2200-01-01 | stock |
000018.XSHE | 神州长城 | SZCC | 1992-06-16 | 2200-01-01 | stock |
000019.XSHE | 深深宝A | SSBA | 1992-10-12 | 2200-01-01 | stock |
000020.XSHE | 深华发A | SHFA | 1992-04-28 | 2200-01-01 | stock |
000021.XSHE | 深科技 | SKJ | 1994-02-02 | 2200-01-01 | stock |
000022.XSHE | 深赤湾A | SCWA | 1993-05-05 | 2200-01-01 | stock |
000023.XSHE | 深天地A | STDA | 1993-04-29 | 2200-01-01 | stock |
000024.XSHE | 招商地产 | ZSDC | 1993-06-07 | 2015-12-30 | stock |
000025.XSHE | 特 力A | TLA | 1993-06-21 | 2200-01-01 | stock |
000026.XSHE | 飞亚达A | FYDA | 1993-06-03 | 2200-01-01 | stock |
000027.XSHE | 深圳能源 | SZNY | 1993-09-03 | 2200-01-01 | stock |
000028.XSHE | 国药一致 | GYYZ | 1993-08-09 | 2200-01-01 | stock |
000029.XSHE | 深深房A | SSFA | 1993-09-15 | 2200-01-01 | stock |
000030.XSHE | 富奥股份 | FAGF | 1993-09-29 | 2200-01-01 | stock |
000031.XSHE | 中粮地产 | ZLDC | 1993-10-08 | 2200-01-01 | stock |
000032.XSHE | 深桑达A | SSDA | 1993-10-28 | 2200-01-01 | stock |
000033.XSHE | *ST新都 | STXD | 1994-01-03 | 2200-01-01 | stock |
... | ... | ... | ... | ... | ... |
603906.XSHG | 龙蟠科技 | LPKJ | 2017-04-10 | 2200-01-01 | stock |
603908.XSHG | 牧高笛 | mgd | 2017-03-07 | 2200-01-01 | stock |
603909.XSHG | 合诚股份 | HCGF | 2016-06-28 | 2200-01-01 | stock |
603918.XSHG | 金桥信息 | JQXX | 2015-05-28 | 2200-01-01 | stock |
603919.XSHG | 金徽酒 | JHJ | 2016-03-10 | 2200-01-01 | stock |
603920.XSHG | 世运电路 | SYDL | 2017-04-26 | 2200-01-01 | stock |
603928.XSHG | 兴业股份 | XYGF | 2016-12-12 | 2200-01-01 | stock |
603929.XSHG | 亚翔集成 | YXJC | 2016-12-30 | 2200-01-01 | stock |
603936.XSHG | 博敏电子 | BMDZ | 2015-12-09 | 2200-01-01 | stock |
603939.XSHG | 益丰药房 | YFYF | 2015-02-17 | 2200-01-01 | stock |
603955.XSHG | 大千生态 | dqst | 2017-03-10 | 2200-01-01 | stock |
603958.XSHG | 哈森股份 | HSGF | 2016-06-29 | 2200-01-01 | stock |
603959.XSHG | 百利科技 | BLKJ | 2016-05-17 | 2200-01-01 | stock |
603960.XSHG | 克来机电 | kljd | 2017-03-14 | 2200-01-01 | stock |
603966.XSHG | 法兰泰克 | FLTK | 2017-01-25 | 2200-01-01 | stock |
603968.XSHG | 醋化股份 | CHGF | 2015-05-18 | 2200-01-01 | stock |
603969.XSHG | 银龙股份 | YLGF | 2015-02-27 | 2200-01-01 | stock |
603977.XSHG | 国泰集团 | GTJT | 2016-11-11 | 2200-01-01 | stock |
603979.XSHG | 金诚信 | JCX | 2015-06-30 | 2200-01-01 | stock |
603986.XSHG | 兆易创新 | ZYCX | 2016-08-18 | 2200-01-01 | stock |
603987.XSHG | 康德莱 | KDL | 2016-11-21 | 2200-01-01 | stock |
603988.XSHG | 中电电机 | ZDDJ | 2014-11-04 | 2200-01-01 | stock |
603989.XSHG | 艾华集团 | AHJT | 2015-05-15 | 2200-01-01 | stock |
603990.XSHG | 麦迪科技 | MDKJ | 2016-12-08 | 2200-01-01 | stock |
603991.XSHG | 至正股份 | zzgf | 2017-03-08 | 2200-01-01 | stock |
603993.XSHG | 洛阳钼业 | LYMY | 2012-10-09 | 2200-01-01 | stock |
603996.XSHG | 中新科技 | ZXKJ | 2015-12-22 | 2200-01-01 | stock |
603997.XSHG | 继峰股份 | JFGF | 2015-03-02 | 2200-01-01 | stock |
603998.XSHG | 方盛制药 | FSZY | 2014-12-05 | 2200-01-01 | stock |
603999.XSHG | 读者传媒 | DZCM | 2015-12-10 | 2200-01-01 | stock |
3272 rows × 5 columns
#获取沪深300指数的所有股票
get_index_stocks('000300.XSHG')
[u'000001.XSHE', u'000002.XSHE', u'000008.XSHE', u'000009.XSHE', u'000027.XSHE', u'000039.XSHE', u'000060.XSHE', u'000061.XSHE', u'000063.XSHE', u'000069.XSHE', u'000100.XSHE', u'000156.XSHE', u'000157.XSHE', u'000166.XSHE', u'000333.XSHE', u'000338.XSHE', u'000402.XSHE', u'000413.XSHE', u'000415.XSHE', u'000423.XSHE', u'000425.XSHE', u'000503.XSHE', u'000538.XSHE', u'000540.XSHE', u'000555.XSHE', u'000559.XSHE', u'000568.XSHE', u'000623.XSHE', u'000625.XSHE', u'000627.XSHE', u'000630.XSHE', u'000651.XSHE', u'000671.XSHE', u'000686.XSHE', u'000709.XSHE', u'000712.XSHE', u'000718.XSHE', u'000725.XSHE', u'000728.XSHE', u'000738.XSHE', u'000750.XSHE', u'000768.XSHE', u'000776.XSHE', u'000778.XSHE', u'000783.XSHE', u'000792.XSHE', u'000793.XSHE', u'000800.XSHE', u'000826.XSHE', u'000839.XSHE', u'000858.XSHE', u'000876.XSHE', u'000895.XSHE', u'000917.XSHE', u'000938.XSHE', u'000963.XSHE', u'000977.XSHE', u'000983.XSHE', u'001979.XSHE', u'002007.XSHE', u'002008.XSHE', u'002024.XSHE', u'002027.XSHE', u'002049.XSHE', u'002065.XSHE', u'002074.XSHE', u'002081.XSHE', u'002085.XSHE', u'002129.XSHE', u'002131.XSHE', u'002142.XSHE', u'002146.XSHE', u'002152.XSHE', u'002153.XSHE', u'002174.XSHE', u'002183.XSHE', u'002195.XSHE', u'002202.XSHE', u'002230.XSHE', u'002236.XSHE', u'002241.XSHE', u'002252.XSHE', u'002292.XSHE', u'002299.XSHE', u'002304.XSHE', u'002310.XSHE', u'002385.XSHE', u'002415.XSHE', u'002424.XSHE', u'002426.XSHE', u'002450.XSHE', u'002456.XSHE', u'002465.XSHE', u'002466.XSHE', u'002470.XSHE', u'002475.XSHE', u'002500.XSHE', u'002568.XSHE', u'002594.XSHE', u'002673.XSHE', u'002714.XSHE', u'002736.XSHE', u'002739.XSHE', u'002797.XSHE', u'300002.XSHE', u'300015.XSHE', u'300017.XSHE', u'300024.XSHE', u'300027.XSHE', u'300033.XSHE', u'300058.XSHE', u'300059.XSHE', u'300070.XSHE', u'300072.XSHE', u'300085.XSHE', u'300104.XSHE', u'300124.XSHE', u'300133.XSHE', u'300144.XSHE', u'300146.XSHE', u'300168.XSHE', u'300182.XSHE', u'300251.XSHE', u'300315.XSHE', u'600000.XSHG', u'600008.XSHG', u'600009.XSHG', u'600010.XSHG', u'600015.XSHG', u'600016.XSHG', u'600018.XSHG', u'600019.XSHG', u'600021.XSHG', u'600023.XSHG', u'600028.XSHG', u'600029.XSHG', u'600030.XSHG', u'600031.XSHG', u'600036.XSHG', u'600037.XSHG', u'600038.XSHG', u'600048.XSHG', u'600050.XSHG', u'600060.XSHG', u'600061.XSHG', u'600066.XSHG', u'600068.XSHG', u'600074.XSHG', u'600085.XSHG', u'600089.XSHG', u'600100.XSHG', u'600104.XSHG', u'600109.XSHG', u'600111.XSHG', u'600115.XSHG', u'600118.XSHG', u'600150.XSHG', u'600153.XSHG', u'600157.XSHG', u'600170.XSHG', u'600177.XSHG', u'600188.XSHG', u'600196.XSHG', u'600208.XSHG', u'600221.XSHG', u'600252.XSHG', u'600256.XSHG', u'600271.XSHG', u'600276.XSHG', u'600297.XSHG', u'600309.XSHG', u'600332.XSHG', u'600340.XSHG', u'600352.XSHG', u'600362.XSHG', u'600369.XSHG', u'600372.XSHG', u'600373.XSHG', u'600376.XSHG', u'600383.XSHG', u'600406.XSHG', u'600415.XSHG', u'600446.XSHG', u'600482.XSHG', u'600485.XSHG', u'600489.XSHG', u'600498.XSHG', u'600518.XSHG', u'600519.XSHG', u'600535.XSHG', u'600547.XSHG', u'600549.XSHG', u'600570.XSHG', u'600582.XSHG', u'600583.XSHG', u'600585.XSHG', u'600588.XSHG', u'600606.XSHG', u'600637.XSHG', u'600648.XSHG', u'600649.XSHG', u'600654.XSHG', u'600660.XSHG', u'600663.XSHG', u'600666.XSHG', u'600674.XSHG', u'600685.XSHG', u'600688.XSHG', u'600690.XSHG', u'600703.XSHG', u'600704.XSHG', u'600705.XSHG', u'600718.XSHG', u'600737.XSHG', u'600739.XSHG', u'600741.XSHG', u'600754.XSHG', u'600783.XSHG', u'600795.XSHG', u'600804.XSHG', u'600816.XSHG', u'600820.XSHG', u'600827.XSHG', u'600837.XSHG', u'600839.XSHG', u'600867.XSHG', u'600871.XSHG', u'600873.XSHG', u'600875.XSHG', u'600886.XSHG', u'600887.XSHG', u'600893.XSHG', u'600895.XSHG', u'600900.XSHG', u'600958.XSHG', u'600959.XSHG', u'600999.XSHG', u'601006.XSHG', u'601009.XSHG', u'601018.XSHG', u'601021.XSHG', u'601088.XSHG', u'601099.XSHG', u'601111.XSHG', u'601118.XSHG', u'601127.XSHG', u'601155.XSHG', u'601166.XSHG', u'601169.XSHG', u'601186.XSHG', u'601198.XSHG', u'601211.XSHG', u'601216.XSHG', u'601225.XSHG', u'601258.XSHG', u'601288.XSHG', u'601318.XSHG', u'601328.XSHG', u'601333.XSHG', u'601336.XSHG', u'601377.XSHG', u'601390.XSHG', u'601398.XSHG', u'601555.XSHG', u'601600.XSHG', u'601601.XSHG', u'601607.XSHG', u'601608.XSHG', u'601611.XSHG', u'601618.XSHG', u'601628.XSHG', u'601633.XSHG', u'601668.XSHG', u'601669.XSHG', u'601688.XSHG', u'601718.XSHG', u'601727.XSHG', u'601766.XSHG', u'601788.XSHG', u'601800.XSHG', u'601818.XSHG', u'601857.XSHG', u'601866.XSHG', u'601872.XSHG', u'601877.XSHG', u'601888.XSHG', u'601899.XSHG', u'601901.XSHG', u'601919.XSHG', u'601928.XSHG', u'601933.XSHG', u'601939.XSHG', u'601958.XSHG', u'601985.XSHG', u'601988.XSHG', u'601989.XSHG', u'601998.XSHG', u'603000.XSHG', u'603885.XSHG', u'603993.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'002113.XSHE', u'002131.XSHE', u'002174.XSHE', u'002175.XSHE', u'002315.XSHE', u'002354.XSHE', u'002439.XSHE', u'002464.XSHE', u'002467.XSHE', u'002517.XSHE', u'002555.XSHE', u'002558.XSHE', u'002624.XSHE', u'300031.XSHE', u'300052.XSHE', u'300059.XSHE', u'300104.XSHE', u'300113.XSHE', u'300226.XSHE', u'300295.XSHE', u'300315.XSHE', u'300343.XSHE', u'300392.XSHE', u'300418.XSHE', u'300431.XSHE', u'300467.XSHE', u'300494.XSHE', u'300571.XSHE', u'600634.XSHG', u'600652.XSHG', u'600804.XSHG', u'600986.XSHG', u'603000.XSHG', u'603258.XSHG', u'603444.XSHG', u'603881.XSHG', u'603888.XSHG']
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