2019/03/17: 观察了两周,没发现改数据 . 哪位大神会设计股指期货回测?
__biz=MzU2NDQ3NTI0OA==&mid=2247484082&idx=1&sn=c6dc788316ebc3ad7394bdc9415f8a47&chksm=f*b26adcb3cafbbf5*82bd341af8f9523f34b37caa88b1d8bce3c0232ca77d4700328a6610&token=1279847976?=zh_CN#rd
2019/02/28:
最近在读“行为金融学”方面的书, 对这方面比较感兴趣,也经常在网上和微信上查资料。 发现“行为金融学”方面的做法和量化界不同, 他们好像是通过互联网的问卷调查预测行情。 拿到问卷调查后怎么研究得出预测的就不太清楚了。就公布的预测结果来看,似乎挺有效。 在这篇文章里我把公布的预测结果拿到平台上来跑跑回测,分析一把:
1.预测结果获取
几个问卷调查预测网站里,找了下面这个预测中证500指数的。把网页下载下来存盘,上传聚宽的研究空间。
__biz=MzU2NDQ3NTI0OA==&mid=2247484048&idx=1&sn=d9cce02378d2fffbfa9069dd4b1fbb54&chksm=f*b268fcb3caf997d154171c06af4b7a822e45f5974421de036117a1916daa03da76e4aef3d&mpshare=1&scene=1&srcid=0228RGlUtu20VHHaBu2Ju9Mk&pass_ticket=GPnK4NUJ2KY/evQtBkBl9WzAjcv/XVNE0EcGOlagl2OgBVNmmWuLJT9keb5ktgja#rd
1.1写几行Python把HTML 文件内的Table读进来转成 dataframe
1.2还不知道怎么在回测空间内读文件。简单点,把dataframe 先转成JSON ,然后手工copy到回测代码里,如下:
2.回测
以中证500指数000905.XSHG 为基准,按预测值每天开盘买卖000905.XSHG。 回测结果:
import pandas as pdwith open("../duyi.htm", "r") as f:s = f.read()df=pd.read_html(s)[0]df=df.set_index(df.columns[0]).sort_index()df['op']=0df.loc[df['操作']=='做多', 'op']=1df.loc[df['操作']=='做空', 'op']=-1df=df[['op']]s=df.to_json()print(s)
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op 2016-11-01 0 2016-11-02 0 2016-11-03 0 2016-11-04 1 2016-11-07 -1 2016-11-08 0 2016-11-09 0 2016-11-10 0 2016-11-11 1 2016-11-14 1 2016-11-15 0 2016-11-16 0 2016-11-17 -1 2016-11-18 0 2016-11-21 0 2016-11-22 0 2016-11-23 0 2016-11-24 0 2016-11-25 0 2016-11-28 1 2016-11-29 -1 2016-11-30 0 2016-12-01 0 2016-12-02 0 2016-12-05 0 2016-12-06 0 2016-12-07 -1 2016-12-08 0 2016-12-09 -1 2016-12-12 0 ... .. 2019-01-11 0 2019-01-14 -1 2019-01-15 1 2019-01-16 1 2019-01-17 0 2019-01-18 1 2019-01-21 1 2019-01-22 1 2019-01-23 1 2019-01-24 0 2019-01-25 0 2019-01-28 0 2019-01-29 0 2019-01-30 1 2019-01-31 0 2019-02-01 0 2019-02-11 1 2019-02-12 1 2019-02-13 1 2019-02-14 1 2019-02-15 0 2019-02-18 1 2019-02-19 1 2019-02-20 0 2019-02-21 0 2019-02-22 1 2019-02-25 1 2019-02-26 1 2019-02-27 1 2019-02-28 1 [568 rows x 1 columns]
df.index
DatetimeIndex(['2016-11-01', '2016-11-02', '2016-11-03', '2016-11-04', '2016-11-07', '2016-11-08', '2016-11-09', '2016-11-10', '2016-11-11', '2016-11-14', ... '2019-02-15', '2019-02-18', '2019-02-19', '2019-02-20', '2019-02-21', '2019-02-22', '2019-02-25', '2019-02-26', '2019-02-27', '2019-02-28'], dtype='datetime64[ns]', length=568, freq=None)
with open("../duyi.htm", "r") as f:s = f.read()df=pd.read_html(s)[0]
df
.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }
Unnamed: 0 | 收盘价 | 价格变化 | 基准 | 净值 | 操作 | |
---|---|---|---|---|---|---|
0 | 2019-02-28 | 5025.29 | 0.15% | 0.772 | 3.295 | 做多 |
1 | 2019-02-27 | 5017.88 | -0.41% | 0.771 | 3.291 | 做多 |
2 | 2019-02-26 | 5038.54 | -0.12% | 0.774 | 3.304 | 做多 |
3 | 2019-02-25 | 5044.35 | 5.59% | 0.775 | 3.308 | 做多 |
4 | 2019-02-22 | 4777.30 | 2.54% | 0.734 | 3.133 | 做多 |
5 | 2019-02-21 | 4659.14 | -0.18% | 0.716 | 3.055 | 平仓 |
6 | 2019-02-20 | 4667.62 | 0.01% | 0.717 | 3.055 | 平仓 |
7 | 2019-02-19 | 4667.28 | 0.13% | 0.717 | 3.055 | 做多 |
8 | 2019-02-18 | 4661.00 | 3.51% | 0.716 | 3.051 | 做多 |
9 | 2019-02-15 | 4502.73 | -0.65% | 0.692 | 2.948 | 平仓 |
10 | 2019-02-14 | 4532.17 | 0.44% | 0.696 | 2.948 | 做多 |
11 | 2019-02-13 | 4512.41 | 1.64% | 0.693 | 2.935 | 做多 |
12 | 2019-02-12 | 4439.48 | 0.83% | 0.682 | 2.887 | 做多 |
13 | 2019-02-11 | 4402.87 | 2.53% | 0.677 | 2.863 | 做多 |
14 | 2019-02-01 | 4294.27 | 2.82% | 0.660 | 2.793 | 平仓 |
15 | 2019-01-31 | 4176.47 | -0.83% | 0.642 | 2.793 | 平仓 |
16 | 2019-01-30 | 4211.62 | -1.00% | 0.647 | 2.793 | 做多 |
17 | 2019-01-29 | 4254.29 | -1.23% | 0.654 | 2.821 | 平仓 |
18 | 2019-01-28 | 4307.22 | -0.26% | 0.662 | 2.821 | 平仓 |
19 | 2019-01-25 | 4318.44 | -0.45% | 0.664 | 2.821 | 平仓 |
20 | 2019-01-24 | 4338.17 | 0.52% | 0.667 | 2.821 | 平仓 |
21 | 2019-01-23 | 4315.68 | 0.17% | 0.663 | 2.821 | 做多 |
22 | 2019-01-22 | 4308.25 | -1.49% | 0.662 | 2.816 | 做多 |
23 | 2019-01-21 | 4373.63 | 0.59% | 0.672 | 2.859 | 做多 |
24 | 2019-01-18 | 4347.81 | 1.02% | 0.668 | 2.842 | 做多 |
25 | 2019-01-17 | 4303.73 | -0.76% | 0.661 | 2.813 | 平仓 |
26 | 2019-01-16 | 4336.88 | -0.24% | 0.666 | 2.813 | 做多 |
27 | 2019-01-15 | 4347.36 | 1.42% | 0.668 | 2.820 | 做多 |
28 | 2019-01-14 | 4286.55 | -0.66% | 0.659 | 2.781 | 做空 |
29 | 2019-01-11 | 4314.88 | 0.77% | 0.663 | 2.763 | 平仓 |
... | ... | ... | ... | ... | ... | ... |
538 | 2016-12-12 | 6249.08 | -4.07% | 0.960 | 1.011 | 平仓 |
539 | 2016-12-09 | 6513.92 | -0.21% | 1.001 | 1.011 | 做空 |
540 | 2016-12-08 | 6527.43 | -0.47% | 1.003 | 1.009 | 平仓 |
541 | 2016-12-07 | 6558.02 | 1.03% | 1.008 | 1.009 | 做空 |
542 | 2016-12-06 | 6490.89 | 0.08% | 0.997 | 1.019 | 平仓 |
543 | 2016-12-05 | 6486.02 | -0.54% | 0.997 | 1.019 | 平仓 |
544 | 2016-12-02 | 6520.97 | -1.56% | 1.002 | 1.019 | 平仓 |
545 | 2016-12-01 | 6624.47 | 0.59% | 1.018 | 1.019 | 平仓 |
546 | 2016-11-30 | 6585.58 | -0.53% | 1.012 | 1.019 | 平仓 |
547 | 2016-11-29 | 6620.79 | -0.75% | 1.017 | 1.019 | 做空 |
548 | 2016-11-28 | 6670.80 | 0.27% | 1.025 | 1.012 | 做多 |
549 | 2016-11-25 | 6652.82 | 0.45% | 1.022 | 1.009 | 平仓 |
550 | 2016-11-24 | 6623.05 | -0.12% | 1.018 | 1.009 | 平仓 |
551 | 2016-11-23 | 6631.17 | -0.45% | 1.019 | 1.009 | 平仓 |
552 | 2016-11-22 | 6661.25 | 0.75% | 1.024 | 1.009 | 平仓 |
553 | 2016-11-21 | 6611.54 | 0.14% | 1.016 | 1.009 | 平仓 |
554 | 2016-11-18 | 6602.33 | -0.51% | 1.015 | 1.009 | 平仓 |
555 | 2016-11-17 | 6636.47 | -0.12% | 1.020 | 1.009 | 做空 |
556 | 2016-11-16 | 6644.19 | 0.06% | 1.021 | 1.008 | 平仓 |
557 | 2016-11-15 | 6640.46 | 0.38% | 1.020 | 1.008 | 平仓 |
558 | 2016-11-14 | 6615.01 | 0.47% | 1.016 | 1.008 | 做多 |
559 | 2016-11-11 | 6583.89 | 0.72% | 1.012 | 1.003 | 做多 |
560 | 2016-11-10 | 6536.97 | 1.29% | 1.004 | 0.996 | 平仓 |
561 | 2016-11-09 | 6453.57 | -0.76% | 0.992 | 0.996 | 平仓 |
562 | 2016-11-08 | 6503.18 | 0.53% | 0.999 | 0.996 | 平仓 |
563 | 2016-11-07 | 6468.82 | 0.10% | 0.994 | 0.996 | 做空 |
564 | 2016-11-04 | 6462.59 | -0.33% | 0.993 | 0.997 | 做多 |
565 | 2016-11-03 | 6483.73 | 0.44% | 0.996 | 1.000 | 平仓 |
566 | 2016-11-02 | 6455.13 | -0.81% | 0.992 | 1.000 | 平仓 |
567 | 2016-11-01 | 6507.95 | 0.00% | 1.000 | 1.000 | 平仓 |
568 rows × 6 columns
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