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%matplotlib inline
import pandas as pd
import numpy as np
import tushare as ts
import matplotlib.pyplot as plt
from setting import get_engine
api = ts.get_apis()
df = ts.bar('300144',conn=api,start_date='2018-01-01')
df.head()
code | open | close | high | low | vol | amount | |
---|---|---|---|---|---|---|---|
datetime | |||||||
2018-03-29 | 300144 | 19.88 | 20.16 | 20.19 | 19.88 | 15050.0 | 30236284.0 |
2018-03-28 | 300144 | 20.26 | 19.86 | 20.63 | 19.85 | 142126.0 | 286976928.0 |
2018-03-27 | 300144 | 19.95 | 20.40 | 20.50 | 19.84 | 125721.0 | 254512320.0 |
2018-03-26 | 300144 | 19.22 | 19.69 | 19.79 | 19.05 | 95743.0 | 186872368.0 |
2018-03-23 | 300144 | 19.11 | 19.20 | 19.80 | 19.06 | 112123.0 | 216926128.0 |
closed = df['close']
closed
datetime 2018-03-29 20.16 2018-03-28 19.86 2018-03-27 20.40 2018-03-26 19.69 2018-03-23 19.20 2018-03-22 19.68 2018-03-21 19.81 2018-03-20 20.45 2018-03-19 20.09 2018-03-16 19.90 2018-03-15 20.29 2018-03-14 20.25 2018-03-13 20.64 2018-03-12 20.50 2018-03-09 20.59 2018-03-08 19.61 2018-03-07 19.75 2018-03-06 19.55 2018-03-05 19.39 2018-03-02 19.16 2018-03-01 19.63 2018-02-28 19.66 2018-02-27 19.46 2018-02-26 19.31 2018-02-23 19.12 2018-02-22 19.06 2018-02-14 18.50 2018-02-13 18.57 2018-02-12 18.72 2018-02-09 17.98 2018-02-08 18.69 2018-02-07 18.37 2018-02-06 17.67 2018-02-05 18.78 2018-02-02 18.87 2018-02-01 18.38 2018-01-31 18.45 2018-01-30 19.19 2018-01-29 19.24 2018-01-26 19.50 2018-01-25 19.41 2018-01-24 19.54 2018-01-23 18.44 2018-01-22 18.64 2018-01-19 18.05 2018-01-18 18.13 2018-01-17 18.30 2018-01-16 17.98 2018-01-15 17.82 2018-01-12 18.18 2018-01-11 18.27 2018-01-10 18.30 2018-01-09 18.50 2018-01-08 18.68 2018-01-05 18.82 2018-01-04 18.87 2018-01-03 18.78 2018-01-02 18.56 Name: close, dtype: float64
year_closed = closed.resample('w',how='ohlc')
c:\python27\lib\site-packages\ipykernel_launcher.py:1: FutureWarning: how in .resample() is deprecated the new syntax is .resample(...).ohlc() """Entry point for launching an IPython kernel.
year_closed
open | high | low | close | |
---|---|---|---|---|
datetime | ||||
2018-01-07 | 18.56 | 18.87 | 18.56 | 18.82 |
2018-01-14 | 18.68 | 18.68 | 18.18 | 18.18 |
2018-01-21 | 17.82 | 18.30 | 17.82 | 18.05 |
2018-01-28 | 18.64 | 19.54 | 18.44 | 19.50 |
2018-02-04 | 19.24 | 19.24 | 18.38 | 18.87 |
2018-02-11 | 18.78 | 18.78 | 17.67 | 17.98 |
2018-02-18 | 18.72 | 18.72 | 18.50 | 18.50 |
2018-02-25 | 19.06 | 19.12 | 19.06 | 19.12 |
2018-03-04 | 19.31 | 19.66 | 19.16 | 19.16 |
2018-03-11 | 19.39 | 20.59 | 19.39 | 20.59 |
2018-03-18 | 20.50 | 20.64 | 19.90 | 19.90 |
2018-03-25 | 20.09 | 20.45 | 19.20 | 19.20 |
2018-04-01 | 19.69 | 20.40 | 19.69 | 20.16 |
apt = (year_closed.high-year_closed.low)/year_closed.low*100
apt
datetime 2018-01-07 1.670259 2018-01-14 2.750275 2018-01-21 2.693603 2018-01-28 5.965293 2018-02-04 4.678999 2018-02-11 6.281834 2018-02-18 1.189189 2018-02-25 0.314795 2018-03-04 2.609603 2018-03-11 6.188757 2018-03-18 3.718593 2018-03-25 6.510417 Freq: W-SUN, dtype: float64
df=df.dropna(axis=0)
df['close']
datetime 2018-03-29 20.16 2018-03-28 19.86 2018-03-27 20.40 2018-03-26 19.69 2018-03-23 19.20 2018-03-22 19.68 2018-03-21 19.81 2018-03-20 20.45 2018-03-19 20.09 2018-03-16 19.90 2018-03-15 20.29 2018-03-14 20.25 2018-03-13 20.64 2018-03-12 20.50 2018-03-09 20.59 2018-03-08 19.61 2018-03-07 19.75 2018-03-06 19.55 2018-03-05 19.39 2018-03-02 19.16 2018-03-01 19.63 2018-02-28 19.66 2018-02-27 19.46 2018-02-26 19.31 2018-02-23 19.12 2018-02-22 19.06 2018-02-14 18.50 2018-02-13 18.57 2018-02-12 18.72 2018-02-09 17.98 2018-02-08 18.69 2018-02-07 18.37 2018-02-06 17.67 2018-02-05 18.78 2018-02-02 18.87 2018-02-01 18.38 2018-01-31 18.45 2018-01-30 19.19 2018-01-29 19.24 2018-01-26 19.50 2018-01-25 19.41 2018-01-24 19.54 2018-01-23 18.44 2018-01-22 18.64 2018-01-19 18.05 2018-01-18 18.13 2018-01-17 18.30 2018-01-16 17.98 2018-01-15 17.82 2018-01-12 18.18 2018-01-11 18.27 2018-01-10 18.30 2018-01-09 18.50 2018-01-08 18.68 2018-01-05 18.82 2018-01-04 18.87 2018-01-03 18.78 2018-01-02 18.56 Name: close, dtype: float64
df['close'].plot()
<matplotlib.axes._subplots.AxesSubplot at 0x83cb850>
engine =get_engine('db_zdt')
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-62-37327cb8c862> in <module>() ----> 1 year_closed.plot() /usr/local/lib/python2.7/dist-packages/pandas/plotting/_core.pyc in __call__(self, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds) 2675 fontsize=fontsize, colormap=colormap, table=table, 2676 yerr=yerr, xerr=xerr, secondary_y=secondary_y, -> 2677 sort_columns=sort_columns, **kwds) 2678 __call__.__doc__ = plot_frame.__doc__ 2679 /usr/local/lib/python2.7/dist-packages/pandas/plotting/_core.pyc in plot_frame(data, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds) 1900 yerr=yerr, xerr=xerr, 1901 secondary_y=secondary_y, sort_columns=sort_columns, -> 1902 **kwds) 1903 1904 /usr/local/lib/python2.7/dist-packages/pandas/plotting/_core.pyc in _plot(data, x, y, subplots, ax, kind, **kwds) 1725 pass 1726 data = series -> 1727 plot_obj = klass(data, subplots=subplots, ax=ax, kind=kind, **kwds) 1728 1729 plot_obj.generate() /usr/local/lib/python2.7/dist-packages/pandas/plotting/_core.pyc in __init__(self, data, **kwargs) 929 930 def __init__(self, data, **kwargs): --> 931 MPLPlot.__init__(self, data, **kwargs) 932 if self.stacked: 933 self.data = self.data.fillna(value=0) /usr/local/lib/python2.7/dist-packages/pandas/plotting/_core.pyc in __init__(self, data, kind, by, subplots, sharex, sharey, use_index, figsize, grid, legend, rot, ax, fig, title, xlim, ylim, xticks, yticks, sort_columns, fontsize, secondary_y, colormap, table, layout, **kwds) 98 table=False, layout=None, **kwds): 99 --> 100 _converter._WARN = False 101 self.data = data 102 self.by = by NameError: global name '_converter' is not defined
from filter_stock import Filter_Stock
█
obj = Filter_Stock()
df = obj.get_new_stock('2015','2015')
df.head()
code | name | industry | area | pe | outstanding | totals | totalAssets | liquidAssets | fixedAssets | ... | bvps | pb | undp | perundp | rev | profit | gpr | npr | holders | 更新日期 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
timeToMarket | |||||||||||||||||||||
2018-05-25 | 300746 | N汉嘉 | 建筑施工 | 浙江 | 36.00 | 0.53 | 2.10 | 84655.75 | 62115.23 | 15889.55 | ... | 3.87 | 2.09 | 29412.24 | 1.40 | 0.00 | 0.00 | 18.70 | 6.51 | 100201.0 | 2018-05-25 |
2017-05-05 | 300643 | 万通智控 | 汽车配件 | 浙江 | 1991.42 | 0.71 | 2.00 | 48480.46 | 35851.01 | 9471.17 | ... | 2.07 | 8.77 | 6885.06 | 0.34 | -4.54 | -94.65 | 26.56 | 0.74 | 17863.0 | 2018-05-25 |
2018-05-23 | 300745 | 欣锐科技 | 汽车配件 | 深圳 | 20.66 | 0.29 | 1.15 | 112515.45 | 98528.06 | 8233.80 | ... | 8.48 | 2.40 | 28390.36 | 2.48 | 0.00 | 0.00 | 29.77 | 18.97 | 56498.0 | 2018-05-25 |
2017-01-11 | 300580 | 贝斯特 | 汽车配件 | 江苏 | 33.83 | 0.66 | 2.00 | 153087.30 | 79671.81 | 51525.51 | ... | 6.43 | 3.32 | 35882.65 | 1.79 | 22.17 | 2.29 | 37.93 | 17.82 | 21940.0 | 2018-05-25 |
2017-11-06 | 300720 | 海川智能 | 电器仪表 | 广东 | 114.29 | 0.18 | 0.72 | 46308.18 | 32482.15 | 9548.55 | ... | 5.94 | 6.23 | 14848.82 | 2.06 | 0.00 | 0.00 | 55.91 | 22.28 | 12821.0 | 2018-05-25 |
5 rows × 23 columns
ret_df = df[(df['pe']< 50) & (df['pe']>0)].sort_values(by='pe',ascending=True)
f = open('new_stock.txt','w')
for i in df['code'].values:
f.write(i)
f.write('\n')
f.close()
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