首先绘制K线图
import pandas as pd
ssec2015=pd.read_csv('ssec2015.csv')
ssec2015=ssec2015.iloc[:,1:]
ssec2015.head()
| Date | Open | High | Low | Close | Volume | |
|---|---|---|---|---|---|---|
| 0 | 2015-03-02 | 3332.720947 | 3336.760010 | 3298.668945 | 3336.284912 | 346400 |
| 1 | 2015-03-03 | 3317.695068 | 3317.695068 | 3260.428955 | 3263.052002 | 382000 |
| 2 | 2015-03-04 | 3264.181885 | 3286.587891 | 3250.483887 | 3279.532959 | 293600 |
| 3 | 2015-03-05 | 3264.084961 | 3266.637939 | 3221.666016 | 3248.476074 | 320700 |
| 4 | 2015-03-06 | 3248.035889 | 3266.933105 | 3234.532959 | 3241.187012 | 282900 |
ssec2015.tail(n=3)
| Date | Open | High | Low | Close | Volume | |
|---|---|---|---|---|---|---|
| 19 | 2015-03-27 | 3686.134033 | 3710.477051 | 3656.831055 | 3691.095947 | 408900 |
| 20 | 2015-03-30 | 3710.612061 | 3795.935059 | 3710.612061 | 3786.568115 | 564700 |
| 21 | 2015-03-31 | 3822.987061 | 3835.566895 | 3737.042969 | 3747.898926 | 561700 |
from matplotlib.dates import DateFormatter,WeekdayLocator,DayLocator,MONDAY,date2num
from datetime import datetime
ssec2015.Date=[date2num(datetime.strptime(date,"%Y-%m-%d")) for date in ssec2015.Date]
ssec15list
[Date 735659.000000
Open 3332.720947
High 3336.760010
Low 3298.668945
Close 3336.284912
Volume 346400.000000
Name: 0, dtype: float64, Date 735660.000000
Open 3317.695068
High 3317.695068
Low 3260.428955
Close 3263.052002
Volume 382000.000000
Name: 1, dtype: float64, Date 735661.000000
Open 3264.181885
High 3286.587891
Low 3250.483887
Close 3279.532959
Volume 293600.000000
Name: 2, dtype: float64, Date 735662.000000
Open 3264.084961
High 3266.637939
Low 3221.666016
Close 3248.476074
Volume 320700.000000
Name: 3, dtype: float64, Date 735663.000000
Open 3248.035889
High 3266.933105
Low 3234.532959
Close 3241.187012
Volume 282900.000000
Name: 4, dtype: float64, Date 735666.000000
Open 3224.313965
High 3307.701904
Low 3198.370117
Close 3302.407959
Volume 321500.000000
Name: 5, dtype: float64, Date 735667.000000
Open 3289.084961
High 3309.915039
Low 3277.094971
Close 3286.068115
Volume 285800.000000
Name: 6, dtype: float64, Date 735668.000000
Open 3289.593994
High 3325.053955
Low 3278.470947
Close 3290.899902
Volume 283000.000000
Name: 7, dtype: float64, Date 735669.000000
Open 3314.812988
High 3360.053955
Low 3300.488037
Close 3349.322998
Volume 357300.000000
Name: 8, dtype: float64, Date 735670.000000
Open 3359.488037
High 3391.254883
Low 3352.149902
Close 3372.910889
Volume 328400.000000
Name: 9, dtype: float64, Date 735673.000000
Open 3391.157959
High 3449.304932
Low 3377.086914
Close 3449.304932
Volume 399100.000000
Name: 10, dtype: float64, Date 735674.000000
Open 3469.603027
High 3504.123047
Low 3459.694092
Close 3502.846924
Volume 520900.000000
Name: 11, dtype: float64, Date 735675.000000
Open 3510.500977
High 3577.662109
Low 3503.853027
Close 3577.301025
Volume 545200.000000
Name: 12, dtype: float64, Date 735676.000000
Open 3576.019043
High 3600.684082
Low 3546.843994
Close 3582.270996
Volume 537300.000000
Name: 13, dtype: float64, Date 735677.000000
Open 3587.083984
High 3632.337891
Low 3569.379883
Close 3617.318115
Volume 516700.000000
Name: 14, dtype: float64, Date 735680.000000
Open 3640.104004
High 3688.249023
Low 3635.485107
Close 3687.728027
Volume 536100.000000
Name: 15, dtype: float64, Date 735681.000000
Open 3692.569092
High 3715.873047
Low 3600.697998
Close 3691.409912
Volume 639600.000000
Name: 16, dtype: float64, Date 735682.000000
Open 3680.952881
High 3693.150879
Low 3634.559082
Close 3660.727051
Volume 521900.000000
Name: 17, dtype: float64, Date 735683.000000
Open 3641.941895
High 3707.318115
Low 3615.010986
Close 3682.094971
Volume 488600.000000
Name: 18, dtype: float64, Date 735684.000000
Open 3686.134033
High 3710.477051
Low 3656.831055
Close 3691.095947
Volume 408900.000000
Name: 19, dtype: float64, Date 735687.000000
Open 3710.612061
High 3795.935059
Low 3710.612061
Close 3786.568115
Volume 564700.000000
Name: 20, dtype: float64, Date 735688.000000
Open 3822.987061
High 3835.566895
Low 3737.042969
Close 3747.898926
Volume 561700.000000
Name: 21, dtype: float64, Date 735659.000000
Open 3332.720947
High 3336.760010
Low 3298.668945
Close 3336.284912
Volume 346400.000000
Name: 0, dtype: float64, Date 735660.000000
Open 3317.695068
High 3317.695068
Low 3260.428955
Close 3263.052002
Volume 382000.000000
Name: 1, dtype: float64, Date 735661.000000
Open 3264.181885
High 3286.587891
Low 3250.483887
Close 3279.532959
Volume 293600.000000
Name: 2, dtype: float64, Date 735662.000000
Open 3264.084961
High 3266.637939
Low 3221.666016
Close 3248.476074
Volume 320700.000000
Name: 3, dtype: float64, Date 735663.000000
Open 3248.035889
High 3266.933105
Low 3234.532959
Close 3241.187012
Volume 282900.000000
Name: 4, dtype: float64, Date 735666.000000
Open 3224.313965
High 3307.701904
Low 3198.370117
Close 3302.407959
Volume 321500.000000
Name: 5, dtype: float64, Date 735667.000000
Open 3289.084961
High 3309.915039
Low 3277.094971
Close 3286.068115
Volume 285800.000000
Name: 6, dtype: float64, Date 735668.000000
Open 3289.593994
High 3325.053955
Low 3278.470947
Close 3290.899902
Volume 283000.000000
Name: 7, dtype: float64, Date 735669.000000
Open 3314.812988
High 3360.053955
Low 3300.488037
Close 3349.322998
Volume 357300.000000
Name: 8, dtype: float64, Date 735670.000000
Open 3359.488037
High 3391.254883
Low 3352.149902
Close 3372.910889
Volume 328400.000000
Name: 9, dtype: float64, Date 735673.000000
Open 3391.157959
High 3449.304932
Low 3377.086914
Close 3449.304932
Volume 399100.000000
Name: 10, dtype: float64, Date 735674.000000
Open 3469.603027
High 3504.123047
Low 3459.694092
Close 3502.846924
Volume 520900.000000
Name: 11, dtype: float64, Date 735675.000000
Open 3510.500977
High 3577.662109
Low 3503.853027
Close 3577.301025
Volume 545200.000000
Name: 12, dtype: float64, Date 735676.000000
Open 3576.019043
High 3600.684082
Low 3546.843994
Close 3582.270996
Volume 537300.000000
Name: 13, dtype: float64, Date 735677.000000
Open 3587.083984
High 3632.337891
Low 3569.379883
Close 3617.318115
Volume 516700.000000
Name: 14, dtype: float64, Date 735680.000000
Open 3640.104004
High 3688.249023
Low 3635.485107
Close 3687.728027
Volume 536100.000000
Name: 15, dtype: float64, Date 735681.000000
Open 3692.569092
High 3715.873047
Low 3600.697998
Close 3691.409912
Volume 639600.000000
Name: 16, dtype: float64, Date 735682.000000
Open 3680.952881
High 3693.150879
Low 3634.559082
Close 3660.727051
Volume 521900.000000
Name: 17, dtype: float64, Date 735683.000000
Open 3641.941895
High 3707.318115
Low 3615.010986
Close 3682.094971
Volume 488600.000000
Name: 18, dtype: float64, Date 735684.000000
Open 3686.134033
High 3710.477051
Low 3656.831055
Close 3691.095947
Volume 408900.000000
Name: 19, dtype: float64, Date 735687.000000
Open 3710.612061
High 3795.935059
Low 3710.612061
Close 3786.568115
Volume 564700.000000
Name: 20, dtype: float64, Date 735688.000000
Open 3822.987061
High 3835.566895
Low 3737.042969
Close 3747.898926
Volume 561700.000000
Name: 21, dtype: float64]
ssec15list=list()
for i in range(len(ssec2015)):
ssec15list.append(ssec2015.iloc[i,:])
## 按照包
%pip install https://github.com/matplotlib/mpl_finance/archive/master.zip
Collecting https://github.com/matplotlib/mpl_finance/archive/master.zip
Downloading https://github.com/matplotlib/mpl_finance/archive/master.zip
[K \ 471kB 8.3kB/s
Requirement already satisfied: matplotlib in /Users/yaochenli/anaconda3/lib/python3.7/site-packages (from mpl-finance==0.10.0) (3.0.3)
Requirement already satisfied: numpy>=1.10.0 in /Users/yaochenli/anaconda3/lib/python3.7/site-packages (from matplotlib->mpl-finance==0.10.0) (1.16.2)
Requirement already satisfied: cycler>=0.10 in /Users/yaochenli/anaconda3/lib/python3.7/site-packages (from matplotlib->mpl-finance==0.10.0) (0.10.0)
Requirement already satisfied: kiwisolver>=1.0.1 in /Users/yaochenli/anaconda3/lib/python3.7/site-packages (from matplotlib->mpl-finance==0.10.0) (1.0.1)
Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Users/yaochenli/anaconda3/lib/python3.7/site-packages (from matplotlib->mpl-finance==0.10.0) (2.3.1)
Requirement already satisfied: python-dateutil>=2.1 in /Users/yaochenli/anaconda3/lib/python3.7/site-packages (from matplotlib->mpl-finance==0.10.0) (2.8.0)
Requirement already satisfied: six in /Users/yaochenli/anaconda3/lib/python3.7/site-packages (from cycler>=0.10->matplotlib->mpl-finance==0.10.0) (1.12.0)
Requirement already satisfied: setuptools in /Users/yaochenli/anaconda3/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib->mpl-finance==0.10.0) (40.8.0)
Building wheels for collected packages: mpl-finance
Building wheel for mpl-finance (setup.py) ... [?25ldone
[?25h Stored in directory: /private/var/folders/q9/2krqssqd16v76z26x_flv77m0000gn/T/pip-ephem-wheel-cache-rvgseok2/wheels/8f/bf/8a/6c858b2a3be945c7b2d658fbf6ab797b451d686cbee8aa2679
Successfully built mpl-finance
Installing collected packages: mpl-finance
Successfully installed mpl-finance-0.10.0
Note: you may need to restart the kernel to use updated packages.
from mpl_finance import candlestick_ohlc
import matplotlib.pyplot as plt
ax=plt.subplot()
mondays=WeekdayLocator(MONDAY)
weekFormatter = DateFormatter('%y %m %d')
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(DayLocator())
ax.xaxis.set_major_formatter(weekFormatter)
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus']=False
ax.set_title('201503 K plot')
candlestick_ohlc(ax,ssec15list,width=0.7,colorup='r',colordown='g')
#plt.setp(plt.gca().get_xticklabels(),rotation=50,horizontalalignment='center')
plt.show()
包装成函数
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter, WeekdayLocator,\
DayLocator, MONDAY,date2num
#from matplotlib.finance import candlestick_ohlc
import numpy as np
def candlePlot(seriesData,title="a"):
#设定日期格式
Date=[date2num(date) for date in seriesData.index]
seriesData.loc[:,'Date']=Date
listData=[]
for i in range(len(seriesData)):
a=[seriesData.Date[i],\
seriesData.Open[i],seriesData.High[i],\
seriesData.Low[i],seriesData.Close[i]]
listData.append(a)
#设定绘图相关参数
ax = plt.subplot()
mondays = WeekdayLocator(MONDAY)
#日期格式为‘15-Mar-09’形式
weekFormatter = DateFormatter('%y %b %d')
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(DayLocator())
ax.xaxis.set_major_formatter(weekFormatter)
#调用candlestick_ohlc函数
candlestick_ohlc(ax,listData, width=0.7,\
colorup='r',colordown='g')
ax.set_title(title) #设定标题
#设定x轴日期显示角度
plt.setp(plt.gca().get_xticklabels(), \
rotation=50,horizontalalignment='center')
return(plt.show())
#蜡烛图与线图
def candleLinePlots(candleData, candleTitle='a', **kwargs):
Date = [date2num(date) for date in candleData.index]
candleData.loc[:,'Date'] = Date
listData = []
for i in range(len(candleData)):
a = [candleData.Date[i],\
candleData.Open[i],candleData.High[i],\
candleData.Low[i],candleData.Close[i]]
listData.append(a)
# 如 果 不 定 长 参 数 无 取 值 ， 只 画 蜡 烛 图
ax = plt.subplot()
# 如 果 不 定 长 参 数 有 值 ， 则 分 成 两 个 子 图
flag=0
if kwargs:
if kwargs['splitFigures']:
ax = plt.subplot(211)
ax2= plt.subplot(212)
flag=1;
# 如 果 无 参 数 splitFigures ， 则 只 画 一 个 图 形 框
# 如 果 有 参 数 splitFigures ， 则 画 出 两 个 图 形 框
for key in kwargs:
if key=='title':
ax2.set_title(kwargs[key])
if key=='ylabel':
ax2.set_ylabel(kwargs[key])
if key=='grid':
ax2.grid(kwargs[key])
if key=='Data':
plt.sca(ax)
if flag:
plt.sca(ax2)
#一维数据
if kwargs[key].ndim==1:
plt.plot(kwargs[key],\
color='k',\
label=kwargs[key].name)
plt.legend(loc='best')
#二维数据有两个columns
elif all([kwargs[key].ndim==2,\
len(kwargs[key].columns)==2]):
plt.plot(kwargs[key].iloc[:,0], color='k',
label=kwargs[key].iloc[:,0].name)
plt.plot(kwargs[key].iloc[:,1],\
linestyle='dashed',\
label=kwargs[key].iloc[:,1].name)
plt.legend(loc='best')
mondays = WeekdayLocator(MONDAY)
weekFormatter = DateFormatter('%y %b %d')
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(DayLocator())
ax.xaxis.set_major_formatter(weekFormatter)
plt.sca(ax)
candlestick_ohlc(ax,listData, width=0.7,\
colorup='r',colordown='g')
ax.set_title(candleTitle)
plt.setp(ax.get_xticklabels(),\
rotation=20,\
horizontalalignment='center')
ax.autoscale_view()
return(plt.show())
#蜡烛图与成交量柱状图
def candleVolume(seriesData,candletitle='a',bartitle='b'):
Date=[date2num(date) for date in seriesData.index]
seriesData.index=list(range(len(Date)))
seriesData['Date']=Date
listData=zip(seriesData.Date,seriesData.Open,seriesData.High,seriesData.Low,
seriesData.Close)
ax1 = plt.subplot(211)
ax2 = plt.subplot(212)
for ax in ax1,ax2:
mondays = WeekdayLocator(MONDAY)
weekFormatter = DateFormatter('%m/%d/%Y')
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(DayLocator())
ax.xaxis.set_major_formatter(weekFormatter)
ax.grid(True)
ax1.set_ylim(seriesData.Low.min()-2,seriesData.High.max()+2)
ax1.set_ylabel('蜡烛图及收盘价线')
candlestick_ohlc(ax1,listData, width=0.7,colorup='r',colordown='g')
plt.setp(plt.gca().get_xticklabels(),\
rotation=45,horizontalalignment='center')
ax1.autoscale_view()
ax1.set_title(candletitle)
ax1.plot(seriesData.Date,seriesData.Close,\
color='black',label='收盘价')
ax1.legend(loc='best')
ax2.set_ylabel('成交量')
ax2.set_ylim(0,seriesData.Volume.max()*3)
ax2.bar(np.array(Date)[np.array(seriesData.Close>=seriesData.Open)]
,height=seriesData.iloc[:,4][np.array(seriesData.Close>=seriesData.Open)]
,color='r',align='center')
ax2.bar(np.array(Date)[np.array(seriesData.Close<seriesData.Open)]
,height=seriesData.iloc[:,4][np.array(seriesData.Close<seriesData.Open)]
,color='g',align='center')
ax2.set_title(bartitle)
return(plt.show())
用Python捕捉K线图的形态
# 早晨之星
# 获取上证指数2012的日度交易数据
ssec2012=pd.read_csv('ssec2012.csv')
ssec2012.index=ssec2012.iloc[:,1]
ssec2012.index=pd.to_datetime(ssec2012.index,format='%Y-%m-%d')
ssec2012=ssec2012.iloc[:,2:]
ssec2012.head(2)
| Open | High | Low | Close | Volume | |
|---|---|---|---|---|---|
| Date | |||||
| 2012-01-04 | 2211.995117 | 2217.520020 | 2168.644043 | 2169.389893 | 49200 |
| 2012-01-05 | 2160.896973 | 2183.404053 | 2145.555908 | 2148.451904 | 58800 |
# 提取收盘数据
Close=ssec2012.Close
# 提取开盘数据
Open=ssec2012.Open
# 计算每个交易日收盘价与开盘价之间差值
ClOp=Close-Open
ClOp.head()
Date
2012-01-04 -42.605224
2012-01-05 -12.445069
2012-01-06 15.248047
2012-01-09 61.148926
2012-01-10 63.911865
dtype: float64
ClOp.describe()
count 243.000000
mean 2.244368
std 22.884043
min -73.685059
25% -11.442016
50% 1.513183
75% 14.296997
max 91.843994
dtype: float64
# 捕捉绿色实体，十字形和红色实体
Shape=[0,0,0]
lag1ClOp=ClOp.shift(1)
lag2ClOp=ClOp.shift(2)
for i in range (3,len(ClOp)):
if all([lag2ClOp[i]<-11, abs(lag1ClOp[i])<2, ClOp[i]>6, abs(ClOp[i])>abs(lag2ClOp[i]*0.5)]):
Shape.append(1)
else:
Shape.append(0)
Shape.index(1)
165
# 定义十字星的位置，十字形实体要在其前后绿色实体和红色实体的下方
lagOpen=Open.shift(1)
lagClose=Close.shift(1)
lag2Close=Close.shift(2)
# 捕捉符合十字形位置的蜡烛图
Doji=[0,0,0]
for i in range(3, len(Open),1):
if all([lagOpen[i]<Open[i],lagOpen[i]<lag2Close[i],lagClose[i]<Open[i],lagClose[i]<lag2Close[i]]):
Doji.append(1)
else:
Doji.append(0)
Doji.count(1)
12
# 定义下跌趋势
ret = Close/Close.shift(1)-1
lag1ret=ret.shift(1)
lag2ret=ret.shift(2)
# 寻找向下趋势
Trend=[0,0,0]
for i in range(3,len(ret)):
if all([lag1ret[i]<0,lag2ret[i]<0]):
Trend.append(1)
else:
Trend.append(0)
# 综合三个条件
StarSig=[]
for i in range(len(Trend)):
if all([Shape[i]==1,Doji[i]==1,Trend[i]==1]):
StarSig.append(1)
else:
StarSig.append(0)
#输出2012年出现早晨之星形态的日期
for i in range(len(StarSig)):
if StarSig[i]==1:
print(ssec2012.index[i])
2012-09-06 00:00:00
#绘制9月6日附近的K线图
ssec201209 = ssec2012['2012-08-21':'2012-09-30']
candlePlot(ssec201209,title='201209 K Plot')
/Users/yaochenli/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py:362: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
self.obj[key] = _infer_fill_value(value)
/Users/yaochenli/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py:543: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
self.obj[item] = s
用Python捕捉乌云盖顶形态
import pandas as pd
ssec2011=pd.read_csv('ssec2011.csv')
ssec2011.index=ssec2011.iloc[:,1]
ssec2011.index=pd.to_datetime(ssec2011.index,format='%Y-%m-%d')
ssec2011=ssec2011.iloc[:,2:]
ssec2011.head()
| Open | High | Low | Close | Volume | |
|---|---|---|---|---|---|
| Date | |||||
| 2011-01-04 | 2825.329102 | 2855.395996 | 2810.107910 | 2852.647949 | 110000.0 |
| 2011-01-05 | 2833.115967 | 2850.492920 | 2824.295898 | 2839.220947 | 104400.0 |
| 2011-01-06 | 2838.435059 | 2853.875000 | 2816.950928 | 2824.197021 | 84800.0 |
| 2011-01-07 | 2820.665039 | 2868.004883 | 2807.870117 | 2838.801025 | 113200.0 |
| 2011-01-10 | 2832.622070 | 2847.357910 | 2786.861084 | 2791.809082 | 90800.0 |
#提取价格数据
Close11=ssec2011.Close
Open11=ssec2011.Open
# 刻画捕捉符合乌云盖顶形态的两个连续蜡烛实体
lagClose11=Close11.shift(1)
lagOpen11 =Open11.shift(1)
Cloud=pd.Series(0,index=Close11.index)
for i in range(1,len(Close11)):
if all([Close11[i]<Open11[i],lagClose11[i]>lagOpen11[i],\
Open11[i]>lagClose11[i],\
Close11[i]<0.5*(lagClose11[i]+lagOpen11[i]),\
Close11[i]>lagOpen11[i]]):
Cloud[i]=1
#定义前期上升趋势
Trend=pd.Series(0,index=Close11.index)
for i in range(2,len(Close)):
if Close11[i-1]>Close11[i-2]>Close11[i-3]:
Trend[i]=1
#寻找乌云盖顶形态
darkCloud=Cloud+Trend
darkCloud[darkCloud==2]
Date
2011-05-19 2
2011-08-16 2
dtype: int64
#绘制5月19日附近K线图
ssec201105=ssec2011['2011-05-01':'2011-05-30']
candlePlot(ssec201105, title='201105 K Plot')
/Users/yaochenli/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py:635: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
self.obj[item_labels[indexer[info_axis]]] = value
# 绘制8月16日前后的K线图
ssec201108=ssec2011['2011-08-01':'2011-08-31']
candlePlot(ssec201108,title='201108 K Plot')
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