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In [1]:

    
import numpy as np # Linear Alg
import pandas as pd # CSV file I/O & data processing

import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import seaborn as sns
import warnings 
from matplotlib import style
from matplotlib.finance import candlestick_ohlc
warnings.filterwarnings("ignore")
style.use('ggplot')

%matplotlib inline
plt.rcParams['figure.figsize'] = (12.0, 8.0)

from subprocess import check_output









    



/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/matplotlib/cbook.py:136: MatplotlibDeprecationWarning: The finance module has been deprecated in mpl 2.0 and will be removed in mpl 2.2. Please use the module mpl_finance instead.
  warnings.warn(message, mplDeprecation, stacklevel=1)



In [2]:

    
data_dir = '../../input'
ripple_price = pd.read_csv('{}/ripple_price.csv'.format(data_dir), parse_dates=['Date'], index_col=0)
ripple_price.dtypes









    Out[2]:





Open          float64
High          float64
Low           float64
Close         float64
Volume         object
Market Cap     object
dtype: object



In [3]:

    
# first fifteen rows of ripple
ripple_price.head(15)









    Out[3]:







  
    
      
      Open
      High
      Low
      Close
      Volume
      Market Cap
    
    
      Date
      
      
      
      
      
      
    
  
  
    
      2017-09-05
      0.205110
      0.218674
      0.190232
      0.215189
      242,636,000
      7,864,700,000
    
    
      2017-09-04
      0.228781
      0.228781
      0.195960
      0.204968
      239,613,000
      8,772,340,000
    
    
      2017-09-03
      0.227229
      0.236443
      0.218662
      0.228811
      186,526,000
      8,712,840,000
    
    
      2017-09-02
      0.249348
      0.249680
      0.217774
      0.226669
      329,928,000
      9,560,940,000
    
    
      2017-09-01
      0.255803
      0.256162
      0.243833
      0.248479
      283,576,000
      9,808,460,000
    
    
      2017-08-31
      0.227558
      0.261038
      0.225907
      0.255630
      432,574,000
      8,725,450,000
    
    
      2017-08-30
      0.218871
      0.229805
      0.217476
      0.228006
      181,186,000
      8,392,340,000
    
    
      2017-08-29
      0.224026
      0.225926
      0.214867
      0.218564
      269,770,000
      8,590,000,000
    
    
      2017-08-28
      0.203425
      0.236260
      0.196610
      0.224508
      542,468,000
      7,800,090,000
    
    
      2017-08-27
      0.212617
      0.212617
      0.197717
      0.202995
      193,971,000
      8,152,540,000
    
    
      2017-08-26
      0.218256
      0.229086
      0.210324
      0.212577
      184,121,000
      8,368,790,000
    
    
      2017-08-25
      0.217200
      0.232184
      0.203022
      0.217828
      504,484,000
      8,328,300,000
    
    
      2017-08-24
      0.252371
      0.288783
      0.212543
      0.218958
      1,342,360,000
      9,676,890,000
    
    
      2017-08-23
      0.240987
      0.297662
      0.219992
      0.246827
      1,770,400,000
      9,240,380,000
    
    
      2017-08-22
      0.194971
      0.254659
      0.171483
      0.239286
      1,450,040,000
      7,475,950,000



In [4]:

    
# last fifteen rows of ripple
ripple_price.tail(15)



In [5]:

    
rip_ohlc = ['Open', 'High', 'Low', 'Close']

for feat in rip_ohlc:
    print('---------------------------------------------')
    print('Statistics for Ripple {} values:'.format(feat))
    print(ripple_price[feat].describe())









    



---------------------------------------------
Statistics for Ripple Open values:
count    1494.000000
mean        0.027803
std         0.063325
min         0.002809
25%         0.005974
50%         0.007478
75%         0.012785
max         0.394931
Name: Open, dtype: float64
---------------------------------------------
Statistics for Ripple High values:
count    1494.000000
mean        0.029456
std         0.067624
min         0.003082
25%         0.006084
50%         0.007698
75%         0.013157
max         0.435170
Name: High, dtype: float64
---------------------------------------------
Statistics for Ripple Low values:
count    1494.000000
mean        0.026254
std         0.059060
min         0.002802
25%         0.005894
50%         0.007296
75%         0.011890
max         0.332222
Name: Low, dtype: float64
---------------------------------------------
Statistics for Ripple Close values:
count    1494.000000
mean        0.027934
std         0.063490
min         0.002810
25%         0.005982
50%         0.007484
75%         0.012859
max         0.394132
Name: Close, dtype: float64



In [6]:

    
# plotting ripple's high, low, close, open values
for feat in rip_ohlc:
    plt.plot(ripple_price[feat], label=feat)

plt.xlabel('Time(Yr-M)')
plt.ylabel('Value(USD)')
plt.legend()
plt.show()



In [7]:

    
#ripple's more recent data
n_days = 365 # number of recent days

for feature in rip_ohlc:
    plt.plot(ripple_price[feature].iloc[:n_days], label=feature)

plt.title('Pricing Trend (last year)')
plt.xlabel('Time(Yr-M)')
plt.ylabel('Value(USD)')
plt.legend()
plt.show()



In [ ]:

	Open	High	Low	Close	Volume	Market Cap
Date
2013-08-18	0.005687	0.006263	0.005585	0.005617	-	44,458,200
2013-08-17	0.003138	0.005684	0.003138	0.005671	-	24,533,400
2013-08-16	0.003082	0.003162	0.002875	0.003134	-	24,092,600
2013-08-15	0.003788	0.003804	0.003067	0.003092	-	29,615,000
2013-08-14	0.004267	0.004267	0.003785	0.003788	-	33,359,300
2013-08-13	0.004463	0.004463	0.004171	0.004245	-	34,893,700
2013-08-12	0.004414	0.004520	0.004359	0.004449	-	34,511,100
2013-08-11	0.004314	0.004421	0.004300	0.004415	-	33,725,000
2013-08-10	0.004291	0.004366	0.004279	0.004314	-	33,547,500
2013-08-09	0.004257	0.004367	0.004253	0.004291	-	33,282,700
2013-08-08	0.004397	0.004424	0.004175	0.004254	-	34,372,500
2013-08-07	0.004669	0.004682	0.004333	0.004417	-	36,503,500
2013-08-06	0.005637	0.005661	0.004629	0.004680	-	44,067,600
2013-08-05	0.005875	0.005980	0.005613	0.005613	-	45,928,400
2013-08-04	0.005874	0.005927	0.005874	0.005882	-	45,921,000

	Open	High	Low	Close	Volume	Market Cap
Date
2017-09-05	0.205110	0.218674	0.190232	0.215189	242,636,000	7,864,700,000
2017-09-04	0.228781	0.228781	0.195960	0.204968	239,613,000	8,772,340,000
2017-09-03	0.227229	0.236443	0.218662	0.228811	186,526,000	8,712,840,000
2017-09-02	0.249348	0.249680	0.217774	0.226669	329,928,000	9,560,940,000
2017-09-01	0.255803	0.256162	0.243833	0.248479	283,576,000	9,808,460,000
2017-08-31	0.227558	0.261038	0.225907	0.255630	432,574,000	8,725,450,000
2017-08-30	0.218871	0.229805	0.217476	0.228006	181,186,000	8,392,340,000
2017-08-29	0.224026	0.225926	0.214867	0.218564	269,770,000	8,590,000,000
2017-08-28	0.203425	0.236260	0.196610	0.224508	542,468,000	7,800,090,000
2017-08-27	0.212617	0.212617	0.197717	0.202995	193,971,000	8,152,540,000
2017-08-26	0.218256	0.229086	0.210324	0.212577	184,121,000	8,368,790,000
2017-08-25	0.217200	0.232184	0.203022	0.217828	504,484,000	8,328,300,000
2017-08-24	0.252371	0.288783	0.212543	0.218958	1,342,360,000	9,676,890,000
2017-08-23	0.240987	0.297662	0.219992	0.246827	1,770,400,000	9,240,380,000
2017-08-22	0.194971	0.254659	0.171483	0.239286	1,450,040,000	7,475,950,000