Waterfall Checkpoint Demo

In [11]:

    

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

# Visualization
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


    

In [2]:

    

data_dir = '../../input'
# Check what files our dataset contain
print('Our dataset contains the following files: \n')
print(check_output(["ls", data_dir]).decode("utf8"))


    

    




Our dataset contains the following files: 

bitcoin_cash_price.csv
bitcoin_dataset.csv
bitcoin_price.csv
bitconnect_price.csv
dash_price.csv
ethereum_classic_price.csv
ethereum_dataset.csv
ethereum_price.csv
iota_price.csv
litecoin_price.csv
monero_price.csv
nem_price.csv
neo_price.csv
numeraire_price.csv
omisego_price.csv
qtum_price.csv
ripple_price.csv
stratis_price.csv
waves_price.csv

In [24]:

    

# Reading in bitcoin price file
bitcoin_price = pd.read_csv('{}/bitcoin_price.csv'.format(data_dir), parse_dates=['Date'], index_col=0)
bitcoin_price.dtypes


    

    
Out[24]:





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

In [4]:

    

# Lets look at the first 10 rows
bitcoin_price.head(10)


    

    
Out[4]:







  

    

      

      
Open
      
High
      
Low
      
Close
      
Volume
      
Market Cap
    
    

      
Date
      

      

      

      

      

      

    
  
  

    

      
2017-09-05
      
4228.29
      
4427.84
      
3998.11
      
4376.53
      
2,697,970,000
      
69,954,400,000
    
    

      
2017-09-04
      
4591.63
      
4591.63
      
4108.40
      
4236.31
      
2,987,330,000
      
75,955,500,000
    
    

      
2017-09-03
      
4585.27
      
4714.08
      
4417.59
      
4582.96
      
1,933,190,000
      
75,841,700,000
    
    

      
2017-09-02
      
4901.42
      
4975.04
      
4469.24
      
4578.77
      
2,722,140,000
      
81,060,600,000
    
    

      
2017-09-01
      
4701.76
      
4892.01
      
4678.53
      
4892.01
      
2,599,080,000
      
77,748,400,000
    
    

      
2017-08-31
      
4555.59
      
4736.05
      
4549.40
      
4703.39
      
1,944,930,000
      
75,322,300,000
    
    

      
2017-08-30
      
4570.36
      
4626.52
      
4471.41
      
4565.30
      
1,937,850,000
      
75,556,600,000
    
    

      
2017-08-29
      
4389.21
      
4625.68
      
4352.13
      
4579.02
      
2,486,080,000
      
72,553,800,000
    
    

      
2017-08-28
      
4384.45
      
4403.93
      
4224.64
      
4382.66
      
1,959,330,000
      
72,467,900,000
    
    

      
2017-08-27
      
4345.10
      
4416.59
      
4317.29
      
4382.88
      
1,537,460,000
      
71,809,200,000
    
  

In [5]:

    

bitcoin_price.tail(10)


    

    
Out[5]:







  

    

      

      
Open
      
High
      
Low
      
Close
      
Volume
      
Market Cap
    
    

      
Date
      

      

      

      

      

      

    
  
  

    

      
2013-05-07
      
112.25
      
113.44
      
97.70
      
111.50
      
-
      
1,248,470,000
    
    

      
2013-05-06
      
115.98
      
124.66
      
106.64
      
112.30
      
-
      
1,289,470,000
    
    

      
2013-05-05
      
112.90
      
118.80
      
107.14
      
115.91
      
-
      
1,254,760,000
    
    

      
2013-05-04
      
98.10
      
115.00
      
92.50
      
112.50
      
-
      
1,089,890,000
    
    

      
2013-05-03
      
106.25
      
108.13
      
79.10
      
97.75
      
-
      
1,180,070,000
    
    

      
2013-05-02
      
116.38
      
125.60
      
92.28
      
105.21
      
-
      
1,292,190,000
    
    

      
2013-05-01
      
139.00
      
139.89
      
107.72
      
116.99
      
-
      
1,542,820,000
    
    

      
2013-04-30
      
144.00
      
146.93
      
134.05
      
139.00
      
-
      
1,597,780,000
    
    

      
2013-04-29
      
134.44
      
147.49
      
134.00
      
144.54
      
-
      
1,491,160,000
    
    

      
2013-04-28
      
135.30
      
135.98
      
132.10
      
134.21
      
-
      
1,500,520,000
    
  

In [6]:

    

print('Date of newest data: {}'.format(bitcoin_price.index[0]))
print('Date of oldest data: {}'.format(bitcoin_price.index[-1]))


    

    




Date of newest data: 2017-09-05 00:00:00
Date of oldest data: 2013-04-28 00:00:00

In [25]:

    

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

for feat in btc_ohlc:
    print('---------------------------------------------')
    print('Statistics for Bitcoin {} values:'.format(feat))
    print(bitcoin_price[feat].describe())


    

    




---------------------------------------------
Statistics for Bitcoin Open values:
count    1592.000000
mean      658.283649
std       723.653014
min        68.500000
25%       258.350000
50%       444.885000
75%       680.187500
max      4901.420000
Name: Open, dtype: float64
---------------------------------------------
Statistics for Bitcoin High values:
count    1592.000000
mean      676.271727
std       749.473657
min        74.560000
25%       263.930000
50%       450.605000
75%       703.960000
max      4975.040000
Name: High, dtype: float64
---------------------------------------------
Statistics for Bitcoin Low values:
count    1592.000000
mean      641.050616
std       699.635365
min        65.530000
25%       253.177500
50%       437.180000
75%       660.775000
max      4678.530000
Name: Low, dtype: float64
---------------------------------------------
Statistics for Bitcoin Close values:
count    1592.000000
mean      660.837795
std       729.447612
min        68.430000
25%       258.592500
50%       444.705000
75%       682.142500
max      4892.010000
Name: Close, dtype: float64

In [26]:

    

# Lets plot the Open, Close, High, Low values on a line plot
for feat in btc_ohlc:
    plt.plot(bitcoin_price[feat], label=feat)

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


    

In [27]:

    

# Lets looks at the more recent data
n_days = 365 # number of recent days

for feature in btc_ohlc:
    plt.plot(bitcoin_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 [29]:

    

# Candlestick graph for Bitcoin Closing Price
btc_close_ohlc = bitcoin_price['Close'][:n_days].resample('10D').ohlc()
btc_close_ohlc.reset_index(inplace=True)
btc_close_ohlc['Date'] = btc_close_ohlc['Date'].map(mdates.date2num)

fig, ax = plt.subplots()

candlestick_ohlc(ax, btc_close_ohlc.values, width=2, colorup='g')
ax.xaxis_date()

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