notebook.community

Edit and run



In [6]:

    
import pandas as pd
import matplotlib.pyplot as plt

%matplotlib inline



In [10]:

    
perf = pd.read_pickle('./results.pickle') # read in perf DataFrame
perf.head()









    Out[10]:






  
    
      
      ETH
      algo_volatility
      algorithm_period_return
      alpha
      benchmark_period_return
      benchmark_volatility
      beta
      capital_used
      ending_cash
      ending_exposure
      ...
      short_exposure
      short_value
      shorts_count
      sortino
      starting_cash
      starting_exposure
      starting_value
      trading_days
      transactions
      treasury_period_return
    
  
  
    
      2016-01-01 23:59:00+00:00
      0.918
      NaN
      0.000000e+00
      NaN
      0.000000
      NaN
      NaN
      0.000000
      1.000000e+07
      0.000
      ...
      0
      0
      0
      NaN
      1.000000e+07
      0.000
      0.000
      1
      []
      0.0227
    
    
      2016-01-02 23:59:00+00:00
      0.956
      0.000000e+00
      0.000000e+00
      NaN
      0.000000
      NaN
      NaN
      0.000000
      1.000000e+07
      0.000
      ...
      0
      0
      0
      NaN
      1.000000e+07
      0.000
      0.000
      2
      []
      0.0227
    
    
      2016-01-03 23:59:00+00:00
      0.960
      9.600184e-07
      -1.047466e-07
      NaN
      0.000000
      NaN
      NaN
      -10.647466
      9.999989e+06
      9.600
      ...
      0
      0
      0
      -9.165151
      1.000000e+07
      0.000
      0.000
      3
      [{'amount': 3, 'dt': 2016-01-03 09:00:00+00:00...
      0.0227
    
    
      2016-01-04 23:59:00+00:00
      0.962
      9.301440e-07
      -2.027499e-07
      NaN
      -0.015304
      NaN
      NaN
      -1.962033
      9.999987e+06
      10.582
      ...
      0
      0
      0
      -11.218769
      9.999989e+06
      9.600
      9.600
      4
      [{'amount': 1, 'dt': 2016-01-04 23:29:00+00:00...
      0.0224
    
    
      2016-01-05 23:59:00+00:00
      0.939
      8.254251e-07
      -2.280629e-07
      -0.000009
      -0.013322
      0.194371
      0.000004
      -3.756130
      9.999984e+06
      14.085
      ...
      0
      0
      0
      -11.115408
      9.999987e+06
      10.582
      10.582
      5
      [{'amount': 2, 'dt': 2016-01-05 19:12:00+00:00...
      0.0225
    
  

5 rows × 39 columns



In [11]:

    
ax1 = plt.subplot(211)
perf.portfolio_value.plot(ax=ax1)
ax1.set_ylabel('portfolio value')
ax2 = plt.subplot(212, sharex=ax1)
perf.ETH.plot(ax=ax2)
ax2.set_ylabel('ETH stock price')









    Out[11]:





<matplotlib.text.Text at 0x113168630>



In [ ]:

	ETH	algo_volatility	algorithm_period_return	alpha	benchmark_period_return	benchmark_volatility	beta	capital_used	ending_cash	ending_exposure	...	sortino	starting_cash	starting_exposure	starting_value	trading_days	transactions	treasury_period_return
2016-01-01 23:59:00+00:00	0.918	NaN	0.000000e+00	NaN	0.000000	NaN	NaN	0.000000	1.000000e+07	0.000	...	NaN	1.000000e+07	0.000	0.000	1	[]	0.0227
2016-01-02 23:59:00+00:00	0.956	0.000000e+00	0.000000e+00	NaN	0.000000	NaN	NaN	0.000000	1.000000e+07	0.000	...	NaN	1.000000e+07	0.000	0.000	2	[]	0.0227
2016-01-03 23:59:00+00:00	0.960	9.600184e-07	-1.047466e-07	NaN	0.000000	NaN	NaN	-10.647466	9.999989e+06	9.600	...	-9.165151	1.000000e+07	0.000	0.000	3	[{'amount': 3, 'dt': 2016-01-03 09:00:00+00:00...	0.0227
2016-01-04 23:59:00+00:00	0.962	9.301440e-07	-2.027499e-07	NaN	-0.015304	NaN	NaN	-1.962033	9.999987e+06	10.582	...	-11.218769	9.999989e+06	9.600	9.600	4	[{'amount': 1, 'dt': 2016-01-04 23:29:00+00:00...	0.0224
2016-01-05 23:59:00+00:00	0.939	8.254251e-07	-2.280629e-07	-0.000009	-0.013322	0.194371	0.000004	-3.756130	9.999984e+06	14.085	...	-11.115408	9.999987e+06	10.582	10.582	5	[{'amount': 2, 'dt': 2016-01-05 19:12:00+00:00...	0.0225