Momentum

1. The SPY is higher than X days ago, buy
2. If the SPY is lower than X days ago, sell your long position.

(optimize for number of days)



In [1]:

    
import pandas as pd
import matplotlib.pyplot as plt
import datetime
from talib.abstract import *

import pinkfish as pf
import strategy

# format price data
pd.options.display.float_format = '{:0.2f}'.format

%matplotlib inline



In [2]:

    
# set size of inline plots
'''note: rcParams can't be in same cell as import matplotlib
   or %matplotlib inline
   
   %matplotlib notebook: will lead to interactive plots embedded within
   the notebook, you can zoom and resize the figure
   
   %matplotlib inline: only draw static images in the notebook
'''
plt.rcParams["figure.figsize"] = (10, 7)

Some global data



In [3]:

    
#symbol = '^GSPC'
symbol = 'SPY'
#symbol = 'DIA'
#symbol = 'QQQ'
#symbol = 'IWM'
#symbol = 'TLT'
#symbol = 'GLD'
#symbol = 'AAPL'
#symbol = 'BBRY'
#symbol = 'GDX'
capital = 10000
start = datetime.datetime(1900, 1, 1)
start = datetime.datetime.strptime(pf.SP500_BEGIN, '%Y-%m-%d')
#end = datetime.datetime(2010, 12, 1)
end = datetime.datetime.now()

Define lookback period



In [4]:

    
periods = range(3, 18+1)
periods = [str(period) for period in periods]

Run Strategy



In [5]:

    
strategies = pd.Series(dtype=object)
for period in periods:
    print("{0}".format(period), end=" ")
    strategies[period] = strategy.Strategy(symbol, capital, start, end, period=int(period))
    strategies[period].run()
    strategies[period].tlog, strategies[period].dbal = strategies[period].get_logs()
    strategies[period].stats = strategies[period].get_stats()









    



3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Summarize results



In [6]:

    
metrics = ('annual_return_rate',
           'max_closed_out_drawdown',
           'drawdown_annualized_return',
           'drawdown_recovery',
           'best_month',
           'worst_month',
           'sharpe_ratio',
           'sortino_ratio',
           'monthly_std',
           'pct_time_in_market',
           'total_num_trades',
           'pct_profitable_trades',
           'avg_points')

df = strategy.summary(strategies, metrics)
df









    Out[6]:







  
    
      
      3
      4
      5
      6
      7
      8
      9
      10
      11
      12
      13
      14
      15
      16
      17
      18
    
  
  
    
      annual_return_rate
      5.60
      6.14
      7.83
      8.44
      7.10
      8.49
      9.61
      8.54
      9.05
      9.31
      8.43
      9.03
      9.46
      9.04
      9.12
      9.69
    
    
      max_closed_out_drawdown
      -26.94
      -26.66
      -24.34
      -24.57
      -29.65
      -22.05
      -26.17
      -30.08
      -25.51
      -22.90
      -32.57
      -29.75
      -33.27
      -33.25
      -33.25
      -27.59
    
    
      drawdown_annualized_return
      -4.81
      -4.34
      -3.11
      -2.91
      -4.17
      -2.60
      -2.72
      -3.52
      -2.82
      -2.46
      -3.86
      -3.30
      -3.52
      -3.68
      -3.65
      -2.85
    
    
      drawdown_recovery
      -4.45
      -3.56
      -3.04
      -0.31
      -2.89
      -1.75
      -1.10
      -0.40
      -1.10
      -0.67
      -0.23
      -0.17
      -0.11
      -0.11
      -0.11
      -2.73
    
    
      best_month
      15.03
      15.02
      15.44
      15.43
      15.45
      15.03
      17.78
      17.78
      17.78
      17.79
      17.77
      17.78
      17.96
      17.95
      17.98
      17.77
    
    
      worst_month
      -18.86
      -18.88
      -18.94
      -18.91
      -18.93
      -18.91
      -22.09
      -22.07
      -18.94
      -18.94
      -29.10
      -29.07
      -29.08
      -29.06
      -29.06
      -18.93
    
    
      sharpe_ratio
      0.51
      0.56
      0.68
      0.71
      0.60
      0.69
      0.76
      0.69
      0.71
      0.71
      0.64
      0.67
      0.69
      0.66
      0.67
      0.73
    
    
      sortino_ratio
      0.53
      0.60
      0.74
      0.78
      0.66
      0.76
      0.85
      0.77
      0.80
      0.82
      0.70
      0.75
      0.77
      0.75
      0.76
      0.84
    
    
      monthly_std
      3.13
      3.16
      3.10
      3.20
      3.34
      3.34
      3.50
      3.45
      3.39
      3.41
      3.55
      3.61
      3.65
      3.69
      3.72
      3.47
    
    
      pct_time_in_market
      70.79
      71.69
      73.12
      74.71
      76.52
      77.47
      78.52
      78.73
      79.57
      81.28
      80.52
      81.62
      81.71
      82.18
      81.72
      81.40
    
    
      total_num_trades
      76.00
      61.00
      55.00
      37.00
      40.00
      29.00
      30.00
      30.00
      29.00
      20.00
      20.00
      16.00
      12.00
      14.00
      16.00
      13.00
    
    
      pct_profitable_trades
      51.32
      40.98
      50.91
      51.35
      47.50
      48.28
      50.00
      46.67
      41.38
      45.00
      40.00
      62.50
      50.00
      50.00
      56.25
      61.54
    
    
      avg_points
      1.79
      2.10
      3.23
      4.91
      3.93
      6.41
      6.35
      6.67
      6.57
      10.32
      7.88
      10.18
      15.00
      12.13
      10.98
      16.82

Bar graphs



In [7]:

    
strategy.plot_bar_graph(df, 'annual_return_rate')
strategy.plot_bar_graph(df, 'sharpe_ratio')
strategy.plot_bar_graph(df, 'max_closed_out_drawdown')

Run Benchmark



In [8]:

    
s = strategies[periods[0]]
benchmark = pf.Benchmark(symbol, capital, s._start, s._end)
benchmark.run()
benchmark.tlog, benchmark.dbal = benchmark.get_logs()
benchmark.stats = benchmark.get_stats()









    



1994-07-05 00:00:00 BUY  223 SPY @ 44.80
2020-07-15 00:00:00 SELL 223 SPY @ 321.85

Equity curve



In [9]:

    
pf.plot_equity_curve(strategies['9'].dbal, benchmark=benchmark.dbal)



In [ ]:

	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18
annual_return_rate	5.60	6.14	7.83	8.44	7.10	8.49	9.61	8.54	9.05	9.31	8.43	9.03	9.46	9.04	9.12	9.69
max_closed_out_drawdown	-26.94	-26.66	-24.34	-24.57	-29.65	-22.05	-26.17	-30.08	-25.51	-22.90	-32.57	-29.75	-33.27	-33.25	-33.25	-27.59
drawdown_annualized_return	-4.81	-4.34	-3.11	-2.91	-4.17	-2.60	-2.72	-3.52	-2.82	-2.46	-3.86	-3.30	-3.52	-3.68	-3.65	-2.85
drawdown_recovery	-4.45	-3.56	-3.04	-0.31	-2.89	-1.75	-1.10	-0.40	-1.10	-0.67	-0.23	-0.17	-0.11	-0.11	-0.11	-2.73
best_month	15.03	15.02	15.44	15.43	15.45	15.03	17.78	17.78	17.78	17.79	17.77	17.78	17.96	17.95	17.98	17.77
worst_month	-18.86	-18.88	-18.94	-18.91	-18.93	-18.91	-22.09	-22.07	-18.94	-18.94	-29.10	-29.07	-29.08	-29.06	-29.06	-18.93
sharpe_ratio	0.51	0.56	0.68	0.71	0.60	0.69	0.76	0.69	0.71	0.71	0.64	0.67	0.69	0.66	0.67	0.73
sortino_ratio	0.53	0.60	0.74	0.78	0.66	0.76	0.85	0.77	0.80	0.82	0.70	0.75	0.77	0.75	0.76	0.84
monthly_std	3.13	3.16	3.10	3.20	3.34	3.34	3.50	3.45	3.39	3.41	3.55	3.61	3.65	3.69	3.72	3.47
pct_time_in_market	70.79	71.69	73.12	74.71	76.52	77.47	78.52	78.73	79.57	81.28	80.52	81.62	81.71	82.18	81.72	81.40
total_num_trades	76.00	61.00	55.00	37.00	40.00	29.00	30.00	30.00	29.00	20.00	20.00	16.00	12.00	14.00	16.00	13.00
pct_profitable_trades	51.32	40.98	50.91	51.35	47.50	48.28	50.00	46.67	41.38	45.00	40.00	62.50	50.00	50.00	56.25	61.54
avg_points	1.79	2.10	3.23	4.91	3.93	6.41	6.35	6.67	6.57	10.32	7.88	10.18	15.00	12.13	10.98	16.82