Double 7's (Short Term Trading Strategies that Work)

1. The SPY is above its 200-day moving average
2. The SPY closes at a X-day low, buy some shares.
   If it falls further, buy some more, etc...
3. If the SPY closes at a X-day high, sell your entire long position.

(optimize for number of positions)



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)
#end = datetime.datetime(2010, 12, 1)
end = datetime.datetime.now()

Define high low trade periods



In [4]:

    
period = 7

Define max number of positions to scale into



In [5]:

    
pos = range(1, 10)
pos = [str(p) for p in pos]

Run Strategy



In [6]:

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









    



1 2 3 4 5 6 7 8 9

Summarize results



In [7]:

    
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',
           'pct_profitable_trades')

df = strategy.summary(strategies, metrics)
df









    Out[7]:







  
    
      
      1
      2
      3
      4
      5
      6
      7
      8
      9
    
  
  
    
      annual_return_rate
      7.21
      6.31
      5.49
      5.22
      5.00
      4.47
      3.95
      3.46
      3.11
    
    
      max_closed_out_drawdown
      -14.63
      -18.58
      -21.96
      -23.56
      -18.74
      -15.45
      -13.98
      -13.94
      -13.52
    
    
      drawdown_annualized_return
      -2.03
      -2.95
      -4.00
      -4.52
      -3.75
      -3.46
      -3.54
      -4.02
      -4.34
    
    
      drawdown_recovery
      -1.84
      -0.13
      -0.13
      -0.13
      -0.13
      -0.13
      -0.18
      -0.18
      -0.18
    
    
      best_month
      12.88
      10.33
      10.41
      12.35
      9.26
      7.64
      8.47
      7.98
      8.15
    
    
      worst_month
      -13.54
      -17.67
      -21.10
      -22.93
      -18.21
      -14.99
      -12.66
      -12.13
      -12.24
    
    
      sharpe_ratio
      0.75
      0.69
      0.62
      0.61
      0.65
      0.64
      0.61
      0.58
      0.56
    
    
      sortino_ratio
      0.55
      0.52
      0.46
      0.46
      0.49
      0.49
      0.46
      0.43
      0.41
    
    
      monthly_std
      2.10
      2.02
      1.93
      1.86
      1.70
      1.52
      1.41
      1.29
      1.20
    
    
      pct_time_in_market
      29.71
      35.86
      39.66
      42.44
      43.96
      44.75
      45.24
      45.63
      45.84
    
    
      total_num_trades
      265.00
      424.00
      522.00
      590.00
      636.00
      660.00
      673.00
      680.00
      685.00
    
    
      pct_profitable_trades
      78.11
      76.89
      76.44
      76.78
      77.20
      77.42
      77.27
      77.21
      77.08
    
    
      avg_points
      0.89
      0.92
      0.96
      1.08
      1.27
      1.36
      1.39
      1.41
      1.42
    
    
      pct_profitable_trades
      78.11
      76.89
      76.44
      76.78
      77.20
      77.42
      77.27
      77.21
      77.08

Bar graphs



In [8]:

    
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 [9]:

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









    



1993-11-11 00:00:00 BUY  215 SPY @ 46.38
2020-07-14 00:00:00 SELL 215 SPY @ 316.03

Equity curve



In [10]:

    
pf.plot_equity_curve(strategies['4'].dbal, benchmark=benchmark.dbal)

	1	2	3	4	5	6	7	8	9
annual_return_rate	7.21	6.31	5.49	5.22	5.00	4.47	3.95	3.46	3.11
max_closed_out_drawdown	-14.63	-18.58	-21.96	-23.56	-18.74	-15.45	-13.98	-13.94	-13.52
drawdown_annualized_return	-2.03	-2.95	-4.00	-4.52	-3.75	-3.46	-3.54	-4.02	-4.34
drawdown_recovery	-1.84	-0.13	-0.13	-0.13	-0.13	-0.13	-0.18	-0.18	-0.18
best_month	12.88	10.33	10.41	12.35	9.26	7.64	8.47	7.98	8.15
worst_month	-13.54	-17.67	-21.10	-22.93	-18.21	-14.99	-12.66	-12.13	-12.24
sharpe_ratio	0.75	0.69	0.62	0.61	0.65	0.64	0.61	0.58	0.56
sortino_ratio	0.55	0.52	0.46	0.46	0.49	0.49	0.46	0.43	0.41
monthly_std	2.10	2.02	1.93	1.86	1.70	1.52	1.41	1.29	1.20
pct_time_in_market	29.71	35.86	39.66	42.44	43.96	44.75	45.24	45.63	45.84
total_num_trades	265.00	424.00	522.00	590.00	636.00	660.00	673.00	680.00	685.00
pct_profitable_trades	78.11	76.89	76.44	76.78	77.20	77.42	77.27	77.21	77.08
avg_points	0.89	0.92	0.96	1.08	1.27	1.36	1.39	1.41	1.42
pct_profitable_trades	78.11	76.89	76.44	76.78	77.20	77.42	77.27	77.21	77.08