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 [11]:

    
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 [12]:

    
# 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 [13]:

    
#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 [14]:

    
period = 7

Define max number of positions to scale into



In [15]:

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

Run Strategy



In [16]:

    
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(merge_trades=True)
    strategies[p].stats = strategies[p].get_stats()









    



1 2 3 4 5 6 7 8 9

Summarize results



In [17]:

    
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[17]:







  
    
      
      1
      2
      3
      4
      5
      6
      7
      8
      9
    
  
  
    
      annual_return_rate
      7.21
      6.70
      5.79
      5.01
      4.33
      3.64
      3.16
      2.77
      2.49
    
    
      max_closed_out_drawdown
      -14.63
      -14.59
      -13.67
      -13.42
      -10.66
      -9.45
      -8.91
      -7.78
      -6.88
    
    
      drawdown_annualized_return
      -2.03
      -2.18
      -2.36
      -2.68
      -2.46
      -2.60
      -2.82
      -2.81
      -2.76
    
    
      drawdown_recovery
      -1.84
      -1.78
      -0.05
      -0.05
      -0.05
      -0.07
      -0.07
      -0.07
      -0.07
    
    
      best_month
      12.88
      10.68
      8.10
      7.42
      6.16
      6.13
      6.16
      5.30
      4.64
    
    
      worst_month
      -13.54
      -13.29
      -12.87
      -12.46
      -9.87
      -8.19
      -7.52
      -6.57
      -5.92
    
    
      sharpe_ratio
      0.75
      0.78
      0.77
      0.75
      0.76
      0.73
      0.72
      0.71
      0.72
    
    
      sortino_ratio
      0.55
      0.55
      0.52
      0.49
      0.49
      0.47
      0.46
      0.45
      0.46
    
    
      monthly_std
      2.10
      1.87
      1.59
      1.35
      1.15
      0.99
      0.87
      0.76
      0.67
    
    
      pct_time_in_market
      29.71
      29.71
      29.71
      29.71
      29.71
      29.71
      29.71
      29.71
      29.71
    
    
      total_num_trades
      265.00
      265.00
      265.00
      265.00
      265.00
      265.00
      265.00
      265.00
      265.00
    
    
      pct_profitable_trades
      78.11
      80.00
      82.64
      85.28
      86.42
      86.79
      86.79
      87.17
      87.17
    
    
      avg_points
      0.89
      1.59
      2.10
      2.48
      2.75
      2.82
      2.87
      2.90
      2.94

Bar graphs



In [18]:

    
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 [19]:

    
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
2020-07-15 00:00:00 SELL 215 SPY @ 321.85

Equity curve



In [20]:

    
pf.plot_equity_curve(strategies['5'].dbal, benchmark=benchmark.dbal)

	1	2	3	4	5	6	7	8	9
annual_return_rate	7.21	6.70	5.79	5.01	4.33	3.64	3.16	2.77	2.49
max_closed_out_drawdown	-14.63	-14.59	-13.67	-13.42	-10.66	-9.45	-8.91	-7.78	-6.88
drawdown_annualized_return	-2.03	-2.18	-2.36	-2.68	-2.46	-2.60	-2.82	-2.81	-2.76
drawdown_recovery	-1.84	-1.78	-0.05	-0.05	-0.05	-0.07	-0.07	-0.07	-0.07
best_month	12.88	10.68	8.10	7.42	6.16	6.13	6.16	5.30	4.64
worst_month	-13.54	-13.29	-12.87	-12.46	-9.87	-8.19	-7.52	-6.57	-5.92
sharpe_ratio	0.75	0.78	0.77	0.75	0.76	0.73	0.72	0.71	0.72
sortino_ratio	0.55	0.55	0.52	0.49	0.49	0.47	0.46	0.45	0.46
monthly_std	2.10	1.87	1.59	1.35	1.15	0.99	0.87	0.76	0.67
pct_time_in_market	29.71	29.71	29.71	29.71	29.71	29.71	29.71	29.71	29.71
total_num_trades	265.00	265.00	265.00	265.00	265.00	265.00	265.00	265.00	265.00
pct_profitable_trades	78.11	80.00	82.64	85.28	86.42	86.79	86.79	87.17	87.17
avg_points	0.89	1.59	2.10	2.48	2.75	2.82	2.87	2.90	2.94