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SMA Percent Band

1. The SPY closes above its upper band, buy
2. If the SPY closes below its lower band, sell your long position.

(optimize band)




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.now()

Define percent bands (from 0 to 10% increment by 0.5)





In [4]:



    


bands = range(0, 100, 5)
bands = [str(band) for band in bands]

Run Strategy





In [5]:



    


strategies = pd.Series(dtype=object)
for band in bands:
    print("{0}".format(band), end=" ")
    strategies[band] = strategy.Strategy(symbol, capital, start, end, sma_period=200, percent_band=int(band)/10)
    strategies[band].run()
    _, strategies[band].tlog, strategies[band].dbal = strategies[band].get_logs()
    strategies[band].stats = strategies[band].get_stats()



    


















    






0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

Summarize results





In [6]:



    


metrics = ('annual_return_rate',
           'ending_balance',
           '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',
           'trades_per_year',
           'avg_points')

df = strategy.summary(strategies, metrics)
df



    


















    
Out[6]:











  
    

      
      0
      5
      10
      15
      20
      25
      30
      35
      40
      45
      50
      55
      60
      65
      70
      75
      80
      85
      90
      95
    

  
  
    

      annual_return_rate
      6.44
      6.37
      6.44
      6.16
      6.17
      6.49
      6.50
      6.45
      6.38
      6.46
      6.38
      5.94
      5.57
      5.42
      4.92
      5.18
      4.94
      5.08
      5.09
      4.85
    

    

      ending_balance
      3061631.13
      2877296.31
      3064011.06
      2404279.91
      2432794.48
      3203066.99
      3231184.92
      3106257.51
      2913748.74
      3125324.64
      2917736.15
      1983737.25
      1439355.71
      1271055.72
      822060.06
      1029797.91
      835955.42
      943765.91
      952106.53
      772450.35
    

    

      max_closed_out_drawdown
      -51.57
      -49.47
      -49.46
      -55.14
      -54.31
      -55.76
      -57.79
      -56.58
      -53.61
      -54.99
      -49.72
      -50.56
      -50.56
      -53.51
      -54.49
      -47.54
      -47.54
      -47.54
      -50.32
      -52.65
    

    

      drawdown_annualized_return
      -8.01
      -7.77
      -7.68
      -8.96
      -8.80
      -8.59
      -8.89
      -8.77
      -8.40
      -8.51
      -7.79
      -8.52
      -9.08
      -9.87
      -11.07
      -9.18
      -9.62
      -9.36
      -9.88
      -10.85
    

    

      drawdown_recovery
      -3.51
      -3.51
      -3.51
      -3.51
      -3.51
      -3.51
      -3.51
      -3.51
      -3.51
      -3.51
      -3.51
      -3.42
      -3.42
      -3.59
      -3.59
      -3.59
      -3.59
      -3.59
      -3.59
      -3.59
    

    

      best_month
      27.01
      27.01
      27.01
      27.02
      27.02
      27.01
      27.00
      27.01
      27.01
      27.02
      27.01
      27.02
      27.02
      22.42
      22.41
      22.41
      22.41
      22.41
      22.42
      22.42
    

    

      worst_month
      -26.88
      -26.88
      -26.89
      -30.46
      -27.07
      -27.06
      -27.06
      -29.72
      -29.72
      -29.72
      -29.72
      -31.45
      -31.46
      -31.45
      -31.47
      -31.46
      -31.47
      -31.45
      -31.46
      -31.45
    

    

      sharpe_ratio
      0.58
      0.57
      0.58
      0.55
      0.55
      0.58
      0.58
      0.57
      0.56
      0.57
      0.56
      0.52
      0.49
      0.48
      0.44
      0.46
      0.44
      0.45
      0.45
      0.43
    

    

      sortino_ratio
      0.59
      0.58
      0.59
      0.56
      0.57
      0.59
      0.59
      0.58
      0.57
      0.58
      0.58
      0.51
      0.49
      0.48
      0.44
      0.46
      0.44
      0.45
      0.45
      0.44
    

    

      monthly_std
      3.33
      3.36
      3.37
      3.39
      3.38
      3.37
      3.36
      3.43
      3.40
      3.43
      3.44
      3.55
      3.56
      3.55
      3.59
      3.53
      3.57
      3.60
      3.61
      3.66
    

    

      pct_time_in_market
      67.11
      66.98
      66.64
      66.62
      66.79
      66.40
      66.75
      67.09
      66.35
      67.35
      68.43
      68.61
      68.97
      69.71
      69.29
      68.63
      69.09
      70.28
      70.50
      72.40
    

    

      total_num_trades
      280.00
      161.00
      120.00
      93.00
      78.00
      60.00
      54.00
      47.00
      45.00
      41.00
      38.00
      36.00
      36.00
      34.00
      34.00
      29.00
      28.00
      26.00
      24.00
      23.00
    

    

      pct_profitable_trades
      25.71
      38.51
      43.33
      44.09
      47.44
      56.67
      61.11
      65.96
      66.67
      68.29
      71.05
      75.00
      72.22
      67.65
      64.71
      72.41
      71.43
      76.92
      79.17
      78.26
    

    

      trades_per_year
      3.05
      1.75
      1.31
      1.01
      0.85
      0.65
      0.59
      0.51
      0.49
      0.45
      0.41
      0.39
      0.39
      0.37
      0.37
      0.32
      0.31
      0.28
      0.26
      0.25
    

    

      avg_points
      7.38
      14.36
      18.73
      26.98
      34.03
      48.78
      56.24
      59.80
      59.15
      65.80
      72.27
      69.24
      65.29
      65.55
      57.99
      63.03
      60.54
      70.79
      66.24
      60.63

Bar graphs





In [7]:



    


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

Run Benchmark





In [8]:



    


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



    


















    






1928-10-16 00:00:00 BUY  460 ^GSPC @ 21.72
2020-07-17 00:00:00 SELL 460 ^GSPC @ 3224.73

Equity curve





In [9]:



    


pf.plot_equity_curve(strategies['35'].dbal, benchmark=benchmark.dbal)

Content source: fja05680/pinkfish

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