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Leverage

Make sure to watch the video and slides for this lecture for the full explanation!

$ Leverage Ratio = \frac{Debt + Capital Base}{Capital Base}$

Leverage from Algorithm

Make sure to watch the video for this! Basically run this and grab your own backtestid as shown in the video. More info:

The get_backtest function provides programmatic access to the results of backtests run on the Quantopian platform. It takes a single parameter, the ID of a backtest for which results are desired.

You can find the ID of a backtest in the URL of its full results page, which will be of the form:

https://www.quantopian.com/algorithms//.

You are only entitled to view the backtests that either:

  • 1) you have created
  • 2) you are a collaborator on




In [ ]:



    


def initialize(context):
    context.amzn = sid(16841)
    context.ibm = sid(3766)
    
    schedule_function(rebalance, 
                      date_rules.every_day(), 
                      time_rules.market_open())
    schedule_function(record_vars,
                      date_rules.every_day(),
                      time_rules.market_close())
    
def rebalance(context,data):
    order_target_percent(context.amzn, 0.5)
    order_target_percent(context.ibm, -0.5)
    
def record_vars(context,data):
    record(amzn_close=data.current(context.amzn, 'close'))
    record(ibm_close=data.current(context.ibm, 'close'))
    record(Leverage = context.account.leverage)
    record(Exposure = context.account.net_leverage)

Backtest Info





In [9]:



    


bt = get_backtest('5986b969dbab994fa4264696')



    


















    






100% Time: 0:00:00|###########################################################|

















In [10]:



    


bt.algo_id



    


















    
Out[10]:








'5986b969dbab994fa4264696'

















In [11]:



    


bt.recorded_vars



    


















    
Out[11]:










  
    

      
      Exposure
      Leverage
      amzn_close
      ibm_close
    

  
  
    

      2016-07-26 00:00:00+00:00
      -0.002203
      0.992140
      735.430
      161.990
    

    

      2016-07-27 00:00:00+00:00
      -0.000207
      0.997486
      736.090
      161.840
    

    

      2016-07-28 00:00:00+00:00
      0.006717
      0.998964
      752.630
      161.440
    

    

      2016-07-29 00:00:00+00:00
      0.000162
      0.996444
      760.120
      160.770
    

    

      2016-08-01 00:00:00+00:00
      0.002431
      1.003876
      767.755
      161.320
    

    

      2016-08-02 00:00:00+00:00
      0.000207
      0.995943
      760.640
      160.510
    

    

      2016-08-03 00:00:00+00:00
      -0.004409
      1.003540
      753.460
      160.600
    

    

      2016-08-04 00:00:00+00:00
      0.003457
      1.001679
      760.960
      161.410
    

    

      2016-08-05 00:00:00+00:00
      -0.002347
      1.008046
      765.890
      163.450
    

    

      2016-08-08 00:00:00+00:00
      0.003158
      0.995528
      766.360
      162.050
    

    

      2016-08-09 00:00:00+00:00
      0.001780
      0.996853
      767.705
      161.750
    

    

      2016-08-10 00:00:00+00:00
      0.000503
      0.999465
      768.480
      162.020
    

    

      2016-08-11 00:00:00+00:00
      -0.002647
      1.007480
      771.410
      163.465
    

    

      2016-08-12 00:00:00+00:00
      0.006231
      0.991732
      772.520
      161.970
    

    

      2016-08-15 00:00:00+00:00
      0.000830
      0.994101
      768.330
      161.870
    

    

      2016-08-16 00:00:00+00:00
      -0.000607
      0.997131
      764.645
      160.730
    

    

      2016-08-17 00:00:00+00:00
      0.001551
      0.996547
      764.192
      160.370
    

    

      2016-08-18 00:00:00+00:00
      -0.000044
      1.001369
      764.230
      161.400
    

    

      2016-08-19 00:00:00+00:00
      -0.000422
      0.994643
      757.420
      160.070
    

    

      2016-08-22 00:00:00+00:00
      0.000555
      1.001931
      759.075
      159.990
    

    

      2016-08-23 00:00:00+00:00
      0.002023
      0.998017
      762.730
      160.340
    

    

      2016-08-24 00:00:00+00:00
      -0.000596
      0.993433
      756.280
      158.910
    

    

      2016-08-25 00:00:00+00:00
      0.003533
      0.995940
      759.135
      158.540
    

    

      2016-08-26 00:00:00+00:00
      0.009068
      0.993721
      769.440
      158.390
    

    

      2016-08-29 00:00:00+00:00
      -0.000505
      1.005921
      771.540
      159.740
    

    

      2016-08-30 00:00:00+00:00
      -0.000020
      0.996528
      767.130
      159.240
    

    

      2016-08-31 00:00:00+00:00
      0.001833
      0.997897
      769.140
      158.900
    

    

      2016-09-01 00:00:00+00:00
      -0.003963
      1.006987
      770.880
      159.580
    

    

      2016-09-02 00:00:00+00:00
      0.002053
      0.996049
      772.535
      159.565
    

    

      2016-09-06 00:00:00+00:00
      0.009555
      1.001295
      789.310
      160.210
    

    

      ...
      ...
      ...
      ...
      ...
    

    

      2017-06-14 00:00:00+00:00
      -0.002005
      0.993778
      974.826
      153.810
    

    

      2017-06-15 00:00:00+00:00
      0.000823
      0.999913
      964.060
      154.210
    

    

      2017-06-16 00:00:00+00:00
      -0.000724
      1.007212
      988.330
      155.240
    

    

      2017-06-19 00:00:00+00:00
      0.001355
      0.992624
      994.400
      154.600
    

    

      2017-06-20 00:00:00+00:00
      -0.002487
      1.000034
      993.020
      154.880
    

    

      2017-06-21 00:00:00+00:00
      0.008139
      0.990287
      1002.435
      153.680
    

    

      2017-06-22 00:00:00+00:00
      0.001759
      0.996882
      1001.520
      154.420
    

    

      2017-06-23 00:00:00+00:00
      0.000366
      0.998513
      1003.230
      154.230
    

    

      2017-06-26 00:00:00+00:00
      -0.007408
      1.002952
      993.520
      155.225
    

    

      2017-06-27 00:00:00+00:00
      -0.006849
      0.998813
      978.668
      154.890
    

    

      2017-06-28 00:00:00+00:00
      0.005374
      1.000617
      989.950
      155.390
    

    

      2017-06-29 00:00:00+00:00
      0.000542
      0.992852
      975.920
      154.210
    

    

      2017-06-30 00:00:00+00:00
      -0.005486
      0.997816
      968.630
      154.010
    

    

      2017-07-03 00:00:00+00:00
      -0.017962
      1.014632
      952.649
      155.840
    

    

      2017-07-05 00:00:00+00:00
      0.013339
      0.987050
      972.526
      153.770
    

    

      2017-07-06 00:00:00+00:00
      0.004378
      0.993076
      965.470
      152.455
    

    

      2017-07-07 00:00:00+00:00
      0.002492
      1.003599
      978.960
      152.970
    

    

      2017-07-10 00:00:00+00:00
      0.005343
      1.005452
      997.910
      153.510
    

    

      2017-07-11 00:00:00+00:00
      0.000796
      1.000549
      993.990
      153.270
    

    

      2017-07-12 00:00:00+00:00
      0.002393
      1.001917
      1006.500
      153.810
    

    

      2017-07-13 00:00:00+00:00
      -0.001796
      1.002179
      999.400
      153.690
    

    

      2017-07-14 00:00:00+00:00
      -0.001548
      1.002784
      1000.700
      154.270
    

    

      2017-07-17 00:00:00+00:00
      0.005278
      0.996348
      1009.650
      153.020
    

    

      2017-07-18 00:00:00+00:00
      0.002428
      1.012750
      1023.440
      154.035
    

    

      2017-07-19 00:00:00+00:00
      0.009528
      0.981475
      1026.214
      147.290
    

    

      2017-07-20 00:00:00+00:00
      -0.002181
      0.999621
      1029.000
      147.820
    

    

      2017-07-21 00:00:00+00:00
      0.006378
      0.996061
      1024.849
      146.950
    

    

      2017-07-24 00:00:00+00:00
      0.008965
      0.994146
      1039.680
      146.010
    

    

      2017-07-25 00:00:00+00:00
      0.003150
      0.998578
      1039.450
      146.130
    

    

      2017-07-26 00:00:00+00:00
      0.008499
      0.993306
      1052.760
      145.270
    

  



253 rows × 4 columns



















In [13]:



    


bt.recorded_vars['Leverage'].plot()



    


















    
Out[13]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f77c62a6fd0>










    
























In [14]:



    


bt.recorded_vars['Exposure'].plot()



    


















    
Out[14]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f77c63d5310>

High Leverage Example

You can actually specify to borrow on margin (NOT RECOMMENDED)





In [ ]:



    


def initialize(context):
    context.amzn = sid(16841)
    context.ibm = sid(3766)
    
    schedule_function(rebalance,
                      date_rules.every_day(),
                      time_rules.market_open())
    schedule_function(record_vars,
                      date_rules.every_day(),
                      time_rules.market_close())
    
def rebalance(context,data):
    order_target_percent(context.ibm, -2.0)
    order_target_percent(context.amzn, 2.0)
    
def record_vars(context,data):
    record(amzn_close=data.current(context.amzn, 'close'))
    record(ibm_close=data.current(context.ibm, 'close'))
    record(Leverage = context.account.leverage)
    record(Exposure = context.account.net_leverage)



    











In [15]:



    


bt = get_backtest('5986bd68ceda5554428a005b')



    


















    






100% Time: 0:00:00|###########################################################|

















In [16]:



    


bt.recorded_vars['Leverage'].plot()



    


















    
Out[16]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f77c6253810>

Set Hard Limit on Leverage

http://www.zipline.io/appendix.html?highlight=leverage#zipline.api.set_max_leverage





In [ ]:



    


def initialize(context):
    context.amzn = sid(16841)
    context.ibm = sid(3766)
    
    set_max_leverage(1.03)
    
    schedule_function(rebalance,
                      date_rules.every_day(),
                      time_rules.market_open())
    schedule_function(record_vars,
                      date_rules.every_day(),
                      time_rules.market_close())
    
def rebalance(context,data):
    order_target_percent(context.ibm, -0.5)
    order_target_percent(context.amzn, 0.5)
    
def record_vars(context,data):
    record(amzn_close=data.current(context.amzn,'close'))
    record(ibm_close=data.current(context.ibm,'close'))
    record(Leverage = context.account.leverage)
    record(Exposure = context.account.net_leverage)

Content source: arcyfelix/Courses

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