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 click Run Full Backtest at the upper right corner of the IDE and grab your own backtestid
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/<algorithm_id>/<backtest_id>
For example:
https://www.quantopian.com/algorithms/5aa0986c36ac88437f4265be/5aa09985b9e6b9458c2f3050
The backtest_id will be 5aa09985b9e6b9458c2f3050
You are only entitled to view the backtests that either:
- you have created
- you are a collaborator on
The following algo will long Amazon and short IBM. Run it in IDE:
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¶
Note: Run the following codes in Notebooks
Use get_backtest() to get the backtest info
bt = get_backtest('5986b969dbab994fa4264696')
Use algo_id to get the backtest_id
bt.algo_id
Use recorded_vars to get back the recorded results
bt.recorded_vars
You can plot the leverage & exposure ratios
bt.recorded_vars['Leverage'].plot()
bt.recorded_vars['Exposure'].plot()
High Leverage Example¶
You can actually specify to borrow on margin (NOT RECOMMENDED) by changing the order_target_percent() from +/- 0.5 to +/- 2.0
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)
bt = get_backtest('5986bd68ceda5554428a005b')
bt.recorded_vars['Leverage'].plot()
Set Hard Limit on Leverage¶
Use set_max_leverage() to set had limit on leverage, note an run-time error will occur if limit is reached
http://www.zipline.io/appendix.html?highlight=leverage#zipline.api.set_max_leverage
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)