def zipline_initialize(context: TradingAlgorithm):
"""Zipline backtest initialization method override.
Initializes context namespace variables used during the portfolio
optimization, and sets zipline configuration options for the simulation.
Arguments:
context {TradingAlgorithm} -- Context variable for the algorithm
"""
# Zipline context namespace variables
context.first_run = True # First run flag
context.synthetics = dict(
) # Dictionary to store synthetic ETF objects
context.port = MinimumVariance() # Initializing portfolio
# Enforcing long trades only
# NOTE: This is commented out because of rounding bugs; i.e. when
# certain positions were reduced to 0, some of the rounding caused
# some positions to go to -1 shares. This is neglegible, and as
# the current optimization enforces no shorts, this
# condition is relaxed here.
# set_long_only()
# Setting the per-trade commission from config
set_commission(PerDollar(cost=config.trade_commission))
# Initializing utilities module
context.util = Utilities()
# Initializing bookkeeping module
context.books = Bookkeeping()
def zipline_handle_data(context: TradingAlgorithm, data: BarData):
"""Zipline `handle_data` method override. Handles all trading
operations for the algorithm. Additionally, this method also handles
synthetic ETF initialization, portfolio balancing, and logging.
In summary, this method initializes all synthetic ETFs on the first
iteration, balances, and opens positions in the necessary assets. After
that, it checks the current date of the simulation against the triggers
for portfolio balancing and ETF restructuring provided in the
configuration file to initialize a rebalancing or restructuring
operation, respectively.
Arguments:
context {TradingAlgorithm} -- Zipline context namespace variable.
data {BarData} -- Instance zipline data bundle.
"""
# Reset log (to handle Zipline's forward-propagation behavior of the
# `record(...)` function)
context.books.cleanLog()
# First run operations
if (context.first_run):
# Validate sector universe
config.sector_universe = Backtest.validateSectorUniverse(
candidate_sector_universe=config.sector_universe,
zipline_data=data)
# Building synthetic sector ETFs
Backtest.buildSyntheticETFs(context=context, zipline_data=data)
# Computing initial portfolio, updating positions
Backtest.rebalancePortfolio(context=context,
zipline_data=data,
update_positions=True,
log_commission=False)
# Updating initial flags for rebalancing/restructuring trigger
context.util.setInitialFlags()
# Logging ETF prices
context.books.etfDataLog(etf_prices=Backtest.getETFPrices(
context, data),
etf_weights=context.port_w)
# Skip rest of logic for first iteration, update iteration flag
context.first_run = False
return
# Portfolio Rebalancing
if context.util.isRebalanceTriggered():
Backtest.rebalancePortfolio(context=context,
zipline_data=data,
update_positions=True,
log_commission=True)
# Synthetic ETF restructuring
if context.util.isRestructureTriggered():
Backtest.restructureETF(context=context,
zipline_data=data,
update_positions=True,
log_commission=True)
# Log ETF prices
context.books.etfDataLog(etf_prices=Backtest.getETFPrices(
context, data),
etf_weights=context.port_w)
