def initialize(context):
psw = paired_switching(context)
context.instrument = {'equity':symbol('SPY'), 'treasury':symbol('TLT')}
context.nbSwitch = 0
context.lookback = 3*21 # 4 months period, 21 trading days per month
context.Periodicity = 1 # every x period ; 1 means every period
context.periodCount = 0
context.cagr_period = 0
context.global_fund_managed = 0.9
context.max_priceslippage = (float(0.5)/100)
'''
get_environment(field='platform')
Returns information about the environment in which the backtest or live algorithm is running.
If no parameter is passed, the platform value is returned. Pass * to get all the values returned in a dictionary.
To use this method when running Zipline standalone, import get_environment from the zipline.api library.
Parameters
arena: Returns IB, live (paper trading), or backtest.
data_frequency: Returns minute or daily.
start: Returns the UTC datetime for start of backtest. In IB and live arenas, this is when the live algorithm was deployed.
end: Returns the UTC datetime for end of backtest. In IB and live arenas, this is the trading day's close datetime.
capital_base: Returns the float of the original capital in USD.
platform: Returns the platform running the algorithm: quantopian or zipline.
'''
context.env = get_environment('platform')
if context.env is 'quantopian' or get_environment('arena') == 'live' or get_environment('arena') == 'IB':
set_commission(commission.PerTrade(cost=4.0))
set_slippage(slippage.FixedSlippage(spread=0.00))
schedule_function(psw.ordering_logic,
date_rule=date_rules.month_start(),
time_rule=time_rules.market_open(hours=1, minutes=0))
schedule_function(get_cagr,
date_rule=date_rules.month_start(),
time_rule=time_rules.market_open(hours=5, minutes=0))
elif context.env == 'zipline':
context.set_commission(commission.PerTrade(cost=4.0))
context.set_slippage(slippage.FixedSlippage(spread=0.00))
context.schedule_function(psw.ordering_logic,
date_rule=date_rules.month_start(),
time_rule=time_rules.market_open(hours=1, minutes=0))
context.schedule_function(get_cagr,
date_rule=date_rules.month_start(),
time_rule=time_rules.market_open(hours=5, minutes=0))
context.startDate = datetime(2004, 1, 1, 0, 0, 0, 0, pytz.utc)
context.endDate = datetime(2015, 1, 1, 0, 0, 0, 0, pytz.utc)
return
def before_trading_start(context, data):
"""
Called every day before market open. Gathers today's pipeline
output and initiates real-time data collection (in live trading).
"""
context.candidates = algo.pipeline_output('pipeline')
context.assets_to_short = []
context.target_value_per_position = -50e3
# Start real-time data collection if we are in live trading
if algo.get_environment("arena") == "trade":
# start real-time tick data collection for our candidates...
sids = [asset.real_sid for asset in context.candidates.index]
if sids:
# collect the trade/volume data
collect_market_data("us-stk-realtime",
sids=sids,
until="09:32:00 America/New_York")
# ...and point Zipline to the derived aggregate db
# For Interactive Brokers databases:
algo.set_realtime_db("us-stk-realtime-1min",
fields={
"close": "LastPriceClose",
"open": "LastPriceOpen",
"high": "LastPriceHigh",
"low": "LastPriceLow",
"volume": "LastSizeSum"
})
def __init__(self, context, strategies=[], allocation_model=None, regime=None):
if get_environment('platform') == 'zipline':
self.day_no = 0
self.ID = 'algo'
self.strategies = strategies
self.allocation_model = allocation_model
self.regime = regime
context.strategies = self.strategies
self.weights = [0. for s in self.strategies]
context.strategy_weights = self.weights
self.strategy_IDs = [s.ID for s in self.strategies]
self.active = [s.ID for s in self.strategies] + [p.ID for s in self.strategies for p in s.portfolios]
if self.allocation_model == None:
raise ValueError('\n *** FATAL ERROR : ALGO ALLOCATION MODEL CANNOT BE NONE ***\n')
context.metrics = Metrics(context)
self.all_assets = self._set_all_assets()
print('ALL ASSETS = {}'.format([s.symbol for s in self.all_assets]))
self.allocations = pd.Series(0, index=self.all_assets)
self.previous_allocations = pd.Series(0, index=self.all_assets)
self.data = Data(self.all_assets)
context.algo_data = self.data
set_symbol_lookup_date('2016-01-01')
self._instantiate_rules(context)
context.securities = [] # placeholder securities in portfolio
if get_environment('platform') == 'zipline':
context.count = context.max_lookback
else:
context.count = 0
self.rebalance_count = 1 # default rebalance interval = 1
self.first_time = True
return
def initialize(context):
context.global_fund_managed = 0.9
weight1 = 0.5
weight2 = 1-weight1
start1_portf_allocation = weight1 *context.global_fund_managed
start2_portf_allocation = weight2 *context.global_fund_managed
context.s1 = strat1(context, start1_portf_allocation)
context.s2 = strat2(context, start2_portf_allocation)
context.instrument = context.s1.instrument
context.instrument += context.s2.instrument
context.cagr_period = 0
context.env = get_environment('platform')
if context.env is 'quantopian' or get_environment('arena') == 'live' or get_environment('arena') == 'IB':
set_commission(commission.PerTrade(cost=4.0))
set_slippage(slippage.FixedSlippage(spread=0.00))
schedule_function(rebalance,
date_rule=date_rules.month_start(),
time_rule=time_rules.market_open(hours=1, minutes=0))
schedule_function(get_cagr,
date_rule=date_rules.month_start(),
time_rule=time_rules.market_open(hours=5, minutes=0))
elif context.env == 'zipline':
context.set_commission(commission.PerTrade(cost=4.0))
context.set_slippage(slippage.FixedSlippage(spread=0.00))
context.schedule_function(rebalance,
date_rule=date_rules.month_start(),
time_rule=time_rules.market_open(hours=1, minutes=0))
context.schedule_function(get_cagr,
date_rule=date_rules.month_start(),
time_rule=time_rules.market_open(hours=5, minutes=0))
context.startDate = datetime(2004, 1, 1, 0, 0, 0, 0, pytz.utc)
context.endDate = datetime(2015, 1, 1, 0, 0, 0, 0, pytz.utc)
return
def update_data(self, context, data):
if get_environment('platform') == 'zipline':
# allow data buffer to fill in the ZIPLINE ENVIRONMENT
if self.day_no <= context.max_lookback:
self.day_no += 1
return
context.algo_data = self.data.update(context, data)
# print ('ALGO_DATA = {}'.format(context.algo_data))
return context.algo_data
def initialize(context):
context.stocks = [symbol('SPY'), symbol('TLT')]
context.nbSwitch = 0
context.lookback = 4*21 # 4 months period, 21 trading days per month
context.Periodicity = 1 # every x period ; 1 means every period
context.periodCount = 0
context.cagr_period = 0
context.portf_allocation = 0.9
context.max_priceslippage = (float(0.5)/100)
context.limit_order = False
context.env = get_environment('platform')
if context.env is 'quantopian':
set_commission(commission.PerTrade(cost=4.0))
set_slippage(slippage.FixedSlippage(spread=0.00))
schedule_function(ordering_logic,
date_rule=date_rules.month_start(),
time_rule=time_rules.market_open(hours=1, minutes=0))
schedule_function(get_cagr,
date_rule=date_rules.month_start(),
time_rule=time_rules.market_open(hours=5, minutes=0))
elif context.env is 'zipline':
context.set_commission(commission.PerTrade(cost=4.0))
context.set_slippage(slippage.FixedSlippage(spread=0.00))
context.schedule_function(ordering_logic,
date_rule=date_rules.month_start(),
time_rule=time_rules.market_open(hours=1, minutes=0))
context.schedule_function(get_cagr,
date_rule=date_rules.month_start(),
time_rule=time_rules.market_open(hours=5, minutes=0))
context.startDate = datetime(2008, 1, 1, 0, 0, 0, 0, pytz.utc)
context.endDate = datetime(2014, 1, 1, 0, 0, 0, 0, pytz.utc)
return
def initialize(context):
context.stocks = [symbol('SPY'), symbol('TLT')]
context.nbSwitch = 0
context.lookback = return_window * 21 # 4 months period, 21 trading days per month
context.Periodicity = 1 # every x period ; 1 means every period
context.periodCount = 0
context.cagr_period = 0
context.portf_allocation = 0.9
context.max_priceslippage = (float(0.5) / 100)
context.env = get_environment('platform')
if context.env == 'quantopian':
set_commission(commission.PerTrade(cost=4.0))
set_slippage(slippage.FixedSlippage(spread=0.00))
schedule_function(ordering_logic,
date_rule=date_rules.month_start(),
time_rule=time_rules.market_open(hours=1, minutes=0))
schedule_function(get_cagr,
date_rule=date_rules.month_start(),
time_rule=time_rules.market_open(hours=5, minutes=0))
elif context.env == 'zipline':
context.set_commission(commission.PerTrade(cost=4.0))
context.set_slippage(slippage.FixedSlippage(spread=0.00))
context.schedule_function(ordering_logic,
date_rule=date_rules.month_start(),
time_rule=time_rules.market_open(hours=1,
minutes=0))
context.schedule_function(get_cagr,
date_rule=date_rules.month_start(),
time_rule=time_rules.market_open(hours=5,
minutes=0))
context.startDate = datetime(2003, 1, 1, 0, 0, 0, 0, pytz.utc)
context.endDate = datetime(2014, 1, 1, 0, 0, 0, 0, pytz.utc)
'''
get_environment(field='platform')
Returns information about the environment in which the backtest or live algorithm is running.
If no parameter is passed, the platform value is returned. Pass * to get all the values returned in a dictionary.
To use this method when running Zipline standalone, import get_environment from the zipline.api library.
Parameters
arena: Returns IB, live (paper trading), or backtest.
data_frequency: Returns minute or daily.
start: Returns the UTC datetime for start of backtest. In IB and live arenas, this is when the live algorithm was deployed.
end: Returns the UTC datetime for end of backtest. In IB and live arenas, this is the trading day's close datetime.
capital_base: Returns the float of the original capital in USD.
platform: Returns the platform running the algorithm: quantopian or zipline.
'''
context.limit_order = None
if get_environment('arena') == 'backtest':
if get_environment('data_frequency') == 'daily':
context.limit_order = False
elif get_environment('data_frequency') == 'minute':
context.limit_order = True
else:
context.limit_order = True
####################################
context.yahoo = True
####################################
back_year = '2005'
today_year = str(datetime.today().year)
url_start = 'http://ichart.finance.yahoo.com/table.csv?s='
url_end = '&d=1&e=1&f=' + today_year + '&g=d&a=0&b=29&c=' + back_year + '&ignore=.csv'
context.yinstrument = []
for y_id in context.stocks:
y_name = 'y_' + y_id.symbol
context.yinstrument.append(y_name)
url = url_start + y_id.symbol + url_end
print(url)
fetch_csv(url,
date_column='Date',
date_format='%Y-%m-%d',
symbol=y_name,
usecols=['Adj Close'],
post_func=rename_col)
return