def initialize(context):
"""
A function to define things to do at the start of the strategy
"""
# universe selection
context.securities = [symbol('NIFTY-I')]
# define strategy parameters
context.params = {
'indicator_lookback': 375,
'indicator_freq': '1m',
'buy_signal_threshold': 0.5,
'sell_signal_threshold': -0.5,
'SMA_period_short': 15,
'SMA_period_long': 60,
'BBands_period': 300,
'trade_freq': 5,
'leverage': 2
}
# variable to control trading frequency
context.bar_count = 0
# variables to track signals and target portfolio
context.signals = dict((security, 0) for security in context.securities)
context.target_position = dict(
(security, 0) for security in context.securities)
context.entry_price = dict(
(security, 0) for security in context.securities)
context.entry_side = dict((security, 0) for security in context.securities)
context.stoploss = 0.01 # percentage stoploss
# set trading cost and slippage to zero
set_commission(commission.PerShare(cost=0.0, min_trade_cost=0.0))
set_slippage(slippage.FixedSlippage(0.00))
def initialize(context):
"""
API function to define things to do at the start of the strategy.
"""
# set strategy parameters
context.lookback_data = 60
context.lookback_long = 20
context.leverage = 2.0
# reset everything at start
daily_reset(context)
# create our universe
create_universe(context)
# schedule calculation at the end of opening range (30 minutes)
schedule_function(calculate_trading_metrics, date_rules.every_day(),
time_rules.market_open(hours=0, minutes=30))
# schedule entry rules
schedule_function(no_more_entry, date_rules.every_day(),
time_rules.market_open(hours=1, minutes=30))
# schedule exit rules
schedule_function(unwind, date_rules.every_day(),
time_rules.market_close(hours=0, minutes=30))
# set trading costs
set_commission(commission.PerShare(cost=0.0, min_trade_cost=0.0))
set_slippage(slippage.FixedSlippage(0.00))
def initialize(context):
'''
A function to define things to do at the start of the strategy
'''
# universe selection
context.universe = [symbol('NIFTY-I'), symbol('BANKNIFTY-I')]
# define strategy parameters
context.params = {
'indicator_lookback': 375,
'indicator_freq': '1m',
'buy_signal_threshold': 0.5,
'sell_signal_threshold': -0.5,
'SMA_period_short': 15,
'SMA_period_long': 60,
'RSI_period': 300,
'BBands_period': 300,
'ADX_period': 120,
'trade_freq': 15,
'leverage': 1
}
# variable to control trading frequency
context.bar_count = 0
# variables to track target portfolio
context.weights = dict((security, 0.0) for security in context.universe)
# set trading cost and slippage to zero
set_commission(commission.PerShare(cost=0.0, min_trade_cost=0.0))
set_slippage(slippage.FixedSlippage(0.00))
# create the list of experts as well as the agent controlling them
expert1 = Advisor('bbands_ea', expert_advisor_1, context.universe)
expert2 = Advisor('maxover_ea', expert_advisor_2, context.universe)
expert3 = Advisor('rsi_ea', expert_advisor_3, context.universe)
expert4 = Advisor('sup_res_ea', expert_advisor_4, context.universe)
context.agent = Agent([expert1, expert2, expert3, expert4])
# schedule agent weights updates
schedule_function(update_agent_weights, date_rules.week_start(),
time_rules.market_close())
