def initialize(context):
context.lookback = 12*21
context.offset = 1*21
context.size = 5
context.weight = 0
context.candidates = []
context.universe = [
symbol('SPY'), # large cap
symbol('QQQ'), # tech
symbol('VUG'), # growth
symbol('QUAL'), # quality
symbol('MTUM'), # momentum
symbol('IWM'), # small cap
symbol('USMV'), # min vol
symbol('HDV'), # dividend
symbol('VEU'), # world equity
symbol('VWO'), # EM equity
symbol('DBC'), # commodities
symbol('USO'), # oil
symbol('GLD'), # gold
symbol('AGG'), # bonds
symbol('TIP'), # inflation
]
attach_pipeline(make_strategy_pipeline(context), name='strategy_pipeline')
schedule_function(rebalance,
date_rules.month_start(days_offset=0),
time_rules.market_close(hours=2, minutes=30))
def initialize(context):
context.params = {'lookback': 12, 'min_volume': 1E7}
schedule_function(strategy, date_rules.month_start(),
time_rules.market_close(minutes=1))
attach_pipeline(make_strategy_pipeline(context), name='strategy_pipeline')
def initialize(context):
print("{}:inside initialize".format(get_datetime()))
schedule_function(rebalance, date_rule=date_rules.month_start(),
time_rule=time_rules.market_open())
context.frequency = 120
context.loop_count = 0
def initialize(context):
"""
A function to define things to do at the start of the strategy
"""
# set the account currency, only valid for backtests
set_account_currency("USD")
# lot-size (mini-lot for most brokers)
context.lot_size = 1000
# universe selection
context.securities = [
symbol('AUD/USD'),
symbol('EUR/CHF'),
symbol('EUR/JPY'),
symbol('EUR/USD'),
symbol('GBP/USD'),
symbol('NZD/USD'),
symbol('USD/CAD'),
symbol('USD/CHF'),
symbol('USD/JPY'),
]
# 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': 60,
'trade_freq': 30,
'leverage': 1,
'pip_cost': 0.00003
}
# variable to control trading frequency
context.bar_count = 0
context.trading_hours = False
# 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)
# set a timeout for trading
schedule_function(stop_trading, date_rules.every_day(),
time_rules.market_close(hours=0, minutes=31))
# call square off to zero out positions 30 minutes before close.
schedule_function(daily_square_off, date_rules.every_day(),
time_rules.market_close(hours=0, minutes=30))
def initialize(context):
"""
function to define things to do at the start of the strategy
"""
# The context variables can be accessed by other methods
context.params = {'lookback': 12, 'percentile': 0.05, 'min_volume': 1E8}
# Call rebalance function on the first trading day of each month
schedule_function(strategy, date_rules.month_start(),
time_rules.market_close(minutes=1))
# Set up the pipe-lines for strategies
attach_pipeline(make_strategy_pipeline(context), name='strategy_pipeline')
def initialize(context):
"""
A function to define things to do at the start of the strategy
"""
# universe selection
context.universe = [
symbol('AMZN'),
symbol('FB'),
symbol('AAPL'),
symbol('GOOGL'),
symbol('NFLX'),
]
# Call rebalance function on the first trading day of each month after 2.5 hours from market open
schedule_function(rebalance, date_rules.month_start(days_offset=0),
time_rules.market_close(hours=2, minutes=30))
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())
def initialize(context):
"""
A function to define things to do at the start of the strategy
"""
# set the account currency, only valid for backtests
set_account_currency("USD")
# universe selection
context.short_dollar_basket = {
symbol('AUD/USD'): 1,
symbol('EUR/USD'): 1,
symbol('GBP/USD'): 1,
symbol('NZD/USD'): 1,
symbol('USD/CAD'): -1,
symbol('USD/CHF'): -1,
symbol('USD/JPY'): -1,
}
# Call rebalance function on the first trading day of each month after 2.5 hours from market open
schedule_function(rebalance, date_rules.month_start(days_offset=0),
time_rules.market_close(hours=2, minutes=30))
def initialize(context):
"""
function to define things to do at the start of the strategy
"""
context.weights = {} # the weights to trade
# strategy parameters
context.params = {'lookback_vol':252,
'lookback_ret':5,
'percentile':0.05,
'min_volume':1E8,
'universe':100,
}
# Call rebalance function on the first trading day of each week
schedule_function(strategy, date_rules.week_start(),
time_rules.market_close(minutes=30))
# Set up the pipeline
attach_pipeline(make_strategy_pipeline(context),
name='strategy_pipeline')
def initialize(context):
"""
A function to define things to do at the start of the strategy
"""
# universe selection
context.long_portfolio = [
symbol('AMZN'),
symbol('AAPL'),
symbol('WMT'),
symbol('MU'),
symbol('BAC'),
symbol('KO'),
symbol('BA'),
symbol('AXP')
]
# Call rebalance function on the first trading day of each month after 2.5 hours from market open
schedule_function(rebalance,
date_rules.month_start(days_offset=0),
time_rules.market_close(hours=2, minutes=30))
def initialize(context):
"""
A function to define things to do at the start of the strategy
"""
# universe selection
context.long_portfolio = [
symbol('DIVISLAB'),
symbol('SUNPHARMA'),
symbol('MARUTI'),
symbol('AMARAJABAT'),
symbol('BPCL'),
symbol('BAJFINANCE'),
symbol('HDFCBANK'),
symbol('ASIANPAINT'),
symbol('TCS')
]
# Call rebalance function on the first trading day of each month after 2.5 hours from market open
schedule_function(rebalance, date_rules.month_start(days_offset=0),
time_rules.market_close(hours=2, minutes=30))
def initialize(context):
context.lookback = 12 * 21
context.offset = 1 * 21
context.min_volume = 1E8
context.max_size = 10
context.min_size = 5
context.weight = 0
context.universe = []
if context.broker.name == 'regT':
context.hedge = symbol('SPY')
elif context.broker.name == 'nse-backtest':
context.hedge = symbol('NIFTY-I')
else:
raise ValueError(f'this broker not supported:{context.broker.name}')
context.hedge_threshold = 10000
schedule_function(strategy, date_rules.month_start(days_offset=0),
time_rules.market_close(hours=2, minutes=30))
attach_pipeline(make_strategy_pipeline(context), name='strategy_pipeline')
schedule_function(hedge, date_rules.every_day(),
time_rules.market_open(hours=0, minutes=30))
schedule_function(hedge, date_rules.every_day(),
time_rules.market_close(hours=0, minutes=30))
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):
"""
function to define things to do at the start of the strategy
"""
context.x = symbol('AMBUJACEM')
context.y = symbol('ACC')
context.leverage = 5
context.signal = 0
# Trade entry and exit when the z_score is +/- entry_z_score and exit_z_score respectively
context.entry_z_score = 2.0
context.exit_z_score = 0.5
# Lookback window
context.lookback = 200
# used for zscore calculation
context.z_window = 100
# Call strategy function on the first trading day of each week at 10 AM
schedule_function(pair_trading_strategy, date_rules.week_start(),
time_rules.market_open(minutes=30))
def initialize(context):
"""
function to define things to do at the start of the strategy
"""
# set the account currency, only valid for backtests
set_account_currency("USD")
# trading pound parity!
# this should work after the European sovereign crisis settled down
# and before the Brexit noise started (2012-2015)
context.x = symbol('GBP/USD')
context.y = symbol('EUR/USD')
context.leverage = 5
context.signal = 0
# Trade entry and exit when the z_score is +/- entry_z_score and exit_z_score respectively
context.entry_z_score = 2.0
context.exit_z_score = 0.5
# Lookback window
context.lookback = 720
# used for zscore calculation
context.z_window = 360
# Call strategy function after the London open every day
schedule_function(pair_trading_strategy, date_rules.every_day(),
time_rules.market_open(hours=9, minutes=30))
# square off towards to NYC close
context.trading_hours = False
# set a timeout for trading
schedule_function(stop_trading, date_rules.every_day(),
time_rules.market_close(hours=0, minutes=31))
# call square off to zero out positions 30 minutes before close.
schedule_function(daily_square_off, date_rules.every_day(),
time_rules.market_close(hours=0, minutes=30))
