def initialize(context):
"""
A function to define things to do at the start of the strategy
"""
# universe selection
context.securities = [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':60,
'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)
# 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):
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):
""" this function is called once at the start of the execution. """
context.assets = [symbol('AAPL'), symbol('KO')]
context.take_profit = 0.0005
context.stop_loss = 0.0005
context.traded = False
context.entry_price = {}
context.order_monitors = {}
context.data_monitors = {}
def generate_signals(context, data):
try:
pipeline_results = pipeline_output('strategy_pipeline')
except NoFurtherDataError:
context.weights = {}
return
# return if the filtered universe is too short
n = int(context.params['universe'])
rets = pipeline_results.dropna()
if len(rets) < n:
context.weights = {}
return
# select top n assets as universe by liquidity factor
rets = rets.sort_values('liquidity')
rets = rets.iloc[-n:]
# calculate the 1-week broad market return
spx = data.history(symbol('SPY'),'close',10,'1d').dropna()
mkt_ret = spx[-1]/spx[-5] - 1
# calculate mean reversion weights
rets['weight'] = -(rets['momentum']-mkt_ret)
rets['weight'] = rets['weight']/rets['weight'].abs().sum()
context.weights = rets['weight'].to_dict()
def create_universe(context):
""" add list of stocks to create trading universe. """
stocks = ['MSFT','AAPL','AMZN','FB','GOOG','BRK.B','JPM','JNJ',
'XOM','V','BAC','INTC','PG','CSCO','DIS','HD','VZ',
'CVX','MA']
context.universe = [symbol(stock) for stock in stocks]
def initialize(context):
""" this function is called once at the start of the execution. """
context.universe = [symbol('NTPC')]
context.ordered = {asset: False for asset in context.universe}
context.target = {asset: 0 for asset in context.universe}
context.stop = {asset: 0 for asset in context.universe}
context.data_monitors = {}
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):
"""
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):
""" this function is called once at the start of the execution. """
context.asset = symbol('EUR/USD')
context.take_profit = 0.005
context.stop_loss = 0.005
context.traded = False
context.entry_price = None
context.exit_price = None
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):
"""
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))
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):
"""
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
"""
# 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):
context.universe = [symbol("AAPL"), symbol("MSFT")]
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.t1 = pd.Timestamp.now()
context.asset = symbol('NIFTY-I')
def initialize(context):
context.asset = symbol('MARUTI')
context.order_func = order_target
context.freq = 60
context.bar_count = 0
