def initialize(context):
''' Initialize global vars'''
context.long_leverage = 0.1
context.short_leverage = -0.9
context.returns_lookback = 16
context.pct_per_stock = 0.5
context.fastperiod = 12
context.slowperiod = 26
context.signalperiod = 9
context.bar_count = 90
set_commission(commission.PerShare(cost=0.0014, min_trade_cost=1))
# Rebalance on the first trading day of each week at 12AM.
schedule_function(rebalance, date_rules.week_start(days_offset=0),time_rules.market_open(hours=0.5))
# Rebalance mid-week
schedule_function(cut_losses, date_rules.week_start(days_offset=2),time_rules.market_open(hours=0.5))
# Record tracking variables at the end of each day.
schedule_function(record, date_rules.every_day(),time_rules.market_open(minutes=1))
# Create and attach our pipeline (dynamic stock selector), defined below.
attach_pipeline(make_pipeline(context),
'mean_reversion_macd_learning')
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
context.profit_target = 1.0
# 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=0.005, 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.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,
'ROC_period_short': 30,
'ROC_period_long': 120,
'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)
# 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):
"""
Called once at the start of a backtest, and once per day at
the start of live trading.
"""
# Attach the pipeline to the algo
algo.attach_pipeline(make_pipeline(), 'pipeline')
# Set SPY as benchmark
algo.set_benchmark(algo.sid("FIBBG000BDTBL9"))
# identify down gaps immediately after the opening
algo.schedule_function(
find_down_gaps,
algo.date_rules.every_day(),
algo.time_rules.market_open(minutes=1),
)
# at 9:40, short stocks that gapped down
algo.schedule_function(
short_down_gaps,
algo.date_rules.every_day(),
algo.time_rules.market_open(minutes=10),
)
# close positions 5 minutes before the close
algo.schedule_function(
close_positions,
algo.date_rules.every_day(),
algo.time_rules.market_close(minutes=5),
)
# Set commissions and slippage
algo.set_commission(commission.PerShare(cost=0.0))
algo.set_slippage(slippage.FixedBasisPointsSlippage(basis_points=3.0))
def initialize(context):
""" Called once at the start of the algorithm. """
set_slippage(slippage.VolumeShareSlippage(volume_limit=0.025, price_impact=0.1))
set_commission(commission.PerShare(cost=0.01, min_trade_cost=1.00))
set_max_leverage(1.0)
# Rebalance every day, 1 hour after market open.
schedule_function(
context.my_rebalance,
date_rules.every_day(),
time_rules.market_open(hours=1)
)
# Close all positions every day, 30 minutes before market close.
schedule_function(
context.close_positions,
date_rules.every_day(),
time_rules.market_close(minutes=30)
)
# Create risk manager
context.risk_manager = RiskManager(context, daily_risk)
# Create our dynamic stock selector.
attach_pipeline(context.make_screener(), 'stock_screener')
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
context.advisor = Advisor('bbands_ea',expert_advisor, context.universe)
# schedule agent weights updates
pass
def initialize(context):
context.set_commission(commission.PerShare(cost=0.0, min_trade_cost=0))
set_benchmark(symbol('SPY'))
context.asset = symbol('AAPL')
context.has_ordered = False
schedule_function(place_order, None, time_rules.market_open())
def initialize(context):
context.start_date = trading_start
context.agent = agent
context.num_trials = 0
context.action = ACTION
context.values = deque(maxlen=21)
set_commission(commission.PerShare(cost=0.005, min_trade_cost=1.00))
set_slippage(slippage.FixedSlippage(0.00))
context.universe = universe_transform('2018-01-01')
schedule_function(rebalance_portfolio,
date_rules.week_start(days_offset=trading_day),
time_rules.market_open(hours=1))
#schedule_function(cancel_open_orders, date_rules.every_day(), time_rules.market_open())
schedule_function(prime_pipeline,
date_rules.week_start(days_offset=trading_day - 1),
time_rules.market_close())
context.Factor_weights = ENV.current_node.weights
context.weights = None
context.run_pipeline = True #We want to run stock selector immediately
context.weeks_since_rebalance = -1
attach_pipeline(make_pipeline(context), 'my_pipeline')
def initialize(context):
context.time = 0
context.asset = symbol('SPY')
context.set_commission(
commission.PerShare(cost=0.001, min_trade_cost=0))
context.strategy_params_1 = ta_params
set_benchmark(symbol("SPY"))
def initialize(self):
"""
Called once at the start of the algorithm.
The initialize function is the place to set your tradable universe and define any parameters
"""
self.securities = tickers
self.sids = [self.symbol(security) for security in self.securities]
# there needs to be enough data points to make a good model
self.data_points = 100
# amount of prior bars to study
self.window_length = 50
# Use a random forest regressor
self.mdl = RandomForestRegressor()
# stores recent prices
self.recent_prices = OrderedDict()
for security in self.securities:
self.recent_prices[security] = []
# initialise the model
self.imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
self.set_commission(commission.PerShare(cost=0.013, min_trade_cost=1.3))
def initialize(context):
# Set benchmark to short-term Treasury note ETF (SHY) since strategy is dollar neutral
set_benchmark(symbol('AAPL'))
# Schedule our rebalance function to run at the end of each day.
# Modified the timing to 5 mins earlier than the market close to reduce the fail to book warnings
# schedule_function(my_rebalance, date_rules.every_day(), time_rules.market_close(minutes=5))
# Try to change it to open and see what happened -- HY
schedule_function(my_rebalance, date_rules.every_day(),
time_rules.market_open(minutes=5))
# Record variables at the end of each day.
# schedule_function(my_record_vars, date_rules.every_day(), time_rules.market_close())
# Get intraday prices today before the close if you are not skipping the most recent data
# schedule_function(get_prices,date_rules.every_day(), time_rules.market_close(minutes=10))
# Try to get the price data when the market is opening -- HY
# schedule_function(get_prices, date_rules.every_day(), time_rules.market_open(minutes=1))
# Set commissions and slippage to 0 to determine pure alpha
set_commission(commission.PerShare(cost=0, min_trade_cost=0))
set_slippage(slippage.FixedSlippage(spread=0))
# Number of quantiles for sorting returns for mean reversion
context.nq = 5
# Number of quantiles for sorting volatility over five-day mean reversion period
context.nq_vol = 3
# Create our pipeline and attach it to our algorithm.
my_pipe = make_pipeline()
attach_pipeline(my_pipe, 'my_pipeline')
def initialize(context):
context.asset = symbol('510050')
# Explicitly set the commission to the "old" value until we can
# rebuild example data.
# github.com/quantopian/zipline/blob/master/tests/resources/
# rebuild_example_data#L105
context.set_commission(commission.PerShare(cost=.0075, min_trade_cost=1.0))
def initialize(context):
context.asset = symbol("AAPL")
# Explicitly set the commission/slippage to the "old" value until we can
# rebuild example data.
# github.com/quantopian/zipline/blob/master/tests/resources/
# rebuild_example_data#L105
context.set_commission(commission.PerShare(cost=0.0075, min_trade_cost=1.0))
context.set_slippage(slippage.VolumeShareSlippage())
def initialize(context):
context.asset = symbol(asset_symbol)
# To keep track of whether we invested in the stock or not
context.invested = False
# Explicitly set the commission/slippage to the "old" value until we can
context.set_commission(commission.PerShare(**commission_cost))
context.set_slippage(slippage.VolumeShareSlippage())
context.params = params_list
def initialize(context):
attach_pipeline(make_pipeline(), 'my_pipeline')
# If Zipline has trouble pulling the default benchmark, try setting the
# benchmark to something already in your bundle
# set_benchmark(symbol("change this to a symbol in your data"))
# Rebalance periodically
schedule_function(rebalance, REBALANCE_INTERVAL)
context.set_commission(commission.PerShare(cost=.005, min_trade_cost=0))
def initialize(context):
attach_pipeline(make_pipeline(), 'my_pipeline')
# Explicitly set the commission/slippage to the "old" value until we can
# rebuild example data.
# github.com/quantopian/zipline/blob/master/tests/resources/
# rebuild_example_data#L105
context.set_commission(commission.PerShare(cost=.0075, min_trade_cost=1.0))
context.set_slippage(VolumeShareSlippage())
schedule_function(rebalance2, date_rules.every_day())
def initialize(context):
"""Setup: register pipeline, schedule rebalancing,
and set trading params"""
attach_pipeline(compute_factors(), 'factor_pipeline')
schedule_function(rebalance,
date_rules.week_start(),
time_rules.market_open(),
calendar=calendars.US_EQUITIES)
set_commission(us_equities=commission.PerShare(cost=0.00075, min_trade_cost=.01))
set_slippage(us_equities=slippage.VolumeShareSlippage(volume_limit=0.0025, price_impact=0.01))
def initialize(self, context):
comm = commission.PerShare(cost=self.cost,
min_trade_cost=self.min_trade_cost)
set_commission(comm)
# schedule train and review functions
schedule_function(self.train_agent, self.train_date, self.train_time)
schedule_function(self.review_performance, self.review_date,
self.review_time)
def initialize(context):
context.asset = symbol('AAPL')
# To keep track of whether we invested in the stock or not
context.invested = False
# Explicitly set the commission to the "old" value until we can
# rebuild example data.
# github.com/quantopian/zipline/blob/master/tests/resources/
# rebuild_example_data#L105
context.set_commission(commission.PerShare(cost=.0075, min_trade_cost=1.0))
def initialize(algo, eps=1, window_length=5):
algo.stocks = STOCKS
algo.tickers = [symbol(ticker) for ticker in algo.stocks]
algo.m = len(algo.stocks)
algo.price = {}
algo.b_t = np.ones(algo.m) / algo.m
algo.eps = eps
algo.window_length = window_length
algo.set_commission(commission.PerShare(cost=0))
algo.set_slippage(slippage.FixedSlippage(spread=0))
def initialize(context, asset):
context.set_commission(commission.PerShare(cost=0, min_trade_cost=0))
context.set_slippage(slippage.FixedSlippage(spread=0.0))
context.start_date = context.get_datetime()
context.asset = context.sid(asset)
context.counter = count()
# buy 1 first 4 days, close out all last day
context.order_amounts = np.repeat(0, len(self.equity_daily_bar_days))
context.order_amounts[0] = 2 # buy 2 lot on first day
context.order_amounts[69] = -1 # sell 1 lot on 69th day
context.order_amounts[-30] = -1 # close out final lot
def initialize(algo, eps=1, window_length=5):
algo.stocks = STOCKS
algo.sids = [algo.symbol(symbol) for symbol in algo.stocks]
algo.m = len(algo.stocks)
algo.price = {}
algo.b_t = np.ones(algo.m) / algo.m
algo.last_desired_port = np.ones(algo.m) / algo.m
algo.eps = eps
algo.init = True
algo.days = 0
algo.window_length = window_length
algo.set_commission(commission.PerShare(cost=0))
def initialize(algo, eps=1, window_length=5):
algo.stocks = STOCKS
algo.m = len(algo.stocks)
algo.price = {}
algo.b_t = np.ones(algo.m) / algo.m
algo.last_desired_port = np.ones(algo.m) / algo.m
algo.eps = eps
algo.init = True
algo.days = 0
algo.window_length = window_length
algo.add_transform('mavg', 5)
algo.set_commission(commission.PerShare(cost=0))
def initialize(context):
attach_pipeline(make_pipeline(), 'my_pipeline')
# Rebalance each day. In daily mode, this is equivalent to putting
# `rebalance` in our handle_data, but in minute mode, it's equivalent to
# running at the start of the day each day.
schedule_function(rebalance, date_rules.every_day())
# Explicitly set the commission to the "old" value until we can
# rebuild example data.
# github.com/quantopian/zipline/blob/master/tests/resources/
# rebuild_example_data#L105
context.set_commission(commission.PerShare(cost=.0075, min_trade_cost=1.0))
def initialize(context, eps=1, window_length=5):
context.stocks = STOCKS
context.sids = [context.symbol(symbol) for symbol in context.stocks]
context.m = len(context.stocks)
context.price = {}
context.b_t = np.ones(context.m) / context.m
context.last_desired_port = np.ones(context.m) / context.m
context.eps = eps
context.init = True
context.days = 0
context.window_length = window_length
context.set_commission(commission.PerShare(cost=0, min_trade_cost=1.0))
def initialize(self):
self.stocks = STOCKS
self.set_commission(commission.PerShare(cost=0))
self.days = 0
self.short_window = 10
self.long_window = 30
self.price_history = deque(maxlen=self.long_window)
#Exponential Moving Average
self.last_ema_test = None
self.short_ema_trans = EMA(timeperiod=self.short_window)
self.long_ema_trans = EMA(timeperiod=self.long_window)
def initialize(self, eps=1, window_length=5):
self.stocks = STOCKS
self.m = len(self.stocks)
self.price = {}
self.b_t = np.ones(self.m) / self.m
self.last_desired_port = np.ones(self.m) / self.m
self.eps = eps
self.init = True
self.days = 0
self.window_length = window_length
self.add_transform(MovingAverage, 'mavg', ['price'],
window_length=window_length)
self.set_commission(commission.PerShare(cost=0))
def initialize(algo, eps=1, window_length=5):
algo.stocks = ['AMD', 'CERN', 'COST', 'DELL', 'GPS', 'INTC', 'MMM']
algo.sids = [algo.symbol(symbol) for symbol in algo.stocks]
algo.m = len(algo.stocks)
algo.price = {}
algo.b_t = np.ones(algo.m) / algo.m
algo.last_desired_port = np.ones(algo.m) / algo.m
algo.eps = eps
algo.init = True
algo.days = 0
algo.window_length = window_length
algo.set_commission(commission.PerShare(cost=0, min_trade_cost=1.0))
algo.set_slippage(slippage.VolumeShareSlippage())
def initialize(context):
# set_benchmark(symbol('SPY'))
model.init_model()
context.i = 0
context.asset1 = symbol('EBAY')
context.asset2 = symbol('KLAC')
context.model_fee= 1e-3
context.previous_predict_reward=0
context.previous_action=0
context.set_commission(commission.PerShare(cost=0.005, min_trade_cost=1.0))
context.set_slippage(slippage.VolumeShareSlippage())
context.sequence_length=300
context.tb_log_dir='./log/backtest'
context.tensorboard=TensorBoard(log_dir=context.tb_log_dir)
def initialize(context):
# 记录股票代码，通过股票代码获取股票对象
context.asset = symbol('AAPL')
# 定义是否买入股票的标记
context.invested = False
# 设置交易的手续费，股票成交时，手续费按成交金额一定比例收取
# 设置手续费率和最低费用
context.set_commission(commission.PerShare(cost=.0075, min_trade_cost=1.0))
# 设置模拟真实交易的滑价，当实际下单交易时，下单订单将影响市场。买单驱使价格上涨，卖单驱使价格下滑;
# 这通常被称为交易的“价格影响”。价格影响的大小取决于订单与当前交易量相比有多大。
context.set_slippage(slippage.VolumeShareSlippage(volume_limit=0.025, price_impact=0.1))
