def initialize(context):
attach_pipeline(make_pipeline(), 'pipeline')
#Schedule Functions
if not IS_LIVE:
schedule_function(
trade,
#date_rules.every_day(),
#date_rules.week_end(days_offset=1),#0=Fri 1= Thurs
date_rules.month_end(days_offset=3),
time_rules.market_close(minutes=30)
)
schedule_function(record_vars, date_rules.every_day(), time_rules.market_close())
schedule_function(cancel_open_orders, date_rules.week_end(days_offset=2), time_rules.market_close())
context.spy = symbol('SPY') #sid(8554) #SPY
context.TF_filter = False
#context.TF_lookback = 60
#Set number of securities to buy and bonds fund (when we are out of stocks)
context.Target_securities_to_buy = 15 #10 #15 #2 #1 #5 #10 #5
context.bonds = symbol('IEF') #sid(23870) #IEF
context.relative_momentum_lookback = 44 #66 #22 #4 #22 #22 #22 #126 #Momentum lookback
context.momentum_skip_days = 1
context.top_n_relative_momentum_to_buy = 10 #15 #10 #15 #1 #5 #5 #10 #5 #Number to buy
context.stock_weights = pd.Series()
context.bond_weights = pd.Series()
context.auto_close = {} #Initialize portfolio auto_close list.
context.TRACK_ORDERS_ON = False
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):
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())
def initialize(context):
"""
Called once at the start of a backtest, and once per day at
the start of live trading. In live trading, the stored context
will be loaded *after* this function is called.
"""
# Rebalance every day, 1 hour after market open.
algo.schedule_function(
rebalance,
algo.date_rules.month_start(),
algo.time_rules.market_open(hours=1),
)
algo.schedule_function(
bonds,
algo.date_rules.month_start(days_offset=1),
algo.time_rules.market_open(hours=1),
)
algo.set_benchmark(algo.sid("FIBBG000BDTBL9"))
# Create a pipeline to select stocks each day.
algo.attach_pipeline(make_pipeline(), 'pipeline')
# algo.set_min_leverage(0, datetime.timedelta(30))
# algo.set_max_leverage(1.2)
context.trend_filter = False
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):
model = g_models[g_idx]
print("hello world --- :",g_idx,model)
attach_pipeline(make_pipeline(asset_finder=None,algo_mode=model), 'my_pipeline')
schedule_function(rebalance, date_rules.week_start(days_offset=0), half_days=True)
context.posset = {}
context.rbcnt = 0
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):
pipe = Pipeline()
attach_pipeline(pipe, 'example')
# Note that we don't call add_factor on these Factors.
# We don't need to store intermediate values if we're not going to use them
sma_short = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=30)
sma_long = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=100)
sma_val = sma_short/sma_long
# Construct the custom factor
mkt_cap = MarketCap()
# Create and apply a filter representing the top 500 equities by MarketCap
# every day.
mkt_cap_top_500 = mkt_cap.top(500)
remove_penny_stocks = sma_short > 1.0
pipe.add(sma_val, 'sma_val')
pipe.add(mkt_cap, 'mkt_cap')
# Use mkt_cap_top_500 as a mask on rank
pipe.add(sma_val.rank(mask=mkt_cap_top_500), 'sma_rank')
# Use multiple screens to narrow the universe
pipe.set_screen(mkt_cap.top(500) & remove_penny_stocks)
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):
"""
Called once at the start of the algorithm.
"""
c = context
c.etf_universe = StaticAssets(
symbols('XLY', 'XLP', 'XLE', 'XLF', 'XLV', 'XLI', 'XLB', 'XLK', 'XLU'))
c.alphas = pd.DataFrame()
# Rebalance every day, 1 hour after market open.
schedule_function(
rebalance,
date_rules.every_day(),
time_rules.market_open(hours=1),
)
# Record tracking variables at the end of each day.
schedule_function(
record_vars,
date_rules.every_day(),
time_rules.market_close(),
)
# Create our dynamic stock selector.
attach_pipeline(make_pipeline(context), 'pipeline')
attach_pipeline(make_pipeinit(context), 'pipeinit')
c.first_trading_day = True
c.factor_name_list = make_factor().keys()
def initialize(context):
"""
Called once at the start of the algorithm.
"""
set_slippage(slippage.FixedSlippage(spread=0.00))
set_commission(commission.PerShare(cost=0, min_trade_cost=0))
schedule_function(rebalance, TRADE_FREQ,
date_rules.every_day(),
time_rules.market_open(hours=1, minutes=30),
)
schedule_function(record_vars, date_rules.every_day(),
time_rules.market_close())
ml_pipeline = make_ml_pipeline(universe,
n_forward_days=N_FORWARD_DAYS,
window_length=TRAINING_PERIOD)
# Create our dynamic stock selector.
attach_pipeline(ml_pipeline, 'ml_model')
context.past_predictions = {}
context.ic = 0
context.rmse = 0
context.mae = 0
context.returns_spread_bps = 0
def initialize(context):
"""
use our factors to add our pipes and screens.
"""
pipe = Pipeline()
attach_pipeline(pipe, 'ff_example')
mkt_cap = MarketEquity()
pipe.add(mkt_cap, 'market_cap')
book_equity = BookEquity()
# book equity over market equity
bm = book_equity/mkt_cap
pipe.add(bm, 'bm')
# 营运能力
op = OP()
pipe.add(op, 'op')
# 投资因子
inv = INV()
pipe.add(inv, 'inv')
returns = Returns(window_length=2)
pipe.add(returns, 'returns')
dt = get_datetime().normalize()
start_ = dt if dt > START_DATE else START_DATE
context.result = result.loc[start_: , :]
def initialize(context):
context.params = {'lookback':12, 'min_volume':1E7
}
schedule_function(strategy, date_rules.every_day(),
time_rules.market_open(minutes=30))
attach_pipeline(make_strategy_pipeline(context),
name='strategy_pipeline')
def initialize(context):
attach_pipeline(make_pipeline(), 'my_pipeline')
context.stop_loss_list = pd.Series()
context.sector_wise_exposure = dict()
context.sector_stocks = {}
context.turnover_count = 0
if context.live_trading is False:
schedule_function(rebalance, date_rule=date_rules.month_start())
def initialize(context):
__build_deeplearn_strategy(context)
attach_pipeline(make_pipeline(context), 'my_pipeline')
schedule_function(rebalance, date_rules.every_day(),
time_rules.every_minute())
# record my portfolio variables at the end of day
print("initialize over")
pass
def initialize(context):
algo.set_long_only()
algo.attach_pipeline(make_pipeline(), 'pipeline')
algo.schedule_function(
rebalance,
algo.date_rules.every_day(),
algo.time_rules.market_close(minutes=30),
)
def initialize(context):
ws.send(msg_placeholder % "Simulation Start")
context.lookback = 252 # Period to calculate slope and drawdown
context.max_leverage = 1.0 # Leverage
context.profit_take = 1.96 # 95% of bollinger band
context.minimum_return = 0.1 # Enter if and only if annualized slope exceeds this level
context.max_drawdown = 0.10 # Avoid if too much drawdown
context.market_impact = 0.2 # Max order is 10% of market trading volume
context.weights = {} # Slope at time of entry
context.drawdown = {} # Drawdown at time of entry
context.shares = {} # Daily target share
schedule_function(func=stop_loss,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_open(minutes=30))
ws.send(
msg_placeholder %
"Execution of stop loss scheduled at 30 minutes after market open")
schedule_function(func=regression,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_open(minutes=50))
ws.send(
msg_placeholder %
"Execution of regression computation scheduled at 50 minutes after market open"
)
schedule_function(func=trade,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_open(minutes=100))
ws.send(
msg_placeholder %
"Execution of transaction planner scheduled at 100 minutes after market open"
)
for thirty_minute_interval in range(30, 391, 30):
schedule_function(
execute_transactions, date_rules.every_day(),
time_rules.market_open(minutes=thirty_minute_interval
)) # execute every 30 minutes
ws.send(msg_placeholder %
"Execution of transactions scheduled at every 30 minutes")
attach_pipeline(create_high_dollar_volume_pipeline(),
'top_dollar_volume')
ws.send(msg_placeholder %
"High Dollar Volume pipeline filter attached")
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(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):
"""
Function runs once, at the start of the backtest. You must attach_pipeline() here.
:param context: A common namespace to keep variables in
:return:
"""
context.longs_portfolio = {}
context.shorts_portfolio = {}
attach_pipeline(make_pipeline(), 'data_pipe')
def initialize(self, context):
# Schedule our rebalance function to run every day, after a 1 hour, when the market opens.
schedule_function(self.rebalance, date_rules.every_day(),
time_rules.market_open(hours=1, minutes=0))
# max_minute = 6 * 60 + 30
#
# for minute in range(30, max_minute, 60):
# schedule_function(self.rebalance, date_rules.every_day(), time_rules.market_open(minutes=minute))
my_pipe = self.make_pipeline()
attach_pipeline(my_pipe, "my_pipeline")
def initialize(context):
# Create, register and name a pipeline in initialize.
pipe = Pipeline()
attach_pipeline(pipe, 'example')
# Construct a simple moving average factor and add it to the pipeline.
sma_short = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=10)
pipe.add(sma_short, 'sma_short')
# Set a screen on the pipelines to filter out securities.
pipe.set_screen(sma_short > 1.0)
def initialize(context):
"""
Called once at the start of the algorithm.
"""
# Rebalance every day, 1 hour after market open.
schedule_function(
rebalance,
date_rules.every_day(),
time_rules.market_open(hours=1),
)
# Create our dynamic stock selector.
attach_pipeline(make_pipeline(), 'pipeline')
def initialize(context):
'''
A 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': 1E7}
# 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):
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):
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):
__build_factor_basic_strategy(context)
attach_pipeline(make_pipeline(context), 'my_pipeline')
schedule_function(rebalance,
date_rules.week_end(days_offset=0),
half_days=True)
# record my portfolio variables at the end of day
schedule_function(func=recording_statements,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_close(),
half_days=True)
print("initialize over")
pass
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')
# Rebalance every day, 30 minutes before market close.
algo.schedule_function(
rebalance,
algo.date_rules.every_day(),
algo.time_rules.market_close(minutes=30),
)
def initialize(context):
pipeline = Pipeline()
context.vwaps = []
for length in vwaps:
name = vwap_key(length)
factor = VWAP(window_length=length)
context.vwaps.append(factor)
pipeline.add(factor, name=name)
filter_ = (USEquityPricing.close.latest > 300)
pipeline.add(filter_, 'filter')
if set_screen:
pipeline.set_screen(filter_)
attach_pipeline(pipeline, 'test')
def initialize(context):
pipeline = Pipeline()
context.vwaps = []
for length in vwaps:
name = vwap_key(length)
factor = VWAP(window_length=length)
context.vwaps.append(factor)
pipeline.add(factor, name=name)
filter_ = USEquityPricing.close.latest > 300
pipeline.add(filter_, "filter")
if set_screen:
pipeline.set_screen(filter_)
attach_pipeline(pipeline, "test")
def initialize(context):
pipe = Pipeline()
attach_pipeline(pipe, 'my_pipeline')
pb_ratios = PriceBookRatio()
pipe.add(pb_ratios, 'pb_ratio')
# 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 monthly
schedule_function(rebalance, date_rules.month_start())
context.set_commission(commission.PerShare(cost=.0075, min_trade_cost=1.0))
def initialize(context):
pipe = Pipeline()
attach_pipeline(pipe, 'pipeline_tutorial')
_50ma = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=50)
_200ma = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=200)
vwap_10 = VWAP(window_length=10)
my_volatility_from_daily = Volatility_Daily_Annual()
pipe.add(_50ma, '_50ma')
pipe.add(_200ma, '_200ma')
pipe.add(_50ma / _200ma, 'ma_ratio')
pipe.add(vwap_10, 'vwap_10')
pipe.add(my_volatility_from_daily, 'my_volatility_from_daily')
pipe.set_screen(_50ma / _200ma > 1.0)
def initialize(context):
pipeline = attach_pipeline(Pipeline(), "test")
vwap = VWAP(window_length=10)
pipeline.add(vwap, "vwap")
# Nothing should have prices less than 0.
pipeline.set_screen(vwap < 0)
def initialize(context):
pipeline = attach_pipeline(Pipeline(), 'test')
vwap = VWAP(window_length=10)
pipeline.add(vwap, 'vwap')
# Nothing should have prices less than 0.
pipeline.set_screen(vwap < 0)
def initialize(context):
pipe = Pipeline()
attach_pipeline(pipe, "example")
sma_short = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=30)
sma_long = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=100)
# Combined factors to create new factors
sma_val = sma_short / sma_long
# Create and apply a screen to remove penny stocks
remove_penny_stocks = sma_short > 1.0
pipe.set_screen(remove_penny_stocks)
pipe.add(sma_short, "sma_short")
pipe.add(sma_long, "sma_long")
pipe.add(sma_val, "sma_val")
# Rank a factor using a mask to ignore the values we're
# filtering out by passing mask=remove_penny_stocks to rank.
pipe.add(sma_val.rank(mask=remove_penny_stocks), "sma_rank")
def initialize(context):
p = Pipeline('test')
p.add(USEquityPricing.close.latest, 'close')
attach_pipeline(p)
def initialize(context):
p = attach_pipeline(Pipeline(), 'test', chunks=chunks)
p.add(USEquityPricing.close.latest, 'close')
def initialize(context):
p = attach_pipeline(Pipeline(), "test", chunksize=chunksize)
p.add(USEquityPricing.close.latest, "close")
def initialize(context):
attach_pipeline(Pipeline(), "test")
def initialize(context):
attach_pipeline(Pipeline(), "test")
pipeline_output("test")
raise AssertionError("Shouldn't make it past pipeline_output()")
def late_attach(context, data):
attach_pipeline(Pipeline(), "test")
raise AssertionError("Shouldn't make it past attach_pipeline!")
def initialize(context):
pipeline_close = attach_pipeline(Pipeline(), 'test_close')
pipeline_volume = attach_pipeline(Pipeline(), 'test_volume')
pipeline_close.add(USEquityPricing.close.latest, 'close')
pipeline_volume.add(USEquityPricing.volume.latest, 'volume')
def initialize(context):
pipeline = attach_pipeline(Pipeline(), 'my_pipeline')
test_factor = TestFactor()
pipeline.add(test_factor, 'test_factor')
def initialize(context):
attach_pipeline(Pipeline('test'))