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):
# Get continuous futures for Light Sweet Crude Oil...
context.crude_oil = continuous_future('CL', roll='calendar')
# ... and RBOB Gasoline
context.gasoline = continuous_future('RB', roll='calendar')
# If Zipline has trouble pulling the default benchmark, try setting the
# benchmark to something already in your bundle
set_benchmark(context.crude_oil)
# Ignore commissions and slippage for now
set_commission(us_futures=commission.PerTrade(cost=0))
set_slippage(us_futures=slippage.FixedSlippage(spread=0.0))
# Long and short moving average window lengths
context.long_ma = 65
context.short_ma = 5
# True if we currently hold a long position on the spread
context.currently_long_the_spread = False
# True if we currently hold a short position on the spread
context.currently_short_the_spread = False
# Rebalance pairs every day, 30 minutes after market open
schedule_function(func=rebalance_pairs,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_open(minutes=30))
# Record Crude Oil and Gasoline Futures prices everyday
schedule_function(record_price, date_rules.every_day(),
time_rules.market_open())
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 rebalance_scheduler(context):
'''
function to schedule a rebalancing trade.
The rebalancing is done based on the weights determined in the strategy core
for each of the instruments defined in the trading universe.
'''
context.use_handle_data = False
rebalance_freq = context.params.get('rebalance_freq',None)
if rebalance_freq is None:
return
if context.params['verbose']:
print('setting up {} scheduler'.format(rebalance_freq))
if rebalance_freq == 'monthly':
schedule_function(rebalance,
date_rules.month_start(days_offset=0),
time_rules.market_open(hours=5, minutes=30))
elif rebalance_freq == 'weekly':
schedule_function(rebalance,
date_rules.week_start(days_offset=0),
time_rules.market_open(hours=5, minutes=30))
elif rebalance_freq == 'daily':
schedule_function(rebalance,
date_rules.every_day(),
time_rules.market_open(hours=5, minutes=30))
elif rebalance_freq.endswith('m'):
try:
context.trade_freq = int(rebalance_freq.split('m')[0])
print('trade freq {}'.format(context.trade_freq))
context.bar_count = 0
context.use_handle_data = True
except:
raise ValueError('Invalid minute frequency')
elif rebalance_freq.endswith('h'):
try:
context.trade_freq = int(rebalance_freq.split('h')[0])*60
print('trade freq {}'.format(context.trade_freq))
context.bar_count = 0
context.use_handle_data = True
except:
raise ValueError('Invalid hourly frequency')
else:
raise ValueError('Un-recognized rebalancing frequency')
if context.params['no_overnight_position']:
schedule_function(square_off,
date_rules.every_day(),
time_rules.market_close(hours=3, minutes=30))
def initialize(context):
"""
Called once at the start of the algorithm.
"""
feature_num = 11
context.orders_submitted = False
large_num = 9999999
least_num = 0
context.n_components = feature_num
context.security = symbol(SYMBOL) # Trade SPY
set_benchmark(symbol(SYMBOL)) # Set benchmarks
context.model = SVC(kernel='rbf', tol=1e-3, random_state=0, gamma=0.2, C=10.0, verbose=True) # 8.05 for SVM model
context.lookback = 350 # Look back 62 days
context.history_range = 350 # Only consider the past 400 days' history
context.threshold = 4.05
context.longprices = large_num
context.shortprices = least_num
set_long_only()
# Generate a new model every week
schedule_function(create_model, date_rules.week_end(), time_rules.market_close(minutes=10))
"""
# Generate a new model every week
schedule_function(create_model1, date_rules.week_end(), time_rules.market_close(minutes=10))
"""
# Trade at the start of every day
schedule_function(rebalance, date_rules.every_day(), time_rules.market_open(minutes=1))
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):
# 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):
# Ethereum contract
#context.contract = ContractHandler()
# Get blacklist of sectors which returns a list of codes
#context.blacklist = context.contract.getBlacklist()
# for testing purpose without Ethereum exclude Defense, Beer & Alcohol, Tabacco and Coal
context.blacklist = [26.0, 4.0, 5.0, 29.0]
# Only run get_fundamentals when necessary based on the rebalance function
context.initial = True
# Dictionary of stocks and their respective weights
context.stock_weights = {}
# Count of days before rebalancing
context.days = 0
# Number of sectors to go long in
context.sect_numb = 2
# Sector mappings
context.sector_mappings = get_sectors(key='code')
context.ticker_sector_dict = get_sector_code()
# TODO: Update this accordingly (weekly?)
# Rebalance monthly on the first day of the month at market open
schedule_function(rebalance,
date_rule=date_rules.month_start(),
time_rule=time_rules.market_open())
def initialize(context):
"""
Called once at the start of the algorithm.
"""
feature_num = 11
context.orders_submitted = False
large_num = 9999999
least_num = 0
context.n_components = 6
context.security = symbol(SYMBOL) # Trade SPY
set_benchmark(symbol(SYMBOL)) # Set benchmarks
context.model2 = SVC(kernel='rbf', tol=1e-3, random_state=0, gamma=0.2, C=10.0, verbose=True) # 8.05 for SVM model
context.model3 = KNeighborsClassifier(n_neighbors=feature_num, p=3, metric='minkowski') # 7.05 for model
context.model = DecisionTreeClassifier(criterion='entropy', max_depth=feature_num, random_state=0)
context.model4 = RandomForestClassifier(criterion='entropy', n_estimators=feature_num, random_state=1,
n_jobs=2) # 5.2 for randomforest
context.model1 = LogisticRegression(random_state=0, solver='lbfgs', multi_class='multinomial')
context.modellist = {'SVM':context.model2,'KNeighbors':context.model3,'DecisionTree':context.model,'RandomForest':context.model4,'LogisticRegression':context.model1}
context.lookback = 350 # Look back 62 days
context.history_range = 350 # Only consider the past 400 days' history
context.threshold = 4.05
context.longprices = large_num
context.shortprices = least_num
context.time_series = 0
context.init = 0
set_long_only()
# Generate a new model every week
#schedule_function(create_model, date_rules.week_end(), time_rules.market_close(minutes=10))
"""
# Generate a new model every week
schedule_function(create_model1, date_rules.week_end(), time_rules.market_close(minutes=10))
"""
# Trade at the start of every day
schedule_function(rebalance, date_rules.every_day(), time_rules.market_open(minutes=1))
def initialize(self, context):
if self.verbose: print("Starting the robo advisor")
# Populate Portfolios
self.all_portfolios = initialize_portfolio(self.verbose)
# Set Commission model
self.initialize_commission(country=self.country, platform=self.trading_platform)
# Set Slippage model
set_slippage(slippage.FixedSlippage(spread=0.0)) # assume spread of 0
# Schedule Rebalancing check
rebalance_check_freq = date_rules.month_end()
if (self.rebalance_freq == 'daily'): rebalance_check_freq = date_rules.every_day()
elif (self.rebalance_freq == 'weekly'): rebalance_check_freq = date_rules.week_end()
if self.verbose: print('Rebalance checks will be done %s' % self.rebalance_freq)
schedule_function(
func=self.before_trading_starts,
date_rule=rebalance_check_freq,
time_rule=time_rules.market_open(hours=1))
# record daily weights at the end of each day
schedule_function(
func=record_current_weights,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_close()
)
def initialize(context):
"""
Called once at the start of the algorithm.
"""
feature_num = 11
context.orders_submitted = False
large_num = 9999999
least_num = 0
context.n_components = feature_num
context.security = symbol(SYMBOL) # Trade SPY
set_benchmark(symbol(SYMBOL)) # Set benchmarks
context.model = DecisionTreeClassifier(criterion='entropy',
max_depth=feature_num,
random_state=0)
context.lookback = 350 # Look back 62 days
context.history_range = 350 # Only consider the past 400 days' history
context.threshold = 4.05
context.longprices = large_num
context.shortprices = least_num
set_long_only()
# Generate a new model every week
schedule_function(create_model, date_rules.week_end(),
time_rules.market_close(minutes=10))
"""
# Generate a new model every week
schedule_function(create_model1, date_rules.week_end(), time_rules.market_close(minutes=10))
"""
# Trade at the start of every day
schedule_function(rebalance, date_rules.every_day(),
time_rules.market_open(minutes=1))
def initialize(context):
# Schedule our rebalance function to run at the start of
# each week, when the market opens.
schedule_function(func=my_rebalance,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_open())
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):
set_commission(commission.PerDollar(cost=0.0055))
# context.strategy_name = "FOF_{mdd}"
context.mdd = context.namespace["mdd"]
context.fof_code = context.namespace["fof_code"]
context.save2mysql = context.namespace.get("save_method", 0)
context.i = 0
schedule_function(scheduled_rebalance,
date_rules.month_start(days_offset=0),
time_rules.market_open())
context.init_value = pd.Series() # 记录自最近一次调仓以来，组合内各基金所达到的最大净值
context.adjust_date = 0 # 记录距离最近一次季调的时间
context.stop_date = 0 # 记录距离最近一次止损的时间
# 基金池
stock = [
'000478.OF', '050002.OF', '110006.OF', '161120.OF', '000217.OF',
"501018.OF", '000071.OF', '070031.OF', '000342.OF', 'B00.IPE',
"HSCI.HI", '037.CS', "em_bond", "reit", "SPTAUUSDOZ.IDC", '096001.OF',
'SPX.GI', '000905.SH'
]
context.stocks = []
for sym in stock:
context.stocks.append(symbol(sym))
# 指数与基金的对应关系 fixed
index = [
"HSCI.HI", '037.CS', "EM_BOND", "REIT", "SPTAUUSDOZ.IDC", "000905.SH",
"B00.IPE", "NDX.GI", 'SPX.GI'
]
index_etf = ["000071.OF","161120.OF","000342.OF",\
"070031.OF","000217.OF","000478.OF",\
"501018.OF","160213.OF",'096001.OF']
context.df_index = pd.DataFrame(
data=index_etf,
index=index,
)
def initialize(context):
"""
Initialization and trading logic
"""
# Set comission and slippage
if enable_commission:
comm_model = PerDollar(cost=commission_pct)
else:
comm_model = PerDollar(cost=0.0)
set_commission(comm_model)
if enable_slippage:
slippage_model = VolumeShareSlippage(
volume_limit=slippage_volume_limit, price_impact=slippage_impact)
else:
slippage_model = FixedSlippage(spread=0.0)
# Used only for progress output
context.last_month = initial_portfolio
# Store index membership
context.index_members = pd.read_csv('./data/sp500_members.csv',
index_col=0,
parse_dates=[0])
# Schedule rebalance monthly
schedule_function(func=rebalance,
date_rule=date_rules.month_start(),
time_rule=time_rules.market_open())
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):
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):
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):
schedule_function(func=trade,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_open(),
half_days=True)
context.asserts = symbols('SPY', 'SHY', 'TLT', 'GLD')
context.asserts_position = [0.25, 0.25, 0.25, 0.25]
context.rebalance_inteval = 'Q' #'Q', #'D', #'M' #'Q' #'Y'
context.rebalance_date = 0
context.fired = False
def initialize(context):
context.security = symbol(SYMBOL) # Trade
set_benchmark(symbol(SYMBOL)) # Set benchmarks
#print(context.security)
context.start = True
set_long_only()
schedule_function(market_open, date_rules.every_day(),
time_rules.market_open(minutes=1))
context.orders_submitted = False
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):
#set_benchmark(symbol("SPY"))
# Schedule our rebalance function to run at the start of
# each week, when the market opens.
schedule_function(
func=my_rebalance,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_open()
)
# Record variables at the end of each day.
schedule_function(
func=my_record_vars,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_open()
)
# Create our pipeline and attach it to our algorithm.
my_pipe = make_pipeline()
attach_pipeline(pipeline=my_pipe, name='my_pipeline')
def initialize(context):
# Define Symbol
context.security = symbol('MSFT')
# Define standard devation threshold
context.std_dev_threshold = 0.6
schedule_function(enter_position,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_open())
schedule_function(square_off,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_close(minutes=1))
def initialize(context):
set_slippage(VolumeShareSlippage(volume_limit=0.025,price_impact=0.1))
tickers = ['AYI','APA','AMZN','LNT','CTL','ALB','ABBV','AMT','ADM','AON','ORCL']
context.tick_list = tickers
context.tickers = [ symbol(x) for x in tickers ]
context.long= False
context.short= False
start = datetime.datetime(2013,1,3)
end = datetime.datetime(2017,8,1)
sec_list = SecurityList(tickers=tickers)
sec_list.downloadQuandl(start,end)
ts = sec_list.genTimeSeries()
context.adjDiv = sec_list.adjDividends()
context.adjHedge = sec_list.adjSplits()
context.divFactors = sec_list.getAdjFactors()
context.splits = sec_list.getSplits()
context.avg = ts.mean()
context.std = ts.std()
context.leverage = 1
context.share_num = 0
context.diff_thresh = 100
schedule_function(adjust_splits_dividends, date_rules.every_day(), time_rules.market_open())
schedule_function(place_orders, date_rules.every_day(), time_rules.market_open(hours=1, minutes=30))
def initialize(context):
context.i = int(input("Start index?: "))
# 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=.005, min_trade_cost=1.0))
context.set_slippage(slippage.VolumeShareSlippage())
context.schedule_function(func=run,
date_rule=date_rules.month_start(),
time_rule=time_rules.market_open(),
half_days=True,
calendar=None)
def initialize(context):
context.security = sid(8554) # Trade SPY
context.model = RandomForestRegressor()
context.lookback = 3 # Look back 3 days
context.history_range = 400 # Only consider the past 400 days' history
# Generate a new model every week
schedule_function(create_model, date_rules.week_end(),
time_rules.market_close(minutes=10))
# Trade at the start of every day
schedule_function(trade, date_rules.every_day(),
time_rules.market_open(minutes=1))
def initialize(context):
context.asset = symbol('000001.SZ')
# 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(slippage.VolumeShareSlippage())
schedule_function(
my_func,
date_rules.every_day(),
time_rules.market_open(minutes=15)
)
def initialize(context):
# ETFs and target weights for a balanced and hedged portfolio
context.securities = {
'SPY': 0.25,
'TLT': 0.3,
'IEF': 0.3,
'GLD': 0.075,
'DBC': 0.075
}
# Schedule rebalance for once a month
schedule_function(rebalance, date_rules.month_start(),
time_rules.market_open())
# Set up a benchmark to measure against
context.set_benchmark(symbol('SPY'))
def initialize(context):
# slippage model
set_slippage(slippage.FixedSlippage(spread=0))
# stock universe - list of tickers
securities = ['AAPL', 'ADBE', 'AIG', 'AMGN', 'BA']
# change string list of tickers into asset list using symbol function
context.sec_symbols = [symbol(s) for s in securities]
print(len(context.sec_symbols))
# schedule functions
schedule_function(trade_market_open, date_rules.every_day(),
time_rules.market_open())
schedule_function(cancel_open, date_rules.every_day(),
time_rules.market_close())
def initialize(context):
schedule_function(func=trade, date_rule=date_rules.every_day(),
time_rule=time_rules.market_open(),half_days=True)
context.asserts = symbols('SPY','IEF')
context.rebalance_date = 0
context.fired = False
context.rebalance_inteval = 'M'#'Q', #'D', #'M' #'Q' #'Y'
context.asserts_position = [0.5, 0.5]
context.volatility_policy = True
#unused if volatility_policy is false
context.volatility_days = 252
context.volatility_price_history = 66
#set at less than 1 to ensure no leverage
context.leverage_buffer=0.90
