def test_day_after_thanksgiving(self):
early_closes = tradingcalendar.get_early_closes(
tradingcalendar.start,
tradingcalendar.end.replace(year=tradingcalendar.end.year + 1)
)
# November 2012
# Su Mo Tu We Th Fr Sa
# 1 2 3
# 4 5 6 7 8 9 10
# 11 12 13 14 15 16 17
# 18 19 20 21 22 23 24
# 25 26 27 28 29 30
fourth_friday = datetime.datetime(2012, 11, 23, tzinfo=pytz.utc)
self.assertIn(fourth_friday, early_closes)
# November 2013
# Su Mo Tu We Th Fr Sa
# 1 2
# 3 4 5 6 7 8 9
# 10 11 12 13 14 15 16
# 17 18 19 20 21 22 23
# 24 25 26 27 28 29 30
fifth_friday = datetime.datetime(2013, 11, 29, tzinfo=pytz.utc)
self.assertIn(fifth_friday, early_closes)
def test_early_close_independence_day_thursday(self):
"""
Until 2013, the market closed early the Friday after an
Independence Day on Thursday. Since then, the early close is on
Wednesday.
"""
early_closes = tradingcalendar.get_early_closes(
tradingcalendar.start,
tradingcalendar.end.replace(year=tradingcalendar.end.year + 1)
)
# July 2002
# Su Mo Tu We Th Fr Sa
# 1 2 3 4 5 6
# 7 8 9 10 11 12 13
# 14 15 16 17 18 19 20
# 21 22 23 24 25 26 27
# 28 29 30 31
wednesday_before = datetime.datetime(2002, 7, 3, tzinfo=pytz.utc)
friday_after = datetime.datetime(2002, 7, 5, tzinfo=pytz.utc)
self.assertNotIn(wednesday_before, early_closes)
self.assertIn(friday_after, early_closes)
# July 2013
# Su Mo Tu We Th Fr Sa
# 1 2 3 4 5 6
# 7 8 9 10 11 12 13
# 14 15 16 17 18 19 20
# 21 22 23 24 25 26 27
# 28 29 30 31
wednesday_before = datetime.datetime(2013, 7, 3, tzinfo=pytz.utc)
friday_after = datetime.datetime(2013, 7, 5, tzinfo=pytz.utc)
self.assertIn(wednesday_before, early_closes)
self.assertNotIn(friday_after, early_closes)
def initialize(context):
# ポートフォリオの中身
context.vxx = {sid(38054): 1.0}
context.vxz = {sid(38055): 1.0}
context.spy = sid(8554)
fetch_csv(vixUrl, symbol='VIX', skiprows=1, date_column='Date', pre_func=addFieldVIX)
fetch_csv(vxstUrl, symbol='VXST', skiprows=3, date_column='Date', pre_func=addFieldVXST)
fetch_csv(vxvUrl, symbol='VXV', skiprows=2, date_column='Date', pre_func=addFieldVXV)
start_date = context.spy.security_start_date
end_date = context.spy.security_end_date
# コストはとりあえず０
set_slippage(slippage.FixedSlippage(spread=0.00))
set_commission(commission.PerShare(cost=0.0, min_trade_cost=None))
# detect if the market will close early
context.early_closes = tradingcalendar.get_early_closes(start_date, end_date).date
context.slopes = pd.Series()
context.vixprices = pd.Series()
context.vivprices = pd.Series()
schedule_function(func=ordering_logic,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_open(minutes=5))
def test_early_close_independence_day_thursday(self):
"""
Until 2013, the market closed early the Friday after an
Independence Day on Thursday. Since then, the early close is on
Wednesday.
"""
early_closes = tradingcalendar.get_early_closes(
tradingcalendar.start,
tradingcalendar.end.replace(year=tradingcalendar.end.year + 1))
# July 2002
# Su Mo Tu We Th Fr Sa
# 1 2 3 4 5 6
# 7 8 9 10 11 12 13
# 14 15 16 17 18 19 20
# 21 22 23 24 25 26 27
# 28 29 30 31
wednesday_before = datetime.datetime(2002, 7, 3, tzinfo=pytz.utc)
friday_after = datetime.datetime(2002, 7, 5, tzinfo=pytz.utc)
self.assertNotIn(wednesday_before, early_closes)
self.assertIn(friday_after, early_closes)
# July 2013
# Su Mo Tu We Th Fr Sa
# 1 2 3 4 5 6
# 7 8 9 10 11 12 13
# 14 15 16 17 18 19 20
# 21 22 23 24 25 26 27
# 28 29 30 31
wednesday_before = datetime.datetime(2013, 7, 3, tzinfo=pytz.utc)
friday_after = datetime.datetime(2013, 7, 5, tzinfo=pytz.utc)
self.assertIn(wednesday_before, early_closes)
self.assertNotIn(friday_after, early_closes)
def test_day_after_thanksgiving(self):
early_closes = tradingcalendar.get_early_closes(
tradingcalendar.start,
tradingcalendar.end.replace(year=tradingcalendar.end.year + 1)
)
# November 2012
# Su Mo Tu We Th Fr Sa
# 1 2 3
# 4 5 6 7 8 9 10
# 11 12 13 14 15 16 17
# 18 19 20 21 22 23 24
# 25 26 27 28 29 30
fourth_friday = datetime.datetime(2012, 11, 23, tzinfo=pytz.utc)
self.assertIn(fourth_friday, early_closes)
# November 2013
# Su Mo Tu We Th Fr Sa
# 1 2
# 3 4 5 6 7 8 9
# 10 11 12 13 14 15 16
# 17 18 19 20 21 22 23
# 24 25 26 27 28 29 30
fifth_friday = datetime.datetime(2013, 11, 29, tzinfo=pytz.utc)
self.assertIn(fifth_friday, early_closes)
def __init__(self, load=None, bm_symbol="^GSPC", exchange_tz="US/Eastern", max_date=None, extra_dates=None):
self.prev_environment = self
self.bm_symbol = bm_symbol
if not load:
load = load_market_data
self.benchmark_returns, treasury_curves_map = load(self.bm_symbol)
self.treasury_curves = pd.DataFrame(treasury_curves_map).T
if max_date:
self.treasury_curves = self.treasury_curves.ix[:max_date, :]
self.full_trading_day = datetime.timedelta(hours=6, minutes=30)
self.early_close_trading_day = datetime.timedelta(hours=3, minutes=30)
self.exchange_tz = exchange_tz
bi = self.benchmark_returns.index
if max_date:
self.trading_days = bi[bi <= max_date].copy()
else:
self.trading_days = bi.copy()
if len(self.benchmark_returns) and extra_dates:
for extra_date in extra_dates:
extra_date = extra_date.replace(hour=0, minute=0, second=0, microsecond=0)
if extra_date not in self.trading_days:
self.trading_days = self.trading_days + pd.DatetimeIndex([extra_date])
self.first_trading_day = self.trading_days[0]
self.last_trading_day = self.trading_days[-1]
self.early_closes = get_early_closes(self.first_trading_day, self.last_trading_day)
def initialize(context):
context.aapl = sid(24)
# Initializing your start and end dates to check for early closes
start_date = context.aapl.security_start_date
end_date = context.aapl.security_end_date
context.early_closes = get_early_closes(start_date,end_date).date
# Minutes before close that you want to execute your order, so this will execute at 3:55 PM only
context.minutes_early = 5
# Builds a queue for all orders to be executed at the end of the day
context.order_queue = {}
def initialize(context):
context.aapl = sid(24)
# Initializing your start and end dates to check for early closes
start_date = context.aapl.security_start_date
end_date = context.aapl.security_end_date
context.early_closes = get_early_closes(start_date, end_date).date
# Minutes before close that you want to execute your order, so this will execute at 3:55 PM only
context.minutes_early = 5
# Builds a queue for all orders to be executed at the end of the day
context.order_queue = {}
def initialize(context):
# Define the instruments in the portfolio:
context.sidsLongVol = {sid(38054): +1.0}
context.sidsShortVol = {sid(40516): +1.0}
context.sidsShortSPY = {sid(22887): +1.0}
context.sidsLongSPY = {sid(8554): +1.0}
context.spy = symbol('SPY')
context.hedge = symbol('IWM')
context.vxx = symbol('VXX')
context.epsilon = .01
context.ivts=[]
context.ivts_medianfiltered = []
pipe = Pipeline()
attach_pipeline(pipe, 'vix_pipeline')
pipe.add(GetVol(inputs=[cboe_vix.vix_close]), 'vix')
pipe.add(GetVol(inputs=[cboe_vxv.close]), 'vxv')
vxstUrl = 'http://www.cboe.com/publish/scheduledtask/mktdata/datahouse/vxstcurrent.csv'
vx1Url = 'http://www.quandl.com/api/v1/datasets/CHRIS/CBOE_VX1.csv'
vx2Url = 'http://www.quandl.com/api/v1/datasets/CHRIS/CBOE_VX2.csv'
fetch_csv(vxstUrl, symbol='VXST', skiprows=3,date_column='Date', pre_func=addFieldsVXST)
fetch_csv(vx1Url, date_column='Trade Date',date_format='%Y-%m-%d', symbol='v1', post_func=rename_col)
fetch_csv(vx2Url, date_column='Trade Date',date_format='%Y-%m-%d', symbol='v2', post_func=rename_col)
# Calculating the contango ratio of the front and second month VIX Futures settlements
context.threshold = 0.90 #contango ratio threshold
schedule_function(ordering_logic,date_rule=date_rules.every_day(),time_rule=time_rules.market_open(hours=0, minutes=1))
# Rebalance every day, 1 hour after market open.
context.wait_trigger=False
context.vixpipe = None
start_date = context.spy.security_start_date
end_date = context.spy.security_end_date
context.algo_hist={}
context.returns_df = pd.DataFrame()
# Get the dates when the market closes early:
context.early_closes = get_early_closes(start_date, end_date).date
context.slopes = Series()
context.betas = Series()
def __init__(
self,
load=None,
bm_symbol='^GSPC',
exchange_tz="US/Eastern",
max_date=None,
extra_dates=None
):
self.prev_environment = self
self.bm_symbol = bm_symbol
if not load:
load = load_market_data
self.benchmark_returns, treasury_curves_map = \
load(self.bm_symbol)
self.treasury_curves = pd.Series(treasury_curves_map)
if max_date:
self.treasury_curves = self.treasury_curves[:max_date]
self.full_trading_day = datetime.timedelta(hours=6, minutes=30)
self.early_close_trading_day = datetime.timedelta(hours=3, minutes=30)
self.exchange_tz = exchange_tz
bm = None
trading_days_list = []
for bm in self.benchmark_returns:
if max_date and bm.date > max_date:
break
trading_days_list.append(bm.date)
self.trading_days = pd.DatetimeIndex(trading_days_list)
if bm and extra_dates:
for extra_date in extra_dates:
extra_date = extra_date.replace(hour=0, minute=0, second=0,
microsecond=0)
if extra_date not in self.trading_days:
self.trading_days = self.trading_days + \
pd.DatetimeIndex([extra_date])
self.first_trading_day = self.trading_days[0]
self.last_trading_day = self.trading_days[-1]
self.early_closes = get_early_closes(self.first_trading_day,
self.last_trading_day)
def __init__(
self,
load=None,
bm_symbol='^GSPC',
exchange_tz="US/Eastern",
max_date=None,
extra_dates=None
):
self.prev_environment = self
self.bm_symbol = bm_symbol
if not load:
load = load_market_data
self.benchmark_returns, treasury_curves_map = \
load(self.bm_symbol)
self.treasury_curves = pd.DataFrame(treasury_curves_map).T
if max_date:
self.treasury_curves = self.treasury_curves.ix[:max_date, :]
self.full_trading_day = datetime.timedelta(hours=6, minutes=30)
self.early_close_trading_day = datetime.timedelta(hours=3, minutes=30)
self.exchange_tz = exchange_tz
bm = None
trading_days_list = []
for bm in self.benchmark_returns:
if max_date and bm.date > max_date:
break
trading_days_list.append(bm.date)
self.trading_days = pd.DatetimeIndex(trading_days_list)
if bm and extra_dates:
for extra_date in extra_dates:
extra_date = extra_date.replace(hour=0, minute=0, second=0,
microsecond=0)
if extra_date not in self.trading_days:
self.trading_days = self.trading_days + \
pd.DatetimeIndex([extra_date])
self.first_trading_day = self.trading_days[0]
self.last_trading_day = self.trading_days[-1]
self.early_closes = get_early_closes(self.first_trading_day,
self.last_trading_day)
def __init__(
self,
load=None,
bm_symbol='^GSPC',
exchange_tz="US/Eastern",
max_date=None,
env_trading_calendar=tradingcalendar,
full_performance_results=True
):
self.prev_environment = self
self.bm_symbol = bm_symbol
if not load:
load = load_market_data
fetch_treasury = full_performance_results
self.benchmark_returns, treasury_curves_map = \
load(self.bm_symbol, fetch_treasury)
self.treasury_curves = pd.DataFrame(treasury_curves_map).T
if max_date:
tr_c = self.treasury_curves
# Mask the treasury curvers down to the current date.
# In the case of live trading, the last date in the treasury
# curves would be the day before the date considered to be
# 'today'.
self.treasury_curves = tr_c[tr_c.index <= max_date]
self.exchange_tz = exchange_tz
# `tc_td` is short for "trading calendar trading days"
tc_td = env_trading_calendar.trading_days
if max_date:
self.trading_days = tc_td[tc_td <= max_date].copy()
else:
self.trading_days = tc_td.copy()
self.first_trading_day = self.trading_days[0]
self.last_trading_day = self.trading_days[-1]
self.early_closes = get_early_closes(self.first_trading_day,
self.last_trading_day)
self.open_and_closes = env_trading_calendar.open_and_closes.loc[
self.trading_days]
def __init__(
self,
load=None,
bm_symbol='^GSPC',
exchange_tz="US/Eastern",
max_date=None,
extra_dates=None
):
self.prev_environment = self
self.bm_symbol = bm_symbol
if not load:
load = load_market_data
self.benchmark_returns, treasury_curves_map = \
load(self.bm_symbol)
self.treasury_curves = pd.DataFrame(treasury_curves_map).T
if max_date:
self.treasury_curves = self.treasury_curves.ix[:max_date, :]
self.full_trading_day = datetime.timedelta(hours=6, minutes=30)
self.early_close_trading_day = datetime.timedelta(hours=3, minutes=30)
self.exchange_tz = exchange_tz
bi = self.benchmark_returns.index
if max_date:
self.trading_days = bi[bi <= max_date].copy()
else:
self.trading_days = bi.copy()
if len(self.benchmark_returns) and extra_dates:
for extra_date in extra_dates:
extra_date = extra_date.replace(hour=0, minute=0, second=0,
microsecond=0)
if extra_date not in self.trading_days:
self.trading_days = self.trading_days + \
pd.DatetimeIndex([extra_date])
self.first_trading_day = self.trading_days[0]
self.last_trading_day = self.trading_days[-1]
self.early_closes = get_early_closes(self.first_trading_day,
self.last_trading_day)
self.open_and_closes = tradingcalendar.open_and_closes.ix[
self.trading_days]
def __init__(
self,
load=None,
bm_symbol='^GSPC',
exchange_tz="US/Eastern",
max_date=None,
env_trading_calendar=tradingcalendar
):
self.prev_environment = self
self.bm_symbol = bm_symbol
if not load:
load = load_market_data
self.benchmark_returns, treasury_curves_map = \
load(self.bm_symbol)
self.treasury_curves = pd.DataFrame(treasury_curves_map).T
if max_date:
tr_c = self.treasury_curves
# Mask the treasury curvers down to the current date.
# In the case of live trading, the last date in the treasury
# curves would be the day before the date considered to be
# 'today'.
self.treasury_curves = tr_c[tr_c.index <= max_date]
self.exchange_tz = exchange_tz
# `tc_td` is short for "trading calendar trading days"
tc_td = env_trading_calendar.trading_days
if max_date:
self.trading_days = tc_td[tc_td <= max_date].copy()
else:
self.trading_days = tc_td.copy()
self.first_trading_day = self.trading_days[0]
self.last_trading_day = self.trading_days[-1]
self.early_closes = get_early_closes(self.first_trading_day,
self.last_trading_day)
self.open_and_closes = env_trading_calendar.open_and_closes.ix[
self.trading_days]
def initialize(context):
# Define the instruments in the portfolio:
context.sidsLongVol = {
sid(38054): +0.98
#,sid(32268): +1.0
}
context.sidsShortVol = {
sid(40516): +0.98
#,sid(8554): +1.0
}
context.stock = symbol('SPY')
fetch_csv(vixUrl,
symbol='VIX',
skiprows=1,
date_column='Date',
pre_func=addFieldsVIX)
fetch_csv(vxstUrl,
symbol='VXST',
skiprows=3,
date_column='Date',
pre_func=addFieldsVXST)
fetch_csv(vxvUrl,
symbol='VXV',
skiprows=2,
date_column='Date',
pre_func=addFieldsVXV)
# Define the benchmark (used to get early close dates for reference).
context.spy = sid(8554)
start_date = context.spy.security_start_date
end_date = context.spy.security_end_date
# Get the dates when the market closes early:
context.early_closes = get_early_closes(start_date, end_date).date
context.slopes = Series()
schedule_function(ordering_logic,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_open(hours=0, minutes=1))
def initialize(context):
# Define the instruments in the portfolio:
context.USStockMkt = sid(22739)
context.IntlStockMkt = sid(34385)
context.RealEstate = sid(26669)
context.NatResources = sid(32201)
context.Bonds = sid(33652)
# Define the benchmark (used to get early close dates for reference).
context.spy = sid(8554)
start_date = context.spy.security_start_date
end_date = context.spy.security_end_date
# Initialize context variables the define rebalance logic:
context.rebalance_date = None
context.next_rebalance_Date = None
context.rebalance_days = 28
context.rebalance_window_start = 10
context.rebalance_window_stop = 15
# Get the dates when the market closes early:
context.early_closes = get_early_closes(start_date,end_date).date
def initialize(context):
# Define the instruments in the portfolio:
context.USStockMkt = sid(22739)
context.IntlStockMkt = sid(34385)
context.RealEstate = sid(26669)
context.NatResources = sid(32201)
context.Bonds = sid(33652)
# Define the benchmark (used to get early close dates for reference).
context.spy = sid(8554)
start_date = context.spy.security_start_date
end_date = context.spy.security_end_date
# Initialize context variables the define rebalance logic:
context.rebalance_date = None
context.next_rebalance_Date = None
context.rebalance_days = 28
context.rebalance_window_start = 10
context.rebalance_window_stop = 15
# Get the dates when the market closes early:
context.early_closes = get_early_closes(start_date, end_date).date
def initialize(context):
# Define the instruments in the portfolio:
context.sidsLongVol = {sid(38054): +1.0}
context.sidsShortVol = {sid(40516): +1.0}
context.sidsShortSPY = {sid(22887): +1.0}
context.sidsLongSPY = {sid(8554): +1.0}
context.spy = symbol('SPY')
context.hedge = symbol('IWM')
context.vxx = symbol('VXX')
context.epsilon = .01
context.ivts = []
context.ivts_medianfiltered = []
pipe = Pipeline()
attach_pipeline(pipe, 'vix_pipeline')
v = Get_VIX()
pipe.add(v, 'vix')
vixUrl = 'http://www.cboe.com/publish/scheduledtask/mktdata/datahouse/vixcurrent.csv'
vxstUrl = 'http://www.cboe.com/publish/scheduledtask/mktdata/datahouse/vxstcurrent.csv'
vxvUrl = 'http://www.cboe.com/publish/scheduledtask/mktdata/datahouse/vxvdailyprices.csv'
vx1Url = 'http://www.quandl.com/api/v1/datasets/CHRIS/CBOE_VX1.csv'
vx2Url = 'http://www.quandl.com/api/v1/datasets/CHRIS/CBOE_VX2.csv'
fetch_csv(vixUrl,
symbol='VIX',
skiprows=1,
date_column='Date',
pre_func=addFieldsVIX)
fetch_csv(vxstUrl,
symbol='VXST',
skiprows=3,
date_column='Date',
pre_func=addFieldsVXST)
fetch_csv(vxvUrl,
symbol='VXV',
skiprows=2,
date_column='Date',
pre_func=addFieldsVXV)
fetch_csv(vx1Url,
date_column='Trade Date',
date_format='%Y-%m-%d',
symbol='v1',
post_func=rename_col)
fetch_csv(vx2Url,
date_column='Trade Date',
date_format='%Y-%m-%d',
symbol='v2',
post_func=rename_col)
# Calculating the contango ratio of the front and second month VIX Futures settlements
context.threshold = 0.90 #contango ratio threshold
context.vix_h = []
schedule_function(ordering_logic,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_open(hours=0, minutes=1))
# Rebalance every day, 1 hour after market open.
context.wait_trigger = False
context.vixpipe = None
start_date = context.spy.security_start_date
end_date = context.spy.security_end_date
context.algo_hist = {}
context.returns_df = pd.DataFrame()
# Get the dates when the market closes early:
context.early_closes = get_early_closes(start_date, end_date).date
context.slopes = Series()
context.betas = Series()
def initialize(context):
"""
Called once at the start of the algorithm.
"""
# Create list of "triplets"
# Each triplet group must be it's own list with the security to be traded as the first security of the list
context.security_group = list()
context.security_group.append([sid(23921), sid(16581), sid(25752)])
# Create list of only the securities that are traded
context.trade_securities = list()
context.intraday_bars = dict()
context.last = dict()
context.stocks = list()
for group in context.security_group:
# Verify the group has exactly 3 listed securities
if len(group) != 3: raise ValueError('The security group to trade {} must have 3 securities.'.format(group[0].symbol))
# Add first security only to the traded list (if not already added)
if group[0] not in context.trade_securities: context.trade_securities.append(group[0])
# Loop through all stocks in triplet group
for stock in group:
# Add stock to context.stocks (if not already added) and create dictionary objects to hold intraday bars/last prices
if stock not in context.stocks:
context.stocks.append(stock)
context.intraday_bars[stock] = [[],[],[],[]] # empty list of lists (for ohlc prices)
context.last[stock] = None # set last price to None
# Define time zone for algorithm
context.time_zone = 'US/Eastern' # algorithm time zone
# Define the benchmark (used to get early close dates for reference).
context.spy = sid(8554) # SPY
start_date = context.spy.security_start_date
end_date = context.spy.security_end_date
# Get the dates when the market closes early:
context.early_closes = get_early_closes(start_date,end_date).date
# Create variables required for each triplet
context.PT = dict()
context.SL = dict()
context.Highest = dict()
context.Lowest = dict()
context.bars_since_entry = dict()
context.prof_x = dict()
context.entry_price = dict()
context.o1 = dict()
context.o2 = dict()
context.o3 = dict()
context.o4 = dict()
context.o5 = dict()
context.o6 = dict()
context.ord1 = dict()
context.ord2 = dict()
context.max_daily_entries = dict()
context.daily_entries = dict()
context.max_pnl = dict()
context.min_pnl = dict()
context.closed_pnl = dict()
context.trading_periods = dict()
context.status = dict()
context.positions_closed = dict()
context.exit_at_close = dict()
context.time_to_close = dict()
context.reset_filter = dict()
context.reset_minutes = dict()
context.long_position = dict()
context.execution_market_close = dict()
context.execution_market_open = dict()
context.long_on = dict()
context.PT_ON = dict()
context.SL_ON = dict()
context.HH = dict()
context.LL = dict()
context.profitable_closes = dict()
context.max_time = dict()
context.position_amount = dict()
i = 0
for group in context.security_group:
context.Highest[i] = 100000.0
context.Lowest[i] = 0.0
context.bars_since_entry[i] = 0
context.prof_x[i] = 0
context.entry_price[i] = None
context.o1[i] = 0
context.o2[i] = 0
context.o3[i] = 0
context.o4[i] = 0
context.o5[i] = 0
context.o6[i] = 0
context.ord1[i] = None
context.ord2[i] = None
context.daily_entries[i] = 0
context.closed_pnl[i] = 0 # keep track of individual system profits
context.status[i] = 'NOT TRADING' # status of the algo: 'NOT TRADING' or 'TRADING'
context.positions_closed[i] = False # keep track of when pnl exits are triggered
if i == 0:
context.execution_market_close[i] = False
context.execution_market_open[i] = True
context.long_on[i] = 1 # 1 = long, 0 = short
context.PT_ON[i] = 0
context.SL_ON[i] = 1
context.HH[i] = 0
context.LL[i] = 0
context.profitable_closes[i] = 1000001
context.max_time[i] = 2
context.position_amount[i] = 40
context.max_daily_entries[i] = 999999
context.max_pnl[i] = 9999999
context.min_pnl[i] = -9999999
context.trading_periods[i] = ['00:00-23:59'] # format: 'HH:MM-HH:MM'
context.reset_filter[i] = False # Turns on/off resetting the filter variables
context.reset_minutes[i] = 1 # number of minutes after market open to call reset_trade_filters()
context.exit_at_close[i] = False # Turns on/off auto exit x minutes before market close
context.time_to_close[i] = 1 # minutes prior to market close to exit all positions
else:
raise ValueError('No input variables were defined for triplet, index # {}'.format(i))
if context.long_on[i]: # long only strategy
context.PT[i] = 100000.0
context.SL[i] = 0.0
else: # short only strategy
context.PT[i] = 0.0
context.SL[i] = 100000.0
# Update index i
i += 1
# Rebalance intraday.
context.minute_counter = 0
context.intraday_bar_length = 5 # number of minutes for the end of day function to run
# Run my_schedule_task and update bars based on configured inputs above
for i in range(1, 390):
if i % context.intraday_bar_length == 0: # bar close
schedule_function(get_intraday_bar, date_rules.every_day(), time_rules.market_open(minutes=i)) # update bars
if True in context.execution_market_close.values(): # check for True in dictionary
schedule_function(my_schedule_task, date_rules.every_day(), time_rules.market_open(minutes=i))
if i % context.intraday_bar_length == 1: # bar open
if True in context.execution_market_open.values(): # check for True in dictionary
schedule_function(my_schedule_task, date_rules.every_day(), time_rules.market_open(minutes=i))