def initialize(context):
context.MaxCandidates = 100
context.MaxBuyOrdersAtOnce = 30
context.MyLeastPrice = 3.00
context.MyMostPrice = 25.00
context.MyFireSalePrice = context.MyLeastPrice
context.MyFireSaleAge = 6
print(len(context.portfolio.positions))
# Rebalance
EveryThisManyMinutes = 10
TradingDayHours = 6.5
TradingDayMinutes = int(TradingDayHours * 60)
for minutez in range(1, TradingDayMinutes, EveryThisManyMinutes):
schedule_function(my_rebalance, date_rules.every_day(),
time_rules.market_open(minutes=minutez))
# Prevent excessive logging of canceled orders at market close.
schedule_function(cancel_open_orders, date_rules.every_day(),
time_rules.market_close(hours=0, minutes=1))
# Record variables at the end of each day.
schedule_function(my_record_vars, date_rules.every_day(),
time_rules.market_close())
# Create our pipeline and attach it to our algorithm.
my_pipe = make_pipeline(context)
attach_pipeline(my_pipe, 'my_pipeline')
def initialize(context):
context.MaxCandidates = 100
context.MaxBuyOrdersAtOnce = 50
context.MyLeastPrice = 3.00
context.MyMostPrice = 25.00
context.MyFireSalePrice = context.MyLeastPrice
context.MyFireSaleAge = 6
context.buy_factor = .99
context.sell_factor = 1.01
context.MaxInvestment = 150000
# over simplistic tracking of position age
if not hasattr(context, 'age') or not context.age:
context.age = {}
# Rebalance
minutes = 10
trading_hours = 6.5
trading_minutes = int(trading_hours * 60)
for minutez in range(1, trading_minutes, minutes):
schedule_function(my_rebalance, date_rules.every_day(),
time_rules.market_open(minutes=minutez))
# Prevent excessive logging of canceled orders at market close.
schedule_function(cancel_open_orders, date_rules.every_day(),
time_rules.market_close(hours=0, minutes=1))
# Record variables at the end of each day.
schedule_function(my_record_vars, date_rules.every_day(),
time_rules.market_close())
# Create our pipeline and attach it to our algorithm.
my_pipe = make_pipeline(context)
attach_pipeline(my_pipe, 'my_pipeline')
def initialize(context):
context.MaxBuyOrdersAtOnce = 50
context.MyFireSaleAge = 6
context.MaxInvestment = 150000
# over simplistic tracking of position age
if not hasattr(context, 'age') or not context.age:
context.age = {}
# Rebalance
minutes = 10
trading_hours = 6.5
trading_minutes = int(trading_hours * 60)
for minutez in range(1, trading_minutes, minutes):
schedule_function(
rebalance,
date_rules.every_day(),
time_rules.market_open(
minutes=minutez))
# Prevent excessive logging of canceled orders at market close.
schedule_function(
cancel_open_orders,
date_rules.every_day(),
time_rules.market_close(
hours=0,
minutes=1))
# Record variables at the end of each day.
schedule_function(
my_record_vars,
date_rules.every_day(),
time_rules.market_close())
def initialize (context): # runs once when script starts
#context is a python dictionary that contains information on portfolio/performance.
context.idr_losers = pd.Series(([]))
context.day_count = 0
context.daily_message = "Day {}."
context.open_orders = get_open_orders()
context.backup_stocks = symbols('VTI')
#Factor criteria
#dolvol = AverageDollarVolume(window_length = 1)
close_price = USEquityPricing.close.latest
vol = USEquityPricing.volume.latest
ann_var = AnnualizedVolatility()
#daily_return = DailyReturns([USEquityPricing.close], window_length = 2) #-- going to use history instead of pipeline
#screening
mask_custom = (IsPrimaryShare() & (vol < 200000) & (close_price > 1) & (close_price < 3) & (ann_var > 0.815)) # Q3000US(8000000) &
stockBasket = USEquityPricing.close.latest.top(3000, mask = mask_custom)
#Column construction
pipe_columns = {'close_price': close_price, "volume": vol, 'ann_var': ann_var}
#Ceation of actual pipeline
pipe = Pipeline(columns = pipe_columns, screen = stockBasket)
attach_pipeline(pipe, "Stocks")
#Schedule functions
schedule_function(late_day_trade, date_rules.every_day(), time_rules.market_open(hours = 5, minutes = 56)) #offset open tells when to run a user defined function
schedule_function(check_portfolio, date_rules.every_day(), time_rules.market_open(hours = 0, minutes = 1))
schedule_function(morning_day_trade1, date_rules.every_day(), time_rules.market_open(hours = 0, minutes = 15))
#schedule_function(check_portfolio, date_rules.every_day(), time_rules.market_open(hours = 0, minutes = 16))
schedule_function(morning_day_trade2, date_rules.every_day(), time_rules.market_open(hours = 0, minutes = 45))
def initialize(context):
context.MaxCandidates = 40
context.MaxBuyOrdersAtOnce = 30
context.MyLeastPrice = 3.00
context.MyMostPrice = 25.00
context.MyFireSalePrice = context.MyLeastPrice
context.MyFireSaleAge = 6
# over simplistic tracking of position age
context.age = {}
print(len(context.portfolio.positions))
# Rebalance
EveryThisManyMinutes = 1
TradingDayHours = 6.5
TradingDayMinutes = int(TradingDayHours * 60)
for minutez in range(1, TradingDayMinutes, EveryThisManyMinutes):
schedule_function(my_rebalance, date_rules.every_day(),
time_rules.market_open(minutes=minutez))
# Prevent excessive logging of canceled orders at market close.
schedule_function(cancel_open_orders, date_rules.every_day(),
time_rules.market_close(hours=0, minutes=1))
# Record variables at the end of each day.
schedule_function(my_record_vars, date_rules.every_day(),
time_rules.market_close())
def initialize(context):
EveryThisManyMinutes = 1
TradingDayHours = 6.5
TradingDayMinutes = int(TradingDayHours * 60)
for minutez in range(1, TradingDayMinutes, EveryThisManyMinutes):
schedule_function(grab_data, date_rules.every_day(),
time_rules.market_open(minutes=minutez))
schedule_function(handle_trade, date_rules.every_day(),
time_rules.market_open(minutes=minutez))
def initialize(context):
log.info('Initializing Algorithm')
# Adjustable variables
context.avoid_trades = []
context.trade_restrictions = [
'ANDV', 'DDAIF', 'DHR', 'HL', 'FTV', 'LPX', 'DDAIF', 'NNHE', 'NVST',
'PNM', 'SKY', 'XSAU'
]
rebalance_hours = 0.0
rebalance_minutes = 8.0
context.long_exposure = 0.90
context.short_exposure = -0.90
context.flip_signal = False
context.num_longs = 15
context.num_shorts = 15
context.spyleverage = 1.25
context.std_cutoff = 0.17
# Fixed variables
rebalance_start_time = rebalance_hours * 60 + rebalance_minutes
context.combined_restrictions = context.avoid_trades + context.trade_restrictions
context.rebalance_complete = False
context.trade_queue = {}
context.rolling_portfolios = []
context.idays = 3
context.ndays = 0
context.clear_queue_run = 0
context.long_weight = context.long_exposure / context.num_longs
context.short_weight = context.short_exposure / context.num_shorts
context.SPY = symbol('VOO')
attach_pipeline(make_pipeline(), 'pipeline')
# Scheduling functions
schedule_function(rebalance, date_rules.every_day(),
time_rules.market_open(minutes=rebalance_start_time))
schedule_function(eod_operations, date_rules.every_day(),
time_rules.market_open(hours=6, minutes=29))
clear_queue_frequency = 1
clear_queue_duration = 60
clear_queue_start = int(rebalance_start_time) + 4
for minutez in range(clear_queue_start,
clear_queue_start + clear_queue_duration,
clear_queue_frequency):
schedule_function(clear_queue, date_rules.every_day(),
time_rules.market_open(minutes=minutez))
check_order_frequency = 10
for minutez in range(10, 390, check_order_frequency):
schedule_function(check_order_status, date_rules.every_day(),
time_rules.market_open(minutes=minutez))
def initialize(context):
log.debug("Initializing...")
set_long_only()
schedule_function(sell, date_rules.every_day(), time_rules.market_open(hours=0, minutes=5), half_days=False)
schedule_function(buy, date_rules.every_day(), time_rules.market_open(hours=0, minutes=59), half_days=False)
schedule_function(cancel_open_orders, date_rules.every_day(), time_rules.market_close(minutes=1), half_days=False)
schedule_function(my_record_vars, date_rules.every_day(), time_rules.market_close(), half_days=False)
attach_pipeline(make_pipeline(), 'my_pipeline')
context.tracker = dict()
context.tmp_tracker = dict()
context.first_time = True
log.debug("Done initializing.")
def initialize(context):
context.ALLOW_SHORT = False
# False => 754% on $1000 12/31/2016 => 10/31/2018
schedule_function(my_record_vars, date_rules.every_day(), time_rules.market_close())
my_pipe = make_pipeline()
attach_pipeline(my_pipe, 'my_pipeline')
def initialize(context):
log.info("initializing")
context.currently_holding = set()
schedule_function(clear, date_rules.month_start(),
time_rules.market_open())
schedule_function(daily_rebalance, date_rules.every_day(),
time_rules.market_open(hours=1))
schedule_function(rebalance, date_rules.month_start(),
time_rules.market_close(hours=1))
set_benchmark(symbol('SPY'))
context.days_cnt = 0
def initialize(context):
#context.leverage = 1.0
#schedule_function(rebalance, date_rules.every_day(), time_rules.every_minute())
EveryThisManyMinutes = 1
TradingDayHours = 6.5
TradingDayMinutes = int(TradingDayHours * 60)
for minutez in range(1, TradingDayMinutes, EveryThisManyMinutes):
schedule_function(rebalance, date_rules.every_day(),
time_rules.market_open(minutes=minutez))
def initialize(context):
"""Sets up the context"""
context.stocks = {
symbol("TMF"): 0.2,
symbol("UJB"): 0.2,
symbol("TQQQ"): 0.6
}
context.target_leverage = 1
schedule_function(rebalance, date_rules.every_day(),
time_rules.market_open(minutes=11))
def initialize(context):
#set_slippage(slippage.VolumeShareSlippage(volume_limit=.20, price_impact=0.0))
#set_commission(commission.PerTrade(cost=0.0))
#set_long_only()
context.MaxCandidates = 100
context.MaxBuyOrdersAtOnce = 30
context.MyLeastPrice = 3.00
context.MyMostPrice = 20.00
context.MyFireSalePrice = context.MyLeastPrice
context.MyFireSaleAge = 6
# over simplistic tracking of position age
context.age = {}
print(len(context.portfolio.positions))
# Rebalance
EveryThisManyMinutes = 5
TradingDayHours = 6.5
TradingDayMinutes = int(TradingDayHours * 60)
for minutez in range(1, TradingDayMinutes, EveryThisManyMinutes):
schedule_function(my_rebalance, date_rules.every_day(),
time_rules.market_open(minutes=minutez))
# Prevent excessive logging of canceled orders at market close.
schedule_function(cancel_open_orders, date_rules.every_day(),
time_rules.market_close(hours=0, minutes=1))
# Record variables at the end of each day.
schedule_function(my_record_vars, date_rules.every_day(),
time_rules.market_close())
# Create our pipeline and attach it to our algorithm.
my_pipe = make_pipeline(context)
attach_pipeline(my_pipe, 'my_pipeline')
print("test 1 complete")
def initialize (context): # runs once when script starts
log.info("Welcome Vincent Perkins")
#context is a python dictionary that contains information on portfolio/performance.
context.idr_losers = pd.Series(([])) #intraday losing stocks
context.day_count = 0
context.daily_message = "Day {}."
context.open_orders = get_open_orders()
context.backup_stocks = symbols('BAM')
context.age = {} #empty dictionary maps one value to another
#context.usep = USEquityPricing() #USEquityPricing object
#Factor criteria
close_price = USEquityPricing.close.latest
vol = USEquityPricing.volume.latest
ann_var = AnnualizedVolatility()
rsi = RSI()
#screening
mask_custom = (IsPrimaryShare() & (vol < 150000) & (close_price > 1) & (close_price < 3) & (ann_var > 0.815) & (rsi < 50))
stockBasket = USEquityPricing.close.latest.top(3000, mask = mask_custom)
#Column construction
pipe_columns = {'close_price': close_price, 'volume': vol, 'ann_var': ann_var}
#Creation of actual pipeline
pipe = Pipeline(columns = pipe_columns, screen = stockBasket)
attach_pipeline(pipe, 'Stocks')
#Testing
log.info(USEquityPricing.get_loader())
eng = LivePipelineEngine(list_symbols)
df = eng.run_pipeline(pipe)
log.info(df)
#Schedule functions
schedule_function(day_start, date_rules.every_day(), time_rules.market_open(hours = 0, minutes = 1))
schedule_function(late_day_trade, date_rules.every_day(), time_rules.market_open(hours = 5, minutes = 56)) #offset open tells when to run a user defined function
schedule_function(check_portfolio, date_rules.every_day(), time_rules.market_open(hours = 0, minutes = 1))
schedule_function(morning_day_trade1, date_rules.every_day(), time_rules.market_open(hours = 0, minutes = 15))
schedule_function(morning_day_trade2, date_rules.every_day(), time_rules.market_open(hours = 0, minutes = 45))
schedule_function(check_portfolio, date_rules.every_day(), time_rules.market_open(hours = 0, minutes = 48))
schedule_function(cancel_open_orders, date_rules.every_day(),time_rules.market_close(hours=0, minutes=1))
def initialize(context):
'''
Called once at the start of the algorithm.
'''
# Set any algorithm 'constants' you will be using
MIN_CASH = 25.00
MIN_ADJUST_AMT = 200.00
# Here we specify the ETFs and their associated weights
# Ensure the weights sum to 1.0
context.MY_ETFS = pd.DataFrame.from_items(
[
(symbol('TYD'), [0.1]), # Daily 7-10 Year Treasury Bull 3X Shares
(symbol('TMF'), [0.2]), # Daily 20+ Year Treasury Bull 3X Shares
(symbol('EDZ'), [0.2]), # Daily MSCI Emerging Markets Bear 3X Shares
(symbol('SPXL'), [0.5]), # Daily S&P 500 Bull 3X Shares
],
columns = ['weight'], orient='index')
# Create a list of daily orders. Initially it's empty.
context.todays_orders = []
# Create and attach pipeline to get data
attach_pipeline(my_pipeline(context), name='my_pipeline')
# Try to place orders
schedule_function(enter_sells, date_rules.every_day(), time_rules.market_open(minutes = 10))
schedule_function(enter_buys, date_rules.every_day(), time_rules.market_open(minutes = 30))
# Retry any cancelled orders 3 times for 10 minutes
schedule_function(retry_cancelled_order, date_rules.every_day(), time_rules.market_open(minutes = 35))
schedule_function(retry_cancelled_order, date_rules.every_day(), time_rules.market_open(minutes = 45))
schedule_function(retry_cancelled_order, date_rules.every_day(), time_rules.market_open(minutes = 55))
# Record tracking variables at the end of each day.
schedule_function(my_record_vars, date_rules.every_day(), time_rules.market_close())
log.info('Alorithim initialized variables:')
def initialize(context):
"""Initialize context to store data for the algorithm."""
LOG.info('Initializing Algorithm')
# Adjustable variables
context.avoid_trades = []
context.trade_restrictions = ['ANDV', 'DDAIF', 'DHR', 'HL', 'FTV', 'LPX', 'DDAIF',
'NNHE', 'NVST', 'PNM', 'SKY', 'XSAU']
rebalance_hours = 0.0
rebalance_minutes = 8.0
context.long_exposure = 0.85
context.short_exposure = -0.85
context.num_longs = 15
context.num_shorts = 15
context.spyleverage = 1.25
context.std_cutoff = 0.15
context.idays = 3
# Fixed variables
rebalance_start_time = rebalance_hours*60 + rebalance_minutes
context.combined_restrictions = context.avoid_trades + context.trade_restrictions
context.rebalance_complete = False
context.trade_queue = {}
context.rolling_portfolios = []
context.clear_queue_run = 0
context.long_weight = context.long_exposure / context.num_longs
context.short_weight = context.short_exposure / context.num_shorts
context.SPY = symbol('VOO')
redis_data = redis.from_url(os.environ.get("REDIS_URL"))
try:
loaded_state = pickle.loads(redis_data.get('pylivetrader_redis_state'))
loaded_ndays = loaded_state['ndays']
LOG.info('Loaded ndays = {}'.format(loaded_ndays))
except:
loaded_ndays = 0
LOG.info('No state has been loaded: ndays = {}'.format(loaded_ndays))
context.ndays = loaded_ndays
attach_pipeline(make_pipeline(), 'pipeline')
schedule_function(calculate_weights, date_rules.every_day(),
time_rules.market_open(minutes=(rebalance_start_time-7)))
schedule_function(rebalance, date_rules.every_day(),
time_rules.market_open(minutes=rebalance_start_time))
clear_queue_frequency = 1
clear_queue_duration = 56
clear_queue_start = int(rebalance_start_time) + 4
for minutez in range(clear_queue_start,
clear_queue_start + clear_queue_duration,
clear_queue_frequency):
schedule_function(clear_queue,
date_rules.every_day(),
time_rules.market_open(minutes=minutez))
check_order_frequency = 10
check_order_duration = 50
check_order_start = int(rebalance_start_time) + 10
for minutez in range(check_order_start,
check_order_start + check_order_duration,
check_order_frequency):
schedule_function(check_order_status,
date_rules.every_day(),
time_rules.market_open(minutes=minutez))
eod_operations_start_time = int(rebalance_start_time) + 70
schedule_function(eod_operations, date_rules.every_day(),
time_rules.market_open(minutes=eod_operations_start_time))
