Jack,
To prevent that from happening, you can cancel all orders at the end of the day like:
from datetime import datetime, timedelta
import numpy as np
def initialize(context):
#: Settinng our universe to be between the top 99.5% and the top 100% of stocks by Dollar Volume
set_universe(universe.DollarVolumeUniverse(floor_percentile=99.5, ceiling_percentile=100))
#: Setting the number of stocks that we want to long and the number of stocks that we want to short
context.stocks_to_long = 5
context.stocks_to_short = 5
schedule_function(func=rebalance,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_open())
schedule_function(func=day_counter,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_close())
schedule_function(func=close_orders,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_close(minutes=15))
context.rebalance_days = 10
context.current_days_counted = 0
def close_orders(context, data):
open_orders = get_open_orders()
# open_orders is a dictionary keyed by sid, with values that are lists of orders.
if open_orders:
# iterate over the dictionary
for security, orders in open_orders.iteritems():
# iterate over the orders
for oo in orders:
cancel_order(oo)
log.info("Canceling order for %s" % security.symbol)
def day_counter(context, data):
"""
Increments our day counter at the end of day, every day
"""
context.current_days_counted += 1
def rebalance(context, data):
"""
The logic for rebalancing our algorithm
"""
if get_open_orders():
return
#: A quick check to see that we're only rebalancing every X days, defined by
#: context.rebalance_days
if context.current_days_counted % context.rebalance_days != 0:
return
#: Getting 200 days worth of historical data
#: If you wanted an intraday strategy based of minutely data you could change '1d' to '1m'
historical_data = history(200, '1d', 'price')
#: Getting the difference between the 50 day mean and the 200 day mean
past_50_day_mean = historical_data.tail(50).mean()
past_200_day_mean = historical_data.mean()
diff = past_50_day_mean / past_200_day_mean - 1
#: Cleaning up our diffs by removing any NaNs and sorting it in ascending order
diff = diff.dropna()
diff.sort()
#: Recording the stocks that we want to buy and the stocks that we want to sell
#: If the 50 mean is greater than the 200 day mean, add it to the buy
#: Vice versa for the shorts
buys = diff[diff > 0]
sells = diff[diff < 0]
#: Create weights for our securities
buy_length = min(context.stocks_to_long, len(buys))
short_length = min(context.stocks_to_short, len(sells))
buy_weight = 1.0/buy_length if buy_length != 0 else 0
short_weight = -1.0/short_length if short_length != 0 else 0
#: Select securities just above and below the slow moving average (the diff)
buys.sort()
sells.sort(ascending=False)
buys = buys.iloc[:buy_length] if buy_weight != 0 else None
sells = sells.iloc[:short_length] if short_weight != 0 else None
#: Define a 2% stoploss for each security
stops = historical_data.iloc[-1] * 0.02
#: Iterate through each security in data
for sym in data:
#: If the security exists in our sells.index then sell
if sells is not None and sym in sells.index:
log.info('SHORT: %s'%sym.symbol)
order_target_percent(sym, short_weight,
stop_price=data[sym].price - stops[sym])
#: If the security instead, exists in our buys index, buy
elif buys is not None and sym in buys.index:
log.info('LONG: %s'%sym.symbol)
order_target_percent(sym, buy_weight,
stop_price=data[sym].price + stops[sym])
#: If the security is in neither list, exit any positions we might have in that security
else:
order_target(sym, 0)
record(wlong=buy_weight, wshort=short_weight)
def handle_data(context, data):
pass
Disclaimer
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