def closeAnyOpenOrders(stock):
orders = get_open_orders(stock)
if orders:
for order in orders:
message = 'Canceling order for {amount} shares in {stock}'
message = message.format(amount=order.amount, stock=stock)
#log.debug(message)
cancel_order(order)
def cancel_open_orders(context, data):
oo = get_open_orders()
if len(oo) == 0:
return
for stock, orders in oo.items():
for o in orders:
log.info("Canceling order of {} shares in {}", o.amount, stock)
cancel_order(o)
def cancel_open_orders(context, data):
oo = get_open_orders()
if len(oo) == 0:
return
for stock, orders in oo.items():
for o in orders:
# message = 'Canceling order of {amount} shares in {stock}'
# log.info(message.format(amount=o.amount, stock=stock))
cancel_order(o)
def submit_buy(stock, context, data, weight):
cash = min(investment_limits(context)['remaining_to_invest'], context.portfolio.cash)
two_week_price_history = data.history([stock], 'price', 10, '1d')
one_week_price_history = data.history([stock], 'price', 5, '1d')
average_two_week_price = float(two_week_price_history.mean())
average_one_week_price = float(one_week_price_history.mean())
current_price = float(data.current([stock], 'price'))
context.stock = [ symbol('UGAZ'), symbol('DGAZ') ]
if np.isnan(current_price):
pass # probably best to wait until nan goes away
if current_price >= float(1.1 * average_two_week_price): # if the price is 10% greater than or equal to the 10d avg
buy_price = float(current_price)
if current_price >= float(1.15 * average_two_week_price): # if the price is 15% above the 10d avg
buy_price = float(current_price)
if current_price < float(0.95 * average_one_week_price): # if the price is 5% below the 5d avg
buy_price = float(current_price)
else:
buy_price = float(current_price * context.buy_factor)
shares_to_buy = int(weight * cash / buy_price)
# This cancels open sales that would prevent these buys from being submitted if running
# up against the PDT rule
open_orders = get_open_orders()
if stock in open_orders:
for open_order in open_orders[stock]:
cancel_order(open_order)
order(context.stock, shares_to_buy)
def submit_buy(stock, context, data, weight):
cash = min(
investment_limits(context)['remaining_to_invest'],
context.portfolio.cash)
price_history = data.history([stock], 'price', 20, '1d')
average_price = float(price_history.mean())
current_price = float(data.current([stock], 'price'))
if np.isnan(current_price):
pass # probably best to wait until nan goes away
else:
if current_price > float(
1.25 * average_price): # if the price is 25% above the 20d avg
buy_price = float(current_price)
else: # Otherwise buy at a discount
buy_price = float(current_price * context.buy_factor)
buy_price = float(make_div_by_05(buy_price, buy=True))
shares_to_buy = int(weight * cash / buy_price)
max_exposure = int(weight * context.portfolio.portfolio_value /
buy_price)
# Prevent over exposing to a particular stock, never own more than 1/max_buy_orders
# of our account value
positions = context.portfolio.positions
if stock in positions and positions[stock].amount >= max_exposure:
return
# This cancels open sales that would prevent these buys from being submitted if running
# up against the PDT rule
open_orders = get_open_orders()
if stock in open_orders:
for open_order in open_orders[stock]:
cancel_order(open_order)
order(stock, shares_to_buy, style=LimitOrder(buy_price))
def cancel_all_orders(context, data):
for _stock, orders in get_open_orders().items():
for pending_order in orders:
cancel_order(pending_order)
def cancel_open_orders(context, data):
for s in get_open_orders():
for o in get_open_orders(s):
cancel_order(o)
