def is_trade(context, data):
i = 0
for s in get_open_orders():
for o in get_open_orders(s):
if o.amount > 0:
i += 1
i = len(context.portfolio.positions) + i
return False if i > 9 else True
def my_rebalance(context, data):
BuyFactor = .99
SellFactor = 1.01
cash = context.portfolio.cash
cancel_open_buy_orders(context, data)
log.info("My Rebalance.")
# Order sell at profit target in hope that somebody actually buys it
open_orders = get_open_orders()
for stock in context.portfolio.positions:
print(stock.sid)
print(get_open_orders(stock.sid))
if stock in open_orders:
pass
else:
print("no orders")
StockShares = context.portfolio.positions[stock].amount
CurrPrice = float(data.current([stock], 'price'))
CostBasis = float(context.portfolio.positions[stock].cost_basis)
SellPrice = float(make_div_by_05(CostBasis * SellFactor,
buy=False))
if np.isnan(SellPrice):
log.info("My Rebalance: NaN")
pass # probably best to wait until nan goes away
elif (stock in context.age and context.age[stock] == 0):
log.info("My Rebalance: Age")
pass
elif (stock in context.age
and context.MyFireSaleAge <= context.age[stock]
and (context.MyFireSalePrice > CurrPrice
or CostBasis > CurrPrice)):
log.info("My Rebalance: Losing")
if (stock in context.age and context.age[stock] < 2):
pass
elif stock not in context.age:
context.age[stock] = 1
else:
SellPrice = float(
make_div_by_05(.95 * CurrPrice, buy=False))
order(stock, -StockShares, style=LimitOrder(SellPrice))
else:
if (stock in context.age and context.age[stock] < 1):
log.info("My Rebalance: too young")
pass
elif stock not in context.age:
log.info("My Rebalance: no age")
context.age[stock] = 1
else:
log.info("My Rebalance: Place order")
order(stock, -StockShares, style=LimitOrder(SellPrice))
def submit_sell(stock, context, data):
if get_open_orders(stock):
return
# We bought a stock but don't know it's age yet
if stock not in context.age:
context.age[stock] = 0
# Don't sell stuff that's less than 1 day old
if stock in context.age and context.age[stock] < 1:
return
shares = context.portfolio.positions[stock].amount
current_price = float(data.current([stock], 'price'))
cost_basis = float(context.portfolio.positions[stock].cost_basis)
if (context.age[stock] >= context.MyFireSaleAge
and (current_price < context.MyFireSalePrice
or current_price < cost_basis)):
log.info("%s is in fire sale!" % stock.symbol)
sell_price = float(make_div_by_05(.95 * current_price, buy=False))
order(stock, -shares, style=LimitOrder(sell_price))
else:
sell_price = float(
make_div_by_05(cost_basis * context.sell_factor, buy=False))
order(stock, -shares, style=LimitOrder(sell_price))
def submit_sell(stock, context, data):
if get_open_orders(stock):
return
# We bought a stock but don't know it's age yet
if stock not in context.age:
context.age[stock] = 0
# Don't sell stuff that's less than 1 day old
if stock in context.age and context.age[stock] < 1:
return
shares = context.portfolio.positions[stock].amount
current_price = float(data.current([stock], 'price'))
two_week_price_history = data.history([stock], 'price', 10, '1d')
average_two_week_price = float(two_week_price_history.mean())
current_price = float(data.current([stock], 'price'))
context.stocks = [ symbol('UGAZ'), symbol('DGAZ') ]
if (context.age[stock] >= context.MyFireSaleAge and
(current_price < float(0.8 * average_two_week_price))):
log.info("First sell function is working!" % stock.symbol)
if (context.age[stock] >= context.MyFireSaleAge and
(current_price > average_two_week_price)):
log.info("Second sell function is working!" % stock.symbol)
order(context.stock, -shares)
def handle_data(context, data):
if context.first_time is True:
if len(context.portfolio.positions) > 0:
sys.exit("Found positions not traded by the algorithm. Shutting down...")
if len(get_open_orders()) > 0:
sys.exit("Found open orders not traded by the algorithm. Shutting down...")
context.first_time = False
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 check_order_status(context, data):
"""Logs any open orders every 10 minutes."""
open_orders = get_open_orders()
if open_orders:
LOG.info('Open Orders: {}'.format(open_orders))
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 log_open_orders():
oo = get_open_orders()
if len(oo) == 0:
return
for stock, orders in oo.items():
for o in orders:
message = 'Found open order for {amount} shares in {stock}'
log.info(message.format(amount=o.amount, stock=stock))
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 my_rebalance(context, data):
log.info("my_rebalance")
BuyFactor = .99
SellFactor = 1.01
cash = context.portfolio.cash
log.info(cash)
cancel_open_buy_orders(context, data)
# Order sell at profit target in hope that somebody actually buys it
for stock in context.portfolio.positions:
if not get_open_orders(stock):
StockShares = context.portfolio.positions[stock].amount
CurrPrice = float(data.current([stock], 'price'))
CostBasis = float(context.portfolio.positions[stock].cost_basis)
SellPrice = float(make_div_by_05(CostBasis * SellFactor,
buy=False))
if np.isnan(SellPrice):
pass # probably best to wait until nan goes away
elif (stock in context.age and context.age[stock] == 1):
pass
elif (stock in context.age
and context.MyFireSaleAge <= context.age[stock]
and (context.MyFireSalePrice > CurrPrice
or CostBasis > CurrPrice)):
if (stock in context.age and context.age[stock] < 2):
pass
elif stock not in context.age:
context.age[stock] = 1
else:
SellPrice = float(
make_div_by_05(.95 * CurrPrice, buy=False))
order(stock, -StockShares, style=LimitOrder(SellPrice))
else:
if (stock in context.age and context.age[stock] < 2):
pass
elif stock not in context.age:
context.age[stock] = 1
else:
order(stock, -StockShares, style=LimitOrder(SellPrice))
WeightThisBuyOrder = float(1.00 / context.MaxBuyOrdersAtOnce)
for ThisBuyOrder in range(context.MaxBuyOrdersAtOnce):
stock = next(context.MyCandidate)
PH = data.history([stock], 'price', 20, '1d')
PH_Avg = float(PH.mean())
CurrPrice = float(data.current([stock], 'price'))
if np.isnan(CurrPrice):
pass # probably best to wait until nan goes away
else:
if CurrPrice > float(1.25 * PH_Avg):
BuyPrice = float(CurrPrice)
else:
BuyPrice = float(CurrPrice * BuyFactor)
BuyPrice = float(make_div_by_05(BuyPrice, buy=True))
StockShares = int(WeightThisBuyOrder * cash / BuyPrice)
order(stock, StockShares, style=LimitOrder(BuyPrice))
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 hasOrders(stock):
hasOrders = False
orders = get_open_orders(stock)
if orders:
for order in orders:
message = 'Open order for {amount} shares in {stock}'
message = message.format(amount=order.amount, stock=stock)
log.info(message)
hasOrders = True
return hasOrders
def handle_data(context, data):
today = get_datetime().floor('1D')
last_date = getattr(context, 'last_ran_buy', None)
if today != last_date:
my_rebalance(context, data)
context.last_ran_buy = today
else:
for stock, position in context.portfolio.positions.items():
if get_open_orders(stock):
continue
current_price = data.current(stock, 'price')
cost_basis = position.cost_basis
stop_loss = -0.05
current_loss = (current_price - cost_basis) / cost_basis
if current_loss < stop_loss:
log.info('selling early %s (%.2f)' % (stock.symbol, current_loss))
order_target_percent(stock, 0)
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 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_open_orders(context, data):
for s in get_open_orders():
for o in get_open_orders(s):
cancel_order(o)
def check_order_status(context, data):
open_orders = get_open_orders()
if open_orders:
log.info('Open Orders: {}'.format(open_orders))
def cancel_all_orders(context, data):
for _stock, orders in get_open_orders().items():
for pending_order in orders:
cancel_order(pending_order)
def place_sells(context, data):
SellFactor = 1.1
cash = context.portfolio.cash
open_orders = get_open_orders()
place_sell_orders = 1
# exit if we have open sell orders
if len(open_orders) != 0:
for stock, orders in open_orders.items():
for o in orders:
if 0 > o.amount: # it is a sell order
place_sell_orders = 0
if place_sell_orders == 1:
log.info("Place sells")
# Order sell at profit target in hope that somebody actually buys it
for stock in context.portfolio.positions:
if stock in open_orders:
pass
else:
StockShares = context.portfolio.positions[stock].amount
CurrPrice = float(data.current([stock], 'price'))
CostBasis = float(context.portfolio.positions[stock].cost_basis)
SellPrice = float(
make_div_by_05(
CostBasis *
SellFactor,
buy=False))
print(stock)
print(StockShares)
if np.isnan(SellPrice):
pass # probably best to wait until nan goes away
elif (stock in context.age and context.age[stock] < 1):
print("age < 1.0")
pass
elif (
stock in context.age
and context.MyFireSaleAge <= context.age[stock]
and (
context.MyFireSalePrice > CurrPrice
or CostBasis > CurrPrice
)
):
if (stock in context.age and context.age[stock] < 1):
print("age < 1.1")
pass
elif stock not in context.age:
print("age = 1")
context.age[stock] = 1
else:
print("sell loss")
SellPrice = float(
make_div_by_05(.95 * CurrPrice, buy=False))
order(stock, -StockShares,
style=LimitOrder(SellPrice)
)
else:
if (stock in context.age and context.age[stock] < 1):
print("age < 1")
pass
elif stock not in context.age:
print("age = 0")
context.age[stock] = 1
else:
print("sell win")
order(stock, -StockShares,
style=LimitOrder(SellPrice)
)
