I know the fees on GDAX are a problem for everyone that is actively trading. Is there any way in Quant Connect to set a limit order and then wait one minute or one bar and if the limit order has not been filled to convert the order to a market order? Or to cancel the limit order and enter a market order instead? I think that would save a ton of fees...

For the example algo I'm submitting its just one that another quantconnect user brilliantly submitted. Its a simple crossver moving average strategy on the SPY but I assume we could use it on Bitcoin. Could we use a limit order, then wait one bar and if that's not filled enter a market order? Thanks for the help!

class MovingAverageCrossAlgorithm(QCAlgorithm):

def __init__(self):
self.symbol = "SPY"
self.previous = None
self.fast = None
self.slow = None

def Initialize(self):

self.SetStartDate(2009, 01, 01) #Set Start Date
self.SetEndDate(2015, 01, 01) #Set End Date
self.SetCash(100000) #Set Strategy Cash
# Find more symbols here: http://quantconnect.com/data
self.AddSecurity(SecurityType.Equity, self.symbol, Resolution.Minute)

# create a 15 day exponential moving average
self.fast = self.EMA(self.symbol, 15, Resolution.Minute);

# create a 30 day exponential moving average
self.slow = self.EMA(self.symbol, 30, Resolution.Minute);

def OnData(self, data):
'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.

data: TradeBars IDictionary object with your stock data
# a couple things to notice in this method:
# 1. We never need to 'update' our indicators with the data, the engine takes care of this for us
# 2. We can use indicators directly in math expressions
# 3. We can easily plot many indicators at the same time

# wait for our slow ema to fully initialize
if not self.slow.IsReady:

# only once per day
if self.previous is not None and self.previous.Date == self.Time.Date:

# define a small tolerance on our checks to avoid bouncing
tolerance = 0.00015;

holdings = self.Portfolio[self.symbol].Quantity

# we only want to go long if we're currently short or flat
if holdings <= 0:
# if the fast is greater than the slow, we'll go long
if self.fast.Current.Value > self.slow.Current.Value * Decimal(1 + tolerance):
self.Log("BUY >> {0}".format(self.Securities[self.symbol].Price))
self.SetHoldings(self.symbol, 1.0)

# we only want to liquidate if we're currently long
# if the fast is less than the slow we'll liquidate our long
if holdings > 0 and self.fast.Current.Value < self.slow.Current.Value:
self.Log("SELL >> {0}".format(self.Securities[self.symbol].Price))

self.previous = self.Time