I'm trying to impliment this part of a Quantopian algorithm:

returns_overall = Returns(window_length=136)
returns_recent = Returns(window_length=10)
momentum = returns_overall - returns_recent

And then sort my universe selction by momentum in fine selection.

Is there a simple way to do this in QuantConnect?

I found a momentum indicator that works, but I can't figure out how to subtract a mom(126) from a mom(10) and then sort based on that number...

averages = dict()
history = algorithm.History(symbols, 200, Resolution.Daily).close.unstack(0)

for symbol in symbols:
# Remove NaN: symbol does not have 200 daily data points
df = history[symbol].dropna()
if df.empty:
continue

mom = Momentum(136)
for time, close in df.iteritems():
mom.Update(time, close)

# Adds Momentum to dict only if it is ready
if mom.IsReady:
averages[symbol] = mom

# Update with current data
for symbol, mom in averages.items():
c = self.coarse.pop(symbol, None)
mom.Update(c.EndTime, c.AdjustedPrice)

sortedbyMomentum = sorted(averages.items(), key=lambda x: x[1], reverse=True)

Anyone have an idea? Thanks!