def rebalance(context, data):
# Pipeline data will be a dataframe with boolean columns named 'longs' and
# 'shorts'.
pipeline_data = context.pipeline_data
all_assets = pipeline_data.index
longs = all_assets[pipeline_data.longs]
shorts = all_assets[pipeline_data.shorts]
record(universe_size=len(all_assets))
# Build a 2x-leveraged, equal-weight, long-short portfolio.
one_third = 1.0 / 3.0
for asset in longs:
order_target_percent(asset, one_third)
for asset in shorts:
order_target_percent(asset, -one_third)
# Remove any assets that should no longer be in our portfolio.
portfolio_assets = longs | shorts
positions = context.portfolio.positions
for asset in viewkeys(positions) - set(portfolio_assets):
# This will fail if the asset was removed from our portfolio because it
# was delisted.
if data.can_trade(asset):
order_target_percent(asset, 0)
def handle_data_api(context, data):
if context.incr == 0:
assert 0 not in context.portfolio.positions
else:
assert context.portfolio.positions[0].amount == \
context.incr, "Orders not filled immediately."
assert context.portfolio.positions[0].last_sale_price == \
data.current(sid(0), "price"), \
"Orders not filled at current price."
context.incr += 1
order(sid(0), 1)
record(incr=context.incr)
def handle_data(context, data):
# Skip first 300 days to get full windows
context.i += 1
if context.i < 300:
return
# Compute averages
# history() has to be called with the same params
# from above and returns a pandas dataframe.
short_mavg = data.history(context.sym, 'price', 100, '1d').mean()
long_mavg = data.history(context.sym, 'price', 300, '1d').mean()
# Trading logic
if short_mavg > long_mavg:
# order_target orders as many shares as needed to
# achieve the desired number of shares.
order_target(context.sym, 100)
elif short_mavg < long_mavg:
order_target(context.sym, 0)
# Save values for later inspection
record(AAPL=data.current(context.sym, "price"),
short_mavg=short_mavg,
long_mavg=long_mavg)
def handle_data(context, data):
trailing_window = data.history(context.asset, 'price', 40, '1d')
if trailing_window.isnull().values.any():
return
short_ema = EMA(trailing_window.values, timeperiod=20)
long_ema = EMA(trailing_window.values, timeperiod=40)
buy = False
sell = False
if (short_ema[-1] > long_ema[-1]) and not context.invested:
order(context.asset, 100)
context.invested = True
buy = True
elif (short_ema[-1] < long_ema[-1]) and context.invested:
order(context.asset, -100)
context.invested = False
sell = True
record(AAPL=data.current(context.asset, "price"),
short_ema=short_ema[-1],
long_ema=long_ema[-1],
buy=buy,
sell=sell)
def handle_data(self, data):
self.incr += 1
self.record(incr=self.incr)
name = 'name'
self.record(name, self.incr)
record(name, self.incr, 'name2', 2, name3=self.incr)
def handle_data(context, data):
order(context.asset, 10)
record(AAPL=data.current(context.asset, 'price'))