def make_pipeline():
# Screen out penny stocks and low liquidity securities.
dollar_volume = AverageDollarVolume(window_length=20)
is_liquid = dollar_volume.rank(ascending=False) < 1000
# Create the mask that we will use for our percentile methods.
base_universe = (is_liquid)
# Filter down to stocks in the top/bottom 10% by sentiment rank
factor = WeightedSentimentByVolatility()
longs = factor.percentile_between(90, 100, mask=base_universe)
shorts = factor.percentile_between(0, 10, mask=base_universe)
# Add Accern to the Pipeline
pipe_columns = {
'longs':longs,
'shorts':shorts
}
# Set our pipeline screens
pipe_screen = (longs | shorts) & (factor != 0)
# Create our pipeline
pipe = Pipeline(columns = pipe_columns, screen = pipe_screen)
return pipe
def initialize(context):
# Create pipeline
pipe = Pipeline()
pipe = attach_pipeline(pipe, name='factors')
pipe.add(PsychSignal(), "psychsignal_sentiment")
#Screen out penny stocks and low liquidity securities
dollar_volume = AverageDollarVolume(window_length=20)
# Only look at top 1000 most liquid securities
liquidity_rank = dollar_volume.rank(ascending=False) < 200
pipe.set_screen((dollar_volume > 10**7) & (liquidity_rank))
# Set our shorts and longs and define our benchmark
context.spy = sid(8554)
context.shorts = None
context.longs = None
schedule_function(rebalance, date_rules.every_day())
schedule_function(cancel_open_orders, date_rules.every_day(),
time_rules.market_close())
set_commission(commission.PerShare(cost=0,
min_trade_cost=0)) # no cost to trading
set_slippage(slippage.FixedSlippage(spread=0))
def make_pipeline():
# Screen out penny stocks and low liquidity securities.
dollar_volume = AverageDollarVolume(window_length=20)
is_liquid = dollar_volume.rank(ascending=False) < 1000
# Add pipeline factors
impact = alphaone_free.impact_score.latest
sentiment = alphaone_free.article_sentiment.latest
return Pipeline(columns={
'impact': impact,
'sentiment': sentiment,
},
screen=is_liquid)
