def make_pipeline():
base_universe = QTradableStocksUS()
sentiment_score = SimpleMovingAverage(
inputs=[stocktwits.bull_minus_bear],
window_length=3,
)
# Create filter for top 350 and bottom 350
# assets based on their sentiment scores
top_bottom_scores = (
sentiment_score.top(350) | sentiment_score.bottom(350)
)
return Pipeline(
columns={
'sentiment_score': sentiment_score,
},
# Set screen as the intersection between our filter
# and trading universe
screen=(
base_universe
& top_bottom_scores
)
)
def make_pipeline():
base_universe = QTradableStocksUS()
sentiment_score = SimpleMovingAverage(
inputs = [stocktwits.bull_minus_bear],
window_length = 3)
top_bottom_scores = (
sentiment_score.top(350)|sentiment_score.bottom(350))
return Pipeline(
columns = {'sentiment_score': sentiment_score},
screen=(base_universe & top_bottom_scores))
def make_pipeline():
base_universe = QTradableStocksUS()
# STEP TWO, CREATE A FILTER
# sentiment_score is a simple moving average ...
# where the inputs come from stocktwits.bull_minus_bear ...
# ... there's a bit of magic here with how this data works
# but not that it comes from pipeline.data.psychsignal import stocktwits
# so it's obviously a data set that comes from a "psychsignal" library
sentiment_score = SimpleMovingAverage(
inputs=[stocktwits.bull_minus_bear],
window_length=3,
)
# Create filter for top 350 and bottom 350
# assets based on their sentiment scores
# NOTE: The "|" operator here is "OR" and creates a UNION.
# The .top(350) and .bottom(350) is so powerful, wow.
top_bottom_scores = (
sentiment_score.top(350) | sentiment_score.bottom(350)
)
# STEP THREE, return PIPELINE, AKA CREATE DATAFRAME AND SPECIFY SCREENING UNIVERSE.
# In place of "return Pipeline" maybe it's clearer to think...
# return pandas dataframe ...
# What is Pipeline( ... ) doing? It's just defining columns of a pandas dataframe
# and it's also inputing our specific SCREEN, that we've DEFINED
# DEFINED as in... both base_universe and top_bottom_scores (that union we defined earlier)
# so Pipeline(...) is really taking these partial abstractions and running this targeted analysis.
return Pipeline(
columns={
'sentiment_score': sentiment_score,
},
# Set screen as the intersection between our filter
# and trading universe
screen=(
base_universe
& top_bottom_scores
)
)
