def make_strategy_pipeline(context):
pipe = Pipeline()
func = lambda asset: asset.instrument_type != InstrumentType.FUNDS
asset_filter = filter_assets(func)
volume_filter = average_volume_filter(context.lookback, context.min_volume)
screener = asset_filter & volume_filter
screener = volume_filter
pipe.add(period_returns(context.lookback, context.offset), 'momentum')
pipe.add(technical_factor(context.lookback, volatility, 1), 'vol')
pipe.set_screen(screener)
return pipe
def make_strategy_pipeline(context):
pipe = Pipeline()
# get the strategy parameters
lookback = context.params['lookback'] * 21
v = context.params['min_volume']
# Set the volume filter
volume_filter = average_volume_filter(lookback, v)
# compute past returns
momentum = period_returns(lookback)
pipe.add(momentum, 'momentum')
pipe.set_screen(volume_filter)
return pipe
def make_strategy_pipeline(context):
pipe = Pipeline()
lookback = context.params['lookback_vol']
v = context.params['min_volume']
# get the filters and factors
volume_filter = average_volume_filter(lookback, v)
momentum = period_returns(context.params['lookback_ret'])
liquidity = liquidity_factor(lookback, v)
# set up the pipeline
pipe.add(momentum,'momentum')
pipe.add(liquidity,'liquidity')
pipe.set_screen(volume_filter)
return pipe
def make_strategy_pipeline(context):
pipe = Pipeline()
# get the strategy parameters
lookback = context.params['lookback'] * 21
v = context.params['min_volume']
# Set the volume filter
volume_filter = average_volume_filter(lookback, v)
# compute past returns
rsi_factor = technical_factor(lookback, rsi, 14)
pipe.add(rsi_factor, 'rsi')
pipe.set_screen(volume_filter)
return pipe
def make_strategy_pipeline(context):
pipe = Pipeline()
# Set the volume filter, 126 days is roughly 6 month daily data
volume_filter = average_volume_filter(126, 1E7)
# compute past returns
rsi_factor = technical_factor(126, rsi, 14)
ema20_factor = technical_factor(126, ema, 20)
ema50_factor = technical_factor(126, ema, 50)
# add to pipelines
pipe.add(rsi_factor,'rsi')
pipe.add(ema20_factor,'ema20')
pipe.add(ema50_factor,'ema50')
pipe.set_screen(volume_filter)
return pipe
def make_strategy_pipeline(context):
pipe = Pipeline()
# get the strategy parameters
lookback = context.params['lookback'] * 21
v = context.params['min_volume']
# Set the volume filter
volume_filter = average_volume_filter(lookback, v)
# compute past returns
vol_factor = technical_factor(lookback, volatility, 1)
skew_factor = technical_factor(lookback, skewness, None)
pipe.add(vol_factor, 'vol')
pipe.add(skew_factor, 'skew')
pipe.set_screen(volume_filter)
return pipe
