Hello Leo,
Once you have instantiated your factor, you will want to add it as a column to your pipeline's output. For example:
def make_pipeline():
weights = MyFactor() # Replace with your factor
return Pipeline(
columns={
'weights': weights
}
)
Then, you will be able to reference that column from the pipeline's output by using the 'weights' string as an index:
def before_trading_start(context, data):
context.output = pipeline_output('my_pipeline')
context.weights = context.output['weights']
The resulting context.weights variable will be a pandas Series indexed by sid containing the corresponding weight calculated by your factor. You can then iterate through context.weights inside a rebalance function and place orders accordingly:
def my_rebalance(context, data):
for security in context.weights.index:
if data.can_trade(security):
order_target_percent(security, context.weights[security])
This is just a general example. I would recommend you going through the Pipeline Tutorial for more details and examples on how to add factors, filters and classifiers to your pipelines, and integrate them with your algorithms.
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