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csp.dynamic(trigger, sub_graph, graph_args...) → csp.DynamicBasket[ ... ]
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trigger: a csp.DynamicBasket input.
As new keys are added to the basket, they will trigger sub_graph instances to be created.
As keys are removed, they will shutdown their respective sub-graph
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sub_graph - a regular csp.graph method that will be wired as new keys are added on trigger
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graph_args: these are the args passed to the sub_graph at the time of creation.
Note the special semantics of argument passing to dynamic sub-graphs:
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scalars: can be passed as is, assuming they are known at main graph build time
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timeseries - can be passed as is, assuming they are known at main graph build time
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csp.snap(ts): this will convert a timeseries input to a scalar at the time of graph creation, allowing you to get a "dynamic" scalar value to use at sub_graph build time
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csp.snapkey(): this will pass through the key that was added which triggered this dynamic sub-graph.
One can use this to get the key triggering the sub-graph.
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csp.attach(): this will pass through the timeseries of the input trigger for the key which triggered this dynamic sub-graph.
For example, say we have a dynamic basket of { symbol : ts[orders ]} as our input trigger.
As a new symbol is added, we will trigger a sub-graph to process this symbol.
Say we also want to feed in the ts[orders] for the given symbol into our sub_graph, we would pass csp.attach() as the argument.
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output: every output of sub_graph (if there are any) will be returned as a member of a csp.DynamicBasket output.
As new keys are added to the trigger, which generates sub-graphs, keys will be added to the output dynamic basket
(Note, output keys will only generate on first tick of some output data, not upon instantiation of the sub-graph, since csp.DynamicBasket requires all keys to have valid values)
@csp.graph
def my_sub_graph(symbol : str, orders : ts[ Orders ], portfolio_position : ts[int], some_scalar : int) -> ts[Fill]:
... regular csp.graph code ...
@csp.graph
def main():
# position as ts[int]
portfolio_position = get_portfolio_position()
all_orders = get_orders()
# demux fat-pipe of orders into a dynamic basket keyed by symbol
demuxed_orders = csp.dynamic_demultiplex(all_orders, all_orders.symbol)
result = csp.dynamic(demuxed_orders, my_sub_graph,
csp.snap(all_orders.symbol), # Grab scalar value of all_orders.symbol at time of instantiation
#csp.snapkey(), # Alternative way to grab the key that instantiated the sub-graph
csp.attach(), # extract the demuxed_orders[symbol] time series of the symbol being created in the sub_graph
portfolio_position, # pass in regular ts[]
123) # pass in some scalar
# process result.fills which will be a csp.DynamicBasket of { symbol : ts[Fill] }