Base Nodes API

CSP comes with some basic constructs readily available and commonly used. The latest set of base nodes can be found in the csp.baselib module.

All of the nodes noted here are imported directly into the CSP namespace when importing CSP.

These are all graph-time constructs.

Table of Contents

• Table of Contents
• csp.print
• csp.log
• csp.sample
• csp.firstN
• csp.count
• csp.delay
• csp.diff
• csp.merge
• csp.split
• csp.filter
• csp.drop_dups
• csp.unroll
• csp.collect
• csp.flatten
• csp.default
• csp.gate
• csp.apply
• csp.null_ts
• csp.stop_engine
• csp.multiplex
• csp.demultiplex
• csp.dynamic_demultiplex
• csp.dynamic_collect
• csp.drop_nans
• csp.times
• csp.times_ns
• csp.accum
• csp.exprtk

csp.print

csp.print(
    tag: str,
    x: ts['T'])

This node will print (using python print()) the time, tag and value of x for every tick of x

csp.log

csp.log(
    level: int,
    tag: str,
    x: ts['T'],
    logger: typing.Optional[logging.Logger] = None,
    logger_tz: object = None,
    use_thread: bool = False
)

Similar to csp.print, this will log ticks using the logger on the provided level. The default CSP logger is used if none is provided to the node.

Args:

• logger_tz: time zone to use for log entries
• use_thread: if True, the logging calls will occur in a separate thread as to not block graph execution. This can be useful when printing large strings in log calls. If individual time-series values are subject to modification after the log call, then the user must pass in a copy of the time-series if they wish to have proper threaded logging.

csp.sample

csp.sample(
    trigger: ts[object],
    x: ts['T']
) → ts['T']

Use this to down-sample an input. csp.sample will return the current value of x any time trigger ticks. This can be combined with csp.timer to sample the input on a time interval.

csp.firstN

csp.firstN(
    x: ts['T'],
    N: int
) → ts['T']

Only output the first N ticks of the input.

csp.count

csp.count(x: ts[object]) → ts[int]

Returns the ticking count of ticks of the input

csp.delay

csp.delay(
    x: ts['T'],
    delay: typing.Union[timedelta, int]
) → ts['T']

This will delay all ticks of the input x by the given delay, which can be given as a timedelta to delay a specified amount of time, or as an int to delay a specified number of ticks (delay must be positive)

csp.diff

csp.diff(
    x: ts['T'],
    lag: typing.Union[timedelta, int]
) → ts['T']

When x ticks, output difference between current tick and value time or ticks ago (once that exists)

csp.merge

csp.merge( x: ts['T'], y: ts['T']) → ts['T']

Merges the two timeseries x and y into a single series. If both tick on the same cycle, the first input (x) wins and the value of y is dropped. For loss-less merging see csp.flatten

csp.split

csp.split(
    flag: ts[bool],
    x: ts['T']
) → {false: ts['T'], true: ts['T']}

Splits input x onto two outputs depending on the value of flag. If flag is True when x ticks, output 'true' will tick with the value of x. If flag is False at the time of the input tick, then 'false' will tick. Note that if flag is not valid at the time of the input tick, the input will be dropped.

csp.filter

csp.filter(flag: ts[bool], x: ts['T']) → ts['T']

Will only tick out input ticks of x if the current value of flag is True. If flag is False, or if flag is not valid (hasn't ticked yet) then x is suppressed.

csp.drop_dups

csp.drop_dups(x: ts['T']) → ts['T']

Will drop consecutive duplicate values from the input.

csp.unroll

csp.unroll(x: ts[['T']]) → ts['T']

Given a timeseries of a list of values, unroll will "unroll" the values in the list into a timeseries of the elements. unroll will ensure to preserve the order across all list ticks. Ticks will be unrolled in subsequent engine cycles.

csp.collect

csp.collect(x: [ts['T']]) → ts[['T']]

Given a basket of inputs, return a timeseries of a list of all values that ticked

csp.flatten

csp.flatten(x: [ts['T']]) → ts['T']

Given a basket of inputs, return all ticks across all inputs as a single timeseries of type 'T' (This is similar to csp.merge except that it can take more than two inputs, and is lossless)

csp.default

csp.default(
    x: ts['T'],
    default: '~T',
    delay: timedelta = timedelta()
)

Defaults the input series to the value of default at start of the engine, or after delay if delay is provided. If x ticks right at the start of the engine, or before delay if delay is provided, default value will be discarded.

csp.gate

csp.gate(
    x: ts['T'],
    release: ts[bool]
) → ts[['T']]

csp.gate will hold values of the input series until release ticks True, at which point all pending values will be output as a burst. release can tick open / closed repeatedly. While open, the input will tick out as a single value burst. While closed, input ticks will buffer up until they can be released.

csp.apply

csp.apply(
    x: csp.ts['T'],
    f: Callable[['T'], 'O'],
    result_type: 'O'
) → ts['O']

Applies the provided callable f on every tick of the input and returns the result of the callable.

csp.null_ts

csp.null_ts(typ: 'T')

Returns a "null" timeseries of the given type which will never tick.

csp.stop_engine

csp.stop_engine(x: ts['T'])

Forces the engine to stop if x ticks

csp.multiplex

csp.multiplex(
    x: {'K': ts['T']},
    key: ts['K'],
    tick_on_index: bool = False,
    raise_on_bad_key: bool = False
) → ts['T']

Given a dictionary basket of inputs and a key timeseries, tick out ticks from the input basket timeseries matching the current key.

Args:

• x: dictionary basket of timeseries inputs
• key: timeseries of keys that will be used as the multiplex key
• tick_on_index: if True, will tick the current value of the input basket whenever the key ticks (defaults to False)
• raise_on_bad_key: if True an exception will be raised if key ticks with an unrecognized key (defaults to False)

csp.demultiplex

csp.demultiplex(
    x: ts['T'],
    key: ts['K'],
    keys: ['K'],
    raise_on_bad_key: bool = False
) → {key: ts['T']}

Given a single timeseries input, a key timeseries to demultiplex on and a set of expected keys, will output the given input onto the corresponding basket output of the current value of key. A good example use case of this is demultiplexing a timeseries of trades by account. Assuming your trade struct has an account field, you can demultiplex(trades, trades.account, [ 'acct1', 'acct2', ... ]).

Args:

• x: the input timeseries to demultiplex
• key: a ticking timeseries of the current key to output to
• keys: a list of expected keys that will define the shape of the output basket. The list of keys must be known at graph building time
• raise_on_bad_key: if True an exception will be raised of key ticks with an unrecognized key (defaults to False)

csp.dynamic_demultiplex

csp.dynamic_demultiplex(
    x: ts['T'],
    key: ts['K']
) → {ts['K']: ts['T']}

Similar to csp.demultiplex, this version will return a Dynamic Basket output that will dynamically add new keys as they are seen.

csp.dynamic_collect

csp.dynamic_collect(
    x: {ts['K']: ts['T']}
) → ts[{'K': 'T'}]

Similar to csp.collect, this function takes a Dynamic Basket input and returns a dictionary of the key-value pairs corresponding to the values that ticked.

csp.drop_nans

csp.drop_nans(x: ts[float]) → ts[float]

Filters nan (Not-a-number) values out of the time series.

csp.times

csp.times(x: ts['T']) → ts[datetime]

Given a timeseries, returns the time at which that series ticks

csp.times_ns

csp.times_ns(x: ts['T']) → ts[int]

Given a timeseries, returns the epoch time in nanoseconds at which that series ticks

csp.accum

csp.accum(x: ts["T"], start: "~T" = 0) -> ts["T"]

Given a timeseries, accumulate via += with starting value start.

csp.exprtk

csp.exprtk(
    expression_str: str,
    inputs: {str: ts[object]},
    state_vars: dict = {},
    trigger: ts[object] = None,
    functions: dict = {},
    constants: dict = {},
    output_ndarray: bool = False)

Given a mathematical expression, and a set of timeseries corresponding to variables in that expression, tick out the result of that expression, either every time an input ticks, or on the trigger if provided.

Args:

• expression_str: an expression, as per the C++ Mathematical Expression Library (see readme
• inputs: a dict basket of timeseries. The keys will correspond to the variables in the expression. The timeseries can be of float or string
• state_vars: an optional dictionary of variables to be held in state between executions, and assignable within the expression. Keys are the variable names and values are the starting values
• trigger: an optional trigger for when to calculate. By default will calculate on any input tick
• functions: an optional dictionary whose keys are function names that can be used in the expression, and whose values are of the form (("arg1", ..), "function body"), for example {"foo": (("x","y"), "x\*y")}
• constants: an optional dictionary of constants. Keys are constant names and values are their values
• output_ndarray: if True, output ndarray (1D) instead of float. Note that to output ndarray, the expression needs to use return like return [a, b, c]. The length of the array can vary between ticks.