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An abstract data flow framework for quantitative trading

Project description

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python-compton

An abstract data-flow framework for quantitative trading, which decouples data initialization, data composition and data processing.

Install

pip install compton

Usage

from compton import (
  Orchestrator,
  Provider,
  Reducer,
  Consumer
)

Vector

We call a tuple of hashable parameters as a vector which is used to identify a certain kind of data.

from enum import Enum

class DataType(Enum):
    KLINE = 1
    ORDER_BOOK = 2

class TimeSpan(Enum):
    DAY = 1
    WEEK = 2

vector = (DataType.KLINE, TimeSpan.DAY)

Orchestrator(reducers, loop=None)

  • reducers List[Reducer] reducers to compose data
  • loop? Optional[EventLoop] The event loop object to use. In most cases, you should NOT pass this argument, unless you exact know what you are doing.
Orchestrator(
    reducers
).connect(
    provider
).subscribe(
    consumer
).add('TSLA')

orchestrator.connect(provider: Provider) -> self

Connects to a data provider

orchestrator.subscribe(consumer: Consumer) -> self

Subscribes the consumer to orchestrator.

orchestrator.add(symbol: str) -> self

Adds a new symbol to orchestrator, and start the data flow for symbol

Provider

Provider is an abstract class which provides initial data and data updates.

A provider should be implemented to support many symbols

We must inherit class Provider and implement some abstract method before use.

  • @property vector returns an Vector
  • async def init() method returns the initial data
  • There is an protected method self.dispatch(symbol, payload) to set the payload updated, which should only be called in a coroutine, or a RuntimeError is raised.
class MyProvider(Provider):
    @property
    def vector(self):
        return (DataType.KLINE, TimeSpan.DAY)

    async def init(self, symbol):
        return {}

Reducer

Another abstract class which handles data composition.

The reducer.vector could be a generic vector which applies partial match to other vectors

class MyReducer(Reducer):
    @property
    def vector(self):
        # So, MyReducer support both
        # - (DataType.KLINE, TimeSpan.DAY)
        # - and (DataType.KLINE, TimeSpan.WEEK)
        return (DataType.KLINE,)

    def merge(self, old, new):
        # `old` might be `None`, if `new` is the initial data
        if old is None:
            # We could clean the initial data
            return clean(new)

        return {**old, **new}

Consumer

A consumer could subscribes to more than one kind of data types

class MyConsumer(Consumer):
    @property
    def vectors(self):
        # Subscribe to two kinds of data types
        return [
            (DataType.KLINE, TimeSpan.DAY),
            (DataType.KLINE, TimeSpan.WEEK)
        ]

    @property
    def all(self) -> bool:
        """
        `True` indicates that the consumer will only go processing
        if both of the data corresponds with the two vectors have changes

        And by default, `Consumer::all` is False
        """
        return True

    @property
    def concurrency(self) -> int:
        """
        Concurrency limit for method `process()`

        By default, `Consumer::concurrency` is `0` which means no limit
        """
        return 1

    def should_process(self, *payloads) -> bool:
        """
        If this method returns `False`, then the data update will not be processed
        """
        return True

    # Then there will be
    # both `kline_day` and `kline_week` passed into method `process`
    async def process(self, symbol, kline_day, kline_week):
        await doSomething(symbol, kline_day, kline_week)

License

MIT

Project details


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