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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 EventLoop The event loop object to use

The following code shows how to use compton in a non-coroutine environmennt

loop = asyncio.new_event_loop()

orchestrator = Orchestrator(
    Reducers,
    loop
)

orchestrator.add('US.TSLA')

loop.run_forever()

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
  • def when_update(dispatch) registers the dispatcher which is a callable.
class MyProvider(Provider):
    @property
    def vector(self):
        return (DataType.KLINE, TimeSpan.DAY)

    def when_update(self, dispatch):
        pass

    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):
        # `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):
        # concurrency limit for method `process()`

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

    # 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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