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https://github.com/kaelzhang/python-compton

An abstract data flow framework for quantitative trading
https://github.com/kaelzhang/python-compton

data-flow quant quantitative-finance

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An abstract data flow framework for quantitative trading

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# python-compton

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

## Install

```sh
pip install compton
```

## Usage

```py
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.

```py
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.

```py
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.

```py
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

```py
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

```py
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](LICENSE)