mirror of
https://github.com/freqtrade/freqtrade.git
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58 lines
1.6 KiB
Python
58 lines
1.6 KiB
Python
import logging
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from abc import ABC, abstractmethod
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from typing import Any, Dict, Union
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import orjson
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import rapidjson
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from pandas import DataFrame
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from freqtrade.misc import dataframe_to_json, json_to_dataframe
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from freqtrade.rpc.api_server.ws.proxy import WebSocketProxy
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from freqtrade.rpc.api_server.ws_schemas import WSMessageSchemaType
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logger = logging.getLogger(__name__)
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class WebSocketSerializer(ABC):
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def __init__(self, websocket: WebSocketProxy):
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self._websocket: WebSocketProxy = websocket
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@abstractmethod
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def _serialize(self, data):
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raise NotImplementedError()
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@abstractmethod
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def _deserialize(self, data):
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raise NotImplementedError()
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async def send(self, data: Union[WSMessageSchemaType, Dict[str, Any]]):
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await self._websocket.send(self._serialize(data))
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async def recv(self) -> bytes:
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data = await self._websocket.recv()
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return self._deserialize(data)
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class HybridJSONWebSocketSerializer(WebSocketSerializer):
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def _serialize(self, data) -> str:
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return str(orjson.dumps(data, default=_json_default), "utf-8")
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def _deserialize(self, data: str):
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# RapidJSON expects strings
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return rapidjson.loads(data, object_hook=_json_object_hook)
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# Support serializing pandas DataFrames
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def _json_default(z):
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if isinstance(z, DataFrame):
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return {"__type__": "dataframe", "__value__": dataframe_to_json(z)}
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raise TypeError
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# Support deserializing JSON to pandas DataFrames
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def _json_object_hook(z):
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if z.get("__type__") == "dataframe":
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return json_to_dataframe(z.get("__value__"))
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return z
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