minor changes, update candle appending to support overlaps
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@ -167,7 +167,8 @@ class DataProvider:
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producer_name: str = "default"
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) -> Tuple[bool, int]:
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"""
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Append a candle to the existing external dataframe
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Append a candle to the existing external dataframe. The incoming dataframe
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must have at least 1 candle.
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:param pair: pair to get the data for
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:param timeframe: Timeframe to get data for
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@ -176,29 +177,32 @@ class DataProvider:
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"""
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pair_key = (pair, timeframe, candle_type)
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if (producer_name not in self.__producer_pairs_df) \
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or (pair_key not in self.__producer_pairs_df[producer_name]):
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if dataframe.empty:
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# The incoming dataframe must have at least 1 candle
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return (False, 0)
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if (producer_name not in self.__producer_pairs_df
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or pair_key not in self.__producer_pairs_df[producer_name]):
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# We don't have data from this producer yet,
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# or we don't have data for this pair_key
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# return False and 1000 for the full df
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return (False, 1000)
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existing_df, _ = self.__producer_pairs_df[producer_name][pair_key]
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existing_df, la = self.__producer_pairs_df[producer_name][pair_key]
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# Iterate over any overlapping candles and update the values
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for idx, candle in dataframe.iterrows():
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existing_df.iloc[
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existing_df['date'] == candle['date']
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] = candle
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existing_df.reset_index(drop=True, inplace=True)
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# CHECK FOR MISSING CANDLES
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timeframe_delta = to_timedelta(timeframe) # Convert the timeframe to a timedelta for pandas
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local_last = existing_df.iloc[-1]['date'] # We want the last date from our copy of data
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incoming_first = dataframe.iloc[0]['date'] # We want the first date from the incoming data
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# We have received this candle before, update our copy
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# and return True, 0
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if local_last == incoming_first:
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existing_df.iloc[-1] = dataframe.iloc[0]
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existing_data = (existing_df.reset_index(drop=True), _)
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self.__producer_pairs_df[producer_name][pair_key] = existing_data
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return (True, 0)
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candle_difference = (incoming_first - local_last) / timeframe_delta
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# If the difference divided by the timeframe is 1, then this
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@ -228,6 +232,7 @@ class DataProvider:
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# Only keep the last 1500 candles in memory
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existing = existing[-1500:] if len(existing) > 1500 else existing
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existing.reset_index(drop=True, inplace=True)
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return existing
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@ -36,6 +36,9 @@ class Producer(TypedDict):
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ws_token: str
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FULL_DATAFRAME_THRESHOLD = 100
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logger = logging.getLogger(__name__)
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@ -376,8 +379,8 @@ class ExternalMessageConsumer:
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logger.debug(f"Received {len(df)} candle(s) for {key}")
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if len(df) >= 999:
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# This is a full dataframe
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if len(df) >= FULL_DATAFRAME_THRESHOLD:
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# This is likely a full dataframe
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# Add the dataframe to the dataprovider
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self._dp._add_external_df(
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pair,
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@ -388,8 +391,8 @@ class ExternalMessageConsumer:
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producer_name=producer_name
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)
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elif len(df) < 999:
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# This is n single candles
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elif len(df) < FULL_DATAFRAME_THRESHOLD:
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# This is likely n single candles
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# Have dataprovider append it to
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# the full datafame. If it can't,
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# request the missing candles
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@ -403,7 +406,12 @@ class ExternalMessageConsumer:
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)
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if not did_append:
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logger.debug("Holes in data or no existing df, "
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# We want an overlap in candles incase some data has changed
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n_missing += 1
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# Set to None for all candles if we missed a full df's worth of candles
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n_missing = n_missing if n_missing < FULL_DATAFRAME_THRESHOLD else 1500
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logger.warning("Holes in data or no existing df, "
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f"requesting {n_missing} candles "
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f"for {key} from `{producer_name}`")
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@ -1062,7 +1062,7 @@ class RPC:
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self,
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pair: str,
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timeframe: str,
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limit: Optional[int] = None
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limit: Optional[int]
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) -> Tuple[DataFrame, datetime]:
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"""
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Get the dataframe and last analyze from the dataprovider
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@ -1083,7 +1083,7 @@ class RPC:
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def _ws_all_analysed_dataframes(
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self,
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pairlist: List[str],
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limit: Optional[int] = None
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limit: Optional[int]
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) -> Generator[Dict[str, Any], None, None]:
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"""
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Get the analysed dataframes of each pair in the pairlist.
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