Reduce diff by avoiding unnecessary changes
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@ -14,7 +14,7 @@ import pandas
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import rapidjson
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from freqtrade.constants import DECIMAL_PER_COIN_FALLBACK, DECIMALS_PER_COIN
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from freqtrade.enums.signaltype import SignalTagType, SignalType
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from freqtrade.enums import SignalTagType, SignalType
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logger = logging.getLogger(__name__)
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@ -698,11 +698,7 @@ class IStrategy(ABC, HyperStrategyMixin):
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lock_time = timeframe_to_next_date(self.timeframe, candle_date)
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return PairLocks.is_pair_locked(pair, lock_time, side=side)
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def analyze_ticker(
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self,
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dataframe: DataFrame,
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metadata: dict
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) -> DataFrame:
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def analyze_ticker(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Parses the given candle (OHLCV) data and returns a populated DataFrame
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add several TA indicators and entry order signal to it
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@ -716,11 +712,7 @@ class IStrategy(ABC, HyperStrategyMixin):
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dataframe = self.advise_exit(dataframe, metadata)
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return dataframe
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def _analyze_ticker_internal(
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self,
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dataframe: DataFrame,
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metadata: dict
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) -> DataFrame:
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def _analyze_ticker_internal(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Parses the given candle (OHLCV) data and returns a populated DataFrame
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add several TA indicators and buy signal to it
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@ -753,20 +745,16 @@ class IStrategy(ABC, HyperStrategyMixin):
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return dataframe
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def analyze_pair(
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self,
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pair: str
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) -> None:
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def analyze_pair(self, pair: str) -> None:
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"""
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Fetch data for this pair from dataprovider and analyze.
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Stores the dataframe into the dataprovider.
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The analyzed dataframe is then accessible via `dp.get_analyzed_dataframe()`.
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:param pair: Pair to analyze.
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"""
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candle_type = self.config.get('candle_type_def', CandleType.SPOT)
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dataframe = self.dp.ohlcv(pair, self.timeframe, candle_type)
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dataframe = self.dp.ohlcv(
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pair, self.timeframe, candle_type=self.config.get('candle_type_def', CandleType.SPOT)
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)
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if not isinstance(dataframe, DataFrame) or dataframe.empty:
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logger.warning('Empty candle (OHLCV) data for pair %s', pair)
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return
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@ -787,10 +775,7 @@ class IStrategy(ABC, HyperStrategyMixin):
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logger.warning('Empty dataframe for pair %s', pair)
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return
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def analyze(
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self,
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pairs: List[str]
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) -> None:
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def analyze(self, pairs: List[str]) -> None:
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"""
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Analyze all pairs using analyze_pair().
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:param pairs: List of pairs to analyze
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@ -798,7 +783,7 @@ class IStrategy(ABC, HyperStrategyMixin):
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for pair in pairs:
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self.analyze_pair(pair)
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@ staticmethod
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@staticmethod
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def preserve_df(dataframe: DataFrame) -> Tuple[int, float, datetime]:
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""" keep some data for dataframes """
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return len(dataframe), dataframe["close"].iloc[-1], dataframe["date"].iloc[-1]
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@ -1219,9 +1204,6 @@ class IStrategy(ABC, HyperStrategyMixin):
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dataframe = _create_and_merge_informative_pair(
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self, dataframe, metadata, inf_data, populate_fn)
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# If in follower mode, get analyzed dataframe from leader df's in dp
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# otherise run populate_indicators
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return self.populate_indicators(dataframe, metadata)
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def advise_entry(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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@ -406,7 +406,6 @@ def test_api_cleanup(default_conf, mocker, caplog):
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apiserver = ApiServer(default_conf)
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apiserver.add_rpc_handler(RPC(get_patched_freqtradebot(mocker, default_conf)))
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apiserver.start_api()
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apiserver.cleanup()
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assert apiserver._server.cleanup.call_count == 1
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