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Split parse_ticker_dataframe some logic to clean_ohlcv_dataframe.
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@ -37,9 +37,29 @@ def parse_ticker_dataframe(ticker: list, timeframe: str, pair: str, *,
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# and fail with exception...
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frame = frame.astype(dtype={'open': 'float', 'high': 'float', 'low': 'float', 'close': 'float',
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'volume': 'float'})
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return clean_ohlcv_dataframe(frame, timeframe, pair,
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fill_missing=fill_missing,
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drop_incomplete=drop_incomplete)
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def clean_ohlcv_dataframe(data: DataFrame, timeframe: str, pair: str, *,
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fill_missing: bool = True,
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drop_incomplete: bool = True) -> DataFrame:
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"""
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Clense a ohlcv dataframe by
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* Grouping it by date (removes duplicate tics)
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* dropping last candles if requested
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* Filling up missing data (if requested)
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:param data: DataFrame containing ohlcv data.
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:param timeframe: timeframe (e.g. 5m). Used to fill up eventual missing data
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:param pair: Pair this data is for (used to warn if fillup was necessary)
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:param fill_missing: fill up missing candles with 0 candles
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(see ohlcv_fill_up_missing_data for details)
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:param drop_incomplete: Drop the last candle of the dataframe, assuming it's incomplete
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:return: DataFrame
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"""
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# group by index and aggregate results to eliminate duplicate ticks
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frame = frame.groupby(by='date', as_index=False, sort=True).agg({
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data = data.groupby(by='date', as_index=False, sort=True).agg({
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'open': 'first',
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'high': 'max',
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'low': 'min',
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@ -48,13 +68,13 @@ def parse_ticker_dataframe(ticker: list, timeframe: str, pair: str, *,
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})
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# eliminate partial candle
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if drop_incomplete:
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frame.drop(frame.tail(1).index, inplace=True)
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data.drop(data.tail(1).index, inplace=True)
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logger.debug('Dropping last candle')
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if fill_missing:
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return ohlcv_fill_up_missing_data(frame, timeframe, pair)
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return ohlcv_fill_up_missing_data(data, timeframe, pair)
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else:
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return frame
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return data
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def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str) -> DataFrame:
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