178 lines
7.0 KiB
Python
178 lines
7.0 KiB
Python
"""
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Functions to convert data from one format to another
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"""
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import logging
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from datetime import datetime, timezone
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import pandas as pd
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from pandas import DataFrame, to_datetime
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from freqtrade.configuration.timerange import TimeRange
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logger = logging.getLogger(__name__)
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def parse_ticker_dataframe(ticker: list, 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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Converts a ticker-list (format ccxt.fetch_ohlcv) to a Dataframe
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:param ticker: ticker list, as returned by exchange.async_get_candle_history
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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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logger.debug("Parsing tickerlist to dataframe")
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cols = ['date', 'open', 'high', 'low', 'close', 'volume']
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frame = DataFrame(ticker, columns=cols)
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frame['date'] = to_datetime(frame['date'],
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unit='ms',
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utc=True,
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infer_datetime_format=True)
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# Some exchanges return int values for volume and even for ohlc.
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# Convert them since TA-LIB indicators used in the strategy assume floats
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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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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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'close': 'last',
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'volume': 'max',
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})
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# eliminate partial candle
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if drop_incomplete:
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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(data, timeframe, pair)
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else:
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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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"""
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Fills up missing data with 0 volume rows,
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using the previous close as price for "open", "high" "low" and "close", volume is set to 0
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"""
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from freqtrade.exchange import timeframe_to_minutes
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ohlc_dict = {
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'open': 'first',
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'high': 'max',
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'low': 'min',
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'close': 'last',
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'volume': 'sum'
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}
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ticker_minutes = timeframe_to_minutes(timeframe)
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# Resample to create "NAN" values
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df = dataframe.resample(f'{ticker_minutes}min', on='date').agg(ohlc_dict)
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# Forwardfill close for missing columns
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df['close'] = df['close'].fillna(method='ffill')
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# Use close for "open, high, low"
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df.loc[:, ['open', 'high', 'low']] = df[['open', 'high', 'low']].fillna(
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value={'open': df['close'],
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'high': df['close'],
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'low': df['close'],
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})
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df.reset_index(inplace=True)
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len_before = len(dataframe)
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len_after = len(df)
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if len_before != len_after:
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logger.info(f"Missing data fillup for {pair}: before: {len_before} - after: {len_after}")
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return df
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def trim_dataframe(df: DataFrame, timerange: TimeRange, df_date_col: str = 'date') -> DataFrame:
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"""
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Trim dataframe based on given timerange
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:param df: Dataframe to trim
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:param timerange: timerange (use start and end date if available)
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:param: df_date_col: Column in the dataframe to use as Date column
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:return: trimmed dataframe
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"""
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if timerange.starttype == 'date':
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start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
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df = df.loc[df[df_date_col] >= start, :]
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if timerange.stoptype == 'date':
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stop = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
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df = df.loc[df[df_date_col] <= stop, :]
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return df
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def order_book_to_dataframe(bids: list, asks: list) -> DataFrame:
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"""
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Gets order book list, returns dataframe with below format per suggested by creslin
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-------------------------------------------------------------------
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b_sum b_size bids asks a_size a_sum
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-------------------------------------------------------------------
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"""
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cols = ['bids', 'b_size']
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bids_frame = DataFrame(bids, columns=cols)
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# add cumulative sum column
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bids_frame['b_sum'] = bids_frame['b_size'].cumsum()
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cols2 = ['asks', 'a_size']
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asks_frame = DataFrame(asks, columns=cols2)
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# add cumulative sum column
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asks_frame['a_sum'] = asks_frame['a_size'].cumsum()
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frame = pd.concat([bids_frame['b_sum'], bids_frame['b_size'], bids_frame['bids'],
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asks_frame['asks'], asks_frame['a_size'], asks_frame['a_sum']], axis=1,
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keys=['b_sum', 'b_size', 'bids', 'asks', 'a_size', 'a_sum'])
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# logger.info('order book %s', frame )
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return frame
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def trades_to_ohlcv(trades: list, timeframe: str) -> list:
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"""
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Converts trades list to ohlcv list
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:param trades: List of trades, as returned by ccxt.fetch_trades.
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:param timeframe: Ticker timeframe to resample data to
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:return: ohlcv timeframe as list (as returned by ccxt.fetch_ohlcv)
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"""
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from freqtrade.exchange import timeframe_to_minutes
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ticker_minutes = timeframe_to_minutes(timeframe)
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df = pd.DataFrame(trades)
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df['datetime'] = pd.to_datetime(df['datetime'])
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df = df.set_index('datetime')
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df_new = df['price'].resample(f'{ticker_minutes}min').ohlc()
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df_new['volume'] = df['amount'].resample(f'{ticker_minutes}min').sum()
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df_new['date'] = df_new.index.astype("int64") // 10 ** 6
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# Drop 0 volume rows
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df_new = df_new.dropna()
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columns = ["date", "open", "high", "low", "close", "volume"]
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return list(zip(*[df_new[x].values.tolist() for x in columns]))
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