309 lines
12 KiB
Python
309 lines
12 KiB
Python
"""
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Handle historic data (ohlcv).
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Includes:
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* load data for a pair (or a list of pairs) from disk
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* download data from exchange and store to disk
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"""
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import logging
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import operator
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from datetime import datetime
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from pathlib import Path
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from typing import Any, Dict, List, Optional, Tuple
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import arrow
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from pandas import DataFrame
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from freqtrade import OperationalException, misc
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from freqtrade.arguments import TimeRange
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from freqtrade.data.converter import parse_ticker_dataframe
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from freqtrade.exchange import Exchange, timeframe_to_minutes
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logger = logging.getLogger(__name__)
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def trim_tickerlist(tickerlist: List[Dict], timerange: TimeRange) -> List[Dict]:
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"""
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Trim tickerlist based on given timerange
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"""
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if not tickerlist:
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return tickerlist
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start_index = 0
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stop_index = len(tickerlist)
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if timerange.starttype == 'line':
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stop_index = timerange.startts
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if timerange.starttype == 'index':
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start_index = timerange.startts
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elif timerange.starttype == 'date':
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while (start_index < len(tickerlist) and
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tickerlist[start_index][0] < timerange.startts * 1000):
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start_index += 1
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if timerange.stoptype == 'line':
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start_index = len(tickerlist) + timerange.stopts
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if timerange.stoptype == 'index':
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stop_index = timerange.stopts
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elif timerange.stoptype == 'date':
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while (stop_index > 0 and
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tickerlist[stop_index-1][0] > timerange.stopts * 1000):
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stop_index -= 1
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if start_index > stop_index:
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raise ValueError(f'The timerange [{timerange.startts},{timerange.stopts}] is incorrect')
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return tickerlist[start_index:stop_index]
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def load_tickerdata_file(
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datadir: Optional[Path], pair: str,
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ticker_interval: str,
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timerange: Optional[TimeRange] = None) -> Optional[list]:
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"""
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Load a pair from file, either .json.gz or .json
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:return tickerlist or None if unsuccesful
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"""
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filename = pair_data_filename(datadir, pair, ticker_interval)
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pairdata = misc.file_load_json(filename)
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if not pairdata:
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return None
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if timerange:
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pairdata = trim_tickerlist(pairdata, timerange)
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return pairdata
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def load_pair_history(pair: str,
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ticker_interval: str,
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datadir: Optional[Path],
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timerange: TimeRange = TimeRange(None, None, 0, 0),
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refresh_pairs: bool = False,
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exchange: Optional[Exchange] = None,
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fill_up_missing: bool = True,
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drop_incomplete: bool = True
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) -> DataFrame:
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"""
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Loads cached ticker history for the given pair.
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:param pair: Pair to load data for
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:param ticker_interval: Ticker-interval (e.g. "5m")
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:param datadir: Path to the data storage location.
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:param timerange: Limit data to be loaded to this timerange
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:param refresh_pairs: Refresh pairs from exchange.
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(Note: Requires exchange to be passed as well.)
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:param exchange: Exchange object (needed when using "refresh_pairs")
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:param fill_up_missing: Fill missing values with "No action"-candles
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:param drop_incomplete: Drop last candle assuming it may be incomplete.
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:return: DataFrame with ohlcv data
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"""
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# The user forced the refresh of pairs
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if refresh_pairs:
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download_pair_history(datadir=datadir,
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exchange=exchange,
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pair=pair,
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ticker_interval=ticker_interval,
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timerange=timerange)
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pairdata = load_tickerdata_file(datadir, pair, ticker_interval, timerange=timerange)
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if pairdata:
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if timerange.starttype == 'date' and pairdata[0][0] > timerange.startts * 1000:
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logger.warning('Missing data at start for pair %s, data starts at %s',
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pair, arrow.get(pairdata[0][0] // 1000).strftime('%Y-%m-%d %H:%M:%S'))
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if timerange.stoptype == 'date' and pairdata[-1][0] < timerange.stopts * 1000:
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logger.warning('Missing data at end for pair %s, data ends at %s',
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pair,
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arrow.get(pairdata[-1][0] // 1000).strftime('%Y-%m-%d %H:%M:%S'))
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return parse_ticker_dataframe(pairdata, ticker_interval, pair=pair,
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fill_missing=fill_up_missing,
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drop_incomplete=drop_incomplete)
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else:
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logger.warning(
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f'No history data for pair: "{pair}", interval: {ticker_interval}. '
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'Use --refresh-pairs-cached option or download_backtest_data.py '
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'script to download the data'
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)
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return None
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def load_data(datadir: Optional[Path],
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ticker_interval: str,
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pairs: List[str],
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refresh_pairs: bool = False,
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exchange: Optional[Exchange] = None,
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timerange: TimeRange = TimeRange(None, None, 0, 0),
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fill_up_missing: bool = True,
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live: bool = False
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) -> Dict[str, DataFrame]:
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"""
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Loads ticker history data for a list of pairs the given parameters
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:return: dict(<pair>:<tickerlist>)
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"""
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result: Dict[str, DataFrame] = {}
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if live:
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if exchange:
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logger.info('Live: Downloading data for all defined pairs ...')
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exchange.refresh_latest_ohlcv([(pair, ticker_interval) for pair in pairs])
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result = {key[0]: value for key, value in exchange._klines.items() if value is not None}
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else:
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raise OperationalException(
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"Exchange needs to be initialized when using live data."
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)
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else:
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logger.info('Using local backtesting data ...')
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for pair in pairs:
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hist = load_pair_history(pair=pair, ticker_interval=ticker_interval,
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datadir=datadir, timerange=timerange,
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refresh_pairs=refresh_pairs,
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exchange=exchange,
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fill_up_missing=fill_up_missing)
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if hist is not None:
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result[pair] = hist
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return result
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def make_testdata_path(datadir: Optional[Path]) -> Path:
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"""Return the path where testdata files are stored"""
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return datadir or (Path(__file__).parent.parent / "tests" / "testdata").resolve()
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def pair_data_filename(datadir: Optional[Path], pair: str, ticker_interval: str) -> Path:
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path = make_testdata_path(datadir)
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pair_s = pair.replace("/", "_")
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filename = path.joinpath(f'{pair_s}-{ticker_interval}.json')
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return filename
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def load_cached_data_for_updating(filename: Path, ticker_interval: str,
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timerange: Optional[TimeRange]) -> Tuple[List[Any],
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Optional[int]]:
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"""
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Load cached data and choose what part of the data should be updated
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"""
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since_ms = None
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# user sets timerange, so find the start time
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if timerange:
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if timerange.starttype == 'date':
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since_ms = timerange.startts * 1000
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elif timerange.stoptype == 'line':
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num_minutes = timerange.stopts * timeframe_to_minutes(ticker_interval)
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since_ms = arrow.utcnow().shift(minutes=num_minutes).timestamp * 1000
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# read the cached file
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if filename.is_file():
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with open(filename, "rt") as file:
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data = misc.json_load(file)
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# remove the last item, could be incomplete candle
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if data:
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data.pop()
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else:
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data = []
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if data:
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if since_ms and since_ms < data[0][0]:
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# Earlier data than existing data requested, redownload all
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data = []
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else:
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# a part of the data was already downloaded, so download unexist data only
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since_ms = data[-1][0] + 1
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return (data, since_ms)
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def download_pair_history(datadir: Optional[Path],
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exchange: Optional[Exchange],
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pair: str,
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ticker_interval: str = '5m',
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timerange: Optional[TimeRange] = None) -> bool:
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"""
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Download the latest ticker intervals from the exchange for the pair passed in parameters
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The data is downloaded starting from the last correct ticker interval data that
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exists in a cache. If timerange starts earlier than the data in the cache,
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the full data will be redownloaded
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Based on @Rybolov work: https://github.com/rybolov/freqtrade-data
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:param pair: pair to download
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:param ticker_interval: ticker interval
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:param timerange: range of time to download
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:return: bool with success state
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"""
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if not exchange:
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raise OperationalException(
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"Exchange needs to be initialized when downloading pair history data"
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)
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try:
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filename = pair_data_filename(datadir, pair, ticker_interval)
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logger.info(
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f'Download history data for pair: "{pair}", interval: {ticker_interval} '
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f'and store in {datadir}.'
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)
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data, since_ms = load_cached_data_for_updating(filename, ticker_interval, timerange)
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logger.debug("Current Start: %s", misc.format_ms_time(data[1][0]) if data else 'None')
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logger.debug("Current End: %s", misc.format_ms_time(data[-1][0]) if data else 'None')
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# Default since_ms to 30 days if nothing is given
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new_data = exchange.get_history(pair=pair, ticker_interval=ticker_interval,
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since_ms=since_ms if since_ms
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else
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int(arrow.utcnow().shift(days=-30).float_timestamp) * 1000)
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data.extend(new_data)
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logger.debug("New Start: %s", misc.format_ms_time(data[0][0]))
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logger.debug("New End: %s", misc.format_ms_time(data[-1][0]))
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misc.file_dump_json(filename, data)
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return True
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except Exception as e:
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logger.error(
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f'Failed to download history data for pair: "{pair}", interval: {ticker_interval}. '
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f'Error: {e}'
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)
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return False
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def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
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"""
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Get the maximum timeframe for the given backtest data
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:param data: dictionary with preprocessed backtesting data
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:return: tuple containing min_date, max_date
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"""
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timeframe = [
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(arrow.get(frame['date'].min()), arrow.get(frame['date'].max()))
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for frame in data.values()
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]
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return min(timeframe, key=operator.itemgetter(0))[0], \
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max(timeframe, key=operator.itemgetter(1))[1]
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def validate_backtest_data(data: DataFrame, pair: str, min_date: datetime,
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max_date: datetime, ticker_interval_mins: int) -> bool:
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"""
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Validates preprocessed backtesting data for missing values and shows warnings about it that.
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:param data: preprocessed backtesting data (as DataFrame)
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:param pair: pair used for log output.
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:param min_date: start-date of the data
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:param max_date: end-date of the data
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:param ticker_interval_mins: ticker interval in minutes
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"""
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# total difference in minutes / interval-minutes
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expected_frames = int((max_date - min_date).total_seconds() // 60 // ticker_interval_mins)
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found_missing = False
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dflen = len(data)
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if dflen < expected_frames:
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found_missing = True
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logger.warning("%s has missing frames: expected %s, got %s, that's %s missing values",
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pair, expected_frames, dflen, expected_frames - dflen)
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return found_missing
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