Combine ohlcv data in exchange class for live mode
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@ -18,12 +18,12 @@ import ccxt.async_support as ccxt_async
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from cachetools import TTLCache
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from ccxt import ROUND_DOWN, ROUND_UP, TICK_SIZE, TRUNCATE, decimal_to_precision
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from dateutil import parser
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from pandas import DataFrame
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from pandas import DataFrame, concat
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from freqtrade.constants import (DEFAULT_AMOUNT_RESERVE_PERCENT, NON_OPEN_EXCHANGE_STATES, BuySell,
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Config, EntryExit, ListPairsWithTimeframes, MakerTaker,
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PairWithTimeframe)
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from freqtrade.data.converter import ohlcv_to_dataframe, trades_dict_to_list
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from freqtrade.data.converter import clean_ohlcv_dataframe, ohlcv_to_dataframe, trades_dict_to_list
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from freqtrade.enums import OPTIMIZE_MODES, CandleType, MarginMode, TradingMode
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from freqtrade.exceptions import (DDosProtection, ExchangeError, InsufficientFundsError,
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InvalidOrderException, OperationalException, PricingError,
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@ -1850,10 +1850,14 @@ class Exchange:
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return pair, timeframe, candle_type, data
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def _build_coroutine(self, pair: str, timeframe: str, candle_type: CandleType,
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since_ms: Optional[int]) -> Coroutine:
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since_ms: Optional[int], cache: bool) -> Coroutine:
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not_all_data = self.required_candle_call_count > 1
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if cache and (pair, timeframe, candle_type) in self._klines:
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# Not in cache - force multi-calls
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not_all_data = False
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if (not since_ms
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and (self._ft_has["ohlcv_require_since"] or self.required_candle_call_count > 1)):
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and (self._ft_has["ohlcv_require_since"] or not_all_data)):
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# Multiple calls for one pair - to get more history
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one_call = timeframe_to_msecs(timeframe) * self.ohlcv_candle_limit(
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timeframe, candle_type, since_ms)
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@ -1890,8 +1894,9 @@ class Exchange:
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if ((pair, timeframe, candle_type) not in self._klines or not cache
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or self._now_is_time_to_refresh(pair, timeframe, candle_type)):
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input_coroutines.append(self._build_coroutine(
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pair, timeframe, candle_type=candle_type, since_ms=since_ms))
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input_coroutines.append(
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self._build_coroutine(pair, timeframe, candle_type, since_ms, cache))
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else:
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logger.debug(
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@ -1901,6 +1906,25 @@ class Exchange:
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return input_coroutines, cached_pairs
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def _process_ohlcv_df(self, pair: str, timeframe: str, c_type: CandleType, ticks: List[List],
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cache: bool, drop_incomplete: bool) -> DataFrame:
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# keeping last candle time as last refreshed time of the pair
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if ticks:
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self._pairs_last_refresh_time[(pair, timeframe, c_type)] = ticks[-1][0] // 1000
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# keeping parsed dataframe in cache
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ohlcv_df = ohlcv_to_dataframe(ticks, timeframe, pair=pair, fill_missing=True,
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drop_incomplete=drop_incomplete)
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if cache:
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if (pair, timeframe, c_type) in self._klines:
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old = self._klines[(pair, timeframe, c_type)]
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# Reassign so we return the updated, combined df
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ohlcv_df = clean_ohlcv_dataframe(concat([old, ohlcv_df], axis=0), timeframe, pair,
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fill_missing=True, drop_incomplete=False)
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self._klines[(pair, timeframe, c_type)] = ohlcv_df
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else:
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self._klines[(pair, timeframe, c_type)] = ohlcv_df
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return ohlcv_df
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def refresh_latest_ohlcv(self, pair_list: ListPairsWithTimeframes, *,
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since_ms: Optional[int] = None, cache: bool = True,
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drop_incomplete: Optional[bool] = None
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@ -1937,16 +1961,11 @@ class Exchange:
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continue
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# Deconstruct tuple (has 4 elements)
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pair, timeframe, c_type, ticks = res
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# keeping last candle time as last refreshed time of the pair
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if ticks:
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self._pairs_last_refresh_time[(pair, timeframe, c_type)] = ticks[-1][0] // 1000
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# keeping parsed dataframe in cache
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ohlcv_df = ohlcv_to_dataframe(
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ticks, timeframe, pair=pair, fill_missing=True,
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drop_incomplete=drop_incomplete)
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ohlcv_df = self._process_ohlcv_df(
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pair, timeframe, c_type, ticks, cache, drop_incomplete)
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results_df[(pair, timeframe, c_type)] = ohlcv_df
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if cache:
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self._klines[(pair, timeframe, c_type)] = ohlcv_df
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# Return cached klines
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for pair, timeframe, c_type in cached_pairs:
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results_df[(pair, timeframe, c_type)] = self.klines(
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