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https://github.com/freqtrade/freqtrade.git
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ruff format: orderflow / public trades
This commit is contained in:
Joe Schr
@@ -2541,19 +2541,20 @@ class Exchange:
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# fetch Trade data stuff
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def needed_candle_ms(self, timeframe: str, candle_type: CandleType):
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one_call = timeframe_to_msecs(timeframe) * self.ohlcv_candle_limit(
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timeframe, candle_type)
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one_call = timeframe_to_msecs(timeframe) * self.ohlcv_candle_limit(timeframe, candle_type)
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move_to = one_call * self.required_candle_call_count
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now = timeframe_to_next_date(timeframe)
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return int((now - timedelta(seconds=move_to // 1000)).timestamp() * 1000)
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def _process_trades_df(self,
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pair: str,
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timeframe: str,
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c_type: CandleType,
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ticks: List[List],
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cache: bool,
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first_required_candle_date: Optional[int]) -> DataFrame:
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def _process_trades_df(
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self,
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pair: str,
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timeframe: str,
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c_type: CandleType,
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ticks: List[List],
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cache: bool,
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first_required_candle_date: Optional[int],
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) -> DataFrame:
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# keeping parsed dataframe in cache
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trades_df = trades_list_to_df(ticks, True)
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@@ -2563,22 +2564,23 @@ class Exchange:
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# Reassign so we return the updated, combined df
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combined_df = concat([old, trades_df], axis=0)
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logger.debug(f"Clean duplicated ticks from Trades data {pair}")
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trades_df = DataFrame(trades_df_remove_duplicates(combined_df),
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columns=combined_df.columns)
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trades_df = DataFrame(
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trades_df_remove_duplicates(combined_df), columns=combined_df.columns
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)
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# Age out old candles
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if first_required_candle_date:
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# slice of older dates
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trades_df = trades_df[
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first_required_candle_date < trades_df['timestamp']]
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trades_df = trades_df[first_required_candle_date < trades_df["timestamp"]]
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trades_df = trades_df.reset_index(drop=True)
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self._trades[(pair, timeframe, c_type)] = trades_df
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return trades_df
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def refresh_latest_trades(self,
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pair_list: ListPairsWithTimeframes,
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*,
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cache: bool = True,
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) -> Dict[PairWithTimeframe, DataFrame]:
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def refresh_latest_trades(
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self,
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pair_list: ListPairsWithTimeframes,
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*,
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cache: bool = True,
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) -> Dict[PairWithTimeframe, DataFrame]:
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"""
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Refresh in-memory TRADES asynchronously and set `_trades` with the result
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Loops asynchronously over pair_list and downloads all pairs async (semi-parallel).
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@@ -2588,24 +2590,27 @@ class Exchange:
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:return: Dict of [{(pair, timeframe): Dataframe}]
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"""
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from freqtrade.data.history import get_datahandler
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data_handler = get_datahandler(
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self._config['datadir'], data_format=self._config['dataformat_trades']
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self._config["datadir"], data_format=self._config["dataformat_trades"]
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)
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logger.debug("Refreshing TRADES data for %d pairs", len(pair_list))
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since_ms = None
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results_df = {}
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for pair, timeframe, candle_type in set(pair_list):
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new_ticks: List = []
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all_stored_ticks_df = DataFrame(columns=DEFAULT_TRADES_COLUMNS + ['date'])
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all_stored_ticks_df = DataFrame(columns=DEFAULT_TRADES_COLUMNS + ["date"])
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first_candle_ms = self.needed_candle_ms(timeframe, candle_type)
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# refresh, if
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# a. not in _trades
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# b. no cache used
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# c. need new data
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is_in_cache = (pair, timeframe, candle_type) in self._trades
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if (not is_in_cache
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or not cache
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or self._now_is_time_to_refresh_trades(pair, timeframe, candle_type)):
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if (
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not is_in_cache
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or not cache
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or self._now_is_time_to_refresh_trades(pair, timeframe, candle_type)
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):
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logger.debug(f"Refreshing TRADES data for {pair}")
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# fetch trades since latest _trades and
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# store together with existing trades
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@@ -2613,26 +2618,29 @@ class Exchange:
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until = None
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from_id = None
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if is_in_cache:
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from_id = self._trades[(pair, timeframe, candle_type)].iloc[-1]['id']
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from_id = self._trades[(pair, timeframe, candle_type)].iloc[-1]["id"]
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until = dt_ts() # now
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else:
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until = int(timeframe_to_prev_date(timeframe).timestamp()) * 1000
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all_stored_ticks_df = data_handler.trades_load(
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f"{pair}-cached", self.trading_mode)
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f"{pair}-cached", self.trading_mode
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)
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if not all_stored_ticks_df.empty:
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if all_stored_ticks_df.iloc[0]['timestamp'] <= first_candle_ms:
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last_cached_ms = all_stored_ticks_df.iloc[-1]['timestamp']
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if all_stored_ticks_df.iloc[0]["timestamp"] <= first_candle_ms:
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last_cached_ms = all_stored_ticks_df.iloc[-1]["timestamp"]
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# only use cached if it's closer than first_candle_ms
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since_ms = (
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last_cached_ms if last_cached_ms > first_candle_ms
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last_cached_ms
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if last_cached_ms > first_candle_ms
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else first_candle_ms
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)
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# doesn't go far enough
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else:
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all_stored_ticks_df = DataFrame(
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columns=DEFAULT_TRADES_COLUMNS + ['date'])
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columns=DEFAULT_TRADES_COLUMNS + ["date"]
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)
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# from_id overrules with exchange set to id paginate
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# TODO: DEBUG:
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@@ -2643,7 +2651,7 @@ class Exchange:
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pair,
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since=since_ms if since_ms else first_candle_ms,
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until=until,
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from_id=from_id
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from_id=from_id,
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)
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except Exception as e:
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@@ -2652,8 +2660,9 @@ class Exchange:
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raise e
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if new_ticks:
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all_stored_ticks_list = all_stored_ticks_df[DEFAULT_TRADES_COLUMNS
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].values.tolist()
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all_stored_ticks_list = all_stored_ticks_df[
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DEFAULT_TRADES_COLUMNS
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].values.tolist()
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all_stored_ticks_list.extend(new_ticks)
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trades_df = self._process_trades_df(
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pair,
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@@ -2661,11 +2670,12 @@ class Exchange:
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candle_type,
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all_stored_ticks_list,
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cache,
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first_required_candle_date=first_candle_ms
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first_required_candle_date=first_candle_ms,
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)
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results_df[(pair, timeframe, candle_type)] = trades_df
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data_handler.trades_store(
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f"{pair}-cached", trades_df[DEFAULT_TRADES_COLUMNS], self.trading_mode)
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f"{pair}-cached", trades_df[DEFAULT_TRADES_COLUMNS], self.trading_mode
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)
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else:
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raise OperationalException("no new ticks")
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@@ -2677,8 +2687,10 @@ class Exchange:
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) -> bool: # Timeframe in seconds
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trades = self.trades((pair, timeframe, candle_type), False)
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pair_last_refreshed = int(trades.iloc[-1]["timestamp"])
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full_candle = int(timeframe_to_next_date(
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timeframe, dt_from_ts(pair_last_refreshed)).timestamp()) * 1000
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full_candle = (
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int(timeframe_to_next_date(timeframe, dt_from_ts(pair_last_refreshed)).timestamp())
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* 1000
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)
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now = dt_ts()
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return full_candle <= now
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