Batch ohlcv requests to not overwelm ccxt's async throttler
closes #6003
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@ -1317,27 +1317,30 @@ class Exchange:
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)
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cached_pairs.append((pair, timeframe))
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results = asyncio.get_event_loop().run_until_complete(
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asyncio.gather(*input_coroutines, return_exceptions=True))
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results_df = {}
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# handle caching
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for res in results:
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if isinstance(res, Exception):
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logger.warning(f"Async code raised an exception: {repr(res)}")
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continue
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# Deconstruct tuple (has 3 elements)
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pair, timeframe, 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)] = 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=self._ohlcv_partial_candle)
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results_df[(pair, timeframe)] = ohlcv_df
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if cache:
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self._klines[(pair, timeframe)] = ohlcv_df
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# Chunk requests into batches of 100 to avoid overwelming ccxt Throttling
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for input_coro in chunks(input_coroutines, 100):
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results = asyncio.get_event_loop().run_until_complete(
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asyncio.gather(*input_coro, return_exceptions=True))
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# handle caching
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for res in results:
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if isinstance(res, Exception):
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logger.warning(f"Async code raised an exception: {repr(res)}")
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continue
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# Deconstruct tuple (has 3 elements)
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pair, timeframe, 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)] = 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=self._ohlcv_partial_candle)
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results_df[(pair, timeframe)] = ohlcv_df
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if cache:
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self._klines[(pair, timeframe)] = ohlcv_df
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# Return cached klines
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for pair, timeframe in cached_pairs:
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results_df[(pair, timeframe)] = self.klines((pair, timeframe), copy=False)
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