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Improve external_candle aggregation

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Matthias 2022-12-11 08:42:13 +01:00 committed by Timothy Pogue
parent 96edd31458
commit a693495a6d
1 changed files with 9 additions and 24 deletions
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33
freqtrade/data/dataprovider.py
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@ -9,7 +9,7 @@ from collections import deque
from datetime import datetime, timezone from datetime import datetime, timezone
from typing import Any, Dict, List, Optional, Tuple from typing import Any, Dict, List, Optional, Tuple
from pandas import DataFrame, concat, to_timedelta from pandas import DataFrame, to_timedelta
from freqtrade.configuration import TimeRange from freqtrade.configuration import TimeRange
from freqtrade.constants import Config, ListPairsWithTimeframes, PairWithTimeframe from freqtrade.constants import Config, ListPairsWithTimeframes, PairWithTimeframe
@ -191,30 +191,13 @@ class DataProvider:
existing_df, _ = self.__producer_pairs_df[producer_name][pair_key] existing_df, _ = self.__producer_pairs_df[producer_name][pair_key]
# Handle overlapping candles
old_candles = existing_df[
~existing_df['date'].isin(
dataframe['date']
)
]
overlapping_candles = existing_df[
existing_df['date'].isin(
dataframe['date']
)
]
new_candles = dataframe[
~dataframe['date'].isin(
existing_df['date']
)
]
if overlapping_candles:
existing_df = concat([old_candles, overlapping_candles], axis=0)
# CHECK FOR MISSING CANDLES # CHECK FOR MISSING CANDLES
timeframe_delta = to_timedelta(timeframe) # Convert the timeframe to a timedelta for pandas timeframe_delta = to_timedelta(timeframe) # Convert the timeframe to a timedelta for pandas
local_last = existing_df.iloc[-1]['date'] # We want the last date from our copy local_last = existing_df.iloc[-1]['date'] # We want the last date from our copy
incoming_first = new_candles.iloc[0]['date'] # We want the first date from the incoming incoming_first = dataframe.iloc[0]['date'] # We want the first date from the incoming
# Remove existing candles that are newer than the incoming first candle
existing_df1 = existing_df[existing_df['date'] < incoming_first]
candle_difference = (incoming_first - local_last) / timeframe_delta candle_difference = (incoming_first - local_last) / timeframe_delta
@ -225,8 +208,10 @@ class DataProvider:
# so return False and candle_difference. # so return False and candle_difference.
if candle_difference > 1: if candle_difference > 1:
return (False, candle_difference) return (False, candle_difference)
if existing_df1.empty:
appended_df = append_candles_to_dataframe(existing_df, dataframe) appended_df = dataframe
else:
appended_df = append_candles_to_dataframe(existing_df1, dataframe)
# Everything is good, we appended # Everything is good, we appended
self.__producer_pairs_df[producer_name][pair_key] = appended_df, last_analyzed self.__producer_pairs_df[producer_name][pair_key] = appended_df, last_analyzed