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Instead of clearing processed dict, store df_analyzed (one with buy/sell signals) dataframe in it.

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It still saves memory because this dataframe is kept by DataProvider.
Fixes #6179.
Amends #6133 (a715083fc0).
This commit is contained in:
Rokas Kupstys 2022-01-07 12:07:49 +02:00
parent 7f20f6834b
commit 11ace0f867
1 changed files with 2 additions and 5 deletions
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7
freqtrade/optimize/backtesting.py
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@ -270,8 +270,8 @@ class Backtesting:
df_analyzed = self.strategy.advise_sell( df_analyzed = self.strategy.advise_sell(
self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair}).copy() self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair}).copy()
# Trim startup period from analyzed dataframe # Trim startup period from analyzed dataframe
df_analyzed = trim_dataframe(df_analyzed, self.timerange, df_analyzed = processed[pair] = pair_data = trim_dataframe(
startup_candles=self.required_startup) df_analyzed, self.timerange, startup_candles=self.required_startup)
# To avoid using data from future, we use buy/sell signals shifted # To avoid using data from future, we use buy/sell signals shifted
# from the previous candle # from the previous candle
df_analyzed.loc[:, 'buy'] = df_analyzed.loc[:, 'buy'].shift(1) df_analyzed.loc[:, 'buy'] = df_analyzed.loc[:, 'buy'].shift(1)
@ -287,9 +287,6 @@ class Backtesting:
# Convert from Pandas to list for performance reasons # Convert from Pandas to list for performance reasons
# (Looping Pandas is slow.) # (Looping Pandas is slow.)
data[pair] = df_analyzed[headers].values.tolist() data[pair] = df_analyzed[headers].values.tolist()
# Do not hold on to old data to reduce memory usage
processed[pair] = pair_data = None
return data return data
def _get_close_rate(self, sell_row: Tuple, trade: LocalTrade, sell: SellCheckTuple, def _get_close_rate(self, sell_row: Tuple, trade: LocalTrade, sell: SellCheckTuple,