adelorenzo/stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Shift candles after pushing them to dataprovider

Browse Source 
this will ensure that the signals are not shifted in callbacks
closes #6234
This commit is contained in:
Matthias 2022-01-27 17:09:19 +01:00
parent 4b9d55dbe2
commit 5d0c2bcb44
1 changed files with 7 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

10
freqtrade/optimize/backtesting.py
View File

@ -275,6 +275,13 @@ class Backtesting:
# Trim startup period from analyzed dataframe # Trim startup period from analyzed dataframe
df_analyzed = processed[pair] = pair_data = trim_dataframe( df_analyzed = processed[pair] = pair_data = trim_dataframe(
df_analyzed, self.timerange, startup_candles=self.required_startup) df_analyzed, self.timerange, startup_candles=self.required_startup)
# Update dataprovider cache
self.dataprovider._set_cached_df(pair, self.timeframe, df_analyzed)
# Create a copy of the dataframe before shifting, that way the buy signal/tag
# remains on the correct candle for callbacks.
df_analyzed = df_analyzed.copy()
# 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)
@ -282,9 +289,6 @@ class Backtesting:
df_analyzed.loc[:, 'buy_tag'] = df_analyzed.loc[:, 'buy_tag'].shift(1) df_analyzed.loc[:, 'buy_tag'] = df_analyzed.loc[:, 'buy_tag'].shift(1)
df_analyzed.loc[:, 'exit_tag'] = df_analyzed.loc[:, 'exit_tag'].shift(1) df_analyzed.loc[:, 'exit_tag'] = df_analyzed.loc[:, 'exit_tag'].shift(1)
# Update dataprovider cache
self.dataprovider._set_cached_df(pair, self.timeframe, df_analyzed)
df_analyzed = df_analyzed.drop(df_analyzed.head(1).index) df_analyzed = df_analyzed.drop(df_analyzed.head(1).index)
# Convert from Pandas to list for performance reasons # Convert from Pandas to list for performance reasons