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ensure full pair string is used for caching dataframes. If not, revert to old behavior. Update docs.

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robcaulk
2022-10-29 22:26:49 +02:00
parent a9db668082
commit 650bb8b7d7
5 changed files with 92 additions and 74 deletions
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40
freqtrade/freqai/data_kitchen.py
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@@ -1133,17 +1133,19 @@ class FreqaiDataKitchen:
"""
Find the columns of the dataframe corresponding to the corr_pairlist, save them
in a dictionary to be reused and attached to other pairs.
:params:
:dataframe: fully populated dataframe (current pair + corr_pairs)
:return:
:corr_dataframes: dictionary of dataframes to be attached to other pairs in same candle.
:param dataframe: fully populated dataframe (current pair + corr_pairs)
:return: corr_dataframes, dictionary of dataframes to be attached
to other pairs in same candle.
"""
corr_dataframes: Dict[str, DataFrame] = {}
pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
for pair in pairs:
coin = pair.split('/')[0]
pair_cols = [col for col in dataframe.columns if coin in col]
valid_strs = [f"%-{pair}", f"%{pair}", f"%_{pair}"]
pair_cols = [col for col in dataframe.columns if
any(substr in col for substr in valid_strs)]
pair_cols.insert(0, 'date')
corr_dataframes[pair] = dataframe.filter(pair_cols, axis=1)
return corr_dataframes
@@ -1153,10 +1155,10 @@ class FreqaiDataKitchen:
current_pair: str) -> DataFrame:
"""
Attach the existing corr_pair dataframes to the current pair dataframe before training
:params:
:dataframe: current pair strategy dataframe, indicators populated already
:corr_dataframes: dictionary of saved dataframes from earlier in the same candle
:current_pair: current pair to which we will attach corr pair dataframe
:param dataframe: current pair strategy dataframe, indicators populated already
:param corr_dataframes: dictionary of saved dataframes from earlier in the same candle
:param current_pair: current pair to which we will attach corr pair dataframe
:return:
:dataframe: current pair dataframe of populated indicators, concatenated with corr_pairs
ready for training
@@ -1164,8 +1166,8 @@ class FreqaiDataKitchen:
pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
for pair in pairs:
if current_pair not in pair:
dataframe = pd.concat([dataframe, corr_dataframes[pair]], axis=1)
if current_pair != pair:
dataframe = dataframe.merge(corr_dataframes[pair], how='left', on='date')
return dataframe
@@ -1186,15 +1188,15 @@ class FreqaiDataKitchen:
:param base_dataframes: dict = dict containing the current pair dataframes
(for user defined timeframes)
:param metadata: dict = strategy furnished pair metadata
:returns:
:return:
dataframe: DataFrame = dataframe containing populated indicators
"""
# for prediction dataframe creation, we let dataprovider handle everything in the strategy
# so we create empty dictionaries, which allows us to pass None to
# `populate_any_indicators()`. Signaling we want the dp to give us the live dataframe.
tfs = self.freqai_config["feature_parameters"].get("include_timeframes")
pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
tfs: List[str] = self.freqai_config["feature_parameters"].get("include_timeframes")
pairs: List[str] = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
if not prediction_dataframe.empty:
dataframe = prediction_dataframe.copy()
for tf in tfs:
@@ -1219,16 +1221,16 @@ class FreqaiDataKitchen:
)
# ensure corr pairs are always last
for i in pairs:
if pair in i:
for corr_pair in pairs:
if pair == corr_pair:
continue # dont repeat anything from whitelist
for tf in tfs:
if pairs and do_corr_pairs:
dataframe = strategy.populate_any_indicators(
i,
corr_pair,
dataframe.copy(),
tf,
informative=corr_dataframes[i][tf]
informative=corr_dataframes[corr_pair][tf]
)
self.get_unique_classes_from_labels(dataframe)