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

auto populate features based on a prepended % in the strategy (remove feature assignment from config). Update doc/constants/example strategy to reflect change

Browse Source
This commit is contained in:
robcaulk
2022-05-17 18:15:03 +02:00
parent 8664e8f9a3
commit d1d451c27e
6 changed files with 80 additions and 69 deletions
Download Patch File Download Diff File Expand all files Collapse all files

53
freqtrade/freqai/data_kitchen.py
View File

@@ -483,31 +483,38 @@ class FreqaiDataKitchen:
return
def build_feature_list(self, config: dict, metadata: dict) -> list:
"""
Build the list of features that will be used to filter
the full dataframe. Feature list is construced from the
user configuration file.
:params:
:config: Canonical freqtrade config file containing all
user defined input in config['freqai] dictionary.
"""
features = []
for tf in config["freqai"]["timeframes"]:
for ft in config["freqai"]["base_features"]:
for n in range(config["freqai"]["feature_parameters"]["shift"] + 1):
shift = ""
if n > 0:
shift = "_shift-" + str(n)
features.append(metadata['pair'].split("/")[0] + "-" + ft + shift + "_" + tf)
for p in config["freqai"]["corr_pairlist"]:
if metadata['pair'] in p:
continue # avoid duplicate features
features.append(p.split("/")[0] + "-" + ft + shift + "_" + tf)
# logger.info("number of features %s", len(features))
def find_features(self, dataframe: DataFrame) -> list:
column_names = dataframe.columns
features = [c for c in column_names if '%' in c]
assert features, ("Could not find any features!")
return features
# def build_feature_list(self, config: dict, metadata: dict) -> list:
# """
# SUPERCEDED BY self.find_features()
# Build the list of features that will be used to filter
# the full dataframe. Feature list is construced from the
# user configuration file.
# :params:
# :config: Canonical freqtrade config file containing all
# user defined input in config['freqai] dictionary.
# """
# features = []
# for tf in config["freqai"]["timeframes"]:
# for ft in config["freqai"]["base_features"]:
# for n in range(config["freqai"]["feature_parameters"]["shift"] + 1):
# shift = ""
# if n > 0:
# shift = "_shift-" + str(n)
# features.append(metadata['pair'].split("/")[0] + "-" + ft + shift + "_" + tf)
# for p in config["freqai"]["corr_pairlist"]:
# if metadata['pair'] in p:
# continue # avoid duplicate features
# features.append(p.split("/")[0] + "-" + ft + shift + "_" + tf)
# # logger.info("number of features %s", len(features))
# return features
def check_if_pred_in_training_spaces(self) -> None:
"""
Compares the distance from each prediction point to each training data

5
freqtrade/freqai/prediction_models/CatboostPredictionModel.py
View File

@@ -53,9 +53,8 @@ class CatboostPredictionModel(IFreqaiModel):
logger.info("--------------------Starting training--------------------")
# create the full feature list based on user config info
self.dh.training_features_list = self.dh.build_feature_list(self.config, metadata)
self.dh.training_features_list = self.dh.find_features(unfiltered_dataframe)
unfiltered_labels = self.make_labels(unfiltered_dataframe)
# filter the features requested by user in the configuration file and elegantly handle NaNs
features_filtered, labels_filtered = self.dh.filter_features(
unfiltered_dataframe,
@@ -127,7 +126,7 @@ class CatboostPredictionModel(IFreqaiModel):
# logger.info("--------------------Starting prediction--------------------")
original_feature_list = self.dh.build_feature_list(self.config, metadata)
original_feature_list = self.dh.find_features(unfiltered_dataframe)
filtered_dataframe, _ = self.dh.filter_features(
unfiltered_dataframe, original_feature_list, training_filter=False
)