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

Merge remote-tracking branch 'origin/develop' into reduce-indicator-population

Browse Source
This commit is contained in:
robcaulk
2022-10-31 09:42:01 +01:00
parent d59a7fa2f9 ccba651d37
commit 66d8ed6c0b
58 changed files with 971 additions and 472 deletions
Download Patch File Download Diff File Expand all files Collapse all files

49
freqtrade/freqai/data_kitchen.py
View File

@@ -210,7 +210,10 @@ class FreqaiDataKitchen:
const_cols = list((filtered_df.nunique() == 1).loc[lambda x: x].index)
if const_cols:
filtered_df = filtered_df.filter(filtered_df.columns.difference(const_cols))
self.data['constant_features_list'] = const_cols
logger.warning(f"Removed features {const_cols} with constant values.")
else:
self.data['constant_features_list'] = []
# we don't care about total row number (total no. datapoints) in training, we only care
# about removing any row with NaNs
# if labels has multiple columns (user wants to train multiple modelEs), we detect here
@@ -241,7 +244,8 @@ class FreqaiDataKitchen:
self.data["filter_drop_index_training"] = drop_index
else:
filtered_df = self.check_pred_labels(filtered_df)
if len(self.data['constant_features_list']):
filtered_df = self.check_pred_labels(filtered_df)
# we are backtesting so we need to preserve row number to send back to strategy,
# so now we use do_predict to avoid any prediction based on a NaN
drop_index = pd.isnull(filtered_df).any(axis=1)
@@ -350,13 +354,19 @@ class FreqaiDataKitchen:
:param df: Dataframe to be standardized
"""
for item in df.keys():
df[item] = (
2
* (df[item] - self.data[f"{item}_min"])
/ (self.data[f"{item}_max"] - self.data[f"{item}_min"])
- 1
)
train_max = [None] * len(df.keys())
train_min = [None] * len(df.keys())
for i, item in enumerate(df.keys()):
train_max[i] = self.data[f"{item}_max"]
train_min[i] = self.data[f"{item}_min"]
train_max_series = pd.Series(train_max, index=df.keys())
train_min_series = pd.Series(train_min, index=df.keys())
df = (
2 * (df - train_min_series) / (train_max_series - train_min_series) - 1
)
return df
@@ -464,18 +474,16 @@ class FreqaiDataKitchen:
def check_pred_labels(self, df_predictions: DataFrame) -> DataFrame:
"""
Check that prediction feature labels match training feature labels.
:params:
:df_predictions: incoming predictions
:param df_predictions: incoming predictions
"""
train_labels = self.data_dictionary["train_features"].columns
pred_labels = df_predictions.columns
num_diffs = len(pred_labels.difference(train_labels))
if num_diffs != 0:
df_predictions = df_predictions[train_labels]
logger.warning(
f"Removed {num_diffs} features from prediction features, "
f"these were likely considered constant values during most recent training."
)
constant_labels = self.data['constant_features_list']
df_predictions = df_predictions.filter(
df_predictions.columns.difference(constant_labels)
)
logger.warning(
f"Removed {len(constant_labels)} features from prediction features, "
f"these were considered constant values during most recent training."
)
return df_predictions
@@ -957,6 +965,9 @@ class FreqaiDataKitchen:
append_df[f"{label}_mean"] = self.data["labels_mean"][label]
append_df[f"{label}_std"] = self.data["labels_std"][label]
for extra_col in self.data["extra_returns_per_train"]:
append_df[f"{extra_col}"] = self.data["extra_returns_per_train"][extra_col]
append_df["do_predict"] = do_predict
if self.freqai_config["feature_parameters"].get("DI_threshold", 0) > 0:
append_df["DI_values"] = self.DI_values