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fix: avoid duplicate date columns interfering with fit_live_predictions()

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robcaulk
2023-09-17 17:36:01 +02:00
parent 14c5f435aa
commit 1f1abfe798
1 changed files with 2 additions and 11 deletions
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freqtrade/freqai/data_drawer.py
+2 -11
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@@ -282,15 +282,12 @@ class FreqaiDataDrawer:
# historically made during downtime. The newest pred will get appeneded later in # historically made during downtime. The newest pred will get appeneded later in
# append_model_predictions) # append_model_predictions)
new_pred.iloc[:, :] = np.nan new_pred.iloc[:, :] = np.nan
new_pred["date"] = dataframe["date"] new_pred["date_pred"] = dataframe["date"]
hist_preds = self.historic_predictions[pair].copy() hist_preds = self.historic_predictions[pair].copy()
# rename date_pred column to date so that we can merge on date
hist_preds = hist_preds.rename(columns={"date_pred": "date"})
# find the closest common date between new_pred and historic predictions # find the closest common date between new_pred and historic predictions
# and cut off the new_pred dataframe at that date # and cut off the new_pred dataframe at that date
common_dates = pd.merge(new_pred, hist_preds, on="date", how="inner") common_dates = pd.merge(new_pred, hist_preds, on="date_pred", how="inner")
if len(common_dates.index) > 0: if len(common_dates.index) > 0:
new_pred = new_pred.iloc[len(common_dates):] new_pred = new_pred.iloc[len(common_dates):]
else: else:
@@ -299,18 +296,12 @@ class FreqaiDataDrawer:
f"for more than {len(dataframe.index)} candles.") f"for more than {len(dataframe.index)} candles.")
df_concat = pd.concat([hist_preds, new_pred], ignore_index=True, keys=hist_preds.keys()) df_concat = pd.concat([hist_preds, new_pred], ignore_index=True, keys=hist_preds.keys())
# remove last row because we will append that later in append_model_predictions() # remove last row because we will append that later in append_model_predictions()
df_concat = df_concat.iloc[:-1] df_concat = df_concat.iloc[:-1]
# any missing values will get zeroed out so users can see the exact # any missing values will get zeroed out so users can see the exact
# downtime in FreqUI # downtime in FreqUI
df_concat = df_concat.fillna(0) df_concat = df_concat.fillna(0)
# rename date column back to date_pred
df_concat = df_concat.rename(columns={"date": "date_pred"})
self.historic_predictions[pair] = df_concat self.historic_predictions[pair] = df_concat
self.model_return_values[pair] = df_concat.tail(len(dataframe.index)).reset_index(drop=True) self.model_return_values[pair] = df_concat.tail(len(dataframe.index)).reset_index(drop=True)
def append_model_predictions(self, pair: str, predictions: DataFrame, def append_model_predictions(self, pair: str, predictions: DataFrame,