From d6b8801f413be4f1c405314473a9d5efda3b959b Mon Sep 17 00:00:00 2001
From: robcaulk <rob.caulk@gmail.com>
Date: Sun, 5 Jun 2022 04:40:58 +0200
Subject: [PATCH] fix follower bug

---
 freqtrade/freqai/data_kitchen.py     | 28 ++++++++++++++--------------
 freqtrade/freqai/freqai_interface.py |  6 +++---
 2 files changed, 17 insertions(+), 17 deletions(-)

diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py
index 5c26b0598..4f68326fc 100644
--- a/freqtrade/freqai/data_kitchen.py
+++ b/freqtrade/freqai/data_kitchen.py
@@ -638,8 +638,8 @@ class FreqaiDataKitchen:
         )
 
         logger.info(
-            "DI tossed %s predictions for being too far from training data",
-            len(do_predict) - do_predict.sum(),
+            f'DI tossed {len(do_predict) - do_predict.sum():.2f} predictions for '
+            'being too far from training data'
         )
 
         self.do_predict += do_predict
@@ -743,7 +743,7 @@ class FreqaiDataKitchen:
                 logger.warning('FreqAI could not detect max timeframe and therefore may not '
                                'download the proper amount of data for training')
 
-        logger.info(f'Extending data download by {additional_seconds/SECONDS_IN_DAY} days')
+        logger.info(f'Extending data download by {additional_seconds/SECONDS_IN_DAY:.2f} days')
 
         if trained_timestamp != 0:
             elapsed_time = (time - trained_timestamp) / SECONDS_IN_DAY
@@ -770,12 +770,13 @@ class FreqaiDataKitchen:
             data_load_timerange.stopts = int(time)
             retrain = True
 
-        logger.info(f'Total data download needed '
-                    f'{(data_load_timerange.stopts - data_load_timerange.startts)/SECONDS_IN_DAY} '
-                    ' days')
-        logger.info(f'Total training timerange '
-                    f'{(trained_timerange.stopts - trained_timerange.startts)/SECONDS_IN_DAY} '
-                    ' days')
+        # logger.info(
+        #     f'Total data download needed '
+        #     f'{(data_load_timerange.stopts - data_load_timerange.startts)/SECONDS_IN_DAY:.2f}'
+        #     ' days')
+        # logger.info(f'Total training timerange '
+        #             f'{(trained_timerange.stopts - trained_timerange.startts)/SECONDS_IN_DAY} '
+        #             ' days')
 
         # if retrain:
         #     coin, _ = metadata['pair'].split("/")
@@ -861,11 +862,10 @@ class FreqaiDataKitchen:
 
         for pair in self.all_pairs:
             for tf in self.freqai_config.get('timeframes'):
-                history_data[pair][tf] = pd.concat(
-                                            [history_data[pair][tf],
-                                             strategy.dp.get_pair_dataframe(pair, tf).iloc[-1]],
-                                            axis=0
-                                            )
+                lh = len(history_data[pair][tf].index)
+                history_data[pair][tf].loc[lh] = strategy.dp.get_pair_dataframe(pair, tf).iloc[-1]
+
+        logger.info(f'Length of history data {len(history_data[pair][tf])}')
 
     def set_all_pairs(self) -> None:
 
diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py
index 04e819cc4..e192f9b0a 100644
--- a/freqtrade/freqai/freqai_interface.py
+++ b/freqtrade/freqai/freqai_interface.py
@@ -226,7 +226,7 @@ class IFreqaiModel(ABC):
         # if trainable, check if model needs training, if so compute new timerange,
         # then save model and metadata.
         # if not trainable, load existing data
-        if (trainable and not self.follow_mode) or coin_first:
+        if (trainable or coin_first) and not self.follow_mode:
             file_exists = False
 
             if trained_timestamp != 0:  # historical model available
@@ -416,8 +416,8 @@ class IFreqaiModel(ABC):
                                                                           metadata)
             unfiltered_dataframe = dh.slice_dataframe(new_trained_timerange, unfiltered_dataframe)
 
-        except Exception:
-            logger.warning('Mismatched sizes encountered in strategy')
+        except Exception as err:
+            logger.exception(err)
             # self.data_drawer.pair_to_end_of_training_queue(metadata['pair'])
             self.training_on_separate_thread = False
             self.retrain = False