Merge branch 'develop' into no-ticker-2

freqtrade/optimize/hyperopt.py

@@ -23,6 +23,8 @@ from joblib import (Parallel, cpu_count, delayed, dump, load,
                     wrap_non_picklable_objects)
 from pandas import DataFrame, json_normalize, isna
 import tabulate
+from os import path
+import io
 
 from freqtrade.data.converter import trim_dataframe
 from freqtrade.data.history import get_timerange
@@ -330,10 +332,10 @@ class Hyperopt:
             lambda x: '{}/{}'.format(str(x).rjust(len(str(total_epochs)), ' '), total_epochs)
         )
         trials['Avg profit'] = trials['Avg profit'].apply(
-            lambda x: ('{:,.2f}%'.format(x)).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
+            lambda x: '{:,.2f}%'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
         )
         trials['Avg duration'] = trials['Avg duration'].apply(
-            lambda x: ('{:,.1f} m'.format(x)).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
+            lambda x: '{:,.1f} m'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
         )
         trials['Objective'] = trials['Objective'].apply(
             lambda x: '{:,.5f}'.format(x).rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ')
@@ -381,6 +383,62 @@ class Hyperopt:
             )
         print(table)
 
+    @staticmethod
+    def export_csv_file(config: dict, results: list, total_epochs: int, highlight_best: bool,
+                        csv_file: str) -> None:
+        """
+        Log result to csv-file
+        """
+        if not results:
+            return
+
+        # Verification for overwrite
+        if path.isfile(csv_file):
+            logger.error("CSV-File already exists!")
+            return
+
+        try:
+            io.open(csv_file, 'w+').close()
+        except IOError:
+            logger.error("Filed to create CSV-File!")
+            return
+
+        trials = json_normalize(results, max_level=1)
+        trials['Best'] = ''
+        trials['Stake currency'] = config['stake_currency']
+        trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
+                         'results_metrics.avg_profit', 'results_metrics.total_profit',
+                         'Stake currency', 'results_metrics.profit', 'results_metrics.duration',
+                         'loss', 'is_initial_point', 'is_best']]
+        trials.columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Total profit', 'Stake currency',
+                          'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best']
+        trials['is_profit'] = False
+        trials.loc[trials['is_initial_point'], 'Best'] = '*'
+        trials.loc[trials['is_best'], 'Best'] = 'Best'
+        trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
+        trials['Epoch'] = trials['Epoch'].astype(str)
+        trials['Trades'] = trials['Trades'].astype(str)
+
+        trials['Total profit'] = trials['Total profit'].apply(
+            lambda x: '{:,.8f}'.format(x) if x != 0.0 else ""
+        )
+        trials['Profit'] = trials['Profit'].apply(
+            lambda x: '{:,.2f}'.format(x) if not isna(x) else ""
+        )
+        trials['Avg profit'] = trials['Avg profit'].apply(
+            lambda x: '{:,.2f}%'.format(x) if not isna(x) else ""
+        )
+        trials['Avg duration'] = trials['Avg duration'].apply(
+            lambda x: '{:,.1f} m'.format(x) if not isna(x) else ""
+        )
+        trials['Objective'] = trials['Objective'].apply(
+            lambda x: '{:,.5f}'.format(x) if x != 100000 else ""
+        )
+
+        trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
+        trials.to_csv(csv_file, index=False, header=True, mode='w', encoding='UTF-8')
+        print("CSV-File created!")
+
     def has_space(self, space: str) -> bool:
         """
         Tell if the space value is contained in the configuration

freqtrade/optimize/hyperopt_loss_sharpe.py

@@ -36,7 +36,7 @@ class SharpeHyperOptLoss(IHyperOptLoss):
         expected_returns_mean = total_profit.sum() / days_period
         up_stdev = np.std(total_profit)
 
-        if (np.std(total_profit) != 0.):
+        if up_stdev != 0:
             sharp_ratio = expected_returns_mean / up_stdev * np.sqrt(365)
         else:
             # Define high (negative) sharpe ratio to be clear that this is NOT optimal.

freqtrade/optimize/hyperopt_loss_sharpe_daily.py

@@ -51,7 +51,7 @@ class SharpeHyperOptLossDaily(IHyperOptLoss):
         expected_returns_mean = total_profit.mean()
         up_stdev = total_profit.std()
 
-        if (up_stdev != 0.):
+        if up_stdev != 0:
             sharp_ratio = expected_returns_mean / up_stdev * math.sqrt(days_in_year)
         else:
             # Define high (negative) sharpe ratio to be clear that this is NOT optimal.

freqtrade/optimize/hyperopt_loss_sortino.py

@@ -39,7 +39,7 @@ class SortinoHyperOptLoss(IHyperOptLoss):
         results.loc[total_profit < 0, 'downside_returns'] = results['profit_percent']
         down_stdev = np.std(results['downside_returns'])
 
-        if np.std(total_profit) != 0.0:
+        if down_stdev != 0:
             sortino_ratio = expected_returns_mean / down_stdev * np.sqrt(365)
         else:
             # Define high (negative) sortino ratio to be clear that this is NOT optimal.

freqtrade/optimize/hyperopt_loss_sortino_daily.py

@@ -59,7 +59,7 @@ class SortinoHyperOptLossDaily(IHyperOptLoss):
         # where P = sum_daily["profit_percent_after_slippage"]
         down_stdev = math.sqrt((total_downside**2).sum() / len(total_downside))
 
-        if (down_stdev != 0.):
+        if down_stdev != 0:
             sortino_ratio = expected_returns_mean / down_stdev * math.sqrt(days_in_year)
         else:
             # Define high (negative) sortino ratio to be clear that this is NOT optimal.