Merge remote-tracking branch 'origin/develop' into spice-rack

freqtrade/optimize/backtesting.py

@@ -114,10 +114,10 @@ class Backtesting:
         self.timeframe = str(self.config.get('timeframe'))
         self.timeframe_min = timeframe_to_minutes(self.timeframe)
         self.init_backtest_detail()
-        self.pairlists = PairListManager(self.exchange, self.config)
+        self.pairlists = PairListManager(self.exchange, self.config, self.dataprovider)
         if 'VolumePairList' in self.pairlists.name_list:
             raise OperationalException("VolumePairList not allowed for backtesting. "
-                                       "Please use StaticPairlist instead.")
+                                       "Please use StaticPairList instead.")
         if 'PerformanceFilter' in self.pairlists.name_list:
             raise OperationalException("PerformanceFilter not allowed for backtesting.")
 
@@ -158,9 +158,6 @@ class Backtesting:
 
         self.init_backtest()
 
-    def __del__(self):
-        self.cleanup()
-
     @staticmethod
     def cleanup():
         LoggingMixin.show_output = True
@@ -377,10 +374,10 @@ class Backtesting:
             for col in HEADERS[5:]:
                 tag_col = col in ('enter_tag', 'exit_tag')
                 if col in df_analyzed.columns:
-                    df_analyzed.loc[:, col] = df_analyzed.loc[:, col].replace(
+                    df_analyzed[col] = df_analyzed.loc[:, col].replace(
                         [nan], [0 if not tag_col else None]).shift(1)
                 elif not df_analyzed.empty:
-                    df_analyzed.loc[:, col] = 0 if not tag_col else None
+                    df_analyzed[col] = 0 if not tag_col else None
 
             df_analyzed = df_analyzed.drop(df_analyzed.head(1).index)
 
@@ -547,7 +544,7 @@ class Backtesting:
 
         if stake_amount is not None and stake_amount < 0.0:
             amount = amount_to_contract_precision(
-                abs(stake_amount) / current_rate, trade.amount_precision,
+                abs(stake_amount * trade.leverage) / current_rate, trade.amount_precision,
                 self.precision_mode, trade.contract_size)
             if amount == 0.0:
                 return trade
@@ -1052,7 +1049,7 @@ class Backtesting:
             if requested_rate:
                 self._enter_trade(pair=trade.pair, row=row, trade=trade,
                                   requested_rate=requested_rate,
-                                  requested_stake=(order.remaining * order.price),
+                                  requested_stake=(order.remaining * order.price / trade.leverage),
                                   direction='short' if trade.is_short else 'long')
                 self.replaced_entry_orders += 1
             else:

freqtrade/optimize/hyperopt.py

@@ -24,6 +24,7 @@ from pandas import DataFrame
 from freqtrade.constants import DATETIME_PRINT_FORMAT, FTHYPT_FILEVERSION, LAST_BT_RESULT_FN, Config
 from freqtrade.data.converter import trim_dataframes
 from freqtrade.data.history import get_timerange
+from freqtrade.data.metrics import calculate_market_change
 from freqtrade.enums import HyperoptState
 from freqtrade.exceptions import OperationalException
 from freqtrade.misc import deep_merge_dicts, file_dump_json, plural
@@ -61,7 +62,7 @@ class Hyperopt:
     """
     Hyperopt class, this class contains all the logic to run a hyperopt simulation
 
-    To run a backtest:
+    To start a hyperopt run:
     hyperopt = Hyperopt(config)
     hyperopt.start()
     """
@@ -111,6 +112,7 @@ class Hyperopt:
 
         self.clean_hyperopt()
 
+        self.market_change = 0.0
         self.num_epochs_saved = 0
         self.current_best_epoch: Optional[Dict[str, Any]] = None
 
@@ -357,7 +359,7 @@ class Hyperopt:
 
         strat_stats = generate_strategy_stats(
             self.pairlist, self.backtesting.strategy.get_strategy_name(),
-            backtesting_results, min_date, max_date, market_change=0
+            backtesting_results, min_date, max_date, market_change=self.market_change
         )
         results_explanation = HyperoptTools.format_results_explanation_string(
             strat_stats, self.config['stake_currency'])
@@ -425,6 +427,9 @@ class Hyperopt:
         # Trim startup period from analyzed dataframe to get correct dates for output.
         trimmed = trim_dataframes(preprocessed, self.timerange, self.backtesting.required_startup)
         self.min_date, self.max_date = get_timerange(trimmed)
+        if not self.market_change:
+            self.market_change = calculate_market_change(trimmed, 'close')
+
         # Real trimming will happen as part of backtesting.
         return preprocessed
 

freqtrade/optimize/optimize_reports.py

@@ -173,7 +173,7 @@ def generate_tag_metrics(tag_type: str,
     tabular_data = []
 
     if tag_type in results.columns:
-        for tag, count in results[tag_type].value_counts().iteritems():
+        for tag, count in results[tag_type].value_counts().items():
             result = results[results[tag_type] == tag]
             if skip_nan and result['profit_abs'].isnull().all():
                 continue
@@ -199,7 +199,7 @@ def generate_exit_reason_stats(max_open_trades: int, results: DataFrame) -> List
     """
     tabular_data = []
 
-    for reason, count in results['exit_reason'].value_counts().iteritems():
+    for reason, count in results['exit_reason'].value_counts().items():
         result = results.loc[results['exit_reason'] == reason]
 
         profit_mean = result['profit_ratio'].mean()
@@ -361,7 +361,7 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
     winning_days = sum(daily_profit > 0)
     draw_days = sum(daily_profit == 0)
     losing_days = sum(daily_profit < 0)
-    daily_profit_list = [(str(idx.date()), val) for idx, val in daily_profit.iteritems()]
+    daily_profit_list = [(str(idx.date()), val) for idx, val in daily_profit.items()]
 
     return {
         'backtest_best_day': best_rel,