refactor ohlcvdata_to_dataframe to advise_all_indicators

freqtrade/edge/edge_positioning.py

@@ -151,7 +151,7 @@ class Edge:
         # Fake run-mode to Edge
         prior_rm = self.config['runmode']
         self.config['runmode'] = RunMode.EDGE
-        preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
+        preprocessed = self.strategy.advise_all_indicators(data)
         self.config['runmode'] = prior_rm
 
         # Print timeframe

freqtrade/optimize/backtesting.py

@@ -533,7 +533,8 @@ class Backtesting:
             'final_balance': self.wallets.get_total(self.strategy.config['stake_currency']),
         }
 
-    def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, Any], timerange: TimeRange):
+    def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, DataFrame],
+                              timerange: TimeRange):
         self.progress.init_step(BacktestState.ANALYZE, 0)
 
         logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
@@ -552,7 +553,7 @@ class Backtesting:
             max_open_trades = 0
 
         # need to reprocess data every time to populate signals
-        preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
+        preprocessed = self.strategy.advise_all_indicators(data)
 
         # Trim startup period from analyzed dataframe
         preprocessed_tmp = trim_dataframes(preprocessed, timerange, self.required_startup)

freqtrade/optimize/hyperopt.py

@@ -394,7 +394,7 @@ class Hyperopt:
         data, timerange = self.backtesting.load_bt_data()
         logger.info("Dataload complete. Calculating indicators")
 
-        preprocessed = self.backtesting.strategy.ohlcvdata_to_dataframe(data)
+        preprocessed = self.backtesting.strategy.advise_all_indicators(data)
 
         # Trim startup period from analyzed dataframe to get correct dates for output.
         processed = trim_dataframes(preprocessed, timerange, self.backtesting.required_startup)

freqtrade/strategy/interface.py

@@ -732,7 +732,7 @@ class IStrategy(ABC, HyperStrategyMixin):
         else:
             return current_profit > roi
 
-    def ohlcvdata_to_dataframe(self, data: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
+    def advise_all_indicators(self, data: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
         """
         Populates indicators for given candle (OHLCV) data (for multiple pairs)
         Does not run advise_buy or advise_sell!