✨ Allow config difference core trend timeframe

user_data/mgm-config.example.json

@@ -1,6 +1,7 @@
 {
   "monigomani_settings": {
     "timeframe": "30m",
+    "trend_timeframe": "1d",
     "backtest_timeframe": "5m",
     "startup_candle_count": 400,
     "precision": 1,

user_data/strategies/MasterMoniGoManiHyperStrategy.py

@@ -98,6 +98,7 @@ class MasterMoniGoManiHyperStrategy(IStrategy, ABC):
     # Apply the loaded MoniGoMani Settings
     try:
         timeframe = mgm_config['timeframe']
+        trend_timeframe = mgm_config['trend_timeframe'] or mgm_config['timeframe']
         backtest_timeframe = mgm_config['backtest_timeframe']
         startup_candle_count = mgm_config['startup_candle_count']
         precision = mgm_config['precision']
@@ -191,6 +192,7 @@ class MasterMoniGoManiHyperStrategy(IStrategy, ABC):
     # Initialize some parameters which will be automatically configured/used by MoniGoMani
     use_custom_stoploss = True  # Leave this enabled (Needed for open_trade custom_information_storage)
     is_dry_live_run_detected = True  # Class level runmode detection, Gets set automatically
+    core_trend_timeframe = trend_timeframe  # Gets set automatically
     informative_timeframe = timeframe  # Gets set automatically
     timeframe_multiplier = None  # Gets set automatically
     separator = 1.5  # Gets set automatically
@@ -457,6 +459,15 @@ def _populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFram
         """
 
         tfz = 'TimeFrame-Zoom'
+        # Populate core trend indicators
+        core_trend = load_pair_history(pair=metadata['pair'],
+                                       datadir=self.config['datadir'],
+                                       timeframe=self.core_trend_timeframe,
+                                       # ToDo: calculate correct startup_candles needed for HT_TRENDMODE and SAR
+                                       startup_candles=self.startup_candle_count,
+                                       data_format=self.config.get('dataformat_ohlcv', 'json'))
+        core_trend = self._populate_core_trend(core_trend, metadata)
+
         # Compute indicator data during Backtesting / Hyperopting when TimeFrame-Zooming
         if (self.is_dry_live_run_detected is False) and (self.informative_timeframe != self.backtest_timeframe):
             self.mgm_logger('info', tfz, f'Backtesting/Hyperopting this strategy with a informative_timeframe '
@@ -473,8 +484,19 @@ def _populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFram
             # Throw away older data that isn't needed.
             first_informative = dataframe['date'].min().floor('H')
             informative = informative[informative['date'] >= first_informative]
-            # Populate core trend indicators
-            informative = self._populate_core_trend(informative, metadata)
+
+            # Merge core trend to informative data frame
+            informative = merge_informative_pair(
+                informative, core_trend[['date', 'ht_trendmode', 'sar', 'trend']].copy(),
+                self.informative_timeframe, self.core_trend_timeframe, ffill=True)
+            informative['ht_trendmode'] = informative[f'ht_trendmode_{self.core_trend_timeframe}']
+            informative['sar'] = informative[f'sar_{self.core_trend_timeframe}']
+            informative['trend'] = informative[f'trend_{self.core_trend_timeframe}']
+            del informative[f'date_{self.core_trend_timeframe}']
+            del informative[f'ht_trendmode_{self.core_trend_timeframe}']
+            del informative[f'sar_{self.core_trend_timeframe}']
+            del informative[f'trend_{self.core_trend_timeframe}']
+
             # Populate indicators at a larger timeframe
             informative = self.do_populate_indicators(informative.copy(), metadata)
             # Merge indicators back in with, filling in missing values.
@@ -491,8 +513,20 @@ def _populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFram
         else:
             self.mgm_logger('info', tfz,
                             f'Dry/Live-running MoniGoMani with normal timeframe ({self.timeframe} candles)')
-            # Populate core trend indicators
-            dataframe = self._populate_core_trend(dataframe, metadata)
+
+            # Merge core trend to main data frame
+            dataframe = merge_informative_pair(
+                dataframe, core_trend[['date', 'ht_trendmode', 'sar', 'trend', 'mgm_trend']].copy(), 
+                self.timeframe, self.core_trend_timeframe, ffill=True)
+            dataframe['ht_trendmode'] = dataframe[f'ht_trendmode_{self.core_trend_timeframe}']
+            dataframe['sar'] = dataframe[f'sar_{self.core_trend_timeframe}']
+            dataframe['trend'] = dataframe[f'trend_{self.core_trend_timeframe}']
+            dataframe['mgm_trend'] = dataframe[f'mgm_trend_{self.core_trend_timeframe}']
+            del dataframe[f'date_{self.core_trend_timeframe}']
+            del dataframe[f'ht_trendmode_{self.core_trend_timeframe}']
+            del dataframe[f'sar_{self.core_trend_timeframe}']
+            del dataframe[f'trend_{self.core_trend_timeframe}']
+            del dataframe[f'mgm_trend_{self.core_trend_timeframe}']
 
             # Just populate indicators.
             dataframe = self.do_populate_indicators(dataframe, metadata)