🔀 Merge PR 3ylo/feature/rolling_vwap

⚡️ Switched `VWAP` to `Rolling VWAP`
Rikj000 · Nov 27, 2021 · 0720f43 · 0720f43
2 parents 5b52cd9 + ac08540
commit 0720f43
Show file tree

Hide file tree

Showing 2 changed files with 11 additions and 11 deletions.
diff --git a/user_data/mgm_tools/TotalOverallSignalImportanceCalculator.py b/user_data/mgm_tools/TotalOverallSignalImportanceCalculator.py
@@ -68,7 +68,7 @@ def initialize_weighted_buy_signal_names():
         'sma_short_golden_cross',
         'stoch',
         'tema',
-        'vwap_cross'
+        'rolling_vwap_cross'
     ]
 
 
@@ -81,7 +81,7 @@ def initialize_weighted_sell_signal_names():
         'sma_short_death_cross',
         'stoch',
         'tema',
-        'vwap_cross'
+        'rolling_vwap_cross'
     ]
 
 
@@ -94,7 +94,7 @@ def initialize_combined_weighted_signal_names():
         'sma_short_golden_death_cross': ['sma_short_golden_cross', 'sma_short_death_cross'],
         'stoch': ['stoch'],
         'tema': ['tema'],
-        'vwap_cross': ['vwap_cross']
+        'rolling_vwap_cross': ['rolling_vwap_cross']
     }
 
 

diff --git a/user_data/strategies/MoniGoManiHyperStrategy.py b/user_data/strategies/MoniGoManiHyperStrategy.py
@@ -26,8 +26,8 @@
     'macd': lambda df: (df['macd'] > df['macdsignal']),
     # Weighted Buy Signal: MFI crosses above 20 (Under-bought / low-price and rising indication)
     'mfi': lambda df: (qtpylib.crossed_above(df['mfi'], 20)),
-    # Weighted Buy Signal: VWAP crosses above current price
-    'vwap_cross': lambda df: (qtpylib.crossed_above(df['vwap'], df['close'])),
+    # Weighted Buy Signal: Rolling VWAP crosses above current price
+    'rolling_vwap_cross': lambda df: (qtpylib.crossed_above(df['rolling_vwap'], df['close'])),
     # Weighted Buy Signal: Price crosses above Parabolic SAR
     'sar_cross': lambda df: (qtpylib.crossed_above(df['sar'], df['close'])),
     # Weighted Buy Signal: Stochastic Slow below 20 (Under-bought, indication of starting to move up)
@@ -46,8 +46,8 @@
     'macd': lambda df: (df['macd'] < df['macdsignal']),
     # Weighted Sell Signal: MFI crosses below 80 (Over-bought / high-price and dropping indication)
     'mfi': lambda df: (qtpylib.crossed_below(df['mfi'], 80)),
-    # Weighted Sell Signal: VWAP crosses below current price
-    'vwap_cross': lambda df: (qtpylib.crossed_below(df['vwap'], df['close'])),
+    # Weighted Sell Signal: Rolling VWAP crosses below current price
+    'rolling_vwap_cross': lambda df: (qtpylib.crossed_below(df['rolling_vwap'], df['close'])),
     # Weighted Sell Signal: Price crosses below Parabolic SAR
     'sar_cross': lambda df: (qtpylib.crossed_below(df['sar'], df['close'])),
     # Weighted Sell Signal: Stochastic Slow above 80 (Over-bought, indication of starting to move down)
@@ -100,7 +100,7 @@ class MoniGoManiHyperStrategy(MasterMoniGoManiHyperStrategy):
     # Plot configuration to show all Weighted Signals/Indicators used by MoniGoMani in FreqUI.
     # Also loads in MGM Framework Plots for Buy/Sell Signals/Indicators and Trend Detection.
     plot_config = MasterMoniGoManiHyperStrategy.populate_frequi_plots({
-        # Main Plots Signals/Indicators (SMAs, EMAs, Bollinger Bands, VWAP, TEMA)
+        # Main Plots Signals/Indicators (SMAs, EMAs, Bollinger Bands, Rolling VWAP, TEMA)
         'main_plot': {
             'sma9': {'color': '#2c05f6'},
             'sma50': {'color': '#19038a'},
@@ -109,7 +109,7 @@ class MoniGoManiHyperStrategy(MasterMoniGoManiHyperStrategy):
             'ema50': {'color': '#0a8963'},
             'ema200': {'color': '#074b36'},
             'bb_middleband': {'color': '#6f1a7b'},
-            'vwap': {'color': '#727272'},
+            'rolling_vwap': {'color': '#727272'},
             'tema': {'color': '#9345ee'}
         },
         # Sub Plots - Each dict defines one additional plot
@@ -209,8 +209,8 @@ def do_populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFr
         # Volume Indicators
         # -----------------
 
-        # VWAP - Volume Weighted Average Price
-        dataframe['vwap'] = qtpylib.vwap(dataframe)
+        # Rolling VWAP - Volume Weighted Average Price
+        dataframe['rolling_vwap'] = qtpylib.rolling_vwap(dataframe)
 
         return dataframe