Merge pull request #266 from lgrawet/fix_pandas_ta_0_4

Feat/pandas-ta >= 0.4 compatibility

Author: rapcmia

environment_conda.yml

@@ -19,7 +19,6 @@ dependencies:
       - glom
       - defillama
       - statsmodels
-      - pandas_ta==0.3.14b
       - pyyaml
       - pathlib
       - streamlit-authenticator==0.3.2

frontend/visualization/indicators.py

@@ -7,10 +7,10 @@
 
 def get_bbands_traces(df, bb_length, bb_std):
     tech_colors = theme.get_color_scheme()
-    df.ta.bbands(length=bb_length, std=bb_std, append=True)
-    bb_lower = f'BBL_{bb_length}_{bb_std}'
-    bb_middle = f'BBM_{bb_length}_{bb_std}'
-    bb_upper = f'BBU_{bb_length}_{bb_std}'
+    df.ta.bbands(length=bb_length, lower_std=bb_std, upper_std=bb_std, append=True)
+    bb_lower = f'BBL_{bb_length}_{bb_std}_{bb_std}'
+    bb_middle = f'BBM_{bb_length}_{bb_std}_{bb_std}'
+    bb_upper = f'BBU_{bb_length}_{bb_std}_{bb_std}'
     traces = [
         go.Scatter(x=df.index, y=df[bb_upper], line=dict(color=tech_colors['upper_band']),
                    name='Upper Band'),

frontend/visualization/signals.py

@@ -20,23 +20,23 @@ def get_signal_traces(buy_signals, sell_signals):
 def get_bollinger_v1_signal_traces(df, bb_length, bb_std, bb_long_threshold, bb_short_threshold):
     # Add Bollinger Bands
     candles = df.copy()
-    candles.ta.bbands(length=bb_length, std=bb_std, append=True)
+    candles.ta.bbands(length=bb_length, lower_std=bb_std, upper_std=bb_std, append=True)
 
     # Generate conditions
-    buy_signals = candles[candles[f"BBP_{bb_length}_{bb_std}"] < bb_long_threshold]
-    sell_signals = candles[candles[f"BBP_{bb_length}_{bb_std}"] > bb_short_threshold]
+    buy_signals = candles[candles[f"BBP_{bb_length}_{bb_std}_{bb_std}"] < bb_long_threshold]
+    sell_signals = candles[candles[f"BBP_{bb_length}_{bb_std}_{bb_std}"] > bb_short_threshold]
 
     return get_signal_traces(buy_signals, sell_signals)
 
 
 def get_macdbb_v1_signal_traces(df, bb_length, bb_std, bb_long_threshold, bb_short_threshold, macd_fast, macd_slow,
                                 macd_signal):
     # Add Bollinger Bands
-    df.ta.bbands(length=bb_length, std=bb_std, append=True)
+    df.ta.bbands(length=bb_length, lower_std=bb_std, upper_std=bb_std, append=True)
     # Add MACD
     df.ta.macd(fast=macd_fast, slow=macd_slow, signal=macd_signal, append=True)
     # Decision Logic
-    bbp = df[f"BBP_{bb_length}_{bb_std}"]
+    bbp = df[f"BBP_{bb_length}_{bb_std}_{bb_std}"]
     macdh = df[f"MACDh_{macd_fast}_{macd_slow}_{macd_signal}"]
     macd = df[f"MACD_{macd_fast}_{macd_slow}_{macd_signal}"]