Lowered signal tresholds so low that we got signals flowing. Few debug scripts to see way signals were not firing. Fix analyst.py indicator calculation to use TA-lib.

analysis/analyst.py

@@ -121,38 +121,44 @@ analysis_conn.commit()
 
 # ========== Technical Indicator Functions ==========
 def compute_indicators(df):
-    close = df['close']
+    """Compute indicators using TA-Lib for accuracy"""
+    import talib
+    
+    close = df['close'].values
+    high = df['high'].values
+    low = df['low'].values
+    volume = df['volume'].values
+    
     # EMA and SMA
-    df['ema_9'] = close.ewm(span=9, adjust=False).mean()
-    df['ema_21'] = close.ewm(span=21, adjust=False).mean()
-    df['sma_50'] = close.rolling(window=50, min_periods=1).mean()
-    df['sma_200'] = close.rolling(window=200, min_periods=1).mean()
-    # RSI (14): using 14-period gains/losses and RSI formula (100 - 100/(1+RS)):contentReference[oaicite:3]{index=3}
-    delta = close.diff()
-    gain = delta.clip(lower=0)
-    loss = -delta.clip(upper=0)
-    avg_gain = gain.rolling(window=14, min_periods=14).mean()
-    avg_loss = loss.rolling(window=14, min_periods=14).mean()
-    rs = avg_gain / avg_loss.replace(0, pd.NA)
-    df['rsi_14'] = 100 - (100 / (1 + rs))
+    df['ema_9'] = talib.EMA(close, timeperiod=9)
+    df['ema_21'] = talib.EMA(close, timeperiod=21)
+    df['sma_50'] = talib.SMA(close, timeperiod=50)
+    df['sma_200'] = talib.SMA(close, timeperiod=200)
+    
+    # RSI (14) - Proper calculation
+    df['rsi_14'] = talib.RSI(close, timeperiod=14)
+    
     # MACD (12,26,9)
-    ema12 = close.ewm(span=12, adjust=False).mean()
-    ema26 = close.ewm(span=26, adjust=False).mean()
-    macd_line = ema12 - ema26
-    df['macd'] = macd_line
-    df['macd_signal'] = macd_line.ewm(span=9, adjust=False).mean()
-    df['macd_hist'] = df['macd'] - df['macd_signal']
+    macd, macd_signal, macd_hist = talib.MACD(close, fastperiod=12, slowperiod=26, signalperiod=9)
+    df['macd'] = macd
+    df['macd_signal'] = macd_signal
+    df['macd_hist'] = macd_hist
+    
     # Bollinger Bands (20,2)
-    df['bb_middle'] = close.rolling(window=20, min_periods=20).mean()
-    bb_std = close.rolling(window=20, min_periods=20).std()
-    df['bb_upper'] = df['bb_middle'] + 2 * bb_std
-    df['bb_lower'] = df['bb_middle'] - 2 * bb_std
-    # Bollinger Squeeze: detect when BB width is lowest over 20 periods:contentReference[oaicite:4]{index=4}
-    bb_width = df['bb_upper'] - df['bb_lower']
-    rolling_min_width = bb_width.rolling(window=20, min_periods=20).min()
-    df['bb_squeeze'] = (bb_width <= rolling_min_width).astype(int)
-    # Volume moving average (20)
-    df['volume_ma_20'] = df['volume'].rolling(window=20, min_periods=1).mean()
+    bb_upper, bb_middle, bb_lower = talib.BBANDS(close, timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)
+    df['bb_upper'] = bb_upper
+    df['bb_middle'] = bb_middle
+    df['bb_lower'] = bb_lower
+    
+    # Bollinger Squeeze
+    bb_width = bb_upper - bb_lower
+    bb_width_series = pd.Series(bb_width)
+    rolling_min_width = bb_width_series.rolling(window=20, min_periods=20).min()
+    df['bb_squeeze'] = (bb_width_series <= rolling_min_width).fillna(0).astype(int)
+    
+    # Volume MA
+    df['volume_ma_20'] = talib.SMA(volume, timeperiod=20)
+    
     return df
 
 # ========== Health Check Server ==========