Update technical_agent.py

technical_agent.py

@@ -1,4 +1,6 @@
 import ta
+import pandas as pd
+
 
 def analyze_technical(market_data):
 
@@ -6,39 +8,54 @@ def analyze_technical(market_data):
 
     for ticker, df in market_data.items():
 
-        score = 0
+        try:
+
+            df = df.copy()
+
+            # Ensure 1D Series (fix for yfinance issue)
+            close = pd.Series(df["Close"]).squeeze()
+            volume = pd.Series(df["Volume"]).squeeze()
+
+            score = 0
+
+            rsi = ta.momentum.RSIIndicator(close).rsi()
+
+            macd = ta.trend.MACD(close).macd()
+
+            ma20 = close.rolling(20).mean()
+            ma50 = close.rolling(50).mean()
+
+            bb = ta.volatility.BollingerBands(close)
+
+            bb_high = bb.bollinger_hband()
+            bb_low = bb.bollinger_lband()
 
-        df["rsi"] = ta.momentum.RSIIndicator(df["Close"]).rsi()
-        df["macd"] = ta.trend.MACD(df["Close"]).macd()
-        df["ma20"] = df["Close"].rolling(20).mean()
-        df["ma50"] = df["Close"].rolling(50).mean()
+            last = len(close) - 1
 
-        bb = ta.volatility.BollingerBands(df["Close"])
-        df["bb_high"] = bb.bollinger_hband()
-        df["bb_low"] = bb.bollinger_lband()
+            # RSI oversold
+            if rsi.iloc[last] < 35:
+                score += 1
 
-        last = df.iloc[-1]
+            # MACD bullish
+            if macd.iloc[last] > 0:
+                score += 1
 
-        # RSI oversold
-        if last["rsi"] < 35:
-            score += 1
+            # MA crossover
+            if ma20.iloc[last] > ma50.iloc[last]:
+                score += 1
 
-        # MACD bullish
-        if last["macd"] > 0:
-            score += 1
+            # Bollinger bounce
+            if close.iloc[last] < bb_low.iloc[last]:
+                score += 1
 
-        # MA crossover
-        if last["ma20"] > last["ma50"]:
-            score += 1
+            # Volume spike
+            if volume.iloc[last] > volume.rolling(20).mean().iloc[last]:
+                score += 1
 
-        # Bollinger bounce
-        if last["Close"] < last["bb_low"]:
-            score += 1
+            results[ticker] = score
 
-        # Volume spike
-        if df["Volume"].iloc[-1] > df["Volume"].rolling(20).mean().iloc[-1]:
-            score += 1
+        except:
 
-        results[ticker] = score
+            results[ticker] = 0
 
     return results