MonteWalk

Instructions

Implementation Reference

• tools/feature_engineering.py:57-172 (handler)

  The core handler function for the 'get_technical_summary' tool. Downloads 2y historical data using yfinance, computes RSI(14), MACD, SMA50, SMA200. Generates a score-based signal (STRONG BUY to STRONG SELL) with detailed reasons.

  def get_technical_summary(symbol: str) -> str:
      """
      Performs a technical analysis summary (RSI, MACD, Moving Averages).
      Returns a 'Buy', 'Sell', or 'Neutral' signal based on aggregated indicators.
      """
      try:
          # Need enough data for 200 SMA
          df = yf.download(symbol, period="2y", progress=False)
          if df.empty:
              return f"No data found for {symbol}"
              
          # Handle MultiIndex if present
          # yfinance structure: columns are (Price, Ticker)
          if isinstance(df.columns, pd.MultiIndex):
              # If we have a MultiIndex, we want the 'Close' level, and then the specific ticker
              # But usually df['Close'] gives us the Close column(s).
              pass 
              
          close = df['Close']
          if isinstance(close, pd.DataFrame):
              # If multiple tickers or just weird structure, take the first column
              close = close.iloc[:, 0]
              
          # 1. RSI
          rsi = ta.rsi(close, length=14)
          if rsi is None or rsi.empty:
              return f"Not enough data to calculate RSI for {symbol}"
          current_rsi = rsi.iloc[-1]
          
          # 2. MACD
          macd = ta.macd(close)
          if macd is None or macd.empty:
              return f"Not enough data to calculate MACD for {symbol}"
          
          # MACD_12_26_9, MACDh_12_26_9 (hist), MACDs_12_26_9 (signal)
          # Column names depend on pandas_ta version/settings, but usually standard
          macd_line = macd.iloc[-1, 0] # First column is usually MACD line
          macd_signal = macd.iloc[-1, 2] # Third column is usually Signal line
          
          # 3. Moving Averages
          sma_50 = ta.sma(close, length=50)
          sma_200 = ta.sma(close, length=200)
          
          if sma_50 is None or sma_50.empty:
              return "Not enough data for SMA 50"
          if sma_200 is None or sma_200.empty:
              # Fallback if 200 SMA not available (e.g. IPO recently)
              sma_200_val = 0
              has_200 = False
          else:
              sma_200_val = sma_200.iloc[-1]
              has_200 = True
              
          sma_50_val = sma_50.iloc[-1]
          current_price = close.iloc[-1]
          
          # Scoring Logic
          score = 0
          reasons = []
          
          # RSI Logic
          if current_rsi < 30:
              score += 1
              reasons.append(f"RSI is Oversold ({current_rsi:.2f})")
          elif current_rsi > 70:
              score -= 1
              reasons.append(f"RSI is Overbought ({current_rsi:.2f})")
          else:
              reasons.append(f"RSI is Neutral ({current_rsi:.2f})")
              
          # MACD Logic
          if macd_line > macd_signal:
              score += 1
              reasons.append("MACD Bullish Crossover")
          else:
              score -= 1
              reasons.append("MACD Bearish Crossover")
              
          # Trend Logic
          if current_price > sma_50_val:
              score += 1
              reasons.append("Price above 50 SMA (Bullish Trend)")
          else:
              score -= 1
              reasons.append("Price below 50 SMA (Bearish Trend)")
              
          if has_200:
              if current_price > sma_200_val:
                  score += 1
                  reasons.append("Price above 200 SMA (Long-term Bullish)")
              else:
                  score -= 1
                  reasons.append("Price below 200 SMA (Long-term Bearish)")
          else:
              reasons.append("200 SMA not available (Insufficient history)")
              
          # Final Verdict
          if score >= 2:
              signal = "STRONG BUY"
          elif score == 1:
              signal = "BUY"
          elif score == 0:
              signal = "NEUTRAL"
          elif score == -1:
              signal = "SELL"
          else:
              signal = "STRONG SELL"
              
          return (f"Technical Summary for {symbol}:\n"
                  f"Signal: {signal} (Score: {score})\n"
                  f"Price: ${current_price:.2f}\n"
                  f"--------------------------------\n"
                  f"Analysis:\n" + "\n".join([f"- {r}" for r in reasons]))
                  
      except Exception as e:
          return f"Error performing technical analysis: {str(e)}"

• server.py:390-393 (registration)

  Registers the get_technical_summary tool (along with compute_indicators and rolling_stats) as an MCP tool using the register_tools helper function, which applies @mcp.tool() decorator.

  register_tools(
      [compute_indicators, rolling_stats, get_technical_summary],
      "Feature Engineering"
  )

• server.py:16-16 (registration)

  Import statement bringing the get_technical_summary function into the MCP server module for registration.

  from tools.feature_engineering import compute_indicators, rolling_stats, get_technical_summary