diff --git a/tradingagents/dataflows/discovery/risk_metrics.py b/tradingagents/dataflows/discovery/risk_metrics.py
new file mode 100644
index 00000000..df80a383
--- /dev/null
+++ b/tradingagents/dataflows/discovery/risk_metrics.py
@@ -0,0 +1,179 @@
+import pandas as pd
+import yfinance as yf
+
+from tradingagents.dataflows.y_finance import suppress_yfinance_warnings
+from tradingagents.utils.logger import get_logger
+
+logger = get_logger(__name__)
+
+
+def calculate_altman_z_score(ticker: str) -> float:
+    """
+    Calculate the Altman Z-Score for a manufacturing company.
+    If the company is non-manufacturing/service, a modified Z-score is often used,
+    but here we stick to the standard/modified approximation for simplicity.
+    Formula: Z = 1.2(X1) + 1.4(X2) + 3.3(X3) + 0.6(X4) + 1.0(X5)
+    X1 = Working Capital / Total Assets
+    X2 = Retained Earnings / Total Assets
+    X3 = EBIT / Total Assets
+    X4 = Market Value of Equity / Total Liabilities
+    X5 = Sales / Total Assets
+    Returns: float (Z-score). Values < 1.81 indicate distress.
+    """
+    try:
+        with suppress_yfinance_warnings():
+            stock = yf.Ticker(ticker.upper())
+            bs = stock.balance_sheet
+            inc = stock.financials
+            info = stock.info
+
+        if bs.empty or inc.empty:
+            return None
+
+        # Get latest annual data
+        latest_bs = bs.iloc[:, 0]
+        latest_inc = inc.iloc[:, 0]
+
+        total_assets = latest_bs.get("Total Assets")
+        if not total_assets or total_assets == 0:
+            return None
+
+        total_liabilities = latest_bs.get(
+            "Total Liabilities Net Minority Interest", latest_bs.get("Total Liabilities")
+        )
+        current_assets = latest_bs.get("Current Assets", 0)
+        current_liabilities = latest_bs.get("Current Liabilities", 0)
+        retained_earnings = latest_bs.get("Retained Earnings", 0)
+
+        ebit = latest_inc.get("EBIT")
+        if pd.isna(ebit):
+            ebit = latest_inc.get("Operating Income", 0)
+
+        sales = latest_inc.get("Total Revenue", 0)
+
+        market_cap = info.get("marketCap", 0)
+
+        # Handle NaNs
+        total_liabilities = 0 if pd.isna(total_liabilities) else total_liabilities
+        retained_earnings = 0 if pd.isna(retained_earnings) else retained_earnings
+
+        working_capital = current_assets - current_liabilities
+
+        x1 = working_capital / total_assets
+        x2 = retained_earnings / total_assets
+        x3 = ebit / total_assets
+        x4 = market_cap / total_liabilities if total_liabilities > 0 else 0
+        x5 = sales / total_assets
+
+        z_score = 1.2 * x1 + 1.4 * x2 + 3.3 * x3 + 0.6 * x4 + 1.0 * x5
+        return round(z_score, 2)
+    except Exception as e:
+        logger.debug(f"Error calculating Altman Z-Score for {ticker}: {e}")
+        return None
+
+
+def calculate_piotroski_f_score(ticker: str) -> int:
+    """
+    Calculate the Piotroski F-Score (0-9).
+    High score (7-9) indicates strong value/health.
+    Low score (0-3) indicates poor financial health.
+    """
+    try:
+        with suppress_yfinance_warnings():
+            stock = yf.Ticker(ticker.upper())
+            bs = stock.balance_sheet
+            inc = stock.financials
+            cf = stock.cashflow
+
+        if (
+            bs.empty
+            or inc.empty
+            or cf.empty
+            or len(bs.columns) < 2
+            or len(inc.columns) < 2
+            or len(cf.columns) < 1
+        ):
+            return None
+
+        latest_bs = bs.iloc[:, 0]
+        prev_bs = bs.iloc[:, 1]
+
+        latest_inc = inc.iloc[:, 0]
+        prev_inc = inc.iloc[:, 1]
+
+        latest_cf = cf.iloc[:, 0]
+
+        total_assets = latest_bs.get("Total Assets")
+        prev_total_assets = prev_bs.get("Total Assets")
+
+        if not total_assets or not prev_total_assets or total_assets == 0 or prev_total_assets == 0:
+            return None
+
+        score = 0
+
+        # Profitability
+        # 1. ROA > 0
+        net_income = latest_inc.get("Net Income", 0)
+        roa = net_income / total_assets
+        if roa > 0:
+            score += 1
+
+        # 2. Operating Cash Flow > 0
+        cfo = latest_cf.get("Operating Cash Flow", 0)
+        if pd.isna(cfo):
+            cfo = latest_cf.get("Total Cash From Operating Activities", 0)
+        if cfo > 0:
+            score += 1
+
+        # 3. Change in ROA > 0
+        prev_net_income = prev_inc.get("Net Income", 0)
+        prev_roa = prev_net_income / prev_total_assets
+        if roa > prev_roa:
+            score += 1
+
+        # 4. CFO > Net Income
+        if cfo > net_income:
+            score += 1
+
+        # Leverage, Liquidity
+        # 5. Change in Leverage (Long-Term Debt / Assets) < 0
+        ltd = latest_bs.get("Long Term Debt", 0)
+        prev_ltd = prev_bs.get("Long Term Debt", 0)
+        if (ltd / total_assets) < (prev_ltd / prev_total_assets):
+            score += 1
+
+        # 6. Change in Current Ratio > 0
+        ca = latest_bs.get("Current Assets", 0)
+        cl = latest_bs.get("Current Liabilities", 1)  # avoid div by zero
+        prev_ca = prev_bs.get("Current Assets", 0)
+        prev_cl = prev_bs.get("Current Liabilities", 1)
+        cr = ca / cl
+        prev_cr = prev_ca / prev_cl
+        if cr > prev_cr:
+            score += 1
+
+        # 7. Change in Shares Outstanding < 0 (or constant)
+        shares = latest_bs.get("Ordinary Shares Number", 0)
+        prev_shares = prev_bs.get("Ordinary Shares Number", 0)
+        if shares <= prev_shares:
+            score += 1
+
+        # Operating Efficiency
+        # 8. Change in Gross Margin > 0
+        gp = latest_inc.get("Gross Profit", 0)
+        prev_gp = prev_inc.get("Gross Profit", 0)
+        rev = latest_inc.get("Total Revenue", 1)
+        prev_rev = prev_inc.get("Total Revenue", 1)
+        if (gp / rev) > (prev_gp / prev_rev):
+            score += 1
+
+        # 9. Change in Asset Turnover > 0
+        ato = rev / total_assets
+        prev_ato = prev_rev / prev_total_assets
+        if ato > prev_ato:
+            score += 1
+
+        return score
+    except Exception as e:
+        logger.debug(f"Error calculating Piotroski F-Score for {ticker}: {e}")
+        return None
diff --git a/tradingagents/dataflows/discovery/scanners/minervini.py b/tradingagents/dataflows/discovery/scanners/minervini.py
index cb1e1744..470b94b4 100644
--- a/tradingagents/dataflows/discovery/scanners/minervini.py
+++ b/tradingagents/dataflows/discovery/scanners/minervini.py
@@ -65,7 +65,7 @@ class MinerviniScanner(BaseScanner):
         self.sma_200_slope_days = self.scanner_config.get("sma_200_slope_days", 20)
         self.min_pct_off_low = self.scanner_config.get("min_pct_off_low", 30)
         self.max_pct_from_high = self.scanner_config.get("max_pct_from_high", 25)
-        self.max_tickers = self.scanner_config.get("max_tickers", 200)
+        self.max_tickers = self.scanner_config.get("max_tickers", 50)
 
     def scan(self, state: Dict[str, Any]) -> List[Dict[str, Any]]:
         if not self.is_enabled():
diff --git a/tradingagents/default_config.py b/tradingagents/default_config.py
index 1b3878ed..4500aa44 100644
--- a/tradingagents/default_config.py
+++ b/tradingagents/default_config.py
@@ -229,7 +229,7 @@ DEFAULT_CONFIG = {
                 "sma_200_slope_days": 20,  # Days back to check SMA200 slope
                 "min_pct_off_low": 30,  # Must be 30%+ above 52w low
                 "max_pct_from_high": 25,  # Must be within 25% of 52w high
-                "max_tickers": 200,  # Cap universe to keep download under scanner timeout
+                "max_tickers": 50,  # Cap universe to keep download under scanner timeout (~75s for 50 tickers x 1y)
             },
         },
     },