Update analysis.py

analysis.py

@@ -1,29 +1,45 @@
+import pandas as pd
 import yfinance as yf
-import os
-from huggingface_hub import InferenceClient
 
 class FundamentalAnalyst:
-    def analyze(self, ticker):
+    def analyze(self, ticker, session=None):
         try:
-            stock = yf.Ticker(ticker)
+            # Use session for requests if provided
+            stock = yf.Ticker(ticker, session=session)
             info = stock.info
             
-            # THE UNIVERSAL PHYSICS: ROE and Debt-to-Equity
-            roe = info.get('returnOnEquity', 0)
-            debt_to_eq = info.get('debtToEquity', 100)
-            fcf = info.get('freeCashflow', 0)
+            # Physics of Finance (Hard Filters)
+            roe = info.get('returnOnEquity', 0) or 0
+            pe = info.get('trailingPE', 0) or 100
+            profit_growth = info.get('revenueGrowth', 0) or 0
             
             score = 0
-            # Capital Allocation Check: Generating >15% on equity?
-            if roe > 0.15: score += 50 
-            # Risk Premium Check: Is debt low?
-            if debt_to_eq < 50: score += 25
-            # Cash Engine: Is FCF positive?
-            if fcf > 0: score += 25
+            if roe > 0.15: score += 40
+            if profit_growth > 0.10: score += 30
+            if pe < 50: score += 30
             
-            return {
-                "score": score,
-                "metrics": f"ROE: {roe*100:.1f}% | Debt/Eq: {debt_to_eq}",
-                "physics": "Strong Capital Allocation" if score > 70 else "Weak Unit Economics"
-            }
-        except: return None
+            metrics = f"ROE: {roe*100:.1f}% | PE: {pe:.1f} | Growth: {profit_growth*100:.1f}%"
+            return {"score": score, "metrics": metrics}
+        except:
+            return None
+
+class TechnicalAnalyst:
+    def analyze(self, ticker, session=None):
+        try:
+            stock = yf.Ticker(ticker, session=session)
+            # Fetch 1 year of history
+            hist = stock.history(period="1y")
+            if hist.empty: return "NO DATA"
+            
+            # Simple Moving Averages
+            sma_50 = hist['Close'].rolling(50).mean().iloc[-1]
+            sma_200 = hist['Close'].rolling(200).mean().iloc[-1]
+            price = hist['Close'].iloc[-1]
+            
+            trend = "SIDEWAYS"
+            if price > sma_50 > sma_200: trend = "UPTREND"
+            elif price < sma_50 < sma_200: trend = "DOWNTREND"
+            
+            return trend
+        except:
+            return "ERROR"