Upload 20 files

forecaster_cli.py

@@ -135,16 +135,11 @@ def generate_reasoning(features, tier, prob):
         
     return " ".join(reasons)
 
-def run_inference(ticker):
+def get_prediction(ticker):
     tier = get_market_cap_tier(ticker)
-    print(f"[{ticker}] Classified as: {tier} Cap")
-    
-    print(f"[{ticker}] Extracting live fundamental data...")
     features = fetch_live_features(ticker)
-    
     if not features:
-        print(f"[{ticker}] Error: Could not extract live data.")
-        return
+        return {"error": "Could not extract live data."}
         
     df = pd.DataFrame([features])
     model_features = ['Sales_Growth', 'OPM', 'ROCE', 'ROE', 'Debt_to_Equity', 'PE_Ratio']
@@ -155,32 +150,38 @@ def run_inference(ticker):
         imputer = joblib.load(f'imputer_{tier.lower()}.pkl')
         scaler = joblib.load(f'scaler_{tier.lower()}.pkl')
     except Exception as e:
-        print(f"Error loading models: {e}")
-        return
+        return {"error": f"Error loading models: {e}"}
         
     X_imputed = imputer.transform(X)
     X_scaled = scaler.transform(X_imputed)
     
-    prob = model.predict_proba(X_scaled)[0][1]
-        
-    if prob > 0.65:
-        decision = "BUY"
-    else:
-        decision = "PASS"
-        
+    prob = float(model.predict_proba(X_scaled)[0][1])
+    decision = "BUY" if prob > 0.65 else "PASS"
     reasoning = generate_reasoning(features, tier, prob)
     
+    return {
+        "Ticker": ticker,
+        "Tier": tier,
+        "Decision": decision,
+        "Confidence": prob,
+        "Reasoning": reasoning,
+        "Features": features
+    }
+
+def run_inference(ticker):
+    res = get_prediction(ticker)
+    if "error" in res:
+        print(f"[{ticker}] {res['error']}")
+        return
+        
     print("\n" + "="*50)
-    print(f" FORECAST FOR {ticker} ({tier} Cap Model)")
+    print(f" FORECAST FOR {ticker} ({res['Tier']} Cap Model)")
     print("="*50)
-    print(f" Decision:   {decision} (Confidence: {prob*100:.1f}%)")
-    print(f" Reasoning:  {reasoning}")
+    print(f" Decision:   {res['Decision']} (Confidence: {res['Confidence']*100:.1f}%)")
+    print(f" Reasoning:  {res['Reasoning']}")
     print("-" * 50)
-    print(f" Sales Growth: {features['Sales_Growth']:.2f}%")
-    print(f" ROE:          {features['ROE']:.2f}%")
-    print(f" ROCE:         {features['ROCE']:.2f}%")
-    print(f" Debt/Equity:  {features['Debt_to_Equity']:.2f}")
-    print(f" OPM:          {features['OPM']:.2f}%")
+    for k, v in res['Features'].items():
+        print(f" {k}: {v}")
     print("="*50 + "\n")
     return {"Ticker": ticker, "Tier": tier, "Decision": decision, **features}