Feature: IPO Discount, Greenshoe, & Ownership Logic

main.py

@@ -309,6 +309,52 @@ def generate_advisory(signals, macro, fundamentals, last_private_price):
         'risk_matrix': risk_matrix
     }
 
+# ==============================================================================
+# IPO STRUCTURING LOGIC (Professional)
+# ==============================================================================
+
+def calculate_ipo_structure(implied_price, discount_pct, greenshoe_active, existing_shares_m):
+    """
+    Calculates final deal structure based on banking levers.
+    Assumption: Target Capital Raise = $250M (Standard for this segment)
+    """
+    target_raise = 250.0  # $M
+    
+    # 1. Apply Discount
+    discount_factor = (100 - discount_pct) / 100
+    final_price = implied_price * discount_factor
+    
+    if final_price <= 0: final_price = 1.0 # Safety
+    
+    # 2. Calculate Issuance
+    new_shares_m = target_raise / final_price
+    
+    # 3. Greenshoe Adjustment (Standard 15% Over-allotment)
+    greenshoe_shares_m = 0.0
+    if greenshoe_active:
+        greenshoe_shares_m = new_shares_m * 0.15
+        new_shares_m += greenshoe_shares_m
+        target_raise += (greenshoe_shares_m * final_price)
+    
+    # 4. Dilution & Ownership
+    total_shares_m = existing_shares_m + new_shares_m
+    dilution_pct = (new_shares_m / total_shares_m) * 100
+    
+    # Ownership Split
+    ownership = {
+        "Existing Shareholders": round(existing_shares_m, 2),
+        "New Public Investors": round(new_shares_m, 2)
+    }
+    
+    return {
+        "final_price": round(final_price, 2),
+        "capital_raised": round(target_raise, 2),
+        "new_shares": round(new_shares_m, 2),
+        "total_shares": round(total_shares_m, 2),
+        "dilution": round(dilution_pct, 1),
+        "ownership": ownership
+    }
+
 # ==============================================================================
 # ROUTES
 # ==============================================================================
@@ -324,7 +370,10 @@ async def health_check():
 @app.post("/analyze")
 async def analyze(request: Request, 
                   query: str = Form(...), 
-                  last_private: str = Form(None)):
+                  last_private: str = Form(None),
+                  ipo_discount: float = Form(15.0),  # Default 15%
+                  greenshoe: bool = Form(False),     # Default Off
+                  primary_shares: float = Form(100.0)): # Default 100M shares
     
     # 1. Determine Sector
     sector_key = 'SaaS'
@@ -347,9 +396,31 @@ async def analyze(request: Request,
     fundamentals = get_fundamentals(target_tickers)
     
     # 4. The Advisor Engine
+    # Get base advisory first to get the 'High' implied price
     advisory = generate_advisory(signals, macro, fundamentals, last_private)
     
-    # 5. Chart Data
+    # 5. IPO Structuring (The Pro Layer)
+    # We use the midpoint of the implied range as the base for discounting
+    implied_midpoint = (advisory['low'] + advisory['high']) / 2
+    structure = calculate_ipo_structure(implied_midpoint, ipo_discount, greenshoe, primary_shares)
+    
+    # Update Advisory with Final Price Context
+    final_price = structure['final_price']
+    
+    # Re-Run Down-Round Logic on FINAL PRICE
+    down_round_alert = False
+    dr_text = ""
+    if last_private:
+        try:
+            lpp = float(last_private)
+            if final_price < lpp:
+                down_round_alert = True
+                diff = ((final_price - lpp) / lpp) * 100
+                dr_text = f"🚨 <b>DOWN-ROUND ALERT:</b> Final IPO Price (${final_price}) is {diff:.1f}% below Last Private Round (${lpp})."
+        except:
+            pass
+
+    # 6. Chart Data
     normalized = prices / prices.iloc[0] * 100
     chart_data = []
     for col in normalized.columns:
@@ -362,10 +433,12 @@ async def analyze(request: Request,
                 'mode': 'lines'
             })
             
-    # 6. Response
+    # 7. Response
     response_data = {
         'sector': sector_key,
         'advisory': advisory,
+        'structure': structure, # NEW
+        'down_round': {'is_active': down_round_alert, 'text': dr_text}, # NEW
         'macro': macro,
         'metrics': { 
             'avg_momentum': np.mean([s['momentum'] for s in signals.values()]) if signals else 0,