Add multi-model scoring: GPT-4.1-mini, GPT-4.1-nano, Gemini-2.5-flash

- MemelyAlphaStockRanking_multi_model.xlsx: 13-sheet Excel with per-model scores
  - Multi-Model Comparison (cross-model ranking)
  - Model Legend (color coding guide)
  - Scores - GPT-4.1-mini (blue highlights)
  - Scores - GPT-4.1-nano (green highlights)
  - Scores - Gemini-2.5-flash (purple highlights)
  - Rank sheets for each model
- scores_gpt41nano.json: GPT-4.1-nano raw scores
- scores_gemini25flash.json: Gemini-2.5-flash raw scores
- score_multi_model.py: Multi-model scoring script
- build_multi_model_excel.py: Excel generation script

score_multi_model.py

@@ -0,0 +1,152 @@
+#!/usr/bin/env python3
+"""
+Score all stocks using a specified AI model.
+Usage: python3 score_multi_model.py <model_name> <output_json>
+"""
+
+import pandas as pd
+import json
+import os
+import sys
+import time
+from openai import OpenAI
+
+model_name = sys.argv[1]
+output_json = sys.argv[2]
+
+client = OpenAI()
+
+EXCEL_PATH = "/home/ubuntu/upload/MemelyAlphaStockRanking（副本）（副本）.xlsx"
+
+# Read the master list
+df = pd.read_excel(EXCEL_PATH, sheet_name='stock master list')
+df4 = pd.read_excel(EXCEL_PATH, sheet_name='Sheet4')
+
+sector_map = {}
+for _, row in df4.iterrows():
+    sym = row.get('Symbol')
+    if pd.notna(sym):
+        sector_map[sym] = {
+            'sector': row.get('Sector', ''),
+            'theme': row.get('Theme', ''),
+        }
+
+# Build stock info
+stocks_info = []
+seen = set()
+for _, row in df.iterrows():
+    sym = row['Symbol']
+    if pd.isna(sym) or sym in seen:
+        continue
+    seen.add(sym)
+    info = {
+        'symbol': sym,
+        'theme': str(row.get('Theme', '')),
+        'perf_7d': row.get('performance past 7 days', None),
+        'perf_ytd': row.get('performance Year to Date', None),
+        'perf_specific': row.get('performance on specific dates', None),
+        'first_call_kol': str(row.get('first call X kol', '')),
+        'top_contributor': str(row.get('top contributor', '')),
+    }
+    if sym in sector_map:
+        info['sector'] = sector_map[sym].get('sector', '')
+        info['theme_s4'] = sector_map[sym].get('theme', '')
+    stocks_info.append(info)
+
+print(f"[{model_name}] Total stocks to score: {len(stocks_info)}")
+
+def score_batch(batch_stocks, model):
+    stocks_text = ""
+    for s in batch_stocks:
+        perf_7d = f"{s['perf_7d']:.2%}" if pd.notna(s.get('perf_7d')) else "N/A"
+        perf_ytd = f"{s['perf_ytd']:.2%}" if pd.notna(s.get('perf_ytd')) else "N/A"
+        perf_spec = f"{s['perf_specific']:.2%}" if pd.notna(s.get('perf_specific')) else "N/A"
+        sector = s.get('sector', s.get('theme', 'N/A'))
+        theme = s.get('theme_s4', s.get('theme', 'N/A'))
+        stocks_text += f"""
+---
+Symbol: {s['symbol']}
+Sector: {sector}
+Theme: {theme}
+Performance (Past 7 Days): {perf_7d}
+Performance (Year to Date): {perf_ytd}
+Performance (Specific Date): {perf_spec}
+First Call KOL: {s.get('first_call_kol', 'N/A')}
+Top Contributor: {s.get('top_contributor', 'N/A')}
+"""
+
+    prompt = f"""You are a professional stock/crypto analyst. Evaluate each of the following stocks/assets and provide a comprehensive score.
+
+For EACH stock, provide:
+1. **overall_score** (1-100): Overall investment attractiveness score
+2. **momentum_score** (1-100): Based on recent price performance and momentum
+3. **theme_score** (1-100): How strong/relevant is the thematic play (e.g., AI, Defense, Aerospace, Crypto, etc.)
+4. **risk_score** (1-100): Risk level (100 = highest risk)
+5. **social_buzz_score** (1-100): Social media attention and KOL backing strength
+6. **brief_analysis**: 2-3 sentence analysis explaining the scores
+
+Consider these factors:
+- YTD performance indicates momentum strength
+- Thematic relevance to current market trends (AI, Defense, Aerospace, Crypto, Quantum Computing are hot in 2025-2026)
+- Small/micro cap stocks with strong themes get higher theme scores
+- Stocks with notable KOL backing get higher social buzz scores
+- Higher volatility = higher risk score
+
+Here are the stocks to evaluate:
+{stocks_text}
+
+Return ONLY a valid JSON array with objects for each stock. Each object must have: symbol, overall_score, momentum_score, theme_score, risk_score, social_buzz_score, brief_analysis.
+Do not include any text outside the JSON array."""
+
+    for attempt in range(3):
+        try:
+            response = client.chat.completions.create(
+                model=model,
+                messages=[
+                    {"role": "system", "content": "You are a professional financial analyst. Always respond with valid JSON only."},
+                    {"role": "user", "content": prompt}
+                ],
+                temperature=0.3,
+                max_tokens=8000
+            )
+            content = response.choices[0].message.content.strip()
+            if content.startswith("```"):
+                content = content.split("```")[1]
+                if content.startswith("json"):
+                    content = content[4:]
+            results = json.loads(content)
+            return results
+        except Exception as e:
+            print(f"  Attempt {attempt+1} error: {e}")
+            time.sleep(3)
+    return None
+
+# Process in batches
+batch_size = 15
+scored = {}
+total_batches = (len(stocks_info) + batch_size - 1) // batch_size
+
+for i in range(0, len(stocks_info), batch_size):
+    batch = stocks_info[i:i+batch_size]
+    batch_num = i // batch_size + 1
+    symbols_in_batch = [s['symbol'] for s in batch]
+    print(f"[{model_name}] Batch {batch_num}/{total_batches}: {symbols_in_batch}")
+    
+    results = score_batch(batch, model_name)
+    
+    if results:
+        for r in results:
+            sym = r.get('symbol', '')
+            if sym:
+                scored[sym] = r
+                print(f"  ✓ {sym}: Overall={r.get('overall_score')}")
+    else:
+        print(f"  ✗ Batch {batch_num} failed after retries")
+    
+    time.sleep(1)
+
+with open(output_json, 'w') as f:
+    json.dump(scored, f, indent=2, ensure_ascii=False)
+
+print(f"\n[{model_name}] COMPLETE: {len(scored)}/{len(stocks_info)} scored")
+print(f"Saved to: {output_json}")