Add AI scoring results: 121 stocks scored with GPT-4.1-mini
- MemelyAlphaStockRanking_scored.xlsx: Excel with AI scores (yellow highlighted) - research_scores.json: Raw scoring data - score_stocks.py: AI scoring script - write_scores_to_excel.py: Excel writing script - Updated README with methodology and top 10 rankings
这个提交包含在：
196
score_stocks.py
普通文件
196
score_stocks.py
普通文件
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#!/usr/bin/env python3
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"""
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Score all stocks in the MemelyAlpha ranking using AI model.
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Processes in batches to handle rate limits and save progress.
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"""
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import pandas as pd
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import json
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import os
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import time
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from openai import OpenAI
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client = OpenAI()
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EXCEL_PATH = "/home/ubuntu/upload/MemelyAlphaStockRanking（副本）（副本）.xlsx"
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OUTPUT_JSON = "/home/ubuntu/memely-alpha-stock-ranking/research_scores.json"
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PROGRESS_FILE = "/home/ubuntu/memely-alpha-stock-ranking/scoring_progress.json"
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# Read the master list
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df = pd.read_excel(EXCEL_PATH, sheet_name='stock master list')
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# Also read Sheet4 for sector/theme info
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df4 = pd.read_excel(EXCEL_PATH, sheet_name='Sheet4')
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sector_map = {}
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for _, row in df4.iterrows():
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sym = row.get('Symbol')
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if pd.notna(sym):
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sector_map[sym] = {
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'sector': row.get('Sector', ''),
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'theme': row.get('Theme', ''),
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'cap_category': row.get('Cap Category', '')
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}
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# Read Sheet6 for additional YTD data
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df6 = pd.read_excel(EXCEL_PATH, sheet_name='Sheet6')
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ytd_extra = {}
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for _, row in df6.iterrows():
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sym = row.get('Symbol')
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if pd.notna(sym):
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ytd_extra[sym] = row.get('performance Year to Date', None)
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# Build stock info for each symbol
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stocks_info = []
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seen = set()
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for _, row in df.iterrows():
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sym = row['Symbol']
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if pd.isna(sym) or sym in seen:
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continue
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seen.add(sym)
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info = {
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'symbol': sym,
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'theme': str(row.get('Theme', '')),
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'perf_7d': row.get('performance past 7 days', None),
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'perf_ytd': row.get('performance Year to Date', None),
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'perf_specific': row.get('performance on specific dates', None),
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'first_call_kol': str(row.get('first call X kol', '')),
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'top_contributor': str(row.get('top contributor', '')),
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}
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# Add sector info from Sheet4
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if sym in sector_map:
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info['sector'] = sector_map[sym].get('sector', '')
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info['theme_s4'] = sector_map[sym].get('theme', '')
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# Add extra YTD from Sheet6
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if sym in ytd_extra and pd.notna(ytd_extra[sym]):
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info['ytd_extra'] = ytd_extra[sym]
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stocks_info.append(info)
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print(f"Total unique stocks to score: {len(stocks_info)}")
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# Load progress if exists
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scored = {}
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if os.path.exists(PROGRESS_FILE):
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with open(PROGRESS_FILE, 'r') as f:
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scored = json.load(f)
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print(f"Loaded {len(scored)} previously scored stocks")
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def score_batch(batch_stocks):
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"""Score a batch of stocks using AI model."""
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stocks_text = ""
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for s in batch_stocks:
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perf_7d = f"{s['perf_7d']:.2%}" if pd.notna(s.get('perf_7d')) else "N/A"
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perf_ytd = f"{s['perf_ytd']:.2%}" if pd.notna(s.get('perf_ytd')) else "N/A"
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perf_spec = f"{s['perf_specific']:.2%}" if pd.notna(s.get('perf_specific')) else "N/A"
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sector = s.get('sector', s.get('theme', 'N/A'))
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theme = s.get('theme_s4', s.get('theme', 'N/A'))
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stocks_text += f"""
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---
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Symbol: {s['symbol']}
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Sector: {sector}
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Theme: {theme}
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Performance (Past 7 Days): {perf_7d}
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Performance (Year to Date): {perf_ytd}
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Performance (Specific Date): {perf_spec}
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First Call KOL: {s.get('first_call_kol', 'N/A')}
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Top Contributor: {s.get('top_contributor', 'N/A')}
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"""
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prompt = f"""You are a professional stock/crypto analyst. Evaluate each of the following stocks/assets and provide a comprehensive score.
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For EACH stock, provide:
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1. **overall_score** (1-100): Overall investment attractiveness score
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2. **momentum_score** (1-100): Based on recent price performance and momentum
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3. **theme_score** (1-100): How strong/relevant is the thematic play (e.g., AI, Defense, Aerospace, Crypto, etc.)
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4. **risk_score** (1-100): Risk level (100 = highest risk)
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5. **social_buzz_score** (1-100): Social media attention and KOL backing strength
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6. **brief_analysis**: 2-3 sentence analysis explaining the scores
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Consider these factors:
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- YTD performance indicates momentum strength
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- Thematic relevance to current market trends (AI, Defense, Aerospace, Crypto, Quantum Computing are hot in 2025-2026)
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- Small/micro cap stocks with strong themes get higher theme scores
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- Stocks with notable KOL backing get higher social buzz scores
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- Higher volatility = higher risk score
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Here are the stocks to evaluate:
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{stocks_text}
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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.
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Do not include any text outside the JSON array."""
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try:
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response = client.chat.completions.create(
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model="gpt-4.1-mini",
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messages=[
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{"role": "system", "content": "You are a professional financial analyst. Always respond with valid JSON only."},
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{"role": "user", "content": prompt}
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],
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temperature=0.3,
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max_tokens=8000
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)
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content = response.choices[0].message.content.strip()
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# Try to extract JSON from the response
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if content.startswith("```"):
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content = content.split("```")[1]
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if content.startswith("json"):
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content = content[4:]
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results = json.loads(content)
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return results
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except Exception as e:
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print(f"Error in batch scoring: {e}")
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return None
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# Process in batches of 15
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batch_size = 15
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remaining = [s for s in stocks_info if s['symbol'] not in scored]
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print(f"Remaining to score: {len(remaining)}")
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total_batches = (len(remaining) + batch_size - 1) // batch_size
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for i in range(0, len(remaining), batch_size):
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batch = remaining[i:i+batch_size]
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batch_num = i // batch_size + 1
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symbols_in_batch = [s['symbol'] for s in batch]
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print(f"\nBatch {batch_num}/{total_batches}: Scoring {symbols_in_batch}")
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results = score_batch(batch)
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if results:
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for r in results:
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sym = r.get('symbol', '')
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if sym:
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scored[sym] = r
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print(f" ✓ {sym}: Overall={r.get('overall_score')}, Momentum={r.get('momentum_score')}, Theme={r.get('theme_score')}")
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# Save progress after each batch
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with open(PROGRESS_FILE, 'w') as f:
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json.dump(scored, f, indent=2, ensure_ascii=False)
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else:
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print(f" ✗ Batch {batch_num} failed, will retry...")
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# Retry once
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time.sleep(2)
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results = score_batch(batch)
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if results:
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for r in results:
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sym = r.get('symbol', '')
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if sym:
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scored[sym] = r
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with open(PROGRESS_FILE, 'w') as f:
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json.dump(scored, f, indent=2, ensure_ascii=False)
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time.sleep(1) # Rate limit
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# Save final results
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with open(OUTPUT_JSON, 'w') as f:
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json.dump(scored, f, indent=2, ensure_ascii=False)
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print(f"\n=== SCORING COMPLETE ===")
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print(f"Total scored: {len(scored)}/{len(stocks_info)}")
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print(f"Results saved to: {OUTPUT_JSON}")
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