hao
90d58a000b
- 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
534 行
19 KiB
Python
534 行
19 KiB
Python
#!/usr/bin/env python3
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"""
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Build a multi-model Excel file:
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- Copy original sheets for each model
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- Each model gets its own colored score columns
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- Add a cross-model comparison summary sheet
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"""
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import json
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import pandas as pd
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from openpyxl import load_workbook
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from openpyxl.styles import PatternFill, Font, Alignment, Border, Side
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from openpyxl.utils import get_column_letter
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from copy import copy
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EXCEL_SRC = "/home/ubuntu/upload/MemelyAlphaStockRanking（副本）（副本）.xlsx"
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EXCEL_OUT = "/home/ubuntu/memely-alpha-stock-ranking/MemelyAlphaStockRanking_multi_model.xlsx"
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# Model configs: name, json_path, highlight_color, header_color
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MODELS = [
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{
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"name": "GPT-4.1-mini",
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"short": "gpt41mini",
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"json": "/home/ubuntu/memely-alpha-stock-ranking/research_scores.json",
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"fill_color": "BDD7EE", # Light blue
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"header_color": "2F75B5", # Dark blue
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"header_font_color": "FFFFFF",
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},
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{
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"name": "GPT-4.1-nano",
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"short": "gpt41nano",
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"json": "/home/ubuntu/memely-alpha-stock-ranking/scores_gpt41nano.json",
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"fill_color": "C6EFCE", # Light green
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"header_color": "548235", # Dark green
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"header_font_color": "FFFFFF",
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},
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{
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"name": "Gemini-2.5-flash",
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"short": "gemini25flash",
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"json": "/home/ubuntu/memely-alpha-stock-ranking/scores_gemini25flash.json",
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"fill_color": "E2BFEE", # Light purple
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"header_color": "7030A0", # Dark purple
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"header_font_color": "FFFFFF",
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},
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]
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# Load all scores
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all_scores = {}
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for m in MODELS:
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with open(m["json"], 'r') as f:
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all_scores[m["name"]] = json.load(f)
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print(f"Loaded {len(all_scores[m['name']])} scores for {m['name']}")
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# Load original workbook
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wb = load_workbook(EXCEL_SRC)
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thin_border = Border(
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left=Side(style='thin'),
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right=Side(style='thin'),
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top=Side(style='thin'),
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bottom=Side(style='thin')
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)
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# ============================================================
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# For each model, create a copy of "stock master list" sheet
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# with that model's scores appended in its color
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# ============================================================
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# Read original data for reference
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df = pd.read_excel(EXCEL_SRC, sheet_name='stock master list')
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df4 = pd.read_excel(EXCEL_SRC, sheet_name='Sheet4')
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# Get original sheet as template
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orig_ws = wb['stock master list']
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orig_max_col = orig_ws.max_column
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orig_max_row = orig_ws.max_row
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for model_cfg in MODELS:
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model_name = model_cfg["name"]
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scores = all_scores[model_name]
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fill_color = model_cfg["fill_color"]
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header_color = model_cfg["header_color"]
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header_font_color = model_cfg["header_font_color"]
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cell_fill = PatternFill(start_color=fill_color, end_color=fill_color, fill_type="solid")
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hdr_fill = PatternFill(start_color=header_color, end_color=header_color, fill_type="solid")
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hdr_font = Font(bold=True, color=header_font_color, size=11)
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score_font = Font(size=10)
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sheet_name = f"Scores - {model_name}"
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# Truncate sheet name if too long (max 31 chars)
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if len(sheet_name) > 31:
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sheet_name = sheet_name[:31]
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ws = wb.copy_worksheet(orig_ws)
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ws.title = sheet_name
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# Add score columns
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new_headers = [
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f'{model_name} Overall',
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f'{model_name} Momentum',
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f'{model_name} Theme',
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f'{model_name} Risk',
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f'{model_name} Social Buzz',
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f'{model_name} Analysis',
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]
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for j, hdr in enumerate(new_headers):
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col = orig_max_col + 1 + j
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cell = ws.cell(row=1, column=col, value=hdr)
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cell.fill = hdr_fill
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cell.font = hdr_font
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cell.alignment = Alignment(horizontal='center', wrap_text=True)
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cell.border = thin_border
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# Write scores
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for row in range(2, orig_max_row + 1):
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symbol = ws.cell(row=row, column=1).value
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if symbol and symbol in scores:
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s = scores[symbol]
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fields = ['overall_score', 'momentum_score', 'theme_score', 'risk_score', 'social_buzz_score', 'brief_analysis']
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for j, field in enumerate(fields):
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col = orig_max_col + 1 + j
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cell = ws.cell(row=row, column=col, value=s.get(field, ''))
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cell.fill = cell_fill
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cell.font = score_font
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if field != 'brief_analysis':
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cell.alignment = Alignment(horizontal='center')
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else:
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cell.alignment = Alignment(wrap_text=True)
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cell.border = thin_border
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# Set column widths
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widths = [14, 16, 14, 12, 16, 55]
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for j, w in enumerate(widths):
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col_letter = get_column_letter(orig_max_col + 1 + j)
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ws.column_dimensions[col_letter].width = w
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ws.freeze_panes = 'B2'
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print(f"Created sheet: {sheet_name}")
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# ============================================================
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# Create cross-model comparison summary sheet
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# ============================================================
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ws_comp = wb.create_sheet("Multi-Model Comparison", 0)
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# Headers
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comp_headers = ['Rank', 'Symbol', 'Theme']
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for m in MODELS:
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comp_headers.append(f'{m["name"]} Overall')
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comp_headers.extend(['Avg Overall', 'Std Dev', 'Consensus'])
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for m in MODELS:
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comp_headers.append(f'{m["name"]} Momentum')
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for m in MODELS:
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comp_headers.append(f'{m["name"]} Theme')
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for m in MODELS:
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comp_headers.append(f'{m["name"]} Risk')
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for m in MODELS:
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comp_headers.append(f'{m["name"]} Social Buzz')
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# Write headers with styling
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general_hdr_fill = PatternFill(start_color="333333", end_color="333333", fill_type="solid")
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general_hdr_font = Font(bold=True, color="FFFFFF", size=10)
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for col, hdr in enumerate(comp_headers, 1):
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cell = ws_comp.cell(row=1, column=col, value=hdr)
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# Color-code model-specific headers
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matched = False
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for m in MODELS:
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if m["name"] in hdr:
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cell.fill = PatternFill(start_color=m["header_color"], end_color=m["header_color"], fill_type="solid")
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cell.font = Font(bold=True, color=m["header_font_color"], size=10)
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matched = True
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break
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if not matched:
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cell.fill = general_hdr_fill
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cell.font = general_hdr_font
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cell.alignment = Alignment(horizontal='center', wrap_text=True)
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cell.border = thin_border
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# Collect all symbols and compute averages
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symbol_info = {}
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for _, row in df.iterrows():
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sym = row['Symbol']
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if pd.notna(sym) and sym not in symbol_info:
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symbol_info[sym] = str(row.get('Theme', ''))
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import statistics
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ranking_data = []
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for sym in symbol_info:
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overalls = []
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momentums = []
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themes = []
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risks = []
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buzzes = []
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for m in MODELS:
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s = all_scores[m["name"]].get(sym, {})
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o = s.get('overall_score', None)
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if o is not None:
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overalls.append(o)
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mo = s.get('momentum_score', None)
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if mo is not None:
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momentums.append(mo)
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t = s.get('theme_score', None)
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if t is not None:
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themes.append(t)
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r = s.get('risk_score', None)
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if r is not None:
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risks.append(r)
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b = s.get('social_buzz_score', None)
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if b is not None:
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buzzes.append(b)
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avg = statistics.mean(overalls) if overalls else 0
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std = statistics.stdev(overalls) if len(overalls) > 1 else 0
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ranking_data.append({
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'symbol': sym,
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'theme': symbol_info[sym],
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'avg_overall': avg,
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'std_overall': std,
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'overalls': overalls,
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'momentums': momentums,
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'themes': themes,
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'risks': risks,
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'buzzes': buzzes,
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})
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# Sort by average overall score
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ranking_data.sort(key=lambda x: x['avg_overall'], reverse=True)
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def get_score_fill_general(score):
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if score >= 70:
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return PatternFill(start_color="92D050", end_color="92D050", fill_type="solid")
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elif score >= 50:
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return PatternFill(start_color="FFFF00", end_color="FFFF00", fill_type="solid")
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elif score >= 30:
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return PatternFill(start_color="FFC000", end_color="FFC000", fill_type="solid")
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else:
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return PatternFill(start_color="FF6B6B", end_color="FF6B6B", fill_type="solid")
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def get_consensus(avg, std):
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if std <= 5:
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return "Strong Consensus"
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elif std <= 10:
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return "Moderate Consensus"
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elif std <= 15:
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return "Weak Consensus"
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else:
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return "Divergent"
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for rank, rd in enumerate(ranking_data, 1):
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row = rank + 1
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sym = rd['symbol']
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ws_comp.cell(row=row, column=1, value=rank).border = thin_border
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ws_comp.cell(row=row, column=1).alignment = Alignment(horizontal='center')
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cell = ws_comp.cell(row=row, column=2, value=sym)
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cell.font = Font(bold=True)
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cell.border = thin_border
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theme_val = rd['theme'] if pd.notna(rd['theme']) and rd['theme'] != 'nan' else ''
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ws_comp.cell(row=row, column=3, value=theme_val).border = thin_border
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# Model overall scores with model-specific colors
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col = 4
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for i, m in enumerate(MODELS):
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s = all_scores[m["name"]].get(sym, {})
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val = s.get('overall_score', '')
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cell = ws_comp.cell(row=row, column=col + i, value=val)
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if isinstance(val, (int, float)):
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cell.fill = PatternFill(start_color=m["fill_color"], end_color=m["fill_color"], fill_type="solid")
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cell.alignment = Alignment(horizontal='center')
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cell.border = thin_border
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col += len(MODELS)
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# Average
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avg = rd['avg_overall']
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cell = ws_comp.cell(row=row, column=col, value=round(avg, 1))
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cell.fill = get_score_fill_general(avg)
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cell.font = Font(bold=True, size=11)
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cell.alignment = Alignment(horizontal='center')
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cell.border = thin_border
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col += 1
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# Std Dev
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std = rd['std_overall']
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cell = ws_comp.cell(row=row, column=col, value=round(std, 1))
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cell.alignment = Alignment(horizontal='center')
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cell.border = thin_border
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col += 1
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# Consensus
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consensus = get_consensus(avg, std)
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cell = ws_comp.cell(row=row, column=col, value=consensus)
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if "Strong" in consensus:
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cell.fill = PatternFill(start_color="92D050", end_color="92D050", fill_type="solid")
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elif "Moderate" in consensus:
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cell.fill = PatternFill(start_color="FFFF00", end_color="FFFF00", fill_type="solid")
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elif "Weak" in consensus:
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cell.fill = PatternFill(start_color="FFC000", end_color="FFC000", fill_type="solid")
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else:
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cell.fill = PatternFill(start_color="FF6B6B", end_color="FF6B6B", fill_type="solid")
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cell.alignment = Alignment(horizontal='center')
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cell.border = thin_border
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col += 1
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# Momentum scores per model
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for i, m in enumerate(MODELS):
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s = all_scores[m["name"]].get(sym, {})
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val = s.get('momentum_score', '')
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cell = ws_comp.cell(row=row, column=col + i, value=val)
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if isinstance(val, (int, float)):
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cell.fill = PatternFill(start_color=m["fill_color"], end_color=m["fill_color"], fill_type="solid")
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cell.alignment = Alignment(horizontal='center')
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cell.border = thin_border
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col += len(MODELS)
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# Theme scores per model
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for i, m in enumerate(MODELS):
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s = all_scores[m["name"]].get(sym, {})
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val = s.get('theme_score', '')
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cell = ws_comp.cell(row=row, column=col + i, value=val)
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if isinstance(val, (int, float)):
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cell.fill = PatternFill(start_color=m["fill_color"], end_color=m["fill_color"], fill_type="solid")
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cell.alignment = Alignment(horizontal='center')
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cell.border = thin_border
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col += len(MODELS)
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# Risk scores per model
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for i, m in enumerate(MODELS):
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s = all_scores[m["name"]].get(sym, {})
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val = s.get('risk_score', '')
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cell = ws_comp.cell(row=row, column=col + i, value=val)
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if isinstance(val, (int, float)):
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cell.fill = PatternFill(start_color=m["fill_color"], end_color=m["fill_color"], fill_type="solid")
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cell.alignment = Alignment(horizontal='center')
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cell.border = thin_border
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col += len(MODELS)
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# Social buzz scores per model
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for i, m in enumerate(MODELS):
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s = all_scores[m["name"]].get(sym, {})
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val = s.get('social_buzz_score', '')
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cell = ws_comp.cell(row=row, column=col + i, value=val)
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if isinstance(val, (int, float)):
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cell.fill = PatternFill(start_color=m["fill_color"], end_color=m["fill_color"], fill_type="solid")
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cell.alignment = Alignment(horizontal='center')
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cell.border = thin_border
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# Set column widths for comparison sheet
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ws_comp.column_dimensions['A'].width = 6
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ws_comp.column_dimensions['B'].width = 10
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ws_comp.column_dimensions['C'].width = 18
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for col_idx in range(4, 4 + len(comp_headers) - 3):
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col_letter = get_column_letter(col_idx)
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ws_comp.column_dimensions[col_letter].width = 16
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ws_comp.freeze_panes = 'D2'
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print(f"Created 'Multi-Model Comparison' sheet")
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# ============================================================
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# Also create per-model ranking sheets (sorted by that model's overall score)
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# ============================================================
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for model_cfg in MODELS:
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model_name = model_cfg["name"]
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scores = all_scores[model_name]
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fill_color = model_cfg["fill_color"]
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header_color = model_cfg["header_color"]
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header_font_color = model_cfg["header_font_color"]
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cell_fill = PatternFill(start_color=fill_color, end_color=fill_color, fill_type="solid")
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hdr_fill = PatternFill(start_color=header_color, end_color=header_color, fill_type="solid")
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hdr_font = Font(bold=True, color=header_font_color, size=10)
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sheet_name = f"Rank - {model_name}"
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if len(sheet_name) > 31:
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sheet_name = sheet_name[:31]
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ws_rank = wb.create_sheet(sheet_name)
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rank_headers = ['Rank', 'Symbol', 'Theme', 'Overall', 'Momentum', 'Theme Score', 'Risk', 'Social Buzz', 'YTD Perf', 'Analysis']
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for col, hdr in enumerate(rank_headers, 1):
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cell = ws_rank.cell(row=1, column=col, value=hdr)
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cell.fill = hdr_fill
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cell.font = hdr_font
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cell.alignment = Alignment(horizontal='center', wrap_text=True)
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cell.border = thin_border
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# Sort by overall score
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sorted_scores = sorted(scores.items(), key=lambda x: x[1].get('overall_score', 0), reverse=True)
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for rank, (sym, s) in enumerate(sorted_scores, 1):
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row = rank + 1
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theme = symbol_info.get(sym, '')
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if pd.isna(theme) or theme == 'nan':
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theme = ''
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ws_rank.cell(row=row, column=1, value=rank).border = thin_border
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ws_rank.cell(row=row, column=1).alignment = Alignment(horizontal='center')
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cell = ws_rank.cell(row=row, column=2, value=sym)
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cell.font = Font(bold=True)
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cell.border = thin_border
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ws_rank.cell(row=row, column=3, value=theme).border = thin_border
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# Overall
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overall = s.get('overall_score', 0)
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cell = ws_rank.cell(row=row, column=4, value=overall)
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cell.fill = cell_fill
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cell.font = Font(bold=True, size=11)
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cell.alignment = Alignment(horizontal='center')
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cell.border = thin_border
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# Momentum
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cell = ws_rank.cell(row=row, column=5, value=s.get('momentum_score', ''))
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cell.fill = cell_fill
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cell.alignment = Alignment(horizontal='center')
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cell.border = thin_border
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# Theme Score
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cell = ws_rank.cell(row=row, column=6, value=s.get('theme_score', ''))
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cell.fill = cell_fill
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cell.alignment = Alignment(horizontal='center')
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cell.border = thin_border
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# Risk
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cell = ws_rank.cell(row=row, column=7, value=s.get('risk_score', ''))
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cell.fill = cell_fill
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cell.alignment = Alignment(horizontal='center')
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cell.border = thin_border
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# Social Buzz
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cell = ws_rank.cell(row=row, column=8, value=s.get('social_buzz_score', ''))
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cell.fill = cell_fill
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cell.alignment = Alignment(horizontal='center')
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cell.border = thin_border
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# YTD Perf from original data
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ytd_val = ''
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for _, orig_row in df.iterrows():
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if orig_row['Symbol'] == sym:
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||
ytd_val = orig_row.get('performance Year to Date', '')
|
||
break
|
||
if pd.notna(ytd_val) and ytd_val != '':
|
||
cell = ws_rank.cell(row=row, column=9, value=ytd_val)
|
||
cell.number_format = '0.00%'
|
||
else:
|
||
cell = ws_rank.cell(row=row, column=9, value='N/A')
|
||
cell.alignment = Alignment(horizontal='center')
|
||
cell.border = thin_border
|
||
|
||
# Analysis
|
||
cell = ws_rank.cell(row=row, column=10, value=s.get('brief_analysis', ''))
|
||
cell.fill = cell_fill
|
||
cell.alignment = Alignment(wrap_text=True)
|
||
cell.border = thin_border
|
||
|
||
# Column widths
|
||
rank_widths = [6, 10, 18, 10, 12, 12, 10, 14, 12, 55]
|
||
for i, w in enumerate(rank_widths):
|
||
ws_rank.column_dimensions[get_column_letter(i + 1)].width = w
|
||
|
||
ws_rank.freeze_panes = 'D2'
|
||
print(f"Created ranking sheet: {sheet_name}")
|
||
|
||
# ============================================================
|
||
# Add a legend/info sheet
|
||
# ============================================================
|
||
ws_legend = wb.create_sheet("Model Legend", 1)
|
||
|
||
ws_legend.cell(row=1, column=1, value="Model Color Legend").font = Font(bold=True, size=14)
|
||
ws_legend.merge_cells('A1:D1')
|
||
|
||
ws_legend.cell(row=3, column=1, value="Model Name").font = Font(bold=True, size=11)
|
||
ws_legend.cell(row=3, column=2, value="Provider").font = Font(bold=True, size=11)
|
||
ws_legend.cell(row=3, column=3, value="Color").font = Font(bold=True, size=11)
|
||
ws_legend.cell(row=3, column=4, value="Sheet Names").font = Font(bold=True, size=11)
|
||
|
||
for i, m in enumerate(MODELS):
|
||
row = 4 + i
|
||
ws_legend.cell(row=row, column=1, value=m["name"]).font = Font(bold=True)
|
||
|
||
provider = "OpenAI" if "GPT" in m["name"] or "gpt" in m["name"] else "Google"
|
||
ws_legend.cell(row=row, column=2, value=provider)
|
||
|
||
cell = ws_legend.cell(row=row, column=3, value=f"Sample Color")
|
||
cell.fill = PatternFill(start_color=m["fill_color"], end_color=m["fill_color"], fill_type="solid")
|
||
|
||
sheet_name_scores = f"Scores - {m['name']}"[:31]
|
||
sheet_name_rank = f"Rank - {m['name']}"[:31]
|
||
ws_legend.cell(row=row, column=4, value=f"{sheet_name_scores}, {sheet_name_rank}")
|
||
|
||
ws_legend.cell(row=8, column=1, value="Sheet Overview").font = Font(bold=True, size=12)
|
||
|
||
sheets_info = [
|
||
("Multi-Model Comparison", "Cross-model comparison with all scores side by side, sorted by average overall score"),
|
||
("Model Legend", "This sheet - explains color coding and sheet structure"),
|
||
]
|
||
for m in MODELS:
|
||
sheets_info.append((f"Scores - {m['name']}"[:31], f"Original stock master list + {m['name']} scores in {m['name']} color"))
|
||
sheets_info.append((f"Rank - {m['name']}"[:31], f"Stocks ranked by {m['name']} overall score"))
|
||
|
||
for i, (sn, desc) in enumerate(sheets_info):
|
||
row = 9 + i
|
||
ws_legend.cell(row=row, column=1, value=sn).font = Font(bold=True)
|
||
ws_legend.cell(row=row, column=2, value=desc)
|
||
ws_legend.merge_cells(start_row=row, start_column=2, end_row=row, end_column=4)
|
||
|
||
ws_legend.column_dimensions['A'].width = 30
|
||
ws_legend.column_dimensions['B'].width = 20
|
||
ws_legend.column_dimensions['C'].width = 15
|
||
ws_legend.column_dimensions['D'].width = 60
|
||
|
||
print(f"Created 'Model Legend' sheet")
|
||
|
||
# Save
|
||
wb.save(EXCEL_OUT)
|
||
print(f"\n=== DONE ===")
|
||
print(f"Saved multi-model Excel to: {EXCEL_OUT}")
|
||
print(f"Total sheets: {len(wb.sheetnames)}")
|
||
for sn in wb.sheetnames:
|
||
print(f" - {sn}")
|