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| """
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| 蒸鱼股票分析系统 - 多维因子评分体系
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| 基于QuantGPT iteration.py compute_factor_score适配
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| 6维度评分: IC Mean 15% + IC IR 15% + 稳定性 15% + 反过拟合 15% + 分组回测 15% + Cloud对齐 25%
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| """
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| import numpy as np
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| import pandas as pd
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| from datetime import datetime
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| import warnings
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| warnings.filterwarnings('ignore')
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| SCORE_WEIGHTS = {
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| 'ic_mean': 0.15,
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| 'ic_ir': 0.15,
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| 'stability': 0.15,
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| 'anti_overfit': 0.15,
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| 'backtest': 0.15,
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| 'cloud_alignment': 0.25,
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| }
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| def compute_factor_score(backtest_result, anti_overfit_result=None, cloud_result=None):
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| """
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| 计算因子综合评分（0-100）
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| Parameters
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| ----------
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| backtest_result : dict
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| run_factor_backtest的返回结果
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| anti_overfit_result : dict, optional
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| 反过拟合检测结果
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| cloud_result : dict, optional
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| 云端因子库对齐评分 {'ic_mean': float, 'ic_ir': float, 'turnover': float, 'sufficiency': float}
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| Returns
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| -------
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| dict : 6维度评分 + 综合评分 + 评级
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| """
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| if not backtest_result or not backtest_result.get('success'):
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| return {'overall': 0, 'grade': 'N/A', 'dimensions': {}, 'error': '回测结果无效'}
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| summary = backtest_result.get('summary', {})
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| performance = backtest_result.get('performance', {})
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| scores = {}
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| ic_mean = abs(summary.get('ic_mean', 0))
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| scores['ic_mean'] = min(ic_mean / 0.05, 1.0) * 100
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| ic_ir = abs(summary.get('ic_ir', 0))
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| scores['ic_ir'] = min(ic_ir / 1.5, 1.0) * 100
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| ic_win_rate = summary.get('ic_win_rate', 0)
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| g1_sharpe = performance.get('group_1', {}).get('sharpe', 0)
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| gn_sharpe = performance.get('group_5', {}).get('sharpe', 0)
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| sharpe_consistent = 1 if g1_sharpe * gn_sharpe <= 0 else 0
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| stability_score = ic_win_rate * 100 * 0.6 + sharpe_consistent * 40
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| scores['stability'] = min(stability_score, 100)
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| if anti_overfit_result:
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| ao_score = anti_overfit_result.get('overall_score', 0)
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| scores['anti_overfit'] = min(float(ao_score), 100)
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| else:
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| scores['anti_overfit'] = ic_win_rate * 100 * 0.7
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| ls_sharpe = abs(performance.get('long_short', {}).get('sharpe', 0))
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| mono = abs(performance.get('monotonicity', 0))
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| spread = abs(performance.get('spread', 0))
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| bt_score = (min(ls_sharpe / 2.0, 1) * 40 + min(mono, 1) * 40 + min(spread / 0.1, 1) * 20)
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| scores['backtest'] = min(bt_score, 100)
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| if cloud_result:
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| c_ic = cloud_result.get('ic_mean', 0)
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| c_ir = cloud_result.get('ic_ir', 0)
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| c_turn = cloud_result.get('turnover', 0.5)
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| c_suff = cloud_result.get('sufficiency', 0.5)
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| scores['cloud_alignment'] = (
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| min(abs(c_ic) / 0.05, 1) * 30 +
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| min(abs(c_ir) / 1.0, 1) * 30 +
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| min(c_turn, 1) * 20 +
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| min(c_suff, 1) * 20
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| )
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| else:
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| local_confidence = (scores['ic_mean'] + scores['ic_ir']) / 200
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| scores['cloud_alignment'] = local_confidence * 100
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| overall = 0
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| for key, weight in SCORE_WEIGHTS.items():
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| overall += scores.get(key, 0) * weight
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| overall = round(overall, 2)
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| if overall >= 80:
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| grade = 'S'
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| elif overall >= 70:
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| grade = 'A'
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| elif overall >= 55:
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| grade = 'B'
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| elif overall >= 40:
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| grade = 'C'
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| elif overall >= 25:
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| grade = 'D'
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| else:
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| grade = 'F'
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| return {
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| 'overall': overall,
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| 'grade': grade,
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| 'grade_label': _grade_label(grade),
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| 'weights': SCORE_WEIGHTS,
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| 'dimensions': {k: round(v, 2) for k, v in scores.items()},
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| 'timestamp': datetime.now().isoformat(),
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| }
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| def _grade_label(grade):
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| labels = {
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| 'S': '卓越因子，强烈推荐',
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| 'A': '优秀因子，推荐使用',
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| 'B': '良好因子，可考虑',
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| 'C': '一般因子，需谨慎',
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| 'D': '较弱因子，不建议',
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| 'F': '无效因子，不推荐',
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| }
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| return labels.get(grade, '未知')
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| def compare_factors(factor_results):
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| """
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| 比较多个因子的评分
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| Parameters
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| ----------
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| factor_results : list of dict
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| [{'name': str, 'score': dict from compute_factor_score}, ...]
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| Returns
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| -------
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| dict : 排名结果
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| """
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| ranked = sorted(factor_results, key=lambda x: x['score']['overall'], reverse=True)
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| comparison = {
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| 'factors': ranked,
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| 'n_factors': len(ranked),
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| 'best': ranked[0]['name'] if ranked else None,
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| 'ranking': [],
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| }
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| for i, f in enumerate(ranked):
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| comparison['ranking'].append({
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| 'rank': i + 1,
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| 'name': f['name'],
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| 'overall': f['score']['overall'],
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| 'grade': f['score']['grade'],
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| })
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| return comparison
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|
| FACTOR_EVAL_PRESETS = {
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| 'fast_momentum': {
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| 'name': '快速动量',
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| 'expression': 'ts_rank(ts_delta(close, 5), 20)',
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| 'category': '动量',
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| 'expected_ic': 0.03,
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| },
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| 'medium_momentum': {
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| 'name': '中期动量',
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| 'expression': 'ts_rank(ts_delta(close, 20), 60)',
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| 'category': '动量',
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| 'expected_ic': 0.04,
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| },
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| 'mean_reversion': {
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| 'name': '均值回归',
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| 'expression': '-ts_zscore(close, 20)',
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| 'category': '反转',
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| 'expected_ic': 0.02,
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| },
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| 'volatility': {
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| 'name': '波动率因子',
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| 'expression': 'ts_std(close / delay(close, 1) - 1, 20)',
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| 'category': '波动率',
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| 'expected_ic': 0.01,
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| },
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| 'volume_momentum': {
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| 'name': '量价动量',
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| 'expression': 'ts_corr(close, volume, 20)',
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| 'category': '量价',
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| 'expected_ic': 0.02,
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| },
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| 'boll_deviation': {
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| 'name': '布林偏离',
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| 'expression': '(close - boll_mid(close, 20)) / ts_std(close, 20)',
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| 'category': '技术',
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| 'expected_ic': 0.03,
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| },
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| 'rsi_extreme': {
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| 'name': 'RSI极值',
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| 'expression': 'rsi(close, 14) - 50',
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| 'category': '技术',
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| 'expected_ic': 0.02,
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| },
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| }
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|
