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update pay ui

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This commit is contained in:
zzlgreat
2025-12-09 16:27:56 +08:00
parent e8763331cc
commit b40ca0e23c
22 changed files with 3637 additions and 8176 deletions
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145
ml/backtest_hybrid.py
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@@ -93,12 +93,12 @@ def backtest_single_day_hybrid(
seq_len: int = 30
) -> List[Dict]:
"""
使用融合检测器回测单天数据
使用融合检测器回测单天数据（批量优化版）
"""
alerts = []
# 按概念分组
grouped = df.groupby('concept_id', sort=False)
# 按概念分组，预先构建字典
grouped_dict = {cid: cdf for cid, cdf in df.groupby('concept_id', sort=False)}
# 冷却记录
cooldown = {}
@@ -114,27 +114,46 @@ def backtest_single_day_hybrid(
current_time = all_timestamps[t_idx]
window_start_time = all_timestamps[t_idx - seq_len + 1]
minute_alerts = []
# 批量收集该时刻所有候选概念
batch_sequences = []
batch_features = []
batch_infos = []
for concept_id, concept_df in grouped_dict.items():
# 检查冷却（提前过滤）
if concept_id in cooldown:
last_alert = cooldown[concept_id]
if isinstance(current_time, datetime):
time_diff = (current_time - last_alert).total_seconds() / 60
else:
time_diff = BACKTEST_CONFIG['cooldown_minutes'] + 1
if time_diff < BACKTEST_CONFIG['cooldown_minutes']:
continue
for concept_id, concept_df in grouped:
# 获取时间窗口内的数据
mask = (concept_df['timestamp'] >= window_start_time) & (concept_df['timestamp'] <= current_time)
window_df = concept_df[mask].sort_values('timestamp')
window_df = concept_df.loc[mask]
if len(window_df) < seq_len:
continue
window_df = window_df.tail(seq_len)
window_df = window_df.sort_values('timestamp').tail(seq_len)
# 提取特征序列（给 ML 模型）
# 当前时刻特征
current_row = window_df.iloc[-1]
alpha = current_row.get('alpha', 0)
# 过滤微小波动（提前过滤）
if abs(alpha) < BACKTEST_CONFIG['min_alpha_abs']:
continue
# 提取特征序列
sequence = window_df[FEATURES].values
sequence = np.nan_to_num(sequence, nan=0.0, posinf=0.0, neginf=0.0)
sequence = np.clip(sequence, -BACKTEST_CONFIG['clip_value'], BACKTEST_CONFIG['clip_value'])
# 当前时刻特征（给规则系统）
current_row = window_df.iloc[-1]
current_features = {
'alpha': current_row.get('alpha', 0),
'alpha': alpha,
'alpha_delta': current_row.get('alpha_delta', 0),
'amt_ratio': current_row.get('amt_ratio', 1),
'amt_delta': current_row.get('amt_delta', 0),
@@ -142,41 +161,79 @@ def backtest_single_day_hybrid(
'limit_up_ratio': current_row.get('limit_up_ratio', 0),
}
# 过滤微小波动
if abs(current_features['alpha']) < BACKTEST_CONFIG['min_alpha_abs']:
batch_sequences.append(sequence)
batch_features.append(current_features)
batch_infos.append({
'concept_id': concept_id,
'stock_count': current_row.get('stock_count', 0),
'total_amt': current_row.get('total_amt', 0),
})
if not batch_sequences:
continue
# 批量 ML 推理
sequences_array = np.array(batch_sequences)
ml_scores = detector.ml_scorer.score(sequences_array) if detector.ml_scorer.is_ready() else [0.0] * len(batch_sequences)
if isinstance(ml_scores, float):
ml_scores = [ml_scores]
# 批量规则评分 + 融合
minute_alerts = []
for i, (features, info) in enumerate(zip(batch_features, batch_infos)):
concept_id = info['concept_id']
# 规则评分
rule_score, rule_details = detector.rule_scorer.score(features)
# ML 评分
ml_score = ml_scores[i] if i < len(ml_scores) else 0.0
# 融合
w1 = detector.config['rule_weight']
w2 = detector.config['ml_weight']
final_score = w1 * rule_score + w2 * ml_score
# 判断是否异动
is_anomaly = False
trigger_reason = ''
if rule_score >= detector.config['rule_trigger']:
is_anomaly = True
trigger_reason = f'规则强信号({rule_score:.0f}分)'
elif ml_score >= detector.config['ml_trigger']:
is_anomaly = True
trigger_reason = f'ML强信号({ml_score:.0f}分)'
elif final_score >= detector.config['fusion_trigger']:
is_anomaly = True
trigger_reason = f'融合触发({final_score:.0f}分)'
if not is_anomaly:
continue
# 检查冷却
if concept_id in cooldown:
last_alert = cooldown[concept_id]
if isinstance(current_time, datetime):
time_diff = (current_time - last_alert).total_seconds() / 60
else:
time_diff = BACKTEST_CONFIG['cooldown_minutes'] + 1
# 异动类型
alpha = features.get('alpha', 0)
if alpha >= 1.5:
anomaly_type = 'surge_up'
elif alpha <= -1.5:
anomaly_type = 'surge_down'
elif features.get('amt_ratio', 1) >= 3.0:
anomaly_type = 'volume_spike'
else:
anomaly_type = 'unknown'
if time_diff < BACKTEST_CONFIG['cooldown_minutes']:
continue
# 融合检测
result = detector.detect(current_features, sequence)
if not result.is_anomaly:
continue
# 记录异动
alert = {
'concept_id': concept_id,
'alert_time': current_time,
'trade_date': date,
'alert_type': result.anomaly_type,
'final_score': result.final_score,
'rule_score': result.rule_score,
'ml_score': result.ml_score,
'trigger_reason': result.trigger_reason,
'triggered_rules': list(result.rule_details.keys()),
**current_features,
'stock_count': current_row.get('stock_count', 0),
'total_amt': current_row.get('total_amt', 0),
'alert_type': anomaly_type,
'final_score': final_score,
'rule_score': rule_score,
'ml_score': ml_score,
'trigger_reason': trigger_reason,
'triggered_rules': list(rule_details.keys()),
**features,
**info,
}
minute_alerts.append(alert)
@@ -341,6 +398,8 @@ def main():
help='规则权重 (0-1)')
parser.add_argument('--ml-weight', type=float, default=0.4,
help='ML权重 (0-1)')
parser.add_argument('--device', type=str, default='cuda',
help='设备 (cuda/cpu)，默认 cuda')
args = parser.parse_args()
@@ -355,15 +414,19 @@ def main():
print(f"模型目录: {args.checkpoint_dir}")
print(f"规则权重: {args.rule_weight}")
print(f"ML权重: {args.ml_weight}")
print(f"设备: {args.device}")
print(f"Dry Run: {args.dry_run}")
print("=" * 60)
# 初始化融合检测器
# 初始化融合检测器（使用 GPU）
config = {
'rule_weight': args.rule_weight,
'ml_weight': args.ml_weight,
}
detector = create_detector(args.checkpoint_dir, config)
# 修改 detector.py 中 MLScorer 的设备
from detector import HybridAnomalyDetector
detector = HybridAnomalyDetector(config, args.checkpoint_dir, device=args.device)
# 获取可用日期
dates = get_available_dates(args.data_dir, args.start, args.end)