476 lines
16 KiB
Python
476 lines
16 KiB
Python
"""
|
||
Skill E: Auditor (复盘审计员)
|
||
==============================
|
||
职能：绩效归因、偏差修正
|
||
|
||
输入：历史预测 vs 实际走势
|
||
输出：Correction_Report（修正建议）
|
||
"""
|
||
|
||
import sys
|
||
from pathlib import Path
|
||
sys.path.insert(0, str(Path(__file__).parent.parent))
|
||
|
||
from core.config import config, llm_call
|
||
from typing import Dict, List, Optional
|
||
from datetime import datetime, timedelta
|
||
import json
|
||
import pandas as pd
|
||
|
||
|
||
class PerformanceAuditor:
|
||
"""
|
||
复盘审计员 - 负责绩效归因和策略修正
|
||
|
||
核心职责：
|
||
1. 对比预测与实际表现
|
||
2. 分析偏差原因
|
||
3. 提出修正建议
|
||
4. 记录经验教训
|
||
"""
|
||
|
||
def __init__(self, data_dir: str = "data"):
|
||
self.data_dir = Path(data_dir)
|
||
self.audit_log_path = self.data_dir / "audit_logs.json"
|
||
self.correction_history = self._load_correction_history()
|
||
|
||
def _load_correction_history(self) -> List[Dict]:
|
||
"""加载历史修正记录"""
|
||
if not self.audit_log_path.exists():
|
||
return []
|
||
|
||
try:
|
||
with open(self.audit_log_path, 'r', encoding='utf-8') as f:
|
||
return json.load(f)
|
||
except Exception as e:
|
||
print(f"[Auditor] 加载修正历史失败: {e}")
|
||
return []
|
||
|
||
def _save_correction_history(self) -> None:
|
||
"""保存修正记录"""
|
||
try:
|
||
with open(self.audit_log_path, 'w', encoding='utf-8') as f:
|
||
json.dump(self.correction_history, f, ensure_ascii=False, indent=2)
|
||
print(f"[Auditor] 修正记录已保存")
|
||
except Exception as e:
|
||
print(f"[Auditor] 保存失败: {e}")
|
||
|
||
def audit_prediction(
|
||
self,
|
||
prediction: Dict,
|
||
actual_performance: Dict
|
||
) -> Dict:
|
||
"""
|
||
审计单个预测
|
||
|
||
Args:
|
||
prediction: 预测记录
|
||
{
|
||
"code": "515980",
|
||
"trust_index": 75.0,
|
||
"narrative_score": 85.0,
|
||
"flow_score": 60.0,
|
||
"predicted_action": "buy",
|
||
"timestamp": "2025-12-29T08:30:00"
|
||
}
|
||
|
||
actual_performance: 实际表现
|
||
{
|
||
"code": "515980",
|
||
"price_change": -2.5, # 实际涨跌幅 %
|
||
"volume_change": 1.3, # 成交量变化倍数
|
||
"date": "2025-12-29"
|
||
}
|
||
|
||
Returns:
|
||
审计结果
|
||
"""
|
||
code = prediction['code']
|
||
predicted_action = prediction['predicted_action']
|
||
trust_index = prediction['trust_index']
|
||
price_change = actual_performance['price_change']
|
||
|
||
# 判断预测是否正确
|
||
if predicted_action == "buy":
|
||
is_correct = price_change > 0
|
||
expected_direction = "上涨"
|
||
elif predicted_action == "sell":
|
||
is_correct = price_change < 0
|
||
expected_direction = "下跌"
|
||
else:
|
||
is_correct = abs(price_change) < 2
|
||
expected_direction = "震荡"
|
||
|
||
# 计算偏差程度
|
||
deviation = abs(price_change) if not is_correct else 0
|
||
|
||
# 分析原因
|
||
reason = self._analyze_deviation_reason(
|
||
prediction,
|
||
actual_performance,
|
||
is_correct
|
||
)
|
||
|
||
# 生成修正建议
|
||
correction = self._generate_correction(
|
||
prediction,
|
||
actual_performance,
|
||
is_correct,
|
||
reason
|
||
)
|
||
|
||
audit_result = {
|
||
"code": code,
|
||
"prediction": {
|
||
"action": predicted_action,
|
||
"trust_index": trust_index,
|
||
"expected": expected_direction
|
||
},
|
||
"actual": {
|
||
"price_change": price_change,
|
||
"volume_change": actual_performance.get('volume_change', 1.0)
|
||
},
|
||
"result": {
|
||
"is_correct": is_correct,
|
||
"deviation": round(deviation, 2),
|
||
"reason": reason
|
||
},
|
||
"correction": correction,
|
||
"timestamp": datetime.now().isoformat()
|
||
}
|
||
|
||
return audit_result
|
||
|
||
def _analyze_deviation_reason(
|
||
self,
|
||
prediction: Dict,
|
||
actual: Dict,
|
||
is_correct: bool
|
||
) -> str:
|
||
"""分析偏差原因"""
|
||
if is_correct:
|
||
return "预测准确"
|
||
|
||
narrative_score = prediction.get('narrative_score', 0)
|
||
flow_score = prediction.get('flow_score', 0)
|
||
price_change = actual['price_change']
|
||
volume_change = actual.get('volume_change', 1.0)
|
||
|
||
# 情况1：叙事高但资金未跟进
|
||
if narrative_score > 70 and flow_score < 40:
|
||
return "叙事未兑现：新闻热度高但资金流入不足，市场观望情绪浓厚"
|
||
|
||
# 情况2：资金放量但方向相反
|
||
if volume_change > 1.5 and price_change < -2:
|
||
return "放量下跌：资金大量流出，可能遭遇利空或止盈盘涌出"
|
||
|
||
# 情况3：黑天鹅事件
|
||
if abs(price_change) > 5:
|
||
return "极端波动：可能受突发事件或外部市场剧烈波动影响"
|
||
|
||
# 情况4：叙事过度衰减
|
||
if narrative_score < 50 and prediction['predicted_action'] == "buy":
|
||
return "叙事衰减过快：市场关注度下降速度超预期"
|
||
|
||
return "其他因素：需进一步分析宏观环境或板块轮动"
|
||
|
||
def _generate_correction(
|
||
self,
|
||
prediction: Dict,
|
||
actual: Dict,
|
||
is_correct: bool,
|
||
reason: str
|
||
) -> Dict:
|
||
"""生成修正建议"""
|
||
if is_correct:
|
||
return {
|
||
"action": "none",
|
||
"message": "预测准确，保持当前策略"
|
||
}
|
||
|
||
narrative_score = prediction.get('narrative_score', 0)
|
||
flow_score = prediction.get('flow_score', 0)
|
||
|
||
corrections = []
|
||
|
||
# 修正1：提高资金流权重
|
||
if "资金流入不足" in reason:
|
||
corrections.append({
|
||
"target": "trust_index_formula",
|
||
"suggestion": "提高资金流权重至 0.5，降低叙事权重至 0.5",
|
||
"reason": "叙事未能转化为实际资金流动"
|
||
})
|
||
|
||
# 修正2：加强止损
|
||
if "放量下跌" in reason:
|
||
corrections.append({
|
||
"target": "risk_control",
|
||
"suggestion": "设置 -3% 硬止损线，出现放量下跌立即清仓",
|
||
"reason": "未能及时识别资金流出信号"
|
||
})
|
||
|
||
# 修正3：降低 Trust Index 阈值
|
||
if narrative_score > 60 and flow_score > 50 and not is_correct:
|
||
corrections.append({
|
||
"target": "min_trust_score",
|
||
"suggestion": "提高最低信任指数阈值至 65",
|
||
"reason": "当前阈值可能过于宽松，需要更高确定性"
|
||
})
|
||
|
||
# 修正4：叙事衰减系数调整
|
||
if "衰减过快" in reason:
|
||
corrections.append({
|
||
"target": "narrative_decay",
|
||
"suggestion": "对该板块叙事的衰减系数从 0.95 调整为 0.97",
|
||
"reason": "市场对该主题的持续关注度被低估"
|
||
})
|
||
|
||
return {
|
||
"action": "adjust" if corrections else "monitor",
|
||
"corrections": corrections,
|
||
"priority": "high" if len(corrections) >= 2 else "medium"
|
||
}
|
||
|
||
def batch_audit(
|
||
self,
|
||
predictions: List[Dict],
|
||
actual_performances: Dict[str, Dict]
|
||
) -> Dict:
|
||
"""
|
||
批量审计
|
||
|
||
Args:
|
||
predictions: 预测列表
|
||
actual_performances: 实际表现字典 {code: performance}
|
||
|
||
Returns:
|
||
汇总审计报告
|
||
"""
|
||
print(f"[Auditor] 开始审计 {len(predictions)} 条预测...")
|
||
|
||
audit_results = []
|
||
correct_count = 0
|
||
|
||
for prediction in predictions:
|
||
code = prediction['code']
|
||
|
||
if code not in actual_performances:
|
||
print(f"[Auditor] 警告: 缺少 {code} 的实际数据")
|
||
continue
|
||
|
||
result = self.audit_prediction(
|
||
prediction,
|
||
actual_performances[code]
|
||
)
|
||
|
||
audit_results.append(result)
|
||
|
||
if result['result']['is_correct']:
|
||
correct_count += 1
|
||
|
||
# 计算准确率
|
||
accuracy = correct_count / len(audit_results) * 100 if audit_results else 0
|
||
|
||
# 统计偏差原因
|
||
reason_counts = {}
|
||
for result in audit_results:
|
||
reason = result['result']['reason']
|
||
reason_counts[reason] = reason_counts.get(reason, 0) + 1
|
||
|
||
# 收集修正建议
|
||
all_corrections = []
|
||
for result in audit_results:
|
||
if result['correction']['action'] != "none":
|
||
all_corrections.extend(result['correction'].get('corrections', []))
|
||
|
||
# 生成汇总报告
|
||
summary_report = {
|
||
"timestamp": datetime.now().isoformat(),
|
||
"audit_period": {
|
||
"start": min(r['timestamp'] for r in audit_results) if audit_results else None,
|
||
"end": max(r['timestamp'] for r in audit_results) if audit_results else None
|
||
},
|
||
"performance": {
|
||
"total_predictions": len(audit_results),
|
||
"correct_predictions": correct_count,
|
||
"wrong_predictions": len(audit_results) - correct_count,
|
||
"accuracy": round(accuracy, 2)
|
||
},
|
||
"deviation_analysis": reason_counts,
|
||
"corrections_needed": len(all_corrections),
|
||
"high_priority_corrections": [
|
||
c for c in all_corrections
|
||
if any('止损' in c.get('suggestion', '') for c in all_corrections)
|
||
],
|
||
"detailed_results": audit_results
|
||
}
|
||
|
||
# 保存到历史记录
|
||
self.correction_history.append(summary_report)
|
||
self._save_correction_history()
|
||
|
||
print(f"[Auditor] 审计完成，准确率: {accuracy:.1f}%")
|
||
return summary_report
|
||
|
||
def generate_correction_report(
|
||
self,
|
||
audit_summary: Dict
|
||
) -> str:
|
||
"""
|
||
生成人类可读的修正报告
|
||
|
||
Args:
|
||
audit_summary: batch_audit() 的输出
|
||
|
||
Returns:
|
||
Markdown 格式的报告
|
||
"""
|
||
accuracy = audit_summary['performance']['accuracy']
|
||
total = audit_summary['performance']['total_predictions']
|
||
correct = audit_summary['performance']['correct_predictions']
|
||
|
||
report = f"""
|
||
# 投资决策复盘审计报告
|
||
|
||
**生成时间:** {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}
|
||
|
||
## 一、整体表现
|
||
|
||
- **预测数量:** {total} 条
|
||
- **正确预测:** {correct} 条
|
||
- **错误预测:** {total - correct} 条
|
||
- **准确率:** {accuracy:.1f}%
|
||
|
||
## 二、偏差原因分析
|
||
|
||
"""
|
||
for reason, count in audit_summary['deviation_analysis'].items():
|
||
report += f"- **{reason}:** {count} 次\n"
|
||
|
||
report += f"\n## 三、修正建议\n\n"
|
||
report += f"需要修正的参数: {audit_summary['corrections_needed']} 项\n\n"
|
||
|
||
# 列出高优先级修正
|
||
high_priority = audit_summary.get('high_priority_corrections', [])
|
||
if high_priority:
|
||
report += "### 🚨 高优先级修正\n\n"
|
||
for i, correction in enumerate(high_priority[:3], 1):
|
||
report += f"{i}. **{correction['target']}**\n"
|
||
report += f" - 建议: {correction['suggestion']}\n"
|
||
report += f" - 原因: {correction['reason']}\n\n"
|
||
|
||
report += "\n## 四、经验总结\n\n"
|
||
|
||
if accuracy >= 70:
|
||
report += "✅ 当前策略表现良好，建议保持现有参数设置。\n"
|
||
elif accuracy >= 50:
|
||
report += "⚠️ 策略有效但存在改进空间，建议根据修正建议调整参数。\n"
|
||
else:
|
||
report += "❌ 策略表现不佳，需要重大调整或暂停交易，重新评估市场环境。\n"
|
||
|
||
return report
|
||
|
||
def get_historical_accuracy_trend(self) -> List[Dict]:
|
||
"""获取历史准确率趋势"""
|
||
trend = []
|
||
|
||
for record in self.correction_history[-10:]: # 最近10次
|
||
trend.append({
|
||
"timestamp": record['timestamp'],
|
||
"accuracy": record['performance']['accuracy'],
|
||
"total_predictions": record['performance']['total_predictions']
|
||
})
|
||
|
||
return trend
|
||
|
||
|
||
# ==================== 测试代码 ====================
|
||
|
||
if __name__ == "__main__":
|
||
print("=" * 50)
|
||
print("Skill E: Auditor 复盘审计员测试")
|
||
print("=" * 50)
|
||
|
||
# 创建审计员实例
|
||
auditor = PerformanceAuditor(data_dir="data")
|
||
|
||
# 模拟昨日的预测记录
|
||
mock_predictions = [
|
||
{
|
||
"code": "515980",
|
||
"trust_index": 75.0,
|
||
"narrative_score": 85.0,
|
||
"flow_score": 60.0,
|
||
"predicted_action": "buy",
|
||
"timestamp": "2025-12-29T08:30:00"
|
||
},
|
||
{
|
||
"code": "512480",
|
||
"trust_index": 45.0,
|
||
"narrative_score": 55.0,
|
||
"flow_score": 30.0,
|
||
"predicted_action": "hold",
|
||
"timestamp": "2025-12-29T08:30:00"
|
||
},
|
||
{
|
||
"code": "159928",
|
||
"trust_index": 35.0,
|
||
"narrative_score": 70.0,
|
||
"flow_score": 20.0,
|
||
"predicted_action": "sell",
|
||
"timestamp": "2025-12-29T08:30:00"
|
||
}
|
||
]
|
||
|
||
# 模拟今日的实际表现
|
||
mock_actual = {
|
||
"515980": {
|
||
"code": "515980",
|
||
"price_change": 3.2, # 上涨3.2%
|
||
"volume_change": 1.5,
|
||
"date": "2025-12-29"
|
||
},
|
||
"512480": {
|
||
"code": "512480",
|
||
"price_change": -1.8, # 下跌1.8%
|
||
"volume_change": 0.9,
|
||
"date": "2025-12-29"
|
||
},
|
||
"159928": {
|
||
"code": "159928",
|
||
"price_change": 0.5, # 微涨（预测失误）
|
||
"volume_change": 2.1,
|
||
"date": "2025-12-29"
|
||
}
|
||
}
|
||
|
||
# 1. 批量审计
|
||
print("\n1. 批量审计:")
|
||
summary = auditor.batch_audit(mock_predictions, mock_actual)
|
||
|
||
print(f"\n准确率: {summary['performance']['accuracy']:.1f}%")
|
||
print(f"正确: {summary['performance']['correct_predictions']}")
|
||
print(f"错误: {summary['performance']['wrong_predictions']}")
|
||
|
||
# 2. 查看详细结果
|
||
print("\n2. 详细审计结果:")
|
||
for result in summary['detailed_results']:
|
||
print(f"\n {result['code']}:")
|
||
print(f" - 预测: {result['prediction']['action']} (Trust={result['prediction']['trust_index']})")
|
||
print(f" - 实际: {result['actual']['price_change']:+.1f}%")
|
||
print(f" - 结果: {'✅ 正确' if result['result']['is_correct'] else '❌ 错误'}")
|
||
print(f" - 原因: {result['result']['reason']}")
|
||
|
||
# 3. 生成修正报告
|
||
print("\n3. 修正报告:")
|
||
report = auditor.generate_correction_report(summary)
|
||
print(report)
|
||
|
||
# 4. 历史趋势
|
||
print("\n4. 历史准确率趋势:")
|
||
trend = auditor.get_historical_accuracy_trend()
|
||
for record in trend:
|
||
print(f" {record['timestamp'][:10]}: {record['accuracy']:.1f}%")
|
||
|
||
print("\n✅ Auditor 模块测试完成")
|