feat: 添加8个多尺度分析模块并完善研究报告
新增分析模块: - microstructure: 市场微观结构分析 (Roll价差, VPIN, Kyle's Lambda) - intraday_patterns: 日内模式分析 (U型曲线, 三时区对比) - scaling_laws: 统计标度律 (15尺度波动率标度, R²=0.9996) - multi_scale_vol: 多尺度已实现波动率 (HAR-RV模型) - entropy_analysis: 信息熵分析 - extreme_value: 极端值与尾部风险 (GEV/GPD, VaR回测) - cross_timeframe: 跨时间尺度关联分析 - momentum_reversion: 动量与均值回归检验 现有模块增强: - hurst_analysis: 扩展至15个时间尺度，新增Hurst vs log(Δt)标度图 - fft_analysis: 扩展至15个粒度，支持瀑布图 - returns/acf/volatility/patterns/anomaly/fractal: 多尺度增强 研究报告更新: - 新增第16章: 基于全量数据的深度规律挖掘 (15尺度综合) - 完善第17章: 价格推演添加实际案例 (2020-2021牛市, 2022熊市等) - 新增16.10节: 可监控的实证指标与预警信号 - 添加VPIN/波动率/Hurst等指标的实时监控阈值和案例 数据覆盖: 全部15个K线粒度 (1m~1mo), 440万条记录 关键发现: Hurst随尺度单调递增 (1m:0.53→1mo:0.72), 极端风险不对称 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
171
src/anomaly.py
171
src/anomaly.py
@@ -24,6 +24,9 @@ from sklearn.preprocessing import StandardScaler
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from sklearn.model_selection import cross_val_predict, StratifiedKFold
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from sklearn.metrics import roc_auc_score, roc_curve
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from src.data_loader import load_klines
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from src.preprocessing import add_derived_features
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try:
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from pyod.models.copod import COPOD
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HAS_COPOD = True
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@@ -625,6 +628,164 @@ def plot_feature_importance(precursor_results: Dict, output_dir: Path, top_n: in
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print(f" [保存] {output_dir / 'precursor_feature_importance.png'}")
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# ============================================================
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# 9. 多尺度异常检测
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# ============================================================
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def multi_scale_anomaly_detection(intervals=None, contamination=0.05) -> Dict:
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"""多尺度异常检测"""
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if intervals is None:
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intervals = ['1h', '4h', '1d']
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results = {}
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for interval in intervals:
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try:
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print(f"\n 加载 {interval} 数据进行异常检测...")
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df_tf = load_klines(interval)
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df_tf = add_derived_features(df_tf)
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# 截断大数据
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if len(df_tf) > 50000:
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df_tf = df_tf.iloc[-50000:]
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if len(df_tf) < 200:
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print(f" {interval} 数据不足，跳过")
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continue
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# 集成异常检测
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anomaly_result = ensemble_anomaly_detection(df_tf, contamination=contamination, min_agreement=2)
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# 提取异常日期
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anomaly_dates = anomaly_result[anomaly_result['anomaly_ensemble'] == 1].index
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results[interval] = {
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'anomaly_dates': anomaly_dates,
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'n_anomalies': len(anomaly_dates),
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'n_total': len(anomaly_result),
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'anomaly_pct': len(anomaly_dates) / len(anomaly_result) * 100,
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}
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print(f" {interval}: {len(anomaly_dates)} 个异常 ({len(anomaly_dates)/len(anomaly_result)*100:.2f}%)")
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except FileNotFoundError:
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print(f" {interval} 数据文件不存在，跳过")
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except Exception as e:
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print(f" {interval} 异常检测失败: {e}")
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return results
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def cross_scale_anomaly_consensus(ms_results: Dict, tolerance_hours: int = 24) -> pd.DataFrame:
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"""
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跨尺度异常共识：多个尺度在同一时间窗口内同时报异常 → 高置信度
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Parameters
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----------
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ms_results : Dict
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多尺度异常检测结果字典
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tolerance_hours : int
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时间容差（小时）
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Returns
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-------
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pd.DataFrame
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共识异常数据
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"""
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# 将所有尺度的异常日期映射到日频
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all_dates = []
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for interval, result in ms_results.items():
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dates = result['anomaly_dates']
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# 转换为日期（去除时间部分）
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daily_dates = pd.to_datetime(dates.date).unique()
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for date in daily_dates:
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all_dates.append({'date': date, 'interval': interval})
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if not all_dates:
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return pd.DataFrame()
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df_dates = pd.DataFrame(all_dates)
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# 统计每个日期被多少个尺度报为异常
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consensus_counts = df_dates.groupby('date').size().reset_index(name='n_scales')
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consensus_counts = consensus_counts.sort_values('date')
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# >=2 个尺度报异常 = "共识异常"
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consensus_counts['is_consensus'] = (consensus_counts['n_scales'] >= 2).astype(int)
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# 添加参与的尺度列表
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scale_groups = df_dates.groupby('date')['interval'].apply(list).reset_index()
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consensus_counts = consensus_counts.merge(scale_groups, on='date')
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n_consensus = consensus_counts['is_consensus'].sum()
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print(f"\n 跨尺度共识异常: {n_consensus} 天 (≥2 个尺度同时报异常)")
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return consensus_counts
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def plot_multi_scale_anomaly_timeline(df: pd.DataFrame, ms_results: Dict, consensus: pd.DataFrame, output_dir: Path):
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"""多尺度异常共识时间线"""
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fig, axes = plt.subplots(2, 1, figsize=(16, 10), gridspec_kw={'height_ratios': [2, 1]})
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# 上图: 价格图（对数尺度）+ 共识异常点标注
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ax1 = axes[0]
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ax1.plot(df.index, df['close'], linewidth=0.6, color='steelblue', alpha=0.8, label='BTC 收盘价')
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if not consensus.empty:
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# 标注共识异常点
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consensus_dates = consensus[consensus['is_consensus'] == 1]['date']
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if len(consensus_dates) > 0:
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# 获取对应的价格
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consensus_prices = df.loc[df.index.isin(consensus_dates), 'close']
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if not consensus_prices.empty:
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ax1.scatter(consensus_prices.index, consensus_prices.values,
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color='red', s=50, zorder=5, label=f'共识异常 (n={len(consensus_prices)})',
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alpha=0.8, edgecolors='darkred', linewidths=1, marker='*')
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ax1.set_ylabel('价格 (USDT)', fontsize=12)
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ax1.set_title('多尺度异常检测：价格与共识异常', fontsize=14)
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ax1.legend(fontsize=10, loc='upper left')
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ax1.grid(True, alpha=0.3)
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ax1.set_yscale('log')
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# 下图: 各尺度异常时间线（类似甘特图）
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ax2 = axes[1]
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interval_labels = list(ms_results.keys())
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y_positions = range(len(interval_labels))
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colors = {'1h': 'lightcoral', '4h': 'orange', '1d': 'steelblue'}
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for idx, interval in enumerate(interval_labels):
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anomaly_dates = ms_results[interval]['anomaly_dates']
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# 转换为日期
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daily_dates = pd.to_datetime(anomaly_dates.date).unique()
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# 绘制时间线（每个异常日期用竖线表示）
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for date in daily_dates:
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ax2.axvline(x=date, ymin=idx/len(interval_labels), ymax=(idx+0.8)/len(interval_labels),
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color=colors.get(interval, 'gray'), alpha=0.6, linewidth=2)
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# 标注共识异常区域
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if not consensus.empty:
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consensus_dates = consensus[consensus['is_consensus'] == 1]['date']
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for date in consensus_dates:
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ax2.axvspan(date, date + pd.Timedelta(days=1),
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color='red', alpha=0.15, zorder=0)
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ax2.set_yticks(y_positions)
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ax2.set_yticklabels(interval_labels)
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ax2.set_ylabel('时间尺度', fontsize=12)
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ax2.set_xlabel('日期', fontsize=12)
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ax2.set_title('各尺度异常时间线（红色背景 = 共识异常）', fontsize=12)
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ax2.grid(True, alpha=0.3, axis='x')
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ax2.set_xlim(df.index.min(), df.index.max())
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fig.tight_layout()
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fig.savefig(output_dir / 'anomaly_multi_scale_timeline.png', dpi=150, bbox_inches='tight')
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plt.close(fig)
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print(f" [保存] {output_dir / 'anomaly_multi_scale_timeline.png'}")
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# ============================================================
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# 7. 结果打印
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# ============================================================
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@@ -747,6 +908,14 @@ def run_anomaly_analysis(
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# --- 汇总打印 ---
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print_anomaly_summary(anomaly_result, garch_anomaly, precursor_results)
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# --- 多尺度异常检测 ---
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print("\n>>> [额外] 多尺度异常检测与共识分析...")
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ms_anomaly = multi_scale_anomaly_detection(['1h', '4h', '1d'])
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consensus = None
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if len(ms_anomaly) >= 2:
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consensus = cross_scale_anomaly_consensus(ms_anomaly)
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plot_multi_scale_anomaly_timeline(df, ms_anomaly, consensus, output_dir)
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print("\n" + "=" * 70)
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print("异常检测与前兆模式分析完成！")
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print(f"图表已保存至: {output_dir.resolve()}")
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@@ -757,6 +926,8 @@ def run_anomaly_analysis(
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'garch_anomaly': garch_anomaly,
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'event_alignment': event_alignment,
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'precursor_results': precursor_results,
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'multi_scale_anomaly': ms_anomaly,
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'cross_scale_consensus': consensus,
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}
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