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feat: 添加8个多尺度分析模块并完善研究报告

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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:
riba2534
2026-02-03 16:35:08 +08:00
parent 68b1c6b45d
commit 6f2fede5ba
67 changed files with 8711 additions and 59 deletions
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111
src/hurst_analysis.py
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@@ -307,6 +307,11 @@ def multi_timeframe_hurst(intervals: List[str] = None) -> Dict[str, Dict[str, fl
returns = log_returns(prices).values
# 对1m数据进行截断，避免计算量过大
if interval == '1m' and len(returns) > 100000:
print(f" {interval} 数据量较大（{len(returns)}条），截取最后100000条")
returns = returns[-100000:]
# R/S分析
h_rs, _, _ = rs_hurst(returns)
# DFA分析
@@ -416,9 +421,11 @@ def plot_multi_timeframe(results: Dict[str, Dict[str, float]],
h_avg = [results[k]['平均Hurst'] for k in intervals]
x = np.arange(len(intervals))
width = 0.25
# 动态调整柱状图宽度
width = min(0.25, 0.8 / 3) # 3组柱状图，确保不重叠
fig, ax = plt.subplots(figsize=(12, 7))
# 使用更宽的图支持15个尺度
fig, ax = plt.subplots(figsize=(16, 8))
bars1 = ax.bar(x - width, h_rs, width, label='R/S Hurst', color='steelblue', alpha=0.8)
bars2 = ax.bar(x, h_dfa, width, label='DFA Hurst', color='coral', alpha=0.8)
@@ -429,20 +436,21 @@ def plot_multi_timeframe(results: Dict[str, Dict[str, float]],
ax.axhline(y=TREND_THRESHOLD, color='green', linestyle=':', alpha=0.4)
ax.axhline(y=MEAN_REV_THRESHOLD, color='red', linestyle=':', alpha=0.4)
# 在柱状图上标注数值
# 在柱状图上标注数值（当柱状图数量较多时减小字体）
fontsize_annot = 7 if len(intervals) > 8 else 9
for bars in [bars1, bars2, bars3]:
for bar in bars:
height = bar.get_height()
ax.annotate(f'{height:.3f}',
xy=(bar.get_x() + bar.get_width() / 2, height),
xytext=(0, 3), textcoords="offset points",
ha='center', va='bottom', fontsize=9)
ha='center', va='bottom', fontsize=fontsize_annot)
ax.set_xlabel('时间框架', fontsize=12)
ax.set_ylabel('Hurst指数', fontsize=12)
ax.set_title('BTC 多时间框架 Hurst指数对比', fontsize=13)
ax.set_xticks(x)
ax.set_xticklabels(intervals)
ax.set_xticklabels(intervals, rotation=45, ha='right') # X轴标签旋转45度避免重叠
ax.legend(fontsize=11)
ax.grid(True, alpha=0.3, axis='y')
@@ -453,6 +461,92 @@ def plot_multi_timeframe(results: Dict[str, Dict[str, float]],
print(f" 已保存: {filepath}")
def plot_hurst_vs_scale(results: Dict[str, Dict[str, float]],
output_dir: Path, filename: str = "hurst_vs_scale.png"):
"""
绘制Hurst指数 vs log(Δt) 标度关系图
Parameters
----------
results : dict
多时间框架Hurst分析结果
output_dir : Path
输出目录
filename : str
输出文件名
"""
if not results:
print(" 没有可绘制的标度关系结果")
return
# 各粒度对应的采样周期（天）
INTERVAL_DAYS = {
"1m": 1/(24*60), "3m": 3/(24*60), "5m": 5/(24*60), "15m": 15/(24*60),
"30m": 30/(24*60), "1h": 1/24, "2h": 2/24, "4h": 4/24, "6h": 6/24,
"8h": 8/24, "12h": 12/24, "1d": 1, "3d": 3, "1w": 7, "1mo": 30
}
# 提取数据
intervals = list(results.keys())
log_dt = [np.log10(INTERVAL_DAYS.get(k, 1)) for k in intervals]
h_rs = [results[k]['R/S Hurst'] for k in intervals]
h_dfa = [results[k]['DFA Hurst'] for k in intervals]
# 排序（按log_dt）
sorted_idx = np.argsort(log_dt)
log_dt = np.array(log_dt)[sorted_idx]
h_rs = np.array(h_rs)[sorted_idx]
h_dfa = np.array(h_dfa)[sorted_idx]
intervals_sorted = [intervals[i] for i in sorted_idx]
fig, ax = plt.subplots(figsize=(12, 8))
# 绘制数据点和连线
ax.plot(log_dt, h_rs, 'o-', color='steelblue', linewidth=2, markersize=8,
label='R/S Hurst', alpha=0.8)
ax.plot(log_dt, h_dfa, 's-', color='coral', linewidth=2, markersize=8,
label='DFA Hurst', alpha=0.8)
# H=0.5 参考线
ax.axhline(y=0.5, color='black', linestyle='--', alpha=0.5, linewidth=1.5,
label='H=0.5 (随机游走)')
ax.axhline(y=TREND_THRESHOLD, color='green', linestyle=':', alpha=0.4)
ax.axhline(y=MEAN_REV_THRESHOLD, color='red', linestyle=':', alpha=0.4)
# 线性拟合
if len(log_dt) >= 3:
# R/S拟合
coeffs_rs = np.polyfit(log_dt, h_rs, 1)
fit_rs = np.polyval(coeffs_rs, log_dt)
ax.plot(log_dt, fit_rs, '--', color='steelblue', alpha=0.4, linewidth=1.5,
label=f'R/S拟合: H={coeffs_rs[0]:.4f}·log(Δt) + {coeffs_rs[1]:.4f}')
# DFA拟合
coeffs_dfa = np.polyfit(log_dt, h_dfa, 1)
fit_dfa = np.polyval(coeffs_dfa, log_dt)
ax.plot(log_dt, fit_dfa, '--', color='coral', alpha=0.4, linewidth=1.5,
label=f'DFA拟合: H={coeffs_dfa[0]:.4f}·log(Δt) + {coeffs_dfa[1]:.4f}')
ax.set_xlabel('log₁₀(Δt) - 采样周期的对数（天）', fontsize=12)
ax.set_ylabel('Hurst指数', fontsize=12)
ax.set_title('BTC Hurst指数 vs 时间尺度 标度关系', fontsize=13)
ax.legend(fontsize=10, loc='best')
ax.grid(True, alpha=0.3)
# 添加X轴标签（显示时间框架名称）
ax2 = ax.twiny()
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(log_dt)
ax2.set_xticklabels(intervals_sorted, rotation=45, ha='left', fontsize=9)
ax2.set_xlabel('时间框架', fontsize=11)
fig.tight_layout()
filepath = output_dir / filename
fig.savefig(filepath, dpi=150, bbox_inches='tight')
plt.close(fig)
print(f" 已保存: {filepath}")
# ============================================================
# 主入口函数
# ============================================================
@@ -592,12 +686,17 @@ def run_hurst_analysis(df: pd.DataFrame, output_dir: str = "output/hurst") -> Di
print("【5】多时间框架Hurst指数")
print("-" * 50)
mt_results = multi_timeframe_hurst(['1h', '4h', '1d', '1w'])
# 使用全部15个粒度
ALL_INTERVALS = ['1m', '3m', '5m', '15m', '30m', '1h', '2h', '4h', '6h', '8h', '12h', '1d', '3d', '1w', '1mo']
mt_results = multi_timeframe_hurst(ALL_INTERVALS)
results['多时间框架'] = mt_results
# 绘制多时间框架对比图
plot_multi_timeframe(mt_results, output_dir)
# 绘制Hurst vs 时间尺度标度关系图
plot_hurst_vs_scale(mt_results, output_dir)
# ----------------------------------------------------------
# 7. 总结
# ----------------------------------------------------------