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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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295
src/patterns.py
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@@ -18,7 +18,7 @@ from scipy import stats
from pathlib import Path
from typing import Dict, List, Tuple, Optional
from src.data_loader import split_data
from src.data_loader import split_data, load_klines
# ============================================================
@@ -668,7 +668,275 @@ def plot_hit_rate_with_ci(results_df: pd.DataFrame, output_dir: Path, prefix: st
# ============================================================
# 6. 主流程
# 6. 多时间尺度形态分析
# ============================================================
def multi_timeframe_pattern_analysis(intervals=None) -> Dict:
"""多时间尺度形态识别与对比"""
if intervals is None:
intervals = ['1h', '4h', '1d']
results = {}
for interval in intervals:
try:
print(f"\n 加载 {interval} 数据进行形态识别...")
df_tf = load_klines(interval)
if len(df_tf) < 100:
print(f" {interval} 数据不足，跳过")
continue
# 检测所有形态
patterns = detect_all_patterns(df_tf)
# 计算前向收益
close = df_tf['close']
fwd_returns = calc_forward_returns_multi(close, horizons=[1, 3, 5])
# 评估每个形态
pattern_stats = {}
for name, signal in patterns.items():
n_occ = signal.sum() if hasattr(signal, 'sum') else (signal > 0).sum()
expected_dir = PATTERN_EXPECTED_DIRECTION.get(name, 0)
if n_occ >= 5:
result = analyze_pattern_returns(signal, fwd_returns, expected_dir)
pattern_stats[name] = {
'n_occurrences': int(n_occ),
'hit_rate': result.get('hit_rate', np.nan),
}
else:
pattern_stats[name] = {
'n_occurrences': int(n_occ),
'hit_rate': np.nan,
}
results[interval] = pattern_stats
print(f" {interval}: {sum(1 for v in pattern_stats.values() if v['n_occurrences'] > 0)} 种形态检测到")
except FileNotFoundError:
print(f" {interval} 数据文件不存在，跳过")
except Exception as e:
print(f" {interval} 分析失败: {e}")
return results
def cross_scale_pattern_consistency(intervals=None) -> Dict:
"""
跨尺度形态一致性分析
检查同一日期多个尺度是否同时出现相同方向的形态
返回:
包含一致性统计的字典
"""
if intervals is None:
intervals = ['1h', '4h', '1d']
# 加载所有时间尺度数据
dfs = {}
for interval in intervals:
try:
df = load_klines(interval)
if len(df) >= 100:
dfs[interval] = df
except:
continue
if len(dfs) < 2:
print(" 跨尺度分析需要至少2个时间尺度的数据")
return {}
# 检测每个尺度的形态
patterns_by_tf = {}
for interval, df in dfs.items():
patterns_by_tf[interval] = detect_all_patterns(df)
# 统计跨尺度一致性
consistency_stats = {}
# 对每种形态，检查在同一日期的不同尺度上是否同时出现
all_pattern_names = set()
for patterns in patterns_by_tf.values():
all_pattern_names.update(patterns.keys())
for pattern_name in all_pattern_names:
expected_dir = PATTERN_EXPECTED_DIRECTION.get(pattern_name, 0)
if expected_dir == 0: # 跳过中性形态
continue
# 找出所有尺度上该形态出现的日期
occurrences_by_tf = {}
for interval, patterns in patterns_by_tf.items():
if pattern_name in patterns:
signal = patterns[pattern_name]
# 转换为日期（忽略时间）
dates = signal[signal > 0].index.date if hasattr(signal.index, 'date') else signal[signal > 0].index
occurrences_by_tf[interval] = set(dates)
if len(occurrences_by_tf) < 2:
continue
# 计算交集（同时出现在多个尺度的日期数）
all_dates = set()
for dates in occurrences_by_tf.values():
all_dates.update(dates)
# 统计每个日期在多少个尺度上出现
date_counts = {}
for date in all_dates:
count = sum(1 for dates in occurrences_by_tf.values() if date in dates)
date_counts[date] = count
# 计算一致性指标
total_occurrences = sum(len(dates) for dates in occurrences_by_tf.values())
multi_scale_occurrences = sum(1 for count in date_counts.values() if count >= 2)
consistency_stats[pattern_name] = {
'total_occurrences': total_occurrences,
'multi_scale_occurrences': multi_scale_occurrences,
'consistency_rate': multi_scale_occurrences / total_occurrences if total_occurrences > 0 else 0,
'scales_available': len(occurrences_by_tf),
}
return consistency_stats
def plot_multi_timeframe_hit_rates(mt_results: Dict, output_dir: Path):
"""多尺度形态命中率对比图"""
if not mt_results:
return
# 收集所有形态名称
all_patterns = set()
for tf_stats in mt_results.values():
all_patterns.update(tf_stats.keys())
# 筛选至少在一个尺度上有足够样本的形态
valid_patterns = []
for pattern in all_patterns:
has_valid_data = False
for tf_stats in mt_results.values():
if pattern in tf_stats and tf_stats[pattern]['n_occurrences'] >= 5:
if not np.isnan(tf_stats[pattern].get('hit_rate', np.nan)):
has_valid_data = True
break
if has_valid_data:
valid_patterns.append(pattern)
if not valid_patterns:
print(" 没有足够的数据绘制多尺度命中率对比图")
return
# 准备绘图数据
intervals = sorted(mt_results.keys())
n_intervals = len(intervals)
n_patterns = len(valid_patterns)
fig, ax = plt.subplots(figsize=(max(12, n_patterns * 0.8), 8))
x = np.arange(n_patterns)
width = 0.8 / n_intervals
colors = ['#3498db', '#e74c3c', '#2ecc71', '#f39c12', '#9b59b6']
for i, interval in enumerate(intervals):
hit_rates = []
for pattern in valid_patterns:
if pattern in mt_results[interval]:
hr = mt_results[interval][pattern].get('hit_rate', np.nan)
else:
hr = np.nan
hit_rates.append(hr)
offset = (i - n_intervals / 2 + 0.5) * width
bars = ax.bar(x + offset, hit_rates, width, label=interval,
color=colors[i % len(colors)], alpha=0.8, edgecolor='gray', linewidth=0.5)
# 标注数值
for j, (bar, hr) in enumerate(zip(bars, hit_rates)):
if not np.isnan(hr) and bar.get_height() > 0:
ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 0.01,
f'{hr:.1%}', ha='center', va='bottom', fontsize=6, rotation=0)
ax.axhline(y=0.5, color='red', linestyle='--', linewidth=1.0, alpha=0.7, label='50% baseline')
ax.set_xlabel('形态名称', fontsize=11)
ax.set_ylabel('命中率', fontsize=11)
ax.set_title('多时间尺度形态命中率对比', fontsize=13, fontweight='bold')
ax.set_xticks(x)
ax.set_xticklabels(valid_patterns, rotation=45, ha='right', fontsize=8)
ax.legend(fontsize=9, loc='best')
ax.set_ylim(0, 1)
ax.grid(axis='y', alpha=0.3, linestyle='--')
plt.tight_layout()
fig.savefig(output_dir / "pattern_multi_timeframe_hitrate.png", dpi=150, bbox_inches='tight')
plt.close(fig)
print(f" [saved] pattern_multi_timeframe_hitrate.png")
def plot_cross_scale_consistency(consistency_stats: Dict, output_dir: Path):
"""展示跨尺度形态一致性统计"""
if not consistency_stats:
print(" 没有跨尺度一致性数据可绘制")
return
# 筛选有效数据
valid_stats = {k: v for k, v in consistency_stats.items() if v['total_occurrences'] >= 10}
if not valid_stats:
print(" 没有足够的数据绘制跨尺度一致性图")
return
# 按一致性率排序
sorted_patterns = sorted(valid_stats.items(), key=lambda x: x[1]['consistency_rate'], reverse=True)
names = [name for name, _ in sorted_patterns]
consistency_rates = [stats['consistency_rate'] for _, stats in sorted_patterns]
multi_scale_counts = [stats['multi_scale_occurrences'] for _, stats in sorted_patterns]
total_counts = [stats['total_occurrences'] for _, stats in sorted_patterns]
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, max(6, len(names) * 0.4)))
# 左图：一致性率
y_pos = range(len(names))
colors = ['#2ecc71' if rate > 0.3 else '#e74c3c' for rate in consistency_rates]
bars1 = ax1.barh(y_pos, consistency_rates, color=colors, edgecolor='gray', linewidth=0.5, alpha=0.8)
for i, (bar, rate, multi, total) in enumerate(zip(bars1, consistency_rates, multi_scale_counts, total_counts)):
ax1.text(bar.get_width() + 0.01, i, f'{rate:.1%}\n({multi}/{total})',
va='center', fontsize=7)
ax1.set_yticks(y_pos)
ax1.set_yticklabels(names, fontsize=9)
ax1.set_xlabel('跨尺度一致性率', fontsize=11)
ax1.set_title('形态跨尺度一致性率\n(同一日期出现在多个时间尺度的比例)', fontsize=12, fontweight='bold')
ax1.set_xlim(0, 1)
ax1.axvline(x=0.3, color='blue', linestyle='--', linewidth=0.8, alpha=0.5, label='30% threshold')
ax1.legend(fontsize=8)
ax1.grid(axis='x', alpha=0.3, linestyle='--')
# 右图：出现次数对比
width = 0.35
x_pos = np.arange(len(names))
bars2 = ax2.barh(x_pos, total_counts, width, label='总出现次数', color='#3498db', alpha=0.7)
bars3 = ax2.barh(x_pos + width, multi_scale_counts, width, label='多尺度出现次数', color='#e67e22', alpha=0.7)
ax2.set_yticks(x_pos + width / 2)
ax2.set_yticklabels(names, fontsize=9)
ax2.set_xlabel('出现次数', fontsize=11)
ax2.set_title('形态出现次数统计', fontsize=12, fontweight='bold')
ax2.legend(fontsize=9)
ax2.grid(axis='x', alpha=0.3, linestyle='--')
plt.tight_layout()
fig.savefig(output_dir / "pattern_cross_scale_consistency.png", dpi=150, bbox_inches='tight')
plt.close(fig)
print(f" [saved] pattern_cross_scale_consistency.png")
# ============================================================
# 7. 主流程
# ============================================================
def evaluate_patterns_on_set(df: pd.DataFrame, patterns: Dict[str, pd.Series],
@@ -843,6 +1111,27 @@ def run_patterns_analysis(df: pd.DataFrame, output_dir: str) -> Dict:
plot_forward_return_boxplots(val_patterns_in_set, val_fwd, output_dir, prefix="val")
plot_hit_rate_with_ci(val_results, output_dir, prefix="val")
# ============ 多时间尺度形态分析 ============
print("\n--- 多时间尺度形态分析 ---")
mt_results = multi_timeframe_pattern_analysis(['1h', '4h', '1d'])
if mt_results:
plot_multi_timeframe_hit_rates(mt_results, output_dir)
# ============ 跨尺度形态一致性分析 ============
print("\n--- 跨尺度形态一致性分析 ---")
consistency_stats = cross_scale_pattern_consistency(['1h', '4h', '1d'])
if consistency_stats:
plot_cross_scale_consistency(consistency_stats, output_dir)
print(f"\n 检测到 {len(consistency_stats)} 种形态的跨尺度一致性")
# 打印前5个一致性最高的形态
sorted_patterns = sorted(consistency_stats.items(), key=lambda x: x[1]['consistency_rate'], reverse=True)
print("\n 一致性率最高的形态:")
for name, stats in sorted_patterns[:5]:
rate = stats['consistency_rate']
multi = stats['multi_scale_occurrences']
total = stats['total_occurrences']
print(f" {name}: {rate:.1%} ({multi}/{total})")
print(f"\n{'='*60}")
print(" K线形态识别与统计验证完成")
print(f"{'='*60}")
@@ -853,4 +1142,6 @@ def run_patterns_analysis(df: pd.DataFrame, output_dir: str) -> Dict:
'fdr_passed_train': fdr_passed_train,
'fdr_passed_val': fdr_passed_val,
'all_patterns': all_patterns,
'mt_results': mt_results,
'consistency_stats': consistency_stats,
}