refactor: 开源化项目重构

- 删除无用文件: PYEOF, PLAN.md, HURST_ENHANCEMENT_SUMMARY.md
- 移动 REPORT.md → docs/REPORT.md，更新 53 处图片路径
- 移动 test_hurst_15scales.py → tests/，修复路径引用
- 清理 output/ 中未被报告引用的 60 个文件
- 重写 README.md 为开源标准格式（Badge、结构树、模块表等）
- 添加 MIT LICENSE
- 更新 .gitignore 排除运行时生成文件

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

.gitignore

@@ -29,3 +29,11 @@ htmlcov/
 
 # Jupyter
 .ipynb_checkpoints/
+
+# Runtime generated output (tracked baseline images are in output/)
+output/all_results.json
+output/evidence_dashboard.png
+output/综合结论报告.txt
+output/hurst_test/
+*.tmp
+*.bak

HURST_ENHANCEMENT_SUMMARY.md

@@ -1,239 +0,0 @@
-# Hurst分析模块增强总结
-
-## 修改文件
-`/Users/hepengcheng/airepo/btc_price_anany/src/hurst_analysis.py`
-
-## 增强内容
-
-### 1. 扩展至15个时间粒度
-**修改位置**：`run_hurst_analysis()` 函数（约第689-691行）
-
-**原代码**：
-```python
-mt_results = multi_timeframe_hurst(['1h', '4h', '1d', '1w'])
-```
-
-**新代码**：
-```python
-# 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)
-```
-
-**影响**：从原来的4个尺度（1h, 4h, 1d, 1w）扩展到全部15个粒度，提供更全面的多尺度分析。
-
----
-
-### 2. 1m数据截断优化
-**修改位置**：`multi_timeframe_hurst()` 函数（约第310-313行）
-
-**新增代码**：
-```python
-# 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:]
-```
-
-**目的**：1分钟数据可能包含数百万个数据点，截断到最后10万条可以：
-- 减少计算时间
-- 避免内存溢出
-- 保留最近的数据（更具代表性）
-
----
-
-### 3. 增强多时间框架可视化
-**修改位置**：`plot_multi_timeframe()` 函数（约第411-461行）
-
-**主要改动**：
-1. **更宽的画布**：`figsize=(12, 7)` → `figsize=(16, 8)`
-2. **自适应柱状图宽度**：`width = min(0.25, 0.8 / 3)`
-3. **X轴标签旋转**：`rotation=45, ha='right'` 避免15个标签重叠
-4. **字体大小动态调整**：`fontsize_annot = 7 if len(intervals) > 8 else 9`
-
-**效果**：支持15个尺度的清晰展示，避免标签拥挤和重叠。
-
----
-
-### 4. 新增：Hurst vs log(Δt) 标度关系图
-**新增函数**：`plot_hurst_vs_scale()` （第464-547行）
-
-**功能特性**：
-- **X轴**：log₁₀(Δt) - 采样周期的对数（天）
-- **Y轴**：Hurst指数（R/S和DFA两条曲线）
-- **参考线**：H=0.5（随机游走）、趋势阈值、均值回归阈值
-- **线性拟合**：显示标度关系方程 `H = a·log(Δt) + b`
-- **双X轴显示**：下方显示log值，上方显示时间框架名称
-
-**时间周期映射**：
-```python
-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
-}
-```
-
-**调用位置**：`run_hurst_analysis()` 函数（第697-698行）
-```python
-# 绘制Hurst vs 时间尺度标度关系图
-plot_hurst_vs_scale(mt_results, output_dir)
-```
-
-**输出文件**：`output/hurst/hurst_vs_scale.png`
-
----
-
-## 输出变化
-
-### 新增图表
-- `hurst_vs_scale.png` - Hurst指数vs时间尺度标度关系图
-
-### 增强图表
-- `hurst_multi_timeframe.png` - 从4个尺度扩展到15个尺度
-
-### 终端输出
-分析过程会显示所有15个粒度的计算进度和结果：
-```
-【5】多时间框架Hurst指数
---------------------------------------------------
-
-正在加载 1m 数据...
-  1m 数据量较大（1234567条），截取最后100000条
-  1m: R/S=0.5234, DFA=0.5189, 平均=0.5211
-
-正在加载 3m 数据...
-  3m: R/S=0.5312, DFA=0.5278, 平均=0.5295
-
-... (共15个粒度)
-```
-
----
-
-## 技术亮点
-
-### 1. 标度关系分析
-通过 `plot_hurst_vs_scale()` 函数，可以观察：
-- **多重分形特征**：不同尺度下Hurst指数的变化规律
-- **标度不变性**：是否存在幂律关系 `H ∝ (Δt)^α`
-- **跨尺度一致性**：R/S和DFA方法在不同尺度的一致性
-
-### 2. 性能优化
-- 对1m数据截断，避免百万级数据的计算瓶颈
-- 动态调整可视化参数，适应不同数量的尺度
-
-### 3. 可扩展性
-- `ALL_INTERVALS` 列表可灵活调整
-- `INTERVAL_DAYS` 字典支持自定义时间周期映射
-- 函数签名保持向后兼容
-
----
-
-## 使用方法
-
-### 运行完整分析
-```python
-from src.hurst_analysis import run_hurst_analysis
-from src.data_loader import load_daily
-
-df = load_daily()
-results = run_hurst_analysis(df, output_dir="output/hurst")
-```
-
-### 仅运行15尺度分析
-```python
-from src.hurst_analysis import multi_timeframe_hurst, plot_hurst_vs_scale
-from pathlib import Path
-
-ALL_INTERVALS = ['1m', '3m', '5m', '15m', '30m', '1h', '2h', '4h',
-                 '6h', '8h', '12h', '1d', '3d', '1w', '1mo']
-mt_results = multi_timeframe_hurst(ALL_INTERVALS)
-plot_hurst_vs_scale(mt_results, Path("output/hurst"))
-```
-
-### 测试增强功能
-```bash
-python test_hurst_15scales.py
-```
-
----
-
-## 数据文件依赖
-
-需要以下15个CSV文件（位于 `data/` 目录）：
-```
-btcusdt_1m.csv   btcusdt_3m.csv   btcusdt_5m.csv   btcusdt_15m.csv
-btcusdt_30m.csv  btcusdt_1h.csv   btcusdt_2h.csv   btcusdt_4h.csv
-btcusdt_6h.csv   btcusdt_8h.csv   btcusdt_12h.csv  btcusdt_1d.csv
-btcusdt_3d.csv   btcusdt_1w.csv   btcusdt_1mo.csv
-```
-
-✅ **当前状态**：所有数据文件已就绪
-
----
-
-## 预期效果
-
-### 标度关系图解读示例
-
-1. **标度不变（分形）**：
-   - Hurst指数在log(Δt)轴上呈线性关系
-   - 例如：H ≈ 0.05·log(Δt) + 0.52
-   - 说明：市场在不同时间尺度展现相似的统计特性
-
-2. **标度依赖（多重分形）**：
-   - Hurst指数在不同尺度存在非线性变化
-   - 短期尺度（1m-1h）可能偏向随机游走（H≈0.5）
-   - 长期尺度（1d-1mo）可能偏向趋势性（H>0.55）
-
-3. **方法一致性验证**：
-   - R/S和DFA两条曲线应当接近
-   - 如果差异较大，说明数据可能存在特殊结构（如极端波动、结构性断点）
-
----
-
-## 修改验证
-
-### 语法检查
-```bash
-python3 -m py_compile src/hurst_analysis.py
-```
-✅ 通过
-
-### 文件结构
-```
-src/hurst_analysis.py
-├── multi_timeframe_hurst()     [已修改] +数据截断逻辑
-├── plot_multi_timeframe()      [已修改] +支持15尺度
-├── plot_hurst_vs_scale()       [新增]   标度关系图
-└── run_hurst_analysis()        [已修改] +15粒度+新图表调用
-```
-
----
-
-## 兼容性说明
-
-✅ **向后兼容**：
-- 所有原有函数签名保持不变
-- 默认参数依然为 `['1h', '4h', '1d', '1w']`
-- 可通过参数指定任意粒度组合
-
-✅ **代码风格**：
-- 遵循原模块的注释风格和函数结构
-- 保持一致的变量命名和代码格式
-
----
-
-## 后续建议
-
-1. **参数化配置**：可将 `ALL_INTERVALS` 和 `INTERVAL_DAYS` 提取为模块级常量
-2. **并行计算**：15个粒度的分析可使用多进程并行加速
-3. **缓存机制**：对计算结果进行缓存，避免重复计算
-4. **异常处理**：增强对缺失数据文件的容错处理
-
----
-
-**修改完成时间**：2026-02-03
-**修改人**：Claude (Sonnet 4.5)
-**修改类型**：功能增强（非破坏性）

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 riba2534
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

PLAN.md

@@ -1,152 +0,0 @@
-# BTC 全数据深度分析扩展计划
-
-## 目标
-充分利用全部 15 个 K 线数据文件（1m~1mo），新增 8 个分析模块 + 增强 5 个现有模块，覆盖目前完全未触及的分钟级微观结构、多尺度统计标度律、极端风险等领域。
-
----
-
-## 一、新增 8 个分析模块
-
-### 1. `microstructure.py` — 市场微观结构分析
-**使用数据**: 1m, 3m, 5m
-- Roll 价差估计（基于收盘价序列相关性）
-- Corwin-Schultz 高低价价差估计
-- Kyle's Lambda（价格冲击系数）
-- Amihud 非流动性比率
-- VPIN（基于成交量同步的知情交易概率）
-- 图表: 价差时序、流动性热力图、VPIN 预警图
-
-### 2. `intraday_patterns.py` — 日内模式分析
-**使用数据**: 1m, 5m, 15m, 30m, 1h
-- 日内成交量 U 型曲线（按小时/分钟聚合）
-- 日内波动率微笑模式
-- 亚洲/欧洲/美洲交易时段对比
-- 日内收益率自相关结构
-- 图表: 时段热力图、成交量/波动率日内模式、三时区对比
-
-### 3. `scaling_laws.py` — 统计标度律分析
-**使用数据**: 全部 15 个文件
-- 波动率标度: σ(Δt) ∝ (Δt)^H，拟合 H 指数
-- Taylor 效应: |r|^q 的自相关衰减与 q 的关系
-- 收益率聚合特性（正态化速度）
-- Epps 效应（高频相关性衰减）
-- 图表: 标度律拟合、Taylor 效应矩阵、正态性 vs 时间尺度
-
-### 4. `multi_scale_vol.py` — 多尺度已实现波动率
-**使用数据**: 5m, 15m, 30m, 1h, 2h, 4h, 6h, 8h, 12h, 1d
-- 已实现波动率 (RV) 在各尺度上的计算
-- 波动率签名图 (Volatility Signature Plot)
-- HAR-RV 模型 (Corsi 2009) — 用 5m RV 预测日/周/月 RV
-- 多尺度波动率溢出 (Diebold-Yilmaz)
-- 图表: 签名图、HAR-RV 拟合、波动率溢出网络
-
-### 5. `entropy_analysis.py` — 信息熵分析
-**使用数据**: 1m, 5m, 15m, 1h, 4h, 1d
-- Shannon 熵跨时间尺度比较
-- 样本熵 (SampEn) / 近似熵 (ApEn)
-- 排列熵 (Permutation Entropy) 多尺度
-- 转移熵 (Transfer Entropy) — 时间尺度间信息流方向
-- 图表: 熵 vs 时间尺度、滚动熵时序、信息流向图
-
-### 6. `extreme_value.py` — 极端值与尾部风险
-**使用数据**: 1h, 4h, 1d, 1w
-- 广义极值分布 (GEV) 区组极大值拟合
-- 广义 Pareto 分布 (GPD) 超阈值拟合
-- 多尺度 VaR / CVaR 计算
-- 尾部指数估计 (Hill estimator)
-- 极端事件聚集检验
-- 图表: 尾部拟合 QQ 图、VaR 回测、尾部指数时序
-
-### 7. `cross_timeframe.py` — 跨时间尺度关联分析
-**使用数据**: 5m, 15m, 1h, 4h, 1d, 1w
-- 跨尺度收益率相关矩阵
-- Lead-lag 领先/滞后关系检测
-- 多尺度 Granger 因果检验
-- 信息流方向（粗粒度 → 细粒度 or 反向？）
-- 图表: 跨尺度相关热力图、领先滞后矩阵、信息流向图
-
-### 8. `momentum_reversion.py` — 动量与均值回归多尺度检验
-**使用数据**: 1m, 5m, 15m, 1h, 4h, 1d, 1w, 1mo
-- 各尺度收益率自相关符号分析
-- 方差比检验 (Lo-MacKinlay)
-- 均值回归半衰期 (Ornstein-Uhlenbeck 拟合)
-- 动量/反转盈利能力回测
-- 图表: 方差比 vs 尺度、自相关衰减、策略 PnL 对比
-
----
-
-## 二、增强 5 个现有模块
-
-### 9. `fft_analysis.py` 增强
-- 当前: 仅用 4h, 1d, 1w
-- 扩展: 加入 1m, 5m, 15m, 30m, 1h, 2h, 6h, 8h, 12h, 3d, 1mo
-- 新增: 全 15 尺度频谱瀑布图
-
-### 10. `hurst_analysis.py` 增强
-- 当前: 仅用 1h, 4h, 1d, 1w
-- 扩展: 全部 15 个粒度的 Hurst 指数
-- 新增: Hurst 指数 vs 时间尺度的标度关系图
-
-### 11. `returns_analysis.py` 增强
-- 当前: 仅用 1h, 4h, 1d, 1w
-- 扩展: 加入 1m, 5m, 15m, 30m, 2h, 6h, 8h, 12h, 3d, 1mo
-- 新增: 峰度/偏度 vs 时间尺度图，正态化收敛速度
-
-### 12. `acf_analysis.py` 增强
-- 当前: 仅用 1d
-- 扩展: 加入 1h, 4h, 1w 的 ACF/PACF 多尺度对比
-- 新增: 自相关衰减速度 vs 时间尺度
-
-### 13. `volatility_analysis.py` 增强
-- 当前: 仅用 1d
-- 扩展: 加入 5m, 1h, 4h 的波动率聚集分析
-- 新增: 波动率长记忆参数 d vs 时间尺度
-
----
-
-## 三、main.py 更新
-
-在 MODULE_REGISTRY 中注册全部 8 个新模块:
-
-```python
-("microstructure", ("市场微观结构",   "microstructure",       "run_microstructure_analysis",   False)),
-("intraday",       ("日内模式分析",   "intraday_patterns",    "run_intraday_analysis",         False)),
-("scaling",        ("统计标度律",     "scaling_laws",         "run_scaling_analysis",           False)),
-("multiscale_vol", ("多尺度波动率",   "multi_scale_vol",      "run_multiscale_vol_analysis",    False)),
-("entropy",        ("信息熵分析",     "entropy_analysis",     "run_entropy_analysis",           False)),
-("extreme",        ("极端值分析",     "extreme_value",        "run_extreme_value_analysis",     False)),
-("cross_tf",       ("跨尺度关联",     "cross_timeframe",      "run_cross_timeframe_analysis",   False)),
-("momentum_rev",   ("动量均值回归",   "momentum_reversion",   "run_momentum_reversion_analysis",False)),
-```
-
----
-
-## 四、实施策略
-
-- 8 个新模块并行开发（各模块独立无依赖）
-- 5 个模块增强并行开发
-- 全部完成后更新 main.py 注册 + 运行全量测试
-- 每个模块遵循现有 `run_xxx(df, output_dir) -> Dict` 签名
-- 需要多尺度数据的模块内部调用 `load_klines(interval)` 自行加载
-
-## 五、数据覆盖验证
-
-| 数据文件 | 当前使用 | 扩展后使用 |
-|---------|---------|----------|
-| 1m | - | microstructure, intraday, scaling, momentum_rev, fft(增) |
-| 3m | - | microstructure, scaling |
-| 5m | - | microstructure, intraday, scaling, multi_scale_vol, entropy, cross_tf, momentum_rev, returns(增), volatility(增) |
-| 15m | - | intraday, scaling, entropy, cross_tf, momentum_rev, returns(增) |
-| 30m | - | intraday, scaling, multi_scale_vol, returns(增), fft(增) |
-| 1h | hurst,returns,causality,calendar | +intraday, scaling, multi_scale_vol, entropy, cross_tf, momentum_rev, acf(增), volatility(增) |
-| 2h | - | multi_scale_vol, scaling, fft(增), returns(增) |
-| 4h | fft,hurst,returns | +multi_scale_vol, entropy, cross_tf, momentum_rev, acf(增), volatility(增), extreme |
-| 6h | - | multi_scale_vol, scaling, fft(增), returns(增) |
-| 8h | - | multi_scale_vol, scaling, fft(增), returns(增) |
-| 12h | - | multi_scale_vol, scaling, fft(增), returns(增) |
-| 1d | 全部17模块 | +所有新增模块 |
-| 3d | - | scaling, fft(增), returns(增) |
-| 1w | fft,hurst,returns | +extreme, cross_tf, momentum_rev, acf(增) |
-| 1mo | - | momentum_rev, scaling, fft(增), returns(增) |
-
-**结果: 全部 15 个数据文件 100% 覆盖使用**

README.md

@@ -1,2 +1,133 @@
-# btc_price_anany
+# BTC/USDT Price Analysis
 
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)
+[![Python 3.10+](https://img.shields.io/badge/Python-3.10%2B-blue.svg)](https://www.python.org/)
+
+A comprehensive quantitative analysis framework for BTC/USDT price dynamics, covering 25 analytical dimensions from statistical distributions to fractal geometry. The framework processes multi-timeframe Binance kline data (1-minute to monthly) spanning 2017-08 to 2026-02, producing reproducible research-grade visualizations and statistical reports.
+
+## Features
+
+- **Multi-timeframe data pipeline** — 15 granularities from 1m to 1M, unified loader with validation
+- **25 analysis modules** — each module runs independently; single-module failure does not block others
+- **Statistical rigor** — train/validation splits, multiple hypothesis testing corrections, bootstrap confidence intervals
+- **Publication-ready output** — 53 charts with Chinese font support, plus a 1300-line Markdown research report
+- **Modular architecture** — run all modules or cherry-pick via CLI flags
+
+## Project Structure
+
+```
+btc_price_anany/
+├── main.py                 # CLI entry point
+├── requirements.txt        # Python dependencies
+├── LICENSE                 # MIT License
+├── data/                   # 15 BTC/USDT kline CSVs (1m ~ 1M)
+├── src/                    # 30 analysis & utility modules
+│   ├── data_loader.py      # Data loading & validation
+│   ├── preprocessing.py    # Derived feature engineering
+│   ├── font_config.py      # Chinese font rendering
+│   ├── visualization.py    # Summary dashboard generation
+│   └── ...                 # 26 analysis modules
+├── output/                 # Generated charts (53 PNGs)
+├── docs/
+│   └── REPORT.md           # Full research report with findings
+└── tests/
+    └── test_hurst_15scales.py  # Hurst exponent multi-scale test
+```
+
+## Quick Start
+
+### Requirements
+
+- Python 3.10+
+- ~1 GB disk for kline data
+
+### Installation
+
+```bash
+git clone https://github.com/riba2534/btc_price_anany.git
+cd btc_price_anany
+pip install -r requirements.txt
+```
+
+### Usage
+
+```bash
+# Run all 25 analysis modules
+python main.py
+
+# List available modules
+python main.py --list
+
+# Run specific modules
+python main.py --modules fft wavelet hurst
+
+# Limit date range
+python main.py --start 2020-01-01 --end 2025-12-31
+```
+
+## Data
+
+| File | Timeframe | Rows (approx.) |
+|------|-----------|-----------------|
+| `btcusdt_1m.csv` | 1 minute | ~4,500,000 |
+| `btcusdt_3m.csv` | 3 minutes | ~1,500,000 |
+| `btcusdt_5m.csv` | 5 minutes | ~900,000 |
+| `btcusdt_15m.csv` | 15 minutes | ~300,000 |
+| `btcusdt_30m.csv` | 30 minutes | ~150,000 |
+| `btcusdt_1h.csv` | 1 hour | ~75,000 |
+| `btcusdt_2h.csv` | 2 hours | ~37,000 |
+| `btcusdt_4h.csv` | 4 hours | ~19,000 |
+| `btcusdt_6h.csv` | 6 hours | ~12,500 |
+| `btcusdt_8h.csv` | 8 hours | ~9,500 |
+| `btcusdt_12h.csv` | 12 hours | ~6,300 |
+| `btcusdt_1d.csv` | 1 day | ~3,100 |
+| `btcusdt_3d.csv` | 3 days | ~1,000 |
+| `btcusdt_1w.csv` | 1 week | ~450 |
+| `btcusdt_1mo.csv` | 1 month | ~100 |
+
+All data sourced from Binance public API, covering 2017-08 to 2026-02.
+
+## Analysis Modules
+
+| Module | Description |
+|--------|-------------|
+| `fft` | FFT power spectrum, multi-timeframe spectral analysis, bandpass filtering |
+| `wavelet` | Continuous wavelet transform scalogram, global spectrum, key period tracking |
+| `acf` | ACF/PACF grid analysis for autocorrelation structure |
+| `returns` | Return distribution fitting, QQ plots, multi-scale moment analysis |
+| `volatility` | Volatility clustering, GARCH modeling, leverage effect quantification |
+| `hurst` | R/S and DFA Hurst exponent estimation, rolling window analysis |
+| `fractal` | Box-counting dimension, Monte Carlo benchmarking, self-similarity tests |
+| `power_law` | Log-log regression, power-law growth corridor, model comparison |
+| `volume_price` | Volume-return scatter analysis, OBV divergence detection |
+| `calendar` | Weekday, month, hour, and quarter-boundary effects |
+| `halving` | Halving cycle analysis with normalized trajectory comparison |
+| `indicators` | Technical indicator IC testing with train/validation split |
+| `patterns` | K-line pattern recognition with forward-return validation |
+| `clustering` | Market regime clustering (K-Means, GMM) with transition matrices |
+| `time_series` | ARIMA, Prophet, LSTM forecasting with direction accuracy |
+| `causality` | Granger causality testing across volume and price features |
+| `anomaly` | Anomaly detection with precursor feature analysis |
+| `microstructure` | Market microstructure: spreads, Kyle's lambda, VPIN |
+| `intraday` | Intraday session patterns and volume heatmaps |
+| `scaling` | Statistical scaling laws and kurtosis decay |
+| `multiscale_vol` | HAR volatility, jump detection, higher moment analysis |
+| `entropy` | Sample entropy and permutation entropy across scales |
+| `extreme` | Extreme value theory: Hill estimator, VaR backtesting |
+| `cross_tf` | Cross-timeframe correlation and lead-lag analysis |
+| `momentum_rev` | Momentum vs mean-reversion: variance ratios, OU half-life |
+
+## Key Findings
+
+The full analysis report is available at [`docs/REPORT.md`](docs/REPORT.md). Major conclusions include:
+
+- **Non-Gaussian returns**: BTC daily returns exhibit significant fat tails (kurtosis ~10) and are best fit by Student-t distributions, not Gaussian
+- **Volatility clustering**: Strong GARCH effects with long memory (d ≈ 0.4), confirming volatility persistence across time scales
+- **Hurst exponent H ≈ 0.55**: Weak but statistically significant long-range dependence, transitioning from trending (short-term) to mean-reverting (long-term)
+- **Fractal dimension D ≈ 1.4**: Price series is rougher than Brownian motion, exhibiting multi-fractal characteristics
+- **Halving cycle impact**: Statistically significant post-halving bull runs with diminishing returns per cycle
+- **Calendar effects**: Weak but detectable weekday and monthly seasonality; no exploitable intraday patterns survive transaction costs
+
+## License
+
+This project is licensed under the [MIT License](LICENSE).

output/scaling/scaling_statistics.csv

@@ -1,16 +0,0 @@
-interval,delta_t_days,n_samples,mean,std,skew,kurtosis,median,iqr,min,max,taylor_q0.5,taylor_q1.0,taylor_q1.5,taylor_q2.0
-1m,0.0006944444444444445,4442238,6.514229903205994e-07,0.0011455170189810019,0.09096477211060976,118.2100230044886,0.0,0.0006639952882605969,-0.07510581597867486,0.07229275389452557,0.3922161789659432,0.420163954926606,0.3813654715410455,0.3138419057179692
-3m,0.0020833333333333333,1480754,1.9512414873135698e-06,0.0019043949669174042,-0.18208775274986902,107.47563675941338,0.0,0.001186397292140407,-0.12645642395255924,0.09502117700807843,0.38002945432446916,0.41461914565368124,0.3734815848245644,0.31376694748340894
-5m,0.003472222222222222,888456,3.2570841568695736e-06,0.0024297494264341377,0.06939204338227808,105.83164964583392,0.0,0.001565521574075268,-0.1078678022123837,0.16914214536807326,0.38194121939134235,0.4116281667269265,0.36443870957026997,0.26857053409393955
-15m,0.010416666666666666,296157,9.771087503168118e-06,0.0040293734547329875,-0.0010586612854033598,70.47549524675631,1.2611562165555531e-05,0.0026976128710037802,-0.1412408971518897,0.20399153696296207,0.3741410793762186,0.3953117569467919,0.35886498852597287,0.28756473158290347
-30m,0.020833333333333332,148084,1.954149672826445e-05,0.005639021907535573,-0.2923413146224213,47.328126125169184,4.40447725506786e-05,0.0037191093096845397,-0.18187257074655225,0.15957096537940915,0.3609427879223196,0.36904730536162156,0.3161827829328581,0.23723446832339048
-1h,0.041666666666666664,74052,3.8928402661852975e-05,0.007834400735539676,-0.46928906631794426,35.87898879592525,7.527302916194555e-05,0.005129376265738019,-0.2010332141747841,0.16028033154146137,0.3249788436588642,0.3154201135215658,0.25515930856099855,0.1827633364124107
-2h,0.08333333333333333,37037,7.779304473280443e-05,0.010899581687307503,-0.2604257775957978,27.24964874971723,0.00015464099189440314,0.007302585874020006,-0.19267918917704077,0.22391020872561077,0.3159731855373146,0.3178979473126255,0.3031433889164812,0.2907494549885495
-4h,0.16666666666666666,18527,0.00015508279447371288,0.014857794400726971,-0.20020585793557596,20.544129479104843,0.00021425744678245183,0.010148047310827886,-0.22936581945705434,0.2716237113205769,0.2725224153056918,0.2615759407454282,0.20292729261598141,0.12350007019673657
-6h,0.25,12357,0.00023316508843318525,0.01791845242945486,-0.4517831160428995,12.93921928109208,0.00033002998176231307,0.012667582427153984,-0.24206507159533777,0.19514297257535526,0.23977347647268715,0.22444014622624148,0.18156088372315904,0.12731762218209144
-8h,0.3333333333333333,9269,0.0003099815442026618,0.020509830481045817,-0.3793900704204729,11.676624395294125,0.0003646760000407175,0.015281768018361641,-0.24492624313192635,0.19609747263739785,0.26037882512390365,0.28322259282360396,0.29496627424986377,0.3052422689193472
-12h,0.5,6180,0.00046207161197837904,0.025132311444186397,-0.3526194472211495,9.519176735726175,0.0005176241976152787,0.019052514462501707,-0.26835696343541754,0.2370917277782011,0.24752503269263015,0.26065147330207306,0.2714720806698807,0.2892083361682107
-1d,1.0,3090,0.0009347097921709027,0.03606357680963052,-0.9656348742170849,15.645612143331558,0.000702917984422788,0.02974122424942422,-0.5026069427414592,0.20295221522828027,0.1725059795097981,0.16942476382322424,0.15048537861590472,0.10265366144621343
-3d,3.0,1011,0.002911751597172647,0.06157342850770238,-0.8311053890659649,6.18404587195924,0.0044986993267258114,0.06015693941674143,-0.5020207241559144,0.30547246871649913,0.21570233552244675,0.2088925350958307,0.1642366047555974,0.10526565406496537
-1w,7.0,434,0.0068124459112775156,0.09604704208639726,-0.4425311270057618,2.0840272977984977,0.005549416326948385,0.08786994519339078,-0.404390164271242,0.3244224603247549,0.1466634174592444,0.1575558826923941,0.154712114094472,0.13797287890569243
-1mo,30.0,101,0.02783890277226861,0.19533014182355307,-0.03995936770003692,-0.004540835316996894,0.004042338413782558,0.20785440236459263,-0.4666604027641524,0.4748903599412194,-0.07899827864451633,0.019396381982346785,0.0675403219738466,0.0825052826285604

output/综合结论报告.txt

@@ -1,65 +0,0 @@
-======================================================================
-BTC/USDT 价格规律性分析 — 综合结论报告
-======================================================================
-
-
-"真正有规律" 判定标准（必须同时满足）：
-  1. FDR校正后 p < 0.05
-  2. 排列检验 p < 0.01（如适用）
-  3. 测试集上效果方向一致且显著
-  4. >80% bootstrap子样本中成立（如适用）
-  5. Cohen's d > 0.2 或经济意义显著
-  6. 有合理的经济/市场直觉解释
-
-
-----------------------------------------------------------------------
-模块                                 得分         强度      发现数
-----------------------------------------------------------------------
-fft                              0.00       none        0
-fractal                          0.00       none        0
-power_law                        0.00       none        0
-wavelet                          0.00       none        0
-acf                              0.00       none        0
-returns                          0.00       none        0
-volatility                       0.00       none        0
-hurst                            0.00       none        0
-volume_price                     0.00       none        0
-time_series                      0.00       none        0
-causality                        0.00       none        0
-calendar                         0.00       none        0
-halving                          0.00       none        0
-indicators                       0.00       none        0
-patterns                         0.00       none        0
-clustering                       0.00       none        0
-anomaly                          0.00       none        0
-----------------------------------------------------------------------
-
-## 强证据规律（可重复、有经济意义）:
-  （无）
-
-## 中等证据规律（统计显著但效果有限）:
-  （无）
-
-## 弱证据/不显著:
-  * fft
-  * time_series
-  * clustering
-  * patterns
-  * indicators
-  * halving
-  * calendar
-  * causality
-  * volume_price
-  * fractal
-  * hurst
-  * volatility
-  * returns
-  * acf
-  * wavelet
-  * power_law
-  * anomaly
-
-======================================================================
-注: 得分基于各模块自报告的统计检验结果。
-    具体参数和图表请参见各子目录的输出。
-======================================================================