diff --git a/.gitignore b/.gitignore index 9ec2924..7b1a9f3 100644 --- a/.gitignore +++ b/.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 diff --git a/HURST_ENHANCEMENT_SUMMARY.md b/HURST_ENHANCEMENT_SUMMARY.md deleted file mode 100644 index 3e8ef9e..0000000 --- a/HURST_ENHANCEMENT_SUMMARY.md +++ /dev/null @@ -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） diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..d9ed902 --- /dev/null +++ b/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. diff --git a/PLAN.md b/PLAN.md deleted file mode 100644 index b12ba79..0000000 --- a/PLAN.md +++ /dev/null @@ -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** diff --git a/README.md b/README.md index 6527943..5b8e2e0 100644 --- a/README.md +++ b/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). diff --git a/REPORT.md b/docs/REPORT.md similarity index 100% rename from REPORT.md rename to docs/REPORT.md diff --git a/output/acf/acf_decay_vs_scale.png b/output/acf/acf_decay_vs_scale.png deleted file mode 100644 index 88d1a72..0000000 Binary files a/output/acf/acf_decay_vs_scale.png and /dev/null differ diff --git a/output/acf/acf_multi_scale.png b/output/acf/acf_multi_scale.png deleted file mode 100644 index 92eb537..0000000 Binary files a/output/acf/acf_multi_scale.png and /dev/null differ diff --git a/output/acf/significant_lags_heatmap.png b/output/acf/significant_lags_heatmap.png deleted file mode 100644 index 82462bb..0000000 Binary files a/output/acf/significant_lags_heatmap.png and /dev/null differ diff --git a/output/all_results.json b/output/all_results.json deleted file mode 100644 index 2de7768..0000000 --- a/output/all_results.json +++ /dev/null @@ -1,908 +0,0 @@ -{ - "fft": { - "daily_peaks": " period_days frequency power noise_level snr\n0 39.615385 0.025243 0.007486 0.001178 6.355355\n1 3.111782 0.321359 0.007130 0.001352 5.273187\n2 14.372093 0.069579 0.006190 0.001187 5.216536\n3 13.261803 0.075405 0.006166 0.001188 5.188618", - "multi_tf_peaks": "{'4h': period_days frequency power noise_level snr\n0 2.182214 0.458250 0.001304 0.000203 6.414376\n1 39.587607 0.025260 0.001277 0.000201 6.340524\n2 2.210332 0.452421 0.001260 0.000203 6.199798\n3 3.075531 0.325147 0.001228 0.000202 6.069181\n4 13.309626 0.075134 0.001128 0.000201 5.598435\n5 3.382074 0.295677 0.001118 0.000202 5.527193\n6 2.482181 0.402871 0.001101 0.000203 5.428543\n7 4.893555 0.204350 0.001070 0.000202 5.303870\n8 4.184056 0.239003 0.001055 0.000202 5.222734\n9 2.789371 0.358504 0.001057 0.000203 5.219469\n10 2.177598 0.459222 0.001057 0.000203 5.197185\n11 14.362016 0.069628 0.001025 0.000201 5.089240, '1d': period_days frequency power noise_level snr\n0 39.615385 0.025243 0.007486 0.001178 6.355355\n1 3.111782 0.321359 0.007130 0.001352 5.273187\n2 14.372093 0.069579 0.006190 0.001187 5.216536\n3 13.261803 0.075405 0.006166 0.001188 5.188618, '1w': period_days frequency power noise_level snr\n0 39.454545 0.025346 0.057044 0.009711 5.873872}", - "bandpass_variance_ratios": { - "7d": 14.917052544334325, - "30d": 3.770230414627656, - "90d": 2.4054786415083576, - "365d": 0.748511130559413, - "1400d": 0.23278898254402042 - }, - "bandpass_components": "{'7d': array([-2.97061917e-03, 8.09307366e-04, 1.64398528e-03, ...,\n -1.99758500e-02, -1.92389106e-02, -8.40156425e-05]), '30d': array([0.00811365, 0.01061458, 0.0131885 , ..., 0.00061353, 0.00056402,\n 0.00048904]), '90d': array([-3.81400112e-02, -3.57430742e-02, -3.31503607e-02, ...,\n -3.86752623e-05, -3.43679665e-05, -3.02800847e-05]), '365d': array([-0.00037487, -0.00025267, -0.00013042, ..., -0.00074066,\n -0.00061892, -0.00049698]), '1400d': array([-5.08113855e-05, -4.99293812e-05, -4.90661763e-05, ...,\n -5.35684912e-05, -5.26311118e-05, -5.17120193e-05])}", - "ar1_rho": -0.04993111698604458, - "daily_spectrum": "{'freqs': array([3.23624595e-04, 6.47249191e-04, 9.70873786e-04, ...,\n 4.99029126e-01, 4.99352751e-01, 4.99676375e-01]), 'periods': array([3.09000000e+03, 1.54500000e+03, 1.03000000e+03, ...,\n 2.00389105e+00, 2.00259235e+00, 2.00129534e+00]), 'power': array([0.00062218, 0.00373931, 0.00464221, ..., 0.00234932, 0.00027632,\n 0.00022944]), 'noise_mean': array([0.0011765 , 0.0011765 , 0.0011765 , ..., 0.00143682, 0.00143682,\n 0.00143682]), 'noise_threshold': array([0.00352447, 0.00352448, 0.00352448, ..., 0.00430432, 0.00430433,\n 0.00430433])}", - "multi_tf_results": "{'4h': {'freqs': array([3.23851676e-04, 6.47703352e-04, 9.71555028e-04, ...,\n 2.99919037e+00, 2.99951422e+00, 2.99983807e+00]), 'periods': array([3.08783333e+03, 1.54391667e+03, 1.02927778e+03, ...,\n 3.33423316e-01, 3.33387317e-01, 3.33351326e-01]), 'power': array([0.00010316, 0.00062397, 0.0007734 , ..., 0.00028183, 0.00066969,\n 0.00094334]), 'noise_mean': array([0.0002014 , 0.0002014 , 0.0002014 , ..., 0.00024194, 0.00024194,\n 0.00024194]), 'noise_threshold': array([0.00060335, 0.00060335, 0.00060335, ..., 0.00072478, 0.00072478,\n 0.00072478]), 'peaks': period_days frequency power noise_level snr\n0 2.182214 0.458250 0.001304 0.000203 6.414376\n1 39.587607 0.025260 0.001277 0.000201 6.340524\n2 2.210332 0.452421 0.001260 0.000203 6.199798\n3 3.075531 0.325147 0.001228 0.000202 6.069181\n4 13.309626 0.075134 0.001128 0.000201 5.598435\n5 3.382074 0.295677 0.001118 0.000202 5.527193\n6 2.482181 0.402871 0.001101 0.000203 5.428543\n7 4.893555 0.204350 0.001070 0.000202 5.303870\n8 4.184056 0.239003 0.001055 0.000202 5.222734\n9 2.789371 0.358504 0.001057 0.000203 5.219469\n10 2.177598 0.459222 0.001057 0.000203 5.197185\n11 14.362016 0.069628 0.001025 0.000201 5.089240, 'log_ret': array([ 0.01761637, -0.01707629, -0.00624831, ..., -0.01471412,\n -0.00540965, -0.00268024]), 'label': '4h'}, '1d': {'freqs': array([3.23624595e-04, 6.47249191e-04, 9.70873786e-04, ...,\n 4.99029126e-01, 4.99352751e-01, 4.99676375e-01]), 'periods': array([3.09000000e+03, 1.54500000e+03, 1.03000000e+03, ...,\n 2.00389105e+00, 2.00259235e+00, 2.00129534e+00]), 'power': array([0.00062218, 0.00373931, 0.00464221, ..., 0.00234932, 0.00027632,\n 0.00022944]), 'noise_mean': array([0.0011765 , 0.0011765 , 0.0011765 , ..., 0.00143682, 0.00143682,\n 0.00143682]), 'noise_threshold': array([0.00352447, 0.00352448, 0.00352448, ..., 0.00430432, 0.00430433,\n 0.00430433]), 'peaks': period_days frequency power noise_level snr\n0 39.615385 0.025243 0.007486 0.001178 6.355355\n1 3.111782 0.321359 0.007130 0.001352 5.273187\n2 14.372093 0.069579 0.006190 0.001187 5.216536\n3 13.261803 0.075405 0.006166 0.001188 5.188618, 'log_ret': array([-0.04211287, 0.0076646 , -0.01305349, ..., -0.00461393,\n -0.06774794, -0.02277265]), 'label': '1d'}, '1w': {'freqs': array([0.00032916, 0.00065833, 0.00098749, 0.00131666, 0.00164582,\n 0.00197498, 0.00230415, 0.00263331, 0.00296248, 0.00329164,\n 0.0036208 , 0.00394997, 0.00427913, 0.00460829, 0.00493746,\n 0.00526662, 0.00559579, 0.00592495, 0.00625411, 0.00658328,\n 0.00691244, 0.00724161, 0.00757077, 0.00789993, 0.0082291 ,\n 0.00855826, 0.00888743, 0.00921659, 0.00954575, 0.00987492,\n 0.01020408, 0.01053325, 0.01086241, 0.01119157, 0.01152074,\n 0.0118499 , 0.01217907, 0.01250823, 0.01283739, 0.01316656,\n 0.01349572, 0.01382488, 0.01415405, 0.01448321, 0.01481238,\n 0.01514154, 0.0154707 , 0.01579987, 0.01612903, 0.0164582 ,\n 0.01678736, 0.01711652, 0.01744569, 0.01777485, 0.01810402,\n 0.01843318, 0.01876234, 0.01909151, 0.01942067, 0.01974984,\n 0.020079 , 0.02040816, 0.02073733, 0.02106649, 0.02139566,\n 0.02172482, 0.02205398, 0.02238315, 0.02271231, 0.02304147,\n 0.02337064, 0.0236998 , 0.02402897, 0.02435813, 0.02468729,\n 0.02501646, 0.02534562, 0.02567479, 0.02600395, 0.02633311,\n 0.02666228, 0.02699144, 0.02732061, 0.02764977, 0.02797893,\n 0.0283081 , 0.02863726, 0.02896643, 0.02929559, 0.02962475,\n 0.02995392, 0.03028308, 0.03061224, 0.03094141, 0.03127057,\n 0.03159974, 0.0319289 , 0.03225806, 0.03258723, 0.03291639,\n 0.03324556, 0.03357472, 0.03390388, 0.03423305, 0.03456221,\n 0.03489138, 0.03522054, 0.0355497 , 0.03587887, 0.03620803,\n 0.0365372 , 0.03686636, 0.03719552, 0.03752469, 0.03785385,\n 0.03818302, 0.03851218, 0.03884134, 0.03917051, 0.03949967,\n 0.03982883, 0.040158 , 0.04048716, 0.04081633, 0.04114549,\n 0.04147465, 0.04180382, 0.04213298, 0.04246215, 0.04279131,\n 0.04312047, 0.04344964, 0.0437788 , 0.04410797, 0.04443713,\n 0.04476629, 0.04509546, 0.04542462, 0.04575379, 0.04608295,\n 0.04641211, 0.04674128, 0.04707044, 0.04739961, 0.04772877,\n 0.04805793, 0.0483871 , 0.04871626, 0.04904542, 0.04937459,\n 0.04970375, 0.05003292, 0.05036208, 0.05069124, 0.05102041,\n 0.05134957, 0.05167874, 0.0520079 , 0.05233706, 0.05266623,\n 0.05299539, 0.05332456, 0.05365372, 0.05398288, 0.05431205,\n 0.05464121, 0.05497038, 0.05529954, 0.0556287 , 0.05595787,\n 0.05628703, 0.05661619, 0.05694536, 0.05727452, 0.05760369,\n 0.05793285, 0.05826201, 0.05859118, 0.05892034, 0.05924951,\n 0.05957867, 0.05990783, 0.060237 , 0.06056616, 0.06089533,\n 0.06122449, 0.06155365, 0.06188282, 0.06221198, 0.06254115,\n 0.06287031, 0.06319947, 0.06352864, 0.0638578 , 0.06418697,\n 0.06451613, 0.06484529, 0.06517446, 0.06550362, 0.06583278,\n 0.06616195, 0.06649111, 0.06682028, 0.06714944, 0.0674786 ,\n 0.06780777, 0.06813693, 0.0684661 , 0.06879526, 0.06912442,\n 0.06945359, 0.06978275, 0.07011192, 0.07044108, 0.07077024,\n 0.07109941]), 'periods': array([3038. , 1519. , 1012.66666667, 759.5 ,\n 607.6 , 506.33333333, 434. , 379.75 ,\n 337.55555556, 303.8 , 276.18181818, 253.16666667,\n 233.69230769, 217. , 202.53333333, 189.875 ,\n 178.70588235, 168.77777778, 159.89473684, 151.9 ,\n 144.66666667, 138.09090909, 132.08695652, 126.58333333,\n 121.52 , 116.84615385, 112.51851852, 108.5 ,\n 104.75862069, 101.26666667, 98. , 94.9375 ,\n 92.06060606, 89.35294118, 86.8 , 84.38888889,\n 82.10810811, 79.94736842, 77.8974359 , 75.95 ,\n 74.09756098, 72.33333333, 70.65116279, 69.04545455,\n 67.51111111, 66.04347826, 64.63829787, 63.29166667,\n 62. , 60.76 , 59.56862745, 58.42307692,\n 57.32075472, 56.25925926, 55.23636364, 54.25 ,\n 53.29824561, 52.37931034, 51.49152542, 50.63333333,\n 49.80327869, 49. , 48.22222222, 47.46875 ,\n 46.73846154, 46.03030303, 45.34328358, 44.67647059,\n 44.02898551, 43.4 , 42.78873239, 42.19444444,\n 41.61643836, 41.05405405, 40.50666667, 39.97368421,\n 39.45454545, 38.94871795, 38.4556962 , 37.975 ,\n 37.50617284, 37.04878049, 36.60240964, 36.16666667,\n 35.74117647, 35.3255814 , 34.91954023, 34.52272727,\n 34.13483146, 33.75555556, 33.38461538, 33.02173913,\n 32.66666667, 32.31914894, 31.97894737, 31.64583333,\n 31.31958763, 31. , 30.68686869, 30.38 ,\n 30.07920792, 29.78431373, 29.49514563, 29.21153846,\n 28.93333333, 28.66037736, 28.39252336, 28.12962963,\n 27.87155963, 27.61818182, 27.36936937, 27.125 ,\n 26.88495575, 26.64912281, 26.4173913 , 26.18965517,\n 25.96581197, 25.74576271, 25.52941176, 25.31666667,\n 25.10743802, 24.90163934, 24.69918699, 24.5 ,\n 24.304 , 24.11111111, 23.92125984, 23.734375 ,\n 23.5503876 , 23.36923077, 23.19083969, 23.01515152,\n 22.84210526, 22.67164179, 22.5037037 , 22.33823529,\n 22.17518248, 22.01449275, 21.85611511, 21.7 ,\n 21.54609929, 21.3943662 , 21.24475524, 21.09722222,\n 20.95172414, 20.80821918, 20.66666667, 20.52702703,\n 20.38926174, 20.25333333, 20.1192053 , 19.98684211,\n 19.85620915, 19.72727273, 19.6 , 19.47435897,\n 19.35031847, 19.2278481 , 19.10691824, 18.9875 ,\n 18.86956522, 18.75308642, 18.63803681, 18.52439024,\n 18.41212121, 18.30120482, 18.19161677, 18.08333333,\n 17.97633136, 17.87058824, 17.76608187, 17.6627907 ,\n 17.56069364, 17.45977011, 17.36 , 17.26136364,\n 17.16384181, 17.06741573, 16.97206704, 16.87777778,\n 16.78453039, 16.69230769, 16.6010929 , 16.51086957,\n 16.42162162, 16.33333333, 16.2459893 , 16.15957447,\n 16.07407407, 15.98947368, 15.90575916, 15.82291667,\n 15.74093264, 15.65979381, 15.57948718, 15.5 ,\n 15.4213198 , 15.34343434, 15.26633166, 15.19 ,\n 15.11442786, 15.03960396, 14.96551724, 14.89215686,\n 14.8195122 , 14.74757282, 14.6763285 , 14.60576923,\n 14.53588517, 14.46666667, 14.39810427, 14.33018868,\n 14.2629108 , 14.19626168, 14.13023256, 14.06481481]), 'power': array([5.04731820e-03, 2.54202438e-02, 3.29256798e-02, 2.32601150e-02,\n 7.19168741e-03, 4.07702144e-03, 1.31016337e-02, 1.33647991e-02,\n 2.25619065e-02, 9.06932138e-03, 2.12605364e-02, 1.30420666e-02,\n 1.75090968e-02, 2.59934318e-02, 1.56038741e-02, 1.13190286e-02,\n 3.38425939e-03, 3.90450547e-03, 1.01345109e-02, 6.52712796e-03,\n 1.93218089e-03, 8.54169510e-03, 1.81711957e-02, 4.76819376e-03,\n 1.36862009e-03, 1.26675786e-02, 2.36832798e-02, 3.31122805e-03,\n 6.88655529e-03, 6.19429647e-03, 7.54168482e-04, 7.42115115e-03,\n 1.51507514e-02, 1.33937217e-02, 8.45247520e-03, 5.34075010e-03,\n 5.70067629e-03, 2.37223308e-02, 5.37421870e-03, 4.41235670e-04,\n 2.03187341e-04, 2.50551066e-03, 8.50939182e-03, 1.17597909e-02,\n 4.94199844e-03, 2.12761785e-03, 6.56113855e-03, 1.01714343e-02,\n 5.62639737e-03, 1.27106690e-02, 1.47849324e-02, 5.64655462e-03,\n 5.24277718e-03, 1.27782820e-02, 2.95455273e-02, 2.51233871e-02,\n 1.53276602e-03, 6.65242716e-03, 8.22040762e-03, 4.86617282e-03,\n 8.44698516e-03, 9.38048137e-04, 4.41104449e-04, 1.93021895e-04,\n 3.38715064e-03, 1.45972399e-02, 2.25114006e-02, 9.92147536e-03,\n 2.11296217e-03, 4.85833662e-03, 6.40711760e-03, 1.09797288e-02,\n 9.39549071e-03, 4.81773075e-03, 1.30206993e-02, 3.66594547e-02,\n 5.70439711e-02, 2.67748831e-02, 6.14358316e-03, 5.64149450e-03,\n 1.10069855e-03, 4.69164314e-03, 7.95593765e-03, 9.03137294e-03,\n 4.76333873e-03, 4.00992543e-03, 1.22710735e-02, 5.29245950e-03,\n 4.54922183e-04, 9.84103767e-04, 3.40059340e-04, 1.03051997e-03,\n 1.10483270e-02, 1.48678769e-02, 1.94958651e-02, 1.33427874e-02,\n 4.81017827e-03, 5.12395285e-03, 7.63732110e-03, 8.75867708e-03,\n 7.92267913e-03, 1.24800768e-02, 1.16172127e-02, 2.14708148e-02,\n 3.62692710e-02, 6.14068109e-03, 9.66533909e-03, 1.28510341e-02,\n 1.88876566e-02, 2.99603175e-02, 1.67991343e-02, 7.87212982e-03,\n 8.06670187e-03, 5.75849696e-03, 3.69292303e-03, 2.58726515e-03,\n 8.42621068e-04, 3.91378652e-03, 1.63645128e-02, 2.32006253e-02,\n 1.14690692e-03, 1.05130926e-03, 2.42622719e-03, 1.11230317e-03,\n 3.44273041e-03, 1.10922301e-02, 3.11889325e-03, 1.18718829e-02,\n 4.89384194e-03, 4.69456656e-03, 5.85533653e-03, 3.76823190e-03,\n 1.85373799e-03, 3.47645535e-03, 7.08100917e-04, 6.83875814e-03,\n 2.65207722e-03, 6.21014222e-03, 1.71526609e-02, 8.59171734e-03,\n 5.10323590e-03, 5.17942219e-03, 6.27372616e-03, 8.53180991e-03,\n 6.24270221e-03, 5.81497566e-04, 9.92337152e-03, 1.10193192e-02,\n 4.20970018e-03, 7.13428111e-03, 3.45337543e-03, 1.49498112e-03,\n 6.19954877e-03, 1.27991689e-02, 1.89395421e-02, 2.08645939e-02,\n 1.92147679e-02, 4.73156141e-04, 4.70553348e-03, 1.53947405e-04,\n 2.79408196e-04, 6.46436647e-03, 5.36361384e-03, 4.24775412e-03,\n 1.03779139e-02, 9.92528633e-03, 1.02800484e-03, 6.41102843e-04,\n 1.18737652e-03, 6.83700084e-03, 6.54711043e-04, 4.17566209e-03,\n 6.80877762e-03, 5.62059165e-03, 3.78821711e-03, 5.97856240e-03,\n 5.65389710e-03, 2.21457802e-03, 1.00680969e-02, 5.15797595e-03,\n 1.55669373e-03, 9.91450808e-04, 1.15631886e-02, 6.12263193e-03,\n 6.17465150e-03, 9.54098034e-04, 1.12148938e-02, 1.43629315e-02,\n 5.06139869e-03, 2.02965805e-04, 1.49012102e-03, 1.49465744e-03,\n 1.34334335e-04, 6.45759159e-04, 4.39451280e-03, 5.81617286e-03,\n 1.02399821e-02, 6.96526834e-03, 9.10156740e-04, 1.59545347e-02,\n 6.03389614e-03, 7.15972092e-05, 2.73526221e-04, 1.61236508e-03,\n 5.19602619e-03, 4.58085183e-03, 3.05963602e-03, 1.02597652e-02,\n 1.70438653e-03, 2.35821281e-02, 2.92636788e-02, 2.63374666e-02,\n 6.22409185e-03, 2.35373999e-04, 6.90831245e-04, 1.42413214e-02]), 'noise_mean': array([0.01059496, 0.01059442, 0.01059352, 0.01059225, 0.01059063,\n 0.01058865, 0.01058631, 0.01058361, 0.01058056, 0.01057715,\n 0.01057339, 0.01056927, 0.01056481, 0.01055999, 0.01055483,\n 0.01054933, 0.01054348, 0.01053729, 0.01053077, 0.01052391,\n 0.01051672, 0.0105092 , 0.01050135, 0.01049318, 0.01048469,\n 0.01047589, 0.01046677, 0.01045734, 0.01044761, 0.01043757,\n 0.01042724, 0.01041661, 0.01040569, 0.01039449, 0.010383 ,\n 0.01037124, 0.01035921, 0.0103469 , 0.01033434, 0.01032151,\n 0.01030843, 0.0102951 , 0.01028153, 0.01026771, 0.01025367,\n 0.01023939, 0.01022488, 0.01021016, 0.01019522, 0.01018008,\n 0.01016473, 0.01014918, 0.01013343, 0.0101175 , 0.01010139,\n 0.0100851 , 0.01006863, 0.010052 , 0.01003521, 0.01001826,\n 0.01000116, 0.00998392, 0.00996654, 0.00994902, 0.00993137,\n 0.0099136 , 0.00989571, 0.00987771, 0.0098596 , 0.00984139,\n 0.00982308, 0.00980468, 0.00978619, 0.00976762, 0.00974897,\n 0.00973026, 0.00971148, 0.00969263, 0.00967374, 0.00965479,\n 0.00963579, 0.00961676, 0.00959769, 0.00957859, 0.00955946,\n 0.00954031, 0.00952114, 0.00950196, 0.00948278, 0.00946359,\n 0.0094444 , 0.00942522, 0.00940605, 0.00938689, 0.00936776,\n 0.00934864, 0.00932956, 0.0093105 , 0.00929148, 0.0092725 ,\n 0.00925357, 0.00923468, 0.00921584, 0.00919706, 0.00917834,\n 0.00915967, 0.00914108, 0.00912255, 0.0091041 , 0.00908573,\n 0.00906743, 0.00904922, 0.00903109, 0.00901305, 0.00899511,\n 0.00897726, 0.00895951, 0.00894186, 0.00892432, 0.00890688,\n 0.00888955, 0.00887234, 0.00885524, 0.00883826, 0.0088214 ,\n 0.00880466, 0.00878805, 0.00877157, 0.00875522, 0.008739 ,\n 0.00872291, 0.00870697, 0.00869116, 0.00867549, 0.00865997,\n 0.00864459, 0.00862936, 0.00861428, 0.00859935, 0.00858457,\n 0.00856995, 0.00855548, 0.00854117, 0.00852703, 0.00851304,\n 0.00849922, 0.00848556, 0.00847206, 0.00845874, 0.00844558,\n 0.00843259, 0.00841978, 0.00840713, 0.00839467, 0.00838237,\n 0.00837026, 0.00835832, 0.00834656, 0.00833498, 0.00832358,\n 0.00831236, 0.00830132, 0.00829047, 0.0082798 , 0.00826932,\n 0.00825903, 0.00824892, 0.00823901, 0.00822928, 0.00821974,\n 0.00821039, 0.00820123, 0.00819227, 0.00818349, 0.00817491,\n 0.00816653, 0.00815834, 0.00815034, 0.00814254, 0.00813494,\n 0.00812753, 0.00812033, 0.00811332, 0.0081065 , 0.00809989,\n 0.00809347, 0.00808726, 0.00808125, 0.00807543, 0.00806982,\n 0.00806441, 0.0080592 , 0.00805419, 0.00804938, 0.00804478,\n 0.00804037, 0.00803618, 0.00803218, 0.00802839, 0.0080248 ,\n 0.00802142, 0.00801824, 0.00801526, 0.00801249, 0.00800993,\n 0.00800756, 0.00800541, 0.00800346, 0.00800171, 0.00800017,\n 0.00799883, 0.0079977 , 0.00799678, 0.00799606, 0.00799554,\n 0.00799523]), 'noise_threshold': array([0.03173966, 0.03173804, 0.03173534, 0.03173155, 0.03172669,\n 0.03172076, 0.03171375, 0.03170567, 0.03169652, 0.0316863 ,\n 0.03167503, 0.03166271, 0.03164933, 0.03163491, 0.03161945,\n 0.03160296, 0.03158544, 0.03156691, 0.03154736, 0.03152681,\n 0.03150527, 0.03148274, 0.03145924, 0.03143476, 0.03140933,\n 0.03138295, 0.03135563, 0.03132739, 0.03129823, 0.03126817,\n 0.03123721, 0.03120537, 0.03117267, 0.0311391 , 0.0311047 ,\n 0.03106946, 0.03103341, 0.03099655, 0.03095891, 0.03092048,\n 0.0308813 , 0.03084137, 0.03080071, 0.03075932, 0.03071724,\n 0.03067446, 0.03063102, 0.03058691, 0.03054216, 0.03049679,\n 0.0304508 , 0.03040422, 0.03035706, 0.03030933, 0.03026106,\n 0.03021225, 0.03016293, 0.03011311, 0.03006281, 0.03001204,\n 0.02996081, 0.02990915, 0.02985708, 0.0298046 , 0.02975173,\n 0.02969849, 0.0296449 , 0.02959097, 0.02953672, 0.02948216,\n 0.02942731, 0.02937218, 0.0293168 , 0.02926117, 0.02920532,\n 0.02914925, 0.02909299, 0.02903654, 0.02897992, 0.02892316,\n 0.02886626, 0.02880923, 0.0287521 , 0.02869488, 0.02863758,\n 0.02858021, 0.02852279, 0.02846534, 0.02840786, 0.02835038,\n 0.0282929 , 0.02823544, 0.028178 , 0.02812062, 0.02806329,\n 0.02800603, 0.02794885, 0.02789177, 0.02783479, 0.02777794,\n 0.02772121, 0.02766462, 0.02760819, 0.02755193, 0.02749584,\n 0.02743993, 0.02738423, 0.02732873, 0.02727345, 0.0272184 ,\n 0.02716359, 0.02710903, 0.02705473, 0.02700069, 0.02694694,\n 0.02689346, 0.02684029, 0.02678742, 0.02673486, 0.02668262,\n 0.02663072, 0.02657915, 0.02652793, 0.02647706, 0.02642655,\n 0.02637641, 0.02632665, 0.02627728, 0.02622829, 0.0261797 ,\n 0.02613151, 0.02608374, 0.02603638, 0.02598945, 0.02594295,\n 0.02589688, 0.02585125, 0.02580607, 0.02576134, 0.02571707,\n 0.02567327, 0.02562993, 0.02558707, 0.02554469, 0.02550279,\n 0.02546137, 0.02542045, 0.02538003, 0.02534011, 0.0253007 ,\n 0.02526179, 0.0252234 , 0.02518553, 0.02514817, 0.02511134,\n 0.02507505, 0.02503928, 0.02500405, 0.02496935, 0.0249352 ,\n 0.0249016 , 0.02486854, 0.02483603, 0.02480408, 0.02477268,\n 0.02474184, 0.02471157, 0.02468185, 0.02465271, 0.02462413,\n 0.02459613, 0.02456869, 0.02454183, 0.02451555, 0.02448985,\n 0.02446473, 0.0244402 , 0.02441625, 0.02439288, 0.02437011,\n 0.02434792, 0.02432632, 0.02430532, 0.02428491, 0.0242651 ,\n 0.02424588, 0.02422727, 0.02420925, 0.02419183, 0.02417501,\n 0.0241588 , 0.02414319, 0.02412819, 0.02411379, 0.0241 ,\n 0.02408681, 0.02407423, 0.02406227, 0.02405091, 0.02404016,\n 0.02403002, 0.0240205 , 0.02401158, 0.02400328, 0.02399559,\n 0.02398852, 0.02398206, 0.02397621, 0.02397098, 0.02396636,\n 0.02396236, 0.02395897, 0.0239562 , 0.02395404, 0.02395251,\n 0.02395158]), 'peaks': period_days frequency power noise_level snr\n0 39.454545 0.025346 0.057044 0.009711 5.873872, 'log_ret': array([ 5.33027564e-02, 4.51529173e-02, -8.77261502e-02, -1.10036874e-01,\n -1.08615051e-02, 1.79233020e-01, 5.80127340e-02, 2.07502906e-01,\n 4.11773085e-02, 3.63010155e-02, 1.74325575e-01, -2.34263335e-01,\n 3.24422460e-01, 1.27168508e-01, 2.01471803e-01, 2.88539264e-01,\n 2.35684467e-01, -3.34354652e-01, 1.58995955e-02, 1.63332626e-01,\n -1.81086533e-01, -1.55883040e-01, 2.98997709e-02, -3.72572108e-01,\n -1.48851671e-02, 2.52816436e-01, -7.94903774e-02, 1.82929644e-01,\n -1.88831279e-01, -1.51906577e-01, 3.36355412e-02, -2.17714198e-01,\n 2.96442576e-02, 1.74381885e-01, 5.04154706e-02, 6.94532932e-02,\n 2.51622327e-02, -1.06903041e-01, -1.77528637e-02, -1.50034023e-01,\n 4.98693332e-02, -1.31309781e-01, -4.77411196e-02, -4.96885289e-02,\n 3.51955442e-02, 5.43851885e-02, -5.49831514e-02, 1.51846982e-01,\n 1.04699021e-01, -1.56114813e-01, -1.07488924e-01, 2.64682826e-02,\n 3.37682615e-02, 8.60421263e-02, -1.55438597e-01, 3.98600566e-02,\n 3.07297320e-02, -1.22135190e-02, -1.70822352e-03, -4.26044393e-02,\n 3.87953782e-02, -1.53182955e-02, -6.28863950e-04, -5.57220787e-03,\n -1.30273486e-01, -3.26264534e-01, 1.82451807e-02, -1.60115306e-01,\n -9.35722392e-02, 1.96495344e-01, -3.30620598e-02, 4.76859741e-02,\n -1.37074339e-01, 1.78081099e-02, 3.26087859e-03, -2.64619982e-02,\n 6.22068593e-02, -3.39701237e-03, 2.05049863e-02, 1.70014332e-02,\n 2.82416366e-02, 1.62881104e-02, 2.76923717e-03, 2.76124769e-02,\n 2.30974987e-01, -7.56587314e-03, 2.40378787e-02, 9.72776579e-03,\n 8.45209271e-02, 1.87583601e-01, 1.56602196e-01, 5.56072973e-02,\n 1.28660786e-02, -1.34462053e-01, 1.60182800e-01, 1.97293985e-01,\n -4.77662618e-03, 4.96176853e-02, -1.14307436e-01, 4.00050841e-02,\n -1.04203324e-01, 1.35785953e-01, 5.52419882e-02, -1.13880908e-01,\n -1.60486821e-02, -4.20554160e-02, 6.52967185e-02, -7.65552960e-03,\n -2.68710882e-02, -2.20563839e-01, -2.38493082e-02, 5.21853432e-02,\n -6.26246110e-03, 1.47459704e-01, -3.58090537e-02, -1.70277743e-02,\n -6.12520678e-02, -2.08351816e-01, 6.82514998e-02, 1.60019521e-02,\n -5.35404407e-02, 5.23853300e-02, -1.52054552e-02, -3.99946556e-03,\n 1.06410686e-01, 6.12176415e-02, -1.00135389e-02, 7.98919753e-02,\n 8.42492591e-02, -2.33684226e-02, 1.92710022e-03, -1.52395046e-01,\n -6.02130886e-02, -4.04390164e-01, 8.14389252e-02, 1.11528212e-02,\n 1.41113408e-01, 1.91588984e-02, 3.09411164e-02, 7.73121706e-02,\n 1.45071058e-01, -1.94580336e-02, 1.04129185e-01, -1.04407806e-01,\n 8.01734280e-02, 3.11268634e-02, -4.24274530e-02, -5.08779702e-03,\n -1.93737647e-02, -5.16229228e-03, 2.54028430e-02, -1.01298745e-02,\n 7.55353708e-02, 1.08645139e-01, 5.36611122e-02, 1.94405402e-02,\n -2.23166908e-02, 5.39658123e-03, -1.32659833e-01, 7.44380419e-03,\n 5.52954061e-02, -1.34625830e-02, -1.00388514e-02, 6.37719357e-02,\n 1.17279293e-02, 1.24544553e-01, 5.47102439e-02, 1.17357442e-01,\n 3.06653238e-02, 1.43236992e-01, -1.25380679e-02, 6.25815611e-02,\n -9.57126389e-03, 2.01499036e-01, 1.13765496e-01, 2.27637719e-01,\n 1.45017205e-01, -6.27795457e-02, -1.04921763e-01, 2.54962024e-02,\n 1.58987983e-01, 2.24857273e-01, 1.67027165e-01, -2.40520974e-01,\n 1.21598936e-01, 1.45728623e-01, -2.78001364e-02, -2.78271517e-02,\n 4.25469973e-02, 3.04651712e-02, -6.63582021e-02, -1.34844836e-01,\n 1.42441905e-01, 2.89628981e-02, -2.26608719e-01, -2.92528879e-01,\n 2.80550068e-02, 4.34057938e-03, 8.62441275e-02, -9.17388108e-02,\n -2.56006471e-02, 1.67512527e-02, -2.95517259e-02, -7.51571635e-02,\n 1.07383670e-01, 1.18832451e-01, 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-0.20646269+0.21707895j -0.34924539-0.00701636j\n ... -0.25097443+0.39542646j -0.4658674 +0.05792538j\n -0.33212563-0.31214098j]\n [-0.05380191+0.28136408j -0.27530143+0.17218711j -0.35580267-0.10443455j\n ... -0.29294848+0.33406657j -0.45377476-0.01811872j\n -0.27502538-0.35805475j]\n [-0.14159683+0.27926617j -0.32962257+0.11214316j -0.33855591-0.21560006j\n ... -0.35441039+0.28241528j -0.45698499-0.09949252j\n -0.20063119-0.3801326j ]\n ...\n [ 0.77259572+0.56913313j 0.09758785-0.78071374j -1.63931615+2.88811983j\n ... -1.01527336-0.81246447j -0.80740554+0.29699673j\n -0.88811535+0.19742563j]\n [-3.71582342-1.56739578j 0.84204529+2.33937542j -0.73357428+0.85279372j\n ... 0.23236656+1.79168259j -0.95454735-0.74439472j\n -1.29585406+0.00785753j]\n [ 1.84842209-1.76757202j -0.20839682+3.25082443j -1.55862037+0.87078631j\n ... 0.50350641+1.02651633j -0.4299536 -0.54308497j\n -0.98618845+0.31493207j]]", - "power": "[[ 0.06841587 0.08975011 0.12202157 ... 0.21935025 0.22038779\n 0.20773942]\n [ 0.08206039 0.10543928 0.13750212 ... 0.19741928 0.20623982\n 0.20384217]\n [ 0.09803925 0.12122713 0.16110349 ... 0.20536512 0.21873404\n 0.18475367]\n ...\n [ 0.92081667 0.61903733 11.02859358 ... 1.6908785 0.74011076\n 0.82772576]\n [16.2640732 6.18171765 1.26538835 ... 3.26412073 1.46528414\n 1.67929949]\n [ 6.54097509 10.61128869 3.18756627 ... 1.30725448 0.47980138\n 1.07174987]]", - "periods": "[ 7. 7.14889947 7.30096623 7.45626766 7.61487256\n 7.7768512 7.94227535 8.11121829 8.28375487 8.45996154\n 8.63991637 8.82369908 9.0113911 9.20307557 9.39883744\n 9.59876343 9.80294211 10.01146395 10.22442133 10.44190861\n 10.66402213 10.8908603 11.12252364 11.35911476 11.6007385\n 11.8475019 12.09951429 12.35688733 12.61973504 12.88817387\n 13.16232277 13.44230318 13.72823915 14.02025737 14.31848721\n 14.6230608 14.93411309 15.25178187 15.57620791 15.90753492\n 16.24590971 16.59148218 16.94440545 17.30483588 17.67293315\n 18.04886034 18.43278402 18.82487427 19.22530481 19.63425305\n 20.05190018 20.47843122 20.91403515 21.35890497 21.81323778\n 22.27723485 22.75110177 23.23504849 23.7292894 24.23404349\n 24.74953437 25.27599045 25.81364496 26.36273611 26.92350717\n 27.49620659 28.0810881 28.67841083 29.28843942 29.91144415\n 30.54770103 31.19749196 31.86110481 32.53883361 33.23097862\n 33.93784649 34.6597504 35.39701017 36.14995246 36.91891086\n 37.70422603 38.50624593 39.32532587 40.16182875 41.01612517\n 41.88859364 42.77962069 43.6896011 44.61893801 45.56804319\n 46.53733711 47.52724922 48.53821811 49.57069167 50.62512734\n 51.70199228 52.8017636 53.92492854 55.07198472 56.24344033\n 57.43981439 58.66163695 59.90944932 61.18380435 62.48526664\n 63.81441279 65.17183167 66.5581247 67.97390605 69.41980298\n 70.8964561 72.40451963 73.94466171 75.51756469 77.12392545\n 78.76445568 80.4398822 82.15094731 83.89840909 85.68304175\n 87.50563596 89.36699921 91.26795618 93.20934907 95.19203802\n 97.21690144 99.28483644 101.39675923 103.55360546 105.75633073\n 108.00591095 110.30334279 112.6496441 115.04585442 117.49303538\n 119.99227118 122.54466911 125.15136 127.81349873 130.53226475\n 133.3088626 136.14452245 139.04050061 141.99808015 145.01857141\n 148.1033126 151.25367041 154.47104059 157.75684859 161.11255017\n 164.53963206 168.03961262 171.6140425 175.26450534 178.99261846\n 182.80003359 186.68843759 190.6595532 194.71513982 198.85699425\n 203.08695153 207.40688572 211.81871075 216.32438127 220.9258935\n 225.62528612 230.42464117 235.326085 240.33178917 245.44397145\n 250.66489675 255.9968782 261.4422781 267.00350903 272.68303486\n 278.48337189 284.40708994 290.45681348 296.63522282 302.94505529\n 309.38910644 315.97023126 322.69134552 329.55542697 336.56551671\n 343.72472055 351.03621034 358.50322541 366.12907398 373.91713468\n 381.87085796 389.99376769 398.28946271 406.76161837 415.41398825\n 424.25040574 433.27478579 442.49112661 451.90351146 461.51611047\n 471.33318247 481.35907687 491.5982356 502.0551951 512.73458826\n 523.64114657 534.77970213 546.15518983 557.77264954 569.63722834\n 581.75418277 594.12888122 606.76680625 619.67355704 632.85485188\n 646.31653068 660.06455761 674.10502367 688.44414944 703.08828781\n 718.04392681 733.31769248 748.91635182 764.84681573 781.11614217\n 797.73153919 814.70036818 832.03014712 849.72855389 867.80342973\n 886.26278262 905.11479092 924.36780694 944.03036065 964.11116349\n 984.61911216 1005.56329264 1026.95298418 1048.79766339 1071.10700847\n 1093.89090351 1117.15944282 1140.92293543 1165.19190968 1189.97711784\n 1215.28954091 1241.14039345 1267.54112861 1294.50344311 1322.03928252\n 1350.16084648 1378.88059415 1408.2112497 1438.16580797 1468.75754022\n 1500. ]", - "scales": "[ 7. 7.14889947 7.30096623 7.45626766 7.61487256\n 7.7768512 7.94227535 8.11121829 8.28375487 8.45996154\n 8.63991637 8.82369908 9.0113911 9.20307557 9.39883744\n 9.59876343 9.80294211 10.01146395 10.22442133 10.44190861\n 10.66402213 10.8908603 11.12252364 11.35911476 11.6007385\n 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65.17183167 66.5581247 67.97390605 69.41980298\n 70.8964561 72.40451963 73.94466171 75.51756469 77.12392545\n 78.76445568 80.4398822 82.15094731 83.89840909 85.68304175\n 87.50563596 89.36699921 91.26795618 93.20934907 95.19203802\n 97.21690144 99.28483644 101.39675923 103.55360546 105.75633073\n 108.00591095 110.30334279 112.6496441 115.04585442 117.49303538\n 119.99227118 122.54466911 125.15136 127.81349873 130.53226475\n 133.3088626 136.14452245 139.04050061 141.99808015 145.01857141\n 148.1033126 151.25367041 154.47104059 157.75684859 161.11255017\n 164.53963206 168.03961262 171.6140425 175.26450534 178.99261846\n 182.80003359 186.68843759 190.6595532 194.71513982 198.85699425\n 203.08695153 207.40688572 211.81871075 216.32438127 220.9258935\n 225.62528612 230.42464117 235.326085 240.33178917 245.44397145\n 250.66489675 255.9968782 261.4422781 267.00350903 272.68303486\n 278.48337189 284.40708994 290.45681348 296.63522282 302.94505529\n 309.38910644 315.97023126 322.69134552 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[-5.72331943e-02, 1.61365641e-02],\n [-1.87599401e-02, 5.46384304e-02],\n [-3.14962287e-02, 4.19239417e-02],\n [-3.76671131e-02, 3.57548985e-02],\n [-2.06257139e-02, 5.27963596e-02],\n [-6.69113119e-02, 6.52828336e-03],\n [-4.35082325e-02, 2.99930585e-02],\n [-4.10127774e-02, 3.24916030e-02],\n [-2.78272660e-02, 4.56783425e-02],\n [-7.73345715e-03, 6.57775406e-02],\n [-6.40353401e-02, 9.53261280e-03],\n [-5.29181960e-02, 2.06999377e-02],\n [-3.92967615e-02, 3.43388991e-02],\n [-5.87846234e-02, 1.48514521e-02],\n [-1.76460187e-02, 5.60226358e-02],\n [-6.63129747e-02, 7.38052908e-03],\n [-1.52282185e-02, 5.85238513e-02],\n [-3.49082245e-02, 3.88754359e-02]]), 'significant_lags': [1, 2, 10, 17, 33, 44, 59, 60, 65, 75], 'n_obs': 3090, 'threshold': np.float64(0.03525955538509657)}, 'squared_return': {'values': array([ 1.00000000e+00, 1.02653192e-01, 3.20545399e-02, 2.29973442e-02,\n 7.78292151e-02, 4.34819920e-02, 3.30282872e-02, 6.67284036e-02,\n 2.35056717e-02, 1.98387597e-02, 2.98393393e-02, 5.66689263e-02,\n 2.47920754e-02, 1.04139545e-02, 2.87428229e-02, 4.24149402e-02,\n 3.45524415e-02, 3.06341072e-02, 2.67352816e-02, 2.09007391e-02,\n 2.57768613e-02, 4.33097679e-02, 1.37203718e-02, 2.03443463e-02,\n 3.05603266e-02, 4.28286931e-02, 1.40305762e-02, 1.54338134e-02,\n 1.92783134e-02, 3.06205711e-02, 1.18403307e-02, 1.70852974e-02,\n 1.84187526e-02, 1.48458009e-02, 7.62125866e-03, 3.41023023e-02,\n 2.62772331e-02, 2.09576181e-02, 1.37041975e-02, 1.72937635e-02,\n 2.70472006e-02, 3.70961355e-02, 9.38186857e-03, 4.29858164e-03,\n 2.32871014e-02, 2.39570419e-02, 1.60131530e-02, 9.24196843e-03,\n 5.48078835e-02, 5.81256896e-03, 4.63221537e-03, 1.46050136e-03,\n 7.96728343e-03, 7.11812679e-03, 9.99116446e-03, 2.54949418e-03,\n 4.19762237e-02, 1.40952448e-02, 3.13433925e-02, 2.98914714e-02,\n 1.94419913e-02, 1.20048936e-02, 3.18753266e-02, 1.12525941e-02,\n 1.22923275e-02, 1.12236197e-02, 1.85317299e-02, 1.09289717e-02,\n 2.21909607e-03, 8.50253557e-03, 2.19780239e-02, 2.27763020e-03,\n 1.44769808e-02, 5.75677585e-03, 2.85303209e-03, 2.86174334e-04,\n -2.20485889e-03, 1.17541113e-02, 7.21400106e-03, 2.70018625e-03,\n 3.14322385e-03, 3.10666008e-02, 2.18093183e-02, 2.08393812e-02,\n 2.29260992e-02, 2.81165093e-02, 2.45944498e-02, -5.17530450e-04,\n 5.83012550e-03, 6.37845587e-03, 9.85075304e-04, 1.67279255e-02,\n 7.53665602e-03, 7.01917059e-03, 8.25443902e-03, 1.06326000e-02,\n 6.99491171e-03, 4.28281196e-03, 1.03125233e-02, 9.49174507e-03,\n 7.27489475e-03]), 'confint': array([[ 1.00000000e+00, 1.00000000e+00],\n [ 6.73942847e-02, 1.37912100e-01],\n [-3.57397731e-03, 6.76830571e-02],\n [-1.26670075e-02, 5.86616959e-02],\n [ 4.21464325e-02, 1.13511998e-01],\n [ 7.58879010e-03, 7.93751939e-02],\n [-2.93034045e-03, 6.89869149e-02],\n [ 3.07320814e-02, 1.02724726e-01],\n [-1.26441039e-02, 5.96554473e-02],\n [-1.63300120e-02, 5.60075314e-02],\n [-6.34295785e-03, 6.60216364e-02],\n [ 2.04560493e-02, 9.28818033e-02],\n [-1.15308807e-02, 6.11150316e-02],\n [-2.59300325e-02, 4.67579416e-02],\n [-7.60487364e-03, 6.50905194e-02],\n [ 6.03899808e-03, 7.87908823e-02],\n [-1.88493258e-03, 7.09898157e-02],\n [-5.84397733e-03, 6.71121917e-02],\n [-9.77477168e-03, 6.32453349e-02],\n [-1.56336446e-02, 5.74351229e-02],\n [-1.07723842e-02, 6.23261068e-02],\n [ 6.73792879e-03, 7.98816070e-02],\n [-2.29151740e-02, 5.03559175e-02],\n [-1.62975869e-02, 5.69862795e-02],\n [-6.09564647e-03, 6.72162997e-02],\n [ 6.14105921e-03, 7.95163269e-02],\n [-2.27191618e-02, 5.07803141e-02],\n [-2.13225833e-02, 5.21902102e-02],\n [-1.74861391e-02, 5.60427658e-02],\n [-6.15644669e-03, 6.73975888e-02],\n [-2.49683682e-02, 4.86490296e-02],\n [-1.97281361e-02, 5.38987309e-02],\n [-1.84045374e-02, 5.52420425e-02],\n [-2.19889407e-02, 5.16805425e-02],\n [-2.92209207e-02, 4.44634380e-02],\n [-2.74183697e-03, 7.09464416e-02],\n [-1.06061260e-02, 6.31605922e-02],\n [-1.59490074e-02, 5.78642436e-02],\n [-2.32172201e-02, 5.06256151e-02],\n [-1.96339772e-02, 5.42215042e-02],\n [-9.89060720e-03, 6.39850084e-02],\n [ 1.33714553e-04, 7.40585564e-02],\n [-2.76268078e-02, 4.63905450e-02],\n [-3.27130514e-02, 4.13102146e-02],\n [-1.37251523e-02, 6.02993550e-02],\n [-1.30734220e-02, 6.09875058e-02],\n [-2.10365743e-02, 5.30628803e-02],\n [-2.78163620e-02, 4.63002988e-02],\n [ 1.77466879e-02, 9.18690792e-02],\n [-3.13492538e-02, 4.29743918e-02],\n [-3.25307377e-02, 4.17951684e-02],\n [-3.57031695e-02, 3.86241722e-02],\n [-2.91964588e-02, 4.51310256e-02],\n [-3.00477388e-02, 4.42839924e-02],\n [-2.71763959e-02, 4.71587248e-02],\n [-3.46214049e-02, 3.97203933e-02],\n [ 4.80510717e-03, 7.91473402e-02],\n [-2.31347554e-02, 5.13252450e-02],\n [-5.89324136e-03, 6.85800263e-02],\n [-7.37794687e-03, 6.71608897e-02],\n [-1.78572194e-02, 5.67412021e-02],\n [-2.53069135e-02, 4.93167007e-02],\n [-5.44128210e-03, 6.91919352e-02],\n [-2.60978481e-02, 4.86030364e-02],\n [-2.50623290e-02, 4.96469840e-02],\n [-2.61360653e-02, 4.85833046e-02],\n [-1.88321466e-02, 5.58956064e-02],\n [-2.64463296e-02, 4.83042731e-02],\n [-3.51601780e-02, 3.95983702e-02],\n [-2.88769023e-02, 4.58819735e-02],\n [-1.54038183e-02, 5.93598660e-02],\n [-3.51202726e-02, 3.96755330e-02],\n [-2.29210944e-02, 5.18750560e-02],\n [-3.16482657e-02, 4.31618174e-02],\n [-3.45531109e-02, 4.02591751e-02],\n [-3.71202392e-02, 3.76925878e-02],\n [-3.96112751e-02, 3.52015573e-02],\n [-2.56524665e-02, 4.91606891e-02],\n [-3.01971681e-02, 4.46251702e-02],\n [-3.47127122e-02, 4.01130847e-02],\n [-3.42699169e-02, 4.05563646e-02],\n [-6.34686821e-03, 6.84800699e-02],\n [-1.56362069e-02, 5.92548435e-02],\n [-1.66219321e-02, 5.83006945e-02],\n [-1.45496233e-02, 6.04018218e-02],\n [-9.37664526e-03, 6.56096638e-02],\n [-1.29249081e-02, 6.21138077e-02],\n [-3.80569257e-02, 3.70218648e-02],\n [-3.17092786e-02, 4.33695296e-02],\n [-3.11620739e-02, 4.39189856e-02],\n [-3.65568017e-02, 3.85269524e-02],\n [-2.08139836e-02, 5.42698347e-02],\n [-3.00145183e-02, 4.50878303e-02],\n [-3.05338842e-02, 4.45722254e-02],\n [-2.93002467e-02, 4.58091248e-02],\n [-2.69243412e-02, 4.81895412e-02],\n [-3.05657715e-02, 4.45555949e-02],\n [-3.32794907e-02, 4.18451146e-02],\n [-2.72503864e-02, 4.78754330e-02],\n [-2.80746842e-02, 4.70581744e-02],\n [-3.02945159e-02, 4.48443054e-02]]), 'significant_lags': [1, 4, 5, 7, 11, 15, 21, 25, 41, 48, 56], 'n_obs': 3090, 'threshold': np.float64(0.03525955538509657)}, 'abs_return': {'values': array([1. , 0.16941215, 0.11681494, 0.11477814, 0.18185448,\n 0.14259819, 0.14312719, 0.13894497, 0.12687252, 0.10913528,\n 0.10631101, 0.12944533, 0.11684757, 0.09762454, 0.15017366,\n 0.12411898, 0.12326963, 0.11562807, 0.0844657 , 0.07607039,\n 0.09505224, 0.16106874, 0.07148222, 0.09370097, 0.113692 ,\n 0.11214878, 0.07064554, 0.09680625, 0.10452395, 0.09941853,\n 0.06644419, 0.07644115, 0.10051518, 0.07849206, 0.07073167,\n 0.1368152 , 0.10616077, 0.07564097, 0.07883957, 0.07680061,\n 0.07310946, 0.11918992, 0.07922182, 0.06456951, 0.08403174,\n 0.07692813, 0.08377337, 0.04391864, 0.10519398, 0.07786352,\n 0.05243764, 0.02742507, 0.04606311, 0.05652268, 0.04697355,\n 0.04920116, 0.12268232, 0.0759553 , 0.09233128, 0.05483661,\n 0.06063155, 0.05068447, 0.10495384, 0.07299428, 0.05869396,\n 0.06155175, 0.06108528, 0.05599143, 0.04059884, 0.04826835,\n 0.09459542, 0.04167287, 0.06421634, 0.03641472, 0.0238428 ,\n 0.02949051, 0.028975 , 0.07587434, 0.04076047, 0.02183433,\n 0.04071859, 0.07915313, 0.04777162, 0.06885369, 0.07274957,\n 0.04513606, 0.07586587, 0.0313361 , 0.03066215, 0.03402811,\n 0.02781086, 0.07376816, 0.04940179, 0.03161297, 0.05145948,\n 0.06577359, 0.02750272, 0.03582021, 0.06441121, 0.03604022,\n 0.03358751]), 'confint': array([[ 1.00000000e+00, 1.00000000e+00],\n [ 1.34153246e-01, 2.04671061e-01],\n [ 8.05582047e-02, 1.53071676e-01],\n [ 7.80564948e-02, 1.51499789e-01],\n [ 1.44689510e-01, 2.19019451e-01],\n [ 1.04342965e-01, 1.80853416e-01],\n [ 1.04216766e-01, 1.82037610e-01],\n [ 9.93854553e-02, 1.78504490e-01],\n [ 8.67108828e-02, 1.67034149e-01],\n [ 6.84784368e-02, 1.49792129e-01],\n [ 6.52915878e-02, 1.47330437e-01],\n [ 8.80847870e-02, 1.70805869e-01],\n [ 7.49864184e-02, 1.58708731e-01],\n [ 5.53598547e-02, 1.39889230e-01],\n [ 1.07629564e-01, 1.92717764e-01],\n [ 8.09209100e-02, 1.67317058e-01],\n [ 7.96304592e-02, 1.66908809e-01],\n [ 7.15581367e-02, 1.59698007e-01],\n [ 4.00202076e-02, 1.28911190e-01],\n [ 3.14257836e-02, 1.20714992e-01],\n [ 5.02467872e-02, 1.39857694e-01],\n [ 1.16013302e-01, 2.06124187e-01],\n [ 2.57165398e-02, 1.17247899e-01],\n [ 4.77967033e-02, 1.39605246e-01],\n [ 6.75505582e-02, 1.59833433e-01],\n [ 6.56603885e-02, 1.58637179e-01],\n [ 2.38220132e-02, 1.17469073e-01],\n [ 4.98503969e-02, 1.43762100e-01],\n [ 5.73206374e-02, 1.51727268e-01],\n [ 5.19283470e-02, 1.46908710e-01],\n [ 1.86959667e-02, 1.14192413e-01],\n [ 2.85781179e-02, 1.24304181e-01],\n [ 5.25006120e-02, 1.48529739e-01],\n [ 3.02166086e-02, 1.26767506e-01],\n [ 2.22978210e-02, 1.19165516e-01],\n [ 8.82531058e-02, 1.85377291e-01],\n [ 5.71218244e-02, 1.55199710e-01],\n [ 2.63171452e-02, 1.24964795e-01],\n [ 2.93717487e-02, 1.28307398e-01],\n [ 2.71768192e-02, 1.26424393e-01],\n [ 2.33381275e-02, 1.22880796e-01],\n [ 6.92852573e-02, 1.69094584e-01],\n [ 2.89645069e-02, 1.29479134e-01],\n [ 1.41571878e-02, 1.14981834e-01],\n [ 3.35167098e-02, 1.34546776e-01],\n [ 2.62396159e-02, 1.27616650e-01],\n [ 3.29399180e-02, 1.34606825e-01],\n [-7.08615530e-03, 9.49234398e-02],\n [ 5.41421874e-02, 1.56245767e-01],\n [ 2.65429673e-02, 1.29184069e-01],\n [ 9.70431440e-04, 1.03904842e-01],\n [-2.41085090e-02, 7.89586539e-02],\n [-5.48861036e-03, 9.76148350e-02],\n [ 4.91981281e-03, 1.08125544e-01],\n [-4.70622811e-03, 9.86533242e-02],\n [-2.53167160e-03, 1.00933984e-01],\n [ 7.08913550e-02, 1.74473292e-01],\n [ 2.38042974e-02, 1.28106298e-01],\n [ 4.00429278e-02, 1.44619622e-01],\n [ 2.34596598e-03, 1.07327256e-01],\n [ 8.06973624e-03, 1.13193368e-01],\n [-1.96422361e-03, 1.03333163e-01],\n [ 5.22445180e-02, 1.57663154e-01],\n [ 2.00257931e-02, 1.25962764e-01],\n [ 5.60057060e-03, 1.11787355e-01],\n [ 8.37775031e-03, 1.14725742e-01],\n [ 7.82277750e-03, 1.14347775e-01],\n [ 2.64190697e-03, 1.09340951e-01],\n [-1.28236898e-02, 9.40213645e-02],\n [-5.19251627e-03, 1.01729224e-01],\n [ 4.10803944e-02, 1.48110436e-01],\n [-1.20496255e-02, 9.53953621e-02],\n [ 1.04536716e-02, 1.17979004e-01],\n [-1.74432220e-02, 9.02726533e-02],\n [-3.00457398e-02, 7.77313351e-02],\n [-2.44111395e-02, 8.33921614e-02],\n [-2.49467077e-02, 8.28967029e-02],\n [ 2.19332808e-02, 1.29815397e-01],\n [-1.33131085e-02, 9.48340436e-02],\n [-3.22774314e-02, 7.59460881e-02],\n [-1.34041188e-02, 9.48413041e-02],\n [ 2.49923475e-02, 1.33313912e-01],\n [-6.53277812e-03, 1.02076025e-01],\n [ 1.44970700e-02, 1.23210313e-01],\n [ 1.82846279e-02, 1.27214510e-01],\n [-9.44954947e-03, 9.97216733e-02],\n [ 2.12338768e-02, 1.30497858e-01],\n [-2.34267105e-02, 8.60989043e-02],\n [-2.41229492e-02, 8.54472398e-02],\n [-2.07783124e-02, 8.88345370e-02],\n [-2.70218246e-02, 8.26435428e-02],\n [ 1.89179440e-02, 1.28618377e-01],\n [-5.57162947e-03, 1.04375204e-01],\n [-2.34156124e-02, 8.66415478e-02],\n [-3.59167249e-03, 1.06510634e-01],\n [ 1.06626737e-02, 1.20884515e-01],\n [-2.77057056e-02, 8.27111426e-02],\n [-1.94052402e-02, 9.10456682e-02],\n [ 9.15688357e-03, 1.19665545e-01],\n [-1.93073728e-02, 9.13878221e-02],\n [-2.17892566e-02, 8.89642734e-02]]), 'significant_lags': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 77, 78, 80, 81, 82, 83, 84, 85, 86, 91, 92, 94, 95, 97, 98, 99], 'n_obs': 3090, 'threshold': np.float64(0.03525955538509657)}, 'volume': {'values': array([1. , 0.89231835, 0.81972046, 0.79411199, 0.78488161,\n 0.76746046, 0.78558329, 0.80074225, 0.7502537 , 0.69843297,\n 0.68093924, 0.67189199, 0.67136443, 0.69291745, 0.71696258,\n 0.68159883, 0.64649686, 0.64016457, 0.63638299, 0.63722759,\n 0.6702784 , 0.6956126 , 0.66121152, 0.62442741, 0.62038527,\n 0.62485345, 0.63041244, 0.65331817, 0.67503497, 0.64368851,\n 0.60795348, 0.60475173, 0.60388998, 0.60859536, 0.63361714,\n 0.65690603, 0.62538197, 0.5897418 , 0.58138651, 0.58239673,\n 0.58570772, 0.61105092, 0.63535735, 0.60272863, 0.56910026,\n 0.55028958, 0.54658496, 0.55471161, 0.5798828 , 0.59119837,\n 0.55674306, 0.52942871, 0.5216846 , 0.51598195, 0.52168578,\n 0.55186523, 0.57864299, 0.54795346, 0.52391621, 0.52105344,\n 0.51781606, 0.51474923, 0.53668783, 0.55946177, 0.53080459,\n 0.50084144, 0.48232368, 0.47273645, 0.47226484, 0.49453748,\n 0.51165976, 0.48139221, 0.45445747, 0.44718943, 0.43952767,\n 0.44420806, 0.46662036, 0.48865304, 0.46132897, 0.4369049 ,\n 0.43427533, 0.42918134, 0.42685324, 0.44946594, 0.47015197,\n 0.44810237, 0.42646501, 0.4187618 , 0.41185523, 0.41157762,\n 0.43753807, 0.46203514, 0.43947459, 0.41311037, 0.40156217,\n 0.40221022, 0.4031838 , 0.43138235, 0.45709229, 0.43832808,\n 0.41973737]), 'confint': array([[1. , 1. ],\n [0.85706515, 0.92757155],\n [0.76295881, 0.8764821 ],\n [0.72416882, 0.86405516],\n [0.70451068, 0.86525253],\n [0.67806976, 0.85685117],\n [0.68834805, 0.88281853],\n [0.69591552, 0.90556898],\n [0.63808256, 0.86242485],\n [0.58018979, 0.81667615],\n [0.55767556, 0.80420292],\n [0.54403879, 0.7997452 ],\n [0.53919587, 0.80353299],\n [0.55657651, 0.82925839],\n [0.57631314, 0.85761202],\n [0.5364784 , 0.82671926],\n [0.49745096, 0.79554276],\n [0.48767344, 0.79265571],\n [0.48058773, 0.79217825],\n [0.47823457, 0.79622061],\n [0.50814243, 0.83241436],\n [0.53006873, 0.86115647],\n [0.49207404, 0.830349 ],\n [0.45210741, 0.79674741],\n [0.44527578, 0.79549475],\n [0.44703338, 0.80267351],\n [0.44988418, 0.81094069],\n [0.47007444, 0.83656191],\n [0.48891895, 0.86115099],\n [0.45455422, 0.8328228 ],\n [0.41611594, 0.79979102],\n [0.41053451, 0.79896896],\n [0.40734643, 0.80043352],\n [0.40975921, 0.80743151],\n [0.43247927, 0.83475501],\n [0.45330267, 0.86050939],\n [0.41916141, 0.83160253],\n [0.38117758, 0.79830602],\n [0.37076005, 0.79201298],\n [0.36978521, 0.79500824],\n [0.37112271, 0.80029274],\n [0.39448819, 0.82761365],\n [0.41666238, 0.85405231],\n [0.38175157, 0.8237057 ],\n [0.34608944, 0.79211109],\n [0.32548111, 0.77509804],\n [0.32010865, 0.77306128],\n [0.32660176, 0.78282146],\n [0.35010263, 0.80966297],\n [0.35960662, 0.82279011],\n [0.32328325, 0.79020287],\n [0.29432465, 0.76453277],\n [0.2851035 , 0.75826569],\n [0.27797548, 0.75398841],\n [0.28229315, 0.7610784 ],\n [0.31106387, 0.79266659],\n [0.3362749 , 0.82101108],\n [0.30387452, 0.7920324 ],\n [0.27831321, 0.76951921],\n [0.27406539, 0.76804148],\n [0.26946566, 0.76616645],\n [0.26506065, 0.76443781],\n [0.28568388, 0.78769177],\n [0.30703572, 0.81188782],\n [0.27684222, 0.78476697],\n [0.245504 , 0.75617889],\n [0.22576823, 0.73887913],\n [0.21505655, 0.73041636],\n [0.21350934, 0.73102034],\n [0.23471297, 0.75436199],\n [0.25066806, 0.77265146],\n [0.21915686, 0.74362757],\n [0.19112615, 0.71778879],\n [0.18288518, 0.71149367],\n [0.17428477, 0.70477057],\n [0.17806153, 0.71035458],\n [0.19955403, 0.7336867 ],\n [0.22057539, 0.75673068],\n [0.19214662, 0.73051131],\n [0.16674175, 0.70706805],\n [0.1632355 , 0.70531515],\n [0.15727814, 0.70108455],\n [0.15410943, 0.69959706],\n [0.17589315, 0.72303873],\n [0.19566298, 0.74464096],\n [0.1726144 , 0.72359035],\n [0.15007269, 0.70285734],\n [0.1415529 , 0.6959707 ],\n [0.13386125, 0.6898492 ],\n [0.13282636, 0.69032888],\n [0.15803259, 0.71704355],\n [0.18167974, 0.74239054],\n [0.15817446, 0.72077472],\n [0.13095825, 0.6952625 ],\n [0.11865934, 0.684465 ],\n [0.1185999 , 0.68582055],\n [0.11886547, 0.68750214],\n [0.14635435, 0.71641036],\n [0.17125404, 0.74293054],\n [0.15158285, 0.7250733 ],\n [0.13216062, 0.70731411]]), 'significant_lags': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], 'n_obs': 3091, 'threshold': np.float64(0.03525385133986227)}}", - "pacf": "{'log_return': {'values': array([ 1.00000000e+00, -4.99311170e-02, 4.33900230e-02, 9.23036809e-03,\n 8.18649253e-03, 2.25859092e-02, 1.08753469e-02, -1.00537506e-02,\n -2.64333041e-02, 8.16874978e-03, 4.80345725e-02, -8.72829103e-03,\n -2.23183314e-03, 2.03449106e-02, -8.43022566e-03, 8.60247795e-03,\n -2.51125774e-02, 4.36334443e-02, 3.77581992e-03, 8.26840455e-03,\n 2.71619216e-02, -2.20098621e-02, -1.70741813e-02, -2.99492331e-02,\n 2.19931901e-02, 2.42602576e-02, 7.32939669e-03, 4.41252105e-04,\n 1.34148338e-02, -3.79342601e-02, 6.55922416e-03, 3.46333098e-02,\n -2.36390122e-02, 4.56576583e-02, -6.47888130e-03, -5.76066449e-03,\n -3.46499714e-02, 2.10708312e-02, 9.73902402e-03, 1.62381715e-02,\n 2.76939727e-02]), 'confint': array([[ 1.00000000e+00, 1.00000000e+00],\n [-8.51900245e-02, -1.46722095e-02],\n [ 8.13111555e-03, 7.86489305e-02],\n [-2.60285394e-02, 4.44892756e-02],\n [-2.70724150e-02, 4.34454000e-02],\n [-1.26729983e-02, 5.78448166e-02],\n [-2.43835606e-02, 4.61342544e-02],\n [-4.53126581e-02, 2.52051569e-02],\n [-6.16922115e-02, 8.82560342e-03],\n [-2.70901577e-02, 4.34276573e-02],\n [ 1.27756650e-02, 8.32934800e-02],\n [-4.39871985e-02, 2.65306165e-02],\n [-3.74907406e-02, 3.30270743e-02],\n [-1.49139968e-02, 5.56038181e-02],\n [-4.36891331e-02, 2.68286818e-02],\n [-2.66564295e-02, 4.38613854e-02],\n [-6.03714849e-02, 1.01463301e-02],\n [ 8.37453685e-03, 7.88923518e-02],\n [-3.14830876e-02, 3.90347274e-02],\n [-2.69905029e-02, 4.35273120e-02],\n [-8.09698588e-03, 6.24208291e-02],\n [-5.72687696e-02, 1.32490454e-02],\n [-5.23330888e-02, 1.81847262e-02],\n [-6.52081405e-02, 5.30967443e-03],\n [-1.32657174e-02, 5.72520976e-02],\n [-1.09986498e-02, 5.95191651e-02],\n [-2.79295108e-02, 4.25883042e-02],\n [-3.48176554e-02, 3.57001596e-02],\n [-2.18440737e-02, 4.86737413e-02],\n [-7.31931676e-02, -2.67535262e-03],\n [-2.86996833e-02, 4.18181316e-02],\n [-6.25597633e-04, 6.98922173e-02],\n [-5.88979196e-02, 1.16198953e-02],\n [ 1.03987508e-02, 8.09165658e-02],\n [-4.17377888e-02, 2.87800262e-02],\n [-4.10195720e-02, 2.94982430e-02],\n [-6.99088789e-02, 6.08936041e-04],\n [-1.41880763e-02, 5.63297386e-02],\n [-2.55198835e-02, 4.49979315e-02],\n [-1.90207359e-02, 5.14970790e-02],\n [-7.56493478e-03, 6.29528802e-02]]), 'significant_lags': [1, 2, 10, 17, 29, 33], 'n_obs': 3090}, 'squared_return': {'values': array([ 1.00000000e+00, 1.02653192e-01, 2.17460146e-02, 1.77413548e-02,\n 7.39038779e-02, 2.79217712e-02, 2.24279580e-02, 5.83814243e-02,\n 3.96823112e-03, 9.01011557e-03, 2.07117331e-02, 4.13952079e-02,\n 8.07840032e-03, -5.34218188e-04, 1.84199368e-02, 2.80457870e-02,\n 1.98428487e-02, 1.80050750e-02, 1.09748160e-02, 7.03584386e-03,\n 1.40689820e-02, 2.89451441e-02, -5.60858789e-03, 9.39476228e-03,\n 1.91062551e-02, 2.62675594e-02, -2.86436891e-03, 3.41980066e-03,\n 5.21343161e-03, 1.74205386e-02, -2.36957364e-03, 4.82802442e-03,\n 3.86515697e-03, 3.46754946e-03, -2.44553615e-03, 2.35198824e-02,\n 9.35939171e-03, 9.89788821e-03, 3.32702025e-03, 3.93295112e-03,\n 1.35836465e-02]), 'confint': array([[ 1. , 1. ],\n [ 0.06739428, 0.1379121 ],\n [-0.01351289, 0.05700492],\n [-0.01751755, 0.05300026],\n [ 0.03864497, 0.10916279],\n [-0.00733714, 0.06318068],\n [-0.01283095, 0.05768687],\n [ 0.02312252, 0.09364033],\n [-0.03129068, 0.03922714],\n [-0.02624879, 0.04426902],\n [-0.01454717, 0.05597064],\n [ 0.0061363 , 0.07665412],\n [-0.02718051, 0.04333731],\n [-0.03579313, 0.03472469],\n [-0.01683897, 0.05367884],\n [-0.00721312, 0.06330469],\n [-0.01541606, 0.05510176],\n [-0.01725383, 0.05326398],\n [-0.02428409, 0.04623372],\n [-0.02822306, 0.04229475],\n [-0.02118993, 0.04932789],\n [-0.00631376, 0.06420405],\n [-0.0408675 , 0.02965032],\n [-0.02586415, 0.04465367],\n [-0.01615265, 0.05436516],\n [-0.00899135, 0.06152647],\n [-0.03812328, 0.03239454],\n [-0.03183911, 0.03867871],\n [-0.03004548, 0.04047234],\n [-0.01783837, 0.05267945],\n [-0.03762848, 0.03288933],\n [-0.03043088, 0.04008693],\n [-0.03139375, 0.03912406],\n [-0.03179136, 0.03872646],\n [-0.03770444, 0.03281337],\n [-0.01173903, 0.05877879],\n [-0.02589952, 0.0446183 ],\n [-0.02536102, 0.0451568 ],\n [-0.03193189, 0.03858593],\n [-0.03132596, 0.03919186],\n [-0.02167526, 0.04884255]]), 'significant_lags': [1, 4, 7, 11], 'n_obs': 3090}, 'abs_return': {'values': array([ 1.00000000e+00, 1.69412153e-01, 9.07181160e-02, 8.45160567e-02,\n 1.48113259e-01, 8.36243602e-02, 8.39548850e-02, 7.37673497e-02,\n 5.10596543e-02, 3.28447583e-02, 2.96940137e-02, 5.42549634e-02,\n 3.63184226e-02, 1.85683191e-02, 8.05149603e-02, 3.77250817e-02,\n 4.12392165e-02, 3.41284438e-02, -9.72034046e-03, -9.84520886e-03,\n 1.27001117e-02, 8.39925053e-02, -2.28472785e-02, 2.13091973e-02,\n 4.15301452e-02, 2.18423321e-02, -1.18171075e-02, 2.07553150e-02,\n 1.81763460e-02, 1.51906758e-02, -1.32874581e-02, -9.00555399e-04,\n 2.38174973e-02, 2.25403203e-03, 3.63227988e-03, 6.64400444e-02,\n 2.52184620e-02, -3.92201324e-03, 3.82418903e-03, -6.13910297e-03,\n -4.26325672e-03]), 'confint': array([[ 1. , 1. ],\n [ 0.13415325, 0.20467106],\n [ 0.05545921, 0.12597702],\n [ 0.04925715, 0.11977496],\n [ 0.11285435, 0.18337217],\n [ 0.04836545, 0.11888327],\n [ 0.04869598, 0.11921379],\n [ 0.03850844, 0.10902626],\n [ 0.01580075, 0.08631856],\n [-0.00241415, 0.06810367],\n [-0.00556489, 0.06495292],\n [ 0.01899606, 0.08951387],\n [ 0.00105952, 0.07157733],\n [-0.01669059, 0.05382723],\n [ 0.04525605, 0.11577387],\n [ 0.00246617, 0.07298399],\n [ 0.00598031, 0.07649812],\n [-0.00113046, 0.06938735],\n [-0.04497925, 0.02553857],\n [-0.04510412, 0.0254137 ],\n [-0.0225588 , 0.04795902],\n [ 0.0487336 , 0.11925141],\n [-0.05810619, 0.01241163],\n [-0.01394971, 0.0565681 ],\n [ 0.00627124, 0.07678905],\n [-0.01341658, 0.05710124],\n [-0.04707601, 0.0234418 ],\n [-0.01450359, 0.05601422],\n [-0.01708256, 0.05343525],\n [-0.02006823, 0.05044958],\n [-0.04854637, 0.02197145],\n [-0.03615946, 0.03435835],\n [-0.01144141, 0.0590764 ],\n [-0.03300488, 0.03751294],\n [-0.03162663, 0.03889119],\n [ 0.03118114, 0.10169895],\n [-0.01004045, 0.06047737],\n [-0.03918092, 0.03133689],\n [-0.03143472, 0.0390831 ],\n [-0.04139801, 0.0291198 ],\n [-0.03952216, 0.03099565]]), 'significant_lags': [1, 2, 3, 4, 5, 6, 7, 8, 11, 12, 14, 15, 16, 21, 24, 35], 'n_obs': 3090}, 'volume': {'values': array([ 1. , 0.89231835, 0.11527043, 0.21942261, 0.1554976 ,\n 0.06485283, 0.25620594, 0.13648006, -0.19802398, -0.08229763,\n 0.00989658, 0.00234771, 0.09028939, 0.12117182, 0.13904393,\n -0.08325209, -0.00542368, 0.03812697, -0.01864103, 0.03616058,\n 0.13651273, 0.07230708, -0.08350945, -0.0322421 , 0.02475962,\n 0.04246932, 0.0395711 , 0.03403851, 0.06132399, -0.0559987 ,\n -0.02640301, 0.0326686 , -0.02576092, 0.03724004, 0.05686875,\n 0.06370599, -0.04903855, -0.03191649, -0.01561977, 0.00630843,\n 0.00846462]), 'confint': array([[ 1.00000000e+00, 1.00000000e+00],\n [ 8.57065146e-01, 9.27571553e-01],\n [ 8.00172271e-02, 1.50523634e-01],\n [ 1.84169404e-01, 2.54675811e-01],\n [ 1.20244394e-01, 1.90750801e-01],\n [ 2.95996237e-02, 1.00106031e-01],\n [ 2.20952737e-01, 2.91459144e-01],\n [ 1.01226860e-01, 1.71733267e-01],\n [-2.33277182e-01, -1.62770775e-01],\n [-1.17550835e-01, -4.70444278e-02],\n [-2.53566232e-02, 4.51497839e-02],\n [-3.29054919e-02, 3.76009152e-02],\n [ 5.50361839e-02, 1.25542591e-01],\n [ 8.59186213e-02, 1.56425028e-01],\n [ 1.03790727e-01, 1.74297134e-01],\n [-1.18505292e-01, -4.79988851e-02],\n [-4.06768818e-02, 2.98295253e-02],\n [ 2.87376303e-03, 7.33801701e-02],\n [-5.38942318e-02, 1.66121753e-02],\n [ 9.07379314e-04, 7.14137864e-02],\n [ 1.01259525e-01, 1.71765933e-01],\n [ 3.70538724e-02, 1.07560279e-01],\n [-1.18762654e-01, -4.82562473e-02],\n [-6.74953046e-02, 3.01110252e-03],\n [-1.04935824e-02, 6.00128247e-02],\n [ 7.21611805e-03, 7.77225251e-02],\n [ 4.31790064e-03, 7.48243077e-02],\n [-1.21469134e-03, 6.92917157e-02],\n [ 2.60707872e-02, 9.65771943e-02],\n [-9.12519074e-02, -2.07455003e-02],\n [-6.16562097e-02, 8.85019736e-03],\n [-2.58460841e-03, 6.79217987e-02],\n [-6.10141262e-02, 9.49228091e-03],\n [ 1.98683953e-03, 7.24932466e-02],\n [ 2.16155489e-02, 9.21219560e-02],\n [ 2.84527896e-02, 9.89591967e-02],\n [-8.42917577e-02, -1.37853506e-02],\n [-6.71696908e-02, 3.33671624e-03],\n [-5.08729734e-02, 1.96334337e-02],\n [-2.89447760e-02, 4.15616311e-02],\n [-2.67885833e-02, 4.37178238e-02]]), 'significant_lags': [1, 2, 3, 4, 5, 6, 7, 8, 9, 12, 13, 14, 15, 17, 19, 20, 21, 22, 25, 26, 28, 29, 33, 34, 35, 36], 'n_obs': 3091}}", - "ljungbox": "{'log_return': lag lb_stat lb_pvalue\n0 10 25.059139 0.005235\n1 20 38.928469 0.006805\n2 50 87.268713 0.000866\n3 100 148.676050 0.001151, 'squared_return': lag lb_stat lb_pvalue\n0 10 84.891973 5.482156e-14\n1 20 117.535801 8.139704e-16\n2 50 172.418996 2.287873e-15\n3 100 211.175390 6.046088e-10, 'abs_return': lag lb_stat lb_pvalue\n0 10 582.314852 1.081991e-118\n1 20 981.305481 3.779643e-195\n2 50 1767.938519 0.000000e+00\n3 100 2294.606367 0.000000e+00, 'volume': lag lb_stat lb_pvalue\n0 10 18825.923581 0.0\n1 20 32655.505952 0.0\n2 50 67729.164244 0.0\n3 100 103242.290924 0.0}", - "periodic_patterns": { - "log_return": [ - { - "period": 2, - "hits": [ - 1, - 10, - 17, - 17, - 33, - 33, - 44, - 59, - 59, - 65, - 65, - 75, - 75 - ], - "count": 13, - "fft_note": "若FFT频谱在 f=0.5000 (1/2天) 处存在峰值，则交叉验证通过" - }, - { - "period": 3, - "hits": [ - 2, - 10, - 17, - 33, - 44, - 59, - 65, - 75 - ], - "count": 8, - "fft_note": "若FFT频谱在 f=0.3333 (1/3天) 处存在峰值，则交叉验证通过" - }, - { - "period": 5, - "hits": [ - 10, - 44, - 59, - 65, - 75 - ], - "count": 5, - "fft_note": "若FFT频谱在 f=0.2000 (1/5天) 处存在峰值，则交叉验证通过" - }, - { - "period": 11, - "hits": [ - 10, - 33, - 44, - 65 - ], - "count": 4, - "fft_note": "若FFT频谱在 f=0.0909 (1/11天) 处存在峰值，则交叉验证通过" - } - ], - "squared_return": [ - { - "period": 2, - "hits": [ - 1, - 4, - 5, - 7, - 11, - 11, - 15, - 15, - 21, - 21, - 25, - 25, - 41, - 41, - 48, - 56 - ], - "count": 16, - "fft_note": "若FFT频谱在 f=0.5000 (1/2天) 处存在峰值，则交叉验证通过" - }, - { - "period": 3, - "hits": [ - 4, - 5, - 11, - 15, - 21, - 25, - 41, - 48, - 56 - ], - "count": 9, - "fft_note": "若FFT频谱在 f=0.3333 (1/3天) 处存在峰值，则交叉验证通过" - }, - { - "period": 5, - "hits": [ - 4, - 11, - 15, - 21, - 25, - 41, - 56 - ], - "count": 7, - "fft_note": "若FFT频谱在 f=0.2000 (1/5天) 处存在峰值，则交叉验证通过" - }, - { - "period": 7, - "hits": [ - 7, - 15, - 21, - 41, - 48, - 56 - ], - "count": 6, - "fft_note": "若FFT频谱在 f=0.1429 (1/7天) 处存在峰值，则交叉验证通过" - }, - { - "period": 11, - "hits": [ - 11, - 21, - 56 - ], - "count": 3, - "fft_note": "若FFT频谱在 f=0.0909 (1/11天) 处存在峰值，则交叉验证通过" - } - ], - "abs_return": [ - { - "period": 2, - "hits": [ - 1, - 3, - 5, - 7, - 9, - 11, - 13, - 15, - 17, - 19, - 21, - 23, - 25, - 27, - 29, - 31, - 33, - 35, - 37, - 39, - 41, - 43, - 45, - 47, - 49, - 52, - 53, - 55, - 57, - 59, - 61, - 63, - 65, - 67, - 69, - 71, - 73, - 77, - 77, - 80, - 81, - 83, - 85, - 91, - 91, - 94, - 95, - 97, - 99 - ], - "count": 49, - "fft_note": "若FFT频谱在 f=0.5000 (1/2天) 处存在峰值，则交叉验证通过" - }, - { - "period": 3, - "hits": [ - 2, - 5, - 8, - 11, - 14, - 17, - 20, - 23, - 26, - 29, - 32, - 35, - 38, - 41, - 44, - 47, - 50, - 53, - 56, - 59, - 62, - 65, - 68, - 71, - 77, - 80, - 83, - 86, - 91, - 92, - 95, - 98 - ], - "count": 32, - "fft_note": "若FFT频谱在 f=0.3333 (1/3天) 处存在峰值，则交叉验证通过" - }, - { - "period": 5, - "hits": [ - 4, - 9, - 14, - 19, - 24, - 29, - 34, - 39, - 44, - 49, - 54, - 59, - 64, - 69, - 80, - 84, - 91, - 94, - 99 - ], - "count": 19, - "fft_note": "若FFT频谱在 f=0.2000 (1/5天) 处存在峰值，则交叉验证通过" - }, - { - "period": 7, - "hits": [ - 6, - 13, - 20, - 27, - 34, - 41, - 48, - 55, - 62, - 69, - 77, - 83, - 91, - 97 - ], - "count": 14, - "fft_note": "若FFT频谱在 f=0.1429 (1/7天) 处存在峰值，则交叉验证通过" - }, - { - "period": 11, - "hits": [ - 10, - 21, - 32, - 43, - 54, - 65, - 77, - 98 - ], - "count": 8, - "fft_note": "若FFT频谱在 f=0.0909 (1/11天) 处存在峰值，则交叉验证通过" - }, - { - "period": 13, - "hits": [ - 12, - 25, - 38, - 52, - 64, - 77, - 91 - ], - "count": 7, - "fft_note": "若FFT频谱在 f=0.0769 (1/13天) 处存在峰值，则交叉验证通过" - }, - { - "period": 17, - "hits": [ - 16, - 33, - 50, - 67, - 84 - ], - "count": 5, - "fft_note": "若FFT频谱在 f=0.0588 (1/17天) 处存在峰值，则交叉验证通过" - }, - { - "period": 19, - "hits": [ - 18, - 37, - 56, - 77, - 94 - ], - "count": 5, - "fft_note": "若FFT频谱在 f=0.0526 (1/19天) 处存在峰值，则交叉验证通过" - }, - { - "period": 23, - "hits": [ - 22, - 45, - 68, - 91 - ], - "count": 4, - "fft_note": "若FFT频谱在 f=0.0435 (1/23天) 处存在峰值，则交叉验证通过" - }, - { - "period": 29, - "hits": [ - 28, - 57, - 86 - ], - "count": 3, - "fft_note": "若FFT频谱在 f=0.0345 (1/29天) 处存在峰值，则交叉验证通过" - }, - { - "period": 31, - "hits": [ - 30, - 61, - 92 - ], - "count": 3, - "fft_note": "若FFT频谱在 f=0.0323 (1/31天) 处存在峰值，则交叉验证通过" - } - ], - "volume": [ - { - "period": 2, - "hits": [ - 1, - 3, - 5, - 7, - 9, - 11, - 13, - 15, - 17, - 19, - 21, - 23, - 25, - 27, - 29, - 31, - 33, - 35, - 37, - 39, - 41, - 43, - 45, - 47, - 49, - 51, - 53, - 55, - 57, - 59, - 61, - 63, - 65, - 67, - 69, - 71, - 73, - 75, - 77, - 79, - 81, - 83, - 85, - 87, - 89, - 91, - 93, - 95, - 97, - 99 - ], - "count": 50, - "fft_note": "若FFT频谱在 f=0.5000 (1/2天) 处存在峰值，则交叉验证通过" - }, - { - "period": 3, - "hits": [ - 2, - 5, - 8, - 11, - 14, - 17, - 20, - 23, - 26, - 29, - 32, - 35, - 38, - 41, - 44, - 47, - 50, - 53, - 56, - 59, - 62, - 65, - 68, - 71, - 74, - 77, - 80, - 83, - 86, - 89, - 92, - 95, - 98 - ], - "count": 33, - "fft_note": "若FFT频谱在 f=0.3333 (1/3天) 处存在峰值，则交叉验证通过" - }, - { - "period": 5, - "hits": [ - 4, - 9, - 14, - 19, - 24, - 29, - 34, - 39, - 44, - 49, - 54, - 59, - 64, - 69, - 74, - 79, - 84, - 89, - 94, - 99 - ], - "count": 20, - "fft_note": "若FFT频谱在 f=0.2000 (1/5天) 处存在峰值，则交叉验证通过" - }, - { - "period": 7, - "hits": [ - 6, - 13, - 20, - 27, - 34, - 41, - 48, - 55, - 62, - 69, - 76, - 83, - 90, - 97 - ], - "count": 14, - "fft_note": "若FFT频谱在 f=0.1429 (1/7天) 处存在峰值，则交叉验证通过" - }, - { - "period": 11, - "hits": [ - 10, - 21, - 32, - 43, - 54, - 65, - 76, - 87, - 98 - ], - "count": 9, - "fft_note": "若FFT频谱在 f=0.0909 (1/11天) 处存在峰值，则交叉验证通过" - }, - { - "period": 13, - "hits": [ - 12, - 25, - 38, - 51, - 64, - 77, - 90 - ], - "count": 7, - "fft_note": "若FFT频谱在 f=0.0769 (1/13天) 处存在峰值，则交叉验证通过" - }, - { - "period": 17, - "hits": [ - 16, - 33, - 50, - 67, - 84 - ], - "count": 5, - "fft_note": "若FFT频谱在 f=0.0588 (1/17天) 处存在峰值，则交叉验证通过" - }, - { - "period": 19, - "hits": [ - 18, - 37, - 56, - 75, - 94 - ], - "count": 5, - "fft_note": "若FFT频谱在 f=0.0526 (1/19天) 处存在峰值，则交叉验证通过" - }, - { - "period": 23, - "hits": [ - 22, - 45, - 68, - 91 - ], - "count": 4, - "fft_note": "若FFT频谱在 f=0.0435 (1/23天) 处存在峰值，则交叉验证通过" - }, - { - "period": 29, - "hits": [ - 28, - 57, - 86 - ], - "count": 3, - "fft_note": "若FFT频谱在 f=0.0345 (1/29天) 处存在峰值，则交叉验证通过" - }, - { - "period": 31, - "hits": [ - 30, - 61, - 92 - ], - "count": 3, - "fft_note": "若FFT频谱在 f=0.0323 (1/31天) 处存在峰值，则交叉验证通过" - } - ] - }, - "summary": "{'log_return': {'label': '对数收益率', 'acf_significant_count': 10, 'pacf_significant_count': 6, 'ljungbox_rejects_white_noise': np.True_, 'periodic_patterns_count': 4, 'periodic_periods': [2, 3, 5, 11]}, 'squared_return': {'label': '平方收益率', 'acf_significant_count': 11, 'pacf_significant_count': 4, 'ljungbox_rejects_white_noise': np.True_, 'periodic_patterns_count': 5, 'periodic_periods': [2, 3, 5, 7, 11]}, 'abs_return': {'label': '绝对收益率', 'acf_significant_count': 88, 'pacf_significant_count': 16, 'ljungbox_rejects_white_noise': np.True_, 'periodic_patterns_count': 11, 'periodic_periods': [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31]}, 'volume': {'label': '成交量', 'acf_significant_count': 100, 'pacf_significant_count': 26, 'ljungbox_rejects_white_noise': np.True_, 'periodic_patterns_count': 11, 'periodic_periods': [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31]}}", - "status": "success" - }, - "returns": { - "normality": { - "ks_statistic": 0.09736697106568132, - "ks_pvalue": 5.971577286037212e-26, - "jb_statistic": 31996.30395577554, - "jb_pvalue": 0.0, - "ad_statistic": 64.17928613929007, - "ad_critical_values": { - "15.0%": 0.575, - "10.0%": 0.655, - "5.0%": 0.786, - "2.5%": 0.917, - "1.0%": 1.091 - } - }, - "fat_tail": { - "excess_kurtosis": 15.645612143331558, - "skewness": -0.9656348742170849, - "exceed_3sigma_actual": 0.015533980582524271, - "exceed_3sigma_normal": 0.002699796063260207, - "exceed_3sigma_ratio": 5.753760735455633, - "exceed_4sigma_actual": 0.005501618122977346, - "exceed_4sigma_normal": 6.334248366623996e-05, - "exceed_4sigma_ratio": 86.85510583964641 - }, - "multi_timeframe": "{'1h': datetime\n2017-08-17 05:00:00 0.001505\n2017-08-17 06:00:00 0.002090\n2017-08-17 07:00:00 0.005912\n2017-08-17 08:00:00 0.002457\n2017-08-17 09:00:00 0.018925\n ... \n2026-02-01 19:00:00 -0.011011\n2026-02-01 20:00:00 -0.000930\n2026-02-01 21:00:00 -0.007512\n2026-02-01 22:00:00 0.009407\n2026-02-01 23:00:00 -0.003646\nName: close, Length: 74052, dtype: float64, '4h': datetime\n2017-08-17 08:00:00 0.017616\n2017-08-17 12:00:00 -0.017076\n2017-08-17 16:00:00 -0.006248\n2017-08-17 20:00:00 -0.009326\n2017-08-18 00:00:00 0.001704\n ... \n2026-02-01 04:00:00 -0.006682\n2026-02-01 08:00:00 0.005100\n2026-02-01 12:00:00 -0.014714\n2026-02-01 16:00:00 -0.005410\n2026-02-01 20:00:00 -0.002680\nName: close, Length: 18527, dtype: float64, '1d': datetime\n2017-08-18 -0.042113\n2017-08-19 0.007665\n2017-08-20 -0.013053\n2017-08-21 -0.017351\n2017-08-22 0.005958\n ... \n2026-01-28 0.000560\n2026-01-29 -0.053474\n2026-01-30 -0.004614\n2026-01-31 -0.067748\n2026-02-01 -0.022773\nName: close, Length: 3090, dtype: float64, '1w': datetime\n2017-08-21 0.053303\n2017-08-28 0.045153\n2017-09-04 -0.087726\n2017-09-11 -0.110037\n2017-09-18 -0.010862\n ... \n2026-01-05 -0.005654\n2026-01-12 0.028802\n2026-01-19 -0.077700\n2026-01-26 -0.118719\n2026-02-02 0.020847\nName: close, Length: 434, dtype: float64}", - "garch": "{'model_summary': ' Constant Mean - GARCH Model Results \\n==============================================================================\\nDep. Variable: close R-squared: 0.000\\nMean Model: Constant Mean Adj. R-squared: 0.000\\nVol Model: GARCH Log-Likelihood: -8091.64\\nDistribution: Normal AIC: 16191.3\\nMethod: Maximum Likelihood BIC: 16215.4\\n No. Observations: 3090\\nDate: Tue, Feb 03 2026 Df Residuals: 3089\\nTime: 11:15:47 Df Model: 1\\n Mean Model \\n==========================================================================\\n coef std err t P>|t| 95.0% Conf. Int.\\n--------------------------------------------------------------------------\\nmu 0.1295 5.806e-02 2.231 2.568e-02 [1.573e-02, 0.243]\\n Volatility Model \\n==========================================================================\\n coef std err t P>|t| 95.0% Conf. Int.\\n--------------------------------------------------------------------------\\nomega 0.4388 0.207 2.121 3.393e-02 [3.329e-02, 0.844]\\nalpha[1] 0.0962 4.760e-02 2.022 4.319e-02 [2.944e-03, 0.190]\\nbeta[1] 0.8768 4.647e-02 18.866 2.158e-79 [ 0.786, 0.968]\\n==========================================================================\\n\\nCovariance estimator: robust', 'omega': np.float64(0.43881933719318955), 'alpha': np.float64(0.09623144766619043), 'beta': np.float64(0.876807221573444), 'persistence': np.float64(0.9730386692396344), 'log_likelihood': -8091.636710514733, 'aic': 16191.273421029466, 'bic': np.float64(16215.417126509034), 'conditional_volatility': datetime\n2017-08-18 0.045564\n2017-08-19 0.045227\n2017-08-20 0.042910\n2017-08-21 0.040965\n2017-08-22 0.039354\n ... \n2026-01-28 0.024847\n2026-01-29 0.024192\n2026-01-30 0.029081\n2026-01-31 0.028085\n2026-02-01 0.034557\nName: cond_vol, Length: 3090, dtype: float64, 'result_obj': Constant Mean - GARCH Model Results \n==============================================================================\nDep. Variable: close R-squared: 0.000\nMean Model: Constant Mean Adj. R-squared: 0.000\nVol Model: GARCH Log-Likelihood: -8091.64\nDistribution: Normal AIC: 16191.3\nMethod: Maximum Likelihood BIC: 16215.4\n No. Observations: 3090\nDate: Tue, Feb 03 2026 Df Residuals: 3089\nTime: 11:15:47 Df Model: 1\n Mean Model \n==========================================================================\n coef std err t P>|t| 95.0% Conf. Int.\n--------------------------------------------------------------------------\nmu 0.1295 5.806e-02 2.231 2.568e-02 [1.573e-02, 0.243]\n Volatility Model \n==========================================================================\n coef std err t P>|t| 95.0% Conf. Int.\n--------------------------------------------------------------------------\nomega 0.4388 0.207 2.121 3.393e-02 [3.329e-02, 0.844]\nalpha[1] 0.0962 4.760e-02 2.022 4.319e-02 [2.944e-03, 0.190]\nbeta[1] 0.8768 4.647e-02 18.866 2.158e-79 [ 0.786, 0.968]\n==========================================================================\n\nCovariance estimator: robust\nARCHModelResult, id: 0x1256660a0}", - "status": "success" - }, - "volatility": { - "realized_vol": " rv_7d rv_30d rv_90d\ndatetime \n2017-08-24 0.508880 NaN NaN\n2017-08-25 0.412318 NaN NaN\n2017-08-26 0.419513 NaN NaN\n2017-08-27 0.411345 NaN NaN\n2017-08-28 0.411973 NaN NaN\n... ... ... ...\n2026-01-28 0.263750 0.322936 0.386842\n2026-01-29 0.467542 0.369624 0.400849\n2026-01-30 0.468716 0.368485 0.400856\n2026-01-31 0.676833 0.435210 0.423360\n2026-02-01 0.664176 0.440095 0.419459\n\n[3084 rows x 3 columns]", - "acf_power_law": "{'d': np.float64(0.6351287691927425), 'd_nonlinear': np.float64(0.3448903462999068), 'r_squared': np.float64(0.42313817191006875), 'slope': np.float64(-0.6351287691927425), 'intercept': np.float64(-0.4744814497920893), 'p_value': np.float64(5.8241517539033605e-25), 'std_err': np.float64(0.053242166415478624), 'lags': array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n 196, 197, 198, 199, 200]), 'acf_values': array([ 0.16941215, 0.11681494, 0.11477814, 0.18185448, 0.14259819,\n 0.14312719, 0.13894497, 0.12687252, 0.10913528, 0.10631101,\n 0.12944533, 0.11684757, 0.09762454, 0.15017366, 0.12411898,\n 0.12326963, 0.11562807, 0.0844657 , 0.07607039, 0.09505224,\n 0.16106874, 0.07148222, 0.09370097, 0.113692 , 0.11214878,\n 0.07064554, 0.09680625, 0.10452395, 0.09941853, 0.06644419,\n 0.07644115, 0.10051518, 0.07849206, 0.07073167, 0.1368152 ,\n 0.10616077, 0.07564097, 0.07883957, 0.07680061, 0.07310946,\n 0.11918992, 0.07922182, 0.06456951, 0.08403174, 0.07692813,\n 0.08377337, 0.04391864, 0.10519398, 0.07786352, 0.05243764,\n 0.02742507, 0.04606311, 0.05652268, 0.04697355, 0.04920116,\n 0.12268232, 0.0759553 , 0.09233128, 0.05483661, 0.06063155,\n 0.05068447, 0.10495384, 0.07299428, 0.05869396, 0.06155175,\n 0.06108528, 0.05599143, 0.04059884, 0.04826835, 0.09459542,\n 0.04167287, 0.06421634, 0.03641472, 0.0238428 , 0.02949051,\n 0.028975 , 0.07587434, 0.04076047, 0.02183433, 0.04071859,\n 0.07915313, 0.04777162, 0.06885369, 0.07274957, 0.04513606,\n 0.07586587, 0.0313361 , 0.03066215, 0.03402811, 0.02781086,\n 0.07376816, 0.04940179, 0.03161297, 0.05145948, 0.06577359,\n 0.02750272, 0.03582021, 0.06441121, 0.03604022, 0.03358751,\n 0.01675996, 0.0422471 , 0.06591494, 0.06885818, 0.04818789,\n 0.04963618, 0.03163956, 0.01832079, 0.01964259, 0.01123289,\n 0.04754563, 0.07196065, 0.05327285, 0.01016024, 0.02099561,\n 0.03435864, 0.02275624, 0.0509572 , 0.07064521, 0.01823375,\n 0.03585885, 0.04181538, 0.03457026, 0.03414933, 0.0637637 ,\n 0.07646433, 0.03986097, 0.02894514, 0.03613122, 0.04108859,\n 0.00557398, 0.02362922, 0.05843189, 0.07093583, 0.01379967,\n 0.03433454, 0.04788753, 0.03138599, 0.08687262, 0.06597871,\n 0.04592148, 0.02997983, 0.03153215, 0.03551219, 0.02675903,\n 0.03524658, 0.05154921, 0.03958578, 0.03302279, 0.03481268,\n 0.02006116, 0.00173977, 0.05858255, 0.03622785, 0.01146138,\n 0.02265825, 0.04005955, 0.01007684, 0.01566085, 0.02396771,\n 0.03186424, 0.04805276, 0.01635648, 0.01976088, 0.02343394,\n 0.00273027, 0.02632813, 0.04456983, 0.01092182, 0.05303205,\n 0.01297113, 0.010092 , 0.02187145, 0.05982095, 0.07711737,\n -0.00194965, 0.00163255, 0.01963515, 0.01550529, 0.00465648,\n 0.0280234 , 0.04069632, 0.00792728, 0.00806326, 0.01348062,\n 0.00751679, 0.01850354, 0.00878234, 0.02196925, 0.00988049,\n 0.00236659, -0.00252685, 0.00163724, -0.00829965, 0.02765656,\n 0.04718163, 0.03509459, 0.01161449, -0.01577184, 0.00061623]), 'lags_positive': array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n 170, 171, 172, 173, 174, 175, 177, 178, 179, 180, 181, 182, 183,\n 184, 185, 186, 187, 188, 189, 190, 191, 193, 195, 196, 197, 198,\n 200]), 'acf_positive': array([0.16941215, 0.11681494, 0.11477814, 0.18185448, 0.14259819,\n 0.14312719, 0.13894497, 0.12687252, 0.10913528, 0.10631101,\n 0.12944533, 0.11684757, 0.09762454, 0.15017366, 0.12411898,\n 0.12326963, 0.11562807, 0.0844657 , 0.07607039, 0.09505224,\n 0.16106874, 0.07148222, 0.09370097, 0.113692 , 0.11214878,\n 0.07064554, 0.09680625, 0.10452395, 0.09941853, 0.06644419,\n 0.07644115, 0.10051518, 0.07849206, 0.07073167, 0.1368152 ,\n 0.10616077, 0.07564097, 0.07883957, 0.07680061, 0.07310946,\n 0.11918992, 0.07922182, 0.06456951, 0.08403174, 0.07692813,\n 0.08377337, 0.04391864, 0.10519398, 0.07786352, 0.05243764,\n 0.02742507, 0.04606311, 0.05652268, 0.04697355, 0.04920116,\n 0.12268232, 0.0759553 , 0.09233128, 0.05483661, 0.06063155,\n 0.05068447, 0.10495384, 0.07299428, 0.05869396, 0.06155175,\n 0.06108528, 0.05599143, 0.04059884, 0.04826835, 0.09459542,\n 0.04167287, 0.06421634, 0.03641472, 0.0238428 , 0.02949051,\n 0.028975 , 0.07587434, 0.04076047, 0.02183433, 0.04071859,\n 0.07915313, 0.04777162, 0.06885369, 0.07274957, 0.04513606,\n 0.07586587, 0.0313361 , 0.03066215, 0.03402811, 0.02781086,\n 0.07376816, 0.04940179, 0.03161297, 0.05145948, 0.06577359,\n 0.02750272, 0.03582021, 0.06441121, 0.03604022, 0.03358751,\n 0.01675996, 0.0422471 , 0.06591494, 0.06885818, 0.04818789,\n 0.04963618, 0.03163956, 0.01832079, 0.01964259, 0.01123289,\n 0.04754563, 0.07196065, 0.05327285, 0.01016024, 0.02099561,\n 0.03435864, 0.02275624, 0.0509572 , 0.07064521, 0.01823375,\n 0.03585885, 0.04181538, 0.03457026, 0.03414933, 0.0637637 ,\n 0.07646433, 0.03986097, 0.02894514, 0.03613122, 0.04108859,\n 0.00557398, 0.02362922, 0.05843189, 0.07093583, 0.01379967,\n 0.03433454, 0.04788753, 0.03138599, 0.08687262, 0.06597871,\n 0.04592148, 0.02997983, 0.03153215, 0.03551219, 0.02675903,\n 0.03524658, 0.05154921, 0.03958578, 0.03302279, 0.03481268,\n 0.02006116, 0.00173977, 0.05858255, 0.03622785, 0.01146138,\n 0.02265825, 0.04005955, 0.01007684, 0.01566085, 0.02396771,\n 0.03186424, 0.04805276, 0.01635648, 0.01976088, 0.02343394,\n 0.00273027, 0.02632813, 0.04456983, 0.01092182, 0.05303205,\n 0.01297113, 0.010092 , 0.02187145, 0.05982095, 0.07711737,\n 0.00163255, 0.01963515, 0.01550529, 0.00465648, 0.0280234 ,\n 0.04069632, 0.00792728, 0.00806326, 0.01348062, 0.00751679,\n 0.01850354, 0.00878234, 0.02196925, 0.00988049, 0.00236659,\n 0.00163724, 0.02765656, 0.04718163, 0.03509459, 0.01161449,\n 0.00061623]), 'is_long_memory': np.True_}", - "model_comparison": "{'GARCH': {'params': {'mu': np.float64(0.12952422372058514), 'omega': np.float64(0.43881933719318955), 'alpha[1]': np.float64(0.09623144766619043), 'beta[1]': np.float64(0.876807221573444)}, 'aic': 16191.273421029466, 'bic': np.float64(16215.417126509034), 'log_likelihood': -8091.636710514733, 'conditional_volatility': datetime\n2017-08-18 0.045564\n2017-08-19 0.045227\n2017-08-20 0.042910\n2017-08-21 0.040965\n2017-08-22 0.039354\n ... \n2026-01-28 0.024847\n2026-01-29 0.024192\n2026-01-30 0.029081\n2026-01-31 0.028085\n2026-02-01 0.034557\nName: cond_vol, Length: 3090, dtype: float64, 'result_obj': Constant Mean - GARCH Model Results \n==============================================================================\nDep. Variable: close R-squared: 0.000\nMean Model: Constant Mean Adj. R-squared: 0.000\nVol Model: GARCH Log-Likelihood: -8091.64\nDistribution: Normal AIC: 16191.3\nMethod: Maximum Likelihood BIC: 16215.4\n No. Observations: 3090\nDate: Tue, Feb 03 2026 Df Residuals: 3089\nTime: 11:15:48 Df Model: 1\n Mean Model \n==========================================================================\n coef std err t P>|t| 95.0% Conf. Int.\n--------------------------------------------------------------------------\nmu 0.1295 5.806e-02 2.231 2.568e-02 [1.573e-02, 0.243]\n Volatility Model \n==========================================================================\n coef std err t P>|t| 95.0% Conf. Int.\n--------------------------------------------------------------------------\nomega 0.4388 0.207 2.121 3.393e-02 [3.329e-02, 0.844]\nalpha[1] 0.0962 4.760e-02 2.022 4.319e-02 [2.944e-03, 0.190]\nbeta[1] 0.8768 4.647e-02 18.866 2.158e-79 [ 0.786, 0.968]\n==========================================================================\n\nCovariance estimator: robust\nARCHModelResult, id: 0x12606b700}, 'EGARCH': {'params': {'mu': np.float64(0.11417527305176238), 'omega': np.float64(0.11719203779011349), 'alpha[1]': np.float64(0.17325396230660126), 'beta[1]': np.float64(0.9600892426630561)}, 'aic': 16209.480929812882, 'bic': np.float64(16233.62463529245), 'log_likelihood': -8100.740464906441, 'conditional_volatility': datetime\n2017-08-18 0.045606\n2017-08-19 0.046114\n2017-08-20 0.043460\n2017-08-21 0.041720\n2017-08-22 0.040522\n ... \n2026-01-28 0.024748\n2026-01-29 0.023667\n2026-01-30 0.027635\n2026-01-31 0.026737\n2026-02-01 0.031800\nName: cond_vol, Length: 3090, dtype: float64, 'leverage_param': nan, 'result_obj': Constant Mean - EGARCH Model Results \n==============================================================================\nDep. Variable: close R-squared: 0.000\nMean Model: Constant Mean Adj. R-squared: 0.000\nVol Model: EGARCH Log-Likelihood: -8100.74\nDistribution: Normal AIC: 16209.5\nMethod: Maximum Likelihood BIC: 16233.6\n No. Observations: 3090\nDate: Tue, Feb 03 2026 Df Residuals: 3089\nTime: 11:15:48 Df Model: 1\n Mean Model \n==========================================================================\n coef std err t P>|t| 95.0% Conf. Int.\n--------------------------------------------------------------------------\nmu 0.1142 5.823e-02 1.961 4.991e-02 [4.720e-05, 0.228]\n Volatility Model \n==========================================================================\n coef std err t P>|t| 95.0% Conf. Int.\n--------------------------------------------------------------------------\nomega 0.1172 4.701e-02 2.493 1.266e-02 [2.506e-02, 0.209]\nalpha[1] 0.1733 5.445e-02 3.182 1.463e-03 [6.653e-02, 0.280]\nbeta[1] 0.9601 1.594e-02 60.213 0.000 [ 0.929, 0.991]\n==========================================================================\n\nCovariance estimator: robust\nARCHModelResult, id: 0x124f5a760}, 'GJR-GARCH': {'params': {'mu': np.float64(0.08097224546042321), 'omega': np.float64(0.48386363868276294), 'alpha[1]': np.float64(0.06779768885138529), 'gamma[1]': np.float64(0.06548062265277206), 'beta[1]': np.float64(0.8693004769146425)}, 'aic': 16170.677755626308, 'bic': np.float64(16200.857387475768), 'log_likelihood': -8080.338877813154, 'conditional_volatility': datetime\n2017-08-18 0.045540\n2017-08-19 0.045790\n2017-08-20 0.043293\n2017-08-21 0.041271\n2017-08-22 0.039661\n ... \n2026-01-28 0.025648\n2026-01-29 0.024905\n2026-01-30 0.031310\n2026-01-31 0.030075\n2026-02-01 0.038224\nName: cond_vol, Length: 3090, dtype: float64, 'leverage_param': np.float64(0.06548062265277206), 'result_obj': Constant Mean - GJR-GARCH Model Results \n==============================================================================\nDep. Variable: close R-squared: 0.000\nMean Model: Constant Mean Adj. R-squared: 0.000\nVol Model: GJR-GARCH Log-Likelihood: -8080.34\nDistribution: Normal AIC: 16170.7\nMethod: Maximum Likelihood BIC: 16200.9\n No. Observations: 3090\nDate: Tue, Feb 03 2026 Df Residuals: 3089\nTime: 11:15:48 Df Model: 1\n Mean Model \n===========================================================================\n coef std err t P>|t| 95.0% Conf. Int.\n---------------------------------------------------------------------------\nmu 0.0810 5.352e-02 1.513 0.130 [-2.392e-02, 0.186]\n Volatility Model \n===========================================================================\n coef std err t P>|t| 95.0% Conf. Int.\n---------------------------------------------------------------------------\nomega 0.4839 0.237 2.044 4.094e-02 [1.993e-02, 0.948]\nalpha[1] 0.0678 2.275e-02 2.979 2.887e-03 [2.320e-02, 0.112]\ngamma[1] 0.0655 6.205e-02 1.055 0.291 [-5.613e-02, 0.187]\nbeta[1] 0.8693 4.827e-02 18.009 1.663e-72 [ 0.775, 0.964]\n===========================================================================\n\nCovariance estimator: robust\nARCHModelResult, id: 0x124ec2d60}}", - "leverage_effect": "{'5d': {'pearson_correlation': np.float64(-0.061984247861208444), 'pearson_pvalue': np.float64(0.0005717052330372451), 'spearman_correlation': np.float64(-0.013703868249034022), 'spearman_pvalue': np.float64(0.44672985128105513), 'n_samples': 3085, 'return_series': datetime\n2017-08-18 -0.042113\n2017-08-19 0.007665\n2017-08-20 -0.013053\n2017-08-21 -0.017351\n2017-08-22 0.005958\n ... \n2026-01-23 0.000453\n2026-01-24 -0.004193\n2026-01-25 -0.029053\n2026-01-26 0.019161\n2026-01-27 0.010168\nName: return, Length: 3085, dtype: float64, 'future_vol_series': datetime\n2017-08-18 0.012436\n2017-08-19 0.020490\n2017-08-20 0.019523\n2017-08-21 0.018687\n2017-08-22 0.018765\n ... \n2026-01-23 0.012627\n2026-01-24 0.022483\n2026-01-25 0.017596\n2026-01-26 0.027313\n2026-01-27 0.029834\nName: future_vol, Length: 3085, dtype: float64}, '10d': {'pearson_correlation': np.float64(-0.033668665650756026), 'pearson_pvalue': np.float64(0.06171938986587776), 'spearman_correlation': np.float64(0.0022075568332161587), 'spearman_pvalue': np.float64(0.9025308817972548), 'n_samples': 3080, 'return_series': datetime\n2017-08-18 -0.042113\n2017-08-19 0.007665\n2017-08-20 -0.013053\n2017-08-21 -0.017351\n2017-08-22 0.005958\n ... \n2026-01-18 -0.015620\n2026-01-19 -0.011188\n2026-01-20 -0.046439\n2026-01-21 0.011548\n2026-01-22 0.001172\nName: return, Length: 3080, dtype: float64, 'future_vol_series': datetime\n2017-08-18 0.015548\n2017-08-19 0.019258\n2017-08-20 0.018660\n2017-08-21 0.020583\n2017-08-22 0.022289\n ... \n2026-01-18 0.013394\n2026-01-19 0.017622\n2026-01-20 0.013440\n2026-01-21 0.019060\n2026-01-22 0.021220\nName: future_vol, Length: 3080, dtype: float64}, '20d': {'pearson_correlation': np.float64(-0.017638367572489176), 'pearson_pvalue': np.float64(0.32858040373635033), 'spearman_correlation': np.float64(0.006272837436601455), 'spearman_pvalue': np.float64(0.7282723382926714), 'n_samples': 3070, 'return_series': datetime\n2017-08-18 -0.042113\n2017-08-19 0.007665\n2017-08-20 -0.013053\n2017-08-21 -0.017351\n2017-08-22 0.005958\n ... \n2026-01-08 -0.002896\n2026-01-09 -0.005048\n2026-01-10 -0.001508\n2026-01-11 0.005608\n2026-01-12 0.003100\nName: return, Length: 3070, dtype: float64, 'future_vol_series': datetime\n2017-08-18 0.029149\n2017-08-19 0.033324\n2017-08-20 0.032953\n2017-08-21 0.033616\n2017-08-22 0.034255\n ... \n2026-01-08 0.012185\n2026-01-09 0.014607\n2026-01-10 0.014762\n2026-01-11 0.017869\n2026-01-12 0.018853\nName: future_vol, Length: 3070, dtype: float64}}", - "status": "success" - }, - "hurst": { - "R/S Hurst": 0.599066670965807, - "DFA Hurst": 0.5868487366138886, - "交叉验证": { - "R/S Hurst": 0.599066670965807, - "DFA Hurst": 0.5868487366138886, - "两种方法差异": 0.012217934351918425, - "平均值": 0.5929577037898478 - }, - "综合Hurst": 0.5929577037898478, - "综合解读": "趋势性 (H=0.5930 > 0.55)：序列具有长程正相关，价格趋势倾向于持续", - "滚动Hurst": { - "窗口数": 87, - "趋势占比": 0.9885057471264368, - "随机游走占比": 0.011494252873563218, - "均值回归占比": 0.0, - "Hurst范围": [ - 0.548657473865875, - 0.6540287499669682 - ], - "Hurst均值": 0.5913167465022056 - }, - "多时间框架": { - "1h": { - "R/S Hurst": 0.5551829664830917, - "DFA Hurst": 0.5559270762382792, - "平均Hurst": 0.5555550213606855, - "数据量": 74052, - "解读": "趋势性 (H=0.5556 > 0.55)：序列具有长程正相关，价格趋势倾向于持续" - }, - "4h": { - "R/S Hurst": 0.5749044947852355, - "DFA Hurst": 0.577134099743992, - "平均Hurst": 0.5760192972646138, - "数据量": 18527, - "解读": "趋势性 (H=0.5760 > 0.55)：序列具有长程正相关，价格趋势倾向于持续" - }, - "1d": { - "R/S Hurst": 0.599066670965807, - "DFA Hurst": 0.5868487366138886, - "平均Hurst": 0.5929577037898478, - "数据量": 3090, - "解读": "趋势性 (H=0.5930 > 0.55)：序列具有长程正相关，价格趋势倾向于持续" - }, - "1w": { - "R/S Hurst": 0.6863567334278854, - "DFA Hurst": 0.6551931131151767, - "平均Hurst": 0.670774923271531, - "数据量": 434, - "解读": "趋势性 (H=0.6708 > 0.55)：序列具有长程正相关，价格趋势倾向于持续" - } - }, - "status": "success" - }, - "fractal": { - "盒计数分形维数": 1.33981806810231, - "维数解读": "序列较为光滑，具有一定趋势持续性", - "Hurst(从D推算)": 0.66018193189769, - "蒙特卡洛检验": "{'BTC分形维数': np.float64(1.33981806810231), '随机游走均值': np.float64(1.380548625079417), '随机游走标准差': np.float64(0.029469822925662244), '随机游走范围': (np.float64(1.2773941560871607), np.float64(1.4351289056018914)), 'Z统计量': np.float64(-1.3821106791123259), 'p值': np.float64(0.16693771997826756), '显著性(α=0.05)': np.False_}", - "多尺度自相似性": { - "缩放指数(H估计)": 0.5274203951268943 - }, - "status": "success" - }, - "power_law": { - "r_squared": 0.5678120109582484, - "power_exponent": 0.7699636561390698, - "intercept": 4.629820314237845, - "corridor_prices": { - "0.05": 16879.14611194412, - "0.5": 51706.664285887106, - "0.95": 119339.81281975961 - }, - "model_comparison": { - "power_law_aic": 68300.50392355697, - "power_law_bic": 68312.57642344123, - "exponential_aic": 67807.4540823288, - "exponential_bic": 67819.52658221306, - "preferred": "exponential" - }, - "current_price": 76968.21, - "current_percentile": 67.87447428016823, - "status": "success" - }, - "volume_price": { - "spearman": { - "correlation": 0.3214920649731082, - "p_value": 3.1129979914822277e-75, - "n_samples": 3090 - }, - "lead_lag": { - "significant_lags": [] - }, - "granger": { - "volume_to_returns_sig_lags": [], - "returns_to_volume_sig_lags": [] - }, - "obv_divergences": { - "total": 82, - "bearish": 49, - "bullish": 33 - }, - "status": "success" - }, - "calendar": { - "status": "success", - "findings": [] - }, - "halving": { - "status": "success", - "findings": [] - }, - "indicators": { - "train_results": " n_buy n_sell ... ic_rejected any_fdr_pass\nindicator ... \nSMA_5_20 47.0 48.0 ... False False\nEMA_5_20 53.0 54.0 ... False False\nSMA_10_50 21.0 22.0 ... False False\nEMA_10_50 19.0 20.0 ... False False\nSMA_20_100 7.0 8.0 ... False False\nEMA_20_100 9.0 10.0 ... False False\nSMA_50_200 4.0 5.0 ... False False\nEMA_50_200 6.0 7.0 ... False False\nRSI_7_30_70 66.0 78.0 ... False False\nRSI_7_25_75 48.0 62.0 ... False False\nRSI_7_20_80 21.0 41.0 ... False False\nRSI_14_30_70 24.0 47.0 ... False False\nRSI_14_25_75 15.0 27.0 ... False False\nRSI_14_20_80 4.0 17.0 ... False False\nRSI_21_30_70 14.0 29.0 ... False False\nRSI_21_25_75 4.0 16.0 ... False False\nRSI_21_20_80 2.0 11.0 ... False False\nMACD_12_26_9 65.0 65.0 ... False False\nMACD_8_17_9 92.0 92.0 ... False False\nMACD_5_35_5 123.0 123.0 ... False False\nBB_20_2 39.0 59.0 ... False False\n\n[21 rows x 23 columns]", - "val_results": " n_buy n_sell ... ic_rejected any_fdr_pass\nindicator ... \nSMA_5_20 21.0 21.0 ... False False\nEMA_5_20 17.0 17.0 ... False False\nSMA_10_50 7.0 7.0 ... False False\nEMA_10_50 8.0 8.0 ... False False\nSMA_20_100 4.0 4.0 ... False False\nEMA_20_100 3.0 3.0 ... False False\nSMA_50_200 2.0 1.0 ... False False\nEMA_50_200 2.0 1.0 ... False False\nRSI_7_30_70 16.0 27.0 ... False False\nRSI_7_25_75 9.0 16.0 ... False False\nRSI_7_20_80 4.0 17.0 ... False False\nRSI_14_30_70 4.0 17.0 ... False False\nRSI_14_25_75 3.0 6.0 ... False False\nRSI_14_20_80 1.0 7.0 ... False False\nRSI_21_30_70 1.0 7.0 ... False False\nRSI_21_25_75 0.0 9.0 ... False False\nRSI_21_20_80 0.0 7.0 ... False False\nMACD_12_26_9 22.0 23.0 ... False False\nMACD_8_17_9 28.0 29.0 ... False False\nMACD_5_35_5 42.0 43.0 ... False False\nBB_20_2 12.0 26.0 ... False False\n\n[21 rows x 23 columns]", - "fdr_passed_train": [], - "fdr_passed_val": [], - "permutation_results": { - "RSI_14_30_70": { - "observed_diff": -0.004977440100087348, - "perm_pval": 0.5664335664335665 - }, - "RSI_14_25_75": { - "observed_diff": -0.03017610738336842, - "perm_pval": 0.014985014985014986 - }, - "RSI_21_30_70": { - "observed_diff": -0.012247499113796413, - "perm_pval": 0.2677322677322677 - }, - "RSI_7_25_75": { - "observed_diff": -0.014302431427126703, - "perm_pval": 0.02097902097902098 - }, - "RSI_21_20_80": { - "observed_diff": -0.0252918754365221, - "perm_pval": 0.3026973026973027 - } - }, - "all_signals": "{'SMA_5_20': datetime\n2017-08-17 0\n2017-08-18 0\n2017-08-19 0\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 0\n2026-01-29 0\n2026-01-30 0\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64, 'EMA_5_20': datetime\n2017-08-17 0\n2017-08-18 -1\n2017-08-19 0\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 0\n2026-01-29 0\n2026-01-30 0\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64, 'SMA_10_50': datetime\n2017-08-17 0\n2017-08-18 0\n2017-08-19 0\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 0\n2026-01-29 0\n2026-01-30 0\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64, 'EMA_10_50': datetime\n2017-08-17 0\n2017-08-18 -1\n2017-08-19 0\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 0\n2026-01-29 0\n2026-01-30 0\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64, 'SMA_20_100': datetime\n2017-08-17 0\n2017-08-18 0\n2017-08-19 0\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 0\n2026-01-29 0\n2026-01-30 0\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64, 'EMA_20_100': datetime\n2017-08-17 0\n2017-08-18 -1\n2017-08-19 0\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 0\n2026-01-29 0\n2026-01-30 0\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64, 'SMA_50_200': datetime\n2017-08-17 0\n2017-08-18 0\n2017-08-19 0\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 0\n2026-01-29 0\n2026-01-30 0\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64, 'EMA_50_200': datetime\n2017-08-17 0\n2017-08-18 -1\n2017-08-19 0\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 0\n2026-01-29 0\n2026-01-30 0\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64, 'RSI_7_30_70': datetime\n2017-08-17 0\n2017-08-18 0\n2017-08-19 0\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 0\n2026-01-29 0\n2026-01-30 0\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64, 'RSI_7_25_75': datetime\n2017-08-17 0\n2017-08-18 0\n2017-08-19 0\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 0\n2026-01-29 0\n2026-01-30 0\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64, 'RSI_7_20_80': datetime\n2017-08-17 0\n2017-08-18 0\n2017-08-19 0\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 0\n2026-01-29 0\n2026-01-30 0\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64, 'RSI_14_30_70': datetime\n2017-08-17 0\n2017-08-18 0\n2017-08-19 0\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 0\n2026-01-29 0\n2026-01-30 0\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64, 'RSI_14_25_75': datetime\n2017-08-17 0\n2017-08-18 0\n2017-08-19 0\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 0\n2026-01-29 0\n2026-01-30 0\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64, 'RSI_14_20_80': datetime\n2017-08-17 0\n2017-08-18 0\n2017-08-19 0\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 0\n2026-01-29 0\n2026-01-30 0\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64, 'RSI_21_30_70': datetime\n2017-08-17 0\n2017-08-18 0\n2017-08-19 0\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 0\n2026-01-29 0\n2026-01-30 0\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64, 'RSI_21_25_75': datetime\n2017-08-17 0\n2017-08-18 0\n2017-08-19 0\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 0\n2026-01-29 0\n2026-01-30 0\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64, 'RSI_21_20_80': datetime\n2017-08-17 0\n2017-08-18 0\n2017-08-19 0\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 0\n2026-01-29 0\n2026-01-30 0\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64, 'MACD_12_26_9': datetime\n2017-08-17 0\n2017-08-18 -1\n2017-08-19 0\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 0\n2026-01-29 0\n2026-01-30 0\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64, 'MACD_8_17_9': datetime\n2017-08-17 0\n2017-08-18 -1\n2017-08-19 0\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 0\n2026-01-29 0\n2026-01-30 0\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64, 'MACD_5_35_5': datetime\n2017-08-17 0\n2017-08-18 -1\n2017-08-19 0\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 0\n2026-01-29 0\n2026-01-30 0\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64, 'BB_20_2': datetime\n2017-08-17 0\n2017-08-18 0\n2017-08-19 0\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 0\n2026-01-29 0\n2026-01-30 0\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64}", - "status": "success" - }, - "patterns": { - "train_results": " n_occurrences ... any_fdr_pass\npattern ... \nDoji 219.0 ... False\nHammer 49.0 ... False\nInverted_Hammer 17.0 ... False\nShooting_Star 6.0 ... False\nPin_Bar_Bull 116.0 ... False\nPin_Bar_Bear 57.0 ... False\nBullish_Engulfing 159.0 ... False\nBearish_Engulfing 149.0 ... False\nMorning_Star 23.0 ... False\nEvening_Star 20.0 ... False\nThree_White_Soldiers 11.0 ... False\nThree_Black_Crows 4.0 ... False\n\n[12 rows x 41 columns]", - "val_results": " n_occurrences ... any_fdr_pass\npattern ... \nDoji 81.0 ... True\nHammer 12.0 ... False\nInverted_Hammer 6.0 ... False\nShooting_Star 3.0 ... False\nPin_Bar_Bull 28.0 ... True\nPin_Bar_Bear 20.0 ... False\nBullish_Engulfing 69.0 ... True\nBearish_Engulfing 47.0 ... False\nMorning_Star 5.0 ... False\nEvening_Star 6.0 ... False\nThree_White_Soldiers 4.0 ... False\nThree_Black_Crows 0.0 ... False\n\n[12 rows x 41 columns]", - "fdr_passed_train": [], - "fdr_passed_val": [ - "Doji", - "Pin_Bar_Bull", - "Bullish_Engulfing" - ], - "all_patterns": "{'Doji': datetime\n2017-08-17 1\n2017-08-18 0\n2017-08-19 1\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 1\n2026-01-29 0\n2026-01-30 0\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64, 'Hammer': datetime\n2017-08-17 0\n2017-08-18 0\n2017-08-19 0\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 0\n2026-01-29 0\n2026-01-30 1\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64, 'Inverted_Hammer': datetime\n2017-08-17 0\n2017-08-18 0\n2017-08-19 0\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 0\n2026-01-29 0\n2026-01-30 0\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64, 'Shooting_Star': datetime\n2017-08-17 0\n2017-08-18 0\n2017-08-19 0\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 0\n2026-01-29 0\n2026-01-30 0\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64, 'Pin_Bar_Bull': datetime\n2017-08-17 0\n2017-08-18 0\n2017-08-19 1\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 0\n2026-01-29 0\n2026-01-30 1\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64, 'Pin_Bar_Bear': datetime\n2017-08-17 1\n2017-08-18 0\n2017-08-19 0\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 1\n2026-01-29 0\n2026-01-30 0\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64, 'Bullish_Engulfing': datetime\n2017-08-17 0\n2017-08-18 0\n2017-08-19 0\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 0\n2026-01-29 0\n2026-01-30 0\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64, 'Bearish_Engulfing': datetime\n2017-08-17 0\n2017-08-18 1\n2017-08-19 0\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 0\n2026-01-29 1\n2026-01-30 0\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64, 'Morning_Star': datetime\n2017-08-17 0\n2017-08-18 0\n2017-08-19 0\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 0\n2026-01-29 0\n2026-01-30 0\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64, 'Evening_Star': datetime\n2017-08-17 0\n2017-08-18 0\n2017-08-19 0\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 0\n2026-01-29 1\n2026-01-30 0\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64, 'Three_White_Soldiers': datetime\n2017-08-17 0\n2017-08-18 0\n2017-08-19 0\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 0\n2026-01-29 0\n2026-01-30 0\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64, 'Three_Black_Crows': datetime\n2017-08-17 0\n2017-08-18 0\n2017-08-19 0\n2017-08-20 0\n2017-08-21 0\n ..\n2026-01-28 0\n2026-01-29 0\n2026-01-30 0\n2026-01-31 0\n2026-02-01 0\nLength: 3091, dtype: int64}", - "status": "success" - }, - "clustering": { - "kmeans": "{'best_k': 3, 'labels': array([1, 1, 2, ..., 0, 1, 0], dtype=int32), 'cluster_desc': log_return abs_return vol_7d ... state_cn count pct\ncluster_K-Means ... \n0 -0.000096 0.012406 0.465074 ... 横盘整理 2253 73.6\n1 -0.056358 0.056570 0.951770 ... 急剧下跌 361 11.8\n2 0.052789 0.052932 0.876194 ... 强势上涨 447 14.6\n\n[3 rows x 14 columns], 'all_results': {3: {'silhouette': np.float64(0.33788983182490917), 'inertia': 21341.973506600978, 'labels': array([1, 1, 2, ..., 0, 1, 0], dtype=int32), 'model': KMeans(max_iter=500, n_clusters=3, n_init=20, random_state=42)}, 4: {'silhouette': np.float64(0.2383690756014495), 'inertia': 19697.42665658707, 'labels': array([3, 3, 0, ..., 1, 2, 3], dtype=int32), 'model': KMeans(max_iter=500, n_clusters=4, n_init=20, random_state=42)}, 5: {'silhouette': np.float64(0.1748258615401955), 'inertia': 18264.213417509138, 'labels': array([4, 4, 0, ..., 2, 3, 4], dtype=int32), 'model': KMeans(max_iter=500, n_clusters=5, n_init=20, random_state=42)}, 6: {'silhouette': np.float64(0.17344301823232902), 'inertia': 17141.26396179242, 'labels': array([2, 2, 0, ..., 3, 3, 3], dtype=int32), 'model': KMeans(max_iter=500, n_clusters=6, n_init=20, random_state=42)}, 7: {'silhouette': np.float64(0.17323332216663148), 'inertia': 16243.10046274255, 'labels': array([3, 4, 1, ..., 5, 5, 5], dtype=int32), 'model': KMeans(max_iter=500, n_clusters=7, n_init=20, random_state=42)}}}", - "gmm": "{'best_k': 7, 'labels': array([3, 1, 3, ..., 6, 5, 6]), 'cluster_desc': log_return abs_return vol_7d ... state_cn count pct\ncluster_GMM ... \n0 0.010128 0.010128 0.357685 ... 温和上涨 698 22.8\n1 -0.072434 0.072434 1.041344 ... 急剧下跌 122 4.0\n2 -0.012261 0.012261 0.381069 ... 温和下跌 783 25.6\n3 0.059049 0.059049 1.051605 ... 强势上涨 241 7.9\n4 0.025606 0.025606 0.656672 ... 强势上涨 634 20.7\n5 -0.043943 0.043943 0.803047 ... 急剧下跌 290 9.5\n6 -0.011326 0.011326 0.700769 ... 温和下跌 293 9.6\n\n[7 rows x 14 columns], 'all_results': {3: {'bic': np.float64(7219.829483951697), 'aic': np.float64(6032.609586979451), 'silhouette': np.float64(0.06549802389899598), 'labels': array([0, 2, 0, ..., 2, 2, 1]), 'model': GaussianMixture(max_iter=500, n_components=3, n_init=5, random_state=42)}, 4: {'bic': np.float64(-101.52417196679198), 'aic': np.float64(-1686.4928669094352), 'silhouette': np.float64(0.05590661985706378), 'labels': array([3, 2, 3, ..., 2, 2, 0]), 'model': GaussianMixture(max_iter=500, n_components=4, n_init=5, random_state=42)}, 5: {'bic': np.float64(-2057.235977022373), 'aic': np.float64(-4039.9534699354135), 'silhouette': np.float64(0.03830870117896256), 'labels': array([2, 4, 2, ..., 0, 0, 0]), 'model': GaussianMixture(max_iter=500, n_components=5, n_init=5, random_state=42)}, 6: {'bic': np.float64(-1810.3804844055708), 'aic': np.float64(-4190.846775289008), 'silhouette': np.float64(0.04026910587877369), 'labels': array([2, 5, 0, ..., 3, 3, 3]), 'model': GaussianMixture(max_iter=500, n_components=6, n_init=5, random_state=42)}, 7: {'bic': np.float64(-3434.449804148429), 'aic': np.float64(-6212.664893002264), 'silhouette': np.float64(0.01891584304771941), 'labels': array([3, 1, 3, ..., 6, 5, 6]), 'model': GaussianMixture(max_iter=500, n_components=7, n_init=5, random_state=42)}}}", - "hdbscan": "{'labels': array([-1, -1, -1, ..., -1, -1, -1]), 'info': {'n_clusters': 0, 'n_noise': np.int64(3061), 'noise_pct': np.float64(100.0), 'labels': array([-1, -1, -1, ..., -1, -1, -1]), 'model': HDBSCAN(min_cluster_size=30, min_samples=10)}}", - "markov": "{'transition_matrix': array([[0.81971581, 0.07726465, 0.10301954],\n [0.45152355, 0.2299169 , 0.31855956],\n [0.5458613 , 0.23042506, 0.22371365]]), 'stationary_distribution': array([0.73645018, 0.11757031, 0.14597951]), 'holding_time': array([5.54679803, 1.29856115, 1.28818444])}", - "features": "{'df_clean': open high ... log_return_lag1 log_return_lag2\ndatetime ... \n2017-09-16 3674.01 3950.00 ... 0.148619 -0.212657\n2017-09-17 3685.23 3748.21 ... 0.004032 0.148619\n2017-09-18 3690.00 4123.20 ... -0.004035 0.004032\n2017-09-19 4060.00 4089.97 ... 0.086679 -0.004035\n2017-09-20 3910.04 4046.08 ... -0.031461 0.086679\n... ... ... ... ... ...\n2026-01-28 89249.99 90600.00 ... 0.010168 0.019161\n2026-01-29 89300.00 89348.00 ... 0.000560 0.010168\n2026-01-30 84650.16 84735.75 ... -0.053474 0.000560\n2026-01-31 84260.50 84270.02 ... -0.004614 -0.053474\n2026-02-01 78741.10 79424.00 ... -0.067748 -0.004614\n\n[3061 rows x 24 columns], 'X_scaled': array([[ 0.08513551, -0.72523644, 4.04658793, ..., 0.26911597,\n 4.12039156, -5.93472476],\n [-0.14073076, -0.72513592, 4.04721932, ..., 0.06727386,\n 0.08334177, 4.10039685],\n [ 2.39898724, 2.3484655 , 4.39080301, ..., 2.5979057 ,\n -0.14191333, 0.08422571],\n ...,\n [-0.15693614, -0.70360882, -0.3029424 , ..., -0.17615044,\n -1.52232198, -0.012228 ],\n [-1.92450248, 1.64441202, 0.13494686, ..., -1.89826004,\n -0.15807487, -1.51313393],\n [-0.66532683, -0.02826674, 0.12068419, ..., -0.68265688,\n -1.92085845, -0.15594238]]), 'scaler': StandardScaler()}", - "status": "success" - }, - "time_series": { - "metrics": "{'Random Walk': {'name': 'Random Walk', 'rmse': np.float64(0.02531781370478331), 'rmse_ratio_vs_rw': np.float64(1.0), 'direction_accuracy': np.float64(0.0), 'dm_stat_vs_rw': nan, 'dm_pval_vs_rw': nan, 'predictions': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 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1.14294597e-02 3.29356814e-02\n -4.37035149e-03 4.13002181e-02 1.63645777e-03 4.69424618e-03\n -9.27991780e-03 9.54003229e-03 -6.98552773e-03 9.30648803e-03\n -7.86551053e-03 -1.08781742e-02 -1.06686554e-02 1.61092509e-02\n 3.11006168e-03 5.17532124e-02 4.59372758e-02 9.03734251e-02\n -2.10607909e-02 2.03525710e-02 -6.44214361e-03 1.80129369e-02\n 7.81724353e-02 -6.85531603e-02 3.62145168e-02 1.12739452e-02\n 1.92824863e-02 2.77167460e-03 9.36284219e-03 4.42833736e-02\n -8.72421781e-03 2.24248318e-02 -2.33079119e-02 -2.68183611e-02\n -6.23229103e-02 4.62758144e-02 -1.15217453e-02 -8.76315556e-02\n 9.10353116e-02 -3.50899774e-02 -2.63690074e-02 3.02645304e-03\n 4.90949198e-02 3.89577312e-02 1.54417041e-03 -7.42893802e-03\n 1.86900911e-02 -1.32271353e-02 -3.84931645e-03 2.41074234e-02\n -2.31360947e-02 -6.19796083e-02 7.59800309e-03 3.75572576e-02\n -9.78920270e-03 1.57318427e-02 6.71783411e-03 3.20584093e-02\n -3.51541534e-02 2.12500631e-02 -8.88603617e-03 -4.21540247e-02\n -4.87273575e-02 2.68150789e-02 -3.47388234e-02 5.87046854e-03\n -4.02389732e-02 3.51580070e-02 5.46682516e-03 1.74374130e-02\n 8.62322910e-06 2.85107851e-02 -6.08438265e-03 -3.25101569e-02\n 3.24176626e-03 -1.13564699e-02 -4.84203072e-03 -5.42517324e-03\n 1.17713252e-02 -5.13048318e-02 -3.87664255e-02 1.18483236e-02\n 6.26959085e-02 1.59346707e-02 1.87578180e-03 -1.33172075e-02\n -1.35980508e-02 -1.81220133e-02 3.02755047e-02 -3.67191743e-02\n 4.27540241e-04 1.07596510e-02 2.34063470e-02 -2.18867810e-02\n 7.24822638e-02 -1.47792916e-02 2.70514104e-02 -1.62461809e-03\n -9.62833181e-03 7.51527831e-02 -1.83384548e-02 -1.40937664e-02\n -1.74570943e-02 8.50197811e-03 1.07455618e-02 -1.13631121e-02\n 1.34659479e-02 -1.50623714e-02 -1.09661600e-02 1.02901847e-02\n -1.19531480e-02 3.35297431e-03 -1.80030647e-05 1.52924957e-02\n 2.48016735e-02 8.05054465e-03 -4.35412429e-03 -2.05988305e-02\n -6.51060564e-04 4.86016216e-03 -1.55386834e-03 -3.25303224e-02\n 1.40105906e-02 -2.20834378e-02 -1.09779218e-02 2.78607392e-03\n 6.75100709e-03 -2.59105784e-03 -2.01861887e-02 -3.08798522e-03\n -1.60777137e-03 -1.12614168e-02 1.84493607e-03 -1.65059577e-02\n -4.72416882e-02 2.47796225e-02 -1.53424764e-02 1.37306551e-02\n -2.09388618e-02 9.20555377e-03 2.88537938e-02]", - "status": "success" - }, - "causality": { - "daily_results": " cause effect ... significant_raw significant_corrected\n0 volume log_return ... False False\n1 volume log_return ... False False\n2 volume log_return ... False False\n3 volume log_return ... False False\n4 volume log_return ... False False\n5 log_return volume ... False False\n6 log_return volume ... False False\n7 log_return volume ... False False\n8 log_return volume ... False False\n9 log_return volume ... False False\n10 abs_return volume ... True True\n11 abs_return volume ... True True\n12 abs_return volume ... True True\n13 abs_return volume ... True True\n14 abs_return volume ... True True\n15 volume abs_return ... False False\n16 volume abs_return ... False False\n17 volume abs_return ... False False\n18 volume abs_return ... False False\n19 volume abs_return ... True True\n20 taker_buy_ratio log_return ... True False\n21 taker_buy_ratio log_return ... False False\n22 taker_buy_ratio log_return ... False False\n23 taker_buy_ratio log_return ... True False\n24 taker_buy_ratio log_return ... True False\n25 log_return taker_buy_ratio ... True True\n26 log_return taker_buy_ratio ... True True\n27 log_return taker_buy_ratio ... True True\n28 log_return taker_buy_ratio ... True True\n29 log_return taker_buy_ratio ... True True\n30 squared_return volume ... False False\n31 squared_return volume ... True True\n32 squared_return volume ... True True\n33 squared_return volume ... True True\n34 squared_return volume ... True True\n35 volume squared_return ... False False\n36 volume squared_return ... False False\n37 volume squared_return ... False False\n38 volume squared_return ... False False\n39 volume squared_return ... False False\n40 range_pct log_return ... False False\n41 range_pct log_return ... False False\n42 range_pct log_return ... False False\n43 range_pct log_return ... False False\n44 range_pct log_return ... True False\n45 log_return range_pct ... False False\n46 log_return range_pct ... True False\n47 log_return range_pct ... True False\n48 log_return range_pct ... True False\n49 log_return range_pct ... True True\n\n[50 rows x 8 columns]", - "cross_timeframe_results": " cause effect ... significant_raw significant_corrected\n0 hourly_intraday_vol log_return ... False False\n1 hourly_intraday_vol log_return ... False False\n2 hourly_intraday_vol log_return ... False False\n3 hourly_intraday_vol log_return ... False False\n4 hourly_intraday_vol log_return ... True True\n5 hourly_volume_sum log_return ... False False\n6 hourly_volume_sum log_return ... False False\n7 hourly_volume_sum log_return ... False False\n8 hourly_volume_sum log_return ... False False\n9 hourly_volume_sum log_return ... False False\n10 hourly_max_abs_return log_return ... False False\n11 hourly_max_abs_return log_return ... False False\n12 hourly_max_abs_return log_return ... False False\n13 hourly_max_abs_return log_return ... False False\n14 hourly_max_abs_return log_return ... True False\n\n[15 rows x 8 columns]", - "all_results": " cause effect ... significant_raw significant_corrected\n0 volume log_return ... False False\n1 volume log_return ... False False\n2 volume log_return ... False False\n3 volume log_return ... False False\n4 volume log_return ... False False\n.. ... ... ... ... ...\n60 hourly_max_abs_return log_return ... False False\n61 hourly_max_abs_return log_return ... False False\n62 hourly_max_abs_return log_return ... False False\n63 hourly_max_abs_return log_return ... False False\n64 hourly_max_abs_return log_return ... True False\n\n[65 rows x 8 columns]", - "status": "success" - }, - "anomaly": { - "anomaly_result": " log_return abs_return ... anomaly_votes anomaly_ensemble\ndatetime ... \n2017-09-05 0.062941 0.062941 ... 3 1\n2017-09-06 0.056390 0.056390 ... 0 0\n2017-09-07 0.015431 0.015431 ... 0 0\n2017-09-08 -0.091169 0.091169 ... 3 1\n2017-09-09 -0.005617 0.005617 ... 1 0\n... ... ... ... ... ...\n2026-01-28 0.000560 0.000560 ... 0 0\n2026-01-29 -0.053474 0.053474 ... 1 0\n2026-01-30 -0.004614 0.004614 ... 0 0\n2026-01-31 -0.067748 0.067748 ... 1 0\n2026-02-01 -0.022773 0.022773 ... 0 0\n\n[3072 rows x 11 columns]", - "garch_anomaly": "datetime\n2017-08-18 0\n2017-08-19 0\n2017-08-20 0\n2017-08-21 0\n2017-08-22 0\n ..\n2026-01-28 0\n2026-01-29 0\n2026-01-30 0\n2026-01-31 0\n2026-02-01 0\nLength: 3090, dtype: int64", - "event_alignment": " anomaly_date event_date event_name diff_days\n0 2020-05-10 2020-05-11 第三次减半 1\n1 2017-12-16 2017-12-17 2017年牛市顶点 1\n2 2017-12-19 2017-12-17 2017年牛市顶点 2\n3 2017-12-20 2017-12-17 2017年牛市顶点 3\n4 2017-12-21 2017-12-17 2017年牛市顶点 4\n5 2017-12-22 2017-12-17 2017年牛市顶点 5\n6 2018-12-20 2018-12-15 2018年熊市底部 5\n7 2020-03-08 2020-03-12 新冠黑色星期四 4\n8 2020-03-12 2020-03-12 新冠黑色星期四 0\n9 2020-03-13 2020-03-12 新冠黑色星期四 1\n10 2020-03-14 2020-03-12 新冠黑色星期四 2\n11 2020-03-15 2020-03-12 新冠黑色星期四 3\n12 2020-03-16 2020-03-12 新冠黑色星期四 4\n13 2020-03-17 2020-03-12 新冠黑色星期四 5\n14 2022-06-13 2022-06-18 Luna/3AC 暴跌 5\n15 2022-06-16 2022-06-18 Luna/3AC 暴跌 2\n16 2022-06-18 2022-06-18 Luna/3AC 暴跌 0\n17 2022-06-19 2022-06-18 Luna/3AC 暴跌 1\n18 2022-11-08 2022-11-09 FTX 崩盘 1\n19 2022-11-09 2022-11-09 FTX 崩盘 0\n20 2022-11-10 2022-11-09 FTX 崩盘 1", - "precursor_results": "{'auc': np.float64(0.993544609941045), 'feature_importances': range_pct_max_5d 0.085575\nrange_pct_std_5d 0.083581\nabs_return_std_5d 0.060453\nabs_return_max_5d 0.058301\nrange_pct_deviation_20d 0.056184\n ... \nvolume_ratio_min_20d 0.000683\nvol_7d_min_5d 0.000681\nrange_pct_min_20d 0.000581\nvolume_ratio_min_5d 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a/output/volume_price/taker_buy_lead_lag.png b/output/volume_price/taker_buy_lead_lag.png deleted file mode 100644 index 83c1ac0..0000000 Binary files a/output/volume_price/taker_buy_lead_lag.png and /dev/null differ diff --git a/output/综合结论报告.txt b/output/综合结论报告.txt deleted file mode 100644 index 9a2e1dc..0000000 --- a/output/综合结论报告.txt +++ /dev/null @@ -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。 -====================================================================== \ No newline at end of file diff --git a/PYEOF b/tests/__init__.py similarity index 100% rename from PYEOF rename to tests/__init__.py diff --git a/test_hurst_15scales.py b/tests/test_hurst_15scales.py similarity index 100% rename from test_hurst_15scales.py rename to tests/test_hurst_15scales.py