refactor: 开源化项目重构
- 删除无用文件: PYEOF, PLAN.md, HURST_ENHANCEMENT_SUMMARY.md - 移动 REPORT.md → docs/REPORT.md，更新 53 处图片路径 - 移动 test_hurst_15scales.py → tests/，修复路径引用 - 清理 output/ 中未被报告引用的 60 个文件 - 重写 README.md 为开源标准格式（Badge、结构树、模块表等） - 添加 MIT LICENSE - 更新 .gitignore 排除运行时生成文件 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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README.md
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README.md
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# btc_price_anany
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# BTC/USDT Price Analysis
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[](LICENSE)
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[](https://www.python.org/)
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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.
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## Features
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- **Multi-timeframe data pipeline** — 15 granularities from 1m to 1M, unified loader with validation
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- **25 analysis modules** — each module runs independently; single-module failure does not block others
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- **Statistical rigor** — train/validation splits, multiple hypothesis testing corrections, bootstrap confidence intervals
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- **Publication-ready output** — 53 charts with Chinese font support, plus a 1300-line Markdown research report
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- **Modular architecture** — run all modules or cherry-pick via CLI flags
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## Project Structure
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```
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btc_price_anany/
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├── main.py # CLI entry point
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├── requirements.txt # Python dependencies
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├── LICENSE # MIT License
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├── data/ # 15 BTC/USDT kline CSVs (1m ~ 1M)
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├── src/ # 30 analysis & utility modules
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│ ├── data_loader.py # Data loading & validation
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│ ├── preprocessing.py # Derived feature engineering
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│ ├── font_config.py # Chinese font rendering
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│ ├── visualization.py # Summary dashboard generation
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│ └── ... # 26 analysis modules
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├── output/ # Generated charts (53 PNGs)
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├── docs/
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│ └── REPORT.md # Full research report with findings
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└── tests/
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└── test_hurst_15scales.py # Hurst exponent multi-scale test
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```
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## Quick Start
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### Requirements
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- Python 3.10+
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- ~1 GB disk for kline data
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### Installation
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```bash
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git clone https://github.com/riba2534/btc_price_anany.git
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cd btc_price_anany
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pip install -r requirements.txt
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```
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### Usage
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```bash
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# Run all 25 analysis modules
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python main.py
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# List available modules
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python main.py --list
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# Run specific modules
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python main.py --modules fft wavelet hurst
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# Limit date range
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python main.py --start 2020-01-01 --end 2025-12-31
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```
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## Data
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| File | Timeframe | Rows (approx.) |
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|------|-----------|-----------------|
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| `btcusdt_1m.csv` | 1 minute | ~4,500,000 |
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| `btcusdt_3m.csv` | 3 minutes | ~1,500,000 |
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| `btcusdt_5m.csv` | 5 minutes | ~900,000 |
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| `btcusdt_15m.csv` | 15 minutes | ~300,000 |
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| `btcusdt_30m.csv` | 30 minutes | ~150,000 |
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| `btcusdt_1h.csv` | 1 hour | ~75,000 |
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| `btcusdt_2h.csv` | 2 hours | ~37,000 |
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| `btcusdt_4h.csv` | 4 hours | ~19,000 |
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| `btcusdt_6h.csv` | 6 hours | ~12,500 |
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| `btcusdt_8h.csv` | 8 hours | ~9,500 |
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| `btcusdt_12h.csv` | 12 hours | ~6,300 |
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| `btcusdt_1d.csv` | 1 day | ~3,100 |
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| `btcusdt_3d.csv` | 3 days | ~1,000 |
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| `btcusdt_1w.csv` | 1 week | ~450 |
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| `btcusdt_1mo.csv` | 1 month | ~100 |
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All data sourced from Binance public API, covering 2017-08 to 2026-02.
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## Analysis Modules
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| Module | Description |
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|--------|-------------|
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| `fft` | FFT power spectrum, multi-timeframe spectral analysis, bandpass filtering |
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| `wavelet` | Continuous wavelet transform scalogram, global spectrum, key period tracking |
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| `acf` | ACF/PACF grid analysis for autocorrelation structure |
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| `returns` | Return distribution fitting, QQ plots, multi-scale moment analysis |
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| `volatility` | Volatility clustering, GARCH modeling, leverage effect quantification |
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| `hurst` | R/S and DFA Hurst exponent estimation, rolling window analysis |
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| `fractal` | Box-counting dimension, Monte Carlo benchmarking, self-similarity tests |
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| `power_law` | Log-log regression, power-law growth corridor, model comparison |
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| `volume_price` | Volume-return scatter analysis, OBV divergence detection |
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| `calendar` | Weekday, month, hour, and quarter-boundary effects |
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| `halving` | Halving cycle analysis with normalized trajectory comparison |
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| `indicators` | Technical indicator IC testing with train/validation split |
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| `patterns` | K-line pattern recognition with forward-return validation |
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| `clustering` | Market regime clustering (K-Means, GMM) with transition matrices |
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| `time_series` | ARIMA, Prophet, LSTM forecasting with direction accuracy |
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| `causality` | Granger causality testing across volume and price features |
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| `anomaly` | Anomaly detection with precursor feature analysis |
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| `microstructure` | Market microstructure: spreads, Kyle's lambda, VPIN |
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| `intraday` | Intraday session patterns and volume heatmaps |
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| `scaling` | Statistical scaling laws and kurtosis decay |
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| `multiscale_vol` | HAR volatility, jump detection, higher moment analysis |
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| `entropy` | Sample entropy and permutation entropy across scales |
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| `extreme` | Extreme value theory: Hill estimator, VaR backtesting |
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| `cross_tf` | Cross-timeframe correlation and lead-lag analysis |
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| `momentum_rev` | Momentum vs mean-reversion: variance ratios, OU half-life |
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## Key Findings
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The full analysis report is available at [`docs/REPORT.md`](docs/REPORT.md). Major conclusions include:
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- **Non-Gaussian returns**: BTC daily returns exhibit significant fat tails (kurtosis ~10) and are best fit by Student-t distributions, not Gaussian
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- **Volatility clustering**: Strong GARCH effects with long memory (d ≈ 0.4), confirming volatility persistence across time scales
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- **Hurst exponent H ≈ 0.55**: Weak but statistically significant long-range dependence, transitioning from trending (short-term) to mean-reverting (long-term)
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- **Fractal dimension D ≈ 1.4**: Price series is rougher than Brownian motion, exhibiting multi-fractal characteristics
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- **Halving cycle impact**: Statistically significant post-halving bull runs with diminishing returns per cycle
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- **Calendar effects**: Weak but detectable weekday and monthly seasonality; no exploitable intraday patterns survive transaction costs
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## License
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This project is licensed under the [MIT License](LICENSE).
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