BTC/USDT Price Analysis

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

git clone https://github.com/riba2534/btc_price_anany.git
cd btc_price_anany
pip install -r requirements.txt

Usage

# 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. 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.