riba2534
f4c4408708
Complete statistical analysis pipeline covering: - FFT spectral analysis, wavelet CWT, ACF/PACF autocorrelation - Returns distribution (fat tails, kurtosis=15.65), GARCH volatility modeling - Hurst exponent (H=0.593), fractal dimension, power law corridor - Volume-price causality (Granger), calendar effects, halving cycle analysis - Technical indicator validation (0/21 pass FDR), candlestick pattern testing - Market state clustering (K-Means/GMM), Markov chain transitions - Time series forecasting (ARIMA/Prophet/LSTM benchmarks) - Anomaly detection ensemble (IF+LOF+COPOD, AUC=0.9935) Key finding: volatility is predictable (GARCH persistence=0.973), but price direction is statistically indistinguishable from random walk. Includes REPORT.md with 16-section analysis report and future projections, 70+ charts in output/, and all source modules in src/. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
18 lines
285 B
Plaintext
18 lines
285 B
Plaintext
pandas>=2.0
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numpy>=1.24
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scipy>=1.11
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matplotlib>=3.7
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seaborn>=0.12
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statsmodels>=0.14
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PyWavelets>=1.4
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arch>=6.0
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scikit-learn>=1.3
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# pandas-ta 已移除，技术指标在 indicators.py 中手动实现
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hdbscan>=0.8
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nolds>=0.5.2
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prophet>=1.1
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torch>=2.0
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pyod>=1.1
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plotly>=5.15
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pmdarima>=2.0
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