Add comprehensive BTC/USDT price analysis framework with 17 modules

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>

src/preprocessing.py

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+"""数据预处理模块 - 收益率、去趋势、标准化、衍生指标"""
+
+import pandas as pd
+import numpy as np
+from typing import Optional
+
+
+def log_returns(prices: pd.Series) -> pd.Series:
+    """对数收益率"""
+    return np.log(prices / prices.shift(1)).dropna()
+
+
+def simple_returns(prices: pd.Series) -> pd.Series:
+    """简单收益率"""
+    return prices.pct_change().dropna()
+
+
+def detrend_log_diff(prices: pd.Series) -> pd.Series:
+    """对数差分去趋势"""
+    return np.log(prices).diff().dropna()
+
+
+def detrend_linear(series: pd.Series) -> pd.Series:
+    """线性去趋势"""
+    x = np.arange(len(series))
+    coeffs = np.polyfit(x, series.values, 1)
+    trend = np.polyval(coeffs, x)
+    return pd.Series(series.values - trend, index=series.index)
+
+
+def hp_filter(series: pd.Series, lamb: float = 1600) -> tuple:
+    """Hodrick-Prescott 滤波器"""
+    from statsmodels.tsa.filters.hp_filter import hpfilter
+    cycle, trend = hpfilter(series.dropna(), lamb=lamb)
+    return cycle, trend
+
+
+def rolling_volatility(returns: pd.Series, window: int = 30) -> pd.Series:
+    """滚动波动率（年化）"""
+    return returns.rolling(window=window).std() * np.sqrt(365)
+
+
+def realized_volatility(returns: pd.Series, window: int = 30) -> pd.Series:
+    """已实现波动率"""
+    return np.sqrt((returns ** 2).rolling(window=window).sum())
+
+
+def taker_buy_ratio(df: pd.DataFrame) -> pd.Series:
+    """Taker买入比例"""
+    return df["taker_buy_volume"] / df["volume"].replace(0, np.nan)
+
+
+def add_derived_features(df: pd.DataFrame) -> pd.DataFrame:
+    """添加常用衍生特征列"""
+    out = df.copy()
+    out["log_return"] = log_returns(df["close"])
+    out["simple_return"] = simple_returns(df["close"])
+    out["log_price"] = np.log(df["close"])
+    out["range_pct"] = (df["high"] - df["low"]) / df["close"]
+    out["body_pct"] = (df["close"] - df["open"]) / df["open"]
+    out["taker_buy_ratio"] = taker_buy_ratio(df)
+    out["vol_30d"] = rolling_volatility(out["log_return"], 30)
+    out["vol_7d"] = rolling_volatility(out["log_return"], 7)
+    out["volume_ma20"] = df["volume"].rolling(20).mean()
+    out["volume_ratio"] = df["volume"] / out["volume_ma20"]
+    out["abs_return"] = out["log_return"].abs()
+    out["squared_return"] = out["log_return"] ** 2
+    return out
+
+
+def standardize(series: pd.Series) -> pd.Series:
+    """Z-score标准化"""
+    return (series - series.mean()) / series.std()
+
+
+def winsorize(series: pd.Series, lower: float = 0.01, upper: float = 0.99) -> pd.Series:
+    """Winsorize处理极端值"""
+    lo = series.quantile(lower)
+    hi = series.quantile(upper)
+    return series.clip(lo, hi)