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>
2026-02-03 10:29:54 +08:00
parent 3ab7ba6c7f
commit f4c4408708
96 changed files with 13218 additions and 0 deletions
--- a/src/wavelet_analysis.py
+++ b/src/wavelet_analysis.py
@@ -0,0 +1,817 @@
+"""小波变换分析模块 - CWT时频分析、全局小波谱、显著性检验、周期强度追踪"""
+
+import matplotlib
+matplotlib.use('Agg')
+
+import numpy as np
+import pandas as pd
+import pywt
+import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
+from matplotlib.colors import LogNorm
+from scipy.signal import detrend
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple
+
+from src.preprocessing import log_returns, standardize
+
+
+# ============================================================================
+# 核心参数配置
+# ============================================================================
+
+WAVELET = 'cmor1.5-1.0'          # 复Morlet小波 (bandwidth=1.5, center_freq=1.0)
+MIN_PERIOD = 7                     # 最小周期（天）
+MAX_PERIOD = 1500                  # 最大周期（天）
+NUM_SCALES = 256                   # 尺度数量
+KEY_PERIODS = [30, 90, 365, 1400]  # 关键追踪周期（天）
+N_SURROGATES = 1000                # Monte Carlo替代数据数量
+SIGNIFICANCE_LEVEL = 0.95          # 显著性水平
+DPI = 150                          # 图像分辨率
+
+
+# ============================================================================
+# 辅助函数：尺度与周期转换
+# ============================================================================
+
+def _periods_to_scales(periods: np.ndarray, wavelet: str, dt: float = 1.0) -> np.ndarray:
+    """将周期（天）转换为CWT尺度参数
+
+    Parameters
+    ----------
+    periods : np.ndarray
+        目标周期数组（天）
+    wavelet : str
+        小波名称
+    dt : float
+        采样间隔（天）
+
+    Returns
+    -------
+    np.ndarray
+        对应的尺度数组
+    """
+    central_freq = pywt.central_frequency(wavelet)
+    scales = central_freq * periods / dt
+    return scales
+
+
+def _scales_to_periods(scales: np.ndarray, wavelet: str, dt: float = 1.0) -> np.ndarray:
+    """将CWT尺度参数转换为周期（天）"""
+    central_freq = pywt.central_frequency(wavelet)
+    periods = scales * dt / central_freq
+    return periods
+
+
+# ============================================================================
+# 核心计算：连续小波变换
+# ============================================================================
+
+def compute_cwt(
+    signal: np.ndarray,
+    dt: float = 1.0,
+    wavelet: str = WAVELET,
+    min_period: float = MIN_PERIOD,
+    max_period: float = MAX_PERIOD,
+    num_scales: int = NUM_SCALES,
+) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """计算连续小波变换（CWT）
+
+    Parameters
+    ----------
+    signal : np.ndarray
+        输入时间序列（建议已标准化）
+    dt : float
+        采样间隔（天）
+    wavelet : str
+        小波函数名称
+    min_period : float
+        最小分析周期（天）
+    max_period : float
+        最大分析周期（天）
+    num_scales : int
+        尺度分辨率
+
+    Returns
+    -------
+    coeffs : np.ndarray
+        CWT系数矩阵 (n_scales, n_times)
+    periods : np.ndarray
+        对应周期数组（天）
+    scales : np.ndarray
+        尺度数组
+    """
+    # 生成对数等间隔的周期序列
+    periods = np.logspace(np.log10(min_period), np.log10(max_period), num_scales)
+    scales = _periods_to_scales(periods, wavelet, dt)
+
+    # 执行CWT
+    coeffs, _ = pywt.cwt(signal, scales, wavelet, sampling_period=dt)
+
+    return coeffs, periods, scales
+
+
+def compute_power_spectrum(coeffs: np.ndarray) -> np.ndarray:
+    """计算小波功率谱 |W(s,t)|^2
+
+    Parameters
+    ----------
+    coeffs : np.ndarray
+        CWT系数矩阵
+
+    Returns
+    -------
+    np.ndarray
+        功率谱矩阵
+    """
+    return np.abs(coeffs) ** 2
+
+
+# ============================================================================
+# 影响锥（Cone of Influence）
+# ============================================================================
+
+def compute_coi(n: int, dt: float = 1.0, wavelet: str = WAVELET) -> np.ndarray:
+    """计算影响锥（COI）边界
+
+    影响锥标识边界效应显著的区域。对于Morlet小波，
+    COI对应于e-folding时间 sqrt(2) * scale。
+
+    Parameters
+    ----------
+    n : int
+        时间序列长度
+    dt : float
+        采样间隔
+    wavelet : str
+        小波名称
+
+    Returns
+    -------
+    coi_periods : np.ndarray
+        每个时间点对应的COI周期边界（天）
+    """
+    # e-folding time for Morlet wavelet: sqrt(2) * s
+    # COI period = sqrt(2) * s * dt / central_freq
+    central_freq = pywt.central_frequency(wavelet)
+    # 从两端递增到中间
+    t = np.arange(n) * dt
+    coi_time = np.minimum(t, (n - 1) * dt - t)
+    # 转换为周期：COI_period = sqrt(2) * coi_time * central_freq (反推)
+    # 实际上 COI boundary in period space: period = sqrt(2) * dt * index / central_freq * central_freq
+    # 简化: coi_period = sqrt(2) * coi_time
+    coi_periods = np.sqrt(2) * coi_time
+    # 最小值截断到最小周期
+    coi_periods = np.maximum(coi_periods, dt)
+    return coi_periods
+
+
+# ============================================================================
+# AR(1) 红噪声显著性检验（Monte Carlo方法）
+# ============================================================================
+
+def _estimate_ar1(signal: np.ndarray) -> float:
+    """估计信号的AR(1)自相关系数（lag-1 autocorrelation）
+
+    Parameters
+    ----------
+    signal : np.ndarray
+        输入时间序列
+
+    Returns
+    -------
+    float
+        lag-1自相关系数
+    """
+    n = len(signal)
+    x = signal - np.mean(signal)
+    c0 = np.sum(x ** 2) / n
+    c1 = np.sum(x[:-1] * x[1:]) / n
+    if c0 == 0:
+        return 0.0
+    alpha = c1 / c0
+    return np.clip(alpha, -0.999, 0.999)
+
+
+def _generate_ar1_surrogate(n: int, alpha: float, variance: float) -> np.ndarray:
+    """生成AR(1)红噪声替代数据
+
+    x(t) = alpha * x(t-1) + noise
+
+    Parameters
+    ----------
+    n : int
+        序列长度
+    alpha : float
+        AR(1)系数
+    variance : float
+        原始信号方差
+
+    Returns
+    -------
+    np.ndarray
+        AR(1)替代序列
+    """
+    noise_std = np.sqrt(variance * (1 - alpha ** 2))
+    noise = np.random.normal(0, noise_std, n)
+    surrogate = np.zeros(n)
+    surrogate[0] = noise[0]
+    for i in range(1, n):
+        surrogate[i] = alpha * surrogate[i - 1] + noise[i]
+    return surrogate
+
+
+def significance_test_monte_carlo(
+    signal: np.ndarray,
+    periods: np.ndarray,
+    dt: float = 1.0,
+    wavelet: str = WAVELET,
+    n_surrogates: int = N_SURROGATES,
+    significance_level: float = SIGNIFICANCE_LEVEL,
+) -> Tuple[np.ndarray, np.ndarray]:
+    """AR(1)红噪声Monte Carlo显著性检验
+
+    生成大量AR(1)替代数据，计算其全局小波谱分布，
+    得到指定置信水平的阈值。
+
+    Parameters
+    ----------
+    signal : np.ndarray
+        原始时间序列
+    periods : np.ndarray
+        CWT分析的周期数组
+    dt : float
+        采样间隔
+    wavelet : str
+        小波名称
+    n_surrogates : int
+        替代数据数量
+    significance_level : float
+        显著性水平（如0.95对应95%置信度）
+
+    Returns
+    -------
+    significance_threshold : np.ndarray
+        各周期的显著性阈值
+    surrogate_spectra : np.ndarray
+        所有替代数据的全局谱 (n_surrogates, n_periods)
+    """
+    n = len(signal)
+    alpha = _estimate_ar1(signal)
+    variance = np.var(signal)
+    scales = _periods_to_scales(periods, wavelet, dt)
+
+    print(f"  AR(1) 系数 alpha = {alpha:.4f}")
+    print(f"  生成 {n_surrogates} 个AR(1)替代数据进行Monte Carlo检验...")
+
+    surrogate_global_spectra = np.zeros((n_surrogates, len(periods)))
+
+    for i in range(n_surrogates):
+        surrogate = _generate_ar1_surrogate(n, alpha, variance)
+        coeffs_surr, _ = pywt.cwt(surrogate, scales, wavelet, sampling_period=dt)
+        power_surr = np.abs(coeffs_surr) ** 2
+        surrogate_global_spectra[i, :] = np.mean(power_surr, axis=1)
+
+        if (i + 1) % 200 == 0:
+            print(f"    Monte Carlo 进度: {i + 1}/{n_surrogates}")
+
+    # 计算指定分位数作为显著性阈值
+    percentile = significance_level * 100
+    significance_threshold = np.percentile(surrogate_global_spectra, percentile, axis=0)
+
+    return significance_threshold, surrogate_global_spectra
+
+
+# ============================================================================
+# 全局小波谱
+# ============================================================================
+
+def compute_global_wavelet_spectrum(power: np.ndarray) -> np.ndarray:
+    """计算全局小波谱（时间平均功率）
+
+    Parameters
+    ----------
+    power : np.ndarray
+        功率谱矩阵 (n_scales, n_times)
+
+    Returns
+    -------
+    np.ndarray
+        全局小波谱 (n_scales,)
+    """
+    return np.mean(power, axis=1)
+
+
+def find_significant_periods(
+    global_spectrum: np.ndarray,
+    significance_threshold: np.ndarray,
+    periods: np.ndarray,
+) -> List[Dict]:
+    """找出超过显著性阈值的周期峰
+
+    在全局谱中检测超过95%置信水平的局部极大值。
+
+    Parameters
+    ----------
+    global_spectrum : np.ndarray
+        全局小波谱
+    significance_threshold : np.ndarray
+        显著性阈值
+    periods : np.ndarray
+        周期数组
+
+    Returns
+    -------
+    list of dict
+        显著周期列表，每项包含 period, power, threshold, ratio
+    """
+    # 找出超过阈值的区域
+    above_mask = global_spectrum > significance_threshold
+
+    significant = []
+    if not np.any(above_mask):
+        return significant
+
+    # 在超过阈值的连续区间内找峰值
+    diff = np.diff(above_mask.astype(int))
+    starts = np.where(diff == 1)[0] + 1
+    ends = np.where(diff == -1)[0] + 1
+
+    # 处理边界情况
+    if above_mask[0]:
+        starts = np.insert(starts, 0, 0)
+    if above_mask[-1]:
+        ends = np.append(ends, len(above_mask))
+
+    for s, e in zip(starts, ends):
+        segment = global_spectrum[s:e]
+        peak_idx = s + np.argmax(segment)
+        significant.append({
+            'period': float(periods[peak_idx]),
+            'power': float(global_spectrum[peak_idx]),
+            'threshold': float(significance_threshold[peak_idx]),
+            'ratio': float(global_spectrum[peak_idx] / significance_threshold[peak_idx]),
+        })
+
+    # 按功率降序排列
+    significant.sort(key=lambda x: x['power'], reverse=True)
+    return significant
+
+
+# ============================================================================
+# 关键周期功率时间演化
+# ============================================================================
+
+def extract_power_at_periods(
+    power: np.ndarray,
+    periods: np.ndarray,
+    key_periods: List[float] = None,
+) -> Dict[float, np.ndarray]:
+    """提取关键周期处的功率随时间变化
+
+    Parameters
+    ----------
+    power : np.ndarray
+        功率谱矩阵 (n_scales, n_times)
+    periods : np.ndarray
+        周期数组
+    key_periods : list of float
+        要追踪的关键周期（天）
+
+    Returns
+    -------
+    dict
+        {period: power_time_series} 映射
+    """
+    if key_periods is None:
+        key_periods = KEY_PERIODS
+
+    result = {}
+    for target_period in key_periods:
+        # 找到最接近目标周期的尺度索引
+        idx = np.argmin(np.abs(periods - target_period))
+        actual_period = periods[idx]
+        result[target_period] = {
+            'power': power[idx, :],
+            'actual_period': float(actual_period),
+        }
+
+    return result
+
+
+# ============================================================================
+# 可视化模块
+# ============================================================================
+
+def plot_cwt_scalogram(
+    power: np.ndarray,
+    periods: np.ndarray,
+    dates: pd.DatetimeIndex,
+    coi_periods: np.ndarray,
+    output_path: Path,
+    title: str = 'BTC/USDT CWT 时频功率谱（Scalogram）',
+) -> None:
+    """绘制CWT scalogram（时间-周期-功率热力图）含影响锥
+
+    Parameters
+    ----------
+    power : np.ndarray
+        功率谱矩阵
+    periods : np.ndarray
+        周期数组（天）
+    dates : pd.DatetimeIndex
+        时间索引
+    coi_periods : np.ndarray
+        影响锥边界
+    output_path : Path
+        输出文件路径
+    title : str
+        图标题
+    """
+    fig, ax = plt.subplots(figsize=(16, 8))
+
+    # 使用对数归一化的伪彩色图
+    t = mdates.date2num(dates.to_pydatetime())
+    T, P = np.meshgrid(t, periods)
+
+    # 功率取对数以获得更好的视觉效果
+    power_plot = power.copy()
+    power_plot[power_plot <= 0] = np.min(power_plot[power_plot > 0]) * 0.1
+
+    im = ax.pcolormesh(
+        T, P, power_plot,
+        cmap='jet',
+        norm=LogNorm(vmin=np.percentile(power_plot, 5), vmax=np.percentile(power_plot, 99)),
+        shading='auto',
+    )
+
+    # 绘制影响锥（COI）
+    coi_t = mdates.date2num(dates.to_pydatetime())
+    ax.fill_between(
+        coi_t, coi_periods, periods[-1] * 1.1,
+        alpha=0.3, facecolor='white', hatch='x',
+        label='影响锥 (COI)',
+    )
+
+    # Y轴对数刻度
+    ax.set_yscale('log')
+    ax.set_ylim(periods[0], periods[-1])
+    ax.invert_yaxis()
+
+    # 标记关键周期
+    for kp in KEY_PERIODS:
+        if periods[0] <= kp <= periods[-1]:
+            ax.axhline(y=kp, color='white', linestyle='--', alpha=0.6, linewidth=0.8)
+            ax.text(t[-1] + (t[-1] - t[0]) * 0.01, kp, f'{kp}d',
+                    color='white', fontsize=8, va='center')
+
+    # 格式化
+    ax.xaxis_date()
+    ax.xaxis.set_major_locator(mdates.YearLocator())
+    ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))
+    ax.set_xlabel('日期', fontsize=12)
+    ax.set_ylabel('周期（天）', fontsize=12)
+    ax.set_title(title, fontsize=14)
+
+    cbar = fig.colorbar(im, ax=ax, pad=0.08, shrink=0.8)
+    cbar.set_label('功率（对数尺度）', fontsize=10)
+
+    ax.legend(loc='lower right', fontsize=9)
+    plt.tight_layout()
+    fig.savefig(output_path, dpi=DPI, bbox_inches='tight')
+    plt.close(fig)
+    print(f"  Scalogram 已保存: {output_path}")
+
+
+def plot_global_spectrum(
+    global_spectrum: np.ndarray,
+    significance_threshold: np.ndarray,
+    periods: np.ndarray,
+    significant_periods: List[Dict],
+    output_path: Path,
+    title: str = 'BTC/USDT 全局小波谱 + 95%显著性',
+) -> None:
+    """绘制全局小波谱及95%红噪声显著性阈值
+
+    Parameters
+    ----------
+    global_spectrum : np.ndarray
+        全局小波谱
+    significance_threshold : np.ndarray
+        95%显著性阈值
+    periods : np.ndarray
+        周期数组
+    significant_periods : list of dict
+        显著周期信息
+    output_path : Path
+        输出路径
+    title : str
+        图标题
+    """
+    fig, ax = plt.subplots(figsize=(10, 7))
+
+    ax.plot(periods, global_spectrum, 'b-', linewidth=1.5, label='全局小波谱')
+    ax.plot(periods, significance_threshold, 'r--', linewidth=1.2, label='95% 红噪声显著性')
+
+    # 填充显著区域
+    above = global_spectrum > significance_threshold
+    ax.fill_between(
+        periods, global_spectrum, significance_threshold,
+        where=above, alpha=0.25, color='blue', label='显著区域',
+    )
+
+    # 标注显著周期峰值
+    for sp in significant_periods:
+        ax.annotate(
+            f"{sp['period']:.0f}d\n({sp['ratio']:.1f}x)",
+            xy=(sp['period'], sp['power']),
+            xytext=(sp['period'] * 1.3, sp['power'] * 1.2),
+            fontsize=9,
+            arrowprops=dict(arrowstyle='->', color='darkblue', lw=1.0),
+            color='darkblue',
+            fontweight='bold',
+        )
+
+    # 标记关键周期
+    for kp in KEY_PERIODS:
+        if periods[0] <= kp <= periods[-1]:
+            ax.axvline(x=kp, color='gray', linestyle=':', alpha=0.5, linewidth=0.8)
+            ax.text(kp, ax.get_ylim()[1] * 0.95, f'{kp}d',
+                    ha='center', va='top', fontsize=8, color='gray')
+
+    ax.set_xscale('log')
+    ax.set_yscale('log')
+    ax.set_xlabel('周期（天）', fontsize=12)
+    ax.set_ylabel('功率', fontsize=12)
+    ax.set_title(title, fontsize=14)
+    ax.legend(loc='upper left', fontsize=10)
+    ax.grid(True, alpha=0.3, which='both')
+
+    plt.tight_layout()
+    fig.savefig(output_path, dpi=DPI, bbox_inches='tight')
+    plt.close(fig)
+    print(f"  全局小波谱 已保存: {output_path}")
+
+
+def plot_key_period_power(
+    key_power: Dict[float, Dict],
+    dates: pd.DatetimeIndex,
+    coi_periods: np.ndarray,
+    output_path: Path,
+    title: str = 'BTC/USDT 关键周期功率时间演化',
+) -> None:
+    """绘制关键周期处的功率随时间变化
+
+    Parameters
+    ----------
+    key_power : dict
+        extract_power_at_periods 的返回结果
+    dates : pd.DatetimeIndex
+        时间索引
+    coi_periods : np.ndarray
+        影响锥边界
+    output_path : Path
+        输出路径
+    title : str
+        图标题
+    """
+    n_periods = len(key_power)
+    fig, axes = plt.subplots(n_periods, 1, figsize=(16, 3.5 * n_periods), sharex=True)
+    if n_periods == 1:
+        axes = [axes]
+
+    colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b']
+
+    for i, (target_period, info) in enumerate(key_power.items()):
+        ax = axes[i]
+        power_ts = info['power']
+        actual_period = info['actual_period']
+
+        # 标记COI内外区域
+        in_coi = coi_periods < actual_period  # COI内=不可靠
+        reliable_power = power_ts.copy()
+        reliable_power[in_coi] = np.nan
+        unreliable_power = power_ts.copy()
+        unreliable_power[~in_coi] = np.nan
+
+        color = colors[i % len(colors)]
+        ax.plot(dates, reliable_power, color=color, linewidth=1.0,
+                label=f'{target_period}d (实际 {actual_period:.1f}d)')
+        ax.plot(dates, unreliable_power, color=color, linewidth=0.8,
+                alpha=0.3, linestyle='--', label='COI 内（不可靠）')
+
+        # 对功率做平滑以显示趋势
+        window = max(int(target_period / 5), 7)
+        smoothed = pd.Series(power_ts).rolling(window=window, center=True, min_periods=1).mean()
+        ax.plot(dates, smoothed, color='black', linewidth=1.5, alpha=0.6, label=f'平滑 ({window}d)')
+
+        ax.set_ylabel('功率', fontsize=10)
+        ax.set_title(f'周期 ~ {target_period} 天', fontsize=11)
+        ax.legend(loc='upper right', fontsize=8, ncol=3)
+        ax.grid(True, alpha=0.3)
+
+    axes[-1].xaxis.set_major_locator(mdates.YearLocator())
+    axes[-1].xaxis.set_major_formatter(mdates.DateFormatter('%Y'))
+    axes[-1].set_xlabel('日期', fontsize=12)
+
+    fig.suptitle(title, fontsize=14, y=1.01)
+    plt.tight_layout()
+    fig.savefig(output_path, dpi=DPI, bbox_inches='tight')
+    plt.close(fig)
+    print(f"  关键周期功率图 已保存: {output_path}")
+
+
+# ============================================================================
+# 主入口函数
+# ============================================================================
+
+def run_wavelet_analysis(
+    df: pd.DataFrame,
+    output_dir: str,
+    wavelet: str = WAVELET,
+    min_period: float = MIN_PERIOD,
+    max_period: float = MAX_PERIOD,
+    num_scales: int = NUM_SCALES,
+    key_periods: List[float] = None,
+    n_surrogates: int = N_SURROGATES,
+) -> Dict:
+    """执行完整的小波变换分析流程
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        日线 DataFrame，需包含 'close' 列和 DatetimeIndex
+    output_dir : str
+        输出目录路径
+    wavelet : str
+        小波函数名
+    min_period : float
+        最小分析周期（天）
+    max_period : float
+        最大分析周期（天）
+    num_scales : int
+        尺度分辨率
+    key_periods : list of float
+        要追踪的关键周期
+    n_surrogates : int
+        Monte Carlo替代数据数量
+
+    Returns
+    -------
+    dict
+        包含所有分析结果的字典:
+        - coeffs: CWT系数矩阵
+        - power: 功率谱矩阵
+        - periods: 周期数组
+        - global_spectrum: 全局小波谱
+        - significance_threshold: 95%显著性阈值
+        - significant_periods: 显著周期列表
+        - key_period_power: 关键周期功率演化
+        - ar1_alpha: AR(1)系数
+        - dates: 时间索引
+    """
+    if key_periods is None:
+        key_periods = KEY_PERIODS
+
+    output_dir = Path(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    # ---- 1. 数据准备 ----
+    print("=" * 70)
+    print("小波变换分析 (Continuous Wavelet Transform)")
+    print("=" * 70)
+
+    prices = df['close'].dropna()
+    dates = prices.index
+    n = len(prices)
+
+    print(f"\n[数据概况]")
+    print(f"  时间范围: {dates[0].strftime('%Y-%m-%d')} ~ {dates[-1].strftime('%Y-%m-%d')}")
+    print(f"  样本数: {n}")
+    print(f"  小波函数: {wavelet}")
+    print(f"  分析周期范围: {min_period}d ~ {max_period}d")
+
+    # 对数收益率 + 标准化，作为CWT输入信号
+    log_ret = log_returns(prices)
+    signal = standardize(log_ret).values
+    signal_dates = log_ret.index
+
+    # 处理可能的NaN/Inf
+    valid_mask = np.isfinite(signal)
+    if not np.all(valid_mask):
+        print(f"  警告: 移除 {np.sum(~valid_mask)} 个非有限值")
+        signal = signal[valid_mask]
+        signal_dates = signal_dates[valid_mask]
+
+    n_signal = len(signal)
+    print(f"  CWT输入信号长度: {n_signal}")
+
+    # ---- 2. 连续小波变换 ----
+    print(f"\n[CWT 计算]")
+    print(f"  尺度数量: {num_scales}")
+
+    coeffs, periods, scales = compute_cwt(
+        signal, dt=1.0, wavelet=wavelet,
+        min_period=min_period, max_period=max_period, num_scales=num_scales,
+    )
+    power = compute_power_spectrum(coeffs)
+
+    print(f"  系数矩阵形状: {coeffs.shape}")
+    print(f"  周期范围: {periods[0]:.1f}d ~ {periods[-1]:.1f}d")
+
+    # ---- 3. 影响锥 ----
+    coi_periods = compute_coi(n_signal, dt=1.0, wavelet=wavelet)
+
+    # ---- 4. 全局小波谱 ----
+    print(f"\n[全局小波谱]")
+    global_spectrum = compute_global_wavelet_spectrum(power)
+
+    # ---- 5. AR(1) 红噪声 Monte Carlo 显著性检验 ----
+    print(f"\n[Monte Carlo 显著性检验]")
+    significance_threshold, surrogate_spectra = significance_test_monte_carlo(
+        signal, periods, dt=1.0, wavelet=wavelet,
+        n_surrogates=n_surrogates, significance_level=SIGNIFICANCE_LEVEL,
+    )
+
+    # ---- 6. 找出显著周期 ----
+    significant_periods = find_significant_periods(
+        global_spectrum, significance_threshold, periods,
+    )
+
+    print(f"\n[显著周期（超过95%置信水平）]")
+    if significant_periods:
+        for sp in significant_periods:
+            days = sp['period']
+            years = days / 365.25
+            print(f"  * {days:7.0f} 天 ({years:5.2f} 年) | "
+                  f"功率={sp['power']:.4f} | 阈值={sp['threshold']:.4f} | "
+                  f"比值={sp['ratio']:.2f}x")
+    else:
+        print("  未发现超过95%显著性水平的周期")
+
+    # ---- 7. 关键周期功率时间演化 ----
+    print(f"\n[关键周期功率追踪]")
+    key_power = extract_power_at_periods(power, periods, key_periods)
+    for kp, info in key_power.items():
+        print(f"  {kp}d -> 实际匹配周期: {info['actual_period']:.1f}d, "
+              f"平均功率: {np.mean(info['power']):.4f}")
+
+    # ---- 8. 可视化 ----
+    print(f"\n[生成图表]")
+
+    # 8.1 CWT Scalogram
+    plot_cwt_scalogram(
+        power, periods, signal_dates, coi_periods,
+        output_dir / 'wavelet_scalogram.png',
+    )
+
+    # 8.2 全局小波谱 + 显著性
+    plot_global_spectrum(
+        global_spectrum, significance_threshold, periods, significant_periods,
+        output_dir / 'wavelet_global_spectrum.png',
+    )
+
+    # 8.3 关键周期功率演化
+    plot_key_period_power(
+        key_power, signal_dates, coi_periods,
+        output_dir / 'wavelet_key_periods.png',
+    )
+
+    # ---- 9. 汇总结果 ----
+    ar1_alpha = _estimate_ar1(signal)
+
+    results = {
+        'coeffs': coeffs,
+        'power': power,
+        'periods': periods,
+        'scales': scales,
+        'global_spectrum': global_spectrum,
+        'significance_threshold': significance_threshold,
+        'significant_periods': significant_periods,
+        'key_period_power': key_power,
+        'coi_periods': coi_periods,
+        'ar1_alpha': ar1_alpha,
+        'dates': signal_dates,
+        'wavelet': wavelet,
+        'signal_length': n_signal,
+    }
+
+    print(f"\n{'=' * 70}")
+    print(f"小波分析完成。共生成 3 张图表，保存至: {output_dir}")
+    print(f"{'=' * 70}")
+
+    return results
+
+
+# ============================================================================
+# 独立运行入口
+# ============================================================================
+
+if __name__ == '__main__':
+    from src.data_loader import load_daily
+
+    print("加载 BTC/USDT 日线数据...")
+    df = load_daily()
+    print(f"数据加载完成: {len(df)} 行\n")
+
+    results = run_wavelet_analysis(df, output_dir='outputs/wavelet')