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>
547 lines
20 KiB
Python
547 lines
20 KiB
Python
"""BTC 减半周期分析模块 - 减半前后价格行为、波动率、累计收益对比"""
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import matplotlib
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matplotlib.use('Agg')
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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import matplotlib.ticker as mticker
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from pathlib import Path
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from scipy import stats
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# 中文显示配置
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plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans']
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plt.rcParams['axes.unicode_minus'] = False
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# BTC 减半日期（数据范围 2017-2026 内的两次减半）
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HALVING_DATES = [
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pd.Timestamp('2020-05-11'),
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pd.Timestamp('2024-04-20'),
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]
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HALVING_LABELS = ['第三次减半 (2020-05-11)', '第四次减半 (2024-04-20)']
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# 分析窗口：减半前后各 500 天
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WINDOW_DAYS = 500
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def _extract_halving_window(df: pd.DataFrame, halving_date: pd.Timestamp,
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window: int = WINDOW_DAYS):
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"""
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提取减半日期前后的数据窗口。
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Parameters
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----------
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df : pd.DataFrame
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日线数据（DatetimeIndex 索引，含 close 和 log_return 列）
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halving_date : pd.Timestamp
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减半日期
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window : int
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前后各取的天数
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Returns
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-------
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pd.DataFrame
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窗口数据，附加 'days_from_halving' 列（减半日=0）
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"""
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start = halving_date - pd.Timedelta(days=window)
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end = halving_date + pd.Timedelta(days=window)
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mask = (df.index >= start) & (df.index <= end)
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window_df = df.loc[mask].copy()
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# 计算距减半日的天数差
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window_df['days_from_halving'] = (window_df.index - halving_date).days
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return window_df
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def _normalize_price(window_df: pd.DataFrame, halving_date: pd.Timestamp):
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"""
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以减半日价格为基准（=100）归一化价格。
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Parameters
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----------
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window_df : pd.DataFrame
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窗口数据（含 close 列）
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halving_date : pd.Timestamp
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减半日期
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Returns
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-------
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pd.Series
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归一化后的价格序列（减半日=100）
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"""
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# 找到距减半日最近的交易日
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idx = window_df.index.get_indexer([halving_date], method='nearest')[0]
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base_price = window_df['close'].iloc[idx]
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return (window_df['close'] / base_price) * 100
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def analyze_normalized_trajectories(windows: list, output_dir: Path):
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"""
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绘制归一化价格轨迹叠加图。
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Parameters
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----------
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windows : list[dict]
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每个元素包含 'df', 'normalized', 'label', 'halving_date'
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output_dir : Path
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图片保存目录
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"""
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print("\n" + "-" * 60)
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print("【归一化价格轨迹叠加】")
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print("-" * 60)
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fig, ax = plt.subplots(figsize=(14, 7))
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colors = ['#2980b9', '#e74c3c']
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linestyles = ['-', '--']
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for i, w in enumerate(windows):
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days = w['df']['days_from_halving']
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normalized = w['normalized']
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ax.plot(days, normalized, color=colors[i], linestyle=linestyles[i],
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linewidth=1.5, label=w['label'], alpha=0.85)
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ax.axvline(x=0, color='gold', linestyle='-', linewidth=2,
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alpha=0.8, label='减半日')
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ax.axhline(y=100, color='grey', linestyle=':', alpha=0.4)
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ax.set_title('BTC 减半周期 - 归一化价格轨迹叠加（减半日=100）', fontsize=14)
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ax.set_xlabel(f'距减半日天数（前后各 {WINDOW_DAYS} 天）')
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ax.set_ylabel('归一化价格')
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ax.legend(fontsize=11)
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ax.grid(True, alpha=0.3)
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fig_path = output_dir / 'halving_normalized_trajectories.png'
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fig.savefig(fig_path, dpi=150, bbox_inches='tight')
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plt.close(fig)
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print(f"图表已保存: {fig_path}")
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def analyze_pre_post_returns(windows: list, output_dir: Path):
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"""
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对比减半前后平均收益率，进行 Welch's t 检验。
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Parameters
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----------
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windows : list[dict]
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窗口数据列表
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output_dir : Path
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图片保存目录
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"""
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print("\n" + "-" * 60)
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print("【减半前后收益率对比 & Welch's t 检验】")
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print("-" * 60)
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all_pre_returns = []
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all_post_returns = []
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for w in windows:
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df_w = w['df']
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pre = df_w.loc[df_w['days_from_halving'] < 0, 'log_return'].dropna()
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post = df_w.loc[df_w['days_from_halving'] > 0, 'log_return'].dropna()
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all_pre_returns.append(pre)
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all_post_returns.append(post)
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print(f"\n{w['label']}:")
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print(f" 减半前 {WINDOW_DAYS}天: 均值={pre.mean():.6f}, 标准差={pre.std():.6f}, "
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f"中位数={pre.median():.6f}, N={len(pre)}")
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print(f" 减半后 {WINDOW_DAYS}天: 均值={post.mean():.6f}, 标准差={post.std():.6f}, "
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f"中位数={post.median():.6f}, N={len(post)}")
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# 单周期 Welch's t-test
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if len(pre) >= 3 and len(post) >= 3:
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t_stat, p_val = stats.ttest_ind(pre, post, equal_var=False)
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print(f" Welch's t 检验: t={t_stat:.4f}, p={p_val:.6f}")
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if p_val < 0.05:
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print(" => 减半前后收益率在 5% 水平下存在显著差异")
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else:
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print(" => 减半前后收益率在 5% 水平下无显著差异")
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# 合并所有周期的前后收益率进行总体检验
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combined_pre = pd.concat(all_pre_returns)
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combined_post = pd.concat(all_post_returns)
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print(f"\n--- 合并所有减半周期 ---")
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print(f" 合并减半前: 均值={combined_pre.mean():.6f}, N={len(combined_pre)}")
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print(f" 合并减半后: 均值={combined_post.mean():.6f}, N={len(combined_post)}")
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t_stat_all, p_val_all = stats.ttest_ind(combined_pre, combined_post, equal_var=False)
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print(f" 合并 Welch's t 检验: t={t_stat_all:.4f}, p={p_val_all:.6f}")
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# --- 可视化: 减半前后收益率对比柱状图（含置信区间） ---
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fig, axes = plt.subplots(1, len(windows), figsize=(7 * len(windows), 6))
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if len(windows) == 1:
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axes = [axes]
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for i, w in enumerate(windows):
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df_w = w['df']
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pre = df_w.loc[df_w['days_from_halving'] < 0, 'log_return'].dropna()
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post = df_w.loc[df_w['days_from_halving'] > 0, 'log_return'].dropna()
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means = [pre.mean(), post.mean()]
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# 95% 置信区间
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ci_pre = stats.t.interval(0.95, len(pre) - 1, loc=pre.mean(), scale=pre.sem())
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ci_post = stats.t.interval(0.95, len(post) - 1, loc=post.mean(), scale=post.sem())
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errors = [
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[means[0] - ci_pre[0], means[1] - ci_post[0]],
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[ci_pre[1] - means[0], ci_post[1] - means[1]],
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]
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colors_bar = ['#3498db', '#e67e22']
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axes[i].bar(['减半前', '减半后'], means, yerr=errors, color=colors_bar,
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alpha=0.8, capsize=5, edgecolor='black', linewidth=0.5)
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axes[i].axhline(y=0, color='grey', linestyle='--', alpha=0.5)
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axes[i].set_title(w['label'] + '\n日均对数收益率（95% CI）', fontsize=12)
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axes[i].set_ylabel('平均对数收益率')
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plt.tight_layout()
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fig_path = output_dir / 'halving_pre_post_returns.png'
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fig.savefig(fig_path, dpi=150, bbox_inches='tight')
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plt.close(fig)
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print(f"\n图表已保存: {fig_path}")
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def analyze_cumulative_returns(windows: list, output_dir: Path):
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"""
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绘制减半后累计收益率对比。
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Parameters
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----------
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windows : list[dict]
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窗口数据列表
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output_dir : Path
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图片保存目录
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"""
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print("\n" + "-" * 60)
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print("【减半后累计收益率对比】")
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print("-" * 60)
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fig, ax = plt.subplots(figsize=(14, 7))
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colors = ['#2980b9', '#e74c3c']
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for i, w in enumerate(windows):
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df_w = w['df']
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post = df_w.loc[df_w['days_from_halving'] >= 0].copy()
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if len(post) == 0:
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print(f" {w['label']}: 无减半后数据")
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continue
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# 累计对数收益率
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post_returns = post['log_return'].fillna(0)
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cum_return = post_returns.cumsum()
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# 转为百分比形式
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cum_return_pct = (np.exp(cum_return) - 1) * 100
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days = post['days_from_halving']
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ax.plot(days, cum_return_pct, color=colors[i], linewidth=1.5,
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label=w['label'], alpha=0.85)
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# 输出关键节点
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final_cum = cum_return_pct.iloc[-1] if len(cum_return_pct) > 0 else 0
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print(f" {w['label']}: 减半后 {len(post)} 天累计收益率 = {final_cum:.2f}%")
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# 输出一些关键时间节点的累计收益
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for target_day in [30, 90, 180, 365, WINDOW_DAYS]:
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mask_day = days <= target_day
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if mask_day.any():
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val = cum_return_pct.loc[mask_day].iloc[-1]
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actual_day = days.loc[mask_day].iloc[-1]
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print(f" 第 {actual_day} 天: {val:.2f}%")
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ax.axhline(y=0, color='grey', linestyle=':', alpha=0.4)
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ax.set_title('BTC 减半后累计收益率对比', fontsize=14)
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ax.set_xlabel('距减半日天数')
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ax.set_ylabel('累计收益率 (%)')
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ax.legend(fontsize=11)
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ax.grid(True, alpha=0.3)
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ax.yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f'{x:,.0f}%'))
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fig_path = output_dir / 'halving_cumulative_returns.png'
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fig.savefig(fig_path, dpi=150, bbox_inches='tight')
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plt.close(fig)
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print(f"\n图表已保存: {fig_path}")
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def analyze_volatility_change(windows: list, output_dir: Path):
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"""
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Levene 检验：减半前后波动率变化。
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Parameters
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----------
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windows : list[dict]
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窗口数据列表
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output_dir : Path
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图片保存目录
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"""
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print("\n" + "-" * 60)
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print("【减半前后波动率变化 - Levene 检验】")
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print("-" * 60)
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for w in windows:
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df_w = w['df']
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pre = df_w.loc[df_w['days_from_halving'] < 0, 'log_return'].dropna()
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post = df_w.loc[df_w['days_from_halving'] > 0, 'log_return'].dropna()
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print(f"\n{w['label']}:")
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print(f" 减半前波动率（日标准差）: {pre.std():.6f} "
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f"(年化: {pre.std() * np.sqrt(365):.4f})")
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print(f" 减半后波动率（日标准差）: {post.std():.6f} "
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f"(年化: {post.std() * np.sqrt(365):.4f})")
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if len(pre) >= 3 and len(post) >= 3:
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lev_stat, lev_p = stats.levene(pre, post, center='median')
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print(f" Levene 检验: W={lev_stat:.4f}, p={lev_p:.6f}")
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if lev_p < 0.05:
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print(" => 在 5% 水平下，减半前后波动率存在显著变化")
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else:
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print(" => 在 5% 水平下，减半前后波动率无显著变化")
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def analyze_inter_cycle_correlation(windows: list):
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"""
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两个减半周期归一化轨迹的 Pearson 相关系数。
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Parameters
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----------
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windows : list[dict]
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窗口数据列表（需要至少2个周期）
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"""
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print("\n" + "-" * 60)
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print("【周期间轨迹相关性 - Pearson 相关】")
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print("-" * 60)
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if len(windows) < 2:
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print(" 仅有1个周期，无法计算周期间相关性。")
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return
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# 按照 days_from_halving 对齐两个周期
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w1, w2 = windows[0], windows[1]
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df1 = w1['df'][['days_from_halving']].copy()
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df1['norm_price_1'] = w1['normalized'].values
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df2 = w2['df'][['days_from_halving']].copy()
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df2['norm_price_2'] = w2['normalized'].values
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# 以 days_from_halving 为键进行内连接
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merged = pd.merge(df1, df2, on='days_from_halving', how='inner')
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if len(merged) < 10:
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print(f" 重叠天数过少（{len(merged)}天），无法可靠计算相关性。")
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return
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r, p_val = stats.pearsonr(merged['norm_price_1'], merged['norm_price_2'])
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print(f" 重叠天数: {len(merged)}")
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print(f" Pearson 相关系数: r={r:.4f}, p={p_val:.6f}")
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if abs(r) > 0.7:
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print(" => 两个减半周期的价格轨迹呈强相关")
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elif abs(r) > 0.4:
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print(" => 两个减半周期的价格轨迹呈中等相关")
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else:
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print(" => 两个减半周期的价格轨迹相关性较弱")
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# 分别看减半前和减半后的相关性
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pre_merged = merged[merged['days_from_halving'] < 0]
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post_merged = merged[merged['days_from_halving'] > 0]
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if len(pre_merged) >= 10:
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r_pre, p_pre = stats.pearsonr(pre_merged['norm_price_1'], pre_merged['norm_price_2'])
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print(f" 减半前轨迹相关性: r={r_pre:.4f}, p={p_pre:.6f} (N={len(pre_merged)})")
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if len(post_merged) >= 10:
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r_post, p_post = stats.pearsonr(post_merged['norm_price_1'], post_merged['norm_price_2'])
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print(f" 减半后轨迹相关性: r={r_post:.4f}, p={p_post:.6f} (N={len(post_merged)})")
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# --------------------------------------------------------------------------
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# 主入口
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# --------------------------------------------------------------------------
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def run_halving_analysis(
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df: pd.DataFrame,
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output_dir: str = 'output/halving',
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):
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"""
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BTC 减半周期分析主入口。
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Parameters
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----------
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df : pd.DataFrame
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日线数据，已通过 add_derived_features 添加衍生特征（含 close、log_return 列）
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output_dir : str or Path
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输出目录
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Notes
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-----
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重要局限性: 数据范围内仅含2次减半事件（2020、2024），样本量极少，
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统计检验的功效（power）很低，结论仅供参考，不能作为因果推断依据。
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"""
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output_dir = Path(output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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print("\n" + "#" * 70)
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print("# BTC 减半周期分析 (Halving Cycle Analysis)")
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print("#" * 70)
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# ===== 重要局限性说明 =====
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print("\n⚠️ 重要局限性说明:")
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print(f" 本分析仅覆盖 {len(HALVING_DATES)} 次减半事件（样本量极少）。")
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print(" 统计检验的功效（statistical power）很低，")
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print(" 任何「显著性」结论都应谨慎解读，不能作为因果推断依据。")
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print(" 结果主要用于描述性分析和模式探索。\n")
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# 提取每次减半的窗口数据
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windows = []
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for i, (hdate, hlabel) in enumerate(zip(HALVING_DATES, HALVING_LABELS)):
|
||
w_df = _extract_halving_window(df, hdate, WINDOW_DAYS)
|
||
if len(w_df) == 0:
|
||
print(f"[警告] {hlabel} 窗口内无数据，跳过。")
|
||
continue
|
||
|
||
normalized = _normalize_price(w_df, hdate)
|
||
|
||
print(f"周期 {i + 1}: {hlabel}")
|
||
print(f" 数据范围: {w_df.index.min().date()} ~ {w_df.index.max().date()}")
|
||
print(f" 数据量: {len(w_df)} 天")
|
||
print(f" 减半日价格: {w_df['close'].iloc[w_df.index.get_indexer([hdate], method='nearest')[0]]:.2f} USDT")
|
||
|
||
windows.append({
|
||
'df': w_df,
|
||
'normalized': normalized,
|
||
'label': hlabel,
|
||
'halving_date': hdate,
|
||
})
|
||
|
||
if len(windows) == 0:
|
||
print("[错误] 无有效减半窗口数据，分析中止。")
|
||
return
|
||
|
||
# 1. 归一化价格轨迹叠加
|
||
analyze_normalized_trajectories(windows, output_dir)
|
||
|
||
# 2. 减半前后收益率对比
|
||
analyze_pre_post_returns(windows, output_dir)
|
||
|
||
# 3. 减半后累计收益率
|
||
analyze_cumulative_returns(windows, output_dir)
|
||
|
||
# 4. 波动率变化 (Levene 检验)
|
||
analyze_volatility_change(windows, output_dir)
|
||
|
||
# 5. 周期间轨迹相关性
|
||
analyze_inter_cycle_correlation(windows)
|
||
|
||
# ===== 综合可视化: 三合一图 =====
|
||
_plot_combined_summary(windows, output_dir)
|
||
|
||
print("\n" + "#" * 70)
|
||
print("# 减半周期分析完成")
|
||
print(f"# 注意: 仅 {len(windows)} 个周期，结论统计功效有限")
|
||
print("#" * 70)
|
||
|
||
|
||
def _plot_combined_summary(windows: list, output_dir: Path):
|
||
"""
|
||
综合图: 归一化轨迹 + 减半前后收益率柱状图 + 累计收益率对比。
|
||
|
||
Parameters
|
||
----------
|
||
windows : list[dict]
|
||
窗口数据列表
|
||
output_dir : Path
|
||
图片保存目录
|
||
"""
|
||
fig, axes = plt.subplots(2, 2, figsize=(16, 12))
|
||
colors = ['#2980b9', '#e74c3c']
|
||
linestyles = ['-', '--']
|
||
|
||
# (0,0) 归一化轨迹
|
||
ax = axes[0, 0]
|
||
for i, w in enumerate(windows):
|
||
days = w['df']['days_from_halving']
|
||
ax.plot(days, w['normalized'], color=colors[i], linestyle=linestyles[i],
|
||
linewidth=1.5, label=w['label'], alpha=0.85)
|
||
ax.axvline(x=0, color='gold', linewidth=2, alpha=0.8, label='减半日')
|
||
ax.axhline(y=100, color='grey', linestyle=':', alpha=0.4)
|
||
ax.set_title('归一化价格轨迹（减半日=100）', fontsize=12)
|
||
ax.set_xlabel('距减半日天数')
|
||
ax.set_ylabel('归一化价格')
|
||
ax.legend(fontsize=9)
|
||
ax.grid(True, alpha=0.3)
|
||
|
||
# (0,1) 减半前后日均收益率
|
||
ax = axes[0, 1]
|
||
x_pos = np.arange(len(windows))
|
||
width = 0.35
|
||
pre_means, post_means, pre_errs, post_errs = [], [], [], []
|
||
for w in windows:
|
||
df_w = w['df']
|
||
pre = df_w.loc[df_w['days_from_halving'] < 0, 'log_return'].dropna()
|
||
post = df_w.loc[df_w['days_from_halving'] > 0, 'log_return'].dropna()
|
||
pre_means.append(pre.mean())
|
||
post_means.append(post.mean())
|
||
pre_errs.append(pre.sem() * 1.96) # 95% CI
|
||
post_errs.append(post.sem() * 1.96)
|
||
|
||
ax.bar(x_pos - width / 2, pre_means, width, yerr=pre_errs, label='减半前',
|
||
color='#3498db', alpha=0.8, capsize=4, edgecolor='black', linewidth=0.5)
|
||
ax.bar(x_pos + width / 2, post_means, width, yerr=post_errs, label='减半后',
|
||
color='#e67e22', alpha=0.8, capsize=4, edgecolor='black', linewidth=0.5)
|
||
ax.set_xticks(x_pos)
|
||
ax.set_xticklabels([w['label'].split('(')[0].strip() for w in windows], fontsize=9)
|
||
ax.axhline(y=0, color='grey', linestyle='--', alpha=0.5)
|
||
ax.set_title('减半前后日均对数收益率（95% CI）', fontsize=12)
|
||
ax.set_ylabel('平均对数收益率')
|
||
ax.legend(fontsize=9)
|
||
|
||
# (1,0) 累计收益率
|
||
ax = axes[1, 0]
|
||
for i, w in enumerate(windows):
|
||
df_w = w['df']
|
||
post = df_w.loc[df_w['days_from_halving'] >= 0].copy()
|
||
if len(post) == 0:
|
||
continue
|
||
cum_ret = post['log_return'].fillna(0).cumsum()
|
||
cum_ret_pct = (np.exp(cum_ret) - 1) * 100
|
||
ax.plot(post['days_from_halving'], cum_ret_pct, color=colors[i],
|
||
linewidth=1.5, label=w['label'], alpha=0.85)
|
||
ax.axhline(y=0, color='grey', linestyle=':', alpha=0.4)
|
||
ax.set_title('减半后累计收益率对比', fontsize=12)
|
||
ax.set_xlabel('距减半日天数')
|
||
ax.set_ylabel('累计收益率 (%)')
|
||
ax.legend(fontsize=9)
|
||
ax.grid(True, alpha=0.3)
|
||
ax.yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f'{x:,.0f}%'))
|
||
|
||
# (1,1) 波动率对比（滚动30天）
|
||
ax = axes[1, 1]
|
||
for i, w in enumerate(windows):
|
||
df_w = w['df']
|
||
rolling_vol = df_w['log_return'].rolling(30).std() * np.sqrt(365)
|
||
ax.plot(df_w['days_from_halving'], rolling_vol, color=colors[i],
|
||
linewidth=1.2, label=w['label'], alpha=0.8)
|
||
ax.axvline(x=0, color='gold', linewidth=2, alpha=0.8, label='减半日')
|
||
ax.set_title('滚动30天年化波动率', fontsize=12)
|
||
ax.set_xlabel('距减半日天数')
|
||
ax.set_ylabel('年化波动率')
|
||
ax.legend(fontsize=9)
|
||
ax.grid(True, alpha=0.3)
|
||
|
||
plt.suptitle('BTC 减半周期综合分析', fontsize=15, y=1.01)
|
||
plt.tight_layout()
|
||
fig_path = output_dir / 'halving_combined_summary.png'
|
||
fig.savefig(fig_path, dpi=150, bbox_inches='tight')
|
||
plt.close(fig)
|
||
print(f"\n综合图表已保存: {fig_path}")
|
||
|
||
|
||
# --------------------------------------------------------------------------
|
||
# 可独立运行
|
||
# --------------------------------------------------------------------------
|
||
if __name__ == '__main__':
|
||
from data_loader import load_daily
|
||
from preprocessing import add_derived_features
|
||
|
||
# 加载数据
|
||
df_daily = load_daily()
|
||
df_daily = add_derived_features(df_daily)
|
||
|
||
run_halving_analysis(df_daily, output_dir='output/halving')
|