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>
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src/volatility_analysis.py
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639
src/volatility_analysis.py
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"""波动率聚集与非对称GARCH建模模块
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分析内容：
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- 多窗口已实现波动率（7d, 30d, 90d）
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- 波动率自相关幂律衰减检验（长记忆性）
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- GARCH/EGARCH/GJR-GARCH 模型对比
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- 杠杆效应分析：收益率与未来波动率的相关性
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"""
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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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from scipy import stats
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from scipy.optimize import curve_fit
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from statsmodels.tsa.stattools import acf
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from pathlib import Path
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from typing import Optional
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from src.data_loader import load_daily
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from src.preprocessing import log_returns
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# ============================================================
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# 1. 多窗口已实现波动率
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# ============================================================
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def multi_window_realized_vol(returns: pd.Series,
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windows: list = [7, 30, 90]) -> pd.DataFrame:
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"""
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计算多窗口已实现波动率（年化）
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Parameters
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----------
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returns : pd.Series
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日对数收益率
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windows : list
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滚动窗口列表（天数）
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Returns
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-------
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pd.DataFrame
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各窗口已实现波动率，列名为 'rv_7d', 'rv_30d', 'rv_90d' 等
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"""
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vol_df = pd.DataFrame(index=returns.index)
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for w in windows:
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# 已实现波动率 = sqrt(sum(r^2)) * sqrt(365/window) 进行年化
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rv = np.sqrt((returns ** 2).rolling(window=w).sum()) * np.sqrt(365 / w)
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vol_df[f'rv_{w}d'] = rv
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return vol_df.dropna(how='all')
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# ============================================================
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# 2. 波动率自相关幂律衰减检验（长记忆性）
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# ============================================================
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def volatility_acf_power_law(returns: pd.Series,
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max_lags: int = 200) -> dict:
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"""
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检验|收益率|的自相关函数是否服从幂律衰减：ACF(k) ~ k^(-d)
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长记忆性判断：若 0 < d < 1，则存在长记忆
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Parameters
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----------
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returns : pd.Series
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日对数收益率
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max_lags : int
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最大滞后阶数
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Returns
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-------
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dict
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包含幂律拟合参数d、拟合优度R²、ACF值等
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"""
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abs_returns = returns.dropna().abs()
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# 计算ACF
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acf_values = acf(abs_returns, nlags=max_lags, fft=True)
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# 从lag=1开始（lag=0始终为1）
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lags = np.arange(1, max_lags + 1)
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acf_vals = acf_values[1:]
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# 只取正的ACF值来做对数拟合
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positive_mask = acf_vals > 0
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lags_pos = lags[positive_mask]
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acf_pos = acf_vals[positive_mask]
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if len(lags_pos) < 10:
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print("[警告] 正的ACF值过少，无法可靠拟合幂律")
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return {
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'd': np.nan, 'r_squared': np.nan,
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'lags': lags, 'acf_values': acf_vals,
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'is_long_memory': False,
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}
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# 对数-对数线性回归: log(ACF) = -d * log(k) + c
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log_lags = np.log(lags_pos)
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log_acf = np.log(acf_pos)
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slope, intercept, r_value, p_value, std_err = stats.linregress(log_lags, log_acf)
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d = -slope # 幂律衰减指数
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r_squared = r_value ** 2
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# 非线性拟合作为对照（幂律函数直接拟合）
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def power_law(k, a, d_param):
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return a * k ** (-d_param)
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try:
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popt, pcov = curve_fit(power_law, lags_pos, acf_pos,
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p0=[acf_pos[0], d], maxfev=5000)
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d_nonlinear = popt[1]
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except (RuntimeError, ValueError):
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d_nonlinear = np.nan
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results = {
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'd': d,
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'd_nonlinear': d_nonlinear,
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'r_squared': r_squared,
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'slope': slope,
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'intercept': intercept,
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'p_value': p_value,
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'std_err': std_err,
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'lags': lags,
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'acf_values': acf_vals,
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'lags_positive': lags_pos,
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'acf_positive': acf_pos,
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'is_long_memory': 0 < d < 1,
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}
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return results
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# ============================================================
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# 3. GARCH / EGARCH / GJR-GARCH 模型对比
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# ============================================================
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def compare_garch_models(returns: pd.Series) -> dict:
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"""
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拟合GARCH(1,1)、EGARCH(1,1)、GJR-GARCH(1,1)并比较AIC/BIC
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Parameters
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----------
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returns : pd.Series
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日对数收益率
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Returns
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-------
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dict
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各模型参数、AIC/BIC、杠杆效应参数
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"""
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from arch import arch_model
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r_pct = returns.dropna() * 100 # 百分比收益率
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results = {}
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# --- GARCH(1,1) ---
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model_garch = arch_model(r_pct, vol='Garch', p=1, q=1,
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mean='Constant', dist='Normal')
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res_garch = model_garch.fit(disp='off')
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results['GARCH'] = {
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'params': dict(res_garch.params),
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'aic': res_garch.aic,
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'bic': res_garch.bic,
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'log_likelihood': res_garch.loglikelihood,
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'conditional_volatility': res_garch.conditional_volatility / 100,
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'result_obj': res_garch,
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}
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# --- EGARCH(1,1) ---
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model_egarch = arch_model(r_pct, vol='EGARCH', p=1, q=1,
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mean='Constant', dist='Normal')
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res_egarch = model_egarch.fit(disp='off')
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# EGARCH的gamma参数反映杠杆效应（负值表示负收益增大波动率）
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egarch_params = dict(res_egarch.params)
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results['EGARCH'] = {
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'params': egarch_params,
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'aic': res_egarch.aic,
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'bic': res_egarch.bic,
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'log_likelihood': res_egarch.loglikelihood,
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'conditional_volatility': res_egarch.conditional_volatility / 100,
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'leverage_param': egarch_params.get('gamma[1]', np.nan),
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'result_obj': res_egarch,
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}
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# --- GJR-GARCH(1,1) ---
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# GJR-GARCH 在 arch 库中通过 vol='Garch', o=1 实现
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model_gjr = arch_model(r_pct, vol='Garch', p=1, o=1, q=1,
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mean='Constant', dist='Normal')
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res_gjr = model_gjr.fit(disp='off')
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gjr_params = dict(res_gjr.params)
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results['GJR-GARCH'] = {
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'params': gjr_params,
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'aic': res_gjr.aic,
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'bic': res_gjr.bic,
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'log_likelihood': res_gjr.loglikelihood,
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'conditional_volatility': res_gjr.conditional_volatility / 100,
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# gamma[1] > 0 表示负冲击产生更大波动
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'leverage_param': gjr_params.get('gamma[1]', np.nan),
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'result_obj': res_gjr,
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}
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return results
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# ============================================================
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# 4. 杠杆效应分析
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# ============================================================
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def leverage_effect_analysis(returns: pd.Series,
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forward_windows: list = [5, 10, 20]) -> dict:
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"""
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分析收益率与未来波动率的相关性（杠杆效应）
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杠杆效应：负收益倾向于增加未来波动率，正收益倾向于减少未来波动率
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表现为 corr(r_t, vol_{t+k}) < 0
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Parameters
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----------
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returns : pd.Series
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日对数收益率
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forward_windows : list
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前瞻波动率窗口列表
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Returns
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-------
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dict
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各窗口下的相关系数及显著性
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"""
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r = returns.dropna()
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results = {}
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for w in forward_windows:
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# 前瞻已实现波动率
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future_vol = r.abs().rolling(window=w).mean().shift(-w)
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# 对齐有效数据
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valid = pd.DataFrame({'return': r, 'future_vol': future_vol}).dropna()
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if len(valid) < 30:
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results[f'{w}d'] = {
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'correlation': np.nan,
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'p_value': np.nan,
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'n_samples': len(valid),
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}
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continue
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corr, p_val = stats.pearsonr(valid['return'], valid['future_vol'])
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# Spearman秩相关作为稳健性检查
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spearman_corr, spearman_p = stats.spearmanr(valid['return'], valid['future_vol'])
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results[f'{w}d'] = {
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'pearson_correlation': corr,
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'pearson_pvalue': p_val,
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'spearman_correlation': spearman_corr,
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'spearman_pvalue': spearman_p,
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'n_samples': len(valid),
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'return_series': valid['return'],
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'future_vol_series': valid['future_vol'],
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}
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return results
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# ============================================================
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# 5. 可视化
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# ============================================================
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def plot_realized_volatility(vol_df: pd.DataFrame, output_dir: Path):
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"""绘制多窗口已实现波动率时序图"""
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fig, ax = plt.subplots(figsize=(14, 6))
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colors = ['#1f77b4', '#ff7f0e', '#2ca02c']
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labels = {'rv_7d': '7天', 'rv_30d': '30天', 'rv_90d': '90天'}
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for idx, col in enumerate(vol_df.columns):
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label = labels.get(col, col)
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ax.plot(vol_df.index, vol_df[col], linewidth=0.8,
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color=colors[idx % len(colors)],
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label=f'{label}已实现波动率（年化）', alpha=0.85)
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ax.set_xlabel('日期', fontsize=12)
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ax.set_ylabel('年化波动率', fontsize=12)
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ax.set_title('BTC 多窗口已实现波动率', fontsize=14)
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ax.legend(fontsize=11)
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ax.grid(True, alpha=0.3)
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fig.savefig(output_dir / 'realized_volatility_multiwindow.png',
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dpi=150, bbox_inches='tight')
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plt.close(fig)
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print(f"[保存] {output_dir / 'realized_volatility_multiwindow.png'}")
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def plot_acf_power_law(acf_results: dict, output_dir: Path):
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"""绘制ACF幂律衰减拟合图"""
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fig, axes = plt.subplots(1, 2, figsize=(14, 5))
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lags = acf_results['lags']
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acf_vals = acf_results['acf_values']
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# 左图：ACF原始值
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ax1 = axes[0]
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ax1.bar(lags, acf_vals, width=1, alpha=0.6, color='steelblue')
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ax1.set_xlabel('滞后阶数', fontsize=11)
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ax1.set_ylabel('ACF', fontsize=11)
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ax1.set_title('|收益率| 自相关函数', fontsize=12)
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ax1.grid(True, alpha=0.3)
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ax1.axhline(y=0, color='black', linewidth=0.5)
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# 右图：对数-对数图 + 幂律拟合
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ax2 = axes[1]
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lags_pos = acf_results['lags_positive']
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acf_pos = acf_results['acf_positive']
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ax2.scatter(np.log(lags_pos), np.log(acf_pos), s=10, alpha=0.5,
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color='steelblue', label='实际ACF')
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# 拟合线
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d = acf_results['d']
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intercept = acf_results['intercept']
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x_fit = np.linspace(np.log(lags_pos.min()), np.log(lags_pos.max()), 100)
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y_fit = -d * x_fit + intercept
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ax2.plot(x_fit, y_fit, 'r-', linewidth=2,
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label=f'幂律拟合: d={d:.3f}, R²={acf_results["r_squared"]:.3f}')
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ax2.set_xlabel('log(滞后阶数)', fontsize=11)
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ax2.set_ylabel('log(ACF)', fontsize=11)
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ax2.set_title('幂律衰减拟合（双对数坐标）', fontsize=12)
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ax2.legend(fontsize=10)
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ax2.grid(True, alpha=0.3)
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fig.tight_layout()
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fig.savefig(output_dir / 'acf_power_law_fit.png',
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dpi=150, bbox_inches='tight')
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plt.close(fig)
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print(f"[保存] {output_dir / 'acf_power_law_fit.png'}")
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def plot_model_comparison(model_results: dict, output_dir: Path):
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"""绘制GARCH模型对比图（AIC/BIC + 条件波动率对比）"""
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fig, axes = plt.subplots(2, 1, figsize=(14, 10))
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model_names = list(model_results.keys())
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aic_values = [model_results[m]['aic'] for m in model_names]
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bic_values = [model_results[m]['bic'] for m in model_names]
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# 上图：AIC/BIC 对比柱状图
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ax1 = axes[0]
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x = np.arange(len(model_names))
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width = 0.35
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bars1 = ax1.bar(x - width / 2, aic_values, width, label='AIC',
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color='steelblue', alpha=0.8)
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bars2 = ax1.bar(x + width / 2, bic_values, width, label='BIC',
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color='coral', alpha=0.8)
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ax1.set_xlabel('模型', fontsize=12)
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ax1.set_ylabel('信息准则值', fontsize=12)
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ax1.set_title('GARCH 模型信息准则对比（越小越好）', fontsize=13)
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ax1.set_xticks(x)
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ax1.set_xticklabels(model_names, fontsize=11)
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ax1.legend(fontsize=11)
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ax1.grid(True, alpha=0.3, axis='y')
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# 在柱状图上标注数值
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for bar in bars1:
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height = bar.get_height()
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ax1.annotate(f'{height:.1f}',
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xy=(bar.get_x() + bar.get_width() / 2, height),
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xytext=(0, 3), textcoords="offset points",
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ha='center', va='bottom', fontsize=9)
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for bar in bars2:
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height = bar.get_height()
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ax1.annotate(f'{height:.1f}',
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xy=(bar.get_x() + bar.get_width() / 2, height),
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xytext=(0, 3), textcoords="offset points",
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ha='center', va='bottom', fontsize=9)
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# 下图：各模型条件波动率时序对比
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ax2 = axes[1]
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colors = {'GARCH': '#1f77b4', 'EGARCH': '#ff7f0e', 'GJR-GARCH': '#2ca02c'}
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for name in model_names:
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cv = model_results[name]['conditional_volatility']
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ax2.plot(cv.index, cv.values, linewidth=0.7,
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color=colors.get(name, 'gray'),
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label=name, alpha=0.8)
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ax2.set_xlabel('日期', fontsize=12)
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ax2.set_ylabel('条件波动率', fontsize=12)
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ax2.set_title('各GARCH模型条件波动率对比', fontsize=13)
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ax2.legend(fontsize=11)
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ax2.grid(True, alpha=0.3)
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fig.tight_layout()
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fig.savefig(output_dir / 'garch_model_comparison.png',
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dpi=150, bbox_inches='tight')
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plt.close(fig)
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print(f"[保存] {output_dir / 'garch_model_comparison.png'}")
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def plot_leverage_effect(leverage_results: dict, output_dir: Path):
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"""绘制杠杆效应散点图"""
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# 找到有数据的窗口
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||||
valid_windows = [w for w, r in leverage_results.items()
|
||||
if 'return_series' in r]
|
||||
n_plots = len(valid_windows)
|
||||
if n_plots == 0:
|
||||
print("[警告] 无有效杠杆效应数据可绘制")
|
||||
return
|
||||
|
||||
fig, axes = plt.subplots(1, n_plots, figsize=(6 * n_plots, 5))
|
||||
if n_plots == 1:
|
||||
axes = [axes]
|
||||
|
||||
for idx, window_key in enumerate(valid_windows):
|
||||
ax = axes[idx]
|
||||
data = leverage_results[window_key]
|
||||
ret = data['return_series']
|
||||
fvol = data['future_vol_series']
|
||||
|
||||
# 散点图（采样避免过多点）
|
||||
n_sample = min(len(ret), 2000)
|
||||
sample_idx = np.random.choice(len(ret), n_sample, replace=False)
|
||||
ax.scatter(ret.values[sample_idx], fvol.values[sample_idx],
|
||||
s=5, alpha=0.3, color='steelblue')
|
||||
|
||||
# 回归线
|
||||
z = np.polyfit(ret.values, fvol.values, 1)
|
||||
p = np.poly1d(z)
|
||||
x_line = np.linspace(ret.min(), ret.max(), 100)
|
||||
ax.plot(x_line, p(x_line), 'r-', linewidth=2)
|
||||
|
||||
corr = data['pearson_correlation']
|
||||
p_val = data['pearson_pvalue']
|
||||
ax.set_xlabel('当日对数收益率', fontsize=11)
|
||||
ax.set_ylabel(f'未来{window_key}平均|收益率|', fontsize=11)
|
||||
ax.set_title(f'杠杆效应 ({window_key})\n'
|
||||
f'Pearson r={corr:.4f}, p={p_val:.2e}', fontsize=11)
|
||||
ax.grid(True, alpha=0.3)
|
||||
|
||||
fig.tight_layout()
|
||||
fig.savefig(output_dir / 'leverage_effect_scatter.png',
|
||||
dpi=150, bbox_inches='tight')
|
||||
plt.close(fig)
|
||||
print(f"[保存] {output_dir / 'leverage_effect_scatter.png'}")
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 6. 结果打印
|
||||
# ============================================================
|
||||
|
||||
def print_realized_vol_summary(vol_df: pd.DataFrame):
|
||||
"""打印已实现波动率统计摘要"""
|
||||
print("\n" + "=" * 60)
|
||||
print("多窗口已实现波动率统计（年化）")
|
||||
print("=" * 60)
|
||||
summary = vol_df.describe().T
|
||||
for col in vol_df.columns:
|
||||
s = vol_df[col].dropna()
|
||||
print(f"\n {col}:")
|
||||
print(f" 均值: {s.mean():.4f} ({s.mean() * 100:.2f}%)")
|
||||
print(f" 中位数: {s.median():.4f} ({s.median() * 100:.2f}%)")
|
||||
print(f" 最大值: {s.max():.4f} ({s.max() * 100:.2f}%)")
|
||||
print(f" 最小值: {s.min():.4f} ({s.min() * 100:.2f}%)")
|
||||
print(f" 标准差: {s.std():.4f}")
|
||||
|
||||
|
||||
def print_acf_power_law_results(results: dict):
|
||||
"""打印ACF幂律衰减检验结果"""
|
||||
print("\n" + "=" * 60)
|
||||
print("波动率自相关幂律衰减检验（长记忆性）")
|
||||
print("=" * 60)
|
||||
print(f" 幂律衰减指数 d (线性拟合): {results['d']:.4f}")
|
||||
print(f" 幂律衰减指数 d (非线性拟合): {results['d_nonlinear']:.4f}")
|
||||
print(f" 拟合优度 R²: {results['r_squared']:.4f}")
|
||||
print(f" 回归斜率: {results['slope']:.4f}")
|
||||
print(f" 回归截距: {results['intercept']:.4f}")
|
||||
print(f" p值: {results['p_value']:.2e}")
|
||||
print(f" 标准误: {results['std_err']:.4f}")
|
||||
print(f"\n 长记忆性判断 (0 < d < 1): "
|
||||
f"{'是 - 存在长记忆性' if results['is_long_memory'] else '否'}")
|
||||
if results['is_long_memory']:
|
||||
print(f" → |收益率|的自相关以幂律速度缓慢衰减")
|
||||
print(f" → 波动率聚集具有长记忆特征，GARCH模型的持续性可能不足以刻画")
|
||||
|
||||
|
||||
def print_model_comparison(model_results: dict):
|
||||
"""打印GARCH模型对比结果"""
|
||||
print("\n" + "=" * 60)
|
||||
print("GARCH / EGARCH / GJR-GARCH 模型对比")
|
||||
print("=" * 60)
|
||||
|
||||
print(f"\n {'模型':<14} {'AIC':>12} {'BIC':>12} {'对数似然':>12}")
|
||||
print(" " + "-" * 52)
|
||||
for name, res in model_results.items():
|
||||
print(f" {name:<14} {res['aic']:>12.2f} {res['bic']:>12.2f} "
|
||||
f"{res['log_likelihood']:>12.2f}")
|
||||
|
||||
# 找到最优模型
|
||||
best_aic = min(model_results.items(), key=lambda x: x[1]['aic'])
|
||||
best_bic = min(model_results.items(), key=lambda x: x[1]['bic'])
|
||||
print(f"\n AIC最优模型: {best_aic[0]} (AIC={best_aic[1]['aic']:.2f})")
|
||||
print(f" BIC最优模型: {best_bic[0]} (BIC={best_bic[1]['bic']:.2f})")
|
||||
|
||||
# 杠杆效应参数
|
||||
print("\n 杠杆效应参数:")
|
||||
for name in ['EGARCH', 'GJR-GARCH']:
|
||||
if name in model_results and 'leverage_param' in model_results[name]:
|
||||
gamma = model_results[name]['leverage_param']
|
||||
print(f" {name} gamma[1] = {gamma:.6f}")
|
||||
if name == 'EGARCH':
|
||||
# EGARCH中gamma<0表示负冲击增大波动
|
||||
if gamma < 0:
|
||||
print(f" → gamma < 0: 负收益（下跌）产生更大波动，存在杠杆效应")
|
||||
else:
|
||||
print(f" → gamma >= 0: 未观察到明显杠杆效应")
|
||||
elif name == 'GJR-GARCH':
|
||||
# GJR-GARCH中gamma>0表示负冲击的额外影响
|
||||
if gamma > 0:
|
||||
print(f" → gamma > 0: 负冲击产生额外波动增量，存在杠杆效应")
|
||||
else:
|
||||
print(f" → gamma <= 0: 未观察到明显杠杆效应")
|
||||
|
||||
# 打印各模型详细参数
|
||||
print("\n 各模型详细参数:")
|
||||
for name, res in model_results.items():
|
||||
print(f"\n [{name}]")
|
||||
for param_name, param_val in res['params'].items():
|
||||
print(f" {param_name}: {param_val:.6f}")
|
||||
|
||||
|
||||
def print_leverage_results(leverage_results: dict):
|
||||
"""打印杠杆效应分析结果"""
|
||||
print("\n" + "=" * 60)
|
||||
print("杠杆效应分析：收益率与未来波动率的相关性")
|
||||
print("=" * 60)
|
||||
print(f"\n {'窗口':<8} {'Pearson r':>12} {'p值':>12} "
|
||||
f"{'Spearman r':>12} {'p值':>12} {'样本数':>8}")
|
||||
print(" " + "-" * 66)
|
||||
for window, data in leverage_results.items():
|
||||
if 'pearson_correlation' in data:
|
||||
print(f" {window:<8} "
|
||||
f"{data['pearson_correlation']:>12.4f} "
|
||||
f"{data['pearson_pvalue']:>12.2e} "
|
||||
f"{data['spearman_correlation']:>12.4f} "
|
||||
f"{data['spearman_pvalue']:>12.2e} "
|
||||
f"{data['n_samples']:>8d}")
|
||||
else:
|
||||
print(f" {window:<8} {'N/A':>12} {'N/A':>12} "
|
||||
f"{'N/A':>12} {'N/A':>12} {data.get('n_samples', 0):>8d}")
|
||||
|
||||
# 总结
|
||||
print("\n 解读:")
|
||||
print(" - 相关系数 < 0: 负收益（下跌）后波动率上升 → 存在杠杆效应")
|
||||
print(" - 相关系数 ≈ 0: 收益率方向与未来波动率无关")
|
||||
print(" - 相关系数 > 0: 正收益（上涨）后波动率上升（反向杠杆/波动率反馈效应）")
|
||||
print(" - 注意: BTC作为加密货币，杠杆效应可能与传统股票不同")
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 7. 主入口
|
||||
# ============================================================
|
||||
|
||||
def run_volatility_analysis(df: pd.DataFrame, output_dir: str = "output/volatility"):
|
||||
"""
|
||||
波动率聚集与非对称GARCH分析主函数
|
||||
|
||||
Parameters
|
||||
----------
|
||||
df : pd.DataFrame
|
||||
日线K线数据（含'close'列，DatetimeIndex索引）
|
||||
output_dir : str
|
||||
图表输出目录
|
||||
"""
|
||||
output_dir = Path(output_dir)
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
print("=" * 60)
|
||||
print("BTC 波动率聚集与非对称 GARCH 分析")
|
||||
print("=" * 60)
|
||||
print(f"数据范围: {df.index.min()} ~ {df.index.max()}")
|
||||
print(f"样本数量: {len(df)}")
|
||||
|
||||
# 计算日对数收益率
|
||||
daily_returns = log_returns(df['close'])
|
||||
print(f"日对数收益率样本数: {len(daily_returns)}")
|
||||
|
||||
# 设置中文字体（兼容多系统）
|
||||
plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans']
|
||||
plt.rcParams['axes.unicode_minus'] = False
|
||||
|
||||
# 固定随机种子以保证杠杆效应散点图采样可复现
|
||||
np.random.seed(42)
|
||||
|
||||
# --- 多窗口已实现波动率 ---
|
||||
print("\n>>> 计算多窗口已实现波动率 (7d, 30d, 90d)...")
|
||||
vol_df = multi_window_realized_vol(daily_returns, windows=[7, 30, 90])
|
||||
print_realized_vol_summary(vol_df)
|
||||
plot_realized_volatility(vol_df, output_dir)
|
||||
|
||||
# --- ACF幂律衰减检验 ---
|
||||
print("\n>>> 执行波动率自相关幂律衰减检验...")
|
||||
acf_results = volatility_acf_power_law(daily_returns, max_lags=200)
|
||||
print_acf_power_law_results(acf_results)
|
||||
plot_acf_power_law(acf_results, output_dir)
|
||||
|
||||
# --- GARCH模型对比 ---
|
||||
print("\n>>> 拟合 GARCH / EGARCH / GJR-GARCH 模型...")
|
||||
model_results = compare_garch_models(daily_returns)
|
||||
print_model_comparison(model_results)
|
||||
plot_model_comparison(model_results, output_dir)
|
||||
|
||||
# --- 杠杆效应分析 ---
|
||||
print("\n>>> 执行杠杆效应分析...")
|
||||
leverage_results = leverage_effect_analysis(daily_returns,
|
||||
forward_windows=[5, 10, 20])
|
||||
print_leverage_results(leverage_results)
|
||||
plot_leverage_effect(leverage_results, output_dir)
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("波动率分析完成！")
|
||||
print(f"图表已保存至: {output_dir.resolve()}")
|
||||
print("=" * 60)
|
||||
|
||||
# 返回所有结果供后续使用
|
||||
return {
|
||||
'realized_vol': vol_df,
|
||||
'acf_power_law': acf_results,
|
||||
'model_comparison': model_results,
|
||||
'leverage_effect': leverage_results,
|
||||
}
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 独立运行入口
|
||||
# ============================================================
|
||||
|
||||
if __name__ == '__main__':
|
||||
df = load_daily()
|
||||
run_volatility_analysis(df)
|
||||
Reference in New Issue
Block a user