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/causality.py
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615
src/causality.py
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"""Granger 因果检验模块
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分析内容：
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- 双向 Granger 因果检验（5 对变量，各 5 个滞后阶数）
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- 跨时间尺度因果检验（小时级聚合特征 → 日级收益率）
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- Bonferroni 多重检验校正
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- 可视化：p 值热力图、显著因果关系网络图
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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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import warnings
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from pathlib import Path
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from typing import Optional, List, Tuple, Dict
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from statsmodels.tsa.stattools import grangercausalitytests
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from src.data_loader import load_hourly
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from src.preprocessing import log_returns, add_derived_features
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# ============================================================
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# 1. 因果检验对定义
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# ============================================================
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# 5 对双向因果关系，每对 (cause, effect)
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CAUSALITY_PAIRS = [
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('volume', 'log_return'),
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('log_return', 'volume'),
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('abs_return', 'volume'),
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('volume', 'abs_return'),
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('taker_buy_ratio', 'log_return'),
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('log_return', 'taker_buy_ratio'),
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('squared_return', 'volume'),
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('volume', 'squared_return'),
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('range_pct', 'log_return'),
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('log_return', 'range_pct'),
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]
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# 测试的滞后阶数
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TEST_LAGS = [1, 2, 3, 5, 10]
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# ============================================================
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# 2. 单对 Granger 因果检验
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# ============================================================
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def granger_test_pair(
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df: pd.DataFrame,
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cause: str,
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effect: str,
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max_lag: int = 10,
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test_lags: Optional[List[int]] = None,
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) -> List[Dict]:
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"""
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对指定的 (cause → effect) 方向执行 Granger 因果检验
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Parameters
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----------
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df : pd.DataFrame
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包含 cause 和 effect 列的数据
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cause : str
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原因变量列名
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effect : str
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结果变量列名
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max_lag : int
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最大滞后阶数
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test_lags : list of int, optional
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需要测试的滞后阶数列表
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Returns
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-------
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list of dict
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每个滞后阶数的检验结果
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"""
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if test_lags is None:
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test_lags = TEST_LAGS
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# grangercausalitytests 要求: 第一列是 effect，第二列是 cause
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data = df[[effect, cause]].dropna()
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if len(data) < max_lag + 20:
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print(f" [警告] {cause} → {effect}: 样本量不足 ({len(data)})，跳过")
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return []
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results = []
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try:
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# 执行检验，maxlag 取最大值，一次获取所有滞后
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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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gc_results = grangercausalitytests(data, maxlag=max_lag, verbose=False)
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# 提取指定滞后阶数的结果
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for lag in test_lags:
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if lag > max_lag:
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continue
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test_result = gc_results[lag]
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# 取 ssr_ftest 的 F 统计量和 p 值
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f_stat = test_result[0]['ssr_ftest'][0]
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p_value = test_result[0]['ssr_ftest'][1]
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results.append({
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'cause': cause,
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'effect': effect,
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'lag': lag,
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'f_stat': f_stat,
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'p_value': p_value,
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})
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except Exception as e:
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print(f" [错误] {cause} → {effect}: {e}")
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return results
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# ============================================================
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# 3. 批量因果检验
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# ============================================================
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def run_all_granger_tests(
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df: pd.DataFrame,
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pairs: Optional[List[Tuple[str, str]]] = None,
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test_lags: Optional[List[int]] = None,
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) -> pd.DataFrame:
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"""
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对所有变量对执行双向 Granger 因果检验
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Parameters
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----------
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df : pd.DataFrame
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包含衍生特征的日线数据
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pairs : list of tuple, optional
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变量对列表 [(cause, effect), ...]
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test_lags : list of tuple, optional
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滞后阶数列表
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Returns
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-------
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pd.DataFrame
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所有检验结果汇总表
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"""
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if pairs is None:
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pairs = CAUSALITY_PAIRS
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if test_lags is None:
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test_lags = TEST_LAGS
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max_lag = max(test_lags)
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all_results = []
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for cause, effect in pairs:
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if cause not in df.columns or effect not in df.columns:
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print(f" [警告] 列 {cause} 或 {effect} 不存在，跳过")
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continue
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pair_results = granger_test_pair(df, cause, effect, max_lag=max_lag, test_lags=test_lags)
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all_results.extend(pair_results)
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results_df = pd.DataFrame(all_results)
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return results_df
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# ============================================================
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# 4. Bonferroni 校正
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# ============================================================
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def apply_bonferroni(results_df: pd.DataFrame, alpha: float = 0.05) -> pd.DataFrame:
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"""
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对 Granger 检验结果应用 Bonferroni 多重检验校正
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Parameters
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----------
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results_df : pd.DataFrame
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包含 p_value 列的检验结果
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alpha : float
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原始显著性水平
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Returns
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-------
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pd.DataFrame
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添加了校正后显著性判断的结果
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"""
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n_tests = len(results_df)
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if n_tests == 0:
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return results_df
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out = results_df.copy()
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# Bonferroni 校正阈值
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corrected_alpha = alpha / n_tests
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out['bonferroni_alpha'] = corrected_alpha
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out['significant_raw'] = out['p_value'] < alpha
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out['significant_corrected'] = out['p_value'] < corrected_alpha
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return out
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# ============================================================
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# 5. 跨时间尺度因果检验
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# ============================================================
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def cross_timeframe_causality(
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daily_df: pd.DataFrame,
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test_lags: Optional[List[int]] = None,
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) -> pd.DataFrame:
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"""
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检验小时级聚合特征是否 Granger 因果于日级收益率
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具体步骤：
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1. 加载小时级数据
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2. 计算小时级波动率和成交量的日内聚合指标
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3. 与日线收益率合并
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4. 执行 Granger 因果检验
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Parameters
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----------
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daily_df : pd.DataFrame
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日线数据（含 log_return）
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test_lags : list of int, optional
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滞后阶数列表
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Returns
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-------
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pd.DataFrame
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跨时间尺度因果检验结果
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"""
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if test_lags is None:
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test_lags = TEST_LAGS
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# 加载小时数据
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try:
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hourly_raw = load_hourly()
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except (FileNotFoundError, Exception) as e:
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print(f" [警告] 无法加载小时级数据，跳过跨时间尺度因果检验: {e}")
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return pd.DataFrame()
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# 计算小时级衍生特征
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hourly = add_derived_features(hourly_raw)
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# 日内聚合：按日期聚合小时数据
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hourly['date'] = hourly.index.date
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agg_dict = {}
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# 小时级日内波动率（对数收益率标准差）
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if 'log_return' in hourly.columns:
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hourly_vol = hourly.groupby('date')['log_return'].std()
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hourly_vol.name = 'hourly_intraday_vol'
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agg_dict['hourly_intraday_vol'] = hourly_vol
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# 小时级日内成交量总和
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if 'volume' in hourly.columns:
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hourly_volume = hourly.groupby('date')['volume'].sum()
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hourly_volume.name = 'hourly_volume_sum'
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agg_dict['hourly_volume_sum'] = hourly_volume
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# 小时级日内最大绝对收益率
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if 'abs_return' in hourly.columns:
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hourly_max_abs = hourly.groupby('date')['abs_return'].max()
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hourly_max_abs.name = 'hourly_max_abs_return'
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agg_dict['hourly_max_abs_return'] = hourly_max_abs
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if not agg_dict:
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print(" [警告] 小时级聚合特征为空，跳过")
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return pd.DataFrame()
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# 合并聚合结果
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hourly_agg = pd.DataFrame(agg_dict)
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hourly_agg.index = pd.to_datetime(hourly_agg.index)
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# 与日线数据合并
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daily_for_merge = daily_df[['log_return']].copy()
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merged = daily_for_merge.join(hourly_agg, how='inner')
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print(f" [跨时间尺度] 合并后样本数: {len(merged)}")
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# 对每个小时级聚合特征检验 → 日级收益率
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cross_pairs = []
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for col in agg_dict.keys():
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cross_pairs.append((col, 'log_return'))
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max_lag = max(test_lags)
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all_results = []
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for cause, effect in cross_pairs:
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pair_results = granger_test_pair(merged, cause, effect, max_lag=max_lag, test_lags=test_lags)
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all_results.extend(pair_results)
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results_df = pd.DataFrame(all_results)
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return results_df
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# ============================================================
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# 6. 可视化：p 值热力图
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# ============================================================
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def plot_pvalue_heatmap(results_df: pd.DataFrame, output_dir: Path):
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"""
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绘制 p 值热力图（变量对 x 滞后阶数）
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Parameters
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----------
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results_df : pd.DataFrame
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因果检验结果
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output_dir : Path
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输出目录
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"""
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if results_df.empty:
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print(" [警告] 无检验结果，跳过热力图绘制")
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return
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# 构建标签
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results_df = results_df.copy()
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results_df['pair'] = results_df['cause'] + ' → ' + results_df['effect']
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# 构建 pivot table: 行=pair, 列=lag
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pivot = results_df.pivot_table(index='pair', columns='lag', values='p_value')
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fig, ax = plt.subplots(figsize=(12, max(6, len(pivot) * 0.5)))
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# 绘制热力图
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im = ax.imshow(-np.log10(pivot.values + 1e-300), cmap='RdYlGn_r', aspect='auto')
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# 设置坐标轴
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ax.set_xticks(range(len(pivot.columns)))
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ax.set_xticklabels([f'Lag {c}' for c in pivot.columns], fontsize=10)
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ax.set_yticks(range(len(pivot.index)))
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ax.set_yticklabels(pivot.index, fontsize=9)
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# 在每个格子中标注 p 值
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for i in range(len(pivot.index)):
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for j in range(len(pivot.columns)):
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val = pivot.values[i, j]
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if np.isnan(val):
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text = 'N/A'
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else:
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text = f'{val:.4f}'
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color = 'white' if -np.log10(val + 1e-300) > 2 else 'black'
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ax.text(j, i, text, ha='center', va='center', fontsize=8, color=color)
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# Bonferroni 校正线
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n_tests = len(results_df)
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if n_tests > 0:
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bonf_alpha = 0.05 / n_tests
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ax.set_title(
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f'Granger 因果检验 p 值热力图 (-log10)\n'
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f'Bonferroni 校正阈值: {bonf_alpha:.6f} (共 {n_tests} 次检验)',
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fontsize=13
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)
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cbar = fig.colorbar(im, ax=ax, shrink=0.8)
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cbar.set_label('-log10(p-value)', fontsize=11)
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fig.savefig(output_dir / 'granger_pvalue_heatmap.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 / 'granger_pvalue_heatmap.png'}")
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# ============================================================
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# 7. 可视化：因果关系网络图
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# ============================================================
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def plot_causal_network(results_df: pd.DataFrame, output_dir: Path, alpha: float = 0.05):
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"""
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绘制显著因果关系网络图（matplotlib 箭头实现）
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仅显示 Bonferroni 校正后仍显著的因果对（取最优滞后的结果）
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Parameters
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----------
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results_df : pd.DataFrame
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含 significant_corrected 列的检验结果
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output_dir : Path
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输出目录
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alpha : float
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显著性水平
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"""
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if results_df.empty or 'significant_corrected' not in results_df.columns:
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print(" [警告] 无校正后结果，跳过网络图绘制")
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return
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# 筛选显著因果对（取每对中 p 值最小的滞后）
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sig = results_df[results_df['significant_corrected']].copy()
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if sig.empty:
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print(" [信息] Bonferroni 校正后无显著因果关系，绘制空网络图")
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# 对每对取最小 p 值
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if not sig.empty:
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sig_best = sig.loc[sig.groupby(['cause', 'effect'])['p_value'].idxmin()]
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else:
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sig_best = pd.DataFrame(columns=results_df.columns)
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# 收集所有变量节点
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all_vars = set()
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for _, row in results_df.iterrows():
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all_vars.add(row['cause'])
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all_vars.add(row['effect'])
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all_vars = sorted(all_vars)
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n_vars = len(all_vars)
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if n_vars == 0:
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return
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# 布局：圆形排列
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angles = np.linspace(0, 2 * np.pi, n_vars, endpoint=False)
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positions = {v: (np.cos(a), np.sin(a)) for v, a in zip(all_vars, angles)}
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fig, ax = plt.subplots(figsize=(10, 10))
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# 绘制节点
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for var, (x, y) in positions.items():
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circle = plt.Circle((x, y), 0.12, color='steelblue', alpha=0.8)
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ax.add_patch(circle)
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ax.text(x, y, var, ha='center', va='center', fontsize=8,
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fontweight='bold', color='white')
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# 绘制显著因果箭头
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for _, row in sig_best.iterrows():
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cause_pos = positions[row['cause']]
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effect_pos = positions[row['effect']]
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# 计算起点和终点（缩短到节点边缘）
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dx = effect_pos[0] - cause_pos[0]
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dy = effect_pos[1] - cause_pos[1]
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dist = np.sqrt(dx ** 2 + dy ** 2)
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if dist < 0.01:
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continue
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# 缩短箭头到节点圆的边缘
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shrink = 0.14
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start_x = cause_pos[0] + shrink * dx / dist
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start_y = cause_pos[1] + shrink * dy / dist
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end_x = effect_pos[0] - shrink * dx / dist
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end_y = effect_pos[1] - shrink * dy / dist
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# 箭头粗细与 -log10(p) 相关
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width = min(3.0, -np.log10(row['p_value'] + 1e-300) * 0.5)
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ax.annotate(
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'',
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||||
xy=(end_x, end_y),
|
||||
xytext=(start_x, start_y),
|
||||
arrowprops=dict(
|
||||
arrowstyle='->', color='red', lw=width,
|
||||
connectionstyle='arc3,rad=0.1',
|
||||
mutation_scale=15,
|
||||
),
|
||||
)
|
||||
# 标注滞后阶数和 p 值
|
||||
mid_x = (start_x + end_x) / 2
|
||||
mid_y = (start_y + end_y) / 2
|
||||
ax.text(mid_x, mid_y, f'lag={int(row["lag"])}\np={row["p_value"]:.2e}',
|
||||
fontsize=7, ha='center', va='center',
|
||||
bbox=dict(boxstyle='round,pad=0.2', facecolor='yellow', alpha=0.7))
|
||||
|
||||
n_sig = len(sig_best)
|
||||
n_total = len(results_df)
|
||||
ax.set_title(
|
||||
f'Granger 因果关系网络 (Bonferroni 校正后)\n'
|
||||
f'显著链接: {n_sig}/{n_total}',
|
||||
fontsize=14
|
||||
)
|
||||
ax.set_xlim(-1.6, 1.6)
|
||||
ax.set_ylim(-1.6, 1.6)
|
||||
ax.set_aspect('equal')
|
||||
ax.axis('off')
|
||||
|
||||
fig.savefig(output_dir / 'granger_causal_network.png',
|
||||
dpi=150, bbox_inches='tight')
|
||||
plt.close(fig)
|
||||
print(f" [保存] {output_dir / 'granger_causal_network.png'}")
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 8. 结果打印
|
||||
# ============================================================
|
||||
|
||||
def print_causality_results(results_df: pd.DataFrame):
|
||||
"""打印所有因果检验结果"""
|
||||
if results_df.empty:
|
||||
print(" [信息] 无检验结果")
|
||||
return
|
||||
|
||||
print("\n" + "=" * 90)
|
||||
print("Granger 因果检验结果明细")
|
||||
print("=" * 90)
|
||||
print(f" {'因果方向':<40} {'滞后':>4} {'F统计量':>12} {'p值':>12} {'原始显著':>8} {'校正显著':>8}")
|
||||
print(" " + "-" * 88)
|
||||
|
||||
for _, row in results_df.iterrows():
|
||||
pair_label = f"{row['cause']} → {row['effect']}"
|
||||
sig_raw = '***' if row.get('significant_raw', False) else ''
|
||||
sig_corr = '***' if row.get('significant_corrected', False) else ''
|
||||
print(f" {pair_label:<40} {int(row['lag']):>4} "
|
||||
f"{row['f_stat']:>12.4f} {row['p_value']:>12.6f} "
|
||||
f"{sig_raw:>8} {sig_corr:>8}")
|
||||
|
||||
# 汇总统计
|
||||
n_total = len(results_df)
|
||||
n_sig_raw = results_df.get('significant_raw', pd.Series(dtype=bool)).sum()
|
||||
n_sig_corr = results_df.get('significant_corrected', pd.Series(dtype=bool)).sum()
|
||||
|
||||
print(f"\n 汇总: 共 {n_total} 次检验")
|
||||
print(f" 原始显著 (p < 0.05): {n_sig_raw} ({n_sig_raw / n_total * 100:.1f}%)")
|
||||
print(f" Bonferroni 校正后显著: {n_sig_corr} ({n_sig_corr / n_total * 100:.1f}%)")
|
||||
|
||||
if n_total > 0:
|
||||
bonf_alpha = 0.05 / n_total
|
||||
print(f" Bonferroni 校正阈值: {bonf_alpha:.6f}")
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 9. 主入口
|
||||
# ============================================================
|
||||
|
||||
def run_causality_analysis(
|
||||
df: pd.DataFrame,
|
||||
output_dir: str = "output/causality",
|
||||
) -> Dict:
|
||||
"""
|
||||
Granger 因果检验主函数
|
||||
|
||||
Parameters
|
||||
----------
|
||||
df : pd.DataFrame
|
||||
日线数据（已通过 add_derived_features 添加衍生特征）
|
||||
output_dir : str
|
||||
图表输出目录
|
||||
|
||||
Returns
|
||||
-------
|
||||
dict
|
||||
包含所有检验结果的字典
|
||||
"""
|
||||
output_dir = Path(output_dir)
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
print("=" * 70)
|
||||
print("BTC Granger 因果检验分析")
|
||||
print("=" * 70)
|
||||
print(f"数据范围: {df.index.min()} ~ {df.index.max()}")
|
||||
print(f"样本数量: {len(df)}")
|
||||
print(f"测试滞后阶数: {TEST_LAGS}")
|
||||
print(f"因果变量对数: {len(CAUSALITY_PAIRS)}")
|
||||
print(f"总检验次数（含所有滞后）: {len(CAUSALITY_PAIRS) * len(TEST_LAGS)}")
|
||||
|
||||
# 设置中文字体
|
||||
plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans']
|
||||
plt.rcParams['axes.unicode_minus'] = False
|
||||
|
||||
# --- 日线级 Granger 因果检验 ---
|
||||
print("\n>>> [1/4] 执行日线级 Granger 因果检验...")
|
||||
daily_results = run_all_granger_tests(df, pairs=CAUSALITY_PAIRS, test_lags=TEST_LAGS)
|
||||
|
||||
if not daily_results.empty:
|
||||
daily_results = apply_bonferroni(daily_results, alpha=0.05)
|
||||
print_causality_results(daily_results)
|
||||
else:
|
||||
print(" [警告] 日线级因果检验未产生结果")
|
||||
|
||||
# --- 跨时间尺度因果检验 ---
|
||||
print("\n>>> [2/4] 执行跨时间尺度因果检验（小时 → 日线）...")
|
||||
cross_results = cross_timeframe_causality(df, test_lags=TEST_LAGS)
|
||||
|
||||
if not cross_results.empty:
|
||||
cross_results = apply_bonferroni(cross_results, alpha=0.05)
|
||||
print("\n跨时间尺度因果检验结果:")
|
||||
print_causality_results(cross_results)
|
||||
else:
|
||||
print(" [信息] 跨时间尺度因果检验无结果（可能小时数据不可用）")
|
||||
|
||||
# --- 合并所有结果用于可视化 ---
|
||||
all_results = pd.concat([daily_results, cross_results], ignore_index=True)
|
||||
if not all_results.empty and 'significant_corrected' not in all_results.columns:
|
||||
all_results = apply_bonferroni(all_results, alpha=0.05)
|
||||
|
||||
# --- p 值热力图（仅日线级结果，避免混淆） ---
|
||||
print("\n>>> [3/4] 绘制 p 值热力图...")
|
||||
plot_pvalue_heatmap(daily_results, output_dir)
|
||||
|
||||
# --- 因果关系网络图 ---
|
||||
print("\n>>> [4/4] 绘制因果关系网络图...")
|
||||
# 使用所有结果（含跨时间尺度）
|
||||
if not all_results.empty:
|
||||
# 重新做一次 Bonferroni 校正（因为合并后总检验数增加）
|
||||
all_corrected = apply_bonferroni(all_results.drop(
|
||||
columns=['bonferroni_alpha', 'significant_raw', 'significant_corrected'],
|
||||
errors='ignore'
|
||||
), alpha=0.05)
|
||||
plot_causal_network(all_corrected, output_dir)
|
||||
else:
|
||||
print(" [警告] 无可用结果，跳过网络图")
|
||||
|
||||
print("\n" + "=" * 70)
|
||||
print("Granger 因果检验分析完成！")
|
||||
print(f"图表已保存至: {output_dir.resolve()}")
|
||||
print("=" * 70)
|
||||
|
||||
return {
|
||||
'daily_results': daily_results,
|
||||
'cross_timeframe_results': cross_results,
|
||||
'all_results': all_results,
|
||||
}
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 独立运行入口
|
||||
# ============================================================
|
||||
|
||||
if __name__ == '__main__':
|
||||
from src.data_loader import load_daily
|
||||
from src.preprocessing import add_derived_features
|
||||
|
||||
df = load_daily()
|
||||
df = add_derived_features(df)
|
||||
run_causality_analysis(df)
|
||||
Reference in New Issue
Block a user