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/volume_price_analysis.py
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577
src/volume_price_analysis.py
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"""成交量-价格关系与OBV分析
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分析BTC成交量与价格变动的关系，包括Spearman相关性、
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Taker买入比例领先分析、Granger因果检验和OBV背离检测。
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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 statsmodels.tsa.stattools import grangercausalitytests
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from pathlib import Path
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from typing import Dict, List, Tuple
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# 中文显示支持
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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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# =============================================================================
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# 核心分析函数
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# =============================================================================
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def _spearman_volume_returns(volume: pd.Series, returns: pd.Series) -> Dict:
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"""Spearman秩相关: 成交量 vs |收益率|
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使用Spearman而非Pearson，因为量价关系通常是非线性的。
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Returns
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-------
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dict
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包含 correlation, p_value, n_samples
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"""
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# 对齐索引并去除NaN
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abs_ret = returns.abs()
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aligned = pd.concat([volume, abs_ret], axis=1, keys=['volume', 'abs_return']).dropna()
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corr, p_val = stats.spearmanr(aligned['volume'], aligned['abs_return'])
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return {
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'correlation': corr,
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'p_value': p_val,
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'n_samples': len(aligned),
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}
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def _taker_buy_ratio_lead_lag(
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taker_buy_ratio: pd.Series,
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returns: pd.Series,
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max_lag: int = 20,
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) -> pd.DataFrame:
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"""Taker买入比例领先-滞后分析
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计算 taker_buy_ratio(t) 与 returns(t+lag) 的互相关，
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检验买入比例对未来收益的预测能力。
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Parameters
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----------
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taker_buy_ratio : pd.Series
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Taker买入占比序列
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returns : pd.Series
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对数收益率序列
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max_lag : int
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最大领先天数
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Returns
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-------
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pd.DataFrame
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包含 lag, correlation, p_value, significant 列
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"""
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results = []
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for lag in range(1, max_lag + 1):
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# taker_buy_ratio(t) vs returns(t+lag)
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ratio_shifted = taker_buy_ratio.shift(lag)
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aligned = pd.concat([ratio_shifted, returns], axis=1).dropna()
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aligned.columns = ['ratio', 'return']
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if len(aligned) < 30:
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continue
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corr, p_val = stats.spearmanr(aligned['ratio'], aligned['return'])
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results.append({
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'lag': lag,
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'correlation': corr,
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'p_value': p_val,
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'significant': p_val < 0.05,
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})
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return pd.DataFrame(results)
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def _granger_causality(
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volume: pd.Series,
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returns: pd.Series,
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max_lag: int = 10,
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) -> Dict[str, pd.DataFrame]:
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"""双向Granger因果检验: 成交量 ↔ 收益率
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Parameters
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----------
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volume : pd.Series
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成交量序列
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returns : pd.Series
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收益率序列
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max_lag : 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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'volume_to_returns': 成交量→收益率 的p值表
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'returns_to_volume': 收益率→成交量 的p值表
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"""
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# 对齐并去除NaN
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aligned = pd.concat([volume, returns], axis=1, keys=['volume', 'returns']).dropna()
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results = {}
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# 方向1: 成交量 → 收益率 (检验成交量是否Granger-cause收益率)
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# grangercausalitytests 的数据格式: [被预测变量, 预测变量]
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try:
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data_v2r = aligned[['returns', 'volume']].values
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gc_v2r = grangercausalitytests(data_v2r, maxlag=max_lag, verbose=False)
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rows_v2r = []
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for lag_order in range(1, max_lag + 1):
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test_results = gc_v2r[lag_order][0]
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rows_v2r.append({
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'lag': lag_order,
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'ssr_ftest_pval': test_results['ssr_ftest'][1],
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'ssr_chi2test_pval': test_results['ssr_chi2test'][1],
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'lrtest_pval': test_results['lrtest'][1],
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'params_ftest_pval': test_results['params_ftest'][1],
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})
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results['volume_to_returns'] = pd.DataFrame(rows_v2r)
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except Exception as e:
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print(f" [警告] 成交量→收益率 Granger检验失败: {e}")
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results['volume_to_returns'] = pd.DataFrame()
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# 方向2: 收益率 → 成交量
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try:
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data_r2v = aligned[['volume', 'returns']].values
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gc_r2v = grangercausalitytests(data_r2v, maxlag=max_lag, verbose=False)
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rows_r2v = []
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for lag_order in range(1, max_lag + 1):
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test_results = gc_r2v[lag_order][0]
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rows_r2v.append({
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'lag': lag_order,
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'ssr_ftest_pval': test_results['ssr_ftest'][1],
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'ssr_chi2test_pval': test_results['ssr_chi2test'][1],
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'lrtest_pval': test_results['lrtest'][1],
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'params_ftest_pval': test_results['params_ftest'][1],
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})
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results['returns_to_volume'] = pd.DataFrame(rows_r2v)
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except Exception as e:
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print(f" [警告] 收益率→成交量 Granger检验失败: {e}")
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results['returns_to_volume'] = pd.DataFrame()
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return results
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def _compute_obv(df: pd.DataFrame) -> pd.Series:
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"""计算OBV (On-Balance Volume)
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规则:
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- 收盘价上涨: OBV += volume
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- 收盘价下跌: OBV -= volume
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- 收盘价持平: OBV 不变
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"""
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close = df['close']
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volume = df['volume']
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direction = np.sign(close.diff())
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obv = (direction * volume).fillna(0).cumsum()
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obv.name = 'obv'
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return obv
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def _detect_obv_divergences(
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prices: pd.Series,
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obv: pd.Series,
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window: int = 60,
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lookback: int = 5,
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) -> pd.DataFrame:
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"""检测OBV-价格背离
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背离类型:
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- 顶背离 (bearish): 价格创新高但OBV未创新高 → 潜在下跌信号
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- 底背离 (bullish): 价格创新低但OBV未创新低 → 潜在上涨信号
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Parameters
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----------
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prices : pd.Series
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收盘价序列
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obv : pd.Series
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OBV序列
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window : int
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滚动窗口大小，用于判断"新高"/"新低"
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lookback : int
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新高/新低确认回看天数
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Returns
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-------
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pd.DataFrame
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背离事件表，包含 date, type, price, obv 列
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"""
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divergences = []
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# 滚动最高/最低
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price_rolling_max = prices.rolling(window=window, min_periods=window).max()
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price_rolling_min = prices.rolling(window=window, min_periods=window).min()
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obv_rolling_max = obv.rolling(window=window, min_periods=window).max()
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obv_rolling_min = obv.rolling(window=window, min_periods=window).min()
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for i in range(window + lookback, len(prices)):
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idx = prices.index[i]
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price_val = prices.iloc[i]
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obv_val = obv.iloc[i]
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# 价格创近期新高 (最近lookback天内触及滚动最高)
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recent_prices = prices.iloc[i - lookback:i + 1]
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recent_obv = obv.iloc[i - lookback:i + 1]
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rolling_max_price = price_rolling_max.iloc[i]
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rolling_max_obv = obv_rolling_max.iloc[i]
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rolling_min_price = price_rolling_min.iloc[i]
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rolling_min_obv = obv_rolling_min.iloc[i]
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# 顶背离: 价格 == 滚动最高 且 OBV 未达到滚动最高的95%
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if price_val >= rolling_max_price * 0.998:
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if obv_val < rolling_max_obv * 0.95:
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divergences.append({
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'date': idx,
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'type': 'bearish', # 顶背离
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'price': price_val,
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'obv': obv_val,
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})
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# 底背离: 价格 == 滚动最低 且 OBV 未达到滚动最低(更高)
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if price_val <= rolling_min_price * 1.002:
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if obv_val > rolling_min_obv * 1.05:
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divergences.append({
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'date': idx,
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'type': 'bullish', # 底背离
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'price': price_val,
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'obv': obv_val,
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})
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df_div = pd.DataFrame(divergences)
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# 去除密集重复信号 (同类型信号间隔至少10天)
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if not df_div.empty:
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df_div = df_div.sort_values('date')
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filtered = [df_div.iloc[0]]
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for _, row in df_div.iloc[1:].iterrows():
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last = filtered[-1]
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if row['type'] != last['type'] or (row['date'] - last['date']).days >= 10:
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filtered.append(row)
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df_div = pd.DataFrame(filtered).reset_index(drop=True)
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return df_div
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# =============================================================================
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# 可视化函数
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# =============================================================================
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def _plot_volume_return_scatter(
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volume: pd.Series,
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returns: pd.Series,
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spearman_result: Dict,
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output_dir: Path,
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):
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"""图1: 成交量 vs |收益率| 散点图"""
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fig, ax = plt.subplots(figsize=(10, 7))
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abs_ret = returns.abs()
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aligned = pd.concat([volume, abs_ret], axis=1, keys=['volume', 'abs_return']).dropna()
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ax.scatter(aligned['volume'], aligned['abs_return'],
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s=5, alpha=0.3, color='steelblue')
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rho = spearman_result['correlation']
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p_val = spearman_result['p_value']
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ax.set_xlabel('成交量', fontsize=12)
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ax.set_ylabel('|对数收益率|', fontsize=12)
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ax.set_title(f'成交量 vs |收益率| 散点图\nSpearman ρ={rho:.4f}, p={p_val:.2e}', fontsize=13)
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ax.grid(True, alpha=0.3)
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fig.savefig(output_dir / 'volume_return_scatter.png', dpi=150, bbox_inches='tight')
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plt.close(fig)
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print(f" [图] 量价散点图已保存: {output_dir / 'volume_return_scatter.png'}")
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def _plot_lead_lag_correlation(
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lead_lag_df: pd.DataFrame,
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output_dir: Path,
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):
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"""图2: Taker买入比例领先-滞后相关性柱状图"""
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fig, ax = plt.subplots(figsize=(12, 6))
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if lead_lag_df.empty:
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ax.text(0.5, 0.5, '数据不足，无法计算领先-滞后相关性',
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transform=ax.transAxes, ha='center', va='center', fontsize=14)
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fig.savefig(output_dir / 'taker_buy_lead_lag.png', dpi=150, bbox_inches='tight')
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plt.close(fig)
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return
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colors = ['red' if sig else 'steelblue'
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for sig in lead_lag_df['significant']]
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bars = ax.bar(lead_lag_df['lag'], lead_lag_df['correlation'],
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color=colors, alpha=0.8, edgecolor='white')
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# 显著性水平线
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ax.axhline(y=0, color='black', linewidth=0.5)
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ax.set_xlabel('领先天数 (lag)', fontsize=12)
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ax.set_ylabel('Spearman 相关系数', fontsize=12)
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ax.set_title('Taker买入比例对未来收益的领先相关性\n(红色=p<0.05 显著)', fontsize=13)
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ax.set_xticks(lead_lag_df['lag'])
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ax.grid(True, alpha=0.3, axis='y')
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fig.savefig(output_dir / 'taker_buy_lead_lag.png', dpi=150, bbox_inches='tight')
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plt.close(fig)
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print(f" [图] Taker买入比例领先分析已保存: {output_dir / 'taker_buy_lead_lag.png'}")
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def _plot_granger_heatmap(
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granger_results: Dict[str, pd.DataFrame],
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output_dir: Path,
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):
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"""图3: Granger因果检验p值热力图"""
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fig, axes = plt.subplots(1, 2, figsize=(16, 6))
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titles = {
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'volume_to_returns': '成交量 → 收益率',
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'returns_to_volume': '收益率 → 成交量',
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}
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for ax, (direction, df_gc) in zip(axes, granger_results.items()):
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if df_gc.empty:
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ax.text(0.5, 0.5, '检验失败', transform=ax.transAxes,
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ha='center', va='center', fontsize=14)
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ax.set_title(titles[direction], fontsize=13)
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continue
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# 构建热力图矩阵
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test_names = ['ssr_ftest_pval', 'ssr_chi2test_pval', 'lrtest_pval', 'params_ftest_pval']
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test_labels = ['SSR F-test', 'SSR Chi2', 'LR test', 'Params F-test']
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lags = df_gc['lag'].values
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heatmap_data = df_gc[test_names].values.T # shape: (4, n_lags)
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im = ax.imshow(heatmap_data, aspect='auto', cmap='RdYlGn',
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vmin=0, vmax=0.1, interpolation='nearest')
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ax.set_xticks(range(len(lags)))
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ax.set_xticklabels(lags, fontsize=9)
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ax.set_yticks(range(len(test_labels)))
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ax.set_yticklabels(test_labels, fontsize=9)
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ax.set_xlabel('滞后阶数', fontsize=11)
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ax.set_title(f'Granger因果: {titles[direction]}', fontsize=13)
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# 标注p值
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for i in range(len(test_labels)):
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for j in range(len(lags)):
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val = heatmap_data[i, j]
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color = 'white' if val < 0.03 else 'black'
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ax.text(j, i, f'{val:.3f}', ha='center', va='center',
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fontsize=7, color=color)
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fig.colorbar(im, ax=axes, label='p-value', shrink=0.8)
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fig.tight_layout()
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fig.savefig(output_dir / 'granger_causality_heatmap.png', dpi=150, bbox_inches='tight')
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plt.close(fig)
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print(f" [图] Granger因果热力图已保存: {output_dir / 'granger_causality_heatmap.png'}")
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def _plot_obv_with_divergences(
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df: pd.DataFrame,
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obv: pd.Series,
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divergences: pd.DataFrame,
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output_dir: Path,
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):
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"""图4: OBV vs 价格 + 背离标记"""
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fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(16, 10), sharex=True,
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gridspec_kw={'height_ratios': [2, 1]})
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# 上图: 价格
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ax1.plot(df.index, df['close'], color='black', linewidth=0.8, label='BTC 收盘价')
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ax1.set_ylabel('价格 (USDT)', fontsize=12)
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ax1.set_title('BTC 价格与OBV背离分析', fontsize=14)
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ax1.set_yscale('log')
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ax1.grid(True, alpha=0.3, which='both')
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# 下图: OBV
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ax2.plot(obv.index, obv.values, color='steelblue', linewidth=0.8, label='OBV')
|
||||
ax2.set_ylabel('OBV', fontsize=12)
|
||||
ax2.set_xlabel('日期', fontsize=12)
|
||||
ax2.grid(True, alpha=0.3)
|
||||
|
||||
# 标记背离
|
||||
if not divergences.empty:
|
||||
bearish = divergences[divergences['type'] == 'bearish']
|
||||
bullish = divergences[divergences['type'] == 'bullish']
|
||||
|
||||
if not bearish.empty:
|
||||
ax1.scatter(bearish['date'], bearish['price'],
|
||||
marker='v', s=60, color='red', zorder=5,
|
||||
label=f'顶背离 ({len(bearish)}次)', alpha=0.7)
|
||||
for _, row in bearish.iterrows():
|
||||
ax2.axvline(row['date'], color='red', alpha=0.2, linewidth=0.5)
|
||||
|
||||
if not bullish.empty:
|
||||
ax1.scatter(bullish['date'], bullish['price'],
|
||||
marker='^', s=60, color='green', zorder=5,
|
||||
label=f'底背离 ({len(bullish)}次)', alpha=0.7)
|
||||
for _, row in bullish.iterrows():
|
||||
ax2.axvline(row['date'], color='green', alpha=0.2, linewidth=0.5)
|
||||
|
||||
ax1.legend(fontsize=10, loc='upper left')
|
||||
ax2.legend(fontsize=10, loc='upper left')
|
||||
|
||||
fig.tight_layout()
|
||||
fig.savefig(output_dir / 'obv_divergence.png', dpi=150, bbox_inches='tight')
|
||||
plt.close(fig)
|
||||
print(f" [图] OBV背离分析已保存: {output_dir / 'obv_divergence.png'}")
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# 主入口
|
||||
# =============================================================================
|
||||
|
||||
def run_volume_price_analysis(df: pd.DataFrame, output_dir: str = "output") -> Dict:
|
||||
"""成交量-价格关系与OBV分析 — 主入口函数
|
||||
|
||||
Parameters
|
||||
----------
|
||||
df : pd.DataFrame
|
||||
由 data_loader.load_daily() 返回的日线数据，含 DatetimeIndex,
|
||||
close, volume, taker_buy_volume 等列
|
||||
output_dir : str
|
||||
图表输出目录
|
||||
|
||||
Returns
|
||||
-------
|
||||
dict
|
||||
分析结果摘要
|
||||
"""
|
||||
output_dir = Path(output_dir)
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
print("=" * 60)
|
||||
print(" BTC 成交量-价格关系分析")
|
||||
print("=" * 60)
|
||||
|
||||
# 准备数据
|
||||
prices = df['close'].dropna()
|
||||
volume = df['volume'].dropna()
|
||||
log_ret = np.log(prices / prices.shift(1)).dropna()
|
||||
|
||||
# 计算taker买入比例
|
||||
taker_buy_ratio = (df['taker_buy_volume'] / df['volume'].replace(0, np.nan)).dropna()
|
||||
|
||||
print(f"\n数据范围: {df.index[0].date()} ~ {df.index[-1].date()}")
|
||||
print(f"样本数量: {len(df)}")
|
||||
|
||||
# ---- 步骤1: Spearman相关性 ----
|
||||
print("\n--- Spearman 成交量-|收益率| 相关性 ---")
|
||||
spearman_result = _spearman_volume_returns(volume, log_ret)
|
||||
print(f" Spearman ρ: {spearman_result['correlation']:.4f}")
|
||||
print(f" p-value: {spearman_result['p_value']:.2e}")
|
||||
print(f" 样本量: {spearman_result['n_samples']}")
|
||||
if spearman_result['p_value'] < 0.01:
|
||||
print(" >> 结论: 成交量与|收益率|存在显著正相关（成交量放大伴随大幅波动）")
|
||||
else:
|
||||
print(" >> 结论: 成交量与|收益率|相关性不显著")
|
||||
|
||||
# ---- 步骤2: Taker买入比例领先分析 ----
|
||||
print("\n--- Taker买入比例领先分析 ---")
|
||||
lead_lag_df = _taker_buy_ratio_lead_lag(taker_buy_ratio, log_ret, max_lag=20)
|
||||
if not lead_lag_df.empty:
|
||||
sig_lags = lead_lag_df[lead_lag_df['significant']]
|
||||
if not sig_lags.empty:
|
||||
print(f" 显著领先期 (p<0.05):")
|
||||
for _, row in sig_lags.iterrows():
|
||||
print(f" lag={int(row['lag']):>2d}天: ρ={row['correlation']:.4f}, p={row['p_value']:.4f}")
|
||||
best = sig_lags.loc[sig_lags['correlation'].abs().idxmax()]
|
||||
print(f" >> 最强领先信号: lag={int(best['lag'])}天, ρ={best['correlation']:.4f}")
|
||||
else:
|
||||
print(" 未发现显著的领先关系 (所有lag的p>0.05)")
|
||||
else:
|
||||
print(" 数据不足，无法进行领先-滞后分析")
|
||||
|
||||
# ---- 步骤3: Granger因果检验 ----
|
||||
print("\n--- Granger 因果检验 (双向, lag 1-10) ---")
|
||||
granger_results = _granger_causality(volume, log_ret, max_lag=10)
|
||||
|
||||
for direction, label in [('volume_to_returns', '成交量→收益率'),
|
||||
('returns_to_volume', '收益率→成交量')]:
|
||||
df_gc = granger_results[direction]
|
||||
if not df_gc.empty:
|
||||
# 使用SSR F-test的p值
|
||||
sig_gc = df_gc[df_gc['ssr_ftest_pval'] < 0.05]
|
||||
if not sig_gc.empty:
|
||||
print(f" {label}: 在以下滞后阶显著 (SSR F-test p<0.05):")
|
||||
for _, row in sig_gc.iterrows():
|
||||
print(f" lag={int(row['lag'])}: p={row['ssr_ftest_pval']:.4f}")
|
||||
else:
|
||||
print(f" {label}: 在所有滞后阶均不显著")
|
||||
else:
|
||||
print(f" {label}: 检验失败")
|
||||
|
||||
# ---- 步骤4: OBV计算与背离检测 ----
|
||||
print("\n--- OBV 与 价格背离分析 ---")
|
||||
obv = _compute_obv(df)
|
||||
divergences = _detect_obv_divergences(prices, obv, window=60, lookback=5)
|
||||
|
||||
if not divergences.empty:
|
||||
bearish_count = len(divergences[divergences['type'] == 'bearish'])
|
||||
bullish_count = len(divergences[divergences['type'] == 'bullish'])
|
||||
print(f" 检测到 {len(divergences)} 个背离信号:")
|
||||
print(f" 顶背离 (看跌): {bearish_count} 次")
|
||||
print(f" 底背离 (看涨): {bullish_count} 次")
|
||||
|
||||
# 最近的背离
|
||||
recent = divergences.tail(5)
|
||||
print(f" 最近 {len(recent)} 个背离:")
|
||||
for _, row in recent.iterrows():
|
||||
div_type = '顶背离' if row['type'] == 'bearish' else '底背离'
|
||||
date_str = row['date'].strftime('%Y-%m-%d')
|
||||
print(f" {date_str}: {div_type}, 价格=${row['price']:,.0f}")
|
||||
else:
|
||||
bearish_count = 0
|
||||
bullish_count = 0
|
||||
print(" 未检测到明显的OBV-价格背离")
|
||||
|
||||
# ---- 步骤5: 生成可视化 ----
|
||||
print("\n--- 生成可视化图表 ---")
|
||||
_plot_volume_return_scatter(volume, log_ret, spearman_result, output_dir)
|
||||
_plot_lead_lag_correlation(lead_lag_df, output_dir)
|
||||
_plot_granger_heatmap(granger_results, output_dir)
|
||||
_plot_obv_with_divergences(df, obv, divergences, output_dir)
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print(" 成交量-价格分析完成")
|
||||
print("=" * 60)
|
||||
|
||||
# 返回结果摘要
|
||||
return {
|
||||
'spearman': spearman_result,
|
||||
'lead_lag': {
|
||||
'significant_lags': lead_lag_df[lead_lag_df['significant']]['lag'].tolist()
|
||||
if not lead_lag_df.empty else [],
|
||||
},
|
||||
'granger': {
|
||||
'volume_to_returns_sig_lags': granger_results['volume_to_returns'][
|
||||
granger_results['volume_to_returns']['ssr_ftest_pval'] < 0.05
|
||||
]['lag'].tolist() if not granger_results['volume_to_returns'].empty else [],
|
||||
'returns_to_volume_sig_lags': granger_results['returns_to_volume'][
|
||||
granger_results['returns_to_volume']['ssr_ftest_pval'] < 0.05
|
||||
]['lag'].tolist() if not granger_results['returns_to_volume'].empty else [],
|
||||
},
|
||||
'obv_divergences': {
|
||||
'total': len(divergences),
|
||||
'bearish': bearish_count,
|
||||
'bullish': bullish_count,
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
from data_loader import load_daily
|
||||
df = load_daily()
|
||||
results = run_volume_price_analysis(df, output_dir='../output/volume_price')
|
||||
Reference in New Issue
Block a user