feat: 添加过滤周期后显示图标

ftt/ftt.py

@@ -1,7 +1,7 @@
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
-from scipy.fft import fft, fftfreq
+from scipy.fft import fft, ifft, fftfreq
 from scipy.signal import hilbert, find_peaks
 import pywt
 from datetime import datetime, timedelta
@@ -47,8 +47,8 @@ def compute_basic_metrics(self):
         # 计算波动率
         self.df['vol_21'] = self.df['log_return'].rolling(window=21).std() * np.sqrt(252)
 
-    def perform_fft(self):
-        """执行傅里叶变换"""
+    def perform_fft(self, filter_threshold=None):
+        """执行傅里叶变换，可选择性地过滤高频成分"""
         # 准备数据
         returns = self.df['log_return'].values
         n = len(returns)
@@ -57,6 +57,19 @@ def perform_fft(self):
         fft_result = fft(returns)
         freqs = fftfreq(n, d=1)
 
+        # 如果指定了过滤阈值，过滤高频成分
+        if filter_threshold is not None:
+            # 创建低通滤波器
+            filter_mask = np.abs(freqs) < filter_threshold
+            fft_result_filtered = fft_result * filter_mask
+
+            # 执行逆傅里叶变换获取过滤后的收益率
+            filtered_returns = np.real(ifft(fft_result_filtered))
+            self.df['filtered_returns'] = filtered_returns
+
+            # 从过滤后的收益率重建价格序列
+            self.df['filtered_price'] = self.df['Close'].iloc[0] * np.exp(filtered_returns.cumsum())
+
         # 计算功率谱
         power_spectrum = np.abs(fft_result)**2
 
@@ -67,6 +80,16 @@ def perform_fft(self):
 
         return self.periods, self.power_spectrum
 
+    def filter_high_frequency(self, cutoff_period=21):
+        """
+        过滤高频成分
+        参数:
+            cutoff_period: 截止周期（天），高于此频率的成分将被过滤
+        """
+        filter_threshold = 1/cutoff_period  # 将周期转换为频率
+        self.perform_fft(filter_threshold=filter_threshold)
+        return self.df['filtered_price']
+
     def find_significant_periods(self, n_peaks=5):
         """找出显著周期"""
         peaks, _ = find_peaks(self.power_spectrum)
@@ -108,10 +131,14 @@ def detect_regime_changes(self):
         change_dates = [self.df.index[i] for i in regime_changes]
         return change_dates, energy, threshold
 
-    def plot_comprehensive_analysis(self):
-        """绘制综合分析图"""
+    def plot_comprehensive_analysis(self, show_filtered=True):
+        """绘制综合分析图，包括过滤后的价格"""
         # 准备数据
-        self.perform_fft()
+        if show_filtered:
+            self.filter_high_frequency()  # 默认使用21天作为截止周期
+        else:
+            self.perform_fft()
+
         self.find_significant_periods()
         self.wavelet_analysis()
         self.hilbert_phase_analysis()
@@ -122,10 +149,13 @@ def plot_comprehensive_analysis(self):
 
         # 1. 价格和移动平均线
         ax1 = plt.subplot(511)
-        ax1.plot(self.df['Date'], self.df['Close'], label='价格')
+        ax1.plot(self.df['Date'], self.df['Close'], label='原始价格', alpha=0.7)
+        if show_filtered and 'filtered_price' in self.df.columns:
+            ax1.plot(self.df['Date'], self.df['filtered_price'], 
+                    label='过滤后价格', color='red', linewidth=2)
         ax1.plot(self.df['Date'], self.df['MA21'], label='21日均线')
         ax1.plot(self.df['Date'], self.df['MA63'], label='63日均线')
-        ax1.set_title('价格走势和移动平均')
+        ax1.set_title('价格走势比较')
         ax1.legend()
         ax1.grid(True)
 
@@ -158,6 +188,26 @@ def plot_comprehensive_analysis(self):
 
         plt.tight_layout()
         return plt
+
+    def analyze_with_different_filters(self, periods=[5, 21, 63]):
+        """使用不同的过滤周期进行分析"""
+        plt.figure(figsize=(15, 8))
+
+        # 绘制原始价格
+        plt.plot(self.df['Date'], self.df['Close'], 
+                label='原始价格', alpha=0.5, color='gray')
+
+        # 使用不同的过滤周期
+        colors = ['blue', 'green', 'red']
+        for period, color in zip(periods, colors):
+            filtered_prices = self.filter_high_frequency(cutoff_period=period)
+            plt.plot(self.df['Date'], filtered_prices, 
+                    label=f'过滤周期 {period}天', color=color)
+
+        plt.title('不同过滤周期的价格对比')
+        plt.legend()
+        plt.grid(True)
+        return plt
 
     def get_trading_signals(self):
         """生成交易信号"""
@@ -182,6 +232,10 @@ def get_trading_signals(self):
 
     def print_analysis_summary(self):
         """打印分析摘要"""
+        # 确保先执行FFT分析和寻找显著周期
+        self.perform_fft()
+        self.find_significant_periods()
+
         print("\n=== 股票分析摘要 ===")
 
         # 基本统计
@@ -244,20 +298,15 @@ def main():
     # 使用示例
     analyzer = StockSpectralAnalysis(data)
 
-    # 执行分析
-    analyzer.perform_fft()
-    analyzer.find_significant_periods()
-
-    # 打印分析摘要
+    # 基本分析
     analyzer.print_analysis_summary()
 
-    # 生成交易信号
-    signals = analyzer.get_trading_signals()
-    print("\n最新交易信号:")
-    print(signals.iloc[-1])
+    # 使用不同的过滤周期进行分析
+    plt = analyzer.analyze_with_different_filters(periods=[5, 21, 63])
+    plt.show()
 
-    # 绘制分析图表
-    plt = analyzer.plot_comprehensive_analysis()
+    # 显示综合分析图
+    plt = analyzer.plot_comprehensive_analysis(show_filtered=True)
     plt.show()
 
 if __name__ == "__main__":