量化交易策略:结合震荡量指标(CHO)与商品渠道指数(CCI)的赫兹量化分析震荡量指标(CHO, Chaikin Oscillator)和商品渠道指数(CCI, Commodity Channel Index)是分析市场趋势和动量的两个重要工具。这篇文章将探讨如何将这两个指标与赫兹量化分析结合,形成一个综合的量化交易策略,并通过Python代码进行实现。1. 震荡量指标(CHO)概述CHO是由Marc Chaikin开发的,主要用于衡量市场买卖压力的变化。它通过计算指数移动平均线(EMA)之差来分析积累/分配线(ADL)的动量,为交易者提供潜在的买卖点。2. 商品渠道指数(CCI)概述CCI是一个统计指标,通过比较商品的当前交易价格与其平均价格来衡量价格的偏离程度。该指标帮助交易者识别市场的过度买入或过度卖出状态,从而寻找交易机会。3. 赫兹量化分析本策略中,赫兹量化分析将用于识别CHO和CCI指标的周期性和频率响应,通过快速傅里叶变换(FFT)来分析数据的频率特性,以辅助决策过程。4. 交易策略设计与Python代码实现步骤1: 导入必要的库并加载数据pythonCopy codeimport numpy as npimport pandas as pdimport matplotlib.pyplot as pltfrom scipy.fft import fft# 假定数据在CSV文件中,包含Open, High, Low, Close, Volume等列data = pd.read_csv('stock_data.csv', parse_dates=True, index_col='Date')步骤2: 计算CHO和CCI指标pythonCopy code# 计算ADLdata['ADL'] = (2 * data['Close'] - data['High'] - data['Low']) / (data['High'] - data['Low']) * data['Volume']data['ADL'] = data['ADL'].cumsum()# 计算CHOshort_ema = 3long_ema = 10data['CHO'] = data['ADL'].ewm(span=short_ema, adjust=False).mean() - data['ADL'].ewm(span=long_ema, adjust=False).mean()# 计算CCItp = (data['High'] + data['Low'] + data['Close']) / 3data['CCI'] = (tp - tp.rolling(window=20).mean()) / (0.015 * tp.rolling(window=20).std())步骤3: 赫兹量化分析pythonCopy code# 对CHO和CCI进行FFT变换cho_fft = fft(data['CHO'].dropna())cci_fft = fft(data['CCI'].dropna())frequencies = np.fft.fftfreq(len(cho_fft))# 找到主要频率成分cho_main_freq = frequencies[np.argmax(np.abs(cho_fft))]cci_main_freq = frequencies[np.argmax(np.abs(cci_fft))]步骤4: 策略实施pythonCopy code# 定义买入卖出信号data['Buy'] = (data['CHO'] > 0) & (data['CCI'] < -100)data['Sell'] = (data['CHO'] < 0) & (data['CCI'] > 100)# 绘制交易信号图plt.figure(figsize=(14, 7))plt.plot(data['Close'], label='Close Price')plt.plot(data.index, data['Buy'] * data['Close'], '^', markersize=10, color='g', lw=0, label='Buy Signal')plt.plot(data.index, data['Sell'] * data['Close'], 'v', markersize=10, color='r', lw=0, label='Sell Signal')plt.title('Trade Signals based on CHO and CCI with Hz Analysis')plt.legend()plt.sh