python做 macd底背离

可以使用Python来计算和绘制MACD（Moving Average Convergence Divergence）指标以及找出MACD底背离。下面是一个简单的示例代码：

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# 假设已经有了价格数据，存储在一个名为df的DataFrame中，包含日期和价格两列
# 例如：df = pd.read_csv('price_data.csv')

# 计算MACD指标
def calculate_macd(df, short_period=12, long_period=26, signal_smoothing=9):
    df['EMA_short'] = df['Close'].ewm(span=short_period, adjust=False).mean()
    df['EMA_long'] = df['Close'].ewm(span=long_period, adjust=False).mean()
    df['MACD'] = df['EMA_short'] - df['EMA_long']
    df['Signal'] = df['MACD'].ewm(span=signal_smoothing, adjust=False).mean()
    df['Histogram'] = df['MACD'] - df['Signal']
    return df

# 找出MACD底背离
def find_macd_divergence(df):
    divergence_points = []
    for i in range(2, len(df)-2):
        if df.iloc[i-2]['Histogram'] > 0 and df.iloc[i-1]['Histogram'] > 0 and \
           df.iloc[i]['Histogram'] < 0 and df.iloc[i+1]['Histogram'] < 0:
            if df.iloc[i]['Close'] < df.iloc[i-2]['Close'] and df.iloc[i]['Close'] < df.iloc[i+1]['Close']:
                divergence_points.append(df.iloc[i])
    return pd.DataFrame(divergence_points)

# 绘制价格图和MACD指标
def plot_macd(df):
    fig, ax1 = plt.subplots()
    ax1.plot(df['Date'], df['Close'], color='blue')
    ax1.set_xlabel('Date')
    ax1.set_ylabel('Price', color='blue')
    ax2 = ax1.twinx()
    ax2.plot(df['Date'], df['MACD'], color='red', label='MACD')
    ax2.plot(df['Date'], df['Signal'], color='green', label='Signal')
    ax2.bar(df['Date'], df['Histogram'], color='gray', alpha=0.5, label='Histogram')
    ax2.set_ylabel('MACD', color='black')
    plt.legend()
    plt.show()

# 调用函数计算MACD指标和找出底背离点
df = calculate_macd(df)
divergence_df = find_macd_divergence(df)

# 打印底背离点
print("MACD底背离点：")
print(divergence_df)

# 绘制图表
plot_macd(df)

请注意，以上代码仅为示例，实际使用时需要根据你的数据和需求进行适当的修改。