利用MA计算股票波峰与波谷点

算法原理

趋势判断

1、利用股票日K数据的MA与收盘价的对比，确定股票的上升与下降趋势时间段；
2、在时间段内求极值点

代码实现

import talib
import pandas as pd
def get_peak_and_buttom(bar_df:pd.Series, ma_length:int)->(list, list):
    """获取序列的波峰与波谷

    Args:
        bar_df (Series): 股票收盘价序列
        ma_length(int):MA长度 

    Returns:
        _type_: _description_
    """    
    if len(bar_df) <=ma_length:
        return None, None
    ma = talib.MA(bar_df, ma_length)
    compare_flag = ma > bar_df
    df_list = []
    start_idx = 0
    prev_flag = compare_flag.iloc[0]
    def add_to_list(df, flag):
        if len(df_list) > 0 and flag == df_list[-1]['flag']:
            tmp_df = df_list.pop()
            new_df = pd.concat([tmp_df['df'], df], axis=0)
            df_list.append({'flag':flag, 'df':new_df})
        else:
            df_list.append({"flag":flag, "df":df})
    # 利用MA数据与收盘价的对比计算上升与下降区间
    for i,flag in enumerate(compare_flag):
        if flag != prev_flag:
            end_idx = i
            # 屏蔽个别异常数据
            if end_idx - start_idx <= 1:
                prev_flag = not prev_flag
                continue
            t_df = bar_df.iloc[start_idx:end_idx]
            add_to_list(t_df, prev_flag)
            start_idx = i
            prev_flag = flag
    end_idx = len(compare_flag)
    t_df = bar_df.iloc[start_idx:]
    add_to_list(t_df, prev_flag)
    # 计算各个区间内的极值点
    high_idx = [t_df["df"].idxmax() for t_df in df_list if not t_df["flag"]]
    low_idx = [t_df["df"].idxmin() for t_df in df_list if t_df["flag"]]
    return high_idx, low_idx

测试结果

MA 长度为5情况
MA长度为10情况
MA长度为15情况

由图可以看出，MA的长度越长，就会屏蔽中间的次级波动