python中60分钟周期下MA60>MA120>MA250,30分钟周期下MA60>MA120>MA250,15分钟周期下MA60>MA120>MA250 用python怎么写代码

假设你已经导入了pandas和numpy库，可以按照以下步骤实现：

1. 获取60分钟周期的数据，计算MA60、MA120、MA250指标：

import pandas as pd
import numpy as np

# 获取60分钟周期的数据
data_60min = pd.read_csv('data_60min.csv')
data_60min['MA60'] = data_60min['Close'].rolling(window=60).mean()
data_60min['MA120'] = data_60min['Close'].rolling(window=120).mean()
data_60min['MA250'] = data_60min['Close'].rolling(window=250).mean()

2. 获取30分钟周期的数据，计算MA60、MA120、MA250指标：

# 获取30分钟周期的数据
data_30min = pd.read_csv('data_30min.csv')
data_30min['MA60'] = data_30min['Close'].rolling(window=60).mean()
data_30min['MA120'] = data_30min['Close'].rolling(window=120).mean()
data_30min['MA250'] = data_30min['Close'].rolling(window=250).mean()

3. 获取15分钟周期的数据，计算MA60、MA120、MA250指标：

# 获取15分钟周期的数据
data_15min = pd.read_csv('data_15min.csv')
data_15min['MA60'] = data_15min['Close'].rolling(window=60).mean()
data_15min['MA120'] = data_15min['Close'].rolling(window=120).mean()
data_15min['MA250'] = data_15min['Close'].rolling(window=250).mean()

4. 判断MA指标是否符合条件：

if data_60min['MA60'].iloc[-1] > data_60min['MA120'].iloc[-1] > data_60min['MA250'].iloc[-1] and \
   data_30min['MA60'].iloc[-1] > data_30min['MA120'].iloc[-1] > data_30min['MA250'].iloc[-1] and \
   data_15min['MA60'].iloc[-1] > data_15min['MA120'].iloc[-1] > data_15min['MA250'].iloc[-1]:
    print('符合条件')
else:
    print('不符合条件')

需要注意的是，这里假设数据已经按照时间顺序排序，且最近一条数据在DataFrame中的索引为-1。如果需要判断多个股票是否符合条件，可以将上述代码写入一个循环中，依次对每个股票进行判断。